Livestock Research for Rural Development 24 (4) 2012 Guide for preparation of papers LRRD Newsletter

Citation of this paper

A comparative study on dairy production and revenue of the dairy farms supported by a dairy cooperative with those supported by a private organization in Central Thailand

S Koonawootrittriron, M A Elzo*, S Yeamkong 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

The objective of this study was to compare monthly milk yield and milk revenue of dairy farms supported by a dairy cooperative (DC) with those supported by a private organization (PO) in Central Thailand. The dataset contained monthly milk yield per farm (MYF), monthly milk yield per cow (MYC), monthly milk revenue per farm (MRF), and monthly milk revenue per cow (MRC) records from 642 DC farms and 449 PO farms collected between 2006 and 2009. The dataset was analyzed using a mixed model with the fixed effects of year-season-organization subclass and farm location-farm size-organization subclass, and the random effects of farm and residual. Least squares means were estimated for each trait and were compared using a Bonferroni t-test.

 

Monthly milk production and revenue varied across year-season-organization subclasses and farm location-farm size-organization subclasses. Across year-season subclasses, farms supported by DC had lower milk productions and revenues than farms supported by PO (P < 0.01). Farms supported by DC and PO had similar milk production and revenue across farm location-farm size subclasses, except for small size farms in a district (Muak Lek). The farm to total variance ratio ranged from 0.47 (MRC) to 0.59 (MYF) indicating that differences among farms were an important source of variation for all traits. This result suggests that dairy organizations need to take into account individual farm characteristics and support them accordingly. To help improve milk production and revenue of dairy farms, sharing experiences and information within and between dairy organizations should be encouraged.

Key words: dairy farming, milk production, organization, revenue, tropic


Introduction

The highly competitive environment of the dairy industry in Thailand and other Asian countries is forcing dairy organizations and individual farmers to enhance their productive and economic efficiencies as well as their qualitative standards to survive in this business. To remain competitive, dairy organizations and farmers have to strive to reduce costs and improve the quality of their milk. Milk collection centers play an important role in countries where most dairy farms are small. Farmers deliver raw milk to the milk collection center, and later the accumulated raw milk from farms is transported to the dairy factory (Belavadi and Niyogi 1999; Devendra 2001). Milk collection centers constitute an efficient strategy to decrease expenditures, reduce transportation time from farms to milk processing factories, and facilitate delivery of milk from dairy farms that are far away from milk processing factories (Tantajinna 2001).

 

Dairy organizations that operate milk collection centers could be classified into 2 groups: dairy cooperatives and private organizations. Both types of organization collect and purchase raw milk from dairy farms, transport and sell the collected milk to dairy factories, and provide services to their farmers. Dairy cooperatives are formed by groups of dairy farmers and have a legal status. They have a set of bylaws that are approved by their members. Normally, decisions are made by committees that meet at regular times and this may cause delays when making decisions. These dairy cooperatives receive assistance and support from the government such as loans with government department interest rates (Rhone et al 2008b). A successful prototype of a dairy cooperative is Anan pattern, which has been promoting dairying (Operation Flood program) since 1969 and has increased dairy production in India to the level of self-sufficiency (Belavadi and Niyogi 1999; Devendra 2001; Rajendran and Mohanty 2004). The business potential of collecting milk from farmers and then selling the accumulated milk to processing factories has attracted private investors and resulted in the creation of private dairy organizations similar to cooperatives. These private dairy organizations are owned entirely by private individuals. Their administrative structure tends to be hierarchical, thus decisions are made by a few individuals, in many cases by a single individual (the owner of the private organization). This could make the decision process substantially faster than in dairy cooperatives that depend on committee decisions, or cooperatives sponsored by government institutions. Thus, these two types of cooperatives could have substantially different management structure and strategies to support their members.

 

The success of a dairy cooperative rests on a successful strategy for their members to produce milk of high quality as efficiently and as cheaply as possible to obtain appropriate revenues for the cooperative and their members. To achieve this goal, knowledge of factors that affect milk yield and revenue of dairy farms in each type of cooperative is necessary. Independent studies on factors affecting monthly milk yield and revenue of farms supported by a dairy cooperative (Seangjun and Koonawootrittriron 2007; Rhone et al 2008a, b, c, d) and by a private organization (Yeamkong et al. 2010a, 2010b) in Thailand have been conducted. Comparison of the performance of dairy cooperatives and private organizations has been done in Europe (Soboh et al 2011), India (Rajendran and Mohanty 2004), and the United States (Parliament et al 1990). However, a study comparing milk yield and revenues of farms in these two types of organizations has not yet to be conducted. Thus, the objective of this study was to compare milk yield and milk revenue of dairy farms supported by a private organization with those supported by a dairy cooperative in Central Thailand.


