Investment Behaviour in Conventional and Emerging Farming Systems under Different Policy Scenarios

Transcription

1 Investment Behaviour in Conventional and Emerging Farming Systems under Different Policy Scenarios Vittorio Gallerani, Sergio Gomez y Paloma, Meri Raggi and Davide Viaggi EUR EN

2 The mission of the IPTS is to provide customer-driven support to the EU policy-making process by researching science-based responses to policy challenges that have both a socio-economic and a scientific or technological dimension. European Commission Joint Research Centre Institute for Prospective Technological Studies Contact information Address: Edificio Expo. c/ Inca Garcilaso, s/n. E Seville (Spain) jrc-ipts-secretariat@ec.europa.eu Tel.: Fax: Legal Notice Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*): (*) Certain mobile telephone operators do not allow access to numbers or these calls may be billed. A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server JRC40561 EUR EN ISBN ISSN DOI /94554 Luxembourg: Office for Official Publications of the European Communities European Communities, 2008 Reproduction is authorised provided the source is acknowledged Printed in Spain

3 Investment behaviour in conventional and emerging farming systems under different policy scenarios Vittorio Gallerani, Sergio Gomez y Paloma, Meri Raggi, and Davide Viaggi JRC Scientific and Technical Report Institute for Prospective Technological Studies February /187

4 ACKNOWLEDGEMENTS This study is based on ideas developed at the Sustainable Agriculture (SUSTAG) action of DG JRC IPTS AGRILIFE Unit. It has been carried out by a network of researchers coordinated by Vittorio Gallerani and Davide Viaggi (Bologna University) 1, and, from the JRC IPTS side, by Sergio Gomez y Paloma. The authors would like to specifically acknowledge the efforts of Daniel Deybe (European Commission, DG Research and Technological Development) and Wolfgang Munch (European Commission, DG Agriculture) for having substantially contributed to steer it through intensive and frequent discussions on the methodological approach, findings and interpretations. The authors would like to acknowledge also the contribution of Wolfgang Britz (University of Bonn) and Francois Bonnieux (INRA-ECONOMIE, Rennes), who peer reviewed the study. A number of experts external to the JRC contributed to the study, particularly in the implementation of case studies. Their names and affiliations are included in the list below. Laure Latruffe and Yann Desjeux of INRA Rennes Dirk Aderhold of Produkt+Markt Basil Manos, Christina Moulogianni and Thomas Bournaris of Aristotle University - Thessaloniki Julio Berbel of Cordoba University Zoltan Karacsonyi of Debrecen University Jack Peerlings and Nico Polman of Wageningen University Edward Majewski and Lukasz Cyganski of Warsaw Agricultural University Meri Raggi, Sabrina Di Pasquale, Antonella Samoggia, Gianluca Selva, Luca Giardini, Gabriele Carminati, Alice Gabaldo, Andrea Ghinassi of Bologna University. Special thanks to Per Sørup (AGRILIFE Head of Unit) and Tomas Ratinger (SUSTAG action leader) and Paulo Barbosa (IPTS) for the comments provided, to Dimitre Nikolov (IPTS visiting scientist and IAE-Sofia fellow) who contributed to follow-up the early phases of the study and to Rafael Castillo (IPTS) for technical support with this publication. 1 Department of Agricultural Economics and Engineering, viale Fanin, , Bologna, Italy, contact: davide.viaggi@unibo.it. 2/187

5 TABLE OF CONTENTS EXECUTIVE SUMMARY BACKGROUND AND OBJECTIVES LITERATURE REVIEW A REFERENCE FRAMEWORK: DEFINITIONS, REPRESENTATIONS AND DETERMINANTS Definition and classification of farm investments Decision-making process Basic economic representations of firm-level investment decisions Determinants of farm investment behaviour TOPICS IN THE ECONOMICS OF INVESTMENT BEHAVIOUR Overview Objectives of investment decisions Asset fixity and adjustment costs Contracts, investment, and information asymmetries Uncertainty and information Farm structure Investment and technical change Household characteristics of interest On-farm vs. off-farm investment Labour allocation Credit constraints and portfolio issues Other issues POLICY EFFECTS ON INVESTMENTS Decoupling and investment Other policies Policy expectations and uncertainty A CLASSIFICATION OF QUANTITATIVE METHODS DISCUSSION: ASSESSMENT OF THE LITERATURE ON FARM INVESTMENT BEHAVIOUR METHODOLOGY OVERVIEW SCENARIO DEFINITION AND CHARACTERIZATION THE MODEL Motivation of the chosen approach The theoretical model The empirical model objective function The empirical model constraints and feasibility set Output indicators Time horizon Model implementation procedure, calibration and validation CASE STUDIES: DESCRIPTION AND SAMPLE SELECTION COVERAGE AND SAMPLING RATIONALE DATA COLLECTION CASE STUDY AREAS CASE STUDIES AND SAMPLE DESCRIPTION RESULTS FARM PERSPECTIVES AND STRATEGIES INVESTMENT BEHAVIOUR CAP REFORM AND DECOUPLING SIMULATION OF SCENARIOS IMPACT /187

6 5.4.1 Economic effects of scenarios Social effects of scenarios Environmental effects of scenarios POLICY IMPLICATIONS DISCUSSION GENERAL FINDINGS METHODOLOGY FURTHER RESEARCH REFERENCES ANNEX I QUESTIONNAIRE ANNEX II DETAILED DESCRIPTIVES BY CASE STUDY ANNEX III VALIDATION PARAMETERS ANNEX IV DETAILS OF FARM BEHAVIOUR UNDER DIFFERENT SCENARIOS GENERAL REMARKS FRANCE PLAIN - ARABLE GERMANY MOUNTAIN - ARABLE GERMANY MOUNTAIN - LIVESTOCK GERMANY PLAIN - ARABLE GERMANY PLAIN - LIVESTOCK GREECE PLAIN - ARABLE HUNGARY PLAIN - ARABLE HUNGARY PLAIN - LIVESTOCK ITALY MOUNTAIN - ARABLE ITALY MOUNTAIN - LIVESTOCK ITALY PLAIN - ARABLE ITALY PLAIN - LIVESTOCK THE NETHERLANDS PLAIN - LIVESTOCK POLAND MOUNTAIN - LIVESTOCK POLAND PLAIN - ARABLE POLAND PLAIN - LIVESTOCK SPAIN PLAIN - TREES FIGURES Figure 1 Classification of the various income sources of a farm household 13 Figure 2 Classification of farm household investments 15 Figure 3 The investment decision process 18 Figure 4 An overview of the methodology 45 TABLES Table 1 Factors affecting investment behaviour Table 2 Case study areas Table 3 Implementation of the CAP and CAP reform in case study areas Table 4 Summary of case studies and farms surveyed (number of questionnaires) Table 5 Sample descriptives Table 6 Number of models /187

7 Table 7 Main objectives of farm households (number of answers per ranking position) Table 8 Main constraints to farm development (number of answers per ranking position) Table 9 Credit accessed by farms (% of the total number of farms in each system) Table 10 Production contracts in place (number of farms per number of production contracts per farm) Table 11 Expectations of prices and payments direction of change (%) Table 12 Expectations about prices and payments sizes of changes Table 13 Main intended investments Table 14 SFP payments received (euro/farm) Table 15 Stated use of SFP (% of money received) Table 16 - Stated effects of SFP (%) Table 17 Correlation between the use of SFP and selected explanatory variables* Table 18 Relationship between the stated effect of decoupling and selected explanatory variables Table 19 Change in farming income compared to Agenda 2000 (%, standard deviation in italics) Table 20 Change in household income compared to Agenda 2000 (%, standard deviation in italics) Table 21 - Change in used land compared to Agenda 2000 (%) Table 22 Changes in major crop/livestock across scenarios (% area/number change) Table 23 Change in net investment compared to Agenda 2000 (%, standard deviation in italics) Table 24 Prevailing direction of change by type of investment across scenarios Table 25 Changes in total labour use on-farm in different scenarios (average ; %, standard deviation in italics) Table 26 Change in nitrogen usage compared to Agenda 2000 (%, standard deviation in italics) Table 27 Changes in water usage compared to Agenda 2000 (%, standard deviation in italics) Table 28 A policy-related classification of farms/systems Table 29 Legal status of the farms in the sample (% of individual/family) Table 30 Average size of the farms in the sample (ha per farm) Table 31 Percentage of land rented on an average-sized farm (%) Table 32 Average age of the farm head in the sample (years) Table 33 Average labour availability in the sample (hours per year per household) Table 34 Average share of off-farm labour in the sample (%) Table 35 Validation parameters and the model chosen Table 36 Summary of farm case studies modelled - France Plain - Arable Table 37 - Summary of baseline (Agenda 2000) of farm case studies modelled - France - Plain - Arable Table 38 Impact of the scenarios on income from farming - France Plain - Arable Table 39 Impact of the scenarios on household income - France Plain - Arable Table 40 Impact of the scenarios on investment - France Plain - Arable Table 41 Impact of the scenarios on labour - France Plain - Arable Table 42 Impact of the scenarios on nitrogen use - France Plain - Arable Table 43 Impact of the scenarios on selected activities - France Plain - Arable Table 44 - Impact of the scenarios on selected investments - France Plain - Arable Table 45 Summary of farm case studies modelled - Germany Mountain - Arable Table 46 Summary of baseline (Agenda 2000) of farm case studies modelled - Germany - Mountain - Arable117 Table 47 Impact of the scenarios on income from farming - Germany Mountain - Arable Table 48 Impact of the scenarios on household income - Germany Mountain - Arable Table 49 Impact of the scenarios on investment- Germany Mountain Arable Table 50 Impact of the scenarios on labour - Germany Mountain Arable Table 51 Impact of the scenarios on nitrogen use - Germany Mountain - Arable Table 52 Impact of the scenarios on selected activities - Germany Mountain - Arable Table 53 Impact of the scenarios on selected investments - Germany Mountain - Arable Table 54 Summary of farm case studies modelled - Germany Mountain - Livestock Table 55- Summary of baseline (Agenda 2000) of farm case studies modelled - Germany - Mountain - Livestock Table 56 Impact of the scenarios on income from farming - Germany Mountain - Livestock Table 57 Impact of the scenarios on household income - Germany Mountain - Livestock Table 58 Impact of the scenarios on investment - Germany Mountain - Livestock Table 59 Impact of the scenarios on labour - Germany Mountain - Livestock /187

8 Table 60 Impact of the scenarios on nitrogen use - Germany Mountain - Livestock Table 61 Impact of the scenarios on selected activities - Germany Mountain Livestock Table 62 Impact of the scenarios on selected investments - Germany Mountain Livestock Table 63 Summary of farm case studies modelled - Germany Plain - Arable Table 64 - Summary of baseline (Agenda 2000) of farm case studies modelled - Germany - Plain - Arable Table 65 Impact of the scenarios on income from farming - Germany Plain Arable Table 66 Impact of the scenarios on household income - Germany Plain Arable Table 67 Impact of the scenarios on investment - Germany Plain - Arable Table 68 Impact of the scenarios on labour - Germany Plain - Arable Table 69 Impact of the scenarios on nitrogen use - Germany Plain - Arable Table 70 Impact of the scenarios on selected activities - Germany Plain - Arable Table 71 Impact of the scenarios on selected investment - Germany Plain - Arable Table 72 Summary of farm case studies modelled - Germany Plain - Livestock Table 73 - Summary of baseline (Agenda 2000) of farm case studies modelled - Germany - Plain - Livestock Table 74 Impact of the scenarios on income from farming - Germany Plain - Livestock Table 75 Impact of the scenarios on household income - Germany Plain - Livestock Table 76 Impact of the scenarios on investment - Germany Plain - Livestock Table 77 Impact of the scenarios on labour - Germany Plain - Livestock Table 78 Impact of the scenarios on nitrogen use - Germany Plain - Livestock Table 79 Impact of the scenarios on selected activities - Germany Plain - Livestock Table 80 Impact of the scenarios on selected investments - Germany Plain - Livestock Table 81 Summary of farm case studies modelled - Greece Plain - Arable Table 82 - Summary of baseline (Agenda 2000) of farm case studies modelled - Greece - Plain - Arable Table 83 Impact of the scenarios on income from farming - Greece Plain - Arable Table 84 Impact of the scenarios on household income - Greece Plain - Arable Table 85 Impact of the scenarios on investment - Greece Plain - Arable Table 86 Impact of the scenarios on labour - Greece Plain - Arable Table 87 Impact of the scenarios on nitrogen use - Greece Plain - Arable Table 88 Impact of the scenarios on water use - Greece Plain - Arable Table 89 Impact of the scenarios on selected activities - Greece Plain - Arable Table 90 Impact of the scenarios on selected investments - Greece Plain - Arable Table 91 Summary of farm case studies modelled - Hungary Plain - Arable Table 92 - Summary of baseline (Agenda 2000) of farm case studies modelled - Hungary - Plain - Arable Table 93 Impact of the scenarios on income from farming - Hungary Plain - Arable Table 94 Impact of the scenarios on household income - Hungary Plain - Arable Table 95 Impact of the scenarios on investment - Hungary Plain - Arable Table 96 Impact of the scenarios on labour - Hungary Plain - Arable Table 97 Impact of the scenarios on nitrogen use - Hungary Plain - Arable Table 98 Impact of the scenarios on selected activities - Hungary Plain - Arable Table 99 Impact of the scenarios on selected investment - Hungary Plain - Arable Table 100 Summary of farm case studies modelled - Hungary Plain - Livestock Table Summary of baseline (Agenda 2000) of farm case studies modelled - Hungary - Plain Livestock. 142 Table 102 Impact of the scenarios on income from farming - Hungary Plain - Livestock Table 103 Impact of the scenarios on household income - Hungary Plain - Livestock Table 104 Impact of the scenarios on investment - Hungary Plain - Livestock Table 105 Impact of the scenarios on labour - Hungary Plain - Livestock Table 106 Impact of the scenarios on nitrogen use - Hungary Plain - Livestock Table 107 Impact of the scenarios on selected activities - Hungary Plain - Livestock Table 108 Impact of the scenarios on selected investments - Hungary Plain - Livestock Table 109 Summary of farm case studies modelled - Italy Mountain - Arable Table Summary of baseline (Agenda 2000) of farm case studies modelled - Italy - Mountain - Arable Table 111 Impact of the scenarios on income from farming - Italy Mountain - Arable Table 112 Impact of the scenarios on household income - Italy Mountain - Arable Table 113 Impact of the scenarios on investment - Italy Mountain - Arable Table 114 Impact of the scenarios on labour - Italy Mountain - Arable /187

9 Table 115 Impact of the scenarios on nitrogen use - Italy Mountain - Arable Table 116 Impact of the scenarios on selected activities - Italy Mountain - Arable Table 117 Impact of the scenarios on selected investments - Italy Mountain - Arable Table 118 Summary of farm case studies modelled - Italy Mountain - Livestock Table Summary of baseline (Agenda 2000) of farm case studies modelled - Italy - Mountain - Livestock. 151 Table 120 Impact of the scenarios on income from farming - Italy Mountain - Livestock Table 121 Impact of the scenarios on household income - Italy Mountain - Livestock Table 122 Impact of the scenarios on investment - Italy Mountain - Livestock Table 123 Impact of the scenarios on labour - Italy Mountain - Livestock Table 124 Impact of the scenarios on nitrogen use - Italy Mountain - Livestock Table 125 Impact of the scenarios on selected activities - Italy Mountain Livestock Table 126 Impact of the scenarios on selected investments - Italy Mountain Livestock Table 127 Summary of farm case studies modelled - Italy Plain - Arable Table Summary of baseline (Agenda 2000) of farm case studies modelled - Italy Plain - Arable Table 129 Impact of the scenarios on income from farming - Italy Plain - Arable Table 130 Impact of the scenarios on household income - Italy Plain - Arable Table 131 Impact of the scenarios on investment - Italy Plain - Arable Table 132 Impact of the scenarios on labour - Italy Plain - Arable Table 133 Impact of the scenarios on nitrogen use - Italy Plain - Arable Table 134 Impact of the scenarios on water use - Italy Plain - Arable Table 135 Impact of the scenarios on selected activities - Italy Plain - Arable Table 136 Impact of the scenarios on selected investments - Italy Plain - Arable Table 137 Summary of farm case studies modelled - Italy Plain - Livestock Table Summary of baseline (Agenda 2000) of farm case studies modelled - Italy - Plain - Livestock Table 139 Impact of the scenarios on income from farming - Italy Plain - Livestock Table 140 Impact of the scenarios on household income - Italy Plain - Livestock Table 141 Impact of the scenarios on investment - Italy Plain - Livestock Table 142 Impact of the scenarios on labour - Italy Plain - Livestock Table 143 Impact of the scenarios on nitrogen use - Italy Plain - Livestock Table 144 Impact of the scenarios on water use - Italy Plain - Livestock Table 145 Impact of the scenarios on selected activities - Italy Plain - Livestock Table 146 Impact of the scenarios on selected investment - Italy Plain - Livestock Table 147 Summary of farm case studies modelled - The Netherlands Plain - Livestock Table Summary of baseline (Agenda 2000) of farm case studies modelled - The Netherlands Plain - Livestock Table 149 Impact of the scenarios on income from farming - The Netherlands Plain - Livestock Table 150 Impact of the scenarios on household income - The Netherlands Plain - Livestock Table 151 Impact of the scenarios on investment - The Netherlands Plain - Livestock Table 152 Impact of the scenarios on labour - The Netherlands Plain - Livestock Table 153 Impact of the scenarios on nitrogen use - The Netherlands Plain - Livestock Table 154 Impact of the scenarios on selected activities The Netherlands Plain - Livestock Table 155 Impact of the scenarios on selected investment The Netherlands Plain - Livestock Table 156 Summary of farm case studies modelled - Poland Mountain - Livestock Table Summary of baseline (Agenda 2000) of farm case studies modelled - Poland Mountain - Livestock Table 158 Impact of the scenarios on income from farming - Poland Mountain - Livestock Table 159 Impact of the scenarios on household income - Poland Mountain - Livestock Table 160 Impact of the scenarios on investment - Poland Mountain - Livestock Table 161 Impact of the scenarios on labour - Poland Mountain - Livestock Table 162 Impact of the scenarios on nitrogen use - Poland Mountain - Livestock Table 163 Impact of the scenarios on selected activities Poland Mountain - Livestock Table 164 Impact of the scenarios on selected investments Poland Mountain - Livestock Table 165 Summary of farm case studies modelled - Poland Plain - Arable Table Summary of baseline (Agenda 2000) of farm case studies modelled - Poland Plain - Arable Table 167 Impact of the scenarios on income from farming - Poland Plain - Arable /187

10 Table 168 Impact of the scenarios on household income - Poland Plain - Arable Table 169 Impact of the scenarios on investment - Poland Plain - Arable Table 170 Impact of the scenarios on labour - Poland Plain - Arable Table 171 Impact of the scenarios on nitrogen use - Poland Plain - Arable Table 172 Impact of the scenarios on selected activities Poland Plain - Arable Table 173 Impact of the scenarios on selected investments Poland Plain - Arable Table 174 Summary of farm case studies modelled - Poland Plain - Livestock Table Summary of baseline (Agenda 2000) of farm case studies modelled - Poland Plain - Livestock Table 176 Impact of the scenarios on income from farming - Poland Plain - Livestock Table 177 Impact of the scenarios on household income - Poland Plain - Livestock Table 178 Impact of the scenarios on investment - Poland Plain - Livestock Table 179 Impact of the scenarios on labour - Poland Plain - Livestock Table 180 Impact of the scenarios on nitrogen use - Poland Plain - Livestock Table 181 Impact of the scenarios on selected activities Poland Plain - Livestock Table 182 Impact of the scenarios on selected investments Poland Plain - Livestock Table 183 Summary of farm case studies modelled - Spain Plain - Trees Table Summary of baseline (Agenda 2000) of farm case studies modelled - Spain Plain - Trees Table 185 Impact of the scenarios on income from farming -Spain Plain - Trees Table 186 Impact of the scenarios on household income - Spain Plain - Trees Table 187 Impact of the scenarios on investment - Spain Plain - Trees Table 188 Impact of the scenarios on labour - Spain Plain - Trees Table 189 Impact of the scenarios on nitrogen use - Spain Plain - Trees Table 190 Impact of the scenarios on water use - Spain Plain - Trees Table 191 Impact of the scenarios on selected activities Spain Plain Trees Table 192 Impact of the scenarios on selected investments Spain Plain Trees /187

11 Executive summary The objective of this study is to carry out an analysis of investment behaviour among farming systems of selected EU regions, and to assess the impact of the 2003 CAP reform on producers' investment behaviour, and on their sustainability. It includes a review of the literature, a description of the methodology, the results of the empirical analysis and conclusions. The review of the literature on farm investment behaviour focuses on: a) the determinants of investment; b) the effects of policy on investment; c) the classification of quantitative tools for analysing farm investment behaviour; and d) the choice of methodology for the empirical analysis. Contributions on this issue have been relatively less numerous than for other fields of agricultural economics research, despite its evident importance for the representation of farm behaviour. The analysis of investment at firm level became an important issue in the general economic literature during the 1950s and 1960s, and in the agricultural economic literature during the 1990s. Early approaches, based on the neoclassical theory of the firm, were subsequently discussed and improved. The investment literature during the last two decades has focused on a number of investment-related topics such as asset fixity and adjustment costs, uncertainty and information, risk and other objectives, household characteristics, on-farm vs off-farm investment, investment and labour allocation, investment and farm structure, investment and technical change, investment and contracts and investment and credit constraints. Despite the variety of themes and approaches, the present understanding of farm investment behaviour is considered to be, to a large extent, unsatisfactory. The main research gaps include the need for: a) more adequate instruments for ex-ante analysis; b) model adaptation to incorporate empirical information about farm preferences and expectations; c) closer attention to the connection between investment, technical change and learning; and d) a more empirically relevant treatment of the decision maker s (farm household s, firm s) objectives. The amount of literature and the state of the art appear particularly unsatisfactory as far as policy analysis is concerned, and particularly for ex-ante policy evaluation. Although a few recent studies tackled this issue, focusing to a large extent on decoupling, the analysis of policy impact on investment behaviour still appears to be a particularly challenging task. This may be attributed to the fact that policy scenarios interact with all other (numerous) determinants, particularly whole household/firm management, risk perception, asset liquidity and output prices. The methodology adopted in this study is based on the integration of empirical primary information - collected through a survey of about 250 farm households - and modelling individual farms surveyed to simulate reactions to policy and price scenarios. The core model is a multicriteria dynamic programming model of farm households. The model is calibrated on primary data from a survey of single farms through a questionnaire. Case studies were developed for France, Germany, Greece, Hungary, Italy, Poland, Spain and The Netherlands. In the majority of cases, farmers stated their investment decisions were indifferent to decoupling. Where any change occurred, the impact of decoupling was highly differentiated. Differences in reaction are better explained by different individual household/farm characteristics, 9/187

12 rather than by association with a specific agricultural system. In the more efficient and expansionoriented farms, decoupling is perceived as an opportunity for farm investment, while in small, poorer performing farms the introduction of the Single Farm Payment (SFP) is viewed rather as an opportunity for extensification, i.e. shifting to less input intensive production techniques. Scenario analysis showed that CAP as a whole is very important for the sustainability of farming systems. However, prices (in the range simulated) appeared to be more important than policy in determining farmers' choices. In turn, adaptation of farm activities was more important than investment as a reaction to both policy and prices. Decoupled CAP appeared from the interviews to be very much a policy with very different impacts depending on the context in which it is cast. From the interviews and modelling it appeared that decoupling tend to reinforce the strategy already adopted by farm-households, either in terms of expansion or abandonment. This result hints at the fact that a number of wider issues should be addressed in order to understand farm household investment behaviour with respect to policies. In particular, demographic trends, job and land use opportunities and technological options seem to be major drivers of a farm household s reaction to CAP. The results confirm the need for better empirical information in this field, contextualized within the present stage of EU agriculture and policy. They also highlight the importance of combining information about intentional behaviour with modelling outcomes. The results show that the methodology is able to fit project expectations, though further refinement is required. Future studies will be needed focusing on ex-ante policy analysis and design, taking into account emerging technologies and market scenarios, as well as future farming/rural agents (households, firms with legal attributes) as the most appropriate decision-making units. Opportunities and strategies for correlating the results to upper scale modelling should also be considered. 10/187

13 1 Background and objectives The background to and motivation for this study are provided by the 2003 CAP reform that de-links farm subsidies from production and concentrates the former in a Single Farm Payment (SFP) supporting producers income (Regulation EC 1782/2003). The SFP represents a large fraction of EU expenditure on agriculture and rural development (in 2005, approximately 62%). Ex-ante studies highlight the relevant expected impact of the 2003 reform on land allocation to different crops, with particular emphasis on reallocation towards more efficient ways of farming (European Commission, 2003). This should also contribute to the competitiveness of the system. In the medium to long term, however, the results of the reform will be largely determined by changes in farm investment behaviour, particularly with respect to more efficient technologies and emerging production processes. Agricultural policy will certainly have a role in determining the propensity to invest. However, recent studies on the impact of the 2003 reform, as well as on farming structures in new member states, emphasise the role of non-policy and non-farm variables associated with farm households (e.g. demography, ageing) and the surrounding economic environment (e.g. shadow wages in farm households, return on capital, quality of life in rural areas) in determining farmers behaviour (European Commission, 2003; Baum et al., 2004). This is particularly true for investment and decommissioning. The objectives of this study are: to perform an ex-ante analysis of investment behaviour among farming systems clustered by the use of conventional and emerging production practices; to assess the impact of the 2003 CAP reform with special focus on the SFP on producers investment behaviour using scenario analysis (8 12 years horizon); to evaluate the consequences of investment behaviour with respect to the sustainability of farming systems, and to make appropriate policy recommendations. This report focuses on five main items: an analysis of the literature on farm investment behaviour; a detailed description of the methodology adopted for this study, including scenario analysis and a description of the case studies performed; an analysis of conventional and emerging farming systems in terms of current and future investment activities with special focus on their determinants, based on empirical evidence from the case studies; an analysis of the impact of alternative policy scenarios on investment behaviour, and of the latter on the sustainability of farming systems; policy recommendations developed based on the results of the study. The document is divided into seven sections including: 1) background and objectives; 2) a review of the literature on farm investment behaviour; 3) an illustration of the methodology used; 4) a description of the case studies to which the methodology is applied; 5) discussion of the results, divided into a descriptive analysis of the outcome of the survey and the results of the modelling exercise; 6) policy recommendations; and 7) discussion of possible future development of the research. 11/187

14 2 Literature review A reference framework: definitions, representations and determinants Definition and classification of farm investments Investment can be generally defined as the purchase of new capital goods by firms (Begg et al., 1991, p. 357). Hence, investment represents a positive change of capital stock over time. This definition requires several qualifications. First, capital changes can be either positive or negative, i.e., investments or disinvestments. Hence, an investment decision may be to increase capital stock (investment) or reduce capital stock (disinvestment); however, these operations are not symmetrical in terms of costs/revenues, and their asymmetric treatment affects a large branch of the literature. Investment and disinvestment add to depreciation in determining the net change in capital stock over time. When investment is limited to the replacement rate of existing capital, no net change in stock occurs. Replacement may also take a more qualitative meaning: it helps in distinguishing investments that bring to the farm new (types of) capital goods (including new technologies) from those that simply substitute old capital goods with others of the same kind. When capital stock is allowed to decline to zero or to grow from zero to some positive amount of capital (zero capital stock implies zero production), investment behaviour may also include entry and exit from markets (Chavas, 1994). Investment is not limited to purchased capital; indeed, farms can invest by producing capital goods with the aim of generating output in the future (e.g. buildings, drainage systems, orchards). In using the term investment behaviour, we mean the behaviour of farmers toward a number of decisions regarding investments, including: which investments to undertake; when (timing, speed of growth over time); with what intensity (e.g. how many farms, what percentage of farms); where (different regions/countries); how investments are funded. The classification of investments is concerned with both the sources and destination of the capital allocated to invest. The connection between sources and destination reflects the fact that investment possibilities are constrained by cash flow, i.e., money available at any moment in time. Hence, there is a close connection between the sources of liquid assets and the possibility of investment. In the context of a single farm (household), the liquid assets available for investment depend on income (corrected for depreciation and other figurative costs), consumption choices, and access to credit. For a limited company, consumption can be replaced by dividends. In terms of a farm household, the sources of income are various (Figure 1). 2 A previous version of this chapter is included in Di Pasquale et al., 2006a; 2006b. 12/187

15 Figure 1 Classification of the various income sources of a farm household + Market receipts + Budgetary payments + Other receipts = Gross receipts - Cash expenses = Net operating income - = Farm income Depreciation + = Total farm household income + Gross wages and salaries + Property income + Social transfers + Other income = Off-farm income - = Disposable farm household income Taxes and mandatory contributions Source: OECD, 2002 Disposable income is obtained as the difference between total farm household income and taxes. Within total farm household income, a major distinction can be drawn between farm and offfarm income. Off-farm income includes gross wages and salaries, property income from investments, and social transfers from pension, health, and unemployment schemes and other social safety nets. Farm income is defined as the difference between gross receipts and the sum of cash expenses and depreciation. Gross receipts include both those earned on the market and those provided by public payments. In most EU OECD countries, farm income accounts for between 40 and 75% of total rural household income. In the US, farm income is only about 10% of total rural household income, and the figure for Canada is approximately 20%. This share is steadily decreasing over time in most countries (OECD, 2002). In addition, in many areas rural households are gradually becoming disconnected from rural business in a general sense, beyond farming itself (Roberts, 2005). In 75% of OECD countries, wages and salaries are the main source of off-farm income (OECD, 2002). The available cash flow is connected to income via two important qualifications: 1. access to credit should be added as an additional determinant of the cash that is available for investment; it may materialise in the form of the additional cash that is 13/187

16 available when new loans prevail or in a reduction in cash flow relative to income when net repayments prevail; 2. depreciation costs subtracted from farm receipts do not actually correspond to a negative monetary flow and should be added as available cash for investment at a given moment in time. Available cash may head for different destinations. A macro classification, suitable in classifying disposable household income, can be established in terms of consumption and savings. By consumption we mean the acquisition of non-durable, non-production goods and expenditure related to leisure. Savings may be addressed to different forms of investment. Much of the literature treats investment as undifferentiated change in the monetary value of capital stock. While this is useful in terms of analytical tractability, this representation is usually unsatisfactory. Common distinctions employed in the literature include those between on-farm and off-farm investments (e.g. Andersson et al., 2005), between land and capital, and between land, buildings, and machinery (e.g. Elhorst, 1993); however, to the best of our knowledge a comprehensive and universally adopted classification of investments has yet to be established in the literature. From the perspective of a farm household, a classification of investments can be structured in the manner presented in Figure 2. 14/187

17 Figure 2 Classification of farm household investments Physical Land On-farm Buildings Machinery Productive investments Non-Physical: rights (quotas etc.) education and training Trees Livestock Landscape and biological Household investments Other Chain-related Off-farm Durable goods aimed at providing services to the household: house transport (e.g., car) non-physical capital (e.g., education) other Other industries Real estate Financial markets Others Within farm/rural household investment, a major distinction is made between productive investments and durable goods that are used to produce services for the household (e.g. home improvements and car). Productive investments can be classified into on-farm and off-farm investments 3. On-farm investment may entail both physical and non-physical investment. The former is traditionally the more significant; however, a growing amount of resources is devoted to the latter. Non-physical investments include the acquisition of knowledge such as the training of farmers and the acquisition of rights, such as marks, contracts, quotas, and payment entitlements. We include in this group only that training and knowledge acquisition that is aimed at the productive activity of the farm; however, analogous knowledge-related investment could be related to off-farm activities and to general education. The same may apply to non-productive knowledge-based investments aimed at improving the household s quality of life (e.g. hobby-related courses). Knowledge acquisition is 3 Most of the literature distinguishes on-farm and off-farm investment, with the latter including both productive and non-productive investments (e.g. Goodwin and Mishra, 2005). Other papers use the terms productive and nonproductive (e.g. Petrick, 2004), although they are mostly used as synonyms of on-farm and off-farm, respectively. In our case, we exploit the different meaning of the two distinctions, attempting to combine them in a unified framework. 15/187

18 considered a growing issue, as it establish a connection with human, social and cultural capital. When moving toward such issues, however, boundaries between production and consumption investment appear to weaken, as improved human and cultural capital are considered horizontal resources that may add to the ability of the household to perform at a higher level in any activity. Physical on-farm investment is usually further classified into the traditional physical categories of capital: land, buildings, machinery, trees (e.g. fruit trees), and livestock. This is usually the most common classification referred to in existing accounting systems. For example, in the EAA (Economic Accounts for Agriculture), a distinction is made between five types of elements of gross fixed capital formation (EUROSTAT, 1997): plantations yielding repeated products; livestock; tangible and intangible fixed assets: - machinery and other capital goods; - transport equipment; - farm buildings (non-residential); - other structures with the exception of land improvement (other buildings and structures, etc.); - other (computer software, etc.); land improvement; costs associated with the transfer of ownership of non-produced assets such as land and production rights. According to EU regulation 2377/77 and modifications, the EU Farm Accounting Data Network (FADN) collects information (values of stock and stock variations) concerned with: - land; - machinery; - buildings and land improvements; - livestock (in terms of animal and species); - permanent crops; - production quota and other rights. FADN also records changes in farm debts and their connections to certain investments. Land is largely a peculiar type of asset that justifies a dedicated literature, mostly devoted to the explanation of land uses and land values. It also usually represents the main limiting factor to size adjustment and, as such, interacts with the willingness to carry out other investments. Buildings are a more or less important feature of farms, depending on the nature of farm specialisation. In building-intensive systems (e.g. livestock farming), buildings may be highly demanding in terms of investment. In many cases, buildings are a key strategic investment in terms of farm diversification (e.g. for agri-tourism or product processing). Machinery is an increasingly important component of farm assets. Within machinery, a growing group of items is that related to advanced technology such as improved information systems (e.g. precision farming) and machinery-control systems. In most cases, these aspects of innovation are related to machinery management and plant control. Landscape and biological capital may assume a relevant and autonomous importance either in view of the conservation of productivity (e.g. soil fertility) or the production of conservationrelated services. In many cases, the latter may be directly connected to EU regulations that provide rural-development payments. 16/187

19 Other dimensions and classifications of physical capital that are potentially transversal to the classification presented above may also be relevant. First, as noted above, a major distinction is made between replacements and increases in capital stocks. In qualitative terms, it is important to distinguish replacements from innovation investments, i.e., capital goods that bring new technologies to the farm. Second, investments can be classified in terms of their functional role regarding relevant policy categories. In particular, it is possible to broadly define conventional vs. alternative investments as those connected respectively with the normal means of farming and those connected with alternative technologies (e.g. organic farming), alternative economic activities (e.g. the production of biomass energy), or agriculture-related diversification (e.g. tourism). Alternative investments may be connected with multifunctional or rural-development concepts. This dimension intersects the classification provided above. For example, machinery as a physical category may include both conventional machinery and machinery used for alternative activities (e.g. hedgerow maintenance). The multipurpose nature of most investments such as machinery, land, and knowledge is a further horizontal issue and is a major factor that characterises farm investments and the interaction of investments with off-farm activities, including the contribution of farming households to rural development. On the investment side, this poses the problem of cost allocation among different activities. From a more general perspective, this supports the degree of importance attributed to the complementary nature of and linkages between different activities, including market and nonmarket goods, that form the basis of the literature on multifunctionality in agriculture (OECD, 2001b). For the purposes of our classification, however, this makes it difficult to allocate investments to different policy-related categories such as traditional farming and conservation activities. In addition, on-farm investment may be truly oriented to farm-production programmes or oriented toward producing services for other farms (e.g. the renting of machinery), which could make a difference in terms of the future development of the farm. Finally, a distinction can be drawn between final and intermediate investments, where intermediate investments are those intended to accumulate sufficient resources to finance larger investments. Off-farm investment may be represented by a variety of assets (see Bowles and Bosworth, 2001 for a review of the main categories of assets used in US statistics). In certain contexts, a major role is played by investment located in other steps of the same production chain as that of the farm itself. These investments can be defined as off-farm but farming-related. Examples include investments in marketing structures, such as investments made via cooperatives. In this case, investment is carried out by a legal entity other than the farm, but it is allowed by virtue of the destination of part of the farm profits (Hamlin et al., 1998; Bogetoft and Olesen, 2004). The above example demonstrates how the demarcation between on-farm and off-farm is commonly not clear-cut: relevant grey areas commonly emerge. A second example is given by diversification activities that are sometimes difficult to distinguish from other industries. For example, food processing may be configured as agriculture, diversification or other industries depending on the degree of connection with the farming activity. As an analogy, we might consider that investment in machinery by farms that specialise in providing mechanical services to other farms may largely develop via a strong relation with the farm itself. In most cases, off-farm investment can be identified with investment in government securities, stock markets, etc.; however, investment in physical assets may also be of some importance (e.g. 17/187

20 buildings). These investments largely have the aim of maintaining capital values and providing additional low-risk income; however, in larger farms they may also be interpreted as part of the overall financial strategy of the farm. Finally, investment in other industries is also possible. In some cases, this may be connected to the shift of the family farm to other activity sectors. Non-productive investment may be identified as durable consumption-oriented goods. While not strictly connected to productivity, such investments may play a major role in interpreting the quality of life and willingness to maintain settlements in rural areas. Consequently, non-productive investments may be an important indicator in relation to rural-development perspectives. To distinguish such investments from rent-seeking investments that can be analogous in nature, they can be identified as those investments that directly aim to provide a flow of services to the farm household Decision-making process An analysis of investment determinants and modelling requires a closer view of the investment decision process. This may be represented as a cyclical process, where business-level decisions interact with the external business environment (Figure 3). Figure 3 The investment decision process Business environment Demand conditions Objectives Demand information Investment criteria Government policy Policy information Decision data Decision Investment expenditure Supply information Forecasting Forecasting Supply conditions Business environment Source: modified from Hay and Morris (1991), p /187

21 The business environment determines product demand and factor supply conditions (quantity and prices) that affect forecasting ability and determine demand and supply information. Specific investment-related parameters such as those connected to capital and that affect the choice of the discount rate (e.g. interest rates or rates of return) may be an explicit part of the forecast process and are included among the capital supply conditions. Figure 3 indicates that government policies have an effect on forecasts by affecting demand and supply conditions; however, direct forecasts of policy changes are also relevant, as it is their connection to demand and supply conditions. Information may be affected by a higher or lower degree of uncertainty and subjectivity. Together, all the above factors contribute to decision data. On the other side, firms express their objectives and translate them into selected investment criteria. Applying investment criteria to decision data yields decisions, which in turn translate into investment expenditure. This modifies the business environment and the cycle resumes. This framework may form the basis of several considerations related to investments, all of which are strictly connected to the identification of investment determinants. First, it shows the interaction between firm decisions and other firms (and consumers) decisions via the business environment. This interaction happens via demand and supply conditions that affect expectations and decisions concerning farm investment. In turn, investment choices affect the business environment. This interaction between the micro and the macro level may take different forms depending on the chain structure. For example, farms may be vertically integrated in the foodprocessing stage and may cooperate in buying inputs or selling outputs through horizontal integration. Second, this information must match the firm s objectives and related criteria for investment selection. These objectives may be various and are related to the nature and structure of the decision-making entity. Third, evidence (see below) supports the idea that the investment decision is a process within which information processing is a major issue. Information is not an objective issue; rather, information sources and format are selected on the basis of investment criteria. For example, expectations and preferences for different kinds of investment may already concentrate the collection of information on the features of a subset of all available investment options. While Figure 3 focuses on the interaction between prices, policies, and decision makers in determining investment behaviour, this is not the whole story. In particular, technical change and innovation play a major role as a driver of investment. Agricultural technology has witnessed impressive changes over the last 50 years, with the substitution of labour by and capital. Finally, the figure is oriented to represent firms behaviour; however, it could be further extended to consider the rural household as the decision-making unit. Narrowly speaking, taking the household as a reference would require the revision and enlargement of the objectives that are relevant to decision making. In a much broader view, households would be required to connect to the framework with the widest economic, social, and demographic context within which opinions, values, and human resources are formed Basic economic representations of firm-level investment decisions A substantial literature has addressed investment behaviour at the sector-level. Microeconomic theory and the theory of firms have paid relatively little attention to investment behaviour compared to other issues such as price formation and other fields of economic theory 19/187

