Livestock Research for Rural Development 22 (2) 2010 Guide for preparation of papers LRRD News

Citation of this paper

Milk quantity, quality and revenue in dairy farms supported by a private organization in Central Thailand

S Yeamkong*, S Koonawootrittriron*, M A Elzo** and T Suwanasopee*

* Department of Animal Science, Kasetsart University, Bangkok 10900, Thailand
agrskk@ku.ac.th
** Department of Animal Sciences, University of Florida, Gainesville, FL 32611-0910, USA

Abstract

This study evaluated factors affecting milk yield per farm and per cow, milk quality (percent of fat, protein, lactose, solid-not-fat, and total solids and somatic cell count), and revenue per farm and per cow in dairy farms supported by a private organization in Central Thailand. The dataset included 34,133 monthly farm records collected from September 2003 to December 2007. Seasons were defined as winter, summer and rainy. Farms were located in  Muak Lek, Wang Muang, Phattana Nikhom and Pak Chong districts. Farm sizes were classified as small, medium and large based on number of cows milked per day. The model for each trait contained year-season and farm location-farm size as subclass fixed effects, and individual farm and residual as random effects.

 

All traits were affected by year-season and farm location-farm size effects, except for protein percentage. Monthly milk yield per farm and per cow tended to decrease and somatic cell count tended to increase from 2003 to 2007.  Milk production per cow was similar across farm sizes and locations. Large farms had higher somatic cell counts than small and medium farms. Revenues per farm and per cow depended primarily on milk yields per farm and per cow. Better training, support and commercial opportunities for farmers are needed to stimulate improvements in dairy production and farm revenues in Central Thailand.

Key words: dairy farming, economic, production, tropics


Introduction

The main objectives of the dairy promotion program in Thailand are to increase milk quantity, milk revenue and milk quality as well as to increase product safety and satisfaction of consumers. Despite considerable efforts on the part of the government, private organizations and dairy farmers, 305-day milk production in Thailand only averages 3,900 1,100 kg (Dairy Farming Promotion Organization 2007). Although milk consumption, number of dairy farms, total quantity of dairy products and land used for dairy production have all increased yearly since the beginning of dairy production in Thailand, progress in terms of milk quality and efficiency of production has been slow (Dairy Farming Promotion Organization 2007; Office of Agricultural Economics 2008).

 

Dairy farming has become an increasingly competitive business in Thailand, due particularly to importation of dairy products (e.g., powdered milk and butter) from other countries (i.e., Australia and New Zealand) as a result of international free trade agreements (Department of Trade Negotiations 2005; Office of Agricultural Economics 2008).  Survival of dairy farming in Thailand depends on the ability of dairy farmers to increase the profitability and efficiency of their dairy operations. Revenues are directly related to amount of milk produced. In addition, milk quality factors (fat percentage, bacterial contamination and somatic cell count) also impact the price of raw milk paid to dairy producers ( Muak Lek Dairy Cooperative Limited 2005). Thus, both quantity and quality of milk determine the amount of revenue of dairy farmers in Thailand (Seangjun and Koonawootrittriron 2007; Rhone et al 2008b, c).

 

The central area is the most important dairy region in Thailand. In 2006, this region had 12,253 dairy households (60% of the country), 280,289 dairy cattle (68% of the country), and 113,080 milking cows (69% of the country) that produced 805,083 kg of milk per day (66% of the country). In addition, there were 14 dairy cooperatives and 17 private organizations in this region (Department of Livestock Development 2007). The role of dairy cooperatives and private organizations is to purchase milk from dairy farmers and to provide services to their members. Dairy cooperatives belong to farmers, are supported by the government, and are managed by a committee of elected farmers. In contrast, private dairy organizations belong to a business person who also manages the business and makes decisions. Although these two types of organizations have similar objectives, they could have dissimilar performance because of different management styles.

