Towards Systematic Evaluation of Crop Model Outputs for Global Land-use Models

Leclère D, Azevedo L, Skalsky R, Balkovič J, Havlik P, & Obersteiner M (2015). Towards Systematic Evaluation of Crop Model Outputs for Global Land-use Models. In: Systems Analysis 2015 - A Conference in Celebration of Howard Raiffa, 11 -13 November, 2015, Laxenburg, Austria.

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Abstract

Land provides vital socioeconomic resources to the society; however, at the cost of large environmental degradation (Verburg et al., 2013). At the crossroads of these dimensions, agriculture becomes increasingly interconnected to various natural and human systems across various scales. In order to inform the design of policies to navigate land use towards a more sustainable operating space, comprehensive global assessment models are increasingly being used. They rely partly on the loose coupling of biophysical crop models to global economic models, via one-way exchange of output variables (Rosenzweig et al. 2013). Accuracy of variables exchanged strongly influences the outcomes assessed at various scales, and its improvement is likely to require iterative improvements. Yet there has been little effort to document, evaluate and compare these exchange variables across models (Mueller & Robertson et al. 2014).
We here present a novel dataset (the Hypercube) generated by the Environmental Policy Integrated Model (EPIC) crop model and providing the Global Biosphere Management Model (GLOBIOM) with high-resolution information at global scale on the yield, water, and nutrient needs of 16 crops for 15 different combinations of management. We present the dataset and its links to the EPIC and GLOBIOM model, and the rationale for developing a systematic evaluation of the data, before illustrating them with preliminary results.

Item Type: Conference or Workshop Item (Poster)
Research Programs: Ecosystems Services and Management (ESM)
Depositing User: Michaela Rossini
Date Deposited: 18 Jan 2016 15:42
Last Modified: 21 Jun 2016 13:09
URI: http://pure.iiasa.ac.at/11756

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