Estimating the environmental footprint of barley with improved nitrogen uptake efficiency—a Swedish scenario study

Abstract

Abstract Plant breeding is a powerful tool for improving nitrogen (N) uptake efficiency and thus reducing the environmental impact relating to crop production. This study evaluated the environmental impact of current barley production systems in two Swedish agricultural areas (South and East) compared with scenarios with improved N uptake efficiency at two levels, in which the fraction of mineral N available for daily crop uptake was increased by 50 and 100%. Life cycle assessment (LCA) methodology was used to quantify energy use, global warming potential (GWP) and acidification and eutrophication potentials along the production chain for spring barley with differing N uptake efficiency, but similar N application rate. The functional unit, to which all energy use and emissions were related, was 1 Mg barley grain. Energy use, GWP and acidification proved to be higher for the East production system, mainly due to lower yield, while eutrophication was higher for South. The two impacts most affected by improved N uptake efficiency were eutrophication and GWP, with GWP decreasing due to a combination of higher yield, soil carbon sequestration and lower indirect emissions of N 2 O due to lower N leaching. Accounting for land savings due to increased yield, reducing the pressure to transform land elsewhere, would further lower the carbon footprint. Potential eutrophication per Mg grain was reduced by 15% in the production system with the highest N uptake efficiency in southern Sweden. Crops with improved N uptake efficiency can thus be an important complementary measure for reducing N losses to water, provided that the N application rate does not increase. However, incentives for farmers to maintain or even lower the N application rate might be required. Using simulation modelling is a promising approach for assessment of expected effects of improved crop varieties when no long-term experimental data are available. However, advanced crop models are required to better reflect the effect of plant breeding on e.g. expected yield. Future model development should involve expertise in plant breeding, plant physiology and dynamic crop and soil modelling.