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1. Modeling the Effects of Irrigation Water Salinity on Growth, Yield and Water Productivity of Barley in Three Contrasted Environments

Author

Zied Hammami, Asad S. Qureshi, Ali Sahli, Arnaud Gauffreteau, Zoubeir Chamekh, Fatma Ezzahra Ben Azaiez, Sawsen Ayadi, and Youssef Trifa

Subjects
salinity, environments, AquaCrop model, water productivity, scenarios, tolerant, Agriculture
Abstract

Freshwater scarcity and other abiotic factors, such as climate and soil salinity in the Near East and North Africa (NENA) region, are affecting crop production. Therefore, farmers are looking for salt-tolerant crops that can successfully be grown in these harsh environments using poor-quality groundwater. Barley is the main staple food crop for most of the countries of this region, including Tunisia. In this study, the AquaCrop model with a salinity module was used to evaluate the performance of two barley varieties contrasted for their resistance to salinity in three contrasted agro-climatic areas in Tunisia. These zones represent sub-humid, semi-arid, and arid climates. The model was calibrated and evaluated using field data collected from two cropping seasons (2012–14), then the calibrated model was used to develop different scenarios under irrigation with saline water from 5, 10 to 15 dS m−1. The scenario results indicate that biomass and yield were reduced by 40% and 27% in the semi-arid region (KAI) by increasing the irrigation water salinity from 5 to 15 dS m−1, respectively. For the salt-sensitive variety, the reductions in biomass and grain yield were about 70%, respectively, although overall biomass and yield in the arid region (MED) were lower than in the KAI area, mainly with increasing salinity levels. Under the same environmental conditions, biomass and yield reductions for the salt-tolerant barley variety were only 16% and 8%. For the salt-sensitive variety, the biomass and grain yield reductions in the MED area were about 12% and 43%, respectively, with a similar increase in the salinity levels. Similar trends were visible in water productivities. Interestingly, biomass, grain yield, and water productivity values for both barley varieties were comparable in the sub-humid region (BEJ) that does not suffer from salt stress. However, the results confirm the interest of cultivating a variety tolerant to salinity in environments subjected to salt stress. Therefore, farmers can grow both varieties in the rainfed of BEJ; however, in KAI and MED areas where irrigation is necessary for crop growth, the salt-tolerant barley variety should be preferred. Indeed, the water cost will be reduced by 49% through growing a tolerant variety irrigated with saline water of 15 dS m−1.

Published
2020
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3. Optimized cultivar deployment improves the efficiency and stability of sunflower crop production at national scale

Author

Philippe Debaeke, Emmanuelle Mestries, Nicolas B. Langlade, Ronan Trépos, Amélia Landré, Patrick Vincourt, Arnaud Gauffreteau, Julien Sarron, Pierre Casadebaig, 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), Agronomie, AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire des Interactions Plantes Microbes Environnement (LIPME), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Terres Inovia, Fonctionnement agroécologique et performances des systèmes de cultures horticoles (UPR HORTSYS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), and Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
0106 biological sciences, Population, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Agricultural engineering, Performance de culture, 01 natural sciences, F30 - Génétique et amélioration des plantes, Crop, Production végétale, F01 - Culture des plantes, Genetics, Cultivar, Plant breeding, education, 2. Zero hunger, education.field_of_study, Abiotic stress, business.industry, cultivar selection [EN], Helianthus annuus, General Medicine, 04 agricultural and veterinary sciences, 15. Life on land, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Sunflower, Rendement des cultures, Agriculture, 040103 agronomy & agriculture, Helianthus, 0401 agriculture, forestry, and fisheries, Environmental science, France, business, Agronomy and Crop Science, Cropping, Biotechnology, 010606 plant biology & botany
Abstract

Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance.In this study, we aim is to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype × environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars × environments combinations leading to increased yield expectations.Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions.Key messageCrop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time.

Published
2022

4. Evaluation of pedoclimatic factors and cultural practices effects on carotenoid and sugar content in carrot root

Author

Wilfried Chevalier, Sitti-Anlati Moussa, Miguel Medeiros Netto Ottoni, Cécile Dubois-Laurent, Sébastien Huet, Christophe Aubert, Elsa Desnoues, Brigitte Navez, Valentine Cottet, Guillaume Chalot, Michel Jost, Laure Barrot, Gerald Freymark, Maarten Uittenbogaard, François Chaniet, Arnaud Gauffreteau, Anita Suel, Marie-Hélène Bouvier Merlet, Latifa Hamama, Valérie Le Clerc, Mathilde Briard, Didier Peltier, and Emmanuel Geoffriau

Subjects
Soil Science, Plant Science, Agronomy and Crop Science
Published
2022
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6. Modeling the Effects of Irrigation Water Salinity on Growth, Yield and Water Productivity of Barley in Three Contrasted Environments

Author

Youssef Trifa, Sawsen Ayadi, Zoubeir Chamekh, Arnaud Gauffreteau, Ali Sahli, Asad Sarwar Qureshi, Zied Hammami, Fatma Ezzahra Ben Azaiez, Agronomie, and AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Irrigation, Soil salinity, Biomass, 01 natural sciences, salinity, lcsh:Agriculture, Crop, environments, ComputingMilieux_MISCELLANEOUS, 2. Zero hunger, scenarios, lcsh:S, 04 agricultural and veterinary sciences, 15. Life on land, Saline water, Arid, 6. Clean water, tolerant, Salinity, Agronomy, AquaCrop model, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, water productivity, Agronomy and Crop Science, Groundwater, 010606 plant biology & botany
Abstract

Freshwater scarcity and other abiotic factors, such as climate and soil salinity in the Near East and North Africa (NENA) region, are affecting crop production. Therefore, farmers are looking for salt-tolerant crops that can successfully be grown in these harsh environments using poor-quality groundwater. Barley is the main staple food crop for most of the countries of this region, including Tunisia. In this study, the AquaCrop model with a salinity module was used to evaluate the performance of two barley varieties contrasted for their resistance to salinity in three contrasted agro-climatic areas in Tunisia. These zones represent sub-humid, semi-arid, and arid climates. The model was calibrated and evaluated using field data collected from two cropping seasons (2012&ndash, 14), then the calibrated model was used to develop different scenarios under irrigation with saline water from 5, 10 to 15 dS m&minus, 1. The scenario results indicate that biomass and yield were reduced by 40% and 27% in the semi-arid region (KAI) by increasing the irrigation water salinity from 5 to 15 dS m&minus, 1, respectively. For the salt-sensitive variety, the reductions in biomass and grain yield were about 70%, respectively, although overall biomass and yield in the arid region (MED) were lower than in the KAI area, mainly with increasing salinity levels. Under the same environmental conditions, biomass and yield reductions for the salt-tolerant barley variety were only 16% and 8%. For the salt-sensitive variety, the biomass and grain yield reductions in the MED area were about 12% and 43%, respectively, with a similar increase in the salinity levels. Similar trends were visible in water productivities. Interestingly, biomass, grain yield, and water productivity values for both barley varieties were comparable in the sub-humid region (BEJ) that does not suffer from salt stress. However, the results confirm the interest of cultivating a variety tolerant to salinity in environments subjected to salt stress. Therefore, farmers can grow both varieties in the rainfed of BEJ, however, in KAI and MED areas where irrigation is necessary for crop growth, the salt-tolerant barley variety should be preferred. Indeed, the water cost will be reduced by 49% through growing a tolerant variety irrigated with saline water of 15 dS m&minus, 1.

