ABSTRACT
Key words
INTRODUCTION
). The ability to be minimally affected by disturbances and to quickly recover if affected is called resilience (
).
;
;
;
). Many studies quantified resilience using indicators based on fluctuations in longitudinal traits. The theory behind the use of fluctuations in longitudinal traits is that many longitudinal traits are sensitive to disturbances. Therefore, the fluctuation pattern of such longitudinal traits is indicative of the resilience of an animal (
). Examples of resilience indicators based on longitudinal traits in dairy cattle are the variance and the lag-1 autocorrelation (rauto) of milk yield (
;
) and activity (
). Here, low variance and autocorrelation indicate a stable pattern over time and thus good resilience. Such resilience indicators are useful to distinguish resilient from less resilient animals. Some of these studies have investigated the use of resilience indicators in genetic selection (
;
). The natural log-transformed variance (LnVar) and rauto of daily deviations from expected milk yield were heritable, and genetically associated with sensitivity of milk yield to disturbances and recovery time (
), and with health traits and longevity (
,
).
;
). Positive effects of resilience on profitability are expected through a smaller yield loss upon disturbances (
) and fewer labor costs, due to fewer health problems and fewer cows with alerts generated by automated systems (
). Positive effects are also expected through favorable associations with health, longevity, and fertility. These traits all have an effect on cow profitability, for example, through decreasing treatment costs with fewer diseases (
;
), dilution of rearing costs per kilogram of milk with increasing productive life (
;
), and decreasing insemination costs and increasing the number of productive days in peak lactation with improved fertility (
).
;
;
). However, feed efficiency is often considered only on a lactation level. It is important to recognize that through a positive effect of resilience on longevity, the association between resilience and feed efficiency may actually be favorable because of more milk produced per cow, which dilutes feed costs during rearing. It is, therefore, important to take costs and revenues throughout the whole lifetime into account and not only throughout the first lactation.
;
;
). Earlier, this type of information was mainly available on research farms. Research farms still do have an advantage over commercial farms in that they often measure feed intake (e.g.,
;
), whereas commercial farms do not. Still, the advantage of commercial farms is that data are available on more cows, and cows are not subjected to experiments. We believe that the amount and frequency of data on commercial farms that are available currently, despite the lack of feed intake data, provide a good opportunity to investigate the association between resilience and lifetime profitability.
) with the lifetime gross margin of cows, expressed as the sum of all revenues throughout life minus the sum of all costs throughout life.
MATERIALS AND METHODS
), Pandas (
), and Statsmodels (
) packages from Python 3.6 and 3.8.5, the AWK programming language (
), and R (R v 3.2.2; R Project for Statistical Computing). No animals were used in this study, and ethical approval for the use of animals was thus deemed unnecessary.
Data
). The data set contained 4,186,231 records on 893,260 cows in 9,200 herds, from May 23, 2010, to September 5, 2020. A total of 4,701 herds had records in all 4 data sets, but with varying degrees of data quantity and quality.
Selection of Herds
Selection of Cows
, the first milk yield record in first lactation was required to be within 2 wk after calving, and at least 50 milk yield records were required until 350 DIM, excluding the first and last 10 DIM. Finally, at least 5 cows were required to be born within the same herd in the same year, to be able to include a contemporary group effect in the analyses. Herd-birth year groups with less than 5 cows were removed. Finally, 1,325 cows were suitable for analysis, with 3,500,720 AMS records, 38,160 milk production registration records, 11,247 insemination records, and 13,145 disease records.
Data Preparation
), and the deviation was larger than 6 times the standard deviation of the deviation of all cows in the same parity, year of calving, and day of lactation (
). Because still some high milk yield records were present that were deemed outliers based on visual inspection, milk yield records larger than 90 kg were removed as well. In total, 770 daily milk yield records were removed, resulting in 1,282,693 final daily milk yield records.
Calculation of Resilience Indicators
. First, potential daily milk yield until 350 DIM in absence of disturbances was estimated using polynomial quantile regression on the 0.7 quantile:
where yt is the observed milk yield on DIM t, tn are DIM to the power of n, where n is 1, 2, 3, or 4, βn are regression coefficients describing the relationships between tn and yt, and ε is the error term. The quantreg package (
) and the poly function in R were used. The 0.7 quantile causes the expected milk yield to be less influenced by low milk yield records than by high records, resulting in a lactation curve that is closer to what is expected in absence of disturbances. Deviations from the regression curve were calculated by subtracting the expected milk yield from the observed milk yield, and they represent the short-term fluctuation pattern in milk yield due to disturbances. The resilience indicators were then calculated on all deviations until 350 DIM per cow, excluding the first and last 10 deviations. The resilience indicators were the LnVar and the rauto of the daily deviations. In addition to the resilience indicators, the average daily milk yield until 350 DIM in parity 1 (ADMY) was calculated for each cow. The ADMY was needed to investigate relationships between the resilience indicators and lifetime gross margin, independent of milk yield level, because especially LnVar, but also rauto are positively associated (genetically and at herd level) with ADMY (
,
,
).
Calculation of Lifetime Gross Margin
All revenues and costs taken into account in this study will be explained below.
