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Composts from green sources show an increased suppressiveness to soilborne plant pathogenic fungi: Relationships between physicochemical properties, disease suppression, and the microbiome

机译:绿色来源的堆肥显示出对土壤植物致病性真菌的抑制力增加:物理化学性质,疾病抑制和微生物组之间的关系

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The need for studying tailored composts, as new feedstocks become available with time, increases the in-depth studies of suppressive composts derived from green sources and agro-wastes recycling. The composition, diversity and variability of microbiomes within a collection of 10 composts were investigated by amplicon sequencing and bioinformatics analyses. The observed differences in microbiome structure were related to the different compost origin. The multi-suppressive properties of composts from agricultural residues, agro-industrial co/by-products, and plant green-waste showed the most complex microbiome structure, which included either biocontrol agents associated with the control of Rhizoctonia damping-off in bean and Verticillium wilt in eggplant or microbial consortia for controlling Pythium damping-off in cucumber and zucchini and Phytophthora root rot in tomato and azalea. In contrast, the pathogen-specific property of composts from municipal solid waste and co-composted cow manure household waste showed a microbiome that overall included biocontrol agents against Fusarium wilt in tomato, melon and basil. The highest correlations between physicochemical properties, disease suppression, and the microbiome have allowed to make a helpful matrix to know how relationships among these variables of composts could be established and quantified for predicting their suppressive properties basing on the physicochemical properties and the microbiome.
机译:新原料随着时间的推移可用,需要研究量身定制的堆肥,增加了抑制堆肥的深入研究,源自绿色来源和农业废物回收。通过扩增子测序和生物信息学分析研究了10个堆肥集合中微生物体内的组成,多样性和可变性。观察到的微生物组结构的差异与不同的堆肥原点有关。来自农业残留物,农业工业股份,植物绿色废物的堆肥的多抑制性质显示出最复杂的微生物组结构,其中包括与控制豆类和牙植物中的rhizoctonia禁止的硼酮药物相关的生物控制剂在茄子或微生物混合中枯萎,用于控制黄瓜和西葫芦和番茄藻的植物和植物中的植物和番茄藻生根腐烂。相反,市政固体废物和共堆肥牛粪家庭废物的堆肥的病原体特性表现出一种微生物组,其总体包括对番茄,甜瓜和罗勒的枯萎病毒的生物控制剂。物理化学性质,疾病抑制和微生物组之间的最高相关性使得能够建立有用的基质,以知道如何建立和量化这些堆肥变量之间的关系,以预测其抑制性质基于物理化学性质和微生物组。

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