Increasing Herd Size Research Articles

Bovine tuberculosis trends in Wales between 2010 and 2021.

Bovine tuberculosis (bTB) is the most important animal health concern in Wales. Annual testing across all cattle herds in Wales commenced in 2010. In 2017, a new geographic division of Wales was conceived, with bespoke cattle controls and eradication milestones reflecting the geographical heterogeneity of bTB distribution. This observational study uses descriptive analysis and Poisson regression modelling to analyse bTB surveillance data from all herds in Wales. Since 2010, there has been a significant decrease (p < 0.0001) in bTB incidence (8.6%‒6.3%) and plateauing prevalence across Wales. Conversely, there has been an increase in bTB incidence and prevalence in discrete areas. Recurrence and persistence remain important drivers of bTB infection. One of the sharpest declines in bTB incidence was observed in an intensive action area where enhanced cattle control and wildlife vaccination were implemented in an area of high bTB prevalence. Increased herd size, dairy herd type and herd location are important risk factors affecting the rate of bTB incidents in Wales. This study includes data from Wales only. Improvements in trends of bTB occurred from 2010 to 2021, but the spatial variations described in this paper support the continued need for regionally adapted surveillance and control measures.

Internet of Things (IoT): Sensors Application in Dairy Cattle Farming

The expansion of dairy cattle farms and the increase in herd size have made the control and management of animals more complex, with potentially negative effects on animal welfare, health, productive/reproductive performance and consequently farm income. Precision Livestock Farming (PLF) is based on the use of sensors to monitor individual animals in real time, enabling farmers to manage their herds more efficiently and optimise their performance. The integration of sensors and devices used in PLF with the Internet of Things (IoT) technologies (edge computing, cloud computing, and machine learning) creates a network of connected objects that improve the management of individual animals through data-driven decision-making processes. This paper illustrates the main PLF technologies used in the dairy cattle sector, highlighting how the integration of sensors and devices with IoT addresses the challenges of modern dairy cattle farming, leading to improved farm management.

Developing sustainable dairy farms in the tropics: From policy to practice

CONTEXTSustainable dairy production is included in the policy agenda of many countries in the tropics to address, among others, their commitment to the Paris Agreement. To the best of our knowledge, however, a study to assess the impact of the proposed interventions for sustainable dairy production is still lacking for most of those countries. Using policy goals as entry points to develop scenarios can provide insight into the impact of policy interventions on dairy farming practices. OBJECTIVEThis study aimed to evaluate the implication of interventions towards sustainable dairy development identified by the governments of Indonesia and Costa Rica. METHODSInformation about current farming practices (i.e. the baseline) were collected on 32 smallholder dairy farms in Indonesia and 24 dairy farms in Costa Rica. Scenarios were designed based on policy goals for dairy development and climate change mitigation in each country. The scenarios for Indonesia encompassed relocation of the dairy sector to Sumatra to allow coupling of livestock to land combined with a restriction on manure production to ensure all manure to be applied to grow forage, and a restriction on the amount of purchased feeds, at two levels: maximally 100% and 50% of the baseline. The scenarios for Costa Rica included a silvopastoral system and a reduction in the amount of purchased feeds, at two levels: 50% and 80% lower than the baseline. We estimated greenhouse gas (GHG) emissions at chain level and carbon (C) stocks at farm level. RESULTS AND CONCLUSIONSThe scenarios for Indonesia increased herd size and milk output by 240–360%, and GHG emissions per farm by 269–455%, while decreased GHG emissions per kg milk by 1–10%, compared to the baseline. C stocks per farm were higher in the scenarios than in the baseline, but compared to natural vegetation much more C is lost under the scenarios because more land is being used. The scenarios for Costa Rica reduced herd size and milk output by 5–25% and GHG emissions per farm by 17–35%, while GHG emissions per kg milk decreased by 10%, compared to the baseline. C stocks per farm were comparable. SIGNIFICANCETo achieve the multiple policy goals for sustainable dairy development, the governments need to consider the trade-off between increasing milk production and reducing GHG emissions. In Indonesia, relocation of the dairy sector needs a strict policy to avoid the expansion of dairy farms into tropical forest land. Furthermore, the Costa Rican government needs to incentivise dairy farmers to implement a silvopastoral system to reduce GHG emissions and land use. This, however, will be at the expense of milk output.

