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Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation.

Simple Summary The collagen architecture in the extracellular matrix (ECM) is highly remodeled in high grade serous ovarian cancer (HGSOC). Many of these tumors begin in the fallopian tubes (FT) before metastasizing to the ovaries and it is important to study ECM alterations in carcinogenesis. Here, we used Second Harmonic Generation (SHG) microscopy to classify changes in the collagen fiber morphology in normal FT, and precursor pure p53 signatures and serous tubal intraepithelial carcinoma (STICs) in tissues with no HGSOC. Using a machine learning approach based on image features, we were able to discriminate the tissue groups with good classification accuracy. We additionally performed mass spectrometry analysis of normal and HGSOC tissues to associate the differential expression of collagen isoforms with fiber morphology alterations. This work provides new insights into ECM remodeling in early stage HGSOC and suggests the combined use of SHG microscopy and mass spectrometry as a new diagnostic/prognostic approach. Abstract Background: The collagen architecture in high grade serous ovarian cancer (HGSOC) is highly remodeled compared to the normal ovary and the fallopian tubes (FT). We previously used Second Harmonic Generation (SHG) microscopy and machine learning to classify the changes in collagen fiber morphology occurring in serous tubal intraepithelial carcinoma (STIC) lesions that are concurrent with HGSOC. We now extend these studies to examine collagen remodeling in pure p53 signatures, STICs and normal regions in tissues that have no concurrent HGSOC. This is an important distinction as high-grade disease can result in distant collagen changes through a field effect mechanism. Methods: We trained a linear discriminant model based on SHG texture and image features as a classifier to discriminate the tissue groups. We additionally performed mass spectrometry analysis of normal and HGSOC tissues to associate the differential expression of collagen isoforms with collagen fiber morphology alterations. Results: We quantified the differences in the collagen architecture between normal tissue and the precursors with good classification accuracy. Through proteomic analysis, we identified the downregulation of single α-chains including those for Col I and III, where these results are consistent with our previous SHG-based supramolecular analyses. Conclusion: This work provides new insights into ECM remodeling in early ovarian cancer and suggests the combined use of SHG microscopy and mass spectrometry as a new diagnostic/prognostic approach.

International audience; In this study, we analyzed the patterns of relationships between multiple ecosystem services in apple orchards by considering the cascade that links agricultural practices to ecosystem functions and then to ecosystem services. Five major ecosystem services were considered: fruit production, soil nitrogen availability, climate regulation, water cycle maintenance and regulation, including water quality, and pest and disease control. We derived indicators of ecosystem functions and of ecosystem services from model simulations of orchards driven by virtual cropping systems combining various modalities of nitrogen fertilization, irrigation, and pest control. We deciphered the links between practices and ecosystem functions and between those functions and ecosystem services and clustered cropping systems according to their ecosystem service supply. Noticeable synergies were found between yield, fruit mass and sequestrated carbon. The contribution of carbon allocation to fruit in sequestrated carbon was considerable. Nitrogen absorption, impacted by fertilization and irrigation, was a major driver of these relationships. The typology built from these virtual cropping systems clearly followed a gradient of provisioning and regulating ecosystem services. Five cropping systems optimized the compromise between provisioning and regulating services and were essentially characterized by organo-mineral fertilization, comfort irrigation, apple scab-resistant cultivars and exclusion nets against codling moth. Our approach could contribute to the design of cropping systems that would provide an acceptable compromise between multiple ecosystem services in orchards.

Different forage grass models are used to simulate forage yield and nutritive attributes, but these models are seldom compared, particularly those for timothy (Phleum pratense L.), a widely grown forage grass species in agricultural regions with a cold temperate climate. We compared the models BASGRA, CATIMO and STICS for their predictions of timothy crude protein (CP) concentration, neutral detergent fibre (NDF) concentration and NDF digestibility (dNDF), three important forage nutritive attributes. Data on CP and NDF concentrations, and dNDF and the associated weather and soil data for seven cultivars, taken from eight field experiments in Canada, Finland, Norway, and Sweden, were divided into calibration and validation datasets. Model parameters were estimated for each cultivar separately (cultivar-specific calibration) and for all cultivars together (generic calibration), using different methods for the three models. Normalized root mean square error (RMSE) in prediction of CP concentration varied between 16 and 26% for BASGRA, 45 and 101% for CATIMO and 23 and 40% for STICS across the two calibration methods and the calibration and validation datasets. Normalised RMSE in prediction of NDF concentration varied between 8 and 13% for BASGRA, 14 and 21% for CATIMO and 8 and 12% for STICS, while for dNDF it varied between 7 and 22% for BASGRA, 7 and 38% for CATIMO and 5 and 6% for STICS. Cultivar-specific calibration improved the performance of CATIMO and STICS, but not BASGRA, compared with generic calibration. The prediction accuracy for NDF concentration and dNDF with the three models was within the same range or better than that for forage dry matter (DM) yield of timothy. Overall, the three models performed well in predicting some nutritive attributes and yield in Northern Europe and Canada, but improvements are required, particularly to increase the prediction accuracy of CP concentration.

