Modélisation statistique et dynamique de la composition de la graine de tournesol (Helianthus annuus L.) sous l’influence de facteurs agronomiques et environnementaux

Pour répondre à la demande mondiale croissante en huile et en protéines, le tournesol apparaît comme une culture très compétitive grâce à la diversification de ses débouchés et son attractivité environnementale et nutritionnelle. Pourtant, les teneurs en huile et protéines sont soumises à des effets génotypiques et environnementaux qui les rendent fluctuantes et difficilement prédictibles. Nous argumentons qu’une meilleure connaissance des effets les plus importants et leurs interactions devrait permettre de mieux prédire ces teneurs. Deux approches de modélisation ont été développées. Dans la première, trois modèles statistiques ont été construits puis comparés à un modèle simple existant. L’approche dynamique est basée sur l’analyse des relations source-puits au champ et en serre (2011 et 2012) pendant le remplissage. Les performances et domaines de validité des deux types de modélisation sont comparés. ABSTRACT : Considering the growing global demand for oil and protein, sunflower appears as a highly competitive crop, thanks to the diversification of its markets and environmental attractiveness and health. Yet the protein and oil contents are submitted to genotypic and environmental effects that make them fluctuating and hardly predictable. We argue that a better knowledge of most important effects and their interactions should permit to improve prediction. Two modeling approaches are proposed: statistical one, where we compared three types of statistical models with a simple existing one. The dynamic approach is based on source-sink relationships analysis (field and greenhouse experiments in 2011 and 2012) during grain filling. Performances of both modeling types and their validity domain are compared

Prediction of sunflower grain oil concentration as a function ofvariety, crop management and environment using statistical models

Sunflower (Helianthus annuus L.) raises as a competitive oilseed crop in the current environmentallyfriendly context. To help targeting adequate management strategies, we explored statistical models astools to understand and predict sunflower oil concentration. A trials database was built upon experi-ments carried out on a total of 61 varieties over the 2000–2011 period, grown in different locations inFrance under contrasting management conditions (nitrogen fertilization, water regime, plant density).25 literature-based predictors of seed oil concentration were used to build 3 statistical models (multiplelinear regression, generalized additive model (GAM), regression tree (RT)) and compared to the refer-ence simple one of Pereyra-Irujo and Aguirrezábal (2007) based on 3 variables. Performance of modelswas assessed by means of statistical indicators, including root mean squared error of prediction (RMSEP)and model efficiency (EF). GAM-based model performed best (RMSEP = 1.95%; EF = 0.71) while the simplemodel led to poor results in our database (RMSEP = 3.33%; EF = 0.09). We computed hierarchical contribu-tion of predictors in each model by means of R2and concluded to the leading determination of potentialoil concentration (OC), followed by post-flowering canopy functioning indicators (LAD2 and MRUE2),plant nitrogen and water status and high temperatures effect. Diagnosis of error in the 4 statistical mod-els and their domains of applicability are discussed. An improved statistical model (GAM-based) wasproposed for sunflower oil prediction on a large panel of genotypes grown in contrasting environments

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.HighlightsWe propose a new approach to deriving calibration recommendations for system modelsApproach is based on analyzing calibration in multi-model simulation exercisesResulting recommendations are holistic and anchored in actual practiceWe apply the approach to calibration of crop models used to simulate phenologyRecommendations concern: objective function, parameters to estimate, software usedCompeting Interest StatementThe authors have declared no competing interest

Statistical and dynamic modeling of sunflower (Helianthus annuus L.) grain composition under agronomic and environmental factors effects

