Harvi G Vora¹, JB Gajera²* and SJ Devra³

¹Ph.D. Student, Department of Genetics and Plant Breeding, College of Agriculture, Junagadh Agricultural University, Junagadh (Gujarat), India
²Research Associate (GKMS), (AMFU-IMD), Agrometeorology Cell, Department of Agronomy, College of Agriculture, Junagadh Agricultural University, Junagadh (Gujarat), India
³Ph.D. Scholar, Department of Agricultural Statistics, College of Agriculture, Junagadh Agricultural University, Junagadh (Gujarat), India

*Corresponding Author: JB Gajera, Research Associate (GKMS), (AMFU-IMD), Agrometeorology Cell, Department of Agronomy, College of Agriculture, Junagadh Agricultural University, Junagadh (Gujarat), India.

Received: September 30, 2025; Published: November 13, 2025

Abstract

Climate-responsive farming, or climate-smart agriculture, is a strategy that optimizes agricultural productivity while minimizing the negative impacts of weather variability and climate change. It involves integrating specific weather conditions and forecasts into decision-making processes, such as crop selection, planting schedules, irrigation, and pest control. This approach enhances farmers' resilience to climate-related risks, ensuring sustainable food production in the face of evolving climatic conditions. As climate change threatens agriculture, livelihoods, and global food security, climate-smart agriculture is crucial in addressing these challenges. Climate resilience practices, such as agroforestry, crop diversification, precision farming, and technological advancements, are essential for food security, economic benefits, environmental sustainability, and social equity. Precision agriculture, a key element of weather-responsive farming, uses advanced technologies like sensors, drones, and AI to optimize farming practices based on real-time data. Successful case studies from Australia, Brazil, and the Netherlands demonstrate the potential of precision agriculture in reducing resource use, improving crop quality and mitigating environmental impacts. Future trends involve integrating AI, machine learning, and drone technology into weather-responsive farming systems. The significance of using precision agriculture as a proactive strategy to address the issues brought on by climate change in agricultural systems is emphasized, as is the possibility for weather-responsive farming to improve climate resilience.

Keywords: Climate Resilience; Sustainability; Weather-Responsive Farming; Precision Agriculture; Weather Monitoring

References

1. Mekouar M. “United Nations Food and Agriculture Organization (FAO)”. Yearbook of International Environmental Law 27 (2016): 488-506.
2. Balasundram SK., et al. “The Role of Digital Agriculture in Mitigating Climate Change and Ensuring Food Security: An Overview”. Sustainability6 (2023): 5325.
3. Shilpa and Bijalwan P. “Climate Resilient Vegetable Farming: A Review”. Journal of Experimental Agriculture International (2021): 55-69.
4. Asseng S. “Uncertainties of Climate Change Impacts in Agriculture”. Procedia Environmental Sciences 29 (2015): 304.
5. Harris E., et al. “Promoting Social Equity and Building Resilience through Value-Inclusive Design”. Buildings8 (2023): 2081.
6. Frimpong CA. “Promoting Climate Resilience in Building Construction Industry in Ghana”. International Journal of Global Sustainability 1 (2023): 65.
7. Paul W. “Prospects for controlled application of water and fertiliser, based on sensing permittivity of soil”. Computers and Electronics in Agriculture2-3 (2002): 151-163.
8. Kwon S H and Lee S. “Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery”. Water Resources Management (2023).
9. Manandhar A., et al. “Techno-economic impacts of using a laser-guided variable-rate spraying system to retrofit conventional constant-rate sprayers’. Precision Agriculture5 (2020): 1156-1171.
10. Yadav A., et al. “Emerging Frontiers in Nanotechnology for Precision Agriculture: Advancements, Hurdles and Prospects”. Agrochemicals2 (2023): 220-256.
11. Sishodia R P., et al. “Applications of Remote Sensing in Precision Agriculture: A Review”. Remote Sensing19 (2020): 31-36.
12. Wang T., et al. “Factors affecting farmer perceived challenges towards precision agriculture”. Precision Agriculture6 (2023): 2456-2478.
13. McBratney A., et al. “Future Directions of Precision Agriculture”. Precision Agriculture1 (2005): 7-23.
14. Holtorf L., et al. “UAV-Based Wireless Data Collection from Underground Sensor Nodes for Precision Agriculture”. AgriEngineering 5.1 (2023): 338-354.
15. Zualkernan I., et al. “Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey. Drones6 (2023): 382.
16. Bhatti S., et al. “Toward automated irrigation management with integrated crop water stress index and spatial soil water balance”. Precision Agriculture6 (2023): 2223-2247.
17. Bramley R., et al. “Vineyard variability in Marlborough, New Zealand: characterising variation in vineyard performance and options for the implementation of Precision Viticulture”. Australian Journal of Grape and Wine Research1 (2011): 72-78.
18. Thakare A. “Intelligent Automated Irrigation System Based on Soil Sensors and Weather Forecast Data”. International Journal of Next-Generation Computing (2021).
19. Nkuh A., et al. “Pesticide Usage in Pest Management by Vegetable Producers in the Foumbot Production Basin of the Western Highlands (Cameroon)”. American Journal of Agriculture2 (2023): 36-52.
20. Rethnaraj J. “Future of Smart Farming Techniques: Significance of Urban Vertical Farming Systems Integrated with IoT and Machine Learning”. Open Access Journal of Agricultural Research3 (2023): 1-11.

Citation

Citation: JB Gajera., et al. “Weather-Responsive Farming: Harnessing Precision Agriculture for Climate Resilience". Acta Scientific Agriculture 9.11 (2025): 01-09.

Copyright

Copyright: © 2025 JB Gajera., et al . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.