The climate impact and land use of cultivated meat: Evaluating agricultural feedstock production

scenario, with varying levels of optimism across regions, most notably higher in Brazil than in Europe. Furthermore, understanding the future role of agriculture in CM production necessitates a comprehensive analysis of resource utilization and the sustainability of CM compared to tradi- tional meat. This includes evaluating the land use requirements of CM to ascertain its environ- mental benefits and value-added potential in the agricultural sector. 3. Methods and scenarios 3.1 Modeling structure Our model uses linear programming to maximize CM production within a 100-hectare refer- ence area, incorporating crop rotation and nutrient constraints specific to southern Germany. Linear programming is a mathematical strategy frequently used across various disciplines for optimization purposes [77]. In agriculture, it is often employed to optimize resource utiliza- tion, encompassing factors such as land and crop rotation optimization [78–81]. In the context of our research, we employed a linear programming model with several spe- cific components in mind. Primarily, we defined an objective function that represents our optimization goal: maximizing the production of CM per 100 hectares of land. In the modeling structure, the 100 hectares serve as a land area reference scale for comparison. The results are expressed in kilograms of meat per square meter. Our model also incorporates constraints reflecting the crop restrictions specific to southern Germany. We focus on southern Germany since this region has environmental conditions that allow the production of a wide variety of

crops with relatively high yields (Table 1). These constraints include crop rotation practices, solar energy use, and meeting CM nutrient requirements. Although future methods may pro- duce some amino acids, we focused on a closed, plant-based system. By integrating these com- ponents, the linear programming model utilized in this study is designed to identify the optimal land use strategies. The ultimate objective is to maximize the production of CM within the confines of limited land availability. Hence, the model offers valuable insights into efficient land practices and optimal crop mixtures for achieving the highest possible output of CM. Table 1. Average crop yields in southern Germany, Germany, the world, and continents in 2021. Crop Southern Germany (Bavaria) (t/ha)a Germany (t/ha)b Africa (t/ha)b Americas (t/ha)b Asia (t/ha)b Europe (t/ha)b World (t/ha)b Barley 6.75 6.76 1.77 2.87 1.59 3.96 2.97 Lupine 2.88 1.84 0.70 1.61 2.25 1.58 1.40 Maize 10.52 10.36 2.27 7.80 5.58 7.20 5.87 Oats 4.45 4.32 1.30 2.38 2.03 2.52 2.36 Peas (general) 2.99 3.06 1.19 1.47 1.45 2.32 1.76 Potatoes 42.26 43.79 15.02 29.44 19.15 23.62 20.74 Rapeseed 3.57 3.50 1.89 1.53 1.72 2.81 1.93 Rye 5.52 5.27 1.87 2.43 2.29 3.23 3.05 Soybean 3.17 3.11 1.44 3.27 1.43 2.09 2.86 Sunflower 2.70 2.60 1.05 1.92 2.02 2.06 1.97 Triticale 4.88 5.81 2.30 2.80 2.06 4.12 3.87 Wheat 7.54 7.30 3.03 2.83 3.39 4.28 3.49 aLfL, 2023 [82] bFAOSTAT, 2023 [83] Note: t/ha = tons per hectare.

Crop rotation restrictions are detailed in Table 2. For grain legumes, total acreage cannot exceed 20% of the farm area, with specific caps for individual crops: lupine, field bean, and pea are each limited to 15% [84]. https://doi.org/10.1371/journal.pone.0316480.t001 PLOS ONE Evaluating agricultural feedstock for cultivated meat PLOS ONE | https://doi.org/10.1371/journal.pone.0316480 January 22, 2025 7 / 27