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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]

Studies have reported improvements in soil quality in integrated crop‐livestock systems (ICLS) when compared to conventional systems. The hypothesis of this study was that ICLS improve Oxisol quality due to the use of intercropping, adding pasture and cattle to the systems. The aim of the study was to evaluate the chemical and physical characteristics of an Oxisol and pasture production in the integrated system versus conventional system. We compared two conventional systems: crop system (maize grain production) and livestock system (beef cattle on single pasture) and, four ICLS: maize plus Marandu palisade grass sown simultaneously without herbicide (ICLS‐1); maize plus Marandu palisade grass sown simultaneously with herbicide (ICLS‐2); maize plus Marandu palisade grass in lagged sowing (ICLS‐3); and maize plus Marandu palisade grass sown simultaneously in maize rows and inter‐rows with herbicide (ICLS‐4). A randomized block design with six treatments and three replicates was used. The results suggest improvements in soil quality, including soil organic carbon (ICLS‐4), total nitrogen, and nitrogen stocks (ICLS‐1, ICLS‐2, and ICLS‐4). The intercropping technique influenced pasture production and soil quality during the implementation of integrated systems. The lowest soil strength was obtained for the crop system compared to systems with grazing animals but there were no changes in the soil physical characteristics that could compromise the production system. [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 Songnen typical black soil belt is distributed with black soil resources with high organic matter and high fertility. Long-term overloaded farming has led to a significant reduction in soil organic matter content. Therefore, it is of great significance to study the reserve of soil nutrient elements for the protection of black soil resources and national food security. The Songnen typical black soil belt was took as the research area, based on 1 057 field soil sample measured data, using geostatistical analysis and other methods, the spatial distribution characteristics and influencing factors of TC (total carbon), TN (total nitrogen) and TP (total phosphorus) soil in the 0 ~20 cm tillage layer were studied. The results show these as follows. The contents of soil TC, TN and TP in the Songnen typical black soil belt were 19. 25, 1. 88 and 1. 28 g/ kg, respectively, which were higher than the national level. The high-density calorific value area and high aggregation areas of soil TC, TN and TP are mainly distributed in the northern part of the Songnen typical black soil belt, and TC, TN and TP showed a trend from north to south decrease. The spatial aggregation of C∶ N, C∶ P and N∶ P is higher in the northern part of this region. The soil silt content was more than 30%, and the contents of TC, TN and TP were high, ranging from 6. 86 to 74. 74 g/ kg, 0. 94 to 5. 93 g/ kg and 0. 66 to 3. 76 g/ kg, respectively. The content of soil TC, TN and TP are greatly affected by soil pH, average annual temperature, average annual precipitation and topography and other factors. The research results can provide a scientific basis for the protection of black soil resources and sustainable agricultural development. [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]

Soil microorganisms are involved in the litter decomposition process and are closely related to nutrient cycling in ecosystems, especially carbon (C) and nitrogen (N) cycling. For grassland ecosystems, most grassland biomass is invested in the root system. Therefore, to determine the influence of root decomposition on soil microorganisms in different grassland species, an in‐situ root decomposition experiment was conducted with two species (gramineous forage: Bothriochloa ischaemum and leguminous forage: Lespedeza davurica) over three decomposition times (90, 270 and 450 days). Total organic carbon (TOC) and total nitrogen (TN) in the roots of the two species decreased gradually. And L. davurica had higher soil organic carbon (SOC) and soil total nitrogen (STN) in the late stage. Proteobacteria, Chloroflexi and Acidobacteria were the dominant bacteria, and Ascomycota and Basidiomycota were the dominant fungi in the two species. STN is the most important factor driving changes in soil microbial communities. The alpha diversity index of bacteria in both species showed an increasing trend, while in fungi, it decreased rapidly at the early stage and increased slightly at the late stage. Compared with the bacteria in B. ischaemum, L. davurica increased some submetabolic system pathway genes related to carbon cycle metabolism. FUNGuild revealed that saprotrophic fungi on the 90th day were significantly lower than those on the 270th and 450th days. Our results show that leguminous forages have better performance in improving SOC and STN, and microbial characteristics are also affected by species during root decomposition. [ABSTRACT FROM AUTHOR]

