What question did this study set out to answer?

The study aims to model and assess bolting in sugar beet hybrids under varying autumn cultivation conditions.

February 28, 2026Open Access

Predictive modeling of sugar beet bolting via vernalization-intensity model and resilience assessment in diverse autumn cultivation environments

Key Points

The study aims to model and assess bolting in sugar beet hybrids under varying autumn cultivation conditions.
Evaluated ten experimental sugar beet hybrids and two controls over two cropping years.
Implemented randomized complete block design with four replications for agronomic evaluations.
Utilized vernalization–intensity model to estimate vernalization thresholds and sensitivity.
Conducted ANOVA and AMMI analysis to assess genotype-environment interactions.
Genotype BOL436 had the highest vernalization threshold but was sensitive to bolting.
BOL435 and BOL434 exhibited low sensitivity to bolting, making them more suitable for variable conditions.
AMMI analysis showed significant genotype-environment interaction contributing to yield variation.
Stable high-performing genotypes for white sugar yield identified include BOL375, BOL239, and BOL376.

Abstract

Climate change and increasing pressure on water resources have renewed interest in autumn cultivation of sugar beet, a practice that benefits from seasonal precipitation and reduces dependence on irrigation. However, bolting remains a major limitation, substantially affecting yield stability and the economic viability of production. This study evaluated ten experimental sugar beet hybrids and two bolting-resistant control varieties across three environments over two cropping years (2022–2023 and 2023–2024). Randomized complete block design with four replications was implemented for agronomic evaluations. The vernalization–intensity model was used to estimate vernalization threshold (VT) and bolting sensitivity. Genotype BOL436 exhibited the highest VT (134 h) but also showed pronounced sensitivity to bolting. In contrast, genotypes BOL435 and BOL434, with similarly high VT values, displayed low bolting sensitivity and thus greater suitability for fluctuating winter conditions. Combined ANOVA revealed significant genotype and environment effects (P < 0.01) for all measured traits, while genotype–environment interaction (GEI) significantly influenced white sugar yield (WSY) and root yield (RY). Additive main effects and multiplicative interactions (AMMI) analysis indicated that the first two interaction principal components (IPCs) accounted for 75.20% of GEI variation in WSY, whereas IPC1 alone explained 57.60% for RY. WAASB-based stability analysis identified BOL375, BOL239, BOL376, and BOL068 as stable, high-performing genotypes for WSY, with BOL239, BOL375, BOL068, and BOL435 showing comparable superiority for RY. Results from the WAASBY index were consistent with these findings. Multi-trait stability index (MTSI) analysis further highlighted BOL434, BOL376, and BOL239 as the most stable genotypes across agronomic and qualitative traits. Overall, the results demonstrate that autumn cultivation, supported by robust modeling and stability analysis, can contribute to reduced irrigation needs, while the identified genotypes offer promising options for enhancing productivity and minimizing bolting risk in water-limited environments.

Predictive modeling of sugar beet bolting via vernalization-intensity model and resilience assessment in diverse autumn cultivation environments

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Abstract

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