MONITORING OF GROWTH AND YIELD AND RESPONSE TO
SILVICULTURAL TREATMENTS DAVID D. M A R S H A L L Department of Forest Resources, Oregon State University, Corvallis, Oregon 97331, U. S. A. (Received: June 1992)
Abstract. The objectives for monitoring the growth and yield of forests can range from collecting data to validate current management on an ownership to collecting data at a regional level to develop models, measure treatment response, or observe long-term growth trends. Traditional inventory and research data each have drawbacks for observing growth and yield and treatment response. The actual design of a monitoring system should try to minimize these problems, but will depend on the specific objectives. Monitoring of operations may take an inventory-type approach, while gathering regional information to build models requires a design more like research. Each organization must be responsible for monitoring its own operations, however, cooperatives offer a viable and cost effective alternative to gathering regional growth and response data. Monitoring programs will lead to confidence and credibility in management practices and in the use of tested models.
1. Introduction
Managers have long realized that stands, once established, can be manipulated to achieve a wide range of yield, tree size, and ecological objectives. Yet rarely has much effort been put into checking the assumptions used in a management decision or to validate the expected results. The basic operation of a growth and yield monitoring program is the collection of data over time throughout a defined area, and for a specific purpose. The specific purpose of a monitoring system can vary widely depending on the expectations of how the information it will provide will be used and can have important implications in the design of the monitoring system to be used. Objectives of a growth and yield monitoring system may include: (a)
To assess whether operational treatments are being done as intended and whether the treatments applied are effective.
(b)
Provide data for making timely management decisions.
(c)
To provide long-term data to validate predictions of growth and yield models.
(d)
To provide long-term data for updating or building growth and yield models.
(e)
To provide long-term records of local stand dynamics and treatments as a demonstration and teaching tool for new personnel.
(f)
To observe long-term trends in forest health.
Environmental Monitoring and Assessment 26: 195-201, 1993. (~) 1993 Kluwer Academic Publishers. Printed in the Netherlands.
196
DAVID D. MARSHALL
Observing current management practices and testing the anticipated response is the basis for accountability and credibility in the profession. Management decisions are based on a prediction of response to treatments. Growth and yield models offer our most important tool for looking into the future at the probable development of stands. This is important for projecting inventories, calculating harvest levels, and to evaluate the desirability (both economically and environmentally) of specific management practices. Future growth and management can be a moving target with changing environmental conditions, operating constraints, and management objectives which result in different or new silvicultural practices that are probably not currently represented 'on the ground'. Curtis (1972) suggests that the "ideal yield table or set of yield functions should provide estimates of potential yield for any specified biologically feasible management regime." Testing and updating models requires a continuing effort and is essential to build confidence and credibility in the model. For this reason, models are usually tied closely to monitoring. 2. Where Can We Get Growth Data?
Research has long been the major source of information on growth and response of trees and stands to silvicultural treatment. Research plots can offer high quality data to consider the effects of extremes in treatments and a view of the forest of the future. Treatments such as planting genetically improved stock, wide initial or early respacing, and repeated fertilizations are often seen as research results long before they are applied operationally. Research programs are expensive, making it difficult for even large organizations to collect enough data to cover only a limited range of stand conditions and treatments. This has lead to cooperative data pooling among organizations. Hyink et al. (1988) points out the problems found in working with one such large data set collected from several organizations in the Pacific Northwest to develop growth and yield information. They found a poor distribution of data (geographically and by site index and age) and data which reflected a wide range of research objectives and measurement practices. They also found that most of the data came from experiments designed to test specific hypotheses in older stands, were for short observation periods, and that relatively little data was from plantations or in management practices that are of current interest. Another major problem associated with research plots is that they typically differ in measurements, stand conditions, and treatments from typical stands and operations (Bruce, 1977). This has been termed 'operational falldown' and is an important consideration in applying models developed from research plots to operational stands. Inventories generate large amounts of data representative of ownerships, but have traditionally been concerned only with the current state of the forest in such terms as species composition, stand structure, volume, and value. Flewelling (1981) discusses the use of inventory-type data for growth and yield. For most inventories growth can only be estimated as the total change in yield from successive
MONITORING SILVICULTURAL TREATMENTS
197
remeasurements, adjusted for harvest and changes in the ownership. In-place type inventories offer yield estimates at the stand level and successive estimates are usually based on independent samples. This could be corrected by selecting stands and establishing permanent plots for remeasurement as suggested by Flewelling (1981). Continuous Forest Inventory (CFI) systems are based on remeasured permanent plots and can offer more efficient estimates of growth. In-place and CFI inventory data often suffer from several common problems making them difficult to use for growth monitoring (Curtis and Hyink, 1985): (1)
Use small plots that are subject to large edge effects and don't describe within stand variability.
