Land Use Change Determinants by Ownership and Forest Type in Alabama and Georgia Rao V. Nagubadi1 and Daowei Zhang
Abstract: This paper analyzes the determinants of timberland use by ownership and forest type in Alabama and Georgia during 1972-2000. Higher forestry returns help to increase the share of timberland ownership by forest industry and NIPF. Hardwood sawtimber prices and poor land quality appear to increase timberland use towards hardwood forest types at the expense of oak-pine mixed, and softwood types. Increases in population and per-capita income have a negative impact on forestry and agricultural land use as well as timberland use by ownership and forest type. Key Words: Timber land, land quality, federal incentives, multinomial logit. INTRODUCTION This study deals with changes in the timberland by ownership and by forest types. Recently, there have been several attempts to model and project land use along the lines of major uses, i.e., forestry, agriculture, and urban uses (Alig 1986; Hardie and Parks 1997; Ahn, Plantinga, and Alig 2000, 2001; Hardie et al. 2000). Few studies have dealt with the changes in the timberland by ownership and by forest types.1 Between 1972 and 2000, ownership pattern of timberland has witnessed change (Table 1). In Alabama, while the timberland under public and non-industrial private forest (NIPF) ownership increased by 20 and 11 percent, private forest industry ownership declined by 11 percent. In Georgia, the timberland under public and forest industry ownerships increased respectively by 11 and 13 percent, the NIPF ownership of timberland declined by 9 percent. Changes have also been taking place in the timberland area under different forest types. There has been a shift towards increasing the timberland under hardwood forest type at the expense of softwood and mixed forest types (Table 1). While the timberland under softwoods increased marginally by 3 percent, there was a decline in the timberland under oak-pine mixed forest type, and a dramatic increase of 25 percent in the timberland under hardwood forest type in Alabama between 1972 and 2000. In Georgia, the timberland under softwood and mixed forest types each declined by nearly 13 percent between 1972 and 1997. During the same period, the timberland under hardwood forest type increased by 12 percent. The changes in timberland ownership pattern and forest type have implications for recreational activities, biodiversity, and water quality. Changes in forestland could imply a significant impact on the condition of forests and their ability to provide wildlife habitat, recreation, and environmental amenities (Wear 2002). Due to increasing population and economic growth, the U.S. South experienced dramatic growth in urban sprawl. Increasing population and economic growth have also spurred the demand for
1
Post-doctoral Fellow, 108 M. White Smith Hall, Auburn University, AL 36849-5418.
[email protected]. (334) 844-1052 (v); (334) 844-1084 (Fax). The authors wish to thank Changyou Sun and Jeffrey D. Kline for helpful comments on a previous draft. The authors gratefully acknowledge financial assistance from Center for Forest Sustainability and Environmental Institute of Auburn University.
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recreation, especially forest-based outdoor recreation, while access to recreational land is increasingly limited to owners themselves (Cordell and Tarrant 2002). This paper examines changes in the ownership pattern and forest types of timberland using county level data for Alabama and Georgia. The next section briefly reviews previous literature and describes the analytical framework used in this study. The third section describes Table 1. Changes in timberland use by ownership and forest type in Alabama and Georgia Particulars Alabama (1,000 Acres) Georgia (1,000 Acres) 1972 2000 % change 1972 1997 % change -4.2 24,839.0 23,795.3 7.5 22,926.5 21,333.1 By ownership: 11.4 1,751.2 1,571.5 20.5 1,230.0 1,020.5 Public 13.3 4,890.7 4,318.2 -11.0 3,740.4 4,204.9 Forest industry -9.5 18,949.3 17,153.4 11.5 17,956.1 16,107.7 NIPF
By forest type: Softwood Oak-pine Hardwood a
21,333.1 7,863.7 5,016.9 8,456.5
7.5 2.9 -16.4 25.9
22,926.5 8,089.0 4,193.7 10,577.9
24,839.0 12,325.2 4,142.9 8,370.9
23,795.5 10,805.3 3,613.3 9,376.9
-4.2 -12.3 -12.8 12.0
Misc. area includes water area, unproductive forests and productive reserve forests.
data and their sources. The fourth section presents and discusses the results. The final section concludes.