Polish Dairy Farm Transformations and Competitiveness 20 Years after Poland's Accession to the European Union.

Poland is one of the leading milk producers in the EU, being the fifth largest after countries such as Germany, France, Italy, and the Netherlands. From Poland's accession to the European Union in 2004 up to 2022, Polish milk production experienced dynamic development. In this, there occurred a strong decline in the number of dairy farms (by -78%) and the number of cows (by -21%), an increase in dairy herd size (3.5 times) and increase in milk production (+60%) and milk yield per cow (by +62%). These were among the highest growth dynamics among the analysed countries. As a result of this significant transformation, Poland maintained an important position in milk exports, with a 31% export share in production in 2022. The scale of milk production was the basic factor determining the efficiency and competitiveness of dairy farms in Poland. Milk yield, farmland productivity, labour productivity, milk price, and the Corrected Competitiveness Index (based on labour and land opportunity costs) all showed a positive relationship with cow herd size on the farm. Milk production is highly uncompetitive for smaller farms (<15 cows). Despite substantial public support, the smaller farms, where subsidies equal up to 47% of total production value, could not earn sufficient income to cover the cost of capital, risk, and management in 2008, and even more so in 2021. This is because the farm income is too small to cover the extremely high opportunity cost of labour. The larger farms (with 30 cows and more) are competitive and responsible for the majority (~60-70%) of milk produced and delivered to the market. The most challenging from the sectoral policy point of view are medium farms (10-29 cows), whose share in production and deliveries is still important. To survive as economically viable units, these farms have to increase in scale and improve productivity. Otherwise, they will be gradually supplanted by larger farms.

Phylogenetic Analysis and Certain Risk Factors of Chlamydia Abortus in Ewes in Nineveh Governorate, Iraq

Chlamydia is an obligate intracellular Gram-negative organism that causes abortion, or "enzootic abortion of ewes", which is characterized by causing economic loss in sheep breeding. Chlamydia abortus spread could be stopped, and monitoring efforts could be more successful by gathering data on the condition of the disease. This ground-breaking study focused on the phylogenetic analysis and certain risk factors of Chlamydia abortus in ewes in Nineveh governorate, Iraq. 52 placenta samples were collected, representing 52 flocks (8660 ewes) who suffered from abortion in various Nineveh provincial locations between August 2022 and January 2023. Two local sequences were submitted to GenBank and assigned the accession numbers OR334580 and OR334581. The Chlamydia abortus 16S rRNA sequences were analyzed using the GenomeNet multiple sequence alignment tool (CLUSTALW), an online resource. After that, the sequences were compared to other Chlamydia abortus sequences that were accessible in the GenBank using NCBI BLAST (BLASTn) of NCBI. The Tamura-Nei model in the MEGA11 software program was used to create the phylogenetic trees. Results indicate that 15.38% (8/52) of the ewes had positive results for Chlamydia abortus, which was significantly affected by the increase in herd size and interspecies farming. Two sequences were put through individual sequence analysis, which revealed new Chlamydia abortus sequences, which were recorded for the first time in the governorate of Nineveh. We concluded that a higher risk of infection occurs in farms that contain more than 100 ewes and are mixed with other species of animals.