Soil is a reservoir of natural capital that provides several ecosystem services, ensuring human well-being and sustainable socioeconomic development. Many researchers nevertheless argue that there is no consensus on practical indicators to assess soil ecosystem services (SES). As many policy decisions rely on metrics and indicators to communicate concise and relevant information, an assessment of ecosystem service indicators can help identifying gaps hindering policymakers from more fully adopting ecosystem service approaches. The aim of this study was to develop a method to quantitatively evaluate six SES using a set of indicators derived from dynamic soil and crop modelling using the STICS model developed by INRA. In a 6 775 km2 study area in north-western France (Brittany), 64 soil sampling points, located in agricultural areas, were selected following a stratified random sampling design based on soil parent material stratification. STICS inputs required climate data, soil property data and soil and landscape management practices. Over a baseline period from 1988 to 2018, similar crop management practices were simulated that reflected the dominant one performed by conventional farmers across the study site: applying organic and mineral fertilizers to a maize-wheat-catch crop rotation. Also, STICS outputs were used to derive six biophysical indicators characterizing six SES. Both mean and annual SES indicators were calculated and analyzed. Interrelations among SES indicators and soil properties were investigated using a Pearson correlation matrix, multivariate variance analysis and regression-based methods. The main results revealed that pedological and inter-annual variability were the main drivers of SES provision, particularly for ground water recharge, plant biomass provision, plant water provision and carbon sequestration. All SES were strongly correlated, except carbon sequestration and plant nitrogen provision, which showed weak correlations with the rest of SES. Moreover, analyzing interrelations between SES indicators and soil properties such as soil depth, soil texture and its related variables such as available water capacity played a predominant role in ensuring high levels of water-related SES indicators. Meanwhile, physicochemical soil properties were strongly correlated with carbon sequestration but less so with plant nitrogen provision. Overall, these findings characterize the effect of soil variability on SES provision using modelling and field measures of several soil properties, which can be useful for policy makers.

In Europe, most of vineyards are managed under rainfed conditions, where water deficit has become increasingly an issue. The flowering-veraison phenophase represents an important period for vine response to water stress, which is known to depend on variety characteristics, soil and climate conditions. In this paper, we have carried out a retrospective analysis for important European wine regions over 1986-2015, with objectives to assess the mean Crop Water Stress Indicator (CWSI) during flowering-veraison phase, and potential Yield Lose Rate (YLR) due to seasonal cumulative water stress. Moreover, we also investigate if advanced flowering-veraison phase can lead to alleviated CWSI under recent-past conditions, thus contributing to reduced YLR. A process-based grapevine model is employed, which has been extensively calibrated for simulating both flowering and veraison stages using location-specific observations representing 10 different varieties. Subsequently, grid-based modelling is implemented with gridded climate and soil datasets and calibrated phenology parameters. The findings suggest wine regions with higher mean CWSI of flowering-veraison phase tend to have higher potential YLR. However, contrasting patterns are found between wine regions in France-Germany-Luxembourg and Italy Portugal-Spain. The former tends to have slight-to-moderate drought conditions (CWSI0.5) and substantial YLR (>40%). Wine regions prone to a high drought risk (CWSI>0.75) are also identified, which are concentrated in southern Mediterranean Europe. Advanced flowering-veraison phase over 1986-2015, could have benefited from more spring precipitation and cooler temperatures for wine regions of Italy-Portugal-Spain, leading to reduced mean CWSI and YLR. For those of France-Germany-Luxembourg, this can have reduced flowering-veraison precipitation, but prevalent reductions of YLR are also found, possibly due to shifted phase towards a cooler growing-season with reduced evaporative demands. Our study demonstrates flowering-verasion water deficit is critical for potential yield, which can have different impacts between Central and Southern European wine regions. This phase can be advanced under a warmer climate, thus having important implications for European rainfed vineyards. The overall outcome may provide new insights for appropriate viticultural management of seasonal water deficits under climate change. This study was funded by Clim4Vitis project-"Climate change impact mitigation for European viticulture: knowledge transfer for an integrated approach", funded by the European Union's Horizon 2020 Research and Innovation Programme, under grant agreement no. 810176; it was also supported by FCT-Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020. We acknowledge the data provisions from members of the PEP725 project, from IPHEN project and from the Consejo Regulador of Ribera de Duero and Rioja DOCa.

