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A real-time control system for individual dairy cow food intake
Abstract
A real-time control system for individual food intake of group-housed dairy cows was designed and implemented. The system consists of 40 feeding stations, each position comprising an automatic opening and closing yoke-gate. The specially designed electro-pneumatic yoke-gate combines off-the-shelf identification, weighing and control systems in a unique mechanical implementation. The yoke-system enables real-time control of food intake, which can be specified for each cow, and for each station through an interactive user interface. In addition, the system enables measurement and utilization of on-line data related to individual food intake and feeding behavior information such as food access frequency, meal duration, intake rate and food quantity for each visit of a cow kept in the group. The advantages of this design are: (i) the system is based on commercial components, (ii) simplicity and high reliability resulting in low maintenance, and (iii) continuous measurement of all feeding parameters, including during the meal. Although the yoke-system was built for a research institute, it is a step towards a commercial system.
... Cattle feed constitutes more than 60% of farm expenses (Ben Meir et al., 2018). Identifying and replacing inefficient cows and breeding more efficient cows may improve the farm's financial situation (Halachmi et al., 1998;Herd et al., 2003). In order to predict the feed intake throughout the lactation, feed intake (DM per milk produced) and eating behavior should be monitored for three weeks (Ben Meir et al., 2019;Richter et al., 2016;Halachmi et al., 2016), allowing to estimate the feed efficiency of the entire herd by a small feed intake measurement system. ...
... The accuracy of the feed mass measuring (120 g) was lower than that of the off-the-shelf commercial systems (±10 g such as GrowSafe) used by Mendes et al. (2014), Stajnko et al. (2010), Chizzotti et al. (2015) and others, and similar to that of self-designed systems built by Halachmi et al. (1998), Bach et al. (2004 and Dahlke et al. (2008). The sensitivity (93.9%) of the feeding visits was lower than in previous studies (99.6% at Bach et al., 2004;99.64% at Chizzotti et al. 2015). ...
... The system was designed to minimize human participation in the cleaning process, consuming significant amounts of time and labor, by an ability to turn the containers over easily by pushing a button. That contrasts with the reviewed commercial and selfdesigned measurement systems, which require manual cleaning around the feeding containers, and/or manual cleaning inside the containers, and/or manually turning over the containers (Mendes et al., 2014;Chapinal et al., 2007;Halachmi et al., 1998). ...
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.
... Attempts are being made to develop a sensor to measure individual daily food intake (Bloch et al., 2019), but so far it is limited to costly research facilities all over the world. For example, such research is being conducted in the Volcani Center in Israel (Halachmi et al., 1998) and in research dairy farms in universities, like the University of Guelph in Canada and University of Florida. In such facilities, individual food intake can be measured for cows kept in a group in conditions that are close to commercial (i.e. ...
... Daily food intake, MY and BW of a cow kept in a group of 42 cows. Individual measurements were made in the barn at the Volcani Center(Halachmi et al., 1998) during 14 successive days at mid lactation. (a) Describes the daily measured values and (b) describes the running averages of these same data over periods of 5 d (days 1-5, 2-6 and so on). ...
... Correlation between daily milk energy and food intake (as is) of a TMR, according to NRC (2001) based on a running average of 5 d. Individual measurements done on 42 cows mid lactation, over a period of 14 d in the individual measurements barn at the Volcani Center(Halachmi et al., 1998). Each point represents 1 d of one cow. ...
This review deals with the prospects and achievements of individual dairy cow management (IDCM) and the obstacles and difficulties encountered in attempts to successfully apply IDCM into routine dairy management. All aspects of dairy farm management, health, reproduction, nutrition and welfare are discussed in relation to IDCM. In addition, new IDCM R&D goals in these management fields are suggested, with practical steps to achieve them. The development of management technologies is spurred by the availability of off-the-shelf sensors and expanded recording capacity, data storage, and computing capabilities, as well as by demands for sustainable dairy production and improved animal wellbeing at a time of increasing herd size and milk production per cow. Management technologies are sought that would enable the full expression of genetic and physiological potential of each cow in the herd, to achieve the dairy operation's economic goals whilst optimizing the animal's wellbeing. Results and conclusions from the literature, as well as practical experience supported by published and unpublished data are analyzed and discussed. The object of these efforts is to identify knowledge and management routine gaps in the practical dairy operation, in order to point out directions and improvements for successful implementation of IDCM in the dairy cows' health, reproduction, nutrition and wellbeing.
... Depending on the stage of lactation-from 15 to 30% of calcium is in a "mobile" state; it can pass from bone tissue into the blood and other tissues-it is essential to control this indicator at the peak of lactation when a large amount of minerals is released with a large volume of milk. It is also important to note that Phosphorus (P) is an antagonist of Calcium (Ca) and their ratio must equal Ca/P = 1.5 − 2/1 [26][27][28][29][30]. ...
... Sulfur(S) helps improve the utilization of non-protein nitrogen, the digestion of fiber and starch in the rumen [30][31][32][33]. ...
Feeding cattle on livestock farms is a labor-intensive operation that requires considerable capital investments to purchase equipment and cover labour costs. The global trends in developing technological equipment for feeding cattle include the robotization of various processes. The operation of feed pushing on the feeding table is an integral part of the feeding process, which has a significant impact on livestock productivity. This study concentrates on the simulation modeling of a feed pusher robot using Simulink tools in the Matlab environment to facilitate robot modernization or optimize the final cost for artificial testing of typical system elements and reduce production costs. Based on a simulation model, an experimental sample was designed with a controlled dispenser of feed additives, which can significantly facilitate the feeding process and optimize the dosing of concentrated additives.
... Traditional feed intake monitoring systems utilize individual weighing balances (electronic scales) and RFID antennas in feeding stalls to measure the amount of feed consumed by each animal. The electronic scale is the oldest (Halachmi et al., 1998) and likely the most straightforward sensor for measuring feed intake in group housing and feedlot settings. An electronic scale is placed in a feeding station and measures each feed's weight consumed by each animal during each meal at each feed bin. ...
... Several companies have developed electronic feed weighing systems, including the Calan Broadbent Feeding System, the Controlling and Recording Feed Intake system, the GrowSafe System, Intergado Efficiency, and the Roughage Intake Control system. Numerous researchers have evaluated these weighing systems (Halachmi et al., 1998;DeVries et al., 2003;Bach et al., 2004;Ferris et al., 2006;Wang et al., 2006;Chapinal et al., 2007;Stajnko et al., 2010;Mendes et al., 2011;Chizzotti et al., 2015), but, unfortunately, they have been infrequently used in commercial operations due to their high price and frequent cleaning and maintenance that many cannot afford (Wang et al., 2006;Stajnko et al., 2010). Furthermore, some of these systems also maintain full control over the collected data, and data manipulation is performed without a transparent process to the end user. ...
Remote-monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 years. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; feed intake and greenhouse gas emissions to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and co-variances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link“ to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.
... Cows were housed in individual free-stall dairy barns at the Agricultural Research Organization (Rishon LeZion, Israel) equipped with an individual cow feed-intake-recognition system (Halachmi et al., 1998). The system includes 42 feeders, with an antenna at each feeder that recognizes a specific sensor tied to the cow's front leg. ...
... Each individual feeder is located on top of a weighing balance. This experimental setup, described in Halachmi et al. (1998), records each entry and exit time of each cow for its specific feeder, along with computerized online monitoring of feed weight consumed from the feeder during each visit. Since each cow was assigned to a single feeder, hierarchy interruption among cows was minimized, and the system enabled detection of each visit to the feeder. ...
We assessed the effects of replacing wheat silage and hay with barley silage and hay in total mixed rations (TMRs) on: eating behavior, digestibility, and performance of individually fed high-yielding lactating cows. The barley cultivar Ma’anit and the wheat cultivar Omer were grown by dryland farming (210 mm precipitation) in a commercial field with sandy loam soil. The two cultivars were harvested simultaneously for silage or hay at a similar dry matter (DM) content (35.6%). Barley yields exceeded wheat yields by 19% (5.68 vs. 4.79 t DM/ha, P = 0.01). Two-thirds of each crop's DM yield was chopped and transferred for direct ensiling in polyethylene-wrapped bales, and one-third was wilted in the field for 2 weeks and then pressed into hay bales. The chemical composition and in-vitro DM digestibility of the wheat and barley silages and hays were similar. The two experimental TMRs contained either barley silage (280 g/kg DM) and hay (60 g/kg DM) or similar contents of wheat silage and hay, as the sole source of roughage. High-yielding multiparous Israeli Holstein cows were paired into two experimental groups of 21 cows each and fed either wheat- or barley-based TMR ad libitum for 28 days. DM intake of cows fed the wheat TMR was higher than that of the barley TMR-fed cows (28.0 vs. 27.5 kg/day) due to a faster eating rate (137.9 vs. 132.9 g DM/min) and larger meal size. Energy corrected milk production was similar in the two groups (42.4 and 42.8 kg/day), while milk fat yield was higher in barley-fed cows than wheat-fed cows (1.62 vs. 1.57 kg/day). Daily rumination time of the barley-fed cows was longer (577 vs. 537 min/day) and their rumen pH higher during the day and night (P < 0.01) compared to wheat-fed cows. Consequently, in-vivo DM digestibility and neutral detergent fiber digestibility were higher in the barley-fed cows. Our results thus show the benefit of barley as an alternative forage crop to wheat in semiarid regions, and its advantage as a substitute for wheat as silage and hay for high-yielding lactating cows’ TMR.
