United States 

Department of 

Agriculture 

Forest Service 

Economic Factors 

Influencing Land Use Changes 

in the South-Centra 

United States 

Southeastern Forest 

Exper~rnent Statton Ralph J. Alrr;, Frea C Lhdhite. 

Research Paper 

SE-272 and Brian C. Murray 
Economic Factors 

Influencing Land Use Changes 

in the South-Central 

United States 

Ralph J. Aiig, Research Forester 

U.S. Department of Agriculture, Forest Service 

Southeastern %rest Experiment Station 

Research Triangle Park. North Carolina 27709 

Fred 6. White, Professor 

Agricultural Economics Department 

Univers~ly of Georgia, 

Athens, Georgia 30602 

Brian C. Murmy, Forester 

U.S. Department of Agriculture, Forest Service 

Southeastern Forest Experiment Station 

Research Triangle Park, 

North Carolina 27709 
Economic Factors Influencing Land Use 

Changes in the South-Central United States 

Ecunometr~c models of land use change were estimated for rpio physiographic 

regrons in the South-Central United States. Results are consistent with the 

economic hterarshy of land use, wth population and penonat income being signtflcanr eqLanaroryivar~ables. Findrngs regarding the Importance of relat~ve 

agr~culturai and forestry market-based incomes in tnfiuenczng regional land use snlfts suggest that farm programs play an important, but contrnuaiiy changing, 

role in iand use change. 

Keyxlords: Econometrrc analysis, acreage, forest land. 

Private owners control over 90 percent of the Iand in the South. 

Their reallocation of land among uses such as forestry is influenced by 

Pactors related ro production and consumption activities. Although 

drea changes among major land uses arc related through complex sets 

ob' intcrdependent Iiokages, understanding the dynamics of Iand usz 

changes and [he effects of government policies on Iand reallocarion is 

imptxtani in planning for efficient resource utilization at the regional 

Lt:vel. Long-term projections for any ses"i~r: ot the economy ehar is 

closely tied co the land, such as agricuiturs: or Porestry, must take into 

account cilmpe tition for iand from or her secrors. 

In the study described here, determinants of area changes for ail 

major pri~ake uses oi' land were analyzed for the South-Central United 

Staecs - Aidbama, Arkansas, Louisiana, ikfississippi, Oklahoma (eastern 18 counties), Tcnncssee, dnd Texas (eastern 66 counties), Effects 

or1 land reatlocation from changes in ~xpecred relative land rents for aitcsnritlve enterprises were estimated, This research study was designed 

ro support foresr resource supply analyses for the C'SDA Forest Service Q 1988) study of the prospective forest resource situaeioo in the 

South, 

Related Literature 

Insights about variables difecting land use changes in the South 

can be gained from pretrious investigarions, ivhich have been cdnducted 

primarily from an agrlcuitural perspective, Stall and others (1484) esrlrnaled land ust: changes among cropland, pasture, and forest Lagid for 

13 Sourherra Stales. In a majority sf cases, statistical ctsefficienes on 

population and lacome were negarire, indicating urbanizing pressures 

on paae"x"!nnd and forest land. Overall, in 13 out of 6 cases, cseffhcienls for subsritute or competing land use variables were negative and 

saaeis~icadiy Bignificant. In a similar study for Georgia, White and Flcmiag (19863) found correlations seemingly inconsistent with the 
hypothesis that incomes from land-based enterprises drive land use 

changes (e.g., negative coefficient for beef income in a pasture-acreage 

equation). 

AIig (1986) estimated ra set of land useiownership equations for 

the three major physiographic: regions - Coastal Plain, Piedmont, and 

Mountains - in the Southeastern United States (Florida, Georgia, 

North Carolina, South Carolina, and Virginia). Restrictions were imposed across equations so that the sum of statistical coefficients for 

each explanatory variable summed lo zero ro reflect the essentially 

fixed nature of the total land base. Population and personal income 

were major explanatory variables across the equations for major land 

uses, and were negatively correlated with changes in percentages of the 

land base occupied by agriculture and forestry. Increases in popnlation and real personal income tend to intensifi development pressure, 

with urbanization reducing forest area (Alig and Healy 1987). Variables reflecting government programs designed to improve soil conservation and encourage tree planting, such its the Soil Bank Program in 

the late 1950's and early 1960's, had significant positive coefficients in 

miscellaneous private forest equations and negative ones in crop and 

pascure:range equations. 

