Economic evaluation of agricultural land to assess land use changes
Introduction
The European landscape is largely dominated by agricultural land uses; in fact, more than 35% of all land in the EU has an agricultural use. Thus agricultural land uses have a central role in terms of the potential impacts of land uses on the sustainability of the wider European environment. An understanding of the spatial dynamics of agricultural land cover is therefore crucial, even more so because these land-use changes are highly interrelated with many economic, social, political and environmental processes. These processes vary through time and space to include a complex range of interactions between human factors and the environment. Land-use models can be used to capture the interactions between many factors that drive land-use changes, and can be used to predict future changes in the land-use patterns. For a review of various land-use model types, we refer to Briassoulis (2000), Veldkamp and Lambin (2001), Parker et al. (2003), and Verburg et al. (2004). Land-use models are increasingly used in ex-ante policy evaluation. For example, the LUISA (Land-Use Integrated Sustainability Assessment) modelling platform is an operational model that is repeatedly used by the European Commission for ex-ante policy evaluation (see Baranzelli et al., 2014, Lavalle et al., 2011).
Physical and political factors are well captured in land-use models, as demonstrated by various studies in the literature (see Hoyman, 2010, Te Linde et al., 2011). However, there have been few efforts to model the economic processes underneath land-use change. This is unfortunate especially because modelling such economic processes allows a deductive approach to land-use modelling, which is found to yield more accurate results (Overmars et al., 2007) and enables the straightforward evaluation of financial and fiscal policy instruments. Koomen et al. (2015) present an example of an approach to integrate economic theories of the land market into a land-use modelling framework. The economic theories mentioned here derive from the theoretical work of Alonso (1964) and others, who assume that there is a competition for a parcel of land where economic agents express their willingness to pay through bid-prices. In Koomen et al. (2015), statistical and utility-based approaches are undertaken for the spatial distribution of bid land prices, which are subsequently used to define local suitability values for all modelled land-use types. This approach implies change of perspective in land-use models: where many land-use models induce land-use dynamics from observed behaviour, Koomen et al. (2015) model land-use changes by deducing model dynamics from agent behaviour. Among few other studies that used such a deductive approach, Overmars et al. (2007) linked land-use changes to single sector processes (e.g. agriculture) and Ettema et al. (2007) focused specifically on residential development (we refer to Koomen et al., 2015 for a detailed review).
The purpose of this article is to analyse, quantify and integrate agricultural land production values in order to deduce land-use changes for European member states. The results are primarily used for the LUISA model, but may serve many additional purposes. The Net Present Value (NPV) method, which provides a basis for the valuation of agricultural land in a wide variety of economic valuation studies, is used to represent the economic values regarding the agricultural land-use transitions in EU-28. This integrated land-use modelling framework aims at combining the economic processes with the physical and political factors, instead of focusing only on specific forces in determining the land-use changes in urban and rural areas. The agricultural land values provided in this study can be integrated to the low-scale spatially distributed suitability maps regarding the modelled land use. Following Koomen et al. (2015), the idea here is to integrate the bid-price theoretical work of Alonso (1964) and others as a measure of local suitability to express the societal sectors willingness to buy or rent a piece of land in a particular location. Those bid prices are assumed to be the result of the net profits that a farmer may obtain from a piece of land with maximum yield and average costs. NPVs may vary spatially by local differences in the amount of crop yield that a land may provide.
The paper is structured as follows. The next section summarises the theoretical and empirical literature focusing on the used NPV approach. Section 3 discusses the inputs used for this article. It provides a review of the CAPRI model from which many inputs have been obtained and introduces the physical input costs, the labour costs, the revenues and the net cash flow processes. Section 4 summarises the main results of the NPV application and Section 5 offers the conclusions of the study. Finally, four annexes offer a more graphical and detailed information about the whole procedure.
Section snippets
Modelling agricultural land-use changes in an economic framework
The concept of economic rent has its foundations in the classical economic theories first developed by Ricardo (1817) and Von Thunen (1826). These theories point to economic rent regarded as a value in excess of real production. In other words, land rent at a specific location is equal to the annual net revenue the user receives at that location. The research on agricultural land values has expanded in the last century (Bean, 1938, Scofield, 1957, Johnson and Haigh, 1970, Pope and Goodwin,
Inputs
Fig. 1 shows the interaction and main sources of the overall inputs according to Eq. (1). Land production values are computed for seven agricultural land-use classes that are consistent in the LUISA model and in some cases include substantial heterogeneity. Most inputs, especially the capital cost, labour hours and the income indicators were determined from the CAPRI model for all modelled land uses except for energy crops. The CAPRI model will be discussed in the following section. Since CAPRI
Results of the NPV analysis
The balance of cash flows representing net total revenues over total production costs for each year starting from 2010 and ending in 2030 were utilised for the computation of NPV by applying the formula given in Eq. (1). The positive values obtained from the NPV analysis indicate a gain of agricultural income from agricultural land operations for the production of a specific agricultural output (cereals, root crops, maize etc.). By contrast, negative values represent losses regarding an
Conclusions
In the current work, agricultural land values in all EU-28 countries, having an extensive coverage in Europe, have been estimated using the NPV methodology. The results of the analysis indicate that agricultural land values in Europe vary substantially, depending on a number of factors. The factors causing variations of the agricultural land value in EU are: 1) differences in production costs (Table 3, Table 4); 2) revenues from agricultural production; 3) the growth rate assumptions of the
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