Climate change and maize yield in southern Africa: what can farm management do?
Bibliographic record
Abstract
There is concern that food insecurity will increase in southern Africa due to climate change. We quantified the response of maize yield to projected climate change and to three key management options - planting date, fertilizer use and cultivar choice - using the crop simulation model, agricultural production systems simulator (APSIM), at two contrasting sites in Zimbabwe. Three climate periods up to 2100 were selected to cover both near- and long-term climates. Future climate data under two radiative forcing scenarios were generated from five global circulation models. The temperature is projected to increase significantly in Zimbabwe by 2100 with no significant change in mean annual total rainfall. When planting before mid-December with a high fertilizer rate, the simulated average grain yield for all three maize cultivars declined by 13% for the periods 2010-2039 and 2040-2069 and by 20% for 2070-2099 compared with the baseline climate, under low radiative forcing. Larger declines in yield of up to 32% were predicted for 2070-2099 with high radiative forcing. Despite differences in annual rainfall, similar trends in yield changes were observed for the two sites studied, Hwedza and Makoni. The yield response to delay in planting was nonlinear. Fertilizer increased yield significantly under both baseline and future climates. The response of maize to mineral nitrogen decreased with progressing climate change, implying a decrease in the optimal fertilizer rate in the future. Our results suggest that in the near future, improved crop and soil fertility management will remain important for enhanced maize yield. Towards the end of the 21st century, however, none of the farm management options tested in the study can avoid large yield losses in southern Africa due to climate change. There is a need to transform the current cropping systems of southern Africa to offset the negative impacts of climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".