Analysis on Carbon Sink Benefits of Comprehensive Soil and Water Conservation in the Red Soil Erosion Areas of Southern China
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School of Geographical Sciences, School of Carbon Neutrality Future Technology, Fujian Normal University, Fuzhou 350117, China
2
Institute of Geography, Fujian Normal University, Fuzhou 350117, China
3
Fujian Shuzhi Tansuo Technology Co., Ltd., Fuzhou 350117, China
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Author to whom correspondence should be addressed.
Forests 2025, 16(10), 1551; https://doi.org/10.3390/f16101551
Submission received: 29 August 2025
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Revised: 5 October 2025
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Accepted: 7 October 2025
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Published: 8 October 2025
(This article belongs to the Section Forest Ecology and Management)
Abstract
Soil erosion is an increasingly severe problem and a global focus. As one of the countries facing relatively serious soil erosion, China encounters significant ecological challenges. This study focuses on the carbon sink benefits of comprehensive soil and water conservation management in the red soil erosion area of southern China, conducting an in-depth analysis using the Ziyang small watershed in Shangyou County, Jiangxi Province, as a typical case. Research methods involved constructing an integrated monitoring approach combining basic data, measured data, and remote sensing data. Changes in soil and vegetation carbon storage in the Ziyang small watershed across different years were determined by establishing a baseline scenario and applying inverse distance spatial interpolation, quadrat calculation, feature extraction, and screening. The results indicate that from 2002 to 2023, after 21 years of continuous implementation of various soil and water conservation measures under comprehensive watershed management, the carbon storage of the Ziyang small watershed increased significantly, yielding a net carbon sink of 54,537.28 tC. Tending and Management of Coniferous and Broad-leaved Mixed Forest, Low-efficiency Forest Improvement, and Thinning and Tending contributed substantially to the carbon sink, accounting for 72.72% collectively. Furthermore, the carbon sink capacity of the small watershed exhibited spatial variation influenced by management measures: areas with high carbon density were primarily concentrated within zones of Tending and Management of Coniferous and Broad-leaved Mixed Forest, while areas with low carbon density were mainly found within zones of Bamboo Forest Tending and Reclamation. The increase in watershed carbon storage was attributed to contributions from both vegetation and soil carbon pools. Comprehensive management of soil erosion demonstrates a significant carbon accumulation effect. The annual growth rate of vegetation carbon storage was higher than that of soil carbon storage, yet the proportion of soil carbon storage increased yearly. This study provides a theoretical basis and data foundation for the comprehensive management of soil and water conservation in small watersheds in the southern red soil erosion region of China and can offer technical and methodological support for other soil and water conservation carbon sink projects in this area.
1. Introduction
Soil erosion refers to the process in which soil and its parent material are destroyed, eroded, transported, and deposited under the action of natural external forces such as water power, wind power, gravity, freeze–thaw, and glaciers, as well as human activities such as reclamation, mining, and road construction. It is one of the natural phenomena of material circulation on the Earth’s surface; however, due to human interference, this process has been accelerated, leading to soil and water loss, which seriously affects human survival and production. Given that the problem of soil and water loss has become increasingly prominent at the current stage and has become the focus of environmental concerns in countries around the world, the United Nations has listed it as one of the three major environmental issues [1,2].
To address the severe situation caused by soil and water loss, soil and water conservation has emerged as a response. With the core goal of protecting, improving, and rationally utilizing soil and water resources, it curbs soil erosion and maintains the dynamic balance of soil and water resources through a series of measures including engineering, biological, and agricultural ones, and serves as a key link in ensuring the healthy operation of ecosystems. As practices have advanced, people have gradually realized that single soil and water conservation measures are difficult to fundamentally solve complex ecological problems, thus making comprehensive soil and water conservation management an inevitable trend [3,4]. This comprehensive management model emphasizes adapting measures to local conditions and the synergy of multiple measures; it combines the rapid effectiveness of engineering measures, the long-term effectiveness of biological measures, and the practicality of agricultural measures to form a complete and systematic prevention and control system, which has played an important role in improving the regional ecological environment and enhancing land use efficiency [5].
In recent years, as the issue of global climate change has become increasingly prominent, carbon sink has attracted widespread attention as an important approach to addressing climate change, and the carbon sink benefits of soil and water conservation have gradually come into the research perspective. In the process of comprehensive soil and water conservation management, measures such as vegetation restoration and soil improvement can not only effectively control soil and water loss but also fix carbon dioxide in the atmosphere through plant photosynthesis and promote the accumulation of soil organic carbon, thereby forming a significant carbon sink effect [6]. Since China introduced the goals of “carbon peak and carbon neutrality”, carbon sink research has received significant attention at national and societal levels. The Ministry of Water Resources has also emphasized the need to study the carbon sink capacity of soil and water conservation, initiating pilot projects to quantify this capacity. This highlights that evaluating the carbon sink benefits of soil and water conservation has become a critical task in supporting the strategic objectives of carbon neutrality in the field of soil and water conservation during the new era. In-depth exploration of the internal connection between comprehensive soil and water conservation management and carbon sink benefits, as well as revealing its carbon sink mechanism and potential, holds important theoretical value and practical significance for enriching the theoretical system of soil and water conservation, expanding carbon sink sources, and realizing the coordinated promotion of ecological protection and climate governance.
Studies by Sitch et al. have shown that soil erosion not only leads to a sharp decline in soil fertility but also accelerates the loss of soil organic carbon, thereby seriously weakening the carbon sink function of the soil [7]. In this context, the implementation of a series of effective soil and water conservation measures, such as building terraces, setting up retaining walls, and adopting vegetation coverage, can effectively reduce the frequency and intensity of soil erosion, thus creating favorable conditions for the accumulation of soil organic carbon and ultimately achieving a significant improvement in soil carbon sink capacity [8]. As pointed out by Powers et al., increasing vegetation coverage and root biomass is conducive to enhancing carbon sink benefits, because plants convert carbon dioxide in the atmosphere into organic matter through photosynthesis and fix it in plants and soil, thereby achieving carbon storage and fixation [9]. Soil and water conservation measures can effectively improve soil structure by increasing the content of soil organic matter, enhancing the aggregation between soil particles, and thus improving the water-holding capacity of the soil. The improvement of such soil structure is not only beneficial to plant growth but also can reduce the risk of carbon loss, laying a solid foundation for long-term carbon storage. Lal’s research results have also fully confirmed this view, that is, soil and water conservation measures can promote the accumulation and stability of soil organic carbon, thereby enhancing the carbon sink function of the entire ecosystem [10]. Studies by Pimentel et al. and Wang et al. have shown that adopting agricultural soil and water conservation measures such as reducing tillage intensity and increasing organic fertilizer application can effectively increase soil organic carbon storage and achieve the coordinated development of agricultural production and carbon sink functions [11,12]. A comparative study by Peng et al. on the Loess Plateau clearly demonstrated that after converting slope farmland to forestland, soil organic matter began to increase significantly after more than five years of vegetation restoration, while soil carbon, nitrogen, and available nutrients increased noticeably after ten years. In contrast, the control plots that continued with traditional farming showed no significant change or even a slight decrease. This compelling contrast directly confirms the positive role of soil and water conservation measures in carbon sequestration [13].
