Enhancing AquaCrop model precision for accurate simulation of sweet potato and taro landraces
Bibliographic record
Abstract
Neglected and underutilised crop species (NUS) such as orange-fleshed sweet potato (OFSP) and taro are nutrient-dense, climate-resilient crops with high potential to diversify food systems. While the AquaCrop model has been calibrated to simulate canopy cover (CC), biomass, and yield for both crops, independent testing across diverse agro-ecological zones is required to critically assess model robustness. We, therefore, evaluated AquaCrop’s ability to simulate the growth and yield of OFSP and taro at three locations in the KwaZulu-Natal province, South Africa. Critical recalibration adjustments included reducing taro’s maximum rooting depth, modifying soil water depletion thresholds to better reflect water stress, and parameterising phenology based on tuber mass stabilisation. Recalibration improved model performance for CC (R 2 , coefficient of determination, up to 0.954 for OFSP; 0.632 for taro), biomass (NSE, Nash-Sutcliffe efficiency, up to 0.975), and final yield (absolute deviations ≤ 6% under optimal irrigation). Validation across three locations confirmed that AquaCrop reliably simulates growth and yield under non-stressed conditions, although performance declined under water-limited environments. The model was run in growing degree-day mode to account for climate variability, which is recommended for future validations. These results demonstrate that, with high-quality calibration datasets representing multiple landraces, AquaCrop can provide reliable yield predictions for NUS. This enables more accurate water management, operational yield predictions, and climate risk assessments for both smallholder and commercial farmers. By bridging the modelling gap for NUS, this work supports their integration into climate adaptation strategies, strengthens food and nutrition security, and promotes resilient agricultural diversification under variable climatic conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".