Suitability evaluation of tea cultivation is very important for improving the yield and quality of tea, which can avoid blind expansion and achieve sustainable development; however, to date, relevant research at town and village scales is lacking. This study selected Xinming Township in Huangshan City, Anhui Province, as the study area, which is the main production area of Taiping Houkui Tea—one of the ten most famous teas in China. We proposed a machine learning-based tea cultivation suitability evaluation model by comparing logistic regression (LR), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), random forest (RF), Gaussian Naïve Bayes (GNB), and multilayer perceptron (MLP) to calculate the weight accuracy of the evaluation factors. We then selected 12 factors, including climate, soil, terrain, and ecological economy factors, using the RF with the highest accuracy to calculate the evaluation factor weights and obtained the suitability evaluation results. The results show that the highly suitable area, moderately suitable area, generally suitable area, and unsuitable area land categories for tea cultivation were 14.13%, 27.25%, 32.46%, and 26.16%, respectively. Combined with field research, the highly suitable areas were mainly distributed in northwest Xinming Town, which is in line with the distribution of tea cultivation at the Xinming township level. The results provide a scientific reference to support land allocation decisions for tea cultivation and sustainable green agricultural development at the town and village scales.

Understanding the relationship between drought and the water use efficiency (WUE) in terrestrial ecosystems can help reduce drought risk. It remains unclear what the correlation between the cropland water use efficiency (CWUE) and drought during drought events. We aim to identify the spatiotemporal relationship between drought and the CWUE and to ensure the service capacity of cultivated land ecosystems. In this study, the cubist algorithm was used to establish a monthly integrated surface drought index (mISDI) dataset for the Huang–Huai–Hai Plain (HHHP), and the run theory was used to identify drought events. We assessed the spatio-temporal variations of drought in the HHHP during 2000–2020 and its influence on the CWUE. The research results were as follows: from the overall perspective of the HHHP, the mISDI showed a downward trend. Drought had an enhanced effect on the CWUE of the HHHP, and the enhancement of the CWUE in the eastern hilly area was more significant. The CWUE response to drought had a three-month lag period and a significant positive correlation, and it was shown that the cultivated land ecosystems in this area had strong drought resistance ability. This study provides a new framework for understanding the response of the CWUE to drought and formulating reasonable vegetation management strategies for the HHHP.

Natural disasters occur frequently causing huge economic losses and reduced grain production. Therefore, it is important to thoroughly explore the spatial correlations between grain, disaster, and the economy. Based on inter-provincial panel data in China in 2019, this study integrates complex network and co-occurrence theory into a coupled grain–disaster–economy (GDE) multilayer network, which provides a new perspective to further explore the spatial correlation between these three systems. We identify the spatial coupled characteristics of the GDE multilayer network using three aspects: degree, centrality, and community detection. The research results show the following: (1) Provinces in the major grain-producing regions have a stronger role in allocating and controlling grain resources, and the correlation between grain and disasters in these provinces is stronger and more prone to disasters. Whereas provinces in the Beijing–Tianjin–Hebei economic zone, and the Yangtze River Delta and Pearl River Delta economic zones, such as Beijing, Tianjin, Jiangsu, Shanghai, and Zhejiang, have a high level of economic development, thereby a stronger ability to allocate economic resources. (2) The economic subsystem assumes a more important, central role compared with the grain and disaster subsystems in the formation and development of the coupled GDE multilayer network, with a stronger coordination for the co-development between the complex grain, disaster, and economy systems in the nodal provinces of the network. (3) The community modularity of the coupled GDE multilayer network is significantly higher than that of the three single-layer networks, indicating a more reasonable community division after coupling the three subsystems. The identification of the spatial characteristics of GDE using multilayer network analysis offers a new perspective on taking various measures to improve the joint sustainable development of grain, disaster, and the economy in different regions of China according to local conditions.