Productive and reproductive profile of dairy farms from Realeza, Paraná, Brazil

N L Santos Júnior, A Pinto Neto, F Skonieki, M F Mota, A C Martinez¹, L S Merlini² and R C A Berber³

Campus Realeza. Universidade Federal da Fronteira Sul.
Rua Edmundo Gaievisky, 1000  Caixa Postal 253 CEP: 85770-000, Brazil
adalgiza.neto@uffs.edu.br
¹ Campus Umuarama. Universidade Estadual de Maringá
² Campus Umuarama. Universidade Paranaense
³ Campus Sinop. Universidade Federal do Mato Grosso

Abstract

Milk production is explored in many rural properties as a main income. In Brazil, 64.4% of producers making less than 50 liters of milk a day represent approximately 800,000 small family farmers. Realeza, located in southwestern Paraná, has approximately 540 milk producers with 11,000 animals, with the majority in family production system. The correct reproductive management is essential to obtain the calving interval close to 12 months. In this sense, the present study aimed to evaluate the productive and reproductive profile of the dairy farms fromRealeza, Parana, Brazil. To this end, 255 milk producers responded to a questionnaire during the period from November/2014 to August/2015. Data were analyzed by ANOVA, the means were compared by Duncan test, and correlations by the Pearson method, using the SAS computer program.

The results showed that 96% of the workforce is familiar and 91.7% have properties of up to 50 hectares, of which 64.2% of the herd consist of crossbred breeds (undefined breed and 19.1 crossbred Holstein and Jersey). Milk production is the main activity in 67.8% of the properties. The exclusive use of artificial insemination (AI) is done in 36.1% and 6.2% is associated with natural mating (NM). The presence of own breeding was noted in 93.9% of 63.9% of properties that used NM, and 98.1% of breeders have never been tested for andrological examinations and only 23.3% of the owner wanted to test free bulls. Still, the results showed that 2.75% of the properties used timed AI as a tool in increasing the number of breeding females, females in lactation and milk production. In conclusion, the present study data, considering the significant number of properties analyzed, showed that milk production in Realeza is little technified and properties are characterized as family subsistence production.

Keywords: cattle, milk production, subsistence production

Introduction

Brazil is fourth largest milk producer in the world ranking and, in recent years, Brazilian production increased from 21.6 billion liters in 2002 to 32.3 billion in 2012. The areas of highest concentration of milk production are in the Minas Gerais, Goiás, Paraná, Rio Grande do Sul, São Paulo and Santa Catarina (IBGE 2012).

The State of Paraná had a significant increase in the milk production chain. In 2012, milk production recorded 3.9 billion liters of milk / year. Its the main dairy basins are located in the regions East-Central, West and South-West State, which concentrate 48.5% of producers and 53% of production. The Southwest region was the fastest increasing region at levels ofproduction and herd, being currently the largest dairy region of Parana state, producing about 1.1 billion liters of milk / year from 321,000 animals in production (SEAB 2013).

Realeza, in its rural areas, is made up of small properties characterized by family farms exploring dairy cattle (approximately 540 producers with 11,000 animals) as a major financial impact activity in family income (EMATER 2013).

In Brazil there are few studies on the adoption of technologies in rural areas. Every year new alternatives emerge.However, it’s not known exactly the degree of adoption by producers. Unfortunately, technologies that don’t consider the opinion of farmers meet only part of the needs of producers (GORDO 2011). Currently, there are several studies related to milk quality, but they lack information on the productive and reproductive herd management considering the regional and/or local, leading to growth in productivity. Considering the impact of milk production in Paraná Southwest region, especially Realeza area, this study aimed to establish the productive and reproductive profile of the dairy farms of this municipality.

Materials and methods

Realeza has an area of 353 Km², located 480 meters above sea level, 25°46 '49' 'South Latitude and 53° 32' 37 '' West Longitude (IBGE 2012). Considering the data published by EMATER (2013) according to which Realeza has approximately 540 dairy farms, 255 properties were visited during the period from November 2014 to August 2015.

In each property, we applied an objective questionnaire to farmers on data ownership and herd questioning about location, size of property, principal and secondary activity of property, number of animals, number of lactating cows, breeds, use of AI, semen origin, use of sexed semen, presence of inseminator in property, NM usage, presence of bull in the property, bull stay with females, the use of NM advantage and AI mating data, reproductive evaluation of the bulls, use of reproductive biotechnologies and type of labor used.

The data were tested by variance (ANOVA) being theaverages tested by Duncan test, and correlation analysis by Pearson's method, considering a 5% significance using the statistical program SAS (Statistical Analysis System).

Results and discussion

Of the 255 dairy farms it was found that family labor is prevalent in 96% (244/255) and in 4% (11/255) there was at least one employee to assist in performing the tasks. These results were similar to those reported by Pereira and Bazotti (2010) and Oliveira and Mera (2011), who reported that 88.4% and 84%, respectively of labor used in milk production was exclusively family farms in the Southwest Parana. Souza and Waquil (2008) state that milk production is very common in Brazilian family farming. According to the authors, the absence of barriers to start the activity, product quality both for domestic consumption and for marketing or processing, obtaining a monthly income, the possibility of using non-noble land and the intensification of family labor are conditions that facilitate the deployment of milk production.

The results of Table 1 suggested that the increase in production and the number of animals could generate more work, resulting in employing workers for these activities as related by Ozaki and Neves (2013).

