Biometric identification of dairy cows via real-time facial recognition

Biometrics methods, which currently identify humans, can potentially identify dairy cows. Given that animal movements cannot be easily controlled, identification accuracy and system robustness are challenging when deploying an animal biometrics recognition system on a real farm. Our proposed method performs multiple-cow face detection and face classification from videos by adjusting recent state-of-the-art deep-learning methods. As part of this study, a system was designed and installed at four meters above a feeding zone at the Volcani Institute's dairy farm. Two datasets were acquired and annotated, one for facial detection and the second for facial classification of 77 cows. We achieved for facial detection a mean average precision (at Intersection over Union of 0.5) of 97.8% using the YOLOv5 algorithm, and facial classification accuracy of 96.3% using a Vision-Transformer model with a unique loss-function borrowed from human facial recognition. Our combined system can process video frames with 10 cows' faces, localize their faces, and correctly classify their identities in less than 20 ms per frame. Thus, up to 50 frames per second video files can be processed with our system in real-time at a dairy farm. Our method efficiently performs real-time facial detection and recognition on multiple cow faces using deep neural networks, achieving a high precision in real-time operation. These qualities can make the proposed system a valuable tool for an automatic biometric cow recognition on farms.

Machine vision-based automatic lamb identification and drinking activity in a commercial farm

Using ear tags, farmers can track specific data for individual lambs such as age, medical records, body condition scores, genetic abnormalities; to make data-based decisions. However, automatic reading of ear tags using Radio Frequency Identification requires (a) an antenna, (b) a reader, (c) comparable reading standards; consequently, such a system can be expensive and impractical for a large group of lambs, especially in situations where animals are not required to have a compulsory Electronic identification, contrary to the case in Europe, where it is mandatory. Therefore, this paper proposes a machine vision system for indoor animals to identify individual lambs using existing ear tags. Using a camera that is installed such that the trough is visible, the drinking behaviour of the lambs can be automatically monitored. Data from different lamb groups in two different pens were collected. The identification algorithm includes a number of steps: (1) Detecting the lambs' face, and its ear tags in each image; (2) Cropping each ear tag image and discerning the digits on it to obtain the tag number; (3) Tracking each lamb throughout the visit using a tracking algorithm; (4) Recovering the ear tag number using an algorithm that incorporates a list of the ear tag numbers of the lambs in each pen, and the predictions for each lamb in each frame. The You Only Look Once deep learning object detection algorithm was applied to locate and localise the lamb's face and the digits in an image. The models' datasets contained 1 160 and 2 165 images for the training set, and 325 and 616 images for the validation set, respectively. The algorithm output includes the identity of each lamb that came to drink, and its duration. The identification system resulted in a total accuracy of 93% for the data tested, which consisted of approximately 900 visits to the drinking stations, and was collected in real time in a natural environment. The ground truth of each video of a visit was obtained by human observation by studying the video. We checked if there was indeed a visit to the water trough and if so we registered the ear tag number of each lamb whose head was above the water trough. Thus, identifying lambs in a commercial pen using a relatively inexpensive and easily installed system consisting of a RGB camera and a computer vision-based algorithm has potential for farm management.

Design a system for measuring individual cow feed intake in commercial dairies

Monitoring individual cow feed intake is necessary for calculating the cow individual feed efficiency. The cost and maintenance time necessary for research systems make them impractical for most of the commercial producers. We developed a measurement system with producer convenience and low investment as key design criteria. The goal of this study was to design the system and validate its ability to rank cows by their feed conversion efficiency in commercial farms. The new system consisted of three principal parts: (a) a hanging weighing system, (b) a visual cow identification system and (c) an automatic cleaning system. The weighing system consisted of hanging a single load cell to provide feed mass measurements. The image-based cow identification system (replacing Radio-Frequency Identification) entailed cameras installed above the feeding area and an image processing algorithm that recognized cows by their collar numbers. The new system worked within normal farm routines: the feed supplying truck distributed the animal feed, and a tractor cleaned feed residual. To validate the accuracy and convenience of the system and to rank the cows by their efficiency, an experiment with six scales and 12 cows was conducted in a research barn, succeeded by eight-scale system in a commercial farm with 16 cows. The feed intake of each cow participating in the experiments was monitored for one month. The validation experiment showed that the system had the following specification: scales were accurate within 120 g; the visual cow identification rate was greater than 96%; feeding duration was accurate to 52 s; and routine farm practices (feed distribution, pushing, and residual removal) continued as usual. The cost for a feeding station (utilized consequently for a number of cows) was about 1 500 USD. An example of application of the system to rank cows by their efficiency under commercial conditions was shown. The system can potentially be used for ranking cows by their efficiency in commercial facilities.

