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1. Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models

Author

Bruce A. Kimball, Kelly R. Thorp, Kenneth J. Boote, Claudio Stockle, Andrew E. Suyker, Steven R. Evett, David K. Brauer, Gwen G. Coyle, Karen S. Copeland, Gary W. Marek, Paul D. Colaizzi, Marco Acutis, Seyyedmajid Alimagham, Sotirios Archontoulis, Faye Babacar, Zoltán Barcza, Bruno Basso, Patrick Bertuzzi, Julie Constantin, Massimiliano De Antoni Migliorati, Benjamin Dumont, Jean-Louis Durand, Nándor Fodor, Thomas Gaiser, Pasquale Garofalo, Sebastian Gayler, Luisa Giglio, Robert Grant, Kaiyu Guan, Gerrit Hoogenboom, Qianjing Jiang, Soo-Hyung Kim, Isaya Kisekka, Jon Lizaso, Sara Masia, Huimin Meng, Valentina Mereu, Ahmed Mukhtar, Alessia Perego, Bin Peng, Eckart Priesack, Zhiming Qi, Vakhtang Shelia, Richard Snyder, Afshin Soltani, Donatella Spano, Amit Srivastava, Aimee Thomson, Dennis Timlin, Antonio Trabucco, Heidi Webber, Tobias Weber, Magali Willaume, Karina Williams, Michael van der Laan, Domenico Ventrella, Michelle Viswanathan, Xu Xu, and Wang Zhou

Subjects
Atmospheric Science, Global and Planetary Change, Forestry, Agronomy and Crop Science
Published
2023

2. Seasonal forecast of land indicators for decision-making in the field of agriculture, forestry and hydrology

Author

André Chanzy, Patrick bertuzzi, Elisa Kamir, Hendrik Davi, Jean-Luc Dupuy, Nicolas Martin, Guillaume Pouget, Marc Lagier, Olivier Maury, Inaki Garcia de Cortazar, Christian Viel, and Jean-Michel Soubeyroux

Abstract

Many decisions in the field of agriculture, forestry and/or hydrology can get profit from seasonal forecast. However, the skill of such forecast is a critical issue to promote their use in operational context and get profitable decisions. If many methods to assess meteorological forecast performances are available, they are mostly implemented on raw climate variables, while their implementation in sectorial application remains limited to some case studies. In this study a wide range of indicators covering most of the decision-making needs in agriculture, forestry and in some extent to hydrology were considered. These indicators are either direct climate variables, a combination of climate variables, or variables calculated by dynamic models (e.g. a crop model). The study was implemented in southern France using the Méteo-France system 6 1993-2016 hindcast, downscaled using the UERRA reanalysis and the ADAMONT methods available in CS-Tools R package developed in the frame of the MEDSCOPE project. These computed indicators need various climate variables as wind speed, radiation and air humidity while most of the downscaling methods were designed for air temperature and precipitation. The main results are the following.We showed that all variables led to comparable level of accuracy. Seasonal forecasts provide added value compared to climatological forecasts with Brier Skill Scores between 0.05 and 0.20. The predictability of the number of rainy days or the number of days with temperature above a threshold is comparable to those of the corresponding scalar quantities such as cumulative precipitation or mean air temperature. However seasonal forecast of extreme events such as heat waves or drought episodes was not possible. Indicators combining several climatic information such as potential evapotranspiration or fire weather index have comparable predictability than the individual climate variables used in the calculation. With indicators based on dynamic models, the memory effect, i.e. the effect of the system state at the beginning of the forecast period, has a strong impact on the skill scores. We propose a methodology based on an ANOVA to qualify this memory effect by using the F-value. It is shown that when the memory effect is strong (F-value >10) the seasonal forecast does not bring any added value compared to the climatological forecast. An evaluation of the interest of a seasonal forecast in a decision-making framework was carried out by an economic approach. We have based our analysis on the decision making based on the forecast of an event. We show that there is a generic relationship between the AUC score and the gain from the forecast. We show that this relationship depends on the frequency of the decision event, the rarer the event the higher the AUC value must be to have a profitable decision. In our case, a decision based on the detection of a tercile leads to a profitable decision in more than half of the indicators while no indicator leads to a profitable decision when it is based on the detection of a quintile.

Published
2022

3. Availability and Integration of Agro-Environmental Data: The French Case

Author

Philippe Debaeke, Jean-Noël Aubertot, Marion Bardy, Patrick Bertuzzi, Julie Constantin, Patrick Durand, Laurence Guichard, Catherine Mignolet, Nicolas Munier-Jolain, Olivier Therond, Jean-Pierre Wigneron, Rémy Ballot, Pierre Cellier, Eric Justes, Frédéric Huard, Christine Le Bas, and Guy Richard

Published
2022

5. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa

Author

Elizabeth A. Meier, Isaac N. Alou, Eckart Priesack, Bruno Basso, Edward Gérardeaux, Heidi Webber, Eric Justes, Michel Giner, Saseendran S. Anapalli, Delphine Deryng, Marcelo Valadares Galdos, Alex C. Ruane, Bouba Sidi Traoré, Dominique Ripoche, Ward Smith, Babacar Faye, Thomas Gaiser, Patrick Bertuzzi, Folorunso M. Akinseye, Dilys S. MacCarthy, Frédéric Baudron, Alain Ndoli, Brian Grant, Claas Nendel, Kenneth J. Boote, Bernardo Maestrini, Louise Leroux, Christian Baron, Tracy E. Twine, Kokou Adambounou Amouzou, Upendra Singh, Sumit Sinha, Amit Kumar Srivastava, Yi Chen, Michael van der Laan, Gerrit Hoogenboom, Marc Corbeels, Dennis Timlin, M. Elsayed, Anthony M. Whitbread, Fulu Tao, Soo-Hyung Kim, Tesfaye Shiferaw Sida, Bahareh Kamali, Jon I. Lizaso, Myriam Adam, Kurt Christian Kersebaum, Peter J. Thorburn, François Affholder, Esther S. Ibrahim, Andrew J. Challinor, Sebastian Gayler, Lajpat R. Ahuja, Gatien N. Falconnier, Cheryl Porter, Fasil Mequanint, Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), University of Florida [Gainesville] (UF), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), University of Ghana, GISS Climate impacts group, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC)-NASA Goddard Space Flight Center (GSFC), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), International Crops Research Institute for the Semi-Arid Tropics [Niger] (ICRISAT), International Crops Research Institute for the Semi-Arid Tropics [Inde] (ICRISAT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Département Environnements et Sociétés (Cirad-ES)

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Mali, 01 natural sciences, exploitant agricole, smallholder farming systems, Leaching (agriculture), uncertainty, General Environmental Science, 2. Zero hunger, Global and Planetary Change, Biomass (ecology), Ecology, U10 - Informatique, mathématiques et statistiques, Rendement des cultures, model intercomparison, Fertilizer, Crop simulation model, crop simulation model, Nitrogen, P40 - Météorologie et climatologie, Climate Change, Climate change, engineering.material, 010603 evolutionary biology, Zea mays, Petite exploitation agricole, ensemble modelling, Environmental Chemistry, Leaf area index, Fertilizers, 0105 earth and related environmental sciences, Changement climatique, Agriculture faible niveau intrants, Nutrient management, Modélisation des cultures, Engrais azoté, Modèle de simulation, 15. Life on land, Agronomy, 13. Climate action, Soil water, engineering, Système d'exploitation agricole, Environmental science, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
Abstract

International audience; Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Published
2020

6. Conceptual basis, formalisations and parameterization of the STICS crop model, second edition

Author

Nicolas Beaudoin, Dominique Ripoche, Strullu, L., Bruno Mary, Marie Launay, Joël Léonard, Patrice Lecharpentier, François Affholder, Patrick Bertuzzi, Samuel Buis, Eric Casellas, Julie Constantin, Dumont, B., Jean-Louis Durand, Inaki Garcia de Cortazar Atauri, Fabien Ferchaud, Anne-Isabelle Graux, Jego, G., Christine Le Bas, Florent Levavasseur, Gaétan Louarn, Alain Mollier, Francoise Ruget, Eric Justes, Transfrontalière BioEcoAgro (Transfrontalière BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Association pour le Suivi Agronomique des Epandages (ASAE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), 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), Université de Liège, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agriculture and Agri-Food [Ottawa] (AAFC), InfoSol (InfoSol), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Transfrontalière BioEcoAgro - UMR 1158 (BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO 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), and Agriculture and Agri-Food (AAFC)

Subjects
[SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2020

7. AgGlob: Workflow for simulation of agronomic models at a global scale

Author

RAYNAL, Helene, Le Bas, Christine, Patrick, Bertuzzi, Cahuzac, Eric, Eric, Casellas, Constantin, Julie, Thomas, Pomeon, Toutain, Benoit, Raynal, Hélène, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), InfoSol (InfoSol), Agroclim (AGROCLIM), Département Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2), AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy, parallel computing, Global simulation, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Crop modeling, Computational modeling, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

8. The PERPHECLIM ACCAF Project – perennial fruit crops and forest phenology evolution facing climatic changes

Author

C. Van Leeuween, I. Garcia de Cortazar-Atauri, Jean-Michel Legave, Helene Raynal, C. Anger, Hendrik Davi, Christian Pichot, Jean-Marc Audergon, Isabelle Chuine, Marc Bonhomme, Eric Duchêne, Patrick Bertuzzi, Sylvain Delzon, Biodiversité, Gènes & Communautés (BioGeCo), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB), Ecophysiologie et Génomique Fonctionnelle de la Vigne (UMR EGFV), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-Institut des Sciences de la Vigne et du Vin (ISVV)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Génétique et Amélioration des Fruits et Légumes (GAFL), Génétique et Biomasse Forestières Orléans (GBFOR), 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), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), 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), 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), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), 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), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), and Ecologie des Forêts Méditerranéennes (URFM)

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Perennial plant, Climate change, Context (language use), Information System, Horticulture, 010603 evolutionary biology, 01 natural sciences, Process Based Models, Databases, Documentation, Information system, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Adaptation (computer science), Phenological Stages, 0105 earth and related environmental sciences, 2. Zero hunger, business.industry, Phenology, Environmental resource management, 15. Life on land, Geography, 13. Climate action, Agriculture, business, Protocols
Abstract

International audience; Phenology is a bio-indicator of climate evolution. Measurements of phenological stages on perennial species provide actually significant illustrations and assessments of the impact of climate change. Phenology is also one of the main key characteristics of the capacity of adaptation of perennial species, generating questions about its consequences on plant growth and development or on fruit quality. Predicting phenology evolution and adaptive capacities of perennial species needs to override three main methodological limitations: 1) existing observations and associated databases are scattered and sometimes incomplete, rendering difficult implementation of multi-site study of genotype-environment interaction analyses 2) there are not common protocols to observe phenological stages 3) access to generic phenological models platforms is still very limited. In this context, the PERPHECLIM project, which is funded by the Adapting Agriculture and Forestry to Climate Change Meta-Program ( ACCAF) from INRA ( French National Institute of Agronomic Research), aims to develop the necessary infrastructure at INRA level ( observatories, information system, modeling tools) to enable partners to study the phenology of various perennial species ( grapevine, fruit trees and forest trees). Currently, the PERPHECLIM project involves 28 research units in France, mainly from INRA institutes. Five activities have been developed: define protocols and observation forms to observe phenology for various species of interest for the project : organize observation training, develop generic modeling solutions to simulate phenology ( Phenological Modelling Platform software and modelling platform solutions), support the building of research projects at national and international levels, develop environment/genotype observation networks for fruit-tree species, and develop an information system to manage data and documentation concerning phenology. Finally, the PERPHECLIM project aims to build strong collaborations with public ( Observatoire des Saisons) and private ( technical institutes) sector partners in order to allow a more direct transfer of knowledge.

Published
2018

9. Simulation of maize evapotranspiration: An inter-comparison among 29 maize models

Author

Amit Kumar Srivastava, Sotirios V. Archontoulis, Qianjing Jiang, Kenneth J. Boote, Delphine Deryng, Sophie Moulin, Jean-Louis Durand, Munir P. Hoffmann, Lajpat R. Ahuja, Sebastian Gayler, Jerry L. Hatfield, Philip Parker, Thomas Gaiser, Dennis Timlin, Tracy E. Twine, Claudio O. Stöckle, Benjamin Dumont, Jon I. Lizaso, Claas Nendel, Soo-Hyung Kim, Kelly R. Thorp, Karina Williams, Christian Baron, Tommaso Stella, Zhiming Qi, Bruno Basso, Heidi Webber, F. Ewert, Taru Palosuo, Fulu Tao, Magali Willaume, Eckart Priesack, Julie Constantin, Patrick Bertuzzi, Bruce A. Kimball, USDA-ARS : Agricultural Research Service, Agronomy Department, University of Florida [Gainesville] (UF), National laboratory for agriculture and the environment, Agricultural Systems Research Unit, USDA, Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, Purdue University [West Lafayette], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Université de Montpellier (UM), Michigan State University [East Lansing], Michigan State University System, 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, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), IRI THESys, Humboldt State University (HSU), Université de Liège, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institute of Crop Science and Resource Conservation [Bonn], Rheinische Friedrich-Wilhelms-Universität Bonn, Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Department of Bioresource Engineering, McGill University = Université McGill [Montréal, Canada], School of Environmental and Forest Sciences, University of Washington [Seattle], Technical University of Madrid, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Spatial Business Integration, Partenaires INRAE, Natural Resources Institute Finland (LUKE), Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Institute of Geographic Sciences and Natural Resources Research, CAS, Crop Systems and Global Change Research Unit, Met Office Hadley Centre, (BMBF, Germany) FKZ031A258B, and German Federal Ministry of Education and Research 01LL1304A

Subjects
0106 biological sciences, Atmospheric Science, Yield, 010504 meteorology & atmospheric sciences, maïs, F60 - Physiologie et biochimie végétale, Crop water use, Eddy covariance, Zea mays, 01 natural sciences, Maize, Simulation, Evapotranspiration, Water Use, Model, Statistics, Range (statistics), [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Squared deviations, 0105 earth and related environmental sciences, Mathematics, 2. Zero hunger, Global and Planetary Change, U10 - Informatique, mathématiques et statistiques, Phenology, Forestry, Évapotranspiration, 13. Climate action, Besoin en eau, Agronomy and Crop Science, Modèle mathématique, Water use, 010606 plant biology & botany
Abstract

International audience; Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006-2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first "blind" phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET.

