Designing crop management systems by simulation

Europ. J. Agronomy 32 (2010) 3–9

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European Journal of Agronomy journal homepage: www.elsevier.com/locate/eja

Designing crop management systems by simulation J.E. Bergez a,∗ , N. Colbach b , O. Crespo c , F. Garcia c , M.H. Jeuffroy d , E. Justes a , C. Loyce e , N. Munier-Jolain b , W. Sadok f a

INRA, UMR 1248 AGIR, Toulouse, France INRA, UMR 1210 Biologie et Gestion des Adventices, Dijon, France c INRA, UR BIA, Toulouse, France d INRA, UMR 211 Agronomie INRA/AgroParisTech, Grignon, France e AgroParisTech, UMR 211 Agronomie INRA/AgroParisTech, Grignon, France f Agronomy-Physiology Lab, University of Florida, Gainesville, FL, United States b

a r t i c l e

i n f o

Article history: Received 13 January 2009 Received in revised form 27 May 2009 Accepted 2 June 2009 Keywords: Design Simulation Crop management system Modelling Evaluation

a b s t r a c t To help agricultural advisors to propose innovative crop management systems, simulation models can be a complementary tool to field experiments and prototyping. Crop management systems can be modelled either by using a vector representing dates and quantities used as input parameters in crop models or by developing specific decision models linked with biophysical models. The general design process of crop management systems by simulation follows a four-step loop (GSEC): (i) generation; (ii) simulation; (iii) evaluation; (iv) comparison and choice. The Generation step can follow different approaches: from blind generation before simulation to optimization procedures using artificial intelligence algorithms during the loop process. Simulation is mainly an engineering problem. Evaluation process means assigning a vector of indicators to the simulated crop management systems. A three-point evaluation can be carried out on the simulated crop management systems: global, agronomic and analytical. Comparison and choice of different simulated crop management systems raise the question of “monetary” versus “non-monetary” comparison and how to aggregate different quantities such as drainage, nitrogen fertilisers, labour, etc. Different examples are given to illustrate the GSEC loop on the basis of research programs conducted in France. Methodological advances and challenges are then discussed. © 2009 Elsevier B.V. All rights reserved.

1. Introduction The rapid change in the agricultural industry driven by continuously arising challenges (climate change, market globalisation, environmental concerns) requires the development of new methods of production in order to guarantee sustainable agriculture. Among the tools available for evaluating cropping systems or investigating alternative cropping systems, field-based approaches such as regional agronomic diagnosis (Doré et al., 1997, 2008) and prototyping (Vereijken, 1997; Rapidel et al., 2006; Lanc¸on et al., 2007) have been tested and used successfully. However, these approaches are too slow to provide timely responses to such rapid contextual changes and are unable to explore a large number of systems (Rossing et al., 1997). In silico approaches, based

∗ Corresponding author. E-mail addresses: [email protected] (J.E. Bergez), [email protected] (N. Colbach), [email protected] (O. Crespo), [email protected] (F. Garcia), [email protected] (M.H. Jeuffroy), [email protected] (E. Justes), [email protected] (C. Loyce), [email protected] (N. Munier-Jolain), walid.sadok@ufl.edu (W. Sadok). 1161-0301/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.eja.2009.06.001

on the study of a wider range of possible systems through modelling and simulation offer the possibility of identifying more quickly new systems to tackle current social, political and environmental concerns (Malézieux et al., 2001; Loyce and Wéry, 2006). Loyce and Wéry (2006) propose a four-step approach to design cropping systems: (1) define goals and constraints for the new cropping systems. Constraints may result from soil and climate but also from environmental or economic concerns; (2) design cropping systems compatible with the set of constraints; (3) evaluate the cropping systems; (4) test and transfer the most innovative ones to practitioners. In this paper, we focus on the use of simulation models to design and evaluate cropping systems in steps 2 and 3. Although steps 1 and 4 are extremely important, we will not address them in this paper. Following the typology of Malézieux et al. (2001), the goals of agroecosystem models can be sorted into four groups: (1) models that represent knowledge, concepts and methods for scientists; (2) models as tools for communication; (3) models as tools to manage or run systems; (4) models as tools to assist debate. The third type is the one to use to design crop management systems.

