Analysis of spatio-temporal variation of crop yield in China using stepwise multiple linear regression

Yingxia Liu, Gerard B.M. Heuvelink, Zhanguo Bai, Ping He*, Xinpeng Xu, Wencheng Ding, Shaohui Huang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


With increasing discrepancies between population growth and food production in China, the monitoring of crop yield is essential to support food security policies. However, current studies about spatio-temporal variation of yield mainly focus on the influence of climatic factors on grain crops, and do not explore the contributions of agricultural, environmental and economic factors on crop yield in China. In this study, a large yield dataset, covering 31 provinces and a 38-year period from 1978 to 2015, and related explanatory variables were collected for analyzing the spatio-temporal variation of different yield aggregations using stepwise multiple linear regression. At the national scale, the average aggregate yield increased from 3.04 Mg ha−1 in 1978 to 10.04 Mg ha−1 in 2015. Overall, the average aggregate yield increased in all provinces but the average annual growth rates varied: it was smaller than 2.5 % in Heilongjiang, Guizhou, Beijing, Qinghai and Jilin, more than 4.0 % in Hainan, Guangxi, Ningxia, Hebei and Shaanxi, and between 2.5 % and 4.0 % in other provinces. The spatial patterns of the average yield from 1978 to 2015 were different for different crop aggregations. Most of the regression models explained more than 60 % of the yield variance, except for rice, potato and cotton models. Agricultural management practices, soil and economic covariates were important explanatory variables in all models. Topography and climatic covariates were also important for some of the crop models. The regression model of the aggregate yield for all crops explained 95 % of the yield variance, which was mainly explained by planting area index of vegetables (20 %), followed by farmer income (14 %), planting area index of other crops (orchards 11 %, melons 8 %, sugar 6 %, cereals 6 %), and density of agricultural diesel engines (5 %). Although the regression residual of the aggregate yield model was zero on average, the trends were different in different provinces: most provinces demonstrated a small negative or positive residual; the yield was substantially lower (< -0.20 Mg ha−1 y−1) than predicted by the regression model in three provinces in central China (Hebei, Shaanxi and Anhui) and substantially higher (> 0.20 Mg ha−1 y−1) in four provinces (Shanxi, Shandong, Sichuan and Guangdong). These systematic over- and underpredictions may be caused by other factors, such as plagues, pests, natural hazards, market structures (such as competition for labor or impediments to market access) and farmer's management skills. With the increasing population and limited agricultural land resources, enhancing economic growth might be an adequate solution to meet the growing demand for food. It can also promote agricultural efficiency in China, certainly when combined with better management practices, crop composition, breeding and planting technologies.

Original languageEnglish
Article number108098
JournalField Crops Research
Publication statusE-pub ahead of print - 20 Feb 2021


  • China
  • Crop yield
  • Explanatory variables
  • Stepwise multiple linear regression

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