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1. Current knowledge and future research opportunities for modeling annual crop mixtures. A review

Author

Gaudio, Noémie, Escobar-Gutiérrez, Abraham J., Casadebaig, Pierre, Evers, Jochem B., Gérard, Frédéric, Louarn, Gaëtan, Colbach, Nathalie, Munz, Sebastian, Launay, Marie, Marrou, Hélène, Barillot, Romain, Hinsinger, Philippe, Bergez, Jacques-Eric, Combes, Didier, Durand, Jean-Louis, Frak, Ela, Pagès, Loïc, Pradal, Christophe, Saint-Jean, Sébastien, Van Der Werf, Wopke, and Justes, Eric

Subjects
Quantitative Biology - Populations and Evolution, Quantitative Biology - Quantitative Methods
Abstract

Growing mixtures of annual arable crop species or genotypes is a promising way to improve crop production without increasing agricultural inputs. To design optimal crop mixtures, choices of species, genotypes, sowing proportion, plant arrangement, and sowing date need to be made but field experiments alone are not sufficient to explore such a large range of factors. Crop modeling allows to study, understand and ultimately design cropping systems and is an established method for sole crops. Recently, modeling started to be applied to annual crop mixtures as well. Here, we review to what extent crop simulation models and individual-based models are suitable to capture and predict the specificities of annual crop mixtures. We argued that: 1) The crop mixture spatio-temporal heterogeneity (influencing the occurrence of ecological processes) determines the choice of the modeling approach (plant or crop centered). 2) Only few crop models (adapted from sole crop models) and individual-based models currently exist to simulate annual crop mixtures. 3) Crop models are mainly used to address issues related to crop mixtures management and to the integration of crop mixtures into larger scales such as the rotation, whereas individual-based models are mainly used to identify plant traits involved in crop mixture performance and to quantify the relative contribution of the different ecological processes (niche complementarity, facilitation, competition, plasticity) to crop mixture functioning. This review highlights that modeling of annual crop mixtures is in its infancy and gives to model users some important keys to choose the model based on the questions they want to answer, with awareness of the strengths and weaknesses of each of the modeling approaches., Comment: 42 pages, 5 figures

Published
2019
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3. Evaluation of Plant Responses to Atmospheric Nitrogen Deposition in France Using Integrated Soil-Vegetation Models

Author

Probst, Anne, Obeidy, Carole, Gaudio, Noémie, Belyazid, Salim, Gégout, Jean-Claude, Alard, Didier, Corket, Emmanuel, Party, Jean-Paul, Gauquelin, Thierry, Mansat, Arnaud, Nihlgård, Bengt, Leguédois, Sophie, Sverdrup, Harald U., Alloway, Brian J., Series editor, Trevors, Jack T., Series editor, de Vries, Wim, editor, Hettelingh, Jean-Paul, editor, and Posch, Maximilian, editor

Published
2015
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4. Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model

Author

Vezy, Rémi, Munz, Sebastian, Gaudio, Noemie, Launay, Marie, Lecharpentier, Patrice, Ripoche, Dominique, Justes, Eric, Vezy, Rémi, Munz, Sebastian, Gaudio, Noemie, Launay, Marie, Lecharpentier, Patrice, Ripoche, Dominique, and Justes, Eric

Abstract

The growing demand for sustainable agriculture is raising interest in intercropping for its multiple potential benefits to avoid or limit the use of chemical inputs or increase the production per surface unit. Predicting the existence and magnitude of those benefits remains a challenge given the numerous interactions between interspecific plant-plant relationships, their environment, and the agricultural practices. Soil-crop models are critical in understanding these interactions in dynamics during the whole growing season, but few models are capable of accurately simulating intercropping systems. In this study, we propose a set of simple and generic formalisms (i.e. the structure and mathematical representation necessary for designing a model) for simulating key interactions in bi-specific intercropping systems that can be readily included into existing dynamic crop models. This requires simulating important processes such as development, light interception, plant growth, N and water balance, and yield formation in response to management practices, soil conditions, and climate. These formalisms were integrated into the STICS soil-crop model and evaluated using observed data of intercropping systems of cereal and legumes mixtures, including Faba bean-Wheat, Pea-Barley, Soybean-Sunflower, and Wheat-Pea mixtures. We demonstrate that the proposed formalisms provide a comprehensive simulation of soil-plant interactions in various types of bispecific intercrops. The model was found consistent and generic under a range of spring and winter intercrops (nRMSE = 25% for maximum leaf area index, 23% for shoot biomass at harvest, and 18% for grain yield). This is the first time a complete set of formalisms has been developed and published for simulating bi-specific intercropping systems and integrated into a soil-crop model. With its emphasis on being generic, sufficiently accurate, simple, and easy to parameterize, STICS is well-suited to help researchers designing in silico

