Cloud-Native Agri-Environmental Mitigation: A Serverless Machine Learning Architecture for Scaling Decision Support Systems in Catchment Water Quality

The mitigation of agricultural impacts on freshwater quality requires the precise orchestration of land management strategies across complex hydrological catchments. While advanced agronomic decision support systems exist to model nutrient leaching and recommend farm-level interventions, their deployment at a national scale is frequently constrained by the computational friction of localized software and manual data aggregation. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for agri-environmental decision support. By deploying asynchronous Python middleware integrated with eXtreme Gradient Boosting regression, the proposed system programmatically ingests high-frequency telemetry from remote soil moisture sensors, meteorological Application Programming Interfaces, and geospatial land-use databases. The system translates these inputs into a dynamic Catchment Mitigation Index, instantly identifying critical runoff vulnerabilities and autonomously generating optimized intervention strategies. Preliminary architectural evaluations demonstrate that decoupling data ingestion from the core agronomic inference engine significantly accelerates the processing of catchment-wide spatial data, providing environmental scientists and policymakers with a deterministic, highly scalable tool for operationalizing water quality preservation.