Lifetime Revenues
where Pmilk, Pfat, Pprotein, and Plactose were the prices of milk and milk contents (Table 1), and Milk, Fat, Protein, and Lactose were the total amount of milk, fat, protein, and lactose calculated for the entire life (kg).
| Parameter | Value | Unit | Source |
|---|---|---|---|
| Milk | −0.0067 | €/kg | |
| Protein | 5.5084 | €/kg | |
| Fat | 2.7542 | €/kg | |
| Lactose | 0.5508 | €/kg | |
| Feed costs | 0.1619 | €/kVEM | |
| Slaughter price cow | 665 | €/cow | |
| Insemination costs | 31.75 | €/insemination | |
| Calf price | 75 | €/calf | |
| Calf management costs | 180 | €/calving | |
| Collection and destruction costs | 61.80 | €/cow |
.
Lifetime Costs
). Daily VEM requirements depended on milk yield, maintenance requirements, gestation stage, and growth.
)
where BW is body weight (kg) and FPCM is fat- and protein-corrected milk. Because no records on BW were available, it was assumed that BW was 540 kg at the start of lactation 1, 595 kg at the start of lactation 2, and 650 kg at the start of lactation 3 (
), and BW increased linearly in between (
). Daily fat- and protein-corrected milk yield was calculated for each cow as follows (
):
where fat and protein were fat and protein content (%) and milk was amount of milk (kg). In addition to the daily VEM requirements for milk yield and maintenance, extra daily VEM requirements for gestation were 450, 850, 1,500, and 2,700 VEM per day in the 6, 7, 8, and 9 mo of pregnancy, respectively (
). Extra daily VEM requirements for growth were 660 VEM per day for cows in first parity and 330 VEM per day for cows in second parity (
). The total lifetime VEM requirement of each cow was then calculated by summing all daily VEM requirements throughout life. The lifetime feed costs were then calculated by multiplying lifetime VEM requirement by the VEM price (Table 1).
, who found an average age of calving of 25 mo in their simulation of rearing costs of Dutch heifers, with an estimated rearing cost of €1,567. For each cow, a cost was subtracted or added to €1,567 when the age at first calving was earlier or later than 25 mo. The amount of money extracted or added was €2.19 per day, based on
, who estimated the difference in costs between a heifer calving at 24 and 30 mo of age to be €400.
| Disorder or treatment | Medicine price (€/complete treatment) | Labor costs veterinarian + call-out fee (€/complete treatment) | Labor time farmer (min/complete treatment) | Labor costs farmer (€/complete treatment) | Total cost (€/complete treatment) | Treatment duration (d) | Waiting time (d) |
|---|---|---|---|---|---|---|---|
| Uterus prolapse | 15 | 185 | 90 | 27 | 227 | 1 | 5 |
| Abnormal vaginal discharge | 12 | — | 15 | 4.5 | 16.5 | 1 | 0 |
| Uterus infection | 24 | — | 15 | 4.5 | 28.5 | 3 | 5 |
| Retained placenta | 9 | — | 15 | 4.5 | 13.5 | 1 | 4 |
| Swollen hock | 25 | — | 30 | 9 | 34 | 3 | 4 |
| Joint infection | 12 | — | 30 | 9 | 21 | 3 | 4 |
| Ketosis | 65 | 85 | 30 | 9 | 159 | 1 | 3 |
| Displaced abomasum | 60 | 147 | 90 | 27 | 234 | 3 | 2 |
| Pneumonia | 30 | 30 | 9 | 39 | 3 | 5 | |
| Clinical mastitis | 85 | 60 | 18 | 103 | 6 | 4 | |
| Milk fever | 32 | 180 | 54 | 86 | 4 | 0 | |
| Cystic ovaries | 17 | 10 | 3 | 20 | 1 | 0 | |
| Inactive ovaries | 17 | 48 | 10 | 3 | 68 | 1 | 0 |
| Euthanasia | 20 | 60 | 20 | 6 | 86 | 1 | — |
| Disorder | Medicine cost (€/ treatment) | Labor cost, farmer (€/complete treatment) | Cost, claw trimmer (€/complete treatment) | Labor cost, veterinarian + call-out fee (€/complete treatment) | Total cost (€/complete treatment) | Treatment duration (d) | Waiting time (d) |
|---|---|---|---|---|---|---|---|
| Panaritium | 13 | 1.5 | 7.75 | — | 22.25 | 1 | 5 |
| Interdigital dermatitis | 0.6 | 1.5 | 7.75 | — | 9.85 | 1 | 0 |
| Digital dermatitis | 2.61 | 1.5 | 7.75 | — | 11.35 | 1 | 1 |
| Sole hemorrhage | 8.10 | 1.5 | 7.75 | — | 17.35 | 1 | 0 |
| Interdigital hyperplasia | 42.88 | 12 | — | 215 | 269.88 | 1 | 0 |
| White line defect | 8.10 | 1.5 | 7.75 | — | 17.35 | 1 | 0 |
| Toe necrosis | 42.88 | 12 | — | 215 | 269.88 | 1 | 0 |
| Laminitis | — | 1.5 | 7.75 | — | 9.25 | 1 | 0 |
Analyses
