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    Author(s): Todd A. Ontl; Kirsten S. Hofmockel; Cynthia A. Cambardella; Lisa A. SchulteRandall K. Kolka
    Date: 2013
    Source: New Phytologist. 199(3): 727-737.
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: View PDF  (465.33 KB)


    Successful modeling of the carbon (C) cycle requires empirical data regarding species-specific root responses to edaphic characteristics. We address this need by quantifying annual root production of three bioenergy systems (continuous corn, triticale/sorghum, switchgrass) in response to variation in soil properties across a toposequence within a Midwestern agroecosystem. Using ingrowth cores to measure annual root production, we tested for the effects of topography and 11 soil characteristics on root productivity. Root production significantly differed among cropping systems. Switchgrass root productivity was lowest on the floodplain position, but root productivity of annual crops was not influenced by topography or soil properties. Greater switchgrass root production was associated with high percent sand, which explained 45% of the variation. Percent sand was correlated negatively with soil C and nitrogen and positively with bulk density, indicating this variable is a proxy for multiple important soil properties. Our results suggest that easily measured soil parameters can be used to improve model predictions of root productivity in bioenergy switchgrass, but the edaphic factors we measured were not useful for predicting root productivity in annual crops. These results can improve C cycling modeling efforts by revealing the influence of cropping system and soil properties on root productivity.

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    Ontl, Todd A.; Hofmockel, Kirsten S.; Cambardella, Cynthia A.; Schulte, Lisa A.; Kolka, Randall K. 2013. Topographic and soil influences on root productivity of three bioenergy cropping systems. New Phytologist. 199(3): 727-737.


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    bioenergy, carbon allocation, carbon cycle, ecosystem modeling, root production, scaling, soil variability

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