Literature Review and Analytical Framework Typically forest returns, timber price, timber-to-crop income ratio act positively on increasing the timberland shares (Alig 1986; Ahn, Plantinga, and Alig 2001; 2002). Increases in timber establishment costs and planting costs discourage forestland use. Increasing farm expenditures discourage agricultural use and promote conversion into either urban land or forestland. Personal income, household income and per capita income affect negatively on the timberland and agricultural land use and in favor of urban land use. Increasing inflation favors conversion of land into forestry use (Alig 1986; Hardie and Parks 1997). Population density is a key variable in converting the forest land and agricultural land to urban use (Alig 1986; Ahn, Plantinga, and Alig 2001; Hardie and Parks 1997; Hardie et al. 2000). As population increased, more land was needed for home sites, roads, airports, school, commercial, and industrial sites, parks, open space, and other uses to satisfy the demands of urbanized areas (Vesterby and Heimlich 1991; Reynolds 2001). The proportions of rural population and urban population have also been shown to be affecting the land use (Alig 1986). The quality of land has a major influence governing the use of land for agricultural or forestry purposes (Parks and Murray 1994; Hardie and Parks 1997; Plantinga, Mauldin, and Alig 1999; Ahn, Plantinga, and Alig 2001). Higher quality land is naturally put to higher income uses in agriculture and lower quality land to forestry uses. In empirical analyses, the proportion of two higher land quality classes in the total land of a county has been shown to affect whether the land is put to agricultural or forestry use. 57
Empirical analyses have shown that distance to city has a negative influence on the agricultural and urban land use, while it has positive influence on forest land use under NIPF and forest industry ownership (Ahn, Plantinga, and Alig 2001; 2002). Distance to interstate highways may act positively on the forest land use and negatively on agricultural and urban land uses. Slope of land has also been an influence on how land is used, agricultural land use preferring land with lower slope in comparison with forest land use (Parks and Murray 1994). Since the start of Agricultural Conservation Program in 1930s, several programs such as Soil Bank Program of 1950’s, Forestry Incentives Program (1972), tree planting program of Conservation Reserve Program (1985), and Stewardship Incentives Program (1993) have influenced landowners’ behavior in favor of forestry. These programs resulted in increasing forestry land use and decreasing the agricultural land use (Alig 1986) and significantly influenced forest tree planting (Kline, Butler, and Alig 2002). Following Miller and Plantinga (1999), and Hardie et al. (2000), a model of land use is developed from the viewpoint of landowners’ decision problem of allocating a fixed amount of land to alternative uses. Optimal (or expected) land use shares, pikt (proportion of land in k-th use, in i-th county, at time t), in the total land, are specified as multinomial logistic functions of a linear combination of vector of explanatory variables, Xit, and vector of unknown parameters, βk (1)
p ikt
=
exp( β k' X it ) K
∑ exp( β k =1
' k
X it )
The land uses can be non-industrial timberland, industrial timberland, agricultural land, and urban/other land (i.e., k = 1, …, K-1, K). The explanatory variables, Xit, used in literature often include (a) economic variables: forest returns, agricultural returns, urban rent, and per capita income; (b) demographic variables: population density, urban/rural population ratio, and average age; (c) land quality variables: average land quality and the proportion of two higher quality classes; (d) geographical variables: distance to city, slope and travel time; and (e) policy variables: government forestry cost-share programs and farm assistance programs. The logistic specification is convenient because it constrains the sum of land use shares to one. If we normalize equation system (1) by one of the land use shares (for example, k=4) and by constraining β4 = 0, the multinomial logit model becomes exp( β k' X it ) for k = 1, …, K-1 (2) p ikt = K −1 ' 1 + ∑ exp( β k X it ) k =1
and the share for the omitted land use is recovered as 1 . (3) pi 4 t = K −1 1 + ∑ exp( β k' X it ) k =1
Logarithmic transformation of equation system (2) yields a three equation system p (4) for k = 1, …, K-1. ln ikt = β k X it pi 4t 58
Since the optimal land use shares, pikt, are not observable and may be different from actual land use shares due to random factors, they are replaced with actual (or observed) land use shares, yikt, and error terms are introduced in the system. The system of equations in (4) then becomes y ln ikt = β k X it + ε ikt for k = 1, …, K-1 (5) y i 4t The logarithmic transformation and use of both time series and cross sectional data may induce heteroskedasticity problem which is corrected by White’s (1980) estimate of covariance matrix. For ease of interpretation, marginal effects and elasticities are estimated at mean levels of variables. Marginal effects are estimated as per formula given by Greene (1990, p.666), and acreage elasticities are calculated with the help of formula given by Wu and Segerson (1995, p.1037). Marginal effects and acreage elasticities for the multinomial logit function are not monotonic, but depend on the point of evaluation and can vary in sign and magnitude according to the value of x, and the proportions of land use pikt.
