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Prediction and validation of residual feed intake and dry matter intake in Danish lactating dairy cows using mid-infrared spectroscopy of milk.

Feed efficiency does not receive sufficient attention in the dairy industry. It is always important, even more so during periods of decreased profit margins. Feed-efficient cows consume less energy and emit less methane. Thus, improving feed efficiency in dairy cows will result in both economic and environmental benefits. Dairy cow efficiency can be measured in several ways. Among these, a commonly used method is residual feed intake (RFI). Residual feed intake is an attractive and effective measure, as it takes into account the contribution of mobilization of body reserves to the cow’s energy supply. It is defined as the difference (in energy units) between feed intake energy and the sum of energy found in milk and that used in maintenance. The reason why cows differ in RFI is that either more efficient cows convert feed energy to milk energy more efficiently or they have a lower maintenance requirement.

Selection for improved feed efficiency by RFI is highly attractive, but practical implementation might be challenging, primarily because individual feed intake records are unavailable in commercial dairy herds. Dry matter intake is the key component to calculate feed efficiency. A promising indirect method could be the use of mid-infrared spectroscopy on milk samples to predict DMI or RFI traits; mid-infrared spectroscopy is a rapid and cost-effective tool and routinely done on standard milk sample analysis in milk recording. It does however require calibration and to estimate RFI body weight and milk yield must be included in the estimation model. This was the basis of the study by Dr N. Shetty and co-workers which they published in the Journal of Dairy Science, Volume 100 of 2917, page 253 to 264, the title being: Prediction and validation of residual feed intake and dry matter intake in Danish lactating dairy cows using mid-infrared spectroscopy of milk.

The authors studied the effectiveness of the so-called Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for dry matter intake (DMI) and residual feed intake (RFI). The regression models which were developed were validated with external test sets. The data included 1 044 records from 140 cows; 97 were Danish Holstein and 43 Danish Jersey. Residual feed intake was calculated as the residual from a linear regression model, where DMI is regressed on the energy content in milk (ECM) and metabolic body weight (MBW):

RFI = DMI –β1 × ECM –β2 × MBW, where ECM was calculated as: ECM = (milk yield/3,140) × [(383 × fat) + (242 × protein) + (157 × lactose) + 20.7]. Milk yield is in kilograms; fat, protein, and lactose contents are in percent; and MBW represents weekly metabolic BW (i.e., BW0.75).

Milk yield (MY) was the largest contributor to DMI prediction, accounting for 59% of the variation and the model error of prediction (EP) was 2.24 kg DMI. The model was improved by adding body weight (BW) as an additional predictor trait, which increased the variation accounted for from 59 to 72% and decreased the EP from 2.24 to 1.83 kg DMI. When only the milk FT-IR spectral profile was used in DMI prediction, a lower prediction ability was obtained, the variation being accounted for only 30% and the EP 2.91kg DMI. However, once the spectral information was added to MY and BW as predictors, the model accuracy improved and the variation accounted for increased to 81% and the EP decreased to 1.49 kg DMI. Interesting, prediction accuracies of RFI changed throughout lactation. The RFI prediction model for the early-lactation stage was better compared with across lactation or mid- and late-lactation stages. The most important spectral wave numbers that contributed to DMI and RFI prediction models included fat, protein and lactose peaks. Comparable prediction results were obtained when using infrared-predicted fat, protein, and lactose instead of full spectra, indicating that FT-IR spectral data do not add significant new information to improve DMI and RFI prediction models. Therefore, in practice, if full FT-IR spectral data are not stored, it is possible to achieve similar DMI or RFI prediction results based on standard milk control data. This is significant as in many cases only standard milk testing results are available.

Bottom line: The prediction accuracy of 81% and associated error of 1.49 kg DMI shows that one can select using RFI without measurement of DMI as the accuracy of prediction of DMI is highly satisfactory. Often the DMI difference between cows with the same MY and BW is 15 to 20%, meaning that if the DMI required for a MY of 30 kg per day is 20 kg, the less efficient cow could have a DMI of 22 kg and the more efficient cow a DMI of 18 kg. The error of estimate of 1.49 kg DMI is much less than the difference of 4 kg DMI between the cows, which means than one will be able to select the more efficient cow.