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Exploring Global Response to Food Insecurity in Sub-Saharan Africa by Colleen C. Chinake Research Design and Analysis Abstract This research study pertains to food insecurity in Sub-Saharan Africa. Rising African agricultural production is crucial for food security to be achieved. Africa has the poorest agricultural output in the world despite its enormous potential and must import much of its food. The research study examines the international solution to and how to solve the problems facing food security in Sub-Saharan Africa. For most Sub-Saharan dwellers food relationship and food insecurity impacts health resulting in starvation or malnutrition. Food insecurity occurs often if food safety is limited. While there is an obvious definition of food security, as a measure of the availability of food in our daily lives, there are some variables that might limit access to the availability such as cost. On the other hand, food insecurity is not a single case of not having something to eat for a day or two. From the research paper, for Sub-Saharan Africa, food insecurity can mean daily perpetual hunger as Oxford Dictionary (n.d.) defines food insecurity as being with no easy resources of affordable nutritious foods. The dictionary also notes the more than 800 million people who do not have access to enough affordable nutritious food worldwide. However, food security does not just mean getting enough calories, but having a varied and nutritious diet that supports a healthy lifestyle. The research study led to a conclusion by analysing the findings and drawing conclusions on global agencies challenges and effects on the failure on support of Sub-Saharan regions. Keywords: Food Insecurity, Sub-Saharan Africa, Global Response Agencies, Hunger in Africa, SAP. Table of Contents Abstract 2 CHAPTER 1 4 Background of the Research 4 Statement of the Problem 7 Rationale 7 Research Aim 8 Hypotheses 8 Research Objectives 9 Research Questions 9 Definition of Terms 10 Limitations (of the Study) 11 CHAPTER 2 11 Theoretical Framework 11 Theory of Justice 12 Malthusian questions of sustainability regarding population 13 Literature Highlights 15 Chronological increase of hunger in African nations 15 Global bodies and organizations’ working towards hunger in Africa 16 Objectives and challenges for global bodies towards evaluation of hunger African nations 17 Strategic response of UN, World hunger and national government 18 CHAPTER 3 20 Methodology 20 Data Analysis 20 Conclusion 23 References 25 CHAPTER 1 Background of the Research Whenever food security is limited, food insecurity occurs. More than 820 million people worldwide are suffering from malnutrition and the largest percentage of them is in Africa (UN Food and Agricultural Organization, 2019). The estimated number of malnourished people in the world has grown from 777 million to 821 million in the two-year period 2015-17. (Worldhunger.org, 2018). As a continent, Africa alone shares the largest number of malnourished people, comprising 20 percent of its total population. In the eastern part of the continent, the state of malnourished people has deteriorated as more than 33 percent of people in this region’s countries live and die in malnourished conditions (Fao.org, 2019). In these deprived countries, malnutrition is associated with other problems, such as food insecurity and low levels of education. The highest number of malnourished people is also due to the large number of populations it holds in the African nations, which totals 520 million people (Fao.org, 2019). No basic freedom has been so often and stupendously abused as of late as the privilege to food, notwithstanding the way that it is one of the most reliably cherished rights in worldwide basic liberties law, as continually reaffirmed by governments. Concerns created by the food emergency of the mid-1970s prompted world pioneers tolerating unexpectedly the basic obligation of the worldwide network to cancel yearning and lack of healthy sustenance. All things considered, somewhere in the range of 1980 and 1998 for each capital food utilization in the 48 Least Developed Countries declined, while for non-industrial nations in general it improved. Worldwide the patterns are disturbing as progress in lessening hunger in the creating scene has eased back to a slither and in many districts the quantity of undernourished individuals is really developing, regardless of the way that world food creation has become quicker than total populace in the previous thirty years. About 28% of the African population (200 million people) are facing malnutrition. Out of these, approximately 38 million people living in Africa are facing danger due to insecurity, instability, and lack of peace (World Bank, 2018). The United Nation Security Council acknowledges that the food crisis in Africa is a threat to the security and peace in the region. Data gathered by Food and Agricultural Organization from 2014 to 2017 in 149 nations revealed that the education, poverty status and the area of habitation are important determinants of the variance in food security levels amid the men and women (Jones, 2017). It was also found that the gender gap in case of food insecurity is highest amongst the uneducated women and poverty-stricken population (Oxfarm Briefing Paper, 2019). The global organizations such as UN, FAO, WHO, World Hunger and national governments operating in the Sub-Saharan African nations have continued their efforts to eradicate hunger or at least to minimize the level of hunger in these nations through goals, programs, and strategic operations. Malabo Goals of 2025 and Sustainable Development Goal 