Distribution Patterns and Influencing Factors Controlling Soil Carbon in the Heihe River Source Basin, Northeast Qinghai–Tibet Plateau

Land, Journal Year: 2025, Volume and Issue: 14(2), P. 409 - 409

Published: Feb. 16, 2025

Soil organic carbon (SOC) and soil inorganic (SIC) are key components of pools in arid ecosystems, playing a crucial role regional cycling climate change mitigation. However, the interactions between these two forms alpine ecosystems remain underexplored. This study was conducted Heihe River Basin (HRB) northeastern Qinghai–Tibet Plateau, focusing on distribution dynamics SOC SIC deep layers. Using data from 329 samples collected 49 profiles extending to bedrock, combined with path analysis, we explored inter-relationships quantified influence environmental factors. The results showed that (1) exhibited unimodal elevation, peaking at 3300–3600 m, while continuously decreased reduction rates ranging −0.39% −31.18%; (2) were significantly positively correlated (r = 0.55, p < 0.01), decreasing depth showing an inflection point 50 cm depth; (3) primarily driven by nutrient factors, such as total nitrogen (TN), coefficient 0.988, influenced abiotic including potential evapotranspiration (PET), −1.987; (4) density accounted for 81.62% pool, dominant storage, whereas dynamic changes, particularly depths 110–150 cm. These findings advance our understanding provide critical improving management strategies similar regions.

Language: Английский

---

Mapping sub-surface distribution of soil organic carbon stocks in South Africa's arid and semi-arid landscapes: Implications for land management and climate change mitigation

Geoderma Regional, Journal Year: 2024, Volume and Issue: 37, P. e00817 - e00817

Published: May 23, 2024

Soil organic carbon (SOC) stocks are critical for land management strategies and climate change mitigation. However, understanding SOC distribution in South Africa's arid semi-arid regions remains a challenge due to data limitations, the complex spatial sub-surface variability driven by desertification degradation. Thus, support soil land-use practices as well advance mitigation efforts, there is an urgent need provide more precise stock estimates within regions. Hence, this study adopted remote-sensing approaches determine of influence environmental co-variates at four depths (i.e., 0-30 cm, 30-60 60-100 100-200 cm). Using two regression-based algorithms, i.e., Extreme Gradient Boosting (XGBoost) Random Forest (RF), found former (RMSE values ranging from 7.12 t/ha 29.55 t/ha) be superior predictor comparison latter 7.36 31.10 t/ha). Nonetheless, both models achieved satisfactory accuracy (R

Language: Английский

---

Machine learning-driven modeling for soil organic carbon estimation from multispectral drone imaging: a case study in Corvera, Murcia (Spain)

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(3), P. 3473 - 3494

Published: March 6, 2024

Language: Английский

---

Spatial variations of organic matter concentration in cultivated land topsoil in North China based on updated soil databases

Soil and Tillage Research, Journal Year: 2025, Volume and Issue: 248, P. 106445 - 106445

Published: Jan. 8, 2025

Language: Английский

---

Spatial and Temporal Variations in Soil Organic Carbon in Northwestern China via Comparisons of Different Methods

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 420 - 420

Published: Jan. 26, 2025

Soil organic carbon (SOC) is a crucial component for investigating cycling and global climate change. Accurate data exhibiting the temporal spatial distributions of SOC are very important determining soil sequestration potential formulating strategies. An scheme mapping to establish link between environmental factors via different methods. The Shiyang River Basin third largest inland river basin in Hexi Corridor, which has closed geographical conditions relatively independent cycle system, making it an ideal area research arid areas. In this study, 65 samples were collected 21 assessed from 2011 2021 Basin. linear regression (LR) method two machine learning methods, i.e., support vector (SVR) random forest (RF), applied estimate distribution SOC. RF slightly better than SVR because its advantages comparison classification. When latitude, slope, normalized vegetation index (NDVI) used as predictor variables, best performance shown. Compared with Harmonized World Database (HWSD), optimal improved accuracy significantly. Finally, tended increase, total increase 135.94 g/kg across whole basin. northwestern part middle decreased by 2.82% industrial activities. Minqin County increased approximately 62.77% 2021. Thus, variability increased. This study provides theoretical basis basins. addition, can also provide effective scientific suggestions projects, offer key understanding cycle, change adaptation mitigation

Language: Английский

---

Regenerative agriculture amplifies productivity and profitability while negating greenhouse gas emissions

