Plant Production Systems; Animal Production Systems PLANT AND ANIMAL PRODUCTION MODULE OF QUANTITATIVE ANALYSIS OF AGRO-ECOSYSTEMS,

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1 Pieter de Visser Martin van Ittersum Nico de Ridder Herman van Keulen Henk Lido Chair groups: Plant Production Systems; Animal Production Systems Course F Wageningen, October 2000 PLANT AND ANIMAL PRODUCTION MODULE OF QUANTITATIVE ANALYSIS OF AGRO-ECOSYSTEMS, QUASI-PAP F o ivw

2 Plant and Animal Production - QU 1. Introduction

3 QUASI Plant and Animal Production

4 Table of contents CHAPTER 1. GENERAL METHODOLOGY OF EXPLORATIVE AND PREDICTIVE STUDIES IN LAND USE INVENTORIES INTRODUCTION QUANTIFICATION OF PRODUCTION ACTIVITIES OUTLINE OF SYLLABUS 12 CHAPTER 2. CONCEPTS IN DEFINITION OF ALTERNATIVE PRODUCTION ACTIVITIES PRODUCTION ECOLOGY AND LIVESTOCK SCIENCE 13 Production level 13 Physical environment 15 Production technique 18 Production activity 18 Production orientation 18 Aggregation level and time horizon of input-output combinations PLANT-ANIMAL INTERACTIONS 19 CHAPTER 3. QUANTIFICATION OF PRODUCTION PRODUCTION ORIENTATION AND PRODUCTION GOALS PLANT PRODUCTION Yield defining factors Yield limiting factors Yield reducing factors Production at the level of the cropping system ANIMAL PRODUCTION Physiological limits to animal production Production limiting factors at animal level Production reducing factors From individual animal to herd Livestock as capital asset OUTPUTS TO THE ENVIRONMENT PLANT-ANIMAL INTERACTIONS Introduction Direct interactions in grazing systems Indirect interactions: fodder, manure and animal traction 46 CHAPTER 4. QUANTIFICATION OF INPUTS AND OUTPUTS OF A PRODUCTION ACTIVITY: DATA HANDLING AND MODELLING TOOLS ACTUAL OR ALTERNATIVE PRODUCTION TECHNIQUES? COLLECTION OF INPUT-OUTPUT DATA FOR ACTUAL PRODUCTION ACTIVITIES INFORMATION SOURCES FOR QUANTIFICATION OF INPUTS AND OUTPUTS OF ALTERNATIVE ACTIVITIES POTENTIAL AND WATER-LIMITED CROP PRODUCTION NUTRIENT INPUTS AND OUTPUTS IN CROP PRODUCTION SOIL LOSS Introduction 5

5 4.7 CROP PROTECTION INPUTS: LABOUR, MACHINERY AND BIOCIDES MILK, MEAT AND TRACTION PRODUCTION: A TARGET-ORIENTED APPROACH FOR THE KOUTIALA REGION IN MALI 63 Selection of production level. 63 Calculation of required inputs DYNAMIC SIMULATION OF INPUTS AND OUTPUTS OF LIVESTOCK SYSTEMS 65 Input-driven modelling: energy intake calculation 66 Output levels on basis of consumed feed 66 Weight gain 67 Milk production 67 Traction 68 Reproduction 68 Aggregation of animal production to herd level NITRATE LOSSES FROM DAIRY FARMING SYSTEMS IN THE NETHERLANDS UNCERTAINTY OF INPUT AND OUTPUT COEFFICIENT VALUES 70 Uncertain TC value: recovery of crop residue-n! 71 N-leaching, varying per crop type, and effects at system level 72 CHAPTER 5. IDENTIFICATION AND COMPILATION OF FUTURE LAND USE ACTIVITIES DETERMINATION OF PRODUCTION ORIENTATION AND ACTIVITY DEFINITION CRITERIA TO DELINEATE PRODUCTION ACTIVITIES THE SELECTION OF DEFINITION CRITERIA IN RELATION TO SPATIAL SCALE AND PRODUCTION INTENSITY EXAMPLE OF THE APPLICATION OF DEFINITION CRITERIA (FROM HENGSDIJK, 2000) 79 Physical environment 79 Crop type 80 Production technique EXAMPLES OF TECHNICAL COEFFICIENT GENERATORS 81 Introduction 81 A TCG for livestock in the tropics 82 A TCG for dairy farming systems on sandy soil in the Netherlands 84 A TCG for cropping activities in the European Union UTILIZATION OF TECHNICAL COEFFICIENTS IN EXPLORATION AND PROTOTYPING OF FARMING SYSTEM AND LAND USE OPTIONS 86 The use in Linear Programming or Simulation models 86 Technical Coefficients in farm prototyping 87 CHAPTER 6. REFERENCES 90 QUASI Plant and Animal Production 6

6 Chapter 1. General methodology of explorative and predictive studies in land use inventories 1.1 Introduction As a consequence of the growing world population, the demand for agricultural land and its products is increasing. At the same time, there is increasing attention for other purposes of land use (e.g. forestry, nature and recreation), and an increasing knowledge and awareness of the vulnerability of the environment. Thus, an efficient use of land and other natural resources, and low emissions to the environment are key issues. In some areas new technologies may improve agricultural productivity per hectare or per unit of product, in others reduction of environmental losses is highly relevant. Decisions on land use should be based on profound knowledge of the possibilities of the land. On the basis of climate and soil qualities at a given location, a certain use may be most appropriate, in comparison with the possibilities of other land in the region, country or continent under study. With concern to agricultural production, the potentials are first of all determined by biophysical characteristics. In this syllabus we focus on the biophysical possibilities of the land. Two major aims and their methodologies for model-based land use studies were discriminated in the Introduction Module of QUASI, (I) exploration of future options and (II) prediction of future options (see Figures 1.1. and 1.2 respectively). These methodologies both study the agricultural production options of the land, but they basically differ in time horizon, spatial scale and structure of the systems studied. Explorative studies might be carried out for various hierarchical levels, i.e. farm, region, country or continent. In predictive (or intervention) studies, the farm level is addressed; in an aggregation procedure this farm level information is aggregated to the regional level of scale. In order to be able to 'predict' land use, farmers' decision making should be part of the predictive modeling approach. Economic, institutional, educational, social and cultural aspects are relevant in the decision making. Economic factors and social and cultural aspects can be included through definition of particular constraints (e.g. limited labour participation of particular groups, number of religious holidays) and so are endogenous to the predictive model. On the contrary, in exploration these aspects are exogenous to the model. 1. Introduction 7

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9 Explorations try to answer what-if questions and make explicit assumptions about behaviour of decision-makers, while the decision making itself is exogenous to the model. The time horizon of explorative studies is usually much longer than that of predictions. Generally, the time horizon of explorative studies is over 5 years and may be up to years, and such that 'trend breaks' are easily imaginable. The time horizon of predictive studies is usually within the range of 2-5 years. 1.2 Quantification of production activities In the QUASI-PAP course we will focus on the quantification of inputs and outputs of production systems (box 2 in Fig. 1.1 and 1.2). We will especially focus on the biophysical properties, that are studied from a background in production ecology and livestock science. Thorough knowledge about the quantification of inputs and outputs is necessary to (1) analyse the current situation, (2) suggest possible short-term improvements or (3) contribute to the exploration of strategic land use options. As a consequence, this QUASI module focuses on input-output coefficients for three types of, so called, production activities: actual, improved and alternative. A production activity stands for the growth of a crop, cropping system, animal or herd as fully characterized by its inputs and outputs. The production activity is defined by the physical production environment, the target production level and the techniques applied. Input-output combinations for actual land use activities are quantified, in general, based on the results of inventories with farmers. Such inventories are acquired through techniques such as Rapid Rural Appraisals (RRA), in which farmers are interviewed with respect to their current practices. In the econometric approach, alternative input-output combinations are often derived from so-called production functions, based on actual practice, statistics and extrapolation. These combinations quantify the relation between a single input and product output, while other inputs are kept constant. The production functions are of limited value to land use planning, because they are valid only at small time scales and in only a few, discrete conditions that are difficult to inter- or extrapolate. If several inputs vary simultaneously and mutually interact, outputs have to be predicted in another way. In plant and animal sciences, the prediction of effects of short-term interventions or technical innovations may be based on more process-oriented information. Results are used of either a simulation of the underlying mechanisms of the process at hand, or from an experimentally applied intervention on a prototype farm or subsystem. Process-oriented knowledge is necessary to simulate the impact of novelties. To derive improved production activities (mainly for predictive studies), the same information of the actual situation is used to predict the effect on production of a certain intervention, e.g. the change of one input. The effect of this intervention may be known for QUASI Plant and Animal Production 10

10 another system or an experimental farm, and by means of simulation, the change in inputoutput relations and coefficients in the current situation is predicted. Interventions or innovations in agricultural systems are established by short-term changes in parts of the existing production system (for instance one input). The impact on production may follow instantly or within approximately one year, depending on the frequency of the related processes. A change in grassland composition by seeding will only result in effects the next growing season, while the use of a new feeding strategy will instantly have effects on animal production. Alternative production activities may differ completely from current land use activities (other crops, new animal species) in both biophysical and economic aspects. Therefore, statistics and extrapolation of the current data cannot be applied, and other techniques are required. One of the problems then is, that many possibilities are open: different inputs can be combined in numerous ways for the production of different outputs. However, inputs are not used haphazardly, but with a specific aim, derived from the desired or unwanted outputs, but also related to factors such as attitude to risk and uncertainty. Detailed specification of the required inputs to realize a particular production level (grain, milk, meat, manure), or a particular level of other, undesirable outputs (pesticide emission to the environment, nitrogen content in urine) in a certain physical environment is possible, following the target oriented approach. In this approach, the particular output level is first specified, often based on production ecological principles ('potential yield of a crop in a given environment', 'energy-limited milk production of a goat in a given environment'). Subsequently, the required inputs are quantified based on knowledge of the physical, chemical, physiological and ecological processes involved in crop and animal production. The whole set of inputs to realize a certain production level is considered in combination, because various inputs may interact. The potential outputs primarily depend on the (genetic) crop (variety) and animal (breed) characteristics and the climatic conditions under which the production takes place. In addition, dual-purpose animals have to allocate their production to more then one output and production levels may drop below potential. Based on the specified output, the required amounts of for instance water and nutrients for a crop and water, energy and nutrients for an animal, can be quantified, using knowledge on uptake and utilization efficiency of resources. Subsequently, the required level of crop protection or veterinary care for realization of the output is quantified. In the target-oriented approach, complete information is supposed, i.e. the output, that can be realized, and the required inputs are known a priori. Factors like risk and uncertainty (e.g. due to weather conditions) are treated as much as possible explicitly, for instance by using series of historical weather data. For more stochastic phenomena, such as occurrence of and damage due to pests and diseases no generally accepted method is available. In biophysically oriented studies, the required inputs are quantified under the assumption that 1. Introduction 11

11 not more water, nutrients, pesticides or feed is used than strictly necessary, according to the available knowledge. This is called the concept of best technical means: the most efficient combination of inputs is used to realize a particular production level. 1.3 Outline of syllabus The quantification of production and related processes is required to define production activities and generate input-output tables. The input-output tables represent the quantification of different production activities on specific land units. They quantify the required inputs to produce certain outputs on a land unit with specific climatic and soil characteristics. In the next chapters the methodology for this quantification is presented. First, a general and systematic approach is dealt with for quantification of production processes in production ecology and livestock science (Chapter 2). Subsequently, the production process and target output per production activity are quantified, based on knowledge of the relevant disciplines (Chapter 3). This knowledge is used to quantify the input-output relations of the examined production activity and are expressed as Technical Coefficients (Chapter 4). Next, the Technical Coefficients for a wide range of production activities is calculated with the aid of Technical Coefficient Generators to generate inputoutput tables (Chapter 5). This chapter presents concepts and procedures that enable a targeted identification, generation and quantification of a broad range of relevant production activities to be used in land use studies, that for instance apply Multiple Goal Linear Programming. QUASI Plant and Animal Production 12

