Agricultural Modeling Under Climate Change: Mapping the Transition from Crop Simulation to Artificial Intelligence-Based Decision Systems

Pablo de Frutos  Madrazo; A.M.  Yessirkepova; Pedro  Antonio Martin Cervantes; D.M.  Makhmud

doi:10.48161/qaj.v6n3a2542

Authors

Pablo de Frutos Madrazo Department of Applied Economics, University of Valladolid, 47002 Valladolid, Spain; https://orcid.org/0000-0002-8206-984X
A.M. Yessirkepova Department of Economics, M. Auezov South Research Kazakhstan University, Shymkent 160012, Republic of Kazakhstan; https://orcid.org/0000-0002-5028-238X
Pedro Antonio Martin Cervantes Department of Financial Economics and Accounting, University of Valladolid, Valladolid 47011, Spain. https://orcid.org/0000-0002-8375-1613
D.M. Makhmud Department of Economics, M. Auezov South Research Kazakhstan University, Shymkent 160012, Republic of Kazakhstan; https://orcid.org/0009-0005-6737-903X

DOI:

https://doi.org/10.48161/qaj.v6n3a2542

Keywords:

Agricultural modeling, Climate change adaptation, Bibliometric analysis, Artificial intelligence, Crop simulation models.

Abstract

This study examines the knowledge evolution of agricultural modeling under climate change through a bibliometric science mapping approach. The analysis is performed based on 860 documents published between the years 1992-2025 and indexed in the Scopus database, which are processed with the Bibliometrix package in R. A PRISMA-based screening procedure was applied to ensure thematic relevance and transparency. The study identifies publication dynamics, the leading sources, productive authors, country level contributions, collaboration patterns, as well as thematic development. The results show a sharp increase in research activity after 2020, indicating that this domain has moved from a limited technical niche to a rapidly expanding interdisciplinary field. The most active publication outlets are concentrated in environmental, agricultural, water management, and digital technology-oriented journals, with Science of the Total Environment emerging as the most productive source. Thematic mapping shows four large conceptually defined directions: climate change; agriculture; crops; and learning systems. These clusters show that the field is moving from process-based crop and resource models toward spatial, data-driven, and AI-assisted systems. The findings also reveal an important imbalance. Environmental, crop-oriented, and digital themes are expanding quickly, while their economic interpretation remains less developed. For agricultural economists this would be beneficial for future research to directly link the knowledge and understanding of the climate and ecosystem needs of agriculture with the contribution of food security and market resilience to scarcity, adaptation costs, investment priorities, and risks related to agri-food supply-chains.

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