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Open AccessArticle

Determination of Fertility Rating (FR) in the 3-PG Model for Loblolly Pine Plantations in the Southeastern United States Based on Site Index

Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute, 228 Cheatham Hall, Blacksburg, VA 24061, USA
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Academic Editors: Eric J. Jokela and Philip J. Polglase
Forests 2015, 6(9), 3002-3027; https://doi.org/10.3390/f6093002
Received: 2 June 2015 / Revised: 19 August 2015 / Accepted: 24 August 2015 / Published: 28 August 2015
Soil fertility is an important component of forest ecosystems, yet evaluating soil fertility remains one of the least understood aspects of forest science. We hypothesized that the fertility rating (FR) used in the model 3-PG could be predicted from site index (SI) for loblolly pine in the southeastern US and then developed a method to predict FR from SI to test this hypothesis. Our results indicate that FR values derived from SI when used in 3-PG explain 89% of the variation in loblolly pine yield. The USDA SSURGO dataset contains SI values for loblolly pine for the major soil series in most of the counties in the southeastern US. The potential of using SI from SSURGO data to predict regional productivity of loblolly pine was assessed by comparing SI values from SSURGO with field inventory data in the study sites. When the 3-PG model was used with FR values derived using SI values from SSURGO database to predict loblolly pine productivity across the broader regions, the model provided realistic outputs of loblolly pine productivity. The results of this study show that FR values can be estimated from SI and used in 3-PG to predict loblolly pine productivity in the southeastern US. View Full-Text
Keywords: soil fertility; process-based models; LAI; regional productivity estimation soil fertility; process-based models; LAI; regional productivity estimation
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Subedi, S.; Fox, T.R.; Wynne, R.H. Determination of Fertility Rating (FR) in the 3-PG Model for Loblolly Pine Plantations in the Southeastern United States Based on Site Index. Forests 2015, 6, 3002-3027.

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