... Cows were housed in the ARO experimental dairy barn (Rishon Lezion, Israel), which is equipped with an individual cow feed intake-recognition system (Halachmi et al., 1998). The system included 24 feeders, with an antenna at each feeder that recognized a sensor tied to a front leg of each cow. ...
... Each individual feeder was located on top of weighing balances. This experimental setup, described by Halachmi et al. (1998), recorded entry and exit time of each cow to its specific feeder, along with computerized online monitoring of how much feed was consumed. Because each cow had its unique feeder, hierarchy interruption among cows was minimized, and the system enabled detection of each visit to the feeder. ...
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.
... Various methods to investigate ingestive behaviour that are found in the literature include the following: visual observation (Bü chel & Sundrum, 2014;Oudshoorn et al., 2013) (also used as a gold standard to validate other methods), animal weighing (Enriquez-Hidalgo et al., 2014;Giger-Reverdin, Lebarbier, Duvaux-Ponter, & Desnoyers, 2012), measuring sward height (Kennedy, O'Donovan, Delaby & O'Mara, 2008), oesphageally fistulated animals (Mayes, 1989), faecal markers (Hellwing et al., 2015), and individual feeding stations (Halachmi et al., 1998). (Bü chel & Sundrum, 2014;Oudshoorn et al., 2013) (also used as a gold standard to validate other methods), animal weighing (Enriquez-Hidalgo et al., 2014;Giger-Reverdin et al., 2012), measuring sward height (Kennedy, O'Donovan, Delaby, & O'Mara, 2008), oesphageally fistulated animals (Mayes, 1989), faecal markers (Hellwing et al., 2015), and individual feeding stations (Halachmi et al., 1998). ...
... Various methods to investigate ingestive behaviour that are found in the literature include the following: visual observation (Bü chel & Sundrum, 2014;Oudshoorn et al., 2013) (also used as a gold standard to validate other methods), animal weighing (Enriquez-Hidalgo et al., 2014;Giger-Reverdin, Lebarbier, Duvaux-Ponter, & Desnoyers, 2012), measuring sward height (Kennedy, O'Donovan, Delaby & O'Mara, 2008), oesphageally fistulated animals (Mayes, 1989), faecal markers (Hellwing et al., 2015), and individual feeding stations (Halachmi et al., 1998). (Bü chel & Sundrum, 2014;Oudshoorn et al., 2013) (also used as a gold standard to validate other methods), animal weighing (Enriquez-Hidalgo et al., 2014;Giger-Reverdin et al., 2012), measuring sward height (Kennedy, O'Donovan, Delaby, & O'Mara, 2008), oesphageally fistulated animals (Mayes, 1989), faecal markers (Hellwing et al., 2015), and individual feeding stations (Halachmi et al., 1998). Although these methods have been validated, they might be laborious to implement, time consuming and sometimes invasive. ...
The demand for quality control and environmentally acceptable animal production are important requirements in livestock monitoring and control. Monitoring the ingestive behaviour of grazing ruminants has applications in measuring animal welfare, detection of animal diseases and modelling the animal production system. In this work, a non-invasive method for fibre intake estimation on ruminants is presented using a surface electromyography (sEMG) based sensor system. In order to acquire bite/chewing sEMG signals, superficial disposable electrodes were placed on three uncastrated male goats’ masseter muscle, housed in individual pens, and data was sampled during eating using an analogue-to-digital converter. Feed samples and-left-overs were weighed before and after the experiment, respectively, to estimate the total intake. Electromyographic preprocessed data was sent to a computer, where seven signal features were extracted. Feed intake was modelled by fitting sEMG features as a predictor by means of a linear model. Results indicate that fibre intake could be successfully predicted in goats eating several forages (Tifton 68 and Tifton 85 grass hay, bad quality Tifton hay, and forage oat hay) with coefficients of determination (R2) higher than 0.867, using the signal feature called Slope Sign Change (SSC) as a predictor, which expresses signal frequency characteristics and indicates physiological aspects of bite/chewing. The present work demonstrates that sEMG based systems can be used as a non-invasive feed fibre intake estimation tool.
... Various methods to investigate ingestive behaviour that are found in the literature include the following: visual observation (Bü chel & Sundrum, 2014;Oudshoorn et al., 2013) (also used as a gold standard to validate other methods), animal weighing (Enriquez-Hidalgo et al., 2014;Giger-Reverdin, Lebarbier, Duvaux-Ponter, & Desnoyers, 2012), measuring sward height (Kennedy, O'Donovan, Delaby & O'Mara, 2008), oesphageally fistulated animals (Mayes, 1989), faecal markers (Hellwing et al., 2015), and individual feeding stations (Halachmi et al., 1998). (Bü chel & Sundrum, 2014;Oudshoorn et al., 2013) (also used as a gold standard to validate other methods), animal weighing (Enriquez-Hidalgo et al., 2014;Giger-Reverdin et al., 2012), measuring sward height (Kennedy, O'Donovan, Delaby, & O'Mara, 2008), oesphageally fistulated animals (Mayes, 1989), faecal markers (Hellwing et al., 2015), and individual feeding stations (Halachmi et al., 1998). ...
... Various methods to investigate ingestive behaviour that are found in the literature include the following: visual observation (Bü chel & Sundrum, 2014;Oudshoorn et al., 2013) (also used as a gold standard to validate other methods), animal weighing (Enriquez-Hidalgo et al., 2014;Giger-Reverdin, Lebarbier, Duvaux-Ponter, & Desnoyers, 2012), measuring sward height (Kennedy, O'Donovan, Delaby & O'Mara, 2008), oesphageally fistulated animals (Mayes, 1989), faecal markers (Hellwing et al., 2015), and individual feeding stations (Halachmi et al., 1998). (Bü chel & Sundrum, 2014;Oudshoorn et al., 2013) (also used as a gold standard to validate other methods), animal weighing (Enriquez-Hidalgo et al., 2014;Giger-Reverdin et al., 2012), measuring sward height (Kennedy, O'Donovan, Delaby, & O'Mara, 2008), oesphageally fistulated animals (Mayes, 1989), faecal markers (Hellwing et al., 2015), and individual feeding stations (Halachmi et al., 1998). Although these methods have been validated, they might be laborious to implement, time consuming and sometimes invasive. ...
... The present study was conducted to compare the results related to growth and methane emissions obtained using an automated feeding system with those of previous studies. This comparison was motivated by the recognition that feed costs can account for as much as 65-75% of the total operating costs, which typically constitute approximately one-third of the total expenses in confined ruminant operations [19,20], and that available technologies have mostly been tested on experimental farms, which were primarily validated for adult cows [20,21]. ...
This study investigated the impact of feeding systems on the determination of enteric methane (CH 4) emissions factor in cattle. Real-time feed intake data, a crucial CH 4 conversion rate (Y m value) parameter, were obtained using a roughage intake control (RIC) unit within a smart farm system. Greenhouse gas (GHG) emissions, including CH 4 and carbon dioxide (CO 2), from Holstein steers were monitored using a GreenFeed (GF) 344 unit. The results revealed satisfactory body weight (383 ± 57.19 kg) and daily weight gain (2.00 ± 0.83 kg), which are crucial factors. CO 2 production exhibited positive correlations with the initial body weight (r = 0.72, p = 0.027), feed intake (r = 0.71, p = 0.029), and feed conversion ratio (r = 0.69, p = 0.036). Five different emission factors (EFs), EF A (New Equation 10.21A) and Equation 10.21 (EF B , EF C , EF D , and EF E), were used for GHG calculations following the Intergovernmental Panel on Climate Change (IPCC) Tier 2 approach. The estimated CH 4 EFs using these equations were 69.91, 69.91, 91.79, 67.26, and 42.60 kg CH 4 /head/year. These findings highlight the potential for further exploration and adoption of smart farming technology , which has the potential to enhance prediction accuracy and reduce the uncertainty in Y m values tailored to specific countries or regions.
