Simulation of forage management strategies considering farm-level land diversity: Example of dairy farms in the Auvergne

Computers and Electronics in Agriculture 55 (2007) 36–48

Simulation of forage management strategies considering farm-level land diversity: Example of dairy farms in the Auvergne Nadine Andrieu a,∗ , Christophe Poix b , Etienne Josien c , Michel Duru d a CIRAD, TA 60/15, 73 rue Jean Fran¸ cois Breton, 34398 Montpellier Cedex 5, France UMR METAFORT, ENITA de Clermont-Ferrand, Site de Marmilhat, BP 35, 63370 Lempdes, France c CEMAGREF, Dynamiques et Fonctions des Espaces Ruraux, 63172 Aubi` ere Cedex, France UMR 1248 INRA-ENSAT, Agrosyst`emes Cultiv´es et Herbagers, BP 27, 31326 Castanet-Tolosan, France

b d

Received 20 May 2005; received in revised form 21 November 2006; accepted 21 November 2006

Abstract Land diversity is a characteristic of low-input farming systems. Land diversity can refer to between-field diversity of grassland vegetation types that are a result of management practices (fertilisation, grazing, cutting), and environmental factors (altitude, aspect, soil water capacity) that have an influence on herbage production. Land diversity can also concern other characteristics of the field, like its distance to the cowshed or its suitability for mechanization for hay-making that are key characteristics affecting the spatial organization of management practices. The purpose of this study was to evaluate, using a simulation model, the consequences for yield of taking the land diversity at farm level into account in the decision-making process. We made the assumption that it could be an asset rather than a constraint for the management of the forage system, and that it could lead to an improvement in the forage balance in dairy farming systems. The model is able to simulate the decision-making process in managing this diversity during a growing season. The decision process was translated into three management strategies of the forage system representing increasing consideration of the farmland diversity. We then simulated these three strategies for two weather series and two farmland diversity levels. We noticed that the strategy giving the most consideration to the farmland diversity improves the forage self-sufficiency (due to higher yields and quality of forage supplies, and a decrease in the amount of hay fed during the grazing period). We thus concluded that the management of between-field diversity could be an asset for extensive farming systems. In the context of extensification of agriculture and increasing concern about its environmental impact on biodiversity, this approach must be pursued. The oriented-object structure of the model will facilitate improvements. © 2006 Elsevier B.V. All rights reserved. Keywords: Simulation model; Farmland diversity; Forage practices; Livestock dairy farm

1. Introduction Since the beginning of the nineties, agricultural policies have supported agricultural extensification either for environmental benefits or to reduce overproduction (Bischoff and Mahn, 2000; Tomasoni et al., 2003; Pacini et al., 2004). These policies have led to an increase in the area of farmland, a reduction in the amounts of inputs used per hectare ∗

Corresponding author. Tel.: +33 4 67615555; fax: +33 4 67614415. E-mail address: [email protected] (N. Andrieu).

