熊鹰, 何鹏. 绿色防控技术采纳行为的影响因素和生产绩效研究——基于四川省水稻种植户调查数据的实证分析[J]. 中国生态农业学报(中英文), 2020, 28(1): 136-146. DOI:10.13930/j.cnki.cjea.190673
引用本文: 熊鹰, 何鹏. 绿色防控技术采纳行为的影响因素和生产绩效研究——基于四川省水稻种植户调查数据的实证分析[J]. 中国生态农业学报(中英文), 2020, 28(1): 136-146.DOI:10.13930/j.cnki.cjea.190673
XIONG Ying, HE Peng. Impact factors and production performance of adoption of green control technology: An empirical analysis based on the survey data of rice farmers in Sichuan Province[J]. Chinese Journal of Eco-Agriculture, 2020, 28(1): 136-146. DOI:10.13930/j.cnki.cjea.190673
Citation: XIONG Ying, HE Peng. Impact factors and production performance of adoption of green control technology: An empirical analysis based on the survey data of rice farmers in Sichuan Province[J].Chinese Journal of Eco-Agriculture, 2020, 28(1): 136-146.DOI:10.13930/j.cnki.cjea.190673

绿色防控技术采纳行为的影响因素和生产绩效研究——基于四川省水稻种植户调查数据的实证分析

Impact factors and production performance of adoption of green control technology: An empirical analysis based on the survey data of rice farmers in Sichuan Province

  • 摘要:绿色防控技术的应用推广顺应了当前农业绿色发展的迫切需要,但现有针对农户绿色防控技术采纳行为的研究在影响因素的综合分析和生产绩效的分析方法上存在不足。本文以四川省水稻种植户为例,对采纳绿色防控技术的影响因素和生产绩效进行定量分析,旨在为促进四川省农业绿色防控技术的有效推广提供政策参考,丰富绿色防控技术应用推广的理论研究。本文基于四川省水稻种植户调查数据,采用Logit模型分析影响农户绿色防控技术采纳行为的主要因素并估算绩效倾向得分,运用DEA-PSM模型分析农户采纳绿色防控技术对生产绩效的影响效应。结果表明:性别为男性、受教育程度越高、种植面积越大、加入合作社、距离乡镇和县城越近、参加过绿色防控技术培训、采纳绿色防控技术生产的安全农产品能够获得市场溢价、认为农业生态环境质量不好、愿意减施农药的农户,采纳绿色防控技术的可能性越大。调查农户的水稻生产绩效介于0.103~1.000,绩效平均值为0.471,在不改变技术水平以及投入规模的情况下,仍有52.9%的绩效提升空间;大部分农户的生产绩效处于0.4~0.6,农户生产效率普遍不高。而从调查区域来看,邛崃、宣汉、泸县的水稻生产绩效均值分别为0.558、0.379和0.467,区域间生产绩效存在显著差异。采用最近相邻匹配法、半径匹配法和核匹配法,测算绿色防控技术采纳对水稻生产绩效的影响效应,结果表明是否采纳绿色防控技术的生产绩效差异较小,且绿色防控技术采纳对水稻生产绩效的影响也不显著。因此,营造农户绿色防控技术采纳的外部环境、激发农户绿色防控技术采纳的内生动力,发挥绿色防控技术的节本增收效应,是推动该技术应用推广的关键。

    Abstract:The application and promotion of crop green control technology addresses the urgent need for green agricultural development. However, existing researches on the adoption of green control technologies suffer from a lack of comprehensive analysis of the influencing factors and methods of production performance. A quantitative analysis of impact factors and production performance of the adoption of green control technology by rice farmers in Sichuan Province could provide policy directions to promote green control technologies and enrich our understanding of their application and promotion. Using data from a survey of rice farmers in Sichuan Province, a Logit model identified the main factors affecting adoption of green control technologies and estimated the propensity scores of production performance. DEA-PSM model analyzed the effects on production performance of the adoption of green control technology by farmers. The results showed that the male gender, higher education levels, greater planting area, cooperative membership, proximity to urban areas, green control technology training, ability to obtain a market premium for safe agricultural products produced by green control technology, consideration of the weak quality of agricultural ecological environments, and willingness to reduce pesticide use were associated with a greater likelihood of adopting green control technology. Rice production performance of the surveyed farmers was 0.103-1.000, and the average performance was 0.471. Without changing the technological level and input scale, there was still room for a 52.9% improvement in performance. Production performance of most farmers was between 0.4 and 0.6, and production efficiency was generally not high. The average rice production performance in Qionglai, Xuanhan, and Luxian counties was 0.558, 0.379, and 0.467 respectively, indicating significant differences among regions. The nearest-neighbor, radius, and kernel matching methods were applied to measure the effects of adoption of green control technology on rice production. The results showed that whether farmers adopting green control technology had little difference on production performance and the effect of farmer adoption of green control technology on rice production performance was not significant. Application and promotion of green control technology will therefore require the creation of a supportive external environment that empowers adoption of green control technology and focuses on reducing costs and increasing incomes.

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