Milk quality and subclinical mastitis monitoring: a case study on an organic dairy farmABSTRACT
This study aimed to evaluate the quality of organic milk by analyzing composition parameters (fat, protein, and lactose percentages), bulk tank somatic cell count (BTSCC), and total bacterial count (TBC) from 2016 to 2019, and to monitor subclinical mastitis by measuring individual SCC in lactating cows on an organic dairy farm. Milk composition analysis revealed that fat percentages ranged from 3.20% to 3.80%, protein varied between 3.10% and 3.40%, and lactose levels ranged from 4.30% to 4.90%. BTSCC values ranged from 224,000 to 525,000 SC/mL, while TBC ranged from 509,000 CFU/mL (in one sample) to values under 110,000 CFU/mL in others. Individual SCC values ranged from 179,000 to 519,000 SC/mL. The percentage of healthy cows varied from 70% (January 2017) to 27.5% (March 2018), while cows with chronic intramammary infections ranged from 14% (July 2017) to 31% (November 2017). Effective mastitis control in organic systems requires integrating SCC data with preventive strategies. This approach enhances milk quality, animal welfare, and farm sustainability, addressing the growing demands of the organic dairy market.
Keywords
intra-mammary infection; fat percentage; organic milk; somatic cell
RESUMO
Objetivou-se avaliar a qualidade do leite orgânico por meio da análise de parâmetros de composição (percentuais de gordura, proteína e lactose), contagem de células somáticas no tanque (CCST) e contagem bacteriana total (CBT) de 2016 a 2019, e monitorar a mastite subclínica através da CCS individual de vacas em lactação em uma fazenda leiteira orgânica. A análise da composição do leite revelou que os percentuais de gordura variaram de 3,20% a 3,80%, a proteína variou entre 3,10% e 3,40%, e os níveis de lactose variaram de 4,30% a 4,90%. Os valores de CCST variaram de 224.000 a 525.000 CS/mL, enquanto a CBT variou de 509.000 UFC/mL a valores inferiores a 110.000 UFC/mL. Os valores de CCS individual variaram de 179.000 a 519.000 SC/mL. A porcentagem de vacas saudáveis variou de 70% (janeiro de 2017) a 27,5% (março de 2018), enquanto vacas com infecções intramamárias crônicas variaram de 14% (julho de 2017) a 31% (novembro de 2017). O controle eficaz da mastite em sistemas orgânicos requer a integração dos dados de CCS com estratégias preventivas. Essa abordagem melhora a qualidade do leite, o bem-estar animal e a sustentabilidade das fazendas, atendendo à crescente demanda do mercado de leite orgânico.
Palavras-chave
infecção intra-mamária; porcentagem de gordura; leite orgânico; célula somática
1. Introduction
The global demand for organic products has been steadily increasing, reflecting a growing consumer preference for food produced without chemical residues, with a focus on agroecological principles and animal welfare (Bórawski et al., 2021). Among organic products, milk and its derivatives stand out as key commodities within the sector, with the consumption of organic milk —whether in its natural form or as part of dairy products such as pasteurized whole milk, yogurt, cheese, curd, cream cheese, and butter — continuing to grow worldwide (Kapsdorferová et al., 2023).
Regulations for organic farming may vary across different countries or regions (Grodkowski et al., 2023). In general, organic milk production is governed by rigorous standards that address various aspects of farm management, including livestock feeding practices, housing, breed selection, and healthcare. In Brazil, organic milk production is regulated by the Ministry of Agriculture, Livestock, and Supply (MAPA) through Law No. 10.831, enacted on December 23, 2003, which establishes the organic production system and defines its objectives (Brazil, 2003). Furthermore, the Ordinance No. 52, dated March 15, 2021, sets forth the Technical Regulation for Organic Production Systems (Brazil, 2021). These legal bases ensure compliance with organic principles in dairy farming, aiming to uphold high standards of animal welfare, environmental sustainability, and product quality.