22 such as macroeconomics (Hay and Morris, 1991). For a long time, investment was to a large extent dealt with almost exclusively in macroeconomics. A neoclassical theory of firm investment behaviour was developed during the 1960s, in particular by Jorgenson (1963, 1967). The literature on the issue grew exponentially during the 1960s and 1970s. Agricultural economics literature on investments saw an increasing number of contributions, particularly from the mid-1990s (e.g. Chavas, 1994; Abel and Eberly, 1994; Cf. infra section 2.2). As stated above, there are at least two interlinked dimensions of capital: capital stock and variation in capital stock over time. These two dimensions are connected as follows: t ( ) K t I t K 1 δ + (2.1) = 1 where: δ = depreciation rate; I = investment; K = capital stock; t and t-1 refer to time t and time t-1, respectively. The depreciation rate can be interpreted in different ways depending on the micro or macro context and the kind of information available. In a macro context, or when using accounting information, the depreciation rate can be interpreted as the (legislation-driven) depreciation rate used for accounting purposes. This is different from the actual depreciation rate determined by the lifetime of capital goods in the production process. The latter is the most commonly used interpretation of δ in microanalyses conducted from a strictly technical perspective. In a static framework, the problem of investment behaviour can be identified as that to define the optimal capital stock. Optimal capital stock depends on the (instantaneous) production function of capital stock, the prices of products, and the cost of capital. Simple optimality conditions require the following (Hay and Morris, 1991, following Jorgenson, 1963, 1967): cd K = pdq, or dk = dq p c (2.2) where: c = cost of capital/investment; dk = change in capital stock; p = product price; dq = change in production due to a change in capital stock. This formulation states that optimal capital stock occurs when the marginal productivity of capital is equal to the ratio between prices of products and cost of capital 4. It is relatively common that actual capital stock differs from optimal stock. Investment may be seen as an attempt by the 4 For a discussion of the variables included in the cost of capital, see Hay and Morris (1991). Note that changes in capital and production represent movement from a couple of points to another on the production function of capital rather than in a dynamic sense (changes represented in the formula are not changes over time). 20/187

23 firm to fill the gap between actual and optimal capital stock by moving the capital stock toward the optimal size. A large branch of the literature on investment is devoted to explaining the way in which investment decisions are connected to the perceived optimal stock and how this adaptation occurs over time (see Section 2.2). Investment is generally defined above as an increase in capital stock. When investment occurs, money is usually used to buy capital goods that serve the production process along their lifetime. This is related to the idea of investment as an anticipated cost aimed at gaining returns in the future. This feature is the basis for the micro-foundation to this topic, expressing a firm investment decision in terms of the discounted cash flow: the net present value model. The discounted-cash-flow approach enables calculation of the net present value, the internal rate of return, and other indicators. The net present value represents the value of the investment for the firm; it is obtained as the sum of discounted cash flows over the period within which the investment is expected to produce its effects: n At NPV = (2.3) t q t = 0 where: NPV = Net Present Value; n = lifetime of investment; t = time t= 0, 1,, n; A t = net cash flow at time t, calculated as the difference between revenues and cost for each time period; t q = discount factor, equal to 1+r, with r being the discount rate. Investment profitability depends on the magnitude of discounted returns, i.e., it decreases with initial asset price and capital cost and increases with net cash flows. The profitability of investment is higher with lower discount rates. The internal rate of return is the discount rate that produces a zero NPV. It represents the return on the capital cost generated by the investment. The internal rate of return considers a different perspective than that of NPV, as profitability is assessed based on a comparison of the value of the parameter and the weighted average cost of capital. The two criteria are not equivalent. Taking the farm/household perspective leads to an expansion of the decision problem over a number of possible investments that can be combined with different production activities and implemented at different points in time. This decision problem can be represented as a dynamic optimisation problem over time. To generalise the representation, t is expanded to infinity and the variables in the decision-making function can be treated as stochastic, mediated by expectations: max t= 0 1 Et Ft ( K t, c, ) (2.4) t q subject to (2.1). 21/187

24 where: E = expectation operator based on the subjective probability distribution of future outcomes; F ( K,c, ) t t t = flow of receipts of the firm, expressed as a function of capital stock, cost, and, if relevant, other variables. The basic determinants of investment behaviour are included in this representation, i.e., capital productivity and costs, the discounting factor, and the expectation operator. In principle, any part of the receipt function (prices, technology) and the discount rate can be considered to be stochastic. The result of the optimisation is an optimal path of investment over time. This formulation can be further qualified on at least two points (see Section 2.2). First, the content of the receipt function is not defined and can be specified in different ways depending on the approach taken, particularly the approach taken for the investment component. Second, the maximisation assumes a profitmaximising behaviour and should be substituted by some utility function when objectives other than profit are considered. This possible extension may involve changes in the content of the output flow F ( K,c, ) t t, where monetary receipts are no longer sufficient to support an evaluation of the investment outcome and additional indicators are necessary Determinants of farm investment behaviour This section provides a basic summary of findings from the literature in terms of the ex-post empirical importance of the different determinants of farm investment behaviour. Explanations, theories, and mechanisms are discussed in the following sections. The issue of determinants of farm investment behaviour is one of the most complex in the literature. Figure 1 and Section are sufficient in illustrating the vast number of factors that may affect farm decision. An additional element of complexity is that factors that affect capital stock may differ significantly from those that affect changes in such stock (investment), despite the fact that the two variables are connected (Hay and Morris, 1991). For example, changes in capital stock may be affected by the initial stock itself at a given point in time, as well as temporary credit constraints, while capital stock may depend on long-term trends in determinants. A number of studies have been dedicated to the empirical ex-post analysis of factors that affect investment behaviour. The most common approach is to estimate the effects of explanatory variables on the decision taken by farmers with the aid of econometric techniques (e.g. Elhorst, 1993). Table 1 summarizes the main findings of selected (and non-exhaustive) studies in this field, in terms of both explanatory and dependent variables. 22/187

25 Table 1 Factors affecting investment behaviour Thijssen, 1996 Thijssen, 1996 Andersson et al., 2005 Gardebroeck and Oude Lansik, 2004 Gardebroeck and Oude Lansik, 2004 Elhorst, 1993 Elhorst, 1993 Elhorst, 1993 Ahituv and Kimhi, 2002 Gardebroeck, 2004 Gardebroeck, 2004 Serra et al., 2004 Dependent variable Factor demand Factor demand Share of farm assest on total family assets Investment threshold Investment threshold Amount of investment Amount of investment Amount of investment Capital stock Adjustment cost Adjustment cost Share of non farm assets Static expectations Rational expectations Variant investment characteristics x x x x x farm characteristics location x x x labour labour off farm/on farm Buildings land percent of farm owned + owned land rented land value of buildings /+ value of machinery specialisation x x x debt/asset ratio - + +/- farm profitability + + +/- yields +/- product markets price of output factor markets capital cost - + interest rate - - land price - rental price buildings - rental price machinery - variable input price - - policy government payments - others technical change + + household characteristics and farmer's attitudes age /- +/- non farm income - household wages + household rents & dividends - successor - education + + household worth +/- Legend: + = higher values encourage investment; - = lower values encourage investment; x = relevant but non-directional. Only significant variables are shown. First, it is important to note the great variety of dependent variables, ranging from capital composition to proper investment and composition of investment. These dependent variables may be basically connected to the different components of Equation (2.1) (capital stock or investment at different points in time), possibly with some qualification about the type of capital good (e.g. machinery, buildings). Machinery Land Buildings Machinery x Buildings Machinery 23/187

26 The determinants found in the literature relate more directly to Equation 2.4. They can be qualified as technical (investment characteristics, farm characteristics, technical change) or economic (product markets, factor markets, policy) factors that affect the dynamic outcome of investment. In addition, an important set of variables concerns household characteristics and farmers attitudes 5. These variables may be interpreted either as affecting resource availability (labour) or, most importantly, determining the subjective evaluation of the outcomes of Equation 2.4 in terms of expected flows of utility derived from the effects of investments. Investment characteristics can be interpreted in a variety of forms (see Section 2.1), but the literature usually only distinguishes investments according to their technical nature (land, buildings, and machinery) or according to their on-farm vs. off-farm nature. These distinctions are always relevant when considered (i.e., differ in terms of reaction to explanatory variables), although this is commonly favoured by the design of the survey (e.g. different functions are estimated for different types of capital goods). Farm characteristics may include size, location (relating to issues such as soil and climate), type of farming or farm specialisation (prevailing farming activities that characterize different groups of farms), labour availability, and existing capital stock. The literature on investment also points to issues related to the financial characteristics of farms, such as debt/asset ratio. All of these variables appear to be generally significant, although commonly with contradictory signs. Location may affect investment in a variety of ways, such as indirect effects on other determinants (e.g. yields, specialisation). Labour and land confirm their prevailing character of complementarity with capital, as they are positively correlated with investment. In contrast, land appears to be negatively correlated with off-farm investment. The generally widespread availability of some capital goods tends to encourage further investment in other capital goods while discouraging investment in the widely available good (e.g. farms with high land availability invest in buildings and machinery, but not in land). Specialisation, debt asset ratio, and yields may push investment in different directions. As expected, profitability generally tends to encourage investment. Product market, represented by price levels, has a uniform positive effect on investment, as expected. Factor markets may include capital, labour, and land markets. Relevant variables are the price levels of factors and availability. For factors that may be either supplied by the household or from outside sources (e.g. capital, labour), different opportunity costs are also relevant. Capital markets are also a key issue in the literature, including cost of capital services, interest rates, equity yields, depreciation, replacement costs of capital. The literature mainly focuses on capital costs and interest rates, land prices, rental prices for buildings and machinery, and prices of variable inputs. In this case, the signs are all consistently negative, although with a number of exceptions that can to some extent be attributed to unsatisfactory features of the model (Thijssen, 1996). Policies affect product and factor markets via measures that are more strongly (e.g. price support) or less strongly (e.g. single farm payment) coupled. They may also provide direct support 5 Attitudes represent patterns of consistent behaviour or preferences expressed by individuals. More formally, they are the results of the degree of trust or subjective probability concerning an event multiplied by the probability that such event happens (Lynne et al., 1988). 24/187

27 to investment (e.g. some measures of the 'second pillar' of the current CAP). Finally, they affect saving/consumption decisions through general taxation. The literature reported in Table 1 does not attribute major importance to policy variables; however, government farm payments are found to be a relevant determinant in terms of discouraging off-farm investment. Among other issues, technology plays a major role, with higher rates of technical change having a positive effect on investment; however, most previous studies do not consider this issue because it requires extended time series that are generally unavailable. Household characteristics may include a number of components: gender, age, education, presence of successor, and household wealth. These variables show consistent behaviour across different studies. Age and education appear to be the most relevant variables in this group. The different signs taken by age may reflect non-linearities in the age investment relationship; however, the literature generally confirms that older farmers tend to invest less. Increasing levels of education appear to encourage greater investment, while off-farm income (labour) discourages investment. Farmers attitudes, mainly in terms of risk aversion, attitude towards savings and investment, and attitude towards specific technologies, are considered to be important in some of the literature on investment behaviour, but do not appear in the majority of literature that makes expost enquiries into the determinants of investments. 2.2 Topics in the economics of investment behaviour Overview Economic literature dealt with the issue of investment behaviour under different perspectives, with a number of cross-cutting issues. For this reason, it appears to be particularly difficult to provide a consistent summary of the different perspectives. In this section, some of the main branches of the literature related to investments are examined and classified under the following topics: 1. Multiple objectives of investment decisions (Section 2.2.2) 2. Farm perspectives (technology and information): o Asset fixity and adjustment costs (2.2.3) o Asset specificity, transaction costs (2.2.4) o Uncertainty and information (2.2.5) o Farm structure (2.2.6) o Technical change (2.2.7) 3. Household perspectives o Household characteristics of interest (2.2.8) o On-farm vs. off-farm investment (2.2.9) o Labour allocation (2.2.10) 4. Finance perspectives: credit constraints and portfolio (2.2.11) 5. Other issues (2.2.12) The first point relates to the objectives that drive investment behaviour, the related problem of the subjective perception of investment outcomes and their economic representation, and 25/187

28 modelling, i.e., the way the outcome of Equation 2.4 is perceived by the decision maker in evaluating investments opportunities such as those shown in Figure 3. The second point is concerned with farm-level issues in terms of investments that involve, in particular, the technical, economic, and informational characteristic of investment. This basically involves an elaboration of the contents of Equations 2.1 to 2.4 in the treatment of their components and variants. The third group of issues concerns the household perspective on investment. This involves reading the previous two points from the perspective of the rural household, including household characteristics that determine investment behaviour and resource (labour and capital) allocation both on- and offfarm. The fourth point concerns the financial perspective on investment and deals mostly with financial constraints that determine the outcome of Equations 2.1 and 2.4. In particular, this includes credit issues and portfolio analysis. A number of remaining issues are dealt with in point Objectives of investment decisions Most work on investments is based on a single criterion, i.e., profit maximisation; however, the insight that decision-makers may pursue objectives other than profit maximisation is well established in the agricultural economics literature, including a wide literature on quantitative instruments to support decision-making under multiple objectives (Romero and Rehman, 2003). Some of these objectives (e.g. firm expansion and debt/asset ratio management) may be thought of as intermediate objectives in a strategy that (especially within a long-term framework) may still be thought of as driven by a profit-maximising objective; however, their explicit consideration as distinct objectives may be driven by an attempt to provide a representation that is closer to the cognitive position of the decision makers; one that looks at future outcomes via proxies. Others objectives may be derived from individual objectives, possibly in contrast to the economic results of the firm. Examples of the latter may be managers own objectives in large firms (wages, fringe benefits, number of staff), or personal attitudes and household objectives in small family firms such as most agricultural holdings (preferences for farm work, leisure, household consumption, use of farm for residential and hobby purposes). Among objectives other than profit that are treated in the investment literature risk reduction (i.e. risk-averse behaviour) is the most common. Risk as the main non-profit objective is a wellestablished issue in economics and agricultural economics literature, particularly in relation to farm management and farm finance issues (Barry and Stanton, 2003). When applied to investmentbehaviour issues, risk aversion becomes particularly relevant due to the long-term effects of decisions, which can potentially amplify the variability and reduce the predictability of outcomes, and the extent of irreversible costs. Risk-related considerations also support some of the main branches of risk-related literature, i.e., portfolio theory. Different approaches to risk representation are provided in the literature; however, they may produce contrasting results (e.g. Nelson and Escalante, 2004; Fleming and Sheu, 2000). Despite the large literature available, Just and Pope (2003) note that the understanding of risk-related behaviour is largely unsatisfactory. While empirical work provides evidence that farmers are risk-responsive, it is unclear as to what extent risk response is due to proper preferences (risk aversion), technology characteristics/decisions that lead to non-linearities, physical constraints, or financial asymmetries. To deal with further household-oriented objectives, Wallace and Moss (2002) present a recursive strategic (dynamic) weighted goal-programming model, including adaptive expectation formation, where household consumption objectives are mediated with farm expansion and other 26/187

29 farm-related objectives. Contrary to most of the literature in this field, they also cast the problem in a dynamic framework and include investment concepts among the objectives. Despite the fact that the adopted objectives may be interpreted as proxies of long-term profit maximisation, the adoption of a multiobjective framework can help in dealing with the nonseparability of some on-farm or off-farm resource allocation or product consumption and the perceptions of the farmer that drive his final utility objectives through intermediate objectives. Wallace and Moss (2002) demonstrate that different farm households attribute different weights and have different abilities to reach key goals concerning farm profitability, family consumption, farm investment, farm growth, and cash flow. Taking the view of the farm household, the trade-off/complementarities between consumption and financial objectives appear particularly significant. In addition, different objectives structures can be traced back to different stages of the family life-cycle and farm structure. Preferences for on-farm vs. off-farm labour have been studied previously (see Fall and Magnac, 2004) and can be expressed using an adequate objective structure. Among other recent literature on this issue, Martins and Marques (2006) developed a model of investment in soil tillage technologies using a compromise programming approach that takes risk behaviour into account. Stirn (2006) provides an example of the integration of multicriteria techniques (fuzzy analytic hierarchy process) with dynamic programming involving investment (forests), but assuming a public view of the decision-making problem, i.e., using social rather than private objectives Asset fixity and adjustment costs The divergence between actual and optimal adaptations of capital stock (Section 2.1.3) and the sluggishness involved in the adaptation of capital over time have been explained using different approaches. Following Gardebroeck and Oude Lansik (2004), it is possible to classify the investment literature under two broad branches: those studies based on adjustment costs; those studies based on asset fixity. The two approaches are combined in Abel and Eberly (1994), who present a unified model of farm investment under uncertainty, including both farm adjustment costs and irreversibility. Adjustment cost theory has been the main approach used since the early literature on investment to explain why firms in each period only partially adapt their capital stock (i.e., invest) to the optimal capital stock. This difference can be modelled using adjustment coefficients determined by adjustment costs. Following this rationale, gross investment can be represented as follows (Hay and Morris, 1991, p. 445): * I t = β [ K t + ( δ 1) K t 1 ] (3.1) where: β = share of optimal change in capital stock; δ = depreciation rate; I t = investment at time t; * K t = optimal amount of capital stock; 27/187

30 K t 1 = capital stock at time t-1. In this model, gross investment is a function of (i) depreciation, i.e. of the difference between optimal capital stock and the capital stock already available at time t-1, and (ii) the coefficient of optimal change in capital stock. Adjustment costs are one of the most complex issues in the investment literature, as a number of interlinked factors can affect their size (e.g. transaction costs, existing investment, financial structure of the firm, location). The important assumption made by the literature is that adjustment costs are convex in investment for each time period. This assumption is used by many authors to explain and model why firms prefer to spread investment over time. Although this is not the only possible rationale that can be employed to delay adjustment, it may be useful from an analytical point of view because it allows non-linear optimisation with a unique global optimum. However, the convexity of adjustment cost is debated in the literature (see Hay and Morris, 1991, p.443 for a discussion of the reasons for and against convexity in adjustment costs). Asset fixity is determined by the difference between the cost of capital acquisition and the salvage price of capital. Since the early literature, it has been hypothesised that the farmer invests if the on-farm value of a productive asset (i.e., the NPV in Equation 2.3) is higher than the purchase price; conversely, the farmer will disinvest if the on-farm value of a productive asset is lower than its selling price. When the cost of capital purchase and selling are different, this provides a rationale to explain investment, disinvestment, and immobility (when the on-farm value of capital is intermediate between the purchase and selling prices). The difference between prices may be related to transaction costs that are directly connected to selling and purchasing factors, as well as to incompleteness in the asset market (De Waegenaere et al., 2002). Examples for specific kinds of investments can be found in the literature (e.g. see Carey and Zilberman, 2002, for irrigation technology) (see also Section 2.2.4). Asset fixity reflects the cost of capital determined by the following equation (Chavas, 1994): + C s I + S I (3.2) = t t t t where: C = cost of capital; I = investment; + t I t = disinvestment; s = purchase price of capital; t S = selling price or unit salvage value. t Following Gardebroek and Oude Lansik (2004) (on the basis of Chavas, 1994 and Abel and Eberly, 1994), the problem of combining adjustment costs and asset fixity can be defined as maximising (Equation 2.4) subject to (Equation 2.1.), with a further set of constraints qualifying certain components of Equation 2.4: ( x K Z ) w x Ψ( I ) si + + SI F,, (3.3) t = pt y t t t t t t t t + t = I t I t I (3.4) 28/187

31 x 0 (3.5) t ( 1 ) Kt 1 I δ (3.6) t where: y = production function; p t = output prices; Z t = vector of fixed inputs; x t = flow of inputs; w t = vector of input prices; Ψ = adjustment cost function. In Equation (3.3), the flow of receipts in the presence of investment is given by the receipts from production (expressed as a function of variable, quasi-fixed, and fixed inputs) minus the cost of variable inputs and adjustment costs, minus investment costs, plus receipts from disinvestment. The remaining (dis)equations simply state that net investment is equal to the difference between investment and disinvestment, that inputs cannot be negative, and that in each period it is not possible to disinvest more than the initial capital stock minus depreciation. Decision variables are investment and variable inputs. Both F ( ) and Ψ ( ) can be assumed to be convex functions (quadratic in the specification assumed by the authors), enabling a solution to the maximisation problem in Equation 2.4. This model can be further qualified. For example, Gardebroek and Oude Lansik (2002) studied how adjustment costs are differentiated among farmers. Previous studies have also provided evidence of asymmetric behaviour with respect to phases of market expansion and contraction (e.g. adjustment costs vary with the direction of change) (Pietola and Meyers, 2000). Sunk costs, irreversibility, and asset fixity interact with the objective discussed in Section For example, sunk costs and risk may lead to specific strategies such as investment in human capital and insurance (see Barham and Chavas, 1999) Contracts, investment, and information asymmetries Investment issues have a profound effect on at least some branches of contract theory. The explicit consideration of contract theory in investment studies is less common but equally relevant. A general discussion of the issue, with empirical examples, is given by Bogetoft and Olesen (2004). Integration and production under contract is a condition that is attracting increasing attention in agriculture as the share of production under contract becomes increasingly important. Typical examples are on-farm investments connected to production contracts (such as livestock, industrial crops). Vertical and horizontal integration of farms may play a highly specific role in investment. In particular, investment incentives may become stronger for farms engaged in long-term contracts, while access to credit may be easier for integrated farms. There are at least three different issues concerned with investment and contract theory: the hold-up problem; the horizon problem; 29/187

32 the portfolio problem. The hold-up problem arises when a specific investment is associated with an incomplete contract, i.e., not all possible rules dealing with all possible future events are included in the contract. In this case, when renegotiating the contract, the asset specificity causes the value of the investment in the existing contractual relationship to be higher than that in other contractual relationships. This provides incentives for the agent to stay in the relationship and offers the contractor opportunities to reduce payments to the farm once the investment has been carried out (hold-up). In some cases, the incompleteness of the contract setting may arise from incomplete definitions of property rights in the output. This involves additional costs to competitors related to appropriate outputs and affects the profitability of the investment (e.g. see Konrad, 2002). The horizon problem is related to the degree of consistency between contract duration and the time period of the investment. When the former is shorter than the latter, an incentive to underinvest (i.e., an investment that is less than optimal) occurs. Finally, portfolio problems arise when the farm chooses among different investments whose returns are positively correlated. In this case, the farm may under-invest because of motives related to risk reduction (see Section 2.2.2). Investment specificity and incomplete contracts are key concepts in determining transaction costs and related institutional forms (Williamson, 1985, 1996). Coriat and Weinstein (1995) provide a general overview in relation to the modern theory of the firm. As asset specificity and complexity are distinguished features of agriculture, the above problems may play an important role in the economics of the sector. This field of research tends to concentrate on in-depth analyses of those factors that affect the different forms of governance concerning the use of assets (e.g. ownership vs. rent). Allen and Lueck (2002) provide a detailed example concerning different farm assets in US agriculture. An important determinant of farm investment behaviour connected to contract issues is the tenure regime. Short-term leases are associated with increased uncertainty. Eliminating such a threat will increase the subjective pay-off from long-term investments and thereby the farmer s willingness to undertake them. The tenure security issue has been addressed in the settings of both developing (e.g. Besley, 1995) and developed economies (Allen and Lueck, 2002) Uncertainty and information Uncertainty and information are key issues in investment decision-making. A wide branch of economic literature is concerned with the problem of uncertainty, also providing different concepts and a definition. A basic distinction provided by Knight (1921) is that between risk, defined as a situation where an individual is able to assign objective probabilities to future events, and uncertainty, which refers to a situation in which no objective probabilities can be attached to future events and only possible alternative outcomes are identified. Much of the recent literature disregards this distinction and argues that risk and uncertainty mean the same thing, as truly objective probabilities are never possible (Hirshleifer and Riley, 1992). The distinction proposed by Knight is often substituted by the use of subjective probabilities, i.e., degree of belief in a future outcome (Savage, 1954). This also appears to be the concept that is most commonly used in the investment literature that relies largely on expectations formulated as subjective probability distributions of future outcomes of a stochastic process. This concept may also support a connection with the improvement of expectations over time and with learning. 30/187

33 Uncertainty may originate from a number of different sources. Obvious examples concern yields, product, and factor markets and policy (see below). Technical performance and duration of investments may also involve a degree of uncertainty. The literature on investment-related uncertainty flourished in the 1990s; see Carruth et al. (2000) for a review. The general result obtained from these studies is that increasing uncertainty can lead to reduced investment. Carruth et al. (2000) conclude that, despite the varied approaches and mathematical formulations, the nature of the adopted methods mean that caution is required in interpreting the results, as they commonly show only moderate reliability. However, as the effects of uncertainty appear to be significant, their omission would lead to the underestimation of an important component of decision-making behaviour. Uncertainty and irreversibility were largely absent from empirical literature on farm investment until the end of the 1980s, despite the fact that the theoretical foundation for such concepts had already been developed (Purvis et al., 1995). OECD (2005a) argued that almost no econometric studies of investment in agriculture simultaneously incorporate both dynamics and uncertainty considerations. This may be due to the fact that such a combination is extremely demanding in terms of data and methodologies; however, at least some stochastic dynamic simulation exercises are available from the literature using programming models (see below). An important stream of literature on uncertainty is concerned with the application of real options theory, following Pindyck (1991) and Abel et al. (1996). Real options address the issue of the choice of investment timing. At each point in time, a decision maker will evaluate the option to invest against the option to wait, using an attached value. The investment will only be carried out when the value of the investment at time t is sufficiently high to overcome the value of waiting. Bowman and Moskovitz (2001) discuss the limitations of the real option approach when used in quantitative studies to support strategic decision-making. A specific issue connected to an uncertain and incompletely informed environment is the learning process. The concept of optimal capital stock associated with adjustment coefficients on lagged variables was used in early works on farm-level investment paths (Trevena and Keller, 1974). More recently, a more explicit modelling of the process of learning has been attempted. For example, Li and Weinberg (2003) use the contrasting learning process between small and large firms as a means of explaining the difference in investment volatility among firms. Uncertainty and information issues may be closely connected to efficiency issues. The literature on efficiency frontiers, e.g. based on Data Envelopment Analysis (DEA), supports the idea that a large part of the farming community is not technically or economically efficient. Different reasons can be used to explain such sub-optimal behaviour, including transaction and learning costs, multiple objectives, time lags, and asset fixity Farm structure Farm structure is an important independent issue in agricultural economics, mainly in relation to farm size (expressed as land). Farm structure can also be viewed from other perspectives such as the economic dimension. This classification option is also highlighted by the FADN, which uses Economic Size Units (ESU) to organise accounting results. Farm size is the main aspect of farm structure that the literature connects to investment. Land markets are important from this perspective because farmland acquisition and entry exit from the market determine changes in farm structure in terms of physical size (Ahearn et al., 2005). A recent review of the literature on the land market is provided by Le Mouel (2004), while 31/187

34 Balkhausen and Balse (2004) provide a review of related modelling approaches. Various studies have attempted to explain farm structure with respect to a number of determinants that are to a large extent the same as those that affect investment (e.g. see Atwood et al., 2002). The fact that determinants for investment and farm structure may to some extent coincide is not surprising, as land and capital are to some extent complements and land itself is commonly included among investment options. Consequently, much of the reasoning concerned with farm structure is also applicable to explaining the choice of capital stock. Farm structure can also be used as an explanatory variable for investment. For example, Feinerman and Peerlings (2005) analysed how farm investment is affected by uncertainty concerning the amount of land available to the farm. Uncertainty about land availability arises from difficulties in predicting the supply of land on the market, especially in relation to the probability of quitting by neighbouring farms. Farm size has also been examined in relation to policy, either exante (Henningsen et al., 2005) or ex-post (Ahearn et al., 2005) Investment and technical change There is an unsurprising affinity between the literature on technical change and literature on investment. While the former focuses more on the qualitative aspects of technology, the latter focuses more on the process of value accumulation. Technical change literature often involves the acquisition of assets that are investments in nature. It tends to focus on factors that affect technology profitability (e.g. location, farm structure), the timing and attitude towards innovation of different farmers (e.g. age), and the production effects of technology change (e.g. physical production, monetary benefits). The process of adoption of new technologies has been widely discussed in the literature. Gimenez (2006) emphasise the importance of technology acquisition and learning costs, the speed of the innovation process, and expectations of future technological development. Feichtinger et al. (2006) used an optimal control model to elaborate on the connection between the changing productivity of capital, learning, and the investment process. They showed that learning may explain why firms invest in older technologies even when more efficient ones are available. This also helps to understand why machinery is older (on average) during recessions. They also show that under conditions of rapid technological development, investment is faster, although more sensitive to output price. Investment-specific technological changes (i.e., aspects of technical progress bound to specific investments) are estimated to account for approximately 30% of output fluctuation in the post- WWII US economy (Greenwood et al., 2000). Although these results are not specific to farming, they corroborate the need for a joint understanding of investment and technological change behaviour; however, the agricultural economics literature provides few examples of attempts to connect the two perspectives, even though the methods and contents commonly overlap. For example, Asseldonk et al. (1999) incorporate technical change in a dynamic programming model used to determine optimal investments in information technology on dairy farms. Recent examples are provided by studies that consider the adoption of precision-farming, one of the potentially significant contemporary avenues for technology innovation (Batte and Arnholt, 2003; Khanna, 2001). An important point highlighted by a number of papers on technical change is the problem of representing the joint decision of technology adoption and land allocation (i.e., crop mix). In this regard, Moreno and Sunding (2005) provide an example concerning irrigation technology. 32/187

35 A specific issue connected to technical change is the shift towards environmentally friendly technologies or conservation activities. For example, Kerselaers et al. (2005) presented a mathematical programming model, including investment cost, that simulates the economic potential of conversion to organic crops. This example represents a case of conservation technologies that require relevant investment cost due to the shift from chemical to mechanical weed control Household characteristics of interest It is recognised in the economic literature that capital structure and investment decisions are affected by the interplay of social, family, and financial factors. Romano et al. (2001) provides a wide (and non-agricultural-specific) framework of the interplay of different variables and the resulting impact on capital structure. This problem is well understood in the agricultural economics literature, where several factors related to households are used to explain investment behaviour (see Section 2.1.4). The mechanisms through which household characteristics affect investment behaviour are often connected to the choice of the household assets portfolio and household labour allocation (see the following sections), as well as the farmers objectives discussed in Section One of the main household characteristics concerned with investment is the household lifecycle. By life-cycle we mean the different stages of the evolution of the household composition, i.e., from young and single to young and married and finally old and married or old and single. A commonly used proxy of household life-cycle is the farmer s age (Andersson et al., 2005; Gardebroeck and Oude Lansik, 2004; Elhorst, 1993; Ahituv and Kimhi, 2002; Serra et al., 2004). Andersson et al. (2005) found that age is the single most important (positive) determinant of the share of on-farm investment of total investment; however, the effect of age on investment is not linear. In an econometric model applied to a sample of Israeli farms, Ahituv and Kimhi (2000, 2002) examined the connection between off-farm labour, family life-cycle, and investment. They found that the maximum capital accumulation is achieved by farmers of approximately 45 years of age. Farmers with a full-time job off the farm tend to anticipate this time, while farmers who are devoted exclusively to farming tend to continue capital accumulation for longer, often up to the end of their working life. Key complementary variables also include the presence of a successor and to some extent education (Gardebroeck and Oude Lansik, 2004; Ahituv and Kimhi, 2002) On-farm vs. off-farm investment Off-farm investment is a growing issue in the literature. This is justified by: the fact that many farms are conducted by pluriactive households living in rural areas; the fact that off-farm financial assets are a good way of reducing risk, as their return is not correlated with on-farm returns. The choice of off-farm vs. on-farm investment may be seen as a special problem related to the wealth composition of farm families. While the broader issue is mostly relevant to rural development and general income policy (e.g. Bowles and Bosworth, 2001), off-farm investment is directly relevant in addressing the connection between household savings and changes in capital stock. 33/187

36 Serra et al. (2004) presented an econometric model of farm households off-farm investment. The authors found that variability in farm income leads to increased off-farm investment, suggesting that off-farm assets are used as farm risk-management tools. The choice is further dependent on a number of other variables. In particular, highly diversified farms and those expecting large government payments are associated with lower off-farm investment. Wealth, farmer s age, and farm size affect the composition of off-farm investment. Household income is also invested differently depending on the source of the income and the return rates of the different investment alternatives. Andersson et al. (2003, 2005) discuss the issue in connection to off-farm labour. Off-farm investment may also be related to agriculture activity. In fact, the accumulation of off-farm capital to produce services for farms is a major aspect of many agricultural systems. Smallsize farming structures found in large parts of the EU favour the establishment of forms of cooperative capital stocks whereby formal legal entities own machinery or buildings. Investment in cooperatives is an issue that is specifically treated in the literature, particularly in connection to the efficiency of investment choices (e.g. see Russo and Sabbatini, 2005). On-farm investment that is intended to sell services outside the farm is also an issue in many areas; e.g. when single farmers provide services based on capital components, notably machinery Labour allocation Labour allocation is a key factor in farm behaviour, particularly as most farms are run by households and off-farm income accounts for a growing share of total rural household income. As the rigidity of labour markets means that labour decisions between on- and off-farm employment are not separable, a major characteristic of farm-household decisions relates to utility maximisation via the allocation of household time to on-farm, off-farm, household, and leisure activities. It is straightforward to understand that investments can interact with labour allocation both within the farm and in the choice between on-farm and off-farm labour; however, the way that such interaction occurs may differ markedly from one case to another. As argued by Ahituv and Kimhi (2002), many studies corroborate the idea that land, labour, and investments are complementary. For example, asset value is often an explanatory variable for labour choices, as well as farm structure (Weiss, 1997; Ahituv and Kimhi, 2002; Mishra and Goodwin, 1998; Kimhi and Rappaport, 2004), although in this case it was not found to be significant. Ahituv and Kimhi (2000) demonstrated that off-farm labour is negatively associated with farm investment, although farmers with greater ability are able to both work off-farm and maintain high capital on the farm. Many investments are a clear substitute for labour (e.g. machinery), and such substitutability is a key starting point of much technological change literature. Generally speaking, while complementary effects may be expected to be more relevant in small farms and labour-intensive systems (e.g. horticulture), substitution may be more evident in more extensive and capitalintensive systems or in the transition between the two. All of these findings strengthen the policy relevance of a joint analysis of labour and investment decisions. A quantitative approach to connecting labour-allocation choices (including on-farm vs. offfarm labour allocation) is given by non-separable household models. A review of farm household models is provided by Taylor and Adelman (2003). An extension of such models in connection to General Computable Equilibrium models can be found in Löfgren and Robinson (1999). Non- Separable Household models are widely used to explain the behaviour of farmers in developing countries; however, recent extensions show that they are also useful in gaining an improved insight 34/187

37 into farmer choices in the present complex rural environment of the Netherlands, where labour and capital flow in and out of the farm. This approach has been used in a number of cases to evaluate farmers investment behaviour as a consequence of the 2003 EU CAP reform (Peerlings, 2005). Andersson et al. (2003) developed a dynamic portfolio choice model with labour income to explain asset choice when access to off-farm sources of income leads to reduced risk. The same model is tested in Andersson et al. (2005). Ahituv and Kimhi (2002) also examined the effects of off-farm labour on investment and found that farmers with an off-farm job tend to acquire a capital stock that is some 40% lower than that of full-time on-farm workers. In connecting investment with labour, it is important to recall that on- vs. off-farm labour is itself a complicated issue. For example, Fall and Magnac (2004) estimated the preferences of farmers for on-farm labour. They found that such preferences are significant and differ across different countries, gender, age, and study level. This has implications, for example, for the identification of the opportunity cost of labour in household models. In addition, the behaviour of labour shifting outside or towards the farm is not symmetric with respect to the direction of change. Weiss (1997) noted that while increases in off-farm wages positively affect the increase of labour allocation outside the farm, decreases in off-farm wages do not have an opposite effect of the same magnitude Credit constraints and portfolio issues If capital markets are perfectly competitive and no collateral is required as guarantee by lenders, production and investment decisions are independent of consumption decisions, as they can be optimally funded according to marginal cost and profitability of capital without being subject to any particular restrictions. The level of investment will then be decided on the basis of the rate of return (OECD, 2001a). In the real world, the importance of financial constraints, as expressed by firm assets, liquidity, and imperfect markets, is proven by most of the empirical evidence, thereby rejecting the neoclassical assumption of perfect financial markets with no information asymmetries and transaction costs (Benjamin and Phimister, 1997; Hart and Lence, 2004). Kuiper and Thijssen (1996) contrast neoclassical theory of investment and empirical evidence using cointegration analysis. The analysis reveals that neglecting debt and financial constraints leads to wrong expectations concerning capital growth and investment. Bierlen and Featherstone (1998), studying the issue of cash constraints on long-term panel data from US agriculture, demonstrate that this issue changes over time, with a tendency to become increasingly relevant in relation to profitability and credit conditions. They also show that the debt level is the single most important determinant of credit constraints. Studies on this issue in transition economies show evidence of imperfections in credit markets that lead to higher costs for external funding and that affect investment decisions. Such situations may also be associated with asymmetric information and differentiated access to funding across farms (Latruffe 2003, 2004; Petrick, 2004). Access to subsidised credit may be expected to encourage farm investment by reducing capital cost; however, Petrick s (2004) study of credit markets in Poland found that the elasticity of investment to borrowing was less than one, implying that funds were used for purposes other than on-farm-investment. 35/187

38 Financial constraints interact not only with credit policy, but also with property-rights policy. For example, Carter and Olinto (2003) found that the reform of property rights 6 in Paraguay did not lead to the expected effects for small, liquidity-constrained farmers; they continued to carry out limited investment, mostly related to land. A different approach, from a financial perspective, may arise from portfolio analysis. Examples of this approach in relation to the issue under discussion come mostly from literature concerning farm-household investment behaviour rather than farm investment behaviour itself (Andersson et al., 2003; Andersson et al. (2005). This is discussed elsewhere in this section Other issues In parallel to physical capital, biological capital (breeding stock, perennial stands of trees, soil fertility) plays an important role in agricultural production. There are several perspectives under which biological capital interacts with investments. First, biological capital may be a determinant of investment, as it affects investment returns. Second, biological capital may itself be viewed as a form of capital investment. More generally, biological processes interact with farmers action in determining production outcomes and uncertainty. Integrating biological processes into economic models is a difficult task; this may explain why there are few empirical attempts in this direction (e.g. see French et al., 1985). Recently, attempts to integrate biological aspects into economic models have become a major task in the economic environmental literature connected to resource conservation. A wide but little-studied issue is the overall effect of investments and their characteristics on the functioning of the economic system. For example, investments affect production costs and the markets via changes in (equilibrium) price, and sunk costs, irreversibilities, and asset fixity influence (limit) the role of competitive markets in achieving an efficient allocation of resources (Chavas, 1994). 2.3 Policy effects on investments Decoupling and investment The economic analysis of the effects of policy reform on the farming sector is one of the richest fields in agricultural economics. This branch of the literature has been stimulated by frequent reforms of agricultural policy, particularly in Europe; however, few recent studies have focussed on the effects of agricultural policy on investment. Notably, many studies focus on decoupling. The focus on this issue is justified by the recent evolution of agricultural policies in OECD countries away from price support toward direct payments. OECD (2001a, 2005b) reviewed the literature on the expected effects of decoupling policy and presented a mathematical formulation of decoupling, including connections to investment. They note that policies may produce dynamic effects because: 6 Property rights may include a number of different concepts. In this case, it has to do with the formalisation of property through adequate titles that allow land to be used as a guarantee to access credit. This kind of issue may also be of some interest in Eastern Europe. 36/187

