Supplementary feeding at milking and minimum milking interval effects on cow traffic and milking performance in a pasture-based automatic milking system

Randomized-controlled study assessing the effect of milking permission settings and concentrate supplementation on milking frequency and milk yield in a pasture-based automatic milking system

This study aimed to verify the effect of milking permission (MPE) and concentrate supplementation (CS) on milking frequency (milkings per cow per day) and milk yield (kilograms per cow per day) in a farm using a pasture-based automatic milking system (AMS). Sixty-eight cows milked using this AMS unit were randomly assigned to 1 of 4 groups homogeneous for parity, DIM, and milk yield. Treatments used were frequent or restricted MPE, that granted cows permission to milk after 6 to 8 h or 9.6 to 14 h of the previous milking, respectively; and low (LC) or high (HC) CS of 0.5 kg or 3.5 kg/cow per day, respectively. The combination of the 2 levels of MPE and the 2 levels of CS resulted in the 4 treatment combinations (frequent HC [FHC], restricted HC [RHC], frequent LC [FLC], and restricted LC [RLC]). This study was designed as a 2 × 2 factorial arrangement with treatment crossover: each of the 4 cow groups was randomly assigned to 1 of the 4 treatment combinations for a 5-wk experimental period (1 pretreatment week and 4 treatment weeks), and after each 5-wk period groups crossed over to another treatment combination until they experienced all. Statistical analysis assessed the effect of MPE, CS, and their interaction on milk yield, milking frequency, box time, milking time, and average milk-flow rate. This was done using a mixed model analysis with repeated measures to account for repeated observations on the experimental unit (cow). Milk yield per cow per day and milkings per cow per day were significantly higher with the frequent compared with the restricted MPE (1.5 kg and 0.65 milkings, respectively). Milk yield per cow per day and milkings per cow per day were significantly higher with the HC compared with the LC CS (3.1 kg and 0.25 milkings, respectively). Additionally, milk yield per cow per day was affected by the interaction of MPE and CS and it was highest with the FHC (20.1 kg) treatment combination, followed by RHC (18.2 kg) treatment combination. The number of milkings per cow per day were also affected by the interaction of MPE and CS. The highest estimated number of milkings per cow per day was recorded for the FHC (2.12) and the FLC (1.77) treatment combinations, followed by the RHC (1.38) and RLC (1.23) treatment combinations. Similarly, milking interval was 2.5 h longer for the RLC treatment combination compared with RHC. The shortest milking interval was observed for the FHC (11 h) and FLC (12.8 h) treatment combinations. In conclusion, the study showed that allowing access to the robot between 6 to 8 h after the previous milking was sufficient (even with a minimal level of CS) to achieve acceptable milk production and milking performance in a pasture-based AMS.

Effect of the Meal Interval Setting of an Automated Concentrate Feeding System on Feed Intake and Feeding Behavior in Fattening Hanwoo Steers

An automatic concentrate feeding system (ACFS) divides the day into several intervals, allowing cattle to consume a predetermined amount of concentrate mix per interval. This study investigated the impact of changing these intervals (four vs. six) in an ACFS on its precision in monitoring the feed intake and feeding behavior of fattening Hanwoo steers. The experiment, involving 29 fattening Hanwoo steers (688 ± 43.3 kg of body weight, 24 months old), employed a switchback design with two interval settings: four and six per day. Both individual forage and concentrate intakes and feeding behaviors were automatically recorded; however, the ACFS measured feed supply, not actual intake. The precision of the ACFS's intake recordings was tested by manually assessing feed residuals per visit using video recordings. Although no difference was observed in the concentrate intake (p > 0.05), the six-interval setting reduced concentrate residuals by 0.2 kg per visit (p < 0.05). The increased interval setting also resulted in fewer visits for forage consumption and decreased forage and total dry matter intakes (p < 0.05). In conclusion, the increased interval setting for the ACFS reduced the visit frequency for forage consumption and actual forage consumption while improving the precision of the ACFS's intake recordings.