Published
2020
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7. Azodyn-Barley, a winter-barley crop model for predicting and ranking genotypic yield, grain protein and grain size in contrasting pedoclimatic conditions

Author

Maud Benezit, Ronan Trépos, Damien Beillouin, Marie-Hélène Jeuffroy, Arnaud Gauffreteau, Margot Leclère, Corentin M. Barbu, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), and CASDAR, ECO2MALT

Subjects
0106 biological sciences, Atmospheric Science, [SDV]Life Sciences [q-bio], Yield (finance), 01 natural sciences, Crop, Protein content, Parameter estimation, [INFO]Computer Science [cs], Grain retention fraction, [MATH]Mathematics [math], Crop management, Model evaluation, Grain protein content, Mathematics, 2. Zero hunger, Hordeum vulgare L, Global and Planetary Change, Ecological agriculture, Forestry, 04 agricultural and veterinary sciences, 15. Life on land, Grain size, Genotype-by-environment interaction, Ranking, Agronomy, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Grain yield, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

International audience; Increasing societal demand for a more ecological agriculture stimulates demand for farm management systems adapted to non-standard situations such as low-nitrogen systems. However, trade-offs between traits of interest could make difficult to reach high agronomic and environment performances. For malting barley, premium prices depend on grain protein content and grain size with strong trade-offs with yield, such traits being highly dependent on practices and genotypes. Here we assume that such trade-offs can be implicitly embedded in models taking into account varietal traits. Starting with an existing wheat-crop model, we developed a parsimonious barley-crop model on how genotypic traits, crop management practices and pedo-climatic environment determine yield, grain protein content and grain size. Grain size predictions required the development of a new module. We parameterized the model with published and new experimental data using 200 genotype-by-environment combinations of high- and low-input management situations. The model was then assessed on 280 other situations, including high and low level of nitrogen input. The relative root mean square error of prediction of the model on hold-out data was below 15% for grain yield, grain protein content and grain retention fraction in high and low-input situations. In addition, the model correctly ranked 72% of the genotype pairs for grain yield, 55% for grain protein content and 85% for grain retention fraction. Our model can be used to predict grain protein content and retention fraction, and rank yield and calibrated yield performance of existing combinations of genotype by management by environment combinations. It also could help to explore new combinations, to support breeding.

Published
2018
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8. Envirotypes Based on Seed Yield Limiting Factors Allow to Tackle G × E Interactions

Author

Erwan Corlouer, Nathalie Nesi, Anne Laperche, Christine Bissuel-Belaygue, Anne-Sophie Bouchet, Arnaud Gauffreteau, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Unité Génétique et Amélioration des Plantes Fourragères, Institut National de la Recherche Agronomique (INRA), ANR-11-BTBR-0004, Agence Nationale de la Recherche, Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, ANR-11-BTBR-0004,RAPSODYN,Optimisation de la teneur et du rendement en huile chez le colza cultivé sous contrainte azotée(2011), 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), Unité de recherche Génétique et Amélioration des Plantes Fourragères (UGAPF), AGROCAMPUS OUEST-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, genotype × environment interaction, Rapeseed, 01 natural sciences, [SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics, seed yield, Brassica napus L, Partial least squares regression, partial least square regression, Plant breeding, Gene–environment interaction, ComputingMilieux_MISCELLANEOUS, 2. Zero hunger, Genetic diversity, business.industry, 04 agricultural and veterinary sciences, Vernalization, 15. Life on land, [SDV.BV.AP]Life Sciences [q-bio]/Vegetal Biology/Plant breeding, Agronomy, Agriculture, Yield (chemistry), 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, business, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

One challenge in plant breeding is to ensure optimized production under fluctuating environments while reducing the environmental impacts of agriculture. Thus, new rapeseed varieties should be adapted to a wide range of pedoclimatic conditions and constraints. Addressing this issue requires identifying the critical factors limiting production and the genotype by environment (G &times, E) interaction. Our goal was to characterize the effects of environment and G &times, E interaction on the seed yield of rapeseed grown over a large field network. First, we defined a pedoclimatic indicator set with the ability to highlight the potential limiting factors along the crop cycle by analyzing the yield of two genotypes grown under 20 environments. Out of the 84 pedoclimatic indicators, 10 were identified as limiting after a partial least squares regression analysis. The environments were then clustered into five envirotypes, each characterized by few major limiting factors: low winter temperatures and heat stress during seed filling (1), low solar radiation during seed filling (3), vernalization conditions during winter (4) and high temperatures at flowering (5). A larger genetic diversity was evaluated in a subset of 11 environments to analyze the impact of envirotyping on genotype ranking. Their results were discussed in light of field network management and plant breeding purposes.

Published
2019
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9. Design as a source of renewal in the production of scientific knowledge in crop science

Author

Veronique Souchere, Quentin Toffolini, Jérôme Enjalbert, Marie-Hélène Jeuffroy, Chantal Loyce, Arnaud Gauffreteau, Amélie Lefèvre, Lorène Prost, Philippe Martin, Chloé Salembier, Jean-Marc Meynard, Muriel Valantin-Morison, Isabelle Goldringer, Gaëlle van Frank, Julie Borg, Sciences pour l'Action et le Développement : Activités, Produits, Territoires (SADAPT), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agronomie, Laboratoire Interdisciplinaire Sciences, Innovations, Sociétés (LISIS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Gustave Eiffel, Liaoning Technical University [Fuxin] (LNTU), Domaine Expérimental Alénya-Roussillon, and Institut National de la Recherche Agronomique (INRA)

Subjects
Sociology of scientific knowledge, 010504 meteorology & atmospheric sciences, Computer science, media_common.quotation_subject, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, 01 natural sciences, Plot (graphics), Design theory, Originality, Diagnosis, Production (economics), Duration (project management), Innovation, 0105 earth and related environmental sciences, media_common, 2. Zero hunger, Management science, Scale (chemistry), Farmers' practices, 04 agricultural and veterinary sciences, 15. Life on land, Variety (cybernetics), 040103 agronomy & agriculture, Designtheory, Research management, 0401 agriculture, forestry, and fisheries, Animal Science and Zoology, Agronomy and Crop Science, Agroecology
Abstract

International audience; Innovation is central to the strategic orientations of many agronomic research institutes. Little attention has been paid, however, to the links between knowledge production and design processes, defined as processes resulting in the creation of new objects in pursuit of specific goals. Our aim was to analyze the conditions and specificities of the production of scientific knowledge on agro-ecosystems through design processes. Drawing on design theory, we carried out a cross-analysis of nine research projects that included design processes and produced innovative objects (for example blending rules for variety mixtures in low-input crop management routes). These projects were managed by researchers from a range of disciplines (agronomists, geneticists, crop physiologists, and ecologists), and varied in their duration (from three to 15 years) and scale (from plot to landscape). We combined semi-structured interviews with these researchers and the analysis of various documents (scientific papers, PhD theses, technical publications, and research projects or reports). Our findings show that in all case studies, original and general scientific knowledge on agro-ecosystem functioning was produced at various stages throughout the design processes. The originality of this knowledge lies with the new representations that emerged, either of the agro-ecosystem processes at stake, or of farmers' practices. We show that these representations were formed gradually, through successive iterations of both refined formulations of the design target and new knowledge produced, required for its design. Finally, our results highlight the role of confrontation with real-life situations (particularly through agronomic diagnosis or experiments) in the evolution of these representations. Engaging in design processes can thus be seen as a research practice that leads to the production of original knowledge, allowing for a greater diversity of actors' ways of knowing to be taken into account.