39 investment decisions taken in one period (on the basis of existing policies) continue to affect production in later years as long as production is a function of existing capital stock; farmers have expectations concerning government behaviour that influence their decision making; expectations particularly affect investment behaviour, as the results of investment will be determined by the long-term context of the policy. Hence, the effects of policy on production, where investments are affected, will lag in time and be strongly related to expectations. In his review of the literature on decoupling, Andersson (2004) identifies at least three potential effects of decoupling: a higher propensity to investment due to the relaxing of financial constraints, particularly in the presence of credit restrictions and imperfect credit markets; a higher propensity to consumption that may be motivated by greater risk-free earnings; a lower propensity to technological innovation because of lower coupled incentives. The issue of the effect of decoupling on risk is at least twofold. In general, when the introduction of decoupled payments is considered in itself, it may be claimed to reduce risk. For example, Roche and McQuinn (2004) noted that a decoupled payment can be regarded as a risk-free return. Using portfolio theory applied to a choice of crop mix under decoupled payments, they showed that farmers that actively produce (in contrast to those that keep some or all of their available land in an idle state) are more likely to be willing to increase their share of riskier crops; however, when decoupling is associated with a reduction in area payments or price support, the latter may lead to higher price volatility and increased overall risk. The balance between the two effects is to a large extent an empirical issue. Overall, the available literature appears to corroborate with the idea that decoupling in itself is risk-reducing, while the overall effect of the 2003 reform will be risk-increasing. The impact of decoupling on investment will also largely depend on the degree of imperfection in the credit market (OECD, 2001a, 2005b). If capital markets are perfect, fully decoupled payments will not affect investment decisions, whereas under imperfect capital markets (e.g. a significant gap exists between borrowing and lending rates and/or the presence of binding debt constraints for the farmer willing to invest) investment decisions will be affected by all kinds of agricultural programmes that affect farmers income. In particular, policies that lead to an increase in income or that provide cash unrelated to production will translate to a higher propensity to investment. Recent key papers on the empirical analysis of the effects of decoupling on investment have been produced by the OECD. OECD (2005a) provides a non-technical summary of the findings of OECD (2005d), later developed in Sckokai and Moro (2006) and OECD (2005c). Both these latter papers develop econometric models and use them ex-ante to predict the effects of policy reforms undertaken in Italy and Manitoba, Canada. The tools used for the analyses are a structural econometric model for Italy, based on individual profit maximisation, and reduced form models with distributed lags for Manitoba. Policies were found to have a significant investment effect on machinery, buildings and equipment. The Italian case study assesses the effect of decoupling of payments and predicts a reduction of investment by 14%. The Manitoba study considers the Gross Revenue Insurance Programme (GRIP) programme, which provides payments linked to (low) prices, and the expected effects on average prices and insurance effects for farmers. The GRIP 37/187

40 program in Manitoba is predicted to increase investments by 22%. The studies also confirm the hypothesis of risk aversion. The risk-related effects of policy (insurance) are found to be larger than relative price effects in Italy and are of the same size in Manitoba; production and wealth effects appear to be less relevant. These studies confirm that different policy designs may lead to contrasting effects on investment, although no generalisations are possible in terms of the effects. The results of these studies also corroborate the need to use models that take account of uncertainty and dynamics in order to evaluate the impacts of policy measures on investment. Peerlings (2005) uses a non-separable farm household model to evaluate ex-ante the impact of the 2003 reforms on farmers investment behaviour in The Netherlands. The model assumes nonseparability between on-farm and off-farm labour allocation as well as between on-farm and offfarm investment decisions. To the best of our knowledge, this is also the only example in the literature of household production models applied to investment behaviour. The empirical model is a normative optimisation model, calibrated using data for individual farms. The model results show that farm payments of the 2003 CAP reform do not fully compensate the income loss caused by the reduction in milk price. The study also finds that savings are reduced and investment shifts from onfarm to off-farm investment; however, decoupled direct-income payments themselves (without any other changes) increase savings and thereby increase investment levels for some farms (although not all). A specific aspect of policy is how funding provided through (decoupled) state intervention is used by farmers. Goodwin and Mishra (2005) studied this issue in connection to decoupling in the US over a sample of over 4000 farms. They provide evidence that only about two-thirds of the revenue from direct payments is used on the farm. Among on-farm uses, about half is used for operating costs, while less than half (about 14% of the total) is used for farm investment. An equivalent amount is directed toward debt repayment and household consumption. The main determinants of on-farm use of decoupled payments are: farm size (+), debt/assets ratio (+), household net wealth (+), age (-), insurance (-), sole proprietor (-), retiring (-), and off-farm work (- ). Unfortunately, the paper does not directly address the issue of on-farm vs. off-farm investment; nor does it compare the findings with those of other policy instruments/payments. A relatively common issue connected to policy is structural change (see Henningsen et al., 2005; Ahearn et al., 2005; Happe et al., 2004b). This is relevant to the present review because, as noted above, factors and mechanisms that affect structural change are to a large extent the same as those used to explain investment behaviour, and land itself is a major component of investment. Some aspects of structural changes, such as buildings and machinery, are directly connected to investment, as the components of structure are capital goods. Other structural aspects, e.g. household structure and labour force, interact with investment decisions via preferences, resource (labour) availability, and the (differentiated) cost of labour. Here we focus mainly on the connection between policy and farmland distribution. The effects of policy on structural change are generally found to be relevant. For example, Ahearn et al. (2005) applied a household model to econometrically estimate the effects of different policies on farm structure (intended as land distribution between different kinds of farms) in the US. They found that commodity payments during the period resulted in a reduction in the share of small farms, an increase in the share of large farms, and an increase in farm exits. This is consistent with the assumption that farmers use commodity payments to expand their farms and can 38/187

41 be explained by the strongly asymmetric distribution of payments between different farms (40% of farms receive payments in the US; most of the large farms are included in this share) and crops. This may be reasonable in the framework adopted by the authors, where heterogeneous actors compete for a finite resource (land). The AgriPoliS (Agricultural Policy Simulator) model has been used to assess the impact of decoupling in regions of Europe (Happe and Balmann, 2003; Happe et al., 2004a; Happe, 2004, 2005; Sahrbacher, 2005). AgriPoliS is a normative spatial and dynamic model of agricultural structural development at the regional level. The model includes a mathematical representation of farm-investment behaviour. The model distinguishes the stage of planning, when the investment intentions are defined, and the stage of decision. The agents can choose among 29 investment options of different kinds (buildings, machinery, and facilities) and size. The number, kind, and combination of investments are not restricted. A farm agent will only invest in one object if the expected average return on investment, determined in the farm planning problem, is positive, i.e., if total household income increases. Each investment object has associated investment costs, maintenance costs, maximum useful life, labour substitution in hours, and production capacity. The same investment may take different sizes, with implications for economies of scale (cost) and labour saving. Despite the details of investment modelling, model results available from the literature focus mainly on structural change and not on investment behaviour as output. In particular, Happe (2004) shows that decoupling may increase the average farm size and to some extent the speed of adaptation over time Other policies Other policy instruments may be connected to investment, especially tax policy, environmental and product regulation policies, and agricultural investment policy. Many studies on the connection between tax policy and investment are available in the general economic literature. Jensen (1998) provides an example of examining the effects of tax and depreciation policy on a fishing fleet, and found it to be an effective means of controlling the capitalisation of fishing fleets. In terms of environmental regulation, Weninger and Just (2002) provide an analysis of farm dynamics and investment under a tradable output permits policy. One specific issue concerns the effects of investment-oriented agricultural policies on onfarm behaviour, as such policies involve the screening of farms that apply for funding, enabling the creation of a profile for investors. For example, Bryla (2005) found that farmers applying for SAPARD payments had larger and more market-oriented farms and were younger (also due to the constraints posed by the programme), but did not clearly differ from the average in terms of education Policy expectations and uncertainty A number of policy-related issues can be traced back to the single fields of investmentrelated studies discussed in Section 3. A major aspect of the perception of policies is the degree of certainty involved in future policy settings. Policy uncertainty in itself is generally found to account for substantial differences in long-run capital price, investment, and output across countries and sectors (Jeong, 2002). Tax-policy uncertainty has been found to be a particularly important issue in general economic literature on investment and capital formation (Hasset and Metcalf, 1999; Alvarez 39/187

42 et al., 1998). With a focus on agriculture, Lagerkvist (2005) investigated the effects of uncertainties in expected policy reform on farmland investment, with an application to Swedish farmers. Much of the background literature summarised in Lagerkvist is drawn from a rich body of literature concerned with farmland value. The main outcome of such literature is that variability in farm incomes (including policy parameters) negatively affects land values; however, farmland values are generally more responsive to non-farm factors (e.g. urban expansion, rates of return) than to farm revenues. Lagerkvist (2005) develops a dynamic stochastic business-level land-valuation model to analyse how policy uncertainty related to the introduction of a single farm payment will affect farmland investment incentives. An important distinction made in the paper is between the timing and magnitude of the reform. The expectations of farmers in regard to these policy parameters and their correlation played a major role in the analysis. The paper demonstrates the importance of policy uncertainty in inducing higher volatility in investment incentives and possibly in inducing inefficient investment behaviour. As a consequence, it confirms the need for certain long-term decisions concerning policy as a key policy parameter as far as the consequences for investment are concerned. 2.4 A classification of quantitative methods This section is intended to provide a classification of the main methodological options available for the analysis of farm investment behaviour emerging from the literature. This is undertaken to support further discussion. Compared to the previous section, with which its content naturally overlaps, it is focused more on methodological aspects than on single determinants or fields of research. The classification refers mainly to studies described in the previous sections. For this reason, citations are generally omitted; however, a number of extensions or related fields appear to be equally relevant and these are briefly described and referred to in the discussion. Starting from the representation of the relevant decision-making unit and maintaining the microeconomic perspective of modelling single entities, an important distinction is made between farm models and farm-household models. The latter have been more commonly used in recent years to understand the relationship between whole farm decisions and investment, as well as in relation to new approaches such as portfolio analysis. While farm models enable a more simplified (and less information-sensitive) understanding of farm behaviour, farm household models enable a treatment of investment in connection to other choices (labour, off-farm investment). The latter is an appealing approach, although it implies an important expansion of relevant objectives, types of investment and labour alternatives, and, finally, of data requirements. Both farm and farm-household models can be treated in an aggregated way. In this case, microeconomic studies usually offer an alternative between representing them as an area or as a farming system. The distinction between the two approaches has to do with the fact that area modelling is related to all of the (agricultural) economic units that exist in a region, while the farm/household or farming system approach models a single farm or aggregates based on combinations of farm specialisation or location. In the literature, area-based models are mostly used to understand structural change, as they enable an explicit representation of markets of finite factors such as land, breeding livestock, and in most cases, labour. This may be managed conventionally 40/187

43 through area-based models where total factor availability is constrained and may be of great importance for representing investment decisions. On the other hand, investments affect product markets and price by changing the production costs at the farm level. Modelling such effects would require representation at an appropriate scale of the demand side as well as the supply side; however, investment behaviour is generally studied on single farms or groups of farms that represent incomplete parts of an area. The second major economic distinction is between ex-post (positive) and ex-ante (normative) analyses. Ex-post approaches based on cross-sectional or time series data prevail in the investment literature (Purvis et al., 1995); however, in recent years policy-making and policy design have shown a growing demand for ex-ante analysis, as it enables the user to anticipate expected behaviour and check the ability of decisions to fit the decision-maker s objectives. An important methodological option is that between programming models and econometric models. The two approaches differ mainly in terms of computational tools and data sources. Programming models are optimisation tools based on a detailed engineering process of structural model-building, often obtained from point observations, and are calibrated based on the value of technical coefficients, resource availability, and constraints settings. Econometric models are based on statistical estimations of parameters based on time series or cross-sectional data, commonly with underlying optimisation assumptions (Howitt, 2005). The agricultural economics literature on investments offers a number of examples of econometric models and several examples of optimisation (programming) models (e.g. Asseldonk et al., 1999; Happe et al., 2004a). Generally speaking, both approaches may be used for either ex-ante or ex-post analyses. To some extent, the programming approach tends to be more common in ex-ante problems that involve technical change and changing policy options, as it enables a more detailed technical analysis of farm activities, expected problems, and future choices. This approach may also be necessary where large data sets are unavailable; however, programming models are commonly constructed using non-codified calibration procedures that leave a lot to the wisdom of the modeller. Econometric studies look to focus more on ex-post analysis, partly because a great part of the work of the modeller is concerned with parameter estimation and partly because such studies are actually used to understand determinants of past behaviour. Compared with programming models, econometric models benefit from well-established estimation procedures; however many previous studies struggled with limited databases that were commonly characterised by aggregated data that did not enable a satisfactory estimation of relevant parameters given the high spatial and personal heterogeneity of agricultural activities (including investment). In perspective, the development of more complete and detailed databases based on panel data might be expected to improve the outcome of such approaches. Both approaches have undergone an important evolution over the past two decades. First, it must be stressed that the net distinction between the econometric and programming approaches has become somewhat reduced thanks to recent methodological developments such as Positive Mathematical Programming (PMP). As far as econometric models are concerned, a wide menu of options exists, differentiated by: type of model (parametric vs. non parametric); structure of the model (single equations or structural equations); types of data (cross-sectional or panel data); 41/187

44 types of estimators (Ordinary Least Squares, Generalised Least Squares, Maximum Likelihood). In the context of programming approaches, calibration techniques and sensitivity analysis have been a major issue in recent years. A dual approach has been used in most of the recent literature from production analysis, both for econometric and programming models (e.g. PMP). Multi-agent models have been developed to deal with interaction between different groups of actors at a territorial level and to accurately model the timing of actions. DEA (Data Envelopment Analysis) has also been used to assess technical efficiency in connection to capital stock or financial structure and could be an interesting tool for a deeper understanding of suboptimal investment behaviour (Davidova et al., 2005). A relevant distinction for investment analysis is that between static and dynamic (or multiperiod) models. While comparative static models are common in the economic literature and have been used for investments, particularly in the early literature, dynamic models are used in most of the recent literature on investment because of their consistency with the nature of the investment problem and with the fact that the timing of choices is a major variable in decision-making. This point is emphasised by recent literature on uncertainty and information issues. Among the dynamic models, different options have been developed over time (e.g. recursive dynamic, fully dynamic, discrete time vs. continuous time, etc.). Improved computational tools, at least in the investment literature, have given rise to a trend toward fully dynamic, discrete time models. A critical issue is how investments are represented in the model. Investments are commonly represented in economic models as (non-differentiated) capital accumulation. A different perspective is adopted in studies in which the technical details of investments are explicitly included in the model. As discussed in Section 2.1, the classification of investment may take very different forms. While the distinction between land, buildings, and machinery is common in most studies, further qualifications are used only in relation to the specific objectives of the analysis. Greater detail is usually only used in programming models, as it is required for a satisfactory calibration and enabled by the scope of the study. In contrast, econometric studies often suffer from insufficient detail in the available data. In terms of information assumptions, it is relevant to distinguish between models under certainty (non-stochasticity) and models under uncertainty (stochasticity) with regard to investment performances and the economic context. Uncertainty is clearly a major issue in investment, and it constitutes a distinguishing feature of recent approaches such as real options or learning models. It is amplified when the economic viability of the sector under analysis is strongly affected by frequent and very open policy reforms. The treatment of uncertainty, and more generally the issue of prediction and expectations, has taken several paths in recent developments in economics, with somewhat contrasting implications and applications for modelling. One issue may be the distinction between the representation of optimal behaviour and suboptimal decisions (i.e., with technical or economic inefficiency), as a large part of the farming community may behave according to the latter. Differences may be found in linear and non-linear specifications. Non-linear specifications prevail in the literature on investment as a consequence of the need to represent tractable production 42/187

45 functions and investment costs, the need to represent uncertainty and risk, and the binary nature of many investment decisions. In most cases, non-linearity (meaning well-behaved non-linearity) is a necessary condition for model estimation/calibration/optimisation, or at the very least can improve the calibration; however, linear models can be more tractable when a dynamic programming framework is adopted. Finally, different hypotheses can be developed regarding the objective function, which may be either mono-objective or multi-objective. The simplest multi-objective function is based on a combination of profit and risk-reduction objectives. Many objectives seem to be particularly pertinent for the household as a decision-making unit, which, however, makes the objectives more strongly differentiated and difficult to attach to objective indicators. The treatment of different objectives may take different forms; e.g. weighted sum, maximum worst case, fuzzy aggregations. Goal programming or ideal point methods may help in modelling conflict resolution between different objectives. Constraints may also be used to include objectives in programming models. 2.5 Discussion: assessment of the literature on farm investment behaviour This section attempts to provide an assessment of the literature in relation to the final target of our research project. The objective is not to undertake an assessment of the literature per se, but to contribute to making appropriate decisions concerning the contents and methodologies for the project. From this standpoint, the main questions concern the ability of the different approaches and techniques to: produce sufficient detail of the expected type and timing of investment; consider the wide set of drivers that are relevant for household investment behaviour; analyse investment behaviour under a policy scenario. Generally speaking, our understanding of farm investment behaviour is considered to be largely unsatisfactory, despite the evident importance of the representation of farm behaviour 7. While this is true for many fields of economics, difficulties with the treatment of investments may be perceived as being more relevant because of their long-term effects, irreversibility, and sunk costs. In addition, contributions on this issue are less numerous than those for other fields of agricultural economics research. The reasons for the unsatisfactory state of the art may be traced back to three main problems (partially following Elhorst, 1993): investment drivers are numerous and affect decisions in a discontinuous way, with different key (significant) determinants emerging depending on the time and context; researchers develop problem-oriented but partly theory-driven models that by necessity only represent part of the decision process; most of the research has been carried out at the meso or macro level based on aggregated data, within which specific causal factors are commonly difficult to detect. 7 For example, Lagerkvist (2005) opens his article with the sentence Variability of farmland investment remains an enigma. 43/187

46 This review of the literature on-farm investments behaviour enables us to confirm and further qualify these problems. The available literature shows an agreement on the need to represent investment problems in a dynamic framework; however, the expected investment behaviour based on optimisation (either in a static or a dynamic context) as modelled in the early literature does not appear to produce much empirically relevant information, at least in agriculture. In addition, the literature appears to commonly over-simplify capital representation (e.g. via a continuous adaptation of undifferentiated stock over time). This issue may be particularly important when investments interact with technical change, as in most practical problems. Previous studies have attempted many different ways of interpreting actual behaviour via the study of a number of determinants. The approach taken is often ex-post, with the support of econometric instruments, whereas ex-ante contributions are few in number. Determinants are often taken singularly or incorporated in models that enable a sufficient understanding of only some of the important factors. Recent works highlight a change in perspective regarding decision-makers in terms of farm investment. In particular, household/firm management appears to be increasing in importance as the most appropriate perspective. The analysis of the impact of policy on on-farm investment behaviour appears a particularly challenging task, as policy scenarios interact with all other (numerous) determinants, particularly risk perception, liquidity, and output prices. Unsurprisingly, the volume of literature and the state of the art appear particularly unsatisfactory as far as policy analysis is concerned, particularly for exante policy evaluation. Most documents related to ex-ante policy analysis, including ex-ante analysis of the 2003 CAP reform, barely consider the issue of investment (European Commission, 2003). Several recent studies, however, which focus on decoupling in particular, deal with investments in more detail. In terms of both decoupling and the role of the household, the US literature appears more advanced because of earlier policy and social changes in agriculture. Some of the main gaps in our knowledge and research needs include: more adequate instruments for ex-ante analysis; models adaptation to incorporate empirical information about farm preferences and expectations, e.g. stochastic decision models with expectations collected via ad hoc surveys; closer attention to the connection between investment, technical change, and learning; a more empirically relevant treatment of the objectives of the decision-maker (farm household, firm). 44/187

47 3 Methodology 3.1 Overview The methodology adopted in this study integrates stated investment intentions by farmers and modelling of farm behaviour under different scenarios. An overview of the steps of methodology is given in Figure 4. Figure 4 An overview of the methodology Scenarios variables Secondary data Questionnaires Mathematical programming models T2.2 - Farmer investment behaviour under different scenarios T2.1 - Current and future investment activities T2.3 Policy relevant economic conclusions Policy-relevant conclusions are derived from two kinds of results: stated current and future investment activities on the one hand, and expected farm investment behaviour under different scenarios on the other. The former are obtained directly from a survey on a sample of farm households through questionnaires. The latter is obtained by mathematical modelling of individual farm-households. The modelling exercise is performed under different scenarios. The combination of stated behaviour and modelling is aimed at a better interpretation of the farmers reaction to policy by compensating for the disadvantages of each of the approaches taken separately. Stated behaviour may be not completely incentive-compatible, or may be incomplete, and applies to the strict individual planning horizon. Models may not take into account important decision factors, but may be adapted to hypothetical situations and used to simulate the effects of different scenarios on the basis of completely controlled decision making mechanisms. 45/187

48 As for the construction of the mathematical programming model, both questionnaires and secondary data have been used where available. It is relevant that questionnaires have at least a twofold objective: to produce directly information about future investment behaviour, and to support modelling. The information collected about present and future investment behaviour, as well as about other decision making aspects of the surveyed sample is processed through standard statistical descriptives and a correlation exercise concerning the main policy variables. The reminder of this chapter focuses on the other two main building blocks of the methodology, i.e. the scenario analysis and the model. 3.2 Scenario definition and characterization The definition of scenarios went through the following steps: 1. scenario identification; 2. scenario description through storyline; 3. scenario characterization through quantitative variables; 4. definition of values for scenario variables. Step 4 produced the input for the model (section 3.3.). The scenarios adopted for this project (and their quantitative parameters) were identified through discussion internal to the project s Steering Committee 8, and after checks with all project partners. Simple scenarios have been preferred, to assess the impact of single variables. To this aim, the quantitative definition of scenarios is differentiated only on the basis of the following variables: - amount of public payments; - mechanism of payments (area-based vs. decoupled); - market prices for agricultural products. Selected scenarios implemented in the model are: 1) Baseline 1: agenda 2000+current prices 2.1) Decoupling 1: 2003 reform+current prices 2.2) Decoupling 2: 2003 reform+lower prices (WTO scenario) 3.1) Payment cut 1: 2003 reform (up to 2013)+no payment after 2013+current prices 3.2) Payment cut 2: 2003 reform (up to 2013)+gradual reduction of payments after 2013+current prices 3.3) Payment cut 3: 2003 reform (up to 2013)+gradual reduction of payments after 2013+lower prices Scenario 1 represents the baseline used as a reference to assess the impact of decoupling and alternative scenarios. In the EU 15 countries, it represents the hypothesis of maintaining the Agenda 2000 conditions (last year of application) up to the end of the time horizon (2030). Nevertheless, payments have been reduced by 10% due to financial discipline. In Poland and Hungary, this hypothesis is substituted with the agricultural policy currently in force. Single Area Payment 8 See p /187

49 Scheme (SAPS) presently in place. SAPS provides increasing payments up to 2013, at a changing rate. In our baseline scenarios, such increasing payments have been assumed up to 2013, then payments are assumed to stabilise at the 2013 rate, till the end of the planning horizon. Prices are assumed to be the current ones (2006) and to undergo no change till the end of the time horizon. Scenario 2.1 shows the impact of decoupling. In the EU15 Countries, this scenario is built by assuming the present decoupled policy in each country, up to the end of the time horizon. In Poland and Hungary, it is assumed a total decoupling based on the payment at 2007, starting in 2007 and lasting till the end of the time horizon. Scenario 2.2 illustrates the impact of the decoupling policy as described in scenario 2.1, associated with possible lower prices of agricultural products due to WTO negotiations. Due to the lack of empirical support for the prices under this scenario, price reduction has been set to 20% by the "expert judgement" of the steering committee this reduction has been identified as a reasonable range of change during the time horizon. All of the scenarios described up to now assume the continuation of payments after 2013 as before (either area based as in scenario 1, or decoupled as in scenarios 2). The following scenarios, i.e. 3.1, 3.2, and 3.3, formulate different hypotheses on what will happen after Scenario 3.1 considers the extreme hypothesis that there will be no further direct payments or support; scenario 3.2 provides for a gradual reduction of payments. This latter hypothesis is associated with lower market prices in scenario 3.3. The proposed gradual reduction of payments after 2013 is calculated as a linear reduction that reaches zero in This is consistent with the duration of current reforms and would end the payments at a date that is significant for other current modelling exercises (e.g. Scenar 2020). Local conditions (e.g. labour opportunities and costs, factor prices) are kept constant across scenarios. Set-aside prescriptions and milk quotas (or other quota systems, e.g. for vineyards) are assumed not to change across scenarios, but are relevant for the characterization and explanation of farm behaviour in individual case studies. Also the rules for the eligibility to the SFP (e.g. crops admitted for cultivation) are the same across scenarios. Technology does not change across scenarios and over time (see later). Payments for organic farming under rural development programmes are assumed not to change across scenarios. Models assume stable real prices (no inflation is accounted for). Sources for payments are official documents about payments in each area, as reported by each local expert. 3.3 The model Motivation of the chosen approach On the basis of the analysis of the literature performed in section 2, the chosen model is a multi-criteria dynamic programming model of the farm household. The choice is motivated by the following considerations. 1. Future models have to face a break in the policy design framework (decoupling) and in the social and economic context (product markets, income sources) characterizing farming-related decisions. Two major points in this changing context are the increasing role of rural households/rural firms as the agents (decision makers) affected by the policy, and the changed role of agriculture in rural economies and in 47/187

50 the overall economy. These issues will modify farming in ways that could not be understood exclusively on the basis of past behaviour. 2. The need to search for innovative simulation tools or for innovative applications of existing tools to improve the ex-ante understanding of policy reforms is recognized in the literature. 3. Given the specific need to analyse policy behaviour in the next 8 12 years, an exante approach is explicitly requested by the objectives of the study. 4. For the nature of investments, a dynamic approach seems necessary, as recognized by all recent literature on this issue. 5. Optimization (programming) models are preferred to econometric models, to focus on future issues and not be conditioned by data available from previous surveys. This choice is also due to the possibility of treating different kinds of investment in greater detail and with explicit technical connections to farm activities, farm resources and financial constraints. 6. Literature on farm investment behaviour seems to lack in particular a technical representation of current and future assets in which to invest. This may be irrelevant for some capital goods (e.g. land) but a key issue for others (e.g. machinery, plant connected to new activities such as energy production). The approach adopted allows such representation by integrating engineeristic with economic information. 7. As the focus of the project is on how farmers will react to the SFP, modelling of the payments under Agenda 2000, the Luxembourg compromise and alternative scenarios will have a major part in the model. 8. As labour decisions and savings are key determinants of investments and interact with income production and consumption, as well as whole-family risk management, a farm household model that is non-separable in labour and capital management is considered preferable. This will also allow the derivation of information about the destination of money from the SFP in the trade-off between different on-farm and off-farm investments. 9. Uncertainty should be considered, at least as related to policy and market variables. 10. Particularly when taking a household perspective, it is expected that a multiobjective model improves the reliability of model outcomes. However, the relevance of different objectives is an empirical matter and should be checked case by case. 11. A non-linear specification of the model is preferable to prevent the model from generating extreme unrealistic solutions and to allow for greater flexibility; however, in the context of the chosen methodology, discontinuous linear approximation is considered more consistent and computationally tractable. 12. Output includes economic, social and environmental indicators. 13. The project objectives call for the use of a farming system approach. For this reason, the model is (individually) applied to a selection of farm households based on location and specialization criteria, and not to a whole territory. Extensions to territorial level could be considered in the future, particularly to manage more directly the issue of the connection between land exchanges, farm structure and investment. The modelling approach suggested requires adequate (important) data collection from single farms. For this reason, it is necessary to manage carefully the trade-off between sufficient detail and a satisfactory degree of representativeness. 48/187

51 Given the purposes of the project, the latter is somewhat sacrificed to the former. In other words, the research is expected to produce empirical, relevant information on investment mechanisms and policy impact, even if this is done at the expense of the degree of statistical representativeness. However, differentiation of the main drivers across farms is considered, according to the safeguard of sufficiently reliable model calibration, given the study coverage requested. References for the modelling part of the study have been identified around three main works. Gardebroeck and Oude Lansik (2004) provide a comprehensive theoretical model, building on the main literature concerning investments. Asseldonk et al. (1999) provides a programming approach to farm technology adoption, including technology change. This is only an example of the vast literature using dynamic programming as a computational instrument for investment behaviour. Wallace and Moss (2002) provide a multi-criteria model applied to strategic decisions from the viewpoint of the farm household. Relatively few papers use multi-criteria analysis in combination with multi-period planning. These have been integrated with papers from the household modelling, credit analysis and policy evaluation literature (see section 2). Given the specific aim of the project, papers directly devoted to assessing the impact of decoupling on investment have been also considered to identify relevant variables, particularly Andersson (2004), OECD (2001; 2005a; 2005b; 2005c; 2005d), Roche and McQuinn (2004), Sckokai and Moro (2006), Peerlings (2005), Goodwin and Mishra (2005), Happe (2004) and Lagerkvist (2005) The theoretical model The model is designed to simulate farm investment behaviour in the face of external scenarios. It receives as input the values of scenario parameters (exogenous to the farm represented) and produces as output a computation of sustainability indicators for each scenario considered. The impact of different scenarios is assessed through comparison with the baseline scenario. The theoretical model for household-level decision making, based on the multi-criteria approach, follows the following maximization approach (see symbols box at the end of the section): [ ] ( x) F z ( x) z ( x),... z ( x) z ( x) Z = 1, 2 q.., Q (1) s.t. x X (2) x 0 (3) The objective function is a representation of household utility. The farm household is expected to maximize a function defined as a combination of multiple criteria, each defined as a function of the set of decision variables. The maximization is subject to constraints on decision variables, represented by the feasible set and by non-negativity constraints. 49/187

52 In translating this to the empirical model, we need to define in more detail the structure of the objective function and the feasible set The empirical model objective function Models attempting to interpret household behaviour (instead of simple farm optimization) tend to move from the net present value (NPV) approach to a consideration of multiple criteria (e.g. consumption, household worth). However, whether farm households make investment choices in a way that is better represented through multi-criteria decision making is an empirical issue. In practice, in the long run, the objective function may often be relaxed to a unique objective represented by the maximization of the net household cash flow. This may be a debatable issue in theoretical terms, but it is acceptable to think that there could be cases where criteria other than profit maximization add little to the fitness of the model, and cases where they may be determinant. In all cases, the NPV model can be taken as a benchmark. The choice of this study is to run models in both the mono and multicriteria form and to choose the best fitting version for simulation (see annex III). More in detail the model has been finally solved in following two forms: 1. a fully dynamic NPV maximisig model; 2. a recursive dynamic multiobjective model. In the former case, the objective function is expressed by a standard NPV calculation over the time horizon. In the latter, the model is first solved as fully dynamic for a shorter time horizon (n). Then, the choices for year 1 are kept fixed and the model is solved for the horizon from time 1+1 to n+1. The procedure continues in the same way, moving ahead 1 year at each step, until the initial time is equal to m, which is the number of years for which we want to generate results from the model. The time horizon is chosen according to that most likely used by the farms, which derives from their answers concerning future variables. In case 1 the objective function takes the following form: Max ρ ty t (4) t where: a l c I tc p Y t = yt + yt + yt + yt yt + yt (5) a p p y = x, gm v υ (6) t i i t i m m l out out in in y t = lh, t wh l j, t w j (7) h j c + + t = ct r ct r (8) I + t = I m, t, τ km, τ I m, t, τ km, τ (9) m τ m τ tc + + t = TC I m t, τ km, τ + TC I m, t, τ km, τ m τ m τ p d t = xi t i, t + Ψt i y y y y, (10), ψ (11) 50/187

53 In case 2, the objective function took a multi-criteria structure. Depending on the information collected, the achievement of objectives is treated following two ways: objectives absolutely to be achieved are incorporated as constraints in the model, particularly if related to the consumption component; objectives for which there is a degree of flexibility (compensability/trade-off) are incorporated in the objective function. The two conditions do not exclude each other, and we may find for the same objective a minimum constraint up to some level, and the possibility of maximizing the objective above that level. If the farmer states that there is a minimum level of certain objectives below which he is not willing to accept the plan, constraints are added, such as: z (12) min q z q These constraints are to be handled carefully, as they could lead to infeasibility and should be used only when there is realistic strong opposition to some very low value of an objective. In most cases, these minimum requirements are included in C t (consumption). If the model reduces to a NPV maximizing model with some constraints on objectives, such as consumption, C t can take the form of a parameter. In the other cases, C t and other objectives are not defined as parameters, but as variables taking values defined by the maximization of the objective function and bounded by the inequality constraints. For flexible objectives, which the household accepts to trade-off against each another a simple multi-criteria objective function was used (Romero and Rehman, 2003): Max Z = ω (16) q q z q The value of each attribute is calculated using a specific procedure, depending on the nature of the objective (e.g. household net worth, leisure). Consumption is a free variable, bounded by income possibility and investment requirements. Some objectives derive from the activities performed on the farm (e.g. crops) and are generally calculated as the average value of objective/attribute z q over time: 1 = T z q aiq xi, t t i Objectives have been collected through the questionnaire. A list has been proposed taking into account potential household objectives (e.g. consumption level, leisure, household wealth). Weights are derived from the ranking of objectives given by the household, using the rank reciprocal formula in the first instance (Wallace and Moss, from Stillwell et al., 1981). (17) The empirical model constraints and feasibility set The constraints and equations defining the feasibility set are the following. 51/187

54 Investment and capital: + I I + I I (18) k m, t, τ = m, t 1, τ 1 m, t, τ m, t, τ = γ k (19) m, τ m, τ m,0 K I = I,, k, + χ (20) t m τ i m 1,τ I m, τ m t τ m τ t, = (21) I = I m, T, τ m, T,τ This group of equations, i.e. from (18) to (22), describes capital and investment relations. In equation (18) capital at time t is related to capital at time t-1, plus investments, minus disinvestments. The value of each capital good is calculated in equation (19), while the value of the total household capital is calculated in equation (20). Equations (21) and (22) assign the initial capital endowment and, respectively, force the model to sell all capital at time T (this is necessary to force the model to take into account the salvage value of all capital when taking decisions close to the end of the time horizon). Activities: x, a rhs (23) i t i, s s i i tai, z I m, t, vm, z i m gm i, t i pi, t ei, t x, τ + v (24) p m = µ (25) Equation (23) is the standard set of constraints of a mathematical programming model ensuring that the solution is feasible. Equation (24) ensures that the amount of investment services (e.g., hours of work of a specific machinery) required by farm activities is available from capital goods plus rented services. Equation (25) is a simple computation of gross margin. Liquidity, credit and external investment: S = Y C (26) t t t = χ t 1 + S t 1 χ t (27) + + in in tc I m, t, τ km, τ + l j, t w j t + yt + xi ei + ct χ t + θt (28), m q j i θ δ (29) t K t This group of equations, i.e. from (26) to (29), defines the relationships between capital, liquidity and investment. First, savings are defined as the difference between income and consumption (equation 26), and liquidity at year t is defined as the sum of liquidity of year t-1 and the savings of year t-1 (equation 27). In addition, liquidity requirements are constrained to liquidity availability (equation 28) and access to credit is constrained to some share of total capital owned (equation 29). Labour: (22) 52/187

55 l out t in x i, tai + l h, t Lh, t + l j, t (30) i Equation (30) constraints labour use (on- and off-farm) to labour availability (own and purchased). Payments: Ψ d t = SFP i x i, t n n u i (31) Payments are calculated based on owned entitlements, after adjustment based on eligible land uses. Positivity constraints: x,, l in i t j, out h l, I m,t, τ, + I m,t,τ, I m,t,τ, + c t, c t, S t, θ t, χt 0 (32) Depreciation is linear with age. Equation (23) covers relevant technical constraints. These are very different from case to case and have been designed as the most appropriate. In general, the most common issues have been: - management of intermediate products, such as feeding with own-produced fodder, use/handling of organic waste from animals; - crop rotation; - market constraints; - land, quotas and production rights are generally treated in the category of investments. As the model refers to individual farms, it is not particularly appropriate for the treatment of structural change and land exchanges. To keep the model conservative (i.e. avoiding unrealistic increase of the farm through land purchase), the possibility of expansion of the farm is allowed only when land purchase is already planned. In other cases, land availability is considered as fixed and propensity to expansion will be judged on the basis of the marginal value of land. Transaction costs have been included to avoid unrealistic indifference about buying and selling an item or keeping it. However, transaction costs are a very complicated issue, and we could not consider collecting the needed amount of information through the survey. Accordingly, during the testing, we tried to estimate a reasonable time for the conclusion of transactions, plus the associated administrative costs. It was found that this value may vary considerably from a farm to another, therefore it has been approximated as a uniform percentage of asset value (20%). Further clarification is required about the following issues: uncertainty and risk aversion; non-linearities; technical change. 53/187

56 The model described above is deterministic. However, uncertainty is a major component of investment decisions in many circumstances and is the main point of much of the literature concerning investment. Many of the parameters of the model could be treated as uncertain from the decision maker s viewpoint. When dealing with such issues using the model explained above, however, we must consider that much uncertainty or risk consideration may have already been captured, in either the decision rules or the objectives. For example, multi-criteria analysis may already incorporate many aspects of uncertainty; crop combinations or rotations may solve risk concerns. For these reasons, the main idea here is to try first to treat such problems through the constraints and objective function of the basic model. Whether this is satisfactory is checked through the calibration and validation process; that is, by checking whether the values generated by the model are reasonably close to those planned (in our case those that the farmer has stated as intentions for next 5 years) (see section 3.3. for details). The model is designed as a linear problem primarily for simplicity of computation. Also, as the model requires mixed integer solutions for investment decisions, adding non-linearities to integer variables could make the solution more difficult. Non-linear components have been treated through piecewise or discontinuous linear functions, for all aspects for which the model reaches a sufficient degree of detail. For example, household labour have been attributed a different opportunity cost depending on stated off-farming salary of each component. This is a widely used solution in linear programming models (Hazell and Norton, 1986; Hillier and Lieberman, 2005). The analysis of technical change, though relevant, is not an explicit objective of this study. In the model, technical change is considered only as incorporated in possible investments and not as a separate variable. This means that there will not be differences (e.g. yields) across scenarios, or regular changes in yields over time. However, investment in a different (e.g. technically improved) piece of machinery may imply different labour productivity. This choice is driven by the attempt to limit the number of variables determining the results of the model and make them more interpretable Output indicators Output indicators include the following. Economic: farming income; total household income; net investment. Social: labour use. Environmental: nitrogen use on land; 54/187

