A Hedonic Study of the Effects of Erosion Control and Drainage on Farmland Values

Palmquist, Raymond B.; Danielson, Leon E.

doi:10.2307/1241774

Cited by 149 publications

(78 citation statements)

References 9 publications

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“…One track of research has focused on the formation of land prices and rents. Theoretical studies [for example, see Capozza and Helsley (1989)] and empirical studies [for instance, see Palmquist and Danielson (1989) and Geoghehan and Bockstael 19971 have shown how land prices are affected by location and other factors. Another track of research has focused on estimating land-use shares as functions of land prices or rents [see Alig (1986) and Hardie and Parks (1997), for example].…”

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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Section: Introductionmentioning

confidence: 99%

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

1998

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“…In addition, the loss of productivity is not due solely to the erosion process. Calculating the costs based on the drop in land values would entail using an ample and consistent historical land price data set, rendering this method impracticable (Ervin and Mill, 1985;Fletcher, 1985;Hertzler et al, 1985;Palmquist and Danielson, 1989). Hertzler et al (1985) conducted a study on the cost of soil use, based on a generalized Leontief function, split into two parts: nutrient losses and soil physical degradation.…”

Section: Soil Erosion Cost Valuation Methodsmentioning

confidence: 99%

“…This happens because the price of the product is increased by the amounts spent on repairing the damage that soil erosion causes on the farming Bennett (1933), Baver (1951), Marques et al (1961), Larson et al (1983), Clark (1985), Ervin and Mill (1985), Gardner and Barrows (1985), Fletcher (1985), Hertzler et al (1985), Crosson (1985Crosson ( , 2007, Crowder (1987), Moore and McCarl (1987), Foster et al (1987), Holmes (1988), Ribaudo et al (1989), Palmquist and Danielson (1989), , Robertson and Colletti (1994), Pimentel et al (1995), Alfsen et al (1996), Tenberg et al (1998), Pimentel and Kounang (1998), Marques (1998), Uri (1999Uri ( , 2000Uri ( , 2001, Gunatilake and Vieth (2000), Pretty et al (2000), Bandara et al (2001) land (represented by the shaded area in Figure 1). This off-site impact shifts C to C', generating a shift along curve D from point A to point B, so that the quantity produced drops from Q 1 to Q 2 , and the price increases from P 1 to P 2 (Figure 1).…”

Section: Losses and Costs Incurred By Soil Erosionmentioning

confidence: 99%

Valuation and assessment of soil erosion costs

Telles

Dechen

Souza³

et al. 2013

Sci. agric. (Piracicaba, Braz.)

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Soil is an essential natural resource for humans and an important part of the environment. However, soil is often used and managed inappropriately, causing its erosion and degradation, with concomitantly negative social, political and economic impacts. This study aimed to discuss sustainable development; and losses and problems caused by soil erosion, and to suggest a model for assessing erosion costs. The relevance of economic models for costing soil erosion is stressed. Based on an economic theory, it presents a procedure for assessing economic costs of soil erosion, centered on the on-site and off-site costs that are generated. The physical processes of soil erosion are described and their economic effects reviewed, drawing on theoretical and empirical sources. Limited data and information is available on the economic losses resulting from erosion, which hampers assessment and valuation.

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“…Turning to the HPM for agricultural lands, Palmquist (1989) and Palmquist and Danielson (1989) may be considered as the seminal papers, respectively, for rental values and for property values. One may also refer to Maddison (2000) for an application of the model to agricultural land.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The different articles focus either on farmland values (e.g. Palmquist and Danielson, 1989 ;Sklenicka et al, 2013) or rental values (e.g. Palmquist, 1989 ;Donoso and Vicente, 2001).…”

Section: Literature Reviewmentioning

confidence: 99%

Determinants of agricultural land values in Argentina

Choumert

Phélinas

2015

Ecological Economics

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Avertissement :Les commentaires et analyses développés n'engagent que leurs auteurs qui restent seuls responsables des erreurs et insuffisances. AbstractIn the context of the rapid development of the cultivation of genetically modified soybeans in Argentina, we conduct a hedonic analysis of agricultural land values. The main objective is to evaluate the impact of land tenure systems and agricultural practices on these values. Data on 338 parcels, located in the Pampas region, are analyzed. The tenure appears to be a particularly important variable. We find that plots rented either by physical persons or by companies are negatively valued in relation to plots owned. Results also highlight the importance, though not to a large degree, of a diversified cropping pattern compared to soybean monoculture. Soil quality, location of the plots, distance to markets, as well as to the nearest city, were also found to affect land values.

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A Hedonic Study of the Effects of Erosion Control and Drainage on Farmland Values

Cited by 149 publications

References 9 publications

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

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

Valuation and assessment of soil erosion costs

Determinants of agricultural land values in Argentina

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