Based on the role of soil and water conservation in enhancing carbon sequestration, this study evaluates the carbon sink benefits of comprehensive management in southern China’s red soil erosion areas. These regions experience a subtropical monsoon climate, with highly erodible soils—low in organic matter and weak in structure—combined with heavy seasonal rainfall and unsustainable land use. These conditions lead to serious soil erosion and organic carbon loss.
Although vegetation restoration is widely used for erosion control in these areas, there is still no reliable method to quantify the carbon sequestration effects of integrated soil and water conservation projects. In particular, a practical approach that combines field measurements and remote sensing to assess both soil and vegetation carbon stocks at the small watershed scale is lacking.
To address this gap, we conducted a case study in the Ziyang small watershed in Jiangxi Province. We integrated field surveys, soil and vegetation sampling, and satellite data to build a monitoring framework for tracking carbon changes after conservation measures were implemented. This study provides a usable method for carbon sink assessment in similar regions and supports the development of carbon trading systems based on soil and water conservation efforts.
2. Materials and Methods
2.1. Study Area
The Ziyang small watershed is a typical red-soil erosion area in the south, belonging to the hilly–mountainous landform. It is located in Ziyang Township, 49 km away from Shangyou County seat in Jiangxi Province, bordering Heyuan Town of Suichuan County in the west, Shengli Village in the north, Xiazuo Village in the south and Xiuluo Village in the east. The area of the small watershed is 22.43 square kilometers, and the altitude is between 334 m and 829 m. The geological structure belongs to the Cathaysian structural system and the Neocathaysian structural system, with obvious fault structures. The strata exposed in the small watershed are mainly Sinian System, Cambrian and Quaternary red soil, and the lithology exposed is mainly granite.
The small watershed is located in the subtropical humid monsoon climate zone, featuring a mild climate, moderate rainfall, a long frost-free period, and distinct four seasons. However, there are significant regional differences, and the three-dimensional agricultural climate between mountainous areas and flatlands is evident. Specifically, it is characterized by distinct rainy and dry seasons, significant seasonal variations, and large regional differences: the northwest is cool and rainy, the central river valley area has alternating and significant changes, the southeast has favorable light and heat conditions, and the lakeside area of the southwest reservoir region is foggy and humid. The annual average sunshine duration is 1603 h, the annual average temperature is 19.0 °C, and the annual average rainfall is 1488.1 mm. Affected by the monsoon climate, the dry and wet climates are distinct. Generally, the dry season lasts from October to February of the following year, accounting for 34% of the annual rainfall, while the rainy season is from March to September, accounting for 66% of the annual rainfall.
Most of the mountain soils in this small watershed are red soils developed from granite and glutenite, characterized by high gravel content, coarse texture, leakage of water and fertilizers, low content of available nutrients such as organic matter, phosphorus, and nitrogen, looseness and easy erosion, acidity, low natural fertility, and strong acidity. Once the vegetation is destroyed, severe soil and water loss is highly likely to occur under the scouring of heavy rains and surface runoff. Due to natural factors and long-term unreasonable logging and utilization, the primary vegetation has been decreasing continuously. The existing vegetation mainly includes Mixed Coniferous and Broad-leaved Forest, coniferous forests, and barren hills with shrubs and grasses, among which pure Masson pine forests have the widest distribution.
According to the available data, since 2002, comprehensive soil and water conservation management has been implemented in the Ziyang Small Watershed. A series of measures have been carried out, including the establishment of 15.89 km2 of soil and water conservation forests, 3.52 km2 of closed restoration areas, 1.1 km2 of economic forests, 6.9 km of production roads, 5.1 km of drainage ditches, 200 m of stone retaining walls, 2000 m2 of greening, 2.0 km of ecological revetments, 4 water weirs, 3 water storage tanks, 32 sand sedimentation tanks, 0.2 km2 of small wetlands, and 3 ecological purification ponds. After more than 20 years of unremitting efforts, key soil and water conservation management has been continuously implemented in this watershed. By adapting measures to local conditions and setting up protections according to specific hazards, the three measures of biological, engineering, and tillage have been organically combined. A large number of arbors and shrubs such as Chinese Fir, Bamboo Forest, and Tea Oil Camellia have been successively planted, changing the situation of a single tree species and achieving favorable benefits, with the soil erosion rate continuously decreasing. The soil and water conservation management measures in this watershed include eight types: Thinning and Tending, Planting and Tending of Economic Forest, Construction of Soil and Water Conservation Forest, Low-efficiency Forest Improvement, Tending and Management of Coniferous and Broad-leaved Mixed Forest, Tending and Management of Fir Forest, Bamboo Forest Tending and Reclamation, and Closed Restoration. The project boundary and regional location of the Ziyang Small Watershed are shown in Figure 1 below.
2.2. Technical Route for Monitoring and Evaluation of Small Watershed Carbon Sink Capacity
The carbon reservoirs within the Ziyang Small Watershed are classified into two primary types: vegetative and soil-based. In light of practical conditions and data accessibility, the monitoring activities spanned a period from 7 November 2002 to 17 November 2023. According to field surveys, interviews, and government records, before soil and water conservation measures were implemented, the area was primarily covered by low-yield Tea Oil Camellia and degraded coniferous forests (such as severely degraded Masson pine forests and low-efficiency Chinese Fir forests). These land use types were characterized by low economic returns and poor ecological functionality, which were the primary drivers of the severe soil erosion at the time. They represent the prevailing condition that the watershed would have most likely sustained in the absence of targeted conservation funding and planned interventions. These land types were therefore selected as the baseline scenarios. For both baseline types, sample plots were established for vegetation and soil surveys. Typical baseline sampling points were identified using high-resolution imagery to support further analysis.
A multi-stage procedure is implemented to monitor and evaluate the carbon sink capacity of small watersheds. The initial phase involves synthesizing existing and historical remote sensing data to establish a sampling plan. Following this, pre- and post-period forest carbon storage is modeled by integrating supplementary data, ground-sampled measurements, carbon-specific models, and satellite images. Techniques including baseline scenario establishment, spatial interpolation (e.g., inverse distance weighting), sample calculation, and feature extraction are applied. These steps allow for the determination of annual soil and vegetation carbon stocks. The analysis of their variation patterns ultimately leads to an assessment of the watershed’s total carbon storage and its overall carbon sink potential [14,15]. This technical route is shown in Figure 2.
