@phdthesis{Isasa2024,
  author    = {Isasa, Emilie},
  title     = {Relationship between wood properties, drought-induced embolism and environmental preferences across temperate diffuse-porous broadleaved trees},
  doi       = {10.25972/OPUS-30356},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-303562},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2024},
  abstract  = {In the scope of climate warming and the increase in frequency and intensity of severe heat waves in Central Europe, identification of temperate tree species that are suited to cope with these environmental changes is gaining increasing importance. A number of tree physiological characteristics are associated with drought-stress resistance and survival following severe heat, but recent studies have shown the importance of plant hydraulic and anatomical traits for predicting drought-induced tree mortality, such as vessel diameter, and their potential to predict species distribution in a changing climate. A compilation of large global datasets is required to determine traits related to drought-induced embolism and test whether embolism resistance can be determined solely by anatomical traits. However, most measurements of plant hydraulic traits are labour-intense and prone to measurement artefacts. A fast, accurate and widely applicable technique is necessary for estimating xylem embolism resistance (e.g., water potential at 50\% loss of conductivity, P50), in order to improve forecasts of future forest changes. These traits and their combination must have evolved following the selective pressure of the environmental conditions in which each species occurs. Describing these environmental-trait relationships can be useful to assess potential responses to environmental change and mitigation strategies for tree species, as future warmer temperatures may be compounded by drier conditions.},
  subject      = {Pflanzen{\"o}kologie},
  language  = {en}
}
@phdthesis{Dhillon2023,
  author    = {Dhillon, Maninder Singh},
  title     = {Potential of Remote Sensing in Modeling Long-Term Crop Yields},
  doi       = {10.25972/OPUS-33052},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-330529},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Accurate crop monitoring in response to climate change at a regional or field scale plays a significant role in developing agricultural policies, improving food security, forecasting, and analysing global trade trends. Climate change is expected to significantly impact agriculture, with shifts in temperature, precipitation patterns, and extreme weather events negatively affecting crop yields, soil fertility, water availability, biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable information combined with crop growth models (CGMs) for yield assessment by monitoring crop development, detecting crop changes, and assessing the impact of climate change on crop yields. This dissertation aims to investigate the potential of RS data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR) for the Free State of Bavaria (70,550 km2), Germany. The first chapter of the dissertation describes the reasons favouring the importance of accurate crop yield predictions for achieving sustainability in agriculture. Chapter second explores the accuracy assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5-6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more suitable for agricultural monitoring than the other synthetic products fused. Chapter third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter 2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal (8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light use efficiency approach (LUE)) for accurate crop yield estimations of both crop types. Chapter third observes that the observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately measuring the yield of WW and OSR. The chapter investigates that the simple light use efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17\%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35\%) with higher input parameters. Chapter four researches the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for WW and OSR using the LUE model for Bavaria from 2001 to 2019. The chapter states the high positive correlation coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter analyses the impact of climate variables on crop yield predictions by observing an increase in R2 (0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when the climate effect is included in the model. The fifth chapter suggests that the coupling of the LUE model to the random forest (RF) model can further reduce the relative root mean square error (RRMSE) from -8\% (WW) and -1.6\% (OSR) and increase the R2 by 14.3\% (for both WW and OSR), compared to results just relying on LUE. The same chapter concludes that satellite-based crop biomass, solar radiation, and temperature are the most influential variables in the yield prediction of both crop types. Chapter six attempts to discuss both pros and cons of RS technology while analysing the impact of land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that the modelled biomass of both crops is positively impacted by land use diversity to the radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The chapter also discusses the future implications by stating that including some dependent factors (such as the management practices used, soil health, pest management, and pollinators) could improve the relationship of RS-modelled crop yields with biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving accurate crop yield predictions for precision farming. In addition, the chapter highlights the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher crop yield accuracies.},
  subject      = {Ernteertrag},
  language  = {en}
}
@article{SchenkMitesserHovestadtetal.2018,
  author    = {Schenk, Mariela and Mitesser, Oliver and Hovestadt, Thomas and Holzschuh, Andrea},
  title     = {Overwintering temperature and body condition shift emergence dates of spring-emerging solitary bees},
  series = {PeerJ},
  volume    = {6},
  journal   = {PeerJ},
  doi       = {10.7717/peerj.4721},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228544},
  pages     = {e4721, 1-17},
  year      = {2018},