DATA The variables used and the data sources are listed in Table 2. This analysis used data for 67 Alabama counties for the years 1972, 1982, 1990, and 2000 and 159 Georgia counties for the years 1972, 1982, 1989, and 1997 obtained from Forest Inventory and Analysis (FIA) surveys. Land in agricultural use includes cropland, pastureland, and rangeland available from agricultural censuses at 5-year intervals. To conform to FIA survey years, these numbers were interpolated for 1972 and 1990 for Alabama and for 1972 and 1989 for Georgia, using annual compound growth rates between the relevant agricultural census years. The area under agricultural land use for the year 2000 for Alabama was obtained by extrapolating from 1997 using annual compound growth rates between 1992 and 1997. The implicit assumption is that the agricultural land use changed at the same compound growth rates between the relevant years. Land in other category includes urban land, roads and rural transportation and was estimated by subtracting water area, productive and unproductive reserve forest land, timberland, agricultural land from the total land area of counties. Total land and water area were from 2000 population census while productive and unproductive reserve forest land area were from FIA.
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Table 2. Description and data sources of variables used in the analysis Variable Description WTDSTPR Sawtimber price weighted by pine sawtimber and oak sawtimber removals ($/MBF) Pine sawtimber price ($/MBF) PSTPR Oak sawtimber price ($/MBF) OSTPR Average value per acre of farm real estate. AGVAL Persons per acre of total land area of county PD Average per capita personal income of county in thousand INC8284 dollars Weighted average land capability class of counties AVLCC Proportion of highest land quality classes I and II in the total land LCC1N2 of the counties CSACRES State average of tree planting acres in thousands for the previous period. PLCOST Average planting cost in 1992 dollars (1992=100) for US South
Source Timber Mart-South Timber Mart-South Timber Mart-South NASS Census Bureau REIS of BEA USDA USDA NRCS Kline; Dubois et al.
To represent the returns to forest land use, a county level weighted sawtimber price (Dollars per MBF) was calculated using Timber Mart-South prices and county level sawtimber removals for softwoods and hardwoods available from FIA as weights. For 1972, pine sawtimber and oak sawtimber prices were obtained by tracing backwards from 1977 using the percentage changes in Louisiana prices (Howard 2001). Three area prices before 1992 were converted to two area prices using conversion weights developed by Prestemon and Pye (2000). The sawtimber prices were deflated using PPI for all commodities (1982=100). Average value per acre of farm real estate from various agricultural censuses is used as proxy for representing the county agricultural returns. Interpolations were used for the years corresponding to FIA years using the method explained above. These values were deflated using Consumer Price Index-Urban (CPIU), 1982-84=100. Population density was estimated as number of persons per acre of total land area of county using Census Bureau’s mid-year estimates from Regional Economic Information System (REIS) of Bureau of Economic Analysis (BEA). County level per capita personal income numbers were obtained from REIS. These numbers were deflated using CPI-U. Two land quality variables were used in the analysis. The U.S.D.A. classifies land into 8 land capability classes (LCC) in the decreasing order of land quality (Klingebiel and Montgomery 1961). The ratings for a land parcel range from 1 to 8 where 1 is the most productive land and 8 is the least productive land. The average land quality index (average LCC) was calculated as a weighted average of acres in each land class in the county. The second variable is the proportion of LCC 1 and 2 in the total land area. The values of the two land quality variables were different for counties, but same for all the analysis years. For acreage under cost share programs, a state level variable for annual average number of acres of trees planted under various cost share programs was constructed using the cost share acres for the previous 7 to10 years for each FIA survey.2 These numbers 60
change over years but same for every county in each particular year since state level numbers were used. Cost share acres for tree planting for period 1962-85 were obtained from USDA Forest Service (1986) and for subsequent years from serial publication Agricultural Statistics, Farm Service Agency and Natural Resource Conservation Service. The data on tree planting cost is the index in 1992 dollars (1992=100) for the U.S. South obtained from Kline (Per. Comm.).
