Climate Change Impacts And Adaptation Barriers Among Smallholder Cassava Farmer
Bibliographic record
Abstract
Abstract Climate variability/change has varied impacts on crop yields in many Sub-Saharan African countries. Enhancing the adaptive capacity of rural farmers is a major challenge as climate change becomes threatened to agricultural activities in the region. In this study, the impacts of climate change on cassava crops were examined, and farmers’ perceptions of climate, change and their experienced adaptation methods were assessed. Historical climate data and social datasets were used; the adaptation option and barriers to the use of adaptation methods were obtained through questionnaires, semistructured interviews and focus group discussions. Correlation statistics and multiple regressions were utilized to show the impacts of climate on the yield of cassava. The results showed a high variation in climatic variables together with an obvious anomaly index with severity. Minimum and maximum temperatures correlated strongly and positively with the yield of cassava, with 0.86, 0.82 and 0.87, respectively, which were significant at p>0.05. The results of multiple regression showed that climate parameters accounted for 75% of the changes in yield. The results also showed a very strong relationship between crops and rainfall in the early growing season at the p<0.05 level of significance. The majority of the farmers perceived that lack of capital and financial, physical and human capital accounted for 70% of barriers to the implementation of climate change adaptation methods. The key findings here are that the cropping system has been impacted by climate change and that the adaptive capacity of rural farmers in the study area is generally low. The study concludes that although climate change is obvious, there is generally a need to enhance the adaptation options available to farmers in the region.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".