Dairy Farms In Australia Research Articles

Meta-Analysis on the Prevalence of Failed Transfer of Passive Immunity in Calves from Pasture-Based Dairy Farms in Australasia.

Monitoring and minimizing the prevalence of failed transfer of passive immunity (FTPI) in dairy replacement calves within the first week of life is crucial for calf health and farm profitability. In this study, a systematic literature search and meta-analysis were conducted on papers reporting the prevalence of FTPI in calves from pasture-based dairy farms in Australia and New Zealand. Two search methods, a "traditional method" and a "search engine method", were conducted to identify published studies on FTPI in Australia and New Zealand. Data from a total of 13,430 calves from eight studies in Australasia were included in the analysis for FTPI within 8 days of birth. The meta-analysis revealed that the average prevalence of FTPI was 33% across the two countries, with the lowest FTPI (9%) in Western Australia and the highest FTPI (59%) in New Zealand. Using farm data from three studies, the average prevalence of FTPI at the farm level in Australasia was 38%, with the lowest prevalence found in a farm in South Australia (6%). In conclusion, the meta-analysis confirmed the need for good management of cows and newborn calves after birth in pasture-based systems to reduce FTPI in calves. Collecting newborn calves from pasture at least twice per day after birth and providing colostrum of sufficient quantity and quality as soon as possible were the best practices for preventing FTPI in Australasian dairy systems.

Predicting Immediate Marginal Milk Responses and Evaluating the Economics of Two-Variable Input Tactical Feeding Decisions in Grazing Dairy Cows.

Simple SummaryIn Australia, feeding grazing dairy cows concentrate and forage supplements is common. Dairy farmers face the challenge of profitably feeding their cows in situations where there is significant variation in feed costs and milk price. We used the results of grazing experiments to develop equations that predict the yield of milk fat and milk protein when different combinations of concentrates and pasture + forage are fed to grazing lactating dairy cows. We applied economic principles to these predictions to estimate the optimal combination of these feeds for given costs and prices. Feed is the largest variable cost in dairying. The allocation of pasture and supplements that are based on better estimates of milk responses to supplements should lead to increased profit for farmers.Feed is the largest variable cost for dairy farms in Australia, and dairy farmers are faced with the challenge of profitably feeding their cows in situations where there is significant variation in input costs and milk price. In theory, the addition of 5.2 MJ of metabolisable energy to a lactating cow’s diet should be capable of supporting an increase in milk production of one litre of milk of 4.0% fat, 3.2% protein and 4.9% lactose. However, this is almost never seen in practice, due to competition for energy from other processes (e.g., body tissue gain), forage substitution, associative effects and imbalances in rumen fermentation. Pasture species, stage of maturity, pasture mass, allowance and intake, stage of lactation, cow body condition and type of supplement can all affect the milk protein plus fat production response to additional feed consumed by grazing dairy cows. We developed a model to predict marginal milk protein plus fat response/kg DM intake when lactating dairy cows consume concentrates and pasture + forages. Data from peer reviewed published experiments undertaken in Australia were collated into a database. Meta-analysis techniques were applied to the data and a two-variable quadratic polynomial production function was developed. Production economic theory was used to estimate the level of output for given quantities of input, the marginal physical productivity of each input, the isoquants for any specified level of output and the optimal input combination for given costs and prices of inputs and output. The application of the model and economic overlay was demonstrated using four scenarios based on a farm in Gippsland, Victoria. Given that feed accounts for the largest input cost in dairying, allocation of pasture and supplements that are based on better estimates of marginal milk responses to supplements should deliver increased profit from either savings in feed costs, or in some cases, increased output to approach the point where marginal revenue equals marginal costs. Such data are critical if the industry is to take advantage of the opportunities to use supplements to improve both productivity and profitability.

Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy?

