Factors influencing milk production on small farms in Alegre, Espírito Santo: a multivariate analysisABSTRACT
This study aimed to identify the factors influencing milk production in Alegre, a municipality in the Caparaó region of Espírito Santo, from May 2023 and May 2024. Data from 30 farms were collected through questionnaires addressing demographic characteristics, management practices, and milk production specifics. Descriptive statistics and principal component analysis (PCA) identified key determinants of productivity. The findings revealed that most farms were small-scale (86.66%) and primarily relied on family labor (76.67%), with only 43% receiving technical assistance. The average herd size was 49.6 ± 35.5 animals, 32% of which were lactating cows, producing an average 6.9 ± 6.1 liters of milk per day. Productivity showed significant variability, influenced by management practices such as feed utilization and rotational grazing systems. The study concluded that dairy farming in Alegre, ES, exhibits considerable productivity differences due to diverse management approaches and the predominance of family labor. Strategies such as improved forage management, the adoption of rotational grazing systems, and targeted feed supplementation hold potential to enhance productivity. Nonetheless, challenges like dependence on family labor and limited disease management continue to restrict productivity, highlighting the need for expanded technical assistance and modernization efforts.
Keywords:
family farming; cattle; productive efficiency; survey; pastures
RESUMO
O objetivo do estudo foi identificar os fatores que afetam a produção de leite em Alegre, município da região do Caparaó, no Espírito Santo, no período de maio de 2023 a maio de 2024. Os dados foram coletados em 30 propriedades rurais, por meio de questionários abrangendo caraterísticas demográficas, práticas de manejo e particularidades da produção de leite. A estatística descritiva e a análise de componentes principais (PCA) foram utilizadas para identificar os principais determinantes da produtividade. Constatou-se que a maioria das fazendas eram pequenas (86,66%) e dependiam predominantemente de mão de obra familiar (76,67%), sendo que apenas 43% recebiam assistência técnica. O tamanho médio do rebanho era de 49,6 ± 35,5 animais, dos quais 32% eram vacas em lactação, produzindo 6,9 ± 6,1 litros de leite por dia. A produtividade mostrou uma variabilidade influenciada pelas práticas de gestão, como a utilização de alimentos e sistemas de pastoreio rotativo. O estudo concluiu que a pecuária leiteira em Alegre, ES, apresenta considerável variação de produtividade devido às diferentes abordagens de manejo e à predominância da mão de obra familiar. Estratégias como a melhoria do manejo alimentar, a adoção de sistemas de pastejo rotacionado e a suplementação alimentar direcionada têm o potencial de aumentar a produtividade. No entanto, desafios como a dependência da mão de obra familiar e o manejo limitado de doenças continuam a limitar a produtividade, evidenciando a necessidade de ampliar a assistência técnica e os esforços de modernização.
Palavras-chave:
agricultura familiar; bovino; eficiência produtiva; inquérito; pastagens
INTRODUCTION
Livestock production is vital to Brazil's agribusiness, contributing around 27.6% of the country's GDP and supporting extensive production chains (PIB…, 2022). As home to the world's largest commercial cattle herd, the Brazilian livestock industry continually evolves to meet rising demand for animal products. The annual milk production, ranging from 33.3 to 35.3 billion liters, reflects an increase in per capita milk consumption in Brazil, which is between 167 and 176 liters per year (Anuário…, 2023).
Espírito Santo is an example of this growth; in 2022, the state was responsible for producing 345 million liters of milk out of the total 35.3 billion liters produced nationwide (Produção…, 2023). The municipality of Alegre stands out among the three municipalities with the largest milk herd in the State, and the fourth concerning quantity produced, characterized mainly by the presence of small farms and family agriculture. The municipality has 623 milk producers from 2,318 farms. However, more than 80% of these producers do not have access to technical assistance, which exacerbates the existing challenges and can affect the economic viability of production (Cidades…, 2022).
Although studies on the region's production profile of the region are limited, existing research highlights the significant socioeconomic importance of dairy farming despite the challenges it faces, especially in terms of health management and vaccination (Viana and Zanini, 2009). Considering this, understanding the factors that influence milk production on small farms in the municipality of Alegre is essential, not only to identify the challenges faced by producers, but also to propose interventions that can promote improvements in production efficiency and the economic sustainability of these farms. In addition to contributing to the strengthening of the local production chain, this study provides input for public policies and technical assistance programs aimed at the dairy sector. The use of descriptive and multivariate analyses allows for a more comprehensive and integrated view, identifying the main bottlenecks and highlighting potential solutions that can directly impact the quality of life of producers and the competitiveness of the sector in the State of Espírito Santo.
