scholarly journals Influence of estrus on dry matter intake, water intake and BW of dairy cows

animal ◽  
2014 ◽  
Vol 8 (5) ◽  
pp. 748-753 ◽  
Author(s):  
S. Reith ◽  
M. Pries ◽  
C. Verhülsdonk ◽  
H. Brandt ◽  
S. Hoy
Keyword(s):  
Dairy Cows ◽  
Water Intake ◽  
Dry Matter ◽  
Dry Matter Intake ◽  
Intake Water

scholarly journals Productive and economic performance of goats of different genetic groups

2018 ◽  
Vol 19 (4) ◽  
pp. 381-390 ◽  
Author(s):  
Ana Barros Oliveira ◽  
Wandrick Hauss Sousa ◽  
Flávio Gomes Oliveira ◽  
Felipe Queiroga Cartaxo ◽  
Edgard Cavalcante Pimenta Filho ◽  
...  
Keyword(s):  
Weight Gain ◽  
Water Intake ◽  
Economic Performance ◽  
Dry Matter ◽  
Profit Margin ◽  
Dry Matter Intake ◽  
Genetic Groups ◽  
Significant Difference ◽  
The Cost ◽  
Intake Water

SUMMARY This study aimed to evaluate the bio-economic performance in confinement crossbred goats from different genetic groups. Were used 30 goats, crossbred (F1) intact male, 10 goats Boer x SPRD (undefined breed), 10 x Savannah SPRD and 10 Oberhasli x SPRD, with an average weight of 15 kg and an average age of 100 days. The initial weight was evaluated, final body weight, average daily gain, total weight gain, dry matter intake, water intake, feed conversion and days on feed. As an economic indicator was calculated gross profit margin (MB), the average dry matter intake, the confinement period, the cost of each diet and the cost of vaccines and medicines. We used the 5% Tukey test for comparisons between treatment means. For the variables weight gain, dry matter intake, water intake and body condition score averages observed did not differ between the genetic groups. There was significant effect (P> 0.05) of genetic groups on days on feed. The biological performance of the goats finished in feedlot was not influenced by genetic group. In bioeconomic analysis was no significant difference (P> 0.05) between the evaluated racial groups. Gross profit margin was negative for the mestizos Pardo Alpine x SPRD. The cross between the Boer breed and without defined breed results in premature animals, reaching slaughter weights with reduced confinement period. In the feedlot finishing system crossbred Boer goats x SPRD showed better economic performance, providing greater profitability to the creator.

Replacement of grain maize with spineless cactus in the diet of dairy goats

2021 ◽  
pp. 1-5
Author(s):  
Genildo Fonseca Pereira ◽  
João Virgínio Emerenciano Neto ◽  
Ângela Patrícia Alves Coelho Gracindo ◽  
Yhêlda Maria de Oliveira Silva ◽  
Gelson dos Santos Difante ◽  
...  
Keyword(s):  
Milk Yield ◽  
Water Intake ◽  
Dry Matter ◽  
Animal Feed ◽  
Production Costs ◽  
Dry Matter Intake ◽  
Soluble Carbohydrates ◽  
Dairy Goats ◽  
Four Levels ◽  
Intake Water

Abstract Spineless cactus (Nopalea cochenillifera) is widely used in animal feed in semi-arid regions, due to the adaptive characteristics to such conditions and for having high levels of soluble carbohydrates. This research article describes the effect of replacing grain maize with spineless cactus in the diet of dairy goats on dry matter intake, water intake, milk yield, milk physicochemical characteristics and diet production costs. Eight multiparous Anglo Nubian goats were fed diets in which grain maize was replaced with spineless cactus at four levels (0, 33, 66, and 100%) in a double 4 × 4 Latin square design. Milk yield was measured and samples collected in the last three days of each period for physicochemical analysis and for determining nutrient intake. Diet production costs were also determined. Replacing maize with spineless cactus did not influence dry matter intake. Water intake via the drinker decreased linearly in response to the increasing levels of spineless cactus in the diet. The replacement of maize with spineless cactus did not change milk yield or physicochemical parameters. Total feed cost and the percentage of revenue losses from feed decreased with the replacement. Therefore, spineless cactus can fully replace grain maize in the diet of dairy goats, as it does not change dry matter intake or milk yield, but rather reduces feed costs and the drinking-water intake of goats.

A note on some of the factors that affect the water consumption of lactating dairy cows at pasture

1983 ◽  
Vol 36 (2) ◽  
pp. 303-306 ◽  
Author(s):  
C. R. Stockdale ◽  
K. R. King
Keyword(s):  
Dairy Cows ◽  
Water Intake ◽  
Water Consumption ◽  
Dry Matter ◽  
Dry Matter Intake ◽  
Total Water ◽  
Total Water Consumption ◽  
Lactating Dairy Cows ◽  
Dry Matter Concentration ◽  
Matter Concentration

ABSTRACTThe influence of the level of dry-matter intake and the dry-matter concentration in the diet on the water consumption of dairy cows in early lactation was investigated for a 2-month period from early August to early October. The importance of the various components of weather on voluntary water intake was also examined. The cows used in the experiment either grazed pasture alone, or were offered pasture and pasture hay supplements. Mean voluntary water intake increased by 2·30 kg per cow per day for every additional kg dry matter consumed and also increased by 0·053 kg per cow per day for each g/kg increase in dry matter concentration. Of the climatic factors, rainfall had the greatest single influence on the daily fluctuations in voluntary water intake and this was negative. Intake was also negatively related to minimum temperature, relative humidity and wind, and positively related to sunshine and evaporation. Although maximum temperature per se had no apparent influence on intake, it showed a positive relationship after the removal of the effects of rainfall. A model for the prediction of total water consumption is:Total water consumption (kg per cow per day) = 11·34 + 4·63 dry-matter intake (kg per cow per day) –0·036 dry-matter concentration (g/kg) + 0·84 mean temperature (°C).This can only be used to predict the water requirements of lactating dairy cows in different environments.

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

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.

scholarly journals 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 ◽  
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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