Abstract

AbstractThe current trend of biodiversity deterioration in rural systems is a complex issue that operates across multiple spatial scales. Agroforestry practices have the potential to positively contribute towards addressing these trends by shaping the structure of agricultural landscapes and their underlying ecological functions. This study aims to test a multi-scale analytical approach to understand and account for these processes. Specifically, the study seeks to assess the contributions that agroforestry practices at the farm scale can make towards supporting biodiversity, in response to the wider-scale landscape eco-mosaic structural and functional challenges and requirements (both at the local and extra-local landscape systems). To achieve this, a series of landscape ecology analyses are conducted on an agroforestry-based rice farm located in the western Po Plain region of Northern Italy. These analyses examine various landscape structural traits (such as matrix composition, patch size, shape complexity, and diversity indices) and functional traits (including connectivity and bionomic indices), with different levels of detail for each scale of analysis. This allows for the evaluation of the current ecological status of both the extra-local and local scale landscape systems (including drivers of vulnerability and resilience) and the assessment of the farm's current contributions to biodiversity support. Based on these findings, strategic agroforestry interventions are identified at the farm scale to enhance its capacity to address the wider-scale ecological gaps. Two design scenarios are assessed, wherein functional ecological traits such as landscape diversity, connectivity, and ecological stability are improved. The results confirm the role of farm scale agroforestry management as a buffering tool, demonstrating how it contributes to the restoration of broader-scale landscape vulnerabilities. The applied approach provides cost-effective assessments of biodiversity-related ecological processes, with the accuracy of the findings dependent on the comprehensive multi-scale analysis conducted.

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