Asian Journal of Dairy and Food Research, volume 40 issue 1 (march 2021) : 25-29

Comparative Study of Automation and Conventional System on Production Performance in Dairy Farms

K. Abhijeet2, S.B. Prasanna1, P.S. Mahesh2, M.D. Gouri1, M.P. Vivek3, S.K. Bhandekar4, S.M. Ali1, K.D. Masood1, Prabha Karan5
1Department of Livestock Production and Management, Veterinary College, Hebbal, Bangalore-560 024, Karnataka, India.
2Central Poultry Development Organization and Training Institute, Hessarghatta, Bangalore-560 088, Karnataka, India.
3Department of Livestock Research and Information Center, Deoni Farm, Bidar-585 401, Karnataka, India.
4Livestock Officer, Central Cattle Breeding Farm, Bangalore-560 088, Karnataka, India.
5First Class Veterinary Hospital, Girihinda Chowk-811 105, Govt of Bihar, India.
Cite article:- Abhijeet K., Prasanna S.B., Mahesh P.S., Gouri M.D., Vivek M.P., Bhandekar S.K., Ali S.M., Masood K.D., Karan Prabha (2021). Comparative Study of Automation and Conventional System on Production Performance in Dairy Farms . Asian Journal of Dairy and Food Research. 40(1): 25-29. doi: 10.18805/ajdfr.DR-1607.
Background: The Indian dairy industry has progress consistently ever since the White revolution of the 1970s, making India, the world’s largest and fastest producer of milk with 17 per cent global share. The Indian dairy market is expected to double within the next 10 years, primarily driven by over 16-20 per cent growth in value added dairy segment. To catch this high growth potential and to meet the rising demand, a sustainable and strong dairy production system will be critical. 

Methods: A study was conducted between December 2018 and February 2019 at four different dairy farms. The farms were identified based on rearing systems practiced. The farms were divided into two groups where the first one (n=10 dairy cattle) utilized automatic rearing systems (the ARS farms), while the second group (n=10) had conventional rearing systems (the CRS farms).

Result: Based on the results, the effect of different rearing systems on the average lactation yield in the fourth lactation was significantly higher (P≤0.05) in automatic rearing system. The lactation yield of both the treatment groups was not significant till third lactation. There was no significant difference observed in persistency of milk production in both the rearing systems. Reproductive performance of the ARS houses had better age at first calving and service period as compared to conventional house type with significant difference. By using an ARS it is possible to save time and achieve greater flexibility. The experiment indicates less man power minutes required for routine daily work like feeding, watering and milking in automatic rearing system as compare to conventional rearing system. A significant (P≤0.01) reduction in working time by comparison with a different feeding, watering and management system however can only be expected in the case of sizeable herds. It appears that not much time can be saved with herds numbering 60 animals, but flexibility for the farm manager becomes significantly greater. In view of the relatively high amount invested in ARS, the profitability of such a system must be decided on a farm by farm basis. In principle an ARS can be a good opportunity for optimizing working time and workload in dairy farming. 
The Indian dairy industry has progress consistently ever since the White revolution of the 1970s, making India, the world’s largest and fastest producer of milk with 17 per cent global share. According to (BAHS, 2019;20th Livestock census, 2019), India ranks first in Milk production with 187.7 MT/year with a growth rate of 6.5 per cent.The Indian dairy market is expected to double within the next 10 years, primarily driven by over 16-20 per cent growth in value added dairy segment. To catch this high growth potential and to meet the rising demand, a sustainable and strong dairy farming base will be critical. For achieving this, it becomes critical to address key problems or challenges faced by the industry such as, low milk yield, improper breeding, improper nutrition, deficient veterinary care, poor farm management and inadequate financial inclusion among others. With dairy farming in India dominated by smallholder marginal farmers, with an average herd size of less than 2 or 3, it becomes all the more challenging to address these problems, in the specific context of making small holder dairy farming globally competitive. Dairy farmers are increasingly modernizing their farms: automatic concentrate dispensers and automatic milking systems (AMS) have been utilized for years and several manufacturers have introduced automatic feeding systems (AFS) during the past decade (Belle et al., 2012).
 