Hardiek ((5984) results suggest thar timber has a comparative advantage on some high fertility sites in the South and that timber growing might become a competitive land use at the intensive margin of the 

region's farmland base. He also suggests that industrial owners of timberland may assign relatively lower discount rates to investments, such 

thar industrial owners may find timberland to be an attractive invesrment under the same circumstances that nonindustrial landowners find 

it advantageous ro disinvest. 

An important finding from pxvious econometric analyses involving competing agricultural crops pertains to complications related to 

government intervention in associated markets. Lee and Helmberger 

(1985) review difficulties encountered in estimating acreage supply 

response (or annual planting) for major agricultural crops, noting that 

supply estimation is particularly difficult because farm programs tend 

to change every 3 to 5 years. This complicates supply estimation because relevant variables and structural parameters may change over 

time. Recent research has attempted to analyze aggregate supply 

response under "free market'knd '"arm program" regimes, including 

taking inlo account farm programs when modeling acreage response 

for nonprograrn crops. 

Pm contrast to the focus on annual planting in studies of agricultural acreage response, perennial crop supply response involves factors 

influencing both planting and removals, more sirnilar to the areachange emphasis in our study. Perennial crop studies have estimated 

functions that relate the planting and removal to measures of past 
pr~fitabiliy~ potential future production from existing acreage, and 

structural changes associated ~th market intervention program (e.g., 

French and others 1985). Researchers inrrestigatirag changes in aggregate forest area are not likely to ha= aaess to acseage data with the 

richness of detail (e,g,, annual time series) available for many annul 

agricultural crops and perennial crops, such as peaches, but results 

from such studies regarding the importance of government programs 

provide useful insights when investigating forest area changes. 

Conceptual Framework 

Eeoxnomic theory postulates that land will be devoted to the use 

 bat yields the greatest returns to tfae Land resource. Returns to land 

are often expressed in terms of land rent -the portion of total returns 

that accrues rs land after payment of total msts (Barlowe 1978). To 

determine the economically efficient allocation of land, demand Euncelms for land as an input must be derived. Consider the case in which 

agricuftilral products such as crops, Lkestock, and loresrry products are 

prcsiiuced by independent technologies through the use of varible inputs and the fixed. allocable input of land. This production reiaiioaship for a particular amd downer can be represented as: 

al -I- a2 ... + am 5 2 

where 

qi = quantity of ith commodiey, 

XI = the vector of variable input quantiries, x~,~, used 

to produce the i6h csmmodi~, 

a, = the quantity 0% land allocated ao the itl-9 commodie)', and 

5 = the total land area. 

Profit from multiple uses, m, of a he$ quantity of land is 

described by r he Lagrangian primal: 

where 

pl = the product prie for the iah land-based productg 

9 --- the purchase price of the jrh input; j = 1,3, ...a, and 

xfj = quantity of the jth variable input used in production 

of the irh esmmodi~);, 
Assuming regularity conditions on Equation (3), optimum values 

for xil, a,, and h can be obtained from the first-order conditions. These 

solutions are a function of all product prices, variable input prices, and 

total quantity of available land (Shumway and others 1984). Optimum 

use of variable inputs is beyond the scope of this paper. Attention in 

this paper is focused on efficient allocation of land as determined by 

the first-order conditions from Equation (3): 

aj =I hJ (P, R, 8) (4) 

where 

P = a vector of product prices and 

R = a vector of input prices, 

Since cash-flows for alternatives are often uneven, discounting is 

needed to account for the time value of money. Using the soil expectation approach, the expected net cash-flows for a particular land use are 

discounted and compared with any discounted cost outlays. 

Model Specification 

The basis for the structural model is derived from economic 

theory and Equation (4). The dependent variables are obtained by 

forming a ratio of acreage in each category to total acreage 2. The 

proportion of total acreage devoted to a particular land use is affected 

by variables representing expected land rent from all potential land 

uses: 

al/B= fl(P, R) 

a 2 / B = f2(P, R) 

where m = number of land uses. 

Four rnajor uses of the land were considered in this study: crop 

agriculture, pasture agriculture, urban and developed uses, and forest. 

The private forest categoq was subdivided into three major ownership 

categories - farm forest* forest industry, and miscellaneous private -because of the distinctly different behaviors, and resultant differences in 

forest area trends (fig. 1)- of these owner groups. Public land was included in a categor.) of urban and other land because ii comprises less 

than 10 percent of the land in the South and because public forest land 

seldom shifts to other categories. Therefore, six (rn = 6) land 

useiownership categories were included. 
Total Psrbale 

Year 

Figure I. -- Area trends far private timberland in the South-Central 

region, 4952-1987 (USDA Forest Service 1988). 