(2)
Ages are categorized by broad classes.
(3)
Tree heights are inaccurately measured or poorly sampled.
(4)
Small trees of low or no value are omitted.
(5) Samples may be weighted to older, high volume stands or in stands of little interest in future management (young stands, genetically improved stock, etc.). (6)
Measurements differ from those used to develop growth and yield models.
In addition, both CFI and in-place inventories offer only information based on how current stands have been managed in the past and do not offer comparisons to no treatment. Neither the typical inventory system nor research program can serve as a complete monitoring system. A fully implemented monitoring program for growth and yield will have elements common to both, however, the exact design for monitoring, and whether it is more like an inventory system or a research program will depend on the objectives. To answer questions about operations, monitoring may take on more the appearance of an inventory system. To answer unanticipated questions by operations and develop models, a monitoring system may look more like research and require a robust design, especially one that anticipates treatment extremes.
3. Design Considerations for a Monitoring Program Because it would be impossible to monitor every stand, a sample of monitoring stands must be selected. Sampling an entire ownership would be prohibitively expensive, so some meaningful strata should be used (i.e. young stands likely to be managed). Selection of monitoring stands should be representative of the strata of interest if any meaningful inferences are to be made. As Cochran (1983) points out "estimates of the effect of a treatment or program from observational studies are
198
DAVID D. MARSHALL
likely to be biased". This means that monitoring stand selection should be made randomly over some meaningful strata of the ownership. Within monitoring stands, permanent plots should be established. The location of plots should be monumented on the ground and documented on operational ownership maps to protect them. If available, plot locations should be stored in Geographic Information Systems (GIS). The actual plot design can use either fixed area plots or variable plot techniques. Fixed area plots have often been favored because they have the advantage of being simpler (Iles, 1983). Variable plots are more consistent with operational inventory methods and will concentrate measurements in the larger trees. A single variable plot contains too few trees for most monitoring purposes, so a cluster or grid of points should be used. This will require more area than for a single fixed plot, but may better sample within stand variability. To more efficiently sample trees in all size classes, a nested or concentric plot designs using fixed and/or variable plots can be used. In the past, the tendency in both research and inventory has been to use small plots in order to reduce the number of measurements and the amount of area taken up by plots. Small plots, however, can give poor mortality estimates, erratic stand and growth estimates, and in many cases may not accurately characterize within stand variability. The actual plot size used will depend on the anticipated number and size of trees over the life of the plot and the variability of the stand being sampled and the treatments being applied. In general, the larger the trees and the more variability encountered, the larger the plots should be. Within a plot all trees should be permanently tagged for identification in remeasurements. Curtis (1983) gives an excellent discussion of the procedures for establishing and maintaining permanent plots, including plot sizes and tree measurements. Systems designed to monitor operations should take on more of the appearance of an inventory. Comparisons can be made between an operational treatment and no treatment by dividing a stand selected for monitoring and randomly assigning the treatment. This can provide a check and suggest possible adjustments for operational falldown in growth models developed from research plots, but applied in the 'real world'. This also builds confidence with model users. Two problems encountered with this type of monitoring system are: (1) it may require large areas if a cluster or grid of plots are used, and (2) the difficulty coordinating treatments with measurements if done on an inventory cycle. While similar in design to an inventory system, the problems associated with most inventory systems should be avoided. These include small plot sizes and incompatibility of measurements with the data used to develop yield estimates. Systems like this must be flexible and adapt to changes in management. Monitoring systems intended to provide data to test potential questions about management, for building models, or observing long-term growth trends will look more like research, but will look at response surfaces rather than test specific hypotheses. Designs should consist of a range of treatments and stand conditions. This may include plots representing current management practices along with a
MONITORING SILVICULTURAL TREATMENTS
199
range of densities, fertilization and other treatments. Replication would be done by representing the treatments at different locations rather than within a specific site. Beginning monitoring of this type at planting would allow the use of genetically improved stock (which may not be used operationally for several years) and a range of planting densities. These designs should provide data to validate and update models, make decisions on the timing and intensity of thinning, evaluate fertilizer response, and study the effects of management on site productivity and wood quality. They may not, however, represent the 'real world' because of the difficulty in treating small areas (plots), not capturing stand variability on small plots, and making measurements differently than for operations. The resulting 'falldown' effect has implications on applying results or models developed form this type of data and emphasizes the need for operational monitoring programs as well. It is important that comparisons of observed growth to model estimates must be made over multiple measurement cycles. Inferences about model validity based on single measurement periods can suffer from large fluctuations in growth and should be avoided. One of the most important aspects of any monitoring system is quality control and is particularly critical during data collection. Crews must be trained and continually checked to insure consistency and accuracy. Data management must not only include data storage, retrieval and analysis, but extensive checking for measurement errors. This is best done as soon after data collection as possible in order that corrections can be made. The use of well programmed electronic data recorders will help catch most errors in the field.