Dynamic cooling strategy based on individual animal response mitigated heat stress in dairy cows

Technological progress enables individual cow's temperatures to be measured in real time, using a bolus sensor inserted into the rumen (reticulorumen). However, current cooling systems often work at a constant schedule based on the ambient temperature and not on monitoring the animal itself. This study hypothesized that tailoring the cooling management to the cow's thermal reaction can mitigate heat stress. We propose a dynamic cooling system based on in vivo temperature sensors (boluses). Thus, cooling can be activated as needed and is thus most efficacious. A total of 30 lactating cows were randomly assigned to one of two groups; the groups received two different evaporative cooling regimes. A control group received cooling sessions on a preset time-based schedule, the method commonly used in farms; and an experimental group, which received the sensor-based (SB) cooling regime. Sensor-based was changed weekly according to the cow's reaction, as reflected in the changes in body temperatures from the previous week, as measured by reticulorumen boluses. The two treatment groups of cows had similar milk yields (44.7 kg/d), but those in the experimental group had higher milk fat (3.65 vs 3.43%), higher milk protein (3.23 vs 3.13%), higher energy corrected milk (ECM, 42.84 vs 41.48 kg/d), higher fat corrected milk 4%; (42.76 vs 41.34 kg/d), and shorter heat stress duration (5.03 vs 9.46 h/day) comparing to the control. Dry matter intake was higher in the experimental group. Daily visits to the feed trough were less frequent, with each visit lasting longer. The sensor-based cooling regime may be an effective tool to detect and ease heat stress in high-producing dairy cows during transitional seasons when heat load can become severe in arid and semi-arid zones.

Effect of forage-to-concentrate ratio on production efficiency of low-efficient high-yielding lactating cows

Feed is usually the costliest input in lactating cow's farms. Therefore, the developing of methods for a better adjustment of feed intake to cow's energetic needs in order to improve efficiency is desired. The aim of this study was to improve feed efficiency of low-efficient (LE) cows through a moderate increase in diet forage-to-concentrate ratio. We studied the effects of replacing 8.2% corn grains in a control low-fiber (LF) diet that contained 17.5% forage neutral detergent fiber (NDF) with 7.5% wheat straw +0.7% soybean meal for a high-fiber (HF) diet that contained 23.4% forage NDF. Based on efficiency data of individual cows from the Agricultural Research Organization's herd measured in our previous study, 15 pairs of pre-classified LE multiparous mid-lactating Israeli Holstein dairy cows were selected, each pair with similar performance, intake, and efficiency data; each member of a pair was then adapted for 2 weeks to one or the other dietary treatment. Traits examined during the 5 weeks of the experiment were DM intake (DMI), eating behavior, milk production, in vivo digestibility, and estimation of feed efficiency [energy-corrected milk (ECM)/DMI and energy balance]. Cows fed the HF diet showed slower eating rate, smaller visit and meal sizes, longer daily eating time, higher visit frequency, and longer meal duration, compared to those fed the LF diet. The DMI of cows fed the HF diet was 9.1% lower, their DM digestibility decreased from 65.7 to 62.2%, and their ECM yield was 7.0% lower than in cows fed the LF diet. Feed efficiency, measured as net energy captured/digestible energy intake, improved in the cows fed the HF vs. LF diet while feed efficiency measured as ECM/DMI remained similar. Our results thus show the potential of improving feed efficiency for milk production in LE cows by increasing the forage-to-concentrate ratio.