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This paper presents a framework to design cropping systems based on the latest work conducted by various French research units on model-based design of crop management systems. Section 2 focuses on the importance of linking decision models with biophysical models in order to develop innovative management systems. Section 3 describes the general conceptual loop to design management systems. Section 4 illustrates the different steps of the designing loop with examples mostly taken from French agronomical research works. A final section opens the discussion on the international use of simulations to design crop management systems and on several general methodological problems as well as various proposals to address them. 2. Why developing decision models? Since 1970, many efforts have been made to develop biophysical models (Whisler et al., 1986; Baker, 1996; Boote et al., 1996; Bouman et al., 1996; Sinclair and Seligman, 1996). Crop models such as EPIC (Kiniry et al., 1992), CROPSYST (Stockle, 1997), STICS (Brisson et al., 1998) and CERES (Gabrielle et al., 2002) were developed synthesizing knowledge from physiology, ecophysiology and agronomy at the field level. However, many of these models have failed to different extents in efficiently transferring their simulation results to farmers and extension advisors as a way to develop and implement new crop management systems. Meanwhile, various researchers have extensively studied farmers’ management practices (Matthews, 2002; Stone and Hochman, 2004; Ascough et al., 2005; Cox, 1996; McCown et al., 2002). They thoroughly analyzed decision-making processes from on-farm observations. Specific methodologies derived from social sciences were developed, using iterative cycles of observation and formalisation of farm practices, including discussions around farmers’ kitchen tables. This led to the design of a model of decision-making processes in technical management and the concept of a “model for action” (Aubry et al., 1998). However, there was still a gap between the two types of models, i.e. the biophysical model (or crop model) and the decision model. In the first type of model decisions were not taken into account while in the second type, the state and the dynamics of the system were not used. This opened the path to the development of biodecisional models in the nineties. A key feature of these models is that they link the biophysical and decisional approaches in a single operating model (Keating et al., 2003; Chatelin et al., 2005; Bergez et al., 2006) using the concept of decision-making modelling based on a set of decision rules (Aubry et al., 1998; Chatelin and Mousset, 1997). Such models therefore integrate a description of the actions of the farmers and of the impact of these actions on the biophysical system. It is not an easy task to describe crop management practices and their impact on the biophysical system. As stated by Boiffin et al. (2002), the sequences of technical acts to manage crops are interdependent and have multiple and prolonged effects on the agro ecosystem dynamic. Strategies of management practices (Fig. 1) are defined as sequences of goal-oriented, carefully thought out and interdependent actions (the goal often being yield-related). In designing these strategies, one must take into account a wide range of interdependent temporal and spatial scales. It is essential for the scales of the decisions and those of the biophysical system to be mutually consistent. In this context, a general modelling exercise should quite naturally be based on three components: i) A model of the biophysical system (the soil-crop model). ii) A model of human actions on the system (the decision model). iii) Dynamic constraints which have to be taken into account such as, for example, water resources changing during the growing season.

Fig. 1. Conceptual modeling framework of a cropping systems (from Boiffin et al., 2002).

