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1. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

Author: Müller, Christoph, Elliott, Joshua, Kelly, David, Arneth, Almut, Balkovic, Juraj, Ciais, Philippe, Deryng, Delphine, Folberth, Christian, Hoek, Steven, Izaurralde, Roberto C., Jones, Curtis D., Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A. M., Reddy, Ashwan, Rosenzweig, Cynthia, Ruane, Alex C., Sakurai, Gen, Schmid, Erwin, Skalsky, Rastislav, Wang, Xuhui, de Wit, Allard, Yang, Hong, Potsdam Institute for Climate Impact Research (PIK), University of Chicago, Department of Mathematics, University of Manitoba [Winnipeg], Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), International Institute for Applied Systems Analysis, Ecosystem Services and Management Program, affiliation inconnue, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), 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), ICOS-ATC (ICOS-ATC), 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), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), Department of Geography, University of Liverpool, Centre for Geo-Information, ALTERRA, Department of Geographical Sciences [College Park], University of Maryland [College Park], University of Maryland System-University of Maryland System, International Institute for Applied Systems Analysis [Laxenburg] (IIASA), Groupe Immunité des Muqueuses et Agents Pathogènes (GIMAP), Université Jean Monnet [Saint-Étienne] (UJM), Department of Physical Geography and Ecosystem Science, Lund University, Institute for Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), National Aeronautics and Space Administration, Partenaires INRAE, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Soil Science and Conservation Research Institute, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École des Ponts ParisTech (ENPC)-École polytechnique (X)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Wageningen University and Research Centre (WUR), 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)-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 Jean Monnet - Saint-Étienne (UJM), Lund University [Lund], National Aeronautics and Space Administration (NASA), Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
Subjects: Statistics and Probability, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Earth Observation and Environmental Informatics, Data Descriptor, Library and Information Sciences, PE&RC, Computer Science Applications, Education, Environmental sciences, Earth sciences, Aardobservatie en omgevingsinformatica, ddc:550, Life Science, Computational models, lcsh:Q, Statistics, Probability and Uncertainty, lcsh:Science, Plant sciences, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Agroecology, Information Systems
Abstract: The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives., Design Type(s)modeling and simulation objective • data integration objective • software variation design • time series designMeasurement Type(s)cultivated environmentTechnology Type(s)computational modeling techniqueFactor Type(s)crop • atmospheric weather • atmospheric wind speed • atmospheric water vapour • atmospheric carbon dioxide • agricultural environmental material • agricultural featureSample Characteristic(s)land Machine-accessible metadata file describing the reported data (ISA-Tab format)
Published: 2019
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2. 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
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3. How precisely do maize crop models simulate the impact of climate change variables on yields and water use?

