生物灾害科学2023，Vol.46Issue(4)：484-492,9.DOI:10.3969/j.issn.2095-3704.2023.04.73
种植规模、政府支持对农户气象灾害适应性行为的影响
Effects of Scale Farming and Government Support on Farmers'Adaptation to Meteorological Disasters:Evidence from Jiangxi Province
摘要
Abstract
[Objective]The expansion of the planting scale provides the possibility for farmers to adopt adaptive behaviors to face meteorological disasters.In the context of the reality of frequent disasters,it is crucial to encourage farmers to adopt adaptive behaviors to cope with meteorological disasters.[Method]Based on 271 questionnaire survey data from farmers in various regions of Jiangxi Province,this paper used Ordered-probit model to analyze the effects of planting scales,government support and the interaction terms between the planting scale and government support on farmers'adaptive behaviors to face meteorological disasters.[Result]The results showed that the planting scale had a positive effect on farmers'adaptative behaviors to cope with meteorological disasters,and the larger the planting scale,the more willing farmers were to adopt adaptive behaviors to deal with meteorological disasters,which indicated that promoting the appropriate scale of land management could significantly activate farmers to adopt adaptive behaviors to cope with meteorological disasters.Also,government support positively influenced farmers to adopt adaptive behaviors to fight against meteorological disasters,which could enhance the influence of planting scales on farmers'adaptive behaviors to meet meteorological disasters.[Conclusion]It is suggested to promote the land management on an appropriate scale according to local conditions,cultivate and develop large-scaled households according to household conditions,and emphasize the effective implementation of government support policies.关键词
种植规模/政府支持/气象灾害/适应性行为Key words
planting scale/government support/meteorological disaster/adaptive behavior分类
农业科技引用本文复制引用
王敏,武昕哲,赵明慧,李辉婕..种植规模、政府支持对农户气象灾害适应性行为的影响[J].生物灾害科学,2023,46(4):484-492,9.基金项目
国家自然科学基金项目(71963020)、江西省自然科学基金项目(20181BAA208055)和江西省2022年度研究生创新专项资金项目(YC2022-s400) （71963020）