Although crop diversification is one of the main strategy of agroecological transition, a major obstacle is the lack of local references regarding new crops. Land suitability methods can provide a rapid screening of crop suitability in a region. However, mainstream methods are mainly based on assessing soil and climate suitability, whereas it is fundamental to know where and how a new crop can be introduced into existing crop rotations and whether this introduction would be profitable. Our method based on recent advances in the characterization of cropping systems at the regional level can be exploited to evaluate: (1) the yield potential of the new crop, (2) the potential of this new crop being successfully introduced into ongoing crop rotations, (3) the economic benefits of such an introduction. The method was tested for the possible introduction of new soybean varieties in northern France. The method developed has relevance beyond the case study. Our method could also be easily adapted to rapidly assess the potential for introducing new crops in cases where there climate database, soil map information and a Land Parcel Identification System are available.

International audience; 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 PâturSTICS. 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 highresolution 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 PâturSTICS 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 northwestern France, the north-western Massif Central, the French Alps and the western Pyrénées, all of which have environmental conditions favourable for grass growth. The metamodels developed are interesting proxies for PâturSTICS' 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 selfsufficiency and N fertilisation management in ruminant livestock systems.

Climate change projections for Portugal showed warming and drying trends, representing a substantial threat for the sustainability of forage production in perennial grassland. The objective of the present study was to assess climate change impacts on seasonal dry matter yield (DMY) in three locations (North-west-, Central-inner and South-Portugal) with different climatic conditions, for two grassland production systems deviating in growing season length, either early cuts in spring (ES) or late cuts in summer (LS). Impacts were estimated using the STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) crop model, by comparing a historical baseline period (1985–2006) with simulated projections over future periods (2021–2080). For this purpose, the STICS crop model was driven by high-resolution climate data from a coupled Global Climate Model/Regional Climate Model chain. As a result, we obtained that, during the baseline period, DMY of LS was consistently much higher than that of ES in all three locations. For LS, significant reductions in mean DMY were forecasted during 2061–2080, ranging from mild (–13%) in the north to severe (–31%) in the south of Portugal. In contrast, seasonal DMY was largely maintained for ES among sites until 2080, benefiting from low water deficits, the expected atmospheric CO2 rise and the forecasted temperature increase during cool season. Thus, the yield gap was projected to gradually decrease between the two regimes, in which mean DMY for ES was foreseen to exceed that of LS over 2061–2080 in the southern site. Moreover, ES was projected to have very low exposure to extreme heat and severe water stresses. Conversely, LS, subjected to high summer water deficit and irrigation needs, was projected to experience increased summertime water stress (9–11%) and drastically increased heat stress (33–57%) in 2061–2080, with more pronounced heat stress occurring in the south. Frequency of occurrence of extreme heat stress was projected to gradually increase in summer over successive study periods, with a concomitant increased intensity of DMY response to inter-annual variability of heat stress during 2061–2080. Heat stress tended to be more important than water stress under the prescribed irrigation strategy for LS, potentially being the main limiting factor for summertime DMY production under climate change scenario.

International audience; The assessment of yield variability at the territory scale is often difficult due to the lack of knowledge on various factors involved, e.g., the agricultural practices of farmers and phenological calendars. Remote sensing can help to provide precise and timely information on crops particularly with the unprecedented amount of free Sentinel data within the Copernicus programme. This study focuses on the evaluation of the contribution of the new Sentinel 2 data to provide phenological information on rice cropping systems in the Camargue region in the South-Eastern France. Dense time series of data acquired at high spatial resolution (10m) were analyzed for 2016 and 2017 and were used to map rice, compute Leaf Area Index. Various methods combining remote sensing information were compared with two different crop models (STICS and SAFY) to assess the yields. The performances are discussed according to surveys made with farmers.

Projected warming and drying trends over the Mediterranean region represent a substantial threat for wheat production. The present study assesses winter wheat yield response to potential climate change and estimates the quantitative effectiveness of using early flowering cultivars and early sowing dates as adaptation options for the major wheat production region of Portugal. A crop model (STICS) is used for this purpose, which is calibrated for yield simulations before projecting future yields. Climate projections over 2021–2050 and 2051–2080 under two emission scenarios (RCP4.5 and RCP8.5) are retrieved from bias-adjusted datasets, generated by a ten-member climate model ensemble. Projected intensification of water deficits and more frequent high-temperature events during late spring (April–June), coinciding with the sensitive grain filling stage, primarily result in continuous mean yield losses (relative to 1981–2010) by − 14% (both scenarios) during 2021–2050 and by − 17% (RCP4.5) or − 27% (RCP8.5) during 2051–2080, also accompanied by increased yield variabilities. Of evaluated adaptation options at various levels, using earlier flowering cultivars reveals higher yield gains (26–38%) than that of early sowings (6–10%), which are able to reverse the yield reductions. The adopted early flowering cultivars successfully advance the anthesis onset and grain filling period, which reduces or avoids the risks of exposure to enhanced drought and heat stresses in late spring. In contrast, winter warming during early sowing window could affect vernalization fulfillment by slowing effective chilling accumulation, thus increasing the pre-anthesis growth length with limited effects on advancing reproductive stage. Crop yield projections and explored adaptation options are essential to assess food security prospects (availability and stability) of dry Mediterranean areas, providing crucial insights for appropriate policymaking.

