23.4: Laboratory Soil Health Testing

Comprehensive Soil Tests

Growers are used to taking soil samples and having them analyzed for available nutrients, pH and total organic matter by a university, government or commercial lab. In arid regions it is common to also determine whether the soil is saline (too much salt) or sodic (too much sodium). This provides information on the soil’s chemical health and potential imbalances. As we discussed in Chapter 21, you get the most benefit from soil tests with regularly scheduled analyses (at least every two years) and good records. If your soil test report includes information on cation exchange capacity (CEC), you should expect it to increase with higher organic matter levels, especially in coarse-textured soils. And, as discussed in Chapter 20, soil CEC increases after liming a soil, even if there is no increase in organic matter.

Table 23.3 USDA-NRCS-Evaluated Laboratory Soil Health Indicators and Methodologies, and Associated Soil Processes They Measure

Soil process	Soil health indicator	Method^1
Organic matter cycling and C sequestration	Soil organic matter content	Dry combustion Wet oxidation Loss on ignition
Structural stability	Aggregation	ARS wet aggregate stability NRCS wet aggregation Cornell sprinkle
General microbial activity	Short-term C mineralization	CO2 respired—4 day CO2 respired—24 hours
General microbial activity	Enzyme activity	BG NAG Phosphomonoesterases Arylsulfatase
C food source	Readily available pool	POXC POM 28-day mineralization WEOC Soluble carbohydrates Substrate-induced respiration Microbial biomass C
Biological available N	Available organic N pool	ACE protein WEON Correlation with short-term mineralization 7-day anaerobic PMN 28-day PMN Illinois soil N test NAG Protease
Microbial diversity	Community structure	PLFA EL-FAME
^1Acronyms are: BG = β-Glucosidase; NAG = N-acetyl-β-D-glucosaminidase; POXC = Permanganate oxidizable C; POM = Particulate organic matter; WEOC = Cold/hot water-extractable organic C; ACE = Autoclaved citrate extractable (protein); WEON = Cold water-extractable organic C; PMN = Potentially mineralizable N; PLFA = Phospholipid fatty acid; EL-FAME = Ester-linked fatty acid methyl ester profile. Source: USDA (2019)

Figure 23.5. Example of a soil health test report.

The traditional soil test does not, however, make a comprehensive assessment of soil health, which probably led to the “chemical bias” in soil management. In other words, the widespread availability of good chemical soil tests, although a very useful management tool, may also have encouraged the quick-fix use of chemical fertilizers over the longer-term holistic approach promoted in this book. Several soil health tests have been developed to provide a more comprehensive soil assessment through the inclusion of soil biological and physical indicators in addition to chemical ones. Indicators were selected based on the soil processes that they represent, and thereby the tests provide insights into a soil’s ability to provide ecosystem services (like growing healthy crops). They also consider cost, consistency and reproducibility of the methodologies, as well as relevance to soil management.

In this context, the USDA evaluated a set of indicators and methodologies in an attempt to encourage standardization in soil health testing (Table 23.3). The proposed methods have all proven to provide useful insights into aspects of soil health. Currently (in the year 2020) there is no single standard soil health test, but there is universal agreement that a comprehensive soil health test should include indicators that represent all three types of soil processes: biological, physical and chemical (Figure 23.5). Also, measured values need to be interpreted based on inherent variation in soils as a result of different climates, soil textures, etc.

Some soil health indicators have become more widely adopted. For physical indicators, aggregate stability (Figure 23.6) relates to infiltration, crusting and shallow rooting, and represents the “tilth” of the soil. It generally shows a fast response after the introduction of new management practices like reduced tillage, cover cropping or manure or compost additions. Available water capacity relates to plant-available water and is relevant to drought resistance. It is more sensitive to inherent soil texture differences than to changes in management.

Figure 23.6. Results of an aggregate stability test for silt loam vegetable soils: organic (70% stable, left) and conventional (20% stable, right) management.

For biological indicators, the most common indicator is total soil organic matter (SOM) content, which affects almost all important soil processes, including water and nutrient retention, and biological activities. It is often the single most important measurement of soil health, but unfortunately it is not very sensitive to management. It takes many years to measure a real change in SOM, and farmers would generally want to know earlier about the benefits of a management change. Active carbon is an inexpensive test that relates to a small fraction of the organic material that is more actively engaged with biological functions, and it has shown to be very sensitive to changes in soil management. It is therefore a good early indicator of soil health improvements. Active C is assessed as the portion of soil organic matter that is oxidized by potassium permanganate, and the results can be measured with an inexpensive spectrophotometer (Figure 23.7). Similarly, soil protein content is an indicator of the soil organic nitrogen potentially available to microorganisms, and it also shows strong response to management changes, especially when more legumes are introduced. Respiration (CO₂ released by soil organisms) is widely measured as an indicator that integrates both abundance and metabolic activity of soil microbes; it is also correlated with nitrogen mineralization potential. Ammonia losses from amino sugars in the soil is a related measurement. There are a number of other biological indicators. The bean root rot bioassay provides an effective and inexpensive assessment of root health and overall disease pressure from various sources (plant-parasitic nematodes; the fungi Fusarium, Pythium, Rhizoctonia; Figure 23.8).

