Sreeram Harshitha

MSc. (Agriculture),
Lovely Professional University, Punjab, India
Email- sreeramharshitha22@gmail.com

Shahid Amin

Associate Professor,
Department of Management, ITM Gwalior, M.P, India
Email- dr.shahidamin15@gmail.com

India, with a population exceeding 1.4 billion and a leading position among the world’s largest agricultural producers, faces a pressing challenge: feeding a rapidly growing population while managing the impacts of climate change. Agriculture is the backbone of human survival because it provides food and nutrition for a healthy and active life. Beyond food, it plays a vital role in sustaining rural livelihoods and shaping the way natural resources like land and water are used. The type of farming practiced directly affects biodiversity, ecological balance, and social security. Agriculture also supports industries by supplying essential raw materials that drive production, trade, and commerce. At the same time, it is both affected by climate change and a contributor to it, which makes adaptation and emission reduction crucial. The sector faces many challenges and opportunities that are constantly changing, with some being global in nature and others specific to individual countries (Chand, 2022). Rising temperatures, irregular rainfall, extreme droughts, and floods are already disrupting traditional cropping patterns, threatening both yields and livelihoods.

As the impacts of climate change intensify, conventional farming methods alone cannot guarantee food security. This has led to the emergence of climate-resilient farming. This strategy integrates technology, sustainable practices, and data-driven decision-making to enhance productivity, reduce risks, and conserve natural resources. At the heart of climate-resilient agriculture in India is the adoption of advanced technologies, particularly Artificial Intelligence (AI) and Remote Sensing (RS), which are transforming farming practices.

AI algorithms enable predictive modeling of crop yields, optimize irrigation and fertilization schedules, and provide early warnings for pest and disease outbreaks. For instance, deep learning models have been successfully deployed to detect diseases in crops like cassava and maize. In contrast, machine learning techniques are increasingly applied to forecast crop performance under various environmental conditions. By analyzing historical and real-time data, AI assists farmers in making informed decisions about planting times, resource allocation, and risk management.

Remote sensing complements AI by providing high-resolution imagery from satellites and drones, offering real-time insights into key variables like soil health, moisture levels, crop stress, and land-use changes. UAVs (drones) have been deployed to monitor fields for nutrient deficiencies and pest infestations. At the same time, satellite imagery helps forecast seasonal weather trends, enabling adaptive planning at both farm and policy levels. Together, AI and RS form a powerful combination, allowing precise monitoring and management of crops.

In India, platforms such as CropX and IBM Watson Decision platform for Agriculture are being leveraged to integrate satellite and ground-level data, providing actionable insights to farmers and agribusinesses. Beyond technology, climate-resilient agriculture emphasizes practical, sustainable interventions. Crop diversification, the introduction of drought and heat-tolerant varieties, and improved water-use efficiency are essential strategies. Techniques like drip and sprinkler irrigation, solar-powered pumps, and rainwater harvesting reduce reliance on erratic monsoon rains. At the same time, integrated nutrient and pest management minimizes chemical input use and protects soil health. Precision agriculture tools also optimize the use of herbicides and fertilizers, reducing environmental impact while maintaining high productivity. In livestock farming, AI-powered sensors and decision-support systems enable continuous monitoring of animal health, facilitating early disease detection and feed optimization, thereby enhancing both animal welfare and productivity.

However, the adoption of climate-resilient practices in India is not without challenges. Smallholder farmers, who make up the majority of India’s agricultural workforce, often lack access to affordable technologies, digital literacy, and timely extension services, all of which hinder their ability to adopt climate-resilient practices. Fragmented landholdings further complicate the implementation of precision farming. Social and institutional barriers—such as risk aversion, lack of awareness, and inadequate support mechanisms—limit the scaling of innovative practices. Addressing these obstacles requires a holistic approach, including policy support, capacity building, and community engagement.  Government subsidies, low-interest financing, and public-private partnerships can make technologies more accessible, while tailored training programs empower farmers to adopt AI, RS, and climate-smart methods effectively. Integrating traditional knowledge with modern tools also fosters trust and accelerates adoption in rural communities.

