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http://dx.doi.org/10.5333/KGFS.2017.37.1.80

A Research on Yield Prediction of Mixed Pastures in Korea via Model Construction in Stages  

Oh, Seung Min (Department of Animal Life Science, Kangwon National University)
Kim, Moon Ju (Institute of Animal Resources, Kangwon National University)
Peng, Jinglun (Department of Animal Life Science, Kangwon National University)
Lee, Bae Hun (Department of Animal Life Science, Kangwon National University)
Kim, Ji Yung (Department of Animal Life Science, Kangwon National University)
Kim, Byong Wan (Department of Animal Life Science, Kangwon National University)
Jo, Mu Hwan (Foundation for the Rural Youth)
Sung, Kyung Il (Department of Animal Life Science, Kangwon National University)
Publication Information
Journal of The Korean Society of Grassland and Forage Science / v.37, no.1, 2017 , pp. 80-91 More about this Journal
Abstract
The objective of this study was to select a model showing high-levels of interpretability which is high in R-squared value in terms of predicting the yield in the mixed pasture using the factors of fertilization, seeding rate and years after pasture establishment in steps, as well as the climate as a basic factor. The processes of constructing the yield prediction model for the mixed pasture were performed in the sequence of data collection (forage and climatic data), preparation, analysis, and model construction. Through this process, six models were constructed after considering climatic variables, fertilization management, seeding rates, and periods after pasture establishment years in steps, thereafter the optimum model was selected through considering the coincidence of the models to the forage production theories. As a result, Model VI (R squared = 53.8%) including climatic variables, fertilization amount, seeding rates, and periods after pasture establishment was considered as the optimum yield prediction model for mixed pastures in South Korea. The interpretability of independent variables in the model were decreased in the sequence of climatic variables(24.5%), fertilization amount(17.8%), seeding rates(10.7%), and periods after pasture establishment(0.8%). However, it is necessary to investigate the reasons of positive correlation between dry matter yield and days of summer depression (DSD) by considering cultivated locations and using other cumulative temperature related variables instead of DSD. Meanwhile the another research about the optimum levels of fertilization amounts and seeding rates is required using the quadratic term due to the certain value-centered distribution of these two variables.
Keywords
Mixed pasture; Yield prediction model; Application in stages;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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