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Intermediate wheatgrass (Thinopyrum intermedium; IWG) is a temperate perennial grass capable of performing in dual-purpose perennial grain cropping systems. It is valued for its ecosystem services and forage yield and quality that can be utilized in many livestock systems. Development for potential perennial grain yield has been the focus of breeding programs for more than two decades, and agronomic management is becoming important, as commercialization of the crop has occurred. This research focused on nitrogen management and intercropping on grain yield and yield components in western Canada. Treatments consisting of a non-fertilized control, an interseeded crop with IWG/Trifolium hybridum, and a fertilized treatment (50 kg N ha−1) were investigated at four locations. Drought conditions were experienced in some years, resulting in the loss of the interseeded crop at three locations. Fertilization with nitrogen increased grain yield in harvest years two and three and influenced yield components in at least one instance across locations. Third-year grain harvests were higher or equal to year one yield at the two locations harvested, with applied N increasing yield on average by 200 kg ha−1 in year three. Inflorescence density is an important yield component after the first production year. The potential for consistent grain yields across three reproductive years was demonstrated, enhancing the potential for sustained productivity.

The objective of this study was to evaluate the effect of dried distillers grains with solubles (DDGS) and red-osier dogwood (ROD) extract on in vitro fermentation characteristics, nutrient disappearance, and microbial profiles using the rumen simulation technique. The experiment was a completely randomized design with a 2 × 2 factorial arrangement of treatments and four replicates per treatment. A basal diet [10% barley silage, 87% dry-rolled barley grain, and 3% vitamin and mineral supplement, dry matter (DM) basis] and a DDGS diet (as per basal diet with 25% of wheat DDGS replacing an equal portion of barley grain) were supplemented with ROD extract at 0 and 1% (DM basis), respectively. The experimental period was 17 d, consisting 10 days of adaptation and 7 days of data and sample collection. The substitution of wheat DDGS for barley grain did not affect gas production; disappearances of DM, organic matter, and crude protein; total volatile fatty acid (VFA) production; and microbial protein production. However, replacing barley grain with wheat DDGS increased (P = 0.01) fermenter pH and molar proportion of branched-chain VFA, switched (P = 0.06) the fermentation pattern to higher acetate production due to increased (P = 0.01) disappearance of neutral detergent fiber (NDF), and decreased (P = 0.08) methane (CH4) production. In the basal barley diet, the ROD extract increased the acetate to propionate (A:P) ratio (P = 0.08) and reduced the disappearance of starch (P = 0.06) with no effect on any other variables. No effects of ROD in the DDGS diet were observed. The number of operational taxonomic unit (OTUs) and the Shannon diversity index of the microbial community had little variation among treatments. Taxonomic analysis revealed no effect of adding the ROD extract on the relative abundance of bacteria at the phylum level with either the basal diet or DDGS diet, while at the genus level, the microbial community was affected by the addition of both DDGS and the ROD extract. Prevotella and Fibrobacter were the most abundant genera in the basal diet; however, Treponema became the most abundant genus with the addition of the ROD extract. These results indicated that the substitution of wheat DDGS for barley grain may mitigate enteric CH4 emissions. The trend of reduced starch fermentability and increased NDF disappearance with the addition of ROD extract suggests a reduced risk of rumen acidosis and an improvement in the utilization of fiber for cattle-fed high-grain diet.

In vitro gas production systems are utilized to screen feed ingredients for inclusion in ruminant diets. However, not all in vitro systems are set up to measure methane (CH4) production, nor do all publications report in vitro CH4. Therefore, the objective of this study was to develop models to predict in vitro CH4 production from total gas and volatile fatty acid (VFA) production data and to identify the major drivers of CH4 production in these systems. Meta-analysis and machine learning (ML) methodologies were applied to a database of 354 data points from 11 studies to predict CH4 production from total gas production, apparent DM digestibility (DMD), final pH, feed type (forage or concentrate), and acetate, propionate, butyrate and valerate production. Model evaluation was performed on an internal dataset of 107 data points. Meta-analysis results indicate that equations containing DMD, total VFA production, propionate, feed type and valerate resulted in best predictability of CH4 on the internal evaluation dataset. The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess generalization ability on unrelated data. Between the ML methodologies assessed, artificial neural networks and support vector regression resulted in very similar predictability, but differed in fitting, as assessed by behaviour analysis. The models developed can be utilized to estimate CH4 emissions in vitro.