0168-1699/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2006.11.004

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and consequently less standardization of the fields. Thus, new research questions have emerged about the management of the diversity of the internal resources of farming systems, particularly in the case of livestock farms (Van Keulen, 2006). In these systems, the farmer has to manage the “forage system”, i.e. a set of means and techniques intended to link cropping and livestock systems (Duru and Hubert, 2003). Many studies have been carried out on the management strategies of the forage system in dairy systems considering the land diversity. Some studies focusing on between-field diversity of topological characteristics (distance to the cowshed, suitability for mechanization for hay-making) have shown how they can influence the spatio-temporal distribution of the practices (cutting versus grazing) and, sometimes, be a constraint (Gibon et al., 1989; Morlon and Benoit, 1990). Others, focusing on the between-field diversity of grassland vegetation, have shown the benefit of having functionally diverse species for their tolerance of the climatic constraints and the variable rates of animal intake which they allow (White et al., 2004). In other words, in this case, the between-field diversity is an asset. However, these authors did not quantify the effects of this diversity on yields and did not take into account the differences of altitude or aspect which can also play a role (Andrieu, 2004). In our study, we tested the assumption that the between-field diversity is an asset rather than a constraint for the management of the forage system, allowing forage production in dairy farming systems to be improved. We thus tried to evaluate how taking farmland diversity into account could influence the balance between the supply and demand of forage that we called forage balance. We considered between-field differences in grassland vegetation types, altitude, aspect or soil water capacity but also the differences in distance or suitability for mechanization for hay-making. In other words, we studied the main characteristics that could play a role in forage management practices. To carry out this study, modelling appeared particularly relevant. Indeed, it allows the expected yields from wide range of alternatives under a wide range of weather conditions to be quickly tested (Hansen, 2002), what would be slow and expensive with field experiments or farm management surveys. Models that are capable of simulating the management of forage systems are numerous (Cacho et al., 1995; Col´eno, 1997; Kristensen et al., 1997; Cros et al., 2001; Romera et al., 2004). They simulate, in a more or less detailed way, the animal and/or plant components of the forage system. However, none of these models accounts for the role of farmland diversity on the production of herbage and on forage management decisions. We therefore built our own model of the forage system in order to represent the farmland diversity and to simulate the effect of the decision rules on the forage balance. The resulting model was for research, since its objective was not to help farmers’ decision-making (Cox, 1996) but to improve the knowledge of the forage system while allowing the study of scientific hypotheses (Bywater and Cacho, 1994; Boote et al., 1996). In this article, we present the structure of the model. Then we show how this model allowed different strategies to be tested for taking farmland diversity into account. 2. General structure of the model For grass-based dairy farming systems, the model simulates dynamically, at a daily time step, the farmer’s decisions on the forage system management during the growing season. These decisions are a function of weather conditions, the characteristics of the fields of the farm, and of the herbage growth. The model was aimed to produce long-term simulations of the dynamic interaction between weather variations and farm management at daily, seasonal and annual scales in order to compare different management strategies. Fig. 1 shows the general model architecture. This model links a biophysical sub-model with a decision making sub-model. The biophysical sub-model simulates the production of herbage according to the farmland diversity, weather data and cultural practices. For its construction we largely took as a starting point the work by Cros et al. (2003). The decision-making sub-model allows different management strategies of the forage system during the growing season to be simulated, taking weather data and farmland diversity into account. The decision-making sub-model was based on enquiries and of follow-up of practices from a network of 26 dairy farmers in central France (Andrieu, 2004). We used an object-oriented analysis which is the most suitable to account for complex systems (Coquillard and Hill, 1997; Keating and McCown, 2001). We must define more precisely the words “class” and “attributes”, used in the rest of this paper. In object-oriented analysis (and programming), objects are made of combinations of code and data. They are built by using classes. These classes act like templates or blueprints. Their task is to represent the functionality of the objects. In this context, code is named “methods” and data is named “attributes”. Python (www.python.org) was used as the implementation language. Python is an interpreted, interactive, object-oriented programming language that runs on many operating systems (UNIX, Windows, MacOS, etc.) with minimal hardware configurations.

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Fig. 1. Unified modelling language class diagram of the model showing the relationship between the main classes, their attributes and operations. Each frame represents a class; the upper part of the frame gives its name; the intermediate part, its attributes, and the bottom part its operations.

2.1. The biophysical sub-model The biophysical sub-model is described by four classes: field, herbage growth, cattle, weather. 2.1.1. Field class The field class represents the diversity of the characteristics of a field. A field is described by seven major attributes. Some of them influence, simultaneously, the herbage production and decisions, e.g. altitude, aspect, soil water capacity, type of vegetation. Others influence decisions only, e.g. the distance to the cowshed, the suitability for mechanization for hay-making and the ability of the soil to carry grazing animals. Three modalities of the aspect can be described: a field can be “flat”, “south” or “north” oriented. Two modalities are possible to describe the soil’s ability to carry grazing animals: “carrying” or “not carrying”, bearing in mind that this state may change in the course of the simulation. The vegetation of the field can belong to one of five types. Each type corresponds to a multi-specific herbaceous cover composed of species having common biological characteristics (leaf lifespan, phenology) and reflecting specific adaptation to practices of defoliation and levels of availability in nitrogen and phosphorus (Cruz et al., 2002; Ansquer et al., 2004). Thus type 1 corresponds to grasslands composed of species such as perennial ryegrass (Lolium perenne L.), orchardgrass (Dactylis glomerata L.) that flower early in the season, growing in nutrient-rich environments due to high fertilisation rate. Conversely, type 5 includes species such as matgrass (Nardus stricta L.), wavy hairgrass (Deschampsia flexuosa L.) and some bushes that flower late in the season and are characteristic of nutrient-poor environments. The field class fulfils three main functions: it returns to the other classes of the model the values of the different attributes, calculates whether the soil can carry grazing animals and estimates the hay quality. The values of the different attributes of the “field” class are used by the “herbage growth” class and by the classes of the decisional sub-model. The herbage growth class uses the values of the altitude, aspect, soil water capacity and type

