摘要
为探索中药材防治5种苹果主要真菌病害的应用价值,采用菌丝生长速率法,对供试的12种中药材浸提物进行室内毒力测定,并开展强抑菌作用的中药材浸提物的病害田间防治试验.结果表明,土荆皮Pseudolarix ama-bilis 浸提物对苹果轮纹病菌 Botryosphaeria dothidea、霉心病菌 Trichothecium roseum、炭疽病菌 Glomerella cinin-gularia、斑点落叶病菌Alternaria alternata、苹果树腐烂病菌Valsa mali的抑菌活性较强,EC50分别为0.91、1.98、0.65、0.81、0.95 g/L.混配后,12种中药材等量混配浸提物对苹果树腐烂病菌、轮纹病菌、霉心病菌增效作用最强,增效系数分别为2.71、4.43、16.21.田间防治试验中,土荆皮浸提物对苹果斑点落叶病防效为54.10％;刮除苹果枝干轮纹病瘤后,土荆皮浸提物对苹果轮纹病防效达54.78％.本研究明确了土荆皮及其与其他中药材混配的浸提物对苹果主要病害病原具有良好的室内抑菌作用,土荆皮浸提物在田间也表现出进一步开发为植物源杀菌剂的潜力.
Abstract
In order to explore the application value of Chinese herbal medicines in prevention of five major pathogenic fungal diseases of apple,extracts of 12 Chinese herbal medicines were subjected to toxicity measurement by using the mycelial growth rate method,and field effects were measured using herb extracts with high inhibitory effects.The results showed water extract of Pseudolarix amabilis presented strong antifungal activity against Botryosphaeria dothidea,Trichothecium roseum,Glomerella ciningularia,Alternaria alternata and Valsa mali,with EC50 values of 0.91,1.98,0.65,0.81 and 0.95 g/L,respectively.The extract of 12 Chinese herbal medicines by equal mixing had the strongest synergistic effect on V.mali,B.dothidea and T.roseum,with synergic ratio of 2.71,4.43,and 16.21,respectively.In the field experiments,the control efficacy of P.amabilis extract to apple Alternaria blotch was 54.10％,and reached 54.78％after scraping disease spots of apple ring rot.The above results demonstrated that the extracts of P.amabilis and its mixture with other Chinese herbal medicines had good indoor inhibitory effect on the pathogens of main apple diseases,and the extract of P.amabilis also presented the potential to be further developed as a botanical fungicide in the field.
基金项目
山东省果品产业技术体系病虫防治与质量控制岗位专家项目(SDAIT-06-11)
山东省科技型中小企业创新能力提升工程(2023TSGC0894)
山东省科技型中小企业创新能力提升工程(2023TSGC0829)
烟台市科技计划(2021NYNC015)
烟台市科技计划(2022XCZX094)
山东省自然科学基金重点项目(ZR2020KC026)
农业农村部农作物病虫鼠害疫情监测与防治项目(152307026)
山东省重点研发计划(2021CXGC010602)
山东省重点研发计划(2021CXGC010802)
国家科技期刊平台
NSTL
万方数据