The impact of AI-enabled climate intelligence on agricultural adaptation and farm resilience
DOI:
https://doi.org/10.68065/joccd.05.01.171Keywords:
Agricultural Adaptation, Artificial Intelligence, Climate Intelligence, Farm Resilience, Predictive Analytics, Smart AgricultureAbstract
This study examined the impact of climate intelligence, integrated with an artificial intelligence (AI) system, on agricultural adaptation and farm resilience. The research used a quantitative cross-sectional design with a structured questionnaire using a 5-point Likert scale, along with qualitative questions, and a sample of 300 Farmers and Agricultural Stakeholders. The climate intelligence, agricultural adaptation, and farm resilience parameters were determined using AI. The data were analyzed using descriptive statistics, reliability analysis, Pearson correlation, and regression analysis. AI climate intelligence was strongly and positively correlated with both agricultural adaptation (r = 0.68, p < .001) and farm resilience (r = 0.64, p < .001). Regression analysis found that AI-based climate intelligence was significantly related to agricultural adaptation (β = 0.68, p < .001, R² = .462) and farm resilience (β = 0.64, p < .001, R² = .410).
Downloads
References
Ajiboye, B. O., Ayojimi, W., Kolapo, A., Adekanye, K. O., Sibanda, M., & Khumalo, N. Z. (2026). Leveraging climate-smart agriculture for improved resource-use efficiency: Evidence from rice farmers in Kwara State, Nigeria. Frontiers in Climate, 8, 1878309. https://doi.org/10.3389/fclim.2026.1878309
Alahyari, M. S., Berjan, S., El Bilali, H., Ben Hassen, T., & Marzban, S. (2025). Assessing the use of ChatGPT among agri-food researchers: A global perspective. Journal of Agriculture and Food Research, 19, 101616. https://doi.org/10.1016/j.jafr.2024.101616
Dhanaraj, R. K., Maragatharajan, M., & Sureshkumar, A. (2025). On-device AI for climate-resilient farming with intelligent crop yield prediction using lightweight models on smart agricultural devices. Scientific Reports, 15, 31195. https://doi.org/10.1038/s41598-025-16014-4
El Bilali, A., Hadri, A., Taleb, A., Tanarhte, M., El Khalki, E. M., & Kharrou, M. H. (2025). A novel hybrid modeling approach based on empirical methods, PSO, XGBoost, and multiple GCMs for forecasting long-term reference evapotranspiration in a data scarce-area. Computers and Electronics in Agriculture, 232, 110106. https://doi.org/10.1016/j.compag.2025.110106
Jumadi, J., Musiyam, M., Amin, C., Fikriyah, V. N., Rohman, A., Nugroho, M. T., & Rudiyanto, R. (2026). Can machine learning see maladaptation? A systematic evidence map of AI in Global South agricultural adaptation to climate change. Forum Geografi, 40(3), 489–510. https://doi.org/10.23917/forgeo.19002
Kolapo, A., & Otitoju, M. A. (2026). Digital innovations in climate-smart agriculture: A review of technologies for climate adaptation and resilience with implications for Sub-Saharan Africa. Frontiers in Agronomy, 8, 1902177. https://doi.org/10.3389/fagro.2026.1902177
Mandour, H., Helmi, R. Y., Safhi, F. A., Alshaya, D. S., Jalal, A. S., Alharbi, K., Aljwaizea, N. I., Al Aboud, N. M., Fayad, E., Sait, L. M., & Hassanin, A. A. (2025). Artificial intelligence for climate-smart agriculture: Enhancing food security and plant adaptation. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 53(4), 14796. https://doi.org/10.15835/nbha53414796
Mmbando, G. S. (2025). Harnessing artificial intelligence and remote sensing in climate-smart agriculture: The current strategies needed for enhancing global food security. Cogent Food & Agriculture, 11(1), https://doi.org/10.1080/23311932.2025.2454354
