The reed planthopper (Pentastiridius leporinus) is the most important vector of the bacterial pathogens of the Syndrome Basses Richesses (SBR) in sugar beet, bacterial potato tuber wilt and, increasingly, various disease patterns in vegetable crops. In view of its increasing spread, a harmonized nationwide monitoring program, covering sugar beet, potato, and sporadically vegetables, was introduced in Germany for the first time in 2025. Between calendar weeks 20 and 35, a total of 50,974 planthoppers were recorded in 914 locations using transparent sticky traps. P. leporinus has been detected in nearly all federal states, with clear regional variations in terms of trap catches. High population densities occurred mainly in southern Germany and in parts of Rhineland-Palatinate, Hesse, and Brandenburg, while large cultivation areas of sugar beet and potato in northern Germany have largely remained without records to date. Peak flight activity occurred mainly during calendar week 26. Within the distribution area of the vector P. leporinus and the pathogens a cultivation area was estimated about 119,000 hectares of sugar beet and 39,000 hectares of potatoes in 2025. For the first time, the results provide a reliable basis for occurrence and risk evaluation, as well as future plant protection decisions.
Due to their nitrogen-fixing properties, legumes are an important crop in organic farming. Controlling insects that attack legumes is difficult and often not economically viable due to the small number of authorised preparations. While the damage caused by insects, especially aphids, is often negligible, the viruses transmitted by aphids have a greater potential for damage. In order to gain an overview of the viruses currently occurring on legumes in organic farming in Lower Saxony, a virus monitoring was carried out on field beans, peas, soya, lentils and lupins in 2024. The detected virus species corresponded to the results of previous monitoring programmes; however, the frequent detection of alfalfa mosaic virus (AMV) on field beans was surprising. So far, AMV has not been detected on legumes in Lower Saxony. The results show that plant viruses also occurred in non-symptomatic crops. In order to be able to successfully cultivate legumes in the future, efforts to breed virus-resistant varieties should be intensified.
Peronospora is the largest genus of the Oomycota, responsible for causing downy mildew on a wide range of cultivated and ornamental plants worldwide. Although more than 400 Peronospora species have been described, many host-pathogen relationships have not been thoroughly explored, particularly in relation to the phylogenetic connections of pathogens from type hosts and additional hosts reported for the various described species. At the same time, infections of Peronospora on economically important hosts are an emerging threat, often with uncertainty regarding the causal agents. In this study, as an example for obligate biotrophic pathogens, 105 specimens of Peronospora parasitising species of the genus Veronica and one specimen parasitising the related host genus Paederota (both Plantaginaceae), were analysed for their morphology and phylogenetic relationships using multi-locus reconstructions. As a result, nine new Peronospora species parasitising Veronica were identified and are described in this manuscript, while the morphology of seven previously described Peronospora species was re-examined. While generally, a high degree of host specialisation was found, Peronospora verna and P. grisea were found to be indifferentiable, suggesting a recent host shift, and P. silvestris was found to also infect Globularia nudicaulis. Likewise it was found that infections on the ornamental subgenus Hebe caused by P. palustris and P. petricosa are the result of host shifts of European species onto non-native hosts. The presence of Peronospora on nearly 500 Veronica species that are not type hosts for any described Peronospora species should be re-examined, as these occurrences likely include many previously overlooked species with unknown pathogenicity.
Accurate detection of sow postures provides valuable insights into animal activity, which is a key indicator for health, welfare, and productivity. While activity monitoring using wearable sensors has become a standard approach for certain livestock species like dairy cattle, its application in pig production remains limited due to practical challenges. Advances in computer vision (CV) and artificial intelligence (AI) offer an alternative, enabling non-invasive monitoring of pig behavior. However, the development of robust CV models requires large and diverse datasets that capture the variability of housing environments and visual conditions. To address this limitation, we present SowPostureDS, a dataset comprising 14,400 annotated images of sows from different farrowing systems. Images were extracted from long-term video recordings and manually annotated into four posture classes following a consistent scheme. The dataset reflects heterogeneity in housing design, lighting conditions, and the presence of piglets, providing a valuable resource for implementing CV models with improved robustness across diverse environments. A use case demonstrates the dataset’s applicability, showing that models trained on SowPostureDS can achieve high accuracy and can be effectively adapted to unseen environments using transfer learning with minimal additional data.
Continuous activity monitoring using motion sensors provides a non-invasive method to assess pig general behavioural activity and detect potential welfare issues on group level in commercial housing systems. In the current study, motion sensors were installed in rearing and fattening pens on four farms (18 pens in total, 18–40 pigs per pen) over multiple trials. Based on daily tail inspections 4977 observation days were classified into control (C, without tail lesion), pre-tail lesion (P, last seven days before tail lesion) and tail lesion (TL) days. Binary classification ablation studies were used to evaluate and understand model robustness, using pairs of classes and varying time windows for P days. Results indicated that a time window of 7 d before the occurrence of tail lesions showed improved classification performance compared to other time windows, allowing the binary distinction of C, P and TL days. Feature selection revealed that not only motion sensor data but also time of year and housing-related variables influenced model performance. The final multiclass model, based on a 7-day time window for P days, achieved a mean testing accuracy >0.7, ranging from 0.75 to 0.85 for the individual classes and lower accuracies for P days compared to TL and C days. However, leave-one-out cross-validation revealed limited generalisation to unseen farms, likely because the general behavioural activity features are not fully independent of farm. Overall, this study demonstrates the potential of motion sensor data for the early detection of tail lesion while indicating that further research is required to improve robustness and generalisability across farm environments.
