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The tendency for more frequent events of El Nino!Southern Oscillation Event (ENSO) since the late 1970's has been questioned about how anticipate the water management strategies of the crops. Nino events are characterised by high rain and moderated temperatures, whereas Nina events produce the opposite effects. In this context, a research was carried out to evaluate the effects of both climatic phenomena on the water balance of a grapevine cv. Cabernet sauvignon in central valley of Chile. Water balance was simulated with STICS model which was combined with a climatic database of four periods of Nino and four Nina events on the soil type of Talca series. The irrigation scheduling was managed automatically by the model and then it varied between years. A descriptive climatic analysis for the normal grapevine growth cycle (between middle September and end of March) showed a climate water deficit (Precipitation - Potential evapotranspiration) significantly higher (average of +27%) in Nina events (-861 mm) compared to Nino events (-677 mm). However, simulation results showed that grapevine transpiration was only 4% higher in Nina events (196 mm) than in Nino events (189 mm). Also seasonal irrigation application calculated by the model was 170 mm in dry years compared to 154 mm in humid years. These results can be explained by: i) the grapevine phenology, with a total grapevine growth period (budbreak to harvest) 10 days longer in Nino than Nina years, and ii) the parameterised soil, which was thin (0.6 m). This last condition has a consequence that the higher rainfall in years "Nino" was not used by the vine, it was runned-off and deep percolated.

The Shuttleworth and Wallace (SW) model with variable canopy resistance was evaluated to estimate evapotranspiration (ETv) from a drip-irrigated Merlot ( Vitis vinifera L.) vineyard trained on a vertical shoot-positioned (VSP) system. This vineyard is located in the Talca Valley, Region del Maule, Chile (35°25′ LS; 71°32′ LW; 125 m a.s.l). The performance of the SW model was evaluated using the eddy-covariance method on a 30 min time interval. Also, sub-models to estimate net radiation (Rn) and soil heat flux ( G ) were used in the SW model. A good agreement between observed and estimated values of Rn was found with a root mean square error (RMSE) of 33 W m −2 and a mean absolute error (MAE) of 24 W m −2 . Also, the SW model was able to estimate latent heat flux with RMSE and MAE of 34 and 21 W m −2 , respectively. On a daily basis, results indicate that the SW model was able to predict the ETv with RMSE and MAE values of 0.51 and 0.41 mm d −1 , respectively. These results suggest that it is possible to directly estimate ETv over unstressed grapevines using meteorological data and soil moisture measurements.

A modeling study was carried out into pea–barley intercropping in northern Europe. The two objectives were (a) to compare pea–barley intercropping to sole cropping in terms of grain and nitrogen yield amounts and stability, and (b) to explore options for managing pea–barley intercropping systems in order to maximize the biomass produced and the grain and nitrogen yields according to the available resources, such as light, water and nitrogen. The study consisted of simulations taking into account soil and weather variability among three sites located in northern European countries (Denmark, United Kingdom and France), and using 10 years of weather records. A preliminary stage evaluated the STICS intercrop model's ability to predict grain and nitrogen yields of the two species, using a 2-year dataset from trials conducted at the three sites. The work was carried out in two phases, (a) the model was run to investigate the potentialities of intercrops as compared to sole crops, and (b) the model was run to explore options for managing pea–barley intercropping, asking the following three questions: (i) in order to increase light capture, would it be worth delaying the sowing dates of one species? (ii) How to manage sowing density and seed proportion of each species in the intercrop to improve total grain yield and N use efficiency? (iii) How to optimize the use of nitrogen resources by choosing the most suitable preceding crop and/or the most appropriate soil? It was found that (1) intercropping made better use of environmental resources as regards yield amount and stability than sole cropping, with a noticeable site effect, (2) pea growth in intercrops was strongly linked to soil moisture, and barley yield was determined by nitrogen uptake and light interception due to its height relative to pea, (3) sowing barley before pea led to a relative grain yield reduction averaged over all three sites, but sowing strategy must be adapted to the location, being dependent on temperature and thus latitude, (4) density and species proportions had a small effect on total grain yield, underlining the interspecific offset in the use of environmental growth resources which led to similar total grain yields whatever the pea–barley design, and (5) long-term strategies including mineralization management through organic residue supply and rotation management were very valuable, always favoring intercrop total grain yield and N accumulation.

Aims: An analysis of the asynchrony and dynamics of dry matter accumulation and water content in the grape berries growth period (Vitis vinifera L.) was carried out on a macroscopic scale (berry population). Experimental studies suggest a framework to evaluate the asynchronous nature of grapevine maturation and to propose a simple model to simulate dry matter and water dynamics.Methods and results:Amethodology based on the measurement of the density of the berries was used, providing information on the dry matter, water content and distribution of the berry populations during the veraison - harvest period. The main experiment was carried out in Rhone valley vineyards on the«Grenache N » and « Syrah » varieties. Additional information was gathered in Burgundy vineyards on « Pinot noir » and « Chardonnay » varieties. A relationship between the sugar content (°Brix) and water content of a berry was observed, which was robust and identical for all varieties. The asynchronous nature of berry growth was confirmed and densimetry allows quantifying the homogeneity of harvest. A model was proposed to modelling dry matter growth, based on thermal time and final potential dry weight. A single relationship was obtained for the two varieties studied which seemed to be independent of field conditions (soil and year). To model water content dynamics (or °Brix, using the relationship obtained between them), two components were defined, one of which is related to the berry phenological stage and the other depends on the water status of the plant.Conclusion: The results provide a conceptual framework to better understand and represent the dynamics of dry matter accumulation and water content of a population of grape berries, and to evaluate the asynchronous nature of grapevine maturity.Significance and impact of the study: The models and relationships proposed could be used as a tool to better identify and understand key factors and processes occurring in the field until the detailed processes of berry growth are better understood and can be incorporated into the model. This model could be incorporated into a crop model of vine growth.

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 (R2???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. Compares interannual climate response of 27 wheat models at four locations.Calculates the diminishing return of constructing multi-model ensembles for assessment.Identifies similarities and major differences of model responses.Differentiates between interannual temperature sensitivity and climate change response.

International audience; Epistics is a model combining a biophysical and a decisional model designed to generate irrigation and N fertilisation schedules in apple orchards. These techniques were chosen since they are key elements in the management of fruit tree cropping systems. The biophysical model representing water and N dynamics in orchards was based on the water and N dynamics of Stics and was completed using a crop water and N requirement estimation method adapted to orchards. It was linked to an agronomic decision rule in a combined model able to generate N fertilisation and irrigation schedules. The Epistics evaluation process dealt with numerical evaluation of state variables (water and N soil content) and qualitative evaluation of model-generated schedules. The numerical evaluation, which concerned the biophysical model of Epistics, was performed on the basis of (i) soil nitrate and water content at the end of winters 2002 and 2003, and on (ii) nitrate and water dynamics during spring and summer 2003. The mean Root Mean Squared Error (RMSE) between observed and simulated values at the end of winter was 3.3% water per horizon and 56 kg N/ha, which is relatively good owing to the high spatial and temporal variability of soil water and nitrate content. The qualitative evaluation of generated schedules was performed during interviews with farmers. Farmers were asked to evaluate the model with reference to their own practices. A sharp difference between farmers and the model concerned the beginning of the irrigation period. This suggested that the model should take into account the constraints imposed by scab and codling moth control practices and irrigation rounds. The difference between model-generated and farmers' fertilisation practices suggested that the model may take plot vigour into account in the fertilisation decision rule. Such a study is a first step towards the design of models linking sound agronomic decision rules to crop modelling and representing interactions between practices. (c) 2006 Elsevier Ltd. All rights reserved.