The average milk production (liters/cow) was similar in properties (Table 1), regardless of the type of labor (hired or family), which could be explained by genetic standardization and management of animals. Of all animals, 45.1% (3571/7917) were undefined breed and 19.1% (1513) were crossbreds (Holstein and Jersey) (Figure 1). These results differed from Ipardes (2009) reported that 63% of Paraná Southwest herd is composed of Holstein cattle (42%) and Jersey (21%), and the remaining 37% of crossbred cattle.

Figure 1. Breeds of cattle in dairy herds in Realeza, Paraná.

As to the size of the properties, 31.7% (81/255) are between 10 and 20 hectares, 23.9% (61/255) of 20 to 30 hectares, 22.7% (58/255) between 5 to 10 hectares, 8.2% (21/255) over 50 hectares, 5.1% (13/255) less than 5 hectares, 4.3% (11/255) from 30 to 40 hectares and 3.9% (10/255) between 40 and 50 hectares. The southwestern Paraná is characterized by a major presence of small properties. Pereira and Bazotti (2010) reported that 90% of family farming establishments have less than 50 hectares, coinciding with the results of this study.

The properties presented 31.0 +20.8 animals in the herd, 19.9 +13.8 females of reproductive age and 12.3 + 9.5 lactating females. The daily production was 135+161 L/property and the milk productionwas 9.5 + 4.4 liters/cow/day. The results of Table 2 were similar toBazotti et al (2012) reported data of properties from Paraná State.

The area of farm affects milk production per cow, but it cannot be said that the increase in the farm size would lead to an increase in production of milk liters per cow (Table 2).

Milk production is the main activity in 67.8% of the properties and the only activity at 38.0%, followed by the production of grain (27.8%), beef cattle (1.5%), tobacco (1.5%) and poultry (1.2%). These data coincide with those presented by the IBGE (2006). Secondary activities, milk production is observed in 31.7% of the properties, followed by production of grains (24.3%), tobacco (3.53%), swine (0.8%), beef cattle (0.8%) and poultry (0.4%). Perhaps these results are justified by the purpose of increasing income and, concomitantly, to find ways of productive employment for their children, and most of thefarmers have to deal with diversification of land use. In addition, the diversification of income results in greater financial security to families involved (Simonettiet al 2011).

The southwestern Paraná region has been reoriented since the 2000s, when the systems almost exclusivelyfocused on the production of grain began to incorporate the production of milk for delivery to dairies. Milk production provides a source of monthly income, unlike grain production that pays only seasonely.Milk production allows for greater liquidity, since owners can sell the herd animals (Khatounianand De Lannoy2006).The diversification of production systems with the integration of grain and milk production was observed in 52% (132/255) of the properties.

As to biotechnologies of reproduction applied to cattle, 36.1% of the properties just used the AI, and 6.3% AI was associated with NM(Table 3). All semen used came from AI centers and 12.0% were using sexed semen. Bazotti et al (2012) reported that in Paraná AI is used in 32.6% of the herd, similar to that found in this study.

Currently the spread of IA technique associated with quality of bulls and low cost do not justify the use of natural breeding in dairy cows (Ulrich 2010). Several researches have proved that AI and synchronization ovulationare affordable and efficient. Thus, regardless of herd size, the number of females of reproductive age and the number of females in lactation, IA is recommended because of the advantages that it presents (Alvarez 2008). Usually, the milk production(liters/day and liters/cow)was higher in herds using IA to herds using NM, as observed in this experiment (Table 3).

From the properties that used AI, 29.63% had inseminator in the property and 70.4% hired this service. 32.4% had semen stored in the property, being 15.7% for community use and 16.6% for private use. The other properties (67.6%) did not have bottled nitrogen. Although few drawbacks of the IA, the success of AI may be negatively influenced by the need of observation of animals in estrus, by poorly trained labor and/or the difficulty of storing semen (Alvarez 2008). Similar aspects were found in this study (Table 5).

The bull remained with the cows in 68.1% of the properties and in 31.9% the bull was used only with female presence in estrus. The mount was noted in 50.9%. In this sense, a lack of notation of mounts entails difficulties forplanning the drying of lactating animals, and predisposes to disease if it is not done at the right time. Ferreira (2001) reported that few dairy farms perform animal identification and adequate zootechnical bookkeeping.

In 98.1% of the properties the bull has never been evaluated and in 1.8% the producers made this evaluation at least once. It is known that bulls remain infertile throughout the reproductive life, and can present problems at any time, thus the andrological exam ensures the reproductive profile for a period of 60 days (CBRA 2013). In addition, only 23.3% of the owners have shown interest in making the andrological exam for free and 76.6% have no interest in having this test done. The lack of reproductive ability of bulls negatively impacts on the reproductive efficiency of livestock (CBRA 2013). Still, only 2.75% (7/248) of the properties performed artificial insemination in fixed time (TAI) (Table 4).

Conclusion

• The farms fromRealeza are characterized by having family labor, less technical training, whose reproductive and productive profile of their herds need to be improved. In addition, the integration of agricultural activities was also detected as better security conditions and economic returns of farmers.
  
  
• Thus, the diagnosis of this study will form the basis for implementing more efficient productive strategies.

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Received 27 May 2016; Accepted 27 July 2016; Published 1 September 2016

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