High Nature Value Farming Systems in Italy: an Economic Perspective

Transcription

1 High Nature Value Farming Systems in Italy: an Economic Perspective Antonella Trisorio a, Andrea Povellato a, Davide Bortolozzo a National Institute of Agricultural Economics (INEA), Italy 1. Introduction High nature value (HNV) farming, as defined by Baldock et al. (1993), came to attention during the nineties when the positive role of agriculture towards biodiversity was debated both in theory and in practice. Thus the concept of HNV farming has been evolving in the framework of both the integration of the environmental concern into the Common Agricultural Policy, and the adoption of the European model of multifunctional agriculture. Within this framework HNV farmland and the associated farming systems have increased their policy relevance. The crucial bidirectional link between farming practices and the conservation value of farmland has favoured the development of studies on HNV farmland from both perspectives: the farming practices and the farmland. On the basis of the agreed definition of the concept of HNV farmland, including three broad categories 1, Andersen, et al. (2003) developed three different and complementary approaches to identify HNV farming area indicators: land cover, farming systems and the distribution of wild species, specifically birds. As specified by Cooper et al. (2007) it is the combination of suitable land covers and features, the state, with appropriate management, the driving force, that creates the conditions for a farming system to be HNV. Refinements and updating of methodological issues have been conducted with particular attention to the land cover approach (i.e Paracchini et al. 2006). Furthermore, different approaches have been tested, mainly at national level (i.e Pointereau et al., 2007). Nevertheless, the difficulties linked to both methodology and availability of data did not allow a clear identification of HNV farmland in European Union member States. HNV farmland in Italy has been firstly estimated by Andersen et al. (2003) on the basis of both the land cover approach and the farming system approach: this exercise indicates in average 21% of UAA. A further assessment (Paracchini et al. 2006) has been based on the update of CLC data combined with environmental information (i.e. Natura 2000 sites and IBAs). Recently, Trisorio (2006) and Povellato and Trisorio (2007) showed, on the basis of a combination of CLC data and vertebrate species richness data, that 30% of Italian territory is covered by HNV farmland. The farming system approach still shows some weaknesses particularly with respect to the definition of the HNV farm types and to data reliance. It focuses on the management of farmland and of natural resources, and aims at identifying types of farming systems according to their ability to maintain or enhance the nature conservation value of the farmland. They are characterised in terms of dynamic pressures for the HNV state. The focus on the intensity of farming allows to derive information on environmental pressures of farming 2. This approach provides insights into the management needs of HNV farmlands and contributes to validate and/or further refine the results of the land cover approach. Andersen et al. (2003) identified 6 types of HNV farming systems in Europe derived from Farm Accountancy Data Network (FADN) data and based on a definition of land use intensity (mainly associated to the input use 3 ). A different typology has been proposed by Cooper et al. (2007), within the Common Monitoring and Evaluation Framework: farming systems type are distinguished according to farmland use, and the definition of land use intensity is linked to a wider range of indicators. Within this methodological framework in evolution the fixed points are that: a. the farm is the crucial level at which decisions are taken on land use and management; b. the economic viability is the first Corresponding author. address: trisorio@inea.it 1 The agreed categories of potentially HNV farmland are the following: Type 1: farmland with high proportion of semi-natural vegetation; Type 2: farmland with a mosaic of habitats and/or land uses; Type 3: farmland supporting rare species or a high proportion of European or World population. Afterwards Type 2 definition has been partly revised as following: farmland with a mosaic of low intensity agriculture and natural and structural elements, such as field margins, hedgerows, stone walls, patches of woodland or scrub, small rivers etc. (Paracchini et al., 2006). 2 The pressure indicators are generally able to signal future environmental change before it actually occurs. Moreover, environmentally objectives set in policy measures are often expressed in terms of changing pressure on the environment, based on assumption about the relations between such variables and the environmental state. 3 Input use includes cost of fertiliser, crop protection and concentrate feedstuff. 1