India has already initiated several programs to enhance climate resilience. The National Mission for Sustainable Agriculture (NMSA) promotes soil health management, water-use efficiency, and climate-resilient crop varieties. State-level initiatives like solar-powered micro-irrigation schemes in Maharashtra and precision farming projects in Punjab demonstrate the potential of technology-driven solutions. Additionally, mobile-based platforms such as Hello Tractor allow smallholders to access mechanization services on demand, increasing efficiency and reducing labor constraints. These efforts show that scalable interventions, combined with data-driven tools, can significantly improve productivity and reduce vulnerability to climate shocks.

Data-driven decision-making is particularly important for mitigating climate risks. By integrating weather forecasts, soil and crop monitoring, and AI-based predictive models, farmers can plan for extreme events such as floods, droughts, or pest outbreaks. Early warning systems, informed by remote sensing and AI, enable targeted interventions, minimizing crop losses and stabilizing yields. For instance, in regions like Sub-Saharan Africa and parts of India, predictive analytics have guided planting schedules and irrigation strategies, allowing farmers to adapt proactively to changing conditions. This approach not only protects food production but also ensures the economic sustainability of small and medium-scale farms.

The environmental benefits of climate-resilient farming are equally significant. Optimized irrigation reduces water wastage, precision fertilizer application lowers nitrogen runoff, and integrated pest management reduces chemical usage, collectively supporting soil health and biodiversity. Crop residue management and mulching enhance soil organic matter, improving water retention and carbon sequestration. When combined with renewable energy solutions, such as solar-powered irrigation, these practices create an agriculture system that is productive, sustainable, and resilient to climate stress. Yet, realizing the full potential of climate-resilient agriculture in India requires addressing several systemic issues. High costs and limited availability of AI and RS tools can exclude resource-poor farmers. Internet connectivity and digital infrastructure are inconsistent in rural areas, further widening the technology adoption gap.

Ethical concerns surrounding data privacy, ownership, and usage must also be addressed to ensure that farmers retain control over information collected from their lands. Establishing clear regulatory frameworks, data protection policies, and equitable benefit-sharing mechanisms is essential for responsible technology integration. Future research should also focus on developing cost-effective, localized AI and RS solutions tailored to India’s diverse agro-ecological zones. Integrating interdisciplinary approaches—combining agronomy, climatology, and computer science—can improve predictive models for yield, pest outbreaks, and climate risks. Public-private partnerships can drive innovation, facilitate knowledge transfer, and enhance infrastructure. Simultaneously, community-based approaches and participatory training programs can empower farmers to adopt new technologies confidently, ensuring that climate-resilient practices are widely implemented and sustainable.

Ultimately, India’s capacity to sustainably feed its growing population amid climate change hinges on a multifaceted strategy. By combining AI and RS technologies, sustainable farming practices, and supportive policies, the country can transform vulnerabilities into opportunities. Data-driven agriculture enhances productivity, reduces environmental impact, and strengthens resilience to climate shocks. Scaling up these practices requires investment in infrastructure, farmer education, and inclusive technology adoption, ensuring that both smallholders and large-scale producers’ benefit.

References-

• Chand, R. (2022). Indian Agriculture Towards 2030—Need for a Transformative Vision. In: Chand, R., Joshi, P., Khadka, S. (eds) Indian Agriculture Towards 2030. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-19-0763-0_1
• Gallopin, G. C. (1996). Environmental and sustainability indicators and the concept of situational indicators. A systems approach. Environmental modeling & assessment, 1(3), 101-117.
• Jat, R. K., Meena, V. S., Durgude, S., Sohane, R. K., Jha, R. K., Kumar, A., … & Lenka, A. K. (2025). Bridging the gap: challenges and adoption of climate-resilient agriculture technologies in agricultural landscapes across agro-climatic zones of Bihar, India. Frontiers in Sustainable Food Systems, 8, 1504388.
• Naik, B. M., Singh, A. K., Venkatesan, P., Maji, S., Sunil, J., & Naik, M. R. (2025). Assessing the contribution of climate resilient agricultural technologies in advancing sustainable development goals in Telangana, India. Discover Sustainability, 6(1), 1-14.