182 research outputs found
Data and code: High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method
No full textData and code supporting the research article:High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method -
Fasil M. Rettie, Sebastian Gayler, Tobias KD Weber, Kindie Tesfaye, Thilo Streck. Please, find detail description of the codes and datasets in readme file
A Comparative Analysis of Agrivoltaic Designs and their Impact on Grassland Growth Dynamics Across Three Locations in Europe
No full textThe global demand for food and energy is growing, and the competition for land is intensifying. A possible solution to this problem is the dual use of agricultural area, utilizing agrivoltaic systems. Agrivoltaic systems combine the agricultural food production and the generation of renewable energy simultaneously, thereby increasing the land use efficiency. Given the financial investment required for the construction of an agrivoltaic system, the systems must be well planned to ensure their successful implementation. This thesis aims to optimize and further develop agrivoltaic systems with a focus ongrasslands in Europe. The grassland growth dynamics are investigated by comparing three agrivoltaic system designs: vertical system, 1P tracking system, and 2P tracking system. The analysis focuses on three locations across Europe: Vastogirardi in Italy, Montlucon in France, and Magdeburg in Germany, plus four types of soils to investigate the importance of soil texture. The comparison is based on a modelling approach using the Hurley Pasture grassland model within the software Expert-N.
The findings show that grassland can tolerate shading rates up to 50%. Beyond this point, the yield reduction accelerates. The vertical system is the most suitable design in this comparison as it does not cause shading rates above 50%. Magdeburg is the most favorable location with the lowest yield reduction for all systems under comparison. Agrivoltaic systems seem to perform best under high radiation, cooler temperatures, and sufficient water availability. A sandy soil showed to benefit the most from the shading of the agrivoltaic system, causing the lowest reduction in yield. Additionally, the timing of shading needs to be considered, as shade in the morning reduces yield more than shade in the afternoon. The improved nutritional values of the harvested biomass suggest a partial compensation for the yield loss due to shading
Spatiotemporal climatic signals in cereal yield variability and trends in Ethiopia
No full textClimatic variability and recurrent drought can strongly affect the variability of crop yield and are therefore frequently considered a risk to food security in Ethiopia. A better understanding of how crop yields vary in space and time, and their relationship to climatic and other driving factors, can assist in enhancing agricultural production and adapting to and mitigating the impacts of climate change. We applied a multiple linear regression model to examine the spatiotemporal climatic signal (air temperature, precipitation, and solar radiation) in the yields of the most important crops (maize, sorghum, tef, and wheat) over the period 1995–2018. An analysis of the climatic data indicated that growing season temperature increased significantly in most regions, but the trends in precipitation were not significant. The yields of maize, sorghum, tef, and wheat tended to increase across most crop-growing areas, particularly in the west, but was highly variable. The results highlight large spatial differences in the contribution of climatic trends to crop-yield variability across Ethiopian regions. The trends in climatic variability did not significantly affect crop yields in some areas, whereas in the main crop-growing areas, up to − 39.2% of yield variability could be attributed to the climatic trends. Specifically, the climatic trends negatively affected maize yields but positively affected sorghum, tef, and wheat yields. Nationally, the average impacts of climatic trends on crop yields was relatively small, ranging from a 3.2% decrease for maize to a 0.7% increase for wheat. In contrast, technological advancements contributed substantially more to yield gains, with annual increases ranging from 4.3% for wheat to 5.1% for sorghum. These findings highlight the dominant role of non-climatic drivers, particularly improved agricultural technology, in shaping crop yield trends. Our findings underscore the spatial heterogeneity of climate impacts on agriculture and highlight the critical importance of technological progress in enhancing crop productivity. They also provide actionable insights for designing crop- and location-specific adaptation strategies, and stress the need for integrated, climate-resilient development pathways in the region.Open Access funding enabled and organized by Projekt DEAL.Universität Hohenheim (3153
Unveiling Wheat’s Future Amidst Climate Change in the Central Ethiopia Region
No full textQuantifying how climatic change affects wheat production, and accurately predicting its potential distributions in the face of future climate, are highly important for ensuring food security in Ethiopia. This study leverages advanced machine learning algorithms including Random Forest, Maxent, Boosted Regression Tree, and Generalised Linear Model alongside an ensemble approach to accurately predict shifts in wheat habitat suitability in the Central Ethiopia Region over the upcoming decades. An extensive dataset consisting of 19 bioclimatic variables (Bio1–Bio19), elevation, solar radiation, and topographic positioning index was refined by excluding collinear predictors to increase model accuracy. The analysis revealed that the precipitation of the wettest month, minimum temperature of the coldest month, temperature seasonality, and precipitation of the coldest quarter are the most influential factors, which collectively account for a significant proportion of habitat suitability changes. The future projections revealed that up to 100% of the regions currently classified as moderately or highly suitable for wheat could become unsuitable by 2050, 2070, and 2090, illustrating a dramatic potential decline in wheat production. Generally, the future of wheat cultivation will depend heavily on developing varieties that can thrive under altered conditions; thus, immediate and informed action is needed to safeguard the food security of the region
Mode of competition for light and water amongst juvenile beech and spruce trees under ambient and elevated levels of O-3 and CO2
No full text
PLATHO - A Dynamic Plant Growth Model Considering Competition between Individuals and Allocation to Carbon-Based Secondary Compounds
No full textHigh-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method
No full textHigh-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized the bias correction constructed analogues with quantile mapping reordering (BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using a perfect sibling approach and detailed results are presented by taking two contrasting (the worst and best performing models) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs.44
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