Author: "Delphine Deryng" / Publication Year Range: Last 10 years - Searchworks@Jio Institute Digital Library Search Results

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49 results on '"Delphine Deryng"'

1. A global dataset for the projected impacts of climate change on four major crops

Author: Toshihiro Hasegawa, Hitomi Wakatsuki, Hui Ju, Shalika Vyas, Gerald C. Nelson, Aidan Farrell, Delphine Deryng, Francisco Meza, and David Makowski
Subjects: Science
Abstract: Measurement(s) relative yield change Technology Type(s) crop simulation model Factor Type(s) geographic location • current average temperature • current annual precipitation • future mid-point • climate scenario • temperature change • annual precipitation change • CO2 ppm Sample Characteristic - Organism Zea mays • Oryza sativa • Glycine max • Triticum aestivum Sample Characteristic - Environment climate change Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.17427674
Published: 2022
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2. Global irrigation contribution to wheat and maize yield

Author: Xuhui Wang, Christoph Müller, Joshua Elliot, Nathaniel D. Mueller, Philippe Ciais, Jonas Jägermeyr, James Gerber, Patrice Dumas, Chenzhi Wang, Hui Yang, Laurent Li, Delphine Deryng, Christian Folberth, Wenfeng Liu, David Makowski, Stefan Olin, Thomas A. M. Pugh, Ashwan Reddy, Erwin Schmid, Sujong Jeong, Feng Zhou, and Shilong Piao
Subjects: Science
Abstract: There are big uncertainties in the contribution of irrigation to crop yields. Here, the authors use Bayesian model averaging to combine statistical and process-based models and quantify the contribution of irrigation for wheat and maize yields, finding that irrigation alone cannot close yield gaps for a large fraction of global rainfed agriculture.
Published: 2021
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3. State-of-the-art global models underestimate impacts from climate extremes

Author: Jacob Schewe, Simon N. Gosling, Christopher Reyer, Fang Zhao, Philippe Ciais, Joshua Elliott, Louis Francois, Veronika Huber, Heike K. Lotze, Sonia I. Seneviratne, Michelle T. H. van Vliet, Robert Vautard, Yoshihide Wada, Lutz Breuer, Matthias Büchner, David A. Carozza, Jinfeng Chang, Marta Coll, Delphine Deryng, Allard de Wit, Tyler D. Eddy, Christian Folberth, Katja Frieler, Andrew D. Friend, Dieter Gerten, Lukas Gudmundsson, Naota Hanasaki, Akihiko Ito, Nikolay Khabarov, Hyungjun Kim, Peter Lawrence, Catherine Morfopoulos, Christoph Müller, Hannes Müller Schmied, René Orth, Sebastian Ostberg, Yadu Pokhrel, Thomas A. M. Pugh, Gen Sakurai, Yusuke Satoh, Erwin Schmid, Tobias Stacke, Jeroen Steenbeek, Jörg Steinkamp, Qiuhong Tang, Hanqin Tian, Derek P. Tittensor, Jan Volkholz, Xuhui Wang, and Lila Warszawski
Subjects: Science
Abstract: Impact models projections are used in integrated assessments of climate change. Here the authors test systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions.
Published: 2019
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4. Consistent negative response of US crops to high temperatures in observations and crop models

Author: Bernhard Schauberger, Sotirios Archontoulis, Almut Arneth, Juraj Balkovic, Philippe Ciais, Delphine Deryng, Joshua Elliott, Christian Folberth, Nikolay Khabarov, Christoph Müller, Thomas A. M. Pugh, Susanne Rolinski, Sibyll Schaphoff, Erwin Schmid, Xuhui Wang, Wolfram Schlenker, and Katja Frieler
Subjects: Science
Abstract: Future agricultural productivity is threatened by high temperatures. Here, using 9 crop models, Schaubergeret al. find that yield losses due to temperatures >30 °C are captured by current models where yield losses by mild heat stress occur mainly due to water stress and can be buffered by irrigation.
Published: 2017
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5. Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble.

Author: Christian Folberth, Joshua Elliott, Christoph Müller, Juraj Balkovič, James Chryssanthacopoulos, Roberto C Izaurralde, Curtis D Jones, Nikolay Khabarov, Wenfeng Liu, Ashwan Reddy, Erwin Schmid, Rastislav Skalský, Hong Yang, Almut Arneth, Philippe Ciais, Delphine Deryng, Peter J Lawrence, Stefan Olin, Thomas A M Pugh, Alex C Ruane, and Xuhui Wang
Subjects: Medicine, Science
Abstract: Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.
Published: 2019
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6. Global patterns of crop yield stability under additional nutrient and water inputs.

Author: Christoph Müller, Joshua Elliott, Thomas A M Pugh, Alex C Ruane, Philippe Ciais, Juraj Balkovic, Delphine Deryng, Christian Folberth, R Cesar Izaurralde, Curtis D Jones, Nikolay Khabarov, Peter Lawrence, Wenfeng Liu, Ashwan D Reddy, Erwin Schmid, and Xuhui Wang
Subjects: Medicine, Science
Abstract: Agricultural production must increase to feed a growing and wealthier population, as well as to satisfy increasing demands for biomaterials and biomass-based energy. At the same time, deforestation and land-use change need to be minimized in order to preserve biodiversity and maintain carbon stores in vegetation and soils. Consequently, agricultural land use needs to be intensified in order to increase food production per unit area of land. Here we use simulations of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 to assess implications of input-driven intensification (water, nutrients) on crop yield and yield stability, which is an important aspect in food security. We find region- and crop-specific responses for the simulated period 1980-2009 with broadly increasing yield variability under additional nitrogen inputs and stabilizing yields under additional water inputs (irrigation), reflecting current patterns of water and nutrient limitation. The different models of the GGCMI ensemble show similar response patterns, but model differences warrant further research on management assumptions, such as variety selection and soil management, and inputs as well as on model implementation of different soil and plant processes, such as on heat stress, and parameters. Higher variability in crop productivity under higher fertilizer input will require adequate buffer mechanisms in trade and distribution/storage networks to avoid food price volatility.
Published: 2018
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7. Crop productivity changes in 1.5 °C and 2 °C worlds under climate sensitivity uncertainty

Author: Carl-Friedrich Schleussner, Delphine Deryng, Christoph Müller, Joshua Elliott, Fahad Saeed, Christian Folberth, Wenfeng Liu, Xuhui Wang, Thomas A M Pugh, Wim Thiery, Sonia I Seneviratne, and Joeri Rogelj
Subjects: 1.5 °C, GGCMI, HAPPI, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: Following the adoption of the Paris Agreement, there has been an increasing interest in quantifying impacts at discrete levels of global mean temperature (GMT) increase such as 1.5 °C and 2 °C above pre-industrial levels. Consequences of anthropogenic greenhouse gas emissions on agricultural productivity have direct and immediate relevance for human societies. Future crop yields will be affected by anthropogenic climate change as well as direct effects of emissions such as CO _2 fertilization. At the same time, the climate sensitivity to future emissions is uncertain. Here we investigate the sensitivity of future crop yield projections with a set of global gridded crop models for four major staple crops at 1.5 °C and 2 °C warming above pre-industrial levels, as well as at different CO _2 levels determined by similar probabilities to lead to 1.5 °C and 2 °C, using climate forcing data from the Half a degree Additional warming, Prognosis and Projected Impacts project. For the same CO _2 forcing, we find consistent negative effects of half a degree warming on productivity in most world regions. Increasing CO _2 concentrations consistent with these warming levels have potentially stronger but highly uncertain effects than 0.5 °C warming increments. Half a degree warming will also lead to more extreme low yields, in particular over tropical regions. Our results indicate that GMT change alone is insufficient to determine future impacts on crop productivity.
Published: 2018
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8. Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets

Author: Richard Wartenburger, Sonia I Seneviratne, Martin Hirschi, Jinfeng Chang, Philippe Ciais, Delphine Deryng, Joshua Elliott, Christian Folberth, Simon N Gosling, Lukas Gudmundsson, Alexandra-Jane Henrot, Thomas Hickler, Akihiko Ito, Nikolay Khabarov, Hyungjun Kim, Guoyong Leng, Junguo Liu, Xingcai Liu, Yoshimitsu Masaki, Catherine Morfopoulos, Christoph Müller, Hannes Müller Schmied, Kazuya Nishina, Rene Orth, Yadu Pokhrel, Thomas A M Pugh, Yusuke Satoh, Sibyll Schaphoff, Erwin Schmid, Justin Sheffield, Tobias Stacke, Joerg Steinkamp, Qiuhong Tang, Wim Thiery, Yoshihide Wada, Xuhui Wang, Graham P Weedon, Hong Yang, and Tian Zhou
Subjects: ISIMIP2a, evapotranspiration, uncertainty, cluster analysis, hydrological extreme events, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%–40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.
Published: 2018
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9. Implications of climate mitigation for future agricultural production

