The impact of different policy environments on agricultural land use in Europe

By H. Meijl, T. Rheenen, Andrzej Tabeau, B. Eickhout. 2006

448 citations recorded by the source corpus.

Subfields: Economics and Econometrics, General Agricultural and Biological Sciences

View Paper: https://www.semanticscholar.org/paper/54e7fb338d4a8577c7509c60c162b91f2cb39ea8

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Citations in the corpus

Trading more food: Implications for land use, greenhouse gas emissions, and the food system (2012, result): “Similar studies by van Meijl et al. (2006) and Eickhout et al. (2009) show that trade liberalisation leads only to small land-use shifts in Europe but 80 dramatic shifts in Africa and other developing regions resulting in negative implications for the environment.”
Land‐use change trajectories up to 2050: insights from a global agro‐economic model comparison (2014, methodology): “This is the approach adopted by Meijl et al. (2006) and Eickhout et al. (2009) in their specification of the MAGNET 175 model, which is also used in the ENVISAGE model (for more details on this, see Hertel et al., 2009).”
Perceptions and responses to climate policy risks among California farmers (2013, background): “…– farmers across the globe deal with multiple policy risks that influence their decisions and collectively scale up to affect the global food supply, environment, and agricultural markets in an increasingly global world (e.g. Cassells and Meister, 2001; Mihyo, 2003; van Meijl et al., 2006).”
Challenges in using land use and land cover data for global change studies (2011, background): “Demands for commodities are often determined by (multi-)sectoral economic models and are expressed in the units of the goods or service under consideration, such as agricultural production (Meijl et al., 2006; Lotze-campen et al., 2008).”
Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios (2015, background): “Final yields (YTOT) are higher than exogenous yields (YEXO) as endogenous management decisions lead to intensification in times of economic growth (Meijl et al 2006, Eickhout et al 2009).”
Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs (2012, methodology): “0 land use scenarios for 2000–2030 (Meijl et al., 2006; Verburg et al., 2006; Westhoek et al., 2006; Verburg et al., 2009), which are based on the four IPCC SRES land use scenarios.”
Will EU biofuel policies affect global agricultural markets (2008, methodology): “A distinguishing feature of our method is the introduction of a land supply curve to include the process of land conversion and land abandonment endogenously (Meijl et al., 2006; Eickhout et al., forthcoming).”
Life cycle inventory modelling of land use induced by crop consumption (2007, methodology): “To make the simulation of land markets more realistic, van Meijl et al. (2006) suggest the introduction of so-called land supply curves, which determine the regional relationships between land price and land supply (see Fig.”
Impact of EU biofuel policies on world agricultural production and land use (2011, methodology): “[3] Van Meijl H, van Rheenen T, Tabeau A, Eickhout B.”
Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation (2018, unknown): “Land is modelled as an explicit production factor described by a land supply curve, constructed with land availability data provided by IMAGE-Land-Management (Van Meijl et al., 2006).”
Analysis of farm performance in Europe under different climatic and management conditions to improve understanding of adaptive capacity (2007, background): “Projected impacts on European agricultural land use were less severe when the global food market and regional land supply curves were included in the modelling framework ( van Meijl et al. 2006 ).”