Bavarian State Research Center for Agriculture

Post-disturbance recovery is a central element of forest resilience against intensifying disturbance regimes. Although recovery signals are strong across Central European forests, the relative roles of different factors contributing to recovery remain incompletely understood. As climate change increasingly challenges recovery, elucidating these processes is essential to adapt forest management to changing climate and disturbance regimes. We extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany. We combined 23,036 ha of quality-filtered photogrammetric canopy height model data with a Landsat-based disturbance map, a forest ownership map and environmental covariates in a Bayesian modelling framework. Post-disturbance growth rates were governed primarily by forest type and site conditions, whereas management strongly influenced disturbance legacies, i.e. the remaining post-disturbance vegetation height structure on site. Legacies varied widely across management types: Federal and set-aside forests retained the highest level of disturbance legacies, while private forests had the lowest legacy levels. Despite marginally lower growth rates, set-aside areas had recovery trajectories that were comparable to managed forests. The median recovery time to 5 m mean canopy height was 14.3 years over all forest and management types. Set-aside areas exhibited the greatest variation in recovery trajectories. We here show that (i) management affects disturbance legacies more strongly than post-disturbance tree growth, (ii) set-aside areas do not differ in recovery speed from managed areas, and (iii) legacies are diversifying forest recovery trajectories, with potential implications for future forest resilience. Our results underline that the post-disturbance reorganization window is a crucial period for management to influence long-term forest development. The framework presented here provides a scalable approach to monitor structural recovery and guide adaptive forest policy and management under increasing disturbance. • Forest management in Central Europe affects post-disturbance recovery more via legacies than tree growth rates. • Set-aside forests recover their canopy height equally fast as managed forests in Central Europe. • Homogenizing and removing disturbance legacies can reduce forest canopy variation across forest stand development. • We combined a biological growth model with remote sensing data to assess forest canopy recovery.

Camera Trap Distance Sampling (CTDS) enables population density estimation of unmarked species from camera trap data by incorporating animal-to-camera distances. In this study, we systematically evaluate the sensitivity of CTDS-derived density estimates to variation in key pipeline parameters, using a high-resolution camera trap dataset from Central Europe. Over a 12-month period, 110 cameras were deployed across a forested area of 94 km 2 . Animal-to-camera distances were estimated via monocular depth estimation. To reduce manual annotation effort, we introduce an automated masking approach for calibration images based on the Segformer model, saving an estimated minimum of two minutes per image. Using red deer ( Cervus elaphus ) as model species, a full-factorial sensitivity study (n = 135 parameter configurations) was conducted, varying MegaDetector confidence threshold, depth sampling method, mean trigger intervals and right-hand distance truncation schemes. Results show that population density estimates are highly sensitive to the choice of depth sampling method, MegaDetector confidence threshold and mean trigger interval. Parameter-induced variation led to differences of up to 200% in population estimates relative to the lowest estimate, twice as much as the variation observed between spatial subsets of the study area differing in vegetation characteristics. However, relative differences between the two sub-areas remained largely consistent across configurations. These results highlight the critical impact of technical choices within CTDS workflows and emphasize the need for transparent reporting and standardization of key pipeline parameters. All CTDS analyses using automated pipelines should therefore be accompanied by dataset-specific sensitivity analyses to account for parameter uncertainty and validation challenges. Overall, our study demonstrates both the methodological challenges and practical applicability of automated CTDS for large-scale wildlife monitoring. • Automated masking reduces manual effort in depth calibration by ∼ 2 min per image. • CTDS parameter variation caused 200% difference, twice our spatial density variation. • Depth sampling, confidence thresholds and trigger intervals affect density estimates. • Automated CTDS requires standardization and dataset-specific sensitivity analyses.

The use of digital tools in agroecological crop production can help mitigate current farming challenges such as labour shortage and climate change. The aim of this study was to map digital tools used in crop production, assess their impacts across economic, environmental, and social dimensions, and determine their potential as enablers of agroecology. A systematic search and screening process, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology, identified 453 relevant studies. The results showed that most digital tools are applied for crop monitoring (83.4%), with unmanned aerial vehicles (37.7%) and camera sensors (75.2% combined) being the most frequently used technologies. Farm Management Information Systems (57.6%) and Decision Support Systems (25.2%) dominated the tool categories, while platforms for market access, social networking, and collaborative learning were rare. Most tools addressed the first tier of agroecology, which refers to input reduction, highlighting a strong focus on efficiency improvements rather than systemic redesign. Although digital tools demonstrated positive contributions to social, environmental, and economic dimensions, studies concentrated mainly on economic benefits. Future research should investigate the potential role of digital technologies in advancing higher tiers of agroecology, emphasising participatory design, agroecosystem services, and broader coverage of the agricultural value chain.

