No abstract

In this study, the floor area covered by individual finishing pigs in various body positions was measured using a contrast-based planimetric method for computer-assisted analysis of two-dimensional images. Two hundred and thirty-two finishing pigs were weighed during the last fifth of the fattening period and measured in different body positions using contrast-based planimetry. Thirteen body positions were defined based on characteristic directions of the head, legs and body. The lowest average covered floor area was found for body position A (pig standing up straight, nose touching the ground) with 0.288 ± 0.026 m2. The highest average covered floor area for a standing pig amounted to 0.335 ± 0.030 m2 in body posture ES (pig standing curved sideways, head raised above the dorsal line) and, for a lying pig, 0.486 ± 0.040 m2 (posture LL, pig lying in fully lateral recumbent position). The covered floor surface significantly depended on the weight of the animal and the body posture. Allometric estimations previously described for calculating the floor area physically covered by a pig's body are not consistently precise in depicting the actual areas covered. The minimal floor area offered in animal transportation vehicles, according to European legislation, is insufficient in the case of all pigs lying in the fully recumbent position simultaneously, without the pigs being forced to partially overlap one another. Therefore, both allometric formulas and legislation should be modified on the basis of these results and further studies with pigs of modern genetic origin should be conducted.

The available floor space is an important welfare factor for cull sows during transportation. Sows of modern genetics reach a size and weight far exceeding those of fattening pigs. In most countries, there are no binding, consistent regulations for the maximum loading densities, especially for sows during road transportation. As a first step towards such recommendations, the physical floor space requirement (static space) of 100 sows of a current breed, while standing and lying down, were determined using contrast-based planimetry. An average sow covered about 0.42–0.47 m2 (standing postures) up to 0.53–0.63 m2 (lying postures). The largest measured area was 0.72 m2 for a sow lying in the belly-chest position. We detected a significant dependency of the covered floor area and the live weight, which supports the common practice to derive space requirements and recommendations based on live weight. Also, our results suggest that especially heavy sows, under currently usual loading densities, are at risk of having insufficient floor space requirements during transport. The results cannot be used to define the space required by a sow to carry out movements or sustain the individual need for distance (social/dynamic space) but provide data on the static space covered by sows of current breeds.

Regional benchmarking data enables farmers to compare their animal health situation to that of other herds and identify areas with improvement potential. For the udder health status of German dairy cow farms, such data were incomplete. Therefore, the aim of this study was (1) to describe the incidence of clinical mastitis (CM), (2) to describe cell count based udder health indicators [annual mean test day average of the proportion of animals without indication of mastitis (aWIM), new infection risk during lactation (aNIR), and proportion of cows with low chance of cure (aLCC); heifer mastitis rate (HM)] and their seasonal variation, and (3) to evaluate the level of implementation of selected measures of mastitis monitoring. Herds in three German regions (North: n = 253; East: n = 252, South: n = 260) with different production conditions were visited. Data on CM incidence and measures of mastitis monitoring were collected via structured questionnaire-based interviews. Additionally, dairy herd improvement (DHI) test day data from the 365 days preceding the interview were obtained. The median (Q0.1, Q0.9) farmer reported incidence of mild CM was 14.8% (3.5, 30.8%) in North, 16.2% (1.9, 50.4%) in East, and 11.8% (0.0, 30.7%) in South. For severe CM the reported incidence was 4.0% (0.0, 12.2%), 2.0% (0.0, 10.8%), and 2.6% (0.0, 11.0%) for North, East, and South, respectively. The median aWIM was 60.7% (53.4, 68.1%), 59.0% (49.7, 65.4%), and 60.2% (51.5, 67.8%), whereas the median aNIR was 17.1% (13.6, 21.6%), 19.9% (16.2, 24.9%), and 18.3% (14.4, 22.0%) in North, East, and South, respectively with large seasonal variations. Median aLCC was ≤1.1% (≤ 0.7%, ≤ 1.8%) in all regions and HM was 28.4% (19.7, 37.2%), 35.7% (26.7, 44.2%), and 23.5% (13.1, 35.9%), in North, East and South, respectively. Participation in a DHI testing program (N: 95.7%, E: 98.8%, S: 89.2%) and premilking (N: 91.1%, E: 93.7%, S: 90.2%) were widely used. Several aspects of udder health monitoring, including exact documentation of CM cases, regular microbiological analysis of milk samples and the use of a veterinary herd health consultancy service were not applied on many farms. The results of this study can be used by dairy farmers and their advisors as benchmarks for the assessment of the udder health situation in their herds.

Good calf health is crucial for a successfully operating farm business and animal welfare on dairy farms. To evaluate calf health on farms and to identify potential problem areas, benchmarking tools can be used by farmers, herd managers, veterinarians, and other advisory persons in the field. However, for calves, benchmarking tools are not yet widely established in practice. This study provides hands-on application for on-farm benchmarking of calf health. Reference values were generated from a large dataset of the “PraeRi” study, including 730 dairy farms with a total of 13,658 examined preweaned dairy calves. At herd level, omphalitis (O, median 15.9%) was the most common disorder, followed by diarrhea (D, 15.4%) and respiratory disease (RD, 2.9%). Abnormal weight bearing (AWB) was rarely detected (median, 0.0%). Calves with symptoms of more than one disorder at the same time (multimorbidity, M) were observed with a prevalence of 2.3%. The enrolled farms varied in herd size, farm operating systems, and management practices and thus represented a wide diversity in dairy farming, enabling a comparison with similar managed farms in Germany and beyond. To ensure comparability of the data in practice, the reference values were calculated for the whole data set, clustered according to farm size (1–40 dairy cows (n = 130), 41–60 dairy cows (n = 99), 61–120 dairy cows (n = 180), 121–240 dairy cows (n = 119) and farms with more than 240 dairy cows (n = 138), farm operating systems (conventional (n = 666), organic (n = 64)) and month of the year of the farm visit. There was a slight tendency for smaller farms to have a lower prevalence of disorders. A statistically significant herd-size effect was detected for RD (p = 0.008) and D (p < 0.001). For practical application of these reference values, tables, diagrams, and an Excel® (Microsoft®) based calf health calculator were developed as tools for on-farm benchmarking (https://doi.org/10.6084/m9.figshare.c.6172753). In addition, this study provides a detailed description of the colostrum, feeding and housing management of preweaned calves in German dairy farms of different herd sizes and farm type (e.g., conventional and organic).