... Cows were housed in the open shade Agricultural Research Organization (ARO) experimental dairy barn (Rishon-Letzion, Israel), which is equipped with a cow recognition system (Halachmi et al., 1998). Cows can move and recumbence freely within the barn while each cow has access to its individual feeder. ...
Wastewater from dairy farms has become a major environmental and economical concern. Sodium residue in treated and untreated wastewater from dairy farms used for irrigation can lead to soil and groundwater salinization, with the risk of soil degradation. We examined the effect of reducing sodium fed to mid to late lactating cows from 0.61% (high sodium [HS]) to 0.45% (low sodium [LS]) of dry matter on: dry matter intake (DMI), milk and milk-component yields, eating behavior, apparent total track digestibility, feed efficiency, and sodium excretion into the environment. We randomly assigned 28 multiparous high-yielding (>35 kg milk/d) cows to 1 of 2 treatment groups (LS or HS) in a crossover design, with 7 d of adaptation and 28 d of data collection. Reducing sodium in the diet reduced sodium intake from 171 to 123 g/d while lowering sodium excreted in the manure by 22%. Energy corrected milk (ECM) yield (37.4 kg/d) and sodium excretion in the milk (33.7 g/d) were similar for both groups. The DMI of LS cows was lower than that of HS cows (27.3 vs. 28 kg/d) and consequently, feed efficiency of the LS cows was higher (1.40 vs. 1.35 ECM/DMI). Eating rate, meal and visit frequency, and eating time were similar for both treatments; meal and visit duration were longer for the HS cows, and meal and visit sizes tended to be larger. Digestibility of DM and amylase-treated neutral detergent fiber remained similar. Based on the results of this study, and discussed considerations, we recommend lowering the dietary sodium content for mid to late lactating cows in commercial herds to 0.52% of DM, in order to reduce sodium excretion to the environment via urine.
... Different feed intake measurement systems have been developed, including electronic scales in the feeding stalls to measure the feed consumed by each cow. These weighing systems have been used by several researchers (Bach et al., 2004;Halachmi et al., 1998;Chapinal et al., 2007). Both self-designed weighing systems and commercial systems are available primarily for research institutions, rather than for commercial cowsheds, due to their high cost, additional infrastructure, high maintenance, and frequent cleaning requirements, all of which make them impractical for most commercial farms (Stajnko et al., 2010;Wang et al., 2006). ...
Data on individual feed intake of dairy cows, an important variable for farm management, are currently unavailable in commercial dairies. A real-time machine vision system including models that are able to adapt to multiple types of feed was developed to predict individual feed intake of dairy cows. Using a Red-Green-Blue-Depth (RGBD) camera, images of feed piles of two different feed types (lactating cows' feed and heifers' feed) were acquired in a research dairy farm, for a range of feed weights under varied configurations and illuminations. Several models were developed to predict individual feed intake: two Transfer Learning (TL) models based on Convolutional Neural Networks (CNNs), one CNN model trained on both feed types, and one Multilayer Perceptron and Convolutional Neural Network model trained on both feed types, along with categorical data. We also implemented a statistical method to compare these four models using a Linear Mixed Model and a Generalised Linear Mixed Model, showing that all models are significantly different. The TL models performed best and were trained on both feeds with TL methods. These models achieved Mean Absolute Errors (MAEs) of 0.12 and 0.13 kg per meal with RMSE of 0.18 and 0.17 kg per meal for the two different feeds, when tested on varied data collected manually in a cowshed. Testing the model with actual cows’ meals data automatically collected by the system in the cowshed resulted in a MAE of 0.14 kg per meal and RMSE of 0.19 kg per meal. These results suggest the potential of measuring individual feed intake of dairy cows in a cowshed using RGBD cameras and Deep Learning models that can be applied and tuned to different types of feed.
... Each individual feeder was located on top of weighing balances. This individual feed measuring system, designed by Halachmi et al. (1998), records the time each cow starts and finishes eating, and the weight of the feed consumed in the feeder. This system, wherein each cow has her unique feeder, prevents hierarchy interruptions among the cows, and enables detection of each valid visit. ...
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.
... Cows were housed in the Agricultural Research Organization's research dairy barn (Rishon Lezion, Israel), which is a free-stall barn equipped with an individual cow feed intake-recognition system (Halachmi et al., 1998). The system includes 42 feeders, with an antenna at each feeder that recognizes a sensor tied to each cow's front leg. ...
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.
... However, these indicators are not easy to measure for individual cows, especially in grazing systems. Some technologies based on trough scales (Halachmi et al., 1998), pressure sensors (Leiber et al., 2016) or accelerometers (Oudshoorn et al., 2013) exist, but they are still mostly used at research level. ...
This Research Reflection addresses the possibilities for Welfare Quality ® to evolve from an assessment method based on data gathered on punctual visits to the farm to an assessment method based on sensor data. This approach could provide continuous and objective data, while being less costly and time consuming. Precision Livestock Farming (PLF) technologies enabling the monitorisation of Welfare Quality ® measures are reviewed and discussed. For those measures that cannot be assessed by current technologies, some options to be developed are proposed. Picturing future dairy farms, the need for multipurpose and non-invasive PLF technologies is stated, in order to avoid an excessive artificialisation of the production system. Social concerns regarding digitalisation are also discussed.
... Several feed intake measurement systems have been developed (Bach et al., 2004;Chapinal et al., 2007;Chizzotti et al., 2015;Halachmi et al., 1998). They are commercially available primarily for research institutions, and not for commercial farms, due to their high price and frequent cleaning required, which most commercial farms cannot afford (Stajnko et al., 2010;Wang et al., 2006). ...
This study entailed the design and implementation of a computer vision system for cow individual feed intake measurement, based on deep Convolutional Neural Networks (CNNs) models, and a low-cost RGB-D (Red, Green, Blue, Depth) camera. Individual feed intake of dairy cows is an important variable currently unavailable in commercial dairies. An RGB-D camera was positioned above the feeding area in an open cowshed. Feed intake was estimated by combining information from the RGB and depth images. Cow identification was conducted using the RGB image. Deep learning algorithms for identification and intake estimation were developed using CNN models. Data for CNN training were acquired by a specially developed automatic data acquisition system. A range of feed weights under varied configurations were collected over a period of seven days with the setup, which included an automatic scale, cameras, and a micro-controller. Test data for feed intake was acquired in an open cowshed research dairy farm, wherein the cows were fed Total Mix Ration (TMR). Images of cows eating over a period of 36 h provided the test data for cow identification. The system was able to accurately identify 93.65% of the cows. The amount of feed consumed, which ranged from 0 to 8 kg per meal, was measured with mean absolute and square errors (MAE and MSE) of 0.127 kg, and 0.034 respectively. The analysis showed that the amount and diversity of data are important for model training. Better results were achieved for the model that was trained with high-diversity data than the model trained with homogeneous data (MAE of 1.025 kg, and MSE of 2.845 kg^2 for a model trained on shadow conditions only). Additionally, the training analysis shows that the model based on RGB-D data shows better results than the model based on depth channel data without RGB (MAE of 0.241 kg, and MSE of 0.106 kg^2). These results suggest the potential of low-cost cameras for individual feed intake measurements in advanced dairy farms.
... Each individual feeder is located on top of weighing balances. This barn described in more details by Halachmi et al. (1998), records enter and exit time of each cow to feeder, while computerized monitoring on-line of feed weight consumed. In this system, where each cow had its individual feeder, hierarchy interruptions among cows were minimized, and the system enables detection of each valid visit (that includes eating) in the feeder. ...
There is absence knowledge about the effects of lactation trimester and parity on eating behavior, production and efficiency of dairy cows. Objective of this study was to identify and characterize in 340 dairy cows, the 20% high efficient (HE), 20% low efficient (LE) and 60% mid efficient (ME) cows according to their individual residual feed intake (RFI) values, within and between lactation trimesters and between 1st and 2nd parities. Efficiency effect within each lactation trimester, was exhibited in daily dry matter intake (DMI), eating rate and meal size, that were the highest in LE cows, moderate in the ME cows and lowest in the HE group. Daily eating time, meal frequency, yields of milk and energy-corrected milk (ECM) and BW were similar in the three efficiency groups within each trimester. The lower efficiency of the LE cows in each trimester attributes to their larger metabolic energy intake, heat production and energy losses. In subgroup of 52 multiparous cows examined along their 1st and 2nd trimesters, milk and ECM production, DMI, eating behavior and efficiency traits were similar with high Pearson's correlation (r=0.78 to 0.89) between trimesters. In another subgroup of 42 multiparous cows measured at their 2nd and 3rd trimesters, milk and ECM yield, DMI and eating time were reduced (P<0.01) at the 3rd trimester, but eating rate, meal frequency and meal size remained similar with high Pearson's correlation (r=0.74 to 0.88) between trimesters. In subgroup of 26 cows measured in 1st and 2nd parities, DMI, BW, milk and ECM yield, and ECM/DMI increased in the 2nd lactation, but eating behavior and RFI traits were similar in both parities. These findings encourage accurate prediction of DMI based on a model that includes eating behavior parameters, together with individual measurement of ECM production. This can be further used to identify HE cows in commercial herd, a step necessary for potential genetic selection program aimed to improve herd efficiency.