Published
2020
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10. Whole-genome prediction of reaction norms to environmental stress in bread wheat (Triticum aestivum L.) by genomic random regression

Author

Agathe Mini, Sylvie Huet, Jean-Luc Jannink, Etienne Paux, Arnaud Gauffreteau, Stéphane Lafarge, Jacques Le Gouis, Delphine Ly, Gaëtan Touzy, Gilles Charmet, Fabien Cormier, Renaud Rincent, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, BIOGEMMA, USDA-ARS : Agricultural Research Service, Cornell University [New York], Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), BreedWheat project - French Government ANR-10-BTBR-03, France AgriMer, French Fund, NRA metaprogram SELGEN, and Auvergne Region

Subjects
0106 biological sciences, 0301 basic medicine, [SDV]Life Sciences [q-bio], Soil Science, Biology, 01 natural sciences, Cross-validation, F30 - Génétique et amélioration des plantes, 03 medical and health sciences, chemistry.chemical_compound, drought adaptation, Molecular marker, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], Plant breeding, [MATH]Mathematics [math], Selection (genetic algorithm), genomic prediction, 2. Zero hunger, business.industry, fungi, food and beverages, Variance (accounting), Regression, Biotechnology, 030104 developmental biology, chemistry, reaction norm, 13. Climate action, Agriculture, [SDE]Environmental Sciences, genotype-by-environment interaction, Adaptation, business, Agronomy and Crop Science, 010606 plant biology & botany, factorial regression
Abstract

International audience; Plant breeding has always sought to develop crops able to withstand environmental stresses, but this is all the more urgent now as climate change is affecting the agricultural regions of the world. It is currently difficult to screen genetic material to determine how well a crop will tolerate various stresses. Multi-environment trials (MET) which include a particular stress condition could be used to train a genomic selection model thanks to molecular marker information that is now readily available. Our study focuses on understanding how and predicting whether a plant is adapted to a particular environmental stress. We propose a way to use genomic random regression, an extension of factorial regression, to model the reaction norms of a genotype to an environmental stress: the factorial regression genomic best linear unbiased predictor (FR-gBLUP). Twenty-eight wheat trials in France (3 years, 12 locations, nitrogen or water stress treatments) were split into two METs where different stresses limited grain number and yield. In MET1, drought at flowering was responsible for 46.7% of the genotype-by-environment (G x E) interactions for yield while in MET2, heat stress during booting was identified as the main factor responsible for G x E interactions, but that explained less of the interaction variance (33.6%). Since drought at flowering explained a fairly large variance in G x E in MET1, the FR-gBLUP model was more accurate than the additive gBLUP across all types of cross validation. Accuracy gains varied from 2.4% to 12.9% for the genomic regression to drought. In MET2 accuracy gains were modest, varying from 5.7% to 2.4%. When a major stress influencing G x E is identified, the FR-gBLUP strategy makes it possible to predict the level of adaptation of genotyped individuals to varying stress intensities, and thus to select them in silico. Our study demonstrates how genome-wide selection can facilitate breeding for adaptation.

Published
2018
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11. Unfolding the potential of wheat cultivar mixtures : a meta-analysis perspective and identification of knowledge gaps

Author

Jérôme Enjalbert, Isabelle Goldringer, Lars Pødenphant Kiær, Arnaud Gauffreteau, Sébastien Barot, Christophe Lecarpentier, Sébastien Saint-Jean, Julie Borg, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11)-Institut National de la Recherche Agronomique (INRA), Department of Plant and Environmental Sciences [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut d'écologie et des sciences de l'environnement de Paris (iEES Paris), Institut National de la Recherche Agronomique (INRA)-Sorbonne Université (SU)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), and Institut d'écologie et des sciences de l'environnement de Paris (IEES (UMR_7618 / UMR_D_242 / UMR_A_1392 / UM_113) )

Subjects
0106 biological sciences, Biodiversity, Soil Science, Stress gradient hypothesis, Meta-regression, Plant disease resistance, 01 natural sciences, Disease reduction, Cultivar, Agroecology, Mathematics, 2. Zero hunger, Phenology, [SDE.IE]Environmental Sciences/Environmental Engineering, food and beverages, 04 agricultural and veterinary sciences, Overyielding, 15. Life on land, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, Agronomy, Homogeneous, Cultivar mixtures, Meta-analysis, Wheat, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Agronomy and Crop Science, Cropping, 010606 plant biology & botany
Abstract

International audience; Increasing the biodiversity of cropped plants is a key leverage for agroecology, aiming to replace chemical inputs by ecological processes and regulations. Cultivar mixtures are a straightforward way to increase within-crop diversity, but they have so far been poorly used by farmers and they are not encouraged by advisory services. Based on the methodology developed by Kiær et al. (2009), we achieved a meta-analysis of cultivar mixtures in wheat. Among the 120 publications dedicated to wheat, we selected 32 studies to analyze various factors that may condition the success or failure of wheat mixtures by calculating overyielding, i.e. the difference in productivity of a variety mixture compared with the weighted mean of its component varieties in pure stand. The analysis highlighted a significant global overyielding of 3.5%, which reached 6.2% in condition of high disease pressures. Overyielding was not affected by seeding density or plot size. Under high disease pressure, overyielding increased by 3.2% point per added component variety. Overyielding was respectively 5.3% and 3.3% higher for mixtures heterogeneous in disease resistance or phenology than for homogeneous ones, and did not vary when considering height. Overyielding reached its highest values in the 1980s and 1990s, which reflects the predominance of disease-focused studies during this period. Our results confirm that cultivar mixtures are a potential way to increase yield relatively to pure varieties, especially under low pesticide cropping systems. Literature suggests that mixture practice is impeded by the lack of general rules that could help to mixing varieties. To design such rules it is needed to (1) achieve new experiments manipulating the heterogeneity in variety traits, (2) determine experimentally the ecological mechanisms underlying mixture performance and (3) develop new models allowing testing and analyzing these mechanisms.