4. The Bad News and the Good News of Monitoring The bad news is that monitoring programs are expensive. Monitoring of operations and model application must be done by each organization on its own lands. The design used should be similar to the inventory system used by the organization while also being as consistent as possible with the type of data used in developing the models used. Research-type monitoring will usually be more expensive and is justifiable only to very large organizations which are able to spread the costs and benefits over many acres. The good news is that cooperatives offer a cost effective means for many organizations to gather this type of monitoring data at a regional level. They offer a method of coordinating the distribution of installations geographically by physiographic region, age, and site index and offer the mechanism for standardization of design and measurements. They are not a substitute for operational monitoring at the ownership level. One example of a cooperative effort is the Stand Management Cooperative (SMC), which was established in 1985 by 22 organizations in Oregon, Washington, and British Columbia. These organizations agreed to cooperate in order to "provide a continuing source of high-quality data on the long-term effects of silvicultural
200
DAVID D. MARSHALL
treatments and treatments regimes on stand and tree development and wood quality" (Chappell et aL, 1987) that is compatible and consistent. Of major interest to the cooperators was the effect of early density control, particularly wide spacings. The establishment of SMC installations is designed to achieve a balanced geographic distribution by region and site. Results will look at regional response surfaces rather than testing hypotheses at specific locations. To date the species of primary interest have been Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and western hemlock (Tsuga heterophylla (Raf.) Sarg.), although other species and species mixes could be considered in the future. Each installation was established to achieve high uniformity in site conditions and stand density across all plots. To date, three types of installations have been established (Maguire et al., 1991). Type I studies are in well-established juvenile stands that have not experienced substantial inter-tree competition. The core treatments are three initial densities (including half and a quarter of the original numbers of trees) with subsequent later thinning in the two highest density treatments. Supplemental treatments include pruning, fertilization, and leave tree selection. Type II studies are in plantations or respaced natural stands that could receive a commercial thinning. Treatments include no thinning, a single thinning, a delayed thinning and repeated high and low density thinnings. The type III installations are established to provide areas of known history and planting stock for future studies. This has been done by operationally planting uniform areas in blocks representing six initial spacings. Target densities range from 254 to 3073 trees per hectare (100-1210 trees per acre). As of 1990, 32 Type I, 12 Type II, and 26 Type III installations have been established (Maguire et al., 1991). 5. Conclusions High quality data are expensive to collect, but they are also valuable. Data on growth and yield have traditionally been seen primarily in terms of a cost: the cost of data collection and processing. But data are quickly becoming a resource or asset that can benefit its owner economically. The value of data depends on the ability to answer information needs. This goes beyond immediate application and includes values that may be realized later (Cronin and Davenport, 1991) and the cost of basing decisions on few, poor, or no data. Many organizations are now realizing that data can also be a commodity to be sold or traded. Like other resources and commodities, information has acquisition costs, is processed, and differs in its quality and performance (Horton, 1985). Good data may also conserve other resources - quick fixes usually cost more (Horton, 1985). A monitoring program cannot replace inventories or research. We still need to know the status of the whole ownership and to investigate specific biological and mensurational relationships. However, it is necessary to base decisions on evidence supported by data and to consider the dangers of acting on untested and unsupported assumptions. Through monitoring of growth and yield, managers and
MONITORINGSILVICULTURALTREATMENTS
201
t h o s e t h e y are a c c o u n t a b l e to can gain c o n f i d e n c e in their decisions and the m o d e l s u s e d to m a k e t h o s e decisions. T h e c o m p l e x and l o n g - t e r m nature o f the resources w e m a n a g e require these kinds o f data collection efforts, w h i c h in turn require stability and c o m m i t m e n t at the h i g h e s t levels o f an organization.
