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Impact of generated solar radiation on simulated crop growth and yield

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  • Garcia y Garcia, Axel
  • Guerra, Larry C.
  • Hoogenboom, Gerrit

Abstract

The availability of observed daily solar radiation (OSR) is restricted to recent years. Its estimation through different methods is necessary to develop long-term data sets for agricultural and environmental applications. The objective of this study was to analyze the impact of using generated daily solar radiation (GSR) on simulated growth and yield of cotton, maize, and peanut. Nine locations representing Georgia's major crop belt were selected. Daily weather data from the Georgia Automated Environmental Monitoring Network (AEMN), including solar radiation, maximum and minimum temperature, and precipitation, were duplicated. The OSR was removed from one set and then generated using a stochastic procedure. The Cropping System Models (CSM)-CROPGRO-Cotton, CERES-Maize, and CROPGRO-Peanut of the Decision Support System for Agrotechnology Transfer (DSSAT) v4 were used to simulate crop growth and yield at each location with both OSR and GSR and for rainfed and irrigated conditions. The statistical analysis included summary statistics, Pearson's coefficient of correlation, mean squared deviation (MSD) and its components, namely: squared bias (SB), squared difference between standard deviations (SDSD), lack of correlation weighted by the standard deviations (LCS), and regressions. Within locations, for the three crops under rainfed and irrigated conditions, GSR did not significantly affect simulated total evapotranspiration and aboveground biomass and yields. For the three crops, deviations of simulated water use and yields from GSR with respect to simulated water use and yields from OSR were lower for the rainfed than for the irrigated conditions. Yields from the CSM-CROPGRO-Cotton and -Peanut models had lower deviations than yields from the CSM-CERES-Maize model. LCS was the major component of the MSD suggesting that the extent of the difference between standard deviations of GSR and OSRG could affect the outputs of the crop models. Nevertheless, for most locations none of the MSD components of the GSR showed significant correlation with simulated yields and the overall performance of the models was not affected. It can be concluded based on the results of this study that GSR can be used as an input for crop model simulation models when OSR is not available.

Suggested Citation

  • Garcia y Garcia, Axel & Guerra, Larry C. & Hoogenboom, Gerrit, 2008. "Impact of generated solar radiation on simulated crop growth and yield," Ecological Modelling, Elsevier, vol. 210(3), pages 312-326.
    • Handle: RePEc:eee:ecomod:v:210:y:2008:i:3:p:312-326
    DOI: 10.1016/j.ecolmodel.2007.08.003
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    1. Kiniry, James R. & Williams, J. R. & Gassman, Philip W. & Debacke, P., 1992. "General, Process-Oriented Model for Two Competing Plant Species (A)," Staff General Research Papers Archive 483, Iowa State University, Department of Economics.
    2. Bert, Federico E. & Laciana, Carlos E. & Podesta, Guillermo P. & Satorre, Emilio H. & Menendez, Angel N., 2007. "Sensitivity of CERES-Maize simulated yields to uncertainty in soil properties and daily solar radiation," Agricultural Systems, Elsevier, vol. 94(2), pages 141-150, May.
    3. Rivington, M. & Matthews, K.B. & Bellocchi, G. & Buchan, K., 2006. "Evaluating uncertainty introduced to process-based simulation model estimates by alternative sources of meteorological data," Agricultural Systems, Elsevier, vol. 88(2-3), pages 451-471, June.
    4. Hunt, L. A. & White, J. W. & Hoogenboom, G., 2001. "Agronomic data: advances in documentation and protocols for exchange and use," Agricultural Systems, Elsevier, vol. 70(2-3), pages 477-492.
    5. Xie, Yun & Kiniry, James R. & Williams, Jimmy R., 2003. "The ALMANAC model's sensitivity to input variables," Agricultural Systems, Elsevier, vol. 78(1), pages 1-16, October.
    6. Hartkamp, A. D. & White, J. W. & Hoogenboom, G., 2003. "Comparison of three weather generators for crop modeling: a case study for subtropical environments," Agricultural Systems, Elsevier, vol. 76(2), pages 539-560, May.
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    1. Persson, Tomas & Garcia y Garcia, Axel & Paz, Joel & Jones, Jim & Hoogenboom, Gerrit, 2009. "Maize ethanol feedstock production and net energy value as affected by climate variability and crop management practices," Agricultural Systems, Elsevier, vol. 100(1-3), pages 11-21, April.
    2. Qin, Jun & Chen, Zhuoqi & Yang, Kun & Liang, Shunlin & Tang, Wenjun, 2011. "Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products," Applied Energy, Elsevier, vol. 88(7), pages 2480-2489, July.
    3. Zheng, Zhen & Hoogenboom, Gerrit & Cai, Huanjie & Wang, Zikai, 2020. "Winter wheat production on the Guanzhong Plain of Northwest China under projected future climate with SimCLIM," Agricultural Water Management, Elsevier, vol. 239(C).
    4. Liu, Xuehua & Cheng, Xiangnan & Skidmore, Andrew K., 2011. "Potential solar radiation pattern in relation to the monthly distribution of giant pandas in Foping Nature Reserve, China," Ecological Modelling, Elsevier, vol. 222(3), pages 645-652.
    5. Sebastian Kloss & Raji Pushpalatha & Kefasi Kamoyo & Niels Schütze, 2012. "Evaluation of Crop Models for Simulating and Optimizing Deficit Irrigation Systems in Arid and Semi-arid Countries Under Climate Variability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(4), pages 997-1014, March.
    6. Li, Meng & Du, Yingji & Zhang, Fucang & Bai, Yungang & Fan, Junliang & Zhang, Jianghui & Chen, Shaoming, 2019. "Simulation of cotton growth and soil water content under film-mulched drip irrigation using modified CSM-CROPGRO-cotton model," Agricultural Water Management, Elsevier, vol. 218(C), pages 124-138.
    7. Salazar, M.R. & Hook, J.E. & Garcia y Garcia, A. & Paz, J.O. & Chaves, B. & Hoogenboom, G., 2012. "Estimating irrigation water use for maize in the Southeastern USA: A modeling approach," Agricultural Water Management, Elsevier, vol. 107(C), pages 104-111.

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