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Crop Suitability Analysis in the Bijnor District, UP, Using Geospatial Tools and Fuzzy Analytical Hierarchy Process

机译:Bijnor区的作物适用性分析,使用地理空间工具和模糊分析层次流程

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Land suitability analysis allows identifying limiting factors for agricultural production and enables decision-makers to formulate efficient agricultural management plans. In the present study, GIS-based multi-criteria decision-making (MCDM) land suitability analysis was carried out for sustainable development of agriculture. Ten variables such as drainage, depth, rainfall, pH, slope, soil texture, distances to a major road and the closest town, and flooding risk were investigated to access cropland suitability in Bijnor. These suitability factors were ranked by Fuzzy analytical hierarchy process and the resulting weights were used to generate suitability map layers. The final suitability maps of major crops were using weighted overlay analysis. The findings revealed that sugarcane, wheat, paddy, and oilseed had largest area under moderately suitable class as land in this category had minor limitations. Alkaline soil reaction, gentle slope and shallow soil depth were the main limiting factors in this category. A fairly remarkable area under each crop selected was also found under marginal suitable category. These areas could be made suitable by modifying land-quality parameters. The methodology adopted and its application procedures can be utilized to evaluate land suitability and to suggest best agricultural practices.
机译:土地适用性分析允许识别农业生产的限制因素,使决策者能够制定高效的农业管理计划。在本研究中,基于GIS的多标准决策(MCDM)土地适用性分析,用于农业可持续发展。 10个变量,如排水,深度,降雨,pH,坡度,土壤质地,距离主要道路的距离以及最近的城镇,以及洪水风险,以便在Bijnor中获取裁剪适用性。通过模糊分析层次结构排序,这些适合性因素被排序,并使用得到的重量来产生适用性映射层。主要作物的最终适用性地图使用加权覆盖分析。结果表明,甘蔗,小麦,稻谷和油籽在中等合适的课程下具有最大的面积,因为这一类别的土地具有轻微的限制。碱性土壤反应,温和的坡度和浅层土壤深度是该类别的主要限制因素。在每个作物下的一个相当卓越的区域也在边际合适的类别下发现。这些领域可以通过修改土地质量参数来实现。采用的方法及其申请程序可用于评估土地适用性,并建议最好的农业实践。

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