Climate-sensitive Stand-level Biomass Models Of Larch Plantations In Northeast China

Climate effects have played a critical role in studying forest biomass.However,only a limited number of studies have been conducted on forest biomass management based on climate effects,particularly at the stand level.Thus,an allometric additive biomass equation based on conventional and climate-based stand biomass models was developed and compared for larch trees(Larix spp.).A total of 160 non-destructive sampling experimental plots of larch plantations have been collected in Heilongjiang Province,Northeast China.Based on a nonlinear model system of biomass additivity,we developed two types of the stand-level biomass basic models(SBBM model type 1 and type 2,M-1 and M-2)with stand variables(stand basal area(BA)and stand mean height(Hm))as the predictors and two types of the climate-sensitive stand-level biomass models,namely stand-level biomass climate-based models(SBCM model type 3 and type 4,M-3 and M-4)with stand variables(BA and Hm)and climatic variables(mean annual temperature(MAT)and annual precipitation(AP))as the predictors.The nonlinear seemingly unrelated regression(NSUR)method has been applied to the parameter estimate of the model system,and the weight function has been applied to overcome the heteroscedasticity of the model residuals.Accordingly,this study evaluated the effects of climatic variables(MAT and AP)and stand variables(BA and Hm)on the model’s performance.Model fitting and validation results revealed that the climatic variables significantly improved the model performance of the fitted equation by increasing the coefficient of determination(R~2)values and reducing the root mean square error(RMSE)values.A higher R~2 and a lower RMSE were consistently generated by M-2 and M-4,whereas M-1 and M-3 consistently generated a lower R~2 and a higher RMSE.We found that the climate-sensitive stand-level biomass model type 4(M-4)performed better than the other models and slightly better than previous studies in climate-sensitive models.The specific results are as follows:1)The climate variables(MAT and AP)significantly improved the stand-level biomass estimations under climate effects.Based on the goodness-of-fit,the SBCM models performed good biomass predictions.The lower R~2 values found in the basic model(SBBM model type 1 and 2,M-1 and M-2)ranged from 0.9322 to 0.9844,which gradually increased once the climate variables incorporated into the models as predictor variables(SBCM model type 3 and type 4,M-3 and M-4)ranged from 0.9438 to 0.9874.2)The role of stand mean height(Hm)was found significantly complement the predictors.As evident,as the RMSE decreased with a range from 3.72%to 30.64%,the biomass components of the climate-sensitive stand-level biomass models(SBCM)as the multiple variable proposed model that includes BA,Hm,and climatic variables(M-4)performed better than the stand-level biomass climate-based model without Hm(M-3).3)Based on the comparison to the four stand age groups(“Young”,“Middle”,“Near-mature”,and“Mature”),the proposed SBCM model type 4(M-4)performed better than other stand-level biomass models(M-1,M-2,and M-3)at different stand ages.4)Based on the predicted value and actual value accuracy comparison,the climate-sensitive stand-level biomass models type 4(SBCM model type 4,M-4)performed slightly better than the previously published climate-sensitive stand biomass model.5)This study revealed that the stand basal area(BA)as the main predictor for stand-level biomass estimation was found to have stable performance.It might appear as the correction for the several previous research that mentioned BA is not recommended predictor for biomass modeling.This study provided an additional and beneficial method of analyzing stand-level biomass estimation under climate effects.The results of this study are novel to the development of stand-level biomass estimation under climate effects for larch plantations,especially in Northeast China.Based on these findings,to implement these biomass models in other regions,intensive consideration must be given since climatological and environmental disparities in regions could result in different allometric relationships between predictors as well as biomass totals and components.