Examples of such models are MODERATO (Bergez et al., 2001a), DECIBLE (Chatelin et al., 2005) or SEPATOU (Cros et al., 2004). The representation of crop management practices in standard crop models is mostly restricted to the technical operations used to manage a limited number of production factors. Indeed, research in agronomy has accumulated data on the effect of water and nitrogen on plant production over many years while knowledge on the functioning of biotic components (e.g. pests, earthworms) remains insufficient. Recent works attempted to fill this gap for a particular biotic component, i.e. weeds, by developing both biophysical (ALOMYSYS, Colbach et al., 2007) and decision-aid models (DECID’Herb, Munier-Jolain et al., 2005). Other models focused on the links between crop management and major crop diseases such as eyespot (Colbach et al., 1999) or Phomopsis stem canker (Debaeke and Estragnat, 2003), without detailing all biophysical processes. More general models simulate a comprehensive pest complex on a given crop, e.g. rice with the model RICEPEST (Willocquet et al., 2000, 2002). However, a lot of work on these aspects remains to be done. In the international literature, crop management practices are often addressed in a section called “management options” (see, e.g. Hearn, 1994; Ghaffari et al., 2001). Actually, this term is very generic and covers a wide spectrum of meanings. The simplest and most often used approach is to represent crop management practices as static parameters of the crop model. The parameter values are then initialized prior to any simulation and are stored in a table (a lookup table). They do not depend on the state of the crop. In order to avoid determining a priori values for the decision parameters and to link them to the evolution of the crop environment, decision rules are used: IF THEN ELSE Though critical, the definition of these decision variables is often considered as secondary. Although great care has often been taken in describing the biophysical processes of the model (forms of the equations, experiments used for parameterization, estimation methods, criteria of quality for the soil–crop model, etc.), few models handle the decision aspect. In many models, decision rules are associated with a look-up table: some parameter values are fixed at the start of the simulation, others are determined during execution. For each time t, the set of rules is evaluated and the rule for which the trigger condition is true is applied: a value is assigned to the relevant parameters. A logical combination (AND, OR) of soil/crop indicators usually defines the trigger condition. The decision rule, which simulates the adaptive characteristics of the decision-making process, is the elementary block of the decision model. To include scheduling, the elementary rules

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Fig. 3. The two ways to generate strategies: (A) before the simulation process; (B) during the simulation process using various optimization methods to improve the strategy step by step. Fig. 2. The four-step loop that aims to design crop management systems by simulation: (i) from some initial value, a first generation procedure is activated (of either date and quantity (D/Q) or of parameters for a decision model (param)); (ii) simulation of the generated crop management systems is then performed with a biophysical model (bm) possibly combined with a decision model (dm); (iii) the outputs of the simulation are then used to evaluate the crop management systems by assessing some indicators (Indic); (iv) comparison and choice of some promising strategies is performed using indicators and multi-attribute aggregation (MAA) (or non-aggregation techniques). At this step it may be decided that, improvement through optimization is requested, which re-starts the loop until no better crop management options are found.

are gathered in a set of rules usually representing a given homogeneity, generally a specific technical operation of management. For instance, sowing is described by the date of sowing, the cultivar earliness and the sowing density. Sequential, iterative and adaptive processes of decisions are accounted for by the decision model that organizes the scheduling of the decisions. For instance, in DECIBLE (Chatelin et al., 2005), the technical operations are organized in sequences; in MODERATO (Bergez et al., 2001a), in loops; in SEPATOU (Cros et al., 2004), in parallel. 3. Designing crop management systems with simulations 3.1. A four-step loop to design crop management systems Designing crop management systems with simulations can follow a four-step iterative process, the GESC loop (Fig. 2): i) G: Generation of a set of candidate crop management plans either randomly created or provided by the user. ii) S: Simulation of the management plans in soil/weather/constraints contexts. iii) E: Evaluation of the simulated management plans. iv) C: Comparison and choice of the most satisfying crop management options and/or improvement through a new generation process that loops to step 1. The loop will stop when no better crop management options are found. The model used in this loop may be a biophysical model using look-up table, or a biodecisional model including a decision model. Accordingly, different methodologies are used to carry out the GSEC loop. 3.2. Generation There are two ways of developing the set of management plans to be simulated. A first approach (Fig. 3A) is to create a large set

