New York and Vermont Corn Silage Hybrid Trials

The corn silage hybrid evaluation program expanded to 77 hybrids in 2018. Hybrid evaluation at multiple environments helps in decision making and expands the reach of this type of data to more farmers. With this in mind Cornell, UVM, and seed companies collaborate to bring this robust evaluation. This year, hybrids were either entered into the 80-95 day relative maturity (RM) group (Early-Mid) and were tested at two locations in NY (n = 20; Hu-Lane Farm in Albion and the Willsboro Research Farm in Willsboro) and one location in VT (n = 20; Borderview Farm in Alburgh) or were entered into the 96-110 day relative maturity group (Mid-Late) and were tested at two locations in NY (n = 57; Greenwood Farms in Madrid and the Musgrave Research Farm in Aurora) and one location in VT (n = 55; Borderview Farm in Alburgh). The average Growing Degree Days (GDD; 86-50°F system) from May through August for years 2005 to 2018 is 2053 GGD at Albion, 2039 at Willsboro, 1979 at Alburgh, 2078 at Aurora and 1953 at Madrid (Table 1a and 1b)

Can herbage nitrogen fractionation in Lolium perenne be improved by herbage management?

peer-reviewedThe high degradability of grass protein is an important factor in the low nitrogen (N) utilization of grazing bovines in intensive European grassland systems. We tested the hypothesis that protein degradability as measured by the Cornell Net Carbohydrate and Protein System (CNCPS) protein fractionation scheme, can be manipulated by herbage management tools, with the aim to reduce N loss to the environment. A field experiment comprising the factorial combinations of three fertilizer N application rates (0, 90 and 390 kg N ha−1 year−1), three regrowth periods (2–3, 4–5, and 6–7 weeks), two perennial ryegrass (Lolium perenne L.) cultivars [Aberdart (high sugar content) and Respect (low sugar content)] and two cutting heights (approximately 8 and 12 cm) was conducted at Teagasc, Johnstown Castle Research Centre, Wexford, Ireland. The plots were sampled during four seasons [September/October 2002 (late season), April 2003 (early season), May/June 2003 (mid season) and September 2003 (late season)] and protein fractions were determined in both sheath and lamina material. The protein was highly soluble and on average 19% and 28% of total N was in the form of non-protein N, 16% and 19% in the form of buffer-soluble protein, 52% and 40% in the form of buffer-insoluble protein, and 12% and 13% in the form of potentially available cell wall N for lamina and sheath material, respectively. In both materials only 0.9% of total N was present as unavailable cell wall N. In general the herbage management tools investigated did not have much effect on protein fractionation. The effects of regrowth period, cultivar and cutting height were small and inconsistent. High N application rates significantly increased protein degradability, especially during late season. This is relevant, as it has been shown that enhanced protein degradation increases the potential N loss through urine excretion at a time when urine-N excreted onto pasture is prone to leaching. However, the effect was most evident for sheath material, which forms only a small proportion of the animals' intake. It was concluded that there appears to be little scope for manipulating the herbage-N fractionation through herbage management. The consequences for modelling herbage quality could be positive as there does not seem to be a need to model the individual N fractions; in most cases the N fractions can be expressed as a fixed proportion of total N instead

Measuring Fiber Digestibility

Through neutral detergent chemistry, feed is divided into a soluble fraction that is rapidly and almost completely available, and a fiber fraction that is more slowly and incompletely degraded by microbial enzymes. However, this fiber fraction might contain contaminants, such as starch, protein and ash, that can artificially inflate the concentration of NDF measured. If an artificially high NDF concentration is measured, for example in feeds with high soil contamination, the diet formulation becomes difficult, especially when balancing to low levels of diet NDF concentration. To overcome these issues, David Mertens published a method that included the option of using alpha-amylase, sodium sulfite and correcting for ash contamination and abbreviated aNDFom. Nutritionally, this is the most useful approach as it reduces the unwanted variability and contaminants in the measurement of the cell wall material. From the analysis required to quantify the intrinsic plant factors involved in aNDFom digestion we can determine the size of the fast, slow and undigested aNDFom pools and their respective rates of degradation. Within the construct of the CNCPS, this information is integrated with animal factors such as dry matter intake, passage rate, rumen pH and ammonia levels in a dynamic mechanistic approach. The latest version of CNCPS 7.0 has the capability to predict rumen pools of fast, slow and undigested aNDFom over time. These calculations are based on the constant battle between degradation and passage out of the rumen. Such an approach can help forward predict the animal response to variable feed quality, such as this years corn silage inventory