2 (SDG2) are some of the efforts of the UN that are directed to provide strategic responses to these initiatives (Un.org, 2018). Apart from these organizational programs, some NGOs such as World Hunger, Kofi Annan Foundations and Bread are also contributing to the development of the problematic situation in this matter. An anecdotal fact is that some of the top 100 Food and Beverage Companies like Nestlé, PepsiCo Incorporated, JBS, and Associated British Foods have been marketing foods to Sub-Saharan Africa without orientating the products for nutrition for the region. Sub-Saharan people have been forced to use socially unacceptable means to include stealing, scavenging, or other coping strategies to survive yet they belong to a natural resources’ rich continent. Companies like market Infant Formula which per Mayo Clinic Staff (2020) do not ‘contain the immunity-boosting elements of breast milk that only your body can provide to the baby’. The result becomes a child deprived of ‘potent cocktail of hormones’ from a mother’s milk and stunted growth or under development. In 2020’s global pandemic due to COVID-19 virus (coronavirus), is anybody paying attention to food insecurity in Sub-Saharan Africa? (Hirtzer and Durisin, 2020). The World Bank posted numbers to indicate food supply chain crisis and other effects of food production in addition to the pandemic resulting in straining developing Sub-Saharan Africa with issues of food security and insecurity which they were not in at risk to begin with. Even as the World Bank (2020) acknowledge food shortage, diets are still important to the health status of people around the world including Africa. If major companies are still supplying food products to the continent, are they orienting for the proper nutrition tor the development for children and malnourished adults? According to a report released by Agriculture Organization of the UN (FAO) and the WFP, the following countries are at high risk of food insecurity: Sudan, Zimbabwe, Cameroon, Burkina Faso, Nigeria, Ethiopia, Somalia, Mozambique, Sierra Leone, Mali, Niger, Central African Republic, and the Democratic Republic of Congo (“UN names 10 countries facing food insecurity,” 2016). Thus, this proposed quantitative archival study will focus on these countries. Specifically, Global Hunger Index (GHI) data from 2015 – 2020 will be analyzed in order to assess any significant trends. The Global Hunger Index (GHI) was created in order to measure hunger (Global Hunger Index – A Peer-Reviewed Publication, 2020). The GHI scores are based on four indicators: undernourishment (the proportion of the population with insufficient caloric intake), child waste (the proportion of children under the age of five with low height, reflecting acute undernourishment), child stunting (the proportion of children under the age of five with low height, reflecting chronic undernourishment), and child mortality (mortality rate of children under age five, reflecting inadequate nutrition and unhealthy environments). Thus, the GHI will be used in this study to measure quantitatively food insecurity. GHI scores range from 0 (no hunger) to 100 (extremely alarming). Specifically, ≤ 9 “low”, 10.0 – 19.9 “moderate”, 20.0 – 34.9 “serious”, 35.0 – 49.9 “alarming”, and ≥ 50.0 “extremely alarming” (Global Hunger Index – A Peer-Reviewed Publication, 2020). Additionally, socio-economic factors of poverty level will be examined as well from 2015-2020 in these countries in order to determine the relationships between poverty level, GHI, and time period (2015 – 2020). Statement of the Problem Despite many goals and strategic operations towards eradication of hunger in African nations, the hunger and food insecurity problems have persisted and have not yet been resolved. Rather, the number of hungry people in African nations is continuously growing. As recent as 2017 (Un.org, 2017) data of these nations, civil wars and internal political conflicts have been identified as the major reason for increase in number of hungry people in Africa. Rationale There is considerable gap in the inclusion of socio-political factors such as civil wars, terrorism, and unemployment in many studies and research do not consider these factors significant in their action plans. The related issues such as poverty and hunger are also the root causes for these chronic hunger issues. Therefore, the programs and goals for eradicating hunger in Sub-Saharan African nations focuses more on feeding the hungry people by providing direct assistance of foods. The research proposes to evaluate the socio-economic factors to review the root causes for continuous hunger in African nations. The research will also examine the present programs that are in line with goals and strategies to minimize hunger to indicate the viability of these global responses. Research Aim The aim of this proposed quantitative research is to evaluate the global responses towards the minimization and eradication of hunger in Sub-Saharan Africa. It will explore the challenges faced by the global agencies in providing food aid to Sub-Saharan Africa and the ways for tackling these challenges. Just as food insecurity affects each person in each household differently many World Programmes as The United Nations (U.N.