Soil health testing is certainly popular, but much less popular should be scoring everyone against the same yardstick –  despite the location. Presently, that’s common practice with labs offering soil health tests according to Woods End Lab. A new approach offered with Woods End’s Soil Health Audit modifies the generalized “universal” ranking or indexing adding a calculation that takes account of the soil’s eco-physiographic zone. The result is a region-specific scaling for the test, making it more accurate. There are in the USA 12 Soil Orders each with many sub-orders, arranged more or less vertically across multiple horizontally expressed climatic zones, making for much local variation within the same soil type. Woods End subdivides the country into about 1,000 parcels, from which the soil orders, suborders, climate zones and rainfall patterns are deduced.  Based on 40 years of soil testing, Wood End assembled these features into an algorithm to estimate the likely soil health score, altering among other things N-min predictions and expected respiration quotients (ratio of CO2 released per unit of soil carbon). What this means in practice is that the soil health audit now shows two needles: one is the universal calculation, “not necessarily very meaningful” – and the other is the expected soil health given your actual location. The results have been rewarding thus far: a summary of 1,000 soil tests covering 8 climatic zones and 9 soil orders gave the remarkable result that most farms were scoring close to their expected soil health benchmark.  “What this means is that farm soils included in our survey are operating close to steady state for soil health for their region”, says Brinton, lab director.  And this changes everything. It’s not realistic to expect a piedmont soil to score anywhere close to a Nebraska Mollisol,- the real question is, given your specific location, your soil series and particular rainfall pattern, what’s realistic?  The exciting part of the process that Woods End tests offer is that growers can see how they have deflect upwards from the regional pattern, as in the example shown in the image, where a farmer significantly improved his health score over what was expected.  This is rare though, since large deflections imply years or decades of commitment to changed management.