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Die Anzahl der unterernährten Menschen in der Welt steigt seit 2017 wieder an. Der Klimawandel wird den Druck auf die Landwirtschaft und die Ernährungssicherheit weiter erhöhen, insbesondere für kleinbäuerliche und von Subsistenzwirtschaft geprägte Agrarsysteme in den Tropen. Um die Widerstandsfähigkeit der Ernährungssysteme und die Ernährungssicherheit zu stärken, bedarf es eines Klimarisikomanagements und Klimaanpassung. Dies kann sowohl die Antizipation als auch die Reaktion auf die Auswirkungen der globalen Erwärmung ermöglichen. Eine zentrale Rolle spielen in dieser Hinsicht landwirtschaftliche Modelle. Sie können die Reaktionen von Pflanzen auf Veränderungen in den Klimabedingungen quantifizieren und damit Risiken identifizieren. Diese Dissertation demonstriert anhand dreier in Peru, in Tansania und in Burkina Faso durchgeführten Fallstudien, wie statistische Ertragsmodelle das Klimarisikomanagement und die Anpassung in der tropischen Landwirtschaft unterstützen können. Während die erste Studie zeigt, wie Klimaanpassungsbestrebungen unterstützt werden können, werden in Studie zwei und drei statistische Modelle genutzt, um Ertrags- und Produktionsvorhersagen zu erstellen. Die Ergebnisse können dazu beitragen, Frühwarnsysteme für Ernährungsunsicherheit zu unterstützen. In den drei Veröffentlichungen werden neue Ansätze statistischer Ertragsmodellierung auf verschiedenen räumlichen Ebenen vorgestellt. Ein besonderer Fokus liegt hierbei auf der Weiterentwicklung von bisherigen Ertragsvorhersagen, insbesondere in Bezug auf unabhängige Modellvalidierungen, eine stärkere Berücksichtigung von Wetterextremen und die Übertragbarkeit der Modelle auf andere Regionen. The number of undernourished people in the world has been increasing since 2017. Climate change will further exacerbate pressure on agriculture and food security, particularly for smallholder and subsistence-based farming systems in the tropics. Anticipating and responding to global warming through climate risk management is needed to increase the resilience of food systems and food security. Crop models play an indispensable role in this regard. They allow quantifying crop responses to changes in climatic conditions and thus identify risks. This dissertation demonstrates how statistical crop modelling can inform climate risk management and adaptation in tropical agriculture in the case studies of Peru, Tanzania and Burkina Faso. While the first study shows how statistical crop models can support climate adaptation, studies two and three provide yield and production forecasts. The results can contribute to supporting early warning systems on food insecurity. The three publications present novel approaches of statistical yield modelling at different spatial scales. A particular focus is on further developing existing yield forecasts, especially with regard to independent rigorous model validations, improved consideration of weather extremes, and the transferability of the models to other regions.

Despite their ecological importance, dry forests’ contribution to climate change adaptation is often neglected. Hence, this study was initiated to assess the socioeconomic contribution of dry forests to climate change adaptation in Tigray Region, Ethiopia. A mixed quantitative and qualitative research design was used to examine the role of dry forests in climate change adaptation. Household questionnaire survey, key informants, and a focus group discussion were used to collect data. The results indicated that 94% of all households visited a dry forest at least once a month to access the forest and forest products. While the dry forest income level varied significantly (p < 0.05), the overall dry forest income level contributed to 16.8% of the total household income. Dry forest income enabled the reduction of the area between the line of equality and the Lorenz curve by 21% in dry evergreen Afromontane Forest users, by 3.02% in Combretum–Terminalia woodland users, and by 3% in Acacia–Commiphora woodland users. Gender, occupation, wealth status, and distance from the forest to their homes are all factors that significantly affected Combretum–Terminalia woodland users’ income level. Among Acacia–Commiphora woodland users, the respondents’ age influenced the dry forest income level, whereas, among dry evergreen Afromontane Forest users, the family size of the household influenced the dry forest income level. The findings of this study could help policy makers understand the crucial role of dry forest income in the livelihood of the community and in climate change adaptation. Policymakers could reduce the pressure on dry forests by introducing policies that recognize the role of dry forest income in reducing poverty and income inequality and by establishing farmer cooperation in commercializing the non-timber forest products which support the long-term coping and adaptation strategy. Further research is needed to understand the increasing role of dry forest products in climate change adaptation over time and its contribution to the national economy at large. Ethiopian Environment and Forestry Research Institute Open Access Fund of the Leibniz Association Peer Reviewed

