Grasslands contribute greatly to increasing soil organic carbon (SOC) storage in cropland. Models can be used to predict effects of grasslands under different soil, weather, and management conditions. We calibrated the STICS soil-crop model to simulate the grassland yield and SOC dynamics of productive-grassland systems, taking into account the contribution of roots to soil carbon (C) inputs. We used observations from three contrasted long-term (13-27 years) French experiments that included different grassland durations and management practices (i.e. temporary/permanent, mown/grazed, fertilised/unfertilised). We optimised some root parameters using a subset of treatments from one of the three sites and then evaluated STICS using the remaining treatments. STICS accurately predicted the observed dynamics of grassland root carbon and nitrogen at one site, and the predicted ranges of root variables (e.g. root:shoot ratio and root C:N ratio) at the three sites were consistent with the literature. STICS satisfactorily predicted SOC dynamics for all three sites and treatments, with low relative error (nRMSE) and bias, ranging from 2.5 to 5.9% and -2.5 to 0.6 t C ha-1, respectively. It also reproduced the observed positive effect of grasslands on SOC stocks. Although STICS slightly underpredicted effects of the sites and treatments on grassland yield and nitrogen content, these predictions were considered satisfactory. Therefore, STICS could be used to predict net C footprints of cattle farms based on productive grasslands or to develop predictive metamodels of SOM dynamics, which would support implementation of better practices in livestock farming.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia ((similar to)34 083 km(2)), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 x 1 km to 100 x 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (< 10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale.

Grasslands offer many environmental and economic advantages that put them at the heart of future sustainable ruminant production systems. This study aimed to quantify and map the dry matter yield (DMY) and nitrogen yield (NY) of French grasslands resulting from cutting and grazing practices, based on the existing diversity of grassland vegetation, management, soil and climate conditions, using a research version of the STICS crop model called PaturSTICS. This model simulates daily dry matter (DM), nitrogen (N) and water fluxes involved in the functioning of grasslands and crops in response to management and environmental conditions. It was improved to represent deposition of animal waste on grassland soils during grazing and to simulate DM production and N content of grasses and legumes more accurately. Simulations were performed for locations across France on a high-resolution grid composed of pedoclimatic units (PCU) obtained by combining the spatial resolutions of climate and soil. The main grassland types and associated management types were determined for each PCU and then simulated over 30 years (1984-2013). Using the simulated values, predictive metamodels of annual grassland DMY and NY were developed from easily accessible explanatory variables using a random forest approach. Annual model predictions were aggregated and averaged at the PCU scale, then compared to regional observations. Predicted DMY agreed with available observations, except in semi-mountainous and mountainous regions, where PaturSTICS tended to overpredict DMY, probably because it ignores effects of snow, frost and slope, and due to how it represents effects of temperature and water stress on plant growth. According to results, three-quarters of French grasslands produce and export at least 7.6 t DM ha(-1)yr(-1) and 172 kg N ha(-1)yr(-1), respectively. One-quarter of French grasslands produce and export at least 10.7 t DM ha(-1) yr(-1) and 254 kg N ha(-1) yr(-1), respectively. The latter are located mainly in north-western France, the north-western Massif Central, the French Alps and the western Pyrenees, all of which have environmental conditions favourable for grass growth. The metamodels developed are interesting proxies for PaturSTICS' predictions of grassland DMY and NY. Our results provided valuable knowledge that promotes better use of the potential forage production of French and European grasslands to improve protein self-sufficiency and N fertilisation management in ruminant livestock systems.

Les ecosystemes agricoles sont l'un des six volets de l'Evaluation francaise des ecosystemes et des services ecosystemiques (EFESE), programme lance en 2012 par le Ministere en charge de l'Environnement pour apporter des connaissances sur l'etat actuel et l'utilisation durable des ecosystemes (voir encadre 2). En 2014, le Ministere de l'Environnement a sollicite l'Inra pour prendre en charge le volet relatif aux ecosystemes agricoles. Le programme federateur de recherche EcoSerV (Services rendus par les ecosystemes), lance par l'Inra en 2013, a egalement soutenu cette etude qu'il va ensuite completer et etendre. L'ecosysteme agricole, vu comme l'ensemble des parcelles dediees a la production de biomasse agricole, est configure et gere par l'agriculteur qui combine, dans ses pratiques de production, processus ecologiques et apports d'intrants exogenes. L'un des enjeux forts associes a l'analyse des services ecosystemiques est la conception de systemes de production reposant sur la valorisation de ces services, donc peu consommateurs en intrants exogenes et repondant aux enjeux de societe tels que la conservation de la biodiversite ou la limitation des impacts environnementaux.

