Effect of climate change on maize yield in Western Ethiopia
Bibliographic record
Abstract
The main objective of the study was to examine trends of maize yield and climate change variables and assess the effect of climate change variables on maize yield in the study area. The data were analyzed using the Mann-Kendall trend test and Sen’s slope estimator to describe the trends of maize yield and climate change variables and Autoregressive Distributed Lag (ARDL) model to estimate the effect of climate change on maize yield. The result of the Bound co-integration test shows that, there is only short-run relationship between the maize yield and rainfall, average minimum and maximum temperature. The finding of the study shows that the average maize yields of western Ethiopia was 29.13 quintals for the last 33 years. The results of the ARDL model revealed that an increase in rainfall has a positive and significant effect on maize yield at 10% significance level and average annual minimum temperature has also a positive and significant effect on maize yield at 5% significance level. Therefore, the government should strengthen its effort to implement the green economy strategy to reduce possible effect of change in annual rainfall, average minimum and maximum temperature on maize yield to enhance agricultural productivity and improve the food insecurity of farm households in Ethiopia.
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 | 0.000 |
| 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".