Difference in Soil Fertility Agricultural Training, Local Livestock Feed Use and Weather Information Access: A Comparative Study of Small-Scale Farmers in Mozambique and Zambia
Bibliographic record
Abstract
This study employs a mixed-method approach, including surveys with 498 smallholder farmers as respondents (186 in Mozambique and 312 in Zambia) and focus group discussions, to compare sustainable soil management and livestock feed management practices. This study shows critical gaps in agricultural extension, significant differences were found, with a higher proportion of Zambian farmers receiving training on soil fertility management (42.2% versus 3.2% in Mozambique, p < 0.001) and using locally produced feeds (78.5% versus 1.6%, p < 0.001). Whereas access to weather information was higher in Mozambique (50.5%) than in Zambia (22.8%). The findings show critical gaps in agricultural extension in Mozambique and Zambia in areas under cowpea, oilseed crops, and vegetables (t = 8.375, p < 0.001; t = 4.138, p < 0.001; and t = 3.104, p < 0.002, respectively). We recommend targeted investment in farmer training programs, including feed formulation and context-specific weather information dissemination to enhance climate resilience and food security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".