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of vegetation attributes to simulate the herbage biomass (Section 2.1.2). The classes of the decisional sub-model use these same values but also the values of the distance, the suitability for mechanization for hay-making and the ability to carry grazing animals to simulate the forage practices. The class calculates the ability of the soil to carry grazing animals according to the following rule: if B(j) > SWC for three consecutive days, then the field is not carrying with (1) B(j) = R(j − 1) − AET(j − 1) + AW(j − 1), (2) AET(j − 1) = min(PET(j), AW(j)), (3) AW(j − 1) = min(B(j − 1), SWC). B is the water balance; R the rainfall; AET the actual evapo-transpiration, PET the potential evapo-transpiration; AW the available water and SWC the soil water capacity. The class also calculates the quality of the hay harvested. This quality depends on the phenological stage at harvest time. Phenological stages are calculated from 1 February, i.e. the average starting date of vegetative growth in the region of study, and expressed in degree-days (accumulated average daily temperatures over a given period, base 0 ◦ C). They depend on the type of vegetation (Ansquer et al., 2004). Thus, quality is estimated roughly by three categories:“good ”, “average ” or “poor” “. Quality is • good: if the forage is harvested 200 degree-days before flowering; • average: if it is harvested between 200 degree-days before flowering and flowering; • poor: if it is harvested after flowering. 2.1.2. Herbage growth class This class is closely connected to the previous one and simulates the growth of the herbage as a function of the characteristics (altitude, aspect, soil water capacity, vegetation) of each field. We used the equations of the herbage growth model by Cros et al. (2003). This model was selected because it uses a limited number of parameters and takes account of various defoliation regimes, growth and senescence processes being dissociated. However, we adapted this model to our study. In its initial version the model did not allow representation of the diversity of altitude, aspect and vegetation, and simulated only vegetative regrowths. In order to take these characteristics of the field into account we have modified it from studies on the effect of aspect and altitude (Legros et al., 1997) and of vegetation types and the reproductive phase in spring (Calvi`ere and Duru, 1999; Duru and Ducrocq, 2002; McCall and Bishop-Hurley, 2003; Ansquer et al., 2004). 2.1.3. Cattle class The cattle class simulates the animal intake of conserved forage or grazing herbage and is described by the size of the herd and its location (on a given field, in the cowshed). We assume that the herd is composed of identical animals. Each animal consumes, every day, 15 kg of herbage dry matter (DM) (Cros et al., 2003). The kind of feed consumed depends on the location. If the herd is in the cowshed, then the hay is eaten, regardless of its quality. On the other hand, if the herd is grazing, two scenarios are possible: • The herd grazes exclusively grass if the available biomass in a given field is sufficient to feed the cows and higher than a residual biomass (herbage mass left after grazing) of 125 g DM m−2 . This amount is the minimal biomass that can be left after grazing because it is an optimum maximizing net herbage growth (minimizing losses by senescence; Parsons, 1988) and intake of cows (Mayne et al., 1987). A lower value (linked to a higher intensity of grazing) leads to an increase in losses by senescence and consequently a lower growth after defoliation. This value of 125 g DM m−2 has been considered to be acceptable in earlier models (Col´eno and Duru, 1999). • If the available biomass is more than a residual biomass of 125 g DM m−2 but insufficient to feed the cows, some hay is given to fulfil the animals’ requirement. The hay consumed by the herd can come from the farm or be purchased if necessary.

