Comparing the Predictive Capacity of Allometric Models in Estimating Grass Biomass in a Desert Grassland

Adam T. Naito, Steven R. Archer, Philip Heilman

Research output: Contribution to journalArticlepeer-review

Abstract

Allometric models provide a rapid, nondestructive means for estimating aboveground biomass (AGB) of perennial grass species. In the absence of site-specific models, allometric relationships developed at other sites at other times are often used. This implicitly assumes that size-biomass relationships are highly robust. In this study, we assess the comparability of allometric relationships developed at two points in time (2005 and 2015) on different soils on a Sonoran Desert savanna in southern Arizona. We used peak growing season field measurements to develop single-species and multispecies regression models using basal diameter and height to predict the current year's AGB for seven perennial grass species. Basal diameter exhibited the strongest relationship with AGB among single-species (adjusted R2 = 0.54 to 0.87) and multispecies models (adjusted R2 = 0.73). Inclusion of height did little to improve biomass predictions. Our models generally underestimated observed 2015 AGB on the loamy site, whereas models developed in 2005 on a sandier site overestimated the 2015 AGB. Results suggest site-specific allometric models should be used when possible. However, in lieu of such models, relationships developed at other sites or at other times may be appropriate depending on the level of precision needed to address a specific research question.

Original languageEnglish (US)
Pages (from-to)72-76
Number of pages5
JournalRangeland Ecology and Management
Volume93
DOIs
StatePublished - Mar 2024

Keywords

  • Santa Rita Experimental Range
  • aboveground biomass
  • allometry
  • desert grassland
  • nondestructive sampling
  • size-biomass relationships

ASJC Scopus subject areas

  • Ecology
  • Animal Science and Zoology
  • Nature and Landscape Conservation
  • Management, Monitoring, Policy and Law

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