Automatic milking systems (AMS) have been available in India since the beginning of 1998. The major advantages of AMS are the reduction of labor for milking (Dijkhuizen and Morris 1997) and the enhanced production per cow due to higher milking frequency than conventional milking parlour (CMP) (Klei et al., 1997). Milk yield increases from 2 per cent to 8 per cent (Millogo et al., 2008) and labour decreases by about 18 per cent. Automatic milking systems (AMS) present an opportunity for dairy farmers to not only improve their lifestyle and conditions of work, but also save on labour costs and/or increase the time available to focus on overall farm management (Clark et al., 2016). Carolan, (2020) conceptualize ‘automation’ and ‘skill’ provide sufficient analytic and conceptual clarity to critically engage the dairy works. However, there is no published scientific data on the merits of using automation over conventional system of rearing and hence the present study will be taken up to compare the automation and conventional systems on production performance in poultry and dairy farms.
A study was conducted in collaboration with Department of Livestock production and Management, Veterinary College Hebbal, Bangalore between December 2018 and February 2019 at four different dairy farms. The farms were identified based on rearing systems practiced. The farms were divided into two groups where the first one (n=10 dairy cattle) utilized automatic rearing systems (the ARS farms), while the second group (n=10) had conventional rearing systems (the CRS farms). The Farms were designated as ARS-1 Dairy Farm, ARS-2 Dairy Farm, CRS-1 Dairy farm and CRS-2 Dairy farm. Each visit focused on general management practices. During the visit, the following information was obtained (Table 1) i) Farm characteristics ii) Feeding systems iii) Feeding strategies iv) Age at first calving v) Persistency in milk production vi) Lactation length vi) Types of automated milking system vii) Service period viii) Farm economics and ix) Working time measurement.
 

Table 1: Questionnaire format used for data in different dairy farms.


 
Statistical analysis method
 
The descriptive statistics for productive traits were analyzed using SPSS version 16.0. Student T-test was carried out to compare the effect of automation and conventional system on productive performance of poultry and dairy farms.
Feeding strategies
 
In ARS Dairy Farm-1, the animals are fed with maize silage grown on 17 acre land. Silage making is carried out on land with Bunker silo method above the ground level using tractor driven harvester cum chopper. To meet concentrate feed requirements they procure the feed with 34% protein containing Distilled Dry Grain Soluble (DDGS)  offered 3 kg per animal per day and Tapioca, GNC and Bengal gram based total mixed ration offered 3.5 kg per animal per day. Water is provided adlibitum which is automatically controlled by ball valve. There is intense labour saving with most of machine operations. In ARS Dairy Farm-2, the animals are fed with maize silage grown on 25 acre land. Silage making is carried out on land with Bunker silo method above the ground level using tractor driven harvester cum chopper. To meet concentrate feed requirements they procure the compounded feed from Charoen popkhoend feed pvt.ltd. containing maize, soybean meal, wheat bran as major component, offered 3 kg per animal per day. Watering system is similar to ARS Dairy farm-1. Concentrate feeding schedule to calves up to six months for 10 kg body weight.
 
In CRS Dairy Farm-1 and Farm-2, Calf was fed first four days on colostrums, later on milk was fed based on 10 per cent of body weight in CRS Dairy Farm-1 and soya milk was fed based on 10% of body weight CRS Dairy Farm-2. Calf rations starts from thirty days and ends up to 180 days in both conventional dairy farms. Calf ration is a mixture of greens and concentrates and Guinea grass, Rhodes grass are primarily used as green feeding and concentrate fed based on 12 per cent of body weight in CRS Dairy Farm-1 where as Calf ration is a mixture of green, concentrates and sprouted maize fodder and Napier and sprouted green fodder are primarily used as green feeding and concentrate fed based on 12% of body weight in CRS Dairy Farm-2. In both the conventional dairy farms, heifer was fed greens @22.5 kg per animal and concentrates was fed for maintenance@ 3kg per animal. Dry fodder fed @10 per cent of green fodder i.e. 2.25 kgs. Lactating cow was fed greens@45kg per animal and concentrates was fed for maintenance and production@ 3 kg per animal and 40 per cent of milk production, respectively. Dry fodder was fed @ 10 per cent of green fodder. However, the leftover residue after soya milk production will be fed to dairy cow by replacing 20% of total concentrates feeding.
 