Published
2019

10. How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield?

Author

Patrick Bertuzzi, Jon I. Lizaso, Jean-Louis Durand, Dennis Timlin, Julián Ramírez Villegas, Fulu Tao, Kurt Christian Kersebaum, Sabine I. Seidel, Lajpat R. Ahuja, Christoph Müller, Delphine Deryng, Amit Kumar Srivastava, Bruno Basso, James W. Jones, Heidi Webber, F. Ewert, Dominique Ripoche, Eckart Priesack, Christian Biernath, Cynthia Rosenzweig, Remy Manderscheid, Alex C. Ruane, Hans Johachim Weigel, Thomas Gaiser, Christian Baron, Claas Nendel, Tracy E. Twine, Enli Wang, Kenneth J. Boote, Saseendran S. Anapalli, Soo-Hyung Kim, Zhigan Zhao, Sebastian Gayler, Florian Heinlein, Albert Olioso, Reimund P. Rötter, Kenel Delusca, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville] (UF), CEIGRAM, Technical University of Madrid, Johann Heinrich von Thünen Institut, NASA Goddard Space Flight Center (GSFC), CPSRU, USDA-ARS : Agricultural Research Service, Department of Geological Sciences, University of Oregon [Eugene], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agroclim (AGROCLIM), Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Computation Institute, Loyola University of Chicago, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Institute of Soil Science and Land Evaluation, Section Biogeophysics, University of Hohenheim, School of Environmental and Forest Sciences, University of Washington [Seattle], Potsdam Institute for Climate Impact Research (PIK), Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), School of Earth and Environment (UWA), The University of Western Australia (UWA), International Center for Tropical Agriculture [Colombie] (CIAT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Natural resources institute Finland, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Crop Systems and Global Change Laboratory, Department of Soil, Water, & Climate, University of Minnesota System, Land and Water, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), China Agricultural University (CAU), University of Florida [Gainesville], Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), UE Agroclim (UE AGROCLIM), Institute of Crop Science and Resource Conservation (INRES), CGIAR Research Program on Climate Change Colombia International Center for Tropical Agriculture (CIAT), Agriculture and Food Security (CCAFS), Natural Resources Institute Finland, China Agricultural University, and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Water supply, Plant Science, 01 natural sciences, modèle de culture, Atmospheric carbon dioxide concentration, Evapotranspiration, Zea Mays, Atmospheric Carbon Dioxide Concentration, Multi-model Ensemble, Stomata Conductance, Grain Number, Water Use, Photosynthèse, Transpiration, 2. Zero hunger, Multi-model ensemble, U10 - Informatique, mathématiques et statistiques, 04 agricultural and veterinary sciences, Rendement des cultures, Stomatal conductance, Irrigation, Grain number, Soil Science, approvisionnement eau, Zea mays, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Leaf area index, weather data, 0105 earth and related environmental sciences, carbonic anhydride, business.industry, culture de mais, Modèle de simulation, 15. Life on land, Évapotranspiration, donnée météorologique, F61 - Physiologie végétale - Nutrition, Agronomy, 13. Climate action, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, business, estimation de rendement, Agronomy and Crop Science, Water use, concentration atmosphérique, Dioxyde de carbone
Abstract

Conference: International Crop Modelling Symposium on Crop Modelling for Agriculture and Food Security under Global Change (iCropM) - Proceedings Paper Berlin, GERMANY 2016; This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thünen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 models was less than 1 Mg ha−1. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.

Published
2017

11. Main lessons drawn from the analysis of the literature

Author

Alexandre Joannon, Julien Tournebize, Françoise Montfort, Carolyne Dürr, Eric Justes, Raphaël Charles, Patrick Bertuzzi, Jean-Pierre Sarthou, Christine Le Bas, Julie Constantin, Nicolas Beaudoin, Laurent Ruiz, Bruno Mary, Veronique Souchere, 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, Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), 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)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-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), Agroressources et Impacts environnementaux (AgroImpact), Institut National de la Recherche Agronomique (INRA), Agroclim (AGROCLIM), Research Institute of Organic Agriculture - Forschungsinstitut für biologischen Landbau (FiBL), Institut de Recherche en Horticulture et Semences (IRHS), 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)-Institut National de la Recherche Agronomique (INRA)-Université d'Angers (UA), Biodiversité agroécologie et aménagement du paysage (UMR BAGAP), Institut National de la Recherche Agronomique (INRA)-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), InfoSol (InfoSol), 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, Sol Agro et hydrosystème Spatialisation (SAS), Sciences pour l'Action et le Développement : Activités, Produits, Territoires (SADAPT), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Université de Toulouse (UT)-Université de Toulouse (UT), Université d'Angers (UA)-Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST-Ecole supérieure d'Agricultures d'Angers (ESA), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, and Institut National de la Recherche Agronomique (INRA)-AgroParisTech

Subjects
porosity, [SDV]Life Sciences [q-bio], Agricultural engineering, 010501 environmental sciences, migration, 01 natural sciences, [SHS]Humanities and Social Sciences, Crop, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], Cover crop, 0105 earth and related environmental sciences, 2. Zero hunger, biomass, Sowing, 04 agricultural and veterinary sciences, clay, 15. Life on land, Soil tillage, Weed control, Mineral nitrogen, sugar, [SDE]Environmental Sciences, 040103 agronomy & agriculture, Erosion, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract

International audience; Planting and destruction are the two main phases in cover crop management techniques. Their dates and implementation conditions play a key role in terms of the various functions targeted for these crops (nitrate-trapping, erosion prevention, weed control, effect on next crop, etc.). The analysis of the literature therefore focused on cover crop planting and destruction techniques. The aim was to identify the different methods and, if possible, their effectiveness with respect to the success of sowing and destruction. The issue of the implementation of these techniques by farmers was then examined by analysing the constraints in terms of organization of labour and implementation costs.

Published
2017

12. Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 29 Maize Models

Author

Kimball, Bruce A., Boote, Kenneth J., Hatfield, Jerry L., Ahuja, Laj R., Stöckle, Claudio O., Archontoulis, Sotiris V., Christian Baron, Bruno Basso, Patrick Bertuzzi, Julie Constantin, Delphine Deryng, Benjamin Dumont, Franck Ewert, Thomas Gaiser, Griffis, Timothy J., Hoffmann, Munir P., Qianjing Jiang, Soo-Hyung Kim, Jon Lizaso, Sophie Moulin, Philip Parker, Taru Palusuo, Zhiming Qi Z., Amit Srivastava, Tao, F., Thorp, K., Dennis Timlin, Heidi Webber, Magali Willaume, Williams, K., Ming Chen, Jean-Louis Durand, Sebastian Gayler, Eckart Priesack, Tracy Twine, USDA-ARS : Agricultural Research Service, Agronomy Department, University of Florida [Gainesville] (UF), Agricultural Systems Research Unit, USDA, Biological Systems Engineering, University of Wisconsin-Madison, Department of Agronomy, Purdue University [West Lafayette], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, 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, Computation Institute, Loyola University of Chicago, Dpt. Agronomy, Bio- Engineering and Chemistry, Crop Science Unit, Université de Liège, Gembloux Agro-Bio Tech [Gembloux], Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Soil, Water, and Climate, University of Minnesota System, Crop Production Systems in the Tropics, Georg-August-University [Göttingen], Department of Bioresource Engineering [Montréal] (BIOENG), McGill University = Université McGill [Montréal, Canada], Center for Urban Horticulture, University of Washington, Dept. Producción Agraria-CEIGRAM, Universidad Politécnica de Madrid (UPM), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Natural resources institute Finland, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Crop Systems and Global Change Research Unit, Climate Adaptation Scientist Meteorological Office, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Department of Soil, Water and Climate, University of Florida [Gainesville], Agricultural Research Service, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), UE Agroclim (UE AGROCLIM), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Georg-August-Universität Göttingen, McGill University, Natural Resources Institute Finland, and United States Department of Agriculture - Agricultural Research Service

Subjects
consommation en eau, comparaison de modèles, [SDV]Life Sciences [q-bio], evapotranspiration, évapotranspiration, culture de mais, croissance des cultures, analyse de rendement, modèle de croissance, caracteristique variétale
Abstract

An important aspect that determines the ability of crop growth models to predict growth and yield is their ability to predict the rate of water consumption or evapotranspiration (ET) of the crop, especially for rain-fed crops. If, for example, the predicted ET rate is too high, the simulated crop may exhaust its soil water supply before the next rain event, thereby causing growth and yield predictions that are too low. In a prior inter-comparison among maize growth models, ET predictions varied widely, but no observations of actual ET were available for comparison. Therefore, another study has been initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). This time observations of ET using the eddy covariance technique from an 8-year-long experiment conducted at Ames, IA are being used as the standard. Simulation results from 29 models have been completed. In the first “blind” phase for which only weather, soils, and management information were furnished to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. A detailed statistical analysis of the daily ET data from 2011, a “typical” rainfall year, showed that, as expected, the median of all the models was more accurate across several criteria (correlation, root mean square error, average difference, regression slope) than any particular model. However, some individual models were better than the median for a particular criteria. Predictions improved somewhat in later stages when the modelers were provided additional leaf area and growth information that allowed them to “calibrate” some of the parameters in their models to account for varietal characteristics, etc.

Published
2016

13. Multi-wheat-model ensemble responses to interannual climate variability

Author

Carlos Angulo, Frank Ewert, Tom M. Osborne, Pramod K. Aggarwal, Senthold Asseng, Pasquale Steduto, Kurt Christian Kersebaum, Eckart Priesack, Patrick Bertuzzi, Roberto C. Izaurralde, Dominique Ripoche, Thilo Streck, Joost Wolf, Pierre Stratonovitch, Alex C. Ruane, Richard Goldberg, Robert F. Grant, Taru Palosuo, Iurii Shcherbak, Kenneth J. Boote, Christian Biernath, Garry O'Leary, J. Hooker, Peter J. Thorburn, Joachim Ingwersen, Soora Naresh Kumar, Lee Heng, Maria I. Travasso, Pierre Martre, Katharina Waha, Nicholas I. Hudson, Claas Nendel, Fulu Tao, Christoph Müller, Andrew J. Challinor, Jørgen E. Olesen, Reimund P. Rötter, Davide Camarrano, L. A. Hunt, Sebastian Gayler, Nadine Brisson, Daniel Wallach, Mikhail A. Semenov, Claudio O. Stöckle, Iwan Supit, Jordi Doltra, Jeffrey W. White, Bruno Basso, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], Agricultural & Biological Engineering Department, University of Florida [Gainesville], The James Hutton Institute, Institute of Crop Science and Resource Conservation, Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, 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), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Research Program on Climate Change, Agriculture and Food Security, International Water Management Institute, International Water Management Institute, Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, Cantabrian Agricultural Research and Training Centre, Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), School of Agriculture, Policy and Development, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Potsdam Institute for Climate Impact Research (PIK), Landscape & Water Sciences, Department of Environment of Victoria, Department of Agroecology, Aarhus University, National Centre for Atmospheric Science, Department of Meteorology, Environmental Impacts Group, Natural Resources Institute Finland, Georg-August-Universität Göttingen, Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council, Institute for Future Environments, Queensland University of Technology, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Earth System Science-Climate Change and Adaptive Land-use and Water Management, Wageningen University and Research Center (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institute for Climate and Water [Castelnar], Instituto Nacional de Tecnología Agropecuaria (INTA), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, USDA-ARS, Arid-Land Agricultural Research Center, United States Department of Agriculture, Plant Production Systems, Modelling European Agriculture with Climate Change for Food Security (MACSUR), JPI FACCE, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn], 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), Agroclim (AGROCLIM), Aarhus University [Aarhus], Natural resources institute Finland, Georg-August-University [Göttingen], Wageningen University and Research [Wageningen] (WUR), 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 National de la Recherche Agronomique (INRA)-AgroParisTech, Natural Resources Institute Finland (LUKE), Georg-August-University = Georg-August-Universität Göttingen, Biotechnology and Biological Sciences Research Council (BBSRC), Queensland University of Technology [Brisbane] (QUT), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Université de Toulouse (UT)-Université de Toulouse (UT), USDA Agricultural Research Service [Maricopa, AZ] (USDA), United States Department of Agriculture (USDA), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Center for Climate Systems Research [New York] ( CCSR ), University of Florida, University of Bonn (Rheinische Friedrich-Wilhelms), Leibniz Centre for Agricultural Landscape Research (ZALF), 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 ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Commonwealth Scientific and Industrial Research Organisation, Department of Geological Sciences and Kellogg Biological Station, Michigan State University, UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), University of Maryland, Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Potsdam Institute for Climate Impact Research ( PIK ), Washington State University ( WSU ), Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), Institute for Climate and Water, and Instituto Nacional de Tecnología Agropecuaria

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Temperature sensitivity, 010504 meteorology & atmospheric sciences, Precipitation, 01 natural sciences, modèle de croissance, wheat, uncertainty, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, climate impacts, 2. Zero hunger, Multi-model ensemble, changement climatique, Ecological Modeling, Temperature, Uncertainty, 04 agricultural and veterinary sciences, PE&RC, Plant Production Systems, Climatology, PRECIPITATION, Climate impacts, Environmental Engineering, interannual variability, Yield (finance), australie, variation interannuelle, Growing season, Climate change, multi-model ensemble, Earth System Science, Interannual variability, blé, température, pays bas, global change, 0105 earth and related environmental sciences, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, précipitation, business.industry, argentine, Simulation modeling, temperature, Global change, 15. Life on land, inde, Climate Resilience, 13. Climate action, Agriculture, Klimaatbestendigheid, Plantaardige Productiesystemen, 040103 agronomy & agriculture, AgMIP, 0401 agriculture, forestry, and fisheries, Environmental science, Leerstoelgroep Aardsysteemkunde, Crop modeling, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, Software
Abstract

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2???0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Compares interannual climate response of 27 wheat models at four locations.Calculates the diminishing return of constructing multi-model ensembles for assessment.Identifies similarities and major differences of model responses.Differentiates between interannual temperature sensitivity and climate change response.