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2.1.4. Weather class This class provides and maintains the weather information entered by the user as a text file. This information drives the simulation. The main function of this class is to return for a given day in the list of daily weather data created by the class, its temperature, rainfall, potential evapo-transpiration or radiation that are used by the “herbage growth” and “field” classes or the classes of the decisional sub-model. The “weather” class can also calculate the sequence of days without rain that will trigger decisions on the harvest of a field (Section 2.2.1) or accumulate average daily temperatures over a given period, useful to estimate phenological stages for the “herbage growth” or “field” classes. 2.2. Decision-making sub-model The decision-making sub-model mimics in a simplified way the management decisions of the forage system. To mimic these decisions we chose a formalism based on the work of Col´eno and Duru (1998). The forage system is seen as a set of forage production units (the grazing production units for the different batches of animals and the conserved forage production units), to fulfill the animals’ requirements. A forage production unit is defined by Coleno and Duru as a spatio-temporal management entity, being characterized by specific practices and know-how to achieve a yield objective that is a function of animal requirements. The growing season is structured in three sequences: spring, summer and autumn. For each sequence, the virtual farmer designs and manages specific practices and consequently specific forage production units. Grazing and conserved forage production units for spring, summer or autumn are thus distinguished. In order to manage each production unit, the virtual farmer uses dimensioning and scheduling rules (Col´eno and Duru, 1999). Rules of dimensioning allocate an amount of resources to the forage production unit (in the study, the considered resource is the “land resource” or, in other words, the fields), a calendar period and a duration. The rules of scheduling control the order in which the fields are to be used within the production units. These rules address two different levels of decision-making: planning by the virtual farmer on a seasonal or annual scale of the organization of the practices to be followed during the growing season as a function of production objectives (Aubry et al., 1998), and monitoring on a daily scale (Chatelin et al., 1993), i.e. implementation of planning according to the weather conditions of the growing season that will determine the effective organization of the practices. In the two following sections, we describe the contents of these rules. 2.2.1. Dimensioning Dimensioning rules determine decisions on: • The size of each forage production unit as a function of animal requirements for the growing season and the winter. • The fields to be chosen according to their attributes. Thus, a strategy can consist of assigning the fields closest to the cowshed to a given production unit or the most productive ones as a function of their altitude, aspect, vegetation and soil water capacity. In this last case, to sort the farm fields and because each field has a specific combination of attributes liable to influence productivity; a system of classification was established according to the criteria or productivity. This system of classification gives a score to each field attribute, their sum making it possible to score the field according to its combination of attributes. The fields having the best scores are assigned to the production unit. • The triggering indicators of the production unit. They vary from one production unit to another. For example, to start the first cut on a field assigned to the conserved forage production units, it is necessary to satisfy three indicators: the phenological stage (300 degree-days before flowering), a minimum of herbage allowance (3 tonnes DM/ha), a minimal sequence of days without rain to allow the farmer to mow (4 days). In the case of grazing, the start is triggered by the turnout date, which depends on the ability of the soil of the fields to carry grazing animals and on a herbage availability allowing a minimum of 15 days of grazing for the herd. • Buffer fields, i.e. a reserve area to cope with weather variations. The final assignment of these fields to a given production unit is fixed only in the course of the growing season according to its level of production and its ability to fulfil the animals’ feed requirements. The weather characteristics, by activating the indicators triggering the events, monitor dimensioning and thus determine the effective attribution of the fields to the production units and in particular that of the buffer fields.

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2.2.2. Scheduling The scheduling rules organize, for a given production unit, the order of use of the fields according to their characteristics, exit or entry indicators (grazing) or material and labour constraints (conserved forage production units). During grazing, the herbage availability to feed the herd for one additional day on the currently grazed field or on the next field to be grazed controls the field entry or exit. The time step of the model being a day, this rule limits the amount of hay eaten while grazing. The material and labour constraints are represented by a maximum number of fields that can be harvested per day. The choice of the next field to be grazed (or to be harvested in the case of the conserved forage production units) depends on the strategy tested. Thus the next field can be selected according to its distance from the cowshed, its productivity or earliness of grass growth in spring using a system of classification as described for dimensioning. The weather also monitors scheduling and determines the effective use order of the fields within the production unit. 2.3. Model inputs The model uses three kinds of input: a weather database, a management strategy and a set of farm characteristics. 2.3.1. Weather data The weather is specified in a text file containing for each day, the following information: • • • •

minimum and maximum temperatures (◦ C); global radiation (MJ m−2 ); rainfall (mm); potential evapotranspiration (mm).

