Effects of temperature changes on maize production in Mozambique
Bibliographic record
Abstract
We examined intraseasonal changes in maize phenology and heat stress exposure over the 1979-2008 period, using Mozambique meteorological station data and maize growth requirements in a growing degree-day model. Identifying historical effects of warming on maize growth is particularly important in Mozambique because national food security is highly dependent on domestic food production, most of which is grown in already warm to hot environments. Warming temperatures speed plant development, shortening the length of growth periods necessary for optimum plant and grain size. This faster phenological development also alters the timing of maximum plant water demand. In hot growing environments, temperature increases during maize pollination threaten to make midseason crop failure the norm. In addition to creating a harsher thermal environment, we find that early season temperature increases have caused the maize reproductive period to start earlier, increasing the risk of heat and water stress. Declines in time to maize maturation suggest that, independent of effects to water availability, yield potential is becoming increasingly limited by warming itself. Regional variations in effects are a function of the timing and magnitude of temperature increases and growing season characteristics. Continuation of current climatic trends could induce substantial yield losses in some locations. Farmers could avoid some losses through simple changes to planting dates and maize varietal types.
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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".