Published
2016

14. Broadening the scope for ecoclimatic indicators to assess crop climate suitability according to ecophysiological, technical and quality criteria

Author

Frédéric Huard, Nathalie de Noblet-Ducoudré, Anne-Isabelle Graux, Patrick Bertuzzi, Marie Launay, Julie Caubel, Iñaki García de Cortázar-Atauri, Environnement et Grandes Cultures (EGC), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Chercheur indépendant, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), UR 0874 Unité de recherche sur l'Ecosystème Prairial, Institut National de la Recherche Agronomique (INRA)-Unité de recherche sur l'Ecosystème Prairial (UREP)-Ecologie des Forêts, Prairies et milieux Aquatiques (EFPA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Agroclim (AGROCLIM), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Unité de recherche sur l'Ecosystème Prairial (UREP), French National Research Agency within the ORACLE (Opportunities and Risks of Agrosystems & forests in response to CLimate, socio-economic and policy changEs in France) project, Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
0106 biological sciences, Atmospheric Science, Crop phenology, 010504 meteorology & atmospheric sciences, media_common.quotation_subject, 01 natural sciences, Crop, Quality (business), Crop management, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, media_common, 2. Zero hunger, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Global and Planetary Change, Scope (project management), business.industry, Environmental resource management, Forestry, 15. Life on land, Adaptation strategies, 13. Climate action, Agriculture, Assessment methods, Environmental science, business, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

International audience; The cultivation of crops in a given area is highly dependent of climatic conditions. Assessment of how the climate is favorable is highly useful for planners, land managers, farmers and plant breeders who can propose and apply adaptation strategies to improve agricultural potentialities. The aim of this study was to develop an assessment method for crop-climate suitability that was generic enough to be applied to a wide range of issues and crops. The method proposed is based on agroclimatic indicators that are calculated over phenological periods (ecoclimatic indicators). These indicators are highly relevant since they provide accurate information about the effect of climate on particular plant processes and cultural practices that take place during specific phenological periods. Three case studies were performed in order to illustrate the potentialities of the method. They concern annual (maize and wheat) and perennial (grape) crops and focus on the study of climate suitability in terms of the following criteria: ecophysiological, days available to carry out cultural practices, and harvest quality. The analysis of the results revealed both the advantages and limitations of the method. The method is general and flexible enough to be applied to a wide range of issues even if an expert assessment is initially needed to build the analysis framework.The limited number of input data makes it possible to use it to explore future possibilities for agriculture in many areas. The access to intermediate information through elementary ecoclimatic indicators allows users to propose targeted adaptations when climate suitability is not satisfactory.

Published
2015

15. The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Author

Sonali P. McDermid, Alexander C. Ruane, Cynthia Rosenzweig, Nicholas I. Hudson, Monica D. Morales, Prabodha Agalawatte, Shakeel Ahmad, L. R. Ahuja, Istiqlal Amien, Saseendran S. Anapalli, Jakarat Anothai, Senthold Asseng, Jody Biggs, Federico Bert, Patrick Bertuzzi, Virender S. Bhatia, Marco Bindi, Ian Broad, Davide Cammarano, Ramiro Carretero, Ashfaq Ahmad Chattha, Uran Chung, Stephanie Debats, Paola Deligios, Giacomo De Sanctis, Thanda Dhliwayo, Benjamin Dumont, Lyndon Estes, Frank Ewert, Roberto Ferrise, Thomas Gaiser, Guillermo Garcia, Sika Gbegbelegbe, Vellingiri Geethalakshmi, Edward Gerardeaux, Richard Goldberg, Brian Grant, Edgardo Guevara, Jonathan Hickman, Holger Hoffmann, Huanping Huang, Jamshad Hussain, Flavio Barbosa Justino, Asha S. Karunaratne, Ann-Kristin Koehler, Patrice K. Kouakou, Soora Naresh Kumar, Arunachalam Lakshmanan, Mark Lieffering, Xiaomao Lin, Qunying Luo, Graciela Magrin, Marco Mancini, Fabio Ricardo Marin, Anna Dalla Marta, Yuji Masutomi, Theodoros Mavromatis, Greg McLean, Santiago Meira, Monoranjan Mohanty, Marco Moriondo, Wajid Nasim, Lamyaa Negm, Francesca Orlando, Simone Orlandini, Isik Ozturk, Helena Maria Soares Pinto, Guillermo Podesta, Zhiming Qi, Johanna Ramarohetra, Muhammad Habib ur Rahman, Helene Raynal, Gabriel Rodriguez, Reimund Rötter, Vaishali Sharda, Lu Shuo, Ward Smith, Val Snow, Afshin Soltani, K. Srinivas, Benjamin Sultan, Dillip Kumar Swain, Fulu Tao, Kindie Tesfaye, Maria I. Travasso, Giacomo Trombi, Alex Topaj, Eline Vanuytrecht, Federico E. Viscarra, Syed Aftab Wajid, Enli Wang, Hong Wang, Jing Wang, Erandika Wijekoon, Lee Byun-Woo, Yang Xiaoguang, Ban Ho Young, Jin I. Yun, Zhigan Zhao, and Lareef Zubair

Subjects
P40 - Météorologie et climatologie, U10 - Informatique, mathématiques et statistiques, business.industry, Impact assessment, Crop yield, Environmental resource management, Climate change, Water resources, F01 - Culture des plantes, Agriculture, Soil retrogression and degradation, Environmental science, Climate model, business, Environmental planning, Downscaling
Abstract

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)...

Published
2015

16. Multimodel ensembles of wheat growth: many models are better than one

Author

Pierre Martre, Bruno Basso, James W. Jones, Jordi Doltra, Garry O'Leary, Pramod K. Aggarwal, Christian Biernath, Jeffrey W. White, Sebastian Gayler, R. Goldberg, Eckart Priesack, Robert F. Grant, Nadine Brisson, Patrick Bertuzzi, Thilo Streck, Daniel Wallach, Joachim Ingwersen, Davide Cammarano, J. Hooker, Fulu Tao, Christoph Müller, Carlos Angulo, Soora Naresh Kumar, Claas Nendel, Jørgen E. Olesen, Lee Heng, Maria I. Travasso, Iurii Shcherbak, Mikhail A. Semenov, Claudio O. Stöckle, Tom M. Osborne, L. A. Hunt, Alex C. Ruane, Frank Ewert, Kenneth J. Boote, Andrew J. Challinor, Reimund P. Rötter, Iwan Supit, Jerry L. Hatfield, Roberto C. Izaurralde, Senthold Asseng, Cynthia Rosenzweig, Pasquale Steduto, Kurt Christian Kersebaum, Dominique Ripoche, Peter J. Thorburn, Pierre Stratonovitch, Joost Wolf, Katharina Waha, Taru Palosuo, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), 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, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation, Rheinische Friedrich-Wilhelms-Universität Bonn, Plant Production Research, Agrifood Research Finland, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), National Laboratory for Agriculture and Environment, Consultative Group on International Agricultural Research, Research Program on ClimateChange, Agriculture and Food Security, International Water Management Institute, Department of Geological Sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Soil Ecology, German Research Center for Environmental Health, Helmholtz-Zentrum München (HZM), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, School of Earth and Environment [Leeds] (SEE), University of Leeds, Cantabrian Agricultural Research and Training Centre, Water & Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), School of Agriculture, Policy and Development, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, Universität Stuttgart, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Primary Industries, Landscape & Water Sciences, Université Paris Diderot - Paris 7 (UPD7), Department of Agroecology, Aarhus University [Aarhus], National Centre for Atmospheric Science, Department of Meteorology, Institute of Soil Ecology German Research Center for Environmental Health, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization of the United Nations, Washington State University (WSU), University of Hohenheim, Wageningen University and Research Centre [Wageningen] (WUR), Institute Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), CIRN, Institute forClimate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Arid-Land Agricultural Research Center, United States Department of Agriculture, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn], Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Wageningen University and Research [Wageningen] (WUR), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ) -Institut National de la Recherche Agronomique ( INRA ), Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, University of Bonn (Rheinische Friedrich-Wilhelms), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Commonwealth Scientific and Industrial Research Organisation, Kellogg Biological Station, UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Helmholtz-Zentrum München ( HZM ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, School of Earth and Environment [Leeds] ( SEE ), University of Alberta [Edmonton], International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Leibniz Centre for Agricultural Landscape Research, Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Washington State University ( WSU ), Wageningen University and Research Centre [Wageningen] ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Instituto Nacional de Tecnología Agropecuaria, Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Helmholtz Zentrum München = German Research Center for Environmental Health, and Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC)

Subjects
Calibration (statistics), Climate, Statistics, process-based model, grain, uncertainty, Triticum, General Environmental Science, Mathematics, 2. Zero hunger, Global and Planetary Change, Ecology, Mathematical model, Estimator, PE&RC, simulation, ecophysiological model, Europe, [ SDE.MCG ] Environmental Sciences/Global Changes, model intercomparison, Plant Production Systems, Wheat (Triticum aestivum L.), climate-change, wheat (Triticum aestivum L.), Centre for Crop Systems Analysis, impact, Seasons, simulations, europe, ensemble modeling, Climate Change, [SDE.MCG]Environmental Sciences/Global Changes, australia, crop production, Environment, Models, Biological, Consistency (statistics), Approximation error, Environmental Chemistry, Alterra - Centrum Bodem, impacts, Hydrology, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, Ensemble forecasting, Crop yield, Simulation modeling, Soil Science Centre, Australia, yield, calibration, Climate Resilience, 13. Climate action, Klimaatbestendigheid, Plantaardige Productiesystemen, biodiversity conservation, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Environmental Sciences, billion
Abstract

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Published
2015

17. Statistical analysis of large simulated yield datasets for studying climate change effects

Author

David Makowski, Senthold Asseng, Frank Ewert, Simona Bassu, Jean-Louis Durand, Pierre Martre, Myriam Adam, Pramod K. Aggarwal, Carlos Angulo, Christian Baron, Bruno Basso, Patrick Bertuzzi, Christian Biernath, Hendrik Boogaard, Kenneth J. Boote, Nadine Brisson, Davide Cammarano, Andrew J. Challinor, Sjakk J. G. Conijn, Marc Corbeels, Delphine Deryng, Giacomo De Sanctis, Jordi Doltra, Sebastian Gayler, Richard Goldberg, Patricio Grassini, Jerry L. Hatfield, Lee Heng, Steven Hoek, Josh Hooker, Tony L. A. Hunt, Joachim Ingwersen, Cesar Izaurralde, Raymond E. E. Jongschaap, James W. Jones, Armen R. Kemanian, Christian Kersebaum, Soo-Hyung Kim, Jon Lizaso, Christoph Müller, Naresh S. Kumar, Claas Nendel, Garry J. O'Leary, Jorgen E. Olesen, Tom M. Osborne, Taru Palosuo, Maria V. Pravia, Eckart Priesack, Dominique Ripoche, Cynthia Rosenzweig, Alexander C. Ruane, Fredirico Sau, Mickhail A. Semenov, Iurii Shcherbak, Pasquale Steduto, Claudio Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Edmar I. Teixeira, Peter Thorburn, Denis Timlin, Maria Travasso, Reimund Rötter, Katharina Waha, Daniel Wallach, Jeffrey W. White, Jimmy R. Williams, Joost Wolf, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, University of Florida [Gainesville] (UF), Rheinische Friedrich-Wilhelms-Universität Bonn, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), 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), Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Michigan State University [East Lansing], Michigan State University System, Agroclim (AGROCLIM), German Research Center for Environmental Health, Centre for Geo-Information, University of Leeds, International Center for Tropical Agriculture, Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Sciences [Changchun Branch] (CAS), University of East Anglia, Catabrian Agricultural Research and Training Center (CIFA), University of Tübingen, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), University of Nebraska [Lincoln], University of Nebraska System, National Laboratory for Agriculture and Environment, International Atomic Energy Agency [Vienna] (IAEA), University of Reading (UOR), University of Guelph, University of Hohenheim, Joint Global Change Research Institute, Instituto Nacional de Investigación Agropecuaria (INIA), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), University of Washington, Universidad Politécnica de Madrid (UPM), Potsdam Institute for Climate Impact Research (PIK), Indian Agricultural Research Institute (IARI), Department of Environment and Primary Industries, Landscape and Water Sciences, Aarhus University [Aarhus], Agrifood Research Finland, Pennsylvania State University (Penn State), Penn State System, Rothamsted Research, FAO Sub-regional Office for Eastern Africa [Addis Ababa, Ethiopie] (FAO), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Washington State University (WSU), Plant & Food Research, Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS : Agricultural Research Service, Institute for Climate and Water, 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, Arid-Land Agricultural Research Center, Texas A&M University System, Hillel, D., Rosenzweig, C., Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, University of Florida, University of Bonn (Rheinische Friedrich-Wilhelms), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Université Clermont Auvergne ( UCA ), Université Blaise Pascal (Clermont Ferrand 2) ( UBP ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement, CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Michigan State University, UE Agroclim ( UE AGROCLIM ), Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), Catabrian Agricultural Research and Training Center ( CIFA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Nebraska Lincoln ( UNL ), International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Instituto Nacional de Investigación Agropecuaria, Leibniz Centre for Agricultural Landscape Research, Universidad Politécnica de Madrid ( UPM ), Potsdam Institute for Climate Impact Research ( PIK ), Indian Agricultural Research Institute ( IARI ), Aarhus University, PennState University [Pennsylvania] ( PSU ), Food and Agricultural Organization ( FAO ), Washington State University ( WSU ), New Zealand Institute for Plant and Food Research Limited, Commonwealth Scientific and Industrial Research Organisation, United States Department of Agriculture - Agricultural Research Service, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Texas A and M University ( TAMU ), AgroParisTech-Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville], Génétique Diversité et Ecophysiologie des Céréales - Clermont Auvergne (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Université Blaise Pascal (Clermont Ferrand 2) (UBP), UE Agroclim (UE AGROCLIM), Wageningen University and Research Center (WUR), Food and Agricultural Organization (FAO), Helmholtz Zentrum München = German Research Center for Environmental Health, University of East Anglia [Norwich] (UEA), University of Nebraska–Lincoln, Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)

Subjects
analyse de données, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Earth Observation and Environmental Informatics, 010504 meteorology & atmospheric sciences, Yield (finance), data analysis, Climate change, 01 natural sciences, Agro Water- en Biobased Economy, statistical analysis, Effects of global warming, Aardobservatie en omgevingsinformatica, Life Science, Alterra - Centrum Bodem, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, global change, 0105 earth and related environmental sciences, 2. Zero hunger, changement climatique, WIMEK, Mathematical model, analyse statistique, Crop yield, Soil Science Centre, Global change, Statistical model, 04 agricultural and veterinary sciences, 15. Life on land, PE&RC, Climate resilience, Climate Resilience, Plant Production Systems, Klimaatbestendigheid, 13. Climate action, Plantaardige Productiesystemen, Climatology, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.