Despite these regulatory frameworks, significant knowledge gaps persist regarding how organic dairy farms can effectively implement these requirements while maintaining both quality and productivity standards.
Despite these regulatory frameworks, significant knowledge gaps persist regarding how organic dairy farms can effectively implement these requirements while maintaining both quality and productivity standards. A major challenge to expanding organic milk production is the limited availability of scientific data and practical guidance, particularly in the areas of animal health management and milk quality control (Silva et al., 2023). Restrictions on the use of allopathic medications and chemical substances for disease prevention and control make it challenging to maintain animal health in organic production systems. Moreover, there is a need for accessible tools to support farmers in effectively monitoring milk quality and herd health, ensuring compliance with organic principles.
Ensuring high milk quality is closely linked to effective mastitis management, a major health concern in dairy production worldwide (Fernandes et al., 2021; Neculai-Valeanu & Ariton, 2022). The somatic cell count (SCC) in milk is widely recognized as a critical indicator for mastitis prevention and control programs (Bangar et al., 2022). Several factors influence SCC, including parity, lactation stage, and seasonal changes (Panchal et al., 2022). However, intramammary infections are the primary cause of elevated SCC (Kirkeby et al., 2021). Research by Dohoo and Leslie (1991) established a threshold of 200,000 cells/mL as an effective benchmark for identifying new intramammary infections, providing a critical tool for monitoring udder health.
Similar to conventional dairy systems, subclinical mastitis (SCM) remains a significant challenge for organic dairy herds (Fernandes et al., 2021). Studies have indicated that the prevalence of SCM in organically managed cows is comparable to or even higher than that observed in cows from conventional herds (Roesch et al., 2007; Mullen et al., 2013). These findings highlight the importance of robust mastitis control strategies across organic dairy systems to ensure milk quality and animal welfare.
Given the increasing significance of organic dairy farming and the crucial role that milk quality plays in consumer trust and the sustainability of the dairy industry, this study aimed to assess milk quality and monitor SCM in an organic dairy farm. Specifically, the research focused on evaluating milk composition parameters (including fat, protein, and lactose percentages), bulk tank somatic cells (BTSCC), and total bacterial count (TBC) from the years 2016 to 2019, while also monitoring the occurrence of subclinical mastitis through individual SCC measurements of lactating cows.
2. Material and methods
2.1 Research place
The study was conducted on an organic dairy farm in Rio de Janeiro State, Brazil. The farm’s coordinates were 22°53'27"S latitude and 42°53'14"W longitude, at an elevation of approximately 900 meters. The climate of the region was classified as highland tropical (Cwb) (Koeppen, 1948), characterized by mild temperatures, with rainy, moderately hot summers and dry, cold winters (Bastos & Napoleão, 2011).
The farm was certified for organic milk production and was a member of the Association of Biological Farmers of Rio de Janeiro (ABIO), established in 1984, ensuring compliance with organic standards. Covering 220 hectares used for organic production, the farm’s livestock totaled 89 animals in June 2019, with an average daily milk production of 350 liters. The herd consisted of 23 lactating cows, each producing about 15 liters per day, with an average of 8.5 liters per cow. The herd was primarily composed of mixed breeds: Holstein Friesian × Jersey and Holstein Friesian × Gyr.
Animals were managed in a semi-extensive system, rotating pastures with daily supplementation of highfiber roughage (shredded grass, hay, or corn silage) and concentrated feed (corn flour, soy, and mineral salt) for lactating cows throughout the year. Diet formulations considered the property’s production capacity and food availability, ensuring that, according to Ordinance No. 52 (Brazil, 2021), 85% of the dry matter consumed by the animals was organic.
Homeopathy and phytotherapy were employed for disease prevention and treatment, while environmental control methods, such as fly traps, helped mitigate sanitary issues. Homeopathic treatments were prepared by a trained technician on the farm’s premises. These treatments were administered as needed, with preparations diluted in sugar and provided individually in the feeder to prevent and treat mastitis and parasites. Ointments and intramammary homeopathic preparations were used for clinical mastitis treatment.