The reclamation of coastal wetlands for agriculture has expanded considerably over the last 40 years in China. Wetland reclamation could substantially alter soil properties, but so far, a systematic analysis of the changes in soil chemical elements after reclamation is missing on a national scale. In this study, we synthesized 418 pairs of data from 41 experimental studies and investigated the responses of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents to coastal wetlands reclamation in China. The results showed that SOC significantly decreased by 24.77% after reclamation, while TN and TP were significantly increased by 23.57% and 13.55%, respectively. The changes in SOC, TN, and TP after reclamation of coastal wetlands mainly occurred in the 0–40 cm soil layer but not in the deep soil layer. For different land use types, SOC was significantly reduced by 49.48%, 44.57%, and 32.91% after reclaiming wetlands to woodlands, paddy fields, and drylands, respectively. TN and TP were significantly increased by 32.22% and 13.54% after the reclamation of wetlands to drylands, respectively. The reclamation duration also had significant effects on those elements. Specifically, SOC gradually decreased with increasing reclamation duration, while TN and TP increased with the increased reclamation duration. Anthropogenic factors, such as land use types and reclamation duration, rather than climatic factors, dominated changes in SOC, TN, and TP contents after coastal reclamation. Further, our study showed a significant SOC reduction following reclamation, and given the importance of coastal blue carbon, it is essential to reduce wetland reclamation to protect coastal blue carbon. [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]

Purpose: Identifying 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. Methods: In 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. Results: Latitudinal 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. Conclusion: Our 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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Appropriate nitrogen (N) fertilization in vegetable-maize rotations is effective in improving agricultural production and mitigating environmental impacts to ensure sustainable food security. The dynamic and sustainability of crop yields, crop root biomasses, and accumulations and sequestration rates of soil organic C (SOC) and total N (STN) were explored and analyzed under four N application rates (kg N ha−1 year−1): 0 (Control), 285 (Low-N), 480 (Medium-N), and 720 (High-N). The Low-N and Medium-N applications increased Chinese cabbage yield by 22% and 20.7% than the High-N application, respectively. The Medium-N application gained the highest grain yield and root biomass in the maize season when compared with the Low-N application. The sustainable yield index had no difference between Low-N and Medium-N treatments in Chinese cabbage-maize rotation. Compared to the Low-N application, the Medium-N application improved the storage and sequestration rates of SOC and STN. Simultaneously, the Medium-N application significantly enhanced the soil C:N ratio than the High-N application. In conclusion, the Medium-N application is a promising fertilization strategy in the Chinese cabbage-maize cropping system as it increases the yield and root biomass of crops, improves the accumulation and sequestration rates of SOC and STN, and maintains a high soil C:N ratio. [ABSTRACT FROM AUTHOR]

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.

Effects of exclosures on restoring degraded lands may vary with soil type, exclosure age, and conditions before the establishment of exclosures. Yet, studies investigating the effectiveness of exclosures in restoring degraded lands under different environmental conditions are lacking. This study aims at investigating the changes in woody species richness and diversity, and ecosystem carbon stocks after implementing exclosures in the Central Rift Valley, Ethiopia. Vegetation and soil data were gathered from 120 nested plots established in exclosures of eight and 30‐years‐old and adjacent grazing lands. Results showed that exclosures contained a higher number of economically important woody species compared to their respective adjacent grazing lands. However, the exclosures and respective adjacent grazing lands did not differ significantly in the diversity of tree and shrub species, and both the exclosures and adjacent grazing lands were dominated by few tree and shrub species. The older exclosure (30 years old) displayed significantly (p < 0.01) higher soil organic carbon and soil total nitrogen content and stocks than the adjacent grazing land, whereas the youngest exclosure (8 years old) did not show a significant difference in these variables. The results suggest that a longer time (e.g., ≥10 years) is needed to detect significant differences in soil organic carbon and total soil nitrogen. However, exclosures could bring considerable changes in woody species density in a relatively shorter period (e.g., ≤10 years) and support to restore degraded native woody species. [ABSTRACT FROM AUTHOR]

