Maize (Zea mays L.) stover has been identified as an important feedstock for the production of cellulosic ethanol. Our objectives were to measure hybrid effect and combining ability patterns of traits related to cellulosic ethanol production, determine if germplasm and mutations used for silage production would also be beneficial for feedstock production, and examine relationships between traits that are relevant to selective breeding. We evaluated grain hybrids, germplasm bred for silage production, brown‐midrib hybrids, and a leafy hybrid. Yield and composition traits were measured in four environments. There was a 53% difference in stover yield between commercial grain hybrids that were equivalent for other production‐related traits. Silage germplasm may be useful for increasing stover yield and reducing lignin concentration. We found much more variation among hybrids than either in vitro ruminal fermentability or polysaccharide concentration. Correlations between traits were mostly favorable or nonexistent. Our results suggest that utilizing standing genetic variation of maize in breeding programs could substantially increase the amount of biofuels produced from stover per unit area of land.
Corn stover, the above-ground, non-grain portion of the crop, is a large, currently available source of biomass that potentially could be collected as a biofuels feedstock. Biomass conversion process economics are directly affected by the overall biochemical conversion yield, which is assumed to be proportional to the carbohydrate content of the feedstock materials used in the process. Variability in the feedstock carbohydrate levels affects the maximum theoretical biofuels yield and may influence the optimum pretreatment or saccharification conditions. The aim of this study is to assess the extent to which commercial hybrid corn stover composition varies and begin to partition the variation among genetic, environmental, or annual influences. A rapid compositional analysis method using near-infrared spectroscopy/ partial least squares multivariate modeling (NIR/PLS) was used to evaluate compositional variation among 508 commercial hybrid corn stover samples collected from 47 sites in eight Corn Belt states after the 2001, 2002, and 2003 harvests. The major components of the corn stover, reported as average (standard deviation) % dry weight, whole biomass basis, were glucan 31.9 (2.0), xylan 18.9 (1.3), solubles composite 17.9 (4.1), and lignin (corrected for protein) 13.3 (1.1). We observed wide variability in the major corn stover components. Much of the variation observed in the structural components (on a whole biomass basis) is due to the large variation found in the soluble components. Analysis of variance (ANOVA) showed that the harvest year had the strongest effect on corn stover compositional variation, followed by location and then variety. The NIR/PLS rapid analysis method used here is well suited to testing large numbers of samples, as tested in this study, and will support feedstock improvement and biofuels process research.
New, rapid, and inexpensive methods that monitor the chemical composition of corn stover and corn stover-derived samples are a key element to enabling the commercialization of processes that convert stover to fuels and chemicals. These new techniques combine near infrared (NIR) spectroscopy and projection to latent structures (PLS) multivariate analysis to allow the compositional analysis of hundreds of samples in 1 d at a cost of about $10 each. The new NIR/PLS rapid analysis methods can also be used to support a variety of research projects that would have been too costly to pursue by traditional methods.
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