... Each individual feeder was located on top of weighing balances. This barn, described by Halachmi et al. (1998), recorded enter and exit time of each cow to her specific feeder with computerized monitoring on line of feed weight consumed in the feeder. In this system, where each cow had a unique feeder, hierarchy interruptions among cows were prevented and the system enabled detection of each valid visit (that included eating) to the feeder. ...
This study aimed to identify individual characteristics differing among high-efficiency (HE; upper 20%, n = 31), low-efficiency (LE; lower 20%, n = 31), and mid-efficiency (ME, 60% n = 93) lactating cows. Primiparous (37) and multiparous (118) high-producing milking cows at 30 to 180 d in milk were fed individually a low-roughage diet [31.6% of dry matter (DM)] for 4 wk. Daily average DM intake, rate of eating, visit duration, meal size, and daily rumination time were higher in LE compared with HE cows. On the other hand, HE cows exhibited higher digestibility of DM, crude protein, and neutral detergent fiber than the LE cows. Daily eating time was similar in the HE and LE groups and higher than that of the ME cows. Visit and meal frequency, average visit and meal duration, daily lying time, and pedometer activity were similar in the HE, LE, and ME groups. The HE cows produced 1.75% more milk, but similar energy-corrected milk compared with the LE cows. Milk fat and protein content were lower by 1.8 and 3.8%, respectively, in the HE cows than in the LE group. Body weight (BW) and BW gain were similar in the 3 efficiency groups. Diurnal distribution of DM intake showed 6 distinct major meals, each composed of 1.1 to 1.6 visits. Higher intake peaks (greater meal size) were found in the LE cows compared with the HE group. Daily DM intake was highly correlated (affected) with energy-corrected milk production (r = 0.61), BW (r = 0.4), eating rate (r = 0.57), and visit size (r = 0.54). Energy balance showed that the lower efficiency of the LE cows was attributed to their excess heat production and energy loss.
... Twenty-six healthy calves were housed for 405 d in a shaded open feedlot with 5 m maximal roof height that provided 10 m 2 /calf under the roof and 8 m 2 /calf in the open yard. The feedlot contained six individual feed troughs controlled by electro-pneumatic yokes (Halachmi et al., 1998) in a feeding area (15 × 3 m) that included two drinking troughs, which allowed free access to water. The feeding area was separated from the resting area by a fence. ...
This study investigated the possible mechanisms for explaining inter-animal 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 3 periods (P1, P2 and P3) while fed a high-quality diet (calculated 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, 127 d, respectively. Initial ages of the calves in P1, P2, and P3 were 7, 11, and 15 mo, respectively, and initial BW were 245, 367, and 458 kg, respectively. Individual DMI, 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 3 periods. The L-RFI group had lowest DMI, MEI, HP, retained energy (RE) and RE/ADG. Chemical analysis of the longissimus dorsi muscle show 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/d (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.
... Production data Variables including the body weight, feed intake, milk yield, and conception rate are frequently recorded on-farms using a number of PLF devices in dairy and other livestock industries. Porto et al. (2015); Halachmi et al. (1998); ; ; Banhazi et al. (2011) Modelling on Heat Stress -the Thermal Indices The modelling of heat stress is the first step to provide precise control of heat stress. Initially, a simple temperature and humidity index (THI), was established as discomfort index (THI-1) using Tdb and Twb based on evaluating human comfort (Thom, 1959). ...
Regular occurrence of heat stress conditions is a significant challenge in dairy farming. Dairy cattle under heat stress will encounter sub-optimal welfare that can ultimately results in production loss for farmers. In precision livestock farming (PLF), studies aimed at modelling the influence of heat stress have been undertaken for several decades. Mitigation solutions including optimal shed structure, ventilation systems, targeted feeding regimes, improved farm management and genetic selection have also been used widely on farms. However, under different on-farm conditions, the heat tolerance and coping ability of cattle can vary significantly. Until now, the results from different developed models can only provide generalized heat stress thresholds for on-farm use. Similar problems exist in relation to mitigation solutions. This review will summarize current developments and analyse the differences in current on-farm research. The results related to thermal indices, animal responses, production loss and mitigation approaches will be compared and analysed. Instead of conducting large international studies to identify and overcome inconsistencies between different study results, alternative solution may be developed via more robust modelling or programming for specific farm and building systems. The developed model or program should be self-recalibrated based on the real-time collection of information on key parameters. The feasibility of the proposed system will be supported by innovative PLF and information technologies.
... Other studies focused on the daily incidence of lying and standing behaviours for oestrus detection (Firk et al., 2002) and early diagnosis of lameness (Pastell et al., 2009). Other research works focused on the monitoring and analysis of feeding behaviour with the aim to optimise intake under different feeding managements (Halachmi et al., 1998;DeVries et al., 2004;O'Driscoll et al., 2009). ...
Among the causes that influence cow welfare, heat stress induced by microclimatic conditions is one of the most relevant and many studies have investigated the efficacy of different cooling systems on animal health status. Nevertheless, the direct influence of the cooling systems on possible modifications of dairy cow behaviour has been addressed in a few studies and the related results were affected by the presence of a paddock, which gave a refuge from hot temperature. Since an alteration of the daily time budget spent by dairy cows in their usual activities can be associated with changes in their health status, this study investigated the effects of the alternation of two different cooling systems on lying, standing, and feeding behaviour of a group of dairy cows bred in a free-stall dairy house where animals had no access to a paddock. The barn was equipped with a fogging system associated with forced ventilation installed in the resting area and a sprinkler system associated with forced ventilation installed in the feeding area. The two systems were activated alternately. The results demonstrated that the management of the two cooling systems affected the analysed behaviours. Though the activation of the cooling system installed in the resting area encouraged the decubitus of animals in the stalls, the activation of that one of the feeding alley could not be able to influence the standing behaviour and had only a moderate positive influence on the feeding activity.
... Cows with history of diseases, miscarriages and twin pregnancies or that were above first trimester were not included in the experiment. The experimental dairy farm is equipped with a facility that is specially designed to individually monitor all of the animal's functions, feed intake and different physiological parameters (Halachmi et al., 1998). The animals were divided into seven groups according to lactation period such that each cow was between 50 and 150 days of lactation when monitored. ...
Ruminants have the remarkable ability to convert human-indigestible plant biomass into human-digestible food products, due to a complex microbiome residing in the rumen compartment of their upper digestive tract. Here we report the discovery that rumen microbiome components are tightly linked to cows' ability to extract energy from their feed, termed feed efficiency. Feed efficiency was measured in 146 milking cows and analyses of the taxonomic composition, gene content, microbial activity and metabolomic composition was performed on the rumen microbiomes from the 78 most extreme animals. Lower richness of microbiome gene content and taxa was tightly linked to higher feed efficiency. Microbiome genes and species accurately predicted the animals' feed efficiency phenotype. Specific enrichment of microbes and metabolic pathways in each of these microbiome groups resulted in better energy and carbon channeling to the animal, while lowering methane emissions to the atmosphere. This ecological and mechanistic understanding of the rumen microbiome could lead to an increase in available food resources and environmentally friendly livestock agriculture.The ISME Journal advance online publication, 6 May 2016; doi:10.1038/ismej.2016.62.
... Recurrent methods for investigate ingestive behaviour are found in literature, as visual observation [5], [6] (also used in order to validate other methods), animal weighting [7], [8], measuring sward height [9], oesphageally fistulated animals [10], faecal markers [11] and individual feeding stations [12]. Such methods are valid, although might be laborious and time expensive. ...
... The Insentec monitoring system performed well in the study by Chapinal et al. (2007), but it still interfered with the natural feeding behaviors of the cow as a physical barrier was placed between the cow and the feed. The ideal system measures, controls, and monitors individual feed intake of the free-housed cow while not interfering with feeding habits and not introducing additional work or inhibiting workflow on the farm (Halachmi et al., 1998). ...