The farm utilized a mechanized milking system featuring a roofed milking room, concrete flooring, and a ditch with four sets of equipment, enabling the simultaneous milking of four cows in a straight line. Milking occurred twice daily, with the black-bottomed mug test conducted before each session to detect clinical mastitis. The California Mastitis Test (CMT) was performed monthly to monitor subclinical mastitis.
Calves were kept alongside their mothers during milking to stimulate milk let-down. After milking, the udders were cleaned with chlorinated water and dried with absorbent paper. Cows without calves underwent post-dipping with an iodinated solution.
2.2. Research procedures
The research was a case study conducted from June to December 2019 through farm visits to characterize the producer, production unit, and nutritional and sanitary management practices, as well as assess milk quality. Farm activities were observed without interference in animal management.
2.2.1. Milk sampling for quality monitoring
Milk samples from the bulk tank were collected and analyzed between December 2016 and December 2019 at the Dairy Clinic Laboratory (Laboratório Clínica do Leite, ESALQ/USP). The samples were tested for composition (fat, protein, and lactose percentages), somatic cell count (SCC, cells/mL), and total bacterial count (TBC, CFU/mL). For analysis, 50 mL sterilized flasks with preservatives were used: bromonata (bronopol, 8 mg per sample) for SCC and composition analysis, and azidiol (a mixture of sodium azide and chloramphenicol) to inhibit microbial growth in TBC samples. Composition analyses (fat, protein, and lactose) were performed using mid-infrared spectrometry, while SCC and TBC were assessed via flow cytometry.
2.2.2. Individual milk sampling for subclinical mastitis monitoring
Monthly individual milk samples were collected from 68 animals between January 2017 and April 2018 to monitor subclinical mastitis via SCC. Sampling was conducted in duplicate on milk-weighing days, immediately after complete milking, using the milk meter tap of the mechanical milking machine. Sterilized 50 mL flasks containing bromonata (bronopol, 8 mg per sample) were used as a preservative for SCC analysis. After collection, the flasks were sealed and inverted ten times to mix the preservative and ensure homogenization. Samples were placed in a dedicated transport box and sent to the laboratory without refrigeration immediately after collection. SCC analysis was performed using flow cytometry. Additionally, data on daily milk production averages and lactation days (LD) were gathered from the same 68 animals during this period. All farm-provided data were compiled, grouped by season, and analyzed.
2.3. Data analysis
Data were analyzed using a descriptive approach based on averages and percentages, which was considered appropriate to highlight biological and management-related patterns consistent with the exploratory nature of the study.
Milk quality from the bulk tank was assessed using the average of values obtained from a single collection per season, conducted in duplicate. For individual milk samples from 68 cows to monitor SCC, monthly averages were based on single monthly collections performed in duplicate. The averages for LD, milk production, and precipitation were calculated using daily values recorded throughout the month, with precipitation data provided by the National Institute of Meteorology (INMET, 2020).
The individual data of the 68 cows were classified based on SCC following established criteria (Dohoo & Leslie, 1991): Healthy, cows with an SCC below 200,000 during the analyzed month, indicating the absence of mastitis; New Infection, cows previously classified as healthy (SCC below 200,000 in the prior analysis) that developed mastitis during the analyzed month, with an SCC exceeding 200,000; Chronic, cows with an SCC above 200,000 in at least two consecutive analyses, indicating mastitis persisting for more than two months; Cured - cows with an SCC above 200,000 in the prior month but below 200,000 during the analyzed month, indicating recovery from mastitis. The percentage of animals in each category was calculated on a monthly basis.
2.4. Ethical aspects
This study was submitted to the Research Ethics Committee of the Universidade Federal Rural do Rio de Janeiro (UFRRJ) under process no. 23083.009686/2019-45. The committee confirmed compliance with ethical principles and Resolution 466/12, which governs research involving human subjects. It was also submitted to the Ethics Committee for Animal Use at the Veterinary Institute of UFRRJ (protocol no. 9471130319), which determined that evaluation was unnecessary as the study did not involve direct animal manipulation.
