Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization

2025-08-08

AI Summary

Agricultural landscapes face dual pressures of ensuring food security and conserving biodiversity, yet existing policies often fail to enhance ecological connectivity due to misalignment between farm-level economic decisions and landscape-scale ecological planning. This study proposes a hierarchical optimization framework: (1) a bottom layer integrating ecological enhancement modeling with graph-theoretic connectivity assessment; (2) a middle layer employing Bayesian optimization to jointly maximize biodiversity connectivity and farm profitability; and (3) a top layer generating scalable, non-spatial incentive instruments. Applied empirically in Canadian agricultural landscapes, the framework increased habitat connectivity by 32% without compromising net farm income. It represents the first approach to dynamically reconcile cross-scale economic–ecological objectives while ensuring policy implementability. The methodology provides a transferable paradigm for designing integrated agricultural-environmental policies worldwide.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 outcomes into simple, cost effective, and scalable policy instruments, such as subsidies and eco-premiums, using non-spatial, farm-level policy parameters. Applying the framework to a Canadian agricultural landscape, we demonstrate how it enhances connectivity under real-world economic constraints. Our approach provides a globally relevant tool for aligning farm incentives with biodiversity goals, advancing the development of agri-environmental policies that are economically viable and ecologically effective.

Problem

• Balancing food production and biodiversity loss in agriculture
• Linking farm economics with landscape-scale ecological planning
• Designing cost-effective policies for ecological connectivity and farm profitability

Innovation

• Hierarchical optimization for farm-landscape integration
• Bayesian Optimization for cost-effective policy design
• Ecological Intensification and Connectivity models synergy