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Rapid, accurate and non-destructive acquisition of soil total nitrogen (TN) content in the black soil zone is significant for achieving precise fertilization. In this study, the soil types of corn and soybean fields in Jilin Agricultural University, China, were selected as the study area. A total of 162 soil samples were collected using a five-point mixed sampling method. Then, spectral data were obtained and the noisy edge were initially eliminated. Subsequently, the denoised spectral data underwent smoothing by using the Savitzky–Golay (SG) method. After performing the first-order difference (FD) and second-order difference (SD) transformations on the data, it was input to the model. In this study, a hybrid deep learning model, CBiResNet-BiLSTM, was designed for precise prediction of soil TN content. This model was optimized based on ResNet34, and its capabilities were enhanced by incorporating CBAM in the residual module to facilitate additional eigenvalue extraction. Also, Bidirectional Long Short-Term Memory (BiLSTM) was integrated to enhance model accuracy. Besides, partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and back propagation neural network (BP), as well as ResNet(18, 34, 50, 101, 152) models were taken for comparative experiments. The results indicated that the traditional machine learning model PLSR achieved good performance, with R2 of 0.883, and the hybrid deep learning model CBiResNet-BiLSTM had the best inversion capability with R2 of 0.937, with the R2 being improved by 5.4%, compared with the PLSR model. On this basis, we present the LUCAS dataset to demonstrate the generalisability of the model. Therefore, the CBiResNet-BiLSTM model is a fast and feasible hyperspectral estimation method for soil TN content. [ABSTRACT FROM AUTHOR]

To understand the role of paddy soils in the global carbon cycle, it is necessary to analyze the characteristics of the organic carbon pool at different soil depths. It was hypothesized that soil organic carbon fractions including labile organic carbon fraction I (LOCF-I), labile organic carbon fraction II (LOCF-II), and recalcitrant organic carbon (ROC) distributed differently within the soil profile. In this study, soil was collected from 27 typical rice fields in Guangdong Province, south China. The carbon fractions of the paddy field soils were analyzed and compared over a 0–60 cm depth profile. The relationship between carbon content and the physical and chemical properties of the soils was further analyzed using correlation analysis and structural equation modeling. The results showed that soil total organic carbon concentration in paddy fields was increased by 22.1% during the last four decades. In the soil organic carbon pool of 0–60 cm profile, the proportion of 67.31 to 70.31% in ROC, 21.75 to 22.06% in LOCF-I, and 7.7 to 10.63% was recorded, respectively, indicating that ROC was the dominating fraction. Storage of soil total organic carbon and fractions all decreased with the increase in soil depth. Correlation and path analysis showed that total nitrogen was the main driving factor affecting the soil carbon fractions, whereas pH and soil bulk density indirectly affected the content of carbon fractions by influencing total nitrogen. The results imply the importance of soil total nitrogen in paddy carbon management of rice cultivation. [ABSTRACT FROM AUTHOR]

Abstract Rapid, accurate and non-destructive acquisition of soil total nitrogen (TN) content in the black soil zone is significant for achieving precise fertilization. In this study, the soil types of corn and soybean fields in Jilin Agricultural University, China, were selected as the study area. A total of 162 soil samples were collected using a five-point mixed sampling method. Then, spectral data were obtained and the noisy edge were initially eliminated. Subsequently, the denoised spectral data underwent smoothing by using the Savitzky–Golay (SG) method. After performing the first-order difference (FD) and second-order difference (SD) transformations on the data, it was input to the model. In this study, a hybrid deep learning model, CBiResNet-BiLSTM, was designed for precise prediction of soil TN content. This model was optimized based on ResNet34, and its capabilities were enhanced by incorporating CBAM in the residual module to facilitate additional eigenvalue extraction. Also, Bidirectional Long Short-Term Memory (BiLSTM) was integrated to enhance model accuracy. Besides, partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and back propagation neural network (BP), as well as ResNet(18, 34, 50, 101, 152) models were taken for comparative experiments. The results indicated that the traditional machine learning model PLSR achieved good performance, with R 2 of 0.883, and the hybrid deep learning model CBiResNet-BiLSTM had the best inversion capability with R 2 of 0.937, with the R 2 being improved by 5.4%, compared with the PLSR model. On this basis, we present the LUCAS dataset to demonstrate the generalisability of the model. Therefore, the CBiResNet-BiLSTM model is a fast and feasible hyperspectral estimation method for soil TN content. Graphical abstract

Earthworm communities influence soil carbon and nitrogen circulation by altering the diversity and composition of microbial communities, which improves soil fertility. Studying the soil nutrient composition and bacterial communities change in response to earthworm community natural invasion may be key to exploring earthworm ecological functions and accurately assessing C and N mineralization in artificial forests and croplands. In this study, we examined the communities of five earthworm species in ecosystems characterized by six different land-use types, such as buttonwood forest, walnut forest, apple orchard, kiwi orchard, ryegrass land, and corn field. The Metaphire baojiensis (d) and Amynthas carnosus planus were dominant earthworm species. Among different land-use types, earthworm densities ranged from 2 to 27 ind·m−2 in summer and 15 to 40 ind·m−2 in spring. However, surface vermicompost weight in summer (296.7 to 766.0 g·m−2) was greater than in spring. There was a positive correlation between the weight of the vermicompost and earthworm numbers in the same season. Soil carbon (C) and total nitrogen (N) of vermicompost ranged from 5.12 to 20.93 g·kg−1 and from 0.52 to 1.35 g·kg−1, respectively. Compared with soil, the contents of vermicompost C and N increased 2.0 to 4.3 times and 1.6 to 7.7 times, respectively. The average C/N of vermicompost (9.5~23.5) was higher than in the soil (7.3~19.8). Due to the higher abundances of C and N in the soil of corn fields and kiwi orchards, which cultivate higher abundances of earthworms and more vermicompost, the C and N and C/N of vermicompost is higher than in the soil. C and N were accumulated by earthworms' excreting and feeding activity instead of vegetation in vermicompost. Earthworm community structure plays key roles in decreasing bacterial diversity and adding Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes, and Chloroflex in vermicompost, resulting in enriching soil C and N content and increasing C/N in vermicompost. Therefore, the evaluation of different vegetation ecosystems in soil C and N pool accumulation and mineralization should be given more attention regarding the function of earthworm communities in the future. [ABSTRACT FROM AUTHOR]

Introduction: Fast and accurate estimation and spatial mapping of soil total nitrogen (TN) content is important for the development of modern precision agriculture, such as soil fertility monitoring and land reclamation decision-making. Hyperspectral remote sensing has been demonstrated to be an accurate real-time technique for rapid estimation and mapping of soil TN content. Methods: To solve the problem of poor accuracy and generalization of estimation models caused by soil environmental heterogeneity in estimating and mapping soil TN content using hyperspectral images, 502 soil samples were collected from a typical black soil area in Yushu City, Jilin Province, China, as a test area, and three sample grouping strategies were established by soil environmental variables (soil type, thickness of the black soil layer, and topographic factors), and Pearson correlation coefficient and competitive adaptive reweighted sampling algorithm were used to determine the TN characteristic bands of each sample set under different strategies. Based on the data characteristics of the sub-sample set, the local regression estimation model based on sample grouping was constructed using the CatBoost algorithm, and the estimation and distribution mapping of soil TN content was carried out. Results and Discussion: The results showed that after dividing the samples according to the differences in soil environmental factors, the characteristic information of the samples is more targeted, with more abundant numbers and distribution ranges of TN characteristic bands. Compared to the global regression estimation with all samples, the local regression based on the grouping of soil environment differences showed improved accuracy, with the local regression estimation model constructed with the ST-G strategy exhibiting the highest estimation accuracy (R²p = 0.839). The results can provide a reference for large-area soil properties mapping, and technical support for soil quality digitization and precision fertilization. [ABSTRACT FROM AUTHOR]

