Distribution Of Soil Total Nitrogen Research Articles

Data-driven assessment of soil total nitrogen on the Qinghai-Tibet Plateau

The investigation of soil total nitrogen (STN) holds significant importance in the preservation and sustainability of Earth's ecosystems. The Qinghai-Tibet Plateau (QTP), renowned as the world's most expansive plateau and characterized by its exceptionally delicate ecosystem, demands an in-depth exploration of its STN content. In this study, we use a machine learning approach to extrapolate point-scale measured STN stocks to the entire QTP and calculated STN storage from 0 to 2 m. Our results show that the XGB algorithm performs well in modeling STN despite variations in simulation accuracy for specific depth ranges. The spatial distribution of STN across the QTP exhibits pronounced heterogeneity, especially for the 0-50 cm soil layer, with relatively higher STN stocks in the southeast and lower stocks in the northwest of QTP. The vertical distribution reveals a gradual decrease in STN storage with increasing depth. The 0–50 cm soil layer holds the highest STN stocks, averaging around 0.78 kg/m2, which is almost the sum of STN stocks in the 50–100 cm and 100–200 cm soil layers. Meanwhile, the STN stocks are smaller in permafrost zone than that in non-permafrost zone. We also investigate the impact factors that control the spatiotemporal distribution of STN. It indicates that vegetation, precipitation, temperature, and elevation are the major factors for STN distribution, while physical properties of the soil have a relatively smaller impact. These findings are crucial for understanding the distribution and evolution of STN on the QTP.

Spatial patterns and drivers of soil total nitrogen in anthropogenic shrub encroachment in desert steppe

Nitrogen is the most important driving factor in primary production and decomposition in arid and semi-arid ecosystems. The effects of shrub encroachment on nitrogen cycling have been investigated at the site scale but seldomly conducted at the landscape scale. Here, we selected 43 shrubland sites distributing across 3000 km2 area in temperate desert grassland in eastern Yanchi County of Ningxia Hui Autonomous. We investigated the spatial heterogeneity and driving factors of soil total nitrogen (STN) at the landscape scale by using geostatistical analysis and the geographical detector method. Our results showed that the average soil total nitrogen decreased in the order of 0–5 cm (0.21 g kg−1) > 5–15 cm (0.19 g kg−1) > 15–40 cm (0.18 g kg−1). Geostatistical analysis showed that soil total nitrogen exhibited the strong spatial autocorrelation in the 0–5 and 5–15 cm soil layers and the moderate spatial autocorrelation in the 15–40 cm soil layer. Furthermore, the geographic detector method indicated that soil physicochemical properties exhibited the stronger effects than these of topographic and vegetation biomass in determining the spatial distribution of soil total nitrogen. Specifically, soil water content in the 0–20 cm soil layer explained 35% of variation in soil total nitrogen spatial pattern in the 0–5 cm soil layer, while soil organic carbon content in the 15–40 cm soil layer explained 64% and 45% of variation in soil total nitrogen spatial patterns in the 5–15 cm and 15–40 cm soil layers, respectively. It was concluded that soil water content and organic carbon content primarily drove the formation of soil total nitrogen spatial heterogeneity in shrubland at the landscape scale, indicating that anthropogenic shrub encroachment evidently affected soil water content and redistribution in dryland.

Effect of different planting pattern arrangements on soil organic matter and soil nitrogen content under a maize/soybean strip relay intercropping system.

