Climate and Soil Geochemistry Influence the Soil Organic Carbon Content in Drylands of the Songliao Plain,Northeast China

The understanding of the spatial distribution of soil organic carbon(SOC)and its influencing factors is crucial for comprehending the global carbon cycle.However,the impact of soil geochemical and climatic conditions on SOC remains limited,particularly in dryland farming areas.In this study,we aimed to enhance the understanding of the factors influencing the distribution of SOC in the drylands of the Songliao Plain,Northeast China.A dataset comprising 35,188 measured soil samples was used to map the SOC distribution in the region.Multiple linear regression(MLR)and random forest models(RFM)were employed to assess the importance of driving indicators for SOC.We also carried out partial correlation and path analyses to further investigate the relationship between climate and geochemistry.The SOC content in dryland soils of the Songliao Plain ranged from 0.05%to 11.63%,with a mean value of 1.47%±0.90%.There was a notable increasing trend in SOC content from the southwest to the northeast regions.The results of MLR and RFM revealed that temperature was the most critical factor,demonstrating a significant negative correlation with SOC content.Additionally,iron oxide was the most important soil geochemical indicator affecting SOC variability.Our research further suggested that climate may exert an indirect influence on SOC concentrations through its effect on geochemical properties of soil.These insights highlight the importance of considering both the direct and indirect impact of climate in predicting the SOC under future climate change.

Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model.This method uses three techniques(window offset,scaling,and rotation)to enhance the number of training data for the model.A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits.Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area.This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset,meaning that the deep-learning method can be effectively used for deposit prospecting prediction.Using soil active geochemical measurement data,this method was applied in the Daqiao area,Gansu Province,for which seven favorable gold prospecting target areas were predicted.The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3-4 g/t.In 2020,the project team drilled and verified the K prediction area,and found 66 m gold mineralized bodies.The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.

Stoichiometry of base cations and silicon during weathering of a deep soil profile derived from granite

Evaluation of the stoichiometry of base cations(BCs,including K^(+),Na^(+),Ca^(2+),and Mg^(2+))and silicon(Si)(BCs:Si)during soil mineral weathering is essential to accurately quantify soil acidification rates.The aim of this study was to explore the differences and influencing factors of BCs:Si values of different soil genetic horizons in a deep soil profile derived from granite with different extents of mineral weathering.Soil type was typic acidi-udic Argosol.Soil samples were collected from Guangzhou,China,which is located in a subtropical region.To ensure that the BCs and Si originated from the mineral weathering process,soil exchangeable BCs were washed with an elution treatment.The BCs:Si values during weathering were obtained through a simulated acid rain leaching experiment using the batch method.Results showed that soil physical,chemical,and mineralogical properties varied from the surface horizon to saprolite in the soil profile.The BCs:Si values of soil genetic horizons during weathering were 0.3–3.7.The BCs:Si value was 1.7 in the surface horizon(A),1.1–3.7 in the argillic horizon(Bt),and 0.3–0.4 in the cambic(Bw)and transition(BC)horizons,as well as in horizon C(saprolite).The general pattern of BCs:Si values in the different horizons was as follows:Bt>A>Bw,BC,and C.Although BCs:Si values were influenced by weathering intensity,they did not correlate with the chemical index of alteration(CIA).The release amounts of Si and BCs are the joined impact of soil mineral composition and physical and chemical properties.A comprehensive analysis showed that the BCs:Si values of the soil derived from granite in this study were a combined result of the following factors:soil clay,feldspar,kaolinite,organic matter,pH,and CIA.The main controlling factors of BCs:Si in soils of different parent material types require extensive research.The wide variance of BCs:Si values in the deep soil profile indicated that H+consumed by soil mineral weathering was very dissimilar in the soils with different weathering intensities derived from the same parent material.Therefore,the estimation of the soil acidification rate based on H+biogeochemistry should consider the specific BCs:Si value.