Soil systems-powerpoint-compressed-final

Editor's Notes

• #2: Traditionally, most soil fertility and health assessments have been done on field basis and for surface soil layers. However, natural processes underlie these fields and provide a broader context beyond the field fences. We will illustrate these well known and established concepts through few examples and make a case for why Soil Health and Stewardship can benefit from such approach. Next Slide.
• #3: Soil System definition from: Daniels, R.B., Buol, S.W., Kleiss, J., Ditzler, C.A. 1999. Soil Systems in North Carolina. Technical Bulletin # 314, Soil Science Dept. North Carolina State University, Raleigh, NC. The key concept that emerges from this illustration is Soil Landscape Model. This is a model that brings into focus not only the water movement and redistribution thought landscape but more importantly at a relevant scale – the management/human scale where most of the operational/daily decisions are made. Next Slide. Figure from: DeGloria, S.D., D.E. Beaudette, J.R. Irons, Z. Libohova, P.E. O’Neill, P.R. Owens, P.J. Schoeneberger, L.T. West, and D.A. Wysocki, 2014. Emergent Imaging and Geospatial Technologies for Soil Investigations. Photogrammetric Engineering & Remote Sensing 80(4):289-294
• #4: More detailed aspects of Soil Systems can be put to work. The model provides an underpinning of many other disciplines and natural processes with some important implications regarding the assessment of the status of resources and their trajectory. Next Slide.
• #5: Through this mechanistic simplified representation we can demonstrate the unique role(s) that soils play in the overall circulation of matter and energy. The “Abiotic” part of soil development captures the influence of climate (precipitation, temperature, wind , etc.) and its interaction with the local variability due to topography and parent material. However, organisms (humans, animals, vegetation, micro fauna/flora, etc.), interact with the “abiotic” cycle and more importantly can influence the direction of processes at landscape scale and beyond. Next Slide. Once we understand the complexity and interrelationships between different parts of the system we can establish relevant cause-effect relationships, which in turn guides our decisions. These decisions will hopefully, reverse the negative trends and encourage the positive ones leading to a healthier/functional soil. However, because of the complexity, the functionality of a system for benefiting humans is also complex and one analysis or practice alone may not be adequate. The following examples will illustrate the validity of a comprehensive approach to Soil Health via Soil Systems. Of course, other approaches could also be elaborated as long as they are based on scientific facts and principles. Next Slide.
• #6: The soil systems approach can provide context for soil quality / soil health (capability and condition) assessment and management activities that are currently often focused on individual fields or farms and on the surface horizon
• #7: Most soil health information is collected as point data Point data is typically up-scaled by field (ownership boundary) As previously pointed out the major challenge is how to best capture the variability that is predictable at a practical scale while learning to live with “other” variability that at this chosen scale appears “random”. From the practical aspect this translates in selecting the appropriate sampling design. Next Slide.
• #8: Soil systems leads to a focus beyond an individual site: The soil face captured by the rectangle displays an ideal soil-landscape-vegetation relationship until you know the context. So far we talked about spatial variability. However, of significance importance is temporal variability. For this simplified example we will ignore the interaction between spatial and temporal variability. Next Slide.
• #9: This is rain-fed wheat in western NE. Loess over Ogallala sands and gravel. The precipitation varies between 15-20 inches. The white horizon represents CaCO3 accumulation which is common in areas where evapotranspiration dominates over precipitation. Most of the observations are made at point or profile scale mostly for practical reasons and not scientific ones. Thus: 1) Most people envision a soil as a profile (tidy layers, ≈ static properties). Approx. = a soil kind. Variability is perceived as short range, random “noise”. Characterization data determined/acquired at (applies to) this pedon scale (numerous properties & details); functionally = point data. Commonly, this point data is then applied to much larger areas by necessity (not necessarily accurate). None of this provides a context (how this pedon/point data varies within the polypedon/same soil, nor how this soil relates to its neighbors (fits within a catena). However, if we look at the entire profile and the variability of the depth to the CaCO3 (Calcic horizon) varies. Well, this is not “NEWS”, however, if we are unaware or overlook this, we may fail to characterize it accurately and understand the process and their impact on our management decisions. How would one characterize the depth to this restrictive layer? Is it 50 cm or 85 cm. Of course, it is easy to say that the average would be 60 cm now that we have the ability to look at this entire profile. But imagine when we sample at one point only as shown by the yellow arrows. What impact this would have on our decision? Can we ignore this variability? Well, we can for some cases but not for others, so it depends, but the point is if we understand the systems in their natural settings and appropriate scales we can make better decisions and live with some of the shortcoming as long as we are aware of them. Next Slide.
• #10: Increasing the length of our observation from a field setting to a broader landscape such as map units introduces another variability that can only be captured at larger scales. Not only depth to CaCO3 varies but the thickness also. The simplistic point data /pedon view will not capture comprehensively natural soil body (which encompasses variability/complexity). For example: Track the whitish CaCO3 layer laterally: lateral gradations within a soil; and not constant within 1 map unit. Some of the complexity varies in predictable ways (= Non-Random variability/ Complexity = Soil landscape models). Some of the complexity remains beyond our understanding or ability to predict (= Random variability/ Complexity). Spatial Variability of soils is a combination of both Random and Non-random complexity. Next Slide.