57 water use. The calculation of economic and social sustainability indicators, as well as the share of noncultivated land is straightforward, as the variables included may be directly derived from the core model and the objective function. Nitrogen and water use indicators are directly derived from the combination of activities through appropriate environmental coefficients: E o = xi, tai, o (33) i Time horizon Results focus on farm investment and its impact over an 8 12-year period from the time that the survey is performed. As investments are decided on the basis of a reasonable time horizon over which their effects are evaluated by the decision maker, a longer time period was considered in the model, to justify investment choices during the last years of the period considered. Taking into account these requirements, models were solved on a 25 years time horizon (by steps of 8 years in the recursive version), setting the final year at This period appears to be long enough to assess the profitability of most investments and is consistent with the timescale of at least some of the scenario exercises available at present (even if most of them stop between 2015 and 2020). In order to avoid problems with choices related to the final period of the planning horizon (e.g. lack of investment, forced selling of capital good in the final year), results are given as average of two shorter periods: and The first period corresponds to the present programming period of the CAP and the final year is consistent with the expected end of such period. For the initial year, the decisions on the farm were already taken when the information was collected. Thus, the actual planning horizon is 7 years and 2006 represents the initial conditions (e.g. existing capital). BOX 1 Symbols used Parameters and variables (v in parentheses=variable) Z = objective function; z = value of attribute/objective q; q min z q = minimum achievement required for each objective; X = feasible set; x = vector of decision variables; ρ t = discounting factor; Y t = total farm household income (v); a y t = household cash flow from production activities, including farming (v); l y t = household cash flow from labour: external household labour minus hired labour (v); 55/187

58 c y t = household cash flow from liquid capital management: rents from investment in non-durable goods minus cost of credit (v); y = cash flow from investment and disinvestment activities (v); I t tc y t = transaction costs connected to investment/disinvestment (v); p y t = cash flow from agricultural policy payments (v); x, = degree of activation of productive activity i (v); i t gm i = gross margin from productive activity i; in l, = labour purchase of type j (v); j t in w j = cost of labour purchase of type j; l, = labour selling (v); out h t out w h = wages from labour selling of type h; + c t, c t = purchase of liquidity (access to credit), investment of liquidity in non-durable goods outside the farm (v); r +, r = interest rate paid on credit, interest rate gained on liquidity and related uses (e.g. bonds); I I I = number of capital goods, investment and disinvestment activities of type m and + m, t, τ, m, t, τ, m, t, τ age τ at time t (v); k = value of capital goods m, depending on age; m,τ TC +, TC = transaction costs on, respectively, investment and disinvestment as a percentage of the value of investment/disinvestment; d ψ, Ψ = area based and decoupled payment (v), respectively; i, t t C t = consumption; a = coefficient of the objective q for the activity i; iq objective q as a result of a unit increase in activity i; ω = weight of attribute q; q d, d = normalized distances from goal; + q q g q = goal for attribute q; χ t = liquidity; γ m,τ i I m τ = depreciation coefficient for capital goods;, = stock of capital good m on the farm in the initial year (2006); aiq quantifies the change in the value of rhs s = right hand side: availability of resource s; l, a, a a = technical coefficients with respect to farm resource s, investment, labour use and a i, s i, z i, i, o environmental impact; v, = amount of investment service z produced by investment m; m z p v m = purchased amount of investment service z; 56/187

59 p υ m = price of purchased investment service z; S = savings (v); t p, = product price of activity i; i t µ i = yield of activity i; e, = variable costs of activity i; i t θ t = credit (v); K t = value of household s capital stock (v); δ = maximum debt/asset ratio allowed; t L, = labour availability of type h in the household; h t SFP = single farm payment; u n, n t = total and used payment entitlements (v) in each year, where the latter depends on the crops cultivated; E = value of output indicator o. o Sets q = objectives; t=1, 2,T = time/years in the planning period, with T = time horizon; i = activities (e.g. crops); j = labour type for purchase (non household); h = labour type for selling (household); m = types of capital goods; τ = age of capital goods; s = farm resources and constraints (different from land, labour or capital); z = investment services; o = output indicator Model implementation procedure, calibration and validation As described in Figure 4, the models were built based on primary data collected through the questionnaires and secondary data derived from available sources. The calibration and validation of models under the selected modelling approach is a particularly challenging task. As a general definition: calibration involves changing the parameterization of the model in such a way that the model fits the observed farm behaviour well; validation involves measuring the distance of the calibrated model from the observed behaviour (exogenous to the model). Appropriate calibration has been a key issue in this modelling exercise, as the model has been used for policy analysis; that is, used to generate results under varying values of exogenous 57/187

60 variables, such as payments and prices (Howitt, 2005). However, there is almost nothing in the literature on calibration of dynamic programming models. The main reference for validation of these models is still given by Hazell and Norton (1986) that identifies the following points: - check whether the model constraint set allows the base year production; - comparing the model s output (activity set) with the behaviour of the real farmers in the base year; - check whether the marginal costs of production, including the implicit opportunity costs of fixed inputs, are equal to the output price; - check whether the dual value on land is equal to actual rental values; - check whether input use corresponds to base year input use. These criteria basically allow to check the ability of the model to reproduce the base years in static models but may be of little help in judging the ability to react to external changes, particularly shocks and to reproduce dynamics. On the other hand, our approach can not profit easily from the many recent approaches developed by programming theory (e.g. PMP) and related statistically robust estimation techniques. In fact, there would be limits to the application of such techniques due to the data set we rely on, and simple calibration on past behaviour could be misleading in understanding policy breaks. We model individual farms, where effects due to average values are less relevant and discontinuities and extreme solutions are not so unusual compared with territorial models. Also, the number of activities entering the plan may be not as high as for an aggregated model. Calibration has been performed for each farm separately (i.e. one model has been produced for each farm case). The calibration process used mainly data from the questionnaire designed for the purposes of the project. Existing secondary data have been considered where available and needed. Calibration on primary data is the focus of the project, particularly as it relies on meaningful information on farm constraints, household behaviour, expectations and attitudes towards investment. The collection of primary information on the farm and its decision-making process plays a key role here, and this justifies the choice to focus on a limited number of farms with accurate data collection and interpretation for each farm. The calibration process is performed by including in the model parameters/decision rules/constraints derived from the questionnaire, particularly concerning: - allowable activities (derived from past, present and possible future activities as stated by the farmers); - land and labour availability (by type/quality, if required); - rotations and interconnections between activities (e.g. forage and livestock); - contracts; - liquidity and credit. The degree to which these constraints vary over time may be an issue and has been evaluated case by case. Final validation of the model is performed by: 58/187

61 1. comparing the model s output (activity set and investment) with the behaviour of the real farmers in the base year; 2. comparing model s output (activity set and investment) with the intentions stated by the farmers for the next 5 years under the actual conditions. The first comparison reflects the classical approach to model validation that can be found in the literature (Howitt, 2005). The second comparison is used specifically given that information about activity and investment intended behaviour for the next years was available from the survey. Specifically, this includes checks on: - the feasibility of the stated investment and activity plan; - the distance between the stated investment and activity plan on one side, and on the other side the planned investment and activity plan generated by the model. To test the stability of the model, a sensitivity analysis has been performed on the main calibration parameters connected to activities and constraints showing the lowest marginal values. This has been performed case by case and the outcomes are not given in this report. Finally, to test the validity of the model output, for a small number of farms, the results have been discussed with farmers. The model implementation and elaboration has been performed using GAMS, the most common software for economic optimization models (McCarl, 2004). Given the nature of investment choices (and possibly of other components of decision making), the model is cast as a mixed integer problem. Preliminary analysis on test farms gave satisfactory solution performance using the solver CPLEX, with a 1% tolerance in the search for integer solutions. 4 Case studies: description and sample selection 4.1 Coverage and sampling rationale The study covers the following combinations of areas, types of farming and farming systems defined ex-ante: (i) plain continental regions, (ii) plain Mediterranean regions, (iii) hilly/mountainous continental regions, and (iv) hilly/mountainous Mediterranean regions; for each area, the types of farming are: (i) predominantly crop farming systems, (ii) predominantly livestock farming systems, and (iii) predominantly orchard/vineyard/forest (tree) farming systems; for each area and types of farming, both conventional and emerging farming systems are considered. In the following, the combination of the above variables is referred to as a case study, while the individual farm-households surveyed are referred as farm-household case studies. Farming systems are defined as in the final report of the study Prospective Analysis of Agricultural Systems (Libeau-Dulos and Rodriguez Cerezo, 2004). Accordingly, the term conventional farming system refers to the ongoing most common production techniques. The term emerging farming system refers to organic farming, integrated agriculture, conservation 59/187

62 agriculture, and guaranteed quality systems. However, in the present study, among emerging farming systems, organic farming has received the most attention since it is of greatest relevance compared to the other emerging systems in several selected countries, including Germany and Italy. The classification of a farm into one of the systems is primarily based on the type of farming (using Eurostat categories). However, mixed farms have also been considered and classified according to their predominant business activity, based on the judgment of the local expert involved in the sample selection. Wherever very specialised farms have been selected, this could lead to the detection of small changes as a reaction to different scenarios, as it would be expected that mixed farms may be more flexible and able/willing to consider different crops when reacting to external pressures. However, given the study design, it was important to include at least some farms that are specialized enough to characterize the system to which they belong and to allow the formulation of comparisons based on that system. Given the relatively small number of questionnaires, the samples have been selected through a non-completely-random methodology based on a proportional stratified sample rationale complemented with expert judgment in order to deal with the (un)availability of secondary information related to some of the important variables in investment behaviour, e.g. personal attitudes. Sample selection involved the following steps. In each study region, case studies ( predominantly crop, predominantly livestock, etc. ) were selected according to the following criteria: a) land allocation; b) number of farms; c) the value of production. Land allocation, as well as number of farms, are available from general statistics (2000 harmonised census) and for this reason are identified as the main criteria. The value of production has been taken from local sources and represents an additional (though possibly correlated) criterion. For each case study, single farms were selected in order to reflect the expected heterogeneity of farm investment behaviour, based on the literature review. In spite of the number of potentially relevant variables, only farm size and farmer s age were known ex ante and were used for sample selection. A proportional stratified sample rationale was used. However, in most cases, expert judgement was required to adapt proportions to the small number of households in the sample and to compensate lack of statistical information (e.g. age, farm size, non-farming activities). 4.2 Data collection A questionnaire was designed to collect data about the farm and the household, their perspectives and intended investment behaviour, their reaction to policy changes. It was also aimed at collecting technical and economic information on production processes, to feed the models. The structure of the questionnaire is the following 9 : 1. Location and contact details 2. Farm structure 3. Household structure and labour management 9 The full questionnaire is included in this document as Annex I. A version with further explanations and accompanying instructions is available in the interim report 2 (Gallerani et al., 2006). 60/187

63 4. Farm activities and production 5. Farm organisation, constraints and connections 6. Policy and decoupling 7. Farm household assets and past investments/disinvestments 8. Vision of the future & expectations 9. Household status and objectives 10. Foreseen farm-household and farm developments 11. Activity-related details Section 6 is devoted to collect straight information about the household s reaction to decoupling. It includes the collection of the following information: a) Single farm payment received b) Use of money from the Single farm payment c) Other payments received (e.g. axis 1 RDP, etc.) d) Use of money from other payments received e) What are or are expected to be the changes in the farm/household as a reaction to the introduction of the single farm payment The survey was conducted through face-to-face interviews, by the local teams. The interview took between 1 and 3 hours depending on the complexity of each farm interviewed. Most of the questionnaire was properly completed by the farmers that accepted to be interviewed. Problems where encountered more frequently with section 7 and 11. Section 7 includes some types of information, such as family wealth and incomes, that were sensitive enough to induce a refusal to answer by many interviewees. Secondary data have been used when necessary, based on average salaries or average asset prices. Section 11 includes a large amount of detailed information about crops and livestock. In many cases this was not available to the farmer. In other cases the questions were found to be too detailed and tedious to be collected through interviews. However, at least crop and livestock yields were asked in order to allow for cross-checking across the farms interviewed and with the secondary data used. Secondary data, when necessary for the modelling exercise, have been preferentially obtained from local studies. The Farm Accountancy Data Network (FADN) has been used when data have been judged consistent with the specific case studies. This source has been used for yields, prices and production costs. Information from local sources haves been used for labour inputs and asset values. In all cases expert knowledge has been used to ensure, as far as possible, consistency with individual data from the questionnaires. 4.3 Case study areas A summary of case study areas is reported in Table 2, while Table 3 provides a summary of CAP reform implementation in the selected case study areas. 61/187

64 Table 2 Case study areas Country NUTS 1 NUTS 2 NUTS 3 Area Germany Schleswig- Holstein, Niedersachsen, Nordrhein- Westfalen, Rhein- Pfalz, Hessen, Baden- Württemberg, Bayern Greece Central Macedonia Pieria, Kilkis, Thessaloniki Spain Andalucia Cordoba, Sevilla France Centre Eure-et-Loir Beauce Chartraine Italy Hungary Emilia Romagna Eszak-Alfold (North Great Plain) Bologna, Modena, Ferrara, Ravenna Hajdú-Bihar Netherlands Poland Gelderland Mazowieckie, Swietokrzyskie, Malopolskie, Kujawskopomorskie, Pomorskie Kamerik, De Glind, Lunteren, Voorthuizen, Ede, Wageningen, Putten, Bennekom 62/187

65 Table 3 Implementation of the CAP and CAP reform in case study areas Country Start SPS Model Sectors remaining coupled Second wave of CAP SPS dynamic - hops payments 25% coupled Germany 2005 hybrid moving to a flat rate model - tobacco coefficient for decoupling: 0.4 Greece 2006 SPS historical Spain 2006 SPS historical France 2006 SPS historical Italy 2005 SPS historical - seeds - Article 69 application: = 10% of the ceiling for arable crops, = 10% of the ceiling for the beef sector, = 5% of the ceiling for the sheep and goat sector. - seeds 100% - arable crops 25% - sheep and goat premiums 50% - suckler cow premium 100% - slaughter premium calves 100% - slaughter premium bovine adults 40% - Article 69 application: = 7% of the ceiling for the bovine sector, = 10% of the ceiling for dairy payments - outermost regions 100% - arable crops 25% - sheep and goat premium 50% - suckler cow premium 100% - slaughter premium calves 100% - slaughter premium bovine adults 40% - seeds (some species) - outermost regions 100% - seeds 100% - Article 69 for quality production = 8% of the ceiling for the arable sector, = 7% of the ceiling for the bovine sector, - Article 69 application: = 2% of the ceiling for tobacco, = 4% of the ceiling for olive oil, =10% of the ceiling for sugar - 2% deduction in the olive oil sector for the funding of working programmes established by producer organisations (Art 110 (i) of 1782/2003 and Art. 8 of Reg. 865/2003). Annex VII point H and I: - tobacco and olive oil coefficient for decoupling: 1 - tobacco coefficient for decoupling: olive oil coefficient for decoupling: Article 69 application: = 5% of the ceiling for the tobacco sector, = 10% of the ceiling for the cotton sector, = 10% of the ceiling for sugar - 10% deduction in the olive oil sector for the funding of working programmes established by producer organisations (Art 110 (i) of 1782/2003 and Art. 8 of Reg. 865/2003) - hops payments 25% coupled Annex VII point H and I: - olive oil coefficient for decoupling: 1 - tobacco coefficient for decoupling: Article 69 application: = 8% of the ceiling for sugar - 5% deduction in the olive oil sector for the funding of working programmes established by producer organisations 63/187

66 = 5% of the ceiling for the sheep and goat sector (Art 110 (i) of 1782/2003 and Art. 8 of Reg. 865/2003) Annex VII point H and I: - olive oil coefficient for decoupling is increased to 1 - tobacco coefficient for decoupling: for the region Puglia, the decoupling coefficient for tobacco is 100% Hungary SAPS - separate sugar payments Netherlands 2006 SPS historical - slaughter premium calves 100% - slaughter premium bovine adults 100% - seeds for fibre flax 100% Poland SAPS - separate sugar payments Source: European Commission, /187

67 Germany, Italy and Poland were the main target of the study. Germany and Italy were the largest countries implementing the SFP since 2005, adopting different decoupling mechanisms. Poland provides a case of a country in Eastern Europe, with an important agricultural sector and with a different policy setting, characterised by increasing area payments through the SAPS scheme. The cases studies in the other countries were selected by following complementarity criteria. In particular: Spain and Greece complement the results from Italy with purely Mediterranean areas; Hungary complements Poland with case studies from a different new Member State; Netherlands complements other case studies as a typical central-northern EU country; France represents an important share of agriculture in the EU 15, furthermore it is a benchmark country for the SFP implementation, as it applied a partial decoupling. 4.4 Case studies and sample description A summary of the case studies analysed in the study with the number of questionnaires is shown in Table 4. Table 4 Summary of case studies and farms surveyed (number of questionnaires) Technology Area Specialisation DE ES FR GR HU IT NL PL Total Mountain Arable Livestock Trees Conventional Plain Arable Livestock Trees Mountain Arable Livestock Emerging Trees Plain Arable Livestock Trees Total Altogether, 248 farms were surveyed, distributed into 43 case studies. Of these, 33 were located in the three countries chosen as the main targets of the study (Italy, Germany and Poland). Of the 248 household case studies, 195 were conducted in Italy, Germany or Poland. Questionnaires were asymmetrically distributed among conventional and emerging farming systems, with a higher number for the former (166) compared to the latter (82). It must be emphasised once again that the main point of the study was to understand the mechanisms through which farmers are reacting to policy. Accordingly, the choice of the number of cases in the survey came from an assessment of the trade-off between in-depth analysis and coverage, given resource constraints, being aware that it may be somewhat unsatisfactory in terms of extrapolation and generalisation of the results. Sample composition in Italy, Germany and Poland was designed to cover all the production specialisations that were chosen ex-ante (Table 4). However, for some of them, namely emerging mountain arable and trees in Poland as well as emerging plain trees in Germany, it was not possible to identify relevant examples (with the exception of very peculiar cases that were excluded). Case studies in Spain dealt with 16 farms growing olive and citrus fruit (plus one arable); selected farms are both conventional and organic. Case studies in Greece included 12 arable production farms, 65/187

68 both conventional and organic. In the Netherlands case studies involved dairy farming and included 12 farms. Selected farms are conventional and organic. Case studies in France involved arable farming. The 6 selected farms are conventional only. The Hungarian case study considered arable crops and livestock (dairy, beef and pork) production. Six farms were considered altogether; they are all conventional. Basic sample statistics are given in Table 5, while more detailed statistics by case study are provided in Annex II. Table 5 Sample descriptives Sample descriptive statistics Minimum Maximum Mean Std. Deviation % of farms with positive value Family farms (%) Age of farm head (years) % Succesor (% of yes) % - - Household head labour on farm (hours/year) % Household head labour off farm (hours/year) % Household labour on farm (hours/year) % Household labour off farm (hours/year) % Total external labour purchased (hours/year) % Owned land (ha) % Land rented in (ha) % Land rented in (% of total farm area) Land rented out (ha) % Total land (ha) % Share of organic products (%) % Debt/asset ratio % SFP amount in 2005 (euro/farm) % SFP amount in 2006 (euro/farm) % The legal status of the farms was normally individual/family farms. These were the main target of the project and also comprise the only type of farm in Greece, the Netherlands and Poland. In Hungary farms are equally distributed between family farms and limited liability companies, and the latter category is of major importance in this country compared with other case studies. In Italy, the majority of farms are family/individual farms; however, a relevant number of limited liability companies was included. Those were, in most cases, formalisations of relationships between family members who are in fact the only participants in the company. At least for Italy this hints at a possible evolution of the most professional family farms, where different members take part in farming activities. The age of the farm head/manager covered a very wide range, though in the majority of cases it was concentrated between the mid-forties and mid-fifties, making the sample younger than the national average in most countries. About 50% of the farm heads have a successor to maintain farming. The average labour availability per household was rather varied across countries. The share of off-farm labour was even more varied across cases. While in Greece and Hungary all labour is dedicated to the farm, in France and Spain off-farm labour tends to prevail. Italy is in an intermediate position. Livestock and fruit farming tend to require greater participation of household labour on the farm. The farms in the sample were rather large compared with the respective national averages. The largest farms are those in Hungary and the annual crop producers in France and Italy, as well as emerging livestock producers. 66/187

69 Renting plays a major role in land availability, particularly for annual crops and livestock. In most case studies except Poland, the Netherlands and, to a lesser extent, Italy, rented land accounted for a share of the farm area equal to or higher than owned land. This reflects the difficulties for farmers trying to expand by buying land and, at the same time, hints at the important role of rent in the structural adjustment process. This is consistent with a general trend throughout Europe. At the same time, this feature qualifies the sample as being composed of mainly expanding farms. This issue will be further developed in the following section. Contrary to the primary role that renting land plays for annual crops and livestock, tree crops tend to encourage ownership-based land usage. The modelling exercise has been performed on a subsample of the households surveyed. A summary of the models built is given in Table 6. Table 6 Number of models Area Specialisation Technology DE ES FR GR HU IT NL PL Total Arable Conventional Emerging Mountain Livestock Conventional Emerging Arable Conventional Emerging Plain Livestock Conventional Emerging Trees Conventional 4 4 Total The households modelled were qualitatively identified as those representing the mode of each group within each country case study, that is less affected by individual specificities. Exclusion criteria included: fruit and other tree farms, as only marginally affected by CAP, with the exception of olive production in Spain; farms/households with prevailing non-farming, agritourism or agro-industry activities; the main reason is that economic data collected for non-farming activities were found to be rather unreliable; livestock farms that were more strongly specialised in pigs and poultry production, as opposed to mainly dairy farms, that were maintained as the main focus of the research; farms/households with very singular individual features, e.g. exceptionally large or small, with special constraints on land quality, etc. In the chosen methodological approach, which is based on a small number of farms, it should be emphasised that, given the criteria for selecting the farms to be modelled, the quality and reliability of the results do not strictly depend on the number of models. However, enlarging the sample of modelled farms helps in understanding the variety of possible reactions to policy scenarios. It can also help in moderating the effects of extreme behaviours (e.g. abandonment) on the average outcome. As average results can not be expected to be statistically representative of whole areas and do not fully account for the variability of effects across different farms, results for individual farm-households are a complementary important information for allowing a deeper understanding of the mechanisms of change. Such results are provided in Annex IV. 67/187

70 5 Results 5.1 Farm perspectives and strategies In this chapter the main results are presented. The first section is dedicated to the description of household perspectives, objectives and strategies. Among the objectives, reducing income uncertainty was by far the most highly ranked (about 70% of households put it first or second), followed by household worth (value of household assets), which was most often ranked second (Table 7). Table 7 Main objectives of farm households (number of answers per ranking position) Rank Reducing income uncertainty Household worth Household consumption Household debt/asset ratio Leisure time Diversification in household activities Others From qualitative insights gained from the interviews, the main component of uncertainty was associated with unclear expectations about the future of farming income, mostly related to a general concern for the future of the farming sector as a whole and to the trends in agricultural prices in recent years. A simple interpretation of uncertainty in terms of income variability is not possible. The leading constraint to farm development identified by the respondents was related to the market share of key products; that was ranked first or second by about half of the interviewees (Table 8). Table 8 Main constraints to farm development (number of answers per ranking position) Market share/contract of key products Liquidity availability Land availability from neighbouring Total household labour availability External labour availability in key periods Household labour availability in key periods Total external labour availability Long term credit availability Short term credit availability Others It was followed by land and liquidity availability, of more or less the same importance. The importance of land availability is consistent with the growing role of rent. It is interesting to note that, while liquidity was listed as an important constraint, credit availability itself was not perceived as a major problem, which could suggest problems with the cost, rather than with availability, of hired capital. Labour constraints had a minor relevance and mainly impacted on the more labour intensive systems. Altogether, the main problems are perceived on the output (product market) side; however, limits remain also on the input side, in which constraints due to land and capital markets are almost equally important, followed by labour. 68/187

71 A high proportion of the farms in the sample use credit (Table 9). Short-term credit is used by about half of the farms, especially livestock and orchard farms. Emerging systems show remarkable differences in the use of credit compared to conventional in single systems. However it is not possible to identify a generally different attitude. Long-term credit is more frequently used in livestock farms in plain areas, where more than half use this type of credit. Table 9 Credit accessed by farms (% of the total number of farms in each system) Technology Area Specialisation short term medium term long term no credit Crop Mountain Livestock CONVENTIONAL Orchard/vineyard/forest Crop Plain Livestock Orchard/vineyard/forest Crop Mountain Livestock EMERGING Orchard/vineyard/forest Crop Plain Livestock Orchard/vineyard/forest Most farms show a high degree of integration in the food chain, as quantified by production contracts (Table 10). 69/187

72 farm) Table 10 Production contracts in place (number of farms per number of production contracts per Technology Area Specialisation Crop Mountain Livestock CONVENTIONAL Orchard/vineyard/forest Crop Plain Livestock Orchard/vineyard/forest Crop Mountain Livestock EMERGING Orchard/vineyard/forest Crop Plain Livestock Orchard/vineyard/forest Only a small number of farms have no production contracts with buyers (concentrated in the tree sector). Most farms have at least one contract in place, but crop producers often have three to four contracts. Many farmers stated that, according to their expectations, in the future it will not be possible to produce without contracts with the processing industry. The picture emerging from the sample hints at an important bias towards younger, dynamic and expanding farms. This is largely due to the selection effect produced by the complexity of the questionnaire and by the ease of approach for this kind of farm. This effect should be carefully kept in mind when providing the following analysis and comments. On one hand, the average results from the sample cannot be generalised as many aspects of behaviour, reaction to policy in particular, can take opposing directions depending on farm characteristics. On the other hand, the results are somehow more representative of the farms that likely will survive and develop with productive purposes. The results may, therefore, be used as an indication of what the farms of tomorrow will be and do. Expectations about the future (in five years time for prices and after 2013 for policies) are for increases in prices for both agricultural products and production means (Table 11). Table 11 Expectations of prices and payments direction of change (%) Decrease Increase Stable No reply Product prices Agricultural labour cost Cost of agricultural capital goods Cost of other production means Decoupled payments Rural development payments Payments for organic production Coupled payments However, the number of farmers who believe that agricultural product prices will increase is only about two-thirds that of farmers who believe that costs will increase. Payments are expected to decrease with the exception of rural development and organic payments. Expectations about 70/187

73 payments received a lower number of responses, probably revealing difficulties in predicting future policy developments. Prices and payments are not expected to vary a lot on average (again in five years time and after 2013 respectively) (Table 12). Table 12 Expectations about prices and payments sizes of changes Minimum Maximum Mean Std. Deviation Product prices Agricultural labour cost Cost of agricultural capital goods Cost of other production means Decoupled payments Rural development payments Payments for organic production Coupled payments The responses show that the cost of production factors is expected to increase more rapidly than product prices, revealing expectations of a further narrowing of incomes. Reductions in payments are on average of minor importance, with the exception of coupled payments. The expectations show an important range of variability, with extreme expectations forecasting total abolition of payments. However, the standard deviation reveals that a substantial proportion of responses is distributed close to the average. 5.2 Investment behaviour The households in the sample show a positive attitude with respect to investment (Table 13). Off-farm investments On farm investment Table 13 Main intended investments Number of farm households (%) 13% Number investments (%) House (new or 71% restructuring) New car 18% Land 31% Farm buildings 36% Cow houses and milking rooms of 29% Machinery buildings 16% Barns and sheds 11% Machinery 50% Tractors 30% Forage harvesting 12% Soil cultivation 9% 71/187

74 Out of 248 households, 33 (13%) state the intention to carry out a off-farm investment in the next five years. In more than half of households, such investments are expected to be building a new house or restructuring an existing one, in most cases for household use. About one-fifth of offfarm investments consists of a new car. With respect to farming related investments, about 31% of farms state the intention to buy land. The amount of land that is predicted to be bought is only about 7% of the total land already owned and it is concentrated among a few farms. Land purchase intentions are to a large extent concentrated in Poland. The emerging profile is that of purchases aimed at complementary land acquisition, while rent remains the main expansion mechanism. Stated expected prices of land range from about 2700 euro/ha in Hungary to euro/ha in The Netherlands. Out of 248 households, 90 (36%) state the intention to make an investment involving farm buildings, for a total of about 130 investments (roughly 0.5 per farm). In most cases these are cow houses and related parts of buildings (i.e. cow house restructuring and improvements, milking rooms, etc.). A second group of investments, far less relevant, is machinery recovery and analogous items. About 50% of the farms reported intentions to invest in machinery, with about one piece of machinery per farm. Among machinery types, the most frequently cited were tractors (more than 30%). Other types of machinery were very varied, depending largely on the farm specialisation. 14). 5.3 CAP reform and decoupling The amount of CAP payments received by farms varies substantially across systems (Table Table 14 SFP payments received (euro/farm) Technology Area Specialisation DE ES FR GR HU IT NL PL Crop Mountain Livestock CONVENTIONAL Orchard/vineyard/forest Crop Plain Livestock Orchard/vineyard/forest Crop Mountain Livestock EMERGING Orchard/vineyard/forest Crop Plain Livestock Orchard/vineyard/forest Arable crop systems and livestock receive much higher revenues from CAP payments, both as an average per number of hectares and as a total amount per farm. It is relevant to point out that in some systems/countries/farms the CAP payment does not reach an amount high enough to justify any relevant effects on household/farm decision-making. In Italy, for example, payments are limited to a few hundred euros for tree cultivation and are never big sums in mountain areas, except for livestock. On-farm use of SFP is widespread, reaching in many circumstances 100% of the SFP received, while off-farm use is almost irrelevant, with a few small exceptions (Table 15). 72/187

75 Table 15 Stated use of SFP (% of money received) Technology Area Specialisation CONVENTIONAL EMERGING Mountain Plain Mountain Plain Off farm productive current expenditure Off farm productive investment Off farm Off farm nonproductivproductive non- intermediate durable consumption goods On farm current expenditure On farm investment Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Among on-farm uses, covering current expenditure is the main use of SFP money. Basically, use for investment mainly occurs for livestock. Otherwise, only crops in southern Europe show a relevant use for investment. In spite of this clear-cut response, it should be noted that the question itself is problematic. There is no such thing as a specific destination for money. This was noted by many farmers and anticipated in constructing the questionnaire. The SFP contributes to the overall revenue and the revenue is distributed across items of expenditure. However, as the money comes at some stage of the year and as a whole sum, it tends to be associated with some specific use depending on the financial conditions of the farm. In an analogous way, the use of SFP does not provide direct information about changes brought about by decoupling, as it does not contains any information about additional effects solely due to the policy change. For this reason a further question about the impact of the introduction of the SFP (i.e. decoupling) has been asked. For the majority of respondents, the shift to SFP has had no relevant effects on farm choices (55% of the total) (Table 16). Table 16 - Stated effects of SFP (%) Increase investment Technology Area Specialisation On farm Off farm productiv e Off farm nonproductiv CONVENTIONAL EMERGING Mountain Plain Mountain Plain Decrease investment On farm Off farm productiv e Off farm nonproductiv Changes in crop mix Changes in other activities Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest None This occurs in particular for systems where the absolute values of the payments per farm are lower. This is consistent with the expectation that farmers are not sensitive to small changes in payments or to changes in the way small payments are related to production. Among farmers reporting changes, most of the respondents (27%) reported an increase in on-farm investment. This behaviour was concentrated in livestock farms and, to some extent, in trees. It was more frequent on plains. However, a small cluster of farms (6%) also stated the 73/187

76 opposite, by reporting disinvestment. This was more frequent among livestock farms in mountain areas. About 8% reported a change in crop mix. This group mainly belongs to livestock and crop producers. Minor changes (which are difficult to interpret) were reported in off-farm activities. Merging these results with composition of the sample by country, a consistent interpretation can be stated as follows. SFP does not encourage, but at least allows, a higher degree of investment in Eastern Europe (positive answers to the question on increasing investment were largely driven by Polish farms) and in the most competitive systems; this effect is also associated with lower asset endowment (Poland again), or with a greater need for investment (livestock again). SFP tends to encourage disinvestment in less productive (mountain) areas, with a reallocation of activities towards plain areas. It must be made clear that these trends have to be regarded cautiously. In Italy the survey was carried out at the end of the second year after the decoupling occurred and the stated behaviour seems to correspond in most cases to the actual behaviour. This is true at least as far as the effects on farm activities are concerned. Decisions in terms of investments may take longer, but most respondents gave answers detailed enough to justify the expected reliability. The opposite happened in Poland and Hungary, where the question was submitted in a totally hypothetical way. In such cases, the effect of the decoupled SFP could be more realistically interpreted as the total effect of the CAP support. Table 17 illustrates the relationships between the use of SFP and selected variables, defined by a simple one-to-one correlation exercise between each explanatory variable and the dependent variable. Table 17 Correlation between the use of SFP and selected explanatory variables* On farm current expenditure On farm investment Off farm productive current expenditure Off farm productive investment Off farm nonproductive intermediate consumption Off farm nonproductive durable Variable SFP amount in Total external labour purchased + Household head labour on farm - - SFP/revenue Household head labour off farm + Number of production contracts Succesor Age of farm head Number of partial workers Land rented in % of total farm area + Household labour off farm Household labour on farm Total land * + = positive significative correlation; - = negative significative correlation; no sign = no significative correlation; significativity at 5%. Use for current expenditure was correlated to employment of external labour only. On-farm investment was positively correlated to the SFP/revenue ratio and the share of rented land to the total farm area. Use for off-farm current production expenditure was correlated to farm heads 74/187

77 labouring off-farm. Off-farm productive investment was positively correlated to SFP amount and the SFP/revenue ratio and negatively correlated to farm heads labouring on-farm. Off-farm nonproductive consumption was only correlated to the SFP/revenue ratio, while non-farming and nonproductive durable goods investments were negatively correlated to farm heads labouring on-farm, and positively correlated the SFP/revenue ratio. These results confirm the consistency of farm responses with most of the literature on investment, particularly: the joint choices of labour and investment directions, the interest of farms in joint residential and labour choices and the importance thresholds of the absolute and relative values of SFP as a prerequisite to any effect on farm choices. The same kind of exercise is performed in Table 18, where explained variables are those related to the stated effect of decoupling. Table 18 Relationship between the stated effect of decoupling and selected explanatory variables Increase investment Off farm productive Off farm nonproductive Decrease investment Changes in crop Off farm Off farm nonproductive mix On farm productive Changes in other activities Variable On farm SFP amount in Total external labour purchased Household head labour on farm - SFP/revenue Household head labour off farm Number of production contracts - + Succesor Age of farm head - + Number of partial workers - Land rented in % of total farm area + + Household labour off farm + Household labour on farm - + Total land + * + = positive significative correlation; - = negative significative correlation; no sign = no significative correlation; significativity at 5%. An increase in on-farm investments is positively associated with SFP amount, successor, and total land, while it is negatively correlated with production contracts, farm head age and parttime working. An increase in off-farm productive investment is negatively correlated with on-farm labour. Increase in off-farm productive investments is negatively correlated with household head labour on farm. These results are consistent with theory, and say that bigger farms, with younger farmers and a higher share of labour allocated to farming see in the decoupling an opportunity to expand through on-farm investment. The fact that an increase in off-farm non-productive investment is positively correlated with the successor is more difficult to explain, though it may be caused by the fact that households with a successor are more willing to invest in non-farm assets on the farm (typically a new house). Decreases are more difficult to explain, also because the number of positive answers was far lower than to the previous question. Only off-farm non productive investments are positively correlated to SFP amount and percentage of rent on total available land, which may identify a strategy based on exploitation of farming activity as a source of income to be used for consumption or rent seeking activities outside the farm. Changes in crop mix are positively correlated with total labour off-farm. No changes are positively correlated with production contracts, farm head age or total labour on-farm, but negatively correlated to the availability of a successor. This is consistent with the expectation that there will be no reaction by specialised fruit farmers (typically based on high amount of labour), by farms more strongly constrained by relationships with the other stages of the crop chain (contracts), and by oldest farmers without successor. None 75/187

78 The results confirm that the SFP tends to contribute to and is consistent with the general strategy of the farm, i.e. increasing investment in farms that already have a positive attitude to investment and enlargement. 5.4 Simulation of scenarios impact Economic effects of scenarios Decoupling yields strongly contrasting results in terms of income (Table 19). Table 19 Change in farming income compared to Agenda 2000 (%, standard deviation in italics) Scenario 2.1 Scenario 2.2 Scenario 3.1 Scenario 3.2 Scenario Poland Plain Livestock 3% -5% -36% 5% 0% 75% 3% 52% -36% 24% 16% 7% 7% 119% 10% 245% 15% 163% 28% 175% Poland Plain Arable -10% -11% -60% -66% -23% -43% -23% -37% -51% -53% 8% 9% 9% 39% 31% 49% 31% 43% 22% 46% Poland Mountain Livestock 0% 4% -50% -63% -4% -32% 0% -28% -43% -63% 6% 9% 9% 35% 5% 40% 6% 38% 28% 30% The Netherlands Plain Livestock -5% -5% -68% -70% -7% -33% -5% -28% -68% -73% 9% 12% 12% 32% 14% 37% 15% 35% 27% 30% Italy Plain Livestock 5% 6% -32% -41% 4% -14% 5% -7% -31% -67% 7% 8% 8% 34% 6% 8% 7% 10% 6% 38% Italy Plain Arable -2% 7% -28% -26% -2% -22% 0% -5% -33% -51% 15% 25% 25% 11% 15% 49% 17% 29% 9% 34% Italy Mountain Livestock -1% -2% -34% -34% -2% -14% -3% -12% -35% -39% 3% 4% 4% 14% 4% 15% 4% 13% 13% 14% Italy Mountain Arable 13% 3% -35% -48% 10% -33% 13% -23% -38% -54% 41% 20% 20% 36% 42% 39% 41% 27% 32% 25% Hungary Plain Livestock -33% -35% -43% -42% -34% -100% -33% -81% -44% -88% 29% 32% 32% 21% 29% 0% 29% 19% 22% 7% Hungary Plain Arable -8% -16% -33% -38% -8% -67% -8% -51% -33% -72% 12% 22% 22% 5% 12% 33% 12% 32% 1% 15% Greece Plain Arable 47% 96% 18% 64% 47% 30% 47% 54% 18% 22% 113% 147% 147% 145% 113% 123% 113% 117% 111% 116% France Plain Arable -5% -3% -26% -25% -5% -35% -5% -23% -27% -45% 5% 6% 6% 5% 5% 15% 5% 11% 4% 10% Spain Plain Trees 0% 0% -31% -31% 0% 0% 0% 0% -31% -31% 2% 2% 2% 13% 2% 2% 2% 2% 13% 13% Germany Plain Livestock 13% -8% -22% -53% 13% -24% 14% -19% -12% -44% 38% 9% 9% 26% 38% 9% 39% 8% 55% 15% Germany Plain Arable 1% 0% -15% 0% 1% -1% 1% -1% -15% -1% 2% 0% 0% 0% 2% 1% 2% 1% 21% 1% Germany Mountain Livestock -4% -5% -6% -35% -4% -13% -6% -10% -6% -38% 8% 10% 10% 41% 8% 14% 12% 18% 49% 42% Germany Mountain Arable -3% -1% -50% -51% -2% -60% -3% -43% -52% -65% 15% 13% 13% 31% 41% 42% 15% 37% 44% 31% In fact, the reform 2003 scenario (2.1) shows increases as well as decreases in farming income compared to the baseline scenario represented by Agenda Considering the data by system, higher income improvements appear in Greece, in mountain arable crops in Italy and in Germany plain livestock, while the worst results (income decreasing) appear in plain livestock in 10 This section includes a summary of the main outcomes of the simulation exercise. Details of validation parameters and impacts of scenarios on selected individual farm modelled are given, respectively, in Annexes III and IV. Results are given as percentage differences from Agenda 2000 and standard deviations of the differences in each case study area (small italics font in the tables). The distinction between emerging and conventional systems has purposefully not been maintained here as the results are not significantly different (or differences may depend on other factors). A comparison of conventional and emerging systems based on individual farm results may be found in Annex IV. 76/187