Udder health outcomes in Irish herds participating in CellCheck dry cow consults

The CellCheck Dry Cow Consult (DCC) was developed by the CellCheck Technical Working Group to enable farmers to engage with their nominated vet to develop farm-specific selective dry cow therapy (SDCT) plans, where appropriate. This study evaluated the effect of the DCC on farmer decision-making around dry cow therapy, and the udder health impact of implementing SDCT, in study herds over the 2019 and 2020 dry periods. The DCC was a 3-h consult, delivered and funded as part of the Targeted Advisory Service on Animal Health (TASAH). Herds that completed a DCC were invited to register for a Dry Cow Review the following year. The combined data set for analysis across both years comprised of 439 herds and 25,357 cows. Available herd size ranged from 25 to 800. The median SCC of cows dried off with teat sealant only was 47,000 cells/ml before drying off in 2019 and 48,000 cells/ml at first milk recording in 2020, and 43,000 cells/ml before drying off in 2020 and 39,000 cells/ml at first milking recording in 2021. Following both the 2019 and 2020 dry periods, cows tended to converge toward a similar median SCC early in the following lactation, irrespective of prior dry cow treatment strategy. The uptake of SDCT was greater in Review herds, with 21% of cows receiving teat sealant only in 2020, compared with 16.3% of cows in herds participating in a Consult for the first time in 2020. At an individual cow level, in both years dry period new infection rate (NIR) was approximately 2.7% higher for cows treated with teat sealant only, than for those treated with both dry cow antibiotic tubes and teat sealant, and 1.2% higher than cows treated with antibiotic only. Regardless of treatment, there was a significant association between increasing parity and the risk of a dry period new infection. Increasing herd size had a statistically significant effect on the risk of dry period new infection rates. At a herd level, there was no statistically significant increase in NIR when SDCT was used compared with herds where blanket dry cow therapy was used. While not without risk, SDCT can be successfully implemented in Irish herds; however, constant attention to hygiene and management is essential. Though there are challenges to face, facilitating continued farmer education and engagement with professional guidance will be important.

Partial herd hoof trimming results in a higher economic net benefit than whole herd hoof trimming in dairy herds.

Sole ulcers, a common cause of lameness is the costliest non-infectious foot lesion in dairy cows and one of the most prevalent non-infectious foot lesions in freestall housing systems. Costs associated with sole ulcers are treatment costs, plus increased labor and decreased productivity and fertility. Routine hoof trimming is part of a strategy to manage sole ulcers. However, hoof trimming strategies differ among farms. The two most frequently applied strategies are: 1) partial herd hoof trimming with a 2-month interval between trims; and 2) whole herd hoof trimming at 6-month intervals. A Markov model was developed to investigate whether every 2 months partial herd hoof trimming or whole herd hoof trimming every 6 months was the most cost-effective strategy to avoid costs associated with sole ulcers. In this model, the net benefits for a 100-cow herd and the average productive life span of a dairy cow in intensive dairy systems of 3 years were evaluated. Partial herd hoof trimming was the most cost-effective strategy 100% of the time compared to whole herd hoof trimming, with a difference in 3-year net benefits of US$4,337 (95% CI: US$2,713-US$5,830). Based on sensitivity analyses, variables that were the sources of the biggest uncertainty in the model were herd size, the probability of being trimmed in a partial herd trim, and the prevalence of sole ulcers. To further investigate the impacts of herd size and of probability of being trimmed, various scenario analyses were conducted. With increasing herd size, the difference in net benefits in favor of partial herd hoof trimming increased even more. Scenario analyses about the probability of getting trimmed all indicated that targeted intervention increased the difference in net benefits in favor of partial herd hoof trimming. However, if the selection of cows to be trimmed in a partial herd trim was random, the whole herd hoof trimming strategy became cost-effective. Therefore, targeted selection and early intervention are necessary to decrease costs associated with sole ulcers.

Assessing technical efficiency and its determinants for dairy cattle farms in northern Algeria: The two-step DEA-Tobit approach

The purpose of this paper is to analyze the technical efficiency of dairy cattle farms in Tizi Ouzou region as one of the main dairy basins in Algeria. To do so, a two-step analysis was applied. First, Data Envelopment Analysis (DEA) was used to quantify the technical efficiency of dairy cattle farms. In the second step, a Tobit model to examine factors affecting farms’ technical efficiency is used. The analysis used cross-sectional data collected from 146 dairy cattle farmers. The study found that the average technical efficiency under VRS assumption is relatively high, suggesting that farms can reduce their inputs by an average of 17% while maintaining the same level of output. The study also highlights the crucial role that agricultural advisory system and traditional insemination play in enhancing technical efficiency. In this region, where cattle breeding is traditionally practiced on a small scale, increasing herd size can result in reduced performance. The study also recommended that agricultural policies should be adapted to local specificities and that a more supportive strategy should be adopted for small-scale family dairy farms instead of promoting the large farm model.

