Dry cow therapy (DCT) in the Netherlands changed from mainly blanket to selective antimicrobial DCT. This transition was supported by a national guideline, with the individual somatic cell count (SCC) at the last milk recording before dry-off as the main selection criterion for antimicrobial DCT. The aim of this retrospective observational study is to evaluate the SCC dynamics during the dry period at the herd and individual dry period level following the national transition from mainly blanket to selective antimicrobial DCT. At the herd level, we used 2 data sets to evaluate the SCC dynamics during the dry period: (1) a national data set containing 3,493 herds with data available from 2011 through 2015 and (2) a veterinary practice data set containing 280 herds with data available from 2013 through 2015. The herd level analysis was carried out using key performance indicators provided via milk recording (CRV, Arnhem, the Netherlands): the percentage of cows that developed a new intramammary infection (IMI) during the dry period and the percentage of cows cured of an IMI during the dry period. The effect of DCT at individual dry period level was analyzed with a mixed-effects logistic regression model based on 4,404 dry periods from 2,638 cows in 20 herds within the veterinary practice data set. For these 20 herds, individual SCC data from milk recordings and individual cow DCT were available from 2013 through 2015. No significant changes were observed to the SCC dynamics during the dry period at the herd level. The percentage of cows that developed a new IMI during the dry period ranged between 16 and 18%, and the percentage of cows cured from an IMI during the dry period ranged between 74 and 76%. At the individual dry period level, a low SCC at the first milk recording following a dry period was associated with the use of intramammary antimicrobial DCT with or without the concurrent use of an intramammary teat sealer [odds ratio (OR) = 2.16 and OR = 2.07, respectively], the use of DCT with an intramammary teat sealer only (OR = 1.35), and a low SCC at the last milk recording before dry-off (OR = 1.78). This study demonstrates that the selection of cows for DCT without antimicrobials based on SCC thresholds at the last milk recording is possible without significant changes to udder health and reduced the use of antimicrobials.

Since the seminal work by Hans-Jörgen Hansen in 1952, it has been assumed that intervertebral disc (IVD) degeneration in chondrodystrophic (CD) dogs involves chondroid metaplasia of the nucleus pulposus, whereas in nonchondrodystrophic (NCD) dogs, fibrous metaplasia occurs. However, more recent studies suggest that IVD degeneration in NCD and CD dogs is more similar than originally thought. Therefore, the aim of this study was to compare the histopathology of IVD degeneration in CD and NCD dogs. IVDs with various grades of degeneration (Thompson grade I–III, n = 7 per grade) from both CD and NCD dogs were used (14 CD and 18 NCD dogs, 42 IVDs in total). Sections were scored according to a histological scoring scheme for canine IVD degeneration, including evaluation of the presence of fibrocyte-like cells in the nucleus pulposus. In CD dogs, the macroscopically non-degenerated nucleus pulposus contained mainly chondrocyte-like cells, whereas the non-degenerated nucleus pulposus of NCD dogs mainly contained notochordal cells. The histopathological changes in degenerated discs were similar in CD and NCD dogs and resembled chondroid metaplasia. Fibrocytes were not seen in the nucleus pulposus, indicating that fibrous degeneration of the IVD was not present in any of the evaluated grades of degeneration. In conclusion, intervertebral disc degeneration was characterized by chondroid metaplasia of the nucleus pulposus in both NCD and CD dogs. These results revoke the generally accepted concept that NCD and CD dogs suffer from a different type of IVD degeneration, in veterinary literature often referred to as chondroid or fibroid degeneration, and we suggest that chondroid metaplasia should be used to describe the tissue changes in the IVD in both breed types.

Highly pathogenic (HP) avian influenza viruses (AIV) can spread globally through migratory birds and cause massive outbreaks in commercial poultry. AIV outbreaks have been associated with proximity to waterbodies, presence of waterfowl or wild bird cases near poultry farms. In this study, we compared densities of selected HPAI high‐risk wild bird species around 7 locations (H farms) infected with HPAIV H5N8 in the Netherlands in 2016–2017 to densities around 21 non‐infected reference farms. Nine reference farms were in low‐lying water‐rich areas (R‐W) and 12 in higher non‐water‐rich areas (R‐NW). Average monthly numbers/km² of Eurasian wigeons, tufted ducks, Anatidae (ducks, geese and swans) and Laridae (gulls) were calculated between September and April in rings of 0–1, 1–3, 3–6 and 6–10 km around the farms. Linear mixed model analyses showed generally higher bird densities for H and R‐W compared to R‐NW farms between October and March. This was most striking for Eurasian wigeons, with in peak month December 105 (95% CI:17–642) and 40 (7–214) times higher densities around H and R‐W farms, respectively, compared to R‐NW farms. Increased densities around H farms for Eurasian wigeons and Anatidae were more pronounced for distances up to 10 km compared to 0–1 km that mostly consists of the farm yard, which is an unattractive habitat for waterfowl. This distance effect was not observed in gulls, nor in tufted ducks that live on large open waterbodies which are unlikely to be within 0–1 km of farms. This study provides insights into spatio‐temporal density dynamics of HPAI high‐risk birds around farms and their associations with poultry outbreaks. The outcomes indicate that knowledge of environmental and ecological drivers for wild bird presence and abundance may facilitate identification of priority areas for surveillance and biosecurity measures and decisions on establishments of poultry farms to reduce risk of HPAI outbreaks.

To improve management on dairy herds, sensor systems have been developed that can measure physiological, behavioral, and production indicators on individual cows. It is not known whether using sensor systems also improves measures of health and production in dairy herds. The objective of this study was to investigate the effect of using sensor systems on measures of health and production in dairy herds. Data of 414 Dutch dairy farms with (n=152) and without (n=262) sensor systems were available. For these herds, information on milk production per cow, days to first service, first calving age, and somatic cell count (SCC) was provided for the years 2003 to 2013. Moreover, year of investment in sensor systems was available. For every farm year, we determined whether that year was before or after the year of investment in sensor systems on farms with an automatic milking system (AMS) or a conventional milking system (CMS), or whether it was a year on a farm that never invested in sensor systems. Separate statistical analyses were performed to determine the effect of sensor systems for mastitis detection (color, SCC, electrical conductivity, and lactate dehydrogenase sensors), estrus detection for dairy cows, estrus detection for young stock, and other sensor systems (weighing platform, rumination time sensor, fat and protein sensor, temperature sensor, milk temperature sensor, urea sensor, β-hydroxybutyrate sensor, and other sensor systems). The AMS farms had a higher average SCC (by 12,000 cells/mL) after sensor investment, and CMS farms with a mastitis detection system had a lower average SCC (by 10,000 cells/mL) in the years after sensor investment. Having sensor systems was associated with a higher average production per cow on AMS farms, and with a lower average production per cow on CMS farms in the years after investment. The most likely reason for this lower milk production after investment was that on 96% of CMS farms, the sensor system investment occurred together with another major change at the farm, such as a new barn or a new milking system. Most likely, these other changes had led to a decrease in milk production that could not be compensated for by the use of sensor systems. Having estrus detection sensor systems did not improve reproduction performance. Labor reduction was an important reason for investing in sensor systems. Therefore, economic benefits from investments in sensor systems can be expected more from the reduction in labor costs than from improvements in measures of health and production in dairy herds.