Chemical soil health indicators are discussed in Chapter 21 on conventional soil testing and include macro and micronutrients, and soil reaction (pH). Undesirable elements like salts and sodium should be evaluated in arid regions and covered areas, such as inside greenhouses and high tunnels. In urban or industrial environments, toxic elements like heavy metals, salts, radioactive materials, solvents and petroleum products should be considered when assessing soil health, as discussed in Chapter 22.

Figure 23.7. Assessment of active carbon using permanganate oxidation and a portable spectrophotometer. Photo by David Wolfe.

Interpreting test results is the next step towards identifying specific soil constraints (see Figure 23.5). This particular report (based on the CASH test) is for a soil that had been under grain production for many years. For each indicator, the report provides a measured value and the associated score (1–100), which is an interpretation of the measured result. If scores are low (less than 20), specific constraints are listed. An overall soil health score, also standardized to a scale of 1–100, is provided at the bottom of the report, which is especially useful for tracking soil health changes over time. The test report in Figure 23.6 is somewhat typical for grain production fields in the northeastern United States. It shows the soil in good shape regarding the chemical indicators but severely underperforming with respect to the physical and biological indicators. Why is that the case? In this situation, the farmer was diligent about using the conventional soil test and keeping nutrients and pH at optimal levels. But intensive cropping caused an unbalanced soil health profile for this field. The test identified these constraints and allows for more targeted management, which we’ll discuss in the next chapter.

You might wonder how measured soil health test values are interpreted through scores. In traditional chemical soil tests, the measured values are related to potential crop response (likely yield increase or decline depending on whether it is a nutrient or a toxic element). For biological and physical indicators, scientists have developed normative scoring functions where test results are compared to a larger population of analyzed soil samples in similar soils and cropping systems (similar to how we interpret cholesterol and potassium levels in human blood samples). This approach allows a sample to be scored and interpreted without knowing the precise impact of high or low values. This normative scoring is typically done by calculating mean and standard deviation values for a population group (say, medium-textured soils in grain crop systems in the midwestern United States) and using the cumulative normal distribution function as a fuzzy scoring curve.

Microbial Soil Tests

Soils can also be tested for specific biological characteristics—for potentially harmful organisms relative to beneficial organisms (for example, nematodes that feed on plants versus those that feed on dead soil organic matter) or, more broadly, for macro- and microbiology. Two common tests—the phospholipid fatty acid (PLFA) and fatty acid methyl ester (EL-FAME) assays—have shown sensitivity to management changes and are offered by some commercial soil testing labs. They produce an estimate of the soil’s living biomass. Also, the biomarkers, or signature fatty acids, identify the presence or absence of various groups of interest such as different bacteria, actinomycetes, arbuscular mycorrhizal fungi, rhizobia and protozoa. The relative amounts or activities of each type of microorganism provide insights into the characteristics of the soil ecosystem. Bacterial-dominated soil microbial communities are generally associated with highly disturbed systems with external nutrient additions (organic or inorganic), fast nutrient cycling and annual plants. Fungal-dominated soils are more common with low amounts of disturbance and are characterized by internal, slower nutrient cycling, and high and stable organic matter levels. Thus, the systems with more weight of bacteria than fungi are associated with intensive agricultural production (especially soils that are frequently plowed), while systems with a greater weight of fungi than bacteria are typical of natural and less disturbed systems. The significance of these differences for the purposes of modifying practices is somewhat unclear, but modifying practices causes biological changes to occur. For example, adding organic matter, reducing tillage and growing perennial crops all lead to a greater ratio of fungi to bacteria. Since networks of mycorrhizal fungal filaments help plants absorb water and nutrients, their presence suggests more efficient nutrient and water use. But we generally want to do these practices for many other reasons—improving soil water infiltration and storage, increasing CEC, using less energy, etc.—that may or may not be related to the ratio of bacteria to fungi.

The study of genetic material recovered directly from soil has advanced in recent years. Routinely characterizing the genetic profile of a soil’s organic matter to obtain a picture of the organisms present is thus becoming commercially feasible. It is challenging to extract specific genetic material from soils due to the high complexity of soil organic matter, and DNA profiling is mostly used for descriptive purposes (for example, how prevalent different types of pseudomonas bacteria are). Some tests are showing promise with identifying specific pathogens that may help farmers better manage their fields.

Sensing methods are increasingly considered for soil health assessment. Visible near-infrared and mid-infrared reflectance spectroscopy methods are non-destructive approaches that measure the optical reflectance properties of soil, which is influenced by chemical bonds like O-H (abundant in clay minerals), C-H (abundant in organic matter), etc. They therefore can assess certain soil properties rapidly and at low cost. Such methods appear to be especially efficient when combined with a subset of laboratory-measured properties that can be compared with the spectroscopy results through advanced statistical and machine learning techniques.

Figure 23.8.Examples of root rot bioassays on bean plants: conventional (left) and organic (right) soil management.Photos by George Abawi.