Smooth additive mixed models for predicting aboveground biomass

Mariola Sánchez-González, María Durbán (), Dae-Jin Lee, Isabel Cañellas and Hortensia Sixto
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Mariola Sánchez-González: CIFOR-INIA
María Durbán: Universidad Carlos III de Madrid
Dae-Jin Lee: BCAM - Basque Center for Applied Mathematics, Applied Statistics
Isabel Cañellas: CIFOR-INIA
Hortensia Sixto: CIFOR-INIA

Journal of Agricultural, Biological and Environmental Statistics, 2017, vol. 22, issue 1, No 2, 23-41

Abstract: Abstract Aboveground biomass estimation in short-rotation forestry plantations is an essential step in the development of crop management strategies as well as allowing the economic viability of the crop to be determined prior to harvesting. Hence, it is important to develop new methodologies that improve the accuracy of predictions, using only a minimum set of easily obtainable information i.e., diameter and height. Many existing models base their predictions only on diameter (mainly due to the complexity of including further covariates), or rely on complicated equations to obtain biomass predictions. However, in tree species, it is important to include height when estimating aboveground biomass because this will vary from one genotype to another. This work proposes the use of a more flexible and easy to implement model for predicting aboveground biomass (stem, branches and total) as a smooth function of height and diameter using smooth additive mixed models which preserve the additive property necessary to model the relationship within wood fractions, and allows the inclusion of random effects and interaction terms. The model is applied to the analysis of three trials carried out in Spain, where nine clones at three different sites are compared. Also, an analysis of slash pine data is carried out in order to compare with the approach proposed by Parresol (Can J For Res 31:865–878, 2001). Supplementary materials accompanying this paper appear on-line

Keywords: Populus hybrids; Additivity; Tree biomass; Penalized splines; Mixed models (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13253-016-0271-4

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