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当前位置: 首页 > 文章 > 基于神经网络模型的耕地撂荒风险评价——以广东兴宁市为例 农业现代化研究 2019,40 (6) 1002-1010
Position: Home > Articles > Risk assessment of farmland abandonment based on the neural network model: A case study of Xingning, Guangdong Province Research of Agricultural Modernization 2019,40 (6) 1002-1010

基于神经网络模型的耕地撂荒风险评价——以广东兴宁市为例

作  者：
吴茗华;王薇;刘光盛;王红梅
单  位：
广东省土地利用与整治重点实验室;华南农业大学水利与土木工程学院;华南农业大学公共管理学院
关键词：
耕地撂荒;地块尺度;撂荒规律;风险评价;人工神经网络模型
摘  要：
耕地是农业生产的基础,经过多年的耕地保护,耕地数量和质量获得较大的提升,但受青壮劳动力析出,务农人口老龄化的影响,耕地撂荒问题成为耕地保护亟待关注的新问题。面对日益严峻的耕地撂荒问题以及乡村振兴的发展要求,耕地撂荒风险评价及治理措施研究具有重要现实意义。本文以粤北山区兴宁市为例,从基础条件、自然地理环境、土壤质量和基础设施四个方面选取指标,并构建人工神经网络模型进行撂荒风险测度,探析不同撂荒风险耕地特征,最后提出分类治理对策。结果表明,耕地撂荒风险测度模型PCM系数为72.8%,能较好地测度耕地撂荒风险;兴宁市约三成耕地为高撂荒风险状态,其主要分布在土壤质量较差、基础设施条件不完善、耕作便利性较差的高海拔坡耕地上,且主要为田块细碎、形状不规整的旱地。为避免耕地大规模撂荒,建议建立耕地撂荒风险动态监测体系,加强土地流转,通过扶持种植大户,吸纳外出务工人员返乡创业等方式推广适度规模经营。
译  名：
Risk assessment of farmland abandonment based on the neural network model: A case study of Xingning, Guangdong Province
作  者：
WU Ming-hua;WANG Wei;LIU Guang-sheng;WANG Hong-mei;College of Water Conservancy and Civil Engineering, South China Agricultural University;Guangdong Province Key Laboratory of Land Use and Consolidation;College of Public Management, South China Agricultural University;
关键词：
farmland abandonment;;patch scale;;law of farmland abandonment;;risk assessment;;the artificial neutral network model
摘  要：
Farmland is the base of agriculture production. After years of farmland protection, the quantity and quality of farmland has been greatly improved. However, influenced by the emergence of young and middle-aged labor force and the aging of farming population, farmland abandonment has become a new issue of urgent concern for farmland protection. Faced with an increasingly serious trend of farmland abandonment and the development of rural revitalization, the research on the risk assessment and management measures of farmland abandonment has practical significance. Taking Xingning in the mountainous area of northern Guangdong as an example and adopting some critical indicators, including the basic conditions, the physical geographical environment, the soil quality and infrastructure, this paper constructed an artificial neural network model for risk assessment of farmland abandonment and calculated the Percent Correct Metric (PCM) index to analyze the characteristics of cultivated land with different abandonment risk. Results show that the PCM coefficient of the risk measurement model for farmland abandonment is 72.8%, which can better measure the risk of farmland abandonment. About 30% of the farmland in Xingning is at high risk of abandonment. Abandoned farmland mainly distributes on high-altitude sloping areas with poor soil quality, imperfect infrastructure and poor farming convenience and on some dry land with fragmented fields and irregular shapes. To avoid large-scale farmland abandonment, this paper suggests to establish a dynamic risk monitoring system for farmland abandonment, to strengthen farmland transfer, and to promote moderate scale management by supporting large-scale planters and absorbing migrant workers.

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