If these issues are not properly identified and addressed, they could potentially reduce the profitability of production. Therefore, there is a need to identify the main challenges faced in production and to look for solutions and guidelines that can help and support producers. In this context, the aim of this study was to identify the factors influencing milk production in the municipality of Alegre, in the Caparaó region of Espírito Santo, using descriptive and multivariate analysis to examine different production data and to identify the main problems encountered on farms.
MATERIAL AND METHODS
The study was conducted between May 2023 and May 2024, and involved the collection of data and samples from 30 farms in Alegre, located in the Caparaó region of Espírito Santo, in southern Brazil. This region spans an area of 756.860km2 and is situated 205.2km from the state capital. Alegre is positioned at a latitude of 20°45'49'' and a longitude of 41°31'57''. The municipality has a population of 29,177 with a density of 38.55 inhabitants per square kilometer, and a GDP per capita of 19,255.36 reais (Cidades…, 2022).
Ethical approval was obtained from the Human Ethics Committee of the Federal University of Espírito Santo (UFES) under registration number 060638/2023, CCAE 70252123.0.0000.8151. Data collection was conducted using a survey designed to gather comprehensive information on socio-economic factors and agricultural practices, including animal management, nutrition, health, and reproduction. The survey, completed by either the farm owner or the technical manager, was organized into several categories: general farm and producer data, general management, nutrition, facilities, health, and reproduction.
The farms were selected based on milk production as the main activity and the suitability of the location regardless of infrastructure. The goal was to identify productive environments that could provide comprehensive data needed to identify factors influencing production. The survey was completed by either the farm owner or the technical manager, ensuring accurate and relevant information on each farm's operations.
Mapping with the territorial limits of the Municipality of Alegre and the Flat Coordinate System, UTM projection, Zone 24 South and SIRGAS 2000 Datum, incorporating the geographical coordinates of the studied farms.
The data were organized and initially analyzed descriptively using Microsoft Excel 2024. Principal Component Analysis (PCA) was then performed to identify the primary factors influencing milk production in the region. The PCA was carried out using R statistical software with the FactoMiner and Factoextra packages.
The results were interpreted through visualization techniques: vectors at angles close to 0° indicated positive correlations, angles near 180° represented negative correlations, and angles of 90° signified uncorrelated variables. The length of the vectors illustrated the extent of data variation, while the proximity of arrows indicated the strength of relationships. To ensure comparability, the data were standardized by subtracting the mean and dividing by the standard deviation. Visualizations, including PCA plots, barplots, and dotplots, were generated using R version 4.2.2 (2022) and the ggplot2 package.
RESULTS AND DISCUSSION
The 30 farmers involved in the research collectively manage a total of 1,488 animals, of which 481 are lactating cows, producing a combined total of 2,943 liters of milk per day (Tab. 1). These farms span a total area of 1271.5 hectares (ha). However, there is considerable variation in farms sizes, with an average area of 42.3 ± 40.6 ha, ranging from as small as 2 ha to as large as 154 ha. Notably, 86.66% of these farms are less than 96 ha, which corresponds to four fiscal modules in the municipality (Programa…, 2023), classifying them as small properties according to Law 8.629 of February 25, 1993, which defines small properties as those with up to four fiscal modules (Brasil, 1993).
Descriptive statistics of the variables related to the production data of the farms evaluated in Alegre - ES, analyzed with 95% confidence for the mean
A similar pattern was observed by Werncke et al. (2016), who identified farms in different regions of Brazil with a mean area of 30±20 ha. These results, along with the higher milk production achieved in smaller areas dedicated to dairy farming, demonstrate the viability of dairy systems on small farms. Such systems excel compared to other agricultural activities due to their competitiveness on a small scale and lower production costs, mainly attributed to the use of family labor.
Most producers relied on family labor, representing 76.67% of all respondents in the Alegre-ES region. Only 6.67% of them reported using hired labor, while 16.67% used a combination of both family and hired labor. This reliance on family labor is largely due to the high cost of hiring outside help, which can make the activity unprofitable. However, only 6.7% of the farms evaluated produced more than 200 liters of milk per day during the study period, as production must reach 200 liters per person per day for dairy farming to be profitable (Oliveira Silva et al., 2018). The use of family labor is reinforced by the significant presence of owners who live on the properties where milk production takes place, representing 83.33% of the cases.