Published
2015

18. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

Author

Joost Wolf, Xinyou Yin, Pierre Martre, Zhengtao Zhang, H. K. Soo, Manuel Marcaida, Nadine Brisson, Patrick Bertuzzi, Soo-Hyung Kim, Yan Zhu, Roberto C. Izaurralde, L. A. Hunt, Maria I. Travasso, Christian Baron, James W. Jones, R.E.E. Jongschaap, T. Palosuo, Daniel Wallach, Jerry L. Hatfield, Christian Biernath, G. De Sanctis, Senthold Asseng, H. Yoshida, Donald S. Gaydon, Edmar Teixeira, Davide Cammarano, Alex C. Ruane, C. Nendel, T. Hasegawa, Thilo Streck, Garry O'Leary, Upendra Singh, Frank Ewert, Delphine Deryng, R. Goldberg, Bas A. M. Bouman, Peter J. Thorburn, Tao Li, Roberto Confalonieri, Myriam Adam, Jes Olesen, Reimund P. Rötter, Tamon Fumoto, Patricio Grassini, Joachim Ingwersen, Robert F. Grant, Katharina Waha, James Williams, Fulu Tao, Eckart Priesack, Pramod K. Aggarwal, Liang Tang, Sebastian Gayler, Jordi Doltra, L. Heng, Christoph Müller, J.G. Conijn, Iwan Supit, S. Naresh Kumar, Iurii Shcherbak, Jeffrey W. White, Hendrik Boogaard, Kenneth J. Boote, David Makowski, Federico Sau, Jean-Louis Durand, Mikhail A. Semenov, Claudio O. Stöckle, Marc Corbeels, Steven Hoek, Simone Bregaglio, Hiroshi Nakagawa, Philippe Oriol, Anthony Challinor, R. A. Kemanian, Carlos Angulo, Pasquale Steduto, Bruno Basso, Kurt Christian Kersebaum, Cynthia Rosenzweig, Dennis Timlin, J. Hooker, Samuel Buis, Maria Virginia Pravia, Françoise Ruget, Dominique Ripoche, Simona Bassu, Pierre Stratonovitch, Jon I. Lizaso, Balwinder Singh, Tom M. Osborne, Paul W. Wilkens, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Int Rice Res Inst, Los Banos, Philippines, Université Paris Diderot - Paris 7 (UPD7), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), International Water Management Institute, Research Program on Climate Change, Agriculture and Food Security, CGIAR, Institute of Crops Science and Resource Conservation INRES, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Geological Sciences [East Lansing], Agroclim (AGROCLIM), German Research Center for Environmental Health, Institute of Soil Ecololgy, Helmholtz-Zentrum München (HZM), Center for Geo-information, Alterra, Department of Agronomy, University of Florida [Gainesville] (UF), Cassandra Lab, University of Milan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The James Hutton Institute, CGIAR ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, School of Earth and Environment [Leeds] (SEE), University of Leeds, Plant Research International, Wageningen University and Research [Wageningen] (WUR), Embrapa Cerrados, Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Tyndall Centre for Climate Change Research, School of Environmental Science, University of East Anglia [Norwich] (UEA), European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Tsukuba, National Institute of Agro-Environmental Sciences (NIAES), Agriculture Flagship, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), WESS Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Departement of Renewable Resources, University of Alberta, Department of Agronomy and Horticulture, University of Nebraska [Lincoln], University of Nebraska System-University of Nebraska System, National Laboratory for Agriculture and Environment, International Atomic Energy Agency [Vienna] (IAEA), Centre for Geo-Information, Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, 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, Instituto Nacional de Investigación Agropecuaria (INIA), Institute of Landscape System Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), College of the Environment, School of Environmental and Forest Sciences, University of Washington, Department Produccion Vegetal, Fitotecnia, Universidad Politécnica de Madrid (UPM), Potsdam Institute for Climate Impact Research (PIK), National Agriculture and Food Research Organization (NARO), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Department of Economic Development Jobs, Transport and Resources, Grains Innovation Park, Department of Agroecology, Aarhus University [Aarhus], Walker Institute, NCAS Climate, Natural Resources Institute Finland, Department of Plant Science, Pennsylvania State University (Penn State), Penn State System-Penn State System, German Research Center for Environmental Health, Institute of Soil Ecology, Department Biologia Vegetal, Computational and Systems Biology Department, Rothamsted Research, Department of Geological Sciences and W.K. Kellogg Biological Station, International Maize and Wheat Improvement Centre [Inde] (CIMMYT), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), International Fertilizer Development Center (IFDC), College of the Environment, School of Environmental and Forest Science, University of Washington [Seattle], FAO Sub-regional Office for Eastern Africa [Addis Ababa, Ethiopie] (FAO), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Biological Systems Engineering, Washington State University (WSU), Plant Production Systems and Earth System Science, National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Sustainable Production, Plant & Food Research, ARS Crop Systems and Global Change Laboratory, United States Department of Agriculture, CIRN, Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Agriculture, Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Arid-Land Agricultural Research Center, Texas AgriLife Research and Extension, Texas A&M University System, Centre for Crop Systems Analysis, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), Metaprogramme ACCAF, 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), Helmholtz Zentrum München = German Research Center for Environmental Health, Università degli Studi di Milano = University of Milan (UNIMI), University of Nebraska–Lincoln, Université de Toulouse (UT)-Université de Toulouse (UT), Natural Resources Institute Finland (LUKE), Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Nanjing Agricultural University (NAU), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Agricultural and Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation INRES, International Rice Research Institute, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), UE Agroclim (UE AGROCLIM), Wageningen University and Research Centre [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (Cirad-Persyst-UPR 115 AIDA), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Eberhard Karls Universität Tübingen, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), National Agriculture and Food Research Organization, International Maize and Wheat Improvement Centre (CIMMYT), Food and Agricultural Organization (FAO), New Zealand Institute for Plant and Food Research Limited, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Beijing Normal University, Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, University of Bonn (Rheinische Friedrich-Wilhelms), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), 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 ), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Territoires, Environnement, Télédétection et Information Spatiale ( UMR TETIS ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ) -AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), Department of Geological Sciences, W.K. Kellogg Biological Station, Michigan State Univ, Dept Geol Sci, E Lansing, MI 48823 USA, UE Agroclim ( UE AGROCLIM ), Helmholtz-Zentrum München ( HZM ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Institut National de la Recherche Agronomique ( INRA ), Invergowrie, School of Earth and Environment [Leeds] ( SEE ), Wageningen University and Research Centre [Wageningen] ( WUR ), Agro-ecologyand Sustainable Intensification of Annual Crops, Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), University of East Anglia [Norwich] ( UEA ), European Commission - Joint Research Centre [Ispra] ( JRC ), National Institute for Agro-Environmental Sciences, Commonwealth Scientific and Industrial Research Organisation, NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Nebraska-Lincoln, International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), UMR 1248 Agrosystèmes et Développement Territorial (AGIR), Agro-ecology and Sustainable Intensification of Annual Crops, Instituto Nacional de Investigación Agropecuaria, Leibniz Centre for Agricultural Landscape Research, Universidad Politécnica de Madrid ( UPM ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), PennState University [Pennsylvania] ( PSU ), W.K. Kellogg Biological Station, Department of Geological Sciences, International Maize and Wheat Improvement Centre ( CIMMYT ), International Fertilizer Development Center ( IFDC ), Food and Agricultural Organization ( FAO ), Washington State University ( WSU ), Instituto Nacional de Tecnología Agropecuaria, Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, and Texas A and M University ( TAMU )

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, F62 - Physiologie végétale - Croissance et développement, 01 natural sciences, Statistics, Aardobservatie en omgevingsinformatica, Climate change, Crop model, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, Triticum, Mathematics, 2. Zero hunger, Global and Planetary Change, Mathematical model, Air, Forestry, Regression analysis, 04 agricultural and veterinary sciences, PE&RC, [ SDE.MCG ] Environmental Sciences/Global Changes, Rendement des cultures, Plant Production Systems, Statistical model, Modèle mathématique, Atmosphère, Earth Observation and Environmental Informatics, Yield, Crop Physiology, P40 - Météorologie et climatologie, [SDE.MCG]Environmental Sciences/Global Changes, Oryza sativa, Zea mays, Earth System Science, Emulator, Agro Water- en Biobased Economy, Alterra - Centrum Bodem, Precipitation, Croissance, 0105 earth and related environmental sciences, Meta-model, Changement climatique, Hydrology, Modélisation des cultures, Crop yield, Simulation modeling, Soil Science Centre, 15. Life on land, Température, Laboratorium voor Phytopathologie, Climate Resilience, 13. Climate action, Klimaatbestendigheid, Yield (chemistry), Plantaardige Productiesystemen, Laboratory of Phytopathology, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Leerstoelgroep Aardsysteemkunde, Plante de culture, Agronomy and Crop Science, Dioxyde de carbone
Abstract

Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. (C) 2015 Elsevier B.V. All rights reserved.

Published
2015

19. PERPHECLIM ACCAF Project perennial fruit crops and forest phenology evolution facing climatic changes

Author

Iñaki Garcia de Cortazar-Atauri, Jean Marc Audergon, Patrick Bertuzzi, Christel Anger, Marc Bonhomme, Isabelle Chuine, Hendrik Davi, Sylvain Delzon, Eric Duchêne, Jean-Michel Legave, Helene Raynal, Christian Pichot, Cornelis van Leeuwen, Perpheclim Team, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Génétique et Amélioration des Fruits et Légumes (GAFL), Génétique et Biomasse Forestières Orléans (GBFOR), 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), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), 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), 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), Ecologie des Forêts Méditerranéennes (URFM), Biodiversité, Gènes & Communautés (BioGeCo), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB), Santé de la vigne et qualité du vin (SVQV), Institut National de la Recherche Agronomique (INRA)-Université de Strasbourg (UNISTRA), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), 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), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Ecophysiologie et Génomique Fonctionnelle de la Vigne (UMR EGFV), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-Institut des Sciences de la Vigne et du Vin (ISVV)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Métaprogramme ACCAF, 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]), Institut National de la Recherche Agronomique (INRA)-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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and ProdInra, Migration

Subjects
modelling, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, changement climatique, phenologie forestière, blooming period, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, adaptation, fruit, phenology, ComputingMilieux_MISCELLANEOUS, abiotic stresses
Abstract

International audience; Phenology is a bio-indicator of climate evolutions. Measurements of phenological stages on perennial species provide actually significant illustrations and assessments of the impact of climate change. Phenology is also one of the main key characteristics of the capacity of adaptation of perennial species, generating questions about their consequences on plant growth and development or on fruit quality. Predicting phenology evolution and adaptative capacities of perennial species needs to override three main methodological limitations: 1) existing observations and associated databases are scattered and sometimes incomplete, rendering difficult the implementation of multi-site study of genotype-environment interaction analyses; 2) there are no common protocols to observe phenological stages; 3) access to generic phenological models platforms is still very limited. In this context, the PERPHECLIM project, which is funded by the Adapting Agriculture and Forestry to Climate Change Meta-Program (ACCAF) from INRA, has the objective to develop the necessary infrastructure at INRA level (observatories, information system, modeling tools) to enable partners to study the phenology of various perennial species (grapevine, fruit trees and forest trees). Currently the PERPHECLIM project involves 27 research units in France. The main activities currently developed are: defining protocols and observation forms to observe phenology for various species of interest for the project; organizing observation training; developing generic modeling solutions to simulate phenology; supporting the building of research projects at national and international level; developing environment/genotype observation networks for fruit trees species; developing an information system managing data and documentation concerning phenology. Finally, PERPHECLIM aims to build strong collaborations with public (Observatoire des Saisons) and private sector partners (technical institutes) in order to allow a more direct transfer of knowledge.

Published
2015

20. Understanding dormancy release in apricot flower buds ([i]Prunus armeniaca[/i] L.) using several process-based phenological models

Author

Jean-Marc Audergon, Raffaella Viti, L. Andreini, Jean Michel Legave, Patrick Bertuzzi, Isabelle Chuine, José Antonio Campoy, David Ruiz, Iñaki García de Cortázar-Atauri, Susanna Bartolini, Unité de recherche Génétique et amélioration des fruits et légumes (GALF), Institut National de la Recherche Agronomique (INRA), UE Agroclim (UE AGROCLIM), Department of Agriculture, Food and Environment, Sant’Anna School of Advanced Studies, Spanish National Research Council (CSIC), Biologie du fruit et pathologie (BFP), Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), 'MAP C1-2009-Abricotier/CEP Innovation Project' (France), Génétique et Amélioration des Fruits et Légumes (GAFL), Agroclim (AGROCLIM), Scuola Universitaria Superiore Sant'Anna [Pisa] (SSSUP), 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), Architecture et Fonctionnement des Espèces Fruitières [AGAP] (AFEF), 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)-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 de la Recherche Agronomique (INRA)-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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and 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)-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)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, Atmospheric Science, dormancy, 010504 meteorology & atmospheric sciences, Prunus armeniaca, Dormancy Modelling Phenology Prunus armeniaca Genetic variability, phenology, 01 natural sciences, modelling, Species level, Botany, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Cultivar, [SDV.BDD]Life Sciences [q-bio]/Development Biology, 0105 earth and related environmental sciences, Global and Planetary Change, biology, Bud, Phenology, food and beverages, Forestry, penetic variability, biology.organism_classification, Horticulture, Release date, Dormancy, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières; The present study was an initial attempt to calibrate a phenological process-based model of dormancy release with experimental data for apricot flower buds. A large experimental database (88 data points) on dormancy release, concerning several cultivars grown at different geographical sites, was used for the model parameterization. We compared five phenological models. None of them provided accurate prediction of the date of dormancy release at the species level. This inaccuracy appeared to be due to the high variance in dormancy release dates among cultivars. Models fitted for different dormancy release precocity groups provided much more accurate predictions. Parameter estimate analysis of the best model for each cultivar group showed very marked differences in apricot flower bud response to temperature within the species. While in early cultivars dormancy release seemed to be driven by the daily minimum temperature, the daily mean temperature appeared to be the controlling factor in intermediate and late cultivars. Our results show that the apricot dormancy release date cannot be predicted accurately at the species level and that different models should be used for different precocity groups.