of strategies, either based on date and quantity parameters or on decision-rule parameters. Each strategy is simulated and evaluated, and then a selection method is applied to the large set of results. Methods range from blind generation up to complex algorithms (Table 1). In the blind generation, combinatory methods create a set of parameters that are simulated with the model. The size of this set may be reduced using different filters (e.g. agronomical filters) before or after the creation process. Though these generation methods are easy to apply, the resulting collection does not necessarily comprise the best set of strategies, because: (i) of the filtering process leaving aside some good strategies or (ii) the generation process does not go into enough detailed combinations of crops × crop successions × cropping techniques or (iii) the best set of strategies is out of the current knowledge of practitioners. To address this drawback, a second approach (Fig. 3B) consists of generating new strategies from the initial population during the simulation process by an evolutionary process concentrating on the most promising population within the decision space. The process starts with an initial population, either mathematically built (random, grid, etc.) or specified by a decision maker, which is simulated and evaluated. New strategies are then generated iteratively by optimization until validation via a stopping criterion (such as ‘no improvement, ‘time spent’, ‘number of evaluations’, etc.). The optimization process can be either control-based or simulationbased. In the control-based optimization approach, the rules are initially unknown. They will be determined by combining the different variables calculated by the crop model and by evaluating the results of the simulated actions. These rules can be obtained through approaches originating from artificial intelligence, (such as reinforcement learning, dynamic programming, etc.). Most of the optimization methods are based on Markov decision problems. For example, Bergez et al. (2001b) tested a QR Learning approach using CMAC approximation to define when to start irrigation in a maize system. Cumulative thermal units and soil water deficit were used to define the correct time to start irrigation and a relationship between these two variables was determined. In this approach, few a priori assumptions regarding the structure of the decision are given. However, using control-based methods is difficult as they are quite complex, which is hardly suitable for handling complex objects such as cropping systems. In addition, the results from the simulation must still be translated into transferable rules. In the simulation-based optimization approach, the structure of the rules is already known. The procedure seeks the optimal set of parameters and generates a new set to be tested at every new

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Table 1 Methods to generate a priori crop management systems and ways to reduce the number of simulations. Authors

Generation process

Restriction of the number of simulations

Model

Bontemps and Couture (2002) Munier-Jolain et al. (2005) Justes et al. (2003) Colbach and Debaeke (1998) Loyce et al. (2002)

Combinatory method Combinatory method Agronomical expertise Random generation Agronomical expertise

None Multi-criteria analysis None Sensitivity analysis Constraint satisfaction problem

STICS DECID’Herb STICS GENESYS BETHA

loop. Several methods exist and the usual typologies sort them into methods applied to continuous or to discrete decision variables (Andradóttir, 1998; Òlafsson and Kim, 2002). Some of them are quite well known now such as gradient-based optimization, systematic optimization, genetic algorithms, simulated annealing or neural networks. As an example, Crespo et al. (2009) used a branch and bound algorithm to optimise an eight-parameter-based irrigation strategy for maize that wrote: “The main irrigation period starts from T1 as soon as the soil water deficit reaches D1. An amount I1 is applied. Once an irrigation cycle ends, a new cycle starts when the soil water deficit reaches D2. An amount I2 is applied. For the irrigation cycle following T3, if the soil water deficit is greater than D3 before this irrigation cycle starts, a last irrigation cycle is performed; otherwise the irrigation period ends. An amount I3 is applied.” The optimization process seeks to find the best set of values for T1 and T3, D1–D3 and I1–I3. 3.3. Simulation Generated strategies must be simulated in different soils and climates in order to evaluate them. This is mostly an engineering problem if we do not consider the scientific problem of selecting the proper cropping system model taking into account the output variables needed for the evaluation step and the available data for calibration. This issue should consider: (i) linking different types of models and algorithms, and (ii) managing large amounts of data. Here, processor clock speed and RAM capacity are critical. To give an idea, in a simulation study performed on a corn-based cropping system with four different models (i.e. APES, Donatelli et al., 2008; MOU STICS, Bergez and Charron, 2007; STICS, Brisson et al., 1998; MODERATO, Bergez et al., 2001a), simulation time of a single strategy varied from 0.1 to 8 s (Bergez et al, unpublished). As several millions of simulations may be required, speed and a proper experimental plan for virtual experiments are therefore essential. 3.4. Evaluation Evaluating a simulated management means assigning it a vector of indicators for which values are calculated from the simulation output. Indicators are chosen to help selecting management plans that will be kept for a new iteration of the general loop. There are several points to consider (Girardin, 1999): 1. Defining proper indicators is a difficult task when different holistic aspects are to be addressed. This is especially the case when the dimensions of the sustainability (social, economic and environment) are to be considered explicitly. 2. Indicators have to be calculated from the simulation outputs. 3. Calculating the indicator requires answering several questions such as: How to account for the weather variability? Should we use a single average value? Do we take into account variance or quantiles of the distribution of results? For each strategy, evaluation may be performed on three different aspects (Meynard et al., 1996):

