Change of Tillage Layer Thickness of Farmland in Anhui Province of Eastern China

Tillage layer thickness (TLT) of farmland could be regarded as one of physical indexes in assessing soil productivity and quality. In recent years, tillage layer shallowing was found in China in various regions, mainly due to the adoption of non-tillage or rotary tillage practices, but only little rough and non-quantitative information is available so far on the issue. This research took Anhui, a typical agricultural province in Eastern China as an example and compared the TLTs of 87 typical profiles on provincial scale and 210 on county scale from 1980s to 2010s. The results showed that TLTs of 3.7% and 17.2% of samples in 1980s and 2010s respectively were larger than 20 cm. From 1980s to 2010s the mean TLT increased from 16.3 to 17.4 cm on the provincial scale and from 15.0 to 15.5 cm on the county scale respectively. In the middle and southern regions the mean TLTs increased by 0.4-0.7 cm on the provincial scale and 0.3-3.2 cm on the county scale respectively, but decreased by 2.0 cm in northern region on the county scale. The mean TLT increased by 0.8 cm for paddy-field and 1.4 cm for dry-land on the provincial scale. TLT was influenced comprehensively by the factors of soil texture, the depth of rotary tillage and the farming positivity of the farmers. Generally, TLT of farmland with coarse soil texture was higher than that of farmland with fine soil texture, in 1980s TLT in region of poor-economic condition usually was deeper than in region of good-economic condition, and the adoption of rotary tillage led widely TLTs of farmlands to about 15 cm in 2010s.

Content, Density, Illuviation Mode and Depth of CaCO₃in Soils of Semiarid-Arid Qilian Mountains—An Altitude Sequence Study of the Hulugou Watershed

The parental material of soils in the Qilian Mountains of northwest China is mainly aeolian loess containing CaCO3 which may remain in soils under the semiarid-arid climate. To disclose the CaCO3 characteristics change with the altitude and the terrain attributes, we surveyed 18 soil profiles in an altitude sequence from 3076 m to 4510 m in the Hulugou Watershed in the Qilian Mountains, measured CaCO3 contents of all genetic horizon samples, analyzed the densities, illuviation modes and depths of CaCO3 in the profiles, extracted values of the terrain attributes of the profiles including altitude slope, aspect, plane curvature, profile curvature and terrain wetness index (TWI) from the 90 m resolution SRTM3 DEM data on ArcGIS 9.3 platform. We found that CaCO3 weighted content of the profiles ranged from 1.30 g·kg-1 to 93.09 g·kg-1, CaCO3 density from 0.05 kg/m2 to 75.69 kg/m2, CaCO3 illuviation depth from 12 cm to 54 cm. CaCO3 illuviation modes could be divided into three types, i.e., no illuviation mode in which the profile has only A horizon or CaCO3 content -1, middle illuviation mode in which CaCO3 accumulated in a middle horizon, and down illuviation mode in which CaCO3 content increases with the depth. CaCO3 weighted content, density and illuviation depth had significant correlation with certain terrain attributes. In general, the altitude sequence is an effective way to study CaCO3 characteristics in the alpine region, and the data of terrain attributes which can influence the precipitation and its redistribution in soil are potential in predicting soil CaCO3 characteristics in the alpine region.

Mapping high resolution National Soil Information Grids of China

Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.