Non-linear models of 15N partitioning kinetics in late lactation dairy cows from individually-labeled feed ingredients.

Few studies have examined the N kinetics of individual feeds with stable isotope tracing. We hypothesized that N partitioning to milk and excreta pools as well as the rates of the processes that drive this partitioning would differ for alfalfa silage, corn silage, corn grain, and soybean meal. Feed ingredients were endogenously labeled with 15N and included in 4 diets to create treatments with the same dietary composition and different labeled feed. Diets were fed to 12 late-lactation dairy cows for 4 d (96 h) and feces, urine, and milk collection proceeded during the 4 d of 15N enrichment and for 3 d (80 h) after cessation of label feeding. Nonlinear models of 15N enrichment and decay were fit to milk (MN), urine (UN), and fecal N (FN) in R with the nlme package and feed-specific parameter estimates were compared. The estimated proportions of feed N that were excreted in feces supported our understanding that N from soybean meal and corn grain is more digestible than N from alfalfa and corn silage. Estimates for the N partitioning between milk (MN) and urine (UN) from the 2 concentrate feeds (soybean meal and corn grain) indicated that UN:MN ratios were less than or equal to 1:1 indicating either more or equal nitrogen partitioning to milk compared with urine. It is important to maintain factual accuracy in representing the results rather than implying a desired outcome unsupported by the data. In contrast, UN:MN ratios for forage feeds (corn and alfalfa silage) were > 1:1, indicating more N partitioning to urine than milk. The modeled proportion of total FN that originated from feed N was 82.2% which is in line with previous research using a similar 15N measurement timeframe. However, the proportion of urinary and MN originating from feed N was much lower (60.5% for urine, 57.9% for milk), suggesting that approximately 40% of urinary and MN directly originate from body N sources related to protein turnover.

Farm specific estimates of forage variability result in larger optimal control limits for forage quality monitoring.

Variation in forage composition decreases the accuracy of diets delivered to dairy cows. However, variability of forages can be managed using a renewal reward model (RRM) and genetic algorithm (GA) to optimize sampling and monitoring practices for farm conditions. Specifically, use of quality-control-charts to monitor forage composition can identify changes in composition for which adjustment in the formulated diet will result in a better match of the nutrients delivered to cows. The objectives of this study were 1) assess the use of a clustering algorithm to estimate the mean time the process is stable or in-control (d) (TStable) and the magnitude of the change in forage composition between stable periods (ΔForage) for corn silage and alfalfa-grass silage which are input parameters for the RRM; 2) compare optimized farm-specific sampling practices (number of samples (n), sampling interval (TSample) and control limits (ΔLimit) using previously proposed defaults and our estimates for the TStable and ΔForage input parameters; and 3) conduct a simulation study to compare the number of recommended diet changes costs of quality control under the proposed sampling and monitoring protocols. We estimated the TStable and ΔForage parameters for corn silage NDF and starch and alfalfa-grass silage NDF and CP using a k-means clustering approach applied to forage samples collected from 8 farms, 3x/week during a 16-week period. We compared 4 sampling and monitoring protocols that resulted from the 2 methods for estimating TStable and ΔForage (default values and our proposed method) and either optimizing only the control limit (Optim1) or optimizing the control limits, the number of samples, and the number of days between sampling (Optim2). We simulated the outcomes of implementing the optimized monitoring protocols using a quality control chart for corn silage and alfalfa-grass silage of each farm. Estimates of T^Stable and Δ^Forage from the k-means clustering analysis were, respectively, shorter and larger than previously proposed default values. In the simulated quality control monitoring, larger Δ^Forage estimates increased the optimized ΔLimit resulting in fewer detected shifts in composition of forages and a lower frequency of false alarms and a lower quality control cost ($/d). Recommended diet reformulation intervals from the simulated quality control analysis were specific for the type of forage and farm management practices. The median of the diet reformulation intervals for all farms using our optimal protocols was 14 d (Q1 = 8, Q3 = 26) for corn silage and 16 d (Q1 = 8, Q3 = 26) for alfalfa-grass silage.

Partitioning of nutrient variation in alfalfa and corn silage by source on New York dairy farms.

