Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (T-eff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand. (C) 2016 Elsevier B.V. All rights reserved.

Soil nitrogen mineralisation simulated by crop models across different environments and the consequences for model improvment. iCROPM2016 International Crop Modelling Symposium

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R-2 <= 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Published by Elsevier Ltd.

Crop models are the state-of-the-art tool to predict crop yields in the context of climate change and food security. The uncertainty associated with their use can be partly overcome by using multi-model ensembles (mme), though model improvement

AgMIP is an international program bringing together research projects on climate, crop modelling and regional agriculture adaptation to climate change. One objective is to better assess the projections of global food availability depending on different staple crops (wheat, rice and maize), taking into account the projections of climate change for the end of century and the uncertainty attached to them. The need for robust estimates, i.e. good crop models for yields and use of natural resources is a prerequisite to benchmark the various cropping systems and local solutions that will ultimately be explored in order to cope with climate change, without bringing about any negative side effects on the environment. Modelers hence work together internationally in order to compare and improve process-based crop simulation models. Maize is a strategic crop, exhibiting high potential radiation and water use efficiencies and is cultivated worldwide. In a first phase, the impacts of CO2 and temperature on the maize yields and water use were studied using 23 crop models on 4 sites with contrasted cool or hot climate conditions, under no water limitation (Lusignan in France, Ames in the United States, Morogoro in Tanzania and Rio Verde in Brasil). Models were run using local soil conditions and climate variables for 30 years (1980-2010) after adjusting the cultivar parameters to the ones used in one experiment in each site. At the four sites studied, the average values across models of simulated yields were closer to the observed local experimental results than the simulation of any individual model. This indicated that ensemble modelling could be a relevant way to approach the impact of climate change on maize yields. There was also a broad agreement between models to simulate a reduction in maize yield in response to temperature, roughly - 0.5 Mg ha-1 per °C increase, with no significant impact on water use, although the latter variable was estimated with a large variability between models. Plant phenology was the mostly altered process with increasing temperature. Shortening of the duration from flowering to maturity in particular reduced the gain in grain weight during that phase. This suggests that genetics could hence play a key role in adapting maize production to climate change, at least under high water availability. Doubling [CO2] from 360 to 720 μmole mole-1 increased grain yield by 7.5% on average across models and sites, with a slight decrease of water use, bringing about an increase in water use efficiency. However, the variability of the response to [CO2] was very high, bringing about the need to better simulate the role of CO2, especially on plant transpiration. In a second phase, models are therefore now being tested against Free Air CO2 Enrichment experimental data, so that variability can be reduced and the actual impact of global change on water use can be assessed with a relevant precision to adaptating agricultural practices. (Texte integral)

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.

AgMIP is an international program bringing together research projects on climate, crop modelling and regional agriculture adaptation to climate change. One objective is to better assess the projections of global food availability depending on different staple crops (wheat, rice and maize), taking into account the projections of climate change for the end of century and the uncertainty attached to them. The need for robust estimates, ie good crop models for yields and use of natural resources is a prerequisite to benchmark the various cropping systems and local solutions that will ultimately be explored in order to cope with climate change, without bringing about any negative side effects on the environment. Modelers hence work together internationally in order to compare and improve process-based crop simulation models. Maize is a strategic crop, exhibiting high potential radiation and water use efficiencies and is cultivated worldwide. In a first phase, the impacts of CO2 and temperature on the maize yields and water use were studied using 23 crop models on 4 sites with contrasted cool or hot climate conditions, under no water limitation (Lusignan in France, Ames in the United States, Morogoro in Tanzania and Rio Verde in Brasil). Models were run using local soil conditions and climate variables for 30 years (1980-2010) after adjusting the cultivar parameters to the ones used in one experiment in each site. At the four sites studied, the average values across models of simulated yields were closer to the observed local experimental results than the simulation of any individual model. This indicated that ensemble modelling could be a relevant way …

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 rerunning 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 degrees 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]. (C) 2015 Elsevier B.V. All rights reserved.

Generic models can be relevant tools for exploring and comparing the impact of climate change on the development of crop diseases. The infection process of foliar pathogens is mainly driven by surface wetness duration (SWD) and temperature. Hence, our objective was to propose a generic response function that attempts to be robust (adapted to the infection process of many foliar pathogens), and easy to apply (well-informed parameters in the literature). This model was then used to quantify the impact of climate change on four foliar fungal pathogens, in the French context. First, the infection was modeled as a function of, both, temperature and SWD, using a simplified version of the sigmoidal Weibull equation. The upper asymptote of this equation was described by linear functions between four cardinal temperatures. Second, this model was fitted and validated with published data from 19 controlled laboratory studies. Especially low relative root mean square errors (RRMSE) were obtained for species in the genera Colletotrichum (about 0.11) and Puccinia (about 0.23). Last, the model was used to assess the CC impact on the infection rate and frequency of Albugo occidentalis, Phytophthora ramorum, Cercospora carotae and Botrytis cinerea. The simulations showed different trends, according to the four pathogens and climate sites: an increased frequency of infection and of the rate of infection of A. occidentalis and P. ramorum, in northern France, and the maintaining or the decrease of the frequency and rate of infection, at more or less long-term, for C. carotae and B. cinerea, in southern France. The importance of microclimate in the canopy, to explain the infection process, could justify coupling this generic model of infection to a mechanistic model based on energy balances, to simulate temperature and SWD, within the crop cover, rather than at the weather station scale.

