• Energy Research

Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2°C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].

Adaptation de l’agriculture irriguee au changement climatique (aicha) : une modelisation trans-disciplinaire d’un bassin-versant en inde du sud. 10. Colloque Modèle de culture STICS

Adaptation of crops to climate change has to be addressed locally due to the variability of soil, climate and the specific socio-economic settings influencing farm management decisions. Adaptation of rainfed cropping systems in the Mediterranean is especially challenging due to the projected decline in precipitation in the coming decades, which will increase the risk of droughts. Methods that can help explore uncertainties in climate projections and crop modelling, such as impact response surfaces (IRSs) and ensemble modelling, can then be valuable for identifying effective adaptations. Here, an ensemble of 17 crop models was used to simulate a total of 54 adaptation options for rainfed winter wheat (Triticum aestivum) at Lleida (NE Spain). To support the ensemble building, an ex post quality check of model simulations based on several criteria was performed. Those criteria were based on the “According to Our Current Knowledge” (AOCK) concept, which has been formalized here. Adaptations were based on changes in cultivars and management regarding phenology, vernalization, sowing date and irrigation. The effects of adaptation options under changed precipitation (P), temperature (T), [CO2] and soil type were analysed by constructing response surfaces, which we termed, in accordance with their specific purpose, adaptation response surfaces (ARSs). These were created to assess the effect of adaptations through a range of plausible P, T and [CO2] perturbations. The results indicated that impacts of altered climate were predominantly negative. No single adaptation was capable of overcoming the detrimental effect of the complex interactions imposed by the P, T and [CO2] perturbations except for supplementary irrigation (sI), which reduced the potential impacts under most of the perturbations. Yet, a combination of adaptations for dealing with climate change demonstrated that effective adaptation is possible at Lleida. Combinations based on a cultivar without vernalization requirements showed good and wide adaptation potential. Few combined adaptation options performed well under rainfed conditions. However, a single sI was sufficient to develop a high adaptation potential, including options mainly based on spring wheat, current cycle duration and early sowing date. Depending on local environment (e.g. soil type), many of these adaptations can maintain current yield levels under moderate changes in T and P, and some also under strong changes. We conclude that ARSs can offer a useful tool for supporting planning of field level adaptation under conditions of high uncertainty.

Evapotranspiration (ET) can be mapped using thermal infrared and spectral reflectance data. Various ET models have been developed but there was no competitive evaluation of them over a large range of situations. Ensemble model averaging is a tool that can be used for deriving ET from multi-model simulations. In this study, we used bayesian model averaging, which consists in weighting each model according to their performances when deriving the ensemble average. It was applied to the monitoring of ET over a saltmarsh scrub area in South France from MODIS data. ET monitoring was improved ( $\mathrm{RMSE}=0.57 \mathrm{mm}\ \mathrm{d}^{-1}$ ) when using a weighted averaging procedure as compared to the performances of a simple average or to the performances of each individual model.

Climate change is expected to influence the development and occurrence of fungal crop diseases. We therefore need to understand and predict the impacts of climate change on crop biotic stresses. A clearer understanding of these impacts requires consideration of how plants respond to climate change, as host plants provide a microclimate and physical and trophic support for disease development. Models have been developed to predict disease pressure on grapevine, but climate change is expected to generate complex responses that require a more integrated view of plant-pathogen interactions. We present here a new, integrated approach using the process-based MILA model coupled with the STICS crop model in order to understand and predict the potential impacts of climate change on downy mildew epidemics affecting grapevine (Plasmopara viticola). We first describe MILA and its calibration to downy mildew. The MILA-STICS combination has then been applied to future climatic data. Analysis of the general trend for future disease pressure, on the one hand, and the effects of the host plant on the course of certain processes, on the other hand, have demonstrated the value of applying MILA to the context of climate change. As a model that attempts to integrate the different mechanisms thatwhich explain involved in disease development, MILA is an appropriate tool to understand and assess the contribution of different effects on disease pressure. Finally, we describe some of the limitations of applying process-based models to the context of climate change. It is necessary to overcome these obstacles to ensure their effective use.