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New Forests

, Volume 46, Issue 3, pp 387–407 | Cite as

Dynamic growth and yield model including environmental factors for Eucalyptus nitens (Deane & Maiden) Maiden short rotation woody crops in Northwest Spain

  • Marta González-GarcíaEmail author
  • Andrea Hevia
  • Juan Majada
  • Rosa Calvo de Anta
  • Marcos Barrio-Anta
Article

Abstract

A dynamic model consisting of two projection functions, dominant height and basal area, was developed for the prediction of stand growth in Eucalyptus nitens bioenergy plantations aged 2–6 years and with stockings of 2,300 and 5,600 trees ha−1. The data came from 40 permanent sample plots, representing site quality variability across the distribution area of E. nitens crops. Three inventories were carried out to collect tree data and determine stand variables. Additionally, edaphic, physiographic and climatic information were obtained and included in the model. For both functions, an ADA growth model was selected, which achieved high accuracy. The corresponding growth curves developed in this study had values of 5, 8, 11 and 14 m for dominant height and 4, 14, 24 and 34 m2 ha−1 for basal area at the base age of 4 years. The inclusion of environmental factors (i.e. soil and climatic variables) as parameters in the model resulted in good estimations and increased the model’s flexibility to adapt to small variations in site conditions. The model developed here is thus shown to be useful for simulating the growth of E. nitens crops when environmental information is available. A prediction function was fitted for use in stands without diameter inventories or not previously occupied by E. nitens, and including environmental variables improved its accuracy. Biomass mean annual increment varied from 3.25 to 18.45 Mg ha−1 year−1 at the end of the rotation, projected as between 6 and 12 years depending on site quality.

Keywords

Eucalyptus Site quality Bioenergy Algebraic difference equations Climate Soil 

Notes

Acknowledgments

The authors wish to thank the company “ENCE (Energía&Celulosa)” for allowing data to be collected from their plantations. We acknowledge the collaboration of CETEMAS and SERIDA field workers. We also thank Ronnie Lendrum for revising the English of the manuscript. Marta González-García was supported by the PhD research fellowship Severo Ochoa from “PCTI-Gobierno del Principado de Asturias”.

