Vegetation restoration in the cropland has been widely implemented to protect the ecological environment of the Loess Plateau, China. However, a quantitative and comprehensive understanding of the changes in soil inorganic carbon (SIC) after vegetation restoration is lacking. Based on 637 pieces of data from 35 studies on the Loess Plateau, we performed a meta-analysis to quantify the variations in SIC after vegetation restoration in the cropland and analyze the influences of environment factors on the variations in SIC. Overall, SIC significantly increased by 6.73% after vegetation restoration. The conversions of cropland to broadleaf forestland, coniferous forestland and grassland significantly increased the SIC contents by 3.16, 14.77 and 6.32%, respectively. The response ratio of SIC (RR-SIC) to vegetation restoration was positively related with restoration age, the response ratio of soil organic carbon and the soil pH respectively, but was significantly negatively correlated with mean annual precipitation and mean annual temperature. The results demonstrated that vegetation restoration in cropland has a high potential to SIC sequestration on the Loess Plateau. The relationship of RR-SIC with environmental factors indicated that the production of pedogenic inorganic carbon plays a crucial role in SIC sequestration after vegetation restoration in the cropland on the Loess Plateau. Our findings highlight that SIC is as vital to soil carbon sequestration as soil organic carbon after vegetation restoration, and SIC should not be neglected for assessing carbon sequestration capacity of vegetation restoration on the Loess Plateau.

Changes in soil inorganic carbon following vegetation restoration in the cropland on the Loess Plateau in China: A meta-analysis

Accurately mapping soil organic carbon (SOC) is conducive to evaluating carbon storage and soil quality. However, the high spatial heterogeneity of SOC caused by river-related factors and agricultural management brings challenges to digital soil mapping in floodplain farmland. Moreover, current studies focus on the non-linear relationship between SOC and covariates, but ignore the effective range of environmental variables on SOC, which prevents the revelation of the SOC differentiation mechanism. Using the 375 samples collected from the Jiangchang Town near Han River, we aim to determine the main controlling factors of SOC, reveal the effective range of environmental variables, and obtain the spatial map of SOC by using the gradient boosting decision tree (GBDT) model and partial dependence plots. Linear regression was used as a reference. Results showed that GBDT outperformed linear regression. GBDT results show that the distance from the river was the most important SOC factor, confirming the importance of the Han River to the SOC pattern. The partial dependence plots indicate that all environmental variables have their effective ranges, and when their values are extremely high or low, they do not respond to changes in SOC. Specifically, the influential ranges of rivers, irrigation canals, and rural settlements on SOC were within 4000, 200, and 50 m, respectively. The peak SOC was obtained with high clay (≥31%), total nitrogen (≥1.18 g/kg), and total potassium contents (≥11.1 g/kg), but it remained steady when these covariates further increased. These results highlight the importance of revealing the effective range of environmental variables, which provides data support for understanding the spatial pattern of SOC in floodplain farmland, achieving carbon sequestration in farmland and precision agriculture. The GBDT with the partial dependence plot was effective in SOC fitting and mapping.

/pdf/mapping-soil-organic-carbon-in-floodplain-farmland-2751t2pk.pdf

Mapping Soil Organic Carbon in Floodplain Farmland: Implications of Effective Range of Environmental Variables

Soil pH is crucial for the quality of the farmland and crop growth. The objective of this study is to analyze the spatial and temporal variations of farmland soil pH in Xinjiang (XJ), and to provide a scientific basis for soil improvement and agricultural production optimization. Based on soil pH data from XJ farmland in two periods, 2008~2010 and 2019~2021, geostatistical methods and kriging spatial interpolation techniques were employed to analyze the spatiotemporal changes in soil pH and to investigate the relationship between relevant influencing factors and pH over more than a decade. The results indicate that the spatiotemporal distribution of soil pH in XJ farmlands is uneven. Between 2019 and 2021, the average soil pH was 8.11, generally being on the higher side, with a coefficient of variation (CV) of 3.85%, indicating minimal spatial variability. In the farmland soil of Southern Xinjiang (S-XJ), the average pH value was 8.17, significantly higher than that of Northern Xinjiang (N-XJ), which was 8.10, demonstrating a spatial pattern of “higher in the south, lower in the north”. Over the past decade, soil pH in XJ has significantly increased from 8.11 to 8.13, with a 0.05 unit increase in the north and a 0.01 unit increase in the south (p &lt; 0.05). Regionally, Altay saw the largest increase of 0.22 units, while Bortala Mongol Autonomous Prefecture (Bortala) experienced the most significant decrease, dropping by 0.59 units. Furthermore, this study found that factors such as topography, nutrients, and irrigation methods all have certain influences on the spatial distribution of soil pH in XJ farmland, while variations in climate factors and fertilization levels may affect its long-term temporal changes. These research findings will provide new insights for adjusting and updating agricultural management measures related to soil pH regulation in XJ. 

