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 CO₂/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 CO₂/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.

近 40% 的地球陆地面积用于农业，为人类提供以植物和动物为基础的食物、纤维或生物能源。农业土地利用、畜牧业和相关环境压力的未来趋势取决于食品部门、农业生产力、技术和许多其他影响因素的发展。情景分析有助于理解它们复杂的相互作用并获得定量洞察。我们在此对农业土地利用模型 BioBaM-GHG 2.0（“BioBaM”）进行了深入描述，该模型旨在评估基于粮食系统、作物产量和其他方面的外生假设而组装的大量农业和畜牧生产情景。因素。BioBaM 确定特定参数组合的可行性以及来自农业活动、畜牧业、土地利用变化和其他活动的相应温室气体 (GHG) 排放。我们提供了软件环境、模型的数据结构、输入和输出变量以及模型算法的描述。为了说明模型的功能和模型应用的范围，我们描述了使用 BioBaM 进行的两个示例性研究：我们评估了农业生态创新的影响及其广泛应用的可行性，以说明它们在农业自给自足和温室气体排放。第一个案例研究将少数个别场景与定性故事情节相结合。我们还展示了“生物物理选择空间方法”，它代表了关于主要影响参数固有的多维不确定性的综合敏感性分析，即对饮食和产量的预测；关于农田用于生物能源和草地集约化的假设。森林再生在减缓气候变化方面的全球潜力是第二种方法的一个例子。选项空间包括 90 个情景，涵盖了关于 2050 年造林温室气体减排（0.5-7 Gt CO 森林再生在减缓气候变化方面的全球潜力是第二种方法的一个例子。选项空间包括 90 个情景，涵盖了关于 2050 年造林温室气体减排（0.5-7 Gt CO 森林再生在减缓气候变化方面的全球潜力是第二种方法的一个例子。选项空间包括 90 个情景，涵盖了关于 2050 年造林温室气体减排（0.5-7 Gt CO₂ /年）。它进一步表明，在某些饮食-产量组合下，潜力为零。假设零能源作物种植和全球趋于健康参考饮食，造林的封存潜力在 2050 年上升到 10 Gt CO ₂ /年。这些示例性应用说明了使用 BioBaM 开发的选项空间如何补充基于场景的评估，这些评估通常侧重于少量个别情景：期权空间将注意力转移到更广泛的可想象未来，从而支持对系统关系和依赖性的全面看法，而对少数情景的分析允许理解更详细的情景叙述和限定。