2.3. Data Collection and Preprocessing
2.3.1. Collection of Basic Data
In the subtropical study area, cloud cover and the synchronization of plant phenology with historical imagery were key considerations for data selection. To address this, bi-temporal satellite imagery was acquired from the Landsat series (U.S.), utilizing Level-2 atmospherically corrected products comprising 6 multispectral bands at a 30 m resolution. The scenes selected were from 7 November 2002, and the date closest to the field survey of 17 November 2023. Furthermore, to enhance the precision of vegetation coverage extraction and its temporal alignment with ground observations, a fused multispectral and orthophoto base map from China’s Ziyuan-3 satellite (dated 14 February 2024) with a high spatial resolution of 2 m was employed. Spatial overlay analysis was subsequently conducted using vector data from the Third National Land Survey and the Second-Class Forest Inventory. This process integrated different land cover categories, with a specific focus on incorporating the distribution of dominant tree species within forested areas. Finally, 9 types of land cover were formed, including Mixed Coniferous and Broad-leaved Forest, Coniferous Forest, Chinese Fir Forest, Bamboo Forest, Tea Oil Camellia, farmland, residential areas, bare soil, and water bodies (Figure 3).
Among these, the latter four types are non-forest lands and are not included in the carbon sink measurement in accordance with the principle of conservatism in carbon sink quantification. For the first five coverage types, field surveys were conducted, and their corresponding areas and the number of sample plots are shown in Table 1.
Moreover, the considerable variation in carbon storage among the five identified vegetation types, attributable to their distinct species composition and growth dynamics, significantly influences the spatial distribution of vegetation carbon density across the watershed. To quantitatively represent these types in subsequent modeling, each was converted into binary raster layers. The spatial resolution of these layers was standardized to match that of the Landsat imagery, thereby generating the essential dummy variables for the vegetation carbon storage model. Concurrently, a suite of vegetation indices was derived through calculations applied to various spectral bands of the Landsat data. These indices include the Normalized Difference Vegetation Index (NDVI), the Fractional Vegetation Coverage (FVC) estimated from NDVI, the Enhanced Vegetation Index (EVI), the Soil-Adjusted Vegetation Index (SAVI), and the Modified Normalized Difference Vegetation Index (MNDVI).
2.3.2. Field Data Collection
Based on the proportional area of different vegetation types, sample plots were allocated accordingly. Ultimately, a total of 33 quadrats, each measuring 20 m × 20 m, were established to ensure both spatial distribution uniformity and ecological representativeness (see Figure 1). The setup procedure utilized the Real-Time Kinematic (RTK) positioning technique for precise navigation and azimuth determination. Specifically, the four vertices of each quadrat were accurately located and marked with PVC pipes as permanent markers. Final coordinates with fixed solutions were recorded using the RTK system to ensure high positional accuracy.
For the trees within the sample plots, a tree-by-tree measurement (caliper survey) was conducted to measure the diameter at breast height (DBH). The height of standard trees was measured by diameter class, and the names of tree species were recorded; for shrubs and other plants, the ground diameter at 5 cm above the ground was measured for each individual. The basic information of the vegetation survey in the sample plots is shown in the following table (Table 2).
Soil sampling was conducted simultaneously with the vegetation survey. Within each sample plot, three soil profiles were excavated along a diagonal S-shaped pattern. For each profile, a cutting ring sample was taken from the 0–30 cm soil layer and placed in a sealed bag. After bringing the samples to the laboratory, the fresh weight of the soil was first measured. A portion of the soil was then placed in an aluminum box and weighed before drying. The soil was oven-dried to a constant weight to determine the dry weight, allowing the moisture content to be calculated, and finally the soil bulk density was derived. Another portion of the soil was air-dried in a shaded area. Roots and gravel were removed, and the soil was ground and sequentially passed through 10-mesh and 100-mesh sieves. Soil organic carbon content was finally determined using the acid washing centrifugation-elemental analysis method.
2.3.3. Indoor Data Preprocessing
Aboveground biomass for individual plants was calculated based on field quadrat surveys, using tree species, diameter at breast height (or ground diameter), and corresponding allometric growth models listed in Table 3. Carbon storage per plant was obtained by applying locally relevant biomass conversion parameters and carbon content rates cited in published studies. After scaling these values to each sample plot, carbon density—represented as tons of carbon per hectare (t C/ha)—was determined. A spatial data layer was subsequently established by linking the surveyed data from 30 quadrats with their precise geographical coordinates.
2.4. Construction of Vegetation Carbon Storage Model and Carbon Sink Assessment
Spectral variables for model construction were extracted from the 2023 remote sensing images at the center locations of the field sample plots. The initial set comprised 12 predictors, including five vegetation indices, six multispectral bands, and five land cover dummy variables. Owing to spectral saturation in areas of high carbon density, most predictors demonstrated a pronounced exponential relationship with the measured values. Consequently, a logarithmic transformation was applied to the dependent variable (carbon density). Furthermore, a correlation analysis was performed on the independent variables to identify and remove those exhibiting strong collinearity. Subsequently, multiple stepwise linear regression was employed for variable selection and model construction, and the obtained results are presented in the table below (Table 4).
The developed model achieved a coefficient of determination (R2) of 0.808. The estimation was characterized by a mean absolute error of 10.472 t/ha, a relative error of 33.25%, and a mean error of −1.907 t/ha. The consistent negative mean error indicates a general underestimation of the actual measured values. This systematic bias is primarily attributed to the saturation effect of multispectral remote sensing signals in regions with dense vegetation cover.
Subsequently, the vegetation carbon storage for the year 2023 was quantified. By applying the established model, the spatial distribution of the estimated carbon storage was generated and is presented in Figure 4.
The model, constructed using radiometrically calibrated and atmospherically corrected data, demonstrates strong transferability to the 2002 satellite imagery. Consequently, the spatial distribution of vegetation carbon storage for that year was retrieved by inputting the corresponding vegetation indices and land cover dummy variables, with the result visualized in Figure 5.
2.5. Evaluation of Soil Carbon Pool Storage
The study area is a relatively homogeneous red soil small watershed. Following the implementation of large-scale soil and water conservation measures, the spatial heterogeneity of the underlying surface conditions has been reduced to some extent. During the initial stage of the project, we were unable to obtain continuous environmental covariates with sufficient resolution and accuracy to cover the entire watershed, which are essential for successfully applying methods like regression kriging or random forest models. Therefore, in the absence of reliable covariates, the Inverse Distance Weighting (IDW) method was selected as a robust deterministic interpolation approach. Its results depend primarily on the spatial relationships among the sampling points themselves, avoiding the introduction of additional uncertainty that could arise from poor-quality covariate data.
The IDW method is straightforward in principle, and its parameters are easily interpretable, making it well-suited for the preliminary monitoring and assessment of carbon sink benefits from soil and water conservation in this watershed. It also provides a reliable baseline reference for future comparisons with more complex models. Furthermore, the layout of our sampling points was carefully designed to represent the different land cover types and management practices within the watershed, ensuring good spatial representation. The IDW method effectively captures the overall spatial trends based on the spatial autocorrelation of these sampling points.