Agribusinesses need to answer to societal growing concerns regarding the use of natural resources. In this context, the digital technologies described by Industry 4.0 appear as allies in the optimization of agribusiness. The use of these technologies is closely related to digital transformation, leading to this work's goal: to analyze the dimensions of Digital Transformation (DT) in the Modern Agricultural (MA) context. The methodology of this study was divided into three main parts. Firstly, a Systematic Literature Review (SLR) was performed with the help of the SciMAT® software. Then, a content analysis was performed using a semantic technique to define the dimensions a posteriori. Finally, a Latent Dirichlet Allocation (LDA) model using the RStudio® software was made to validate the results found in the semantic analysis. To the best of our knowledge, there are only five studies that define dimensions of DT in MA, however none of them used a robust SLR with content analysis. Two of these studies did empirical researches with specialists to define the dimensions, and the other three defined specifics dimensions only a priori. Our study brings as an innovation the definition of the dimensions a posteriori through the SLR. Additionally, no studies that validated the results using a LDA model were found. As a result of our studies, we have identified eight dimensions: Economical, Government, Sustainability; Infrastructure, Technological, Cooperation, Change, and People/Knowledge/Skills. The first three dimensions are more influenced by external factors, while external and internal factors influence the following three, and finally, the last two are more affected by internal factors. For each set of the dimensions listed, the main pillars/drivers/motivators and the main barriers/challenges/difficulties were identified. For example, for the mixed dimension, the first set can be the creation of regional centers that broadcast digital technology content. Regarding the second set, we can cite the difficulty in transforming data into useful information for the same dimension. The results serve as a foundation for both practical business actions and the academic field. They presented a robust theoretical basis for developing guidelines for agricultural management (of farms and other related startups and companies) and public policies to encourage the adoption and implementation of technological resources guided by sustainable development goals. The impacts can be viewed as: reduced production through sustainable and responsible production, assistance in the conscious and sustainable use of water resources, and sustainable actions to mitigate climate change. In conclusion, we highlight that the environment in which the framework will be applied should influence the emphasis given to each set of dimensions. For instance, legislators that seek to develop policies for DT in MA should pay greater attention to the external dimensions. In contrast, farmers that seek to implement DT should be more focused on the internal dimensions.

The Arctic Ocean is undergoing a major transition from a year-round sea ice cover to ice-free summers with global consequences. Sea ice thickness is at the center of the ongoing changes because the thickness regulates key processes of the Arctic climate system and in the last six decades, the mean thickness has more than halved. With the most scientific attention on the increased melting and delayed freezing of Arctic sea ice, dynamic thickness change caused by sea ice deformation has remained less studied. Dynamic thickness change alters the sea ice thickness through colliding floes that raft or form pressure ridges or floes breaking apart resulting in leads. Because sea ice grows faster in open water and under thin ice, new ice formation is enhanced in those leads compared to the surrounding ice during the growth season. Because thinner ice is easier to break and move, the ongoing thinning of Arctic sea ice may result in more ridges and leads, which, in turn, could increase ice thickness in winter. However, our limited quantitative understanding of dynamic thickness change has hampered any robust prediction if and to which extent such increased dynamic thickening in winter could mitigate summer thinning in the warming Arctic. To address this gap, we need more robust estimates of the current magnitude as well as a better understanding and representation of the different processes in state-of-the-art sea ice models. Thus, the overarching goal of this thesis is to resolve and quantify dynamic thickness change and to link it to the corresponding sea ice deformation. I focus on the freezing period addressing the following research questions: (1) How large is the dynamic contribution to the mean sea ice thickness in different dynamic regimes? (2) How is deformation shaping the ice thickness distribution? (3) How can high-resolution microwave synthetic aperture radar (SAR) satellite data be used to estimate dynamic thickness change? I answer them in two regional case studies: a unique month-long deformation event during the closing of a polynya north of Greenland and in the Transpolar Drift along the drift track of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The combination of available high-resolution electromagnetic (EM) induction sounding ice thickness data and high-resolution deformation data offer unique research opportunities to study the highly localized and intermittent dynamic thickness changes. My results show that dynamic thickness change plays an important role in both convergent and divergent drift regimes. Studying the polynya closing event reveals that convergence can locally double the thickness of young, thin (<1 m) ice and restore the mean thickness of 2 m of the surrounding multi-year ice within one month. In more divergent regimes like the Transpolar Drift, new ice formation in leads contributes 30% to the sea ice mass balance. There are indicators that this fraction may increase in a more seasonal Arctic sea ice cover. Besides the mean changes, I show how deformation shapes the ice thickness distribution (ITD) with a particular focus on the transfer of observational results into modeling concepts. I identify the ice that participates in ridging, show that the current ridging parameterization in state-of-the-art models is not able to reproduce the observed changes in the shape of the ITD, and suggest an updated parameterization that relates the shape of the ITD proportionally to the observed deformation. Lastly, I demonstrate that SAR-derived deformation can successfully be used to describe sea ice dynamics and to estimate the dynamic contribution to the ice thickness on regional scales. In conclusion, this dissertation substantially advances our understanding of dynamic thickness change with robust and quantitative estimates. The high-resolution EM ice thickness data with simultaneously collected high-resolution deformation data provide an excellent opportunity to deepen our process understanding and to evaluate and improve the modeling of the dynamic processes shaping the ITD. With the increasing availability of SAR data in the Arctic and the presented deformation datasets and methods, new opportunities are opening up to derive dynamic thickness change on Arctic-wide scales and to study the temporal trends in dynamic thickness change over the last decade.