}. Food and Agriculture Organization (FAO), the International Fund for Agricultural Development (IFAD), the United Nations Children’s Fund (UNICEF), the World Food Programme (WFP), and the World Health Organization (WHO) have worked together globally to eliminate food insecurity and the challenges outline a greater scope on hunger, malnutrition, and health in Sub-Saharan Africa with differing proposed outcomes. Hypotheses H0: There is no significant predictive relationship between GHI, poverty level, and time period (2015-2020). H1: There is a significant predictive relationship between GHI, poverty level, and time period (2015-2020). Research Objectives The proposed study will comprise of the following research objectives: To investigate global agencies directional response to food insecurity in Sub-Saharan Africa and the impact and the problematic fallout from food insecurity.To explore the challenges faced by the global agencies in providing food aid to Sub-Saharan Africa.To examine the ways in which the global agencies can handle the challenges they face in providing food aid to Sub-Saharan Africa.To measure the relationship between GHI, poverty level, and time period (2015-2020). Research Questions The Research paper tries to find solutions for the following: Are there significant differences in GHI from 2015-2012 in the countries Sudan, Zimbabwe, Cameroon, Burkina Faso, Nigeria, Ethiopia, Somalia, Mozambique, Sierra Leone, Mali, Niger, Central African Republic, and the Democratic Republic of Congo?Is there a significant relationship between GHI and poverty level?Is there a predictive relationship between GHI, poverty level, and time period (2015-2020).? Definition of Terms Due to the need of uniformity the following definition are taken from ‘What is Hunger?’ Learner, M. (n.d.) and will be used in the study to explain circumstances. Hunger: is the continual need of sufficient food or calories, essential nutrients, or both that occurs to individual who have ongoing primary need for food. Food security: Food security can be said to be a situation when everyone have easy access to nutritional food sufficiently. Food insecurity: This is a situation where the access to quality food and enough for the personal satisfaction and for normal living becomes hard and not available. Malnutrition: Malnutrition is said to be when the population or a group of people or even an individual fails to acquire the quality food required for normal body functioning with all nutrients. In most cases the children suffer deficiency diseases like anemia, and Marasmus. Hidden hunger: This when someone is getting something to eat but on a rare circumstance. A good example is when someone gets breaks-first and supper but skips having lunch due to financial issues. Limitations of the Study There are many factors that limit the studies of food insecurity in Sub-Saharan Africa to include socio-political factors such as civil wars, terrorism, gender gaps, unemployment, geographical study patterns, and timing of studies, sampling size, global warming and climate changes. Additional limitations include changes and impact of data available from the global COVID-19 pandemic can or has altered since the pandemic onset and for many years to come including the directional responses of food insecurity in SSA. The food insecurity is not only affecting SSA but developed nations as well and until there is economic recovery and food security is re-established in these developed nations then the world’s poor and hungry might have to wait with disastrous consequences. There is considerable amount of literature available on the impact of food insecurity in developed nation following the 2020 pandemic but with global travel restrictions only time will tell when studies will be conducted and documented for Sub-Saharan Africa. CHAPTER 3 Introduction The aim of this proposed quantitative research is to evaluate the global responses towards the minimization and eradication of hunger in Sub-Saharan Africa. In order to do this, the relationships between hunger, (as measured by the GHI), poverty level, and time period (2015-2020) will be examined. Knowing the factors that contribute to hunger will help determine the direction of global response. Archival data from 2015-2020 will be collected from various internet sources such as the Global Hunger Index (https://www.globalhungerindex.org) and the World Health Organization (https://www.who.int/). The following quantitative research questions will be addressed: RQ1.Are there significant differences in GHI from 2015-2012 in the countries Sudan, Zimbabwe, Cameroon, Burkina Faso, Nigeria, Ethiopia, Somalia, Mozambique, Sierra Leone, Mali, Niger, Central African Republic, and the Democratic Republic of Congo? RQ2. Is there a significant relationship between GHI and poverty level? RQ3.Is there a predictive relationship between GHI, poverty level, and time period (2015-2020).? Chapter 3 contains an overview of the methodology used for this study. This overview will include the study design, population, sampling method, sample size, instrumentation, and data analysis methods. Ethical considerations and study limitations are also described. Statement of the Problem Despite many goals and strategic operations towards eradication of hunger in African nations, the hunger and food insecurity problems have persisted and have not yet been resolved. Rather, the number of hungry people in African nations is continuously growing. As recent as 2017 (Un.org, 2017) data of these nations, civil wars and internal political conflicts have been identified as the major reason for increase in number of hungry people in Africa. This study will address this problem by examining the relationships between hunger and poverty through the years 2015-2019 in African countries. Research Questions and Hypotheses The following research questions and hypotheses will be addressed in this study: RQ1.Are there significant differences in GHI from 2015-2012 in the countries Sudan, Zimbabwe, Cameroon, Burkina Faso, Nigeria, Ethiopia, Somalia, Mozambique, Sierra Leone, Mali, Niger, Central