Enteric Methane Emissions Prediction in Dairy Cattle and Effects of Monensin on Methane Emissions: A Meta-Analysis

Greenhouse gas emissions, such as enteric methane (CH4) from ruminant livestock, have been linked to global warming. Thus, easily applicable CH4 management strategies, including the inclusion of dietary additives, should be in place. The objectives of the current study were to: (i) compile a database of animal records that supplemented monensin and investigate the effect of monensin on CH4 emissions; (ii) identify the principal dietary, animal, and lactation performance input variables that predict enteric CH4 production (g/d) and yield (g/kg of dry matter intake DMI); (iii) develop empirical models that predict CH4 production and yield in dairy cattle; and (iv) evaluate the newly developed models and published models in the literature. A significant reduction in CH4 production and yield of 5.4% and 4.0%, respectively, was found with a monensin supplementation of ≤24 mg/kg DM. However, no robust models were developed from the monensin database because of inadequate observations under the current paper’s inclusion/exclusion criteria. Thus, further long-term in vivo studies of monensin supplementation at ≤24 mg/kg DMI in dairy cattle on CH4 emissions specifically beyond 21 days of feeding are reported to ensure the monensin effects on the enteric CH4 are needed. In order to explore CH4 predictions independent of monensin, additional studies were added to the database. Subsequently, dairy cattle CH4 production prediction models were developed using a database generated from 18 in vivo studies, which included 61 treatment means from the combined data of lactating and non-lactating cows (COM) with a subset of 48 treatment means for lactating cows (LAC database). A leave-one-out cross-validation of the derived models showed that a DMI-only predictor model had a similar root mean square prediction error as a percentage of the mean observed value (RMSPE, %) on the COM and LAC database of 14.7 and 14.1%, respectively, and it was the key predictor of CH4 production. All databases observed an improvement in prediction abilities in CH4 production with DMI in the models along with dietary forage proportion inclusion and the quadratic term of dietary forage proportion. For the COM database, the CH4 yield was best predicted by the dietary forage proportion only, while the LAC database was for dietary forage proportion, milk fat, and protein yields. The best newly developed models showed improved predictions of CH4 emission compared to other published equations. Our results indicate that the inclusion of dietary composition along with DMI can provide an improved CH4 production prediction in dairy cattle

Palmitic Acid, Milk Fat, and Hard Butter - What's All of the Fuss About?

The purpose of this fact sheet is to provide information on how milk fat is synthesized and to provide additional context for the consumer and those involved in the discussion

Predicting orthophosphate in feces and manure from dairy cattle

Dairy cattle excreta are a valuable source of orthophosphate (Ortho-P), an inorganic form of phosphorus (P) that is readily available for microorganisms, plant growth, and development. There is, however, a growing environmental concern about the potential negative environmental impact of excessive amounts of Ortho-P excretion, which can lead to the eutrophication of water bodies. As a result, the development of mathematical equations to quantify and manage Ortho-P excretion on dairy farms could prove valuable for environmental sustainability. This study aimed to use literature data to develop empirical predictions for Ortho-P (g/kg dry matter [DM]) excretion using total P (TP [g/kg DM]) content of dairy cattle feces (Ortho-Pf) and manure (Ortho-Pm). Data sets from studies that evaluated and characterized the different forms of P in feces and manure from dairy cattle were compiled. After outlier exclusion, the final retained database for feces included 37 treatment means from 4 published papers while the manure comprised 23 treatment means from 7 published papers. A linear-mixed model was used to develop the predictive equations, incorporating the random effect of the study. A leave-one-out cross-validation procedure was used to evaluate the predictive ability of the developed models, whereby studies were regarded as folds. The fecal equation was determined as Ortho-Pf (g/kg DM) = −2.447 (0.572) + 0.966 (0.083) × TP (g/kg DM) (R2 = 0.79) and resulted in a root mean square prediction error as a percentage of mean observed value (RMSPE, %) of 32.8% and error due to random sources of 97.6%. Additionally, the manure equation was determined as Ortho-Pm (g/kg) = −0.204 (0.446) + 0.590 (0.065) × TP (g/kg) (R2 = 0.77) and had an RMSPE of 43.3% with a random error of 93.9%. Both models revealed minimal mean and slope biases on feces and manure data. Findings suggest that these sets of equations can be used to estimate excreted Ortho-P from total excreted P, helping nutritionists and farmers to understand the impact of dietary P changes on the environment. Further, these equations can be incorporated into extant models such as the Cornell Net Carbohydrate and Protein System (CNCPS) to aid in understanding and mitigating P and Ortho-P excretion from dairy cattle and to clarify the portion of P that migrates more rapidly into watersheds