 

Under the current social conditions and high level of economic competition in Thailand, increasing efficiency and lowering production costs while producing high quality milk would increase farmer profitability. Determination of important factors affecting milk quantity and quality would help dairy farmers manage their limited resources more effectively and provide opportunities to increase the efficiency of their dairy operations. This information would also help dairy organizations to provide more appropriate and effective support to their members. Studies on factors affecting milk quantity and quality in Central Thailand have been conducted to date only for members of a dairy cooperative (Seangjun and Koonawootrittriron 2007; Rhone et al 2008b, c). Similar studies for members of a dairy private organization do not exist. Thus, the objective of this research was to determine factors affecting milk quantity, quality and revenue in dairy farms supported by a private organization in the Central Thailand.

 

Materials and methods 

Data, traits and farms

 

The dataset was composed of 34,133 farm records of milk production, revenue and quality. These records were from 1,101 farms supported by a private dairy organization (Midland Dairy Limited Partnership, Saraburi, Thailand) from September 2003 to December 2007. No records were taken from individual animals.

 

The dataset contained farm records for 2 monthly milk production traits, 2 monthly milk revenue traits and 6 monthly milk quality traits.  The 2 monthly milk production traits were milk yield per farm (MYF; kg) and milk yield per cow (MYC; kg).  MYF was computed as the sum of daily milk yields per farm for each month.  MYC was calculated by dividing MYF by the average number of milking cows for a particular farm and month.

 

The 2 monthly milk revenue traits were milk revenue per farm (RVF; baht) and milk revenue per cow (RVC; baht).  The average exchange rate during the years of the study was 37.71 2.54 baht per USD.  RVF was the amount of money that farms received from selling their milk to the private dairy organization for each month.  RVF was computed as the sum of daily milk income per farm for each month.  RVC was calculated by dividing RVF by the average number of milking cows for a particular farm and month.

 

The monthly milk quality traits were fat percentage (FAT; %), protein percentage (PRO; %), lactose percentage (LAC; %), solids-not-fat percentage (SNF; %), total-solids percentage (TS; %) and somatic cell count (SCC; 103 cells/ml). The values of FAT, PRO, LAC, SNF, TS and SCC were obtained from milk samples taken randomly once a month from milk containers from individual farms.

 

The farm identification number created by the private dairy organization was used for the analyses and also to link all related information. The address of individual farms was used to assign farms to 4 locations:   Muak Lek (Saraburi province), Wang Muang (Saraburi province), Phattana Nikhom (Lop Buri province) and Pak Chong (Nakhon Rachasima province). The average number of milking cows for the duration of the study was used to classify farms into 3 sizes: small = less than 10 milking cows per day, medium = between 10 and 19 milking cows per day and large = 20 or more milking cows per day). Seasons were winter (November to February: cool [21 C to 32 C] and dry [70% RH, precipitation 124 mm/year]), summer (March to June: hot [25 C to 36 C] and dry [69% RH, precipitation 187 mm/year]) and rainy season (July to October: hot [24 C to 33 C] and humid [79% RH, precipitation 903 mm/year]) as described by Koonawootrittriron et al 2009).

 

Cattle, feeding and management practices

 

The majority of dairy cattle in the population were over 75% Holstein (H). The remaining fraction (25% or less) contained genes from one or more of the following breeds: Brahman, Jersey, Red Dane, Red Sindhi, Sahiwal and Thai Native. Farms primarily used artificial insemination (AI) to breed cows. Most farms used semen from Holstein bulls to breed cows. Farmers used their own experience, sire information (EBV and daughters’ production) and suggestions from government and private organization advisors to choose sires. The private organization provided veterinary services to farms including AI and healthcare for animals. Calves were vaccinated against Hemorrhagic Septicemia between 4 and 8 months of age. All animals were vaccinated against Foot and Mouth Disease twice a year. All farms treated their cows with antihelmintics twice a year.

 

Farm feeding and nutritional management varied among seasons. Grasses fed to dairy cattle included Brachiaria mutica (para grass), Brachiaria ruziziensis (ruzi grass), Pennisetum purpureum (napier grass) and Panicum maximum (guinea grass). However, during the dry seasons (cold and hot) grasses were usually insufficient because of lack of irrigation. Thus, rice straw, hay and silage were used as supplements. All farmers milked cows twice a day, once in the morning and once in the afternoon. Almost all farms used machine rather than hand milking. After each milking, either the farmer or a private carrier took the raw milk to the private milk collection center.