Individual dairy cow feed intake is closely related to the health and productive output of each cow, with healthy cows generally eating more feed than unhealthy cows. Incorporating the use of an automated system to monitor feed consumption for each cow may be beneficial for dairy farm management. This study examined the use of an inexpensive 3-dimensional video camera to measure feed volume, from which we derived feed weight. Proof-of-concept testing was conducted to determine the effectiveness and capability of the machine vision feed-scanning system and its possible use in feed intake monitoring. Such systems are ideal because they do not impede the workflow of the farm or interrupt feeding behavior. This is an improvement over existing systems that are labor and cost intensive. Our conducted experiments involve measuring feed volume at known weights, up to 22.68 kg, with the resulting volume and weight values analyzed by means of linear and quadratic least squares t-test regression analysis. The effects of feed positioning in the bin and near-range sensor limitations were also examined. The results showed that an estimation of feed weight from 3-dimensional scan of volume measurements could be made to within 0.5 kg of the physically measured feed weight using a digital scale. Future efforts will focus on extending this work to active bunks with multiple cows eating throughout the day and testing total mixed rations of varied composition.
... We have recently started to assemble Baladi individuals from smallholders in the north of the country, in order to establish a genetic resource population for purposes of bio-agricultural research, to promote practical conservation decisions and a future functional genomic based program of re-introduction of Baladi into the Israeli beef cattle herd. In a preliminary study, we used an infra red (IR) identification system (SCR Engineering Ltd, Israel) for real-time individual food intake (Halachmi et al., 1998(Halachmi et al., , 2011 measurements. The system is located in an open cowshed and has one-way gates to direct the cows' traffic from the feeding lane via a walk-through scale in order to enable body weight determination few times per day. ...
Generally taken, breeds of Bostaurus ancestry are considered more productive, in comparison with Bosindicus derived breeds that present enhanced hardiness and disease resistance, low nutritional requirements and higher capability of feed utilization. While breeds of B. taurus have been mostly selected for intensive production systems, indigenous cattle, developed mostly from indicine and African taurines, flourish in extensive habitats. Worldwide demographic and economic processes face animal production with new challenges - the increasing demand for animal food products. Intensification of animal husbandry is thus a desired goal in stricken parts of the world. An introduction of productive traits to indigenous breeds might serve to generate improved biological and economic efficiencies. For this to succeed, the genetic merit of traits like efficiency of feed utilization and product quality should be revealed, encouraging the conservation initiatives of indigenous cattle populations, many of which are already extinct and endangered. Moreover, to overcome potential genetic homogeneity, controlled breeding practices should be undertaken. The Baladi cattle are a native local breed found throughout the Mediterranean basin. Purebred Baladi animals are rapidly vanishing, as more European breeds are being introduced or used for backcrosses leading to improved production. The superiority of Baladi over large-framed cattle, in feedlot and on Mediterranean pasture, with respect to adaptability and efficiency, is highlighted in the current review.
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... The CVBS proposed in this study is also suitable to assess the performances of other systems developed for cow behaviour monitoring (Darr & Epperson, 2009;Gygax, Neisen, & Bollhalder, 2007;Halachmi et al., 1998;Huhtala, Suhonen, M€ akel€ a, Hakoj€ arvi, & Ahokas, 2007;Mattachini, Antler, Riva, Arbel, & Provolo, 2013;Porto, Arcidiacono, Anguzza, Giummarra, et al., 2013;Porto, Arcidiacono, Giummarra, Anguzza, & Cascone, 2014). ...
Changes in cow behaviour may occur in relation to health disorders. In literature the suitability of using behavioural changes to provide an early indication of disease is studied. The possibility of achieving a real-time analysis of a number of specific changes in behaviours, such as lying, feeding, and standing, is crucial for disease prevention.Cow feeding and standing behaviour detectors were modelled and validated by defining a methodology based on the Viola-Jones algorithm and using a multi-camera video-recording system to obtain panoramic top-view images of an area of the barn.Assessment of the detection results was carried out by comparison with the results generated by visual recognition. The ability of the system to detect cow behaviours was shown by the high values of its sensitivity achieved for the behaviours of feeding and standing which were about 87% and 86%, respectively. Branching factor values for the two behaviours showed that one false positive was detected for every 13 and 6 well-detected cows, respectively. On the basis of these research outcomes, the proposed system is suitable for computing cow behavioural indices and the real-time detection of behavioural changes.
... Advances in the application of the outlined procedure for adoption of essential enterprise processes will depend more and more on the automated measurement, [14,15] . [16] Improved egg incubators via synchronisation of hatching [17] Intelligent ventilation control in livestock buildings [7,[18][19][20] Weight estimation of pigs via machine vision tools [21] Dairy management to maximise profit [22,23] Improving profitability via precision feeding for pigs [24] Sensor placement robot for pigs [25,26] Cattle monitoring system [27] Udder health and hygiene monitoring in dairy cattle [28][29][30] Poultry carcass inspection [31] Automated egg counting and identification [32] Carcass composition prediction for pigs [33][34][35] Automated fish sizing and sorting [36,37] Improved thermal control for pigs via machine vision [38][39][40] Cough recognition in pigs ...
Precision Livestock Farming (PLF) is potentially one of the most powerful developments amongst a number of interesting new and upcoming technologies that have the potential to revolutionise the livestock farming industry. If properly implemented, PLF or Smart Farming could (1) improve or at least objectively document animal welfare on farms, (2) reduce GHG emission and improve environmental performance of farms, (3) facilitate product segmentation and better marketing of livestock products, (4) reduce illegal trading of livestock products and (5) improve the economic stability of rural areas. However, there are only a few examples of successful commercialisation of PLF technologies introduced by a small number of commercial companies which are actively involved in the PLF commercialisation process. To ensure that the potential of PLF is taken to the industry, we need to: (1) establish a new service industry, (2) verify, demonstrate and publicise the benefits of PLF, (3) better coordinate the efforts of different industry and academic organisations interested in the development and implementation of PLF technologies on farms, and (4) encourage commercial sector to assist with professionally managed product development.
... monitoring of lactating cow feed intake can improve DMI (Halachmi et al., 1998) and on-farm 57 implementation of feeding suggestions from a precision management model can improve milk 58 yield and income over feed costs (Andre et al., 2007). These results indicate that precision 59 feeding may be one method to concurrently improve nutrient-use efficiency and productivity. ...
The objective of this study was to use a precision nutrition model to simulate the relationship between diet formulation frequency and dairy cattle performance across various climates. Agricultural Modeling and Training Systems (AMTS) CattlePro diet-balancing software (Cornell Research Foundation, Ithaca, NY) was used to compare 3 diet formulation frequencies (weekly, monthly, or seasonal) and 3 levels of climate variability (hot, cold, or variable). Predicted daily milk yield (MY), metabolizable energy (ME) balance, and dry matter intake (DMI) were recorded for each frequency-variability combination. Economic analysis was conducted to calculate the predicted revenue over feed and labor costs. Diet formulation frequency affected ME balance and MY but did not affect DMI. Climate variability affected ME balance and DMI but not MY. The interaction between climate variability and formulation frequency did not affect ME balance, MY, or DMI. Formulating diets more frequently increased MY, DMI, and ME balance. Economic analysis showed that formulating diets weekly rather than seasonally could improve returns over variable costs by $25,000 per year for a moderate-sized (300-cow) operation. To achieve this increase in returns, an entire feeding system margin of error of <1% was required. Formulating monthly, rather than seasonally, may be a more feasible alternative as this requires a margin of error of only 2.5% for the entire feeding system. Feeding systems with a low margin of error must be developed to better take advantage of the benefits of precision nutrition.
... Individual DMI measurements are practicable in tie-stall barns, in small-scale operations, or in research centers (Halachmi et al., 1998). In other locations DMI can be measured automatically only for a group of cows, e.g., by weighing mixer wagons; in a large-scale operation individual DMI can be evaluated only by means of models, which should have been well calibrated in research centers. ...
The voluntary dry matter intake (DMI) of a cow is an important parameter in dairy farming; however, it cannot be measured directly for individual cows within a group, therefore a model is needed to estimate the individual cow DMI.In a previous study a DMI model–the ‘2004 Model'–was developed for Israeli Holstein cows milked in a milking parlor. The present study aimed to apply the 2004 Model to robotically milked dairy cows of three different breeds: Danish Holstein, Danish Red, and Danish Jersey.Robotic milking by means of an Automatic Milking System (AMS) influences both milk yield and feeding behavior. The 2004 Model incorporates individual animal factors: milk yield (MY), daily changes in body weight (BW), the MY/BW relationship, and milk fat content. A modern milking robot automatically measures BW and milk yield of the cow in its milking stall.The 2004 Model differs from the US National Research Council (NRC) model in its incorporation of continuously changing parameters: 1) coefficients appropriate to each day in lactation; 2) daily values of and changes in BW; 3) values of MY normalized according to BW; and 4) data from only the previous 2 d rather than from the whole lactation.The model was developed on the basis of three data sets acquired from 2002 through 2005 at the Danish Cattle Research Centre (DCRC) in Foulum, Denmark, and which covers a total of 206 lactations (62 Holstein cows, 77 Danish Reds, and 67 Jerseys).Results suggest that: (1) on the individual cow level, the 2004 Model takes daily fluctuations into account; (2) on the group level, the average Mean Absolute Error (MAE) of the DMI as calculated by the 2004 Model was significantly (t-test, p=0.05) lower than that given by the NRC model. The MAE values from the 2004 Model and the NRC models, respectively were: 2.0 vs. 2.4kg, 2.2 vs. 2.5kg, and 1.8 vs. 2.1kg for the Danish Red, Holstein and Jersey cows, respectively.DMI estimation is important for ration formulation, therefore, the 2004 Model may be considered for on-line decision-making, such as daily adjustment of the amount of concentrate supplied by the AMS.