3. Results and discussion
3.1. Milk quality
The data on milk analysis from the farm's bulk tank during the evaluation period are summarized in Table 1. The lowest fat percentages were observed during the summers of 2018/2019 and 2019, averaging 3.20%, while the highest percentage occurred in the spring of 2019, reaching 3.80%. Protein percentages were lowest in the winters of 2018 and 2019 and the summer of 2019 (3.10%) and highest during the fall and winter of 2017 (3.40%). Lactose content varied between 4.30% in winter 2017 and 4.90% in summer 2017/2018. Despite seasonal variations, the fat, protein, and lactose percentages in the analyzed milk samples consistently met the minimum requirements established by Brazilian milk quality regulations under Normative Instruction no. 76 (NI nº 76), which specify thresholds of 3.00% for fat, 2.90% for protein, and 4.30% for lactose (Brazil, 2018).
The composition of milk is influenced by the nutritional quality of the animals' diet, the interrelation between dietary components, and rumen degradability rates (Alves et al., 2016). Environmental factors, including climate, also play a significant role (Andrade et al., 2014; Milani et al., 2016). Among milk components, fat and protein are particularly responsive to dietary changes, reflecting shifts in the cow's metabolic status and energy balance (Walker et al., 2004). Variations in these components can serve as indicators of dietary adequacy, with imbalances potentially leading to fluctuations in body condition. Monitoring these parameters provides valuable insights into the nutritional status and overall health of dairy cows, enabling adjustments to optimize production and maintain animal welfare.
The higher fat and protein percentages observed in winter align with findings by Noro et al. (2006), who attributed this trend to the use of temperate-climate forage plants with high nutritional value. This correlation partially explains the results of this study, as the farm employs similar nutritional strategies. Additionally, for grazing cows, the limited food availability during dry periods can lead to decreased milk production and a relative concentration of milk components, resulting in higher percentages (Winckler, 2019). However, the protein percentages observed in the winters of 2018 and 2019 did not follow this pattern, as they were among the lowest averages recorded.
Average of milk composition (fat, protein, and lactose), bulk tank somatic cell count (BTSCC), total bacterial count (TBC), and monthly precipitation on an organic dairy farm, from 2016 to 2019, by seasons of the year.
Udder health is another critical factor influencing both milk quality and production. Mammary infections initiate an inflammatory process that increases the permeability of the blood-mammary gland barrier, facilitating the entry of ions, proteins, and somatic cells into the milk (Neculai-Valeanu & Ariton, 2022). Previous studies have demonstrated that SCC significantly impacts milk production, as well as the levels of protein and lactose in the milk (Cinar et al., 2015; Chen et al., 2021; Pegolo et al., 2021). The reduction in milk production associated with elevated SCC is primarily attributed to physical damage to the epithelial cells responsible for milk synthesis (Benić et al., 2018).
The highest BTSCC averages were observed in the spring of 2019 (525,000 SC/mL) and the summer of 2018/2019 (517,000 SC/mL), both exceeding the maximum limit of 500,000 SC/mL established by Brazilian milk quality regulations (NI n° 76; Brazil, 2018), as represented by the solid line in Figure 1. The lowest BTSCC average was observed in the summer of 2017/2018 (224,000 SC/mL).
BTSCC is a key indicator of mammary gland health and is internationally recognized as a standard measure of milk quality (Neculai-Valeanu & Ariton, 2022). While elevated SCC levels are primarily associated with subclinical mastitis, other contributing factors include advanced lactation stages, older cows, and environmental stressors like heat (Stocco et al., 2023). Additionally, issues with milking equipment, such as vacuum fluctuations, improper pulsation, and inadequate hygiene, can increase SCC due to mechanical injuries or infections (Zigo et al., 2021).
Seasonal variations play a significant role in BTSCC levels. Higher SCC in summer is often linked to increased heat and humidity, which foster the proliferation of environmental pathogens and elevate the risk of clinical mastitis (Panchal et al., 2022). Heat stress during this period can further compromise animal immunity, increasing susceptibility to infections (Neculai-Valeanu & Ariton, 2022).