79 Hungary and in plain arable in Poland. However, the standard deviations show how variable these results are for single farms. In fact, in almost all cases study areas both positive and negative results arise in individual farms (compare also Annex IV). This encourages us to think that individual farm characteristics play a far more important role than system characteristics in determining the impact of decoupling. In particular, the individual results may be the outcome of at least four contrasting factors. First of all, as expected from the literature, decoupling may increase flexibility, allowing a better adaptation to profit seeking and increasing income. However, and this is the second point, when a household is already heading for a retirement strategy, decoupling may encourage such a strategy, yielding a reduction in farm income. In addition, there may be a discrepancy (in some cases a strong one) between the historic crop mix that gave rise to payments rights and the present crop mix; this can generate an effect due to the reform in the direction of either positive or negative income change. Finally, there were farms that, in the baseline, were pursuing an expansion strategy driven by payments; for these farms, the baseline in fact included an increase of payments through an increase of paymentbenefiting activities (e.g. milk production, cereals). In this case, decoupling causes a reduction of income as expansion is slowed, or stopped, due to the lack of pulling effects of area -or headcoupled payments. As a result, when read on a single farm basis, decoupling may yield results in any direction. In the period , the results may tend to become more extreme in some areas (i.e. completing the adaptation process) and to reverse in others (less frequently). The other scenarios show very sharp reductions in income in cases where a reduction in prices (by 20%, see scenario description) is assumed (2.2 and 3.3). The results for livestock show the sharpest decrease (up to 68% in the period and up to 70% later). This shows the difficulties of these systems, where the difference between revenues and costs is already very narrow and there is a need to face important costs for purchased production means. These results show that we are at a critical point for farm profitability; this is true under the present price expectations, but things can change in the case of (even slightly) better price conditions. Scenarios with a total (3.1) or gradual (3.2) reduction of payments after 2013 yield stronger effects in Hungary, and most often stronger negative results for arable and mountain farms. It is remarkable, in addition, that farms anticipate adaptation, with a reduction in income already before 2013 (assuming they know about policy changes in advance). As a consequence of the previous outcome, the total household income also changes in different scenarios (Table 20). 77/187

80 Table 20 Change in household income compared to Agenda 2000 (%, standard deviation in italics) Scenario 2.1 Scenario 2.2 Scenario 3.1 Scenario 3.2 Scenario Poland Plain Livestock 2% 0% -31% -17% 0% 22% 3% 18% -31% -13% 10% 11% 11% 51% 6% 100% 11% 68% 15% 67% Poland Plain Arable -9% -11% -45% -55% -18% -33% -18% -29% -41% -50% 8% 9% 9% 30% 22% 34% 22% 32% 22% 31% Poland Mountain Livestock 0% 0% -27% -29% -3% -20% 0% -14% -27% -36% 3% 4% 4% 26% 4% 25% 3% 22% 21% 31% The Netherlands Plain Livestock -3% -4% -55% -56% -5% -23% -4% -19% -55% -59% 7% 11% 11% 21% 12% 20% 13% 19% 20% 20% Italy Plain Livestock 6% 7% -24% -29% 5% -10% 6% -3% -24% -50% 6% 8% 8% 10% 6% 7% 6% 8% 7% 32% Italy Plain Arable 3% 7% -17% -19% 3% -12% 4% -2% -18% -32% 8% 13% 13% 8% 9% 24% 10% 16% 9% 15% Italy Mountain Livestock -1% -1% -30% -29% -2% -11% -2% -9% -30% -33% 3% 3% 3% 6% 3% 12% 4% 10% 8% 7% Italy Mountain Arable 3% 0% -14% -9% 3% -7% 3% -5% -15% -12% 11% 7% 7% 13% 11% 8% 11% 6% 16% 11% Hungary Plain Livestock -34% -35% -44% -42% -34% -99% -34% -80% -45% -87% 30% 31% 31% 21% 30% 0% 29% 18% 23% 7% Hungary Plain Arable -4% -16% -30% -37% -4% -67% -4% -51% -30% -72% 7% 22% 22% 5% 7% 34% 7% 32% 3% 16% Greece Plain Arable 46% 94% 17% 62% 46% 32% 46% 55% 17% 23% 111% 144% 144% 142% 111% 119% 111% 115% 109% 114% France Plain Arable -5% -4% -26% -27% -5% -32% -5% -21% -27% -44% 4% 5% 5% 5% 5% 14% 5% 10% 3% 8% Spain Plain Trees 0% 0% -26% -26% 0% 0% 0% 0% -26% -26% 2% 2% 2% 6% 2% 2% 2% 2% 6% 6% Germany Plain Livestock 12% -10% -21% -49% 13% -22% 13% -18% -11% -42% 38% 10% 10% 20% 38% 9% 39% 9% 54% 9% Germany Plain Arable 0% 0% -1% 0% 0% -1% 0% -1% -1% -1% 0% 0% 0% 0% 0% 1% 0% 1% 0% 1% Germany Mountain Livestock -3% -5% -2% -31% -3% -12% -5% -10% -2% -33% 7% 9% 9% 32% 7% 12% 12% 16% 45% 32% Germany Mountain Arable 1% 2% -21% -19% 6% -21% 1% -12% -19% -20% 6% 5% 5% 18% 21% 27% 6% 14% 24% 13% The various differences between this table and the previous one are explained by the different shares of off-farm income for different farms, which, in addition, change as a reaction to scenarios. However, most of the farms in the simulation exercise are characterised by a prevailing allocation of labour on-farm and this explains the small differences with the previous table for the majority of cases. Assessing the reliability of these figures is certainly difficult, as off-farm revenue opportunities (alternative accessible job supply) are considered stable among the scenarios and household dynamics (e.g. children growing up) are not considered at all. The change in used land compared to agenda 2000 reveal a tendency towards a reduction of farm area (Table 21). Table 21 - Change in used land compared to Agenda 2000 (%) USED LAND % -16% -3% -4% -21% % -22% -28% -15% -42% This is done by giving up some of the land rented in or by selling land or by not carrying out land purchases planned under the Agenda 2000 scenario. Given the use of individual household (farm) models these results only say that the shadow value of land tend to decrease compared with the (exogenous) prices adopted for land purchasing and rent. Decoupling contributes marginally to this effect, while price or payment reductions show substantial changes. 78/187

81 Table 22 summarizes the changes in areas cultivated for some major activities (crops and livestock). Taking into account the reduction of total cultivated area, most of the major crops show minor adaptations. Generally speaking all cereals tend to decrease, while forages tend to increase due to decoupling 11. In the short run, only tobacco and sunflower show major negative reactions to decoupling. In the long run, also wheat, vegetables and other cereals show important negative changes. In case of price reduction, vegetables, alfalfa and cereals are the more reactive. Due to the type of approach taken, this result is certainly one of the most contentious, as it does not take into account chain effects, territorial constraints and complementarities. However it confirms a trend towards extensification due to decoupling and/or price reduction. Table 22 Changes in major crop/livestock across scenarios (% area/number change) Alfaalfa -10% -38% 4% 2% -38% Cereals -16% -35% -12% -15% -42% Forages 10% -13% 12% 10% -22% Maize -12% -20% -3% -4% -33% Pasture 0% 0% 0% 0% 0% Sugar beet 0% 0% 0% 0% 0% Sunflower -56% -25% 8% -25% -41% Tobacco -100% -100% -100% -100% -100% Wheat -12% -21% -7% -8% -29% Vegetables -6% -40% -9% -7% -39% Fruits 0% -1% 0% 0% -1% Dairy Cows -10% -12% -9% -14% -24% Alfaalfa -12% -26% 1% -5% -28% Cereals -25% -50% -40% -37% -56% Forages 6% -48% -22% -15% -71% Maize -17% -23% -29% -21% -37% Pasture 0% 0% -100% - -53% Sugar beet 0% 0% 0% 0% 0% Sunflower -58% -36% -36% -36% -36% Tobacco -96% -97% -96% -96% -97% Wheat -21% -31% -46% -31% -41% Vegetables -26% -44% -30% -27% -45% Fruits -1% -1% -1% -1% -1% Dairy Cows -14% -15% -23% -18% -32% 11 This makes sense under the assumption, consistent with our microeconomic framework, that changes in production do not affect market prices. 79/187

82 The variety of investment choices and of values for single investments makes the variability of results so wide as not to allow generalised conclusions about the effects of scenarios on investment (Table 23). Table 23 Change in net investment compared to Agenda 2000 (%, standard deviation in italics) Scenario 2.1 Scenario 2.2 Scenario 3.1 Scenario 3.2 Scenario Poland Plain Livestock 3% 35% -39% -35% -25% -33% -6% -52% -30% -16% 31% 98% 98% 45% 66% 48% 17% 86% 96% 44% Poland Plain Arable 18% -6% 374% -52% 246% -59% -10% -96% 501% -59% 51% 11% 11% 48% 664% 90% 103% 158% 1202% 87% Poland Mountain Livestock 43% -4% -22% -43% -32% -20% 41% -29% -53% -99% 102% 27% 27% 40% 80% 23% 104% 69% 113% 183% The Netherlands Plain Livestock 3% 4% 237% -48% 72% -22% 0% -100% 234% -85% 78% 17% 17% 58% 175% 46% 101% 199% 1289% 108% Italy Plain Livestock 1% 0% 18% -9% 7% 0% 2% 0% -14% -47% 2% 0% 0% 16% 10% 2% 5% 2% 116% 37% Italy Plain Arable -610% -9% -759% 2% -2554% -10% -654% -16% -2368% -35% 1433% 68% 68% 29% 6191% 67% 1481% 61% 6322% 62% Italy Mountain Livestock -13% -8% -117% -40% -45% -28% -49% -28% -134% -41% 19% 17% 17% 55% 78% 44% 76% 43% 173% 54% Italy Mountain Arable 0% 0% 132% -70% 3% -24% 2% -31% 385% -42% 0% 1% 1% 55% 5% 41% 4% 49% 923% 49% Hungary Plain Livestock -146% -58% -147% -60% -200% -86% -168% -86% -172% -100% 131% 52% 52% 47% 102% 25% 146% 24% 149% 0% Hungary Plain Arable -50% -33% -74% -49% -51% -32% -52% -33% -73% -49% 71% 46% 46% 67% 72% 45% 73% 46% 103% 67% Greece Plain Arable 0% -11% 0% -16% 0% -23% 0% -17% 0% -19% 0% 22% 22% 28% 0% 36% 0% 26% 0% 30% France Plain Arable -4% -97% -6% -94% -5% -99% -10% -95% -201% -70% 8% 187% 187% 190% 11% 186% 16% 182% 399% 156% Spain Plain Trees -6% -6% -41% -41% -5% -5% -6% -6% -40% -40% 12% 12% 12% 67% 13% 13% 12% 12% 66% 66% Germany Plain Livestock -65% 6% -65% -16% -64% -6% -64% 7% -64% 6% 130% 12% 12% 34% 129% 16% 128% 11% 128% 11% Germany Plain Arable 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Germany Mountain Livestock -7% -13% 73% -10% -2% -8% -6% 4% 82% 8% 23% 30% 30% 27% 26% 33% 21% 54% 203% 13% Germany Mountain Arable -92% -24% -193% -32% -355% -120% -178% -570% -285% -139% 166% 58% 58% 96% 643% 274% 287% 975% 564% 258% However, decoupling brings generally a reduction in investments, with exceptions such as in Poland and Italy. Payment reduction and, more importantly, price reduction lead to a collapse in investment in most cases, again with some exceptions. It must be noted that changes in policy and prices may yield to individual transitory periods with prevailing investments. Changes in investment according to asset type, represented by the prevailing direction of change in terms of number of case studies, yield a substantially stable picture (Table 24). 80/187

83 Table 24 Prevailing direction of change by type of investment across scenarios Land = = = = = Farm buildings = - = = - Tractors = - - +/- - Tillage machinery = - = = - Harvesting machinery = = = = = Land = = = = = Farm buildings = = = = = Tractors = + +/- = - Tillage machinery = = = = =/- Harvesting machinery = = = = = Decoupling tends to cause no major changes. Price reduction tends to decrease investments, with the exception of tractors in the second period (that, however, move in different directions in scenario 2.2 compared to 3.3). Gradual payment reduction affects mainly tractors (with contrasting effects, however) and seems to cause more effects in the first period compared to the second (assuming farmers can anticipate the change) Social effects of scenarios The social effects of scenarios are captured exclusively using labour information. Scenarios different from the baseline generally bring a reduction in on-farm labour (Table 25). Exceptions are some individual cases where decoupling brings incentives for a small increase in labour. Price reduction or payments cuts, on the contrary, bring unambiguously a substantial reduction in employment. The effect is particularly strong in Hungary. The variability of the results across households in the same case study and across case studies is mostly lower than for the changes in economic indicators discussed above (income and, particularly, investments). This because changes in labour use tend to measure an aggregated organisational shift of the farm, where compensation between processes are possible and effects due to changes of price and payments are not relevant up until they cause a change in the activity mix. On the other hand, it should be kept in mind that a strong reaction in terms of organisation and labour allocation may be driven in this household model by the direct inclusion of opportunity revenues that can be derived by off-farm use of capital and labour (see model description). Compared to the model, real life reactions may be smoother (but in some cases also sharper), depending on individual objectives not formalised in the model, on trends in external labour and credit markets, and on contingent opportunities for employment and capital allocation, as well as on the stage of the household life cycle. 81/187

84 Table 25 Changes in total labour use on-farm in different scenarios (average ; %, standard deviation in italics) Scenario 2.1 Scenario 2.2 Scenario 3.1 Scenario 3.2 Scenario Poland Plain Livestock 2% -5% -23% 46% 0% 83% 3% 59% -23% 80% 16% 8% 8% 177% 10% 256% 16% 168% 45% 258% Poland Plain Arable -5% -6% -36% -54% -18% -38% -17% -32% -27% -37% 9% 8% 8% 45% 27% 50% 27% 44% 38% 56% Poland Mountain Livestock 9% 18% -27% -44% -2% -16% 9% -7% -20% -43% 27% 30% 30% 52% 5% 48% 27% 52% 40% 46% The Netherlands Plain Livestock -4% -3% -55% -59% -7% -24% -6% -21% -55% -62% 5% 8% 8% 46% 10% 40% 11% 36% 38% 45% Italy Plain Livestock 0% 1% -20% -32% -1% -3% 0% -1% -17% -60% 1% 3% 3% 39% 1% 6% 1% 7% 3% 49% Italy Plain Arable -8% -9% -21% -33% -7% -19% -7% -8% -21% -46% 11% 14% 14% 28% 11% 41% 11% 14% 26% 41% Italy Mountain Livestock -1% -1% -32% -40% -1% -20% -2% -21% -32% -40% 2% 2% 2% 55% 2% 45% 3% 44% 44% 55% Italy Mountain Arable 0% -12% -43% -56% -3% -22% 0% -21% -43% -39% 1% 31% 31% 46% 5% 41% 1% 40% 39% 49% Hungary Plain Livestock -60% -66% -65% -67% -56% -100% -56% -73% -65% -88% 52% 57% 57% 56% 49% 0% 49% 45% 56% 20% Hungary Plain Arable -35% -50% -55% -50% -37% -50% -37% -50% -55% -50% 44% 70% 70% 70% 47% 71% 47% 70% 62% 70% Greece Plain Arable -20% -26% -22% -27% -20% -20% -20% -24% -22% -27% 33% 31% 31% 29% 33% 40% 33% 33% 31% 30% France Plain Arable 0% 3% -1% 3% 0% 2% 0% 3% -1% -2% 0% 7% 7% 7% 0% 5% 0% 5% 2% 2% Spain Plain Trees 0% 0% -9% -9% 0% 0% 0% 0% -9% -9% 1% 1% 1% 18% 1% 1% 1% 1% 18% 18% Germany Plain Livestock 19% -3% 20% -25% 19% -6% 20% -6% -25% -25% 39% 6% 6% 50% 39% 12% 40% 13% 50% 50% Germany Plain Arable 0% 0% -50% -50% 0% 0% 0% 0% -50% -50% 0% 0% 0% 71% 0% 0% 0% 0% 71% 71% Germany Mountain Livestock -5% -7% -18% -40% -5% -7% -5% -7% -18% -39% 12% 16% 16% 49% 12% 18% 12% 18% 43% 49% Germany Mountain Arable -5% -7% -46% -43% -10% -49% -5% -37% -67% -69% 9% 11% 11% 39% 25% 45% 9% 39% 40% 40% Environmental effects of scenarios Table 26 summarises the main effects on the environment, represented by the usage of nitrogen. Decoupling brings generally no change or a decrease in nitrogen use, hence reinforcing the expectation of an extensification effect. Arable crops show a further relative reduction in nitrogen use shifting to the further scenarios. The strongest reduction occurs for the case of decoupling with lower prices. On the other hand, in the scenarios with reductions in payments, this effect is counterbalanced by the search for profit by cultivating more intensive crops. 82/187

85 Table 26 Change in nitrogen usage compared to Agenda 2000 (%, standard deviation in italics) Scenario 2.1 Scenario 2.2 Scenario 3.1 Scenario 3.2 Scenario Poland Plain Livestock -2% -2% -19% 48% -4% 82% -1% 59% -19% 79% 11% 15% 15% 183% 6% 257% 11% 168% 38% 258% Poland Plain Arable -4% -5% -52% -62% -18% -38% -17% -31% -36% -34% 7% 6% 6% 47% 26% 50% 26% 44% 36% 59% Poland Mountain Livestock -14% 20% -27% -27% -7% 10% -14% 5% -23% -17% 17% 76% 76% 73% 9% 90% 17% 91% 39% 63% The Netherlands Plain Livestock -2% -4% -48% -60% -5% -24% -5% -21% -48% -62% 6% 8% 8% 46% 12% 40% 11% 36% 44% 46% Italy Plain Livestock 18% 11% 27% -8% 20% -18% 18% 5% 21% -68% 31% 18% 18% 13% 31% 23% 31% 16% 38% 37% Italy Plain Arable -21% -34% -24% -42% -22% -46% -22% -35% -24% -54% 22% 23% 23% 37% 23% 35% 23% 23% 34% 44% Italy Mountain Livestock -16% -38% -48% -60% -32% -58% -32% -58% -40% -60% 31% 52% 52% 55% 40% 53% 40% 53% 49% 55% Italy Mountain Arable -9% -21% -49% -70% -11% -40% -9% -36% -49% -57% 20% 33% 33% 39% 20% 47% 20% 42% 41% 48% Hungary Plain Livestock -1% -31% -41% -81% -4% -38% -1% -38% -41% -63% 52% 58% 58% 58% 49% 58% 49% 58% 56% 58% Hungary Plain Arable -36% -50% -55% -50% -39% -50% -39% -50% -55% -50% 47% 71% 71% 71% 50% 71% 50% 71% 63% 71% Greece Plain Arable -1% -4% -15% -32% -1% -31% -1% -8% -15% -43% 8% 8% 8% 51% 8% 39% 8% 9% 42% 46% France Plain Arable 0% 3% -3% -1% 0% 2% 0% 2% -4% -5% 0% 6% 6% 10% 0% 3% 0% 3% 6% 6% Spain Plain Trees 0% 0% -9% -9% 0% 0% 0% 0% -9% -9% 1% 1% 1% 18% 1% 1% 1% 1% 18% 18% Germany Plain Livestock 20% -3% 12% -35% 20% -17% 20% -16% 11% -17% 38% 7% 7% 47% 39% 20% 39% 20% 47% 21% Germany Plain Arable 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Germany Mountain Livestock 0% 0% 31% -19% 0% -5% 0% -5% 31% -17% 1% 1% 1% 40% 1% 10% 1% 11% 78% 41% Germany Mountain Arable -5% -7% -52% -50% -10% -55% -5% -41% -56% -58% 9% 11% 11% 45% 25% 49% 9% 41% 47% 49% Analogous trends than those observed for nitrogen use occur for water usage in Mediterranean regions (Table 27). Table 27 Changes in water usage compared to Agenda 2000 (%, standard deviation in italics) Scenario 2.1 Scenario 2.2 Scenario 3.1 Scenario 3.2 Scenario Italy Plain Livestock -4% 4% 14% -13% -2% -35% -4% -10% 14% -34% 13% 8% 8% 26% 16% 45% 13% 12% 18% 47% Italy Plain Arable -6% -7% -6% -8% -6% -20% -6% -7% -6% -20% 10% 12% 12% 13% 10% 40% 10% 12% 11% 40% Italy Mountain Livestock 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Italy Mountain Arable 0% 0% 0% -13% -2% 0% 0% 0% 0% 0% 0% 0% 0% 31% 5% 0% 0% 0% 0% 0% Greece Plain Arable -12% -13% -17% -22% -12% -20% -12% -14% -17% -26% 26% 24% 24% 25% 26% 26% 26% 23% 26% 24% Spain Plain Trees 0% 0% -9% -9% 0% 0% 0% 0% -9% -9% 1% 1% 1% 18% 1% 1% 1% 1% 18% 18% 6 Policy implications The main policy implications may be found in the qualitative descriptions of decoupling effects on different farm/system types provided in Table /187

86 Table 28 A policy-related classification of farms/systems Type of farm/system Farms/systems Main role of decoupling CAP-indifferent Very small farms, fruit farms None Income-CAP-dependent Farming-CAP-dependent retiring Eastern Europe, disadvantaged areas Old farmers, high labour opportunities Income support, more cropneutral Encourages land retention, but with extensification Farming-CAP-dependent expanding Livestock, large arable crops, young farmers Encourages investment This qualitative (and tentative) classification of farm types aims to qualify different actor groups that could be identified as different policy addressees with respect to future policies. First, for CAP-indifferent agents, the policy has no relevance in the sense that it does not affect decisions and, at the same time, does not have a relevant impact on income. These payments likely do not respond to any policy objectives and their final reduction or elimination could be considered in order to save money, if necessary. Income-CAP-dependent households are those in which the main effect of CAP is to support incomes, with little effect on production, due to environmental or technical constraints. The relevance of the CAP payments is socially dependent: they may be very relevant in some areas and not relevant at all in others, depending on opportunities of external income. If this is to be the role of the policy, it may be worthwhile to take into consideration the option to re-address the policy towards more objectives-compatible mechanisms, e.g. social cross-compliance. However, where agriculture is no longer a social issue in rural areas (see allocation of labour and income on- vs offfarm), there appears to be no clear need to use CAP as a vested social policy. For farming-cap-dependent retiring households, the decoupling policy may have different effects depending on the overall household strategy. Here, incentives clearly interact with human factors and background household strategy. In many cases, due to ageing or alternative sources of income, there may be reasons to give up farming and this may be encouraged by a decoupled policy. This choice may depend on the overall profitability of farming. However, the fine-tuning choices about activities to be carried out on the farm may be more dependent on personal considerations rather than profit driven choices. In many cases, giving up farming may be not associated with selling land, which may encourage the diffusion of a category of smallholders' residents renting out land. Finally, there are farms that, because of their characteristics, tend to retain farming activity and expand if possible. In most cases they are found in more labour-intensive and more value added systems (livestock or fruit) or are large arable farms, with younger farmers and a relatively large household mostly working on-farm. They may be driven by the lack of external opportunities, farmer expertise, and household critical mass. These farms tend to consider the CAP payment as a useful integration of income and use it to cover current expenditure, but also for investments. This is also pushed by the fact that most of them already experience shortages of liquidity and use credit. In this case, the decoupling may have a role in providing steady and market-independent revenues that can be used for farm expansion. This is clearly the case in systems in which capital intensive activities are associated with potentially extensive land management (livestock production, some 84/187

87 tree production). This case was rather important in the sampled farms, though it is likely much less frequent in the whole population. This farm strategy makes the decoupled payment still maintaining some coupled effects. This could be regarded as an unwanted effect when evaluated against the objective of reducing policy influence on the markets. However, when investment is perceived as a legitimate policy objective, this could deliver a rationale to allocate public expenditure in order to address it directly: e.g. funding investments under rural development programmes, risk reduction schemes, debt guarantee schemes, etc. 7 Discussion 7.1 General findings The main research question addressed in this report is the impact of SFP on investment behaviour in different farming systems. This issue is positioned at the intersection of a number of areas of research and requires an understanding of the interplay of all the main factors affecting farming activities and rural life. The literature review has shown the importance of investment behaviour and the number of issues it touches, such as household structure, attitudes and evolution, long-term expectations, perceived opportunities and costs of resources, which focus attention on the wider socio-economic context. As a consequence, also, the set of methodological options considered is rather broad. The present study relies on a combination of primary information collected through a survey of about 250 farm households in eight European countries and the evaluation of scenario effects based on multicriteria dynamic programming models. The main outcome of the study is that, in most cases, farms show minor or no reaction to decoupling. Where some changes occur, the impact of decoupling is highly differentiated across different technology systems, i.e. conventional and organic, and, even more importantly, across different farm types, i.e. crops, livestock and trees. The effects very often lead farms in the same system in opposite directions if they differ in resource endowments, structure and human capital. Scenario analysis shows that CAP as a whole is crucial for the economic and social sustainability of farming systems. It also predicts differentiated outcomes, where prices appear more important than policy and adaptations of farm activities appear more important than investment as a reaction to both policy and prices. Altogether, post-decoupling CAP looks very much like a policy with multiple impacts but uncertain objectives, which takes different roles depending on the context in which it is cast. As a result, the study hints at the fact that a number of wider issues should be addressed more directly in order to understand farm household behaviour with respect to policies. In particular, demographic trends, extra-farming labour, capital and land use opportunities, technological options and personal strategies seem to be increasingly major drivers of farm reaction to CAP. 7.2 Methodology For its characteristics, the methodology used was flexible enough to fit the project s objectives, though very demanding in terms of data gathering and processing. The jont use of primary information concerning stated behaviour in relation to policies and modelling tools revealed a good strategy in order to interpret a wide variety of decision mechanisms. The modelling activity produced a set of locally adapted models that could be further developed into a true farm 85/187

88 investment model or structural adjustment model. As it was designed, the modelling part tended to overestimate whole farm management choices (i.e. selling land) and to underestimate crop mix changes. Should the study be replicated or expanded, the experience gained up to now already hints at possible improvements. For example, the methodology could be improved through the use of an interactive procedure that allows for checking the model s outcome with farmers. However, the big issue remains that of coverage and extrapolation of detailed decision mechanisms into a full view of EU farming systems. Hence, further analysis may also be devoted to extrapolation and expansion of the results, connecting models with upper scale models and general statistics as well as exploring meta-modelling opportunities. However, a relevant background work would need to be done to allow a fruitful integration of existing methodologies. At the same time, the literature on farm investment behaviour also shows the existence of a variety of methodologies that could be used in order to get complementary insights, particularly should the scope of the research be revised towards more defined objectives. This connects to the next issue. 7.3 Further research This work clearly hints at possible developments of the research that could be considered after the end of the project. First of all, the decision to try to interpret policy-related behaviour in the light of whole household management proved fruitful and relevant. However, the characteristics of the selected farms encourage us to look further ahead. New societal forms and chain connections through contracts are major structural and decision-related features in the sample that also deserve greater attention in a future study of farming-related behaviour. On the supply side, the issue of machinery service providers was also marginally dealt with here, while the relationship between on-farm and off-farm mechanisation will likely be a major driver of future investment behaviour in rural areas. In addition, land markets and the interactions between farms through land rent should be better understood. Finally, technology change was also partially considered here. Three main points to be developed are the further possibility of exploiting economies of scale (or at least an improved land/capital ratio), the technologies deriving from new crops (e.g. energy crops, GMOs), electronic technologies and precision farming. Putting together these issues, the research suggests that further development is needed in order to understand how tomorrow s farm (or, better, tomorrow s farming/rural agents/systems) will look and behave. The research also calls for a deeper prospective analysis of the role of CAP in rural areas. The fact that the varied effects of decoupled payment seem to depend on the household and context characteristics more than on farming system features may require further research to understand the action of drivers external to farming, and to identify local policy objectives and policy designs aimed at achieving such objectives. Non-farming effects are clearly important and far from being understood in the literature. 86/187

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96 Annex I Questionnaire Presentation and treatment of personal data The questionnaire focuses on the future of rural households and their investment behaviour. It will ask for a number of information related to on-farm and off-farm activities, including personal objectives and expectations. The data collected will be treated in a completely anonymous form. Add here a sentence about treatment of personal data according to national law. Questionnaire code 1 Location and contact details 1) Country 2) Region/area 3) Post code 4) Address 5) Name of interviewee 6) Name of Interviewer 7) Date 8) Time for filling-in 2 Farm type, structure and specialisation 2.1 Legal status of the farm 1) Individual/family farm 2) Limited company 3) Cooperative farm 4) Other, namely 2.2 Land ownership (ha) Type Owned Rent in Rent out Other (specify ) Area 94/187

97 2.3 Location 1) Plain 2) Hill/mountain 2.4 Farm specialisation 1. Crops 2. Livestock 3. Orchard/vineyard/forest Comments 2.5 Type of production 3) Mostly conventional 4) Mostly organic 2.6 If organic, which share of the products are marketed as organic products % 3 Household structure and labour management 3.1 Household structure Member (role relative to farm head) Male/ Female Age (years) Farm head Education level (see 12) On farm labour (hours/year) Off-farm employment (description) Education type (agricultural vs. nonagricultural) Offfarm income ( /year) 3.2 The farmer has a successor? 1) Yes 2) No 3) Do not know 95/187

98 3.3 Other people working on the farm Worker (description) Labour time (hours/year) 4 Farm organisation, constraints and connections 4.1 Constraints determining current farm activities (rank 1= most important, 2= second most important,, put a bar - for those not important at all) Constraint Rank Specify Market share/contract of key products Total household labour availability Total external labour availability Household labour availability in key periods External labour availability in key periods Land availability from neighbouring Liquidity availability Short term credit availability Long term credit availability Others 4.2 Crop rotations/sequence (describe) 4.3 Production contracts in place Product Established year Length (years) Amount of product (t/year) 4.4 Public contracts in place Policy Tick Specify Rural development contracts (reg. 1257/99) Local/national conservation contracts Others 96/187

99 4.5 What organisations or persons provide advice to the farm (please tick only those considered most important) Organisation Tick Specify Public extension service Private advice Farmer association or union advice service Agri-input provider enterprise Downstream food processing enterprise and cooperative association advice service Bank Other farmers Family Machinery services 4.6 Type of credit used (in 2006) Credits Tick Paid interest rate (%) None Short term (<1 year) Medium term (1-5 years) Long term (>5 years) Specify use of money 4.7 Debt/asset ratio 1) % 4.8 Limits to accessing credit (please rank: 1=most important, etc.) 1) High interest rate 2) Insufficient collateral 3) Other guarantees requested 4) Others 5) No limit 5 Policy and decoupling 5.1 Single farm payment received Year Euro Number of rights (ha) Money from Single farm payment is used for (describe): a) Off-farm b) On-farm 97/187

100 5.3 Summarise the destination of money coming from Single farm payments (express % of the Single farm payment) Current expenditure Investment On-farm Off-farm productive Immediate consumption Durable goods Off-farm non-productive 5.4 Other payments received (e.g. axis 1 RDP, etc.) Type Surface (ha) or heads (n.) Total amount 5.5 Money from other payments received is used for (describe): a) Off-farm b) On-farm 5.6 Summarise the destination of money coming from other payments (express % of the other payments Current expenditure Investment On-farm Off-farm productive Immediate consumption Durable goods Off-farm non-productive 5.7 What are or are expected to be the changes in your farm/household as a reaction to the introduction of the single farm payment Sectors Tick Specify None Increase investment On-farm Off-farm productive Off-farm non-productive Decrease investment On-farm Off-farm productive Off-farm non-productive Changes in crop mix Changes in other activities 98/187

101 6 Perspectives & expectations 6.1 What are the expected changes in the social/economic environment influencing the farm-household (e.g. new roads, infrastructures)? 6.2 What conditions do you expect for household related activities in 5 year time (2006=100%) Confidence in response % (High, Medium, Low) Price of consumption goods Price of housing Level of off-farm salaries Interest rates Comments 6.3 What conditions do you expect for farm-related markets in 5 year time about the activities /crops that you are carrying out (2006=100%) Confidence in response % (High, Medium, Low) Product prices Agricultural labour cost (price) Cost of agricultural capital goods (price) Cost of other production means (price) Comments 6.4 How will be the conditions of agricultural policy after 2013 (2006=100%) Confidence in response % (High, Medium, Low) Decoupled payments Rural development payments Payments for organic production Coupled payments (specify) Others payments (specify) Comments 99/187

102 7 Household status and objectives 7.1 Household wealth and asset management Unit Household total revenue 000 /year Household consumption 000 /year Household Debt/asset ratio % Household net worth 000 Amount 7.2 Objectives, targets and importance Objective Importance (rank) Minimum acceptable (% of 2006) Household worth Household consumption Household debt/asset ratio Diversification in household activities Income certainty Leisure time Others Rank 1=most important, 2=second most important, ecc. Target by 2013 (% of 2006) Comments 7.3 How important is the role of the farm in the overall household income 1) It is the main economic activity 2) It is an important integration of income 3) It is a secondary integration of income 4) It is a net loss 5) Others (specify) 7.4 How important is the role of the farm in the overall household asset management 1) Does not have any particular role 2) Serves as a low-risk asset for investment differentiation 3) Has a strong affection value and we ll never leave it 4) Others 100/187

103 8 Present and future farm/household activities 8.1 Crops Crop (description) Area in 2006 (ha) Cultivated in the last 5 years Year Area (ha) Considered/planned for the next 5 years Year Area (ha) 8.2 Animals on farm Animals on the farm (description) Number of animals (2006) Number expected in 5 years Grazing (yes/no) 8.3 Other activities carried out on the farm Description Measurement Unit Size/amount Starting date (year) Continued in the future (Yes/No) 8.4 Off-farm activities (only activities different from employment in question 3.1) Description Measurement Unit Size/amount Starting date (year) Continued in the future (Yes/No) 101/187

104 9 Past and future farm/household assets and investments/disinvestments 9.1 Main non-farm assets (stocks) Presently owned Description Purchase year Unit Amount Purchase value Expected end of life/ disinvestment (year) Replaced (Yes/No) Expected investment (excluding replacements) in the next 5 years (flows) Description Purchase year Unit Amount Approximate value Disinvestments (excluding replacements) in the last 5 years (flows) Description Purchase year Unit Amount 102/187

105 9.2 Agricultural assets at present on the farm (stocks) Land existing and disinvestment Description Purchase year Ha Purchase value Expected disinvestment (year) Land investment Description Purchase year Decided (Y/N) Area (ha) Approximate value Buildings existing and disinvestment Description Purchase year Size Purchase value unit amount Expected end of life/ disinvestment (year) Replaced (Yes/No) Used for crops/activities Buildings investments Description Purchase year Decided Size (Y/N) unit amount Approximate value 103/187

106 Machinery existing and disinvestment Description Purchase year Size Purchase value unit amount Expected end of life/ disinvestment (year) Replaced (Yes/No) Used for crops/activities Machinery Investments Description Purchase year Decided Size (Y/N) unit amount Approximate value Other equipment (e.g. PC) existing and disinvestment Description Purchase year Size Purchase value Expected end of life/disinvest ment (year) unit amount Replaced (Yes/No) Used for crops/activities Other equipment (e.g. PC) investment Description Purchase year Decided Size (Y/N) unit amount Approximate value 104/187

107 Quota and production rights Description Purchase year unit Size Purchase value Used % Expected disinvestment (year) amount Quota and production rights investments Description Purchase year Decided Size (Y/N) unit amount Approximate value 9.3 Main farm assets sold in the last 5 years (e.g. machinery, livestock, land, etc.) (flows) Category (as above) Description Year Unit Amount 9.4 Others (including training) Investment/disinvestment Description Year Decided Investment/disinvestment Size (Y/N) (I/D) unit amount Approximate value 105/187

108 10 Activity-related details - average (please include future activities planned) Annual crops Yield (t/ha) Crop/group of crops Price ( /t) Variable costs ( /ha) Cash anticipation ( /ha) Labour (h/ha) Of which in peek periods (h/ha) Specify period Use of nitrogen (kg/ha/year) Use of pesticides (kg/ha/year) Notes Crop/group of crops 10.2 Tree crops Yield (t/ha) Price ( /t) Variable costs ( /ha) Cash anticipation ( /ha) Labour (h/ha) Of which in peek periods (h/ha) Specify period Use of nitrogen (kg/ha/year) Use of pesticides (kg/ha/year) Duration (years) Cost of plantation (euro/ha) Notes 10.3 Dairy Livestock Livestock Stay in productio n (years) Milk yield (t/year) Milk price ( /t) Variable costs ( /head) Cash anticipation ( /head) Labour (h/head) Of which in peek periods (h/ha) Specify period Selling age (year) Weight at selling age (t) Price at end career ( /t) 12 To be filled either by the farmer, through expert opinion or using secondary data. See instructions /187

109 10.4 Other Livestock Livestock Selling age Weight at selling age Price at end career ( /t) Variable costs ( /head) Cash anticipation ( /head) Labour (h/head) Of which in peek periods (h/ha) Specify period Notes 10.5 Other activities Activity Size (choose-unit) Price ( /unit) Variable costs ( /unit) Cash anticipation ( /unit) Labour (h/unit) Of which in peek periods (h/unit) Specify period Notes 10.6 Land productivity compared with the average of the region % 107/187

110 Annex II Detailed descriptives by case study13 Table 29 Legal status of the farms in the sample (% of individual/family) Individual/ family farm CONVENTIONAL EMERGING Mountain Plain Mountain Plain COUNTRY DE ES FR GR HU IT NL PL Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Table 30 Average size of the farms in the sample (ha per farm) tot land CONVENTIONAL EMERGING Mountain Plain Mountain Plain COUNTRY DE ES FR GR HU IT NL PL Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Table 31 Percentage of land rented on an average-sized farm (%) rent su tot land CONVENTIONAL EMERGING Mountain Plain Mountain Plain COUNTRY DE ES FR GR HU IT NL PL Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest For comments on these data, please refer back to chapter /187

111 Table 32 Average age of the farm head in the sample (years) Farm head age CONVENTIONAL EMERGING Mountain Plain Mountain Plain COUNTRY DE ES FR GR HU IT NL PL Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Table 33 Average labour availability in the sample (hours per year per household) 14 tot labour onoff CONVENTIONAL EMERGING Mountain Plain Mountain Plain COUNTRY DE ES FR GR HU IT NL PL Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Table 34 Average share of off-farm labour in the sample (%) share off CONVENTIONAL EMERGING Mountain Plain Mountain Plain COUNTRY DE ES FR GR HU IT NL PL Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Crop Livestock Orchard/vineyard/forest Data about labour were not always reported in detail in the questionnaires (often only number of household components was included, not working hours). For this reason they are not included in these tables for some case studies. For the purposes of modelling, questionnaires were complemented with secondary data. 109/187