Published
2018
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12. Using ideotypes to support selection and recommendation of varieties

Author

Arnaud Gauffreteau, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, and Université Paris Saclay (COmUE)

Subjects
0106 biological sciences, Computer science, [SDV]Life Sciences [q-bio], design, lcsh:TP670-699, Multiple cropping, 01 natural sciences, Biochemistry, genotype x environment interaction, Plant traits, Input reduction, Selection (genetic algorithm), 2. Zero hunger, model, Ideotype, 04 agricultural and veterinary sciences, 15. Life on land, Variety (cybernetics), ideotype, Risk analysis (engineering), [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, lcsh:Oils, fats, and waxes, Agronomy and Crop Science, Cropping, 010606 plant biology & botany, Food Science
Abstract

International audience; Climate change, synthetic input reduction and new cropping practices contribute to renew deeply and rapidly the cropping conditions and the required attributes of the varieties. To provide varieties adapted to those multiple cropping situations and objectives, stakeholders of selection, assessment and recommendation need new methods. Indeed, selecting a variety on a large number of objectives simultaneously may be complicated and inefficient. To improve the efficiency of the selection, Donald (1968) proposed an ideotype-based approach. It consists first in building a plant model by pointing out the mechanisms and plant traits to reach the goals and then only selecting varieties on those traits. To do so, a three-step method is presented: (i) defining the varietal specifications; (ii) designing and building an ideotype; and (iii) selecting varieties according to the ideotype and assessing their ability to meet the specifications. Tools available for helping the implementation of such a method are identified in different fields (computing, design, genetics ...). This ideotype approach is presented as a valuable framework to support a collective design as it eases the interactions between stakeholders from different scientific and technical fields and the use of different forms of knowledge either scientific or empirical.

Published
2018
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13. Characterization of spatial and temporal combinations of climatic factors affecting yields: an empirical model applied to the French barley belt

Author

Marie-Hélène Jeuffroy, Damien Beillouin, Arnaud Gauffreteau, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris Saclay (COmUE), Florimond Desprez, and French Ministry of Agriculture (CASDAR Eco2malt)

Subjects
0106 biological sciences, Atmospheric Science, Yield (finance), Water stress, [SDV]Life Sciences [q-bio], 01 natural sciences, Partial least squares regression, Cultivar, Precipitation, Environment characterization, Mathematics, 2. Zero hunger, Global and Planetary Change, Temperature, Forestry, Regression analysis, 04 agricultural and veterinary sciences, Vernalization, Target population of environments, 15. Life on land, Breed, Agronomy, Frost, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

International audience; The adaptation of genotypes to environmental conditions is one of the main levers for maintaining an acceptable level of production, both in terms of quality and quantity. To breed suitable genotypes and for the farmers to choose the most adapted one to his farm conditions, the factors affecting production must be precisely characterized. Here, we analyzed the impacts of the climatic factors on winter barley yield in 35 departements (French geographic units) over 25 years, by partial least squares (PLS) regression analysis. Using ascendant hierarchical clustering based on PLS results, we defined the main combinations of climatic factors affecting yield in the French barley belt, hereafter referred as "climatic-stress patterns" (CPs). Four CPs captured 27% of total yield variability and widely differ in term of yield. Crops experienced low winter rainfall and few days with heat stress (31.8% of environments- mean yield of 7.2 t ha(-1)), high winter frost levels and high amounts of precipitation during stem elongation (34.7% of environments- mean yield of 6.5 t ha(-1)), high temperature during grain filling either with low vernalization temperatures (28.9% of environments-mean yield of 6.2 t ha(-1)), or high winter rainfall (4.6% of environments -mean yield of 5.5 t ha(-1)). Two-thirds of the French departements experienced all the four CPS over the years studied. Three clusters of regions with homogeneous frequencies of the CPs were identified. The regions with similar climatic-stress patterns could help breeders to design genotypes better adapted to the different local French growing conditions. It could also help farmers to choose the most appropriate cultivars to grow.

Published
2018
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14. Delayed and reduced nitrogen fertilization strategies decrease nitrogen losses while still achieving high yields and high grain quality in malting barley

Author

Ronan Trépos, Marie-Hélène Jeuffroy, Arnaud Gauffreteau, Damien Beillouin, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), and Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, DECISION-SUPPORT, Yield, Nitrogen, [SDV]Life Sciences [q-bio], WHEAT, Soil Science, chemistry.chemical_element, CROPPING SYSTEMS, Pareto-based ranking, Plant Science, 01 natural sciences, Protein content, Human fertilization, Azodyn crop model, Reduced nitrogen, USE EFFICIENCY, WINTER-BARLEY, Grain quality, MANAGEMENT, N use efficiency, [INFO]Computer Science [cs], Grain retention fraction, Leaching (agriculture), Crop model, [MATH]Mathematics [math], Grain protein content, Mathematics, 2. Zero hunger, Nitrogen management, 04 agricultural and veterinary sciences, DEFICIENCY, MODEL, SOIL, Nitrogen fertilizer, chemistry, Agronomy, [SDE]Environmental Sciences, 040103 agronomy & agriculture, AZODYN, 0401 agriculture, forestry, and fisheries, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

International audience; Nitrogen fertilizer applications are essential to achieve high yield and malting specifications in barley, but may also have a deleterious effect on the environment. Most strategies currently being implemented aim to optimize quantitative and qualitative production without taking environmental concerns into account. We used a barley crop model to pinpoint new nitrogen management plans maximizing the calibrated yields (yield of grains larger than 2.5 mm) and the grain quality whilst reducing N gaseous emission and N leaching. We compared the currently recommended N fertilization strategy in France and 44 new ones defined based on expertise knowledge, and differing in the amount of N applied, splitting patterns and time of application. The strategies providing the best compromise between the three criteria were identified by considering Pareto optimal solutions over 25 years in 35 French departements (equivalent to a county). We also identified Pareto optimal strategies for the seven years with the lowest yields and for years in which climatic conditions were unfavorable for efficient use of the early N supplies. The current recommended N fertilization strategy resulted in a high proportion of situations satisfying malting grain protein content requirements, but also to high N losses. We pinpointed new N strategies resulting in better compromise between the three outputs studied. Some Pareto optimal strategies were particularly efficient to reduce N losses in all tested environments regardless of the climatic conditions. They, however, also slightly reduced calibrated yield compared to the reference strategy. Others interesting strategies performed better than the reference simultaneously for all three studied outputs, but depended on the region considered. A common feature of these strategies was later application of smaller doses of N. Our results, thus demonstrated that low-N strategies are possible for malting barley.