Balancing dairy cattle diets for rumen nitrogen and methionine or all essentail amino acids relative to metabolizable energy

Improving the ability of diet formulation models to more accurately predict AA supply while appropriately describing requirements for lactating dairy cattle pro- vides an opportunity to improve animal productivity, reduce feed costs, and reduce N intake. The goal of this study was to evaluate the sensitivity of a new version of the Cornell Net Carbohydrate and Protein System (CNCPS) to formulate diets for rumen N, Met, and all essential AA (EAA). Sixty-four high-producing dairy cattle were randomly assigned to 1 of the 4 following diets in a 14-wk longitudinal study: (1) limited metabo- lizable protein (MP), Met, and rumen N (Base), (2) adequate Met but limited MP and rumen N (Base + M), (3) adequate Met and rumen N, but limited MP (Base + MU), and (4) adequate MP, rumen N, and bal- anced for all EAA (Positive). All diets were balanced to exceed requirements for ME relative to maintenance and production, assuming a nonpregnant, 650-kg ani- mal producing 40 kg of milk at 3.05% true protein and 4.0% fat. Dietary MP was 97.2, 97.5, 102.3, and 114.1 g/kg of dry matter intake for the Base, Base + M, Base + MU, and Positive diets, respectively. Differences were observed for dry matter intake and milk yield (24.1 to 24.7 and 39.4 to 41.1 kg/d, among treatments). Energy corrected milk, fat, and true protein yield were greater (2.9, 0.13, and 0.08 kg/d, respectively) in cows fed the Positive compared with the Base diet. Using the updat- ed CNCPS, cattle fed the Base, Base + M, and Base + MU diets were predicted to have a negative MP balance (−231, −310, and −142 g/d, respectively), whereas cattle fed the Positive diet consumed 33 g of MP/d excess to ME supply. Bacterial growth was predicted to be depressed by 16 and 17% relative to adequate N supply for the Base and Base + M diets, respectively, which corresponded with the measured lower apparent total-tract NDF degradation. The study demonstrates that improvements in lactation performances can be achieved when rumen N and Met are properly supplied and further improved when EAA supply are balanced relative to requirements. Formulation using the revised CNCPS provided predictions for these diets, which were sensitive to changes in rumen N, Met, all EAA, and by extension MP supply

Best timing of harvest for brown midrib forage sorghum yield, nutritive value, and ration performance

Forage sorghum is a drought- and heat-tolerant warm-season grass that can be used for silage on dairy farms. Since it requires a soil temperature of at least 60°F for planting, the recommended planting time for New York is early June, unlike corn, which is usually planted earlier in the spring. This would allow time for a forage winter cereal harvest in mid- to late-May prior to sorghum planting. Forage sorghum also has comparable forage quality to corn silage for most parameters except for starch, which is typically lower in forage sorghum. The main question for this research was: Can forage sorghum be harvested in time for establishment of a fall cover crop or winter cereal double-crop in New York? To answer this question, we conducted seven trials in central New York from 2014 through 2017 to evaluate the impact of harvesting at the boot, flower, and milk growth stages versus the traditional soft dough stage on the yield and forage quality of a brown midrib (BMR) forage sorghum variety