Pregnancy diagnosis is an essential part of successful breeding programs on dairy farms. Milk composition alters with pregnancy, and this is well documented. Fourier-transform mid-infrared (MIR) spectroscopy is a rapid and cost-effective method for providing milk spectra that reflect the detailed composition of milk samples. Therefore, the aim of this study was to assess the ability of MIR spectroscopy to predict the pregnancy status of dairy cows. The MIR spectra and insemination records were available from 8,064 Holstein cows of 19 commercial dairy farms in Australia. Three strategies were studied to classify cows as open or pregnant using partial least squares discriminant analysis models with random cow-independent 10-fold cross-validation and external validation on a cow-independent test set. The first strategy considered 6,754 MIR spectra after insemination used as independent variables in the model. The results showed little ability to detect the pregnancy status as the area under the receiver operating characteristic curve was 0.63 and 0.65 for cross-validation and testing, respectively. The second strategy, involving 1,664 records, aimed to reduce noise in the MIR spectra used as predictors by subtracting a spectrum before insemination (i.e., open spectrum) from the spectrum after insemination. The accuracy was comparable with the first approach, showing no superiority of the method. Given the limited results for these models when using combined data from all stages after insemination, the third strategy explored separate models at 7 stages after insemination comprising 348 to 1,566 records each (i.e., progressively greater gestation) with single MIR spectra after insemination as predictors. The models developed using data recorded after 150 d of pregnancy showed promising prediction accuracy with the average value of area under the receiver operating characteristic curve of 0.78 and 0.76 obtained through cross-validation and testing, respectively. If this can be confirmed on a larger data set and extended to somewhat earlier stages after insemination, the model could be used as a complementary tool to detect fetal abortion.

An investigation of dairy calf management practices, colostrum quality, failure of transfer of passive immunity, and occurrence of enteropathogens among Australian dairy farms

Calf preweaning morbidity and mortality risks have been reported as high in several countries, with average values approximating 35 and 7%, respectively. However, limited data are available for calf morbidity and mortality risks on Australian dairy farms. The aims of this study were (1) to investigate current calf management practices on dairy farms in Australia and their association with herd-level morbidity and mortality using a questionnaire-based, cross-sectional study; and (2) to estimate the prevalence of common enteropathogens causing diarrhea, the failure of passive transfer of immunity, and poor colostrum quality in a sample of Australian dairy farms. We analyzed 106 completed questionnaires and samples from 23 farms (202 fecal, 253 calf serum, and 221 colostrum samples). Morbidity and mortality risks reported by farmers in preweaned heifers were 23.8 and 5.6%, respectively. These risks were above the Australian dairy industry targets in 75.5 and 66.7% of respondents. The zoonotic pathogens Cryptosporidium spp. and Salmonella spp. were the most prevalent enteropathogens, with a true prevalence of 40.9 and 25.2%, respectively. Salmonella O-group D was present in 67.9% of Salmonella-positive samples, followed by O-groups B (17.9%) and C (10.7%). Failure of transfer of passive immunity (IgG <10 g/L) was observed in 41.9% of calves (mean herd-level prevalence of 36.2%), and only 19.5% of colostrum samples met the standards for immunoglobulin content and microbiological quality. Collectively, these data indicate that there is still considerable room for improvement in calf-rearing practices on Australian dairy farms, particularly with regard to colostrum management and feeding hygiene.

Retrospective study

Downer cows, defined as a bright and alert cow recumbent for more than 1 day, are often challenging cases for veterinarians and farmers. This article reviews findings for 37 downer dairy cows that originally become recumbent from hypocalcemia ( milk fever) on commercial dairy farms in Australia. Clinical examination determined that they had apparently recovered from primary hypocalcemia, and remained recumbent from secondary damage. There was a wide range of secondary damage, which mainly affected the musculoskeletal system, but other parts of the body were also affected. It was concluded that downer cow syndrome is due to clinically important secondary damage, defined as "secondary damage that can cause recumbency in its own right, or delay or prevent recovery from the primary cause of the recumbency". Clinically important secondary damage in downer cows can present in a wide range of types, and is not necessarily confined to the musculoskeletal system. This study demonstrated a wider range of secondary damage than considered in the literature. When managing recumbent dairy cows, in addition to the primary etiology of the recumbency, veterinarians must consider secondary damage.

