Modelling coffee leaf rust risk in Colombia with climate reanalysis data

Abstract

Many fungal plant diseases are strongly controlled by weather, and global climate change is thus likely to have affected fungal pathogen distributions and impacts. Modelling the response of plant diseases to climate change is hampered by the difficulty of estimating pathogen-relevant microclimatic variables from standard meteorological data. The availability of increasingly sophisticated high-resolution climate reanalyses may help overcome this challenge. We illustrate the use of climate reanalyses by testing the hypothesis that climate change increased the likelihood of the 2008–2011 outbreak of Coffee Leaf Rust (CLR, Hemileia vastatrix) in Colombia. We develop a model of germination and infection risk, and drive this model using estimates of leaf wetness duration and canopy temperature from the Japanese 55-Year Reanalysis (JRA-55). We model germination and infection as Weibull functions with different temperature optima, based upon existing experimental data. We find no evidence for an overall trend in disease risk in coffee-growing regions of Colombia from 1990 to 2015, therefore, we reject the climate change hypothesis. There was a significant elevation in predicted CLR infection risk from 2008 to 2011 compared with other years. JRA-55 data suggest a decrease in canopy surface water after 2008, which may have helped terminate the outbreak. The spatial resolution and accuracy of climate reanalyses are continually improving, increasing their utility for biological modelling. Confronting disease models with data requires not only accurate climate data, but also disease observations at high spatio-temporal resolution. Investment in monitoring, storage and accessibility of plant disease observation data are needed to match the quality of the climate data now available.This article is part of the themed issue ‘Tackling emerging fungal threats to animal health, food security and ecosystem resilience’.

Many unreported crop pests and pathogens are probably already present

“…However, most of the epidemiological data required for this pest was unavailable, and the risk assessment was thus based on expert opinion or data from related pests (Jeger et al, 2017). For example, coffee leaf rust fungus (Hemileia vastatrix) has affected coffee production for more than a century, but a recent infection model relied upon temperature response functions derived from the single available study published three decades previously (Bebber, Castillo, & Gurr, 2016). For example, coffee leaf rust fungus (Hemileia vastatrix) has affected coffee production for more than a century, but a recent infection model relied upon temperature response functions derived from the single available study published three decades previously (Bebber, Castillo, & Gurr, 2016).…”

Expecting the unexpected: factors influencing the emergence of fungal and oomycete plant pathogens

“…For example, reductions in the diurnal thermal amplitude decreased the latency period of coffee rust in Central and South America. Together with economic factors, such as the use of susceptible cultivars as a result of high initial investment needed for cultivar replacement, variable rainfall and extreme weather conditions thus led to coffee rust outbreaks in 2008-2013 which directly affected the livelihoods of thousands of smallholder farmers (Avelino et al, 2015;Bebber et al, 2016) (Fig. 1).…”

Tackling emerging fungal threats to animal health, food security and ecosystem resilience

“…B 371: 20160332 categories of pathogen. For instance, both Clare et al [17] and Bebber et al [23] argue that correlative statistical models parametrized from statistical data (such as climate variables), while having utility as a tool to indicate future trends, are prone to great uncertainty. Clearly, preparedness in combating fungal disease emergence needs refined epidemiological tools, and forward process-based models based on experimentally derived variables were cited as one example; evidently, the mathematical modelling of fungal diseases is an area that has the potential to give much but that also needs further funding to make this field attractive for quantitative epidemiologists.…”

“…However, and as shown by several articles in this issue [5,12,23,29], newly virulent strains of crop pathogens have the potential to cause losses on a scale that can precipitate famines. Godfray et al [30] examined this issue in some depth by studying the effects of an outbreak of rice disease that resulted in 80% loss of yield across Southeast Asia using the IMPACT economic model [31].…”