Dietary restriction improved feed efficiency of inefficient lactating cows

The aim of this study was to reduce voluntary dry matter intake (DMI) to increase feeding efficiency of preclassified inefficient (INE) dairy cows through restricted feeding. We studied the effects of dietary restriction on eating behavior, milk and energy-corrected milk (ECM) production, in vivo digestibility, energy balance, and measures of feed efficiency [residual feed intake (RFI) and ECM/DMI]. Before the experiment, 12 pairs of cows were classified as INE. The 2 dietary treatments consisted of ad libitum feeding versus restricted feeding of the same total mixed ration containing 36.5% roughage. Inefficient cows fed the restricted total mixed ration had a shorter eating time and lower meal and visit frequency, but a similar rate of eating, meal size, and meal duration compared with INE cows fed ad libitum. Compared with the INE cows fed ad libitum, restricted INE cows had 12.8% lower intake, their dry matter and neutral detergent fiber digestibility remained similar, and their ECM yield was 5.3% lower. Feed efficiency, measured as RFI, ECM/DMI, and net energy retained divided by digestible energy intake, was improved in the restricted INE cows as compared with the ad libitum cows. Our results show that moderate DMI restriction has the potential to improve feed efficiency of preclassified INE cows.

Consistency of feed efficiency ranking and mechanisms associated with inter-animal variation among growing calves

This study investigated the possible mechanisms for explaining interanimal variation in efficiency of feed utilization in intact male Holstein calves. Additionally, we examined whether the feed efficiency (FE) ranking of calves (n = 26) changed due to age and/or diet quality. Calves were evaluated during three periods (P1, P2, and P3) while fed a high-quality diet (calculated mobilizable energy [ME] of 11.8 MJ/kg DM) during P1 and P3, and a low-quality diet (calculated ME of 7.7 MJ/kg DM) during P2. The study periods were 84, 119, and 127 d, respectively. Initial ages of the calves in P1, P2, and P3 were 7, 11, and 15 mo, respectively, and initial body weight (BW) were 245, 367, and 458 kg, respectively. Individual dry matter intake (DMI), average daily gain (ADG), diet digestibility, and heat production (HP) were measured in all periods. The measured FE indexes were: residual feed intake (RFI), the gain-to-feed ratio (G:F), residual gain (RG), residual gain and intake (RIG), the ratio of HP-to-ME intake (HP/MEI), and residual heat production (RHP). For statistical analysis, animals' performance data in each period, were ranked by RFI, and categorized into high-, medium-, and low-RFI groups (H-RFI, M-RFI, and L-RFI). RFI was not correlated with in vivo digestibility, age, BW, BCS, or ADG in all three periods. The L-RFI group had lowest DMI, MEI, HP, retained energy (RE), and RE/ADG. Chemical analysis of the longissimus dorsi muscle shows that the L-RFI group had a higher percentage of protein and a lower percentage of fat compared to the H-RFI group. We suggested that the main mechanism separating L- from H-RFI calves is the protein-to-fat ratio in the deposited tissues. When efficiency was related to kg/day (DMI and ADG) and not to daily retained energy, the selected efficient L-RFI calves deposited more protein and less fat per daily gain than less efficient H-RFI calves. However, when the significant greater heat increment and maintenance energy requirement of protein compared to fat deposition in tissue were considered, we could not exclude the hypothesis that variation in efficiency is partly explained by efficient energy utilization. The ranking classification of calves to groups according to their RFI efficiency was independent of diet quality and age.