Fertiliser strategies for improving nitrogen use efficiency in grazed dairy pastures

Evidence from farm level studies indicates that there is potential to improve nitrogen (N) use efficiency of the predominately pasture-based dairy farms in Australia. This is possible via several ways which includes modifying the timing and rates of N fertiliser applied to pasture. Traditionally fertiliser strategies have been based on a “recipe” approach where N fertiliser, primarily urea, is applied a set rate following grazing. The aim of this study was to compare the pasture dry matter response, N loss and response rate of fertiliser strategies which used increasing knowledge of plant and soil conditions in different ways. The study was conducted under grazing conditions using the biophysical model, DairyMod and repeated at several locations and farming systems in the dairy regions of Australia. In comparison to set rates this study showed that strategic approaches to N fertiliser have the potential to be more efficient in N use and lower both N inputs and N losses with little impact of pasture production. This was evident across all seasons and locations studied. Strategies that used the plant N status to trigger fertiliser timing and rates were more efficient and had lower environmental N losses than those that used fixed rates or soil N information. Fertilising per plant N requirements was the most efficient – and therefore should be the priority for development – particularly in view of the greater expense of fertilisers that are slow release. Precision fertiliser management strategies have the value in terms of reducing fertiliser use and loss during autumn and to a lesser extent in summer, with the least value in winter. However, for the strategies to be properly evaluated for pasture based dairy farms with grazing, a whole farm analysis needs to be conducted that incorporates other sources of feed. This is a necessary inclusion in any subsequent studies.

Genomic characterization of coagulase-negative staphylococci including methicillin-resistant Staphylococcus sciuri causing bovine mastitis

Methicillin-resistant coagulase-negative staphylococci (MRCoNS) have recently emerged as a significant cause of bovine mastitis worldwide. Here we describe the isolation of MRCoNS from cases of bovine mastitis from a single dairy farm in Australia. Fourteen CoNS isolates were identified as MRCoNS on the basis of having an oxacillin MIC of ≥0.5 μg/mL. The isolates were speciated as S. chromogenes (n = 1) S. fleurettii (n = 1), S. haemolyticus (n = 2), S. sciuri (n = 5), S. simulans (n = 1) S. succinus (n = 2) and S. xylosus (n = 2). Five of the isolates (S. fleuretti, S. haemolyticus S. sciuri and two S. succinus) were mecA-positive. We also detected a previously described S. sciuri mecA homolog in four oxacillin-resistant S. sciuri isolates. The remainder of the putative MRCoNS did not contain any mecA-related resistance determinants in their genomes. Comparative genomic analysis of three previously published S. sciuri isolates, from humans, a squirrel and a cereal crop (rice), and a representative isolate from our study demonstrated clustering and a high degree of genetic homogeneity (>95%), suggesting S. sciuri has low host specificity. In conclusion, CoNS, in particular S. sciuri, may act as a reservoir for SCCmec elements that can easily be spread between different host species by direct cross-infection.

New breeding objectives and selection indices for the Australian dairy industry

This study comprises an update of the economic values for dairy traits for the Australian industry and the formulation of updated selection indices. An economic model, which calculates partial economic values for each trait individually, was developed to determine the economic implications of selective dairy breeding, based on the effect of trait changes on the profit of commercial dairy farms in Australia. Selection indices were developed from economic values, which were transformed into base economic weights by including the discounted genetic expressions coefficients. Economic weights (in Australian dollars) were 1.79, 6.92, -0.10, -5.44, 8.84, 7.68, 1.07, 4.86, 1.91, 3.51, 4.90, 0.31, 2.03, 2.00, and 0.59, for milk fat (kg), milk protein (kg), milk volume (L), body weight (kg), survival (%), residual survival (%), somatic cell count (cells/mL), fertility (%), mammary system [Australian Breeding Value (ABV) unit], temperament (ABV unit), milking speed (ABV unit), udder depth (%), overall type (%), fore udder attachment (%), and pin set (%), respectively. The updated economic weights presented in this study constituted the basis of the definition for 3 new indices. These indices were developed from combination of bioeconomic principles, patterns of farmer preferences for trait improvements, and desired gains approaches. The 3 indices, Balanced Performance Index, Health Weighted Index, and Type Weighted Index, have been released to the industry.