12 Chapter 2. Concepts in definition of alternative production activities 2.1 Production ecology and livestock science Agricultural input-output combinations according to production ecological principles can be characterised with the concepts production level, physical environment, production technique and production orientation. For crop production, these concepts have been described and applied extensively (cf. Van Ittersum & Rabbinge, 1997). For animal production, these concepts have recently been developed analogous to crop production (De Ridder et al., submitted). The target-oriented approach has been explained in the preceding chapter and in the course QUASI-Intro. In this chapter the other terms are treated. Production level Production level refers to the level of primary output per unit of production, for cropping activities generally a unit area, for animal production activities an individual animal or a herd. Three production levels are distinguished, following the concepts of growth-defining, growthlimiting and growth-reducing factors (Figure 2.1). Crops Livestock potential limited radiation sexe temperature purpose Defining factors phenology development physiological properties/metabolism architecture body shape, breed temperature water water Limiting factors nitrogen feed r phosphorus foraging time actual pests social stress r Reducing factors diseases diseases weeds anti-nutritional factors pollutants pollutants Figure 2.1 Production levels and associated principal growth factors (largely adopted from Van Ittersum and Rabbinge, 1997; De Ridder et al., submitted). 2. Definition of production activities 13

13 Growth-defining factors are those that, at optimum supply of all inputs, determine potential growth and potential production level. For crop production, this concept is rather straightforward. Dry matter production, either of the whole crop (for example, grass) or of specific organs (for example, grains, tubers) is in general the production objective. Potential production is then determined by genetic plant characteristics (related to the physiology and phenology of the plant: species and variety) and temperature and solar radiation level as determined by the latitude and season at the place and time of production. For animal production the production objectives are much more diverse (Udo & Cornelissen 1998) and in addition to milk, meat and eggs, may include manure and draught power, and even such intangible objectives as saving and investment. For such a variety of production objectives it is difficult to identify unequivocally the growth-defining factors. As a first approximation, therefore, they have been defined for the objectives milk and meat production and they include genetic animal characteristics (species, breed and sex) and the prevailing climatic conditions as determined by latitude and time of production of the relevant location. Growth-limiting factors refer to those factors, that, when in sub-optimal supply limit growth and production. For crop production they comprise the essential abiotic resources water and nutrients. For animal production, water and nutrients (minerals) are also growthlimiting factors, but in addition also the quality of the available feed, which is commonly expressed in digestible energy and digestible protein content, and for ruminants the fiber content. Moreover, shortage of vitamins may negatively affect the growth rate. Growth-reducing factors reduce or hamper growth and comprise for crop production biotic factors such as weeds, pests and diseases, and abiotic factors such as pollutants; for animal production they comprise diseases, pollutants and anti-nutritional factors, that negatively affect the utilization of the energy and protein in the feed. The three production levels associated with these three groups of production factors are potential production level, water- and nutrient-limited production level and actual production level. The growth-defining factors, i.e. climatic factors that cannot be easily modified, such as incoming solar radiation and temperature and genetic characteristics of crop and animal determine the potential production level. This production level is realised when there are no growth-limiting factors (water, nutrients, feed quantity and quality are optimal) and complete protection is provided against growth-reducing factors. Water- and nutrientlimited production levels are lower than the potential, as in that situation the supply of water and/or nutrients is sub-optimal. In the production system, water and nutrients are supplied from internal and/or external sources. The internal sources are in general associated with the 'natural supply' from the local physical environment (see next section), based on climatic and soil conditions. For animal production, the 'natural supply' of feed (quantity and quality) is based on the supply from grazing and fodder production on the land within the production system (see Fig. 2.2: system within dashed lines). Since livestock may move around from one soil-plant system to another (for instance free-ranging cattle that consumes the vegetation along roads), the borders of their 'natural' habitat is sometimes difficult to define. The actual QUASI Plant and Animal Production 14

14 production level may be below the water- and nutrient-limited level, when under natural supply of the growth-increasing factors, growth-reducing factors negatively influence growth and production. At one given, climate-related, potential production level, various water- and nutrientlimited and actual production levels may be established. In a given climate, a variety of soil types may be present that will result in a variation of the natural supply of water and nutrients and feed. The degree of production reduction depends on such factors as (weed) seed bank (size and composition) and degree of infestation with and development of pests and diseases which depend, among others, on humidity conditions and condition of the animals. Figure 2.2 Inputs and outputs of a mixed production system. Physical environment Input-output combinations are location specific. The location can be characterized by the physical environment or production situation, i.e. the conditions under which a crop or livestock activity takes place. The physical environment is difficult to modify (although of course in glasshouse cultivation that is done), and affects the potential production level and the inputs required to realize a particular production level. On the other hand, on the short 2. Definition of production activities 15

15 term agricultural activities hardly affect the physical environment: the time coefficient of the physical environment is much bigger than that of agricultural activities. The physical environment refers to: 1. climate factors: temperature, solar radiation level and humidity; 2. soil characteristics that affect the uptake efficiency of inputs: e.g. water retention characteristics, soil depth, texture, organic matter content; 3. abiotic factors in soil and atmosphere that affect agricultural activities such as anaerobic conditions or presence of air pollutants. The physical environment does not include water and inorganic nutrient pools in the soil, neither drinking water for animals, as they are relatively easy to manipulate by cultural practices. Potential production levels may differ between locations because of differences in the climatic component of the physical environment (Figure 2.3). In theory, potential production levels are the same for two physical environments that do not differ in climate but do differ in, e.g. soil type. Favorable physical environments may be defined as environments in which inputs are used more efficiently (which means a high output / input ration) than in other, less favorable, environments. Consequently, at the same or even lower input levels, water or nutrient-limited and actual production levels will be higher in the favorable than in the less favorable physical environments (Figure 2.3). This interaction between physical environment, yield and input levels is illustrated by an example of sugar beet growth on two soil types (see table 1 of chapter 19, Introductory Module). In 'new land reclamations with loamy clay' ('Centraal Kleigebied'), with its favorable physical environment, yields are higher, whereas input levels are not higher or even lower than in 'Peat-harvested areas with sandy soils' ('Veenkoloniën') with a less favorable physical environment (poor sandy soils with low water-holding capacity). The differences relate to higher utilization efficiencies by the crop of external inputs, by higher nutrient retention and water-holding capacity of the clay soil (effect of physical-chemical soil properties and associated organic matter conservation), and on historically built-up nutrient reserves that may originate from external (fertilizer, manure) or internal (soil nutrient pool) sources. These soil-related differences do not affect the potential production levels at the given climatic conditions, but only the water- and nutrient-limited and actual production levels. QUASI Plant and Animal Production 16

16 Production level Defining factors Potential Potential Limiting factors Potential Limited Limited Reducing factors Limited Actual Actual Actual Poor physical environment (unfavorable climate or poor breed/ poor soil) Poor physical environment (favorable climate or good breed/ poor soil) Good physical environment (favorable climate or good breed/ good soil) Figure 2.3 Physical environments, production levels and associated principal growth factors for plants and livestock. The input levels are the same in all three situations (Van Ittersum & Rabbinge, 1997; De Ridder et al., submitted). For animals, the physical environment and the pressure of plagues and diseases at a specific location may favour the local animal species or breed. In analogy to the production situation in production ecology, certain animal production functions depend on the climate at that location. This conditioning becomes clear in adverse, extreme conditions, such as long periods of cold or sudden heat. Western cattle in the tropics will loose much of their appetite due to the high temperatures, and thus feed intake and production will reduce relative to the levels at optimum, temperate conditions. Yet, for a wide range of latitudes no effect of climate on body functions of production animals is apparent. In addition to these climatic and "risk on infection" boundary conditions, being the growth-defining factors, the main growth-limiting factor for animals is nutrition. Growth, conception, reproduction and mortality are strongly influenced by nutrition, in interaction with the animal's genetic properties. Maximum growth and offspring of animals are genetically determined, but the water- and nutrient-limited level is determined by feed intake. The actual level again, is influenced by growth-reducing factors and depend on health care, living conditions, etc. 2. Definition of production activities 17

17 Feed supply for animals depends on the system boundaries. A rich situation might be defined as a situation where the local feed resources are more than enough and of the appropriate quality to feed the local stock. So, limitations are in first instance set by the biophysical properties of the system. The living conditions for animals can relatively easily be changed by the farmer. For example, it is technically feasible to adapt animal housing climate and feed for exotic breeds. These technical options, in combination with the animals' tolerance for a range of conditions, increase the number of options for animal production activities. This is illustrated by the use of highly productive, western (Holstein-Friesian breed) cows in dairy farming systems in e.g. Kenia. The selection of these breeds with the aim of high animal production may have negative side-effects, e.g. effects of high temperature on production, higher risks of tropical diseases and import of large quantities of high-quality feed. However, it is a possible option in tropical zones. An intermediate option is to use the productive capacities of crossbreds in cross-breeding. Production technique To realize a particular production level in a certain physical environment (e.g. 8 tons of flower bulbs on a clay soil in region x; 8000 kg milk annually from a cow in region y) various inputs are required: production-increasing and production-protecting inputs such as irrigation water, nutrients, pesticides and vaccines. These inputs and the way they are applied characterize the production technique. Because some inputs may be mutually substitutable (e.g. fossil energy for labor, machinery for draught power, or pesticides for hand labor), a particular production level in a certain physical environment may be realized with various production techniques. Then, the aim of production (production orientation) determines the preferred (selected) production technique. Production activity A production activity (or an input-output relation) is defined as cultivation of a specific crop species (variety) or crop rotation, or keeping a specific animal species (breed) in a particular physical environment, completely specified by its inputs and outputs. These inputs and outputs are fully determined by the production technique and the physical environment. When a particular production technique is applied in two different physical environments, the output levels may differ and thus two production activities can be distinguished. Production orientation Production orientations refer to value-driven aims, i.e. in addition to objectives related directly to agricultural production processes, other aims such as landscape conservation, keeping indigenous animal breeds only, maintenance of employment and (for animals) ease QUASI Plant and Animal Production 18

18 of conversion into cash (market conditions) may be considered. A more elaborate discussion on production orientation is presented in Chapter 3. Aggregation level and time horizon of input-output combinations The input-output combinations are derived for the level of crop or cropping system, animal or herd. The time horizon of an explorative study should be reflected in the time horizon of the input-output combinations. Input-output combinations in long term explorative studies (biophysically oriented studies) should be defined in such a way that they hold for many years, cycles in the crop rotation, or life cycles of the animals. This implies that the resource pools in the soil, and the associated production levels, should not change as a result of the production activity, unless explicitly desired as for instance for P-saturated soils. Further, the physical environment should be maintained, e.g. the application of soil tillage to maintain or repair soil structure. In short-term studies where actual production activities prevail, the agro-ecological sustainability is less important as long as it is possible to continue a certain production activity for a few years. It may even be possible to permit a certain level of non-sustainability (for instance depletion of soil nutrients) in the short run, considering investments for recovery of the resource base in subsequent periods. Relative to arable farming systems, animal production systems need an extra definition for their biophysical boundaries. Since an animal production system depends to some extent on external feed supplies, its land use exceeds the field boundaries. If a specific LP problem in dairy production systems focuses on optimal use of home-grown forage, the import of concentrate feed (expressed in kg per kg produced milk) can be fixed implicitly in the model, and the boundaries for home-grown feed are farm-size determined. Yet, if certain inputs or outputs will affect properties outside the studied production system, those relations should be explicitly quantified in the analysis. If for instance environmental problems that are associated to the growth of the imported feed crop, they have to be explicitized if one wants to quantify all environmental impacts of the examined system. A way to explicitize the effects of energy use of the farm on the global warming, is to determine its "ecological footprint" (Wackernagel, 1996). The spatial scale of water use is also hard to define, eg. irrigation water may be derived from a large aquifer but environmental effects are not predictable. In such cases sometimes governemental legislation defines the acceptable input level. 2.2 Plant-animal interactions In land use studies, we can distinguish two types of interaction: (1) direct interactions between the soil-plant systems, i.e. the pastures, and livestock that grazes these pastures, (2) indirect interaction between the soil-plant systems and the livestock systems, with livestock that do not (or only partly) graze pastures. 2. Definition of production activities 19