A flexible control system designed for lab-scale simulations and optimization of composting processes

Understanding and optimization of composting processes can benefit from the use of controlled simulators of various scales. The Agricultural Research Organization Composting Simulator (ARO-CS) was recently built and it is flexibly automated by means of a programmable logic controller (PLC). Temperature, carbon dioxide, oxygen and airflow are monitored and controlled in seven 9-l reactors that are mounted into separate 80-l water baths. The PLC program includes three basic heating modes (pre-determined temperature profile, temperature-feedback ("self-heating"), and carbon dioxide-dependent temperature), three basic aeration modes (airflow dependence on temperature, carbon dioxide, or oxygen) and enables all possible combinations among them. This unique high flexibility provides a robust and valuable research tool to explore a wide range of research questions related to the science and engineering of composting. In this article the logic and flexibility of the control system is presented and demonstrated and its potential applications are discussed.

The effect of routine hoof trimming on locomotion score, ruminating time, activity, and milk yield of dairy cows

The objective of this study was to quantify the effect of hoof trimming on cow behavior (ruminating time, activity, and locomotion score) and performance (milk yield) over time. Data were gathered from a commercial dairy farm in Israel where routine hoof trimming is done by a trained hoof trimmer twice per year on the entire herd. In total, 288 cows spread over 6 groups with varying production levels were used for the analysis. Cow behavior was measured continuously with a commercial neck activity logger and a ruminating time logger (HR-Tag, SCR Engineers Ltd., Netanya, Israel). Milk yield was recorded during each milking session with a commercial milk flow sensor (Free Flow, SCR Engineers Ltd.). A trained observer assigned on the spot 5-point locomotion scores during 19 nighttime milking occasions between 22 October 2012 and 4 February 2013. Behavioral and performance data were gathered from 1wk before hoof trimming until 1wk after hoof trimming. A generalized linear mixed model was used to statistically test all main and interactive effects of hoof trimming, parity, lactation stage, and hoof lesion presence on ruminating time, neck activity, milk yield, and locomotion score. The results on locomotion scores show that the proportional distribution of cows in the different locomotion score classes changes significantly after trimming. The proportion of cows with a locomotion score ≥3 increases from 14% before to 34% directly after the hoof trimming. Two months after the trimming, the number of cows with a locomotion score ≥3 reduced to 20%, which was still higher than the baseline values 2wk before the trimming. The neck activity level was significantly reduced 1d after trimming (380±6 bits/d) compared with before trimming (389±6 bits/d). Each one-unit increase in locomotion score reduced cow activity level by 4.488 bits/d. The effect of hoof trimming on ruminating time was affected by an interaction effect with parity. The effect of hoof trimming on locomotion scores was affected by an interaction effect with lactation stage and tended to be affected by interaction effects with hoof lesion presence, indicating that cows with a lesion reacted different to the trimming than cows without a lesion did. The results show that the routine hoof trimming affected dairy cow behavior and performance in this farm.

Time required to determine performance variables and production efficiency of lactating dairy cows

Thirty-five lactating dairy cows throughout weeks of lactation (WOL) 16 to 30 were used to determine optimal time needed for reliable measurement of performance variables, and to classify the cows into high-, medium-, and low-efficiency groups. Individual performance variables [body weight (BW), dry matter intake (DMI), and milk production] were measured daily with a computerized monitoring system. Body condition was visually scored weekly and used to calculate retained or depleted body energy as a result of fat content change (REF). Milk composition was analyzed weekly. Body weight, DMI, and total recovered energy (RE), which represents energy in milk production plus REF, were summarized weekly. Efficiency was calculated as RE/DMI and as residual feed intake (RFI; i.e., the difference between actual and expected DMI), which was calculated from multiple linear regression of DMI dependence on BW(0.75) and RE. Unexpectedly, it was found that BW did not affect DMI and RE/DMI. Changes and relative changes in phenotypic coefficient of variation and correlations among data from shortened tests ranging from 1 wk (WOL 16) to a sequence of 15-wk tests were used to determine optimal test period durations for 5 traits: BW, DMI, RE, RE/DMI, and RFI. Traits were fitted into a mixed model with repeated measures. For each week, the traits were summarized as a sequence of cumulative data, starting from WOL 16 and cumulated over periods that increased in 1-wk steps up to WOL 16 to 29. Weekly cumulations were compared with those for entire test period (WOL 16 to 30). Consistency of each cow's efficiency classification as high, medium, or low was tested by the total-agreement procedure; the kappa index P-value was used. Throughout WOL 16 to 30, the effects of increasing test period duration on between-animal coefficient of variation differed with respect to the various performance variables and RE/DMI: it tended to change with respect to BW, did not change with respect to DMI, and decreased with respect to RE and RE/DMI. In conclusion, compared with a 15-wk study, a 2-wk study can classify RFI and RE/DMI to 3 efficiency levels, with an individual correlation coefficient of 0.6. When the study was carried out over 3 wk or more, the lowest significant index of the classification was P<0.004, the lowest individual correlation coefficient was 0.65, and its lowest significance was P<0.01. The current study indicated that the insignificant effect of the BW of dairy lactating cows on their DMI should be validated in more studies.

Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity

The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm's daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow's performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY=0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well.

Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle

Currently, diagnosis of lameness at an early stage in dairy cows relies on visual observation by the farmer, which is time consuming and often omitted. Many studies have tried to develop automatic cow lameness detection systems. However, those studies apply thresholds to the whole population to detect whether or not an individual cow is lame. Therefore, the objective of this study was to develop and test an individualized version of the body movement pattern score, which uses back posture to classify lameness into 3 classes, and to compare both the population and the individual approach under farm conditions. In a data set of 223 videos from 90 cows, 76% of cows were correctly classified, with an 83% true positive rate and 22% false positive rate when using the population approach. A new data set, containing 105 videos of 8 cows that had moved through all 3 lameness classes, was used for an ANOVA on the 3 different classes, showing that body movement pattern scores differed significantly among cows. Moreover, the classification accuracy and the true positive rate increased by 10 percentage units up to 91%, and the false positive rate decreased by 4 percentage units down to 6% when based on an individual threshold compared with a population threshold.

Feeding soyhulls to high-yielding dairy cows increased milk production, but not milking frequency, in an automatic milking system

To attract a cow into an automatic milking system (AMS), a certain amount of concentrate pellets is provided while the cow is being milked. If the milking frequency in an AMS is increased, the intake of concentrate pellets might increase accordingly. Replacing conventional starchy pellets with nonstarchy pellets increased milk yield, milk fat, and milk protein and decreased body weight. The hypothesis was that a nonroughage by-product rich in digestible neutral detergent fiber, such as soyhulls and gluten feed, could replace starchy grain in pellets fed in an AMS. Sixty cows were paired by age, milk yield, and days in milk, and were fed a basic mixture ad libitum [16.2 +/- 0.35 (mean +/- SE) kg of dry matter intake/d per cow] plus a pelleted additive (6 to 14 kg of dry matter/d per cow) that was consumed in the AMS and in a concentrate self-feeder, which could only be entered after passing through the AMS. The 2 feeding regimens differed only in the composition of the pelleted additives: the control group contained 52.9% starchy grain, whereas the experimental group contained 25% starchy grain, plus soyhulls and gluten feed as replacement for part of the grain. Wheat bran in the control ration, a source of fiber with low digestibility, was replaced with more digestible soyhulls and gluten. During the first 60 d in milk, a cow received 10 to 12 kg of concentrate pellets. After 60 DIM, concentrate feed was allocated by milk production: < or =25 kg/d of milk entitled a cow to 2 kg/d of concentrate feed; >25 kg/d of milk entitled a cow to receive 1 kg/d of additional concentrate feed per 5 kg/d of additional milk production, and >60 kg/d of milk entitled a cow to receive 9 kg of concentrate. The concentrate feed was split between the AMS and concentrate self-feeder. The 2 diets resulted in similar frequencies of voluntary milking (3.12 +/- 0.03 to 2.65 +/- 0.03 visits/d per cow vs. 3.16 +/- 0.00 to 2.60 +/- 0.01 visits/d per cow). Average milk yields were higher in the experimental group (42.7 +/- 0.76 to 39.09 +/- 0.33 kg/d per cow vs. 39.69 +/- 0.68 to 37.54 +/- 0.40 kg/d per cow) and percentages of milk protein (3.02 +/- 0.06 to 3.12 +/- 0.05% vs. 3.07 +/- 0.04 to 3.20 +/- 0.04%) and milk fat (3.42 +/- 0.17 to 3.44 +/- 0.08% vs. 3.38 +/- 0.13 to 3.55 +/- 0.06%) were similar in the 2 groups. The results suggest that the proposed pellets high in digestible neutral detergent fiber can be allocated via the AMS to selected high-yielding cows without a negative effect on appetite, milk yield, or milk composition while maintaining a high milking frequency.

