350 Nutrient Movement in the Environment: Confined versus Grazing Systems

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 192-193
Author(s):  
Andre F Brito ◽  
Kleves V Almeida

Abstract Grazing systems perform multiple ecosystem services including food production, climate regulation, nutrient cycling, and erosion control. Ruminants can also express their natural behaviors on pasture, with recent research revealing that dairy cows were more motivated to go outside for grazing than stay indoors consuming fresh TMR offered immediately after the afternoon milking. In addition, consumers often associate grazing systems with “healthier and happier cows” and are willing to pay premiums for “grass-fed” dairy products. However, milk production and nutrient utilization generally decrease in pasture-based compared with confinement systems, which may reduce farm profitability depending on milk pay prices. It should be noted that there is limited research reporting milk N efficiency (milk N/N intake) or methane emissions in confined versus grazing dairy cows using data collected from the same experiments. Therefore, our overarching objective was to build data sets to compare nutrient utilization in dairy cows under confinement or grazing management where milk N efficiency or methane emissions or both were reported in the same study. Dietary strategies to mitigate methane emissions in grazing dairy systems such as the use of high-quality forages (e.g., brassicas, perennial ryegrass), concentrate and seaweed supplementation, and forage species and management will be explored. For instance, Jersey cows grazing forage canola offered at 40% of the total DM emitted 31% less methane than those kept indoors and fed TMR (419 vs. 289 g/d, respectively) in an experiment conducted at the University of New Hampshire. Methane yield and methane intensity also decreased (P < 0.001) by 29.3% and 23.4%, respectively, in the same study. Irish researchers reported that methane production (-37%), yield (-11.5%), and intensity (-13%) decreased significantly in Holstein-Friesian cows offered perennial ryegrass herbage versus TMR. Data from whole-farm models comparing confinement and grazing systems will be presented and discussed.

2020 ◽  
Vol 60 (1) ◽  
pp. 143 ◽  
Author(s):  
Bríd McClearn ◽  
Trevor Gilliland ◽  
Clare Guy ◽  
Michael Dineen ◽  
Fergal Coughlan ◽  
...  

Grazed grass is considered the cheapest feed available for dairy cows in temperate regions, and to maximise profits, dairy farmers must utilise this high-quality feed where possible. Recent research has reported that including white clover (Trifolium repens L.) in grass swards can have a positive effect on milk production. The aim of the present study was to quantify the effect of tetraploid and diploid perennial ryegrass (Lolium perenne L.; PRG) swards sown with and without white clover on the milk production of grazing dairy cows. Four grazing treatments were used for the study; tetraploid-only PRG swards, diploid-only PRG swards, tetraploid PRG with white clover swards and diploid PRG with white clover swards. Thirty cows were assigned to each treatment and swards were rotationally grazed at a stocking rate of 2.75 cows/ha and a nitrogen-fertiliser application rate of 250 kg/ha annually. There was no significant effect of ploidy on milk production. Over the present 4-year study, cows grazing the PRG–white clover treatments had greater milk yields (+597 kg/cow.year) and milk-solid yield (+48 kg/cow.year) than cows grazing the PRG-only treatments. This significant increase in milk production suggests that the inclusion of white clover in grazing systems can be effectively used to increase milk production of grazing dairy cows.


2003 ◽  
Vol 93 (4) ◽  
pp. 467-477 ◽  
Author(s):  
W. F. Pfender

A weather-based infection model for stem rust of perennial ryegrass seed crops was developed and tested using data from inoculated bioassay plants in a field environment with monitored weather. The model describes favorability of daily weather as a proportion (0.0 to 1.0) of the maximum possible infection level set by host and inoculum. Moisture duration and temperature are combined in one factor as wet degree-hours (DHw) (i.e., degree-hours > 2.0°C summed only over time intervals when) moisture is present). Degree-hours are weighted as a function of temperature, based on observed rates of urediniospore germination. The pathogen Puccinia graminis subsp. graminicola requires favorable conditions of temperature and moisture during the night (dark period) and also at the beginning of the morning (light period), and both periods are included in the model. There is a correction factor for reduced favorability if the dark wet period is interrupted. The model is: proportion of maximum infection = 1 - e(-0.0031) × (DHw Index), where DHw Index is the product of interruption-adjusted overnight weighted DHw multiplied by morning (first 2 h after sunrise) weighted DHw. The model can be run easily with measurements from automated dataloggers that record temperature and wetness readings at frequent time intervals. In tests with three independent data sets, the model accounted for 80% of the variance in log(observed infection level) across three orders of magnitude, and the regression lines for predicted and observed values were not significantly different from log(observed) = log(predicted). A simpler version of the model using nonweighted degree hours (>2.0°C) was developed and tested. It performed nearly as well as the weighted-degree-hour model under conditions when temperatures from sunset to 2 h past sunrise were mostly between 4 and 20°C, as is the case during the growing season in the major U.S. production region for cool-season grass seed. The infection model is intended for use in combination with measured or modeled estimates of inoculum level, to derive estimates of daily infection.