Author: Christoph Müller, Joshua Elliott, James Chryssanthacopoulos, Delphine Deryng, Christian Folberth, Thomas A M Pugh, and Erwin Schmid
Subjects: climate change, climate mitigation, agriculture, crop model, AgMIP, ISI-MIP, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: Climate change is projected to negatively impact biophysical agricultural productivity in much of the world. Actions taken to reduce greenhouse gas emissions and mitigate future climate changes, are thus of central importance for agricultural production. Climate impacts are, however, not unidirectional; some crops in some regions (primarily higher latitudes) are projected to benefit, particularly if increased atmospheric carbon dioxide is assumed to strongly increase crop productivity at large spatial and temporal scales. Climate mitigation measures that are implemented by reducing atmospheric carbon dioxide concentrations lead to reductions both in the strength of climate change and in the benefits of carbon dioxide fertilization. Consequently, analysis of the effects of climate mitigation on agricultural productivity must address not only regions for which mitigation is likely to reduce or even reverse climate damages. There are also regions that are likely to see increased crop yields due to climate change, which may lose these added potentials under mitigation action. Comparing data from the most comprehensive archive of crop yield projections publicly available, we find that climate mitigation leads to overall benefits from avoided damages at the global scale and especially in many regions that are already at risk of food insecurity today. Ignoring controversial carbon dioxide fertilization effects on crop productivity, we find that for the median projection aggressive mitigation could eliminate ∼81% of the negative impacts of climate change on biophysical agricultural productivity globally by the end of the century. In this case, the benefits of mitigation typically extend well into temperate regions, but vary by crop and underlying climate model projections. Should large benefits to crop yields from carbon dioxide fertilization be realized, the effects of mitigation become much more mixed, though still positive globally and beneficial in many food insecure countries.
Published: 2015
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10. Strong Regional Influence of Climatic Forcing Datasets on Global Crop Model Ensembles

Author: Alex C. Ruane, Meridel Phillips, Christoph Müller, Joshua Elliott, Jonas Jägermeyr, Almut Arneth, Juraj Balkovic, Delphine Deryng, Christian Folberth, Toshichika Iizumi, Roberto C. Izaurralde, Nikolay Khabarov, Peter Lawrence, Wenfeng Liu, Stefan Olin, Thomas A. M. Pugh, Cynthia Rosenzweig, Gen Sakurai, Erwin Schmid, Benjamin Sultan, Xuhui Wang, Allard de Wit, and Hong Yang
Subjects: Meteorology And Climatology
Abstract: We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.
Published: 2021
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11. Narrowing Uncertainties in the Effects of Elevated CO2 on Crops

Author: Andrea Toreti, Delphine Deryng, Francesco N Tubiello, Christoph Muller, Bruce A Kimball, Gerald Moser, Kenneth Boote, Senthold Asseng, Thomas A M Pugh, Eline Vanuytrecht, Hakan Pleijel, Heidi Webber, Jean-Louis Durand, Frank Dentener, Andrej Ceglar, Xuhui Wang, Franz Badeck, Remi Lecerf, Gerard W Wall, Maurits van den Berg, Petra Hoegy, Raul Lopez-Lozano, Matteo Zampieri, Stefano Galmarini, Garry J O’Leary, Remy Manderscheid, Erik Mencos Contreras, and Cynthia Rosenzweig
Subjects: Meteorology And Climatology
Abstract: Plant responses to rising atmospheric carbon dioxide (CO2) concentrations, together with projected variations in temperature and precipitation will determine future agricultural production. Estimates of the impacts of climate change on agriculture provide essential information to design effective adaptation strategies, and develop sustainable food systems. Here, we review the current experimental evidence and crop models on the effects of elevated CO2 concentrations. Recent concerted efforts have narrowed the uncertainties in CO2-induced crop responses so that climate change impact simulations omitting CO2 can now be eliminated. To address remaining knowledge gaps and uncertainties in estimating the effects of elevated CO2 and climate change on crops, future research should expand experiments on more crop species under a wider range of growing conditions, improve the representation of responses to climate extremes in crop models, and simulate additional crop physiological processes related to nutritional quality.
Published: 2020
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12. Mixed farming systems: potentials and barriers for climate change adaptation in food systems

Author: Emily Baker, Rachel Bezner Kerr, Delphine Deryng, Aidan Farrell, Helen Gurney-Smith, and Philip Thornton
Subjects: General Social Sciences, General Environmental Science
Published: 2023

13. Modelling adaptation and transformative adaptation in cropping systems: recent advances and future directions

Author: Aidan D Farrell, Delphine Deryng, and Henry Neufeldt
Subjects: General Social Sciences, General Environmental Science
Published: 2023

14. Narrowing uncertainties in the effects of elevated CO2 on crops

Author: Kenneth J. Boote, Franz Badeck, Thomas A. M. Pugh, Andrej Ceglar, Gerard W. Wall, Remi Lecerf, Raúl López-Lozano, Matteo Zampieri, Andrea Toreti, Håkan Pleijel, Jean-Louis Durand, Frank Dentener, Remy Manderscheid, Francesco N. Tubiello, Gerald Moser, Christoph Müller, Bruce A. Kimball, Senthold Asseng, Erik Mencos Contreras, Garry O'Leary, Xuhui Wang, Eline Vanuytrecht, Cynthia Rosenzweig, Delphine Deryng, Stefano Galmarini, Heidi Webber, Maurits van den Berg, Petra Hoegy, European Commission - Joint Research Centre [Ispra] (JRC), NewClimate Institute, IRI THESys, Humboldt State University (HSU), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Potsdam Institute for Climate Impact Research (PIK), USDA Agricultural Research Service [Maricopa, AZ] (USDA), United States Department of Agriculture (USDA), Justus-Liebig-Universität Gießen (JLU), University of Florida [Gainesville] (UF), University of Birmingham [Birmingham], Lund University [Lund], Flemish Institute for Technological Research (VITO), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), University of Gothenburg (GU), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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), Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University [Beijing], Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), University of Hohenheim, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agriculture Victoria (AgriBio), Thünen Institute of Biodiversity, 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], Justus-Liebig-Universität Gießen = Justus Liebig University (JLU), 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), and Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria = Council for Agricultural Research and Economics (CREA)
Subjects: 0106 biological sciences, CARBON-DIOXIDE ENRICHMENT, PROTEIN-CONCENTRATION, 010504 meteorology & atmospheric sciences, Natural resource economics, Climate change, Nutritional quality, 01 natural sciences, Crop, WHEAT-GRAIN QUALITY, FUTURE CO2, USE EFFICIENCY, Sustainable agriculture, Precipitation, ATMOSPHERIC CO2, Agricultural productivity, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 0105 earth and related environmental sciences, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, 2. Zero hunger, Carbon dioxide in Earth's atmosphere, Science & Technology, FACE EXPERIMENT, business.industry, FOOD SECURITY, fungi, food and beverages, 15. Life on land, CLIMATE-CHANGE IMPACT, MODEL, 13. Climate action, Agriculture, Food Science & Technology, Environmental science, Animal Science and Zoology, business, Life Sciences & Biomedicine, Agronomy and Crop Science, 010606 plant biology & botany, Food Science
Abstract: International audience; Plant responses to rising atmospheric carbon dioxide (CO2) concentrations, together with projected variations in temperature and precipitation will determine future agricultural production. Estimates of the impacts of climate change on agriculture provide essential information to design effective adaptation strategies, and develop sustainable food systems. Here, we review the current experimental evidence and crop models on the effects of elevated CO2 concentrations. Recent concerted efforts have narrowed the uncertainties in CO2-induced crop responses so that climate change impact simulations omitting CO2 can now be eliminated. To address remaining knowledge gaps and uncertainties in estimating the effects of elevated CO2 and climate change on crops, future research should expand experiments on more crop species under a wider range of growing conditions, improve the representation of responses to climate extremes in crop models, and simulate additional crop physiological processes related to nutritional quality.Understanding of the effects of elevated CO2 on crops has improved sufficiently that modelling future climatic effects on agriculture should eliminate 'no CO2' simulations. Further advancement in the estimation of the effects can be realized by studying a wider variety of crop species under a wider range of growing conditions, improving the representation of responses to climate extremes in crop models and simulating additional crop physiological processes related to nutritional quality.
Published: 2020

15. Screening Protocol:Feasible Socioeconomic Measures to Create Sustainable Food Systems - A Systematic Review

Author: Daniel Chrisendo, Mika Jalava, Delphine Deryng, Matias Heino, and Matti Kummu
Abstract: In recent years, many scientific studies have analyzed potential solutions and opportunities to improve food systems towards sustainability. Various socioeconomic factors play crucial roles in determining the successful or unsuccessful implementation. Yet, systematic reviews, which provide a comprehensive picture of food systems-socioeconomic relationships, are still lacking. Such studies could integrate crucial information, particularly important for stakeholders who rely on scientific articles when making decisions. Therefore, we aim to systematically screen and review existing literature to draw common patterns of feasible socioeconomic measures essential for sustainable food systems. The objective of this protocol is to outline the methods we use and to avoid bias, especially when screening relevant articles.
Published: 2022

16. How Can CO2 Help Agriculture in the Face of Climate Change?

Author: Delphine, Deryng, Elliott, Joshua, Folberth, Christian, Mueller, Christoph, Pugh, Thomas A. M, Boote, Kenneth J, Conway, Declan, Ruane, Alexander C, Gerten, Dieter, Jones, James W, Khabarov, Nikolay, Olin, Stefan, Schaphoff, Sibyll, Schmid, Erwin, Yang, Hong, and Rosenzweig, Cynthia
Subjects: Meteorology And Climatology
Abstract: Humans are increasing the amount of carbon dioxide (CO2) in the air through CO2 emissions. This is changing the climate, making life harder for many plants in areas that suffer from heat and drought. However, plants need CO2 to grow, and more CO2 can make them grow better. So will plants overall benefit from increased CO2 level or suffer from it? We wanted to test if the positive effect would offset the negative ones. To do so, we used scientific models to calculate future crop production and water use of four important crops all over the world under different scenarios of CO2 emissions and climate change. Our calculations show that although there will be large reductions in crop yield due to climate change over the next century, some crops will still be able to grow well. This is also because crops can grow with less water when CO2 levels are raised.
Published: 2017