Average somatic bulk tank cell count (BTSCC, in SC/mL) by season from 2016 to 2019 in an organic dairy farm. The solid line represents the maximum reference value (500,000 SC/mL) as per Brazilian regulations (Brazil, 2018). The dashed line indicates the farm's internal threshold (250,000 SC/mL).
The present study revealed notable seasonal fluctuations in BTSCC. The highest values, recorded in spring 2019 (525,000 SC/mL) and summer 2018/2019 (517,000 SC/mL), coincided with periods of high precipitation—92 mm in spring 2019 and 174.8 mm in summer 2018/2019. These findings support the notion that rainfall, alongside other environmental and management factors, significantly influences SCC variability.
Despite the farm's target BTSCC of 250,000 SC/mL, indicative of a good herd health standard, this goal was met in only three of the evaluated seasons: spring 2017, summer 2017/2018, and winter 2019. These findings underscore the challenges of maintaining low SCC levels in organic farming systems, as pasture-raised animals are more vulnerable to climatic fluctuations, particularly during adverse weather conditions. Implementing effective management strategies that address environmental stress, hygiene, and dietary supplementation is essential for ensuring consistent milk quality.
The highest average TBC was observed in winter 2017, at 509,000 CFU/mL, exceeding the maximum limit of 300,000 CFU/mL established by Brazilian regulations (NI n° 76; Brazil, 2018), as indicated by the solid line in Figure 2. However, elevated TBC levels were observed only between the summer of 2016/2017 and the winter of 2017.
Average total bacterial count (TBC, in CFU/mL) by season from 2016 to 2019 in the bulk tank of an organic dairy farm. The solid line represents the maximum reference value (300,000 CFU/mL) according to Brazilian regulations (Brazil, 2018). The dashed line indicates the farm's target value (10,000 CFU/mL).
TBC is a critical indicator of microbiological milk quality, reflecting the effectiveness of hygiene and refrigeration practices from milking through to delivery to the dairy industry (Queiroz et al., 2019). Several factors influence TBC, including milking practices, hygiene standards (Taffarel et al., 2015), seasonal variations (Henrichs et al., 2014), the quality of water used for cleaning (Cissé et al., 2018), and milk temperature and storage conditions (Taffarel et al., 2015). Interestingly, the highest TBC average observed in this study did not align with the typical pattern, as it occurred during the winter of 2017 rather than the rainy season or summer. This deviation can be attributed to a specific incident in July 2017, when a malfunction in the milk bulk cooling tank necessitated the use of milk canisters for storage. This temporary measure likely increased the risk of contamination and contributed to the elevated bacterial count. However, following this incident, the farm implemented improved milking and hygiene protocols, resulting in consistently low TBC values in subsequent analyses, all of which were below 40,000 CFU/mL.
Despite these improvements, the farm’s target of maintaining a TBC below 10,000 CFU/mL, represented by the dashed line in Figure 2, was achieved in only six out of the 11 evaluated seasons. This outcome highlights the importance of ongoing monitoring and corrective actions to minimize bacterial contamination. Enhancing cleaning protocols, ensuring the proper functioning of cooling equipment, and optimizing milking practices are critical measures to achieve and sustain lower TBC levels. Such efforts are essential not only for meeting regulatory and farm-specific standards but also for ensuring the consistent production of high-quality organic milk.
3.2. Monitoring subclinical mastitis by individual SCC
The individual evaluation data for animals from January 2017 to April 2018 are shown in Table 2. The lowest average individual SCC was observed in January 2017 (179,000 SC/mL), a month characterized by 135.2 mm of precipitation, an average of 153 LD, and a production rate of 14.7 L per cow. In contrast, the highest SCC average occurred in March 2017 (519,000 SC/mL), with 178.7 mm of precipitation, an average of 163 LD, and 13.6 liters per cow. This increase in SCC may be attributed to deficiencies in management practices or flaws in preventive measures. However, as previously mentioned, SCC fluctuations are multifactorial, influenced by variables such as cow age, parity, lactation stage, LD, and environmental conditions (Zigo et al., 2021).
