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In this graph, we compared our sites’ soil pH with their longitude (east-west location). We found that sites further east out on the plains tend to have higher pH than sites closer to the Front Range foothills and up in the mountains. This could be due to several things.
Precipitation is higher in the mountains and foothills than further out on the plains. Higher rainfall is associated with more acidic soil. Also, a site’s original parent soil material is more acidic in the mountains and foothills than on the plains. Furthermore, the pH of irrigation water can change soil pH with repeated applications. Irrigation water becomes more alkaline as it travels further east away from the mountains, picking up tailwater, salts and minerals. All this means that the location of a field might determine its soil pH as well as its soil health, since soil pH has a significant effect on soil health. (As pH increases and becomes more alkaline, soil health decreases.)
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All the sites in our project fall into one of seven crop groups. Most groups include both organic and conventional fields.
We calculated each of our 7 crop group’s average longitude and average pH, which is shown in the following two graphs. No surprise, trees are located to the west in our forests, with dryland gains and commodity crops located to the east, where large sections of undeveloped agricultural lands remain. In the second graph, you can see how the order of the average pH of the 7 groups closely corresponds to their relative longitude, as shown in the first graph. Groups further east had the highest pH, while groups further west had the lowest pH. These 2 graphs suggest that some crop groups face more of a disadvantage than others when it comes to soil health, since their location can determine their soil pH, which in turn can make improving their soil’s health more difficult.
For each of our 7 crop groups, we calculated their average use of 6 different parameters that effect soil health: days of supplemental irrigation water, days of living cover, tons of organic matter added, number of grazing days, their tillage intensity score, and their soil Ph.
We then examined those averages to see if we could predict which crop groups would have the lowest and highest soil health scores. The graphs above show the average soil health practices for all 7 crop groups. See if you can predict which crop groups will have the best and worst soil health scores, just by looking at their relative rankings on soil health practices. Remember that you are looking for HIGH water days, HIGH days of living cover, HIGH organic matter inputs and HIGH grazing days, but LOW tillage intensity and LOW soil pH to predict the highest soil health scores. It’s just the opposite for the lowest soil health scores. If you guessed that Dryland Grains would have the lowest average soil health scores, and that Trees, Wild Grasslands and Home Gardens would have the highest scores, you would hit the jackpot. Dryland Grains have no supplemental water, no organic matter inputs, the shortest days of living cover, and high pH, which all gang up to give the group some of the lowest soil health scores. Home Gardens have the most supplemental water available, huge organic matter inputs, very low tillage intensity and low soil pH, which raises them to the top. Although Grasslands and Trees have no supplemental irrigation water generally and no organic matter inputs, they have the most days of living cover, no tillage and the lowest soil pH, so they do very well too. The chart below has the average soil health scores of each of our 7 crop groups, for Soil Organic Matter, Soil Respiration, Organic Nitrogen, Organic Carbon, Soil Health Score, Total Microbial Biomass, and Number of Fungi.
Please remember that the numbers in these tables and graphs are averages, a mathematical construct. There is no grower named “Average”, nor a field called “Average”. We are talking about an imaginary mathematically constructed “average” site in these tables and charts. Our real world is much more varied and complicated. Most of the sites in the CSSHP fall into 3 main crop categories: Perennial Hay/Alfalfa/Pasture, Commodity Row Crops and Commercial Veg/Flower/Fruit. See if you can predict their relative soil health scores just by looking at their soil health practices.
Perennial Hay/Alfalfa/Pastures: The Pasture group has the highest average soil health scores of these three crop groups. Although the Pasture group has lower supplemental water days and lower organic matter added, their very high days of living cover and very high grazing days, along with their very low tillage intensity and lower soil pH seem to more than make up for their water challenges, in terms of soil health.
Commodity Row Crops: The Commodity crop group has the lowest average scores of these three groups. Although they have done an excellent job of reducing their tillage intensity, that fact alone cannot make up for their high soil pH, lowest days of living cover and lowest organic matter added. They have only 2/3rds of the water availability as the Commercial Veg/Flower/Fruit group, which explains their lower days of cover crops that often require fall seeding and fall water. Inter-seeding cover crops aerially or when the main crop is still small are work-arounds but not always practical. Low commodity prices mean the cost of additional organic matter inputs like compost and manure are hard to justify. Commercial Veg/Flower/Fruit: The Commercial Veg group has the highest tillage intensity by far, but also triple the organic matter inputs of the other 2 groups. These huge organic matter inputs, along with their longer water season, greater use of cover crops, and lower soil pH overpower their intense tillage and boost their average soil health scores above the commodity crops’ averages. Their longer water season means they can plant more fall cover crops and string together succession plantings for a longer growing season. Their high value vegetables mean that they can afford organic matter input costs and hauling fees. In the top graph above, 71 sites with both grazing animals and organic matter inputs (OMI) are each represented by a quadruplet of data points connected by a vertical black line (a blue square for 2019, red circle for 2020, green triangle for 2021, and yellow diamond for 2022). Each square-circle-triangle-diamond-black-line combo represents the Soil Organic Matter (SOM) values for one site for 4 years. According to the literature, SOM is supposed to be quite stable and very difficult to change, and yet we are seeing large swings in individual sites’ SOM data, especially when grazing animals are present or organic matter is imported to the site, as is the case in the top graph above.
We only have 12 sites in our study which have no grazing animals or imported organic matter for 3 or more years. The second lower graph shows that the variability in SOM values for these 12 sites is much less than for sites with grazing animals or organic matter inputs. We examined our 28 sites which have the most variability in their soil health scores. We call these sites our “Swingers”, and they are evenly split between organic and conventional growing methods. Over half the “Swinger” sites are pastures with the rest split evenly between home gardens and commercial vegetable sites. Their most common crop is grass hay with mixed vegetables coming in second. Their average water season is 127 days long. “Swinger” sites have an average soil health score of 27.6, which is very high, especially for Colorado. The growers of these “Swinger” sites are all Soiley Award winners or nominees. They have adopted many soil health practices, as you can see in the following graph. The lesson here seems to be that no good deed goes unpunished. It seems that one result of adopting good soil health practices may be a great deal of variability in soil health lab results. If you see your Haney test results bouncing around a lot, year-to-year, it does not necessarily mean that you are doing anything wrong. It may mean that you are doing many things right! We will explore this hypothesis further in coming years as we gather more data.
We sorted our sites into 3 groups and calculated the average variability for each group. This bar graph shows that the groups which grazed animals or added organic matter to their sites for 2 consecutive years have approximately three times as much variability in their lab results as the group with NO grazing animals and NO organic matter inputs.
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AuthorElizabeth Black is the producer of the Citizen Science Soil Health Project Archives
March 2024
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