This paper compares the performance of econometric land use models based on three proxies for agricultural land rent: farmers' revenues, land prices, and shadow land prices derived from a mathematical programming model. We consider different land use classes (agriculture, pasture, forest, urban, and other), different determinants (economic, physical, and demographic) of land use shares, and different spatial econometric specifications. It is found that the inclusion of spatial components significantly improves the quality of predictions. In terms of economic interpretation, the shadow land prices provide the most stable and intuitive results.

Interaction between mitigation and adaptation is a key question for the design of climate policies. In this paper, we study how land use adaptation to climate change impacts land use competition in the agriculture, forest and other land use (AFOLU) sector and how a mitigation policy in agriculture might affect this competition. We use for this purpose two sector-specific bio-economic models of agriculture and forest combined with an econometric land use shares model to simulate the impacts of two climate change scenarios (A2 and B1, 2100 horizon), and a greenhouse gas emissions from agriculture policy consisting of a tax of between 0 and 200 e/tCO 2 equivalent. Our results show that both climate change scenarios lead to an increase in the area devoted to agriculture at the expense of forest which could have a negative impact on reducing greenhouse gas emissions responsible for climate change. The mitigation policy would curtail agricultural expansion, and thus could counteract the effects of land use adaptation to climate change.In other words, accounting for land use competition results in a reduction of the abatement costs of the mitigation policy in the agricultural sector.