Evidence map›Paper›PMID 39085329›Full record

ArticleScientific reports2024

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

Yantao Ma, Jie Xue, Xinlong Feng, Jianping Zhao, Junhu Tang, Huaiwei Sun, Jingjing Chang, Longke Yan

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yantao MaCollege of Mathematics and System Science, Xinjiang University, Urumqi, 830046, China.
Jie XueState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, Xinjiang, China. xuejie11@ms.xjb.ac.cn.
Xinlong FengCollege of Mathematics and System Science, Xinjiang University, Urumqi, 830046, China. fxlmath@gmail.com.
Jianping ZhaoCollege of Mathematics and System Science, Xinjiang University, Urumqi, 830046, China.
Junhu TangCollege of Ecology and Environment, Xinjiang University, Urumqi, 830046, China.
Huaiwei SunSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Jingjing ChangState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, Xinjiang, China.
Longke YanCollege of Mathematics and System Science, Xinjiang University, Urumqi, 830046, China.

Funding

National Natural Science Foundation of China 42071259
6 · The paper itself

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 optimal timescale (R

Indexed as

Crop suitability assessmentCrop water productivityDynamic Bayesian networkDynamic planting distributionTimeGAN

Identifiers

PMID39085329
PMCPMC11754617

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.