References Bruce, D.: 1977, 'Yield Differences Between Research Plots and Managed Forests', J. Forestry 75, 14-17. Chappell, H.N., Curtis, R.O., D.M. Hyink, and Maguire, D.A.: 1987, 'The Pacific Northwest Stand Management Cooperative and its Field Installation Design', In: Ek, A.R., Shirley, S.R., and Burk, T.E. (Eds.), Forest Growth Modelling and Prediction, Volume 2, pp. 1073-1080. USDA Forest Service. North Central Forest Experiment Station, St. Paul, Minnesota. General Technical Report NC-120. 1149 pp. Cochran, W.G.: 1983, Planning and Analysis of Observational Studies, John Wiley and Sons, N.Y. 145 pp. Cronin, B. and Davenport, G." 1991, Elements oflnformation Management, The Scarecrow Press, Inc. Metuchen, N.J. 207 pp. Curtis, R.O.: 1972, 'Yield Tables Past and Present', J. Forestry 70, 28-32. Curtis, R.O.: 1983, Proceduresfor Establishing and Maintaining Permanent Plots for Silvicultural and Yield Research. USDA Forest Service. Pacific Northwest Forest and Range Experiment Station, Portland, Oregon. General Technical Report PNW-155.56 pp. Curtis, R.O.: 1987, 'A Report on the Permanent Plot Task Force'. In: Ek, A.R., Shirley, S.R., and Burk, T.E. (Eds.), Forest Growth Modelling and Prediction, Volume 2, pp. 1081-1088. USDA Forest Service. North Central Forest Experiment Station, St. Paul, Minnesota. General Technical Report NC-120. 1149 pp. Curtis, R.O. and Hyink, D.M.: 1985, 'Data for Growth and Yield Models'. In: Van Hooser, D.D. and Van Pelt, N. (Eds.), Proceedings-Growth and Yield and Other Mensurational Tricks: A Regional Technical Conference, pp. 1-5. USDA Forest Service, General Technical Report INT193, Intermountain Research Station, Ogden, Utah. 98 pp. Flewelling, J.W.: 1981, 'Sampling for Forest Growth'. In: Brann, T.B., House IV, L.O., and Lund, H.G. (Eds.), In-Place Resource Inventories: Principles & Practices, pp. 410-412. Society of American Foresters. Washington, D.C. 1101 pp. Horton, EW. Jr.: 1985, Information Resources Management: Harnessing Information Assests for Productivity Gains in the Office, Factory, and Laboratory, Prentice-Hall Inc. Englewood Cliffs, N.J. 263 pp. Hyink, D.M., Scott, W., and Leon, R.M.: 1988, 'Some Important Aspects in the Development of a Managed Stand Growth Model for Western Hemlock'. In: Ek, A.R., Shirley, S.R., and Burk, T.E. (Eds.), Forest Growth Modelling and Prediction, Volume 2, pp. 9-21. USDA Forest Service. North Central Forest Experiment Station, St. Paul, Minnesota. General Technical Report NC-120. 1149 pp. Iles, K.: 1983, 'Some Thoughts on Growth Measurement Techniques'. In: Bell, J.E and Atterbury, T. (Eds.), Renewable Resource Inventories for Monitoring Changes and Trends, pp. 259-260. College of Forestry, Oregon State University, Corvallis, Oregon. 737 pp. Maguire, D.A., Bennett, W.S., Kershaw, J.A., Gonyea, R., and Chappell, H.N.: 1991, Establishment Report: Stand Management Cooperative Silviculture Project Field Installations, Institute of Forest Resources Contribution No. 72. College of Forest Resources, University of Washington, Seattle. 42 pp.