 

The volume of milk received from each farm after each milking (morning and afternoon) was recorded at the private milk collecting center. Milk from individual farms was randomly sampled every ten days. Milk samples were sent to the Department of Livestock Development office for milk quality analyses (i.e., FAT, PRO, LAC, SNF, TS and SCC). If a farm had low milk quality (i.e., FAT < 3.2%, PRO < 2.8%, SNF < 8.25%, TS < 12% or SCC > 500 103 cells/ml), then personnel from the private organization visited that farm and provided suggestions to solve the problems that were identified (e.g., improvements in feeding, housing or milking conditions; Midland Dairy Limited Partnership 2007).

 

Statistical analysis

 

Single-trait mixed models were used to analyze MYF, MYC, RVF, RVC, FAT, PRO, LAC, SNF, TS and SCC.  Computations were carried out using the mixed procedure of the SAS software package (SAS 2004).  The mixed model used for all traits contained the subclasses of year-season and farm location-farm size as fixed effects.  Year-season subclasses contained the effects of year, season and year season interactions.  Similarly, farm location-farm size subclasses contained the effects of farm location, farm size and farm location x farm size interactions.  Random effects were farm and residual. The model in matrix notation was as follows.

y = Xb + Zfuf + e

where

y               =   vector of single-trait records (MYF, MYC, RVF, RVC, FAT, PRO, LAC, SNF, TS or SCC),

b               =   vector of fixed effects (year-season and farm location-farm size),

uf              =   vector of random farm effects,

X              =   incidence matrix relating records to elements of b,

Zf              =   incidence matrix relating records to elements of u,

e               =   vector of residual effects.

 

Random farm effects were assumed to have mean zero, a common variance σf2 and uncorrelated. Similarly, random residual effects were assumed to have mean zero, common variance σe2 and uncorrelated. Variances for random effects were estimated using restricted maximum likelihood using option REML in the mixed procedure of SAS. Year-season and farm location-farm size least squares means (LSM) were estimated for all traits, and then compared using t-tests.

 

Results and discussion 

Milk yield, quality and revenue of dairy farms included in this study were as shown in Table 1.


Table 1.  Number of observation, mean and standard deviation of milk quantity, milk quality and revenue

Trait

Number of observations

Mean

Standard deviation

Milk yield per farm, kg

23,052

3,232

2,553

Milk yield per cow, kg

23,052

367

163

Revenue per farm, baht

23,052

37,522

29,850

Revenue per cow, baht

23,052

4,256

1,895

Fat, %

28,612

3.40

0.46

Protein, %

28,729

3.01

0.22

Lactose, %

28,727

4.57

0.28

Solid not fat, %

28,729

8.25

0.36

Total solid, %

28,700

11.7

0.67

Somatic cell count,  x103 cells/ml

26,103

657

679


Averages for these traits were similar to those from farms supported by a dairy cooperative in Central Thailand (Seangjun and Koonawootrittriron 2007; Rhone et al 2008a, b, c)

 

Year-season subclass effects

 

Year-season LSM ranged from 4,352 89.5 kg (2006-Rainy) to 5,027 84.7 kg (2005-Summer) for MYF (Figure 1a) and from 284 7.73 kg (2007-Rainy) to 369 6.63 kg (2005-Summer) for MYC (Figure 1b). Year-season LSM for MYF tended to increase from 2003 to 2007 (4.57 17.0 kg/year-season; P < 0.79; P > 0.05). In contrast, year-season LSM for MYC decreased over this same period (-6.23 1.42 kg/year-season; P < 0.01). These trends for MYF and MYC indicated that the increase in milk production per farm that occurred in this dairy cattle population from 2003 to 2007 was not because of an increase in individual MYC, but it was due to an increase in the average number of cows milked per day. The average number of cows milked per day increased from 8.54 6.31 cows in 2003 to 10.7 7.80 cows in 2007.