Published
2018
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15. Nitrogen nutrition index predicted by a crop model improves the genomic prediction of grain number for a bread wheat core collection

Author

Pierre Martre, Sylvie Huet, Karine Chenu, Arnaud Gauffreteau, David Gouache, Delphine Ly, Jacques Bordes, Renaud Rincent, Gilles Charmet, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland [Brisbane], Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), Terres Inovia, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Enterprise Competitiveness Fund Project 'Semences de Demain', INRA metaprogram SELGEN, Auvergne Region, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), QAAFI, AgroParisTech-Institut National de la Recherche Agronomique (INRA), 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), and Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, 0301 basic medicine, Nitrogen stress, prédiction génétique, Soil Science, Agricultural engineering, Biology, 01 natural sciences, Crop, 03 medical and health sciences, blé, wheat, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Plant breeding, Gene–environment interaction, Crop model, Environment characterization, 2. Zero hunger, rendement en grain, Genomic prediction, business.industry, Genotype-by-environment interactions, fungi, Crop growth, Grain number, food and beverages, genotype environment interaction, Biotechnology, interaction génotype environnement, 030104 developmental biology, stress environnemental, business, index de nutrition azotée, Agronomy and Crop Science, Genomic selection, modèle de production, 010606 plant biology & botany
Abstract

In plant breeding, one of the major challenges of genomic selection is to account for genotype-by-environment (G × E) interactions, and more specifically how varieties are adapted to various environments. Crop growth models (CGM) were developed to model the response of plants to environmental conditions. They can be used to characterize eco-physiological stresses in relation to crop growth and developmental stages, and thereby help to dissect G × E interactions. Our study aims at demonstrating how environment characterization using crop models can be integrated to improve both the understanding and the genomic predictions of G × E interactions. We evaluated the usefulness of using CGM to characterize environments by comparing basic and CGM-based stress indicators, to assess how much of the G × E interaction can be explained and whether gains in prediction accuracy can be made. We carried out a case study in wheat (Triticum aestivum) to model nitrogen stress in a CGM in 12 environments defined by year × location × nitrogen treatment. Interactions between 194 varieties of a core collection and these 12 different nitrogen conditions were examined by analyzing grain number. We showed that (i) CGM based indicators captured the G × E interactions better than basic indicators and that (ii) genomic predictions were slightly improved by modeling the genomic interaction with the crop model based characterization of nitrogen stress. A framework was proposed to integrate crop model environment characterization into genomic predictions. We describe how this characterization promises to improve the prediction accuracy of adaptation to environmental stresses.

Published
2017
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16. Designing mixtures of varieties for multifunctional agriculture with the help of ecology. A review

Author

Jérôme Enjalbert, Audrey Niboyet, Isabelle Goldringer, Jean-Christophe Lata, Xavier Le Roux, Amélie A. M. Cantarel, Sébastien Barot, Vincent Allard, Arnaud Gauffreteau, Emmanuelle Porcher, Institut d'écologie et des sciences de l'environnement de Paris (IEES (UMR_7618 / UMR_D_242 / UMR_A_1392 / UM_113) ), Institut National de la Recherche Agronomique (INRA)-Sorbonne Université (SU)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Génétique Diversité et Ecophysiologie des Céréales - Clermont Auvergne (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Laboratoire d'Ecologie Microbienne - UMR 5557 (LEM), Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Vétérinaire de Lyon (ENVL)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11)-Institut National de la Recherche Agronomique (INRA), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Tomsk Polytechnic University [Russie] (UPT), AgroParisTech, Centre d'Ecologie et des Sciences de la COnservation (CESCO), Muséum national d'Histoire naturelle (MNHN)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR Wheatamix, INRA meta-programme EcoServ through the project SolFaMi, Institut d'écologie et des sciences de l'environnement de Paris (iEES Paris), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Ecole Nationale Vétérinaire de Lyon (ENVL)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Institut National de la Recherche Agronomique (INRA), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Ecole Nationale Vétérinaire de Lyon (ENVL), IRD, CNRS, UPEC, Institut d'Ecologie et des Sciences de l'Environnement de Paris ( IEES Paris ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Ecologie microbienne ( EM ), Centre National de la Recherche Scientifique ( CNRS ) -Ecole Nationale Vétérinaire de Lyon ( ENVL ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique ( INRA ) -VetAgro Sup ( VAS ), Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) ( GQE-Le Moulon ), Institut National de la Recherche Agronomique ( INRA ) -Université Paris-Sud - Paris 11 ( UP11 ) -AgroParisTech-Centre National de la Recherche Scientifique ( CNRS ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, IRD CNRS UPEC, Department of Geoecology and Geochemistry, Institute of Natural Resources, Tomsk Polytechnic University, Centre d'Ecologie et des Sciences de la COnservation ( CESCO ), and Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Muséum National d'Histoire Naturelle ( MNHN )

Subjects
0106 biological sciences, Environmental Engineering, [SDV]Life Sciences [q-bio], Biodiversity, Sampling effect, Biology, 01 natural sciences, Crop traits, Multifunctional agriculture, Mixing ability, Crop breeding, Complementarity effect, Mixtures of varieties, Ecosystem, 2. Zero hunger, Sustainable development, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Genetic diversity, [ SDV ] Life Sciences [q-bio], Ecology, business.industry, 04 agricultural and veterinary sciences, 15. Life on land, Agriculture, Complementarity (molecular biology), 040103 agronomy & agriculture, Trait, 0401 agriculture, forestry, and fisheries, Agricultural biodiversity, business, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

International audience; Abstract - The study of natural ecosystems and experiments using mixtures of plant species demonstrates that both species and genetic diversity generally promote ecosystem functioning. Therefore, mixing crop varieties is a promising alternative practice to transform modern high-input agriculture that is associated with a drastic reduction of within-field crop genetic diversity and is widely recognized as unsustainable. Here, we review the effects of mixtures of varieties on ecosystem functioning, and their underlying ecological mechanisms, as studied in ecology and agronomy, and outline how this knowledge can help designing more efficient mixtures. We recommend the development of two complementary strategies to optimize variety mixtures by fostering the ecological mechanisms leading to a positive relationship between biodiversity and ecosystem functioning and its stability through time, i.e., sampling and complementarity effects. (1) In the “trait-blind” approach, the design of high-performance mixtures is based on estimations of the mixing abilities of varieties. While this approach is operational because it does not require detailed trait knowledge, it relies on heavy experimental designs to evaluate mixing ability. (2) The trait-based approach is particularly efficient to design mixtures of varieties to provide particular baskets of services but requires building databases of traits for crop varieties and documenting the relations between traits and services. The performance of mixtures requires eventually to be evaluated in real economic, social, and agronomic contexts. We conclude that the need of a multifunctional low-input agriculture strongly increases the attractiveness of mixtures but that new breeding approaches are required to create varieties with higher mixing abilities, to foster complementarity and selection effects through an increase in the variance of relevant traits and to explore new combinations of trait values.

Published
2017
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17. L'innovation à l'épreuve d'un climat et d'un monde changeant rapidement : intérêt de la co-conception dans le domaine des semences

Author

Laurent Hazard, Arnaud Gauffreteau, Julie Borg, Marie-Hélène Moirez-Charron, Matthew Deo, Jerome Enjalbert, Vladimir Goutiers, Gressier, E., Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11)-Institut National de la Recherche Agronomique (INRA), Association Vétérinaires Eleveurs du Millavois (AVEM), Climagie, Métaprogramme ACCAF, ANR O2LA (ANR-09-STRA-09), ANR Agrobiosphere (ANR-13-AGRO-0008-04), Mélibio, Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), and Jean-Louis Durand

Subjects
agroecology, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, farmers' practices, agroécologie, agricultural development, environmental factor, pratiques des agriculteurs, mélange fourrager, fourrage, aide à la décision, prairie, forage, facteur milieu, innovation, méthode, approches participatives, forage mixture, développement agricole, participatory approaches, plant association, method, cultivar breeding, aid to decision, grassland, sélection variétale, association végétale, cultivar
Abstract

