Assessment of Factors Influencing Use of Insecticide among Smallholders Farmers in Dale Sadi District of Kellem Wallega Zone, Ethiopia
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2026.17.2.01Keywords:
Insecticide Adoption;, Probit Model, Smallholder Farmers, Agricultural Technology, EthiopiaDimensions Badge
Issue
Section
License
Copyright (c) 2026 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The research aimed to evaluate influences upsetting the application of insecticide among smallholder farmers in Dale Sadi District. The data collection method is employed by randomly selecting 138 farmers, and the data type used is a cross-sectional type of data. Descriptive and econometric analysis was employed for data analysis. Descriptive analysis revealed that 72.46% percent of sample respondents applied insecticide, and the rest, 27.54%, did not apply it. Probity model analysis revealed that education status, farm size, total livestock owned, credit access, frequency of extension contact, and farmer’s experience in the use of chemical pesticides have a positive influence and significantly affect the probability of being an insecticide user. Therefore, stakeholders should focus in enhancing continuous training, conserving existing farmland, improving market infrastructure, and increasing access to credit services, enhance the use of chemical insecticide to increase farm productivity among smallholder farmers with less cost to transform and enhance the role of agriculture.Abstract
How to Cite
Downloads
Similar Articles
- Sandip Sane, Diksha Tripathi, Nitin Ranjan, Digital transformation in management education: Bridging theory and practice , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- J. Pavithra, Status of investment in startup in India – An analysis , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
- Mudassir Peeran A, A.R. Mohamed Shanavas, A Hybrid Post-Quantum Cryptography and Machine Learning and Framework for Intrusion Detection and Downgrade Attack Prevention throughout PQC Migration , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Vijay Kumar, Priya Thapliyal, Rajesh Rayal, Baljeet Singh Saharan, Arun Kumar, Shweta Sahni, The Molecular Profiling and HCV RNA Quantification to Study the Distribution of Different HCV Genotypes in Accordance to Geographical Condition , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
- Amita Pal, Richa Trivedi, Amit Jain, Sudhir Jain, Diurnal and seasonal variation of GPS-TEC during a low solar activity period at EIA region (Bhopal) , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Gitesh Kalita, NEP 2020 policies for inclusive education , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Krishna Deo Verma, EFFICACY OF SOME NOVEL PESTICIDES AGAINST MUSTARD APHID , The Scientific Temper: Vol. 8 No. 1&2 (2017): The Scientific Temper
- Sheena Edavalath, Manikandasaran S. Sundaram, MARCR: Method of allocating resources based on cost of the resources in a heterogeneous cloud environment , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Rajeshwar Mukherjee, Uday S. Dixit, Understanding cosmopsychism based on stochastic electrodynamics from the perspective of the Indian knowledge system , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- M. Jayakandan, A. Chandrabose, An ensemble-based approach for sentiment analysis of covid-19 Twitter data using machine learning and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
<< < 41 42 43 44 45 46 47 48 49 50 > >>
You may also start an advanced similarity search for this article.