17. Research trends and gaps in climate change impacts and adaptation potentials in major crops

Author: Hitomi Wakatsuki, Hui Ju, Gerald C Nelson, Aidan D Farrell, Delphine Deryng, Francisco Meza, and Toshihiro Hasegawa
Subjects: General Social Sciences, General Environmental Science
Published: 2023

18. Global irrigation contribution to wheat and maize yield

Author: Joshua Elliot, Erwin Schmid, Thomas A. M. Pugh, Feng Zhou, Jonas Jägermeyr, Xuhui Wang, David Makowski, Laurent Li, Wenfeng Liu, Delphine Deryng, Philippe Ciais, Hui Yang, Christoph Müller, Nathaniel D. Mueller, Chenzhi Wang, Stefan Olin, Su-Jong Jeong, Christian Folberth, James S. Gerber, Shilong Piao, Ashwan Reddy, Patrice Dumas, Peking University [Beijing], Potsdam Institute for Climate Impact Research (PIK), University of Chicago, Colorado State University [Fort Collins] (CSU), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), 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), ICOS-ATC (ICOS-ATC), 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), Centre International de Recherche sur l'Environnement et le Développement (CIRED), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École des hautes études en sciences sociales (EHESS)-AgroParisTech-École des Ponts ParisTech (ENPC)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-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), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Ludwig-Maximilians-Universität München (LMU), China Agricultural University (CAU), Agronomie, AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Lund University [Lund], University of Maryland [College Park], University of Maryland System, Seoul National University [Seoul] (SNU), Chinese Academy of Sciences [Beijing] (CAS), Mathématiques et Informatique Appliquées (MIA-Paris), 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)-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), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), and Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay
Subjects: Irrigation, 010504 meteorology & atmospheric sciences, Science, Yield (finance), [SDE.MCG]Environmental Sciences/Global Changes, 0208 environmental biotechnology, General Physics and Astronomy, Climate change, Water supply, 02 engineering and technology, Agricultural engineering, 01 natural sciences, Article, General Biochemistry, Genetics and Molecular Biology, Rainfed agriculture, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, 2. Zero hunger, [STAT.AP]Statistics [stat]/Applications [stat.AP], Multidisciplinary, business.industry, Crop yield, Yield gap, General Chemistry, 15. Life on land, 6. Clean water, 020801 environmental engineering, Environmental sciences, 13. Climate action, [SDE]Environmental Sciences, Environmental science, business, Agroecology, Water use
Abstract: Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts., There are big uncertainties in the contribution of irrigation to crop yields. Here, the authors use Bayesian model averaging to combine statistical and process-based models and quantify the contribution of irrigation for wheat and maize yields, finding that irrigation alone cannot close yield gaps for a large fraction of global rainfed agriculture.
Published: 2021

19. A global dataset for the projected impacts of climate change on four major crops

Author: Toshihiro Hasegawa, Hitomi Wakatsuki, Hui Ju, Shalika Vyas, Gerald C. Nelson, Aidan Farrell, Delphine Deryng, Francisco Meza, and David Makowski
Subjects: Statistics and Probability, Library and Information Sciences, Statistics, Probability and Uncertainty, Computer Science Applications, Education, Information Systems
Abstract: Reliable estimates of the impacts of climate change on crop production are critical for assessing the sustainability of food systems. Global, regional, and site-specific crop simulation studies have been conducted for nearly four decades, representing valuable sources of information for climate change impact assessments. However, the wealth of data produced by these studies has not been made publicly available. Here, we develop a global dataset by consolidating previously published meta-analyses and data collected through a new literature search covering recent crop simulations. The new global dataset builds on 8703 simulations from 202 studies published between 1984 and 2020. It contains projected yields of four major crops (maize, rice, soybean, and wheat) in 91 countries under major emission scenarios for the 21st century, with and without adaptation measures, along with geographical coordinates, current temperature and precipitation levels, projected temperature and precipitation changes. This dataset provides a solid basis for a quantitative assessment of the impacts of climate change on crop production and will facilitate the rapidly developing data-driven machine learning applications.
Published: 2021

20. A global dataset for the projected impacts of climate change on four major crops

Author: Vyas S, Gerald C. Nelson, Ju H, Hitomi Wakatsuki, Delphine Deryng, David Makowski, Meza F, Toshihiro Hasegawa, and Aidan D. Farrell
Subjects: Crop, business.industry, Impact assessment, Crop production, Global warming, Environmental resource management, Sustainability, Climate change, Food systems, Environmental science, business, Geographic coordinate system
Abstract: Reliable estimates of the impacts of climate change on crop production are critical for assessing the sustainability of food systems. Global, regional, and site-specific crop simulation studies have been conducted for nearly four decades, representing valuable sources of information for climate change impact assessments. However, the wealth of data produced by these studies has not been made publicly available. Here, we develop a global dataset by consolidating previously published meta-analyses and data collected through a new literature search covering recent crop simulations. The new global dataset builds on 8314 simulations from 203 studies published between 1984 and 2020. It contains projected yields of four major crops (maize, rice, soybean, and wheat) in 91 countries under major emission scenarios for the 21st century, with and without adaptation measures, along with geographical coordinates, current temperatures, local and global warming levels. This dataset provides a basis for a comprehensive understanding of the impacts of climate change on crop production and will facilitate the rapidly developing data-driven machine learning applications.
Published: 2021

21. The global adaptation mapping initiative (GAMI)

Author: Tabea Lissner, A. R. Siders, James D. Ford, Adelle Thomas, Alexandra Lesnikowski, Katharine J. Mach, Kathryn Bowen, Lindsay C. Stringer, Marjolijn Hassnoot, Erin Coughlan de Perez, Elisabeth A. Gilmore, Susan J. Elliott, Diana Reckien, Maarten van Aalst, Jan C. Minx, Robbert Biesbroek, Michael D. Morecroft, Chris Trisos, A. Paige Fischer, Alexandre K. Magnan, Matthias Garschagen, Rachel Bezner Kerr, Mark New, Shuaib Lwasa, Nicholas Philip Simpson, Chandni Singh, Neal R. Haddaway, Max Callaghan, Lea Berrang-Ford, Delphine Deryng, Sherliee Harper, Edmond Totin, UT-I-ITC-PLUS, Faculty of Geo-Information Science and Earth Observation, Department of Urban and Regional Planning and Geo-Information Management, Department of Earth Systems Analysis, and UT-I-ITC-4DEarth
Subjects: Political science, Adaptation (computer science), ITC-GOLD, Data science
Abstract: Context: It is now widely accepted that the climate is changing, and that societal responses will need to be rapid and comprehensive to prevent the most severe impacts. A key milestone in global climate governance is to assess progress on adaptation. To-date, however, there has been negligible robust, systematic synthesis of progress on adaptation or adaptation-relevant responses globally. Aim: The purpose of this review protocol is to outline the methods used by the Global Adaptation Mapping Initiative (GAMI) to systematically review human adaptation responses to climate-related changes that have been documented globally since 2013 in the scientific literature. The broad question underpinning this review is: Are we adapting to climate change? More specifically, we ask ‘what is the evidence relating to human adaptation-related responses that can (or are) directly reducing risk, exposure, and/or vulnerability to climate change?’ This work responds to the recognition of the need for high-level syntheses of adaptation research to inform global and regional climate assessments.Methods: We review scientific literature 2013-2019 to identify documents empirically reporting on observed adaptation-related responses to climate change in human systems that can directly reduce risk. We exclude non-empirical (theoretical & conceptual) literature and adaptation in natural systems that occurs without human intervention. Included documents were coded across a set of questions focused on: Who is responding? What responses are documented? What is the extent of the adaptation-related response? What is the evidence that adaptation-related responses reduce risk, exposure and/or vulnerability? Once articles are coded, we conduct a quality appraisal of the coding and develop ‘evidence packages’ for regions and sectors. We supplement this systematic mapping with an expert elicitation exercise, undertaken to assess bias and validity of insights from included/coded literature vis a vis perceptions of real-world adaptation for global regions and sectors, with associated confidence assessments. Related protocols: This protocol represents Part 1 of a 5-part series outlining the phases of methods for this initiative. Part 1 provides an introduction to the Global Adaptation Mapping Initiative (GAMI) and an overview of methods.
Published: 2021

