Towards standardized ecosystem condition assessments: Selecting agroecosystem condition indicators based on SEEA EA with an example application in Lower Saxony, Germany

• A literature review revealed more than 200 used agroecosystem condition indicators. • Indicators were grouped to compile agroecosystem-specific characteristics. • Enabling and quality criteria were applied in an indicator filtering process. • Nine indicators were selected for comprehensive agroecosystem condition assessments. Human well-being strongly relies on agroecosystems in good condition to ensure food security and a sustainable Ecosystem Services (ES) supply. However, agroecosystems face growing threats from environmental pressures, increasing the need for conservation and restoration measures. Their targeted and efficient implementation requires reliable, comprehensive and standardized methods to assess and monitor agroecosystem condition. To support such integrated assessments, we aimed to identify a set of suitable agroecosystem condition indicators. We applied the UN System of Environmental Economic Accounting – Ecosystem Accounting (SEEA EA), to support standardized assessments. In a literature review, we identified the most relevant agroecosystem characteristics based on expert knowledge and current practice. Subsequently, we applied the SEEA EA selection criteria to filter the most suitable indicators and datasets under conceptual and practical aspects. Finally, we mapped the selected indicators and checked the entire set for redundancies. We used the Federal State of Lower Saxony (Germany) as an exemplary study area, including both agricultural croplands and grasslands. The review revealed 19 relevant physical, chemical, compositional, structural, functional and landscape agroecosystem state characteristics. The subsequent filtering process resulted in a comprehensive set of nine ecosystem condition indicators. The selected indicators are suitable to support comprehensive agroecosystem condition assessments for various purposes. However, high-resolution data availability remains a key limiting factor, restricting the suitability of the selected indicators to regional-scale assessments. While the indicator set already can support monitoring procedures, future research is needed to define reference levels and to calculate condition indicators with a normative meaning.