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]

The inability of humans and many farm animals to synthesize certain amino acids has long triggered tremendous interest in increasing the levels of these so-called essential amino acids in crop plants. Hence, a two-year field experiment was conducted to evaluate the effects of straw mulch (8 t ha−1) and biochar applied at various rates (0, 4, 12, 36 t ha−1) on the chlorophyll, photosynthesis, total nitrogen in soil, essential and nonessential amino acid (AA) contents of maize grain. The maize straw mulch and biochar significantly increased chlorophyll contents, photosynthesis rate of maize crop and total soil nitrogen. The application of biochar (12 t ha−1) increased the chlorophyll contents and photosynthesis rate and total soil nitrogen during both years. However, excessive biochar applications (more than 12 t ha−1) had negative effects on chlorophyll contents, photosynthesis rate of maize crop and total soil nitrogen of soil. The AAs were significantly affected by biochar, depending on the application rate. In conclusion, the application of straw mulch and biochar improved the rate of leaf chlorophyll contents, photosynthesis rate and amino acid contents in maize grain when applied at appropriate rates, but the effects were negative when biochar was overused. [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]

[Objectives] The paper was to investigate the distribution characteristics of soil total nitrogen in low-efficiency forest land in the northern windy desert area of Jingbian County. [Methods] The distribution of soil total nitrogen in the 0-40 cm soil layer of 5 towns in the northern windy desert area of Jingbian County was studied through field sampling and laboratory detection. [Results] The average soil total nitrogen contents of Hongdunjie Town, Haizetan Town, Huanghaojie Town, Ningtiaoliang Town, Dongkeng Town and windy desert area in the 0-20 cm soil layer were 0.259, 0.224, 0.242, 0. 248, 0.431 and 0.275 g/kg, respectively. The soil total nitrogen content in Dongkeng Town was higher than those in other towns, while there was little difference among other regions. The average total nitrogen contents in the 20 -40 cm soil layer were 0.239, 0.285, 0.113, 0.262, 0.349 and 0.241 g/kg, respectively. The soil total nitrogen content in Huanghaojie Town was slightly lower than those in other towns, while that in Dongkeng town was higher. The variation coefficient of soil total nitrogen content in the survey area was greater than 30%, and there was great difference in spatial distribution. With the increase of soil depth, there was little change in soil total nitrogen content, namely the soil total nitrogen contents in 5 towns and windy desert area were not statistically different in the 0-40 cm soil layer. According to the nutrient grading standard of the second national soil survey, the soil total nitrogen content in the survey area was in the deficiency grade. [Conclusions] The research will provide a scientific guidance for the healthy and sustainable development of vegetation in arid regions. [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]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

[Display omitted] • Using miniaturized NIR spectrometers (mini-NIRs) for soil nutrient tests is feasible. • The deployment strategy of multiple mini-NIRs based on spectral transfer is feasible. • The DS algorithm can successfully transfer spectra from different spectrometers. Developing timely, convenient, and low-cost methods for high-frequency characterization of soil nutrients is necessary for implementing precise soil nutrient management. With the current availability of numerous calibration models of laboratory benchtop near-infrared (NIR) spectrometers for rapid soil nutrient characterization and the appearance of low-cost, convenient miniaturized NIR spectrometers, this study proposes an efficient deployment strategy to address model failure due to inter-device variation based on spectral transfer. The strategy involves using Direct Standardization (DS) to migrate the spectra from multiple miniaturized NIR spectrometers with a laboratory benchtop NIR spectrometer and then directly applying the existing calibration models of the laboratory benchtop instrument to the transferred spectra for soil nutrient analysis. The results indicated that the DS method successfully transferred the spectra of miniaturized devices to be consistent with the spectra of the laboratory benchtop instrument. The soil organic matter (SOM) predictions using the transferred spectra and the calibration models of the laboratory benchtop instrument were even more accurate than those using the respective models developed for each miniaturized devices, with root mean square error (RMSE) of 0.177 %, 0.177 %, and 0.150 %, respectively, while the performances of total nitrogen (TN) predictions were comparable to those using the respective models, with RMSE of 0.013 %, 0.012 %, and 0.010 %, respectively. Bland-Altman plots demonstrated good consistency between the strategy proposed in this study and the strategy of developing respective models for each miniaturized device, with no difference in predictions for the independent validation set compared to the laboratory benchtop instrument. This study proved the feasibility of deployment strategy of multiple miniaturized NIR spectrometers based on spectral transfer, offering a new solution for high-frequency on-site soil nutrient characterization. [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.