Materials and methods

Dataset, variables, and traits

 

The dataset contained a total of 70,143 records of monthly milk yields and revenues collected in 1,091 dairy farms from January 2006 to December 2009. Seventy two percent of the records in the dataset (50,168 records) were from 642 farms (59% of all farms) that were members of a dairy cooperative (DC; Muak Lek Dairy Cooperative, Saraburi, Thailand), and the remaining 28% (19,975 records) came from 449 farms (41% of all farms) that were members of a private organization (PO; Midland Dairy Limited Partnership, Saraburi, Thailand).

 

Farms were located in the districts of Muak Lek (470 farms supported by DC, and 343 farms supported by PO), Wang Muang (11 farms supported by DC, and 68 farms supported by PO), and Pak Chong (161 farms supported by DC, and 38 farms supported by PO). These districts are located in Central Thailand. Muak Lek (ML), the largest district, is located in the east of the Saraburi province. This district is a plateau with a hillside complex. Dairy farm areas in this location generally have high and low stream flows through several lines as well as capillary water. Wang Muang (WM) is a district in the Saraburi province located north of ML. Dairies in this district are upland, have less water available than those in Muak Lek, and they have an artificial irrigation system with water from the Pasak Jolasit Dam. Pak Chong (PC) is located in the Nakhon Ratchasima province and it is the gateway to the Northeast of Thailand from the central region. Areas for dairy farming in this district are flat, locate at the foot of the hills, and generally have fertile soils. Geographically, ML located between WM (in the North-West) and PC (in the South-East).

 

Seasons in these districts can be classified as winter (November to February), summer (March to June), and rainy (July to October), as described by the Thai Meteorological Department (2007). Farms were classified according to their size in terms of their average number of milking cows per day into 3 categories: 1) small size farms (less than 10 milking cows per day), 2) medium size farms (from 10 to 19 milking cows per day), and 3) large size farms (more than 19 milking cows).

 

Traits in this study were monthly milk yield per farm (MYF), monthly milk yield per cow (MYC; computed as MYF divided by the average number of milking cows in a particular month), monthly milk revenue per farm (MRF) and monthly milk revenue per cow (MRC; computed as MRF divided by the average number of milking cows in a particular month). Descriptive statistics for MYF, MYC, MRF, and MRC of the dairy farms supported by PO and those supported by DC are presented in Table 1.

 

Table 1: Descriptive statistics for milk production and revenue by a private organization and a dairy cooperative

Organization

Trait1

Number of Observations

Mean

Standard Deviation

Min

Max

Private organization

MYF, kg

19,883

3,773

2,795

203

19,731

MYC, kg

19,809

320

88

101

599

MRF, baht

19,848

50,773

38,902

1,011

313,444

MRC, baht

19,702

4,276

1,247

1,018

8,399

Dairy cooperative

MYF, kg

36,572

3,959

3,351

201

35,940

MYC, kg

25,225

292

111

101

599

MRF, baht

36,625

53,574

45,494

1,010

379,176

MRC, baht

25,939

4,008

1,669

1,002

8,394

Overall

MYF, kg

56,455

3,893

3,167

201

35,940

MYC, kg

45,034

305

102

101

599

MRF, baht

56,473

52,589

43,312

1,010

379,176

MRC, baht

45,641

4,123

1,507

1,002

8,399

1MYF = milk yield per farm; MYC = milk yield per cow; MRF = milk revenues per farm; MRC = milk revenue per cow

 

Farm and management

 

