La lixiviation d’azote nitrique dans les rotations céréalières avec colza : un diagnostic à partir de l’analyse de résultats d’expérimentations pluriannuelles et de modélisations

Nitrate leaching was measured during 6 years in a crop rotation including oilseed rape (OSR) and winter wheat in two field trials, on two rendzina soils differing by their water content, in Le Magneraud (West of France) and Martincourt (East of France). Results show that nitrate losses are variable within the year and drainage: between 20 and 95 kgN.ha–1.an–1. When the soil remains without cover crop after OSR, losses under wheat following OSR are definitely higher than under OSR following wheat: respectively 54.1 kgN.ha–1.an–1 and 14.8 kgN.ha–1.an–1 during the first three years in Le Magneraud. Results even show that nitrate losses differed according to N strategy management. Among the treatments of nitrogen management, the least losses are observed with optimized fertilization plus OSR volunteers used as cover crop between OSR and wheat; and the highest losses are observed with high fertilization without OSR volunteers as catch crop : respectively in Le Magneraud (second phases of three years) 29.9 and 61.1 kgN.ha–1.an–1, and at Martincourt 43.0 and 61.4 kgN.ha–1.an–1 on average. Simulations realized with LIXIM and DEAC models for new years, soils and regions, show the same trend that experimental results: nitrate losses are higher under wheat after OSR, and the high performance cropping systems are based on optimized N fertilization management of OSR and the catch of nitrate at the end of summer by OSR (after wheat) or its volunteers (before wheat)

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

Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

This article aims to evaluate the potential of multitemporal and multiorbital remote sensing data acquired both in the microwave and optical domain to derive rapeseed biophysical parameters (crop height, dry mass, fresh mass, and plant water content). Dense temporal series of 98 Landsat-8 and Sentinel-2 images were used to derive normalized difference vegetation index (NDVI), green fraction cover (fCover), and green area index (GAI), while backscattering coefficients and radar vegetation index (RVI) were obtained from 231 mages acquired by synthetic aperture radar (SAR) onboard Sentinel-1 platform. Temporal signatures of these remote sensing indicators (RSI) were physically interpreted, compared with each other to ground measurements of biophysical parameters acquired over 14 winter rapeseed fields throughout the 2017–2018 crop season. We introduced new indicators based on the cumulative sum of each RSI that showed a significant improvement in their predictive power. Results particularly reveal the complementarity of SAR and optical data for rapeseed crop monitoring throughout its phenological cycle. They highlight the potential of the newly introduced indicator based on the VH polarized backscatter coefficient to estimate height (R2 = 0.87), plant water content (R2 = 0.77, from flowering to harvest), and fresh mass (R2 = 0.73) and RVI to estimate dry mass (R2 = 0.82). Results also demonstrate that multiorbital SAR data can be merged without significantly degrading the performance of SAR-based relationships while strongly increasing the temporal sampling of the monitoring. These results are promising in view of assimilating optical and SAR data into crop models for finer rapeseed monitoring

Le raisonnement et les avancées techniques permettent de réduire la fertilisation azotée : le cas de Farmstar-colza®

The practices of nitrogen fertilization are really changing, due to the new farming context including environmental, economical, and energetic constraints. In 2005, 90% of the producers said they used a method to adapt nitrogen supplies in their fields and more than 40% among them used the CETIOM method “réglette azote”. Since 2004, a new system, called Farmstar-colza® based on satellite observation has been developed by Infoterra France, a subsidiary of the EADS group. With this technology, it’s possible to draw a map of the fields with nitrogen supply advices and to practice modular apply on the crops. Moreover, this technology increases the precision of the crop nitrogen absorption assessment. Therefore, in the future it seems possible to improve the estimated need of nitrogen to grow oil seed rape in order to get a good energetic balance with an optimum oil rate as well as high yields

La lixiviation d’azote nitrique dans les rotations céréalières avec colza : un diagnostic à partir de l’analyse de résultats d’expérimentations pluriannuelles et de modélisations

Nitrate leaching was measured during 6 years in a crop rotation including oilseed rape (OSR) and winter wheat in two field trials, on two rendzina soils differing by their water content, in Le Magneraud (West of France) and Martincourt (East of France). Results show that nitrate losses are variable within the year and drainage: between 20 and 95 kgN.ha–1.an–1. When the soil remains without cover crop after OSR, losses under wheat following OSR are definitely higher than under OSR following wheat: respectively 54.1 kgN.ha–1.an–1 and 14.8 kgN.ha–1.an–1 during the first three years in Le Magneraud. Results even show that nitrate losses differed according to N strategy management. Among the treatments of nitrogen management, the least losses are observed with optimized fertilization plus OSR volunteers used as cover crop between OSR and wheat; and the highest losses are observed with high fertilization without OSR volunteers as catch crop : respectively in Le Magneraud (second phases of three years) 29.9 and 61.1 kgN.ha–1.an–1, and at Martincourt 43.0 and 61.4 kgN.ha–1.an–1 on average. Simulations realized with LIXIM and DEAC models for new years, soils and regions, show the same trend that experimental results: nitrate losses are higher under wheat after OSR, and the high performance cropping systems are based on optimized N fertilization management of OSR and the catch of nitrate at the end of summer by OSR (after wheat) or its volunteers (before wheat)

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

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