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

The Korean Society of Grassland and Forage Science (한국초지조사료학회)
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A Yield Estimation Model of Forage Rye Based on Climate Data by Locations in South Korea Using General Linear Model

  • 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 : 2016.07.04
  • Accepted : 2016.08.04
  • Published : 2016.09.30
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Abstract

The objective of this study was to construct a forage rye (FR) dry matter yield (DMY) estimation model based on climate data by locations in South Korea. The data set (n = 549) during 29 years were used. Six optimal climatic variables were selected through stepwise multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the six climatic variables and cultivated locations as dummy variables was constructed as follows: DMY = 104.166SGD + 1.454AAT + 147.863MTJ + 59.183PAT150 - 4.693SRF + 45.106SRD - 5230.001 + Location, where SGD was spring growing days, AAT was autumnal accumulated temperature, MTJ was mean temperature in January, PAT150 was period to accumulated temperature 150, SRF was spring rainfall, and SRD was spring rainfall days. The model constructed in this research could explain 24.4 % of the variations in DMY of FR. The homoscedasticity and the assumption that the mean of the residuals were equal to zero was satisfied. The goodness-of-fit of the model was proper based on most scatters of the predicted DMY values fell within the 95% confidence interval.

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References

  1. Abdi, H. 2007. Part (semi partial) and partial regression coefficients. Encyclopedia of measurement and statistics. 736-740.
  2. Ahn, J.H., Kim, K.D. and Lee, J.T. 2014. Growth Modeling of Chinese Cabbage in an Alpine Area. Korean Journal of Agricultural and Forest Meteorology. 16(4):309-315. https://doi.org/10.5532/KJAFM.2014.16.4.309
  3. Allison, P.D. 1999. Multiple regression: A primer. Pine Forge Press, Newbury Park, CA, U.S.A. p142.
  4. Bushuk, W. 1993. Rye Production and Uses Worldwide. In: R. Macrae, R. K. Robinson, and M. J. Sadler (Ed.), Encyclopaedia of Food Science and Technology, vol. 6. Academic Press. London. UK. pp. 3946-3950.
  5. Cao, W. and Moss, D.N. 1997. Modelling phasic development in wheat: a conceptual integration of physiological components. The Journal of Agricultural Science. 129(2), 163-172. https://doi.org/10.1017/S0021859697004668
  6. Chang, J. and Clay, D.E. 2005. Identifying factors for corn yield prediction models and evaluating model selection methods. Korean Journal of Crop Science. 50(4):268-275.
  7. Cohen, J., Cohen, P., West, S.G. and Aiken, L.S. 2003. Applied multiple regression/correlation analysis for the behavioral sciences. Routledge, New York, U.S.A. pp. 310-316.
  8. Dahikar, S.S. and Rode, S.V. 2014. Agricultural crop yield prediction using artificial neural network approach. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering. 2(1):683-686.
  9. Delgado, C.L. 2003. Rising consumption of meat and milk in developing countries has created a new food revolution. The journal of nutrition. 133(11):3907S-3910S. https://doi.org/10.1093/jn/133.11.3907S
  10. Farrar, D.E. and Glauber, R.R. 1967. Multicollinearity in regression analysis: the problem revisited. The Review of Economic and Statistics. 92-107.
  11. Harvey, M. and Pilgrim, S. 2011. The new competition for land: food, energy, and climate change. Food Policy. 36:S40-S51. https://doi.org/10.1016/j.foodpol.2010.11.009
  12. Hauke, J. and Kossowski, T. 2011. Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data. Quaestiones geographicae. 30(2):87-93.
  13. Huang, J. and Han, D. 2014. Meta-analysis of influential factors on crop yield estimation by remote sensing. International Journal of Remote Sensing. 35(6):2267-2295.
  14. Johansson, D.J.A. and Azar, C. 2007. A scenario based analysis of land competition between food and bioenergy production in the US. Climatic Change. 82(3-4):267-291. https://doi.org/10.1007/s10584-006-9208-1
  15. Kent, N.L. 1983. Rye and triticale. In: Technology of Cereals. 3rd ed. Pergamon Press, Oxford. pp.175-183.
