*
 

iForest - Biogeosciences and Forestry

*

Using soil-based and physiographic variables to improve stand growth equations in Uruguayan forest plantations

Cecilia Rachid-Casnati (1)   , Euan G Mason (2), Richard C Woollons (2)

iForest - Biogeosciences and Forestry, Volume 12, Issue 3, Pages 237-245 (2019)
doi: https://doi.org/10.3832/ifor2926-012
Published: May 03, 2019 - Copyright © 2019 SISEF

Research Articles


Information provided by traditional growth models is an essential input in decision making processes for managing planted forests. They are generally fitted using inventory data guaranteeing robustness and simplicity. The introduction of explanatory factors affecting tree development in age-based sigmoidal growth and yield equations attempts not only to improve the quality of predictions, but also to add useful information underpinning forest management decisions. This study aimed to assess the use of the following soil-based and physiographic predictors: potentially available soil water (PASW), elevation (Elev), aspect (α) and slope (β) in a system of empirical stand equations comprising: dominant height (hdom), basal area (G), maximum diameter at breast height (dmax), and standard deviation of diameters (SDd). Augmented models were compared with the base models through precision and bias of estimations for two contrasting species: Pinus taeda (L.), and Eucalyptus grandis (Hill ex. Maiden), planted commercially in Uruguay. Soil-based and physiographic information significantly improved predictions of all the state variables fitted for E. grandis, but just hdom and G for P. taeda. Only PASW was consistently significant for the augmented models in P. taeda and E. grandis, while the contribution of other predictors varied between species. From a physiological point of view, predictors on the augmented models showed consistency. Models with such augmentation produced decrease of errors between 3 to 10.5%, however decreases in the prediction errors calculated with the independent dataset were lower. Results from this study contributed to add information to the decision-making process of plantations’ management.

  Keywords


Forest Modelling, Soil Variables, Physiographic Variables, Pinus taeda, Eucalyptus grandis

Authors’ address

(1)
Cecilia Rachid-Casnati 0000-0002-8621-7061
Forestry Research Programme, National Institute of Agricultural Research (INIA Uruguay), Road 5, Km 386, 45000 Tacuarembó (Uruguay)
(2)
Euan G Mason 0000-0001-9024-9106
Richard C Woollons
School of Forestry, University of Canterbury, Private Bag 48000, Christchurch (New Zealand)

Corresponding author

 
Cecilia Rachid-Casnati
crachid@tb.inia.org.uy

Citation

Rachid-Casnati C, Mason EG, Woollons RC (2019). Using soil-based and physiographic variables to improve stand growth equations in Uruguayan forest plantations. iForest 12: 237-245. - doi: 10.3832/ifor2926-012

Academic Editor

Emanuele Lingua

Paper history

Received: Jul 19, 2018
Accepted: Mar 16, 2019

First online: May 03, 2019
Publication Date: Jun 30, 2019
Publication Time: 1.60 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 15875
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 11420
Abstract Page Views: 1075
PDF Downloads: 2837
Citation/Reference Downloads: 4
XML Downloads: 539

Web Metrics
Days since publication: 2048
Overall contacts: 15875
Avg. contacts per week: 54.26

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Nov 2020)

Total number of cites (since 2019): 2
Average cites per year: 1.00

 

