Simulation of the pea crop development using AquaCrop model in Chichaoua region, Morocco: Application for irrigation management
Bibliographic record
Abstract
This study aims to calibrate and validate the AquaCrop model to accurately simulate key growth parameters of pea crops in the semi-arid Chichaoua region of central Morocco. It specifically targets, canopy cover (CC), actual evapotranspiration (ETa), total soil water content (SWC), biomass (B), and grain yield (GY). The model was firstly calibrated using observed data from the 2022 growing season, while data from the 2018 growing season were used for validation. The calibration process focused on identifying key parameters, including water productivity (WP), harvest index (HI), and maximum crop transpiration coefficient (KcTr,x), by minimizing the differences between observed data and simulated model outputs. The optimal values obtained were 14.6 g/m² for WP, 39 % for HI, and 0.95 for KcTr,x. Overall, it was found that the model effectively simulated all the growth parameters during the calibration and validation processes. The average Root Mean Square Error (RMSE) between observed and simulated CC was 3.7 %. In addition, biomass and grain yields for the calibration season were simulated with RMSE values of 100 and 74 kg ha −1 , respectively. Furthermore, the model performed well in simulating ETa and SWC with RMSE values for ETa and SWC of 0.57 mm/day and 20 mm/m. For the validation phase, ETa and SWC were well retrieved, the RMSE values were 0.80 mm/day and 59.74 mm/m, respectively. This calibrated model can serve as a valuable tool for decision-makers in agriculture by supporting the development of efficient irrigation strategies, optimizing water use, and maintaining crop productivity under water-limited conditions. Finally, the AquaCrop model was used to schedule irrigation based on the root zone water depletion threshold (Dr, threshold) across the field. The findings demonstrated that irrigating at 40 % depletion of TAW serves as an effective threshold for enhancing pea irrigation management. This threshold allows for a significant reduction of 40 mm in water use over the growing season. This analysis revealed important insights into irrigation efficiency, showing to farmers that excessive water applications often fail to translate into yield improvements while contributing to significant water losses. In semi-arid regions, irrigation practices are the most important factor in agriculture, so our study highlights to farmers the importance of proper irrigation timing and the importance of meeting the actual crop requirements. • AquaCrop was well calibrated and validated for estimating key growth parameters of pea. • The average RMSE are 3.7% for CC, 100 kg ha -1 for biomass, and 74 kg ha -1 for grain yield. • The Main calibration parameters are 39% for HI, 14.6 g/m² for WP*, and 0.95 for KcTr,x. • Irrigating at 40% TAW depletion reduces water use by 40 mm over the season.
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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 | 0.001 |
| 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) | 0.000 | 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".