Ma Yantao, Xue Jie, Feng Xinlong, Zhao Jianping, Tang Junhu, Sun Huaiwei, Chang Jingjing, Yan Longke
College of Mathematics and System Science, Xinjiang University, Urumqi, 830046, China.
State 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.
Sci Rep. 2024 Jul 31;14(1):17695. doi: 10.1038/s41598-024-68523-3.
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 > 0.8). Based on the spatial distribution of crop suitability analysis, wheat and corn are most suitable for cultivation in the central and eastern parts of Qira oasis while cotton is unsuitable for planting in the western region. The walnuts and Chinese dates are mainly unsuitable in the southeastern part of the oasis. Maximizing crop water productivity while ensuring food security has led to increased acreage for cotton, Chinese dates and walnuts. Under the combined action of the five optimization objectives, the average increase of crop water productivity is 14.97%, and the average increase of ecological benefit is 3.61%, which is much higher than the growth rate of irrigation water consumption of cultivated land. It will produce a planting structure that relatively reduced irrigation water requirement of cultivated land and improved crop water productivity. This proposed framework can serve as an effective reference tool for decision-makers when determining future cropping plans.
提高作物水分生产率对于区域水资源管理和农业可持续发展至关重要，特别是在干旱地区。然而，面对数据限制评估作物水分生产率的空间异质性和时间动态性是一项挑战。在本研究中，我们提出了一个框架，该框架整合了遥感数据、时间序列生成对抗网络（TimeGAN）、动态贝叶斯网络（DBN）和优化模型，以评估作物水分生产率，并在策勒绿洲有限的水资源分配条件下优化作物种植结构。结果表明，TimeGAN和DBN的结合能更好地提高模型动态预测的准确性，特别是对于以4年为最佳时间尺度的短期预测（R > 0.8）。基于作物适宜性分析的空间分布，小麦和玉米最适合在策勒绿洲的中部和东部种植，而棉花不适合在西部地区种植。核桃和红枣在绿洲东南部主要不适宜种植。在确保粮食安全的同时最大化作物水分生产率导致棉花、红枣和核桃的种植面积增加。在五个优化目标的共同作用下，作物水分生产率平均提高14.97%，生态效益平均提高3.61%，远高于耕地灌溉用水量的增长率。这将产生一种相对减少耕地灌溉用水需求并提高作物水分生产率的种植结构。这个提出的框架可以作为决策者在确定未来种植计划时的有效参考工具。
