MONitor and DETect

Projektinformationen

MONET

ID Finanzhilfevereinbarung: 101106698

DOI

10.3030/101106698

Projekt abgeschlossen

EK-Unterschriftsdatum 20 Juli 2023

Startdatum 1 Februar 2024

Enddatum 31 Januar 2026

Finanziert unter

• Marie Skłodowska-Curie Actions (MSCA)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 215 534,40

Koordiniert durch

TEAGASC - AGRICULTURE AND FOOD DEVELOPMENT AUTHORITY
Ireland

Periodic Reporting for period 1 - MONET (MONitor and DETect)

Berichtszeitraum: 2024-02-01 bis 2026-01-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Aphids are among the major global pests, causing significant economic damage to food and commodity crops. Barley/Cereal yellow dwarf virus (B/CYDV) is vectored by aphids and is one of the most important viral diseases in cereals and grass species in the family Poaceae, with yield losses ranging from 5% to 80%. Potato Virus Y (PVY) is transmitted by more than 40 aphid species and is fifth in the world’s top ten most important plant viruses. In potatoes, it causes substantial yield reduction and loss of tuber quality. The EU has indicated, through the EU Biodiversity Strategy 2030, the need to reduce pesticide use by 50% by 2030. Better diagnostic capabilities for B/CYDV and PVY enable targeted pest control, reducing unnecessary pesticide applications. MONET addressed these challenges by: (1) Advancing detection of B/CYDV and PVY; (2) Identifying the vector responsible for infection through surveying crops and aphid vectors. Knowing the principal vector and viral strain is crucial for improving DSS for farmers; (3) Investigating virus-vector interaction using viral transmission assays and life history analysis with primary aphid vectors and the most common strains of virus found in Ireland.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

New molecular diagnostics: RT-LAMP assays were developed and validated for BYDV-PAS, BYDV-MAV, and PVY, alongside a Real-time RT-PCR protocol for Yellow Dwarf Virus detection. These tools provide greater strain-level precision than the serological methods previously used in routine surveillance. More than sixty plant samples were tested using the newly developed RT-LAMP assay, confirming sensitivity and specificity. All three protocols were published openly on protocols.io and are freely available to researchers and diagnostic laboratories internationally.
National aphid and virus monitoring: Systematic monitoring was conducted using a network of suction and yellow pan traps across three locations in Ireland. More than 1000 aphids were processed and tested, generating a comprehensive seasonal dataset on aphid flight activity and species composition across Ireland's main arable regions. Monitoring confirmed BYDV-MAV as the dominant strain in Irish cereal crops and revealed that the rose grain aphid consistently occurs in spring barley and winter barley colonization, demonstrating distinct seasonal phenology compared to the English grain aphid.
Vector-virus interactions: Life-history experiments showed that an insecticide-resistant clone of the English grain aphid incurs a significant fitness cost relative to a susceptible clone, a finding with important implications for understanding how resistance evolves in field populations. Transmission efficiency experiments demonstrated that the rose grain aphid is an efficient vector for BYDV-MAV, a moderate vector for BYDV-PAS, and a poor vector for BYDV-PAV. A four-arm olfactometer bioassay was developed to investigate whether aphids prefer virus-infected plants to healthy ones, providing new insight into virus-vector manipulation.
Mathematical modelling: Life history and transmission data generated in MONET contributed to a collaborative modelling study, in which a stage-structured model was built to estimate aphid dispersal and virus spread at the field scale for specific vector-virus combinations. This work, carried out with external collaborators, provides a framework for building future decision support tools for growers.
Scientific outputs: A major output of the work will be four peer-reviewed publications: two are published, one is submitted, and one is under review. Three open diagnostic protocols and one open dataset were also published.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

Strain-specific diagnostics now openly available: Rapid, accessible molecular tools are now available for distinguishing between B/CYDV strains circulating in Irish crops. Publishing these protocols openly ensures that other research groups and diagnostic services can adopt and build on them without duplication of effort.
Rose grain aphid identified as a key vector: The rose grain aphid is an efficient vector of BYDV-MAV, the dominant strain in Irish cereal crops, and occurs regularly in spring and winter barley. This challenges the previous focus on the English grain aphid as the primary vector of concern in Ireland and has direct implications for how monitoring programmes and control decisions are structured.
Insecticide resistance affects vector biology: Life-history experiments on healthy barley plants showed that the insecticide-resistant English grain aphid clone (SA3) has significantly poorer life-history traits than the susceptible clone (SA27), suggesting that resistance comes at a biological cost. In a separate experiment, the susceptible clone showed no differences in life-history traits when fed on BYDV-MAV-infected plants compared with those fed on healthy plants, indicating that this virus strain does not manipulate the fecundity or longevity of the English grain aphid.
Field-scale modelling framework: The stage-structured model, developed in collaboration with Biomathematics and Statistics Scotland and the University of Liverpool, utilising life history and transmission data generated in MONET, provides a new tool for estimating how specific vector-virus combinations drive disease spread, creating the framework to move beyond oversimplified single-threshold systems.
Further uptake requires integrating these tools into national surveillance platforms and decision-support systems for farmers, and sustained monitoring of aphid populations and viral strain diversity as climate change reshapes pest pressure across Ireland and the UK.

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