Climate change and agriculture in the Sudan: Impact pathways beyond changes in mean rainfall and temperature

Several environmental changes have occurred in the Sudan in the past; several are ongoing; and others are projected to happen in the future. The Sudan has witnessed increases in temperature, floods, rainfall variability, and concurrent droughts. In a country where agriculture, which is mainly rainfed, is a major contributor to gross domestic product, foreign exchange earnings, and livelihoods, these changes are especially important, requiring measurement and analysis of their impact. This study not only analyzes the economy-wide impacts of climate change, but also consults national policy plans, strategies, and environmental assessments to identify interventions which may mitigate the effects. We feed climate forcing, water demand, and macro-socioeconomic trends into a modelling suite that includes models for global hydrology, river basin management, water stress, and crop growth, all connected to the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT). The outcomes of this part of the modeling suite are annual crop yields and global food prices under various climate change scenarios until 2050. The effects of such changes on production, consumption, macroeconomic indicators, and income distribution are assessed using a single country dynamic Computable General Equilibrium (CGE) model for the Sudan. Additionally, we introduce yield variability into the CGE model based on stochastic projections of crop yields until 2050. The results of the model simulations reveal that, while the projected mean climate changes bring some good news for the Sudan, extreme negative variability costs the Sudan cumulatively between 2018 and 2050 US$109.5 billion in total absorption and US$ 105.5 billion in GDP relative to a historical mean climate scenario without climate change

Improving the Depiction of Uncertainty in Simulation Models by Exploiting the Potential of Gaussian Quadratures

Simulationsmodelle sind ein etabliertes Instrument zur Analyse von Auswirkungen exogener Schocks in komplexen Systemen. Die in jüngster Zeit gestiegene verfügbare Rechenleistung und -geschwindigkeit hat die Entwicklung detaillierterer und komplexerer Simulationsmodelle befördert. Dieser Trend hat jedoch Bedenken hinsichtlich der Unsicherheit solcher Modellergebnisse aufgeworfen und daher viele Nutzer von Simulationsmodellen dazu motiviert, Unsicherheiten in ihren Simulationen zu integrieren. Eine Möglichkeit dies systematisch zu tun besteht darin, stochastische Elemente in die Modellgleichungen zu integrieren, wodurch das jeweilige Modell zu einem Problem (mehrfacher) numerischer Integrationen wird. Da es für solche Probleme meist keine analytischen Lösungen gibt, werden numerische Approximationsmethoden genutzt. Die derzeit zur Quantifizierung von Unsicherheiten in Simulationsmodellen genutzt en Techniken, sind entweder rechenaufwändig (Monte Carlo [MC] -basierte Methoden) oder liefern Ergebnisse von heterogener Qualität (Gauß-Quadraturen [GQs]). In Anbetracht der Bedeutung von effizienten Methoden zur Quantifizierung von Unsicherheit im Zeitalter von „big data“ ist es das Ziel dieser Doktorthesis, Methoden zu entwickeln, die die Näherungsfehler von GQs verringern und diese Methoden einer breiteren Forschungsgemeinschaft zugänglich machen. Zu diesem Zweck werden zwei neuartige Methoden zur Quantifizierung von Unsicherheiten entwickelt und in vier verschiedene, große partielle und allgemeine Gleichgewichtsmodelle integriert, die sich mit Agrarumweltfragen befassen. Diese Arbeit liefert methodische Entwicklungen und ist von hoher Relevanz für angewandte Simulationsmodellierer. Obwohl die Methoden in großen Simulationsmodellen für Agrarumweltfragen entwickelt und getestet werden, sind sie nicht durch Modelltyp oder Anwendungsgebiet beschränkt, sondern können ebenso in anderen Zusammenhängen angewandt werden.Simulation models are an established tool for assessing the impacts of exogenous shocks in complex systems. Recent increases in available computational power and speed have led to simulation models with increased levels of detail and complexity. However, this trend has raised concerns regarding the uncertainty of such model results and therefore motivated many users of simulation models to consider uncertainty in their simulations. One way is to integrate stochastic elements into the model equations, thus turning the model into a problem of (multiple) numerical integration. As, in most cases, such problems do not have analytical solutions, numerical approximation methods are applied. The uncertainty quantification techniques currently used in simulation models are either computational expensive (Monte Carlo [MC]-based methods) or produce results of varying quality (Gaussian quadratures [GQs]). Considering the importance of efficient uncertainty quantification methods in the era of big data, this thesis aims to develop methods that decrease the approximation errors of GQs and make these methods accessible to the wider research community. For this purpose, two novel uncertainty quantification methods are developed and integrated into four different large-scale partial and general equilibrium models addressing agro-environmental issues. This thesis provides method developments and is of high relevance for applied simulation modelers who struggle to apply computationally burdensome stochastic modeling methods. Although the methods are developed and tested in large-scale simulation models addressing agricultural issues, they are not restricted to a model type or field of application

