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Chemura A, Gleixner S, Gornott C. Dataset of the suitability of major food crops in Africa under climate change. Sci Data 2024; 11:294. [PMID: 38485989 PMCID: PMC10940296 DOI: 10.1038/s41597-024-03118-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
Understanding the extent and adapting to the impacts of climate change in the agriculture sector in Africa requires robust data on which technical and policy decisions can be based. However, there are no publicly available comprehensive data of which crops are suitable where under current and projected climate conditions for impact assessments and targeted adaptation planning. We developed a dataset on crop suitability of 23 major food crops (eight cereals, six legumes & pulses, six root & tuber crops, and three in banana-related family) for rainfed agriculture in Africa in terms of area and produced quantity. This dataset is based on the EcoCrop model parameterized with temperature, precipitation and soil data and is available for the historical period and until mid-century. The scenarios used for future projections are SSP1:RCP2.6, SSP3:RCP7.0 and SSP5:RCP8.5. The dataset provides a quantitative assessment of the impacts of climate change on crop production potential and can enable applications and linkages of crop impact studies to other socioeconomic aspects, thereby facilitating more comprehensive understanding of climate change impacts and assessment of options for building resilience.
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Affiliation(s)
- Abel Chemura
- Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands.
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany.
| | - Stephanie Gleixner
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Christoph Gornott
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- Agroecosystem Analysis and Modelling, Faculty of Organic Agricultural Sciences, University of Kassel, Kassel, Germany
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2
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Rodríguez A, van Grinsven HJM, van Loon MP, Doelman JC, Beusen AHW, Lassaletta L. Costs and benefits of synthetic nitrogen for global cereal production in 2015 and in 2050 under contrasting scenarios. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169357. [PMID: 38128654 DOI: 10.1016/j.scitotenv.2023.169357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/01/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023]
Abstract
Cereals are the most important global staple crop and use more than half of global cropland and synthetic nitrogen (N) fertilizer. While this synthetic N may feed half of the current global population, it has led to a massive increase in reactive N loss to the environment, causing a suite of impacts, offsetting the benefits of N fertilizers for food security and agricultural economy. To address these complex issues, the NBCalCer model was developed to quantify the global effects of N input on crop yields, N budgets and environmental impacts and to assess the associated social benefits and costs. Three Shared Socioeconomic Pathway scenarios (SSPs) were considered with decreasing N agri-environmental ambitions, through contrasting climate and N policy ambitions: sustainability (SSP1H), middle-of-the-road (SSP2M) and fossil-fueled development (SSP5L). In the base year the contribution of synthetic N fertilizer to global cereal production was 44 %. Global modelled grain yield was projected to increase under all scenarios while the use of synthetic N fertilizer decreases under all scenarios except SSP5L. The total N surplus was projected to be reduced up to 20 % under SSP1H but to increase under SSP5L. The Benefit-Cost-Ratio (BCR) was calculated as the ratio between the market benefit of increased grain production by synthetic N and the summed cost of fertilizer purchase and the external cost of the N losses. In base year the BCR was well above one in all regions, but in 2050 under SSP1H and SSP5L decreased to below one in most regions. Given the concerns about food security, environmental quality and its interaction with biodiversity loss, human health and climate change, the new paradigm for global cereal production is producing sufficient food with minimum N pollution. Our results indicate that achieving this goal would require a massive change in global volume and distribution of synthetic N.
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Affiliation(s)
- Alfredo Rodríguez
- Department of Economic Analysis and Finances, Universidad de Castilla-La Mancha, Toledo, Spain.
| | | | - Marloes P van Loon
- Plant Production Systems, Wageningen University & Research, 6700 AK Wageningen, the Netherlands
| | - Jonathan C Doelman
- PBL Netherlands Environmental Assessment Agency, The Hague, the Netherlands
| | - Arthur H W Beusen
- PBL Netherlands Environmental Assessment Agency, The Hague, the Netherlands
| | - Luis Lassaletta
- CEIGRAM-Agricultural Production, ETSIAAB, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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3
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van Voorn GAK, Boer MP, Truong SH, Friedenberg NA, Gugushvili S, McCormick R, Bustos Korts D, Messina CD, van Eeuwijk FA. A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library. FRONTIERS IN PLANT SCIENCE 2023; 14:1172359. [PMID: 37389290 PMCID: PMC10303120 DOI: 10.3389/fpls.2023.1172359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/30/2023] [Indexed: 07/01/2023]
Abstract
Introduction Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies. Methods Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values. Results We demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation. Discussion A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.
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Affiliation(s)
- George A. K. van Voorn
- Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
| | - Martin P. Boer
- Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
| | | | | | - Shota Gugushvili
- Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
| | - Ryan McCormick
- Research & Development, Corteva Agriscience, Johnston, IA, United States
- Gro Intelligence, New York, NY, United States
| | - Daniela Bustos Korts
- Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
- Institute of Plant Production and Protection, Faculty of Agricultural Sciences, Universidad Austral de Chile, Valdivia, Chile
| | - Carlos D. Messina
- Research & Development, Corteva Agriscience, Johnston, IA, United States
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
| | - Fred A. van Eeuwijk
- Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
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4
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McDermid SS, Hayek M, Jamieson DW, Hale G, Kanter D. Research needs for a food system transition. CLIMATIC CHANGE 2023; 176:41. [PMID: 37034009 PMCID: PMC10074344 DOI: 10.1007/s10584-023-03507-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 03/04/2023] [Indexed: 06/19/2023]
Abstract
The global food system, and animal agriculture in particular, is a major and growing contributor to climate change, land system change, biodiversity loss, water consumption and contamination, and environmental pollution. The copious production and consumption of animal products are also contributing to increasingly negative public health outcomes, particularly in wealthy and rapidly industrializing countries, and result in the slaughter of trillions of animals each year. These impacts are motivating calls for reduced reliance on animal-based products and increased use of replacement plant-based products. However, our understanding of how the production and consumption of animal products, as well as plant-based alternatives, interact with important dimensions of human and environment systems is incomplete across space and time. This inhibits comprehensively envisioning global and regional food system transitions and planning to manage the costs and synergies thereof. We therefore propose a cross-disciplinary research agenda on future target-based scenarios for food system transformation that has at its core three main activities: (1) data collection and analysis at the intersection of animal agriculture, the environment, and societal well-being, (2) the construction of target-based scenarios for animal products informed by these new data and empirical understandings, and (3) the evaluation of impacts, unintended consequences, co-benefits, and trade-offs of these target-based scenarios to help inform decision-making.