RESULTS AND DISCUSSION The analysis is accomplished at two levels. First, forest ownership is studied. The total area of land in this category is the sum of timberland owned by private forest industry, NIPF landowners, agricultural land, and urban/other land, and excludes all types of public land. Second, three forest type groups are examined. Softwood forest type includes longleaf-slash pine, loblolly-shortleaf forest species groups. Mixed forest type is comprised of oak-pine forest type group. Hardwood forest type includes oak-hickory, oak-gum-cypress, elm-ash-cottonwood, and non-stocked forest species groups. The total land area in this category is the sum of land under softwood, mixed, hardwood forest types, agricultural land and urban/other land.
Ownership The explanatory powers of the estimated equations according to ownership type are between 0.26 and 0.43 (Table 3). The effect of timber price is positive and significant on the share of both forest industry and NIPF ownership. Agricultural land values have a significantly negative impact on the shares of forest industry ownership, but not a significant effect on the share of non-industrial forestry ownership. Agricultural land values have no influence on the share of agriculture as indicated by its insignificant coefficient. As expected, population density has significant negative impact on the shares of forest industry and NIPF land ownerships as well as agricultural land use. The per capita personal income has the expected negative influence on the shares of all types of ownerships but the effect is not significant for the share of agricultural land use. The LCC1N2 has significant negative impact on the shares of both private and nonindustrial forestry ownerships, while it has significant positive impact on the share of agricultural land use. The AVLCC has the expected negative impact on the agricultural land use share, but the coefficients for both the forestry ownerships are negative, contrary to our expectations. Acreage under cost share programs has insignificant impact on the shares of both private forest industry and non-industrial private forest ownership. Elasticity estimates indicate that the sawtimber prices and cost share acres have positive effect, while agricultural land values, planting costs, per capita personal income and proportion of higher land quality classes have negative effect on forest industry timberland ownership. For NIPF timberland ownership, personal income and LCC1N2 have negative effect and declining AVLCC has positive effect. For agriculture land use, declining AVLCC and increasing sawtimber prices are negative factors and increasing proportion of LCC1N2 is a positive factor.
Forest types 61
The explanatory powers of the four estimated equations in this category range from 0.32 to 0.52 (Table 4). In the softwood and mixed forest type equations, except for AVLCC and planting cost, all coefficients are significant and have the expected signs. In the hardwood forest type equation, the pine and oak sawtimber prices have the expected reverse impact on the share of hardwood forest type. The effect of agricultural land value is negative and significant on the share of hardwood forest type. The coefficients for population density and per capita income are significant and have expected negative impact on the share of hardwood forests. The cost shared acres is not a significant variable in affecting the share of hardwood forests. In the agricultural land use equation all coefficients are significant and as expected, except for oak sawtimber price. Pine sawtimber price exerted a significant negative influence on the share of agriculture land use. Among the remaining variables, agricultural land values, LCC1N2, and planting cost had positive influence on the share of agriculture land use, while population density, per capita personal income, and AVLCC had significant negative effect.