International audience; The Penman-Monteith model with a variable surface canopy resistance (rcv) was evaluated to estimate hourly and daily crop evapotranspiration (ETc) over a soybean canopy for different soil water status and atmospheric conditions. The hourly values of rcv were computed as a function of environmental variables (air temperature, vapor pressure deficit, net radiation) and a normalized soil water factor (F), which varies between 0 (wilting point, thetaWP) and 1 (field capacity, thetaFC). The performance of the Penman-Monteith model (ETPM) was evaluated using hourly and daily values of ETc obtained from the combined aerodynamic method (ETR). On an hourly basis, the overall standard error of estimate (SEE) and the absolute relative error (ARE) were 0.06 mm h–1 (41 W m–2) and 4.2%, respectively. On a daily basis, the SEE was 0.47 mm day–1 and the ARE was 2.5%. The largest disagreements between ETPM and ETR were observed, on the hourly scale, under the combined influence of windy and dry atmospheric conditions. However, this did not affect daily estimates, since nighttime underestimations cancelled out daytime overestimations. Thus, daily performances of the Penman-Monteith model were good under soil water contents ranging from 0.31 to 0.2 (thetaFC and thetaWP being 0.33 and 0.17, respectively) and LAI ranging from 0.3 to 4.0. For this validation period, calculated values of rcv and F ranged between 44 s m–1 and 551 s m–1 and between 0.19 and 0.88, respectively.

International audience; The STICS crop model was recently coupled with radiative transfer (RT) models in the solar and microwave domains. This permits the simulation of the temporal variation of remotely-sensed data over wheat canopies. This paper presents the first results in assimilating remotely sensed data into STICS: comparing simulated and actual remotely-sensed data allows the recalibration of some STICS parameters so as to constrain the evolution of the simulated canopy variables. A sensitivity analysis of the coupled STICS+RT model was carried out to select the parameters of STICS with the largest influence on remotely-sensed observables and on some canopy variables (dry biomass and LAI), leading us to assess the parameters that should be recalibrated in the assimilation process, in order to adequately simulate the time course of the remotely-sensed data. In a second step, the benefits of assimilating optical data alone, radar data alone and both optical and radar data into STICS+RT were compared.; Assimilation de données de télédétection optique et radar dans le modèle STICS de culture de blé. Le modèle de culture STICS a été récemment couplé avec des modèles de transfert radiatif (RT) dans les domaines optique (du visible au moyen infrarouge) et radar. Ceci permet de simuler l'évolution temporelle de variables obtenues par télédétection au dessus d'un couvert de blé. Cet article présente les premiers résultats d'assimilation de données de télédétection dans le modèle STICS : la comparaison de données de télédétection modélisées aux données réelles permet de re-étalonner certains des paramètres de STICS, de manière à contraindre la simulation de l'évolution temporelle des variables du couvert. Une analyse de sensibilité du modèle couplé STICS+RT permet de sélectionner les paramètres de STICS ayant le plus d'effet sur les données de télédétection simulées, ainsi que sur certaines variables caractéristiques du couvert (indice foliaire et biomasse aérienne). On peut ainsi choisir les paramètres devant être ajustés dans la procédure d'assimilation, afin d'améliorer la simulation des données de télédétection. On compare ensuite les résultats obtenus en assimilant des données de télédétection optique, des données radar, et simultanément des données optiques et radar.

Oxisols have a high likelihood of NO3 − leaching which may strongly reduce N availability for tropical crops. The aim of this work was to evaluate the N and the water submodels of the STICS crop model for its ability to estimate N availability in N-fertilised field maize crops on two oxisols in Guadeloupe (French West Indies) with and without Al toxicity: a non-limed plot (NLI, pHKCl 3.9, 2.1 cmol Al3+ kg−1), and a limed plot (LI, pHKCl 4.5, 0 cmol Al3+ kg−1). An uncropped plot (UC, pHKCl 4.5, 0 cmol Al3+ kg−1) was used in order to fit some model parameters for soil evaporation, nitrification and NO3 − transport. The model was modified in order to describe nitrification as a partially inhibited process in acid soils, and to take into account NO3 − retention in oxisols. Nitrification was described as the result of the multiplicative effects of soil acidity, temperature and soil water content. Soil moisture and NO3 − and NH4 + content up to 0.8 m soil depth, above-ground biomass and N uptake by crops, and their leaf area index (LAI), were measured from sowing to the beginning of grain filling. The model described correctly the changes in soil water content during the moist and the dry periods of the experiment, and there was some evidence that capillary rise occurred in the dry period. Nitrogen mineralization, nitrification in UC, NO3 − transport and plant uptake were satisfactorily simulated by the model. Because of the effect of Al toxicity on plant growth, LAI at flowering was three times higher in LI than in NLI. Some discrepancies between observed and simulated data were found for the distribution of NO3 − and NH4 + in the cropped plots. This was probably due to the change of the ionic N form absorbed by the crops as a function of soil acidity and available P in the soil. No leaching was observed below 0.8 m depth and this was associated with NO3 − retention in the soil. The results showed that partial inhibition of nitrification and NO3 − retention should be taken into account by crop models to obtain realistic estimates of N availability for plants in tropical acid soils.

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.

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.

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.

