The reactions of land use to price and policy signals
Recent food price spikes and their potential link to an increased demand of biofuels and food, ongoing land use changes, such as conversion of tropical forest to agricultural land, and their relation to Green House Gas Emissions as well as discussions about the so called bio-economy – all these factors have renewed societal and scientific interest in better understanding how agricultural land use reacts to price and policy signals.
For scientists, especially economists, it is crucial to integrate these issues into models.
The core question is how to represent land use and management by farms and firms in models. SUSFANS researchers at the University of Bonn have now classified and compared key mechanisms with regard to land use in various model applications, in a way which was not conducted before. The outcomes are published in the SUSFANS working paper "Spatial heterogeneity of the agricultural sector in economic models".
The SUSFANS researchers have been focussing on spatial heterogeneity. The paper is the first to provide a comprehensive and theory-based discussion of approaches to better represent spatial and land heterogeneity in economic models. Most economic models take land by its input, size and output relation, thus assuming a land homogeneity that mostly does not exist in real-life conditions at farm level.
Plots play a key role in assessing spatial heterogeneity. Plots are typically distinguished by biophysical conditions, like soil properties, local climate, or if they are flat or hilly. Furthermore, size, shape, distance to markets (transport costs), the use of a plot in previous management seasons, for example rotational effects, are all factors contributing to land heterogeneity.
If moving to a higher aggregation level as for example a group of farms (firms) situated in the same region differences in production functions will increase with higher variability in biophysical conditions and other aspects. Whereas the production factor land is immobile, firms clearly have varying production factors such as labor, knowledge and capital. Consequently, even identical plots from a bio-physical perspective might deliver a different output at given input quantities, since the technology of the firms and/or the utility function of the managers operating these plots usually differ.
All these factors contribute to spatial heterogeneity. Spatial heterogeneity can be understood as the combination and interaction of land and firm heterogeneity, while taking additionally into account spatial differences in market and policy signals.
Whereas market signals for commodities like grains or oilseeds might be quite similar within a country, other markets like fruits and vegetables might receive market signals that differ spatially. Consumers may prefer regional food for example, willing to pay higher prices.
Policy signals also differ in space: Political interventions affecting agricultural land use can differ at an even higher spatial resolution, e.g. at regional level (e.g. to support so-called Less Favoured Areas) and, typically, between nations.
Access to input and outputs markets, transport cost differences and policy interventions such as border protection also clearly differentiate prices in space and lead to spatial specialization in agriculture.
Challenges for economic modelling
The challenge for economic modeling is that only some of the spatially heterogeneous drivers can be observed directly, whereas other drivers, related to the behavioral model and production functions, can only be assumed. Accordingly, a clear separation of land from other spatial heterogeneity factors is hardly possible in empirical applications.
These reflections lead to the following questions on how to apply quantitative modeling approaches with respect to their representation of spatial heterogeneity:
Are attributes of spatial heterogeneity such as soil or climate considered explicitly in the production function or only indirectly by calibrating observed input/output relations?
Is there a clear separation between land heterogeneity and other factors which are (potentially) spatially differentiated (other production factors, behavioral model, market and policy signals)?
The researchers based their overview on an analysis of global (agro-)economic models comparing the representation of land heterogeneity, like GTAP-AEZ or the OECD’s and FAO’s Aglink-Cosimo, CAPRI or IMPACT-WATER model.
Econometric specification of the intensive margin
For an empirical determination of a production function based taking potential yields potential yields and their relation to land heterogeneity into account, statistical-econometric pre-work is required.
The proposed approach requires a broad multidisciplinary cooperation between meteorologists, crop scientists and economists. Work on this differentiation has just started.
Regional aggregated model for Germany as an example
The authors discuss the modeling of land from a micro-economic perspective. Based on these findings, they explore the importance of spatial distribution of potential crop yields for simulations. They especially discuss how regional yield potentials depend on the actual landallocation of crops. For this, they applied a regionally aggregated model exemplarily for Germany, giving an outlook on potential solutions for the parameterization of the approach.
First comprehensive and theory-based discussion of approaches
The main contribution of the paper to the literature is that it provides the first comprehensive and theory-based discussion of approaches of how to represent spatial and land heterogeneity in economic models and how this representation could be improved.
„We limit the review of different modeling approaches of land heterogeneity to a few selected models for illustrating their implications. Based on a test case we conducted with crop yields in Germany, we demonstrate that the error rate associated with assuming that average regional potential yields are independent of the ‘real’ spatial land allocation is quite low we assume that crops will not move freely across a country“, explains leading author Marcel Adenäuer.
A full verification of this at European or global scale is left for future model applications. „Also, we just show a basic illustration of how economic yield functions that take biophysical aspects into account could potentially be parameterized based on transferring applications in the literature to our approach. The refinement of the parameterization and its adaptation to our purposes is beyond the scope of this paper and will be discussed in future research“, tells Adenäuer.