19 Grazing and browsing of (natural or sown) pastures by animals directly affects the photosynthetic capacity of the vegetation, i.e. plant growth, through defoliation. The poaching and trampling will influence the water balance through changes in soil compaction and resulting changes in infiltration capacity. The resulting changes in water availability affects plant growth and production. Selective grazing of palatable species results in changes in botanical composition of the swards. The botanical composition co-determines the forage production of pastures. High stocking rates may result in reduced plant cover and, finally, lead to soil erosion, which in its turn affects both water and nutrient balances at the spot. A large part of the consumed nutrients is returned to the soil via excreta. However, losses occur in the transformation processes of plant material through the animals to excreta, resulting in a lowering nutrient availability for plant growth. Nevertheless, in nutrient-poor habitats the animals' digestion of slowly degradable organic matter (forage/fodder), i.e. transforming it into quickly degradable organic matter (manure), may accelerate nutrient cycling compared to a situation where transformations by decomposition of the plant material, directly returned to the soil, takes place without the intermediate of animals. When livestock is uncoupled from the soil-plant system, many types of systems can be realized, e.g. cattle-fodder, pigs-maize, cut-and-carry. The interaction then is indirect. It involves the effects of quantity and quality of fodder, produced in the cropping systems, on animal intake and, subsequently, production and quality of manure. The manure that is produced and used in the cropping systems affects the physical, chemical and biological processes in the soil-crop system. Following are some examples to illustrate this indirect relation. In the same physical environment, a C 4 crop renders a much higher straw production than a C 3 crop, but generally of a lower quality. The fodder of a C 4 crop may result in a higher manure production, but of a lower quality. The N/P ratio in pig manure is more favorable for grass growth compared to cattle manure. Depending on the manure and soil characteristics, each manure-soil combination will render a specific working coefficient of the plant nutrients N, P and K. Another interaction between the crop and the animal production system is ploughing by the aid of animal traction. Ploughing, compared to zero-tillage or manual cultivation, affects soil structure and as such growth and production of a crop. The ploughing energy the animals need for draught, is partly determined by the specific soil resistance (Van der Lee et al., 1994). At farm level, the total available traction energy determines the size of the cropping area to be ploughed. QUASI Plant and Animal Production 20

20 Chapter 3. Quantification of production 3.1 Production orientation and production goals In land use analysis and land use planning, in principle, for each production situation or physical environment, i.e. each unique combination of land unit and climate an infinite number of production activities, both for plant production and for animal production can be defined. These production activities may differ in production level, and/or in production technique, as characterized in their input-output coefficients. To structure identification of the relevant production activities in a land use study, the concept of production orientation has been introduced (Van Ittersum & Rabbinge, 1997). Production orientation refers to value-driven aims, defined in general terms, of specific (groups of) stakeholders. In contrast to the goals or objectives defined in the land use studies, production orientation does not have to be defined in terms that can be translated in explicit characteristics for which in each production activity a technical coefficient is defined. Production orientation may be defined, for instance, as yield-oriented agriculture or environment-oriented agricultural production. The first orientation would require definition (and quantification) of activities characterized by high land productivity, low emissions per unit product, high resource use efficiencies, etc., while the second orientation would require definition of activities characterized by restricted use of agrochemicals (fertilizers and biocides), low emissions per unit area, etc. Production orientation is not necessarily directed to preference for aims related to agricultural production. Orientations in land use may also be directed to nature conservation, production of drinking water, maintenance of rural employment, etc. Animal production activities, in many instances, form an integral part of agricultural land use, and therefore the treatment of production orientation also applies to that category of activities. A special case for these activities is formed by the observation that animals not only (or not even in first instance) are kept for production purposes, but for their function as a capital asset in situations where financial institutions, like insurance companies, banks and markets are absent or functioning imperfectly. Keeping animals (mainly) for that production purpose is therefore also considered a production orientation. This classification in production orientations thus allows systematic identification of the production activities that are relevant in a specific land use study. Depending on the purpose, in a given situation, activities classified under different production orientations may be included. This implies that the targets or aspirations of 3. Quantification of production 21

21 various stakeholders, expressed in the objective function(s) of a linear programming model, usually comprise various production orientations. Agricultural activities may be practiced with many different goals: in arable farming, producing grains for human consumption, grains for animal feed, grains for biofuel, a combination of grains for human consumption and straw for animal feed, covering the soil to prevent soil erosion or nitrate leaching, etc; in livestock farming goals may include, producing meat, and/or milk for human consumption, providing draught power for soil tillage, producing dung as a fertilizer, controlling weeds in plantation crops, producing eggs for human consumption, producing wool, hair and skins as raw material for agro-industries, etc. In pursuing these primary production goals, very often 'by- products' are also produced that may form very valuable commodities: in developing countries crop residues (by-product of grain or tuber production) are vital in their function as animal feed, animal manure (byproduct of meat and/or milk production) is very often the most important fertiliser source for arable cropping systems, meat (by-product of production of draught power) is an important component in calculating the economic viability of integrated crop-livestock systems, groundwater (by-product of production of plant material in environments with a precipitation surplus in the growing season), may be a valuable raw material for drinking water production. These examples can be supplemented with many others, that can be derived from the primary goals defined above. The basis of all these production goals is formed by the growth, production and development of crops and livestock. Therefore we will first focus on the principles governing these growth and development processes that, by definition, occur at the plant and animal level. For animals, production of milk and traction is intimately linked to these processes and will also be dealt with, and finally, principles will be treated to quantify the intangible function of livestock as capital asset. 3.2 Plant Production Yield defining factors In crops or vegetation, the process of photosynthesis transforms carbon dioxide and water, through the solar energy absorbed by the leaves into biomass, according to the overall reaction: Sun energy 6 H C0 2 C 6H (1) This process, also called gross C0 2 assimilation, transforms C0 2 from the air into glucose (C 6 H ). The glucose is partly respired to provide energy to support functioning of crops or vegetation (maintenance) and is partly converted into structural plant dry matter (growth). The energy expenditure in these processes is referred to as maintenance and growth QUASI Plant and Animal Production 22

22 respiration. The net assimilation, which is the gross assimilation minus maintenance respiration, is the basis of crop production: A W = (A - R) * E c (2) with: AW A g R E c Dry matter production Gross assimilation Maintenance respiration Conversion efficiency Potential production, that is the maximum possible dry matter production of a crop variety with given phenological characteristics in a given climatic environment, is determined entirely by the quantity of available light, C0 2 concentration, prevailing temperatures and crop characteristics. Potential production conditions are characterised by the absence of adverse environmental conditions, that would limit water or nutrient supply or reduce (weeds, pests and diseases) crop growth. For quantification of inputs and outputs associated with potential production, mechanistic models exist that are based on insights in the physical and physiological processes underlying crop growth. As an example, a simple model will be described below. This dynamic model explains the time course of crop growth (expressed as dry matter accumulation) from processes that occur one aggregation level below plant level, i.e. organ level. Plant growth is a dynamic process, in which the quantity of. plant biomass (W) continuously changes. The change per unit time is defined as the rate of change (dw/dt). The most simple model of growth of a crop surface is that in which it is assumed that the rate of growth is proportional to the amount of biomass present. This is referred to as exponential growth, and can be described by the equation dw/dt = r * W, in which r is defined as the relative growth rate. That situation continues as long as the increase in plant biomass is fully invested in organs that contribute to growth capacity, i.e. leaves, and these leaves can fully express that capacity, i.e. lead to a proportional increase in the absorption of sunlight. In practice, such a situation can be maintained only during a limited time in the early stages of crop development, as the morphological changes associated with increase in biomass will result in below exponential growth. Leaf area will increase, resulting in mutual shading, new plant parts develop that do not contribute to photosynthesis (cf. stems) and differ in maintenance requirements, etc. A more realistic model will deal with the effects of growth-defining factors in relation to the current state (i.e. plant biomass and its composition, leaf area and development stage) of the plant. In this way shading and maintenance is accounted for. Over a certain time interval, the increase in biomass (AW) depends on gross assimilation (A g ), maintenance respiration (R) and conversion efficiency of carbohydrate into 5. Identification and compilation 23

23 dry matter (E c ), characteristics that are affected by radiation, temperature, plant weight and composition: A g = f (Radiation, Temperature) R = f (Weight, Composition, Temperature) E c = f(composition) Gross assimilation, expressed per unit ground surface, depends on the area of leaves that intercept radiation. In models, Leaf Area Index (LAI) is used to express the relative cover of the ground surface with leaves, and ranges from a very low value at plant emergence to a species-specific maximum of 2 to 9 (coniferous trees). The maximum C0 2 -assimilation rate per unit leaf area (A^J, determined by the transport capacity of the leaf surface for carbon dioxide (generally expressed by its inverse, the stomatal resistance), differs between C 3 - and C 4 -species 1 and is easily reached on a clear summer day at the top of the canopy, under optimal water and nutrient conditions. Actual gross assimilation is determined by available radiation, that depends on season and time of the day (angle of incidence), presence of clouds and air clarity. is 40 kg C0 2 h" 1 ha" 1 (leaf) for C 3 -plants and this may result in biomass production of maximally 200 kg ha" 1 d" 1 in the Netherlands, assuming an overall loss by respiration of 40% and an overcast sky during 60% of the day. Phenological plant development is characterised by the order and rate of appearance of vegetative and reproductive plant organs. In models, plant development is often characterised by its development stage (DVS), a unitless characteristic, assigned by convention, the value 0 at emergence, 1 at flowering and 2 at maturity. The rate of change, that thus by the same convention has the unit d" 1 is defined as a function of temperature (for photoperiod-sensitive species or cultivars corrected for daylength). In the course of development the distribution of carbohydrates among different plant organs varies: in annual species typically first to roots and leaves, subsequently to stems, and finally to storage organs (grains, tubers, etc.). DVS can also be expressed in the so-called temperature sum (d C). Note that this is a descriptive formulation, as it does not explain the relation between physiological/phenological processes and dry matter distribution. Growth and phenological development are to a large extent independent processes: only in extreme situations or for specific crops (e.g. millet), phenological development is affected by growth rate of the vegetation. Consequently, in the course of development, AW will change for each plant organ, thus changing plant morphological composition, and hence the energy requirements for maintenance and growth, that are specific for each plant part. The relation between DVS and carbohydrate partitioning has been established experimentally for a wide range of crop species and cultivars, as illustrated for rice in Figure The two groups of species differ in photosynthetic pathway, and the identification of the groups is derived from the length of the first stable carbon component. QUASI Plant and Animal Production 24

24 fraction of total dry-matter increase fraction of shoot dry-matter increase development stage development stage Figure 3.1 Partitioning factors for plant parts for rice in the course of development to anthesis (From Van Keulen and Wolf (1986), Figure 12). In principle thus, these equations can be used to quantitatively describe growth of a crop from emergence to maturity, under optimum growing conditions (potential production) Yield limiting factors Water Water requirements Water is one of the most important substances to sustain plant and animal life. From equation (1) it is evident that in the process of assimilation, water is an indispensable component. As for energy (only 3-5 % of the energy absorbed by the vegetation is used in assimilation), the amount of water used in assimilation by a growing vegetation is only a very small proportion of total crop water use. Most of the water is required to dissipate the energy not used in photosynthesis, to prevent heating of the vegetation ('cooling'). The process of photosynthesis requires an open connection of the plant with the atmosphere to allow entrance of C0 2. This connection is provided by small openings in the epidermis of the leaves, the stomata. When the stomata are open, water vapour diffuses from the sub-stomatal cavity (that is saturated with water vapour) to the air (that is only saturated in extreme cases), under the influence of that concentration gradient ('transpiration'). When not enough water is available in the root medium (in the field situation, the soil) to replenish the water lost to the atmosphere, the stomata close as a result of turgor loss and C0 2 assimilation is reduced, and eventually approaches zero. Plant growth is thus strongly influenced by water availability. At extreme drought, the plant is desiccated and eventually may die. As transport of carbon dioxide and water through the stomata follows the same pathway (and thus involves the same (diffusion) resistances), a proportional relation exists between assimilation (and hence 5. Identification and compilation 25

25 growth) and water use. In some models water-limited assimilation is therefore derived from the ratio of water-limited (so-called 'actual') and potential transpiration (T a /T p ), that is derived from the availability of water to the roots in the soil. Actual assimilation is then calculated as potential assimilation times the ratio T a /T p, or in other words, assimilation is derived from water availability. An alternative is to take a target dry matter production as a starting point and derive water requirements from the crop's water use efficiency, WUE. The target biomass should be based on realistic figures, for instance 15 t ha" 1 maize (including residues) in The Netherlands. WUE is expressed as dry matter production per unit transpired water (T w ), and is a very 'conservative' plant characteristic, that can be empirically derived from experiments (Fig. 3.2). grass ly grass 2y luzerne luzerne ly 2y maize beets Figure 3.2. Water use of perennial ryegrass and some arable crops in a greenhouse trial in 1995 at 4 different water regimes (Grashoff pers.comm.). Age of grass and luzerne after seeding is indicated. WUE is species-specific and rather constant over a wide range of light intensities and soil moisture conditions. Subsequently, water requirements to maintain optimal (soil) moisture conditions are then calculated as AW/ WUE. Water supply Water supply to the rooted zone of the soil may originate from precipitation, flooding, dew, irrigation, or groundwater. Plant roots extract water from the soil, when present in sufficient quantities. The driving force for uptake of water is the potential difference between the water in the plant and that in the soil, created by the loss of water through transpiration. As the forces that can be exerted by the plant are limited, water uptake is increasingly QUASI Plant and Animal Production 26

