Explore the vast range of titles available.

Rice straw and stubble burning is widely practiced to clear fields for new crops. However, questions remain about the effects of fire on soil bacterial communities and soil properties in paddy fields. Here, five adjacent farmed fields were investigated in central Thailand to assess changes in soil bacterial communities and soil properties after burning. Samples of soil prior to burning, immediately after burning, and 1 year after burning were obtained from depths of 0 to 5 cm. The results showed that the pH, electrical conductivity, NH4-N, total nitrogen, and soil nutrients (available P, K, Ca, and Mg) significantly increased immediately after burning due to an increased ash content in the soil, whereas NO3-N decreased significantly. However, these values returned to the initial values. Chloroflexi were the dominant bacteria, followed by Actinobacteria and Proteobacteria. At 1 year after burning, Chloroflexi abundance decreased remarkably, whereas Actinobacteria, Proteobacteria, Verrucomicrobia, and Gemmatimonadetes abundances significantly increased. Bacillus, HSB OF53-F07, Conexibacter, and Acidothermus abundances increased immediately after burning, but were lower 1 year after burning. These bacteria may be highly resistant to heat, but grow slowly. Anaeromyxobacter and Candidatus Udaeobacter dominated 1 year after burning, most likely because of their rapid growth and the fact that they occupy areas with increased soil nutrient levels after fires. Amidase, cellulase, and chitinase levels increased with increased organic matter levels, whereas β-glucosidase, chitinase, and urease levels positively correlated with the soil total nitrogen level. Although clay and soil moisture strongly correlated with the soil bacterial community’s composition, negative correlations were found for β-glucosidase, chitinase, and urease. In this study, rice straw and standing stubble were burnt under high soil moisture and within a very short time, suggesting that the fire was not severe enough to raise the soil temperature and change the soil microbial community immediately after burning. However, changes in soil properties due to ash significantly increased the diversity indices, which was noticeable 1 year after burning.

Soil total nitrogen (STN) is a crucial component of the ecosystem’s nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distribution in the northeastern coastal region of Hebei Province, China, employing the random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods. The purpose was to compare the ability of remote sensing images (Landsat-8, Sentinel-1, and Sentinel-2) with different machine learning methods for predicting STN content. The research results show the following: (1) The three machine learning methods accurately predicted the STN content and the optimal model provided by the XGBoost method, with an R2 of 0.627, RMSE of 0.127 g·kg−1, and MAE of 0.092 g·kg−1. (2) The combination of optical and synthetic aperture radar (SAR) images improved prediction accuracy, with the R2 improving by 45.5%. (3) The importance of optical images is higher than that of SAR images in the RF, GBM, and XGBoost methods, with optical images accounting for 87%, 76%, and 77% importance, respectively. (4) The spatial distribution of STN content predicted by the three methods is similar. Higher STN contents are distributed in the northern part of the study area, while lower STN contents are distributed in coastal areas. The results of this study can be very useful for inventories of soil nitrogen and provide data support and method references for revealing nitrogen cycling.

Background and Aims Abandonment of paddy fields is a significant threat to soil organic carbon (SOC) stocks owing to the associated shift from anaerobic to aerobic conditions. However, the impact of this transition on the dynamics of soil total nitrogen (TN) and its relationship with SOC in bulk soil and soil aggregates remains unclear. Methods A long-term experiment was conducted to examine abandoned paddy fields with different fertilizer treatments over a 16-year period before abandonment, followed by an 8-year period after abandonment. Results The abandonment of paddy fields led to a significant decrease in TN content by an average of 14.0%, resulting in a mean annual loss rate of 0.08 t N ha −1 . The loss of TN was as sensitive as that of SOC, and there was a positive correlation between SOC and TN in both bulk soil and soil aggregates. The loss of SOC and TN was mainly caused by reductions in the middle- and micro-aggregate-associated SOC and TN, which together explained approximately 87.3% of C loss and 81.3% of N loss. The weaker protective capacity of soil aggregates (> 53 μm) was evidenced by a significant decrease in aggregate-associated C (average of 8.7%) and N (average of 9.1%). Abandonment maintained stoichiometric stability, with bulk soil C:N ratios ranging from 9.4 to 9.6 following abandonment. Conclusions Paddy soil aggregate-associated SOC and TN were sensitive to loss owing to the weaker protective capacity of soil aggregates following the abandonment of paddy fields. The C:N ratios remained relatively consistent after abandonment.