Farm Characteristics (Table 2)
 
Feeding System
 
ARS Farm-1, ARS Farm-2 and CRS Farm-1 used feed mixer wagon alley without robotic pusher with the help of tractor whereas CRS Farm-2 done manual conventional feeding. The feeding of cows was done twice a day in all the studied farms.
 

Table 2: Farm characteristics of the identified farms.


 
Types of milking system
 
ARS Dairy Farm-1 has Herringbone (Fishbone) milk Parlour where 12 cows can be milked at one time. Cows stand on an elevated platform in a 45° angled or herringbone manner with their back to the centre of milking area (Veysset et al., 2001). This exposes enough of the back half of the cow to access to milk her from the side. The milking cup was attached from the sides (Pichler et al., 1998). There was a single entry and exit point for this milking parlour. ARS Dairy Farm-2 was equipped with Parallel (Side by side) milk parlour for the lactating cow. Cows stand on an elevated platform at a 90o facing away from the operator area (Axelsson et al., 2012). Access to the udder between the rear legs, reduces the visibility of fore quarters. This configuration makes the walking distance shorter than in herringbone parlour. The cow platform is wider than a herringbone parlour to accommodate the length of the cow. To assure that each position is filled in order, a series of interlocking fronts prevent a position from being used until the one next to it has been occupied. Most parallel parlours use rapid exit stall fronts and use dual return lanes. Both CRS Dairy Farm-1 and CRS Dairy Farm-2 equipped with Bucket automatic milk parlour for the lactating cow. The simplest autonomous machine milking included vacuum pump, single or dual buckets and pulsator for milking one or two animals simultaneously.
 
Lactation yield and persistency of milk production
 
There was significant difference (P ≤ 0.05) in lactation yield of animals in automation over conventional system. The average lactation yield at fourth lactation (Table 3) for dairy cattle rear in automatic rearing system recorded 6115.45 litres as compare to 5785.20 litres of milk production of dairy cattle rear in conventional rearing system. In the present study increased in Automation occurred at all four lactation stages. AMS feeding programs involves concentrates that completing the nutrient to meet the requirements of animal and increased the production. The findings of current study were in agreement with those of de Koning (2010), Jacobs and Siegford, (2012), Prescott et al., (1998), Rodenburg, (2011) who reported increase in milk yield due to AM system. Total mixed ration feeding strategies strive to maintain a constant nutrient composition to encourage milk production and a more accurate understanding of nutrient consumption (Coppock, 1977). However there was no significant (P ≤ 0.05) influence recorded in persistency of milk production (Table 3) among two rearing systems. A typical lactation curve can be described as increasing from initial yield at calving to maximum peak yield, a plateau maintaining peak yield and a decrease from peak yield to the end of the lactation (Grossman and Koops, 2003).
 

Table 3: Effect of rearing systems on lactation yield (kg) and persistency (%) in milk production up to four lactation of ARS and CRS dairy farms.


 
Age at first calving, lactation length and service period
 
The comparison between automation and conventional system of rearing showed (Table 4) that there was significant difference (P ≤ 0.01) in Age at first Calving and service period. The average age at first calving and service period in automatic rearing system was recorded 843.65 days and 122.15 days respectively where as average age at first calving and service period in conventional rearing system was recorded 903.05 days and 142.55 days. However, there was no significant difference in lactation length. These results will be in agreement of the findings of (Ali et al., 2015) who reported that location differences in reproductive performance are often results of difference in feed and feeding strategies, microclimatic conditions including temperature and humidity and management practices. As reported by (Obese et al., 1999; Domecq et al., 1997), cows reared under very limited resources and unfavourable climate of extensive management systems may fail to become pregnant. On contrary to these findings, (Carson et al., 2002) reported that effect of rearing regime does not influence on reproductive traits like age at first calving, fertility etc in Friesian cattle. However, reproductive performance, as indicated by the number of services per conception, was somewhat poorer than previous work with Friesian heifers (Leaver, 1977) but similar to that reported, more recently, by other workers using Holstein heifers (Pirlo et al., 2000; Lammers et al., 1999; Carson et al., 2000).
 