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Affiliation(s)
| | - Matthew Hayek
- Department of Environmental Studies, New York University, New York, NY USA
| | - Dale W. Jamieson
- Department of Environmental Studies, New York University, New York, NY USA
| | - Galina Hale
- Department of Economics, University of California at Santa Cruz, Santa Cruz, CA USA
- National Bureau of Economic Research, Cambridge, MA USA
- Centre for Economic Policy Research, London, England
| | - David Kanter
- Department of Environmental Studies, New York University, New York, NY USA
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5
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Cammarano D, Jamshidi S, Hoogenboom G, Ruane AC, Niyogi D, Ronga D. Processing tomato production is expected to decrease by 2050 due to the projected increase in temperature. NATURE FOOD 2022; 3:437-444. [PMID: 37118037 DOI: 10.1038/s43016-022-00521-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 04/19/2022] [Indexed: 04/30/2023]
Abstract
The global production of processing tomatoes is concentrated in a small number of regions where climate change could have a notable impact on the future supply. Process-based tomato models project that the production in the main producing countries (the United States, Italy and China, representing 65% of global production) will decrease 6% by 2050 compared with the baseline period of 1980-2009. The predicted reduction in processing tomato production is due to a projected increase in air temperature. Under an ensemble of projected climate scenarios, California and Italy might not be able to sustain current levels of processing tomato production due to water resource constraints. Cooler producing regions, such as China and the northern parts of California, stand to improve their competitive advantage. The projected environmental changes indicate that the main growing regions of processing tomatoes might change in the coming decades.
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Affiliation(s)
- Davide Cammarano
- Department of Agroecology, iClimate, Centre for Circular Bioeconomy (CBIO), Aarhus University, Tjele, Denmark.
| | - Sajad Jamshidi
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
| | - Gerrit Hoogenboom
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA
| | - Alex C Ruane
- NASA Goddard Institute for Space Studies, New York, NY, USA
| | - Dev Niyogi
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
- Department of Geological Sciences, Jackson School of Geosciences, and Department of Civil, Architectural, and Environmental Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Domenico Ronga
- Department of Pharmacy, University of Salerno, Fisciano, Italy
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6
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Ollenburger M, Kyle P, Zhang X. Uncertainties in estimating global potential yields and their impacts for long-term modeling. Food Secur 2022. [DOI: 10.1007/s12571-021-01228-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractEstimating realistic potential yields by crop type and region is challenging; such yields depend on both biophysical characteristics (e.g., soil characteristics, climate, etc.), and the crop management practices available in any site or region (e.g., mechanization, irrigation, crop cultivars). A broad body of literature has assessed potential yields for selected crops and regions, using several strategies. In this study we first analyze future potential yields of major crop types globally by two different estimation methods, one of which is based on historical observed yields (“Empirical”), while the other is based on biophysical conditions (“Simulated”). Potential yields by major crop and region are quite different between the two methods; in particular, Simulated potential yields are typically 200% higher than Empirical potential yields in tropical regions for major crops. Applying both of these potential yields in yield gap closure scenarios in a global agro-economic model, GCAM, the two estimates of future potential yields lead to very different outcomes for the agricultural sector globally. In the Simulated potential yield closure scenario, Africa, Asia, and South America see comparatively favorable outcomes for agricultural sustainability over time: low land use change emissions, low crop prices, and high levels of self-sufficiency. In contrast, the Empirical potential yield scenario is characterized by a heavy reliance on production and exports in temperate regions that currently practice industrial agriculture. At the global level, this scenario has comparatively high crop commodity prices, and more land allocated to crop production (and associated land use change emissions) than either the baseline or Simulated potential yield scenarios. This study highlights the importance of the choice of methods of estimating potential yields for agro-economic modeling.
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7
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Franke JA, Müller C, Minoli S, Elliott J, Folberth C, Gardner C, Hank T, Izaurralde RC, Jägermeyr J, Jones CD, Liu W, Olin S, Pugh TAM, Ruane AC, Stephens H, Zabel F, Moyer EJ. Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change. GLOBAL CHANGE BIOLOGY 2022; 28:167-181. [PMID: 34478595 DOI: 10.1111/gcb.15868] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/04/2021] [Indexed: 06/13/2023]
Abstract
Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.
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Affiliation(s)
- James A Franke
- Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois, USA
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois, USA
| | - Christoph Müller
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Sara Minoli
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Joshua Elliott
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois, USA
| | - Christian Folberth
- Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Charles Gardner
- Program on Global Environment, University of Chicago, Chicago, Illinois, USA
| | - Tobias Hank
- Ludwig-Maximilians-Universitat Munchen (LMU), Munich, Germany
| | | | - Jonas Jägermeyr
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- NASA Goddard Institute for Space Studies, New York City, New York, USA
- Center for Climate Systems Research, Columbia University, New York City, New York, USA
| | - Curtis D Jones
- Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA
| | - Wenfeng Liu
- College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China
| | - Stefan Olin
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
| | - Thomas A M Pugh
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
- Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
| | - Alex C Ruane
- NASA Goddard Institute for Space Studies, New York City, New York, USA
| | - Haynes Stephens
- Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois, USA
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois, USA
| | - Florian Zabel
- Ludwig-Maximilians-Universitat Munchen (LMU), Munich, Germany
| | - Elisabeth J Moyer
- Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois, USA
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois, USA
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8
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Peng B, Guan K. Harmonizing climate-smart and sustainable agriculture. NATURE FOOD 2021; 2:853-854. [PMID: 37117516 DOI: 10.1038/s43016-021-00407-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Affiliation(s)
- Bin Peng
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana Champaign, Urbana, IL, USA.
