What Do High Producing Herds Really Feed? (original) (raw)
Related papers
Developing nutrition programs for high producing dairy herds
Journal of dairy science, 1993
Herd average milk production continues to increase in the US. Average milk production in Holstein herds enrolled in DHI testing programs surpassed 9000 kg in some states in 1991. Individual dairy herds have produced > 14,000 kg per cow per lactation. The upper limit for milk production per cow continues to increase. A challenge exists in developing nutrition programs for these herds. The goal is to attain efficient and profitable levels of milk production while maintaining herd health and reproductive performance. Evaluation of rations currently fed to high producing herds indicate that these rations are consistent with current nutrient requirement guidelines. Many high producing herds have average DMI > 4% of BW. Ration formulation principles and nutrient requirements used in development of feeding programs for high producing herds are similar to methods already in use. Optimizing DMI, optimizing rumen fermentation, and providing supplemental nutrients are key factors in meet...
Animal Feed Science and Technology, 2008
A simulation rumen model has been developed to function under non-steady state conditions in order to allow prediction of nutrient availability in dairy cows managed under discontinuous feeding systems. The model simulates availability of glycogenic, aminogenic and lipogenic nutrients to lactating dairy cows fed discontinuously. The model structure considers input of up to three different feeds fed independently at any time during the day. Feeds are described by their nitrogen (N), carbohydrate and fatty acid fractions. The N containing feed fractions include ruminally undegraded crude protein (CP), ruminally insoluble but potentially degradable CP, ruminally soluble CP and ammonia N. The feed carbohydrate fractions include ruminally undegradable neutral detergent fibre (NDF), ruminally degradable NDF, ruminally insoluble starch, ruminally soluble starch and sugars. The fatty acids in the feeds are divided between long chain fatty acids and volatile fatty acids (VFA). Additionally four pools were defined representing absorption of amino acids, glucose, long chain fatty acids and ଝ This paper is part of a special issue entitled "Mathematical Methods that Predict the Effects of Feed Characteristics on Animal Performance" guest edited by Essi Evans, Daniel Sauvant and Peter Udén. 149 volatile fatty acids. The rumen microbial population is represented as a single pool. Besides a flexible structure, new features to the extant model include adoption of the concept of chewing efficiency (or chewing effectiveness) during eating, variable fractional ruminal absorption rates of VFA and variable fractional ruminal degradation rates of NDF as a function of rumen liquid pH, as well as a variable rumen volume which directly affects rumen concentrations of metabolites. The model continuously (i.e., by minute) predicts release of soluble components from the feeds in the rumen, concentration and absorption of fermentation end products in the rumen, rumen pools of nutrients and microbial biomass dynamics, as well as passage of microbial biomass and non-fermented nutrients from the rumen, in response to various feeding strategies. Model evaluation covered a wide range of feeding strategies that included pasture and housed feeding systems. Overall, the mean square prediction error (MSPE) as a percentage of the observed mean was relatively low (<10%) with a high amount of the total variation explained by random variation (>65%). Deviation from unity varied between 23% (rumen dry matter content) and 25% (NDF), indicating some consistent over and/or under prediction. A more detailed evaluation was done based on studies available that reported diurnal behaviour of key model outputs such as rumen pools, rumen pH, and rumen VFA. The predictions broadly simulated the observed values quantitatively, relative to general diurnal patterns, and relative to differences between treatments in the predicted diurnal patterns. Results show that the model provides a tool to assess potential outcomes of changing feeding strategies which may be particularly valuable in assessing selection of feeds, amounts and times of the day to offer the feeds. The continuous nature of the simulated output also allows determination of the time(s) of the day that ruminal (and/or postruminal) delivery of nutrients may limit ruminal output of nutrients (and/or availability of nutrients) to support milk nutrient synthesis.