where y is LGM, revenues, costs, or an underlying trait and PL is productive life (d), expressed as the time between first calving and culling date. If the productive life was less than 365 d, the correction was not applied and yyearly was assumed to be y (
). As a result, yyearly was lower for cows with a productive life shorter than 365 d than for cows with a longer productive life. To correct for this, an additional covariate was included in the models for the traits expressed per productive year. This covariate was the productive life (d) divided by 365, and was set to 1 if the productive life was 365 or longer. The residuals of all models were checked using residual plots. For the models with rauto, heteroskedastic residuals were observed, which was due to a skewed distribution of rauto. This was solved by removing outliers for rauto using the interquartile range method applicable to skewed distributions, where records outside the first and third quartile ±1.5 × the interquartile range were removed (
). As a result, the analysis for rauto included 1,301 records. The residuals of the models of both LnVar and rauto then were homoscedastic. There were no signs of nonlinear relationships between the resilience indicators and the dependent variables. For most models, the residuals were roughly normally distributed upon visual inspection, although the residuals were often left-skewed (LGM per productive year, slaughter revenues per productive year) or right-skewed (LGM, lifetime milk revenues, lifetime calving revenues and costs, lifetime feed costs, lifetime rearing costs, lifetime insemination costs, lifetime disease costs, calving revenues and costs per productive year, rearing costs per productive year, insemination costs per productive year, disease costs per productive year, destruction costs per productive year, calving age, lifetime yield and components, lifetime VEM, lifetime discarded milk and components, number of inseminations per productive year, disease count per productive year for individual diseases). This skewness seems due to characteristics of the data (e.g., some cows had long productive life and, therefore, high lifetime figures, and most cows had 0 or close to 0 cases of a disease per productive year, except for some cows with high disease count), and there is no reason to believe that it is due to errors in the data. Therefore, no data were removed and the skewness of residuals should be kept in mind when interpreting results.
RESULTS
Descriptive Statistics
| Trait | Mean | Median | Minimum | Maximum | SD | Interquartile range |
|---|---|---|---|---|---|---|
| LnVar | 1.33 | 1.31 | −0.97 | 4.12 | 0.66 | 0.85 |
| rauto | 0.56 | 0.58 | 0.067 | 0.92 | 0.18 | 0.24 |
| ADMY | 24.98 | 24.64 | 10.82 | 40.66 | 4.74 | 6.87 |
| Item | Lifetime | Per year of productive life | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | Median | Minimum | Maximum | SD | IQR | Mean | Median | Minimum | Maximum | SD | IQR | |
| Gross margin | 4,361.78 | 3,943.45 | −1,428.15 | 16,715.60 | 3,386.78 | 4,692.38 | 1,229.39 | 1,365.31 | −1,428.15 | 2,505.99 | 673.28 | 700.86 |
| Milk revenues | 9,152.98 | 8,501.31 | 270.3140 | 27,855.50 | 5,501.10 | 7,754.69 | 2,909.18 | 3,024.19 | 270.31 | 4,409.65 | 659.49 | 670.54 |
| Calving revenues | 231.00 | 225.00 | 75.00 | 675.00 | 113.26 | 150.00 | 79.73 | 76.15 | 30.32 | 150.00 | 16.44 | 16.11 |
| Slaughter revenues | — | — | — | — | — | — | 258.41 | 207.64 | 0.00 | 665.00 | 200.64 | 223.66 |
| Feed costs | 3,077.77 | 2,915.71 | 125.61 | 9,080.90 | 1,790.02 | 2,512.93 | 985.59 | 1,023.47 | 125.61 | 1,359.85 | 194.93 | 166.01 |
| Calving costs | 554.40 | 540.00 | 180.00 | 1,620.00 | 271.83 | 360.00 | 191.36 | 182.75 | 72.76 | 360.00 | 39.46 | 38.66 |
| Rearing costs | 1,587.70 | 1,551.67 | 1,297.63 | 2,324.74 | 153.72 | 157.68 | 724.60 | 567.34 | 189.03 | 2,305.03 | 441.06 | 531.94 |
| Insemination costs | 205.41 | 190.50 | 0.00 | 1,079.50 | 140.80 | 190.50 | 69.06 | 63.26 | 0.00 | 384.32 | 39.12 | 40.93 |
| Total treatment costs | 145.09 | 9.85 | 0.00 | 3,580.56 | 297.89 | 167.73 | 43.19 | 3.32 | 0.00 | 593.51 | 76.91 | 54.50 |
| Destruction costs | — | — | — | — | — | — | 4.14 | 0.00 | 0.00 | 61.80 | 11.34 | 0.00 |