Mathematical optimization to improve cows' artificial insemination services

Engineering tools and mathematical optimization are applied in this study to plan the work of the agents of the cow artificial insemination service (inseminator) in Israel. Time is crucial in insemination as the chances of conception decline with increasing delay between the start of estrus and insemination. About 1,090 artificial inseminations of cows are performed daily in Israel. They involve 412 farms in 283 villages, and are performed by 29 inseminators; the work plan should balance the work load among the inseminators. To this end, the working time of an inseminator in each village is required. Thus, a model to predict the working time in a village was developed. Subsequently, a mathematical optimization model was designed and solved, which aims to allocate customers to trips and to determine the itinerary of each trip to minimize total distance/time. The main benefits included a 21.4% reduction in total traveling time and a 55% reduction in the difference between the lengths of the longest and shortest working days. Moreover, the longest delay in reaching an estrous cow is reduced from 7.6 to 5.9 h (i.e., by 1.7 h), which may increase the conception ratio by some 7%. In addition, the trade-off between work balance and total traveling time was studied.

Cow body shape and automation of condition scoring

The feasibility of including a body shape measure in methods for automatic monitoring of body reserves of cattle was evaluated. The hypothesis tested was that the body shape of a fatter cow is rounder than that of a thin cow and, therefore, may better fit a parabolic shape. An image-processing model was designed that calculates a parameter to assess body shape. The model was implemented, and its outputs were validated against ultrasonic and thermal camera measurements of the thickness of fat and muscle layers, and manual body condition scoring of 186 Holstein-Friesian cows. The thermal camera overcomes some of the drawbacks of a regular camera; the hooks and the tailhead nadirs of a thin cow diverged from the parabolic shape. The correlation between thermal camera's measurements and fat and muscle thickness was 0.47. Mean body condition scorings were 2.18, 2.15, and 2.23, with no significant difference found across the assessment methods. Further research is needed to achieve fully automatic, accurate body condition scoring.

Comparing two concentrate allowances in an automatic milking system

This study investigated the potential for applying an automatic milking system (AMS) to the management of high-yielding cows offered a total mixed ration (TMR). The null hypothesis was that it is desirable to maintain even in AMS, the TMR feeding management practice recommended for high-yielding cows and therefore it can be attained by ‘reducing the concentrate allocation in the robot without reducing the number of milkings’. Two feeding regimes were used: the ‘candy concept’, with only 1·2 kg of food concentrate – the minimum to attract the cow – provided at each visit to the milking robot; and the provision of a maximum of 7 kg of food concentrate per day. Approximately 100 cows were subjected to one or other of these two treatments. Although the cows in the first treatment consumed approximately 3·5 kg of concentrate per day and those in the second treatment approximately 5 kg per day, no significant differences were observed in the numbers of voluntary milkings.

Stochastic Models for Simulating Parallel, Rotary, and Side-Opening Milking Parlors

Simulation models of parallel, rotary, and side-opening milking parlors were built that could predict milking parlor performance according to herd size, number of milking stalls, labor quality, and cow characteristics. The models were validated by statistically comparing the duration of the simulated milking process with actual data collected at 3 dairy farms during 12 mo. Various scenarios were generated to study parlor performance, and the results indicated that for a parlor with up to 14 milking stalls, a side-opening design provided greater capacity than parallel or rotary parlors. Performance of a side-opening parlor was reduced by enlargement up to 20 milking stalls. For 10 to 40 milking stalls, a rotary design gave better performance than a double parallel design in terms of milking process duration and stall utilization. The presented models can serve as a practical tool in designing new parlors or changing operations of existing ones.

