How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis
Numerous studies have been published during the past two decades that use simulation models to assess crop yield gaps (quantified as the difference between potential and actual farm yields), impact of climate change on future crop yields, and land-use change. However, there is a wide range in qualit...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Elsevier
2015
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/76583 |
| _version_ | 1855529757202972672 |
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| author | Grassini, Patricio Bussel, Lenny G. J. van Wart, Justin van Wolf, Joost Claessens, Lieven Yang, Haishun Boogaard, H. Groot, Hugo de Ittersum, Martin K. van Cassman, Kenneth G. |
| author_browse | Boogaard, H. Bussel, Lenny G. J. van Cassman, Kenneth G. Claessens, Lieven Grassini, Patricio Groot, Hugo de Ittersum, Martin K. van Wart, Justin van Wolf, Joost Yang, Haishun |
| author_facet | Grassini, Patricio Bussel, Lenny G. J. van Wart, Justin van Wolf, Joost Claessens, Lieven Yang, Haishun Boogaard, H. Groot, Hugo de Ittersum, Martin K. van Cassman, Kenneth G. |
| author_sort | Grassini, Patricio |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Numerous studies have been published during the past two decades that use simulation models to assess crop yield gaps (quantified as the difference between potential and actual farm yields), impact of climate change on future crop yields, and land-use change. However, there is a wide range in quality and spatial and temporal scale and resolution of climate and soil data underpinning these studies, as well as widely differing assumptions about cropping-system context and crop model calibration. Here we present an explicit rationale and methodology for selecting data sources for simulating crop yields and estimating yield gaps at specific locations that can be applied across widely different levels of data availability and quality. The method consists of a tiered approach that identifies the most scientifically robust requirements for data availability and quality, as well as other, less rigorous options when data are not available or are of poor quality. Examples are given using this approach to estimate maize yield gaps in the state of Nebraska (USA), and at a national scale for Argentina and Kenya. These examples were selected to represent contrasting scenarios of data availability and quality for the variables used to estimate yield gaps. The goal of the proposed methods is to provide transparent, reproducible, and scientifically robust guidelines for estimating yield gaps; guidelines which are also relevant for simulating the impact of climate change and land-use change at local to global spatial scales. Likewise, the improved understanding of data requirements and alternatives for simulating crop yields and estimating yield gaps as described here can help identify the most critical “data gaps” and focus global efforts to fill them. A related paper (Van Bussel et al., 2015) examines issues of site selection to minimize data requirements and up-scaling from location-specific estimates to regional and national spatial scales. |
| format | Journal Article |
| id | CGSpace76583 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace765832025-02-19T14:32:26Z How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis Grassini, Patricio Bussel, Lenny G. J. van Wart, Justin van Wolf, Joost Claessens, Lieven Yang, Haishun Boogaard, H. Groot, Hugo de Ittersum, Martin K. van Cassman, Kenneth G. climate change agriculture food security crop simulation yield gap yield potential weather data cropping system Numerous studies have been published during the past two decades that use simulation models to assess crop yield gaps (quantified as the difference between potential and actual farm yields), impact of climate change on future crop yields, and land-use change. However, there is a wide range in quality and spatial and temporal scale and resolution of climate and soil data underpinning these studies, as well as widely differing assumptions about cropping-system context and crop model calibration. Here we present an explicit rationale and methodology for selecting data sources for simulating crop yields and estimating yield gaps at specific locations that can be applied across widely different levels of data availability and quality. The method consists of a tiered approach that identifies the most scientifically robust requirements for data availability and quality, as well as other, less rigorous options when data are not available or are of poor quality. Examples are given using this approach to estimate maize yield gaps in the state of Nebraska (USA), and at a national scale for Argentina and Kenya. These examples were selected to represent contrasting scenarios of data availability and quality for the variables used to estimate yield gaps. The goal of the proposed methods is to provide transparent, reproducible, and scientifically robust guidelines for estimating yield gaps; guidelines which are also relevant for simulating the impact of climate change and land-use change at local to global spatial scales. Likewise, the improved understanding of data requirements and alternatives for simulating crop yields and estimating yield gaps as described here can help identify the most critical “data gaps” and focus global efforts to fill them. A related paper (Van Bussel et al., 2015) examines issues of site selection to minimize data requirements and up-scaling from location-specific estimates to regional and national spatial scales. 2015-06 2016-08-25T11:51:18Z 2016-08-25T11:51:18Z Journal Article https://hdl.handle.net/10568/76583 en Open Access Elsevier Grassini P, van Bussel LGJ, Van Wart J, Wolf J, Claessens L, Yang H, Boogaard H, de Groot H, van Ittersum MK, Cassman KG. 2015. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Research 177:49-63. |
| spellingShingle | climate change agriculture food security crop simulation yield gap yield potential weather data cropping system Grassini, Patricio Bussel, Lenny G. J. van Wart, Justin van Wolf, Joost Claessens, Lieven Yang, Haishun Boogaard, H. Groot, Hugo de Ittersum, Martin K. van Cassman, Kenneth G. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis |
| title | How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis |
| title_full | How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis |
| title_fullStr | How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis |
| title_full_unstemmed | How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis |
| title_short | How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis |
| title_sort | how good is good enough data requirements for reliable crop yield simulations and yield gap analysis |
| topic | climate change agriculture food security crop simulation yield gap yield potential weather data cropping system |
| url | https://hdl.handle.net/10568/76583 |
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