Estimation of soil organic carbon and total nitrogen in different soil layers using VNIR spectroscopy: Effects of spiking on model applicability

Geoderma ◽  
2017 ◽  
Vol 293 ◽  
pp. 54-63 ◽  
Author(s):  
Qinghu Jiang ◽  
Qianxi Li ◽  
Xinggang Wang ◽  
Yu Wu ◽  
Xiaolu Yang ◽  
...  
2020 ◽  
Vol 12 (16) ◽  
pp. 6443
Author(s):  
Zhiwei Cao ◽  
Xi Fang ◽  
Wenhua Xiang ◽  
Pifeng Lei ◽  
Changhui Peng

The study was to investigate the change patterns of soil organic carbon (SOC), total nitrogen (TN), and soil C/N (C/N) in each soil sublayer along vegetation restoration in subtropical China. We collected soil samples in four typical plant communities along a restoration chronosequence. The soil physicochemical properties, fine root, and litter biomass were measured. Our results showed the proportion of SOC stocks (Cs) and TN stocks (Ns) in 20–30 and 30–40 cm soil layers increased, whereas that in 0–10 and 10–20 cm soil layers decreased. Different but well-constrained C/N was found among four restoration stages in each soil sublayer. The effect of soil factors was greater on the deep soil than the surface soil, while the effect of vegetation factors was just the opposite. Our study indicated that vegetation restoration promoted the uniform distribution of SOC and TN on the soil profile. The C/N was relatively stable along vegetation restoration in each soil layer. The accumulation of SOC and TN in the surface soil layer was controlled more by vegetation factors, while that in the lower layer was controlled by both vegetation factors and soil factors.


Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 517
Author(s):  
Sunwei Wei ◽  
Zhengyong Zhao ◽  
Qi Yang ◽  
Xiaogang Ding

Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability to estimate SOCS. In the first stage, an artificial neural network (ANN) model is adopted to estimate SOCS based on 255 soil samples with five soil layers (20 cm increments to 100 cm) in Luoding, Guangdong Province, China. This method is compared with three common methods: The soil type method (STM), ordinary kriging (OK), and radial basis function (RBF) interpolation. In the second stage, a linear model is introduced to capture the regional differences and further improve the estimation accuracy of the Luoding-based ANN model when extending it to Xinxing, Guangdong Province. This is done after assessing the generalizability of the above four methods with 120 soil samples from Xinxing. The results for the first stage show that the ANN model has much better estimation accuracy than STM, OK, and RBF, with the average root mean square error (RMSE) of the five soil layers decreasing by 0.62–0.90 kg·m−2, R2 increasing from 0.54 to 0.65, and the mean absolute error decreasing from 0.32 to 0.42. Moreover, the spatial distribution maps produced by the ANN model are more accurate than those of other methods for describing the overall and local SOCS in detail. The results of the second stage indicate that STM, OK, and RBF have poor generalizability (R2 < 0.1), and the R2 value obtained with ANN method is also 43–56% lower for the five soil layers compared with the estimation accuracy achieved in Luoding. However, the R2 of the linear models built with the 20% soil samples from Xinxing are 0.23–0.29 higher for the five soil layers. Thus, the ANN model is an effective method for accurately estimating SOCS on a regional scale with a small number of field samples. The linear model could easily extend the ANN model to outside areas where the ANN model was originally developed with a better level of accuracy.


2021 ◽  
pp. 1-19
Author(s):  
Yingcong Ye ◽  
Yefeng Jiang ◽  
Lihua Kuang ◽  
Yi Han ◽  
Zhe Xu ◽  
...  

2011 ◽  
Vol 57 (1) ◽  
pp. 21-30
Author(s):  
Božena Šoltysová ◽  
Martin Danilovič

Tillage in Relation to Distribution of Nutrients and Organic Carbon in the SoilChanges of total nitrogen, available phosphorus, available potassium and soil organic carbon were observed on gleyic Fluvisols (locality Milhostov) at the following crops: grain maize (2005), spring barley (2006), winter wheat (2007), soya (2008), grain maize (2009). The experiment was realized at three soil tillage technologies: conventional tillage, reduced tillage and no-tillage. Soil samples were collected from three depths (0-0.15 m; 0.15-0.30 m; 0.30-0.45 m). The ratio of soil organic carbon to total nitrogen was also calculated.Soil tillage affects significantly the content of total nitrogen in soil. The difference between the convetional tillage and soil protective tillages was significant. The balance showed that the content of total nitrogen decreased at reduced tillage by 5.2 rel.%, at no-tillage by 5.1 rel.% and at conventional tillage by 0.7 rel.%.Similarly, the content of organic matter in the soil was significantly affected by soil tillage. The content of soil organic carbon found at the end of the research period was lower by 4.1 rel.% at reduced tillage, by 4.8 rel.% at no-tillage and by 4.9 rel.% at conventional tillage compared with initial stage. The difference between the convetional tillage and soil protective tillages was significant.Less significant relationship was found between the soil tillage and the content of available phosphorus. The balance showed that the content of available phosphorus was increased at reduced tillage (by 4.1 rel.%) and was decreased at no-tillage (by 9.5 rel.%) and at conventional tillage (by 3.3 rel.%).Tillage did not significantly affect the content of available potassium in the soil.


Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 208
Author(s):  
Małgorzata Szostek ◽  
Ewa Szpunar-Krok ◽  
Renata Pawlak ◽  
Jadwiga Stanek-Tarkowska ◽  
Anna Ilek

The aim of the study was to compare the effect of conventional, simplified, and organic farming systems on changes in the content of soil organic carbon, organic matter fractions, total nitrogen, and the enzymatic activity. The research was conducted from 2016–2018 on arable land in the south-eastern part of Poland. The selected soils were cultivated in conventional tillage (C_Ts), simplified tillage (S_Ts), and organic farming (O_Fs) systems. The analyses were performed in soil from the soil surface layers (up to 25 cm depth) of the experimental plots. The highest mean contents of soil organic carbon, total nitrogen, and organic matter fractions were determined in soils subjected to the simplified tillage system throughout the experimental period. During the study period, organic carbon concentration on surface soil layers under simplified tillage systems was 31 and 127% higher than the soil under conventional tillage systems and organic farming systems, respectively. Also, the total nitrogen concentration in those soils was more than 40% and 120% higher than conventional tillage systems and organic farming systems, respectively. Moreover, these soils were characterised by a progressive decline in SOC and Nt resources over the study years. There was no significant effect of the analysed tillage systems on the C:N ratio. The tillage systems induced significant differences in the activity of the analysed soil enzymes, i.e., dehydrogenase (DH) and catalase (CAT). The highest DH activity throughout the experiment was recorded in the O_Fs soils, and the mean value of this parameter was in the range of 6.01–6.11 μmol TPF·kg−1·h−1. There were no significant differences in the CAT values between the variants of the experiment. The results confirm that, regardless of other treatments, such as the use of organic fertilisers, tillage has a negative impact on the content of SOC and organic matter fractions in the O_Fs system. All simplifications in tillage reducing the interference with the soil surface layer and the use of organic fertilisers contribute to improvement of soil properties and enhancement of biological activity, which helps to maintain its productivity and fertility.


PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e54827 ◽  
Author(s):  
Chengchen Pan ◽  
Halin Zhao ◽  
Xueyong Zhao ◽  
Huibang Han ◽  
Yan Wang ◽  
...  

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