Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network.

ABSTRACT: Enhancing crop water productivity is crucial for regional water resource management and agricultural sustainability, particularly in arid regions. However, evaluating the spatial heterogeneity and temporal dynamics of crop water productivity in face of data limitations poses a challenge. In this study, we propose a framework that integrates remote sensing data, time series generative adversarial network (TimeGAN), dynamic Bayesian network (DBN), and optimization model to assess crop water productivity and optimize crop planting structure under limited water resources allocation in the Qira oasis. The results demonstrate that the combination of TimeGAN and DBN better improves the accuracy of the model for the dynamic prediction, particularly for short-term predictions with 4 years as the op

SUBMITTER: Ma Y 

PROVIDER: S-EPMC11754617 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature