Climate Change Anomalies and Beans Production in Nigeria
Bibliographic record
Abstract
This study evaluated the impact of climate change on yield and net revenue of beans production in Nigeria using Feasible Generalised Least Square and Hedonic Ricardian Approach. Secondary data were used for this study; monthly rainfall and temperature data from 1981 to 2019 were obtained from Nigeria Meteorological agency while data on socioeconomic and demographic characteristics as well as farm production for 2000 beans farmers across the six agro-ecological zones were obtained from General household survey wave IV. The study reveals that beans crop is sensitive to infinitesimal change in temperature than rainfall. The marginal impact analysis of increasing temperature and rainfall indicated that a unit increase in rainfall decreased the net revenue of beans by N14,997 per hectare, while a unit increase in temperature increased the net revenue of the beans production in Nigeria by N15,316 per hectare. The adjusted mean yield of beans will increase by 1.058kg per hectare with a unit increase in temperature while it will reduce 0.173 per hectare with a unit increase in rainfall. The study also examined the impact of predicted climate scenarios from two models namely Canadian Climate Change and Parallel Climate Model on net revenue for the years 2050 and 2100. All these models indicated increasing temperature would have a positive impact on the net revenue from beans production for the year 2050 but the impact will be negative by year 2100. This means there will be more profit from beans production in year 2050 while in 2100 the beans farmers will produce at loss. Nigeria government should therefore consider designing and implementing adaptation policies to counteract the harmful impacts of climate change on beans production.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".