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首页> 外文期刊>International Journal of Biometeorology: Journal of the International Society of Biometeorology >BotRisk: simulating the annual bunch rot risk on grapevines (Vitis vinifera L. cv. Riesling) based on meteorological data
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BotRisk: simulating the annual bunch rot risk on grapevines (Vitis vinifera L. cv. Riesling) based on meteorological data

机译：BOTRISK：基于气象数据模拟葡萄树（葡萄vinifera L.CV.RIESLING）的年度束腐败风险

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摘要

The aim of the present investigations was to simulate the annual risk of bunch rot (Botrytis cinerea) on Vitis vinifera L. cv. Riesling grapes based on three long-term (n = 3 x 7 = 21 cases) assessment data sets originating from three Central European grape-growing regions. Periods when meteorological parameters were significantly (p < 0.01) correlated with the cumulative degree day (CDD7;18;24) reaching 5% disease severity were determined by Window Pane analysis. Analyses revealed five critical weather constellations ("events") influencing annual epidemics: relatively low temperatures after bud break, dry conditions during flowering, high temperatures after flowering, and low temperatures and high precipitation sums during/after veraison were all associated with thermal-temporal early epidemics. Meteorological data in each of the five events served as input for the bunch rot risk model "BotRisk." The multiple linear regression model resulted in an adjusted coefficient of determination (R-adj.(2)) of 0.63. BotRisk enables (i) the simulation of the thermal-temporal position of the annual epidemic and, based on this, (ii) the classification of the annual bunch rot risk into three classes: low, medium, or high risk. According to leave-one-out cross-validation, 11 of 21 case studies were correctly classified. No systematic bias caused by location was observed, indicating that the transfer of the model into other locations with comparable climatic conditions could be possible. BotRisk (i) represents a novel viticultural decision support tool for crop cultural and chemical measures against bunch rot and (ii) enables an estimation of the bunch rot risk under changing environmental conditions.
机译：目前调查的目的是模拟在肺炎血管血管腐蚀（Botrytis Cinerea）的年风险。基于三个长期（n = 3 x 7 = 21例）评估数据集的RIESLING葡萄来自三个中欧葡萄种植区的评估数据集。当气象参数显着（p <0.01）与累积度日相关（CDD7; 18; 24）达到5％疾病严重程度，通过窗单分析确定。分析揭示了影响年度流行病的五个临界天气星座（“事件”）：芽突破的温度相对较低，开花期间的干燥条件，开花后的高温和温度和维森期间的低温和高降水量都与热颞有关早期的流行病。五个事件中的每一个中的气象数据都是束腐败风险模型“BotRisk”的输入。多元线性回归模型导致调整的判定系数（R-ACM。（2））为0.63。 BOTRISK使（i）仿真年度疫情的热时间位置，并基于这一点，（ii）将年度群体腐败风险分为三类：低，中等或高风险。根据休假交叉验证，21个案例研究中的11项被正确分类。未观察到由位置引起的系统偏差，表明该模型将模型转移到具有可比性气候条件的其他位置。 BOTRISK（i）代表了一种新的葡萄酒决策支持工具，用于作物文化和对抗串的化学措施，（ii）可以在不断变化的环境条件下估算群腐败风险。

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