Skip Navigation Links.
Journal of Geosciences and Geomatics. 2017, 5(5), 251-258
DOI: 10.12691/JGG-5-5-4
Original Research

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
Full Text PDF

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

Copyright

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/

References

[1]  C. L. Niblett, L. E. Claflin, and others, “Corn lethal necrosis-a new virus disease of corn in Kansas.,” Plant Dis. Report., vol. 62, no. 1, pp. 15-19, 1978.
 
[2]  B. Doupnik Jr, “Status of corn lethal necrosis [virus diseases in the United States]: 1979 update,” in Proceedings of the... annual corn and sorghum research conference (USA), 1979.
 
[3]  X. Q. Jiang, L. J. Meinke, R. J. Wright, D. R. Wilkinson, and J. E. Campbell, “Maize chlorotic mottle virus in Hawaiian-grown maize: vector relations, host range and associated viruses,” Crop Prot., vol. 11, no. 3, pp. 248-254, 1992.
 
[4]  A. W. Wangai et al., “First report of maize chlorotic mottle virus and maize lethal necrosis in Kenya,” Plant Dis., vol. 96, no. 10, pp. 1582-1582, 2012.
 
[5]  I. P. Adams et al., “First report of maize lethal necrosis disease in Rwanda,” New Dis. Rep., vol. 29, pp. 22-22, 2014.
 
[6]  D. Makumbi and A. Wangai, “Maize lethal necrosis (MLN) disease in Kenya and Tanzania: facts and actions,” CIMMYT/KARI, 2013.
 
[7]  F. Kagoda, R. Gidoi, and B. E. Isabirye, “Status of maize lethal necrosis in eastern Uganda,” Afr. J. Agric. Res., vol. 11, no. 8, pp. 652-660, 2016.
 
[8]  M. Lukanda et al., “First report of Maize chlorotic mottle virus infecting maize in the Democratic Republic of the Congo,” Plant Dis., vol. 98, no. 10, pp. 1448-1448, 2014.
 
[9]  G. Mahuku et al., “Maize lethal necrosis (MLN), an emerging threat to maize-based food security in sub-Saharan Africa,” Phytopathology, vol. 105, no. 7, pp. 956-965, 2015.
 
[10]  F. H. Kiruwa, T. Feyissa, and P. A. Ndakidemi, “Insights of maize lethal necrotic disease: A major constraint to maize production in East Africa,” Afr. J. Microbiol. Res., vol. 10, no. 9, pp. 271-279, 2016.
 
[11]  H. De Groote, F. Oloo, S. Tongruksawattana, and B. Das, “Community-survey based assessment of the geographic distribution and impact of maize lethal necrosis (MLN) disease in Kenya,” Crop Prot., vol. 82, pp. 30-35, 2016.
 
[12]  R. Margaret, “Vector-virus interactions in maize agroecosystems in East Africa,” presented at the 2016 International Congress of Entomology, 2016.
 
[13]  C. L. Niblett, L. E. Claflin, and others, “Corn lethal necrosis-a new virus disease of corn in Kansas.,” Plant Dis. Report., vol. 62, no. 1, pp. 15-19, 1978.
 
[14]  A. W. Wangai et al., “First report of maize chlorotic mottle virus and maize lethal necrosis in Kenya,” Plant Dis., vol. 96, no. 10, pp. 1582-1582, 2012.
 
[15]  G. Mahuku et al., “Maize lethal necrosis (MLN), an emerging threat to maize-based food security in sub-Saharan Africa,” Phytopathology, vol. 105, no. 7, pp. 956-965, 2015.
 
[16]  S. M. Makone, “EXTENT TO WHICH MAIZE LETHAL NECROSIS DISEASE AFFECTS MAIZE YIELD: A CASE OF KISII COUNTY, KENYA,” Kisii University, 2014.
 
[17]  L. R. Nault et al., “Transmission of maize chlorotic mottle virus by chrysomelid beetles,” Phytopathology, vol. 68, no. 7, pp. 1071-1074, 1978.
 
[18]  D. Cabanas, S. Watanabe, C. H. V. Higashi, and A. Bressan, “Dissecting the mode of Maize chlorotic mottle virus transmission (Tombusviridae: Machlomovirus) by Frankliniella williamsi (Thysanoptera: Thripidae),” J. Econ. Entomol., vol. 106, no. 1, pp. 16-24, 2013.
 
