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.
Highlights
The soil microbiome has been a great interest for its potentials in improving plant nutrient utilization and suppressing soil-borne diseases (Müller et al, 2016)
The other part of each sample was used for CHN analysis (Microanalysis Laboratory, University of Illinois, Urbana, IL, U.S.A.), and were quantified for other 26 characteristics, including one biotic feature: the soybean cyst nematode (SCN) eggs counts, and 25 abiotic characteristics: latitude and longitude of sampling areas, percentage of clay, sand, and silt, 12 elements (B, Ca, Cu, Fe, Mg, Mn, N, P, K, Na, S, and Zn), percent saturation (PS) of five elements (PS.Ca, PS.H, PS.K, PS.Mg, and PS.Na), cationexchange capacity (CEC), organic matter, and water pH
Soil Characteristics Were Not Associated with Crop Productivity
Summary
The soil microbiome has been a great interest for its potentials in improving plant nutrient utilization and suppressing soil-borne diseases (Müller et al, 2016). A soil microbiome composition could depend on abiotic and biotic factors, and variations in these factors may cause differences in crop productivity (Tkacz and Poole, 2015). A hypothesis for the crop productivity difference is that some beneficial and/or detrimental abiotic or biotic factors are unequally distributed in the bulk soils among areas in a field. A couple of studies have suggested the link between yield performances and soil microbiome differences for grape and millet (Debenport et al, 2015; Xu et al, 2015). This could be the case for field crops
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