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Evaluating Fertilizer Use Efficiency and Spatial Correlation of Its Determinants in China: A Geographically Weighted Regression Approach

机译:评估中国决定因素的肥料利用效率及空间相关性:地理加权回归方法

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摘要

Improving fertilizer use efficiency (FUE) is an effective means to reduce fertilizer use and environmental contamination. Few studies have considered the spatial effects of FUE and its determinants. This paper calculated the FUE of agricultural production by adopting panel data on 31 provinces in China from 2007 to 2017 using a stochastic frontier method with a heteroscedastic inefficiency term, and discussed the spatial characteristics. Further, the geographical weighted regression model (GWR) was employed to examine the spatial impact of factors on FUE and revealed the spatial dispersion and agglomeration effect. The results show that averaged FUE in China was 0.722, and had a significantly decreasing trend with a significant regional difference and spatial positive correlation in different provinces. The non-agricultural employment ratio was the leading factor for increasing FUE, and its degree of influence showed a decreasing trend from eastern to western China. The different agricultural industry development modes, crop planting patterns adjustment, labor transfer, and policy incentive systems for increasing the non-agricultural employment ratio should be developed for different regions. Farmers’ income had a negative impact on FUE, but the influence degree decreased annually. Education level had a negative impact on FUE and was relatively weak, but the influence degree was increasing. This should strengthen the exploration of a scientific and practical technical training system for farmers on fertilizer use while improving educational levels in different regions on the basis of local characteristics. The impact of disasters on FUE depended on their severity, and a combined weather and disaster forecasting mechanism should be developed.
机译:提高肥料使用效率(FUE)是减少肥料使用和环境污染的有效手段。很少有研究考虑过FUE及其决定因素的空间效应。本文从2007年至2017年通过具有异镜效率低下术语的随机前沿方法,通过2007年至2017年在中国采用了31个省份的小组数据计算了农业生产发电。此外,采用地理加权回归模型(GWR)来检查大规模因素的空间影响,并揭示了空间分散和聚集效应。结果表明,中国的平均水平为0.722,并在不同省份具有显着的区域差异和空间正相关的趋势显着降低。非农业就业比例是提高疲劳的主要因素,其影响程度从东部到中国的东部趋势下降。应为不同地区制定不同的农业产业发展模式,作物种植模式调整,劳动转移和政策激励系统,以增加非农业就业比例。农民收入对疲劳产生负面影响,但受影响程度每年减少。教育水平对大黄产生负面影响,相对较弱,但影响程度正在增加。这应该加强对农民肥料使用的科学和实践技术培训系统的探索,同时在局部特征的基础上提高不同地区的教育水平。灾害对疲劳的影响取决于他们的严重程度,以及应制定出综合的天气和灾难预测机制。

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