Most dairy farms in this study were small size [255 farms supported by DC, and 327 farms supported by PO], followed by medium size farms [253 farms supported by DC, and 293 farms supported by of PO], and large size farms [154 farms supported by DC, and 115 farms supported by PO]. Nearly all dairy cattle raised by farms from the two organizations were crossbred Holstein (H) with H fractions ranging from 25% H to 99% H. The other breeds of dairy cattle present in this population were Brown Swiss, Jersey, Red Danish, Red Sindhi, Sahiwal, Brahman and Thai native. Individual animals could have fractions from 2 to more than 7 different breeds. Farms primarily used semen from purebred H rather than crossbred H sires and sires from other breed (e.g., Jersey, Brown Swiss) to artificially inseminate their cows. Sixty seven percent of all farms used artificial insemination services provided by their organization (DC or PO) rather than from others organizations (e.g., the department of livestock development, the dairy farming promotion organization, or commercial services). Dairy farmers made sire selection decisions based on their own experience and suggestions from staff provided by their organizations. Both organizations provided veterinary services to their members. Farms in both organizations vaccinated their cows against Foot and Mouth Disease, Hemorrhagic Septicemia and parasites twice a year. Antibiotics were typically given to treat infections such as mastitis.

 

Farms supported by both organizations (DC and PO) had nutritional management programs that varied depending on seasons and their own resources. Grasses present in farms from both organizations were Brachiaria mutica (para grass), Brachiaria ruziziensis (ruzi grass), Pennisetum purpureum (napier grass), and Panicum maximum (guinea grass). Legumes were Stylosanthes hamata cv. Verano (Verano stylo), Stylosanthes guianensis (Thapra stylo) or Leucaena leucocephala (leucaena). Most farms used both cut-and-carry and pasture grazing (57% of all farms), 39% of farms used only cut-and-carry, and the remaining 4% of farms used pasture grazing only. Approximately, 30 to 40 kg of grass, which was about 10% of cattle’s body weight, was fed to the dairy cows daily. Forages were more abundant in the rainy season than in the dry seasons (summer and winter). Forages were insufficient in the summer and winter seasons due to lack of irrigation in the areas of the study. Thus, during these dry seasons, cattle were additionally fed with purchased rice straw, silage, and hay. Less than 10% of all farms in both organizations made and used their own silage. Some small farmers fed their cattle with native grasses by cut and carry from public areas. Concentrates used by farms of both organizations were either made by farmers themselves or purchased from their dairy organization (DC or PO) of local private companies. The ingredients of concentrates included cassava, rice bran, palm meal, cotton meal, soybean meal, and brewers’ dries grains. The amount of concentrate fed to individual cows depended on the amount of milk produced, cost, and farmer’s knowledge and personal opinions. The usual rule was to give 1 kg of concentrate per 2 kg of milk.

 

Farms supported by both organizations usually milked their cows by machine (bucket type) twice a day (morning and afternoon). Milk was collected in 30-litter buckets at each milking time and then transported to the milk collection center of the organization (DC or PO). The amount of milk of each farm at each milking time was recorded at the milk collection center. The price paid for raw milk to each farm was determined based on milk composition (i.e., fat percentage and total solids) and bacterial contamination (i.e., methylene blue test) according to a scale used by the organization (DC or PO). Both organizations computed milk prices for individual farms every 10 days (3 times a month), and recorded number of milking cows per farm monthly.

 

The DC was established by a group of farmers in 1972, and it presently has a membership of 683 farms, and produces approximately 91 tons of milk per day from 7,260 milking cows. In contrast, the PO was established by a business family in 1992, and it currently has 690 member dairy farms that produce 94 tons of milk per day from 7,835 milking cows. Both organizations have member dairy farms distributed across the same locations in Central Thailand (i.e., Muak Lek, Wang Muang, and Pak Chong). However, they differ in terms of administrative personnel (farmer representatives vs. hired individuals), decision making processes (committees vs. primarily the owner), and funding (revenues from the cooperative vs. primarily owner’s resources).

 

Statistical Analysis

 

Traits were analyzed using univariate mixed models. Fixed effects were year-season-organization subclasses and farm location-farm size-organization subclasses. Random effects were farm and residual. Random farm effects were assumed to have mean zero, a common variance (σf2), and be uncorrelated. Similarly, random residual effects were assumed to have mean zero, a common variance (σe2), and uncorrelated. Variances for random effects were estimated using restricted maximum likelihood (REML). Least squares means (LSM) were estimated for fixed subclass effects for each trait, and then pairwise comparisons were tested for significance using a Bonferroni t-test. The significance level for pairwise comparisons was α = 0.05. Analyses were performed using the MIXED procedure of the Statistical Analysis System (SAS 2003).


