Assessment of climate change-induced hazard potential of locusts (Acrididae) as pest for future German agriculture

Der Klimawandel begünstigt die Ansiedlung neuer Schadorganismen in Deutschland, die hier nun geeignete Lebensräume finden. Feldheuschrecken treten in südeuropäischen Ländern immer wieder als Landwirtschaftsschädlinge auf. Es wird daher untersucht, ob durch die klimawandelbedingte Nordverschiebung wärmerer Zonen klimatisch geeignete Lebensräume für Feldheuschrecken in Deutschland entstehen und landwirtschaftlich genutzte Flächen dadurch betroffen sein können. Mit der Software CLIMEX wird die mögliche Verbreitung der Italienischen Schönschrecke (Calliptamus italicus (L., 1758)), der Marokkanischen Wanderheuschrecke (Dociostaurus maroccanus (Thunberg, 1815)) und der Europäischen Wanderheuschrecke (Locusta migratoria (L., 1758)) für 20 Standorte in Deutschland in sechs Szenarien modelliert. Diese Szenarien werden durch die Kombination der beiden Betrachtungszeiträume 2021–2050 und 2071–2100 mit den drei Klimaprojektionen RCP2.6, RCP4.5 und RCP8.5 definiert. Aufgrund der Untersuchung ist zu erwarten, dass C. italicus sich in Deutschland stark verbreiten wird, während D. maroccanus und L. migratoria nur kleine und lokale Populationen ausbilden könnten. Heuschreckenschwärme können an den betrachteten Standorten potentiell pflanzliche Erzeugnisse auf etwa 10 – 25 % der landwirtschaftlichen Fläche in Deutschland bedrohen, ihr Auftreten wird allerdings als unwahrscheinlich eingeschätzt, da die intensive Nutzung von Grünlandflächen bisher nur unzureichende Vermehrungsbedingungen bietet. Die Schaffung größerer Brachflächen im Rahmen von Umweltschutz- und Klimaanpassungsmaßnahmen könnte dies zukünftig ändern. Zusätzlich sollten Schwarmbildungen im Ausland und mögliche Migrationsrouten nach Deutschland untersucht werden. Weiterhin wird die Entwicklung von Konzepten zur Prävention und Intervention für den Fall einer Heuschreckeninvasion empfohlen. Insgesamt ist jedoch aktuell von einem geringen Gefahrenpotential von Feldheuschrecken für die deutsche Landwirtschaft auszugehen.Climate change favors the establishment of new pests in Germany, which now find suitable habitats here due to the changed climate. Field locusts occur repeatedly as agricultural pests in southern European countries. Therefore, it is investigated whether the climate change-induced northward shift of warmer zones can create climatically suitable habitats for field locusts in Germany and whether agricultural areas can be affected by this. The CLIMEX software is used to model the possible distribution of the Italian locust (Calliptamus italicus (L., 1758)), the Moroccan locust (Dociostaurus maroccanus (Thunberg, 1815)) and the Migratory locust (Locusta migratoria (L., 1758)) for 20 locations in Germany in six scenarios. These result from the combination of the two observation periods 2021 – 2050 and 2071 – 2100 with the three climate projections RCP2.6, RCP4.5 and RCP8.5. Based on the study, C. italicus is expected to spread widely in Germany, whereas D. maroccanus and L. migratoria might form only small and local populations. Locust swarms can potentially threaten crop products on around 10 – 25% of the agricultural area in Germany at the sites considered, but are unlikely to occur, since the intensive use of grassland areas provides insufficient conditions for reproduction. The creation of lager fallow areas as part of environmental protection and climate adaptation measures could change this in the future. In addition, swarm formation in neighbouring countries and possible migration routes to Germany should be investigated. Furthermore, the development of concepts for prevention and intervention in the event of a locust invasion is recommended. Overall, however, a low risk potential of field locusts for German agriculture is currently assumed

Research Trends on Greenhouse Engineering Using a Science Mapping Approach

Horticultural protected cultivation has spread throughout the world as it has proven to be extremely effective. In recent years, the greenhouse engineering research field has become one of the main research topics within greenhouse farming. The main objectives of the current study were to identify the major research topics and their trends during the last four decades by analyzing the co-occurrence network of keywords associated with greenhouse engineering publications. A total of 3804 pertinent documents published, in 1981-2021, were analyzed and discussed. China, the United States, Spain, Italy and the Netherlands have been the most active countries with more than 36% of the relevant literature. The keyword cluster analysis suggested the presence of five principal research topics: energy management and storage; monitoring and control of greenhouse climate parameters; automation of greenhouse operations through the internet of things (IoT) and wireless sensor network (WSN) applications; greenhouse covering materials and microclimate optimization in relation to plant growth; structural and functional design for improving greenhouse stability, ventilation and microclimate. Recent research trends are focused on real-time monitoring and automatic control systems based on the IoT and WSN technologies, multi-objective optimization approaches for greenhouse climate control, efficient artificial lighting and sustainable greenhouse crop cultivation using renewable energy

