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Detecting multiple structural breaks in systems of linear regression equations with integrated and stationary regressors
Full text linkIn this paper, we propose a two‐step procedure based on the group LASSO estimator in combination with a backward elimination algorithm to detect multiple structural breaks in linear regressions with multivariate responses. Applying the two‐step estimator, we jointly detect the number and location of structural breaks and provide consistent estimates of the coefficients. Our framework is flexible enough to allow for a mix of integrated and stationary regressors, as well as deterministic terms. Using simulation experiments, we show that the proposed two‐step estimator performs competitively against the likelihood‐based approach in finite samples. However, the two‐step estimator is computationally much more efficient. An economic application to the identification of structural breaks in the term structure of interest rates illustrates this methodology.German Research Foundation 10.13039/50110000165
On-farm use of recycled liquid ammonium sulphate in Southwest Germany using a participatory approach
No full textFor political and environmental reasons, there is an urgent need for alternatives to energy-intensive synthetic fertilizers. One solution is the targeted recycling of nutrients within agriculture. In this study, liquid ammonium sulphate (LAS) as a recycling product derived from digestate treatment was compared to calcium ammonium nitrate, manure and original digestates in an on-farm experiment using a participatory approach. Based on regular meetings with the farmers involved, a flexible experimental design was developed which integrated the fertilization legislation and the farmers’ operational structures already in place, such as crop rotation, available application techniques and manure management demands. The aim was to achieve both implementation practicability and acceptance of the study results by the farmers. Results from the year 2020 showed that LAS applied with three-jet nozzles in barley and wheat had significantly lower yields than the other fertilizers. Applied with a slurry tanker trailing shoe applicator in 2021, LAS had comparable yields to the other fertilizers in maize (51.2 t ha −1 ) and comparable yields to digestate in rapeseed (4.4 t ha −1 ). Application techniques that minimize environmental impacts and lower the LAS pH could potentially increase the effectiveness of the fertilizer. We recommend that farmers use this fertilizer not as a single solution but as a mineral compensatory fertilizer in addition to organic fertilizers following local fertilizer legislation. In this case, LAS could potentially substitute calcium ammonium nitrate (CAN).Open Access funding enabled and organized by Projekt DEAL.Agriplus HohenloheUniversität Hohenheim (3153
Phylogenomic approach to integrative taxonomy resolves a century‐old taxonomic puzzle and the evolutionary history of the Acromyrmex octospinosus species complex
No full textAccurately delimiting species boundaries is essential for understanding biodiversity. Here, we assessed the taxonomy of the leaf‐cutting ants in the Acromyrmex octospinosus (Reich) species complex using an integrative approach incorporating morphological, population genetic, phylogenetic and biogeographical data. We sampled populations across the biogeographic distribution of the species complex and reconstructed their evolutionary relationships using ultraconserved elements (UCEs) as molecular markers. We evaluated traditional morphological characters used to distinguish putative taxa and performed species delimitation analyses to investigate divergence between evolutionary lineages. Our results support the hypothesis that the A. octospinosus species complex consists of two species: the widely distributed and polymorphic species A. octospinosus and its inquiline social parasite A. insinuator Schultz et al. We consider A. echinatior (Forel) syn. nov . and A. volcanus Wheeler syn. nov . as well as the subspecies A. octospinosus cubanus Wheeler syn. nov ., A. octospinosus ekchuah Wheeler syn. nov . and A. octospinosus inti Wheeler syn. nov . as junior synonyms of A. octospinosus . We also investigated the biogeographic history of the species complex and the evolutionary origin of the social parasite A. insinuator . We inferred that A. octospinosus originated during the late Miocene approximately 6.9 Ma ago in the Neotropical rainforest. Acromyrmex insinuator shared a common ancestor with A. octospinosus approximately 3.4 Ma ago, with a crown‐group age of approximately 0.9 Ma. Our phylogeny supports the hypothesis that the inquiline social parasite speciated via the intra‐specific route of social parasite evolution in direct sympatry from its host. Our findings reshape our understanding of the A. octospinosus species complex and provide a foundation for future studies of Acromyrmex leaf‐cutting ants.Arizona State University 10.13039/100007482Universität Hohenheim 10.13039/100009613U.S. National Science Foundation 10.13039/100000001Smithsonian Tropical Research Institute 10.13039/100009201Carl‐Zeiss‐Stiftun
