About

Peter Ojiambo is a Professor and Director of International Programs in the Department of Entomology and Plant Pathology at North Carolina State University. His research interests encompass botanical epidemiology and the integrated management of plant diseases, with a focus on understanding the basic biology and ecology of plant pathogens. He employs mathematical, statistical, and computer models to describe the dynamics of plant diseases in space and time, aiming to develop management decisions based on risk assessment and outbreak prediction. His work bridges botanical epidemiology and pathogen population genetics, emphasizing the importance of understanding environmental factors, pathogen populations, host resistance, and cropping practices in disease management. Ojiambo's laboratory utilizes advanced statistical and ecological techniques, computer technology, and large weather and disease databases to create decision-support tools for disease control. He has contributed to ongoing research projects such as developing spatially explicit network models for epidemic spread, characterizing epidemic dynamics across landscapes, and assessing ecological spillovers affecting disease dissemination. His educational background includes a B.S. and M.S. in Agriculture and Plant Pathology from the University of Nairobi, Kenya, and a Ph.D. in Plant Pathology from the University of Georgia. He has published extensively on plant disease epidemiology, management strategies, and the application of modeling techniques in plant pathology.

Research topics

• Biology
• Microbiology
• Botany
• Internal medicine
• Statistics
• Biochemistry
• Horticulture
• Medicine
• Biotechnology
• Mathematics
• Chemistry

Selected publications

• Combining ability and heterosis in partial resistance to gray leaf spot in elite maize ( <i>Zea mays</i> L.) genotypes

  Crop Science · 2026-01-01

  articleOpen accessSenior authorCorresponding

  Abstract Six sets of 48 maize ( Zea mays L.) inbred lines, and their 96 single crosses generated using the North Carolina Mating Design II system, were evaluated for resistance to gray leaf spot (GLS). The aim of the study was to assess the implications of combining ability, heterotic effects, and potence ratio in breeding for GLS resistance in maize. Inbred lines, crosses, and two local commercial checks were artificially inoculated with Cercospora zeina and evaluated across nine field environments in Western Kenya from 2012 to 2014. Analysis of variance revealed significant ( p ≤ 0.05) differences in disease resistance among inbred lines and their single crosses. Both general combining ability (GCA) and specific combining ability (SCA) effects were significant ( p ≤ 0.05), suggesting that resistance was influenced by both additive and non‐additive genetic factors. However, the sums of squares of GCA were two to four times larger than those of SCA, indicating a greater role of additive genetic effects, although the magnitude varied depending on the genetic set under investigation. Further, there was a strong and significant correlation ( p ≤ 0.05) between the GCA effects of the parental lines and the resistance levels of their hybrid crosses, indicating GCA effects were predictive of hybrid resistance and that reciprocal recurrent selection may be a potential strategy of breeding for GLS resistance. Inbred lines CML202, CML210, and CML373 were the strongest general combiners. The highest SCA effects were observed in crosses such as CML373 × CML168, CML371 × CML168, and CML219 × CML205. Crosses derived from parents within compatible heterotic patterns exhibited significant ( p ≤ 0.05) mid‐parent heterosis, with the cross CML219 × CML123 showing the most pronounced heterotic effect. Other crosses with notable mid‐parent heterosis included CML371 × CML390, CML204 × CML160, and CML394 × CML168. Although the variation in potence ratio estimates indicated that the interactions between loci ranged from complementary to inhibitory epistasis, the most prevalent genetic interaction was overdominance. These findings suggest that breeding strategies to improve GLS resistance in maize should be adapted to the specific genetic backgrounds of parental material. While reciprocal recurrent selection would suit most of the evaluated genotypes, half‐sib and genomic selection could be the most effective in different contexts.

