• Energy Research

Quantifying crop responses to nitrogen and avenues to improve nitrogen-use efficiency

Comprendre les différences entre des sorties de modèles plutôt que les moyenner ?. 10. Colloque Modèle de culture STICS

AbstractExtreme climatic events are expected to increase in frequency and magnitude as a consequence of global warming. Grasslands cover a large proportion of the European continent and contribute to both agricultural production and ecosystem services through inter and intraspecific genetic variability. This study analysed the effects of summer droughts and heat waves on the persistence and production of perennial forage grasses. Mediterranean and temperate populations of Dactylis glomerata L. and Festuca arundinacea (Schreb.) were compared at both Mediterranean and temperate sites in France. By manipulating canopy temperatures and water availability, grass swards in the field were subjected to cumulative summer and spring water deficits (CSSWD) ranging from 329 to 707 mm to test different projected climatic conditions and extreme summer events. Under controlled summer heat waves (6–21 days at a mean daily canopy temperature higher than 30–35 °C), there was no increase in membrane damage to surviving aerial tissues. Plant stress was thus mainly generated through greater soil water deficit. Under the greatest CSSWD, annual biomass production was reduced on average by 60% and 30% with temperate and Mediterranean populations, respectively. Thresholds for a significant increase in summer tiller mortality were seen at CSSWD higher than 450 mm for temperate populations and 550 mm for Mediterranean populations. The latter displayed lower predawn leaf water potentials in summer and recovered through intense tillering in the subsequent seasons. Under the most extreme CSSWD, fewer than 20% of tillers of temperate populations survived and their nitrogen uptake ability was drastically altered. The higher potential productivity of Mediterranean populations in winter was associated with greater frost sensitivity. The identification of thresholds for vulnerability and the determination of the role of genetic diversity will improve the management of plant resilience and the design of new plant material to cope with climate change.

Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compared. See more from this Division: Special Sessions See more from this Session: Symposium--Perspectives on Climate Effects on Agriculture: The International Efforts of AgMIP

This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thunen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 models was less than 1 Mg ha−1. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.

Climate change will produce a decrease in rainfall over French territory, especially in western France. Cropping systems pattern is a key factor in water resources management at catchment basin level. In the frame of the ANR French project ―Climator‖, we have undertaken an analysis of the relationship between rainfall and the annual supply of water to the aquifers under several cropping systems and ecosystems. This was performed through crop modelling using agroclimatic data provided either by measurements at 12 experimental sites in France (1971-2000) or by using regionalised outputs of the French climatological model Arpege (2021-2050 and 2071-2100). The simulations highlight the important differences in aquifers recharge between cropping systems (rainfed vs irrigated but also winter vs spring crops and annual crops vs perennial vegetation). For the 12 sites, they also give an estimate of the decrease with time of the annual recharge under each cropping system (at least 2/3 of rain decrease). In the driest locations, that decrease may lead to a partial change in cropping systems pattern in order to match the total water demand at catchment level. Such change could be devoted either to increase annual recharge when irrigation water is pumped from large aquifers or to reduce summer water demand when irrigation water comes from rivers. Both cases are illustrated.