References

  1. Bailey RL, Clutter JL (1974) Base-age invariant polymorphic site curves. For Sci 20:155–159Google Scholar
  2. Barrio-Anta M, Castedo-Dorado F, Diéguez-Aranda U et al (2006) Development of a basal area growth system for maritime pine in northwestern Spain using the generalized algebraic difference approach. Can J For Res 36:1461–1474. doi: 10.1139/x06-028 CrossRefGoogle Scholar
  3. Barrio-Anta M, Sixto-Blanco H, De Vinas ICR, Castedo-Dorado F (2008) Dynamic growth model for I-214 poplar plantations in the northern and central plateaux in Spain. For Ecol Manag 255:1167–1178CrossRefGoogle Scholar
  4. Bertalanffy LV (1949) Problems of organic growth. Nature 163:156–158CrossRefPubMedGoogle Scholar
  5. Bertalanffy LV (1957) Quantitative laws in metabolism and growth. Q Rev Biol 32:217–231CrossRefGoogle Scholar
  6. Booth TH, Pryor LD (1991) Climatic requirements of some commercially important eucalypt species. For Ecol Manag 43:47–60. doi: 10.1016/0378-1127(91)90075-7 CrossRefGoogle Scholar
  7. Bravo F, Álvarez-González JG, del Rio M et al (2011) Growth and yield models in Spain: historical overview, contemporary examples and perspectives. For Syst 20:315–328Google Scholar
  8. Bravo-Oviedo A, Tomé M, Bravo F et al (2008) Dominant height growth equations including site attributes in the generalized algebraic difference approach. Can J For Res 38:2348–2358. doi: 10.1139/X08-077 CrossRefGoogle Scholar
  9. Bravo-Oviedo A, Roig S, Bravo F et al (2011) Environmental variability and its relationship to site index in Mediterranean maritime pine. For Syst 20:50–64Google Scholar
  10. Breusch TS, Pagan AR (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47:1287. doi: 10.2307/1911963 CrossRefGoogle Scholar
  11. Castedo-Dorado F, Diéguez-Aranda U, Barrio-Anta M, Álvarez-González JG (2007) Modelling stand basal area growth for radiata pine plantations in northwestern Spain using the GADA. Ann For Sci 64:609–619CrossRefGoogle Scholar
  12. Cieszewski CJ (2001) Three methods of deriving advanced dynamic site equations demonstrated on inland Douglas-fir site curves. Can J For Res 31:165–173CrossRefGoogle Scholar
  13. Cieszewski CJ (2002) Comparing fixed- and variable-base-age site equations having single versus multiple asymptotes. For Sci 48:7–23Google Scholar
  14. Cieszewski CJ (2003) Developing a well-behaved dynamic site equation using a modified Hossfeld IV function Y-3 = (ax(m))/(c + x(m-1)), a simplified mixed-model and scant subalpine fir data. For Sci 49:539–554Google Scholar
  15. Cieszewski CJ (2004) GADA derivation of dynamic site equations with polymorphism and variable asymptotes from Richards, Weibull, and other exponential functions. University of Georgia, AthensGoogle Scholar
  16. Cieszewski CJ, Bailey RL (2000) Generalized algebraic difference approach: theory based derivation of dynamic site equations with polymorphism and variable asymptotes. For Sci 46:116–126Google Scholar
  17. Cieszewski CJ, Bella IE (1989) Polymorphic height and site index curves for lodgepole pine in Alberta. Can J For Res 19:1151–1160. doi: 10.1139/x89-174 CrossRefGoogle Scholar
  18. Cieszewski CJ, Harrison M, Martin SW (2000) Practical methods for estimating non-biased parameters in self-referencing growth and yield models. University of Georgia PMRC-TR 2000-7Google Scholar
  19. Clutter JL (1963) Compatible growth and yield models for loblolly pine. For Sci 9:354–371Google Scholar
  20. Clutter JL, Fortson JC, Pienaar LV et al (1983) Timber management: a quantitative approach. Wiley, New YorkGoogle Scholar
  21. Durbin J, Watson GS (1951) Testing for serial correlation in least squares regression. II. Biometrika 38:159–178. doi: 10.2307/2332325 CrossRefPubMedGoogle Scholar
  22. FAO (1981) El Eucalyptus en la repoblación forestal. Food and Agriculture Organization of the United Nations, RomeGoogle Scholar
