Genetic analysis of lactation consistency in US Holsteins using temporal variation in daily milk weights

The ability of a dairy cow to perform reliably over time is an interesting trait to include in dairy cattle breeding programs aimed at improving dairy cow resilience. Consistency, defined as the quality of performing as expected each day of the lactation, could be highly associated with resilience, defined as animal's ability to maintain health and performance in the presence of environmental challenges, including pathogens, heat waves, and nutritional changes. A total of 51,415,022 daily milk weights collected from 2018 to 2023 were provided for 255,191 multiparous Holstein cows milked 3 times daily in conventional parlor systems on farms in 32 states. The temporal variance (TEMPVAR) of milk yield from 5 to 305 d postpartum was computed as the log-transformed variance of daily deviations between observed and expected individual milk weights. Lower values of TEMPVAR imply smaller day-to-day deviations from expectations, indicating consistent performance, whereas larger values indicate inconsistent performance. Expected daily milk weights were computed using 3 nonparametric and parametric regression models: (1) loceally estimated scatterplot smoothing regression with a 0.75 span; (2) polynomial quantile regression using the median (0.5 quantile), and (3) polynomial quantile regression using a 0.7 quantile. The univariate statistical model included age at first calving and herd-year-season as fixed effects and cow as a random effect. Heritability estimates (standard errors) of TEMPVAR phenotypes calculated over the entire lactation ranged between 0.227 (0.011) and 0.237 (0.011), demonstrating that cows are genetically predisposed to display consistent or inconsistent performance. Estimated genetic correlations calculated using a multiple-trait model between TEMPVAR traits and between lactations were high (>0.95), indicating TEMPVAR is repeatable across lactations and robust to the model used to compute expected daily milk yield. Higher TEMPVAR phenotypes reflect more variation in performance, hence greater inconsistency, which is undesirable. Therefore, correlations between predicted transmitting abilities (PTA) for TEMPVAR and milk yield of 0.57 indicate that high-producing cows exhibit more day-to-day variation in performance. Correlations with productive life and livability were -0.38 and -0.48, respectively. Correlations between PTA for TEMPVAR and those of postpartum health traits were also negative, ranging from -0.41 to -0.08. Given that health traits are derived from disease resistance measurements, and higher health trait PTA are preferred, our results indicate that more consistent cows tend to have fewer health problems and greater longevity. Overall, our findings suggest that temporal variation in daily milk weights can be used to identify consistent animals that maintain expected performance throughout the lactation, which will enable selection for greater resilience to management and environmental perturbations.

Peri-estrus activity and mounting behavior and its application to estrus detection in Hanwoo (Korea Native Cattle)

This study was conducted to investigate the change in activity and mounting behavior in Hanwoo (Korean Native Cattle) during the peri-estrus period and its application to estrus detection. A total of 20 Hanwoo cows were fitted with a neck-collar accelerometer device, which measured the location and acceleration of cow movements and recorded the number of instances of mounting behavior by the altitude data. The data were analyzed in three periods (24-, 6-, and 2-h periods). Blood samples were collected for 5 days after the prostaglandin F2α (PGF2α) injection, and the concentrations of estradiol, progesterone, follicle-stimulating hormone, and luteinizing hormone were determined by enzyme-linked immunosorbent assays. Activity and mounting behavior recorded over 2-h periods significantly increased as estrus approached and were more efficient at detecting estrus than over 24- and 6-h periods (p < 0.05). Endocrine patterns did not differ with the variation of individual cows during the peri-estrus period (p > 0.05). Activity was selected as the best predictor through stepwise discriminant analysis. However, activity alone is not enough to detect estrus. We suggest that a combination of activity and mounting behavior may improve estrus detection efficiency in Hanwoo. Further research is necessary to validate the findings on a larger sample size.

Effect of voluntary waiting period on metabolism of dairy cows during different phases of the lactation