Water pricing under climate uncertainty – an economy-wide model considering precipitation stochastics

So far uncertainty of input parameters has not often been systematically analyzed in computable general equilibrium (CGE) modeling. Especially this is true when it comes to climate uncertainty. Instead, CGE models are mostly applied in a deterministic setup making their findings highly dependent on point estimates of key exogenous variables. In this study, we employ a Monte Carlo approach to simulate uncertainty of annual rainfall-induced freshwater recharge in Israel. A novelty of our approach is that we systematically determine the sufficient sample size. The CGE model includes a detailed depiction of water supply and demand it also considers alternative water sources, such as desalination and reclamation of wastewater. We apply our approach to determine the minimum water price that should be charged in order to avoid overexploitation of natural freshwater resources with 90% confidence under different desalination-capacity regimes. Our findings suggest that the current pricing scheme guarantees only in about 45% of the years that water demand remains below the annual renewable freshwater recharge rate. In order to avoid overdrafting of in 90% of the years, the water price would need to be doubled. Yet, the overall effect on the economy of such a price increase would be relatively small, resulting in a drop of GDP by 0.02%, as water constitutes only a small expenditure share in the production of most commodities and services and there are alternative water sources which serve as substitutes. On the household level, welfare effects are overall negative and result in a more unequal distribution. In case the desalination capacity is doubled, potable water prices would only need to be increased by only 21% in order to avoid overdrafting of freshwater resources in 90% of the years. The effect on GDP would not be much different, but effects on household-welfare would be much more balanced and less negative

Stochastic simulation with informed rotations of Gaussian quadratures

Given the fast growth of available computational capacities and the increasing complexity of simulation models addressing agro-environmental issues, uncertainty analysis using stochastic techniques has become a standard modeling practice. However, conventional uncertainty/sensitivity analysis methods are either computationally demanding (Monte Carlo-based methods) or produce results with varying quality (Gaussian quadratures). In this article, we present a computationally inexpensive and reliable uncertainty analysis method for simulation models called informed rotations of Gaussian quadratures (IRGQ). We also provide a linear programming model that generates IRGQ points based on the required input data. The results demonstrate that this method is able to produce approximations that are close to the estimated benchmarks at low computational costs. The method is tested in three different simulation models using different input data in order to demonstrate the independence of the proposed method on specific model types and data structures. This is a methodological paper for practitioners rather than theorists

Stochastic simulation with informed rotations of Gaussian quadratures

Given the fast growth of available computational capacities and the increasing complexity of simulation models addressing agro-environmental issues, uncertainty analysis using stochastic techniques has become a standard modeling practice. However, conventional uncertainty/sensitivity analysis methods are either computationally demanding (Monte Carlo-based methods) or produce results with varying quality (Gaussian quadratures). In this article, we present a computationally inexpensive and reliable uncertainty analysis method for simulation models called informed rotations of Gaussian quadratures (IRGQ). We also provide a linear programming model that generates IRGQ points based on the required input data. The results demonstrate that this method is able to produce approximations that are close to the estimated benchmarks at low computational costs. The method is tested in three different simulation models using different input data in order to demonstrate the independence of the proposed method on specific model types and data structures. This is a methodological paper for practitioners rather than theorists