Assessing the spatial distribution of organic matter and total nitrogen in soil is essential for management and optimum utilization of fertilizers. Therefore, the present field experiment was conducted to evaluate the impact of different planting pattern arrangements on the spatial distribution of soil total nitrogen and organic matter content under a maize/soybean strip relay intercropping system. The planting was arranged in a manner such that soil sampling could be done from continuous maize/soybean relay strip intercropping (MS1), maize/soybean relay strip intercropping in rotation (MS2), traditional maize/soybean intercropping (MS3), sole maize (M), sole soybean (S), and fallow land (FL) from 2018 to 2020. The results showed significant variations for soil organic matter and total nitrogen content under different planting pattern arrangements of maize and soybean in the strip relay intercropping system. Across all systems, the highest soil organic matter (29.19 g/kg) and total nitrogen (10.19 g/kg) were recorded in MS2. In contrast, the lowest soil organic matter (1.69 g/kg) and total nitrogen (0.64 g/kg) were observed in FL. Soil organic matter and total nitrogen in MS2 increased by 186.45% and 164.06%, respectively, when compared with FL. Soil organic matter and total nitrogen in MS2 increased by 186.45% and 164.06%, respectively, when compared with FL. Furthermore, under MS2, the spatial distribution of soil organic matter was higher in both maize and soybean crop rows as compared with other cropping patterns, whereas the soil total nitrogen was higher under soybean rows as compared with maize in all other treatment. However, correlation analysis of the treatments showed variations in organic matter content. It can be concluded that different planting patterns can have varying effects on soil organic matter and total nitrogen distribution under the strip relay intercropping system. Moreover, it is recommended from this study that MS2 is a better planting pattern for the strip relay intercropping system, which can increase the spatial distribution of soil organic matter and total nitrogen, thereby improving soil fertility, C:N ratio, and crop production. This study will serve as a foundation towards the scientific usage of chemical fertilizers in agricultural sector.

Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area

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.

Vertical distribution of STN and STP in watershed of loess hilly region

Abstract In order to explore the effects of land use change on the contents of total nitrogen and total phosphorus in deep soil, four land use types (cropland, grassland (7 years), grassland (30 years), and Jujube orchard) were selected from the Yuanzegou watershed in the loess hilly region of northern China. Soil samples at 0–10 m depth were collected to measure the contents of soil total nitrogen (STN) and soil total phosphorus (STP), and their stocks were estimated. The results showed that the STN content showed a decreasing trend with the increase in soil depth, and the lowest STN content of grassland (7 years) was 0.09–0.17 g kg−1. The range of STN content in the watershed was 0.12–0.22 g kg−1 and the coefficient of variation was 10.52–25.90%, which belonged to medium variation. The STP content is stable regionally with the change in soil depth, and does not change much (except for grassland [30 years]). STP content of the watershed is 0.81–1.05 g kg−1 and the coefficient of variation is 9.37–54.69%, which is a high variation. The change trend of STN and STP stocks is consistent with the nitrogen and phosphorus content. The results revealed the dynamic changes in STN and STP after land use change, and estimated the stocks of STN and STP in deep loess, which provided scientific basis for land and soil resource management and sustainable development of the project of returning farmland to forest or grassland in small watershed of loess hilly-gully region.

Effects of land use types and environmental factors on spatial distribution of soil total nitrogen in a coalfield on the Loess Plateau, China

Soil total nitrogen (TN) is one of the most important nutrients for plant and crop growth. It is essential to estimate the spatial distribution of TN to evaluate soil productivity, land reclamation efficiency, and agricultural management. This study was performed to investigate the spatial distribution of TN and its influencing factors in a coalfield in the Loess Plateau, China. A total of 143 topsoil samples were collected from five land use types and different slope positions. Classical statistical and geostatistical methods were used to quantify the spatial variation in TN. Redundancy analysis (RDA) was used to detect correlations between environmental factors and TN, whereas the random forest (RF) algorithm and LightGBM were applied to simulate the correlations. The results showed that the degree of spatial dependence ranged from 31.7%–45.2%, indicating moderate spatial variability in TN. Land use type significantly affected TN. The highest TN contents were found in the conservation tillage lands, followed by conventional tillage land, shrub land, grassland, and coal mining areas. The spatial distribution of the TN was also influenced by the slope position. The sites located downslope had higher TN than those located on the upper and middle slopes. RDA indicated that available potassium (AK) and soil organic matter (SOM) significantly affected the spatial distribution of TN. It was also demonstrated by LightGBM and RF that the TN content could be simulated by AK, SOM, normalised vegetation index, elevation, and available phosphorus. These results are helpful in understanding the effects of anthropogenic and natural factors on soil TN in coalfield areas.