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

In crop models, yield depends on complex interactions between biomass production, crop development and assimilate partitioning, particularly with crop species exhibiting indeterminate patterns of development and submitted to limiting soil resources. The present study aimed at: (i) evaluating the adaptability of the STICS model based on the boxcartrain approach for the simulation of the fruit development and growth of tomato cultivars with different growth habits and (ii) evaluating the hypothesis that both carbon and nitrogen limitations alter fruit setting. Four experiments were carried out to test the model, one on a vine type tomato cultivar grown in a greenhouse and three on bush type tomato cultivars grown in open field, at the experimental station Panguilemo that belongs to Talca University in Chile. Total dry matter, nitrogen uptake, leaf area index and fruit number were satisfactorily simulated (model efficiency (EF) >82%), whereas the fruit dry matter production was predicted with a lower precision (EF=63%). The simulations showed that the indeterminate growth features incorporated in the STICS model were appropriate to simulate the crop growth and development for both the vine and bush types of tomato cultivars. Introducing a stress nitrogen index in the algorithm to calculate fruit set improved the quality of prediction of the fruit number. (C) 2014 Elsevier B.V. All rights reserved.

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-of-season 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 (e-mean) 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 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.

Since weather has a major influence on the occurrence and development of crop diseases, valuable insight toward future agricultural planning emerges with assessment tools to evaluate fungal disease pressure and crop regional suitability under projected future climatic conditions. The aim of this study was to develop two climatic indicators, the average infection efficiency (AIE) and the number of infection days (NID), to quantify the potential effects of weather on the intensity and occurrence of pathogen infection. First, a simple and continuous infection function accounting for daily temperature and leaf wetness duration variations was implemented. The function was then parameterized from published data sets for five major contrasting fungal diseases affecting crops in Northern France: phoma of oilseed rape, late blight of potato, downy mildew of grape, leaf rust of wheat and net blotch of barley. Finally, AIE and NID were calculated for the recent past (1970-2000) and the future A1B climate scenario (2070-2100). An overall decrease in the risk of infection was shown for potato late blight and downy mildew of grapevine for all months during the period when the host plant is susceptible to infection. There were greater differences for the other three diseases, depending on the balance between warmer temperatures and lower humidity. The future climate would result in a later onset of disease and higher infection pressure in late autumn. In spring, for brown rust of wheat and net blotch of barley, the climatic risk for infection is expected to occur earlier but would result in lower infection pressure in May. These findings highlighted the need to use an infra-annual (monthly or seasonally) scale to achieve a relevant analysis of the impact of climate change on the infection risk. The described indicators can easily be adapted to other pathogens and may be useful for agricultural planning at the regional scale and in the medium term, when decision support tools are required to anticipate future trends and the associated risks of crop diseases. (C) 2014 Elsevier B.V. All rights reserved.

Large standardized model intercomparison projects enable the quantification of uncertainty in projecting the impacts of climate change. One of the largest studies so far indicates that individual crop models are able to simulate wheat yields accurately under a range of environments, but that differences between crop models are a major source of uncertainty. Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1,3 are difficult4. 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 andpolicymaking.

In the coming years, water resources and vegetation production of Mediterranean areas will be drastically affected by climate changes as well as intense and rapid changes in the land use. The impact of climate and land-use changes on water balance and vegetation production can be analysed and predicted through land surface models, provided that the uncertainties associated to these models and to the data used to run them are evaluated. Vegetation phenology is generally poorly taken into account in land surface models and may be a substantial source of uncertainties for global change scenario studies. In this paper, we discuss the improvement obtained in Soil Vegetation Atmosphere Transfer (SVAT) modelling by taking into account the phenology using a crop growth model, focusing on the water budget, over a Mediterranean crop site. The STICS model (Brisson et al, 1998) is used to simulate crop processes (growth and development, taking into account water and nitrogen exchanges between the environment and the crop). STICS describes the vegetation phenology very accurately and was validated for many types of crop and various pedoclimatic conditions. The SVAT model being analyzed is the a-gs version (Calvet et al., 1998) of the ISBA model (Noilhan et al, 1989), which simulates the photosynthesis and calculates the plant biomass and the Leaf Area Index (LAI) using a simple growth model. In STICS, the phenology is driven by the sum of daily air temperatures, which is quite realistic, while in ISBA, the phenology is driven by the plant carbon assimilation. Measurements (vegetation characteristics, soil properties, agricultural practises