Evaluation of Application Levels to Agricultural Extension Agents in Precision Agriculture Techniques to Improve The Efficiency of Agricultural Resource Usage in Salah Al-Din Governorate
This research evaluated the application levels for agricultural extension agents in precision agriculture techniques for improving agricultural resource usage in Salah Al-Din governorate. It examined their effectiveness, organizational planning, monitoring, implementation, and evaluating abilities and analysed differences based on specific variables. These included productivity gains from precision agriculture techniques, agricultural extension fields, qualifications, length of service, job grade, and participation in activities related to precision agriculture techniques. For this purpose, a questionnaire was prepared (with verified virtual and content validity) covering two areas, namely personal characteristics, and 30 norms comprising five measures that were developed after reviewing the literature and conducting interviews with specialists. Data was collected from 1/11/2024-28/1/2025. The research community represented 14.54% of the total 385 respondents, with the sample of 56 employees in Tikrit Governorate. Twenty employees were excluded from the final research sample. The statistical methods employed were the Kruskal-Wallis and Mann-Whitney U tests, and the Z-score. Key findings on the effectiveness of precision agriculture revealed a mixed picture, with two factors being particularly significant, i.e., agricultural extension and office qualifications. These had a substantial impact on how precision agriculture is evaluated. The main conclusions highlight the crucial role of specialists and senior staff in extension programs, who play a vital part in precision agriculture
Authors
- W. Ayyed
- A. Al_Hafidh
Institutions
- Ministry of Agriculture and Forestry (LA)
- University of Tikrit (IQ)
Publication Details
- Journal
- Anbar Journal of Agricultural Sciences
- Published
- 2025-12-31
- DOI
- https://doi.org/10.32649/ajas.2025.189369
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00