The Influence of Climate, Soil Properties and Vegetation on Soil Nitrogen in Sloping Farmland

Soil nitrogen in farmland ecosystems is affected by climate, soil physical and chemical properties and planting activities. To clarify the effects of these factors on soil nitrogen in sloping farmland quantitatively, the distribution of soil total nitrogen (TN) content, nitrate nitrogen (NO3-N) content and ammonium nitrogen (NH4-N) content at depth of 0–100 cm on 11 profiles of the Luanhe River Basin were analyzed. Meanwhile, soil physical and chemical properties, climatic factors and NDVI (Normalized Difference Vegetation Index) were used to construct a structural equation which reflected the influence mechanism of environmental factors on soil nitrogen concentration. The results showed that TN and NO3-N content decreased with the increase of soil depth in the Luanhe River Basin, while the variation of NH4-N content with soil depth was not obvious. Soil organic carbon (SOC) content, soil pH, soil area average particle size (SMD) and NDVI6 (NDVI of June) explained variation of TN content by 77.4%. SOC was the most important environmental factor contributing to the variation of TN content. NDVI5 (NDVI of May), annual average precipitation (MAP), soil pH and SOC explained 49.1% variation of NO3-N content. Among all environmental factors, only NDVI8 (NDVI of August) had significant correlation with soil NH4-N content, which explained the change of NH4-N content by 24.2%. The results showed that soil nitrogen content in the sloping farmland ecosystem was mainly affected by natural factors such as soil parent material and climate.

Effects of freeze-thaw cycles on the spatial distribution of soil total nitrogen using a geographically weighted regression kriging method

Freeze-thaw cycles (FTCs) change the soil physicochemical properties and biogeochemical cycles and possibly also change the spatial heterogeneity of soil total nitrogen (TN) in the watershed. In this study, 912 soil samples were collected at 0–5 cm, 5–10 cm and 10–20 cm soil depths in the autumn and the spring of next year after FTCs of 2016–2017 and 2017–2018 in a Mollisol watershed (1.86 km2) of northeast China. The field investigations combined with classical statistics and geographically weighted regression kriging (GWRK) were used to explore the spatiotemporal distribution of TN before and after FTCs. Terrain information (e.g., slope aspect) and land management (e.g., tillage method) was main covariates were used for GWRK. The results showed the following. (1) TN decreased by 3.7–5.7% after FTCs at 0–20 cm soil depths at the watershed scale, decreasing more than 60% of the total watershed area. (2) The spatial pattern of TN did not change in the field with slope aspects and tillage methods after FTCs, but it changed with slope steepness and land uses. (3) TN was mainly influenced by snowmelt erosion during FTCs. TN increased in parts of the top slope, at land use intersection, in gully banks and at the watershed outlet. (4) Simulation accuracy of GWRK was higher than ordinary kriging (OK) for predicted TN at 0–20 cm soil depths before and after FTCs. (5) Spatial distribution of soil TN after FTCs can be predicted (R2 = 0.521, p < 0.0001) and validated (R2 = 0.494, p < 0.0001) using the data before FTCs based on GWRK. Generally, to reduce N loss and increase farmland fertility after FTCs, conservational techniques, e.g., tillage and straw amendment, could be used, especially in the middle slope positions. Moreover, fertilization should be appropriately reduced in parts of the watershed after FTCs, especially on the top slope, land use intersection and watershed outlet.

Effects of different tillage practices on the distribution of soil total nitrogen and carbon/nitrogen ratio at different soil depths in a double rice cropping system

ABSTRACT Soil total nitrogen (STN) and the carbon/nitrogen ratio (C/N) influence both crop production and microbial activity, but their distributions under different tillage practices are not clearly understood. This study investigated the effects of tillage practices on the vertical and temporal distributions of STN and the C/N ratio as well as on the distributions of STN fractions in a double rice cropping system from 2012 to 2017. Four tillage practices were established in 2005 in Southern China, i.e. no-till with rice straw mulch (NTS), rotary tillage with rice straw retention (RTS), plough tillage with rice straw retention (CTS), and with rice straw removal (CT). Even at 20–30 cm soil depth, long-term rice straw retention significantly increased STN and its fractions compared to those for CT. Noticeably, at 0–5 cm soil depth, the C/N ratio under NTS was 1.6–6.5% lower than those of other treatments, with a larger amount of rice straw input. In addition, long-term rice straw retention (NTS, RTS, and CTS) increased rice yield by 6.2–14.2% compared to that for CT. Therefore, long-term rice straw retention is recommended in this region due to its ability to increase N accumulation (even at deeper soil depths), which might improve the rice production.

