Spatial 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.

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.

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.

Effect of Land Use Types on the Spatial Prediction of Soil Nitrogen

Mapping the spatial distribution of soil nutrient contents from sample data has received much attention in the recent decade. Accurately mapping soil nutrients purely based on sample data, however, is difficult due to the sparsity and high cost of samples. Land use types usually influence the contents of soil nutrients at the local level and it is desirable to integrate such information into predictive mapping. The area-and-point kriging (AAPK) method, which was proposed recently, may provide an interpolation technique for such purposes. This study mapped the soil total nitrogen (TN) distribution of Hanchuan County, China, using AAPK with sample data (consisting of 402 points) and land use information. Ordinary kriging (OK) and residual kriging (RK) were compared to evaluate the performance of AAPK. Results showed that: (1) land use types had important impacts on the spatial distribution of soil TN; (2) measured data at 135 validation locations had stronger correlation with the data predicted by AAPK than by RK and OK, and the mean error and root mean square error with AAPK were lower than with RK and OK; and (3) AAPK generated smaller error variances than RK and OK did. This suggests that AAPK represents an effective method for increasing the interpolation accuracy of soil TN. It should be pointed out that some of the land use polygons used in this study are very large and complex, which might impact the effectiveness of AAPK in improving the prediction accuracy. Segmenting them into simple smaller areas might be helpful.