2.3.2. The strategy The rules characterising a strategy are stored in a file. They can model the management decisions of the forage system according to the general framework described in Section 2.2. Parameters describing the different rules of dimensioning and scheduling can be modified e.g. the value of the triggering indicators of the events. 2.3.3. The farm characteristics The model user must enter: • the initial stock of hay in the building and the maximum stocking rate; • the number of animals; • the different fields, each being described by 10 attributes: ◦ some attributes influencing herbage production and decision rules e.g. altitude (m), soil water capacity (mm), type of vegetation (1–5 for the five types characterised), aspect (0, 1 or 2, i.e. flat, south or north); ◦ some attributes influencing only decisions e.g. distance from the cowshed (m), suitability for mechanization (yes or no), ability of the soil to carry grazing animals (yes or no); ◦ other attributes arising from internal functions of the model e.g. the name (a character string), initial herbage mass (g DM m−2 ), allocation of the field (grazing or conserved forage production units). Some of these field attributes (ability of the soil to carry grazing animals, initial herbage mass, allocation of the field for cutting or grazing) will vary according to the decision rules and weather conditions. Others are fixed. The initial stock of hay in the building initialises the simulation and then evolves. 2.4. Model outputs The outputs of the model can be plotted (e.g. the evolution of hay stocks in the buildings) or exported as four types of files, which can then be processed using a spreadsheet (e.g. Excel):

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• The “General” file contains the daily simulated herbage mass produced on each field. • The “Detail” file is useful to the validation of the herbage growth model. This files contains, for each day and field, the simulated herbage growth and senescence rates, and other attributes simulated by the herbage growth model. • The “Activities” file contains daily simulated data like the amount of hay harvested, purchased, eaten during the grazing sequences, or during winter, the location of the herd, etc. • The “Calendar” file presents the daily use of the fields; the amount and the quality of the forage harvested are indicated. 3. Application of the model 3.1. Description of the experiment To study the consequences of the management of the farmland diversity on the forage balance, we studied three strategies corresponding to an increasing consideration of the between-field diversity in the dimensioning and scheduling decisions. We compared a control strategy with two improved strategies. The control strategy corresponds to a management strategy where dimensioning or scheduling rules take into account the between-field diversity of distance to the cowshed or suitability for mechanization. This component of diversity is normally taken into account by dairy farmers. In this control strategy, to decide on the number or nature of the fields allocated to the different forage production units (dimensioning rules), as well as on their order of use in each production unit (scheduling rules), the simulated farmer considers the distance from the cowshed or the suitability for mechanization for hay-making. Altitude, aspect, vegetation and soil water capacity differences are not decisional factors (Table 1). For strategy 1, the consideration of between-field diversity is improved with dimensioning decisions (to decide on the nature and number of fields to allocate to each production unit) being based on vegetation, altitude, aspect or soil water capacity differences. For example, one dimensioning rule of this strategy is “to assign the most productive fields (according to their altitude, aspect, vegetation and soil water capacity) to the conserved forage production units”. For the scheduling, the rules are the same as the control strategy thus to decide on the use order of the fields in a given production unit, this strategy prioritizes the distance from the cowshed. For strategy 2, the farmer takes the between-field differences of altitude, aspect, soil water capacity and type of vegetation into account in the dimensioning rules (as for strategy 1), but also in scheduling. For example, the most productive fields are allocated to the conserved forage production units (dimensioning), their order of use within these forage production units being a function of the earliness of grass growth in spring according to their characteristics of vegetation or aspect (scheduling). To test the generality of the results we ran these simulations for 11 years, using two existing weather databases (meteorological stations 1 and 2) likely to generate different weather constraints and data from two farms (A and B) differing in their degree of land diversity. For station 1 (altitude: 1100 m, latitude: 45.32◦ N, longitude: 2.83◦ E) the high rainfall could be a constraint for the beginning of harvest and turnout date (Fig. 2); for station 2 (altitude: 1100 m, latitude: 44.85◦ N, longitude: 3.83◦ E) the low rainfall could limit the production of the forage units notably during summer. Table 1 Examples of decision rules (case of spring grazing production unit) Dimensioning

Scheduling

Control strategy

The fields the closest to the cowshed are allocated to grazing

Strategy 1

The least productive fields and those maturing more slowly are allocated to grazing

Strategy 2

The least productive fields and those maturing more slowly are allocated to grazing

If there is not sufficient herbage mass on the field currently grazed then the animals are assigned to the field the nearest to the cowshed provided that it has a minimal herbage availability If there is not sufficient herbage mass on the field currently grazed then the animals are assigned to the field the nearest to the cowshed provided that it has a minimal herbage availability If there is not sufficient herbage mass on the field currently grazed then the animals are assigned to the field having the most herbage production provided that it has a minimal herbage availability