112 Annex III Validation parameters Table 35 reports the value of the validation parameter used and the form (mono- or multiobjective) chosen for each model. The validation parameter is the average of the normalised sum of the distances between the simulated and the expected activity mix over five years ( ). The expected activity mix is the one stated by the farmer. Where data collection about activity intentions was not complete, the validation parameter has not been computed. The choice between monoobjective or multi-objective models depends first on the stated objectives by the farmers. When both were acceptable based on the farmer s interview, the better fitting model (according to the validation parameter described above) was chosen for simulation. When both types of models simulated the baseline with less than 0.1 error, the multi-objective model was chosen for simulation. The baseline was taken to be the present CAP as applied in each country. Table 35 Validation parameters and the model chosen Validation Type of model Validation Type of model PO P C L multi IT M E A mono PO P C L mono IT M E A multi PO P C L multi IT M E A multi PO P C L multi HU P C L multi PO P C L mono HU P C L multi PO P E L multi HU P C L mono PO P C A mono HU P C A mono PO P C A mono HU P C A mono PO P C A mono GR P C A multi PO P C A multi GR P C A multi PO P E A multi GR P C A 09 - multi PO M C L mono GR P C A 10 - multi PO M C L mono GR P C A 12 - multi PO M E L multi GR P E A multi PO M E L multi FR P C A multi PO M E L mono FR P C A multi NE P C L mono FR P C A mono NE P C L mono FR P C A mono NE P C L mono ES P C T mono NE P E L mono ES P C T 08 - mono NE P E L mono ES P C T mono NE P E L 03 - multi ES P C T mono IT P C L multi DE P C L multi IT P C L mono DE P C L mono IT P C L multi DE P C L mono IT P E L mono DE P E L multi IT P C A mono DE P C A mono IT P C A mono DE P E A multi IT P C A multi DE M C L multi IT P C A mono DE M C L 30 - multi IT P E A multi DE M C L multi IT P E A multi DE M C L multi IT M C L mono DE M E L multi IT M C L multi DE M E L multi IT M C L mono DE M C A mono IT M E L mono DE M C A mono IT M E L mono DE M C A mono IT M C A multi DE M E A 02 - mono IT M C A multi DE M E A multi IT M C A mono DE M E A 46 - mono 110/187

113 Acronyms are composed as follows: Country: PO = Poland; NE = Netherlands; IT = Italy; HU = Hungary; GR = Greece; FR = France; ES = Spain; DE = Germany; Area: P = Plain; M = Hill/Mountain; Technology: C = Conventional; E = Emerging; Specialisation: A = Mainly arable crops; L = Mainly livestock; Number: is the numerical identifier of each farm in each case study area. 111/187

114 Annex IV Details of farm behaviour under different scenarios General remarks In this annex, results are illustrated clustering farms by case study. The results reported include either mono-objective (NPV) maximising or multi-objective models according to Annex III. For each farm, averages over the years and are reported. With the exception of the first table for each case study, the numbers in the columns represent the scenarios (please refer to chapter 3 for scenario description). In all cases, the results are reported as percentage variations with respect to agenda The indicators reported here are farming income, household income, investment, labour, nitrogen use and water use. In addition, specific investments and crop information are reported. Investment refers to the value of net investment in all capital goods, including non-farm. However, due to model characteristics, non-farm investment in non productive capital goods (e.g. house) is limited to replacement. Purely financial investments are not accounted. Incomes are measured as gross margins, without subtracting investment expenditure (that is accounted for by a separate indicator), as well as the cost for the use of own land, labour and capital. 112/187

115 France Plain - Arable The French plain arable case study includes four farms (Table 36). Table 36 Summary of farm case studies modelled - France Plain - Arable Household CODE FRPCA01 FRPAC04 FRPCA05 FRPCA06 Legal Status Family run Family run Family run Family run N. household members Age farmer Use external labour yes yes no yes Members working off farm yes yes yes yes Houshold debt/asset ratio Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Conventional SFP (euro) SFP/income ratio 184% 407% 30% 147% Number of rights (ha) The remaining two farms were not modelled due to the presence of livestock and off-farm activities. While all of the farms modelled are family run, the differences in size are remarkable, ranging from 40 to 240 hectares. The debt/asset ratio was high in FRPCA06, while it could not be detected for the other cases. For the purposes of the modelling exercise, this has been assumed to be zero. Baseline indicators for France Plain Arable are shown in Table 37. Table 37 - Summary of baseline (Agenda 2000) of farm case studies modelled - France - Plain - Arable Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) FRPCA FRPCA FRPCA FRPCA FRPCA FRPCA FRPCA FRPCA Incomes from farming are relatively good. Household incomes are not substantially different from farming income, due to the low weight of off-farm labour. The degree of investment is very low as well as is the intensity of labour use. Nitrogen use is relatively high, while water use is negligible. The results for the scenarios in terms of income are reported in Table /187

116 Table 38 Impact of the scenarios on income from farming - France Plain - Arable FRPCA1-4% -25% -4% -4% -25% FRPCA4 1% -21% 2% 2% -22% FRPCA5-9% -30% -9% -9% -30% FRPCA6-7% -30% -8% -7% -30% FRPCA1 2% -20% -48% -30% -52% FRPCA4 2% -21% -16% -9% -33% FRPCA5-9% -30% -48% -33% -54% FRPCA6-7% -30% -28% -21% -43% Decoupling generally brought a reduction in income in the range of 10%, with the exception of farm FRPAC04, whose income increased slightly as an effect of higher flexibility (scenario 2.1). A reduction in prices by 20% led to a reduction in income of between 20 and 30% (scenario 2.2). A total cut in payments in the period led to reductions in income of up to about 50% in this period (scenario 3.1), attenuated in the case of a gradual reduction (scenario 3.2.). The concurrent reduction of prices and payments (scenarios 3.3) between 2014 and 2021 led to stronger effects in the second period, while the behaviour in the first period (scenario 2.2, ) remained unchanged when payments remained the same (scenarios 2.3). The effect on household income was similar to the effect on farm income, due to the low relevance of non-farming income, in spite of the fact that all households have some member working off-farm (Table 39). Table 39 Impact of the scenarios on household income - France Plain - Arable FRPCA1-4% -25% -4% -5% -25% FRPCA4 1% -21% 1% 2% -23% FRPCA5-8% -28% -8% -8% -28% FRPCA6-8% -31% -8% -8% -31% FRPCA1 0% -23% -44% -28% -50% FRPCA4 2% -21% -14% -8% -33% FRPCA5-9% -30% -43% -30% -51% FRPCA6-8% -31% -26% -19% -43% In the first period, the scenarios generally led to no relevant changes in investment with the exception of FRPCA4, i.e. the largest farm and the one with a positive debt (Table 40). 114/187

117 Table 40 Impact of the scenarios on investment 15 - France Plain - Arable FRPCA1 0% 0% 0% -6% -6% FRPCA4-17% -26% -21% -33% -799% FRPCA5 0% 0% 0% 0% 0% FRPCA6 0% 0% 0% 0% 0% FRPCA1-378% -378% -378% -368% 25% FRPCA4-11% 6% -16% -10% -303% FRPCA5 0% 0% 0% 0% 0% FRPCA6 0% -2% -2% -2% -2% In the second period ( ), negative signs prevailed for farms FRPCA1, 4 and 6, though, in all cases at least one price reduction scenario led to an increase in investment. As investment in the baseline was negative (prevailing disinvestment), this means in fact an increase of investment. Labour use on the farms was stable, with the exception of farm FRPCA5, in which it slightly decreased and farm FRPCA1 that showed an increase in the second period (Table 41). Table 41 Impact of the scenarios on labour - France Plain - Arable FRPCA1 0% 0% 0% 0% 0% FRPCA4 0% 0% 0% 0% -3% FRPCA5 0% -2% 0% 0% -2% FRPCA6 0% 0% 0% 0% 0% FRPCA1 14% 14% 10% 10% 0% FRPCA4 0% 0% 0% 0% -5% FRPCA5 0% -2% 0% 0% -2% FRPCA6 0% 0% 0% 0% 0% Nitrogen was also stable and only farm FRPCA1 reacts, in this case with a moderate increase in all scenarios except 3.3 (Table 42). Table 42 Impact of the scenarios on nitrogen use - France Plain - Arable FRPCA1 0% 0% 0% 0% 0% FRPCA4 0% 0% 0% 0% -3% FRPCA5 0% -13% 0% 0% -13% FRPCA6 0% 0% -1% 0% 0% FRPCA1 11% 11% 7% 7% 0% FRPCA4 0% 0% 0% 0% -5% FRPCA5 0% -13% 0% 0% -13% FRPCA6 0% 0% 0% 0% 0% 15 Land, farm buildings and tractors investment was already negative in the baseline scenario. 115/187

118 The opposite trend was noted for farm FRPCA5 in the scenario involving price reduction. Changes in activity mix are reported in Table 43. Table 43 Impact of the scenarios on selected activities - France Plain - Arable durum wheat 0% 0% 0% 0% 0% maize 0% 47% 0% 0% 47% potatoes 0% 0% 0% 0% 0% rapeseed 0% -15% -1% 0% -15% set-aside 0% 0% 0% 0% 0% soft wheat 0% 0% 0% 0% 0% spring barle 0% 0% 0% 0% 0% sugar beet 0% 0% 0% 0% 0% winter barle 0% 0% 0% 0% 0% durum wheat 0% 0% 0% 0% 0% maize 0% 52% 0% 0% 52% potatoes 0% 0% 0% 0% 0% rapeseed 4% -12% 4% 4% -16% set-aside -25% -25% -25% -25% -26% soft wheat 1% 1% 0% 0% 0% spring barle 5% 5% 5% 5% 0% sugar beet 0% 0% 0% 0% 0% winter barle 7% 7% 7% 7% 0% The decoupling brings no change in the short run. The changes due to other scenarios appear negligible, except for the increase of maize and the decrease of rapeseed when all prices decrease by 20%. Some Good Agricultural and Environmental Conditions (GAEC) practices could be adopted, though in practice this could be irrelevant. Generally speaking, farms do not seem to have major alternatives in terms of crop choices, and the present crop mix already responded to a reasonable differentiation. Changes in selected investments are reported in Table 44. Table 44 - Impact of the scenarios on selected investments - France Plain - Arable Land = = = = = Farm buildings = = = = = Tractors Tillage machinery Harvesting machinery = = = = = Land = = = = = Farm buildings = = = = = Tractors Tillage machinery Harvesting machinery = Only tractors and tillage machinery (and harvesting machinery in the second period) showed some reaction, in all cases with a positive change, with the exception of the scenario 3.3. in the second period, when changes are negative. 116/187

119 Germany Mountain - Arable The German mountain arable case study is represented by six farms, of which two are organic (Table 45). Table 45 Summary of farm case studies modelled - Germany Mountain - Arable Household CODE DEMCA44 DEMCA45 DEMCA47 DEMEA02 DEMEA43 DEMEA46 Legal Status Family run Family run Family run Family run Family run Family run N. household members Age farmer Use external labour no no no no yes no Members working off farm yes yes yes yes no yes Houshold debt/asset ratio 5% 0% 0% 8% 0% 4% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Organic Organic Organic SFP (euro) SFP/income ratio Number of rights (ha) Arable The sizes vary between 40 and 240 hectares, with two cases out of five that rent out land. Summary of baseline indicators for Germany Mountain Arable is shown in Table 46. Table 46 Summary of baseline (Agenda 2000) of farm case studies modelled - Germany - Mountain - Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) DEMCA DEMCA DEMCA DEMEA DEMEA DEMEA DEMCA DEMCA DEMCA DEMEA DEMEA DEMEA Incomes from farming are rather high. Household incomes are even higher, due to the high importance of non-farming labour. The degree of investment is rather high, though it shows a mix 117/187

120 of positive and negative trends. Labour use is rather high for arable crops. Nitrogen use is relatively high, while water use is negligible. The results of the scenarios in terms of income are reported in Table 47. Table 47 Impact of the scenarios on income from farming - Germany Mountain - Arable DEMCA44-14% -45% -14% -14% -54% DEMCA45-5% -64% -5% -5% -64% DEMCA47 18% -9% 18% 18% -9% DEMEA2 2% -94% -55% 2% -94% DEMEA43 8% 11% 67% 8% 8% DEMEA46-24% -101% -24% -24% -101% DEMCA44-15% -45% -100% -100% -100% DEMCA45-5% -68% -60% -41% -68% DEMCA47 18% -9% -7% 2% -24% DEMEA2 2% -100% -100% -65% -100% DEMEA43 7% -39% -81% -41% -41% DEMEA46-40% -94% -135% -91% -130% Decoupling caused an increase in income in two cases, while it causes a decrease in other three cases. The stronger increase effect was likely due to a discrepancy between the crop mix in the reference years and the crop mix in recent ones (scenario 2.1). A reduction in prices by 20% led to reduction in income of up to more than 100% (scenario 2.2). A reduction in payments in (scenario 3.1) would lead to a reduction in incomes of up to the same amount and abandonment of farming by two households (in scenarios 3.3). The impact on the household income was attenuated in all cases with respect to the percent change in income from farming (Table 48). Table 48 Impact of the scenarios on household income - Germany Mountain - Arable DEMCA44 0% -5% 0% 0% -6% DEMCA45-4% -36% -4% -4% -36% DEMCA47 9% -4% 9% 9% -4% DEMEA2 1% -16% -9% 1% -16% DEMEA43 7% -5% 45% 7% 7% DEMEA46-7% -57% -7% -7% -57% DEMCA44 0% -5% -11% -11% -13% DEMCA45-4% -36% -27% -19% -36% DEMCA47 10% -5% -2% 3% -11% DEMEA2 1% -17% -16% -10% -17% DEMEA43 8% -46% -72% -34% -34% DEMEA46-19% -63% -87% -59% -87% Four farms out of six showed no change in investment activity due to decoupling (scenario 2.1) (Table 49). 118/187

121 Table 49 Impact of the scenarios on investment- Germany Mountain Arable DEMCA44-142% -158% -660% -660% -660% DEMCA45 0% 570% 479% 0% 570% DEMCA DEMEA2 0% -739% -725% 0% -739% DEMEA43 0% 49% 49% 0% 0% DEMEA46-410% -878% -1277% -410% -878% DEMCA44 0% 0% -25% -25% -25% DEMCA45 0% -52% -52% -243% -52% DEMCA47 0% 0% -1% 0% 0% DEMEA2 0% -100% -100% -2491% -100% DEMEA43 0% 119% 119% 0% 0% DEMEA46-55% -54% -54% -371% -271% On the contrary, price (scenario 2.2) and payment changes (scenarios 3.1, 3.2 and 3.3) dramatically affected the degree of investment. The effect was normally negative, as expected. However, farm DEMEA45 is an example of a farm continuing to invest in the first period, at an even stronger pace, and then decreasing investment in the second period. Stronger reductions in income were accompanied by even stronger reductions in farm labour (Table 50). Table 50 Impact of the scenarios on labour - Germany Mountain Arable DEMCA44-19% -48% -19% -19% -48% DEMCA45 0% -56% 0% 0% -56% DEMCA47 0% 0% 0% 0% 0% DEMEA2 0% -97% -50% 0% -97% DEMEA43 0% 24% 24% 0% -100% DEMEA46-13% -100% -13% -13% -100% DEMCA44-22% -48% -100% -100% -100% DEMCA45 0% -64% -64% -40% -64% DEMCA47 0% 0% 0% 0% 0% DEMEA2 0% -100% -100% -63% -100% DEMEA43 0% 0% -13% 0% -100% DEMEA46-28% -100% -100% -55% -100% In terms of nitrogen, the models showed opposite tendencies for farm DEMCA43, which tended to intensify use, and the others, which tended to reduce nitrogen use as a consequence of production abandonment (Table 51). 119/187

122 Table 51 Impact of the scenarios on nitrogen use - Germany Mountain - Arable DEMCA44-20% -50% -20% -20% -50% DEMCA45 0% -88% 0% 0% -88% DEMCA47 0% 0% 0% 0% 0% DEMEA2 0% -98% -50% 0% -98% DEMEA43 0% 25% 25% 0% 0% DEMEA46-13% -100% -13% -13% -100% DEMCA44-23% -50% -100% -100% -100% DEMCA45 0% -100% -100% -63% -100% DEMCA47 0% 0% 0% 0% 0% DEMEA2 0% -100% -100% -63% -100% DEMEA43 0% 0% -13% 0% 0% DEMEA46-28% -100% -100% -55% -100% The activity mix was rather stable in the decoupling scenario, with minor decreases in cereals and clover (Table 52). Table 52 Impact of the scenarios on selected activities - Germany Mountain - Arable Cereals -6% -53% -8% -6% -89% Clover -13% -100% -13% -13% -100% Grassland -2% -83% -2% -2% -83% Maize 1% 15% 19% 1% -82% Potatoes 0% -24% -14% 0% -24% Uncultivated 0% 0% 0% 0% -87% Cereals -11% -63% -73% -46% -97% Clover -28% -100% -100% -55% -100% Grassland -3% -94% -100% -67% -100% Maize 0% -12% -21% -8% -83% Potatoes 0% -28% -37% -26% -37% Uncultivated 0% 0% 0% 0% -87% Strong reduction in all selected crops is associated to the scenarios where farming activity is abandoned by some farms (and the total usable land of the group of farms is reduced). Some GAEC was introduced on residual land. Possible substitutions between cereals were not detected here because only information about a unique category cereals was collected. Investments decrease in all of the scenarios during the first period, while a balance of increasing and decreasing happens during the second period (Table 53). Table 53 Impact of the scenarios on selected investments - Germany Mountain - Arable 120/187

123 Land Farm buildings = = = = + Tractors = - - = - Tillage machinery = - - = - Harvesting machinery = - - = Land Farm buildings = Tractors = Tillage machinery = Harvesting machinery = /187

124 Germany Mountain - Livestock The mountain livestock case study involved six farms, of which two are organic (Table 54). Table 54 Summary of farm case studies modelled - Germany Mountain - Livestock Household CODE DEMCL12 DEMCL30 DEMCL39 DEMCL40 DEMEL26 DEMEL36 Legal Status Family run Family run Family run Family run Family run Family run N. household members Age farmer Use external labour no no yes yes yes no Members working off farm no yes yes no no yes Houshold debt/asset ratio 20% 6% 95% 8% 5% 7% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Conventional Organic Organic SFP (euro) SFP/income ratio Number of rights (ha) The farms selected were strongly specialised in dairy livestock. In all cases except DEMCL39 the land rented prevails over owned land. The farmer s age varied substantially, as well as the debt/asset ratio. Baseline indicators for Germany Mountain Livestock are showed in Table 55. Table 55- Summary of baseline (Agenda 2000) of farm case studies modelled - Germany - Mountain - Livestock Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) DE M C L DE M C L DE M C L DE M C L DE M E L DE M E L DE M C L DE M C L DE M C L DE M C L DE M E L DE M E L Incomes from farming are again rather high (as in the previous case study). Household incomes are substantially higher, due to the high importance of non-farming labour. The degree of investment is rather high in some cases, while it shows a strong net disinvestment in at least one case and moderate disinvestments in other two cases. Labour use is rather high, which is reasonable for livestock farms. Nitrogen use is very variable. 122/187

125 The results of the scenarios in terms of change in farm income are reported in Table 56. Table 56 Impact of the scenarios on income from farming - Germany Mountain - Livestock DE M C L 12 2% -23% 2% 2% -23% DE M C L 30 2% -22% 2% 3% -23% DE M C L 39-5% -30% -5% -5% -30% DE M C L 40-18% -44% -18% -29% -42% DE M E L 26-6% -6% -6% -6% -6% DE M E L 36 2% -26% 2% 2% -26% DE M C L 12 2% -21% -12% -7% -30% DE M C L 30 2% -22% -6% 3% -28% DE M C L DE M C L 40-24% -48% -40% -45% -47% DE M E L 26-8% -8% -9% -6% -6% DE M E L 36 2% -26% -10% -6% -33% Decoupling caused contrasting but changes, with prevailing negative effects in three farms and small positive changes in the others (scenario 2.1). Price reduction brought an income drop of up to about 50% (scenario 2.2). Reductions in payments in the second period led to a reduction in income of mostly less than 20% (scenarios 3.1, 3.2 and 3.3). The effects on household income appeared again to be slightly attenuated with respect to farm income, due to the (small) effect of off-farm income (Table 57). Table 57 Impact of the scenarios on household income - Germany Mountain - Livestock DE M C L 12 2% -24% 2% 2% -24% DE M C L 30 1% -15% 0% 1% -15% DE M C L 39-1% -7% -1% -1% -7% DE M C L 40-16% -46% -17% -28% -45% DE M E L 26-7% -7% -7% -7% -7% DE M E L 36 2% -27% 2% 2% -27% DE M C L 12 3% -24% -10% -5% -32% DE M C L 30 1% -15% -4% 1% -18% DE M C L 39 0% -1% 0% 0% -1% DE M C L 40-22% -50% -35% -43% -52% DE M E L 26-10% -10% -10% -8% -8% DE M E L 36 2% -29% -9% -5% -36% Investment trends tended to follow and emphasise income trends, though with important discontinuities and reversing the direction of change in the case of farm DEMEL26 (Table 58). 123/187

126 Table 58 Impact of the scenarios on investment 16 - Germany Mountain - Livestock DE M C L 12-3% -19% -1% 6% -17% DE M C L 30 13% -6% 35% 5% -24% DE M C L 39 0% 0% 0% 0% 0% DE M C L 40-53% -36% -47% -49% 10% DE M E L 26-1% -1% -4% -4% -4% DE M E L 36 0% 0% 0% 0% 0% DE M C L 12 15% 28% 12% 14% 15% DE M C L 30-6% -4% 0% -9% -1% DE M C L 39-10% -33% 20% 95% 5% DE M C L 40 0% 0% 0% 0% 0% DE M E L 26-72% -48% -72% -75% 30% DE M E L 36 0% 0% 0% 0% 0% While some farms tended to show no change as a reaction to decoupling (scenario 2.1), the reactions were mostly stronger in case of price reductions (scenarios 2.2 and 3.3). Impact on on-farm labour followed pretty much income trends (Table 59). Table 59 Impact of the scenarios on labour - Germany Mountain - Livestock DE M C L 12 0% 0% 0% 0% 0% DE M C L 30 0% 0% 0% 0% 0% DE M C L 39 0% 0% 0% 0% 0% DE M C L 40-29% -29% -29% -29% -26% DE M E L 26 2% -100% 2% 2% -100% DE M E L 36 0% 0% 0% 0% 0% DE M C L 12 0% 0% 0% 0% 0% DE M C L 30 0% 0% 0% 0% 0% DE M C L DE M C L 40-40% -41% -44% -44% -31% DE M E L 26 0% -100% 1% 2% -100% DE M E L 36 0% 0% 0% 0% 0% Farm DEMCL40 and DEMEL26 tended to have by far the strongest reaction, probably because all of their land is rented. Similarly, nitrogen use tended to reflect closely the changes in income (Table 60). 16 Investments were already negative in the baseline scenario in the farm DEMEL26 124/187

127 Table 60 Impact of the scenarios on nitrogen use - Germany Mountain - Livestock DE M C L 12 0% 0% 0% 0% 0% DE M C L 30 0% 0% 0% 0% 0% DE M C L 39 0% 0% 0% 0% 0% DE M C L 40 2% -7% 2% 2% -4% DE M E L 26 0% 0% 0% 0% 0% DE M E L 36 0% 0% 0% 0% 0% DE M C L 12 0% 0% 0% 0% 0% DE M C L DE M C L DE M C L 40 1% -14% -26% -26% 0% DE M E L 26 0% 0% -5% 0% 0% DE M E L 36 0% 0% 0% 0% 0% The impact of decoupling in terms of activity mix was mainly represented by a small decrease in dairy cows and fattening cattle and a small increase of cereals (Table 61). Table 61 Impact of the scenarios on selected activities - Germany Mountain Livestock Cereals 4% -6% 4% 4% -4% Clover -1% 195% -1% -1% 194% Maize -5% -4% -5% -5% -2% Uncultivated 0% -17% 0% 0% -17% Dairy Cows -5% -7% -4% -4% -5% Cereals 4% -21% -18% -18% -12% Clover -3% -100% -1% -1% -100% Maize -8% -8% -14% -14% 0% Uncultivated 0% -17% 0% 0% -17% Dairy_Cows -7% -23% -13% -13% -16% The main investments items showed no reaction to decoupling (scenario 2.1), while all farms speeded up disinvestment in all of the scenarios (scenarios 2.2 and 3.3) with a price reduction (Table 53). 125/187

128 Table 62 Impact of the scenarios on selected investments - Germany Mountain Livestock Land = Farm buildings = - = = - Tractors = - = = - Tillage machinery = = = = = Harvesting machinery = - = = Land = = = = - Farm buildings = = = = = Tractors = - - = - Tillage machinery = = = = = Harvesting machinery = - = = - 126/187

129 Germany Plain - Arable The case German plain arable system is represented by two farms, one conventional and one organic (Table 63). Table 63 Summary of farm case studies modelled - Germany Plain - Arable Household CODE DEPCA33 DEPEA03 Legal Status Family run Others N. household members 5 4 Age farmer Use external labour no yes Members working off farm yes yes Houshold debt/asset ratio 20% 20% Land owned (ha) 47 0 Land rented in (ha) 125 Land rented out (ha) Technology Conventional Organic SFP (euro) SFP/income ratio Number of rights (ha) Both households have some members working off-farm. However, their land possession strategy is completely different (total ownership in one case, total rent in the other). SFP plays a quite important role in both cases. Baseline indicators for Germany Plain Arable are shown in Table 64. Table 64 - Summary of baseline (Agenda 2000) of farm case studies modelled - Germany - Plain - Arable Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) DEPCA DEPEA DEPCA DEPEA Incomes from farming are very diverse in this case. DEPEA3 has very high income per hectare thanks to high value organic production. Household incomes are higher than farming income due to the non-farming labour. While this integration is minimal for DEPEA3, it is substantial for DEPCA33. The degree of investment is low, though it shows a mix of positive and negative trends. Labour use is minimum. Nitrogen use is relatively low. The results of the scenarios in terms of income are reported in Table /187

130 Table 65 Impact of the scenarios on income from farming - Germany Plain Arable DEPCA33 3% -30% 3% 3% -30% DEPEA3 0% 0% 0% 0% 0% DEPCA DEPEA3 0% 0% -2% -1% -1% The results showed a minimum effect of decoupling, with an increase in income in one case and no change in the other (scenario 2.1). Price and payments reduction (scenarios 2.2, 3.1, 3.2 and 3.3) showed moderate impacts on one farm (the organic one), while strongly affecting the other in the first period. Such effects are, however, of little importance when compared to the total farm income, whose changes were almost negligible (Table 66). Table 66 Impact of the scenarios on household income - Germany Plain Arable DEPCA33 0% -1% 0% 0% -1% DEPEA3 0% 0% 0% 0% 0% DEPCA33 0% 0% 0% 0% 0% DEPEA3 0% 0% -1% -1% -1% Investment trends tended to stay stable in the different scenarios, in which they mostly followed income trends (Table 67). Table 67 Impact of the scenarios on investment - Germany Plain - Arable DEPCA33 0% 0% 0% 0% 0% DEPEA3 1% 1% 1% 1% 1% DEPCA DEPEA3 0% 0% 0% 0% 0% The main reason is that farm investment endowment is rather simplified here and, as a consequence, corner points in the model tend to be quite stable. The impact on on-farm labour followed pretty much investment trends, with relevant reactions only in the case of scenario 2.2 and 3.3 and in farm DEPEA3 (Table 68). Table 68 Impact of the scenarios on labour - Germany Plain - Arable DEPCA33 0% 0% 0% 0% 0% DEPEA3 0% -100% 0% 0% -100% DEPCA DEPEA3 0% -100% 0% 0% -100% The same applies to nitrogen use (Table 69). 128/187

131 Table 69 Impact of the scenarios on nitrogen use - Germany Plain - Arable DEPCA33 0% 0% 0% 0% 0% DEPEA3 0% 0% 0% 0% 0% DEPCA DEPEA3 0% 0% 0% 0% 0% The changes are reflected and explained in the activity mix, which basically witnesses the abandonment of farming in case of both price reduction and payment reduction (Table 70). Table 70 Impact of the scenarios on selected activities - Germany Plain - Arable Beans 0% -100% 0% 0% -100% Cereals 0% -98% 0% 0% -98% Uncultivated 0% -86% 0% 0% -86% Horses 0% 0% 0% 0% 0% Beans 0% -100% 0% 0% -100% Cereals 0% -100% 0% 0% -100% Uncultivated 0% -100% 0% 0% -100% The main investments items showed no reaction to decoupling (Table 71). Table 71 Impact of the scenarios on selected investment - Germany Plain - Arable Land = = = = + Farm buildings = = = = = Tractors = = = = = Tillage machinery Harvesting machinery = = = = = Land = = = = = Farm buildings = = = = = Tractors = = = = = Tillage machinery = - = = - Harvesting machinery = = = = = Altogether, this reflects a situation with small profit margins and few alternatives, with few opportunities for adjustment, but where many opportunities of off-farm income can easily drive labour out of agriculture. 129/187

132 Germany Plain - Livestock The case of German plain livestock is represented by four farms, of which one is organic (Table 72). Table 72 Summary of farm case studies modelled - Germany Plain - Livestock Household CODE DEPCL09 DEPCL19 DEPCL38 DEPEL37 Legal Status Family run Family run Family run Family run N. household members Age farmer Use external labour yes no yes yes Members working off farm no yes no yes Houshold debt/asset ratio 40% 21% 10% 4% Land owned (ha) Land rented in (ha) Land rented out (ha) 1 Technology Conventional Conventional Conventional Organic SFP (euro) SFP/income ratio Number of rights (ha) The farms selected were strongly specialised in dairy livestock, with a mix of owned and rented land. All of them are around 100 hectares, with the exception of the organic one that is more than 300. The debts/asset ratio was rather high in one case, and relevant in two others. Table 73 shows the baseline indicators for Germany Plain Livestock. Table 73 - Summary of baseline (Agenda 2000) of farm case studies modelled - Germany - Plain - Livestock Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) DEPCL DEPCL DEPCL DEPEL DEPCL DEPCL DEPCL DEPEL Incomes from farming are rather good. Household incomes are higher, but the difference is not so important, due to the low importance of non-farming labour. Net investment is usually positive, with one exception. Labour use is rather high, consistently with the livestock specialisation. Nitrogen use is relatively high. The results of the scenarios in terms of income are reported in Table /187

133 Table 74 Impact of the scenarios on income from farming - Germany Plain - Livestock DEPCL09 69% 31% 70% 71% 71% DEPCL19-13% -39% -13% -13% -39% DEPCL38-9% -35% -9% -9% -35% DEPEL37 4% -45% 4% 4% -45% DEPCL09-15% -91% -35% -32% -32% DEPCL19-13% -39% -16% -15% -41% DEPCL38-9% -35% -16% -14% -39% DEPEL37 4% -45% -28% -16% -65% In two cases out of four, decoupling brought a reduction of income (scenario 2.1). Price reduction caused falls in income up to as high as 45% in the first period and 91% in the second (scenario 2.2). Payment cuts added about a 5 to 60% reduction in income in the second period (scenarios 3.1, 3.2 and 3.3). Such an effect was never anticipated by changes in the first period. Due to the negligible importance of off-farm labour, the effects on the total household income were basically the same as on-farm income ( Table 75). Table 75 Impact of the scenarios on household income - Germany Plain - Livestock DEPCL09 68% 30% 69% 70% 70% DEPCL19-13% -39% -13% -13% -39% DEPCL38-9% -36% -9% -9% -36% DEPEL37 4% -39% 4% 4% -39% DEPCL09-20% -78% -35% -31% -31% DEPCL19-13% -41% -16% -15% -43% DEPCL38-9% -36% -16% -13% -40% DEPEL37 4% -40% -20% -11% -54% The farms showed basically no changes in investment as a reaction to policy scenarios, with the exception of DEPCL09 (Table 76). Table 76 Impact of the scenarios on investment - Germany Plain - Livestock DEPCL09-261% -259% -257% -256% -256% DEPCL19 0% 0% 0% 0% 0% DEPCL38 0% 0% 0% 0% 0% DEPEL37 0% 0% 0% 0% 0% DEPCL09 25% -68% -29% 23% 23% DEPCL19 0% 0% 0% 0% 0% DEPCL38 0% 0% 0% 0% 0% DEPEL37 0% 3% 4% 4% 3% 77). The same applies to labour use, where only one farm showed some sensible reaction (Table 131/187

134 Table 77 Impact of the scenarios on labour - Germany Plain - Livestock DEPCL09 77% 81% 78% 79% -100% DEPCL19 0% 0% 0% 0% 0% DEPCL38 0% -1% 0% 0% -1% DEPEL37 0% 0% 0% 0% 0% DEPCL09-13% -100% -25% -25% -100% DEPCL19 0% 0% 0% 0% 0% DEPCL38 0% 0% 0% 0% 0% DEPEL37 0% 0% 0% 0% 0% Again, only one farm showed some reaction in terms of nitrogen use, with a strong increase in the first period and a strong reduction in the second (Table 78). Table 78 Impact of the scenarios on nitrogen use - Germany Plain - Livestock DEPCL09 77% 83% 78% 79% 79% DEPCL19 0% 0% 0% 0% 0% DEPCL38 0% -3% 0% 0% -3% DEPEL37 2% -32% 2% 2% -31% DEPCL09-13% -100% -25% -25% -25% DEPCL19 0% 0% 0% 0% 0% DEPCL38 0% 0% 0% 0% 0% DEPEL37 3% -42% -42% -40% -42% In terms of activity mix, decoupling brought about a reduction in numbers of young cattle (scenario 2.1), while price reductions were reflected more sharply in cereal reduction (scenarios 2.2 and 3.3) (Table 79). Table 79 Impact of the scenarios on selected activities - Germany Plain - Livestock Cereals 7% -14% 7% 7% -28% Grassland 28% 29% 29% 29% -37% Maize 15% 17% 15% 15% -17% Uncultivated 0% -1% 0% 0% -1% Dairy Cows 10% 12% 10% 10% -11% Cereals 4% -10% -22% -21% -32% Grassland 2% 33% 5% 5% -34% Maize 1% 14% 4% 4% -15% Uncultivated 0% 0% 0% 0% 0% Dairy Cows 2% 21% 5% 5% -10% Compared with the German plain arable case, this witnesses a situation with the largest profit margins but fewer alternatives, with small opportunities for adjustment but more resistance towards off-farm income. 132/187

135 This also reflects on the kind of investments that usually reflects land reduction and increase of specialisation in livestock production (Table 80). Table 80 Impact of the scenarios on selected investments - Germany Plain - Livestock Land - = Farm buildings Tractors Tillage machinery Harvesting machinery = = = = = Land = = = = = Farm buildings = = = + - Tractors + = Tillage machinery + = = + - Harvesting machinery = = = = = 133/187

136 Greece Plain - Arable Six farms were selected for modelling in Greece (Table 81). Table 81 Summary of farm case studies modelled - Greece Plain - Arable Household CODE GRPCA07 GRPCA08 GRPCA09 GRPCA10 GRPCA12 GRPEA01 Legal Status Family run Family run Family run Family run Family run Family run N. household members Age farmer Use external labour yes yes yes yes yes yes Members working off farm no no no no no no Houshold debt/asset ratio Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Conventional Conventional Organic SFP (euro) SFP/income ratio 73% 28% 150% 24% 34% 4% Number of rights (ha) All of them are highly specialised in farming as there is no off-farm activity reported. Young farmers prevail and there is a strong specialisation in arable production and also a lack of nonfarming income (see also Table 89 at the end of this section). Farms with a high degree of nonfarming activities and/or relevant livestock production were not modelled. Baseline indicators for Greece Plain Arable are shown in Table 82. Table 82 - Summary of baseline (Agenda 2000) of farm case studies modelled - Greece - Plain - Arable Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) GR P C A GR P C A GR P C A GR P C A GR P C A GR P E A GR P C A GR P C A GR P C A GR P C A GR P C A GR P E A Incomes from farming are rather good and very high in some farms. The exceptionally income of GRPEA01 is generated by high value added organic product, fruit refrigeration, processing, packaging and selling activities on the farm, associated to small farm size. Household incomes are higher then farm incomes, but the difference is not so important, due to the low importance of non-farming labour in most of the households. Net investment is usually negative or zero, with an exception. Labour use is very high, consistently with intensive arable specialisation. 134/187

137 Nitrogen use is relatively high; water use is very important in this system, characterised by a key role for irrigation. The results of the scenarios in terms of income are reported in Table 83. Table 83 Impact of the scenarios on income from farming - Greece Plain - Arable GR P C A % 245% 277% 277% 245% GR P C A 08-4% -22% -4% -4% -22% GR P C A 09 7% -28% 7% 7% -28% GR P C A 10 1% -41% 1% 1% -41% GR P C A 12 1% -21% 1% 1% -21% GR P E A 01 0% -24% 0% 0% -24% GR P C A % 256% -32% 90% 51% GR P C A 08-5% -22% -49% -32% -49% GR P C A 09 8% -27% -6% -1% -38% GR P C A 10 0% -47% -12% -7% -54% GR P C A 12 1% -21% -1% 0% -22% GR P E A 01-73% -25% 0% 0% -24% Decoupling brought mixed effects (scenario 2.1). In most cases there was a slight increase in income. An exception was GRPCA7 in which the increase in income was substantial due to the appropriation of benefits from tobacco decoupling. In two cases only there was an income decrease, due mainly to inconsistencies between the historic areas used for SFP calculation and the recent crop mix. Changes in prices brought decreases in farming income of between 20 and 42%, except again for farm GRPCA7 where they were more than compensated by gains from decoupling (scenario 2.2). Given the concentration of work on-farm and the small proportion of off-farm activities, the effect did not change substantially when changes in total household income were considered (Table 84). Table 84 Impact of the scenarios on household income - Greece Plain - Arable GR P C A % 240% 272% 272% 240% GR P C A 08-4% -21% -4% -4% -21% GR P C A 09 7% -28% 7% 7% -28% GR P C A 10 1% -41% 1% 1% -41% GR P C A 12 1% -21% 1% 1% -21% GR P E A 01 0% -25% 0% 0% -25% GR P C A % 251% -19% 97% 59% GR P C A 08-5% -22% -46% -30% -47% GR P C A 09 8% -27% -6% -1% -38% GR P C A 10 0% -47% -11% -6% -53% GR P C A 12 1% -21% -1% 0% -22% GR P E A 01-70% -26% 0% 0% -25% 135/187

138 The impact of decoupling on investment was basically irrelevant in the first period, due to previous farm investments, but became important in the period (Table 85). Table 85 Impact of the scenarios on investment - Greece Plain - Arable GR P C A 07 0% 0% 0% 0% 0% GR P C A 08 0% 0% 0% 0% 0% GR P C A 09 0% 0% 0% 0% 0% GR P C A 10 0% 0% 0% 0% 0% GR P C A 12 0% 0% 0% 0% 0% GR P E A 01 0% 0% 0% 0% 0% GR P C A GR P C A 08-56% -69% -56% -56% -69% GR P C A 09-11% -25% -81% -43% -45% GR P C A 10-2% -2% -2% -2% -2% GR P C A GR P E A % 0% 0% 0% 0% This reflects a situation where capital endowment is relatively simplified, as most of the farmers had already done their machinery investments in previous years. In contrast with income effects, the impact on labour use appeared substantial in at least three cases (Table 86). Table 86 Impact of the scenarios on labour - Greece Plain - Arable GR P C A 07-42% -35% -42% -42% -35% GR P C A 08-78% -78% -78% -78% -78% GR P C A 09 0% -18% 0% 0% -18% GR P C A 10 0% 0% 0% 0% 0% GR P C A 12 0% 0% 0% 0% 0% GR P E A 01 0% 0% 0% 0% 0% GR P C A 07-39% -37% -39% -39% -40% GR P C A 08-75% -76% -77% -75% -77% GR P C A 09 0% -12% 37% 9% -12% GR P C A 10 0% 0% 0% 0% 0% GR P C A 12 0% 0% 0% 0% 0% GR P E A 01-71% 0% 0% 0% 0% In addition, the effect of decoupling appears here as much more relevant than price changes, as the effects of the latter are almost negligible. Farmers, due to decoupling, changed their crop plans abandoning tobacco, cotton and other highly labor intensive crops. All scenario variables (decoupling, price reduction and payment reduction) brought about a decrease in nitrogen use as well as in water use (Table 87 and Table 88). 136/187