Author: Durand, J. L., Bassu, Simona, Brisson, Nadine, Boote, Kenneth J., Lizano, Jon, Jones, James W., Rosenzweig, Cynthia, Ruane, Alex C., Myriam ADAM, Baron, Christian, Basso, Bruno, Biernath, Christian, Boogaard, Hendrik, Conijn, Sjaak, Corbeels, Marc, Deryng, Delphine, Sanctis, Giacomo, Gayler, Sebastian, Grassini, Patricio, Hatfield, Jerry L., Hoek, Steven B., Izaurralde, Cesar, Jongschaap, Raymond, Kemanian, Armen, Kersebaum, Kurt Christian, Kim, Soo-Hyung, Kumar, Naresh S., Makowski, David, Müller, Christoph, Nendel, Claas, Priesack, Eckart, Pravia, Maria Virginia, Sau, Federico, Shcherbak, Iurii, Tao, Fulu, Teixeira, Edmar, Timlin, Dennis, Waha, Katharina, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Department of agronomy, University of Florida [Gainesville] (UF), Department Produccion vegetal, Fitotecnia, Universidad Politécnica de Madrid (UPM), 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), National Aeronautics and Space Administration, Partenaires INRAE, 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), 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), 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), Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department crop systems, forestry and environmental sciences, University of Basilicata, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Centre for Geo-Information, ALTERRA, Plant Research International (PRI), Wageningen University and Research [Wageningen] (WUR), 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, University of East Anglia [Norwich] (UEA), Agroclim (AGROCLIM), Water and earth system science [Tübingen] (WESS), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Department of agronomy and horticulture, University of Nebraska [Lincoln], University of Nebraska System-University of Nebraska System, United States Department of Agriculture (USDA), Pacific Northwest National Laboratory (PNNL), University of Maryland [College Park], University of Maryland System, Department of Plant Science, Pennsylvania State University (Penn State), Penn State System-Penn State System, Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), University of Washington [Seattle], Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Potsdam Institute for Climate Impact Research (PIK), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Sustainable Production, Plant & Food Research, ARS Crop Systems and Global Change Laboratory, United States Department of Agriculture, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, and ProdInra, Migration
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, F01 - Culture des plantes, P40 - Météorologie et climatologie, U10 - Informatique, mathématiques et statistiques, F62 - Physiologie végétale - Croissance et développement, P10 - Ressources en eau et leur gestion, ComputingMilieux_MISCELLANEOUS
Abstract: AgMIP is an international program bringing together research projects on climate, crop modelling and regional agriculture adaptation to climate change. One objective is to better assess the projections of global food availability depending on different staple crops (wheat, rice and maize), taking into account the projections of climate change for the end of century and the uncertainty attached to them. The need for robust estimates, i.e. good crop models for yields and use of natural resources is a prerequisite to benchmark the various cropping systems and local solutions that will ultimately be explored in order to cope with climate change, without bringing about any negative side effects on the environment. Modelers hence work together internationally in order to compare and improve process-based crop simulation models. Maize is a strategic crop, exhibiting high potential radiation and water use efficiencies and is cultivated worldwide. In a first phase, the impacts of CO2 and temperature on the maize yields and water use were studied using 23 crop models on 4 sites with contrasted cool or hot climate conditions, under no water limitation (Lusignan in France, Ames in the United States, Morogoro in Tanzania and Rio Verde in Brasil). Models were run using local soil conditions and climate variables for 30 years (1980-2010) after adjusting the cultivar parameters to the ones used in one experiment in each site. At the four sites studied, the average values across models of simulated yields were closer to the observed local experimental results than the simulation of any individual model. This indicated that ensemble modelling could be a relevant way to approach the impact of climate change on maize yields. There was also a broad agreement between models to simulate a reduction in maize yield in response to temperature, roughly - 0.5 Mg ha-1 per °C increase, with no significant impact on water use, although the latter variable was estimated with a large variability between models. Plant phenology was the mostly altered process with increasing temperature. Shortening of the duration from flowering to maturity in particular reduced the gain in grain weight during that phase. This suggests that genetics could hence play a key role in adapting maize production to climate change, at least under high water availability. Doubling [CO2] from 360 to 720 μmole mole-1 increased grain yield by 7.5% on average across models and sites, with a slight decrease of water use, bringing about an increase in water use efficiency. However, the variability of the response to [CO2] was very high, bringing about the need to better simulate the role of CO2, especially on plant transpiration. In a second phase, models are therefore now being tested against Free Air CO2 Enrichment experimental data, so that variability can be reduced and the actual impact of global change on water use can be assessed with a relevant precision to adaptating agricultural practices. (Texte intégral)
Published: 2015

4. 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
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5. Analyse des incertitudes associées aux prédictions de la teneur en argile obtenues par imagerie hyperspectrale Vis-NIR aéroportée (0.4-2.5µm)

Author: Gomez, Cécile, Drost, ARTHUR, Roger, Jean-Michel, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-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), Centre for Geo-Information, ALTERRA, Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
Subjects: analyse d'incertitude, argile, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, géolocalisation, spectrographie en proche infrarouge, imagerie hyperspectrale, qualité du sol
Abstract: National audience; Depuis quelques années, un nombre croissant d’études ont montré que l’imagerie spectroscopique Visible Proche-Infrarouge (0.4-2.5μm) aéroportée peut fournir une estimation géo-localisée de plusieurs propriétés de sol telles que l’Argile, la matière Organique, le Carbonate de Calcium ou encore le Fer. Ces estimations sont généralement réalisées par le biais de modèles de régression multi-variés, construit en Cross-validation sur des bases de données d’étalonnage, puis validées sur des bases de données indépendantes. La performance globale de ces modèles est étudiée à travers l’analyse d’indices tels que l’Erreur Standard de Prédiction (SEP), le coefficient de détermination (R2), ou encore l’Erreur quadratique moyenne (RMSEP). Au-delà de ces indices reflétant la performance globale des modèles, l’analyse de l’erreur et de l’incertitude affectant chaque nouvelle prédiction reste un enjeu. On peut définir l’erreur de prédiction comme « l’écart entre la valeur prédite et la valeur vraie ». Et on peut définir l’incertitude comme « la variance des prédictions ». Cette étude s’intéresse à l’estimation et l’interprétation de l’incertitude associée à chaque nouvelle prédiction. Les données spectrales considérées dans cette étude sont des données aéroportées acquises par le capteur AISA-DUAL (280 bandes spectrales entre 0.4-2.5μm), sur le Bassin Versant du Lebna (300 km², Tunisie), avec une résolution spatiale de 5 m. Un modèle de régression PLS (Partial Least Square) a été construit à partir d’une base de données de 96 individus afin de prédire le taux d’Argile et validé par une base de données indépendante de 32 individus. Trois expressions de l’incertitude associée aux prédictions d’Argile, développées initialement par Fernandez-Ahumada et al. (2012) pour des modèles de régression calés sur des données spectrales de Laboratoire, ont été testées sur nos données aéroportées : i) un 1er terme exprime l’incertitude liée au modèle de régression, ii) un 2ème terme exprime l’incertitude liée au spectre permettant la prédiction, et iii) un 3ème terme exprime la dépendance des deux premiers. De plus des expressions d’incertitude plus communément employées telles que le Leverage, la distance de Mahalanobis et la variance de prédiction par Boostrap, ont également été testées. La dimension spatiale de nos données a été prise en compte dans l’étude de cette incertitude associée aux prédictions. Une analyse de ces différentes expressions d’incertitude a permis de mieux comprendre l’origine des différentes sources d’incertitude et de mettre en évidence des zones à fort risque de mauvaises prédictions.
Published: 2014