An extended calving interval (CInt) by extending the voluntary waiting period (VWP) could be associated with altered metabolism in dairy cows. The aim of this study was first to evaluate the effects of VWP on metabolism and body condition during the first 305 d after the first calving in the experiment (calving 1), around the end of the VWP, and during pregnancy (280 d before calving 2). Second, the effects of the VWP on metabolism were determined from 2 wk before until 6 wk after calving 2. Third, individual cow characteristics were used to predict milk production and body condition of cows after different VWP. Holstein-Friesian cows (N = 154, 41 primiparous [PP], 113 multiparous [MP]) were blocked for parity, milk production, and lactation persistency, randomly assigned to a VWP of 50, 125, or 200 d (VWP50, VWP125, or VWP200) and followed from calving 1 until 6 wk after calving 2. In the first 6 wk after calving 1 and from 2 wk before until 6 wk after calving 2, weekly plasma samples were analyzed for nonesterified fatty acids (NEFA), β-hydroxybutyrate, glucose, insulin, and insulin-like growth factor 1 (IGF-1). From wk 7 after calving 1 until 2 wk before calving 2, insulin and IGF-1 were analyzed every 2 wk. Fat- and protein-corrected milk (FPCM) and body weight (BW) gain were measured weekly. Cows were divided in two parity classes based on calving 1 (PP and MP) and remained in these classes after calving 2. During pregnancy, MP cows in VWP200 had greater plasma insulin and IGF-1 concentration and lower FPCM compared with MP cows in VWP125 (insulin: 18.5 vs. 13.9 µU/mL, CI 13.0-19.7, P < 0.01; IGF-1: 198.5 vs. 175.3 ng/mL ± 5.3, P = 0.04; FPCM: 22.6 vs. 30.0 kg/d ± 0.8, P < 0.01) or VWP50 (insulin: 15.8 µU/mL, P < 0.01; IGF-1: 178.2 ng/mL, P < 0.01; FPCM: 26.6 kg/d, P < 0.01) and had a greater daily BW gain compared with cows in VWP50 (3.6 vs. 2.5 kg/d ± 0.2; P < 0.01). After calving 2, MP cows in VWP200 had greater plasma NEFA concentration (0.41 mmol/liter) compared with MP cows in VWP125 (0.30 mmol/liter, P = 0.04) or VWP50 (0.26 mmol/liter, P < 0.01). For PP cows, the VWP did not affect FPCM or body condition during the first lactation in the experiment, or metabolism after calving 2. Independent of the VWP, higher milk production and lower body condition before insemination were associated with higher milk production and lower body condition at the end of the lactation. Variation in these characteristics among cows could call for an individual approach for an extended VWP.

Contribution of Precision Livestock Farming Systems to the Improvement of Welfare Status and Productivity of Dairy Animals

Although the effects of human–dairy cattle interaction have been extensively examined, data concerning small ruminants are scarce. The present review article aims at highlighting the effects of management practices on the productivity, physiology and behaviour of dairy animals. In general, aversive handling is associated with a milk yield reduction and welfare impairment. Precision livestock farming systems have therefore been applied and have rapidly changed the management process with the introduction of technological and computer innovations that contribute to the minimization of animal disturbances, the promotion of good practices and the maintenance of cattle’s welfare status and milk production and farms’ sustainability and competitiveness at high levels. However, although dairy farmers acknowledge the advantages deriving from the application of precision livestock farming advancements, a reluctance concerning their regular application to small ruminants is observed, due to economic and cultural constraints and poor technological infrastructures. As a result, targeted intervention training programmes are also necessary in order to improve the efficacy and efficiency of handling, especially of small ruminants.

XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification

Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state-of-the-art MTS classifier is a heavyweight deep learning approach, which outperforms the second-best MTS classifier only on large datasets. Moreover, this deep learning approach cannot provide faithful explanations as it relies on post hoc model-agnostic explainability methods, which could prevent its use in numerous applications. In this paper, we present XCM, an eXplainable Convolutional neural network for MTS classification. XCM is a new compact convolutional neural network which extracts information relative to the observed variables and time directly from the input data. Thus, XCM architecture enables a good generalization ability on both large and small datasets, while allowing the full exploitation of a faithful post hoc model-specific explainability method (Gradient-weighted Class Activation Mapping) by precisely identifying the observed variables and timestamps of the input data that are important for predictions. We first show that XCM outperforms the state-of-the-art MTS classifiers on both the large and small public UEA datasets. Then, we illustrate how XCM reconciles performance and explainability on a synthetic dataset and show that XCM enables a more precise identification of the regions of the input data that are important for predictions compared to the current deep learning MTS classifier also providing faithful explainability. Finally, we present how XCM can outperform the current most accurate state-of-the-art algorithm on a real-world application while enhancing explainability by providing faithful and more informative explanations.

