Optimization of black-box models with uncertain climatic inputs. Application to sunflower ideotype design

Accounting for the interannual climatic variations is a well-known issue for simulation-based studies of environmental systems. It often requires intensive sampling (e.g., averaging the simulation outputs over many climatic series), which hinders many sequential processes, in particular optimization algorithms. We propose here an approach based on a subset selection in a large basis of climatic series, using an ad-hoc similarity function and clustering. A non-parametric reconstruction technique is introduced to estimate accurately the distribution of the output of interest using only the subset sampling. The proposed strategy is non-intrusive and generic (i.e. transposable to most models with climatic data inputs), and can be combined to most ªoff-the-shelfº optimization solvers. We apply our approach to sunflower ideotype design using the crop model SUNFLO. The underlying optimization problem is formulated as a multi-objective one to account for risk-aversion. Our approach achieves good performances even for limited computational budgets, outperforming significantly standard strategies

Delayed and reduced nitrogen fertilization strategies decrease nitrogen losses while still achieving high yields and high grain quality in malting barley

International audienceNitrogen fertilizer applications are essential to achieve high yield and malting specifications in barley, but may also have a deleterious effect on the environment. Most strategies currently being implemented aim to optimize quantitative and qualitative production without taking environmental concerns into account. We used a barley crop model to pinpoint new nitrogen management plans maximizing the calibrated yields (yield of grains larger than 2.5 mm) and the grain quality whilst reducing N gaseous emission and N leaching. We compared the currently recommended N fertilization strategy in France and 44 new ones defined based on expertise knowledge, and differing in the amount of N applied, splitting patterns and time of application. The strategies providing the best compromise between the three criteria were identified by considering Pareto optimal solutions over 25 years in 35 French departements (equivalent to a county). We also identified Pareto optimal strategies for the seven years with the lowest yields and for years in which climatic conditions were unfavorable for efficient use of the early N supplies. The current recommended N fertilization strategy resulted in a high proportion of situations satisfying malting grain protein content requirements, but also to high N losses. We pinpointed new N strategies resulting in better compromise between the three outputs studied. Some Pareto optimal strategies were particularly efficient to reduce N losses in all tested environments regardless of the climatic conditions. They, however, also slightly reduced calibrated yield compared to the reference strategy. Others interesting strategies performed better than the reference simultaneously for all three studied outputs, but depended on the region considered. A common feature of these strategies was later application of smaller doses of N. Our results, thus demonstrated that low-N strategies are possible for malting barley

Retrieval of soil water capacity at intra-plot scale using a data driven approach by combining unsupervised classification, crop modeling and Sentinel-2 remote sensing

International audienceWater fluxes and soil properties are notoriously hard to estimate because of their high-spatial variability. The inversion of agronomic models in water stress conditions can deliver accurate retrieval of soil parameters at local and basin scales. In this work we provide a methodology for regional scale inversion of the soil water capacity at an intra-plot resolution using high resolution optical data provided by satellites such as Sentinel2, Formosat2 or SPOT5. The methodology relies on intra-field unsupervised classification of remotely-sensed GAI followed by bayesian retrieval to reduce the number of inverted/simulated entities and observational noise. Several configurations for the unsupervised classification are tested to retain the soil heterogeneity while increasing retrieval efficiency using multi-temporal images. The identified classes are compared in a multi-annual framework. A prior per-field model calibration is also applied to account for cultivar variability and constrain the vegetation module of the crop model. Only a restricted set of vegetation parameters are used in this step. The inversion scheme is applied on sunflower fields chosen for their proneness to exhibit water stress in the agro-climatic context of southern France. We use a sum of temperature and FAO-56 based crop model (SAFYE) in a bayesian inversion algorithm (DREAM). The results show that the use of unsupervised classification reduces the computational needs significantly (x100) while preserving the identifiability of soil classes and providing a good accuracy (RMSE Available Water Content = 11mm, RMSE evapotranspiration = 0.56 mm/day and RMSE soil moisture = 0.02 - 0.07 m3/m3). This study provides new insights on the significant efficiency of the combined use of machine learning, physical modeling and remote sensing data to solve agronomic problems

A hidden semi-Markov model for inferring the structure of migratory bird flyway networks

International audienc

A hidden semi-Markov model for inferring the structure of migratory bird flyway networks

International audienc

Apprentissage par renforcement pour l’optimisation de la conduite de culture du colza

L’agriculture intégrée consiste à adapter les pratiques agricoles pour limiter l’usage des produits phytosanitaires tout en contenant la pression des biogresseurs et avec une productivité suffisante pour garantir un revenu satisfaisant à l’agriculteur. A l’échelle de l’année culturale, le problème peut être formulé comme un problème d’optimisation de décisions d’un itinéraire technique (ITK). Nous avons mis en oeuvre une méthode d’apprentissage par renforcement pour adopter une méthode d’optimisation basée sur l’expérimentation virtuelle dans le cadre de l’optimisation d’ITK pour la conduite de culture du colza. Ce projet a requis le couplage de plusieurs modèles mécanistes de simulation. Des résultats préliminaires de l’optimisation sont présentés

Apprentissage par renforcement pour l’optimisation de la conduite de culture du colza

L’agriculture intégrée consiste à adapter les pratiques agricoles pour limiter l’usage des produits phytosanitaires tout en contenant la pression des biogresseurs et avec une productivité suffisante pour garantir un revenu satisfaisant à l’agriculteur. A l’échelle de l’année culturale, le problème peut être formulé comme un problème d’optimisation de décisions d’un itinéraire technique (ITK). Nous avons mis en oeuvre une méthode d’apprentissage par renforcement pour adopter une méthode d’optimisation basée sur l’expérimentation virtuelle dans le cadre de l’optimisation d’ITK pour la conduite de culture du colza. Ce projet a requis le couplage de plusieurs modèles mécanistes de simulation. Des résultats préliminaires de l’optimisation sont présentés

VLE: Virtual Laboratory Environment

VLE, Virtual Laboratory Environment, est une plateforme de modélisation, multi-modélisation et de simulation à événements discrets

Forecasting Sunflower Grain Yield by Assimilating Leaf Area Index into a Crop Model

International audienceForecasting sunflower grain yield a few weeks before crop harvesting is of strategic interest for cooperatives that collect and store grains. With such information, they can optimize their logistics and thus reduce the financial and environmental costs of grain storage. To provide these predictions, data assimilation approaches involving the crop model SUNFLO are used. The methods are based on the re-estimation of soil conditions and on the sequential update of crop model states using an ensemble Kalman filter. They combine the simulation of the crop model and time series of leaf area index (LAI) derived from remote sensors and extracted over 281 fields near Toulouse, France. A sensitivity analysis is used to identify the most relevant model inputs to consider into the data assimilation process. Results show that data assimilation leads to statistically significant better predictions than the simulation alone (from an RMSE of 9.88 q.ha(-1) to an RMSE 7.49 q.ha(-1)). Significant improvement is achieved by relying on smoothed LAI rather than raw LAI. Nevertheless, there is still an over estimation of the grain yield that can be partially explained by the limiting factors observed on the fields and the forecast yield still need improvements to meet the required applications' accuracy