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Jeffrey W. White

Researcher at Agricultural Research Service

Publications -  160
Citations -  11764

Jeffrey W. White is an academic researcher from Agricultural Research Service. The author has contributed to research in topics: Climate change & Crop simulation model. The author has an hindex of 55, co-authored 160 publications receiving 9961 citations. Previous affiliations of Jeffrey W. White include International Maize and Wheat Improvement Center & International Center for Tropical Agriculture.

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Rising Temperatures Reduce Global Wheat Production

Senthold Asseng, +59 more
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.
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Uncertainty in Simulating Wheat Yields Under Climate Change

Senthold Asseng, +53 more
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.
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Field-based phenomics for plant genetics research

TL;DR: This work defines key criteria, experimental approaches, equipment and data analysis tools required for robust, high-throughput field-based phenotyping (FBP), and focuses on simultaneous proximal sensing for spectral reflectance, canopy temperature, and plant architecture.
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Methodologies for simulating impacts of climate change on crop production

TL;DR: In this paper, the authors reviewed 221 peer-reviewed papers that used crop simulation models to examine diverse aspects of how climate change might affect agricultural systems, focusing on wheat, maize, soybean and rice.
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Multimodel ensembles of wheat growth: many models are better than one

Pierre Martre, +52 more
TL;DR: It is concluded that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics, and argued that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.