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基于宏观和微观营养素的土壤肥力分区：运用模糊逻辑和地理空间技术

Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques.

作者信息

Venkateswarlu Meeniga, Rallapalli Srinivas, Singh Amit, Chalapathi G Sai Sesha, Kumar Suresh, Katpatal Yashwant Bhaskar, Sujatha Gouligari

机构信息

Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India.

School of Interdisciplinary Research and Entrepreneurship, Birla Institute of Technology and Science, Pilani, Rajasthan, India.

出版信息

Sci Rep. 2025 Jul 23;15(1):26772. doi: 10.1038/s41598-025-12184-3.

DOI:10.1038/s41598-025-12184-3
PMID:40702069
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12287334/
Abstract

Modeling the spatial variability and uncertainty of soil fertility parameters is crucial for sustainable agriculture but remains a challenge due to complex interactions between soil properties. Traditional models often assess individual parameters, such as pH or nitrogen (N), without considering their combined influence and uncertainty. This study develops a fuzzy logic and geoinformatics-based approach to simultaneously assess multiple soil fertility parameters. The model integrates 80 fuzzy rules to evaluate macro- and micronutrients, incorporating 250 soil samples analyzed using the PUSA Soil Test and Fertilizer Recommendation Meter (STFR). Experimental results showed soil fertility parameter ranges: pH (7.46-8.26), ECe (0.267-0.807 dS m), organic carbon (0.24-0.56%), N (85.56-146.32 kg ha), P (21.99-34.28 kg ha), K (116.41-156.16 kg ha), S (5.60-20.86 mg kg), Fe (1.065-5.095 mg kg), Mn (2.058-2.637 mg kg), Zn (0.748-1.105 mg kg), B (0.372-0.530 mg kg), and Cu (0.230-0.788 mg kg). The fuzzy model-derived fertility scores ranged from 41.55 to 52.60, with pH, organic carbon, nitrogen, phosphorus, potassium, and iron as critical parameters influencing fertility. Geostatistical kriging interpolation estimated fertility values at unsampled locations, generating a continuous, high-resolution soil fertility map for precision agriculture. Validation with crop yield data ranked suitability as: Pearl millet (0.919) > Mustard (0.890) > Wheat (0.863) > Barley (0.861). Multi-criteria decision analysis confirmed pearl millet as the most suitable crop based on fertility and yield potential. The study categorizes soil into low and moderate fertility zones across Jhunjhunu, Rajasthan, ensuring a systematic assessment for optimal nutrient management. By integrating fuzzy logic with GIS-based spatial modeling, this study enhances soil fertility classification, site-specific nutrient recommendations, and sustainable crop planning, reinforcing the role of fuzzy-GIS frameworks in precision agriculture.

摘要

对土壤肥力参数的空间变异性和不确定性进行建模对于可持续农业至关重要，但由于土壤性质之间复杂的相互作用，这仍然是一个挑战。传统模型通常评估单个参数，如pH值或氮（N），而不考虑它们的综合影响和不确定性。本研究开发了一种基于模糊逻辑和地理信息学的方法，以同时评估多个土壤肥力参数。该模型整合了80条模糊规则来评估大量营养素和微量营养素，并纳入了使用PUSA土壤测试和肥料推荐仪（STFR）分析的250个土壤样本。实验结果显示了土壤肥力参数范围：pH值（7.46 - 8.26）、电导率（ECe）（0.267 - 0.807 dS m）、有机碳（0.24 - 0.56%）、氮（85.56 - 146.32 kg/ha）、磷（21.99 - 34.28 kg/ha）、钾（116.41 - 156.16 kg/ha）、硫（5.60 - 20.86 mg/kg）、铁（1.065 - 5.095 mg/kg）、锰（2.058 - 2.637 mg/kg）、锌（0.748 - 1.105 mg/kg）、硼（0.372 - 0.530 mg/kg）和铜（0.230 - 0.788 mg/kg）。模糊模型得出的肥力得分在41.55至52.60之间，其中pH值、有机碳、氮、磷、钾和铁是影响肥力的关键参数。地统计克里格插值法估计了未采样地点的肥力值，生成了用于精准农业的连续、高分辨率土壤肥力图。用作物产量数据进行验证后，适宜性排名为：珍珠粟（0.919）＞芥菜（0.890）＞小麦（0.863）＞大麦（0.861）。多标准决策分析证实，基于肥力和产量潜力，珍珠粟是最合适的作物。该研究将拉贾斯坦邦琼朱努地区的土壤分为低肥力区和中等肥力区，确保了对养分进行优化管理的系统评估。通过将模糊逻辑与基于GIS的空间建模相结合，本研究加强了土壤肥力分类、特定地点养分推荐和可持续作物规划，强化了模糊 - GIS框架在精准农业中的作用。

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