Browsing by Subject "Crop model"

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    Developing cropping systems for the ancient grain chia (Salvia hispanica L.) in two contrasting environments in Egypt and Germany
    (2020) Mack, Laura; Graeff-Hönninger, Simone
    Chia (Salvia hispanica L.) seeds have been revived as functional “superfood” for human nourishment especially for vegan and vegetarian diets and are becoming increasingly widespread and present in new food products in Europe. The seeds are beneficial because of being gluten-free, containing antioxidants and a high concentration of α-linolenic acid, and having a high content of dietary fiber and high-quality protein. Chia is originally adapted to short-day conditions and grows naturally in tropical and subtropical environments. Nevertheless, it can survive under water stress and could, therefore, be cultivated in arid regions. Egypt has been classified as a water-scarce state. Due to its drought tolerance, chia might contribute to saving the scarce source “water” in Egypt and offer the chance to export these high value seeds, generating foreign exchange for reimporting e.g. wheat characterized by a higher water demand. Worldwide, the biggest problems and key challenges under climate change (CC) are water and food security in arid and semiarid regions. In the future, CC and water scarcity will significantly threaten agriculture and sustainable development. A rising population requires on the one hand an increase in food grain production, but also a change toward environmentally sound sustainable agriculture. Chia has been suggested as a favorably economic alternative for common field crops sustaining diversification and stabilization of the local agricultural economy. However, broad experience in growing chia in new environments is missing. The agronomic management has not been improved from formerly small-scale production systems. Most of the previous studies focused on seed characteristics. Information on fertilization, plant protection, and improved varieties is scarce, which are reasons for its low productivity in the countries of origin. Field experiments were conducted at the experimental station “Ihinger Hof” of the University of Hohenheim in southwestern Germany from 2015 to 2017 and in Egypt during the cropping season 2015 to 2016 at SEKEM’s experimental station located 50 km Northeast of Cairo. The present doctoral thesis was based on a project embedded in the graduate school Water-People-Agriculture (WPA) at the University of Hohenheim funded by the Anton-&-Petra-Ehrmann foundation that focuses on key water issues and water related challenges of todays society. On a final note, the main results of this thesis provide further information and expanded knowledge on chia cultivation in two contrasting environments (including a desert region) out of its center of origin. Overall, the current doctoral thesis presents a combined approach of experimental field research and crop modeling to support the optimization of farming practices of chia in new environments. A universal and nondestructive LA estimation model for chia was developed. Further, the CROPGRO model was adapted for chia to provide a preliminary model for a realistic simulation of crop growth variables. The approaches presented in this thesis may contribute to testing new environments for chia cultivation and to improving its production. Moreover, this study helped to develop further general model source codes to simulate the growth of tiny seeds. The adaptation to other Salvias should be much easier with this developed model. Future research requirements and issues requiring model improvement such as N-response and the development of code relationships that can simulate parameters of seed quality could improve the plant growth model for chia.
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    Reducing uncertainty in prediction of climate change impacts on crop production in Ethiopia
    (2024) Rettie, Fasil Mequanint; Streck, Thilo
    Ethiopia, with an economy heavily reliant on agriculture, is among the countries most vulnerable to climate change. It faces recurrent climate extreme events that result in devastating impacts and acute food shortages for millions of people. Studies that focus on their influence on agriculture, especially crop productivity, are of particular importance. However, only a few studies have been conducted in Ethiopia, and existing studies are spatially limited and show considerable spatial invariance in predicted impacts, as well as discrepancies in the sign and direction of impacts. Therefore, a robust, regionally focused, and multi-model assessment of climate change impacts is urgently needed. To guide policymaking and adaptation strategies, it is essential to quantify the impacts of climate change and distinguish the different sources of uncertainty. Against this backdrop, this study consisted of several key components. Using a multi-crop model ensemble, we began with a local climate change impact assessment on maize and wheat growth and yield across three sites in Ethiopia . We quantified the contributions of different sources of uncertainty in crop yield prediction. Our results projected a of 36 to 40% reduction in wheat grain yield by 2050, while the impact on maize was modest. A significant part of the uncertainty in the projected impact was attributed to differences in the crop growth models. Importantly, our study identified crop growth model-associated uncertainty as larger than the rest of the model components. Second, we produced a high-resolution daily projections database for rainfall and temperature to serve the requirement for impact modeling at regional and local levels using a statistical downscaling technique based on state-of-the-art GCMs under a range of emission scenarios called Shared Socioeconomic Pathways (SSPs). The evaluated results suggest that the downscaling strategy significantly reduced the biases between the GCM outputs and the observation data and minimized the errors in the projections. Third, we explored the magnitude and spatial patterns of trends in observed and projected changes in climate extremes indices based on downscaled high-resolution daily climate data to serve as a baseline for future national or regional-level impact assessment. Our results show largely significant and spatially consistent trends in temperature-derived extreme indices, while precipitation-related extreme indices are heterogeneous in terms of spatial distribution, magnitude, and statistical significance coverage. The projected changes in temperature-related indices are dominated by the uncertainties in the GCMs, followed by uncertainties in the SSPs. Unlike the temperature-related indices, the uncertainty from internal climate variability constitutes a considerable proportion of the total uncertainty in the projected trends. Fourth, we examined the regional-scale impact of climate change on maize and wheat yields by crop modeling, in which we calibrated and validated three process-based crop models to guide the design of national-level adaptation strategies in Ethiopia. Our analysis showed that under a high-emissions scenario, the national-level median wheat yield is expected to decrease by 4%, while maize yield is expected to increase by 2.5% by the end of the century. The CO2 fertilization effect on the crop simulations would offset the projected negative impact. Crop model spread followed by GCMs was identified as the largest contributor to overall uncertainty to the estimated yield changes. In summary, our study quantifies the impact of climate change and demonstrates the importance of a multi-model ensemble approach. We highlight the significant impacts of climate change on wheat yield in Ethiopia and the importance of crop model improvements to reduce overall uncertainty in the projected impact.