                        (a)




           (b)



                        (c)



                   (d)


Figure 1.  Year-season subclass least squares means for monthly milk yield per farm (a), milk yield per cow (b), milk revenue per farm (c) and milk revenue per cow (d)


Year-season LSM for farm revenues ranged from 50,322 1,044 baht (2006-Rainy) to 65,441 1,155 baht (2007-Winter) for RVF (Figure 1c), and 3,430 87 baht (2006-Rainy) to 4,435 104 baht (2007-Winter) for RVC (Figure 1d). There was a positive trend of 623 249 baht/year-season (P < 0.03) for RVF from 2003 to 2007. On the other hand, RVC tended to decrease (-15 23 baht/year-season; P < 0.53) between 2003 and 2007. The direction of the RVF and RVC trends were the same as those for MYF and MYC. This occurred because the milk revenue system in Thailand depends primarily on milk quantity, thus revenues are proportional to amounts of milk purchased by milk collection centers.

 

The decreasing trend in MYC found in this dairy population between 2003 and 2007 may have been a consequence of the deterioration of the economic situation of dairy farmers during that period. The price for raw milk yield remained at 12.5 baht/kg from September 2003 to September 2007 (Office of Agricultural Economics 2008; Department of Livestock Development 2007). However, dairy production costs (e.g., feed, fuel, labor, equipment and services) and living expenses (e.g., food, clothes and health care) increased dramatically from 2003 to 2007 (Prateepavanid 2006; Suriyasathaporn et al 2006; Ndambi et al 2008). In order to maintain their revenue the farmers were forced to increase their farm size which could have also led to their inability to supply the required inputs for production. Increased costs may have forced farmers to decrease the quantity and quality of feed supplied to cows, and perhaps to lower the level of management and health care. Decreased levels of nutrition, management, and health care may, in turn, have increased stress on dairy cows resulting in lower MYC.

 

Price of milk was increased to 14.5 baht during the last quarter of 2007 (Office of Agricultural Economics 2008; Department of Livestock Development 2007). Information collected from dairy farms in this study in 2008 and later years will help determine whether this price increase for milk resulted in a corresponding increase in MYC. Additional training on improving the efficiency of milk production, cost-effective feeding, management and health care practices of dairy farmers in this private organization may also help increase the productivity and profitability of their dairy farms.

 

Table 2 shows LSM and standard errors for the 6 milk quality traits (FAT, PRO, LAC, SNF, TS and SCC) by year-season subclass.


Table 2. Least squares means for fat (FAT), protein (PROT), lactose (LAC), solids not fat (SNF), total solids (TS) and somatic cell count (SCC) by year-season subclass