2 condition for farm being at work. Indeed, one of the main threat to HNV farmland, that is abandonment of farming, strictly derives from the vulnerable economy of the farming systems associated. Moreover, the level of economic viability might affect the farm responsiveness to policy measures or, conversely, the policy measures in order to be effective should be differentiated according to the economic viability of farms. The assessment of the economic characteristics of farms in connection to their environmental performance provides useful insights to policy design, allowing to better target instruments or to differentiate farming policies. Policy responses, indeed, are aimed at affecting farm behaviour that is mainly driven by economic factors. Therefore, also environmental performance of farms can be modified through economic measures. The aim of this paper is to investigate the potential economic viability of HNV farming system through a set of basic economic indicators. The hypothesis is that income and productivity of production factors are the main driving forces of farm behaviour with respect to land use and farming practices. The analysis has been structured in three steps: 1) identification of HNV farming systems in Italy; 2) economic and structural specification of HNV farms; 3) identification of economically viable HNV farms. It was decided to use data from Italian Farm Accountancy Data Network (FADN) for they allow both to identify HNV farming systems and to analyse them from an economic perspective. Moreover FADN dataset is updated yearly allowing indicators provided by the analysis to be suitable for monitoring short term changes in farming systems. The identification of HNV farming systems in Italy has been based on Andersen et al. (2003) typology. The outcome of the first step shows the regional importance of HNV farming in terms of both the share of UAA and of farms managed through HNV farming systems. HNV farms have been, subsequently, analysed from an economic perspective. A social and structural specification completes the characterization of HNV farms. HNV farms are then stratified according to economic indicators allowing to investigate the economic viability and the possible differences in economic viability throughout regions and altitude. 2. The extent of HNV farmland in Italy Before exploring the economic performance of HNV farms a brief survey of works on the assessment of HNV farmland in Italy can be useful to figure out its relevance and extent. High Nature Value Farmland plays in Italy an important role in the conservation of biodiversity and traditional rural landscape accounting for a large part of agricultural area, even if not evenly widespread. There are few attempts to provide an estimate of HNV farmland in Italy, most of them included in a wider European perspective. Andersen et al. (2003) provides the general framework for national investigations. The results show that the share of HNV farmland on UAA varies from 12% based on farming systems approach to 30% based on CLC approach. A following study (Paracchini et al., 2006), introducing CLC update data and refinements on the basis of biodiversity data layers, produced more precise estimates. The first attempt to assess HNV farmland on the national level has been carried out by Trisorio (2006) and Povellato and Trisorio (2007): the approach proposed by Andersen et al. (2003) is adapted to the Italian context in terms of CLC classes 4, and a national biodiversity data set related to the richness of species of vertebrates is overlaid. Results of this explorative exercise show that more than 9 million hectares can be potentially defined as HNV farmland. This accounts for a large part (31%) of the whole national area and, for more (46%) of agricultural area. The distribution is not uniform throughout Italian territory, but HNV farmland is mainly concentrated (44%) in Southern Italy and along the main mountainous ridges. The classification of HNV according to species of vertebrates richness show that the highest class accounts for 12% of agricultural area. This class includes farmland that has the highest probability to be of HNV and can be considered the minimum estimate according to this approach. More accurate estimates have been carried out at regional level, in accomplishment of the Common Monitoring and Evaluation Framework 5. For example, Lazio Region has identified HNV farmland on the basis of regional data layers referring both to land use and environment 6, in addition to CLC data and ortho-photo. A comprehensive picture based on the regional assessments, would not give a 4 The estimate is based on CLC classes included in the Andersen et al. (2003) maximum estimate excluding CLC class 211 (non-irrigated arable land) and considering only olive groves for CLC class 222 (fruit trees and berry plantation). 5 In Italy the Rural Development Policy is implemented by the Regional administrations. As a consequence there are as many Rural Development Programmes as the number of Regions, i.e Environmental data refers to Natura 2000 sites, Natural Parks, hydrography, Important Bird Areas, Nitrate Vulnerable zones. 2

