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• We present the diagnostic biophysical land-system and GHG emission model BioBaM-GHG 2.0. • BioBaM is designed for evaluating the feasibility and associated GHG emissions of large numbers of agro-food system and land-use scenarios at various scales. • We present model algorithms, data structures and the software environment. • As illustrative example, we analyse scenarios for the expansion of agro-ecological measures in the European Union. • As second example, we present an assessment of global potentials for afforestation as climate mitigation measure. Close to 40% of Earth's land area is used for agriculture to provide humankind with plant- and animal-based food, fibers or bioenergy. Future trends in agricultural land use, livestock husbandry and associated environmental pressures are determined by developments in the food sector, agricultural productivity, technology, and many other influencing factors. Scenario analysis helps to understand their complex interaction and obtain quantitative insight. We here present an in-depth description of the agricultural land use model BioBaM-GHG 2.0 ("BioBaM"), designed for evaluating large numbers of agricultural and livestock production scenarios assembled on the basis of exogenous assumptions on food systems, crop yields and other factors. BioBaM determines the feasibility of specific parameter combinations and the corresponding greenhouse gas (GHG) emissions from agricultural activities, livestock husbandry, land-use change and other activities. We provide a description of the software environment, the model's data structures, input and output variables and model algorithms. To illustrate the model's capabilities and the scope of model applications, we describe two exemplary studies performed with BioBaM: We assess implications of agro-ecological innovations and the feasibility of their widespread application in order to illustrate their implications in terms of agricultural self-sufficiency and GHG emissions. This first case study aligns a small number of individual scenarios with qualitative storylines. We also showcase a "biophysical option space approach", which represents a comprehensive sensitivity analysis regarding the multidimensional uncertainties inherent to main influencing parameters, i.e. projections for diets and yields; assumptions on cropland use for bioenergy, and regarding grassland intensification. The global potential of forest regeneration for climate change mitigation serves as an example for this second approach. The option space comprises 90 scenarios and encompasses the full range of literature estimates on GHG mitigation from afforestation in 2050 (0.5 – 7 Gt CO 2 /yr). It further shows that the potential is zero under certain diet-yield-combinations. Assuming zero energy crop cultivation and global convergence to a healthy reference diet, the sequestration potential of afforestation rises to 10 Gt CO 2 /yr in 2050. These exemplary applications illustrate how option spaces developed with BioBaM can complement scenario-based assessments that usually focus on small numbers of individual scenarios: Option spaces shift attention to a wider scope of conceivable futures and thus support a comprehensive view on systemic relations and dependencies, whereas analyses with few scenarios allow apprehension of much more detailed scenario narratives and qualifications. [ABSTRACT FROM AUTHOR]