Nutritive characteristics, mineral concentrations and dietary cation–anion difference of feeds used within grazing-based dairy farms in Australia

Feed types consumed by cows on 44 contrasting dairy farms from all major dairy regions in Australia were identified and sampled on five occasions over a 15-month period to determine nutritive characteristics and mineral concentrations. Perennial ryegrass was the most dominant and commonly used forage, although a broad range of other home-grown forages was also grazed or conserved. Almost all farms utilised cereal or legume grains or grain-based concentrates, as well as a range of by-products and mineral additives. The present study has documented, for the first time, the nutritive characteristics, mineral concentrations and dietary cation–anion difference (DCAD) of the broad variety of feed types used on dairy farms across Australia, and has also highlighted the substantial variation that can occur among and within feed types. While there were effects of season, region and some defined farm-management practices on important nutrients for specific feeds, among-farm variation was generally large. These results suggest that farmers and nutritionists should be mindful when formulating dairy rations using feed nutritive characteristics and mineral concentrations reported in feed databases, as there is a potential for considerable variation in specific feeds. In particular, these defined differences suggest that appropriate feed testing of grazed pasture and conserved forage should be undertaken on at least a seasonal basis to monitor changes in nutritive characteristics and mineral concentrations, so as to optimise dietary formulation for the nutrition of grazing dairy cows.

Networks of practice for co-construction of agricultural decision support systems: Case studies of precision dairy farms in Australia

The on-farm use of commercial decision support systems (DSSs) presents learning and adaptation challenges for farmers and their social learning networks. A study of six Australian dairy farms installing new precision dairy farming technology was undertaken to develop an in-depth picture of the issues occurring at the interface where precision farming data and decision-making meet. A qualitative exploratory case study method was used, with farmers each interviewed up to five times from pre-installation until 2years of use. A three-phase learning trajectory was observed amongst farmers involving early learning, consolidation, and advanced use. Farmers exhibited experiential learning but also learned via interaction with a network of on- and off-farm contacts forming a network of practice around the new users. This precision dairy farming network of practice formed a vital method of exchanging knowledge on how to best use technology and data in farming systems, with DSSs acting as a boundary object for learning. Externalisation of tacit knowledge into an explicit form suitable for DSSs was a major focus of this social learning. Co-construction of DSS knowledge in the emerging network was impeded by the absence of potentially important agents, in addition to the incomplete links between existing agents such as technology retailers and farmers. A technological innovation systems perspective is used to propose an improved framework to make greater use of translators and intermediaries. It is aimed at improving links amongst the community to more effectively aid farmers in creating new knowledge in agricultural DSS use.

Farm-scale nitrogen, phosphorus, potassium and sulfur balances and use efficiencies on Australian dairy farms

Efficient and effective nutrient management decisions are critical to profitable and sustainable milk production on modern Australian dairy farms. Whole-farm nutrient balances are commonly used as nutrient management tools and also for regulatory assessment on dairy farms internationally, but are rarely used in Australia. In this study, nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) imports and exports were measured during a standardised production year on 41 contrasting Australian dairy farms, representing a broad range of geographic locations, milk production, herd and farm size, reliance on irrigation, and soil types. The quantity of nutrients imported varied markedly – with feed and fertiliser generally the most substantial imports – and were principally determined by stocking rate and type of imported feed. Milk exports were the largest source of nutrient exports. Nitrogen balance ranged from 47 to 601 kg N/ha.year. Nitrogen-use efficiency ranged from 14 to 50%, with a median value of 26%. Phosphorus balance ranged from –7 to 133 kg P/ha.year, with a median value of 28 kg P/ha. Phosphorus-use efficiencies ranged from 6 to 158%, with a median value of 35%. Potassium balances ranged from 13 to 452 kg K/ha, with a median value of 74 kg K/ha; K-use efficiency ranged from 9 to 48%, with a median value of 20%. Sulfur balances ranged from –1 to 184 kg S/ha, with a median value of 27 kg S/ha; S-use efficiency ranged from 6 to 110%, with a median value of 21%. Nitrogen, P, K and S balances were all positively correlated (P &lt; 0.001) with stocking rate and milk production per ha. Poor relationship between P, K and S fertiliser inputs and milk production from home-grown pasture reflected the already high soil fertility levels measured on many of these farms. The results from this study demonstrate that increasing milk production per ha will be associated with greater nutrient surpluses at the farm scale, with the potential for greater environmental impacts. We suggest that simplified and standardised nutrient balance methodologies should be used on dairy farms in Australia to help identify opportunities for improvements in nutrient management decisions and to develop appropriate industry benchmarks and targets.

