In the context of a global decline of bird populations around the world, the combination of passive acoustic monitoring (PAM) and automated bird vocalization identification tools such as BirdNET offers new opportunities to investigate the environmental factors structuring bird communities and populations. These approaches enable the identification of bird species from long-term acoustic recordings collected across numerous sampling sites. Although the use of BirdNET has rapidly expanded in recent years, few studies have applied this tool to assess how environmental factors influence bird communities and population. This gap raises key questions about how to process and interpret BirdNET derived data and how to minimize biases that may affect the reliability of ecological relationships inferred from such data. We propose a general pipeline designed to identify and mitigate the effects of factors influencing bird detectability and BirdNET efficiency. The pipeline includes: (1) raw data filtering; (2) selection of the species list according to the research question; and (3) listening tests to maximize the proportion of correct BirdNET identifications and identify the main sources of error. It was applied to a case study investigating the effects of agricultural practices on bird communities in apple orchards. The application demonstrates the value of each step in reducing bias and improving the reliability of environmental inferences, but also reveals data loss induced by filtering. It also allowed us to evaluate three metrics derived from BirdNET data: community activity density, species activity density (based on BirdNET detection counts), and species richness. Our results show that combining PAM and BirdNET provides a powerful framework to explore environmental drivers of bird community and species activity densities across multiple sampling sites. However, producing reliable and reproducible estimates of species richness remains more challenging. This approach is not a turnkey solution, some steps are time-consuming, and requires careful attention to sampling design, data processing, and metric selection.