The relationship between soil properties and environmental covariates is mostly nonlinear for human impacted areas. Herein, we proposed a nonlinear model for mapping soil organic carbon (SOC) and total nitrogen (TN) in a typical human impacted area (a small watershed of Poyang Lake, China), namely radial basis function neural network combined with agricultural land use (RBFNN_ALU). The results showed that the RBFNN_ALU performs better than the ordinary kriging (OK), the OK combined with agricultural land use (OK_ALU), the geographically weighted regression (GWR), the multiple linear regression (MLR), the MLR combined with agricultural land use (MLR_ALU) and the RBFNN. In addition, RBFNN_ALU provided a more detailed and accurate description of the spatial SOC and TN patterns. The results indicate that when predicting spatial distribution of SOC and TN for human impacted areas, non-linear models are critical for predicting the spatial distribution of soil properties. [ABSTRACT FROM AUTHOR]

【Objective】 The experiment was conducted to study the effect of different planting patterns arrangements of maize and soybean intercropping on spatial distribution of soil total nitrogen and organic matter contents, in order to provide reference for optimal management and fertilizer utilization and better crop yield at lower fertilizer cost.【Method】 The soil samples of continuous maize/soybean relay strip intercropping (MS1), maize/soybean relay strip intercropping in rotations (MS2), Traditional maize/soybean intercropping (MS3), sole maize (M), sole soybean (S), and fallow land (FL) were used as tests from 2018 to 2020 to analyze the differences of soil organic matter and total nitrogen contents under different planting patterns.【Result】 The highest content of soil organic matter (29.19 g/kg) and total nitrogen (10.19 g/kg) were detected under MS2. The minimum content of organic matter (1.69 g/kg) and total nitrogen (0.64 g/kg) were under FL. Soil total nitrogen in soybean row was higher than that in maize row, and organic matter content was significantly different under different planting patterns (P<0.05). Compared with other planting patterns, soil organic matter changed greatly under MS2 planting patterns, and the spatial distribution was the most dense. The variation coefficient of soil organic matter content was used to classify the soil. It was found that the variation of MS1, MS2, MS3, M and S was moderate, the variation of FL was weak, and the variation of MS2 was the largest. The spatial distribution density of total nitrogen in maize row was lower than that in soybean row, but the maximum and minimum values of total nitrogen and organic matter had the same distribution pattern. The moderate variation of soil total nitrogen was MS1, MS2, MS3, M, and S, the weak variation was FL, and the maximum variation was MS2. At the same time, the correlation analysis of soil nitrogen and organic mass variability showed that there was a high correlation between soil organic matter and soil total nitrogen, indicating that the change rule of total nitrogen and organic matter was consistent, and soil organic matter and total nitrogen content showed a significant positive correlation under different planting patterns. 【Conclusion】 There were significant variations for soil organic matter and total nitrogen under different planting patterns of maize and soybean in intercropping. MS2 was a better planting pattern for strip relay intercropping, it could increase spatial distribution of soil organic matter and total nitrogen unit content, and improve the soil fertility and carbon to nitogen ratio. [ABSTRACT FROM AUTHOR]