Uptake And Use Of Climate Information Services To Enhance Agriculture And Food Production Among Smallholder Farmers In Eastern And Southern Africa Region.
Bibliographic record
Abstract
This study evaluates the contribution of climate information services (CIS) to agriculture and food production, and rural household incomes in selected Climate Change Adaptation in Africa projects in Eastern and Southern Africa region. It establishes the resilience of projects after completion; factors influencing the sustainable use of CIS in the project area and beyond; and the benefits of institutionalization of climate information services through organized groups and extension services. Existing project documents were reviewed; questionnaires and interviews conducted with farmers, field project officers and key informants to achieve the study objectives. The results showed that institutionalization of climate information services through organized groups such as farmer groups and extension services enhance climate resilient agriculture. Access, consistency, reliability and relevance of the climate information to farmers? needs were fundamental in integration of climate information into household decision making. Thus translation and communication of the seasonal forecasts in local languages empowers farmers to make informed farm management decisions. Use of CIS increases agricultural yields by between 5% to more than 75% and gender, age, education levels and household sizes influence the use of CIS in farm decisions. Therefore, investment in adult literacy and women involvement are key to use of CIS for increased productivity in Africa.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".