22. Mapping evidence of human adaptation to climate change

Author: Emily Baker, Gina Marie Maskell, Malcolm Araos, Lolita Shaila Safaee Chalkasra, Caitlin Grady, Souha Ouni, Rachel Bezner Kerr, Justice Issah Musah-Surugu, Matthew Jurjonas, Raquel Ruiz-Díaz, Julia B. Pazmino Murillo, Robbert Biesbroek, Lindsay C. Stringer, Deepal Doshi, Nikita Charles Hamilton, Stephanie L. Barr, Carys Richards, Kathryn Bowen, Greeshma Hedge, Avery Hill, Custodio Matavel, Vhalinavho Khavhagali, Tara Chen, Timo Leiter, Steven Koller, Portia Adade Williams, Oliver Lilford, Patricia Nayna Schwerdtle, Asha Sitati, Sherilee L. Harper, Eranga K. Galappaththi, Philip Antwi-Agyei, Tabea Lissner, Megan Lukas-Sithole, Alexandra Harden, Gabrielle Wong-Parodi, Bianca van Bavel, Kathryn Dana Sjostrom, Leah Gichuki, Eunice A Salubi, Gabriela Nagle Alverio, Jordi Sardans, Joshua Mullenite, Alexandre K. Magnan, Andrew Forbes, Delphine Deryng, Lea Berrang-Ford, Emily Duncan, Donovan Campbell, Garry Sotnik, Ivan Villaverde Canosa, Mia Wannewitz, Jan C. Minx, Katherine E. Browne, Katy Davis, Kripa Jagannathan, Neal R. Haddaway, Roopam Shukla, Vasiliki I. Chalastani, Mohammad Aminur Rahman Shah, Elphin Tom Joe, Shaugn Coggins, Lam T. M. Huynh, Diana Reckien, Carolyn A. F. Enquist, Tanvi Agrawal, Christine J. Kirchhoff, Luckson Zvobgo, Neha Chauhan, Stephanie E. Austin, Adelle Thomas, Nicola Ulibarri, Indra D. Bhatt, Elisabeth A. Gilmore, Katharine J. Mach, Brian Pentz, Nicole van Maanen, Sienna Templeman, Julia Pelaez Avila, Emily Theokritoff, Alexandra Paige Fischer, Josep Peñuelas, Matthias Garschagen, Maarten van Aalst, William Kakenmaster, Yuanyuan Shang, Christa Anderson, Mark New, Pratik Pokharel, Jennifer Niemann, Mariella Siña, Giulia Scarpa, Erin Coughlan de Perez, Ingrid Arotoma-Rojas, Warda Ajaz, Edmond Totin, Marjolijn Haasnoot, Idowu Ajibade, Chandni Singh, Max Callaghan, Jan Petzold, A. R. Siders, James D. Ford, Jiren Xu, Miriam Nielsen, Michael D. Morecroft, Thelma Zulfawu Abu, Lynée L. Turek-Hankins, Alcade C Segnon, Cristina A. Mullin, Hasti Trivedi, Praveen Kumar, Tom Hawxwell, Carol Zavaleta-Cortijo, Alexandra Lesnikowski, Susan J. Elliott, Abraham Marshall Nunbogu, Anuszka Mosurska, Aidan D. Farrell, Nicholas Philip Simpson, Shuaib Lwasa, Christopher H. Trisos, Alyssa Gatt, Rebecca R. Hernandez, Zinta Zommers, and Shinny Thakur
Subjects: Geography, business.industry, Environmental resource management, Climate change, business, Adaptation (computer science)
Abstract: We present the first systematic, global stocktake of the academic literature on human adaptation. We screen 48,316 documents and identify 1,682 articles that present empirical research documenting human efforts to reduce risk from climate change and associated hazards. Coding and synthesizing this literature highlights that the overall extent of adaptation across global regions and sectors is low. Adaptations are largely local and incremental rather than transformative. Behavioural adjustments by individuals and households are more prevalent than any other type of response, largely motivated by drought and precipitation variability. Local governments and civil society are engaging in risk reduction across all sectors and regions, particularly in response to flooding. Urban technological and infrastructural adaptations to flood risk are prevalent in Europe, while shifts in farming practices dominate reporting from Africa and Asia. Despite increasing evidence of adaptation responses, evidence that these responses are reducing risks (observed and projected) remains limited.
Published: 2021

23. Nature Climate Change

Author: Susan J. Elliott, Tom Hawxwell, Alcade C Segnon, Cristina A. Mullin, Hasti Trivedi, Shinny Thakur, Aidan D. Farrell, Nicholas Philip Simpson, Carys Richards, Neal R. Haddaway, Tanvi Agrawal, Portia Adade Williams, Indra D. Bhatt, Maarten van Aalst, Shuaib Lwasa, Praveen Kumar, Deepal Doshi, Alexandre K. Magnan, Sherilee L. Harper, Christopher H. Trisos, Jordi Sardans, Alyssa Gatt, Jiren Xu, Miriam Nielsen, A. R. Siders, Carol Zavaleta-Cortijo, Steven Koller, Michael D. Morecroft, Marjolijn Haasnoot, Nicole van Maanen, Avery Hill, James D. Ford, Rebecca R. Hernandez, Christine J. Kirchhoff, Diana Reckien, Bianca van Bavel, Jan Petzold, Jennifer Niemann, Erin Coughlan de Perez, Luckson Zvobgo, Brian Pentz, Katherine E. Browne, Mohammad Aminur Rahman Shah, Chandni Singh, Lea Berrang-Ford, Alexandra Lesnikowski, Matthias Garschagen, Elphin Tom Joe, Thelma Zulfawu Abu, Donovan Campbell, Mia Wannewitz, Nikita Charles Hamilton, Roopam Shukla, Lynée L. Turek-Hankins, Neha Chauhan, Tara Chen, Oliver Lilford, Patricia Nayna Schwerdtle, Greeshma Hegde, William Kakenmaster, Custodio Matavel, Vhalinavho Khavhagali, Stephanie L. Barr, Zinta Zommers, Eranga K. Galappaththi, Tabea Lissner, Yuanyuan Shang, Alexandra Paige Fischer, Megan Lukas-Sithole, Delphine Deryng, Leah Gichuki, Katharine J. Mach, Ivan Villaverde Canosa, Alexandra Harden, Max Callaghan, Matthew Jurjonas, Andrew Forbes, Giulia Scarpa, Garry Sotnik, Stephanie E. Austin, Adelle Thomas, Julia B. Pazmino Murillo, Vasiliki I. Chalastani, Caitlin Grady, Lolita Shaila Safaee Chalkasra, Eunice A Salubi, Abraham Marshall Nunbogu, Anuszka Mosurska, Kathryn Dana Sjostrom, Robbert Biesbroek, Christa Anderson, Joshua Mullenite, Emily Baker, Mark New, Gina Marie Maskell, Lam T. M. Huynh, Sienna Templeman, Elisabeth A. Gilmore, Emily Theokritoff, Josep Peñuelas, Pratik Pokharel, Souha Ouni, Rachel Bezner Kerr, Justice Issah Musah-Surugu, Idowu Ajibade, Raquel Ruiz-Díaz, Edmond Totin, Timo Leiter, Carolyn A. F. Enquist, Asha Sitati, Warda Ajaz, Kathryn Bowen, Gabrielle Wong-Parodi, Malcolm Araos, Shaugn Coggins, Julia Pelaez Avila, Mariella Siña, Kripa Jagannathan, Emily Duncan, Katy Davis, Nicola Ulibarri, Ingrid Arotoma-Rojas, Lindsay C. Stringer, Philip Antwi-Agyei, Gabriela Nagle Alverio, Jan C. Minx, UT-I-ITC-PLUS, Faculty of Geo-Information Science and Earth Observation, Department of Urban and Regional Planning and Geo-Information Management, Department of Earth Systems Analysis, UT-I-ITC-4DEarth, and Publica
Subjects: Civil society, PERCEPTIONS, STRATEGIES, AGRICULTURE, Environmental Studies, Climate change, Environmental Sciences & Ecology, WASS, Scientific literature, Environmental Science (miscellaneous), URBAN, Political science, Global network, Meteorology & Atmospheric Sciences, Life Science, 0502 Environmental Science and Management, SMALLHOLDER FARMERS, Adaptation (computer science), NATIONAL-LEVEL, Environmental planning, WIMEK, Corporate governance, Public Administration and Policy, Private sector, OPPORTUNITIES, VARIABILITY, Transformational leadership, ITC-ISI-JOURNAL-ARTICLE, Physical Sciences, COMMUNITY-LEVEL, LOCAL-LEVEL, Bestuurskunde, 0401 Atmospheric Sciences, 0406 Physical Geography and Environmental Geoscience, Social Sciences (miscellaneous), Environmental Sciences
Abstract: Assessing global progress on human adaptation to climate change is an urgent priority. Although the literature on adaptation to climate change is rapidly expanding, little is known about the actual extent of implementation. We systematically screened >48,000 articles using machine learning methods and a global network of 126 researchers. Our synthesis of the resulting 1,682 articles presents a systematic and comprehensive global stocktake of implemented human adaptation to climate change. Documented adaptations were largely fragmented, local and incremental, with limited evidence of transformational adaptation and negligible evidence of risk reduction outcomes. We identify eight priorities for global adaptation research: assess the effectiveness of adaptation responses, enhance the understanding of limits to adaptation, enable individuals and civil society to adapt, include missing places, scholars and scholarship, understand private sector responses, improve methods for synthesizing different forms of evidence, assess the adaptation at different temperature thresholds, and improve the inclusion of timescale and the dynamics of responses. Determining progress in adaptation to climate change is challenging, yet critical as climate change impacts increase. A stocktake of the scientific literature on implemented adaptation now shows that adaptation is mostly fragmented and incremental, with evidence lacking for its impact on reducing risk.
Published: 2021

24. Strong regional influence of climatic forcing datasets on global crop model ensembles

Author: Jonas Jägermeyr, Allard de Wit, Alex C. Ruane, Thomas A. M. Pugh, Wenfeng Liu, Joshua Elliott, Toshichika Iizumi, Gen Sakurai, Nikolay Khabarov, Peter Lawrence, Benjamin Sultan, Stefan Olin, Christoph Müller, Xuhui Wang, Hong Yang, A. Arneth, Juraj Balkovic, Delphine Deryng, Erwin Schmid, Christian Folberth, Robert C. Izaurralde, Meridell Phillips, Cynthia Rosenzweig, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), NASA GODDARD INSTITUTE FOR SPACE STUDIES NEW YORK USA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), University of Chicago, Potsdam Institute for Climate Impact Research (PIK), Institute of Geography, University of Edinburgh, International Institute for Applied Systems Analysis [Laxenburg] (IIASA), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), Department of Geography, University of Liverpool, National Institute of Agro-Environmental Sciences (NIAES), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), 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), 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)
Subjects: Atmospheric Science, Earth Observation and Environmental Informatics, 010504 meteorology & atmospheric sciences, Global Gridded Crop Model Intercomparison (GGCMI), Forcing (mathematics), 01 natural sciences, Crop productivity, Crop, 03 medical and health sciences, Crop production, Climatic Forcing Datasets, Aardobservatie en omgevingsinformatica, Precipitation, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0105 earth and related environmental sciences, 2. Zero hunger, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, 0303 health sciences, Global and Planetary Change, business.industry, Agroclimate, Extreme events, Forestry, Climate Impacts, PE&RC, Agricultural Model Intercomparison and Improvement Project (AgMIP), 13. Climate action, Agriculture, Climatology, Environmental science, business, Agronomy and Crop Science
Abstract: We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.
Published: 2021