Based on the central coordinates of each sample plot, Kriging spatial interpolation was performed on the measured soil data, with the neighborhood radius set as the range value, resulting in the spatial distribution raster data of soil carbon density in 2023 with a spatial resolution of 30 m (Figure 6). Finally, the distribution map of carbon layer types was used for spatial statistics to obtain the soil organic carbon density of each carbon layer.
3. Results and Discussion
3.1. Baseline Carbon Sink Amount
Carbon sink capacity refers to the net increment of carbon storage under the project scenario relative to the baseline scenario within a specific period. The baseline scenarios of the Ziyang Small Watershed in Shangyou County, Jiangxi Province can be divided into two types: one is degraded coniferous forests, such as severely degraded Masson pine forests and low-efficiency Chinese Fir forests; the other is low-yield Tea Oil Camellia on degraded steep slopes. For both types of baselines, sample plots were setup for vegetation and soil surveys, and three sample areas were mapped by combining interpretation of high-resolution images. The changes in baseline vegetation carbon storage were derived from the average of the remote sensing estimated values of three baseline sample areas, namely degraded Masson pine forests, low-efficiency Chinese Fir forests, and degraded Tea Oil Camellia forests. The average vegetation carbon density of the baseline sample plots in 2002 was 5.91 tC/ha, and that in 2023 was 12.08 tC/ha.
For soil, the reduction in carbon density is mainly caused by existing soil erosion. In accordance with the principle of conservatism, it can be assumed that there was no change in soil carbon density between 2002 and 2023, and the carbon density measured in June 2023 was used as a substitute for the soil carbon density and organic carbon content before the implementation of the comprehensive management project (i.e., in 2002). Considering the significant differences in types and measures such as Closed Restoration, artificial forest management, and economic forest management, combined with field surveys, the soil baselines were determined as follows: for the five management measures including Tending and Management of Fir Forest, Thinning and Tending, Low-efficiency Forest Improvement, Tending and Management of Coniferous and Broad-leaved Mixed Forest, and Construction of Soil and Water Conservation Forest, the baseline was set as the low-efficiency Chinese Fir forest in sample plot P06, with a carbon density of 38.60 t C/ha in the 0–30 cm soil layer; for Closed Restoration, the baseline was sample plot P13, with a soil organic carbon density of 29.14 t C/ha; for Bamboo Forest Tending and Reclamation, the baseline was sample plot P31, with a soil organic carbon density of 32.75 t C/ha; and for Planting and Tending of Economic Forest, the baseline was the Tea Oil Camellia sample plot P27, with a soil organic carbon density of 45.85 t C/ha.
3.2. Vegetation Carbon Sink Amount
The established remote sensing model for vegetation carbon storage was employed to derive spatial patterns of carbon density for both 2002 and 2023, utilizing the corresponding satellite imagery and associated variables. Subsequently, zonal statistics were performed on the resulting carbon density raster data, using vector boundaries representing different land cover types. The final quantitative outcomes from this analysis are summarized in Table 5.
Over the 21-year period, the vegetation carbon density across the entire small watershed increased from 14.62 t C/ha to 32.70 t C/ha, representing a significant increase of 123.5%, which indicates that the carbon sink capacity of forests per unit area has more than doubled. Under the implementation of project management measures, the total vegetation carbon storage increased from 27,245.19 t C in 2002 to 60,944.33 t C in 2023, with an increase of 33,699.13 t C in vegetation carbon storage attributed to the project. In the baseline scenario, the vegetation carbon density increased from 5.91 t C/ha to 12.08 t C/ha, resulting in a baseline carbon sink of 11,512.89 t C, while the net vegetation carbon sink amounted to 22,186.24 t C. This implies that the project implementation has sequestered an additional 22,186.24 t C compared to natural restoration. The net carbon sink accounts for 65.8% of the project’s total carbon sink, demonstrating a highly significant additionality.
Significant differences are observed in the changes in vegetation carbon density under different management measures across the entire small watershed. Among these measures, Tending and Management of Coniferous and Broad-leaved Mixed Forest yields the highest net carbon sink, making the greatest contribution to carbon sink, with a net carbon sink amount of 8124.59 t C, accounting for 36.6% of the total net carbon sink. This is mainly attributed to its largest implementation area and a relatively high increase in carbon density (18.83 t C/ha); although the increase in carbon density per unit area is not the highest, the effect of large-area implementation is extremely prominent. Low-efficiency Forest Improvement ranks second in terms of carbon sink contribution, with a net carbon sink amount of 4739.39 t C, accounting for 21.4% of the total net carbon sink, and an increase in carbon density of 18.08 t C/ha, showing a significant improvement effect, which indicates that the improvement measures have effectively enhanced the productivity of low-efficiency forest stands. Thinning and Tending achieves the highest increase in carbon density, reaching 19.59 t C/ha, which suggests that scientific and reasonable thinning can most effectively promote the growth of reserved trees and significantly increase the carbon sink rate per unit area. In contrast, the carbon sink increment of Bamboo Forest Tending is relatively small, which may be related to the growth characteristics of bamboo forests (such as rapid growth but relatively low individual biomass/carbon density and fast carbon turnover) or the specific methods of tending and reclamation (including inorganic fertilizer application, cutting, tillage, and regular removal of understory vegetation) [21].
3.3. Soil Carbon Sink Amount
Taking the survey data of the two baseline scenarios as controls, the eight management measures assumed no change in soil carbon density under the baseline scenarios. The measured carbon density of the control sample plots was assigned to each corresponding measure and used as the soil carbon density before the project implementation (i.e., in 2002), based on which the soil carbon sink amount was calculated. The results are presented in Table 6.
The various vegetation management measures implemented in the Ziyang Small Watershed have not only significantly enhanced vegetation carbon sink but also greatly promoted the accumulation of soil organic carbon. Under the implementation of soil and water conservation management in the entire small watershed, the soil carbon storage has reached 105,778.10 t C. Compared with the soil carbon storage of 71,779.97 t C under the baseline scenario, the project implementation has resulted in a soil carbon sink amount of 33,998.13 t C. This soil carbon sink amount even exceeds the net vegetation carbon sink amount, highlighting the significant contribution of the soil carbon pool to the regional carbon balance and indicating that these measures have effectively improved soil health and enhanced the function of soil as a carbon pool.
The project carbon density under Tending and Management of Coniferous and Broad-leaved Mixed Forest has increased by 18.70 t C/ha compared with the baseline carbon density, and the soil carbon sink amount is as high as 12,012.85 t C, accounting for 35.3% of the total soil carbon sink amount. This is closely related to its largest implementation area and relatively high project carbon density. The increment of carbon sink per unit area under Tending and Management of Fir Forest is the highest, which is almost the same as that under Low-efficiency Forest Improvement, indicating that the tending and management of Chinese Fir forests are highly conducive to the accumulation of soil organic carbon, and the improvement of low-efficiency forests also has an excellent effect on enhancing the soil carbon pool. The soil carbon sink amount under Bamboo Forest Tending and Reclamation is the lowest, only 757.85 t C, which is mainly limited by its smallest implementation area. However, the increase in its soil carbon density is much higher than that in its vegetation carbon density, indicating that Bamboo Forest Tending and Reclamation has a relatively more prominent effect on improving the soil carbon pool.