Table 4: Effect of rearing systems on age at first calving, lactation length, service period and working time measurement of ARS and CRS dairy farms.


 
Working time measurement
 
The comparison between automation and conventional system of rearing showed in Table 4, that there was significant difference (P ≤ 0.01) in manpower minutes for feeding, watering and milking in automatic rearing system than conventional rearing system. Automatic feeding systems are relatively expensive and require a high initial investment. The reason is that if at all possible they should be used for all feeding groups, including dry cows and young animals. The storage containers for the various feed components, particularly roughage, account for a substantial proportion of the investment cost, so the number of basic ration components used has a major effect on investment cost. Working time measurement modelling showed a significantly lower time requirement for feeder-mixer wagon than for a conventional manual feeding system. This supports corresponding statements by farmers in the survey conducted previously. Bisaglia et al., (2012) arrived at a similar result in a simulated comparison of working times between automatic feeder-mixer wagons versus conventional feeding system. Working time measurement of feeder wagon was also studied by Grothmann et al., (2010) and reported the similar manpower minutes requirement in these system. However there should be extensive work and research required to understand the economics of modern dairy farming in India.
This study indicated the significant influence on lactation yield, age at first calving and working time measurement. However there was no significant difference in persistency of milk production in both rearing system. There was lot of dissimilarities’ in feeding strategies, feeding system, farm characteristics and types of milking system among the identified dairy farms. Thus, these results indicated that automation rearing system for commercial dairy farms is beneficial for eliciting optimum production performance in crossbred dairy cattle.

  1. Ali, T., Lemma, A. and Yilma, T. (2015). Effect of management practices on reproductive performance of small holder dairy cattle. Austin Journal of Veterinary Science and Animal Husbandry. 2(3): 10-15.

  2. Anonymous, (2017). Establishment of HI-tech dairy farming unit and connecting India to the world. Vibrant Gujarat Global Summit. 08(1): 03-05.

  3. Axelsson, T. and Birk, U. Delaval Holding Ab, (2012). Milking parlour and method for operating the same. U.S. Patent 8,281,743.

  4. Basic Animal Husbandry and Fisheries Statistics. (2019). Government of India, New Delhi, India.

  5. Belle, Z., André, G. and Pompe, J.C.A.M. (2012). Effect of automatic feeding of total mixed rations on the diurnal visiting pattern of dairy cows to an automatic milking system. Biosystems Engineering. 111(1): 33-39.

  6. Bisaglia, C., Belle, Z., Van Den Berg, G. and Pompe, J.C. (2012). Automatic vs. conventional feeding systems in robotic milking dairy farms: a survey in the Netherlands. In International Conference of Agricultural Engineering CIGR-AgEng: Agriculture and Engineering for a Healthier Life. Federation de Gremios de Editores de Espana, p.1-6.

  7. Carolan, M. (2020). Automated agrifood futures: robotics, labor and the distributive politics of digital agriculture. The Journal of Peasant Studies. 47(1):184-207.

  8. Carson, A.F., Dawson, L.E.R., Mccoy, M.A., Kilpatrick, D.J. and Gordon, F.J. (2002). Effects of rearing regime on body size, reproductive performance and milk production during the first lactation in high genetic merit dairy herd replacements. Animal Science. 74(3): 553-565.

  9. Carson, A.F., Wylie, A.R.G., Mcevoy, J.D.G., Mccoy, M. and Dawson, L.E.R. (2000). The effects of plane of nutrition and diet type on metabolic hormone concentrations, growth and milk production in high genetic merit dairy herd replacements. Animal Science. 70(2): 349-362.

  10. Clark, C.E.F., Farina, S.R., Garcia, S.C., Islam, M.R., Kerrisk, K.L. and Fulkerson, W.J. (2016). A comparison of conventional and automatic milking system pasture utilization and pre and post grazing pasture mass. Grass and Forage Sci. 71(1): 153-159.