  PublisherOA PDFDOI

• A comprehensive review of Pseudoperonospora cubensis: biology, epidemiology, and disease management

  Frontiers in Horticulture · 2025-09-19 · 1 citations

  articleOpen accessSenior authorCorresponding

  Over the past two decades, significant changes in the population structure of Pseudoperonospora cubensis have been reported worldwide. These changes have been associated with, among other things, severe epidemics of cucurbit downy mildew that are now much more destructive particularly on cucumber, than has previously been reported. Host specificity has complicated disease control as host resistance and fungicides that were previously effective in controlling the disease have become less effective. In response to this resurgence, significant research efforts have been made to better understand disease epidemiology, pathogen biology and host resistance, to generate information to improve disease management. Oospores have been reported under natural field settings in the United States, however, uncertainty remains regarding their role as a source of inoculum for initial disease outbreaks in northern latitudes that experience hard frost. Further, recent work indicates that the initial source of inoculum in the continental United States is southern Florida and along the edge of the Gulf of Mexico. Network analysis of disease outbreaks has identified key locations in the eastern United States that could be critical for disease monitoring in an effort to limit epidemic spread during the growing season. Lineage-specific biosurveillance of P. cubensis using spore traps complements existing disease monitoring efforts and is providing opportunities for precision management by determining cucurbit crops at risk of infection during the season. This review summarizes the substantial progress that has been made in understanding the biology of P. cubensis , disease epidemiology and control, which could inform better the management of cucurbit downy mildew.

  PublisherOA PDFDOI

• Predicting the Establishment Risk of Pale Cyst Nematode in Idaho Using a Process-Based, Stage-Structured Population Dynamics Model

  Open MIND · 2025-12-08

  articleSenior author

  Abstract Potato cyst nematodes are among the most economically important pathogens affecting potatoes worldwide, capable of reducing yields by up to 80% if not controlled. These nematodes can remain dormant in soil as cysts for up to 30 years without a suitable host. One group of these nematodes, the pale cyst nematode (Globodera pallida), was detected in the United States in 2006 and is currently quarantined in Bingham and Bonneville Counties, Idaho. Spatial modeling has been done to describe the current infestation and spread of G. pallida in fields where it is present. However, no studies have examined its potential establishment in other potato-growing areas within the state. To address this, we developed a random forest model to predict the daily average soil temperature at a depth of 20 cm using longitude, latitude, elevation and daily mean air temperature from 43 weather stations in Idaho, and the Julian days that covered the potato-growing season of April to September 2024. The model performed well, with an R2 value of 0.98 and a root mean square error of 0.98. We then used a process-based, temperature-driven population dynamics model to estimate the multiplication ratio (i.e., the final population density divided by the initial population density in eggs/gram of soil) of pale cyst nematodes in the following locations: Ashton, Grace, Malta, Parma, Picabo, and Shelley. Our results showed that G. pallida could reproduce in major potato-growing regions in Idaho other than the counties where it is currently contained. Moreover, its reproduction coincided with the potato growing season, during which the bulk of planting occurs from mid-April to mid-May and ends in mid-August to late September. A typical long growing season (~18 weeks) associated with russet potato production, along with a reduced proportion of nematodes requiring diapause, suggests the possibility of completing two generations across all locations. Compared to using actual soil temperatures, we observed overestimation and underestimation at specific planting dates x proportion of obligatory diapause in all locations, underscoring the need for more accurate estimation of soil temperature. Keywords: Pale cyst nematode, Establishment risk , Predictive modelling, Soil temperatureNote: This poster was submitted and presented during the 2025 Annual Meeting of the Society for Risk Analysis last December 7-10, 2025 at Washington D.C., USA.

  DOI

• Predicting the Establishment Risk of Pale Cyst Nematode in Idaho Using a Process-Based, Stage-Structured Population Dynamics Model