The predominance of family labor, observed in 76.67% of the producers interviewed, reflects a common scenario in small farms in Brazil, where the cost of hiring external labor is a significant barrier. This model, although economical, has important limitations for growth and production efficiency. The low adherence to hired labor, found in only 6.67% of cases, can be attributed to the limited economic structure of the properties, which limits investments in increasing scale or technologies that require greater specialization in management.
This dependence on family labor is aggravated by the fact that only 6.7% of farms reach the level of production considered profitable (200 liters per person per day). This data shows that most farms are unable to reach production levels sufficient to guarantee the economic sustainability of the activity, making them more vulnerable to market fluctuations and input costs.
In addition, the high percentage of owners who live on the properties (83.33%) suggests a direct relationship between home and work, which can be advantageous in terms of dedication and control of activities. However, this proximity can also create challenges related to work overload and the lack of a clear separation between personal and professional life, which affects the quality of life of the families involved.
Finally, this scenario reinforces the importance of technical assistance and training programs for small producers, which can help optimize the use of family labor, increase productivity per person and, consequently, improve profitability. Solutions such as the adoption of accessible technologies, efficient management and associations can offer viable alternatives to address the structural and economic challenges faced by these farms.
The study also showed that on all the farms where only hired labor was used, the owners lived away from the farm. This data underscores the socio-economic importance of dairy production in rural areas and highlights the need for producers to be directly involved in the supervision and management of production to ensure better control.
As presented in Table 1, producers had an average of 49.6±38.5 animals, ranging from 6 to 160. Of these, an average of 16±9 were lactating cows (32.3%), with daily milk production averaging 98.1±93.7 liters, ranging from 20 to 500 liters. According to Viana and Zanini (2009), in a study also conducted in Alegre - ES, farms at that time had a maximum of 40 lactating cows with an average production of 200 liters per day, indicating a potential shift in the city's dairy production systems. This fact reveals an interesting panorama of the characteristics of dairy production in Alegre-ES, highlighting both advances and challenges in the production context. It can be suggested that changes in production systems have occurred, possibly because of changes in the profile of properties, management or economic context. The reduction in the average number of lactating cows and the variation in daily production per farm point to a diversification of production systems, reflecting different strategies among producers, possibly influenced by resource limitations or a focus on local markets.
The results observed in this study are like those observed in Santa Catarina by Werncke et al. (2016), where the average herd was 51 cows, with 29 lactating cows on an average total area of 30±20 hectares. Despite the similarities, production in the southern region of the country reached 258.2 liters, which could be attributed to the different breeds used, as the Jersey breed was predominant in the south. This suggests that investment in genetics and technology could be a way to improve production efficiency in Alegre-ES.
The average milk production per cow per day in the study was 6.9 ± 6.1L, ranging from 2.5 to 35L (Table 1). According to the IBGE (Cidades…, 2022), the municipality's total production was 13,281,000L/year with 10,466 lactating cows, resulting in an average of 3.47L of milk cow per day. This discrepancy may suggest a greater selection of animals and producers in the sample studied, or the presence of a greater number of producers with satisfactory production volumes, which raised the average. However, the municipality's production remains below the Brazilian average, which in 2023 was 2.7 thousand liters per cow per year (Estudo…, 2023b), approximately 7.39L per cow per day. These results indicate that improving dairy production in Alegre-ES depends on a multifaceted approach, including investments in genetics, nutritional management and technical training. In addition, public policies that encourage the adoption of accessible technologies and promote technical assistance can play a key role in increasing the production levels and competitiveness of the dairy sector in the municipality.
In total, seven breeds were identified (Fig. 2), with most animals belonging to the dairy production type, particularly those with genetic composition that is a cross between Gyr and Holstein, which are present on 96.67% of the farms. Additionally, about 36.6% of the farms raise Nelore animals, which are primarily beef cattle. The preference for Nelore bulls is driven by the ease of selling the calves. However, this choice directly impacts the lower milk performance of replacement animals, thereby affecting the specialization of the cows, as observed by Azevedo et al. (2011).
This research showed that 43% of producers receive some form of technical assistance, while 57% do not. These results differ from the expectations generated by the data collected by the IBGE (Produção…, 2023), which indicate that more than 80% of producers in the municipality do not receive technical assistance. However, this figure refers to all the 2318 farms and not only to the 623 milk producers, indicating a higher presence of technical assistance within the dairy sector compared to other agricultural activities.
The number of times cattle breeds were reported to be present in the herds of surveyed farms.