Associations Between Resilience Indicators and Profit, Revenues, and Costs, and Underlying Traits
| Trait expression | Trait | Intercept | β LnVar | SE LnVar | LnVar (P-value) |
|---|---|---|---|---|---|
| Lifetime | Gross margin | 1,517.72 | −454.61 | 160.89 | 0.005 |
| Milk revenues | 5,118.19 | −730.62 | 259.99 | 0.005 | |
| Calving revenues | 229.17 | −18.12 | 5.53 | 0.001 | |
| Feed costs | 2,022.12 | −240.79 | 84.86 | 0.005 | |
| Calving costs | 550.01 | −43.50 | 13.26 | 0.001 | |
| Rearing costs | 1,725.94 | 1.43 | 7.01 | 0.839 | |
| Insemination costs | 84.39 | −15.05 | 6.83 | 0.028 | |
| Total treatment costs | −90.27 | 1.78 | 13.92 | 0.898 | |
| Per year of productive life | Gross margin | −2,678.35 | −53.31 | 21.63 | 0.014 |
| Milk revenues | −1,640.34 | −15.63 | 15.31 | 0.307 | |
| Calving revenues | 63.22 | 0.32 | 0.83 | 0.699 | |
| Slaughter revenues | 849.15 | 16.18 | 8.76 | 0.065 | |
| Feed costs | −414.98 | −1.77 | 3.79 | 0.640 | |
| Calving costs | 151.74 | 0.77 | 1.99 | 0.699 | |
| Rearing costs | 2,257.38 | 49.64 | 18.24 | 0.007 | |
| Insemination costs | −9.85 | 1.06 | 1.96 | 0.589 | |
| Total treatment costs | −40.17 | 4.01 | 3.57 | 0.261 | |
| Destruction costs | 6.27 | 0.47 | 0.60 | 0.434 |
| Trait | Intercept LnVar | β LnVar | SE LnVar | LnVar (P-value) | Intercept rauto | β rauto | SE rauto | rauto(P-value) |
|---|---|---|---|---|---|---|---|---|
| Herd life (d) | 1,926.27 | −87.03 | 27.90 | 0.002 | 1,913.57 | 6.18 | 94.12 | 0.948 |
| Productive life (d) | 1,090.70 | −87.68 | 28.19 | 0.002 | 1,077.60 | 7.031 | 95.14 | 0.941 |
| Age at first calving (d) | 835.58 | 0.65 | 3.20 | 0.839 | 835.97 | −0.85 | 10.76 | 0.937 |
| Lifetime milk yield (kg) | 11,093.66 | −1,875.18 | 768.80 | 0.015 | 10,655.69 | 386.87 | 2,590.47 | 0.881 |
| Lifetime fat yield (kg) | 717.16 | −98.56 | 34.10 | 0.004 | 702.78 | 3.02 | 115.15 | 0.979 |
| Lifetime protein yield (kg) | 531.80 | −76.44 | 27.71 | 0.006 | 515.68 | 9.83 | 93.47 | 0.916 |
| Lifetime lactose yield (kg) | 522.78 | −92.06 | 35.05 | 0.009 | 501.80 | 14.91 | 118.18 | 0.900 |
| Lifetime VEM requirement | 12,489,898 | −1,487,287 | 524,120 | 0.005 | 12,207,609 | 185,318 | 1,768,362 | 0.917 |
| Lifetime discarded milk (kg) | −111.03 | 1.70 | 11.05 | 0.877 | −130.33 | 46.79 | 37.29 | 0.210 |
| Lifetime discarded fat (kg) | −4.05 | 0.0060 | 0.49 | 0.991 | −4.91 | 2.08 | 1.64 | 0.204 |
| Lifetime discarded protein (kg) | −3.62 | 0.052 | 0.39 | 0.892 | −4.27 | 1.59 | 1.30 | 0.223 |
| Lifetime discarded lactose (kg) | −5.04 | 0.066 | 0.50 | 0.895 | −5.94 | 2.16 | 1.68 | 0.199 |
| Milk yield per productive year (kg) | −5,761.04 | 47.49 | 44.50 | 0.286 | −5,761.51 | −43.31 | 148.91 | 0.771 |
| Fat yield per productive year (kg) | −197.87 | −2.77 | 2.23 | 0.215 | −200.44 | −2.53 | 7.52 | 0.737 |
| Protein yield per productive year (kg) | −179.93 | −1.40 | 1.67 | 0.402 | −182.64 | −2.48 | 5.62 | 0.658 |
| Lactose yield per productive year (kg) | −259.36 | 0.071 | 2.11 | 0.973 | −260.67 | −1.76 | 7.063 | 0.803 |
| VEM requirement per productive year | −2,563,173 | −10,929 | 23,377 | 0.640 | −2,591,731 | −12,726 | 78,510 | 0.871 |
| Nr. inseminations per productive year | −0.31 | 0.033 | 0.062 | 0.589 | −0.27 | 0.052 | 0.21 | 0.802 |
| Nr. disease events per productive year | −0.72 | 0.018 | 0.040 | 0.659 | −0.75 | 0.17 | 0.13 | 0.198 |
| Trait | Intercept LnVar | β LnVar | SE LnVar | LnVar (P-value) | Intercept rauto | β rauto | SE rauto | rauto(P-value) |
|---|---|---|---|---|---|---|---|---|
| Uterus prolapse | −0.0081 | −0.0032 | 0.0033 | 0.34 | −0.00080 | −0.0095 | 0.011 | 0.39 |
| Vaginal discharge | −0.014 | 0.0011 | 0.0053 | 0.83 | −0.00080 | −0.026 | 0.018 | 0.15 |
| Uterus infection | 0.10 | −0.012 | 0.011 | 0.26 | 0.089 | 0.025 | 0.037 | 0.50 |
| Retained placenta | −0.0073 | −0.0030 | 0.0033 | 0.36 | −0.0017 | −0.016 | 0.011 | 0.15 |
| Hock abscess | 0.0093 | 0.0042 | 0.0054 | 0.44 | 0.018 | −0.0029 | 0.018 | 0.87 |
| Joint infection | −0.021 | 0.0017 | 0.0025 | 0.49 | −0.019 | 0.0011 | 0.0084 | 0.90 |
| Ketosis | −0.0038 | −0.0035 | 0.0015 | 0.017 | −0.0036 | −0.0033 | 0.0050 | 0.51 |
| Abomasal displacement | −0.0075 | −0.00070 | 0.0017 | 0.67 | −0.0052 | −0.0044 | 0.0057 | 0.44 |
