AI Application in Climate-Smart Agricultural Technologies: A Synthesis Study

Authors

  • Petros Chavula World Agroforestry Centre, St Eugene Office Park 39P Lake Road, P.O. Box 50977, Kabulonga, Lusaka, Zambia & African Centre of Excellence for Climate-Smart Agriculture and Biodiversity Conservation, Haramaya University, Dire-Dawa, Ethiopia Author https://orcid.org/0000-0002-7153-8233
  • Fredrick Kayusi Department of Environmental Sciences, School of Environmental and Earth Sciences, Pwani University, Kilifi, Kenya & Department of Environmental Studies, Geography and Planning, Maasai Mara University, Narok, Kenya Author https://orcid.org/0000-0003-1481-4016
  • Gilbert Lungu School of Natural Resources Management, Copperbelt University, P.O. Box 21692, Kitwe, Zambia Author https://orcid.org/0009-0008-7767-6371
  • Hockings Mambwe World Agroforestry Centre, St Eugene Office Park 39P Lake Road, P.O. Box 50977, Kabulonga, Lusaka, Zambia Author https://orcid.org/0009-0009-2826-807X
  • Agnes Uwimbabazi Department of Nature Conservation Rwanda Polytechnic-Integrated Polytechnic Regional College of Kitabi, Rwanda, P.O. Box 330 Huye Rwanda & School of Natural Resources Management, Copperbelt University, P.O. Box 21692, Kitwe, Zambia Author https://orcid.org/0009-0001-3415-0192

DOI:

https://doi.org/10.62486/latia2025330

Keywords:

Climate-smart agriculture, Artificial intelligence, Machine learning, Precision farming, Greenhouse gas emissions, Sustainability, Climate resilience, Digital agriculture, Food security

Abstract

Climate change poses significant challenges to global agriculture, necessitating innovative solutions to enhance sustainability and productivity. Artificial intelligence (AI) has emerged as a key enabler in climate-smart agricultural technologies (CSAT), offering data-driven approaches to optimize resource use, mitigate climate risks, and improve decision-making. This study aims to evaluate AI's integration into CSAT, focusing on its applications, benefits, and adoption challenges, particularly in climate-vulnerable regions. A bibliographic review employing machine learning (ML) and natural language processing (NLP) techniques was conducted to analyze over 40,000 scientific articles from global academic databases. Topic modeling and classification algorithms were applied to identify key trends, adoption barriers, and implementation pathways for AI-driven CSAT. The study also incorporated expert validation through the Delphi method to refine AI-generated insights and ensure their alignment with real-world agricultural challenges. Findings indicate that AI enhances decision-making in conservation agriculture, precision farming, water management, and market intelligence. AI-powered tools facilitate early pest detection, optimize irrigation schedules, and provide real-time climate advisory services, significantly improving agricultural resilience and food security. However, major barriers to AI adoption include high implementation costs, limited digital literacy, and inadequate infrastructure, particularly in low-income regions. Despite these challenges, AI-driven CSAT presents significant potential to transform agriculture, especially in climate-affected areas. Strategic investments in digital literacy, infrastructure development, and supportive policy frameworks are essential to facilitate AI adoption. Strengthening interdisciplinary collaboration among researchers, policymakers, and farmers will be crucial in advancing sustainable agricultural practices and ensuring long-term food security.

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Published

2025-03-25

Issue

Vol. 3 (2025): LatIA

Section

Review

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Copyright (c) 2025 Petros Chavula, Fredrick Kayusi , Gilbert Lungu , Hockings Mambwe , Agnes Uwimbabazi (Author)

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How to Cite

1.
Chavula P, Kayusi F, Lungu G, Mambwe H, Uwimbabazi A. AI Application in Climate-Smart Agricultural Technologies: A Synthesis Study. LatIA [Internet]. 2025 Mar. 25 [cited 2025 Apr. 6];3:330. Available from: https://latia.ageditor.uy/index.php/latia/article/view/330