Effect of Minimum Milking Interval on Traffic and Milk Production of Cows Milked by a Pasture Based Automatic Milking System

Simple Summary

Several studies have demonstrated that combining grazing and robotic milking is possible. However, there is often a decrease in milking frequency, which leads to a decrease in milk production. The objective of this study was to investigate the effect of two strategies to improve traffic in a pasture-based automatic milking system. Therefore we formed four groups differing based on concentrate allocation and based on minimum milking interval (MMI) necessary to access the robot for milking. Therefore four groups (high concentrate–short MMI, high concentrate–long MMI, low concentrate–short MMI, low concentrate–long MMI) were constituted. We compared these four groups with regard to traffic parameters (milkings, refused milkings) and animal production. The study highlighted the positive effect of high concentrate–short MMI on traffic to the robot by reducing the number of refused milkings. High concentrate allocation allowed to maintain high milk production over the experiment duration.

Abstract

In dairy farms automatic milking systems and grazing, traffic to the robot is the cornerstone of profitability as higher milking frequency enhances milk yield. In this study, we investigated whether shortening the minimum milking interval (MMI), i.e., the required time between two milkings for an animal to get access to the milking unit, coupled with high concentrate allocation, could increase the daily milking frequency (MF, milking/cow/day) and consequently the milk yield of grazing cows. Two groups of cows (n = 19 and n = 20) belonging to the same herd were discriminated based on concentrate supply (high vs. low: 4 vs. 2 kg/cow/day) and then further divided on the basis of MMI (4 h vs. 6 h) so that four groups were formed (HC4 h–HC6 h–LC4 h and finally LC6 h). Higher concentrate allocation induced a rise in milk yield (MY, kg/cow/day) and allowed to stabilize it in periods of grass shortage but did not influence milking frequency, while shorter MMI (4 h) was correlated with higher MF without effect on MY. A combination of both strategies (4 h and high concentrate) improved the traffic globally to the robot. This result was linked to a reduction of refused milking and, therefore, the decrease in returns to the robot. This strategy could be advised to maximize the system’s efficiency during periods of high milk sales. When the economic conditions do not favour the increase in concentrate supply, short MMI could facilitate the traffic and increase the efficiency of returns.

Systematic Review and Meta-Analysis: Identification of Factors Influencing Milking Frequency of Cows in Automatic Milking Systems Combined with Grazing

More dairy farms (up to more than one in four in some countries) are equipped with automatic milking systems (AMS) worldwide. Because of the positive impacts of grazing, e.g., on animal welfare or on production costs, numerous researchers have published papers on the combination of AMS with grazing. However, pasture-based AMS usually causes a reduction in milking frequency (MF) compared to indoors systems. The objectives of this meta-analysis were to review publications on the impacts of pasture-based AMS on MF and mitigation strategies. First, data from 43 selected studies were gathered in a dataset including 14 parameters, and on which a Principal Component Analysis (PCA) was performed, leading to the description of four clusters summarizing different management practices. Multiple pairwise comparisons were performed to determine the relationship between the highlighted parameters of MF on milk yield (MY). From these different analyses, the relationship between MF and MY was confirmed, the systems, i.e., Clusters 1 and 2, that experienced the lowest MF also demonstrated the lowest MY/cow per day. In these clusters, grazed grass was an essential component of the cow's diet and low feeding costs compensated MY reduction. The management options described in Clusters 3 and 4 allowed maintenance of MF and MY by complementing the cows' diets with concentrates or partial mixed ration supplied at the AMS feeding bin or provided at barn. The chosen management options were closely linked to the geographical origin of the papers indicating that other factors (e.g., climatic conditions or available grasslands) could be decisional key points for AMS management strategies.

The effect of concentrate allocation on traffic and milk production of pasture-based cows milked by an automatic milking system