The role of hired labour on technical efficiency in an expanding dairy sector: The case of Ireland

AbstractThe 2015 EU milk quota abolition initiated considerable expansion in the dairy sector. This expansion has increased the demand for additional labour in some EU countries, most significantly in Ireland. This paper explores the role of hired labour on Irish dairy farms' technical efficiency (TE). We use a detailed farm‐level panel data set of a representative sample from 2000 to 2018. To estimate transient, persistent, and overall TE over time, we apply a 4‐component stochastic frontier model. Our findings show significant variation in TE scores over the period. We also control for endogeneity to obtain marginal effects of hired labour on TE. The results reveal that hired labour has a significant yet small, positive effect on farms' TE. Our findings suggest that the effect of hired labour on TE is larger for small and medium‐sized farms. This effect is larger when herd size increases.

Real-time automatic integrated monitoring of barn environment and dairy cattle behaviour: Technical implementation and evaluation on three commercial farms

Due to increasing herd sizes and automation on dairy farms there is an important need for automated monitoring of cow production, health, and welfare. Despite much progress in automatic monitoring techniques, there is still a need to integrate data from multiple sources to create a comprehensive overview and accurate diagnosis of a cow’s state. To aid the technological development of data integration, a prototype of an open and customizable automatic system that integrates data from multiple sensors relating to barn environment and cow behaviour was developed. The system integrates data from sensors that measure barn climate (e.g., temperature, humidity, wind speed), air quality (e.g., CO2 concentration), water use and temperature, the moisture and temperature of the litter and cow behaviour (e.g., lying, eating, ruminating). An external weather system and video recording system are also included. The system’s architecture consists of four main elements: sensors, nodes, gateways, and backend. The data are recorded by sensors, then locally processed on custom-developed sensor nodes, and then transmitted via radio channels to local gateways that combine the data from multiple nodes and transmit them to distributed digital storage (“the cloud”) via a 3G/4G cellular network. On the cloud, the data are further processed and stored in a database. The data are then presented to the user continuously and in real time on a dashboard that can be accessed via the internet. In the design of the local wireless network, care was taken to avoid data packet collision and thus to minimize data loss. To test the system’s performance, the system was installed and operated on three commercial dairy cattle farms for one year. The system provided high data stability with minimal loss and outliers, showing that the system is reliable and suitable for long term application on commercial dairy farms. The system’s architecture, communication network, and data processing and visualization applications form an open framework for research and development purposes, allowing it to be customized and fine-tuned before being deployed as a management assistant on commercial dairy farms. Missing elements that should be added in the future are the integration of the data from the milking parlour and cow identification. Algorithms to integrate information from multiple sensors can be added to provide a comprehensive system that monitors all aspects related to cow welfare, health, and production automatically, remotely and in real time, thereby supporting farmers in important management decision-making.

Evaluating environmental effects of adopting automatic milking systems on Norwegian dairy farms

Abstract We present a novel procedure based on eco-efficiency for assessing farm-level effects of technology adoption while considering secondary effects. Secondary effects are defined as structural and behavioural adaptations to technology that may impact environmental, social or economic outcomes. We apply the procedure to automatic milking systems (AMS) in Norway and find that AMS induces secondary effects, most strongly by decreasing labour per cow and increasing herd sizes. For estimating effects of AMS we employ a novel causal machine learning approach. AMS induce heterogenous effects on eco-efficiency, negatively associated with herd expansion and labour per cow.