Because the land base is fixed, and we are interested in how it is 

proportioned among competing rases, we selected the proportion 

devoted to a given use as the dependent variable, The share of the land 

base in a particular land use should be positively correlated with the 

ratio of expeceed real net income from than Band use (serving as a proq 

for land rent) to expected real riel incgme from a8ternative uses. This 

formulation suggests that landowners are concerned with relative expected laad-based incomes ic realloeating acreage among competing 

uses and would not respond 1.0 a proportional change in all! expected 

land use incomes. Thus, the response function is homogeneous of degree zero rn use ~ntcornes, 

-4 crucial step in the specification of regisnaj land use models is 

securing independent \ariables that can be empiricaily evaluated. Because adequate series of historical land rent data (or Band values for alternailire uses) are not avaiiiable, proxies were used such as reai net 

income for land enterprises or other measures, such as popuBatZon, 

which are thought to significant%> influence land rent, 

Given the complex set of interdependent linkages among major 

land uses indicated by the system of equations in (5), six categories of 

candidate explanatory variables were considered: (1) rural and urban 

population; (2) per-capita personal income; (3) relative land-based 
Incomes from alternative enterprises; (3) risk measures; (5) income 

from processing of timber; and (6) go~ernment programs invoiving subsidies for tree planting, 

Direct measures of land income are nor available for urban and related developed uscs. Therefore, urban and rlarat populations and percapita income were ased as proxies ta the land use equations. Because 

developed land uses command the top of the economic hierarchy of 

land use, development pressures from increases in populations and incomes cause direct and indirect conversion of forest land. Clearing of 

forest for agriculture to replace agricultural acres hat are deveioped 

would be an indirect ~onsdersion. Figure 3 illustrates the marked increases in population and personal income over ttmc last several 

decades. 

Time differences in production cycles between competing land 

uses complicate the analysis sf factors that prompt shifts in iand use, 

Major agricultural crops in the South, such as soy'~eans or corn, have 

production cycles equai to one growing season. In contrast, culture of 

a timber crop Gram seedlilzgs requires at. least several decades, The impiicaeir>ns arc that income expectations of the Landrrwner for various 

land enterprises are quite different. Given the time differentials in 

Population in Millions Income in Thousands of 1980 % 

m 10 

Figure 2, --- Changes in population and per-capita personal income in 

the South-Central region, 1950-1980 (CSDC, Bureau of the Census 

1984). 
production cycles, agriculture would likely be favored when the discount rate increases because of the relative reduction in the future 

value of timber products. A variable to reflect changes in real interest 

raies was tested, based on long-term inkerest sates, 

Because expectations of landowners in aggregate cannot be obsenfed, a major concern is specification of expectations and their 

dynamic nature. Based on the Herlove (1958) model, partial-adjustrneni dynamic models in agriculture have attributed lags to technological and psychological inertia. Future expectations of land incomes 

formulated in the present study were conditioned by incomes obtained 

in the recent past, FOP. exampie, a Ibgged 2-year nnaving average was 

used for timber income. 

Little empirical research exists ro guide tbe modeling sf landowner perceptions of risk differentiais for alternative land enterprises. 

One measure of the risk associated with land use is the deviation sf actual returns from expected returns. Behrman (1968) specified a risk 

variable as a moving standard deviaiion of past prices. dVe used a 

similar approach by measuring risk on the basis of a moving standard 

deviation of returns per acre fc~selected land uses. 

Property tax rates have often keen hypothesized as contributing 

to the conversion of farmland act nonagricu6tura'a uses (Conkiin and 

tesher 1977) and of Coresr land to more intensive uses (Stier and 

Chaag 1983). In adchiinon, income taxes may impash differential irnpacts across land uses (Durst 1987; Murray 1987). Studies of the effects ol rmtislra and differential assessment on land use have yielded 

mked results. Stoll and others 11984) conclude that differential assessment for tax purposeGn the South generally does not appear to haare 

forestalled the conversion of eligible land eo gtoaeligible uses. Further, 

differential irssessment practices vary markedly among Soutbere States. 

Although the impacts of taxes on land uses have long been 

debated, empirical siudie have not offered concBuslve evidence of differential impaecs across land uses. Tm-induced reductions in aaer income stredms for a land use are compounded by grogere tax 

infiuenees, brat the net effect in a region is no! known. 

