Saturday, 15 July 2006

Spatial Modelling of Soil Organic Carbon in a Tropical Agricultural Landscape of Western Kenya.

Tom Owiyo1, Keith Shepherd2, and Stephen D. DeGloria1. (1) Cornell Univ, Dept Crop and Soil Sciences, Ithaca, NY 14853-1901, (2) World Agroforestry Centre (ICRAF), ICRAF House, PO Box 30677-00100, Nairobi, Kenya

One major problem in interpolating point estimates of soil properties is the presence of factors known to influence the distribution of the property under investigation, but for which measurements are not available for the entire landscape. In modeling Soil Organic Carbon (SOC), management related factors, like use of fertilizers and adoption of soil conservation practices, may influence the levels of SOC. In some circumstances data on such factors do not exist for the entire landscape being studied, earning them the dubious title of “nuisance factors”. A study was designed to test a procedure for controlling the effect of such factors in developing a spatial continuity model for interpolating SOC in a tropical agricultural landscape of western Kenya. Soils were sampled at nine sites, each of which had a radius of 1 km. A Y-design was established at each site and three farms were selected on each arm of the Y and one at the center, making ten farms per site. Each farm was divided into fields according to the land use history and type of enterprises. Soil samples were collected from each field for analysis of SOC, pH and particle size distribution. Mixed effects autoregression modeling was used to estimate the effect of landscape covariates and to control for this effect in the estimation of the spatial continuity function. Size of the plot (p<0.0001) and distance of field from homestead (p<0.08) were the landscape factors that had significant influence on the spatial structure of the SOC in the landscape. A variogram model was developed after removing the effect of these factors and used to interpolate SOC. An r2 of 0.55 and mean squared error (MSE) of 9.3 g kg-1were obtained from the predictions. The Prediction Accuracy (PA) was 55 percent. These results demonstrate a protocol for controlling for such “nuisance” factors.

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