6. How do various maize crop models vary in their responses to climate change factors?

Author: Federico Sau, Sjaak Conijn, Delphine Deryng, Jean-Louis Durand, Katharina Waha, Edmar Teixeira, Iurii Shcherbak, R.E.E. Jongschaap, James W. Jones, Kenneth J. Boote, Maria Virginia Pravia, Jerry L. Hatfield, Alex C. Ruane, Christian Biernath, Patricio Grassini, H.L. Boogaard, Steven Hoek, K. Christian Kersebaum, Fulu Tao, Christian Baron, David Makowski, Claas Nendel, Sebastian Gayler, Dennis Timlin, Marc Corbeels, Christoph Müller, Nadine Brisson, Jon I. Lizaso, Naresh S. Kumar, Cynthia Rosenzweig, Simona Bassu, Armen R. Kemanian, Cesar Izaurralde, Bruno Basso, Giacomo De Sanctis, Myriam Adam, Soo-Hyung Kim, Eckart Priesack, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Department of agronomy, University of Florida [Gainesville], Department Produccion vegetal, Fitotecnia, Universidad Politécnica de Madrid (UPM), Department of agricultural and biological engineering, GISS Climate impacts group, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC)-NASA Goddard Space Flight Center (GSFC), 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), 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, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department crop systems, forestry and environmental sciences, University of Basilicata, Centre for Geo-Information, ALTERRA, WUR-Plant research international, 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), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), School of Environmental Sciences [Norwich], UE Agroclim (UE AGROCLIM), Water and earth system science [Tübingen] (WESS), Eberhard Karls Universität Tübingen, Department of agronomy and horticulture, University of Nebraska [Lincoln], University of Nebraska System-University of Nebraska System, Department of plant science, University of Pensylvania, Institute of Lanscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), School of environmental and forest sciences, University of Washington [Seattle], Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of landscape systems analysis, Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Universidad Politécnica de Madrid ( UPM ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ) -NASA Goddard Space Flight Center ( GSFC ), 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 ), 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 ), Wageningen University and Research Centre [Wageningen] ( WUR ), Annual cropping systems, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Tyndall Centre for climate change research and school of environmental sciences, University of East Anglia [Norwich] ( UEA ), UE Agroclim ( UE AGROCLIM ), Water and earth system science (WESS) competence cluster, University of Nebraska-Lincoln, Leibniz Centre for Agricultural Landscape Research, Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), University of Florida [Gainesville] (UF), 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), 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), Wageningen University and Research [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agroclim (AGROCLIM), and Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, nitrogen dynamics, Atmospheric sciences, maize, 01 natural sciences, Standard deviation, F01 - Culture des plantes, wheat, Aardobservatie en omgevingsinformatica, water-use efficiency, uncertainty, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, General Environmental Science, agriculture, 2. Zero hunger, Global and Planetary Change, elevated co2, Ecology, Geography, Phenology, U10 - Informatique, mathématiques et statistiques, Agricultura, 04 agricultural and veterinary sciences, simulation, Rendement des cultures, model intercomparison, climate change, CO2, Crop simulation model, Modèle mathématique, Crops, Agricultural, simulation-model, Earth Observation and Environmental Informatics, air co2 enrichment, P40 - Météorologie et climatologie, Agmip, Climate, Maize, Model Intercomparison, Simulation, Temperature, Uncertainty, Climate change, carbon-dioxide, Models, Biological, Zea mays, Agro Water- en Biobased Economy, Environmental Chemistry, Water-use efficiency, climate, 0105 earth and related environmental sciences, Changement climatique, systems simulation, Modélisation des cultures, Crop yield, Simulation modeling, Water, temperature, Modèle de simulation, Carbon Dioxide, yield, Température, Agronomy, 13. Climate action, Yield (chemistry), 040103 agronomy & agriculture, AgMIP, 0401 agriculture, forestry, and fisheries, Environmental science, Dioxyde de carbone
Abstract: Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
Published: 2014
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7. Digital soil mapping: Approaches to integrate sensing techniques to the prediction of key soil properties

