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The U.S. Corn Belt, known for its agricultural productivity, faces significant challenges regarding environmental sustainability. More holistic quantification of trade-offs related to best management practice (BMP) adoption is needed to help farmers, consumers, supply chain actors, and policymakers develop a nuanced understanding of the agricultural system’s configuration and constraints. A nuanced understanding could guide the actors in developing broad and robust support for farmers and landowners who strive to make different land use and management decisions toward balancing agricultural production and environmental goals. The overarching goal of this dissertation is to develop a deeper understanding of the economic and environmental trade-offs associated with the adoption of BMPs in intensively modified agricultural landscapes. I employed multiple approaches, including process-based modeling, non-dominating sorting genetic optimization algorithms, and digital game-based learning tools (DGBL), to achieve this goal. In Chapter 2, I assess the Agricultural Production System sIMulator (APSIM) model’s ability to predict long-term soil organic carbon (SOC) and yield dynamics in the U.S. Corn Belt. Results show that APSIM’s SOC predictions, evaluated using over a century of field data from the Morrow Plots, demonstrated acceptable prediction accuracy, supporting its potential use in carbon programs. The complexity of agricultural management often hinders stakeholder discussions about trade-offs among production and environmental outcomes associated with BMP adoption, and opportunities to support farmers financially through environmental markets. Thus, in Chapter 3, I examine these trade-offs in-depth, including the effect of adopting cover crops, contour prairie strips, reduced tillage, and residue retention through an integrated multi-objective optimization framework. The framework integrates output from the calibrated APSIM model with publicly available geographical and economic data. Land use scenarios developed from optimizing BMP placement locations generated higher ecosystem service benefits than the baseline scenario based on historical cropping systems. The monetization results indicate that environmental markets could span profitability gaps caused by BMP implementation costs and yield reductions. However, sensitivity analysis indicated that environmental credit prices must increase by at least 50% to improve the economic prospects of BMP adoption. In Chapter 4, I integrate a greenhouse gas (GHG) prediction framework into the People in Ecosystems Watershed Integration (PEWI) DGBL tool to advance a spatially explicit ecosystem service trade-off analysis to support environmental education. This module enables students to explore GHG emissions and SOC storage for different land uses represented within PEWI. Training students to effectively evaluate complex information toward maximizing synergies and minimizing trade-offs is crucial for achieving multidimensional agricultural and natural resource goals. To this end, in Chapter 5, I evaluate PEWI’s educational effectiveness in teaching land-use trade-offs. Results from a survey of 58 volunteer students enrolled in an undergraduate natural resource economics class at Iowa State University indicate that PEWI enhances critical thinking, comprehension, and self-efficacy in designing agricultural landscapes for food, fiber, and fuel production. This case study highlights PEWI's contributions to teaching and learning experiences that prepare students to holistically evaluate complex information related to agricultural land uses and ecosystem service outcomes.