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Fig. 2. Water deficit (R-PET) at the stations 1 (dark blue) and 2 (pale blue) calculated from 11 years of meteorological data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

The two farms A and B (Fig. 3) were built (data for the altitude and vegetation having being obtained from the farm surveys) so that they differ only in their level of diversity and not by their level of herbage production. In order to estimate their level of diversity, we calculated at the farm level the standard deviation of each criterion of diversity (altitude, aspect, soil water capacity, etc.). For each scenario (“weather”/“strategy”/“farm”), we calculated indicators of the forage balance efficiency: the turnout date (day of year), the amount of hay harvested, the proportion of hay harvested after flowering (that is an indicator of quality, expressed as a percentage), the amount of hay sold and the amount of hay fed during the grazing sequences. A late turnout date, a low amount of hay harvested, a high proportion of hay harvested after flowering or fed during the grazing sequences translates a bad efficiency of the forage system. The amount of hay harvested and eaten during the grazing sequences were expressed in days of consumption for the herd (dividing the output of the model expressed in tons by the daily feed requirement of the herd) to compute an aggregated index (AI). Expressed in days of consumption for the herd, the AI aggregates the effects of these indicators and translates the total herbage intake for grazing (considering the duration of the grazing production unit and hay feeding during this period) and hay harvesting. AI = cowshed date − turnout date − hay feeding at grazing + amount of hay harvested

(4)

Fig. 3. Characteristics of the fields of the farms A and B. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

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Table 2 The indicators of the forage balance

Harvests of stored forage (days of consumption for the herd) Hay feeding at grazing (days of consumption for the herd) Sales of hay (days of consumption for the herd) Turnout day (day of year) Proportion of hay of poor quality (%) Aggregated index (days of consumption for the herd)

Control strategy

Strategy 1

Strategy 2

227.9 22.0 48.2 103 17.1 400.0

244.1 22.1 57.2 111 20.7 408.0

241.0 12.8 60.6 111 9.9 414.0

3.2. Simulations 3.2.1. Harvests of stored forage Regardless the weather or the level of diversity of the farmland, strategy 1 allowed harvests of stored forage that were higher than those of the control strategy (Table 2). The average profit as compared to the control strategy was 16.2 days of feed. Strategy 2 also allowed harvests always greater than those of the control strategy (13.1 days of additional feed), although slightly less than with strategy 1. These differences among strategies were significant (Table 3). The more significant herbage production of strategies 1 and 2 as compared to the control strategy were related to the assignment of the most productive fields to the conserved forage production units. Strategy 1 allowed harvests that were slightly higher than those of the strategy 2 because it was designed to harvest first the fields that have the earliest spring growth and are the most productive. The amounts harvested with this strategy were therefore lower than if the fields had been harvested last (as is possible with strategy 1) but with a beneficial effect on the quality, which we discuss in Section 3.2.5. 3.2.2. Hay feeding at grazing Strategy 1 did not modify the amount of hay feeding at grazing. On the other hand, strategy 2 involved a reduction of 9.3 days in the consumption of hay during the grazing period. Thus it was the taking into account of diversity in the scheduling of the grazing production unit (strategy 2) which made it possible to decrease the feeding of hay at grazing as compared to the control strategy. 3.2.3. Hay sales Hay sales were increased with strategy 1. Compared to the control strategy, this increase corresponds to 9 days of feeding. Strategy 2 also resulted in an increase in hay sales, corresponding to 12.4 days of feed. The more significant sales with strategies 1 and 2 were related to the increase in harvests and also (for strategy 2 in particular) to the smaller amount of hay feeding at grazing. 3.2.4. The turnout date With strategies 1 and 2, the turnout date was 8.3 days later than for the control strategy. The same tendencies between strategies 1 and 2 stress that it was the assignment of the fields to the grazing (and not their order of use) which delayed the turnout date. Saving the most productive fields for the conserved forage production units (strategies 1 and 2) delayed the turnout date as compared to the control strategy. Table 3 Analysis of variance among strategies of the forage balance indices (the F test was done with all the scenarios “weather/farm/strategy” that are independent)