【Background and objective】 Gold pear is a main fruit in northern China and its production relies on irrigation and fertilization. The purpose of this paper is to study the effects of different combinations of irrigation and nitrogen-fertilization on nitrogen uptake by roots and its translocation in the trees. 【Method】 The experiment was conducted in an orchard; it consisted of three irrigation treatments by keeping the lowest soil water content controlled for irrigation at 75 (HW), 65 (MW) and 55% (LW) of the field capacity, respectively, and three nitrogen treatments by applying 486 kg/hm2 (HF), 324 kg/hm2 (MF) and 162 kg/hm2 (LF), respectively. Standard irrigation and fertilization used by local farmers was taken as the control (CK). In each treatment, we measured nitrogen content in different parts of the tree at different growth stages. 【Result】 At fruit-expansion stage, the total nitrogen content in spring shoots and leaves were the highest in MW+MF, increasing by 26.20% and 8.66% respectively, compared to CK; the total nitrogen content differed significantly in spring shoots but not in leaves between the treatments. At maturity stage, the total nitrogen content in spring shoots was the highest in MW+MF, reaching 0.74 g/kg, and least in HW +MF, dropping to 0.54 g/kg. The total nitrogen content in the leaves in HW+MF was the highest, reaching 1.83 g/kg, and least in HW+LF being 1.70 g/kg. The nitrogen content in fruits in LW+HF was the highest, reaching 0.82 g/kg, a 52.70% increase compared to CK. The treatments did not result in considerable changes in total nitrogen contents in spring shoots and leaves, but significantly changed the total nitrogen contents in shoots and fruits. Correlation analysis did not find correlation between total nitrogen contents in spring shoots, leaves and fruits. The total nitrogen content in spring shoots and fruit were positively but insignificantly correlated, while total nitrogen content in leaves was negatively but insufficiently correlated with the total nitrogen content in spring shoots and fruits. 【Conclusion】 For all treatments we compared, keeping the lowest soil water controlled for irrigation at 65% of the field capacity combined with 300~350 kg/hm2 of nitrogen fertilization is optimal for yield and fruit quality of the golden pear in the studied area.

As a precious soil resource, black soils in Northeast China are currently facing severe land degradation. Visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–25,000 nm) have shown great potential to predict soil properties. However, there is still limited research on using MIR in situ. The aim of this study was to explore the feasibility of in situ MIR for the prediction of soil total nitrogen (TN) and total phosphorus (TP) and to compare its performance with the use of laboratory MIR, as well as the use of in situ and laboratory vis-NIR. A total of 450 samples from 90 soil profiles, along with their in situ and laboratory spectra of MIR and vis-NIR, were collected in a field with ten different tillage and management practices in a typical black soil area of Northeast China. Partial least square regression (PLSR), random forest (RF) and multivariate adaptive regression splines (MARS) were used to generate the calibrations between the spectra and the two properties. The results showed that both MIR and vis-NIR were able to predict the TN whether in laboratory or in situ conditions, but neither of them could predict the TP quantitatively since there was no sensitive band on both spectra regarding the TP. The prediction accuracy of the TN with laboratory spectra was higher than that with in situ spectra, for both vis-NIR and MIR. The optimal prediction accuracy of the TN with laboratory MIR (RMSE = 0.11 g/kg, RPD = 3.12) was higher than that of laboratory vis-NIR (RMSE = 0.14 g/kg, RPD = 2.45). The optimal prediction accuracy of in situ MIR (RMSE = 0.20 g/kg, RPD = 1.80) was lower than that of in situ vis-NIR (RMSE = 0.16 g/kg, RPD = 2.14). The prediction performance of the spectra followed laboratory MIR > laboratory vis-NIR > in situ vis-NIR > in situ MIR. The performance of in situ MIR was relatively poor, mainly due to the fact that MIR was more influenced by soil moisture. This study verified the feasibility of in situ MIR for soil property prediction and provided an approach for obtaining rapid soil information and a reference for soil research and management in black soil areas.

Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. In this study, a geographical weighted regression (GWR) model, a multiple stepwise regression (MLSR) model, and a boosted regression trees (BRT) model were compared to obtain the best prediction of SOC and STN stocks of the forest ecosystems in northeastern China. Five-hundred and thirteen topsoil (0–30 cm) samples (10.32 kg m−2 (±0.53) for SOC, 1.21 kg m−2 (±0.32) for STN), and 9 remotely-sensed environmental variables were collected and used for the model development and verification. By comparing with independent verification data, the best model (BRT) achieved R2 = 0.56 and root mean square error (RMSE) = 00.85 kg m−2 for SOC stocks, R2 = 0.51 and RMSE = 0.22 kg m−2 for STN stocks. Of all the remotely-sensed environment variables, soil adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI) are of the highest relative importance in predicting SOC and STN stocks. The spatial distribution of the predicted SOC and STN stocks gradually decreased from northeast to southwest. This study provides an attempt to rapidly predict SOC and STN stocks in the dense vegetation covered area. The results can help evaluate soil quality and facilitate land policy and regulation making by the government in the region.