Publication Metrics

by Dimensions ©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

 
(1)
Albaugh TJ, Allen HL, Dougherty PM, Kress LW, King JS (1998)
Leaf area and above- and belowground growth responses of loblolly pine to nutrient and water additions. Forest Science 44: 317-328.
Online | Gscholar
(2)
Albaugh TJ, Allen HL, Dougherty PM, Johnsen KH (2004)
Long term growth responses of loblolly pine to optimal nutrient and water resource availability. Forest Ecology and Management 192: 3-19.
CrossRef | Gscholar
(3)
Altamirano A, Da Silva H, Durán A, Echevarría A, Panario D, Puentes R (1976)
Carta de Reconocimiento de Suelos del Uruguay, Tomo I: Clasificación de suelos [Soil Survey of Uruguay, Vol 1: Soil Classification]. MAP-DSF, Montevideo, Uruguay, pp. 96. [in Spanish]
Gscholar
(4)
Castaño JP, Giménez A, Ceroni M, Furest J (2011)
Caracterización agroclimática del Uruguay 1980-2009 [Climatic characterization of Uruguay 1980-2009]. Technical Series 193, INIA, Montevideo, Uruguay. pp. 34. [in Spanish]
Gscholar
(5)
Clutter JL (1963)
Compatible growth and yield models for loblolly pine. Forest Science 9: 354-370.
Online | Gscholar
(6)
Clutter JL, Fortson JC, Pienaar LV, Brister GH, Bailey RL (1983)
Timber management: a quantitative approach (1st edn). John Wiley and Sons, USA, pp. 333.
Online | Gscholar
(7)
ESRI (2013)
ArcGis for Desktop. Environmental Systems Research Institute, Redlands, CA, USA.
Gscholar
(8)
Ferraz Filho AC, Scolforo JRS, Oliveira AD, Mello JM (2015)
Modeling growth and yield of loblolly pine stands under intensive management. Pesquisa Agropecuária Brasileira 50: 707-717.
CrossRef | Gscholar
(9)
Fontes L, Tom M, Thompson F, Yeomans A, Luis JS, Savill P (2003)
Modelling the Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) site index from site factors in Portugal. Forestry 76: 491-507.
CrossRef | Gscholar
(10)
García O (1998)
Estimating top height with variable plot sizes. Canadian Journal of Forest Research 28: 1509-1517.
CrossRef | Gscholar
(11)
Goulding CJ (1979)
Validation of growth models used in forest managament. New Zealand Journal of Forestry 24: 108-124.
Gscholar
(12)
Grosenbaugh LR (1965)
Generalization and reparameterization of some sigmoid and other nonlinear functions. Biometrics 21: 708-714.
CrossRef | Gscholar
(13)
Henning JG, Burk TE (2004)
Improving growth and yield estimates with a process model derived growth index. Canadian Journal of Forest Research 34: 1274-1282.
CrossRef | Gscholar
(14)
Kimsey MJ, Moore J, McDaniel P (2008)
A geographically weighted regression analysis of Douglas-fir site index in north central Idaho. Forest Science 54: 356-366.
Online | Gscholar
(15)
Kuru GA, Whyte AGD, Woollons RC (1992)
Utility of reverse Weibull and extreme value density functions to refine diameter distribution growth estimates. Forest Ecology and Management 48: 165-174.
CrossRef | Gscholar
(16)
Landsberg JJ, Sands P (2011)
Physiological ecology of forest production (1st edn). Terrestrial Ecology Series, Elsevier, USA, pp. 331.
Gscholar
(17)
Maestri R (2003)
Modelo de crescimento e produção para povoamentos clonais de Eucalyptus grandis considerando variáveis ambientais [Modelling growth and yield of clonal Eucalyptus grandis using environmental variables]. PhD thesis, Federal University of Paraná, Curitiba, Brazil, pp. 143. [in Portuguese]
Gscholar
(18)
Mason EG, Whyte AGD, Woollons RC, Richardson B (1997)
A model of the growth of juvenile radiata pine in the Central North Island of New Zealand: links with older models and rotation-length analyses of the effects of site preparation. Forest Ecology and Management 97: 187-195.
CrossRef | Gscholar
(19)
Methol R (2001)
Comparisons of approaches to modelling tree taper, stand structure and stand dynamics in forest plantations. PhD thesis, University of Canterbury, Christchurch, New Zealand, pp. 298.
Gscholar
(20)
Methol R (2006)
