Biomass Estimation for Four Common Grass Species in Northern Arizona Ponderosa Pine
Issue Date
1986-09-01Keywords
Festuca arizonicaMuhlenbergia montana
Poa fendleriana
Coconino National Forest
sitanion longefolium
ground cover plants
basal area
biomass determination
allometry
Pinus ponderosa
prescribed burning
rangelands
Arizona
Metadata
Show full item recordCitation
Andariese, S. W., & Covington, W. W. (1986). Biomass estimation for four common grass species in northern Arizona ponderosa pine. Journal of Range Management, 39(5), 472-473.Publisher
Society for Range ManagementJournal
Journal of Range ManagementDOI
10.2307/3899456Additional Links
https://rangelands.org/Abstract
Vegetation allometric relations were examined for 4 important grass species in southwestern ponderosa pine (Pinus ponderosa). Logarithmic regressions were developed relating aboveground biomass to basal area, height, and number of seedheads, as well as 3 factors: overstory type (pole, yellow pine), burning treatment (unburned, prescribed burn 2-, 5-, and 7-yr previously), and site (3 locations). Basal area was defined as longest basal diameter multiplied by the widest perpendicular diameter. Of the metric variables, basal area proved to be the best predictor of biomass. Height and number of seedheads did little to increase R2 values. Burning treatment was a significant factor for Sitanion longefolium and Muhlenbergia montana. Overstory type significantly affected Poa fenderiana and Festuca arizonica equations. Site effects were important for all but Sitanion longefolium. When biomass regressions are used for species such as these, sampling efficiency can be improved by including factors such as overstory type, burning history, and locale. Final regression equations relating biomass of each species to basal area and significant factors were significant at p<0.05 and had adjusted R2 values ranging from 0.81 to 0.87. A validation test using 20% of the data not used in developing the regressions indicated that these equations are adequate predictors. When used with double sampling, weight prediction based on basal area indices should provide a more objectively measured predictor than percent cover.Type
textArticle
Language
enISSN
0022-409Xae974a485f413a2113503eed53cd6c53
10.2307/3899456