As agronomists, and as conservationists, we generally acknowledge that good soil conservation supports higher yield. We have been looking for a definitive answer on whether soil erosion causes yield drag, but the relationship is complicated. With the ability to accurately calculate soil loss in 9×9 meter grids, Agren is working with agronomists to determine “if and when” soil erosion is causing a yield reduction.
We started by generating 9×9 meter grid erosion maps for five fields having five different operators in Western Iowa. All fields had different management systems and variability in topography (a combination of hilly land and flat land). Within the five fields, soil erosion varied based on the soil type, slope steepness, and slope length. Erosion ranged from 0 to 3 tons/acre/year in the fields with good soil conservation practices (terraces and no-till). Fields with poor soil conservation practices had erosion ranging from 0 to 20 tons/acre/year.
Greg Reisz, owner and president of E4 Sons/E4 Crop Intelligence in Woodbine, Iowa recently began defining the correlation between yield and soil erosion with Agren. Reisz ran a simple correlation between soil erosion and yield data collected with yield monitors. In the fields that had a tradition of good soil conservation, there did not seem to be any yield correlation between areas of higher and lower soil erosion (0 to 3 tons/acre/year). At first glance, I was surprised there was no correlation. But after careful thought, it makes sense. The whole field has very little erosion. Many other factors can easily mask the small difference that may show up.
The answer was a little different in fields with high levels of soil erosion (0 to 20 tons/acre/year). There was a correlation. Areas with low erosion had higher yields and high erosion areas had lower yields (see Figure 1 and Figure 2).
Based on the Iowa State statistics class that I had many, many years ago, I realize I can’t draw any real conclusions from five observations in one small part of Western Iowa. As with all data layers in precision agriculture, there are apt to be a lot of considerations and variability. Is the yield drag on some soil associations impacted more by erosion than on other soil associations? How long do you have to apply good conservation practices to mitigate the erosion effects from previous years (1 year, 5 years, 10 years)? Does the time required to mitigate these differences change from one soil association to another? Are certain soil conservation practices more effective at mitigating the yield drag due to past erosion? Do different varieties of corn respond differently to a poor soil quality? Is there more impact in dry years than wet years?
The list goes on and on, but that is the beauty of using precision agriculture. Many companies have the necessary yield information, but only those companies that have collected information on rotations and tillage will have the data needed to calculate soil erosion. It is those companies with both data sets that have the ability to crack the code on soil erosion and yield drag. Big Data?…Oh Yes! Then, there are more questions. Should past erosion help predict the correct population we should use when planting corn? You can use your imagination to come up with a multitude of other questions related to precision agriculture.
Imagine when we finally solve this question of soil erosion and yield drag. Imagine what this means to land valuation. Imagine what it means to agronomic decision-making! Imagine what it means to landowner and farmer interest in soil erosion. And remember, this is possible since we can now calculate soil erosion in 9×9 meter grids.
In my next post, I will provide a more in-depth discussion on how soil erosion is calculated using 9×9 meter grid.