Disc mower versus bar mower: Evaluation of the direct effects of two common mowing techniques on the grassland arthropod fauna
No full text1. In Central Europe, species‐rich grasslands are threatened by intensive agriculture with frequent mowing, contributing to the reduction of arthropods such as insects and spiders. However, comprehensive and standardised studies on the direct effects of the two most agriculturally relevant mowing techniques, e.g., double‐blade bar mower versus disc mower, are lacking. 2. In a 2‐year experiment, we have investigated the direct effect of mowing on eight abundant arthropod groups in grassland, covering two seasonal mowing events in both years, using a randomised block design. We compared (a) an unmown control, (b) a double‐blade bar mower and (c) a disc mower. 3. For most of the taxonomic groups studied, a significantly lower number of individuals was found in the experimental plots immediately after mowing, regardless of the mowing technique, compared to an unmown control. This was not the case for Orthoptera and Coleoptera, which did not show a significant reduction in the number of individuals for both mowing techniques (Orthoptera) or only for the double‐blade bar mower (Coleoptera). 4. Between both mowing techniques, no significant differences were found for all taxonomic groups investigated. 5. Synthesis and applications: Our findings suggest that mowing in general has a negative impact on abundant arthropod groups in grassland, regardless of the method used. Tractor‐driven double‐blade bar mowers do not seem to be a truly insect‐friendly alternative to a conventional disc mower. Other factors such as cutting height and mowing regimes should be seriously considered to protect spiders and insects from the negative effects of mowing. In addition, we strongly recommend the maintenance of unmown refugia. Insects and spiders that are spared by mowing can take refuge in these unmown areas to avoid subsequent harvesting and thermally unfavourable conditions that arise on mown areas. Further, unmown refugia are basic habitat structures for a subsequent recolonisation of mown areas once the flora has recovered.Bundesamt für Naturschutz http://dx.doi.org/10.13039/50110001041
Hydroxylated transformation products obtained after UV irradiation of the current-use brominated flame retardants hexabromobenzene, pentabromotoluene, and pentabromoethylbenzene
Full text linkHexabromobenzene (HBB), pentabromotoluene (PBT), and pentabromoethylbenzene (PBEB) are current-use brominated flame retardants (cuBFRs) which have been repeatedly detected in environmental samples. Since information on hydroxylated transformation products (OH-TPs) was scarcely available, the three polybrominated compounds were UV irradiated for 10 min in benzotrifluoride. Fractionation on silica gel enabled the separate collection and identification of OH-TPs. For more insights, aliquots of the separated OH-TPs were UV irradiated for another 50 min (60 min total UV irradiation time). The present investigation of polar UV irradiation products of HBB, PBT, and PBEB was successful in each case. Altogether, eight bromophenols were detected in the case of HBB (three Br3-, four Br4-, and one Br5-isomer), and nine OH-TPs were observed in the case of PBT/PBEB (six Br3- and three Br4-congeners). In either case, Br➔OH exchange was more relevant than H➔OH exchange. Also, such exchange was most relevant in meta- and ortho-positions. As a further point, and in agreement with other studies, the transformation rate decreased with decreasing degree of bromination. UV irradiation of HBB additionally resulted in the formation of tri- and tetrabrominated dihydroxylated compounds (brominated diphenols) that were subsequently identified. These dihydroxylated transformation products were found to be more stable than OH-TPs.Open Access funding enabled and organized by Projekt DEAL.Universität Hohenheim (3153
Projecting the impact of climate change on honey bee plant habitat distribution in Northern Ethiopia
Full text linkClimate change significantly affects the diversity, growth, and survival of indigenous plant species thereby influencing the nutrition, health and productivity of honey bees ( Apis mellifera ). Hypoestes forskaolii (Vahl) is one of the major honey bee plant species in Ethiopia’s Tigray region. It is rich in pollen and nectar that typically provides white honey, which fetches a premium price in both local and inter-national markets. Despite its socio-economic and apicultural significance, the distribution of H. forskaolii has been declining, raising concerns regarding its conservation efforts. However, there is limited knowledge on how environmental and climatic factors affect its current distribution and response to future climate change. The study investigates the current and projected (the 2030s, 2050s, 2070s, and 2090s) habitat distributions of H. forskaolii under three future climate change scenarios (ssp126, ssp245, and ssp585) using the Maximum Entropy Model (MaxEnt). The results show that land use (50.1%), agro-ecology (28%), precipitation during the Driest Quarter (11.2%) and soil texture (6.1%) predominantly influence the distribution of H. forskaolii, collectively explaining 95.4% of the model's predictive power. Habitats rich in evergreen trees and mosaic herbaceous with good vegetation cover are identified as the most suitable for H. forskaolii . The spatial distribution of H. forskaolii is concentrated in the highlands and mid-highlands of the eastern and southern parts of Tigray, characterized by a colder temperature. Across the three climate change scenarios, the size of suitable habitat for H. forskaolii is projected to decrease over the four time periods studied. Predictions under the ssp585 scenario reveal alarming results, indicating a substantial decrease in the suitable habitat for H. forskaolii from 4.26% in the 2030s to 19.09% in the 2090s. Therefore, given the challenges posed by climate change, research efforts should focus on identifying and evaluating new technologies that can help the H. forskaolii species in adapting and mitigating the effects of climate change
Mapping knowledge domains of regenerative agriculture with a focus on on-farm nitrogen fertilization experimentation and response surface regression
Full text linkIn the face of growing environmental concerns and the global demand for sustainable agriculture, achieving balanced nitrogen (N) management is critical for both maximizing crop productivity and maintaining environmental health. This dissertation proposes an innovative framework to address this challenge within the scope of regenerative agriculture, which emphasizes sustainable farming practices. Regenerative agriculture aims to reduce chemical inputs while maintaining yield levels yet implementing these practices at scale is complex due to the intricate interactions between biological, environmental, and technological factors on farms. This research tackles these challenges by introducing a Knowledge Domain Mapping (KDM)-based framework, integrating advanced technologies—including remote sensing, Internet of Things (IoT) telemetry, geospatial sciences, statistical modeling, machine learning, and cloud computing—to create a holistic and scalable system for optimizing nitrogen applications. Central to this research is the accurate estimation and spatial allocation of the Economic Optimum Nitrogen Rate (EONR), a crucial element for reducing nitrogen use and enhancing yield. A key contribution of this study is the development of a robust Response Surface Model (RSM) that leverages multispectral indices (MSIs) from Sentinel-2 imagery, historical IoT telemetry data, and on-machine nitrogen sensors. This RSM approach allows for precise EONR predictions tailored to field-specific conditions, reducing the need for traditional plot-based trials and achieving an average prediction error of just 14.5%. Applied to a 7-hectare winter wheat field, the model successfully predicted EONR values ranging from 43 kg/ha to 75 kg/ha across zones, showcasing the adaptability and accuracy of RSM for field-specific nitrogen recommendations. This precisionfocused approach exemplifies the study’s goal of minimizing environmental impacts while ensuring sustainable yield improvements. Beyond the initial field-level implementation, this research examines the generalizability of the RSM framework using two modeling strategies: a single RSM across fields and a weighted average model that aggregates individual field-specific RSMs. The weighted model demonstrated superior adaptability and high prediction accuracy, with a root mean square error (RMSE) of 11 kg N/ha for the EONR, highlighting the framework’s potential for broader application across different agricultural settings. Such generalizability supports the framework’s adoption in diverse farming environments, enabling precise and informed nitrogen management at scale. To facilitate widespread adoption and practical application, the dissertation also introduces a cloud-based infrastructure that integrates the KDM framework with real-time IoT data and satellite imagery. Leveraging cloud services like Amazon Web Services (AWS) Batch for job orchestration, Amazon S3 for scalable data storage, and RDS Postgres for structured data management, this8 infrastructure allows for seamless handling of both real-time and historical data. Spatial interpolation techniques, such as Kriging, enhance the model’s capability to generate real-time nitrogen prescription maps, enabling precise nutrient management for large-scale agricultural operations. Automated data quality control and data harmonization embedded within this cloud architecture provide a strong foundation for managing increasing data volumes and diverse field conditions, making the system cost-effective, adaptable, and efficient for modern agriculture. In summary, this dissertation maps regenerative agriculture via a comprehensive roadmap for translating regenerative agriculture principles into practical, operational nitrogen management practices. Through KDM an interdisciplinary approach is mapped by the integration of advanced modeling, data