Author: Ulrike Werban, Steffen Zacharias, Peter Dietrich, Harm Bartholomeus, Gilles Grandjean, Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research (UFZ), Centre for Geo-information, and Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)
Subjects: 2. Zero hunger, Soil map, Hydrology, 0207 environmental engineering, Soil Science, Soil science, 04 agricultural and veterinary sciences, 02 engineering and technology, 15. Life on land, PE&RC, GeneralLiterature_MISCELLANEOUS, Laboratory of Geo-information Science and Remote Sensing, 13. Climate action, Digital soil mapping, Vadose zone, [SDE]Environmental Sciences, 040103 agronomy & agriculture, Key (cryptography), Life Science, 0401 agriculture, forestry, and fisheries, Environmental science, Soil properties, Laboratorium voor Geo-informatiekunde en Remote Sensing, Soil parameters, 020701 environmental engineering
Abstract: International audience; Knowledge about soil properties and their variation in space and time is a key challenge for understanding processes in the vadose zone. As we consider more complex models, applied to larger geographical regions, the demand for information on key soil parameters and thus input variables becomes harder to meet. In particular, classical soil maps and soil sampling has limitations in resolution, and information might be subjective. In the face of the new challenges in the context of climate and global change and the increasing need of multidisciplinarity, a more sophisticated use of soil information beyond the "classical" soil information is needed. Predictive soil mapping (Scull et al., 2003) or digital soil function mapping (Ugbemuna and Reuter, 2013) are promising ways to enhance the information content of soil maps. The application of pedometrical approaches, e. g., digital soil mapping (DSM) as one focus of pedometrics, provides the potential to overcome these issues. Today, DSM is well established in soil science, providing tools for improving, advancing, and increasing a more objective collection and spatial analysis of soil data. A broad variety of tools helping to improve the quality of diagnoses, to refine their resolution by using correlations between soil properties, sensor data, and environmental covariates is available and intensively discussed in the soil science community (Adamchuk and Viscarra Rossel, 2010; McBratney et al., 2003; Behrens and Scholten, 2006). However, Carre et al. (2007) asked the DSM community to be much more aware of end-user requirements and coined the term digital soil assessment for the quantitative modeling of soil attributes for assessing soil threats and soil functions. In addition, Grunwald (2009) identified a need for more sophisticated technologies to measure soil properties. Finke (2012) indicated the potential of integration of sensors in making maps and providing model input. Concerning soil moisture, Vereecken et al. (2008) described the need to develop strategies that combine hydrogeophysical measurement techniques with remote sensing methods and, moreover, the development of novel upscaling methods for predicting effective moisture fluxes and disaggregation schemes
Published: 2013
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8. Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution

Author: Michael E. Schaepman, Lucie Homolová, Radek Pokorny, Pavel Cudlín, Jean-Philippe Gastellu-Etchegorry, Z. Malenovsky, Raul Zurita-Milla, Emmanuel Martin, Jan G. P. W. Clevers, Centre for Geo-information, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute of Systems Biology and Ecology (AS CR), Institute of Systems Biology and Ecology AS CR, Faculty of Geo-Information Science and Earth Observation, Department of Geo-information Processing, UT-I-ITC-STAMP, Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), University of Zurich, and Malenovský, Z
Subjects: Canopy, modis-lai product, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Laboratory of Geo-information Science and Remote Sensing, spectral properties, 910 Geography & travel, 1111 Soil Science, remotely-sensed data, vegetation indexes, computer.programming_language, GIP, biology, 1903 Computers in Earth Sciences, Geology, PE&RC, radiative-transfer models, 10122 Institute of Geography, bidirectional reflectance, Norway spruce canopy, Woody elements, leaf-area index, ADLIB-ART-2707, Soil Science, Red edge, Normalized Difference Vegetation Index, AISA, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Radiative transfer, Laboratorium voor Geo-informatiekunde en Remote Sensing, Computers in Earth Sciences, Leaf area index, 1907 Geology, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Tree canopy, Dart, High spatial resolution, net primary production, Picea abies, 15. Life on land, biology.organism_classification, gross primary production, LAI, Spectroradiometer, biophysical variables, ITC-ISI-JOURNAL-ARTICLE, Environmental science, DART, computer
Abstract: A detailed sensitivity analysis investigating the effect of woody elements introduced into the Discrete Anisotropic Radiative Transfer (DART) model on the nadir bidirectional reflectance factor (BRF) for a simulated Norway spruce canopy was performed at a very high spatial resolution (modelling resolution 0.2 m, output pixel size 0.4 m). We used such a high resolution to be able to parameterize DART in an appropriate way and subsequently to gain detailed understanding of the influence of woody elements contributing to the radiative transfer within heterogeneous canopies. Three scenarios were studied by modelling the Norway spruce canopy as being composed of i) leaves, ii) leaves, trunks and first order branches, and finally iii) leaves, trunks, first order branches and small woody twigs simulated using mixed cells (i.e. cells approximated as composition of leaves and/or twigs turbid medium, and large woody constituents). The simulation of each scenario was performed for 10 different canopy closures (CC = 50–95%, in steps of 5%), 25 leaf area index (LAI = 3.0–15.0 m2 m− 2, in steps of 0.5 m2 m− 2), and in four spectral bands (centred at 559, 671, 727, and 783 nm, with a FWHM of 10 nm). The influence of woody elements was evaluated separately for both, sunlit and shaded parts of the simulated forest canopy, respectively. The DART results were verified by quantifying the simulated nadir BRF of each scenario with measured Airborne Imaging Spectroradiometer (AISA) Eagle data (pixel size of 0.4 m). These imaging spectrometer data were acquired over the same Norway spruce stand that was used to parameterise the DART model. The Norway spruce canopy modelled using the DART model consisted of foliage as well as foliage including robust woody constituents (i.e. trunks and branches). All results showed similar nadir BRF for the simulated wavelengths. The incorporation of small woody parts in DART caused the canopy reflectance to decrease about 4% in the near-infrared (NIR), 2% in the red edge (RE) and less than 1% in the green band. The canopy BRF of the red band increased by about 2%. Subsequently, the sensitivity on accounting for woody elements for two spectral vegetation indices, the normalized difference vegetation index (NDVI) and the angular vegetation index (AVI), was evaluated. Finally, we conclude on the importance of including woody elements in radiative transfer based approaches and discuss the applicability of the vegetation indices as well as the physically based inversion approaches to retrieve the forest canopy LAI at very high spatial resolution.
Published: 2008
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9. Spatial analysis of chronic obstructive pulmonary disease and its risk factors in an urban area of Trivandrum, Kerala, India.

