On The Application Of Computational Modeling To Complex Food Systems Issues

Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section. Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity. Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena. Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool

Grass-Based Dairy in Vermont: Benefits, Barriers, and Effective Public Policies

A comprehensive literature review was undertaken in order to define and assess the sustainability and resiliency characteristics associated with grass-based and confinement dairy farming. Primarily as a result of reduced input costs, grass-based dairy farming often enhances profitability over confinement systems, especially on small farms. Further, conversion of tilled soil to permanent pasture has been shown to significantly reduce harmful sediment and nutrient transport into waterways. Perennial forage also acts as a carbon sink, curtailing or even negating a grass-based farm\u27s carbon footprint. Finally, social benefits derived from enhanced nutrition and higher quality of life are also associated with grass-based dairy farming. Given that policy goals of the State of Vermont include both bolstering farm viability and reducing farm-related runoff, two questions are then raised. What is the most effective way to incentivize the adoption of rotational grazing in Vermont? And what types of farms are best suited to its use? A series of interviews with dairy experts and farmers was conducted as a preliminary investigation into these questions. This qualitative evidence suggested that farmers generally adopted grass-based dairying after observing a peer\u27s success with the method, suggesting that a key leverage point may be peer-based learning. A behavioral economics game was developed to evaluate the role of peer networks in facilitating decision-making under conditions of uncertainty. A computerized game platform simulated networks of small dairy farm enterprises, with participants acting as farm managers. Treatments varied the size of peer networks, as well as the inclusion of a perfectly-performing automated \u27seed player.\u27 Participants could base their decisions upon the successes of their peers. They received a cash incentive based on their farms\u27 performance. Results indicated that players with higher numbers of peers made better economic decisions on average. The inclusion of a \u27seed player\u27 within a network, which modeled the ideal behavior, also facilitated better decision-making. Both of these correlations were statistically significant. Furthermore, the shape of the \u27diffusion curve\u27 of new adoptees confirmed literature on the dynamics of innovation diffusion. Public policy implications from this work include an increased focus on facilitating peer-to-peer learning among farmers where Best Management Practice adoption is a policy goal. To further evaluate the potential for peer learning to facilitate positive change, the Dairy Farm Transitions Agent Based Model (DFTABM) was developed. The model was calibrated using existing datasets along with the qualitative and quantitative results described above. It forecasts effects on farm profitability, attrition, and soil loss arising from varying assumptions about peer network connectivity, peer emulation, macroeconomic trends, and agri-environmental policy. Nine experimental treatments were assessed. Overall, it was found that high rates of emulation coupled with high rates of connectivity\u27especially targeted connectivity among smaller farms\u27yielded the best balance of farm viability and reduction in soil loss. The establishment of a performance-based tax credit had no clear correlation with the resulting soil loss figures predicted by the model. Policy implications from this study include the finding that direct payment schemes for reduction in environmental harm may not always have their intended effects, whereas policies that enhance peer-to-peer learning opportunities, especially among the proprietors of smaller farms, may present an effective and relatively affordable means by which to bolster farm profitability while also reducing environmental degradation