Search

With global warming, grapevine is expected to be increasingly exposed to water deficits occurring at various development stages. In this study, we aimed to investigate the potential impacts of projected climate change on water deficits from the flowering to veraison period for two main white wine cultivars (Riesling and Müller-Thurgau) in Germany. A process-based soil-crop model adapted for grapevine was utilized to simulate the flowering-veraison crop water stress indicator (CWSI) of these two varieties between 1976–2005 (baseline) and 2041–2070 (future period) based on a suite of bias-adjusted regional climate model (RCM) simulations under RCP4.5 and RCP8.5. Our evaluation indicates that the model can capture the early-ripening (Müller-Thurgau) and late-ripening (Riesling) traits, with a mean bias of prediction of ≤2 days and a well-reproduced inter-annual variability for more than 60 years. Under climate projections, the flowering stage is advanced by 10–20 days (higher in RCP8.5) between the two varieties, whereas a slightly stronger advancement is found for Müller-Thurgau than for Riesling for the veraison stage. As a result, the flowering-veraison phenophase is mostly shortened for Müller-Thurgau, whereas it is extended by up to two weeks for Riesling in cool and high-elevation areas. The length of phenophase plays an important role in projected changes of flowering-veraison mean temperature and precipitation. The late-ripening trait of Riesling makes it more exposed to increased summer temperature (mainly in August), resulting in a higher mean temperature increase for Riesling (1.5–2.5 °C) than for Müller-Thurgau (1–2 °C). As a result, an overall increased CWSI by up to 15% (ensemble median) is obtained for both varieties, whereas the upper (95th) percentile of simulations shows a strong signal of increased water deficit by up to 30%, mostly in the current winegrowing regions. Intensified water deficit stress can represent a major threat for high-quality white wine production, as only mild water deficits are acceptable. Nevertheless, considerable variabilities of CWSI were discovered among RCMs, highlighting the importance of efforts towards reducing uncertainties in climate change impact assessment.

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.

Intercropping of two or more species on the same piece of land can enhance biodiversity and resource use efficiency in agriculture. Traditionally, intercropping systems have been developed and improved by empirical methods within a specific local context. To support the development of promising intercropping systems, the individual species that are part of an intercrop can be subjected to breeding. Breeding for intercropping aims at resource foraging traits of the admixed species to maximize niche complementarity, niche facilitation, and intercrop performance. The breeding process can be facilitated by modeling tools that simulate the outcome of the combination of different species' (or genotypes') traits for growth and yield development, reducing the need of extensive field testing. Here, we revisit the challenges associated with breeding for intercropping, and give an outlook on applying crop growth models to assist breeding for intercropping. We conclude that crop growth models can assist breeding for intercropping, provided that (i) they incorporate the relevant plant features and mechanisms driving interspecific plant-plant interactions; (ii) they are based on model parameters that are closely linked to the traits that breeders would select for; and (iii) model calibration and validation is done with field data measured in intercrops. Minimalist crop growth models are more likely to incorporate the above elements than comprehensive but parameter-intensive crop growth models. Their lower complexity and reduced parameter requirement facilitate the exploration of mechanisms at play and fulfil the model requirements for calibration of the appropriate crop growth models., This research was funded by the DIVERSify project, a grant from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 727284. GV and MW were also supported by a grant from the Swedish Research Council Formas (Grant Number 2018-02321).

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.

Cultivar recommendation is a key factor in cropping system management. Classical approaches based on comparative multi-environmental trials can hardly explore the agro-climatic and management heterogeneity farmers may have to face. Moreover, they struggle to keep up with the number of genotypes commercially released each year. We propose a new approach based on the integration of in silico ideotyping and functional trait profiling, with the common bean (Phaseoulus vulgaris L.) in Northern Italy as a case study. Statistical distributions for six functional traits (light extinction coefficient, radiation use efficiency, thermal time to first pod and maturity, seed weight, plant height) were derived for 24 bean varieties. The analysis of soil, climate and management in the study area led us to define 21 homogeneous contexts, for which ideotypes were identified using the crop model STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard), the E-FAST (Extended Fourier Amplitude Sensitivity Test) sensitivity analysis method, and the distributions of functional traits. For each context, the 24 cultivars were ranked according to the similarity (weighted Euclidean distance) with the ideotype. Context-specific ideotypes mainly differed for phenological adaptation to specific combinations of climate and management (sowing time) factors, and this reflected in the cultivar recommendation for the different contexts. Feedbacks from bean technicians in the study area confirmed the reliability of the results and, in turn, of the proposed methodology.

