Ecology and economy are etymological siblings. Eco was originally oikos, a Greek word meaning home or household, and logy refers to “the study of” while “onomy” is “the management of.” Etymologically, this makes ecology the study of our home and economy its management.
Ecology and economy are also intertwined as forces in our lives. But these connections belie the norm in the systems of knowledge surrounding them – the study of our collective home and the study of its management have historically worked in silos.
This is changing though, and FEWscapes represents how ecology and economics can come together in research to deepen understandings about the world in ways that are useful to decision-making.
FEWscapes combines a suite of ecological models with a global economic model to create a space for these systems to interact – even if theoretically – to understand the pulls and pushes each make on each other and the potential consequences for our homes and lives.
The economic model used in FEWscapes is called SIMPLE-G, and it’s the brainchild of Thomas Hertel, a professor of agricultural economics at Purdue University, along with collaborators. In a nutshell, SIMPLE-G can estimate the effects of potential economic policy changes on factors related to agricultural supply and demand, land use, and water resources.
Hertel and collaborators designed SIMPLE-G to be true to its name – simple, and specifically, simple to integrate with other models and theories from other disciplines to enrich the insights that are possible. In FEWscapes, the project team will pair economic analyses of US agriculture from SIMPLE-G with Agro-IBIS, an agroecological model that can simulate fundamental ecological processes, such as the nutrient, carbon, and water cycles.
“Having thoughtful, accurate characterizations of the ecological processes and ecosystem services is essential for the economic analysis to have meaning and value,” said Hertel.
The models will pass data back and forth as they simulate the interplay between economic and ecological changes in agricultural systems. For example, SIMPLE-G can simulate how global drivers – such as population, income, and climate – could influence crop production, fertilizer use, and cropland use, and then pass those outputs to Agro-IBIS to calculate what the changes could mean for nutrient loads, water availability, and other natural benefits people need from agroecosystems.
There is certainly a lot more going on under the hood of this modeling game of toss, but there are a couple of important details about SIMPLE-G that stand out as key attributes of its value to dialogue and decision-making about food, energy, water, and ecosystem security in the Upper Mississippi River Basin.
A Global-Local-Global Framework
SIMPLE-G accounts for drivers and feedbacks on both the supply and demand sides, and at multiple scales – from global to local and the reverse.
Global population, income, and climate have local consequences for agriculture, such as crop production and fertilizer use. These local consequences, in turn, can stress land and water resources in various ways depending on the location and its climate, soils, and socio-economic context.
“The agricultural sector is not monolithic,” said Hertel.
Indeed, there will be varying local responses to that stress, sometimes leading to local policy change, which can then have feedbacks that scale up globally.
An example of this global-local-global feedback loop, and one that SIMPLE-G is able to simulate, is the production of corn ethanol for biofuel. Global climate change and rising energy prices drove policies that upped the demand for and supply of biofuels in the Midwest. Biofuel production has influenced fertilizer use, which has stressed land and water resources to varying degrees – in particular, contributions to nutrient runoff to the Mississippi River.
Meanwhile, how states respond to mitigate the nutrient runoff – which is varied and still evolving – could subsequently have national and global effects, such as on crop production and fertilizer prices.
“When local policies react to these stresses, they may feed back to the global level,” said Hertel.
The variation in local impacts and feedbacks is indicative of the next key attribute.
Economics on a Grid
Unlike many global economic models – including those often used to make policies – SIMPLE-G does not assume uniform behavior across an entire region. Hertel says the beauty of SIMPLE-G is the G, which stands for grid, because the model lays the supply side of the economic equation on a grid to represent the range of local impacts and feedbacks that may occur.
“Most of the policy analysis that is delivered to national policymakers is at a level that is too aggregated to be useful. In a lot of [food, energy, and water] systems work, it’s essential that it be done at a finer scale, but you can’t just do it in one or two locations or you are likely to reach misleading conclusions,” said Hertel.
This means, rather than averaging agricultural production across the whole United States or using one or two localized case studies to generalize for the whole country, SIMPLE-G calculates economic activity across thousands of grid cells. For example, a version of the model that represents all irrigated cropland in the US has 70,000 grid cells, with each cell representing roughly 15,000 acres of land.
“So, the cool thing here is being able to look at a policy across the whole landscape and capture the way that might influence agricultural markets, the price of fertilizer, or other metrics of interest,” said Hertel.
For example, in one modeling project Hertel did with collaborators, including FEWscapes principal investigator Chris Kucharik and the Agro-IBIS model, they simulated what could happen to corn ethanol production and nitrate leaching if coal-fired power plants in the Upper Midwest were converted to co-fire with biofuels.
While a region-wide view of the consequences showed a less-than-5% increase in nitrate leaching across the Upper Midwest overall, the gridded analysis revealed local hotspots where leaching could increase by as much as 60%, worsening local water quality significantly.
“The framework helps to identify which policies will be most effective in which locations and what their unintended consequences might be,” said Hertel.
In this same experiment, the gridded analysis also illuminated potential market-mediated spillover effects, where an increase in corn stover production on farmland around power plants leads to more corn grain on the market, which then depresses corn prices and production in other locations. A spillover like this could have global consequences given the importance of US agriculture to global food production.
“Having this nested within a global context means that we aren’t abstracting from assumptions about how the rest of the world is going to react,” said Hertel.
In another experiment that paired SIMPLE-G with Agro-IBIS, Hertel, Kucharik and collaborators explored how well different nitrogen-loss mitigation strategies would work across the U.S. Corn Belt, comparing nutrient management, controlled drainage, wetland construction, and a charge on leaching. This study is still in review, but the general conclusion is that the efficacy of these policies varies by location – and sometimes, in ways that challenge assumptions.
We’re eager to embark on explorations like these through FEWscapes with our local contributors who represent the food, energy, water, and conservation sectors in the Upper Mississippi River Basin. And we’ll surely be reporting back what we find in future posts – although it might take a couple of years!