Mots cles Pesticides, modelisation spatio-temporelle, dynamique, transfert, paysage Resume L'utilisation des pesticides en agriculture entraine une contamination de la plupart des compartiments des ecosystemes (sol, eau, air) comme en temoignent les differents monitoring mis en place. Pour estimer le risque de contamination de ces compartiments et identifier les moyens de limiter cette contamination, il est necessaire de developper une approche de modelisation qui decrive completement les dynamiques et les voies de transfert des pesticides depuis l'echelle de la parcelle jusqu'a l'echelle du paysage, integrant l'ensemble des compartiments. Jusque-la, le plus souvent, les approches de modelisation sont partielles car se focalisant chacune sur des dynamiques et compartiments specifiques comme la dispersion atmospherique d'une part, le transport par ruissellement de surface d'autre part ou encore la percolation vers les nappes souterraines. Cette communication presente un projet collaboratif pour la modelisation du devenir des pesticides qui rassemble six unites de recherche, soit une vingtaine de chercheurs et ingenieurs, pour developper un modele integre du devenir des pesticides a l'echelle du paysage. Ce modele integre permettra de predire les concentrations en pesticides dans le sol, l'eau et l'air ainsi que les echanges dans et entre les differents compartiments sous l'influence conjointe de l'organisation spatio-temporelle des paysages et des pratiques agricoles a l'echelle d'un bassin versant. Le principe fondamental de cette demarche collaborative n'est pas de developper un nouveau modele mais de reutiliser les approches de modelisation deja developpees dans chaque groupe de recherche et de les coupler via des plateformes de modelisation et simulation concues pour faciliter la modelisation des agroenvironnements (plateformes OpenFLUID, SolVirtuel et RECORD). Cette communication presente alors les principes de la modelisation integree des pesticides a l'echelle du paysage qui est actuellement en cours de developpement, et plus particulierement les choix retenus pour i) la representation spatiale des paysages agricoles (objets spatiaux, proprietes et connectivite spatiale), ii) les principaux processus consideres (distribution entre sol, culture et atmosphere ; transferts dans le sol et a la surface ; equilibres physico-chimiques ; emission vers l'atmosphere par volatilisation et derive ; dispersion atmospherique de la fraction volatile ; deposition gazeuse ; …) et iii) leur couplage spatio-temporel. Un des enjeux cles est de reussir le couplage a l'echelle du paysage de la modelisation de la dispersion atmospherique avec la modelisation mise en oeuvre pour la modelisation des processus hydrologiques, et de representer les echanges a l'interface surface/atmosphere a cette echelle. Les premiers resultats obtenus sur un bassin-versant viticole seront presentes.

Farming systems are complex and have several dimensions that interact in a dynamic and continuous manner depending on farmers' management strategies. This complexity peaks in Indian semi-arid regions, where small farms encounter a highly competitive environment for markets and resources, especially unreliable access to water from rainfall and irrigation. NAMASTE, a dynamic computer model for water management at the farm level, was developed to reproduce interactions between decisions (investment and technical) and processes (resource management and biophysical) under scenarios of climate-change, socio-economic and water-management policies. The most relevant and novel aspects are i) system-based representation of farming systems, ii) description of dynamic processes via management flexibility and adaptation, iii) representation of farmers' decision-making processes at multiple temporal and spatial scales, iv) management of shared resources. NAMASTE's ability to simulate farmers' adaptive decision-making processes is illustrated by simulating a virtual Indian village composed of two virtual farms with access to groundwater.

Developing sustainable crop systems is a major challenge. Presently, management practices are simulated using either biophysical models or simple farmer decision models. As a result, there is a lack of generic models integrating both biophysical parameters and farmer decision parameters. Here, we developed an original graphical plug-in to sketch and implement decision-making models and to link them with biophysical models. For that, we used the RECORD platform, standing for REnovation and COORDination of agro-ecosystem modeling. Different pop-up windows allow to create the model using a decision formalism then to implement the model under the RECORD platform. The sequence of technical operations is formally modeled as a direct multi-graph without retroaction. The plug-in allows defining activities, relation between activities, and decision rules to trigger the different activities. The resulting model is independent of any biophysical model and can then be linked with different crop models. An example is given on an innovative cropping systems part of the MicMac-Design project. The decision-making model is then linked with the STICS crop model.