26 restricted at lower soil moisture content, and hence lower potentials of water in the soil. The water content may even decrease to the 'permanent wilting point' (pf (negative value of the logarithm of soil water pressure in cm) 4.2 = hpa water pressure) at which water uptake completely ceases. Also at high soil water contents water uptake is hampered as a consequence of insufficient root functioning due to oxygen shortage (e.g. under water logging). An empirical relation between soil water pressure (potential) and the ratio T a /T p can be used to calculate transpiration reduction as a result of drying of the soil and oxygen shortage (Fig. 3.3). a Pressure head (p) Figure 3.3 Relative water uptake (), equal to the ratio TJT p, in relation to soil hydraulic pressure head p (From Feddes et al., 1978). p2 represents field capacity. The value of p3 depends on soil characteristics and also on the transpiration flux (potential flux Tpot). The point p4 represents the wilting point. In the field situation, a soil can retain water against the forces of gravity as a consequence of capillary forces. Of course, the gravity forces depend on the depth at which 'free' water (a groundwater table for example) is present. For practical purposes, an upper limit for plant available water is set between pf 2.0 (- 100 cm water pressure) and pf 2.4 (- 150 cm water pressure), and is referred to as 'field capacity'. Hence, total available soil moisture is expressed as the difference in moisture content of the soil between pf 2(.4) and pf Nutrition Nutrient demand In addition to carbon dioxide and water (Eqn. 1) plants need inorganic elements to produce organic components. Since the discovery of these nutritional demands in the middle of the 19th century, to which the names of Von Liebig (1855) in Germany and Lawes and Gilbert in the UK are intimately connected, a large number of elements have been shown to 5. Identification and compilation 27

27 be indispensable for proper functioning of the plant. In practice, however, mainly N (nitrogen), P (phosphorus) and K (potassium) are likely to limit growth, since plant demand for these (macro)nutrients is high. This high demand reflects the importance of these nutrients in plant functioning: N is essential for protein formation, P as well, in addition to its function in energy transfers, and K is most important in the plant's water economy. At optimal nutrient supply, plant species are characterised by specific (development-stage dependent) contents of macro- and micro nutrients in their tissues. Therefore, associated with the increase in dry weight, a distinct demand for nutrients is created. Plant growth is negatively affected when nutrients are in short supply. Also the balance in supply may affect growth: excess supply of K may hamper Mg (magnesium) uptake (cation antagonism) and result in Mg deficiencies in grass and cattle; shortage of N may limit uptake of phosphorus and vice versa. The ratio between P and N in plant tissue, related to the functions of both elements, may fluctuate within reasonable limits. Ideal is a P/N ratio of 0.1, but the range is from a relative shortage of P (0.05) to a relative shortage of N (0.14). In crop modelling, the simplest approach to calculation of plant nutrient demand is based on the minimum nutrient concentrations observed in the various plant organs at maturity. Hence, the dry weights of the plant organs associated with the potential or waterlimited production levels are multiplied with these minimum concentrations to estimate the total amounts of plant nutrients necessary to realize the calculated production levels. As the concentrations are assumed to be constant in the relevant range, yields are reduced in proportion to the supply of a certain nutrient. In more complex models dealing with nutrient limited conditions, the demand for plant nutrients is calculated dynamically, i.e. at any moment it is set equal to the difference between the (development-stage dependent) optimum quantity (optimum concentration times dry weight) and the actual quantity in the tissue(s) at that time. Sub-optimal concentrations in the tissue then affect processes in the plant, such as gross assimilation, and dry matter allocation. The dynamics of nutrients within the plant may also be taken into account by assuming that nutrients can be translocated from (older) tissue to newly formed tissues, thus contributing to efficient use of nutrients in the plant. Nutrient supply A distinction is generally made between the supply of nutrients from 'natural sources', such as the soil, biological fixation (symbiotic and free-living microorganisms) and deposition, and from external sources, such as (green) manures and fertilizers. All the N originates from the atmosphere, and has been partially incorporated in living and dead biomass (i.e. soil organic matter, peat, oil, etc.) through time. The soil is a source of nutrients as a result of weathering of the mineral fraction (especially cations such as Ca (calcium), Mg, K and Fe (iron)) and through mineralization during decomposition of organic matter (predominantly N, P and S (sulfur)). Mineralization is the result of decomposition of organic matter through microorganisms, in which part of the nutrients is incorporated in microbial biomass, and the possible excess (partly originating QUASI Plant and Animal Production 28

28 from carbon compounds that are respired during decomposition, forming C0 2 ) is released in inorganic form. As the processes of incorporation in microbial biomass and release in inorganic form proceed concurrently, for plant growth the net rate of mineralization is important. This rate may have a strong impact on plant growth, since large quantities of N and P are stored in soil organic matter. In simple models, net mineralization (M) is usually estimated by first-order kinetics: M = c * NuSOM, where c is the relative mineralisation rate (y" 1 ) and NuSOM is the pool of nutrients in the soil organic matter. Other sources of nutrients are biological fixation of N from the air by microorganisms (in symbiosis with leguminous plant species, such as clovers, beans, etc., or free living in association with living plants that supply energy), atmospheric deposition of anthropogenic N and S and imports through seasalts and dust. External sources of nutrient inputs consist of artificial fertilizer, animal manure, crop residues and sediments from flooding. Uptake of nutrients from these external sources, such as applied fertilizer, by the crop, 'recovery', is never 100%. Inevitably, part of the nutrients is not available for uptake by the crop, due to losses, through leaching or gaseous emissions, adsorption on the exchange complex or immobilization by micro-organisms. For artificial fertilisers, the recovery fraction depends on soil type, application rate, timing, application technique and weather conditions. For nitrogen, the fraction may vary between 0.0 and 0.9, for phosphorus between 0.0 and 0.5. (External) nutrient requirements for cropping activities are then estimated from the total requirement, taking into account the supply from natural sources and the efficiency of uptake (recovery) of nutrients from these external sources. As for crop growth, more complex models have been developed for nutrient balances in the soil-crop system, that describe the dynamics of the nutrients in the soil in time intervals of one day. Although nutrient dynamics have been subject of extensive research for many years, it appears that our understanding of the basic processes is at best fragmentary, and that quantitative estimates are difficult. Hence, for most applications within the context of land use planning such complex models hardly warrant the efforts Yield reducing factors Weeds, pests, diseases and pollutants At any production level (potential, water-limited, nutrient-limited), pests, diseases, weeds and pollutants may hamper growth, resulting in yield reductions. The degree of damage due to these factors depends on many factors. A highly-productive continuous monoculture is most likely more susceptible to these growth-reducing factors than a mixture of species in a wide rotation. Pests and diseases are more likely to have a strong impact in warm and moist climates than in cold and/or dry climates. Taking into account the effects of these factors, following quantification of the effects of growth-determining and growthlimiting factors, results in estimates of current or actual production. 5. Identification and compilation 29

29 The strongest effect of weeds on crop growth is through the reduction in resource availability, i.e. light, water and nutrients for the crop. Even in a simple situation with one crop and one weed, the competitive effects can only be analysed properly by simulation, although analytical solutions have been proposed (de Wit, 1960). In mixed cropping systems modelling approaches are also being used. The competition for light, and water has been explicitized in growth models (example; see Kropff and van Laar, 1993). At each time step, growth and concurrent resource use of the separate species are calculated. The outcome depends mainly on differences in seedling density, relative growth rate and development rate among the species. Competition for nutrients is much more complex, and though attempts have been made, sofar no satisfactory descriptions appear to have been developed (Spitters, 1989). Pests may interact with crop growth in various ways (QUASI-Intro, 10.6), and each of these interactions requires a specific treatment in coupling pest and disease development and crop growth models (Rabbinge and Bastiaaans (1989). Certain fungi growing on the leaf surface, affect the effective assimilation area, and/or modify the light relations of the leaf, thus decreasing assimilation rate per unit dry weight (A g ). Effects of insects that consume leaves or total plants will be expressed in reductions in biomass and LAI, reducing assimilation as well as respiration per unit ground area. Lice and mites are typical examples of assimilate consumers, not reducing the biomass directly. In most growth models the assimilate pool is quantified, thus enabling calculation of the growth reduction through such organisms. Development and spread of infections are complex processes, that depend on many factors, such as wetness of the surface and temperature. Therefore, to predict the level of infection and the possible damage, in general very detailed information is required on the specific conditions. In the framework of explorative land use studies, such information is seldomly available, and effects of pests and diseases are therefore often described in very general terms (de Koning et al., 1992; 1995). Airborne and soil-related pollutants may strongly hamper crop growth. Extensive studies exist on the effects of Zn, Cd, Cu and Pb on the growth and physiology of vegetables (e.g. Lexmond,1980). The effects of air pollutants (ozone, NO x, NH 3 and F) on crops and trees have been quantified in recent years. The toxic compound may operate through ion antagonism, reduction of enzymatic functioning (e.g. photosynthetic phosphorylation), membrane functioning, etc. The effects on growth and production can be quantified if the mechanisms of the toxicological process are known. Ozone operates via reduced assimilation and so assimilation-based simulation models can incorporate its effects. At the current atmospheric composition, yield reductions are estimated at 10% of the total arable production in The Netherlands. QUASI Plant and Animal Production 30

30 Effectiveness of crop protection measures Except in very specific cases (outbreak of a new disease, loss of resistance), biocides (herbicides, pesticides, fungicides, acaricides for Acaridae) are available to control weeds, pests and diseases. However, application of these biocides may have serious side-effects: human health hazards during application or in the form of residues, negative effects on benign organisms, residual effects on other crops in the rotation, pollution of ground- and surface water, etc. Therefore, especially in formulating alternative production techniques, for instance directed towards environment-oriented agriculture, alternative control measures have to be specified, and their effects on crop yields and the environment. For weed control, mechanical weeding may be very effective, provided it is carried out at the proper time and at the proper intensity. However, it is very labour-intensive and requires inputs of either manpower, animal traction or fuel. The effect of biocide spraying depends on the amount of active ingredient (a.i.) and its effectiveness, which depends on the degree of infestation, timing of the control measure, environmental conditions, and can be described in the dose-response relationship. The amounts needed vary strongly among crops : currently for flower bulbs 120 kg ha"' of a.i. are needed, while maize is already protected at 4 kg ha" 1. In recent years, extensive attention has been paid to biological control of insect infestations. On the basis of models that simulate development and reproduction (for instance on the fruit tree red spider mite, see Rabbinge and Bastiaans, 1989), the required timing and density of predator insects are estimated. At the level of cropping systems, rotations can be used to control the level of pests and diseases to a large extent. Distinct rotation schemes are necessary for, for instance, potatoes (preventing nematode and Verticellum dahliae infections), sugarbeets (nematodes) or maize {Phythium and Fusarium) Production at the level of the cropping system From crop system to cropping system we arrive one level higher in hierarchy. In the study of cropping systems we deal with a field, having a defined spatial border, and a sequence of crop systems in time. Within the specific field, one or more crops can be cultivated in space and time, and thus an aggregation of the data of the different crop activities is necessary. The order in which (different) crop species are cultivated in time is referred to as rotation. Different rotation types can be distinguished: monocropping, multiple cropping, and a sequence of cropping and fallow. Mixed cropping and intercropping refer to systems with more crops in one field at the same time, reflecting an aggregation in space. Such systems may utilize radiation and water more efficiently than individual species, which will result in input-output relations that differ from those of the crops separately. 5. Identification and compilation 31

