Regional Differences in the Effect of Climate and Soil Texture on Soil Organic Carbon

The agricultural soil carbon pool plays an important role in mitigating greenhouse gas emission and understanding the soil organic carbon-climate-soil texture relationship is of great significance for estimating cropland soil carbon pool responses to climate change.Using data from 900 soil profiles,obtained from the Second National Soil Survey of China,we investigated the soil organic carbon(SOC)depth distribution in relation to climate and soil texture under various climate regimes of the cold northeast region(NER)and the warmer Huang-Huai-Hai region(HHHR)of China.The results demonstrated that the SOC content was higher in NER than in HHHR.For both regions,the SOC content at all soil depths had significant negative relationships with mean annual temperature(MAT),but was related to mean annual precipitation(MAP)just at the surface 0–20 cm.The climate efect on SOC content was more pronounced in NER than in HHHR.Regional diferences in the efect of soil texture on SOC content were not found.However,the dominant texture factors were diferent.The efect of sand content on SOC was more pronounced than that of clay content in NER.Conversely,the efect of clay on SOC was more pronounced than sand in HHHR.Climate and soil texture jointly explained the greatest SOC variability of 49.0%(0–20 cm)and 33.5%(20–30 cm)in NER and HHHR,respectively.Moreover,regional diferences occurred in the importance of climate vs.soil texture in explaining SOC variability.In NER,the SOC content of the shallow layers(0–30 cm)was mainly determined by climate factor,specifically MAT,but the SOC content of the deeper soil layers(30–100 cm)was more afected by texture factor,specifically sand content.In HHHR,all the SOC variability in all soil layers was predominantly best explained by clay content.Therefore,when temperature was colder,the climate efect became stronger and this trend was restricted by soil depth.The regional diferences and soil depth influence underscored the importance of explicitly considering them in modeling long-term so