Results and discussion

Year-season-organization subclasses

 

Year-season-organization subclasses were important (P < 0.0001) for monthly milk yield (MYF and MYC) and monthly milk revenue (MRF and MRC). The LSM for MYF ranged from 3,830 206 kg (2008-Rainny) to 4,765 209 kg (2006-Winter) for farms supported by DC, and from 4,359 146 kg (2008-Rainny) to 5,033 146 kg (2007-Summer) for farms supported by PO. The MYF tended to decrease for farms supported by DC (-14 15 kg/year-season; P = 0.36) and PO (-8 24 kg/year-season; P = 0.74). Furthermore, farms supported by DC had lower MYF than those farms supported by PO across year-season subclasses (333 181 kg, P < 0.01; Figure 1a).

 

The LSM for MYC ranged from 262 8 kg (2008-Rainny) to 318 8 kg (2009-Summer) for farms supported by DC, and from 312 6 kg (2008-Rainny) to 345 6 kg (2006-Summer) for farms supported by PO. The MYC in farms supported by DC showed a positive trend (1.4 1.2 kg/year-season; P = 0.28), whereas a negative trends was estimated for farms supported by PO (-1.2 0.7 kg/year-season; P = 0.12). However, farms supported by DC had lower MYC than farms supported by PO across year-season subclasses (-37 13 kg, P < 0.01; Figure 1b).

 

 

 

Figure 1a: Trends for year-season-organization least squares means for monthly milk yield per farm from 2006 to 2009

Figure 1b: Trends for year-season-organization least squares means for monthly milk yield per cow from 2006 to 2009

 

The LSM for MRF ranged from 49,349 2,798 baht (2006-Rainny) to 73,405 2,791 baht (2009-Winter) for farms supported by DC, and from 56,490 1,989 baht (2006-Rainny) to 77,592 2,007 baht (2009-Winter) for farms supported by PO. The MRF tended to increase significantly for both farms supported by DC (1,721 315 baht/year-season; P = 0.0002) and PO (1,380 262 baht/year-season; P = 0.0003) during the period of the study period (2006-Winter to 2010-Winter). Farms supported by DC had lower MRF than farms supported by PO across year-season subclasses, except for 2009-Summer (5,826 2,579 baht, P < 0.01; Figure 1c).

 

 

 

Figure 1c: Trends for year-season-organization least squares means for monthly revenue per farm from 2006 to 2009

Figure 1d: Trends for year-season-organization least squares means for monthly revenue per cow from 2006 to 2009

 

The LSM for MRC ranged from 3,336 112 baht (2006-Rainny) to 4,787 112 baht (2009-Winter) for farms supported by DC, and from 3,752 82 baht (2006-Rainny) to 5,197 84 baht (2009-Winter) for farms supported by PO. Similar to MRF, the MRC tended to increase significantly across year-season subclasses for both farms supported by DC (112 17 baht/year-season; P < 0.0001) and PO (110 19 baht/year-season; P = 0.0001). Also, farms supported by DC had lower MRC than those supported by PO, except for 2009-Summer (406 248 baht, P < 0.01; Figure 1d).

 

Monthly milk yield (MYF and MYC) and revenue (MRF and MRC) of both farms supported by DC and PO varied across year and season subclasses. Monthly milk production (MYF and MYC) and revenue (MRF and MRC) in both farms supported by DC and those farms supported by PO dropped every year during the rainy season (Figure 1). This decrease in milk production and revenue could be due to environmental stress and stage of lactation.

 

The rainy season in Thailand normally has higher temperature and humidity (24C to 33C and 79% RH) than winter (21C to 32C and 70% RH) and summer (25C to 36C and 69%RH; Thai Meteorological Department 2009). Higher temperatures and humidity would increase the level of stress of dairy cows, causing lowering their milk production ability and efficiency (West et al 2003; Rhone et al 2008c). Furthermore, with large amounts of water on the ground and limited land capacity, cows in many farms (especially small ones) have insufficient space to lie down to relax or sleep, increasing their stress level.

