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Currently accepted pedotransfer functions show negligible effect of managementinduced 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.

The Walnut Gulch Experimental Watershed is a semi-arid experimental watershed and long-term agro-ecosystem research (LTAR) site managed by the USDA-Agricultural Research Services (ARS) Southwest Watershed Research Center for which highresolution, long-term hydroclimatic data are available across its 149-km 2 drainage area. Quality control and quality assurance of the massive data set are a major challenge. We present the analysis of 50 years of data sets to develop a strategy to identify errors and inconsistencies in historical rainfall and runoff databases. A multiple regression model was developed to relate rainfall, watershed properties, and the antecedent conditions to runoff characteristics in 12 subwatersheds ranging in area from 0.002-94 km 2. A regression model was developed based on 18 predictor variables, which produced predicted runoff with correlation coefficients ranging from 0.4-0.94 and Nash efficiency coefficients up to 0.76. The model predicted 92% of runoff events and 86% of no-runoff events. The modeling approach is a complement to existing quality assurance and quality control (QAQC) procedures and provides a specific method for ensuring that rainfall and runoff data in the USDA-ARS Walnut Gulch Experimental Watershed database are consistent and contain minimal error. The model has the potential for making runoff predictions in similar hydroclimatic environments with available high-resolution observations.

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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.

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 (θFC_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 θFC_INTACT is not. This study informs selection of soil health indicators to measure soil hydraulic function.