東華大學圖書館 |

Global Food Security Under Climate-Water-Energy Nexus.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Global Food Security Under Climate-Water-Energy Nexus./
作者:	Najafi, Ehsan .
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	178 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-08, Section: B.
Contained By:	Dissertations Abstracts International81-08B.
標題:	Water resources management. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27736194
ISBN:	9781658407328

Global Food Security Under Climate-Water-Energy Nexus.
Najafi, Ehsan .

Global Food Security Under Climate-Water-Energy Nexus. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 178 p.

Source: Dissertations Abstracts International, Volume: 81-08, Section: B.

Thesis (Ph.D.)--The City College of New York, 2020.

This item must not be sold to any third party vendors.

During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water and energy crisis, and extreme weather, climate change, etc. threaten the global food security. The focus of this study is to advance the understanding of the associations between crops productivity and large scale and local scale climatic variables, climate change, technology enhancement and assessing food-water-energy nexus to enhance global food security in a sustainable manner considering current available resources. As decision-makers try to improve food security, it is important to identify the impact of technology enhancement and major climatic patterns that impact crop yields, quantify and predict their impacts and find the trade strategies to minimize current and potential food shortages. Annually crop yields of the global countries are impacted by climatic variables deferentially in magnitude and sign. Some of these climate variables triggers simultaneous impacts on crops that can cause synergistic crops variability and volatility across the globe. Considering these climatic patterns as well as other factors such as population distribution, available freshwater, cropland and energy resources, a global optimization model can enhance global food security through maximizing crop production. In this study we use historical records of crops yield, production, cropland area, international crops trade data, a broad range of climatic and non-climatic data, energy and freshwater resources. We implement data mining, statistical analysis, predictive and optimization modeling tools to shed light on the food security topic. This study will be performed at the global scale to the extent that data coverage allows. A global exploration of food security provides a broader understanding of this issue, and depending on the implemented methodology, may inform us about space-time interconnections of the desired variables. Diagnosing potential predictability of global crop yields in the near term is of utmost importance for ensuring food supply and preventing socio-economic consequences. While agricultural influence of climate is well-established, a detailed account of the characteristics of synergistic multi-national variability and world-wide volatility of crop yields, whereby many countries undergo harmonizing influences of climate to thwart or facilitate crops productivity, remains largely unexplored. History indicates that such synchronous volatility-led crop yields losses can leave major ramification for global price and food security. Previous studies suggest that a substantial proportion of global yields depends on local climate and larger-scale ocean-atmospheric patterns. It is however unclear whether synergistic variability and volatility (major departure from the normal) of multi-national crop yields can be potentially predicted by larger-scale climate drivers. Using observed data of yields and climate variability from 1961-2013, we diagnose that yields of 5 staple crops, namely maize, rice, sorghum, soybean and wheat vary synergistically across key producing nations and can also be concurrently volatile, as a function of shared larger-scale climate drivers. We use a statistical approach called Robust Principal Component Analysis, to decouple and quantify the leading modes of global yield variability. Sea surface temperature anomalies, multiple atmospheric and oceanic indices, air temperature anomalies and Palmer Drought Severity Index are used to study the association between yields variability/volatility and climate. Results show that large-scale climate, especially El Nino-Southern Oscillation and North Atlantic Oscillation are strongly correlated with persistent and anomalous yield variability.The impact of local climate variability in both concurrent and lag phases vary among different countries. In addition to extreme wet conditions across sorghum croplands in South America, extensive significant hot or drought patterns are recognized across maize croplands of South America and south of Asia, rice harvesting regions of Oceania and south of Asia and sorghum and soybean growing regions of North America, south and southeast of Asia. Results show that warmer-than-normal winter time sea surface temperature anomalies in the Pacific Ocean exerts the most dominating influence on global rice and sorghum yield volatility. In addition, extreme soybean and maize volatility are associated with mutual climatic teleconnection patterns. We diagnose that wheat yields can be concurrently volatile, as a function of shared larger-scale climate drivers. Results also demonstrate that world-wide wheat yield volatility has become more common in the current most decades, associating with warmer northern Pacific and Atlantic oceans, negative North Atlantic Oscillation, negative Scandinavian Pattern, and positive Southern Annular Mode, leading mostly to global wheat supply shortage. We found out not only do the same crops in many countries co-vary significantly, but different crops co-vary in a same/different manner. Then we present a predictive model of the changes in the crop yields and how they relate to different large-scale and regional climate and climate change variables and technology in a unified framework. A new Bayesian multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Nino-Southern Oscillation, Palmer Drought Severity Index, geopotential height anomalies, historical carbon dioxide concentration and country-based time series of Gross Domestic Product per capita -as an approximation of technology measurement- are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. We found out these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-o fsample verification. While some countries were not generally affected by climatic factors, Palmer Drought Severity Index and geopotential height anomalies acted both positively and negatively in different regions for crop yields in many countries. In the next step we assess approaches to maximize total production of barley, maize, rice, sorghum, soybean and wheat across the global countries. The model is tested based on the current available freshwater resources and croplands area as well as a energy constraint. The results show that total production of these crops in many countries can be increased substantially. This analysis will provide the essential scientific motivation at the national and global scale to discover feasible regions and different crop choices that can support the sustainable area expansion and crop production enhancement. The results of this research try to improve three pillars of food security namely availability, access, and stability. Our work tries to enhance the knowledge of global food security field, which is of relevance to policy initiatives, decision makers, water and energy managers, government and non-government organizations such as United States Department of Agriculture, Food and Agricultural Organization of United Nations, stakeholders, insurance companies and scientists with similar interests to ours.

