Saline–alkali land is an important land resource with high development potential and an important ecological product with high potential ecosystem service value (ESV). It is of great significance for rational development and protection of saline–alkali land resources to carry out research on the evaluation methods of the ESV of saline–alkali land and to scientifically quantify the ESV of saline–alkali land development. Taking typical saline–alkali land in the Yellow River Delta on the Huanghe Island of China as an example, this research constructed an ESV evaluation index system from the perspective of functional value containing three first-level indicators such as economic value, four second-level indicators such as supply service, and eight lowest-level indicators such as food production. It also considered production and operation costs and evaluated the ESV of the Huanghe Island using the ecological economics method. The results showed that the ESV of the Huanghe Island is CNY 133.586 million, and the unit area value is 50,200 CNY/hm², which is a relatively high value. Among them, the supply service value was the highest, accounting for more than half. In recent years, the development of the Huanghe Island has produced high supply and service value, but due to weak background conditions and low vegetation coverage, regulation and support service values are relatively low, the cultural service value is still to be explored, and there is great potential for ecological and social value. This research can provide references for scientific evaluation of the ESV of saline–alkali land and provide a basis for rational development and utilization of the Huanghe Island.

The use of vegetable greenhouse production systems has increased rapidly because of the increasing demand for food materials. The vegetable greenhouse production industry is confronted with serious environmental problems, due to their high agrochemical inputs and intensive utilization. Besides this, antibiotic-resistant bacteria, carrying antibiotic-resistance genes (ARGs), may enter into a vegetable greenhouse with the application of animal manure. Bacterial communities and ARGs were investigated in two typical vegetable-greenhouse-using counties with long histories of vegetable cultivation. The results showed that Proteobacteria, Firmicutes, Acidobacteria, Chloroflexi, and Gemmatimonadetes were the dominant phyla, while aadA, tetL, sul1, and sul2 were the most common ARGs in greenhouse vegetable soil. Heatmap and principal coordinate analysis (PCoA) demonstrated that the differences between two counties were more significant than those among soils with different cultivation histories in the same county, suggesting that more effects on bacterial communities and ARGs were caused by soil type and manure type than by the accumulation of cultivation years. The positive correlation between the abundance of the intI gene with specific ARGs highlights the horizontal transfer potential of these ARGs. A total of 11 phyla were identified as the potential hosts of specific ARGs. Based on redundancy analysis (RDA), Ni and pH were the most potent factors determining the bacterial communities, and Cr was the top factor affecting the relative abundance of the ARGs. These results might be helpful in drawing more attention to the risk of manure recycling in the vegetable greenhouse, and further developing a strategy for practical manure application and sustainable production of vegetable greenhouses.

To advance site-specific management of soil volumetric moisture content (VMC), this study analyzed the spatio-temporal evolution characteristics of soil VMC using the method of sequential Gaussian simulation (SGS) during the different growth stages of winter wheat. This was compared with data measured by time domain reflectometry (TDR) which is a well-established electromagnetic technique to measure soil VMC. The spatial autocorrelation coefficient of VMC indicated the strongest clustering of VMC in the tillering stage, and the least clustering of VMC in the harvest stage. A threshold of VMC in topsoil in the jointing stage of winter wheat was put forward. This threshold is 26, signifying that at a lower value, irrigation should be performed and irrigation efficiencies can be improved. Stable and sub-stable areas in the spatial variability maps of VMC were identified in the winter wheat jointing stage. Furthermore, the optimal irrigation stage was the early jointing stage, and irrigation was performed once as a guide. A loose-couple spatial model was constructed using the VMC in topsoil and the volume of water for irrigation. The VMC in the jointing stage of winter wheat was linked with efficient and water-saving irrigation.

Quickly obtaining accurate soil quality information is the premise for accurate agricultural production and increased crop yield. With the development of the digital information industry, smart agriculture has become a new trend in agricultural development and there is increasing demand for efficiently and intelligently acquiring good soil quality information. Scientists worldwide have developed many remote sensing quantitative inversion models, which need to be systematized and intelligent for agricultural personnel to enjoy the dividends of information technology such as 3S (remote sensing, geographic information system, and global navigation satellite system) techniques. Accordingly, to meet the need of farmers, agricultural managers, and agricultural researchers to acquire timely information on regional soil quality, in this paper, we designed a cloud platform for inversion analysis of moisture, nutrient, salinity, and other important soil quality indicators. The platform was developed using ArcGIS (The software is produced by the Environmental Systems Research Institute, Inc. of America in Redlands, CL, USA) and GeoScene (The software is produced by GeoScene Information Technology Co.,Ltd., Beijing, China) software, with Java and JavaScript as programing languages and SQL Server as the database management system with a PC client, a web client, and a mobile app. On the basis of the existing quantitative remote sensing models, the platform realizes mapping functions, intelligent inversion of soil moisture–nutrient–salinity (SMNS) content, data analysis mining, soil knowledge base, platform management, and so on. It can help different users acquire, manage, and analyze data and make decisions based on the data. In addition, the platform can customize model parameters according to regional characteristics, improving analysis accuracy and expanding the application area. Overall, the platform employs 3S techniques, Internet technology, and mobile communication technology synthetically and realizes intelligent inversion and decision analysis of significant soil quality information, such as moisture–nutrient–salinity content. This platform has been applied to the analysis of soil indicators in several areas and has produced good operational results and benefits. This study will enable rapid data analysis and provide technical support for regional agriculture production, contributing to the development of smart agriculture.