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Growth and yield models are essential tools in modern forestry, especially for intensively managed loblolly pine plantations in the southeastern United States. While model developers often have a good idea of where these models should be used with respect to geographic location, determining geographic bounds for model usage can be daunting. Such bounds provide suitable areas where model predictions are likely to behave as expected or identify areas where models may do a poor job of characterizing the growth of a resource. In this research, we adapted a niche model methodology, commonly used to identify suitable spots for species occurrence (maximum entropy), to identify areas for using growth and yield models built from plots established in the Lower Coastal Plain and Piedmont/Upper Coastal Plain in the southeastern United States. The results from this analysis identify areas with similar climatic envelopes and soil properties to the areas where data was collected to fit these growth and yield models. These areas show notable overlap with the areas prescribed for use by the evaluated growth and yield models and support practitioners use of these models throughout these regions. Furthermore, this methodology can be applied to different forest models built using large regional extents as long as climatic and soil values are available for each site. 

Abstract Mid-rotation silvicultural treatments (MRT) are commonly applied to loblolly pine (Pinus taeda L.) plantations in the southeastern United States to improve pine productivity. Competing vegetation is often present in operational plantations and limits site resource availability. The benefits of MRT for pine productivity are well known, but competing vegetation growth has not been extensively studied. Pine and competing vegetation growth within two regions of the southeastern United States was monitored for 8 years following a one-time post-thin application of either fertilization (224 kg ha-1 of nitrogen plus 28 kg ha-1 phosphorus), chemical herbicide (0.8 oz glyphosate and 0.8 oz triclopyr L-1 of water) or their combination. Fertilization significantly increased pine volume growth in the Lower Coastal Plain (LCP, 2.67-4.01 m3ha-1yr-1) and the Upper Coastal Plain/ Piedmont (UCPIE, 0.20-3.72 m3ha-1yr-1). Chemical herbicide application in both the LCP (0.34-4.87 m3 ha-1yr-1) and UCPIE (0.89-1.97 m3 ha-1yr-1) also significantly increased pine volume. Chemical herbicide application, individually and combined, did not result in significant decreases in herbaceous vegetation, but reduced woody vegetation by up to -2.40 m3 ha-1yr-1 in the LCP and -5.67 m3 ha-1yr-1 in the UCPIE. Consequently, we suggest that competing vegetation response should be considered within site-specific management plans aimed at maximizing pine productivity. 

Vegetation indices calculated from remotely sensed satellite imagery are commonly used within empirically derived models to estimate leaf area index in loblolly pine plantations in the southeastern United States. The data used to parameterize the models typically come with observation errors, resulting in biased parameters. The objective of this study was to quantify and reduce the effects of observation errors on a leaf area index (LAI) estimation model using imagery from Landsat 5 TM and 7 ETM+ and over 1500 multitemporal measurements from a Li-Cor 2000 Plant Canopy Analyzer. Study data comes from a 16 quarter 1 ha plot with 1667 trees per hectare (2 m × 3 m spacing) fertilization and irrigation research site with re-measurements taken between 1992 and 2004. Using error-in-variable methods, we evaluated multiple vegetation indices, calculated errors associated with their observations, and corrected for them in the modeling process. We found that the normalized difference moisture index provided the best correlation with below canopy LAI measurements (76.4%). A nonlinear model that accounts for the nutritional status of the stand was found to provide the best estimates of LAI, with a root mean square error of 0.418. The analysis in this research provides a more extensive evaluation of common vegetation indices used to estimate LAI in loblolly pine plantations and a modeling framework that extends beyond the typical linear model. The proposed model provides a simple to use form allowing forest practitioners to evaluate LAI development and its uncertainty in historic pine plantations in a spatial and temporal context.