Daily activity measures and milk yield immediately before and after a fertile estrus and during the period of expected return to estrus after insemination in dairy cows

The objective of this study was to characterize changes in milk yield and other physical measures during a 7-d periestrual period encompassing estrus (d 0) and during a 16-d period of expected return to estrus beginning at d 17 after artificial insemination (AI) until pregnancy status was determined on d 32. Lactating dairy cows milked thrice daily were fitted with CowManager SensOor ear tags (Agis) capable of assessing real-time eating, rumination, resting, high activity (estrus), ear-surface temperature, and heat alerts. Data were uploaded to the cloud, downloaded daily into Excel (Microsoft Corp.) spreadsheets, averaged to produce daily means for each activity, and analyzed as repeated measures relative to estrus or to d 17 after AI. Daily milk was unchanged during the periestrual period but was greater in nonpregnant cows that failed to return to estrus (NP-NR) during d 21 through 26 compared with NP cows that returned to estrus (NP-R) and pregnant (PREG) cows during that same period. Daily ear-surface temperature was greater during d 1 to 3 compared with d 0 and averaged 0.6 to 1.7°C greater from d 17 through 32 in NP-NR cows compared with NP-R and PREG cows. Daily rumination and resting times reached nadirs on d 0, with decreases occurring 48 h before estrus. Both rumination and resting times increased by 25 or 81% on the day after estrus, respectively. Rumination and resting times were less in NP-R cows during d 22 through 26 compared with NP-NR and PREG cows. In contrast, daily eating time was greatest on the day of estrus compared with 3 d before and after estrus. The NP-R cows spent more time eating during d 17 through 32 compared with NP-NR and PREG cows. High activity increased by 97% during 48 h before estrus, peaked at estrus, and decreased to a constant level during d 1 through 3. The NP-R cows had greater high activity on d 22 through 26 compared with NP-NR and PREG cows. We conclude that resting and rumination activity decreased to daily nadirs, whereas eating and high activity peaked on the day of estrus. Fertile estrus was associated with 12% greater high activity, 11% less resting time, and 6% less rumination time. In addition, cows that returned to estrus after AI had greater daily eating and high activity times and less rumination and resting time during the period of expected return to estrus after AI compared with pregnant cows and cows failing to return to estrus.

Milk losses and dynamics during perturbations in dairy cows differ with parity and lactation stage

Milk yield dynamics during perturbations reflect how cows respond to challenges. This study investigated the characteristics of 62,406 perturbations from 16,604 lactation curves of dairy cows milked with an automated milking system at 50 Belgian, Dutch, and English farms. The unperturbed lactation curve representing the theoretical milk yield dynamics was estimated with an iterative procedure fitting a model on the daily milk yield data that was not part of a perturbation. Perturbations were defined as periods of at least 5 d of negative residuals having at least 1 day that the total daily milk production was below 80% of the estimated unperturbed lactation curve. Every perturbation was characterized and split in a development and a recovery phase. Based hereon, we calculated both the characteristics of the perturbation as a whole, and the duration, slopes, and milk losses in the phases separately. A 2-way ANOVA followed by a pairwise comparison of group means was carried out to detect differences between these characteristics in different lactation stages (early, mid-early, mid-late, and late) and parities (first, second, and third or higher). On average, 3.8 ± 1.9 (mean ± standard deviation) perturbations were detected per lactation in the first 305 d after calving, corresponding to an estimated 92.1 ± 135.8 kg of milk loss. Only 1% of the lactations had no perturbations. On average, 2.3 kg of milk was lost per day in the development phase, while the recovery phase corresponded to an average increase in milk production of 1.5 kg/d, and these phases lasted an average of 10.1 and 11.6 d, respectively. Perturbation characteristics were significantly different across parity and lactation stage groups, and early and mid-early perturbations in higher parities were found to be more severe with faster development rates, slower recovery rates, and higher milk losses. The method to characterize perturbations can be used for precision phenotyping purposes that look into the response of cows to challenges or that monitor applications (e.g., to evaluate the development and recovery of diseases and how these are affected by preventive actions or treatments).

Economic Viability of Adoption of Automated Oestrus Detection Technologies on Dairy Farms: A Review

Simple Summary

The accurate and timely detection of oestrus is a central element of good dairy herd management as it ultimately determines the level of milk production and is core to the economic viability of the farm business. However, the traditional method of oestrus detection, which occurs by observing the dairy cows standing immobile while being mounted, is usually time-consuming, repetitive and requires considerable skill and experience on the part of the farmer to attain a reasonable level of efficiency. Given the limitation of the traditional method of oestrus detection, a number of automated oestrus detection (AOD) technologies have been developed. However, the rate of adoption of these technologies remains low. One reason that has been proposed for farmers’ low adoption of such technologies has been their lack of knowledge around the potential economic returns from investing in AOD technologies. In this paper, we review the empirical literature on the viability of investment in AOD technologies from an economic perspective. The conclusion of this study provides evidence from which farmers can make more informed decisions in relation to investing in AOD technologies. The review and analysis is also of importance for informing policy, as it provides an examination of the incentives and levers that could improve productivity on dairy farms.

