Jean-Pierre Rellier

Modelling and simulating work practices in agriculture

Research has shown that the managerial capacities and work practices of farmers play a major role in explaining differences in economic and environmental performances. This paper presents a computer simulation framework that enables work organisation issues in agricultural production systems to be studied. This framework relies on a purposive frame-based ontology of such production systems. The paper focuses on a subpart of the ontology that concerns production activities, flexible plans and material resources. The paper also outlines the interpretation algorithms that operate on instances of these ontology concepts in any production system model constructed in compliance with the ontology.

A simulation framework for the design of grassland-based beef-cattle farms

Grassland-based beef-cattle farms are dynamic systems that are difficult to manage, particularly because of their sensitivity to uncontrollable environmental factors such as weather. The design of farms and management strategies capable of coping with a wide range of conditions is thus a challenging issue. The SEDIVER discrete-event simulation framework presented in this article has been developed to support the construction of dynamic simulation models of grassland-based beef-cattle farms for evaluation and empirical design purposes. The originality of the models built with SEDIVER lies in the explicit representation of: (i) management strategies as the planning and coordination of activities in time and space through which the farmer controls the biophysical processes occurring within the system and (ii) the diversity in plant, animals, grassland and farmland, and the management opportunities and difficulties that this might induce. An application example illustrates the kind of simulation-based investigations enabled by SEDIVER. A grassland-based beef-cattle farm in France is examined for two contrasted management strategies: the first one corresponding to the actual practice and the second one paying increased attention to and exploiting plant and grassland diversity. The simulation results showed that the second one could roughly double fodder yields and thus ensure farm self-sufficiency for fodder. Thanks to the capacity of a SEDIVER-based model to take practical production considerations into account, it is possible to increase the realism of farm simulations and the credibility and relevance of the farming systems which can thus be designed.

Modelling adaptive management of intercropping in vineyards to satisfy agronomic and environmental performances under Mediterranean climate

In the Mediterranean area, rainfed viticulture is exposed to irregular rainfall distribution. The impacts on production and environment can be mitigated by appropriate management practices like, for instance, the introduction of cover crop in the inter-rows in vineyards. This paper presents the VERDI simulation model created to study various adaptive intercrop management strategies at field scale. The purpose is to design management strategies that are responsive to the water status of the biophysical system (soil - grapevine - intercrop) and the past and current climatic conditions. VERDI realistically reproduces the dynamic interactions between the biophysical system and the decision system in varying Mediterranean rain regime. The decision system works as an interpreter of a management strategy, defined as a set of soil surface management activities (e.g. mechanical weeding of the intercrop) that are linked by temporal constraints (e.g. sequencing, synchronisation) and organisational or programmatic specifications (e.g. iteration). The adaptive capabilities of the strategies are distinguished according to the different sources of flexibility to be exploited at operational, tactical, and strategic levels. A simulation study is reported that involves more or less flexible strategies under different climate scenarios. The simulation results proved that, in case of severe drought, the most flexible strategy yields the best trade-off between agricultural production and environmental services over the years.

MELODIE: a whole-farm model to study the dynamics of nutrients in dairy and pig farms with crops.

In regions of intensive pig and dairy farming, nutrient losses to the environment at farm level are a source of concern for water and air quality. Dynamic models are useful tools to evaluate the effects of production strategies on nutrient flows and losses to the environment. This paper presents the development of a new whole-farm model upscaling dynamic models developed at the field or animal scale. The model, called MELODIE, is based on an original structure with interacting biotechnical and decisional modules. Indeed, it is supported by an ontology of production systems and the associated programming platform DIESE. The biotechnical module simulates the nutrient flows in the different animal, soil and crops and manure sub-models. The decision module relies on an annual optimization of cropping and spreading allocation plans, and on the flexible execution of activity plans for each simulated year. These plans are examined every day by an operational management sub-model and their application is context dependent. As a result, MELODIE dynamically simulates the flows of carbon, nitrogen, phosphorus, copper, zinc and water within the whole farm over the short and long-term considering both the farming system and its adaptation to climatic conditions. Therefore, it is possible to study both the spatial and temporal heterogeneity of the environmental risks, and to test changes of practices and innovative scenarios. This is illustrated with one example of simulation plan on dairy farms to interpret the Nitrogen farm-gate budget indicator. It shows that this indicator is able to reflect small differences in Nitrogen losses between different systems, but it can only be interpreted using a mobile average, not on a yearly basis. This example illustrates how MELODIE could be used to study the dynamic behaviour of the system and the dynamic of nutrient flows. Finally, MELODIE can also be used for comprehensive multi-criterion assessments, and it also constitutes a generic and evolving framework for virtual experimentation on animal farming systems.