  23. FAO (1991) Digitized soil map of the world. Volume 1: Africa. Volume 2: North and Central America. Volume 3: Central and South America. Volume 4: Europe and West of the Urals. Volume 5: North East Asia. Volume 6: Near East and Far East. Volume 7: South East Asia and Oceania. Release 1.0Google Scholar
  24. Fischer G, Prieler S, van Velthuizen H (2005) Biomass potentials of miscanthus, willow and poplar: results and policy implications for Eastern Europe, Northern and Central Asia. Biomass Bioenergy 28:119–132CrossRefGoogle Scholar
  25. Fontes L, Bontemps JD, Bugmann H et al (2010) Models for supporting forest management in a changing environment. For Syst 19:8–29Google Scholar
  26. García O (1988) Growth modelling—a (re)view. N Z For 33:14–17Google Scholar
  27. Gee GW, Bauder JW (1996) Particle size analysis. In: Klute A (ed) Methods of soil analysis: part 1, 2nd edn. American Society of Agronomy, Madison, pp 383–411Google Scholar
  28. González-García M, Hevia A, Majada J, Barrio-Anta M (2013) Above-ground biomass estimation at tree and stand level for short rotation plantations of Eucalyptus nitens (Deane & Maiden) Maiden in Northwest Spain. Biomass Bioenergy 54:147–157. doi: 10.1016/j.biombioe.2013.03.019 CrossRefGoogle Scholar
  29. González-García M, Almeida AC, Hevia A, Majada J, Beadle C (2015) Application of a process-based model for predicting the productivity of Eucalyptus nitens bioenergy plantations in Spain. Glob Change Biol Bioenergy. Under reviewGoogle Scholar
  30. Harvey AC (1976) Estimating regression models with multiplicative heteroscedasticity. Econometrica 44:461. doi: 10.2307/1913974 CrossRefGoogle Scholar
  31. Hossfeld JW (1822) Mathematik für Forstmänner. Ökonomen und Cameralisten, GothaGoogle Scholar
  32. Krumland B, Eng H (2005) Site index systems for major young-growth forest and woodland species in northern California. California Department of Forestry and Fire Protection, CaliforniaGoogle Scholar
  33. Laureysens I, Bogaert J, Blust R, Ceulemans R (2004) Biomass production of 17 poplar clones in a short-rotation coppice culture on a waste disposal site and its relation to soil characteristics. For Ecol Manag 187:295–309. doi: 10.1016/j.foreco.2003.07.005 CrossRefGoogle Scholar
  34. Lundqvist B (1957) On height growth in cultivated stands of pine and spruce in Northern Sweden. Medd Fran Statens Skogfoesk 47:1–64Google Scholar
  35. McDill ME, Amateis RL (1992) Measuring forest site quality using the parameters of a dimensionally compatible height growth function. For Sci 38:409–429Google Scholar
  36. Mehlich A (1953) Determination of P, Ca, Mg, K, Na, and NH4. Raleigh, North Carolina Soil Test DivisionGoogle Scholar
  37. Myers R (1986) Classical and modern regression with applications. Duxbury Press, MasssachusettsGoogle Scholar
  38. Newnham RM (1988) A modification of the Ek-Payandeh nonlinear regression model for site index curves. Can J For Res 18:115–120CrossRefGoogle Scholar
  39. Ni C, Liu C (2008) Evaluating behaviours of factors affecting the site index estimate on the basis of a single stand using simulation approach. Can J For Res 38:2762–2770. doi: 10.1139/X08-095 CrossRefGoogle Scholar
  40. Nunes L, Patricio M, Tomé J, Tomé M (2011) Modeling dominant height growth of maritime pine in Portugal using GADA methodology with parameters depending on soil and climate variables. Ann For Sci 68:311–323CrossRefGoogle Scholar
  41. Oliver CD, Larson BC (1996) Forest stand dynamics. Wiley, New YorkGoogle Scholar
  42. Pallardy SG, Kozlowski TT (1979) Relationships of leaf diffusion resistance of Populus clones to leaf water potential and environment. Oecologia 40:371–380. doi: 10.1007/BF00345333 CrossRefGoogle Scholar
  43. Parresol BR, Vissage JS (1998) White pine site index for the southern forest survey. Res. Pap. SRS-10. US Deparment of Agriculture, Forest Service, Southern Research Station, AshevilleGoogle Scholar