... Control actions concerned exclusively with the plant-soil complex fall within the realm of traditional precision agriculture with the caveat that in rangeland systems there are significant impediments to the use of traditional machinery and methods. Control that concerns exclusively the animal component is fairly well developed for confined systems such as in dairy production (Schellberg et al., 2008) where behavior, health, weight, production and feed intake are routinely monitored for uniquely identified individual animals (Gonzalez et al., 2008; Halachmi et al., 1998; Mottram, 1997; Pastell et al., 2008a; Pastell et al., 2008b; Peiper et al., 1993) and animals are fed and managed intensively. The novel areas that require the most development are in the realm of controlling animals and plant-animal interactions in complex grasslands and rangeland systems. ...
Precision livestock production (PLP) is the augmentation of precision agriculture (PA) concepts to include all components of agroecosystems, particularly animals and plant-animal interactions. Soil, plants and soil-plant interactions are the subjects of PA or site-specific farming, where the main principle is to exploit natural spatial heterogeneity to increase efficiency and reduce environmental impacts. For the most part, PA has been studied and developed for intensive cropping systems with little attention devoted to pastoral and agropastoral systems. PLP focuses on the animal component and exploits heterogeneity in space and among individual animals towards more efficient and environmentally friendly production. Within PLP, precision grazing consists of the integration of information and communication technologies with knowledge about animal behavior and physiology to improve production of meat, milk and wool in grazing conditions. Two main goals are to minimize overgrazing of sensitive areas and to maximize the quality of the product through enhanced traceability. An integrated precision grazing system is outlined with its components: sensors of animal position, behavior and physiological status, real-time transmission of information to a decision support system, and feed-back through a series of actuators. Control of animal movement and diets is based on knowledge about species specific responses to various stimuli within the paradigms of flavor aversions and operant conditioning. Recent advances in the technologies and instrumentation available are reviewed briefly and linked to current livestock identification systems. The precision grazing vision is presented in full and the areas that need further research and development are discussed.
In this study a 3D camera system was setup to measure individual feed intake of dairy cows in a commercial in house setting. The system was developed to both identify the cows while eating, predicting body weight based on the curvature of the back of the cow as well as to quantify the amount of feed eaten by the cow at each visit of eating. The identification of the cow was based on recognizing the patterns, colors and curvatures of the back from a reference database obtained in a corridor after milking, where images were taken of all cows with a simultaneous reading of the electronic eartag. Also the body weight is predicted using the curvatures of the back of the cow. Feed intake is quantified as the difference in surface of the feed a cow can reach before and after a visit is initiated. This estimate is in liter but transferred to kilos knowing the density of the feed in the specific herd. A total of 9142 cows were measured in 19 herds across 3 breeds - Jersey (2513 cows), Red Dairy cattle (2813 cows) and Holstein (3816 cows). Mean daily feed intake was higher for Red Dairy cattle (61.72 kg) and Holstein (64.59 kg) than for Jersey (55.74 kg). Repeatability estimates for daily feed intake as a weekly average was 0.62, 0.65 and 0.63 for Jersey, Red Dairy cattle and Holstein respectively. Mean body weight was higher for Red Dairy cattle (647.9 kg) and Holstein (683.8 kg) than for Jersey (469.6 kg). Repeatability estimates for body weight as a weekly average was 0.83, 0.85 and 0.88 for Jersey, Red Dairy cattle and Holstein respectively. The perspectives in having such records available is huge both for the farmer and for the dairy industry. The records can both be used for improving management in farms on an individual cow level and herd level, but also for genetic evaluation and selection as well as testing feeding regimens. Feed intake can be measured on an individual level using a 3D camera system.
A challenge in modern farming is to find a sustainable way of achieving sufficient production. Precision in dosage, timing and allocation of water, biocides, fertilizer and other inputs is essential, as are such management actions as harvesting, pruning and weeding. Despite the increasing availability of sensor and actuator technologies, decision-making is still largely left to the farmer. This is creating a strong demand for support in operational management.
This paper presents an overview of methods involving the use of technology and data to develop model-based management support and automation for productive and input-efficient farming. For each method, the main advantages and drawbacks relating to typical farm characteristics are discussed and summarized. Three case studies are presented, to illustrate the design steps involved in developing a model, observer and controller. The overall design procedure is summarized in a flowchart, and serves as a basic guide for method selection and model development.
We address the hypothesis that individual cow feed intake can be measured in commercial farms through the use of a photogrammetry method. Feed intake and feed efficiency have a significant economic value for the farmer. A common method for measuring feed mass in research is a feed mass weighing system, which is excessively expensive for commercial farms. However, feed mass can be estimated by its volume, which can be measured by photogrammetry. Photogrammetry applies cameras along the feed-lane, photographing the feed before and after the cow visits the feed-lane, and calculating the feed volume. In this study, the precision of estimating feed mass by its volume was tested by comparing measured mass and calculated volume of feed heaps. The following principal factors had an impact on the precision of this method: camera quality, lighting conditions, image resolution, number of images, and feed density. Under laboratory conditions, the feed mass estimation error was 0·483 kg for heaps up to 7 kg, while in the cowshed the estimation error was 1·32 kg for up to 40 kg. A complementary experiment showed that the natural feed compressibility causes about 85% of uncertainty in the mass estimation error.
Agricultural production involves almost all aspects of cultivation, harvesting, processing, storage, and transportation of crops, animals, food, and fiber. Agricultural production depends heavily on the availability of agricultural inputs such as labor, water, arable land, and other resources (energy, fertilizer, etc.), which are significantly affected by the type and scale of farm practices. This chapter focuses on an evaluation of relevant technology in the mechanization of agricultural
production in developing countries, with a view to bridging the gap in the information as presented.
First, the levels of agricultural mechanization technology are discussed. Then, mechanization in both
large- and small-scale agricultural fields are examined. Opportunities concerning the adoption of
specific advanced technology are also evaluated. Finally, case studies of agricultural mechanization
are presented, and the challenges of mechanization are discussed. Overall, the main emphasis of this
chapter is to focus on the key factors affecting mechanization, with a future outlook to improving
agricultural productivity and reducing cost.
In order to carry out precision feeding for dairy cow, the authors developed a flute-wheel concentrate feeding mechanism which was controlled with ARM techniques. The structure and the operating principle of the flute-wheel concentrate feeding mechanism were introduced. By the experiments, some optimal parameters and the configuration of the parts were concluded, the brush with flush-sided end was fixed in the middle position, the working length of the flute-wheel with 16 flutes was 38 mm when the flute-wheel was used for discharging the small mass of the small pellet feed, while for discharging large mass of the large pellet feed, the working length of the flute wheel was 112 mm. The results also showed that the pellet level of the concentrate in the hopper did not have a significant influence on the full coefficient of the flute-wheel. It was confirmed that the average error of the discharging mass was less than 2%.Copyright
The main expense in the dairy operation is food. Individual feeding enables tailoring a precise ration for each cow. New technologies had improved capabilities to ration concentrates individually. However, the absence of an on-line milk composition analyzer restricts the efficient exploitation of these technologies. This hurdle was recently overcome. A new sensor (Afilab™) that measures milk composition (fat, protein and lactose and gives indication of blood and SCC) for each cow every milking was developed. These results in the acquisition of milk component data as milk yield and body weight data. The objective of this study was to incorporate the on-line milk composition data into the concentrates allocation decision making in order to improve cow performance under a defined economical policy of a commercial dairy by using duel channel computer controlled concentrates dispensers. 23 multiparous cows were fed forages in the common trough and all the concentrates through dual channel computer controlled self feeders. Concentrates were rationed individually according to performance. The NRC 2001 formula for predicting dry matter intake (DMI) of individual cow was applied to calculate protein and ration density. For 11 cows daily concentrates were allocated by using daily milk composition data and for the rest of the cows milk composition recorded by periodical milk test was used. On average milk yield was lower but 4% fat corrected milk, was significantly higher and relatively less concentrates were allocated to the cows for which decisions were done according to on-line milk composition data. In addition, they had a higher dry matter intake and lost less weight during transition time. The periodical milk test failed to provide the needed information regarding milk fat decrease during transition time which led to rationing concentrates in a level that most likely caused a greater decrease in milk fat during this period.
