Forages are a major source of nutrients for milk production, but little is known about the effects of forage species on whole-farm profitability and environmental performance. Using a whole-farm approach, the N-CyCLES (Nutrient-Cycling Crops-Livestock-Environment-Soil) optimization model, and experimentally measured data, this study aimed to compare the profitability and environmental performance of dairy farms when four alfalfa (Medicago sativa L.)-grass binary mixtures harvested and stored as silage and hay are used in ration formulation of heifers and lactating and dry cows: (a) alfalfa-timothy (Phleum pratense L.) (Al+Tim), (b) alfalfa-tall fescue [Schedonorus arundinaceus (Schreb.) Dumort.] (Al+TF), (c) alfalfa-meadow fescue fescue [Schedonorus pratensis (Huds.) P. Beauv.] (Al+MF), and (d) alfalfameadow bromegrass (Bromus biebersteinii Roem. & Schult.) (Al+Bro). Simulations were conducted on two virtual eastern Canadian dairy farms: southwestern (SWQ) and eastern Quebec (EQ). Comparisons were based on net income, greenhouse gas emissions, and nutrient balances. In SWQ, Al+Bro and Al+Tim led to the highest net income, but only slight differences were calculated between the four mixtures (maximum difference of 1.4%). In EQ, results suggested that Al+TF is the most viable alternative to Al+Tim mixtures as it allowed the farm to reach the highest profit (maximum difference of 8%). The choice of grass in alfalfa-grass binary mixture had little impact on the farm's total greenhouse gas emissions: the lowest total greenhouse gas emissions were with Al+ MF in SWQ and with Al+TF in EQ. In SWQ, phosphorus balance was low for the four mixtures and the lowest nitrogen balance was Abbreviations: Al+Bro, alfalfa with meadow bromegrass; Al+MF, alfalfa with meadow fescue; Al+TF, alfalfa with tall fescue; Al+Tim, alfalfa with timothy; aNDF, neutral detergent fiber assayed with a heat-stable amylase and sodium sulfite; CO 2 eq, CO 2 -equivalent; CP, crude protein; DM, dry matter; EQ, eastern Quebec; FPCM, fat and protein corrected milk; GDD5, growing degree days based on 5 ˚C; GHG, greenhouse gas; N-CyCLES, Nutrient-Cycling Crops-Livestock-Environment-Soil; NDF, neutral detergent fiber; SWQ, southwestern Quebec; TDN, total digestible nutrients.
Although it has been acknowledged for a long time that a single period of hydrolysis, normally 21 to 24 h, is not the optimal time for most of the AA, a single period is routinely used due to time and cost constraints. As models to balance dairy rations for proteins are evolving toward balancing for AA, it becomes critical to improve the predictions of AA supply from digested proteins. Our objective was to develop correction factors that could systematically be applied to AA concentrations obtained after a 24-h hydrolysis of proteins to account for incomplete recovery and therefore determine their true AA composition. Thirteen substrates were selected to represent different types of proteins commonly used to estimate the supply of AA in ration formulation models: feed ingredients (grass silage, corn silage, soybean meal, canola meal, high-protein corn dried distillers grains, and wheat dried distillers grains plus solubles), 16-h rumen residues (soybean meal and canola meal), digesta (duodenal digesta and feces), and rumen microorganisms (fluid-associated bacteria, particle-associated bacteria and protozoa). Each protein was hydrolyzed in 6 N HCl for multiple hydrolysis times: 13 (2, 4, 8, 12, 18, 21, 24, 30, 48, 72, 96, 120, and 168 h) for feed ingredients, rumen residues, and digesta, and 9 (2, 4, 8, 18, 24, 30, 48, 96, and 168 h) for rumen microorganisms; all analyses were conducted in triplicate. Using nonlinear regression, the AA composition in the protein before the hydrolysis (A 0 ) was derived for each AA in each protein. Two ratios were calculated as potential correction factors: A 0 /24-h concentration (A 0 /24h) and the maximal concentration/24-h concentration (max/24h). Both ratios were tested to determine if the type of proteins was affecting them. The ratios A 0 /24h were not affected by the type of proteins, whereas the ratios max/24h were also not affected by the type of proteins except for 3 nonessential AA (Ala, Glu, and Gly). In an attempt to propose correction factors, our results were combined with results from the literature reporting ratios A 0 /24h, ratios max/24h, or the ratio of the AA composition calculated from gene structure/24 h. The correction factors proposed for individual AA varied from 1.02 (Asp) to 1.12 (Thr). For the essential AA, the highest ratios were obtained, as expected, for the branched-chain AA and Thr. Formulation programs balancing dairy rations for essential AA would need to acknowledge the incomplete recovery of AA when obtained from 24-h hydrolysis and include correction factors, specific for each AA, but the same across different types of proteins, to correctly estimate the true AA supply to dairy cows.