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Table 3. Land use determinants by ownership, Alabama and Georgia, (K=4; obs.=833) Variable Coefficient Standard Error Marginal Effects Elasticities (a) Industry owned timberland use, Dep. variable: Ln(Ind/Urban&Other), Adj. R2 = 0.42 Constant 3.6541*** 0.8524 WTDSTPR 0.0034*** 0.0007 0.0002 0.3308 AGVAL -0.0013*** 0.0002 -0.0001 -1.0549 PD -0.0018*** 0.0004 -0.0001 -0.0650 INC8284 -0.1475*** 0.0373 -0.0077 -0.7212 AVLCC -0.2262* 0.1163 -0.0034 -0.1373 LCC1N2 -2.9064*** 0.5449 -0.2145 -0.6043 CSACRES 0.0035 0.0027 0.0004 0.1769 PLCOST -0.0007 0.0038 -0.0006 -0.6734 (b) NIPF owned timberland use, Dep. variable: Ln(NIPF/Urban&Other), Adj. R2 = 0.26 Constant 2.3687*** 0.5167 WTDSTPR 0.0017*** 0.0005 0.0003 0.0753 AGVAL -0.00007 0.0001 0.00005 0.0824 PD -0.0013*** 0.0002 0.00001 0.0026 INC8284 -0.0887*** 0.0235 -0.0101 -0.1648 AVLCC -0.1689** 0.0691 0.0136 0.0946 LCC1N2 -1.1277*** 0.3144 -0.2033 -0.0993 CSACRES -0.0001 0.0016 0.0004 0.0324 PLCOST 0.0075*** 0.0024 0.0013 0.2658 (c) Agricultural land use, Dep. variable: Ln(Agri/Urban&Other), Adj. R2 = 0.43 Constant 1.8342*** 0.6314 WTDSTPR -0.0007 0.0006 0.0001 -0.2922 AGVAL 0.0001 0.0001 0.00002 0.2107 PD -0.0021*** 0.0002 0.0000 -0.0975 INC8284 -0.0272 0.0326 -0.0031 0.4164 AVLCC -0.4012*** 0.0806 0.0042 -0.8457 LCC1N2 0.9594** 0.3340 -0.0621 0.4933 CSACRES -0.0060*** 0.0020 0.0001 -0.2287 PLCOST 0.0050* 0.0028 0.0004 -0.0182 *, **, *** indicate significance levels at 0.10, 0.05, and 0.01 probability.
Estimates of elasticities for forest type indicate that increasing pine sawtimber prices and cost share acres act positively and both the land quality variables act negatively for the shares of softwood forest type. Increasing proportion of higher land quality, personal income, and pine sawtimber prices act negatively, while increasing oak sawtimber prices and decreasing average land quality act positively on the shares of land use under oakpine mixed forest type. For hardwood forest land use, planting costs, personal income, and pine sawtimber prices exert negative influence, whereas poorer land quality and oak sawtimber prices exert positive influence. Increasing proportion of higher land quality, agricultural land values impact agricultural land use shares positively, while poorer average land quality, pine sawtimber prices and cost share acres impact negatively.
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Table 4. Land Use Determinants by Forest Types, Alabama and Georgia, (K=5, obs.=878) Variable Coefficient Standard Error Marginal Effects Elasticities (a) Softwood forest type land use, Dep. variable: Ln(Soft/Urban&Other), Adj. R2 = 0.32 Constant 2.4945*** 0.6088 PSTPR 0.0041*** 0.0010 0.0012 0.7146 OSTPR -0.0019** 0.0009 -0.0011 -0.3759 AGVAL -0.0003*** 0.0001 0.0000 -0.1551 PD -0.0007*** 0.0001 0.0000 0.0101 INC8284 -0.1612*** 0.0253 -0.0109 -0.3576 AVLCC -0.2532*** 0.0778 -0.0364 -0.5051 LCC1N2 -2.5040*** 0.3936 -0.4819 -0.4660 CSACRES 0.0064*** 0.0020 0.0011 0.1651 PLCOST 0.0098*** 0.0029 0.0014 0.5648 (b) Mixed forest type land use, Dep. variable: Ln(Mixed/Urban&Other), Adj. R2 = 0.35 Constant 