English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Seventy-two infiltration tests were conducted to model soil water infiltration variability using scaling in the first and third terraces of Yangling District, respectively. The relationship between the scaling factor of the Kostiakov–Lewis equation and soil properties for multiple scales was determined through two-dimensional continuous wavelet transform (2D-CWT) and path analyses, and the pedotransfer functions (PTFs) based on the support vector machine (SVM) and back propagation (BP) neural network were developed for estimating the scaling factor. The results indicated that when scaling factor was calculated using the least square method ( F S), the best match was achieved between the predicted infiltration and measured values. In the first and third terraces, the results of 2D-CWT and path analyses indicated that  F Swere positively correlated with sand content, silt content, and soil organic matter content. However,  F Swas negatively correlated with bulk density in the first terrace, and which was negatively correlated with clay content and initial water content in the third terrace. The accuracy of SVM-based PTF is higher than BP-based PTF, the mean absolute value of relative errors of SVM-based PTF were 11.3% and 8.16% for the first and third terrace, respectively. Therefore, the SVM is preferred for PTF development.

Scaling of Kostiakov–Lewis equation and estimation of scaling factors at field scale