Une evaluation du modele generique de culture STICS, decrit en details dans Brisson et al. [11], est presentee. Elle repose sur une base de donnees agronomiques qui reunit des situations variees de culture de ble et de mais en France. L'accent est mis sur la necessite d'utiliser des references standards pour le parametrage des varietes, qui concerne surtout les stades de developpement. La validation est realisee sur les variables de sortie du modele, definie comme etant les variables finales d'interet agronomique (rendement et composantes, biomasse aerienne, dates de floraison et de maturite, teneurs en azote dans la plante et dans le grain, quantite d'eau et d'azote dans le sol) au moyen de plusieurs criteres mathematiques (erreurs quadratiques, ecarts moyens, efficacite). Il ressort que le comportement des deux cultures sont assez proches avec des erreurs quadratiques de 1,6 t.ha -1 pour le rendement du ble et de 2,4 t.ha -1 pour le rendement du mais. La simulation des deux composantes du rendement: nombre de grains et poids du grain est plus mauvaise, de meme que les simulations concernant l'azote aussi bien dans la plante que dans le sol qui apparaissent avec un biais systematique. En revanche l'eau dans le sol est correctement simulee. L'analyse de cinetiques d'evolution de variables d'etat majeures du systeme, telles que l'indice foliaire ou l'indice de nutrition azotee, sur quelques cas extraits de la base de donnee permet de mettre en evidence les disfonctionnements du modele et de proposer des modifications pour les corriger. On retiendra essentiellement l'introduction d'une relation entre le nombre de grains et le poids maximal du grain, ce qui rend la variable « nombre de grains » dependante de la variete, la prise en compte de la senescence des feuilles liee aux stress environnementaux, l'arret de l'absorption azotee au debut du remplissage du grain. Ces modifications permettent d'ameliorer les resultats de modelisation concernant les composantes du rendement et le bilan azote. Elles ont peu d'effet sur la biomasse et le rendement qui restent a des niveaux d'erreur de l'ordre de 15 %; cette incompressibilite de l'erreur sur la biomasse et par consequent le rendement est une illustration de la robustesse du modele.

This study was aimed at analysing and quantifying the response of the root system dynamics of a wheat crop to waterlogging. Two experiments were carried out in parallel: one under controlled conditions with semi-permanent water tables using lysimeters equipped with oxygen measurers, and the other under conditions of artificially drained plots by continually monitoring their hydraulic functioning. The root system was observed frequently using the root mapping method, and this made it possible to measure the growth of the root front, to estimate root densities, and infer growth indices from them. The results showed that the anoxic medium for wheat roots consisted of a water-saturated soil with an oxygen concentration of below a critical threshold estimated to be 0.12 mol m−3 water. The results also showed that the area which was unfavourable to root growth corresponded to the water table topped with a capillary zone of approximately 6 cm. Once the critical threshold had been reached, it was the water-table duration that explained root behaviour and the first effects were perceptible after approximately 48 h. On the basis of these results, two stress variables were analysed: water-table duration in the root zone (WTD) and proportion of roots in the water table (RPWT). The RPWT variable gave the best results within the two experimental contexts. In the case of the permanent regime, this variable made it possible to consider the root proliferation observations made above the saturated zone. Equations linking the stress variable RPWT to the growth indices are proposed that offer new perspectives to modelling waterlogging effects.

Agriculture is the result of a balance between soil, climate, plants and cropping techniques applied by the farmers. Once one of those elements, in this case the climate, is perceptibly modified in a sustainable way, it is important that the other elements are also modified to respect the balance. To adapt to climate change farmers can modify their strategic or tactic decisions. But the very first point is to have good ideas of what are to be expected in terms of climate modifications and their agronomic consequences: acceleration of crop cycles, increase of summer droughts and increase in inter-season and inter-annual variability. Concerning strategic choices, one must not expect revolution and climate change will not deeply modify cropping systems. Yet rainfed crops will be more difficult in the south while summer crops and crop successions will become easier in the north. Escape strategies in terms of crop and genotype choices could be advantageous. An important point of discussion is about sowing dates. If they could theoretically be advanced for summer crops, the soil water status may not be adequate for allowing farmers to enter fields with heavy machines. It is also because of soil dryness that winter crops sowing should be delayed. In order to provide the farmers with more quantified results an ANR project, CLIMATOR, is proceeding.

In crop models maximum transpiration is an important component of the computation of water stress factors. It depends on reference climatic variables and leaf area index, and also on soil evaporation which modifies the actual air properties around the plants. This last effect is not accounted for in classical approaches used in crop models. Yet Shuttleworth and Wallace theory offers a framework to simulate canopy and soil evaporation fluxes in a coupled way. In this paper an adaptation and a parameterisation of the basic equations from Shuttleworth and Wallace is proposed, allowing use of the model to calculate maximum transpiration by using daily variables. The adaptation concerns soil evaporation. A potential soil evaporation is calculated assuming that, when the soil surface is wet, total evaporative flux consumes the whole available energy. It is used as an input to a two-staged model to calculate actual soil evaporation. The parameterisation relies on two field experiments performed on well-irrigated soybean. Measurements of net radiation balance show that radiation extinction within the canopy is less than generally admitted. Simulations of daily soil evaporation exhibit the same dynamics as microlysimeter measurements, which can be high even when the crop is fully developed. Bulk canopy resistances derived from Bowen ratio measurements agree closely with values obtained from classical formulae using a mean stomatal resistance of 250 ms−1. The modified and properly parameterised model shows that the contribution of plants to total evapotranspiration is highly variable as a result of the interactions between direct soil evaporation and plant transpiration.

International audience; 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.

Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract 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 lmol 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.

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.

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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. 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 and policymaking.

Climate variability influences the development of crop diseases, including through an effect on crop structure and hence on the microclimate. In a context of climate change, emerging and/or more aggressive plant diseases are thus expected. It is therefore critical to understand, anticipate and quantify the effects of climate variability and climate change on numerous host plant/pathogen systems. For this purpose, an homogeneous and integrative approach to the disease dynamics of all airborne fungal pathogens affecting crops is necessary. It enables to identify when plant-climate-pathogen interactions lead to the onset or development of one or more pathosystem(s) at a local or regional scale. We therefore describe here the conceptual design of a mechanistic model of foliar disease dynamics coupled with a process-based crop model. This conceptual design proposes generic response functions based on existing response functions in published models to simulate climate-based epidemiological processes. The dispersal and deposition, infection, latency and secondary inoculum production processes are the modules in this generic model. Input variables are either climate-related (rain, wind, air temperature, and air relative humidity) or plant-related (canopy relative humidity, canopy temperature, host surface wetness, plant phenological stage, plant and tissue age, organ surfaces, plant nitrogen content and varietal resistance). We evaluated the general applicability of the conceptual design using a number of airborne fungal plant pathogens with contrasted biological behaviours. We successfully completed proof-of-concept tests, during which disease models for two airborne fungal pathogens, Plasmopara viticola and Puccinia triticina, were coupled with the grapevine and wheat versions of the generic crop model STICS. This revealed the ability of our conceptual design to be transposed into functional models and then coupled with a classical crop model. This conceptual design could be a valuable tool for agronomists who might now be wanting to consider biotic stresses as additional constraints in their crop models.