3.4. Small Watershed Carbon Sink Amount
A quantitative analysis was conducted to evaluate the differences among three key metrics: the net carbon sink of vegetation and soil under the baseline scenario, the actual net carbon sink of the project, and the anthropogenic net carbon sink. The results of this analysis, expressed in terms of both carbon storage (t C) and carbon dioxide equivalent (t CO2e), are presented in Table 7.
The 21-year comprehensive management measures in the Ziyang Small Watershed have achieved extremely significant carbon sink benefits. Compared with the baseline scenario without project intervention, the project has generated an additional anthropogenic net carbon sink of up to 56,184.37 tC. Among this, the contribution of soil carbon sink accounts for 60.5%, exceeding the 39.5% contribution of vegetation carbon sink, which highlights the crucial role of the soil carbon pool in the carbon balance of the ecosystem.
Under the implementation of the project’s soil and water conservation management measures in the entire small watershed, the actual net carbon sink amount has reached 67,697.26 tC, which represents the actual increase in carbon storage of the ecosystem in the project area over the 21-year period. To more intuitively understand the climate benefits, the carbon sink amount was multiplied by a coefficient of 3.67 (where 1 ton of carbon is equivalent to 3.67 tons of carbon dioxide), resulting in an actual net carbon sink amount of 248,223.29 t CO2e for the project, which is a very considerable emission reduction amount.
In the baseline scenario assuming no project implementation, the ecosystem would also naturally accumulate a certain amount of carbon sink, mainly derived from the natural growth of vegetation; the baseline net vegetation carbon sink amount is 11,512.89 t C (converted to 42,213.92 t CO2e). The anthropogenic net carbon sink amount is a core indicator for evaluating the additionality and actual climate benefits of the project, which is equal to the “actual net carbon sink amount of the project” minus the “baseline net carbon sink amount”; this represents the additional amount of carbon fixed in the atmosphere by the project implementation compared to the baseline scenario and is the net contribution of the project to mitigating climate change. The anthropogenic net vegetation carbon sink is 81,349.56 t CO2e, and the anthropogenic net soil carbon sink is 124,659.81 t CO2e, with the latter being slightly higher than the former. This fully demonstrates that the management measures implemented in the Ziyang Small Watershed (such as tending and management, improvement, Closed Restoration, and afforestation) have not only promoted the growth of aboveground biomass but, more importantly, have greatly improved and enhanced soil health and significantly increased soil organic carbon storage. Accordingly, increasing soil organic carbon (SOC) content is the optimal strategy for reducing soil erodibility and mitigating soil erosion [22]. Ignoring the soil carbon pool would seriously underestimate the overall carbon sink benefits and climate value of the project, and this finding emphasizes the importance of including soil carbon monitoring and measurement in ecosystem carbon sink projects (such as forestry carbon sink and ecological restoration).
In summary, soil serves as the principal contributor to the observed increase in carbon sink, whereas vegetation plays a supplemental role through targeted management practices. The project’s implementation has substantially boosted both the regional carbon sequestration capacity and overall carbon storage. This enhancement is vital for carbon dioxide mitigation and climate change adaptation.
4. Conclusions
This study integrated field investigations, remote sensing monitoring, and model simulation to develop a carbon sink benefit model for comprehensive soil and water conservation management in small watersheds. The carbon sink of the Ziyang small watershed from 2002 to 2023 was monitored, and its carbon sequestration benefits were analyzed and evaluated, aiming to provide theoretical and data support for the management of the red soil erosion region in southern China.
Based on an in-depth study of the Ziyang small watershed, the following key management strategies are summarized: First, integrate scientific planning with site-specific measures by developing tailored solutions based on regional geographical conditions and soil erosion characteristics. Second, promote multi-stakeholder participation in collaborative governance, encouraging involvement from government, enterprises, social organizations, and local residents. Third, establish a long-term monitoring and dynamic adjustment mechanism. Modern technologies such as satellite remote sensing and IoT sensors should be used to monitor key indicators—including soil, vegetation, climate, and carbon sink capacity—in real time. Management measures can then be adjusted promptly based on feedback to ensure project compliance and continuous improvement in outcomes.
It should be noted that this study, based on the Ziyang small watershed, has certain limitations. For instance, the determination of the baseline scenario could be further refined, and the soil sampling was limited to a depth of 0–30 cm due to practical constraints. These limitations highlight directions for future research to achieve a more precise assessment of carbon sink benefits. Building on existing research in the Ziyang small watershed, the following future research directions are proposed: improve carbon sink measurement methods by refining model accuracy and incorporating more influencing factors to enhance estimation reliability; and establish a comprehensive ecosystem service value assessment system to fully evaluate the integrated benefits of soil and water conservation carbon sink projects.
Author Contributions
Methodology, Y.W.; Conceptualization, Y.W. and J.W.; Data curation, Y.W., J.W. and S.K.; Funding acquisition, Y.W., S.K. and X.Z.; Supervision, S.K. and J.W.; Software, Y.W. and S.K.; Validation, Y.W., X.Z., J.W. and S.K.; Investigation, J.W., S.K. and X.Z.; Resources, X.Z., J.W. and S.K.; Writing—original draft, Y.W. and S.K.; Writing—review and editing, J.W., S.K. and X.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported by the Major science and technology project of the Ministry of Water Resources “Research and Demonstration of Carbon Sink Effect and Measurement Technology for Different Control Measures in Southern Red Soil” [grant number SKS2022083] and Fujian Provincial Water Conservancy Project “Study of Southern Red Soil” [grant number MSK202311].
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest. Shennan Kuang is employed by Fujian Shuzhi Tansuo Technology Co., Ltd. His employer was not involved in this study, and there is no relevance between this research and their company.
