Biochar has been considered as a stable-carbon source for improving soil quality and long-term sequestration of carbon. However, in view of ecological environmental feedback and the tightly coupled system of carbon-nitrogen cycling, further attention has shifted to the effect of biochar on soil net nitrogen mineralization (SNNM). Recently, ecological evaluations of biochar were mostly based on laboratory incubation or pot experiments, ignoring external and uncontrollable natural factors. Therefore, the essential characteristics of local environments were not accurately described.
Materials and methods
In this paper, a nonlinear stochastic model of SNNM based on least squares support vector machine (LS-SVM) was set up to study the effect of biochar on nitrogen cycling in a field experiment. In order to explore this effect in natural conditions, partial derivative (PaD) sensitivity analysis of LS-SVM was firstly proposed, evaluated by the data from a known equation, and then applied to open the “black-box” stochastic model of SNNM.
Results and discussion
Comparing with the sensitivity analysis of artificial neural networks (ANNs), the RD values of LS-SVM PaD1 algorithm were almost the same as those of ANNs PaD1 algorithm. However, the RSD values of LS-SVM PaD2 algorithm were closer to the given equation. In the SNNM model, RD values of LS-SVM PaD1 algorithm of initial nitrogen, time, and precipitation were 21, 15, and 14 %, and the biochar RD value was only 0.51 %, implying that biochar did not influence SNNM directly. However, the cumulative RSD of the PaD2 algorithm of biochar with the other factors was 15.05 %, the maximum of the interactions, implying that it could greatly enhance the tendency for SNNM by interacting with other factors.
Conclusions
PaD sensitivity analysis of LS-SVM was a stable and reliable data mining method. In the SNNM model, initial nitrogen, time, and precipitation were the main controlling factors of the SNNM model. Biochar did not directly influence SNNM; however, it could greatly enhance the tendency for SNNM by interactions with other factors by decreasing the inhibitory effect of initial nitrogen on SNNM and modifying soil condition to change the effect of other factors on SNNM.
Macropores have important effects on the movement of soil water, air, and chemical substances. However, the quantitative relationship between complex 3D soil macropore networks and forest communities remains unclear in the northern mountainous area in China. The objectives of this study were to (1) use industrial computed tomography (CT) scanning and image analysis to quantitatively analyze macropore networks in intact soil columns and (2) identify characteristics of soil macropore networks in different forest communities.
Materials and methods
Intact soil columns (100-mm diameter, 300 mm long) were taken from six local forest communities with three replicates for a total of 18 samples. Industrial X-ray CT was used to scan soil samples; then, the scanned images were used to obtain the 3D images of rock fragments and macropore structures. Next, the macropore structure was quantified, including volume, diameter, surface area, length, angle, tortuosity, and number of macropores. This technique provided an accurate method to quantify the structure of macropores.
Results and discussion
The analysis and results revealed that different forest communities influence soil macropore 3D structure significantly and in different ways. Macropores in mixed Pinus tabulaeformis, Castanea mollissima, and Ulmus pumila forest had the largest diameter, surface area, network density, and length density of macropores as well as the smallest mean tortuosity of soil macropores. This is caused by the fact that mixed forest soils had more complex root systems, better soil structure, and more biotic activity. Within the soils of a single forest community, macropore porosity, network density, surface area density, and length of macropores decreased with increased soil depth, because more roots and more biological activity were present in the surface soil.
Conclusions
Advanced industrial CT technology can allow an accurate quantification of soil macropore structure. This is important because this type of structure has significant effects on soil water, air, and chemical transport. The results suggest that mixed forest is the best afforestation model in the northern mountainous area in China because of its ability to improve soil structure.
