Cellulosic bioenergy is a primary land-based climate mitigation strategy, with soil carbon (C) storage and nitrogen (N) conservation as important mitigation elements. Here, we present 13 years of soil C and N change under three cellulosic cropping systems: monoculture switchgrass (Panicum virgatum L.), a five native grasses polyculture, and no-till corn (Zea mays L.). Soil C and N fractions were measured four times over 12 years. Bulk soil C in the 0–25 cm depth at the end of the study period ranged from 28.4 (± 1.4 se) Mg C ha⁻¹ in no-till corn, to 30.8 (± 1.4) Mg C ha⁻¹ in switchgrass, and to 34.8 (± 1.4) Mg C ha⁻¹ in native grasses. Mineral-associated organic matter (MAOM) ranged from 60% to 90% and particulate organic matter (POM) from 10% to 40% of total soil C. Over 12 years, total C as well as both C fractions persisted under no-till corn and switchgrass and increased under native grasses. In contrast, POM N stocks decreased 33% to 45% across systems, whereas MAOM N decreased by less than 13% and only in no-till corn. Declining POM N stocks likely reflect pre-establishment land use, which included alfalfa and manure in earlier rotations. Root production and large soil aggregate formation explained 69% (p < 0.001) and 36% (p = 0.024) of total soil C change, respectively, and 60% (p = 0.020) and 41% (p = 0.023) of soil N change, demonstrating the importance of belowground productivity and soil aggregates for producing and protecting soil C and conserving soil N. Differences between switchgrass and native grasses also indicate a dependence on plant diversity. Soil C and N benefits of bioenergy crops depend strongly on root productivity and pre-establishment land use.

See the Materials and Methods of the associated publication for procedures on sampling and processing, and section 2.9 Statistical analysis for statistical models. The R software was used for all analyses (R Core Team, 2014); the R scripts are provided in the file Statistical_Analysis.R.

R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.Rproject.org/