  abstract  = {Solitary bees in seasonal environments must align their life-cycles with favorable environmental conditions and resources; the timing of their emergence is highly fitness relevant. In several bee species, overwintering temperature influences both emergence date and body weight at emergence. High variability in emergence dates among specimens overwintering at the same temperatures suggests that the timing of emergence also depends on individual body conditions. However, possible causes for this variability, such as individual differences in body size or weight, have been rarely studied. In a climate chamber experiment using two spring-emerging mason bees (Osmia cornuta and O. bicornis), we investigated the relationship between temperature, emergence date, body weight, and body size, the last of which is not affected by overwintering temperature. Our study showed that body weight declined during hibernation more strongly in warm than in cold overwintering temperatures. Although bees emerged earlier in warm than in cold overwintering temperatures, at the time of emergence, bees in warm overwintering temperatures had lower body weights than bees in cold overwintering temperatures (exception of male O. cornuta). Among specimens that experienced the same overwintering temperatures, small and light bees emerged later than their larger and heavier conspecifics. Using a simple mechanistic model we demonstrated that spring-emerging solitary bees use a strategic approach and emerge at a date that is most promising for their individual fitness expectations. Our results suggest that warmer overwintering temperatures reduce bee fitness by causing a decrease in body weight at emergence. We showed furthermore that in order to adjust their emergence dates, bees use not only temperature but also their individual body condition as triggers. This may explain differing responses to climate warming within and among bee populations and may have consequences for bee-plant interactions as well as for the persistence of bee populations under climate change.},
  language  = {en}
}
@phdthesis{Bangelesa2022,
  author    = {Bangelesa, Freddy Fefe},
  title     = {Impacts of climate variability and change on Maize (\(Zea\) \(mays\)) production in tropical Africa},
  doi       = {10.25972/OPUS-25934},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259347},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Climate change is undeniable and constitutes one of the major threats of the 21st century. It impacts sectors of our society, usually negatively, and is likely to worsen towards the middle and end of the century. The agricultural sector is of particular concern, for it is the primary source of food and is strongly dependent on the weather. Considerable attention has been given to the impact of climate change on African agriculture because of the continent's high vulnerability, which is mainly due to its low adaptation capac- ity. Several studies have been implemented to evaluate the impact of climate change on this continent. The results are sometimes controversial since the studies are based on different approaches, climate models and crop yield datasets. This study attempts to contribute substantially to this large topic by suggesting specific types of climate pre- dictors. The study focuses on tropical Africa and its maize yield. Maize is considered to be the most important crop in this region. To estimate the effect of climate change on maize yield, the study began by developing a robust cross-validated multiple linear regression model, which related climate predictors and maize yield. This statistical trans- fer function is reputed to be less prone to overfitting and multicollinearity problems. It is capable of selecting robust predictors, which have a physical meaning. Therefore, the study combined: large-scale predictors, which were derived from the principal component analysis of the monthly precipitation and temperature; traditional local-scale predictors, mainly, the mean precipitation, mean temperature, maximum temperature and minimum temperature; and the Water Requirement Satisfaction Index (WRSI), derived from the specific crop (maize) water balance model. The projected maize-yield change is forced by a regional climate model (RCM) REMO under two emission scenarios: high emission scenario (RCP8.5) and mid-range emission scenario (RCP4.5). The different effects of these groups of predictors in projecting the future maize-yield changes were also assessed. Furthermore, the study analysed the impact of climate change on the global WRSI. The results indicate that almost 27 \% of the interannual variability of maize production of the entire region is explained by climate variables. The influence of climate predictors on maize-yield production is more pronounced in West Africa, reaching 55 \% in some areas. The model projection indicates that the maize yield in the entire region is expected to decrease by the middle of the century under an RCP8.5 emission scenario, and from the middle of the century to the end of the century, the production will slightly recover but will remain negative (around -10 \%). However, in some regions of East Africa, a slight increase in maize yield is expected. The maize-yield projection under RCP4.5 remains relatively unchanged compared to the baseline period (1982-2016). The results further indicate that large-scale predictors are the most critical drivers of the global year-to-year maize-yield variability, and ENSO - which is highly correlated with the most important predictor (PC2) - seems to be the physical process underlying this variability. The effects of local predictors are more pronounced in the eastern parts of the region. The impact of the future climate change on WRSI reveals that the availability of maize water is expected to decrease everywhere, except in some parts of eastern Africa.},
  language  = {en}
}
@article{CorneliusLeingaertnerHoissetal.2012,
  author    = {Cornelius, C. and Leing{\"a}rtner, A. and Hoiss, B. and Krauss, J. and Steffan-Dewenter, I. and Menzel, A.},
  title     = {Phenological response of grassland species to manipulative snowmelt and drought along an altitudinal gradient},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-77969},
  year      = {2012},
  abstract  = {Plant communities in the European Alps are assumed to be highly affected by climate change since temperature rise in this region is above the global average. It is predicted that higher temperatures will lead to advanced snowmelt dates and that the number of extreme weather events will increase. The aims of this study were to determine the impacts of extreme climatic events on flower phenology and to assess whether those impacts differed between lower and higher altitudes. In 2010 an experiment simulating advanced and delayed snowmelt as well as drought event was conducted along an altitudinal transect ca. every 250m (600-2000 m a.s.l.) in the Berchtesgaden National Park, Germany. The study showed that flower phenology is strongly affected by altitude; however there were few effects of the manipulative treatments on flowering. The effects of advanced snowmelt were significantly greater at higher than at lower sites, but no significant difference was found between both altitudinal bands for the other treatments. The response of flower phenology to temperature declined through the season and the length of flowering duration was not significantly influenced by treatments. The stronger effect of advanced snowmelt at higher altitudes might be a response to differences in treatment intensity across the gradient. Consequently, shifts in the date of snowmelt due to global warming may affect species more at higher than at lower altitudes since changes may be more pronounced at higher altitudes. Our data indicate a rather low risk of drought events on flowering phenology in the Bavarian Alps.},
  subject      = {Biologie},
  language  = {en}
}