| Pneumonia | 0.00050 | 0.0037 | 0.0038 | 0.32 | −0.0039 | 0.017 | 0.013 | 0.20 |
| Clinical mastitis | −0.025 | 0.036 | 0.014 | 0.011 | −0.018 | 0.051 | 0.048 | 0.28 |
| Milk fever | −0.031 | −0.0041 | 0.0042 | 0.33 | −0.028 | −0.0010 | 0.014 | 0.94 |
| Cystic ovaries | −0.051 | −0.012 | 0.0070 | 0.10 | −0.042 | −0.024 | 0.024 | 0.31 |
| Inactive ovaries | −0.16 | −0.015 | 0.0094 | 0.12 | −0.19 | 0.030 | 0.032 | 0.35 |
| Claw disorder | −0.50 | 0.025 | 0.031 | 0.42 | −0.55 | 0.13 | 0.10 | 0.18 |
| Trait expression | Trait | Intercept | β rauto | SE rauto | rauto(P-value) |
|---|---|---|---|---|---|
| Lifetime | Gross margin | 1,470.18 | −50.79 | 543.85 | 0.93 |
| Milk revenues | 4,981.13 | 68.087 | 877.38 | 0.94 | |
| Calving revenues | 227.70 | −2.44 | 18.68 | 0.90 | |
| Feed costs | 1,976.41 | 30.00 | 286.30 | 0.92 | |
| Calving costs | 546.48 | −5.85 | 44.83 | 0.90 | |
| Rearing costs | 1,726.79 | −1.87 | 23.56 | 0.94 | |
| Insemination costs | 89.64 | −12.75 | 23.09 | 0.58 | |
| Total treatment costs | −118.24 | 66.29 | 45.99 | 0.15 | |
| Per year of productive life | Gross margin | −2,726.39 | −134.52 | 72.80 | 0.065 |
| Milk revenues | −1,663.06 | −21.32 | 51.45 | 0.68 | |
| Calving revenues | 64.05 | −0.69 | 2.78 | 0.80 | |
| Slaughter revenues | 870.70 | −15.65 | 29.56 | 0.60 | |
| Feed costs | −419.60 | −2.06 | 12.71 | 0.87 | |
| Calving costs | 153.71 | −1.66 | 6.67 | 0.80 | |
| Rearing costs | 2,310.53 | 70.35 | 61.43 | 0.25 | |
| Insemination costs | −8.59 | 1.66 | 6.61 | 0.80 | |
| Total treatment costs | −43.90 | 24.46 | 11.93 | 0.040 | |
| Destruction costs | 5.92 | 4.10 | 2.02 | 0.043 |
LnVar
Rauto
DISCUSSION
;
). Even though we relied to a larger extent on real data, we did have to make large assumptions on feed intake and BW, because we had no data on these traits. Feed intake was calculated as a function of milk yield and BW (which was assumed the same between cows), which neglects possible differences in feed required for maintenance between cows. Because resilient cows likely use more feed for maintenance (
;
), and feed is the largest contributor to costs (Table 5), results may change when real feed intake data and BW would be used. Furthermore, slaughter price was assumed to be the same for all cows. However, as resilience likely has an association with body condition (
), slaughter weight and, therefore, slaughter price may be associated with resilience. As such, repeating this analysis on farms with many years of feed intake and BW data, in addition to milk yield, inseminations, and treatment data, would be of great value. Such commercial farms may not be available, but research farms may offer a solution. For example,
had detailed feed intake, milk yield, health, and insemination data available from a research farm and, therefore, were able to calculate realized profit. Nevertheless, the data used in this study still gave us novel insights into the association between resilience and at least several revenues and costs, while taking into account differences in lifespan.
), and have incomplete treatment registrations. Nevertheless, when the requirement on the treatment registrations was lifted and the analyses were repeated (64 farms with 4,583 cows then met all requirements), regression coefficients were similar to what we found in this study, although rauto did have stronger, but still insignificant, associations with lifetime milk revenues and feed costs (data not shown). The similar results suggest that the requirement on treatment registrations does not create bias. Moreover, the lifetime figures of the cows in our study were close to the mean lifetime figures across the Netherlands (Table 10). In summary, our data seems representative for the Dutch situation, but the applicability of our results for farms with milking parlors remains to be tested.
| Item | This study, birth year <2011 | Dutch situation (2016–2020) |
|---|---|---|
| Herd life | 2,099.3 | 2,076.2 |
| Productive life | 1,311.8 | 1,256.4 |
| Number of parities | 3.6 | 3.5 |
| Age at first calving | 787.5 | 781.2 |
| Lifetime milk yield | 33,318.7 | 31,341.6 |
| Lifetime fat yield | 1,450.1 | 1,363.8 |
| Lifetime protein yield | 1,188.4 | 1,110.4 |
.