1.1183* 0.5768 PSTPR -0.0017* 0.0010 -0.0002 -0.2602 OSTPR 0.0047*** 0.0009 0.0003 0.2599 AGVAL -0.0001 0.0001 0.0000 0.0244 PD -0.0009*** 0.0001 0.0000 -0.0253 INC8284 -0.1523*** 0.0270 -0.0031 -0.2724 AVLCC -0.1339* 0.0751 0.0006 0.0216 LCC1N2 -2.2232*** 0.3742 -0.1515 -0.3867 CSACRES 0.0035* 0.0020 0.0001 0.0421 PLCOST 0.0104*** 0.0028 0.0006 0.6398 (c) Hardwood forest type land use, Dep. variable: Ln(Hard/Urban&Other), Adj. R2 = 0.35 Constant 1.9418*** 0.4931 PSTPR -0.0007 0.0008 -0.0002 -0.0925 OSTPR 0.0034*** 0.0007 0.0004 0.1332 AGVAL -0.0001** 0.0001 0.0000 0.0018 PD -0.0008*** 0.0001 0.0000 -0.0029 INC8284 -0.1505*** 0.0206 -0.0077 -0.2552 AVLCC 0.0899 0.0649 0.0633 0.8857 LCC1N2 -0.3658 0.3071 0.1413 0.1375 CSACRES 0.0020 0.0016 -0.0002 -0.0230 PLCOST -0.0007 0.0023 -0.0016 -0.6441 2 (d) Agricultural land use, Dep. variable: Ln(Agri/Urban&Other), Adj. R = 0.52 Constant 2.2131*** 0.5839 PSTPR -0.0057*** 0.0010 -0.0009 -0.9391 OSTPR 0.0064*** 0.0010 0.0007 0.4196 AGVAL 0.0000 0.0001 0.0000 0.1429 PD -0.0013*** 0.0001 -0.0001 -0.0973 INC8284 -0.0988*** 0.0282 0.0042 0.2429 AVLCC -0.3953*** 0.0729 -0.0445 -1.0815 LCC1N2 1.3775*** 0.3016 0.3720 0.6296 CSACRES -0.0018 0.0019 -0.0007 -0.1849 PLCOST 0.0065** 0.0026 0.0003 0.1840 *, **, *** indicate significance levels at 0.10, 0.05, and 0.01 probability.
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CONCLUSIONS This paper has analyzed the determinants of land use changes using county level data from Alabama and Georgia for the period 1972 to 2000. This analysis is accomplished at two levels: ownership, and forest type. During the period of analysis, timberland area in Alabama increased, while it declined in Georgia. Land in agricultural use declined and land in urban/other uses increased dramatically in both Alabama and Georgia. Industry ownership timberland declined and NIPF ownership of timberland increased in Alabama, while the situation in Georgia was reverse. Among the forest types, area under hardwood forest type increased, while area under oak-pine mixed forest type declined in both the states. Area under softwood forest types slightly increased in Alabama and whereas it showed a decline in Georgia. Higher forestry returns help to increase the share of timberland ownership by forest industry and NIPF, while higher income levels and higher proportion of good quality land may lead to declines in the share of timberland ownership by forest industry. The NIPF landowners appear to increase their ownership share of lower quality lands, while on the contrary, higher income levels and higher proportion of good quality of land may lead to declining shares of ownership by NIPF landowners. The trend of increasing hardwood forest type land use, at the expense of oak-pine mixed, and softwood forest types, is driven by increases in population and per-capita income. Increasing softwood sawtimber prices and tree planting under cost share programs have been favorable towards increasing the share of land in softwood forest types. Increasing hardwood sawtimber prices and poorer land quality have promoted increasing the land use shares in hardwood forests.