  Zenodo (CERN European Organization for Nuclear Research) · 2025-12-08

  articleOpen accessSenior author

  Abstract Potato cyst nematodes are among the most economically important pathogens affecting potatoes worldwide, capable of reducing yields by up to 80% if not controlled. These nematodes can remain dormant in soil as cysts for up to 30 years without a suitable host. One group of these nematodes, the pale cyst nematode (Globodera pallida), was detected in the United States in 2006 and is currently quarantined in Bingham and Bonneville Counties, Idaho. Spatial modeling has been done to describe the current infestation and spread of G. pallida in fields where it is present. However, no studies have examined its potential establishment in other potato-growing areas within the state. To address this, we developed a random forest model to predict the daily average soil temperature at a depth of 20 cm using longitude, latitude, elevation and daily mean air temperature from 43 weather stations in Idaho, and the Julian days that covered the potato-growing season of April to September 2024. The model performed well, with an R2 value of 0.98 and a root mean square error of 0.98. We then used a process-based, temperature-driven population dynamics model to estimate the multiplication ratio (i.e., the final population density divided by the initial population density in eggs/gram of soil) of pale cyst nematodes in the following locations: Ashton, Grace, Malta, Parma, Picabo, and Shelley. Our results showed that G. pallida could reproduce in major potato-growing regions in Idaho other than the counties where it is currently contained. Moreover, its reproduction coincided with the potato growing season, during which the bulk of planting occurs from mid-April to mid-May and ends in mid-August to late September. A typical long growing season (~18 weeks) associated with russet potato production, along with a reduced proportion of nematodes requiring diapause, suggests the possibility of completing two generations across all locations. Compared to using actual soil temperatures, we observed overestimation and underestimation at specific planting dates x proportion of obligatory diapause in all locations, underscoring the need for more accurate estimation of soil temperature. Keywords: Pale cyst nematode, Establishment risk , Predictive modelling, Soil temperatureNote: This poster was submitted and presented during the 2025 Annual Meeting of the Society for Risk Analysis last December 7-10, 2025 at Washington D.C., USA.

  PublisherDOI

• Predicting the Potential Risk of Red Ring Nematode Establishment Using Palm Distribution in the United States

  Open MIND · 2025-08-02

  article

  Abstract The red ring nematode (Bursaphelenchus cocophilus), is a plant pathogen causing serious damage to palms and is present in Mexico, Central America, South America, and the Caribbean. While the nematode is absent in the contiguous U.S., its vector, the South American palm weevil (Rhynchophorus palmarum), has been detected in California and Texas. Limited data exist to determine the establishment of RRN within U.S. Thus, its potential distribution was modeled using palm trees (family Arecaceae) as a proxy. Using palm records from the Global Biodiversity Information Facility (GBIF), and bioclimatic variables and elevation data as predictors, nine correlative species distribution models (SDMs), logistic regression, general additive model, naïve bayes, classification and regression tree, random forest, k-nearest neighbor, support vector machine, artificial neural network, and maximum entropy, were trained. Optimal cutoffs were determined by maximizing Youden’s J, slightly improving some of the models’ test performance when compared to a default threshold of 0.5. Overall, the random forest model performed best with an overall accuracy = 0.967, Kappa = 0.934, Youden’s J = 0.934, and AUC = 0.993, using a threshold of 0.5. The mean temperature of the coldest quarter and mean precipitation of the warmest quarter were identified as the most important bioclimatic variables. Model predictions across the contiguous U.S. showed that much of the western and southern parts of the country, where palms are native and cultivated, could be at risk of red ring nematode establishment.Note: This poster was submitted and presented at the 2025 Annual Meeting of American Phytopathological Society last August 2-5, 2025 at the Honolulu Convention Center, Honolulu, Hawaii, USA.

  DOI

• Predicting the Potential Risk of Red Ring Nematode Establishment Using Palm Distribution in the United States

  Zenodo (CERN European Organization for Nuclear Research) · 2025-08-02

  articleOpen access

  Abstract The red ring nematode (Bursaphelenchus cocophilus), is a plant pathogen causing serious damage to palms and is present in Mexico, Central America, South America, and the Caribbean. While the nematode is absent in the contiguous U.S., its vector, the South American palm weevil (Rhynchophorus palmarum), has been detected in California and Texas. Limited data exist to determine the establishment of RRN within U.S. Thus, its potential distribution was modeled using palm trees (family Arecaceae) as a proxy. Using palm records from the Global Biodiversity Information Facility (GBIF), and bioclimatic variables and elevation data as predictors, nine correlative species distribution models (SDMs), logistic regression, general additive model, naïve bayes, classification and regression tree, random forest, k-nearest neighbor, support vector machine, artificial neural network, and maximum entropy, were trained. Optimal cutoffs were determined by maximizing Youden’s J, slightly improving some of the models’ test performance when compared to a default threshold of 0.5. Overall, the random forest model performed best with an overall accuracy = 0.967, Kappa = 0.934, Youden’s J = 0.934, and AUC = 0.993, using a threshold of 0.5. The mean temperature of the coldest quarter and mean precipitation of the warmest quarter were identified as the most important bioclimatic variables. Model predictions across the contiguous U.S. showed that much of the western and southern parts of the country, where palms are native and cultivated, could be at risk of red ring nematode establishment.Note: This poster was submitted and presented at the 2025 Annual Meeting of American Phytopathological Society last August 2-5, 2025 at the Honolulu Convention Center, Honolulu, Hawaii, USA.

