Livestock Research for Rural Development 15 (9) 2003

Citation of this paper

Evolution of Milk Production Systems in Tropical Latin America
 and its interrelationship with Markets:
An Analysis of the Colombian Case

Federico Holmann, Libardo Rivas, Juan Carulla, Bernardo Rivera,
Luis A Giraldo, Silvio Guzman, Manuel Martinez,
Anderson Medina and Andrew Farrow

International Center for Tropical Agriculture (CIAT) and
International Livestock Research Institute (ILRI)
F.Holmann@cgiar.org


Abstract

The objectives were to: (1) identify and quantify the effect of technological change on productivity, profitability, and competitiveness in different milk production systems and regions of the country; (2) analyze the relationship between productivity, technological change, profitability, and competitiveness; (3) analyze the evolution of milk production systems in Colombia; and (4) discuss the market concentration and its impact on the formation of milk price. Data came from a survey to 545 farms during the year 2000 in five regions: Caribbean and Piedmont in the lowlands, Coffee Growing, Antioquia, and the Cundiboyacense altiplanicie in the highlands. The survey was designed to quantify inputs and products in order to determine costs and prices at the farm level in order to calculate (a) variable costs for feed supplementation, labor, health, reproduction, fertilization, and irrigation; (b) gross income from the sale of milk and beef, and (c) to characterize farms according to productivity level and management practices. The statistical analysis of multiple correspondence and general linear models were used to explain the variability observed between productivity and profitability as a function of technological change.

Independent of the production system or the region where farms were located, the increase in competitiveness was in direct relationship with herd size. Thus, as herd size increased, production costs per unit of milk and beef decreased, net incomes per cow increased, and the return to capital investment improved. However, when this increase in competitiveness was associated with increases in productivity, this trend was not observed, which suggested that highly productive farms were not necessarily competitive. The dual-purpose system was the most profitable one in the Piedmont, Caribbean, and Coffee growing regions while in Antioquia and in the Cundiboyacense altiplanicie the most profitable was the specialized dairy system.

With regards to technological change, the adoption of improved pastures and the investment in pasture divisions for a more efficient rotation generated higher productivity and income in all regions and production systems, as well as increased competitiveness through a reduction in production costs per unit of milk and beef. The use of strategic feed supplementation to the basal diet of forage had mixed effects. The best economic response to this supplementation in lowland regions (i.e., Piedmont and Caribbean) was with low quantities (i.e., < 0.5 kg DM/cow/day) of feed supplements while in highland regions (i.e., Coffee Growing area, Antioquia and the Cundiboyacense altiplanicie) was with moderate quantities (i.e., between 0.5 and 2 kg DM/cow/day). The use of fertilization and irrigation increased productivity, but reduced net income and increased production costs, except in the Cundiboyacense altiplanicie. The practice of milking twice a day increased both productivity and profitability and reduced production costs, except in the Caribbeanregion. Farms that de-wormed milking cows with low frequency against internal and external parasites obtained higher incomes and lower production costs in comparison with farms that de-wormed cows with higher frequency although there were no differences in productivity. The amount of years of experience of farmers at producing milk was a key factor to increase profits, although not productivity. Farms located in sites where the commercial value of land was high (>US$6,000/ha) and near market centers had higher productivity that those with commercial value of land medium ($3,000 to $6,000/ha) and low (<$3,000/ha) but were less profitable in all regions.

Box 1: Recommendations to researchers and extensionists

Identify profitable technologies. Technologies that increase productivity are not necessarily profitable, which suggest the need to determine appropriate ways to evaluate them economically. This was the case of fertilization and irrigation. It is necessary to determine the best economic response to various levels of N2 and H2O to different species of improved grasses under various soil types and conditions.

The most competitive and profitable breed group in the dual-purpose system was the crossbred with low (24% European-76% Zebu genes) and medium levels of dairy genes (55% European-45% Zebu genes) but had lower productivity than the purebred group (98% European genes). In the specialized dairy system, the purebred group was slightly more profitable, productive and competitive than the crossbred group with medium level of dairy genes, but this difference was not significant.

The Colombian dairy sector has become more productive and competitive, but less profitable. Comparing the evolution of dairy farms with studies 12 years ago, milk production per hectare increased by 44% in dual-purpose herds and 14% in specialized dairies. This increase in productivity reduced the milk production cost by 16% and 10% in dual-purpose and specialized dairies, respectively, due to an increase in stocking rate by 15% and 17% in dual-purpose and specialized dairies as well as to an increase in investment in infrastructure and equipment by 258% and 37% in dual-purpose and specialized dairies, respectively. However, the net income per hectare during this period decreased by 27% and 69% in dual-purpose and specialized dairies due to a reduction in the producer's price of milk and beef of 22% and 20% in dual-purpose systems, and of 41% and 27% in specialized dairies.

Nevertheless, this reduction in price to producers was never translated in lower prices to consumers, but remained in the hands of supermarkets and milk processing plants which expanded and modernized with long-life technology. Development agencies must internalize the fact that policies oriented to markets will be increasingly "oriented to supermarkets". If one adds that in Colombia exists 3 or 4 supermarket chains that control the food retail market, the conclusion is that sectoral policies will need to learn how to deal with a handful of giant companies. This in a huge challenge, and demands an urgent review of ideas and strategies.

Box 2: Recommendations to decision makers

Regionalize research. Due to the fact that the most profitable production systems are region-specific, Colombia should have different strategies for research and technology transfer in order to exploit more efficiently the comparative advantages of each region and production system.
 
Promote collective action. It is necessary to promote cooperatives and associations to help small producers to adapt to new patterns with higher levels of competition. Otherwise, the new rules of the game could induce a massive exodus of producers in the short term and in a relatively brief period of time.

It is possible, in the short run, to adopt technologies that increase milk productivity and reduce production costs while profits are reduced as a result of falling real prices as occurred in Colombia during the 90's. However, in the long run, this situation is simply unsustainable.

The proposals and challenges presented in this case study have illustrated the problems and opportunities of the dairy sector in Colombia. However, these systems could represent similar situations in other countries of Latin America. Given the phenomenon of globalization and higher degree of efficiency that these systems are being exposed to, the issues of productivity, technological change, competitiveness, and markets, are critical and of enormous relevance for the performance and survival of the livestock sector in the next decades.

Box 3: Recommendations to producers

Without research there is no future. Efficiency goes hand in hand with technology and this depends on research and technology transfer. However, public funds allocated to agricultural research are being reduced. The challenge consists that producers in Colombia take greater control of livestock research by building alliances with local, regional and international organizations leaders in forage and livestock research. For this it is necessary that producers define and fund their own research agenda.

Production-to-consumption participation
. In the coming years, producers cannot limit themselves to participate only in the primary phase of production, but to expand their scope of action to other phases of the market chain to have a higher participation in the formation of milk prices and to capture a greater piece of the final price.


Introduction

Cattle production has traditionally been one of the main activities of Latin America's agricultural sector. The region's extensive savannas and profuse forests make it most suitable for livestock production. Latin America and the Caribbean (LAC) currently have a total area of 602 million hectares under permanent pastures and a cattle inventory of 359 million heads, of which 40 million (11%) correspond to milk cows (FAO 2002).

The tropical belt of LAC harbors most of the forage and livestock resources - 72% of grazing land, 82% of total livestock and 88% of milk cows (FAO 2002). In 2001, livestock production in tropical LAC accounted for 13% of world livestock production and 35% of that of developing countries all together.

Despite the region's enormous provision of forage resources, livestock production in tropical LAC faces serious problems in terms of quantity, quality, and productivity of pastures, especially during prolonged dry periods. The problem is widespread, mainly because a high fraction of the available forage base is composed by native pastures that are adapted but present low productivity and by highly degraded introduced species. Multiple production systems coexist in tropical livestock production at different thermal floors, with different degrees of intensification, and located in highly diverse socioeconomic environments.

Furthermore, a great deal of internal discussion exists within LAC countries regarding the viability of these systems in an open economic environment, especially now that the entry into the Free Trade Area of the Americas (ALCA, its Spanish acronym) is being negotiated to compete openly with North America. This study attempts to analyze milk production systems, using Colombia as case study, and determine their importance, limitations, and economic and technical possibilities within the context of small livestock producers and the competitiveness of regional livestock production.

The Case of Colombia
Growth and milk production systems

The dairy activity in Colombia has been very dynamic over the past 30 years. In the 1970s, it grew at an annual rate of 4.7%, then presented an exceptional, sustained growth of 6.5% during the 1980s, and grew at an annual rate of 3.8% during the 1990s, producing approximately 5,877 million liters of fluid milk in 2001 (Balcázar 1992; FEDEGAN 2002).

Two types of milk production systems exist in Colombia: (1) specialized dairy and (2) dual-purpose. Of the estimated 25 million heads composing the national herd, the cattle population found in milk producing farms is estimated at some 6 million, of which 89% are in dual-purpose production systems that account for 55% of the country's milk production (CORPOICA 1998).

The specialized dairy system consists in milking the cow without the calf close by and the male calf is usually sold a few days after birth. Cows are usually purebred or with a high percentage of genes from European Bos taurus (i.e., Holstein) breeds and are supplemented with feed concentrates. As a result, milk productivity is high. On the other hand, the dual-purpose system consists in raising the male calf and selling it after weaning. The cow is milked with the calf close by. Furthermore, these cows have a high percentage of Bos indicus (i.e., Brahman) genes or are crossed with Bos taurus breeds. Their feeding is based on extensive pasture-based systems with low milk and beef productivity (Arias et al 1990).

Compared with specialized dairy production systems, several advantages of the dual-purpose system are: (1) reduced risk because of variations in milk and beef prices; (2) lower incidence of mastitis because of suckling of calves; (3) reduced need for capital investment; and (4) fewer requirements of technical support (Seré 1983).

Most specialized dairy systems are located in the tropical highlands, in regions with cool-to-cold climates located near urban centers. Dual-purpose systems, on the other hand, are usually located in lowland regions with high temperatures and further away from markets. Twelve years ago, Aldana (1990) reported that specialized dairy production systems were more profitable than dual-purpose milk production systems. He also reported that this profitability was directly associated with the level of technology used by the producers. That is, farms presenting higher productivity were more profitable regardless of the production system used.

Contribution of the livestock sector to the Colombian economy

Around 1994, milk and beef production accounted for 25.2% of Colombia's agricultural gross domestic product (GNP) (Lorente 1996), more than doubling the 12.2% attributable to coffee and higher than that of all semi-annual crops put together (24%). By year 2000, livestock production had increased its share to 29.9% of agricultural GNP (DANE 2002). Livestock production as generator of employment has gained importance within the agricultural sector and in the economy as a whole. In 1999, this sub-sector generated 1,400,000 permanent jobs, equivalent to 19.8% of the permanent work force in rural areas (Martínez et al 2002).

Livestock products (beef and milk) play a significant role in the economy's aggregate demand. According to national records, beef, milk, and their derivatives accounted for nearly 13% of total domestic consumption expenditures and for more than 45% of food expenditures in 1989 (Balcázar 1992). The rapid growth of the dairy sector allowed milk consumption by the population to increase from 57 liters per capita in 1970 to 136 liters in 2001, representing an increase of 138% in 30 years (Balcázar 1992; FEDEGAN 2002).

In terms of foreign trade, Colombia is practically self-sufficient in milk production. During the 1990s, the country imported an average of 2% of its production per year (FEDEGAN 2002). Although from time to time the country has been a net exporter of beef over the past 20 years, the relative importance of this activity has visibly diminished since the early 1990s. In 1991, 5% of domestic production was exported. From then on, exports have been decreasing and since 1996 the country exports less than 1% of its total beef production (FEDEGAN 2002).