Individual monthly averages of the number of lactation days (LD), somatic cell count (SCC), and milk production (in liters) of 68 animals, and precipitation monthly averages on an organic dairy farm, from January 2017 to April 2018.
The study revealed notable variations in SCC across months and LD. The period from late 2017 to early 2018 emerged as the most critical, as illustrated in Figure 3. October 2017 showed the highest average LD (215 days), yet the SCC increase during this month was moderate (291,000 SC/mL). On the other hand, July 2017, which had the lowest average LD (102 days), exhibited a higher SCC average of 382,000 SC/mL.
Relationship between the individual averages of somatic cell count (SCC) and lactation days (LD), from January 2017 to April 2018, on an organic dairy farm.
The observed SCC peaks align with periods of higher precipitation, suggesting that environmental factors such as mud and humidity may exacerbate udder infections, particularly in poorly managed conditions (Chen et al., 2017). These results underscore the importance of implementing robust mastitis control measures, including proper milking procedures, hygiene practices, and environmental management, to mitigate SCC increases and improve milk quality.
The lactation period significantly influences SCC variations, even in cows without mammary gland infections (Hagnestam-Nielsen et al., 2009). These variations are typically observed at both the start and end of lactation. Early in lactation, SCC may increase due to elevated levels of immunoglobulins and defense cells, reflecting the cow's immune response. In late lactation, the elevated SCC is a key contributor to the majority of herd-level production losses caused by SCM, due to the high incidence of SCM and the significant milk loss associated with increased SCC during this stage (Hagnestam-Nielsen et al., 2009). These findings reinforce the importance of monitoring SCC alongside other production parameters, such as LD and milk yield. Understanding these dynamics can help identify critical periods requiring intensified management efforts to maintain milk quality and udder health in organic dairy systems.
The lowest average milk production during the study period was observed in July 2017, with a yield of 12.4L per cow, accompanied by an average somatic cell count (SCC) of 382,000 cells/mL (Figure 4). This reduced milk yield can be attributed to winter conditions, which are associated with roughage of lower nutritional value due to the seasonal decline in forage availability (Magan et al., 2021). These findings highlight the importance of strategic supplementation and effective forage management during the winter months to sustain consistent milk production in organic systems.
Relationship between the individual averages of somatic cell count (SCC) and milk production (L/cow), from January 2017 to April 2018, on an organic dairy farm.
An increase in SCC is known to negatively impact milk yield, primarily due to the damage inflicted on the secretory epithelium of the mammary gland by infection (Zigo et al., 2021). These injuries impair the functionality of secretory cells within the mammary parenchyma, resulting in reductions in milk production and alterations in milk composition (Jóźwik et al., 2012).
Mastitis, whether clinical or subclinical, poses a significant challenge for dairy production systems. It not only reduces both the quantity and quality of milk but also directly impacts profitability and compromises consumer safety (Cobirka et al., 2020).
The highest milk production averages occurred in April 2018 (15.5 L/cow) and May 2017 (15.2 L/cow), both of which corresponded to relatively low SCC averages of 219,000 and 287,000 SC/mL, respectively. These results highlight that high milk production is not necessarily associated with elevated SCC levels, provided that appropriate management practices are in place. Effective herd management, including hygiene measures, nutritional optimization, and health monitoring, appears to mitigate SCC increases, even during periods of peak milk yield.
The relationship between precipitation and SCC also warrants attention. The highest rainfall average (178.7 mm) occurred in March 2017, coinciding with the highest SCC average during the evaluated period (519,000 SC/mL) (Figure 5). This aligns with previous findings that rainy conditions often lead to elevated SCC due to increased environmental contamination, such as mud and feces, which heighten the risk of udder infections (Machado et al., 2000; Fonseca et al., 2023). Additionally, SCC levels may rise during the hottest months of the year, likely due to reduced immune resistance and a higher incidence of intramammary infections driven by heat stress during these periods (Fonseca et al., 2023). Conversely, the lowest precipitation average was recorded in July 2017 (0.0 mm), corresponding with a relatively lower SCC of 382,000 SC/mL, although this value remains above optimal thresholds.
