Imperial College London
Michael J Jeger

Michael J Jeger

Faculty of Natural Sciences, Department of Life Sciences (Silwood Park)

Emeritus Professor of Horticulture
 
 
 
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Contact

 

+44 (0)1398 332 941m.jeger Website

 
 
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Location

 

Home working 13 Brook Street, Bampton, Devon EX16 9LUSilwood ParkSilwood Park

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Summary

 

Publications

Citation

BibTex format

@article{Hilker:2017:10.1094/PHYTO-03-17-0080-F1,
author = {Hilker, FM and Allen, LJS and Bokil, VA and Briggs, CJ and Feng, Z and Garrett, KA and Gross, LJ and Hamelin, FM and Jeger, MJ and Manore, CA and Power, AG and Redinbaugh, MG and Rua, MA and Cunniffe, NJ},
doi = {10.1094/PHYTO-03-17-0080-F1},
journal = {Phytopathology},
pages = {1095--1108},
title = {Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya},
url = {http://dx.doi.org/10.1094/PHYTO-03-17-0080-F1},
volume = {107},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Maize lethal necrosis (MLN) has emerged as a serious threat to foodsecurity in sub-Saharan Africa. MLN is caused by coinfection with twoviruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcanemosaic virus. To better understand the dynamics of MLN and to provideinsight into disease management, we modeled the spread of the virusescausing MLN within and between growing seasons. The model allows fortransmission via vectors, soil, and seed, as well as exogenous sources ofinfection. Following model parameterization, we predict how managementaffects disease prevalence and crop performance over multipleseasons. Resource-rich farmers with large holdings can achieve goodcontrol by combining clean seed and insect control. However, croprotation is often required to effect full control. Resource-poor farmerswith smaller holdings must rely on rotation and roguing, and achievemore limited control. For both types of farmer, unless management issynchronized over large areas, exogenous sources of infection can thwartcontrol. As well as providing practical guidance, our modeling frameworkis potentially informative for other cropping systems in which coinfectionhas devastating effects. Our work also emphasizes how mathematicalmodeling can inform management of an emerging disease even whenepidemiological information remains scanty.
AU  - Hilker,FM
AU  - Allen,LJS
AU  - Bokil,VA
AU  - Briggs,CJ
AU  - Feng,Z
AU  - Garrett,KA
AU  - Gross,LJ
AU  - Hamelin,FM
AU  - Jeger,MJ
AU  - Manore,CA
AU  - Power,AG
AU  - Redinbaugh,MG
AU  - Rua,MA
AU  - Cunniffe,NJ
DO  - 10.1094/PHYTO-03-17-0080-F1
EP  - 1108
PY  - 2017///
SN  - 0031-949X
SP  - 1095
TI  - Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya
T2  - Phytopathology
UR  - http://dx.doi.org/10.1094/PHYTO-03-17-0080-F1
UR  - https://www.ncbi.nlm.nih.gov/pubmed/28535127
UR  - http://hdl.handle.net/10044/1/55557
VL  - 107
ER  -
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