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ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #214589
Title: Experience with the Pasture Condition Score System in On-Farm Research

Author
item Sanderson, Matt

Submitted to: American Forage and Grassland Council Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 10/1/2007
Publication Date: 1/26/2008
Citation: Sanderson, M.A. 2008. Experience with the Pasture Condition Score System in On-Farm Research. American Forage and Grassland Council Proceedings Paper Number 1441. CDROM.

Interpretive Summary: An interepretive summary is not required.

Technical Abstract: Renewed interest in grassland-based livestock systems have created a need for new methods of assessing and monitoring pastures. The Pasture Condition Score (PCS) system was developed by the USDA-NRCS as a monitoring and management tool. We have worked with the PCS system for several years. In a survey study, we used the PCS system to assess pastures on 31 farms across the northeast. Results indicated that few pastures fell into the lowest or highest score categories, whereas greater than 80% fell into the score category of “Needs some improvement.” Scores for the indicator “percent legume” had the lowest rating of all indicators, which suggests that producers should focus management on establishing and maintaining legumes. Pasture condition score was negatively related to plant species richness. This may indicate that focusing strictly on increasing the number of species in a pasture without regard to the species composition may not be wise. In a second on-farm study, we examined how PCS results vary within and among grazing seasons and within and among farms. We applied the PCS on five farms across the northeast during 3 yr. Our data indicated that a strategy of assessing PCS at the start of the grazing season in spring, during stressful growing conditions (typically mid summer) and near the end of the season (to determine the extent of recovery) would be useful. Grouping pastures managed for different classes of cattle (e.g., heifer, dry cow, or holding pastures) and monitoring representative subsets of these pastures, may reduce the monitoring work load.