This higher coverage in the dairy sector may be related to its economic and social importance in the region, with special efforts being made to support milk production. However, the fact that 57% of dairy farmers still do not receive any support indicates the need to expand the provision of technical services, as technical assistance is a crucial factor in improving production efficiency, farm management and product quality.
Expanding technical assistance can help modernize management practices, adopt technologies and train producers, thereby strengthening the sector and reducing regional disparities. Targeted programs and effective public policies are key to overcoming this challenge and ensuring that more producers have access to the support they need to improve their production and economic sustainability.
Brachiaria was the most used forage plant, with 23.3% of the producers surveyed using continuous grazing and 76.6% employing rotational grazing. The farms had an average of 4.6 ± 3.9 paddocks, ranging from 1 to 18, and an average stocking rate of 1.17 animals per hectare (number of animals/total farm area). Notably, the farm with the largest number of paddocks also had the highest milk production per day, achieving 38.5 liters per hectare. This result is higher than that reported by Santos et al. (2017), where only four out of ten livestock practiced pasture rotation. Furthermore, the milk production per cow per day was 3.44 ± 1.1 L in their study, compared to 6.9 ± 6.1 L found in this study, demonstrating the correlation between improved forage utilization through pasture rotation and increased milk production.
These data reinforce the importance of proper pasture management for dairy production. The dominance of Brachiaria as a forage species, together with the high adoption of rotational grazing, shows a positive trend towards more efficient management practices. By allowing pastures to rest and recover, rotational grazing helps to improve forage quality and availability, which has a direct impact on herd productivity.
These results highlight the need to promote the adoption of pasture management practices, especially rotational grazing, among producers through technical assistance and training. The combination of good forage selection and rotational management can be a viable strategy for increasing milk production in a sustainable manner, contributing to the profitability and sustainability of dairy farms in the region.
All respondents reported providing mineral supplements to their animals, with 41.9% using commercial mineral mixes of various brands, 19.3% using only white salt (sodium chloride), 16.1% using white salt with a high concentration of garlic, and 22.5% using a combination of white salt and a homemade mineral mix. These findings indicate that many of the producers in the study already recognize the importance of mineral supplementation, which is essential due to the variations in the quality and quantity of the animals’ feed throughout the year. Proper mineral supplementation enhances animal productivity but can account for up to 30% of the production costs for pasture-raised animals (Giacomel et al., 2022).
The results show that producers are aware of the importance of mineral supplementation and nutritional management strategies, especially during periods of drought, although there is still room for improvement. The fact that all the respondents use some kind of mineral supplement shows significant progress compared to traditional practices, recognizing the importance of this input for the productive and reproductive performance of the animals. However, the variability in the practices adopted, with 19.3% using only white salt and 22.5% opting for homemade combinations, may indicate gaps in access to technical advice and standardization of supplementation.
In times of drought, different management strategies are adopted to meet the nutritional needs of animals. In this study, we mainly observed the supply of sugarcane (Saccharum officinarum L.) and elephant grass (Pennisetum purpureu), in addition to the use of maize silage or high-productivity forage as the main forage conservation method, adopted by 76.6% of producers. This result is lower than that found by Werncke et al. (2016), where 92% of producers ensiled some plant material. Besides using additional roughage, concentrated supplements such as cornmeal (13.3%), balanced commercial feed (3.3%), and protein mineral salt (13.3%) were also identified. Only one producer reported that they did not use any kind of nutritional management during the dry season.
The use of alternative roughage, such as sugar cane and elephant grass, demonstrates concern about nutritional challenges, but nutritional strategies such as the use of preserved foods, such as silage, hay and pre-dried foods, could also be options to consider. These findings underscore the need to expand access to technical assistance and training programs that can help producers improve the planning and implementation of more balanced nutrition strategies. Promoting the use of forage conservation technologies, combined with more efficient mineral and concentrate supplementation, can significantly contribute to the resilience of farms in the face of climate variability, thereby promoting greater sustainability and profitability in dairy production.
The primary source of water for animals on all 30 farms was natural: from rivers, streams, dams, and reservoirs, which are generally of low quality. The supply of poor-quality water can increase the level of pathogen infestation, causing diarrhea, mastitis outbreaks, and problems with the quality of the farm's milk (Guerra et al., 2011). According to Resolution 357 of the National Environment Council (CONAMA), which classifies water for use, the minimum standard for animal watering would be water of class 3, which imposes limits on the parameters of water for animal production (Brasil, 2005). Thus, it is crucial to raise awareness among producers and technicians in the region about the importance of improving water supply for the herd, given its impact on herd health and milk quality.
