Substantial soil phosphorus (P) losses often occur in the northern temperate regions owing to soil freeze-thaw cycles (FTCs). Presumably, biochar amendment is an efficient method of conserving P and sustaining agricultural production in the black soil region of northeast China. However, how biochar interacts with FTCs to affect soil P adsorption and desorption is unclear. A simulated laboratory FTC experiment was conducted on untreated and biochar-amended soil with varying moisture content to assess their effects on P adsorption and desorption. Soil P adsorption and desorption values were fitted with Langmuir and Freundlich isotherms to determine the interaction of the frequency of FTCs with moisture content and biochar amendment. Higher soil moisture content increased soil P adsorption, whereas biochar amendment mitigated decreased P retention by decreasing soil P adsorption capacity. Biochar amendment significantly increased the desorption ratio (D_avg) under all the FTCs. The desorption ratio of soil and biochar-amended soil in saturated moisture content treatment was significantly higher than that of 12 FTCs. The FTCs decreased the P availability of biochar-amended soil by enhancing P desorbability. Our results suggest that biochar amendment in arable black soil should not be conducted during FTCs, particularly during snowmelt.

Rainfall kinetic energy has been linked to linear, exponential, logarithmic, and power-law functions using rainfall intensity as an independent variable. The power law is the most suitable mathematical expression used to relate rainfall kinetic energy and rainfall intensity. In evaluating the rainfall kinetic energy, the empirical power laws have shown a larger deviation than other functions. In this study, universal power law between rainfall kinetic energy and rainfall intensity was proposed based on the rainfall power theory under an ideal assumption that drop-size is uniformly distributed in constant rainfall intensity. An exponent of the proposed power law was 11/9 and coefficient was estimated at 10.3 from the empirical equations of the existing power-law relation. The rainfall kinetic energy calculated by universal power law showed >95% concordance rate in comparison to the average values calculated from exponential and logarithmic functions used in soil erosion model such as USLE, RUSLE, EUROSEM, and SEMMA and <5% relative difference as compared to the average rainfall kinetic energies calculated by other empirical functions. Therefore, it is expected that power law of ideal assumption may be utilized as a universal power law in evaluating rainfall kinetic energy.

During forest operations, canopy removal results in the soil surface being vulnerable to deformation, negatively impacting soil fertility and water quality. This study utilized unmanned aerial vehicle (UAV) photogrammetry to accurately detect soil surface deformation (SSD). Two-dimensional images were safely collected on a steep slope without real-time kinematics by conducting vertically parallel flights (VPFs). A high-resolution digital surface model (DSM) with a <3 cm resolution was acquired for precise SSD detection. Using DSM of difference (DoD), SSDs were calculated from DSMs acquired in June, July, September, and October 2022. By checking spatial distances at ground control points, errors of DSM alignments were confirmed as only 3 cm, 11.1 cm, and 4 cm from July to June, September to June, and October to June, respectively. From the first month of monitoring, erosion and deposition of approximately 7 cm and 9 cm, respectively, were detected at validation points (VPs). However, from total monitoring, cumulative SSD was assessed as having deposition tendencies at all VPs, even compared to ground truths. Although UAV photogrammetry can detect SSDs, spatial distortion may occur during UAV surveys. For vegetation growth issues, UAV photogrammetry may be unable to capture data on the soil surface itself.

Freeze–thaw cycles stimulate the release of available soil phosphorus (P) in winter, and biochar as a soil amendment could improve P availability. Nevertheless, it is unclear how freeze–thaw cycles and biochar amendment interact to affect the soil P fractions and their availability in winter, particularly under different soil water conditions. We simulateda freeze–thaw cycle experimentto assess the effects of three factors on soil P fractions: soil moisture content (22%, 31%, and 45%), frequencies of freeze–thaw cycles (0, 1, 3, 6, and 12 times) and biochar amendment (soil and biochar-amended soil). Modified Hedley sequential P fractionation was conducted to measure the soil P fractions. Increasing the number of freeze–thaw cycles increased soil labile P fractions in the soil with the lowest moisture content (22%). After biochar amendment, the content of labile P decreased as the number of freeze–thaw cycles increased. Biochar amendment enhanced P availability in Mollisols owing to the direct effect of NaOH-P_o, which has a large direct path coefficient. Principal components analysis showed that moisture content was a major factor influencing the variation in the P fractions. The P fractions were separated by the interactive effects of biochar amendment and freeze–thaw cycles in soils with a higher moisture content (45%), indicating that the effects of freeze–thaw cycles on P availability appear to be more pronounced in biochar-amended Mollisols of higher water contents.

Machine traffic during timber harvesting operations induces soil compaction, which is particularly evident in the formation of ruts. Visual inspection of rut formation is labor-intensive and limits the volume of data that can be collected. This study aims to contribute to the limited knowledge base regarding the extent of soil physical disturbance caused by machine traffic on steep slopes and to evaluate the utility of LiDAR and UAV photogrammetry techniques. The selected traffic trails included single-pass uphill, single-pass downhill, three-pass round trip, and five-pass round trip trails, with an average slope of 70.7%. Traditional methods were employed to measure rut depth using a pin board and to assess soil bulk density (BD) and soil porosity (SP) from soil samples. The results revealed that the average rut depth was 19.3 cm, while the deepest ruts were observed after a single pass (uphill: 20.0 cm; downhill: 22.7 cm), where BD and SP showed the most significant changes. This study provides a rare quantitative evaluation of the applicability of remote sensing methods in forestry by comparing surface height data collected via a pin board with that derived from a Mobile LiDAR System (MLS) and UAV photogrammetry using structure-from-motion (SfM). When compared to pin board measurements, the MLS data showed an R² value of 0.74 and an RMSE of 4.25 cm, whereas the SfM data had an R² value of 0.62 and an RMSE of 5.27 cm. For rut depth estimation, SfM (16.0 cm) significantly underestimated values compared to the pin board (19.3 cm) and MLS (19.9 cm). These findings not only highlight the potential and limitations of remote sensing methods for assessing soil disturbance in steep forest environments but also contribute to addressing the knowledge gaps surrounding the effects of soil compaction in steep terrain.

Pollutants can exist in the soil for a long time and alter the bacterial community. Using lubricants to prevent the wear of chainsaw blades is necessary for thinning activities and wood harvesting. We investigated the influences of soil contamination with chainsaw lubricants on soil bacterial communities. Bio-oil, mineral oil, and recycled oil were scattered on each treatment to investigate variations in soil bacterial structure during treated periods using the Illumina MiSeq sequencing platform. The results obtained were 5943 ASVs, 5112 ASVs, and 6136 ASVs after treatment at one month, six months, and twelve months, respectively. There was a significant difference in Shannon and Simpson indices between treatments and controls. A total of 46 bacterial genera with an average relative abundance of more than 1.0% were detected in all soil samples. Massilia was the most common genus detected in control at one month, with an average relative abundance of 14.99%, while Chthoniobacter was the most abundant genus detected in bio-oil, mineral oil, and recycled oil treatments at one month, with an average relative abundance of 13.39%, 14.32%, and 10.47%, respectively. Among the three chainsaw lubricants, bio-oil and mineral oil had fewer impacts than recycled oil. The abundances of several functional bacteria groups in the bio-oil treatment were higher than in other treatments and controls. Our results indicated that different chainsaw lubricants and their time of application affected the soil bacterial community composition.