The phenological development of cereal crops from emergence through flowering to maturity is largely controlled by temperature, but also affected by day length and potential physiological stresses. Responses may vary between species and varieties. Climate change will affect the timing of cereal crop development, but exact changes will also depend on changes in varieties as affected by plant breeding and variety choices. This study aimed to assess changes in timing of major phenological stages of cereal crops in Northern and Central Europe under climate change. Records on dates of sowing, flowering, and maturity of wheat, oats and maize were collected from field experiments conducted during the period 1985–2009. Data for spring wheat and spring oats covered latitudes from 46 to 64°N, winter wheat from 46 to 61°N, and maize from 47 to 58°N. The number of observations (site–year–variety combinations) varied with phenological phase, but exceeded 2190, 227, 2076 and 1506 for winter wheat, spring wheat, spring oats and maize, respectively. The data were used to fit simple crop development models, assuming that the duration of the period until flowering depends on temperature and day length for wheat and oats, and on temperature for maize, and that the duration of the period from flowering to maturity in all species depends on temperature only. Species-specific base temperatures were used. Sowing date of spring cereals was estimated using a threshold temperature for the mean air temperature during 10 days prior to sowing. The mean estimated temperature thresholds for sowing were 6.1, 7.1 and 10.1°C for oats, wheat and maize, respectively. For spring oats and wheat the temperature threshold increased with latitude. The effective temperature sums required for both flowering and maturity increased with increasing mean annual temperature of the location, indicating that varieties are well adapted to given conditions. The responses of wheat and oats were largest for the period from flowering to maturity. Changes in timing of cereal phenology by 2040 were assessed for two climate model projections according to the observed dependencies on temperature and day length. The results showed advancements of sowing date of spring cereals by 1–3 weeks depending on climate model and region within Europe. The changes were largest in Northern Europe. Timing of flowering and maturity were projected to advance by 1–3 weeks. The changes were largest for grain maize and smallest for winter wheat, and they were generally largest in the western and northern part of the domain. There were considerable differences in predicted timing of sowing, flowering and maturity between the two climate model projections applied. The phenological development of cereal crops from emergence through flowering to maturity is largely controlled by temperature, but also affected by day length and potential physiological stresses. Responses may vary between species and varieties. Climate change will affect the timing of cereal crop development, but exact changes will also depend on changes in varieties as affected by plant breeding and variety choices. This study aimed to assess changes in timing of major phenological stages of cereal crops in Northern and Central Europe under climate change. Records on dates of sowing, flowering, and maturity of wheat, oats and maize were collected from field experiments conducted during the period 1985–2009. Data for spring wheat and spring oats covered latitudes from 46 to 64°N, winter wheat from 46 to 61°N, and maize from 47 to 58°N. The number of observations (site–year–variety combinations) varied with phenological phase, but exceeded 2190, 227, 2076 and 1506 for winter wheat, spring wheat, spring oats and maize, respectively. The data were used to fit simple crop development models, assuming that the duration of the period until flowering depends on temperature and day length for wheat and oats, and on temperature for maize, and that the duration of the period from flowering to maturity in all species depends on temperature only. Species-specific base temperatures were used. Sowing date of spring cereals was estimated using a threshold temperature for the mean air temperature during 10 days prior to sowing. The mean estimated temperature thresholds for sowing were 6.1, 7.1 and 10.1°C for oats, wheat and maize, respectively. For spring oats and wheat the temperature threshold increased with latitude. The effective temperature sums required for both flowering and maturity increased with increasing mean annual temperature of the location, indicating that varieties are well adapted to given conditions. The responses of wheat and oats were largest for the period from flowering to maturity. Changes in timing of cereal phenology by 2040 were assessed for two climate model projections according to the observed dependencies on temperature and day length. The results showed advancements of sowing date of spring cereals by 1–3 weeks depending on climate model and region within Europe. The changes were largest in Northern Europe. Timing of flowering and maturity were projected to advance by 1–3 weeks. The changes were largest for grain maize and smallest for winter wheat, and they were generally largest in the western and northern part of the domain. There were considerable differences in predicted timing of sowing, flowering and maturity between the two climate model projections applied.

National audience; Physical degradation of agricultural and forest soils due to compaction is an important concern for the actors in agriculture, forest and environment. Many questions are still pending on the importance of this degradation, the evaluation of its impact on environment, the effect of changes of climate and practices, its natural remediation. The DST (Agricultural and Forest Soil Degradation induced by Compaction ?) project has proposed a research action on soil compaction in metropolitan France in order to : quantify the impacts of soil compaction on important soil environmental functions ; identify the conditions leading to compaction ; prevent the occurrence of these conditions ; monitor the evolution of soil structure after compaction. The project was structured in five working packages : impact, prediction, prevention, follow-up and cartography. It was based on modelling (hydrology, soil deformation, soil-crop interactions models) to study the occurrence of compaction and its impacts on crop growth and the environment. It also aimed at developing non-destructive methods to monitor soil structure deformation. The project associated research laboratories working in agriculture, geophysics or civil engineering and extension services members and was also based on field and laboratory experimentations, performed in various conditions (field crops, vineyards, forests). Mains results concerned : (i) impacts of compaction, as we demonstrated the existence of a threshold value for the structural void ratio (0,15 m3m3 in loamy soils) beyond which crop performances and hydrological properties are systematically affected ; (ii) modelling, with a 2-D model of soil deformation ; (iii) compaction assessment, based on non-destructive geophysical methods ; (iv) prevention of compaction, with a predictive model of soil water content and (v) mapping of compaction risks in France, based on the knowledge acquired during the project.; La degradación física de los suelos agrícolas y forestales ligada a la compactación durante los pasajes de maquinarias está una preocupación importante de los actores de la agricultura y de la gestión forestal. Pero más allá de este círculo, concierne igualmente todos los que se preocupan de las consecuencias ambientales de la agricultura y de la gestión forestal. En efecto, la compactación de los suelos modifica sus propiedades y así varias funciones importantes para los procesos tales que erosión hídrica, denitrificación, almacenamiento del carbono, preservación de la biodiversidad. En este cuadro, el proyecto DST propuso cinco direcciones de investigación : impacto, previsión, prevención, seguimiento y cartografía de la compactación. Se basó en la utilización de modelos de compactación de los suelos y de funcionamiento del sistema para estudiar las consecuencias sobre los cultivos y el medio ambiente, vía la modificación de las propiedades (almacenamiento, transferencia, mecánicas) de los suelos. Este proyecto transversal, que asocia laboratorios de investigación que trabajan en agricultura, geofísica, o ingeniera civil y los organismos profesionales, se apoyo también sobre experimentaciones variadas, en laboratorio y en el campo. En este articulo se presenta los principales resultados que conciernen : (i) el impacto de la compactación, con la puesta en evidencia de un umbral de índice de vacios en suelo limoso, más allá del cual las performances de los cultivos y las propiedades hidrodinámicas están, desde luego, impactadas, (ii) la modelización, el proyecto desemboco sobre la realización de un modelo 2-D de previsión de la deformación de los suelos, (iii) el seguimiento, con la evaluación de métodos geofísicos, no destructivos que permiten la cartografía de las zonas compactadas, (iv) la prevención, a través del desarrollo de una herramienta de previsión de la humedad del suelo y de los días disponibles y (v) la cartografía de las zonas con riesgos en Francia.; La dégradation physique des sols agricoles et forestiers liée au tassement lors des passages d’engins est une préoccupation importante des acteurs de l’agriculture et de la forêt. Mais au-delà de ce cercle, elle concerne également tous ceux qui se préoccupent des conséquences environnementales de l’agriculture et de la gestion forestière. En effet, le tassement des sols modifie a des conséquences lourdes sur plusieurs processus tels que l’érosion hydrique, la dénitrification, le stockage du carbone, la préservation de la biodiversité, la production. Dans ce cadre, le projet DST (Dégradation physique des Sols agricoles et forestiers liée au Tassement) proposait cinq volets de recherche : impact, prévision, prévention, suivi et cartographie du tassement en France métropolitaine. Il a reposé sur l’utilisation de modèles de tassement des sols et de fonctionnement du système sol/plante pour étudier les conséquences sur les cultures et l’environnement, via la modification des propriétés (stockage, transfert, mécaniques) des sols. Ce projet transversal, associant des laboratoires de recherche travaillant en agriculture, en géophysique ou en génie civil et des organismes professionnels, s’est aussi appuyé sur des expérimentations variées, en laboratoire et sur le terrain. Les principaux résultats sont présentés dans cet article. Ils concernent : (i) l’impact du tassement, avec la mise en évidence d’un seuil d’indice des vides en sol limoneux (0,15 m3.m 3 dans les sols limoneux), au-delà duquel les performances des cultures et les propriétés hydrodynamiques sont à coup sûr affectées, (ii) la modélisation, le projet ayant débouché sur la mise au point d’un modèle 2-D de prévision de la déformation des sols, (iii) le suivi, avec l’évaluation de méthodes géophysiques, non destructrices, permettant la cartographie des zones tassées, (iv) la prévention, à travers le développement d’un outil de prévision de l’humidité du sol et des jours disponibles et, (v) la cartographie des zones à risques en France.