427 A Survey of Mississippi Beef Cattle Producer Attitudes, Practices, and Demographics

Abstract As input costs increase, producers need to be mindful of management practices to maintain profit margins and it is important to understand current management practices. Therefore, the objective of this study was to investigate current beef marketing strategies and beliefs of beef producers in the state of Mississippi in 2019. This survey was distributed to cattle producers in Mississippi in the June/July issue of “Cattle Business in Mississippi” magazine with return postage. There also was a link included in the “Monday Memo” email sent to members of the Mississippi Cattlemen’s Association. The survey consisted of 22 questions that were divided into three sections: current practices, current cow-calf marketing practice, and general information. Responses from the survey were anonymous and confidential and were received via paper mail or electronic responses. Responses were stored and analyzed using Qualtrics. Data collected from this study were compared with 1999 data from a similar study conducted by Little et al. (2000). Out of 148 respondents, there was a 95% completion rate of surveys taken. Most respondents were 51 years and older with 54% of the respondents being part of the cattle industry for over 31 years. Ninety percent of respondents were members of the Mississippi Cattlemen’s Association and almost 60% were certified in Beef Quality Assurance. The majority, 122 of 148 producers, stated their cattle operation was not the primary source of income for their household. The average number of cows, bulls, stockers/yearling, replacement heifers and cattle on-feed on an operation was greater in the 2019 data compared with 1999. On average, the number of cows per operation increased from 40 to 68 animals, bulls increased from 2 to 5 animals, and replacement heifers increased from 7 to 17 animals from 1999 to 2019. There was also an average increase from 30 to76 animals of stocker/yearling and an increase from 2 to 27 animals of on-feed cattle. Most producers vaccinate (81%) and deworm (85%) according to recommendations and indicated a lack of labor and time as reasons for not implementing some common management practices such as preconditioning. Respondents mainly purchased calves directly from a producer (61%) or from special sales (59%). In 1999, over one-half of respondents purchased from an auction. When it came to selling cattle, 58% of respondents sold at auction regularly in 2019. In 1999, 73% of respondents sold at auctions. In 2019, 72% of respondents stated they did not retain ownership while in 1999 only 58% of respondents did not retain ownership. In conclusion, our results suggest there have been key changes within the cattle industry regarding herd size increases and a shift in selling and purchasing habits since 1999.

Exploring policy options to transform traditional dairy system in Ethiopia: A system dynamics approach

Despite having a larger herd size and a favourable climate, Ethiopia generally lags behind developing nations in terms of both production and consumption of dairy products. For the purpose of finding and assessing ways to increase milk production by the traditional dairy system in Ethiopia's West Shewa zone, we integrated system dynamics (SD) modelling with a participatory model building approach. The main objective of the research was to develop SD model for West Shewa dairy value chain and apply it to assess ex-ante milk production and dairy household profit impacts of various interventions. The interventions evaluated in the study include enhanced urea treatment of crops residue, increased production of improved feed, and investment in more dairy cows. Analysis reveals that policies targeting feed development can boost milk production and household profits above the baseline. They also lead to a higher seasonal variability in milk production. Through the feed development strategy producers can achieve a 70 % and 735 % increase in milk production and household profit, respectively. However, the implementation of improved feed policy still leaves a gap in the average feed protein requirement of a dairy cattle in the study area. On the other hand, policy of increasing cow herd is not profitable. In addition, improved feed policy in drought results in reduced herd size, yet it leads to a higher milk output and household profit. Hence, with the feed development options explored in this study, increasing herd size is not a recommended course of action for improvement of the West Shewa dairy value chain. Therefore, future research should explore for further enhancement of the supply as well as quality of feed resources and the potential of investment in improved breeds.

Genomic Selection for Dairy Cattle Behaviour Considering Novel Traits in a Changing Technical Production Environment.

