Xiaosan Jiang

Heavy metal pollution in soil–crop systems has attracted great attention globally, caused by rapid urbanization and intensive industrialization. The research aims to investigate the environmental quality of the agricultural production area in Taizhou City, a typical economic region that is along the Yangtze River in the Southeast of China. A total of 370 sampling sites were chosen, with 370 soil, rice (Oryza sativa L.) and wheat (Triticum aestivum L.) samples collected, respectively, for measuring and analyzing the status, spatial distribution and pollution level of different heavy metals. The mean values of soil Cr, Pb, Cd, As and Hg were 66.78, 32.88, 0.23, 8.16 and 0.16 mg/kg, which were lower than the risk control standard values (RCV). However, the mean values of Pb, Cd and Hg were 1.25-, 1.77- and 2-fold larger than their soil background values (SBV) due to the intensive anthropogenic activities. The average content of Cd in rice exceeded its food safety limiting values (FCV) by 0.05 mg/kg, and the average contents of Pb in rice and wheat both exceeded the relevant FSV by 0.42 and 0.186 mg/kg, respectively. In addition, the maximum As and Cr contents in rice and wheat could be 0.13, 0.46 mg/kg and 0.63, 3.5 mg/kg larger than the relative FCVs in certain areas. Most of the high-value areas of soil and crop heavy metals were mainly located in Xinghua City, Taixing City and Jiangyan District, which had a similar distribution pattern with local industries or anthropogenic activities. The heavy metal pollution in soils and crops was found to be inconsistent, as 8.94% of the arable land possessed lightly metal pollution, while 3.18% of the area of rice and 4.0% of the area of wheat suffered severe pollution, with excessive accumulation of Cr, Pb and Cd. Based on the heavy metal pollution assessment of soil–crop systems, approximately 83% of the study area possessed medium or higher environmental quality, which was preferable for agricultural production. Our results implied that the spatial distribution and pollution level of the heavy metals in soil–crop systems were significantly influenced by industrial activities, followed by agricultural sources, transportation emissions and so on. Therefore, continuous monitoring and source control of heavy metals, especially for Cr, Pb and Cd, should be conducted to ensure the regional environmental quality and food security.

The use of rotations is an effective strategy to control crop diseases and improve plant health. The soil bacterial communities in the rhizosphere are highly important for maintaining soil productivity. However, the composition and structure of soil bacterial communities in the rotations of vegetable crops remain unclear. In this study, we explored the bacterial diversity and community structure of the tomato rhizosphere, including enzyme activities, yield, and fruit quality, under three different cropping systems: tomato-tomato (Solanum lycopersicum) continuous cropping (TY1), eggplant (Solanum melongena)-tomato rotation (TY2) and arrowhead (Sagittaria trifolia)-tomato rotation (TY3). The composition and diversity of the rhizosphere bacterial communities differed significantly. The diversity was more in the TY2 and TY3 treatments than those in the TY1 treatment. Chujaibacter and Rhodanobacter were two predominant and unique strains detected only in TY1, while the relative abundances of Curvibacter and Luteimonas were the highest in TY2 and TY3, respectively. Moreover, Lysobacter was a relatively abundant type of biocontrol bacterium found only in the TY3 treatment, which could contribute to alleviating the obstacle of tomato continuous cropping. Compared with the TY1 treatment, the activities of catalase were significantly higher in the TY2 and TY3 treatments. In addition, compared with TY1, the TY2 and TY3 plots increased the following parameters: tomato yields by 24–46%, total soluble solids by 37-93%, total organic acid by 10-15.7% and soluble protein by 10-21%, while the content of nitrate was significantly reduced by 23%. Altogether, compared with the tomato monoculture, the rotations of tomato with eggplant and arrowhead shifted the rhizosphere bacterial communities and improved the yield and quality of the tomato. Moreover, a tomato rotation, particularly with arrowhead, was an effective way to alleviate the obstacles of continuous cropping.

