Agrivoltaic (AV) systems impact fruit orchards in multiple ways. These influences vary according to the design of the specific AV system as well as agronomic system and environmental factors determined by the location of the respective agricultural plot. This study suggests a holistic approach to optimize the output of AV systems that includes management practices, optimized AV characteristics as well as adapted plant types and varieties. To contribute to this approach, experiments were conducted at two sites in Karlsruhe (static as well as tracked modules over apple fruits) and Nußbach-Oberkirch (two different tracking algorithms over apple and plum fruits) in South Germany in 2024 and early 2025. Sugar content of plum fruits was reduced under full-sun tracking for all cultivars, but not for agronomic tracking for the cultivars Moni and Franzi. Apple trees showed impacts on flower formation at the Vollmer farm and delayed ripening of up to two weeks at the LTZ site. Additionally, fruit characteristics showed low suitability for long-term storage. The prediction of optimal harvest dates by assessing the Streif Index was difficult. These problems could be caused by low starch assimilation during the vegetation period, which impacts assumed starch degradation and thus the Streif Index. Therefore, it might be helpful to adjust the calculation of the Streif Index for AV systems.
The order Albuginales includes the causal agents of white blister rust disease, which affect a wide range of weedy plants, both cultivated and wild. The genus Pustula is one of the five major lineages of white blister rusts identified to date. It causes disease on asterids, the largest group of flowering plants. Pustula is represented by several phylogenetically distinct, host-specific lineages, many of which remain formally undescribed. In this study, samples of white blister rust collected from the economically valuable ornamental plant Echinacea purpurea were investigated. Phylogenetic analysis based on COX2 mtDNA, supported by ITS and LSU rDNA sequence comparisons, combined with detailed morphological investigations, revealed a distinct and independent lineage, separate from previously known species of Pustula. As a result, a new species, Pustula echinaceae sp. nov., infecting Echinacea purpurea, is described and illustrated.
With increasing demand for crop yields and increasingly harsh growing conditions due to climate change, breeding more resilient plant phenotypes becomes necessary. Assessment of plant growth while breeding is still primarily conducted manually on randomly sampled plants in the field. Automatic imaging systems such as UAVs are becoming increasingly common, making it possible to provide more complete datasets of entire fields or experimental parcels. To process those increasing volumes of raw image data, automatic processing from field-level data to plant-level growth parameters is required. Existing approaches in literature mainly focus on specialized assessment of specific plant phenotypes, growth stages, and assessment parameters, which provide limited scalability towards new crops or parameters. We propose to learn generalized features using a Multi-Net architecture with one single backbone shared by multiple heads, providing assessment for a selection of different plant parameters. We implemented a ResNet-based backbone in combination with Faster RCNN, Mask RCNN, and HTC assessment head architectures. We evaluated our approach both on experimental parcel data as well as the CVPPP dataset, which contains arabidopsis and tobacco plants of varying growth stages. We compare our Multi-Task Learning (MTL) model with its single task instances and all task combinations as permutations to showcase performance across multiple MTL scenarios. Our proposed model architecture allows for multi-plant and multi-parameter assessment, improving the plant selection process in breeding new plant phenotypes by enabling the evaluation of complete field datasets.
A winter wheat composite cross population (CCP), created in the UK in 2001, has been grown in Germany, Hungary, and the UK since 2005 (F-5 generation). In 2008/09 (F-8), a cycling pattern for the populations was developed between partners to test the effects of rapidly changing environments on agronomic performance and morphological characteristics. One CCP was grown by eight partners for one year and subsequently sent to the next partner, creating "cycling CCPs" with different histories. In 2013, all eight cycling CCPs and the three non-cycling CCPs (from Germany, Hungary, and the UK) were included in a two-year experiment in Germany with three line varieties as references. Differing seed weights of the F-13 at sowing affected some agronomic parameters under drought conditions in 2014/15 but not under less stressful conditions in 2013/14. In both experimental years, the CCPs were comparable to the line varieties in terms of agronomic performance, with some CCPs yielding more than the varieties under the drought conditions of 2015. The results highlight the potential of CCPs to compete with line varieties, while the overall similarity of the CCPs based on their origin and cycling history for agronomic traits indicates a high buffering potential under highly variable environmental conditions.
Black canker, caused by fungi of the genus Diplodia, poses an increasing threat to pome fruit production in Germany. In order to determine the spread of the disease in Germany, 423 bark samples with suspicious symptoms from all federal states were examined in a non-representative monitoring. In 62% of the samples examined, fungi of the genus Diplodia were isolated, with the species D. bulgarica dominating the Diplodia species spectrum with 56.6%, followed by D. seriata with 28.3%. D. malorum (7.8%), D. mutila (4.9%), D. juglandis (1.6%) and D. intermedia (0.8%), on the other hand, took up only a small part of the Diplodia species spectrum, with the latter two species being first records on apple in Germany.