The assessment of supplementation requirements of grazing ruminants using nutrition models
Translational Animal Science
This paper was aimed to summarize known concepts needed to comprehend the intricate interface between the ruminant animal and the pasture when predicting animal performance, acknowledge current efforts in the mathematical modeling domain of grazing ruminants, and highlight current thinking and technologies that can guide the development of advanced mathematical modeling tools for grazing ruminants. The scientific knowledge of factors that affect intake of ruminants is broad and rich, and decision-support tools (DST) for modeling energy expenditure and feed intake of grazing animals abound in the literature but the adequate predictability of forage intake is still lacking, remaining a major challenge that has been deceiving at times. Despite the mathematical advancements in translating experimental research of grazing ruminants into DST, numerous shortages have been identified in current models designed to predict intake of forages by grazing ruminants. Many of which are mechanistic ...
2001
Studies were conducted to determine: 1) the effect of pasture allowance on substitution rate, pasture and total dry matter intake, milk production and composition, rumen digestion, and grazing behavior of unsupplemented and supplemented high producing dairy cows in early-mid lactation, and 2) the effect of feeding systems combining pasture and total mixed rations on dry matter intake, milk production and composition, body weight and body condition score changes, rumen digestion and grazing behavior of high producing dairy cows in early-mid lactation. Pastures grazed in the studies were based on smooth bromegrass (Bromus inermis L.) and orchardgrass (Dactylis glomerata L.). In the first study, twenty multiparous Holstein cows (4 ruminally cannulated) in five 4 x 4 Latin squares with 21-d periods were used to study the effect of concentrate supplementation when grazed at two pasture allowances. The four dietary treatments resulted from the combination of two pasture allowance targets ...
Mi lk Production of Dairy Cows Fed Total Mixed Rations After a Grazing Period
Twenty multiparous Holstein cows were used in a completely randomized design with repeated measures to study milk production of cows supplemented or not supplemented with concentrate when they were switched to a total mixed ration (TMR) after grazing. In one group, cows grazed an or-chardgrass/bromegrass pasture and were assigned to one of two treatments: 1) unsupplemented (U; 1 kg/d mineral mix) or 2) concentrate supplemented (CS; 1 kg corn-based concen-trate/4 kg milk). Total DMI was greater (26.5 vs 22.0 kg/d), but pasture DMI was less (16.8 vs 21.2 kg/ d), for CS cows because of the substitution rate of 0.49 kg pasture/kg concentrate. Overall, CS cows had greater 3.5% fat-corrected milk (FCM) (32.9 vs 26.5 kg/d), but less milk urea N (MUN; 9.6 vs 14.7 mg/dL) and milk fat (3.13% vs 3.88%), than U cows. Milk response to supplementation averaged 1.08 kg milk/kg concentrate. Cows assigned to both treatments lost BW (−17 kg/d) and body condition score (BCS) (−0.33). At the end of the 6-wk grazing period, all cows were switched to a TMR fed in confinement for 11 wk. Overall, DMI (24.3 kg/d), 1 To whom correspondence should be addressed: lmuller@psu.edu 3.5% FCM (30.6 kg/d), milk fat (3.26%), milk true protein (2.87%), and MUN (12.7 mg/dL) did not differ between treatments. Cows gained BW (53 kg) and BCS (0.33). A significant treatment × time interaction was found for milk yield. During the first day of TMR feeding, milk yield was greater (30.9 vs 19.3 kg/d) for CS cows. After 10 d on a TMR, milk yields between cows that had previously been on the U or CS treatments did not differ (35.5 kg/d). When cows were switched from only pasture to a TMR, milk yield was comparable with that of cows fed CS after 10 d. Lack of carry-over effects of previous treatments and increased production suggest improvement in nutrition and the potential for greater animal well-being for cows housed in a tie-stall barn and fed a nutritionally complete TMR.
