Reliable data on individual cow DMI and GFE is key to achieving improvement in the efficiency of feed utilisation in dairy cattle. Availability of such data on a large scale would enable the estimation of accurate breeding values for feed efficiency traits, thereby facilitating their inclusion in the breeding objectives (McParland et al. 2014). Generating large amounts of DMI records through direct measurement is, however, challenging. In the current study, it was hypothesised that milk production traits and live weight, which are easy and cheap to measure, can be used as reliable predictors of DMI and GFE.
Performance Statistics For Milk Production, Live Weight, Dmi And Gfe
Each cow in the current study consumed an average of 21.91 kg/day dry matter of a total mixed ration, to produce mean energy-corrected milk and milk yield of 28.46 kg/day and 34.26 kg/day, respectively. This resulted in a gross feed efficiency of 1.32 per animal per day, which is within the expected range for Holstein cows in first lactation (Heinrichs and Ishler 2016). There is, however, scarcity of information on DMI from studies in a sub-tropical environment with which to compare the results of the current study. Our mean for DMI was, however, lower than that observed in Canadian first-parity Holstein cows (Beard 2018). This variation could be attributed to various factors including the different environmental conditions where animals were reared, the level of production and genetic merit of the cows, which may highly influence feed intake. A Holstein cow in the heat of the Limpopo Valley, where the current study was carried out, is expected to have much lower intake compared to one in freezing Canada. Maintenance requirements would also be higher for the cow in Limpopo, as it would need more energy to cool its body. Generally, the means for milk production, live weight, DMI and GFE found in this study are within the range for Holstein animals in first-parity (ICAR 2012; Poncheki et al. 2015; Heinrichs and Ishler 2016; Krattenmacher et al. 2019).
Correlations Between Milk Production Traits, Live Weight, Dmi And Gfe
A preliminary step in the development of prediction models was to quantify the phenotypic association of milk production traits and live weight with DMI and GFE, in order to determine those traits that could be the best candidates as predictors for DMI and GFE. Milk yield was moderately and positively associated with DMI (0.32), meaning that increased feed intake led to higher milk production, which is in agreement with findings from other studies (e.g., Ben Meir et al. 2018; Zhang et al. 2020; Liang et al. 2021). This is attributable to more nutrients being available for milk production, after meeting requirements for other physiological functions such as growth and maintenance (Erickson and Kalscheur, 2020). The association between MY and DMI demonstrates that MY could be a useful predictor for DMI. Accordingly, MY has been widely considered alone or together with other traits (e.g. butterfat, protein, lactose contents, LW, mid-infrared spectra of milk, parity and stage of lactation) in most prediction models for DMI in dairy cows (NRC 2001; Lindgren et al. 2001; Shetty et al. 2017; Lahart et al. 2019; Liang et al. 2021).
Butterfat percent had a moderate antagonistic association with DMI (-0.55), although a previous study reported a poor positive association between these traits in first-parity Holstein cows in Belgium (Zhang et al. 2020). This relationship implies that cows consuming more dry matter produced milk with less butterfat. The biological basis of this relationship is not clear. In extreme cases, too much feed in the cow rumen, particularly rations low in fibre, may cause digestibility problems, which may result in low milk butterfat production (Heinrichs and Jones 2016). This association between butterfat percent and DMI presented butterfat percent as a candidate predictor in developing our models for predicting DMI. Other studies have also demonstrated the importance of BFP in prediction models for DMI in dairy cows (e.g. Lahart et al. 2019).
Live weight had a strong positive association with DMI (0.76), in concurrence with findings from other studies on first-parity Holstein cows (e.g. Zhang et al., 2020). This relationship has, however, been reported to be moderate in multiparous cows (Zhang et al., 2020; Liang et al., 2021). Thus, heavier animals consume more feed due to their higher requirements for body maintenance, and this has been documented in many studies (Searle et al. 1982; Vallimont et al. 2011; Guinguina et al. 2019). Searle et al. (1982) indicated that LW is one of the factors most closely related to net energy for maintenance. The strong association between LW and DMI observed in the current study points to LW as a major candidate for predicting DMI. Consequently, LW has been widely considered in most prediction models of DMI in dairy cows (NRC 2001; Lahart et al. 2019; Martin et al. 2021; Liang et al. 2021).
