Economic Analysis of Pre and Postpartum Alphatocopherol Supplementation for Milk Performance and Dry Matter Intake of Dairy Cows in Tropical Region

The main aim of this study was to investigate experimentally the effect of different composition of non-structural carbohydrates (NFC) in prepartum feed rations administered to high-yielding dairy cows at a high concentration of NFC in the diet on dry matter intake both before and after parturition and on subsequent milk performance, body condition and physiological traits of rumen fluid and blood. Thirty-six high-yielding dairy cows were allocated into one of the three well-balanced groups (K, O, and C), and each group received a different feeding rations. Feeding rations differed in non-structural carbohydrate (NFC) structure. The "K" (control) group received a feeding ration with NFC in the form of maize starch in particular, while the feeding rations of the other two (experimental) groups contained either (besides maize starch) saccharose from dried sugar beet (the "O" group) or a dominant amount of NFC was in the form of saccharose (the "C" group). After calving, all dairy cows were given the same feeding ration from the first day after parturition. The experiment was conducted for 21 days before and 50 days after calving. FR in the form of total mixed ration was offered ad libitum. Dry matter intake, milk performance, body condition, live weight, and blood and rumen parameters were recorded for the duration of the experiment. Average daily dry matter intake before calving was highest in the "K" group (14.32 kg per head). Differences among groups were statistically significant (P < 0.05). Prepartum dry matter consumption dropped as the rate of saccharose in the diet of cows increased. Dry matter consumption levelled off after calving. Milk yield was also highest in the "K" group (43.71 kg/head/day), but fatness of milk and thus the production of fat corrected milk were lowest in this group. The highest milk fat content (4.10%) and fat corrected milk production (44.03 kg/head/day) were recorded in the "C" group, whereas the highest milk protein concentration was found in the milk of the "O" group. The composition of NFC affected dry matter intake before parturition, but these concentrations did not significantly affect dry matter intake, milk yield, milk composition, live weight, body condition or blood serum and rumen fluid parameters after calving

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Non-structural carbohydrates in the nutrition of high-yielding dairy cows during a transition period

Czech Journal of Animal Science ◽

10.17221/1710-cjas ◽

2010 ◽

Vol 55 (No. 1) ◽

pp. 468-478 ◽

Cited By ~ 1

Author(s):

K. Poláková ◽

V. Kudrna ◽

A. Kodeš ◽

B. Hučko ◽

Z. Mudřík

Keyword(s):

Dairy Cows ◽

Milk Yield ◽

Body Condition ◽

Dry Matter ◽

Rumen Fluid ◽

Live Weight ◽

Dry Matter Intake ◽

Control Group ◽

Maize Starch ◽

Milk Performance

The main aim of this study was to investigate experimentally the effect of different composition of non-structural carbohydrates (NFC) in prepartum feed rations administered to high-yielding dairy cows at a high concentration of NFC in the diet on dry matter intake both before and after parturition and on subsequent milk performance, body condition and physiological traits of rumen fluid and blood. Thirty-six high-yielding dairy cows were allocated into one of the three well-balanced groups (K, O, and C), and each group received a different feeding rations. Feeding rations differed in non-structural carbohydrate (NFC) structure. The "K" (control) group received a feeding ration with NFC in the form of maize starch in particular, while the feeding rations of the other two (experimental) groups contained either (besides maize starch) saccharose from dried sugar beet (the "O" group) or a dominant amount of NFC was in the form of saccharose (the "C" group). After calving, all dairy cows were given the same feeding ration from the first day after parturition. The experiment was conducted for 21 days before and 50 days after calving. FR in the form of total mixed ration was offered ad libitum. Dry matter intake, milk performance, body condition, live weight, and blood and rumen parameters were recorded for the duration of the experiment. Average daily dry matter intake before calving was highest in the "K" group (14.32 kg per head). Differences among groups were statistically significant (P < 0.05). Prepartum dry matter consumption dropped as the rate of saccharose in the diet of cows increased. Dry matter consumption levelled off after calving. Milk yield was also highest in the "K" group (43.71 kg/head/day), but fatness of milk and thus the production of fat corrected milk were lowest in this group. The highest milk fat content (4.10%) and fat corrected milk production (44.03 kg/head/day) were recorded in the "C" group, whereas the highest milk protein concentration was found in the milk of the "O" group. The composition of NFC affected dry matter intake before parturition, but these concentrations did not significantly affect dry matter intake, milk yield, milk composition, live weight, body condition or blood serum and rumen fluid parameters after calving.

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Effect of pre-grazing herbage mass and daily herbage allowance on perennial ryegrass swards structure, pasture dry matter intake and milk performance of Holstein-Friesian dairy cows

Spanish Journal of Agricultural Research ◽

10.5424/sjar/20110901-126-10 ◽

2011 ◽

Vol 9 (1) ◽

pp. 86 ◽

Cited By ~ 4

Author(s):

A. I. Roca-Fernandez ◽

M. A. O’Donovan ◽

J. Curran ◽

A. Gonzalez-Rodriguez

Keyword(s):

Dairy Cows ◽

Perennial Ryegrass ◽

Dry Matter ◽

Dry Matter Intake ◽

Holstein Friesian ◽

Milk Performance

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Progressive inclusion of pearl millet herbage as a supplement for dairy cows fed mixed rations: Effects on methane emissions, dry matter intake, and milk production

Journal of Dairy Science ◽

10.3168/jds.2020-18894 ◽

2020 ◽

Author(s):

M. Civiero ◽

R. Delagarde ◽

A. Berndt ◽

Jusiane Rosseto ◽

M.N. de Souza ◽

...

