Reinspecting the Climate-Crop Yields Relationship at a Finer Scale and the Climate Damage Evaluation: Evidence from China

Yongbin Zhu,Zhenbo Wang,Changxin Liu,Yajuan Shi,Bing Lyu

doi:10.1155/2020/9424327

Abstract

This paper reinvestigated the climate-crop yield relationship with the statistical model at crops’ growing stage scale. Compared to previous studies, our model introduced monthly climate variables in the production function of crops, which enables separating the yield changes induced by climate change and those caused by inputs variation and technique progress, as well as examining different climate effects during each growing stage of crops. By applying the fixed effect regression model with province-level panel data of crop yields, agricultural inputs, and the monthly climate variables of temperature and precipitation from 1985 to 2015, we found that the effects of temperature generally are negative and those of precipitation generally are positive, but they vary among different growth stages for each crop. Specifically, GDDs (i.e., growing degree days) have negative effects on spring maize’s yield except for the sowing and ripening stages; the effects of precipitation are negative in September for summer maize. Precipitation in December and the next April is significantly harmful to the yield of winter wheat; while, for the spring wheat, GDDs have positive effects during April and May, and precipitation has negative effects during the ripening period. In addition, we computed climate-induced losses based on the climate-crop yield relationship, which demonstrated a strong tendency for increasing yield losses for all crops, with large interannual fluctuations. Comparatively, the long-term climate effects on yields of spring maize, summer maize, and spring wheat are more noticeable than those of winter wheat.

Highlights

Guest Editor: Jun Yang is paper reinvestigated the climate-crop yield relationship with the statistical model at crops’ growing stage scale
Our model introduced monthly climate variables in the production function of crops, which enables separating the yield changes induced by climate change and those caused by inputs variation and technique progress, as well as examining different climate effects during each growing stage of crops
By applying the fixed effect regression model with province-level panel data of crop yields, agricultural inputs, and the monthly climate variables of temperature and precipitation from 1985 to 2015, we found that the effects of temperature generally are negative and those of precipitation generally are positive, but they vary among different growth stages for each crop

Summary

Introduction

Guest Editor: Jun Yang is paper reinvestigated the climate-crop yield relationship with the statistical model at crops’ growing stage scale. Schilenker and Lobell [3, 11] and Chen et al [12] studied the nonlinear effects of weather on crop yields and the impacts of climate change on agriculture at a finer county scale. In their studies, they found the inverted U-shape between yield and climate variables, based on which they derived the optimal climate conditions for the crops. They found the inverted U-shape between yield and climate variables, based on which they derived the optimal climate conditions for the crops. e authors in [13, 14] tried to unpack the climate drivers of agricultural yields with statistical models. e former study found that the largest drivers of yield loss were freezing temperatures in the fall and extreme heat events in the spring; the latter argued that exposure to relatively high temperature (>30°C) over warmer parts of the growing season and exposure to relatively cool temperatures appeared detrimental to most crops

Methods

Findings

Discussion

Conclusion