Les cultures intermédiaires multi-services (CIMS) sont un des piliers de l’agroécologie et elles peuvent être introduites dans les systèmes de culture actuels sans les modifier fortement. Cependant, dans un contexte de changement climatique avec une modification des régimes pluviométriques, l’impact des CIMS sur la dynamique et le bilan hydrique pourrait être négatif. Le bassin Adour-Garonne (BAG), connaît des problèmes de gestion de la quantité et de la qualité (nitrates, pesticides) de ses masses d’eau. Quantifier l’impact des CIMS en fonction de leur gestion sur la dynamique et le bilan hydrique des sols agricoles du territoire du BAG représente donc un enjeu social et scientifique crucial. Mon objectif est d’évaluer l’impact des CIMS sur le bilan hydrique et les flux d’eau à l’échelle de la parcelle, en fonction de leur mode de gestion, et à l’échelle du territoire, pour évaluer les conséquences possibles sur la recharge des nappes en cas de réduction de drainage. J’ai mis en place une démarche scientifique couplant 3 types de méthodes : méta-analyse de la littérature scientifique, expérimentations et mesures de terrains et simulations avec le modèle de culture STICS à l’échelle de la parcelle, puis par extrapolation à l’échelle du territoire du BAG. J’ai analysé l’impact des CIMS sur l’évapotranspiration, le drainage, la lixiviation des nitrates et les stocks d’eau et d’azote en fonction de divers facteurs : pédoclimat, espèce de CIMS (et mélange d’espèces), date de semis et de destruction et gestion des résidus (enfouissement par travail du sol ou laissés en mulch). Les résultats indiquent que les CIMS diminuent le drainage par une augmentation de l’évapotranspiration réelle en situation avec couvert végétal, en comparaison à un sol nu. Toutefois, si les CIMS sont bien gérées, avec une adaptation des espèces composant le couvert végétal, des dates de semis et de destruction, elles n’induisent pas de stress hydrique et azoté entrainant des baisses de rendements des cultures principales suivantes. La généralisation des CIMS sur l’ensemble du BAG, sans adaptation locale de leur gestion et avec des destructions tardives (cas de l’agriculture de conservation), pourrait cependant modifier le grand cycle de l’eau en augmentant l’évapotranspiration et diminuant le drainage, qui est le processus déterminant de la recharge des nappes. Ces modifications pourraient accentuer le déficit entre les besoins en eau grandissant et les ressources toujours plus restreintes dans un contexte de changement climatique.

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.

Modélisation du fonctionnement des agrosystèmes et des pathosystèmes dans un contexte de changement climatique Mémoire présenté en vue de l'obtention du diplôme d'habilitation à Diriger des recherches par

International audience; Cette epopee doit beaucoup a la regrettee Nadine Brisson, premiere capitaine de« l' embarcation Sties ». Nous lui dedions avec emotion ce chapitre, dont les axes reprennent ceux de l'hommage qui lui a ete rendu le 16 octobre 2012 conjointement par le departement Environnement et Agronomie (EA) de l'Inra et !'Association frans;aise d' agronomie 123. Le modele Sties (Simulateur multidisciplinaire pour les cultures standard) a plusde vingt ans. Il fait maintenant partie des cinq modeles de culture les plus cites au monde. Cette success story s' est fondee sur un projet collectif dont le creuset a ete le departement EA naissant. Ce texte tente une analyse reflexive des interactions entre un groupe elargi de chercheurs modelisateurs et la necessite de mobiliser des moyens sur une tres longue duree, marquee par le renouvellement des questions posees face a l'evolution rapide des usages et des contextes d' application.

