IMPACT OF PROJECTED CLIMATE CHANGE ON AGRICULTURAL PRODUCTION IN\nSEMI-ARID AREAS OF TANZANIA: A CASE OF SAME DISTRICT
Bibliographic record
Abstract
Sub-Saharan Africa is one of the most vulnerable regions in the World to climate change because of widespread poverty and limited adaptive capacity.The future climate change is likely to present an additional challenge to the agricultural sector.Therefore, the effects of climate change on the current agronomic management practices were investigated using Same District, Tanzania as a case study area.APSIM software was used to investigate the response of maize (Zea mays L.) yield to different agronomic management practices using current and future (2046 -2065) climate data.The climate change projections data from global climate models were downscaled using self-organising maps technique.Under the conventional practices, results show that during long rainy season (from March to May) there is yield decline of 13% for cultivar Situka, no change for cultivar Kito and increase of 10% and 15% for cultivars Sc401 and TMV1, respectively.Under the recommended practices, cultivars TMV1 and Sc401 are projected to register a 10% yield increase whereas cultivars Situka and Kito are projected to register a decrease of 10% and 45%, respectively.Also, under both conventional and recommended management practices, results showed that during short rainy season (from October to December/January) all cultivars are projected to register between 75% and 146% increase in maize yields.This implies that future climate change is going to have positive effects on current management practices during short rainy seasons and it will have negligible impact during long rainy seasons.
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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 | 0.001 | 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".