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Peter Ojiambo


Epidemiology and integrated management of plant diseases

Partners Building III 239



Program Overview:

My research interest encompasses botanical epidemiology and integrated management of plant diseases, with emphasis on understanding the basic biology and ecology of plant pathogens and the use of this information to develop disease management tools.  Application of mathematical and statistical models and computer technology to describe the dynamics of plant diseases in space and time is an integral aspect of my research program whose primary goal is to develop management decisions based on risk assessment and prediction of outbreaks of epidemics. I am also exploring research that bridges botanical epidemiology and pathogen population genetics.

Effective disease management through disease prediction and risk assessment requires an understanding of the biology and ecology of plant pathogens. Basic research is routinely conducted to understand the effects of changing weather patterns, shifts in pathogen populations, host resistance, and changes in cropping practices on the appearance of new and resurgence of existing plant diseases. My laboratory employs mathematical, statistical and computer models to study these effects and their possible consequences on disease development in space and time. I take advantage of modern statistical and ecological techniques, advanced computer technology and large weather and disease databases to develop and use prediction and risk assessment models as decision-making tools in the integrated management of plant diseases.

Ongoing research projects:

  1. Relating initial epidemic area to the velocity of spread of cucurbit downy mildew in eastern United States.
  2. Incorporating random effects in survival analysis of plant disease epidemics and developing sampling techniques for assessment of disease incidence.
  3. Developing a risk assessment model for Stagonospora nodorum blotch in winter wheat.
  4. Within-season risk assessment model for cucurbit downy mildew and validation of the downy mildew forecasting system (
  5. Population biology and ecology of downy mildew of cucurbits in the eastern United States.
  6. Impact of the soil population structure and ecology of members of Aspergillus section Favi on the efficacy of bio-control of aflatoxin in corn.

Current Graduate Students:

  1. Jane Marian Luis (PhD) — Started Fall 2015. Project: Ecological Implications of the Genetics of Atoxigenic Strains in Efficacy of Biocontrol of Aflatoxin Contamination in Corn.
  2. Urmila Adhikari (PhD) — Started Fall 2016. Project: Environmental factors affecting secondary inoculum production in S. nodorum and components of disease resistance in wheat.
  3. Maureiq Ojwang (PhD — Biomathematics program) — Started Fall 2016. Project: Network modeling of spread and transmission of P. cubensis in the eastern United States.
  4. Isaac Kikway (PhD) — Fall 2017.


  1. PP 790: Epidemiology: Theory & Application (Fall semester).
  2. PP 790-004: Advances in Evolutionary Ecology & Population Biology (Alternate Spring semester).

Selected Publications:

