Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia
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 se...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2025
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/175693 |
| _version_ | 1855527169543897088 |
|---|---|
| author | Andersen Onofre, K.F. Delaquis, E. Newby, J.C. Haan, Stef de Thuy, C.T.L. Minato, N. Legg, James P. Cuéllar, Wilmer Jose Briseño, R.I.A. Garrett, K.A. |
| author_browse | Andersen Onofre, K.F. Briseño, R.I.A. Cuéllar, Wilmer Jose Delaquis, E. Garrett, K.A. Haan, Stef de Legg, James P. Minato, N. Newby, J.C. Thuy, C.T.L. |
| author_facet | Andersen Onofre, K.F. Delaquis, E. Newby, J.C. Haan, Stef de Thuy, C.T.L. Minato, N. Legg, James P. Cuéllar, Wilmer Jose Briseño, R.I.A. Garrett, K.A. |
| author_sort | Andersen Onofre, K.F. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace175693 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1756932026-01-07T14:32:48Z Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia Andersen Onofre, K.F. Delaquis, E. Newby, J.C. Haan, Stef de Thuy, C.T.L. Minato, N. Legg, James P. Cuéllar, Wilmer Jose Briseño, R.I.A. Garrett, K.A. cassava seed systems plant disease control plant viruses 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. 2025-10 2025-07-18T20:42:00Z 2025-07-18T20:42:00Z Journal Article https://hdl.handle.net/10568/175693 en Open Access application/pdf Elsevier Andersen Onofre, K.F.; Delaquis, E.; Newby, J.C.; De Haan, S.; Thuy, C.T.L.; Minato, N.; Legg, J.P.; Cuellar, W.J.; Briseño, R.I.A.; Garrett, K.A. 2025. Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia. Agricultural Systems. ISSN 1873-2267. 229, 104435. https://doi.org/10.1016/j.agsy.2025.104435 |
| spellingShingle | cassava seed systems plant disease control plant viruses Andersen Onofre, K.F. Delaquis, E. Newby, J.C. Haan, Stef de Thuy, C.T.L. Minato, N. Legg, James P. Cuéllar, Wilmer Jose Briseño, R.I.A. Garrett, K.A. Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia |
| title | Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia |
| title_full | Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia |
| title_fullStr | Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia |
| title_full_unstemmed | Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia |
| title_short | Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia |
| title_sort | decision support for managing an invasive pathogen through efficient clean seed systems cassava mosaic disease in southeast asia |
| topic | cassava seed systems plant disease control plant viruses |
| url | https://hdl.handle.net/10568/175693 |
| work_keys_str_mv | AT andersenonofrekf decisionsupportformanaginganinvasivepathogenthroughefficientcleanseedsystemscassavamosaicdiseaseinsoutheastasia AT delaquise decisionsupportformanaginganinvasivepathogenthroughefficientcleanseedsystemscassavamosaicdiseaseinsoutheastasia AT newbyjc decisionsupportformanaginganinvasivepathogenthroughefficientcleanseedsystemscassavamosaicdiseaseinsoutheastasia AT haanstefde decisionsupportformanaginganinvasivepathogenthroughefficientcleanseedsystemscassavamosaicdiseaseinsoutheastasia AT thuyctl decisionsupportformanaginganinvasivepathogenthroughefficientcleanseedsystemscassavamosaicdiseaseinsoutheastasia AT minaton decisionsupportformanaginganinvasivepathogenthroughefficientcleanseedsystemscassavamosaicdiseaseinsoutheastasia AT leggjamesp decisionsupportformanaginganinvasivepathogenthroughefficientcleanseedsystemscassavamosaicdiseaseinsoutheastasia AT cuellarwilmerjose decisionsupportformanaginganinvasivepathogenthroughefficientcleanseedsystemscassavamosaicdiseaseinsoutheastasia AT brisenoria decisionsupportformanaginganinvasivepathogenthroughefficientcleanseedsystemscassavamosaicdiseaseinsoutheastasia AT garrettka decisionsupportformanaginganinvasivepathogenthroughefficientcleanseedsystemscassavamosaicdiseaseinsoutheastasia |