  11. Coppock, C.E. (1977). Feeding methods and grouping systems. Journal of Dairy Science. 60: 1327-1336.

  12. De Koning, K. (2010). Automatic milking-Common practice on dairy farms. Automatic milking-Common practise on dairy farms Proc. First North American Conference on Precision Dairy Management, Toronto, Canada, pp. 52-67. 

  13. Dijkhuisen, A.A. and Morris, R.S. (1997). Animal Health Economics. Principles and Applications. Post Graduate Foundation in Veterinary Science, University of Sydney.

  14. Domecq, J.J., Skidmore, A.L., Lloyd, J.W. and Kaneene, J.B. (1997). Relationship between body condition scores and conception at first artificial insemination in a large dairy herd of high yielding holstein cows. Journal of Dairy Science. 80(1): 113-120.

  15. Grossman, M. and Koops, W.J. (2003). Modeling extended lactation curves of dairy cattle: A biological basis for the multiphasic approach. Journal of Dairy Science. 86(3): 988-998.

  16. Grothmann, A., Nydegger, F., Häußermann, A. and Hartung, E. (2010). Automatic feeding system (AFS)-potential for optimization in dairy farming. Landtechnik. 65(2):129-131.

  17. Jacobs, J.A. and Siegford, J.M. (2012). Invited review: The impact of automatic milking systems on dairy cow management, behaviour, health and welfare. Journal of Dairy Science. 95(5): 2227-2247.

  18. Klei, L.R., Lynch, J.M., Barbano, D.M., Oltenacu, P.A., Lednor, A.J. and Bandler, D.K. (1997). Influence of milking three times a day on milk quality. Journal of Dairy Science. 80(3): 427-436.

  19. Lammers, B.P., Heinrichs, A.J. and Kensinger, R.S. (1999). The effects of accelerated growth rates and estrogen implants in prepubertal Holstein heifers on growth, feed efficiency and blood parameters. Journal of Dairy Science. 82(8): 1746-1752.

  20. Leaver, J.D. (1977). Rearing of dairy cattle. Effect of level of nutrition and body condition on the fertility of heifers. Animal Science. 25(2): 219-224.

  21. Livestock Census (2019). 20th Livestock Census All India Report, Ministry of Agriculture, Department of Animal Husbandry, Dairying and Fisheries, Government of India, New Delhi. www.india.gov.in. 

  22. Millogo, V., Ouédraogo, G.A., Agenäs, S. and Svennersten-Sjaunja, K. (2008). Survey on dairy cattle milk production and milk quality problems in peri-urban areas in Burkina Faso. African. Journal of Agricultural Research. 3(3): 215-224.

  23. Obese, F.Y., Okantah, S.A., Oddoye, E.O.K. and Gyawu, P. (1999). Post-partum reproductive performance of Sanga cattle in smallholder peri-urban dairy herds in the Accra plains of Ghana. Tropical Animal Health and Production. 31(3): 181-190.

  24. Pichler, O. and Oliver, C.K., DeLaval International AB, (1998). Herringbone-type rotary milking parlour. U.S. Patent. 5: 718-185.

  25. Pirlo, G., Miglior, F. and Speroni, M. (2000). Effect of age at first calving on production traits and on difference between milk yield returns and rearing costs in Italian Holsteins. Journal of Dairy Science. 83(3): 603-608.

  26. Prescott, N.B., T.T. Mottram and A.J.F. Webster. (1998). Relative motivations of dairy cows to be milked or fed in a Y-maze and an automatic milking system. Applied Animal Behavioural Science. 57: 23-33.

  27. Rodenburg, J. (2011). Designing feeding systems for robotic milking. Proc Tri-state dairy nutrition on conference. pp. 127-136. 

  28. Veysset, P., Wallet, P. and Prugnard, E. (2001). Automatic Milking Systems: Characterizing the Farms Equipped with AMS, Impact and Economic Simulations. ICAR Technical Series. 7: 141-150. 

Editorial Board

View all (0)