Prediction of the methane conversion factor (Ym) for dairy cows on the basis of national farm data

2016 ◽  
Vol 56 (3) ◽  
pp. 535
Author(s):  
A. L. F. Hellwing ◽  
M. R. Weisbjerg ◽  
M. Brask ◽  
L. Alstrup ◽  
M. Johansen ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Methane Conversion ◽  
Dry Matter ◽  
Conversion Factor ◽  
Dry Matter Intake ◽  
Intergovernmental Panel ◽  
Feed Composition ◽  
Greenhouse Gas Inventories ◽  
Gross Energy ◽  
Jersey Cows

Methane constitutes a significant loss of feed gross energy in ruminants, and there is an ongoing struggle for identifying feed and animal characteristics feasible for documentation of National Greenhouse Gas Inventories. The aim of the current study was to develop a model that predicts the methane conversion factor (Ym, % of gross energy) for dairy cows on the basis of data obtained from a range of our respiration studies, and, subsequently, to use this model to predict Ym for Holstein and Jersey cows on the basis of compiled average national farm data on dry matter intake, yield of energy-corrected milk and dietary composition. In total, 183 observations were compiled, including 41 rations from 10 experiments with Holstein dairy cows where methane emission was measured by means of indirect calorimetry using the same experimental equipment. Two models were developed; one using dry matter intake and feed composition as variables, and one using yield of energy corrected milk and feed composition as variables. The methane conversion factor was significantly reduced with increased content of starch and fat in the ration, whereas neutral detergent fibre content surprisingly did not have a significant effect in any model. On the basis of compiled data from practical Danish farms, the predicted Ym for dairy cows was 6.02% and 5.98% of gross energy intake for Holstein and Jersey cows, respectively, in the model with dry matter intake and 6.13% and 6.00% for Holstein and Jersey cows, respectively, in the model with energy-corrected milk yield. In conclusion, the Intergovernmental Panel on Climate Change default value for Ym of 6.5% overestimates. Ym for both Holstein and Jersey cows fed rations typically used in intensive dairy producing countries in northern Europe.

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.

scholarly journals Prepartum 2,4-Thiazolidinedione Alters Metabolic Dynamics and Dry Matter Intake of Dairy Cows

2007 ◽  
Vol 90 (8) ◽  
pp. 3660-3670 ◽  
Author(s):  
K.L. Smith ◽  
S.E. Stebulis ◽  
M.R. Waldron ◽  
T.R. Overton
Keyword(s):  
Dairy Cows ◽  
Dry Matter ◽  
Dry Matter Intake

scholarly journals Genetic parameters of forage dry matter intake and milk produced from forage in Swedish Red and Holstein dairy cows

2021 ◽  
Vol 104 (4) ◽  
pp. 4424-4440
Author(s):  
Getinet Mekuriaw Tarekegn ◽  
Johanna Karlsson ◽  
Cecilia Kronqvist ◽  
Britt Berglund ◽  
Kjell Holtenius ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Dry Matter ◽  
Genetic Parameters ◽  
Dry Matter Intake ◽  
Holstein Dairy Cows
Sign in / Sign up

Export Citation Format

Share Document