  • K.N. Neufeld, A.P. Keinath, B.K. Gugino, M.T. McGrath, E.J. Sikora, S.A. Miller, M.L. Ivey, D.B. Langston, B. Dutta, T. Keever, A. Sims and P.S. Ojiambo. 2018. Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model. International Journal of Biometeorology 62: (in press). s00484-017-1474-2.
  • K.N. Neufeld, A.P. Keinath and P.S. Ojiambo. 2018. Evaluation of a model for predicting the infection risk of squash and cantaloupe by Pseudoperonospora cubensisPlant Disease 102: (in press).
  • P.S. Ojiambo, J. Yuen, F. van den Bosch, and L. V. Madden. 2017. Epidemiology: past, present, and future impacts on understanding disease dynamics and improving plant disease management — A summary of focus issue articles. Phytopathology 107: 1092-1094.
  • P.S. Ojiambo, D.H. Gent, L.K. Mehra, D. Christie, and R. Magarey. 2017. Focus expansion and stability of the spread parameter estimate of the power law model for dispersal gradients. PeerJ 5:e3465; DOI 10.7717/peerj.3465.
  • K.N. Neufeld, A.P. Keinath and P.S. Ojiambo. 2017. A model to predict the risk of infection of cucumber by Pseudoperonospora cubensisMicrobial Risk Analysis 6: 21-30.
  • A. Thomas, I. Carbone, K. Choe, L.M. Quesada-Ocampo and P.S. Ojiambo. 2017. Resurgence of cucurbit downy mildew in the United States: Insights from comparative genomic analysis of Pseudoperonospora cubensisEcology and Evolution 7: 6231-6246.
  • L.K. Mehra, C. Cowger and P.S. Ojiambo. 2017. A model for predicting onset of Stagonospora nodorum blotch in winter wheat based on pre-planting and weather factors. Phytopathology 107: 635-644.
  • A. Thomas, I. Carbone, A. Lebeda and P.S. Ojiambo. 2017. Virulence structure within the populations of Pseudoperonospora cubensis in the United States. Phytopathology 107: 777-785.
  • A. Thomas, I. Carbone, Y. Cohen and P.S. Ojiambo. 2017. Occurrence and distribution of mating types of Pseudoperonospora cubensis in the United States. Phytopathology 107: 313-321.
  • L.K. Mehra, C. Cowger, K. Gross and P.S. Ojiambo. 2016. Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models. Frontiers in Plant Science 7:390. doi: 10.3389/fpls.2016.00390.
  • S. Withers, E. Gongora-Castillo, D.H. Gent, A. Thomas, P.S. Ojiambo and L.M. Quesada-Ocampo. 2016. Using next-generation sequencing to develop molecular diagnostics for Pseudoperonospora cubensis, the cucurbit downy mildew pathogen. Phytopathology 106: 1105-1116.
  • J. Atehnkeng, M. Donner, P.S. Ojiambo, B. Ikotun, J. Augusto, P.J. Cotty and R. Bandyopadhyay. 2016. Genetic diversity of vegetative compatibility groups of Aspergillus flavus and selection of atoxigenic isolates for biocontrol of aflatoxin contamination. Microbial Biotechnology 9: 75-88.
  • L.K. Mehra, C. Cowger, R. Weisz and P.S. Ojiambo. 2015. Quantifying the effects of wheat residue on severity of Stagonospora nodorum blotch and yield in winter wheat. Phytopathology 105: 1417-1426.
  • Y. Cohen, K.M. Van den Langenberg, T.C. Wehner, P.S. Ojiambo, M.K. Hausbeck, L.M. Quesada-Ocampo, A. Lebeda, H. Sierotzki and U. Gisi. 2015. Resurgence of Pseudoperonospora cubensis — the causal agent of cucurbit downy mildew. Phytopathology 105: 998-1012.
  • P.S. Ojiambo, D.H. Gent, L.M. Quesada-Ocampo, M.K. Hausbeck and G.J. Holmes. 2015. Epidemiology and population biology of Pseudoperonospora cubensis: A model system for management of downy mildews. Annual Review of Phytopathology 53: 223-246.
  • G.J. Holmes, P.S. Ojiambo, M.K. Hausbeck, L.M. Quesada-Ocampo and A.P. Keinath. 2015. Resurgence of cucurbit downy mildew in the United States: A  watershed event for research and extension. Plant Disease 99: 428-441.
  • M. Rahman, P.S. Ojiambo and F. Louws. 2015. Initial inoculum and spatial dispersal of Colletotrichum gloeosporiodies, the causal agent of strawberry anthracnose crown rot. Plant Disease99:80-86.
  • M. Twiyezimana, P.S. Ojiambo, R. Bandyopadhyay and G.L. Hartman. 2014. Use of quantitative traits to assess aggressiveness of Phakospora pachyrhizi isolates from Nigeria and the United States. Plant Disease 98: 1261-1266.
  • J. Atehnkeng, P.S. Ojiambo, P.J. Cotty and R. Bandyopadhyay. 2014. Field efficacy of mixtures of atoxigenic Aspergillus flavus vegetative compatibility groups in mitigating aflatoxin contamination in maize. Biological Control 72:62-70.
  • P.S. Ojiambo and E.L. Kang. 2013. Modeling spatial frailties in survival analysis of cucurbit downy mildew epidemics. Phytopathology 103: 216-227.


BS, Agriculture, University of Nairobi, Kenya (1994)
MS, Plant Pathology, University of Nairobi, Kenya (1997)
Ph.D, Plant Pathology, The University of Georgia (2004)