ARS | Publication request: Use of Direct and Indirect Estimates of Crown Dimensions to Predict One Seed Juniper Woody Biomass Yield for Alternative Energy Uses

Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: September 9, 2009
Publication Date: February 10, 2010
Citation: Utsumi, S., Racher, B., Borland, D., Booth, D.T., Bobo, M., Cox, S.E., Cox, S., Dunlap, R., Sandoval, A., Reid, K. 2010. Use of Direct and Indirect Estimates of Crown Dimensions to Predict One Seed Juniper Woody Biomass Yield for Alternative Energy Uses. Society for Range Management Meeting Abstracts.

Technical Abstract: Throughout the western United States there is increased interest in utilizing woodland biomass as an alternative energy source. We conducted a pilot study to predict one seed juniper (Juniperus monosperma) chip yield from tree-crown dimensions measured on the ground or derived from Very Large Scale Aerial (VLSA) digital imagery. Our study was conducted in January 2009, at the Corona Range and Livestock Research Center, in central New Mexico. We harvested 57 individual trees from a 0.10 ha plot and 17 tree clumps (72 individual trees) from a neighboring site which had been recently surveyed with VLSA digital imagery. We recorded crown and stem measurements on all trees shortly before the harvest date. A Volvo timber extractor and a Vermeer horizontal grinder with a 10 cm screen were used to harvest and grind individual trees. Juniper chips from each tree were collected and placed on a load bars scale to determine green weight. Random grab samples were extracted from each tree to determine dry matter content ([dry mass/green mass]*100) of chips which was 67.1 % (± 0.04; n=156). Overall, tree crown dimensions accounted for >70% of the variation in chip yield and were better predictors of chip yield of tree clumps in aerial images than of individual trees measured on the ground. Longest tree clump crown diameter was the best predictor of tree clump chip yield (y=81.4x-1,137.8;R2=0.94;p=0.05). We conclude VLSA image analysis is a powerful tool for monitoring piñon-juniper woodlands and that image-derived measurements are strong predictors of juniper chip yield.