31 The most important aspect of cropping systems is the effect of carry-over of soilrelated factors from one year to the next. Growth factors related to soil characteristics, such as organic matter content, influencing N supply from natural sources (plant available soil N) and diseases (soil pathogens) are to a certain extent carried over to the following growing season(s). These factors are external to the crop, but internal to the cropping system. Apart from soil chemistry and biology, also the physical properties of the soil maybe influenced by preceding crop activities. Soil structure can for example be affected by the use of heavy machinery. The impact of all the mentioned factors also depends on weather conditions. For example, in temperate climates, a period of intensive frost in winter may alleviate the adverse effects of soil compaction by perturbation of soil aggregates, or it may lead to death of soil pathogens; carry-over of soil N is strongly influenced by the precipitation regime in winter. The carry-over effect of a crop activity to subsequent years can be described by the dynamics of the relevant processes. Survival of a root pathogen is very improbable if the specific crop species (host) or an alternative host species is not present the year after; retention of inorganic N is very unlikely if precipitation between seasons is very high, etc. In summary, the effect of one crop on the next is transmitted through the crop's impact on biological, physical and chemical soil properties and its persistence (over time). In general, rotation effects in quantification of cropping activities are largely based on expert judgement (De Koning et al., 1992; Habekotté, 1996), as reliable, accurate dynamic descriptions are hardly available. 3.3 Animal Production Physiological limits to animal production Potential growth In analogy to the potential limits of plant growth at unrestricted availability of resources, animals have a potential limit to growth. This maximum is, like in plants, fixed in the animal's genetic make-up. The interaction of these species-specific characteristics and the environment results in the potential demand for energy and proteins per unit body weight. These species and sex-specific factors are, for instance, development, body shape and energy metabolism (see Fig. 2.1). On the day the elephant and the mouse are born, their maximum adult size is predetermined, and no ominous feed consumption can change this. There are only a few exceptions where potential growth levels are affected by feed, due to alterations of genetic expression at extremely high or low levels of feed supply. Temperature and daylength are the main climatic boundary conditions to realize potential production, mainly via their effects on feed intake. Cold and heat stress increase cq. decrease feed intake. The specific requirements based on the potential demand are described below. QUASI Plant and Animal Production 32

32 Potential growth cannot be calculated mechanistically. Growth is described on basis of empirical data of well nourished animals that have realized potential growth rates and potential adult weight. Growth follows a logistic curve like: W(t) = WP - (WP-WB)* e v *' (3) where: t = Age (yr), Wt = Weight at age t, WP = potential weight (kg), WB = birth weight (kg), v = relative growth rate (yr 1 ). The weight gain AW between two time steps can be calculated with this equation, where AW represents its slope. Another approach can estimate weight gain on basis of the current weight at age t (Wt): AW = c, * (WP/Wt) c2 + c 3 * (Wt/WP) c4 (4) This function is also derived from a logistic growth curve (used in PCHerd, see Brouwer, 1994). The value of WP and the constants c, to c 4 are species and sex specific. On basis of Eqn. 3 and 4 the feed requirements for potential growth can be calculated. The required net energy is assumed to be twice the energy content of the new formed body tissue, so 50% of the added energy is lost (NRC, 1988). The associated feed consumption is based on voluntary intake. Voluntary or so-called ad libitum (ad lib) intake of high quality feed by growing animals will result in energy and protein intake that fulfills the demands, and the potential, maximum adult weight may be achieved. Also vitamins and other nutrients than N are necessary, but are generally abundantly available in the offered feed and not further elaborated upon. The intake that meets the demand for energy is based on the energy required for growth and maintenance of 1 kg metabolic weight. In general however, the supply does not meet the demand because unfavourable feed or environmental characteristics may result in a negative feedback on feed intake. This intake constraint due to lower quality of supplied feed is a growth limiting factor (Fig. 2.1), just like the feed availability in total. We will further elaborate on this in the next section ( 3.3.2) Energy demands The genetic properties of the animal set the limits to maximum production. In reality, the feed resources mostly govern animal growth, functioning and production. Although feed availability can be controlled by transportation to the animal and is as such not a 'defining' but a 'limiting' factor, its similarity to radiation as a defining factor for plants is almost convincing. For that reason, the subject 'feed/energy demand' is placed in this section as well as in section 'limiting factors'. Feed demand is driven by the animal's genes as well as by the environment, i.e. the animal's habitat resources. 5. Identification and compilation 33

33 Growth Animals need energy and proteins from feed to grow and maintain their body functions. Animals are heterotrophs, and derive their energy and nutrient needs from biomass of primary consumers (plants) or other heterotrophs. The energy is obtained by dissimilation of energy compounds, and this is the reverse of the process described in Eqn. 1. In analogy to plant growth (Eqn. 2), animal growth is a function of available energy x conversion of energy to tissue, and specified as follows: AW = (energy supply - energy for maintenance) x 'energy to tissue' conversion = (IDOM a * [ME] dom * C NE/ME - NE m ) * (5) with per animal: AW Weight gain (kg) IDOM animal specific Intake of Digestible Organic Matter (DOM) (kg) [ME] dom content of Metabolisable Energy in DOM (MJ kg" 1 ) C N E/ME Conversion factor of Metabolisable to Net Energy (-/-) NE m Net Energy required for maintenance (MJ animal" 1 ) Cenergy»gain factor to transform net energy to animal tissue (kg MJ" 1 ) DOM refers to plant carbohydrates that are digestible in the animal's tract, like starch and cellulose. On basis of numerous measurements, a default value of 15.8 M J metabolisable energy per kg DOM is used. This gross energy offers ca. 60% net energy (C NE/ME = 0.6). The maintenance requirement (NEjJ utilizes a part of the energy supplied by the feed (see below at "maintenance" for details). The remaining energy may be used for tissue formation, and the value of C encrgy _, gain will differ between energy-rich fat and energy-poor protein tissue, as it may vary between animal species. Eqn. 5 suggests that growth can be completely controlled by energy supply without limitations. However, maximum intake and growth are basically determined by the animals' genotype in combination with some external, environmental conditions (see "potential growth", above). The actual feed intake rate (IDOM per day) depends on the feed quality and may thus limit growth (see further at and Eqn. 9). For growth animals also require amino acids. These N compounds are derived from the consumed proteins. Some other, species-specific requirements are fibres (for ruminants) or stones (birds). Maintenance On the basis of research on oxygen consumption and carbon dioxide production, the net energy demand for maintenance (NE,) is estimated from body weight W QUASI Plant and Animal Production 34

34 as A * W 0 ' 75. This relation between energy requirement and "metabolic size" of the animal, i.e. W to the power 3 /4, is quite general with only a few exceptions. The constant "A" equals Net Energy required for maintenance of 1 kg of metabolic body weight. For cattle the default value of A is ca MJ per kg live weight or 0.32 MJ per kg 0 75 metabolic weight. This value must be corrected for animal species, breed, sex and age. This will result in the following calculation of maintenance requirement (in MJ day" 1 ) per animal: NE m (W) = f s * e q(age " b) * A * W 0 7S (6) A = NE m per unit metabolic body weight (0.32 M J kg" 075 ) f s = 1.0 for females, 1.25 for males (-) q = negative value to account for decreased maintenance by aging (-) b = age at which q was estimated (y) During grazing in summer requirements are ca. 10% higher. Maintenance requirements increase during pregnancy and may also deviate between stable types. Other feed components are also needed for maintenance. For N a gross requirement of 0.5 g per kg metabolic weight is assumed, required for tissue renewal and other maintenance losses that are excreted in the form of urea and organic N. Energy requirements of lactating animals Extra energy is necessary during lactation. The total intake IDOM L per animal can be derived from intake by non-lactating animals (IDOM, Eqn. 5) as follows: IDOM L = (C MIIKLEVEL - R* LD) * IDOM (7) IDOM = Intake of digestible organic matter (kg animal" 1 ) Cmiikievei = factor that depends on the milk production level (-/-) LD = lactation day R = factor for lactation stage (-/-) The constant C may differ between 1.1 for domestic cattle breeds in the tropics (producing maximally 2000 kg milk per year), 1.3 for crossbreds up to 3.2 for HF cows in the Netherlands (production levels of 6000 to kg milk per year). Milk production, and subsequently feed intake, is decreasing during lactation, which is roughly described by the term R x LD. The value of R is increasing with age. If the milk production and its composition are known exactly, the net energy for lacatation (MJ per kg milk) that has been used, can be calculated subsequently, by: NE = 1, ,0406 x fat content (g kg" 1 milk) (8) 5. Identification and compilation 35

35 Traction Animal traction often forms an important part of smallholder farming systems. In order to model it as an essential link between the cropping and livestock subsystem, concepts must be integrated from the disciplines agricultural engineering, soil science, agronomy and animal production. The forces necessary for a certain traction operation can be expressed in an amount of net energy. The energy costs can be discriminated for a number of operations (Van der Lee et al., 1993): pulling, elevation, carrying loads and live weight, walking. The traction supply by a single animal depends on its condition score, live weight, optimal weight, harnessing score, team efficiency and team size. Traction requirements are animal -independent and depend on loads to elevate or pull, the surface area to be ploughed, the soil characteristics, etc Production limiting factors at animal level Introduction So far, we could calculate the theoretically required energy for growth and other animal functions, i.e. the potential level of production. In practice feed intake varies due to availability and quality of feed. These factors limit animal production relative to potential. Below we will elaborate on the quantification of the production functions of the individual animal, which is being offered a range of feed levels and qualities. One has to realise, that this feed level is mostly managed by the farmer and artificial. The feed level may change radically at herd level if the farmer decides to reduce the number of animals. Consequently, the actual level of individual animal production as part of a livestock system is actually decided upon at herd level, where the feed allocation is managed. The production at herd level will be treated in Feed intake regulation The animal's feed demand is basically driven by its energy requirements (see 3.3.1). Yet the feed availability and quality influence the amount of energy consumed. Low-quality feeds will hamper energy intake due to their lack of taste and tough composition, and higher supply rates will not promote the intake rates. The intake mechanisms are usually quantified by intake equations. Ketelaars and Tolkamp (1991) based ad lib intake equations on a set of extensive intake studies on sheep): on the basis of the digestibility of the feed, the total ad lib dry matter or intake of organic matter (IOM) in g organic matter per kg metabolic weight (kg 0 75 d" 1 ) for sheep can be estimated as follows: IOM = *OMD *OMD *N *OMD*N (9) QUASI Plant and Animal Production 36

36 OMD = organic matter digestibility (% or g 100g') N= N-content in organic matter (or crude protein/ 6.25) (% or g 100g" 1 ) Other empirical relations of IOM, dry matter intake or digestible organic matter intake with OMD and N are known as well and the choice for a particular function may be based on its goodness of fit of the regression. The intake amounts of digestible organic matter have to be converted to energy amounts, since the life processes are energy-driven. IOM is multiplied by OMD to arrive at IDOM and this is converted into IME (Intake of Metabolisable Energy), assuming that 1 g of digestible organic matter is equivalent to 15.8 kj ME. A correction factor of 1.33 is used to convert the sheep intake equations to intake by local Indian cattle, to account for the on average higher metabolism of cattle per unit metabolic body weight. The actual IDOM, based on Eqn. 9, has to substituted in Eqn. 5 and will so determine the animals' growth rate. The relationships between feed quantity, quality and growth are schematised in Fig Consequently, the actual growth will be equal or lower than potential growth (see Eqn. 3 and 4). However, the supposed potential adult weight Wp ( 3.3.1) may to some extent be influenced by the rate of voluntary intake. If new feed strategies enhance voluntary intake, adult animals may well become larger than expected. This illustrates that an exact definition of the potential animal production is rather difficult Allocation of the limited energy The total of animal functions can be grouped into the maintenance of body functions, in foraging, in maturation, in reproduction and in the production of milk, meat, manure, traction. All these processes need energy and proteins. It is assumed that at energy limitation, the production of milk and meat are decreased first. In PCHerd (a model that will be used in supply intake conversions DM Ash OM N IOM in kg > day 1 W 0 75 "T IDOM *15.8 IME f(%n, OMD) physical limitation f(omd) digestibility r maint *0.6 gain

37 Chapter 4), traction is a stronger "sink" for energy compounds then the other production processes. So, a strong demand for traction requirements will go at the expense of e.g. milk production. The competition for energy allocation to these processes is not well known and the information is mostly based on field expertise. The maintenance requirements always have to be fulfilled in order to survive. Figure 3.4 Scheme of intake and digestion of organic matter by animals. The 'supply' box represents a pool, the grey boxes represent flows of substances (see text for details). As with crops, animals may built up reserves that are utilised in times of low feed supply. The seasonality of feed supply may lead to fluctuations in milk production and gain. Therefore on an annual basis the relation between feed supply and production will not be linear. The fluctuation in live weight development is shown in Fig. 3.5, where compensatory growth in nutritious periods "compensates" depressed growth in poor times. potential A limited restricted until week Age (wk) 60 Figure 3.5. Compensatory growth in dwarf goats (from Tolkamp, 1996). Simulated optimum growth, growth at supply of poor feed from 10 weeks onwards (triangles) and growth at restricted access to feed from week 14 to week 27 and unrestricted feed supply from 27 weeks onwards (circles) Production reducing factors Animals may be affected by diseases and will then show a certain degree of malfunctioning. Infections may cause fever and body heat loss, resulting in depressed feed intake. Depending on the character of the infection (species and type), the infected animal may eventually die. So both at animal (reduced production) and herd level (reduced reproduction) production will drop. Especially in tropical countries scientific research has QUASI Plant and Animal Production 38