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.

Incident information sharing is being encouraged and mandated as a way of improving overall cyber intelligence and defense, but its take up is slow. Organisations may well be justified in perceiving risks in sharing and disclosing cyber incident information, but they tend to express such worries in broad and vague terms. This paper presents a specific and granular analysis of the risks in cyber incident information sharing, looking in detail at what information may be contained in incident reports and which specific risks are associated with its disclosure. We use the STIX incident model as indicative of the types of information that might be reported. For each data field included, we identify and evaluate the threats associated with its disclosure, including the extent to which it identifies organisations and individuals. The main outcome of this analysis is a detailed understanding of which information in cyber incident reports requires protection, against specific threats with assessed severity. A secondary outcome of the analysis is a set of guidelines for disciplined use of the STIX incident model in order to reduce information security risk.

International audience; Climate change is likely to strengthen abiotic stresses on crops in temperate zones. Grain legumes and the associated provision of ecosystem services are the cornerstone of more sustainable cropping systems, yet the impact of climate change on their performance has not been extensively quantified. Based on previous experiments carried out in south-western France with low biotic stress, we calibrated the STICS soil-crop model for spring pea (SP), winter pea (WP) and winter faba bean (WF) and evaluated its quality of prediction on an independent dataset. STICS was used to explore the effect of climate change scenarios on the legumes performance. Assuming no change in crop management, mean and inter-annual variability of grain yield and N2 fixation were assessed for historical (1995-2015), mid-term (2020-2040) and long-term (2060-2080) periods, considering projections from two Global Circulating Models (GCM) and two Representative CO2 Concentration Pathways (RCP), i.e. RCP 4.5 and RCP 8.5. The GCMs consistently predicted no significant change in rainfall amounts and patterns but indicated a 1.7°C and 2.5°C increase in average temperature over the growth period in the long term under RCP 4.5 and RCP 8.5 respectively. Therefore, simulations indicated no extra water stress on grain yield and N2 fixation of these legumes. The increase in temperature entailed a shortening in crop duration and a slight but significant increase in the temperature stress factor values for grain filling, for photosynthesis and for N2 fixation during the reproductive period (+1% to +13 % depending on temperature stress, crop and RCP). Under RCP 4.5, yield decreased by 23 to 34% (depending on crop) in the long term. Average fixed N2 decreased by 16% to 34%. Probability of yield failure (i.e. yield below the 20th percentile of historical yield) increased from 20 to 50, 54 and 58% for WF, WP and SP respectively. Probability of N2 fixation failure increased from 20 to 34, 50 and 53% for WP, WF and SP respectively. In contrast, under RCP 8.5, the CO2 fertilisation effect would offset the decrease in yield due to the increase in temperature and simulations predicted a 8 to 13 % average yield increase in the long term. Average N2 fixation would benefit from the increase in biomass and increase by 15 to 23%. Probability of yield failure would increase slightly, from 20 to 21, 25 and 27% for WF, WP and SP respectively. Probability of N2 fixation failure would increase for spring pea (from 20 to 31%) but decrease for winter faba bean (from 20 to 13%) and winter pea (from 20 to 11%). The increased probability of yield and N2 fixation failure simulated with the RCP 4.5 scenario indicates the need for technical and transformational adaptations for grain legumes to deliver the expected ecosystem services with future climate. Under RCP 8.5, better yield and N2 fixation highlight the opportunity represented by climate change for inclusion of grain legume in cropping systems.

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.