Ce numéro de la revue Fourrages présente les résultats des travaux du projet Climagie du métaprogramme Adaptation au changement climatique de l'agriculture et de la forêt (ACCAF) de l'Inra; Valoriser la biodiversité cultivée permet d'augmenter la résilience des cultures face aux aléas tout en réduisant l'utilisation des intrants chimiques. Implémenter une telle agriculture basée sur la biodiversité nécessite le développement d'approches de co-conception pour tenir compte de la singularité des situations locales et de la variabilité accrue des conditions de culture.La production agricole doit réduire l'usage des intrants à un moment où elle fait face aux effets du changement climatique. Or l'utilisation des intrants, si elle permet d'augmenter la production, tamponne également la variabilité spatiale et temporelle des conditions de culture, atténuant notamment les effets des aléas climatiques. L'utilisation d'une diversité d'espèces et de variétés, de populations génétiquement diversifiées constitue une alternative agroécologique à l'utilisation des intrants synthétiques. Le défi est alors de créer des innovations génétiques (variétés, mélanges des variétés, mélanges d'espèces) qui soient adaptées aux conditions locales. Cela nécessite donc de raisonner à l'échelle locale et d'impliquer l'utilisateur dans ce travail de conception de ces innovations. Nous verrons à travers la présentation de différents travaux de co-conception que travailler avec les acteurs de terrain permet (i) d'affiner les objectifs poursuivis et d'ainsi faire évoluer les questions de recherche et les critères d'évaluation des innovations, (ii) de produire des connaissances et des innovations adaptées aux usagers (idéotypage participatif d'associations variétales), (iii) de concevoir des outils leur permettant de créer leurs propres solutions (outil Capflor® d'aide à la conception de prairies à flore variée), ou (iv) d'accompagner le développement d'un programme de la sélection paysanne. Ces travaux rompent avec la logique du transfert de connaissances des chercheurs vers les agriculteurs au profit d'apprentissages croisés favorisant l'adaptation locale dans le processus d'innovation.

Published
2016

18. Increasing within-field diversity to foster agro-ecosystem services and cope with climate change

Author

Jerome Enjalbert, Xavier Le Roux, Vincent Allard, Bruno Andrieu, Sébastien Barot, Julie Borg, Dominique Descoureaux, Claude Pope de Vallavieille, Arnaud Gauffreteau, Isabelle Goldringer, Stephane Lemarié, Sébastien Saint-Jean, Emmanuelle Porcher, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11)-Institut National de la Recherche Agronomique (INRA), Laboratoire d'Ecologie Microbienne - UMR 5557 (LEM), Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Ecole Nationale Vétérinaire de Lyon (ENVL), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut de Recherche pour le Développement (IRD [France-Ouest]), Chambre d'Agriculture du Loir et Cher (CA 41), BIOlogie et GEstion des Risques en agriculture (BIOGER), Agronomie, Laboratoire d'Economie Appliquée = Grenoble Applied Economics Laboratory (GAEL), Université Pierre Mendès France - Grenoble 2 (UPMF)-Institut National de la Recherche Agronomique (INRA), Muséum national d'Histoire naturelle (MNHN), Wheatamix Project, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) ( GQE-Le Moulon ), Institut National de la Recherche Agronomique ( INRA ) -Université Paris-Sud - Paris 11 ( UP11 ) -AgroParisTech-Centre National de la Recherche Scientifique ( CNRS ), Ecologie microbienne ( EM ), Centre National de la Recherche Scientifique ( CNRS ) -Ecole Nationale Vétérinaire de Lyon ( ENVL ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique ( INRA ) -VetAgro Sup ( VAS ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes ( ECOSYS ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Institut de Recherche pour le Développement ( IRD [France-Ouest] ), Chambre d'Agriculture du Loir et Cher ( CA 41 ), BIOlogie GEstion des Risques en agriculture - Champignons Pathogènes des Plantes ( BIOGER-CPP ), Laboratoire d'Economie Appliquée de Grenoble ( GAEL ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Institut National de la Recherche Agronomique ( INRA ), Muséum National d’Histoire Naturelle ( MNHN ), Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Université de Lyon-Ecole Nationale Vétérinaire de Lyon (ENVL)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Vétérinaire de Lyon (ENVL)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Laboratoire d'Economie Appliquée de Grenoble (GAEL), and Institut National de la Recherche Agronomique (INRA)-Université Pierre Mendès France - Grenoble 2 (UPMF)

Subjects
[ SDE.MCG ] Environmental Sciences/Global Changes, Diversité génétique, [SDE.MCG]Environmental Sciences/Global Changes, Variabilité génotypique, Couvert végétal, Association culturale, Triticum
Abstract

During the 20th century, agriculture experienced major gains in productivity via homogenization and intensive use of inputs. This model is jeopardized by the awareness of rapid global change, in particular climate change, and the need for greater agricultural sustainability. Crop genetic diversity should play an essential role in this context, as it could promote various ecosystem services essential for adaptation to climate change. Increasing within field diversity through the use of cultivar mixtures is a timely option, testified by with some significant “success stories” in the past. Despite the abundant bibliography demonstrating the interest of intra-specific crop diversity, cultivar mixtures are poorly developed worldwide. In this context, the WHEATAMIX project studies the interest of mixing wheat genotypes to reinforce the sustainability and resilience of agricultural production and the provision of various ecosystem services. Based on a highly multidisciplinary approach we analyze the interactions among genotypes and with the environment, to develop new methods for breeding and/or combining wheat varieties to obtain performing blends in a global change context. Complementary experimental approaches are being deployed: i) a main diversity experiment (Eighty- eight wheat plots with 1, 2, 4 or 8 varieties, under low input) to quantify over several years the variety diversity effects on ecosystem services; ii) replicates of the same diversity experiment in 4 sites across France, to test the robustness of wheat diversity effects under a wide range of soil and climate conditions; iii) a network of 50 farms, encompassing agro-climatic variability in the Paris basin, to compare the ecological and techno-economic performance of blends with that of monocultures, using direct links with key stakeholders. The first results provide a comprehensive characterization of the multiple ecosystem services provided by genetic diversity (yield stability; regulation of foliar diseases; insect pest and weed biocontrol; maintenance of soil fertility; biodiversity conservation), and the trade-offs and synergies that exist among ecosystem services. The result also guide the selection of variety mixtures and corresponding bundles of functional traits that can deliver particular groups of services to tackle the climate change issue.