Published
2023

5. Exploring complementarities between modelling approaches that enable upscaling from plant community functioning to ecosystem services as a way to support agroecological transition

Author

Gaudio, Noemie, Louarn, Gaëtan, Barillot, Romain, Meunier, Clémentine, Vezy, Rémi, Launay, Marie, Gaudio, Noemie, Louarn, Gaëtan, Barillot, Romain, Meunier, Clémentine, Vezy, Rémi, and Launay, Marie

Abstract

Promoting plant diversity through crop mixtures is a mainstay of the agroecological transition. Modelling this transition requires considering both plant–plant interactions and plants' interactions with abiotic and biotic environments. Modelling crop mixtures enables designing ways to use plant diversity to provide ecosystem services, as long as they include crop management as input. A single modelling approach is not sufficient, however, and complementarities between models may be critical to consider the multiple processes and system components involved at different and relevant spatial and temporal scales. In this article, we present different modelling solutions implemented in a variety of examples to upscale models from local interactions to ecosystem services. We highlight that modelling solutions (i.e. coupling, metamodelling, inverse or hybrid modelling) are built according to modelling objectives (e.g. understand the relative contributions of primary ecological processes to crop mixtures, quantify impacts of the environment and agricultural practices, assess the resulting ecosystem services) rather than to the scales of integration. Many outcomes of multispecies agroecosystems remain to be explored, both experimentally and through the heuristic use of modelling. Combining models to address plant diversity and predict ecosystem services at different scales remains rare but is critical to support the spatial and temporal prediction of the many systems that could be designed.

Published
2022

6. Species choice and N fertilisation influence the biodiversity effect and its components in intercrops

Author

Mahmoud, Remi, Casadebaig, Pierre, Hilgert, Nadine, Gaudio, Noemie, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Gaudio, Noémie

Subjects
[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, [SDV.EE] Life Sciences [q-bio]/Ecology, environment, dominance effect, biodiversity effect, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, N fertilisation, Complementarity effect, intercropping
Abstract

International audience; Loreau & Hector, (2001) proposed a framework to estimate the net biodiversity effect (over-or under-yielding) on the productivity of plant communities and to further decompose this effect into dominance and complementarity effects. Here we applied this method to analyse the performance of eight cereal-legume intercropping experiments grown in diverse environmental conditions in France, with three combinations of species mixtures: i) durum wheat/faba bean ii) soft wheat/pea and iii) durum wheat/pea. For each species mixture, we tested whether N fertilisation influenced dominance or complementarity effects using a Bayesian approach. Our results showed that the Biodiversity Effect (BE) was positive in the three intercrops under unfertilised conditions, mainly thanks to complementarity effect. The relative importance of BE and both of its components depends on the species intercropped. Finally, we showed that the impact of N fertilisation on BE of intercrops strongly depends on the competitive ability of the species intercropped.

Published
2021

7. Calibration and Evaluation of the STICS Intercrop Model for Two Cereal-Legume Mixtures

Author

Munz, Sebastian, Vezy, Rémi, Gaudio, Noemie, Bedoussac, Laurent, Justes, Eric, and Paff, Kirsten

Subjects
ecological intensification, crop modeling, pea, barley, durum wheat
Abstract

