Effectiveness of milking management practices for SCC and TBC levels in milk

Author Contributions

Conceptualization: M.O. Daneluz, M.D. Canever, H.G. Lima and R.F. Bermudes. Data curation: M.O. Daneluz, M.D. Canever and H.G. Lima. Formal analysis: M.O. Daneluz, M.D. Canever, H.G. Lima and F.G. Ribeiro. Funding acquisition: M.O. Daneluz, M.D. Canever and H.G. Lima. Investigation: M.O. Daneluz, M.D. Canever and H.G. Lima. Methodology: M.O. Daneluz, M.D. Canever, H.G. Lima and F.G. Ribeiro. Project administration: M.O. Daneluz, M.D. Canever and H.G. Lima. Resources: M.O. Daneluz and M.D. Canever. Software: M.O. Daneluz and M.D. Canever. Supervision: M.O. Daneluz, M.D. Canever and H.G. Lima. Validation: M.O. Daneluz, M.D. Canever, H.G. Lima and F.G. Ribeiro. Visualization: M.O. Daneluz, M.D. Canever and H.G. Lima. Writing-original draft: M.O. Daneluz, M.D. Canever, H.G. Lima, R.F. Bermudes and F.G. Ribeiro. Writing-review & editing: M.O. Daneluz, M.D. Canever, H.G. Lima, R.F. Bermudes and F.G. Ribeiro.

Variable		Use (%)
Variable		Pelotas (RS)	Castro (PR)	Total
Milking practice	Uses pre-dip	35.2	100.0	57
	Uses post-dip	48.6	100.0	65.8
	Performs individual milk control	21.0	60.4	34.2
	Keeps individual animal records with information concerning milk quality	6.7	62.3	25.3
	Knows which active principle of antibiotic to use during the animal dry period	25.7	59.6	36.9
	Analyzes quality of water used during milking	16.3	71.7	35.0
	Controls the temperature of the water used to sterilize milking equipment	78.1	98.1	84.8
	Controls subclinical and/or clinical mastitis relapse	64.8	81.1	70.3
	Uses the California Mastitits Test biweekly	36.2	66.0	46.2
	Does the dark bottom mug test every day before milking	22.9	79.2	41.8

			Pelotas (RS)	Castro (PR)	Total
Control	Production system (%)
		1 – Extensive	86.7	84.9	86.1
		2 – Semi-confined	13.7	0	8.9
		3 – Confined	0	15.1	5.1
	Schooling (%)
		1 – Incomplete elementary school	72.1	29.4	58.1
		2 – Complete elementary school	27.9	70.6	41.9
	Milk as main source of income (%)
		1 – Yes	73.3	86.0	77.4
		2 – No	26.7	14.0	22.6
Farmer characterization	Age (years)		48.51	43.92	47.03
	Sex
		Male (%)	33.3	22.6	29.7
		Female (%)	66.7	77.4	70.3
	Dairy activity experience (years)		27.47	17.66	24.29
Farm characterization	Prevailing breed in dairy herd
		Jersey (%)	45.2	20.8	36.9
		Holstein (%)	41.3	52.8	45.2
		Mixed breed (%)	12.5	15.1	13.4
		Mixed (Jersey and Holstein) (%)	0	9.4	3.2
		Other (%)	1.0	1.9	1.3
	Number of lactating cows (heads)		17.13	76.60	37.10
	Liters produced by cow per day (L/day)		11.44	17.29	13.39
	Area (hectares)		50.37	67.78	56.06
	Liters produced by hectare (L/ha/year)		3,422.93	15,964.09	7,521.35
	Daily production (L)		239.75	1,718.96	729.68
	Average price received (R$/L)		0.83	1.33	1.00
	Average monthly income (R$)
		Up to R$ 2,000.00 (%)	41.9	0	28.2
		From R$ 2,000.00 to R$ 10,000.00 (%)	40.0	27.4	35.9
		Over R$ 10,000.00 (%)	18.1	72.5	36.0
	Milk as main source of income?		73.3	86.0	77.4
Quality	SCC (average, in number of cells/mL of milk)		527.53	331.76	453.37
Quality	TBC (average, in cfu/mL of milk)		684.43	10.03	669.49
Number of dairy farmers			105	53	158

Variable	Model
	SCC		TBC
	(1)	(2)	(1)	(2)
Regions ¹ 1 Castro region = 1 and Pelotas region = 2.	−0.144	−0.197	−0.571 ^* ***** significant at 1%.	−0.613 ^* ***** significant at 1%.
System type ² 2 System type (extensive; semi-confined; confined); base system = extensive system.	−0.006	−0.019	0.067	0.059
Schooling ³ 3 Schooling (incomplete elementary school and complete elementary school).	0.047	0.033	−0.051	−0.065
Milk as main source of income ⁴ 4 Milk as main source of income (yes or no).	−0.127	−0.105	−0.067	−0.046
Number of practices ⁵ 5 Number of management practices used by the producer.	−0.375 ^* ***** significant at 1%.	−0.988 ^* ***** significant at 1%.	−0.301 ^* ***** significant at 1%.	−0.825 ^* ***** significant at 1%.
Number of practices ⁶ 6 Number of management practices squared.		0.680 ^* ***** significant at 1%.		0.577 ^* ***** significant at 1%.
N	129	129	123	123
Adjusted R²	0.224	0.248	0.707	0.727

