Optimizing selective breeding of livestock and forage crops to reduce the environmental impacts of grass-based dairy production by combining life cycle assessment and machine learning

Global demand for ruminant milk-based products is increasing, contributing to increases in associated environmental impacts. Yet, most efforts to reduce the total environmental impact of dairy production are based on livestock breeding and manipulation of feed and manure systems. Despite the critical need for low-environmental impact breeding, life cycle assessment (LCA) has not been significantly utilized for forage and livestock trait prioritization. By combining LCA and machine learning, we inventory reductions in footprints of grass-fed dairy systems achievable by selective breeding. To measure the environmental impacts of traits and other farm changes, a pasture-centric dairy system representative of Ireland was modelled using the GOBLIN model. Milk produced in ryegrass-based dairy systems in Ireland was associated with environmental impacts of 1.08 kg CO₂-eq (global warming), 0.0066 kg PO₄-eq (eutrophication), 0.013 kg SO₂-eq (acidification), and 1.62 MJ-eq (fossil resource depletion) per kg fat- and protein-corrected milk (FPCM), translating to annual per-hectare loads of 9281.21 kg CO₂-eq, 56.75 kg PO₄-eq, 108.21 kg SO₂-eq, and 14,025.16 MJ-eq, respectively. Using a machine learning model trained on farm-level LCA outputs, complementarities and trade-offs were revealed across 10,000 graduated scenarios analyzed. The XGBoost Regressor achieved an outstanding R² value of 99 % in estimating the LCA impacts. Principal component analysis and explainable artificial intelligence analyses identified dry matter digestibility, crude protein, and chemical nitrogen use as key drivers of environmental impacts in dairy systems. By optimizing input parameters, the environmental impacts of grass-based milk can be substantially reduced by breeding for ‘LCA-designed ideotypes’. The optimal ryegrass ideotype identified for grass-based dairy systems can reduce; (a) global warming potential by 36.7 %, (b) acidification potential by 31 %, (c) eutrophication potential by 29 %, and (d) fossil resource depletion by 11 %, compared to current levels. We conclude that the environmental performance of ryegrass-based dairy systems can be substantially increased by new LCA-designed forage ideotypes. A more systems-based approach to livestock and forage breeding is therefore needed, as a sole focus on low-footprint livestock may overlook critical gains from forage grass improvement.