The effects of iax code changes embodied in the 19886 Tax Weform 

Aer arc roo recent to afiorki empir~caf sesling of associated impaces on 

land use shifrs. Lofgrere and others' 6 1986) study provides theoretical 

economic Implicaaions for changes in fc>rest management intensity 

resulllag from tax code changes. Lofpren and orhers ( 1986) used a 

utibity mmimizaiion approach to analyze the effects of unequal t~ug 

of Income ion a iandowiier"q allocation of tima: among forestry, agricultural, and other acbikfihies A relative increase in taatioa for forestryrelated income reduces optimal lebeis of timber managemeni activltt 

and long-term supply of roundwood from forest farmers. By varying 
assurnption~or income rargets and input substirurability, however, 

Lofgren and others (1986) derive a quite differen! resulr. Ef the landowner must produce a certain amount of ancome to saris@ some 

specific need, such as college tuition for an offspring, and labor 

devoted cs nonlimber activity cannot be varied, they derive a bachardbending supply curve for forest managemen1 activity. As the relative 

ea on forestry activity rises and rkze opportunity to shift labor co nontimber activity is heavily constrained, fc~restry ae~iviq must be increased to meet the IradiviQuaB's target income. 

Area change %-or C~rest industry ownership is byputbesized to eorrelate with changes in capacity and expzceed returns from timber 

processing, relared to costs of timber conversion, prc~dareti\~iry trends, 

and demand for timber produces. Concern for the availability of raw 

materials influences tactical and strategic planning, because industq 

draws heavily on noncaprive sources of rimher. However, empirical investigation of such behavior is hampered by unavaiiabilisy of a cornpieke set of aggregate data. 

Dummy variables were included 10 test lor sigaificanr differences 

in %and use among States in each physiographic region. Arkansas was 

singled oue in the Craasral Plain region because of its northernmost 

Cropbnd Harvest 

Year 

Figure 3. -Area trends for seiected m;lg:,r uses cf the land in the SouthCentral region, 4950-6980 [Fret and Hexcrn 1984: LtSDA Foresf Service $988)- 
locar ion, Likewise, Tennessee was targeted because of its location at 

the northern edge of the Interior Highlands. Examination of the importance of certain attributes of land, such as location with respecr to 

markets, geographic features, and other resources, was not feasible in 

this regional study. 

Area in forest, sometimes viewed as a residual use, can be augmented by human factors, naturai forces, or a combination of the two. 

For example, in the early twentieth century agricultural income per 

acre declined, crop fields were left unplanted, and many seeded 

naturally to lobloliy pine. Trends in areas of cropland haniested and 

timberland have tended to move in opposite directions (fig. 3). Passive 

reversion of agricultural land to forest represents decisions by landowners in many cases to abandon crop fields because expected net 

returns were negative. This decision process has been complicated in 

farer years by the availability of government programs that have supported agricultural incomes. 

Government programs for agriculture have often focused on 

reducing crop acreage in order ro reduce surpluses. One would therefore expect payments in pt-i~graols for diverting land from agricultural 

production to be intiersel~, relaaed to crop acreage and ao be directly related to targeted uses, such as forest. 

Data and Variable it%easuremernts 

The seven Sr~uthesn S~aics chosen for model building contain apprc~ximately 180 million acres. About 85 percent of this area is in the 

coastal Pldin and Interior Highlands physnographic regions. The 

remainder is in the Lower h4ississippi Alluvial Plain, often referred to 

as the "Delia." The Delta was omitted from our analysis because data 

on land use there were 100 limited. 

According to periodic surveys by the regional Forest Inventory 

aad Analysis (F6) unit of ahe USDA Forest Service (Akig and others 

1986), net changes in the areas of major land uses occur s10~l.r at the 

regional level. Forests cover about 58 percent of the land in the SouthCentral region; a large majori~ ofthis forest arises from natural sources, Crops are the next largest use, occupyrag approxirnaiely 15 

percent of the land, Paslure occupies abolse 13 percent, and urban and 

other uses occupy 14 percent. FIA sumegis each State on a staggered 8- 

to 10-year cycle. The 7 States contain 35 suraiey units or sampling 

units, which are ZPTU~~~GOUFIZ~ aggregations of from 3 million to 13 million acres. These surveys, in conjunction with periodic Census of 

Agricuiture surkreys by the U.S. Deparemenr 06 Commerce, Bureau of 

the Census, provided estimates of areas in the major land uses ai either 

mo or three dates from 3950 trs 1985. Acreage estimates were transformed into percentages of a survey unit's total laad area occupied by 

the six land uses!ownershtps in a pariicular year. 
For isolation of critical pairs of competing land uses, quotients of 

land incomes for alternative enterprises were used, based on ratios of 

output prices to input prices. For example, a ratio was formed from 

the index of prices received for so$eam and other crops and the index 

of prices paid for factors of production. Crop and livestock prices 

were derived from several sources, inclding annuai issues by the U.S. 