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL, USA.
| | - Kaiyu Guan
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana Champaign, Urbana, IL, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, USA
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9
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Zabel F, Müller C, Elliott J, Minoli S, Jägermeyr J, Schneider JM, Franke JA, Moyer E, Dury M, Francois L, Folberth C, Liu W, Pugh TAM, Olin S, Rabin SS, Mauser W, Hank T, Ruane AC, Asseng S. Large potential for crop production adaptation depends on available future varieties. GLOBAL CHANGE BIOLOGY 2021; 27:3870-3882. [PMID: 33998112 DOI: 10.1111/gcb.15649] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 04/08/2021] [Indexed: 06/12/2023]
Abstract
Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5-8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1-2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro-ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5-8.5. The results highlight that region-specific breeding efforts are required to allow for a successful adaptation to climate change.
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Affiliation(s)
- Florian Zabel
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany
| | - Christoph Müller
- Climate Resilience, Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Joshua Elliott
- Center for Climate Systems Research, Columbia University, New York, NY, USA
| | - Sara Minoli
- Climate Resilience, Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Jonas Jägermeyr
- Climate Resilience, Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- Center for Climate Systems Research, Columbia University, New York, NY, USA
- NASA Goddard Institute for Space Studies, New York, NY, USA
| | - Julia M Schneider
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany
| | - James A Franke
- Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA
| | - Elisabeth Moyer
- Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA
| | | | | | - Christian Folberth
- International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
| | - Wenfeng Liu
- Center for Agricultural Water Research in China, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China
| | - Thomas A M Pugh
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
- Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
| | | | - Sam S Rabin
- Institute of Meteorology and Climate Research - Atmospheric Environmental Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Wolfram Mauser
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany
| | - Tobias Hank
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany
| | - Alex C Ruane
- NASA Goddard Institute for Space Studies, New York, NY, USA
| | - Senthold Asseng
- School of Life Sciences, Technical University of Munich (TUM), München, Germany
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10
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Falconnier GN, Corbeels M, Boote KJ, Affholder F, Adam M, MacCarthy DS, Ruane AC, Nendel C, Whitbread AM, Justes É, Ahuja LR, Akinseye FM, Alou IN, Amouzou KA, Anapalli SS, Baron C, Basso B, Baudron F, Bertuzzi P, Challinor AJ, Chen Y, Deryng D, Elsayed ML, Faye B, Gaiser T, Galdos M, Gayler S, Gerardeaux E, Giner M, Grant B, Hoogenboom G, Ibrahim ES, Kamali B, Kersebaum KC, Kim SH, van der Laan M, Leroux L, Lizaso JI, Maestrini B, Meier EA, Mequanint F, Ndoli A, Porter CH, Priesack E, Ripoche D, Sida TS, Singh U, Smith WN, Srivastava A, Sinha S, Tao F, Thorburn PJ, Timlin D, Traore B, Twine T, Webber H. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa. GLOBAL CHANGE BIOLOGY 2020; 26:5942-5964. [PMID: 32628332 DOI: 10.1111/gcb.15261] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 05/19/2020] [Accepted: 06/22/2020] [Indexed: 06/11/2023]
Abstract
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.
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Affiliation(s)
| | - Marc Corbeels
- AIDA, Univ Montpellier, CIRAD, Montpellier, France
- CIMMYT, Nairobi, Kenya
| | | | | | - Myriam Adam
- CIRAD, UMR AGAP, Bobo-Dioulasso, Burkina Faso
- AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | - Dilys S MacCarthy
- Soil and Irrigation Research Centre, School of Agriculture, College of Basic and Applied Science, University of Ghana, Accra, Ghana
| | - Alex C Ruane
- Climate Impacts Group, National Aeronautics and Space Administration Goddard Institute for Space Studies, New York, NY, USA
| | - Claas Nendel
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
| | - Anthony M Whitbread
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Dar es Salaam, Tanzania
| | - Éric Justes
- PERSYST, Univ Montpellier, CIRAD, Montpellier, France
| | | | - Folorunso M Akinseye
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Kano, Nigeria
| | - Isaac N Alou
- Department of Plant and Soil Sciences, University of Pretoria, Pretoria, South Africa
| | - Kokou A Amouzou
- West Africa Program, African Plant Nutrition Institute (APNI), Yamoussoukro, Cote d'Ivoire
| | | | - Christian Baron
- CIRAD, UMR TETIS, Montpellier, France
- TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, IRSTEA, Montpellier, France
| | - Bruno Basso
- Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, USA
| | | | | | - Andrew J Challinor
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK
| | - Yi Chen
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Delphine Deryng
- Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Berlin, Germany
- NewClimate Institute, Berlin, Germany
| | - Maha L Elsayed
- MALR-ARC, Central Laboratory for Agricultural Climate (CLAC), Giza, Egypt
| | - Babacar Faye
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany
| | - Thomas Gaiser
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany
| | - Marcelo Galdos
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK
| | - Sebastian Gayler
- Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany
| | | | - Michel Giner
- AIDA, Univ Montpellier, CIRAD, Montpellier, France
| | - Brian Grant
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | | | - Esther S Ibrahim
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
| | - Bahareh Kamali
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
| | | | - Soo-Hyung Kim
- School of Environmental and Forest Sciences, University of Washington, Seattle, USA
| | - Michael van der Laan
- Department of Plant and Soil Sciences, University of Pretoria, Pretoria, South Africa
| | - Louise Leroux
- AIDA, Univ Montpellier, CIRAD, Montpellier, France
- CIRAD, UPR AIDA, Dakar, Senegal
| | - Jon I Lizaso
- CEIGRAM-Universidad Politécnica de Madrid, ETSIAAB, Madrid, Spain
| | - Bernardo Maestrini
- Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, USA
