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Die Dissertation beschäftigt sich mit der Wahrnehmung ländlicher Armut als räumlichem Phänomen und untersucht die Maßnahmen der Agrar-, Sozial- und Raumordnungspolitik, die als strukturpolitisch wirksam zur Bekämpfung sowie Vermeidung ländlicher Armut in den zeitgenössischen Diskursen angenommen wurden. Im Mittelpunkt der Analyse stehen dabei die traditionell landwirtschaftlich geprägten Bundesländer Rheinland-Pfalz und Baden-Württemberg. Untersucht wird, wie das ländliche Armutsproblem in Aussagen von Agrar- und Sozialpolitikern sowie Experten definiert und beschrieben wurde und welche Maßnahmen aufgrund dieser Annahmen als geeignet erschienen, Armut in den agrarwirtschaftlich geprägten Regionen zu bekämpfen. Die Forschungsarbeit basiert auf der Analyse und Bewertung von Förderprogrammen und Förderungshilfen von Bund, Ländern und Kommunen und untersucht diese auf ihre Wirksamkeit hinsichtlich der Existenzsicherung landwirtschaftlicher Betriebe im südwestdeutschen Raum zwischen 1949 und 1974. Ländliche Armut wird so als vor allem bäuerliche Armut beschrieben, die in den zeitgenössischen Diskursen als räumlich abgrenzbare Existenzgefährdung in sogenannten "strukturschwachen" Räumen konstruiert wurde. Im Zusammenhang mit der Untersuchung wirtschaftspolitischer Debatten werden die sozialpolitischen Probleme diskutiert, gesetzliche Durchführungsbestimmungen der Sozialpolitik auf ihre konkreten Ziele hinsichtlich Armutsbekämpfung auf dem Land untersucht sowie die Verflechtungen von Wirtschafts- und Sozialpolitik analysiert.
In order to discuss potential sustainability issues of expanding silage maize cultivation in Rhineland-Palatinate, spatially explicit monitoring is necessary. Publicly available statistical records are often not a sufficient basis for extensive research, especially on soil health, where risk factors like erosion and compaction depend on variables that are specific to every site, and hard to generalize for larger administrative aggregates. The focus of this study is to apply established classification algorithms to estimate maize abundance for each independent pixel, while at the same time accounting for their spatial relationship. Therefore, two ways to incorporate spatial autocorrelation of neighboring pixels are combined with three different classification models. The performance of each of these modeling approaches is analyzed and discussed. Finally, one prediction approach is applied to the imagery, and the overall predicted acreage is compared to publicly available data. We were able to show that Support Vector Machine (SVM) classification and Random Forests (RF) were able to distinguish maize pixels reliably, with kappa values well above 0.9 in most cases. The Generalized Linear Model (GLM) performed substantially worse. Furthermore, Regression Kriging (RK) as an approach to integrate spatial autocorrelation into the prediction model is not suitable in use cases with millions of sparsely clustered training pixels. Gaussian Blur is able to improve predictions slightly in these cases, but it is possible that this is only because it smoothes out impurities of the reference data. The overall prediction with RF classification combined with Gaussian Blur performed well, with out of bag error rates of 0.5% in 2009 and 1.3% in 2016. Despite the low error rates, there is a discrepancy between the predicted acreage and the official records, which is 20% in 2009 and 27% in 2016.
The study analyzes the long-term trends (1998–2019) of concentrations of the air pollutants ozone (O3) and nitrogen oxides (NOx) as well as meteorological conditions at forest sites in German midrange mountains to evaluate changes in O3 uptake conditions for trees over time at a plot scale. O3 concentrations did not show significant trends over the course of 22 years, unlike NO2 and NO, whose concentrations decreased significantly since the end of the 1990s. Temporal analyses of meteorological parameters found increasing global radiation at all sites and decreasing precipitation, vapor pressure deficit (VPD), and wind speed at most sites (temperature did not show any trend). A principal component analysis revealed strong correlations between O3 concentrations and global radiation, VPD, and temperature. Examination of the atmospheric water balance, a key parameter for O3 uptake, identified some unusually hot and dry years (2003, 2011, 2018, and 2019). With the help of a soil water model, periods of plant water stress were detected. These periods were often in synchrony with periods of elevated daytime O3 concentrations and usually occurred in mid and late summer, but occasionally also in spring and early summer. This suggests that drought protects forests against O3 uptake and that, in humid years with moderate O3 concentrations, the O3 flux was higher than in dry years with higher O3 concentrations.
Das Working Paper beleuchtet den Themenbereich‚Demografie und Migration und arbeitet Herausforderungen für die Raumentwicklung der Großregion ab. Insbesondere legt es einen Fokus auf die grenzüberschreitende Wohnmobilität an den Grenzen des Großherzogtums; die Bevölkerungsalterung und die Sicherung der Daseinsvorsorge im Gesundheitsbereich in ländlichen Gebieten.
Soil loss by water-erosion is world-wide one of the main risks in soil protection. However, the reasons for soil erosion and its degree differ within the different regions of the world. The main focus of the PhD-Theses was to investigate the main factors for soil erosion in two regions differing in parent material, climate, soil type, and land use, namely Rhineland-Palatinate (Germany) and Turkey (Middle Anatolia). The results can be summarized as followed: 1. The salt content and the electric conductivity of the soils in Middle Anatolia were higher compared with the soils in Rhineland-Palatinate. This resulted in a reduced aggregate stability, a reduced water infiltration rate, and consequently in a compaction of the soil surface. 2. The amounts of soil organic matter were lower in the soils of Middle Anatolia compared to the German soils. 3. According to the universal soil loss equation (USLE) the K-factor was higher in the soils of Middle Anatolia compared to the German soils. 4. Just as the K-factor the R-factor was higher in the soils of Middle Anatolia compared to the German soils.