National audience; Le concept des services écosystémiques permet d’enrichir l’analyse des agroécosystèmes en intégrant leurs externalités positives. Mais comment analyser ces services au sein d’un verger de pommier ? Quels sont les liens de conflits et de synergies entre services écosystémiques multiples ? Une thèse (Demestihas C., 2017) réalisée dans le cadre d’une bourse CIFRE Ctifl – INRA vise à répondre à ces questions avec une démarche novatrice combinant mesures expérimentales, modélisation et analyse statistique.Cinq services ont été sélectionnés suite à une analyse bibliographique des services écosystémiques en vergers (Demestihas et al, 2017) : production de fruits, disponibilité de l’azote dans le sol, régulation du climat reposant sur la prévention de la dénitrification de l’azote et sur la séquestration du carbone, maintenance et régulation du cycle de l’eau y compris de sa qualité, et régulation des bioagresseurs. Les nuisances environnementales dues aux produits phytosanitaires ont aussi été considérées. Les services et fonctions écosystémiques ont été décrits par un ou plusieurs indicateurs et quantifiés à l’aide de modèles dans les cas suivants : (i) neuf systèmes de culture réels sur deux dispositifs expérimentaux « systèmes de culture » en pommiers dans le sud-est de la France et (ii) 150 systèmes de culture fictifs conçus à partir de la combinaison de cinq leviers de pratiques et de leurs modalités, dans des conditions pédoclimatiques identiques. Les deux modèles utilisés ont été STICS, un modèle générique de simulation du fonctionnement du sol et des cultures sous l’influence des pratiques, qui a nécessité une paramétrisation et une évaluation sur pommier à partir de mesures expérimentales, et IPSIM, une plateforme de modélisation simulant les effets des pratiques et des conditions du milieu sur les dégâts aux cultures causés par les bioagresseurs. IPSIM a été paramétré sur pommier, sur la base d’une importante étude bibliographique et à dires d’experts. Les simulations des modèles ont fait l’objet d’analyses statistiques pour analyser les liens entre services et fonctions écosystémiques, et l’impact des conditions pédoclimatiques et des pratiques agricoles.L’analyse des neufs systèmes de culture réels a permis de quantifier l’impact de pratiques agricoles sur certains services et l’influence importante des caractéristiques pédoclimatiques de chaque site. Ceci conforte la nécessité d’une prise en compte explicite de l’interdépendance « pratiques x conditions du milieu » pour analyser les services. L’analyse des résultats des systèmes de culture fictifs a permis de préciser l’impact sur les fonctions et services écosystémiques de la fertilisation, de l’irrigation et des pratiques de protection contre le carpocapse, la tavelure et le puceron cendré. Une analyse exploratoire a permis d’identifier les systèmes aboutissant aux compromis les plus favorables entre services au sein des systèmes virtuels de pommiers.Cette démarche centrée sur la cascade « pratiques culturales – fonctions écosystémiques – services écosystémiques », combinant observation, simulation à partir de modèles, et analyse statistique, devrait contribuer à l’optimisation des systèmes de culture en vergers sous l’angle des services écosystémiques. Pour la mettre en oeuvre, il sera cependant nécessaire de créer une nouvelle génération de modèles capables de mieux intégrer des services multiples et les effets d’une gamme élargie de pratiques, notamment la biodiversité planifiée et la biodiversité associée.

