Reed canary grass biomass yield and energy use efficiency in Northern European pedoclimatic conditions

Abstract

Reed canary grass is a potential bio-energy crop in Northern Europe. As plantation biomass production depends on local pedoclimatic conditions, it is important to evaluate yield in the context of soil-specific characteristics. The current study used regression models to evaluate reed canary grass yield variability in the context of soil nitrogen (N) content and in applied mineral N fertilisers. Reed canary grass bio-energy potential was evaluated in a soil-specific manner to calculate the production and energy use efficiency. Soils with low N content produce yields of almost 1 Mg ha −1 in years with unsuitable weather conditions for plant growth. The average dry matter yield of 6–7 Mg ha −1 is achievable within limited years on soils with N contents of more than 0.6%. Fertilisation increases the yield and decreases yield variability in humus-poor soils, but on soils with high N content, production risks increase with increasing N fertiliser applications. Energy use efficiency decreases with increasing input on Histosols; increasing the input from 6 to 31 GJ ha −1 results in energy use efficiency decreasing from 9 to 2 GJ GJ −1 . As a consequence of energy use efficiency, a diminishing return occurs on Haplic Albeluvisol, as optimum efficiency peaks at 5.2 GJ GJ −1 using 198 kg N ha −1 . The current study integrated the developed models in the soil Geographic Information System and calculated the energy use efficiency of selected areas. This approach enables researchers to evaluate production risks in the region and provides a framework for knowledge-based bio-energy production. ► We modelled RCG yield probability dependent on soil total N content and mineral N fertilisation. ► N fertilisation efficiency decreases with increasing soil N content. ► N fertilisation decreases production risks on nitrogen-poor soils. ► Energy use efficiency and net energy yield are dependent on soil type. ► We performed site-specific RCG yield analysis using a geographic information system.