The strong positive relationship observed between BFY and GFE (0.83) implies that cows producing higher quantities of butterfat were more efficient at converting feed into milk. There is, however, scarcity of information on the relationship between these two traits in the literature, with which to compare these findings. Thus, BFY also appeared as a promising predictor of GFE in the development of our prediction models.
A moderate positive association (0.36) was observed between MY and GFE. Comparable results have been reported in Holstein cows (Ben Meir et al. 2018); however, a previous study found a stronger relationship between these two traits (Spurlock et al. 2012). This association implies that as MY increases, corresponding gains in feed efficiency are achieved. Milk yield, therefore, came out as another strong potential predictor of GFE.
There was a low antagonistic relationship (-0.23) between LW and GFE, which was however not significant. This negative association is a well-documented phenomenon, and is attributable to the fact that larger cows demand more nutrients for body maintenance, resulting in less feed being available for milk production (Linn et al., 2009; Vallimont et al., 2011; Ben Meir et al., 2018; Guinguina et al., 2019). Thus, LW could contribute towards the prediction of GFE. A recent study by Guinguina et al. (2019) found the inclusion of LW to be useful in models for predicting GFE.
Developed Prediction Models For Dmi And Gfe
Reliable prediction of DMI and/or GFE from easy-to-measure traits could assist in generating large quantities of data for the estimation of accurate breeding values. Such predictions can be obtained from basal linear models, with milk production and live weight as independent variables (VandeHaar et al. 2016). These easy-to-measure traits are known to greatly influence feed efficiency, as they are important drivers of feed intake (VandeHaar et al. 2016). It is unclear which trait, between DMI and GFE, can be predicted more reliably than the other from milk production and live weight. In the current study, stepwise regression analyses were performed to develop models for predicting DMI and GFE using milk production traits and live weight, as independent variables, in first-parity Holstein cows.
Dry matter intake
Live weight (kg/day) was the best predictor of DMI (kg/day), followed by MY (kg/day). This confirms the correlation results of our preliminary analysis, which found these two traits to be good potential predictors of DMI. 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 influence on DMI. Our best model for predicting DMI had a greater prediction power (R2 of 0.79) prior validations, than the one recently developed from multiparous Holstein data in China (R2 of 0.46) (Liang et al. 2021). A high prediction accuracy (R2 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). Differences in the methods used to measure DMI, model development approaches, milk production traits considered, parity and stage of lactation may be responsible for the disparity in accuracy of prediction between studies. Based on the magnitude of the R2 value, our model may be considered sufficiently reliable for application to obtain large quantities of DMI data at low cost. High accuracy of phenotypes may be dispensed with as a requirement for obtaining accurate EBVs if large quantities of phenotypic records are obtainable (Calus et al. 2013; McParland and Berry 2016). Given the fact that a significantly high number of the South African Holstein cattle population is performance recorded (NMRIS 2020), the models developed in the current study provide an opportunity to generate large quantities of DMI data, which can be utilised to produce high accuracy EBVs for feed efficiency. There is, however, a need to determine the genetic variability of the predicted DMI, so as to determine the potential to improve it through selection.
Gross feed efficiency
Butterfat yield (kg/day) was the best predictor of GFE, followed by LW (kg/day) and MY (kg/day). This supports the results of our preliminary analysis (i.e. correlations), which established these three traits to be good potential predictors of GFE. A model including BFY, MY and LW achieved the best accuracy of prediction, compared to one with only one of these traits. These traits have also been found to be associated with GFE and, therefore, good predictor traits for GFE in several other studies (Linn et al. 2009; Vallimont et al. 2011; Ben Meir et al. 2018; Guinguina et al. 2019). Our best model for predicting GFE had an exceptionally strong prediction power (R2 = 0.98), which was much higher than for one developed previously from multiparous Holstein data (R2 = 0.76) (Guinguina et al. 2019). Beard (2018) developed models with much lower prediction ability (R2 = 0.45) using primiparous Canadian Holstein data, and including milk yield, milk components and live weight only. The disparity in prediction power among the different studies may be attributed to variation in methods used to measure DMI, parities considered and approaches used to develop the models. The stage of lactation at which DMI is measured may also influence accuracy of prediction (Lahart et al. 2019). There are limited studies on primiparous cows in the literature, with which compare our results. Given its high prediction power (large R2), our best model could be applied to generate large quantities of reliable GFE data at low cost, which can be used to estimate accurate EBVs for GFE. It is, however, necessary to first determine the genetic variability of this predicted trait, in order to determine the extent to which it is under genetic control.
