UAV Phenotyping and Genomic Prediction of Ground Cover can Accelerate Organic Spring Cereal Breeding

Abstract

Abstract Ground cover is a key trait in organic cereal production, contributing to weed suppression, soil protection, and yield stability. Assessing canopy development manually is labor-intensive, whereas unmanned aerial vehicle (UAV) phenotyping offers a high-throughput and objective alternative. In this study, we evaluated the potential of UAV-derived ground cover measurements combined with genomic prediction to support breeding for organic spring oat ( Avena sativa L.) and wheat ( Triticum aestivum L.). A diverse panel of 461 oat genotypes and 218 spring wheat genotypes were phenotyped using UAVs in May and June. Ground cover exhibited substantial variation in May (oat: mean = 0.43, SD = 0.31; wheat: mean = 0.36, SD = 0.35) and approached to maturity in June for Oat. Narrow-sense heritabilities were intermediate for both species and growth stages (0.30–0.50), indicating potential for breeding for increased ground cover. Genomic prediction models showed higher accuracy for early-season ground cover (May: 0.45) compared with later stages (June: 0.35), consistent with greater phenotypic variation at early growth. Positive correlations were observed between genetic values for early ground cover and grain yield suggesting that selection for early canopy development can be very useful for organic farming. These results demonstrate that UAV-based phenotyping, integrated with genomic prediction, provides an efficient strategy for selecting competitive, high-yielding cultivars in organic spring cereals, particularly through early-season canopy traits.

Plant phenotyping relevance

UAVによる地上被覆の大規模な表現型取得とゲノム予測への統合が研究の中心であり、育種利用を技術的に評価している。

Code and data availability

The supplied article blocks contain no data availability statement, no public phenotype dataset, UAV imagery, or author code repository. The paper describes UAV ground cover phenotyping and genomic prediction (DMU software, R), but no public deposit or URL for the underlying data or analysis scripts is provided.

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