3 consistent figure because Regions have not followed the same methodology for the implementation of the baseline indicator 18 UAA of HNV farmland. In fact, Rural Development Programmes for the programming period pay for a methodological framework not well defined yet. 3. The economic viability of HNV farming systems The territorial dimension, mainly identified through the land cover approach, remains crucial for understanding the extent and the location of HNV farmland. Nevertheless, the investigation of socioeconomic features of HNV farms could provide insights in the drivers of farmer choices towards desirable patterns of land use and management. Data sources The analysis is based on the FADN made up of a sample of farms selected through a casual criterion from the large number of over-4 Economic Size Unit (ESU) farms. That is farms getting an over 4,800 euros (1 ESU = 1,200 euros) Standard Gross Margin and regarding in Italy as commercial farms. The analysis was carried out on three-year (2003, 2004 and 2005) average values, allowing more robust estimates. Moreover, the technological and price evolution occurred in these three years are by no means regarded as significantly influent on the validity of these three-year averages. One of the weakness of FADN for HNV farms analysis is linked to the exclusion of economically small farms and non-professional farms that may in fact represent a interesting share of potentially HNV farms. In Italy they generally have an extremely reduced significance in terms of surface and income. Moreover, a large part of the small farms areas is run by contractor services or by other neighbouring farms. Nevertheless, in some territories they represent an important social pattern characterized by the presence of semi-natural elements (hedgerows, stonewalls, buffer strips, etc.). According to the Farm Structural Survey, referring to 2003 and 2005, the over-4 ESU farms in Italy account for respectively 43% of the total number of farms, 88% of the agricultural area and 93% of the Standard Gross Margin. Methods The analysis has been carried out following three steps: 1) identification of HNV farming systems in Italy; 2) economic and structural specification of HNV farms; 3) identification of economically viable HNV farms. Step 1. The criteria to identify HNV farming systems have been drawn from the work by Andersen et al. (2003). The parameters used to classify HNV farming systems are mainly based on the intensity of farming (input cost, stocking density, presence of irrigation) and the presence of extensive habitats such as permanent grassland, fallow and pastures, hence the soil productivity results far lower than non-hnv farms. Moreover, as the paper s aim is that of inquiring into the economic features of farms types, the study of new typologies of HNV farming systems has not been deemed appropriate, even though refining Andersen et al. (2003) classification criteria would be necessary. It should be added that, presently, the FADN dataset, besides economic and financial variables, contains a number of structural ones. Nevertheless, the description of farm practices and of unfarmed land use is very patchy and it would be, therefore, very difficult to identify more appropriate classifying variables. All farms identified as HNV have been subsequently grouped, in order to concentrate the analysis on two groups of farms: HNV farms and the rest (non-hnv farms). This simplification on the one hand would not affect the results of the analysis and on the other hand would make the results more readable. A further distinction of farms based on specific discriminators and threshold values according to the maximum and minimum approach (Anderson et al., 2003) was used exclusively to test the presence of significant differences within the HNV farms group. In order to detect some of the most significant productive structure and land use differences characterising Italian agriculture the analysis of farms groups has been made by large districts (North, Centre and South and Island) and altitude (plain, hill and mountain). This moves the analysis on nine areas 7. Step 2. New stratifying structural and economic variables (see Table 2) are introduced in order to economically characterise the two groups of farms (HNV and non-hnv). In addition to purely economic ones, some variables linked to the labour factor, in terms of both quantity and quality, have 7 The nine geographical areas in which the territory has been divided are the following: Northern-plain, Northern-hill, Northernmountain, Centre-plain, Centre-hill, Centre-mountain, Southern-plain, Southern-hill, Southern-mountain. 3