Year-Season subclass

Milk quality

FAT, %

PRO, %

LAC, %

SNF, %

TS, %

SCC, 103 cells/ml

2003 - Winter

3.48 0.017a

3.13 0.008

4.61 0.008

8.44 0.012

11.93 0.023

674 26.5

2004 - Summer

3.50 0.016

3.09 0.007

4.61 0.007

8.40 0.011

11.90 0.022

770 24.3

2004 - Rainy

3.31 0.016

2.90 0.008

4.01 0.007

7.61 0.011

11.92 0.022

755 25.0

2004 - Winter

3.36 0.016

2.93 0.007

4.33 0.007

8.11 0.011

11.73 0.022

672 24.6

2005 - Summer

3.39 0.019

2.99 0.009

4.62 0.008

8.19 0.013

11.82 0.025

799 30.9

2005 - Rainy

3.45 0.017

3.08 0.008

4.64 0.008

8.30 0.012

11.97 0.023

736 26.2

2005 - Winter

3.46 0.016

2.95 0.008

4.61 0.007

8.23 0.011

11.76 0.022

764 25.0

2006 - Summer

3.41 0.016

3.02 0.008

4.66 0.007

8.27 0.011

11.79 0.022

775 24.8

2006 - Rainy

3.52 0.018

3.10 0.008

4.65 0.008

8.36 0.012

11.98 0.025

795 27.6

2006 - Winter

3.52 0.017

3.02 0.008

4.68 0.008

8.35 0.012

11.92 0.023

740 26.1

2007 - Summer

3.42 0.016

3.02 0.008

4.71 0.007

8.36 0.011

11.85 0.022

843 24.9

2007 - Rainy

3.68 0.016

3.12 0.008

4.68 0.007

8.47 0.011

12.18 0.022

868 24.5

 2007 - Winter

3.59 0.018

3.05 0.009

4.63 0.008

8.35 0.012

11.97 0.024

838 28.0

a Standard error


Year-season LSM tended to increase for FAT (0.015 0.006%/year-season; P < 0.04), LAC (0.024 0.013%/year-season; P < 0.09), SNF (0. 021 0.016%/year-season; P < 0.21), TS (0.033 0.021%/year-season; P < 0.13) and SCC (11.31 3.07 103 cells/ml/year-season; P < 0.01), and to remain unchanged for PRO (0.003 0.006%/year-season; P < 0.58). Similar trends across year-seasons for milk quality traits were also found by Azad et al (2007), Seangjun and Koonawootrittriron (2007) and Rhone et al (2008a).

 

Trends for milk quality traits in this dairy population were low but favorable, except for SCC. Year-season LSM for SCC were all above the recommended maximum of 500,000 cells/ml (Department of Livestock Development 2003; Thai Agriculture Commodity and Food Standard 2005).  Thus, improving management and health care of dairy cows to reduce and maintain SCC below the recommended maximum should be a priority for farmers in this private organization. This will likely result not only in lower SCC, but it may also increase milk production.

 

All milk quality traits were likely influenced by quantity and quality of roughage, management, health care (particularly for SCC) and changing climatic conditions across years and seasons.  Thus, variability in environmental conditions across year-seasons should be accounted for when devising management strategies to improve milk quality in Central Thailand.

 

Farm location-farm size subclass effects

 

Farm location-farm size subclass was important for all traits (P < 0.05; Figure 2a). Farm location-farm size LSM for MYF ranged from 6,157 625 kg in Phattana Nikhom to 8,675 228 kg in  Muak Lek for large farms, from 4,113 286 kg in Phattana Nikhom to 4,759 194 kg in Pak Chong for medium farms and from 1,566 188 kg in Phattana Nikhom to 1,823 135 kg in Pak Chong for small farms.

 

Farm location-farm size subclass LSM for MYC ranged from 162 47.4 kg in Phattana Nikhom to 356 17.3 kg in   Muak Lek for large farms, from 319 22.1 kg in Phattana Nikhom to 378 14.5 kg in Pak Chong for medium farms and from 337 14.7 kg in Phattana Nikhom to 353 10.2 kg in Pak Chong for small farms. Except for large farms in Phattana Nikhom, ranges for MYC were similar in all farm location by farm size (Figure 2b).




                (a)

)
                (b)



                (c)




              (d)


Figure 2.  Farm location-farm size subclass least squares means for monthly milk yield per farm (a), milk yield per cow (b), milk revenue per farm (c) and milk revenue per cow (d)


The low LSM values for MYF and MYC in large farms in Phattana Nikhom was likely due to the low quality and quantity of feed provided to dairy cows. Large farms in Phattana Nikhom made extensive use of rice straw particularly during the dry season because of limited availability and high cost of good quality forage. Most farms in Phattana Nikhom (82%) did not grow grass or legume for their cows, and approximately 84% of the land in Phattana Nikhom was used for crop production (cassava, sugarcane, corn and sunflower).

 

The pattern of farm location-farm size LSM for RVF across locations was similar to that for MYF (Figure 2c).  Farm location-farm size LSM for RVF ranged from 72,015 7,273 baht in Phattana Nikhom to 100,929 2,655 baht in  Muak Lek for large farms, from 47,987 3,334 baht in Phattana Nikhom to 56,338 2,253 baht in Pak Chong for medium farms and from 19,449 2,184 baht in Phattana Nikhom to 21,960 1,574 baht in Pak Chong for small farms.

