Simulation of the pea crop development using AquaCrop model in Chichaoua region, Morocco: Application for irrigation management
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».