Department of Agriculture on agricultural statistics and agricuttural 

prices for each State. Timber income was computed with a ?-year 

mo.r+ng average of weighted prices for southern pine sawtimber and 

pulpwood stumpage (Ultich 1983). 

Per-capita incomes and populations were obtained from the Census of Population (for example, U.S. Department of Commerce, 

Bureau of the Census 1984). Information on government programs 

was obtained from annual reports by State from the U.S. Department 

of Agriculture, Agricultural Stabilization and Consewarioo Sewice. 

This information included government payments to farmers to establish tree cotier under long-term programs such as the Soil Bank 

Program. 

Estimation Procedure 

Seemingly unrefated regression equations (SURE) were estimated to maximize efficiency gains in estimation, in view of the likely 

correlation of error terms across land use equations. Correlation 

among the error terms is inevitable because of the fixed total- land 

base, frequent transfers of land among usesiownerships, and related 

patterns in the economic determinants. 

Based oe the structural model in Equation (9, the set of Equations in (6) can be viewed as reduced form equations in a system of 

demand and supply for land: 

ill = f(P,R)? i=1, ..., 6 (6) 

Relative prices of land, for which a complete set of data is not available, would not necessarily be a part of the vectors of variables P or W, 

bur only those more fundamental variables that cause shifts in the 

demand and supply equations for land. 

The fixed nature of the Land base was reflected in estimation by irnposing a series of restrictions. Because the size of each survey unit is 

essentially Exed, changes among the dependent variables are interdependent, An increase in the share of oae use necessarily reduces the 

area of one or more other uses, Restricted SURE estimation satisfies 

the fied land-base constraint that the sum of the coefficients of any explanatory variable across all Iand use equations is identically equal to 

zero, and that rhl; intercept terms sum to one (Alig 1986). 
hlufrit~ariate logii is an alternative to restricted SURE estimation. 

Both methods assure that predictions sum to desired totals, bur the 

reslricled SURE approach provides results that are easier to interpret. 

In it, eacb Land use is represented in estimation, whereas in the multivariate approach, one equation b excluded and the estimated equations 

are in relative terms, Regression coefficients fur exogenous variables 

in the multivariate regression approach indicate bovri each variable affects a Band use relative no the Iaad use excluded from estimation. Ths 

multivariate lugir approach also requires use of the same set of isdependent variables in each equation, which preciudes specifying the link 

of vsriabks lsdiciduaiily for each 'land use, 

Logarithmic transformaiiions of the independent variables provide 

a cc)ncar;.c functional form, posited for the relarionship between the 

fraction of land in a. particular use and expected [and incomes, The 

eoncape functional form implies that as a greater percentage of the 

toss1 land area is comcesatrared in a particuiar use, land rent differenss 

betmeen uses should tend 10 equalize. 

The sens of equations for the two physiographic regions were also 

estimated hy using first differences of the independent and dependent 

variables, The first difference for a particular variable is equal eo the 

difference in its value between successit~e paints af observation, t-(t-I). 

This alternative esilmatlon %as undertaken to test the sensititiity of 

results to aliernative specifications, 

Results 

The SURE econometric results are presented in tables 1 and 2 by 

physiographic region. The first-difference results are given in tables 3 

and 4. The following discussion refers ao the first set of results, unless 

otherwise noted. Results for the PNO estimation sets are slmiiar in 

genera!. 

As in a nationwide study by Alig and Healy (1987), hypothesized 

signs for the significant coefficients for the income and population variahles were obtained in most cases, For emmple. the significant coefficients for urban popia8atiora ass positive in she usbadother equation 

and negative in the forest ownership equalions for the Interior HighHands, However, specific net effecbs of urban and rural populafion 

changes on the distribution of foresf ownerships warrant further examination ins future studies, particuhaxly for the misceltanaeslas private 

class, 
Table 1.- tim results for the Plain regid 
Table 2. xxsul.~ fmthe otf 

l?arm Forest 

farest industry 

- - 

em = 0e5-4 

le size, p1 == 33 
le size, n = 28 
ems for Lhe 

le size, n .= 22 
Hypotheses about relative income variables were rejected. For example, we expected the coefficient f~r theratlo of timber-to-crop income to be negative in the crop equarion when there was net movement 

of cropland to timberland. In fact, when this coefficient was significant, it was positise in the Interior Highlands region, contrary to 

the hypothesized relationship, Results for other measures related to 

land-based incomes were similar. Variables representing inndowner 

risk, using moving averages of standard deviations of land income 

returns, and interest rates were not significant in preliminary testing. 

