Clarifying Soil Texture and Salinity Using Local Spatial Statistics (Getis-Ord Gi* and Moran’s I) in Kazakh–Uzbekistan Border Area, Central Asia

Abstract

The purpose of this paper was to study the spatial characteristics and possible influencing factors of farmland soil texture and salt content in the Syr Darya River Basin. Data on the soil grain size and salt content were collected at 56 sampling sites in the southern part of the Shardara Reservoir and the left bank of the Syr Darya River irrigation area. With the methods of local spatial statistics (Getis-Ord Gi* and Moran’s I), the hotspots of soil salinity and grain size in the study area were revealed, and along with the use of correlation analysis, the possible factors affecting soil salt distribution were discussed. Among the 56 soil sampling sites, sandy loam, loamy loam, and chalky loam accounted for 20%, 50%, and 30%, respectively, and mildly, moderately, and severely saline soils accounted for 80.36%, 14.28%, and 5.36%, respectively. There was statistically significant spatial autocorrelation between sand, silt, and clay content in the soils, but the spatial autocorrelation for salt content was weak. The results show that high and high-cluster areas (hotspots) with statistically significant salt content are mainly distributed in the northwest of the study area and that the hotspot distribution of salt content is mainly affected by topography (altitude), but the effect of soil texture on salt content is not significant. The control of soil salinity should prioritize low-altitude areas, especially in the northwestern region. The results are of great significance for the regulation and control of soil salinity and the sustainable utilization of soil in arid Central Asia.

Characterization of the water bodies of Extremadura (SW Spain)

“…SWBs were classified by size in five classes (the class classification is in Table 2) proposed by Meybeck (1995). The patterns of spatial distribution were characterized with a kernel density estimator (Lin et al, 2011;Spencer et al, 2017) and other local spatial statistics such as semivariogram modeling (Abdennour et al, 2020;Goovaerts, 2001;Juan et al, 2011;Mahmood & Batool, 2020) and Getis-Ord Gi* and Moran's I values (Liu et al, 2022).…”

Identificación de zonas con presencia de retamo espinoso (ulex europaeus), utilizando teledetección espacial, en el municipio de Sibaté [Cundinamarca]

“…El cuarto paso consistió en aplicar un análisis estadístico espacial, denominado "Optimized Hot Spot Analysis", al entorno del software ArcMap (Environmental Systems Research Institute [Esri], 2020). Esta técnica está muy bien documentada y validada, como una de las mejores para encontrar patrones espaciales, basados en la autocorrelación espacial de las entidades geográficas (Goovaerts, 2001;Liu et al, 2022).…”

Spatial and In-Depth Distribution of Soil Salinity and Heavy Metals (Pb, Zn, Cd, Ni, Cu) in Arable Irrigated Soils in Southern Kazakhstan

“…Crop production in the Syr Darya river basin is essential for the economy and almost entirely depends on irrigation [8][9][10]. Therefore, this area provides many examples of the adverse effects of irrigation, such as the formation of water-logged and saline soils along unlined canals or the formation of spotty saline fields, due to the lack of proper drainage installations for the evacuation of saline subsoil water.…”

“…These types of soil degradation can be observed, e.g., in the irrigation zone of the Arys-Turkestan canal, which irrigates approximately 70,000 ha in southern Kazakhstan. Apart from irrigation-related factors, such as the quality of groundwaters and salt content in soils, a range of heavy metals is also affected by geochemistry and geology, relief, eolian dust, wetting of the soil profile, and rainfall infiltration [8,[11][12][13][14][15][16].…”