Abstract The application of near-infrared spectroscopy (NIRS) for rapid quantitative analysis of soil total nitrogen (STN) is of great significance to recycling nitrogen in the ecosystem and crops growth. However, collecting thousands of soil samples and chemical analysis are impracticable, more importantly a deviation from NIRS advantages of rapid, inexpensive and nondestructive. To more efficiently improve the estimation performance and reduce uncertainty of the model when working with small sample sizes (less than 100), solutions from soil particle size decomposition and model fusion were investigated. Elaborately, 123 Latosols samples were collected and decomposed them according to particle sizes to extend limited data at multiple scales. Based on all soil groups decomposed, a hyperspectral data recapture and model decision fusion method were implemented. The results demonstrated that the proposed method increased the scale of spectral data, extracted more STN-related spectral information, improved estimation accuracy, and reduced uncertainty. The fused model based on data from all decomposed groups yielded the best estimated results (root mean square error $$(RMSE) = 0.075g.kg^{-1}$$ ( R M S E ) = 0.075 g . k g - 1 , $$R^2 = 0.784$$ R 2 = 0.784 , and a ratio of performance to inter-quartile distance $$(RPIQ) = 3.787$$ ( R P I Q ) = 3.787 ) on the validation set. Through a tenfold cross-validation, the weighted fusion model with six groups of particle sizes data showed an improvement of 0.307 in $$R^2_cv$$ R c 2 v and an improved RPIQ of 1.015 compared to models constructed using conventional machine learning (ML) techniques and limited pristine data ( $$R^2_cv = 0.442, RMSE = 0.119$$ R c 2 v = 0.442 , R M S E = 0.119 ). Therefore, when utilizing NIRS to build rapid and accurate STN predictive models, the proposed method demonstrates great potential in improving the reliability of soil spectral models under small sample sizes. Graphical Abstract

Rational utilization of soil total nitrogen is one of the keys to achieving sustainable agricultural development. By accurately measuring the content of total nitrogen in the soil, the utilization efficiency of nitrogen in the soil can be improved, and the scientific use of chemical fertilizers can reduce the pressure of agriculture on natural resources and realize the sustainable development of agriculture. In order to measure soil total nitrogen content simply and accurately, combined with the method of artificial olfactory systems, a new method of soil total nitrogen content detection based on convolutional noise reduction autoencoder (CDAE)–whale optimization algorithm (WOA)–deep residual shrinkage network (DSRN) is proposed. In order to obtain more salient features for fusion, the channel mechanism of the DSRN is improved by adding global Max pooling. The model uses a CDAE for the first filtering stage to automatically obtain data that filters simple noise and uses the WOA to automatically optimize hyperparameters. Finally, the optimized hyperparameters were used to train the DRSN for secondary filtering and predict the soil total nitrogen content. Experimental results show that the R2 of CAE-WOA-DSRN test set is 0.968, which is significantly better than the R2 of a traditional algorithm (0.873) and a simple BP network (0.877), and it can more accurately measure soil total nitrogen content. [ABSTRACT FROM AUTHOR]

The rapid and accurate diagnosis of nitrogen content in apple orchard soil is of great significance for the rational application of nitrogen fertilizer in orchards to improve apple yield and quality. An apple orchard in Shuangquan Town, Changqing District, Jinan City, Shandong Province, was taken as the experimental area. The optimal method for extracting spectral characteristic bands and screening spectral characteristic indices (SCIs) of soil total nitrogen (TN) for independent and comprehensive fertilization periods was explored. Independent and comprehensive soil TN content estimation models were constructed and optimized for each and the entire fertilization period, respectively. The results show that compared with the correlation coefficient method, stepwise multiple linear regression (SMLR) performs better in extracting hyperspectral characteristic bands of soil TN content. It helps to achieve a higher modeling accuracy, smaller root mean square error (RMSE), and is more conducive to avoiding the influence of multicollinearity of model variables. The sensitive areas of soil TN content in the SCI do not undergo significant changes due to different fertilization periods. Among them, the ratio spectral indices (RSIs) are in the range of 800–900 nm, 1900–1950 nm, and 2200–2300 nm, while the sensitive areas of the difference spectral index (DI) and Normalized difference spectral index (NDSI) are in the range of 1900–1950 nm and 2200–2300 nm. The combination of SCI and characteristic bands significantly improves the prediction accuracy of soil TN estimation models. The independent and comprehensive estimation models for each fertilization period based on the BP (back propagation) neural network optimized by the Mind Evolution Algorithm (MEA-BPNN) can achieve a more stable and accurate estimation of soil TN. Finally, using soil spectral characteristic bands selected through continuum removal (CR) transformation and SMLR, combined with SCI, the model based on the MEA-BPNN (CR-SCI-MEA-BPNN) has the best prediction performance. The modeling determination coefficients R2 for each fertilization period reached 0.94, 0.95, 0.92, and 0.94, respectively, with RMSE of 0.0032, 0.0024, 0.0035, and 0.0027. The R2 and RMSE of the modeling and validation set of the entire fertilization period comprehensive model are 0.899, 0.0038, and 0.89, 0.0041, respectively. The results of this article provide technical support for promoting the timely monitoring of soil TN content and guiding rational fertilization in apple orchards. [ABSTRACT FROM AUTHOR]

The application of near-infrared spectroscopy (NIRS) for rapid quantitative analysis of soil total nitrogen (STN) is of great significance to recycling nitrogen in the ecosystem and crops growth. However, collecting thousands of soil samples and chemical analysis are impracticable, more importantly a deviation from NIRS advantages of rapid, inexpensive and nondestructive. To more efficiently improve the estimation performance and reduce uncertainty of the model when working with small sample sizes (less than 100), solutions from soil particle size decomposition and model fusion were investigated. Elaborately, 123 Latosols samples were collected and decomposed them according to particle sizes to extend limited data at multiple scales. Based on all soil groups decomposed, a hyperspectral data recapture and model decision fusion method were implemented. The results demonstrated that the proposed method increased the scale of spectral data, extracted more STN-related spectral information, improved estimation accuracy, and reduced uncertainty. The fused model based on data from all decomposed groups yielded the best estimated results (root mean square error (R M S E) = 0.075 g. k g - 1 , R 2 = 0.784 , and a ratio of performance to inter-quartile distance (R P I Q) = 3.787 ) on the validation set. Through a tenfold cross-validation, the weighted fusion model with six groups of particle sizes data showed an improvement of 0.307 in R c 2 v and an improved RPIQ of 1.015 compared to models constructed using conventional machine learning (ML) techniques and limited pristine data ( R c 2 v = 0.442 , R M S E = 0.119 ). Therefore, when utilizing NIRS to build rapid and accurate STN predictive models, the proposed method demonstrates great potential in improving the reliability of soil spectral models under small sample sizes. [ABSTRACT FROM AUTHOR]

The variation and correlation of leaf economics and vein traits are crucial for predicting plant ecological strategies under different environmental changes. However, correlations between these two suites of traits and abiotic factors such as soil water and nitrogen content remain ambiguous. We measured leaf economics and vein traits as well as soil water and nitrogen content for two different shade-tolerant species (Betula platyphylla and Acer mono) in four mixed broadleaved-Korean pine (Pinus koraiensis) forests along a latitudinal gradient in Northeast China. We found that leaf economics traits and vein traits were decoupled in shade-intolerant species, Betula platphylla, but significantly coupled in a shade-tolerant species, A. mono. We found stronger correlations among leaf traits in the shade tolerant species than in the shade intolerant species. Furthermore, leaf economic traits were positively correlated with the soil water gradient for both species, whereas vein traits were positively correlated with soil water gradient for the shade intolerant species but negatively correlated in the shade tolerant species. Although economic traits were positively correlated with soil nitrogen gradient in shade intolerant species but not correlated in shade tolerant species, vein traits were negatively correlated with soil nitrogen gradient in shade tolerant species but not correlated in shade intolerant species. Our study provides evidence for distinct correlations between leaf economics and vein traits and local abiotic factors of species differing in light demands. We recommend that the ecological significance of shade tolerance be considered for species when evaluating ecosystem functions and predicting plant responses to environmental changes.

