Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques
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 consid...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-12184-3 |
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| author | Meeniga Venkateswarlu Srinivas Rallapalli Amit Singh G. Sai Sesha Chalapathi Suresh Kumar Yashwant Bhaskar Katpatal Gouligari Sujatha |
| author_facet | Meeniga Venkateswarlu Srinivas Rallapalli Amit Singh G. Sai Sesha Chalapathi Suresh Kumar Yashwant Bhaskar Katpatal Gouligari Sujatha |
| author_sort | Meeniga Venkateswarlu |
| collection | DOAJ |
| description | 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−1), organic carbon (0.24–0.56%), N (85.56–146.32 kg ha−1), P (21.99–34.28 kg ha−1), K (116.41–156.16 kg ha−1), S (5.60–20.86 mg kg−1), Fe (1.065–5.095 mg kg−1), Mn (2.058–2.637 mg kg−1), Zn (0.748–1.105 mg kg−1), B (0.372–0.530 mg kg−1), and Cu (0.230–0.788 mg kg−1). 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. |
| format | Article |
| id | doaj-art-0b8e990582bb4d97ac0804e95098a663 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-0b8e990582bb4d97ac0804e95098a6632025-08-20T03:05:22ZengNature PortfolioScientific Reports2045-23222025-07-0115112210.1038/s41598-025-12184-3Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniquesMeeniga Venkateswarlu0Srinivas Rallapalli1Amit Singh2G. Sai Sesha Chalapathi3Suresh Kumar4Yashwant Bhaskar Katpatal5Gouligari Sujatha6Department of Civil Engineering, Birla Institute of Technology and ScienceDepartment of Civil Engineering, Birla Institute of Technology and ScienceDepartment of Mechanical Engineering, Birla Institute of Technology and ScienceDepartment of Electrical and Electronics Engineering, Birla Institute of Technology and ScienceAgriculture, Forestry and Ecology Group, Indian Institute of Remote SensingDepartment of Civil Engineering, Visvesvaraya National Institute of TechnologyNational Remote Sensing CentreAbstract 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−1), organic carbon (0.24–0.56%), N (85.56–146.32 kg ha−1), P (21.99–34.28 kg ha−1), K (116.41–156.16 kg ha−1), S (5.60–20.86 mg kg−1), Fe (1.065–5.095 mg kg−1), Mn (2.058–2.637 mg kg−1), Zn (0.748–1.105 mg kg−1), B (0.372–0.530 mg kg−1), and Cu (0.230–0.788 mg kg−1). 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.https://doi.org/10.1038/s41598-025-12184-3Crop productivityFuzzy logicGISSoil fertilityZone mapping |
| spellingShingle | Meeniga Venkateswarlu Srinivas Rallapalli Amit Singh G. Sai Sesha Chalapathi Suresh Kumar Yashwant Bhaskar Katpatal Gouligari Sujatha Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques Scientific Reports Crop productivity Fuzzy logic GIS Soil fertility Zone mapping |
| title | Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques |
| title_full | Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques |
| title_fullStr | Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques |
| title_full_unstemmed | Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques |
| title_short | Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques |
| title_sort | macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques |
| topic | Crop productivity Fuzzy logic GIS Soil fertility Zone mapping |
| url | https://doi.org/10.1038/s41598-025-12184-3 |
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