[19]  T. G. Atkinson, M. N. Grant, and others, “An evaluation of streak mosaic losses in winter Wheat.,” Phytopathology, vol. 57, no. 2, pp. 188-192, 1967.
 
[20]  J. K. Uyemoto, “Biology and Control,” Plant Dis., vol. 67, no. 1, p. 7, 1983.
 
[21]  B. E Isabirye and I. Rwomushana, “Current and future potential distribution of maize chlorotic mottle virus and risk of maize lethal necrosis disease in Africa,” J. Crop Prot., 2016.
 
[22]  K. Scheets, “Maize chlorotic mottle machlomovirus and wheat streak mosaic rymovirus concentrations increase in the synergistic disease corn lethal necrosis,” Virology, vol. 242, no. 1, pp. 28-38, 1998.
 
[23]  S. M. Makone, “EXTENT TO WHICH MAIZE LETHAL NECROSIS DISEASE AFFECTS MAIZE YIELD: A CASE OF KISII COUNTY, KENYA,” Kisii University, 2014.
 
[24]  A. Wangai et al., “REPORT ON STATUS OF MAIZE LETHAL NECROSIS DISEASE AND GENERAL MAIZE PERFORMANCE:STAKEHOLDERS’ MAIZE TOUR,” English, Sotik, Jul. 2012.
 
[25]  R. E. Hill and Z. B. Mayo, “Distribution and abundance of corn rootworm species as influenced by topography and crop rotation in eastern Nebraska,” Environ. Entomol., vol. 9, no. 1, pp. 122-127, 1980.
 
[26]  S. C. Morsello, The role of temperature and precipitation on thrips populations in relation to the epidemiology of Tomato spotted wilt virus. ProQuest, 2007.
 
[27]  C. J. H. Franssen and P. Huisman, “The biology and control of Thrips angusticeps Uzel,” Dutch Versl. Landbouwkd. Onderz., vol. 64, pp. 1-104, 1958.
 
[28]  W. D. J. Kirk, T. Lewis, and others, “Distribution, abundance and population dynamics.,” Thrips Crop Pests, pp. 217-257, 1997.
 
[29]  S. F. Bailey, “A winter study of the onion thrips in California,” CADA Bull, vol. 23, pp. 149-152, 1934.
 
[30]  T. Lewis and others, “Flight and dispersal.,” Thrips Crop Pests, pp. 175-196, 1997.
 
[31]  R. Yadav and N.-T. Chang, “Effects of temperature on the development and population growth of the melon thrips, Thrips palmi, on eggplant, Solanum melongena,” J. Insect Sci., vol. 14, no. 1, p. 78, 2014.
 
[32]  J. Davidson and H. G. Andrewartha, “The influence of rainfall, evaporation and atmospheric temperature on fluctuations in the size of a natural population of Thrips imaginis (Thysanoptera),” J. Anim. Ecol., pp. 200-222, 1948.
 
[33]  I. A. Pearsall and J. H. Myers, “Spatial and temporal patterns of dispersal of western flower thrips (Thysanoptera: Thripidae) in nectarine orchards in British Columbia,” J. Econ. Entomol., vol. 94, no. 4, pp. 831-843, 2001.
 
[34]  L. Wang, C. Hui, H. S. Sandhu, Z. Li, and Z. Zhao, “Population dynamics and associated factors of cereal aphids and armyworms under global change,” Sci. Rep., vol. 5, 2015.
 
[35]  M. Wains, M. Ali, M. Hussain, J. Anwar, M. Zulkiffal, and W. Sabir, “Aphid dynamics in relation to meteorological factors and various management practices in bread wheat,” J. Plant Prot. Res., vol. 50, no. 3, pp. 386-392, 2010.
 
[36]  A. Kumar, “Population Dynamics of Black Aphid (Aphis craccivora Koch) in Different Genotypes of Indian Bean (Dolichus lablab) in Relation to Weather Parameters at Kanpur, UP,” Environ. Ecol., vol. 15, pp. 318-321, 1997.
 
