Technology Forecasting of Agricultural Implements for Central India using Structural Time Series Model
DOI:
https://doi.org/10.52151/jae2025621.1907Keywords:
demand forecast, farm mechanization, Kalman filter, one-step ahead forecastAbstract
This study investigates the technology forecasting of agricultural implements in Central India, focusing on enhancing farm mechanization to improve productivity and farmers’ income. The Structural Time Series (STS) model was applied to predict the future demand for 14 commonly used farm implements for the year 2020, 2025 and 2030 in Madhya Pradesh, using sales data from 138 manufacturers (2000-2018). Implements such as graders and animal-drawn tools showed declining trends, with higher mean absolute percentage error (MAPE). The findings revealed an increasing demand for modern agricultural machinery, including seed drills, rotavators, and paddy threshers, driven by factors such as labor shortages, evolving cropping patterns, and government policies supporting mechanization. The study predicts a 19.5% annual increase in the demand for paddy threshers, reflecting a shift in farming practices from soybean to paddy cultivation. In contrast, the demand for seed drills is expected to rise by only 3% annually due to the growing preference for seed-cum-fertilizer drills. The results underline the importance of technology forecasting in shaping farm mechanization strategies and guiding policy decisions. The study also offers valuable insights for manufacturers and suppliers to efficiently plan production and ensure timely access to the required implements, thus contributing to the overall development of Indian agriculture.
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