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Securing the Earth’s soil assets for future generations requires a comprehensive, community-based approach that combines traditional wisdom with modern innovation. This chapter begins by emphasizing the urgent need for soil conservation and presents a vision that integrates Traditional Ecological Knowledge (TEK) with advanced digital technologies. Bridging these two domains enables the development of adaptive, sustainable, and resilient soil management systems. Advanced tools such as Artificial Intelligence (AI), Machine Learning (ML), Geographic Information Systems (GIS), Internet of Things (IoT) sensors, and remote sensing help bridge knowledge gaps. These technologies empower farmers, researchers, and policymakers to predict patterns of soil degradation and respond proactively, thus addressing the limitations of traditional methods across vast and diverse landscapes. At the same time, the value of TEK is recognized with traditional practices that have long sustained soil fertility, ecological balance, and social trust. However, these practices often face challenges of scalability and adaptability under rapidly changing climatic and land-use conditions. By merging TEK with AI-driven insights, a hybrid framework emerges that promotes ecological balance, operational efficiency, and long-term resilience. Through real-world applications, this chapter explores how this integration can enhance soil health, strengthen food security, reduce carbon emissions, and preserve cultural heritage.

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Hybrid Approaches to Soil Conservation: Integrating AI with Indigenous Knowledge for Sustainable Soil Conservation

  • 章节

  • 发表日期：

    2026年

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  • G. Jyoshna Rani,
  • K. B. Behura,
  • A. K. Pradhan,
  • S. Gowthami,
  • P. K. Paramaguru,
  • Tarannum,
  • R. Surya Vardhan Raju,
  • Y. Poorna Chandra Rao

摘要

Securing the Earth’s soil assets for future generations requires a comprehensive, community-based approach that combines traditional wisdom with modern innovation. This chapter begins by emphasizing the urgent need for soil conservation and presents a vision that integrates Traditional Ecological Knowledge (TEK) with advanced digital technologies. Bridging these two domains enables the development of adaptive, sustainable, and resilient soil management systems. Advanced tools such as Artificial Intelligence (AI), Machine Learning (ML), Geographic Information Systems (GIS), Internet of Things (IoT) sensors, and remote sensing help bridge knowledge gaps. These technologies empower farmers, researchers, and policymakers to predict patterns of soil degradation and respond proactively, thus addressing the limitations of traditional methods across vast and diverse landscapes. At the same time, the value of TEK is recognized with traditional practices that have long sustained soil fertility, ecological balance, and social trust. However, these practices often face challenges of scalability and adaptability under rapidly changing climatic and land-use conditions. By merging TEK with AI-driven insights, a hybrid framework emerges that promotes ecological balance, operational efficiency, and long-term resilience. Through real-world applications, this chapter explores how this integration can enhance soil health, strengthen food security, reduce carbon emissions, and preserve cultural heritage.