To understand the role of paddy soils in the global carbon cycle, it is necessary to analyze the characteristics of the organic carbon pool at different soil depths. It was hypothesized that soil organic carbon fractions including labile organic carbon fraction I (LOCF-I), labile organic carbon fraction II (LOCF-II), and recalcitrant organic carbon (ROC) distributed differently within the soil profile. In this study, soil was collected from 27 typical rice fields in Guangdong Province, south China. The carbon fractions of the paddy field soils were analyzed and compared over a 0–60 cm depth profile. The relationship between carbon content and the physical and chemical properties of the soils was further analyzed using correlation analysis and structural equation modeling. The results showed that soil total organic carbon concentration in paddy fields was increased by 22.1% during the last four decades. In the soil organic carbon pool of 0–60 cm profile, the proportion of 67.31 to 70.31% in ROC, 21.75 to 22.06% in LOCF-I, and 7.7 to 10.63% was recorded, respectively, indicating that ROC was the dominating fraction. Storage of soil total organic carbon and fractions all decreased with the increase in soil depth. Correlation and path analysis showed that total nitrogen was the main driving factor affecting the soil carbon fractions, whereas pH and soil bulk density indirectly affected the content of carbon fractions by influencing total nitrogen. The results imply the importance of soil total nitrogen in paddy carbon management of rice cultivation.

Earthworm communities influence soil carbon and nitrogen circulation by altering the diversity and composition of microbial communities, which improves soil fertility. Studying the soil nutrient composition and bacterial communities change in response to earthworm community natural invasion may be key to exploring earthworm ecological functions and accurately assessing C and N mineralization in artificial forests and croplands. In this study, we examined the communities of five earthworm species in ecosystems characterized by six different land-use types, such as buttonwood forest, walnut forest, apple orchard, kiwi orchard, ryegrass land, and corn field. The Metaphire baojiensis (d) and Amynthas carnosus planus were dominant earthworm species. Among different land-use types, earthworm densities ranged from 2 to 27 ind·m−2 in summer and 15 to 40 ind·m−2 in spring. However, surface vermicompost weight in summer (296.7 to 766.0 g·m−2) was greater than in spring. There was a positive correlation between the weight of the vermicompost and earthworm numbers in the same season. Soil carbon (C) and total nitrogen (N) of vermicompost ranged from 5.12 to 20.93 g·kg−1 and from 0.52 to 1.35 g·kg−1, respectively. Compared with soil, the contents of vermicompost C and N increased 2.0 to 4.3 times and 1.6 to 7.7 times, respectively. The average C/N of vermicompost (9.5~23.5) was higher than in the soil (7.3~19.8). Due to the higher abundances of C and N in the soil of corn fields and kiwi orchards, which cultivate higher abundances of earthworms and more vermicompost, the C and N and C/N of vermicompost is higher than in the soil. C and N were accumulated by earthworms’ excreting and feeding activity instead of vegetation in vermicompost. Earthworm community structure plays key roles in decreasing bacterial diversity and adding Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes, and Chloroflex in vermicompost, resulting in enriching soil C and N content and increasing C/N in vermicompost. Therefore, the evaluation of different vegetation ecosystems in soil C and N pool accumulation and mineralization should be given more attention regarding the function of earthworm communities in the future.

Soil total nitrogen is one of the most important basic indicators for fertiliser decision making, but tens of millions of soil total nitrogen sampling data have been accumulated, forming a huge database. In this large database, there is a large amount of anomalous data, which can interfere with data analysis, affect the construction of spatial interpolation and prediction models, and then affect the accuracy of nutrient management decisions. The traditional method of identifying soil total nitrogen anomalies based on boxplots suffers from the problems of not being able to identify local anomalies, which can easily lead to misclassification of soil total nitrogen data anomalies, and the detection efficiency is not high. We propose a method to identify soil total nitrogen outliers by combining the Isolation Forest algorithm and local spatial autocorrelation analysis, which can simultaneously detect global and local outliers from large amounts of data and combine organic matter as an auxiliary indicator in the spatial analysis to help judge local outliers. Finally, the results of global and local anomalies were combined to provide a comprehensive assessment of the soil nitrogen data, avoiding the misjudgement or omission of judgement that can occur when using a single method. Using 25,930 soil test data from Yunnan Province in 2009 as an example, we compared and analysed the typical boxplot method and the unsupervised OneClassSVM method and evaluated the performance of each method in terms of correct detection rate, false positive rate and false negative rate. The results show that the proposed method has a correct detection rate (TR) of 99.97%, a false positive rate (FPR) of 8.06% and a false negative rate (FNR) of 0.01% on the data, which shows high validity and accuracy; it is also comparable to the independent isolated forests (FNR = 4.76%), boxplot (FNR = 3.90%) and OneClassSVM (FNR = 4.77%), and the false negative rate is reduced by 4.75%, 3.89% and 4.76%, respectively. [ABSTRACT FROM AUTHOR]

The rapid and accurate diagnosis of nitrogen content in apple orchard soil is of great significance for the rational application of nitrogen fertilizer in orchards to improve apple yield and quality. An apple orchard in Shuangquan Town, Changqing District, Jinan City, Shandong Province, was taken as the experimental area. The optimal method for extracting spectral characteristic bands and screening spectral characteristic indices (SCIs) of soil total nitrogen (TN) for independent and comprehensive fertilization periods was explored. Independent and comprehensive soil TN content estimation models were constructed and optimized for each and the entire fertilization period, respectively. The results show that compared with the correlation coefficient method, stepwise multiple linear regression (SMLR) performs better in extracting hyperspectral characteristic bands of soil TN content. It helps to achieve a higher modeling accuracy, smaller root mean square error (RMSE), and is more conducive to avoiding the influence of multicollinearity of model variables. The sensitive areas of soil TN content in the SCI do not undergo significant changes due to different fertilization periods. Among them, the ratio spectral indices (RSIs) are in the range of 800–900 nm, 1900–1950 nm, and 2200–2300 nm, while the sensitive areas of the difference spectral index (DI) and Normalized difference spectral index (NDSI) are in the range of 1900–1950 nm and 2200–2300 nm. The combination of SCI and characteristic bands significantly improves the prediction accuracy of soil TN estimation models. The independent and comprehensive estimation models for each fertilization period based on the BP (back propagation) neural network optimized by the Mind Evolution Algorithm (MEA-BPNN) can achieve a more stable and accurate estimation of soil TN. Finally, using soil spectral characteristic bands selected through continuum removal (CR) transformation and SMLR, combined with SCI, the model based on the MEA-BPNN (CR-SCI-MEA-BPNN) has the best prediction performance. The modeling determination coefficients R2 for each fertilization period reached 0.94, 0.95, 0.92, and 0.94, respectively, with RMSE of 0.0032, 0.0024, 0.0035, and 0.0027. The R2 and RMSE of the modeling and validation set of the entire fertilization period comprehensive model are 0.899, 0.0038, and 0.89, 0.0041, respectively. The results of this article provide technical support for promoting the timely monitoring of soil TN content and guiding rational fertilization in apple orchards.

Since fire is still necessary for rotational shifting cultivation (RSC), the vertical distribution and slope effect on soil properties and soil surface loss after a fire remain unclear. To address these research gaps, the study aims to achieve the following objectives: 1) investigating post-fire soil properties and soil surface loss in RSC, and 2) assessing the vertical distribution and slope effect on soil properties and soil surface loss in RSC. Soil samples were collected from two stages of RSC: 6 years (RSC-6Y) and 12 years (RSC-12Y), located in Chiang Mai Province, Northern Thailand. A continuous 15-year left fallow field (CF-15Y) was used as the reference site. Soil samples were collected from the upper, middle, and lower slopes at depths of 0–5, 5–10, 10–20, and 20–30 cm at five different time points: before burning, 5 min, 3 months, 6 months, and 9 months post-fire. The results indicated that older fallow fields had a tendency to accumulate more soil organic carbon (SOC) and soil organic nitrogen (STN). The color of the ash was altered by the fire, resulting in dark reddish-brown ash with higher levels of pH, organic matter (OM), electrical conductivity, total nitrogen, and soil nutrients when compared to gray and white ashes. The combustion of OM during the fire was found to release soil nutrients, which could explain the increase after burning. SOC stock increased at deeper layers (5–10 cm) with higher values than pre-burning levels, especially at lower slope positions, while STN stock decreased at the surface soil post-fire but increased in deeper layers at all slope positions. The average soil surface loss ranged from 1.6 to 3.1 cm, with the highest loss observed 9 months after the fire (during the rainy season) at the upper slope. In terms of the impact of slope on soil properties following the fire event, our study indicated a significant correlation between lower slopes and variables including SOC, STN, electrical conductivity, nitrate–nitrogen (NO3-N), ammonium nitrogen (NH4-N), exchangeable calcium, and exchangeable magnesium. Further study is required to investigate and develop appropriate post-fire management strategies to effectively reduce nutrient loss and minimize soil surface erosion.