Regarding the cooling and storage of milk after milking on the evaluated farms, it was found that 50% of producers used community expansion tanks, 46.6% used individual tanks, and 3.3% did not send their milk to dairies. Despite the widespread use of cooling tanks (96%), encouraged by dairy cooperatives and aimed at complying with Normative Instruction No. 76 of November 26, 2018 (Brasil, 2018), which requires milk to be stored at 4 degrees Celsius. The use of community tanks raises concerns about milk quality due to the risk of contamination (Siqueira et al., 2014).
The single tank option offers greater control over milk quality by eliminating the risk of cross-contamination between batches. This model, although more expensive, may be an alternative for producers seeking to add value to their product, especially in more demanding markets. However, these results point to the need for continued efforts to educate and train producers in milk hygiene, transport and handling, especially for those who rely on shared tanks. In addition, incentives for the purchase of individual tanks or improvements to community systems can help ensure milk quality, thereby strengthening the competitiveness and sustainability of the region's dairy sector.
In reference to milk collection, the main forms found were manual milking (50%) and mechanical bucket milking (50%), while closed circuit milking was less common (10%). This outcome denotes an evolution in the municipality compared to the study by Viana and Zanini (2009), where only 6.66% of the milking was mechanized, reflecting a greater investment in technology. The adoption of automation is directly related to the scarcity and high cost of labor in dairy farming, which also affects the competitiveness of the farm in the agrarian and commercial trade sectors (Anuário…, 2023a). Mechanization of milking not only optimizes management but can also improve milk quality by reducing the risk of contamination during the process. In addition, this modernization allows producers to become more competitive in the market, especially in chains that demand high standards of hygiene and efficiency.
These results suggest the need for incentive programs to promote access to more advanced milking technologies, such as specific credit lines, subsidies for equipment and technical training. Such initiatives could accelerate the modernization process, making farms more efficient and resilient to market changes, while improving working conditions and product quality.
Concerning health management, all producers reported having implemented compulsory vaccination against brucellosis. The vaccination rate in the region is currently higher than that found by Viana and Zanini (2009), where only 40% of the farms were vaccinated, and according to the IDAF (Dados…, 2022), the vaccination rate for brucellosis in female cattle aged three to eight months in Alegre - ES was 76.7% in 2021. This progress can be attributed to the implementation and compliance with the normative instruction that established the National Program for the Control and Eradication of Brucellosis and Animal Tuberculosis (Brazil, 2001).
Vaccination against rabies and clostridiosis was present in 93% of the farms. The widespread vaccination against rabies justifies the only confirmed case of rabies in the municipality in 2021, out of 50 cases recorded in the state (Dados…, 2022). However, cases with neurological signs were also reported, which may indicate underreporting of the disease in the region. The problems of underreporting and difficulty in diagnosis have been highlighted by MAPA (Brasil, 2017).
The most prevalently documented diseases were bovine babesiosis and anaplasmosis complex, accounting for 30% of cases. This finding is in line with the study by Freitas Junior et al. (2008), where ticks were reported as the main parasite found by 88% of producers in the Caparaó micro-region, a factor mainly attributed to the difficulty in controlling ticks, resulting in increased transmission of the agents of bovine parasitic sadness.
Mastitis, reported by 10% of the producers in this study, was also observed by Vieira et al. (2013) in the same municipality, where 73.24% of the udder quarters evaluated showed bacterial growth on microbiological examination, indicating a persistent problem with good milking practices. There were also reports of diarrhea (6.6%), leptospirosis (3.3%), papillomatosis (3.3%), dewlap oedema (3.3%), uterine prolapse (3.3%), clostridiosis (3.3%) and rabies (3.3%), while 43.3% of producers reported no common diseases in the herd. The occurrence of cases with neurological signs, with rabies suspected in 3.3% of farms, supports the suspicion of under-reporting and diagnostic difficulties previously mentioned.
To identify the most significant patterns in the data collected, a principal component analysis was conducted, including a total of fifteen variables. The variables with the greatest influence on the variability of the data for principal components 1 and 2 were Number of animals, Total milk (L/day), Total farm area, Milk production (L/ha), Lactating animals, Type of production, Milk (L/ani/day) and Living. In contrast, the variables Number of paddocks, Milking system, Incidence of disease, Technical assistance, Type of production, Labor, Feed supplementation and Bulk tank milk had no significant effect on the data set (P>0.05).