[EN] The European research project AGADAPT (Climate-KIC) intended to do the evaluation of model water resources in the context of climate change. Combining innovative technologies based on modelling and knowledge, AGADAPT demonstrated the added value offered by the state of the art research results for the management of water resources in agriculture. The project was divided into three working groups. The second working group of the project (WP2) aimed to test different models to assess water resources at river basin level. Therefore, they were evaluated and implemented various models for forecasting water resources, considering the drivers of global change (climate change and land use). For each model, was required data description and evaluation of the various pilot sites and provided simulation scenarios. WP2 dealt with water resources at the regional scale in the global context of climate change, using 2001-2010 as the current scenario and the prospects for change in the future scenario 2037-2065. The project demonstrated the potential of the state of the art of hydrometeorological models combined with models of water demand, for prospective studies on available water resources for agriculture. The initial activities were devoted to the study of the evolution of the models and an inventory of existing modelling framework. This allowed having an opinion about them and emphasizing the added value of the proposed model compared with the existing approaches. Thus several models and pilot sites were considered. INRA led the implementation of STICS model with the multi-simulation module in the areas of La Crau and La Mancha Oriental. STICS is a daily crop model, developed by INRA since 1996 (N. Brisson et al., 2003) with input variables related to climate, soil, crops and agronomic techniques, and output variables related to yield in terms quantity and quality and to the environment and in terms of drainage and nitrate leaching. STICS is applied to crop parcel scale. The specific objectives of this Master’s Final Work can be shown in the following contributions to the project with the work developed at INRA for the simulations with the STICS model: (1) Direct participation in the work of WP2, developing the database required for the pilot study area in the Júcar river basin, with particular interest in the details needed in La Mancha Oriental area where simulations were implemented. The database elaborated contains information on climate, soil and land use, crop species and agronomic techniques used in the study area. This database will be used by different models to evaluate in the project. Reviews and corrections were also needed in the database of La Crau area where simulations were also implemented. (2) Preparation and configuration of input variables needed in the simulation process using the STICS model and implementation of the model simulations on the Crau aquifer belonging to Rhone basin (Albert Olioso et al., 2013) and the area of La Mancha Oriental in the Jucar river basin. As a new point it was evaluated, reviewed and applied the STICS model of the study areas using Multisim STICS, one of its latest developments executing simultaneous and spatialized simulation in all the plots. This process of Multi-simulation also provides the ability to run simulations using combinations of input variables, such as variation in the sowing date or the amount of water used in irrigation stages. With these combinations it was possible to obtain an aggregation of results that fit in better as the reality of the process. (3) It is intended as the ultimate objective, to demonstrate the validity of the model in different places of study, obtaining reasonable results with available data. Then this model was applied in the context of future climate change. Implementation of the model over the Crau was more accurate because in this area there was a lot of information available and it was done lot of work by INRA in previous projects. Therefore the model had been calibrated before in this area and it was possible to obtain good indicators for assessing the tool concerning aquifer piezometry, water consumption for irrigation and agricultural production. These indicators provided satisfactory results, reflecting an adaptation of the model to the reality of the process showing a decrease in groundwater levels and drainage in years of drought and some increase in irrigations in those years to obtain an optimum crop production. For the future climate scenarios although rainfall was overestimated, a slight decrease in the recharges to the aquifer and increase of crop water needs were observed. The Implementation of the model over La Mancha Oriental area was a simple approximation, but results have shown that with simple assumption and without any calibration we arrive in first attempt to interesting results on water increasing needs for irrigation in dry years and good yield results in the study area comparing with statistics based on real data., [ES] Dentro del proyecto Europeo de investigación AGADAPT (Climate-KIC), se pretendía realizar la evaluación de modelos de recursos hídricos en un contexto de cambio climático. Combinando el conocimiento basado en las innovadoras tecnologías y la modelización, AGADAPT demostró el valor añadido ofrecido por el estado del arte de los resultados de investigación para la gestión de los recursos hídricos en la