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Frontiers of Agricultural Science and Engineering

ISSN 2095-7505

ISSN 2095-977X(Online)

CN 10-1204/S

邮发代号 80-906

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Frontiers of Agricultural Science and Engineering  2019, Vol. 6 Issue (2): 181-187   https://doi.org/10.15302/J-FASE-2019252
  本期目录
Evaluation of automated in-line precision dairy farming technology implementation in three dairy farms in Italy
Maria CARIA, Giuseppe TODDE(), Antonio PAZZONA
Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy
 全文: PDF(634 KB)   HTML
Abstract：

In recent decades, dairy farms have been exposed to wide variation in profit levels due to a considerable variability of milk price, and energy and feed costs. Consequently, it is necessary for the dairy industry to improve efficiency and productivity by adopting innovative technologies. The automated in-parlour milk analysis and separation is mainly useful to produce low or high quality milk and to monitor the animal health status. Milk with high levels of protein and fat contents may reduce the intensity of standardization during cheesemaking process, reducing production costs. The study aimed to evaluate the efficiency of real-time milk separation during milking and the performance of the milking machine after implementation of AfiMilk MCS. In addition, the economic aspects were assessed. The separation of milk required the existing milking parlors to be equipped with an additional milkline to allow channeling milk with low and high coagulation properties into two different cooling tanks. The results showed that the high coagulation milk fraction, compared to the bulk milk, increased in fat (from 18% to 43%) and protein (from 3% to 7%) concentration. The technology tested has given promising results showing reliability and efficiency in milk separation in real time with affordable implementation costs.
Key words： cheese yield    infrared analysis    milk quality    real-time measurement    sensor
收稿日期: 2018-10-18      出版日期: 2019-05-22
Corresponding Author(s): Giuseppe TODDE   
 引用本文:   
. [J]. Frontiers of Agricultural Science and Engineering, 2019, 6(2): 181-187.
Maria CARIA, Giuseppe TODDE, Antonio PAZZONA. Evaluation of automated in-line precision dairy farming technology implementation in three dairy farms in Italy. Front. Agr. Sci. Eng. , 2019, 6(2): 181-187.
 链接本文:  
https://academic.hep.com.cn/fase/CN/10.15302/J-FASE-2019252
https://academic.hep.com.cn/fase/CN/Y2019/V6/I2/181
Fig.1  
Analysis (n) CF* DF*
Milk yield/(× 103 kg) Total milk yield/% Fat/% Protein/% Casein/% (Cas/Prot)/% Total bacterial count/(×103 CFU·mL−1) Somatic cell counts/ (×103 cell) Milk yield (×103 kg) Total milk yield/% Fat/% Protein/% Casein/% (Cas/Prot)/% Total bacterial count/(×103 CFU·mL−1) Somatic cell count/ (×103 cell)
Farm A
7 2.1±0.27 37±4 5.36a±0.18 3.34a±0.06 2.61a±0.03 78.3±0.3 10±40 213±38 3.7±0.55 63±4 2.83b±0.14 3.09b±0.05 2.41b±0.02 77.9±0.6 13±70 171±24
72 3.1±0.22 50±2 5.00a±0.12 3.28a±0.07 2.55a±0.06 77.8±0.5 13±30 206a±36 3.1±0.19 50±2 2.50b±0.11 3.05b±0.06 2.37b±0.05 77.5±0.5 13±22 167b±42
17 4.8±0.92 71±1 4.39a±0.06 3.17a±0.02 2.49a±0.02 78.5±0.5 5a±10 172±21 2.0±0.11 29±1 2.11b±0.06 2.91b±0.02 2.28b±0.02 78.3±0.6 8b±20 160±35
Farm B
6 2.5±0.14 39±3 4.87a±0.30 3.35a±0.06 2.61a±0.06 77.8±0.6 17±80 358±95 3.9±0.25 61±3 2.75b±0.33 3.04b±0.03 2.34b±0.03 77.1±0.6 19±15 275±76
24 3.3±0.24 51±3 4.78a±0.12 3.35a±0.03 2.61a±0.02 77.9±0.5 17±11 305a±45 3.2±0.19 49±3 2.67b±0.11 3.06b±0.04 2.37b±0.04 77.2±0.7 15±3 238b±29
8 4.2±0.17 68±1 4.68a±0.16 3.35a±0.05 2.60a±0.05 77.8±0.4 18±40 280±38 2.0±0.15 32±1 2.62b±0.18 3.01b±0.03 2.31b±0.04 76.8±0.4 18±4 252±52
Farm C
4 2.3±0.11 38±1 5.26c±0.03 3.49c±0.03 2.73c±0.02 78.1±0.4 12±20 289c±36 3.8±0.76 62±1 2.96d±0.06 3.13d±0.03 2.42d±0.03 77.5±0.4 15±40 139d±25
62 3.2±0.23 50±2 4.89a±0.08 3.36a±0.07 2.61a±0.07 77.7±0.6 27±26 226a±46 3.1±0.18 50±2 2.58b±0.09 3.09b±0.05 2.39b±0.04 77.4±0.5 31±23 149b±26
2# 3.8±0.86 69±4 5.05±0.19 3.33±0.19 2.59±0.18 77.7±0.9 32±19 234±21 1.7±0.81 31±4 2.71±0.16 3.08±0.11 2.37±0.08 77.1±0.1 26±18 118±23
Tab.1  
Farm Separation ratio per farm Total milk/(×103 kg) Fat/% Protein/% Casein/% (Cas/Prot)/%
A 40:60 5.8±0.63 3.75±0.21 3.18±0.06 2.48±0.03 78.1±0.50
50:50 6.1±0.35 3.75±0.09 3.17±0.06 2.46±0.05 77.7±0.42
70:30 6.8±0.11 3.72±0.05 3.09±0.01 2.43±0.02 78.4±0.40
B 40:60 6.4±0.14 3.58±0.25 3.16±0.03 2.44±0.03 77.4±0.57
50:50 6.4±0.24 3.74±0.08 3.21±0.03 2.49±0.03 77.6±0.57
70:30 6.1±0.28 3.80±0.14 3.20±0.04 2.48±0.04 77.4±0.37
C 40:60 6.1±0.17 3.83±0.06 3.26±0.01 2.54±0.02 77.7±0.33
50:50 6.3±0.32 3.74±0.07 3.23±0.06 2.50±0.05 77.5±0.50
70:30 5.5±0.17 4.21±0.09 3.23±0.15 2.51±0.14 77.5±0.56
Tab.2  
Fig.2  
Farm A Farm B Farm C
Sensor 1 Sensor 2 Sensor 1 Sensor 2 Sensor 1 Sensor 2
0.8 0.8 0.4 0.4 0.3 0.4
Tab.3  
Farm Construction works/EUR Electrical works/EUR AfiMilk MCS/EUR Milk tank/EUR Total/EUR Average cost per head/EUR Average total cost per stall/EUR
A 2000 4000 68800 25200 100000 200 6200
B 1800 3000 134300 25200 164300 274 5500
C 2000 2000 83600 25200 112800 235 6200
Tab.4  
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