Evaluating the correlation between the variables (Fig. 3), the positive influence of the number of animals on the holdings is explained by the fact that, on average, holdings with a higher number of animals tend to have higher production, in addition to having a higher number of lactating cows, more area, and more paddocks to accommodate the animals. Similarly, the incidence of diseases and technical assistance also showed a strong positive correlation with the other variables due to their direct influence on milk production. The only variable that showed a negative correlation for dimensions 1 and 2 was bulk tank milk, which may be due to the large number of community expansion tanks (50%) found in the study, which can negatively impact production.
Correlation of Variables with Principal Component analysis plot for dimensions 1 and 2, according to the contribution of the variables.
Figure 4 illustrates the contribution of these farms correlated with the variables, showing how much each variable can contribute to the productive improvement of the farm. The figure also shows that some farms, such as 1 and 6, are well correlated with variables such as the number of animals and the area of the farm, but they are quite far from the production variables such as milk production/day, average production/ha and average production per animal. Therefore, there is still a need to improve the productivity of these areas and the animals. On the other hand, farm 16 made a significant contribution to the production variables, although it did not have a strong influence on the other variables, showing a higher productivity per area.
Farm 16 stood out because it showed above-average results in all aspects evaluated. Key highlights include its higher milk production per day (500 liters compared to the average of 98.1 liters), higher productivity per area (38.4> 6.3 liters/ha) and higher number of paddocks (18> 4.6 paddocks). In addition, this farm had the second highest average production per animal (17.9> 6.9 liters) and the fourth highest average number of lactating animals (28> 16 females). The only variable where this farm was below average was the total number of animals (34 < 49.6 animals), which together with the other results indicates a high level of technology and specialization on this farm.
Farm 28 was the closest to farm 16 due to its above average results in the production variables, notably having second highest milk production per day in the study (300L) and the third highest average milk per animal per day (10.34L). The highest average milk per animal per day went to farm 17 with 35L, well above the second place (F.16 = 17.86L), despite being the farm with the lowest number of animals (6) and lactating animals (2). On the other hand, farms closer to the center of the graph, such as farm 5, exerted less influence on the study variables.
Principal component analysis plot for dimensions 1 and 2, according to the contribution of variables correlated with farms.
Analyzing the sets of variables by means of cluster analysis (Fig. 5), four groups of elements have been classified, which are similar concerning the variability of the data. The first cluster (Cluster 1) includes the variables “bulk tank milk” and “living”, because statistically 60% of the farms where producers lived off the farm used individual expansion tanks.
Cluster 2 included more productive and directly correlated variables, as total milk production is strongly influenced by the number of animals in lactation. There was also a correlation between these variables and the number of paddocks available, since good management and use of forage are necessary to increase production and the number of lactating animals. For instance, farms that efficiently use forage within a paddock system can sustain a higher number of lactating animals and, consequently, achieve a higher total milk production per day. Another variable in this group was milking systems, notably because the farms with the highest number of lactating animals used mechanical milking.
Cluster analysis of principal components for dimensions 1 and 2. The variables within each circle are those that are most like each other concerning the variability of the data.
The analysis also showed that there was a correlation between milk production (L/ha), milk (L/ani/day) and feed supplementation in cluster 3, emphasizing the importance of feed supplementation in increasing average productivity. Higher production per animal and per hectare indicates better utilization and greater production efficiency.
Cluster 4 showed a complex grouping with six variables. There was a tendency for producers with a larger total area to also have a greater number of animals. In addition, the larger number of animals has an influence on several characteristics of a farm, such as the need for hired labor, and a direct relationship with the type of production, since the farms with the largest number of animals were mixed farms.
Looking at the elements of labor and type of production in Cluster 4, 71.4% of the farms with mixed types of production (beef and milk) employed hired labor or a combination of family and hired labor, while 91.3% of the exclusively milk farms had family labor. This shows a strong link between the need for more labor when diversifying the type of production and increasing the area.
Other variables that were also correlated in this group were technical support and disease incidence. This is due to the pursuit of higher production and increased animal demand, especially with the assistance of technical support, which can lead to greater efficiency and specialization. However, this can also make animals more vulnerable and potentially increase disease incidence (Sundrum, 2015).