4 also been considered. The variables linked to the age of farmer and to the presence of family labour units less than 45 years old could be useful to provide insights into the social aspects of farm viability. Step 3. The farms included in the two groups have been further divided into three classes of economic viability (i.e. low, medium, high) according to position value of economic variables. Structural and economic features of the HNV farms The analysis highlights first HNV farms significance in the nine areas; then the structural, economic and social features of the two typologies by means of some synthesis indicators. Finally the degree of farms economic viability is described. When the common level 8 is adopted, HNV farming systems in Italy manage 33% of the agricultural area; they decrease to 25% if we adopt the maximum level 9 and get as low as 7% when referring to the minimum 10 level. Moreover, no significant differences in economic terms have resulted between the farms just falling within the "maximum level" and those respecting the "minimum level" criteria. Following the maximum typology, the share of farms identified as HNV on the total number of commercial farms (ESU>4) is 13%. In terms of land area they manage 24% of UAA, whereas in economic terms, they contribute by 7% of the value added. These figures largely vary among the nine geographical areas in which the national territory has been divided (table 1). Table 1 Geographical distribution of the HNV farms (% of total) Number of holdings Utilised Agricultural (UAA) Area Net Value Added (NVA) Northern plain 1,8 2,7 0,8 Northern hill 6,3 14,4 5,4 Northern mountain 30,5 58,9 18,8 Central plain 5,7 21,6 7,0 Central hill 20,7 26,5 11,8 Central mountain 33,6 42,5 16,0 Southern plain 5,5 17,1 4,1 Southern hill 15,9 33,2 11,4 Southern mountain 25,1 42,1 23,5 Italy The frequency, in terms of UAA, grows with the increase of altitude. In the plain the percentages are under 10%, whereas in the mountain areas nearly 50% of UAA is classified as HNV. In geographical terms the percentage significantly increases from the Northern to the Centre-Southern regions. The larger differences within HNV and non-hnv farming systems is found in Northern Italy. Indeed, the 8 See Andersen et al. (2003) 9 See note See note 8. 4

5 frequency of extensive systems in Northern plain is really marginal (3% of UAA), given the suitability of this land to intensive producing systems, whereas in the mountain areas HNV systems reach very high values (59%) because of the soil-climatic difficulties which do not allow the presence of many farming systems. The latter confirms that climate and soil characteristics can affect the degree of intensity of the productive system. Moreover, it also confirms the outcome of the land cover approach. As to farm features, the size of HNV holdings is larger than non-hnv ones only in terms of farming area (28 ha vs. 13 ha), whereas the economic size as well as the number of worker units are definitely larger in non-hnv farms (table 2). The choice of adopting low intensive farming practices seems to depend mostly on soil-climatic factors. Nevertheless, the lack of good opportunities for the economic development seems to limit the ability of holdings to produce an adequate income. An HVN farm gets a net value added of euros while a non-hnv reaches euros. The difference is particularly evident in holdings in the plains and in the Northern regions. Table 2 Structural and economic profile of HNV and non-hnv farms HNV non-hnv Total Utilised Agricultural Area (UAA) 28,1 13,2 15,0 Net Value Added (NVA) Annual Working Unit (AWU) 1,0 1,4 1,4 Net Value Added per ha Net Value Added per AWU Net Farm Income (NFI) Total Assets (TA) Return on Investment (NFI / TA) 3,9 6,0 5,7 Family AWU (< 45 years old) 0,2 0,3 0,3 Farmer age 57,3 56,6 56,6 Source: FADN, Italy The larger economic size and the possibility of allocating the production factors in a more effective way determine a remarkable difference in terms of labour and capital productivity. The labour productivity of non-hnv farms is on average 33% higher than HNV ones on a national level, with even larger differences in mountain areas and in Northern regions. We can make the hypothesis that in the Northern regions the favourable soil-climatic conditions allow the farmer to choose among a larger number of productive combinations, thus favouring the specialised and intensive holdings. The greater difference detected in mountain areas, where structural and social factors very likely determine a higher difference, is less intuitive. Also the return on investment is definitely higher in non-hnv farms than in HNV ones. Finally, we have tried to establish whether there are significant differences as to the farmers age. Even though a slightly higher average age was found in HNV farms, the average values are on the whole very much alike. Even taking into consideration a parameter measuring the percentage share of annual worker units below 45 years old on the total worker units, the differences are very small. The considered socio-demographic factors - synthesised by the age proxies - do not seem to have such an effect on the farmers' choices as to generate differences between HNV and non-hnv farming systems, probably due to the influence of other factors not currently taken into account. The analysis of the economic performance of farms was based on Net value added per AWU, used as discriminator to form three classes of economic viability. It shows that the lower class of economic 5

