Living grass mulching (LGM) is an important orchard floor management that has been applied worldwide. Although LGM can effectively enhance soil nutrient availability and fertility, its effects on microbial-mediated soil nutrient cycling and main drivers are unclear. Meanwhile, the variation of enzyme activities and soil nutrient availability with LGM duration have been rarely studied. This study aims to explore the effects of mulching age and soil layer on enzyme activities and soil nutrients in citrus orchards. In this study, three LGM (Vicia villosa) treatments were applied, i.e., mulching for eight years, mulching for four years, and no mulching (clean tillage). Their effects on the enzyme activities and soil nutrients were analyzed in different soil layers of citrus orchards in subtropical China, i.e., 0-10, 10-20, and 20-40 cm. Compared to clean tillage, mulching for four years had fewer effects on enzyme activities and soil nutrients. In contrast, mulching for eight years significantly increased available nitrogen (N), phosphorus (P) nutrients, β-glucosidase, and cellobiohydrolase activities in the soil layer of 0-20 cm. In the soil layer of 0-40 cm, microbial biomass carbon (C), N, P, N-acetylglucosaminidase, leucine aminopeptidase, and acid phosphatase activities also increased (P < 0.05). Mulching for eight years significantly promoted C, N, and P-cycling enzyme activities and total enzyme activities by 2.45-6.07, 9.29-54.42, 4.42-7.11, and 5.32-14.91 times, respectively. Redundancy analysis shows that mulching treatments for eight and four years had soil layer-dependent positive effects on soil enzyme activities. Microbial C and P showed the most significant positive correlation with enzyme activities, followed by moisture content, organic C, and available N (P < 0.05). Available nutrients contributed almost 70% to affect enzyme activities significantly and were the main drivers of the enzyme activity variation. In summary, LGM could improve soil enzyme activities by increasing available nutrients. The promotion effect was more significant under mulching for eight years. Therefore, extending mulching age and improving nutrient availability are effective development strategies for sustainable soil management in orchard systems. Our study can provide valuable guidelines for the design and implementation of more sustainable management practices in citrus orchards.

Soil extracellular enzymes (SEEs) affect the decomposition of organic matter and microbial nutrient demand. However, the seasonal dynamics of SEE activity for differently aged plantations is still unclear. To analyze the seasonal variations of SEE activity and nutrient limitation for differently aged plantations, this study employed the “space-for-time substitution” method and Pinus massoniana plantations of varying ages (6, 13, 29, 38, and 57 years) in subtropical China to determine SEE activity and nutrient limitations in the growing and non-growing seasons. The results showed that SEE activity varied notably with the growth stage and season. In particular, β-1,4-glucosidase activity was higher in the growing season than in the non-growing season, while the opposite was observed for acid phosphatase and leucine-amino-peptidase activity. Acid phosphatase gradually increased with stand age, peaking in the 38-year plantation for the growing and non-growing seasons. Microbial carbon (C)-limitation was higher in the growing season and gradually decreased with forest stand development. Phosphorus (P)-limitation was higher in the growing season than in the non-growing season and was maximum in the 38-year plantation. Moreover, the physicochemical properties and microbial biomass explained the microbial C- and P-limitations, respectively. Compared to the non-growing season, the C- and P-limitations of different stand ages were stronger during the growing season, and the physicochemical properties and microbial biomass were important factors affecting their changes. The study reveals the balance status between soil microorganisms and nutrients in subtropical forest ecosystems and provides guidance for the development of afforestation strategies.

Living vegetation volume (LVV), one of the most difficult tree parameters to calculate, is among the most important factors that indicates the biological characteristics and ecological functions of the crown. Obtaining precise LVV estimates is, however, challenging task because the irregularities of many crown shapes are difficult to capture using standard forestry field equipment. Terrestrial light detection and ranging (T-LiDAR) can be used to record the three-dimensional structures of trees. The primary branches of Larix olgensis and Quercus mongolica in the Qingyuan Experimental Station of Forest Ecology at the Chinese Academy of Sciences (CAS) were taken as the research objects. A new rapid LVV estimation method called the filling method was proposed in this paper based on a T-LiDAR point cloud. In the proposed method, the branch point clouds are divided into leaf points and wood points. We used RiSCAN PRO 1.64 to manually separate the leaf points and wood points under careful visual inspection, and calculated that leaf points and wood points accounted for 91% and 9% of the number of the point clouds of branches. Then, the equation LVV = V₁N (where N is the number of leaf points, and V₁ is cube size) is used to calculate LVV. When the laser transmission frequency is 300,000 points/second and the point cloud is diluted to 30% using the octree method, the point cloud can be replaced by a cube (V₁) of 6.11 cm³ to fill the branch space. The results showed that good performance for this approach, the measuring accuracy for L. olgensis and Q. mongolica at the levels of α = 0.05 and α = 0.01, respectively (94.35%, 90.01% and 91.99%, 85.63%, respectively). The results suggest that the proposed method can be conveniently used to calculate the LVV of coniferous and broad-leaf species under specific scanning settings. This work is explorative because hypotheses or a theoretical framework have not been previously defined. Rather, we would like to contribute to the formation of hypotheses as a background for further studies.

The Three Gorges Reservoir Area (TGRA) in China has extensive non-timber product plantations (NTPP), in which integrated management based on intensive fertilization and weeding were required to maintain and improve yields for a long time. Uncertainties still existed regarding the compound effects of environment and the long-term integrated management on soil organic carbon content (SOC) in NTPP. Data from 341 sampling plots covering six primary NTPP types were collected to investigate the influence of environment and management on topsoil (0–10 cm) SOC of NTPP using a coupled algorithm based on machine learning and structural equation modeling. Results showed significant differences and spatial variabilities in SOC content among different types of NTPP. Integrated management accounted for approximately 53% of the accumulation of topsoil organic carbon, surpassing the total contribution of topography, climate, vegetation, and soil properties in NTPP of TGRA. SOC content increased with available nitrogen for NTPP at all altitudes in TGRA. The study highlighted the potential of enhancing SOC storage through adaptive integrated management in NTPP of vast areas. Improving soil organic carbon stock in large area of non-timber production plantations would benefit the realization of carbon neutralization in next decades.