AgEvidence: a dataset to explore agro-ecological effects of conservation agriculture.

Conservation agriculture (CA) is a set of principles thought to be able to enhance crop productivity while minimizing impacts on the environment. The evidence base for CA can be challenging to synthesize because it encompasses many different practices and social and agroecological outcomes. To facilitate synthesis of CA evidence we have created a dataset organizing 218 response variables from five common categories of CA: cover crops, tillage management, pest management, nutrient management, and crop diversification. These data cover the Midwestern United States (U.S.) from 1980-2020. The dataset is also summarized and visualized on the AgEvidence website, which enables users to interactively explore, filter, and download data. We hope this dataset will help a wide variety of stakeholders, including researchers, policy makers, advocacy groups, and growers access the evidence needed to make informed and impactful decisions about how to produce food with less negative environmental impact.

Selection pressure on the rhizosphere microbiome can alter nitrogen use efficiency and seed yield in Brassica rapa.

Microbial experimental systems provide a platform to observe how networks of groups emerge to impact plant development. We applied selection pressure for microbiome enhancement of Brassica rapa biomass to examine adaptive bacterial group dynamics under soil nitrogen limitation. In the 9th and final generation of the experiment, selection pressure enhanced B. rapa seed yield and nitrogen use efficiency compared to our control treatment, with no effect between the random selection and control treatments. Aboveground biomass increased for both the high biomass selection and random selection plants. Soil bacterial diversity declined under high B. rapa biomass selection, suggesting a possible ecological filtering mechanism to remove bacterial taxa. Distinct sub-groups of interactions emerged among bacterial phyla such as Proteobacteria and Bacteroidetes in response to selection. Extended Local Similarity Analysis and NetShift indicated greater connectivity of the bacterial community, with more edges, shorter path lengths, and altered modularity through the course of selection for enhanced plant biomass. In contrast, bacterial communities under random selection and no selection showed less complex interaction profiles of bacterial taxa. These results suggest that group-level bacterial interactions could be modified to collectively shift microbiome functions impacting the growth of the host plant under soil nitrogen limitation.

Soil salinization accelerates microbiome stabilization in iterative selections for plant performance.

Climate change-related soil salinization increases plant stress and decreases productivity. Soil microorganisms are thought to reduce salt stress through multiple mechanisms, so diverse assemblages could improve plant growth under such conditions. Previous studies have shown that microbiome selection can promote desired plant phenotypes, but with high variability. We hypothesized that microbiome selection would be more consistent in saline soils by increasing potential benefits to the plants. In both salt-amended and untreated soils, we transferred forward Brassica rapa root microbiomes (from high-biomass or randomly selected pots) across six planting generations while assessing bacterial (16S rRNA) and fungal (ITS) composition in detail. Uniquely, we included an add-back control (re-adding initial frozen soil microbiome) as a within-generation reference for microbiome and plant phenotype selection. We observed inconsistent effects of microbiome selection on plant biomass across generations, but microbial composition consistently diverged from the add-back control. Although salt amendment strongly impacted microbial composition, it did not increase the predictability of microbiome effects on plant phenotype, but it did increase the rate at which microbiome selection plateaued. These data highlight a disconnect in the trajectories of microbiomes and plant phenotypes during microbiome selection, emphasizing the role of standard controls to explain microbiome selection outcomes.