[1] To simulate multivariate daily time series (minimum and maximum temperatures, global radiation, wind speed, and precipitation intensity), we propose a weather state approach with a multivariate closed skew-normal generator, WACS-Gen, that is able to accurately reproduce the statistical properties of these five variables. Our weather generator construction takes advantage of two elements. We first extend the classical wet and dry days dichotomy used in most past weather generators to the definition of multiple weather states using clustering techniques. The transitions among weather states are modeled by a first-order Markov chain. Second, the vector of our five daily variables of interest is sampled, conditionally on these weather states, from a closed skew-normal distribution. This class of distribution allows us to handle nonsymmetric behaviors. Our method is applied to the 20 years of daily weather measurements from Colmar, France. This example illustrates the advantages of our approach, especially improving the simulation of radiation and wind distributions.

Publication Inra prise en compte dans l'analyse bibliométrique des publications scientifiques mondiales sur les Fruits, les Légumes et la Pomme de terre. Période 2000-2012. http://prodinra.inra.fr/record/256699; International audience; No consensus currently exists about how climate change should affect the status of soil organic matter (SOM) in the tropics. In this study, we analyse the impact of climate change on the underlying mechanisms controlling SOM dynamics in a ferralsol under two contrasting tropical crops: maize (C4 plant) and banana (C3 plant). We model the effect of microbial thermal adaptation on carbon (C) mineralisation at the crop system scale and introduce it in the model STICS, which was previously calibrated for the soil-crop systems tested in this study. Microbial thermal adaptation modelling is based on a reported theory for thermal acclimation of plant and soil respiration. The climate is simulated from 1950 to 2099 for the tropical humid conditions of Guadeloupe (French Antilles), using the ARPEGE model and the IPCC emission scenario A1B. The model predicts increases of 3.4 °C for air temperature and 1100 mm yr−1 for rainfall as a response to an increase of 375 ppm for atmospheric carbon dioxide concentration in the 2090–2099 decade compared with the 1950–1959 decade. The results of the STICS model indicate that the crop affects the response of SOM to climate change by controlling the change in several variables involved in C dynamics: C input, soil temperature and soil moisture. SOM content varies little until 2020, and then it decreases faster for maize than for banana. The decrease is weakened under the hypothesis of thermal adaptation, and this effect is greater for maize (−180 kg C ha−1 yr−1 without adaptation and −140 kg C ha−1 yr−1 with adaptation) than for banana (−60 kg C ha−1 yr−1 and −40 kg C ha−1 yr−1, respectively). The greater SOM loss in maize is mainly due to the negative effect of warming on maize growth decreasing C input from residues. Climate change has a small effect on banana growth, and SOM loss is linked to its effect on C mineralisation. For both crops, annual C mineralisation increases until 2040, and then it decreases continuously. Thermal adaptation reduces the initial increase in mineralisation, but its effect is lower on the final decrease, which is mainly controlled by substrate limitation. No stabilisation in SOM status is attained at the end of the analysed period because C mineralisation is always greater than C input. Model predictions indicate that microbial thermal adaptation modifies, but does not fundamentally change the temporal pattern of SOM dynamics. The vegetation type (C3 or C4) plays a major role in SOM dynamics in this tropical soil because of the different impact of climate change on crop growth and then on C inputs

The last two decades are witnessing a decline in the growth trend of cereal yields in many European countries. The present study analyses yield trends in France using various sources of data: national and regional statistics, scattered trials, results of agroclimatic models using climatic data. Effects in genetic changes through breeding, agronomy and climate are investigated as possible causes. Our results show that genetic progress has not declined but it was partly counteracted, from 1990 on, by climate change which in general is unfavorable to cereal yields in temperate climates because of heat stress during grain filling and drought during stem elongation. We cannot however, from the decade beginning in 2000, rule out agronomic causes, related to policy and economy, in particular the decline of legumes in the cereal rotations, replaced by oilseed rape and to a lesser extent the decrease in nitrogen fertilization

International audience; A marked increase in air temperature has been observed in France since the end of the 1980s for fruit trees and vine regardless of cropping areas. As phenology is mainly influenced by temperature, changes in vegetative and flowering phenology timing will have agronomic consequences on these perennial productions. It is thus necessary to characterize the present and future trends of bud phenology in the context of global warming. Firstly, long-term series of phenological data extracted from a national data base devoted to fruit trees and vine (called PhénoClim®) have revealed changes in the time-course variations in flowering and vegetative phenology. Surprisingly, quite similar trends toward advances of flowering dates in various fruit trees and bud break dates in vine have been observed in different French cropping areas. In apple and pear trees, a statistical study of phenological series using multiple change-point models clearly support the idea of an abrupt change in the mean dates of flowering stages since the end of the 1980s, the most likely period for this discontinuity being between 1988 and 1989. Similar results were obtained considering phenological series of bud break dates in vine. The concomitancy with the global warming recorded in France since the end of 1980s leads to conclude that these advances in bud phenology could be actual impacts of this warming. Secondly, regarding the analysis of the phenological evolution in terms of temperature effects, modelling works based on the same conception of successive chilling and heat effects were developed to analyze the past and future impacts on bud phenology. For apple tree, a user-oriented software (called Pollenoscope) was exploited to understand the recent advance of mean flowering dates. The results suggest that two opposite trends simultaneously occurred in France since the end of the 1980s: a slower rate of completion of chilling requirements and a higher rate of completion of heat requirements. Nevertheless, the observed advance of mean flowering dates could result of more pronounced increases of temperatures from January to April, corresponding to the period of heat requirements completion for buds in France. In vine, a new model (called Brin) was developed introducing a chilling sub-model contrary to many traditional vine models. The optimal parameters were defined using the PhénoClim® database and the software STICS. The prediction of bud break date was improved by the model Brin. Moreover, a simulation over a future period (2070-2099) was realized using the Brin phenological model and the Arpege climatic model on the basis of A2 and B2 scenarios of IPCC. An increase of the mean advance of bud break could be observed for vine in France, although the dormancy break by chilling would have a marked tendency to be later, as previously suggested for apple tree in a recent past.