Keyword(s):

Dairy Cows ◽

Pearl Millet ◽

Milk Production ◽

Dry Matter ◽

Methane Emissions ◽

Dry Matter Intake ◽

Progressive Inclusion

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Fourier transform infrared spectroscopy of milk samples as a tool to estimate energy balance, energy- and dry matter intake in lactating dairy cows

Journal of Dairy Research ◽

10.1017/s0022029920001004 ◽

2020 ◽

pp. 1-8

Author(s):

Amira Rachah ◽

Olav Reksen ◽

Nils Kristian Afseth ◽

Valeria Tafintseva ◽

Sabine Ferneborg ◽

...

Keyword(s):

Dairy Cows ◽

Dry Matter ◽

External Validation ◽

Ftir Spectra ◽

Dry Matter Intake ◽

Energy Status ◽

Test Set ◽

Milk Samples ◽

External Test ◽

Body Energy

Abstract The objective of the study was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) analysis of milk samples to predict body energy status and related traits (energy balance (EB), dry matter intake (DMI) and efficient energy intake (EEI)) in lactating dairy cows. The data included 2371 milk samples from 63 Norwegian Red dairy cows collected during the first 105 days in milk (DIM). To predict the body energy status traits, calibration models were developed using Partial Least Squares Regression (PLSR). Calibration models were established using split-sample (leave-one cow-out) cross-validation approach and validated using an external test set. The PLSR method was implemented using just the FTIR spectra or using the FTIR together with milk yield (MY) or concentrate intake (CONCTR) as predictors of traits. Analyses were conducted for the entire first 105 DIM and separately for the two lactation periods: 5 ≤ DIM ≤ 55 and 55 < DIM ≤ 105. To test the models, an external validation using an independent test set was performed. Predictions depending on the parity (1st, 2nd and 3rd-to 6th parities) in early lactation were also investigated. Accuracy of prediction (r) for both cross-validation and external test set was defined as the correlation between the predicted and observed values for body energy status traits. Analyzing FTIR in combination with MY by PLSR, resulted in relatively high r-values to estimate EB (r = 0.63), DMI (r = 0.83), EEI (r = 0.84) using an external validation. Only moderate correlations between FTIR spectra and traits like EB, EEI and dry matter intake (DMI) have so far been published. Our hypothesis was that improvements in the FTIR predictions of EB, EEI and DMI can be obtained by (1) stratification into different stages of lactations and different parities, or (2) by adding additional information on milking and feeding traits. Stratification of the lactation stages improved predictions compared with the analyses including all data 5 ≤ DIM ≤105. The accuracy was improved if additional data (MY or CONCTR) were included in the prediction model. Furthermore, stratification into parity groups, improved the predictions of body energy status. Our results show that FTIR spectral data combined with MY or CONCTR can be used to obtain improved estimation of body energy status compared to only using the FTIR spectra in Norwegian Red dairy cattle. The best prediction results were achieved using FTIR spectra together with MY for early lactation. The results obtained in the study suggest that the modeling approach used in this paper can be considered as a viable method for predicting an individual cow's energy status.

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Predicting Daily Dry Matter Intake Using Feed Intake of First Two Hours after Feeding in Mid and Late Lactation Dairy Cows with Fed Ration Three Times Per Day

Animals ◽

10.3390/ani11010104 ◽

2021 ◽

Vol 11 (1) ◽

pp. 104

Author(s):

Shulin Liang ◽

Chaoqun Wu ◽

Wenchao Peng ◽

Jian-Xin Liu ◽

Hui-Zeng Sun

Keyword(s):

Dairy Cows ◽

Milk Yield ◽

Dry Matter ◽

Prediction Equation ◽

Dry Matter Intake ◽

Validation Dataset ◽

Nutrient Requirements ◽

Independent Dataset ◽

Lactating Dairy Cows ◽

Proposed Model

The objective of this study was to evaluate the feasibility of using the dry matter intake of first 2 h after feeding (DMI-2h), body weight (BW), and milk yield to estimate daily DMI in mid and late lactating dairy cows with fed ration three times per day. Our dataset included 2840 individual observations from 76 cows enrolled in two studies, of which 2259 observations served as development dataset (DDS) from 54 cows and 581 observations acted as the validation dataset (VDS) from 22 cows. The descriptive statistics of these variables were 26.0 ± 2.77 kg/day (mean ± standard deviation) of DMI, 14.9 ± 3.68 kg/day of DMI-2h, 35.0 ± 5.48 kg/day of milk yield, and 636 ± 82.6 kg/day of BW in DDS and 23.2 ± 4.72 kg/day of DMI, 12.6 ± 4.08 kg/day of DMI-2h, 30.4 ± 5.85 kg/day of milk yield, and 597 ± 63.7 kg/day of BW in VDS, respectively. A multiple regression analysis was conducted using the REG procedure of SAS to develop the forecasting models for DMI. The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × BW (kg/day) (R2 = 0.46, mean bias = 0 kg/day, RMSPE = 1.26 kg/day). Moreover, when compared with the prediction equation for DMI in Nutrient Requirements of Dairy Cattle (2001) using the independent dataset (VDS), our proposed model shows higher R2 (0.22 vs. 0.07) and smaller mean bias (−0.10 vs. 1.52 kg/day) and RMSPE (1.77 vs. 2.34 kg/day). Overall, we constructed a feasible forecasting model with better precision and accuracy in predicting daily DMI of dairy cows in mid and late lactation when fed ration three times per day.

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