Published
2014

21. Environmental Consequences of Apple Waste Deposition on Soil

Author

V. Bouisson, Patrick Bertuzzi, Vincent Valles, B. Cabibel, Ghislain Sevenier, Laurent Bruckler, A. M. de Cockborne, ProdInra, Migration, Unité de Science du Sol, and Institut National de la Recherche Agronomique (INRA)

Subjects
[SDE] Environmental Sciences, Biogeochemical cycle, Malus, Environmental Engineering, Denitrification, [SDV]Life Sciences [q-bio], Alkalinity, chemistry.chemical_element, 010501 environmental sciences, Management, Monitoring, Policy and Law, 01 natural sciences, Waste Management and Disposal, Dissolution, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Water Science and Technology, 2. Zero hunger, biology, Environmental engineering, 04 agricultural and veterinary sciences, Biodegradable waste, 15. Life on land, biology.organism_classification, Pollution, Nitrogen, 6. Clean water, [SDV] Life Sciences [q-bio], Deposition (aerosol physics), chemistry, GEOCHIMIE, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract

In the Provence Alpes Cote d'Azur region (France), overproduction of apple (Malus domestica Borkh.) can result in a significant surplus (0-230 000 Mg per year between 1986 and 1995) which has to be used or destroyed. Among the possibilities for disposing of overproduction, land application is flexible and inexpensive. A field study was undertaken to identify the environmental risks and main biogeochemical processes involved when apple waste is applied to farm land. Three experimental plots receiving 0, 500, or 1000 Mg of apple ha -1 were studied. Soil solutions were collected at three depths (0-0.3, 0.3-0.6, and 0.6-0.9 m) at different times, pH and Eh were measured, major ions, metals, and total and carbonated alkalinity were analyzed. Rapid pH and Eh decrease (from 7.0-5.5 for the pH and from +500 to -300 mV for the redox potential) were observed. The soil became reduced, thus causing the dissolution of Mn. Nitrogen immobilization and denitrification resulting from waste application explained NO - 3 depletion in the soil. The formation of organic anions complexed the metals and major cations. Finally, the oxidation of the soil during drying created conditions which polluted less, emphasizing the importance of the choice of season for land application of organic waste.

Published
1999

22. Estimation of energy fluxes from thermal infrared, spectral reflectances, microwave data and SVAT modeling

Author

P. Bessemoulin, Patrick Bertuzzi, K. Bergaoui, Habiba Chauki, Albert Olioso, Jean-Pierre Wigneron, Andre Chanzy, J.-C. Clavet, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), and Unité de Science du Sol

Subjects
Canopy, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Energy balance, 02 engineering and technology, Vegetation, 15. Life on land, 01 natural sciences, Physics::Geophysics, Atmosphere, [SDE]Environmental Sciences, Radiative transfer, Astrophysics::Solar and Stellar Astrophysics, General Earth and Planetary Sciences, Environmental science, Leaf area index, 020701 environmental engineering, Water content, ComputingMilieux_MISCELLANEOUS, Microwave, 0105 earth and related environmental sciences, Remote sensing
Abstract

The ALiBi model, a Soil-Vegetation-Atmosphere Transfer model, was developed to simulate mass and energy exchanges between vegetation canopies, the soil and the atmosphere. In the present work, it was used in conjunction with remote sensing data in thermal, solar and microwave domains to assess energy balance of various vegetation canopies (two soybean crops, one wheat crop and one fallow canopy). Inversions of radiative transfer formulations were performed to retrieve model parameters: -i) canopy structure parameters (leaf area index and canopy height) from red and near-infrared reflectances, when available; -ii) surface soil moisture from microwave measurements, when available and -iii) plant water transfer parameters from thermal infrared measurements. The estimates of energy balance fluxes were in good agreement with measured values, in particular for well-developed canopies. It can be noticed that it was not necessary to know accurately surface soil moisture and leaf area index to assess energy balance fluxes. Conversely, the vegetation height must be known with a good accuracy.

Published
1999

23. Estimating the root-zone soil moisture from the combined use of time series of surface soil moisture and SVAT modelling

Author

Albert Olioso, Jean-Christophe Calvet, Andre Chanzy, Jean-Pierre Wigneron, Patrick Bertuzzi, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), and Unité de Science du Sol

Subjects
Surface (mathematics), 010504 meteorology & atmospheric sciences, télédétection, [SDV]Life Sciences [q-bio], 0207 environmental engineering, MICROONDES, Soil science, réserve en eau de la plante, 02 engineering and technology, 01 natural sciences, Thermal, humidité du sol, 020701 environmental engineering, Water content, ComputingMilieux_MISCELLANEOUS, modélisation, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, système racinaire, Series (mathematics), Vegetation, 15. Life on land, transfert sol végétation atmosphère, Root zone soil moisture, Remote sensing (archaeology), [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, Microwave
Abstract

This work investigates the possibility to retrieve the root zone soil moisture from the combined use of time series of surface soil moisture, that can be estimated from microwave remote sensing instruments, and Soil-Vegetation-Atmosphere-Transfer (SVAT) modeling. The analysis is based on the ISBA surface scheme and two data sets acquired over' soybean crops, in 1989 and 1990. They include very detailed measurements of soil and vegetation characteristics, mass and energy transfers in the soil-plant-atmosphere system and crop remote sensing signatures in the thermal and microwave domains during a 2- or 3-month period. The 3-month experiment in 1989, made it possible to investigate the accuracy of the soil reservoir retrievals, as a function of the time period and repetitivity of the surface measurements included in the retrieval process. This work contributes to a better definition of the requirements for the use of remotely-sensed microwave observations of surface soil moisture. This is a crucial problem, if we consider the increasing potential of microwave remote sensing technology and the fundamental needs in atmospheric and hydrological models for water transfer characterization in soil.

Published
1999

24. Uncertainty in simulating wheat yields under climate change

Author

J. Hooker, Pramod K. Aggarwal, Joost Wolf, Pierre Martre, Iurii Shcherbak, L. A. Hunt, Kenneth J. Boote, L. Heng, James W. Jones, Jerry L. Hatfield, Katharina Waha, Christian Biernath, Iwan Supit, Eckart Priesack, Pasquale Steduto, S. Naresh Kumar, Davide Cammarano, Joachim Ingwersen, Kurt Christian Kersebaum, Fulu Tao, Christoph Müller, Jordi Doltra, Thilo Streck, Senthold Asseng, Alex C. Ruane, Jeffrey W. White, Roberto C. Izaurralde, Tom M. Osborne, Patrick Bertuzzi, Sebastian Gayler, Andrew J. Challinor, Taru Palosuo, Reimund P. Rötter, Jørgen E. Olesen, Peter J. Thorburn, Nadine Brisson, Mikhail A. Semenov, Claudio O. Stöckle, Maria I. Travasso, Daniel Wallach, James Williams, Garry O'Leary, Cynthia Rosenzweig, Carlos Angulo, Bruno Basso, R. Goldberg, Robert F. Grant, Frank Ewert, Dominique Ripoche, Pierre Stratonovitch, Claas Nendel, Agricultural and Biological Engineering Department, University of Florida [Gainesville], Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), National Laboratory for Agriculture and Environment, Department of Agronomy, Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Research, Agrifood Research Finland, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), CCAFS, IWMI, NASC Complex, DPS Marg, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, Centro de Investigaciòn y Formenta Agrario (CIFA), WESS-Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, nstitute of Soil Science and Land Evaluation, University of Hohenheim, Joint Global Change Research Institute, University of Maryland [College Park], University of Maryland System-University of Maryland System, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Landscape and Water Sciences, Department of Primary Industries, Department of Agroecology, Aarhus University [Aarhus], NCAS-Climate, Walker Institute, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Institute of Soil Science and Land Evaluation, Plant Production Systems and Earth System Science-Climate Change, Wageningen University and Research Centre [Wageningen] (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), 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, Arid-Land Agricultural Research Center, Texas A&M University System, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), University of Florida [Gainesville] (UF), Agroclim (AGROCLIM), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Wageningen University and Research [Wageningen] (WUR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut für Nutzpflanzenwissenschaften und Ressourcenschutz ( INRES ), University of Bonn (Rheinische Friedrich-Wilhelms), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Commonwealth Scientific and Industrial Research Organisation, Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Helmholtz-Zentrum München ( HZM ), Institute for Climate and Atmospheric Science [Leeds] ( ICAS ), University of Leeds, Centro de Investigaciòn y Formenta Agrario ( CIFA ), University of Alberta [Edmonton], International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Leibniz Centre for Agricultural Landscape Research, Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Washington State University ( WSU ), Wageningen University and Research Centre [Wageningen] ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Instituto Nacional de Tecnología Agropecuaria, Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, Texas A and M University ( TAMU ), Helmholtz Zentrum München = German Research Center for Environmental Health, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université de Toulouse (UT)-Université de Toulouse (UT)

Subjects
010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Climate change, projection, crop production, adaptation, Environmental Science (miscellaneous), 01 natural sciences, Greenhouse effect, Uncertainty analysis, 0105 earth and related environmental sciences, 2. Zero hunger, model, [ SDV ] Life Sciences [q-bio], food, Simulation modeling, ensemble, temperature, 04 agricultural and veterinary sciences, 15. Life on land, Transient climate simulation, scenario, 13. Climate action, Greenhouse gas, Climatology, 040103 agronomy & agriculture, impact, 0401 agriculture, forestry, and fisheries, Environmental science, co2, Climate model, Crop simulation model, Social Sciences (miscellaneous)
Abstract

Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking. Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.

Published
2013

26. French contribution to the international AgMIP project: leading maize pilot and STICS simulations for wheat, maize and rice

Author

Bassu, F., Durand, J. L., Sanctis, G., Dominique Ripoche, Francoise Ruget, Samuel Buis, Patrick Bertuzzi, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Agroclim (AGROCLIM), and Déposants HAL-Avignon, bibliothèque Universitaire

Subjects
[SDU] Sciences of the Universe [physics], [SDU]Sciences of the Universe [physics], ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2012

27. Random Boolean Functions: Non-Parametric Estimation of the Intensity. Application to Soil Surface Roughness

Author

Michel Goulard, Patrick Bertuzzi, Joël Chadoeuf, Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), and Unité de Science du Sol

Subjects
Statistics and Probability, Discrete mathematics, Series (mathematics), Boolean model, REPARTITION SPATIALE, [SDV]Life Sciences [q-bio], Mathematical analysis, Random function, Nonparametric statistics, 04 agricultural and veterinary sciences, 15. Life on land, Horizontal plane, 01 natural sciences, Point process, 010104 statistics & probability, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, 0101 mathematics, Statistics, Probability and Uncertainty, Boolean function, ComputingMilieux_MISCELLANEOUS, Intensity (heat transfer), Mathematics
Abstract

A non-parametric estimation method is proposed for the intensity of the Poisson process associated to a Boolean random function. The case studied is that of marks being half-spheres as primary functions, these half-spheres lying on a given horizontal plane. The proposed method is derived from the method presented in Gyorfi, Hardle, Sarda et Vieu [1989] for the case of time series. An application in soil science is presented.

Published
1994

29. An overview of the crop model STICS

Author

Jean-Pierre Gaudillère, Daniel Zimmer, Nadine Brisson, Philippe Burger, Bruno Mary, Jorge Sierra, Bernard Seguin, Pierre Cellier, Philippe Debaeke, Hervé Sinoquet, Catherine Hénault, Romain Roche, Florent Maraux, Yves-Marie Cabidoche, Christian Gary, Eric Justes, Patrick Bertuzzi, Dominique Ripoche, François Bussière, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Institut National de la Recherche Agronomique (INRA), 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, Unité d'agronomie de Laon-Péronne, Unité de Recherche AgroPédoClimatique de la zone caraïbe (APC), Département Environnement et Agronomie (DPT_EA), Unité d'agronomie, Microbiologie du Sol et de l'Environnement (MSE), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB), 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), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Unité d'Agronomie de Laon-Péronne ( LILL LAON AGRO), Département Environnement et Agronomie (DEPT EA), M.K.van Ittersum, M.Donatelli, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Institut National de la Recherche Agronomique ( INRA ), Unité de recherche Plantes et Systèmes de Culture Horticoles ( PSH ), Institut National de la Recherche Agronomique ( INRA ), Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, Unité de Recherche AgroPédoClimatique de la zone caraïbe ( APC ), Département Environnement et Agronomie ( DPT_EA ), Microbiologie du Sol et de l'Environnement ( MSE ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ), 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 ), Ouvrages pour le drainage et l'étanchéité (UR DEAN), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, Tige, F62 - Physiologie végétale - Croissance et développement, Plant Science, Agricultural engineering, DONNEES D'ENTREE, 01 natural sciences, Water balance, Range (statistics), [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, media_common, Mathematics, Generality, Ecology, U10 - Informatique, mathématiques et statistiques, Bilan hydrique du sol, 04 agricultural and veterinary sciences, Rendement des cultures, [SDE]Environmental Sciences, Phénologie, MODELE STICS, CEMAGREF, F40 - Écologie végétale, media_common.quotation_subject, Azote, DEAN, Soil Science, Adaptability, Crop, Croissance, 0105 earth and related environmental sciences, business.industry, Modèle de simulation, BILAN AZOTE, 15. Life on land, Agronomy, Agriculture, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Plante de culture, business, Agronomy and Crop Science, Cropping, Racine
Abstract

International audience; STICS is a model that has been developed at INRA (France) since 1996. It simulates crop growth as well as soil water and nitrogen balances driven by daily climatic data. It calculates both agricultural variables (yield, input consumption) and environmental variables (water and nitrogen losses). From a conceptual point of view, STICS relies essentially on well-known relationships or on simplifications of existing models. One of the key elements of STICS is its adaptability to various crops. This is achieved by the use of generic parameters relevant for most crops and on options in the model formalisations concerning both physiology and management, that have to be chosen for each crop. All the users of the model form a group that participates in making the model and the software evolve, because STICS is not a fixed model but rather an interactive modelling platform. This article presents version 5.0 by giving details on the model formalisations concerning shoot ecophysiology, soil functioning in interaction with roots, and relationships between crop management and the soil-crop system. The data required to run the model relate to climate, soil (water and nitrogen initial profiles and permanent soil features) and crop management. The species and varietal parameters are provided by the specialists of each species. The data required to validate the model relate to the agronomic or environmental outputs at the end of the cropping season. Some examples of validation and application are given, demonstrating the generality of the STICS model and its ability to adapt to a wide range of agro-environmental issues. Finally, the conceptual limits of the model are discussed.

