Integrating soil- and agro-ecosystem models into life cycle assessments of sustainable management of agricultural residues: a review in the context of Sustainable Development Goals and planetary boundaries

Abstract Purpose This review assessed the suitability of soil- and agro-ecosystem models for assessing the sustainability of agricultural residue management within the frameworks of Sustainable Development Goals (SDG) and planetary boundaries (PB). It aimed to assist LCA practitioners in selecting appropriate models for site-specific impact assessments, providing an overview of available models, their applications, required input data, and examples of LCA studies that integrated ecosystem models into inventory analysis. Methods The Web of Science database was used to identify LCA studies related to soil- and agro-ecosystem models by including 118 ecosystem models, sub-models, and farming assessment tools in the search string, which yielded 117 eligible studies that used 51 different ecosystem models. These models were analyzed and categorized based on the indicators they simulated and the input data they used, such as soil and weather databases, surveys, and field experiments. The review analyzed how these models contribute to LCA methods and their potential advantages for replacing generalized datasets or direct measurements. The results were condensed into a table that shows which ecosystem models were used to simulate which indicators and how frequently they were used. Results and discussion Soil- and agro-ecosystem models are already widely accepted in LCA methods for assessing impacts of various agricultural practices, including residue management. Compared to generalized LCA datasets, these models provide more site-specific and potentially accurate predictions, which are particularly useful for addressing IPCC Tier 1 emissions and replacing direct field measurements. However, using these models involves increased complexity and effort to predict reliable life-cycle inventories. Most studies reviewed used soil and weather databases as input data, while fewer used surveys or field experiments. A broad range of indicators were predicted using ecosystem models, reflecting complex soil interactions and providing detailed assessment of model-specific environmental impacts. These indicators align with key sustainability goals in the SDG and PB frameworks, although few studies referred to them explicitly. Conclusions Soil- and agro-ecosystem models improve LCA methods by predicting more site-specific and potentially accurate data for assessing agricultural residue management, providing comprehensive assessment of environmental impacts while aligning with sustainability frameworks, although integrating these models explicitly into SDG and PB assessments remains limited. Integrating these models into agricultural LCA could increase the accuracy and relevance of impact assessments while reducing reliance on on-site measurements.