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https://hal.archives-ouvertes.fr/hal-02293097/document

About modeling and control strategies for scheduling crop irrigation

Le modele de culture STICS est developpe a l'INRA depuis 1996 en collaboration avec d'autres centres de recherche et instituts techniques. Ce modele synthetise, illustre et concretise une part importante des connaissances agronomiques francaises, comme un point de vue sur la parcelle et le fonctionnement des systemes de culture. Les formalisations du modele STICS presentees dans cet ouvrage peuvent etre considerees comme des references en agrophysiologie. L'ouvrage est structure selon la facon dont le modele concoit le fonctionnement du systeme sol-culture. Chaque chapitre est consacre a un ensemble important de fonctions telles que le demarrage en croissance, la mise en place des composantes du rendement, la consommation en eau, la transformation de la matiere organique etc. L'un des chapitres traite de la problematique des systemes de culture et des simulations a long terme. Le dernier chapitre aborde l'implication de l'utilisateur en termes de choix d'options et de parametrisation. Cet ouvrage est principalement destine aux scientifiques qui utilisent le modele STICS mais il interessera egalement les agronomes, les modelisateurs de cultures, etudiants et techniciens a la recherche de formalisations elementaires du fonctionnement du systeme culture-sol.

Conceptual basis, formalisations and parameterization of the STICS crop model, second edition

This paper discusses an innovative framework to use crop models which combines sensitivity analysis, uncertainty analysis and constrained optimisation runs for irrigation optimisation purposes, facing competing constraints on several agricultural variables (e.g. crop yield, total irrigation amount, financial expectations). For simplicity, this ex-post optimisation relies on direct calculations only, exploiting the dispersions on the target variables. The screening of the parameter space for sensitivity analysis yields a reference dispersion which is expectedly reduced by reducing the uncertainties in the sensitive parameters and/or climatic forcings. Additional dispersions are calculated to evaluate if the management controls on irrigation strategies (amounts, triggers, periods) are more influential on model predictions than the remaining uncertainties on the soil, plant, irrigation and climatic inputs, eventually allowing optimisation. As a case study, the Optirrig model is used. A discussion proposes future ways to convert diagnostics into real-time near-optimal decision rules, for example through learning algorithms. This ex-post optimisation of irrigation strategies involves direct calculations only.The Optirrig model (Irstea Montpellier, France) is chosen for application.Both the feasibility and the achievement of irrigation optimisation are handled.Dispersion is reduced through sensitivity, uncertainty then optimisation runs.Perspectives are discussed to convert diagnostics into real-time decision rules.

A framework to use crop models for multi-objective constrained optimization of irrigation strategies

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A framework to use crop models for multi-objective constrained optimization of irrigation strategies