Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects

Christopher K. Black, Michael D. Masters, David S. LeBauer, Kristina J. Anderson-Teixeira, Evan H. DeLucia

Research output: Contribution to journalArticlepeer-review


Aims: Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods: We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results: The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions: Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool.

Original languageEnglish (US)
Pages (from-to)391-404
Number of pages14
JournalPlant and Soil
Issue number1-2
StatePublished - Oct 1 2017


  • Bayesian modeling
  • Minirhizotron
  • Root allocation
  • Root volume
  • Stan

ASJC Scopus subject areas

  • Soil Science
  • Plant Science

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