“I couldn’t repair your brakes, so I made your horn louder.” — Steven Wright
Unless you live on the moon, you must have noticed — soil health is all the rage. It is hot. It is cool. It is everywhere. You can’t open a farm magazine without seeing an article on soil heath. You can’t go to a farm meeting without hearing about soil health. It’s the hip chatter in conservation. But for all the talk, there isn’t much to listen to.
There are quantitative tests to determine soil health, there are qualitative tools to assess soil health, and there are predictive models to project changes in soil health. But after all the work, there is very little meaningful data to answer the most important question: does soil health improve farm economics? If soil health is so “everything,” then why can’t we speak unequivocally about the positive economics of soil health and get on with it? Unfortunately, like many things in agriculture, soil health is complicated.
Any comprehensive soil health assessment consists of physical, chemical, and biological components. If we included all of these parameters in a full-scale research study, the cost would be enormous. Since full scale soil health assessments are likely infeasible, maybe we should consider a different approach. I suggest we study how to simplify the assessment by comparing multiple independent tests correlated to yield.
In the reports generated by Agren® SoilCalculator, we place an actual value on a ton of topsoil. We do this by calculating the nutrient value plus the future value of production from a ton of topsoil. When calculating the nutrient value in a ton of topsoil, we talked to several leading soil scientists. They helped us ascertain available nutrient content of nitrogen, phosphorus, and potassium in soil. To assess the future value of production lost from a ton of topsoil, we used research by Dr. Richard Cruse, Iowa State University. From 2014 through 2017, Dr. Cruse set out to estimate the impact of soil erosion on crop yield. He collected yield maps from different Iowa farm fields from 2007 to 2013. For each field, he pulled soil cores from 40 locations to determine topsoil depth at each location. Each soil probe location was georeferenced so that the yields obtained from the combine monitors could be matched with the topsoil depth. From these matched data pairs, the relationship between topsoil depth and crop yield was calculated for each field. From Dr. Cruse’s research we then converted the ton/acre value to inches per acre average loss over a ten year period.
Needless to say, our economic analysis generates considerable interest from our customers. We all understand that the depth of topsoil is a critical element of soil health; therefore it makes sense there would be a correlation between the depth of topsoil and yield.
As with most university research, Dr. Cruse was only able to sample a limited number of fields. It is reasonable to think each soil type will have its one response curve. This research could be expanded to more soil types in more states. This could be done for a very reasonable cost.
Although Dr. Cruse’s work is very compelling and a great place to start, we have spent significant time and effort thinking about different correlations on economics (i.e., yield) and soil health that could add to our economic understanding of soil health. Here are some simple ideas we have considered for cracking the code on the economics of soil health.
#1: Soil organic carbon (SOC) vs. yield: SOC may be the best single measurement of soil health. Therefore, it seems reasonable to determine the correlation between SOC and yield. Note: To date, SOC tests are expensive ($35/sample), but according to soil scientists, organic matter would be an excellent surrogate for soil health.
#2: Research demonstrates that soil erosion reduces SOC by preferentially transporting the light organic component (containing the most SOC) to a site of deposition. As a result, it makes sense to correlate SOC levels with levels of soil erosion; to determine the effect of soil erosion on soil health and ultimately yield.
#3: If depth of topsoil is related to crop yields, then another way of looking at this relationship is correlating within field erosion to the yield. Correlating yield maps to soil erosion maps (from SoilCalculator) would seem to be a reasonable approach.
#4: According to Dr. Daniel Yoder, University of Tennessee, the best way to model the improvement (or degradation) of soil health is with the Soil Conditioning Index (SCI). SCI is a tool that can predict the consequences of cropping systems and tillage practices on soil organic matter. Agren® SoilCalculator can create a SCI map on a 3-meter grid. This in return could be correlated with yield.
In addition to the correlations I’ve outlined above, there are other possibilities that need to be considered. These correlations may be used as single variables or combined in multiple variables. These simple parameters include soil compaction, aggregate stability, soil pH, etc. A robust data set could be developed, at a reasonable level of effort, to begin to crack the code on the correlation of soil health and yield. If there was ever a time for a precision ag company to roll out their big data and prove it’s worth, now is the time!!
I am a very strong believer in soil health. I have worked on soil health issues for years. But I don’t want to be the mechanic that couldn’t repair the brakes, so I just made the horn louder. Likewise if I can’t figure out the economics of soil health, I don’t want to add to the hype. There is nothing more I would like to do than crack the code on soil health and economics…on a large scale. But I can’t do it alone. I need the help of someone who has access to a lot of field data. If your company is interested in collaborating with us to crack the code on soil health and economics, please contact me today.
A special thanks to Tim Mundorf, Midwest Laboratories, for his help in understanding this complicated system and providing me with images for this post.