Modeling the Measurable or Measuring the Modelable: A Hierarchical Approach to Isolating Meaningful Soil Organic Matter Fractionations
Abstract
The approaches of modeling the measurable and measuring the modelable are both valuable for advancing our understanding of soil organic matter (SOM). In the former case, we assume that the measurements we make are the best representation of nature, and that model structure should follow. While this may simplify model testing, it may not yield a particularly useful description of SOM dynamics. There is no question that most of our knowledge about SOM is derived from experimentation, but models are being increasingly used to further our understanding. In the latter case, model structure and parameters are modified in reasonable ways to obtain the best fit of simulation with observation, thus deducing the “correct” structure. In this case, it is conceivable that more than one structure can result in a good fit to the data. Good predictions may be produced, but an incorrect structure may be derived. In either case, there are no clear criteria for obtaining the “truth” except for repeatedly testing our methods/models against new information, preferably obtained from definitive experiments. A combination of approaches is best. We elaborate a particular approach which is mindful of the habitat in which microbes and their substrates reside and relate this theory to methods developed to separately isolate plant and microbially derived SOM, which may be physically protected from microbial attack in the soil.
Department
Soil Biogeochemistry and Microbial Ecology
Publication Date
1-1-1996
Journal Title
Evaluation of Soil Organic Matter Models
Publisher
Springer
Digital Object Identifier (DOI)
Document Type
Article
Recommended Citation
Elliott, E.T., Paustian, K., Frey, S.D. (1996). Modeling the Measurable or Measuring the Modelable: A Hierarchical Approach to Isolating Meaningful Soil Organic Matter Fractionations. In: Powlson, D.S., Smith, P., Smith, J.U. (eds) Evaluation of Soil Organic Matter Models. NATO ASI Series, vol 38. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-61094-3_12