International audience; Climate change could undermine grain legumes ability to fix atmospheric nitrogen and their contribution to increase cropping systems sustainability. Pea (Pisum sativum L.) and faba bean (Vicia faba L.) are the two most widely grown grain legumes in Europe, yet the potential impact of climate change on their performances has not been quantified. We calibrated and evaluated the STICS soil-crop model for spring pea, winter pea and winter faba bean using experimental data from southwestern France and explored the effect of contrasting climate change scenarios. After calibration, STICS accurately simulated grain yield and amount of N2 fixed for the experimental growing seasons. Assuming no change in crop management, mean and inter-annual variability of grain yield and fixed N2 were assessed for historical (1995−2015), mid-term (2020−2040) and long-term (2060−2080) periods in one location in southwestern France. We considered projections from three climate models and two Representative [CO2] Pathways (RCP 4.5 and RCP 8.5). The climate models spanned a wide range of changes in temperature (+0.3 to +4.1 °C) and rainfall (−15% to +8%) depending on time horizon and RCP. Simulated grain yield increased over the long term in most scenarios (+1 to +25%), and spring pea tended to benefit less than winter pea and winter faba bean. Nevertheless, for the climate scenario with a decrease in rainfall and the strongest increase in temperature, simulated spring pea grain yield decreased by 28% while winter legumes yields were less affected (−14% for pea and no decrease for faba bean). Simulated changes in the amount of N2 fixed followed the grain yield response. Temperature rise caused a shortening in crop cycle duration. Simulated temperature stress significantly increased for spring and winter pea in most climate change scenarios while winter faba bean was rather unaffected due to greater upper temperature thresholds. N2 fixation of spring pea was reduced by above-optimal temperature during its vegetative growth in spring while N2 fixation of winter legumes was enhanced by the increase in temperature during their vegetative growth in winter. Simulated drought stress only increased in the climate scenario predicting a decrease in rainfall. Overall, [CO2] increase would allow offsetting negative effects of temperature and drought on grain yield and N2 fixation, except for climate scenarios involving a decrease in rainfall and the strong increase in temperature. The contrasted simulated response of winter and spring grain legumes to climate change in southwestern France points to the opportunity to tap grain legume diversity and cultivar choice as an adaptation strategy.

Wheat yield potentials under rainfed Mediterranean conditions have been long limited by late-in-season occurrence of enhanced water deficits and high temperatures, coinciding with sensitive reproductive stages. Present study aims to quantify and separate the impacts of two main abiotic stresses (drought & heat) on potentially attainable wheat yields, in a typical Mediterranean environment of southern Portugal (Alentejo) over 1986—2015. We also evaluate how possible adaptation options could mitigate potential yield losses (reduce the gap between actual and potential yield). Previously calibrated STICS soil-crop model is used for these purposes, which has been satisfactorily evaluated herein for yield simulations using additional field data before running at regional level. By coupling with high-resolution gridded soil and climate datasets, STICS simulations reliably reproduce the inter-annual variability of 30-year regional yield statistics, together with reasonable estimations of experimental potential yields. Therefore, the model is useful to explore the source of yield gap in the region. The quantified impacts, though with some uncertainties, identify the prolonged terminal drought stress as the major cause of yield gap, causing 40–70% mean potential yield losses. In contrast, a short-duration of crop heat stress (≥38 °C) during late grain-filling phase only results in small-to-moderate reductions (up to 20%). Supplemental Irrigation (SI) during reproductive stages provides good adaptive gains to recover potential yield losses by 15–30%, while the proposed early-flowering cultivar is more useful in escaping the terminal heat stress (5–15% adaptive gains) than avoiding prolonged drought stress. In addition, advancing sowing date generally favours wheat production with a robust spatial-temporal pattern. Therefore, combined options based on application of SI, using balanced early-flowering cultivar and early sowing date, may contribute to considerably reduce local yield gap, where current yields can account for 60% of potential yields (26–32% without adaptation). Regional impact assessment and adaptation modelling studies are essential to support agricultural policy development under climate change and variability. The recommended combined adaptation may also represent a promising adaptation strategy for rainfed wheat cropping system in other regions with similar Mediterranean conditions. However, the existing spatial-temporal variability of adaptation response highlights the need to address adaptation strategies at a more detailed local scale with better flexible design.

