Towards an empirical model to estimate the spatial variability of grapevine phenology at the within field scale

The aim of this study is to propose an empirical spatial model to estimate the spatial variability of grapevine phenology at the within-field scale. This spatial model allows the characterization of the spatial variability of a given variable of the fields through a single measurement performed in the field (reference site) and a combination of site-specific coefficients calculated through historical information. This approach was compared to classical approaches requiring extensive sampling and phenology models based on climatic data, which do not consider the spatial variability of the field. The study was conducted on two fields of Vitis vinifera, one of cv Cabernet Sauvignon (CS, 1.56 ha) and the other one of cv Chardonnay (CH, 1.66 ha) located in Maule Valley, Chile. Date of occurrence of grapevine phenology (budburst, flowering and veraison) were observed at the within field level following a regular sampling grid during 4 seasons for cv CS and 2 seasons for cv CH. The best results were obtained with the devised spatial model in almost all cases, with a Root Mean Square Errors (RMSE) lower than 3 days. However, if the variability of phenology is low, the traditional method of sampling could lead to better results. This study is the first step towards a modeling of the spatial variability of grapevine phenology at the within-field scale. To be fully operational in commercial vineyards, the calibration process needs simplification, for example, using low cost, inexpensive ancillary information to zone vineyards according to grapevine phenology

Model inter-comparison on crop rotation effects ? an intermediate report

Data of diverse crop rotations from five locations across Europe were distributed to modelers to investigate the capability of models to handle complex crop rotations and management interactions

Adaptation du modèle STICS à la vigne (Vitis vinifera L. ) : utilisation dans le cadre d'une étude d'impact du changement climatique à l'échelle de la France

Crop models, which have been developed over the last thirty years, offer a conceptual framework for studying the dynamic interactions between plant, soil, climate and cultivation techniques at plot scale. The aim of this project was to adapt a generic crop model, the STICS model, to grapevines on the scale of France's major vineyards, and then apply it to a study of the impact of climate change on the same scale. To carry out this work, an extensive network of collaborations was set up with various research, technical and professional organizations in the Côtes du Rhône, Languedoc, Bordeaux, Cognac, Burgundy, Anjou and Champagne regions. Two databases were created on the basis of these collaborations: an ecophysiological database containing data from experiments carried out over a two-year period in the various vineyards, and a historical database based on data from earlier experiments. For the adaptation of the model, we implemented a methodology consisting firstly in analyzing the various existing formalisms and proposing new ones likely to improve the description of vine functioning; secondly, we parameterized the model on the basis of the ecophysiological database and the bibliography. Finally, we evaluated the model on the basis of historical data. The results obtained are satisfactory, particularly for use as a prospective tool in impact studies. What's more, the robustness of the model adapted to grapevines means that it can be used throughout France. The study of the impact of climate change on French vineyards as a whole was carried out using both the STICS model and climate data simulated by the ARPEGE-Climat model (Météo-France). To study these impacts, we defined planting structures and traditional techniques for each region. The main results show a significant change in phenology, as well as an increase in vegetative biomass and yield (except in the Côtes du Rhône and Languedoc vineyards, where a decrease is observed), an increase in water stress at the end of the cycle and a significant change in climatic conditions during the veraison-harvest period. Based on these results, we have proposed various technical combinations to adapt vine management to climate changes. The result is a set of proposed adaptation strategies for each region.Les modèles de cultures, qui se sont développés depuis une trentaine d’années, offrent un cadre conceptuel pour étudier les interactions dynamiques entre la plante, le sol, le climat et les techniques culturales à l’échelle parcellaire. Le travail a eu comme objectif d'adapter un modèle générique de cultures, le modèle STICS, à la vigne à l'échelle des grands vignobles de France pour ensuite l'appliquer à une étude d'impact du changement climatique à la même échelle. Pour réaliser ce travail, un important réseau de collaborations avec différents organismes de recherche, techniques et professionnels a été mis en place dans les régions de Côtes du Rhône, Languedoc, Bordeaux, Cognac, Bourgogne, Anjou et Champagne. Deux bases des données ont été créées à partir de ces collaborations : une base de données écophysiologiques regroupant les données d'expérimentations menées pendant deux ans dans les différents vignobles, et une base de données historiques constituée à partir de données d'expérimentations anciennes. Pour l'adaptation du modèle, nous avons mis en place une méthodologie consistant dans un premier temps à faire l'analyse des différents formalismes existants et à en proposer de nouveaux susceptibles d'améliorer la description du fonctionnement de la vigne; dans un deuxième temps, nous avons réalisé le paramétrage du modèle à partir de la base de données écophysiologiques et de la bibliographie. Enfin, nous avons évalué le modèle sur la base de données historiques. Les résultats obtenus s'avèrent satisfaisants, en particulier pour que le modèle serve d'outil de prospective dans le cadre d'études d'impact. De plus, la robustesse du modèle ainsi adapté à la vigne permet de l'utiliser à l'échelle de la France. L'étude d'impact du changement climatique à l'échelle de l'ensemble du vignoble français a été réalisée à partir de l'utilisation conjointe du modèle STICS et des données climatiques simulées par le modèle ARPEGE-Climat (Météo-France). Pour étudier ces impacts, nous avons défini des structures de plantation et des techniques traditionnelles pour chaque région. Les principaux résultats montrent une importante modification de la phénologie, ainsi qu'une augmentation de la biomasse végétative et du rendement (sauf dans les vignobles de Côtes du Rhône et Languedoc pour lesquels on observe une diminution), une augmentation du stress hydrique à la fin du cycle et une importante modification des conditions climatiques de la période véraison-récolte. Suite à ces résultats, nous avons proposé différentes combinaisons techniques afin d'adapter la conduite de la vigne aux modifications du climat. Il en résulte de propositions de stratégies d'adaptation pour chacune des régions

