| 摘要: |
| 农业是受气候变化影响最显著的国民经济行业，农业生产不仅关乎一个国家粮食供给安全，也为其他产业部门提供基础原材料，因此在全球气候变化的大背景下研究气候变化对农业产量的影响是评估气候变化影响经济的重要方面，受到学界普遍关注。以极端气候多发的水稻主产区之一广东省为例，基于作物生产函数模型并引入月度气候因子变量，综合测度农用物资投入与气候因子对作物单产的影响，利用1992—2016年广东省各地市水稻作物单产以及农用物资投入、月度平均积温和降水的面板数据，对影响水稻单产的关键气候因子进行识别，并在此基础上定量估算了历史气候对广东省水稻单产的气候损失或收益。研究结果发现：（1）有效积温增加有利于水稻单产提高，而降水增多对水稻单产有不利影响；（2）生育期不同阶段的气候因子对水稻单产影响不同，4月和6月的有效积温以及6—7月的降水对早稻单产有显著影响，晚稻全生育期（6—11月）除7月以外其余月份的积温和降水均是影响晚稻单产的关键气候因子；（3）历史气候对水稻单产的影响表现为明显的年际波动现象，而未观察到明显的趋势性特征，且近年来气候变化对广东省水稻单产的影响表现为增产效应；（4）早稻单产的气候损失呈现由北向南逐渐增强的趋势，晚稻单产的气候损失则呈现由东向西逐渐增强的趋势；（5）早稻对气候变化的敏感性高于晚稻，主要原因在于早稻生育期较短，且生育期内的降水及气温的波动幅度更大，早稻难以在较短生育期内对气候进行有效适应，抗风险能力较低。 |
| 关键词: 气候因子 影响估算 月度积温 月度降水 水稻单产 |
| DOI：10.7515/JEE212020 |
| CSTR：32259.14.JEE212020 |
| 分类号: |
| 基金项目:国家重点研发计划（2016YFA0602703）；北京市哲学社会科学首都流通业研究基地项目（JD-ZD-2021-022） |
| 英文基金项目:National Key Research and Development Program of China (2016YFA0602703); Capital Circulation Research Base Project of Beijing Philosophy and Social Science Foundation (JD-ZD-2021-022) |
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| Identification of climate factors affecting rice yield and climate impact estimation in Guangdong Province |
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XIONG Wen, LIU Jia, ZHU Yongbin
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1. School of Economics, Beijing Technology and Business University, Beijing 100048, China
2. Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
3. School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China
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| Abstract: |
| Background, aim, and scope Climate change has significantly impacted agriculture, which is not only related to food security, but also to industrial production through the harvesting of raw materials. Therefore, it is essential to study the impact of climate change on agricultural production in the context of global climate change. The present study was undertaken to identify the key climate factors that have significant impacts on rice yields and evaluate historical climate effects based on the estimation of the climate—yield relationship. As Guangdong Province is a major rice producing area and experiences frequent extreme climate events, we limited our scope to early and late rice grown in Guangdong. Materials and methods Many approaches have been proposed for climate—yield relationship estimation, such as the yield decomposition method, which decomposes yield variation into different parts associated with technique, climate and other random factors, and then regresses the climate yield with climate factors. This study applied the production function approach, which reflects the relationship between rice yield and material inputs such as fertilizer, and then introduced the monthly climate factors of temperature and precipitation into that function. This approach enabled us not only to estimate the effects of material inputs and climate factors on crop yields simultaneously, but also to identify the key climate factors at a finer time scale. We thus collected data on rice yield, material inputs (such as fertilizer, labor, agrichemicals, and films), and climate factors, including temperature, denoted by growing degree days (GDD), and precipitation at the prefecture-level in Guangdong Province during 1992—2016. With the above data, we performed fixed effect panel regression. Results The study successfully identified the key climate factors affecting rice crops and found that the climate effects of monthly GDD and precipitation on the rice yield are distinct between early rice and late rice. (1) The GDD in April and June, as well as precipitation from June to July, have a significant impact on the yield of early rice. (2) The GDD and precipitation during the full growth period (June—November), except July, are the key climatic factors affecting the late rice yield. The analysis indicates that the increase in effective GDD is generally conducive to increases in rice yield, while the increase in precipitation has an adverse effect on rice yield. Moreover, we evaluated the historical climate effects on the rice yield and obtained historical trends in climate losses/gains of rice yield. This analysis shows the following: (1) obvious inter-annual fluctuations of climate-related yield change are observed, but there are no significant long-term trends; (2) the impact of climate change on rice yield has shown a positive effect in recent years. Discussion Theoretically, there should exist optimal climate conditions for each crop. However, this study failed to characterize the nonlinear relationship between the yield and climate factors, or to estimate the optimal climatic conditions corresponding to the highest yield. Previous studies that found such a nonlinear relationship have used the mean value of climate factors, while this study introduced climate factors at a finer time scale to identify the key climate factors during different growing stages. Another explanation for this discrepancy is that our model is linear because quadratic terms of climate variables would complicate the model, and the current dataset are insufficient to estimate such a non-linear statistical model. Conclusions The main conclusions include the following: (1) the historical aggregated climate effects on rice yield are positive, although the key influencing climate factors are distinct among crop varieties and growing stages; (2) early rice is more sensitive to climate change compared to late rice, and the climate loss gradually increases from north to south for early rice, but gradually increases from east to west for late rice. Therefore, the counter-measures to adapt to climate change should be specific for different rice varieties and regions. Recommendations and perspectives This study has proposed a novel method that simultaneously estimates the effects of input factors and climate factors on yields, specifically at a finer monthly time scale. Although some progress has been made, it still failed to provide a thorough picture of all major crops and across the whole scope of China. Therefore, we suggest future research to complete this picture by identifying the key climate factors affecting the yield of all other crops and to evaluate the climate losses or gains of historical as well as future climate change. |
| Key words: climate factors impact evaluation monthly GDD monthly precipitation rice yield |