processing, and cloud technologies. This framework enables sustainable crop management and aligns with global goals for environmentally responsible food production. The innovations introduced here support a scalable, data-driven approach to agricultural sustainability, bridging scientific research with real-world applications to meet the evolving demands of modern agriculture.Angesichts wachsender Umweltbedenken und der globalen Nachfrage nach nachhaltiger Landwirtschaft ist ein ausgewogenes Stickstoffmanagement (N-Management) entscheidend, um sowohl die Produktivität der Kulturen zu maximieren als auch die Umweltgesundheit zu erhalten. Diese Dissertation schlägt ein innovatives Rahmenkonzept vor, um diese Herausforderung im Kontext der regenerativen Landwirtschaft zu bewältigen, die nachhaltige Anbaumethoden betont. Ziel der regenerativen Landwirtschaft ist es, den Einsatz chemischer Betriebsmittel zu reduzieren und gleichzeitig stabile Erträge zu gewährleisten. Die Umsetzung solcher Praktiken im großen Maßstab ist jedoch aufgrund der komplexen Wechselwirkungen zwischen biologischen, ökologischen und technologischen Faktoren auf landwirtschaftlichen Betrieben äußerst anspruchsvoll. Diese Forschung begegnet diesen Herausforderungen durch die Einführung eines auf Knowledge Domain Mapping (KDM) basierenden Rahmenwerks, das fortschrittliche Technologien wie Fernerkundung, Telemetrie des Internets der Dinge (IoT), Geowissenschaften, statistische Modellierung, maschinelles Lernen und Cloud-Computing integriert, um ein ganzheitliches und skalierbares System zur Optimierung des Stickstoffeinsatzes zu schaffen. Im Mittelpunkt dieser Forschung steht die präzise Schätzung und räumliche Verteilung der wirtschaftlich optimalen Stickstoffdosis (Economic Optimum Nitrogen Rate, EONR), ein entscheidendes Element zur Reduzierung des Stickstoffeinsatzes und zur Ertragssteigerung. Ein zentraler Beitrag dieser Arbeit ist die Entwicklung eines robusten Response Surface Models (RSM), das multispektrale Indizes (MSIs) aus Sentinel-2-Satellitenbildern, historische IoT- Telemetriedaten und Sensordaten von Stickstoffapplikationsgeräten nutzt. Dieser RSM-Ansatz ermöglicht genaue EONR-Vorhersagen, die auf feldspezifische Bedingungen abgestimmt sind, wodurch der Bedarf an traditionellen, parzellenbasierten Versuchen reduziert wird. Mit einem durchschnittlichen Vorhersagefehler von nur 14,5 % wurde das Modell in einem 7 Hektar großen Winterweizenfeld angewendet und konnte EONR-Werte von 43 kg/ha bis 75 kg/ha in verschiedenen Zonen erfolgreich vorhersagen. Dies verdeutlicht die Anpassungsfähigkeit und Genauigkeit des RSM für feldspezifische Stickstoffempfehlungen. Dieser präzisionsorientierte Ansatz illustriert das Ziel der Studie, Umweltbelastungen zu minimieren und gleichzeitig nachhaltige Ertragssteigerungen zu gewährleisten. Über die anfängliche Anwendung auf Feldebene hinaus untersucht diese Arbeit die Generalisierbarkeit des RSM-Rahmenwerks mit zwei Modellierungsstrategien: einem einheitlichen RSM über mehrere Felder hinweg und einem gewichteten Durchschnittsmodell, das individuelle, feldspezifische RSMs zusammenfasst. Das gewichtete Modell zeigte eine überlegene Anpassungsfähigkeit und hohe Vorhersagegenauigkeit mit einem Root Mean Square Error (RMSE) von 11 kg N/ha für die EONR und unterstreicht das Potenzial des Rahmenwerks für eine breitere Anwendung in verschiedenen landwirtschaftlichen Kontexten. Diese Generalisierbarkeit unterstützt die Akzeptanz des Rahmenwerks in unterschiedlichen Anbausystemen und ermöglicht eine präzise und informierte Stickstoffbewirtschaftung im großen Maßstab. Um eine breite Anwendung und praktische Umsetzung zu fördern, führt die Dissertation zudem eine cloudbasierte Infrastruktur ein, die das KDM-Rahmenwerk mit Echtzeit-IoT-Daten und Satellitenbildern integriert. Durch den Einsatz von Cloud-Diensten wie AWS-Batch für die Job- Orchestrierung, Amazon S3 für skalierbare Datenspeicherung und RDS Postgres für die strukturierte Datenverwaltung ermöglicht diese Infrastruktur eine nahtlose Verarbeitung von Echtzeit- und historischen Daten. Räumliche Interpolationstechniken wie Kriging erweitern die Fähigkeit des Modells, Echtzeit-Stickstoffempfehlungskarten zu erstellen, was eine präzise Nährstoffbewirtschaftung für großflächige landwirtschaftliche Betriebe ermöglicht. Automatisierte Qualitätssicherung und Datenharmonisierung, die in diese Cloud-Architektur eingebettet sind, bieten eine solide Grundlage für die Bewältigung wachsender Datenmengen und vielfältiger Feldbedingungen und machen das System kosteneffizient, anpassungsfähig und leistungsfähig für die moderne Landwirtschaft. Zusammenfassend bietet diese Dissertation eine umfassende Roadmap, um Prinzipien der regenerativen Landwirtschaft in praktische, operative Stickstoffmanagementpraktiken zu übersetzen. Durch KDM wird ein interdisziplinärer Ansatz abgebildet, der fortschrittliche Modellierung, Datenverarbeitung und Cloud-Technologien integriert. Dieses Rahmenwerk ermöglicht eine nachhaltige Pflanzenbewirtschaftung und unterstützt die globalen Ziele einer umweltbewussten Nahrungsmittelproduktion. Die eingeführten Innovationen fördern einen skalierbaren, datengetriebenen Ansatz für landwirtschaftliche Nachhaltigkeit und schlagen eine Brücke zwischen wissenschaftlicher Forschung und realen Anwendungen, um den sich wandelnden Anforderungen der modernen Landwirtschaft gerecht zu werden
