Leveraging Text Mining for Better Policy Making to Combat Climate Change: A Bibliometric Analysis Correlating Precipitation Changes with Agriculture in India

India is widely considered as an agrarian economy, where agriculture defines the livelihood of a large population. However, our current agriculture practices are facing formidable challenges due to rapid progression in climate change. Climate change is leading to various unprecedented spatio-temporal alterations in weather patterns resulting in devastating events such asdrought, heat waves, erratic and intense rainfall. The impact of climate change on agriculture is widely accepted, and the possible threats and its affects are continuously being studied. The idea is to gain enough understanding of various factors affecting the precipitation, to be able to predict changes in precipitation patterns as well as design mitigation strategies to minimize the impact of such disturbances, through effective policy making. However, policy making requires synthesis of disparate factors, involving not only detailed understanding of the individual component but also the correlation between them. Establishing such correlations can be challenging, given the diverse array of information available, especially if presented in an unstructured format. Scientific literature often explores a problem from varied facets, which can be highly useful but hard to correlate manually. However, text mining can serve as a useful approach to generate knowledge out of such varigated information pool in minimal time. Text mining is an evidence-based method for knowledge generation, which works through quantification of information present in texts, helping in understanding the correlation between various key terms.The present study is an extension of our goal at Semantic Climate’s Climate knowledge hunt hackathon organised on 26-27^th February 2024 (https://semanticclimate.github.io/p/en/past-events/climate_knowledge_hunt_Feb24/). Here we intended to leverage the text mining strategy to extract pertinent information from literature sources, such as scientific papers, related to precipitation patterns in India. The study relies solely on the extraction of published data and no ground data related to agriculture or precipitation has been used directly. The key findings emerging from the information corpus through text mining will have potential to direct the policy makers towards comprehending the gaps created in our agricultural practices due to climate change. This understanding will allow policy makers to devise more effective and potent solutions to combat the effects of climate changeon Indian agriculture, thereby safeguarding the broader socio-economic fabric of the country.