Implications of water management on methane emissions and grain yield in paddy rice: A case study under subtropical conditions in Brazil using the CSM-CERES-Rice model
Notice bibliographique
Résumé
Rice ( Oryza sativa L.) is a staple food and plays a crucial role in the food security of many countries. However, rice cultivation is associated with significant methane (CH 4 ) emissions, contributing to overall greenhouse gas emissions and, thus, climate change. In this context, process-based crop models are useful tools for understanding and predicting the complex interactions between crop production, environmental factors, and sustainability. The objective of this study was to evaluate the performance of the Cropping System Model (CSM)-CERES-Rice model and DSSAT-GHG module to predict daily methane emissions and rice grain yield for different irrigation practices in a subtropical environment. The study employed a comprehensive approach, including measurements of daily CH 4 emissions, phenological stages, final aboveground biomass, and grain yield for rice cultivars BRS Pampa, BRS Pampeira, A705, and XP113 conducted over four consecutive crop seasons (2019–2023) and two irrigation systems: continuous flooding (CF) or alternate wetting and drying (AWD) in Capão do Leão, RS, Brazil. We followed a four-step methodology involving initial calibration of cultivar parameters, sensitivity analysis (soil-related parameters associated with CH 4 emissions), final cultivar parameters calibration, and long-term simulation analysis. Based on the sensitivity analysis and comparison to observed emissions, modifications were made to soil-related parameters such as soil buffer regeneration after drainage events (BRAD) and the fraction of soil water-filled porosity above which methane production occurs (WFPS thresh ) to enhance the accuracy of methane production. Optimal parameter combinations (WFPS thresh = 70 %, BRAD = 0.070 d −1 ) were selected based on a comparative analysis, enabling CH 4 simulations under non-flooded conditions. The predictive capability of the CERES-Rice model exhibited an average bias for grain yield of 485 kg ha −1 under CF and 592 kg ha −1 under AWD conditions. The results showed that the GHG module of DSSAT, after BRAD and WFPS thresh parameter adjustments, was able to simulate daily CH 4 emissions in paddy rice with a very good agreement (average index of agreement (D-Statistic) of 0.87 for CF and 0.70 for AWD). Following the model evaluation, long-term simulations for different irrigation practices revealed the impact on grain yield, cumulative methane emissions, and seasonal applied irrigation. The highest crop water-methane productivity (CWMP = 52 %) was observed under sprinkler irrigation at 50 % soil water depletion, identifying it as the most sustainable option in this subtropical environment. Thus, the CSM-CERES-Rice model combined with the DSSAT-GHG module proved to be a potential tool for agricultural and environmental management of rice fields under subtropical conditions. • Simulations of methane emissions from crop model were compared with measured data. • Sensitivity analysis showed the model simulated methane only under flooded conditions. • Soil parameters were adjusted to simulate methane under non-flooded conditions. • The crop model can be a potential tool for predicting methane production in rice.
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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 | 0,000 |
| 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 ».