Feeding of Pellets Rich in Digestible Neutral Detergent Fiber to Lactating Cows in an Automatic Milking System

If the milking frequency in an automatic milking system (AMS) is increased, the intake of concentrated pellets in the robot may be raised accordingly. Consumption of a large quantity of starchy grains within a short time can impair the appetite, decrease voluntary visits to the milking stall, and lower intakes of dry matter (DM) and neutral detergent fiber (NDF). Therefore, the hypothesis to be tested in this study was whether conventional starchy pellets fed in the AMS could be replaced with pellets rich in digestible NDF without impairing the cows' motivation to visit a milking stall voluntarily. Fifty-four cows were paired according to age, milk yield, and days in milk, and were fed a basic mixture along the feeding lane (19.9 kg of DM/cow per d), plus a pelleted additive (approximately 5.4 kg of DM/cow per d) that they obtained in the milking stall and in the concentrate self-feeder that they could enter only after passing through the milking stall. The 2 feeding regimens differed only in the composition of the pelleted additive, which, for the control group, contained 49% starchy grain, and for the experimental group contained 25% starchy grain plus soy hulls and gluten feed as replacement for part of the grain and other low-digestible, NDF-rich feeds. Both diets resulted in similar rates of voluntary milkings (3.31 vs. 3.39 visits/cow per d). Average yields of milk and percentages of milk protein were also similar in the 2 groups. The results suggest that an alternative pellet composition can be allocated in the AMS in conjunction with basic mixture in the feeding lane, without any negative effect on appetite, milk yield, milk composition, or milking frequency of the cows. It also opens the opportunity to increase yields of milk and milk solids by increasing the amount of pelleted concentrates that can be allocated to selected high-yielding cows via the AMS, because this can be done while maintaining a high frequency of voluntary milkings.

Predicting Feed Intake of the Individual Dairy Cow

The voluntary feed intake of the dairy cow is an important variable in dairy operation but is impossible to measure individually when cows are kept in groups or grazing. Existing formulas that calculate dry matter intake (DMI) from ration and performance variables are not applicable to an individual cow for online decision-making, such as daily ration density adjustment by computerized feeders in a milking robot. This led to a new DMI modeling approach of using only animal factors that are measurable online on an individual basis. In 1997 we published a small-scale attempt of this approach using milk yield (MY) and body weight (BW). In 2001, this approach was adopted by the National Research Council (NRC), using 4% fat-corrected milk rather than MY together with BW and time after calving. In the present study, we increased the number of cows. The model is a multiple regression, where the descriptive variables are the interrelation MY/BW, daily BW change, and milk fat including the effect of previous 2 d. The coefficients are calculated on daily basis, i.e., each day has its own coefficients. Our model differs from that of the NRC by: 1) the descriptive variable, 2) using daily coefficients to deal with the ever-changing physiological state of lactation, and 3) considering previous performance. Two data sets (60 cows together) acquired in 2 intervals of the Volcani Center herd were used to calibrate (18 cows) and test (42 cows) the model. Model validity was statistically tested, compared to that of the NRC, and was not rejected with 99.5% confidence.

Effects of replacing roughage with soy hulls on feeding behavior and milk production of dairy cows under hot weather conditions