International audience; Fruit tree production faces the major challenge of ensuring maximal productivity with due consideration for the environment and human health. The increasingly recognized concept of ecosystem service could help to address this duality. In this paper, we propose an analytical framework based on a soil crop model to investigate how agricultural management and pedoclimatic conditions affect the joint production of marketed and non-marketed ecosystem services through underlying ecosystem functions in apple orchards. The ecosystem services considered on an annual scale were soil nitrogen availability, climate regulation, water regulation and fruit production. Ecosystem functions and services were described by specific indicators that were quantified using the STICS soil crop model. This model was parameterized using data collected on two experimental apple orchard sites under conventional and low-input or organic management in southeastern France. The interdependencies between environmental components, cultural operations and ecosystem functions were dynamically integrated by the model and highlighted significant interactions between the indicators of ecosystem services. Thus, the service indicators soil organic nitrogen variation and the prevention of nitrogen denitrification and of leaching were positively correlated and in conflict with soil mean nitrate concentration and mean soil humidity. They were also linked negatively to nitrogen mineralization enhanced by irrigation and positively to soil carbon sequestration impacted by fertilization; these two functions were impacted by soil conditions. Yield and carbon sequestration presented a strong synergy and were positively correlated to nitrogen absorption increased by mineral fertilization. Globally, nitrogen fertilization management and planting density were particularly important for the delivery of multiple ecosystem services, but soil and climate effects were far from negligible, especially for nitrogen and water-related services. The ecosystem service profiles of the studied cropping systems were diversified, with contrasted profiles showing high yield and carbon sequestration but low prevention of nitrogen denitrification and of nitrogen leaching, and more balanced profiles. The STICS crop model made it possible to quantify and analyze profiles of ecosystem services and should be helpful in instrumenting the dialogue between fruit growers and other stakeholders by simulating scenarios to optimize multiple services. However, it has to be improved to address the impact of grass cover on soil functions and the long-term functioning of apple orchards.

International audience; The creation of an environmental tool diagnosis from existing modules in other models in order to contribute to improve the nitrogen management. Did the simplification of soil description, in terms of horizons number and of the shape’s curve of root profile in the French model of culture STICS, allows us to simulate correctly the quantity of leaching nitrogen?. The main objective is to feign the consequences of the variations of the environment (climatic parameters and soil properties) on the system of culture on agricultural plot production (wheat, corn and mustard) and on the environment (nitrogen leaching).

Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10 cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC components. These results show the potential of crop model inversion for estimating the AWC components of two-layered soils and may guide the sampling of representative years and fields to use this technique for mapping soil properties that are relevant for distributed hydrological modelling. (C) 2017 Elsevier B.V. All rights reserved.

The future of global rice productions in top producing countries is undermined by the impact of climate change threatening food security in the near future. In those European Mediterranean areas where rice is cultivated, this peculiar cropping system plays a crucial role in terms of sociocultural and ecological issues, and the climate change impact is still scarcely investigated. In this study, we explored the future trends of potential rice yields in the region considering the multiple sources of uncertainty associated with climate and yield predictions. Two rice crop models (STICS and WARM) were calibrated using 20 field experiments carried out in two main European rice producing areas − i.e., the Italian Lomellina and the French Camargue. These models were then applied under a range of climate change scenarios in 2030 and 2070 time frames, considering projections from the combination of four General Circulation Models and two extreme Representative CO2 Concentration Pathways (RCP 2.6 and 8.5). We compared the simulated yield levels with no adaptation, and designed adaptation strategies based on the anticipation of sowing date and the adoption of varieties with longer crop cycle. Our results showed that with no adaptation yields would decrease on average by 8% in 2030 and 12% in 2070 in Camargue and Lomellina. Future simulated yields in the two areas were lower than in the baseline in 67% (Camargue) and 84% (Lomellina) of the cases. The implementation of both adaptation strategies proved to be effective in reversing the situation, leading to an average yield increase of 28% and 25% in 2030 and 2070, respectively. The associated probability of lower yields than in current conditions was 24% in the two sites. Despite the uncertainty in predictions, mainly related to site, GCM and RCP, our findings indicate that the European rice sector has the potential to enhance current production levels in a changing climate, if longer cycle varieties will be grown in Mediterranean rice areas.

