What drives yield formation in sugar beet? Quantifying functional components across genotypes and irrigation managements

Abstract

Improving sugar beet yield under variable environmental conditions requires a detailed understanding of the physiological mechanisms that drive yield formation. In sugar beet, canopy development determines resource capture, while radiation use efficiency (RUE) regulates the transformation efficiency of primary resources, and assimilate partitioning regulates the allocation of dry matter to the storage root. High-throughput phenotyping offers opportunities to quantify these physiological processes across diverse environments and genetic backgrounds, thereby identifying key traits for yield improvement. A scalable drone-based pipeline was established and validated to estimate physiological yield components – leaf area index (LAI), radiation interception efficiency (RIE), RUE, and harvest index (HI). Unmanned Aerial Vehicle (UAV)-derived multispectral imagery, combined with environmental records and harvest measurements, was used across more than 1300 field plots in Germany and Italy (2023–2024), covering three contrasting environments, two irrigation managements, and up to 171 genotypes. LAI estimation was calibrated and validated under different water regimes in northern Germany (mean absolute error, MAE = 0.30 m² m⁻²). Dynamic UAV-based LAI enabled continuous estimation of radiation interception and biomass accumulation. Total dry matter correlated strongly with cumulative effective (temperature-dependent) radiation interception (R² = 0.81), indicating a comparatively stable RUE across diverse conditions. Genotypic variation in yield formation was mainly driven by canopy-level processes: RIE accounted for 65 % of variation under water-limited conditions, while RUE accounted for 46 % under irrigation. Partitioning traits (HI and Sugar HI) contributed minimally in both irrigation managements. The results highlight the dominant role of canopy development and radiation use in sugar beet yield formation under contrasting environmental conditions. The proposed UAV-based framework provides a transferable, high-throughput approach to quantify physiological yield drivers in field settings. This enables targeted trait selection for breeding and facilitates integration of functional yield components into crop improvement strategies.

Plant phenotyping relevance

UAVマルチスペクトル画像によるLAI等の生理的形質推定パイプラインを構築・検証しており、フェノタイピング手法が研究の中心である。

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