Spatial Variability of Soil Aggregate Stability in a Disturbed River Watershed

Zachary Gichuru Mainuri and James Odhiambo. Owino
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Zachary Gichuru Mainuri: Egerton University, Department of Crops, Horticulture and Soils, Egerton, Kenya.
Authors registered in the RePEc Author Service: Vania Petrova Ivanova

European Journal of Economics and Business Studies Articles, 2017, vol. 3

Abstract: Analysis of spatial distribution of soil properties like soil aggregate stability presents an important outset for precision agriculture. The study area was classified into different landscape units according to physiographic features namely: mountains, plateaus, uplands, valleys, pen plains, alluvial plains, lacustrine plains and hills and maps were drawn. The objectives of this study were to evaluate the effects of landscape and land use interaction on the spatial variability of aggregate stability. The variability of aggregate stability exhibited spatial dependence (SDP) which helped in the generation of a spatial dependence index (SDI) that was described using semivariogram models. SPD Gaussian( percent) ≤ 25 percent gave a weak spatial dependence, moderate spatial dependence was given by 25 percent ( SDP ( percent ) ≤ 75 percent and strong spatial dependence by SDP ( percent) ) 75 percent, while SDI Gaussian ( percent) ≤ 25 percent gave a strong spatial dependence index while moderate spatial dependence index was indicated by 25 percent ( SDI ( percent) ≤ 75 percent, and weak spatial dependence index SDI ( percent ) ) 75 percent. Mean Weight Diameters (MWD) of 0.25 - 0.45 represented unstable soils mostly found in wetlands occurring in valleys, mountains, plains, and depressions in hills, 0 55 -0.62 represented moderately stable soils mostly in agricultural and grassland areas which include plateaus, uplands, and plains, while 0.62 - 0.92 represented stable and very stable soils being found in forested areas, mountains and hills. Various interpolation (kriging) techniques capitalized on the spatial correlation between observations to predict attribute values at unsampled locations using information related to one or several attributes that helped in the construction of an aggregate stability prediction map using Empirical Bayesian kriging (EBK) technique.

Keywords: land scape; Spatial Variability; semivariogram; Geostatitics; aggregate stability; kriging 1. (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eur:ejesjr:179

DOI: 10.26417/ejes.v9i1.p278-290

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