Spatial and Temporal Variations of Soil pH in Farmland in Xinjiang, China over the Past Decade

Graphene oxide (GO) was prepared by a waterless synthesis route to generate GO sheets, which were then applied to coat calcined oyster shell with fertilizer (OSF) pellets, resulting in the creation of an OSF-GO particle. The GO sheets (ID/IG = 0.86) were characterized by Raman spectroscopy, which showed that the GO-coated OSF pellet features a compact coating approximately 13.68 μm thick. SEM and AFM analyses revealed that the GO sheets displayed a monolayer configuration with a crinkled topography (about 0.91 nm). The EDS analysis confirmed that the core was primarily composed of Ca, K, P, O, N, and C elements. The hydroponic experiment results showed that a GO concentration of 80 mg/L significantly enhanced plant height, stem thickness, and root length in loose-leaf lettuce, while higher concentrations induced oxidative stress. In pot experiments, the OSF-GO composite effectively raised the soil pH from 5.38 to 6.41 and improved nutrient availability. OSF-GO composite functions effectively as both a soil conditioner and slow-release fertilizer (SRF), simultaneously remediating degraded soils and optimizing nutrient delivery. 

Waste Oyster Shell/Graphene Oxide Composite as a Dual-Functional Soil Conditioner and SRF: Impacts on Soil pH and Nutrient Availability

Soil pH is an essential indicator for assessing soil quality and soil health. In this study, based on the Chinese farmland soil survey dataset and meteorological dataset, the spatial distribution characteristics of soil pH in coastal eastern China were analyzed using kriging interpolation. The relationships between hydrothermal conditions and soil pH were explored using regression analysis with mean annual precipitation (MAP), mean annual temperature (MAT), the ratio of precipitation to temperature (P/T), and the product of precipitation and temperature (P*T) as the main explanatory variables. Based on this, a model that can rapidly estimate soil pH was established. The results showed that: (a) The spatial heterogeneity of soil pH in coastal eastern China was obvious, with the values gradually decreasing from north to south, ranging from 4.5 to 8.5; (b) soil pH was significantly correlated with all explanatory variables at the 0.01 level. In general, MAP was the main factor affecting soil pH (r = −0.7244), followed by P/T (r = −0.6007). In the regions with MAP < 800 mm, soil pH was negatively correlated with MAP (r = −0.4631) and P/T (r = −0.7041), respectively, and positively correlated with MAT (r = 0.6093) and P*T (r = 0.3951), respectively. In the regions with MAP > 800 mm, soil pH was negatively correlated with MAP (r = −0.6651), MAT (r = −0.5047), P/T (r = −0.3268), and P*T (r = −0.5808), respectively. (c) The estimation model of soil pH was: y = 23.4572 − 6.3930 × lgMAP + 0.1312 × MAT. It has been verified to have a high accuracy (r = 0.7743, p < 0.01). The mean error, the mean absolute error, and the root mean square error were 0.0450, 0.5300, and 0.7193, respectively. It provides a new path for rapid estimation of the regional soil pH, which is important for improving the management of agricultural production and slowing down soil degradation.

https://www.mdpi.com/1660-4601/19/24/16855/pdf?version=1671456717

Spatial Distribution and Estimation Model of Soil pH in Coastal Eastern China

Soil organic carbon (SOC) plays a key role in soil improvement, carbon sequestration, and increasing crop yield. In this study, the distribution characteristics and the influence of hydrothermal conditions on farmland SOC content in eastern China were studied. The results showed that the spatial heterogeneity of SOC content in eastern China was obvious. In the area with the mean average temperature (MAT) below 10.42 °C, the SOC content was negatively correlated with MAT and ≥10 °C accumulated temperature, but positively correlated with the ratio of precipitation to temperature (P/T). In the area with the MAT between 10.42 °C and 20.75 °C, the SOC content was negatively correlated with mean average precipitation (MAP), MAT, P/T and ≥10 °C accumulated temperature. In the area with the MAT above 20.75 °C, the SOC content was negatively correlated with MAT and ≥10 °C accumulated temperature, but positively correlated with MAP and P/T. In the area with the MAP below 400 mm, the SOC content was negatively correlated with P/T, but positively correlated with MAP, MAT and ≥10 °C accumulated temperature. In the area with the MAP between 400 mm and 800 mm, the SOC content was negatively correlated with P/T, but positively correlated with MAT and ≥10 °C accumulated temperature. In the area with the MAP more than 800 mm, the SOC content was negatively correlated with MAP, MAT, P/T and ≥10 °C accumulated temperature. Based on the above results, a model for predicting SOC content was established. This is of great significance for the rapid estimation of SOC content on a regional large scale.

https://iopscience.iop.org/article/10.1088/2515-7620/ac71a2/pdf

Distribution characteristics and prediction model of farmland soil organic carbon in eastern China

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Spatial Distribution and Estimation Model of Soil pH in Coastal Eastern China

Distribution characteristics and prediction model of farmland soil organic carbon in eastern China