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BotRisk:基于气象数据模拟酿酒葡萄(Vitis vinifera L. cv. Riesling)的年度穗轴腐烂风险。

BotRisk: simulating the annual bunch rot risk on grapevines (Vitis vinifera L. cv. Riesling) based on meteorological data.

机构信息

Environmental Research and Innovation (ERIN) Department, LIST - Luxembourg Institute of Science and Technology, 41, rue du Brill, L-4422, Belvaux, Luxembourg.

Hochschule Geisenheim University, Institute of Phytomedicine, Von-Lade-Str. 1, D-65366, Geisenheim, Germany.

出版信息

Int J Biometeorol. 2020 Sep;64(9):1571-1582. doi: 10.1007/s00484-020-01938-5. Epub 2020 May 20.

Abstract

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 × 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 (CDD) 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) 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.

摘要

本研究旨在基于三个来自中欧葡萄种植区的长期(n = 3 × 7 = 21 个案例)评估数据集,模拟酿酒葡萄(Vitis vinifera L. cv. Riesling)上的年度束腐病(Botrytis cinerea)风险。通过窗格分析确定与累积度日(CDD)达到 5%严重度呈显著正相关(p < 0.01)的气象参数的时期。分析揭示了影响年度流行的五个关键天气组合(“事件”):芽出后温度较低、开花期干燥条件、开花后高温、转色期和转色后低温和高降水总量都与热时间早期流行有关。每个事件中的气象数据都用作束腐病风险模型“BotRisk”的输入。多元线性回归模型得到的调整决定系数(R)为 0.63。BotRisk 能够模拟年度流行的热时间位置,并基于此对年度束腐病风险进行分类:低、中或高风险。根据留一法交叉验证,21 个案例研究中有 11 个被正确分类。没有观察到由于位置引起的系统偏差,这表明可以将模型转移到具有类似气候条件的其他地点。BotRisk (i)代表了一种针对束腐病的作物文化和化学措施的新型葡萄栽培决策支持工具,(ii)可以估计在不断变化的环境条件下的束腐病风险。

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