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Climate change is fundamentally reshaping global agricultural dynamics, threatening food security through rising temperatures, erratic precipitation, extreme weather events, and shifting ecological zones. This chapter provides a comprehensive analysis of how integrating Artificial Intelligence (AI) with climate-adaptive agricultural strategies can revolutionize food production, resource management, and resilience building in the face of climate variability. This chapter begins by examining the diverse and intensifying impacts of climate change on agriculture, with a particular focus on yield instability and increased vulnerability to pests and diseases. It then introduces the foundational principles of climate-adaptive agriculture, emphasizing risk assessment, ecological resilience, and resource efficiency. Established adaptation practices such as crop diversification, efficient water management, agroforestry, conservation agriculture, climate-resilient crop varieties, and early warning systems are explored as essential responses. The transformative potential of AI is then analyzed in depth, highlighting its role in enabling, enhancing, and scaling these climate-adaptive strategies. Key applications include precision agriculture for input optimization, remote sensing and analytics for yield forecasting, and predictive models for early warning systems. Furthermore, this chapter demonstrates how AI can be synergistically integrated with adaptive practices, including selecting optimal crop combinations, improving irrigation scheduling, crop diversification, designing agroforestry systems, and accelerating climate-resilient breeding programs. Critical challenges, including limited data availability, digital infrastructure gaps, algorithmic fairness, ethical concerns, and farmer adoption, are critically analyzed. Looking forward, this chapter explores emerging innovations, including edge computing, quantum computing, predictive breeding, explainable AI (XAI), blockchain-based supply chain traceability, and AI-driven policy modeling. Together, these insights offer a strategic roadmap for policymakers, researchers, industry leaders, and farming communities to collaboratively build climate-resilient, intelligent, and future-ready agricultural systems.

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Computational Intelligence in Climate-Adaptive Agriculture: Pathways to Resilient Food Systems

  • 章节

  • 发表日期：

    2026年

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  • AFM Tariqul Islam,
  • Kowshik Kumar Saha,
  • M. Golam Mahboob

摘要

Climate change is fundamentally reshaping global agricultural dynamics, threatening food security through rising temperatures, erratic precipitation, extreme weather events, and shifting ecological zones. This chapter provides a comprehensive analysis of how integrating Artificial Intelligence (AI) with climate-adaptive agricultural strategies can revolutionize food production, resource management, and resilience building in the face of climate variability. This chapter begins by examining the diverse and intensifying impacts of climate change on agriculture, with a particular focus on yield instability and increased vulnerability to pests and diseases. It then introduces the foundational principles of climate-adaptive agriculture, emphasizing risk assessment, ecological resilience, and resource efficiency. Established adaptation practices such as crop diversification, efficient water management, agroforestry, conservation agriculture, climate-resilient crop varieties, and early warning systems are explored as essential responses. The transformative potential of AI is then analyzed in depth, highlighting its role in enabling, enhancing, and scaling these climate-adaptive strategies. Key applications include precision agriculture for input optimization, remote sensing and analytics for yield forecasting, and predictive models for early warning systems. Furthermore, this chapter demonstrates how AI can be synergistically integrated with adaptive practices, including selecting optimal crop combinations, improving irrigation scheduling, crop diversification, designing agroforestry systems, and accelerating climate-resilient breeding programs. Critical challenges, including limited data availability, digital infrastructure gaps, algorithmic fairness, ethical concerns, and farmer adoption, are critically analyzed. Looking forward, this chapter explores emerging innovations, including edge computing, quantum computing, predictive breeding, explainable AI (XAI), blockchain-based supply chain traceability, and AI-driven policy modeling. Together, these insights offer a strategic roadmap for policymakers, researchers, industry leaders, and farming communities to collaboratively build climate-resilient, intelligent, and future-ready agricultural systems.