IntroductionAs part of the Energy Independence and Security Act (EISA) of 2007, a revised Renewable Fuels Standard mandates the use of 36 billion gallons of renewable fuels by 2022, 16 billion gallons of which would be derived from cellulosic biofuels. Crop residue, including maize stover, from eligible lands was included as a feedstock for cellulosic biofuels production in the EISA, increasing economic incentives for maize corn stover removal. There exists, however, natural resources-related constraints for the removal of maize stover in maize-based production systems. Maize stover removal can enhance erosion, deplete soil organic matter and other nutrients, impacting the health of land and water resources external to specific operations.

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Integrating perennial groundcovers (PGC) — sometimes referred to as living mulches or perennial cover crops — into annual cash-crop systems could address root causes of bare-soil practices that lead to negative impacts on soil and water quality. Perennial groundcovers bring otherwise absent functional traits — namely perenniality — into cash-crop systems to preserve soil and regenerate water, carbon, and nutrient cycles. However, if not optimized, they can also cause competitive interactions and yield loss. When designing PGC systems, the goal is to maximize complementarity — spatial and temporal separation of growth and resource acquisition — between PGC and cash crops through both breeding and management. Traits of interest include complementary root and shoot systems, reduced shade avoidance response in the cash-crop, and PGC summer dormancy. Successful deployment of PGC systems could increase both productivity and profitability by improving water- and nutrient-use-efficiency, improving weed and pest control, and creating additional value-added opportunities like stover harvest. Many scientific questions about the inherent interactions at the cell, plant, and ecosystem levels in PGC systems are waiting to be explored. Their answers could enable innovation and refinement of PGC system design for multiple geographies, crops, and food systems, creating a practical and scalable pathway towards resiliency, crop diversification, and sustainable intensification in agriculture.

3273ReseaRch M aize (Zea mays L.) stover is harvested for a variety of uses, including as a feedstock for cellulosic ethanol production or for use in livestock operations as bedding or feed. The use of 136 billion L yr −1 of renewable fuels by 2022 is mandated by the Renewable Fuel Standard as established in the Energy Independence and Security Act of 2007, of which cellulosic biofuels would comprise 61 billion L (USDOE, 2011). The promulgated rules identify maize stover as a cellulosic biofuels feedstock (Schnepf, 2013), thus expanding long-term opportunities for stover as an additional revenue stream in the renewable fuels arena. Other uses also enhance the economic incentive to harvest maize stover. For example, chemical processing of residues can increase digestibility by 35 to 62% by decomposition of lignocellulosic bonds (Shreck et al., 2011), making maize stover more attractive for use as a livestock feed, especially when grain prices escalate (Meteer, 2014). The increased harvest of maize stover, however, compounds the already hefty challenges of natural resources conservation in conventional systems.Maize stover provides myriad ecosystems services, and returning stover to soil recycles plant nutrients. Standard fertilization practices can be insufficient to compensate for nutrient loss after residue removal or when soil erosion approaches the soil loss ABSTRACT The Renewable Fuels Standard mandate provides enhanced opportunity for maize (Zea mays L.) stover use as a bioenergy feedstock. Living mulch (LM) offers a possible solution for the natural resources constraints associated with maize stover biomass harvest. A two-site-year study was conducted near Boone and Kanawha, IA, in both maize following maize (MM) and maize following soybean [Glycine max (L.) Merr.] (SM) sequences to evaluate the impact of established and chemically suppressed Kentucky bluegrass (Poa pratensis L.) 'Ridgeline', 'Wild Horse', 'Oasis', and 'Mallard' blend and creeping red fescue (Festuca rubra L.) 'Boreal' as LM on three maize hybrids (population sensitive, population insensitive, and yield stable). Maize grain yield for the no LM treatments in the MM and SM sequences was 12.0 and 13.2 Mg ha −1 , respectively, at Boone and 12.8 and 14.8 Mg ha −1 , respectively, at Kanawha, 23 to 73% greater than the LM treatment. Ethanol yield (L ha −1 ) was 12 to 119% greater, protein concentration was £9% greater, and starch concentration was £1% lower in the no LM treatment maize than in LM treatment maize. Maize hybrid by cover interaction was significant for parameters including total aboveground biomass and protein concentration at Boone, with inconsistent maize hybrid responses to the LM system. Stover yield, stover quality, stover C and N, leaf area index, maize plant density, maize maturity, and sequence year in the MM sequence were also evaluated. Results emphasize the need for maize hybrid and LM system compatibility, as well as effective LM suppression techniques.

The inclusion of perennial groundcover (PGC) in maize (Zea mays L.) production offers a tenable solution to natural resources-related concerns associated with conventional maize; however, insight into system management and key information gaps is needed to guide future research. We therefore extended the Agricultural Production Systems sIMulator (APSIM) to an annual and perennial intercrop by integrating annual and perennial APSIM modules. These were parameterized for Kentucky bluegrass (Poa pratensis L.) or creeping red fescue (Festuca rubra L.) as PGC using a 3-yr dataset. Our objectives for this intercropping modeling study were to: (a) simultaneously model a PGC and annual cash crop using APSIM software; (b) utilize APSIM to understand interactive processes in the maize-PGC system; and (c) utilize the calibrated model to explore both production and environmental benefits via scenario modeling. For the first objective, the integrated model successfully predicted maize total aboveground biomass (relative root mean square error [RRMSE] of 13-27%) and PGC above-and belowground tissue N concentration (RRMSE of 11-18%). The calibrated model captured observed trends in PGC biomass accumulation and soil nitrate. For the second objective, model analysis showed that competition for light was the primary PGC-related maize yield-penalty factor, while water and N affected maize yield later in the growing season. In the third objective, we concluded that effective PGC suppression produces minimal maize-yield loss and significant environmental benefits; conversely, poor groundcover suppression may produce unfavorable environmental consequences and decrease maize grain yield. Effective PGC suppression is key for long-term system success.