Several factors make modeling in~zrdependencies among rural 

land use classes difficult, First, impacts of government cornmodit! 

programs are irregular and difficult to track. Second, pasture may act 

as a buffer between cropland and forest conversions. Although a substantial amount of cropland is grazed, this acreage is not reflected in 

available regional land use data. lo addition, part of the difficult) in 

constructing appropriarc proxies of expected land rent is representing 

the formulation of income expectastons of landowners, 

The influence of government programs on land use shifts in the 

South is reflected by the staristical results. Results ssggesr that governmenr conservation programs have caused agricuirurai land to bc 

diverted to forest land (primarily pine plantation) on farms in both 

physiographic region&. 

Rcsulrs biz- thc firs[-diffcrenccs cquaiiuns drc himijar 10 tho? of 

the: equatlctrrs with the untrawsformed variables. The weighted R'- 

tlalues are silghtlv higher for the systems of firsr-differences equations. 

Most ctf the coeffieiiints on the land use inccime varlabdes had the expected signs, bur were generally not. statistically signifisaa~i. Several of 

the associated coefficaents for orher independent variables are insigaificani or have wrong signs, similar to the other sets of equations. 

ln general, results for the hypothesized land use: relationships 

were mhed. Results for the urban and developed rase classes, wbiek 

are aa the lop of the economic hierarcb. appear to be the most erirnsisaenr with the hypotheses, Results are less congruous for hbe less intensive iand uses, The results support the hypothesis that changes in rural 

iand uses are driven largely by demand forces outside the agricultural 

and forestry sectors, consisiizai wlih the economic hierarchy for land 

uses. 

Results fur the pasrure,'range equarions appear to bi: the Heas: cimsistena with the hypothesized relarkonships, ln large par[, "8listoricaj 

dara for this Band use class reflect a residual land ciassification abar may 

not be IPU'BP;" reflective of use. A contributing factor may be cycles in 

beef production, where inelastic demand for beef in conjuacrion with 
supply shifts (due to land use skftsg would cause price to fail, which 

could then cause income to fall. Additional cornpfications include the 

use of feed grains in livestock production and use of croplands and 

forest for grazing when expected livestock incomes rise. 

Our results generally are consistent u~ith those of related studies, 

Lack of significance for proxies representing relative land rent, 

product prices, or related explanatoq variables is consistent witb Atig's 

(1986) findings for the Coastal Plain and Piedmont physiograpbie 

regions in the Southeastern United States, Parks' findings for North 

Ctrrolina,%ihite and Fleming's (1380) for Georgia, and Carlen and 

Lofgren3s (1988) for Sweden. Statistically significant effects of urban 

population and personal income on land use are likewise consistent 

with ALig's (1986) findings and with the national-level equations estimated by AIig and Healy (1987). The results art: also consistenr with 

other studies (suck as Pope 1985) that indicate "consumptive demand" 

for rural land is a major determinant of land values and therefore of 

land use shifts. 

The failure to detect effects of expecfed relative incomes from 

agriculture and forestry enterprises on use shifts suggests a need for additional research. Notable differences berween forest and cropland 

enterprises are the heterogeneous nature of the land management 

goals of forest owners and the timing of land use incomes. Numerous 

studies of forest ~wners have attempted to isolate characeerisiics that 

correlate highly witb investments in forestry. In general, nonindusarial 

owners of forest demonstrate highly inelastic supply responses.' 

Forests often are passiveiy managed, major management decisions 

(e.g., harvest) may be prompted by a change in ownership, and forest 

incomes tend to be lumpy and periodic. In addition, management 

actions such as har\eiring of trees ma) prcldisposc: stme forested trecis 

to Land use ct,tlver\ritn. 

'~draa, Pcieil 1986 The tnfiurnce of economic and drmugidphic fdciorr on 

torest idnd use derisrans Berkeiet. CA Cntver~ri). of daltfornra 8~ pp. Cnpubirshea 1% D dlssertar~cn 

7 

"Robe., Jdcb 198' Lse and efrecrs of the reforestallon ia.k llncentlves and cost shar- 
Recent Changes in Land Use Outlook 

Analysis of the prospective reallocation of \and among uses in the 

South requires consideration of recent governmental actions that could 

not be malyzed in the regresstoe phase of our study. The 1985 Farm 

act, with its Conservation Reserve provisioas, has considerable implicarions for forestry, Moulton. and Dicks (1987) state that the Conservation Reserve Program is expected ro become the largest tree planting 

program in the nation's hlaistoql. The Tax Reform Act of 1986 (TRA) 

has relevant impacts sa financial returns to crop, Iives~ock, aetd timber 

ina~estmenrts, \Ve will focus on the decisions of 'other private" landowners, all forest owners except public agencies and companies with 

wood-processing facilities. This class of owner controls about twotbirds of the forest in the South-Central region (USDA Forest Service 

1988). 