38 been done on the quantitative effects of diseases on production. For instance the heat production of Trypanosome infected dwarf goats increased by ca. 7% (Van Dam, cited by Tolkamp, 1996), resulting in higher energy requirements and possible production loss at a given feed availability. Studies on effects of high temperatures and tropical diseases on growth of European breeds in Australia (Frisch and Vercoe, 1978; see Fig. 3.6) show that actual growth (Field LW) is lower, whereas potential growth by higher appetite in the absence of constraints is higher than that of the local breed (Fig. 3.6). Also pollutants and anti-nutritional substances may indirectly reduce feed intake by decreasing the animals' disease resistance. The supplementation of phytase (in the past: copper) to pig feed increases phosphate retention, but has detrimental effects on the health of the animal..2 ">5 C 0) O a 4- o O O) a 4-1 C a> o b. a> Q. 100% IF 90% 80% 70% 60% 50% 40% 30% - 20% - 10% 0% Brahman BX AX HS Appetite (genetic) El Ticks ED Worms m Heat 0 Pinkeye M Nutritional fluctuations Field LW Figure 3.6 Differences in actual growth (Field Live Weight (LW)) between various cattle breeds in Australia. Brahman is a local breed, BX and AX are crosses between European and local breeds, and HS is European Hereford x Shorthorn. The causes for the growth depressions are shown in the legend From individual animal to herd Reproduction The age at which the first conception takes place is related to the weight of the animal and varies among species depending on their size. The age at first calving is that at first conception plus the gestation period, for cattle ca. 9 months. The intercalving period depends on the length of the post-partum anoestrus and the fertility, which is influenced by feeding conditions. 5. Identification and compilation 39

39 Mortality The mortality probability strongly increases at a poorer condition of the animal, which is generally expressed as the proportion between live weight and optimal weight. A minimum value for body condition to survive is not commonly known. Also the relation between body condition and mortality chance is not much investigated. On basis of a limited number of studies, Hengsdijk et al. (1996) quantified a relationship between energy intake level and mortality as well as abortion rates (Table 3.1). The energy intake level of 1.2 represents ad lib feeding conditions at optimal living conditions at the experimental research station, and 1.05 the conditions of a transhumance herd Table 3.1 Mortality and abortion rates (%) as a function of energy intake level Variable Energy intake level Abortion rate Mortality 0-1 yr Mortality 1-2 yr Mortality >2 yr Mortality 0-3 yr Herd composition At herd level the continuity of animal production is relevant, therefore new aspects appear that did not play a role at animal level. In order to keep a herd, either the animals should reproduce or the farmer has to buy new animals to replace the ageing and dying individuals. Apart from their number, the animals are different in breed, sex, age, etc. This herd composition is controlled by the farmer, following his/her production aim. For instance, the aim of meat production may lead to a herd consisting entirely of male calves kept in stables. For a specific production orientation, a more or less stable distribution among the different types and ages of animals may exist. For the time interval with a constant herd composition it is possible to aggregate the different animals to one LU (Livestock Unit). In the Netherlands for example, a LU or GVE (in Dutch: grootvee-eenheid) is equivalent to one adult cow of 600 kg live weight. An average livestock density at Dutch dairy farms is 2.5 LU ha" 1, consisting of ca adult cows and additional young stock. This LU requires a certain amount and quality of feed, which represents a weighted average of the individual requirements. In some cases of smallholder farming, a livestock production system may refer to one animal only. Many farmers in South-East Asia and Africa keep only one or two animals. In those cases the herd dynamics actually take place at the regional level. QUASI Plant and Animal Production 40

40 Growth factors at farming system level The herd size and composition determines how much of the available feed is allocated to the individual animals. So at herd level the farmer may decide to keep only a few animals and feed them optimally, or create a large stock at limited feed supply. In a 'closed' system, at farming systems level the amount of feed in a specific time interval is determined by plant production. Consequently, the growth factors that result in the actual yield of roughage (grass, maize, beets) also determine animal production. In natural ecosystems, herbivore growth and reproduction are intimately coupled to plant production. In theory potential plant yields would result in potential animal production, under the assumption that the animals have adapted to that particular climate during evolution. At the moment, many farming systems are 'open' systems and feed can also be imported from other systems Livestock as capital asset Most production outputs and their economic revenues can be calculated on basis of existing analytical or empirical relations. This is not the case for intangibles. Below we will present two examples of an estimation of the value of livestock as a capital asset. The first example refers to goat keeping in South Western Nigeria. Here animal scientists collaborated with agricultural economists in a goat improvement program. The economists wondered why farmers kept goats anyway, because the financial returns from goats to labour were far below the returns from cocoa or even cassava. But goats enable farm households to meet unexpected expenditures. How to value this? A concept was developed to estimate the insurance value of keeping livestock and the financing value of animals sold (Bosman et al, 1997). The capital invested in the flock is in fact a guarantee that future requirements are met, for which an insurance premium must be paid in situations where an insurance market exists. The benefits of this can be assessed by considering alternative insurance options. Disposal of animals when required, means that financing through formal or informal agents can be avoided, which in turn means a saving on transaction costs. These transaction costs can be considerable. For goat keeping in Nigeria the insurance and finance functions were four times as important as the meat production function. Now, the benefits from goats per unit of labour were comparable to the benefits from crops (Bosman et al., 1997). In the second example, on the role of cattle in mixed farming systems in East Java, Indonesia, Ifar (1996) came to the same conclusion. Only, if the insurance and financing values were included, the returns to labour from livestock were comparable to labour wages. Table 3.2 shows the relative importance of the various functions of keeping cattle in this marginal area in Indonesia. 5. Identification and compilation 41

41 The various motives for keeping livestock can be conflicting. Selling an animal for urgent cash needs (e.g. a new roof for the house, a household member hospitalised, a wedding) may not coincide with the optimal moment from a meat production or breeding perspective. The farmer has to balance the sometimes conflicting goals with respect to the socio-economic and ecological context of the farming system. Table 3.2 Benefits from cattle for an average household with 1.9 head of cattle in upland mixed farming systems in East Java, Indonesia (Ifar, 1996). Benefits in Rp* 1000 y" 1 Progeny (value of calves) 133 Manure 44 Weight gain (individual animals) 44 Draught 17 Insurance 57 Financing 17 In 1996: 1 US$ = 2100 Rp 3.4 Outputs to the environment In every production system a certain amount of water, chemicals, energy, heat and biological compounds is inevitably lost to the environment. Recently, national (MINAS) as well as international (EU, UN-Rio de Janeiro) legislation have set limits to the emissions. Every production system has to comply to specific, maximum emission levels. Ammonia emissions Almost half of the ammonia emission originates from excreta in animal buildings and one-third results from slurry application to the land. With current knowledge, it is possible to estimate ammonia losses in the winter period, when the cows are indoors. In cattle, protein intake rates which are higher than demand will increase the excretion of ureum. This may result in higher rates of ammonia volatilisation by higher ammonium concentration and/or more frequent urination (Smits et al., 1995). Ammonia emissions are linearly related to dissolved ammonium concentrations according to Henry's law. For a range of ammonium concentrations in slurry in standard animal houses and the emission, an empirical relationship has been determined (LEI, 1999). The most complicated part is the relation between animal nutrition and resulting slurry composition. It was found that the ureum excretion is best explained by the so-called "degraded protein balance" in the rumen (OEB) of the cow. The OEB represents the fraction of degradable proteins that can not be incorporated in the protein of the rumen microbes due to an excess of proteins over degradable carbohydrates, required for the incorporation process. The ureum from urine is transformed into ammonium shortly after excretion by the enzyme urease, abundantly present in slurry and stable. Ammonia emissions are proportional to the flux of excreted ureum, and thus proportional to OEB intake. At current Dutch dairy farms the OEB intake is approximately equal to the recommended protein supply in the feed ration, i.e. 300 g cow"' d" 1. The resulting NH 3 emissions are ca. 6% of total N intake. QUASI Plant and Animal Production 42

42 In Western Europe nitrogen is a major environmental issue in agriculture. N affects the environment along a number of pathways: nitrate leaching pollutes the drinking water, N 2 0 contributes to the greenhouse effect, NH 3 and NO x contribute to eutrofication and are toxic to plants, animals and humans. In Western Europe, dairy husbandry contributes (in The Netherlands ca. 55 %) the major part of the N emissions from livestock to the environment. It is generally agreed that N losses should be drastically reduced. In the definition of alternative production activities a specific target level of nutrient loss can be imposed. In order to design alternative production techniques with low emission levels, the relation between inputs and output of N (see highlighted box for ammonia) and other outputs should be quantified. 3.5 Plant-animal interactions Introduction In Chapter 2, we distinguished two types of plant-animal interactions, i.e. direct interactions between herbivores and pastures or swards and indirect interactions between crop and animal production systems. Below we will highlight some ways to quantify the effects of the two types of plant-animal interactions Direct interactions in grazing systems Livestock can return large quantities of the consumed nitrogen and phosphorus to the soil via excreta. Cattle return up to 70 to 80% of the consumed nitrogen and phosphorus. The utilisation by plants of the excreted nutrients depends on the form of N and P and the spatial distribution of urine and dung patches. At a high protein content of the grass sward, livestock excretes relatively much ureum (see 3.4), which may result in high N leaching losses and grass scorching at the local urine spots. Grassland damage by poaching and urine scorching from cattle has been observed in sandy soils, and this resulted in lower N uptake and harvested biomass (Fig. 3.7). In nutrient-poor grazing systems, the animals consume relatively much undegradable feed compounds that result in manure with a higher dry and organic matter content and a slower decomposition rate in soil. Grazing and browsing of animals physically affect the vegetation through defoliation and poaching and trampling. In sown pastures, defoliation may result in a dense grass tillering and a stronger sod. At high animal densities, however, grasslands may be damaged, e.g. during cold and wet weather in temperate zones. Severe defoliation, with in addition poaching and digging, may result in a bare soil, eventually leading to irreversible soil degradation. This is a common phenomenon in tropical arid and semi-arid zones. Preference of animals for palatable species in natural pastures can favour the growth of unpalatable species, and thus negatively affecting the animal-plant production system. However, in some 5. Identification and compilation 43

43 cases, high-productive grass species can be favoured, positively affecting the animal-plant production system. kvem.ha'vlo 3 20 IG < N-lnrlilizer Ikg N.lia I N-ylold (kg N.lia' 1! GOO«- N-lerlilizer Ikg N.ho I Figure 3.7. N yield of grass and herbage intake (in kvem = 6.9 M J Net Energy for lactation) at different N application rates for two grazing strategies (From Deenen, 1994). (circles = continuous grazing, crosses = weekly cuttings, points = 4-weekly cuttings) Plant-animal interactions on pastures cannot be quantified by calculating the subsystems of plants and animals separately, since the interaction implies that a certain mutual benefit or disadvantage will affect growth processes of both plants and animals. A number of concepts exist that are based on experimental results or developed through mathematical models on these interactions. The most simple concept relates the animal production per animal to the stocking rate per hectare. With increasing stocking rate, the intake per animal decreases. The decreasing intake is caused by a lower supply of forage (effect of grazing on plant production, botanical composition, etc), and because the forage has to be shared among more animals. Up to a certain stocking rate, however, the total intake per hectare may still increase, because grazing may have positive effects on vegetation growth. Beyond a certain point, the herbivore density is above the carrying capacity of the vegetation, and both production per animal and total animal and plant production per hectare are decreasing. The production can be expressed in terms of physical products, i.e. meat and milk, and in economic benefits. The shape of the curves, however, remains the same, although the absolute optimal points in both production per animal and per hectare may change depending on how production is expressed. Furthermore, the optima depend on the characteristics of livestock and pastures. QUASI Plant and Animal Production 44

