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Effect of Heavy Rainfall Events on the Dry Matter Yield Trend of Whole Crop Maize ( Zea mays L.)

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  • Moonju Kim

    (Institute of Animal Resources, Kangwon National University, Chuncheon 24341, Korea)

  • Befekadu Chemere

    (College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Korea)

  • Kyungil Sung

    (Department of Animal Industry Convergence, College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Korea)

Abstract

The objective of this study was to detect the historical dry matter yield (DMY) trend and to evaluate the effects of heavy rainfall events on the observed DMY trend of whole crop maize (WCM, Zea mays L.) using time-series analysis in Suwon, Republic of Korea. The climatic variables corresponding to the seeding to harvesting period, including the growing degree days, mean temperature, etc., of WCM along with the DMY data ( n = 543) during 1982–2011, were used in the analysis. The DMY trend was detected using Autoregressive Integrated Moving Average with the explanatory variables (ARIMAX) form of time-series trend analysis. The optimal DMY model was found to be ARIMAX (1, 1, 1), indicating that the DMY trend follows the mean DMY of the preceding one year and the residual of the preceding one year with an integration level of 1. Furthermore, the SHGDD and SHHR were determined to be the main variables responsible for the observed trend in the DMY of WCM. During heavy rainfall events, the DMY was found to be decreasing by 4745.27 kg/ha ( p < 0.01). Our analysis also revealed that both the intensity and frequency of heavy rainfall events have been increasing since 2005. The forecasted DMY indicates the potential decrease, which is expected to be 11,607 kg/ha by 2045. This study provided us evidence for the correlation between the DMY and heavy rainfall events that opens the way to provide solutions for challenges that summer forage crops face in the Republic of Korea.

Suggested Citation

  • Moonju Kim & Befekadu Chemere & Kyungil Sung, 2019. "Effect of Heavy Rainfall Events on the Dry Matter Yield Trend of Whole Crop Maize ( Zea mays L.)," Agriculture, MDPI, vol. 9(4), pages 1-11, April.
    • Handle: RePEc:gam:jagris:v:9:y:2019:i:4:p:75-:d:222012
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    References listed on IDEAS

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    1. Dixon, Bruce L. & Hollinger, Steven E. & Garcia, Philip & Tirupattur, Viswanath, 1994. "Estimating Corn Yield Response Models To Predict Impacts Of Climate Change," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 19(1), pages 1-11, July.
    2. Intergovernmental Panel on Climate Change IPCC, 2008. "Intergovernmental Panel on Climate Change: Fourth Assessment Report: Climate Change 2007: Synthesis Report," Working Papers id:1325, eSocialSciences.
    3. Befekadu Chemere & Jiyung Kim & Baehun Lee & Moonju Kim & Byongwan Kim & Kyungil Sung, 2018. "Detecting Long-Term Dry Matter Yield Trend of Sorghum-Sudangrass Hybrid and Climatic Factors Using Time Series Analysis in the Republic of Korea," Agriculture, MDPI, vol. 8(12), pages 1-10, December.
    4. Nam, Won-Ho & Hayes, Michael J. & Svoboda, Mark D. & Tadesse, Tsegaye & Wilhite, Donald A., 2015. "Drought hazard assessment in the context of climate change for South Korea," Agricultural Water Management, Elsevier, vol. 160(C), pages 106-117.
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    1. Yongqing Zhao & Rendong Li & Juan Qiu & Xiangdong Sun & Lu Gao & Mingquan Wu, 2019. "Prediction of Human Brucellosis in China Based on Temperature and NDVI," IJERPH, MDPI, vol. 16(21), pages 1-15, November.
    2. Jinglun Peng & Moonju Kim & Kyungil Sung, 2020. "Yield Prediction Modeling for Sorghum–Sudangrass Hybrid Based on Climatic, Soil, and Cultivar Data in the Republic of Korea," Agriculture, MDPI, vol. 10(4), pages 1-11, April.

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