The determinants of crop yields in Uganda: what is the role of climatic and non-climatic factors?
Bibliographic record
Abstract
It is widely accepted that crop yields will be affected by climate change. However, the role played by climate in affecting crop yields vis-a-vis non-climatic stresses, is often unclear, limiting decision choices around efforts to promote increased production in light of multiple stresses. This study quantifies the role of climatic and non-climatic factors affecting multiple crop yields in Uganda, utilizing a systematic approach which involves the use of a two-stage multiple linear regression to identify and characterize the most important drivers of crop yield, examine the location of the key drivers, identify the socio-economic implications of the drivers and identify policy options to enhance agricultural production. We find that non-climatic drivers of crop yields such as forest area dynamics ( p = 0.012), wood fuel ( p = 0.032) and usage of tractors (0.041) are more important determinants of crop yields than climatic drivers such as precipitation, temperature and CO 2 emissions from forest clearance. Climatic drivers are found to multiply existing risks facing production, the significance of which is determined by variability and inadequate distribution of precipitation over the crop growing seasons. The significance and validity of these results is observed in an f -statistic of 50 for the final optimized model when compared to the initial model with an f -statistic of 19.3. Research and agricultural policies have to be streamlined to include not only the climatic elements but also the non-climatic drivers of global, regional and national agricultural systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".