 

A similar pattern of farm location-farm size LSM across locations existed for RVC (Figure 2d) and MYC (Figure 2b). In particular, large farms in Phattana Nikhom had lower RVC as expected from the low LSM for MYC and the milk pricing system in Thailand. Farm location-farm size LSM for RVC ranged from 2,015 549 baht/cow in Phattana Nikhom to 4,208 200 baht/cow in  Muak Lek for large farms, from 3,757 256 baht/cow in Phattana Nikhom to 4,484 168 baht in Pak Chong for medium farms and from 3,996 171 baht/cow in Phattana Nikhom to 4,179 118 baht/cow in Pak Chong for small farms.

 

The RVF and RVC patterns across farm location-farm size subclasses were related to MYF and MYC.  Low milk revenues (RVF and RVC) were due to low milk production (MYF and MYC) rather than low milk quality (FAT, PRO, LAC, SNF, TS and SCC). Thus, factors that affected milk production (e.g., feed and management) had a direct impact on milk revenues. As with MYF and MYC, low LSM values for RVF and RVC in large farms in Phattana Nikhom were likely due to low quality and quantity of feed given to cows.  Thus, feed and management strategies, especially during the dry season, must be improved to increase milk production and revenues in this region.

 

Table 3 shows LSM for FAT, PRO, LAC, SNF, TS and SCC by farm location-farm size subclass.


Table 3.  Least squares means for fat (FAT), protein (PROT), lactose (LAC), solids not fat (SNF), total solids (TS) and somatic cell count (SCC) by farm location-farm size subclass

Farm locationa

Farm sizeb

Milk Quality

FAT, %

PRO, %

LAC, %

SNF, %

TS, %

SCC, 103 cells/ml

ML

Small

3.39 0.011c

3.02 0.005

4.58 0.005

8.26 0.008

11.74 0.016

602 17.6

 

Medium

3.38 0.016

3.01 0.008

4.59 0.007

8.26 0.012

11.73 0.022

611 25.1

 

Large

3.44 0.042

3.03 0.021

4.55 0.020

8.24 0.031

11.77 0.059

744 65.2

WM

Small

3.53 0.023

3.04 0.012

4.59 0.011

8.29 0.017

11.92 0.032

659 36.5

 

Medium

3.51 0.040

3.02 0.020

4.58 0.019

8.26 0.029

11.86 0.056

809 62.3

 

Large

3.55 0.067

3.07 0.033

4.55 0.031

8.28 0.049

11.93 0.093

1,056 103

PN

Small

3.51 0.031

3.02 0.016

4.61 0.014

8.30 0.023

11.90 0.043

536 48.5

 

Medium

3.57 0.048

3.03 0.024

4.59 0.022

8.28 0.035

11.95 0.066

639 74.4

 

Large

3.60 0.098

3.09 0.049

4.58 0.046

8.33 0.072

12.01 0.137

1,000 154

PC

Small

3.37 0.025

3.02 0.013

4.53 0.012

8.21 0.018

11.67 0.036

749 40.1

 

Medium

3.36 0.036

2.99 0.018

4.54 0.017

8.19 0.027

11.64 0.050

792 56.4

 

Large

3.41 0.073

3.05 0.037

4.56 0.034

8.27 0.054

11.77 0.102

1,063 114

aML =  Muak Lek; WM = Wang Muang; PN = Phattana Nikhom; PC = Pak Chong

bSmall = less than 10 milking cows per day; Medium = from 10 to 19 milking cows per day; Large = 20 or more milking cows per day;   c Standard error


Farm location-farm size subclass LSM for FAT, PRO, SNF, TS and SCC tended to be similar in small and medium farms (P > 0.05) and smaller than in large farms (P < 0.05) in almost all locations. In contrast, LSM for LAC tended to decrease as farm size increased in all locations, except for Pak Chong.