Within-herd Validation Of Dmi And Gfe Prediction Models
An assessment of the robustness and accuracy of the models developed for predicting DMI and GFE from milk production and live weight was carried through within-herd validation of predicted data. There were no external data available to carry out such an analysis.
Validation of DMI prediction model
Validation of the model for predicting DMI that included MY and LW only yielded a moderate R2 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. Interestingly though, the model developed by Liang et al. (2021) included dry matter intake of the first 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. Adding milk MIR spectra data to these prediction models resulted in a slightly improved prediction power (Lahart et al. 2019). Similarly, Dórea et al. (2018) predicted DMI from milk yield, body weight and days in milk of multiparous American Holstein cattle with a strong prediction power, which improved with the addition of milk MIR spectra data. Models developed from the different studies are bound to vary in their ability to predict DMI largely due to differences in the predictor traits included, methods used to measure DMI and validate predicted DMI, as well as factors such as breed, parity, stage of lactation and production system (Dórea et al. 2018; Lahart et al. 2019; Liang et al. 2021). Utilization of additional easy-to-measure traits such as milk MIR spectra and days in milk may be useful in improving the accuracy of predicting DMI, as indicated by some previous studies (Shetty et al. 2017; Dórea et al. 2018; Wallén et al. 2018; Lahart et al. 2019). It might also be worthwhile to explore other prediction and validation methods such as the partial least squares approach and artificial neural networks (Felipe et al. 2015; Dórea et al. 2018).
Validation of GFE prediction model
Studies on the prediction of GFE are generally scarce in the literature. The model that we developed for predicting GFE from BFY, LW and MY had a reasonably strong prediction ability, as indicated by a high R2 and small RMSE. This was confirmed by the results of within-herd validation, despite a slight inconsistence in the relative magnitude of the R2 and RMSE values. Guinguina et al. (2019) obtained comparable results, using energy-corrected milk, live weight and estimated dry matter digestibility to predict GFE. In another study on primiparous Canadian Holstein cattle, prediction of weekly feed intake conversion efficiency from milk yield, milk components and live weight yielded a moderate prediction ability, with a much higher RMSE than in the current study (Beard 2018). There is scope to improve the model for predicting GFE developed in the current study by utilising data from novel technologies, such as milk MIR spectra. Extensive research has demonstrated that milk MIR spectra data can considerably improve the performance of models for predicting feed efficiency traits such DMI, residual feed intake and energy intake in dairy cows (McParland et al. 2014; Shetty et al. 2017; Dórea et al. 2018; Wallén et al. 2018; Lahart et al. 2019). The application of other model development approaches, such as partial least squares and artificial neural networks, also warrants investigation for better prediction of GFE (Felipe et al. 2015; Dórea et al. 2018).
Limitations Of The Study And Recommended Future Work
The current study developed models for predicting DMI and GFE utilising data of first-parity Holstein cows, and achieved reasonable prediction accuracies, as determined by within-herd validation. External (across-herd) validation, which examines whether a prediction model can function outside of the dataset used to create the model was, however, not performed in this study. Such validation is essential in ensuring that a model is robust enough to achieve the same power of prediction in other populations (Shetty et al. 2017; Lahart et al. 2019). It is also not clear if the models developed in the current study can be reliably extrapolated to multi-parity cows. Thus, further studies are required to determine if the models developed in this study are applicable in other herds, multi-parity cows, breeds and/or other dairy production systems. Such knowledge is a prerequisite to the wide application of these models in different environments. Furthermore, it is important to determine the genetic variation of the predicted DMI and GFE phenotypes, in order to assess their potential for improvement through selection. Further research is also warranted to determine whether there are genes or parts of the genome that are associated with these predicted traits. Such knowledge may assist in implementing marker-assisted selection, by identifying animals that utilise feed efficiently through DNA analysis.