Author: Surendran S, Mohan A, Valamparampil MJ, Nair S, Balakrishnan SK, Laila AA, Reghunath R, Jose C, Rajeevan A, Vasudevakaimal P, Surendrannair AT, Nujum ZT, Varghese S, and Mohan A
Abstract: Background: Chronic obstructive pulmonary disease (COPD) is the second leading cause of death in India. The objective of this study was to map COPD cases and its risk factors and to determine the association between them using geographic information system (GIS) in a semi-urban area of Trivandrum, South India., Materials and Methods: This community-based cross-sectional, descriptive study (n = 494) was conducted in a subcenter area of a primary health center. Location was mapped and COPD population screener questionnaire was administered to all the study subjects enrolled by census method. Lifetime firewood exposure (person-hours) and tobacco smoking were enquired and distance from road was mapped using portable differential global positioning system. The association with COPD was assessed by kriging and hotspot analysis using ArcGIS software., Results: The prevalence of COPD (6.5%) was comparable to national prevalence estimates. Spatial maps showed COPD case clustering in areas with higher firewood exposure, greater smoking exposure, and in households with closer proximity to local roads. A particular high-risk cluster was obtained which had a significant association with all the risk factors., Conclusion: GIS technology is useful in identification of spatial clustering of COPD cases and its environmental risk factors, making it an important tool for targeted interventions for COPD., Competing Interests: None
Published: 2022
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10. Climate change vulnerability in a tropical region based on environmental and socio-economic factors.

Author: Sarun S, Ghermandi A, Sheela AM, Justus J, and Vineetha P
Subjects: Humans, India, Socioeconomic Factors, Wetlands, Climate Change, Environmental Monitoring methods
Abstract: The understanding of the regional and local dimensions of vulnerability due to climate change is essential to develop appropriate and targeted adaptation efforts. We assessed the local dimensions of vulnerability in the tropical state of Kerala, India, using a purposely developed vulnerability index, which accounts for both environmental and socio-economic factors. The large extents of coastal wetlands and lagoons and high concentration of mangrove forests make the state environmentally vulnerable. Low human development index, large population of socially deprived groups, which are dependent on the primary sector, and high population density make the state vulnerable from a socio-economic point of view. The present study investigates climate change vulnerability at the district level in the State of Kerala relying on a purposely developed composite vulnerability index that encompasses both socio-economic and environmental factors. The Kerala coast contains the socio-economically and ecologically most vulnerable regions, as demonstrated by a composite vulnerability index.
Published: 2018
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11. An integrated pan-tropical biomass map using multiple reference datasets.