NOTES: 1 Exceptions are Plantinga and Buongiorno (1990), and Ahn, Abt, and Plantinga (2001). 2 For the years 1972 and 1982 annual average of cost shared tree planting acres from 1962 to 1971 and 1972 to 1981 were used respectively for Alabama and Georgia. For the years 1990 and 2000 for Alabama, the annual average of cost share tree planting acres for 1982 to 1989, and 1990 to 1999 were used. For the years 1989 and 1997 for Georgia, the annual average of cost share tree planting acres for 1982 to 1998 and 1990 to 1996 were used. LITERATURE CITED Ahn, S., A.J. Plantinga, and R.J. Alig. 2000. “Predicting future forestland area: A comparison of econometric approaches.” For. Sci. 46:363-376. Ahn, S., A.J. Plantinga, and R.J. Alig. 2001. Historical trends and projections of land use for the South Central United States. Res. Pap. PNW-RP-530. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 20 p. Ahn, S., A.J. Plantinga, and R.J. Alig. 2002. “Determinants and projections of land use in the South Central United States.” South. J. Appl. For. 26:78-84. Ahn, S., R.C. Abt, and A.J. Plantinga. 2001. “Land use in the South Central United States: A further investigation on land use practices by forestland ownership.” P. 165-171. In Forest Law and Economics, Zhang, D., and S. Mehmood (eds.). Southern Forest Economics Workgroup. 65
Alig, R.J. 1986. “Econometric analysis of factors influencing forest acreage trends in the Southeast.” For. Sci. 32:119-134. Bureau of Economic Analysis (BEA). Regional Economic Information System (REIS), Regional accounts data, local area personal income. Internet site: http://www.bea.gov/bea/regional/reis. (Accessed on March 15, 2003). Cordell, H.K., and M.A. Tarrant. 2002. “Forest-based outdoor recreation.” Chapter 11,P.269-282 in Wear and Greis (2002). Dubois, M. R., K. McNabb, and T. J. Straka. 1997. “Costs and cost trends for forestry practices in the South.” Forest Landowner Manual, March-April. Greene, W.H. 1990. Econometric analysis. New York: Macmillan Publishing Co. 791 p. Hardie, I.W., and P.J. Parks. 1997. “Land use in a region with heterogeneous land quality: An application of an area base model.” Amer. J. Agr. Econ. 73:300-310. Hardie, I.W., P.J. Parks, P. Gottlieb and D.N. Wear. 2000. “Responsiveness of rural and urban land uses to land rent determinants in the U.S. South.” Land Econ. 78:659673. Howard, J.L. 2001. U.S. Timber Production, Trade, Consumption, and Price Statistics 1965-1999. Gen. Tech. Rep. FPL-GTR-595. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 90 p. Kline, J.D. 2002. Personal communication. Corvallis, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Kline, J.D., B.J. Butler, and R.J. Alig. 2002. “Tree planting in the South: What does the future old?” South. J. Appl. For. 26:99-107. Klingebiel A.A., and P.H. Montgomery. 1961. Land-Capability Classification. U.S.D.A. Agric. Handb. No. 210. Washington, D.C. 21p. Miller, D.J., and A.J. Plantinga. 1999. “Modeling land use decisions with aggregate data.” Amer. J. Agr. Econ. 81:180-194. National Agricultural Statistics Service (NASS). Various Years. Agricultural Statistics. U.S. Department of Agriculture, Washington, D.C. Parks, P.J., and B.C. Murray. 1994. “Land attributes and land allocation: Nonindustrial forest use in the Pacific Northwest.” For. Sci. 40:293-303. Plantinga, A.J., and J. Buongiorno. 1990. “Determinants of changes in nonindustrial private timberland ownership in the United States.” J. World For. Resour. Manage. 5:29-46. Plantinga, A.J., T.E. Mauldin, and R.J. Alig. 1999. Land use in the Lake States region: An analysis of past trends and projections of future changes. Res. Pap. PNW-RP511. Portland, OR: U.S.D.A. Forest Service, Pacific Northwest Research Station. 20p. Prestemon, J.P., and Pye, J.M. 2000. “A technique for merging areas in Timber MartSouth data.” South. J. Appl. For. 24:219-229. Reynolds, J.E. 2001. “Land use change and competition in the South.” J. Agric. Appl. Econ. 33:271-281. Timber-Mart South (TMS). 1977-2000. Timber Mart-South Quarterly Report. Norris Foundation, Daniel B. Warnell School of Forest Resources, Athens, GA: University of Georgia. USDA Forest Service. 1986. A statistical history of tree planting in the South, 19251985. U.S. Department of Agriculture, Forest Service. Misc. Rep. SA-MR 8. Atlanta, GA: Cooperative Forestry, State and Private Forestry, Southern Region. 23 p. 66
Vesterby, M., and R.E. Heimlich. 1991. “Land use and demographic change: Results from fast-growth counties.” Land Econ. 67:279-291. Wear, D.N. 2002. Land use. Chapter 6, P.153-173. In Wear and Greis (2002). Wear, D.N., and J.G. Greis. (eds.). 2002. Southern forest resource assessment. Gen. Tech. Rep. SRS-53. Ashville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 635 p. White, H. 1980. “A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity.” Econometrica. 48:817-838. Wu. J., and K. Segerson. 1995. “The impact of policies and land characteristics on potential ground water pollution in Wisconsin.” Amer. J. Agr. Econ. 77:10331047.
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