Introduction STICS is a soil-crop model capable of simulating crops in succession (Brisson et al., 2003). Intercropping occurs when multiple species are grown simultaneously on the same field. There has been a growing interest in adapting this traditional technique for modern agriculture as a way of ecological intensification, especially for combining leguminous and cereal crops in order to reduce N inputs and potential environmental damage through N losses. Intercropping adds complexity to the system by adding inter-species competition. Crop models are useful tools for analyzing complex systems, as they allow the user far more control over individual variables than is possible in field experiments. A first version of the STICS intercrop model was created by Brisson et al. (2004) and was recently improved by Vezy et al. (2020). The aim of this study was to calibrate and evaluate this improved STICS-Intercrop model by simulating a winter and a spring intercrop mixture: durum wheat-winter pea and barley-spring pea. Materials and Methods: [br/]The data set used for modelling comprised of four years of wheat (Triticum turgidum L.) and pea (Pisum sativum L.) field data from Auzeville, France with multiple levels of nitrogen fertilizer, and four years of barley (Hordeum vulgare L.) and pea field data from Angers, France (Corre-Hellou, 2005), which in some years included two levels of nitrogen fertilizer and two different plant densities of the intercrops. The sole crop trials were used for calibration and the intercrop trials for evaluation, except for a subset of intercrop data that was used to calibrate the parameters unique to the intercrop model. The assumption was that parameters common to both sole and intercropping, such as plant-soil interactions and phenology, would be the same for both. The optimization method used for calibration was based on Wallach et al. (2011). The parameters were broken down into 15 groups (16 for pea including nitrogen fixation) for calibration, each corresponding to a different process. Results and Discussion: The model calibration process is still ongoing. The root mean square error (RMSE) for shoot biomass was 1.92 t/ha for winter pea and 1.37 t/ha for durum wheat. The RMSE for grain yield was 1.84 t/ha for spring pea and 1.15 t/ha for barley. Overall the model captured the dominancy of one species quite well, however the accuracy has to be increased. The phenology and height were correctly simulated. Some of the discrepancies could be due to biological stresses that STICS does not capture. Some parameters for the pea-wheat model have not been calibrated yet, so the RMSE is likely to improve. Conclusions: Intercrop systems are difficult to model in comparison to sole crops due to their complex interactions, but the STICS model, after having completed the calibration, could be a useful tool for better understanding the biological functions of this management practice.

Published
2020

8. A trait-based approach to understand and predict the performance of arable annual mixed crops

Author

Mahmoud, Rémi, Gaudio, Noemie, Casadebaig, Pierre, Gendre, Xavier, Bedoussac, Laurent, Hellou, Guenaelle, Fort, Florian, Journet, Etienne-Pascal, Litrico, Isabelle, Naudin, Christophe, Violle, Cyrille, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut de Mathématiques de Toulouse UMR5219 (IMT), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), École Nationale Supérieure de Formation de l'Enseignement Agricole de Toulouse-Auzeville (ENSFEA), Légumineuses, Ecophysiologie Végétale, Agroécologie (LEVA), Institut National de la Recherche Agronomique (INRA)-Ecole supérieure d'Agricultures d'Angers (ESA), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Laboratoire des interactions plantes micro-organismes (LIPM), Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Ecole Supérieure d'Agricultures d'Angers (ESA d'Angers), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Université de Toulouse (UT)-Université de Toulouse (UT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
[SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SHS]Humanities and Social Sciences
Abstract

National audience; One of the challenges in low-input annual arable mixed crops (i.e. species and variety mixtures) is to determine which species / varieties go well together to improve overall system performance. Theories coming from community ecology suggest that two ecological processes are particularly involved in mixed crops performance by improving resource use efficiency [1]. Niche complementarity can be quantified by the distance between target functional traits between two plants. Phenotypic plasticity can be quantified by the variance of target functional traits between cropping environments (including crop management). We aim to apply this ecological theory in an agronomical context by calculating these complementarity and plasticity metrics to predict crop yield of various mixed crops in different environments. A plant trait database on wheat and pea mixtures was built, gathering ten experiments with various management practices over six years, localized in two French sites (Angers, Toulouse). Plant traits were measured dynamically (crop height, aerial biomass, nitrogen content) to quantify crop growth and development over key phenological stages and at harvest (yield, grain quality) to quantify crop performance. Together with environmental (climate and soil) indicators we considered both trait by trait and multi-traits methods to analyze performance variability.

Published
2018

9. Évaluation et amélioration du module microclimatique de STICS

Author

Ben Othman, Wafa, Gaudio, Noemie, Launay, Marie, Lecharpentier, Patrice, Ripoche, Dominique, Saint-Jean, Sébastien, Saudreau, M., Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Laboratoire de Physique et Physiologie Intégratives de l’Arbre en environnement Fluctuant (PIAF), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), and Université de Toulouse (UT)-Université de Toulouse (UT)

Subjects
formalisations, changement climatique, climate change, formalismes, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, température de culture, expérimentation, crop temperature, field experiments, ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences
Abstract

National audience

Published
2017

10. Does a mixture of pea varieties with different leaf morphology improve crop performance?

Author

Carlsson, Georg, Bedoussac, Laurent, Cupina, Branko, Djordjevic, Vuk, Gaudio, Noemie, Jensen, Eric-Steen, Jeuffroy, Marie-Helene, Journet, Etienne-Pascal, Justes, Eric, Mikic, Aleksandar, Pelzer, Elise, Swedish University of Agricultural Sciences (SLU), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, École Nationale Supérieure de Formation de l'Enseignement Agricole de Toulouse-Auzeville (ENSFEA), University of Novi Sad, Institute of Field and Vegetable Crops [Novi Sad], Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Laboratoire des interactions plantes micro-organismes (LIPM), Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), and This work has received funding from the European Community's Seventh Framework Program under the grant agreement n°FP7-613551, LEGATO project.