Author: Avitabile V, Herold M, Heuvelink GB, Lewis SL, Phillips OL, Asner GP, Armston J, Ashton PS, Banin L, Bayol N, Berry NJ, Boeckx P, de Jong BH, DeVries B, Girardin CA, Kearsley E, Lindsell JA, Lopez-Gonzalez G, Lucas R, Malhi Y, Morel A, Mitchard ET, Nagy L, Qie L, Quinones MJ, Ryan CM, Ferry SJ, Sunderland T, Laurin GV, Gatti RC, Valentini R, Verbeeck H, Wijaya A, and Willcock S
Subjects: Datasets as Topic, Models, Theoretical, Trees, Tropical Climate, Biomass, Maps as Topic
Abstract: We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets., (© 2015 John Wiley & Sons Ltd.)
Published: 2016
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12. Mobile devices for community-based REDD+ monitoring: a case study for Central Vietnam.

Author: Pratihast AK, Herold M, Avitabile V, de Bruin S, Bartholomeus H, Souza CM Jr, and Ribbe L
Subjects: Computers, Handheld, Developing Countries, Ecosystem, Environmental Monitoring methods, Environmental Pollutants, Equipment Design, Geography, Reproducibility of Results, Software, Trees, Vietnam, Carbon analysis, Conservation of Natural Resources, Environmental Monitoring instrumentation
Abstract: Monitoring tropical deforestation and forest degradation is one of the central elements for the Reduced Emissions from Deforestation and Forest Degradation in developing countries (REDD+) scheme. Current arrangements for monitoring are based on remote sensing and field measurements. Since monitoring is the periodic process of assessing forest stands properties with respect to reference data, adopting the current REDD+ requirements for implementing monitoring at national levels is a challenging task. Recently, the advancement in Information and Communications Technologies (ICT) and mobile devices has enabled local communities to monitor their forest in a basic resource setting such as no or slow internet connection link, limited power supply, etc. Despite the potential, the use of mobile device system for community based monitoring (CBM) is still exceptional and faces implementation challenges. This paper presents an integrated data collection system based on mobile devices that streamlines the community-based forest monitoring data collection, transmission and visualization process. This paper also assesses the accuracy and reliability of CBM data and proposes a way to fit them into national REDD+ Monitoring, Reporting and Verification (MRV) scheme. The system performance is evaluated at Tra Bui commune, Quang Nam province, Central Vietnam, where forest carbon and change activities were tracked. The results show that the local community is able to provide data with accuracy comparable to expert measurements (index of agreement greater than 0.88), but against lower costs. Furthermore, the results confirm that communities are more effective to monitor small scale forest degradation due to subsistence fuel wood collection and selective logging, than high resolution remote sensing SPOT imagery.
Published: 2012
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13. Exploring patterns of movement suspension in pedestrian mobility.

Author: Orellana D and Wachowicz M
Subjects: History, 20th Century, History, 21st Century, Safety economics, Safety history, Safety legislation & jurisprudence, Cities economics, Cities ethnology, Cities history, Cities legislation & jurisprudence, Public Health economics, Public Health education, Public Health history, Public Health legislation & jurisprudence, Spatial Behavior, Transportation economics, Transportation history, Transportation legislation & jurisprudence, Walking economics, Walking education, Walking history, Walking legislation & jurisprudence, Walking physiology, Walking psychology
Abstract: One of the main tasks in analyzing pedestrian movement is to detect places where pedestrians stop, as those places usually are associated with specific human activities, and they can allow us to understand pedestrian movement behavior. Very few approaches have been proposed to detect the locations of stops in positioning data sets, and they often are based on selecting the location of candidate stops as well as potential spatial and temporal thresholds according to different application requirements. However, these approaches are not suitable for analyzing the slow movement of pedestrians where the inaccuracy of a nondifferential global positioning system commonly used for movement tracking is so significant that it can hinder the selection of adequate thresholds. In this article, we propose an exploratory statistical approach to detect patterns of movement suspension using a local indicator of spatial association (LISA) in a vector space representation. Two different positioning data sets are used to evaluate our approach in terms of exploring movement suspension patterns that can be related to different landscapes: players of an urban outdoor mobile game and visitors of a natural park. The results of both experiments show that patterns of movement suspension were located at places such as checkpoints in the game and different attractions and facilities in the park. Based on these results, we conclude that using LISA is a reliable approach for exploring movement suspension patterns that represent the places where the movement of pedestrians is temporally suspended by physical restrictions (e.g., checkpoints of a mobile game and the route choosing points of a park).
Published: 2011
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14. Sensing a changing world.

Author: Ligtenberg A and Kooistra L
Published: 2009
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15. Development of a Dynamic Web Mapping Service for Vegetation Productivity Using Earth Observation and in situ Sensors in a Sensor Web Based Approach.

