Climate Smart Agriculture (C.S.A.)
DOI:
https://doi.org/10.66635/wc7fc241Keywords:
Climate-Smart Agriculture, Crop Yield Prediction, Climate Risk Analytics, Machine Learning, Sustainable AgricultureAbstract
Climate variability has emerged as a critical challenge to agricultural sustainability, particularly in regions where crop productivity is highly sensitive to rainfall and temperature fluctuations. This study investigates the influence of historical climate variability on crop yield, develops a machine-learning-based yield prediction model, and constructs a Climate Risk Index (CRI) to quantify climate stress within a Climate-Smart Agriculture framework. Using multi-year climatic and crop yield data (2019–2025), the analysis first examines linear relationships between absolute climatic variables and yield outcomes, followed by anomaly-based assessments to capture deviation-driven impacts.
The results indicate that absolute rainfall and temperature values exhibit weak linear correlations with crop yield, whereas anomaly-based climatic deviations demonstrate moderate sensitivity, particularly for moisture-dependent crops such as pulses and oilseeds. A Random Forest regression model is implemented to capture non-linear climate–yield interactions, achieving high predictive accuracy. However, feature importance analysis reveals that crop-specific structural characteristics play a dominant role in yield determination, while climatic variables contribute secondary but meaningful effects.
To quantify climate stress systematically, a standardized CRI is developed using rainfall and temperature deviations. Comparative analysis between high-risk and low-risk years shows disproportionate yield losses for pulses and oilseeds, while cereals remain relatively stable under moderate climatic stress. Scenario-based projections further suggest that modest warming conditions may not drastically affect cereal productivity but could intensify vulnerability in moisture-sensitive crops.
Overall, the study demonstrates that integrating anomaly-based climate analysis, machine learning modeling, and quantitative risk indexing provides a comprehensive and scalable framework for climate-resilient agricultural planning and policy design.
References
1.CHINEMEREM, C. M. (2025). ROLE OF CLIMATE SMART AGRICULTURE (CSA) IN ADDRESSING CHALLENGES OF FOOD SECURITY AND CLIMATE CHANGE.
2.Balakrishnan, P., & Marimuthu, S. (2026). Climate-Smart Agriculture and Sustainable Land Management. In Climate Change, Land Degradation, and Sustainability: Insight Towards Innovative Solutions (pp. 327-351). Cham: Springer Nature Switzerland.
3.Olatunbosun, A., Ismaila, E. O., Adeoba, M. I., Oluwadare, O. E., Arayombo, T. J., Oladunni, A., ... & Igwenagu, M. O. (2026). AI-Enabled Climate-Smart Agriculture for Smallholder Resilience in Sub-Saharan Africa: A Systematic Review. Journal of Global Agriculture and Ecology, 18(1), 64-78.
4.Ingole, A. D. (2026). AI and IoT and Smart Sensor for Automated Plant Health Monitoring. Anusandhanvallari, 15-33.
5.Recheal, P., Michael, G., & Matthew, J. (2026). Self-Healing and Survivable Wireless Sensor Networks for IoT-Enabled Precision Agriculture.
6.Mehdizadeh, S. A., Moalezadeh, J. M., & Abin, A. (2026). Energy-Autonomous IoT-based Wireless Sensor Networking Architecture for Plant Health Monitoring and Precision Irrigation in Sugarcane. Smart Agricultural Technology, 101800.
7.Samha, A. K., Alzaben, N., Abaker, M., Derouez, F., Bedewi, W., Alahmadi, M., ... & Al Sadig, M. (2025). Advanced Crop Health Monitoring for Smart Agriculture Using Fusion of Transfer Learning With Attentive Convolutional Recurrent Neural Network on Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
8.Swarnkar, S. K., Dewangan, L., Dewangan, O., Prajapati, T. M., & Rabbi, F. (2023, September). AI-enabled crop health monitoring and nutrient management in smart agriculture. In 2023 6th international conference on contemporary computing and informatics (IC3I) (Vol. 6, pp. 2679-2683). IEEE.
9.Sahoo, R. N. (2022). Sensor-based monitoring of soil and crop health for enhancing input use efficiency. In Food, Energy, and Water Nexus: A Consideration for the 21st Century (pp. 129-147). Cham: Springer International Publishing.
10.Morchid, A., Qjidaa, H., Alami, R. E., Mobayen, S., Skruch, P., & Bossoufi, B. (2026). Smart irrigation-based internet of things and cloud computing technologies for sustainable farming. Scientific Reports.