SAG globulus: sistema de apoyo a la gestion de plantacions de Eucalyptus globulus [SAG globulus: decision-support system for managing Eucalyptus globulus’ plantations]. Technical Series 158, INIA, Montevideo, Uruguay, pp. 34. [in Spanish]
Gscholar
(21)
MGAP/DSF (1976)
Carta de reconocimiento de suelos del Uruguay Escala 1:1.000.000 [Soil Survey Map 1:1.000.000]. MGAP, Montevideo, Uruguay, pp. 1. [in Spanish]
Gscholar
(22)
MGAP/RENARE (1994)
Indices de productividad de suelos [Soil productivity indices]. CONEAT Groups, MGAP, Montevideo, Uruguay, pp. 183. [in Spanish]
Gscholar
(23)
MGAP/RENARE (2016a)
Cartas Temáticas [Thematic cartography]. MGAP, Montevideo, Uruguay, Web site. [in Spanish]
Online | Gscholar
(24)
MGAP/RENARE (2016b)
Modelo Digital de Terreno [Digital Terrain Model]. Web site. [in Spanish]
Online | Gscholar
(25)
Molfino JH, Califra A (2001)
Agua disponible de las tierras del Uruguay [Available soil water in Uruguay]. MGAP, Montevideo, Uruguay, pp. 12. [in Spanish]
Online | Gscholar
(26)
Monserud RA, Moody Breuer DW (1990)
A soil-site study for inland Douglas-fir. Canadian Journal of Forest Research 20: 686-695.
CrossRef | Gscholar
(27)
Pinjuv G, Mason EG, Watt M (2006)
Quantitative validation and comparison of a range of forest growth model types. Forest Ecology and Management 236: 37-46.
CrossRef | Gscholar
(28)
QGIS Development Team (2015)
QGIS geographic information system. Open Source Geospatial Foundation Project, web site.
Online | Gscholar
(29)
Rachid-Casnati AC, Hirigoyen A (2015)
SAG taeda: sistema de apoyo a la gestión de plantaciones de Pinus taeda [SAG taeda: decision-support system for managing Pinus taeda’s plantations]. Technical Series 224, INIA, Montevideo, Uruguay, pp. 22. [in Spanish]
Gscholar
(30)
Ritchie MW, Hamann JD (2008)
Individual-tree height-, diameter- and crown-width increment equations for young Douglas-fir plantations. New Forests 35: 173-186.
CrossRef | Gscholar
(31)
Running SW (1984)
Microclimate control of forest productivity: analysis by computer simulation of annual photosynthesis/transpiration balance in different environments. Agricultural and Forest Meteorology 32: 267-288.
CrossRef | Gscholar
(32)
Snowdon P, Jovanovic T, Booth TH (1999)
Incorporation of indices of annual climatic variation into growth models for Pinus radiata. Forest Ecology and Management 117: 187-197.
CrossRef | Gscholar
(33)
Stage AR, Salas C (2007)
Interactions of elevation, aspect, and slope in models of forest species composition and productivity. Forest Science 53: 486-492.
Online | Gscholar
(34)
Stage AR (1976)
Notes: an expression for the effect of aspect, slope, and habitat type on tree growth. Forest Science 22: 457-460.
Gscholar
(35)
Stape J, Binkley D, Ryan MG (2004)
Eucalyptus production and the supply, use and efficiency of use of water, light and nitrogen across a geographic gradient in Brazil. Forest Ecology and Management 193 (1-2): 17-31.
CrossRef | Gscholar
(36)
Temu MJ (1992)
Forecasting yield of Douglas fir in the south island of New Zealand. PhD thesis, University of Canterbury, Christchurch, New Zealand, pp. 249.
Gscholar
(37)
Trimble GR, Weitzman S (1956)
Site index studies of upland oaks in the Northern Appalachians. Forest Science 2: 162-173.
Gscholar
(38)
Verbyla DL, Fisher RF (1989)
Effect of aspect on ponderosa pine height and diameter growth. Forest Ecology and Management 27: 93-98.
CrossRef | Gscholar
(39)
Weiskittel AR, Hann DW, Kershaw JA, Vanclay JK (2011)
Forest growth and yield modeling. John Wiley and Sons, Honoken, NJ, USA, pp. 415.
Gscholar
(40)
Werling JA, Tajchman SJ (1984)
Soil thermal and moisture regimes on forested slopes of an Appalachian watershed. Forest Ecology and Management 7: 297-310.
CrossRef | Gscholar
(41)
Woollons RC, Snowdon P, Mitchell ND (1997)
Augmenting empirical stand projection equations with edaphic and climatic variables. Forest Ecology and Management 98: 267-275.
CrossRef | Gscholar
 

This website uses cookies to ensure you get the best experience on our website. More info