Published
2015

19. WHEATAMIX Project : Increasing within-field wheat diversity to foster ecosystem services in the Parisian basin

Author

Vincent Allard, Bruno Andrieu, Sébastien Barot, Julie Borg, Amélie Cantarel, Céline Cervek, François Coléno, Claude Pope de Vallavieille, Dominique Descoureaux, Florence Dubs, Jerome Enjalbert, Feret, M., Nathalie Galic, Arnaud Gauffreteau, Gilet, J. D., Isabelle Goldringer, Mourad Hannachi, Houivet, G., Sophie Pin, Marie-Helene Jeuffroy, Christian Kerbiriou, Pierre Labarthe, Jean Christophe Lata, Christophe Lecarpentier, Lejars, L., Lemain, B., Stephane Lemarié, Leny, F., Xavier Le Roux, Le Viol, M., Christophe Montagnier, Audrey Niboyet, Omond, B., Piaud, S., Franck Poly, Thomas Pommier, Emmanuelle Porcher, Sébastien Saint-Jean, Sandrine Salmon, Didier Tropee, Tiphaine Vidal, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Environnement et Grandes Cultures (EGC), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut de Recherche pour le Développement (IRD [France-Ouest]), Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11)-Institut National de la Recherche Agronomique (INRA), Laboratoire d'Ecologie Microbienne - UMR 5557 (LEM), Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Vétérinaire de Lyon (ENVL)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Chambre Régionale d'Agriculture du Centre, Sciences pour l'Action et le Développement : Activités, Produits, Territoires (SADAPT), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, BIOlogie et GEstion des Risques en agriculture (BIOGER), CA 41, Chambre d'Agriculture du Loir et Cher, France., Chambre d'Agriculture du Loir et Cher (CA 41), Chambre d'Agriculture de l'Indre (CA 36), Agronomie, FDGEDA Cher, Centre d'Ecologie et des Sciences de la COnservation (CESCO), Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Institut d'écologie et des sciences de l'environnement de Paris (IEES), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Recherche Agronomique (INRA), Chambre d'Agriculture du Loiret, Laboratoire d'Economie Appliquée de Grenoble (GAEL), Institut National de la Recherche Agronomique (INRA)-Université Pierre Mendès France - Grenoble 2 (UPMF), Chambre d'Agriculture de l'Eure (CA 27), Domaine expérimental de Versailles-Grignon (UEVG), Institut National de la Recherche Agronomique (INRA), Chambre d'Agriculture de la Seine et Marne, Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Ecole Nationale Vétérinaire de Lyon (ENVL), Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN), Institut d'écologie et des sciences de l'environnement de Paris (iEES), Laboratoire d'Economie Appliquée = Grenoble Applied Economics Laboratory (GAEL), Université Pierre Mendès France - Grenoble 2 (UPMF)-Institut National de la Recherche Agronomique (INRA), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Environnement et Grandes Cultures ( EGC ), AgroParisTech-Institut National de la Recherche Agronomique ( INRA ), Institut de Recherche pour le Développement ( IRD [France-Ouest] ), Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) ( GQE-Le Moulon ), Institut National de la Recherche Agronomique ( INRA ) -Université Paris-Sud - Paris 11 ( UP11 ) -AgroParisTech-Centre National de la Recherche Scientifique ( CNRS ), Ecologie microbienne ( EM ), Centre National de la Recherche Scientifique ( CNRS ) -Ecole Nationale Vétérinaire de Lyon ( ENVL ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique ( INRA ) -VetAgro Sup ( VAS ), Sciences pour l'Action et le Développement : Activités, Produits, Territoires ( SADAPT ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, BIOlogie GEstion des Risques en agriculture - Champignons Pathogènes des Plantes ( BIOGER-CPP ), Chambre d'Agriculture du Loir et Cher ( CA 41 ), Chambre d'Agriculture de l'Indre ( CA 36 ), Centre d'Ecologie et des Sciences de la COnservation ( CESCO ), Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Muséum National d'Histoire Naturelle ( MNHN ), Institut d'écologie et des sciences de l'environnement de Paris ( IEES ), Institut National de la Recherche Agronomique ( INRA ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris-Est Créteil Val-de-Marne - Paris 12 ( UPEC UP12 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'Economie Appliquée de Grenoble ( GAEL ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Institut National de la Recherche Agronomique ( INRA ), Chambre d'Agriculture de l'Eure ( CA 27 ), Domaine expérimental de Versailles-Grignon ( UEVG ), Institut National de la Recherche Agronomique ( INRA ), Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Université de Lyon-Ecole Nationale Vétérinaire de Lyon (ENVL)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), ProdInra, Archive Ouverte, and Muséum National d'Histoire Naturelle ( MNHN ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
[SDE.MCG] Environmental Sciences/Global Changes, [ SDV.BV ] Life Sciences [q-bio]/Vegetal Biology, [ SDE.MCG ] Environmental Sciences/Global Changes, triticum, [SDE.MCG]Environmental Sciences/Global Changes, région parisienne, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, service écosystémique, winter wheat
Abstract

WHEATAMIX Project : Increasing within-field wheat diversity to foster ecosystem services in the Parisian basin. 3. International Conference on Biodiversity and the UN Millennium Development Goals,Biodiversity and Food Security – From Trade-offs to Synergies

Published
2014

20. Effect of the structural variables of landscapes on the risks of spatial dissemination between GM and non-GM maize

Author

Baptiste Lecroart, Marianne Le Bail, Antoine Messéan, Arnaud Gauffreteau, Frédérique Angevin, Sciences pour l'Action et le Développement : Activités, Produits, Territoires (SADAPT), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité Impacts Ecologiques des Innovations en Production Végétale (ECO-INNOV), Institut National de la Recherche Agronomique (INRA), Agronomie, and AgroParisTech-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, REGIONAL MANAGEMENT, ZEA-MAYS L, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Pollination, Soil Science, Plant Science, Genetically modified crops, COEXISTANCE, Spatial distribution, 01 natural sciences, Agricultural statistics, Landscape level, GENETICALLY MODIFIDED CORP, European commission, 2. Zero hunger, FIELD PATTERN, LANDSCAPE, business.industry, CROSS POLINISATION, 04 agricultural and veterinary sciences, GM, 15. Life on land, TIME, Geography, Agronomy, Agriculture, DISTANCE, 040103 agronomy & agriculture, MEDIATED GENE FLOW, 0401 agriculture, forestry, and fisheries, Landscape ecology, business, POLLEN DISPERSAL, Agronomy and Crop Science, MAIZE, 010606 plant biology & botany
Abstract

Crops grown from non-genetically modified seed should contain no more than 0.9% GM material. The very diverse nature of EU landscapes implies that coexistence measures must be adapted to regional conditions. Rigid rules, such as the requirement of large isolation distances everywhere, may raise the cost of coexistence measures, particularly in densely planted areas, without necessarily meeting the objectives of the European Commission. One of the objectives of the SIGMEA programme (Sustainable Introduction of GM Crops into European Agriculture is a Specific Targeted Research Project in the EC), within which this work was conducted, was to provide tools for the management of coexistence at the landscape level. We compared four contrasting maize production zones (in Alsace and Aragon) using MAPOD ® , a spatially-explicit model of gene flow, and randomly allocating GM crops to maize plots. We calculated two indicators of potential interest to different stakeholders (not only farmers and regulators, but also collecting firms and industry): (1) the proportion of the non-GM maize area not complying with a given threshold and (2) the mean cross pollination rate obtained if the harvests of all the conventional plots were to be mixed in a silo. Our results suggest that it is possible to classify the regional landscapes in terms of the level of risk (for different thresholds depending on market standards—e.g. 0.1%; 0.4%; 0.9%) with few structural characteristics easily calculated from GIS and agricultural statistics data (Mean field area; proportion of the Agricultural Used Area (AUA) devoted to maize, GM maize proportion, maize flowering dynamics and wind characteristics). The mean cross pollination rate increases with GM maize proportion in all the landscapes but the effect of this factor is modulated by structural variables of the landscapes.