25. 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

26. Climate change and agriculture

Author: Delphine Deryng
Subjects: Land use, Agroforestry, business.industry, Agriculture, Crop production, Animal production, Environmental science, Climate change and agriculture, Climate change, Livestock, business
Published: 2020

27. 1.5°C Hotspots: Climate Hazards, Vulnerabilities, and Impacts

Author: Fahad Saeed, Melinda Noblet, Olivia Serdeczny, Tabea Lissner, Mouhamed Ly, Patrick Pringle, Peter Pfleiderer, Adelle Thomas, William Hare, Sarah D'haen, Carl-Friedrich Schleussner, Delphine Deryng, Michiel Schaeffer, Alexander Nauels, and Martin Rokitzki
Subjects: extreme weather events, 010504 meteorology & atmospheric sciences, business.industry, vulnerability, Perspective (graphical), Environmental resource management, Vulnerability, Climatic variables, Climate change, 010501 environmental sciences, small islands, 01 natural sciences, Extreme weather, Environmental Systems Analysis, Geography, Sea level rise, 1.5°C, sea level rise, Milieusysteemanalyse, hotspots, business, 0105 earth and related environmental sciences, General Environmental Science
Abstract: Differentiating the impacts of climate change between 1.5°C and 2°C requires a regional and sector-specific perspective. Whereas for some regions and sectors the difference in climate variables might be indistinguishable from natural variability, other areas especially in the tropics and subtropics will experience significant shifts. In addition to region-specific changes in climatic conditions, vulnerability and exposure also differ substantially across the world. Even small differences in climate hazards can translate into sizeable impact differences for particularly vulnerable regions or sectors. Here, we review scientific evidence of regional differences in climate hazards at 1.5°C and 2°C and provide an assessment of selected hotspots of climate change, including small islands as well as rural, urban, and coastal areas in sub-Saharan Africa and South Asia, that are particularly affected by the additional 0.5°C global mean temperature increase. We interlink these with a review of the vulnerability and exposure literature related to these hotspots to provide an integrated perspective on the differences in climate impacts between 1.5°C and 2°C.
Published: 2018

28. Understanding the weather signal in national crop-yield variability

Author: Delphine Deryng, James P. Chryssanthacopoulos, Joshua Elliott, Erwin Schmid, Stefan Olin, Sibyll Schaphoff, Jacob Schewe, Nikolay Khabarov, Almut Arneth, Christoph Müller, Lila Warszawski, Thomas A. M. Pugh, Bernhard Schauberger, Christian Folberth, Juraj Balkovic, Katja Frieler, and Anders Levermann
Subjects: 2. Zero hunger, 010504 meteorology & atmospheric sciences, Natural resource economics, Yield (finance), Crop yield, Empirical modelling, Climate change, Subsistence agriculture, Context (language use), Loss and damage, 04 agricultural and veterinary sciences, 15. Life on land, Livelihood, 01 natural sciences, 13. Climate action, Climatology, 040103 agronomy & agriculture, Earth and Planetary Sciences (miscellaneous), 0401 agriculture, forestry, and fisheries, Environmental science, 0105 earth and related environmental sciences, General Environmental Science
Abstract: Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.
Published: 2017

29. Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble

Author: Alex C. Ruane, Joshua Elliott, Roberto C. Izaurralde, Nikolay Khabarov, Peter Lawrence, James P. Chryssanthacopoulos, Philippe Ciais, Christoph Müller, Almut Arneth, Thomas A. M. Pugh, Xuhui Wang, Juraj Balkovic, Wenfeng Liu, Erwin Schmid, Stefan Olin, Hong Yang, Delphine Deryng, Curtis D. Jones, Rastislav Skalský, Christian Folberth, Ashwan Reddy, Department of Geography, University of Liverpool, University of Chicago, Potsdam Institute for Climate Impact Research (PIK), Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], 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), Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Soil Science and Conservation Research Institute, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), 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), 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), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), 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), Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU), 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], 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: 010504 meteorology & atmospheric sciences, Climate, Plant Science, 01 natural sciences, Soil, Agricultural Soil Science, Edaphology, Econometrics, ddc:550, Plant Growth and Development, 2. Zero hunger, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Multidisciplinary, Uncertainty, Eukaryota, Agriculture, 04 agricultural and veterinary sciences, Plants, Crop Production, Experimental Organism Systems, Agricultural soil science, Erosion, Soil horizon, Medicine, Core model, Agrochemicals, Research Article, Crops, Agricultural, Science, Soil Science, Crops, Research and Analysis Methods, Model Organisms, Hydrology (agriculture), Plant and Algal Models, Grasses, Fertilizers, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 0105 earth and related environmental sciences, Models, Statistical, Impact assessment, Crop yield, Ecology and Environmental Sciences, Organisms, Biology and Life Sciences, Geomorphology, 15. Life on land, Crop Management, Maize, Earth sciences, 13. Climate action, Animal Studies, Earth Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Scale (map), Crop Science, Developmental Biology
Abstract: Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.
Published: 2019

30. 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

31. Climate Change and Agriculture

Author: Delphine Deryng and Delphine Deryng
Subjects: Crops and climate, Agriculture--Environmental aspects, Climatic changes
Abstract: It has been suggested that agriculture may account for up to 24% of the greenhouse gas emissions (GHGs) contributing to climate change. At the same time climate change is threatening to disrupt agricultural production. This collection reviews key research addressing this challenge. Climate change is the biggest challenge agriculture faces. Part 1 of this collection reviews current research on the impacts of climate change on agriculture, such as the effects of increased temperatures, as well as the ways these impacts can be modelled. Part 2 assesses what we know about the contribution of agriculture to climate change, including the impacts of both crop and livestock production as well as land use. Part 3 surveys mitigation strategies to achieve a more ‘climate-smart'agriculture such as the role of integrated crop-livestock and agroforestry systems.
Published: 2020

32. Climate analogues suggest limited potential for intensification of production on current croplands under climate change

Author: Thomas A. M. Pugh, Stefan Olin, Christian Folberth, Delphine Deryng, Erwin Schmid, Joshua Elliott, Almut Arneth, and Christoph Müller
Subjects: 0106 biological sciences, 010504 meteorology & atmospheric sciences, Yield (finance), Science, General Physics and Astronomy, Climate change, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, Article, Crop, ddc:550, Production (economics), 0105 earth and related environmental sciences, 2. Zero hunger, Multidisciplinary, Land use, business.industry, Agroforestry, Crop yield, General Chemistry, 15. Life on land, Earth sciences, 13. Climate action, Agriculture, Food processing, Environmental science, business, 010606 plant biology & botany
Abstract: Climate change could pose a major challenge to efforts towards strongly increase food production over the coming decades. However, model simulations of future climate-impacts on crop yields differ substantially in the magnitude and even direction of the projected change. Combining observations of current maximum-attainable yield with climate analogues, we provide a complementary method of assessing the effect of climate change on crop yields. Strong reductions in attainable yields of major cereal crops are found across a large fraction of current cropland by 2050. These areas are vulnerable to climate change and have greatly reduced opportunity for agricultural intensification. However, the total land area, including regions not currently used for crops, climatically suitable for high attainable yields of maize, wheat and rice is similar by 2050 to the present-day. Large shifts in land-use patterns and crop choice will likely be necessary to sustain production growth rates and keep pace with demand., Simulations of the impact of future climate change on crop yield vary considerably. Here, the authors use a climate analogue approach to estimate the response of maximum attainable yield to climate change and predict that large shifts in land use and crop choice would be required to meet demand.
Published: 2016

33. Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity

Author: Hong Yang, Alex C. Ruane, Christian Folberth, Joshua Elliott, Sibyll Schaphoff, Stefan Olin, Delphine Deryng, Thomas A. M. Pugh, Christoph Müller, Kenneth J. Boote, Nikolay Khabarov, Dieter Gerten, Declan Conway, Cynthia Rosenzweig, Erwin Schmid, and James W. Jones
Subjects: 2. Zero hunger, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Agroforestry, Crop yield, 15. Life on land, Environmental Science (miscellaneous), 01 natural sciences, Water resources, Water security, 13. Climate action, Consumptive water use, Evapotranspiration, Greenhouse gas, Farm water, Environmental science, Social Sciences (miscellaneous), Water use, GE Environmental Sciences, 010606 plant biology & botany, 0105 earth and related environmental sciences
Abstract: Increasing atmospheric CO2 concentrations are expected to enhance photosynthesis and reduce plant water use. Research now reveals regional disparities in this effect on crops, with potential implications for food production and water consumption. Rising atmospheric CO2 concentrations ([CO2]) are expected to enhance photosynthesis and reduce crop water use1. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments1,2 and global crop models3 to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to evapotranspiration) for wheat, maize, rice and soybean under elevated [CO2] and associated climate change projected for a high-end greenhouse gas emissions scenario. We find CO2 effects increase global CWP by 10[0;47]%–27[7;37]% (median[interquartile range] across the model ensemble) by the 2080s depending on crop types, with particularly large increases in arid regions (by up to 48[25;56]% for rainfed wheat). If realized in the fields, the effects of elevated [CO2] could considerably mitigate global yield losses whilst reducing agricultural consumptive water use (4–17%). We identify regional disparities driven by differences in growing conditions across agro-ecosystems that could have implications for increasing food production without compromising water security. Finally, our results demonstrate the need to expand field experiments and encourage greater consistency in modelling the effects of rising [CO2] across crop and hydrological modelling communities.
Published: 2016