Estimating the spatial distribution of soil total nitrogen and available potassium in coastal wetland soils in the Yellow River Delta by incorporating multi-source data

Abstract Coastal wetland soils play a vital role in providing habitats for wildlife and vegetation, and in affecting water quality and primary production in coastal wetland ecosystem. The spatial prediction of soil properties provides important data support to help sustainable wetland management. This research explored the relationships between soil total nitrogen (TN) and soil available potassium (AK) and multiple environmental variables such as remote sensing spectral indices, geographic attributes and land use types of coastal wetlands in the Yellow River Delta (YRD). Soil moisture and geographic attributes were the major factors controlling the spatial distribution of soil TN. Soil salinity, vegetation and geographic attributes were the key factors affecting the spatial distribution of soil AK. The research established spatial prediction models of soil TN and AK by random forest method, and showed heterogeneous spatial patterns of TN and AK in the study area. Soil TN model showed relatively higher prediction performance compared with soil AK model. The research framework utilized in this study can be promoted to predict and map other soil properties, and has significant implication for the site-specific soil management of coastal wetlands in the YRD.

Mapping stocks of soil total nitrogen using remote sensing data: A comparison of random forest models with different predictors

Estimation of soil total nitrogen (STN) is important for quantifying the spatial distribution of soil N (Nitrogen) stocks, determining the amount absorbed by crops and avoiding yield losses and environmental pollution. The objective of this research was to determine the spatial distribution of STN using a remote sensing approach and to investigate the importance of different predictors under two conditions: the bare soil condition and the vegetated condition. We also investigated whether the Sentinel-2A red-edge bands and relative spectral indices were suitable for STN estimation. Soil data were collected from the topsoil (0–20 cm) at 104 sampling sites on 06/15/2016 from farmland in a black soil region in Northeast China. Two Sentinel-2A Multispectral Instrument (MSI) remote sensing images were acquired on 05/26/2016 and 08/03/2016 to represent the two conditions. Environmental variables included the terrain attributes, temperature and precipitation. Then, 21 predictors, including the original bands (O), normal spectral indices (S), red-edge indices (R) and environmental variables (E), were employed to estimate the spatial distribution of the STN content using a random forest (RF) model. Finally, different predictors were combined to construct RF models, and the prediction model with the best performance was selected to determine the spatial pattern of the STN content. The results showed that the predictors had different levels of importance under the two conditions. However, most environmental variables and normal spectral indices always play a significant role in the estimation of STN. The model with the combination of the original bands, normal spectral indices, red-edge indices and environmental variables (O + S + E + R) under the bare soil condition had the best prediction performance, and the combination of the original bands, normal spectral indices and red-edge indices (O + S + R) model had a performance similar to the O + S + E + R model. Therefore, the selection of suitable predictors is necessary to predict STN. The spatial pattern of STN was related to the crop type and elevation of the study area. The results of this study suggested that the proposed RF-based remote sensing method was able to accurately capture the variation in STN and that the performance of the prediction model can be improved by providing enough types of suitable predictors.

Vertical distribution of soil total nitrogen and soil total phosphorus in the critical zone on the Loess Plateau, China

It is important to determine the vertical distributions of soil total nitrogen (STN) and soil total phosphorus (STP) as well as the factors that influence them in the Earth's critical zone (CZ) in order to understand the N/P cycle in the CZ. However, few data are available regarding the vertical distributions of STN and STP in deep soil profiles in the CZ. Thus, this study investigated the vertical distributions of the STN and STP as well as related factors in a deep soil profile on the Loess Plateau, China. Soil drilling was used to collect 703 soil samples throughout the soil profile from five sites. The STN decreased initially at all of the sampling sites (except at Shenmu), before fluctuating with increasing depth throughout the profile, whereas the STP exhibited a fluctuating trend at all sites. The mean STN and STP concentrations ranged from 0.12 g kg−1 to 0.25 g kg−1 and from 0.45 g kg−1 to 0.58 g kg−1, respectively. The adjusted r2 values based on stepwise multiple linear regressions for STN and STP ranged from 35% to 83% and from 4% to 17%, respectively, and thus, STN was explained better by the measured soil variables compared with STP. The mean STN and STP stocks ranged from 1.87 to 3.90 mg ha−1 and from 6.75 to 9.09 mg ha−1, respectively. The results of this study facilitate evaluations of STN and STP stocks and studies of the N/P cycle in the CZ on the Loess Plateau.