,
) and the negative association between mean LnVar within herd and proportion of cows in a herd that survived to second lactation (
). The association is also in line with the expectation that animals that have good resilience or robustness have long productive life (
). However,
found that cows with variable milk yield in lactation 1 had a higher resilience rank (which was largely determined by lifespan) than cows with stable milk yield. The difference with our study may be because of a difference in definition between productive life and resilience rank or because
did not adjust for ADMY, whereas we did. In summary, the favorable association between having a stable milk yield, which represents good resilience, with LGM is mainly due to an association with lifespan.
;
). However, when looking at separate diseases, rauto was not significantly associated with any of them. The largest coefficients were positive though, so apparently the summed costs of these diseases were sufficient to make the association with total treatment costs significant. Nevertheless, when looking across the total life of cows, having low or high rauto does not seem to have an economic benefit.
;
). Two explanations can be given for the insignificant association between LnVar and yearly treatment costs. The first explanation is that LnVar was favorably associated with some diseases (clinical mastitis; Table 8), but unfavorably with others (ketosis; Table 8), which levels out the economic effects. The significant association with clinical mastitis is in line with
. The second explanation for the insignificant association is that farmers likely did not register all (minor) health problems that required attention. Labor costs due to unregistered disturbances could be dealt with similar to
. Those authors assumed that cows require labor if their milk yield drops below a certain value, and labor costs automatically rise with increasing LnVar. However, the assumption that drops in milk yield require labor has not been tested yet and, therefore, was not applied here. It should be mentioned, too, that the residuals from the models testing the association between the resilience indicators and individual diseases were very skewed for many diseases, which is probably because of the low incidence (Appendix Table A1). Re-testing in herds with higher incidence of diseases may be valuable to obtain better insight into the association between resilience indicators and individual diseases.
. Therefore, the economic importance of treatments seems limited. However, it is important to acknowledge that the disease costs did not include decreased milk production due to disease and increased culling risk, because these were already accounted for by the lifetime milk yield based on daily milk yield records and the length of productive life. Furthermore, it is important to acknowledge that the significant associations with individual diseases can still be important to a farmer. In this study, a standard labor price of €18/h was used, but an hour of labor may be worth much more to a farmer in case of unanticipated extra labor due to health problems.
showed that some farmers valued their hourly rate as high as €200 in case of mastitis. Indeed, when we used a labor price that was 10 times higher than the original labor price (€180/h), total treatment costs were relatively larger compared with other costs and revenues. Now, total treatment costs accounted for 5.0% of total lifetime costs. In addition, with a labor price of €180/h, the association between rauto and total treatment costs per productive year was 1.4 times as strong (β = 36) as previously in Table 9 (β = 25). This illustrates that good health through good resilience is economically important for farmers, as long as they value their time highly enough.
,
,
,
). However, comparable economic analyses on other resilience indicators would be interesting as well. Examples are the lifetime resilience rank, or additional traits describing fluctuations in daily milk yield or daily activity described by
. Furthermore, economic analysis of traits describing resilience to specific disturbances would be interesting, for example heat tolerance based on reaction norm models (
,
). Moreover, the calculation of the resilience indicators based on daily milk yield in this study may be optimized in the future. For example, the effect of different methods for calculating daily milk yield from AMS visit data on the resilience indicators may be investigated. In this study and
,
,
,
), we assigned a proportion of the first milk record of a day to the previous day, with the proportion depending on the milking interval and time since midnight. However, the effect of milking interval length on the amount of milk was not corrected for any further, and therefore the higher amount of milk produced on days with more AMS visits (
;
;
) was not adjusted to the amount that would have been produced with fewer AMS visits. Official ICAR methods do adjust for this effect by relying on weightings based on milk rates on the current and previous days (
;
), which raises the question if this approach should be used in the current study as well. However, for the purpose of developing resilience indicators, this approach is not suitable. Resilience indicators rely on fluctuations between realized milk yield from day to day. Smoothing the realized milk yields, by using weighted averages of milk rates on multiple days and by adjusting for number of milk visits, will remove variation between days that is informative about resilience. For example, if a cow normally visits the AMS 3 times per day, but due to disease this is temporarily reduced to 2 times, this will contribute to larger variation between days, which is exactly the kind of information that is needed for the resilience indicators to reflect resilience. In previous studies it was already shown that the resilience indicators in this study reflect resilience (
,
,
,
), and it is very unlikely that they will improve by smoothing the daily milk yield records. The current study adds even more confirmation that the resilience indicators reflect resilience, given their associations with lifespan and yearly disease costs.