International audience; In a context of increased land and natural resources scarcity, the possibilities for local authorities and stakeholders of anticipating evolutions or testing the impact of envisaged developments through scenario simulation are new challenges. PRECOS's approach integrates data pertaining to the fields of water and soil resources, agronomy, urbanization, land use and infrastructure etc. It is complemented by a socio-economic and regulatory analysis of the territory illustrating its constraints and stakes. A modular architecture articulates modeling software and spatial and temporal representations tools. It produces indicators in three core domains: soil degradation, water and soil resources and agricultural production. As a territory representative of numerous situations of the Mediterranean Basin (urban pressures, overconsumption of spaces, degradation of the milieus), a demonstration in the Crau's area (Southeast of France) has allowed to validate a prototype of the approach and to test its feasibility in a real life situation. Results on the Crau area have shown that, since the beginning of the 16th century, irrigated grasslands are the cornerstones of the anthropic-system, illustrating how successfully men's multi secular efforts have maintained a balance between environment and local development. But today the ecosystem services are jeopardized firstly by urban sprawl and secondly by climate change. Pre-diagnosis in regions of Emilia-Romagna (Italy) and Valencia (Spain) show that local end-users and policy-makers are interested by this approach. The modularity of indicator calculations and the availability of geodatabases indicate that PRECOS may be up scaled in other socio-economic contexts. (C) 2016 Elsevier Ltd. All rights reserved.

International audience; Viticulture is a key socioeconomic sector in Europe. Owing to the strong sensitivity of grapevines to atmospheric factors, climate change may represent an important challenge for this sector. The present study analyses viticultural suitability, yield, phenology, and water and nitrogen stress indices in Europe, for present climates (1980–2005) and future (2041–2070) climate change scenarios (RCP4.5 and RCP8.5). The STICS crop model is coupled with climate, soil and terrain databases, also taking into account CO2 physiological effects, and simulations are validated against observational datasets. A clear agreement between simulated and observed phenology, leaf area index, yield and water and nitrogen stress indices, including the spatial differences throughout Europe, is shown. The projected changes highlight an extension of the climatic suitability for grapevines up to 55ºN, which may represent the emergence of new winemaking regions. Despite strong regional heterogeneity, mean phenological timings (budburst, flowering, veraison and harvest) are projected to undergo significant advancements (e.g. budburst/harvest can be >1 month earlier), with implications also in the corresponding phenophase intervals. Enhanced dryness throughout Europe is also projected, with severe water stress over several regions in Southern regions (e.g. southern Iberia and Italy), locally reducing yield and leaf area. Increased atmospheric CO2 partially offsets dryness effects, promoting yield and leaf area index increases in Central/Northern Europe. Future biomass changes may lead to modifications in nitrogen demands, with higher stress in Northern/Central Europe and weaker stress in Southern Europe. These findings are critical decision support systems for stakeholders from the European winemaking sector.

International audience; During the 1999/2000 agricultural seasons, an experiment was carried out on winter wheat fields in the semiarid Yaqui Valley (Northwest Mexico). This data set was used to calibrate the evolution of the leaf area index (LAI) simulated by STICS, which was found to be in excellent agreement with estimates obtained from field reflectance measurements. After calibration, STICS was able to simulate satisfactorily the seasonal levels and trends observed in net radiation, soil moisture and evapotranspiration, but the crop temperature was overestimated by about 2.5 °C. On a larger scale, STICS was run on 16 fields with contrasting management practices. The simulations indicate that yield predictability is significantly lower for later sowing dates, consistent with observations. The seasonal variations of field and satellite data (Landsat-ETM+, Terra-MODIS and VEGETATION) NDVI were very close. However, some difficulties were noted: saturation of NDVI at high LAI values and smoothed variability on a 1-km spatial scale, as well as the need for a sound methodology for processing satellite data.

The STICS simulation model was adapted for linseed. An original procedure was used. Firstly, options were selected from among the possibilities available in STICS to simulate the processes of crops. Secondly, the model was calibrated following six steps: 1. gathering of information, 2. the use of parameters from the literature or from other models, 3. the use of STICS parameters for other crops if there is an analogy with linseed, 4. the use of the experimental data to determine parameters which can be measured or calculated, 5. the use of the experimental data to determine parameters by testing a range of values, and 6. the checking of consistency between the parameters and their physical or biological meaning. After adaptation to linseed, the simulations of leaf area, biomass, water consumption, plant nitrogen content, seed number and seed yield were in good agreement with the measurements used for calibration. Thirdly, the results of calculations by STICS were compared with measurements not used for calibration. There was little difference between calculations and measurements of leaf area, biomass, plant nitrogen content and seed number, while seed yield was overestimated because of diseases and lodging, which are not taken into account by the model. However, the differences in seed yield between treatments were properly simulated. This work was a first step towards developing a model to improve linseed crop management. To this end, modifications are needed to account for all yield limitations.

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.

