Typically, they allow for endogenous structural adjustments in land use, management, commodity production, and consumption in response to exogenous scenario drivers . However, with several components of productivity parameters endogenously determined, it can be difficult to isolate the potential role of livestock efficiency changes due to technological breakthroughs or policy incentives. For example, as production decreases due to decreasing demand, so could productivity. In this case, a design feature can be a design faw for sensitivity analysis and policy assessment focused on individual key system parameters, even if model results can be further decomposed to disentangle endogenous and exogenous productivity contributions . Accounting-based land sector models, such as the FABLE Calculator, which we also employ in this current study, can offer similarly detailed sector representation, without the governing market mechanisms, thus allowing fully tunable parameters for exploring policy impacts . This feature facilitates quantifying uncertainty and bounding estimates through sensitivity analyses. The FABLE Calculator is a sophisticated land use accounting model that can capture several of the key determinants of agricultural land use change and GHG emissions without the complexity of an optimization based economic model. Its high degree of transparency and accessibility also make it an appealing tool to facilitate stakeholder engagement.This paper explores the impacts of healthier diets and increased crop yields on U.S. GHG emissions and land use,dutch buckets as well as how these impacts vary across assumptions of future livestock productivity and ruminant density in the U.S. We employ two complementary land use modeling approaches.
The first is the FABLE Calculator , a land use and GHG accounting model based on biophysical characteristics of the agricultural and land use sectors with high agricultural commodity representation. The second is a spatially-explicit partial equilibrium optimization model for global land use systems . The combination of these modeling approaches allows us to provide both detailed representation of agricultural commodities with high flexibility in scenario design and a dynamic representation of land use in response to known economic forces , qualities that are difficult to achieve in a single model. Both modeling frameworks allow us to project to 2050 U.S. national scale agricultural production, diets, land-use, and carbon emissions and sequestration under varying policy and productivity assumptions. Our work makes several advances to sustainability research. First, using agricultural and forestry models that capture market and intersectoral dynamics, this is the first non-LCA study to examine the sustainability of a healthier average U.S. diet . Second, using two complementary modeling approaches, this is the first study to explore the GHG and land use effects of the interaction of healthy diets and agricultural productivity. Specifically, we examined key assumptions about diet, livestock productivity, ruminant density, and crop productivity. Two of the key production parameters we consider—livestock productivity and stocking density—are affected by a transition to healthier diets but have not been extensively discussed in the agricultural economic modeling literature. Third, we isolate the effects of healthier diets in the U.S. alone, in the rest of the world, and globally, which is especially important given the comparative advantage of U.S. agriculture in global trade.To model multiple policy assumptions across dimensions of food and land use and have full flexibility in terms of parameter assumptions and choice of underlying data sets, we customized a land use accounting model built in Excel, the FABLE Calculator , for the U.S. Below we describe the design of the Calculator, but for more details we direct the reader to the complete model documentation .
The FABLE Calculator represents 76 crop and livestock products using data from the FAOSTAT database. The model first specifies demand for these commodities under selected scenarios , the Calculator computes agricultural production and other metrics, land use change, food consumption, trade, GHG emissions, water use, and land for biodiversity. The key advantages of the Calculator include its speed, the number and diversity of scenario design elements , simplicity, and its transparency. However, unlike economic models using optimization techniques, the Calculator does not consider commodity prices in generating the results, does not have any spatial representation, and does not represent different production practices. The following assumptions can be adjusted in the Calculator to create scenarios: GDP, population, diet composition, population activity level, food waste, imports, exports, livestock productivity, crop productivity, agricultural land expansion or contraction, reforestation, climate impacts on crop production, protected areas, post-harvest losses, bio-fuels. Scenario assumptions in the Calculator rely on “shifters” or time-step-specific relative changes that are applied to an initial historic value using a user-specified implementation rate. The Calculator performs a model run through a sequence of steps or calculations, as follows: calculate human demand for each commodity; calculate livestock production; calculate crop production; calculate pasture and cropland requirements; compare the land use requirements with the available land accounting for restrictions imposed and reforestation targets; calculate the amount of feasible pasture and cropland; and calculate the feasible crop and livestock production; calculate feasible human demand; calculate indicators . See Figure S1 in the Supplementary Materials for a diagram of these steps. Using U.S. national data sources, we modified or replaced the US FABLE Calculator’s default data inputs and growth assumptions based on Food and Agriculture Organization data.