1. A global evaluation checking whether the targeted general goals of the cropping systems are reached (combining economic or environmental goals). 2. An agronomic evaluation checking whether the agronomical goals are reached, such as production, quality, use of fertilizer or water. 3. An analytical evaluation checking whether the decision rules used for specific system behaviour allowed a correct system functioning (in terms of timing of operation and amount of requested inputs). When dealing with evaluation using simulation, the last two points require a specific in silico experimental plan design and the use of intermediate computed variables in order to give an insight into the system’s behaviour (Chatelin et al., 2007). This evaluation may either be performed by expert analysis or by a grid as in ROTAT (Dogliotti et al., 2003). Expert analysis is difficult to simulate in an automatic process but is used in trial and error approach. 3.5. Comparison and choice Some systems may lead to good results for some indicators and less satisfactory results for others. Integrating the information resulting from all the indicators is often the best way to select the best systems. This process often defines a decision-making problem which can be handled by multi-criteria decision aid (MCDA) algorithms. Ranking, sorting, choosing are typical questions addressed by such tools (Roy, 1985; Schärlig, 1985). Their basic aim is to rationalize planning and decision problems by systematically structuring all relevant aspects of choices. Usually, these choices are not a one-shot activity, but take place in all phases of decision making (Munda et al., 1994). To be effective, this process must be flexible and adaptable to the changing views and judgements regarding the relevance of alternatives or impacts (Nijkamp et al., 1990; Munda et al., 1994). In this way, this process may be considered as a continuous cyclic activity, allowing the adaptation of elements of the evaluation due to continuous consultation between the various parties involved in the planning process: technicians, researchers and planners (Munda et al., 1994). In general terms, one can identify two major kinds of aggregation processes. The so-called ‘monetary’ approach is characterized by an attempt to measure all effects in monetary units, based on cost–benefit and cost-effectiveness approaches (Mishan, 1971; Hanley and Spash, 1993; Spash et al., 2004). These conventional economic optimization models are based on the assumption that different objectives can be expressed with respect to a common denominator by means of trade-offs (complete commensurability) so that a loss in one objective can be evaluated against a gain in another (complete comparability) (Martinez-Alier et al., 1998, 1999). However, these approaches have their limitations, especially in handling multi-dimensional decision problems. For example, in a problem of designing crop management systems for integrated weed management, one has to rank criteria such as: (i) long-term containment of weeds; (ii) reliance on herbicides and associated