Author: Kooistra L, Bergsma A, Chuma B, and de Bruin S
Abstract: This paper describes the development of a sensor web based approach which combines earth observation and in situ sensor data to derive typical information offered by a dynamic web mapping service (WMS). A prototype has been developed which provides daily maps of vegetation productivity for the Netherlands with a spatial resolution of 250 m. Daily available MODIS surface reflectance products and meteorological parameters obtained through a Sensor Observation Service (SOS) were used as input for a vegetation productivity model. This paper presents the vegetation productivity model, the sensor data sources and the implementation of the automated processing facility. Finally, an evaluation is made of the opportunities and limitations of sensor web based approaches for the development of web services which combine both satellite and in situ sensor sources.
Published: 2009
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16. Open source GIS for HIV/AIDS management.

Author: Vanmeulebrouk B, Rivett U, Ricketts A, and Loudon M
Subjects: Acquired Immunodeficiency Syndrome epidemiology, Acquired Immunodeficiency Syndrome prevention & control, Acquired Immunodeficiency Syndrome therapy, Community Networks organization & administration, Developing Countries, Feasibility Studies, Female, HIV Infections prevention & control, Health Care Surveys, Humans, Information Dissemination, Internet, Male, Needs Assessment, Program Development, Program Evaluation, Safety Management, Severity of Illness Index, Software, South Africa epidemiology, Total Quality Management, Geographic Information Systems organization & administration, HIV Infections epidemiology, HIV Infections therapy, Information Management methods
Abstract: Background: Reliable access to basic services can improve a community's resilience to HIV/AIDS. Accordingly, work is being done to upgrade the physical infrastructure in affected areas, often employing a strategy of decentralised service provision. Spatial characteristics are one of the major determinants in implementing services, even in the smaller municipal areas, and good quality spatial information is needed to inform decision making processes. However, limited funds, technical infrastructure and human resource capacity result in little or no access to spatial information for crucial infrastructure development decisions at local level.This research investigated whether it would be possible to develop a GIS for basic infrastructure planning and management at local level. Given the resource constraints of the local government context, particularly in small municipalities, it was decided that open source software should be used for the prototype system., Results: The design and development of a prototype system illustrated that it is possible to develop an open source GIS system that can be used within the context of local information management. Usability tests show a high degree of usability for the system, which is important considering the heavy workload and high staff turnover that characterises local government in South Africa. Local infrastructure management stakeholders interviewed in a case study of a South African municipality see the potential for the use of GIS as a communication tool and are generally positive about the use of GIS for these purposes. They note security issues that may arise through the sharing of information, lack of skills and resource constraints as the major barriers to adoption., Conclusion: The case study shows that spatial information is an identified need at local level. Open source GIS software can be used to develop a system to provide local-level stakeholders with spatial information. However, the suitability of the technology is only a part of the system - there are wider information and management issues which need to be addressed before the implementation of a local-level GIS for infrastructure management can be successful.
Published: 2008
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17. Changes in farmers' knowledge of maize diversity in highland Guatemala, 1927/37-2004.

Author: van Etten J
Subjects: Anthropology, Cultural, Ethnobotany, Genetic Variation, Guatemala, Humans, Social Change, Terminology as Topic, Agriculture, Intergenerational Relations, Knowledge, Zea mays classification, Zea mays genetics
Abstract: Small-scale studies on long-term change in agricultural knowledge might uncover insights with broader, regional implications. This article evaluates change in farmer knowledge about crop genetic resources in highland Guatemala between 1927/37 and 2004. It concentrates on maize (Zea mays ssp. mays L.) in one Guatemalan township, Jacaltenango, an area with much ecological and maize diversity. It relies on a particular type of baseline information: lists of farmer-defined cultivars drawn up by ethnographers in the first half of the twentieth century. A questionnaire format based on two independent lists of local farmer cultivars dating from 1927 and 1937 was used to assess changes in maize diversity. Comparisons between attributes given to each cultivar in the past and in 2004 were used as a partial test of the stability of cultivar identity. In farmers' perceptions, cultivar loss was low and limited to certain cultivars adapted to the warmer environments. Crop production problems were mentioned as the main motives for change. No evidence for a loss of cultivars due to the political violence of the 1980s was found. In the lower areas many newly introduced cultivars were found, which reportedly provide solutions for the production problems the older cultivars have. The article contrasts these findings with those of an earlier study which suggested much cultivar loss due to political violence, and draws conclusions about the methodological implications.
Published: 2006
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18. Do users ignore spatial data quality? A decision-theoretic perspective.

Author: van Oort PA and Bregt AK
Abstract: Risk analysis (RA) has been proposed as a means of assessing fitness for use of spatial data but is only rarely adopted. The proposal is that better decisions can be made by accounting for risks due to errors in spatial data. Why is RA so rarely adopted? Most geographical information science (GISc) literature stresses educational and technical constraints. In this article we propose, based on decision theory, a number of hypotheses for why the user would be more or less willing to spend resources on RA. The hypotheses were tested with a questionnaire, which showed that the willingness to spend resources on RA depends on the presence of feedback mechanisms in the decision-making process, on how much is at stake, and to a minor extent on how well the decision-making process can be modeled.
Published: 2005
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19. Spatial variability and uncertainty in ecological risk assessment: a case study on the potential risk of cadmium for the little owl in a Dutch river flood plain.