Interannual variation in pasture growth rate in Australian and New Zealand dairy regions and its consequences for system management

The profitability of dairy farms in Australia and New Zealand is closely related to the amount of pasture dry matter consumed per hectare per year. There is variability in the pasture growth curve within years (seasonal variation) and between years (interannual variation) in all dairy regions in both countries. Therefore, the biological efficiency of production systems depends on the accuracy and timeliness of the many strategic and tactical decisions that influence the balance between feed supply and demand over an annual cycle. In the case of interannual variation, decisions are made with only limited quantitative information on the range of possible pasture growth outcomes. To address this limitation, we used the biophysical simulation model ‘DairyMod’ to estimate mean monthly herbage accumulation rates of annual or perennial ryegrass-based pastures in 100 years (1907–2006) for five Australian sites (Kyabram in northern Victoria, Terang in south-west Victoria, Ellinbank in Gippsland, Elliott in north-west Tasmania and Vasse in south-west Western Australia) and in 35 years (1972–2006) for three sites in New Zealand (Hamilton in the Waikato, Palmerston North in the Manawatu and Winchmore in Canterbury). The aim was to evaluate whether or not a probabilistic approach to the analysis of pasture growth could provide useful information to support decision making. For the one site where annual ryegrass was simulated, Vasse, the difference between the 25th and 75th percentile years was 20 kg DM/ha.day or less in all months when pasture growth occurred. Irrigation at Kyabram and Winchmore also resulted in a narrow range of growth rates in most months. For non-irrigated sites, the 25th–75th percentile range was narrow (10–15 kg DM/ha.day) from May or June through to September or October, because plant available soil water was adequate to support perennial ryegrass growth, and the main source of interannual variability was variation in temperature. Outside of these months, however, variability in growth was large. There was a positive relationship between total annual herbage accumulation rate and mean stocking for four southern Australian regions (northern Victoria, south-west Victoria, Gippsland and Tasmania), but there was evidence of a negative relationship between the co-efficient of variation in pasture growth and stocking rate. The latter suggests that farmers do account for risk in pasture supply in their stocking rate decisions. However, for the one New Zealand region included in this analysis, Waikato, stocking rate was much higher than would be expected based on the variability in pasture growth, indicating that farmers in this region have well defined decision rules for coping with feed deficits or surpluses. Model predictions such as those presented here are one source of information that can support farm management decision making, but should always be coupled with published data, direct experience, and other relevant information to analyse risk for individual farm businesses.

Leptospira weilii serovar Topaz, a new member of the Tarassovi serogroup isolated from a bovine source in Queensland, Australia

This paper reports on a Leptospira isolate of bovine origin and its identification as belonging to a previously unknown serovar, for which the name Topaz is proposed. The isolate (94-79970/3) was cultured from bovine urine from a north Queensland dairy farm in Australia. Strain 94-79970/3 grew at 30 degrees C in Ellinghausen McCullough Johnson Harris (EMJH) medium but failed to grow at 13 degrees C in EMJH medium or in the presence of 8-azaguanine. Serologically, strain 94-79970/3 produced titres against the Leptospira borgpetersenii serovar Tarassovi, the reference strain for the Tarassovi serogroup; however, no significant titres to any other serovars within the serogroup were obtained. Using 16S rRNA and DNA gyrase subunit B gene analysis, strain 94-79970/3 was identified as a member of the species Leptospira weilii. We propose that the serovar be named Topaz, after the location where the original isolate was obtained. The reference strain for this serovar is 94-79970/3 (=KIT 94-79970/3=LT722).

Clover Hill Dairies: Balancing productivity and treading lightly in the centre of a rural residential development and a rainforest

Clover Hill Dairies is a progressive and highly intensive dairy farm, which currently milks 200 cows on 50 hectares three times a day on steep rainforest hinterland country. Milk production (305 day) currently averages 13 000 litres and 850 kg of milk solids per cow making Clover Hill Dairies one of the highest production dairy farms in Australia. The stud Holstein herd is fed a predominately pasture based diet with supplementary feeding comprising 35-40% of the dietary intake. Clover Hill Dairies aims to optimise the genetic potential of our high end genetic merit herd through close attention to pasture and soil management, biodiversity and the environment. Combining this with the application of the latest scientific principals and technology skills, we produce over 50 000 litres per hectare from the 200 cows. We recognise agriculture is no longer just about farming; it is intrinsically tied in with managing the environment and minimising the impact of human endeavours, hence the ongoing need to balance productivity and progress with minimal environmental footprint. Clover Hill Dairies strives to adopt a holistic decision making process to ensure higher quality of life, financial stability including managing and reducing debt, consistent profitability, and the confidence of knowing that our decisions are improving the environment and the community we live in. The key factors are herd genetics; best practice herd and farm management; the management and operational team; and interaction with the environment and the community.