To address new environmental and social issues, crop models need to widen their scope and be linked to other tools. The crop model STICS was encapsulated in the modelling platform RECORD and new process-based developments were added to address environmental issues. We present plant and soil processes developed recently in STICS and describe three projects using STICS within RECORD: MICMAC-Design aims to design innovative cropping systems at field scale, integrating economic and epidemiological analysis and using a database to represent agricultural management; CRASH aims to develop and evaluate crop-allocation strategies at farm scale that meet water-shortage regulations, using links with databases, optimisation processes and farmers' representation; AICHA aims to analyse impacts of irrigation on the water table at catchment scale using links to a hydrological model, cluster computation, integrated economic and agronomic optimisation. Automated encapsulation procedures allow STICS and RECORD communities to work independently but to benefit from mutual exchanges.

Crop models require different structures for different applications. Modular and flexible crop modelling frameworks, such as the recently developed agricultural production and externalities simulator (APES), support the change of model structure. However, the assembly of different modules to create a model may not always result in the best model structure. We developed and tested a protocol for a systematic selection and evaluation of a crop growth model structure. The novelty of the presented protocol relies on a throughout analysis of the different modelling approaches (modules) and on how to assemble them to create new modelling solutions (i.e. model). We use a case study to demonstrate that we can explicitly express and test the different assumptions behind the choice of a specific modelling approach. Our case study refers to the simulation of crop growth in response to nitrogen management and the importance of an accurate simulation of the nitrogen uptake. Applying the proposed protocol, we identify the need to improve the initially selected nitrogen mineralisation module. We conclude that not only is the protocol suitable to provide guidance for systematic testing of different crop processes modelled, but also its use highlights the importance of the documentation of the modelling process and of the clarification of the uncertainty associated with the model structure.

Cropping system models are powerful tools for regional impact assessment, but their input data requirements for large heterogeneous areas are difficult to fulfil. Hence, the objectives of this paper are to present low-data approaches for specifying detailed management data required by cropping system models, and for calibrating default crop parameters applied to 12 regions in the European Union (EU). Various downscaling and upscaling procedures for different data types are applied to address both objectives. The Agricultural Production and Externalities Simulator (APES) model is used for illustrative purposes.Combining easy-to-collect regional crop management information and expert knowledge enables to develop generic, expert-based rules for specifying crop management. Effects of these expert-based management rules on simulated yields and nitrogen leaching are illustrated using APES. Simulated yields of grain maize, soft wheat and durum wheat using default crop parameters for phenology are compared with crop yields observed in 12 EU regions. The accuracy of the simulated yields was variable, but generally poor. A regional calibration factor Kpheno is developed based on the temperature sum of the average sowing and harvest dates of the three crops in each region. Applying this calibration factor improved the simulated yields in all cases. Results suggest that it is possible to develop expert-based management rules and to capture yield variation across the EU by using the presented low-data approaches. (C) 2010 Elsevier B.V. All rights reserved.

Although existing simulation tools can be used to study the impact of agricultural management on production activities in specific environments, they suffer from several limitations. They are largely specialized for specific production activities: arable crops/cropping systems, grassland, orchards, agro-forestry, livestock etc. Also, they often have a restricted ability to simulate system externalities which may have a negative environmental impact. Furthermore, the structure of such systems neither allows an easy plug-in of modules for other agricultural production activities, nor the use of alternative components for simulating processes. Finally, such systems are proprietary systems of either research groups or projects which inhibits further development by third parties. SEAMLESS aims to provide a tool to integrate analyses of impacts on the key aspects of sustainability and multi-functionality, particularly in Europe. This requires evaluating agricultural production and system externalities for the most important agricultural production systems. It also requires a simulation framework which can be extended and updated by research teams, which allows a manageable transfer of research results to operational tools, and which is transparent with respect to its contents and its functionality. The Agricultural Production and Externalities Simulator (APES) is a modular simulation system aimed at meeting these requirements, and targeted at estimating the biophysical behavior of agricultural production systems in response to the interaction of weather and agro-technical management. APES is a framework which uses components that offer simulation options for different processes of relevance to agricultural production systems. Models are described in the associated help files of components, and a shared ontology is built on the web. Components like these, which are designed to be inherently re-usable, that is not targeted specifically to a given modelling framework, also represent a way to share modelling knowledge with other projects and the scientific community in general. This chapter describes the current state of APES development and presents modelling options in the system, and its software architecture.