Published
2003

30. Denitrification in the upper vadose zone layers: a comparison between a fluvic hypercalcaric cambisol and a haplic calcisol

Author

Christophe Emblanch, François Lafolie, Pierre Renault, Agnès Richaume, Patrick Bertuzzi, Patrice Cannavo, Unité de Science du Sol, Institut National de la Recherche Agronomique (INRA), Laboratoire d'Hydrogéologie d'Avignon (LHA), Avignon Université (AU), 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), ProdInra, Migration, and INRA - Avignon (INRA)

Subjects
[SDE] Environmental Sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, dénitrification, [SDV]Life Sciences [q-bio], zone non saturée, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, 010501 environmental sciences, 01 natural sciences, Nitrogen Protoxide, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Cambisol, Chemistry, bactérie dénitrifiante, Forestry, 04 agricultural and veterinary sciences, Calcisol, 15. Life on land, 6. Clean water, activité spécifique, [SDV] Life Sciences [q-bio], micro-organismes hétérotrophes, [SDU]Sciences of the Universe [physics], [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Agronomy and Crop Science
Abstract

International audience; The vertical distributions of heterotrophs, NO 3--reducers and denitrifiers, specific respiratory and denitrifying activities were studied in the upper vadose zone of two profiles, distinguished from each other by the risk of transitory anaerobic conditions. According to pedological observations, 3 and 4 layers were defined in the fluvic hypercalcaric cambisol and haplic calcisol profiles, respectively. Bacterial enumerations were performed using the Most Probable Number (MPN) method. Aerobic respiratory and denitrifying activities were estimated from the depletion of O 2 and the accumulation of N 2O in the presence of C 2H 2, respectively, for soil in sealed flasks incubated in optimal conditions. The denitrifier - to - heterotroph ratio indicates that the denitrifying ability favors micro-organisms in the competition for deep layer colonization in the poorly aerated site. Whereas specific aerobic respiration does not vary or increase with depth, the specific denitrifying activity decreases with depth and is mostly affected by organic-C in the poorly aerated site. The use of specific denitrifying activity should be accounted for in models of denitrification; Dénitrification dans la zone non saturée superficielle : comparaison entre un fluvic hypercalcaric cambisol et un haplic calcisol . Dans cette étude les distributions verticales des micro-organismes hétérotrophes, des réducteurs de nitrate et des dénitrifiants, ainsi que de leurs activités spécifiques respiratoires et dénitrifiantes, ont été comparées dans la zone non saturée superficielle de deux sites se distinguant par le risque de conditions anoxiques transitoires. Suite à une caractérisation pédologique, 3 et 4 horizons ont été distingués respectivement aux niveaux d'un fluvic hypercalcaric cambisol et d'un haplic calcisol. Les nombres de bactéries ont été estimés par la méthode du nombre le plus probable (MPN). Les activités respiratoire aérobie et dénitrifiante ont été caractérisées par le suivi respectif de la disparition de O 2 ou de l'accumulation de N 2O en présence de C 2H 2, pour du sol incubé en conditions optimales à l'intérieur de flacons scellés. Le rapport dénitrifiants/hétérotrophes indique que l'aptitude à dénitrifier donne un avantage dans la compétition pour coloniser les horizons profonds du site le moins bien aéré. Alors que l'activité respiratoire aérobie spécifique semble constante ou s'accroître avec la profondeur, l'activité spécifique dénitrifiante décroît. Elle est affectée par le C-organique plus faible dans le site moins aéré. L'introduction de l'activité spécifique dénitrifiante dans les modèles de dénitrification pourrait améliorer les prédictions de concentration en NO 3- arrivant à la nappe.

Published
2002

31. A simple approach to monitor crop biomass from C-band radar data

Author

Jean-Pierre Wigneron, Paolo Ferrazzoli, Patrick Bertuzzi, Andre Chanzy, Albert Olioso, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), Unité de Science du Sol, and ProdInra, Migration

Subjects
Canopy, [SPI.OTHER]Engineering Sciences [physics]/Other, 010504 meteorology & atmospheric sciences, C band, [SPI.OTHER] Engineering Sciences [physics]/Other, 0211 other engineering and technologies, Soil Science, Geology, 02 engineering and technology, Vegetation, 15. Life on land, 01 natural sciences, law.invention, Atmospheric radiative transfer codes, law, Simple (abstract algebra), Environmental science, Computers in Earth Sciences, Radar, Water content, Optical depth, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

A simple two-term model of the radar backscattering coefficient of crops, designed for the retrieval of the amount of water in the canopy, is described and analyzed. The principle of the method is to calibrate the simple model from the simulations of a discrete first-order radiative transfer model during crop development. The canopy structure is taken into account in the discrete model to compute the relationships between a) the vegetation direct contribution to backscattering σ ° v and the optical depth τ and b) the optical depth τ and the amount of water in the canopy. The two-term model is tested against C-band radar data acquired over a soybean crop during the whole vegetation cycle. The simulations correlate well with the measurements and the retrieval of the amount of water in the canopy Wc (kg/m 2 ) can be carried out. Accurate temporal information on the crop growth could be derived from the radar data. Ancillary information about soil moisture are required, but it is found that rough estimates on a 4–5 day basis are sufficient.

Published
1999

32. Estimating root zone soil moisture from surface soil moisture data and soil-vegetation-atmosphere transfer modeling

Author

Jean-Christophe Calvet, Jean-Pierre Wigneron, Patrick Bertuzzi, Albert Olioso, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), and Unité de Science du Sol

Subjects
Surface (mathematics), réserve en eau du sol, 010504 meteorology & atmospheric sciences, télédétection, Energy transfer, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Soil science, 02 engineering and technology, TSVA, SYSTEME SOL PLANTE ATMOSPHERE, 01 natural sciences, Atmosphere, Soil thermal properties, Pedotransfer function, humidité du sol, 020701 environmental engineering, Water content, ComputingMilieux_MISCELLANEOUS, modélisation, 0105 earth and related environmental sciences, Water Science and Technology, système racinaire, Vegetation, 15. Life on land, transfert sol végétation atmosphère, microonde passive, Root zone soil moisture, [SDE]Environmental Sciences, Environmental science
Abstract

We studied the possibility of estimating root zone soil moisture through the combined use of a time series of observed surface soil moisture data and soil-vegetation-atmosphere transfer modeling. The analysis was based on the interactions between soil- biosphere-atmosphere surface scheme and two data sets obtained from soybean crops in 1989 and 1990. These data sets included detailed measurements of soil and vegetation characteristics and mass and energy transfer in the soil-plant-atmosphere system. The data measured during the 3-month experiment in 1989 are used to investigate the accuracy of soil reservoir retrievals, as a function of the time period and frequency of measurements of surface soil moisture involved in the retrieval process. This study contributes to better defining the requirements for the use of remotely sensed microwave measurements of surface soil moisture.

Published
1999

33. Estimation of energy fluxes and photosynthesis from thermal infrared, spectral reflectances, microwave data and SVAT modeling

Author

Habiba Chauki, Patrick Bertuzzi, Jean-Pierre Wigneron, Albert Olioso, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), and Unité de Science du Sol

Subjects
Canopy, transfert de masse, échange d'énergie, 010504 meteorology & atmospheric sciences, télédétection, [SDV]Life Sciences [q-bio], RELATION SOL PLANTE ATMOSPHERE, COUVERTURE VEGETALE, 0211 other engineering and technologies, évapotranspiration, glycine max, 02 engineering and technology, Atmospheric model, modèle, 01 natural sciences, Evapotranspiration, Radiative transfer, humidité du sol, photosynthèse, soja, Water content, ComputingMilieux_MISCELLANEOUS, infrarouge thermique, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Transpiration, Moisture, microonde, Vegetation, 15. Life on land, oléoprotéagineux, 6. Clean water, reflectance spectrale, plante légumière, [SDE]Environmental Sciences, Environmental science, radar
Abstract

Soil Vegetation Atmosphere Transfer (SVAT) models have been implemented to simulate energy and mass fluxes between soil, vegetation and atmosphere of various ecosystems. Usually, these models are simple, but they use realistic descriptions of radiative, turbulent and water transfers. These include description of stomatal control of transpiration and CO/sub 2/ fluxes. They can be used for assimilating remote sensing data and derive vegetation canopy evapotranspiration or photosynthesis. Various remote sensing data may provide useful information to drive SVAT models. Surface temperature may be used through inversion procedures to retrieve parameters related to stomatal conductance or root zone soil moisture. Parameters related to vegetation structure (LAI, vegetation height) may be retrieved from reflectance measurements in the solar domain, either through direct relationships with some vegetation index or by inverting radiative transfer formulation against spectral reflectance measurements. The microwave data contribution has not been studied very often in the case of vegetation canopies, but they were proposed for estimating surface soil moisture. In this paper, inversions of the ALiBi model were performed to retrieve canopy evapotranspiration from thermal infrared, spectral reflectances and microwave data on two water stressed soybean crops. In a previous study, thermal infrared data alone were used to invert the model on plant water status parameters, while other parameters, related to canopy structure and soil surface water status, were prescribed from in situ measurements. In the present study, spectral reflectance and radar measurements were used to retrieve canopy structure parameters (LAI and vegetation height) and surface soil moisture by inverting radiative transfer models.

Published
1998

34. Comparison of methods for estimating actual evapotranspiration in a row-cropped vineyard

Author

Marc Voltz, W. Trambouze, Patrick Bertuzzi, Unité de Science du Sol, Institut National de la Recherche Agronomique (INRA), and ProdInra, Migration

Subjects
2. Zero hunger, Hydrology, Atmospheric Science, Global and Planetary Change, 010504 meteorology & atmospheric sciences, Forestry, 04 agricultural and veterinary sciences, 15. Life on land, 01 natural sciences, Vineyard, Water balance, Water potential, [SDV.SA.SF]Life Sciences [q-bio]/Agricultural sciences/Silviculture, forestry, Lysimeter, Evapotranspiration, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, [SDV.SA.SF] Life Sciences [q-bio]/Agricultural sciences/Silviculture, forestry, Agronomy and Crop Science, Water content, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Transpiration
Abstract

The water consumption of grapevines is one of the main factors influencing wine quality. The general characteristics of the vines (deep and sparse rooting system, heterogeneity of canopy size) and of vineyards (sloping ground, stony soils, small fields) make it difficult to estimate the actual evapotranspiration of the vineyard using the soil water balance or energy balance methods. In this paper these two methods are compared to a method relying on separate measurements of soil evaporation and plant transpiration. An experiment was conducted for two months in 1994 in a one-hectare vineyard of Southern France in which five monitoring plots had been installed. In each plot, soil moisture was monitored down to 260 cm under the row and between the rows to take into account the heterogeneity of the rooting system. Water flux at 260 cm depth was computed from measurements of soil water potential and hydraulic properties. Evapotranspiration was then obtained from the soil water balance. Transpiration was monitored with two sap flow gauges per plot and evaporation with three mini-lysimeters installed at different distances from the row. The energy balance was determined with micrometeorological measurements taken in the field. The reliability of the measurements of evaporation from the soil and of plant transpiration was satisfactory. First, soil evaporation computed from mini-lysimeter measurements compared well with that calculated from the water balance of the soil surface. Second, the sap flow followed the diurnal pattern of net radiation at a time scale of less than one hour. The three methods for estimating actual evapotranspiration gave comparable mean field values and showed a similar evolution through the summer. Statistical tests showed that the observed differences between the estimated values were not significant, whatever the length of the period considered (3–31 days) and the rate of evapotranspiration (1.8–3.5 mm d −1 ). The experiment shows the inherent limits and advantages of the three methods. The soil water balance method must be used over periods of longer than one week to provide acceptable precision in estimating field evapotranspiration. The energy balance method gives small time scale information but requires constant technical follow-up. Measuring evaporation and transpiration separately to estimate vineyard evapotranspiration is easily adaptable to a large range of vine-growing conditions. Moreover, it proved to be more precise than the other two methods.

Published
1998

35. Estimating Long-Term Drainage at a Regional Scale Using a Deterministic Model

Author

Patrick Bertuzzi, Ary Bruand, L. Raison, Laurent Bruckler, B. Guillet, S. Ould Mohamed, Service dEtude des Sols de la Carte pédologique France, Institut National de la Recherche Agronomique (INRA), Institut des Sciences de la Terre d'Orléans (ISTO), Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Unité de Science du Sol, Service d'Etude des Sols et de la Carte Pédologique de France (SESCPF), Unité de recherche Science du Sol (USS), and ProdInra, Migration

Subjects
[SDE] Environmental Sciences, Water flow, [SDV]Life Sciences [q-bio], [SDE.MCG]Environmental Sciences/Global Changes, Soil Science, 010502 geochemistry & geophysics, 01 natural sciences, Field capacity, Hydraulic conductivity, Drainage, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Water content, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Hydrology, 04 agricultural and veterinary sciences, 15. Life on land, 6. Clean water, [SDV] Life Sciences [q-bio], Loam, [SDE]Environmental Sciences, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Spatial variability, TENEUR EN EAU DU SOL
Abstract

Ary Bruand : Present Address : Institut des Sciences de la Terre d'Orléans; International audience; This study discusses the use of a deterministic model, at a regional scale, when the soils have small heterogeneity. Long-term soil drainage was simulated on 3500 ha in Beauce (France), taking into account variation in soil thickness, agricultural practices, and climate characteristics for a 32-yr period. The model was based on a numerical solution of Richards' equation, using a finite element method. Hydraulic properties were determined at four sites that are 4 km apart. The results show a small variability of hydraulic parameters, with coefficient of variation (CV) ranges from 1 to 16% for the α parameter, the empirical constant n, the saturated water content θs, and the residual water content θr, and from 60 to 75% for the saturated hydraulic conductivity Ks. Hence, the spatial and temporal variability of drainage are mainly related to soil thickness, agricultural practices, and climate characteristics. Assigning small spatial variability to hydraulic properties, we analyzed the role of soil thickness and initial water content (IWC), induced by agricultural practices and climate characteristics, on drainage prediction. Five IWC were defined: 100, 90, 80, 70, and 55% of field capacity (FC). Four soil thicknesses were studied: 30, 60, 100, and 150 cm. The IWC appeared as a significant factor in drainage prediction for 60% of the 32 yr, regardless of soil thickness. Soil thickness became the most significant factor (20% of the 32 yr) for low IWC. The model was spatially extrapolated by combining the calculated drainage (which varied with soil thickness and IWC) and the spatial distribution of crops and soil thickness classes. Simulated drainage was closely related (r = 0.90) to groundwater recharge during the 32 yr, indicating that the model could be applied to other areas with appropriate parameters.

