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Modelling spatial variability of soil nitrogen using geostatistical methods and remote sensing

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dc.contributor.author Tarwireyi, Fadzisayi
dc.date.accessioned 2018-01-08T09:44:01Z
dc.date.available 2018-01-08T09:44:01Z
dc.date.issued 2016-06
dc.identifier.citation Tarwireyi, F. (2016). Modelling spatial variability of soil nitrogen using geostatistical methods and remote sensing. Harare: University of Zimbabwe. en_US
dc.identifier.uri http://hdl.handle.net/10646/3466
dc.description.abstract The aim of this study was to explore the use of geostatistical approaches involving remote sensing data to model and map soil nitrogen variability for use in precision agriculture. Precision agriculture is a management strategy aimed at reducing the costs of nitrogen fertilizers by matching site-specific nutrient applications with crop requirements and soil properties as both vary across a field. The study was conducted on a 4-hectare plot at the University of Zimbabwe farm in Zimbabwe. Three prediction models were applied to estimate soil nitrogen variability within the plots and these included; (1) Ordinary kriging with sample nitrogen data only, (2) Ordinary co-kriging with sample nitrogen and remotely sensed data as covariate data, and (3) spatial regression with remotely sensed data only. Landsat 8 was used as a source of remotely sensed data. Results showed that the Coastal/Aerosol, Blue and NIR spectral bands were highly correlated to soil nitrogen, and produced good prediction models in co-kriging with R2 values of 0.8593, 0.8606 and 0.8596 respectively. Ordinary kriging with nitrogen sample data alone also yielded similar results with R2 value of 0.8597. Finally, spatial regression analysis (SAR lag model) using the same spectral bands yielded R2 values of 0.527, 0.517 and 0.545 respectively while the other bands like Red, SWIR-1 and SWIR-2, and indices like NDVI, RVI and SAVI yielded values below 0.5. Comparison of the three prediction models indicated that there was no significant difference in the mean prediction values of the models. Thus, the results suggest that remotely sensed data can successfully be used alone and in combination with field sample data to model and map soil nitrogen variability in soils. en_US
dc.language.iso en_ZW en_US
dc.subject Soil nitrogen variability en_US
dc.subject Remote sensing en_US
dc.subject Precision agriculture en_US
dc.subject Geostatistical approaches en_US
dc.title Modelling spatial variability of soil nitrogen using geostatistical methods and remote sensing en_US
dc.contributor.registrationnumber R945105Q en_US
thesis.degree.advisor Murwira, Amon
thesis.degree.country Zimbabwe en_US
thesis.degree.discipline Geography en_US
thesis.degree.faculty Faculty of Science en_US
thesis.degree.grantor University of Zimbabwe en_US
thesis.degree.grantoremail specialcol@uzlib.uz.ac.zw
thesis.degree.level MSc en_US
thesis.degree.name Master of Science degree in Geographic Information Science and Remote Sensing en_US
thesis.degree.thesistype Thesis en_US
dc.date.defense 2016-06


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