| | - Elizabeth A Meier
- CSIRO Agriculture and Food, Queensland Bioscience Precinct, St Lucia, Qld, Australia
| | - Fasil Mequanint
- Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany
| | | | | | - Eckart Priesack
- Institute of Biochemical Plant Pathology, Helmholtz Center Munich, Neuherberg, Germany
| | | | | | - Upendra Singh
- International Center for Soil Fertility and Agricultural Development, Muscle Shoals, AL, USA
| | - Ward N Smith
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Amit Srivastava
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany
| | - Sumit Sinha
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK
| | - Fulu Tao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
- Natural Resources Institute Finland (Luke), Helsinki, Finland
| | - Peter J Thorburn
- CSIRO Agriculture and Food, Queensland Bioscience Precinct, St Lucia, Qld, Australia
| | - Dennis Timlin
- Crop Systems and Global Change Research Unit, USDA-ARS, Beltsville, MD, USA
| | | | - Tracy Twine
- Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN, USA
| | - Heidi Webber
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
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11
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Peng B, Guan K, Tang J, Ainsworth EA, Asseng S, Bernacchi CJ, Cooper M, Delucia EH, Elliott JW, Ewert F, Grant RF, Gustafson DI, Hammer GL, Jin Z, Jones JW, Kimm H, Lawrence DM, Li Y, Lombardozzi DL, Marshall-Colon A, Messina CD, Ort DR, Schnable JC, Vallejos CE, Wu A, Yin X, Zhou W. Towards a multiscale crop modelling framework for climate change adaptation assessment. NATURE PLANTS 2020; 6:338-348. [PMID: 32296143 DOI: 10.1038/s41477-020-0625-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 02/24/2020] [Indexed: 05/18/2023]
Abstract
Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.
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Affiliation(s)
- Bin Peng
- Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Kaiyu Guan
- Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Jinyun Tang
- Climate Sciences Department, Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Elizabeth A Ainsworth
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Senthold Asseng
- Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA
| | - Carl J Bernacchi
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Mark Cooper
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
| | - Evan H Delucia
- Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Joshua W Elliott
- Department of Computer Science, University of Chicago, Chicago, IL, USA
| | - Frank Ewert
- Crop Science Group, INRES, University of Bonn, Bonn, Germany
- Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
| | - Robert F Grant
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
| | | | - Graeme L Hammer
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
- Australian Research Council Centre of Excellence for Translational Photosynthesis, The University of Queensland, Brisbane, Queensland, Australia
| | - Zhenong Jin
- Department of Bioproducts and Biosystems Engineering, University of Minnesota-Twin Cities, St. Paul, MN, USA
| | - James W Jones
- Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA
| | - Hyungsuk Kimm
- Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Yan Li
- State Key Laboratory of Earth Surface Processes and Resources Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | | | - Amy Marshall-Colon
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Donald R Ort
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - James C Schnable
- Department of Agronomy & Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - C Eduardo Vallejos
- Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
| | - Alex Wu
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
- Australian Research Council Centre of Excellence for Translational Photosynthesis, The University of Queensland, Brisbane, Queensland, Australia
| | - Xinyou Yin
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, Wageningen, The Netherlands
| | - Wang Zhou
- Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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12
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Stöckle CO, Kemanian AR. Can Crop Models Identify Critical Gaps in Genetics, Environment, and Management Interactions? FRONTIERS IN PLANT SCIENCE 2020; 11:737. [PMID: 32595666 PMCID: PMC7303354 DOI: 10.3389/fpls.2020.00737] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 05/07/2020] [Indexed: 05/21/2023]
Abstract
Increasing food demand under climate change constraints may challenge and strain agricultural systems. The use of crop models to assess genotypes performance across diverse target environments and management practices, i.e., the genetic × environment × management interaction (GEMI), can help understand suitability of genotype and agronomic practices, and possibly accelerate turnaround in plant breeding programs. However, the readiness of models to support these tasks can be debated. In this article, we point out modeling and data limitations and argue the need for evaluation and improvement of relevant process algorithms as well as model convergence. Under conditions suitable for plant growth, without meteorological extremes or soil limitation to root exploration, models can simulate resource capture, growth, and yield with relative ease. As stresses accumulate, the plant species- and genotype-specific attributes and their interactions with the soil and atmospheric environment generate a large range of responses, including conditions where resources become so limiting as to make yields very low. The space in between high and low yields is where most rainfed production occurs, and where the current model and user skill at representing GEMI varies. We also review studies comparing the performance of a large number of crop models and the lessons learned. The overall message is that improvement of models appears as a necessary condition for progress, and perhaps relevancy. Model ensembles help mitigate data input, model, and user-driven uncertainty for some but not all applications, sometimes at a very high cost. Successful model-based assessment of GEMI not only requires better crop models and knowledgeable users, but also a realistic representation of the environmental conditions of the landscape where crops are grown, which is not trivial given the 3D nature of water and nutrient transport. Models remain the best quantitative repository of our knowledge on crop functioning; they contain a narrative of plant, soil, and atmospheric functioning in computer language and train the mind to couple processes. But in our quest to tame GEMI, will they lead the way or just ride along history?