Low-cost feeding-behavior sensors will soon be available for commercial use in dairy farms. The aim of this study was to develop a feed intake model for the individual dairy cow that includes feeding behavior. In a research farm, the individual cows’ voluntary feed intake and feeding behavior were monitored at every meal. A feed intake model was developed based on data that exist in commercial modern farms: ‘BW,’ ‘milk yield’ and ‘days in milking’ parameters were applied in this study. At the individual cow level, eating velocity seemed to be correlated with feed intake (
R
2
=0.93 to 0.94). The eating velocity coefficient varied among individuals, ranging from 150 to 230 g/min per cow. The contribution of feeding behavior (0.28) to the dry matter intake (DMI) model was higher than the contribution of BW (0.20), similar to the contribution of fat-corrected milk (FCM)/BW (0.29) and not as large as the contribution of FCM (0.49). Incorporating feeding behavior into the DMI model improved its accuracy by 1.3 (38%) kg/cow per day. The model is ready to be implemented in commercial farms as soon as companies introduce low-cost feeding-behavior sensors on commercial level.
The main objective of this review was to assess currently available technologies to be used in Australian Precision Livestock Farming (PLF) systems and the research and development (R&D) work required before these systems can be implemented. Commercially available hardware and software products were assessed and a literature review of current R&D was undertaken to capture emerging technologies. One of the main recommendations of the review was that the implementation of PLF systems within Australia should occur via a coordinated development and linking of existing hardware and software components. The main implementation barriers identified are data compatibility and transfer. In order to achieve these aims investment in (1) industry coordination, (2) awareness raising/training programs (3) effectively targeted R&D projects and in the (4) establishment of demonstration/research sites would be required.
It is widely known that labor costs are continuously and rapidly growing in terms of the raw cost of products in Korea. The increased labor costs are degrading the competitiveness of dairy industry sector as in the other major industrial fields. Furthermore, the number of dairy farms is constantly decreasing while that of dairy cattle is increasing. Thus, mechanized and/or automated stockbreeding management systems are crucially required to support professional stockbreeding management, as well as to enhance the productivity of the sector. Hence this paper develops an IIS (Integrated Information System) for dairy cattle stockbreeding management. IIS is composed of five application modules and associated utility programs. The five modules are individual stock management, milking management, feeding management, propagation management, and disease management. The utility programs are involved in stock farm accounting, and handy unloading of individual stock data into a personal data acquisition device. Compared with existing foreign products, the developed system takes advantages of various stock body measurement data such as body weight, body temperature, milk conductivity, milking amount, and the number of walking steps. All the measured data are transmitted into a programmable logic controller that monitors and controls measurement devices. The transmitted data are finally aggregated into an integrated database located in the main personal computer. The integrated data are analyzed and reformed in the five modules of IIS, then, used for providing farmers with various farm states and information through application module scenes. Hence, IIS keeps the each module work in a systematic and compatible manner, while supervising the whole stockbreeding management system.
The objective of this study was to measure the effect of feeding two total mixed rations (TMRs), differing in their roughage content and in vitro dry matter digestibility, on the respiratory rate, body temperature, eating behavior and energy balance of lactating cows. The partitioning of metabolizable energy intake (MEI) between heat production (HP) and retained energy (RE) of cows held under heat load conditions was measured. Forty-two lactating cows were divided into two similar sub-groups, each of 21 animals, and were fed either a control (CON) ration containing 18% roughage neutral detergent fiber (NDF) or an experimental (EXP) TMR that contained 12% roughage NDF and used soy hulls as partial wheat silage replacer. The in vitro DM digestibility of the CON and EXP TMRs was 75.3 and 78.6%, respectively (P
We examined the effect of replacing wheat hay with soy hulls on thermoregulatory responses and feeding behavior under summer condition in dairy cows. Milk yield, feed intake, meal size, and number, heart rate and heat production were similar in the two dietary groups. Visit duration and time spent eating were shorter, and body temperature was higher in the soy hulls fed cows. It was concluded that supplying soy hulls can enable cows subjected to heat load conditions to reduce the costs of feed ingestion. However, the contribution of this tool to the overall energy budget is rather small.
This study examined the biomass yield and nutritive value for lactating cows of two sorghum silages, a new brown mid-rib BMR-101 variety (BMR) and the commercial hybrid FS-5 widely used in Israel, in comparison with the corn Oropesa silage. Forages were grown in a Mediterranean climate in five replicate plots, harvested by a combine harvester at the soft dough stage and ensiled in bunker silos. The BMR plants had high degree of lodging, and the yield of forage dry matter (DM) was highest for corn, intermediate for FS-5 and lowest for BMR. Ensiling resulted in moderate DM losses for corn and FS-5 and higher losses for BMR, but all silages were stable after exposure to air. The in vitro DM digestibility (IVDMD) of BMR and corn silages were similar and higher than that of the FS-5 silage. Forty-two milking cows were assigned into three groups of 14 cows each, that were similar in their initial performance and DM intake, and fed three total mixed rations (TMR) containing either BMR, FS-5 or corn silages as the basal forage, in a 3×3 Latin square design. Each period consisted of 2 adaptation weeks to the TMR plus 5 data collection weeks. There were no differences in DM intake among cows fed the three silage-based TMR, but the number of voluntary daily meals per cow was higher for the FS-5-fed group versus the BMR and corn groups. In contrast, DM intake per meal and average meal duration were higher for the cows fed the corn and BMR-based TMR. Rate of feed consumption and daily eating duration were similar in the three treatments. Milk yield of the cows fed the corn-based TMR was higher than that of the FS-5 group, while that of BMR group was intermediate (42.1 vs. 40.7 and 41.4kg /d, respectively). The two groups fed the sorghum TMR had higher milk fat production, while the group fed corn silage TMR had higher milk and milk protein production and moderate milk fat yield and content, as well as the lowest loss of body weight (BW). However, silage yield per hectare, digestibility of the silage, and availability of water for irrigation are the dominant parameters when deciding which forage varieties to grow.
This trial was designed to investigate whether increasing the amount of ruminal undegradable crude protein (CP) content of rations given to high producing dairy cows, affected total non-structural carbohydrate (TNC) digestibility and milk production. A total of 36 Israeli-Holstein multiparous dairy cows were assigned to three treatments according to parity number, calving date, milk yield during the previous lactation and body weight (BW) 2 days after calving. Diets were formulated to contain (on a DM basis) 45% forage, 16.5% CP, 1.7 Mcal/kg net energy lactation (NE), and differed in dietary CP undegradability, as follows: (1) low undegradable CP (LUDCP)-UDCP, 36% of dietary CP; (2) medium UDCP (MUDCP)-UDCP, 41% of dietary CP; and (3) high UDCP (HUDCP)-UDCP, 46% of dietary CP. Dietary UDCP was increased by reducing concentrations of soybean meal and urea and increasing those of fish meal and gluten meal. The cows were maintained in the trial for 3 months. Average dry matter intakes of cows on LUDCP, MUDCP and HUDCP treatments were 25.0, 23.3 and 23.3 kg/day, respectively (P>0.1). Treatment averages of milk yield (kg/day), milk fat, protein and lactose (%) were 43.3, 3.01, 2.97 and 4.48, respectively, and no significant differences between diets were observed. Apparent digestibilities of dietary CP and TNC were increased by UDCP supplementation (P
We examined the effect of replacing wheat hay with soya hulls on energy balance and feeding behaviour in mid-lactating dairy cows fed with concentrate diets and raised under summer condition. Cows were individually fed and faecal output was assessed by internal marker. Heat production was estimated from heart rate monitoring. Metabolizable energy intake, milk energy, heat production and energy balance, and pattern feed intake were similar in the two dietary groups. It was concluded that replacing forage NDF with NDF from non forage source, like soya hulls, is not an effi cient means to reduce metabolic heat production in heat stressed cows.
Precision Livestock Farming (PLF) is potentially one of the most powerful developments amongst a number of interesting new and upcoming technologies that have the potential to revolutionise the livestock farming industry. If properly implemented, PLF or Smart Farming could (1) improve or at least objectively document animal welfare on farms; (2) reduce greenhouse gas (GHG) emission and improve environmental performance of farms; (3) facilitate product segmentation and better marketing of livestock products; (4) reduce illegal trading of livestock products; and (5) improve the economic stability of rural areas. However, there are only a few examples of successful commercialisation of PLF technologies introduced by a small number of commercial companies which are actively involved in the PLF commercialisation process. To ensure that the potential of PLF is taken to the industry, we need to: (1) establish a new service industry; (2) verify, demonstrate and publicise the benefits of PLF; (3) better coordinate the efforts of different industry and academic organisations interested in the development and implementation of PLF technologies on farms; and (4) encourage commercial sector to assist with professionally managed product development.