Pour répondre à la demande mondiale croissante en huile et en protéines, le tournesol apparaît comme une culture très compétitive grâce à la diversification de ses débouchés et son attractivité environnementale et nutritionnelle. Pourtant, les teneurs en huile et protéines sont soumises à des effets génotypiques et environnementaux qui les rendent fluctuantes et difficilement prédictibles. Nous argumentons qu’une meilleure connaissance des effets les plus importants et leurs interactions devrait permettre de mieux prédire ces teneurs. Deux approches de modélisation ont été développées. Dans la première, trois modèles statistiques ont été construits puis comparés à un modèle simple existant. L’approche dynamique est basée sur l’analyse des relations source-puits au champ et en serre (2011 et 2012) pendant le remplissage. Les performances et domaines de validité des deux types de modélisation sont comparés.Considering the growing global demand for oil and protein, sunflower appears as a highly competitive crop, thanks to the diversification of its markets and environmental attractiveness and health. Yet the protein and oil contents are submitted to genotypic and environmental effects that make them fluctuating and hardly predictable. We argue that a better knowledge of most important effects and their interactions should permit to improve prediction. Two modeling approaches are proposed: statistical one, where we compared three types of statistical models with a simple existing one. The dynamic approach is based on source-sink relationships analysis (field and greenhouse experiments in 2011 and 2012) during grain filling. Performances of both modeling types and their validity domain are compared

Analysis and modelling of the factors controlling seed oil concentration in sunflower: a review

Sunflower appears as a potentially highly competitive crop, thanks to the diversification of its market and the richness of its oil. However, seed oil concentration (OC) - a commercial criterion for crushing industry - is subjected to genotypic and environmental effects that make it sometimes hardly predictable. It is assumed that more understanding of oil physiology combined with the use of crop models should permit to improve prediction and management of grain quality for various end-users. Main effects of temperature, water, nitrogen, plant density and fungal diseases were reviewed in this paper. Current generic and specific crop models which simulate oil concentration were found to be empirical and to lack of proper evaluation processes. Recently two modeling approaches integrating ecophysiological knowledge were developed by Andrianasolo (2014, Statistical and dynamic modelling of sunflower (Helianthus annuus L.) grain composition as a function of agronomic and environmental factors, Ph.D. Thesis, INP Toulouse): (i) a statistical approach relating OC to a range of explanatory variables (potential OC, temperature, water and nitrogen stress indices, intercepted radiation, plant density) which resulted in prediction quality from 1.9 to 2.5 oil points depending on the nature of the models; (ii) a dynamic approach, based on "source-sink" relationships involving leaves, stems, receptacles (as sources) and hulls, proteins and oil (as sinks) and using priority rules for carbon and nitrogen allocation. The latter model reproduced dynamic patterns of all source and sink components faithfully, but tended to overestimate OC. A better description of photosynthesis and nitrogen uptake, as well as genotypic parameters is expected to improve its performance.Le tournesol apparaît comme une culture potentiellement compétitive grâce à la diversité de ses débouchés et de la richesse en huile de ses graines. Cependant, la teneur en huile de la graine (TH) –critère commercial pour la trituration– dépend d’effets génotypiques et environnementaux ce qui en complexifie parfois la prédiction. Nous faisons l’hypothèse qu’une meilleure compréhension de la physiologie de l’accumulation d’huile combinée à l’utilisation de modèles de culture permettrait d’améliorer la prédiction et la gestion de la qualité du grain pour différents usages. Les principaux effets de la température, de l’eau, de l’azote, de la densité de peuplement et des maladies fongiques sont revus dans cette synthèse. Les modèles de culture génériques et spécifiques apparaissent empiriques pour ce qui concerne TH et manquent d’évaluation pour ce critère. Récemment, deux approches de modélisation intégrant des connaissances écophysiologiques ont été développées par Andrianasolo (2014, Modélisation statistique et dynamique de la composition de la graine de tournesol (Helianthus annuus L.) sous l’influence des facteurs agronomiques et environnementaux, Ph.D. Thesis, INP Toulouse) : (i) une approche statistique reliant la teneur en huile à une gamme de variables explicatives (TH potentielle, température, indices de stress eau et azote, rayonnement intercepté, densité de peuplement) dont la qualité prédictive est de 1.9 à 2.5 points d’huile selon le type de modèle développé ; ( ii) une approche dynamique basée sur les relations ‘source-puits’ incluant les feuilles, les tiges, les réceptacles (en tant que sources), les coques, les protéines et l’huile (en tant que puits ) et mobilisant des règles de priorité pour l’allocation du carbone et de l’azote. Ce modèle reproduit assez bien les dynamiques des composantes « sources » et « puits » avec une tendance à surestimer TH. Une meilleure prise en compte de la photosynthèse et de l’absorption d’azote mais aussi des paramètres génotypiques est nécessaire à l’amélioration des performances d’un tel modèle dynamique