Conséquences sur les terroirs et les vins: premiers éléments de réponse

* INRA Documentation, Domaine St Paul, Site Agroparc, 84914 Avignon cedex 9 Diffusion du document : INRA Documentation, Domaine St Paul, Site Agroparc, 84914 Avignon cedex 9National audienc

Les terroirs viticoles et le réchauffement climatique

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

Face au gel, quelles armes pour toujours avoir des fruits français sur nos étals à l'avenir?

Alors que la production française de fruits a été dévastée par les gelées de ce mois d'avril, l'enjeu pour l'agriculture va être d'éviter de revivre la même situation année après année

Multivariate bias corrections of climate simulations seen through impact model: Results of the COMPROMISE project

Atmospheric variables simulated from climate models often present biases relative to the same variables calculated by reanalysis in the past (SAFRAN reanalysis for example). In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. Currently, the bias correction methods used operationally correct one-dimensional time series and are therefore applied separately, physical variable by physical variable and site by site. Multivariate bias correction methods have been developed to better take into account dependencies between variables and in space. In this work, we propose a comparison between two multivariate bias correction methods (R2D2 and dOTC) and a univariate correction (CDF-t) through several highly multivariate impact models (phenological stage, reference evapo-transpiration, soil water content, forest weather index) integrating the climatic signal throughout a season. The data, the impact models and the statistical methods are first presented. The experimental design is then described. Extensive results are illustrated but not commented

Multivariate bias corrections of climate simulations seen through impact model: Results of the COMPROMISE project

Atmospheric variables simulated from climate models often present biases relative to the same variables calculated by reanalysis in the past (SAFRAN reanalysis for example). In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. Currently, the bias correction methods used operationally correct one-dimensional time series and are therefore applied separately, physical variable by physical variable and site by site. Multivariate bias correction methods have been developed to better take into account dependencies between variables and in space. In this work, we propose a comparison between two multivariate bias correction methods (R2D2 and dOTC) and a univariate correction (CDF-t) through several highly multivariate impact models (phenological stage, reference evapo-transpiration, soil water content, forest weather index) integrating the climatic signal throughout a season. The data, the impact models and the statistical methods are first presented. The experimental design is then described. Extensive results are illustrated but not commented