Specifically, we used crop and livestock productivity assumptions from the U.S. Department of Agriculture , grazing/stock intensity using literature from U.S. studies, miscanthus and switch grass bio-energy feed stock productivity assumptions from the Billion Ton study , updated beef and other commodity exports using USDA data, and created a “Healthy Style Diet for Americans” diet using the 2015–2020 USDA Dietary Guidelines for Americans . See SM Table S6 for all other US Calculator data and assumptions. We used these U.S.-specific data updates to construct U.S. diet, yield, and livestock scenarios and sensitivities . See for a full description of the other assumptions and data sources used in the default version of the FABLE Calculator.As a complement to the FABLE Calculator’s exogenously determined trade flows, we used GLOBIOM [a widely used and well-documented global spatially explicit partial equilibrium model of the forestry and agricultural sectors. Documentation can be found at the GLOBIOM github development site to capture the dynamics of endogenously determined international trade. Unlike the FABLE Calculator, GLOBIOM is a spatial equilibrium economic optimization model based on calibrated demand and supply curves as typically employed in economic models. GLOBIOM represents 37 economic production regions, with regional consumers optimizing consumption based on relative output prices, income, and preferences. The model maximizes the sum of consumer and producer surplus by solving for market equilibrium and using the spatial equilibrium modeling approach described in McCarl and Spreen and Takayama and Judge . Product-specific demand curves and growth rates over time allow for selective analysis of preference or dietary change through augmenting demand shift parameters over time to reflect differences in relative demand for specific commodities . Production possibilities in GLOBIOM apply spatially explicit information aggregated to Simulation Units, which are aggregates of 5 pixels of the same altitude, slope, and soil class, within the same 30 arcmin pixel, and within the same country. Land use, production and prices are calibrated to FAOSTAT from the 2000 historic period. Production systems parameters and emissions coefficients for specific crop and livestock technologies are based on detailed biophysical process models,grow bucket including EPIC for crops and RUMINANT for livestock . Livestock and crop productivity changes are reflected by both endogenous and exogenous components. For crop production, GLOBIOM yields can be shifted exogenously to reflect technological or environmental change assumptions and their associated impact on yields. Exogenous yield changes are accompanied by changes in input use intensity and costs .A similar approach has been applied in other U.S.-centric land sector models, including the intertemporal approach outlined in Wade et al. . Furthermore, reflecting potential yield growth with input intensification per unit area is consistent with observed intensification of some inputs in the U.S. agricultural system. This includes nitrogen fertilizer intensity , which grew approximately 0.4% per year from 1988 to 2018 .
Higher prices can induce production system intensification or crop mix shifts across regions to exploit regional comparative advantages. GLOBIOM accounts for several different crop management techniques, including subsistence-level , low input, high input, and high input irrigated systems. The model simulates spatiotemporal allocation of production patterns and bilateral trade fows for key agriculture and forest commodities. Regional trade patterns can shift depending on changes in market or policy factors that Baker et al. and Janssens et al. explore in greater detail in addition to providing a more comprehensive documentation of the GLOBIOM approach to international trade dynamics, including cost structures and drivers of trade expansion or contraction, or establishing new bilateral trade flows. This approach allows for flexibility in trade adjustments at both the intensive and extensive margins given a policy or productivity change in a given region. GLOBIOM has been applied extensively to a wide range of relevant topics, including climate impacts assessment , mitigation policy analysis , diet transitions , and sustainable development goals . We designed new U.S. and rest-of-the world diet and yield scenarios , and ran all scenarios at medium resolution for the U.S. and coarse resolution for ROW. We chose Shared Socioeconomic Pathway 2 macroeconomic and population growth assumptions for all parameters across all scenarios when not specified or overridden by scenario assumptions .We aligned multiple assumptions in the FABLE Calculator with GLOBIOM inputs and/or outputs to isolate the impacts of specific parameter changes in livestock productivity and ruminant density. Specifically, we used the same set of U.S. healthy diet shifters in both models, but aligned the US FABLE Calculator’s crop yields and trade assumptions with GLOBIOM outputs to isolate the effects of increasing the ruminant livestock productivity growth rate and reducing the ruminant grazing density using the Calculator . While we developed high and baseline crop yield inputs for GLOBIOM, actual yields are reported because of the endogenous nature of yields in GLOBIOM. This two model approach allows us to explore the impact of exogenous changes to the livestock sector that cannot be fully exogenous in GLOBIOM. Subsequent methods sections describe each of these scenarios and sensitivity inputs in greater detail.We constructed a “Healthy U.S. diet” using the “Healthy U.S.-style Eating Pattern” from the USDA and US Department of Health and Human Services’ 2015–2020 Dietary Guidelines for Americans . We use a 2600 kcal average diet. This is a reduction of about 300 kcal from the current average U.S. diet given that the current diet is well over the Minimum Dietary Energy Recommendations of 2075 kcal, computed as a weighted average of energy requirement per sex, age, and activity level and the population projections by sex and age class following the FAO methodology . The DGA recommends quantities of aggregate and specific food groups in units of ounces and cup-equivalents on a daily or weekly basis. We chose representative foods in each grouping to convert volume or mass recommendations into kcal/day equivalents and assigned groupings and foods to their closest equivalent US Calculator product grouping . For DGA food groups that consist of more than one US Calculator product group, e.g., “Meats, poultry, eggs”, we used the proportion of each product group in the baseline American diet expressed in kcal/day and applied it to the aggregated kcal from the DGA to get the recommended DGA kcal for each product group . We made one manual modification to this process by increasing the DGA recommendation for beef from a calculated value of 36 kcal/day to 50 kcal/day, since trends in the last decade have shown per capita beef consumption exceeding that of pork . This process led to a total daily intake of 2576 kcal for the healthy U.S. diet . The Baseline, average U.S. diet is modeled in the US FABLE Calculator using FAO reported values on livestock and crop production by commodity in weight for use as food in the U.S., applying the share of each commodity that is wasted, then allocating weight of each commodity to specific food product groups , converting weight to kcal, and finally dividing by the total population and days in a year to get per capita kcal/day. See the Calculator for more details and commodity specific assumptions . This healthy U.S. diet expressed in kcal was used directly in the Calculator as a basis for human consumption demand calculations for specific crop and livestock commodities.