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    Sequestration by Arkansas forests removes carbon dioxide from the atmosphere, storing this carbon in biomass that fills a number of critical ecological and socioeconomic functions. We need a better understanding of the contribution of forests to the carbon cycle, including the accurate quantification of tree biomass. Models have long been developed to predict aboveground live tree biomass, but few of these have been derived from Arkansas forests. Since there is geographic variability in the growth and yield of pine as a function of genetics, site conditions, growth rate, stand stocking, and other factors, we decided to compare aboveground tree biomass estimates for a naturally regenerated, uneven-aged loblolly pine (Pinus taeda)-dominated stand on the Crossett Experimental Forest (CEF) in southeastern Arkansas. These predictions were made using a new locally derived biomass equation, five regional biomass equations, and the pine model from the National Biomass Estimators. With the local model as the baseline, considerable biomass variation appeared across a range of diameters—at the greatest diameter considered, the minimum value was only 69% of the maximum. Using a recent inventory from the CEF’s Good Farm Forty to compare each model, stand-level biomass estimates ranged from a low of 76.9 Mg/ha (a different Arkansas model) to as much as 96.1 Mg/ha (an Alabama model); the local CEF equation predicted 82.5 Mg/ha. A number of different factors contributed to this variability, including differences in model form and derivation procedures, geographic origins, and utilization standards. Regardless of the source of the departures, their magnitude suggests that care be used when making large-scale biomass estimates.

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    Bragg, D.C.; McElligott, K.M. 2013. Comparing aboveground biomass predictions for an uneven-aged pine-dominated stand using local, regional, and national models. Journal of the Arkansas Academy of Science. 67: 34-41.


    Arkansas, carbon dioxide, carbon sequestration, biomass, loblolly pine, Pinus taeda, Crossett Experimental Forest, biomass estimator

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