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Soil erosion and surface pollution near reservoirs can adversely affect water quality and safety. Soil aggregate stability is an important predictor of soil water loss and erosion resistance that is strongly influenced by land use. This study therefore aimed to identify factors affecting soil aggregate stability near reservoirs to provide empirical and theoretical insights that could guide the development of management measures to increase land quality, optimize land use, and maximize sustainability. This study focuses on the land around the Shitoukoumen Reservoir in China and examines the effects of six land use types, eleven soil physicochemical properties, and five environmental factors. Ninety-four sets of soil samples were collected in 2021 for analysis of soil aggregates and properties. Particle size classification of soil aggregates was carried out using the wet sieve method and four indicators were calculated to evaluate the effects of land use, soil physicochemical properties, and environmental factors on soil aggregate stability: water stable aggregates (WSA), mean weight diameter (MWD), geometric mean diameter (GMD), and fractal dimension (D). Descriptive statistics and geostatistics were used to explore the spatial distributions of soil aggregate stability around the reservoir and the influence of soil properties was studied using correlation analysis and path analysis. The conclusion indicates that land use type significantly affects aggregate stability. The most stable aggregates were found in paddy fields (WSA = 0.77, MWD = 0.76, GMD = 0.57) and forests (WSA = 0.75, MWD = 0.76, GMD = 0.55), followed by an orchard, irrigated land, and grassland. Aggregate stability was worst in upland sites (WSA = 0.61, D = 2.28), where soil aggregates were highly fragmented. There were clear spatial correlations between all four stability indicators. The environmental factors and soil physicochemical characteristics with the strongest influence on aggregate stability were soil organic matter, pH, soil clay content, total nitrogen, and temperature changes. Path analysis revealed that some soil properties affect aggregate stability indirectly, with particularly complex relationships between clay, soil organic matter, and pH. In conclusion, land use type, soil organic matter, pH, soil clay content, total nitrogen, these soil physicochemical properties, and environmental factors, especially temperature, significantly affect soil aggregate stability around reservoirs. In the future, it is necessary to appropriately change upland into paddy land, increase forest land, and appropriately add organic fertilizer to improve soil quality. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N2O) is an important greenhouse gas that plays a significant role in atmospheric photochemical reactions and contributes to stratospheric ozone depletion. Soils are the main sources of N2O emissions. In recent years, it has been demonstrated that soil is not only a source but also a sink of N2O uptake and consumption. N2O emissions at the soil surface are the result of gross N2O production, uptake, and consumption, which are co-occurring processes. Soil N2O uptake and consumption are complex biological processes, and their mechanisms are still worth an in-depth systematic study. This paper aimed to systematically address the current research progress on soil N2O uptake and consumption. Based on a bibliometric perspective, this study has highlighted the pathways of soil N2O uptake and consumption and their driving factors and measurement techniques. This systematic review of N2O uptake and consumption will help to further understand N transformations and soil N2O emissions. [ABSTRACT FROM AUTHOR]

Leymus chinensis (L. chinensis) is the dominant plant in the eastern margins of the Eurasian temperate grasslands. It is a very robust species, exhibiting good saline-alkali resistance and stabilizing soil. In this study, 67 soil samples and L. chinensis were collected in western Jilin province, China. The contents of N, P, K, S, Mn, Fe, Zn, Cu and Na were measured, revealing that the growth of L. chinensis was mainly restricted by N based on the stoichiometric N: P ratios of plant. Furthermore, path analysis indicated that N was significantly correlated with K, S, Cu, and Zn. Imbalances in the homeostasis of these four elements may thus constrain N. The homeostasis index of Cu (HCu) in sites with 100%-70% of vegetation cover was only 0.79, it was classified as a sensitive element. However, K, S and Zn, whose concentrations in L. chinensis were significantly related to those of N, exhibited no homeostatic characteristics. These results suggest that when seeking to treat saline-alkali stress, it is important to add fertilizers containing K, S, and Zn to avoid growth limitation. Na+, an ion associated with high soil alkalinity, exhibited weak homeostasis in L. chinensis even in sites with only 40%-10% of vegetation cover. When soil Na exceeded 16000 mg/kg, the homeostasis mechanism of L. chinensis appeared to be overwhelmed, resulting in rapid and probably harmful accumulation of Na. Proper control of N content can alleviate the toxicity of Na stress in L. chinensis and enhance its Na tolerance. Together, these results suggest that combined fertilization with N, K, S, Zn and Cu should be applied to improve grasslands growth. The results of this study can provide a reference basis for sustainable grassland management. [ABSTRACT FROM AUTHOR]

Ecological stoichiometry is an emerging discipline started in China in recent years. It is the science of studying the balance of energy and elements (i.e. carbon, nitrogen and phosphorus) in ecological processes and ecological interaction, providing an integrative approach to investigate the stoichiometric relationships and rules in the biogeochemical cycling and ecological processes. It has been one of the hotly-discussed issues in ecological research. The contents of carbon, nitrogen, and phosphorus is a core issue in ecological stoichiometry studies. It is necessary to choose a method that can simulate and accurately predict the contents of plant carbon, nitrogen, and phosphorus in order to avoid destructive sampling. There is a complex nonlinear relationship between plant carbon, nitrogen, phosphorus, and soil physical and chemical properties. It is difficult to accurately predict plant carbon, nitrogen, and phosphorus by using traditional methods and models such as linear regression and a BP neural network. As a new artificial neural network model, a RBF (radial basis function) neural network has some advantages of fast learning, getting in the local minimum, and approximating any arbitrary accuracy of the global nonlinear relationship. Therefore, a RBF neural network can show an ability to handle a complex nonlinear relationship. Currently, a RBF neural network is one of the most accepted prediction methods. Taking the prediction of 38 samples as a research sample, this paper established a prediction model based on a RBF Neural network from seven impact indexes including pH, the total soluble salt, total carton, total nitrogen, total phosphorus, available nitrogen, and available phosphorus. Taking the prediction of five samples as a test sample, the results indicated that the relative errors of carbon, nitrogen, and phosphorus contents were only 1.39%, 4.69%, and 7.65%, respectively, and the correlation coefficients were 0.5, 0.93, and 0.94 respectively. Therefore, a RBF neural network had higher prediction accuracy. The statistical results showed that the average contents of carbon, nitrogen, and phosphorus in Leymus chinensis (103 samples) were 411.46, 18.25, and 1.11 mg/g, respectively. They are all lower than the global average contents of carbon, nitrogen, and phosphorus in a terrestrial plant. The values of C/N, C/P, and N/P were 24.70, 429.24, and 17.92, respectively in Leymus chinensis. They were all higher than those in a global terrestrial plant. The N/P was 17.92 in Leymus chinensis. The growth of Leymus chinensis in the research area was limited by phosphorus. [ABSTRACT FROM AUTHOR]