Studying both soil carbon (C) and nitrogen (N) storages in different climate zones and their relationship with climatic factors is of great significance for understanding soil fertility and predicting global climate change. Climate influences soil minerals, which are important for soil organic carbon (SOC) and N retention. However, there are few studies on SOC and soil total nitrogen (STN) storage in different climatic zones, and of the effects of soil oxidation minerals on SOC and STN storage. We measured the storage of SOC and STN and the content of oxidizable minerals in soils from different climatic regions, then obtained climate data from the China Meteorological Data Service Center, and finally investigated the effects of climate factors and soil oxides minerals on SOC and STN. The results showed that climatic factors (mean annual temperature—MAT, mean annual precipitation—MAP, and ≥10 °C mean annual cumulative temperature—MACT) had significant effects on SOC and STN content, and there was significant epistatic clustering of SOC and STN contents in different climatic zones. When MAT, MAP, and MACT increased, SOC and STN storage showed a trend of increasing and then decreasing, and both SOC and STN storages were largest in the middle temperate zone. The content of soil metal oxides (Al2O3, Fe2O3, Na2O, MgO, CaO, K2O, and TiO2) showed significant positive correlation with climatic factors (MAT, MAP, and MACT). The contents of Al2O3, Fe2O3, CaO, K2O, and TiO2 showed significant negative correlation with SOC and STN contents. In summary, our results showed that, although soil metal oxides (SMO) have a protective effect on SOC and STN to some extent, they do not change the influence of climatic factors on SOC and STN storage. [ABSTRACT FROM AUTHOR]

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500–25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0–100 cm) or the shallow layers (0–40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm). A total of 90 soil samples containing 450 subsamples (0–10 cm, 10–20 cm, 20–40 cm, 40–70 cm, and 70–100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSEp = 1.07–3.82 g/kg, RPD = 1.10–5.80; TN: RMSEp = 0.11–0.15 g/kg, RPD = 1.70–4.39) outperformed those based on vis-NIR (OM: RMSEp = 1.75–8.95 g/kg, RPD = 0.50–3.61; TN: RMSEp = 0.12–0.27 g/kg; RPD = 1.00–3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSEp = 1.09–2.97 g/kg, RPD = 2.13–5.80; TN: RMSEp = 0.08–0.19 g/kg, RPD = 1.86–4.39) outperformed those based on the shallow layers (OM: RMSEp = 1.07–8.95 g/kg, RPD = 0.50–3.93; TN: RMSEp = 0.11–0.27 g/kg, RPD = 1.00–2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths. [ABSTRACT FROM AUTHOR]

Precise fertilizer application in agriculture requires accurate and dependable measurements of the soil total nitrogen (TN) concentration. Henan Province is one of the most important grain-producing areas in China. In order to promote the development of precision agriculture in Henan Province, this study took the high-standard basic farmland construction area in central Henan Province as the research area. Using single-phase images acquired from the ZY1-02D satellite hyperspectral sensor on 28 January 2021 (with a spatial resolution of 30 m × 30 m, a spectral range that covered 400–2500 nm, and a revisit period of 3 days) for spectral reflectance transformation and feature spectral band extraction. Based on multiple representation models, such as multiple linear regression, partial least squares regression, and support vector machine (SVM), all bands, feature bands, feature band combinations, and differential evolution (DE) algorithms were used to extract the secondary characteristic variables of the combination of characteristic bands, which were used as model inputs to estimate the content of TN in the study area. It was found that (1) the spectral reflectance transformation could help to improve the accuracy of prediction by reducing the interference from noise in the model, but the optimal spectral transformation method differed between different models and even between the training and test sets of the same model; (2) the estimation accuracy of the TN content model based on the minimum shrinkage and feature selection operator of the feature band was usually better than that of the full band, the feature combination band contained more effective information related to the TN content, and the combination of the DE algorithm and the SVM model achieved a better estimation accuracy for secondary feature extraction and TN content estimation of the feature combination band; and (3) ZY1-02D hyperspectral satellite data have the potential for the dynamic and non-destructive monitoring of regional TN content. [ABSTRACT FROM AUTHOR]

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Abstract Background The rock cut slope (RCS) could cause damage to regional ecological functions and landscapes and requires recovery. Biological soil crusts (BSCs) are pioneer and dominant colonizers during the initial recovery stage. To accelerate the natural recovery of RCS, the development process and influencing agents of BSC should be revealed. Thus, the area index of crevices (I R), BSC coverage (COV) and biomass (BM), soil weight (SW), and major soil nutrients [organic carbon (OC), total nitrogen (TN) and total phosphorus (TP)] content, collected from 164 quadrats on 13 RCSs in the mountainous area of west Sichuan Province, China, were measured, to explore the effect of crevice of RCS on BSC development. Results Soil OC, TN and TP on RCSs ranged from 18.61 to 123.03 g kg−1, 0.96 to 6.02 g kg−1 and 0.52 to 2.46 g kg−1, respectively, and were approximately to or higher than those on natural slopes. The OC, TN and TP contents in soils elevated unsystematically with recovery time of RCSs. BSCs on RCS distributed along crevices generally and firstly. During the first 13 years of natural recovery, COV, BM and SW ranged from 6.5 to 28.2%, 14.43 to 67.25 g m−2, and 127.69 to 1277.74 g m−2, respectively. COV, BM and SW increased linearly with I R on RCSs. The positive correlation between COV and BM and I R was insignificantly impacted by bedrock, slope aspect and altitude within the recovery time less than 13 years. COV and BM on RCSs increased significantly when the recovery time is more than 27 years. Conclusions Crevice on RCSs could be a major environmental factor which is conducive to BSC development and soil accumulation through creating a space for water and soil particle. Furthermore, with the increase of recovery time of RCSs, BSCs may grow and reach a stable state with the promotion of soil nutrients, plant growth and microbial activity. These results provide a development process of BSC that from inside to outside the crevices on RCSs. In the areas with stable rock strata and a low risk of geological disasters, purposeful improvement in crevice density on RCS may effectively accelerate BSC development.

The soil organic matter (SOM) and soil total nitrogen (STN) is a significant concern in Qinghai–Tibet Plateau, China. This study analysed the spatial distribution of SOM and STN and determined their influencing factors to support the conservation of cultivated soil and development of sustainable agricultural strategies in the Plateau. In total, 120 soil samples were collected from the 0–20-cm soil layer in Huzhu County, Qinghai Province. Traditional statistical and geostatistical methods were used to analyse the spatial distribution of SOM and STN; a geographical detector (GeoDetector)was used to explore the factors influencing the spatial variation. The SOM and STN concentrations were 6.92–44.57 and 0.52–2.54 g/kg, respectively. The Cokriging interpolation map showed a similar spatial distribution pattern for SOM and STN concentrations, which decreased from the northeast to southwest directions in the study area. GeoDetector results revealed that the three primary factors influencing the spatial variability of SOM and STN were soil type, annual accumulated precipitation and elevation, with their explanatory power ranging between 38.4% and 59.5%. Two-factor interactions enhanced the explanatory power of the spatial variability of SOM and STN. The research results provide a reference for conservation tillage and precision agriculture.