 

Values of LSM for FAT across farm location-farm size subclasses ranged from 3.41 0.07% in Pak Chong to 3.60 0.10% in Phattana Nikhom for large farms, from 3.36 0.04% in Pak Chong to 3.57 0.05% in Phattana Nikhom for medium farms and from 3.37 0.03% in Pak Chong to 3.53 0.02% in Wang Muang for small farms. Large farms in  Muak Lek, Pak Chong and Wang Muang tended to have similar and higher FAT than small and medium farms. All farm sizes in  Muak Lek had LSM for FAT similar to those in Pak Chong and both of them were lower than FAT values of farms in Phattana Nikhom and Wang Muang (Figure 3a). Variability of FAT across farm location-farm size subclasses could be associated with weather patterns, availability of roughage, agricultural activities, irrigation of pastures and the ability of farmers to manage and utilize local feed resources (Allore et al 1997; Kaewkamcham et al 2001; Garcia et al 2005).



                   (a)



            (b)


Figure 3.  Farm location-farm size subclass least squares means for fat percentage (a) and somatic cell count (b)


Farm location-farm size LSM for SCC ranged from 744 65.2 103 cells/ml in  Muak Lek to 1,063 114 103 cells/ml in Pak Chong for large farms, from 611 25.1 103 cells/ml in   Muak Lek to 809 62.3 103 cells/ml in Wang Muang for medium farms and from 536 48.5 103 cells/ml in Phattana Nikhom to 749 40.1 103 cells/ml in Pak Chong for small farms. These results were similar to those reported by Rhone et al (2008a) and Seangjun and Koonawootrittriron (2007). Several researchers (Hansen et al 2002; Othmane et al 2002; Koivula et al 2005) have indicated positive associations between SCC, mastitis and low quality of management. Thus, the high SCC found here is likely be related to low hygienic level in farms (Surawong et al 2004; Ajariyakhajorn et al 2005; Kivaria et al 2006).

 

Large farms in all locations tended to have higher SCC than smaller size farms (Table 3; Figure 3b). This may be related to lack of training of employees on large farms.  Owners of small and medium farms may be more directly involved in their dairies, thus providing a higher quality of management than personnel on large farms. Thus, in order to reduce SCC to acceptable levels (Department of Livestock Development 2003; Thai Agriculture Commodity and Food Standard 2005), large farms would need to increase the level of training of dairy personnel and to improve the management and sanitary conditions of dairy cows.

 

Individual farm effects

 

Variation among individual farms and ratios of farm variances to total variances are shown in Table 4.


Table 4. Farm variances and ratio of farm variances to total variances for monthly milk quantity, milk revenue and milk quality

Trait

Farm variances

Ratio of farm variances to total variances

Milk yield per farm, kg2

1,497,076 70,996a

0.52

Milk yield per cow, kg2

8,058 421

0.30

Milk revenue per farm, baht2

202,410,000 9,592,411

0.52

Milk revenue per cow, baht2

1,082,181 56,551

0.30

Fat, %2

0.0497 0.00250

0.25

Protein, %2

0.0128 0.00063

0.25

Lactose, %2

0.0109 0.00054

0.25

Solids not fat,%2

0.0281 0.00134

0.29

Total solids, %2

0.0976 0.00484

0.26

Somatic cell count, 106 cells2/ml2

120,054 6,048

0.26

a Standard error


These ratios indicated that variation associated with differences among individual farms explained from 25% to 52% of the total variation for these traits.  Variability among farms could be due to differences in educational background, experience, training, social networks and economic resources of farmers as indicated by Rojanasthien et al (2006) and Rhone et al (2008b).  Thus, to improve monthly milk yield and revenues per farm, increasing the level of training, dairying ability and commercial opportunities for farmers should be considered together with improvements in feeding, management, health care and genetics of dairy cattle.

 

Conclusions 


Acknowledgements
 

The authors would like to thank the Commission on Higher Education, Thailand, for the grant funded under the Strategic Program Scholarships for Frontier Research Networks for the Ph.D. Program at Kasetsart University.  Authors express their appreciation to KURDI for financial support of research project, and to the Midland Dairy Limited Partnership for providing the dataset used in this study.

 

References 

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Received 23 November 2009; Accepted 27 December 2009; Published 7 February 2010

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