34. Crop productivity changes in 1.5 °C and 2 °C worlds under climate sensitivity uncertainty

Author: Wim Thiery, Joshua Elliott, Christian Folberth, Xuhui Wang, Thomas A. M. Pugh, Delphine Deryng, Fahad Saeed, Carl-Friedrich Schleussner, Wenfeng Liu, Joeri Rogelj, Christoph Müller, Sonia I. Seneviratne, Hydrology and Hydraulic Engineering, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), and 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)
Subjects: 010504 meteorology & atmospheric sciences, HAPPI, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Forcing (mathematics), 010501 environmental sciences, Atmospheric sciences, 01 natural sciences, 7. Clean energy, 1.5 °C, Environmental Science(all), ddc:550, Agricultural productivity, Productivity, 0105 earth and related environmental sciences, General Environmental Science, 2. Zero hunger, GGCMI, Renewable Energy, Sustainability and the Environment, Crop yield, Global warming, Public Health, Environmental and Occupational Health, 15. Life on land, Radiative forcing, Earth sciences, 13. Climate action, [SDU]Sciences of the Universe [physics], Greenhouse gas, Climate sensitivity, Environmental science, 1.5 degrees C
Abstract: International audience; Following the adoption of the Paris Agreement, there has been an increasing interest in quantifying impacts at discrete levels of global mean temperature (GMT) increase such as 1.5 °C and 2 °C above pre-industrial levels. Consequences of anthropogenic greenhouse gas emissions on agricultural productivity have direct and immediate relevance for human societies. Future crop yields will be affected by anthropogenic climate change as well as direct effects of emissions such as CO2 fertilization. At the same time, the climate sensitivity to future emissions is uncertain. Here we investigate the sensitivity of future crop yield projections with a set of global gridded crop models for four major staple crops at 1.5 °C and 2 °C warming above pre-industrial levels, as well as at different CO2 levels determined by similar probabilities to lead to 1.5 °C and 2 °C, using climate forcing data from the Half a degree Additional warming, Prognosis and Projected Impacts project. For the same CO2 forcing, we find consistent negative effects of half a degree warming on productivity in most world regions. Increasing CO2 concentrations consistent with these warming levels have potentially stronger but highly uncertain effects than 0.5 °C warming increments. Half a degree warming will also lead to more extreme low yields, in particular over tropical regions. Our results indicate that GMT change alone is insufficient to determine future impacts on crop productivity.
Published: 2018

35. Evapotranspiration simulations in ISIMIP2a-Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets

Author: Wim Thiery, Delphine Deryng, Akihiko Ito, Philippe Ciais, Jinfeng Chang, Guoyong Leng, Yadu Pokhrel, Rene Orth, Simon N. Gosling, Joshua Elliott, Xingcai Liu, Thomas Hickler, Hyungjun Kim, Yusuke Satoh, Nikolay Khabarov, Christian Folberth, Hong Yang, Tian Zhou, Sonia I. Seneviratne, Graham P. Weedon, Thomas A. M. Pugh, Joerg Steinkamp, Yoshihide Wada, Martin Hirschi, Junguo Liu, Lukas Gudmundsson, Yoshimitsu Masaki, Catherine Morfopoulos, Alexandra-Jane Henrot, Christoph Müller, Richard Wartenburger, Tobias Stacke, Erwin Schmid, Kazuya Nishina, Xuhui Wang, Sibyll Schaphoff, Qiuhong Tang, Justin Sheffield, Hannes Müller Schmied, Institute for Atmospheric and Climate Science [Zürich] (IAC), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), 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), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-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 Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), 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), University of Chicago, Unité de Modélisation du Climat et des Cycles Biogéochimiques (UMCCB), Université de Liège, Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Goethe-Universität Frankfurt am Main-Senckenberg – Leibniz Institution for Biodiversity and Earth System Research - Senckenberg Gesellschaft für Naturforschung, Leibniz Association-Leibniz Association, National Institute for Environmental Studies (NIES), Korea Advanced Institute of Science and Technology (KAIST), Southern University of Science and Technology [Shenzhen] (SUSTech), Potsdam Institute for Climate Impact Research (PIK), Department of Civil and Environmental Engineering [Ann Arbor] (CEE), University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, University of Natural Resources and Life Sciences (BOKU), Department of Civil and Environmental Engineering [Princeton], Princeton University, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University [Beijing], European Research Council DROUGHT-HEAT project 617518 Office of Science of the US Department of Energy as part of the Integrated Assessment Research Program US DOE DE-AC05-76RLO1830 DECC GA01101 National Natural Science Foundation of China 41625001 41571022 Southern University of Science and Technology G01296001 Defra Integrated Climate Program - DECC/Defra GA01101, 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), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-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 Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), 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), Southern University of Science and Technology (SUSTech), Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU), and Hydrology and Hydraulic Engineering
Subjects: PARAMETERIZATION, 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, REANALYSIS DATA, 02 engineering and technology, Forcing (mathematics), 01 natural sciences, ISIMIP2a, Environmental Science(all), Evapotranspiration, ddc:550, Range (statistics), Cluster Analysis, Meteorology & Atmospheric Sciences, WATER, Water cycle, uncertainty, General Environmental Science, Uncertainty, Variance (accounting), Explained variation, GLOBAL TERRESTRIAL EVAPOTRANSPIRATION, Variable (computer science), [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, Climatology, Physical Sciences, Life Sciences & Biomedicine, PROJECT, HYDROLOGICAL MODELS, evapotranspiration, Climate change, Environmental Sciences & Ecology, SOIL-MOISTURE, hydrological extreme events, LAND-SURFACE MODEL, cluster analysis, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Hydrological extreme events, 0105 earth and related environmental sciences, Science & Technology, Renewable Energy, Sustainability and the Environment, Public Health, Environmental and Occupational Health, POTENTIAL EVAPOTRANSPIRATION, 020801 environmental engineering, Earth sciences, 13. Climate action, Environmental science, Environmental Sciences, HIGH-RESOLUTION
Abstract: Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and anessential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties thatare impeding a precise attribution of human-induced climate change. Here, we aim at comparing arange of independent global monthly land ET estimates with historical model simulations from theglobal water, agriculture, and biomes sectors participating in the second phase of the Inter-SectoralImpact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use theEartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensembleproduct (LandFlux-EVAL), and an updated collection of recently published datasets thatalgorithmically derive ET from observations or observations-based estimates (diagnostic datasets). Acluster analysis is applied in order to identify spatio-temporal differences among all datasets and tothus identify factors that dominate overall uncertainties. The clustering is controlled by several factorsincluding the model choice, the meteorological forcing used to drive the assessed models, the datacategory (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates,reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers inthe models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we findthat themodel choicemostly dominates (24%–40%of variance explained), except for spatio-temporalpatterns of total ET, where the forcing explains the largest fraction of the variance (29%). The mostdominant clusters of datasets are further compared with individual diagnostic and reanalysis-basedestimates to assess their representation of selected heat waves and droughts in the Great Plains,Central Europe and western Russia. Although most of the ET estimates capture these extreme events,the generally large spread among the entire ensemble indicates substantial uncertainties.
Published: 2018

36. Climate and southern Africa's water–energy–food nexus

Author: Bruce Lankford, Tingju Zhu, Stephen Dorling, Timothy J. Osborn, Claudia Ringler, Delphine Deryng, Karen Lebek, Tobias Krueger, Willem A. Landman, Carole Dalin, Declan Conway, Emma Archer van Garderen, and James Thurlow
Subjects: Food security, business.industry, Natural resource economics, Economic sector, Environmental resource management, HC Economic History and Conditions, HF Commerce, Environmental Science (miscellaneous), Gross domestic product, Geography, Agriculture, Power pool, Climate model, Precipitation, S Agriculture (General), business, Nexus (standard), Social Sciences (miscellaneous), GE Environmental Sciences
Abstract: In southern Africa, the connections between climate and the water-energy-food nexus are strong. Physical and socioeconomic exposure to climate is high in many areas and in crucial economic sectors. Spatial interdependence is also high, driven for example, by the regional extent of many climate anomalies and river basins and aquifers that span national boundaries. There is now strong evidence of the effects of individual climate anomalies, but associations between national rainfall and Gross Domestic Product and crop production remain relatively weak. The majority of climate models project decreases in annual precipitation for southern Africa, typically by as much as 20% by the 2080s. Impact models suggest these changes would propagate into reduced water availability and crop yields. Recognition of spatial and sectoral interdependencies should inform policies, institutions and investments for enhancing water, energy and food security. Three key political and economic instruments could be strengthened for this purpose; the Southern African Development Community, the Southern African Power Pool, and trade of agricultural products amounting to significant transfers of embedded water.
Published: 2015

37. A framework for the cross-sectoral integration of multi-model impact projections: land use decisions under climate impacts uncertainties