Author: Kooistra L, Huijbregts MA, Ragas AM, Wehrens R, and Leuven RS
Subjects: Animals, Computer Simulation, Demography, Feeding Behavior physiology, Monte Carlo Method, Netherlands, Regression Analysis, Risk Assessment, Cadmium toxicity, Conservation of Natural Resources methods, Environment, Food Chain, Models, Theoretical, Soil Pollutants analysis, Strigiformes physiology, Uncertainty
Abstract: This paper outlines a procedure that quantifies the impact of different sources of spatial variability and uncertainty on ecological risk estimates. The procedure is illustrated in a case study that estimates the risks of cadmium for a little owl (Athene noctua vidalli) living in a Dutch river flood plain along the river Rhine. A geographical information system (GIS) was used to quantify spatial variability in contaminant concentrations and habitats. It was combined with an exposure and effect model that uses Monte Carlo simulation to quantify parameter uncertainty. Spatial model uncertainty was assessed by the application of two different spatial interpolation methods (classification and kriging) and foraging ranges. The results of the case study show that parameter uncertainty is the main type of uncertainty influencing the risk estimate, and to a lesser extent spatial variability, while spatial model uncertainty was of minor importance. Compared to the deterministically calculated hazard index for the little owl (0.9), inclusion of spatial variability resulted in a median hazard index that can vary between 0.8 and 1.4. It is concluded that a single estimator for a whole flood plain may over- or underestimate risks for specific parts within the flood plain. Further research that expands the procedure presented in this paper is necessary to improve the incorporation of spatial factors in ecological risk assessment.
Published: 2005
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20. A design and application of a multi-agent system for simulation of multi-actor spatial planning.

Author: Ligtenberg A, Wachowicz M, Bregt AK, Beulens A, and Kettenis DL
Subjects: Communication, Decision Making, Forecasting, Netherlands, Conservation of Natural Resources, Environment Design, Models, Theoretical
Abstract: Multi-agent Systems (MAS) offer a conceptual approach to include multi-actor decision making into models of land use change. The main goal is to explore the use of MAS to simulate spatial scenarios based on modelling multi-actor decision-making within a spatial planning process. We demonstrate MAS that consists of agents representing organizations and interest groups involved in an urban allocation problem during a land use planning process. The multi-actor based decision-making is modelled by generating beliefs and preferences of actors about the location of and relation between spatial objects. This allows each agent to confront these beliefs and preferences with it's own desires and with that of other agents. The MAS loosely resembles belief, desire and intentions architecture. Based on a case study for a hypothetical land use planning situation in a study area in the Netherlands we discuss the potential and limitations of the MAS to build models that enable spatial planners to include the 'actor factor' in their analysis and design of spatial scenarios. In addition, our experiments revealed the need for further research on the representation of spatial objects and reasoning, learning and communication about allocation problems using MAS.
Published: 2004
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20 results on '"Centre for Geo-Information"'

1. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

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

3. How precisely do maize crop models simulate the impact of climate change variables on yields and water use?

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

5. Analyse des incertitudes associées aux prédictions de la teneur en argile obtenues par imagerie hyperspectrale Vis-NIR aéroportée (0.4-2.5µm)

6. How do various maize crop models vary in their responses to climate change factors?

7. Digital soil mapping: Approaches to integrate sensing techniques to the prediction of key soil properties

8. Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution

9. Spatial analysis of chronic obstructive pulmonary disease and its risk factors in an urban area of Trivandrum, Kerala, India.

10. Climate change vulnerability in a tropical region based on environmental and socio-economic factors.

11. An integrated pan-tropical biomass map using multiple reference datasets.

12. Mobile devices for community-based REDD+ monitoring: a case study for Central Vietnam.

13. Exploring patterns of movement suspension in pedestrian mobility.

14. Sensing a changing world.

15. Development of a Dynamic Web Mapping Service for Vegetation Productivity Using Earth Observation and in situ Sensors in a Sensor Web Based Approach.

16. Open source GIS for HIV/AIDS management.

17. Changes in farmers' knowledge of maize diversity in highland Guatemala, 1927/37-2004.

18. Do users ignore spatial data quality? A decision-theoretic perspective.

19. Spatial variability and uncertainty in ecological risk assessment: a case study on the potential risk of cadmium for the little owl in a Dutch river flood plain.

20. A design and application of a multi-agent system for simulation of multi-actor spatial planning.

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