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首页> 外文期刊>Acta Horticulturae >Modeling bud break phenology in 'Chardonnay' grapevine using the chill overlap model framework
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Modeling bud break phenology in 'Chardonnay' grapevine using the chill overlap model framework

机译:使用Chill重叠模型框架在'Chardonnay'Grapevine中建模芽脱损候选

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摘要

Bud break time in grapevines (Vitis vinifera) defines growth cycle onset, and is strongly sensitive to temperature. Delays during this stage can have impacts on the whole growth cycle, making it a key phenological stage. Changing temperatures, due toclimate change, make the need for accurate models to predict phenological patterns increasingly relevant, potentially affecting vineyard management, establishment and adaptability. 'Chardonnay' bud break data from Californian and Spanish wine regions were used to estimate chilling requirements, and the compensatory relationship of overlapping chill and heat phases during specific temperature accumulation periods. Considerable variation in day of the year observation data, and diversification of climatesacross locations, enhanced the performance reliability of the model, leading to more accurate predictions over different climates. Preliminary evaluation of the model yielded acceptable model performance. However, variation due to the use of different criteria to define phenological stages, differences in microclimatic conditions, clonal variability among vineyards and vineyard management practices may be important factors to be considered for further increasing model accuracy. The chill-overlap modelprovided a framework for predicting bud-break in grapes but there is a necessity for deeper analyses in order to develop a more robust global model.
机译:葡萄葡萄酒中的芽休息时间(血管vinifera)定义生长周期发作,对温度非常敏感。在此阶段的延迟可能会对整个生长周期产生影响,使其成为关键的候选阶段。不断变化的温度变化,需要准确的模型来预测诸如越来越相关的毒品模式,可能影响葡萄园管理,建立和适应性。从加州和西班牙葡萄酒地区的“霞多丽的芽中断数据用于估计冷却要求,以及在特定温度累积期间重叠的寒冷和热阶段的补偿关系。在年度观测数据中的一天中的相当大变异,以及攀升的历史流程的多样化,增强了模型的性能可靠性,导致更准确的预测对不同的气候。模型的初步评估产生了可接受的模型性能。然而,由于使用不同标准来定义毒性阶段的变化,微跨越条件的差异,葡萄园和葡萄园管理实践中的克隆变异性可能是为了进一步提高模型准确性而被认为是重要的因素。 Chill-Remplyap Model采用框架来预测葡萄中的萌芽,但是必须更深入的分析,以便开发更强大的全球模型。

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