6 viability is the most numerous for both non-hnv and HNV farms, even though the latter show a slightly larger share of farms included in the high economic viability class. The lower class is particularly represented in Southern regions and in hilly areas. Nevertheless, the results of the economic performance of farms did not provide satisfactory results. The differences between HNV and non-hnv farms appear not significant and mainly attributable to external factors, claiming for further investigation on the relative efficiency based on more refined techniques. The role of the CAP subsidies The total amount of subsidies received by HNV farms is only slightly greater than those received by non-hnv farms (table 3). Nevertheless, public expenditures play an important role in HNV farms, because they represent on average 43% of the net value added, against 20% registered in non-hnv farms. The incidence of farm subsidies does not change according to geographic district variation. The source of the subsidies is slightly different between HNV and non-hnv farms: the latter rely more on direct payments, whereas HNV farms received a more significant part of the payments through the Agri-Environmental Schemes and the Less Favoures Area Allowance. The relatively higher share of HNV farms in mountain and other marginal areas can explain this difference. The choice to adopt a less intensive farming system should be favoured by the AES payments but further investigation is needed. Table 3 Farm subsidies of HNV and non-hnv farms HNV non-hnv Total Subsidies Subsidies on net VA (%) 42,7 19,8 21,5 Distribution of subsidies (%): - Direct Payments 74,3 87,7 85,7 - Agri-Environmental Schemes 13,2 5,2 6,4 - Less Favoured Areas Payments 5,5 1,2 1,9 - Other RD measures 4,5 4,4 4,5 - Other subsidies 2,5 1,4 1,6 Subsidies per hectare Subsidies per AWU Net Value Added per AWU (without subsidies) Source: FADN, Italy Our analysis confirms the result carried out by Osterburg et al. (2008) about the essential contribution of the subsidies to the economic viability of the HNV farms. The subsidies per Annual Worker Unit are greater in HNV farms compared to non-hnv farms, where the amount of subsidies reaches higher levels in terms of area units. Comparing the net-of-subsidies labour productivity (net value added minus subsidies per AWU) the dramatic difference between the two types comes out very clearly: the 6

7 "net" labour productivity of the HNV farms (coming from the market) is half than the productivity of non-hnv farms. 4. Concluding remarks HNV farmland is directly run by farmers who have to care for both economic and environmental objectives. The land cover approach is therefore essential to understand the extent and localisation of these areas. Nevertheless, without an adequate knowledge of the economic and social mechanisms which regulate the farmers behaviour it is not possible to understand the cause-effect relation between farming and the preservation of biodiversity. The investigation of farming systems is therefore essential to provide a complete picture in order to design appropriate measures for HNV farmland conservation. The analysis at farm level can contribute to this aim based on the fact that the farm, as said before, is the crucial level at which decisions are taken on land use and management. The results of this explorative exercise on the economic performance of HNV farms opens to further investigations in this direction. In particular, the analysis of the linkages between efficiency and environmental performance of farms provides insights into the possibilities to influence farmer in adopting more friendly behaviour. Moreover, the efficiency analysis of HNV farms vs non-hnv farms would provide useful insights into the role of subsidies. The latter have to be well targeted if they would really serve as a reward for positive externalities generated by farming activities and, therefore, motivate farmers to modify their behaviour. The FADN sample is more suitable than the one created by the Farm Structure Survey, since the survey of economic and financial aspects allows a more appropriate description of farm-holders behaviour. The farm classification according to HNV criteria must however be further developed (Andersen et al., 2007). In order to exploit to the best the economic and financial information contained in FADN it is necessary to integrate data gathering with information about farming practices, agricultural land use and the management of the unfarmed features within the holdings. It is not an easy task, as at present the availability of financial and human resources for the realisation of reliable