Source and sink indicators for determining nitrogen, plant density andgenotype effects on oil and protein contents in sunflower achenes

Given the diversification of oilseed-based products, sunflower is a competitive crop for obtaining high oil and protein concentrations; however, both are subject to genotypic and environmental variability. We analyzed individual and interaction effects of nitrogen (N), plant density (D) and genotype (G) in a 2-year field experiment. A set of “sink” (oil, protein, and hull concentrations and quantities) and “source” (leaf area duration, nitrogen uptake, nitrogen and biomass remobilizations) indicators were measured at harvest in a split-split-plot design with contrasting nitrogen (N+: 150 kg ha−1 ; N−: no fertilization), plant density (D1: 3 and D2: 4.5 plants m−2 ) and genotype (cv. LG5451HO in 2011, cv. Olledy in 2012 and cv. Kerbel in both years) treatments. We found that nitrogen had a significant positive effect on protein con- centration and plant density had a positive effect on nitrogen uptake after flowering. Oil concentration was not related to oil weight but was related to plant dry matter at flowering and biomass remobilization. Protein concentration was related to protein weight and nitrogen nutrition index at flowering and to nitrogen uptake and leaf area duration after flowering. Significant interaction effects were identified on sink (N × D, D × G) and source (N × G) indicators in the 2012 experiment, which was only partly explained by differences in initial states at flowering. In this study, the genotype that maximized oil concentration depended on nitrogen and plant density conditions, while the genotype that maximized protein concen- tration was the same regardless of cropping conditions. We highlight the importance of analyzing effects of determining factors on oil accumulation during grain filling

Analysis and modelling of the factors controlling seed oil concentration in sunflower: a review

Sunflower appears as a potentially highly competitive crop, thanks to the diversification of its market and the richness of its oil. However, seed oil concentration (OC) – a commercial criterion for crushing industry – is subjected to genotypic and environmental effects that make it sometimes hardly predictable. It is assumed that more understanding of oil physiology combined with the use of crop models should permit to improve prediction and management of grain quality for various end-users. Main effects of temperature, water, nitrogen, plant density and fungal diseases were reviewed in this paper. Current generic and specific crop models which simulate oil concentration were found to be empirical and to lack of proper evaluation processes. Recently two modeling approaches integrating ecophysiological knowledge were developed by Andrianasolo (2014, Statistical and dynamic modelling of sunflower (Helianthus annuus L.) grain composition as a function of agronomic and environmental factors, Ph.D. Thesis, INP Toulouse): (i) a statistical approach relating OC to a range of explanatory variables (potential OC, temperature, water and nitrogen stress indices, intercepted radiation, plant density) which resulted in prediction quality from 1.9 to 2.5 oil points depending on the nature of the models; (ii) a dynamic approach, based on “source-sink” relationships involving leaves, stems, receptacles (as sources) and hulls, proteins and oil (as sinks) and using priority rules for carbon and nitrogen allocation. The latter model reproduced dynamic patterns of all source and sink components faithfully, but tended to overestimate OC. A better description of photosynthesis and nitrogen uptake, as well as genotypic parameters is expected to improve its performance

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in essentially every application of crop models and process models in other fields and has an important impact on simulated values. The goal of this study is to develop a comprehensive list of the decisions involved in calibration and to identify the range of choices made in practice, as groundwork for developing guidelines for crop model calibration starting with phenology. Three groups of decisions are identified; the criterion for choosing the parameter values, the choice of parameters to estimate and numerical aspects of parameter estimation. It is found that in practice there is a large diversity of choices for every decision, even among modeling groups using the same model structure. These findings are relevant to process models in other fields