The Von Thuenen Paradigm, the Industrial‐Urban Hypothesis, and the Spatial Structure of Agriculture

“…This body of work is built on the premise that land generally is employed in its highest-value use, that these values are influenced by various features of the land, and, therefore, that patterns of land use should be organized by these land features. The Von Thiinen model of rural land use [for example, see Samuelson (1983)] and various models of urban development around central places [see Katzman (1974), for example] are built upon the premise that the location of land defines its value for various uses. One track of research has focused on the formation of land prices and rents.…”

Section: Introductionmentioning

confidence: 99%

Orginal Article: Land-Use Changes in Southern Appalachian Landscapes: Spatial Analysis and Forecast Evaluation

1998

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Understanding human disturbance regimes is crucial for developing effective conservation and ecosystem management plans and for targeting ecological research to areas that define scarce ecosystem services. We evaluate and develop a forecasting model for land-use change in the Southern Appalachians. We extend previous efforts by (a) addressing the spatial diffusion of human populations, approximated by building density, (b) examining a long time period (40 years, which is epochal in economic terms), and (c) explicitly testing the forecasting power of the models. The resulting model, defined by linking a negative binomial regression model of building density with a logit model of land cover, was fit using spatially referenced data from four study sites in the Southern Appalachians. All fitted equations were significant, and coefficient estimates indicated that topographic features as well as location significantly shape population diffusion and land use across these landscapes. This is especially evident in the study sites that have experienced development pressure over the last 40 years. Model estimates also indicate significant spatial autocorrelation in land-use observations. Forecast performance of the models was evaluated by using a separate validation data set for each study area.Depending on the land-use classification scheme, the models correctly predicted between 68% and 89% of observed land uses. Tests based on information theory reject the hypothesis that the models have no explanatory power, and measures of entropy and information gain indicate that the estimated models explain between 47% and 66% of uncertainty regarding land-use classification. Overall, these results indicate that modeling land-cover change alone may not be useful over the long run, because changing land cover reflects the outcomes of more than one human process (for example, agricultural decline and population growth). Here, additional information was gained by addressing the spatial spread of human populations. Furthermore, coarse-scale measures of the human drivers of landscape change (for example, population growth measured at the county level) appear to be poor predictors of changes realized at finer scales. Simulations demonstrate how this type of approach might be used to target scarce resources for conservation and research efforts into ecosystem effects.

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“…Additionally, (5) population density in the neighborhood of a site should influence its comparative advantage in different uses. These variables define either the quality of the site (slope, elevation, population density) or the location of the site within a physical/human landscape (access distance and distance to market), and should therefore influence land rents and land uses (Katzman 1974). As part of the second question, we examine whether or not these explanatory variables influence land-cover change within the study areas by developing statistical models and testing three hypotheses:…”

Section: Introductionmentioning

confidence: 99%

Land Ownership and Land‐Cover Change in the Southern Appalachian Highlands and the Olympic Peninsula

Turner¹,

Wear²,

Flamm³

1996

Ecological Applications

279

148

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Social and economic considerations are among the most important drivers of landscape change, yet few studies have addressed economic and environmental influences on landscape structure, and how land ownership may affect landscape dynamics. Watersheds in the Olympic Peninsula, Washington, and the southern Appalachian highlands of western North Carolina were studied to address two questions: (1) Does landscape pattern vary among federal, state, and private lands? (2) Do land‐cover changes differ among owners, and if so, what variables explain the propensity of land to undergo change on federal, state, and private lands? Landscape changes were studied between 1975 and 1991 by using spatial databases and a time series of remotely sensed imagery. Differences in landscape pattern were observed between the two study regions and between different categories of land ownership. The proportion of the landscape in forest cover was greatest in the southern Appalachians for both U.S. National Forest and private lands, compared to any land‐ownership category on the Olympic Peninsula. Greater variability in landscape structure through time and between ownership categories was observed on the Olympic Peninsula. On the Olympic Peninsula, landscape patterns did not differ substantially between commercial forest and state Department of Natural Resources lands, both of which are managed for timber, but differed between U.S. National Forest and noncommercial private land ownerships. In both regions, private lands contained less forest cover but a greater number of small forest patches than did public lands. Analyses of land‐cover change based on multinomial logit models revealed differences in land‐cover transitions through time, between ownerships, and between the two study regions. Differences in land‐cover transitions between time intervals suggested that additional factors (e.g., changes in wood products or agricultural prices, or changes in laws or policies) cause individuals or institutions to change land management. The importance of independent variables (slope, elevation, distance to roads and markets, and population density) in explaining land‐cover change varied between ownerships. This methodology for analyzing land‐cover dynamics across land units that encompass multiple owner types should be widely applicable to other landscapes.

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The Von Thuenen Paradigm, the Industrial‐Urban Hypothesis, and the Spatial Structure of Agriculture

Cited by 50 publications

References 22 publications

Spatial targeting of agri-environmental policy and urban development

Spatial targeting of agri-environmental policy and urban development

Orginal Article: Land-Use Changes in Southern Appalachian Landscapes: Spatial Analysis and Forecast Evaluation

Land Ownership and Land‐Cover Change in the Southern Appalachian Highlands and the Olympic Peninsula

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