Increased economic, societal and environmental challenges facing agriculture are leading to a greater focus on effective way to combine grazing and automatic milking systems (AMS). One of the fundamental aspects of robotic milking is cows' traffic to the AMS. Numerous studies have identified feed provided, either as fresh grass or concentrate supplement, as the main incentive for cows to return to the robot. The aim of this study was to determine the effect of concentrate allocation on voluntary cow traffic from pasture to the robot during the grazing period, to highlight the interactions between grazed pasture and concentrate allocation in terms of substitution rate and the subsequent effect on average milk yield and composition. Thus, 29 grazing cows, milked by a mobile robot, were monitored for the grazing period (4 months). They were assigned to two groups: a low concentrate (LC) group (15 cows) and a high concentrate (HC) group (14 cows) receiving 2 and 4 kg concentrate/cow per day, respectively; two allocations per day of fresh pasture were provided at 0700 and 1600 h. The cows had to go through the AMS to receive the fresh pasture allocation. The effect of concentrate level on robot visitation was calculated by summing milkings, refusals and failed milkings/cow per day. The impact on average daily milk yield and composition was also determined. The interaction between lactation number and month was used as an indicator of pasture availability. Concentrate allocation increased significantly robot visitations in HC (3.60±0.07 visitations/cow per day in HC and 3.10±0.07 visitations/cow per day in LC; P<0.001) while milkings/cow per day were similar in both groups (LC: 2.37±0.02/day and HC: 2.39±0.02/day; Ns). The average daily milk yield over the grazing period was enhanced in HC (22.39±0.22 kg/cow per day in HC and 21.33±0.22 kg/cow per day in LC; P<0.001). However the gain in milk due to higher concentrate supply was limited with regards to the amount of provided concentrates. Milking frequency in HC primiparous compared with LC was increased. In the context of this study, considering high concentrate levels as an incentive for robot visitation might be questioned, as it had no impact on milking frequency and limited impact on average milk yield and composition. By contrast, increased concentrate supply could be targeted specifically to primiparous cows.

Differential response to stocking rates and feeding by two genotypes of Holstein-Friesian cows in a pasture-based automatic milking system

The throughput of automatic milking systems (AMS) is likely affected by differential traffic behavior and subsequent effects on the milking frequency and milk production of cows. This study investigated the effect of increasing stocking rate and partial mixed ration (PMR) on the milk production, dry matter intake (DMI), feed conversion efficiency (FCE) and use of AMS by two genotypes of Holstein-Friesian cows in mid-lactation. The study lasted 8 weeks and consisted in a factorial arrangement of two genotypes of dairy cattle, United States Holstein (USH) or New Zealand Friesian (NZF), and two pasture-based feeding treatments, a low stocking rate system (2 cows/ha) fed temperate pasture and concentrate, or a high stocking rate system (HSR; 3 cows/ha) fed same pasture and concentrate plus PMR. A total of 28 cows, 14 USH and 14 NZF, were used for comparisons, with 12 cows, six USH and six NZF, also used for tracking of animal movements. Data were analyzed by repeated measure mixed models for a completely randomized design. No differences (P>0.05) in pre- or post-grazing herbage mass, DMI and FCE were detected in response to increases in stocking rate and PMR feeding in HSR. However, there was a significant (P<0.05) grazing treatment×genotype×week interaction on milk production, explained by differential responses of genotypes to changes in herbage mass over time (P<0.001). A reduction (P<0.01) in hours spent on pasture was detected in response to PMR supplementation in HSR; this reduction was greater (P=0.01) for USH than NZF cows (6 v. 2 h, respectively). Regardless of the grazing treatment, USH cows had greater (P=0.02) milking frequency (2.51 v. 2.26±0.08 milkings/day) and greater (P<0.01) milk yield (27.3 v. 16.0±1.2 kg/day), energy-corrected milk (24.8 v. 16.5±1.0 kg/day), DMI (22.1 v. 16.6±0.8 kg/day) and FCE (1.25 v. 1.01±0.06 kg/kg) than NZF cows. There was also a different distribution of milkings/h between genotypes (P<0.001), with patterns of milkings/h shifting (P<0.001) as a consequence of PMR feeding in HSR. Results confirmed the improved FCE of grazing dairy cows with greater milk production and suggested the potential use of PMR feeding as a tactical decision to managing HSR and milkings/day in AMS farms.

