Climate-Smart Agriculture: Strategies for Enhancing Productivity, Resilience and Sustainability
Kangjam Sonamani Singh, Ibrahim Kaleel, Pravina P. Solanki, Neetu Swarnkar, Vivek Swarnkar, Vivek Chauhan, Nirjharnee Nandeha, Syed Wasifur, Megha Mandloi
Journal of Scientific Research and Reports · pp. 576–587 · Published 21 Feb 2026
10.9734/jsrr/2026/v32i24007Abstract
Agriculture is essential to food security and rural development in emerging economies, as it provides livelihoods for over 70% of the rural population, especially in areas like South Asia and Sub-Saharan Africa. However, efforts to enhance agricultural productivity have been hindered by multiple interlinked factors, including economic instability, unequal land distribution, increasing climate variability, and the limited dissemination of scientific knowledge and improved planting methods among farmers. Despite the fact that agriculture is rapidly changing technologically thanks to Agriculture 4.0 and the new Agriculture 5.0 paradigm, many rural and resource-constrained areas have not yet embraced these innovations because of their high costs, poor infrastructure, and lack of technical capacity. Organizations like the Food and Agriculture Organization (FAO) have promoted a balanced strategy that incorporates social justice, environmental preservation, and productivity in response to these issues, and the idea of sustainable agriculture has been widely recognized. Sustainable agricultural methods have been clearly beneficial to industrialized farming systems since the 1990s, but for successful global application, both large-scale commercial farms and smallholder systems in poor nations still need focused, site-specific study. Through the promotion of diverse cropping systems, design-oriented spatial planning, and efficient resource flows, climate-smart agriculture (CSA) helps conserve biodiversity by enhancing ecosystem resilience and lowering climate-related risks. The shift to climate-smart, circular agricultural systems is further supported by the integration of spatiotemporal, data-driven sensing, monitoring, and decision support frameworks, which allows for optimal resource usage.
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