Published
1997

36. Error analysis of an evaporation method for determining hydrodynamic properties in unsaturated soil

Author

J. C. Gaudu, M. Bourlet, D. Mohrath, Laurent Bruckler, Patrick Bertuzzi, Unité de Science du Sol, Institut National de la Recherche Agronomique (INRA), and ProdInra, Migration

Subjects
Observational error, Materials science, Calibration curve, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Evaporation, Soil Science, Mineralogy, Soil science, 04 agricultural and veterinary sciences, 02 engineering and technology, 6. Clean water, [SDV] Life Sciences [q-bio], Viscosity, Pressure head, Hydraulic conductivity, 040103 agronomy & agriculture, Calibration, 0401 agriculture, forestry, and fisheries, Richards equation, 020701 environmental engineering, ComputingMilieux_MISCELLANEOUS
Abstract

Solving soil unsaturated flow problems requires knowledge of the water retention θ(h) and unsaturated hydraulic conductivity K(θ) relationships. The purpose of this study was to investigate more thoroughly the properties and accuracy of an evaporation method described by Wind (1969) for determining θ(h) and K(θ) from laboratory cores. Evaporation from a vertical column of soil was first simulated using numerical solution of Richards equation for a given set of soil hydraulic properties. The simulated data were then used to evaluate the ability of Wind's method to provide estimations of the retention and unsaturated conductivity curves when measurement errors were taken into account. The main sources of error were (i) errors due to the position of the tensiometers in the sample, (ii) errors due to the calibration of the transducers used for the pressure head measurements, and (iii) errors due to layering in the soil column. The estimated water retention curves were sensitive only to soil layering. On the other hand, small uncertainties in the position of the tensiometers (1 or 2 mm) and in the calibration curve of the transducers for the pressure head measurements (1-5%) had a great influence on the estimated hydraulic conductivity curves. A correction procedure was proposed and was satisfactory when errors of position in the tensiometers were taken into account. Results also showed that temperature corrections related to viscosity of liquid water were large. Finally, this method gives poor estimates of hydraulic conductivities when raw tensiometric data are corrupted with small errors in their position or calibration.

Published
1997

37. Field Evaluation and Error Analysis of Soil Water Content Measurement using the Capacitance Probe Method

Author

Laurent Bruckler, Patrick Bertuzzi, S. Ould Mohamed, L. Raison, Ary Bruand, Service d'Etude des Sols et de la Carte Pédologique de France (SESCPF), Institut National de la Recherche Agronomique (INRA), Unité de Science du Sol, Institut des Sciences de la Terre d'Orléans (ISTO), Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Unité de recherche Science du Sol (USS), ProdInra, Migration, and Service dEtude des Sols de la Carte pédologique France

Subjects
[SDE] Environmental Sciences, Soil test, Calibration curve, [SDV]Life Sciences [q-bio], [SDE.MCG]Environmental Sciences/Global Changes, 0207 environmental engineering, Soil Science, Mineralogy, Soil science, 02 engineering and technology, Standard deviation, Calibration, 020701 environmental engineering, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Water content, ComputingMilieux_MISCELLANEOUS, Mathematics, SOL ARGILO LIMONEUX, Capacitance meter, 04 agricultural and veterinary sciences, 15. Life on land, 6. Clean water, [SDV] Life Sciences [q-bio], Soil water, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Capacitance probe, TENEUR EN EAU DU SOL
Abstract

Ary Bruand : Present Address : Institut des Sciences de la Terre d'Orléans; International audience; The volumetric water content of a silty clay loam soil was measured using the capacitance probe method (CPM) for three successive winter-spring periods. The results were compared with those obtained using the neutron scattering method (NSM). The CPM was calibrated using gravimetric water content measurements, whereas the NSM was calibrated using a theoretical calibration which requires chemical analysis of soil samples. The calibration curves of CPM were linear for volumetric water content ranging from 0.25 to 0.40 m3 m−3. The correlation coefficients (r) ranged from 0.87 to 0.98. The advantages of the CPM were discussed, and it was demonstrated that, for low differentiated soils, this method can be a better alternative to the NSM, which is commonly used for the determination of soil water content. The CPM and NSM measurements were compared and root mean squared errors between these two methods ranged from 0.0098 to 0.0159 m3 m−3. The precision of NSM and CPM in a field calibration exercise was calculated. For CPM, this study discusses unbiased and biased statistical treatments to establish the calibration curves. The unbiased treatment greatly decreased the total variance in volumetric water content. The standard deviation of volumetric water content ranged from 0.0194 to 0.0248 m3 m−3 and from 0.0108 to 0.0136 m3 m−3 for biased and unbiased statistical method, respectively. Finally, the soil water storage and associated errors were calculated using CPM and NSM.

Published
1997

38. A scaling based estimation of soil unsaturated hydraulic properties at a field scale

Author

Laurent Bruckler, Patrick Bertuzzi, Unité de Science du Sol, Institut National de la Recherche Agronomique (INRA), and ProdInra, Migration

Subjects
Irrigation, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Soil Science, Soil science, 04 agricultural and veterinary sciences, 02 engineering and technology, 6. Clean water, [SDV] Life Sciences [q-bio], Infiltration (hydrology), Hydraulic conductivity, Pedotransfer function, Neutron probe, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Neutron, EAU DANS LE SOL, 020701 environmental engineering, Agronomy and Crop Science, Scaling, ComputingMilieux_MISCELLANEOUS, Water Science and Technology, Mathematics
Abstract

This paper deals with the prediction of the soil water retention h(S) and the soil unsaturated hydraulic conductivity K(S) functions of a clay-loam soil at a field scale (1 ha) where the variable S represents water saturation. The Van Genuchten model and the corresponding Mualem-Van Genuchten model were used to predict h(S) and K(S) functions respectively. The field data (tensiometric and neutron probe measurements) used in this study were provided by the soil water balance (four neutron sites, 0.35 to 1.55 m soil layer) of a soybean crop over a 78 days growing season. The advantages of the scaling approach for describing the field variability of the h(S) function were confirmed. The scaling approach accounted for 73% of the field variability of the soil matrix potential. A simple procedure was proposed in order to predict the K(S) function using scaling theory. This was done by simultaneously applying a ``zero flux method'' and ``deep flux method'' to compute the soil water balance and fit the saturated hydraulic conductivitiy (Ksat), the only unknown parameter in K(S).

Published
1996

39. Stream hydrograph separation on two small Guianese catchments

Author

Thierry Bariac, Millet, A., Bernard Ladouche, Mathieu, R., Grimaldi, C., Michel Grimaldi, Pascale Hubert, Molicova, H., Laurent Bruckler, Patrick Bertuzzi, Boulègue, J., Yves Brunet, Régis Tournebize, Granier, A., Unité de science du sol et de bioclimatologie, Institut National de la Recherche Agronomique (INRA), Unité de Science du Sol, Unité de bioclimatologie, Institut francilien recherche, innovation et société (IFRIS), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.)-Institut National de la Recherche Agronomique (INRA)-École des hautes études en sciences sociales (EHESS)-OST-Université Paris-Est Marne-la-Vallée (UPEM)-ESIEE Paris-Centre National de la Recherche Scientifique (CNRS), and Unité d'écophysiologie forestière

Subjects
GEOCHIMIE, [SDV]Life Sciences [q-bio], HYDROLOGIE, TECHNIQUE DES TRACEURS, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
1995

40. Rainfall runoff modelling : from the hillslope to the continental scale

Author

Thierry Bariac, Millet, A., Bernard Ladouche, Grimaldi, C., Michel Grimaldi, Pascale Hubert, Molicova, H., Laurent Bruckler, Patrick Bertuzzi, Boulègue, J., Yves Brunet, Régis Tournebize, Granier, A., Unité de recherche Science du Sol (USS), Institut National de la Recherche Agronomique (INRA), Unité de Science du Sol, Unité de bioclimatologie, Institut francilien recherche, innovation et société (IFRIS), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.)-Institut National de la Recherche Agronomique (INRA)-École des hautes études en sciences sociales (EHESS)-OST-Université Paris-Est Marne-la-Vallée (UPEM)-ESIEE Paris-Centre National de la Recherche Scientifique (CNRS), Unité associée en sciences forestières, Ecole Nationale du Génie Rural, des Eaux et des Forêts (ENGREF)-Institut National de la Recherche Agronomique (INRA), and Unité de Recherche AgroPédoClimatique de la zone caraïbe (APC)

Subjects
GEOCHIMIE, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

Supplement 2; International audience

Published
1994

41. Sampling strategies for soil water content to estimate evapotranspiration

Author

Andre Chanzy, Patrick Bertuzzi, D. Bay, Laurent Bruckler, Unité de Science du Sol, Institut National de la Recherche Agronomique (INRA), and ProdInra, Migration

Subjects
2. Zero hunger, Hydrology, Topsoil, Field experiment, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Soil Science, Sampling (statistics), Soil science, 04 agricultural and veterinary sciences, 02 engineering and technology, [SDV] Life Sciences [q-bio], SONDE CAPACITIVE, Evapotranspiration, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Soil horizon, Spatial variability, 020701 environmental engineering, Agronomy and Crop Science, Water content, ComputingMilieux_MISCELLANEOUS, Water Science and Technology
Abstract

When the soil water balance method is applied at a field scale, estimation of the spatial variability and confidence interval of actual evapotranspiration is rare, although this method is sensitive to the spatial variability of the soil, and thus to the sampling strategy. This work evaluated the effect of soil sampling strategies for soil water content and water flux at the bottom of the soil profile on the estimation of the daily and cumulative evapotranspirations. To do that, according to the statistical properties of daily measurements in a field experiment with a soybean crop, the water content and flux through the base to the soil profile in space (field scale) and time (daily scale) were simulated. Four different sampling strategies were then compared, and their effects on daily and seasonal cumulative evapotranspirations quantified. Strategy 1 used ten theoretical sites randomly located in the field. The daily water content estimates were assumed to be available each day from these same ten locations, which were located from 0.15 m to 1.55 m in depth, with space steps of 0.10 m. Strategy 2 assumed that daily water content estimates combined two sources: in the 0.00–0.20 m soil layer, ten theoretical sites were selected but changed every day, with thin soil layers for soil moisture sampling, from 1 to 5 cm in thickness. In the 0.20–1.60 m soil layer, the daily water content estimates were assumed to come from the same ten locations (the first soil moisture estimate was located at 0.25 m, and the others were located every 0.10 m until 1.55 m). Strategy 3 used ten theoretical sites located in the field, as in strategy 1, however the water content estimates in the 0.00–0.20-m soil layer were assumed to come from accurate water content measurements (soil layers from 1 to 5 cm in thickness), while for the 0.20–1.60 m soil layer, the strategy was similar to strategies 1 and 2. Strategy 4 used 10 new theoretical locations of measurement every day. Precise water content estimates for thin layers were assumed to be available in the 0.00–0.20 m soil layer as in strategy 2. The layers for water content estimates in the 0.20–1.60 m were similar to those of strategies 1, 2, and 3. Results showed that the spatial variability of the daily actual evapotranspiration may not be negligible, and differences from approximately ±1.0 mm d −1 to ±3.0 mm d −1 were calculated between the four sampling strategies. Strategy 1 gave the worst results, because variations in the water content of the top soil layers were neglected, and thus the daily evapotranspiration was underestimated. Strategy 2 led to a considerable variability for estimating daily evapotranspiration which was explained by the effect of the spatial variability due to the daily site sampling for the top soil layers (0 to 0.2 m). Strategy 3 appeared to be the best practical compromise between practical field considerations and the necessity to obtain accurate evapotranspiration measurements. The accuracy of daily evapotranspiration could reach ± 0.5 mm d−1, and could be further improved by increasing the number of measurement sites. The best results were obtained with strategy 4, although such a destructive and time-consuming strategy is not likely to be practical.