The Conservation Reserve Program (CRP) of the 1985 Farm Act 

could stimulate planting of more than 3 million acres of erodible 

cropland to pine trees over the next decade. That area would equal approximarely three-fifths of the exlstiag nonindustrial area in southern 

pine ~Lantations, Timber products from these plantations will. not 

begin to be marketed until after the turn of the century, but at that 

time impacts on prices far stumpage and wood products could be significant (USDA Foresi Senficc 1988). Currently, the full extent of 

Landowner participation In the CRP is in doubt, but clur regressjon 

results suggesuhar farmers are receptive to such programs, gibten the 

attraciite subsidy rages (up io 50 percent of plantarion establishmt;nt 

costs). 

With respect to the TRA, Dursr's (1987) results indicate rhar 

cropland invesrmen~s arc improved somewhat by the tax reiiisic)ns and 

[has Ib.~estuck (hug) returns are virtually unaffected. After-%a profit 

for the hypothetical cropland investment appears to have improved by 

appro>;lmarely 5 percent after implementation of the new lau. The livestock investment has decreased in ~lalue by about 4 pereenr. These seialively small changes In profirability are not likely to Irave large effects 

on use of agricultural land. Consequently, we focus on the pv~ential irnpact of the ticx changes on timber investments. 

Murra:, (1987) anal! ~eb the effects on individual rmpayzrs of tin-nber-related provisictns in &he 3986 Tax Reform iZc1 for d hypothetical 

iravestmenr in loblo18y pine reforestation in the South, Murray found 

an average decrease in Einanciai returns of 33 percenl under thc ncu 

law due ro: (I) el~mlnaelon of ~$e capital pains iax ~XCIUSBO~ for timber 

sale income, 12) reduced ~alue of annual expensc deducrions resulting 

from the liwering of tax rates on ordinar\ income, (31 timber sdlc 

resenue king taxed at a higher marginal rate than most ordinary income, and (43 elimination of income a.4ieragm"n reporting sporadic 

income. 
Agriculture a 

band 

Rent 

($1 

Decreasing Uses Capecity-------.-.----- 3 

Figure 4. -Comparative land t-enis and rke transfer of land bemeen 

timber and agriculture as a result of tax code changes. 

The margin of translrrence between timber and agriculture prior 

to TRA is represented by point W in figure 4 for assumption a (agriculture outcompetes timber on higher qualiq sites) and poior Z for assumption h (timber outcompetes agriculture on higher quality sires). 

Timber returns are siabstanrlally recduceb by the Tax Reform Act, wbile 

agriculture returns remain virtually unchanged. The effect on timber 

returns is reElecred by the inward shift of the rent curve- AS a result, 

the competiti\~r status of timber investments worsens. The transfer of 

land from timber to agricultural uses is (L2- El) under agriculture assumption a or iL5 - Lj) under assumption b. The inward shift of the 

timber rent triangle (fig. 4) forces the extensive margin from point Lb 

to h, resulting in a timberland loss of (L - h). 
Murray's (1987) analysis indicates that the extensive margin shifts 

inward and that ehe land rerat triangle sdaifr is biased percentagewise 

toward the extensive margin (fig. 4). Reforestarion larestmealis near 

the exteasirit: margin receive a relatively 'idrger percentage reduction in 

expsceed financial returns due a@ the TRA than $0 investments weft 

within the: masgin, %!usray concludes riaas marginal invesemenrs are 

taxed relatively higher than high-return plantainon invesrmenrs, further 

discouraging limber production from these lands. 

The Iikeiy net impact of the GRP and TRA on changes in forest 

area ie the South is difficult to predict at this time. Guidelines and procedures for these recent institutional changes have not been full? 

developed, and landowner responses to aktse complex and dynamic institutional deveiopments are uncertain. Over the next few decades, we 

expect the 1985 Farm Acr to have greater impact on land use than the 

TU- We base chis expectation on r? direct causal link between the 

CRP and esnverslrrn to timber, as compared with an ill-defined link between prospective changes in after-tax timber income and forest conversion. Subsiansiaf forest acreage will continue to be converted rs 

urban and developed uses (AEig and Healy llSb7), and the futurc course 

of agriculture will have an imporlant, but uncertain, impact on forest 

area changes. 