11.Bastam, M., Majeed, D. H., & Babagoli, M. (2026). A Hybrid Ensemble Framework for Smart Irrigation: Optimizing Water Management in Precision Agriculture. Int. J. Eng. Trans. B Appl, 39(3), 705-726.
12.Swain, S. R., & Gupta, R. K. (2026). Smart Water, Soil, and Environment Management System: An Approach Toward Sustainable Farming. In Smart Horticulture Production (pp. 239-252). Apple Academic Press.
13.Sikka, A. K., Islam, A., & Rao, K. V. (2018). Climate‐smart land and water management for sustainable agriculture. Irrigation and Drainage, 67(1), 72-81.
14.Reza, M. N., Lee, K. H., Karim, M. R., Haque, M. A., Bicamumakuba, E., Dey, P. K., ... & Chung, S. O. (2025). Trends of soil and solution nutrient sensing for open field and hydroponic cultivation in facilitated smart agriculture. Sensors, 25(2), 453.
15.Thilakarathne, N. N., Bakar, M. S. A., Abas, P. E., & Yassin, H. (2025). Internet of things enabled smart agriculture: Current status, latest advancements, challenges and countermeasures. Heliyon, 11(3).
16.Elbahnasawy, M., Shamseldin, T., Mansour, A. E., Alkady, Y., & Elashmawi, W. H. (2026). Mobile Mapping Systems: Architecture and Technologies in Smart Agriculture. In Precision and Intelligence in Agriculture: Advanced Technologies for Sustainable Farming (pp. 97-130). IGI Global Scientific Publishing.
17.Ngonjock, J. M., Ali, E., & Soh Wenda, B. D. (2026). Climate-smart agriculture adoption and technical efficiency of smallholder farms in Northern Togo. Mitigation and Adaptation Strategies for Global Change, 31(1), 4.
18.Ishieze, P. U., Enyi, J. I., Adewuyi, S. O., Baiyeri, K. P., Ishieze, H. N., & Aruah, B. C. Machine Intelligence in Horticulture: Automating Pest and Disease Detection in Solanaceous Plants.
19.Abbas, M., Sarwar, N., Hussain, S., Nazi, T., Aftab, J., Saif, S., ... & Ahmad, J. N. (2026). Pest and Disease Management Through Precision Approaches. In Artificial Intelligence and Data Sciences for Precision Agriculture (pp. 39-58). Cham: Springer Nature Switzerland.
20.Srivastav, A. K., & Das, P. (2025). Pest Control and Disease Management: Smart Solutions. In Biotechnology and IoT in Agriculture and Food Production: Green Innovation (pp. 85-94). Berkeley, CA: Apress.
21.Gill, R., & Chawla, P. (2021). A review on various techniques to transform traditional farming to precision agriculture. Turkish Journal of Computer and Mathematics Education, 12(2), 131-135.
22.Bhardwaj, S., Gupta, G., Tomar, A., Tiwari, M., Ahmad, S., & Azrour, M. (2026). Smart Agro-Tech: Using IoT-Powered Intelligence and Sustainable AI Solutions to Revolutionize Crop Management. In IoT and Advanced Intelligence Computation for Smart Agriculture (pp. 163-178). CRC Press.
23.Hu, Y., Koondhar, M. A., & Kong, R. (2023). From traditional to smart: exploring the effects of smart agriculture on green production technology diversity in family farms. Agriculture, 13(6), 1236.
24.Zhang, Y., Dai, J., & Dong, H. (2025). Multi-objective integrated cotton cultivation (MOICC): A synergistic framework for sustainable production. Journal of Integrative Agriculture.
25.Song, Y., Song, Y., & Wang, Q. (2026). Smart Agriculture Development: How Can Rural Digital Transformation Enhance the Resilience of Food Security?. Foods, 15(3), 426.
26.Lu, Z., Dong, J., Li, N., Feng, H., Gou, D., & Xu, M. (2026). Land Consolidation and Smart Agriculture Synergy for Food Security: Pathways Toward Agricultural Carbon Neutrality in China. Agriculture, 16(3), 287.
27.Fauzi, N. A. S. B. M., Zain, S. K. M. B. S. M., Abdullah, N. A. I. B., Ali, N. B., Shah, N. F. B. A., & Ramly, R. B. (2024). Smart Farming System and Its Role in Supporting SDGs. In Opportunities and Risks in AI for Business Development: Volume 1 (pp. 93-101). Cham: Springer Nature Switzerland.