Trees are integral to ecosystems and hold considerable economic importance. Their exceptional longevity and modular structure also make them valuable models for studying the long-term accumulation of somatic mutations and epimutations in plants. Empirical evidence indicates that the annual rate of these stochastic events correlates negatively with generation time, suggesting that species with long lifespans have evolved mechanisms to mitigate the build-up of deleterious somatic variants. It has been hypothesized that this reduction is achieved by slowing growth and minimizing the number of cell divisions per unit time, thereby reducing errors associated with DNA replication. However, a direct test of this “mitotic-rate hypothesis” remains technically challenging. Here we take advantage of a 150 year-old experiment in European beech to show that a thinning-induced growth acceleration increases the annual rate of somatic epimutations in main stems and lateral branches of trees. We demonstrate that this effect is accompanied by a proportional increase in the rate of cell divisions per unit time. These findings support the notion that life-history constraints on growth rates in trees are not merely a trade-off between resource allocation and structural stability but also a strategy to preserve genetic and epigenetic fidelity over extended lifespans.

We applied 15 N labelled cattle slurry over one year to large grassland lysimeters in Southern Germany to study its importance for plant nitrogen (N) nutrition not only in the year of application, but also in the following four years, and for both current and future climatic conditions. In the year of 15 N fertilizer application, the recovery of 15 N in harvested aboveground plant biomass was as low as 16 %, with several fold larger 15 N recovery in soil organic nitrogen (SON). In the first year after its application, the labelled 15 N was of equal importance for plant N nutrition compared to the year of application, as illustrated by a plant 15 N recovery of 9–14 %. The recovery of 15 N fertilizer in mowed plant biomass remained relatively stable and only gradually declined in the following years, ultimately resulting in a cumulative 15 N recovery rate of 33–37 % in mowed biomass after five years. Total 15 N recovery in plant and soil after five years was 67.6 ± 3.8 % for the climate change treatment and 75.6 ± 10.8 % for the climate control treatment, suggesting increased long-term fertilizer N losses under warming. Overall, this study reveals a minimal contribution of recent fertilizer N to plant nutrition, but a surprisingly quick recycling via SON and re-allocation to plants. The findings support a concept that fertilizing such grasslands is largely a fertilization of soils rather than a direct fertilization of plants, with a fast turnover of fertilizer-derived SON promoting the risk of soil N mining. • 15 N labelled cattle slurry was traced over 5 years in grassland lysimeters. • In the year of fertilizer application, soil is fertilized rather than plants. • Plant N uptake largely based on soil organic N. • Fast turnover of fertilizer-derived soil organic N upon allocation to plants. • Climate change increases fertilizer N losses and soil N mining.

Crown plasticity enables trees to acclimate to competition and environmental stress, but how social strata and species-specific behavior modulate crown structure–secondary growth relationships remains poorly understood. We, therefore, tested three hypotheses in close-to-nature forests across wet (Germany) and dry (Spain) sites using terrestrial laser scanning and tree-ring width data of Norway spruce ( Picea abies (L.) H. Karst.) and Silver fir ( Abies alba Mill.) in Germany, and Stone pine ( Pinus pinea L.) in Spain. We examined (HⅠ) crown structure and radial growth are positively correlated and modulated by social strata and species-specific behavior; (HⅡ) local competition and canopy space filling modulate the relationships across sites, (HⅢ) space-use efficiency varies across social strata and species behavior across sites. We observed that crown shape metrics and ring width variability were significantly correlated (p < 0.05), modulated by species and social identity. Dominant and codominant trees showed stronger relationships, while suppressed trees exhibited greater crown plasticity, allowing adaptive responses to canopy gaps. Spruce and fir adjusted crown structure under suppression, while pines maintained static crowns, decoupling structure from growth. Crown-growth relationships were influenced by neighborhood competition and canopy space filling (p < 0.05). Site-specific patterns emerged: wet sites had greater light competition, increasing canopy space filling and growth irregularity; dry sites were dominated by competition for water. Crown space- use efficiency also varied by species and social stratum. Dominant trees occupied more canopy space, but suppressed trees showed higher space-use efficiency especially by spruce and fir, by maintaining plastic crowns and waiting for more favorable light conditions. Despite similar biomass investment, dominant spruce and fir had higher space-use but lower biomass-use efficiency, while suppressed trees invested biomass more efficiently, signaling growth potential. These results show that asymmetric crown plasticity drives a grow or wait strategy across strata, informing precision forestry interventions for climate-adaptive forest management. • The relationship between crown shape and tree ring patterns was studied in close-to-nature forests. • A strong correlation was found between crown shape and tree ring patterns varied depending on species and social group. • The local neighborhood and canopy space filling influenced this relationship. • Dominant trees were effectively occupying canopy space, while suppressed trees exploited better. • The future growth potential of trees can be predicted based on their 3D crown shape.

