Abstract
Breeding for Cercospora resistant sugar beet cultivars requires field experiments for testing resistance levels of candidate genotypes in conditions that are close to agricultural cultivation. Non-invasive spectral phenotyping methods can support and accelerate resistance rating and thereby speed up breeding process. In a case study, experimental field plots with strongly infected beet genotypes of different resistance levels were measured with two different spectrometers. Vegetation indices were calculated from measured wavelength signature to determine leaf physiological status, e.g., greenness with the Normalized Differenced Vegetation Index (NDVI), leaf water content with the Leaf Water Index (LWI) and Cercospora disease severity with the Cercospora Leaf Spot Index (CLSI). Indices values correlated significantly with visually scored disease severity, thus connecting the classical breeders’ scoring approach with advanced non-invasive technology.
Highlights
Sugar beets (Beta vulgaris spp. vulgaris) are frequently infested by the fungal pathogen Cercospora beticola that occurs in moderate climatic areas and causes yield loss between 40% and 100% [1,2].Plant protection against Cercospora leaf spot (CLS) usually includes crop rotation, fungicide application, and the use of resistant cultivars [1]
Breeding for CLS resistant cultivars requires field experiments for testing resistance levels of candidate genotypes in conditions that are close to agricultural cultivation
Germany) trial field were visually scored for CLS disease severity using the KWS scale scoring protocol [1]
Summary
Sugar beets (Beta vulgaris spp. vulgaris) are frequently infested by the fungal pathogen Cercospora beticola that occurs in moderate climatic areas and causes yield loss between 40% and 100% [1,2]. Non-invasive spectral phenotyping methods [4,5] can support disease scoring by providing information on the plant status [6]. This in turn has the potential to accelerate resistance rating and speed up the breeding process. We carried out visual scoring and non-invasive phenotyping on trial fields with sugar beet breeding genotypes with different levels of CLS disease severity. The fungus evokes changes in leaf properties that differ according to developmental stages and disease severity [24] Such changes result in modification of spectral signatures and thereby lead to alterations in index values. We acquired spectral data using a FieldSpec point spectrometer and a Tetracam Agriculture Digital Camera and compared these data with visual disease scoring
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