A Conceptual Model of Grassland-Based Beef Systems

Fulfilling the production objectives of a grassland-based beef system requires a robust management strategy to secure the best practicable use of forage resources with regard to the cattle demand. To address the challenging issue of designing such strategies, this article describes the application of an ontology of agricultural production systems to the generic conceptual model SEDIVER, which supports the representation and dynamic farm-scale simulation of specific grassland-based beef systems. The most salient and novel aspects of SEDIVER concern the explicit modeling of (a) the diversity in plant, grassland, animal and farmland, and (b) management strategies that deal with the planning and coordination of activities whereby the farmer controls the biophysical processes. By using the SEDIVER conceptual framework, part of the subjective and context-specific knowledge used in farm management can be captured and, in this way, enable scientific investigation of management practices.

An artificial-intelligence-based software for designing crop management plans

Abstract A crop management plan is a logical and ordered sequence of cultural operations applied to a crop. When managing a crop with various production objectives or input levels, and taking into account constraints induced by the management of other production activities on the farm, one must establish the whole set of cultural operations simultaneously and coherently. This paper introduces software intended to design crop management plans for wheat crops, based on constraint satisfaction procedures. The software uses concepts of artificial intelligence. In particular, we separate two kinds of knowledge: domain-specific (experimental relationships between variables) and general design knowledge which could be applied in other contexts. This separation allows us to aim, not only at a wheat-specific software, but also at a general software environment for easily solving a class of problems defined in common terms.

Modelling Management Practices in Viticulture while Considering Resource Limitations: The Dhivine Model

Many farming-system studies have investigated the design and evaluation of crop-management practices with respect to economic performance and reduction in environmental impacts. In contrast, little research has been devoted to analysing these practices in terms of matching the recurrent context-dependent demand for resources (labour in particular) with those available on the farm. This paper presents Dhivine, a simulation model of operational management of grape production at the vineyard scale. Particular attention focuses on representing a flexible plan, which organises activities temporally, the resources available to the vineyard manager and the process of scheduling and executing the activities. The model relies on a generic production-system ontology used in several agricultural production domains. The types of investigations that the model supports are briefly illustrated. The enhanced realism of the production-management situations simulated makes it possible to examine and understand properties of resource-constrained work-organisation strategies and possibilities for improving them.

A whole farm-model to simulate the environmental impacts of animal farming system: Melodie

The ex ante environmental evaluation of farming systems is increasingly demanded when proposing new developments of animal farming systems. Modelling is a promising approach to reduce the cost and the delay in studying the relationship between farming management and risky emissions. The simulation of decision is essential to better analyze ex ante changes in farm management, but is rarely considered in environmental models. MELODIE simulates the flows of carbon, nitrogen, phosphorus, copper, zinc and water within the whole pig and dairy farm over the long term. MELODIE upscales dynamic models developed at the field or animal scale by considering the management of the whole farm system coherently with the livestock farming system. The model is structured according to an ontology of agricultural production systems to represent the interactions between the biotechnical system and the decision system. The biotechnical module simulates the nutrient flows at a daily time step for each entity of the sub-models (soil/crop, animal and manure processes). MELODIE represents decisions at two time scales: every year, for drawing annual activity plans and every day for the context-dependent application of this plan. Thanks to the interactions between the biotechnical system and the decision system at different time scales, MELODIE is able to run consistently under different long-term climate series. The goal is to study the emerging properties of the system. Besides, because the nutrient flows within the farm are dynamically simulated, it is possible to study both the spatial and temporal heterogeneity of the environmental risks. This approach enables a better understanding of variability in the farming system according to climate. MELODIE is intended for use in research, not as a decision support system for farm management. It is a framework for virtual experimentation on animal farming systems, and could be extended to deal with other issues than nutrient flows.

Making Sequential Crop Management Decisions: A Constraint Satisfaction Approach

Abstract This paper presents a constraint satisfaction approach of the crop management planning problem. This sequential decision problem can be tackled by solving a sequence of constraint satisfaction problems (CSP) in a way inspired from the backward induction mechanism used in dynamic programming. The main benefit that can be expected from this approach relies on the powerful capabilities that CSP techniques provide for the generation of the states which may be encountered during the cropping season.

Decision in agroecosystems advanced modelling techniques studying global changes in environmental sciences

Abstract In recent years, the sustainable management of agricultural and ecological systems has become a major challenge. Sustainable management has to solve crucial environmental problems linked, in part, to rapid changes in context: climatic changes, agricultural policy objectives changes, and so on. Solving this challenge involves the joint development of research in modelling, simulation, and virtual experimentation. In this chapter, we present some recent work devoted to the modelling and simulation of complex systems involved in agroecosystem management. Then, we present new formalisms for management strategies design, based on the weighted constraint satisfaction problems or the Markov decision processes frameworks. We also show how simulation and conception of strategies can be integrated. Finally, we illustrate the use of the presented approaches on several case studies in agroecosystems management, jointly tackled with research teams in agronomy.