Factors influencing production efficiency of intensively managed loblolly pine plantations in a 1- to 4-year-old chronosequence

Two machine-learning techniques, gradient boosting (GB) and random forests (RF), were used to predict stand mean height (HT), trees per hectare (Tree ha-1) and basal area per hectare (BA ha-1) based on datasets collected from extensively- and intensively-managed loblolly pine plantations in the West Gulf Coastal Plain region. Models were evaluated using coefficient of determination (R2), bias and root mean squared error (RMSE) by applying models to independent dataset and then compared to the model (Coble et al. 2017) currently being used in the region. For extensively-managed plantations, the GB models had less bias, larger R2 and smaller RMSE than RF and HT model was the best, followed by those of Tree ha-1 and BA ha-1. Even for BA ha-1, the GB model had R2 over 0.83. GB and RF models outperformed the Coble et al. (2017); differences were notable for HT and Tree ha-1, but significant for BA ha-1. For intensively-managed plantations, GB and RF were similarly great in predicting HT and Tree ha-1, but GB outperformed RF in predicting BA ha-1. We recommend the use of GB models to predict quantitative information required for managing loblolly pine plantations in the region.

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Assessing Change in Soil-Site Productivity of Intensively Managed Loblolly Pine Plantations

Soil Science Society of America Journal ◽

10.2136/sssaj2004.0065er ◽

2006 ◽

Vol 70 (3) ◽

pp. 1037-1037

Author(s):

M. H. Eisenbies ◽

J. A. Burger ◽

W. M. Aust ◽

S. C. Patterson

Keyword(s):

Loblolly Pine ◽

Pine Plantations ◽

Site Productivity ◽

Soil Site ◽

Intensively Managed

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Management Practices to Support Increased Biodiversity in Managed Loblolly Pine Plantations

EDIS ◽

10.32473/edis-fr236-2008 ◽

2008 ◽

Vol 2008 (3) ◽

Author(s):

Michael Andreu ◽

Kevin Zobrist ◽

Thomas Hinckley

Keyword(s):

Loblolly Pine ◽

Management Practices ◽

Forest Resources ◽

Pine Plantations ◽

Fact Sheet ◽

Minor Revision ◽

Intensively Managed

FOR-183, a 9-page fact sheet by Michael Andreu, Kevin Zobrist, and Thomas Hinckley, reviews the literature to identify a spectrum of practices that support increased biodiversity in intensively managed loblolly pine plantations. Includes references. Published by the UF School of Forest Resources and Conservation, March 2008. Minor revision April 2017.

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Assessing Change in Soil-Site Productivity of Intensively Managed Loblolly Pine Plantations

Soil Science Society of America Journal ◽

10.2136/sssaj2004.0065 ◽

2006 ◽

Vol 70 (1) ◽

pp. 130-140 ◽

Cited By ~ 8

Author(s):

M. H. Eisenbies ◽

J. A. Burger ◽

W. M. Aust ◽

S. C. Patterson ◽

T. R. Fox

Keyword(s):

Loblolly Pine ◽

Pine Plantations ◽

Site Productivity ◽

Soil Site ◽

Intensively Managed

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A New Site Index Model for Intensively Managed Loblolly Pine (Pinus taeda) Plantations in the West Gulf Coastal Plain

Forest Science ◽

10.1093/forsci/fxz050 ◽

2019 ◽

Author(s):

Kynda R Trim ◽

Dean W Coble ◽

Yuhui Weng ◽

Jeremy P Stovall ◽

I-Kuai Hung

Keyword(s):

Pinus Taeda ◽

Loblolly Pine ◽

Coastal Plain ◽

Site Index ◽

The United States ◽

Pine Plantations ◽

Permanent Plots ◽

Gulf Coastal Plain ◽

The West ◽

Intensively Managed

Abstract Site index (SI) estimation for loblolly pine (Pinus taeda L.) plantations is important for the successful management of this important commercial tree species in the West Gulf Coastal Plain of the United States. This study evaluated various SI models for intensively managed loblolly plantations in the West Gulf Coastal Plain using data collected from permanent plots installed in intensively managed loblolly pine plantations across east Texas and western Louisiana. Six commonly used SI models (Cieszewski GADA model, both Chapman-Richards ADA and GADA models, both Schumacher ADA and GADA models, and McDill-Amateis GADA model) were fit to the data and compared. The Chapman-Richards GADA model and the McDill-Amateis GADA model were similar and best in their fit statistics. These two models were further compared to the existing models (Diéguez-Aranda et al. 2006 (DA2006), Coble and Lee 2010 (CL2010)) commonly used in the region. Both the Chapman-Richards GADA and the McDill-Amateis GADA models consistently predicted greater heights up to age 25 than the models of DA2006 and CL2010, with larger height differences for the higher quality sites, but predicted shorter heights thereafter. Ultimately, the McDill-Amateis GADA model was chosen as the best model for its consistency in predicting reasonable heights extrapolated beyond the range of the data. Foresters can use this model to make more informed silvicultural prescriptions for intensively managed loblolly pine plantations in the West Gulf Coastal Plain.

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