Soil degradation is a worsening global phenomenon driven by socio‐economic pressures, poor land management practices and climate change. A deterioration of soil structure at timescales ranging from seconds to centuries is implicated in most forms of soil degradation including the depletion of nutrients and organic matter, erosion and compaction. New soil–crop models that could account for soil structure dynamics at decadal to centennial timescales would provide insights into the relative importance of the various underlying physical (e.g. tillage, traffic compaction, swell/shrink and freeze/thaw) and biological (e.g. plant root growth, soil microbial and faunal activity) mechanisms, their impacts on soil hydrological processes and plant growth, as well as the relevant timescales of soil degradation and recovery. However, the development of such a model remains a challenge due to the enormous complexity of the interactions in the soil–plant system. In this paper, we focus on the impacts of biological processes on soil structure dynamics, especially the growth of plant roots and the activity of soil fauna and microorganisms. We first define what we mean by soil structure and then review current understanding of how these biological agents impact soil structure. We then develop a new framework for modelling soil structure dynamics, which is designed to be compatible with soil–crop models that operate at the soil profile scale and for long temporal scales (i.e. decades, centuries). We illustrate the modelling concept with a case study on the role of root growth and earthworm bioturbation in restoring the structure of a severely compacted soil.

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Remote sensing is an interesting tool for monitoring crop production, energy exchanges and mass exchanges between the soil, the biosphere and the atmosphere. The aim of the Alpilles-ReSeDA program was the development of such techniques combining remote sensing data, and soil and vegetation process models. This article focuses on SVAT models (Soil-Vegetation-Atmosphere Transfer models) which may be used for monitoring energy and mass exchanges by using assimilation of remote sensing data procedures. As a first step, we decided to implement a model comparison experiment with the aim of analyzing the relationships between the models' complexity, validity and potential for assimilating remote sensing data. This experiment involved the definition of three comparison scenarios with different objectives: (i) test the models' capacity to accurately describe processes using input parameters as measured in the field; (ii) test the portability of the models by using a priori information on input parameters (such as pedotransfer functions), and (iii) test the robustness of the models by a calibration/validation procedure. These 3 scenarios took advantage of the experimental network that was implemented during the Alpilles experiment and which combined measurements on different fields that may be used for calibration of models and their validations on independent data sets. The results showed that the models' performances were close whatever their complexity. The simpler models were less sensitive to the specification of input parameters. Significant improvements in the models' results were achieved when calibrating the models in comparison with the first scenario. remote sensing / modeling /experiment / surface energy fluxes / comparison Résumé-Modélisation du transfert Sol-Végétation-Atmosphère sur l'expérimentation Alpilles-ReSeDA : comparaison des modèles TSVA sur champs de blé. Le programme Alpilles-ReSeDA a été mis en place pour développer des méthodes d'utilisation des données de télédétection en combinaison avec des modèles de fonctionnement du sol ou de la végétation, dans le but d'estimer ou de suivre la production des cultures ou leurs échanges de masse et d'énergie avec le sol et l'atmosphère. Parmi ces modèles, cet article se focalise sur les modèles de transfert Sol-Végétation-Atmosphère qui peuvent être utilisés pour suivre les échanges d'énergie et de masse au moyen de procédures d'assimilation des données de télédétection. Dans une première étape, nous avons mis en place une expérience de comparaison de plusieurs modèles, avec l'objectif d'analyser les relations entre la complexité des modèles, leur validité et leur utilisation potentielle pour assimiler des données de télédétection. Nous avons défini trois scénarios qui répondent à des objectifs différents : (i) tester la capacité des modèles à décrire les processus en utilisant les paramètres d'entrée des modèles tels qu'ils ont été mesurés dans les champs ; (ii) tester la portabilité des modèles en utilisant des informations a priori en entrée (comme des f...

No abstract

No abstract