Obsi, Dechassa (2008) Application of Spatial Modeling to the Study of Soil Fertility Pattern. Masters thesis, Addis Ababa University.
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Abstract
Spatial statistical analysis was undertaken to study the variability of potato yield data due to soil fertility pattern. Seed potato yield were measured from field uniformity trial conducted at Hollota and Kulumsa agricultural research centers in year 2001 on an area of 0.15 hectare at each site. The harvested area was divided in to basic units of 1.2x1.5 m and a total of 1658 and 931 plots were considered from Hollota and Kulumsa respectively. The basic units were combined in different plot sizes to acquire the required plot dimension. In this study, the Monte Carlo test for completely spatial randomness is applied and the result shows no complete spatial randomness detected in the series of potato yield data for both sites. Thus, to set a model adjusted for spatial pattern, Moran’s index and Geary’s coefficient were applied to test for global and local spatial autocorrelation respectively. The result shows positive spatial autocorrelation detected among potato yield data using Rook’s weighted neighboring plot relations. The result also shows, increasing plot size will not generally make the observed spatial autocorrelation insignificant. An autoregressive model, that is adjusted for presence of spatial autocorrelation in simulated plot size of 12m2 is fitted for row and column effect for each site. The result shows significant positive association between neighboring plots row effect and the adjusted potato yield. In addition, the result from comparison of model adjusted for presence of spatial autocorrelation and conventional OLS based analysis of variance shows the autocorrelation parameter accounts significant percent of variation among potato yield in both sites. The classical and robust variogram models were used to produce a map of predicted potato yield data, taking in to account plot variation from the model prediction for Kulumsa agricultural research center. The result shows the predicted values at each of the grid locations do not differ greatly for the two variogram models. However, in the comparison of Gaussian and Spherical models the standard error of kriged prediction for the spherical model is subsequently larger than the Gaussian model.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | H Social Sciences > HA Statistics Q Science > QE Geology S Agriculture > S Agriculture (General) |
| Divisions: | Africana |
| Depositing User: | Selom Ghislain |
| Date Deposited: | 02 Oct 2018 12:12 |
| Last Modified: | 02 Oct 2018 12:12 |
| URI: | http://thesisbank.jhia.ac.ke/id/eprint/5789 |
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