Market concentration in hands of supermarkets

A trend recently observed, parallel to the concentration of the population in large urban centers, is the growing participation and concentration of supermarkets in the chain of food distribution and sale. In many countries, distribution is handled by a small number of commercial firms, which give them great negotiation power in terms of deciding which products are offered on the shelves as well as pricing and forms of operation (Castro et al 2001). Reardon and Berdegué (2002) reported that during 2000 supermarkets in Latin America billed 60% of retail food sales. In 1990, this percentage barely reached 20%. In the case of Colombia, by the year 2000, supermarkets billed 38% of retail food sales with a forecasted annual increase of 7% for the first decade of the millennium (Hernández 2000).

This structural change has modified the parameters underlying milk marketing. Nowadays, supermarkets have more negotiating power before the milk processing plants, not popular neighborhood stores as in the past. This change has directly influenced the mechanism of fixing the milk price received by producers.

Addressing the problem

Colombia has a proven capacity to increase milk production and pressing socioeconomic reasons to develop the sector. However, there is internal discussion regarding the most suitable technologies to achieve this development, and whether they alone will be sufficient to make livestock production competitive within and outside the region under a scheme of open, unsubsidized economies (Consejo Nacional Lácteo 1999).


Objectives

The objectives of this study were to: (1) identify and quantify the effect of technologies on the increase in milk production, profitability, and competitiveness in both specialized dairy production and dual-purpose systems in different regions of the country; (2) analyze the relationship between productivity, level of technology, profitability, and competitiveness; (3) analyze the evolution of milk production systems in Colombia; and (4) discuss market concentration and its impact on the formation of milk price received by producers.


Methodology

Data came from a survey of 545 farms carried out from February to November 2000. Farms were distributed in five regions as follows: (a) 145 farms in the savanna piedmont (departments of Arauca, Casanare, and Meta); (b) 116 in the Caribbean region (departments of Atlántico, Guajira, Magdalena, César, Bolivar, and Córdoba); (c) 105 in the coffee-growing region (Department of Quindío, northern Valle del Cauca, Caldas, and Risaralda), (d) 97 in Antioquia's altiplanicie, and (e) 82 farms in the altiplanicie of the departments of Cundinamarca and Boyacá. These five regions account for more than 80% of the nation's milk production (FEDEGAN 2002) and were accordingly chosen because they are Colombia's main dairy areas, together with the southern region (Department of Nariño), which was not covered due to lack of funds. The goal was to survey an average of 100 farms per region (for a total of 500 farms) to obtain the variability needed for statistical analyses.

The survey was designed to quantify inputs and products so that farm-level costs, prices and management practices could be determined. Data were then used to calculate not only the variable costs of supplementation, labor, animal health and reproduction, fertilization, and irrigation, but also the gross receipts from sale of milk and beef, as well as to characterize farms according to levels of productivity and technological change. The survey instrument can be found in Holmann et al (2003).

The variable costs of supplementation, labor, animal health and reproduction, as well as gross sales, net income, and milk and beef production costs were estimated using the methodology described by Holmann et al (1990). A total of 55 additional variables were created to consolidate and synthesize the information gathered in the surveys. These variables are described in Holmann et al (2003). In addition, all figures reported in this study were converted to US dollars at the existing average exchange rate for year 2000 (US$1 = Col$2,094).

For this study, competitiveness was defined as the capacity of the producer to remain in the dairy business and was measured by the unitary milk and/or beef production cost. Thus, the lower the production costs, the greater the competitiveness. Profitability was defined as the annual net income per cow or hectare as well as the farm annual net income divided by the total capital invested in the farm, represented by land, livestock, facilities, and equipment. Technological change was measured using the concept of productivity, which was expressed as milk and beef production per cow and per hectare.

Surveys were carried out under the coordination of the animal production faculties of the following universities: Universidad de los Llanos for the Piedmont region, the Fundación San Martín for the Caribbean region, the Universidad de Caldas for the Coffee-growing region, and the Universidad Nacional (Medellín and Bogotá campuses) for Antioquia and the Cundiboyacense altiplanicie. These academic centers contacted the main milk buyers in each of the regions. In the Coffee-growing region, Antioquia, and Cundiboyacense altiplanicie, buyers were mostly processing plants. In the Caribbean and Piedmont regions, milk buyers were intermediaries and/or owners of small-scale cheese factories and, to a lesser extent, processing plants. These milk buyers provided academic centers with a list of their routes with the names of the farms. Routes were chosen based on their accessibility and whether four-wheel drive vehicles could enter. Interviewers were mostly undergraduate students who visited farmers accompanied by those responsible for collecting milk. This ensured a level of trust that would warrant the success of the survey. The students were also trained to execute the survey and were permanently supervised by the co-authors of this study in their respective regions. Of survey respondents, 73% were farm owners.

During November 2002, two managers from the five largest supermarket chains of the country and two milk-processing plants in the city of Cali were interviewed to understand the marketing mechanisms of dairy products in supermarkets. In addition, a telephone poll was carried out in November 2002 to analyze the evolution of the installed capacity of 30 processing plants belonging to the 13 largest dairy companies in Colombia to relate their marketing strategy with the type of technology used to upgrade installed capacity during the 1990s. Information on the poll can be found in Holmann et al (2003).

Statistical analysis

Descriptive statistics were estimated by region and production system to characterize surveyed farms. The general linear model technique was used to prove several hypotheses about the effect that different technologies have on animal productivity. This technique allows the variability observed in different productivity indicators to be expressed as a function of the different technologies, regions, and production systems. No interactions occurred because of lack of data in the different combinations of technology categories. Therefore the model used only estimated the main effects of each technological change.

Furthermore, the averages of each indicator were estimated and compared for each combination of technology*region and/or technology*production system. A multivariate principal component analysis was performed to analyze milk production systems and also to group those farms with similar characteristics on the basis of component values. Ward's clustering methodology (1963) was also used. Multiple correspondence analysis was used to establish relationships between:

Productivity, defined as milk production per cow per day (Milk);

Profitability, defined as net income per cow per year (Income) and annual return to capital invested (Return); and

Technological change, defined by the following technologies or management practices:


Results

Farm characteristics
Brief description of regions

The Piedmont and Caribbean regions are located in the lowland tropics of Colombia with an annual mean temperature ranging between 27 ºC and 30 ºC, whereas the Coffee-growing region, Antioquia and the Cundiboyacense altiplanicie are located in the colder highland tropics with mean temperatures that fluctuate between 14 ºC and 22 ºC (Table 1). In the Piedmont, the Coffee-growing region and Antioquia, the annual mean precipitation is higher (2,400 to 2,700 mm) and the rainy season lasts about 8 months, whereas in the Caribbean region and the Cundiboyacense altiplanicie, annual mean precipitation is lower (1,100 to 1,200 mm), with a rainy season that last around 6 months.

Similarly, the topography of the Piedmont and Caribbean regions and to a certain extent the Cundiboyacense altiplanicie is very flat, with few undulated areas, whereas the Coffee-growing region and Antioquia are located in mountainous areas where undulated and broken topography prevails (Table 1).

Table 1.  General characteristics, production systems utilized, and existing public infrastructure where surveyed farms were located in each of the five regions during 2000.

 

Parameter

Region

Piedmont

(n=145)

Caribbean

(n=116)

Coffee

(n=105)

Antioquia

(n=97)

Cundi-Boyacense

(n=82)

Mean annual temperature (o C)

27.2

30.4

22.0

15.2

13.9

Mean annual precipitation (mm)

2,698

1,203

2,418

2,374

1,105

Length of rainy season (months)

8.0

6.5

7.7

7.8

6.2

Farm topography  (%)

 

 

 

 

 

- Flat

79.3

74.2

14.7

14.6

51.7

- Ondulated

16.6

22.4

64.2

47.9

34.6

- Steep

4.1

3.2

21.3

37.4

13.1

Farms with following services (%)

 

 

 

 

 

- Electricity

90

65

100

96

98

- Telephone

25

57

83

56

70

- Water from municipality

47

18

59

28

63

- Own water resources

68

95

80

87

100

Production system utilized

 

 

 

 

 

- Specialized dairy (#)

4

0

37

75

60

- Dual-purpose (#)

141

116

68

22

22

In addition, public infrastructure varied broadly among the regions - from fewer services in the Caribbean region, especially electricity, to better infrastructure in the Coffee-growing region and the Cundiboyacense altiplanicie. The dual-purpose production system was most used in the Piedmont (97% of farms surveyed), Caribbean (100%), and Coffee-growing regions (65%), whereas the specialized dairy system prevailed in Antioquia (77%) and Cundiboyacense altiplanicie (73%).

Land use and pasture management

The scale of operation differed broadly among production systems and regions. The dual-purpose farms, located mainly in the Piedmont, Caribbean, and Coffee-growing regions, were larger than specialized dairies located mainly in Antioquia and Cundiboyacense altiplanicie (Table 2). Around 80% of total farm area was sown to pastures in dual-purpose systems compared with more than 90% in specialized dairies. The area under forest was less than 10% of total farm area in all regions and in all production systems.

Table 2.  Land use, proportion of pasture area under improved forages, stocking rate and general pasture management by milk production system and region of the country in 2000

 

 

 

Parameter

Production system

Region

Dual purpose

(n=333)

Specialized dairy

 (n=212)

Piedmont

(n=145)

Caribbean

(n=116)

Coffee

(n=105)

Antioquia

(n=97)

Cundi-boyacense

(n=82)

Land use (ha / farm)

 

 

 

 

 

 

 

- Total area

164

47.5

94.6

300

72.0

50.7

47.6

- Area improved pastures 1

110

30.9

68.7

200

46.0

26.1

34.3

- Area native pastures 2

31.4

8.3

12.7

70.4

15.3

14.0

8.9

- Crops

5.7

1.3

2.2

9.9

5.6

0.7

0.5

- Forest

8.0

3.9

8.8

9.5

4.0

4.7

3.0

- Other

5.3

2.2

2.2

10.5

1.1

5.2

0.9

Improved forages (%)

78.0

78.8

90.8

65.5

77.9

71.3

91.0

Stocking rate (AU / ha)

1.47

2.68

1.22

1.33

2.35

2.66

2.70

Resting period  in pastures grazed by milking cows (# days)

 

 

 

- Rainy season

31.3

42.1

27.0

35.4

33.4

43.4

45.1

- Dry season

32.6

42.0

26.7

38.9

33.4

43.1

45.3

Weed control frequency (# / yr)

3.5

2.2

2.5

2.0

6.2

2.9

1.0

Pastures fertilized? (% farms)

 

 

 

 

- Yes

42.6

85.4

27.6

38.8

78.1

93.8

79.3

- Proportion  fertilized (%)

29.3

71.7

35.5

22.5

47.8

81.8

65.4

- Fertilizer  (kg N/ha/yr)

103

193

77

47

172

213

155

- Frequency applications (# / yr)

4.2

7.0

1.6

1.5

7.1

7.7

6.5

Are pastures irrigated?  (% farms)

 

 

 

 

- Yes

12.6

32.1

1.4

24.1

15.2

20.6

53.7

- Proportion irrigated (%)

58.8

51.0

27.7

40.0

32.2

36.8

52.7

Grazing paddocks (# / farm)

17.2

26.5

8.8

14.4

32.2

29.6

26.2

- paddocks for milking cows

7.3

14.6

4.2

5.1

16.8

15.4

13.2

- paddocks for dry cows

4.1

7.9

2.7

3.3

7.6

8.9

7.5

- paddocks for rest of herd

5.8

4.0

1.9

6.0

7.8

5.3

5.5

1  Brachiaria brizantha, B. decumbens, B. humidicola, B. mutica, Cynodon nlemfuensis, Panicum maximum, Hyparrhenia rufa, Echinocloa polystachya, Dichanthium aristatum, Andropogon gayanus, Penisetum clandestinum, and P. purpureum
2
 Mainly Bothriochloa pertusa found in dual-purpose systems located in lowland areas (Piedmont and Caribbean regions) and Lolium multiflorum, Poa trivialis, Dactylis glomerata, and Paspalum notatum found in specialized dairies in highland areas (Coffee, Antioquia, and Cundiboyacense altiplanicie).