F ratio P value

Harvests

Hay feeding at grazing

Sales of hay

Turnout date

Proportion of hay of poor quality

Aggregated index

113.31 0.0000

50.56 0.0000

178.49 0.0000

299.87 0.0000

39.88 0.0000

178.88 0.0000

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3.2.5. Hay quality With strategy 1, the proportion of poor hay increased by 3.6%. Conversely, with strategy 2 this proportion decreased by 8.1% as compared with the control strategy. We therefore concluded that it was the consideration of diversity in the scheduling of the conserved forage production unit that reduced the proportion of poor quality hay. 3.2.6. The aggregated index Thus strategy 1 increased the amount of hay harvested as compared to the control (Table 2). This increase led to more significant sales in spite of the increase in hay feeding at grazing and of the later turnout dates. The proportion of poor quality hay was also increased. Strategy 2 also increased the amount of hay harvested or sold, with later turnout dates. On the other hand, the proportion of poor hay and the amount of hay fed over the grazing period were decreased as compared with the control strategy. The aggregated index that combines the effects of several indices (Section 3.1), increased on average by 8 days with strategy 1 and by 14 days with strategy 2. In other words the strategies allowing greater consideration of farmland diversity increased the herbage yield through grazing and cutting, strategy 2 being the most efficient. At the same time, strategy 2 increased the herbage yield through grazing and cutting, and improved hay quality. With strategy 1 the harvests were increased but to the detriment of hay quality and grazing, since significant hay feeding was indicated during the grazing period. Improving the consideration of farmland diversity at the same time in dimensioning and scheduling was beneficial. These conclusions were obtained by averaging for each strategy the results of the simulations run with the two sets of weather series and farm data. However, considering separately the results obtained with meteorological station 1 from 2 or farm A from B, these conclusions remained valid but differences between strategies were less pronounced for station 1 and farm A. The AI was therefore increased by 4 days with strategy 1 and by 12 days with strategy 2 running the simulations with farm A and by, respectively, 13 and 16 days with farm B. The AI was increased by 5 days with strategy 1 and by 10 days with strategy 2 running the simulations with meteorological station 1 and by, respectively, 12 and 18 days with station 2. 4. Discussion 4.1. Validation Validation of a model simulating a complex system, according to Brown and Kulusari (1996), is one of the most difficult tasks in its development. The validation must determine if the model is, within its experimental framework, a valid representation of the real system consistent with the desired use (Kleijnen and Sargent, 2000). Balandier et al. (2003) prefer the term “evaluation” to “validation” because of the controversy around the concept. Certain authors underline the impossibility of validation (Boote et al., 1996; Monteith, 1996; Passioura, 1996; Sinclair and Seligman, 1996; Woodward and Rollo, 2002). Others advocate a statistical series of tests for this validation (Kleijnen and Sargent, 2000). Between these two extremes, we chose a “subjective validation” consisting of proposing with experts (i.e. people having a good knowledge of the system) simulations starting from cases similar to their own cases studies and asking them whether the behaviour of the model appears realistic or not. Moreover, it appeared best adapted in the case of a model comprising a decisional sub-model. For Cros et al. (2001), this approach utilizing scientists and grazing experts is the only possible one for models simulating complex systems. Indeed, a method of current validation is the “validation of repetitivity” (Coquillard and Hill, 1997), which consists of comparing the model with other models or reality. However this is not really feasible for models intended to produce estimates of various dynamic processes using a time step varying from a day to several growing seasons and including a decisional component. Indeed the comparison of the outputs of simulations to experimental data is feasible only in models integrating a limited number of variables and parameters. Another argument is that in such models, the human strategic decisions are often taken into account, with the basic assumption that this management strategy does not evolve during the time of simulation (Cournut, 2001). The biophysical sub-model, made up mainly of the grass growth model, was largely validated in earlier studies (Col´eno and Duru, 1999; Cros et al., 2003). For its subjective validation with experts (farmers, researchers, technicians), the question was more particularly to see if the adaptations carried out within the framework of our research (namely modelling of the growth during the reproductive phase as well as the consideration of between-field characteristics) led to consistent or aberrant yield levels. The decision rules (triggering indicators of production units, order of use of the fields, etc.) were also previously validated by the farmers having allowed the construction of the decisional sub-model.