Variation in feed components contributes to variation and uncertainty of diets delivered to dairy cows. Forages often have a high inclusion rate (50 to 70% of DM fed) and variable composition, thus are an important contributor to nutrient variability of delivered diets. Our objective was to quantify the variation and identify the main sources of variability in corn silage and alfalfa-grass haylage composition at harvest (fresh forage) and feed-out (fermented forage) on NY dairy farms. Corn silage and alfalfa-grass haylage were sampled on 8 NY commercial dairy farms during harvest in the summer and fall of 2020 and during their subsequent feed-out in the winter and spring of 2021. At harvest, a composite sample of fresh chopped forage of every 8-ha section of individual fields was collected from piles delivered for silo filling. During a 16-week feed-out period, 2 independent samples of each forage were collected 3 times per week. The fields-of-origin of each forage sample during feed-out were identified and recorded using silo maps created at filling. A mixed-model analysis quantified the variance of corn silage DM, NDF, and starch and haylage DM, NDF, and CP content. Fixed effects included soil type, weather conditions, and management practices during harvest and feed-out while random effects were farm, silo unit, field, and day. At harvest, between-farm variability was the largest source of variation for both corn silage and haylage, but within-farm sources of variation exceeded farm-to-farm variation for haylage at feed-out. At feed-out, haylage DM and NDF content had higher within-farm variability than corn silage. In contrast, corn silage starch showed higher within-farm variation at feed-out than haylage CP content. For DM content at feed-out, day-to-day variation was the most relevant source of within-farm variation for both forages. However, for the nutrient components at feed-out (NDF and CP for haylage; NDF and starch for corn silage) silo-to-silo variation was the largest source of variability. Weather conditions systematically explained a proportion of the farm-to-farm variability for both forages at harvest and feed-out. We concluded that because of the high farm-to-farm variation, corn silage and haylage must be sampled on individual farms. We also concluded that due to the high silo-to-silo variability, and the still significant day-to-day and field-to-field variability within-farm, corn silage and haylage should be sampled within individual silos to better capture changes in forage components at feed-out.

Key nitrogen and phosphorus performance indicators derived from farm-gate mass balances on dairies.

Efficient management of N and P on dairy farms is critical for farm profitability and environmental stewardship. Annual farm-gate nutrient mass balance (NMB) assessments can be used to determine the nutrient-use efficiency of farms, set efficiency targets, and monitor the effect of management changes with minimal inputs required. In New York, feasible N and P balances have been developed as benchmarks for dairy farm NMB, alongside key performance indicators (KPI) that serve as predictors for high NMB. Here, 3 yr of NMB data from 47 farms were used to evaluate the main drivers of N and P balances and identify additional KPI. From the 141 farm records, 26% met both the feasible N balances per hectare and per megagram of milk produced. For P, 53% of the records met both benchmarks. Imports, rather than exports, drove NMB primarily by feed and fertilizer purchases, consistent with earlier findings. Linear regression analysis showed that a selection of KPI currently used, particularly animal density, nutrient-use efficiency, and the amount of home-grown feed, explained a large portion of variation in NMB. Heifer-to-cow ratio and the relative proportion of various forage crops may provide further insight into the drivers of feed and fertilizer imports and ultimately farm-gate NMB. This study provides avenues toward a better assessment of whole-farm nutrient management and means for farms to communicate progress to stakeholders and consumers.

The Ruminant Farm Systems Animal Module: A Biophysical Description of Animal Management.

Dairy production is an important source of nutrients in the global food supply, but environmental impacts are increasingly a concern of consumers, scientists, and policy-makers. Many decisions must be integrated to support sustainable production-which can be achieved using a simulation model. We provide an example of the Ruminant Farm Systems (RuFaS) model to assess changes in the dairy system related to altered animal feed efficiency. RuFaS is a whole-system farm simulation model that simulates the individual animal life cycle, production, and environmental impacts. We added a stochastic animal-level parameter to represent individual animal feed efficiency as a result of reduced residual feed intake and compared High (intake = 94% of expected) and Very High (intake = 88% of expected) efficiency levels with a Baseline scenario (intake = 100% of expected). As expected, the simulated total feed intake was reduced by 6 and 12% for the High and Very High efficiency scenarios, and the expected impact of these improved efficiencies on the greenhouse gas emissions from enteric methane and manure storage was a decrease of 4.6 and 9.3%, respectively.