Published
1994

42. Monitoring surface changes of bare soils due to slaking using spectral measurements

Author

Dominique Courault, Patrick Bertuzzi, Michel-Claude Girard, Unité de science du sol et de bioclimatologie, Institut National de la Recherche Agronomique (INRA), Unité de Science du Sol, and Science du Sol - Montpellier

Subjects
Surface (mathematics), 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Soil Science, Mineralogy, Environmental science, 04 agricultural and veterinary sciences, 01 natural sciences, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract

International audience

Published
1993

43. Mesure de l'humidite des sols par une methode capacitive : analyse des facteurs influencant la mesure

Author

P. Stengel, Patrick Bertuzzi, Andre Chanzy, R. Guennelon, Laurent Bruckler, J. C. Gaudu, JM Mathieu, JC Fumanal, Revues Inra, Import, Unité de Science du Sol, Institut National de la Recherche Agronomique (INRA), Institut Universitaire de Technologie - Aix-Marseille (IUT AMU), and Aix Marseille Université (AMU)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, sol nu, méthode de mesure, couvert végétal, permittivité diélectrique, structure du sol, teneur en eau, capteur capacitif, Agricultural sciences, [SDV.EE] Life Sciences [q-bio]/Ecology, environment, variabilité spatiale, température, humidité du sol, Agronomy and Crop Science, texture, ComputingMilieux_MISCELLANEOUS, Sciences agricoles, appareillage, salinité
Abstract

On teste en laboratoire et in situ les performances d’un capteur capacitif destiné à la mesure de l’humidité du sol. En laboratoire, on montre que les dérives thermiques propres à l’appareillage sont négligeables, que la mesure de permittivité diélectrique relative est dépendante de la qualité du contact sol-électrodes, et que le volume de mesure a des dimensions de l’ordre de quelques cm3. Les relations linéaires permittivité relative-teneur en eau volumique sont dépendantes de la texture, de la structure (agrégats de 2-3 mm et 4 mm disposés autour des électrodes ou structure continue), de la température (la variation de la permittivité relative en fonction de la température peut atteindre 0,20 par °C dans la gamme de 0 à 45 °C), de la salinité (la mesure de permittivité diélectrique relative est peu sensible à la concentration saline jusqu’à des valeurs de conductance électrique de l’ordre de 2 à 3 mS). In situ, deux expérimentations sont réalisées, l’une sur un sol nu présentant un état structural homogène, l’autre sur un sol couvert (soja) présentant un état structural plus hétérogène. Pour chaque expérimentation, on réalise pendant plusieurs jours (21 et 41 j respectivement) des mesures simultanées de permittivité diélectrique, de température, de conductance électrique, et des contrôles de teneur en eau dans les couches superficielles du sol (0 à 20 cm). Les relations d’étalonnage permittivité-humidité sont généralement linéaires et variables d’un capteur à l’autre. Cette variabilité est probablement due à la variabilité spatiale de la teneur en eau du sol, de la structure à proximité immédiate du capteur, et/ou du contact sol-électrodes. Par ailleurs, les effets thermiques sur la permittivité mesurée sont comparables à ceux obtenus en laboratoire, et la gamme de conductances électriques rencontrées in situ semble compatible avec celle ne présentant pas d’influence notable sur la mesure de permittivité et estimée en laboratoire., The performance of a capacitive probe for soil moisture measurement was studied under laboratory and field conditions. The probe (21-mm ED cylinder) was composed of 2 electrodes connected to an electronic oscillator (38 MHz) located in the probe. Four soils were used to analyse the ’dielectric permittivity-volumetric water content’ relationships under laboratory or field conditions. The sensitivity of the probe including its electronic components in relation to the variations of the temperature (0-45 °C) was very low (< 0.5 permittivity unit). Permittivity measurements were dependent on the quality of the soil-electrodes contact, and on the soil structure near the electrodes. The order of magnitude of the volume of influence was a few cubic centimeters, and the area of the soil most affected by the permissibility measurements was located in the vicinity of the electrodes. The ’dielectric permittivity-volumetric water content’ relationships were linear (r > 0.99), and dependent on soil texture and structure. Moreover, soil temperature influenced the measurements since increases of the order of magnitude of 0.20 permittivity unit per °C could be observed. Permittivity measurements were not sensitive to the concentration of the solution when the electric conductance was < 2-3 mS. Two field experiments were performed using 2 different soil textures. The first soil (C = 10.5%, L = 50.6%, S = 38.8%) was a bare soil, and showed a quite homogeneous soil structure, the second (C = 27.2%, L = 61.7%, S = 11.1%) was cultivated with soybean, and showed a more heterogeneous soil structure. For each experiment field, simultaneous measurements (1 or 2 times per d) of relative dielectric permittivity, soil temperature and gravimetric soil water content were made in the vicinity of the probes (over a 21 and 41 d period, for the 1st and 2nd experiment, respectively). The variation of the relative dielectric permittivity versus time was very similar to the variation of the measurement soil water content. The calibration relationships between the volumetric water content and the relative dielectric permittivity were linear for each capacitive probe. The regression lines for the different capacitive probes exhibited variations between probes, probably due to the spatial variability of the water content, soil structure and/or soil-electrode contact for each sensor. The effects of the soil temperature on the relative dielectric permittivity measurements were similar to the previous results obtained under laboratory conditions, and effects of electrical conductance appeared negligible under our conditions.

Published
1993

44. Agrometeorological soil water balance for crop simulation models

Author

Bernard Seguin, Patrick Bertuzzi, Nadine Brisson, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), Unité de Science du Sol, and ProdInra, Migration

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Soil texture, Soil science, 01 natural sciences, Water balance, [SDV.SA.SF]Life Sciences [q-bio]/Agricultural sciences/Silviculture, forestry, AGROMETEOROLOGIE, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, 2. Zero hunger, Hydrology, Soil map, Global and Planetary Change, Forestry, 04 agricultural and veterinary sciences, 15. Life on land, 6. Clean water, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, RESERVOIR, [SDV.SA.SF] Life Sciences [q-bio]/Agricultural sciences/Silviculture, forestry, Interception, Crop simulation model, Surface runoff, Agronomy and Crop Science, Water use
Abstract

The use of crop simulation models on a large scale for agrometeorological purposes is often limited by their inputs being non-routinely collected data, especially with regard to their soil water balance compartment. The objective of this study is to develop a water balance sub-model which can be run with readily available inputs. The model predicts water use, soil evaporation and crop transpiration throughout the growing season. Physiological reduction factors, as influenced by water stress, are derived from the soil water availability. This is achieved by using empirical relationships such as the reservoir analogy to assess water availability in relation to root development. The framework of applicable conditions is assessed by sensitivity analyses performed on inputs: the model can be run with a time step of ten days and using soil information given by soil maps, i.e. soil texture and depth, which fit agrometeorological purposes. Moreover, the model is shown to describe realistically soil water depletion, crop evapotranspiration and rooting depth. However, wetting processes are not correctly simulated, especially when large amounts of water are supplied. This limitation is emphasized for ten day time steps. Therefore incorporating effective rainfall simulation, i.e. both runoff and rainfall interception by the canopy, would improve the model.

Published
1992

46. Calibration, field-testing, and error analysis of a gamma-ray probe for in situ measurement of dry bulk density

Author

Patrick Bertuzzi, Y. Gabilly, Laurent Bruckler, J. C. Gaudu, Unité de Science du Sol, and Institut National de la Recherche Agronomique (INRA)

Subjects
In situ, Field (physics), Instrumentation, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Soil Science, Mineralogy, 02 engineering and technology, Optics, Calibration, 020701 environmental engineering, ComputingMilieux_MISCELLANEOUS, Observational error, business.industry, Gamma ray, analyse de l'erreur, 04 agricultural and veterinary sciences, densité du sol, gammadensimètre, Bulk density, 040103 agronomy & agriculture, Measuring instrument, 0401 agriculture, forestry, and fisheries, Environmental science, étalonnage, business
Abstract

International audience

Published
1987

47. Régression linéaire avec erreur sur les variables : application à l'étalonnage d'un gammadensimètre à transmission et d'un humidimètre à neutrons

Author

Laurent Bruckler, Christian Gros, Patrick Bertuzzi, Revues Inra, Import, Unité de Science du Sol, Institut National de la Recherche Agronomique (INRA), and Avignon Université (AU)

Subjects
humidimètre à neutrons, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, régression linéaire, Forestry, Agricultural sciences, erreur de mesure, [SDV.EE] Life Sciences [q-bio]/Ecology, environment, modèle linéaire, gammadensimétrie, Linear regression, étalonnage, Agronomy and Crop Science, ComputingMilieux_MISCELLANEOUS, Sciences agricoles, modélisation, Mathematics
Abstract

On se propose de calculer des estimateurs non biaisés de la régression linéaire dans le cas où les variables (x) et (y) de la régression, sont entachées d’erreurs. Dans une première partie, on présente les bases théoriques de la réduction du biais des estimateurs. Ces bases permettent d’aboutir à la construction d’estimateurs sans biais à l’ordre 1. De plus, on décrit la méthodologie permettant de quantifier rigoureusement l’ensemble des sources d’erreur à prendre en compte successivement pour les variables (x) et (y). Dans une seconde partie, ces résultats théoriques sont appliqués à deux instruments de mesure nécessitant un étalonnage préliminaire, un gamma densimètre à transmission (étalonné en laboratoire) et une sonde à neutrons (étalonnée in situ). Les résultats numériques montrent que les erreurs de mesure sont négligeables pour les variables (x) (taux de comptage) et (y) (masse volumique sèche) dans le cas du gamma densimètre à transmission (≤ 0,4 p. 100). Dans le cas de la sonde à neutrons, il en est de même pour la variable (x) (taux de comptage), mais les erreurs deviennent non négligeables (9 à 16 p. 100) pour la variable (y) (teneur en eau volumique). Lorsque les erreurs de mesure restent faibles (cas du gamma densimètre à transmission), il y a peu de différences entre les estimateurs de la droite d’étalonnage avec ou sans prise en compte des erreurs de mesure. Par contre, les différences deviennent plus sensibles lorsque les erreurs deviennent plus importantes (cas de l’humidimètre à neutrons), notamment en ce qui concerne la précision de l’estimation de valeurs prédites à partir de l’étalonnage., This paper proposes unbiased estimators of linear regression when the variables (x, y) present measurement errors. In the first part, theoretical calculations are described to obtain first-order unbiased estimators, and an error analysis is developed to take into account all the error components involved in each variable (x or y). In the second part, these theoretical results were applied to the calibration of a gamma-ray attenuation probe (laboratory calibration) and a neutron probe (field calibration). Numerical results showed that the measurement errors were negligible in the case of the gamma-ray attenuation equipement (≤ 0.4 %), but were greater (9 to 16 %) for the neutron probe, particularly for variable (y). When the measurement errors are small (gamma-ray attenuation probe), there is no difference between the parameters of the calibration estimated according to the classical approach or the proposed model. But when the measurement errors are greater (neutron probe), the differences between these two calibrations become higher especially for the precision of new predicted values.

Published
1987

48. Adaptation de l’abricotier aux changements climatiques : estimation des risques génétiques et géographiques liés aux avortements floraux et à l’époque de floraison Le réseau agroclimatique de l’INRA et la base associée de données

Author

Iñaki Garcia de Cortazar-Atauri, Patrick Bertuzzi, Lucia Andreini, François Madon, Jean Marc Audergon, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), and Génétique et Amélioration des Fruits et Légumes (GAFL)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences

49. Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 31 Maize Models

Author

Bruce Kimball, Boote, K., Hatfield, J., Ahuja, L., Stockle, C., Archontoulis, S., Baron, C., Bruno Basso, Patrick Bertuzzi, Chen, M., Julie Constantin, Derying, D., Dumont, B., Jean-Louis Durand, Ewert, F., Gaiser, T., Gayler, S., Griffis, T., Hoffmann, M., Jiang, Q., Soo-Hyung Kim, Lizaso, J., Sophie Moulin, Nendel, C., Parker, P., Palosuo, T., Priesack, E., Zhiming Qi Z., Srivastava, A., Stella, T., Tao, F., Thorp, K., Timlin, D., Twine, T., Heidi Webber, Magali Willaume, Williams, K., ProdInra, Migration, USDA-ARS : Agricultural Research Service, Agronomy Department, University of Florida [Gainesville], National laboratory for agriculture and the environment, Agricultural Systems Research Unit, USDA, Agricultural Research Service, United States Department of Agriculture - Agricultural Research Service, Biological Systems Engineering, University of Wisconsin-Madison, Department of Agronomy, Purdue University [West Lafayette], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Department of Soil, Water, and Climate, University of Minnesota System, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Computation Institute, Loyola University of Chicago, ULg-GxABT, Gembloux Agro-Bio Tech Faculty, Dpt. Agronomy, Bio-Engineering and Chemistry, Crop Science Unit, Université de Liège, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Crop Production Systems in the Tropics, Georg-August-Universität Göttingen, Department of Bioresource Engineering, Macdonald Campus, Université McGill, Center for Urban Horticulture, University of Washington, Dept. Producción Agraria-CEIGRAM, Universidad Politécnica de Madrid (UPM), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Natural Resources Institute Finland, Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Crop Systems and Global Change Research Unit, Climate Adaptation Scientist Meteorological Office, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), University of Florida [Gainesville] (UF), Agroclim (AGROCLIM), 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, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Georg-August-University [Göttingen], McGill University = Université McGill [Montréal, Canada], Natural resources institute Finland, Washington State University (WSU), Michigan State University System, Department of Soil, Water and Climate, Institute of Crop Science and Resource Conservation [Bonn], Natural Resources Institute Finland (LUKE), Institute of Biochemical Plant Pathology (BIOP), and Agricultural Model Intercomparison and Improvement Project (AgMIP). USA.

Subjects
[SDE] Environmental Sciences, Evapotranspiration, maïs, [SDV]Life Sciences [q-bio], évapotranspiration, analyse de covariance, modèle de croissance, [SHS]Humanities and Social Sciences, caracteristique variétale, Maize, covariance analysis, [SDV] Life Sciences [q-bio], densité de surface foliaire, potentiel de croissance, [SDE]Environmental Sciences, rendement agricole, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, [SHS] Humanities and Social Sciences, Crop model
Abstract

International audience; An important aspect that determines the ability of crop growth models to predict growth and yield is their ability to predict the rate of water consumption or evapotranspiration (ET) of the crop, especially for rain-fed crops. If, for example, the predicted ET rate is too high, the simulated crop may exhaust its soil water supply before the next rain event, thereby causing growth and yield predictions that are too low. In a prior inter-comparison among maize growth models, ET predictions varied widely, but no observations of actual ET were available for comparison. Therefore, another study has been initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). This time observations of ET using the eddy covariance technique from an 8-year-long experiment conducted at Ames, IA are being used as the standard. Simulation results from 31 models have been completed. In the first “blind” phase for which only weather, soils, and management information were furnished to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. A detailed statistical analysis of the daily ET data from 2011, a “typical” rainfall year, showed that, as expected, the median of all the models was more accurate across several criteria (correlation, root mean square error, average difference, regression slope) than any particular model. However, some individual models were better than the median for a particular criteria. Predictions improved in later stages when the modelers were provided additional leaf area, growth, and the actual ET observations that allowed them to “calibrate” some of the parameters in their models to account for varietal characteristics, etc. Bruce

50. Actes du XIe séminaire des utilisateurs de Stics

Author

Nicolas Beaudoin, Samuel Buis, Eric Justes, Dominique Ripoche, Patrick Bertuzzi, Eric Casellas, Julie Constantin, Benjamin Dumont, Jean-Louis Durand, Iñaki Garcia de Cortazar-Atauri, Guillaume Jégo, Marie Launay, Christine Le Bas, Patrice Lecharpentier, Joël Léonard, Bruno Mary, Loic Strullu, Francoise Ruget, Gaëtan Louarn, Anne-Isabelle Graux, François Affholder, Agroressources et Impacts environnementaux (AgroImpact), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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, Agroclim (AGROCLIM), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Faculté Gembloux Agro Bio-Tech, Université de Liège, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Agriculture and Agri-Food [Ottawa] (AAFC), InfoSol (InfoSol), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-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), Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Institut National de Recherche Agronomique (INRA). UR Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (0004).

Subjects
[SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], [MATH]Mathematics [math], ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences
Abstract

National audience

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