Trend Analysis and ARIMA Forecasting of Area, Production, and Yield of Major Crops in Andhra Pradesh, India

Agriculture plays a vital role in the economy of Andhra Pradesh, contributing significantly to food security, employment generation, and rural livelihoods, with diverse agro-climatic conditions supporting a wide range of crops. However, increasing climatic variability, market fluctuations, and structural changes in cropping patterns necessitate systematic time series analysis to understand production dynamics and generate reliable forecasts for informed agricultural planning. This study conducts a comprehensive time series (TS) analysis and forecasting of major crops in Andhra Pradesh, a key agricultural state in India. Focusing on the core crop groups of cereals, millets, pulses, and oilseeds, the research analyzes long-term historical data on area, production, and yield (1975-2023). The analysis covers cereals, millets, pulses, and oilseeds, which together account for most of the state’s cultivated area and food output. Polynomial regression models are employed to identify structural growth patterns, followed by ARIMA models to generate five-year production forecasts. Stationarity testing and model diagnostics were conducted to ensure robust model specification, and forecast accuracy was evaluated using standard error measures. The results reveal significant heterogeneity in the growth patterns and predictability across crop groups. The results reveal significant heterogeneity in growth dynamics and predictability across crop groups. Rice exhibits a strong non-linear upward trend in production (R² = 0.963), driven largely by sustained yield improvements (R² = 0.972), and demonstrates high forecasting accuracy (MAPE = 6.28%), indicating a stable and predictable production system. Millets display pronounced non-linear fluctuations in cultivated area (R² ≈ 0.97), yet ragi production shows relatively strong forecast reliability (MAPE = 11.16%), despite their predominantly rainfed nature. In contrast, pulses and oilseeds exhibit persistent non-stationarity and higher forecast errors (MAPE ranging from 14–20%), reflecting greater sensitivity to climatic variability and market shocks. The study concludes that agricultural planning in Andhra Pradesh requires crop-specific, evidence-based strategies. The findings advocate for continued investment in productivity-enhancing technologies for stable cereals, targeted support for climate-resilient practices in millets, and robust policy interventions, including improved irrigation, varietal adoption, and market support to mitigate volatility in pulses and oilseeds. The forecasts (2024–2028) provide practical guidance for production planning, procurement decisions, price stabilization, and climate-resilient agricultural policy in the state. The findings emphasize the need for crop-specific, evidence-based interventions, including productivity-enhancing technologies for cereals, climate adaptation support for millets, and improved irrigation, varietal innovation, and market mechanisms for pulses and oilseeds. By combining structural trend analysis with ARIMA forecasting, the study offers an empirically grounded framework for regional crop predictability and strengthens the agricultural forecasting literature.