Drought is a major abiotic stress limiting agricultural productivity. Previous field-level experiments have demonstrated that drought decreases microbiome diversity in the root and rhizosphere. How these changes ultimately affect plant health remains elusive. Toward this end, we combined reductionist, transitional and ecological approaches, applied to the staple cereal crop sorghum to identify key root-associated microbes that robustly affect drought-stressed plant phenotypes. Fifty-three Arabidopsis-associated bacteria were applied to sorghum seeds and their effect on root growth was monitored. Two Arthrobacter strains caused root growth inhibition (RGI) in Arabidopsis and sorghum. In the context of synthetic communities, Variovorax strains were able to protect plants from Arthrobacter-caused RGI. As a transitional system, high-throughput phenotyping was used to test the synthetic communities. During drought stress, plants colonized by Arthrobacter had reduced growth and leaf water content. Plants colonized by both Arthrobacter and Variovorax performed as well or better than control plants. In parallel, we performed a field trial wherein sorghum was evaluated across drought conditions. By incorporating data on soil properties into the microbiome analysis, we accounted for experimental noise with a novel method and were able to observe the negative correlation between the abundance of Arthrobacter and plant growth. Having validated this approach, we cross-referenced datasets from the high-throughput phenotyping and field experiments and report a list of bacteria with high confidence that positively associated with plant growth under drought stress. In conclusion, a three-tiered experimental system successfully spanned the lab-to-field gap and identified beneficial and deleterious bacterial strains for sorghum under drought.

Farmers, scientists, and other soil health stakeholders require interpretable indicators of soil hydraulic function. Determining which indicators to use has been difficult because of measurement disconformity, spatial and temporal variability, recently established treatments, and the effect of site characteristics on management practice differences. The North American Project to Evaluate Soil Health Measurements includes 124 sites uniformly sampled across a range of soil health management practices in North America in 2019. We compare and recommend indicators of hydraulic function that best characterize soil health. We assessed the relationship of each indicator to a suite of soil inherent properties and climate variables, the response of each indicator to soil health management practices, the effect that soil inherent properties (clay content, sand content, and pH) and climatic variables (10‐yr mean annual precipitation and temperature) had on response to management practices, and the relationship among the responses of the indicators to soil health management practices. Field capacity measured on intact cores (θ _{FC_INTACT} ) was the best measure of soil hydraulic function, because it responded to management, represents a direct measure of soil hydraulic function, is proximal to stakeholder values, and its response to management was not significantly influenced by inherent and climatic variables. Other suitable indicators are bulk density, soil organic carbon (SOC), and aggregate stability, which are not direct measures of soil hydraulic function but do respond to management and may be practical in situations in which measuring θ _{FC_INTACT} is not. This study informs selection of soil health indicators to measure soil hydraulic function.

Currently accepted pedotransfer functions show negligible effect of management‐induced changes to soil organic carbon (SOC) on plant available water holding capacity (θ _AWHC ), while some studies show the ability to substantially increase θ _AWHC through management. The Soil Health Institute's North America Project to Evaluate Soil Health Measurements measured water content at field capacity using intact soil cores across 124 long‐term research sites that contained increases in SOC as a result of management treatments such as reduced tillage and cover cropping. Pedotransfer functions were created for volumetric water content at field capacity (θ _FC ) and permanent wilting point (θ _PWP ). New pedotransfer functions had predictions of θ _AWHC that were similarly accurate compared with Saxton and Rawls when tested on samples from the National Soil Characterization database. Further, the new pedotransfer functions showed substantial effects of soil calcareousness and SOC on θ _AWHC . For an increase in SOC of 10 g kg ^–1 (1%) in noncalcareous soils, an average increase in θ _AWHC of 3.0 mm 100 mm ^–1 soil (0.03 m ³ m ^–3 ) on average across all soil texture classes was found. This SOC related increase in θ _AWHC is about double previous estimates. Calcareous soils had an increase in θ _AWHC of 1.2 mm 100 mm ^–1 soil associated with a 10 g kg ^–1 increase in SOC, across all soil texture classes. New equations can aid in quantifying benefits of soil management practices that increase SOC and can be used to model the effect of changes in management on drought resilience.

Various soil health indicators that measure a chemically defined fraction of nitrogen (N) or a process related to N cycling have been proposed to quantify the potential to supply N to crops, a key soil function. We evaluated five N indicators (total soil N, autoclavable citrate extractable N, water‐extractable organic N, potentially mineralizable N, and N ‐acetyl‐β‐ D ‐glucosaminidase activity) at 124 sites with long‐term experiments across North America evaluating a variety of managements. We found that 59%–81% of the variation in N indicators was among sites, with indicator values decreasing with temperature and increasing with precipitation and clay content. The N indicators increased from 6%–39% in response to decreasing tillage, cover cropping, retaining residue, and applying organic sources of nutrients. Overall, increasing the quantity of organic inputs, whether from increased residue retention, cover cropping, or rotations with higher biomass, resulted in higher values of the N indicators. Although N indicators responded to management in similar ways, the analysis cost and availability of testing laboratories is highly variable. Further, given the strong relationships of the N indicators with carbon (C) indicators, measuring soil organic C along with 24‐h potential C mineralization could be used as a proxy for N supply instead of measuring potentially mineralizable N or any other N indicator directly.

Low grain protein in hard red winter (HRW) wheat (Triticum aestivum L.) is a serious challenge for rainfed wheat growers, particularly in years with elevated grain yield. Proper nitrogen (N) management with adequate N rate and application timing is critical for optimizing grain yield and protein content. This 2‐yr experiment evaluated the effects of different N rates and application timings (fall, spring, and split) on grain yield and protein of two HRW wheat cultivars. Field studies were conducted at four different sites across Nebraska under rainfed conditions in 2018/2019 (Year 1) and 2019/2020 (Year 2). A split‐plot randomized complete block design with wheat cultivars as the whole plots and factorial combinations of six N rates and three application timings as the subplots was used in four replications. Grain yield was associated positively and grain protein negatively with the water supply to demand ratio (WS/WD) in the season. Freeman cultivar yielded better in a year with higher WS/WD and a newly developed cultivar, Ruth, yielded better in a lower WS/WD year. Nitrogen fertilization significantly increased grain yield in the site‐years with moderately higher WS/WD. There was an increase in grain protein with increasing N rates at all site‐years. Spring and split‐applied N resulted in better grain yield than fall application in the site‐year when there was a risk of N loss. This experiment suggested that an effective N management strategy for winter wheat should account for and be adaptable to weather variability to optimize grain yield and protein content.