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Affiliation(s)
- Claudio O. Stöckle
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
- *Correspondence: Claudio O. Stöckle,
| | - Armen R. Kemanian
- Department of Plant Science, The Pennsylvania State University, University Park, PA, United States
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13
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Mitter H, Techen AK, Sinabell F, Helming K, Kok K, Priess JA, Schmid E, Bodirsky BL, Holman I, Lehtonen H, Leip A, Le Mouël C, Mathijs E, Mehdi B, Michetti M, Mittenzwei K, Mora O, Øygarden L, Reidsma P, Schaldach R, Schönhart M. A protocol to develop Shared Socio-economic Pathways for European agriculture. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019; 252:109701. [PMID: 31629178 DOI: 10.1016/j.jenvman.2019.109701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 10/07/2019] [Accepted: 10/09/2019] [Indexed: 05/16/2023]
Abstract
Moving towards a more sustainable future requires concerted actions, particularly in the context of global climate change. Integrated assessments of agricultural systems (IAAS) are considered valuable tools to provide sound information for policy and decision-making. IAAS use storylines to define socio-economic and environmental framework assumptions. While a set of qualitative global storylines, known as the Shared Socio-economic Pathways (SSPs), is available to inform integrated assessments at large scales, their spatial resolution and scope is insufficient for regional studies in agriculture. We present a protocol to operationalize the development of Shared Socio-economic Pathways for European agriculture - Eur-Agri-SSPs - to support IAAS. The proposed design of the storyline development process is based on six quality criteria: plausibility, vertical and horizontal consistency, salience, legitimacy, richness and creativity. Trade-offs between these criteria may occur. The process is science-driven and iterative to enhance plausibility and horizontal consistency. A nested approach is suggested to link storylines across scales while maintaining vertical consistency. Plausibility, legitimacy, salience, richness and creativity shall be stimulated in a participatory and interdisciplinary storyline development process. The quality criteria and process design requirements are combined in the protocol to increase conceptual and methodological transparency. The protocol specifies nine working steps. For each step, suitable methods are proposed and the intended level and format of stakeholder engagement are discussed. A key methodological challenge is to link global SSPs with regional perspectives provided by the stakeholders, while maintaining vertical consistency and stakeholder buy-in. We conclude that the protocol facilitates systematic development and evaluation of storylines, which can be transferred to other regions, sectors and scales and supports inter-comparisons of IAAS.
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Affiliation(s)
- Hermine Mitter
- University of Natural Resources and Life Sciences Vienna, BOKU, Institute for Sustainable Economic Development, Austria.
| | - Anja-K Techen
- Leibniz Centre for Agricultural Landscape Research, ZALF, Germany
| | | | | | - Kasper Kok
- Wageningen University, WUR, Soil Geography and Landscape Group, the Netherlands
| | - Jörg A Priess
- Helmholtz-Centre for Environmental Research, UFZ, Germany
| | - Erwin Schmid
- University of Natural Resources and Life Sciences Vienna, BOKU, Institute for Sustainable Economic Development, Austria
| | | | | | | | - Adrian Leip
- European Commission, Joint Research Centre, Ispra (VA), Italy
| | - Chantal Le Mouël
- UMR 1302 SMART-LERECO, Institut National de la Recherche Agronomique, INRA, Rennes, France
| | - Erik Mathijs
- University of Leuven, KU Leuven, Division of Bioeconomics, Belgium
| | - Bano Mehdi
- University of Natural Resources and Life Sciences Vienna, BOKU, Division of Agronomy, Austria
| | - Melania Michetti
- Fondazione Centro Euro-Mediterraneo Sui Cambiamenti Climatici, CMCC, Italy
| | | | - Olivier Mora
- UAR 1241 DEPE, Institut National de la Recherche Agronomique, INRA, Paris, France
| | | | - Pytrik Reidsma
- Wageningen University, WUR, Plant Production Systems Group, the Netherlands
| | | | - Martin Schönhart
- University of Natural Resources and Life Sciences Vienna, BOKU, Institute for Sustainable Economic Development, Austria
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14
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Porter JR, Challinor AJ, Henriksen CB, Howden SM, Martre P, Smith P. Invited review: Intergovernmental Panel on Climate Change, agriculture, and food-A case of shifting cultivation and history. GLOBAL CHANGE BIOLOGY 2019; 25:2518-2529. [PMID: 31095820 DOI: 10.1111/gcb.14700] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 04/01/2019] [Indexed: 05/23/2023]
Abstract
Since 1990, the Intergovernmental Panel on Climate Change (IPCC) has produced five Assessment Reports (ARs), in which agriculture as the production of food for humans via crops and livestock have featured in one form or another. A constructed database of the ca. 2,100 cited experiments and simulations in the five ARs was analyzed with respect to impacts on yields via crop type, region, and whether adaptation was included. Quantitative data on impacts and adaptation in livestock farming have been extremely scarce in the ARs. The main conclusions from impact and adaptation are that crop yields will decline, but that responses have large statistical variation. Mitigation assessments in the ARs have used both bottom-up and top-down methods but need better to link emissions and their mitigation with food production and security. Relevant policy options have become broader in later ARs and included more of the social and nonproduction aspects of food security. Our overall conclusion is that agriculture and food security, which are two of the most central, critical, and imminent issues in climate change, have been dealt with an unfocussed and inconsistent manner between the IPCC five ARs. This is partly a result of not only agriculture spanning two IPCC working groups but also the very strong focus on projections from computer crop simulation modeling. For the future, we suggest a need to examine interactions between themes such as crop resource use efficiencies and to include all production and nonproduction aspects of food security in future roles for integrated assessment models.
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Affiliation(s)
- John R Porter
- CIHEAM-IAMM - SupAgro - MUSE University of Montpellier, Montpellier, France
- Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark
| | - Andrew J Challinor
- School of Earth and Environment, Institute for Climate and Atmospheric Science (ICAS), University of Leeds, Leeds, UK
| | | | - Stuart Mark Howden
- Climate Change Institute, Australian National University, Canberra, ACT, Australia
| | - Pierre Martre
- LEPSE, INRA, Montpellier SupAgro, Université Montpellier, Montpellier, France
| | - Pete Smith
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK
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15
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Muller B, Martre P. Plant and crop simulation models: powerful tools to link physiology, genetics, and phenomics. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:2339-2344. [PMID: 31091319 DOI: 10.1093/jxb/erz175] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Affiliation(s)
- Bertrand Muller
- UMR LEPSE, Univ Montpellier, INRA, Montpellier SupAgro, Montpellier, France
| | - Pierre Martre
- UMR LEPSE, Univ Montpellier, INRA, Montpellier SupAgro, Montpellier, France
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16
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Uz SS, Ruane AC, Duncan BN, Tucker CJ, Huffman GJ, Mladenova IE, Osmanoglu B, Holmes TR, McNally A, Peters-Lidard C, Bolten JD, Das N, Rodell M, McCartney S, Anderson MC, Doorn B. Earth observations and integrative models in support of food and water security. REMOTE SENSING IN EARTH SYSTEMS SCIENCES 2019; 2:18-38. [PMID: 33005873 PMCID: PMC7526267 DOI: 10.1007/s41976-019-0008-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 12/26/2018] [Accepted: 01/17/2019] [Indexed: 11/28/2022]
Abstract
Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.