Both production systems also presented a similar proportion of grassland established with improved pastures (79%), although this proportion differed among regions, being greater in the Cundiboyacense altiplanicie and Piedmont and lower in the Caribbean region. Brachiaria species were more common in dual-purpose systems, whereas Penisetum clandestinum was more common in specialized dairy systems. For more information on grass species established on farms per region, please refer to Holmann et al (2003).

The stocking rate in specialized systems and in high-altitude regions was twice that reported in dual-purpose systems located mainly in the Caribbean and Piedmont regions. This was attributed to the fact that a greater proportion of specialized dairies not only fertilized and irrigated their pastures, but also applied a higher level of nitrogen more frequently and supplemented animal diets with greater amounts of feed concentrates than dual-purpose farms (Tables 2 and 4). In addition, specialized dairies had a higher number of grazing paddocks (Table 2), in smaller areas, thus allowing better use of the amount of forage biomass produced. This rotational management in specialized dairies, accompanied with pasture fertilization, could explain the lower frequency of weed control (2.2 times/year) compared with dual-purpose farms (3.5 times/year), which were managed more extensively.

Herd structure and genetic composition

Dual-purpose farms had larger herds composed mainly by crossbreds between Bos indicus (i.e., Brahman) and Bos taurus of European breeds (i.e., Holstein), whereas specialized dairies in high-altitude regions had smaller herds of either purebreds (ie., Holstein) or livestock with a very high proportion of European genes (Table 3).

Table 3.  Herd structure, genetic make-up, reproduction system utilized, mortality, parturition and culling rates, age and weight of breeding heifers, and mature body weight by milk production system and region of the country in 2000.

 

 

Parameter

Production system

Region

Dual purpose

(n=333)

Specialized (n=212)

Piedmont

(n=145)

Caribbean

(n=116)

Coffee

(n=105)

Antioquia

(n=97)

Cundi-boyacense

(n=82)

Herd structure (#)

 

 

 

 

 

 

 

- Milking cows

48.7

36.0

20.9

86.8

33.1

33.9

48.3

- Dry cows

34.2

10.8

14.7

68.3

14.4

10.4

13.3

- Heifers > 2 years

21.9

9.1

10.2

41.5

10.4

9.2

11.4

- Heifers 1-2 years

21.2

9.7

10.7

40.4

9.1

9.4

12.3

- Female calves 0-1 year

24.1

10.0

12.3

44.5

11.4

8.2

15.0

- Male calves 0-1 year

21.2

1.2

10.8

42.2

6.1

1.1

1.0

- Steers 1-2 years

16.2

0.6

6.8

31.0

8.1

0.5

0.4

- Steers > 2 years

13.8

0.3

8.8

18.9

11.0

0.1

0.3

- Bulls

3.3

0.9

1.8

6.4

1.5

1.0

0.8

  Total heads

204

78.6

97.1

379

105

73.8

103

  Total animal units (UA)

155

66.0

72.6

286

84.5

62.7

85.6

Genetic make-up of adult herd (%) 1

 

 

 

 

 

- Cows 100% Zebu

10.3

0.4

8.6

15.9

3.9

0

0

- Cows 75% Zebu – 25% European

22.0

0.2

16.3

40.4

3.2

0

0

- Cows 50% Zebu – 50% European

37.5

6.1

45.3

33.3

23.6

6.8

2.4

- Cows 25% Zebu – 75% European

21.8

7.3

26.5

10.0

35.8

0.5

0

- Cows 100% European

8.4

86.0

3.4

0.3

33.5

92.7

97.6

Reproduction system (% farms)

 

 

 

 

 

- Only natural service

74.5

42.9

79.3

76.7

65.7

47.4

24.4

- Only AI

7.8

38.7

8.3

.9

14.3

35.1

56.1

- Both

17.7

18.4

12.4

22.4

20.0

17.5

19.5

Annual mortality (%)

 

 

 

 

 

 

 

- Adults

1.8

3.5

1.3

1.9

1.3

3.7

3.2

- Calves

7.4

8.7

6.4

7.7

7.5

12.0

6.3

Breeding heifers

 

 

 

 

 

 

 

Age (mths)

26.9

22.4

27.7

27.4

24.2

21.2

23.3

Body weight (kg)

317

337

317

303

336

316

364

Mature body weight (kg)

432

477

427

423

444

472

509

Parturition rate (%)

69.2

74.3

71.3

64.1

73.8

72.3

76.3

Culling rate (%)

15.6

13.4

20.4

14.8

14.0

12.8

13.3

1  More than 90% of cows with Zebu phenotype (Bos indicus) had Brahman genes and more than 95% of cows with European phenotype (Bos taurus) had Holstein genes 

Herd management

Compared with dual-purpose farms, specialized dairies used artificial insemination more frequently, the age of breeding heifers was younger, and body weight of both heifers and mature cows was higher. This reflects the genetic composition of the herd and the better nutrition received by the latter group. Most specialized dairies practiced twice-a-day milking without the calf close by, mainly in grazing paddocks (especially in Antioquia and the Cundiboyacense altiplanicie), whereas in dual-purpose farms cows were milked once a day, in corrals, with the calf close by.

The proportion of cows in milk was greater in specialized dairies systems and in the Coffee-growing region, Antioquia and Cundiboyacense altiplanicie than in dual-purpose farms found mostly in the Piedmont and Caribbean region because of longer lactations and higher reproductive rate in specialized dairy systems (Tables 3 and 4). The levels of supplementation with feed concentrates were also 3 times higher (Table 4) in specialized dairy systems compared with dual-purpose farms.

Table 4.  Milk production, number of daily milkings, location and milking system utilized, proportion of cows in milk, lactation length, and quantity of feed supplement offered to milking cows by production system and region of the country in 2000

 

 

Parameter

Production system

Region

Dual purpose

(n=333)

Specialized (n=212)

Piedmont

(n=145)

Caribbean

(n=116)

Coffee

(n=105)

Antioquia

(n=97)

Cundi-boyacense

(n=82)

Milk production (kg)

 

 

 

 

 

 

 

- ha/yr

1,515

7,605

888

731

4,544

8,045

7,875

- Cow/yr

2,053

4,697

1,904

1,540

3,209

5,064

4,837

- Cow/day

5.63

12.87

5.2

4.2

8.8

13.9

13.3

Number of milkings (% farms)

 

 

 

 

- Once a day

81.7

8.0

94.5

94.0

30.5

2.0

11.0

- Twice a day

18.3

92.0

5.5

6.0

69.5

98.0

89.0

Milking place (% farms)

 

 

 

 

- In grazing paddock

3.9

68.4

0.7

0

27.6

80.4

61.0

- Open-air corral

31.5

3.3

14.5

65.5

8.6

0

7.3

- Roofed shed

54.7

13.7

81.4

32.8

39.0

4.1

12.2

- Milking parlor

9.9

14.6

3.4

1.7

24.8

15.5

19.5

Milking system (% farms)

 

 

 

 

 

- By hand with calf close by

81.4

7.5

91.7

99.1

27.6

1.0

11.0

- By hand without calf

7.5

67.0

3.5

0

44.8

82.5

42.7

- Mechanical with calf close by

3.6

0.5

4.1

0.9

5.7

0

0

- Mechanical without calf 

7.5

25.0

0.7

0

21.9

16.5

46.3

Cows in milk (%)

64.4

77.5

64.2

61.3

71.1

78.4

78.1

Lactation length (days)

261

314

240

267

308

308

308

Amount of feed supplements offered to milking cows (g DM / cow / day)

 

 

Salt and minerals

133

120

142

141

100

126

117

Molasses

102

163

243

77

101

125

105

Comercial concentrate

520

2,549

125

54

1,816

3,174

2,073

Rice millings

21

16

18

32

0

0

0

Wheat millings

72

25

27

112

0

0

174

Other supplements1

112

147

35

264

163

185

115

Total

960

3,020

590

680

2,180

3,610

2,584

1 Cottonseed and/or soybean cake, cottonseed and/or oilpalm hulls, and corn.

Production parameters

Milk production in specialized systems was almost 5 times higher per hectare and 2.3 times higher per cow than that of dual-purpose systems. Similarly, milk production was higher in Antioquia, followed by the Cundiboyacense altiplanicie, the Coffee-growing region, the Piedmont, and finally the Caribbean region. Adult and calf mortality was higher on specialized dairy farms than in dual-purpose herds although this was compensated by a higher calving rate in specialized dairy herds (Table 3).

Production costs and income

 The production cost per kilogram of milk was 14% higher in specialized systems than on dual-purpose farms, including the opportunity cost for family labor, and 21% higher if this opportunity cost was not included. The milk price received in specialized dairies was slightly higher (4%, Table 5). As evidenced further on, the higher milk price obtained in specialized systems in highland areas can be attributed to their being closer to markets and urban centers (more accessible) and possibly to the bonuses given for cold milk, because 27.8% of farms with specialized dairy systems had milk tanks compared with only 7.8% of dual-purpose farms (Holmann et al 2003).

Although differences in productivity among production systems were very pronounced depending on the degree of intensification, in terms of unitary production costs, the dual-purpose systems were highly competitive compared with specialized dairy systems. The latter produced between 3 to 7 times more milk than the former, but when unitary costs were estimated for both systems, these differences were not observed. This was attributed to the fact that the lower productivity of dual-purpose systems was neutralized by a low level of operational expenses.

Production costs were higher in Antioquia and the Coffee-growing regions, followed by the Piedmont and the Cundiboyacense altiplanicie, and were lowest in the Caribbean region. The regions with higher prices received by producers were Antioquia, followed by the Caribbean and Coffee-growing regions, then the Cundiboyacense altiplanicie, and lastly the Piedmont (Table 5).

Table 5.  Milk and beef production costs, prices, and income by production system and region of the country in 2000.

 

 

Parameter

Production system

Region

Dual purpose

(n=333)

Specialized (n=212)

Piedmont

(n=145)

Caribbean

(n=116)

Coffee

(n=105)

Antioquia

(n=97)

Cundi-boyacense

(n=82)

Production costs (US$ / farm / year)

 

 

 

- Feed supplements

9,005

23,639

3,084

8,923

13,493

27,528

27,680

- Permanent hired labor

8,766

7,166

5,125

13,178

7,398

7,133

8,511

- Family labor

1,912

2,074

2,724

1,314

1,246

2,498

1,898

- Temporarily hired labor

392

161

249

554

324

177

160

- Irrigation

3,654

1,688

67

9,174

818

1,192

2,885

- Reproduction

1,316

715

642

2,430

698

680

923

- Animal health

1,144

469

675

1,839

731

408

645

- Fertilization

581

1,538

297

283

1,197

1,942

1,579

- Herbicides

343

47

15

672

361

41

29

  Total

26,599

37,498

12,879

38,907

26,265

41,599

44,311

Annual production (kg / farm)

 

 

 

 

 

- Milk 

92,772

184,547

39,880

125,931

106,432

179,640

256,416

- Beef

15,230

4,475

8,954

27,445

6,389

4,197

5,615

Cost of milk production ($ / kg)

 

 

 

 

- Including opportunity cost of family labor

0.194

0.221

0.200

0.176

0.222

0.242

0.187

- Excluding opportunity cost of family labor

0.159

0.193

0.142

0.160

0.200

0.213

0.159

Product prices ($ / kg)

 

 

 

 

 

 

 

- Milk

0.207

0.215

0.189

0.222

0.213

0.228

0.207

- Beef

0.818

1.24

0.771

0.760

0.847

1.056

1.455

Gross income ($ / farm / year)

 

 

 

 

 

 

 

- Milk

19,204

39,678

7,537

27,957

22,670

40,958

53,078

- Beef

12,464

5,528

6,906

20,849

5,409

4,433

8,168

  Total

31,668

45,206

14,443

48,806

28,079

45,391

61,246

Net income  ($ / cow / year) 1

 

 

 

 

 

- Including opportunity cost of family labor

45.8

6.84

9.9

90.4

-20.6

31.2

122.0

- Excluding opportunity cost of family labor

113.