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Thus, for the subjective validation of the decisional sub-model, the experts had to react to its capacity, once combined with the biophysical sub-model, to simulate a realistic growing season. The experts approved the realistic character of the calendar of the events simulated by the model (turnout date, date of first harvesting, end of grazing, etc.) as well as the levels of production obtained after pre-fixed harvests (in spring and summer). Starting from the first prototype, they only suggested ways of improving the parameter settings (in particular thresholds for triggering events) and enlarging the range of grazing practices considered (they suggested practices that are often observed in such systems). These suggestions were discussed, and were finally incorporated, making this validation an integral part of the process of development of the model. 4.2. Originality of the model The main original feature of our model is its capacity to simulate the effects of between-field diversity in vegetation type, soil water capacity, altitude and aspect on herbage production (level and seasonality through differences in species phenology) and its availability for cutting or grazing. This characteristic of the model accounts for the variation within a farmland and quantifies its effects. The farmland diversity was seldom considered in models that are applied to field crops (Batchelor et al., 2002; Keating et al., 2003) or to livestock farms (Van Keulen, 2006). Indeed, the fields are usually assumed to be identical. In the rare models simulating between-field diversity, the source of diversity is limited to classes of soil fertility, area (Gibon et al., 1989) or distances to the market (Thornton and Jones, 1998). Simulating identical fields is acceptable when the production systems studied are very artificial due to human interference. Since we were studying low-input farming systems, particularly in less favoured areas, this simplification was not acceptable. Indeed, simulating identical fields does not account for the changes in herbage production or plant phenology existing between the fields, which can affect the practices. Paradoxically, diversity was in fact considered at the within-field level (Armstrong et al., 1997; Baumont et al., 2002), primarily to study the processes of diet selection by the animal. But this level is not suitable for studying the management strategies of the stockbreeder, which apply on the field scale. Modelling between-field diversity was justified by the will to simulate these management strategies. Simulating management strategies is another new development of the model that explains why the distance between the cowshed and the fields, and the suitability for mechanization for hay-making were also considered. These characteristics greatly influence the organization of the forage system, whereas they have no effect on herbage growth. To account for the management of this farmland diversity we did not program a calendar of technical operations, but rather formalized a decision-making process. In the majority of studies, the decisional sub-model refers to technical operations on predetermined dates or possibly activated by the weather data. The goal is then to evaluate the consequences of the technical operations on the animal or herbage resource. A few studies have tried to formalize the decision-making process of the stockbreeder (Gibon et al., 1989; Cros et al., 2001; Romera et al., 2004; Chatelin et al., 2005). This structure distinguishes the various levels of decisions made by the stockbreeder. Some levels deal with the strategic aspects (planning of the activities), other levels deal more with the operational aspects (monitoring). Moreover, only this approach makes it possible to account for the decision rules, which the farmer uses to anticipate the use of the fields according to their characteristics. In other words, only this approach can enable one to compare various spatio-temporal management strategies. 4.3. Limits of the model and prospects To build this model we made a certain number of choices, such as restricting the cattle class. These choices were justified by the need to remain focused on our subject of study: farmland diversity and its influence on forage production. Using the model we could quantify the benefit of taking the farmland diversity into account in the management of forage production units. To progress further, we plan to increase the complexity of the model without losing sight of the fact that increasing the number of variables of a model may reduce its predictive capacity (Hakanson, 1995). The modularity of the model permitted by the oriented-object approach (Jones et al., 2001) will make it possible to increase the complexity of certain target classes of the model. Firstly, we are thinking of improving the management by assuming several batches of animals and testing the interaction between the diversity of the batches of animals and farmland diversity. We also plan to simulate management of the hay stock carried forward from one year to the next. Secondly we plan to enlarge the criteria considered to evaluate the management. Thus, we envisage modelling herbage intake at grazing more finely to evaluate strategies in terms of livestock production (amount of milk or meat produced).

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In this way, the model already constitutes a tool for studying the dimensioning of forage production units as a function of the variation in the land and the weather. 5. Conclusion To evaluate whether consideration of farmland diversity in strategies implemented by farmers could improve the forage balance, we built a model capable of simulating a virtual farmer taking into consideration the state of the system and the environmental conditions (weather and farmland diversity) to decide on the actions to be taken. This model enabled us to show that the farmland diversity is a valuable asset for farmers to use. In the context of extensification of agriculture, having tools which can represent the diversity of the internal resources of the farm constitutes a major improvement. References Andrieu, N., 2004. Diversit´e du territoire de l’exploitation d’´elevage et sensibilit´e du syst`eme fourrager aux al´eas climatiques: e´ tude empirique et mod´elisation. Doctoral Thesis. 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