28.Lee, H., & Ahn, S. H. (2026). Inclusive AI and Low-cost Robotics for Sustainable Smart Systems: Bridging the Global Technological Divide. International Journal of Precision Engineering and Manufacturing, 4(1), 11-25.
29.Paul, K., Schweng, S., Kaul, H. P., Gansberger, F., & Holzinger, A. (2026). AI-powered Pheno-Farm Server: Making adaptive farming decisions. Agricultural Systems, 231, 104563.
30.Partey, S. T., Zougmoré, R. B., Ouédraogo, M., & Campbell, B. M. (2018). Developing climate-smart agriculture to face climate variability in West Africa: Challenges and lessons learnt. Journal of cleaner Production, 187, 285-295.
31.Batra, I., Sharma, C., Malik, A., Sharma, S., Kaswan, M. S., & Garza-Reyes, J. A. (2025). Industrial revolution and smart farming: a critical analysis of research components in Industry 4.0. The TQM Journal, 37(6), 1497-1525.
32.Hu, Y., Koondhar, M. A., & Kong, R. (2023). From traditional to smart: exploring the effects of smart agriculture on green production technology diversity in family farms. Agriculture, 13(6), 1236.
33.Sow Badji, A., & Gathu, C. (2026). Leveraging public-private partnerships for climate finance: advancing climate-smart agriculture for NDC implementation in Kenya and Senegal. Climate Policy, 1-15.
34.Bondarenko, V., & Petrenko, O. (2025). TRADITIONAL AND MODERN AGRICULTURE. American Journal of Innovation in Science Research and Development, 2(1), 5-8.
35.Kabato, W., Getnet, G. T., Sinore, T., Nemeth, A., & Molnár, Z. (2025). Towards climate-smart agriculture: Strategies for sustainable agricultural production, food security, and greenhouse gas reduction. Agronomy, 15(3), 565.
36.Ali, I. A., Bukhari, W. A., Adnan, M., Kashif, M. I., Danish, A., & Sikander, A. (2025). Security and privacy in IoT-based Smart Farming: a review. Multimedia Tools and Applications, 84(16), 15971-16031.
37.Majeed, H., & Iftikhar, T. (2026). Industry 6.0 and the Rise of Intelligent Automation in Manufacturing. In Intelligent Manufacturing in Industry 6.0: A Climate Resilience Approach (pp. 293-343). Cham: Springer Nature Switzerland.
38.Lu, Z., Dong, J., Li, N., Feng, H., Gou, D., & Xu, M. (2026). Land Consolidation and Smart Agriculture Synergy for Food Security: Pathways Toward Agricultural Carbon Neutrality in China. Agriculture, 16(3), 287.
39.Aarif KO, M., Alam, A., & Hotak, Y. (2025). Smart sensor technologies shaping the future of precision agriculture: Recent advances and future outlooks. Journal of Sensors, 2025(1), 2460098.
40.Morchid, A., Qjidaa, H., Alami, R. E., Mobayen, S., Skruch, P., & Bossoufi, B. (2026). Smart irrigation-based internet of things and cloud computing technologies for sustainable farming. Scientific Reports.
41.Kabato, W., Getnet, G. T., Sinore, T., Nemeth, A., & Molnár, Z. (2025). Towards climate-smart agriculture: Strategies for sustainable agricultural production, food security, and greenhouse gas reduction. Agronomy, 15(3), 565.
42.Jiani, Z., Yang, H., Zhuxin, L., Ruohan, L., & Xinning, H. (2026). Dynamic assessment of drought disaster risk based on maize growth stages in Northeast China. Journal of Hydrology: Regional Studies, 64, 103135.
43.Pradhan, N. C., Gavhane, K. P., Bhalekar, D. G., & Kiran, P. R. (2026). Sustainable agriculture fundamentals. In Health, Nutrition and Sustainability (pp. 609-626). Academic Press.
44.Karn, S. K., Pathan, M., Mahato, P. K., Giri, A. R., & More, A. S. (2026). Review on AI and ML-Driven Agricultural Systems: Revolutionizing Crop Disease Detection and Management. In Advancements in IoT Sensors and Security: Harnessing AI/ML for Secured IoT Sensor Data (pp. 471-483). Cham: Springer Nature Switzerland.