Cow behaviour is a major factor influencing dairy herd profitability and is an indicator of animal welfare and disease. Behaviour is a complex network of behavioural patterns in response to environmental and social stimuli and human handling. Advances in agricultural technology have led to changes in dairy cow husbandry systems worldwide. Increasing herd sizes, less time availability to take care of the animals and modern technology such as automatic milking systems (AMSs) imply limited human-cow interactions. On the other hand, cow behaviour responses to the technical environment (cow-AMS interactions) simultaneously improve production efficiency and welfare and contribute to simplified "cow handling" and reduced labour time. Automatic milking systems generate objective behaviour traits linked to workability, milkability and health, which can be implemented into genomic selection tools. However, there is insufficient understanding of the genetic mechanisms influencing cow learning and social behaviour, in turn affecting herd management, productivity and welfare. Moreover, physiological and molecular biomarkers such as heart rate, neurotransmitters and hormones might be useful indicators and predictors of cow behaviour. This review gives an overview of published behaviour studies in dairy cows in the context of genetics and genomics and discusses possibilities for breeding approaches to achieve desired behaviour in a technical production environment.

Accurate prediction of calving in dairy cows by applying feature engineering and machine learning.

Prediction of calving is key to dairy cow management. Current trends of increasing herd sizes globally can directly impact the time that farmers spend monitoring individual animals. Automated monitoring on behavioural and physiological changes prior to parturition can be used to develop machine learning solutions for calving prediction. In this study, we developed a machine learning algorithm for the prediction of calving in dairy cows. We demonstrated that temperature and activity index information retrieved from a commercial reticuloruminal bolus sensor can accurately predict calving from 1-day to 5-days in advance. The best prediction solution using data from 82 dairy cows, achieved up to 87.81 % in accuracy, 92.99 % in specificity, 75.84 % in sensitivity, 82.99 % in positive predictive value (PPV), 78.85 % in F-score, and 90.02 % in negative predictive value (NPV) on the test dataset when using information from 2-days in advance and all the subsets of feature characteristics (temperature + drinking + activity). The performance only decreased by 2.45 % points in accuracy, 0.74 % points in specificity, 6.41 % points in sensitivity, 2.45 % points in positive predictive value, 4.91 % points in F-score, and 2.44 % points in negative predictive value on the test dataset when using all feature characteristics and 5-days in advance information compared to using all features and information from 2-days in advance. Full evaluation of the performance of the prediction showed an improvement when using all the different subsets of feature characteristics together (temperature, activity, and drinking) compared to using temperature features only. When adding activity and drinking to the subset of temperature features, an average increase of 2.70, 1.52, 5.40, 4.39, 5.02, 2.13 % points in accuracy, specificity, sensitivity, PPV, F-score, and NPV, respectively, was obtained. Notably, evaluation of feature importance (i.e., relative weight of any given feature in relation to model prediction) showed that 3-5 (depending on the selected days in advance model) of the top ten features were derived from drinking behaviour, showing the relevance that this behaviour can have in the prediction of calving. This algorithm can provide a useful tool for automated calving prediction in dairy cows which has potential for improvement of health, welfare, and productivity in the dairy industry.

Assessment of the current performance of grazing infrastructure across Irish dairy farms

The increased average Irish dairy herd size in a post-quota environment has put heightened pressure on grazing infrastructure. In a rotational grazing system, grazing infrastructure consists of the paddock system, which delineates the grazing areas into appropriately sized grazing parcels, and the roadway network, which connects these paddocks to the milking parlor. Where herd size has increased without corresponding adaptations to the infrastructure, farm management and roadway network performance has been affected. The links between suboptimal grazing infrastructure and roadway network efficiency are poorly understood and not widely documented. The aims of this study were to (1) analyze the effect of herd expansion and paddock size on pasture allocations per paddock, (2) identify the factors that affect the total distance walked per year, and (3) create a metric to compare the efficiency of roadway networks across farms of varying grazing platforms. A sample population of 135 Irish dairy farms with a median herd size of 150 cows was used for this analysis. Herds were split into the following 5 categories: <100 cows, 100 to 149 cows, 150 to 199 cows, 200 to 249 cows, and ≥250 cows. Herds with ≥250 cows had a greater number of paddocks per farm and rotated around the grazing paddocks more frequently, with 46% of paddocks only suitable for 12 h allocations relative to herd size, compared with just 10% to 27% of paddocks for herds with <100 cows to herds with 200-249 cows. When predicting the total distance walked per year on each study farm, the mean distance from a paddock to the milking parlor was the strongest indicator (R2 = 0.8247). Other metrics, such as herd size, have failed to account for the location of the milking parlor relative to the grazing platform. The creation of the relative mean distance from a paddock to milking parlor (RMDMP) metric allowed the calculation of a farm's roadway network efficiency for moving the herd between paddocks and the milking parlor. The analyzed farms increased their efficiency in terms of RMDMP (0.34-40.74%) as they increased herd size post quota. However, the position of new additional paddocks relative to the milking parlor substantially affected their RMDMP.