New Zealand Journal of Agricultural Research, 2010
The main effects of, and the interactions between, stocking rate (SR), supplementation and genotype on dry matter (DM) intake, herbage utilisation, milk production and profitability of grazing dairy systems have been reviewed. The SR determines the average herbage allowance (HA) per cow and therefore has a major effect on herbage intake (HI) and on the productivity of grazing dairy systems. In this review, the effect of HA on HI is presented separately for two groups of studies: those that measured allowance at ground level and those that measured allowance at a cutting height of 3Á5 cm above ground level. HI and milk yield per hectare usually increase as SR increases. However, there is generally an associated reduction in HI and milk yield per cow because of the decrease in average HA at a higher SR. The dual objectives of adequate level of feeding per cow and high herbage utilisation per hectare can be achieved through the inclusion of supplements. The milk response to supplements depends mainly on the size of the relative energy deficit between potential energy demand and actual energy supply. The relative energy deficit determines both energy partitioning within the cow and substitution rate. The relative energy deficit is increased by either a high demand for energy within the cow or by a deficit of dietary energy available to meet the demand. Cows of different genotype differ in their potential for milk yield. Cows with high genetic potential for milk yield undergo higher relative energy deficits under grazing dairy systems, resulting in lower substitution rates, higher milk responses to supplements, but also lower body condition score, which, in turn, leads to lower reproductive performance. Whole-farm experiments in many countries have demonstrated that the inclusion of supplements, with a concomitant increase in SR, can have synergistic effects in improving the productivity of grazing dairy systems. Overall, the level of supplementation required per cow and the optimum SR depend on the genetic potential of the cow, the size of the responses to supplement, the value of milk and the costs of feeding supplements.
Nutritional strategies for small herds
1994
Effective herd health programs are traditionally based on strategies to control disease prevalence so that farm profit is maximized. In the past veterinary services focused on improving production efficiency primarily through reproductive consultation and mastitis control programs. Veterinary nutritional consulting has only recently become an integral component of herd health programs and it is gradually becoming recognized that sound management advice in this area may have a greater impact on economic efficiency than other traditional services. A survey of ration evaluation revealed a mean potential 14% feed cost saving through ration reformulation. Nutrition is an important economic input component for the dairy herd, large or small. Feed can range from 40-60% of the value of milk production depending on herd size and on efficiency of production. Small herds, which do not enjoy the economies of scale effects on feed prices realized by large herds will tend to have higher feed cost...
Evaluation of different feed intake models for dairy cows
The objective of the current study was to evaluate feed intake prediction models of varying complexity using individual observations of lactating cows subjected to experimental dietary treatments in periodic sequences (i.e., change-over trials). Observed or previous period animal data were combined with the current period feed data in the evaluations of the different feed intake prediction models. This would illustrate the situation and amount of available data when formulating rations for dairy cows in practice and test the robustness of the models when milk yield is used in feed intake predictions. The models to be evaluated in the current study were chosen based on the input data required in the models and the applicability to Nordic conditions. A data set comprising 2,161 total individual observations was constructed from 24 trials conducted at research barns in Denmark, Finland, Norway, and Sweden. Prediction models were evaluated by residual analysis using mixed and simple model regression. Great variation in animal and feed factors was observed in the data set, with ranges in total dry matter intake (DMI) from 10.4 to 30.8 kg/d, forage DMI from 4.1 to 23.0 kg/d, and milk yield from 8.4 to 51.1 kg/d. The mean biases of DMI predictions for the National Research Council, the Cornell Net Carbohydrate and Protein System, the British, Finnish, and Scandinavian models were −1.71, 0.67, 2.80, 0.83, −0.60 kg/d with prediction errors of 2.33, 1.71, 3.19, 1.62, and 2.03 kg/d, respectively, when observed milk yield was used in the predictions. The performance of the models were ranked the same, using either mixed or simple model regression analysis, but generally the random contribution to the prediction error increased with simple rather than mixed model regression analysis. The prediction error of all models was generally greater when using previous period data compared with the observed milk yield. When the average milk yield over all periods was used in the predictions of feed intake, the increase in prediction error of all models was generally less than when compared with previous period animal data combined with current feed data. Milk yield as a model input in intake predictions can be substantially affected by current dietary factors. Milk yield can be used as model input when formulating rations aiming to sustain a given milk yield, but can generate large errors in estimates of future feed intake and milk production if the economically optimal diet deviates from the current diet.