Experiments conducted in different breeding conditions have shown that active RFID systems based on the use of position markers can detect the presence of animals equipped with active tags within areas bound by induction loops. However, from these experiences, a number of disadvantages, mainly attributable to the cost of the position markers and the complexity of the installation of the induction loops in the breeding environment, have emerged. This paper describes a configuration of an active RFID system alternative to that making use of position markers for the automatic detection of tags. The automatic detection system proposed in this study employed a specifically implemented algorithm based on the concept of reference tags, and the same hardware utilized in previous experiments, excluding position markers and induction loops. The automatic detection system was initially tested in the laboratory and then applied to a group of pigs housed in a pen consisting of a built and completely roofed resting area (indoor area) and an enclosed open-air area (outdoor area). The laboratory tests revealed a localization error almost equal to that of other similar studies which utilized a greater number of reference tags than used in this research. The field trial carried out in this study demonstrated that it is possible to detect the pigs resting in the indoor area by avoiding the cost of the position markers and the complexity of the induction loops installation. High error rates were observed in the outdoor area mainly due to the building characteristics of the fence.
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.
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.
The complex agricultural environment combined with intensive production requires development of robust systems with short
development time at low cost. The unstructured nature of the external environment increases chances of failure. Moreover,
the machines are usually operated by low-tech personnel. Therefore, inherent safety and reliability is an important feature.
Food safety is also an issue requiring the automated systems to be sanitized and reliable against leakage of contaminations.
This chapter reviews agricultural automation systems including field machinery, irrigation systems, greenhouse automation,
animal automation systems, and automation of fruit production systems. Each section describes the different automation systems
with many application examples and recent advances in the field.
A prototype system has been designed to automate the use of a Cattle Feed Mixer with the aim of optimizing the efficiency, precision and consistency of the mix. Many additional nutrients are added into cattle feed at various times throughout their lactation cycle in order to produce optimum milk quantity and quality. Multiple herds also require various quantities of additional nutrients. At present these nutrients are calculated, weighed and individually added to the mixer by hand. The need for automation was apparent in this field as the quantity and quality of milk varies dramatically depending on the cattle's supplement intake. The reported design uses separate feed tanks to control the quantity of individual powder nutrients. Peristaltic pumps and stepper motors control the quantities of liquid nutrient additives and actuators vary the feed rate of the mix and extension of the feed conveyor. To accomplish this, weigh sensors, strain gauges, rotary encoders, stepper motors, flow sensors, linear actuators and micro controllers were used. The prototype has been tested on a small scale implementation.
To implement precision feeding for dairy cow, an intelligent multi-concentrates feeding system was developed. The system consists
of two parts, one is precision ingredients control subsystem, the other is multi-concentrates discharge subsystem. The former
controls the latter with 4 stepper motors. The precision ingredients control subsystem was designed based on Samsung S3C2440
ARM9 microprocessor and WinCE5.0 embedded operating system. The feeding system identifies the dairy cow with passive transponder
using RFID (Radio frequency identification) reader. According to the differences of based diet intake and individual dairy
cow milk yield, the system can automatically and quantificationally discharge 4 kinds of different concentrates on the basis
of the cow identification ID. The intelligent multi-concentrates feeding system for dairy cow has been designed and implemented.
According to the experiment results, the concentrate feeding error is less than 5%, the cow inditification delay time is less
than 0.5s and the cow inditification error rate is less than 0.01%.
An electronic weighing system combined with an automatic individual identification system was used with a commercial herd to record cow weights three times a day, without interference to normal procedures. Dedicated real-time software and special algorithms were developed to obtain accurate weights on-line. Eighty-five cows were measured during complete lactations over a period of 3 years. The accuracy of the walk through weighing system was 1·5%. No less than 65% of the cows were weighed routinely at least once daily and all cows in the group had at least five weights recorded weekly. Automatic weighing of cows proved to be feasible as an additional real-time on-line parameter.
Voluntary food intake of the dairy cow is an important variable in the dairy herd operation, We are unable to measure it individually in the conventional dairy operation when cows are kept in groups or grazing. This prevents using voluntary food intake as a parameter in the management decision-making process for the individual cow. Existing formulas that calculate dry matter intake (DMI) from ration and performance variables (see Mertens 1985), are not applicable for estimating DMI of an individual cow for on-line decision making such as adjusting ration density by computerized self feeders (Maltz et al. 1991, 1992Maltz and Metz 1994). In this work we calculated the dairy cows' DMI on a daily and individual basis, employing a non-conventional approach involving only body weight (BW) and milk yield (MY). These two variables are measurable on-line daily for individual cows.
To investigate the feasibility of implementing a completely automated milking and feeding under typical dairy farm conditions, a prototype fully integrated system was designed and installed at an experimental farm. The barn had the following sections: a feeding area, a lying area, and an automatic milking system area. The automatic milking system included a milking stall with a robot for automated attachment of the milking cluster and two selection units. Concentrates can be fed in the milking stall as well as in the selection units. Cows enter the automatic milking system from the lying area through one of the two selection units and can be sent to the milking stall or to the feeding area. In the milking stall, cows are milked automatically by the milking robot. In the feeding area, an automated feeding system is available, in which the individual forage intake of cows can be controlled and measured. The process control can be divided into three groups: robot control, cow traffic control, and management control. The system offers much flexibility with regard to the milking frequency, cow traffic, and feed allocation. The system was used for 7 months with a one-way cow routing and an average of 79 planned milkings per day. Many data were collected automatically and could immediately be utilized for on-line decisions. Although many questions still have to be answered, the system proved to be flexible enough to allow automatic milking and feeding with much variation in milking frequency, feedstuff allocation, and cow traffic.
A trial was conducted in a commercial dairy herd in which the concentrate part of the ration was fed individually to a group of cows through computerized self-feeders. Performance results were compared with those of a group fed TMR of 65 to 67% concentrates. Rationing of individual concentrates was according to parity, milk yield, milk yield potential, BW changes, and bunk feed-stuffs. Mean intake of concentrates per cow was about 1 kg/d lower in the individually supplemented cows. This was partly compensated for by a higher intake of bunk feedstuffs. Overall daily milk yield per cow was similar to those receiving a TMR in first parity cows, higher in second parity cows, and lower in third and greater parity cows. The higher performance of the second parity cows was achieved in all milk yield potential classes, and the lower yield in subsequent lactations was due to lower performance in low and high potential classes. The individually supplemented cows gained less BW than those in the TMR group. Milk yield per unit of BW was better than milk yield as a variable to refine individual cow supplementation strategy for allocation of concentrates. Results also suggest that the same criteria used for supplementation of concentrates can be beneficial to cows' assignments and movements among different TMR groups. Computerized dispensing of concentrates, when applied properly, can economize on consumption of concentrates when grouping and feeding different TMR are impossible.
Intake and digestibility of feeds by ruminants are influenced by characteristics of the feed, animal and feeding situation. Integration of these characteristics in mathematical models is critical to future progress in forage evaluation and optimal formulation of diets for ruminants. The physiological and physical theories of intake regulation can be described by simple mathematical equations. These equations indicate that intake is a linear function of animal characteristics, such as body weight and production level, and a reciprocal function of feed characteristics, such as fill effect and energy content. Theoretical equations were developed to predict intake when the neutral detergent fiber and energy content of the diet and the energy requirements of the animal are known. The theoretical model also can be used to predict the maximum intake that will maintain a given level of animal production by solving the physiological and physical intake equations at their intersection. Psychogenic intake regulation, which is related to the animal's behavioral response to factors not related to physiological or physical characteristics, can be described mathematically as a multiplier. Digestibility can be predicted by summing the contents of ideal nutritive entities in feeds, which have true digestibilities near 100%, subtracting their associated endogenous losses and adding the variable digestible fiber content. Steady-state models indicate fractional rates of digestion and passage can be used to define ideal nutritive entities and predict digestibility over a range of kinetic characteristics. The steady-state solutions are particularly useful in understanding and predicting the depression in digestibility associated with changes in rates of passage at high levels of feed intake.
A controlled automatic fodder consumption system and method for feeding livestock using same. Israel Patent Application No. 119109 The body weight of the dairy cow: modeling individual voluntary food intake based on body weight and milk production
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Prospects for a system for individual feeding of forage for cubicle housed dairy cows
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An individual approach to manage the dairy cow: a challenge for research and practice
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A controlled automatic fodder consumption system and method for feeding livestock using same
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