A crop model to evaluate land suitability is described. It has been devised to study spatial variation and uses readily available input data. The case study described is for the maize crop and uses a simple growth model for this crop. The model is incorporated within procedures that allow the descrip tion of crop environment variability both in space and time and the model is run under a Geographical Information System. Input data are stored in soil, climate and crop management data bases, for 20 × 20 km areas and constitute the basic information for crop growth simulation. From the network of synoptic meteorological stations, climatic variables are spatially interpolated to give predicted values for each elementary area. The model computes every ten days : i) potential crop productivity in the absence of any stress ; ii) productivity in limited-water situation. The modelling principles for the soilplant-atmosphere system are simple : development depends on thermal time, growth depends on energy use efficiency and the calculated water balance uses a reservoir model. Because of the ten-day time step, particular attention was given to the way in which water stress affects the growth-development functions. A study proved the model to be reliable for estimating maize productivity in various locations although some discrepancies between measurements and simulations can occur for intermediate variables in extreme environmental conditions. As illustrations of the model performance, map outputs of land suitabilities over France for maize growing are presented.

International audience; Cereal–legume intercrops are gaining increasing interest in Europe. Modelling, by taking into account the complexity of species interactions, can be a very useful tool to study such systems and to test new strategies in various soil and climatic conditions. The present work describes the adaptation of an intercrop model for pea–barley intercrops through the extrapolation of the STICS sole crop model and its parameterisation from experimental data recorded on sole crops. Several improvements have been added to the existing crop model to allow an inversion of dominance in height between species during the crop cycle and a trophic link between crop growth rate and the potential for N2 fixation. A 2-year dataset on pea and barley sole crops grown under non-limiting water conditions and with full crop protection was first used for calibration. The intercrop model was subsequently tested on experimental datasets of pea–barley intercrops grown under the same conditions as the sole crops. The intercrop experiments used to test the intercrop model differed in soil type, soil N supply and plant densities of each species. The development of the intercrop model through the extrapolation of the sole crop model and its parameterisation from experimental data recorded in sole crops allowed an accurate simulation of the interactions for nitrogen between intercropped species. The simulated crop growth, N accumulation and N2 fixation in intercrops were in agreement with the observations from the experimental dataset. Moreover the behaviour of the model was consistent with the general knowledge about those intercropping systems. However the simulation of competition for light needs to be improved, especially the simulation of height growth. A thorough validation on many data sets, independent of those used for parameterisation, is still required. This model needs to be tested under a wider range of situations and for other limiting factors than nitrogen occurring in low-input farming systems

SUMMARYCarbohydrate remobilization from the sugar beet storage root to support leaf regrowth after release from water stress was demonstrated by experimental and modelling approaches. Experimental trials were carried out in northern France in 1994 and 1995 and in southern France in 2005, in conditions that involved a succession of soil moisture stresses and re-hydrations. Drought stress slowed leaf growth and the subsequent release of stress resulted in regrowth. A second trial showed that after total defoliation, sugar beet was able to produce new leaves. It was assumed that this leaf renewal, observed at drought stress release or after defoliation, relied on the possibility of remobilizing carbohydrates from storage roots to above-ground organs. This assumption was tested through a heuristic modelling approach, involving the STICS crop model and its existing sub-model on remobilization. The relevance of these formalizations for sugar beet was tested on the experimental data to validate the plant behaviour concerning remobilization. The model succeeded in reproducing leaf area index (LAI) dynamic trends and particularly leaf re-growth after drought stress release or defoliation, despite an over-estimation of the drought stress effect involving an inaccurate simulation of the changes in LAI. Nevertheless, the model's ability to forecast accurately above-ground and storage root dry weight, as well as trends in LAI dynamics, showed that the assumptions made about remobilization were able to explain sugar beet behaviour.

International audience; The budburst stage is a key phenological stage for grapevine (Vitis vinifera L.), with large site and cultivar variability. The objective of the present work was to provide a reliable agro-meteorological model for simulating grapevine budburst occurrence all over France. The study was conducted using data from ten cultivars of grapevine (Cabernet Sauvignon, Chasselas, Chardonnay, Grenache, Merlot, Pinot Noir, Riesling, Sauvignon, Syrah, Ugni Blanc) and five locations (Bordeaux, Colmar, Angers, Montpellier, Epernay). First, we tested two commonly used models that do not take into account dormancy: growing degree days with a base temperature of 10°C (GDD10), and Riou’s model (RIOU). The errors of predictions of these models ranged between 9 and 21 days. Second, a new model (BRIN) was studied relying on well-known formalisms for orchard trees and taking into account the dormancy period. The BRIN model showed better performance in predicting budburst date than previous grapevine models. Analysis of the components of BRIN formalisms (calculation of dormancy, use of hourly temperatures, base temperature) explained the better performances obtained with the BRIN model. Base temperature was the main driver, while dormancy period was not significant in simulating budburst date. For each cultivar, we provide the parameter estimates that showed the best performance for both the BRIN model and the GDD model with a base temperature of 5°C

We assessed the effects of rising atmospheric CO(2), changing climate, and farmers' practice on the carbon and water balance of European croplands during the past century (1901-2000). The coupled vegetation-crop model ORCHIDEE-STICS is applied over western Europe for C3 crops (winter wheat) and for maize, with prescribed historical agricultural practice changes. Not surprisingly, the enormous crop yield increase observed in all European regions, 300-400% between 1950 and 2000, is found to be dominantly explained by improved practice and varieties selection, rather than by rising CO(2) (explaining a similar to 11% uniform increase in yield) and changing climate (no further change in yield on average, but causing a decrease of similar to 19% in the southern Iberian Peninsula). Agricultural soil carbon stocks in Europe are modeled to have decreased between 1950 and 1970, and since then to have increased again. Thus, the current stocks only differ by 1 +/- 6 tC ha(-1) from their 1900 value. Compensating effects of increasing yields on the one hand (increasing stocks) and of higher harvest index values and ploughing on the other hand (decreasing stocks) occur. Each of these processes taken individually has the potential to strongly alter the croplands soil carbon balance in the model. Consequently, large uncertainties are associated to the estimated change in carbon stocks between 1901 and 2001, roughly +/- 6 tC ha(-1) a(-1). In our most realistic simulation, the current cropland carbon balance is a net sink of 0.16 +/- 0.15 tC ha(-1) a(-1). The annual water balance of cropland soils is influenced by increasing crop water use efficiency, one third of which is caused by rising CO(2). However, increasing water use efficiency occurred mainly in spring and winter, when water is not limiting for plant growth, whereas no strong savings of soil water are achieved in summer through elevated CO(2). Overall, trends in cultivation practices have caused a 3 times larger increase of water use efficiency than rising CO(2).

