eng Enviro Research Publishers Current Agriculture Research Journal 2347-4688 2321-9971 2026-01-10 13 3 848 856 25818 article Abhay Kumar 1 Ashwani 2 Sonali Kumari Suman 3 Veer Singh 4 Department of Geography, K. S. College, Laheriasarai, Darbhanga, Bihar, India Department of Geography, Delhi School of Economics, University of Delhi, Delhi, India Department of Economics, Baba Saheb Bhimrao Ambedkar Central University, Lucknow, Uttar Pradesh, India Department of Geography, Hemvati Nandan Bahuguna Garhwal (Central) University, Srinagar Garhwal, Uttarakhand Wheat is a staple food for India and rotates with rice and basmati for the country's food security, with Uttar Pradesh being the highest-producing state. Production of wheat is forecasted accurately for agricultural planning, procurement and policy recognition. Here, the univariate time series (ARIMA) model for forecasting wheat production in Uttar Pradesh is used for the period 2023-2030 using area, yield and production data from 1997 to 2022 taken from the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare and Government of India. On initial observation, there was a continuous increase in production mainly through yield increases, while the area remained stagnant. After ensuring stationarity with all other diagnostics guaranteed, the model selected to fit was ARIMA (1,1,1). The forecasts indicate that production will continue slowly increasing to 41.64 million tonnes in 2030 as against 38.54 million tonnes in 2022. In addition, the model proved highly accurate had excellent predictive capability and was statistically significant. Undoubtedly, the findings stress the importance of yield-driven growth, with some providing suggestions for the government planners and policymakers. However, the results also indicate a possible productivity plateau which calls for renewed emphasis on climate-resilient varieties, precision farming and input-use efficiency. This study further contributes to the growing literature on agricultural forecasts and offers a replicable model for other major producing regions. https://www.agriculturejournal.org/volume13number3/forecasting-wheat-production-in-uttar-pradesh-using-autoregressive-integrated-moving-average-arima-time-series-modelling-a-yield-driven-approach-based-on-historical-area-and-productivity-trends/ Agriculture; ARIMA; Uttar Pradesh; Wheat production; Yield