Published
2010
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21. Modelling impacts of cropping systems and climate on maize cross-pollination in agricultural landscapes: The MAPOD model

Author

Antoine Messéan, Jean-Marc Meynard, Frédérique Angevin, Etienne K. Klein, Claire Lavigne, Cécile Choimet, Arnaud Gauffreteau, Unité Impacts Ecologiques des Innovations en Production Végétale (ECO-INNOV), Institut National de la Recherche Agronomique (INRA), Biostatistique et Processus Spatiaux (BIOSP), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Département Sciences pour l'Action et le Développement (DEPT SAD), Biostatistique et Processus Spatiaux (BioSP), and Institut National de la Recherche Agronomique (INRA)-AgroParisTech

Subjects
0106 biological sciences, CORN, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Pollination, PLANTE TRANSGENIQUE, Soil Science, Distribution (economics), Plant Science, ADVENTITIOUS PRESENCE, 01 natural sciences, COEXISTENCE, GENE FLOW, Cropping system, 2. Zero hunger, Genetically modified maize, CROPPING SYSTEM, business.industry, GMO, FLOWERING, 04 agricultural and veterinary sciences, Field (geography), Agronomy, Agriculture, POLLINISATION CROISEE, 040103 agronomy & agriculture, Organic farming, 0401 agriculture, forestry, and fisheries, Environmental science, CROSS POLLINATION, business, POLLEN DISPERSAL, Agronomy and Crop Science, Cropping, 010606 plant biology & botany
Abstract

New concerns about crop coexistence in agricultural landscapes are being expressed in reaction to the prospect of introducing transgenic crops into European cropping systems: these include meeting current consumer demand for non-GM products, respecting threshold levels required for organic farming labels as well as keeping food cultures separated from those destined for the pharmaceutical and energy industries. To address these concerns in the case of maize crops, we have chosen a modelling approach. Our aim was to simulate cross-pollination in the case of existing agricultural landscapes, taking into account the effect of climate and cropping techniques in order to forecast gene escape from genetically modified maize to non-GM maize. The resulting spatially explicit model, MAPOD (Matricial Approach to POllen Dispersal), is presented in this paper. A preliminary evaluation is also provided. Pollen exchanges between GM and non-GM maize crops are simulated and influencing factors such as field sizes and shapes, distribution of GM and non-GM fields in the agricultural landscape as well as flowering dates and dynamics are integrated. Model parameter values were either derived from existing models of pollen dispersal or estimated from experimental field studies. The preliminary evaluation of MAPOD was carried out by comparing simulation results with data from two French and one American gene flow field trials. MAPOD was found to provide good average predictive values. Examples of output data illustrate the capacity of the model to simulate a wide range of agricultural contexts. These simulation results provide a basis for designing coexistence rules and monitoring procedure set-up.

Published
2008
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22. How MAPOD can supply useful simulations for the regional debates between stakeholders on the coexistence between GMO and non GMO maize ?

Author

Arnaud Gauffreteau, Marianne Le Bail, Frédérique Angevin, Mathieu Leclaire, Antoine Messean, Agronomie (Agronomie), Institut National de la Recherche Agronomique (INRA)-Institut National Agronomique Paris-Grignon (INA P-G), Systèmes Agraires Développement : Activités, Produits, Territoires (SADAPT), Unité Impacts Ecologiques des Innovations en Production Végétale (ECO-INNOV), Institut National de la Recherche Agronomique (INRA), and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], [SDE] Environmental Sciences, COEXISTENCE, [SDV]Life Sciences [q-bio], GENETICALLY MODIFIED MAIZE, [SDE]Environmental Sciences, [SHS] Humanities and Social Sciences, GENE FLOW MODEL, ComputingMilieux_MISCELLANEOUS, LOCAL DEBATE, [SHS]Humanities and Social Sciences
Abstract

International audience

Published
2006

23. Mélanges variétaux et mélanges plurispécifiques – atouts et contraintes

Author

Jerome Enjalbert, Isabelle Litrico, Elisabeth Fournier, Safia Médiène, Arnaud Gauffreteau, Julie Borg, Guenaelle Hellou, Isabelle Goldringer, Hanachi, M., Etienne-Pascal Journet, Eric Justes, Jean-Benoit Morel, Naudin, C., Hervé Sanguin, Muriel Morison, Valentin Verret, Laurent Bedoussac, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11)-Institut National de la Recherche Agronomique (INRA), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), SFR UA 4207 QUAlité et SAnté du Végétal (QUASAV), Université d'Angers (UA)-Institut National de la Recherche Agronomique (INRA)-Université de Nantes (UN)-AGROCAMPUS OUEST-Ecole supérieure d'Agricultures d'Angers (ESA), Sciences pour l'Action et le Développement : Activités, Produits, Territoires (SADAPT), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Laboratoire des interactions plantes micro-organismes (LIPM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Laboratoire d'Expertise et de Validation des Applications combustibles multi-filières (LEVA), Service d'Etudes de Simulation du Comportement du combustibles (SESC), Département d'Etudes des Combustibles (DEC), CEA-Direction de l'Energie Nucléaire (CEA-DEN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-CEA-Direction de l'Energie Nucléaire (CEA-DEN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Département d'Etudes des Combustibles (DEC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-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), Université d'Angers (UA)-Institut National de la Recherche Agronomique (INRA)-Université de Nantes (UN)-AGROCAMPUS OUEST, 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)-Ecole supérieure d'Agricultures d'Angers (ESA), Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), and Institut National de la Recherche Agronomique (INRA)-Ecole supérieure d'Agricultures d'Angers (ESA)-Université de Nantes (UN)-AGROCAMPUS OUEST-Université d'Angers (UA)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, biodiversité cultivée, F08 - Systèmes et modes de culture, agroécologie, interaction GxE, interactions plante-plante, biodiversité, blé, diversité intra et interspécifique, Agriculture durable, mélange variétal, Triticum, riz, Oryza, Agricultural sciences, Rendement des cultures, Agroécosystème, Diversification, Système de culture, P01 - Conservation de la nature et ressources foncières, Culture associée, Sciences agricoles, Agroecology
Abstract

article issus du colloque « Biodiversité et durabilité des agricultures » qui s’est tenu à Paris le 11 avril 2019https://www6.inra.fr/ciag/CIAg-Environnement/Biodiversite-et-agricultures; Pour réduire les impacts négatifs des systèmes agricoles, la transition agroécologique appelle en premier lieu à l’utilisation d’une plus grande biodiversité cultivée. Une telle diversification peut être conduite à l'échelle de la parcelle, en associant différentes espèces, ou différentes variétés d'une même espèce. Nous abordons les avantages de cette diversification intra-parcellaire, en développant 5 exemples : (1) les mélanges variétaux de blé en France, (2) les populations de riz au YuanYang, (3) le colza associé à des plantes de services, (4) les mélanges d’espèces fourragères et (5) les associations céréales-légumineuses à graines. Nous discutons des avantages conférés par cette diversification sur : (i) l’optimisation et la stabilisation des productions, (ii) les interactions avec les pratiques culturales, (iii) la sélection variétale, et (iv) l’impact de ces peuplements et pratiques sur la biodiversité sauvage associée.

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