Impacts of national vs European carbon pricing on agriculture

The agricultural sector has the potential to contribute to reaching both global and national climate targets. Lately, frequent discussions emerge among academics as well as policymakers regarding whether the agricultural sector should be subject to carbon pricing under different emission trading systems. Germany has set ambitious climate targets envisaging to reach carbon neutrality by 2045, and the EU plans reaching carbon neutrality by 2050. However, the current GHG emission mitigation trends are not in line with this goal. In this study, we quantitatively analyze the environmental and economic effects of the possible inclusion of the agricultural sector into a carbon pricing scheme, once for Germany only, and second for the EU. Moreover, we evaluate the role of already existing and novel technological mitigation options in the GHG emissions mitigation quest. Our findings demonstrate that even the unilateral action by Germany leads to net agricultural emissions reduction, although, the effect obtained by the EU-wide implementation of carbon pricing in agriculture is fivefold larger. The results also highlight the importance of stimulating the use and transferability of the technological options not only in mitigating GHG emissions but also in alleviating the emission leakage to third countries and easing the economic consequences of such a policy

Assessment of Heme and Non-Heme Iron Intake and Its Dietary Sources among Adults in Armenia

Adequate dietary iron (Fe) intake is crucial for preventing Fe-deficient anemia, a recognized global public health concern which is important in Armenia. This study aimed to analyze the intake of Fe, both heme (from animal tissues) and non-heme (more prevalent, but less efficiently absorbed), as well as the Fe dietary sources, among adults in a representative national sample in Armenia. The study was conducted on 1400 individuals aged 18–80 and above, who were enrolled from all regions of Armenia. The Fe intake was assessed through a 24 h dietary recall survey, while Fe occurrence was determined through atomic absorption spectrophotometry (AAS). The results showed a high proportion of adults with a Fe intake lower than the average requirements set by EFSA (65%, 80% and 85% of males, total females and females at fertile age, respectively). Main Fe sources were bread, fruits and vegetables; heme Fe accounted only for p p < 0.05), while the age-group 36–55 years had higher intakes of total Fe. Our data call for comprehensive nutritional security strategies in order to reduce iron deficiency in Armenia, that represents a public health concern

Assessing Dietary Intakes from Household Budget Survey in Armenia, 2008-2019

Household budget surveys are used regularly to estimate dietary intakes. The study aims to assess the trends in food consumption and nutrient intake, according to 14 dietary indicators from household budget surveys in Armenia. Data on food consumption was obtained from Armenian Integrated Living Conditions Surveys, 2008-2019. The results indicate that the consumption of all types of foods, including plant-origin has decreased, whereas the consumption of foods of animal origin has mostly stayed stable. Over time, the energy and macronutrient intakes of Armenians have decreased, while the contribution of each food group to total energy and nutrient intake has not changed. More than 50% of total energy, protein, and carbohydrate intake is attributable to cereals and bakery products. The population is characterized by macronutrient variations; the amounts of energy and carbohydrate intake are below the recommended values set by WHO/FAO, total fat intake is at the highest recommended level, while the amount of protein exceeds the threshold. Based on the findings there is an urgent need to increase awareness of nutritional requirements and a need to change widespread dietary practices, such as irregular meal intake and omission of breakfast.4s

Trans-Fatty Acids in Fast-Food and Intake Assessment for Yerevan's Population, Armenia

There are stringent regulations applicable for trans-fatty acid (TFA) limitations from food supply across the world. However, in Armenia, there is a scarcity of data on TFA content in food products and their consumption levels. Considering that fast-food is among the major contributors to TFA intake, this study aims to assess the dietary exposure of TFAs through the consumption of fast-food in Yerevan, Armenia. Eleven types of fast-food were included in the study. The Food Frequency Questionnaire (FFQ) was used to evaluate daily fast-food consumption. TFA contents in samples were determined using gas chromatography-mass spectrometry. Mean daily fast-food consumption values ranged from 14.68 g/day to 76.09 g/day, with popcorn as the lowest and pastry as the highest consumed food. The study results indicate that the aggregate average daily intake (DI) of TFA is 0.303 g/day. Even though TFA DI values do not exceed the WHO limit of 1%, they substantially contribute to daily TFA intake and may exceed the limit when combined with other foods. Hence, it is recommended to carry out continuous monitoring of TFA content in the food supply to ensure consumer health protection.4s