Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking

No abstract

-STICS (Simulateur mulTJdiscplinaire pour les Cultures Standard) is a crop model constructed as a simulation tool capable of working under agricultural conditions. Outputs comprise the production (amount and quality) and the environment. Inputs take into account the climate, the soi1 and the cropping system. STICS is presented as a model exhibiting the following qualities: robustness, an easy access to inputs and an uncomplicated f~~t u r e evolution thanks to a modular (easy adaptation to various types of plant) nature and generic. However, STICS is not an entirely new model since most parts use classic formalisms or stem from existing models. The main simulated processes are the growth, the development of the crop and the water and nitrogenous balance of the soil-crop system. The seven modules of STICSdevelopment, shoot growth, yield components, root growth, water balance, thermal environment and nitrogen balanceare presented in tum with a discussion about the theoretical choices in comparison to other models. These choices should render the model capable of exhibiting the announced qualities in classic environmental contexts. However, because some processes (e.g. ammoniac volatilization, clrought resistance, etc.) are not taken into account, the use of STICS is presently limited to several cropping systems. (

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-ofseason variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables.Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (emean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 Accepted ArticleThis article is protected by copyright. All rights reserved. models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Nendel 38 | Jørgen Eivind Olesen 37 | Taru Palosuo 44 | John R. Porter 42,45,46 | Eckart Priesack 39 | Dominique Ripoche 47 | Mikhail A. Semenov 48 | Claudio Stöckle 17 | Pierre Stratonovitch 48 | Thilo Streck 33 | Iwan Supit 49 | Fulu Tao 50,44 | Marijn Van der Velde 51 | Daniel Wallach 52 | Enli Wang 53 | Heidi Webber 30,38 AbstractWheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO 2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable 156 |

No abstract

No abstract

No abstract