Allometric Equations for Estimating Above-Ground Biomass Carbon sequestration in Five Tree Species grown in an Intercropping Agroforestry System in Southern Ontario, Canada

This data set includes characteristics of approximately 66 trees that were harvested and weighted. Tree species are: Red Oak (Quercus rubra) [n=12], Black Walnut (Juglans nigra) [n=16], Black Locust (Robinia pseudoacacia) [n=10], White Ash (Fraxinus americana) [n=15], Norway Spruce (Picea abies) [n=13]. From this data allometric equations were developed for estimating above-ground biomass carbon (AGBC) sequestration in five tree species grown in a tree-based intercropping system at the University of Guelph Agroforestry Research Station (GARS), Guelph, Ontario, Canada. A total of 66 representative trees from five above species were selected, harvested and their aboveground biomass and C content were quantified. Three commonly used allometric models were used to develop predictive equations. Regression models were developed and parameterized for each tree species and the best are presented based on information criteria (AIC, AICc, and BIC), mean absolute percentage error (MAPE), over/under estimation (MOUE), root mean square error (RMSE), R2, and regression coefficients (a, b) of the observed/predicted (OP) linear regression analysis.

本数据集涵盖加拿大安大略省圭尔夫大学农林业研究站（Guelph Agroforestry Research Station, GARS）树基间作系统中，经采伐并称重的约66株林木的特征数据。所涉树种包括：红栎（Quercus rubra）[样本量n=12]、黑胡桃（Juglans nigra）[n=16]、刺槐（Robinia pseudoacacia）[n=10]、白蜡（Fraxinus americana）[n=15]、挪威云杉（Picea abies）[n=13]。本研究基于该数据集构建异速生长方程，用于估算上述5个树基间作树种的地上生物量碳（above-ground biomass carbon, AGBC）固存量。研究共选取上述5个树种的66株代表性林木进行采伐，并量化其地上生物量与碳含量。研究采用3种常用异速模型构建预测方程，针对每个树种分别建立回归模型并完成参数化。最终基于赤池信息准则（AIC）、修正赤池信息准则（AICc）、贝叶斯信息准则（BIC）、平均绝对百分比误差（MAPE）、高估/低估误差（MOUE）、均方根误差（RMSE）、决定系数R²，以及观测-预测（OP）线性回归分析的回归系数（a、b）筛选并给出最优模型。