Heat stress compromises dairy production by decreasing feed intake and milk yield, and it may also alter milk composition and feed efficiency. However, little information is available for evaluating such effects across different levels of heat stress and cows enrolled in heat stress studies. The objectives of this study were to evaluate the effects of heat stress on dry matter intake (DMI), energy-corrected milk (ECM), milk composition, and feed efficiency (kg ECM/kg DMI) and to investigate the relationship between such effects and heat stress intervention and animal characteristics by using meta-analytical approaches. Data from 31 studies (34 trials) fulfilled the inclusion criteria and were used for analysis. Results showed that heat stress decreased DMI, ECM, and milk protein concentration, but did not alter milk fat concentration or feed efficiency. Meta-regression confirmed that such reductions in DMI and ECM were significantly associated with increasing temperature-humidity index (THI). Over the period of heat stress, for each unit increase in THI, DMI and ECM decreased by 4.13% and 3.25%, respectively, in mid-lactation cows. Regression models further revealed the existence of a strong interaction between THI and lactation stage, which partially explained the large heterogeneity in effect sizes of DMI and ECM. The results indicated a need for more research on the relationship between the effect of heat stress and animal characteristics. This study calls for the implementation of mitigation strategies in heat-stressed herds due to the substantial decrease in productivity.

Precise energy balance estimates for individual cows are of great importance to monitor health, reproduction, and feed management. Energy balance is usually calculated as energy input minus output (EB(inout)), requiring measurements of feed intake and energy output sources (milk, maintenance, activity, growth, and pregnancy). Except for milk yield, direct measurements of the other sources are difficult to obtain in practice, and estimates contain considerable error sources, limiting on-farm use. Alternatively, energy balance can be estimated from body reserve changes (EB(body)) using body weight (BW) and body condition score (BCS). Automated weighing systems exist and new technology performing semi-automated body condition scoring has emerged, so frequent automated BW and BCS measurements are feasible. We present a method to derive individual EB(body) estimates from frequently measured BW and BCS and evaluate the performance of the estimated EB(body) against the traditional EB(inout) method. From 76 Danish Holstein and Jersey cows, parity 1 or 2+, on a glycerol-rich or a whole grain-rich total mixed ration, BW was measured automatically at each milking. The BW was corrected for the weight of milk produced and for gutfill. Changes in BW and BCS were used to calculate changes in body protein, body lipid, and EB(body) during the first 150 d in milk. The EB(body) was compared with the traditional EB(inout) by isolating the term within EB(inout) associated with most uncertainty; that is, feed energy content (FEC); FEC=(EB(body)+EMilk+EMaintenance+Eactivity)/dry matter intake, where the energy requirements are for milk produced (EMilk), maintenance (EMaintenance), and activity (EActivity). Estimated FEC agreed well with FEC values derived from tables (the mean estimate was 0.21 MJ of effective energy/kg of dry matter or 2.2% higher than the mean table value). Further, the FEC profile did not suggest systematic bias in EB(body) with stage of lactation. The EB(body) estimated from daily BW, adjusted for milk and meal-related gutfill and combined with frequent BCS, can provide a successful tool. This offers a pragmatic solution to on-farm calculation of energy balance with the perspective of improved precision under commercial conditions.

In pigs (Sus scrofa), joint disorders are frequent leg problems, and inappropriate pigpen floors and slippery floor conditions may contribute to these problems. Therefore, this study first aimed to quantify the net joint kinetics (net joint moments and net joint reaction forces) in the forelimbs and hindlimbs of healthy pigs walking on solid concrete floors. Second, this study aimed to examine the effect of floor condition on the net joint kinetics. Kinematic (50-Hz video recordings) and kinetic (1-kHz force plate measurements) data were collected from 30 pigs and combined with body segment parameters from a cadaver study. Net joint kinetics was calculated by using a 2-dimensional inverse dynamic solution. Inverse dynamics have, to our knowledge, not been applied in pigs before. Dry, greasy, and wet floor conditions were tested with 10 pigs each. In the forelimbs, peak joint moment was less (P < 0.01) on greasy (0.184 ± 0.012 Nm/kg, moment of force per kg of BW) than on dry (0.232 ± 0.012 Nm/kg) or wet (0.230 ± 0.012 Nm/kg) conditions. Additionally, the minimum forelimb joint moment was more negative (P < 0.05) on greasy (−0.119 ± 0.009 Nm/kg) than on dry or wet (both −0.091 ± 0.009 Nm/kg) conditions. The forelimb joint reaction forces and the hindlimb joint kinetics were unaffected by floor condition. The greatest (P < 0.001) joint moments occurred in the shoulder (−0.376 ± 0.007 Nm/kg), elbow (0.345 ± 0.009 Nm/kg), hip (0.252 ± 0.009 Nm/kg), and tarsal (0.329 ± 0.009 Nm/kg) joints, which may be related to the greater incidence of joint diseases in some of these joints. In conclusion, the forelimb joints of the pigs responded more markedly to floor condition than did their hindlimb joints, probably because the forelimbs carry more weight. In particular, between the dry and greasy floor conditions, the joint loading differed, most likely because the pigs adapted to a potentially slippery surface. Copyright © 2008. . Copyright 2008 Journal of Animal Science

