Journal of The Korean Society of Grassland and Forage Science (한국초지조사료학회지)

The Korean Society of Grassland and Forage Science (한국초지조사료학회)
권호 표지
DOI QR코드

DOI QR Code

Models for Estimating Yield of Italian Ryegrass in South Areas of Korean Peninsula and Jeju Island

  • Peng, Jing Lun (Department of Feed Science and Technology, College of Animal Life Sciences, Kangwon National University) ;
  • Kim, Moon Ju (Department of Feed Science and Technology, College of Animal Life Sciences, Kangwon National University) ;
  • Kim, Byong Wan (Department of Feed Science and Technology, College of Animal Life Sciences, Kangwon National University) ;
  • Sung, Kyung Il (Department of Feed Science and Technology, College of Animal Life Sciences, Kangwon National University)
  • Received : 2016.08.11
  • Accepted : 2016.09.06
  • Published : 2016.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

The objective of this study was to construct Italian ryegrass (IRG) dry matter yield (DMY) estimation models in South Korea based on climatic data by locations. Obviously, the climatic environment of Jeju Island has great differences with Korean Peninsula. Meanwhile, many data points were from Jeju Island in the prepared data set. Statistically significant differences in both DMY values and climatic variables were observed between south areas of Korean Peninsula and Jeju Island. Therefore, the estimation models were constructed separately for south areas of Korean Peninsula and Jeju Island separately. For south areas of Korean Peninsula, a data set with a sample size of 933 during 26 years was used. Four optimal climatic variables were selected through a stepwise approach of multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the selected four climatic variables and cultivated locations as dummy variables was constructed. The model could explain 37.7% of the variations in DMY of IRG in south areas of Korean Peninsula. For Jeju Island, a data set containing 130 data points during 17 years were used in the modeling construction via the stepwise approach of multiple regression analysis. The model constructed in this research could explain 51.0% of the variations in DMY of IRG. For the two models, homoscedasticity and the assumption that the mean of the residuals were equal to zero were satisfied. Meanwhile, the fitness of both models was good based on most scatters of predicted DMY values fell within the 95% confidence interval.

Keywords

References

  1. Allison, P.D. 1999. Multiple regression: A primer. Pine Forge Press. Newbury Park. pp. 141-143.
  2. Armitage, P., Berry, G. and Matthews, J.N.S. 2008. Statistical methods in medical research. 4th ed, John Wiley & Sons, Malden, USA. pp.83-162.
  3. Choi, S.W., Lee, C.S., Park, C.H., Kim, D.H., Park, S.K., Kim, B.G. and Moon, S.H. 2014. Prediction of nutrient composition and in-vitro dry matter digestibility of corn kernel using near infrared reflectance spectroscopy. Journal of the Korean Society of Grassland and Forage Science. 34(4):277-282. https://doi.org/10.5333/KGFS.2014.34.4.277
  4. Chung, Y.S., Yoon, M.B. and Kim, H.S. 2004. On climate variations and changes observed in South Korea. Climatic Change. 66(1-2):151-161. https://doi.org/10.1023/B:CLIM.0000043141.54763.f8
  5. Chung, C.H. 2007. Vegetation response to climate change on Jeju Island, South Korea, during the last deglaciation based on pollen record. Geosciences Journal. 11(2):147-155. https://doi.org/10.1007/BF02913928
  6. Cohen, J., Cohen, P., West, S.G. and Aiken, L.S. 2013. Applied multiple regression/correlation analysis for the behavioral sciences. Routledge. New York. pp 310-316.
  7. Excel (2010) Microsoft Excel 2010. Microsoft Corp., Redmond, WA, USA.
  8. Farrar, D.E. and Glauber, R.R. 1967. Multicollinearity in regression analysis: the problem revisited. The Review of Economic and Statistics. 49(1):92-107. https://doi.org/10.2307/1937887
  9. Hatfield, J.L., Boote, K.J., Kimball, B.A., Ziska, L.H., Izaurralde, R.C., Ort, D., Thomson, A.M. and Wolfe, D. 2011. Climate impacts on agriculture: implications for crop production. Agronomy Journal. 103(2):351-370. https://doi.org/10.2134/agronj2010.0303
  10. Hong, J.H., Kim, S.K., Kim, D.G., Hong, S.C., Lee, J.B., Moon, K.J., Cha, J.S., Hong, J.S., Ma, Y.I., Kim, S.Y., Jung, H.C. and Choi, Y.E. 2014. Korean Climate Change Assessment Report 2014. Korean National Institute of Environment Research. Incheon. pp.1-45.
  11. Lee, S. and Muller, A.R. 2012. South Korean external strategy qualms: Analysis of Korean overseas agricultural investment within the global food system. Proceedings of Global Land Grabbing II conference. Ithaca. USA. pp. 1-31.
  12. Mardia, K.V., Kent, J.T. and Bibby, J.M. 1979. Multivariate Analysis (Probability and Mathematical Statistics). Academic Press, London-New York-Toronto-Sydney-San Francisco. pp. 1-518.
  13. Mkhabela, M.S., Bullock, P., Raj, S., Wang, S. and Yang, Y. 2011. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology. 151(3): 385-393. https://doi.org/10.1016/j.agrformet.2010.11.012
  14. Oteng-Darko, P., Yeboah, S., Addy, S.N.T., Amponsah, S. and Danquah, E.O. 2013. Crop modeling: a tool for agricultural research-a review. Journal of Agriculture Research and Development. 2(1): 1-6.
  15. Rauff, K.O. and Bello, R. 2015. A review of crop growth simulation models as tools for agricultural meteorology. Agricultural Sciences. 6(9):1098. https://doi.org/10.4236/as.2015.69105
  16. Robinson, J. 2004. Pasture Perfect: The Far-Reaching Benefits of Choosing Meat, Eggs, and Dairy Products from Grass-Fed Animals. Vashon Island Press. Washington. pp. 1-152.
  17. SPSS (2012) IBM SPSS Statistics 21.0. IBM Corp., Somers, New York. USA.
  18. StatsDirect Limited. Unpaired (Two Sample) t Test. Available from: http://www.statsdirect.com/help/default.htm#parametric_methods/unpaired_t.htm%3FTocPath%3DParametric%2520methods%7C_____3. Accessed Aug. 13, 2016.
  19. Sung, K.I., Nejad, J.G., Song, Y.H., Kim, S.Y., Lee, B.H. and Kim, W.H. 2012. A comparison of feeding whole crop barley mixed with Italian ryegrass silage versus tall fescue hay for Holstein growing cattle. Proceedings of the XVI International Silage Conference. Finland. pp. 508-509.
  20. Thornton, P.K. 2010. Livestock production: recent trends, future prospects. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1554), 2853-2867. https://doi.org/10.1098/rstb.2010.0134
  21. Tilman, D., Cassman, K.G., Matson, P.A., Naylor, R. and Polasky, S. 2002. Agricultural sustainability and intensive production practices. Nature. 418(6898):671-677. https://doi.org/10.1038/nature01014

Cited by

  1. Accuracy evaluation of the crop-weather yield predictive models of Italian ryegrass and forage rye using cross-validation vol.20, pp.4, 2017, https://doi.org/10.1007/s12892-017-0090-0
  2. The Relationships between Dry Matter Yield and Days of Summer Depression in different Regions with Mixed Pasture vol.38, pp.1, 2018, https://doi.org/10.5333/KGFS.2018.38.1.53