基于宏观和微量营养素的土壤肥沃度划分区使用模糊逻辑和地理空间技术
Meeniga Venkateswarlu1,2, Srinivas Rallapalli3, Amit Singh4
1Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India. meeniga.venkateswarlu@pilani.bits-pilani.ac.in.
Scientific reports
|July 23, 2025
概括
本研究引入了一个模糊逻辑和地理信息学方法,用于同时评估多个土壤肥力参数. 开发的模型通过创建详细的土壤肥力图和识别最佳作物适应性来增强精确农业,如珍珠小米.
科学领域:
- 农业科学 农业科学
- 土壤科学 土壤科学
- 地理信息学是一种地理信息学.
背景情况:
- 评估土壤肥力变化和不确定性对于可持续农业至关重要.
- 传统模型经常单独分析土壤参数,忽视它们的综合影响和不确定性.
研究的目的:
- 开发一种基于模糊逻辑和地理信息学的方法,用于同时评估多个土壤肥力参数.
- 整合模糊规则和空间建模,以提高土壤肥力分类和营养管理.
主要方法:
- 开发了一个含有80个规则的模糊逻辑模型来评估宏观和微量营养素.
- 地质统计战争插值被用来创建高分辨率的土壤肥力图.
- 普萨的土壤测试和肥料推计 (STFR) 分析了250个土壤样本.
主要成果:
- 包括pH值,有机碳,N,P,K和Fe在内的土壤肥力参数被确定为关键.
- 来自模糊模型的生育率得分从41.55到52.60不等.
- 在拉贾斯坦邦的Jhunjhunu,土壤被分为低和中等肥力区.
结论:
- 模糊的GIS框架改善了土壤肥力分类和特定地点的营养建议.
- 珍珠小米被确定为最适合的作物,基于生育能力和产量潜力.
- 该研究支持在精准农业中可持续的作物规划和营养管理.
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