Spatial dynamics and control of a crop pathogen with mixed-mode transmission

Abstract

Trade or sharing that moves infectious planting material between farms can, for vertically-transmitted plant diseases, act as a significant force for dispersal of pathogens, particularly where the extent of material movement may be greater than that of infected vectors or inoculum. The network over which trade occurs will then effect dispersal, and is important to consider when attempting to control the disease. We consider the difference that planting material exchange can make to successful control of cassava brown streak disease, an important viral disease affecting one of Africa's staple crops. We use a mathematical model of smallholders’ fields to determine the effect of informal trade on both the spread of the pathogen and its control using clean-seed systems, determining aspects that could limit the damage caused by the disease. In particular, we identify the potentially detrimental effects of markets, and the benefits of a community-based approach to disease control.

Cassava brown streak disease: historical timeline, current knowledge and future prospects

“…Studies have shown that CBSD spread and development are enhanced by high disease pressure, the use of susceptible genotypes and high whitefly numbers (Katono et al, 2015). CBSD is dispersed locally and over long distances through the trade transportation of infected planting material, whereas whiteflies are only able to disperse and amplify CBSD locally (McQuaid et al, 2017).…”

“…Mathematical modelling has shown that, in order for the clean seed system to be sustainable, multiplication sites should only be set up in areas with low disease pressure and low vector population density (McQuaid et al, 2015). Modelling has also shown that, to reduce CBSD dispersal and increase cassava yields, virusfree planting material should be distributed to a number of different growers across a widespread area with restricted trade (McQuaid et al, 2017). Once certified virus-clean material has been distributed, farmers must also be thoroughly trained in the identification of disease symptoms to enable sufficient roguing to further reduce CBSD spread McQuaid et al, 2015).…”

Cassava planting material movement and grower behaviour in Zambia: implications for disease management

“…At the same time, in order to attempt control on a regional scale, without which any local attempts at control will ultimately fail, it is important to understand how the viruses spread between fields and across distance. This is particularly relevant in the context of grower behaviour when considering the movement of planting material, which has been shown to be key in the spread of cassava viruses (Legg et al 2014; Legg et al 2011; McQuaid et al 2017b; Patil et al 2015).…”

“…Although it has been shown that both CMD and CBSD pandemics depend strongly on the exchange of planting material (McQuaid et al 2017b), and that growers often share their cuttings with friends and family (Houngue et al 2018; Kombo et al 2012; Ntawuruhunga et al 2007; Teeken et al 2018) there is a lack of studies that assess the distances over which this material is moved depending on sources or destinations. Thus, the primary objective of the current study was to obtain insight into the nature of the flow of cassava planting material into and out of the growers’ fields (specifically the volume moved over distance) depending on sources or destinations.…”

Modeling Epidemics in Seed Systems to Guide Management Strategies: The Case of Sweetpotato in Northern Uganda

“…Seed systems are amenable to network analysis because they are inherently networks with a suite of actors (network nodes) that move both genetic material and information through space and time (dynamic or static network links) (Pautasso 2015). Until recent years, plant disease epidemiologists have given relatively little attention to the study of seed system (and plant trade) networks, although the movement of planting material plays a fundamental role in the spread of plant disease and the persistence of epidemics (Buddenhagen et al 2017;Garrett et al 2017;McQuaid et al 2017;Nelson and Bone 2015;Pautasso 2015;Pautasso and Jeger 2008). Increasing availability of computational tools, coupled with advances in network analysis in the medical and social sciences, makes the implementation of network analysis for plant disease epidemiology more obtainable.…”