Significant loss of soil inorganic carbon at the continental scale.

Widespread soil acidification due to atmospheric acid deposition and agricultural fertilization may greatly accelerate soil carbonate dissolution and CO2 release. However, to date, few studies have addressed these processes. Here, we use meta-analysis and nationwide-survey datasets to investigate changes in soil inorganic carbon (SIC) stocks in China. We observe an overall decrease in SIC stocks in topsoil (0-30 cm) (11.33 g C m-2 yr-1) from the 1980s to the 2010s. Total SIC stocks have decreased by ∼8.99 ± 2.24% (1.37 ± 0.37 Pg C). The average SIC losses across China (0.046 Pg C yr-1) and in cropland (0.016 Pg C yr-1) account for ∼17.6%-24.0% of the terrestrial C sink and 57.1% of the soil organic carbon sink in cropland, respectively. Nitrogen deposition and climate change have profound influences on SIC cycling. We estimate that ∼19.12%-19.47% of SIC stocks will be further lost by 2100. The consumption of SIC may offset a large portion of global efforts aimed at ecosystem carbon sequestration, which emphasizes the importance of achieving a better understanding of the indirect coupling mechanisms of nitrogen and carbon cycling and of effective countermeasures to minimize SIC loss.

A novel method for predicting cadmium concentration in rice grain using genetic algorithm and back-propagation neural network based on soil properties.

Heavy metal pollution is a global ecological safety issue, especially in crops, where it directly threatens regional ecological security and human health. In this study, the back-propagation (BP) neural network optimized by the genetic algorithm (GA) was used to predict the concentration of cadmium (Cd) in rice grain based on influencing factors. As an intelligent information processing system, the GA-BP neural network could learn the laws of Cd movement in the soil-crop system through its own training and use the soil properties to predict the concentration of Cd in grain with high accuracy. The total soil Cd concentration, clay content, Ni concentration, cation exchange capacity (CEC), organic matter (OM), and pH have important impacts and interactions on Cd concentration in rice grain were selected as input factors of the prediction model based on Pearson's correlation analysis and GeoDetector. By using GA to optimize the initial weight, the prediction accuracy of the GA-BP neural network model was optimal compared with the BP neural network model and multiple regression analysis. Based on the Cd concentration predicted in grain by the model, human exposure and health risk can be assessed quickly, enabling measures to be taken in time to reduce the transfer of Cd from soil to the food chain.

[Soil organic carbon storage and its spatial distribution in Henan Province].

By using 1: 200 000 soil database, the soil organic carbon storage in Henan Province was estimated, and its spatial distribution was analyzed. The results showed that in this province, soil organic carbon storage was about 10.27 x 10(8) t, accounting for 1.15% of the total in China, and its density was 7.46 kg m(-2) on average, being lower than that (9.60 kg m(-2)) in this country. The top four soil types in organic carbon storage were fluvo-aquic soil, cinnamon soil, skeletal soil, and yellow cinnamon soil, with the storage all being higher than 1.0 x 10(8) t and totally taken up 69.65% of that in Henan. The organic carbon density was the highest in bog soil (24.54 kg m(-2)), followed by in mountain meadow soil (17.69 kg m(-2)) and brown soil (14.64 kg m(-2)). These three soil types mainly distributed in the mountainous areas of west Henan, and the sum of their organic carbon storage was only accounted for 6.34% of the total in the province. The organic carbon density was the lowest in rocky soil (1.32 kg m(-2)) and aeolian sandy soil (1.38 kg m(-2)). In general, the density of soil organic carbon in Henan province mostly varied from 5 kg m(-2) to 10 kg m(-2).