CLIMATE CHANGE AND VARIABILITY: SMALLHOLDER FARMING COMMUNITIES IN\nZIMBABWE PORTRAY A VARIED UNDERSTANDING
Bibliographic record
Abstract
Increasing awareness of risks associated with climate change and variability among smallholder farmers is critical in building their capacity to develop the necessary adaptive measures.Using farmer participatory research approaches and formal questionnaire surveys, interaction has been made with >800 farmers in two distinct smallholder farming systems of Makoni and Wedza Districts in eastern Zimbabwe to determine the current level of understanding of climate change and variability, current responses to perceived changes, as well as identify sources of agro-meteorological information.The results indicated that farmers portrayed a varied understanding both within and across the study sites.While poor rainfall distribution was seen as the major indicator for climate change by over two-thirds of the respondents in both sites, more farmers in Makoni attributed delay in onset of rains, high incidences of flush floods and unpredictable 'wind movements' yielding cyclones to climate change.In Wedza, it was recurrent droughts, winter and summer temperature extremes, and increased pest and disease incidences for both crops and livestock that indicated climate change.Perceived changes were linked more to natural and human forces (Makoni), unknown forces as well as breakdown in cultural norms and beliefs and rise of Christianity (Wedza).Disparities between the two sites could be attributed to the inherent differences of the communities in terms to their social settings.The national extension, Agritex, was ranked first by 50-60% of the farmers as major source of weather information.Electronic media (radio and television) ranked second with 47% of farmers in Makoni and 35% in Wedza.Concerns were raised over inadequacies of such information, apparent lack of reliability, timing and frequency of dissemination that directly influenced the utilisation of the information.Common coping strategies included early planting which was the more prevalent in Makoni, while combinations of cereals and legumes were a preferred solution in Wedza.We concluded that farmers' interaction with various information sources and knowledge sharing platforms needs to be enhanced in order to deepen their understanding as well as increase their capacity to respond to pressures of climate variability and change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".