  44. Peech M (1947) Methods of soil analysis for soil-fertility investigations. Department of Agriculture, WashingtonGoogle Scholar
  45. Pérez S, Renedo CJ, Ortiz A et al (2006) Energy evaluation of the Eucalyptus globulus and the Eucalyptus nitens in the north of Spain (Cantabria). Thermochim Acta 451:57–64CrossRefGoogle Scholar
  46. Pérez-Cruzado C, Merino A, Rodríguez-Soalleiro R (2011a) A management tool for estimating bioenergy production and carbon sequestration in Eucalyptus globulus and Eucalyptus nitens grown as short rotation woody crops in north–west Spain. Biomass Bioenergy 35:2839–2851. doi: 10.1016/j.biombioe.2011.03.020 CrossRefGoogle Scholar
  47. Pérez-Cruzado C, Muñoz-Saez F, Basurco F et al (2011b) Combining empirical models and the process-based model 3-PG to predict Eucalyptus nitens plantations growth in Spain. For Ecol Manag 262:1067–1077CrossRefGoogle Scholar
  48. Pérez-Cruzado C, Blanco-Souto A, López-Sánchez CA et al (2013) Calidad de estación y productividad en plantaciones forestales de Eucalyptus nitens (Deane & Maiden) Maiden en el noroeste de España. Actas 6o Congr. For. Esp. CD-RomGoogle Scholar
  49. Pérez-Cruzado C, Sánchez-Ron D, Rodríguez-Soalleiro R et al (2014) Biomass production assessment from Populus spp. short-rotation irrigated crops in Spain. GCB Bioenergy 6:312–326. doi: 10.1111/gcbb.12061 CrossRefGoogle Scholar
  50. Pienaar LV, Turnbull KJ (1973) The Chapman-Richards generalization of Von Bertalanffy’s growth model for basal area growth and yield in even—aged stands. For Sci 19:2–22Google Scholar
  51. Richards FJ (1959) A flexible growth function for empirical use. J Exp Bot 10:290–301. doi: 10.1093/jxb/10.2.290 CrossRefGoogle Scholar
  52. Sánchez-Palomares O, Sánchez SF, Carretero-Carrero M (1999) Modelos y cartografía de estimaciones climáticas termopluviométricas para la España peninsular. INIA, Ministerio de Agricultura, Pesca y Alimentación, MadridGoogle Scholar
  53. SAS Institute Inc. (2004a) SAS/ETS® 9.1 user’s guide. Cary. NCGoogle Scholar
  54. SAS Institute Inc. (2004b) SAS/STAT® 9.1 user’s guide. Cary. NCGoogle Scholar
  55. Sims REH, Maiava TG, Bullock BT (2001) Short rotation coppice tree species selection for woody biomass production in New Zealand. Biomass Bioenergy 20:329–335. doi: 10.1016/S0961-9534(00)00093-3 CrossRefGoogle Scholar
  56. Tewari VP, Álvarez-González JG, García O (2014) Developing a dynamic growth model for teak plantations in India. For Ecosyst 1:9. doi: 10.1186/2197-5620-1-9 CrossRefGoogle Scholar
  57. Tomé M, Ribeiro F, Soares P (2001) O modelo Globulus 2.1. Universidad Técnica de Lisboa-ISA, Relatórios Técnico-científicos do GIMREF no 1Google Scholar
  58. Trnka M, Trnka M, Fialová J et al (2008) Biomass production and survival rates of selected poplar clones grown under a short-rotation on arable land. Plant Soil Environ 54:78–88Google Scholar
  59. White H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48:817–838. doi: 10.2307/1912934 CrossRefGoogle Scholar
  60. Wright L (2006) Worldwide commercial development of bioenergy with a focus on energy crop-based projects. Biomass Bioenergy 30:706–714CrossRefGoogle Scholar
  61. Vanclay JK (1994) Modelling forest growth and yield: applications to mixed tropical forests. CAB International, WallingfordGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Marta González-García
    • 1
    Email author
  • Andrea Hevia
    • 1
  • Juan Majada
    • 1
  • Rosa Calvo de Anta
    • 2
  • Marcos Barrio-Anta
    • 3
  1. 1.Sustainable Forest Management Area, Wood and Forest Research Technology Centre of Asturias (CETEMAS)GradoSpain
  2. 2.Departamento de Edafología y Química Agrícola, Facultad de BiologíaUniversidad de Santiago de CompostelaSantiago de CompostelaSpain
  3. 3.Department of Organisms and Systems Biology, Polytechnic School of MieresUniversity of OviedoMieresSpain

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