Technology Forecasting of Agricultural Implements for Central India using Structural Time Series Model

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.

Authors

Institutions

Publication Details

Journal
Journal of Agricultural Engineering (India)
Published
2025-03-22
DOI
https://doi.org/10.52151/jae2025621.1907
Citations
3
Primary Topic
Agricultural Economics and Practices
Type
article
Field-Weighted Citation Impact
14.50
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Technology Forecasting of Agricultural Implements for Central India using Structural Time Series Model

C. R. Mehta, M. B. Tamhankar, Bikram Jyoti, Manoj Kumar et al.
3 citations
Journal of Agricultural Engineering (India)
Agricultural Economics and Practices
14.50
article

Technology Forecasting of Agricultural Implements for Central India using Structural Time Series Model

C. R. Mehta, M. B. Tamhankar, Bikram Jyoti, Manoj Kumar, V. Bhushana Babu
article en
3 citations

Abstract

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.

Journal of Agricultural Engineering (India)Vol. 62(1)
Central Institute of Agricultural Engineering (IN)
Openalex Percentile: Top 2%
Agricultural Economics and Practices
14.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Technology Forecasting of Agricultural Implements for Central India using Structural Time Series Model — C. R. Mehta, M. B. Tamhankar, et al. · Journal of Agricultural Engineering (India) (2025) | TGRS Research Map | TGRS