104

127

111

37

77

211

In terms of net income, dual-purpose farms obtained the highest annual net income per cow, regardless of whether the opportunity cost for family labor was considered or not. Region-wise, the highest income was obtained in the Cundiboyacense altiplanicie, followed by the Piedmont, Caribbean and Antioquia regions. The Coffee-growing region reported the lowest level of income.

Capital investment and returns

About 88% of the investment in dual-purpose farms was represented in land and livestock compared with 80% in specialized dairies. This difference occurred because 20% of the investment in specialized dairies was in infrastructure and equipment whereas this percentage was lower (12%) in dual-purpose farms (Table 6). The market value of land was usually lower in the Piedmont and Caribbean regions where most farms were dual-purpose and higher in high-altitude regions where farms were specialized dairies because of their proximity to urban centers and better public infrastructure. The market value of livestock was higher in high-altitude regions compared with the Piedmont and Caribbean regions.

Table 6.  Capital invested in land, livestock, facilities and equipment by milk production system and region of the country in 2000.

 

 

Parameter

Production system

Region

Dual purpose

(n=333)

Specialized (n=212)

Piedmont

(n=145)

Caribbean

(n=116)

Coffee

(n=105)

Antioquia

(n=97)

Cundi-boyacense

(n=82)

Average prices

 

 

 

 

 

 

 

Value of land where farm is located (US$/ha)

3,600

5,873

3,541

1,938

3,923

3,656

6,869

Labor wage rate (US$/day)

4.57

5.01

5.13

3.65

4.83

5.30

4.84

Value of a milking cow (US$)

511

634

479

413

696

686

579

Value of a culled cow (US$)

225

193

237

219

193

179

225

Value of a pregnant heifer (US$)

355

497

339

274

473

445

608

Capital investment (US$ / farm)

 

 

 

 

 

Land

351,790

203,864

288,205

472,174

240,474

146,612

296,732

Livestock

65,430

40,847

30,979

105,876

52,365

40,305

52,031

Infrastructure

39,715

26,682

36,549

47,694

35,839

14,267

35,398

Equipment

19,742

33,831

7,196

34,834

16,442

16,340

65,250

Total

476,677

305,224

362,929

660,579

345,120

217,524

449,410

Total capital invested ($ / ha)

3,359

7,786

4,459

2,437

5,631

5,432

10,403

Annual return to capital invested  (%)

 

 

 

 

- Including opportunity cost of family labor

1.63

0.32

0.47

3.29

0.23

0.31

2.06

- Excluding opportunity cost of family labor

2.72

2.83

2.21

3.80

1.05

3.15

4.00

Regarding total investment per unit area, use of capital was more intensive in specialized systems, being 2.3 times higher than in dual-purpose farms. This difference was also observed at the regional level, where the highest investment per unit area was in the Cundiboyacense altiplanicie, followed by Antioquia and the Coffee-growing regions, and lastly the Piedmont and Caribbean regions. For more information on the type of investments on farms, please refer to Holmann et al (2003).

Return to capital investment was higher in dual-purpose systems when the opportunity cost for family labor was considered as compared with specialized dairy systems, but was similar when this variable was not considered. Returns to capital investment varied broadly depending on the region, being higher in the Caribbean region and in the Cundiboyacense altiplanicie, lower in Antioquia and in the Piedmont, and lowest in the Coffee-growing region.

Relationship between productivity, technological change, and profitability

Table 7 presents the variables utilized to link productivity and profitability with technological change (with their respective categories and acronyms used), and the diagram in Figure 1 is a perceptual map of the links between these variables.

Table 7.  Variables related to productivity, technological change, and profitability utilized in the multiple correspondence analysis to construct Figure 1

Variable

Category

Code name

Best dimension 2

 Production system

Dual purpose

DPURPSE

1

 

Specialized dairy

SPEC

1

 Región 1

Piedmont

PIEDMT

1

 

Caribbean

CARIBBEAN

1

 

Coffee

COFFEE

1

 

Antioquia

ANTIOQ

1

 

Cundiboyacense

CUNDIB

2

 Amount of feed supplements offered to milking cows (kg DM /cow / day)

< 0.5

ASUP-L

1

 

0.5 to 2.0

ASUP-M

1

 

> 2.0

ASUP-H

1

 Proportion of cows in milk (%)

< 60

MHERD-L

1

 

60 to 80

MHERD-M

2

 

> 80

MHERD-H

1

 Proportion of pasture area with improved forages (%)

< 33

IMPFOR-L

2

 

33 to 67

IMPFOR-M

2

 

> 67

IMPFOR-H

1

 Frequency of milking (# daily)

Once

MILKING1

1

 

Twice

MILKING2

1

 Fertilize pastures

No

NFERPA

1

 

Yes

YFERPA

1

 Irrigate pastures

No

NIRRIPA

1

 

Yes

YIRRIPA

1

 Reproduction system utilized

Natural Service

NS

1

 

Artificial insemination

AI

1

 

Both

BOTH

1

 Number of grazing paddocks used by milking cows

< 10

PADDOCK-L

1

 

10 to 20

PADDOCK-M

2

 

> 20

PADDOCK-H

1

 Mature body weight (Kg)

< 400

BW-L

1

 

400 to 500

BW-M

2

 

> 500

BW-H

1

 Experience producing milk (# years)

< 5

XPROD-L

2

 

5 to 15

XPROD-M

2

 

> 15

XPROD-H

2

 Milk price (US$/Kg)

< 0.18

PRICE-L

1

 

0.18 to 0.24

PRICE-M

2

 

> 0.24

PRICE-H

2

 Internal deworming frequency  ( # / yr)

< 2

IDEW-L

2

 

2 to 3

IDEW-M

2

 

> 3

IDEW-H

2

 External deworming frequency (# / yr)

< 6

EDEW-L

2

 

6 to 12

EDEW-M

2

 

> 12

EDEW-H

2

 Net income per cow ($/yr)

< 50

INCOME-L

2

 

50 to 100

INCOME-M

1

 

> 100

INCOME-H

2

 Annual return to investment  (%)

< 1

RETURN-L

2

 

1 to 3

RETURN-M

2

 

> 3

RETURN-H

2

 Milk production per cow per day (Kg)

< 5

MILK-L

1

 

5 to 10

MILK-M

2

 

> 10

MILK-H

1

 Cost per kilo of milk ($/Kg)

< 0.13

MCOST-L

1

 

0.13 to 0.20

MCOST-M

2

 

> 0.20

MCOST-H

2

 Cost per kilo of beef ($/Kg)

< 0.5

BCOST-L

2

 

0.5 to 0.7

BCOST-M

2

 

> 0.7

BCOST-H

2

 Herd size (mature cows/farm)

< 30

HERD-S

2

 

30 to 100

HERD-M

2

 

> 100

HERD-L

2

 Commercial value of land (US$ / ha)

< 3,000

LAND-L

1

 

3,000 to 6,000

LAND-M

1

 

> 6,000

LAND-H

1

1 This variable was used in the analysis as supplementary.  Thus, its different categories were not used to estimate the dimensions.
2 The best dimension for any particular variable was that in which the weight (coefficient) of the variable was greater than in the other dimension.



Figure 1. Diagram showing the contribution of variables related to productivity, technological change, and profitability that explain both dimensions (DIM 1 y DIM 2) of the perceptual map. For more information on the meaning of each acronym refer to Table 7.


 Variables associated with productivity

The contribution of variables to each of the two dimensions accounted for approximately 25% of total variation. The best dimension for a particular variable is that in which its weight (coefficient) is higher than that of the other dimension. The variables that defined the first dimension were clearly those corresponding to milk productivity. High values in this dimension were associated with farms with high milk yield per cow per day (Milk-H), and low values in this dimension were related to farms presenting low milk yields per cow per day (Milk-L).

Other important variables in this dimension (# 1A) were those related to the different farm management practices, which given their coordinates, explained the differences in levels of productivity. Therefore, farms associated with high levels of productivity were those on which a high level of feed supplements was offered to milking cows (Asup-H), artificial insemination (AI) was used, a specialized dairy production system (Spec) was in place, cows were milked twice a day (Milking2), and pastures were irrigated and fertilized (Yirripa, Yferpa), the mature body weight was high (BW-H), and the proportion of cows in milk and the number of grazing paddocks on the farm for better rotation were both high (Mherd-H, Paddock-H). In addition, the most productive farms had a high market value of land (Land-H).

This situation contrasted with those variables that presented negative values and that were associated with low productivities (dimension # 1B). For example, farms with a dual-purpose production system (Dpurpse) were associated with management practices were pastures were not irrigated nor fertilized (Nirripa, Nferpa)), the reproductive system used was natural service (NS), cows were milked once a day (Milking1), the amount of feed supplements offered to milking cows was low (Asup-L), the proportion of cows in milk and the number of grazing paddocks on the farm was low (Mherd-L, Paddock-L), the mature body weight was low (BW-L), and the price per kilogram milk received was also low (Price-L). This information confirms the well-known fact that high technological levels effectively correlate with high levels of productivity (Aldana 1990).

Farms with low productivity were also associated with a low market value of land (Land-L). In terms of productivity, Antioquia and the Cundiboyacense altiplanicie (Cundib, Antioq) were the most productive, contrasting with the Caribbean and Piedmont regions (Caribbean, Piedmt) that presented the lowest productivities. The Coffee-growing region (Coffee) ranked intermediate in terms of productivity.

Variables associated with profitability

The second dimension was basically defined by income and profitability. High (positive) values of this dimension were determined by a high annual net income per cow (Income-H) and by a high annual return to capital investment (Return-H). Low (negative) values of this dimension were associated with low incomes low returns to capital investment (Income-L, Return-L). According to this dimension (# 2A), the most profitable farms (Return-H, Income-H) were characterized by having large herds (Herd-L), which indicated the existence of economies of scale. In addition, profitable farms were also associated to farmers with many years of experience in producing milk (Xprod-H).

The most profitable farms also seemed to be associated with a low frequency of de-worming milking cows against external parasites (Edew-L). This could be attributed to the low prevalence of ticks in high-altitude areas, where most specialized dairy systems are found, thus explaining the lower frequency of treatment.

In Dimension # 2B, those farms with low incomes per cow (Income-L) and low returns to capital invested (Return-L) were associated with farms having small herds (Herd-L), with a low proportion of pasture area with improved forages (Impfor-L), and high production costs per kilogram milk and beef (Mcost-H, Bcost-H). In addition, low profit farms were related to farmers with little experience in milk production (Xprod-L) and with high frequency of treating milking cows against both internal and external parasites (Idew-H, Edew-H).

In terms of profitability among regions, the second dimension shows that the Cundiboyacense altiplanicie (Cundib) was the most profitable region, followed by the Caribbean, Antioquia, and Piedmont (Caribbean, Antioq, Piedmt) with average profitability, and lastly the Coffee-growing region (Coffee) with the lowest income.

The reasons why the Coffee-growing region was least profitable could be the following:

Effect of technological change on productivity, profitability, and competitiveness

The general linear model technique was used to estimate the effect of each one of the technologies evaluated in the multiple correspondence analysis on productivity (expressed as milk production per hectare per year), profitability (expressed as net income per cow per year), and competitiveness (expressed as production cost per kilogram of milk). The variability observed in productivity, profitability, and competitiveness was accordingly as a function of technological change (Tables 8-10) for the different regions of the country.

Adoption of improved pastures

Regardless of the region or production systems used, the adoption of improved pastures increased productivity, profitability, and competitiveness. When farms with a high proportion of pasture area with improved forages were compared with those with a low proportion, an increase was observed in productivity and in net income in the Piedmont (147% and 31%), in the Caribbean (126% and 350%), in the Coffee-growing region (309% and 69%), and in Antioquia (235% and 181%), while the production cost of milk decreased by 13% in the Piedmont, 15% in the Caribbean, 9% in the Coffee-growing region, and 8% in Antioquia. In the Cundiboyacense altiplanicie, milk productivity increased by 92% and income by 570%, while unitary milk production cost decreased by 41% (Tables 8 to 10).