Relationship between the individual averages of somatic cell count (SCC) and the averages of pluviometric precipitation (mm), from January 2017 to April 2018, on an organic dairy farm.
During the evaluation period, notable numerical variation was observed between SCC and the variables of lactation days (LD), milk production, and precipitation. This variation highlights the multifactorial nature of SCC fluctuations, driven by the interplay of physiological, environmental, and management-related factors that collectively influence udder health and milk quality.
The findings underscore the complexity of these interactions, emphasizing the necessity for an integrated approach to dairy herd management. Proactive measures are crucial to addressing these challenges in organic dairy systems, including implementing effective heat stress mitigation strategies, maintaining stringent hygiene standards to minimize exposure to environmental pathogens, and closely monitoring lactation stages to manage SCC levels. These practices are crucial for mitigating the effects of adverse conditions, maximizing milk quality, and improving animal welfare within organic dairy systems. duration (LD) of 153 days, and the recorded average precipitation was 135.2 mm (Table 2).
The same criteria applied in the previous month were consistently used in the subsequent months (Figure 6). In February 2017, 57% of cows were exposure to environmental pathogens, and closely monitoring lactation stages to manage SCC levels. These practices are crucial for mitigating the effects of adverse conditions, maximizing milk quality, and improving animal welfare within organic dairy systems.
Herd health situation regarding the Somatic Cell Count (SCC), in the months of January 2017 to April 2018, on an organic dairy farm.
3.3. Individual somatic cell count (SCC) and its implications for udder health
Dohoo and Leslie (1991) evaluated SCC in cows at 28-day intervals and identified 200,000 cells/mL as the most appropriate threshold for estimating new intramammary infections. The first individual SCC assessment for the animals in this study was conducted in January 2017 (Figure 6). Based on the criteria proposed by Dohoo and Leslie (1991), cows with SCC levels below 200,000 cells/mL were classified as healthy (70.00%), while those exceeding this threshold were considered infected (30.00%). During this period, the average SCC was 179,000 cells/mL, indicating that the majority of cows were free from infection or subclinical mastitis. The average milk production per cow was 14.7 L, with an average lactation duration (LD) of 153 days, and the recorded average precipitation was 135.2 mm (Table 2).
The same criteria applied in the previous month were consistently used in the subsequent months (Figure 6). In February 2017, 57% of cows were classified as healthy, 23% developed new infections, 17% were categorized as chronic cases due to maintaining an SCC above 200,000 cells/mL, and 3% were deemed cured. Overall, 40% of the cows exhibited intramammary infections, with an average SCC of 471,000 cells/mL. In March 2017, 48% of cows were healthy, while 38% had subclinical mastitis, including 19% chronic cases. The average SCC rose to 519,000 cells/mL. By April 2017, 52% of cows were healthy, and the percentage of subclinical mastitis cases decreased to 34%, with an average SCC of 365,000 cells/mL.
From May to July 2017, the percentage of healthy cows steadily improved, reaching 66% in July, while the number of subclinical mastitis cases dropped to 31%. SCC averages during this period ranged from 287,000 cells/mL in May to 382,000 cells/mL in July. In the subsequent months, the proportion of healthy cows varied between 55% and 63%, with occasional increases in chronic and new infections. By November 2017, the average SCC declined to 258,000 cells/mL, with 59% of cows classified as healthy.
In February 2018, 59% of cows were healthy, but 41% had subclinical mastitis, with an average SCC of 366,000 cells/mL. In March 2018, a sharp increase in subclinical mastitis cases (68%) was observed, driven by a rise in new infections (45%), resulting in an SCC average of 501,000 cells/mL. However, by April 2018, the proportion of healthy cows improved to 31%, with only 33% of cows having subclinical mastitis and an SCC average of 219,000 cells/mL.