 

Although dairy farmers in Thailand normally mate their cows all year round (Sarakul et al 2009), the highest proportion of calvings (54%) occur in winter (November to February; Rhone et al 2008c). During the rainy season, cows that calved in winter would be more than 120 days in lactation (4 months), which is a declining period of milk production (Koonawootrittriron et al 2001; Kaewkamchan 2003; Rhone et al 2008b).

 

To improve milk production in rainy season, farms supported by both organizations should consider improvements in cow management such as: 1) providing dry and clean housing for cows throughout the year, particularly during the rainy season; 2) creating a more comfortable environment for cows by lowering the temperature and reducing the level of humidity of barns, particularly during the rainy season; and 3) distributing matings more uniformly throughout the year to maintain a reasonably even volume of milk production per farm all year round. Advice, training, and support from the dairy organizations (DC and PO) would be essential to help farmers accomplish these tasks.

 

Farms supported by DC had lower monthly milk yield (MYF and MYC) and monthly milk revenue (MRF and MRC, except for 2009-Summer) than farms supported by PO across year-season subclasses (Figure 1). These differences in monthly milk production and revenue suggest the existence of differences in efficiency of dairy production between farms supported by DC and those farms supported by PO, perhaps due to differences in knowledge and ability of farmers themselves (Rojanasthien et al 2006; Rhone et al 2008a; Yeamkong et al 2010a) and differences in member support strategies by the two organizations (Rhone et al 2008b; Rhone et al 2008c). However, differences between farms supported by DC and PO for monthly milk yield and revenue varied across year-season subclasses. Differences were small at the beginning, large in the middle, and then small again at the end of the study period (2006-Winter to 2010-Winter). The trend of differences between farms supported by DC and PO suggests that their differences were likely due to temporary factors, thus farms supported by these two organizations could potentially have similar monthly milk production and revenue. Sharing of experiences among farmers within and across organizations as well as support strategies across organizations may help improve monthly farm milk production and revenue.

 

Monthly milk revenue (MRF and MRC) tended to significantly increase (P < 0.001) across year-season subclasses, while monthly milk yield (MYF and MYC) did not, in both farms supported by DC and farms supported by PO. The standard price given by the government for raw milk (baht/kg) was changed and increased periodically during the period of the study. Raw milk price was 12.50 baht/kg in 2006, 12.50, 13.75, and 14.50 baht/kg in 2007, 14.50 and 18.00 baht/kg in 2008, and 18.00 and 16.50 baht/kg in 2009 (Office of Agricultural Economics 2009). Thus, price for raw milk was one of the factors that affected monthly milk revenue. In addition, the price of raw milk paid to farmers by their organization was also related to milk quality (fat percentage, total solid percentage, bacterial contamination, and somatic cells; Seangjun and Koonawootrittriron 2007; Rhone et al 2008a, b). Farmers received higher milk revenues due to price additions to the government price when the quality of their raw milk was higher than the standard values, and lower milk revenues due to price deductions when raw milk quality was lower than the standard values of their organization (DC or PO). The Thai dairy pricing system provides a strong incentive for farmers to improve not only amount of milk produced per farm, but also milk quality as a means to increase milk revenues. Farm support organizations (DC and PO) will continue to play a decisive role to help farmers achieve higher milk production and milk quality levels through high quality services. These services include dairy production and health care monitoring, training sessions, reproduction services (e.g., artificial insemination), advising and providing updated information (e.g., nutritional, management, genetics) and dairy improvement loans as needed and feasible (Srinoy et al 1999; Wanapat et al 2000; Rhone et al 2008c; Sarakul et al 2009).

 

Farm location-farm size-organization subclasses

 

Monthly milk yield (MYF and MYC) and monthly revenue (MRF and MRC) varied by farms location-farm size-organization (P < 0.0001). Small size farms supported by DC and PO had similar MYF in WM (2,979 958 kg vs. 2,662 240 kg) and PC (2,282 234 kg vs. 2,202 343 kg), except for ML (2,121 145 kg vs. 3,020 109 kg; P = 0.0001). Similar MYF were found among medium size farms supported by DC and PO in ML (3,892 143 kg vs. 4,124 110 kg), WM (3,265 958 kg vs. 4,141 245 kg), and PC (3,935 246 kg vs. 5,159 363 kg). Large size farms supported by DC had MYF higher than, similar to, and lower than those supported by PO in ML (7,981 179 kg vs. 5,413 133 kg; P < 0.0001), WM (5,976 1,106 kg vs. 6,118 281 kg) and PC (7,292 334 kg vs. 9,879 549 kg; P = 0.0086), respectively (Figure 2a).

