Climate Impacts on Chinese Corn Yields: A Fractional Polynomial Regression Model

In this study, we examine the effect of climate on corn yields in northern China using data from ten districts in Inner Mongolia and two in Shaanxi province. A regression model with a flexible functional form is specified, with explanatory variables that include seasonal growing degree days, precipitation, technical change and dummy variables to account for regional fixed effects. Results indicate that a fractional polynomial model in growing degree days explains variability in corn yields better than a linear or quadratic model. Among the tested models, the other factors show steady effects on corn yields. Growing degree days, precipitation in July, August and September, and technical change are important determinants of corn yields.


Issue Date:
2012
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/127966
Total Pages:
24
JEL Codes:
Q15; Q54
Series Statement:
REPA Working Paper
2012-02




 Record created 2017-04-01, last modified 2017-08-26

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)