IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v409y2019ic2.html
   My bibliography  Save this article

Development of a dynamic growth model for sweet chestnut coppice: A case study in Northwest Spain

Author

Listed:
  • Prada, Marta
  • González-García, Marta
  • Majada, Juan
  • Martínez-Alonso, Celia

Abstract

Sweet chestnut coppice (Castanea sativa Mill.) is a species of great importance in the northwest of Spain, due to its potential for producing valuable timber in relatively short rotations. However, abandonment has resulted in unstable and degraded stands. Thus, there is a need to improve forestry decision making tools. The objective of this study is the development of a dynamic stand growth model for the sweet chestnut comprised of three transition functions (dominant height, basal area and number of stems per hectare). They are used to estimate rates of change in the stand between an initial point in time and a point in the future. The data comes from two inventories of an unmanaged network of plots which incorporate all the variability in conditions in the region for the study species (climate, soil, stocking, site quality etc.). ADA and GADA approaches were used to develop the three transition functions. The model achieved high accuracy (explaining >90% of variability). The model incorporates an initialization function (explaining 60% of variability) for predicting initial stand basal area in stands without diameter inventories, which can be used to establish the starting point for the simulation, and, in addition, the biomass expansion factor (BEF) for this species (expressed as a constant value of 0.60) and a new single aboveground biomass equation (explaining almost 80% of variability) were calculated. A case study shows how to apply these decision making tools for the sustainable management of sweet chestnut coppice.

Suggested Citation

  • Prada, Marta & González-García, Marta & Majada, Juan & Martínez-Alonso, Celia, 2019. "Development of a dynamic growth model for sweet chestnut coppice: A case study in Northwest Spain," Ecological Modelling, Elsevier, vol. 409(C), pages 1-1.
  • Handle: RePEc:eee:ecomod:v:409:y:2019:i:c:2
    DOI: 10.1016/j.ecolmodel.2019.108761
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380019302698
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2019.108761?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-465, May.
    2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anjum, Zeba & Burke, Paul J. & Gerlagh, Reyer & Stern, David I., "undated". "Modeling the Emissions-Income Relationship Using Long-Run Growth Rates," Working Papers 249422, Australian National University, Centre for Climate Economics & Policy.
    2. Ansgar Belke & Robert Czudaj, 2010. "Is Euro Area Money Demand (Still) Stable? Cointegrated VAR Versus Single Equation Techniques," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 56(4), pages 285-315.
    3. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2016. "Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 582-594.
    4. Cheng, Tsung-Chi, 2012. "On simultaneously identifying outliers and heteroscedasticity without specific form," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2258-2272.
    5. Dufour, Jean-Marie & Khalaf, Lynda & Bernard, Jean-Thomas & Genest, Ian, 2004. "Simulation-based finite-sample tests for heteroskedasticity and ARCH effects," Journal of Econometrics, Elsevier, vol. 122(2), pages 317-347, October.
    6. Steven Ross & Yves Zenou, 2003. "Shirking, Commuting and Labor Market Outcomes," Working papers 2003-41, University of Connecticut, Department of Economics.
    7. LE GALLO, Julie, 2000. "Econométrie spatiale 2 -Hétérogénéité spatiale," LATEC - Document de travail - Economie (1991-2003) 2001-01, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    8. repec:zbw:rwirep:0171 is not listed on IDEAS
    9. Romano, Joseph P. & Wolf, Michael, 2017. "Resurrecting weighted least squares," Journal of Econometrics, Elsevier, vol. 197(1), pages 1-19.
    10. Roberto Dell’Anno & Désirée Teobaldelli, 2015. "Keeping both corruption and the shadow economy in check: the role of decentralization," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 22(1), pages 1-40, February.
    11. David E. A. Giles, 2004. "Calculating a Standard Error for the Gini Coefficient: Some Further Results," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(3), pages 425-433, July.
    12. Spierdijk, L., 2002. "An Empirical Analysis of the Role of the Trading Intensity in Information Dissemination on the NYSE," Other publications TiSEM d495caf0-2f2a-425f-8e50-e, Tilburg University, School of Economics and Management.
    13. Barabas, György & Kitlinski, Tobias & Schmidt, Christoph M. & Schmidt, Torsten & Siemers, Lars-H. & Brilon, Werner, 2010. "Verkehrsinfrastrukturinvestitionen: Wachstumsaspekte im Rahmen einer gestaltenden Finanzpolitik. Endbericht - Januar 2010. Forschungsprojekt im Auftrag des Bundesministeriums der Finanzen. Projektnumm," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 72601.
    14. Atanu Saha & Arthur Havenner & Hovav Talpaz, 1997. "Stochastic production function estimation: small sample properties of ML versus FGLS," Applied Economics, Taylor & Francis Journals, vol. 29(4), pages 459-469.
    15. Samira Shayanmehr & Shida Rastegari Henneberry & Mahmood Sabouhi Sabouni & Naser Shahnoushi Foroushani, 2020. "Drought, Climate Change, and Dryland Wheat Yield Response: An Econometric Approach," IJERPH, MDPI, vol. 17(14), pages 1-18, July.
    16. Julie Le Gallo, 2000. "Spatial econometrics (2, Spatial heterogeneity) [Econométrie spatiale (2, Hétérogénéité spatiale)]," Working Papers hal-01526969, HAL.
    17. Ansgar Belke & Robert Czudaj, 2010. "Is Euro Area Money Demand (Still) Stable? – Cointegrated VAR versus Single Equation Techniques," Ruhr Economic Papers 0171, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    18. William H. Greene & David A. Hensher, 2008. "Modeling Ordered Choices: A Primer and Recent Developments," Working Papers 08-26, New York University, Leonard N. Stern School of Business, Department of Economics.
    19. Atkinson, Anthony C. & Riani, Marco & Torti, Francesca, 2016. "Robust methods for heteroskedastic regression," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 209-222.
    20. Ragnar Tveteras & Ola Flaten & Gudbrand Lien, 2011. "Production risk in multi-output industries: estimates from Norwegian dairy farms," Applied Economics, Taylor & Francis Journals, vol. 43(28), pages 4403-4414.
    21. Sucarrat, Genaro & Escribano, Álvaro, 2009. "Automated financial multi-path GETS modelling," UC3M Working papers. Economics we093620, Universidad Carlos III de Madrid. Departamento de Economía.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:409:y:2019:i:c:2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.