 

 

 

Figure 2a: Least squares means for monthly milk yield per farm by level of farm size-farm location-organization subclasses [ML = Muak Lek; WM = Wang Muang; PC = Pak Chong]

Figure 2b: Least squares means for monthly milk yield per cow by level of farm size-farm location-organization subclasses [ML = Muak Lek; WM = Wang Muang; PC = Pak Chong]

 

Small size farms supported by DC had similar MYC with those farms supported by PO in ML (313 6 kg vs. 324 4 kg), WM (347 36 kg vs. 335 9 kg), and PC (326 9 kg vs. 332 13 kg). Medium size farms supported by DC had lower MYC than farms supported by PO in ML (284 5 kg vs. 317 4 kg; P = 0.0003) and PC (297 9 kg vs. 362 15 kg; P = 0.0238), but they were similar in WM (221 36 kg vs. 312 9 kg). As with small size farms, large size farms supported by DC had similar MYC to farms supported by PO in ML (289 7 kg vs. 310 6 kg), WM (268 42 kg vs. 305 11 kg) and PC (280 13 vs. 366 24 kg; Figure 2b).

 

Similar to MYF, small size farms supported by DC and PO had similar MRF in WM (39,577 12,982 baht vs. 36,321 3,264 baht) and PC (30,765 3,176 baht vs. 27,588 4,667 baht), except for ML (28,245 1,967 baht vs. 41,657 1,480 baht; P < 0.0001). Medium size farms supported by DC and PO had similar MRF in ML (53,764 1,937 baht vs. 55,298 1,494 baht), WM (44,867 12,988 baht vs. 55,701 3,325 baht), and PC (54,875 3,328 baht vs. 69,758 4,945 baht). Large size farms supported by DC had MRF higher than, similar to, and lower than those supported by PO in ML (109,331 2,424 baht vs. 73,755 1,836 baht; P < 0.0001), WM (84,230 14,990 baht vs. 85,762 3,838 baht) and PC (101,837 4,522 baht vs. 154,090 7,543 baht; P < 0.0001), respectively (Figure 2c).

 

Monthly milk yields per cow of small size farms supported by DC was similar to MYC from small size farms supported by PO in ML (4,314 78 baht vs. 4,390 60 baht), WM (5,086 516 baht vs. 4,527 132 baht), and PC (4,484 127 baht vs. 4,469 191 baht). Medium size farms supported by DC had lower MRC than farms supported by PO in ML (3,890 77 baht vs. 4,259 61 baht; P = 0.0264) and PC (4,092 132 baht vs. 4,989 208 baht; P = 0420), but they were similar in WM (2,912 516 baht vs. 4,160 136 baht). Large size farms supported by DC had similar MRC to those farms supported by PO in ML (3,935 96 baht vs. 4,142 80 baht), WM (3,699 595 baht vs. 4,136 165 baht) and PC (3,824 180 baht vs. 4,818 355 baht; Figure 2d).

 

 

Figure 2c: Least squares means for monthly milk revenue per farm by level of farm size-farm location-organization subclasses [ML = Muak Lek; WM = Wang Muang; PC = Pak Chong]

Figure 2d: Least squares means for monthly milk revenue per cow by level of farm size-farm location-organization subclasses [ML = Muak Lek; WM = Wang Muang; PC = Pak Chong]

 

Similar patterns of LSM among farms supported by DC and farms supported by PO across farm location-farm size subclasses were found for MYF and MRF, and MYC and MRC. Small differences (2 to 3 cows) in the average number of cows in the small (6 2 cows vs. 8 4 cows), medium (13 3 cows vs. 16 6 cows), and large (28 9 cows vs. 25 8 cows) farm size categories existed between DC and PO supported farms. The MYF and MRF within farm sizes across locations were similar for DC and PO supported farms. Significant differences existed only for small farms in ML and small and large farms in PC. Similarly, the MYC and MRC within farm size were overwhelmingly similar in farms supported by DC or PO; only small farms in WM differed significantly for these two traits.

