A combination of Lactobacillus buchneri and Pediococcus pentosaceus extended the aerobic stability of conventional and brown midrib mutants-corn hybrids ensiled at low dry matter concentrations by causing a major shift in their bacterial and fungal community.

We evaluated the effects of applying a combination inoculant to four corn hybrids harvested at high moisture on their nutritive value and microbial populations. The treatment design was the factorial combination of corn hybrids ensiled with (INO) and without (CON) inoculant. The hybrids were TMF2R737 (MCN), F2F817 (MBR), P2089YHR (PCN), and PI144XR (PBR), ensiled at dry matter (DM) concentrations of 30.5%, 26.3%, 31.1%, and 31.5%, respectively; MBR and PBR were brown midrib mutants (BMR). The inoculant contained Lactobacillus buchneri and Pediococcus pentosaceus (4 × 105 and 1 × 105 cfu/g of fresh corn). The experiment had a complete randomized design with treatments replicated six times. Corn was treated or not with inoculant, packed into 7.6 L bucket silos, and stored for 100 d. At d 0, the relative abundance (RA, %) of Enterobacteriaceae was lower in PBR vs. the other hybrids [51.3 vs. x¯ = (average of) 58.4] and in the case of fungi, incertae sedis (i.s.) Tremellales and Mucoraceae were more and less abundant, respectively, in conventional hybrids vs. BMRs (x¯= 25.8 vs. x¯ = 13.9 and x¯ = 3.64 vs. x¯ = 7.52; P < 0.04). After ensiling, INO had higher LAB (9.3 vs. 7.1 log cfu/g of fresh corn) and acetic acid (3.44% vs. 1.32% of DM) and lower yeast (3.1 vs. 4.6) and molds (1.5 vs. 3.0), and also extended the aerobic stability (582 vs. 111 h) but decreased DM recovery (95.6% vs. 97.4%) vs. CON (P < 0.02). Inoculation reduced bacterial phylogenetic diversity (6.75 vs. 14.4) but increased fungal observed taxonomical units (46 vs. 20) vs. CON (P < 0.01). Also, a higher relative abundance (RA) for Lactobacillaceae (99.2% vs. 75.7%) and lower for Enterobacteriaceae (0.28 vs. 9.93) was observed due to inoculation (P < 0.001). For fungi, INO had a lower RA compared to CON for Monascaceae (12.6 vs. 44.7) and increased i.s. Tremellales (8.0 vs. 1.2) and i.s. Saccharomycetales (6.4% vs. 0.3%; P < 0.006). Inoculation changed the diverse bacterial community found in the phyllosphere across hybrids to a taxonomically uneven one dominated by Lactobacillaceae. In the case of fungi, INO application increased the fungal diversity at d 100 mainly by reducing the dominance of Monascaceae vs. CON. In conclusion, the INO treatment overwhelmed the disparate microbial populations found across BMR and conventional hybrids ensiled at low DM concentrations and ensured a significant concentration of acetic acid that modified fungal populations and in turn extended the aerobic stability of all hybrids.

Near-infrared spectroscopic models for analysis of winter pea (Pisum sativum L.) quality constituents.

BACKGROUND: Winter pea (Pisum sativum L.) grows well in a wide geographic region, both as a forage and cover crop. Understanding the quality constituents of this crop is important for both end uses; however, analysis of quality constituents by conventional wet chemistry methods is laborious, slow and costly. Near infrared reflectance spectroscopy (NIRS) is a precise, accurate, rapid and cheap alternative to using wet chemistry for estimating quality constituents. We developed and validated NIRS calibration models for constituent analysis of this crop. RESULTS: Of the 11 constituent models developed, nine constituents including moisture, dry-matter, total-nitrogen, crude protein, acid detergent fiber, neutral detergent fiber, AD-lignin, cellulose and non-fibrous carbohydrate had low standard errors and a high coefficient of determination (R2 = 0.88-0.98; 1 - VR, which is the coefficient of determination during cross-validation = 0.77-0.92) for both calibration and cross-validation, indicating their potential for quantitative predictability. The calibration models for ash (R2 = 0.65; 1 - VR = 0.46) and hemicellulose (R2 = 0.75; 1 - VR = 0.50) also appeared to be adequate for qualitative screening. Predictions of an independent validation set yielded reliable agreement between the NIRS predicted values and the reference values with low standard error of prediction (SEP), low bias, high coefficient of determination (r2 = 0.82-0.95), high ratios of performance to deviation (RPD = SD/SEP; 2.30-3.85) and high ratios of performance to interquartile distance (RPIQ = IQ/SEP; 2.57-7.59) for all 11 constituents. CONCLUSION: Precise, accurate and rapid analysis of winter pea for major forage and cover crop quality constituents can be performed at a low cost using the NIRS calibration models developed. © 2018 Society of Chemical Industry.