Ecologically engineering a sustainable sugar beet landscape matrix informed by molecular tools, satellite imagery and bioeconomics.

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In England, sugar beet is grown on 100,000 ha of arable land, meeting half of domestic sugar demand. Yields are threatened by virus yellows (VY), comprising beet mild yellowing virus, beet chlorosis virus and beet yellows virus. These three viruses, all transmitted by the primary vector Myzus persicae, decrease the ability of the infected leaf to photosynthesize, therefore reducing yield. Given the recent 2022 derogation following our 'Rothamsted Model' forecast, the neonicotinoid seed treatment, Cruiser SB, has been authorised for use. Derogations are a temporary policy measure; our proposal looks to the future. We will address how the threats to sugar beet can be understood and mitigated.

We will develop a low cost, accurate LAMP assay to target all three virus types in the field and use this to quantify the number of effective 'non-crop' plant hosts that act as a virus reservoir for each virus type. We will estimate the weekly rate of virus spread under field conditions from a single inoculated source using field counts, hyperspectral drone technology and confirmatory LAMP assays, underpinning the next generation of models to provide more accurate forecasting and hence support better management decisions. The landscape matrix is key to understanding VY risk. Supported by a wealth of virus incidence data held by Rothamsted and using expertise at Cranfield, we will use satellite imagery to estimate the threat posed by the network of nearby oilseed rape crops and characterise the extent of field margins, a potential source of VY, measuring the spillover of VY into the adjacent crop. A bioeconomic model, led by Bristol, will be used to capture a grower's management decisions under different land use and payment options.

Our work is much needed. It will explain how the VY transmission pathway functions locally and at scale and will show how improved land use and crop rotation planning based on this knowledge could potentially reduce the virus risk.