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environmental impacts; (iii) energy input and emission of greenhouse gas; (iv) labour input; (v) economic profitability. Criteria of a different nature (in terms of information structure and units) are obviously needed to encompass the multi-dimensional aspects of the evaluation problem. The emergence of non-monetary approaches is the direct result of the ‘failure’ of simple ‘monetary aggregation’ to address these problems realistically. These alternative approaches are being increasingly used in the context of sustainable development, and are often referred to as multi-criteria evaluation (MCE) methods. They represent the core of the MCDA algorithms, and were developed within decision theory as a tool to support decision makers (Roy, 1985; Vincke, 1989; Bana e Costa, 1990; Nijkamp et al., 1990; Paruccini, 1994). In an agro-environmental management context, the use of appropriate MCE methodology allows the various dimensions of effects of different projects to be taken into account, such as environmental, social and economic impacts. This is made possible by two major considerations. The first is measuring these impacts in different units (i.e. monetarization of all impacts is no longer necessary) so that strong assumptions about the commensurability and comparability of values are not necessary as they are with costbenefit analysis (Munda et al., 1995; O’Neill, 1997; Martinez-Alier et al., 1998). The second is coping with conflicting objectives existing among technical, social and economic judgements (Nijkamp et al., 1990; Beinat and Nijkamp, 1998; Janssen and Munda, 1999) to reach a satisfactory – rather than optimal – compromise solution (Guitouni and Martel, 1998). In the literature, a huge number of methods and their taxonomies are available, and so far very few have been applied to agricultural problems (see Sadok et al., 2008 for a review). Examples include the use of the ELECTRE methodology in the model MEACROS (Mazzetto and Bonera, 2003) and ELECTRE combined with fuzzy logic functions in BETHA (Loyce et al., 2002), or multi-objective linear programming in ROTAT (Dogliotti et al., 2003) and FSSIM (Louichi et al., 2007). In DECID’Herb (Munier-Jolain et al., 2005), the method used for the ranking of the generated strategies on a multicriteria basis does not require any aggregation of variables. It is an interactive method: the user defines ‘desired’ values for the cost and the environmental impact of the weed control strategy. A ‘target’ efficiency is set as a function of the risk of yield loss in the current crop and of weed community development in the subsequent crops. Then, the various possible strategies are ranked as a function of a distance to the ‘desired’ virtual strategy in the multidimensional space of criteria (Gabrel and Vanderpooten, 2002). In some cases, if the virtual input strategy requires both a low cost and a low environmental impact, the distance to the best generated strategy is so high that the user is asked to revise his requirements, either by increasing the expected cost or by accepting higher environmental impacts. If the risks of yield loss and of any increase in weed infestation in the subsequent crops are low, then the required efficiency of weed control is limited, and the best strategy might be the ‘no control’ option, which is actually associated with a cost and an environmental impact equal of zero. According to different authors (Roy, 1985; Guitouni and Martel, 1998), a multi-criteria evaluation procedure is based on 8 steps. A detailed version of the procedure is as follows:

1. 2. 3. 4. 5. 6. 7. 8.

Choose decision option. Choose evaluation criteria. Obtain performance by simulation. Transform to commensurate units. Weight the criteria. Rank or score the options. Perform sensitivity analysis. Make a decision.

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Four of these [1, 2, 4, 5] are usually performed out of the simulating exercice while the reminding four may be automatised. 4. Discussion and conclusion 4.1. Guidelines for designing crop management plans Designing crop management plans based on models requires four important points to be kept in mind. The first is to describe the system of interest properly, depending on the question to be answered (processes, temporal and spatial scales, constraints and boundaries). The second is to build an adequate “in silico” experimental plan of simulations (virtual design) aiming at designing crop management plans. The third is the simulation work itself. The fourth is how to deal with the simulation data and analyse the results. As previously mentioned, we are interested not only in identifying the best system on one criteria, but also in choosing the least risky, or the most efficient one, considering various outputs (water or nitrogen use efficiency, soil erosion, etc.). A major drawback with crop models at present is the large gap in knowledge and the lack of models for biotic components such as pests (including weeds) but also for beneficial organisms such as earthworms, auxiliaries, etc. To design comprehensive crop management strategies, models must be extended to account for the biotic components, and additional cultivation techniques specifically aimed at some of these components such as fungicides, herbicides, etc., must be included. Furthermore, modelling biotic components usually (but not always) requires the integration of long-term effects and often also of the field neighbourhood or even the entire region (Colbach, 2009). This upscaling process has major consequences: (i) for the cropping system concept which must be extended to the regional level (Benoît and Papy, 1998), (ii) for the decision rules which must also integrate the state of neighbouring fields (e.g. isolation distances between varieties for seed production or in case of co-existing GM and non-GM crops), and (iii) for the simulation methodology. Simulations must now test the co-existence in space of contrasting crops, varieties or cropping systems, the impact of field patterns, and the effects of semi-natural areas where the modelled biotic component can grow (Colbach, 2009). To obtain robust and generic results on cropping systems covering a wide range of situations, simulations must be based on a large number of contrasted field patterns as well as replicates of crop and cropping system locations in the landscape. In addition, the decision makers establishing the rules at the landscape level are mostly interested in simple and individual measurements such as isolation distances or maximum crop areas (instead of the more complex concept of cropping systems), which can be established by analysing this kind of stochastic landscape simulations with regression quantiles as a function of the major factors such as the proportion of “risk” crops (e.g. GM crops, crops favouring erosion or nitrate leaching) in the region or the distance to the nearest “risk” crop (e.g. crop of the same species in case of disease spread, GM crop when looking at genetic harvest impurities). 4.2. GSEC loop: methodological advances and challenges We have seen that for each step (generation/simulation/ evaluation/comparison and choice), many methodologies are available. This situation has some advantages and some drawbacks. Indeed, on the plus side, one can see in the richness of the methodological variants various approaches allowing a wide range of situations to be handled. This is for instance the case of the huge number of MCE methodologies available in the literature. Although many studies have tried to provide taxonomies of these methods, the ‘half-life’ of these taxonomies is increasingly short as new and ‘hybrid’ methods are released continuously from operational