• #11: Natural soil body variability/complexity) is not uniform within 1 map unit. However, some of the complexity varies in predictable ways (= Non-Random variability/ Complexity = Soil landscape models). However, it varies in predictable ways (= Non-Random variability/ Complexity). The CaCO3 layer thins uphill, due to upland erosion, is thickest along the evaporative edge perimeter of the low area, and is thin or absent in the center of the low area. This is an accurate and predictable soil model. The best summary / simplification of the soil continuum is a catena. Two loess increments and shallow depressions control the calcic horizon distribution – soil pattern is control by the deposits during the late Pleistocene and Holocene. How does this relate to Soil Health? When looking at the current Soil Health approach of assessing the soil status few limitations become obvious. Next Slide.
• #12: Most soil health information is collected as point data Point data is typically up-scaled by field (ownership boundary) As previously pointed out the major challenge is how to best capture the variability that is predictable at a practical scale while learning to live with “other” variability that at this chosen scale appears “random”. From the practical aspect this translates in selecting the appropriate sampling design. Next Slide.
• #13: Most current soil quality / health assessments focus on the top (10-20 cm) Public display in Iowa (central US – the “Corn Belt”) that shows changes in the thickness of mollic surface over a 150 year period since European Settlement. It has decreased from 14 to 5.5 inches (35 cm – 15 cm) almost three times. Yet, if one had to just look at this from Many of Soil Health and Quality Assessments focusing merely on the top 6 inches (15 cm) we would not be able to detect the upcoming threat of another dust bowl. The ecosystem functions soil perform rely on the sequence of soil horizons across the landscape not merely the surface horizon. What is lurking around is the fact that these soil-landscapes continue to degrade overall, which may not be apparent if we limit our sampling to the surface layer only. Next Slide.
• #14: Another example of the spatial variability of soil pH laterally and vertically due to landscape position and soil horizonation. While the surface soil pH is relatively similar the change magnitude between upper and bottom horizons is obviously decreasing as we move from summit to toeslope. Also, the soil pH is slightly increasing close to 7 at the toeslope due to lateral movement of carbonates with water flow from upper slope positions and discharging to the lower slope positions. Nutrients can display similar movement, which in turn would affect any nutrient recommendations and C distribution in this field. Again, this brings us back full circle on the question about the context under which we attempt to asses trajectories of Soil Health. Next Slide. Source: Libohova, Z., Winzeler, H. E., Lee, B., Schoeneberger, P.J., Datta, J., Owens, P.R. 2016. Geomorphons: Landformand property predictions in a glacial moraine in Indiana landscapes. Catena 142:66-76. http://dx.doi.org/10.1016/j.catena.2016.01.002.
• #15: Hypothetical index values – how can we use soil systems principles to better understand why we are seeing what we are seeing – add depth and nuance to static numbers
• #16: More detailed aspects of Soil Systems can be put to work. The model provides an underpinning of many other disciplines and natural processes with some important implications regarding the assessment of the status of resources and their trajectory. Next Slide.
• #17: I don’t have all the answers and I leave you with a question. The approach we suggest is a unifying conceptual focus and we hope that this sparks discussions about the future of the Soil Science and the continuing challenge to stay relevant. Consider some quotes from Landscape Conservation Initiative (2009) and NRCS Acting Chef Weller. “the continuation of the work on landscape conservation initiatives and better integrate science, assessment, and monitoring in these initiatives” Weller, J., 2012. Comments by Acting NRCS Chief Jason Weller at NRCS Family Meeting, November 14, 2012 “scientifically-based conservation beyond geopolitical boundaries in order to address natural resource concerns such as species conservation and water quality at landscape scales” NRCS Landscape Conservation Initiative (LCI, 2009)
• #18: This is rain-fed wheat in western NE. Loess over Ogallala sands and gravel. The precipitation varies between 15-20 inches. The white horizon represents CaCO3 accumulation which is common in areas where evapotranspiration dominates over precipitation. Most of the observations are made at point or profile scale mostly for practical reasons and not scientific ones. Thus: 1) Most people envision a soil as a profile (tidy layers, ≈ static properties). Approx. = a soil kind. Variability is perceived as short range, random “noise”. Characterization data determined/acquired at (applies to) this pedon scale (numerous properties & details); functionally = point data. Commonly, this point data is then applied to much larger areas by necessity (not necessarily accurate). None of this provides a context (how this pedon/point data varies within the polypedon/same soil, nor how this soil relates to its neighbors (fits within a catena). Next Slide.
• #19: Through this mechanistic simplified representation we can demonstrate the unique role(s) that soils play in the overall circulation of matter and energy. The “Abiotic” part of soil development captures the influence of climate (precipitation, temperature, wind , etc.) and its interaction with the local variability due to topography and parent material. Next Slide.
• #21: Perturbations, in this case overgrazing, but it could be any human induced activities such as clearing, pavement, tillage, drainage, etc. has a direct impact that is also visible at landscape scale. Erosion that is. However, because the soil system integrates many process, the overgrazing will influence other components such as seed dispersal, surface water flow, ground water recharge just to mention few. The major massage is that Soil is a complex environment (often called “system” by the European School of thoughts, not to be confused with the Soil Systems as a Landscape model that we are elaborating here), and can be better managed under the proper contexts – Soil Landscape. So, we are talking about systems within systems if you wish. This is a key concept that has major implications for selecting the most efficient and relevant way to determine the trajectory of the resource in questions – SOILS and more importantly how to modify the trajectory (change, reverse, encourage, etc.). Next Slide.
• #22: Once we understand the complexity and interrelationships between different parts of the system we can establish relevant cause-effect relationships, which in turn guides our decisions. Next Slide.