The objective of this study was to evaluate the effect of rolled barley grain (RB) supplementation on rumen metabolism, omasal flow of nutrients, and microbial dynamics in lactating dairy cows fed fresh perennial ryegrass (Lolium perenne L.; PRG)-based diets. Ten ruminally cannulated Holstein cows averaging (mean ± standard deviation) 49 ± 23 d in milk and 513 ± 36 kg of body weight were assigned to 1 of 2 treatments in a switchback design. The treatment diets were PRG only (G) or PRG plus 3.5 kg of dry matter RB (G+RB). The study consisted of three 29-d periods where each period consisted of 21 d of diet adaptation and 8 d of data and sample collection. A double marker system was used to quantify nutrient flow entering the omasal canal along with labeled 15 N-ammonium sulfate to measure bacterial, protozoal, and nonmicrobial N flow. Rumen evacuation techniques were used to determine nutrient and microbial pool size, allowing the calculation of fractional rates of digestion and microbial growth. There was no difference in daily milk yield or energy-corrected milk yield between treatments. Milk fat concentration and milk urea N decreased, whereas milk protein concentration increased in cows fed the G+RB diet. During the omasal sampling phase, dry matter intake was higher in cows fed the G+RB diet. Ruminal and total-tract neutral detergent fiber digestibility was lower in G+RB cows; however, no difference was observed in reticulorumen pH. The rumen pool size of fermentable carbohydrate was increased in cows fed the G+RB diet; however, the fractional rate of digestion was decreased. Flow of nonammonia N and bacterial N at the omasal canal increased in cows fed the G+RB diet compared with the G diet. Protozoa N flow was not different between diets; however, protozoa appeared to supply a much larger amount of microbial N and exhibited shorter generation time than previously considered. Feed N ruminal digestibility, corrected for microbial contribution, was similar for both treatments (88.4 and 89.0% for G and G+RB, respectively). In conclusion, RB supplementation did not benefit overall animal performance; however, it reduced ruminal neutral detergent fiber digestibility and increased bacterial N flow. The results demonstrate the large dependence of cows consuming PRG-based diets on microbial N as the main source of nonammonia N supply. Additional quantitative research is required to further describe the supply of nutrients and microbial dynamics in cows consuming PRG-based diets in an effort to determine most limiting nutrients.

Strategies that can improve health and maximize growth in the preweaning period should improve the subsequent production and longevity of replacement animals. Few data are available that quantify feed and water consumption, as well as growth, in healthy versus non-healthy calves-the objective of this study. A database of Holstein calves (<1 wk of age; n = 313) was developed to compare calves that developed diarrhea in the first 21 d after arrival from commercial farms to the research facility versus calves that remained healthy. Individual calf data from 4 experiments included daily intake of milk replacer, free water, electrolyte solution, and starter grain, as well as weekly body weight (BW) and frame measures for 21 d after arrival. Calves with a fecal score of >2 for ≥3 consecutive days over the first 21 d of each experiment were retrospectively classified as diarrheic (DIA; n = 96); the remainder were classified as healthy (HEA; n = 217). Other health issues were minimal. The likelihood of elevated fecal score occurrence and the cumulative number of days with an elevated score were greater for DIA calves than for HEA calves. The initial total protein concentration in blood did not differ between classifications. Cumulative milk replacer dry matter intake (DMI) and water consumed from milk replacer were significantly less for DIA calves than for HEA calves, because DIA calves were more likely to refuse milk replacer. Cumulative starter DMI was decreased for DIA versus HEA calves. As a result, cumulative total DMI was significantly less for DIA calves than for HEA calves. Cumulative free water intake did not differ between classifications. The DIA calves were more likely to receive electrolyte solution and have more days given electrolyte solution than HEA calves. As a result, total cumulative intake of electrolyte solution was greater in DIA calves than in HEA calves. Cumulative total water intake did not differ between classifications. Initial BW did not differ between classifications; however, a classification × time interaction for BW indicated that HEA calves were heavier than DIA calves and had greater ADG. Significant classification × time interactions for hip height and heart girth revealed that HEA calves had a larger frame size. Gain-feed ratios for both milk replacer intake and total DMI differed between classifications: DIA calves were less feed-efficient than HEA calves. In conclusion, diarrhea in young calves decreases DMI, BW gain, and feed efficiency relative to HEA calves within 21 d of arrival.

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. System dynamics methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity, and focuses on feedback processes arising from endogenous system elements. This paper highlights the process of SD modeling using 2 examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model illustrates the benefits of the SD approach in modeling rumen fill and animal performance. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing, and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The paper concludes with recommendations about how to increase awareness and training in SD methods to enhance their appropriate use in research and instruction.

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