PDF(Pubmed)
Abstract：
Areas within an agricultural field in the same season often differ in crop productivity despite having the same cropping history, crop genotype, and management practices. One hypothesis is that abiotic or biotic factors in the soils differ between areas resulting in these productivity differences. In this study, bulk soil samples collected from a high and a low productivity area from within six agronomic fields in Illinois were quantified for abiotic and biotic characteristics. Extracted DNA from these bulk soil samples were shotgun sequenced. While logistic regression analyses resulted in no significant association between crop productivity and the 26 soil characteristics, principal coordinate analysis and constrained correspondence analysis showed crop productivity explained a major proportion of the taxa variance in the bulk soil microbiome. Metagenome-wide association studies (MWAS) identified more Bradyrhizodium and Gammaproteobacteria in higher productivity areas and more Actinobacteria, Ascomycota, Planctomycetales, and Streptophyta in lower productivity areas. Machine learning using a random forest method successfully predicted productivity based on the microbiome composition with the best accuracy of 0.79 at the order level. Our study showed that crop productivity differences were associated with bulk soil microbiome composition and highlighted several nitrogen utility-related taxa. We demonstrated the merit of MWAS and machine learning for the first time in a plant-microbiome study.
摘要：
尽管具有相同的种植历史，但同一季节的农田内的区域通常在作物生产率上有所不同。作物基因型，和管理实践。一种假设是，土壤中的非生物或生物因素在不同地区之间有所不同，从而导致这些生产力差异。在这项研究中,从伊利诺伊州六个农艺田中高生产力和低生产力地区收集的散装土壤样品进行了量化，以确定其非生物和生物特征。从这些散装土壤样品中提取的DNA进行shot弹枪测序。虽然逻辑回归分析显示作物生产力与26种土壤特性之间没有显着关联，主坐标分析和约束对应分析显示，作物生产力解释了土壤微生物群中分类单元差异的主要部分。宏基因组范围关联研究（MWAS）在生产力较高的地区和更多的放线菌中发现了更多的缓生菌和伽玛变形菌，子囊,Planctomycetales,在生产力较低的地区和链霉菌。使用随机森林方法的机器学习基于微生物组组成成功预测了生产率，在订单水平上的最佳精度为0.79。我们的研究表明，作物生产力差异与整体土壤微生物组组成有关，并突出了几个与氮素效用相关的分类单元。我们首次在植物微生物组研究中证明了MWAS和机器学习的优点。