Spatial heterogeneity distribution of soil total nitrogen and total phosphorus in the Yaoxiang watershed in a hilly area of northern China based on geographic information system and geostatistics.

Soil total nitrogen (STN) and total phosphorus (STP) are important indicators of soil nutrients and the important indexes of soil fertility and soil quality evaluation. Using geographic information system (GIS) and geostatistics, the spatial heterogeneity distribution of STN and STP in the Yaoxiang watershed in a hilly area of northern China was studied. The results showed that: (1) The STN and STP contents showed a declining trend with the increase in soil depth; the variation coefficients (C v) of STN and STP in the 0‐ to 10‐cm soil layer (42.25% and 14.77%, respectively) were higher than in the 10‐ to 30‐cm soil layer (28.77% and 11.60%, respectively). Moreover, the C v of STN was higher than that of STP. (2) The maximum C 0/(C 0 + C 1) of STN and STP in the soil layers was less than 25%, this indicated that a strong spatial distribution autocorrelation existed for STN and STP; and the STP showed higher intensity and more stable variation than the STN. (3) From the correlation analysis, we concluded that the topographic indexes such as elevation and slope direction all influenced the spatial distribution of STN and STP (correlation coefficients were 0.688 and 0.518, respectively). (4) The overall distribution of STN and STP in the Yaoxiang watershed decreased from the northwest to the southeast. This variation trend was similar to the watershed DEM trend and was significantly influenced by vegetation and topographic factors. These results revealed the spatial heterogeneity distribution of STN and STP, and addressed the influences of forest vegetation coverage, elevation, and other topographic factors on the spatial distribution of STN and STP at the watershed scale.

BME prediction of continuous geographical properties using auxiliary variables

Using auxiliary information to improve the prediction accuracy of soil properties in a physically meaningful and technically efficient manner has been widely recognized in pedometrics. In this paper, we explored a novel technique to effectively integrate sampling data and auxiliary environmental information, including continuous and categorical variables, within the framework of the Bayesian maximum entropy (BME) theory. Soil samples and observed auxiliary variables were combined to generate probability distributions of the predicted soil variable at unsampled points. These probability distributions served as soft data of the BME theory at the unsampled locations, and, together with the hard data (sample points) were used in spatial BME prediction. To gain practical insight, the proposed approach was implemented in a real-world case study involving a dataset of soil total nitrogen (TN) contents in the Shayang County of the Hubei Province (China). Five terrain indices, soil types, and soil texture were used as auxiliary variables to generate soft data. Spatial distribution of soil total nitrogen was predicted by BME, regression kriging (RK) with auxiliary variables, and ordinary kriging (OK). The results of the prediction techniques were compared in terms of the Pearson correlation coefficient (r), mean error (ME), and root mean squared error (RMSE). These results showed that the BME predictions were less biased and more accurate than those of the kriging techniques. In sum, the present work extended the BME approach to implement certain kinds of auxiliary information in a rigorous and efficient manner. Our findings showed that the BME prediction technique involving the transformation of variables into soft data can improve prediction accuracy considerably, compared to other techniques currently in use, like RK and OK.

Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R2 for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected.

Temporal and spatial variability of nitrogen in rice–wheat rotation in field scale

Traditional statistics and geostatistical methods were used to study the spatial and temporal variation of soil nitrogen in rice–wheat rotation field in Changshu. Soil samples were collected after the wheat and rice harvest by a grid spacing of 30 m in three depths of soil profiles, which were 0–15 cm, 15–28 cm, 28–42 cm. The contents of soil total nitrogen and nitrate nitrogen in rice fields were lower than that in wheat fields. The contents of total nitrogen and nitrate in the surface layer (0–15 cm) were higher than that in subsurface (15–28 cm) and bottom (28–42 cm) layers. The spatial distribution of soil total nitrogen and nitrate contents in the crop growing season was affected by the agricultural fertilization. The study is to provide a basis for rational fertilization.