Creating long-term weather data from thin air for crop simulation modeling

Wart, J V and Grassini, P and Yang, H and Claessens, L and Jarvis, A and Cassman, K G (2015) Creating long-term weather data from thin air for crop simulation modeling. Agricultural and Forest Meteorology, 209-10 (1). pp. 49-58. ISSN 0168-1923

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Simulating crop yield and yield variability requires long-term, high-quality daily weather data, including solar radiation, maximum (Tmax) and minimum temperature (Tmin), and precipitation. In many regions, however, daily weather data of sufficient quality and duration are not available. To overcome this limitation, we evaluated a new method to create long-term weather series based on a few years of observed daily temperature data (hereafter called propagated data). The propagated data are comprised of uncorrected gridded solar radiation from the Prediction of Worldwide Energy Resource dataset from the National Aeronautics and Space Administration (NASA–POWER), rainfall from the Tropical Rainfall Measuring Mission (TRMM) dataset, and location-specific calibration of NASA–POWER Tmax and Tmin using a limited amount of observed daily temperature data. The distributions of simulated yields of maize, rice, or wheat with propagated data were compared with simulated yields using observed weather data at 18 sites in North and South America, Europe, Africa, and Asia. Other sources of weather data typically used in crop modeling for locations without long-term observed weather data were also included in the comparison: (i) uncorrected NASA–POWER weather data and (ii) generated weather data using the MarkSim weather generator. Results indicated good agreement between yields simulated with propagated weather data and yields simulated using observed weather data. For example, the distribution of simulated yields using propagated data was within 10% of the simulated yields using observed data at 78% of locations and degree of yield stability (quantified by coefficient of variation) was very similar at 89% of locations. In contrast, simulated yields based entirely on uncorrected NASA–POWER data or generated weather data using MarkSim were within 10% of yields simulated using observed data in only 44 and 33% of cases, respectively, and the bias was not consistent across locations and crops. We conclude that, for most locations, 3 years of observed daily Tmax and Tmin data would allow creation of a robust weather data set for simulation of long-term mean yield and yield stability of major cereal crops.

Item Type: Article
Divisions: RP-Resilient Dryland Systems
CRP: CGIAR Research Program on Dryland Systems
CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)
Uncontrolled Keywords: Yield potential, Yeild variability, Weather data, Crop simulation model
Subjects: Others > Climate Change
Depositing User: Mr B K Murthy
Date Deposited: 12 Oct 2015 05:23
Last Modified: 24 Jul 2018 06:21
Official URL:
Acknowledgement: Support for various aspects of this research was provided by the Robert Daugherty Water for Food Institute at the University of Nebraska-Lincoln, the Bill and Melinda Gates Foundation, and the CGIAR research program on Climate Change, Agriculture and Food Security (CCAFS). We thank Drs. Shaobing Ping (Huazhong Agriculture University, China), Jingshun Bai (China Agricultural University, China), and Christian K. Kersebaum (Leibniz Centre for Agricultural Landscape Research, Germany) for proving weather data from China and Germany and agronomists contributing to the Global Yield Gap Atlas for providing weather and management data for several countries in Sub-Saharan Africa, including Dr. Korodjouma Ouattara (Institut de l'Environnement et de Recherches agricoles, Burkina Faso), Dr. Ochieng Adimo (Jomo Kenyatta University of Agriculture and Technology, Kenya), and Dr. Regis Chikowo (University of Zimbabwe).
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