Assessment of Factors Influencing Use of Insecticide among Smallholders Farmers in Dale Sadi District of Kellem Wallega Zone, Ethiopia
Downloads
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
- Finney D. Shadrach, Harsshini S, Darshini R, Grapevine leaf species and disease detection using DNN , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Akshay J., G. Mahesh Kumar, B. H. Manjunath, Optimizing durability of the thin white topping applying Taguchi method using desirability function , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Adedotun Adedayo F, Odusanya Oluwaseun A, Adesina Olumide S, Adeyiga J. A, Okagbue, Hilary I, Oyewole O, Prediction of automobile insurance fraud claims using machine learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Hardik N Talsania, Kirit Modi, Interpretable Cardiovascular Diagnosis using Multi-dimensional Feature Fusion and Deep Learning , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
- Ravi Kumar P, C. Gowri Shankar, Optimizing power converters for enhanced electric vehicle propulsion: A novel research methodology , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- N. Saranya, M. Kalpana Devi, A. Mythili, Summia P. H, Data science and machine learning methods for detecting credit card fraud , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Muruganantham P, Harshavardhan J, Rajesh PK , Neelakrishnan S, Implementation of flexible and customizable free-from mirror heads-up display , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Bratati Dey, Poonam Sharma, A comprehensive review of urban growth studies and predictions using the Sleuth model , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Manpreet Kaur, Shweta Mishra, A smart grid data privacy-preserving aggregation approach with authentication , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Pankaj Kumar, Ambrish Pandey, Rajendrakumar Anayath, Comparative study of print quality attributes on bio-based biodegradable plastic using flexography and gravure printing process , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
<< < 8 9 10 11 12 13 14 15 16 17 > >>
You may also start an advanced similarity search for this article.

