Soil microbial legacy drives crop diversity advantage: Linking ecological plant–soil feedback with agricultural intercropping

Abstract

Although the importance of the soil microbiome in mediating plant community structures and functions has been increasingly emphasized in ecological studies, the biological processes driving crop diversity overyielding remain unexplained in agriculture. Based on the plant–soil feedback (PSF) theory and method, we quantified to what extent and how soil microbes contributed to intercropping overyielding. Soils were collected as inocula and sequenced from a unique 10‐year field experiment, consisting of monoculture, intercropping and rotation planted with wheat (Triticum aestivum), maize (Zea mays) or faba bean (Vicia faba). A PSF greenhouse study was conducted to test microbial effects on three crops' growth in monoculture or intercropping. In wheat & faba bean (W&F) and maize & faba bean (M&F) systems, soil microbes drove intercropping overyielding compared to monoculture, with 28%–51% of the overyielding contributed by microbial legacies. The overyielding effects resulted from negative PSFs in both systems, as crops, in particular faba bean grew better in soils conditioned by other crops than itself. Moreover, faba bean grew better in soils from intercropping or rotation than from the average of monocultures, indicating a strong positive legacy effect of multispecies cropping systems. However, with positive PSF and negative legacy benefit effect of intercropping/rotation, we did not observe significant overyielding in the W&M system. With more bacterial and fungal dissimilarities by metabarcoding in heterospecific than its own soil, the better it improved faba bean growth. More detailed analysis showed faba bean monoculture soil accumulated more putative pathogens with higher Fusarium relative abundance and more Fusarium oxysporum gene copies by qPCR, while in heterospecific soils, there were less pathogenic effects when cereals were engaged. Further analysis in maize/faba bean intercropping also showed an increase of rhizobia relative abundance. Synthesis and applications. Our results demonstrate a soil microbiome‐mediated advantage in intercropping through suppression of the negative PSF of pathogens and increasing beneficial microbes. As microbial mediation of overyielding is context‐dependent, we conclude that the dynamics of both beneficial and pathogenic microbes should be considered in designing cropping systems for sustainable agriculture, particularly including combinations of legumes and cereals.

Long-Term Maize-Legume Intercropping Shifts Structure and Composition of Soil Microbiome with Potential Impacts on Ecosystem Services and Food Safety

“…Generally, research supports the later, i.e., an increased diversity of soil microorganisms in intercropping systems, particularly fungal microbes. For example, in wheat-soybean intercropping (Bargaz et al 2017), maize/wheat-faba bean intercropping (Wang et al 2020), millet-mung bean intercropping (Dang et al 2020), and cucumber-cereal intercropping (Li and Wu 2018). Tang et al (2020), observed a higher diversity of both bacterial and fungal species in cassava-peanut intercropping, but also peanut monoculture, suggesting that the leguminous peanut intercrop was responsible for the observed higher soil microbial diversity, a factor we could not correct for in our study.…”

Diverse recruitment to a globally structured atmospheric microbiome

“…Calculation of geographic distances were performed using R package geosphere 35 function distGeo with WGS84 ellipsoid. Source tracking was conducted using FEAST 36 with data from other studies (processed using dada2 following the same parameters as this study) as additional sources/sinks [37][38][39][40][41][42][43] and NCBI BioProject PRJEB42801. For correlation analysis between abiotic and biotic variables the Pearson correlation coefficient for multiple pairwise combinations were calculated using the R package Corrplot 44 .…”

“…Aerosolization of microorganisms not only occurs from soil but also from different terrestrial and aquatic surfaces, e.g. ocean surface waters 3,20 , the phyllosphere 33,39 and stochastic desert dust events 40,41 . We therefore employed a statistical classification algorithm to estimate recruitment to air microbiomes from the surface habitats of different climatic regions (Fig.…”

“…Clear patterns for terrestrial sources were apparent. Dryland soils (dry deserts, polar/alpine and dry continental locations) were pronounced sources for bacteria globally and this may reflect the more readily aerosolised non-cohesive soils typical of these biomes 41 . This expands the influence of deserts to a global scale beyond the well-defined intercontinental desert dust transit routes for microbial dispersal 40 .…”

“…For high-altitude air, major sources were dry deserts and polar/alpine sources and this likely reflects in part the adaptive advantages that taxa from these habitats have in air, e.g. UV repair and desiccation tolerance 41 . The ability to become aerosolized may vary between taxa in marine 34 and terrestrial 42 systems and so deterministic biotic drivers may also be relevant to recruitment from sources, as well as selective deposition during transit 43 .…”