Cows and Climate Change: A Linear Optimization Approach

The project "Cows and Climate Change: A Linear Optimization Approach" addresses the challenge of reducing methane emissions, a significant contributor to global warming, especially in the agricultural sector, where dairy and cattle farming are major sources. Methane has an immense impact, being 80 times more potent than CO2 in the short term. The US EPA's Action Plan aims to significantly cut methane emissions by optimizing agricultural practices.

The study focuses on enteric fermentation in cattle, specifically beef cattle (Black Angus), which produce high levels of methane. The project explores a linear optimization problem to minimize methane emissions by adjusting the composition of cattle feed mixes. Several raw materials, including legume silage, corn silage, and seaweed, are evaluated for their nutritional content, availability, cost, and methane emissions. The goal is to develop an optimal feed mix that reduces emissions while maintaining the nutritional requirements for cattle.

Through linear programming methods, including the Simplex method, and using the Pyomo tool, the study formulates an objective function that minimizes methane emissions while meeting the constraints on nutrient composition, feed production, and budget. The study includes sensitivity analyses to understand how changes in budget and minimum production constraints affect methane emissions and feed composition.

Key findings suggest that certain raw materials, such as wheat silage and seaweed, are preferred in emission reduction scenarios due to their low emissions and cost-effectiveness. However, introducing budget constraints leads to higher emissions, as the model is forced to select less optimal but cheaper materials. When minimum production requirements are introduced, the model increases methane emissions by including less efficient feed mixes.

Overall, the project demonstrates the importance of optimizing feed mix compositions in reducing methane emissions from cattle farming, highlighting the impact of budget and production constraints on environmental outcomes.