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ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #356633

Research Project: Multifunctional Farms and Landscapes to Enhance Ecosystem Services (Bridge Project)

Location: Pasture Systems & Watershed Management Research

Title: Estimating pasture species biomass from canopy cover

item Goslee, Sarah

Submitted to: Crop, Forage & Turfgrass Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/28/2020
Publication Date: N/A
Citation: N/A

Interpretive Summary: Determining the biomass of individual pasture species requires substantial time and effort for clipping, sorting, and weighing. Biomass composition of pastures is useful for managing forage quality, as well as for understanding other ecosystem services provided by pastures. Equations relating aboveground biomass to species canopy cover by season in grazed, multi-species pastures in Pennsylvania were developed for individual species, and for grasses, large forbs, and small forbs, then tested on an independent dataset. The functional group models were best at estimating biomass, with average error less than thirty percent for both spring and autumn. Because these relationships were developed under realistic pasture conditions, they have wide applicability in similar pastures of the northeastern United States.

Technical Abstract: Many tools are available to measure total aboveground plant biomass in pastures, such as the rising plate meter and the Robel pole, but measuring biomass by species requires time-consuming clipping and sorting. Pasture composition determines forage quality, carbon storage, and other ecosystem services. Seasonal allometric coefficients relating canopy cover to biomass were developed for individual species and for functional groups (grass, large forb, small forb) using data from a grazed, multi-species, experimental pasture in Pennsylvania, and tested on an independent grazed experimental study. The models performed better in autumn than in spring, and performance varied greatly among species. The functional group models were generally more accurate than species-specific models, even for estimating single species. Total grass and forb biomasses were well modeled, with bias less than thirty percent regardless of season. Unlike many allometric models, these were developed and tested on multi-year, grazed, multi-species, pastures, so variability was high, but the results will have broad applicability in high-fertility, mesic, pastures.