  PublisherDOI

• Performance and Stability of Winter Wheat Cultivars to Stagonospora nodorum Blotch Epidemics in Multi-Environment Trials

  Phytopathology · 2025-10-01

  articleSenior author

  Field performance of winter wheat genotypes with quantitative resistance to Stagonospora nodorum blotch (SNB) is influenced by genotype-by-environment interactions (GEIs). This phenomenon explains why cultivars may perform inconsistently across environments, affecting decisions on locally adapted genotypes. Further, GEIs can also affect risk assessment when cultivar disease reaction is used as a model predictor under the assumption of stable responses across environments. Thus, this study investigated GEI effects on four disease metrics: final disease severity (SEV), relative area under disease progress stairs (rAUDPS), time to 50% disease incidence (T 50 ), and the apparent rate of disease increase (ω), describing SNB epidemics of 18 commercial soft red winter wheat cultivars planted in 18 environments in North Carolina from 2021 to 2024. Linear mixed models with various variance-covariance structures for random effects were used to analyze the disease data, and a third-order factor analytic model provided the best fit to the data across the metrics examined. Type B genetic correlation ([Formula: see text]), broad-sense heritability ([Formula: see text]), overall cultivar performance ( OP), and global stability (expressed as root mean square deviation [ RMSD]) were estimated using model outputs and the factor analytic selection tool method. For SEV, rAUDPS, and T 50 , values of [Formula: see text] ranged from −0.15 to 0.99, with most environment pairs exhibiting high [Formula: see text] values, indicating an agreement in cultivar rankings, although some low [Formula: see text] values revealed rank instability and non-crossover GEI. Based on OP and RMSD, ‘USG 3230’ was the top-performing and most stable cultivar, whereas ‘TURBO’ and ‘SH7200’ were more unstable cultivars. Cultivar reaction classes derived from OP exhibited consistent class-level means of marginal predictions across environments with varying GEIs, supporting their utility as indicators of SNB susceptibility in risk assessment models. However, the presence of minor non-crossover GEI effects suggests that incorporating environmental drivers of GEI into SNB risk models could enhance prediction accuracy.

  PublisherDOI

• Insect pest and plant disease management in horticultural crop production: recent insights provide opportunities for improved control

  Frontiers in Horticulture · 2025-07-23 · 3 citations

  articleOpen access1st authorCorresponding

  With the world population projected to increase to approximately 8 billion people by 2030, tremendous efforts are needed to produce enough food to feed the population with a decreasing land available for agricultural production. Horticultural crops, characterized by very diverse production systems, continue to play a significant role in food security and safety. However, plant pests and plant diseases continue to negatively impact the production of healthy and safe food in horticultural cropping systems, by affecting produce quality, quantity, and safety. Furthermore, the emergence and re-emergence of pests and pathogens coupled with the rapid development of resistance to available pesticides further exacerbate the challenges of pest and disease control in horticultural systems. Given the recognized need to mitigate climate-change risks, novel pest and disease management strategies are required to achieve net-zero emissions for more sustainable horticultural production. This perspective highlights some recent research insights that could provide opportunities for the improved management of insect pests and plant diseases in horticultural crop production systems.

  PublisherOA PDFDOI

• Leveraging window-pane analysis with environmental factor loadings of genotype-by-environment interaction to identify high-resolution weather-based variables associated with plant disease