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Fire is traditionally used by farmers for clearing fields when the fallow period is short. Soil chemical properties changed significantly after burning and returned to prefire levels after 1 year. Bacillus, HSB OF53-F07, Conexibacter, and Acidothermus abundances increased immediately after burning and then significantly declined, with lower levels 1 year after burning. Anaeromyxobacter and Candidatus Udaeobacter dominated at 1 year after burning. Burning under high soil moisture conditions and within a very short time caused no effect to the bacterial soil communities. 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. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) spectroscopy can improve the efficiency of soil property prediction, such as that of soil total nitrogen (TN) content. However, soil spectra are very sensitive to soil moisture content, which is a crucial factor affecting the accuracy of soil nutrient composition prediction. In response to this issue, the goal of this study is to identify the best model to correct the effect of soil moisture for predicting soil total nitrogen by near-infrared spectroscopy. The 107 collected soil samples were divided into six different water content (0%, 5%, 10%, 15%, 20%, and 25%) sample groups. Then, five correction methods, including direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), spectral space transformation (SST), and slope/bias (S/B), were executed. Finally, partial least squares regression (PLSR) models were established to forecast TN content. The results showed that SST could minimize the influence of moisture. Furthermore, SST–PLSR had the best TN content prediction accuracy: R p 2 (the coefficient of determination of the prediction set) in the range of 0.81–0.82, RMSEP (the root mean square error of the prediction set) in the range of 0.09–0.10 g/kg, and RPD (ratio of performance to deviation) in the range of 2.32–2.40. Therefore, the dry soil prediction model is competent for wet soil samples and could achieve preciseness in TN content prediction. The use of SST can effectively eliminate the influence of moisture and achieve high-precision TN prediction in wet soil samples. Additionally, the introduction of SST expands the application scope of soil nutrient prediction models and increases model robustness. [ABSTRACT FROM AUTHOR]

Substituting mineral fertilizers (MFs) with manure nitrogen (N) can not only reduce environmental pollution, but also improve soil quality. However, the effects of various manure N substitution ratios (SRs, the ratio of manure N over total N applied) on soil properties and vegetable yields in China are poorly studied. Here, through a meta-analysis of 667 observations, we assessed the effects of three manure N SRs (low (SR ≤ 35%), medium (35% < SR ≤ 70%), and high (SR > 70%)) on vegetable yields and soil properties (soil organic carbon, SOC; soil total nitrogen, STN; microbial biomass carbon (C) and nitrogen (N), MBC/N; and available phosphorus and potassium, (AP/AK)) in the 0–20 cm soil under different climatic conditions, initial soil properties, and management practices. The results show that the SOC and STN contents increased by 28.5% and 21.9%, respectively, under the medium SRs compared to the MF, which were the highest among the three SRs. Both soil MBC and MBN increased with the increase in the SRs, and the increased ratios in the high SRs reached 203.4% and 119.3%, respectively. In addition, the AP also increased with the increase in the SR, but the AK was not significantly changed with the low and medium SRs compared with the MF. Overall, the medium SR produced the highest vegetable yield among the three SRs with an increase of 18.6%. Additionally, a random forest analysis indicated that the N application rate, planting years, and mean annual precipitation were the most important factors influencing vegetable yield. In conclusion, the SR of 35–70% is more conducive to increasing soil nutrient contents significantly and improves vegetable yields in Chinese vegetable fields. [ABSTRACT FROM AUTHOR]

Previous studies have observed that increased precipitation positively affects primary production in semiarid grasslands in Inner Mongolia, while soil carbon (C) and nitrogen (N) strongly influence how ecosystems respond to precipitation as well as anthropogenic disturbances under different management strategies. Therefore, in this study, we investigated the storage of organic C and N in four grassland sites with similar flora and landforms but with different grazing intensities to characterize how the storage and concentrations of C and N respond to relief from grazing pressure and precipitation. The concentrations of soil organic carbon (SOC), soil total nitrogen (STN), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN), as well as the contents of soil bacteria and fungi in the 0–50 cm soil layers, were measured. The results showed that SOC, STN, MBC, and MBN storage varied greatly among the four grassland sites (p < 0.05), with all decreasing significantly with grassland degradation and increasing greatly with the exclusion of grazing, i.e., the establishment of natural grassland (NG). More than 90% of C and 95% of N stored in the soil were lost due to heavy grazing, but the losses were smaller in other nutrient pools (including the aboveground biomass, litter, and roots). Interestingly, the proportion of the particle size fractions (clay, silt, and sand) had a stronger effect on limiting the soil and microbial nutrient pools compared to precipitation. The limited range of C and N storage found in these grassland soils indicated that enclosed fencing was a valuable management tool with a high potential to sequester C in the top meter of the soil, showing a stronger effect than precipitation. This study provides a theoretical basis for improving grassland recovery in semiarid areas that have been heavily grazed. [ABSTRACT FROM AUTHOR]

Background: The rock cut slope (RCS) could cause damage to regional ecological functions and landscapes and requires recovery. Biological soil crusts (BSCs) are pioneer and dominant colonizers during the initial recovery stage. To accelerate the natural recovery of RCS, the development process and influencing agents of BSC should be revealed. Thus, the area index of crevices (IR), BSC coverage (COV) and biomass (BM), soil weight (SW), and major soil nutrients [organic carbon (OC), total nitrogen (TN) and total phosphorus (TP)] content, collected from 164 quadrats on 13 RCSs in the mountainous area of west Sichuan Province, China, were measured, to explore the effect of crevice of RCS on BSC development. Results: Soil OC, TN and TP on RCSs ranged from 18.61 to 123.03 g kg−1, 0.96 to 6.02 g kg−1 and 0.52 to 2.46 g kg−1, respectively, and were approximately to or higher than those on natural slopes. The OC, TN and TP contents in soils elevated unsystematically with recovery time of RCSs. BSCs on RCS distributed along crevices generally and firstly. During the first 13 years of natural recovery, COV, BM and SW ranged from 6.5 to 28.2%, 14.43 to 67.25 g m−2, and 127.69 to 1277.74 g m−2, respectively. COV, BM and SW increased linearly with IR on RCSs. The positive correlation between COV and BM and IR was insignificantly impacted by bedrock, slope aspect and altitude within the recovery time less than 13 years. COV and BM on RCSs increased significantly when the recovery time is more than 27 years. Conclusions: Crevice on RCSs could be a major environmental factor which is conducive to BSC development and soil accumulation through creating a space for water and soil particle. Furthermore, with the increase of recovery time of RCSs, BSCs may grow and reach a stable state with the promotion of soil nutrients, plant growth and microbial activity. These results provide a development process of BSC that from inside to outside the crevices on RCSs. In the areas with stable rock strata and a low risk of geological disasters, purposeful improvement in crevice density on RCS may effectively accelerate BSC development. [ABSTRACT FROM AUTHOR]

Soil total nitrogen is one of the most important basic indicators for fertiliser decision making, but tens of millions of soil total nitrogen sampling data have been accumulated, forming a huge database. In this large database, there is a large amount of anomalous data, which can interfere with data analysis, affect the construction of spatial interpolation and prediction models, and then affect the accuracy of nutrient management decisions. The traditional method of identifying soil total nitrogen anomalies based on boxplots suffers from the problems of not being able to identify local anomalies, which can easily lead to misclassification of soil total nitrogen data anomalies, and the detection efficiency is not high. We propose a method to identify soil total nitrogen outliers by combining the Isolation Forest algorithm and local spatial autocorrelation analysis, which can simultaneously detect global and local outliers from large amounts of data and combine organic matter as an auxiliary indicator in the spatial analysis to help judge local outliers. Finally, the results of global and local anomalies were combined to provide a comprehensive assessment of the soil nitrogen data, avoiding the misjudgement or omission of judgement that can occur when using a single method. Using 25,930 soil test data from Yunnan Province in 2009 as an example, we compared and analysed the typical boxplot method and the unsupervised OneClassSVM method and evaluated the performance of each method in terms of correct detection rate, false positive rate and false negative rate. The results show that the proposed method has a correct detection rate (TR) of 99.97%, a false positive rate (FPR) of 8.06% and a false negative rate (FNR) of 0.01% on the data, which shows high validity and accuracy; it is also comparable to the independent isolated forests (FNR = 4.76%), boxplot (FNR = 3.90%) and OneClassSVM (FNR = 4.77%), and the false negative rate is reduced by 4.75%, 3.89% and 4.76%, respectively.