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Affiliation(s)
| | - Alex C. Ruane
- NASA Goddard Institute for Space Studies, Climate Impacts Group, New York, NY, USA
| | | | | | | | - Iliana E. Mladenova
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | | | | | - Amy McNally
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | | | | | - Narendra Das
- NASA Jet Propulsion Laboratory, Pasadena, CA, USA
| | | | - Sean McCartney
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Science Systems and Applications, Inc., Lanham, MD, USA
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17
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Rötter RP, Hoffmann MP, Koch M, Müller C. Progress in modelling agricultural impacts of and adaptations to climate change. CURRENT OPINION IN PLANT BIOLOGY 2018; 45:255-261. [PMID: 29866444 DOI: 10.1016/j.pbi.2018.05.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 05/07/2018] [Accepted: 05/08/2018] [Indexed: 06/08/2023]
Abstract
Modelling is a key tool to explore agricultural impacts of and adaptations to climate change. Here we report recent progress made especially referring to the large project initiatives MACSUR and AgMIP; in particular, in modelling potential crop impacts from field to global using multi-model ensembles. We identify two main fields where further progress is necessary: a more mechanistic understanding of climate impacts and management options for adaptation and mitigation; and focusing on cropping systems and integrative multi-scale assessments instead of single season and crops, especially in complex tropical and neglected but important cropping systems. Stronger linking of experimentation with statistical and eco-physiological crop modelling could facilitate the necessary methodological advances.
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Affiliation(s)
- R P Rötter
- University of Göttingen, Department of Crop Sciences, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Grisebachstraße 6, 37077 Göttingen, Germany; University of Göttingen, Centre for Biodiversity and Sustainable Land Use (CBL), Büsgenweg 1, 37077 Göttingen, Germany.
| | - M P Hoffmann
- University of Göttingen, Department of Crop Sciences, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Grisebachstraße 6, 37077 Göttingen, Germany
| | - M Koch
- University of Göttingen, Department of Crop Sciences, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Grisebachstraße 6, 37077 Göttingen, Germany
| | - C Müller
- Potsdam Institute for Climate Impact Research (PIK), Telegraphenberg A31, 14473 Potsdam, Germany
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18
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Ruane AC, Phillips MM, Rosenzweig C. Climate Shifts within Major Agricultural Seasons for +1.5 and +2.0 °C Worlds: HAPPI Projections and AgMIP Modeling Scenarios. AGRICULTURAL AND FOREST METEOROLOGY 2018; 259:329-344. [PMID: 30880854 PMCID: PMC6415298 DOI: 10.1016/j.agrformet.2018.05.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
This study compares climate changes in major agricultural regions and current agricultural seasons associated with global warming of +1.5 or +2.0 °C above pre-industrial conditions. It describes the generation of climate scenarios for agricultural modeling applications conducted as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments. Climate scenarios from the Half a degree Additional warming, Projections, Prognosis and Impacts project (HAPPI) are largely consistent with transient scenarios extracted from RCP4.5 simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5). Focusing on food and agricultural systems and top-producing breadbaskets in particular, we distinguish maize, rice, wheat, and soy season changes from global annual mean climate changes. Many agricultural regions warm at a rate that is faster than the global mean surface temperature (including oceans) but slower than the mean land surface temperature, leading to regional warming that exceeds 0.5 °C between the +1.5 and +2.0 °C Worlds. Agricultural growing seasons warm at a pace slightly behind the annual temperature trends in most regions, while precipitation increases slightly ahead of the annual rate. Rice cultivation regions show reduced warming as they are concentrated where monsoon rainfall is projected to intensify, although projections are influenced by Asian aerosol loading in climate mitigation scenarios. Compared to CMIP5, HAPPI slightly underestimates the CO2 concentration that corresponds to the +1.5 °C World but overestimates the CO2 concentration for the +2.0 °C World, which means that HAPPI scenarios may also lead to an overestimate in the beneficial effects of CO2 on crops in the +2.0 °C World. HAPPI enables detailed analysis of the shifting distribution of extreme growing season temperatures and precipitation, highlighting widespread increases in extreme heat seasons and heightened skewness toward hot seasons in the tropics. Shifts in the probability of extreme drought seasons generally tracked median precipitation changes; however, some regions skewed toward drought conditions even where median precipitation changes were small. Together, these findings highlight unique seasonal and agricultural region changes in the +1.5°C and +2.0°C worlds for adaptation planning in these climate stabilization targets.
