INTERACTIVE SIMULATION OF VARIOUS MANAGEMENT STRATEGIES

INTERACTIVE SIMULATION OF VARIOUS MANAGEMENT STRATEGIES

GAMEDE: A WHOLE-FARM DYNAMIC MODEL

Crop-livestock farms, especially dairy farms, are very complex systems. The interactions operating in such systems involve structural, decisional, biophysical, and environmental factors. Moreover, as farmers face a large range of management options, they need tools to support their decision-making to reach production levels complying with their objectives and their human and physical resources while controlling their effects on the environment. Computer models may serve both to explore this complexity and to help extension agents and farmers themselves test management scenarios for their farms. GAMEDE, a whole-dairy-farm model, has been developed to represent dynamically the effect of operational decisions on biomass flows within the system, distinguishing between “actionable” (i.e. man-controlled) and biophysical flows.

This article describes the model dealing with decisional and biophysical processes. Output indicators such as milk and forage crop productivity, work time, nitrogen balance, and nitrogen efficiency help assess the system’s overall performances. Six farms with different structures, agro-climatic conditions, and management strategies were used for validation. The results indicate that the model is able to explain the differences found in their sustainability indicators at the year scale. The infra-year variability of biomass flows and stocks is also well explained.

Inquiries and observations within the scope of bimonthly visits

In tune with the experiences of Aubry et al. (1998) and Dounias et al. (2002), we organised regular visits to the farmer’s about the planning of their technical operations, their effective practices and the technical results of their actions (to quantify biomass flows). We have collected those data on a bimonthly basis during two years.The first author of this paper continued to participate in the farm works to maintain the especially confident relationship. The principal objective of this second type of inquiry was to regularly (six times per year) compare with farmers their action plan to their effective actions (plan versus reality). We used farm management schedules as discussion supports. Environmental conditions (weather, fodder market, etc.) were recorded and some observations were made on the grass fields and the cattle, so as to be able to describe operating conditions. Many complementary operational rules, adjustment rules in particular, were identified.

Interactive simulations with the farmer

Information about technical options circulates between extension agents and farmers, and between farmers themselves. The farmers thus already know about options that could be applied to their farms. GAMEDE can be used to improve farmers’ knowledge of the consequences of these hypothetical options on their farm functioning. During an individual meeting aimed at evaluating the realism of the simulations proposed by the model from the farmer’s viewpoint (simulation of the actual scenario), farmer 2 asked if we could simulate the building of 50 cow cubicles for lactating cows. An improvised “kitchen table simulation” compared actual management practices to the hypothetical management options proposed by the farmer . We used graphic representations given by GAMEDE to follow with the farmer the biomass cycle in the farm: dejecta production in barns Æorganic fertiliser available in the slurry pit Æ grassland fertilising Æ forage production Æ silage stocked.