It is necessary to assess and analyze the factors that influence surface water since they are crucial to human activities such as agriculture, raising livestock, and industry. Previous research has mostly focused on how land use and landscape patterns affect the quality of surface waters; it has seldom addressed the industrial and agricultural production activities that are directly connected to human society. Therefore, the research area’s surface water quality was assessed by single factor index (SFI) and composite water quality index (WQI), divided into flood and non-flood periods, and water quality indicators with severe pollution and significant seasonal variations were selected; A total of 28 indicators were selected from three main factors-topography, socio-economic, and land use type-and analyzed using the Spearman correlation coefficient model. (1) SFI data reveal substantial seasonal changes in pH, DO, NH₃-N, TN, and TP water quality indicators. The well-developed agricultural and aquaculture in the studied region is the primary cause of the excess TN and NH3-N concentrations; (2) The sample points’ water quality index (WQI) scores range from 50 to 80, with 62% of them having “medium” water quality; (3) The study area’s seasonal variation in water quality is primarily caused by human socio-economic activities (GDP, industrial effluent discharge, COD discharge, aquatic product quality, and the proportion of primary, secondary, and tertiary industries), as well as land use type (forest, shrubland, and cropland). Topography has little effect on the study area’s surface water quality. This study offers a fresh viewpoint on surface water quality management and driver analysis, and a new framework for managing and safeguarding aquatic ecosystems.

The use of rotations is an effective strategy to control crop diseases and improve plant health. The soil bacterial communities in the rhizosphere are highly important for maintaining soil productivity. However, the composition and structure of soil bacterial communities in the rotations of vegetable crops remain unclear. In this study, we explored the bacterial diversity and community structure of the tomato rhizosphere, including enzyme activities, yield, and fruit quality, under three different cropping systems: tomato-tomato continuous cropping (TY1), eggplant-tomato ( S. melongena ) rotation (TY2) and arrowhead-tomato ( Sagittaria trifolia-Solanum lycopersicum ) rotation (TY3). The composition and diversity of the rhizosphere bacterial communities differed significantly. The diversity was more in the TY2 and TY3 treatments than those in the TY1 treatment. Chujaibacter and Rhodanobacter were two predominant and unique strains detected only in TY1, while the relative abundances of Curvibacter and Luteimonas were the highest in TY2 and TY3, respectively. Moreover, Lysobacterium was a relatively abundant type of biocontrol bacterium found only in the TY3 treatment, which could contribute to alleviating the obstacle of tomato continuous cropping. Compared with the TY1 treatment, the activities of catalase were significantly higher in the TY2 and TY3 treatments. In addition, compared with TY1, the TY2 and TY3 plots increased the following parameters: tomato yields by 24–46%, total soluble solids by 37-93%, total organic acid by 10-15.7% and soluble protein by 10-21%, while the content of nitrate was significantly reduced by 23%. Altogether, compared with the tomato monoculture, the rotations of tomato with eggplant and arrowhead shifted the rhizosphere bacterial communities and improved the yield and quality of the vegetables. Moreover, a tomato rotation, particularly with arrowhead, was an effective way to alleviate the obstacles of continuous cropping.

Rapeseed distribution mapping is a crucial issue for food and oil security, entertainment, and tourism development. Previous studies have used various remote sensing approaches to map rapeseed. However, the time-consuming and labor-intensive sample data used in these supervised classification methods greatly limit the development of large-scale mapping in rapeseed studies. Regarding threshold methods, some empirical thresholding methods still need sample data to select the optimal threshold value, and their accuracies decrease when a fixed threshold is applied in complex and diverse environments. This study first developed the Normalized Difference Rapeseed Index (NDRI), defined as the difference in green and short-wave infrared bands divided by their sum, to find a suitable feature to distinguish rapeseed from other types of crops. Next, a two-stepwise hierarchical adaptive thresholding (THAT) algorithm requiring no training data was used to automatically extract rapeseed in Xinghua. Finally, two adaptive thresholding methods of the standalone Otsu and Otsu with Canny Edge Detection (OCED) were used to extract rapeseed across Jiangsu province. The results show that (1) NDRI can separate rapeseed from other vegetation well; (2) the OCED-THAT method can accurately map rapeseed in Jiangsu with an overall accuracy (OA) of 0.9559 and a Kappa coefficient of 0.8569, and it performed better than the Otsu-THAT method; (3) the OCED-THAT method had a lower but acceptable accuracy than the Random Forest method (OA = 0.9806 and Kappa = 0.9391). This study indicates that the THAT model is a promising automatic method for mapping rapeseed.

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.

The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague, which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus, but also to reveal its cluster rule. This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014, in order to predict plague outbreaks. Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils. Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods. The quantity of M. unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention. The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index. High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high. In terms of time series, the area of the epidemic focus gradually increased from 2005 to 2007, declined rapidly in 2008 and 2009, and then decreased slowly and began trending towards stability from 2009 to 2014. For the spatial change, the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007, and then moved from north to south in 2007 and 2008. The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation. The diversity of temporary and spatial distribution is mainly affected by seasonal variation, the human activity and natural factors.