Effects of Climate Change on the Grain Yield Based on Nonlinear PLSR Model

doi:10.3969/j.issn.1000-6362.2016.06.007

Abstract

Abstract: Considering the multicollinearity of climatic factors，as well as the complex nonlinear relationship between climatic factors and the grain yield, authors attempt to model the climatic factors and climate yield data from 1961 and 2008 in this paper with respect to the cubic B splines function（Spline-PLSR）and internal embedded Generalized regression neural network（GRNN） into the partial least squares regression，on the basis of separating the climatic yield by HP filter. Through?the fitting?test based on the data from 2009 to 2013 and the comparison between the C-D production function and the proposed model，authors determine that the Spline-PLSR model is relatively simple with higher prediction accuracy. Compared with the C-D production function，the Spline-PLSR model requires fewer elements and possesses a better forecasting value. It is worth noting that the fitting result of Spline-PLSR is more stable than that of GRNN-PLSR. Hence，it is a better choice to utilize Spline-PLSR to fit the influence of climatic factors on the grain yield.

Key words: Climatic yield, Partial least squares regression, Cubic B-spline, Generalized regression neural network

CHEN Ji-bo，HU Hui,CHEN Ke-yao,WANG Gui-zhi. Effects of Climate Change on the Grain Yield Based on Nonlinear PLSR Model[J]. Chinese Journal of Agrometeorology, 2016, 37(06): 674-681.

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