Legume/cereal intercropping has the potential to maximize the use of resources to raise yields due to enhanced nitrogen (N) fixation by legume root nodules, while high N fertilization may inhibit the nodulation of legume. However, whether legume/cereal intercropping can promote the accumulation of soil N storage with N fertilization and its underlying mechanism are less clear. Here, we evaluated the long-term (5 years) effects of maize/peanut intercropping and mineral N fertilization on peanut soil total N content and soil N cycling functional genes. The experiment includes two planting patterns (peanut maize intercropping and peanut monocropping) with three N fertilization rates (0, 150, and 300 kg N ha−1). Intercropping increased soil total N content (STN) by average 18.2%, and the positive effect of intercropping on STN decreased with N application rate. Highest N application decreased the nodule fresh weight (NFW) by 64.3% and 46.0% in intercropping and monocropping system, respectively. However, intercropping has no effect on NFW. Intercropping increased the nifH gene abundance by average 26.5%. SEM analysis indicated that NFW and nifH gene abundance combined can explain 46% of the variance of STN. Our results indicate that biological N fixation but not mineral N fertilization enhances the accumulation of N in soil planted with peanut in maize/peanut intercropping system.

In order to satisfy the basic requirements of sustainable agricultural development, it is important to understand the spatial distribution characteristics of soil total nitrogen (TN) content to better guide accurate fertilization to increase grain yield. To this end, this paper constructs three inversion models of partial least squares regression (PLSR), back propagation neural network (BPNN) and support vector machines (SVM) with remote sensing data to predict the TN content in Datong County, Xining City, Qinghai Province, China. The results showed that the average TN content was 1.864 g/kg, and the coefficient of variation (CV) was 30.596%. The prediction accuracy of the SVM model (R2 = 0.676, RMSE = 0.296) among the three inversion models was higher than that of the BPNN model (R2 = 0.560, RMSE = 0.305) and the PLSR model (R2 = 0.374, RMSE = 0.334). The model with the highest accuracy predicted the spatial distribution of TN, and TN content showed a spatial distribution trend which was high in the northwest and low in the southeast, and gradually decreased from north to south. This study provides reference basis and support for soil fertility evaluations and sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Precise fertilizer application in agriculture requires accurate and dependable measurements of the soil total nitrogen (TN) concentration. Henan Province is one of the most important grain-producing areas in China. In order to promote the development of precision agriculture in Henan Province, this study took the high-standard basic farmland construction area in central Henan Province as the research area. Using single-phase images acquired from the ZY1-02D satellite hyperspectral sensor on 28 January 2021 (with a spatial resolution of 30 m × 30 m, a spectral range that covered 400–2500 nm, and a revisit period of 3 days) for spectral reflectance transformation and feature spectral band extraction. Based on multiple representation models, such as multiple linear regression, partial least squares regression, and support vector machine (SVM), all bands, feature bands, feature band combinations, and differential evolution (DE) algorithms were used to extract the secondary characteristic variables of the combination of characteristic bands, which were used as model inputs to estimate the content of TN in the study area. It was found that (1) the spectral reflectance transformation could help to improve the accuracy of prediction by reducing the interference from noise in the model, but the optimal spectral transformation method differed between different models and even between the training and test sets of the same model; (2) the estimation accuracy of the TN content model based on the minimum shrinkage and feature selection operator of the feature band was usually better than that of the full band, the feature combination band contained more effective information related to the TN content, and the combination of the DE algorithm and the SVM model achieved a better estimation accuracy for secondary feature extraction and TN content estimation of the feature combination band; and (3) ZY1-02D hyperspectral satellite data have the potential for the dynamic and non-destructive monitoring of regional TN content.

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500–25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0–100 cm) or the shallow layers (0–40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm). A total of 90 soil samples containing 450 subsamples (0–10 cm, 10–20 cm, 20–40 cm, 40–70 cm, and 70–100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSEp = 1.07–3.82 g/kg, RPD = 1.10–5.80; TN: RMSEp = 0.11–0.15 g/kg, RPD = 1.70–4.39) outperformed those based on vis-NIR (OM: RMSEp = 1.75–8.95 g/kg, RPD = 0.50–3.61; TN: RMSEp = 0.12–0.27 g/kg; RPD = 1.00–3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSEp = 1.09–2.97 g/kg, RPD = 2.13–5.80; TN: RMSEp = 0.08–0.19 g/kg, RPD = 1.86–4.39) outperformed those based on the shallow layers (OM: RMSEp = 1.07–8.95 g/kg, RPD = 0.50–3.93; TN: RMSEp = 0.11–0.27 g/kg, RPD = 1.00–2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths.

Soil nitrogen is very important for crop growth and development. However, the factors affecting the three-dimensional spatial distribution of soil total nitrogen (TN), particularly in coal mining subsidence areas, are unclear. In this study, Markov geostatistics was used to analyse the three-dimensional spatial distribution characteristics and influential factors of TN by examining 180 soil samples from the Zhaogu mine in China. The results showed that the TN content was significantly different at different soil depths (0–20, 20–40, 40–60 cm) and decreased with increasing soil depth. The variation coefficient of the TN content decreased gradually from top to bottom, ranging from 18.18 to 25.62%. In addition, the TN content was greatly affected by mining subsidence, rainfall, irrigation, fertilization and management mode. The factors that influenced the TN content also varied across different slope positions. The TN content of the upslope was the highest, and the TN content of the middle slope was the lowest. These results can provide research ideas and technical countermeasures for ecological environment improvement and sustainable land development in coal mining subsidence areas. [ABSTRACT FROM AUTHOR]

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 nitrogen is an essential nutrient element for crop growth and development, and an important indicator of soil fertility characteristics. This study proposed a method based on pyrolysis and artificial olfaction to quickly and accurately determine the soil total nitrogen (STN) content. A muffle furnace was used to pyrolyze the soil samples, and ten different types of oxide semiconductor gas sensors were used to construct a sensor array to detect the soil samples' pyrolysis gas. The response curves of the sensors were tested at pyrolysis temperatures of 200°C, 300°C, 400°C, and 500°C and at pyrolysis times of 1 min, 3 min, 5 min, and 10 min to obtain the optimal pyrolysis state of the soil samples. The optimal pyrolysis temperature was 400°C, and the pyrolysis time was 3 min. The response area, maximum value, average differential coefficient, variance value, maximum gradient value, average value, and 8th-second transient value of the sensor response curve were extracted to construct an artificial olfactory feature space of 121×10×7 (121 soil samples, ten sensor numbers, seven extracted eigenvalues). Back-propagation neural network algorithm (BPNN), partial least squares regression algorithm (PLSR), and partial least squares regression combined with back-propagation neural network algorithm (PLSR-BPNN) were used to establish a prediction model of artificial olfactory feature space and STN content. Moreover, coefficient of determination (R²), root mean square error (RMSE), and the ratio of performance to deviation (RPD) were used as the performance indicators of the prediction results. The test results showed that the R² of the PLSR, BPNN, and PLSR-BPNN models were 0.89033, 0.81185, and 0.92186, and the RMSE values were 0.24297, 0.37370, and 0.21781, and the RPD were 2.9964, 1.9482, and 3.3426, respectively. The model established by the PLSR-BPNN algorithm has the highest R² and RPD and the smallest RMSE, can achieve the accurate prediction of STN content, and therefore the model is rated as "excellent". The detection method in this study achieves a low-cost, rapid, and accurate determination of STN content, and provides a new reference for the measurement of STN. [ABSTRACT FROM AUTHOR]

Although soil total nitrogen (STN) and soil total phosphorus (STP) play significant roles in terrestrial ecosystem function, their storage and driving factors in the alpine wetlands of the Qinghai-Tibetan Plateau remain unclear. In this study, we estimated STN and STP storage and their controlling factors, including vegetation, soil, and climate characteristics, using data collected from 50 sites across the wetlands in the Three Rivers Source Region. STN and STP storage in the top 30 cm of soil were 62.12 ± 37.55 Tg N and 9.24 ± 2.90 Tg P, respectively. Although STN density did not differ significantly for different vegetation types (i.e., alpine meadow and alpine wetland), belowground biomass showed a positive relationship with STN density. Mean annual precipitation (MAP) showed a significant positive relationship with STN density, whereas the effects of mean annual temperature on STN density were minor. Compared with the effects of vegetation and climatic factors, soil characteristics were found to not only exert a significant effect on STN density, but also influence the effects of climate and vegetation on STN density. For STP density, soil characteristics were found to be a significant controlling factor, whereas the effects of biomass and climatic factors were minor. The studied climate, soil, and vegetation characteristics jointly explained ∼54% of STN variance, whereas soil characteristics explained only 20% of STP variation. MAP indirectly affected STN density via effects on vegetation and soil, and its direct effect on STN density was minor. This indicated a strong relationship between biotic and abiotic effects and STN density. Identification of the factors influencing STN and STP variance in alpine wetlands contributes to our understanding of the biogeochemical cycle in high-altitude regions.

