Climate change impacts on cattle production: analysis of cattle herders’ climate variability/change adaptation strategies in Nigeria
Bibliographic record
Abstract
Abstract The study examines the seasonality in climate and extreme weather events, and its effect on cattle production in the Guinea Savannah ecological zone of Nigeria. The study uses both quantitative and qualitative approaches. Climate data of 34 years were used to examine the trends in rainfall pattern and climate variability while household survey was used to appraise the herders’ awareness of climate variability/change impacts and adaptation strategies. Cumulative Departure Index (CDI) method was used to assess the extreme weather events while descriptive statistics and multinomial logistic (MNL) regression model were used to identify the factors that determined herders’ adaptation strategies to climate change. The results revealed a significant spatiotemporal variation in both rainfall and temperature with CDI ranging from -1.39 to 3.3 and -2.3 to 1.81 respectively. The results revealed a reduction in the amount of water available for cattle production. From survey results, 97.5% of the herders identified drought as the major extreme weather event affecting livestock productivities in the study region. In the herder’s perception, the droughts are more severe in recent years than 34 years ago. The results from MNL revealed that extreme weather events, such as drought, has a positive likelihood on migration, at a 10% level of significance, the events has led to migration of cattle herders from the northern part of the study area toward the southern part in recent years.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".