Analyzing and Forecasting All-India Tur (Arhar) Yields: A Time Series Approach

The agricultural sector plays a vital role in India's economy, with Tur (Arhar) being a significant crop. Accurate yield forecasting is essential for efficient agricultural planning and resource allocation. This study employs time series analysis and forecasting techniques to predict the all-India yield of lentils. Historical yield data were collected, preprocessed, and subjected to exploratory data analysis to identify trends and seasonal patterns. Various models, including ARIMA, SARIMA, and Exponential Smoothing, were evaluated for their forecasting performance. The models were trained on historical data and validated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the chosen models provide reliable forecasts, which can aid policymakers and farmers in making informed decisions. The study highlights the importance of time series analysis in agricultural forecasting and provides a methodological framework for future research in crop yield prediction.