Author
Bruno Basso
Other affiliations: Queensland University of Technology, University of Basilicata, Great Lakes Bioenergy Research Center ...read more
Bio: Bruno Basso is an academic researcher from Michigan State University. The author has contributed to research in topics: Crop yield & Climate change. The author has an hindex of 51, co-authored 241 publications receiving 10747 citations. Previous affiliations of Bruno Basso include Queensland University of Technology & University of Basilicata.
Papers published on a yearly basis
Papers
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University of Florida1, University of Bonn2, Institut national de la recherche agronomique3, Blaise Pascal University4, Stanford University5, Prince of Songkla University6, Agricultural Research Service7, University of Arizona8, International Maize and Wheat Improvement Center9, Kansas State University10, International Water Management Institute11, Washington State University12, Michigan State University13, CGIAR14, University of Leeds15, Counterintelligence Field Activity16, Spanish National Research Council17, University of Tübingen18, University of Guelph19, Texas A&M University20, University of Maryland, College Park21, Aarhus University22, Potsdam Institute for Climate Impact Research23, Indian Agricultural Research Institute24, Goddard Institute for Space Studies25, Rothamsted Research26, University of Hohenheim27, Wageningen University and Research Centre28, Chinese Academy of Sciences29, Commonwealth Scientific and Industrial Research Organisation30, China Agricultural University31, Nanjing Agricultural University32
TL;DR: The authors systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating.
Abstract: Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.
1,461 citations
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University of Florida1, University of Bonn2, Goddard Institute for Space Studies3, Commonwealth Scientific and Industrial Research Organisation4, Agro ParisTech5, Institut national de la recherche agronomique6, Michigan State University7, Blaise Pascal University8, International Water Management Institute9, CGIAR10, University of Leeds11, Counterintelligence Field Activity12, University of Tübingen13, University of Alberta14, International Atomic Energy Agency15, University of Reading16, University of Guelph17, University of Hohenheim18, Joint Global Change Research Institute19, Potsdam Institute for Climate Impact Research20, Indian Agricultural Research Institute21, Aarhus University22, Rothamsted Research23, Washington State University24, Wageningen University and Research Centre25, Chinese Academy of Sciences26, International Trademark Association27, Texas A&M University28
TL;DR: In this article, the authors present the largest standardized model intercomparison for climate change impacts so far, finding that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient.
Abstract: Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.
1,049 citations
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Columbia University1, Goddard Institute for Space Studies2, University of Florida3, United States Department of Agriculture4, Commonwealth Scientific and Industrial Research Organisation5, Oregon State University6, International Food Policy Research Institute7, Wageningen University and Research Centre8, Michigan State University9, University of Bonn10, Institut national de la recherche agronomique11, University of Nebraska–Lincoln12
TL;DR: The Agricultural Model Intercomparison and Improvement Project (AgMIP) as mentioned in this paper is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector.
803 citations
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Agro ParisTech1, Institut national de la recherche agronomique2, University of Florida3, Goddard Institute for Space Studies4, University of Basilicata5, Michigan State University6, Wageningen University and Research Centre7, Empresa Brasileira de Pesquisa Agropecuária8, University of East Anglia9, University of Tübingen10, University of Nebraska–Lincoln11, United States Department of Agriculture12, Pacific Northwest National Laboratory13, Pennsylvania State University14, University of Washington15, Indian Agricultural Research Institute16, Potsdam Institute for Climate Impact Research17, Chinese Academy of Sciences18, Plant & Food Research19
TL;DR: The largest maize crop model intercomparison to date, including 23 different models, is presented, suggesting that using an ensemble of models has merit and there was a large uncertainty in the yield response to [CO2 ] among models.
Abstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per degrees C. Doubling [CO2] from 360 to 720 mu mol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
536 citations
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University of Florida1, Oregon State University2, Michigan State University3, Colorado State University4, University of Chicago5, University of Oxford6, Commonwealth Scientific and Industrial Research Organisation7, University of California, Davis8, Wageningen University and Research Centre9, Columbia University10, University of Reading11
TL;DR: The history of agricultural systems modeling is summarized and lessons learned are identified that can help guide the design and development of next generation of agricultural system tools and methods.
421 citations
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01 Jan 2015
TL;DR: The work of the IPCC Working Group III 5th Assessment report as mentioned in this paper is a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change, which has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.
Abstract: The talk with present the key results of the IPCC Working Group III 5th assessment report. Concluding four years of intense scientific collaboration by hundreds of authors from around the world, the report responds to the request of the world's governments for a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change. The report has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.
3,224 citations
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TL;DR: A forum to review, analyze and stimulate the development, testing and implementation of mitigation and adaptation strategies at regional, national and global scales as mentioned in this paper, which contributes to real-time policy analysis and development as national and international policies and agreements are discussed.
Abstract: ▶ Addresses a wide range of timely environment, economic and energy topics ▶ A forum to review, analyze and stimulate the development, testing and implementation of mitigation and adaptation strategies at regional, national and global scales ▶ Contributes to real-time policy analysis and development as national and international policies and agreements are discussed and promulgated ▶ 94% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again
2,587 citations
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TL;DR: The evidence supports the need for considerable investment in adaptation and mitigation actions toward a “climate-smart food system” that is more resilient to climate change influences on food security.
Abstract: Climate change could potentially interrupt progress toward a world without hunger. A robust and coherent global pattern is discernible of the impacts of climate change on crop productivity that could have consequences for food availability. The stability of whole food systems may be at risk under climate change because of short-term variability in supply. However, the potential impact is less clear at regional scales, but it is likely that climate variability and change will exacerbate food insecurity in areas currently vulnerable to hunger and undernutrition. Likewise, it can be anticipated that food access and utilization will be affected indirectly via collateral effects on household and individual incomes, and food utilization could be impaired by loss of access to drinking water and damage to health. The evidence supports the need for considerable investment in adaptation and mitigation actions toward a “climate-smart food system” that is more resilient to climate change influences on food security.
2,050 citations
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Goddard Institute for Space Studies1, University of Chicago2, Columbia University3, University of East Anglia4, Potsdam Institute for Climate Impact Research5, Karlsruhe Institute of Technology6, University of Florida7, Swiss Federal Institute of Aquatic Science and Technology8, International Institute for Applied Systems Analysis9, Netherlands Environmental Assessment Agency10, University of Natural Resources and Life Sciences, Vienna11
TL;DR: Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.
Abstract: Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.
1,704 citations