agricultura. El proyecto constaba de tres grupos de trabajo. El segundo grupo de trabajo de dicho proyecto (WP2) tenía como objetivo probar diferentes modelos para evaluar los recursos hídricos a escala de cuenca hidrográfica. Por tanto, fueron evaluados e implementados diferentes modelos para hacer previsiones de recursos hídricos, considerando los conductores del cambio global (cambio climático y uso del suelo). Para cada modelo se proporcionó una descripción y datos requeridos, así como la evaluación de los diferentes sitios piloto y escenarios de simulación. El WP2 aborda los recursos hídricos a nivel regional en el contexto global de cambio climático, utilizando el periodo 2001-2010 como escenario actual, y las perspectivas de cambio en el escenario futuro 2037-2065. El proyecto demostró el potencial del estado del arte de los modelos hidrometeorológicos combinados con los modelos de demanda hídrica, para hacer estudios prospectivos sobre los recursos hídricos disponibles para la agricultura. Las actividades iniciales se dedicaron al estudio de la evolución de los modelos y realización de un inventario del marco de modelización existente. Todo ello permitió tener una opinión acerca de ellos y hacer hincapié en el valor añadido del modelo que se propuso en comparación con los enfoques existentes. De esta forma se consideraron varios modelos y sitios piloto. El INRA lideró la implementación del modelo STICS con su módulo de multi-simulación, en las zonas de La Crau y La Mancha Oriental. STICS es un modelo de cultivo de paso diario, desarrollado por el INRA desde 1996 (N. Brisson et al., 2003) con variables de entrada relacionadas con el clima, suelo, cultivos y técnicas, y variables de salida relacionadas con el rendimiento en términos de cantidad y calidad y el medio ambiente en términos de drenaje y lixiviación de nitratos. STICS se aplica a escala de parcela agrícola. Los objetivos específicos de este Trabajo Fin de Máster se concretaron en las siguientes aportaciones al proyecto en base al trabajo desarrollado en el INRA para las simulaciones con el modelo STICS: (1) La participación directa en las tareas del WP2, desarrollando la base de datos necesaria para la zona piloto de estudio en la cuenca del río Júcar, prestando especial interés en el detalle que precisaba la zona de La Mancha Oriental donde se implementaron las simulaciones. La base de datos que se elaboró, contiene información de clima, suelo y usos, especies de cultivo y técnicas agronómicas empleadas en el área de estudio. Dicha base de datos fue utilizada por los diversos modelos a evaluar en el proyecto. También fue necesaria una revisión y correcciones en la base de datos de la zona de La Crau donde también se implementaron las simulaciones. (2) Preparación y parametrización de las variables de entrada necesarias en el proceso de simulación mediante el modelo STICS, y ejecución de las simulaciones mediante dicho modelo sobre el acuífero de la Crau perteneciente al Ródano (Albert Olioso et al., 2013) y la zona de la Mancha Oriental en la cuenca del río Júcar. Como punto novedoso se evaluó, revisó y aplicó el modelo STICS sobre las zonas de estudio usando MultiSim STICS, uno de sus últimos desarrollos en el que se permite la ejecución simultánea y espacializada de simulaciones en todas las parcelas de cultivo. Dicho proceso de Multi-simulación también ofrece la posibilidad de ejecutar las simulaciones utilizando posibles combinaciones de las variables de entrada, como la variación en la fecha de siembra o la cantidad de agua empleada en las etapas de riego. Con estas combinaciones se pudo obtener posteriormente una agregación de resultados que se ajustaran en mejor medida a la realidad del proceso. (3) Se pretendía como último objetivo, demostrar la validez de los modelos en diferentes lugares de estudio, obteniendo resultados razonables con datos accesibles. Posteriormente se pudo aplicar dicho modelo en un contexto de cambio climático futuro. La implementación del modelo sobre La Crau fue más precisa ya que en esta zona se disponía de mucha información y trabajos realizados por el INRA en proyectos anteriores. Por tanto el modelo ya había sido calibrado con anterioridad en esta zona y se pudieron obtener buenos indicadores para la evaluación de los resultados de la herramienta referentes a la piezometría del acuífero, los consumos de agua para el riego y la producción agrícola. Dichos indicadores arrojaron unos resultados satisfactorios y reflejaron una adecuación del modelo a la realidad del proceso, evidenciando descensos de los niveles piezométricos y en los drenajes en las épocas de sequía y aumentos de riego en los dichos periodos para conseguir una producción óptima de los cultivos. En cuanto a los escenarios climáticos futuros aunque la precipitaciones se sobre estimaron se observó un ligero descenso en la recarga sobre el acuífero y un aumento en las necesidades hídricas de los cultivos. La implementación del modelo sobre La Mancha Oriental se trataba de una aproximación sencilla ya que no se habían realizado calibraciones del modelo sobre esta zona de estudio, pero reflejó resultados interesantes en cuanto a las crecientes necesidades de riego para los cultivos en épocas de sequía y la adecuación de los resultados del modelo en cuanto a producciones de cultivos en la zona de estudio comparando con estadísticas oficiales basadas en datos reales.