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Classification and Digital Mapping of Soils in a Semiarid Region of Afghanistan
Eurasian Soil Science ( IF 1.4 ) Pub Date : 2021-02-26 , DOI: 10.1134/s1064229321010142
Emal Wali , Masahiro Tasumi , Yoshinori Shinohara

Abstract

A soil map provides information on soil spatial distribution. Such information is essential for effective use of soil resources for crop production, land evaluation, spatial planning, environmental control, and for other similar purposes. In this study, a digital soil map for a semiarid region of Afghanistan was developed. This map contains soil taxonomic information up to the subgroup level, which is the first such attempt in Afghanistan. A total of 114 soil samples were collected in and around the Khost Province through an intensive soil survey. The collected samples were classified into 14 subgroups of soils, following the USDA soil classification system. A soil land inference model (SoLIM) was applied for mapping the recognized 14 soil subgroups digitally, via an expert knowledge-based fuzzy soil inference scheme, with surface topography and other spatial data as inputs. The overall accuracies from the error matrix and Kappa statistics were 0.74 and 0.71, respectively. This map was also compared with the currently used soil map. A general agreement between the two maps was found in the spatial distribution of soil classes, at the great group level. However, the newly developed map contains more detailed information on soils, which might be useful for the advanced use of soil information, for example, to better determine the crop type for cultivation by considering the detailed soil properties. Throughout this study, 14 different recognized classes of soil subgroups were digitally mapped in the study area.



中文翻译:

阿富汗半干旱地区土壤的分类和数字制图

摘要

土壤图提供了有关土壤空间分布的信息。这些信息对于有效利用土壤资源进行作物生产,土地评估,空间规划,环境控制以及其他类似目的至关重要。在这项研究中,开发了阿富汗半干旱地区的数字土壤图。该地图包含了亚组级别的土壤生物分类信息,这是阿富汗的首次此类尝试。通过深入的土壤调查,总共在霍斯特省及其周边地区收集了114个土壤样品。按照美国农业部土壤分类系统,收集的样品分为14个土壤亚类。通过基于专家知识的模糊土壤推理方案,将土壤土地推理模型(SoLIM)用于数字化识别已识别的14个土壤亚组,以表面地形和其他空间数据作为输入。来自误差矩阵和Kappa统计的总体准确度分别为0.74和0.71。该图还与当前使用的土壤图进行了比较。在伟大的群体层次上,在土壤类别的空间分布中发现了这两个地图之间的总体共识。但是,新开发的地图包含有关土壤的更详细的信息,这可能有助于进一步利用土壤信息,例如,通过考虑详细的土壤特性来更好地确定要种植的作物类型。在整个研究过程中,在研究区域中以数字方式绘制了14个不同的公认的土壤亚类类别。该图还与当前使用的土壤图进行了比较。在伟大的群体层次上,在土壤类别的空间分布中发现了这两个地图之间的总体共识。但是,新开发的地图包含有关土壤的更详细的信息,这可能有助于进一步利用土壤信息,例如,通过考虑详细的土壤特性来更好地确定要种植的作物类型。在整个研究过程中,在研究区域中以数字方式绘制了14个不同的公认的土壤亚类类别。该图还与当前使用的土壤图进行了比较。在伟大的群体层次上,在土壤类别的空间分布中发现了这两个地图之间的总体共识。但是,新开发的地图包含有关土壤的更详细的信息,这可能有助于进一步利用土壤信息,例如,通过考虑详细的土壤特性来更好地确定要种植的作物类型。在整个研究过程中,在研究区域中以数字方式绘制了14个不同的公认的土壤亚类类别。通过考虑详细的土壤特性,更好地确定要种植的作物类型。在整个研究过程中,在研究区域中以数字方式绘制了14个不同的公认的土壤亚类类别。通过考虑详细的土壤特性,更好地确定要种植的作物类型。在整个研究过程中,在研究区域中以数字方式绘制了14个不同的公认的土壤亚类类别。

更新日期:2021-02-26
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