Linking New Information Technologies to Agricultural Economics: The Role of Artificial Intelligence Integration

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 Studies, Geography, and Planning, Maasai Mara University, 861-20500, Narok-Kenya. Author https://orcid.org/0000-0003-1481-4016
  • Bismark Agura Kayus Kisii University, Department of Curriculum, Instructions & Media, School of Education & Human Resource Development, P.O. Box 408-40200, Kisii-Kenya. Author

DOI:

https://doi.org/10.62486/latia2025326

Keywords:

Artificial Intelligence, Agricultural Economics, Precision Farming, Machine Learning, Food Security, Supply Chain Management

Abstract

Artificial Intelligence (AI) is revolutionizing agricultural economics by optimizing productivity, reducing costs, and enhancing decision-making processes. This paper explores the integration of AI technologies—such as machine learning, predictive analytics, and automation—into agricultural economic frameworks. AI-driven innovations, including precision farming, yield forecasting, and supply chain management, are reshaping agricultural practices by improving efficiency and sustainability. Furthermore, AI facilitates data-driven policymaking, enabling governments and stakeholders to address food security, market fluctuations, and resource allocation more effectively. Despite its benefits, AI adoption in agriculture faces challenges, including high implementation costs, data privacy concerns, and the digital divide between developed and developing regions. The study highlights case studies and real-world applications demonstrating AI’s impact on economic growth and sustainable agricultural development. The findings suggest that strategic investment in AI infrastructure, combined with supportive policies and education, can accelerate its adoption and maximize its economic benefits. Ultimately, AI integration holds the potential to transform agricultural economies by fostering innovation, resilience, and sustainability.

References

Chen, T., Lv, L., Wang, D., Zhang, J., Yang, Y., Zhao, Z., Wang, C., Guo, X., Chen, H., Wang, Q., Xu, Y., Zhang, Q., Du, B., Zhang, L., & Tao, D. (2023). Revolutionizing Agrifood Systems with Artificial Intelligence: A Survey. arxiv.org/pdf/2305.01899

Van Dijk, M., Morley, T., Rau, M. L., & Saghai, Y. (2021). A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nature Food. A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050 | Nature Food DOI: https://doi.org/10.1038/s43016-021-00322-9

De Wrachien, D., Schultz, B., & Goli, M. B. (2021). Impacts of population growth and climate change on food production and irrigation and drainage needs: A world‐wide view*. Irrigation and Drainage. https://onlinelibrary.wiley.com/doi/am-pdf/10.1002/ird.2597 DOI: https://doi.org/10.1002/ird.2597

Rejeb, A., Keogh, J. G., & Rejeb, K. (2022). Big data in the food supply chain: a literature review. Journal of Data, Information and Management, 4(1), 33-47. https://doi.org/10.1007/s42488-021-00064-0 DOI: https://doi.org/10.1007/s42488-021-00064-0

Chen, T., Lv, L., Wang, D., Zhang, J., Yang, Y., Zhao, Z., ... & Tao, D. (2024). Empowering agrifood system with artificial intelligence: A survey of the progress, challenges and opportunities. ACM Computing Surveys, 57(2), 1-37. https://doi.org/10.1145/3698589 DOI: https://doi.org/10.1145/3698589

Rejeb, A., Rejeb, K., Zailani, S., Keogh, J. G., & Appolloni, A. (2022). Examining the interplay between artificial intelligence and the agri-food industry. Artificial intelligence in agriculture, 6, 111-128. https://doi.org/10.1016/j.aiia.2022.08.002 DOI: https://doi.org/10.1016/j.aiia.2022.08.002

Taneja, A., Nair, G., Joshi, M., Sharma, S., Sharma, S., Jambrak, A. R., ... & Phimolsiripol, Y. (2023). Artificial intelligence: Implications for the agri-food sector. Agronomy, 13(5), 1397. https://doi.org/10.3390/agronomy13051397 DOI: https://doi.org/10.3390/agronomy13051397

Nath, P. C., Mishra, A. K., Sharma, R., Bhunia, B., Mishra, B., Tiwari, A., ... & Sridhar, K. (2024). Recent advances in artificial intelligence towards the sustainable future of agri-food industry. Food Chemistry, 138945. https://doi.org/10.1016/j.foodchem.2024.138945 DOI: https://doi.org/10.1016/j.foodchem.2024.138945

Ben Ayed, R. & Hanana, M. (2021). Artificial intelligence to improve the food and agriculture sector. Journal of Food Quality. https://doi.org/10.1155/2021/5584754 DOI: https://doi.org/10.1155/2021/5584754

Kutyauripo, I., Rushambwa, M., & Chiwazi, L. (2023). Artificial intelligence applications in the agrifood sectors. Journal of Agriculture and Food Research, 11, 100502. https://doi.org/10.1016/j.jafr.2023.100502 DOI: https://doi.org/10.1016/j.jafr.2023.100502