  16. Kim, K.D., Sung, K.I., Jung, J.I., Lee, E.J., Kim, E.J., Nejad, J.G., Jo, M.H. and Lim, Y.C. 2012. Suitability classes for Italian ryegrass (Loliummultiflorum Lam) using soil and climate digital database in Gangwon Province. The Korean Society of Grassland and Forage Science. 32(4), 437-446. https://doi.org/10.5333/KGFS.2012.32.4.437
  17. Kim, K.D., Sung, K.I., Joo, J.H., Kim, B.W., Peng, J.L., Lee, B.H., Nejad, J.G., Jo, M.H. and Lim, Y.C. 2013. Suitability Classes for Whole Crop Barley Using Soil and Climate Digital Database in Gangwon Province, Journal of Agricultural, Life and Environmental Sciences. 25(3), 26-31.
  18. Kim, K.D., Suh, J.T., Lee, J.N., Yoo, D.L., Kwon, M. and Hong, S.C. 2015. Evaluation of factors related to productivity and yield estimation based on growth characteristics and growing degree days in highland Kimchi cabbage. Korean Journal of Horticultural Science & Technology. 33(6):911-922. https://doi.org/10.7235/hort.2015.15074
  19. Kim, M.R. and Kim, S.G. 2014. Examining Impact of Weather Factors on Apple Yield. Korean Journal of Agricultural and Forest Meteorology. 16(4):274-284. https://doi.org/10.5532/KJAFM.2014.16.4.274
  20. Kim, S.Y., Park, C.K., Gwon, H. S., Khan, M. I., and Kim, P. J. 2015. Optimizing the harvesting stage of rye as a green manure to maximize nutrient production and to minimize methane production in mono-rice paddies. Science of the Total Environment. 537:441-446. https://doi.org/10.1016/j.scitotenv.2015.07.061
  21. Kryvobok, O. 2000. Estimation on the productivity parameters on wheat crops using high resolution satellite data. International Archives of Photogrammetry and Remote Sensing. 33(B7/2; PART 7): 717-722.
  22. Lee, H. and Moon, A. 2014. Development of yield prediction system based on real-time agricultural meteorological information. Proceedings of 16th International Conference on Advanced Communication Technology. IEEE. 1292-1295.
  23. Lee, S.G., Seo, T.C., Jang, Y.A., Lee, J.G., Nam, C.W., Choi, C.S., Yeo, K.H. and Um, Y.C. 2012. Prediction of Chinese cabbage yield as affected by planting date and nitrogen fertilization for spring production. Journal of Bio-Environment Control. 21(3), 271-275.
  24. Lorenz, K.J. 1991. Rye. In: K. J. Lorenz and K. Kulp (Ed.), Handbook of Cereal Science and Technology. Marcel Dekker, New York, U.S.A. pp.331-371.
  25. Na, S.I., Lee, K.D., Baek, S.C. and Hong, S.Y. 2015. Estimation of Chinese Cabbage Growth by RapidEye Imagery and Field Investigation Data. Korean Journal of Soil Science and Fertilizer. 48(5):556-563. https://doi.org/10.7745/KJSSF.2015.48.5.556
  26. IPCC, 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, (Ed.), Cambridge University Press, Cambridge, UK, 976pp.
  27. Peng, J.L., Kim, M.J., Kim, Y.J., Jo, M.H., Nejad, J.G., Lee, B.H., Ji, D.H., Kim, J.Y., Oh, S.M., Kim, B.W., Kim, K.D., So, M.J., Park, H.S. and Sung, K.I. 2015. Detecting the Climate Factors related to Dry Matter Yield of Whole Crop Maize. Korean Journal of Agricultural and Forest Meteorology. 17(3):261-269. https://doi.org/10.5532/KJAFM.2015.17.3.261
  28. Rathmann, R., Szklo, A. and Schaeffer, R. 2010. Land use competition for production of food and liquid biofuels: An analysis of the arguments in the current debate. Renewable Energy. 35(1):14-22. https://doi.org/10.1016/j.renene.2009.02.025
  29. Schlenker, W. and Roberts, M.J. 2006. Estimating the Impact of Climate Change on Crop Yield: The Importance of Non Linear Temperature Effects. No. w13799. National Bureau of Economic Research, 2008.
  30. SPSS (2012). IBM SPSS Statistics 21.0. IBM Corp., Somers, New York. U.S.A.
  31. Takahashi, S. 2002. A Model Predicting Forage Maize Growth Based on Temperature and Solar Radiation. Grassland Science. 48(1), 43-49.
  32. Yun, J.I. 2003. Predicting regional rice production in South Korea using spatial data and crop-growth modeling. Agricultural Systems. 77(1):23-38. https://doi.org/10.1016/S0308-521X(02)00084-7
  33. Zhang, H., Chen, H. and Zhou, G. 2012. The model of wheat yield forecast based on modis-ndvi: a case study of Xinxiang. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress.

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