The occurrence of Campylobacter spp., Listeria monocytogenes and Shiga toxin-producing Escherichia coli in Norwegian dairy cattle farms; a comparison between free stall and tie stall housing systems.

Aims: This study explores how different dairy farm operating systems influence the occurrence of zoonotic bacteria in raw milk. Methods and Results: Samples from bulk tank milk, milk filters, feces, feed, teats and teat milk were collected from eleven farms with loose housing and seven with tie-stall housing every second month over a period of 11 months and analyzed for the presence of Campylobacter spp., L. monocytogenes and STEC. Campylobacter spp., L. monocytogenes and STEC were abundant in samples from the farm environment and were also detected in 4%, 13% and 7% of the milk filters, respectively, and in 3%, 0% and 1% of bulk tank milk samples. Four STEC isolates carried the eae gene, which is linked to the capacity to cause more severe human disease. Conclusion: The results indicate a higher prevalence of L. monocytogenes and Campylobacter spp. in samples collected from loose housed herds compared to tie-stalled herds suggesting that the operating system can influence the food safety of raw milk. Significance and Impact of the study: This study highlights that zoonotic bacteria can be present in raw milk independent of hygienic conditions at the farm and what hosing system is used. Altogether, this study provides an important knowledge base for evaluating the risk of drinking unpasteurized milk.

WGS analysis of Listeria monocytogenes from rural, urban, and farm environments in Norway: Genetic diversity, persistence, and relation to clinical and food isolates

Listeria monocytogenes is a ubiquitous environmental bacterium associated with a wide variety of natural and man-made environments, such as soil, vegetation, livestock, food processing environments, and urban areas. It is also among the deadliest foodborne pathogens, and knowledge about its presence and diversity in potential sources is crucial to effectively track and control it in the food chain. Isolation of L. monocytogenes from various rural and urban environments showed higher prevalence in agricultural and urban developments than in forest or mountain areas, and that detection was positively associated with rainfall. Whole genome sequencing (WGS) was performed for the collected isolates and for L. monocytogenes from Norwegian dairy farms and slugs, in total 218 isolates. The data was compared with available datasets from clinical and food associated sources in Norway collected within the last decade. Multiple examples of clusters of isolates with 0?8 wgMLST allelic differences were collected over time in the same location, demonstrating persistence of L. monocytogenes in natural, urban and farm environments. Furthermore, several clusters with 6?20 wgMLST allelic differences containing isolates collected across different locations, times and habitats were identified, including nine clusters harbouring clinical isolates. The most ubiquitous clones found in soil and other natural and animal ecosystems (CC91, CC11, and CC37) were distinct from clones predominating among both clinical (CC7, CC121, CC1) and food (CC9, CC121, CC7, CC8) isolates. The analyses indicated that ST91 was more prevalent in Norway than other countries and revealed a high proportion of the hypovirulent ST121 among Norwegian clinical cases.

Measuring technology and performance differentials among the Norwegian dairy farms

PurposeThe study measures the technology gap and performance of the Norwegian dairy farms accounting for farm heterogeneity.Design/methodology/approachThe analysis was based on a meta-frontier and unbalanced farm-level panel data for 1991–2014 from 417 Norwegian farms specialized in dairy production in five regions of Norway.FindingsThe result of the analysis provides empirical evidence of regional differences in technical efficiencies, technological gap ratios (TGRs) and input use. Consequently, the paper provides some insights into policies to increase the efficiency of dairy production in the country across all regions.Research limitations/implicationsThe author used a meta-frontier approach for modeling regional differences based on a single-output production function specification. This approach has commonly been used in the economics literature since Battese et al. (2004). To get more informative and useful results, it would be necessary to repeat the analysis within terms of multiple input-output frameworks using, for instance, the input distance function approach. Moreover, the author estimated the meta-frontier using the non-parametric approach, thus it is also a need for further analysis if the values are different by estimating using a parametric approach.Practical implicationsOne implication for farmers (and their advisers) is that dairy farms in all regions used available technology in the area sub-optimally. Thus, those lagging the best-performing farms need to look at the way the best-performing farmers are operating. Policymakers might reduce the gap is through training, including sharing information about relevant technologies from one area to another, provided that the technologies being shared fit the working environment of the lagging area. Moreover, some of the dairy technologies they use may not fit other regions, suggesting that agricultural policies that aim to encourage efficient dairy production, such as innovation of improved technology (like breeding, bull selection and improved feed varieties) through research and development, need to account the environmental differences between regions.Social implicationsFor both taxpayers and consumers, one implication is that the contributions they pay that go to subsidize dairy farmers appear to bring some benefits in terms of more efficient milk production that, in turn, increases the supply of some foods so possibly making food prices more affordable.Originality/valueThe paper contributes to the literature in several ways. In contrast to Battese et al. (2004), the author accounts for farm-level performance differences by applying the model devised by Greene (2005), thus may serve as a model for future studies at more local levels or of other industries. Moreover, the author is fortunate to able to use a large level farm-level panel data from 1991 to 2014.

The Role of Technical Efficiency Achieving Sustainable Development: A Dynamic Analysis of Norwegian Dairy Farms

Growing environmental concerns have prompted governments to make sustainable choices in agricultural resource use. Evaluating the sustainability of agricultural systems is a key issue for the implementation of policies and practices aimed at revealing sustainability. This study aimed to evaluate the performance of Norwegian dairy farms, accounting for marginal effects of environmental (exogenous) variables. We adopted the dynamic parametric approach within the input distance function framework to estimate the performance of Norwegian dairy farms, focusing on the technical efficiency and determinates. For comparison, we also estimated the static parametric model, which was used by previous studies. We used unbalanced farm-level panel data for the period 2000–2018. The result shows a mean technical efficiency score of 0.92 for the dynamic model and 0.87 for the static models. The empirical result shows that the previous studies that focused on the static model reported a biased result on the performance of dairy farms. The dynamic efficiency score suggests that Norwegian dairy farms can reduce the input requirement of producing the average output by 8% if the operation becomes technically efficient. The environmental variables have a different effect on the performance of the farmers; thus, policymakers need to place special focus on these variables for the sustainable development of the dairy sector.