Kaur, K., Priyanka, Kaur, G., Singh, B., Sehgal, S., & Trehan, S. (2023). Artificial intelligence (AI) as a transitional tool for sustainable food systems. In Sustainable Food Systems (Volume II) SFS: Novel Sustainable Green Technologies, Circular Strategies, Food Safety & Diversity (pp. 305-328). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-46046-3_15 DOI: https://doi.org/10.1007/978-3-031-46046-3_15

Khan, N., Ray, R. L., Kassem, H. S., Hussain, S., Zhang, S., Khayyam, M., ... & Asongu, S. A. (2021). Potential role of technology innovation in transformation of sustainable food systems: A review. Agriculture, 11(10), 984. https://doi.org/10.3390/agriculture11100984 DOI: https://doi.org/10.3390/agriculture11100984

Gwagwa, A., Kazim, E., Kachidza, P., Hilliard, A., Siminyu, K., Smith, M., & Shawe-Taylor, J. (2021). Road map for research on responsible artificial intelligence for development (AI4D) in African countries: The case study of agriculture. https://doi.org/10.1016/j.patter.2021.100381 DOI: https://doi.org/10.1016/j.patter.2021.100381

Xu, Y., & Yang, J. (2025, January). Revolutionizing Rural Development Through Ai-Powered Agricultural Practices. In The 3rd International scientific and practical conference “Global trends in the development of educational systems”(January 21–24, 2025) Bergen, Norway. International Science Group. 2025. 321 p. (p. 105).

Altayeb, J. M., Eleyan, H., Wishah, N. D., Elmahmoum, A. E., Khalil, A. J., Abu-Nasser, B. S., & Abu-Naser, S. S. (2024). AI-Driven Innovations in Agriculture: Transforming Farming Practices and Outcomes. www.ijeais.org/ijaar

Agrawal, J. & Arafat, M. Y. (2024). Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture. Drones (2504-446X). https://www.researchgate.net/publication/385734156 DOI: https://doi.org/10.3390/drones8110664

Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in the Agriculture Sector. Advanced Agrochem. https://doi.org/10.1016/j.aac.2022.10.001 DOI: https://doi.org/10.1016/j.aac.2022.10.001

Gupta, D. K., Pagani, A., Zamboni, P., & Singh, A. K. (2024). AI-powered revolution in plant sciences: advancements, applications, and challenges for sustainable agriculture and food security. Exploration of Foods and Foodomics, 2(5), 443-459. https://doi.org/10.37349/eff.2024.00045 DOI: https://doi.org/10.37349/eff.2024.00045

Avasthi, S., Chauhan, R., & Tripathi, S. L. (2025). Artificial intelligence-powered agriculture and sustainable practices in developing countries. In Hyperautomation in Precision Agriculture (pp. 49-62). Academic Press. https://doi.org/10.1016/B978-0-443-24139-0.00005-9 DOI: https://doi.org/10.1016/B978-0-443-24139-0.00005-9

Titirmare, S., Margal, P. B., Gupta, S., & Kumar, D. (2024). AI-powered predictive analytics for crop yield optimization. Agriculture 4.0. DOI: https://doi.org/10.1201/9781003570219-5

Jayadatta, S. (2024). A Study on AI-Driven Agricultural Innovations for Rural and Industrial Development in Indian Context. Journal of Rural and Industrial Development.

Grace Doye, D. (1981). Economic Evaluation of Alternative Sheep Production Systems in Oklahoma.

de Campos, J. L., Kates, A., Steinberger, A., Sethi, A., Suen, G., Shutske, J., ... & Ruegg, P. L. (2021). Quantification of antimicrobial usage in adult cows and pre-weaned calves on 40 large Wisconsin dairy farms using dose-based and mass-based metrics. Journal of Dairy Science, 104(4), 4727-4745. https://doi.org/10.3168/jds.2020-19315 DOI: https://doi.org/10.3168/jds.2020-19315

Neupane, S., Talley, J. L., Swiger, S. L., Pickens, V., Park, Y., & Nayduch, D. (2024). Bacterial Communities of House Flies from Beef and Dairy Cattle Operations Are Diverse and Contain Pathogens of Medical and Veterinary Importance. Current Microbiology, 81(12), 1-14. https://doi.org/10.1007/s00284-024-03870-y DOI: https://doi.org/10.1007/s00284-024-03870-y

Amma, Z., Reiczigel, J., Fébel, H., & Solti, L. (2024). Relationship between milk yield and reproductive parameters on three Hungarian dairy farms. Veterinary Sciences. https://doi.org/10.3390/vetsci11050218 DOI: https://doi.org/10.3390/vetsci11050218

Cantor, M. C., Renaud, D. L., Neave, H. W., & Costa, J. H. C. (2022). Feeding behaviour and activity levels are associated with recovery status in dairy calves treated with antimicrobials for Bovine Respiratory Disease. Scientific reports. https://doi.org/10.1038/s41598-022-08131-1 DOI: https://doi.org/10.1038/s41598-022-08131-1

