Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization
Kevin Bradley Dsouza, Graham Alexander Watt, Yuri Leonenko, Juan Moreno-Cruz

TL;DR
This paper presents a hierarchical and Bayesian optimization framework that aligns farm-level economic decisions with landscape-scale ecological connectivity to improve biodiversity and sustainability in agricultural landscapes.
Contribution
It introduces a novel integrated approach combining ecological and economic models with Bayesian optimization for scalable policy design.
Findings
Enhances landscape connectivity while maintaining farm profitability.
Provides a scalable, cost-effective policy instrument for biodiversity conservation.
Demonstrates applicability to real-world Canadian landscapes.
Abstract
Agricultural landscapes face the dual challenge of sustaining food production while reversing biodiversity loss. Agri-environmental policies often fall short of delivering ecological functions such as landscape connectivity, in part due to a persistent disconnect between farm-level economic decisions and landscape-scale spatial planning. We introduce a novel hierarchical optimization framework that bridges this gap. First, an Ecological Intensification (EI) model determines the economically optimal allocation of land to margin and habitat interventions at the individual farm level. These farm-specific intervention levels are then passed to an Ecological Connectivity (EC) model, which spatially arranges them across the landscape to maximize connectivity while preserving farm-level profitability. Finally, we introduce a Bayesian Optimization (BO) approach that translates these spatial…
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Taxonomy
TopicsSustainable Agricultural Systems Analysis · Agricultural Economics and Policy