Nowadays European agriculture is evolving in a context where policy-making and environmental concerns play a key role. To better assess agro-environmental policies, the AROPAj agricultural supply model needs to take into account the technical characteristics of crop management for different farms. A method to build up specific relationships between yield and nitrogen fertilization that takes into account agronomic techniques is proposed in this paper. The nitrogen response curve is based on an exponential function that integrates economic properties consistently from an agronomic point of view. In AROPAj, individual production systems (farm types) do not have a given location within a specified region and in databases technical information is scarce. The method involves determining technical and physical characteristics, inputs that allow the STICS crop model to assess the yield response to nitrogen of each crop on every farm type. From this information, a nitrogen response curve can be drawn up for each crop of each one of the farms. It can take into account both nitrogen from purchased fertilizer and nitrogen from animal effluents produced on farm. The method was designed to be adaptable to any European region, and tests carried out on two French regions covering a wide range of situations (crops, soils, climates and techniques) showed it was able to cope with varying prices and environments. The agronomic consistency of STICS inputs and curve shapes was also checked. When incorporated into the AROPAj economic model, the response curves can be used to render farms more sensitive to agricultural policy scenarios, by allowing their optimal fertilization level to be adjusted.

International audience; To explore the evolution of a human impacted river, the Seine (France), over the 21st century, three driving factors were examined: climate, agriculture, and point source inputs of domestic and industrial origin. Three future scenarios were constructed, by modification of a baseline representative of recent conditions. A climate change scenario, based on simulations by a general circulation model driven by the SRES-A2 scenario of radiative forcing, accounts for an average warming of + 3.3 °C over the watershed and marked winter increase and summer decrease in precipitation. To illustrate a possible reduction in nitrate pollution from agricultural origin, a scenario of good agricultural practices was considered, introducing catch crops and a 20% decrease in nitrogen fertilisation. Future point source pollution was estimated following the assumptions embedded in scenario SRES-A2 regarding demographic, economic and technologic changes, leading to reductions of 30 to 75% compared to 2000, depending on the pollutants. Four models, addressing separate components of the river system (agronomical model, hydrogeological model, land surface model and water quality model), were used to analyse the relative impact of these scenarios on water quality, in light of their impact on hydrology and crop production. The first-order driving factor of water quality over the 21st century is the projected reduction of point source pollution, inducing a noticeable decrease in eutrophication and oxygen deficits downstream from Paris. The impact of climate change on these terms is driven by the warming of the water column. It enhances algal growth in spring and the loss factors responsible for phytoplankton mortality in late summer (grazers and viruses). In contrast, increased seasonal contrasts in river discharge have a negligible impact on river water quality, as do the changes in riverine nitrate concentration, which never gets limiting. The latter changes have a similar magnitude under the three scenarios. Under climate change, riverine and groundwater nitrate concentrations increase and crop production is advantaged with reduced growing cycles and increased yields. In contrast, nitrate concentrations decrease under the good agricultural practices scenario, with a limited decrease in crop production. When these two scenarios are combined, the changes in nitrate concentrations balance each other and crop yields increase. The results of this numerical exercise indicate that the potential changes to the Seine River system during the 21st century will not lead to severely degraded water quality.

Competition for soil resources plays a key role in the outcome of intercropping systems. In cereal–legume intercrops, competition for soil nitrogen during the vegetative phase greatly influences the final performance of the intercropped species. However, there is a lack of knowledge on the main factors involved in interspecific soil N competitive interactions between species. The dominance of cereals over legumes is often attributed to their faster growing rooting system. Nevertheless, using only field experimental approaches makes it difficult to isolate the effect of one factor because of the strong interactions between processes and the environment. Given the complexity of intercropping systems, dynamic simulation models can be especially helpful for testing hypotheses about the key factors driving competition between species. The present work was designed to investigate, under non-limiting water conditions, through an experimental and modelling approach, whether differences in root depth penetration among pea and barley grown together determined competition for soil N and dry matter accumulation (DM) by each species during the vegetative phase. This hypothesis was tested through several simulated scenarios generated using the STICS crop model. The model was first used to compare competition for soil N according to differences in root depth penetration rates between species. This rooting depth penetration effect was then studied at three levels of soil N supply leading to different degrees of N demand and N stress. A field experiment carried out in 2003 including pea–barley intercrops grown either with 130 kg N ha � 1 or without any fertilizer was used to test the model. Experimental results of aboveground biomass, nitrogen accumulation, N2 fixation and rooting depth monitored regularly during the crop cycle were compared to simulated results. The simulated responses of the intercrops were in agreement with the observations from the experimental dataset. Using the model, it is clear that faster root growth in barley gives it access to more soil nitrogen than pea during the vegetative phase. However, this advantage, which is limited to the vegetative phase, only affects the outcome of the intercrop when soil N supply is low. With higher soil N supplies, soil N sharing is not affected by the differences in rooting depth penetration between species. It appears that with higher N supplies, the differences in N demand between species have more influence on species dominance than differences in rooting depth. # 2007 Elsevier B.V. All rights reserved.

* INRA Centre d'Avignon, Documentation, Domaine St Paul, Site Agroparc, 84914 Avignon cedex 9 Diffusion du document : INRA Centre d'Avignon, Documentation, Domaine St Paul, Site Agroparc, 84914 Avignon cedex 9; National audience

International audience; Soil-Vegetation-Atmosphere Transfer Models (SVAT) and Crop Simulation Models describe physical and physiological processes occurring in crop canopies. Remote sensing data may be used through assimilation procedures for constraining or driving SVAT and crop models. These models provide continuous simulation of processes such as evapotranspiration and, thus, direct means for interpolating evapotranspiration between remote sensing data acquisitions (which is not the case for classical evapotranspiration mapping methods). They also give access to variables other than evapotranspiration, such as soil moisture and crop production. We developed the coupling between crop, SVAT and radiative transfer models in order to implement assimilation procedures in various wavelength domains (solar, thermal and microwave). Such coupling makes it possible to transfer information from one model to another and then to use remote sensing information for retrieving model parameters which are not directly related to remote sensing data (such as soil initial water content, plant growth parameters, physical properties of soil and so on). Simple assimilation tests are presented to illustrate the main techniques that may be used for monitoring crop processes and evapotranspiration. An application to a small agricultural area is also performed showing the potential of such techniques for retrieving evapotranspiration and information on irrigation practices over wheat fields.

