Evaluating sampling designs and deriving biomass equations for young plantations of poplar and willow clones. (December 2015)

• Record Type: Journal Article
• Authors: Lupi, Carlo; Larocque, Guy; DesRochers, Annie; Labrecque, Michel; Mosseler, Alex; Major, John; Beaulieu, Jean; Tremblay, Francine; Gordon, Andrew M.; Thomas, Barb R.; Vézina, André; Bouafif, Hassine; Cormier, Denis; Sidders, Derek; Krygier, Richard; Thevathasan, Naresh; Riopel, Martin; Ferland-Raymond, Bastien
• Is Part Of: Biomass and bioenergy. Volume 83(2015:Dec.)
• Page Start: 196
• Page End: 205
• Publication Date: 2015-12
• Subjects: Random sampling -- Stratified sampling -- Coppice SRIC -- Clone-specific allometric equations -- Biomass -- Multi-stemmed plants; Biomass energy -- Periodicals; Biomass -- Periodicals; Energy-Generating Resources -- Periodicals; Bioénergie -- Périodiques; 333.9539
• DOI: 10.1016/j.biombioe.2015.09.019
• Languages: English
• ISSNs: 0961-9534

Abstract

Abstract: Short-rotation intensive culture (SRIC) for bioenergy production is at its pre-commercial stage in Canada. To be economically viable, these types of plantations need an accurate examination of actual yields, which requires precise and efficient estimation methods (i.e., specific allometric equations and sampling methods). At six SRIC plantations from three Canadian provinces (Quebec, Ontario and Alberta), 6 willow and 10 poplar clones were sampled and clone allometric equations were developed to estimate plant biomass. A stem selection approach was successfully used to develop plant allometric equations, reducing the number of stems to be measured by up to 81% in coppiced plantations relative to traditional stem equations. Clone-specific equations were more accurate than equations for groups of clones, but the difference in terms of RMSE% was generally small (less than 5%). Using extensive measurements of all the plants inside a plantation and a simulation approach, we also compared five sampling methods (simple random sampling, stratified sampling, systematic sampling, random and systematic cluster sampling) to estimate total biomass inside the plantation. Simple random sampling and stratified random sampling were the most efficient methods (i.e., increased precision for equal sample size) for the estimation of average plant biomass, survival and total plantation biomass. Stratified random sampling (based on the position inside the plantation) made it possible to reduce the sample size as compared to simple random sampling, but only at higher levels of precision (e.g., 25 less plants at 5% precision). Applications of sampling using remote sensing techniques and GIS are briefly discussed. Highlights: We developed biomass equations and sampling methods for SRIC plantations. Clone-specific equations are more accurate than equations for groups of clones. … but the difference in terms of RMSE% is generally small (less than 5%). Stem selection reduces the number of stems to be measured by up to 81%. For total biomass, simple random sampling and stratified sampling are the most efficient.