PNNL

The Science

In this study, researchers apply advanced statistical techniques and hindcasting to evaluate and calibrate a new model of future global food consumption for use in human-Earth system dynamics models.

The Impact

The study advances the science of model validation and hindcasting in human-Earth system dynamics models. It also furthers our understanding of future regional food consumption around the world, a major driver of future land use. It is part of a larger effort to better characterize uncertainty in future human-Earth system dynamics. The study and its methods constitute a potential blueprint for other models and model elements to follow as they develop.

Summary

Understanding and characterizing the uncertainty in future projections of terrestrial system changes (e.g., land use, land cover, and land use change) is an active area of research. Food consumption is among the most fundamental drivers of these terrestrial system changes. Food consumption, in turn, is shaped by global change through interactions with socioeconomic changes such as population growth and economic prosperity.

The research develops a new model of food demands for use in human-Earth system dynamics models, and employs hindcasting and advanced statistical techniques to characterize the food demand model's performance and to derive numerical values for model parameters.

The new model addresses a long-standing issue in human-Earth system dynamics modeling. This issue is the evolution of food demand that accompanies large changes in income and agricultural prices occurring in widely varying countries over decades. As people's wealth increases, their diets change, with important ramifications for agricultural and terrestrial systems more generally. Similarly, changes in prices that might emerge, for example, from drought, will affect the foods people eat.

In representing these changes, this study takes a new approach that is rooted in decades of historical data and the latest understanding of how people have changed their diets and their food consumption over time around the world. The model projects the demand for two different types of food: staples commodities, such as grains like corn and wheat, and nonstaples such as fruits and vegetables.

An important element of the study is the application of advanced statistical techniques-specifically, Bayesian Monte Carlo parameter estimation-to establish numerical values for the parameters of the food demand model. The robustness of the model was tested by developing the model parameters using a "training set" and then applying them to a "testing" data set. This approach is a form of "hindcasting" because the projection is not being made into the future, but rather into data from the past; it is therefore testing model performance over history.

These "hindcast" experiments demonstrated that the model performed consistently well in predicting values in both the testing and training data sets. An additional benefit of using these statistical techniques is that that the statistical characterization of the model parameters can be used to create uncertainty distributions for projections of future food demands in coupled human-Earth system models.

The use of hindcasting and advanced statistical techniques is less common in the development of the human system components of coupled human-Earth system models than in the physical science components. Targeted approaches like those described in this paper provide a template for increasing their future use in coupled human-Earth system models.

Acknowledgments

Sponsors: The U.S. Department of Energy Office of Science, Biological and Environmental Research supported this research as part of the Integrated Assessment Research program.

Reference: J.A. Edmonds, R. Link, S.T. Waldhoff, R. Cui, "A Global Food Demand Model for the Assessment of Complex Human-Earth Systems." Climate Change Economics 8(4), 1750012 (22 pages, 2017). [DOI: 10.1142/S2010007817500129]