Comprendre et prédire la phénologie du soja pour adapter la culture à de nouveaux environnements climatiques.

L’exploration de nouvelles stratégies agronomiques (semis très précoce, expansion de la culture à des latitudes plus élevées, double culture) pour augmenter la production de soja en Europe dans un contexte de changement climatique impose de prévoir la phénologie de la culture dans des environnements thermiques et photopériodiques variés. Dans cet objectif, le modèle (Simple Phenology) de prédiction de la phénologie du soja a été calibré et évalué à partir de données expérimentales. Pour la calibration, deux expériences ont été réalisées avec 10 génotypes contrastés (groupes de maturité 000 à II) : 1- suivi phénologique de plantes en pots sur la plateforme HeliaphenINRA Toulouse avec 6 dates de semis, 2- réponse de la germination à la température en conditions contrôlées. L’évaluation du modèle a été réalisée à partir d’essais multi-locaux menés dans le cadre du projet SOJAMIP 2012-15 ainsi qu’à l’INRA Toulouse en 2017 et 2018. Les températures cardinales de germination (Tmin, Topt et Tmax) sont proches de 0, 30 et 40°C ; avec des différences significatives de sensibilité des variétés à la photopériode. Le modèle calibré avec un paramétrage variétal a montré une RMSE de ~ 6 jours pour la prédiction du cycle cultural (i.e. stade cotylédons à maturité physiologique). Coupler l’expérimentation et la modélisation agronomique permettra de positionner le cycle cultural de variétés de soja dans de nouveaux environnements.Developing new cropping strategies (very early sowing, crop expansion at higher latitudes, double cropping) to improve soybean production in Europe under climate change needs a good prediction of phenology in different temperature and photoperiod conditions. For this aim, a soybean phenology model was calibrated and evaluated using experimental data. Two experiments were carried out with 10 contrasting genotypes (maturity group 000 to II): 1- Phenological monitoring of plants in pots on the Heliaphen platform with 6 sowing dates (INRA Toulouse). 2- Response of seed germination to temperature in controlled conditions. Multilocal trials carried out as part of the SOJAMIP 2012-15 project and at INRA Toulouse in 2017and 2018, were used to evaluate the phenology predicted by Simple [br/] Phenology (SP) model. Cardinal temperatures (minimal, optimal and maximal) of germination were close to 0, 30 and 40°C, respectively; with significant differences for photoperiod sensitivity among varieties. The calibrated model with varietal parameters showed an RMSE of less than 6 days for the prediction of crop cycle duration (i.e. cotyledons stage to physiological maturity). Combining experimentation and agronomic modeling will make it possible to predict phenology of soybean genotypes in new environments

Adding a diversity of legumes to a crop decision-support system: Maintaining satisfactory accuracy while keeping the model simple

International audienceIn a context of economic and environmental concerns in agriculture, legumes appear to be suitable alternative crops to diversify current cropping systems and reduce their dependence on synthetic nitrogen (N) fertiliser and protein from imported soya bean. However, legume-based cropping systems may increase N losses through nitrate leaching if the N available from legumes does not coincide with subsequent crop requirements. To help agricultural advisers manage N in these systems, we adapted the decision-support system Syst’N®, designed to assess N losses in cropping systems, to simulate three annual and one perennial legume crops: pea, faba bean, soya bean and lucerne. To this end, we adapted and simplified existing submodels of legume functioning to include them in Syst’N, to keep the latter simple. We adapted the submodels “BNF” (i.e. biological N fixation) from the STICS model and “dormancy” from the CropSyst model. We also added the ability to enter the flowering date to improve predictions (improvement in N fixation’s rRMSE from 57% to 41% and EF from 0.57 to 0.77). The equations and associated parameter set developed for the four legume crops yielded satisfying predictions of crop biomass (rMBE = 9%, EF = 0.82, rRMSE = 39%) and N content (rMBE = 5%, EF = 0.76, rRMSE = 37%). These performances support the philosophy of Syst’N® that requires minimising the number of additional parameters for users when representing new crops or processes

Future area expansion outweighs increasing drought risk for soybean in Europe

The European Union is highly dependent on soybean imports from overseas to meet its protein demands. Individual Member States have been quick to declare self- sufficiency targets for plant- based proteins, but detailed strategies are still lacking. Rising global temperatures have painted an image of a bright future for soybean pro-duction in Europe, but emerging climatic risks such as drought have so far not been included in any of those outlooks. Here, we present simulations of future soybean production and the most prominent risk factors across Europe using an ensemble of climate and soybean growth models. Projections suggest a substantial increase in potential soybean production area and productivity in Central Europe, while southern European production would become increasingly dependent on supplementary irrigation. Average productivity would rise by 8.3% (RCP 4.5) to 8.7% (RCP 8.5) as a result of improved growing conditions (plant physiology benefiting from rising temperature and CO2 levels) and farmers adapting to them by using cultivars with longer phenological cycles. Suitable production area would rise by 31.4% (RCP 4.5) to 37.7% (RCP 8.5) by the mid-century, contributing considerably more than productivity increase to the production potential for closing the protein gap in Europe. While wet conditions at harvest and incidental cold spells are the current key challenges for extending soybean production, the models and climate data analysis anticipate that drought and heat will become the dominant limitations in the future. Breeding for heat-tolerant and water-efficient genotypes is needed to further improve soybean adaptation to changing climatic conditions.Open Access to this publication at: [https://onlinelibrary.wiley.com/share/PHYJQNVCQJ3DCYDTQQMB?target=10.1111/gcb.16562