[37]  M. R. Hasan, M. Ahmad, M. H. Rahman, and M. A. Haque, “Aphid incidence and its correlation with different environmental factors,” J. Bangladesh Agric. Univ., vol. 7, no. 1, pp. 15-18, 2009.
 
[38]  G. F. Backoulou et al., “Relationship between Russian Wheat Aphid 1 Abundance and Edaphic and Topographic Characteristics in Wheat Fields,” Southwest. Entomol., vol. 35, no. 1, pp. 11-18, 2010.
 
[39]  S. Selvaraj, D. Adiroubane, and V. Ramesh, “Population dynamics of leafhopper, Amrasca devastans Distant in Cotton and its relationship with weather parameters,” Ann. Plant Prot. Sci., vol. 19, no. 1, pp. 47-50, 2011.
 
[40]  K. Matsukura, K. Yoshida, and M. Matsumura, “Estimation of climatic factors relating to occurrence of the maize orange leafhopper, Cicadulina bipunctata,” Popul. Ecol., vol. 54, no. 3, pp. 397-403, 2012.
 
[41]  J. Iqbal, M. Ashfaq, U. H. MANSOOR, M. Sagheer, and M. Nadeem, “Influence of abiotic factors on population fluctuation of leaf hopper, Amrasca biguttula biguttula (Ishida) on Okra,” Pak. J Zool, vol. 2, pp. 615-621, 2010.
 
[42]  A. Wilstermann and S. Vidal, “Western corn rootworm egg hatch and larval development under constant and varying temperatures,” J. Pest Sci., vol. 86, no. 3, pp. 419-428, 2013.
 
[43]  K. Maredia and D. Landis, “Corn rootworms: biology, ecology and management,” Ext. Bull. USA, 1993.
 
[44]  B. E Isabirye and I. Rwomushana, “Current and future potential distribution of maize chlorotic mottle virus and risk of maize lethal necrosis disease in Africa,” J. Crop Prot., vol. 5, no. 2, pp. 215-228, 2016.
 
[45]  R. Kyalo, L. Tobias, E. Abdel-Rahman, and N. Johnson, “Mapping Maize Lethal Necrosis severity in Kenya using multi-spectral high to moderate resolution satellite imagery,” in The 2016 European Space Agency Living Planet Symposium, Prague, Czech Republic, 2016.
 
[46]  E. Abdel-Rahman, R. Kyalo, L. Tobias, and P. L. R. Bruno, “Maize pest and disease mapping using RapidEye-based vegetation indices and machine learning methods,” presented at the 11th International African of the Association of Remote Sensing of Remote Sensing (AARSE 2016), Kampala, Uganda, 2016, vol. 11.
 
[47]  P. A. Burrough, R. A. McDonnell, and C. D. Lloyd, Principles of geographical information systems. Oxford University Press, 2015.
 
[48]  B. Li, C. Ti, Y. Zhao, and X. Yan, “Estimating soil moisture with landsat data and its application in extracting the spatial distribution of winter flooded paddies,” Remote Sens., vol. 8, no. 1, p. 38, 2016.
 
[49]  “data mining research standardization and normalization - Google Scholar.” [Online]. Available: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=data+mining+research+standardization+and+normalization&oq=data+mining+research+ standardization+and+normali. [Accessed: 23-Sep-2017].
 
[50]  P. . Sharma, “Measurement of disease and assessment of losses.”
 
[51]  C. CIMMYT, “Maize lethal necrosis (MLN) information portal.”.
 
[52]  D. E. Gray, Doing research in the real world. Sage, 2013.
 
[53]  T. Lillesand, J. Chipman, D. Nagel, H. Reese, and M. Bobo, “Upper Midwest gap analysis program, image processing protocol,” WISCONSIN UNIV-MADISON, 1998.
 
[54]  K. Ani, “Spatial econometrics,” 2013.
 
[55]  A. Getis and D. A. Griffith, “Comparative spatial filtering in regression analysis,” Geogr. Anal., vol. 34, no. 2, pp. 130-140, 2002.
 
[56]  R. J. Hijmans and J. Elith, “Species distribution modeling with R,” R Package Version 08-11, 2013.
 
[57]  H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Autom. Control, vol. 19, no. 6, pp. 716-723, 1974.