139 Table 87 Impact of the scenarios on nitrogen use - Greece Plain - Arable GR P C A 07-13% -100% -13% -13% -100% GR P C A 08 12% 12% 12% 12% 12% GR P C A 09 0% 0% 0% 0% 0% GR P C A 10-2% -2% -2% -2% -2% GR P C A 12 0% 0% 0% 0% 0% GR P E A 01 0% 0% 0% 0% 0% GR P C A 07-12% -95% -12% -12% -100% GR P C A 08 9% 9% -64% -1% -33% GR P C A 09-2% -2% -95% -22% -25% GR P C A 10-3% -3% -3% -3% -3% GR P C A 12 0% 0% 0% 0% 0% GR P E A 01-71% 0% 0% 0% 0% While changes in nitrogen use have a certain variability, decreases in water use seem a more general phenomenon. Table 88 Impact of the scenarios on water use - Greece Plain - Arable GR P C A 07-9% -33% -9% -9% -33% GR P C A 08-64% -64% -64% -64% -64% GR P C A 09 0% -8% 0% 0% -8% GR P C A 10 0% 0% 0% 0% 0% GR P C A 12 0% 0% 0% 0% 0% GR P E A 01 0% 0% 0% 0% 0% GR P C A 07-8% -35% -8% -8% -41% GR P C A 08-61% -62% -61% -61% -62% GR P C A 09 0% -4% -44% -8% -19% GR P C A 10 0% 0% 0% 0% 0% GR P C A 12 0% 0% 0% 0% 0% GR P E A 01-71% 0% 0% 0% 0% In terms of crop mix, the most evident changes are the disappearance of tobacco and a strong drop in sugar beet and tomato in the long run, accompanied by a relevant increase of durum wheat (Table 89). 137/187

140 Table 89 Impact of the scenarios on selected activities - Greece Plain - Arable alfalfa 1% -6% 1% 1% -6% beans 0% 0% 0% 0% 0% durum_wheat 93% 93% 93% 93% 93% maize 0% -6% 0% 0% -6% peas 0% 0% 0% 0% 0% tobacco -100% -100% -100% -100% -100% tomatoes 0% 0% 0% 0% 0% alfalfa 2% -4% 50% 13% 1% beans 0% 0% 0% 0% 0% durum_wheat 40% 40% -93% 21% -36% maize 0% -5% -50% -10% -18% peas 0% 0% 0% 0% 0% tobacco -96% -97% -96% -96% -97% tomatoes -64% 0% 0% 0% 0% No change appears for the main investment items (Table 90). Table 90 Impact of the scenarios on selected investments - Greece Plain - Arable Land = = = = = Farm buildings = = = = = Tractors = = = = = Tillage machinery = = = = = Harvesting machinery = = = = = Land = = = = = Farm buildings = = = = = Tractors = = = = = Tillage machinery = = = = = Harvesting machinery = = = = = 138/187

141 Hungary Plain - Arable In the Hungarian plain arable case study, two farms were modelled (Table 91). Table 91 Summary of farm case studies modelled - Hungary Plain - Arable Household CODE HUPCA04 HUPCA06 Legal Status Limited company Family run N. household members 1 1 Age farmer Use external labour yes yes Members working off farm no no Houshold debt/asset ratio 0% 10% Land owned (ha) Land rented in (ha) Land rented out (ha) 0 0 Technology Conventional Conventional SFP (euro) SFP/income ratio 77% 267% Number of rights (ha) Both of them are quite large, but differ substantially in structure and size. While the first farm is more than 4000 hectares, mostly owned, the second is about 800, mostly rented. Payments played a key role compared to income. Summary of baseline indicators for Hungary Plain Arable is shown in Table 92. Table 92 - Summary of baseline (Agenda 2000) of farm case studies modelled - Hungary - Plain - Arable Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) HUPCA HUPCA HUPCA HUPCA Incomes from farming are rather low and do not differ substantially from household incomes. Net investment is usually positive, but not so important in value. Labour use is rather low in HUPCA6 and high in HUPCA4. Nitrogen use is relatively high. The results of the scenarios in terms of income are reported in Table 93. Table 93 Impact of the scenarios on income from farming - Hungary Plain - Arable HUPCA6 1% -32% 1% 1% -32% HUPCA4-16% -34% -17% -17% -33% HUPCA6-1% -34% -43% -28% -61% HUPCA4-32% -41% -91% -73% -83% 139/187

142 Decoupling had a minor effect on income in one case, but a major negative impact in the other (scenario 2.1). Price reductions had impacts in the range of 30% (scenario 2.2). On the other hand, payment cuts brought income reductions of up to 85% in the farm that is more payment dependent (scenarios 3.1, 3.3 and 3.3). Adjustments of these effects due to off-farm income were basically irrelevant (Table 94). Table 94 Impact of the scenarios on household income - Hungary Plain - Arable HUPCA6 1% -32% 1% 1% -32% HUPCA4-9% -28% -9% -9% -28% HUPCA6-1% -34% -43% -28% -61% HUPCA4-32% -41% -90% -73% -83% With decoupling and, even more so, with payment cuts and price reductions, investment tended to collapse in the case of the family run farm, while it remained basically the same in the other case (Table 95). Table 95 Impact of the scenarios on investment - Hungary Plain - Arable HUPCA6 0% 0% 0% 0% 0% HUPCA4-100% -148% -102% -103% -146% HUPCA6 0% -2% 0% 0% -2% HUPCA4-65% -97% -64% -65% -97% Reductions in payments in the second period were partially anticipated through investment reduction in the first period. Labour use dropped in all cases, with a much higher effect in the family farm (Table 96). Table 96 Impact of the scenarios on labour - Hungary Plain - Arable HUPCA6-4% -11% -4% -4% -11% HUPCA4-67% -99% -71% -71% -99% HUPCA6 0% 0% 0% 0% 0% HUPCA4-99% -99% -100% -99% -99% Again, nitrogen use tended to drop following labour and investment (Table 97). 140/187

143 Table 97 Impact of the scenarios on nitrogen use - Hungary Plain - Arable HUPCA6-3% -11% -3% -3% -11% HUPCA4-70% -100% -74% -74% -100% HUPCA6 0% 0% 0% 0% 0% HUPCA4-100% -100% -100% -100% -100% Labour and nitrogen trends showed that the drops in income and prices would likely translate to abandonment of the family farm. An explanation in terms of crop mix of the previous trends can be found in Table 98, with major drops in the (relatively) most intensive crops accompanied by large adoption of GAEC. Table 98 Impact of the scenarios on selected activities - Hungary Plain - Arable Barley -100% -100% -100% -100% -100% Maize -44% -51% -44% -44% -51% Rape -23% -98% -23% -23% -98% Soya_beans -34% -100% -34% -34% -100% Storage_faci 28% -48% 28% 28% -43% Sunflower -74% -74% -74% -74% -74% Wheat -58% -88% -67% -67% -88% Barley -100% -100% -100% -100% -100% Maize -47% -47% -47% -47% -47% Rape -100% -100% -100% -100% -100% Soya_beans -100% -100% -100% -100% -100% Storage_faci -25% -100% -25% -25% -100% Sunflower -73% -73% -73% -73% -73% Wheat -86% -86% -86% -86% -86% The strong effect here may be justified by the relative low efficiency of the farms, which made the system very much payment-dependent as well as at the limit of price acceptability. A reduction of investments prevailed in all scenarios alternative to the baseline (Table 99). Table 99 Impact of the scenarios on selected investment - Hungary Plain - Arable Land = = = = = Farm buildings Tractors Tillage machinery Harvesting machinery Land = = = = = Farm buildings + = + = = Tractors = = = = = Tillage machinery Harvesting machinery /187

144 Hungary Plain - Livestock A summary of the Hungarian plain livestock case study is given in Table 100. Table 100 Summary of farm case studies modelled - Hungary Plain - Livestock Household CODE HUPCL01 HUPCL02 HUPCL03 Legal Status Limited company Family run Family run N. household members Age farmer Use external labour yes yes yes Members working off farm no no no Houshold debt/asset ratio 0% 50% 50% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional SFP (euro) SFP/income ratio 19% 16% 4% Number of rights (ha) The farms selected were strongly specialised dairy livestock. HUPCL01 was based on rented land only, almost 3000 hectares, while the other farms showed a more equilibrated mix of owned and rented land. Table 101 shows the baseline indicators for Hungary Plain Livestock. Table Summary of baseline (Agenda 2000) of farm case studies modelled - Hungary - Plain Livestock Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) HUPCL HUPCL HUPCL HUPCL HUPCL HUPCL Incomes from farming are rather low and do not differ substantially from household incomes. Net investment is usually positive, but not so important in value. Labour use is rather low in HUPCL1 and high in HUPCL2 and HUPCL3. Nitrogen use is relatively low. The results of the scenarios in terms of income were very varied, as reported in Table /187

145 Table 102 Impact of the scenarios on income from farming - Hungary Plain - Livestock HUPCL1 0% -19% 0% 0% -19% HUPCL2-54% -60% -50% -50% -60% HUPCL3-47% -50% -52% -51% -54% HUPCL1 0% -20% -100% -59% -80% HUPCL2-62% -62% -100% -94% -94% HUPCL3-44% -44% -100% -89% -89% In particular, both negative and positive impacts occurred, particularly in the longer term. The negative effect was the combined result of the (modelling) choice to set the reference year for the SFP at 2007 and the fact that, by decoupling, the farms could not accrue more payments by increasing their production processes, as they would have done at the baseline. A reduction in prices of 20% brought less relevant effects (scenario 2.2). In this case, payment cuts in the second period also would bring a reduction in income high enough to induce abandonment in the same period (scenarios 3.1) In this case, where limited company structure prevails, the impact on total income was basically the same as farming income, except for minor differences due mainly to interest on liquidity (Table 103). Table 103 Impact of the scenarios on household income - Hungary Plain - Livestock HUPCL1 0% -19% 0% 0% -19% HUPCL2-56% -62% -51% -51% -62% HUPCL3-47% -50% -52% -51% -54% HUPCL1 0% -20% -100% -59% -80% HUPCL2-61% -61% -99% -93% -93% HUPCL3-43% -43% -100% -88% -88% The impact of the scenarios on investment was generally negative, including a reduction of more then 100% that implied shifting from positive to negative investment (Table 104). Table 104 Impact of the scenarios on investment - Hungary Plain - Livestock HUPCL1 0% 4% -82% 0% 0% HUPCL2-255% -261% -252% -252% -261% HUPCL3-183% -183% -265% -253% -253% HUPCL1 0% -8% -57% -58% -100% HUPCL2-100% -100% -100% -100% -100% HUPCL3-73% -73% -100% -100% -100% 143/187

146 Impacts on labour were very strong (Table 105) and basically coincided with a dramatic reduction in nitrogen use (Table 106), shifting to extensive GAEC adoption, reductions in dairy production and some major crops (Table 107). Table 105 Impact of the scenarios on labour - Hungary Plain - Livestock HUPCL1 0% 0% 0% 0% 0% HUPCL2-90% -99% -80% -80% -99% HUPCL3-89% -95% -89% -89% -95% HUPCL1 0% -2% -100% -21% -64% HUPCL2-99% -99% -100% -100% -100% HUPCL3-99% -99% -100% -100% -100% Table 106 Impact of the scenarios on nitrogen use - Hungary Plain - Livestock HUPCL HUPCL2-92% -100% -82% -82% -100% HUPCL3-89% -93% -89% -89% -93% HUPCL HUPCL2-100% -100% -100% -100% -100% HUPCL3-100% -100% -100% -100% -100% Table 107 Impact of the scenarios on selected activities - Hungary Plain - Livestock Alfalfa -89% -100% - 0% -100% Dairy_cows -28% -5% 8% 0% -7% Maize -89% -100% - 0% -100% Other_cereal -88% 0% 0% 0% 0% pasture 0% 0% 0% 0% 0% Sunflower -94% 0% 99% 0% -50% Wheat -100% - - 0% -100% Alfalfa -100% Dairy_cows -32% -2% -100% - -55% gaec 4247% 0% -100% - 0% Maize -100% Other_cereal -100% pasture 0% 0% -100% - -53% Sunflower -100% Wheat -100% Impact on selected investments showed in the majority of cases some reduction (Table 108). 144/187

147 Table 108 Impact of the scenarios on selected investments - Hungary Plain - Livestock Land Farm buildings Tractors Tillage machinery Harvesting machinery Land + + = = = Farm buildings = Tractors - - = - - Tillage machinery Harvesting machinery = = = = = The strong effect here may be explained by the relatively low efficiency, which made the system very much payment-dependent as well as at the limit of price acceptability. Maintaining an even more extensive livestock production coupled with pasture and abandoning arable production seems a suitable option in this case. 145/187

148 Italy Mountain - Arable In the case of Italian mountain arable systems, six farms were selected, of which three were organic and three conventional (Table 109). Table 109 Summary of farm case studies modelled - Italy Mountain - Arable Household CODE ITMCA09 ITMCA16 ITMCA21 ITMEA54 ITMEA57 ITMEA63 Legal Status Limited company Limited company Limited company Family run Family run Limited company N. household members Age farmer Use external labour no yes no no no no Members working off farm yes yes yes yes yes no Houshold debt/asset ratio 35% 0% 10% 0% 0% 0% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Organic Organic Organic SFP (euro) SFP/income ratio 6% 7% 6% Number of rights (ha) Farm sizes ranged from 30 to 100 hectares. In all cases except one, there were some family members already working off-farm. SFP played generally a minor role compared to whole farm income. Baseline indicators for Italy Mountain Arable are showed in Table 110. Arable Table Summary of baseline (Agenda 2000) of farm case studies modelled - Italy - Mountain - Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) ITMCA ITMCA ITMCA ITMEA ITMEA ITMEA ITMCA ITMCA ITMCA ITMEA ITMEA ITMEA Incomes from farming are very differentiated in this case. Household incomes are usually higher, due to the non-farming labour. The difference tends to intensify, with a higher allocation of 146/187

149 labour off-farm (and related higher income) in the second period. The degree of investment is low, though it shows a mix of negative (first period) and positive (second period) trends. Labour use is rather high. Nitrogen use is relatively low. One household (ITMCA9) would sell the farm during the first period. The results of the scenarios in terms of income are reported in Table 111. Table 111 Impact of the scenarios on income from farming - Italy Mountain - Arable ITMCA9-1% -37% -1% -1% -37% ITMCA16 4% -48% 1% 1% -48% ITMCA21 1% -28% -12% 1% -28% ITMEA54-27% -64% -27% -27% -83% ITMEA57 93% 14% 93% 93% 14% ITMEA63 8% -47% 8% 8% -47% ITMCA9 0% -41% 0% 0% -41% ITMCA16 5% -67% -30% -23% -47% ITMCA21 1% -82% -10% -6% -35% ITMEA54-26% -75% -55% -44% -100% ITMEA57 37% 14% -100% -65% -65% ITMEA63 9% -48% 0% 3% -53% Decoupling caused various kinds of changes in these farms. In three of them, the effect was a small increase in income, while in another a small decrease was witnessed. In one farm decoupling translated into an important decrease, due to the difference between the historic payment and the actual payment associated with the recent crop mix. In one case the effect was strongly positive due to increased flexibility in crop choices (scenario 2.1). On the other hand, price and payment drops had unambiguously negative effects. Payment cuts in the second period were anticipated in only one farm (scenario 2.2). Payment cuts alone and with price reduction led to abandonment in two organic farms out of three (scenario 3.3). The impact of the scenarios on household income was much narrower due to external labour opportunities and made decoupling almost irrelevant at least in the conventional farms (Table 112). 147/187

150 Table 112 Impact of the scenarios on household income - Italy Mountain - Arable ITMCA9 0% -2% 0% 0% -2% ITMCA16 1% -12% 1% 1% -12% ITMCA21 0% -15% 1% 0% -15% ITMEA54-10% -22% -10% -10% -25% ITMEA57 22% 5% 22% 22% 5% ITMEA63 7% -39% 7% 7% -39% ITMCA9 0% -2% 0% 0% -2% ITMCA16 1% -15% -6% -4% -11% ITMCA21 1% -17% -5% -3% -19% ITMEA54-11% -26% -21% -17% -30% ITMEA57 10% 10% -11% -4% -6% ITMEA63 7% -41% 1% 3% -45% Investment tended to show little reaction to decoupling (scenario 2.1), but reacted sharply to price changes (scenario 2.2) and, to a lesser extent, to payment cuts (scenarios 3.1, 3.2 and 3.3). The reaction tended to be more often characterised by an increase in the first period and by a decrease (in some cases a total halt) in investment in the second period (Table 113). Table 113 Impact of the scenarios on investment - Italy Mountain - Arable ITMCA9 0% 0% 0% 0% 0% ITMCA16 0% 17% 10% 2% 17% ITMCA21 0% 0% 0% 0% 0% ITMEA54 0% 751% 0% 0% 2269% ITMEA57 0% 26% 10% 10% 26% ITMEA63 0% 0% 0% 0% 0% ITMCA9 0% 0% 0% 0% 0% ITMCA16 0% -118% -44% -89% -53% ITMCA21 0% -101% 0% 0% 0% ITMEA54 0% -100% 0% 0% -100% ITMEA57-1% -100% -100% -100% -99% ITMEA63 0% 0% 0% 0% 0% 148/187

151 Impact on farm labour followed pretty much investment trends (Table 114). Table 114 Impact of the scenarios on labour - Italy Mountain - Arable ITMCA9 1% 1% 1% 1% 1% ITMCA16-1% -36% -1% -1% -36% ITMCA21 0% 0% -13% 0% 0% ITMEA54-3% -86% -3% -3% -86% ITMEA57 0% -83% 0% 0% -83% ITMEA63 0% -56% 0% 0% -56% ITMCA9 0% 0% 0% 0% 0% ITMCA16 0% -60% -35% -25% -35% ITMCA21 0% -75% 0% 0% 0% ITMEA54 0% -100% 0% 0% -100% ITMEA57-75% -100% -100% -100% -100% ITMEA63 0% -56% 0% 0% -56% The main driver here appears to be prices, with a reduction of 20% causing abandonment of two of the organic farms (scenario 2.2), at least in the second period and with no payments (scenarios 3.1, 3.2 and 3.3). Nitrogen use showed relevant reductions in all cases (Table 115). Table 115 Impact of the scenarios on nitrogen use - Italy Mountain - Arable ITMCA9-50% -50% -50% -50% -50% ITMCA16-3% -80% -3% -3% -80% ITMCA21 0% 0% -13% 0% 0% ITMEA54-3% -64% -3% -3% -64% ITMEA57 0% -100% 0% 0% -100% ITMEA ITMCA ITMCA16 0% -94% -90% -64% -90% ITMCA21 0% -75% 0% 0% 0% ITMEA54 0% -100% 0% 0% -100% ITMEA57-75% -100% -100% -100% -100% ITMEA Water use is not relevant in mountain areas and the related table is not reported here. The previous effects are associated with changes in the crop mixes that mainly affect alfalfa, wheat, horse beans (not a common crop anyway), barley and other cereals (Table 116). 149/187

152 Table 116 Impact of the scenarios on selected activities - Italy Mountain - Arable Alfaalfa -7% -85% -26% -28% -88% Barley -6% -77% -14% -26% -95% Forages 0% -81% 0% 0% -81% Forest 0% -96% -100% -100% -100% Snow_removal 0% 0% -100% -100% -100% Uncultivated 0% 0% -2% -3% -3% Wheat -7% -69% -14% -24% -85% Alfaalfa -31% -100% -100% -88% -100% Barley -30% -80% -100% -88% -100% Uncultivated 0% 0% -14% -14% -14% Wheat -17% -81% -100% -83% -100% GAEC may have a role in these areas. Effects on single types of investment are mostly negative in the first period, while, during the second, no change seems to prevail (Table 117). Table 117 Impact of the scenarios on selected investments - Italy Mountain - Arable Land Farm buildings = = = = = Tractors = - - = - Tillage machinery = Harvesting machinery - - = = = Land = = = = = Farm buildings = = = = = Tractors = Tillage machinery = = = = = Harvesting machinery /187

153 Italy Mountain - Livestock The case of Italian mountain livestock is represented by five farms, of which two are organic (Table 118). Table 118 Summary of farm case studies modelled - Italy Mountain - Livestock Household CODE ITMCL76 ITMCL77 ITMCL79 ITMEL46 ITMEL61 Legal Status Limited company Family run Family run Limited company Limited company N. household members Age farmer Use external labour no no no no no Members working off farm no no yes no no Houshold debt/asset ratio 30% 2% 0% 0% 0% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Organic Organic SFP (euro) SFP/income ratio 19% 16% 47% Number of rights (ha) In these cases, the majority of households have no member working off-farm and the size varies between 15 and over 150 hectares. The farms selected were highly specialised in dairy livestock, where payments may have a quite substantial role, up to 47% of income in one case. Summary of baseline indicators for Italy Mountain Livestock is shown in Table 119. Table Summary of baseline (Agenda 2000) of farm case studies modelled - Italy - Mountain - Livestock Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) IT M C L IT M C L IT M C L IT M E L IT M E L IT M C L IT M C L IT M C L IT M E L IT M E L Incomes from farming are rather good and high in some farms. Highest income per hectare is generated in those farms with an important share of high value added livestock production (e.g. milk for parmesan cheese) and with higher animal/land ratio. Household incomes are higher than 151/187

154 farmer incomes, but the difference is not so important in most cases, due to the low importance of non-farming labour in most of the households (with the notable exception of ITMCL67 and ITMCL79). Net investment is usually positive. Labour use is high, consistently with intensive livestock specialisation. Nitrogen use is low. The results of the scenarios in terms of income are reported in Table 120. Table 120 Impact of the scenarios on income from farming - Italy Mountain - Livestock IT M C L 67-3% -52% -3% -3% -52% IT M C L 76-5% -28% -2% -5% -28% IT M C L 79 0% -24% 0% 0% -24% IT M E L 46 0% -45% -8% -8% -45% IT M E L 61 2% -23% 2% 2% -23% IT M C L 67-8% -57% -11% -10% -60% IT M C L 76-5% -27% -8% -9% -32% IT M C L 79 0% -24% -5% -3% -27% IT M E L 46 0% -38% -41% -36% -47% IT M E L 61 3% -23% -6% -3% -29% Decoupling brought no change or slightly negative effects (scenario 2.1). The effect of price decreases was a sharp reduction in income (up to 52%) (scenario 2.2). Cessation of payments after 2013 has relevant but not dramatic results, with the exception of ITMEL46 (scenarios 3.1, 3.2 and 3.3). The impact on household income basically reflects the impact on farming income, as the latter represents the highest proportion of the former (Table 121). Table 121 Impact of the scenarios on household income - Italy Mountain - Livestock IT M C L 67-2% -34% -2% -2% -34% IT M C L 76-5% -30% -2% -5% -30% IT M C L 79 0% -21% 0% 0% -21% IT M E L 46 0% -41% -6% -6% -42% IT M E L 61 2% -23% 2% 2% -23% IT M C L 67-4% -36% -6% -6% -38% IT M C L 76-5% -31% -8% -9% -35% IT M C L 79 0% -22% -4% -3% -24% IT M E L 46 0% -32% -32% -27% -41% IT M E L 61 3% -24% -6% -2% -29% Impacts on investment tended to reflect a variety of strategies (Table 122). 152/187

155 Table 122 Impact of the scenarios on investment 17 - Italy Mountain - Livestock IT M C L 67-41% -307% -41% -41% -331% IT M C L 76-25% 17% -5% -25% -24% IT M C L 79 0% 0% 0% 0% 0% IT M E L 46 0% -295% -181% -181% -314% IT M E L 61 0% 0% 0% 0% 0% IT M C L 67-39% -100% -39% -39% -100% IT M C L 76-4% 0% -1% -2% -3% IT M C L 79 0% 0% 0% 0% 0% IT M E L 46 0% -100% -100% -100% -100% IT M E L 61 0% 1% 0% 0% 0% Decoupling seemed to bring about a decrease in investment in at one farm, but an increase in the other one (ITML76, see note) while the other showed no changes. This behaviour was maintained even when payments were reduced in the second period (again with an exception). Price reduction caused important negative changes in investments in the first period in three farms, but no relevant changes in the others. The same pattern continued in the second period, with at least two households abandoning farming. Interestingly enough, at least one farm would keep investing in the first period even in the price drop scenario. The impact on farm labour was mostly negative, though four farms out of five showed no reaction to decoupling and three out of five had no reaction even to price and payment reductions (Table 123). Table 123 Impact of the scenarios on labour - Italy Mountain - Livestock IT M C L 67 0% -88% 0% 0% -88% IT M C L 76-5% 0% 0% -5% 0% IT M C L 79 1% 0% 1% 1% 0% IT M E L 46 0% -73% -3% -3% -73% IT M E L 61 0% 0% 0% 0% 0% IT M C L 67 0% -100% 0% 0% -100% IT M C L 76-5% 1% 1% -5% 1% IT M C L 79 0% 0% 0% 0% 0% IT M E L 46 0% -100% -100% -100% -100% IT M E L 61 0% 0% 0% 0% 0% Stronger reactions were detected for nitrogen use (Table 124). 17 Investments were already negative in the baseline scenario in the farm ITML76 153/187

156 Table 124 Impact of the scenarios on nitrogen use - Italy Mountain - Livestock IT M C L 67-72% -100% -72% -72% -100% IT M C L IT M C L IT M E L 46 0% -95% -79% -79% -95% IT M E L 61-8% -44% -8% -8% -44% IT M C L 67-90% -100% -90% -90% -100% IT M C L IT M C L IT M E L 46 0% -100% -100% -100% -100% IT M E L % -100% -100% -100% -100% However, in this case, the strong impact was mostly due to abandonment of some minor crop, while two farms out of five had not reported any relevant use of fertilisers even in the baseline. Water use is not reported here as irrigation is not relevant in mountain areas. Activities change showed a further specialisation in livestock production with an increase in dairy livestock and sheep production and a further substitution of cereals (when not used for feed) with forage crops (Table 125). Table 125 Impact of the scenarios on selected activities - Italy Mountain Livestock Alfaalfa 0% -3% 0% 0% -3% Ewes 2% -72% 2% 2% -72% Forages 0% 0% 0% 0% 0% Uncultivated 0% 0% 0% 0% 0% Dairy cows 0% -3% 0% 0% -3% Alfaalfa 0% 0% 4% 4% -1% Forages 1% 1% 1% 1% 1% Uncultivated 0% 0% 0% 0% 0% Dairy cows 0% 0% 4% 3% -1% Selected investments showed a high stability. However some of them, in particular farm buildings and tractors, showed a decrease in the first period, but an increase in the second in the price reduction scenarios (Table 126). 154/187

157 Table 126 Impact of the scenarios on selected investments - Italy Mountain Livestock Land = = = = = Farm buildings = - = = - Tractors - - = - - Tillage machinery = = = = = Harvesting machinery = = = = = Land = = = = = Farm buildings Tractors = + = = + Tillage machinery = + = = = Harvesting machinery = = = = = 155/187

158 Italy Plain - Arable The case of Italian plain arable systems is represented by six farms, all family run, of which two are organic (Table 127). Table 127 Summary of farm case studies modelled - Italy Plain - Arable Household CODE ITPCA15 ITPCA19 ITPCA23 ITPCA27 ITPEA51 ITPEA66 Legal Status Family run Family run Family run Family run Family run Family run N. household members Age farmer Use external labour yes yes no yes no no Members working off farm yes yes no yes no yes Houshold debt/asset ratio 10% 3% 17% 1% 0% 0% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Conventional Organic Organic SFP (euro) SFP/income ratio 57% 29% 36% 3% 2% Number of rights (ha) Four out of six have some household member working off-farm. The structure is variable from 5 to more than 300 hectares. The role of SFP was very variable, ranging from 2 to 57% of total farm income. Baseline indicators for Italy Plain Arable are shown in Table 128. Table Summary of baseline (Agenda 2000) of farm case studies modelled - Italy Plain - Arable Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) ITPCA ITPCA ITPCA ITPCA ITPEA ITPEA ITPCA ITPCA ITPCA ITPCA ITPEA ITPEA Incomes from farming are rather good and high in some farms. Higher farm income is produced in emerging farms with relevant vegetable production and, in some cases, with direct selling of agricultural products. Household incomes are higher, but the difference is not so important, due to the low importance of non-farming labour in most of the households, though with an important exception, ITPEA66. This is rather peculiar as the household members are young and 156/187

159 already have high income activities off-farm. The strategy is to disinvest farming during the first period ( ) and this yields the high income/land ratio. In the second period farming activity does not take place at all. The degree of investment is low, though it shows a mix of positive and negative trends. Labour use is low, with the exception of ITPEA51. Nitrogen use is relatively high and water use very variable with some high figures in two cases. The peculiar data of ITPEA66 derive from the fact that the optimal strategy for the household is to abandon farming, selling the farm and working elsewhere. This results in strong disinvestment in the first period, high nonfarming income and no data for the second period. The results of the scenarios in terms of income are reported in Table 129. Table 129 Impact of the scenarios on income from farming - Italy Plain - Arable ITPCA15 22% -20% 22% 22% -45% ITPCA19-12% -37% -12% -12% -37% ITPCA23 10% -21% 10% 20% -21% ITPCA27-15% -34% -16% -15% -34% ITPEA51-2% -24% -2% -2% -24% ITPEA66-14% -34% -14% -14% -34% ITPCA15 22% -20% -31% -11% -82% ITPCA19-20% -43% -37% -31% -53% ITPCA23 13% -20% -8% 7% -33% ITPCA27-17% -36% -100% -39% -100% ITPEA51-1% -24% -3% -3% -26% ITPEA As in previous cases, decoupling yielded effects in either possible direction, ranging from +22% to -15% (scenario 2.1). Again, the explanation may be found mainly in discrepancies between the historic and recent crop mixes, particularly for farms with a small cereal area in the reference period (corresponding to those with lower SFP now). A reduction in prices by 20% led to a reduction in farming income of between 20 and 43% (scenario 2.2). Reductions in payments led to less pronounced effects (scenarios 3.1, 3.2 and 3.3). Coupling payment and price reduction would lead to a sharp reduction in income with abandonment by two farms (the smallest and the largest) in the second period. The impact on household income was adjusted according to the relevance of farming income with respect to total farm income (Table 130). 157/187

160 Table 130 Impact of the scenarios on household income - Italy Plain - Arable ITPCA15 16% -14% 16% 16% -20% ITPCA19-5% -25% -5% -5% -25% ITPCA23 10% -21% 10% 19% -21% ITPCA27-3% -19% -4% -3% -20% ITPEA51-1% -22% -1% -1% -22% ITPEA66 0% 0% 0% 0% 0% ITPCA15 17% -14% -18% -4% -41% ITPCA19-9% -30% -22% -17% -38% ITPCA23 14% -22% -6% 7% -34% ITPCA27-3% -19% -47% -20% -49% ITPEA51-1% -24% -3% -2% -25% ITPEA66 0% 0% 0% 0% 0% Case ITPEA66 is remarkable in that the income from farming is almost irrelevant compared to the whole household income. As a reaction to decoupling, investment tended to either decrease or stay stable (Table 131). Table 131 Impact of the scenarios on investment - Italy Plain - Arable ITPCA15 0% 2% 0% 0% 1154% ITPCA19-127% -127% -127% -127% -127% ITPCA23 0% -2% -4% -120% 0% ITPCA % -4423% % -3675% % ITPEA51 0% 0% 0% 0% 0% ITPEA66 0% 0% 0% 0% 0% ITPCA15 0% -15% 0% 0% -114% ITPCA19-40% -40% -40% -40% -40% ITPCA23 96% 38% 93% 66% 42% ITPCA27-112% 28% -114% -119% -100% ITPEA51 0% 0% 0% 0% 0% ITPEA Farms reacted either immediately or in the second period. Price decreases tended to cause a drop in investments in the second period, though at least one farm reacted with a temporary increase in investment in the first period (scenario 2.2). Payment cuts also caused a reduction in investment, though this was far less relevant than price reduction (scenarios 3.1, 3.2 and 3.3). The impact on farm labour followed to a large extent trends in farming income, particularly in the cases where an extreme reaction (abandonment) was witnessed (Table 132). 158/187

161 Table 132 Impact of the scenarios on labour - Italy Plain - Arable ITPCA15 1% -70% 1% 1% -70% ITPCA19-15% -15% -15% -15% -15% ITPCA23-6% -11% -4% -4% -11% ITPCA27-26% -29% -27% -26% -29% ITPEA51 0% 0% 0% 0% 0% ITPEA66 0% 0% 0% 0% 0% ITPCA15 1% -70% 1% 1% -81% ITPCA19-24% -24% -24% -24% -24% ITPCA23-2% -10% -1% 0% -10% ITPCA27-29% -33% -100% -29% -100% ITPEA51 0% 0% 0% 0% 0% ITPEA The same applies in most cases to nitrogen and water use (Table 133 and Table 134). Table 133 Impact of the scenarios on nitrogen use - Italy Plain - Arable ITPCA15-60% -89% -60% -60% -89% ITPCA19-15% -15% -15% -15% -15% ITPCA23-24% -9% -30% -30% -9% ITPCA27-28% -32% -29% -28% -32% ITPEA51 0% 0% 0% 0% 0% ITPEA66 0% 0% 0% 0% 0% ITPCA15-60% -89% -60% -60% -93% ITPCA19-24% -24% -24% -24% -24% ITPCA23-31% -19% -33% -36% -19% ITPCA27-31% -35% -100% -31% -100% ITPEA51 0% 0% 0% 0% 0% ITPEA Table 134 Impact of the scenarios on water use - Italy Plain - Arable ITPCA ITPCA19-11% -11% -11% -11% -11% ITPCA23 0% 0% 0% 0% 0% ITPCA27-23% -27% -24% -23% -27% ITPEA51 0% 0% 0% 0% 0% ITPEA ITPCA ITPCA19-19% -19% -19% -19% -19% ITPCA23 0% 0% 0% 0% 0% ITPCA27-26% -31% -100% -26% -100% ITPEA51 0% 0% 0% 0% 0% ITPEA /187

162 In terms of the activity mix, decoupling brought about an increase in alfalfa and forage products and a reduction in wheat (Table 135). Table 135 Impact of the scenarios on selected activities - Italy Plain - Arable Alfaalfa 34% -27% 43% 39% -27% Barley 0% -8% 0% 0% -8% Forages 60% -49% 68% 60% -49% gaec 12% -44% 12% 12% -44% Maize 0% 0% 0% 0% 0% Renthouse 0% 0% 0% 0% 0% Sugar_beet 0% 0% 0% 0% 0% Wheat -43% -52% -44% -45% -52% Alfaalfa 59% -22% 55% 54% -33% Forages 83% -40% 70% 60% -62% Maize 0% 0% 0% 0% 0% Renthouse 0% 0% 0% 0% 0% Sugar_beet 0% 0% 0% 0% 0% Wheat -46% -59% -49% -53% -62% This is realistic assuming a local market for forage products was able to absorb increased production. A price reduction would further decrease wheat but also cause a drop in other cereals and forage products. In most cases investment is characterised by stability. Exceptions are most often marked by an increase in the first period and a balance between decrease and increase in the second (Table 136). Table 136 Impact of the scenarios on selected investments - Italy Plain - Arable Land = = = = = Farm buildings = = = = + Tractors = = = + = Tillage machinery = = = = = Harvesting machinery = = + + = Land = = = = = Farm buildings = = = = = Tractors = = Tillage machinery + + = - - Harvesting machinery = = 160/187

163 Italy Plain - Livestock The case of Italian plain livestock is represented by four farms, of which only one is organic (Table 137). Table 137 Summary of farm case studies modelled - Italy Plain - Livestock Household CODE ITPCL08 ITPCL78 ITPCL80 ITPEL59 Legal Status Family run Limited Limited Family run company company N. household members Age farmer Use external labour yes no no yes Members working off farm yes yes no no Houshold debt/asset ratio 0% 2% 8% 0% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Organic SFP (euro) SFP/income ratio 15% 68% 0% Number of rights (ha) The farms selected were highly specialised in dairy livestock, with sizes of between 20 and 250 hectares. Half of them also had household members working off-farm. The SFP varied between 0 and 68% of reported household income. Baseline indicators for Italy Plain Livestock in Table 138. Table Summary of baseline (Agenda 2000) of farm case studies modelled - Italy - Plain - Livestock Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) IT P C L IT P C L IT P C L IT P E L IT P C L IT P C L IT P C L IT P E L Incomes from farming are rather good due to high value added livestock (mostly milk) production. Income per hectare is higher for the farms with the higher animal/land ratio. Household incomes are higher than farming incomes, but the difference is not so important, with the exception of one case, where strong disinvestment in the first period may be associated to an increase in the allocation of labour off-farm, though maintaining income and labour intensity (by hectare) stable. Net investment is usually positive, with some exceptions. Labour use is rather high, consistently 161/187

164 with the livestock specialisation. Nitrogen use is relatively high, while water use is negligible in most cases. The results of the scenarios in terms of income are reported in Table 139. Table 139 Impact of the scenarios on income from farming - Italy Plain - Livestock IT P C L 08-4% -48% -4% -4% -39% IT P C L 78 7% -28% 5% 7% -28% IT P C L 80 4% -29% 4% 4% -31% IT P E L 59 13% -24% 11% 13% -25% IT P C L 08-3% -91% -20% -18% -100% IT P C L 78 8% -17% -10% -3% -29% IT P C L 80 4% -29% -20% -11% -100% IT P E L 59 17% -28% -4% 5% -40% Decoupling always brought about an increase in farming income (scenario 2.1). However, price reductions caused a drop in farming income, which was important only for the smallest farm (scenario 2.2). Payment cuts had, in this case, stronger effects compared to price reductions and some of them were anticipated in the first period (scenario 3.1). A gradual reduction would substantially smooth this effect (scenario 3.2). A reduction in payments coupled with a reduction in prices would lead to dramatic effects, with abandonment by the organic farm, probably already in the first period (scenario 3.3). The effect was only marginally moderated by (minor) off-farming income (Table 140). Table 140 Impact of the scenarios on household income - Italy Plain - Livestock IT P C L 08-2% -23% -2% -2% -19% IT P C L 78 7% -19% 6% 7% -19% IT P C L 80 5% -31% 5% 5% -33% IT P E L 59 13% -23% 12% 13% -23% IT P C L 08-2% -38% -9% -8% -41% IT P C L 78 8% -15% -7% -2% -24% IT P C L 80 5% -34% -20% -10% -96% IT P E L 59 18% -27% -3% 7% -38% Investments tended to stay steady with decoupling, while an increase seemed to prevail with price reductions, at least in the first period (Table 141). 162/187