Becker, S. & Fanzo, J. (2023). Population and food systems: what does the future hold?. Population and Environment. https://doi.org/10.1007/s11111-023-00431-6 DOI: https://doi.org/10.1007/s11111-023-00431-6

Kumar, L., Naresh, R. K., Tiwari, H., Kataria, S. K., Saharan, S., Reddy, B. R., ... & Singh, R. P. (2022). Millets for food and nutritional security in the context of climate resilient agriculture: A Review. Int J Plant Soil Sci, 34(23), 939-953. https://www.academia.edu/download/112239528/5012.pdf DOI: https://doi.org/10.9734/ijpss/2022/v34i232504

Siegel, F. R., & Siegel, F. R. (2021). Global Warming and Water 2050: More People, Yes; Less Ice, Yes; More Water, Yes; More Fresh Water, Probably; More Accessible Fresh Water?. The Earth’s Human Carrying Capacity: Limitations Assessed, Solutions Proposed, 71-85. https://doi.org/10.1007/978-3-030-73476-3_7 DOI: https://doi.org/10.1007/978-3-030-73476-3_7

Mavani, N. R., Ali, J. M., Othman, S., Hussain, M. A., Hashim, H., & Rahman, N. A. (2022). Application of artificial intelligence in food industry—a guideline. Food Engineering Reviews, 14(1), 134-175. https://doi.org/10.1007/s12393-021-09290-z DOI: https://doi.org/10.1007/s12393-021-09290-z

Halder, S., Mamun, Q., Mahboubi, D. A., Walsh, P., Islam, M. Z., & Islam, R. A Survey on Ai Enabled Secure Social Industrial Internet of Things in Agri-Food Supply Chain. Available at SSRN 5096170. http://dx.doi.org/10.2139/ssrn.5096170 DOI: https://doi.org/10.2139/ssrn.5096170

Elbasi, E., Mostafa, N., AlArnaout, Z., Zreikat, A. I., Cina, E., Varghese, G., ... & Zaki, C. (2022). Artificial intelligence technology in the agricultural sector: A systematic literature review. IEEE access, 11, 171-202. 10.1109/ACCESS.2022.3232485 DOI: https://doi.org/10.1109/ACCESS.2022.3232485

Spanaki, K., Sivarajah, U., Fakhimi, M., Despoudi, S., & Irani, Z. (2022). Disruptive technologies in agricultural operations: A systematic review of AI-driven AgriTech research. Annals of Operations Research, 308(1), 491-524. https://doi.org/10.1007/s10479-020-03922-z DOI: https://doi.org/10.1007/s10479-020-03922-z

Ganeshkumar, C., Jena, S. K., Sivakumar, A., & Nambirajan, T. (2023). Artificial intelligence in agricultural value chain: review and future directions. Journal of Agribusiness in Developing and Emerging Economies, 13(3), 379-398.https://doi.org/10.1108/JADEE-07-2020-0140 DOI: https://doi.org/10.1108/JADEE-07-2020-0140

Ryan, M., Isakhanyan, G., & Tekinerdogan, B. (2023). An interdisciplinary approach to artificial intelligence in agriculture. NJAS: Impact in Agricultural and Life Sciences, 95(1), 2168568. tandfonline.com DOI: https://doi.org/10.1080/27685241.2023.2168568

Bhat, S. A. & Huang, N. F. (2021). Big data and ai revolution in precision agriculture: Survey and challenges. Ieee Access. ieee.org DOI: https://doi.org/10.1109/ACCESS.2021.3102227

Mohr, S. & Kühl, R. (2021). Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior. Precision Agriculture. https://doi.org/10.1007/s11119-021-09814-x DOI: https://doi.org/10.1007/s11119-021-09814-x

Ryan, M. (2023). The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature. Ai & Society. https://doi.org/10.1007/s00146-021-01377-9 DOI: https://doi.org/10.1007/s00146-021-01377-9

Abiri, R., Rizan, N., Balasundram, S. K., Shahbazi, A. B., & Abdul-Hamid, H. (2023). Application of digital technologies for ensuring agricultural productivity. Heliyon, 9(12). https://doi.org/10.1016/j.heliyon.2023.e22601 DOI: https://doi.org/10.1016/j.heliyon.2023.e22601

Sridhar, A., Balakrishnan, A., Jacob, M. M., Sillanpää, M., & Dayanandan, N. (2023). Global impact of COVID-19 on agriculture: role of sustainable agriculture and digital farming. Environmental Science and Pollution Research, 30(15), 42509-42525. https://doi.org/10.1007/s11356-022-19358-w DOI: https://doi.org/10.1007/s11356-022-19358-w

Downloads

Published

2024-12-03

Issue

Section

Original

How to Cite

1.
Chavula P, Kayusi F, Agura Kayus B. Linking New Information Technologies to Agricultural Economics: The Role of Artificial Intelligence Integration. LatIA [Internet]. 2024 Dec. 3 [cited 2025 Aug. 17];2:326. Available from: https://latia.ageditor.uy/index.php/latia/article/view/326