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researchers. New and promising methods include ‘mixed’ MCE methods, which – among other proprieties – are able to: (i) handle quantitative/qualitative information on the criteria level, or to (ii) express the preference model solely through ‘qualitative reasoning’. These approaches sound promising for handling ‘holistic’ decision problems, such as those underlying sustainability assessment (Sadok et al., 2008). In addition, either for the evaluation/choice or the generation/simulation steps, one should note the increasing importance of artificial intelligence-based techniques, which can be considered as a paradigm shift in the crop management systems field. Depending on the management model available, either biophysical or biodecisional, current research trends also deal with uncertainty, farmer risk-aversion and participatory methods. These are burning issues when dealing with global change or sustainable agriculture. However, on the minus side, the multiplicity of methodologies hampers to some extent the comparison of different studies (and also their integration/aggregation), and one can realistically wonder if there is a ‘method effect’ on the final results. This issue has been addressed for instance in some studies comparing the results displayed by different MCE methodologies for a given decisional problem (Zanakis et al., 1998; Kangas et al., 2001; Wang and Triantaphyllou, 2008). Ranking differences were observed between the tested methods, and the authors simply suggested the simultaneous use of different MCE methods for a given decisional problem. 4.3. GSEC loop and implementation Using simulation models to design crop management systems is not an alternative to experimenting and prototyping, but it aims to: (i) shorten these steps and (ii) enhance their efficiency during implementation. By designing, simulating and evaluating ex ante and in silico all the realistically possible options and suggesting the most promising ones, implementation may be significantly speeded up as it is no longer necessary to test all the combinations in the field. Moreover, although still in need of further developments, many of the tools presented here allow for a better flexibility of the ‘solutions’, which is important for handling the rapid changes in conditions and objectives now driving agricultural systems. Specific frameworks should be developed to help running the GSEC loop. This is the case of the French RECORD platform that is specifically aimed at developing and simulating cropping systems (Bergez et al., 2007). The RECORD platform has been designed as a modelling and simulation software platform where researchers can build, assemble and couple their own pieces of model to preexisting ones, and then run the resulting models. The RECORD modelling environment will thus have to facilitate the implementation of heterogeneity and interactions between different kinds of spatial dynamic models on different scales. These models can be either deterministic or stochastic, and are described within different modelling formalisms like difference equations, differential equations, partial differential equations, cellular automata for biophysical models, discrete events models or agent-based models for decision models. More formal representation of crop management systems, cropping systems and crop rotation are also required in order to simulate them more easily (Castellazzi et al., 2008). 4.4. Conclusions In this paper, we described the use of simulation models for designing new crop management systems. For this purpose, a general design framework, the GSEC loop was used. Of course, once designed and selected, management plans still have to be tested and then ‘adjusted’ to real-life farms (Debaeke et al., 2009) as modelling is just a “simplification of the reality”. Involving social sciences researchers is necessary to “bridge the gap” between the simulation

results and the on-field application. In addition, field implementation can prove very useful to improving the GSEC loop – mostly developed ex ante – through a feedback based on real situations. For some years now, participatory modelling actions have started to address farmers’ constraints and requests (Bousquet et al., 2007; Etienne et al., 2003). However, to use in interaction models and farmers, other specifications to the simulation models are required, for instance easy access to input data, graphic user interfaces, speed of simulation process, etc. Depending on how to use the model, specification will be different.

Acknowledgements The authors wish to thank the French network of agronomist scientists from INRA and A. Scaife for the English editing.

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