Variable	Coefficient of the models
Variable	General (OLS) ¹ 1 Coefficients obtained by Ordinary Least Squares (OLS) method; pattern error between parenthesis.	10th quantile	50th quantile	90th quantile
Categorization education level	−0.01	0.04	−0.02	−0.07
	(0.02)	(0.12)	(0.04)	(0.11)
System type	−0.00	−0.01	−0.00	0.03
	(0.03)	(0.06)	(0.05)	(0.12)
State	−0.04	−0.03	−0.02	0.02
	(0.03)	(0.11)	(0.04)	(0.07)
Main source of income	−0.05	−0.10	−0.04	−0.07
	(0.04)	(0.13)	(0.06)	(0.15)
Uses pre-dip	0.02	−0.09	0.04	0.05
	(0.06)	(0.34)	(0.09)	(0.27)
Uses post-dip	−0.03	−0.03	−0.01	−0.01
	(0.06)	(0.29)	(0.08)	(0.24)
Controls water temperature hygiene	−0.21 ^* ***** significant parameters at 1%.	−0.26	−0.24 ^* ***** significant parameters at 1%.	−0.18
	(0.05)	(0.15)	(0.08)	(0.22)
Does individual milk control	−0.03	−0.05	−0.04	0.00
	(0.05)	(0.15)	(0.08)	(0.20)
Does individual quality records	0.06	0.07	0.07	−0.01
	(0.07)	(0.25)	(0.10)	(0.25)
Does mastitis return control	0.06	0.05	0.06	0.00
	(0.04)	(0.10)	(0.05)	(0.17)
Uses CMT biweekly	−0.00	−0.06	0.01	0.01
	(0.04)	(0.12)	(0.06)	(0.14)
Knows dry active principle	−0.06	0.02	−0.08	−0.06
	(0.04)	(0.18)	(0.06)	(0.19)
Does water quality test	−0.07	−0.02	−0.14 ^ significant parameters at 5%;	−0.10
	(0.04)	(0.19)	(0.07)	(0.11)
Does dark bottom mug test	−0.01	0.04	0.01	−0.07
	(0.04)	(0.14)	(0.06)	(0.19)
Constant	3.00 ^* ***** significant parameters at 1%.	2.82 ^* ***** significant parameters at 1%.	3.00 ^* ***** significant parameters at 1%.	3.19 ^* ***** significant parameters at 1%.
	(0.10)	(0.37)	(0.15)	(0.31)
Observations	129	129	129	129
R²	0.35

Variable	SCC		TBC
Variable	Wald	P-value	Wald	P-value
Uses pre-dip	0.89	0.4120	1.36	0.2611
Uses post-dip	0.81	0.4487	0.16	0.8500
Controls water temperature hygiene	0.31	0.7309	1.65	0.1960
Does individual milk control	0.16	0.8495	0.14	0.8660
Does individual quality records	0.16	0.8383	0.71	0.4955
Does mastitis return control	0.85	0.4289	4.17	0.0180
Uses CMT biweekly	0.34	0.7112	1.97	0.1447
Knows dry active principle	0.52	0.5967	0.52	0.5947
Does water quality test	0.45	0.6408	0.23	0.7970
Does dark bottom mug test	1.12	0.3285	1.69	0.1890

Variable	General (OLS) ¹ 1 Coefficients obtained by Ordinary Least Squares (OLS) method.	10th quantile	50th quantile	90th quantile
Categorization education level	−0.09	−0.09	−0.02	−0.11
	(0.06)	(0.28)	(0.13)	(0.13)
System type	0.11	0.10	0.07	0.16
	(0.08)	(0.50)	(0.16)	(0.18)
State	−0.63 ^* ***** significant parameters at 1%.	−0.31	−0.62 ^* ***** significant parameters at 1%.	−0.83 ^* ***** significant parameters at 1%.
	(0.07)	(0.35)	(0.12)	(0.10)
Main source of income	−0.15	0.10	−0.21	−0.14
	(0.10)	(0.50)	(0.19)	(0.22)
Uses pre-dip	0.04	−0.13	0.07	−0.23
	(0.16)	(0.68)	(0.28)	(0.31)
Uses post-dip	−0.35 ^ significant parameters at 5%;	−0.16	−0.36	−0.18
	(0.14)	(0.66)	(0.26)	(0.19)
Controls water temperature hygiene	−0.48 ^* ***** significant parameters at 1%.	−0.94	−0.39	−0.22
	(0.14)	(0.64)	(0.26)	(0.19)
Does individual milk control	−0.08	−0.02	0.03	−0.27
	(0.13)	(0.31)	(0.26)	(0.31)
Does individual quality records	0.17	−0.03	−0.01	0.36
	(0.17)	(0.78)	(0.34)	(0.35)
Does mastitis return control	−0.06	−0.07	−0.01	−0.15
	(0.09)	(0.34)	(0.18)	(0.14)
Uses CMT biweekly	0.03	0.03	−0.02	0.08
	(0.09)	(0.36)	(0.18)	(0.15)
Knows dry active principle	−0.29 ^* ***** significant parameters at 1%.	−0.08	−0.34 ^* * Significant parameters at 10%;	−0.16
	(0.10)	(0.53)	(0.20)	(0.24)
Does water quality test	−0.01	−0.17	0.06	0.01
	(0.12)	(0.46)	(0.22)	(0.19)
Does dark bottom mug test	0.05	−0.17	0.02	0.19
	(0.10)	(0.33)	(0.20)	(0.18)
Constant	4.02 ^* ***** significant parameters at 1%.	2.98 ^* ***** significant parameters at 1%.	3.97 ^* ***** significant parameters at 1%.	4.61 ^* ***** significant parameters at 1%.
	(0.25)	(1.08)	(0.48)	(0.44)
Observations	123	123	123	123
R²	0.78

[1] * Corresponding author: maridaneluz22@gmail.com

Brasil

Brasil

Effectiveness of milking management practices for SCC and TBC levels in milk

ABSTRACT