Authors
Mezgebu Aynalem, Zemen Ayalew, Aemro Tazeze Terefe
Published in
Scientific reports. Jul 14, 2026. Epub Jul 14, 2026.
Abstract
This study examines whether climate smart agriculture (CSA) practices reduce technical inefficiency among smallholder vegetable farmers in Northwest Ethiopia. Using cross sectional data from 550 households producing onion, potato, and tomato, the study applies a stochastic frontier analysis to estimate technical inefficiency and derives inefficiency scores for second stage analysis. To identify the determinants of inefficiency, three econometric models, beta regression, Fractional Logit, and Two-limit Tobit, are employed and compared. Model selection based on the loglikelihood, Akaike Information Criterion, and Bayesian Information Criterion confirmed the superiority of the beta regression model. The results reveal that average technical inefficiency levels are 0.170 for onion, 0.195 for potato, and 0.244 for tomato, indicating substantial scope for improving productivity through better resource utilization. The findings consistently show that the adoption of CSA practice combinations significantly reduces technical inefficiency across all vegetable crops. In particular, the integrated adoption of soil and water management practices reduces the largest inefficiency part, followed by climate related and soil based practices. These results highlight the importance of complementarily among agricultural technologies to reduce inefficiency. Socioeconomic and institutional factors, such as education, livestock ownership, credit access, and market proximity, significantly influence inefficiency levels. This study contributes to the literature by explicitly integrating CSA practices into the inefficiency framework and showing that the bundled adoption of climate smart practices enhances production efficiency. These findings provide important policy insights for promoting integrated and context specific CSA interventions to improve smallholder productivity and resilience.
PMID:
42448805
Bibliographic data and abstract were imported from PubMed on 15 Jul 2026.
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