SILVICULTURAL TREATMENTS DAVID D. M A R S H A L L Department of Forest Resources, Oregon State University, Corvallis, Oregon 97331, U. S. A. (Received: June 1992)
Abstract. The objectives for monitoring the growth and yield of forests can range from collecting data to validate current management on an ownership to collecting data at a regional level to develop models, measure treatment response, or observe long-term growth trends. Traditional inventory and research data each have drawbacks for observing growth and yield and treatment response. The actual design of a monitoring system should try to minimize these problems, but will depend on the specific objectives. Monitoring of operations may take an inventory-type approach, while gathering regional information to build models requires a design more like research. Each organization must be responsible for monitoring its own operations, however, cooperatives offer a viable and cost effective alternative to gathering regional growth and response data. Monitoring programs will lead to confidence and credibility in management practices and in the use of tested models.
1. Introduction
Managers have long realized that stands, once established, can be manipulated to achieve a wide range of yield, tree size, and ecological objectives. Yet rarely has much effort been put into checking the assumptions used in a management decision or to validate the expected results. The basic operation of a growth and yield monitoring program is the collection of data over time throughout a defined area, and for a specific purpose. The specific purpose of a monitoring system can vary widely depending on the expectations of how the information it will provide will be used and can have important implications in the design of the monitoring system to be used. Objectives of a growth and yield monitoring system may include: (a)
To assess whether operational treatments are being done as intended and whether the treatments applied are effective.
(b)
Provide data for making timely management decisions.
(c)
To provide long-term data to validate predictions of growth and yield models.
(d)
To provide long-term data for updating or building growth and yield models.
(e)
To provide long-term records of local stand dynamics and treatments as a demonstration and teaching tool for new personnel.
(f)
To observe long-term trends in forest health.
Environmental Monitoring and Assessment 26: 195-201, 1993. (~) 1993 Kluwer Academic Publishers. Printed in the Netherlands.
196
DAVID D. MARSHALL
Observing current management practices and testing the anticipated response is the basis for accountability and credibility in the profession. Management decisions are based on a prediction of response to treatments. Growth and yield models offer our most important tool for looking into the future at the probable development of stands. This is important for projecting inventories, calculating harvest levels, and to evaluate the desirability (both economically and environmentally) of specific management practices. Future growth and management can be a moving target with changing environmental conditions, operating constraints, and management objectives which result in different or new silvicultural practices that are probably not currently represented 'on the ground'. Curtis (1972) suggests that the "ideal yield table or set of yield functions should provide estimates of potential yield for any specified biologically feasible management regime." Testing and updating models requires a continuing effort and is essential to build confidence and credibility in the model. For this reason, models are usually tied closely to monitoring. 2. Where Can We Get Growth Data?
Research has long been the major source of information on growth and response of trees and stands to silvicultural treatment. Research plots can offer high quality data to consider the effects of extremes in treatments and a view of the forest of the future. Treatments such as planting genetically improved stock, wide initial or early respacing, and repeated fertilizations are often seen as research results long before they are applied operationally. Research programs are expensive, making it difficult for even large organizations to collect enough data to cover only a limited range of stand conditions and treatments. This has lead to cooperative data pooling among organizations. Hyink et al. (1988) points out the problems found in working with one such large data set collected from several organizations in the Pacific Northwest to develop growth and yield information. They found a poor distribution of data (geographically and by site index and age) and data which reflected a wide range of research objectives and measurement practices. They also found that most of the data came from experiments designed to test specific hypotheses in older stands, were for short observation periods, and that relatively little data was from plantations or in management practices that are of current interest. Another major problem associated with research plots is that they typically differ in measurements, stand conditions, and treatments from typical stands and operations (Bruce, 1977). This has been termed 'operational falldown' and is an important consideration in applying models developed from research plots to operational stands. Inventories generate large amounts of data representative of ownerships, but have traditionally been concerned only with the current state of the forest in such terms as species composition, stand structure, volume, and value. Flewelling (1981) discusses the use of inventory-type data for growth and yield. For most inventories growth can only be estimated as the total change in yield from successive
MONITORING SILVICULTURAL TREATMENTS
197
remeasurements, adjusted for harvest and changes in the ownership. In-place type inventories offer yield estimates at the stand level and successive estimates are usually based on independent samples. This could be corrected by selecting stands and establishing permanent plots for remeasurement as suggested by Flewelling (1981). Continuous Forest Inventory (CFI) systems are based on remeasured permanent plots and can offer more efficient estimates of growth. In-place and CFI inventory data often suffer from several common problems making them difficult to use for growth monitoring (Curtis and Hyink, 1985): (1)
Use small plots that are subject to large edge effects and don't describe within stand variability.