  Frontiers in Plant Science · 2025-09-11 · 1 citations

  articleOpen accessSenior authorCorresponding

  Designing and identifying biologically meaningful weather-based predictors of plant disease is challenging due to the temporal variability of conducive conditions and interdependence of weather factors. Confounding effects of plant genotype further obscure true environmental signals within observed disease responses. To address these limitations, this study leveraged window-pane analysis with feature engineering and stability selection, to identify weather-based variables associated with latent environmental factors ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="im1"><mml:mover accent="true"><mml:mi>λ</mml:mi><mml:mo>^</mml:mo></mml:mover></mml:math> ) of a factor analytic model explaining genotype-by-environment (GEI) effects on disease severity in multi-environment trials. Using Stagonospora nodorum blotch of wheat as a case study and a two-stage feature engineering procedure, hourly weather data, i.e., air temperature ( T ), precipitation ( R ), and relative humidity ( RH ), were aggregated into 1,530 distinct time series, in the first stage feature engineering procedure. These series were correlated daily with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="im2"><mml:mover accent="true"><mml:mi>λ</mml:mi><mml:mo>^</mml:mo></mml:mover></mml:math> throughout the second half of the wheat growing season. In the second stage procedure, significant daily weather variables were consolidated into optimal epidemiological periods relative to wheat anthesis, yielding 60, 19, and 28 second-level weather-based variables derived from the first ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="im3"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>λ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math> ), second ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="im4"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>λ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> ), and third ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="im5"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>λ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> ) environmental factor loadings, respectively. Among the weather-based predictors identified, fa1.41_18.TRH.13T16nRH.G80.daytime.sum_25 and fa1.11_5.R.S.dawn.sum_10 , were positively associated with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="im6"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>λ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math> (i.e., the dominant environmental gradient underlying variation in SNB severity across environments) pre-anthesis, during a period of 24 and 7 consecutive days, respectively. In contrast, fa1.22_16.TR.19T22nR.G0.2.dawn.sum_20 and fa1.2_-12.RH.L35.daytime.sum_15 were negatively associated with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="im7"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>λ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math> at pre-anthesis and post-anthesis, respectively. Additional predictors derived from T , R , and RH , were identified up to 63 days pre-anthesis. However, no single predictor consistently maintained an association with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="im8"><mml:mover accent="true"><mml:mi>λ</mml:mi><mml:mo>^</mml:mo></mml:mover></mml:math> during the entire study period. This framework advances the development of weather ‘markers’ for detailed environmental profiling of GEI drivers and improves upon prior approaches that limited window-pane analysis to disease outcomes from susceptible hosts to identify weather-based variables for predicting plant disease epidemics.

  PublisherOA PDFDOI

• The maize mitogen‐activated protein kinase kinase kinase gene <i>ZmMAPKKK45</i> is associated with multiple disease resistance

  New Phytologist · 2025-12-18 · 1 citations

  articleOpen access

  Southern leaf blight (SLB), caused by the necrotrophic fungus Cochliobolus heterostrophus, is a major foliar disease of maize (Zea mays) world-wide. A genome-wide association study was performed to dissect the genetic basis of SLB resistance in maize. Functional validation was performed using mutant and transgenic analyses. Molecular experiments provided preliminary insights into the underlying disease resistance mechanisms. Association analyses identified 14 single nucleotide polymorphisms (SNPs) linked to SLB resistance, 13 of which overlapped with known quantitative resistance loci, highlighting 10 candidate genes. Functional studies confirmed ZmMAPKKK45, encoding a mitogen-activated protein kinase kinase kinase (MAPKKK), is the causal gene at a resistance locus on chromosome 3. ZmMAPKKK45 also enhanced resistance to northern leaf blight and gray leaf spot and promotes reactive oxygen species (ROS) accumulation during defense responses. Our results indicate that ZmMAPKKK45 functions outside canonical MAPK cascades and likely enhances disease resistance by upregulating maize respiratory burst oxidase homolog (ZmRBOH) genes, thereby increasing ROS production and contributing to broad-spectrum foliar disease resistance in maize.

  PublisherOA PDFDOI

Frequent coauthors

• Anna Thomas

  43 shared

• Christina Cowger

  North Carolina State University

  40 shared

• L. M. Quesada-Ocampo

  Plant (United States)

  39 shared

• K. N. Neufeld

  Bayer (Germany)

  32 shared

• Richard Nyankanga

  University of Nairobi

  32 shared

• A. Lebeda

  Palacký University Olomouc

  30 shared

• Todd C. Wehner

  North Carolina State University

  29 shared

• M. K. Hausbeck

  Michigan State University

  29 shared

Labs

• Entomology and Plant PathologyPI