Using the EMS process as an integrative farm management tool

The use of a management process, described in the Environmental Management System (EMS) standard AS/NZS ISO 14001:2004, to develop integrated farm management plans was examined on dairy farms in Australia. Key research questions were whether the ‘Plan–Do–Check–Act’ management cycle could: (i) adequately identify diverse target areas highlighted by a range of stakeholders, (ii) assist with setting on-farm management priorities and targets, as well as planning actions and monitoring and (iii) provide for integrated management across relevant business areas. Project outcomes demonstrated that the EMS process could identify diverse targets, highlight monitoring issues, guide development of integrated farm plans and achieve business, environmental and social outcomes. Using the EMS process, and supported by skilled and knowledgeable EMS mentors, farmers devised solutions to natural resource management issues that additionally addressed farm productivity and business issues. Increased adoption of management processes inherent in EMS should lead to enhanced integration of on-farm management of environmental, quality, occupational health and safety, and social issues. It could also assist in meeting and evaluating catchment, regulatory and community outcomes.

Phosphorus uptake and release in surface drains

Irrigated dairy farms in Australia contain extensive surface drainage networks that transport water and nutrients off the property into community drains, rivers and lake systems. These drainage networks are an important hydro-chemical link between the paddock, farm and catchment scales. This paper presents a preliminary investigation into phosphorus transfer in two drains from an irrigated dairy farm in southeastern Australia. The effects of drain condition (bare earth vs. grazed pasture) and management (weed growth and herbicide application) are investigated over four drainage events. The results show that farm drains (160 m long) significantly alter the phosphorus concentrations in drainage water. The changes in phosphorus concentration and to a lesser extent phosphorus form appear to be affected by the condition as well as the management of the drain. For example the wetting front concentrations increased by 4.4 mg P/l down a pasture-based drain and decreased by 1.2 mg P/l down an earthen drain. Understanding the transport of phosphorus between the paddock and farm boundary appears to be an important step in developing strategies to minimise the potential export of phosphorus from irrigated dairy farms.

Farmers' attitudes to the benefits and barriers of adopting automation for surface irrigation on dairy farms in Australia ∗

The study reports farmers' attitudes andperceptions towards the ``barriers'' and ``benefits'' of automaticirrigation systems. Factor analysis was used to identify perceived ``barriers'' and ``benefits'' andthe impact of these on farmers' priority to adopt automation wasexamined. Results suggest that farmers'attitudes, particularly with respect to ``cost'' and``lifestyle'' influenced the priority given to adopting automaticirrigation systems. The area under pasturecultivation was also an importantdeterminant in the farmers' priority foradopting automatic irrigation systems.

Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data

A stochastic frontier production function is defined for panel data on sample firms, such that the disturbances associated with observations for a given firm involve the differences between traditional symmetric random errors and a non-negative random variable, which is associated with the technical efficiency of the firm. Given that the non-negative firm effects are time-invariant and have a general truncated normal distribution, we obtain the best predictor for the firm-effect random variable and the appropriate technical efficiency of an individual firm, given the values of the disturbances in the model. The results obtained are a generalization of those presented by Jondrow et al. (1982) for a cross-sectional model in which the firm effects have half-normal distribution. The model is applied in the analysis of three years of data for dairy farms in Australia.

Isolation of Yersinia enterocolitica from Milk and a Dairy Farm in Australia

Forty‐eight isolates obtained from pasteurized milk and cream, raw milk, and various sites on a dairy farm were identified as Yersinia enterocolitica biotype 1 but were unusual in their ability to ferment lactose. The majority belonged to serotype 5a. Serotypes 5b, 6, 13/15 and 23/15 were also found. A few isolates were not typable. There is no evidence that strains isolated in this study are pathogenic for humans. Their presence in pasteurized products and growth at refrigeration temperatures are, however, cause for concern.