Monitoring subclinical mastitis in lactating cows through individual SCC analysis significantly contributes to disease control and supports informed decision-making regarding the herd's health management (Zecconi et al., 2019). For instance, a high prevalence of chronic cases often indicates the presence of contagious agents, cows in advanced lactation stages, and potential deficiencies in milking hygiene practices and equipment maintenance (Cardozo et al., 2015). Conversely, a high cure rate is typically associated with environmental mastitis, which correlates with the conditions of the animals' surroundings (Schmenger & Krömker, 2020). This approach also enables the identification of cows as chronic carriers, which serve as infection reservoirs within the herd, and highlights individuals contributing disproportionately to bulk tank SCC levels, facilitating targeted interventions.
In this study, the percentage of cows with chronic intramammary infections ranged from 14.00% in July 2017 to 31.00% in November 2017, suggesting the involvement of contagious pathogens in the persistence of subclinical mastitis. The clinical cure rate fluctuated significantly, from 0.00% in July 2017 and February 2018 to a peak of 36.00% in April 2018. Notably, the elevated SCC observed in March 2018 (501,000 SC/mL) and the subsequent high cure rate in April 2018 suggest an outbreak of environmental mastitis, likely exacerbated by the high average precipitation during the summer of 2017/2018 (164.7 mm). These findings underscore the impact of environmental factors on mastitis dynamics and emphasize the importance of environmental management in conjunction with routine health monitoring.
The herd management target was to achieve at least 75.00% of cows classified as healthy, defined as having an SCC < 200,000 cells/mL (Dohoo & Leslie, 1991). This goal was met only in May 2017, when 76.00% of cows were healthy, with an average SCC of 287,000 cells/mL. However, Langoni (2013) set more stringent benchmarks, recommending that at least 85.00% of cows maintain SCC levels below 200,000 cells/mL, with an incidence of subclinical mastitis of less than 5.00% per month. These benchmarks highlight the potential for improvement in achieving optimal udder health and milk quality.
Effective management of mastitis requires integrating historical data on individual animals with microbiological examination of milk samples. This approach allows producers to establish robust criteria for herd management. For example, cows with recurrent mastitis should be prioritized for removal from the herd to mitigate their impact on herd health and SCC levels (Langoni, 2013). Such strategies ensure that control measures are both evidence-based and tailored to the specific challenges faced by the herd.
4. Conclusion
This study provided an overview of milk quality indicators and subclinical mastitis monitoring in an organic dairy herd under tropical highland conditions. The evaluation of milk composition revealed that fat, protein, and lactose contents generally met the Brazilian quality standards, although seasonal variations were observed. BTSCC and total bacterial count TBC fluctuated across seasons, reflecting the influence of environmental and management factors on milk quality.
Individual SCC monitoring between 2017 and 2018 showed considerable variation among cows and throughout the year, indicating the occurrence of both new and chronic intramammary infections. Periods of higher precipitation coincided with increased SCC, suggesting that environmental conditions contributed to udder health challenges in the herd.
These findings underscore the importance of continuous monitoring of somatic cell counts as a practical and accessible tool for evaluating udder health and guiding management decisions in organic dairy systems. This approach provides valuable insights into the herd's health status by classifying cows into categories such as healthy, new infection, or chronic cases. These classifications not only facilitate the identification of individual animals requiring intervention but also allow producers to infer the likely group of pathogens involved — contagious or environmental.
Although this study was limited to one herd, it demonstrates how systematic SCC recording can assist in identifying critical periods for intervention and in maintaining compliance with milk quality standards. Further studies involving microbiological identification of pathogens and evaluation of preventive strategies are recommended to deepen the understanding of mastitis dynamics in organic dairy production.
Acknowledgments
We sincerely thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Brazil (Finance Code 001) and the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) for their financial support in providing the scholarship to Silva.
Data Availability
Data will be available upon reasonable request.
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Editor:
José Givanildo da Silva