Global change biology (2022). doi:10.1111/gcb.16462 Published by Wiley-Blackwell, Oxford [u.a.]

&lt;p&gt;The mid-latitudes of Mars host a large amount of buried ice deposits [1], which bear a record on the very Late Amazonian climate evolution (~10&lt;sup&gt;7&lt;/sup&gt; to 10&lt;sup&gt;8&lt;/sup&gt; yr.) [2] and are a possible resource for future exploration [3]. The reconstruction of the history of these ice reservoirs and the characterization of their current state is critical to enable putting constraints on the interplay between climate change, volatile migration and landform evolution. Examination of terrestrial analogs can help to identify similarities and &amp;#8211; equally important &amp;#8211; differences between the response of buried ice on Mars and Earth to changing environmental parameters. Here we report on our long-term investigations of an ice-cored moraine on Svalbard, a proxy for mid-latitude debris-covered glacier on Mars [4].&lt;/p&gt; &lt;p&gt;Our study area is an ice-cored lateral moraine of Kongsvegen glacier on the southern shore of Kongsforden in NW Spitsbergen (Svalbard). This site is subject to rapid degradation by melting and redistribution of surficial sediment, e.g., by thaw slumps and debris flows [5]. We couple multi-year fieldwork with repeat remote-sensing observations. Fieldwork includes DGPS-supported monitoring of surface changes by several imaging methods comparisons to environmental data (e.g., [6]). In 2008, a flight campaign with HRSC-AX (an airborne version of DLR&amp;#8217;s High Resolution Stereo Camera on Mars Express) provided orthoimages (20 cm/px) and a Digital Elevation Model (DEM; 50 cm ground sampling distance, GSD) [7]. In 2020 a repeat campaign with DLR&amp;#8217;s MACS (Modular Airborne Camera System) achieved ground pixel sizes between 5 cm and 10 cm for RGB images, and 10 cm to 15 cm for NIR images. The associated DEM has the same resolution [8]. MACS also acquires TIR data,which we use to identify regions with anomalous temperatures (e.g., exposed ice). We use for detailed mapping of ice-rich viscous flow features on Mars [9]. This study focuses on the Nereidum Montes area in the northern part of the Argyre basin. Image analysis, mapping of landforms and documentation of results were carried out using the QGIS software.&lt;/p&gt; &lt;p&gt;A difference DEM was generated to determine the elevation differences (i.e. volume changes) on the ice-cored moraine between 2008 and 2020. Hillshade layers were created for each dataset to facilitate quick visual identification of morphological changes. Slump scars were digitized in 2008 and 2020 on orthoimages and hillshade views, and prominent slumps of 2020 (most recent, visible and best preserved) have been geomorphologically mapped with a focus on buried dead ice, collapsed sediment, and the relative activity of mud flows. Image analysis and mapping of Martian landforms is currently ongoing.&lt;/p&gt; &lt;p&gt;&lt;img src=&quot;&quot; alt=&quot;&quot; /&gt;&lt;/p&gt; &lt;p&gt;There are 4 areas of major activity within the ice-cored moraine (Fig.1.a) highlighted by the four red boxes b, c, d and e, with more than 10m of elevation losses. There are many areas showing a neutral balance in terms of topographic evolution (white pixels). Hillshade layers from 2008 (Fig.1.f) and 2020 (Fig.1.g) correspond to the area of box d and show that significant changes have occurred in 12 years, indicating a massive degradation of ice-cored moraine in the last decade. This also demonstrates that the general trend is towards a heterogenous loss of material. The slump scars appear to be concentrated in the same areas described above. In 2008, 85 scars were counted against 157 scars in 2020, i.e. almost twice as many. It also appears that the 2020 scars are following the outlines of high activity areas while the 2008 scars are within these areas.