165 Table 141 Impact of the scenarios on investment - Italy Plain - Livestock IT P C L 08 0% 5% 0% 0% 2% IT P C L 78 0% 113% 22% 0% 137% IT P C L 80 0% 3% 0% 0% -141% IT P E L 59 3% -49% 7% 10% -54% IT P C L 08 0% -23% 0% 1% -41% IT P C L 78 0% 8% 2% 1% -14% IT P C L 80 0% 0% 0% 0% -100% IT P E L 59-1% -22% -4% -3% -32% Such investments have mostly to be interpreted as the need to adapt capital stock as a consequence of changing incentives. A reduction in payments causes mostly a reduction in investments. No major changes in labour were witnessed as a reaction to decoupling or payment cuts. On the contrary, price reductions would lead to a total reshaping in one farm and abandonment in two of them (scenario 3.3) when payments were also reduced (Table 142). Table 142 Impact of the scenarios on labour - Italy Plain - Livestock IT P C L 08-2% -30% -2% -2% -17% IT P C L 78 0% -17% -3% 0% -17% IT P C L 80 0% -11% 0% 0% -13% IT P E L 59 0% -21% -2% 0% -21% IT P C L 08 0% -87% -12% -11% -100% IT P C L 78 0% -1% 0% 1% -1% IT P C L 80 0% -11% 0% 0% -100% IT P E L 59 6% -27% 2% 6% -37% To some extent, the same path observed for labour use applies to nitrogen and water use (but with two exceptions) (Table 143 and Table 144). Table 143 Impact of the scenarios on nitrogen use - Italy Plain - Livestock IT P C L 08 64% 87% 64% 64% 66% IT P C L 78 8% 38% 16% 8% 38% IT P C L 80 0% 0% 0% 0% -3% IT P E L 59 0% -17% -1% 0% -17% IT P C L 08 37% -7% -33% 26% -100% IT P C L 78 0% 2% -43% -13% -36% IT P C L 80 0% 0% 0% 0% -100% IT P E L 59 6% -27% 2% 6% -37% 163/187

166 Table 144 Impact of the scenarios on water use - Italy Plain - Livestock IT P C L 08-23% 17% -23% -23% 17% IT P C L 78 8% 38% 16% 8% 38% IT P C L IT P E L IT P C L 08 16% -52% -95% -25% -100% IT P C L 78 0% 2% -43% -13% -36% IT P C L IT P E L As expected from the previous results, decoupling had no important effects in terms of activity mix (Table 145). Table 145 Impact of the scenarios on selected activities - Italy Plain - Livestock Alfaalfa 0% -5% -1% -1% -5% Calves 0% 0% -74% -100% -100% Dairy_cows 0% -5% -40% -54% -58% Maize 6% -70% 9% 9% -70% Alfaalfa -4% -7% -7% -7% -7% Calves 0% -1% -100% -100% -100% Dairy_cows -4% -7% -65% -65% -65% Maize 17% -99% -99% -68% -99% When prices dropped, the farms tended to concentrate slightly in dairy farms when payments remained. However, a large part of dairy production was dismissed when payments were reduced. Investments decrease for farm buildings and have varied trends for tractors and other machineries (Table 146). Table 146 Impact of the scenarios on selected investment - Italy Plain - Livestock Land = = = = = Farm buildings = Tractors Tillage machinery + = = + = Harvesting machinery = = = = Land = = = = = Farm buildings - = = = = Tractors = Tillage machinery = = = - = Harvesting machinery = = = - = 164/187

167 The Netherlands Plain - Livestock The case study of Dutch plain livestock was based in Gelderland. The farms modelled are described in Table 147. Table 147 Summary of farm case studies modelled - The Netherlands Plain - Livestock Household CODE NEPCL06 NEPCL08 NEPCL11 NEPEL01 NEPEL02 NEPEL03 Legal Status Family run Family run Family run Family run Family run Family run N. household members Age farmer Use external labour no no no no no no Members working off farm no no no yes no no Houshold debt/asset ratio 100% 80% 80% 75% 80% 85% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Organic Organic Organic SFP (euro) SFP/income ratio Number of rights (ha) The farms selected for modelling were highly specialised dairy livestock farms, aside from mixed pig farms. Table 148 shows the baseline indicators for Netherlands Plain Livestock. Table Summary of baseline (Agenda 2000) of farm case studies modelled - The Netherlands Plain - Livestock Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) NEPCL NEPCL NEPCL NEPEL NEPEL NEPEL NEPCL NEPCL NEPCL NEPEL NEPEL NEPEL Incomes from farming are rather good. Household incomes are higher, but the difference with farming incomes is not so important, due to the low importance of non-farming labour in most of the households. The degree of investment is low, though it shows a mix of positive and negative trends. Labour use is high, consistently with intensive livestock specialisation. Nitrogen use is relatively high. 165/187

168 The results of the scenarios in terms of income are reported in Table 149. Table 149 Impact of the scenarios on income from farming - The Netherlands Plain - Livestock NEPCL6-21% -94% -36% -36% -94% NEPCL8-2% -89% -4% -1% -89% NEPCL11 2% -91% -2% 2% -91% NEPEL1-1% -50% -1% -1% -50% NEPEL2 1% -28% 2% 2% -28% NEPEL3-10% -55% -4% 2% -56% NEPCL6-29% -100% -50% -48% -100% NEPCL8 5% -100% -100% -90% -100% NEPCL11 2% -94% -22% -11% -95% NEPEL1-1% -52% -7% -5% -63% NEPEL2 1% -26% -7% -4% -32% NEPEL3-29% -72% -50% -36% -85% In almost all cases decoupling yielded a negative result in the first period (scenario 2.1). This result was partly confirmed in the long period. The small positive differences may be mainly explained by the high degree of flexibility thanks to decoupling while payments were maintained. The strongest negative effects of decoupling were associated with farms that would have expanded their activity thanks to coupled payments in the baseline scenario. A reduction in prices by 20% (scenarios 2.2 and 3.3) would have catastrophic effects on income, with drops as high as more than 90% in the first period and exit by two farms in the second period. In comparison, a reduction in payments in the period would yield a lower impact (scenarios 3.1 and 3.2), though it brings to the same effect of the price reduction in at least one farm. This result is explained by the fact that these farms, though quite profitable, have high costs and a relatively narrow margin between revenues and costs. Effects on total household income are in the same range though attenuated by non-farming incomes Table /187

169 Table 150 Impact of the scenarios on household income - The Netherlands Plain - Livestock NEPCL6-17% -84% -29% -29% -84% NEPCL8 1% -55% -1% 1% -55% NEPCL11 2% -71% 0% 2% -71% NEPEL1-1% -42% -1% -1% -42% NEPEL2 1% -29% 1% 1% -29% NEPEL3-6% -49% -1% 4% -49% NEPCL6-24% -89% -43% -40% -90% NEPCL8 5% -57% -53% -46% -57% NEPCL11 2% -72% -17% -8% -73% NEPEL1-1% -43% -7% -5% -51% NEPEL2 1% -30% -7% -4% -36% NEPEL3-20% -62% -38% -26% -72% It should be recalled that the model allows off-farm income to be earned only by those household members that already report some activity off-farm, so in fact compensation of farming income loss through off-farm employment is underestimated, particularly when extreme impacts occur (e.g. giving up farming). Farms exiting and with the strongest impact on income were the smallest, with a higher debt/asset ratio and conventional farming. The latter characteristic may be associated with the fact that organic farms are also less often specialised in dairy production (though less efficient in dairy production itself). Decoupling affected investments only in three farms, and in two of them with a positive effect (Table 151). Table 151 Impact of the scenarios on investment - The Netherlands Plain - Livestock NEPCL6-118% -364% -185% -185% -364% NEPCL8 7% 189% 158% 114% 189% NEPCL11 0% 2663% 333% 2% 2639% NEPEL1 0% -1213% 0% 0% -1213% NEPEL2 0% 0% 11% 11% 0% NEPEL3 127% 150% 115% 59% 151% NEPCL6-11% -100% -37% -37% -100% NEPCL8 38% -100% -108% -499% -100% NEPCL11 0% -94% -8% -87% -94% NEPEL1-1% -24% -1% 0% -267% NEPEL2 0% 0% 22% 22% 20% NEPEL3-32% -115% -38% -120% -118% In the second period this effect could reverse as well as strengthen. Again the effect was much stronger when accompanied by price decreases. Variations in investment may be very high (e.g. thousands of percent). This is explained by the fact that they are produced by choices of proceeding with or delaying acquisition of single 167/187

170 capital goods. In most cases farms already had a large endowment of capital goods, so the baseline investment pattern was mostly characterised by turnover in dairy cows and capital substitution investment. Abandonment of farming (and investment) was driven here by the effect of the opportunity cost of capital (due to the large amount of capital necessary to carry out dairy production), rather than extra farm labour opportunities. Impact on on-farm labour followed pretty much income trends (Table 152). Table 152 Impact of the scenarios on labour - The Netherlands Plain - Livestock NEPCL6-11% -92% -28% -28% -92% NEPCL8-7% -85% -8% -6% -85% NEPCL11 0% -87% -3% 0% -85% NEPEL1 0% -38% 0% 0% -38% NEPEL2 0% 0% 0% 0% 0% NEPEL3-9% -27% -5% -2% -28% NEPCL6-20% -100% -36% -36% -100% NEPCL8 0% -100% -100% -89% -100% NEPCL11 0% -100% -9% -4% -100% NEPEL1 0% -43% 0% 0% -55% NEPEL2 0% 0% 0% 0% -1% NEPEL The same applied to nitrogen use (Table 153). Table 153 Impact of the scenarios on nitrogen use - The Netherlands Plain - Livestock NEPCL6-11% -92% -28% -28% -92% NEPCL8-8% -81% -8% -7% -86% NEPCL11 0% -87% -3% 0% -78% NEPEL1 0% -38% 0% 0% -38% NEPEL2 0% 0% 2% 2% 0% NEPEL3 6% 9% 4% 1% 6% NEPCL6-20% -100% -36% -36% -100% NEPCL8-2% -100% -100% -89% -100% NEPCL11 0% -100% -9% -4% -100% NEPEL1 0% -43% 0% 0% -55% NEPEL2 0% 0% 2% 2% 0% NEPEL3 15% -73% 15% 13% -73% The main impact on farm activities concerned the number of dairy cows, while other impacts were mainly consequences (Table 154). 168/187

171 Table 154 Impact of the scenarios on selected activities The Netherlands Plain - Livestock Ewes -72% -72% 0% -100% -100% F_cattle_ % -43% -7% -42% -69% Grassland -9% -25% 0% -40% -70% Maize 0% -19% -3% -3% -26% Dairy_cows -20% -37% -5% -33% -58% F_cattle_ % -70% -26% -48% -100% Grassland -10% -35% -26% -45% -100% Maize 0% -27% -6% -6% -57% Dairy_cows -24% -56% -15% -35% -85% On the whole, decoupling has relevant effects on the size of production. This looks numerically more relevant for less important productions (e.g. sheep), but hits sharply also dairy cows, that are the main business of farms in this case study. A reduction in prices by 20% has much stronger effects and causes the exit of some farms, with a strong reduction in the number of dairy cows. Impacts on investments were again rather varied, showing different patterns of behaviour in different farms (Table 155). Table 155 Impact of the scenarios on selected investment The Netherlands Plain - Livestock Land = = Farm buildings = Tractors Tillage machinery = - = = - Harvesting machinery Land = = = = = Farm buildings = = = = - Tractors Tillage machinery = = = = - Harvesting machinery = 169/187

172 Poland Mountain - Livestock A summary of the farms modelled for Polish plain arable systems is reported in Table 156. Table 156 Summary of farm case studies modelled - Poland Mountain - Livestock Household CODE POMCL38 POMCL40 POMEL31 POMEL32 POMEL39 Legal Status Family run Family run Family run Family run Family run N. household members Age farmer Use external labour yes no yes no no Members working off farm no no yes yes no Houshold debt/asset ratio 0% 0% 16% 0% 7% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Organic Organic Organic SFP (euro) SFP/income ratio 18% 11% 5% 10% 19% Number of rights (ha) The farms selected were those more highly specialised in dairy livestock. However, they were in fact mostly mixed farms, cultivating also vegetables and rearing pigs as well as bovines. Two out of five had off-farm labour. Sizes ranged between 4 and 30 hectares. Rent was seldom employed. Baseline indicators for Poland Mountain Livestock are shown in Table 157. Table Summary of baseline (Agenda 2000) of farm case studies modelled - Poland Mountain - Livestock Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) POMCL POMCL POMEL POMEL POMEL POMCL POMCL POMEL POMEL POMEL Incomes from farming are again rather high. Household incomes are substantially higher than farming incomes in at least two cases, due to the high importance of non-farming labour and the high wages earned by the household members. This creates incentives to disinvest partially and to allocate more labour off-farm. This produces a strong increase of the income/land ratio in the 170/187

173 second period. Also, as the activities retained on the farm are the most profitable ones, the average income from farming increases in these farms in the second period. The degree of investment is moderate (with important negative figures) in the first period and tends to become positive in the second. Labour use is rather high, which is reasonable for livestock farms. Nitrogen use is on average values. The results of the scenarios in terms of income are reported in Table 158. Table 158 Impact of the scenarios on income from farming - Poland Mountain - Livestock POMCL38 10% -35% -9% 10% -35% POMCL40-1% -28% -1% -1% -28% POMEL31-1% -69% -1% -1% -32% POMEL32 0% -26% 0% 0% -26% POMEL39-6% -93% -9% -7% -93% POMCL38 13% -35% -31% -6% -82% POMCL40-1% -27% -10% -7% -33% POMEL31 1% -99% 0% 0% -32% POMEL32 12% -52% -18% -37% -69% POMEL39-7% -100% -100% -91% -100% With the exception of POMCL38, decoupling brought about a small income reduction (scenario 2.1). Instead, a reduction in prices of 20% caused a strong reduction in income from farming, with reductions up to 93% in the first period and up to abandonment of farming in the second (scenario 2.2). The negative effect of decoupling was partly influenced by the choice of 2007 as the reference year (decoupling is totally hypothetical in Poland and Hungary), so that decoupled payments did not follow the increases expected for area based payments. The differential in terms of income of payment reductions after 2013 appeared particularly relevant here compared to other countries. The effects on total farm income were weaker, due to the effect of off-farm labour (where present) and pensions (where present) (Table 159). Table 159 Impact of the scenarios on household income - Poland Mountain - Livestock POMCL38 4% -37% -10% 4% -37% POMCL40-1% -29% -1% -1% -29% POMEL31 0% -4% 0% 0% -5% POMEL32 0% -8% 0% 0% -8% POMEL39-4% -56% -6% -4% -56% POMCL38 5% -45% -34% -13% -72% POMCL40-1% -31% -9% -6% -35% POMEL31 0% -2% 0% 0% -5% POMEL32 3% -5% 0% -1% -6% POMEL39-5% -62% -57% -52% -62% 171/187

174 Investment reaction tended to vary greatly, which was also likely as a consequence of varied initial capital endowment (Table 160). Table 160 Impact of the scenarios on investment - Poland Mountain - Livestock POMCL38 226% 0% 16% 226% -16% POMCL40 0% 0% 0% 0% 0% POMEL31 0% 145% 0% 1% 0% POMEL32 0% 0% 0% 4% 4% POMEL39-10% -255% -174% -25% -255% POMCL38 35% -9% 0% 25% -422% POMCL40 0% 0% 0% 0% 0% POMEL31-15% -100% -15% 9% 24% POMEL32-39% -51% -31% -31% -42% POMEL39-3% -54% -54% -148% -54% However, differences even among comparable scenarios were evident (e.g. 2.1 and 3.1) and might be explained as an anticipation of policy changes in the second period. Once again, either decoupling, payment reduction or price reduction might lead investment in any possible direction. Labour changes tended to follow pretty much investment trends, with extreme drops at least in one farm when prices or payments decreased (Table 161). Table 161 Impact of the scenarios on labour - Poland Mountain - Livestock POMCL38 57% 1% 3% 57% 1% POMCL40 0% 0% 0% 0% 0% POMEL31 0% -38% 0% 0% 0% POMEL32-7% -7% -7% -7% -7% POMEL39-5% -92% -8% -6% -92% POMCL38 66% 0% 1% 54% -79% POMCL40 0% 0% 0% 0% 0% POMEL31 0% -100% 0% 0% 0% POMEL32 32% -22% 21% -1% -34% POMEL39-6% -100% -100% -91% -100% In almost all cases there was a reduction in nitrogen use, including the case of positive investment (Table 162). 172/187

175 Table 162 Impact of the scenarios on nitrogen use - Poland Mountain - Livestock POMCL38-41% -1% -2% -41% -2% POMCL40 0% 0% 0% 0% 0% POMEL31 0% -20% 0% 0% 0% POMEL32-21% -21% -21% -21% -21% POMEL39-9% -92% -12% -10% -92% POMCL38-46% -2% -1% -39% -51% POMCL40 0% 0% 0% 0% 0% POMEL31 0% -100% 0% 0% 0% POMEL32 152% 68% 152% 152% 68% POMEL39-6% -100% -100% -91% -100% This may be explained by the fact that only nitrogen from fertilisers is accounted for here, while farm expansion (if any) was generally associated with an increase in the number of livestock. This was clearly reflected also in the changes in activity mix (Table 163). Table 163 Impact of the scenarios on selected activities Poland Mountain - Livestock Barley -6% -6% -6% -6% -6% Calves -45% -50% -45% -45% -49% Heifers -94% -94% -94% -94% -94% Wheat -6% -6% -6% -6% -6% Dairy cows -43% -45% -43% -43% -44% Barley -4% -4% -4% -4% -4% Calves -46% -59% -47% -46% -57% Heifers_for_ -100% -100% -100% -100% -100% Wheat -4% -4% -4% -4% -4% Dairy cows -46% -59% -47% -46% -57% As in the other cases, price decoupling tended to be associated with a further specialisation in dairy production rather than fattening for beef. However, price reductions, particularly when coupled with payment reductions, tended to cause a strong drop also in dairy production. Minor changes in investment patterns were identified (Table 164). 173/187

176 Table 164 Impact of the scenarios on selected investments Poland Mountain - Livestock Land = = = = = Farm buildings = - = = - Tractors = = = = = Tillage machinery = = = = = Harvesting machinery = = = = = Land = = = = = Farm buildings = - = = - Tractors = = = = = Tillage machinery = = = = = Harvesting machinery = = = = = 174/187

177 Poland Plain - Arable A summary of farms modelled for Polish plain arable systems is reported in Table 165. Table 165 Summary of farm case studies modelled - Poland Plain - Arable Household CODE POPCA33 POPCA34 POPCA36 POPCA58 POPEA13 Legal Status Family run Family run Family run Family run Family run N. household members Age farmer Use external labour yes no yes no yes Members working off farm no yes no no no Houshold debt/asset ratio 23% 24% 15% 0% 0% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Conventional Organic SFP (euro) SFP/income ratio 33% 6% 45% 7% 11% Number of rights (ha) Households here were very variable in size and used external labour in at least three cases. Only in one case did household members work off-farm. Farm size was quite large, at least in the case of conventional farms. Baseline indicators for Poland Plain Arable are shown in Table 166. Table Summary of baseline (Agenda 2000) of farm case studies modelled - Poland Plain - Arable Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) POPCA POPCA POPCA POPCA POPEA POPCA POPCA POPCA POPCA POPEA Incomes from farming are rather good and high in some farms. Household incomes are higher than farming incomes, but the difference is not so important, due to the low importance of non-farming labour in most of the households. The degree of investment is rather low in some cases, while it shows a strong net disinvestment in at least one case and moderate disinvestments in 175/187

178 another case. Labour use is low, with the exception of POPEA13. Nitrogen use is relatively high. POPCA58 would abandon farming during the first period attracted by off-farm wages. The results of the scenarios in terms of income are reported in Table 167. Table 167 Impact of the scenarios on income from farming - Poland Plain - Arable POPCA33-10% -76% -76% -76% -52% POPCA34-13% -60% -13% -13% -60% POPCA36-21% -80% -23% -21% -80% POPCA58-2% -23% -2% -2% -23% POPEA13-2% -58% -2% -2% -37% POPCA33-18% -100% -100% -100% -93% POPCA34-13% -65% -15% -14% -66% POPCA36-21% -85% -92% -64% -98% POPCA POPEA13-3% -80% -9% -7% -9% The largely negative effects showed the shocking impact of decoupling on farms growing crops (scenario 2.1), thanks to increasing area payments on the main crops in the baseline scenario. In this case, compared with livestock, this was more important due to the higher relevance of payments to total income and the lack of compensation from livestock expansion. Again, the strong negative effect of decoupling was partly affected by the choice of 2007 as the reference year, so that decoupled payments did not follow the increases expected for area based payments. Effects of price reductions (scenario 2.2) were maybe less strong but more homogeneous compared with livestock farms. The effects of payment reductions after 2013 were again rather important, but very variable across the farms (scenarios 3.1, 3.2 and 3.3). The changes in total household income followed basically farm income, with a few exceptions in which they were attenuated due to other income sources (Table 168). Table 168 Impact of the scenarios on household income - Poland Plain - Arable POPCA33-10% -55% -55% -55% -42% POPCA34-15% -64% -15% -15% -64% POPCA36-19% -57% -20% -19% -57% POPCA58-1% -9% -1% -1% -9% POPEA13-1% -42% -1% -1% -33% POPCA33-18% -78% -78% -78% -72% POPCA34-16% -73% -18% -17% -73% POPCA36-20% -59% -59% -42% -69% POPCA58 0% -4% 0% 0% -4% POPEA13-2% -63% -9% -6% -32% Differently from income, investment may take different directions as a reaction to both decoupling and price cuts (Table 169). 176/187

179 Table 169 Impact of the scenarios on investment - Poland Plain - Arable POPCA33 0% -177% -177% -177% -58% POPCA34 0% -38% 0% 0% -38% POPCA36 108% 2650% 1427% 108% 2650% POPCA58 0% 0% 0% 0% 0% POPEA13-19% -564% -19% 19% -51% POPCA33-25% -100% -100% -100% -185% POPCA34 0% -52% 0% 0% -43% POPCA36-6% -100% -200% -368% -100% POPCA POPEA13 0% -10% 3% -10% 34% In particular, at least one farm showed an increase in investment with decoupling and this reaction was enhanced by price reductions (only in POPCA36). Effects on labour followed substantially income effects (Table 170). Table 170 Impact of the scenarios on labour - Poland Plain - Arable POPCA33 0% -62% -62% -62% -25% POPCA34 0% -9% 0% 0% -9% POPCA36-20% -93% -25% -20% -93% POPCA58 0% -3% 0% 0% -3% POPEA13-3% -13% -3% -3% -3% POPCA33-4% -100% -100% -100% -88% POPCA34-1% -25% 0% 0% -25% POPCA36-20% -99% -86% -54% -100% POPCA POPEA13-5% -46% -5% -5% 28% A similar pattern was followed by nitrogen use (Table 171). Table 171 Impact of the scenarios on nitrogen use - Poland Plain - Arable POPCA33 0% -62% -62% -62% -25% POPCA34 0% -9% 0% 0% -9% POPCA36-15% -93% -20% -15% -93% POPCA58 0% -46% 0% 0% -46% POPEA13-6% -50% -6% -6% -6% POPCA33-4% -100% -100% -100% -88% POPCA34-1% -25% 0% 0% -25% POPCA36-15% -100% -85% -51% -100% POPCA POPEA13-3% -85% -3% -3% 40% 177/187

180 Crop mix changes showed a basic stability to decoupling, while price changes bring sharper effects (Table 172). Table 172 Impact of the scenarios on selected activities Poland Plain - Arable Barley 0% -82% -38% -30% -64% Piglets 1% 1% 1% 1% 1% Pigs_for_fat 1% 1% 1% 1% 1% Potatoes 0% -9% 0% 0% -9% Rape 0% -57% -18% -18% -46% Wheat 0% -66% -29% -29% -48% Barley -3% -100% -93% -76% -93% Other_cereal -72% -79% -72% -72% -79% Potatoes -1% -25% 0% 0% -25% Rape -2% -77% -64% -50% -73% Wheat -2% -91% -82% -70% -84% GAEC options played a relevant role, although this could be attenuated in practice. Finally, impact of scenarios on investments shows mainly a decrease, particularly in the second period (Table 173). Table 173 Impact of the scenarios on selected investments Poland Plain - Arable Land = Farm buildings = = = = = Tractors = Tillage machinery = - = = - Harvesting machinery = Land Farm buildings Tractors Tillage machinery = = - - = Harvesting machinery = = In most cases, however, land investments tend to decrease, while machinery tend to decrease in the first period and to not to change or decrease in the second. 178/187

181 Poland Plain - Livestock A summary of farms modelled for Polish plain livestock systems is reported in Table 174. Table 174 Summary of farm case studies modelled - Poland Plain - Livestock Household CODE POPCL05 POPCL06 POPCL07 POPCL08 POPCL24 POPEL11 Legal Status Family run Family run Family run Family run Family run Family run N. household members Age farmer Use external labour yes yes no yes no yes Members working off farm no no no no no no Houshold debt/asset ratio 13% 36% 33% 50% 12% 5% Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Conventional Conventional Organic SFP (euro) SFP/income ratio 14% 3% 5% 5% 17% 7% Number of rights (ha) The farms selected mainly specialised in dairy livestock, though usually mixed with beef and pig production. Specialised pig producers were not modelled. Only one farm was organic. No households had members working off-farm. Farm sizes were between 20 and 80 hectares. Summary of baseline indicators for Poland Plain Livestock is shown in Table 175. Table Summary of baseline (Agenda 2000) of farm case studies modelled - Poland Plain - Livestock Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) PO P C L PO P C L PO P C L PO P C L PO P C L PO P E L PO P C L PO P C L PO P C L PO P C L PO P C L PO P E L Incomes from farming are rather good and high in some farms. Household incomes are higher than income from farming, but the difference is not so important, due to the low importance of non-farming labour in most of the households. The degree of investment is rather high, though 179/187

182 with two exceptions in the first period and one in the second. Labour use is rather high, consistently with the livestock specialisation. Nitrogen use is relatively high. The results of the scenarios in terms of income are reported in Table 176. Table 176 Impact of the scenarios on income from farming - Poland Plain - Livestock PO P C L 05-2% -27% 0% -1% -25% PO P C L 06-1% -31% -1% -1% -31% PO P C L 07 32% -6% 15% 32% -6% PO P C L 08 2% -34% 3% 2% -35% PO P C L 24-15% -88% -15% -15% -88% PO P E L 11 0% -30% 0% 1% -30% PO P C L 05-10% -27% -5% -11% -32% PO P C L 06-1% -31% -7% -5% -35% PO P C L 07-3% 242% 568% 382% 378% PO P C L 08 2% -36% -7% -4% -41% PO P C L 24-18% -92% -100% -49% -99% PO P E L 11 5% -29% 2% 3% -28% Again, decoupling seemed to cause a prevailing but mostly negligible reduction in income. However at least one farm showed a relevant increase of income due to decoupling (scenario 2.1). On the other hand, price reductions translated into major negative effects on income (scenario 2.2). In addition, payment cuts after 2013 had minor negative effects (scenarios 3.1, 3.2 and 3.3). Only farm POPCL24 seemed to undergo a major negative impact from both reform and price changes. Once again, effects on household income were very relevant though moderated with respect to the farming effects only (Table 177). Table 177 Impact of the scenarios on household income - Poland Plain - Livestock PO P C L 05-2% -27% -1% -2% -26% PO P C L 06-1% -31% -1% -1% -31% PO P C L 07 22% -9% 10% 25% -11% PO P C L 08 2% -33% 3% 2% -34% PO P C L 24-8% -59% -9% -8% -59% PO P E L 11 0% -29% 0% 0% -29% PO P C L 05-5% -14% -3% -3% -17% PO P C L 06-1% -34% -6% -4% -37% PO P C L 07 20% 82% 219% 154% 117% PO P C L 08 2% -38% -5% -3% -43% PO P C L 24-12% -68% -70% -36% -74% PO P E L 11 4% -30% 1% 2% -29% The effect on farm investment was consistently negative here, with stronger effects from price reductions (Table 178). 180/187

183 Table 178 Impact of the scenarios on investment - Poland Plain - Livestock PO P C L 05 1% 5% -2% 1% 3% PO P C L 06 0% -9% 0% 0% -9% PO P C L 07 57% 14% 7% 7% 68% PO P C L 08 0% -29% 4% 0% -29% PO P C L 24-40% -213% -159% -40% -213% PO P E L 11 0% 0% 0% -6% 1% PO P C L 05-10% -13% -4% 2% -12% PO P C L 06 0% -1% 0% 0% 0% PO P C L % -84% -89% -106% 29% PO P C L 08 0% -15% -2% -1% -14% PO P C L 24-15% -100% -100% -204% -100% PO P E L 11 4% 0% 4% 4% 3% Labour effects of decoupling were also consistently negative, with price effects again stronger than payment changes (Table 179). Table 179 Impact of the scenarios on labour - Poland Plain - Livestock PO P C L 05-3% -2% -1% -2% -1% PO P C L 06 0% -3% 0% 0% -3% PO P C L 07 33% 33% 17% 33% 33% PO P C L 08 0% -15% 0% 0% -16% PO P C L 24-15% -92% -16% -15% -92% PO P E L 11-3% -58% -1% -1% -58% PO P C L 05-12% 0% 0% -8% 0% PO P C L 06 0% -1% 0% 0% -1% PO P C L 07 0% 398% 600% 400% 600% PO P C L 08 0% -18% 0% 0% -17% PO P C L 24-17% -100% -100% -36% -100% PO P E L 11 6% -53% 6% 6% -50% Once again the pattern followed mainly investment trends. The same applied basically to nitrogen use changes (Table 180). 181/187

184 Table 180 Impact of the scenarios on nitrogen use - Poland Plain - Livestock PO P C L 05-6% -5% -2% -4% -4% PO P C L 06 0% 0% 0% 0% 0% PO P C L 07 17% 18% 1% 18% 17% PO P C L 08 0% -13% 1% 0% -14% PO P C L 24-13% -92% -14% -13% -92% PO P E L 11-8% -22% -7% -7% -22% PO P C L 05-13% 0% 0% -2% 0% PO P C L 06-4% -3% -4% -4% -3% PO P C L 07 27% 413% 600% 400% 600% PO P C L 08 0% -19% 0% -1% -18% PO P C L 24-17% -100% -100% -36% -100% PO P E L 11-3% -7% -2% -1% -3% Basic farming activities, dairy cows in particular, seemed to be rather stable in the short run and strongly increasing in the long run with respect to decoupling, while they would be reduced with price decreases (Table 181). Table 181 Impact of the scenarios on selected activities Poland Plain - Livestock Cereals -21% -27% -13% -8% -31% Heaveis 0% -13% -5% 0% -13% Heifers 13% 4% 7% 12% 5% Maize -20% -53% -19% -21% -53% Straw 4% -20% 3% 5% -20% Dairy cows 10% -3% 6% 10% -3% Maize -54% -100% -60% -59% -100% Other cereal -41% -87% -60% -68% -100% Straw -13% -20% 10% -1% -11% Sugar beet 0% 0% 0% 0% 0% Dairy cows -4% 11% 44% 26% 29% Farm investments showed a very varied reaction in the short run and a basic stability with some cases of increase in the longer run, particularly under price reduction scenarios (Table 182). 182/187

185 Table 182 Impact of the scenarios on selected investments Poland Plain - Livestock Land = + = + = Farm buildings Tractors Tillage machinery = - = = - Harvesting machinery = = = = = Land = = = = = Farm buildings = + = = + Tractors = + + = + Tillage machinery = = = = = Harvesting machinery = = = = = 183/187

186 Spain Plain - Trees The case study of Spanish plain trees was based in Andalusia, southern Spain. The farms modelled are described in Table 183. Table 183 Summary of farm case studies modelled - Spain Plain - Trees Household CODE ESPCT01 ESPCT08 ESPCT10 ESPCT14 Legal Status Family run Family run Family run Limited company N. household members Age farmer Use external labour yes yes yes yes Members working off farm yes yes no no Houshold debt/asset ratio Land owned (ha) Land rented in (ha) Land rented out (ha) Technology Conventional Conventional Conventional Conventional SFP (euro) SFP/income ratio 33% Number of rights (ha) The farms selected were highly specialised in olive cultivation, although they varied in size and complementary crops. In all cases the farms used external labour, which was particularly relevant in peak periods. While farm ESPCT10 specialised exclusively in olive cultivation, farms ESPCT01, ESPCT08 and ESPCT14 integrated olives with, respectively, cultivation of lemons, arable crops and grapes. SFP was particularly relevant for the largest farms. Table 184 shows the baseline indicators for Spain Plain Trees. Table Summary of baseline (Agenda 2000) of farm case studies modelled - Spain Plain - Trees Income from farming ( /ha) Household income ( /ha) Nitrogen use (kg/ha) Water use (m 3 /ha) Investment ( /ha) Labour (h/ha) ES P C T ES P C T ES P C T ES P C T ES P C T ES P C T ES P C T ES P C T Incomes from farming are rather good. Household incomes are higher than farming incomes, but the difference is not so important, due to the low importance of non-farming labour. 184/187

187 Net investment is usually positive. Labour use is rather high as requested by olive tree cultivation. Nitrogen use is not particularly high, while there is relevant use of water for irrigation. The results of the scenarios in terms of income are reported in Table 185 Table 185 Impact of the scenarios on income from farming -Spain Plain - Trees ES P C T 01 0% -49% 0% 0% -49% ES P C T 08 3% -21% 3% 3% -21% ES P C T 10-2% -25% -2% -2% -25% ES P C T 14 0% -30% 0% 0% -30% ES P C T 01 0% -50% -18% -8% -55% ES P C T 08 3% -21% -19% -11% -34% ES P C T 10-4% -27% -17% -12% -35% ES P C T 14 0% -30% -13% -8% -39% Given the kinds of crops, adaptations to decoupling were minimum (scenario 2.1), while income changes mainly reflected either price or payment reductions in the respective scenarios. Scenarios 2.2 and 3.3 showed the most dramatic reductions (up to about 50%) in income from farming, due to price reductions. The total cut in payments after 2014 caused a reduction of between 13% and 19% of farm revenue (scenario 3.1). A gradual reduction in payments would moderate this effect substantially (scenario 3.2). Impacts on household income are reported in Table 186. Table 186 Impact of the scenarios on household income - Spain Plain - Trees ESPCT1 0% -30% 0% 0% -30% ESPCT10 2% -17% 2% 2% -17% ESPCT14-2% -25% -2% -2% -25% ESPCT8 0% -30% 0% 0% -30% ESPCT1 0% -30% -11% -6% -33% ESPCT10 2% -17% -15% -8% -28% ESPCT14-3% -27% -16% -11% -35% ESPCT8 0% -31% -13% -8% -39% They were more or less the same size as the impacts on farm revenue. The main exception was farm ESPCT1. This farm reacted by shifting household labour outside the farm and compensating for the reduction in prices with additional sources of income off-farm. This was reflected also in a reduction in investments on-farm that shifted from net positive to net negative investments (Table 187). 185/187

188 Table 187 Impact of the scenarios on investment - Spain Plain - Trees ESPCT1 0% -140% 0% 0% -138% ESPCT10 0% 0% 0% 0% 0% ESPCT14-25% -25% -25% -25% -25% ESPCT8 0% 0% 4% 0% 3% ESPCT1 0% -100% -100% 0% -100% ESPCT10 0% 0% 0% 0% 0% ESPCT14 0% 0% 0% 0% 0% ESPCT8-2% 0% -4% 0% -4% The impacts of the scenario on labour use on the farms are summarised in Table 188. Table 188 Impact of the scenarios on labour - Spain Plain - Trees ESPCT1 0% -36% 0% 0% -36% ESPCT10 0% 0% 0% 0% 0% ESPCT14-1% -1% -1% -1% -1% ESPCT8 0% 0% 0% 0% 0% ESPCT1 0% -41% -10% 0% -41% ESPCT10 0% 0% 0% 0% 0% ESPCT14-3% -3% -3% -3% -3% ESPCT8 0% 0% 0% 0% 0% Labour was basically stable everywhere with the exception of farm ESPCT1. Impacts of the scenarios on nitrogen and water use were of the same size as those on labour (Table 189 and Table 190), as the effects were due to a reduction in the farmed areas with olive trees. Table 189 Impact of the scenarios on nitrogen use - Spain Plain - Trees ESPCT1 0% -36% 0% 0% -36% ESPCT10 0% 0% 0% 0% 0% ESPCT14-1% -1% -1% -1% -1% ESPCT8 0% 0% 0% 0% 0% ESPCT1 0% -41% -10% 0% -41% ESPCT10 0% 0% 0% 0% 0% ESPCT14-3% -3% -3% -3% -3% ESPCT8 0% 0% 0% 0% 0% 186/187

189 Table 190 Impact of the scenarios on water use - Spain Plain - Trees ESPCT1 0% -36% 0% 0% -36% ESPCT10 0% 0% 0% 0% 0% ESPCT14-1% -1% -1% -1% -1% ESPCT8 0% 0% 0% 0% 0% ESPCT1 0% -41% -10% 0% -41% ESPCT10 0% 0% 0% 0% 0% ESPCT14-3% -3% -3% -3% -3% ESPCT8 0% 0% 0% 0% 0% Due to system characteristics, impacts on both activity mix and investments were of minor importance (Table 191 and Table 192). Table 191 Impact of the scenarios on selected activities Spain Plain Trees Grapes 0% 0% 0% 0% 0% Olives -1% -1% -1% -1% -1% Sunflower 0% 0% 0% 0% 0% Wheat 0% 0% 0% 0% 0% Grapes 0% 0% 0% 0% 0% Olives -2% -3% -2% -2% -3% Sunflower 0% 0% 0% 0% 0% Wheat 0% 0% 0% 0% 0% Table 192 Impact of the scenarios on selected investments Spain Plain Trees Land Farm buildings = = = = = Tractors Tillage machinery = = = = = Harvesting machinery = = = = = Land = = = = = Farm buildings = = = = = Tractors = = = = = Tillage machinery = = = = = Harvesting machinery = = = = = 187/187

190 European Commission EUR EN Joint Research Centre Institute for Prospective Technological Studies Title: Investment Behaviour in Conventional and Emerging Farming Systems under Different Policy Scenarios Authors: Vittorio Gallerani, Sergio Gomez y Paloma, Meri Raggi, and Davide Viaggi Luxembourg: Office for Official Publications of the European Communities 2008 EUR Scientific and Technical Research series ISSN ISBN DOI /94554 Abstract Objective of this study is to carry out an analysis of investment behaviour among farming systems of selected EU regions, and to assess the impact of the 2003 CAP reform on producers' investment behaviour, and on their sustainability. The study includes a review of the literature, a description of the methodology, the results of the empirical analysis and policy recommendations. The review of the literature on farm investment behaviour focuses on: a) the determinants of investment behaviour; b) the effects of policy on investment behaviour; c) the classification of quantitative tools for analysing farm investment behaviour; and d) the choice of methodology for the empirical analysis of farm investment behaviour. The methodology adopted is based on the integration of empirical primary information collected through a survey of about 250 farm households with a modelling exercise of the individual farms surveyed. The core model is a multi-criteria dynamic programming model of farm households. The model is calibrated on primary data from a survey of single farms through a questionnaire. Case studies were developed for France, Germany, Greece, Hungary, Italy, Poland, Spain and The Netherlands. In the majority of cases, farmers stated they were indifferent to decoupling. Where any change occurred, the impact of decoupling was highly differentiated. Scenario analysis showed that CAP as a whole is very important for the sustainability of farming systems. However, prices (in the range simulated) appeared to be more important than policy and adaptation of farm activities more important than investment as a reaction to both policy and prices. Post-decoupling CAP appeared from the interviews to be very much a policy with multiple objectives that takes on very different roles depending on the context in which it is cast. In particular it seems to tend to reinforce the strategy already adopted by farm-households, either in terms of expansion or abandonment. The results confirm the need for better empirical information, contextualized within the present stage of EU agriculture and policy. How to obtain EU publications Our priced publications are available from EU Bookshop ( where you can place an order with the sales agent of your choice. The Publications Office has a worldwide network of sales agents. You can obtain their contact details by sending a fax to (352)

191 The mission of the JRC is to provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of EU policies. As a service of the European Commission, the JRC functions as a reference centre of science and technology for the Union. Close to the policy-making process, it serves the common interest of the Member States, while being independent of special interests, whether private or national. LF-NA EN-C