2021 ◽  
Vol 11 (03) ◽  
pp. 440-457
Author(s):  
Cecilia Loza ◽  
José Gere ◽  
María Soledad Orcasberro ◽  
Alberto Casal ◽  
Mariana Carriquiry ◽  
...  

1997 ◽  
Vol 129 (4) ◽  
pp. 459-469 ◽  
Author(s):  
P. J. MOATE ◽  
T. CLARKE ◽  
L. H. DAVIS ◽  
R. H. LABY

Results are reported from three experiments conducted at the Dairy Research Institute, Ellinbank, Australia during 1992/93 which examined the composition and kinetics of the gas in the rumen headspace of lactating dairy cows grazing white clover/perennial ryegrass pastures. Before grazing, rumen headspace gas was composed of carbon dioxide 65%, methane 31% and nitrogen 4% whereas, after one hour of active grazing, the headspace gas was composed of carbon dioxide 76%, methane 22% and nitrogen 2%. The composition of headspace gas was not affected by antibloat capsules (which release 250 mg/day of monensin). The headspace gas from bloated cows contained slightly less (P<0·01) carbon dioxide and slightly more nitrogen than that from non-bloated cows.A novel technique which employs ethane as a tracer to measure rumen headspace volume and the kinetics of the rumen headspace gases is described. The tracer technique was used in two experiments in which the influence of grazing, antibloat capsules and bloat on the rumen headspace volume and the kinetics of the headspace gases were examined. It is concluded that our ethane tracer technique provides a simple and inexpensive way to estimate methane production by grazing ruminants.


1995 ◽  
Vol 60 (1) ◽  
pp. 25-30 ◽  
Author(s):  
A. J. Rook ◽  
C. A. Huckle

AbstractThe synchronization of eating, ruminating and idling activity by lactating dairy cows grazing a perennial ryegrass-white clover sward was studied. Synchronization was defined as the number of pairs of cows engaged in a particular activity as a proportion of the total possible number of pairs and was compared with random expectation using a kappa statistic. All three activities were significantly more synchronized than random expectation. This suggests that wherever possible individual cows should not be treated as replicates in grazing experiments.


2006 ◽  
Vol 89 (9) ◽  
pp. 3494-3500 ◽  
Author(s):  
B.M. Tas ◽  
H.Z. Taweel ◽  
H.J. Smit ◽  
A. Elgersma ◽  
J. Dijkstra ◽  
...  

2015 ◽  
Vol 175 ◽  
pp. 37-46 ◽  
Author(s):  
Camila Muñoz ◽  
Sara Hube ◽  
Jorge M. Morales ◽  
Tianhai Yan ◽  
Emilio M. Ungerfeld

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 158-158
Author(s):  
Jan C Plaizier ◽  
Allan Kotz ◽  
Khafipour Khafipour

Abstract Relationships between enteral methane emissions and abundances of bacterial taxa in the feces of dairy cows were determined to assess if these abundances can be used to predict these emissions. Six mature non-lactating Holstein dairy cows on diets with forage (alfalfa/grass hay) to grain ratios of 100:0, 75:25, and 50:50 were used during 5-wk experimental periods in a replicated 3x3 Latin Square Design. Dietary NDF and starch concentrations ranged from 38.7 to 56.0 % DM and from 0.5 to 19.5 % DM among diets, respectively. Methane outputs were measured using an open-hood calorimetric system during two 24 h periods on two separate days during the fifth week of experimental periods. Daily methane emissions ranged from 288.5 to 588.5 L/d among cows and diets, and averaged 413.4 L/d. Feces were sampled twice daily on the days preceding methane measurements. Compositions of the microbiota in feces were determined using Illumina 16S rRNA sequencing. Linear regression models were developed using the MIXED procedure of the SAS to determine the relationships between daily methane emissions (L/d) and the abundances of bacterial taxa in feces (%). Relative abundances of taxa that were significantly correlated with methane emissions and that were present in at least 30% of feces samples were included in the initial model. Abundances with a significance level greater than 0.25 were stepwise removed from the model. Taxa with high correlation coefficients (r &gt; 0.75) were not placed together in models. The final model included the abundances of 6 bacterial families in the feces, and had an R2, CV, and root MSE values of 0.92, 4.93, and 20.2, respectively. These results suggest that enteral methane emissions can be predicted from the bacterial composition of the feces. However, model validation with different data sets and diets is needed.


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