The analysis reveals clear challenges and opportunities for local producers. The dependence on family labor and limitations related to management, infrastructure and disease control are evident. Investment in management practices and infrastructure is needed, along with ongoing technical support. In addition, effective disease and parasite control strategies and genetic improvement of herds are critical to ensure the sustainability and profitability of local dairy production. The multivariate analysis highlights key variables such as number of animals, daily milk production, total farm area and available labor. These baseline data serve as a foundation for short-term research and innovation, as well as the development of medium- and long-term policies and programs to strengthen the region's dairy sector. This study not only sheds light on the current scenario of milk production in Alegre, ES, but also provides essential guidelines for future decisions in favor of the sector's progress and prosperity.
CONCLUSIONS
Milk production in Alegre, ES, shows significant variability in productivity, largely influenced by different management practices. To ensure sustainable growth, it is crucial to analyze and improve the factors that currently influence local milk production. The availability of technical assistance provides a valuable opportunity to promote best practices and encourage innovation, which are essential for improving dairy farm management and increasing regional competitiveness. By focusing on these areas, the dairy sector in Alegre can move towards greater sustainability and profitability.
ACKNOWLEDGEMENTS
This work was supported by the Espírito Santo Research and Innovation Support Foundation (FAPES) through the following funding programs: Call for proposals nº 04/2022 Support Program for Emerging Capixaba Postgraduate Programs (PROAPEM) and Call for Proposals nº 03/2023, Capixaba Researcher Scholarship, Process E-docs 2022-71JG6.
REFERENCES
- ANUÁRIO leite 2023: leite baixo carbono. Juiz de Fora: Embrapa Gado de Leite, 2023. 118p.
- AZEVEDO, R.A.; FELIX, T.M.; PIRIS JÚNIOR, O.D.S. et al. Perfil de propriedades leiteiras ou com produção mista no norte de minas gerais. Rev. Caatinga, v.24, p.153-159, 2011.
- BRASIL. Conselho Nacional do Meio Ambiente - Conama. Resolução 357/2005, Enquadramento dos Corpos Hídricos Superficiais no Brasil. Diário Oficial da União, Brasília, 18 março 2005. Seção 1, n 53, pág.58-63.
- BRASIL. Lei nº 8.629, de 25 de fevereiro de 1993. Dispõe sobre a regulamentação dos dispositivos constitucionais relativos à reforma agrária, previstos no Capítulo 3, Título 7, da Constituição Federal. Brasília, DF, 25 fev. 1993.
-
BRASIL. Ministério da Agricultura Pecuária e Abastecimento - MAPA. Revisão sobre a raiva. 2017 Disponível em: https://www.gov.br/ agricultura/pt-br/assuntos/sanidade-animal-e-vegetal/saude-animal/programas-de-saude-animal/raiva-dos-herbivoros-e-eeb/RevisosobreRaiva2017.pdf/view Acessado em: 25 abr. 2024.
» https://www.gov.br/ agricultura/pt-br/assuntos/sanidade-animal-e-vegetal/saude-animal/programas-de-saude-animal/raiva-dos-herbivoros-e-eeb/RevisosobreRaiva2017.pdf/view -
BRASIL. Ministério da Agricultura, Pecuária e Abastecimento. Instrução Normativa Nº 2, de 10 de janeiro de 2001. Disponível em: https://www.gov.br/agricultura/pt-br/assuntos/sanidade-animal-e-vegetal/saude-animal/programas-de-saude-animal/pncebt/principais-normas-pncebt/in-2-de-10-de-janeiro-de-2001-institui-o-pncebt.pdf/view Acessado em: 25 abr. 2024.
» https://www.gov.br/agricultura/pt-br/assuntos/sanidade-animal-e-vegetal/saude-animal/programas-de-saude-animal/pncebt/principais-normas-pncebt/in-2-de-10-de-janeiro-de-2001-institui-o-pncebt.pdf/view - BRASIL. Ministério da Agricultura, Pecuária e Abastecimento. Instrução Normativa Nº 76, de 26 de novembro de 2018. Aprova os Regulamentos Técnicos que fixam a identidade e as características de qualidade que devem apresentar o leite cru refrigerado, o leite pasteurizado e o leite pasteurizado tipo A. (Alterada pela IN nº 58/2019, Alterada pela IN nº 55/2020). Diário Oficial da União, Brasília, DF, 2018.
-
CIDADES e estados. Rio de Janeiro: IBGE, 2022. Disponível em: https://www.ibge.gov.br/ cidades-e-estados/es/alegre.html Acessado em: 25 abr. 2024.
» https://www.ibge.gov.br/ cidades-e-estados/es/alegre.html -
DADOS e informações epidemiológicas - 2021. Vitória: IDAF, 2022. Disponível em: https://idaf.es.gov.br/dados-e-informacoes-epidemiologicas Acessado em: 25 abr. 2024.
