Journal of Geosciences and Geomatics
ISSN (Print): 2373-6690 ISSN (Online): 2373-6704 Website: http://www.sciepub.com/journal/jgg Editor-in-chief: Maria TSAKIRI
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Journal of Geosciences and Geomatics. 2017, 5(5), 251-258
DOI: 10.12691/jgg-5-5-4
Open AccessArticle

Spatial Modelling of Maize Lethal Necrosis Disease in Bomet County, Kenya

Michael Osunga1, , Felix Mutua1 and Robinson Mugo2

1Department of Geomatics Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

2SERVIR-Eastern and Southern Africa, Regional Centre for Mapping of Resources for Development, Nairobi, Kenya

Pub. Date: October 14, 2017

Cite this paper:
Michael Osunga, Felix Mutua and Robinson Mugo. Spatial Modelling of Maize Lethal Necrosis Disease in Bomet County, Kenya. Journal of Geosciences and Geomatics. 2017; 5(5):251-258. doi: 10.12691/jgg-5-5-4

Abstract

Maize lethal necrosis (MLN) is a disease that attacks maize crops with significant impacts on both food security and nutrition security on smallholder farmers in Kenya. The study used spatial regression analysis to model MLN severity on sampled farm fields in Bomet County, Kenya. The modelling analysis integrated spatial information based on derived crop mask, on-site derived MLN disease severity index at an optimal maize growing season and phenological stage. Relevant ecological variables derived spatially including temperature, rainfall, soil moisture and slope were identified and fed into a spatial regression model. Significant ecological variables were weighted and used as basis for generating spatially explicit MLN severity index map. MLN affected farms have spatial dependence with MLN severity becoming less correlated the further away from each MLN affected farm field. The ecological variables have negative influence on MLN severity except for temperature. Soil moisture, rainfall and slope are the most significant determinants of MLN severity index in Bomet (all<p 0.05), with high MLN severity areas identified in Chebunyo, Sigor and Kipreres. This study would help in MLN epidemiological surveillance and in developing site-specific control measures and interventions. The spatial model used in this study could be replicated and up-scaled to other MLN prone areas in Kenya and in Africa coupled with other statistically significant spatiotemporal ecological variables to fully understand and ascertain MLN disease outbreak.

Keywords:
maize lethal necrosis disease severity index spatial regression

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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