Mollel, M. M., Quiroz, L. F., Varley, C., Firestine, A., McLoughlin, M.-E., Kafunah, J., Kharkar, S., O'Farrell, J., Ndlovu, N., Johnston, A., McKeown, P. C., Brychkova, G., Murray, U., Leiva, S., & Spillane, C. (2025). Digital technologies to accelerate the impact of climate smart agriculture by next-generation farmers in Africa. Frontiers in Sustainable Food Systems, 9, 1462328. https://doi.org/10.3389/fsufs.2025.1462328
Mustafa, G., & Alotaibi, B. A. (2024). Fostering adaptation to climate change among farmers in Pakistan: The influential role of farmers’ climate change knowledge and adaptive capacity. Frontiers in Sustainable Food Systems, 8, 1471238. https://doi.org/10.3389/fsufs.2024.1471238
Nawaz, M., & Babar, M. I. K. (2024). IoT and AI: A panacea for climate change-resilient smart agriculture. Discover Applied Sciences, 6, 517. https://doi.org/10.1007/s42452-024-06228-y
Niu, Z., Chen, J., & Feng, W. (2025). Digital agriculture and climate resilience in agri-food systems: Empirical evidence from China. Food and Energy Security, 14, e70178. https://doi.org/10.1002/fes3.70178 (Wiley Online Library)
Roy, S., Hoque, A., Saikia, P., & Padhiary, M. (2025). Climate-smart agriculture: AI-based solutions for enhancing crop resilience and reducing environmental impact. Asian Research Journal of Agriculture, 18(1), 291–310. https://doi.org/10.9734/arja/2025/v18i1665
Shah, S. A. A., Cheng, J., & Haider, I. (2026). Adapting to climate change: Drivers of technological and socio-economic adaptation strategies among Punjab’s grain growers. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-08321-6
Sharma, G. D., Shah, M. I., Chopra, R., Rao, A., & Shahzad, U. (2025). Impact of technological advancement and greener energy on sustainable agriculture in Asia: Evidence from selected Asian countries. Sustainable Development, 33(1), 221–237. https://doi.org/10.1002/sd.3106
Sharma, H., & Kaur, S. (2025). Deep learning for sustainable development across climate, energy, agriculture and urban systems. Discover Sustainability, 6, 1408. https://doi.org/10.1007/s43621-025-02186-6
Sharma, S. K. (2025). Smart agriculture revolution. In M. Kumar, G. Srivastava, A. K. Singh, & K. Dubey (Eds.), AI-based advanced optimization techniques for edge computing. Wiley. https://doi.org/10.1002/9781394287062.ch10
Tunio, R. A., Li, D., & Khan, N. K. (2024). Maximizing farm resilience: The effect of climate smart agricultural adoption practices on food performance amid adverse weather events. Frontiers in Sustainable Food Systems, 8, 1423702. https://doi.org/10.3389/fsufs.2024.1423702
Ullah, I., & Aruga, K. (2026). Exploring the role of nature-based solutions for climate adaptation in the agriculture sector. Discover Agriculture, 4, 266. https://doi.org/10.1007/s44279-026-00704-2
Ullah, W., Khurshid, M., & Shaofeng, Z. (2026). Adoption of climate smart agriculture for enhancing socio-ecological resilience: A systematic review of evidence from Punjab, Pakistan. Frontiers in Environmental Science, 14, 1853469. https://doi.org/10.3389/fenvs.2026.1853469
Vishwakarma, S. K., & Kumar, V. (2025). Agentic AI-driven decision-support framework for climate-responsive agricultural adaptation using reinforcement learning. International Journal of Applied Mathematics, 38(6S). https://doi.org/10.12732/ijam.v38i6s.399
Yuan, Y., & Sun, X. (2025). Artificial intelligence and sustainable agricultural livelihoods: Enhancing farmers’ decision-making and resilience.
Downloads
Published
License
Copyright (c) 2026 Zunair Imran, Touheed Iqbal (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