References
- Shi, Z.; Liu, Q.; Zhang, H. Recent Decade‘s Research Progress and Prospects on Soil Erosion and Soil and Water Conservation. Acta Pedol. Sin. 2020, 57, 1117–1127. [Google Scholar]
- Lai, S.; Li, X.; Jiang, W.; Li, F.Y.; Wu, H.; Zhang, Z.; Li, Z. Evaluating the Sustainability of Ecological Quality in Inner Mongolia Autonomous Region: Bridging the Millennium Development Goals and Sustainable Development Goals. Land Degrad. Dev. 2025, 35, 104–112. [Google Scholar] [CrossRef]
- Li, Z.; Jiang, X.; Liu, E.; Zhao, H. Status and Development Direction of Soil and Water Conservation Monitoring Technology and Methods in China. Sci. Soil Water Conserv. 2015, 21, 144–148. [Google Scholar]
- Jia, G.; Zhang, L.; Yu, X. Carbon sink Mechanisms, Implementation Pathways, and Carbon Neutrality Strategies of Ecological Restoration. Bull. Soil Water Conserv. 2022, 42, 393–397. [Google Scholar]
- Li, Z. Discussion on Modernization of Soil and Water Conservation Governance System and Capacity in China. Soil Water Conserv. China 2020, 43, 18–20. [Google Scholar]
- Li, X.; Li, Z.; Cheng, H.; Lu, S.; Wu, J.; Zhong, X. Ecological Mechanism of Carbon Sink Effect in Soil and Water Conservation. J. Fujian Norm. Univ. (Nat. Sci. Ed.) 2025, 41, 53–58. [Google Scholar]
- Sitch, S.; Friedlingstein, P.; Gruber, N.; Jones, S.D.; Murray-Tortarolo, G.; Ahlstrom, A.; Doney, S.C.; Graven, H.; Heinze, C.; Huntingford, C.; et al. Recent Trends and Drivers of Regional Sources and Sinks of Carbon Dioxide. Biogeosciences 2015, 12, 653–679. [Google Scholar] [CrossRef]
- Zhang, J.; Zhao, G.; Jin, Q.; Zhang, Y.; Ding, L.; Zhang, X. A Review of Research on Carbon Sinks from Vegetation Measures in Soil and Water Conservation. Soil Water Conserv. China 2024, 32, 6–12. [Google Scholar]
- Powers, J.S.; Read, J.M.; Denslow, J.S.; Guzman, S.M. Estimating Soil Carbon Fluxes Following Land-Cover Change: A Test of Some Critical Assumptions for a Region in Costa Rica. Glob. Change Biol. 2004, 10, 170–181. [Google Scholar] [CrossRef]
- Lal, R. Artificial Intelligence for Assessing Organic Matter Content and Related Soil Properties. J. Soil Water Conserv. 2024, 79, 66–73. [Google Scholar] [CrossRef]
- Pimentel, M.L.; De Oliveira, A.B.; Schiebelbein, B.E.; Carvalho, M.L.; Tenelli, S.; Cherubin, M.R.; Carvalho, J.L.N.; Briedis, C.; Panosso, A.R.; De Oliveira Bordonal, R. Quantity, Quality and Physical Protection of Soil Carbon Associated with Sugarcane Straw Removal in Southern Brazil. Soil Tillage Res. 2024, 237, 105976. [Google Scholar] [CrossRef]
- Wang, R.; Shi, M.; Chen, S.; Gao, Z. Multi-Scale Integration of Soil and Water Conservation Spatial Data. Res. Soil Water Conserv. 2005, 12, 206–209. [Google Scholar]
- Peng, W.; Zhang, K.; Chen, Y. Study on Soil Property Changes After Converting Slope Farmland to Forestland on the Loess Plateau. J. Nat. Resour. 2005, 20, 272–278. [Google Scholar]
- Cao, W.; Zhang, X.; Zhang, Y.; Liu, B.; Wang, Y.; Zhao, Y.; Yin, X.; Han, X. Connotation and Calculation Method of Soil and Water Conservation Carbon Sink. Sci. Soil Water Conserv. 2024, 22, 1–11. [Google Scholar]
- Zhong, X.; Cheng, H.; Li, Z.; Wu, J.; Lu, S.; Li, D.; Xie, J. Monitoring and Evaluation of Carbon Sink Capacity in Comprehensive Management of Small Watersheds: A Case Study of Luodi River Small Watershed in Changting County, Fujian Province. Bull. Soil Water Conserv. 2023, 43, 304–311. [Google Scholar]
- Ministry of Ecology and Environment. Release of Four Methodologies for Voluntary Greenhouse Gas Emission Reduction Projects. Energy Conserv. Environ. Prot. 2023, 5, 1–9. [Google Scholar]
- Lin, K.; Lyu, M.; Jiang, M.; Chen, Y.; Li, Y.; Chen, G.; Xie, J.; Yang, Y. Improved Allometric Equations for Estimating Biomass of the Three Emphasis Castanopsis Carlesii Emphasis; H. Forest Types in Subtropical China. New For. 2017, 48, 115–135. [Google Scholar] [CrossRef]
- State Forestry Administration. Carbon sink Measurement and Monitoring Methodology for Bamboo Forest Projects. World Bamboo Ratt. Commun. 2024, 18, 11–12. [Google Scholar]
- Wang, Z.; Du, H.; Song, T.; Peng, W.; Zeng, F.; Zeng, Z.; Zhang, H. Allometric Models and Forest Biomass Characteristics of Major Tree Species (Groups) in Guangxi. Acta Ecol. Sin. 2023, 17, 4462–4472. [Google Scholar]
- Guo, M.; Yang, Y.; Xu, Y.; Xi, R. Distribution Characteristics of Carbon Storage in Camellia gauchowensis Plantation. J. South China Agric. Univ. 2022, 11, 86–92. [Google Scholar]
- Zhou, X.; Guan, F.; Zhang, X.; Li, C.; Zhou, Y. Response of Moso Bamboo Growth and Soil Nutrient Content to Strip Cutting. Forests 2022, 13, 1293. [Google Scholar] [CrossRef]
- Arunrat, N.; Sereenonchai, S.; Kongsurakan, P.; Hatano, R. Assessing Soil Organic Carbon, Soil Nutrients and Soil Erodibility under Terraced Paddy Fields and Upland Rice in Northern Thailand. Agronomy 2022, 12, 537. [Google Scholar] [CrossRef]
Figure 1.
Project Boundary and Location Map of Ziyang Small Watershed.
Figure 2.
Technical Route Map for Assessment of Small Watershed Carbon Sink Capacity.
Figure 3.
Distribution Map of Land Cover Types in Ziyang Small Watershed.
Figure 4.
Spatial Distribution Map of Vegetation Carbon Storage in Ziyang Small Watershed (2023).
Figure 5.
Spatial Distribution Map of Vegetation Carbon Storage in Ziyang Small Watershed (2002).
Figure 6.
Spatial Distribution Map of Carbon Storage in 0–30 cm Topsoil Layer of Ziyang Small Watershed (2023).
Figure 6.
Spatial Distribution Map of Carbon Storage in 0–30 cm Topsoil Layer of Ziyang Small Watershed (2023).
Table 1.
Sample Plot Survey Statistics Table.
| Land Cover Type | Area (ha) | Number of Sample Plots |
|---|---|---|
| Chinese Fir | 590.03 | 9 |
| TeaOilCamellia | 76.23 | 4 |
| Mixed Coniferous and Broad-leaved Forest | 987.18 | 12 |
| ConiferousForest | 169.6 | 5 |
| BambooForest | 38.81 | 3 |
Table 2.
