The future of sustainable food: Evaluating the effect of dynamic life cycle assessment methods on lettuce production ecoefficiency

Abstract

Agricultural stakeholders must develop and implement greenhouse gas (GHG) mitigation measures in coming decades to meet global climate targets. Life Cycle Assessment (LCA) can help identify optimal ecoefficiency measures, but standard static techniques offer limited insight as they do not incorporate the dynamics of a changing world. Dynamic LCA (DLCA) methods consider product systems over long time periods but are not frequently considered in the agricultural sector. Current methods utilized are limited to singular approaches isolated from other dynamic approaches. In this study, we apply three different DLCA methods to a United States leaf lettuce cradle-to-harvest product system: dynamic weather and crop modeling, upstream supply chain transformation modeling, and on-farm prospective technology adoption modeling. When DLCA methods are adopted, the 2050 lettuce production GWP results range is 0.15–0.19 kg CO₂e kg⁻¹ lettuce across different locations, an average decline of 30 % from 2020 static LCA results. Scenario analysis of different technology change cases demonstrates how practitioners must carefully and transparently account for prospective technology adoption cases while also providing stakeholder insights into the comparative dynamic potential of emerging alternatives like renewable diesel and electric machinery. The results highlight near-term opportunities for emissions reduction in lettuce cultivation, such as irrigation electrification and renewable diesel usage, while exploring the potential of future investments like low-GHG fertilizer purchases and electrified heavy machinery investments which may not be apparent in static LCA. Additionally, the study demonstrates important considerations for LCA practitioners looking to incorporate dynamics in agricultural systems.