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<p>The growing global population and increasing food demand require monitoring soil health and ensuring sustainable agriculture. Generating soil nutrient maps with traditional GIS methods is often a lengthy process, causing delays in decisions. This study introduces PySNM, a new, Python-based, free, and open-source tool designed to fully automate spatial interpolation (IDW) and generate soil nutrient maps. PySNM, a Python-based tool for Soil Nutrient Mapping was evaluated through a case study in which 430 soil samples, including available potassium (Kᵃᵐᵖ), phosphorus (Pᵃᵐᵖ), nitrogen (Nᵃᵐᵖ), pHᵃᵐᵖ, soil organic carbon (OCᵃᵐᵖ), electrical conductivity (ECᵃᵐᵖ), iron (Feᵃᵐᵖ), zinc (Znᵃᵐᵖ), copper (Cuᵃᵐᵖ), boron (Bᵃᵐᵖ), manganese (Mnᵃᵐᵖ), and sulfur (Sᵃᵐᵖ), were collected from agricultural fields in Amarapura Village, India. PySNM completed the automated spatial interpolation and map generation of the study area data in just 22&#xa0;s on a computer with an i9 processor and 32GB of RAM. PySNM was tested on different system configurations to identify the optimal setup. The tool also outperformed the automation features of Model Builder and ArcPy in ArcMap. Spatial variability analysis identified severe deficiencies in several nutrients: Bᵃᵐᵖ (100%), Mnᵃᵐᵖ (99.88%), Feᵃᵐᵖ (99.66%), Sᵃᵐᵖ (98.53%), Nᵃᵐᵖ (96.37%), OCᵃᵐᵖ (97.46%), and Znᵃᵐᵖ (91.7%). These results indicate that urgent action is required to maintain soil health. PySNM is cost-effective, cross-platform compatible, optimizes resources, and does not require specialized skills. It may be an alternative to traditional GIS software, especially suitable for least-developed and developing countries.</p>

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PySNM: an open-source python package for automated Spatial interpolation and soil nutrient mapping for sustainable agriculture— a case study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Ranga Rao Velamala,
  • Pawan Kumar Pant

摘要

The growing global population and increasing food demand require monitoring soil health and ensuring sustainable agriculture. Generating soil nutrient maps with traditional GIS methods is often a lengthy process, causing delays in decisions. This study introduces PySNM, a new, Python-based, free, and open-source tool designed to fully automate spatial interpolation (IDW) and generate soil nutrient maps. PySNM, a Python-based tool for Soil Nutrient Mapping was evaluated through a case study in which 430 soil samples, including available potassium (Kᵃᵐᵖ), phosphorus (Pᵃᵐᵖ), nitrogen (Nᵃᵐᵖ), pHᵃᵐᵖ, soil organic carbon (OCᵃᵐᵖ), electrical conductivity (ECᵃᵐᵖ), iron (Feᵃᵐᵖ), zinc (Znᵃᵐᵖ), copper (Cuᵃᵐᵖ), boron (Bᵃᵐᵖ), manganese (Mnᵃᵐᵖ), and sulfur (Sᵃᵐᵖ), were collected from agricultural fields in Amarapura Village, India. PySNM completed the automated spatial interpolation and map generation of the study area data in just 22 s on a computer with an i9 processor and 32GB of RAM. PySNM was tested on different system configurations to identify the optimal setup. The tool also outperformed the automation features of Model Builder and ArcPy in ArcMap. Spatial variability analysis identified severe deficiencies in several nutrients: Bᵃᵐᵖ (100%), Mnᵃᵐᵖ (99.88%), Feᵃᵐᵖ (99.66%), Sᵃᵐᵖ (98.53%), Nᵃᵐᵖ (96.37%), OCᵃᵐᵖ (97.46%), and Znᵃᵐᵖ (91.7%). These results indicate that urgent action is required to maintain soil health. PySNM is cost-effective, cross-platform compatible, optimizes resources, and does not require specialized skills. It may be an alternative to traditional GIS software, especially suitable for least-developed and developing countries.