Evaluation of Monsoon Rainfall Variability and Its Impact on Agricultural Productivity in South India: A Time-Series Analysis

Authors

Rajashekara Murthy M. S.

Abstract

This study investigates the variability of monsoon rainfall and its impact on agricultural productivity in South India over the decade from 2013 to 2023. Utilizing the Autoregressive Integrated Moving Average (ARIMA) model for time-series forecasting, along with Pearson correlation analysis, the research aims to identify trends and correlations between monsoon rainfall and the yields of major crops, including rice, sorghum, groundnut, and cotton. The findings reveal that monsoon rainfall in South India has remained relatively stable, with low variability contributing to consistent agricultural productivity. Strong positive correlations were found between monsoon rainfall and crop yields, particularly for rice and groundnut, emphasizing the crucial role of reliable monsoon patterns in sustaining agriculture. The study also addresses a significant gap in the literature by integrating long-term time-series analysis with modern predictive modeling techniques specific to South India. These insights are vital for developing adaptive strategies that can enhance agricultural resilience in the face of climate variability. The broader implications of this research extend to other monsoon-dependent regions, underscoring the global importance of predictive modeling in agricultural planning and climate adaptation.

Keywords

Monsoon rainfall variability, agricultural productivity, ARIMA model, South India, climate resilience, time-series analysis

Evaluation of Monsoon Rainfall Variability and Its Impact on Agricultural Productivity in South India: A Time-Series Analysis. Rajashekara Murthy M. S.. 2019. IJIRCT, Volume 5, Issue 4. Pages 1-11. https://www.ijirct.org/viewPaper.php?paperId=2408054