Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya

Journal Article · Tue Aug 01 00:00:00 EDT 2017 · Phytopathology

DOI:https://doi.org/10.1094/PHYTO-03-17-0080-FI· OSTI ID:1409767

Hilker, Frank M. ^[1]; Allen, Linda J. S. ^[2]; Bokil, Vrushali A. ^[3]; Briggs, Cheryl J. ^[4]; Feng, Zhilan ^[5]; Garrett, Karen A. ^[6]; Gross, Louis J. ^[7]; Hamelin, Frédéric M. ^[8]; Jeger, Michael J. ^[9]; Manore, Carrie A.  ^[10]; Power, Alison G. ^[11]; Redinbaugh, Margaret G. ^[12]; Rúa, Megan A. ^[13]; Cunniffe, Nik J. ^[14]

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1. Osnabruck Univ., Osnabruck (Germany)
2. Texas Tech Univ., Lubbock, TX (United States)
3. Oregon State Univ., Corvallis, OR (United States)
4. Univ. of California, Santa Barbara, CA (United States)
5. Purdue Univ., West Lafayette, IN (United States)
6. Univ. of Florida, Gainesville, FL (United States)
7. Univ. of Tennessee, Knoxville, TN (United States)
8. Univ. de Rennes 1, Univ. Bretagne-Loire, Rennes (France)
9. Imperial College London, London (United Kingdom)
10. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
11. Cornell Univ., Ithaca, NY (United States)
12. Ohio State Univ., Wooster, OH (United States)
13. Wright State Univ., Dayton, OH (United States)
14. Univ. of Cambridge, Cambridge (United Kingdom)

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with large holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Finally, our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty.

• Research Organization: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1409767
• Report Number(s): LA-UR-17-22310
• Journal Information: Phytopathology, Vol. 107, Issue 10; ISSN 0031-949X
• Publisher: American Phytopathological Society
• Country of Publication: United States
• Language: English

Related Subjects

60 APPLIED LIFE SCIENCES
59 BASIC BIOLOGICAL SCIENCES
Biological Science
Mathematics