The soil organic matter (SOM) and soil total nitrogen (STN) is a significant concern in Qinghai–Tibet Plateau, China. This study analysed the spatial distribution of SOM and STN and determined their influencing factors to support the conservation of cultivated soil and development of sustainable agricultural strategies in the Plateau. In total, 120 soil samples were collected from the 0–20-cm soil layer in Huzhu County, Qinghai Province. Traditional statistical and geostatistical methods were used to analyse the spatial distribution of SOM and STN; a geographical detector (GeoDetector)was used to explore the factors influencing the spatial variation. The SOM and STN concentrations were 6.92–44.57 and 0.52–2.54 g/kg, respectively. The Cokriging interpolation map showed a similar spatial distribution pattern for SOM and STN concentrations, which decreased from the northeast to southwest directions in the study area. GeoDetector results revealed that the three primary factors influencing the spatial variability of SOM and STN were soil type, annual accumulated precipitation and elevation, with their explanatory power ranging between 38.4% and 59.5%. Two-factor interactions enhanced the explanatory power of the spatial variability of SOM and STN. The research results provide a reference for conservation tillage and precision agriculture. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the possible difference in the amount of organic carbon and total nitrogen in the soil under the crown of tree species in a semi-arid forest at the level of a single tree. Also, the pattern of possible differences between these two main soil materials was observed in different size classes of trees and in top- and subsoil. For this purpose, 60 trees from four species of juniper (Juniperus excelsa), narrow-leafed ash (Fraxinus angustifolia), Brant's oak (Quercus brantii) and hawthorn (Crataegus azarolus) were randomly selected in a mixed stand of central Zagrosian forest that had been enclosd for over 30 years. The amounts of organic carbon and total nitrogen of topsoil (0-15 cm) and subsoil (15-50 cm) were measured and compared. The results showed that, first, both the amount of soil organic carbon and the total nitrogen from the samples beneath the tree crowns were higher than their corresponding values in soils outside the crowns. Second, only the amounts of soil organic carbon were affected by the species, and these values ranged from a minimum of 1.37% in topsoil and 0.52% in subsoil for hawthorn, to maximum values of 1.64 and 0.73 percent for Juniper in top- and subsoil, respectively. Third, the amount of total nitrogen in the soil with an average of 0.21 and 0.08 percent of dry weight in top- and subsoils, respectively were the same beneath the crowns of the four species. Fourth, the size of the trees only affected the amount of organic carbon and total nitrogen in the topsoil. This study showed that there is little difference in soil organic carbon and total nitrogen under the canopy of different tree species in a standard Zagros forest and therefore the whole stand may act as a mega-organism and has more or less homogeneous soil.

Substituting mineral fertilizers (MFs) with manure nitrogen (N) can not only reduce environmental pollution, but also improve soil quality. However, the effects of various manure N substitution ratios (SRs, the ratio of manure N over total N applied) on soil properties and vegetable yields in China are poorly studied. Here, through a meta-analysis of 667 observations, we assessed the effects of three manure N SRs (low (SR ≤ 35%), medium (35% < SR ≤ 70%), and high (SR > 70%)) on vegetable yields and soil properties (soil organic carbon, SOC; soil total nitrogen, STN; microbial biomass carbon (C) and nitrogen (N), MBC/N; and available phosphorus and potassium, (AP/AK)) in the 0–20 cm soil under different climatic conditions, initial soil properties, and management practices. The results show that the SOC and STN contents increased by 28.5% and 21.9%, respectively, under the medium SRs compared to the MF, which were the highest among the three SRs. Both soil MBC and MBN increased with the increase in the SRs, and the increased ratios in the high SRs reached 203.4% and 119.3%, respectively. In addition, the AP also increased with the increase in the SR, but the AK was not significantly changed with the low and medium SRs compared with the MF. Overall, the medium SR produced the highest vegetable yield among the three SRs with an increase of 18.6%. Additionally, a random forest analysis indicated that the N application rate, planting years, and mean annual precipitation were the most important factors influencing vegetable yield. In conclusion, the SR of 35–70% is more conducive to increasing soil nutrient contents significantly and improves vegetable yields in Chinese vegetable fields.

Stock estimates are critical to quantifying carbon and nitrogen sequestration, quantifying greenhouse gas emissions, and understanding key biogeochemical processes (i.e., soil carbon and nutrient cycling). Many studies have assessed soil organic matter and nutrients in different ecosystems. However, the spatial distribution of carbon and nitrogen and the key influencing factors in arid desert steppe remain unclear. Here, we investigated the soil organic carbon (SOC) and soil total nitrogen (STN) to a depth of 100 cm at 126 sites in a desert steppe in northwestern China. SOC and STN contents decreased with increasing depth; the highest average SOC and STN contents were 12.70 and 0.65 g kg−1 in the surface 5 cm, and the lowest were from 80 to 100 cm (4.49 and 0.16 g kg−1, respectively). SOC density (SOCD) and STN density (STND) to a depth of 100 cm averaged 8.94 and 0.45 kg m−2, respectively. The top 1 m of the soils stored approximately 1,041 Tg SOC and 52 Tg STN in the study area. Geostatistical analysis showed strong and moderate spatial autocorrelation for SOCD in different soil layers, but the autocorrelation for STND gradually weakened with increasing depth. SOCD and STND decreased from southwest to northeast in the study area, along an elevation gradient. Both were significantly positively correlated with topographic variables, precipitation, and the normalized-difference vegetation index, but negatively correlated with temperature and aridity. More than 40% of the SOCD and STND spatial variation was explained by elevation, which was the dominant factor. The data and high-resolution maps from this study will support future soil carbon and nitrogen analyses.

Precision farming based on soil total nitrogen status is crucial to informed fertilization strategies while enhancing environmental quality of agricultural ecosystems. Hyperspectral technology was applied for rapid assessment of soil total nitrogen content in a long-term fertilization experiment of wheat-corn rotation system. The first-order derivative of squared spectra and Soil Adjusted Spectral Index transformation provided the highest correlation with soil total nitrogen contents. Inversion modeling results involving multiple linear regression, back propagation neural network (BPNN), and partial least square regression indicated that BPNN provided the best performance with coefficient of determination (R2) reaching 0.8 and 0.9, respectively, for wheat and corn seasons. Model performance with wheat and corn season combined was poor due to seasonal difference in hyperspectral patterns and different decomposition of straws returned to the field. This study provided a quantitative tool for rapid and accurate diagnosis of soil total nitrogen content for precision agriculture.

