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A new grading system for evaluating China's cultivated landquality

机译:一种评估中国培育土匪的新分级系统

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摘要

Cultivated land is crucial to food security, social and economic stability, environmental quality, and sustainable urban-rural development in China and around the world. However, most of the current grading systems for evaluating cultivated land quality in China tend to generate homogeneous results and inadequate spatial descriptions mainly due to the use of a composite index method that often fails to recognize the matching relationship between land use needs and the quality of cultivated land. Thepurpose of this study is to propose a new grading system for evaluating China's cultivated land quality that involves two index systems and a scoring and grading scheme. In the proposed system, cultivated land quality is redefined in terms of productioncapacity quality and environmental quality, and the minimum limiting factor method and the weighted linear model are coupled for factor scoring and grading. The new system is demonstrated by a case study in Yimen Town, a loess plateau region in Western China's Shaanxi Province. The results of this study clearly indicate that the new grading system is superior to the current system when compared using two methods: the spatial pattern comparison method and the crop performance validation method. The results are also favorable in terms of matching relationships between the subclasses of cultivated land quality versus soil types and landforms. The new grading system not only helps to better understand the cultivated land quality but also provides a clear direction for science-based remediation and amelioration of cultivated land.
机译:耕地对粮食安全,社会和经济稳定,环境质量和中国和世界各地的可持续城乡发展至关重要。然而,用于评估中国耕地质量的大多数分级系统倾向于产生均匀的结果,并且空间描述不足主要是由于使用综合指数方法,这些方法通常无法识别土地利用需求与质量之间的匹配关系耕地。本研究的特点是提出一种新的评分系统,用于评估中国培养的土地质量,涉及两个指数系统和评分和评分方案。在所提出的系统中,在生产远处理质量和环境质量方面重新定义耕地质量,并且最小限制因子方法和加权线性模型耦合以进行因子评分和分级。新系统是通过在中国西部陕西省黄土高原地区沂蒙镇的案例研究来证明。该研究的结果清楚地表明,使用两种方法比较时,新的分级系统优于当前系统:空间模式比较方法和作物性能验证方法。结果在培养土地质量与土壤类型和地貌之间的匹配关系方面也有利。新的评分系统不仅有助于更好地了解耕地质量,而且还为科学的修复和耕地改善提供了明确的方向。

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