 

The few significant differences that existed among farms supported by DC and PO may have been due to a variety of factors, although the primary ones may have been differences in the number of cows within farm size categories between DC and PO farms as well as differences in cow performance in farms from these two organizations. Cow performance may have been affected by quality of nutrition, management, and health care, farmer’s knowledge and ability (Skunmun et al 1998; Srinoy et al 1999; Rojanasthien et al 2006; Rhone et al 2008a; Yeamkong et al 2010b), and differences in the level and strategy of farmer (Morton and Miheso 2000; Rhone et al 2008a) support by the DC and PO organizations. The similarity among monthly milk production and revenue in farms supported by DC and PO in the vast majority of farm location-farm size subclasses suggested that membership in either DC or PO would make little difference for monthly milk production and revenue across location-farm size subclasses. This similarity among DC and PO supported farms provides additional support for the idea that sharing support strategies and outcomes among dairy organizations as well as sharing experiences among farmers may facilitate the identification of effective nutritional, management, health, genetic, and economic strategies to increase production and revenues in a sustainable manner.

 

Individual farm effects

 

The farm to total variance ratios were 0.59 for MYF, 0.48 for MYC, 0.58 for MRF, and 0.47 for MRC. These variance ratios indicated that variability among farms accounted for 47% to 59% of the total variance for these four traits. These farm variance ratios were estimated using the complete dataset (i.e., data from farms supported by DC and PO). Farm variance ratios here were higher than values estimated with data from farms supported by PO only (0.30 to 0.52; Yeamkong et al 2010b). This suggests that the variation among farms in the combined dataset (DC and PO) was larger than the variation among farms within the PO organization. The larger variation among farms could be due to additional differences among farms and farmers from the two organizations (DC and PO) perhaps related to experience, education, record keeping, decision making, and support from their respective organizations (Rojanasthien et al 2006; Rhone et al 2008a, Yeamkong et al 2010b).

 

The large farm variance ratios obtained here suggest that it is important to consider the individual characteristics of each farm when trying to improve milk production and revenue. Each farm would require different services and support from the dairy organization because of their particular problems and characteristics (Rhone et al 2008a; Yeamkong et al 2010b). Furthermore, individual farmers would likely have different levels of knowledge, learning ability, and levels of receptiveness to advice and suggestions (Suksawat 2004; Rhone et al 2008a; Yeamkong et al 2010a). Thus, farms would need to be classified into groups according to particular areas of support needed within an organization. Information recorded within individual farms would help in this regard. To successfully increase level of dairying ability and commercial opportunities for farmers, multiple levels of training and services should be implemented by dairy organizations and be provided to individual farmers or to group of farmers as needed. In addition, dairy farm performance and profitability should be periodically monitored (Moran 2009), a critical aspect to ensure the sustainability of dairy enterprises. Furthermore, farmers should strive to acquire appropriate decision making skills, share their knowledge and expertise with the dairy community, and become actively involved in dairy social networks.

 

Thai dairy farmers involved in this study had higher milk yields per cow (8.5 to 11.1 kg/cow/day), had higher number of milking cows per farm (14 cows/farm), and got higher price (12.5 to 18.0 baht/kg) than dairy farmers in India, Pakistan and other countries in Southeast Asia (FAOSTAT 2009; Moran 2009). However, Thai dairy farmers did not produce enough milk to satisfy the demand of consumers in the country (FAOSTAT 2009) as it happened in India and Pakistan. To achieve self-sufficiency dairy farmers in Thailand would need to increase the number of dairy cows, milk yield per cow, or both. Increasing the level of training and support (technical and economic) to farmers from DC and PO may help decrease the time to achieve this goal. In addition, competition among DC and PO would encourage better quality of service and support for their farmers (and perhaps attract new ones), and likely increase dairy production per farm and per cow. Lastly, sharing of experiences and support strategies among organizations would help identify and solve common problems more easily and efficiently. This will speed up improvements in dairy production efficiency, reduce support costs, and improve revenues in Central Thailand and serve as a model for similar strategies at a national level.


Conclusion


Acknowledgements

This research was partially funded by the Program Strategic Scholarships for Frontier Research Network, the Commission on Higher Education, Thailand. Authors express their appreciation to KURDI for its support and to the Midland Dairy Limited Partnership, Ltd., and the Muak Lek, Dairy Cooperative for providing dairy data.


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Received 21 December 2011; Accepted 4 March 2012; Published 2 April 2012

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