Two total mixed rations (TMR) containing different proportions of roughage neutral detergent fiber (NDF) were fed to lactating cows under Israeli summer conditions, and the effects on feeding behavior and milk production were measured. Forty-two lactating cows were divided into 2 groups fed ad libitum a TMR containing either 18% NDF of roughage origin (control group) or only 12% roughage NDF, in which the corn silage component (16.5% of dry matter [DM]) was replaced with soy hulls (experiment group). This and additional adjustments in TMR were reflected in higher net energy for lactation and in vitro digestibility of the experimental TMR. Cow behavior was investigated at the feeding lane during June 2001; about 11,000 cow visits were analyzed. Feed intake per meal and average meal duration were significantly higher in the experiment group (1.51 kg of DM per meal and 12.1 min per meal, respectively) as compared with the control group (1.22 kg of DM per meal and 9.47 min per meal, respectively). However, number of meals per day recorded in the feeding lane was significantly higher in the control group (21.0 vs. 16.6 meals/d per cow). Distribution of meals and feed intake along the day depended more on management practices, such as milking and feed dispensing times, than on feed composition or weather conditions. These differences between groups were expressed in similar daily eating duration (approximately 200 min), and because the rate of feed consumption was similar for both treatments (approximately 127 g DM/min), the daily average DM intake was also similar (25.0 to 25.7 kg). However, NDF intake was higher in the experiment group. Consequently, the average milk yield was higher in the experimental group, and production of milk fat, 4% fat-corrected milk, and economically corrected milk were significantly higher in the experiment group than in the control group. Data imply that the experimental TMR containing only 12% NDF of roughage origin is more suitable for cows under hot climate conditions compared with the control TMR.

Feeding behavior and performance of dairy cows fed pelleted nonroughage fiber byproducts

The potential of pellets made of soy hulls (SH) and corn gluten feed (CGF) to replace starchy pelleted supplement in diets of lactating cows was measured in a feeding regime comparable to automatic milking systems. Twenty-four cows were divided into 2 equal groups and fed for 7 wk in individual feeders monitored by computer on one of the 2 experimental diets. Both diets contained 75% basic total mixed ration plus an additional 25% of pelleted supplement (17% CP), being either high starch pellets (HST) in treatment, or pellets made of SH + CGF (2:1) (SHCG) in treatment. In vitro dry matter digestibility was higher in the HST pellets, whereas neutral detergent fiber (NDF) digestibility was higher in the SHCG pellets. The NDF content was higher in the SHCG diet. Individual cow behavior at the feeding lane was analyzed during the experimental period. Average number of meals and daily eating duration of the SHCG cows were significantly greater, as compared with the HST group. However, intake per meal and rate of eating were greater in the HST cows, whereas meal duration was similar in both groups. Feeding behavior resulted in significantly higher daily dry matter and NDF intake by the SHCG cows (27.1 and 11.1 kg, respectively) as compared with the HST group (24.8 and 7.61 kg, respectively). Consequently, significantly higher milk fat content, milk fat yield, and 4% FCM yield were obtained in the SHCG cows. Milk and milk protein yields were similar in both treatments. Data suggest potential advantages of the SHCG pellets for herds using automatic milking systems.

Designing the Automatic Milking Farm in a Hot Climate

A mathematical simulation model was used to assist 5 farmers in developing design criteria for inclusion of robotic milking systems in each farm situation. The barn layout influences arrivals of cows to the milking robot as well as other cow traffic and must be carefully planned. Each farmer had individual objectives and consequently unique design criteria that determined the optimal solution. Planning factors addressed by simulations on these farms included: 1) optimal cow cooling locations; 2) optimal facility allocation in expanding dairies; 3) concentrate feeder locations and feeding management; 4) number of feeders--based on expected rations; 5) use of a robot in an open cowshed without free stalls; 6) number of robots needed, currently and in the future; 7) eight robots working in harmony; 8) robot locations that allow for expansion; 9) floor space needed in each barn section including maternity and veterinary treatment areas; 10) location of bottlenecks that limit efficiency or expansion; and 11) cow traffic routine as affected by management practices, feed allocation, and farm layout. The simulation allowed farmers to receive a course in managing a "virtual robotic milking farm" before installation of the barn. Therefore, each could be more confident that his future barn would work properly under his unique local conditions and management practices. One farm checked 2 yr after construction achieved an average daily robot utilization of 84%, nearly matching the 85% projected before the barn was built. Important variables considered in the simulations were facility allocation, cow space needed in each farm area, robot utilization, number of cows, milk yield, milk flow rate, feeding method and timing, robot location and orientation, and farm physical layout.