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Impact of derived global weather data on simulated crop yields
Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BlackWell Publishing Ltd
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4288967/ https://www.ncbi.nlm.nih.gov/pubmed/23801639 http://dx.doi.org/10.1111/gcb.12302 |
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author | van Wart, Justin Grassini, Patricio Cassman, Kenneth G |
author_facet | van Wart, Justin Grassini, Patricio Cassman, Kenneth G |
author_sort | van Wart, Justin |
collection | PubMed |
description | Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA-POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method. |
format | Online Article Text |
id | pubmed-4288967 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BlackWell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-42889672015-01-20 Impact of derived global weather data on simulated crop yields van Wart, Justin Grassini, Patricio Cassman, Kenneth G Glob Chang Biol Primary Research Articles Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA-POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method. BlackWell Publishing Ltd 2013-12 2013-09-24 /pmc/articles/PMC4288967/ /pubmed/23801639 http://dx.doi.org/10.1111/gcb.12302 Text en © 2013 John Wiley & Sons Ltd http://creativecommons.org/licenses/by/3.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Primary Research Articles van Wart, Justin Grassini, Patricio Cassman, Kenneth G Impact of derived global weather data on simulated crop yields |
title | Impact of derived global weather data on simulated crop yields |
title_full | Impact of derived global weather data on simulated crop yields |
title_fullStr | Impact of derived global weather data on simulated crop yields |
title_full_unstemmed | Impact of derived global weather data on simulated crop yields |
title_short | Impact of derived global weather data on simulated crop yields |
title_sort | impact of derived global weather data on simulated crop yields |
topic | Primary Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4288967/ https://www.ncbi.nlm.nih.gov/pubmed/23801639 http://dx.doi.org/10.1111/gcb.12302 |
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