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设施栽培条件下月季灰霉病预测模型的建立及应用
李垚1,蔡世熊1,张肇鹏2,徐扬3,王志军4,马男1,徐彦杰1,高俊平1*
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(1.中国农业大学 园艺学院/花卉发育与品质调控北京市重点实验室,北京 100193;2.弥勒品元园艺有限公司,云南 弥勒 652300;3.北京市昌平区园林绿化局,北京 102299;4.北京永安园林绿化有限责任公司,北京 102200)
摘要:
为有效防治月季灰霉病发生,保障切花产品采后品质,以‘粉蝴蝶’(‘Vuvuzela’)和‘雪山’(‘Avalanche’)为试验材料,实地调查设施内灰霉病发病情况,监测温室内温湿度,获取当地降雨量等气象资料,构建了月季切花设施生产灰霉病预测模型,并利用这一模型进行灰霉病防治的实际指导。结果表明:1)‘粉蝴蝶’‘雪山’的灰霉病病情指数与设施内的均温和均湿呈线性相关,据此建立灰霉病预测模型,Y=4.761-0.332X1+4.930X2+0.015X3;这一预测模型经设施内实测验证,平均绝对误差(MAE)=0.20,平均绝对百分误差(MAPE)=0.31,表明可有效预测7日内设施条件下月季切花灰霉病发病情况。2)‘粉蝴蝶’‘雪山’在贮藏和瓶插期的灰霉病发病率与栽培中病情指数预测值呈正相关。当病情指数为Y=0.33的低预测值时,切花产品在模拟运输1 d+冷库贮藏5 d后,发病率为5.0%;当病情指数为Y=2.09的高预测值时,切花产品在相同处理后发病率可达35.0%。3)基于预测模型进行病害爆发前3 d喷药的早期防治处理,采收当天的病情指数从2.33降为1.05,处于中度风险等级;切花瓶插期间的灰霉病发病率从30.0%降低到了10.0%。综上,利用本模型进行早期防治,可提早遏制病害扩散,为月季真菌病害的监测与科学防治提供支撑。
关键词:  月季切花  温室生产  灰霉病  监测  防治
DOI:10.11841/j.issn.1007-4333.2024.04.16
投稿时间:2023-10-07
基金项目:科技部重点基础研究专项(2018YFD1000400)
Establishment and application of a prediction model for gray mold disease in protected cultivation of cut rose
LI Yao1, CAI Shixiong1, ZHANG Zhaopeng2, XU Yang3, WANG Zhijun4, MA Nan1, XU Yanjie1, GAO Junping1*
(1.College of Horticulture, China Agricultural University/Beijing Key Laboratory of Development and Quality Control of Ornamental Crops, Beijing 100193, China;2.Pinyuan Horticulture Company, Mile 652300, China;3.Bureau of Landscape and Forestry of Changping, Beijing 102299, China;4.Yongan Landscaping Company, Beijing 102200, China)
Abstract:
To effectively prevent the occurrence of gray mold disease in cut rose flowers and guarantee their postharvest quality after harvesting, ‘Vuvuzela’ and ‘Avalanche’ were used as experimental materials to conduct an on-site investigation on the incidence of gray mold in the facility, monitor temperature and humidity inside the facility, obtain rainfall data from local meteorological stations, and construct a prediction model for gray mold disease in the production of rose cut flower facility. The results showed that:. 1) Disease index of ‘Vuvuzela’ and ‘Avalanche’ was linearly correlated with the average temperature and humidity inside greenhouse. Based on this, a prediction model of gray mold was established, Y=4.761-0.332X1+4.930X2+0.015X3. The prediction model was validated through on-side measurements, with an average absolute error (MAE) of 0.20 and an average absolute percentage error (MAPE) of 0.31. Therefore, the prediction model can effectively predict the incidence of gray mold in protected cultivation of cut rose within 7 days. 2) Gray mold incidences of ‘Vuvuzela’ and ‘Avalanche’ during storage and vase period were positively correlated with predicted value of disease index in cultivation. When prediction value of disease index was Y=0.33, the gray mold incidence of cut flowers was less than 5.0% after 1 day of simulated transportation and 5 d of cold storage. whereas, when prediction value of disease index was a high predictive value of Y=2.09, the gray mold incidence of cut flowers could reach 35.0% after cold storage. 3) Early prevention and control measures were taken by spraying pesticides three days before the outbreak of the disease in the prediction model. The disease index on the day of harvesting decreased from 2.33 to 1.05, indicating a moderated risk level; the incidence rate of gray mold during vase period reduced from 30.0% to 10.0%. In conclusion, using this prediction model for early prevention and control can prevent the spread of disease in advance, providing support for the monitoring and scientific control of rose fungal disease.
Key words:  cut rose  protected production  gray mold  monitoring  prevention