ARS | Publication request: ESTIMATING ARUNDO DONAX L. SHOOT BIOMASS

Submitted to: Aquatic Botany
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 16, 2005
Publication Date: April 1, 2006
Citation: Spencer, D.F., Liow, P., Chan, W., Ksander, G.G. 2006. Estimating arundo donax l. shoot biomass. Aquatic Botany. 84:272-276.

Interpretive Summary: Giant reed (Arundo donax L.) is a tall perennial plant that has invaded streamside habitats where it changes important ecosystem properties. Plant biomass is typically measured by harvesting plants from sample plots and determining their dry weight. In habitats occupied by giant reed this approach is more difficult. Also in some cases, such as the long-term evaluation of the effects of biocontrol agents, destructive sampling methods may not be desirable. In order to provide rapid biomass estimates in these circumstances we derived an equation for estimating a shoot's weight from its height. We tested it against an independent data set, and the equation provided accurate estimates of shoot dry weight for shoots ranging from 0.3 to 7.06 m height. This equation may give useful biomass estimates in structural plant models and from the application of remote sensing technologies, such as Lidar, which estimate the distribution of heights within a plant canopy.

Technical Abstract: We developed an equation for estimating Arundo donax L. shoot dry weight from shoot length. The equation, shoot dry weight (g) = 14.254 (Standard Error = + 0.275) x shoot height2 (m), was as effective at explaining a high proportion of total variation in shoot dry weight as more complicated equations containing additional morphometric parameters. Tested against an independent data set, the equation provided accurate estimates of shoot dry weight for shoots ranging from 0.3 to 7.06 m height (P < 0.0001, R2 = 0.87). In addition to providing aboveground biomass estimates from stem counts and heights (more rapid than harvest methods), this equation may be useful in structural plant modeling and remote sensing technologies, such as Lidar, which estimate the distribution of heights within a plant canopy.