8. INTERACTIONS AMONG SILVICULTURAL TREATMENTS

Obtaining optimum plantation production and value clearly requires the use of integrated systems that couple intensive management of both site and genetic resources. As a result, interactions among the numerous silvicultural treatment options need to be understood and taken into account. For example, the ability to acquire and utilize nutrients differs among species with longleaf and slash pine having lower nutrient use than loblolly pine. Consequently, loblolly pine is typically more responsive to intensive culture than slash pine (Clark and Saucier 1991, Jokela et al. 2000). Within species, the beneficial effects of improved genetics and intensive culture appear to be at least additive (McKeand et al. 1997, Martin and Shiver 2002). Another example of interaction among treatments occurs on infertile poorly-drained soils where waxy-leaved shrub species can be major competitors to planted pine (Lauer and Glover 1998, Miller et al. 2003). On these sites, the effects of vegetation control and fertilization are generally less than additive for slash (Pienaar and Rheney 1993, Jokela et al. 2000) and additive for loblolly pine (Allen and Lein 1998, Jokela et al. 2000). On upland sites with significant hardwood competition, loblolly pine growth responses to vegetation control and fertilization can be more additive (Allen and Lein 1998) as hardwoods also respond to fertilizer resulting in increased competition for light and water resources with pine crop trees unless they are controlled. In many established stand situations, responses to fertilization, thinning, and vegetation control have been additive (Albaugh et al 2003).

The development and implementation of site specific silvicultural prescriptions requires that the forest manager be able to diagnose time- and site-specific resource limitations. Much of the resource information that is currently available is static (e.g. site index, soil type) and does not reflect the dynamic changes occurring in resource availability and use over the life of a stand (Allen et al. 1990) or in response to treatment. Fortunately, leaf area is a good dynamic indicator of nutrient deficiencies and growth and it is now possible to estimate leaf area of pine plantations (Flores, 2003) using remote sensing. Despite the strong evidence that supports the use of remote sensing to estimate LAI and the opportunities to improve forest productivity by using LAI information, there is no broad scale operational use of remote sensing to estimate LAI as an aid for silvicultural decision making. This is particularly striking in the case of intensively managed plantations where high investments provide a strong incentive to use better information for decision making. Because plantations typically have a single dominant species, are of the same age, are planted at known spacing, and have known boundaries, many of the confounding effects for remote sensing determination of LAI are also reduced. Remote sensing LAI estimates have several potential uses including: 1) identifying pine stands that may be responsive to fertilization due to low leaf area, 2) monitoring changes in LAI after fertilization to determine the efficacy of the applied fertilizer, 3) quantifying competing vegetation LAI to identify stands that need vegetation control and monitoring changes in competing vegetation LAI after vegetation control has been employed, and 4) improving stand growth estimates for inventory updates. Even greater opportunities exist to couple data bases that include stand, soil, landform, and geologic information with dynamic data bases that include leaf area and climatic data using geographical information systems.

< previous | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | next >