Author: Katja Frieler, Taikan Oki, Qiuhong Tang, Jens Heinke, Almut Arneth, Douglas B. Clark, Jacob Schewe, Simon N. Gosling, Mark R. Lomas, Dominik Wisser, Yoshimitsu Masaki, Balázs M. Fekete, Ingjerd Haddeland, Pete Falloon, P. Ciais, Franziska Piontek, Christoph Schmitz, Kazuya Nishina, Hans Joachim Schellnhuber, Elke Stehfest, Anders Levermann, Andrew D. Friend, Petra Döll, C. Gellhorn, Erwin Schmid, Marc F. P. Bierkens, Tobias Stacke, Ryan Pavlick, Veronika Huber, Christian Folberth, K. Neumann, Delphine Deryng, Nikolay Khabarov, Lila Warszawski, Alex C. Ruane, Joshua Elliott, Hermann Lotze-Campen, Hydrologie, Sub NMR Spectroscopy, Sub FG LGH 3e geldstroom, Landscape functioning, Geocomputation and Hydrology, Max-Planck-Institut für Extraterrestrische Physik (MPE), Potsdam Institute for Climate Impact Research (PIK), Department of Physical Geography and Ecosystems Analysis, Geobiosphere Science Centre, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), 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), ICOS-ATC (ICOS-ATC), 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), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Institute of Physical Geography, Norwegian Water Resources and Energy Directorate (NVE), Centre for Terrestrial Carbon Dynamics: National Centre for Earth Observation (CTCD), University of Sheffield [Sheffield], Department of Life Science, Tokyo Institute of Technology [Tokyo] (TITECH), Institute of Industrial Science, Max Planck Institute for Meteorology (MPI-M), Max-Planck-Gesellschaft, Netherlands Environmental Assessment Agency, 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)-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 Department of Geosciences
Subjects: lcsh:Dynamic and structural geology, Natural resource economics, Population, Climate change, 7. Clean energy, Robust decision-making, lcsh:QE500-639.5, Laboratory of Geo-information Science and Remote Sensing, 11. Sustainability, ddc:550, Life Science, Laboratorium voor Geo-informatiekunde en Remote Sensing, lcsh:Science, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, education, ComputingMilieux_MISCELLANEOUS, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, 2. Zero hunger, education.field_of_study, Food security, business.industry, lcsh:QE1-996.5, Global warming, Environmental resource management, 15. Life on land, PE&RC, lcsh:Geology, Earth sciences, Climate change mitigation, Agriculture and Soil Science, 13. Climate action, Greenhouse gas, General Earth and Planetary Sciences, Environmental science, lcsh:Q, Climate model, business
Abstract: Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.
Published: 2015

38. The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0)

Author: Jens Heinke, Nathaniel D. Mueller, Kenneth J. Boote, James P. Chryssanthacopoulos, Toshichika Iizumi, Alex C. Ruane, Justin Sheffield, Joshua Elliott, Michael Glotter, Christoph Müller, Cynthia Rosenzweig, Ian Foster, Roberto C. Izaurralde, Matthias Büchner, Delphine Deryng, and Deepak K. Ray
Subjects: 2. Zero hunger, Coupled model intercomparison project, 010504 meteorology & atmospheric sciences, Meteorology, lcsh:QE1-996.5, Atmospheric Model Intercomparison Project, 04 agricultural and veterinary sciences, 15. Life on land, Radiative forcing, 01 natural sciences, lcsh:Geology, 13. Climate action, Climatology, 040103 agronomy & agriculture, ddc:550, 0401 agriculture, forestry, and fisheries, Hindcast, Environmental science, Model choice, Climate extremes, Historical record, 0105 earth and related environmental sciences, Climate impact assessment
Abstract: We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project's (AgMIP's) Gridded Crop Modeling Initiative (AgGRID). The project includes global simulations of yields, phenologies, and many land-surface fluxes by 12–15 modeling groups for many crops, climate forcing datasets, and scenarios over the historical period from 1948–2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the impacts to agriculture of large-scale climate extremes from the historical record.
Published: 2015

39. Consistent negative response of US crops to high temperatures in observations and crop models

Author: Delphine Deryng, Sibyll Schaphoff, Nikolay Khabarov, Sotirios V. Archontoulis, Wolfram Schlenker, Philippe Ciais, Juraj Balkovic, Christian Folberth, Almut Arneth, Joshua Elliott, Susanne Rolinski, Christoph Müller, Thomas A. M. Pugh, Erwin Schmid, Xuhui Wang, Bernhard Schauberger, Katja Frieler, Potsdam Institute for Climate Impact Research (PIK), Iowa State University (ISU), Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), 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), ICOS-ATC (ICOS-ATC), 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), Department of Computer Science, University of Chicago, University of Chicago, International Institute for Applied Systems Analysis [Laxenburg] (IIASA), School of Geography, Earth and Environmental Sciences and Birmingham Institute of Forest Research, University of Birmingham [Birmingham], Sino-French Institute of Earth System Sciences, College of Urban and Environmental Sciences, Peking University [Beijing], Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-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), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), University of Colombia, Within the framework of the Leibniz Competition (SAW-2013-PIK-5) and by the German Federal Ministry of Education and Research (BMBF, grant no. 01LS1201A1), European Project: 603864,EC:FP7:ENV,FP7-ENV-2013-two-stage,HELIX(2013), European Project: 603542,EC:FP7:ENV,FP7-ENV-2013-two-stage,LUC4C(2013), 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)-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), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris)
Subjects: 630 Landwirtschaft und verwandte Bereiche, 0106 biological sciences, Irrigation, 010504 meteorology & atmospheric sciences, Science, General Physics and Astronomy, Growing season, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, 01 natural sciences, Article, General Biochemistry, Genetics and Molecular Biology, Crop, Yield (wine), ddc:550, ddc:630, Agricultural productivity, 0105 earth and related environmental sciences, 2. Zero hunger, Multidisciplinary, Crop yield, Water stress, fungi, food and beverages, Agriculture, General Chemistry, 15. Life on land, Earth sciences, Agronomy, Negative response, 13. Climate action, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, Environmental science, Climate-change impacts, Agroecology, 010606 plant biology & botany
Abstract: High temperatures are detrimental to crop yields and could lead to global warming-driven reductions in agricultural productivity. To assess future threats, the majority of studies used process-based crop models, but their ability to represent effects of high temperature has been questioned. Here we show that an ensemble of nine crop models reproduces the observed average temperature responses of US maize, soybean and wheat yields. Each day >30 °C diminishes maize and soybean yields by up to 6% under rainfed conditions. Declines observed in irrigated areas, or simulated assuming full irrigation, are weak. This supports the hypothesis that water stress induced by high temperatures causes the decline. For wheat a negative response to high temperature is neither observed nor simulated under historical conditions, since critical temperatures are rarely exceeded during the growing season. In the future, yields are modelled to decline for all three crops at temperatures >30 °C. Elevated CO2 can only weakly reduce these yield losses, in contrast to irrigation., Future agricultural productivity is threatened by high temperatures. Here, using 9 crop models, Schauberger et al. find that yield losses due to temperatures >30 °C are captured by current models where yield losses by mild heat stress occur mainly due to water stress and can be buffered by irrigation.
Published: 2017

40. Spatial and temporal uncertainty of crop yield aggregations

Author: Deepak K. Ray, Peter Lawrence, Alex C. Ruane, Vera Porwollik, Xuhui Wang, Joshua Elliott, Xiuchen Wu, Curtis D. Jones, Wenfeng Liu, Christian Folberth, Ashwan Reddy, Gen Sakurai, Toshichika Iizumi, Allard de Wit, Nikolay Khabarov, Juraj Balkovic, Philippe Ciais, Roberto C. Izaurralde, Almut Arneth, Christoph Müller, Delphine Deryng, Thomas A. M. Pugh, James P. Chryssanthacopoulos, Erwin Schmid, Potsdam Institute for Climate Impact Research (PIK), Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], National Institute of Agro-Environmental Sciences (NIAES), Department of Civil, Environmental and Geo-Engineering [Minneapolis], University of Minnesota [Twin Cities] (UMN), University of Minnesota System-University of Minnesota System, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), 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, University of Maryland [College Park], University of Maryland System, Department of Geographical Sciences [College Park], University of Maryland System-University of Maryland System, International Institute for Applied Systems Analysis [Laxenburg] (IIASA), National Center for Atmospheric Research [Boulder] (NCAR), Swiss Federal Insitute of Aquatic Science and Technology [Dübendorf] (EAWAG), School of Geography, Earth and Environmental Sciences [Birmingham], University of Birmingham [Birmingham], National Agriculture and Food Research Organization (NARO), University of Natural Resources and Life Sciences (BOKU), Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University [Beijing], Wageningen University and Research [Wageningen] (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), and Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU)
Subjects: 0106 biological sciences, Earth Observation and Environmental Informatics, 010504 meteorology & atmospheric sciences, Yield (finance), Harvested area, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Soil Science, Context (language use), Plant Science, Spatial distribution, 010603 evolutionary biology, 01 natural sciences, Agricultural economics, Crop, Aardobservatie en omgevingsinformatica, Statistics, global crop model, Time series, 0105 earth and related environmental sciences, 2. Zero hunger, Crop yields, Crop yield, fungi, food and beverages, crop yields, gridded data, PE&RC, harvested area, Agronomy, Aggregation uncertainty, Global crop model, Spatial ecology, Environmental science, Temporal difference learning, Gridded data, Agronomy and Crop Science
Abstract: International audience; The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05 (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.
Published: 2017

