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

Abstract

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.

Prediction of dry matter intake and gross feed efficiency using milk production and live weight in first-parity Holstein cows

“…Validation of the model for predicting DMI that included MY and LW only yielded a moderate R 2 and low RMSE, indicating modest robustness and accuracy. This prediction power was, however, relatively higher compared to values observed by Liang et al (2021) in a study on multiparous Holstein cattle.…”

“…These two variables are the major determinants of the cow's total nutrient requirements; hence, their large in uence on DMI. Our best model for predicting DMI had a greater prediction power (R 2 of 0.79) prior validations, than the one recently developed from multiparous Holstein data in China (R 2 of 0.46) (Liang et al 2021). A high prediction accuracy (R 2 of 0.71) for DMI, was also obtained from a model including MY, LW and mid-infrared (MIR) spectra data, in a multiparous American Holstein cattle population (Dórea et al 2018).…”

“…milk yield, ECM, butterfat, protein, lactose) and live weight. This was demonstrated in a recent study that was carried out to predict DMI in multi-parous Holstein cows (Liang et al 2021).…”

“…Combining MY and LW achieved better accuracy of prediction, compared to a model with either of the traits only. Previous studies have similarly found MY and LW to be highly correlated with DMI and, thus, included them in prediction models for DMI (Holter et al 1997;NRC 2001;Lahart et al 2019;Liang et al 2021;Martin et al 2021). These two variables are the major determinants of the cow's total nutrient requirements; hence, their large in uence on DMI.…”

“…Interestingly though, the model developed by Liang et al (2021) included dry matter intake of the rst 2 hours after feeding, in addition to MY and LW. On the other hand, Lahart et al ( 2019) developed models that predicted DMI better from milk production traits and LW (MY, fat percent, protein percent, body weight, stage of lactation, and parity) of grazing Irish Holstein-Friesian and Jersey cross-bred cattle.…”