Impact of climate variability on grain yields of spring and summer maize
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Computers and Electronics in Agriculture, 2022, 199, pp. 107101
- Issue Date:
- 2022-08-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S0168169922004185-main.pdf | Published version | 9.28 MB |
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Crop yield is greatly impacted by climate change, and a systemic assessment of its impacts on crop yields is essential. Aiming to investigate the impact of climate change on spring and summer maize yields in main maize growing areas of China, the observed meteorological and maize yield data over 1988–2017 at the 121 sites (including 85 sitess for spring maize and 36 sites for summer maize) in main maize growing areas of China were collected. The first-order difference, Sen's slopes and trend test, multi-collinearity detection, Pearson correlation, stepwise linear and nonlinear regression methods were used, and the best statistical regression models between maize yield and climate variables have been established. Of these, the Sen′s slopes quantify the trend magnitude of the related climate variables and spring/summer maize yields during maize growth period. The Pearson correlation coefficients assess the relationship between pairs of climatic variables and maize yields, while the multi-collinearity analysis determines the mutually independent climatic variables with maize yields. The stepwise multi-variate linear and nonlinear regressions were conducted to obtain the best functions of the one-order-differences of spring (summer)maize yields at the 85 (36) sites. The results indicated that: (1) Generally, the precipitation and temperature during growth seasons was rising, while relative air humidity and sunshine hours was declining. Both the yields of spring and summer maize showed increasing trends. (2) Spring maize yields were more related to relative humidity, sunshine hours and precipitation, while summer maize yields were more related to precipitation and temperature. (3) The multivariate nonlinear functions performed better than the linear relationship. Based on the coefficient of determination, climate change has explained 5.8–87.6% variability of spring maize yield and 6.6–78.5% variability of summer maize yield. (4) The contribution importance rank of climate variables to yields of spring and summer maize was precipitation > relative humidity > sunshine hours > minimum temperature > maximum temperature > average temperature. The wet-cold and wet-warm climate, especially the former, had positive effects on maize yield. In conclusion, climate variables affect spring and summer maize yields and their best relationships were site-specific in China. Our research provides new insights for maize planting management under climate change.
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