AgOSS: A Dataset and Multi-Layer Characterization of Open-Source Agricultural Software

Abstract

Much of agriculture depends on open-source software spanning farm management platforms, cloud services, edge gateways, embedded systems, and field-deployed sensors, forming a domain-specific software supply chain that has drawn little empirical security attention. It is unknown whether this ecosystem's supply chain security posture differs from that of comparable non-agricultural software, and if it does, whether the difference reflects the agricultural domain or the size and maturity of the projects within it. As a step towards securing agricultural open-source software, we present AgOSS, a dataset of 66 repositories across six architectural categories. We assess supply chain security within the dataset via OpenSSF Scorecard, governance metrics, SBOM-based dependency analysis, and KEV matching, and compare against matched non-agricultural controls. We report two findings. First, governance is largely independent of inherited dependency risk. Scorecard tracks community activity, but we detect no association with vulnerability count, density, or known-exploited count, and in regression the exposure signal loads on architectural category rather than domain. Second, agricultural projects score far lower on raw Scorecard, but size and maturity confound the gap: the residual loses significance under matching and regression. Securing this ecosystem means investing in contributor capacity and dependency hygiene, not agriculture-specific controls.