Adoption decisions for climate-smart dairy farming practices: Evidence from smallholder farmers in the Salale highlands of Ethiopia
Bibliographic record
Abstract
The adoption rate of climate-smart livestock production in Ethiopia has remained low, despite its potential to increase animal productivity and reduce greenhouse gas emissions. Understanding the factors that influence smallholder farmers' (SHFs) decisions to adopt improved practices is crucial for tailoring strategies for stakeholders and policymakers. The present study examines factors that determine adoption and the intensity of adoption of multiple climate-smart dairy (CSD) farming practices, including improved breeds, feed, and feeding conditions, forage, and manure management, using data from 480 SHFs in Salale highlands. The study employed a multivariate probit model (MVP) to analyze the simultaneous adoption of multiple CSD farming practices and an ordered probit model to examine factors influencing the degree of adoption. The results indicate that about 90 % of the smallholder farmers have adopted at least two of the CSD farming practices. Improved breed, improved feed, and improved feeding conditions are the most commonly adopted farming practices, whereas improved forage is the least adopted improved practice in the study area. Our result showed that most CSD farming practices have complementary associations. Furthermore, gender, dependency ratio, land size, Tropical Livestock Unit (TLU), off-farm activity, access to extension services, farmer-to-farmer communication, and distance to the nearest market significantly influence smallholder farmers' adoption and intensity of adoption of multiple CSD farming practices. The result suggest that the government bodies should prioritize encouraging the uptake of improved forages and should take the required steps to facilitate their implementation. To accelerate the adoption of CSD farming practices for SHFs and promote their widespread implementation across the region, policymakers and implementers must recognize the synergies between these practices. Interventions that improve access to agricultural resources, supply chain inputs and outputs, as well as service provision, will further facilitate the adoption and effective implementation of CSD farming practices. • Climate Smart Dairy (CSD) farming improve animal productivity, farmers' income, and food security. • Determinant of adoption of CSD farming and its intensities were examined using econometrics model. • Positive interdependence (complementarity) among the CSD practices were reported. • Technological, socio-economic, and institutional factors significantly influenced the uptake of the farming practice. • Providing access to input and output markets, and creating awareness can help improve the uptake of farming practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".