Because the amdpaanls of land devoted to various uses are interdependent, forest area change is notably influenced by macroeconomi~s and demography. consistent with the economic hierarchy of 

land use. The area in urban use grows with population and affluence. 

Changes in personal income also appear as bave altered patterns sf 

forest ownership. Muck farm forest has transferred to other private 

mcsnirsdustsial owners, in part because of the demand for landownership 

fueled by personal income increases. 

Impacts of rechnulogicaIt change on; land-based incomes and land 

use need further stud], but some effects are obvious. lmprovernelnts in 

the per-acre prsducti~n of crops and declining exports in recent years 

have lessened the area of land needed for food produerirpsl, As a result, 

real prices of agriculrural crops have declined, dampening incentives 

for planting crops. Similar situations in thr; past bave Ied io conversion 

of cropland 161: forest in the South, and we believe rhal the same thing 

will happen again, The CRP of ehrlt 1985 Farm Bill is IikcIy to speed 

that process, 
The impacts of the 1986 Tax Reform Act on future land reallocation are much more difficult to predict. It is important not only to 

analyze direct effects of tau changes on timber investments but also to 

evaluate impacts on other uses in direct competition with timberland 

on the land base. Murray's (1987) and Durst's (1987) findings 

separately suggest that recent changes in income tax laws adversely affect the returns to timber investments relative to investments in other 

land-based activities. Evaluating tax impacts on land use shifts is complicated by the absence of definitive empirical tests of land use theory, 

One major gap is the lack of empirical findings supporting the 

hypothesis that differences in expected land incomes guide landowner 

behavior . 

Owners compare land use alternatives on highly subjective scales. 

Theoretically, this subjective valuation results in choice of the land use 

with the highest ut ility for each landowner. In part because of data 

limitations, analysis have had great difficulties in empirically based 

studies in incorporating the influences of nonmarket values in land use 

decisions. Conser~~ation, protection, and preservation are highly valued 

by some owners. The lack of statistical significance of market-based 

variables in our study supports the need for indepth investigation of 

nonmarket influences in regard to landowner behavior. 

Efficient allocation of land among competing uses requires 

dynamic adjustments that do not take place as smoothly in the real 

world as they do in some models. There often are lags in adjustment 

and imperfections in land and capital markets. Mobility of resources 

in shifting among land use enterprises is hindered by rigidities related 

to investments in farm machinery and preferences for an agrarian lifestyle. Finally, landowners often may not have sufficient information 

readily available with which to fully understand the comparative advantages of competing land uses. 

We thank Richard A. Birdsey and William H. McWilliams, 

USDA Forest Service, Southern Forest Experiment Station, Starhille, 

MS, for their assistance in providing timberland area data for this 

study. We also wish to acknowledge the assistance of Timotby D. 

Marly and William G. Hobenstein, USDA Forest Service, 

Southeastern Forest Experiment Station, Research Triangle Park, NC, 

in helping to assemble other data series. 
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103. 
I 

I Alig, Ralph J.; White, Fred C.; Murray, Brian C. 

I 

I Economic factors influencing land use changes in the South1 Central United States. Res, Pap. SE-272. Asheville, NC: U.S. 

I Department of Agriculture, Forest Service, Southeastern Forest 

I 

I Experiment Station. 1988.23 pp. 

Econometric models of land use change were estimated for two 

physiographic regions in the Soutb-Central United States. Results are 

consistent with the economic hierarchy of land use, with population 

and personal income being significant explanatoq variables. Findings 

regarding the importance of relative agricultural and forestry marketbased incomes in influencing regional land use shifts suggest that farm 

programs play an important, but concinualiy changing, role in land use 

change. 

I Keywords: Econometric analysis, acreage, forest land, 

I --------.------------------------ 

I 

I Alig, Ralph J.; White, Fred C.; Murray, Brian C. 

I 

I Economic factors influencing land use changes in the SouthI Central United States. Res, Pap. SE-272, Asheville, NC: U.S. 

I Department of Agriculture, Forest Service, f outheastern Forest 

I Experiment Station. 1988.23 pp. 

i 

Econometric models of land use change were estimated for two 

physiographic regions in the South-Central United States, Results are 

consistent with rhe economic bierarcby of land use, with population 

and persooal income being significant explanatory variables. Findings 

regarding the importance of relative agricultural and forestry marketbased incomes in influencing regional land use shifts suggesr that farm 

programs play an important, but continually changing, role in land use 

change. I 

1 Kcyords: Econometric analysis, acreage, forest land.