Perception of Farmers on Conservation Agriculture for Climate Change Adaptation in Namibia
Bibliographic record
Abstract
Traditional cultivation methods in Namibia are characterised by cultivating the same type of crops persistently on the same piece of land, using a disc or mouldboard plough with minimal to no fertilizer application. This study assessed the knowledge level of farmers' on conservation agriculture and the household factors,which influence farmers to take up conservation agriculture in the Omusati Region of Namibia. Both socioeconomic and biophysical data were collected through household face-to-face interviews from 40 households located in seven constituencies of the Omusati Region. The results showed that technological know-how, limited agricultural inputs and implements for conservation agriculture hindered the uptake of conservation agriculture. In addition, lack of crop residues for mulching purposes and little understanding of the importance of crop rotation were identified as barriers to practice conservation agriculture. Logistic regression analysis showed that age, gender, marital status, education level, crop field size and farming period did not significantly influence the adoption of conservation agriculture. The study indicates that there is a need to encourage the use of climate smart agriculture technologies such as conservation agriculture, which minimizes the negative impacts of dry spells in order to maximize crop production and increase farmers' understanding on the principles of conservation agriculture. Thus, strategies and policies to reduce poverty need to consider local contexts, social norms and values. In this regard, engagement of local farmers and demonstration of the short and long-term benefits of conservation agricultural practices offer promising entry points.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".