can be helpful because humans have the ability to shape soils from a range tools and techniques, including tillage (Reicosky, 2015) and drainage (Dinnes et al., 2002; Skaggs et al., 1994). Similarly, when comparing a range of management practices, selection of two practices with major differences (e.g., moldboard plow vs native prairie grass) will likely show more significant differences due to contrasting levels of soil disruption, plant species, and external inputs (Veum et al., 2014; Veum et al., 2015). Furthermore, the way in which the comparisons are statistically conducted can definitely influence “how much” of an effect was present and detectable (van Es and Karlen, 2019; Roper et al., 2017).
Scale
The scale of measurement is an important consideration for soil health assessments because soil resources can have very different properties when viewed across an individual field, across an entire landscape, as a core representing the soil profile, or as a small sample prepared for one or more analytical measurements. Within‐field variability is another factor that can make soil health assessment useful or futile. For example, changes in SOM could reflect either an increasing level or an unintended over‐sampling from soil series within the field that tend to have higher SOM levels than others. Slope is another common factor in agricultural fields that helps explain differences in SOM (Ladoni et al., 2016; Ontl et al., 2015). Also, if samples are collected from within a row, are the measurements applicable to the entire field or only the 25% of the field that is within a crop row? These questions are relevant not only for SOM but all potential soil health indicators. For example, in a study of carbon dioxide (CO2) flux from no‐till fields, Kaspar and Parkin (2011) estimated each field consisted of 25% rows, 45% untracked inter‐rows, and 30% tracked inter‐rows, thus highlighting the importance of controlling traffic patterns to ensure that only a small portion of each field is disturbed. Quantifying such fine scale variations in soil properties is an important component of soil health assessment since the results may help identify new crop management practices that can improve all functional zones in the soil (Williams et al., 2016).
Figure 3.3 Soil health documentation must recognize inherent (left) and dynamic (right) soil properties. (Photo Credit: Gary Radke, USDA ARS).
Analytical Methods
After selecting the soil function(s) for which indicator comparisons are of interest and the appropriate scale for making the comparisons, the third factor to consider is which method should be used to measure the important or critical changes? These three factors (topic, scale, and methods) are core scientific questions within any field of study. This book and numerous others document that a range of soil sampling and analytical methods exist (Dane and Topp, 2002; Dick, 2011; Sparks et al., 1996, Ulery and Drees, 2008). Therefore, the term “soil health test” can refer to a multitude of in‐field, laboratory, or even remote sensing techniques for quantifying or documenting a specific soil function or indicator of that function (Table 3.3). This then fuels ongoing discussions with regard to the utility or futility of soil health assessment and which soil physical, chemical, and/or biological properties should be documented (Derner et al., 2018; Doran et al., 1994; Doran and Jones, 1996; Elliott et al., 1997; Schindelbeck et al., 2008; Stone et al., 2016).
Common soil health tests (Table 3.3) include in‐field assessments with scorecards or portable soil test kits that can be used to evaluate soils visually and interactively. A more involved type of in‐field assessment can be achieved through the installation of various analytical instruments including edge‐of‐field or end‐of‐drainage‐tile samplers for quantifying soil health impacts on water quality. Assessments can also be made by sending soil samples to a commercial testing laboratory, or by participating in research projects such as the National Corn Grower sponsored Soil Health Partnership, the Soil Health Institute’s national soil health evaluation, or one of many state or national NRCS soil health programs. A third category of soil health tests emerging through technological advances is the use of remote sensing which has the benefit of enabling more frequent assessments of several soil functions, but the challenge of amassing high volumes of data which require more sophisticated storage and interpretation algorithms (Mulder et al., 2011; Shoshany et al., 2013).
Table 3.3 Categories of soil health tests, each with unique characteristics but a common goal.
| Type of test | Characteristics | Common Goal |
|---|---|---|
| On‐farm or in‐field | Portable, generally quite simple, qualitative, interactive, provide general contrasts | Successfully identify if soil properties change |
| Commercial laboratory | Rapid and high throughput, primarily focused on chemical indicators, with a few physical and biological measurements, generally group responses in categories | |
| Research projects | More precise but often very slow turn‐around, capable of identifying fine‐scale differences, difficult to generalize, specific methods may vary |
The three categories of soil health tests listed in Table 3.3 thus serve different purposes. On‐farm or in‐field qualitative tests are generally used to build an awareness of what soil is, how it forms, and the types of functions (sustaining productivity, filtering and buffering, controlling water entry, retention and release). Commercial or research‐based laboratory tests generally require collection of numerous samples and sending them to a separate location for analysis. Also, since the farmer/landowner/interested individual is often not the one collecting or analyzing the samples, there can be disconnects or even lack of communication between the person providing analytical data and the one who will ultimately use the information to modify decisions and/or change soil and crop management practices. Regardless of the specific type of test, a very important cultural change associated with development of soil health concepts has been the act of bringing people together, often in the field, to evaluate the soil and thus better understand benefits that often cannot be easily seen through printouts of laboratory data. For example, an area prone to erosion can often be documented more easily by evaluating the slope, amount of groundcover, and presence of ephemeral or permanent gullies than looking at data showing soil texture, SOM, or fertility changes.
Another difference among the three categories of soil health tests (Table 3.3) is that they are generally applied at different scales. For laboratory tests extensive pre‐processing, such as sieving, grinding or sub‐sampling, will often be required as samples are prepared for analysis using various analytical instruments. In contrast, on‐farm or in‐field tests can often help producers recognize impacts of past and current soil management decisions within a field or landscape more easily than viewing multiple pages of laboratory data. Furthermore, if the small amount of soil submitted for laboratory analysis is not accompanied by an appropriate amount of metadata (i.e., data about the samples, site, and analytical methods) it may be impossible to fully capture what can be seen within‐field by the naked eye. Another visual assessment technique that has become more common since the emergence of soil health conferences and field days is the increased use and familiarity of soil pits to show producers how their crop production practices are influencing plant root systems and soil structure. Rainfall simulators are also used in soil health field days to demonstrate the benefits of keeping soil covered and slake tests are used to show the importance