surveys is often insufficient: the economic indicators lack accuracy as to data gathering and the sampling level does not permit accurate analyses (Vrolijk et al., 2004). An important aspect which demands for further research regards georeferencing farm data which might permit a more direct collation with the land cover approach results. The complexity of the georeferencing process in spite of the progresses achieved in recent years in terms of information technologies and administrative data availability (IACS data) further widens the difficulties related to the availability of resources for this kind of surveys. The comparison of survey costs with the presumable benefits deriving from better information for planning purposes would be an important direction of further investigation. In future perspective the concrete opportunities that can arise for farmers in protecting biodiversity will have to be identified. Moreover, it is important to acknowledge this function to the less intensive and more sustainable farming models. The technological evolution and market trends favour intensive and specialised productive models. For this reason, the conservation of HVN farmland, thanks to some farming types, is attainable through the adoption of farming practices beyond ordinary management. A demand for compensation for the higher costs and missed incomes which farmers must face in order to grant the safeguard of these areas is therefore justified. The introduction of HNV farmland conservation as an objective of planning interventions for rural development should not turn into a new mandatory measure limiting farmers choices or imposing higher burden especially on low intensity farms. The management of these areas should remain on a voluntary basis. Moreover, a better targeting of current policy measures is required in order to appropriately address farmers choices and compensate farmers for the relevant positive externalities they provide. In order to get good results, it is necessary to provide farmers with information and extension services, so that they can compare scientific with the local knowledge. The latter being the root of these low intensity farming systems. Applied research should concentrate on the identification of new technologies suitable to these environments, rather than searching for new processes of farming intensification. The new technologies should allow labour and capital productivity increases in order to grant economic profitability also in the production of environmental services related with the adoption of extensive farming practices and to the management of unfarmed features within farms. Finally, both farmers and policy makers need a better information on the environmental role of HNV farming systems. 7

8 References Andersen, E., Baldock, D., Bennet, H., Beaufoy, G., Bignal, E., Brower, F., Elbersen, B., Eiden, G., Godeschalk, F., Jones, G., McCracken, D.I., Nieuwenhuizen, W., van Eupen, M., Hennekes, S., and Zervas, G., (2003), Developing a high nature value indicator. Report for the European Environment Agency, Copenhagen. Andersen E., Elbersen B., Godeschalkc F., Verhoog D. (2007), Farm management indicators and farm typologies as a basis for assessments in a changing policy environment, Journal of Environmental Management 82 (2007) Baldock, D., Beaufoy, G., Bennett G., Clark, J., (1993), Nature conservation and new directions in the EC Common Agricultural Policy, Institute for European Environmental Policy (IEEP), London. Cooper, T., Arblaster, K., Baldock, D., Farmer, M., Beaufoy, G., Jones, G., Poux, X., McCracken, D., Bignal, E., Elbersen, B., Washer, D., Angelstam, P., Roberge, J.M., Pointereau, P., Seffer, J., and D., Galvanek (2007), Final report for the study on HNV indicators for evaluation, Institute for European Environmental Policy (IEEP), London. Osterburg, B., Nitsch, H., Laggner, A., Wagner S. (2008), Analysis of policy measures for greenhouse gas abatement and compliance with the Convention on Biodiversity, working paper of project MEACAP WP6 D16a. Pointereau, P., Paracchini, M.L., Terres, J.M., Jiguet, F., Bas, Y.and Biala K., (2007), Identification of High Nature Value farmland in France through statistical information and farm practices surveys, JRC report EUR EN. Paracchini, M.L., Terres, JM., Petersen, J.E., and Y. Hoogeveen, (2006), Background document on the methodology for mapping High Nature Value farmland in EU27, EU JRC. Povellato, A. and Trisorio A., (2007), Dimensione geografica e sistemi agricoli nella definizione delle aree ad alto valore naturale. Il caso italiano in Atti convegno. Aree agricole ad alto valore naturalistico: individuazione, conservazione, valorizzazione, APAT, Roma. Trisorio, A., (2006), Le aree agricole ad alto valore naturalistico in Annuario dell agricoltura italiana, Vol. LIX, 2005, INEA, Roma. Vrolijk, H. C. J., Meier, B., Kleinhanss, W., Poppe, K. J. (2004), FADN: buttress for farm policy or a resource for economic analysis? Eurochoices, Vol. 3, No. 3,