Table 8. Comparison of milk productivity per hectare between different technology levels by region of the country in 2000.

Technological change1
Region

Piedmont

Caribbean

Coffee

Antioquia

Cundiboyacense

(kg milk / ha / yr)

Production system

 

 

 

 

 

Dual purpose

861 b

731

4.138 a

5.237 b

5.686 b

Specialized dairy

1.827 a

ND

5.290 a

8.867 a

8.677 a

Amount of feed supplements offered to milking cows (kg DM / cow / d)

< 0.5

840 a

583 b

1.162 c

6.080 a

3.445 c

0.5 to 2.0

961 a

1.118 ab

3.722 b

6.037 a

7.221 b

> 2.0

1.307 a

1.235 a

7.967 a

8.471 a

11.774 a

Proportion of cows in milk (%)

 

 

 

< 60

592 c

569 b

1.782 c

7.414 a

6.000 a

60 to 80

983 a

768 b

4.319 b

8.503 a

6.547 a

> 80

1.487 a

1.420 a

7.838 a

8.930 a

9.269 a

Proportion of pasture area under improved forages (%)

 

 

< 33

471 a

368 a

1.320 a

2.961 a

6.247 a

33 to 67

905 a

678 ab

2.280 ab

6.514 ab

7.250 b

> 67

1161 b

832 b

5.395 b

9.924 b

11.998 b

Frequency of milking (#/d)

 

 

Once

844 b

708 a

984 b

4.438 a

4.378 b

Twice

1.649 a

732 a

6.104 a

8.120 a

8.306 a

Fertilize pastures

 

 

 

No

794 b

663 a

1.029 b

2.082 b

4.013 b

Yes

1.137 a

838 a

5.529 a

8.438 a

8.885 a

Irrigate pastures

 

 

No

888 a

707 a

4.052 b

7.351 b

5.666 b

Yes

934 a

806 a

7.278 a

10.716 a

9.783 a

Reproduction management

 

 

 

Natural Service

867 a

699 a

3.171 b

5.495 b

3.554 b

Artificial insemination

1.127 a

727 a

9.773 a

11.499 a

9.647 a

Both

864 a

745 a

5.317 b

8.036 ab

8.181 a

Number of grazing paddocks used  by milking cows

 

 

< 10

504 a

668 a

4.442 a

7.742 a

7.012 b

10 to 20

985 ab

619 a

4.464 a

7.903 a

7.868 a

> 20

1.212 b

842 a

5.027 a

8.672 a

9.687 ab

Experience producing milk (years)

 

< 5

651 b

608 a

7.093 a

6.441 a

7.282 a

5 to 15

857 ab

738 a

3.243 b

7.889 a

8.300 a

>15

1.059 a

744 a

3.762 b

8.336 a

7.796 a

Internal deworming frequency (#  / yr)

 

 

< 2

713 b

714 a

4.030 b

7.120 a

8.074 a

2 to 3

1.180 a

1.014 a

3.860 b

12.545 a

6.521 a

> 3

1.170 a

496 a

7.652 a

10.454 a

8.830 a

External deworming frequency (#  / yr)

 

 

< 6

830 a

753 a

5.504 a

8.796 a

8.372 a

6 to 12

1.015 a

734 a

3.479 a

9.148 a

7.702 a

> 12

764 a

651 a

4.767 a

6.430 a

6.880 a

Herd size (# cows / farm)

 

 

< 30

997 a

434 a

5.626 a

7.037 a

6.195 b

30 to 100

775 ab

839 a

3.918 ab

9.496 a

8.102 b

> 100

207 b

675 a

2.511 b

8.320 a

10.871 a

< 3,000

633 b

707 b

2.401 b

5.295 b

2.330 c

3,000 to 6,000

1.071

703 b

4.344 b

7.781 b

6.484 b

> 6,000

1.153 a

1.634 a

9.095 a

14.901 a

10.767 a

1 Means with the same letter do not differ signifficantly (p=0.05) according to Tukey’s multiple comparison test (Steel y Torrie 1980)

Rotational management of pastures

Another technological change associated with adoption of improved pastures was the investment in grazing paddocks to increase the efficiency in the quantity and quality of forage produced through rotational management. Thus, farms with a high number of grazing paddocks for the milking herd were 140% more productive in the Piedmont, 13% more productive in the Coffee-growing region, 12% more productive in Antioquia, and 38% more productive in the Cundiboyacense altiplanicie, as compared with farms with few grazing paddocks. Likewise they were 54% more profitable in the Piedmont, 133% in the Coffee-growing region, 100% in Antioquia, and 61% in the Cundiboyacense altiplanicie (Tables 8 to 10). On these farms, milk production costs were also 23% lower in the Piedmont; 22% lower in the Coffee-growing region; 18% lower in Antioquia, and 27% lower in the Cundiboyacense altiplanicie. In the Caribbean region, this technological change increased productivity by 26% but there were no differences in net income. Unitary production cost increased by 11%, indicating that existing grasses may not be the most adapted to this region and, as a result, this investment did not improve the nutritional quality and/or quantity as occurred in other regions.

Table 9. Comparison of net income per cow per year between different technology levels by region of the country in 2000.

Technological change1
Region

Piedmont

Caribbean

Coffee

Antioquia

Cundiboyacense

(US$ / cow / yr)

Production system

 

 

 

 

 

Dual purpose

135 a

111

11 b

53 b

145 b

Specialized dairy

-140 b

ND

86 a

158 a

236 a

Amount of feed supplements offered to milking cows (kg DM / cow / d)

< 0.5

164 a

118 a

117 a

-457 c

163 a

0.5 to 2.0

32 b

102 a

58 a

300 a

263 b

> 2.0

-49 c

47 b

-46 b

39 b

238 c

Proportion of cows in milk (%)

 

 

 

< 60

118 a

72 c

-39 a

-27 b

212 a

60 to 80

123 a

126 b

43 b

83 a

177 a

> 80

178 a

234 a

67 b

86 a

242 a

Proportion of pasture area under improved forages (%)

 

 

< 33

127 a

27 b

29 a

48 a

-358 a

33 to 67

118 a

116 a

37 a

62 a

203 b

> 67

167 a

121 a

49 a

135b

316 c

Frequency of milking (#/d)

 

 

Once

126 a

156 a

17 a

-68 b

173 a

Twice

157 a

108 a

83 b

80 a

216 a

Fertilize pastures

 

 

 

No

148 a

140 a

39 a

130 a

266 a

Yes

72 b

65 b

36 a

74 b

197 b

Irrigate pastures

 

 

No

131 a

121 a

42 a

119 a

189 a

Yes

-107 b

79 b

7 b

-85 b

231 a

Reproduction management

 

 

 

Natural Service

127 a

128 a

53 a

68 a

177 a

Artificial insemination

131 a

51 b

1 b

131 a

213 a

Both

123 a

195 a

16 b

62 a

226 a

Number of grazing paddocks used  by milking cows

 

 

< 10

126 a

102 a

-75 b

-4 c

158 b

10 to 20

148 a

139 a

60 a

112 a

180 b

> 20

194 b

83 a

58 a

102 b

255 a

Experience producing milk (years)

 

< 5

92 a

88 b

-42 b

66 a

144 a

5 to 15

138 b

73 b

50 a

73 a

244 b

>15

162 b

136 a

78 a

91 a

236 b

Internal deworming frequency (#  / yr)

 

 

< 2

151 a

112 b

55 a

302 a

399 a

2 to 3

111 a

179 a

30 a

59 b

173 b

> 3

66 b

-14 c

-27 b

53 b

210 b

External deworming frequency (#  / yr)

 

 

< 6

249 a

139 a

40 b

140 a

201 a

6 to 12

87 b

59 b

76 a

23 b

223 a

> 12

135 b

98 b

-2 c

59 b

222 a

Herd size (# cows / farm)

 

 

< 30

101 b

48 b

-10 b

-11 b

88 c

30 to 100

168 a

123 a

57 a

166 a

234 b

> 100

227 c

109 a

87 a

217 a

422 a

Commercial land value (US$ / ha)

< 3,000

101 b

48 b

-10 b

-11 b

88 c

3,000 to 6,000

145 b

114 a

37 a

114 a

143 b

> 6,000

182 a

126 a

43 a

102 b

246 a

1 Means with the same letter do not differ (p=0.05) according to Tukey’s multiple comparison test (Steel y Torrie 1980)

These results suggest that the adoption of improved pastures with an appropriate paddock rotation system was a low-risk investment to increase productivity, profitability, and competitiveness. Furthermore, it was the basis of technological change that generated synergisms and on which other management practices, such as feed supplementation, were key changes in further increasing productivity and competitiveness.

Use of fertilizer

The use of this technology increased farm productivity but reduced profits. Unitary milk production cost increased in all regions. Farms that fertilized their pastures increased milk productivity by 43% in the Piedmont but net income was reduced by 51% and unitary costs increased by 21%. In the Caribbean region, productivity increased by 26% but income decreased 54% and unitary costs rose 47%. In the Coffee-growing region, productivity increased 437% but income decreased 83% and unitary costs rose 5%. In Antioquia, production increased by 305%, income decreased by 43%, and unitary costs increased by 9%. In the Cundiboyacense altiplanicie, productivity increased 121%, income decreased 26%, and unitary costs went up 19% (Tables 8-10).

The above data indicate that fertilization increases marginal costs more than marginal income, suggesting that livestock producers who used this technology have not learned to manage it successfully and may be applying more N2 than needed.

Table 10.  Comparison of the cost of producing milk between different technology levels by region of the country in 2000.

Technological change1
Region

Piedmont

Caribbean

Coffee

Antioquia

Cundiboyacense

(US$ / kg milk)

Production system

 

 

 

 

 

Dual purpose

0.20 b

0.17

0.23 a

0.21 b

0.21 a

Specialized dairy

0.29 a

ND

0.21 a

0.25 a

0.17 a

Amount of feed supplements offered to milking cows (kg DM / cow / d)

< 0.5

0.19 a

0.16 a

0.19 a

0.26 a

0.17 a

0.5 to 2.0

0.23 ab

0.21 b

0.22 ab

0.17 a

0.17 a

> 2.0

0.26 b

0.24 b

0.25 b

0.31 b

0.20 a

Proportion of cows in milk (%)

 

 

 

< 60

0.22 a

0.19 a

0.24 a

0.24 a

0.20 a

60 to 80

0.20 a

0.16 a

0.23 a

0.24 a

0.19 a

> 80

0.20 a

0.15 a

0.21 a

0.24 a

0.17 a

Proportion of pasture area under improved forages (%)

< 33

0.23 a

0.20 a

0.23 a

0.25  a

0.29 a

33 to 67

0.20 b

0.17 b

0.21 a

0.24 a

0.19 b

> 67

0.20 b

0.17 b

0.21 a

0.23 a

0.17 b

Frequency of milking (#/d)

 

 

Once

0.20 a

0.17 a

0.23 a

0.33 a

0.21 a

Twice

0.17 a

0.22 a

0.20 a

0.24 b

0.18 a

Fertilize pastures

 

 

 

No

0.19 b

0.15 b

0.21 a

0.22 a

0.16 a

Yes

0.23 a

0.22 a

0.22 a

0.24 a

0.19 a

Irrigate pastures

 

 

No

0.20 a

0.16 b

0.22 a

0.23 b

0.17 a

Yes

0.29 a

0.22 a

0.23 a

0.29 a

0.20 a

Reproduction management

 

 

 

Natural Service

0.20 a

0.16 a

0.21 a

0.24 a

0.19 a

Artificial insemination

0.20 a

0.19 a

0.24 a

0.24 a

0.19 a

Both

0.18 a

0.21 a

0.23 a

0.24 a

0.17 a

Number of grazing paddocks used  by milking cows

 