Existing methods for estimating individual dairy cow energy balance typically either need information on feed intake, that is, the traditional input-output method, or frequent measurements of BW and body condition score (BCS), that is, the body reserve changes method (EB(body)). The EB(body) method holds the advantage of not requiring measurements of feed intake, which are difficult to obtain in practice. The present study aimed first to investigate whether the EB(body) method can be simplified by basing EB(body) on BW measurements alone, that is, removing the need for BCS measurements, and second to adapt the EB(body) method for real-time use, thus turning it into a true on-farm tool. Data came from 77 cows (primiparous or multiparous, Danish Holstein, Red or Jersey) that took part in an experiment subjecting them to a planned change in concentrate intake during milking. BW was measured automatically during each milking and real-time smoothed using asymmetric double-exponential weighting and corrected for the weight of milk produced, gutfill and the growing conceptus. BCS assessed visually with 2-week intervals was also smoothed. EB(body) was calculated from BW changes only, and in conjunction with BCS changes. A comparison of the increase in empty body weight (EBW) estimated from EB(body) with EBW measured over the first 240 days in milk (DIM) for the mature cows showed that EB(body) was robust to changes in the BCS coefficients, allowing functions for standard body protein change relative to DIM to be developed for breeds and parities. These standard body protein change functions allow EB(body) to be estimated from frequent BW measurements alone, that is, in the absence of BCS measurements. Differences in EB(body) levels before and after changes in concentrate intake were calculated to test the real-time functionality of the EB(body) method. Results showed that significant EB(body) increases could be detected 10 days after a 0.2 kg/day increase in concentrate intake. In conclusion, a real-time method for deriving EB(body) from frequent BW measures either alone or in conjunction with BCS measures has been developed. This extends the applicability of the EB(body) method, because real-time measures can be used for decision support and early intervention.

The aim of this study was to use an automated behavior-monitoring system to objectively assess the association between lying and activity behavior in the precalving, calving, and postcalving periods between multiparous and primiparous cows with (1) normocalcemia, (2) subclinical hypocalcemia, or (3) clinical hypocalcemia at calving. Behavioral data and blood serum samples were collected from 51 multiparous and 21 primiparous Holstein dairy cattle. Blood samples from the coccygeal vein were taken within 24 h of calving, and serum was analyzed to measure total calcium concentration. Cows were classified into one of 3 categories: normocalcemia (serum calcium concentration ≥ 2.0 mmol/L), subclinical hypocalcemia (serum calcium concentration < 2.0 mmol/L, absence of clinical signs), and clinical hypocalcemia (clinical signs and successful treatment). An activity sensor was fitted to the right hind leg of cows 3 wk before their expected calving date. Data for lying time, standing time, number of steps, and the total number of standing and lying bouts (postural transitions) were automatically collected and summed into 15-min blocks. Behavioral variables were summarized into 2-h and 24-h periods before analyses. Mixed effect models were used to analyze cow behavior in the entire 14 d before calving (d -14 to -1), on the day of calving, and the entire 21 d postcalving (d 1 to 21). In the precalving period, multiparous cows with normocalcemia had fewer postural transitions (18.5 ± 6.9 no./d) compared with cows with subclinical hypocalcemia (23.5 ± 8.0 no./d) and clinical hypocalcemia (23.5 ± 8.6 no./d). However, there was no association between blood calcium status on lying time (min/d) or step count (no./d) for multiparous cows. For primiparous cows, the step count of cows with subclinical hypocalcemia remained constant across the period, and the step count of cows with normocalcemia decreased from 842.8 steps/d on d -14 to 427.5 steps/d on d -1. Postpartum cows with clinical hypocalcemia were less active (fewer steps) and spent 88 min/d (1.5 h) and 125 min/d (2.1 h) more time lying down compared with cows with subclinical hypocalcemia and normocalcemia, respectively. This shows that clinical hypocalcemia is associated with significant long-lasting behavioral effects on cows during the critical postpartum period.

Mastitis, an intramammary bacterial infection, is not only known to adversely affect the health of a dairy cow but also to cause significant economic loss to the dairy industry. The severity and spread of mastitis can be restrained by identifying the early signs of infection in the cows through an intelligent decision support system. Early intervention and control of infection largely depend on the availability of on-site high throughput machinery, which can analyze milk samples regularly. However, due to limited resources, marginal and small farms usually cannot afford such high-end machinery, hence, the financial loss in such farms due to mastitis may become significant. To overcome such limitations, this article proposes a low-complexity yet affordable automated system for accurate prediction of early signs of clinical mastitis infection in dairy cows. In this work, behavioral data collected through Internet of Things (IoT)-enabled wearable sensors for cows is utilized to develop a support vector machine (SVM) model for the daily prediction of mastitis cases in a dairy farm. The dataset from the research herd utilizes the information of 415 cows collected in the span of 4.75 years in which 75 cows had mastitis. In addition to relevant behavioral features, other statistically significant features, such as daily milk yield, lactation period, and age are also utilized as features. Our study indicates that the SVM model comprising a subset of behavioral and nonbehavioral features can deliver a mastitis prediction accuracy of 89.2%.