Modelling Pasture-based Automatic Milking System Herds: Grazeable Forage Options

One of the challenges to increase milk production in a large pasture-based herd with an automatic milking system (AMS) is to grow forages within a 1-km radius, as increases in walking distance increases milking interval and reduces yield. The main objective of this study was to explore sustainable forage option technologies that can supply high amount of grazeable forages for AMS herds using the Agricultural Production Systems Simulator (APSIM) model. Three different basic simulation scenarios (with irrigation) were carried out using forage crops (namely maize, soybean and sorghum) for the spring-summer period. Subsequent crops in the three scenarios were forage rape over-sown with ryegrass. Each individual simulation was run using actual climatic records for the period from 1900 to 2010. Simulated highest forage yields in maize, soybean and sorghum- (each followed by forage rape-ryegrass) based rotations were 28.2, 22.9, and 19.3 t dry matter/ha, respectively. The simulations suggested that the irrigation requirement could increase by up to 18%, 16%, and 17% respectively in those rotations in El-Niño years compared to neutral years. On the other hand, irrigation requirement could increase by up to 25%, 23%, and 32% in maize, soybean and sorghum based rotations in El-Nino years compared to La-Nina years. However, irrigation requirement could decrease by up to 8%, 7%, and 13% in maize, soybean and sorghum based rotations in La-Nina years compared to neutral years. The major implication of this study is that APSIM models have potentials in devising preferred forage options to maximise grazeable forage yield which may create the opportunity to grow more forage in small areas around the AMS which in turn will minimise walking distance and milking interval and thus increase milk production. Our analyses also suggest that simulation analysis may provide decision support during climatic uncertainty.

Milking permission and milking intervals in a pasture-based automatic milking system

In a pasture-based, automatic milking system, a proportion of milking events occur with milking intervals (MI) >16 h (extended MI). Additionally, cows necessarily walk longer distances than in indoor-based systems. The decision to milk a cow is based on milking permission criteria, which are generally based on time since last milking but can often take into account expected yield as well. Any cow arriving at the dairy and that does not receive milking permission is drafted to a pasture allocation, but it is not known whether milking refusal influences total time of return and therefore MI. Data from a 33-day period from the FutureDairy pasture-based, automatic milking system research farm using a prototype robotic rotary were analysed to investigate the hypothesis that a greater proportion of milking events occurring with extended MI would correspond to cows that had experienced a previous milking refusal. If this were the case then management practices could be implemented to deal with cows that visit the dairy soon after the last milking event. Results indicate that one-third of milking events had extended MI, although only 16% of them had a previous milking refusal. The average refusal took place 3 h after the last milking event and caused extended MI in >60% of the cases. This indicated that although attention should be placed on cows that returned to the dairy before milking permission (because they were likely to have an extended MI), milking refusals were not the main cause of extended MI. Therefore, cows that visit the dairy facility earlier than expected could be sorted to a feeding area close to the dairy, yet the greatest impact on overall MI will probably be achieved by reducing time spent in any one pasture allocation.

Planning for whole-farm systems research at a credible scale: subdividing land into farmlets with equivalent initial conditions

Most research comparing different farming systems has been conducted on relatively uniform plots at small scales made necessary by the desire for sufficient replication of the systems and cost limitations. This paper describes an alternative approach to plan the allocation of land to three unreplicated whole-farm management systems such that each farmlet had equivalent starting conditions and yet was at a scale credible to both livestock producers and researchers. The paddocks of each farmlet were distributed across the landscape in a ‘patchwork quilt’ pattern after six iterations of a mapping exercise using a Geographic Information System. Allocation of paddocks took into account those variables of the landscape and natural resource capacity that were not able to be altered. An important benefit of the procedure was that it ensured that the farmlets were co-located with contiguous paddock boundaries so that all farmlets experienced the same climatic as well as biophysical conditions. An electromagnetic survey was conducted of the entire property and used in conjunction with a detailed soils map in order to classify areas into soil conductivity groupings. Equivalent areas of each soil type were allocated across the three farmlets. Similarly, land was distributed according to its topography so that no farmlet would be compromised by being allocated more low lying, flood-prone land than any other farmlet. The third factor used to allocate land to each farmlet was the prior fertiliser history of the original paddocks. This process ensured that each farmlet was objectively allocated equivalent areas of soil type, topography and fertiliser history thus avoiding initial bias among the farmlets. After the plan for all paddocks of each farmlet was finalised, new paddock boundaries were drawn and where necessary, fencing was removed, modified and added, along with re-arranged watering points. The farmlet treatments commenced in July 2000 when the first pasture establishment and differential fertiliser applications were carried out. Evidence from the electromagnetic survey and the Landsat imagery confirmed that the distribution of hydrologic soil conductivity and vegetation greenness were similar between all farmlets just before the commencement of the experiment.