The FAO AquaCrop model has been conceived as a tool for simulating, on a daily scale, the canopy cover (CC), biomass and the actual evapotranspiration and for simulating, on a seasonal scale, the final biomass, the harvested yield, the cumulate actual evapotranspiration, and the crop water use efficiency. This performance was analysed after a series of tests carried on 12 crop cycles, concerning corn and tomato grown in the Mediterranean region, and having three levels of plant water stress: absence of plant water stress (control), moderately stressed and severely stressed. The results highlight the effects of three factors affecting the AquaCrop performances: the species, the level of plant water stress during the crop cycle and the output variable to simulate. The AquaCrop adequately simulates the daily canopy cover (CC) in control treatments of tomato and corn, and in moderate stress treatment of corn. In the severe stressed treatment of corn, the simulated values of CC were close to the measured values only from sowing to 60 days after sowing, after that the simulated values do not fit the measurements. The AquaCrop model adequately simulates the daily biomass accumulation under all treatments in tomato and under non-stressed and moderate stressed treatments in corn. However, the simulated biomass outputs were generally overestimated during the late stages of the crop cycles and, consequently, the yield also exhibited a tendency to be overestimated. Nevertheless, the yield overestimation can be retained as acceptable because the normalised differences ( D ) between the simulations and measured values were less than 15% on average. An exception was the tomato yield simulated in the severely stressed treatment, for which D was greater than 30%. In contrast, in the case of the severely stressed treatment in corn, AquaCrop did not exhibit any aptitude for simulating the biomass or the grain yield. In fact, the model predicts the absence of any yield production, while 5 t ha −1 of grain were actually measured in the severely stressed treatment. The daily actual evapotranspiration simulated by AquaCrop was consistent with the observations only in the case of the control treatments of tomato, in all the three seasons. In contrast, for the other treatments (all treatments in corn and all stressed treatments in tomato), the quality of the evapotranspiration simulation was poor. In general, AquaCrop underestimated the seasonal values of evapotranspiration. The normalised differences between the seasonal values of the observations and simulations are acceptable in the case of the tomato evapotranspiration ( D = −7%). However, in the case of corn, the differences are related to the level of plant water stress, and they become unacceptable ( D = −36%) in the severely stressed treatments. The overestimation of the yield and the underestimation of the seasonal evapotranspiration cause the simulations of the water use efficiency to be overestimated. In the specific case of corn, due to the unacceptable performance of the model under severely stressed treatments, the linear regression between the observations and measurements of water use efficiency is unsatisfactory. The potential uses of the AquaCrop model as a tool for research purposes aimed to enhance the water efficiency and as a tool for managing irrigation have been deeply discussed. The paths that should be followed in the future to improve the model simulations have also been suggested

International audience; Agriculture constitutes a major source of non-point pollution (e.g., nitrates) where overall water resources are affected, in particular, aquifers. Intensive agricultural practices take place in regions with appropriate weather conditions that are usually deficient in water resources. The preservation of water resources in these types of regions depends on the evaluation of the efficiency of agricultural practices for specific crops and conditions. Although water scarcity is a characteristic feature in the Western Mediterranean, it is one of the most appropriate regions in the world for intensive agriculture development for climatic reasons. In the current work, percolation and N leaching from different crops (corn, potato, and rotation of lettuce and melon) under different irrigation methods (surface, sprinkler and drip) were evaluated through experimental plots. Water (irrigation + precipitation) and fertilizer inputs were accurately controlled. Soil water content and nitrate concentration were monitored from time domain reflectometry measurements, and cup lysimeters and destructive sampling, respectively. Percolation and nitrate leaching was simulated from different numerical codes (STICS and GLEAMS, tipping bucket method; HYDRUS-1D, Richards' equation), which were chosen based on the available information and the specific purposes of each experiment. For the studied periods, the obtained results showed high percolation values: 34, 58 and 37% of total applied water for corn, potato, and rotation of lettuce and melon, respectively. Also, high N leaching values across all experiences were observed, even higher than the applied doses in some periods as consequence of remobilizing mineralized N, despite following the recommended agricultural management practices. Percolation and N leaching were mostly controlled by the precipitation regime, namely, unevenly distributed intensive rainfall events, mainly in autumn and spring, which have a great impact in irrigated agriculture due to the permanent high soil water content. In detail, irrigation water applied for frost prevention on potato crops and plastic cover for melon crops, played a very important role for both percolation and N leaching. Whilst for the corn crop, N leaching mainly took place in the fallow period (autumn and winter), where the rain leached N present in soil from previous crops.

