少数民族脱贫地区的高素质农民培育效果评价及障碍因子诊断

Evaluation of cultivation effect and diagnosis of obstacle factors ofhigh-quality farmers in minority areas

随着农业农村现代化快速发展,人才振兴对促进“三农”发展极具重要意义。依托新疆农业大学在南疆四地州开展的高素质农民培育课程,以人力资本理论为基础,运用熵权TOPSIS模型从反应层、学习层、行为层、效果层四个层面选取指标测度实证分析南疆四地州高素质农民培育效果,并以此模型结果为基础,进一步运用障碍度模型,实证诊断分析新疆南疆四地州高素质农民培育效果的制约因素。研究发现:新疆南疆四地州高素质农民培育效果分数处于0.6~0.8之间,处于良好阶段;通过障碍度模型发现,学习层阻碍程度最高,负面影响值为0.042 8;反应层阻碍程度次之,负面影响值为0.005 68;行为层较小,负面影响值为0.005 4。因此,政府部门应改进实施机制,加强宣传引导以及提供政策保障,以进一步削弱并减少影响农民培育效果的阻滞因素,从而有效提升新疆南疆四地州高素质农民培育效果。

With the rapid development of agricultural and rural modernization,talent revitalization is of great significance in promoting the development of agriculture,rural areas,and farmers.Based on the high-quality farmer training courses conducted by Xinjiang Agricultural University in four prefectures of southern Xinjiang,this study uses human capital theory and uses entropy-weighted TOPSIS model to select indicators and measure the effectiveness of high-quality farmer training in the four prefectures from four aspects:reaction,learning,behavior,and outcomes.The study also uses the obstacle degree model to identify constraints affecting the effectiveness of high-quality farmer training in southern Xinjiang.The results showed that the cultivation effect scores of high-quality farmers in the four prefectures of southern Xinjiang were between 0.6 to 0.8,which were in a good stage.However,the obstacle degree model showed that the learning layer has the highest obstacle degree,with a negative impact value of 0.0428,followed by the reaction layer with a negative impact value of 0.00568.The behavior layer showed a small negative impact value of 0.0054.Therefore,it is recommended that the government should improve the implementation mechanism,strengthen the publicity and guidance,and provide policy guarantees to weaken and reduce the blocking factors affecting the effectiveness of farmer training.These measures will effectively improve the effectiveness of high-quality farmer training in the four prefectures of southern Xinjiang.