(2)
Ages are categorized by broad classes.
(3)
Tree heights are inaccurately measured or poorly sampled.
(4)
Small trees of low or no value are omitted.
(5) Samples may be weighted to older, high volume stands or in stands of little interest in future management (young stands, genetically improved stock, etc.). (6)
Measurements differ from those used to develop growth and yield models.
In addition, both CFI and in-place inventories offer only information based on how current stands have been managed in the past and do not offer comparisons to no treatment. Neither the typical inventory system nor research program can serve as a complete monitoring system. A fully implemented monitoring program for growth and yield will have elements common to both, however, the exact design for monitoring, and whether it is more like an inventory system or a research program will depend on the objectives. To answer questions about operations, monitoring may take on more the appearance of an inventory system. To answer unanticipated questions by operations and develop models, a monitoring system may look more like research and require a robust design, especially one that anticipates treatment extremes.
3. Design Considerations for a Monitoring Program Because it would be impossible to monitor every stand, a sample of monitoring stands must be selected. Sampling an entire ownership would be prohibitively expensive, so some meaningful strata should be used (i.e. young stands likely to be managed). Selection of monitoring stands should be representative of the strata of interest if any meaningful inferences are to be made. As Cochran (1983) points out "estimates of the effect of a treatment or program from observational studies are
198
DAVID D. MARSHALL
likely to be biased". This means that monitoring stand selection should be made randomly over some meaningful strata of the ownership. Within monitoring stands, permanent plots should be established. The location of plots should be monumented on the ground and documented on operational ownership maps to protect them. If available, plot locations should be stored in Geographic Information Systems (GIS). The actual plot design can use either fixed area plots or variable plot techniques. Fixed area plots have often been favored because they have the advantage of being simpler (Iles, 1983). Variable plots are more consistent with operational inventory methods and will concentrate measurements in the larger trees. A single variable plot contains too few trees for most monitoring purposes, so a cluster or grid of points should be used. This will require more area than for a single fixed plot, but may better sample within stand variability. To more efficiently sample trees in all size classes, a nested or concentric plot designs using fixed and/or variable plots can be used. In the past, the tendency in both research and inventory has been to use small plots in order to reduce the number of measurements and the amount of area taken up by plots. Small plots, however, can give poor mortality estimates, erratic stand and growth estimates, and in many cases may not accurately characterize within stand variability. The actual plot size used will depend on the anticipated number and size of trees over the life of the plot and the variability of the stand being sampled and the treatments being applied. In general, the larger the trees and the more variability encountered, the larger the plots should be. Within a plot all trees should be permanently tagged for identification in remeasurements. Curtis (1983) gives an excellent discussion of the procedures for establishing and maintaining permanent plots, including plot sizes and tree measurements. Systems designed to monitor operations should take on more of the appearance of an inventory. Comparisons can be made between an operational treatment and no treatment by dividing a stand selected for monitoring and randomly assigning the treatment. This can provide a check and suggest possible adjustments for operational falldown in growth models developed from research plots, but applied in the 'real world'. This also builds confidence with model users. Two problems encountered with this type of monitoring system are: (1) it may require large areas if a cluster or grid of plots are used, and (2) the difficulty coordinating treatments with measurements if done on an inventory cycle. While similar in design to an inventory system, the problems associated with most inventory systems should be avoided. These include small plot sizes and incompatibility of measurements with the data used to develop yield estimates. Systems like this must be flexible and adapt to changes in management. Monitoring systems intended to provide data to test potential questions about management, for building models, or observing long-term growth trends will look more like research, but will look at response surfaces rather than test specific hypotheses. Designs should consist of a range of treatments and stand conditions. This may include plots representing current management practices along with a
MONITORING SILVICULTURAL TREATMENTS
199
range of densities, fertilization and other treatments. Replication would be done by representing the treatments at different locations rather than within a specific site. Beginning monitoring of this type at planting would allow the use of genetically improved stock (which may not be used operationally for several years) and a range of planting densities. These designs should provide data to validate and update models, make decisions on the timing and intensity of thinning, evaluate fertilizer response, and study the effects of management on site productivity and wood quality. They may not, however, represent the 'real world' because of the difficulty in treating small areas (plots), not capturing stand variability on small plots, and making measurements differently than for operations. The resulting 'falldown' effect has implications on applying results or models developed form this type of data and emphasizes the need for operational monitoring programs as well. It is important that comparisons of observed growth to model estimates must be made over multiple measurement cycles. Inferences about model validity based on single measurement periods can suffer from large fluctuations in growth and should be avoided. One of the most important aspects of any monitoring system is quality control and is particularly critical during data collection. Crews must be trained and continually checked to insure consistency and accuracy. Data management must not only include data storage, retrieval and analysis, but extensive checking for measurement errors. This is best done as soon after data collection as possible in order that corrections can be made. The use of well programmed electronic data recorders will help catch most errors in the field.