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 28, 2025

The broad philosophy comprising regenerative agriculture can be deconstructed into several underpinning components, including adaptive multi-paddock grazing (AMP), improved biodiversity, silvopasture, and minimal use of cultivation synthetic fertilisers. Here, we sheep farms positioned across a rainfall gradient to examine how pasture species diversity, antecedent SOC AMP influence soil organic carbon (SOC) accrual, greenhouse gas (GHG) emissions, production enterprise profit. Compared with light intensities for long durations, high-intensity short-duration cell spelling periods (AMP) amplified productivity, improving accrual GHG abatement, increasing profit per animal hectare. Renovation pastures high-yielding, low-emissions ecotypes enhanced removals, albeit lesser extent than that realised from AMP. Adaptive management, where animals were moved in response residual, evoked the greatest but also increased supplementary feed costs. Low stocking rates longer between events most profitable, highlighting need agile, proactive management adapted line seasonal conditions. We conclude (1) whole farm rate quantum have greater on production, SOC, compared diversity (2) individual – rather diversity bearing sward (3) notwithstanding removals via CH₄ enteric fermentation dominates profiles, (4), catalyse lighter conducted only when is harmonised long-term sustainable carrying capacity, latter being function plant-available water capacity drought frequency.

Language: Английский

---

High-Resolution Mapping of Cropland Soil Organic Carbon in Northern China

Agronomy, Journal Year: 2025, Volume and Issue: 15(2), P. 359 - 359

Published: Jan. 30, 2025

Mapping the high-precision spatiotemporal dynamics of soil organic carbon (SOC) in croplands is crucial for enhancing fertility and sequestration ensuring food security. We conducted field surveys collected 1121 samples from cropland Changzhi, northern China, 2010 2020. Random Forest (RF) models combined with 19 environmental covariates were used to map topsoil (0–20 cm) SOC 2020, uncertainty maps calculate dynamic changes between Finally, RF Structural Equation Modeling (SEM) employed explore effects climate, vegetation, topography, properties, agricultural management on variation croplands. Compared prediction model using only natural variables (RF_C), incorporating (RF_A) significantly improved simulation accuracy SOC. The coefficient determination (R2) increased 0.77 0.85, while Root Mean Square Error (RMSE) decreased 1.74 1.53 g kg−1, Absolute (MAE) was reduced 1.10 0.94 kg−1. our predictions low, an average value 0.39–0.66 From Changzhi exhibited overall increasing trend, increase 1.57 Climate change, management, properties strongly influence variation. annual precipitation (MAP), drainage condition (DC), net primary productivity (NPP) drivers variability. Our findings highlight effectiveness predicting Overall, study confirms that has great potential stocks, which may contribute sustainable development.

Language: Английский

---

A prediction model of soil organic carbon into river and its driving mechanism in red soil region

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 10, 2025

Soil erosion contributes to the irreversible loss of soil organic carbon (SOC) into rivers (SOCR), posing risks food security and cycle assessments. Red regions, characterized by high sink potential selenium enrichment, are particularly vulnerable. However, existing studies largely rely on small-scale experiments, with limited understanding basin-scale SOCR dynamics their driving factors. This study integrates Water Assessment Tool (SWAT) for sediment yield simulation a Organic Carbon Content (SOCC) model quantify at basin scale. A Random Forest-based prediction was developed explore spatial-temporal variability mechanisms in Dongjiang River Basin (DRB), representative red region southern China. Results indicate significant heterogeneity, higher observed downstream, human-disturbed areas during flood seasons. The demonstrates excellent performance (R²>0.9). Key drivers include yield, cultivated land area (CULT), urban (TOWN), urbanization showing stronger sensitivity than cultivation due factors such as city size impervious surfaces. proposed framework reveals dynamic change characteristics its mechanism, which has be generalized other basins similar studies, provides technical support resource management cycling erosion-prone region.

Language: Английский

---

Spatial distribution, drivers and future trends of soil organic carbon in cropland of China

Soil Science Society of America Journal, Journal Year: 2025, Volume and Issue: 89(2)

Published: March 1, 2025

Abstract Soil organic carbon (SOC) pool of cropland is one the most active parts global C pool. Hence, it important to estimate SOC stock, drivers, and future evolutionary trends in order improve sequestration emission reduction capacity soil stability food production. In this study, we utilized 856 samples for density (SOCD) at a depth 0–20 cm 544 SCOD 0–100 cm. Using five machine learning models combined with environmental factors data, predicted spatial distribution, key China's croplands. The results were as follows: (1) mean values SOCD 2.98 7.88 kg m −2 , respectively, stocks 5.64 14.91 Pg, which accounted 15.78% 17.25% terrestrial ecosystems, respectively. (2) physicochemical properties consistently explained more variation uniquely than other factors, explaining 50% 43% was mainly driven by nitrogen deposition human impacts; pH, normalized difference vegetation index, annual precipitation, temperature. (3) Under Shared Socioeconomic Pathway 5–8.5 (high‐C emissions), greatest decline trend two‐depth stock. Our study understanding changes enhancing implement mitigation adaptation strategies.

Language: Английский

---

Machine learning-based estimation of soil organic carbon in Thailand’s cash crops using multispectral and SAR data fusion combined with environmental variables

Geo-spatial Information Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 23

Published: April 4, 2025

Language: Английский

---