44 Another concept uses the predator-prey relationship, normally used for animal-animal interactions, to model herbivores ('predators') and pasture biomass ('prey') interaction (Noy- Meir, 1975). The predator-prey models illustrate at what growth and consumption rate the plantanimal relationship becomes unstable and plants or animals may not survive (Figure 3.8). Given a few assumptions on e.g. minimum and maximum growth, the plant biomass (P in kg ha') and herbivore population (H, number of herbivores) can be modelled by: dp/dt = ap(l - P max /P) - bhp (10) dh/dt = chp-mh (11) P max is the attainable, maximum plant biomass in the local physical environment without grazing, a is the growth rate of plants (in kg ha' per time interval dt). The fraction bhp is the consumed biomass during dt, with b as foraging efficiency of the herbivores. Constant c is the efficiency of animals to convert plant biomass to growth and reproduction, while m is the mortality rate of the herbivores, which may be influenced by external factors like predators and diseases. From Eqn. 10 and 11 equilibria (E) of plant biomass and herbivore population can be calculated. This is illustrated in Fig. 3.8 for a range of plant communities, which have a decreasing growth rate at higher plant densities due to plant competition for resources and increasing respiration losses. High herbivore density S I Eî Plant biomass Low herbivore Figure 3.8 Rate of plant growth and consumption per animal as a function of plant biomass (from Noy-Meir). El to E4 denote equilibria of plant growth and consumption. Note that equilibrium El in Figure 3.8 is unstable since plant biomass below this value will be eaten to extinction, whereas at E2 to E4 increased consumption results in lower plant growth that feed backs to concomitantly lower herbivore density. 5. Identification and compilation 45

45 Indirect interactions: fodder, manure and animal traction The second type of plant-animal interactions refers to the indirect interactions between crop and livestock systems. These interactions act at the farm, and sometimes even at the regional level. The livestock is partly or completely detached from the grazing areas as can be the case at farm level, or livestock systems pre-dominantly using pastures (e.g. nomadism, transhumance) or that graze the aftermaths of crop fields during short periods in the year providing nutrients to the crops of arable farmers with their droppings (regional level). Furthermore, animals provide draught power to the cropping system to improve growing conditions and area of crop land that can be cultivated, and subsequently increase production of crops. Already since the first domestication of animals, these type of interactions exist between livestock en cropping systems. Straw and other by-products of arable production were used as feed for livestock. In reverse direction, manure and animal traction is used to improve cropping conditions. E.g. in the Middle Ages in Europe manure mixed with straw or heather was brought from the stable to the cropping land as fertiliser. In developing countries, the exchange of manure and draught power and feed between animal and plant production systems is still of the utmost importance. Chemical fertilisers and machines are often lacking or too expensive, whereas feed delivered by the cropping systems is often indispensable during parts of the year (tropical zones with extended dry seasons). Nowadays, in HEIA systems much nutrients are imported in feed stuffs, amongst them nitrogen. The increased N fluxes in the animal-plant interaction may lead to N excess in the soil and the soil-plant system, resulting in environmental pollution (nitrate leaching to the groundwater and volatilisation of nitrogenous gasses to the air). In general, losses can be reduced if the working coefficient of N of the manure is known for each particular soil-manure combination, and the N-manure application is tuned to the outputs in crop and environment. E.g. to produce 10 ton of grass containing 2.5% N and a fertiliser working coefficient of 0.7, the recommended fertiliser level should be 357 kg N ha" 1, assuming that no other N sources (mineralisation, deposition, etc.) are present. The working coefficient of N in manure, defined as the fraction of applied N that is harvested in the current year, differs between application techniques and soil type. An example is given in Table 3.3 for cattle slurry. Table 3.3. The N working coefficient (W?J of cattle slurry applied in spring, at different application techniques and soil types (from FOMA, 1994). QUASI Plant and Animal Production 46

46 Method Slurry injection Sand Loamy sand Sod fertilisation Sand Clay Peat Sod injection Sand Clay Peat Identification and compilation 47

47 Chapter 4. Quantification of inputs and outputs of a production activity: data handling and modelling tools 4.1 Actual or alternative production techniques? Information on inputs and outputs of actual and alternative production activities is collected differently. Actual production activities can be quantified on the basis of data from either land use surveys on current agricultural practices or experimental, well-monitored farms or stations. The produced output is de facto known, in contrast to the optional target level in alternative activities (or designs). For quantification of inputs and outputs of current ('actual') production activities, the current yield level ('target') and known inputs (derived from measurements, estimates, household surveys or information from extension officers) are used to determine input and output coefficients, for instance nutrient balances and biocide indices. It may well be, that data availability of actual systems is inadequate to fully quantify all their required inputs and/or outputs. Those inputs and/or outputs will have to be quantified then on the basis of general knowledge on the agricultural production process. For example, when only yields (economic products, i.e. grain or tuber yield) have been reported, yields of crop residue may be derived using general information on harvest indices. Actual production activities can be non-sustainable, for instance, nutrient balances can be positive (nutrient accumulation as for phosphorus in Dutch dairy farming systems) or negative (nutrient mining as for all macronutrients in agro-ecosystems in Sub-Saharan Africa). These balances are an unavoidable consequence of the current orientation and technology. Inclusion of such activities in the input-output matrix of static optimisation models limits its applicability, as selection of these activities in the activity mix implies that the quality of the resource base, that directly influences the values of the technical coefficients, changes. As a consequence, the technical coefficients also change. Hence, such a matrix is only valid for a limited period of time in the future, and is therefore not suitable for explorative land use analyses. Mechanistic modelling may be required to generate technical coefficients for which no experimental data are available. Alternative, future-oriented production activities are suggested by a joined effort of agronomic experts and stakeholders, based on what is possible on theoretical and productionecological grounds rather than on what is currently happening on-farm.. The inputs and outputs, i.e. technical coefficients, of such activities can be quantified by statistical data, experimental data, expert knowledge and with the aid of mechanistic modelling (see Figure 4.1). Underlying information required to quantify technical coefficients consists of a mixture of process-based knowledge of physical, chemical, physiological and ecological processes involved, empirical data and standard data with respect to agronomic and livestock QUASI Plant and Animal Production 48

48 relationships. Often, both knowledge and data required to quantify relevant processes are incomplete as a consequence of, for example, inaccuracy of measured data, the stochastic nature of some data, or incomplete process knowledge. Therefore, uncertainty analysis is required to evaluate the validity of the conclusions (see 4.11). Expert estimates are commonly needed to complete knowledge and fill data gaps. Figure 4.1 Scheme of operations to quantify the inputs and outputs of a specific production activity. In Chapter 4 all the steps for quantification are treated, in Chapter 5 TCG computer software is presented First, we will present the approach to quantify inputs and outputs for actual production activities. Those data may later on in the exploration be referred to as the reference situation. An improvement in the current production technique, for instance by introduction of another crop variety or animal breed, is, by definition, not an alternative technique but an intervention in the actual activity. The prediction of the effect of such an intervention on inputs and outputs is addressed in 4.2 as well. In a following paragraph ( 4.3) we discuss the nature of the information sources for quantification of inputs and outputs of alternative activities. Since not all inputs and outputs can be treated here, a selected number is further elaborated in succeeding paragraphs, and a list of available studies on (some) other inputs and outputs is given. Finally ( 4.11), uncertainty analysis is proposed as a 5. Identification and compilation 49

49 method to evaluate the effect of the information source on the results. In Chapter 5, the concept of the TCG (see Fig. 4.1) is presented as a method to automate the repetitive calculations required to generate a range of TC values, that are associated with varying production environments and target levels, with the aid of computer software Collection of input-output data for actual production activities Nowadays, many databases have been established on arable and livestock production. This information is certainly useful for understanding the system at hand, but for complete, explicit quantification of input-output relations generally not enough data are available. In most cases, the scientist should carry out additional inventories. In Western countries, monitoring of data on agricultural production systems at farm level is a regular component of management practices. On the one hand, the data on crops and/or livestock and other farm components are systematically collected for the benefit of the farmers themselves to monitor and direct energy, nutrient, other material and economic flows. On the other hand, institutes such as LEI (the Dutch Agricultural Economics Research Institute), on a routine basis, monitor such information for a representative sample of the Dutch agricultural sector, to maintain databases that can be used to derive overviews of Dutch agriculture, and keep track of developments in the agricultural sector. These databases are also used for analyses of the sector as a basis for policy advice for the Government. Access to such information is relatively easy and (part of) the information is frequently published. In tropical countries, availability of farm household data is, very often, a major constraint for analysis of the agricultural sector. Monitoring farm data is not a regular practice and bookkeeping is not required. Most complete input-output data are collected at experimental stations, but those are generally not representative for actual farming practices. Hence, for information on current farming practices, use must be made of results of farm household surveys that (very often) are (or have been) carried out in the framework of specific research projects. A serious constraint is, that such information remains generally in the 'grey circuit' and is hardly accessible, so that each project tends to start its own data collection component. This phenomenon is aggravated by the fact that in most developing countries the 'institutional memory' is rather short, if the data have not already been 'safeguarded' by the expatriate expert working in the project. Moreover, collection and classification of information is often strongly geared to the specific purposes of the project in which they are collected, and may therefore not be suitable for land use analysis. A rather complete list of information requirements for land use planning purposes, at different spatial scales has been provided by Fresco et al. (1992). For identification of the relevant inputs and outputs in a particular current situation, it may be advisable to consult with local stakeholders, who are familiar with the particulars of the local farming systems. Results of farm household surveys may be also useful, as they can give indications of the types of inputs and outputs considered relevant by QUASI Plant and Animal Production 50

50 the fanning community, and that may not be evident without detailed knowledge of the system. An interesting point in case was encountered in a study on the use of crop residues in mixed crop-livestock systems in Burkina Faso (Savadogo, 2000). From consultations with stakeholders and results of farm surveys it appeared that ownership of a cart was a decisive factor in crop residue management. Hence, for analysis of the possibilities of optimising crop residue use, the cart was an indispensable input. Interventions with other production techniques or plant/animal types may show the possibilities of the current production system to improve its performance. For instance the introduction of crossbred cows in dairy farming systems in India has almost doubled milk production (Patil and Udo, 1997). Yet, this system reflects only a small improvement of the current situation relative to an "alternative" production activity that will drastically differ from the current one (see 4.3). In designing the data collection method, much depends on the complexity of the household economy. A smallholder enterprise in the medium-potential area of Kenya (with, for example a two acre field around the compound of a nuclear family) is much easier to monitor than an enterprise of an extended family (easily comprising several dozen members) in Southern Mali. In the latter case, the division of labour within the family (and between the sexes), and the fragmented mixed cropping system, make quantification of production activities a formidable (if not impossible) task. With respect to the (spatial) scale of data collection, it might be worthwhile to draw attention to the 'funnel-principle' advocated by Fresco et al. (1992): most data collection exercises start at the broad (national, regional) level, and move towards a narrow focus (farm households or individuals) Information sources for quantification of inputs and outputs of alternative activities At the moment an alternative activity is suggested and formulated by the stakeholders, the agronomic experts are challenged to retrieve as much information as possible on the relevant inputs and outputs. Even when the suggested activity has been (or is being) practised elsewhere, the conditions of the alternative may differ with respect to physical environment and production orientation. Quantification has to be based then on a range of information sources, from mechanistic or empirical modelling, experimental data to expert judgement and statistical data. Principles of models for quantifying plant and animal production have been presented in Chapter 3. For a considerable number of crop species, biophysical process knowledge is translated into, more or less, mechanistic models (see Section 4.4). Such models may yield input and output data of a more general applicability than empirical data, that are often only valid for rather specific conditions. Under controlled conditions (irrigation to meet water requirements of the crop, and optimal nutrient supply to fulfil nutrient demands), 5. Identification and compilation 51

51 simulation of growth on the basis of growth defining factors (see Chapter 2) yields realistic results. So, for specific production orientations, such as potential and water-limited production at optimum nutrition, mechanistic models can simulate a target output and associated input requirements. Yet, mechanistic models are only part of the tools to determine inputs and outputs of production. They must be supplemented with other sources of information to establish inputs and outputs of alternative production activities. Agro-ecological process knowledge can also be used pragmatically, not implemented in computer models, but as calculation rules. For instance, data on the relation between temperature sum and plant emergence, or the maintenance requirement of a not-lactating cow, are understood from biological process knowledge but quantified empirically. For innovative production techniques, information from experimental trials is often indispensable. If experiments are set up properly, simple quantifications may be possible and, for extensive trials, data may serve to parameterise and validate simulation models. For example, growth and yield data of a crop growing at optimal water and nutrient supply by means of irrigation and fertilisation, can be related to daily temperature and radiation levels (cf. Nix, 1976; Ortiz-Monasterio et al., 1994). On the basis of such a calibrated relationship a rough quantification of crop growth can be made for other weather conditions. This represents an empirical model that, for example, assumes that yield is linearly related to the ratio of incoming radiation and mean daily temperature (above a base temperature), without taking into account differences in assimilate partitioning. Although theoretically often inferior, often statistical data are used to quantify alternative production activities. In most countries economic yields of (the most important) crops (in terms of dry matter) are being recorded on a routine basis, albeit methods may vary strongly (Supit, 2000). The associated production technique is mostly indicated in general terms (for instance, irrigated rice or biologically produced potatoes). The information on applied inputs and boundary conditions varies per country. In The Netherlands, fertiliser, fuel, labour and monetary inputs per hectare are mostly known. Such a database may serve to investigate e.g. time trends in production, fertiliser use efficiency (if nutrient content of economic yield and crop residues is known) or annual variation. The statistical data may contain important information on production ceiling and yield gaps that may be used in production-ecological modelling. Quantification of crop production and the input requirements is solely possible for the same production situation (and taking into account differences due to weather variations). Quantification of growth for other techniques and situations will require either on-farm trials or simulation modelling. For livestock production activities, predictions and explorations are predominantly based on empirical and statistical data. No matter which approach we use (dynamic model, regression or otherwise), the applied relationships are always based on empirical evidence and are thus validated for the measured circumstances only. Purely analytical models do exist, but are only valid for the experimental, controlled conditions they were made for. For example, the model on N flows in dairy cattle (van Straalen, 1995) has been calibrated using QUASI Plant and Animal Production 52