41. 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

42. Supplementary material to 'Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates'

Author: Christian Folberth, Joshua Elliott, Christoph Müller, Juraj Balkovic, James Chryssanthacopoulos, Roberto C. Izaurralde, Curtis D. Jones, Nikolay Khabarov, Wenfeng Liu, Ashwan Reddy, Erwin Schmid, Rastislav Skalský, Hong Yang, Almut Arneth, Philippe Ciais, Delphine Deryng, Peter J. Lawrence, Stefan Olin, Thomas A. M. Pugh, Alex C. Ruane, and Xuhui Wang
Published: 2016

43. Similar estimates of temperature impacts on global wheat yield by three independent methods

Author: Frank Ewert, Jakarat Anothai, P. V. Vara Prasad, Davide Cammarano, Curtis D. Jones, Elias Fereres, Margarita Garcia-Vila, Soora Naresh Kumar, Eckart Priesack, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Alex C. Ruane, Christian Folberth, Gerrit Hoogenboom, Pierre Martre, Roberto C. Izaurralde, Fulu Tao, Pramod K. Aggarwal, Mohamed Jabloun, Jordi Doltra, Joshua Elliott, Christoph Müller, Bing Liu, Iurii Shcherbak, Jeffrey W. White, Bruno Basso, Senthold Asseng, Pierre Stratonovitch, Peter J. Thorburn, Claas Nendel, Taru Palosuo, Joost Wolf, Ann-Kristin Koehler, Thilo Streck, Jørgen E. Olesen, David B. Lobell, Kurt Christian Kersebaum, Delphine Deryng, L. A. Hunt, Garry O'Leary, Katharina Waha, Giacomo De Sanctis, Daniel Wallach, Yan Zhu, James W. Jones, Elke Stehfest, Mikhail A. Semenov, Christian Biernath, Claudio O. Stöckle, Thomas A. M. Pugh, Matthew P. Reynolds, Enli Wang, Bruce A. Kimball, Erwin Schmid, Iwan Supit, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Cynthia Rosenzweig, Ehsan Eyshi Rezaei, Gerard W. Wall, National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, 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), Potsdam Institute for Climate Impact Research (PIK), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], Computation Institute, Loyola University of Chicago, Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), 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, CGIAR Research Program on Climate Change, Agriculture and Food Security, Borlaug Institute for South Asia, CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Department of Plant and Soil Sciences, Mississippi State University [Mississippi], Department of Plant Science, Faculty of Natural Resources, Prince of Songkla University (PSU), Department of Geological Sciences, University of Oregon [Eugene], W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Soil Ecology [Neuherberg] (IBOE), Helmholtz-Zentrum München (HZM), The James Hutton Institute, 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, European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Department of Agronomy, Purdue University [West Lafayette], Department of Geography, University of Liverpool, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Institute of Soil Science and Land Evaluation, University of Hohenheim, AgWeatherNet Program, Washington State University (WSU), Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], US Arid-Land Agricultural Research Center, United States Department of Agriculture, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Landscape & Water Sciences, Department of Environment of Victoria, The School of Plant Sciences, University of Arizona, Natural resources institute Finland, Institute of Ecology, German Research Center for Environmental Health, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), School of Geography, Earth and Environmental Sciences [Birmingham], University of Birmingham [Birmingham], Birmingham Institute of Forest Research (BIFoR), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Center for Development Research (ZEF), Environmental Impacts Group, Georg-August-University [Göttingen], Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council, Netherlands Environmental Assessment Agency, Department of Biological Systems Engineering, University of Wisconsin-Madison, PPS, WSG and CALM, Wageningen University and Research [Wageningen] (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS, Arid-Land Agricultural Research Center, China Agricultural University (CAU), National High-Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616, 31611130182, 41571088 and 31561143003), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China Scholarship Council., IFPRI through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat, the Agricultural Model Intercomparison and Improvement Project (AgMIP), Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Stanford University [Stanford], 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), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Prince of Songkla University, Texas A&M AgriLife Research and Extension Center, Natural Resources Institute Finland, Georg-August-Universität Göttingen, Wageningen University and Research Center (WUR), China Agricultural University, Division of Plant Nutrition-University of Bonn, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), University of Florida, Potsdam Institute for Climate Impact Research ( PIK ), Leibniz Centre for Agricultural Landscape Research, Institute for Landscape Biogeochemistry, Center for Climate Systems Research [New York] ( CCSR ), 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 ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Consultative Group on International Agricultural Research ( CGIAR ), W.K. Kellogg Biological Station, Institute of Soil Ecology [Neuherberg] ( IBOE ), Helmholtz-Zentrum München ( HZM ), James Hutton Institute, Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, European Commission - Joint Research Centre [Ispra] ( JRC ), International Institute for Applied Systems Analysis ( IIASA ), Washington State University ( WSU ), Texas A and M University ( TAMU ), Leibniz Centre for Agricultural Landscape Research (ZALF), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung ( IMK-IFU ), Karlsruher Institut für Technologie ( KIT ), School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, University of Birmingham, International Maize and Wheat Improvement Center ( CIMMYT ), Bonn Universität [Bonn], University of Natural Resources and Life Sciences, University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Commonwealth Scientific and Industrial Research Organisation, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université de Toulouse (UT)-Université de Toulouse (UT), Helmholtz Zentrum München = German Research Center for Environmental Health, Natural Resources Institute Finland (LUKE), Georg-August-University = Georg-August-Universität Göttingen, Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU), Biotechnology and Biological Sciences Research Council (BBSRC), and Institute of geographical sciences and natural resources research [CAS] (IGSNRR)
Subjects: 0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [ SDV.BV ] Life Sciences [q-bio]/Vegetal Biology, régression statistique, 010504 meteorology & atmospheric sciences, impact sur le rendement, klim, Atmospheric sciences, 01 natural sciences, incertitude, wheat, uncertainty, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, 2. Zero hunger, changement climatique, Regression analysis, statistical regression, simulation, PE&RC, [ SDE.MCG ] Environmental Sciences/Global Changes, sécurité alimentaire, Plant Production Systems, modèle de récolte, Yield (finance), comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, Climate change, Environmental Science (miscellaneous), Earth System Science, blé, température, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, réchauffement climatique, global change, 0105 earth and related environmental sciences, Hydrology, WIMEK, Global temperature, business.industry, Crop yield, Global warming, Climate Resilience, 13. Climate action, Agriculture, Klimaatbestendigheid, Plantaardige Productiesystemen, Environmental science, Leerstoelgroep Aardsysteemkunde, Climate model, business, Social Sciences (miscellaneous), 010606 plant biology & botany
Abstract: The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security. The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.
Published: 2016

44. 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

45. Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)

Author: Katja Frieler, Richard Betts, Eleanor Burke, Philippe Ciais, Sebastien Denvil, Delphine Deryng, Kristie Ebi, Tyler Eddy, Kerry Emanuel, Joshua Elliott, Eric Galbraith, Simon N. Gosling, Kate Halladay, Fred Hattermann, Thomas Hickler, Jochen Hinkel, Veronika Huber, Chris Jones, Valentina Krysanova, Stefan Lange, Heike K. Lotze, Hermann Lotze-Campen, Matthias Mengel, Ioanna Mouratiadou, Hannes Müller Schmied, Sebastian Ostberg, Franziska Piontek, Alexander Popp, Christopher P. O. Reyer, Jacob Schewe, Miodrag Stevanovic, Tatsuo Suzuki, Kirsten Thonicke, Hanqin Tian, Derek P. Tittensor, Robert Vautard, Michelle van Vliet, Lila Warszawski, and Fang Zhao
Abstract: In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a "special report in 2018 on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways". In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impacts projections. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from other human drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the effects of additional warming up to 1.5 °C, including a potential overshoot and long-term effects up to 2299, compared to a no-mitigation scenario (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios but accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and differential bio-energy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impacts projections from a range of impact models across different sectors (global and regional hydrology, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, health, and tropical cyclones).
Published: 2016

46. Supplementary material to 'Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)'

Author: Katja Frieler, Richard Betts, Eleanor Burke, Philippe Ciais, Sebastien Denvil, Delphine Deryng, Kristie Ebi, Tyler Eddy, Kerry Emanuel, Joshua Elliott, Eric Galbraith, Simon N. Gosling, Kate Halladay, Fred Hattermann, Thomas Hickler, Jochen Hinkel, Veronika Huber, Chris Jones, Valentina Krysanova, Stefan Lange, Heike K. Lotze, Hermann Lotze-Campen, Matthias Mengel, Ioanna Mouratiadou, Hannes Müller Schmied, Sebastian Ostberg, Franziska Piontek, Alexander Popp, Christopher P. O. Reyer, Jacob Schewe, Miodrag Stevanovic, Tatsuo Suzuki, Kirsten Thonicke, Hanqin Tian, Derek P. Tittensor, Robert Vautard, Michelle van Vliet, Lila Warszawski, and Fang Zhao
Published: 2016

47. Supplementary material to 'Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications'

Author: Christoph Müller, Joshua Elliott, James Chryssanthacopoulos, Almut Arneth, Juraj Balkovic, Philippe Ciais, Delphine Deryng, Christian Folberth, Michael Glotter, Steven Hoek, Toshichika Iizumi, Roberto C. Izaurralde, Curtis Jones, Nikolay Khabarov, Peter Lawrence, Wenfeng Liu, Stefan Olin, Thomas A. M. Pugh, Deepak Ray, Ashwan Reddy, Cynthia Rosenzweig, Alexander C. Ruane, Gen Sakurai, Erwin Schmid, Rastislav Skalsky, Carol X. Song, Xuhui Wang, Allard de Wit, and Hong Yang
Published: 2016

48. 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

49. 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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