Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia

Abstract

Context

Effective seed systems must distribute high-performing varieties efficiently, and slow or stop the spread of pathogens and pests. Epidemics increasingly threaten crops around the world, endangering the livelihoods of smallholder farmers. Responding to these challenges to food and economic security requires stakeholders to act quickly and decisively during the early stages of pathogen invasions, typically with limited resources. A current threat is the introduction of cassava mosaic virus in Southeast Asia.

Objectives

Our goal in this study is to provide a decision-support framework for efficient management of healthy seed systems, applied to cassava mosaic disease. The specific objectives are to (1) evaluate disease risk in disease-free parts of Cambodia, Lao PDR, Thailand, and Vietnam; (2) incorporate estimated risk of disease establishment with seed exchange survey data and whitefly spread in the landscape to model epidemic spread; and (3) identify candidate regions to be prioritized in seed system management.

Methods

We used machine learning to integrate disease occurrence, climate, topology, and land use, and network meta-population models of epidemic spread. We used scenario analyses to identify candidate priority regions for management.

Results and conclusions

The analyses allow stakeholders to evaluate strategic options for allocating their resources in the field, guiding the implementation of seed system programs and responses. Consistently targeting initially high priority locations with clean seed produced more favorable outcomes in this model, as did prioritization of a higher number of districts for the deployment of smaller volumes of clean seed.

Significance

The decision-support framework presented here can be applied widely to seed systems challenged by the dual goals of distributing seed efficiently and reducing disease risk. Data-driven approaches support evidence-based identification of optimized surveillance and mitigation areas in an iterative fashion, providing guidance early in an epidemic, and revising recommendations as data accrue over time.