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

Santosh Subedi,Randolph Wynne,Thomas Fox

doi:10.3390/f6093002

Abstract

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.

Highlights

The 3-PG model [1], Physiological Principles Predicting Growth, is a process model that predicts forest productivity based on radiation use efficiency, carbon balance, and partitioning. 3-PG and its variants have been calibrated and tested on many commerically important tree species around the globe
When leave-one-out cross validation was carried out among the three model forms, Mean Absolute Error (MAE), Root Means Square Error (RMSE), and Predicted Residual Sums of Square (PRESS) statistics were lowest for the linear model, followed by the sigmoidal model (Table 4)
The sigmoidal model was selected as the best model to predict volume (m3 ha−1 ) from site index at base age 25 (m): 379.57

Summary

Introduction

The 3-PG model [1], Physiological Principles Predicting Growth, is a process model that predicts forest productivity based on radiation use efficiency, carbon balance, and partitioning. 3-PG and its variants have been calibrated and tested on many commerically important tree species around the globe. The 3-PG model [1], Physiological Principles Predicting Growth, is a process model that predicts forest productivity based on radiation use efficiency, carbon balance, and partitioning. 3-PG calculates the amount of photosynthetically active radiation (PAR, φp ) intercepted by a stand (APAR, φpa ) which is converted into gross primary productivity (GPP,PG ) using canopy quantum efficiency (αc ), constrained by enviromental factors such as vapor pressure deficit (D), mean temperature (T), soil moisture (θs ), frost days, and site nutrient status [2]. Αc , is calculated as: αc = fT fN ff φ αCx where αCx is the theoretical maximum canopy quantum efficiency, fT , fN , ff , and φ are temperature, nutrition, frost, and physiology related modifiers, respectively. Pn is allocated to aboveground and belowground biomass production

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Discussion

Conclusion