数字农科院2.0

Evaluation of Food Legumes Pest and Disease Control in China: Evidence Using a Provincial-Level Dataset

文献类型： 外文期刊

作者： Zhang, Huijie;Yin, Guodong;He, Yuhua;Liu, Yujiao;Luo, Hongmei;Zhang, Jijun;Zhou, Bin;Liu, Zhenxing;Zhang, Xiaoyan;Zhu, Xu;Shao, Yang;Lian, Rongfang;Xiang, Chao;Wei, Yunshan;Wang, Xuejun;Yuan, Xingxing;Zhu, Zhendong;Chen, Xin;Jiang, Changyi

作者机构：

关键词： food legumes;pests and diseases control;research and development;China;double machine learning model

期刊名称： AGRONOMY-BASEL

ISSN：

年卷期： 2025 年 15 卷 10 期

页码：

收录情况： SCIE（2025版）

摘要： Food legumes play a pivotal role in China's food security, nutritional health, and green development strategies due to their unique advantages. This paper presents an empirical study on the economic evaluation of scientific research on pest and disease control for food legumes. Using panel data from 31 Chinese provinces from 2008 to 2023, we employ a Double Machine Learning (DML) approach to identify the impact of investment in plant protection research on food legume outputs. The results indicate a steady increase in China's investment in this field, with an average annual growth rate of 5.19% from 2008 to 2023, and the total investment in 2023 was 2.14 times that of 2008. Investment in plant protection research effectively mitigates output losses and leads to significant production increases. Specifically, a 1% increase in research investment corresponds to a 0.2% increase in food legume output. This effect remains robust across various algorithms, time windows, and control variable settings. Based on these findings, we recommend: (1) increasing financial support and talent acquisition for research on food legume pests and diseases to enhance the stability and sustainability of research investment; (2) strengthening cooperation mechanisms between research institutions and enterprises to leverage their respective strengths and promote the commercialization of research outcomes and regional variety extension; (3) establishing a diversified research investment system that explores a co-construction model guided by the government, involving enterprises, and utilizing public-private partnerships to reconcile the conflict between long research cycles and market demands; (4) fostering a dual-track linkage between regional technological innovation and enterprise product commercialization to improve the efficiency of technology transfer and application; and (5) strengthening R&D in cutting-edge fields like Artificial Intelligence to improve the efficiency and precision of pest and disease control.

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