 

< 10

0.22 a

0.18 a

0.27 a

0.27 a

0.22 a

10 to 20

0.20 a

0.16 a

0.21 b

0.24 ab

0.18 b

> 20

0.17 b

0.20 b

0.21 b

0.22 b

0.16 b

Experience producing milk (years)

 

< 5

0.21 a

0.18 a

0.25 a

0.24 a

0.18 a

5 to 15

0.20 a

0.19 a

0.21 ab

0.24 a

0.18 a

>15

0.19 a

0.16 a

0.21 b

0.24 a

0.18 a

Internal deworming frequency (#  / yr)

 

 

< 2

0.18 a

0.15 a

0.22 a

0.19 a

0.14 a

2 to 3

0.22  b

0.17 a

0.22 a

0.24 b

0.17 a

> 3

0.25 b

0.23 b

0.24 a

0.25 b

0.19 a

External deworming frequency (#  / yr)

 

 

< 6

0.18 a

0.16 a

0.21 a

0.21 a

0.17 a

6 to 12

0.19 a

0.16 a

0.23 a

0.26 b

0.17 a

> 12

0.21 a

0.21 b

0.23 a

0.26 b

0.19 a

Herd size (# cows / farm)

 

 

< 30

0.23 a

0.29 a

0.25 a

0.27 a

0.21 a

30 to 100

0.19 a

0.17 b

0.20 b

0.22 b

0.17 ab

> 100

0.16 a

0.17 b

0.20 b

0.19 b

0.14 b

Commercial land value (US$ / ha)

< 3,000

0.18 b

0.15 a

0.22 a

0.23 a

0.18 a

3,000 to 6,000

0.19 b

0.17 a

0.22 a

0.25 a

0.19 a

>6,000

0.24 a

0.27 b

0.23 a

0.25 a

0.19 a

 1 Means with the same letter do not differ (p=0.05) according to Tukey’s multiple comparison test (Steel y Torrie 1980)

Use of irrigation

Just as in the case of fertilization, irrigation technology increased both milk productivity and unitary production costs in all regions, suggesting that additional research is required to determine the level of water needed to produce high-quality forage economically. In addition, irrigation reduced net income in all regions, except for the Cundiboyacense altiplanicie, where the use of this technology increased income by 22% although production costs increased by 17%.

Use of feed supplementation

In all regions, milk productivity and unitary production costs increased on those farms that offered feed supplements to milking cows. However, the best economic yield in terms of net income occurred on farms that provided the milking herd with low levels of supplementation (i.e., < 0.5 kg DM/cow per day) in the Piedmont, Caribbean, and Coffee-growing regions, and intermediate levels of supplementation (i.e., between 0.5 and 2 kg DM/cow per day) in Antioquia and the Cundiboyacense altiplanicie (Tables 8 to 10). Feed supplementation at higher levels increased milk productivity in all regions, but at the expense of reducing net income/cow per year.

During 2000, the milk price received by producers was US$0.21/kg and the average cost of commercial concentrate was US$0.22/kg. Thus, the milk-to-concentrate price ratio was lower than 1 (0.96:1), indicating that the price of commercial concentrate in Colombia was very expensive compared with that of milk. Alternatively, it could be presumed that the reduction of tariffs on imported grains would make the use of supplements more viable and Colombia could rapidly increase its milk productivity.

Production system

In lowland regions (Piedmont and Caribbean), the dual-purpose production system generated higher net income, whereas in highland regions (Coffee, Antioquia and Cundiboyacense altiplanicie), most profits were obtained with the specialized dairy system. In addition, the dual-purpose system produced milk at the lowest unitary cost in all regions, except the Coffee-growing region, suggesting that Colombia should have different research and technology transfer strategies to tap more efficiently the comparative advantages of each region.

Frequency of milking

The twice-a-day milking management practice proved to be 83% to 520% more productive and 25% to 148% more profitable, and unitary production cost decreased between 15% and 27% in all study areas, except for the Caribbean region where twice-a-day milking did not increase productivity but instead adversely affected net income and increased production costs. One reason why this practice did not trigger increases in productivity in the Caribbean region could be the low genetic potential of animals, because more than 56% of the herd were cows with ≥ 75% Zebu genes (i.e., Brahman). In addition, this management practice require farms to have electricity and own a milk cooling system to store the afternoon milk until the following day. As a result, it is not appropriate for livestock producers with limited resources, located in marginal livestock areas and/or owning herds of low genetic potential.

Percentage of cows in milk

This indicator is the result of the interaction between reproductive efficiency (ie., calving interval) and breed (ie., lactation length). Farms with a high proportion of cows in milk (i.e., > 80%) were 20% to 340% more productive and 15% to 225% more profitable in all regions than those farms with a low proportion of cows in milk (i.e., < 60%), meaning that the fixed costs of dry cows were shared by a larger number of cows in production. In addition, milk production cost was 9% to 21% lower with increasing proportion of cows in milk, except for Antioquia where no differences were observed.

Frequency of de-worming

The effect of the frequency of de-worming milking cows against both internal and external parasites did not have a significant impact on productivity but did affect profitability and competitiveness. Farms that seldom de-wormed cows for internal parasites obtained between 77% to 128% more income per cow and unitary production cost was 8% to 35% lower, depending on the region, than those farms that frequently de-wormed cows. Similarly, farms that seldom de-wormed cows for external parasites obtained between 42% to 137% more income, and production costs were 9% to 24% lower, except for the Cundiboyacense altiplanicie where no significant differences in income were observed.

Reproductive management

Farms that used artificial insemination were more productive that those using natural service in highland regions (Coffee, Antioquia, and Cundiboyacense altiplanicie). No differences were observed in production costs or levels of profitability, except in the Coffee-growing and Caribbeanregions where farms using only natural service were more profitable (Tables 8 -10). This suggests that the use of artificial insemination, whether as sole reproduction strategy or as complement to natural service to produce breeding animals of superior genetic quality on the farm, may have helped improve the genetic potential of herds and, as a result, farm productivity.

Herd size

Farms with larger herds were always more profitable and more competitive than farms with small herds across all regions and production systems. Farms with larger herds were also more productive in the Cundiboyacense altiplanicie; in contrast, farms with small herds were more productive in the Caribbean and Coffee-growing regions. As evidenced later on in this document, the scale of operation was the factor that most affected profitability and competitiveness.

Farmer experience in producing milk

Farms where the producer had lots of experience in dairying (i.e. >15 years) obtained higher income in all regions, ranging from 38% in Antioquia to 120% in the Coffee-growing region, compared with farms where producers had little experience (i.e., < 5 years). The amount of experience in dairying only had a positive effect on milk productivity in the Coffee and Piedmont regions. Years of experience in dairying only reduced unitary production costs in the Coffee-growing region. This fact suggests that practical training events could help increase the income of those producers with little experience, especially small-scale producers, because it is more difficult for them to access information on new technologies and/or successful management practices.

Market value of land

Farms located in areas where the market value of land was high (> $6,000/hectare) were always more productive than those with intermediate or low market values, but less profitable in all regions. There were no significant differences in production costs, except in the Piedmont and Caribbean regions, where farms with high market value also produced milk at a higher cost. The most profitable category contained farms with an intermediate market value of land (i.e., from $3,000 to $6,000/hectare). Thus, this range might be used as reference when deciding whether intensification of dairying would prove profitable. Above this range, intensification is not justifiable from the financial viewpoint. Furthermore, intensification of livestock production on lands with a high market value should no longer be based on grazing, but on other systems such as confinement because the proximity to urban centers boosts the cost of land.

Based on the results of this analysis, it was concluded that the performance of diverse technologies affect productivity and profitability in different ways, depending on the region. It also showed that, at a regional level, technologies that increase milk productivity are not necessarily profitable. In general, these results show a trend similar to those associations observed in the multiple correspondence analysis presented in the perceptual map (Figure 1), except that the difference in productivity and profitability among levels of each technology are quantified for each region in the latter analysis.

Effect of breed on profitability, competitiveness, and productivity

The variables that determined the profitability, productivity, and competitiveness of three cattle breed groups, categorized according to the proportion of European genes (i.e., Holstein), were identified to define genetic improvement strategies for different production systems and regions of Colombia. The averages of the seven most relevant variables are presented in Table 11. Farms were grouped as follows:

Table 11.  Effect of breed group on production costs of milk and beef, net income, milk and beef productivity, and return to capital investment by production system and region of the country in 2000.

 

Production system or region

Cost of production 4

Milk production

Beef production
(kg/ha/yr)

Net income
($/cow/yr)

Annual return to investment
(%)

Milk ($/kg)

Beef
($/kg)

Cow
(kg/d)

Hectare (kg/yr)

Dual purpose

 

 

 

 

 

 

 

- Group A 1          (n=104)

0.215    a

0.77    a

9.5  a

4,421  a

195    a

- 10  a

- 0.09  a

- Group B 2          (n=124)

0.195  ab

0.72  ab

5.8  b

1,313  b

156  ab

31  ab

1.49  a

- Group C 3          (n=141)

0.188    b

0.70    b

4.2  c

747     c

138    b

59    b

2.01  b

Specialized dairy

 

 

 

 

 

 

 

- Group A            (n=160)

0.217 a

0.64 ab

13.4 a

8,145 a

191 a

39 a

1.01 a

- Group B              (n=18)

0.236 a

0.72 a

9.8 a

5,587 a

183 a

- 71 a

- 1.28 a

- Group C                (n=0)

NA

NA

NA

NA

NA

NA

NA

Piedmont region

 

 

 

 

 

 

 

- Group A              (n=27)

0.196 a

0.77 a

6.0 a

1,020 a

114 a

14 a

0.50 a

- Group B              (n=66)

0.198 a

0.77 a

5.4 a

913 a

160 a

13 a

0.46 a

- Group C              (n=52)

0.205 a

0.83 a

4.5 b

789 a

160 a

3 a

0.46 a

Caribbean region

 

 

 

 

 

 

 

- Group A                (n=0)

NA

NA

NA

NA

NA

NA

NA

- Group B              (n=33)

0.169 a

0.59 a

4.7 ab

786 a

126 a

89 a

3.88 a

- Group C              (n=83)

0.180 a

0.62 a

4.0 b

716 a

121 a

84 a

3.03 a

Coffee region

 

 

 

 

 

 

 

- Group A              (n=60)

0.231 a

0.76 a

9.5 a

5,326 a

212 a

-56 b

- 0.27 b

- Group B              (n=37)

0.220 a

0.73 a

8.1 a

3,859 ab

181 a

- 10 ab

0.53 ab

- Group C                (n=8)

0.165 b

0.56 b

6.3 b

1,842 b

156 a

196 a

2.54 a

Antioquia region

 

 

 

 

 

 

 

- Group A             (n=97)

0.200

0.71

14.0

8,260

200

36

0.48

- Group B               (n=0)

NA

NA

NA

 NA

NA

NA

NA

- Group C               (n=0)

NA

NA

NA

NA

NA

NA

NA

Cundinoboyacense altiplanicie region

 

- Group A              (n=80)

0.183 b

0.60b

13.3 a

7,974 a

197 a

133 a

2.31 a

- Group B                (n=2)

0.311 a

1.07 a

10.4 a

3,934 a

85 a

-350 b

-8.15 b

- Group C                (n=0)

NA

NA

NA

NA

NA

NA

NA

1 Farms where the milking herd had on average between 75%-100% European genes. The overall   
  average of this group was 98% European genes and 2% Zebu genes
2 Farms where the milking herd had on average between 50%-74% European genes. The overall 
  average of this group was 55% European genes and 45% Zebu genes
3 Farms where the milking herd had on average less than 50% European genes. The overall
  average of this group was 24% European genes and 76% Zebu genes
Means with the same letter do not differ signifficantly (p=0.05) according to Tukey’s multiple
   comparison test (Steel y Torrie 1980)

Effect of breed on production system

Farms with dual-purpose production systems were more profitable (i.e., higher net income and improved profitability) and more competitive (i.e., lower production costs of milk and beef), but less productive when the proportion of European genes was low (group C) and intermediate (group B). In addition, farms with specialized dairy systems were slightly more profitable, competitive, and productive when high levels of European genes (group A) were found, although these differences were not significant compared with the intermediate group (group B).