An observational study of ear-tagged calf mortality (1 to 100 days) on Irish dairy farms and associations between biosecurity practices and calf mortality on farms participating in a Johne's disease control program

Postnatal mortality among replacement stock has a detrimental effect on the social, economic, and environmental sustainability of dairy production. Calf mortality rates vary between countries and show differences in temporal trends; most, however, are characterized by high levels of between-farm variability. Explaining this variation can be difficult because herd-level information on management practices relevant to calf health is often not available. The Irish Johne's Control Programme (IJCP) contains a substantial on-farm monitoring program called the Veterinary Risk Assessment and Management Plan (VRAMP). Although this risk assessment is largely focused on factors relevant to the transmission of paratuberculosis, many of its principles are good practice biocontainment policies that are also advocated for the protection of calf health. The objectives of this study were (1) to quantify mortality in ear-tagged Irish dairy calves between 2016 and 2020 using both survival and risk approaches, (2) to determine risk factors for 100-d cumulative mortality hazard in ear-tagged Irish dairy calves between 2016 and 2020, (3) to determine whether 100-d cumulative mortality hazard was higher in ear-tagged calves within herds registered in the IJCP versus those that were not registered in the IJCP and whether there were differences between these cohorts over time, and (4) within IJCP herds, to determine whether VRAMP score or changes in VRAMP score were associated with 100-d cumulative mortality hazard. Excluding perinatal mortality, the overall 100-d cumulative mortality hazard was 4.1%. Calf mortality was consistently underestimated using risk approaches that did not account for calf censoring. Cox proportional hazards models showed that cumulative mortality hazard was greater in male calves; particularly, calves born to Jersey breed dams and those with a beef breed sire. Mortality hazard increased with increasing herd size, was highest in calves born in herds that contract-reared heifers, and lowest in those born in mixed dairy-beef enterprises. Mortality hazard decreased over time with the mortality hazard in 2020 being 0.83 times that of 2016. Mortality hazard was higher in IJCP-registered herds than nonregistered herds (hazard ratio 1.06, 95% CI 1.01-1.12), likely reflecting differences in herds that enrolled in the national program. However, we detected a significant interaction between IJCP status (enrolled vs. not enrolled) and year (hazard ratio 0.96, 95% CI 0.92-1.00), indicating that the decrease in mortality hazard between 2016 and 2020 was greater in IJCP herds versus non-IJCP herds. Finally, increasing VRAMP scores (indicating higher risk for paratuberculosis transmission) were positively associated with increased calf mortality hazard. Postnatal calf mortality rates in Irish dairy herds declined between 2016 and 2020. Our study suggests that implementation of recommended biocontainment practices to control paratuberculosis in IJCP herds was associated with a reduction in calf mortality hazard.

Factors influencing Scottish dairy farmers' antibiotic use.

Good stewardship of antibiotics can reduce the development and impact of antimicrobial resistance (AMR); therefore, understanding farmers' antibiotic use is of interest to stakeholders. To date, few qualitative studies have looked at farmers' antibiotic use on dairy farms in the UK. Semi-structured interviews were used to explore 15 Scottish dairy farmers' antibiotic use behaviours and the factors influencing their antibiotic use on farms. Using an inductive process, the results from the interviews were analysed, and four key themes were extracted: use of antibiotics, awareness of AMR, determinants of antibiotic use and future aspirations. Some of the farmers interviewed were wary about discussing their antibiotic use, which could mean that some contentious issues were not discussed. The farmers also all belonged to a single milk-buying group, which may limit the generalisability of the findings. The quantities of antibiotics used were felt to be driven by the disease prevalence on farms, cows being indoors more, increasing herd sizes resulting in increased stocking densities, retention of poorer cows and substandard housing. Farmer knowledge of antibiotics varied, and not all farmers interviewed were aware of AMR. The farm veterinarian, the press and peers were found to be the main sources of antibiotic information.