CONCLUSIONS
ACKNOWLEDGMENTS
APPENDIX
| Item | Lifetime incidence | Minimum number of registrations throughout life per cow | Maximum number of registrations throughout life per cow |
|---|---|---|---|
| Uterus prolapse | 0.027 | 0 | 6 |
| Vaginal discharge | 0.063 | 0 | 5 |
| Uterus infection | 0.117 | 0 | 7 |
| Retained placenta | 0.032 | 0 | 3 |
| Hock abscess | 0.027 | 0 | 5 |
| Joint infection | 0.027 | 0 | 4 |
| Ketosis | 0.013 | 0 | 2 |
| Abomasal displacement | 0.010 | 0 | 4 |
| Pneumonia | 0.022 | 0 | 6 |
| Clinical mastitis | 0.392 | 0 | 11 |
| Milk fever | 0.064 | 0 | 3 |
| Cystic ovaries | 0.142 | 0 | 7 |
| Inactive ovaries | 0.187 | 0 | 7 |
| Claw disorder | 1.412 | 0 | 45 |
| Euthanasia | 0.0080 | 0 | 1 |
| Dead on farm | 0.16 | 0 | 1 |
| Trait | Mean | Median | Minimum | Maximum | Standard | IQR |
|---|---|---|---|---|---|---|
| Herd life (d) | 1,846.82 | 1,800 | 783 | 3,923 | 592.36 | 845 |
| Productive life (d) | 1,074.37 | 1,027 | 70 | 3,093 | 589.52 | 834 |
| Age at first calving (d) | 772.45 | 756 | 640 | 1,109 | 70.19 | 72 |
| Lifetime milk yield (kg) | 27,243.60 | 25,284.10 | 799.31 | 89,079.10 | 16,504.50 | 23,270.10 |
| Lifetime fat yield (kg) | 1,191.18 | 1,110.01 | 33.99 | 3,867.28 | 716.64 | 999.63 |
| Lifetime protein yield (kg) | 974.28 | 906.40 | 29.64 | 3,094.12 | 587.79 | 828.74 |
| Lifetime lactose yield (kg) | 1,249.20 | 1,166.04 | 34.08 | 4,173.73 | 751.45 | 1,054.78 |
| Lifetime VEM requirement
(kVEM) |
19,010.34 | 18,009.32 | 775.86 | 56.089.56 | 11,056.35 | 15,521.50 |
| Lifetime discarded milk (kg) | 107.97 | 0.00 | 0.00 | 1,895.78 | 234.30 | 134.89 |
| Lifetime discarded fat (kg) | 4.75 | 0.00 | 0.00 | 86.95 | 10.33 | 6.05 |
| Lifetime discarded protein (kg) | 3.78 | 0.00 | 0.00 | 63.36 | 8.21 | 4.81 |
| Lifetime discarded lactose (kg) | 4.89 | 0.00 | 0.00 | 89.42 | 10.56 | 6.28 |
| Milk yield per productive year (kg) | 8,662.89 | 8,924.81 | 799.31 | 13,238.30 | 2,050.70 | 2,244.99 |
| Fat yield per productive year (kg) | 378.57 | 391.19 | 33.99 | 570.49 | 85.27 | 87.19 |
| Protein yield per productive year (kg) | 309.54 | 319.60 | 29.64 | 480.15 | 71.87 | 76.28 |
| Lactose yield per productive year (kg) | 398.48 | 408.87 | 34.08 | 620.75 | 93.97 | 103.09 |
| VEM requirement per productive year (kVEM) | 6,087.67 | 6,321.62 | 775.86 | 8,399.33 | 1,203.99 | 1,025.39 |
| Number of inseminations per productive year | 2.18 | 1.99 | 0 | 12.11 | 1.23 | 1.29 |
| Number of disease events per productive year | 0.72 | 0.18 | 0 | 7.79 | 1.16 | 1 |
).
| Trait expression | Trait | β ADMY | SE ADMY | ADMY (P-value) |
|---|---|---|---|---|
| Lifetime | Gross margin | 201.27 | 21.26 | 0.00 |
| Milk revenues | 323.33 | 34.36 | 0.00 | |
| Calving revenues | 2.77 | 0.73 | 0.00 | |
| Feed costs | 97.71 | 11.21 | 0.00 | |
| Calving costs | 6.64 | 1.75 | 0.00 | |
| Rearing costs | 4.51 | 0.93 | 0.00 | |
| Insemination costs | 6.06 | 0.90 | 0.00 | |
| Total treatment costs | 4.23 | 1.84 | 0.02 | |
| Per year of productive life | Gross margin | 42.92 | 2.94 | 0.00 |
| Milk revenues | 61.27 | 2.08 | 0.00 | |
| Calving revenues | −0.61 | 0.11 | 0.00 | |
| Slaughter revenues | −6.00 | 1.19 | 0.00 | |
| Feed costs | 15.67 | 0.51 | 0.00 | |
| Calving costs | −1.46 | 0.27 | 0.00 | |
| Rearing costs | −4.19 | 2.48 | 0.09 | |
| Insemination costs | 0.83 | 0.27 | 0.00 | |
| Total treatment costs | 0.58 | 0.49 | 0.23 | |
| Destruction costs | 0.30 | 0.081 | 0.00 |
| Trait | Model with LnVar | Model with rauto | ||||
|---|---|---|---|---|---|---|
| β ADMY | SE ADMY | ADMY (P-value) | β ADMY | SE ADMY | ADMY (P-value) | |
| Herd life (d) | 23.38 | 3.69 | 0.00 | 19.16 | 3.56 | 0.00 |
