234 Fleming et al.: Predicting Participation in Urban and Community Forestry Programs successful in determining the likelihood of participation in U&CF programs. Education2 (high school) and Duties4 (educator) proved to be significant at the 5% level indicating that these variables have a large effect on participation. U&CF program planners should pay close attention to the characteristics defined by the previously mentioned variables when targeting individuals for participation. Our results are consistent with other research on factors affecting participation in volunteer organizations. All the variables identified in the final model are considered primary determinants of participation (Natural Resources Conserva- tion Service 2004). Other studies that discuss determinants of participation consistently use the type of variables in the models discussed here (Smith 1994). Pseudo R2 values are not high. Analysts using biologic or physical data would gen- erally be unhappy with these results. However, for social data of this type and the logit model formulation, these R2 levels are usually considered acceptable (Maddala 2001). We were satisfied that these results are significant and do illustrate valuable explanatory relationships that can be used to esti- mate participation levels. Note that we were limited to data included in the 2003 survey. This model is merely a starting point in establishing factors that affect participation. Additional data will surely strengthen the model. Our main contribution is showing that this technique can be used effectively to estimate participa- tion and we provide a starting point for a more detailed study. Can a model like this be used in day-to-day work of the U&CF professional? Yes, it does provide valuable informa- tion. Notice in our prior example of the 35 year old female forestry consultant that we determined the likelihood of her participation. The variables in the model interact and a simple table of likelihoods by characteristic would be too complex to be usable. However, other variables can be held constant and changes in variables like income level or age can be evalu- ated. The model certainly can be used to estimate likelihood of participation for any individual and would show the pro- gram planner where to best spend his or her time. CONCLUSION The purpose of this study was to provide insight into partici- pation within U&CF programs. A logistic regression model was used with independent variables being qualitative. Two econometric models were evaluated—one using all the avail- able independent variables (model 1) and the other omitting certain variables (model 2). The pseudo-R2 values were not especially high, but they suggest a level of predictability. These low values could mean the model was not properly specified or that relevant variables were omitted. For an econ- ometric study of this type, these are acceptable R2 values. The two models proved to be significant (at the 10% level) in the prediction of participation. Model 2 may prove more ©2006 International Society of Arboriculture useful than the full model in estimating participation as a result of the problem of multicollinearity being corrected. Acknowledgments. Support for this research was provided by a USDA Forest Service Urban and Community Forestry Assistance Grant awarded through the South Carolina Forestry Commission. LITERATURE CITED Alig, R.J., F. Bedford, R.J. Moulton, and L. Lee. 1999. Long- term projection of urban and developed land area in the United States. In: Keep America Growing, Balancing Working Lands and Sevelopment: Conference Proceed- ings (CD-ROM). American Farmland Trust, Washington, DC. Additional information at: www.farmland.org/ (ac- cessed 11/4/05). Alig, R.J., A.J. Plantinga, S. Ahn, and J.D. Kline. 2003. Land Use Changes Involving Forestry in the United States 1952 to 1997, With Projections to 2050. USDA Forest Service General Technical Report PNW-GTR-587. Allison, P.D. 1999. Logistic Regression Using the SASR Sys- tem: Theory and Application. SAS Institute, Cary, NC, p. 46–51. Andresen, J.W. 1989. Tree City USA: volunteer urban for- estry. Journal of Arboriculture 13:333–343. Bloniarz, D.V., and H.D.P. Ryan III. 1996. The use of vol- unteer initiatives in conducting urban forest resource in- ventories. Journal of Arboriculture 22:75–82. Cole, D.W. 1979. Oakland urban forestry experiment: a co- operative approach. Journal of Forestry 77:417–419. Cubbage, F.W., J. O’Laughlin, and C.S. Bullock. 1993. For- est Resource Policy. Wiley, New York, NY. Heimlich, R.E., and W.D. Anderson. 2001. Development at the Urban Fringe and Beyond: Impacts on Agriculture and Rural Land. USDA Economic Reporting Service Agricul- tural Economic Report No. 803. Henderson, N. 1984. Tapping the volunteer spirit: commit- ment in the Sierra. American Forests 90:33–36. Iles, J.K. 1998. Inclusive urban and community forestry pro- grams: using all of your community’s cultural resources. Journal of Arboriculture 24:316–321. London, J.B., and N.L. Hill. 2000. Land Conversion in South Carolina: State Makes the Top 10 List. Jim Self Center on the Future, Clemson University, Clemson, SC. Maddala, G.S. 2001. Introduction to Econometrics, Third Edition. John Wiley, Chichester, UK. Natural Resources Conservation Service. 2004. Guide for Es- timating Participation in Conservation Programs and Pro- jects. Technical Note, Series No. 1801, 2nd Revision. Greensboro, NC. SAS Institute Inc. 2002. The SAS System for Windows Ver- sion 9.0. SAS Institute Inc., Cary, NC.
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