ISBN: 9781658407328Subjects--Topical Terms:

794747
Water resources management.
Subjects--Index Terms:

Climate

Global Food Security Under Climate-Water-Energy Nexus.
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500 $a Source: Dissertations Abstracts International, Volume: 81-08, Section: B.
500 $a Advisor: Khanbilvardi, Reza.
502 $a Thesis (Ph.D.)--The City College of New York, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water and energy crisis, and extreme weather, climate change, etc. threaten the global food security. The focus of this study is to advance the understanding of the associations between crops productivity and large scale and local scale climatic variables, climate change, technology enhancement and assessing food-water-energy nexus to enhance global food security in a sustainable manner considering current available resources. As decision-makers try to improve food security, it is important to identify the impact of technology enhancement and major climatic patterns that impact crop yields, quantify and predict their impacts and find the trade strategies to minimize current and potential food shortages. Annually crop yields of the global countries are impacted by climatic variables deferentially in magnitude and sign. Some of these climate variables triggers simultaneous impacts on crops that can cause synergistic crops variability and volatility across the globe. Considering these climatic patterns as well as other factors such as population distribution, available freshwater, cropland and energy resources, a global optimization model can enhance global food security through maximizing crop production. In this study we use historical records of crops yield, production, cropland area, international crops trade data, a broad range of climatic and non-climatic data, energy and freshwater resources. We implement data mining, statistical analysis, predictive and optimization modeling tools to shed light on the food security topic. This study will be performed at the global scale to the extent that data coverage allows. A global exploration of food security provides a broader understanding of this issue, and depending on the implemented methodology, may inform us about space-time interconnections of the desired variables. Diagnosing potential predictability of global crop yields in the near term is of utmost importance for ensuring food supply and preventing socio-economic consequences. While agricultural influence of climate is well-established, a detailed account of the characteristics of synergistic multi-national variability and world-wide volatility of crop yields, whereby many countries undergo harmonizing influences of climate to thwart or facilitate crops productivity, remains largely unexplored. History indicates that such synchronous volatility-led crop yields losses can leave major ramification for global price and food security. Previous studies suggest that a substantial proportion of global yields depends on local climate and larger-scale ocean-atmospheric patterns. It is however unclear whether synergistic variability and volatility (major departure from the normal) of multi-national crop yields can be potentially predicted by larger-scale climate drivers. Using observed data of yields and climate variability from 1961-2013, we diagnose that yields of 5 staple crops, namely maize, rice, sorghum, soybean and wheat vary synergistically across key producing nations and can also be concurrently volatile, as a function of shared larger-scale climate drivers. We use a statistical approach called Robust Principal Component Analysis, to decouple and quantify the leading modes of global yield variability. Sea surface temperature anomalies, multiple atmospheric and oceanic indices, air temperature anomalies and Palmer Drought Severity Index are used to study the association between yields variability/volatility and climate. Results show that large-scale climate, especially El Nino-Southern Oscillation and North Atlantic Oscillation are strongly correlated with persistent and anomalous yield variability.The impact of local climate variability in both concurrent and lag phases vary among different countries. In addition to extreme wet conditions across sorghum croplands in South America, extensive significant hot or drought patterns are recognized across maize croplands of South America and south of Asia, rice harvesting regions of Oceania and south of Asia and sorghum and soybean growing regions of North America, south and southeast of Asia. Results show that warmer-than-normal winter time sea surface temperature anomalies in the Pacific Ocean exerts the most dominating influence on global rice and sorghum yield volatility. In addition, extreme soybean and maize volatility are associated with mutual climatic teleconnection patterns. We diagnose that wheat yields can be concurrently volatile, as a function of shared larger-scale climate drivers. Results also demonstrate that world-wide wheat yield volatility has become more common in the current most decades, associating with warmer northern Pacific and Atlantic oceans, negative North Atlantic Oscillation, negative Scandinavian Pattern, and positive Southern Annular Mode, leading mostly to global wheat supply shortage. We found out not only do the same crops in many countries co-vary significantly, but different crops co-vary in a same/different manner. Then we present a predictive model of the changes in the crop yields and how they relate to different large-scale and regional climate and climate change variables and technology in a unified framework. A new Bayesian multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Nino-Southern Oscillation, Palmer Drought Severity Index, geopotential height anomalies, historical carbon dioxide concentration and country-based time series of Gross Domestic Product per capita -as an approximation of technology measurement- are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. We found out these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-o fsample verification. While some countries were not generally affected by climatic factors, Palmer Drought Severity Index and geopotential height anomalies acted both positively and negatively in different regions for crop yields in many countries. In the next step we assess approaches to maximize total production of barley, maize, rice, sorghum, soybean and wheat across the global countries. The model is tested based on the current available freshwater resources and croplands area as well as a energy constraint. The results show that total production of these crops in many countries can be increased substantially. This analysis will provide the essential scientific motivation at the national and global scale to discover feasible regions and different crop choices that can support the sustainable area expansion and crop production enhancement. The results of this research try to improve three pillars of food security namely availability, access, and stability. Our work tries to enhance the knowledge of global food security field, which is of relevance to policy initiatives, decision makers, water and energy managers, government and non-government organizations such as United States Department of Agriculture, Food and Agricultural Organization of United Nations, stakeholders, insurance companies and scientists with similar interests to ours.
590 $a School code: 1606.
650 4 $a Water resources management. $3 794747
650 4 $a Agriculture. $3 518588
650 4 $a Atmospheric sciences. $3 3168354
653 $a Climate
653 $a Energy
653 $a Food security
653 $a Water
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710 2 $a The City College of New York. $b Civil Engineering. $3 3183726
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