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
- Rupesh Mandal, Bobby Sharma, Dibyajyoti Chutia , Smart flood monitoring in Guwahati city: A LoRa-based AIoT and edge computing sensor framework , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Amudavalli L, K. Muthuramalingam, Integrated energy-efficient routing and secure data management for location-aware wireless sensor networks with PFO leveraged improved fuzzy unequal clustering algorithm (IFUC) , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Komal Raichura, Asha L. Bavarava, Redefining Classroom Dynamics: AI Tools and the Future of English Language Pedagogy , The Scientific Temper: Vol. 16 No. 11 (2025): The Scientific Temper
- M. Kohila, S. Rethinavalli, A P2ECAM: A Trust-Preserving Cross-Cloud Data Migration Model For Resource-Constrained Mobile Devices Using Certificate-Free Elliptic Curve Cryptography , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
- Deena Merit C K , Haridass M, Analysis of multiple sleeps and N-policy on a M/G/1/K user request queue in 5g networks base station , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Krishna P. Kalyanathaya, Krishna Prasad K, A framework for generating explanations of machine learning models in Fintech industry , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Dadhaniya Deepa Karshanbhai, Nilofar Bhatti, Bioremediation of Textile Dyes Using Native Microorganisms: Sustainable Microbiological Approaches , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Priyanka Patel, Bhaskar Pandya, The idea of Indianness in Indian literature: An analysis of social and cultural themes in the short stories of Rabindranath Tagore, Mulk Raj Anand, and R.K. Narayan , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Abhishek Dwivedi, Shekhar Verma, SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Nisha Patil, Archana Bhise, Rajesh K. Tiwari, Fusion deep learning with pre-post harvest quality management of grapes within the realm of supply chain management , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
<< < 25 26 27 28 29 30 31 32 33 34 > >>
You may also start an advanced similarity search for this article.