This paper addresses the question of summer cover-crop adoption by farmers in presence of a risk of yield loss due to take-all disease and climate variability. To analyze the public incentives needed to encourage farmers to adopt summer cover crops as a means of reducing N leaching, we combine outputs from an economic, an epidemiological and an agronomic model. The economic model is a simple model of choice under risk. The farmer is assumed to choose among a range of summer fallow managements and input uses on the basis of the expected utility criterion (HARA assumption) in presence of both climate and take-all risks. The epidemiological model proposed by Ennaïfar et al. (Eur J Plant Pathol 118:127–143, 2007) is used to determine the impact of take all on yields and N uptake. The crop-soil model (STICS) is used to compute yield developments and N leaching under various management options and climatic conditions. The input parameters are chosen to match the conditions prevailing in Grignon, located in the main wheat-growing area in France. Eight management systems are examined: four summer fallow managements: ‘wheat volunteers’ (WV), ‘bare soil’ (BS), ‘early mustard’, ‘late mustard’, and two input intensities. We show that the optimal systems are BS (WV) when the take-all risk is (not) taken into account by agents. We then compute the minimum payment to each system such that it emerges in the optimum. We thus derive the required amounts of transfer needed to trigger catch-crop adoption. The results of the Monte Carlo sensitivity analysis show that the ranking of management systems is robust over a wide range of input parameters.; Cet article examine la question de l'adoption de cultures intermédiaires en présence d'un risque de rendement dû à une maladie (piétin) et au climat. Pour analyser les incitations requises pour encourager les exploitants à adopter une culture intermédiaire afin de réduire le lessivage d'azote, les auteurs combinent les résultats de trois modèles (économique, épidemiologique et agronomique). Le modèle économique est un simple modèle de choix en situation d'incertitude. L'exploitant est supposé choisir entre les différentes options de gestion de la période intercultures et d'intensité d'intrants sur la base de l'utilité espérée (HARA), le modèle épidemiologique proposé par Ennaifar et al. (2007) est utilisé pour déterminer l'impact du piétin sur le rendement et l'utilisation d'azote par la plante. Le modèle STICS est utilisé pour calculer les rendements et le lessivage d'azote dans les différentes situations. Ces modèles sont calibrés sur les conditions prévalant à Grignon, situé dans la principale zone de production de blé en France. Huit options sont considérées : 4 gestion de l'interculture 'repousses de blé' (WV), 'sol nu' (BS), 'moutarde précoce' (EM), 'moutarde tardive' (LM), et 2 niveaux d'intensité des intrants. Les auteurs montrent que les systèmes optimaux sont BS (WV) quand le risque de piétin est (n'est pas) pris en compte. Les auteurs calculent alors le paiement minimum pour que chaque système soit choisi à l'optimum. Les auteurs pouvent ainsi déduire les montants requis pour qu'une culture intermédiaire soit adoptée. Les résultats de l'analyse de sensibilité par méthode de Monte Carlo montrent que le classement des différents systèmes est robuste pour une large gamme de valeurs des paramètres.

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; Given the complexity of intercropping systems, models can be especially helpful to analyse them comprehensively. The present work puts forward a modelling approach based on an extension of sole crop models, considering the system to be composed of two species instead of one. The chosen sole crop model is STICS and its adaptation to intercropping relies first on a simplified definition of the complex agronomic system, which is subdivided into three sub-systems: the dominant canopy, and the shaded and sunlit parts of the understorey canopy. It is assumed that no inversion of dominancy occurs during the cycle of the shorter duration crop. Each of the sub-systems is embedded in a different light microclimate, estimated from a radiative balance module, assuming that a simple geometric shape can represent the dominant canopy. This radiative balance pilots the differential behaviours of the sub-systems in terms of growth, LAI dynamics, water and nitrogen uptakes and stress indices. The estimation of the water requirements for both crops relies on light partitioning coupled to a resistive scheme and applied at a daily time-step. In the soil, the horizontal differentiation is neglected in favour of the vertical one. As far as below-ground competition for water and nitrogen is concerned, it is assumed that the interactions between the two root systems result from the influence of the soil on each crop root profile through its water content dynamics. Data sets from three different intercrops, two annual and one perennial systems grown in French Antilles, were used for parameterisation of the model and verification of its consistency. The intercrop-specific modules of the model are described and discussed in comparison with the other existing models.

International audience; The aim of this paper was to develop an upscaling approach for the soil-crop model STICS in order to predict the impact of agricultural practices on nitrate leaching on both plot and regional scales. A case study was carried out on a "Nitrate Vulnerable Zone" located in central France. The performance of the spatial approach was evaluated by accounting for all the spatial and temporal variability existing within the studied area. The results indicate that N leaching and nitrate concentration in drainage water were slightly underestimated; by 3 kg N*ha-1 (16%) and 8 mg NO3-*L-1 (11%), respectively. The STICS scaling approach was used to assess the effectiveness of "Good Agricultural Practice" established within the area over a seven-year period. The simulation results provided evidence that such a practice had reduced the nitrate concentration by about 30% (36 mg NO3-*L-1). However, the rate of nitrate leaching remains too large and further improvements to agricultural practices are required.