» https://idaf.es.gov.br/dados-e-informacoes-epidemiologicas -
ESTUDO sobre mercado do leite é destaque na reunião da cadeia produtiva. Mato Grosso do Sul: Embrapa, 2023. Disponível em: https://www.embrapa.br/busca-de-noticias/-/noticia/81695091/estudo-sobre-mercado-do-leite-e-destaque-na-reuniao-da-cadeia-produtiva Acessado em: 25 jul. 2024.
» https://www.embrapa.br/busca-de-noticias/-/noticia/81695091/estudo-sobre-mercado-do-leite-e-destaque-na-reuniao-da-cadeia-produtiva - FREITAS JUNIOR, P.I.; DEMONER, L.C.; AVELAR, B.R. et al. Estudo parasitológico em bovinos leiteiros da microrregião do Caparaó, Espírito Santo, Brasil Parasitological study in dairy cattle of Caparaó microregion, Espírito Santo State, Brazil. Ciênc. Vet., p.151. 2008.
- GIACOMEL, A.; FREITAS, T.C.; COSTA, A.L.B. et al. Suplementação mineral para bovinos de corte-uma revisão sistemática. Res. Soc. Dev., v.11, p.e39211326616, 2022.
- GUERRA, M.G.; GALVÃO JÚNIOR, J.G.B.; NASCIMENTO RANGEL, A.H. et al. Disponibilidade e qualidade da água na produção de leite. Acta Vet. Bras., v.5, p.230-235, 2011.
- OLIVEIRA SILVA, A.R.; RODRIGUES, M.; SILVA, D.C.C. Análise de viabilidade de irrigacão na pecuária leiteira: alternativas para a agricultura familiar na Amazônia. Rev. Estud. Soc., v.20, p.179-191, 2018.
-
PIB Agro CEPEA-USP/CNA. São Paulo: CEPEA, 2022. Disponível em: https://www.cepea.esalq.usp.br/br/pib-do-agronegocio-brasileiro.aspx Acessado em: 25 abr. 2024.
» https://www.cepea.esalq.usp.br/br/pib-do-agronegocio-brasileiro.aspx -
PRODUÇÃO agropecuária/leite. 2023. Rio de Janeiro: IBGE, 2023. Disponível em: https://www.ibge.gov.br/ explica/producao-agropecuaria/leite/br Acessado em: 25 abr. 2024.
» https://www.ibge.gov.br/ explica/producao-agropecuaria/leite/br -
PROGRAMA de assistência técnica e extensão rural - PROATER 2020-2023: Alegre. Vitória: INCAPER, 2023. Disponível em: https://incaper.es.gov.br/ media/incaper/proater/municipios/Alegre.pdf Acessado em: 25 abr. 2024.
» https://incaper.es.gov.br/ media/incaper/proater/municipios/Alegre.pdf - SANTOS, M.A.S.; LOURENÇO JÚNIOR, J.D.B.; SANTANA, A.C. et al. Caracterização do nível tecnológico da pecuária bovina na Amazônia Brasileira. Rev. Ciênc. Agrar. Amazon. J. Agric. Environ. Sci., v.60, p.103-111, 2017.
- SIQUEIRA, A.K.; ALVES, T.D.S.; FERRAZ, M.M.G. et al. Resistência antimicrobiana em coliformes totais isolados de tanques de refrigeração de leite bovino. Atas Saúde Ambient., v.2, p.2-15, 2014.
- SUNDRUM, A. Metabolic disorders in the transition period indicate that the dairy cows’ ability to adapt is overstressed. Animals, v.5, p.978-1020, 2015.
- VIANA, K.F.; ZANINI, M.S. Perfil de produtores frente à vacinação contra doenças infecciosas abortivas em rebanhos bovinos do município de Alegre/ES. Arch. Vet. Sci., v.14, p.103-108, 2009.
- VIEIRA, B.; LORENZONI, L.; SOUZA, M.H. et al. Etiologia infecciosa associada à mastite subclínica em bovinos de propriedades rurais no município de Alegre-ES. Enciclop. Biosfera, v.9, n.16, 2013.
- WERNCKE, D.; GABBI, A.M.; ABREU, A.S.D. et al. Qualidade do leite e perfil das propriedades leiteiras no sul de Santa Catarina: abordagem multivariada. Arq. Bras. Med. Vet. Zootec., v.68, p.506-516, 2016.
