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United States Department of Agriculture

Agricultural Research Service

Title: Estimating Forage Production of a Tallgrass Prairie with Measure of Tiller Development by Key Grasses

Author
item Northup, Brian

Submitted to: International Rangeland Congress
Publication Type: Proceedings
Publication Acceptance Date: February 6, 2003
Publication Date: November 28, 2003
Citation: NORTHUP, B.K. 2003. ESTIMATING FORAGE PRODUCTION OF A TALLGRASS PRAIRIE WITH MEASURE OF TILLER DEVELOPMENT BY KEY GRASSES. INTERNATIONAL RANGELAND CONGRESS. p. 742-744.

Interpretive Summary: The maturity and size of grass plants (tillers) change during the growing season. These changes affect both the timing and amount of forage produced by grasslands, and they could be used to help manage grazing lands. A study was conducted during the 2000 and 2001 growing seasons to determine if tiller development of key grass species could be used to estimate production of a tallgrass pasture in central Oklahoma. Total production of the pasture was determined by clipping plant materials from 5.4 ft^2 rectangular areas located within a set of 16 plots at 2-week intervals from April through August. Seventy-five tillers (per plot) of both big bluestem and little bluestem (the dominant grasses on the area) were collected, maturity was described, and average plant weights were determined. Equations describing relationships between the two tiller measurements and production were non-linear and complex, but more than 80% accurate. Also, sampling time required to describe standing crop with tiller measurements was less than what was required to clip plots. Describing pasture production with clipped plots required 7, 8, and 16 hours in April, June, and August (35, 40, and 80 plots required). In the time required to collect information from five plots (one hour), 120 to 240 grass tillers could be collected, assessed, and used to produce estimators of production. Problems estimating standing crop with such equations include: requirements for experienced well-trained technicians, technician fatigue when large numbers of maturity stages exist, and the need for equations that describe relationships for different kinds of growing seasons. However, the shorter sampling times required make using tiller measurements to define production attractive for use in monitoring pastures.

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 standing crop (SC) of a tallgrass prairie in Oklahoma. Data were collected from 16 plots on an upland site during April-August (period of active plant growth) of both years at 14-day intervals. Total SC 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. Times required to sample quadrats and tiller characteristics were recorded, and required sample numbers were calculated. Data were analyzed by regression techniques, with tiller maturity and weight the independent variables and SC the dependent variable. Significant (R^2 > 0.80, P<0.01) nonlinear relationships were noted between the independent variables and SC, and equations for the two years were different. Times required to adequately sample to define mean SC (±10%, with 95% confidence) by clipping quadrats was high and increased over the growing season (n=35-80; time=7-16 h). Estimating SC by regression equations was more time efficient. In the time required to sample five quadrats (one hour), 120 tillers could be growth staged (adequate to accurately define maturity), or 240 tillers collected to determine mass. Drawbacks to estimating SC include extensive training requirements for technicians, technician fatigue when numerous maturity stages are present, and year-specific equations for different growing seasons. However, their accuracy and time efficiencies make such approaches attractive components for inclusion in monitoring systems.

Last Modified: 10/23/2014
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