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Title: ESTIMATING FORAGE PRODUCTION OF A TALLGRASS PRAIRIE WITH MEASURES OF TILLER DEVELOPMENT BY KEY GRASSES

Author
item Northup, Brian

Submitted to: International Rangeland Congress
Publication Type: Abstract Only
Publication Acceptance Date: 8/14/2002
Publication Date: 12/14/2002
Citation: NORTHUP, B.K. ESTIMATING FORAGE PRODUCTION OF A TALLGRASS PRAIRIE WITH MEASURES OF TILLER DEVELOPMENT BY KEY GRASSES. INTERNATIONAL RANGELAND CONGRESS. 2002. V. 20. p. B1.24.

Interpretive Summary: ABSTRACT ONLY

Technical Abstract: Development of grass tillers during the growing season affects the timing and level of production by prairie communities, and could be a useful tool to help manage grazing lands. A study was conducted in 2000-2001 to determine if tiller development of key grasses could estimate aboveground production (AGP) of a tallgrass prairie in Oklahoma. Data were collected from 16 plots on an upland site during March-August (period of active plant growth) of both years at 14-day intervals. Total AGP was determined on 0.5 m^2 quadrats, and 75 tillers (per plot) of both Andropogon gerardii and Schizachyrium scoparium (dominant species on upland sites) were collected and maturity determined by the Nebraska growth staging system. Data were analyzed by non-linear regressions, with tiller maturity and weight the independent variables and AGP the dependent variable. Sample numbers and times required to adequately sample the different characteristics were also calculated. Significant (R^2>0.85, P<0.01) quadratic relationships were noted between the independent variables and AGP, and equations for the two years were different. Times required to adequately define mean AGP (±10%, 95% confidence) by clipping quadrats was high and increased over the growing season (n=35-80; time=5.9-13.4 h). Adequately sampling for tiller maturity (n=40-255; time=0.3-2.4 h) or weight (n=1510-2625; time=2.1-3.7 h), and estimating production by regression equations, was more time efficient. Drawbacks to estimating AGP include small losses in accuracy, training requirements, and year-specific equations for different growing seasons. However, their time saving capacity makes such techniques attractive components for monitoring systems.