Adoption of dairy technologies in smallholder dairy farms in Ethiopia

The adoption of modern agricultural technologies in Ethiopia’s dairy production system remains underutilized and under-researched yet it is a promising sector to aid in reducing poverty, improving the food security situation and the welfare of rural households, and in ensuring environmental sustainability. This paper uses the Negative Binomial regression model to examine determinants of multiple agricultural technology adoption in the Addis Ababa and Oromia regions of Ethiopia. Data was collected from 159 smallholder dairy farms in Ethiopia’s Addis Ababa and Oromia regions exploring 19 technologies used by the farmers during the study period. The findings show that farm location and herd size impact adoption decisions. Increasing herd size is associated with increased uptake of multiple technologies. Further, as farmer education level increases the more likely farmers are to adopt multiple technologies. The increase in the number of female workers is positively associated with the adoption of multiple dairy technologies. In terms of farmers’/workers’ years of experience, those with no years of work experience are less likely to have adopted multiple technologies than those with more than 5 years of experience. However, this could be due to a number of factors where experience stands as a proxy value. Trust in information from government agencies was associated with a higher propensity to adopt multiple dairy technology as was farmer perception of fellow farmers as peers compared to those who perceive them as competitors. This is an important finding as it may help policymakers or institutions explore knowledge exchange and diffusion of innovation strategies tailored to specific farming and community situations. Studies have shown that farmers within a social group learn from each other more fully about the benefits and usage of new technology. These findings are of value in future technology adoption studies, particularly which factors influence the intensity of adoption of multiple technologies by smallscale producers.

Influence of dairy farmers’ knowledge on their attitudes towards breeding tools and genomic selection

Understanding farmers' attitudes towards traits is critical for developing appropriate breeding goals for dairy production. In response to a research gap in regards to the influence of farmers' knowledge of breeding tools, this study aimed to determine the effect of farmers' knowledge on their attitudes towards the use of breeding tools and traits in typical family-owned farms in Slovenia. An online questionnaire was sent to dairy farmers affiliated with Slovenian breeding associations, and 256 dairy farmers responded. The analysis was conducted in three steps. First, the basic response patterns according to the farmers' knowledge level were determined using latent class analysis. Second, farmers' attitudes towards breeding tools were assessed by 15 statements using principal component analysis. Finally, we were interested in the relationship between farmers' attitudes and knowledge about selection. The results showed that farmers had more knowledge about the benefits of genomic selection, followed by general knowledge about breeding values and the definition of genomic selection, and they had the least knowledge about the reference population. Farmers with more knowledge were statistically significantly more likely than farmers with less knowledge to have higher education, be younger, have a larger herd size, have higher milk production per cow, have the intent to increase herd size and milk quantity, and use genomically tested bulls. No significant relationship was found between belonging to a specific knowledge class and the main breed in the herd, the farmer's gender, production system, or farming in less-favoured areas. The results also show that farmers basically agree that they need written recorded performance data about a bull/cow to know exactly how good the animal is, that the genetic merit (breeding value) of bulls/cows adds to the performance of their progeny, that it is very important to maintain the breed characteristics of bulls/cows, that cooperation in being able to compare animals with other farmers is essential for improving herd performance, and that the possibilities of selecting dairy cows with genomic selection and monogenetic traits must be fully exploited, indicating a positive attitude towards genomic selection. The level of knowledge was shown to influence attitudes towards various aspects of breeding. It was found that the higher the level of knowledge, the more positive the attitude towards genetic and genomic selection, and the more negative the attitude towards traditional selection.