52 measurements on fistulated cows, but model extrapolations to other cows were not feasible. Yet, some well-established feed recommendations (DVE/OEB system, cf. Tamminga et al., 1994) have been developed, using the models' relationships in combination with generally accepted empirical data. Although in theory alternative activities are fully based on production ecological principles and process knowledge, in practice often pragmatic approaches are used, simply because not all knowledge or data are available. In the paragraphs below, a number of pragmatic approaches to quantify common outputs or inputs are presented as an example. As a basis, always a target output or input is defined which is associated with a distinct production orientation. Table 4.1 summarises a greater number of approaches for quantification of a wide range of inputs and outputs for various production environments Potential and water-limited crop production For several production environments, potential yield of crops and harvestable product is known for quite a number of crop species, based on experimental or empirical evidence. In situations where the growth-defining factors do not differ too much from the measured situation, such empirical data can easily be extrapolated. Nowadays, more reliable and sophisticated estimates can be achieved by the aid of computer modelling. A large number of mechanistic models is available for quantification of potential and water-limited production. These models are explanatory in nature and simulate crop production on the basis of driving variables and system status. The driving variables are external to the system (radiation, temperature, water and nutrient inputs, soil physical conditions) and are not affected by its behaviour. The state of the system is simulated for a particular point in time (characterised by such variables as leaf area index, dry weight of the stems, rooting depth, etc.). Such models use equations that describe the rates of change of system states at that particular moment on the basis of the interactions between the external variables and the system states (this approach is often referred to as the 'state variable approach'). In the last decades many such models, either for specific crops or of a more generic nature have been developed. In the footsteps of De Wit, in Wageningen, a wide variety of simulation models has been developed (Bouman et al., 1996). An example is a, relatively, simple model at crop level, called SUCROS (Simple and Universal CROp growth Simulator), that has been and is being widely used (cf. van Laar et al., 1992; Spitters et al., 1987; van Keulen and de Milliano, 1984). Table 4.1 A list of pragmatic approaches and their literature sources to quantify inputs and outputs of alternative production activities Inputs Outputs Literature source Potential crop proc uction Water, nutrients, labour, biocides Yields and N-loss De Koning et al., I/O coefficients for Ground for Choices 5. Identification and compilation 53

53 Nutrient-limited crop production Nitrogen, phosphorus Nutrients, soil Yields, crop residues Yield, nutrients Van Duivenbooden et al., 1991 Janssen et al., QUEFTS Crop production affected by growth reducing factors Biocides, fertilizer, labor Bulb yields, residues of biocides and nitrogen Rössing et al., Flower bulb production Animal production Feed, labour Feed, labour Feed, labour Milk, meat, manure, animals, traction Milk, meat, animals Meat, manure Hengsdijk et al., A TCG for land use activities in the Koutiala region of South Mali Bouman et al, TCG PASTOR (Costa Rica) Savadogo, 2000 Crop and animal production and their interactions Nutrients, crop residues Nutrients, crop residues Soil conservation measure, nutrients, labour, traction Crop residues, nutrients (N, P) Soil nutrients and organic matter balance Soil nutrients and organic matter Yields. Soil and nutrient losses Yields Sissoko, A future for Agriculture? Van Keulen, Sustainability and long-term dynamics of soil organic matter and nutrients under alternative management strategies Lu Changhe, Struif Bontkes, Modelling the dynamics of agricultural development: a process approach. The case of Koutiala (Mali) Employment Labour Labour Yields Yields Van Rheenen, Farm household level optimal resource allocation (Java) Mohamed, An integrated agro-economic and agroecological framework for land use planning and policy analysis WOFOST (an acronym of WOrld Food Studies; Boogaard et al., 1998) is a SUCROS related model calibrated and validated for a wide range of crops and conditions. A more simplified approach is used in LINTUL (Habekotté, 1996; Kooman, 1995) based on light interception and utilisation. In modelling crop growth and dry matter production of various plant parts for potential production (not limited by availability of water and nutrients, and in the absence of effects of weeds, pests, diseases and polluting factors), the Wageningen models all use a similar approach. Under a given regime of daily radiation and temperature, the growth defining factors (Chapter 2), a crop will grow at a rate determined by species (or variety)- specific characteristics, such as assimilation capacity, maintenance requirements, assimilate partitioning to various organs and chemical composition of the growing material. The basic QUASI Plant and Animal Production 54

54 processes have been described in Chapter 3. The simulation models thus require daily climate data, and crop characteristics. The model WOFOST (Boogaard et al., 1998) simulates the above mentioned processes and is illustrated in Fig The driving variables (such as daily radiation and precipitation) determine the rate of internal processes (like growth, water flow), which are depicted as circles in Fig The state variables are quantities or pools within the system (such as biomass or amount of soil water) and are shown as boxes. In addition to a crop growth submodel, WOFOST includes a submodel on soil water dynamics, as described below. At each time step, data are exchanged among the submodels. The crop growth submodel is similar to the SUCROS model (Van Laar et al., 1992) and simulates the ecophysiological processes, as described in Chapter 3. The model's major processes are phenological development, C0 2 -assimilation, respiration, partitioning of assimilates to the various organs, and dry matter formation (see Fig. 4.2). In the model, they are described as functions of the most essential exogenous characteristics of the system, i.e. radiation and temperature. For calculation of water-limited yields, in addition to radiation and temperature, rainfall, air humidity and wind speed are read from the weather tables, and the physical properties of the soil are read from the soil data files. On the basis of weather conditions and crop status (leaf area index), potential canopy transpiration (the loss of water by the canopy when stomata are open all day to allow unrestricted transport of carbon dioxide) is calculated according to the Penman formula. A crop factor is used to account for differences in Penman transpiration among crops. The results of the daily soil water balance calculations (input from rain and/or irrigation, water retention of the selected soil type and output in transpiration, soil evaporation and percolation) provide information on the amount of water available for uptake by the vegetation. When available water exceeds crop water requirements (potential transpiration), the crop transpires at the potential rate 2 and gross assimilation rate equals potential gross assimilation rate. When available water falls short of crop requirements, the crop transpires whatever can be taken up, and gross assimilation is reduced in proportion to the reduction in transpiration (i.e. Ta/Tp (Chapter 3), ratio actual transpiration/potential transpiration). Figure 4.2 General structure of a dynamic, explanatory crop growth model (see Boogaard et al, 1998) 5. Identification and compilation 55

55

56 WOFOST was applied to calculate potential and water-limited production in the European Union, as a basis for the IMGLP study Ground for Choices (Van Lanen et al., 1992; De Koning et al., 1992; 1995). The calculated production levels were the result of a combined qualitative and quantitative land evaluation. In the quantitative land evaluation, soils non-suitable for mechanised cultivation for three crop types (grass, cereals and root crops, applying increasingly severe criteria for soil suitability) were eliminated. In the quantitative land evaluation, thematic maps of the European Community ('the 12') for climate, soils and administrative region were combined, to define about 4200 Land Evaluation Units, each comprising a unique combination of these characteristics. Soil characteristics, such as texture, slope, availability of groundwater and occurrence of phases were derived from the 1 : 1 million EC soil map. Weather data (radiation, minimum and maximum temperature, vapour pressure, precipitation and wind speed) were obtained from 109 weather stations and assigned to climatic zones. Administrative regions comprised the 58 NUTS-1 regions of the EC. For the suitable LEU's yield potential and water use were determined using the WOFOST model. Depending on the length of the available weather records, series of 26 years for most climatic zones were calculated and averaged. From the basic weather data, for each climatic zone the long term average annual precipitation surpluses and precipitation deficits were derived on the basis of monthly water balances. Quantitative production estimates were thus obtained for grass, wheat, maize, sugar beet, potato and oil seed rape. For each of the crops, simulated output comprised, both for potential and water-limited conditions: dry weight of stems, dry weight of leaves, dry weight of storage organs (not for grass) and water use by the crop. Often not enough detailed data are available on crop characteristics and physical environment to use crop growth models. Therefore, criteria to select-tools for quantification of alternative crop production activities will be rather pragmatic: if data are missing to run an elaborate model, empirical data or expert knowledge have to be used. In the explorative land use study for the Sudano-Sahelian zone of Mali (Bakker et al., 1998), water-limited yield estimates were based on relationships between crop transpiration, vapour pressure deficit and yields (Tanner & Sinclair, 1983). Water availability for crop transpiration was based on an elaborate analysis of the water budgets of land use systems including run-off, percolation and evaporation (Quak et al., 1996). Run-off was estimated using intensity and duration of rainfall showers and soil surface storage capacity. Effects of soil and water conservation measures were taken into account, for example, different types of ridges constructed for the purpose increased surface storage capacity. Percolation, i.e. the amount of water lost to soil layers below the rooting zone of crops, was determined according to an empirical equation of Breman & de Ridder (1991). Evaporation, finally, was based on the potential évapotranspiration calculated for ten-day_periods and the development of canopy cover (leaf area) in the course of the growing season. The vapour pressure deficit, i.e. the difference between saturated and actual vapour pressure was calculated according to Goudriaan (1977) QUASI Plant and Animal Production 56

57 for ten-day periods. Subsequently, the estimated water-limited yields were reduced to account for unavoidable losses due to diseases and pests, sub-optimal water supply due to local variability and lack of timeliness. These reduction factors are crop-specific and based on expert estimates. 4.5 Nutrient inputs and outputs in crop production As for dry matter production data, for a rather large number of Dutch farms also nutrient inputs and outputs of cropping systems are reported annually (Landbouwstatistieken, PAV, LEI). It is very likely, that the farmers applied more than enough fertilizer to their fields, i.e. the production was not nutrient-limited. The yields may therefore be used in explorative studies on potential and water-limited production, but the nutrient outputs may well be more than strictly necessary because of over-supply of nutrients. Any extrapolation to other physical environments or to other input levels is however not valid. For example, halving the level of fertiliser input will not per se result in a 50% decrease in yield and associated nutrient output, since the level of overfertilization and the contribution from natural sources (mineralisation from soil organic matter, deposition) is not known, nutrient recovery may not be linear with application rate, nutrient concentrations may increase with yield level, etc. A model based on carbon assimilation, such as WOFOST, can be used to quantify nutrient requirements. First, WOFOST calculates potential and water-limited yields on the basis of carbon assimilation. Subsequently, it calculates nutrient-limited yields according to a 'static' procedure. This procedure is largely based on the QUEFTS-approach (Quantitative Evaluation of the Fertility of Tropical Soils, see Janssen et al., 1990). In the QUEFTS module in WOFOST ; a user-defined nutrient supply per growing season is introduced. In the default situation it uses the measured nutrient uptake of an unfertilised, rain-fed maize crop on an African soil. For other soils and crops the nutrient supply from the soil has to be inputted by the user. Nutrients dealt with are the macro-nutrients nitrogen (N), phosphorus (P) and potassium (K). Two different procedures can be distinguished: (1) for water-limited and potential production a nutrient demand is calculated that should be satisfied by supply from natural sources, mainly soil organic matter, supplemented by fertiliser (= target-oriented), and (2) for nutrient-limited production, yield is calculated from nutrient availability from natural sources only (input-oriented). For water-limited and potential production, the quantities of the three elements required for realisation of the calculated yields are derived from the nutrient contents of the crop biomass. After subtracting the supply from natural sources, fertiliser requirements are derived on the basis of exogenously supplied fertiliser recoveries. These recoveries are defined on the basis of knowledge about fertiliser management strategy, and soil type. For the calculation of nutrient-limited yield, the uptake of each nutrient is calculated on the basis of the potential supply of that nutrient from natural sources, taking into account 5. Identification and compilation 57