| Productive life (d) | 21.32 | 3.73 | 0.00 | 17.06 | 3.60 | 0.00 |
| Age at first calving (d) | 2.06 | 0.42 | 0.00 | 2.10 | 0.41 | 0.00 |
| Lifetime milk yield (kg) | 1,087.30 | 101.60 | 0.00 | 997.50 | 98.03 | 0.00 |
| Lifetime fat yield (kg) | 38.96 | 4.51 | 0.00 | 34.29 | 4.36 | 0.00 |
| Lifetime protein yield (kg) | 35.55 | 3.66 | 0.00 | 31.97 | 3.54 | 0.00 |
| Lifetime lactose yield (kg) | 49.93 | 4.63 | 0.00 | 45.60 | 4.47 | 0.00 |
| Lifetime VEM requirement | 603,547.90 | 69,264.57 | 0.00 | 532,454.90 | 66,920.88 | 0.00 |
| Lifetime discarded milk (kg) | 5.23 | 1.46 | 0.00 | 4.99 | 1.41 | 0.00 |
| Lifetime discarded fat (kg) | 0.19 | 0.064 | 0.00 | 0.18 | 0.062 | 0.00 |
| Lifetime discarded protein (kg) | 0.17 | 0.051 | 0.00 | 0.16 | 0.049 | 0.00 |
| Lifetime discarded lactose (kg) | 0.24 | 0.066 | 0.00 | 0.23 | 0.064 | 0.00 |
| Milk yield per productive year (kg) | 229.13 | 6.04 | 0.00 | 232.11 | 5.76 | 0.00 |
| Fat yield per productive year (kg) | 6.78 | 0.30 | 0.00 | 6.68 | 0.29 | 0.00 |
| Protein yield per productive year (kg) | 6.96 | 0.23 | 0.00 | 6.94 | 0.22 | 0.00 |
| Lactose yield per productive year (kg) | 10.55 | 0.29 | 0.00 | 10.60 | 0.27 | 0.00 |
| VEM requirement per productive year | 96,809.96 | 3,174.26 | 0.00 | 96,771.15 | 3,037.96 | 0.00 |
| Number of inseminations per productive year | 0.026 | 0.0080 | 0.00 | 0.027 | 0.0080 | 0.00 |
| Number of disease events per productive year | 0.021 | 0.0050 | 0.00 | 0.019 | 0.0050 | 0.00 |
).
| Trait | Model with LnVar | Model with rauto | ||||
|---|---|---|---|---|---|---|
| β ADMY | SE ADMY | ADMY (P-value) | β ADMY | SE ADMY | ADMY (P-value) | |
| Uterus prolapse | 0.00039 | 0.00045 | 0.39 | 0.00038 | 0.00043 | 0.37 |
| Vaginal discharge | 0.0018 | 0.00072 | 0.01 | 0.0020 | 0.00069 | 0.00 |
| Uterus infection | 0.00062 | 0.0015 | 0.67 | 0.00027 | 0.0014 | 0.85 |
| Retained placenta | 0.00082 | 0.00044 | 0.07 | 0.00072 | 0.00043 | 0.09 |
| Hock abscess | 0.0018 | 0.00073 | 0.02 | 0.0019 | 0.00071 | 0.01 |
| Joint infection | 0.00017 | 0.00034 | 0.62 | 0.00023 | 0.00033 | 0.49 |
| Ketosis | 0.00033 | 0.00020 | 0.11 | 0.00013 | 0.00019 | 0.49 |
| Abomasal displacement | −0.00007 | 0.00023 | 0.75 | −0.00011 | 0.00022 | 0.63 |
| Pneumonia | −0.00010 | 0.00051 | 0.85 | 0.00003 | 0.00050 | 0.96 |
| Clinical mastitis | −0.0034 | 0.0019 | 0.07 | −0.0022 | 0.0018 | 0.23 |
| Milk fever | 0.00071 | 0.00056 | 0.21 | 0.00043 | 0.00054 | 0.42 |
| Cystic ovaries | 0.0022 | 0.00095 | 0.02 | 0.0018 | 0.00092 | 0.05 |
| Inactive ovaries | 0.0053 | 0.0013 | 0.00 | 0.0045 | 0.0012 | 0.00 |
| Claw disorder | 0.010 | 0.0042 | 0.02 | 0.0094 | 0.0039 | 0.02 |
| Trait expression | Trait | β ADMY | SE ADMY | ADMY (P-value) |
|---|---|---|---|---|
| Lifetime | Gross margin | 180.56 | 20.58 | 0.00 |
| Milk revenues | 288.94 | 33.20 | 0.00 | |
| Calving revenues | 1.93 | 0.71 | 0.01 | |
| Feed costs | 86.20 | 10.83 | 0.00 | |
| Calving costs | 4.64 | 1.70 | 0.01 | |
| Rearing costs | 4.59 | 0.89 | 0.00 | |
| Insemination costs | 5.34 | 0.87 | 0.00 | |
| Total treatment costs | 3.89 | 1.74 | 0.03 | |
| Per year of productive life | Gross margin | 41.22 | 2.82 | 0.00 |
| Milk revenues | 60.93 | 1.99 | 0.00 | |
| Calving revenues | −0.59 | 0.11 | 0.00 | |
| Slaughter revenues | −5.36 | 1.14 | 0.00 | |
| Feed costs | 15.67 | 0.49 | 0.00 | |
| Calving costs | −1.42 | 0.26 | 0.00 | |
| Rearing costs | −2.33 | 2.38 | 0.33 | |
| Insemination costs | 0.85 | 0.26 | 0.00 | |
| Total treatment costs | 0.67 | 0.46 | 0.15 | |
| Destruction costs | 0.32 | 0.078 | 0.00 |
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