Big Data Analytics in Climate Smart Agriculture: A Social Science Perspective

Abstract

In an era of climate volatility and population explosion, increasing productivity of agriculture in a sustainable means has to be emphasized. As the digitization rate exceeding in an accelerated manner, data generation also increases in case of agricultural sector as well. Hence, big data analytics emerged as a transformative tool for climate-smart agriculture (CSA). Big data is characterized by different attributes and have several differences from traditional data. It can be used in structured data analysis, text data analysis, website data analysis, multimedia data analysis, network data analysis, and mobile data analysis, and it has several applications in climate smart agriculture as well. Using different analytical methods including predictive modelling, sentiment analysis, and automated content analysis, researchers will be able to perform social sciences research with the help of various tools (e.g., R, RapidMiner, WEKA). Sentimental analysis of digital platforms can identify behavioural patterns influencing technology adoption, while predictive models can enhance precision in resource allocation, reducing costs and emissions. However, challenges such as infrastructural gaps, data privacy concerns, and digital literacy barriers hinder the widespread usage of big data analytics. The study reveals that the integration of big data into social science frameworks can be explored more, particularly in understanding how socio-economic dynamics, farmer behaviour, and community-level decision-making intersect with environmental data. It also suggests that keeping big data analytics as a bridge between technological innovation and social equity can redefine CSA as a tool not only for climate adaptation but for empowering farmers as key agents of sustainable transformation.