Effect of breed on the region

In lowland regions (ie., Piedmont and Caribbean), no significant differences between breed groups were observed for any of the seven variables analyzed. In the Coffee-growing region, the use of cows of the intermediate or low groups (groups B and C) proved more profitable and competitive but less productive than the use of cows of the high group (group A). In Antioquia and the Cundiboyacense altiplanicie, the only breed group found on farms surveyed belonged to the high level (group A). Thus, it was not possible to compare results.

However, the results reported herein refute those of McDowell et al (1996), who stated that: "for milk farms to be profitable, they must produce over 4,400 kg/lactation (> 14.4 kg/cow per day) using purebreds or cows with a high percentage of Bos taurus genes". The case of Colombia has demonstrated that the profitability and competitiveness of milk production systems are much more complex and depend not only on technological change but also on the interaction of technology change with the effect of the environment and of management practices.

Economies of scale

Based on multivariate principal components analysis, farms were grouped within each production system and region to identify those changes in productivity associated with increased farm competitiveness and profitability. Six groups of farms were formed, and these accounted for 80% of total variability using Ward's minimum variance method (1963).

Results show that improved farm competitiveness and profitability was directly related to herd size (Table 12). In other words, unitary production costs of milk and beef decreased, net income per cow increased, and return on capital investment improved significantly with increasing herd size, suggesting the existence of economies of scale. The logic seems to indicate that fixed costs are distributed among a larger number of cows, meaning that unitary production costs are reduced with increasing herd size.

Table 12.  Multivariate analysis of farms grouped by herd size which contains the cost of  cost of milk production, net income, annual return on investment, and productivity of milk and beef by production system and region of the country in 2000.

Clusters of farms grouped by production system and region

 

 

R2

 Number of farms per cluster

 Herd size
(# cows)

 Cost of milk production
($/kg)

Net income
($/cow/yr)

 Annual return to investment
(%)

 Productivity of milk
(kg/ha/yr)

 Productivity of beef
(kg/ha/yr)

 Retribution to family labor
($/day)

Dual purpose

82.3

 

 

 

 

 

 

 

 

1

 

108

20

0.24

- 66

-0.7

894

140

3.5

2

 

21

35

0.21

58

1.3

2193

247

15.3

3

 

136

83

0.16

106

2.8

734

134

27.7

4

 

17

78

0.20

87

2.6

5472

173

36.4

5

 

13

337

0.13

164

6.0

636

140

135.1

6

 

5

730

0.13

82

6.1

226

78

162.8

Specialized dairy

76.6

 

 

 

 

 

 

 

 

1

 

54

17

0.25

- 152

-2.9

9100

360

1.7

2

 

52

24

0.26

- 163

-3.7

2976

128

1.1

3

 

35

37

0.20

180

4.6

15760

262

30.3

4

 

24

62

0.18

227

6.0

7970

130

40.7

5

 

31

105

0.20

57

1.7

3090

79

19.9

6

 

13

159

0.16

413

6.2

14358

245

183.7

Piedmont

77.4

 

 

 

 

 

 

 

 

1

 

59

19

0.19

12

0.2

1099

178

8.9

2

 

30

23

0.30

-184

-1.9

742

75

1.4

3

 

9

45

0.08

463

8.5

662

392

70.6

4

 

29

56

0.16

61

1.0

728

109

17.9

5

 

5

56

0.28

-182

-1.7

1463

84

2.7

6

 

8

108

0.17

23

0.3

326

109

12.5

Caribbean

84.8

 

 

 

 

 

 

 

 

1

 

9

48

0.32

-130

-1.8

377

56

1.9

2

 

27

73

0.19

25

0.4

750

112

8.1

3

 

35

111

0.14

140

4.8

1028

151

41.8

4

 

17

175

0.11

253

8.8

758

152

116.6

5

 

10

528

0.15

84

2.9

410

116

127.8

6

 

1

926

0.10

280

9.0

108

80

710.4

Coffee

79.1

 

 

 

 

 

 

 

 

1

 

13

8

0.30

-341

-3.6

9300

378

1.1

2

 

28

19

0.24

-55

-0.8

1460

186

1.9

3

 

18

28

0.24

-70

-0.8

10100

291

2.1

4

 

13

76

0.15

115

2.8

600

157

26.4

5

 

29

85

0.19

179

2.8

3800

99

30.2

6

 

1

265

0.15

210

3.1

6400

114

152.5

Antioquia

81.1

 

 

 

 

 

 

 

 

1

 

11

13

0.29

-361

-9.6

8500

428

1.2

2

 

14

18

0.27

-195

-4.8

2370

105

2.6

3

 

12

26

0.25

48

2.7

20200

385

14.5

4

 

36

34

0.23

21

1.5

6090

153

14.0

5

 

10

113

0.20

90

1.7

2800

80

35.7

6

 

10

117

0.20

255

5.6

14600

197

75.2

Cundi-boyacense altiplanicie

82.0

 

 

 

 

 

 

 

 

1

 

10

10

0.25

-178

-4.7

4900

197

4.5

2

 

14

21

0.22

-86

-0.6

10600

263

4.1

3

 

18

38

0.19

25

0.5

2100

126

10.8

4

 

25

72

0.16

278

5.4

9400

183

56.4

5

 

7

170

0.15

567

7.9

15800

279

260.7

6

 

1

330

0.15

591

8.1

12,700

41

534.1

This trend was not observed when increased competitiveness was associated with productivity, suggesting that farms with high productivity are not necessarily competitive nor profitable. Therefore, herd (farm) size is the factor that most influences profitability and competitiveness, regardless of the production system, the level of productivity, or the region where farms were located.

This foregoing has significant implications for the rural/livestock sector in Colombia because approximately 70% of milk producers produce less than 100 kg/day (Consejo Nacional Lácteo,1999). Table 12 shows negative income per cow per year in the case of farms with small herds. This means that the returns to family labor are lower than the official minimum wage rate and the persistence of these systems can be attributed to the opportunity cost of family labor or the limited opportunities of employment in rural areas. Table 12 also indicates the daily return to family labor, expressed in US dollars. As can be observed, returns to family labor on small farms were lower than the official minimum wage in force during the year 2000 of US$ 4.75/day.

Likewise, the objective function of smallholders may not necessarily be to maximize income, especially in the case of small farms. Mixed systems, where other crops (i.e., coffee) are also planted in addition to livestock activities, were usually found in the small farms surveyed in this study, a common strategy to reduce risk and maximize family labor use. It is highly probable that these small farms are involved in livestock activity to generate cash flow, as a source of savings, as a way to diversify risk, for food security, or a combination of the above.

Evolution of milk production systems in Colombia

Table 13 shows the parameters of productivity and profitability reported by Aldana (1990) in 1988 and those found by this study during 2000. Aldana (1990) utilized a methodology similar to that used in this study to calculate milk and beef production costs, gross sales, and net income. Thus, we believe this study can be used as reference to analyze the evolution of milk production systems in Colombia during this period. To be able to compare the economic variables of both studies, the figures of Aldana were expressed in Colombian pesos of year 2000 and then in US currency, at the 2000 market exchange rate (US$1 = Col$2,094) (Estadísticas Financieras Internacionales 2002).

Table 13.  Evolution of productivity, cost of production, capital investment, profitability, and farm-gate prices of milk and beef in dual-purpose and specialized dairy systems in Colombia between 1988 y 2000.

 

Parameter

Milk production system

Dual purpose

Specialized dairy

1988 a

2000

1988 a

2000

Productivity

 

 

 

 

- Milk production (kg/ha/yr)

453

654

4,132

4,708

- Beef production (kg/ha/yr)

115

107

212

114

- Stocking rate (AU/ha)

1.3

1.5

2.3

2.7

Cost of production

 

 

 

 

- Milk (US$/kg)

0.19

0.16

0.21

0.19

- Beef  (US$/kg)

0.73

0.57

0.98

0.60

- Both (US$/ha)

172

174

1,098

903

Capital investment (US$/ha)

 

 

 

 

- Land

1,828

2,479

7,120

5,201

- Livestock

688

461

2,868

1,042

- Equipment and infrastructure

117

419

1,126

1,544

- Total

2,632

3,359

11,114

7,786

Income (US$ / ha / yr)

 

 

 

 

- Gross income

239

223

1,906

1,153

- Net income

67

49

806

250

Farm-gate prices (US$/kg)

 

 

 

 

- Milk

0.27

0.21

0.37

0.22

- Beef

1.02

0.82

1.71

1.24

Annual return to capital investment (%)

4.2

2.7

6.8

2.8

a  Adapted from Aldana (1990). Values in Colombian pesos of 1988 were inflated to constant  pesos of the year 2000 and then expressed in US dollars using the average exchange rate during  2000 of Col$ 2,084 pesos to the dolar (Estadísticas Financieras Internacionales 2002).   Productivity parameters were estimated based on the weighted average of the production  systems’ categories “improved” and  “intensive” calculated by Aldana.

The first major difference is that milk productivity per hectare increased by 44% in dual-purpose systems and by 14% in specialized dairy systems. This increase was partly due to a 15% increase in stocking rate in dual-purpose systems and a 17% increase in specialized dairy systems. This trend was also observed in other regions of Colombia not covered by this study, as was the case of the Colombian Amazon region between 1986 and 1997 (Rivas and Holmann 2000). In addition, beef productivity remained stable in dual-purpose systems but decreased by 46% in specialized dairy systems. Therefore, efforts to increase productivity were exclusively focused on milk production, not on beef.

This increase in productivity reduced, in real terms, the cost of producing milk in dual-purpose systems by 16% and in specialized systems by 10%. Likewise, beef production costs decreased by 22% in dual-purpose systems and by 39% in specialized dairy systems. As a result, Colombia's dairy sector has become more competitive, due partly to a higher stocking rate, but also from an increased investment in infrastructure and equipment (eg., adoption of improved forages, rotational grazing management, forage choppers, irrigation equipment, and facilities). This increase in investment was 258% in dual-purpose systems and 37% in specialized dairy systems.

However, even though milk producers have made technological changes and investments to become more competitive and productive, the net income per hectare during this period decreased by 27% in dual-purpose systems and by 69% in specialized dairy systems. This drastic reduction in income was attributed to the respective decline in real milk and beef prices to the farmer of 22% and 20% in dual-purpose systems and of 41% and 27% in specialized dairy systems.

Figure 2 shows the percentage of the price paid per liter milk by consumers that is retained by milk producers. As can be observed, in 1989 milk producers received 70% of the final price; however, during the 1990s, this percentage was systematically reduced until it reached only 37% in 2001.

Figure 2.  Proportion of milk price paid by consumers that is retained by producers in Colombia compared to Costa Rica, Nicaragua, and Venezuela during the period 1989 to 2001 (Source:  CEGA 2002; DANE 2002; Cámara Nacional de Productores de Leche 2002; CORECA 200; ASOLEP 2000; Gaceta Ganadera 2002)

This dramatic loss occurred because the fall of milk and beef prices to the producer was never translated into a fall of prices to the consumer and, as a result, an important segment of the society (the consumers) did not benefit (Figures 3 and 4). As can be observed, the increases in milk and beef prices to producers were always below the inflation rate, whereas adjustments of prices to the consumer usually surpassed the level of inflation. During this period, the average inflation rate was 20.7% per year, but the increase in milk price to the producer was of the order of 15.4% per year (Figure 3) and that of beef price, 18.2% per year (Figure 4). As a result, the prices of milk and beef received by producers in 2001 were 41.7% and 34.7% lower, in real terms, than those received in 1989 (Aldana 1990). This explains why net income and returns on capital investment decreased significantly when the two studies were compared (Table 13).