Subjects
[SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SHS]Humanities and Social Sciences
Abstract

International audience; Crop diversification in space and time is a key component in the design of resource -efficient cropping systems with reduced dependency on synthetic inputs. A well - known example of crop diversification is the practice of intercropping, i.e. growing two or more crops together in the same field. Intercropping grain legumes and cereals in low-input systems makes use of complementary acquisition of light, water and nutrients to enhance the crop’s resource use efficiency and competitiveness against weeds. Within-species diversification in variety mixtures may also increase yield stability, via e.g. complementary tolerance to abiotic or biotic stress. As part of the European LEGATO project, our study has investigated whether a mixture of pea (Pisum sativum L.) varieties with two types of leaf morphology can increase the legume crop’s standing ability and competitiveness against weeds. The pea varieties Dukat (normal-leafed) and Partner (semi-leafless) were grown as single varieties and variety mixtures with and without a wheat intercrop in field experiments at four locations in Europe: Serbia (Novi Sad), Sweden (Alnarp) and France in central (close to Paris) and southern (Toulouse) regions. Lodging, weed biomass and crop/intercrop grain yields were recorded during two growing seasons per location. The results showed that a variety mixture of pea can combine the beneficial traits of the single varieties with maintained legume grain yield relative to the highest-yielding variety. However, the benefits in terms of weed reduction and standing ability were not as strong in variety mixtures as in pea-wheat intercrops, which remained the most efficient mixtures.

Published
2017

12. RReShar (Regeneration and Resource Sharing): a model to simulate uneven-aged forest stand dynamics

Author

Gaudio, Noemie, Balandier, Philippe, Dones, Nicolas, Rouault de Coligny, Francois, Ginisty, Christian, Laboratoire de Physique et Physiologie Intégratives de l'Arbre Fruitier et Forestier (PIAF), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Ecosystèmes forestiers (UR EFNO), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Irstea Publications, Migration, ProdInra, Archive Ouverte, Écosystèmes forestiers (UR EFNO), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD [France-Sud]), and International Union of Forest Research Organisations (IUFRO). Vienne, AUT. University of Ljubljana.

Subjects
[SDE] Environmental Sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, uneven-aged stand, ARBRE, regeneration, [SDE]Environmental Sciences, capsis, RReShar model, DYNAMIQUE, understorey vegetation
Abstract

International audience; The RReShar model (Regeneration and Resource Sharing), implemented within the CAPSIS simulation platform (http://www.inra.fr/capsis/), is a functional structural model. Stand structure is described including all the strata of the forest ecosystem, i.e. adult trees, suppressed trees, saplings and seedlings growing in the understorey (regeneration) and herbaceous/shrubby vegetation. Growth processes of all those components are based on the interactions for light and water. The simulated scene is a 1 ha plot divided in square cells of chosen size (of a few meters). Adult trees are explicitly spatialised on the plot, whereas understorey vegetation and regeneration are considered as a multi-species layer which characteristics vary at the cell level. Vegetation is described by its height, cover and porosity. Tree regeneration, i.e. seedlings and saplings, are initialised as cohorts characterized by a diameter and height distribution. Overstorey and understorey growth time step is annual, whereas within this annual loop, the light interception and water cycle (interception and evapotranspiration) processes are respectively monthly and daily simulated. Currently, RReShar is calibrated with data coming from uneven-aged mixed Quercus petraea Pinus sylvestris stands with an understorey colonised by Calluna vulgaris, Pteridium aquilinum and Molinia caerulea in temperate conditions.