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Affiliation(s)
- Alex C Ruane
- NASA Goddard Institute for Space Studies, New York, NY, USA
| | - Meridel M Phillips
- Columbia University Center for Climate Systems Research, New York, NY, USA
- NASA Goddard Institute for Space Studies, New York, NY, USA
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19
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Challinor AJ, Adger WN, Benton TG, Conway D, Joshi M, Frame D. Transmission of climate risks across sectors and borders. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2018; 376:20170301. [PMID: 29712795 PMCID: PMC5938635 DOI: 10.1098/rsta.2017.0301] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/19/2018] [Indexed: 05/29/2023]
Abstract
Systemic climate risks, which result from the potential for cascading impacts through inter-related systems, pose particular challenges to risk assessment, especially when risks are transmitted across sectors and international boundaries. Most impacts of climate variability and change affect regions and jurisdictions in complex ways, and techniques for assessing this transmission of risk are still somewhat limited. Here, we begin to define new approaches to risk assessment that can account for transboundary and trans-sector risk transmission, by presenting: (i) a typology of risk transmission that distinguishes clearly the role of climate versus the role of the social and economic systems that distribute resources; (ii) a review of existing modelling, qualitative and systems-based methods of assessing risk and risk transmission; and (iii) case studies that examine risk transmission in human displacement, food, water and energy security. The case studies show that policies and institutions can attenuate risks significantly through cooperation that can be mutually beneficial to all parties. We conclude with some suggestions for assessment of complex risk transmission mechanisms: use of expert judgement; interactive scenario building; global systems science and big data; innovative use of climate and integrated assessment models; and methods to understand societal responses to climate risk. These approaches aim to inform both research and national-level risk assessment.
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Affiliation(s)
- Andy J Challinor
- School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK
| | - W Neil Adger
- Geography, College of Life and Environmental Sciences, University of Exeter, Rennes Drive, Exeter EX4 4RJ, UK
| | - Tim G Benton
- School of Biology, University of Leeds, Leeds LS2 9JT, UK
| | - Declan Conway
- Grantham Research Institute on Climate Change and the Environment, London School of Economics, London WC2A 2AE, UK
| | - Manoj Joshi
- Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
| | - Dave Frame
- NZ Climate Change Research Institute, Victoria University, Wellington, PO Box 600, Wellington 6012, New Zealand
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20
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Rosenzweig C, Ruane AC, Antle J, Elliott J, Ashfaq M, Chatta AA, Ewert F, Folberth C, Hathie I, Havlik P, Hoogenboom G, Lotze-Campen H, MacCarthy DS, Mason-D'Croz D, Contreras EM, Müller C, Perez-Dominguez I, Phillips M, Porter C, Raymundo RM, Sands RD, Schleussner CF, Valdivia RO, Valin H, Wiebe K. Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2018; 376:20160455. [PMID: 29610385 PMCID: PMC5897826 DOI: 10.1098/rsta.2016.0455] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/29/2017] [Indexed: 05/19/2023]
Abstract
The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some breadbasket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO2 effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.
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Affiliation(s)
- Cynthia Rosenzweig
- Goddard Institute for Space Studies, National Aeronautics and Space Administration, 2880 Broadway, New York, NY 10025, USA
- Center for Climate Systems Research, Columbia University, 2880 Broadway, New York, NY 10025, USA
| | - Alex C Ruane
- Goddard Institute for Space Studies, National Aeronautics and Space Administration, 2880 Broadway, New York, NY 10025, USA
- Center for Climate Systems Research, Columbia University, 2880 Broadway, New York, NY 10025, USA
| | - John Antle
- Department of Applied Economics, Oregon State University, 213 Ballard Hall, Corvallis, OR 97331, USA
| | - Joshua Elliott
- Computation Institute, University of Chicago, 5735 South Ellis Avenue, Chicago, IL 60637, USA
| | - Muhammad Ashfaq
- University of Agriculture Faisalabad, University Main Road, Faisalabad, Pakistan
| | - Ashfaq Ahmad Chatta
- University of Agriculture Faisalabad, University Main Road, Faisalabad, Pakistan
| | - Frank Ewert
- INRES-Crop Science, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany
- Leibniz Center for Agricultural Landscape Research (ZALF), Eberswalder Strasse 84, 15374 Müncheberg, Germany
| | - Christian Folberth
- Ecosystems Services and Management Program, International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, Austria
| | - Ibrahima Hathie
- Initiative Prospective Agricole et Rurale, 67 Rond-Point VDN--Ouest Foire, BP 16788, Dakar-Fann, Senegal
| | - Petr Havlik
- Ecosystems Services and Management Program, International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, Austria
| | - Gerrit Hoogenboom
- Department of Agricultural and Biological Engineering, University of Florida, Frazier Rogers Hall, Gainesville, FL 32611, USA
| | - Hermann Lotze-Campen
- Potsdam-Institut fur Klimafolgenforschung eV, PO Box 601203, 14412 Potsdam, Germany
- Humboldt-Universität zu Berlin, 10099 Berlin, Germany
| | - Dilys S MacCarthy
- Soil and Irrigation Research Centre, University of Ghana, PO Box LG 68, Kpong, Ghana
| | - Daniel Mason-D'Croz
- International Food Policy Research Institute, 1201 I Street NW, Washington, DC 20005, USA
- Commonwealth Science and Industrial Research Organisation, 306 Carmody Road, St Lucia, QLD 4067, Australia
| | - Erik Mencos Contreras
- Goddard Institute for Space Studies, National Aeronautics and Space Administration, 2880 Broadway, New York, NY 10025, USA
- Center for Climate Systems Research, Columbia University, 2880 Broadway, New York, NY 10025, USA
| | - Christoph Müller
- Potsdam-Institut fur Klimafolgenforschung eV, PO Box 601203, 14412 Potsdam, Germany
| | | | - Meridel Phillips
- Goddard Institute for Space Studies, National Aeronautics and Space Administration, 2880 Broadway, New York, NY 10025, USA
- Center for Climate Systems Research, Columbia University, 2880 Broadway, New York, NY 10025, USA
| | - Cheryl Porter
- Department of Agricultural and Biological Engineering, University of Florida, Frazier Rogers Hall, Gainesville, FL 32611, USA
| | - Rubi M Raymundo
- Department of Agricultural and Biological Engineering, University of Florida, Frazier Rogers Hall, Gainesville, FL 32611, USA
