Comprehensive Evaluation on Soil Properties and Artemisia ordosica Growth under Combined Application of Fly Ash and Polyacrylamide in North China Abstract: A field experiment was conducted to investigate the combined application effects of fly ash (FA) (0, 5%, 10%, and 15% (ww) soil) and polyacrylamide (PAM) (0, 0.006% and 0.012% (ww) soil) on the edge of Hobq Desert in Inner Mongolia, China from May 2016 to October 2018. Seven different ratios of FA and PAM were selected as evaluation objects, a total of 14 soil property indices and 9 Artemisia ordosica growth indices were selected as evaluation indicators, and the entropy weight method was employed to evaluate the soil physicochemical properties and vegetation growth performances under FA and PAM amendments. The results showed that the F15P1 (15% FA  0.006% PAM) and F5P1 (5% FA  0.006% PAM) were the effective treatments for soil improvement and Artemisia ordosica growth respectively. Considering the soil properties and Artemisia ordosica growth in 2016xe2x80x932018 synthetically, the highest score was observed in the F5P1, followed by the F5P2 (5% FA  0.012% PAM) and F10P1 (10% FA  0.006% PAM) treatments. The optimal amounts for FA and PAM should be recommended as 5% and 0.006%, respectively.

Model prediction of depthspecific soil texture distributions with artificial neural network: A case study in Yunfu, a typical area of Udults Zone, South China Abstract   The depthspecific soil texture map with highresolution (i.e. xe2x89xa410xc2xa0m) is essential for soil management and forest silviculture. The objective of this research was to develop a modelling method to generate highresolution soil texture maps at five depths (D1: 0xe2x80x9320, D2: 20xe2x80x9340, D3: 40xe2x80x9360, D4: 60xe2x80x9380, and D5: 80xe2x80x93100xc2xa0cm) in Yunfu, a typical area of Udults Zone, South China. Taking a coarseresolution soil texture (CST) map with a 1: 2,800,000 scale and nine topohydrologic variables derived from a digital elevation model (DEM) with 10xc2xa0mresolution as input candidates, a series of artificial neural network (ANN) models for five depths were built and evaluated by a 10fold crossvalidation with 385 soil profiles from the Yunfu forest. The results indicated that the optimal model for five depths engaged five, five, five, four, and four DEMgenerated variables as inputs, respectively, and model accuracies for estimating sand and clay contents varied with root mean squared error (RMSE) of 6.8xe2x80x939.7%, R2 of 0.56xe2x80x930.72, and relative overall accuracy (ROA)xc2xa0xc2xb1xc2xa05% of 54xe2x80x9381%, which were better than most of other researches. An extra independent validation with 64 soil profiles outside of the modelbuilding area also indicated that the optimal models had adequate capabilities for generalization with RMSE of 9.2xe2x80x9312.2%, R2 of 0.33xe2x80x930.47, and ROAxc2xa0xc2xb1xc2xa05% of 37xe2x80x9353%. The depthspecific sand and clay content maps with 10xc2xa0mresolution generated from the optimal models in Yunfu showed more detailed information than the CST map, and could reflected the influence of the DEMderived topohydrologic variables. Based on the generated maps, horizontal characteristics of soil texture in the study area exhibited an obvious process of clay translocation from the topsoil (D1) to subsoil (D25), a maximum accumulation of clay in D4, and a dominant sandy soil in the topsoil (D1). Thus, the modelling method, i.e. developing ANNs with kfold crossvalidation, can be used to generate depthspecific soil texture maps in Udults Zone, South China. In addition, the generated highresolution maps can clearly show the changes of soil texture in threedimension.