On the genetic affinity of individual tree biomass allometry in poplar short rotation coppice
Woody biomass is one of our main resources available to enhance the bio-economy, but its production varies considerably depending on the species, the environment and crop management. The variability associated with these crops complicates the estimation of biomass through prediction models. The specificity of environment or genotype level limits the application of many of the models, which are often developed for use at local geographical levels. Although generalizations involve some loss of accuracy, the inclusion of a wide range of data for a wide range of environments and genotypes can improve model applicability. A total of 11,265 data from short-rotation, high-density poplar plantations (from 22 sites in Spain, covering 29 genotypes belonging to 7 different taxonomic groups) were used to develop biomass prediction models under Mediterranean conditions and to test whether similarities in individual tree biomass allometry occur within the taxonomic group level. A general model and both taxonomic group- and genotype-level models were fitted using weighted nonlinear regression. The simplified model, in which only the basal diameter is included, presented the best model performance, explaining 87% of the variability. The allometric similarities among different genotypes were evaluated in order to explore the relationship between the most frequently used poplar genotypes in the Mediterranean area, and although certain groups were identified, it was not possible to relate these similarities among different genotypes to their taxonomic group affinity. This was also confirmed by comparing the performance of the general models with the taxonomic group-level models when predicting at the genotype level. Although estimates made using the general models are relatively precise, the use of genotype-level models is recommended for more accurate predictions. © 2017, Springer Science+Business Media New York.
| Main Authors: | , , , , |
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| Format: | journal article biblioteca |
| Language: | eng |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.12792/3202 |
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| Summary: | Woody biomass is one of our main resources available to enhance the bio-economy, but its production varies considerably depending on the species, the environment and crop management. The variability associated with these crops complicates the estimation of biomass through prediction models. The specificity of environment or genotype level limits the application of many of the models, which are often developed for use at local geographical levels. Although generalizations involve some loss of accuracy, the inclusion of a wide range of data for a wide range of environments and genotypes can improve model applicability. A total of 11,265 data from short-rotation, high-density poplar plantations (from 22 sites in Spain, covering 29 genotypes belonging to 7 different taxonomic groups) were used to develop biomass prediction models under Mediterranean conditions and to test whether similarities in individual tree biomass allometry occur within the taxonomic group level. A general model and both taxonomic group- and genotype-level models were fitted using weighted nonlinear regression. The simplified model, in which only the basal diameter is included, presented the best model performance, explaining 87% of the variability. The allometric similarities among different genotypes were evaluated in order to explore the relationship between the most frequently used poplar genotypes in the Mediterranean area, and although certain groups were identified, it was not possible to relate these similarities among different genotypes to their taxonomic group affinity. This was also confirmed by comparing the performance of the general models with the taxonomic group-level models when predicting at the genotype level. Although estimates made using the general models are relatively precise, the use of genotype-level models is recommended for more accurate predictions. © 2017, Springer Science+Business Media New York. |
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