Figure 3.  Inflation rate and adjustments in the price of milk paid by consumers and received by producers
 in Colombia during the period 1989 to 2001 (
Based on the weighted average of farm-gate price
received by producers for one kg of milk   and the consumer price of one liter of milk
in plastic bag containing 2% butterfat) (Source:  CEGA 2002; DANE 2002)

.

Figure 4.  Inflation rate and adjustments in the price of beef paid by consumers and received
 by producers in Colombia during the period 1989 to 2001 (
Source:  CEGA 2002; DANE 2002)

This trend also agrees with the fact that since 1991 Colombia entered the scheme of complete milk price liberalization. Prior to this date, the government regulated milk prices through a law that stipulated that the producer receive 70% of the final price paid by the consumer and that the processing plant receive the remaining 30% to cover processing and marketing costs (Aldana 1993).

Market concentration

Then the question is - if producers are receiving lower income and consumers are paying more, who has benefited? A probable hypothesis is that a good proportion of this difference has remained in the hands of a sector whose growth has been dramatic in the last decade: supermarkets. Gutman (1998) demonstrated that in Argentina this sector has captured the highest percentage of aggregate value created by the food chain compared with the processing and production segments.

As discussed in the Introduction, this structural change has modified the parameters underlying milk marketing. Nowadays, supermarkets have more negotiating power before the milk processing plants, not the popular neighborhood stores as in the past. As a result, this change has directly affected the mechanism of fixing the milk price received by producers. Supermarkets in Cali demand from dairy plants:

In addition, inventories at supermarkets have been minimized. Thus, its management has been transferred to milk processing plants, forcing them to make frequent trips to maintain adequate supply. These requisites applied by supermarkets to dairy plants in Cali are also applicable in other cities of Colombia and in other countries such as Brazil and Argentina (Farina 2002; Gutman 2002).

The strategy followed by dairy plants in Colombia, most of which are privately owned, has been to transfer these additional marketing costs to milk producers. To illustrate this hypothesis, Figure 2 contains the same information for Costa Rica, Nicaragua, and Venezuela. In Costa Rica, the supermarket boom has been similar to that occurring in Colombia (Reardon and Berdegué 2002); however, 85% of the milk collected by processing plants is controlled by farmer cooperatives, whose main objective is to ensure a market for the milk produced by their members at a price that guarantees a reasonable profit. In this case, Costa Rican producers organized in cooperatives have more negotiating power towards supermarkets and have retained almost 70% of the final consumer price, even though milk price has been liberalized since 1997 (Cámara Nacional de Productores de Leche 2002).

In the case of Nicaragua, producers succeeded in withholding nearly 65% of the consumer price during the 1990's, although all dairy plants are privately owned, as in Colombia. A possible explanation of why the same trend was not observed in Nicaragua as compared to Colombia is that only 15% of the milk volume marketed in Nicaragua is through supermarkets (Reardon and Berdegué 2002), while the remaining percentage is marketed through popular neighborhood stores. In Venezuela, where most milk processing plants are privately owned as in Colombia, this proportion showed a similar trend in the 1990's as in Colombia, although the fall was not as dramatic.

The case of Colombia is similar to that of Brazil, where, in an aggressive competition, supermarkets also transferred these marketing costs to dairy plants, and these in turn, to producers. This situation not only occurs with the dairy sector, but also with other agro-industries, as is the case of the poultry sector in Colombia (FENAVI 2003).

In reply to pressure from low profits, dairy plants began to encourage producers to install milk-cooling tanks at the farm level to reduce transport and collection costs (Farina 2002). To efficiently tap this technology, producers were initially encouraged to shift from once-a-day milking to twice-a-day milking, followed by machine milking and herd genetic improvement. As a result, the investments derived from the required cooling system were multiple, favoring intermediate and large producers at the expense of small producers who, because of the scale of their operations, could not make these investments. This issue has great social importance because thousands of small-scale milk producers are seeing their real income decrease and increasingly fewer options of positioning themselves in the market.

From 1997 to 2000, the number of milk suppliers of the 12 largest companies in Brazil decreased by 35% (and the average number of milk deliveries of the remaining producers increased by 55%). This reduction in the number of suppliers is represented by 60,000 producers who had to shift to either smaller companies or to the informal market of buy-sale of raw milk, or leave the dairy activity (Farina 2002). In Argentina, the dairy sector has also significantly reduced the number of producers supplying the industry, going from 30,500 producers in 1988 to 15,000 in 2001, which represents a 51% reduction in just 13 years (Gutman 2002). Producers who have remained in dairying have increased their production by 90%, although many who did this by accessing loans in US currency now face the risk of losing their properties because of the economic crisis in Argentina.

Similarly, dairy plants in Colombia also benefited from this growing differential gap between the milk price received by the producer and that paid by the consumer by modernizing the industrial sector as part of its medium-term marketing strategy. The increase in installed capacity of these companies over the past three decades has been similar (i.e., an increase in processing capacity of 2.5 million liters milk/day per decade). However, during the last decade, this increase mainly involved new equipment with technology to produce powder milk or ultra-high temperature (UHT) milk for long shelf life. Following this strategy, Colanta installed three modern powder milk plants over the past four years (Perez 2002). In 1996, Nestlé duplicated its capacity for producing powder milk and, in 1998, Parmalat tripled its capacity for producing and processing UHT milk. In 1998 Incolácteos and in 2001 Coolechera also invested in new plants for processing UHT milk. These technologies are much more expensive, but significantly prolong the shelf life of dairy products compared with traditional products that require refrigeration.

As a result, empirical evidence seems to point to the fact that the growing gap between the milk price received by producers and that paid by consumers stayed in the hands of supermarkets and dairy plants that expanded and modernized their processing capacity with long shelf-life technology.


Conclusions

The proposals and challenges presented in this case study have illustrated the problems and opportunities of the dairy sector in Colombia. However, these systems could represent similar situations in other countries of Latin America. Given the phenomenon of globalization and higher degree of efficiency that these systems are being exposed to, the issues of productivity, technological change, competitiveness, and markets, are critical and of enormous relevance for the performance and survival of the livestock sector in the next decades.

It is possible, in the short run, to adopt technologies that increase milk productivity and reduce production costs while profits are reduced as a result of falling real prices as occurred in Colombia during the 90's. However, in the long run, this situation is simply unsustainable.

In addition, it is highly probable that supermarkets are here to stay because their establishment responded to a structural change and expansion of the economy. Development agencies must internalize the fact that market-oriented policies will increasingly be "oriented to supermarkets". If one adds that in Colombia exist 3 or 4 supermarket chains that control the food retail food market, the conclusion is that sectoral policies will need to learn how to deal with a handful of giant companies. This is a huge challenge, and demands an urgent review of ideas and strategies (Reardon and Berdegué 2002).

Associations such as the National Dairy Association (ANALAC, its Spanish acronym) and the Federation of Livestock Producers (FEDEGAN, its Spanish acronym), who represent Colombia's dairy and livestock sectors, are the most affected by the structural change triggered by the supermarket boom. These associations are also responsible for monitoring price relationships, for actively lobbying within the dairy chain to facilitate negotiations with public and private entities, and for effectively documenting the impact of these market practices on the Colombia's rural livestock production sector. One issue that should be analyzed is the extent that market changes, in which the costs of modernizing marketing systems are directly transferred to the producer, can adversely affect technological adoption and hinder increased competitiveness (cost reduction) via technological change. Livestock production is a long-term investment where the decision to enter or leave the business is more complex than in the case of agricultural crops.

Bohórquez (2002), spokesman for the National Milk Producers' Association of Colombia (Asoleche, its Spanish acronym), argued that this task cannot be left to the good will of the private sector, but should respond to an ambitious food security strategy headed by the ministries of Health and Agriculture. In the near future, producers will not be able to limit their participation solely to the primary phase of production, but must expand their scope of action to other links of the chain, increasing their participation in the setting of prices to capture a greater share of the final price.

Small-scale milk producers need quicker, improved access to (a) information on technological change, productivity, and profitability, (b) education and training, and (c) credit to use collective actions as a way of coping with their constraint of operation size. These collective actions, either through cooperatives or associations, are important not only to buy and sell at better prices, but also to help small producers to adapt to new patterns with higher levels of competition. Otherwise, the new rules of game could induce a massive exodus of producers in the short term and in a relatively brief period of time.

The challenge

Livestock production is the most important economic activity of Colombia's agricultural sector, not only in terms of sectorial GDP but also because it responds to the country's unquestionable comparative advantages: abundant land and natural resources suitable for forage and livestock production.

These advantages have been eroded by the colossal interventions (mainly direct or indirect subsidies) of developed countries in favor of milk and beef production, the two categories receiving greatest direct support for production in OECD countries, considered the 'select' club of the world's most developed countries. The level of subsidies that OECD countries grant to their producers amounts to US$1 billion per day, which is more than six times the amount these countries invest in foreign aid to developing countries (UNDP 2002). Therefore, from the viewpoint of sensible public spending, in this part of the world it would not be viable to neutralize those subsidies by granting similar supports (Sanint 2001). Therefore, developing countries have to respond by continuously and permanently improving their levels of efficiency and competitiveness along the entire beef and milk production chain so that the livestock sector can continue to be viable for both society and producers; in other words, to continue to be profitable.

Efficiency goes hand in hand with technology and this depends on research and extension. However, investment in agricultural research is being reduced in Colombia. Figure 5 shows the total budget and net investment in research (i.e., operations) of Colombia's Corporation for Agricultural and Livestock Research (CORPOICA, its Spanish acronym), in constant 1993 dollars since the year the Corporation was established. As can be observed, there has been a real reduction of public funds allocated to agricultural and livestock research. The challenge consists in that producers in Colombia and Latin America assume greater control over livestock research by building alliances with local, regional, and international organizations, leaders in forages and livestock research. For this it is necessary that producers define and fund their own research agenda.

Figure 5.  Annual budget of the Colombian Corporation of Agricultural Research (CORPOICA)
for the period 1993 to 2001 expressed in constant US dolars of 1993 (Source:  MADR 2001)


Both CIAT and ILRI are interested in identifying common needs between countries and regions and in exploring the possibility of establishing alliances with the private sector to support a common livestock research agenda led by the private sector for their benefit. This alliance should have a non-profit, semi-private, limited inter-institutional scheme, in which committed participants provide funds to execute a regional livestock research program and to facilitate the dissemination of results.
 

Acknowledgements

The authors wish to thank the International Livestock Research Institute (ILRI) and the System-wide Livestock Programme (SLP) for partially funding this study.


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Received 15 August 2003; Accepted 22 August 2003

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Livestock economist. International Center for Tropical Agriculture (CIAT) and International Livestock Research Institute (ILRI). F.Holmann@cgiar.org

Agricultural economist, research associate. CIAT. Cali, Colombia. L.Rivas@cgiar.org

Animal nutritionist, Professor, School of veterinary medicine, Universidad Nacional de Colombia. Bogotá. Djcarull@veterinaria.unal.edu.co

Animal scientist, Professor, School of Agricultural Sciences, Universidad de Caldas. Manizales. Brivera@cumanday.ucaldas.edu.co

Animal Scientist, Professor, School of Agricultural Sciences, Universidad Nacional de Colombia. Medellín. Conisilvo@epm.net.co

Veterinary and Animal Scientist, Dean, School of veterinary medicine and Animal Production, Fundación Universitaria San Martin. Barranquilla. Sigupe@latinmail.com

Microbiologist, Professor, School of Animal Science, Universidad de los Llanos, Villavicencio. Telephone (57-0986) 69-8662 and Fax (57-0986) 69-8535.

Animal Scientist. Assistant. CIAT. Cali, Colombia. A.Medina@cgiar.org

Geographer, CIAT. Cali, Colombia. A.Farrow@cgiar.org