dc.contributor.author | Karongo, John | |
dc.date.accessioned | 2023-01-25T09:32:35Z | |
dc.date.available | 2023-01-25T09:32:35Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | http://erepository.uonbi.ac.ke/handle/11295/162073 | |
dc.description.abstract | Linear mixed-effects models (LME) include both fixed-effects and random-effects variables. The
use of LME is powerful in analyzing repeated measurements, longitudinal data or unbalanced data
and the variations between and within-subject observations can be captured by random effects. In
this study, remote sensing data were used to understand sorghum yield variability in a context of
low input low output extensive farming system such in South Sudan. LME modelling approach
helped understanding the sorghum yield variation between the two states of interest in this study
and between two different agricultural seasons.
The unbalanced nature, the repeated measures on same statistical units for remotely derived parameters
and the longitudinal nature of the data dictated the choice and the appropriateness of
Linear Mixed-Effects models (LME) for statistical analysis in this study. The random-effects structures
were used to describe the spatio (between states) and temporal (between seasons) specific
variations of the sorghum yield during the two agricultural seasons (2018-2019); while the size of
cultivated land and the households’ size as a proxy of labor were used as fixed-effects variables in
addition to remotely derived variables. | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Nairobi | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Assessing Field - Level Sorghum Yield Variability in South Sudan Using Remote Sensing | en_US |
dc.title | Assessing Field - Level Sorghum Yield Variability in South Sudan Using Remote Sensing | en_US |
dc.type | Thesis | en_US |