While the spread of plant disease depends strongly on biological factors controlling transmission, epidemics clearly also have a human dimension. Disease control depends on decisions made by individual growers, who are in turn influenced by a broad range of factors. Despite this, human behaviour has rarely been included in plant epidemic models. Considering Cassava Brown Streak Disease, we model how the perceived benefit of disease control influences participation in clean seed systems (CSS). Our models are rooted in game theory, with growers making strategic decisions based on the expected profitability of different control strategies. We find that both the information used by growers to assess profitability and the perception of economic and epidemiological parameters influence long-term participation in the CSS. Over-estimation of infection risk leads to lower participation in the CSS, as growers perceive that paying for the CSS will be futile. Additionally, though the CSS can reduce the burden of disease, and allow a scenario in which all growers control, disease is not eliminated from the system. These results are robust to stochastic and spatial effects. Our work highlights the importance of including human behaviour in plant disease models, but also the significance of how that behaviour is included.

While the spread of plant disease depends strongly on biological factors driving transmission, it also has a human dimension. Disease control depends on decisions made by individual growers, who are in turn influenced by a broad range of factors. Despite this, human behaviour has rarely been included in plant epidemic models. Considering Cassava Brown Streak Disease, we model how the perceived increase in profit due to disease management influences participation in clean seed systems (CSS). Our models are rooted in game theory, with growers making strategic decisions based on the expected profitability of different control strategies. We find that both the information used by growers to assess profitability and the perception of economic and epidemiological parameters influence long-term participation in the CSS. Over-estimation of infection risk leads to lower participation in the CSS, as growers perceive that paying for the CSS will be futile. Additionally, even though good disease management can be achieved through the implementation of CSS, and a scenario where all controllers use the CSS is achievable when growers base their decision on the average of their entire strategy, CBSD is rarely eliminated from the system. These results are robust to stochastic and spatial effects. Our work highlights the importance of including human behaviour in plant disease models, but also the significance of how that behaviour is included.

Population-scale effects of resistant or tolerant crop varieties have received little consideration from epidemiologists. When growers deploy tolerant crop, population-scale disease pressures are often unaffected. This only benefits growers using tolerant varieties, selfishly decreasing yields for others. However, resistant crop can reduce disease pressure for all. We coupled an epidemiological model with game theory to understand how this affects uptake of control. Each time a grower plants a new crop, they must decide whether to use an improved (i.e. tolerant/resistant) or unimproved variety. This decision is based on strategic-adaptive expectations in our model, with growers comparing last season’s profit with an estimate of what is expected from the alternative crop. Despite the positive feedback loop promoting use of a tolerant variety whenever it is available, a mixed unimproved- and tolerant-crop equilibrium can persist. Tolerant crop can also induce bistability between a scenario in which all growers use tolerant crop and the disease-free equilibrium, where no growers do. However, due to ‘free-riding’ by growers of unimproved crop, resistant crop nearly always exists in a mixed equilibrium. This work highlights how growers respond to contrasting incentives caused by tolerant and resistant varieties, and the distinct effects on yields and population-scale deployment.

Disease management often involves genetically improved crops. Resistant varieties are less susceptible, and so less likely to act as reservoirs of inoculum. Tolerant varieties can be highly susceptible, but limit yield loss for those who grow them. Population-scale effects of deploying resistant or tolerant varieties have received little consideration from epidemiologists. We examined how tolerant and resistant crop have opposing consequences upon the uptake of control using a behavioural model based on strategic-adaptive expectations. Growers compared last season's profit with an estimate of what could be expected from the alternative crop type, thereby assessing whether to alter their strategy for the next season. Tolerant crop only benefited growers using it, decreasing yields for others. This incentivises widespread use via a negative feedback loop. Resistant crop was more widely beneficial, with reduced population-scale disease pressure leading to increased yields for all. However, this positive externality allows growers who do not deploy resistant crop to "free-ride" upon the management of others. This work highlights how a community of growers responds to the contrasting incentives caused by tolerant and resistant crop varieties, and how this leads to very distinct effects on yields and population-scale deployment.

Citrus greasy spot (CGS), caused by Zasmidium citri, induces premature defoliation and yield loss in Citrus spp. The epidemiology of CGS is well understood in high humidity areas, but remains unaddressed in Brazil, despite differing climatic conditions and disease management practices. The spatiotemporal dynamics of CGS were characterized in the Recôncavo of Bahia (Brazil) at four hierarchical levels (quadrant, plant, grove, and region). A survey conducted in 19 municipalities found the disease to be present throughout the region with an incidence of 100% in groves and plants, and higher than 70% on leaves. Index of dispersion (D) values suggest the spatial pattern of units with symptoms lies between random and regular. This was confirmed by the parameters of the binary power law for plants and their quadrants (log[A] < 0 and b < 1). No consistent differences were observed in the disease incidence at different plant heights. We introduce a compartmental model synthesizing CGS epidemiology. The collected data allow such a model to be parameterized, albeit with some ambiguity over the proportion of new infections that result from inoculum produced within the grove versus external sources of infection. By extending the model to include two populations of growers—those who control and those who do not—coupled by airborne inoculum, we investigate likely performance of cultural controls accessible to citrus growers in northeastern Brazil. The results show that control via removal of fallen leaves can be very effective. However, successful control is likely to require area‐wide strategies in which a large proportion of growers actively manage disease.