Sample Plot Survey Status Table.
| Sample Plot ID | Management Measure | Vegetation Cover | Species Composition | Average DBH (cm) | ATH (m) | NOI |
|---|---|---|---|---|---|---|
| P01 | Tending and Management of Coniferous and Broad-leaved Mixed Forest | Coniferous and Broad-leaved Mixed | 32F 26BT 1MP 7TO | 9.8 | 6.7 | 66 |
| P02 | Planting and Tending of Economic Forest | Tea Oil Camellia | 123TO 12F | 7.0 | 4.3 | 146 |
| P03 | Construction of Soil and Water Conservation Forest | Coniferous Forest | 37F 11MP | 10.2 | 7.0 | 48 |
| P04 | Bamboo Forest Tending and Reclamation | Bamboo Forest | 70B 3F | 8.4 | 8.8 | 73 |
| P05 | Thinning and Tending | Fir | 51F | 10.5 | 7.8 | 51 |
| P06 | Thinning and Tending | Fir | 54F 14MP 4BT | 10.4 | 8.5 | 72 |
| P07 | Tending and Management of Coniferous and Broad-leaved Mixed Forest | Coniferous and Broad-leaved Mixed | 40BT 3MP 20F 2B | 11.8 | 9.3 | 65 |
| P08 | Tending and Management of Coniferous and Broad-leaved Mixed Forest | Coniferous and Broad-leaved Mixed | 38F 10MP 4BT | 9.2 | 6.3 | 52 |
| P09 | Tending and Management of Coniferous and Broad-leaved Mixed Forest | Coniferous and Broad-leaved Mixed | 24F 7BT 3MP 4TO | 10.4 | 5.4 | 38 |
| P10 | Low-efficiency Forest Improvement | Coniferous and Broad-leaved Mixed | 27BT 24F 13B | 10.7 | 6.2 | 64 |
| P11 | Thinning and Tending | Fir | 91F 4MP | 9.0 | 7.6 | 95 |
| P12 | Low-efficiency Forest Improvement | Coniferous Forest | 86F 2MP | 8.2 | 6.7 | 88 |
| P13 | Baseline Scenario | Coniferous Forest | 57MP 8F 1BT | 3.7 | 3.4 | 66 |
| P14 | Low-efficiency Forest Improvement | Coniferous Forest | 58F 1MP | 10.6 | 7.3 | 59 |
| P15 | Tending and Management of Coniferous and Broad-leaved Mixed Forest | Fir | 69F 3BT 3MP | 10.7 | 8.2 | 75 |
| P16 | Baseline Scenario | Tea Oil Camellia | 82TO | 6.5 | 2.4 | 61 |
| P17 | Tending and Management of Coniferous and Broad-leaved Mixed Forest | Coniferous and Broad-leaved Mixed | 32F 5BT 1MP 4B | 6.8 | 7.0 | 46 |
| P18 | Low-efficiency Forest Improvement | Coniferous and Broad-leaved Mixed | 64F 7MP | 12.0 | 12.5 | 83 |
| P19 | Construction of Soil and Water Conservation Forest | Fir | 78F 1BT | 11.1 | 7.8 | 79 |
| P20 | Tending and Management of Chinese Fir Forest | Fir | 54F 4BT | 10.5 | 6.6 | 58 |
| P21 | Low-efficiency Forest Improvement | Coniferous Forest | 51F 4MP | 11.6 | 12.6 | 91 |
| P22 | Thinning and Tending | Coniferous and Broad-leaved Mixed | 61BT 29F 5MP | 9.0 | 8.9 | 95 |
| P23 | Tending and Management of Chinese Fir Forest | Fir | 59F 2BT 2MP | 13.5 | 10.7 | 63 |
| P24 | Tending and Management of Chinese Fir Forest | Tea Oil Camellia | 254TO 2F | 5.0 | 4.1 | 247 |
| P25 | Construction of Soil and Water Conservation Fores | Fir | 43F 4BT | 11.7 | 10.5 | 47 |
| P26 | Thinning and Tending | Fir | 79F | 11.9 | 10.7 | 79 |
| P27 | Planting and Tending of Economic Forest | Tea Oil Camellia | 77TO | 8.9 | 2.3 | 77 |
| P28 | Bamboo Forest Tending and Reclamation | Bamboo Forest | 79B | 11.9 | 10.7 | 79 |
| P29 | Tending and Management of Chinese Fir Forest | Coniferous and Broad-leaved Mixed | 45F 20BT | 12.4 | 10.1 | 65 |
| P30 | Tending and Management of Coniferous and Broad-leaved Mixed Forest | Coniferous and Broad-leaved Mixed | 70F 3BT 5B | 10.1 | 8.1 | 78 |
| P31 | Bamboo Forest Tending and Reclamation | Bamboo Forest | 97B | 8.5 | 10.7 | 97 |
| P32 | Tending and Management of Coniferous and Broad-leaved Mixed Forest | Coniferous and Broad-leaved Mixed | 51F 9BT 6B | 14.0 | 11.4 | 66 |
| P33 | Tending and Management of Coniferous and Broad-leaved Mixed Forest | Coniferous and Broad-leaved Mixed | 129B 9BT 3TO | 11.2 | 14.7 | 141 |
Notes: In the table, F represents fir, MP represents Masson Pine, BT represents Broad-leaved Trees, TO represents Tea Oil Camellia, B represents, Bamboo. ATH represents Aver-age Tree Height, NOI represents Number of Individuals.
Table 3.
Data or Parameter Registration Table.
| Tree Species | Biomass Equation | RSRAF | CFTotal | Source | |
|---|---|---|---|---|---|
| Chinese Fir | AGB = 0.04363 * DBH2.54589 | (R2 = 0.98) | 0.2332 | 0.4990 | CCER [16] |
| Masson Pine | AGBDBH≧5 = 0.09949 * DBH2.40859 AGBDBH<5 = 0.14769 * DBH2.16312 | (R2 = 0.95) | 0.2053 | 0.5252 | CCER [16] |
| Broad-leaved Trees | AGB = BBleaves + BBbranches + BBtrunk | 0.2610 | 0.4718 | Lin et al. [17] | |
| BBleaves = 0.015 * (DBH2)1.059 | (R2 = 0.899) | ||||
| BBbranches = 0.008 * (DBH2)1.367 | (R2 = 0.962) | ||||
| BBtrunk = 0.108 * (DBH2)1.204 | (R2 = 0.967) | ||||
| Schima | AGB = 0.17685 * DBH2.26314 | (R2 = 0.96) | 0.2610 | 0.4718 | Lin et al. [17] |
| Bamboo | AGB = 0.1697 * DBH2.0812 | (R2 = 0.912) | 0.5110 | 0.5000 | SFA [18] |
| Camellia oleifera | B = 0.151 D2.017 | (R2 = 0.981) | 0.4835 | Z.W. et al. [19,20] | |
Notes: In the table, RSRAF represents Belowground/Aboveground Biomass, CFTotal represents Average Carbon Content.
Table 4.
