New models for estimating the carbon sink capacity of Spanish softwood species

Quantifying the carbon balance in forests is one of the main challenges in forest management. Forest carbon stocks are usually estimated indirectly through biomass equations applied to forest inventories, frequently considering different tree biomass components. The aim of this study is to develop systems of equations for predicting tree biomass components for the main forest softwood species in Spain: Abies alba Mill., A. pinsapo Boiss., Juniperus thurifera L., Pinus canariensis Sweet ex Spreng., P. halepensis Mill., P. nigra Arn., P. pinaster Ait., P. pinea L., P. sylvestris L., P. uncinata Mill. For each species, a system of additive biomass models was fitted using seemingly unrelated regression. Diameter at the breast height and total height were used as independent variables. Diameter appears in all component models, while tree height was included in the stem component model of all species and in some branch component equations. Total height was included in order to improve biomass estimations at different sites. These biomass models were compared to previously available equations in order to test their accuracy and it was found that they yielded better fitting statistics in all cases. Moreover, the models fulfil the additivity property. We also developed root:shoot ratios in order to determine the partitioning into aboveground and belowground biomass. A number of differences were found between species, with a minimum of 0.183 for A. alba and a maximum of 0.385 for P. uncinata. The mean value for the softwood species studied was 0.265. Since the Spanish National Forest Inventory (NFI) records species, tree diameter and height of sample trees, these biomass models and ratios can be used to accurately estimate carbon stocks from NFI data.

[1]  R. Mäkipää,et al.  Uncertainty estimation of biomass expansion factors for Norway spruce in the Czech Republic , 2007, Annals of Forest Science.

[2]  João Santos Pereira,et al.  Biomass allometry and carbon factors for a Mediterranean pine (Pinus pinea L.) in Portugal , 2010 .

[3]  J. Navar,et al.  Allometric equations for tree species and carbon stocks for forests of northwestern Mexico , 2009 .

[4]  L. Sequist,et al.  A Phase II Study of Oxaliplatin, Pemetrexed, and Bevacizumab in Previously Treated Advanced Non-small Cell Lung Cancer , 2008, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[5]  Richard Evans Schultes,et al.  Forest Biomass , 1982, Forestry Sciences.

[6]  F. Bravo,et al.  Carbon sequestration in Spanish Mediterranean forests under two management alternatives: a modeling approach , 2008, European Journal of Forest Research.

[7]  Ilya Gelfand,et al.  Biogeochemical factors contributing to enhanced carbon storage following afforestation of a semi-arid shrubland , 2007 .

[8]  Margarida Tomé,et al.  Effect of tree, stand, and site variables on the allometry of Eucalyptus globulus tree biomass , 2007 .

[9]  P. Muukkonen,et al.  Generalized allometric volume and biomass equations for some tree species in Europe , 2007, European Journal of Forest Research.

[10]  J. Álvarez-González,et al.  Temporal variations and distribution of carbon stocks in aboveground biomass of radiata pine and maritime pine pure stands under different silvicultural alternatives , 2006 .

[11]  J. Ewel,et al.  Allometric equations for four valuable tropical tree species , 2006 .

[12]  E. Cienciala,et al.  Biomass functions applicable to Scots pine , 2006, Trees.

[13]  A. Prokushkin,et al.  Critical analysis of root : shoot ratios in terrestrial biomes , 2006 .

[14]  F. Raulier,et al.  Canadian national tree aboveground biomass equations , 2005 .

[15]  Marcia J. Lambert,et al.  Additive biomass equations for native eucalypt forest trees of temperate Australia , 2004, Trees.

[16]  Christian Wirth,et al.  Generic biomass functions for Norway spruce in Central Europe--a meta-analysis approach toward prediction and uncertainty estimation. , 2004, Tree physiology.

[17]  Sophie E. Hale,et al.  Biomass expansion factors and root: shoot ratios for coniferous tree species in Great britain , 2004 .

[18]  Juha Heikkinen,et al.  Biomass expansion factors (BEFs) for Scots pine, Norway spruce and birch according to stand age for boreal forests , 2003 .