Published
2010

14. A trait-based approach to understand and predict the performance of arable annual mixed crops

Author

Mahmoud , Rémi, Casadebaig, Pierre, Gendre, Xavier, Bedoussac, Laurent, Hellou, Guenaelle, Fort, Florian, Journet, Etienne-Pascal, Litrico, Isabelle, Naudin, Christophe, Violle, Cyrille, and Gaudio, Noemie

Abstract

One of the challenges in low-input annual arable mixed crops (i.e. species and variety mixtures) is to determine which species / varieties go well together to improve overall system performance. Theories coming from community ecology suggest that two ecological processes are particularly involved in mixed crops performance by improving resource use efficiency [1]. Niche complementarity can be quantified by the distance between target functional traits between two plants. Phenotypic plasticity can be quantified by the variance of target functional traits between cropping environments (including crop management). We aim to apply this ecological theory in an agronomical context by calculating these complementarity and plasticity metrics to predict crop yield of various mixed crops in different environments. A plant trait database on wheat and pea mixtures was built, gathering ten experiments with various management practices over six years, localized in two French sites (Angers, Toulouse). Plant traits were measured dynamically (crop height, aerial biomass, nitrogen content) to quantify crop growth and development over key phenological stages and at harvest (yield, grain quality) to quantify crop performance. Together with environmental (climate and soil) indicators we considered both trait by trait and multi-traits methods to analyze performance variability.

Published
2018

15. Combined effect of atmospheric nitrogen deposition and climate change on temperate forest soil biogeochemistry: A modeling approach

Author

Xavier Gendre, Simon Rizzetto, Harald Sverdrup, Noémie Gaudio, Manuel Nicolas, Anne Probst, Salim Belyazid, Arnaud Mansat, Laboratoire Ecologie Fonctionnelle et Environnement (LEFE), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT), Belyazid Consulting & Communication, Institut de Mathématiques de Toulouse UMR5219 (IMT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Office national des forêts (ONF), Lund University [Lund], Ecotoxicologie & Santé des écosystèmes - ECSECO (ECI), Institut National de la Recherche Agronomique (INRA)-Laboratoire Ecologie Fonctionnelle et Environnement (ECOLAB), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Belyazid Consulting and Communication AB, Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Laboratoire Ecologie Fonctionnelle et Environnement (ECOLAB), Office National des Forêts (ONF), Lund University, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National des Sciences Appliquées de Toulouse - INSA (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Belyazid Consulting & Communication AB - BCC (SWEDEN), Lund University (SWEDEN), Office National des Forêts - ONF (FRANCE), Institut de Mathématiques de Toulouse - IMT (Toulouse, France), Institut National Polytechnique de Toulouse - INPT (FRANCE), and Gaudio, Noemie

Subjects
Biogeochemical cycle, Soil nitrogen, 010504 meteorology & atmospheric sciences, Climate change, Soil science, Soil base saturation, 010501 environmental sciences, Atmospheric sciences, 01 natural sciences, [SDU.STU.GC]Sciences of the Universe [physics]/Earth Sciences/Geochemistry, Forest ecology, Atmospheric N deposition, Ecosystem, ForSAFE model, atmospheric, forest, ecosystem, biodiversity, dynamic, Milieux et Changements globaux, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, [STAT.AP]Statistics [stat]/Applications [stat.AP], Ecological Modeling, Géochimie, Temperate forest, Biogeochemistry, Soil carbon, 15. Life on land, Modélisation et simulation, Deposition (aerosol physics), 13. Climate action, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, Modeling and Simulation, Environmental science, Climatologie, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Forest soil biogeochemistry
Abstract

International audience; Atmospheric N deposition is known to severely impact forest ecosystem functioning by influencing soil biogeochemistry and nutrient balance, and consequently tree growth and overall forest health and biodiversity. Moreover, because climate greatly influences soil processes, climate change and atmospheric N deposition must both be taken into account when analysing the evolution of forest ecosystem status over time. Dynamic biogeochemical models have been developed to test different climate and atmospheric N deposition scenarios and their potential interactions in the long term. In this study, the ForSAFE model was used to predict the combined effect of atmospheric N deposition and climate change on two temperate forest ecosystems in France dominated by oak and spruce, and more precisely on forest soil biogeochemistry, from today to 2100. After a calibration step and following a careful statistical validation process, two atmospheric N deposition scenarios were tested: the current legislation in Europe (CLE) and the maximum feasible reduction (MFR) scenarios. They were combined with three climate scenarios: current climate scenario, worst-case climate scenario (A2) and best-case climate scenario (B1). The changes in base saturation and inorganic N concentration in the soil solution were compared across all scenario combinations, with the aim of forecasting the state of acidification, eutrophication and forest ecosystem recovery up to the year 2100. Simulations highlighted that climate had a stronger impact on soil base saturation, whereas atmospheric deposition had a comparative effect or a higher effect than climate on N concentration in the soil solution. Although deposition remains the main factor determining the evolution of N concentration in soil solution, increased temperature had a significant effect. Results also highlighted the necessity of considering the joint effect of both climate and atmospheric N deposition on soil biogeochemistry.

Published
2014
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