4. The Bad News and the Good News of Monitoring The bad news is that monitoring programs are expensive. Monitoring of operations and model application must be done by each organization on its own lands. The design used should be similar to the inventory system used by the organization while also being as consistent as possible with the type of data used in developing the models used. Research-type monitoring will usually be more expensive and is justifiable only to very large organizations which are able to spread the costs and benefits over many acres. The good news is that cooperatives offer a cost effective means for many organizations to gather this type of monitoring data at a regional level. They offer a method of coordinating the distribution of installations geographically by physiographic region, age, and site index and offer the mechanism for standardization of design and measurements. They are not a substitute for operational monitoring at the ownership level. One example of a cooperative effort is the Stand Management Cooperative (SMC), which was established in 1985 by 22 organizations in Oregon, Washington, and British Columbia. These organizations agreed to cooperate in order to "provide a continuing source of high-quality data on the long-term effects of silvicultural
200
DAVID D. MARSHALL
treatments and treatments regimes on stand and tree development and wood quality" (Chappell et aL, 1987) that is compatible and consistent. Of major interest to the cooperators was the effect of early density control, particularly wide spacings. The establishment of SMC installations is designed to achieve a balanced geographic distribution by region and site. Results will look at regional response surfaces rather than testing hypotheses at specific locations. To date the species of primary interest have been Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and western hemlock (Tsuga heterophylla (Raf.) Sarg.), although other species and species mixes could be considered in the future. Each installation was established to achieve high uniformity in site conditions and stand density across all plots. To date, three types of installations have been established (Maguire et al., 1991). Type I studies are in well-established juvenile stands that have not experienced substantial inter-tree competition. The core treatments are three initial densities (including half and a quarter of the original numbers of trees) with subsequent later thinning in the two highest density treatments. Supplemental treatments include pruning, fertilization, and leave tree selection. Type II studies are in plantations or respaced natural stands that could receive a commercial thinning. Treatments include no thinning, a single thinning, a delayed thinning and repeated high and low density thinnings. The type III installations are established to provide areas of known history and planting stock for future studies. This has been done by operationally planting uniform areas in blocks representing six initial spacings. Target densities range from 254 to 3073 trees per hectare (100-1210 trees per acre). As of 1990, 32 Type I, 12 Type II, and 26 Type III installations have been established (Maguire et al., 1991). 5. Conclusions High quality data are expensive to collect, but they are also valuable. Data on growth and yield have traditionally been seen primarily in terms of a cost: the cost of data collection and processing. But data are quickly becoming a resource or asset that can benefit its owner economically. The value of data depends on the ability to answer information needs. This goes beyond immediate application and includes values that may be realized later (Cronin and Davenport, 1991) and the cost of basing decisions on few, poor, or no data. Many organizations are now realizing that data can also be a commodity to be sold or traded. Like other resources and commodities, information has acquisition costs, is processed, and differs in its quality and performance (Horton, 1985). Good data may also conserve other resources - quick fixes usually cost more (Horton, 1985). A monitoring program cannot replace inventories or research. We still need to know the status of the whole ownership and to investigate specific biological and mensurational relationships. However, it is necessary to base decisions on evidence supported by data and to consider the dangers of acting on untested and unsupported assumptions. Through monitoring of growth and yield, managers and
MONITORINGSILVICULTURALTREATMENTS
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t h o s e t h e y are a c c o u n t a b l e to can gain c o n f i d e n c e in their decisions and the m o d e l s u s e d to m a k e t h o s e decisions. T h e c o m p l e x and l o n g - t e r m nature o f the resources w e m a n a g e require these kinds o f data collection efforts, w h i c h in turn require stability and c o m m i t m e n t at the h i g h e s t levels o f an organization.
