Soil-crop long-term feedback matters to assess climate change impact on maize yield in Sub-Saharan Africa
Bibliographic record
Abstract
Sub-Saharan Africa (SSA) faces significant food security risks, primarily due to low soil fertility leading to low crop yields. Climate change is expected to worsen food security issues in SSA due to a combined negative impact on crop yield and soil fertility. A common omission from climate change impact studies in SSA is the interaction between change in soil fertility and crop yield. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, is expected to increase crop yield but it is uncertain how this advantage is maintained with climate change.   We explored the impact of scenarios of change in soil fertility and climate variables (temperature, rainfall, and CO2) on rainfed maize yield in four representative sites in SSA with no input and ISFM management. To do so, we used an ensemble of 15 calibrated soil-crop models. Reset and continuous simulations were performed to assess the impact of soil fertility vs climate change on crop yield. In reset simulations, SOC, soil N and soil water were reinitialized each year with the same initial conditions. In continuous simulations, SOC, soil N and soil water values of a given year were obtained from the simulation of the previous year, allowing cumulative effects on SOC and crop yields.Most models agreed that with current baseline (no input) management, yield changed by a much larger order of magnitude when considering declining soil fertility with baseline climate (-39%), compared with considering constant soil fertility but changes in temperature, rainfall and CO2 (from -12% to +5% depending on the climate variable considered). The interaction between change in soil fertility and climate variables only marginally influenced maize yield (high agreement between models). The model ensemble indicated that when accounting for soil fertility change, the benefits of ISFM systems over no-input systems increased over time (+190%). This increase in ISFM benefits was greater in sites with low initial soil fertility. We advocate for the urgent need to account for soil-crop long-term feedback in climate change studies to avoid large underestimations of climate change and ISFM impact on food production in SSA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".