Vegetation Carbon Storage Estimation Models and Accuracy Analysis.
| Model and Variables | Modeling R2 | Validation R2 | AE (t/ha) | RMSE (t/ha) | RMSEr |
|---|---|---|---|---|---|
| EXP(−27.254 + 68.498XMNDVI − 24.229XFVC-XConiferous Mixed Forest-XFir + 0.782Bamboo + 0.622Tea Oil Camellia + 10.717XB1 − 32.167XB2 − 70.246XB3 + 30.063XB4 + 77.190XB5 − 13.365XB6) | 0.808 | 0.708 | −1.907 | 10.472 | 33.25% |
Notes: In the table, AE represents Average Error, RMSE represents Root Mean Square Error, RMSEr represents Root Mean Square Error Relative.
Table 5.
Statistics of Vegetation Carbon Sink Under Different Management Measures in Ziyang Small Watershed.
Table 5.
Statistics of Vegetation Carbon Sink Under Different Management Measures in Ziyang Small Watershed.
| Management Measure | Area (ha) | CD in 2023 (t C/ha) | CS in 2023 (t C) | CD in 2002 (t C/ha) | CS in 2002 (t C) | PCVCS (t C) | BCCS (t C) | NVCS (t C/ha) |
|---|---|---|---|---|---|---|---|---|
| Thinning and Tending | 314.95 | 36.55 | 11,511.11 | 16.96 | 5340.61 | 6170.50 | 1945.76 | 4224.74 |
| Planting and Tending of Economic Forest | 75.9 | 23.05 | 1749.65 | 6.61 | 501.93 | 1247.72 | 468.91 | 778.81 |
| Construction of Soil and Water Conservation Forest | 133.74 | 28.01 | 3745.79 | 12.13 | 1622.53 | 2123.26 | 826.25 | 1297.01 |
| Low-efficiency Forest Improvement | 397.9 | 32.41 | 12,897.53 | 14.33 | 5699.92 | 7197.61 | 2458.23 | 4739.39 |
| Tending and Management of Coniferous and Broad-leaved Mixed Forest | 642.26 | 32.38 | 20,794.45 | 13.55 | 8701.98 | 12,092.47 | 3967.88 | 8124.59 |
| Tending and Management of Fir Forest | 210.31 | 38.35 | 8064.76 | 21.09 | 4436.28 | 3628.48 | 1299.30 | 2329.18 |
| Bamboo Forest Tending and Reclamation | 38.02 | 24.02 | 913.28 | 11.91 | 452.78 | 460.50 | 234.89 | 225.61 |
| Closed Restoration | 50.45 | 25.13 | 1267.76 | 9.70 | 489.16 | 778.59 | 311.68 | 466.91 |
| Regional Total | 1863.53 | 32.70 | 60,944.33 | 14.62 | 27,245.19 | 33,699.13 | 11,512.89 | 22,186.24 |
Notes: In the table, CD represents Carbon Density, CS represents Carbon Storage, PCVCS represents Projected Change in Vegetation Carbon Storage, BCCS represents Baseline Change in Carbon Storage, NVCS represents Net Vegetation Carbon Sink.
Table 6.
Statistics of Soil Carbon Sink Under Different Management Measures in Ziyang Small Watershed.
Table 6.
Statistics of Soil Carbon Sink Under Different Management Measures in Ziyang Small Watershed.
| Management Measure | Area (ha) | PCD (t C/ha) | PCS (t C) | BCD (t C/ha) | BCS (t C) | SCS (t C/ha) |
|---|---|---|---|---|---|---|
| Thinning and Tending | 314.95 | 52.26 | 16,460.14 | 38.60 | 12,156.45 | 4303.69 |
| Planting and Tending of Economic Forest | 75.90 | 58.42 | 4434.28 | 45.85 | 3480.38 | 953.91 |
| Construction of Soil and Water Conservation Forest | 133.74 | 58.06 | 7764.59 | 38.60 | 5162.10 | 2602.49 |
| Low-efficiency Forest Improvement | 397.90 | 58.41 | 23,241.48 | 38.60 | 15,358.16 | 7883.33 |
| Tending and Management of Coniferous and Broad-leaved Mixed Forest | 642.26 | 57.30 | 36,802.82 | 38.60 | 24,789.97 | 12,012.85 |
| Tending and Management of Fir Forest | 210.31 | 58.42 | 12,285.73 | 38.60 | 8117.55 | 4168.18 |
| Bamboo Forest Tending and Reclamation | 38.02 | 52.69 | 2003.09 | 32.75 | 1245.24 | 757.85 |
| Closed Restoration | 50.45 | 55.22 | 2785.96 | 29.14 | 1470.12 | |
| Regional Total | 1865.33 | 56.76 | 105,778.10 | 38.52 | 71,779.97 | 33,998.13 |
Notes: In the table, PCD represents Project Carbon Density, PCS represents Project Carbon Storage, BCD represents Baseline Carbon Density, BCS represents Baseline Carbon Storage, SCS represents Soil Carbon Sink.
Table 7.
Carbon Sink Table of Ziyang Small Watershed.
| Project | BNCS (t C) | APNCS (t C) | AGNCS (t) | BNCS (t CO2e) | APNCS (t CO2e) | AGNCS (t CO2e) |
|---|---|---|---|---|---|---|
| Vegetation | 11,512.89 | 33,699.13 | 22,186.24 | 42,213.92 | 123,563.49 | 81,349.56 |
| Soil | 0.00 | 33,998.13 | 33,998.13 | 0.00 | 124,659.81 | 124,659.81 |
| Total | 11,512.89 | 67,697.26 | 56,184.37 | 42,213.92 | 248,223.29 | 206,009.37 |
Notes: In the table, BNCS represents Baseline Net Carbon Sink, APNCS represents Actual Project Net Carbon Sink, AGNCS represents Anthropogenic Net Carbon Sink.
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Wu, Y.; Wu, J.; Kuang, S.; Zhong, X. Analysis on Carbon Sink Benefits of Comprehensive Soil and Water Conservation in the Red Soil Erosion Areas of Southern China. Forests 2025, 16, 1551. https://doi.org/10.3390/f16101551
AMA Style
Wu Y, Wu J, Kuang S, Zhong X. Analysis on Carbon Sink Benefits of Comprehensive Soil and Water Conservation in the Red Soil Erosion Areas of Southern China. Forests. 2025; 16(10):1551. https://doi.org/10.3390/f16101551
Chicago/Turabian StyleWu, Yong, Jiechen Wu, Shennan Kuang, and Xiaojian Zhong. 2025. "Analysis on Carbon Sink Benefits of Comprehensive Soil and Water Conservation in the Red Soil Erosion Areas of Southern China" Forests 16, no. 10: 1551. https://doi.org/10.3390/f16101551
APA StyleWu, Y., Wu, J., Kuang, S., & Zhong, X. (2025). Analysis on Carbon Sink Benefits of Comprehensive Soil and Water Conservation in the Red Soil Erosion Areas of Southern China. Forests, 16(10), 1551. https://doi.org/10.3390/f16101551
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