when combined with a soil health assessment framework that provides soil and climate adapted interpretations of raw laboratory values. These book volumes are intended to summarize current best available methods and to identify gaps in our current understanding of soil health measurement and assessment. They are also intended to facilitate and increase in public awareness of soil health assessment and to provide insight and science‐based methods for those evaluations. Furthermore, by encouraging soil health assessments, we hope these volumes will ultimately result in compilation of a national dataset that can be used to support multiple public and private soil health goals and document the value of public and private investments in such assessments.
While qualitative or semi‐quantitative field observations can be used for preliminary identification of soil health constraints or to improve soil and crop management practices, identifying specific underlying causes and/or the management practices needed to address them, often requires quantitative laboratory analysis. We anticipate information in these volumes will be used by a wide group of stakeholders including producers, consultants, technical service providers, conservation planners, and other private and public agricultural service providers, conservation groups, researchers, industry, policymakers, and the general public. Uses will include: (1) identifying soil health problems and planning and implementing soil health management systems; (2) innovating, monitoring, and continually improving soil health management systems and their outcomes; and (3) leveraging diverse partnerships and efforts across multiple organizations and geographical scales for further research and innovation in soil health assessment and management at local, regional, national, and global scales through standardized datasets and sharing information for agricultural lands. Having meaningful, science‐based soil health assessments is also important for planning, implementing, and managing conservation projects, establishing baselines, and documenting soil property and process changes over time to quantify outcomes of such projects.
Soil Health Indicators and Methods
Four main criteria have been developed by the soil health community of researchers, agricultural service providers, and practitioners to select indicators and methods for high‐through‐put soil test laboratories (Larson & Pierce, 1991; Mausbach & Seybold, 1998; Doran & Zeiss, 2000; Moebius et al., 2007; Norris et al., 2020):
1 Soil Health Indicator Effectiveness (short‐term sensitivity to management, usefulness)
2 Production Readiness (ease of use, cost effectiveness for labs and producers)
3 Measurement Repeatability
4 Interpretability for agricultural management decisions (directionally understood, management influence known, regional potential ranges known, outcome thresholds).
These were developed using scientific literature and robust discussions in a series of workshops coordinated by the Farm Foundation and Noble Research Institute through the Soil Renaissance program between 2014 and 2016 (https://www.farmfoundation.org/projects/the‐soil‐renaissance‐knowledge‐to‐sustain‐earths‐most‐valuable‐asset‐1873‐d1/). Understanding that soil health is a dynamic and evolving component of soil science, we recognize that both the indicators and methods recommended within these two volumes could change. Potential factors leading to changes may include identification of: (1) new or different critical soil processes, (2) more‐responsive SH indicators, and/or (3) better methods of assessment. Furthermore, because of the dynamic nature of soil health assessment, we suggest information in these volumes be reviewed in three to 5 yr or a decade at most.
Need for Standardization
Once a suite of soil health indicators has been selected, standard methods for collecting and handling samples in the field, processing them in the laboratory, analyzing them, and interpreting the data are needed for monitoring and making appropriate comparisons (Doran & Parkin, 1994). This is especially true for biological assays which can be more sensitive to how soil samples are collected and processed prior to analysis than to subtle differences in the analytical methods themselves. Currently, soil health measurement protocols vary widely and can therefore lead to inconsistent results and slow progress toward widely validated interpretation. This challenge is best addressed by standardization of a minimum dataset of methods used across organizations that collaborate nationally to make progress on interpretation and science‐based management recommendations (USDA‐NRCS, 2019b). Thus, ongoing efforts among public‐sector and commercial laboratories are needed to ensure preanalytical soil processing (i.e., degree of aggregation, sieving, grinding, etc.) and analytical methods are standardized. As with all soil chemical measurements (e.g., pH, salinity, extractable N, phosphorus, and potassium), biological and physical indicators generally have large spatial and temporal variation. Care thus needs to be taken not only with sampling (i.e., compositing enough subsamples to make inferences about a sampled area) but also sampling methods (soil volume and depth), timing of collection (seasonal or annual), and the statistical methods used for interpretation.
Volume 2 is also intended to help reduce analytical variation in the measurement of soil health indicators. This is important because, as previously shown by the standardization of NRCS inherent soil property characterization methods, standardization makes large‐scale data integration and comparisons feasible. Without rigorous standardization of soil health methods, variation among laboratories will hinder evaluation of changes over time and space and development of interpretations for various soil types and climate scenarios. This will in turn make regional and national compilations of soil health data very difficult to interpret.
Standardization of methods and protocols, along with appropriate proficiency testing, will facilitate collection of high‐quality data with a high degree of interpretability, which is needed to facilitate development and use of regionally‐appropriate interpretation functions (i.e., scoring algorithms). Those algorithms are needed to transform raw laboratory data into unitless (0 to 1) values that shows how well a specific soil is performing a production or environmental function. Such ratings can then be used for on farm management decision making. Private and public soil testing laboratories that use broadly standardized methods will therefore have the advantage of being able to offer broadly validated soil health testing and interpretation using functions and recommendations developed from a large dataset achieved through multiorganization public‐private partnership contributions.
Interpretation of Soil Health Information
Several nationally appropriate tools, including the Revised Universal Soil Loss Equation (RUSLE), Soil Conditioning Index (SCI), Water Erosion Prediction Project (WEPP), Wind Erosion Prediction System (WEPS), AgroEcosystem Performance Assessment Tool (AEPAT), and Soil Management Assessment Framework (SMAF), have been developed to help interpret soil health related data (USDA‐NRCS, 2019b). RUSLE2 estimates soil loss due to rill and inter‐rill erosion caused by rainfall on cropland (Renard et al., 2011; USDA‐ARS, 2015). The SCI combines information from the soil tillage intensity rating tool (STIR), a N‐leaching index, and Version 2 of the Revised Universal Soil Loss Equation (RUSLE2) to provide information to producers regarding how their management decisions are affecting their soil resources and is widely used in NRCS conservation planning. AEPAT is a research‐oriented index methodology that ranks agroecosystem performance among management practices for chosen functions and indicators (Liebig et al., 2004; Wienhold et al., 2006). Water Erosion Prediction Project (WEPP) is a process‐based, distributed parameter, continuous simulation, erosion prediction model for use on personal computers (USDA‐ARS, 2017); Wind Erosion Prediction System (WEPS) predicts many forms of soil erosion by wind including saltation‐creep and suspension (USDA‐ARS, 2018). Without question, wind‐, water‐, and anthropogenic‐induced soil erosion continues to be a global problem (Karlen & Rice, 2015) and must be the first factor mitigated to truly improve soil health, as it is an advanced symptom of degradation including loss
