Cost-Effective Control of Plant Disease When Epidemiological Knowledge Is Incomplete: Modelling Bahia Bark Scaling of Citrus

Abstract

A spatially-explicit, stochastic model is developed for Bahia bark scaling, a threat to citrus production in north-eastern Brazil, and is used to assess epidemiological principles underlying the cost-effectiveness of disease control strategies. The model is fitted via Markov chain Monte Carlo with data augmentation to snapshots of disease spread derived from a previously-reported multi-year experiment. Goodness-of-fit tests strongly supported the fit of the model, even though the detailed etiology of the disease is unknown and was not explicitly included in the model. Key epidemiological parameters including the infection rate, incubation period and scale of dispersal are estimated from the spread data. This allows us to scale-up the experimental results to predict the effect of the level of initial inoculum on disease progression in a typically-sized citrus grove. The efficacies of two cultural control measures are assessed: altering the spacing of host plants, and roguing symptomatic trees. Reducing planting density can slow disease spread significantly if the distance between hosts is sufficiently large. However, low density groves have fewer plants per hectare. The optimum density of productive plants is therefore recovered at an intermediate host spacing. Roguing, even when detection of symptomatic plants is imperfect, can lead to very effective control. However, scouting for disease symptoms incurs a cost. We use the model to balance the cost of scouting against the number of plants lost to disease, and show how to determine a roguing schedule that optimises profit. The trade-offs underlying the two optima we identify—the optimal host spacing and the optimal roguing schedule—are applicable to many pathosystems. Our work demonstrates how a carefully parameterised mathematical model can be used to find these optima. It also illustrates how mathematical models can be used in even this most challenging of situations in which the underlying epidemiology is ill-understood.

Risk‐based management of invading plant disease

“…A model of the spread of citrus canker has been successfully fitted to these data, and we used that model here, thereby following several previous studies that have considered constant radius management strategies (Cook et al ., ; Parnell et al ., , ; Cunniffe et al ., ). While the underlying model has been extensively validated for the spread of citrus canker in Miami (Neri et al ., ), it is flexible, and has been used for several other plant diseases, including huanglongbing (also known as citrus greening) (Parry et al ., ; Cunniffe et al ., ; Parnell et al ., ), Bahia bark scaling (Cunniffe et al ., ) and sudden oak death (Demon et al ., ; Thompson et al ., ).…”

Mathematical models are a powerful method to understand and control the spread of Huanglongbing

“…Their focus is on citrus canker but they include HLB as a second example, with the result that optimal roguing radii can be found dependent on the level of risk aversion of the grower. Similarly, Cunniffe et al (2014), which focuses on Bahia bark scaling of citrus, illustrates that mathematical models are able to provide useful recommendations for roguing and tree spacing strategies, even when epidemiological knowledge of the disease is limited.…”

Grower and regulator conflict in management of the citrus disease Huanglongbing in Brazil: A modelling study

“…Projected yield is important when considering the merits of control scenarios (Cunniffe, Koskella, et al, 2015;Cunniffe, Laranjeira, Neri, DeSimone, & Gilligan, 2014). We estimate yield using equations for yield in terms of disease severity (Bassanezi, Montesino, Gasparoto, Bergamin Filho, & Amorim, 2011), and disease severity in terms of time since infection (Bassanezi & Bassanezi, 2008); details are given in Appendix S1.…”