To improve the accuracy of detecting soil total nitrogen (STN) content by an artificial olfactory system, this paper proposes a multi-feature optimization method for soil total nitrogen content based on an artificial olfactory system. Ten different metal–oxide semiconductor gas sensors were selected to form a sensor array to collect soil gas and generate response curves. Additionally, six features such as the response area, maximum value, average differential coefficient, standard deviation value, average value, and 15th-second transient value of each sensor response curve were extracted to construct an artificial olfactory feature space (10 × 6). Moreover, the relationship between feature space and soil total nitrogen content was used to establish backpropagation neural network (BPNN), extreme learning machine (ELM), and partial least squares regression (PLSR) models were used, and the coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) were selected as prediction performance indicators. The Monte Carlo cross-validation (MCCV) and K-means improved leave-one-out cross-validation (K-means LOOCV) were adopted to identify and remove abnormal samples in the feature space and establish the BPNN model, respectively. There were significant improvements before and after comparing the two rejection methods, among which the MCCV rejection method was superior, where values for R2, RMSE, and RPD were 0.75671, 0.33517, and 1.7938, respectively. After removing the abnormal samples, the soil samples were then subjected to feature-optimized dimensionality reduction using principal component analysis (PCA) and genetic algorithm-based optimization backpropagation neural network (GA-BP). The test results showed that after feature optimization the model indicators performed better than those of the unoptimized model, and the PLSR model with GA-BP for feature optimization had the best prediction effect, with an R2 value of 0.93848, RPD value of 3.5666, and RMSE value of 0.16857 in the test set. R2 and RPD values improved by 14.01% and 50.60%, respectively, compared with those before optimization, and RMSE value decreased by 45.16%, which effectively improved the accuracy of the artificial olfactory system in detecting soil total nitrogen content and could achieve more accurate quantitative prediction of soil total nitrogen content.

Alfalfa in China is mostly planted in the semi-arid or arid Northwest inland regions due to its ability to take up water from deep in the soil and to fix atmospheric N2 which reduces N fertilizer application. However, perennial alfalfa may deplete soil water due to uptake and thus aggravate soil desiccation. The objectives of this study were (1) to determine the alfalfa forage yield, soil property (soil temperature (ST), soil water content (SWC), soil organic carbon (SOC) and soil total nitrogen (STN)) and greenhouse gas (GHG: methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2)) emissions affected by alfalfa stand age and growing season, (2) to investigate the effects of soil property on GHG emissions, and (3) to optimize the alfalfa stand age by integrating the two standard criteria, the forage yield and water use efficiency, and the total GHG efflux (CO2-eq). This study was performed in alfalfa fields of different ages (2, 3, 5 and 7 year old) during the growing season (from April to October) in a typical salinized meadow with temperate continental arid climate in the Northwest inland regions, China. Despite its higher total GHG efflux (CO2-eq), the greater forage yield and water use efficiency with lower GEIhay and high CH4 uptake in the 5-year alfalfa stand suggested an optimal alfalfa stand age of 5 years. Results show that ST, SOC and RBM alone had positive effects (except RBM had no significant effect on CH4 effluxes), but SWC and STN alone had negative effects on GHG fluxes. Furthermore, results demonstrate that in arid regions SWC superseded ST, SOC, STN and RBM as a key factor regulating GHG fluxes, and soil water stress may have led to a net uptake of CH4 by soils and a reduction of N2O and CO2 effluxes from alfalfa fields. Our study has provided insights into the determination of alfalfa stand age and the understanding of mechanisms regulating GHG fluxes in alfalfa fields in the continental arid regions. This knowledge is essential to decide the alfalfa retention time by considering the hay yield, water use efficiency as well as GHG emission. [ABSTRACT FROM AUTHOR]

Temporal variations in soil CO2 efflux and its direct links to soil microbial biomass and environmental factors in high‐elevation regions are poorly understood. In this study, I investigated the variations in soil CO2 efflux over time and their relationships to soil organic matter, soil microbial biomass and environmental factors in an alpine meadow site on the Qinghai–Tibetan Plateau. The results showed notable monthly to seasonal changes in soil CO2 efflux in alpine meadows. The soil CO2 efflux was lower overall from January to May and from October to December than from June to September (p < 0.05); on a seasonal basis, the soil CO2 efflux was lower during the spring, autumn and winter than during the summer (p < 0.05). In addition, linear regression analysis showed that the temporal variations in soil CO2 efflux over the whole year decreased with soil microbial biomass carbon content (p < 0.05). Soil CO2 efflux was also positively related to air temperature and snow cover depth (p < 0.05). Multiregression and principal component regression analyses indicated that temporal variations in soil CO2 efflux were mainly caused by monthly and seasonal changes in air temperature and soil microbial biomass. [ABSTRACT FROM AUTHOR]

Precision farming based on soil total nitrogen status is crucial to informed fertilization strategies while enhancing environmental quality of agricultural ecosystems. Hyperspectral technology was applied for rapid assessment of soil total nitrogen content in a long-term fertilization experiment of wheat-corn rotation system. The first-order derivative of squared spectra and Soil Adjusted Spectral Index transformation provided the highest correlation with soil total nitrogen contents. Inversion modeling results involving multiple linear regression, back propagation neural network (BPNN), and partial least square regression indicated that BPNN provided the best performance with coefficient of determination (R2) reaching 0.8 and 0.9, respectively, for wheat and corn seasons. Model performance with wheat and corn season combined was poor due to seasonal difference in hyperspectral patterns and different decomposition of straws returned to the field. This study provided a quantitative tool for rapid and accurate diagnosis of soil total nitrogen content for precision agriculture. [ABSTRACT FROM AUTHOR]

In China’s Loess Plateau, afforestation and reforestation are considered the foremost practices for sequestering carbon and conserving soil and water. In order to evaluate the carbon storage changes of tree, soil, and litter, and the soil total nitrogen (STN) in two typical artificial forests in the region, we conducted plot surveys for different ages of both artificial forest types. Soil samples were collected at different depths from 0–100 cm. The results indicated that forest ecosystem carbon storage increased with tree development. The rates of mean annual carbon sequestration of Pinus tabulaeformis and Robinia pseudoacacia plantation ecosystems were 3.31 and 3.53 Mg ha−1 year−1, respectively. The rate of mean annual carbon sequestration of R. pseudoacacia plantation ecosystems was higher by 6.65% than that of P. tabulaeformis plantation ecosystems. The soil organic carbon (SOC) and STN decreased at deeper soil depths in both plantations at different stand ages, significantly decreasing in the 0–60 cm of soil (P < 0.05), and the highest SOC content and storage were in the top 0–20 cm of soil. The temporal patterns for SOC and STN changes at different soil sampling depths from 0 to 100 cm all showed an initial decrease during the early stage of restoration, and then an increase that coincided with the development of the two plantation forests. At 0–100 cm depth, the SOC storage was in the range of 40.95–106.79 and 45.13–113.61 Mg ha−1 for the P. tabulaeformis forest and R. pseudoacacia forest, respectively. The STN storage in the 0–100 cm soil layer with the stand age development ranged from 4.16 to 8.34 Mg ha−1 in the R. pseudoacacia plantation and 4.19–7.55 Mg ha−1 in the P. tabulaeformis forest. The results showed a significant positive correlation between SOC and STN. This study suggests that we should pay more attention to changes in soil carbon and nitrogen sequestration during long-term vegetation restoration.

Background Patchy vegetation is a very common phenomenon due to long-term overgrazing in degraded steppe grasslands, which results in substantial uncertainty associated with soil carbon (C) and nitrogen (N) dynamics because of changes in the amount of litter accumulation and nutrition input into soil. Methods We investigated soil C and N stocks beneath three types of monodominant species patches according to community dominance. Stipa krylovii patches, Artemisia frigida patches, and Potentilla acaulis patches represent better to worse vegetation conditions in a grassland in northern China. Results The results revealed that the soil C stock (0–40 cm) changed significantly, from 84.7 to 95.7 Mg ha−1, and that the soil organic carbon content (0–10 cm) and microbial biomass carbon (0–10 and 10–20 cm) varied remarkably among the different monodominant species communities (P < 0.05). However, soil total nitrogen and microbial biomass nitrogen showed no significant differences among different plant patches in the top 0–20 cm of topsoil. The soil C stocks under the P. acaulis and S. krylovii patches were greater than that under the A. frigida patch. Our study implies that accurate estimates of soil C and N storage in degenerated grassland require integrated analyses of the concurrent effects of differences in plant community composition.