Open AccessIdentifying the Main Factors Contributing to the Spatial Variability of Soil Saline–Sodic Properties in a Reclaimed Coastal Area
Author(s) -
Fei Yuanhang,
She Dongli,
Fang Kai
Publication year - 2018
Publication title -
vadose zone journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.036
H-Index - 81
ISSN - 1539-1663
DOI - 10.2136/vzj2018.06.0118
Subject(s) - transect , land reclamation , environmental science , spatial variability , soil salinity , sodium adsorption ratio , sampling (signal processing) , soil science , hydrology (agriculture) , soil test , soil water , geography , geology , ecology , mathematics , irrigation , statistics , geotechnical engineering , oceanography , archaeology , filter (signal processing) , drip irrigation , biology , computer science , computer vision
Core Ideas A spatial model was used for identifying the main factors affecting spatial variability of soil salt. Land‐use pattern was the most influential factor that affected spatial variation of soil salt. The generalized least squares model was suitable for soil salt content in Transect 2. This study is helpful for agronomic management practices in coastal reclamation areas. Studies on changes in soil saline–sodic characteristics have important reference value for reclamation planning and agricultural utilization of coastal regions. The objective of this study was to investigate the spatial distribution of the soil salt content (SSC) and sodium adsorption ratio (SAR) in two transects, which extended through different reclamation areas along a reclaimed coastal area of China. A total of 54 sampling points along Transect 1 were collected to study soil saline–sodic characteristics of a recently reclaimed farmland in 2007, and 50 sampling points were collected along Transect 2 that ran through six farmland areas reclaimed at different times (2007, 1981, 1960, 1950, 1940, and 1916). A spatial model, generalized least squares (GLS), was used to conduct multiple regression equations on SSC or SAR and explanatory variables. The results showed that land‐use pattern was the most influential factor that affected spatial variation of SSC and SAR. The parametric Levene's test and Moran's I test showed that heterogeneity and autocorrelated residuals could only be found for SSC in Transect 2, so the GLS model was suitable for SSC in Transect 2. The GLS method did not perform better than the nonspatial model (ordinary least squares) by incorporating exhaustive ancillary variables but was more suitable when autocorrelation and heteroscedasticity existed in the errors of a model. Using these methods, we obtained information on the spatial variation of soil saline–sodic characteristics and its controlling factors. This information is helpful for land‐use planning, soil partition management, and agronomic management practices in coastal reclamation areas.