[19]  B. Parresol,et al.  Additivity in tree biomass components of Pyrenean oak (Quercus pyrenaica Willd.) , 2003 .

[20]  F. Wagner,et al.  Good Practice Guidance for Land Use, Land-Use Change and Forestry , 2003 .

[21]  T. Gauquelin,et al.  Biomass and nutrient content of a semi-arid mountain ecosystem: the Juniperus thurifera L. woodland of Azzaden Valley (Morocco) , 2002 .

[22]  Christopher B. Field,et al.  FOREST CARBON SINKS IN THE NORTHERN HEMISPHERE , 2002 .

[23]  Didier Bert,et al.  Allometric relationships for branch and tree woody biomass of Maritime pine (Pinus pinaster Aı̈t.) , 2002 .

[24]  Sandra A. Brown.  Measuring carbon in forests: current status and future challenges. , 2002, Environmental pollution.

[25]  C. Peng,et al.  Changes in Forest Biomass Carbon Storage in China Between 1949 and 1998 , 2001, Science.

[26]  Q. Ketterings,et al.  Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests , 2001 .

[27]  Bernard R. Parresol,et al.  Additivity of nonlinear biomass equations , 2001 .

[28]  Jeffrey Q. Chambers,et al.  MEASURING NET PRIMARY PRODUCTION IN FORESTS: CONCEPTS AND FIELD METHODS , 2001 .

[29]  Francis Colin,et al.  Estimating root system biomass from breast-height diameters , 2001 .

[30]  J. Ottorini,et al.  Root biomass and biomass increment in a beech (Fagus sylvatica L.) stand in North-East France , 2001 .

[31]  Kalliopi Radoglou,et al.  Forests of the Mediterranean region : gaps in knowledge and research needs , 2000 .

[32]  N. Montes,et al.  A non-destructive method for estimating above-ground forest biomass in threatened woodlands , 2000 .

[33]  I. Cañellas,et al.  Productividad aérea y dinámica de nutrientes en una repoblación de Pinus pinaster Ait. sometida a distintos regímenes de claras , 1999 .

[34]  B. Parresol.  Assessing Tree and Stand Biomass: A Review with Examples and Critical Comparisons , 1999, Forest Science.

[35]  Dafydd Gibbon,et al.  1 User’s guide , 1998 .

[36]  J. Neirynck,et al.  Biomass and nutrient cycling of a highly productive Corsican pine stand on former heathland in northern Belgium , 1998 .

[37]  R. Birdsey,et al.  Biomass Estimation for Temperate Broadleaf Forests of the United States Using Inventory Data , 1997, Forest Science.

[38]  Eileen H. Helmer,et al.  Root biomass allocation in the world's upland forests , 1997, Oecologia.

[39]  Werner A. Kurz,et al.  Estimation of root biomass and dynamics for the carbon budget model of the Canadian forest sector , 1996 .

[40]  G. Marland,et al.  The role of forest and bioenergy strategies in the global carbon cycle , 1996 .

[41]  R. K. Dixon,et al.  Carbon Pools and Flux of Global Forest Ecosystems , 1994, Science.

[42]  L. Marklund,et al.  Biomass functions for pine, spruce and birch in Sweden , 1988 .

[43]  R. H. Myers.  Classical and modern regression with applications , 1986 .

[44]  B. Lemoine,et al.  Biomasses et croissance du pin maritime. Étude de la variabilité dans un peuplement de 16 ans , 1986 .

[45]  John Pastor,et al.  Biomass prediction using generalized allometric regressions for some northeast tree species , 1984 .

[46]  P. J. Edwards,et al.  World Forest Biomass and Primary Production Data. , 1983 .

[47]  T. Crow,et al.  Alternative models for estimating woody plant biomass. , 1980 .

[48]  A. Cabanettes,et al.  Biomass, mineral mass and productivity of a Pinus pinea ecosystem near the Mediterranean coast. I. Biomass. , 1978 .

[49]  H. Akaike.  A new look at the statistical model identification , 1974 .

[50]  A. Kozak.  Methods for Ensuring Additivity of Biomass Components by Regression Analysis , 1970 .

[51]  A. Zellner.  An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias , 1962 .