International audience; Leaf and crop water measurements were analysed dynamically throughout the cycle of two crops of durum wheat subjected to contrasting conditions of water supply. The objectives were to compare short- and long-term crop reactions to water deficit, based on some hypotheses from the literature. Water status measurements were of the first type and phenological or morphological measurements of the second type. The droughted crop under a rainout shelter received no water between emergence and harvest, while the open-air crop was irrigated. Dynamic plant morphological measurements consisted of leaf area index and specific leaf area, and those characterising plant water status dynamics included predawn leaf water potential, bulk osmotic potential and the ratio of crop water uptake to evaporative demand. Continuous measurement of the surface temperature on the two plots served to calculate a degree-days temperature scale, allowing comparison of the two crops. We found that the droughted crop developed mechanisms of resistance to water deficit: the leaf area index and specific leaf area were reduced (from 6 to 1.5 m2 m-2 and from 300 to 260 cm2 g-1 for the maximum values of, respectively, the LAI and the SLA), the bulk osmotic potential was lower (from -2 to -3 MPa) and the soil layer affected by root uptake was deeper (from 50 to 100 cm) than that of the irrigated crop. The amount of water evapo-transpired by both crops was similar when related to climatic demand. Yet no clear relationship appeared between the specific leaf area index and osmotic potential. Furthermore, there appeared to be a clear difference between the vegetative and reproductive phases of both crops in terms of water functioning. The osmotic potential decreased markedly after anthesis and a peak of water uptake was observed in both crops during the grain-filling phase, which may have been linked to the specific functioning of the flag leaf or the ear. While this study demonstrated the efficiency of drought adaptive features in durum wheat, it also showed that the difference in the water behaviours of two contrasted durum wheat crops could be of the same order of magnitude as the differences between the vegetative and reproductive phases.

National audience

[Departement_IRSTEA]RE [TR1_IRSTEA]RIE / TRANSFEAU; Dans les systèmes agricoles comme dans tout écosystème, les économies de l'eau et de l'azote sont étroitement mêlées. Différentes variables synthétiques caractérisent l'efficacité du système de culture face au déficit hydrique. Une valeur élevée du rapport entre l'azote exporté et l'eau consommée par la culture (efficience de l'eau pour l'azote : NWUE , mg L-1) laisse espérer que le système est moins sujet aux pertes d'azote minéral (ou gazeux). Maximiser la quantité d'azote exportée par rapport à la consommation d'eau est l'une des voies pour une agriculture respectueuse de l'environnement. Nous cherchons à comprendre l'influence du fonctionnement hydrique de la culture sur différentes composantes du fonctionnement azoté : extraction de l'azote minéral du sol, influence sur le fonctionnement trophique et morphogénétique du peuplement cultivé. Ces différents processus interviennent à des échelles diverses, et sont encore mal hiérarchisés. Le principal objectif de ce travail est d'établir le poids respectif de différents processus impliqués dans la contrainte hydrique et son influence sur l'état de nutrition azotée (en termes quantitatifs et de temps de réponse). Un effort particulier a porté sur les situations de compétition entre espèces végétales se partageant la surface cultivée, dans la mesure où ce type d'occupation est souvent perçu comme un avantage pour l'environnement et la régularité de la production agricole. Nous avons étudié ces processus sous différents niveaux de contrainte hydrique et azotée à différents stades de croissance en milieu contrôlé et dans le cas de la prairie temporaire et de la vigne enherbée. Dans les deux cas, l'état de nutrition azoté est souvent limitant, notamment durant l'été.

National audience

International audience; The simulations supplied by a combination of a global climate model and a weather generator allowed the creation of two climate scenarios including an increase and/or monthly variations in temperature for the 2070-2100 horizon, which were compared with two currently available series (1961-1990 and 1990-2000). Three forage systems applied in upland areas of southern France were simulated using the STICS model (silage maize, perennial alfalfa and grasses) and the outputs were introduced into a digital elevation model. We noted changes in precocity which allowed the sowing of silage maize varieties with longer crop cycles at lower altitudes and an enlargement of the crop zone above 700-800 m. When introducing monthly temperature variations, we observed major frost damage which decreased maize yields. As for gramineous and alfalfa grasslands, we obtained a lengthening in the growing period with earlier first cut dates and sometimes the possibility of a supplementary cut.

International audience; There is a strong international demand for quantitative estimates of both carbon sources/sinks, and water availability at the land surface at various spatial scales (regional to global). These estimates can be derived (and usually are) from global biosphere models, which simulate physiological, biogeochemical, and biophysical processes, using a variety of plant functional types. Now, the representation of the large area covered with managed land (e.g., croplands, grasslands) is still rather basic in these models, which were first designed to simulate natural ecosystems, while more and more land is heavily disturbed by man (crops cover ;35% and grasslands ;30%-40% of western Europe's area as a result of massive deforestation mainly in the Middle Ages). In this paper a methodology is presented that combines the use of a dynamic global vegetation model (DGVM) known as Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and a generic crop model [the Simulateur Multidisciplinaire pour les Cultures Standard (STICS)]. This association aims at improving the simulation of water vapor and CO 2 fluxes at the land-atmosphere interface over croplands, and thereby the calculation of the carbon and water budget. Variables that are much better computed in STICS (e.g., leaf area index, root density profile, nitrogen stress, vegetation height) are assimilated daily into ORCHIDEE, which continues to compute its own carbon and water balance from the fluxes simulated at the half-hourly time step. The allocation of photosynthates in ORCHIDEE was modified in order to maintain the coherence between leaf area index and leaf biomass, as well as between root density and root biomass. Soil moisture stress is computed using a more realistic root density profile. The maximum rates of carboxylation and RuBP (ribulosebisphosphate) regeneration were adjusted to more realistic values, while the actual rates can now be reduced following the nitrogen stress. Finally, harvest has been implemented into ORCHIDEE. The improved model (ORCHIDEE-STICS) is evaluated against measurements of total aboveground biomass, evapotranspiration, and net CO 2 flux at four different sites covered with either winter wheat or corn.

International audience

International audience; Agriculture is still accounted for in a very simplistic way in the land-surface models which are coupled to climate models, while the area it occupies will significantly increase in the next century according to future scenarios. In order to improve the representation of croplands in a Dynamic Global Vegetation Model named ORCHIDEE (which can be coupled to the IPSL1 climate model), we have (1) developed a procedure which assimilates some of the variables simulated by a detailed crop model, STICS, and (2) modified some parameterisations to avoid inconsistencies between assimilated and computed variables in ORCHIDEE. Site simulations show that the seasonality of the cropland-atmosphere fluxes of water, energy and CO2 is strongly modified when more realistic crop parameterisations are introduced in ORCHIDEE. A more realistic representation of wheat and corn croplands over Western Europe leads to a drying out of the atmosphere at the end of summer and during autumn, while the soils remain wetter, specially at the time when winter crops are sowed. The seasonality of net CO2 uptake fluxes is also enhanced and shortened.