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.

The frequent occurrence of microcystins (MCs) in freshwater poses serious threats to the drinking water safety and health of human beings. Although MCs have been detected in individual fresh waters in China, little is known about their occurrence over a large geographic scale. An investigation of 30 subtropical lakes in eastern China was performed during summer 2018 to determine the MCs concentrations in water and their possible risk via direct water consumption to humans, and to assess the associated environmental factors. MCs were detected in 28 of 30 lakes, and the highest mean MCs concentrations occurred in Lake Chaohu (26.7 μg/L), followed by Lake Taihu (3.11 μg/L). MC-LR was the primary variant observed in our study, and MCs were mainly produced by Microcystis , Anabaena ( Dolicospermum ), and Oscillatoria in these lakes. Replete nitrogen and phosphorus concentrations, irradiance, and stable water column conditions were critical for dominance of MC-producing cyanobacteria and high MCs production in our study. Hazard quotients indicated that human health risk of MCs in most lakes was at moderate or low levels except Lakes Chaohu and Taihu. Nutrient control management is recommended to decrease the likelihood of high MCs production. Finally, we recommend the regional scale thresholds of total nitrogen and total phosphorus concentrations of 1.19 mg/L and 7.14 × 10 −2  mg/L, respectively, based on the drinking water guideline of MC-LR (1 μg/L) recommended by World Health Organization. These targets for nutrient control will aid water quality managers to reduce human health risks created by exposure to MCs.

Soil aggregates not only store carbon (C) and nitrogen (N) but hold a critical role in determining the nutrients supply, crop productivity, and climate change mitigation. However, the impact of cropping system and N fertilization on aggregate-associated C and N in both topsoil and subsoil remains unclear. Here, we assessed the effect of cropping systems (wheat–soybean vs. wheat–maize cropping systems) and N fertilization rates (0 N; medium N, 120 kg N ha −1 ; high N, 240 kg N ha −1 ) on soil water-stable aggregates distribution, as well as aggregate-associated C and N based on a field study in North China Plain. Our study suggests that the variations of soil organic carbon (SOC) and total nitrogen (TN) stocks were more affected by N fertilization than short-term cropping systems. In the wheat–soybean system, medium N increased the SOC stock by 19.18% and 15.73% as compared to high N in the topsoil and subsoil, respectively. Additionally, medium N resulted in 6.59–18.11% higher TN stock in the topsoil for both wheat–soybean and wheat–maize cropping systems as compared to 0 N and high N. Notably, the water-stable macroaggregates (> 0.25 mm) in the topsoil occupied more than 70% of the soil, which increased under medium N in the wheat–soybean cropping system. In conclusion, medium N fertilization combined with a legume-based cropping could be used to improve SOC stock, promote soil aggregation, and enhance aggregate-associated C.

PurposeIdentifying patterns and drivers of soil total N (TN) and its fractions along latitudinal gradients provides a comprehensive understanding of the response of soil N availability to environmental changes.MethodsIn the present study, we collected soil samples at depths of 0–10 cm, 10–20 cm, 20–30 cm, and 30–50 cm from 98 natural shrublands along a latitudinal gradient (23.2° N to 32.3° N) in the arid valleys of southwest China. We investigated the soil TN density (STND) and inorganic N densities (SIND)—which is the sum of nitrate–N [NO3D] and ammonium-N [NH4D] densities—and their environmental driving factors.ResultsLatitudinal patterns of STND followed an inverse unimodal distribution, whereas those of NH4D followed a unimodal distribution, regardless of the soil layer. SIND in the 0–10 cm layer followed a unimodal distribution with increasing latitude, while NO3D had an inverse unimodal distribution with increasing latitude in the 10–20 cm, 20–30 cm, and 30–50 cm layers. Across the four soil layers, variations in STND with latitude were largely explained by soil organic carbon content. Soil sorption capacity and vegetation composition including shrub cover [SC] and herb cover [HC] strongly influenced the SIND. Variations in NH4D and NO3D with latitude were jointly driven by the mean annual temperature, SC, HC, dissolved organic carbon, clay content, and pH. Although soil properties determined the latitudinal patterns of soil N density in all the four soil layers tested, the relative contributions of climatic and vegetation factors increased with soil depth.ConclusionOur study elucidated the latitudinal patterns in STND and its fractions, and our findings highlight the potential impacts of climatic and vegetation factors on subsoil N pools and dynamics.