ABSTRACTAnimal growth is shaped by a complex interplay of environmental conditions and intrinsic life‐history trade‐offs, yet long‐term datasets allowing the reconstruction of individual growth histories in natural populations remain rare. Here, we use scale analysis to reconstruct age‐specific growth histories of coregonid fish (Coregonus sp.) from Lake Starnberg, Germany, over a 22‐year period, evaluating the roles of lake phosphorus concentrations and spring temperatures as potential drivers. Linear mixed‐effects models revealed age‐specific changes in scale growth over time: while growth remained stable or increased slightly in younger age classes (ages 1 and 2), growth significantly declined over time in mature fish (age 3). During the observed period, phosphorus concentrations decreased, indicating reduced nutrient availability, whereas spring temperatures showed no significant long‐term trend. Our analyses further indicated significant interactions between temperature and age class, suggesting that temperature effects on growth may have varied by age, even though there was no overall change in temperature during the monitored time period. The observed decline in growth among mature individuals aligns with predictions from life‐history theory, reflecting a potential allocation shift from somatic growth to reproductive investment following maturation. This study provides rare empirical evidence from a natural fish population that long‐term environmental changes interact with intrinsic life‐history strategies, resulting in clear age‐specific patterns of growth variation.

Food-based dietary guidelines acknowledge non-fortified dairy foods as a source of multiple essential vitamins and minerals as well as high-quality protein. Considering the cultural significance of dairy foods in our diet and the increasing prevalence of non-communicable diseases, it is essential to continuously evaluate the entirety of data regarding the impact of dairy consumption on various health-related outcomes. A systematic literature search was performed in three databases: Medline, Embase, and Web of Science. Systematic reviews published between January 2014 and February 2024 based on randomized controlled trials (RCTs), prospective cohort studies, case-control studies, and/or cross-sectional studies in adults, focusing on the consumption of bovine dairy products were evaluated for inclusion. Reports from the World Cancer Research Fund on selected cancer outcomes were also included in this review. We identified 95 reports encompassing five dairy exposure categories on 29 different health outcomes. Out of 281 associations identified, 37.7% linked dairy consumption to a reduced risk, while 48.0% showed no association with disease risk. Inconclusive results were found in 10.0% of the associations, and 4.3% indicated an increased risk of adverse health outcomes. Overall, the evidence suggests that consuming dairy is not associated with an increased risk of non-communicable diseases or mortality. In fact, it may moderately reduce the risk of several health outcomes, including adverse cardiovascular outcomes and certain cancers such as bladder, breast, colorectal, liver, oral, and ovarian. Some studies have also linked dairy consumption to improved body composition, lower rates of type 2 diabetes, and better bone health.

Meat inspection (MI) data can be useful as a monitoring tool of animal health and welfare on farm, thereby enhancing the sustainability and productivity of livestock. There is also concern about certain limitations of these data, primarily related to the quality and harmonisation of inspections across various slaughterhouses. In our study, we investigated the development of a cross-slaughterhouse ranking system for farmers using MI data. The integration of new digital tools in Germany, such as the web-based database Qualifood®, offers new opportunities for collecting and utilising MI data across different slaughterhouses, enabling at the same time digital feedback of these information to livestock farmers. To accomplish our research goal, MI data over a period of 5years (2020-2024) was exported from Qualifood®. Our analysis was conducted using MI data from both cattle and pig farms. However, this manuscript focuses on presenting the statistical analysis model using pig data and the category "respiratory health" as a representative case study. We presented an annual overview of reference values -the so-called basic risk- for respiratory health findings using generalised linear mixed models. The basic risk of respiratory health findings for pigs showed a gradual decline from 14.4% in 2020 to approximately 12.0% in 2024. The calculated basic risks establish a reference for normal finding rates and provide a baseline assessment of respiratory health in southern Germany based on MI data. Furthermore, we estimated the variability of key random effects derived. Across all years, SDs for farm and delivery levels remain relatively stable between the selected and full datasets, indicating consistent variability at these levels. However, the slaughterhouse-level SDs are substantially higher in the full dataset compared to the selected slaughterhouses suggesting notable heterogeneity in reporting or detection practices across facilities. This underlines the importance of slaughterhouse selection when conducting cross-facility analyses and benchmarking. Towards a cross-slaughterhouse evaluation, we compare the farmer-specific risks and the basic risk using the concept of relative risk, also known as risk ratio. Our model demonstrates how recent advancements in digitalisation enable the evaluation of MI data across multiple slaughterhouses, underscoring the importance of region-wide, digital, and standardised MI data collection as a foundation for consistent and reliable cross-slaughterhouse assessments. By addressing inconsistencies in recording quality, our model can support a data-driven decision-making for farmers, industry stakeholders, and veterinary authorities, ultimately reinforcing the entire agricultural value chain and animal health and welfare management.