Model-Based Forecasting of Agricultural Crop Disease Risk at the Regional Scale, Integrating Airborne Inoculum, Environmental, and Satellite-Based Monitoring Data

Crop diseases have the potential to cause devastating epidemics that threaten the world's food supply and vary widely in their dispersal pattern, prevalence, and severity. It remains unclear what the impact disease will have on sustainable crop yields in the future. Agricultural stakeholders are increasingly under pressure to adapt their decision-making to make more informed and efficient use of irrigation water, fertilizers, and pesticides. They also face increasing uncertainty in how best to respond to competing health, environment, and (sustainable) development impacts and risks. Disease dynamics involves a complex interaction between a host, a pathogen, and their environment, representing one of the largest risks facing the long-term sustainability of agriculture. New airborne inoculum, weather, and satellite-based technology provide new opportunities for combining disease monitoring data and predictive models—but this requires a robust analytical framework. Integrated model-based forecasting frameworks have the potential to improve the timeliness, effectiveness, and foresight for controlling crop diseases, while minimizing economic costs and environmental impacts, and yield losses. The feasibility of this approach is investigated involving model and data selection. It is tested against available disease data collected for wheat stripe (yellow) rust (Puccinia striiformis f.sp. tritici) (Pst) fungal disease within southern Alberta, Canada. Two candidate, stochastic models are evaluated; a simpler, site-specific model, and a more complex, spatially-explicit transmission model. The ability of these models to reproduce an observed infection pattern is tested using two climate datasets with different spatial resolution—a reanalysis dataset (~55 km) and weather station network township-aggregated data (~10 km). The complex spatially-explicit model using weather station network data had the highest forecast accuracy. A multi-scale airborne surveillance design that provides data would further improve disease risk forecast accuracy under heterogeneous modeling assumptions. In the future, a model-based forecasting approach, if supported with an airborne surveillance monitoring plan, could be made operational to provide agricultural stakeholders with reliable, cost-effective, and near-real-time information for protecting and sustaining crop production against multiple disease threats

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies

Reusing dynamic data marts for query management in an on-demand ETL architecture

Data analysts working often have a requirement to integrate an in-house data warehouse with external datasets, especially web-based datasets. Doing so can give them important insights into their performance when compared with competitors, their industry in general on a global scale, and make predictions as to sales, providing important decision support services. The quality of these insights depends on the quality of the data imported into the analysis dataset. There is a wealth of data freely available from government sources online but little unity between data sources, leading to a requirement for a data processing layer wherein various types of quality issues and heterogeneities can be resolved. Traditionally, this is achieved with an Extract-Transform-Load (ETL) series of processes which are performed on all of the available data, in advance, in a batch process typically run outside of business hours. While this is recognized as a powerful knowledge-based support, it is very expensive to build and maintain, and is very costly to update, in the event that new data sources become available. On-demand ETL offers a solution in that data is only acquired when needed and new sources can be added as they come online. However, this form of dynamic ETL is very difficult to deliver. In this research dissertation, we explore the possibilities of creating dynamic data marts which can be created using non-warehouse data to support the inclusion of new sources. We then examine how these dynamic structures can be used for query fulfillment andhow they can support an overall on-demand query mechanism. At each step of the research and development, we employ a robust validation using a real-world data warehouse from the agricultural domain with selected Agri web sources to test the dynamic elements of the proposed architecture

Computer and Computing Technologies in Agriculture VI: 6th IFIP WG 5.14 International Conference, CCTA 2012, Zhangjiajie, China, October 19-21, 2012

International audienceBook Front Matter of AICT 39

Computer and Computing Technologies in Agriculture VI6th IFIP WG 5.14 International Conference, CCTA 2012, Zhangjiajie, China, October 19-21, 2012, Revised Selected Papers, Part I /

XVIII, 455 p. 237 illus.online resource

Computer and Computing Technologies in Agriculture VI6th IFIP WG 5.14 International Conference, CCTA 2012, Zhangjiajie, China, October 19-21, 2012, Revised Selected Papers, Part II /

XVIII, 487 p. 201 illus.online resource