Abstract

The decision for dairy farmers to invest in automated oestrus detection (AOD) technologies involves the weighing up of the costs and benefits of implementation. In this paper, through a review of the existing literature, we examine the impacts of investment in AOD technologies in relation to the profitability and technical performance of dairy farms. Peer-reviewed articles published between 1970 and 2019 on the investment viability of AOD technologies were collated and analysed. We capture the different measures used in assessing the economic performance of investment in AOD technologies over time which include net present value (NPV), milk production, Benefit-Cost Ratio (BCR), internal rate of return (IRR) and payback period (PBP). The study concludes that investment in AOD technologies is not only worthwhile but also contributes to farm profitability.

Review: extended lactation in dairy cattle

This paper reviews the effects of extended lactation (EXT) as a strategy in dairy cattle on milk production and persistency, reproduction, milk quality, lifetime performance of the cow and finally the economic effects on herd and farm levels as well as the impact on emission of greenhouse gas at product level. Primiparous cows are able to produce equal or more milk per feeding day during EXT compared with a standard 305-d lactation, whereas results for multiparous cows are inconsistent. Cows managed for EXT can achieve a higher lifetime production while delivering milk with unchanged or improved quality properties. Delaying insemination enhances mounting behaviour and allows insemination after the cow's energy balance has become positive. However, in most cases EXT has no effect or a non-significant positive effect on reproduction. The EXT strategy sets off a cascade of effects at herd and farm level. Thus, the EXT strategy leads to fewer calvings and thereby expected fewer diseases, fewer replacement heifers and fewer dry days per cow per year. The optimal lifetime scenario for milk production was modelled to be an EXT of 16 months for first parity cows followed by an EXT of 10 months for later lactations. Modelling studies of herd dynamics indicate a positive effect of EXT on lifetime efficiency (milk per dry matter intake), mainly originating from benefits of EXT on daily milk yield in primiparous cows and the reduced number of replacement heifers. Consequently, EXT also leads to reduced total meat production at herd level. For the farmer, EXT can give the same economic return as a traditional lactation period. At farm level, EXT can contribute to a reduction in the environmental impact of dairy production, mainly as a consequence of the reduced production of beef. A wider dissemination of the EXT concept will be supported by methods to predict which cows may be most suitable for EXT, and clarification of how milking frequency and feeding strategy through the lactation can be organised to support milk yield and an appropriate body condition at the next calving.

Change of daily milk yield during estrous period in Holstein cattle raised under Mediterranean climate

This study was conducted to determine the effect of estrus on the daily milk yield in Holstein cows and to investigate the chance of using the possible milk yield changes in determining the estrus. During the 3-year period of the study, 103 dairy cows were observed 4 days before and 4 days after daily milk yield of 240 estruses and a total of 2174 daily milk yields were evaluated. Variance analysis was used to determine the factors affecting the daily milk yield, and the LSD test was used for multiple comparisons. Insemination year, insemination season, number of lactation, milk yield group, and daily milk yield of lactation period were found to be significant (P < 0.01). On the other hand, the effect of estrus days on milk yield was insignificant. In the days of estrus, the least square mean of milk yield is 31.0 kg, while the lowest and highest milk yields are 10.2 kg and 62.9 kg. The daily milk yield in the estruses decreased by an average of 300 g, which decreased to 400 g by continuing 1 day after the estruses. The next day, however, it increased rapidly by 600 g, and then dropped again, probably due to the effect of metestrus. It was found that, among all estruses, 31.3% of cows decreased their milk yield, whereas 26.5% of cows increased their milk yield. However, 42.2% of cows both decreased and increased their milk yield in different estruses. The interval between birth and the first insemination after were found to be longer (97.5 days and 92.9 days) at high milk-yielding cows compared to the low milk-yielding cows. According to the results of this study, daily milk yield changes could not be used as an estrus indicator.

Inline Progesterone Monitoring in the Dairy Industry

An inline progesterone monitoring system that works automatically and provides real-time physiological information about lactating dairy cows for making farm management decisions is not only a novel tool for scientific research but also improves productivity, food safety, animal well-being, the environment, and the public perception of the dairy industry.