| | - Ronald D Sands
- Economic Research Service, United States Department of Agriculture, 355 E Street SW, Washington, DC 20024, USA
| | - Carl-Friedrich Schleussner
- Potsdam-Institut fur Klimafolgenforschung eV, PO Box 601203, 14412 Potsdam, Germany
- Climate Analytics, Ritterstrasse 3, 10969 Berlin, Germany
| | - Roberto O Valdivia
- Department of Applied Economics, Oregon State University, 213 Ballard Hall, Corvallis, OR 97331, USA
| | - Hugo Valin
- Ecosystems Services and Management Program, International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, Austria
| | - Keith Wiebe
- International Food Policy Research Institute, 1201 I Street NW, Washington, DC 20005, USA
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21
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Ruane AC, Antle J, Elliott J, Folberth C, Hoogenboom G, Mason-D’Croz D, Müller C, Porter C, Phillips MM, Raymundo RM, Sands R, Valdivia RO, White JW, Wiebe K, Rosenzweig C. Biophysical and economic implications for agriculture of +1.5° and +2.0°C global warming using AgMIP Coordinated Global and Regional Assessments. CLIMATE RESEARCH 2018; 76:17-39. [PMID: 33154611 PMCID: PMC7641099 DOI: 10.3354/cr01520] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
This study presents results of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments (CGRA) of +1.5° and +2.0°C global warming above pre-industrial conditions. This first CGRA application provides multi-discipline, multi-scale, and multi-model perspectives to elucidate major challenges for the agricultural sector caused by direct biophysical impacts of climate changes as well as ramifications of associated mitigation strategies. Agriculture in both target climate stabilizations is characterized by differential impacts across regions and farming systems, with tropical maize Zea mays experiencing the largest losses, while soy Glycine max mostly benefits. The result is upward pressure on prices and area expansion for maize and wheat Triticum aestivum, while soy prices and area decline (results for rice Oryza sativa are mixed). An example global mitigation strategy encouraging bioenergy expansion is more disruptive to land use and crop prices than the climate change impacts alone, even in the +2.0°C scenario which has a larger climate signal and lower mitigation requirement than the +1.5°C scenario. Coordinated assessments reveal that direct biophysical and economic impacts can be substantially larger for regional farming systems than global production changes. Regional farmers can buffer negative effects or take advantage of new opportunities via mitigation incentives and farm management technologies. Primary uncertainties in the CGRA framework include the extent of CO2 benefits for diverse agricultural systems in crop models, as simulations without CO2 benefits show widespread production losses that raise prices and expand agricultural area.
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Affiliation(s)
- Alex C. Ruane
- NASA Goddard Institute for Space Studies, New York, NY 10025, USA
- Corresponding author:
| | - John Antle
- Oregon State University, Corvallis, OR 97331, USA
| | | | - Christian Folberth
- International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria
| | | | - Daniel Mason-D’Croz
- International Food Policy Research Institute, Washington, DC 20005, USA
- Commonwealth Science and Industrial Research Organisation, St Lucia, QLD 4067, Australia
| | - Christoph Müller
- Potsdam Institute for Climate Impacts Research, 14473 Potsdam, Germany
| | | | - Meridel M. Phillips
- NASA Goddard Institute for Space Studies, New York, NY 10025, USA
- Columbia University Center for Climate Systems Research, New York, NY 10025, USA
| | | | - Ronald Sands
- USDA Economic Research Service, Washington, DC 20036, USA
| | | | | | - Keith Wiebe
- International Food Policy Research Institute, Washington, DC 20005, USA
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22
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Pleban JR, Mackay DS, Aston TL, Ewers BE, Weinig C. Phenotypic Trait Identification Using a Multimodel Bayesian Method: A Case Study Using Photosynthesis in Brassica rapa Genotypes. FRONTIERS IN PLANT SCIENCE 2018; 9:448. [PMID: 29719545 PMCID: PMC5913710 DOI: 10.3389/fpls.2018.00448] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 03/22/2018] [Indexed: 05/21/2023]
Abstract
Agronomists have used statistical crop models to predict yield on a genotype-by-genotype basis. Mechanistic models, based on fundamental physiological processes common across plant taxa, will ultimately enable yield prediction applicable to diverse genotypes and crops. Here, genotypic information is combined with multiple mechanistically based models to characterize photosynthetic trait differentiation among genotypes of Brassica rapa. Infrared leaf gas exchange and chlorophyll fluorescence observations are analyzed using Bayesian methods. Three advantages of Bayesian approaches are employed: a hierarchical model structure, the testing of parameter estimates with posterior predictive checks and a multimodel complexity analysis. In all, eight models of photosynthesis are compared for fit to data and penalized for complexity using deviance information criteria (DIC) at the genotype scale. The multimodel evaluation improves the credibility of trait estimates using posterior distributions. Traits with important implications for yield in crops, including maximum rate of carboxylation (Vcmax ) and maximum rate of electron transport (Jmax ) show genotypic differentiation. B. rapa shows phenotypic diversity in causal traits with the potential for genetic enhancement of photosynthesis. This multimodel screening represents a statistically rigorous method for characterizing genotypic differences in traits with clear biophysical consequences to growth and productivity within large crop breeding populations with application across plant processes.
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Affiliation(s)
- Jonathan R. Pleban
- Department of Geography, University at Buffalo, Buffalo, NY, United States
- *Correspondence: Jonathan R. Pleban
| | - D. Scott Mackay
- Department of Geography, University at Buffalo, Buffalo, NY, United States
| | - Timothy L. Aston
- Department of Botany, University of Wyoming, Laramie, WY, United States
| | - Brent E. Ewers
- Department of Botany, University of Wyoming, Laramie, WY, United States
- Program in Ecology, University of Wyoming, Laramie, WY, United States
| | - Cynthia Weinig
- Department of Botany, University of Wyoming, Laramie, WY, United States
- Program in Ecology, University of Wyoming, Laramie, WY, United States
- Department of Molecular Biology, University of Wyoming, Laramie, WY, United States
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