Abstract. Several strategies are available for mitigating greenhouse gas (GHG) emissions associated with dairy manure management in barns, storage units, and fields. For instance, incorporation of manure into the soil, solid-liquid separation, composting, enclosed manure storage, and anaerobic digestion have been identified as good options. However, these strategies are not widely adopted in Canada because clear information on their effectiveness to abate the whole-farm GHG footprint is lacking. Better information on the most cost-effective options for reducing on-farm GHG emissions would assist decision making for dairy producers and foster adoption of the most promising approaches on Canadian dairies. In this context, whole-farm modeling provides a tool for evaluating different GHG abatement strategies. An Excel-based linear optimization model (N-CyCLES) was used to assess the economics and the nutrient and GHG footprints of two representative dairy farms in Québec, Canada. The farms were located in regions with contrasting climates (southwestern and eastern Québec). The model was developed to optimize feeding, cropping, and manure handling as a single unit of management, considering the aforementioned mitigation options. Greenhouse gas emissions from the different simulated milk production systems reached 1.27 to 1.85 kg CO2e kg-1 of corrected milk, allowing GHG reductions of up to 25% compared to the base system described in Part I. Solid-liquid separation had the greatest GHG mitigation potential, followed by the digester-like strategy involving a tight cover for gas burning. However, both options implied a decrease in farm net income. Manure incorporation into the soil and composting were associated with high investment relative to their GHG abatement potential. The most cost-effective option was using a loose cover on the manure storage unit. This approach lessened the manure volume and ammonia-N volatilization, thereby reducing fertilizer and manure spreading costs, increasing crop sales and profit, and enhancing the whole-farm N and GHG footprints. Consequently, covering the manure tanks appears to be an economically viable practice for Québec dairy farms. Keywords: Anaerobic digestion, Composting, Dairy cow, Farm net income, Greenhouse gas emission, Incorporation, Nutrient footprint, Solid-liquid separation, Storage cover, Whole-farm model.
<p>This study aimed to characterise the quality of meat from commercially-raised rabbits. Animals came from five different producers and were laughtered in three different plants under provincial or federal inspection jurisdiction. Animal behaviour evaluated by scan sampling prior to feed withdrawal (FW) and transport, as well as blood lactate concentration at exsanguination, did not raise concerns with respect to stress. Stomach pH was higher (<em>P</em>=0.047) when the FW time was short (≤13.5 h), at a mean value of 2.23. All pH values measured 1 h post-mortem from the Biceps femoris (BF) and almost all (97.6%) from the Longissimus lumborum (LL) were higher than 6. Values for ultimate pH measured 24 h postmortem(pH<sub>u</sub>) ranged from 5.80 to 6.83 and from 5.70 to 6.70 for BF and LL muscles, respectively. The maximum meat drip loss recorded was 2.6%, while cooking loss reached 30%. Meat lightness (L*) and colour intensity (C*) for the long FW times (≥23 h) were no different from those with short and intermediate (15.5 to 17.3 h) FW times. However, these colour parameters were higher for the short FW time class compared to the intermediate FW time class (<em>P</em><0.02). A hierarchical cluster analysis based on pH<sub>u</sub>, cooking loss and lightness (L*) from 200 rabbit loins was performed. Of the four clusters created, clusters 1 and 2 had the best and second-best meat quality, respectively. Clusters 3 and 4 had the lowest meat quality and presented DFD-like (dark, firm and dry) characteristics. Meat did not exhibit PSE-like (pale, soft, exudative) characteristics, even for the slaughter lot with the minimum mean pH<sub>u</sub>. Of the eight slaughter lots evaluated, more than 50% of the meat from three of them fell into clusters 3 and 4; all three were in the intermediate FW time class. Overall, the quality of rabbit meat analysed was acceptable for commercial use, but rather variable. This suggests that there are factors within the value chain that are not yet fully controlled and require further investigation.</p>
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