Body and milk traits as indicators of dairy cow energy status in early lactation

摘要

The inclusion of feed intake and efficiency traits indairy cow breeding goals can lead to increased risk ofmetabolic stress. An easy and inexpensive way to monitorpostpartum energy status (ES) of cows is thereforeneeded. Cows’ ES can be estimated by calculatingthe energy balance from energy intake and outputand predicted by indicator traits such as change inbody weight (ΔBW), change in body condition score(ΔBCS), milk fat: protein ratio (FPR), or milk fattyacid (FA) composition. In this study, we used bloodplasma nonesterified fatty acids (NEFA) concentrationas a biomarker for ES. We determined associationsbetween NEFA concentration and ES indicators andevaluated the usefulness of body and milk traits alone,or together, in predicting ES of the cow. Data werecollected from 2 research herds during 2013 to 2016 andincluded 137 Nordic Red dairy cows, all of which had afirst lactation and 59 of which also had a second lactation.The data included daily body weight, milk yield,and feed intake and monthly BCS. Plasma samples forNEFA were collected twice in lactation wk 2 and 3 andonce in wk 20. Milk samples for analysis of fat, protein,lactose, and FA concentrations were taken on the bloodsampling days. Plasma NEFA concentration was higherin lactation wk 2 and 3 than in wk 20 (0.56 ± 0.30,0.43 ± 0.22, and 0.13 ± 0.06 mmol/L, respectively; allmeans ± standard deviation). Among individual indicators,C18:1 cis-9 and the sum of C18:1 in milk hadthe highest correlations (r = 0.73) with NEFA. Sevenmultiple linear regression models for NEFA predictionwere developed using stepwise selection. Of the modelsthat included milk traits (other than milk FA) as wellas body traits, the best fit was achieved by a modelwith milk yield, FPR, ΔBW, ΔBCS, FPR × ΔBW,and days in milk. The model resulted in a cross-validationcoefficient of determination (R~2cv) of 0.51 anda root mean squared error (RMSE) of 0.196 mmol/L.When only milk FA concentrations were considered inthe model, NEFA prediction was more accurate usingmeasurements from evening milk than from morningmilk (R_(cv)~2 = 0.61 vs. 0.53). The best model with milktraits contained FPR, C10:0, C14:0, C18:1 cis-9, C18:1cis-9 × C14:0, and days in milk (R_(cv)~2 = 0.62; RMSE =0.177 mmol/L). The most advanced model using bothmilk and body traits gave a slightly better fit than themodel with only milk traits (R_(cv)~2 = 0.63; RMSE =0.176 mmol/L). Our findings indicate that ES of cowsin early lactation can be monitored with moderatelyhigh accuracy by routine milk measurements.