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Table des matières

RESUME
ABSTRACT
REMERCIEMENTS
PREAMBULE
TABLE DES MATIERES
LISTE DES TABLEAUX
LISTE DES FIGURES
CHAPITRE I.INTRODUCTION GENERALE
I.1DURABILITE ET ELEVAGE DANS LE MONDE
I.2DURABILITE DE LA FILIERE BOVINE LAITIERE A LA REUNION
I.3OBJECTIF ET QUESTIONS DU PROJET GAMEDE
I.4CONTENU DU MANUSCRIT
CHAPITRE II.DE L’ETUDE DES FLUX D’AZOTE A UN MODELE D’EVALUATION DE LA DURABILITE DES SYSTEMES DE PRODUCTION CONSTRUIT EN PARTENARIAT
II.1EXPLAINING THE DIVERSITY OF ENVIRONMENTAL PERFORMANCES ACCORDING TO A TYPOLOGY OF FARMING PRACTICES COMBINATIONS
II.2MODELISER LES PRATIQUES DECISIONNELLES ET LES FLUX D’AZOTE A L’ECHELLE GLOBALE DE L’EXPLOITATION
II.2.1Approche de modélisation
II.2.2Description du modèle
II.2.3Originalité de l’approche et validation du modèle
II.3FARMERS PARTICIPATION IN DESIGNING A WHOLE FARM MODEL
CHAPITRE III.GAMEDE: A WHOLE-FARM DYNAMIC MODEL
III.1MODEL OVERVIEW
III.1.1Computing characteristics
III.1.2Stock-flow model
III.1.3Model inputs and outputs
III.2INTEGRATION OF DECISIONAL AND BIOPHYSICAL SYSTEMS
III.2.1Model architecture
III.2.2Model ontology
III.2.3Decisional system
III.3INTEGRATION OF VARIOUS SOIL, CROP, AND ANIMAL PROCESSES IN THE BIOPHYSICAL SYSTEM
III.3.1The module of green forage production (MPF)
III.3.2The forage conditioning module (MCF)
III.3.3The herd demography module (MD)
III.3.4The herd productions module (MPA)
III.3.5The grazing module (MP)
III.3.6The module of fertiliser evolution (MEA)
III.4MODEL EVALUATION
III.4.1Quantitative validation of the model
III.4.2The DS as a central way to improve the model realism
III.4.3Interest of subjective and qualitative validations
CHAPITRE IV.MODELLING FARMERS’ ACTION: DECISION RULES CAPTURE METHODOLOGY AND FORMALISATION STRUCTURE
IV.1LITERATURE REVIEW
IV.1.1General concepts to define the domain of the present study
IV.1.2Conceptual decision models resulting from the concept of action model
IV.1.3Agricultural production systems ontology
IV.2STUDY AREA DESCRIPTION
IV.3METHODOLOGY: AN ITERATIVE COMBINATION OF APPROACHES TO REVEL DECISION RULES
IV.3.1Immersion
IV.3.2Inquiries and observations within the scope of bimonthly visits
IV.3.3Individual and collective validation from farmers themselves
IV.3.4Definition of the working group based on an iterative process
IV.3.5Complementarities of immersion and rapid appraisal
IV.3.6Validation by simulating farmers’ actions
IV.4RESULTS
IV.4.1From study of dairy farm management constraints: adjustments to be made to decision approaches
IV.4.2The conceptual structure: convergence between the action model and the agricultural production systems ontology
IV.4.3A generic structure offering a pertinent way of interpreting farmer’s actions: illustration by examples
IV.4.4From operational decision rules to simulation of farmer’s actions
IV.5DISCUSSION: VALIDATION AND CO-PRODUCTS OF THE ACTION MODELLING
IV.5.1Quantitative validation of the methodology
IV.5.2Qualitative validation of the SAM
IV.5.3Crossing planned action and situated action theories
IV.5.4Co-products of action modelling: a better comprehension of farmers’ logic of farm management
CHAPITRE V.INTERACTIVE SIMULATION OF VARIOUS MANAGEMENT STRATEGIES
V.1MATERIALS AND METHOD: FROM A MODEL TO INTERACTIVE SIMULATIONS
V.1.1GAMEDE: the material support of simulations
V.1.2Dairy production areas and typical cases of farming systems
V.1.3Definition of “actual” scenarios based on farms survey
V.1.4Identification of “hypothetical” options from different sources
V.2RESULTS: FROM EXTERNAL ASSESSMENTS TO CO-CONSTRUCTED SOLUTIONS
V.2.1Learning from diversity
V.2.2Learning from dynamical graphic representations
V.2.3Learning from testing “hypothetical” options
V.3DISCUSSION: TOWARDS A DECISION SUPPORT TOOL FOR FARMERS
V.3.1GAMEDE validation by its use
V.3.2Comparison with other experiences of DSS implementation
V.3.3Reasons for such success?
V.3.4Model use perspectives
CHAPITRE VI.DISCUSSION GENERALE
VI.1UN MODELE ORIGINAL
VI.2INNOVATION METHODOLOGIQUE
VI.3LIMITES ET GENERICITE DU MODELE GAMEDE
CONCLUSION GENERALE
REFERENCES
LISTE DES ANNEXES