How fluid pseudoplasticity and elasticity affect propeller flows in biogas fermenters
Full text linkMixing in biogas fermenters is complex due to the non‐Newtonian rheology of biogenic substrates, which exhibit both pseudoplasticity and elasticity. It is yet unclear how these non‐Newtonian properties affect propeller flows and the mixing behavior in fermenters. Therefore, propeller flows in Newtonian as well as shear‐thinning inelastic and elastic fluids are compared numerically and validated against particle image velocity (PIV) data. Elastic normal stresses lead to an increase of pumping rates in the laminar regime and a suppression of the formation of a propeller jet in the transitional regime. Thus, flow rates are severely overestimated by the inelastic, shear‐thinning model in this regime. The results indicate that elasticity is critical for an accurate modeling of flows of biogenic substrates.Bundesministerium für Ernährung und Landwirtschaft http://dx.doi.org/10.13039/50110000590
Enhancing weed suppression in plants by artificial stress induction
Full text linkVarious plant species from the Poaceae, Cannabaceae, and Brassicaceae families are used as cover crops to suppress weeds and volunteer crops through competition and allelopathy. This study examined the effects of artificially induced stress on the physiological processes, total phenolic content (TPC), and allelopathic potential of the plant species Avena strigosa, Cannabis sativa , and Sinapis alba at an early growth stage with the aim to increase their weed suppression abilities. Stress was induced at the 3–4 leaf stage in greenhouse-grown plants via harrowing, methyl jasmonate (MeJA) application, insect stress simulation, or a combination of insect stress and harrowing. Maximum quantum yield of photosystem II and shoot dry matter in the three plant species were only minimally or not affected a few days after treatment (DAT). Insect stress caused visible symptoms on treated leaves in all plants. The TPC in the shoot extracts of combined stress-treated C. sativa and insect-stressed S. alba was significantly higher by 1.7 and 1.9 times, respectively, five DAT compared to the shoot extracts from untreated control plants. Additionally, laboratory bioassays with aqueous shoot extracts from the untreated and treated plants were conducted to identify changes in allelopathic potential within the shoot tissues. The application of shoot extracts from MeJA-treated C. sativa and S. alba resulted in the lowest seed germination rates for the two weed species Alopecurus myosuroides and Stellaria media , as well as for the volunteer wheat Triticum aestivum , which were up to 65% lower 10 DAT compared to seeds treated with shoot extracts from non-stressed plants. However, the root-suppressing effect of the shoot extracts on weeds was not influenced by the stress treatments. This study reveals that artificial stress induction can be a suitable management strategy to enhance weed and volunteer cereal suppression in plants in an early growth stage but may vary between stress types and plant species, and requires further optimization and field testing.Open Access funding enabled and organized by Projekt DEAL.Universität Hohenheim (3153
Estimating effects of ocean environmental conditions on summer flounder (Paralichthys dentatus) distribution
Full text linkThe relative abundance of summer flounder ( Paralichthys dentatus ) differs over space and time with changes in environmental factors, such as depth, bottom temperature, sea surface temperature (SST) and bottom salinity. We use the integrated nested Laplace approximation (INLA) approach to account for the random effects arising from either over-dispersion, or spatial and temporal autocorrelation. We explore how the different assumptions in the spatial temporal models result in varying model predictions. The results indicate that the distribution of summer flounder is correlated with depth, regional increases in bottom temperature, SST and bottom salinity. We find that in the Fall relative abundance increased 10–15% with a 1∘C increase in SST, by 12% with each 1∘C increase in bottom temperature and 3–4% with each meter increase in depth across all models. In the spring, relative abundance increased by about 30% with each 1∘C increase in SST with an upper preferred temperature between 10-20∘C. Our study also shows that models that include spatio-temporally correlated variables can inadvertently be over parameterized when including higher order interaction terms between spatial and temporal random effects. This can lead to inflated variances in the estimates and predictions as well as lengthening model convergence times. Therefore, care should be taken in identifying the level of model complexity given the indirect implications of these results on fisheries management and marine ecology