A three level system for estimating the biomass of Castanea sativa Mill. coppice stands in north-west Spain

Abstract Aboveground biomass was studied in Castanea sativa Mill. coppice stands in north-west Spain, and biomass equations were fitted at three levels (individual tree, stool and stand). Four systems of biomass estimation were developed. In two of the systems, the following individual tree variables were taken into account: standing tree variables and stump dimension variables. In the other two systems, biomass was estimated at stool and stand level, respectively. In order to represent the existing range of ages, stand densities and sites in the study area, samples of 120 trees (for the individual tree level), 45 stools (for the stool level) and 70 plots (for the stand level) were chosen for study. The trees were felled and destructively sampled to separate biomass into the following components: wood, bark, thick branches, medium branches, thin branches and leaves. Several equations for quantifying the biomass of the different biomass components were evaluated. Heterocedasticity was corrected for by weighted fitting. To guarantee the additivity of the different biomass components, the equations were fitted simultaneously by nonlinear seemingly unrelated regressions (NSURs). The different biomass levels considered accounted for between 60% and 90% of the total variability, depending on the level and component evaluated. Most of the equations developed in this study were evaluated with an independent dataset, which confirmed the good performance of the biomass equations for prediction purposes.

[1]  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 .

[2]  Elena Angulo Aramburu.  Ministerio de educación y ciencia , 1991 .

[3]  Chuankuan Wang,et al.  Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests , 2006 .

[4]  Fernando Castedo-Dorado,et al.  Development of a stand density management diagram for radiata pine stands including assessment of stand stability , 2009 .

[5]  Laurent Saint-André,et al.  Age-related equations for above- and below-ground biomass of a Eucalyptus hybrid in Congo , 2005 .

[6]  David A. Belsley,et al.  Conditioning Diagnostics: Collinearity and Weak Data in Regression , 1991 .

[7]  G. Alonso,et al.  Mejora del Pinus pinaster Ait. en Galicia , 1993 .

[8]  Thomas P. Ryan,et al.  Modern Regression Methods , 1996 .

[9]  Hal O. Liechty,et al.  Above- and belowground dry matter accumulation pattern derived from dimensional biomass relationships , 1996 .

[10]  M. Tomé,et al.  Total aboveground biomass and net dry matter accumulation by plant component in young Eucalyptus globulus in response to irrigation , 1998 .

[11]  H. Wiant,et al.  A simple technique for estimating cubic foot volume yields , 2004 .

[12]  Richard J. Harper,et al.  Additive prediction of aboveground biomass for Pinus radiata (D. Don) plantations , 2010 .

[13]  M. Ter-Mikaelian,et al.  Biomass equations for sixty-five North American tree species , 1997 .

[14]  M.S. Patricio,et al.  Biomass Equations for Castanea sativa High Forest in the Northwest of Portugal , 2004 .

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

[16]  Oscar García,et al.  Evaluating forest Growth Models , 1997 .

[17]  S. Salazar,et al.  Above-ground tree biomass equations and nutrient pools for a paraclimax chestnut stand and for a climax oak stand in the Sierra de Francia Mountains, Salamanca, Spain , 2010 .

[18]  R. Ruiz-Peinado,et al.  Producción de biomasa y fijación de CO2 por los bosques españoles , 2011 .

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

[20]  I. S. Regina.  Organic matter distribution and nutrient fluxes within a sweet chestnut (Castanea sativa Mill) stand of the Sierra de Gata, Spain , 2000 .

[21]  A. Harvey.  Estimating Regression Models with Multiplicative Heteroscedasticity , 1976 .

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

[23]  R. Birdsey,et al.  National-Scale Biomass Estimators for United States Tree Species , 2003, Forest Science.

[24]  J. Ranger,et al.  Dynamique d'incorporation du carbone et des éléments nutritifs dans un taillis simple de châtaignier (Castanea sativa Miller) , 1990 .

[25]  R. Briggs,et al.  Forcing additivity of biomass tables: some empirical results , 1984 .

[26]  Ben Bond-Lamberty,et al.  Aboveground and belowground biomass and sapwood area allometric equations for six boreal tree species of northern Manitoba , 2002 .

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

[28]  L. Zhang.  Cross-validation of Non-linear Growth Functions for Modelling Tree Height–Diameter Relationships , 1997 .

[29]  R. Hall,et al.  Assessing prediction errors of generalized tree biomass and volume equations for the boreal forest region of west-central Canada , 2008 .

[30]  T. Johansson.  Biomass equations for determining fractions of pendula and pubescent birches growing on abandoned farmland and some practical implications. , 1999 .

[31]  Richard H. Waring,et al.  Forest Ecosystems: Analysis at Multiple Scales , 1985 .

[32]  T. Hassard,et al.  Applied Linear Regression , 2005 .

[33]  G. Moreno,et al.  Nutrient cycling in deciduous forest ecosystems of the Sierra de Gata mountains: nutrient supplies to the soil through both litter and throughfall. , 1998 .

[34]  I. S. Regina,et al.  Biomass, litterfall and nutrient content in Castanea sativa coppice stands of southern Europe. , 1996 .

[35]  Robert A. Monserud,et al.  An evaluation of diagnostic tests and their roles in validating forest biometric models , 2004 .

[36]  G. M. Furnival.  An index for comparing equations used in constructing volume tables , 1961 .

[37]  U. Diéguez-Aranda,et al.  An ecoregional model for estimating volume, biomass and carbon pools in maritime pine stands in Galicia (northwestern Spain) , 2006 .

[38]  A. Kozak,et al.  Effects of multicollinearity and autocorrelation on the variable-exponent taper functions , 1997 .

[39]  E. Green,et al.  A method of forcing additivity of biomass tables when using nonlinear models , 1985 .

[40]  Bruce E. Borders,et al.  Systems of Equations in Forest Stand Modeling , 1989, Forest Science.

[41]  K. Rennolls,et al.  Timber Management-A Quantitative Approach. , 1984 .

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

[43]  E. H. White,et al.  Biomass estimation equations for norway spruce in New York , 1986 .

[44]  A. Kozak,et al.  Does cross validation provide additional information in the evaluation of regression models , 2003 .