&lt;/p&gt; &lt;p&gt;&lt;img src=&quot;&quot; alt=&quot;&quot; /&gt;&lt;/p&gt; &lt;p&gt;Regarding the detailed mapping of slumps, the area corresponding to the buried dead ice, mapped with the TIR channel, can vary in shape and size depending on the shape of headscarp and the other units, i.e. the collapsed sediments and the mudflows (Fig.2.b,d). The collapsed sediments are washed away by the mudflows. The mudflow seems more active near the previously exposed dead ice. Therefore, the degradation of ice-cored moraines takes place mainly through the melting of the ice, which leads to mass wasting and rapid degradation. As Mars does not have the same climatic conditions, it is interesting to see whether the same landforms can be found on it at a very local scale.&lt;/p&gt; &lt;p&gt;&lt;img src=&quot;&quot; alt=&quot;&quot; /&gt;&lt;/p&gt; &lt;p&gt;The Mars case study displays gullies, arcuate ridges with small gullies superposed on their slopes, flow features and also possible sublimation features such as pitted terrain, broad pits, and lineated patterned ground (Fig.3.b). The area is also filled with washboard terrain which is only found at steep slopes, above the lobate flow feature. It is marked by a close alternation of closely-spaced scarps which are parallel to the contour lines (possible evidence for paraglacial sacking [10]). Tension cracks are also found at the ridges and parallel to gully headscarps indicating recent activity. These tension cracks may be similar to headscarp retreats on backwasting processes on ice-cored moraines (Fig.3.d). The ice-cored ridge or arcuate ridge may still undergo degradation. The first results show that the degradation processes are rather related to potential ice sublimation and that signs of melting ice, if they exist on Mars, are not strongly encountered, which is consistent with the studies on very Late Amazonian climate evolution.&lt;/p&gt; &lt;p&gt;&amp;#160;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;[1] Levy, J. S. et al. (2014), &lt;em&gt;J. Geophys. Res. Planets&lt;/em&gt;, 119, 2188&amp;#8211; 2196. [2] Bramson, A. et al. (2021), &lt;em&gt;Bull. Amer. Astron. Soc.&lt;/em&gt;, 53(4), e-id. 115. [3] Putzig, N. et al. (2022) &lt;em&gt;LPSC 53&lt;/em&gt;, #2443. [4] Petersen,E. et al. (2017) &lt;em&gt;LPSC 48&lt;/em&gt;, #2966. [5] Bennett, M. et al. (2000) &lt;em&gt;Geomorphology&lt;/em&gt;, 35, 21-40. [6] Boike, J. et al. (2018), &lt;em&gt;Earth Syst. Sci. Data&lt;/em&gt;, 10, 355&amp;#8211;390. [7] Hauber, E. et al. (2011), &lt;em&gt;Geological Society of America&lt;/em&gt;, 483, 177&amp;#8211;201. [8] DLR - Institut f&amp;#252;r Optische - Sensorsysteme, (2020). [9] Souness, C. and Hubbard, B. (2012), &lt;em&gt;Progress in Phys. Geog&lt;/em&gt;. 36(2), 238-261. [10] Jawin, E. R. et al. (2018), &lt;em&gt;Icarus&lt;/em&gt;, 309, 187-206.&lt;/p&gt;

Peer Reviewed

This chapter presents a novel distributed software infrastructure to enable energy management and simulation of novel control strategies in smart cities. In this context, the following heterogeneous information, describing the different entities in a city, needs to be taken into account to form a unified district information model: internet-of-things (IoT) devices, building information model, system information model, and georeferenced information system. IoT devices are crucial to monitor in (near-) real-time both building energy trends and environmental data. Thus, the proposed solution fulfills the integration and interoperability of such data sources providing also a correlation among them. Such correlation is the key feature to unlock management and simulation of novel energy policies aimed at optimizing the energy usage accounting also for its impact on building comfort. The platform has been deployed in a real-world district and a novel control policy for the heating distribution network has been developed and tested. Finally, experimental results are presented and discussed.