References Bruce, D.: 1977, 'Yield Differences Between Research Plots and Managed Forests', J. Forestry 75, 14-17. Chappell, H.N., Curtis, R.O., D.M. Hyink, and Maguire, D.A.: 1987, 'The Pacific Northwest Stand Management Cooperative and its Field Installation Design', In: Ek, A.R., Shirley, S.R., and Burk, T.E. (Eds.), Forest Growth Modelling and Prediction, Volume 2, pp. 1073-1080. USDA Forest Service. North Central Forest Experiment Station, St. Paul, Minnesota. General Technical Report NC-120. 1149 pp. Cochran, W.G.: 1983, Planning and Analysis of Observational Studies, John Wiley and Sons, N.Y. 145 pp. Cronin, B. and Davenport, G." 1991, Elements oflnformation Management, The Scarecrow Press, Inc. Metuchen, N.J. 207 pp. Curtis, R.O.: 1972, 'Yield Tables Past and Present', J. Forestry 70, 28-32. Curtis, R.O.: 1983, Proceduresfor Establishing and Maintaining Permanent Plots for Silvicultural and Yield Research. USDA Forest Service. Pacific Northwest Forest and Range Experiment Station, Portland, Oregon. General Technical Report PNW-155.56 pp. Curtis, R.O.: 1987, 'A Report on the Permanent Plot Task Force'. In: Ek, A.R., Shirley, S.R., and Burk, T.E. (Eds.), Forest Growth Modelling and Prediction, Volume 2, pp. 1081-1088. USDA Forest Service. North Central Forest Experiment Station, St. Paul, Minnesota. General Technical Report NC-120. 1149 pp. Curtis, R.O. and Hyink, D.M.: 1985, 'Data for Growth and Yield Models'. In: Van Hooser, D.D. and Van Pelt, N. (Eds.), Proceedings-Growth and Yield and Other Mensurational Tricks: A Regional Technical Conference, pp. 1-5. USDA Forest Service, General Technical Report INT193, Intermountain Research Station, Ogden, Utah. 98 pp. Flewelling, J.W.: 1981, 'Sampling for Forest Growth'. In: Brann, T.B., House IV, L.O., and Lund, H.G. (Eds.), In-Place Resource Inventories: Principles & Practices, pp. 410-412. Society of American Foresters. Washington, D.C. 1101 pp. Horton, EW. Jr.: 1985, Information Resources Management: Harnessing Information Assests for Productivity Gains in the Office, Factory, and Laboratory, Prentice-Hall Inc. Englewood Cliffs, N.J. 263 pp. Hyink, D.M., Scott, W., and Leon, R.M.: 1988, 'Some Important Aspects in the Development of a Managed Stand Growth Model for Western Hemlock'. In: Ek, A.R., Shirley, S.R., and Burk, T.E. (Eds.), Forest Growth Modelling and Prediction, Volume 2, pp. 9-21. USDA Forest Service. North Central Forest Experiment Station, St. Paul, Minnesota. General Technical Report NC-120. 1149 pp. Iles, K.: 1983, 'Some Thoughts on Growth Measurement Techniques'. In: Bell, J.E and Atterbury, T. (Eds.), Renewable Resource Inventories for Monitoring Changes and Trends, pp. 259-260. College of Forestry, Oregon State University, Corvallis, Oregon. 737 pp. Maguire, D.A., Bennett, W.S., Kershaw, J.A., Gonyea, R., and Chappell, H.N.: 1991, Establishment Report: Stand Management Cooperative Silviculture Project Field Installations, Institute of Forest Resources Contribution No. 72. College of Forest Resources, University of Washington, Seattle. 42 pp.
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