African Republic, and the Democratic Republic of Congo? H01: There are no significant differences in GHI from 2015-2012 in the countries Sudan, Zimbabwe, Cameroon, Burkina Faso, Nigeria, Ethiopia, Somalia, Mozambique, Sierra Leone, Mali, Niger, Central African Republic, and the Democratic Republic of Congo. H11: There are significant differences in GHI from 2015-2012 in the countries Sudan, Zimbabwe, Cameroon, Burkina Faso, Nigeria, Ethiopia, Somalia, Mozambique, Sierra Leone, Mali, Niger, Central African Republic, and the Democratic Republic of Congo. RQ2. Is there a significant relationship between GHI and poverty level? H02: There is no significant relationship between GHI and poverty level. H12: There is a significant relationship between GHI and poverty level. RQ3.Is there a predictive relationship between GHI, poverty level, and time period (2015-2020).? H03: There is no significant predictive relationship between GHI, poverty level, and time period (2015-2020). H13: There is a significant predictive relationship between GHI, poverty level, and time period (2015-2020) Research Methodology The proposed research will comprise of a non-experimental quantitative study with a correlational design heavily employing online sources to gather data and information pertaining to food insecurity in Sub Saharan Africa. The relationships between GHI, poverty, and time period (2015-2020) will be examined. A nonexperimental quantitative methodology with a correlational design is most appropriate for specific reasons. First, the study includes numerical data that are analysed to test hypotheses (McCusker & Gunaydin, 2015). Second, the choice of a nonexperimental quantitative method with a correlational design ensures research objectivity as the researcher is separated from the research participants (McCusker & Gunaydin, 2015). Third, there is no manipulation of independent variables; thus, this study is a nonexperimental quantitative method with a correlational design (McCusker & Gunaydin, 2015). Additionally, a nonexperimental quantitative method with a correlational design is the correct design for the current study because the objective is to identify and evaluate the relationship between the dependent variable (GHI), and the independent variables (poverty and time period). Sources will include but not limited to review of articles, books, journals, case studies, newspapers, magazines, and websites. The quantitative analysis using SPSS will involve statistical analysis of the data collected from the reports of Food and Agricultural Organization of United Nations, Agriculture Organization (FAO), the International Fund for Agricultural Development (IFAD), the United Nations Children’s Fund (UNICEF), the World Food Programme (WFP), and the World Health Organization (WHO) and any other global agencies fighting food insecurity in the African continent. A qualitative approach is not appropriate because the study will not focus on exploring a phenomenon or establishing a theory, model, or definition (Allwood, 2012). Due to the nature of the research questions posed, multiple regression is the best fit for data analysis for this study. Multiple regression analysis is used to predict a dependent variable measured at the interval level of measurement, GHI, in this case, based on independent variables, poverty level and time period, in this case (Mertler & Vannata, 2013). Multiple regression also allows for a measurement of overall fit of thew regression model in addition to controlling for possible covariates. Population and Sample Selection The highest number of malnourished people is also due to the large number of populations it holds in the African nations, which totals 520 million people (Fao.org, 2019). About 28% of the African population (200 million people) are facing malnutrition. Out of these, approximately 38 million people living in Africa are facing danger due to insecurity, instability, and lack of peace (World Bank, 2018). However, this study will not utilize data at the individual level. Instead, GHI and poverty data will be collected for the 13 countries: Sudan, Zimbabwe, Cameroon, Burkina Faso, Nigeria, Ethiopia, Somalia, Mozambique, Sierra Leone, Mali, Niger, Central African Republic, and the Democratic Republic of Congo. Thus, the sample will consist of these 13 countries the study is investigating. Quantitative sample size. A priori power analysis was conducted using G*Power to determine the required minimum sample size for the study. Four factors were considered in the power analysis: significance level, effect size, the power of the test, and statistical technique. The significance level, also known as Type I error, refers to the chance of rejecting a null hypothesis given that it is true (Haas, 2012). Most quantitative studies make use of a 95% confidenece level because it adequately provides enough statistical evidence of a test (Creswell & Poth, 2017). The effect size refers to the estimated measurement of the relationship between the variables being considered (Cohen, 1988). Cohen (1988) categorizes effect size into small, medium, and large. Berger, Bayarri, and Pericchi (2013) purported that a medium effect size is better as it strikes a balance between being too strict (small) and too lenient (large). The power of test refers to the probability of correctly rejecting a null hypothesis (Sullivan & Feinn, 2012). In most quantitative studies, an 80% power is usually used (Sullivan, & Feinn, 2012). The statistical test to be used for this study is multiple regression. In order to conduct multiple regression to detect a medium effect size, 5% level of significance, with 80% power, at least 68 cases are required (Figure 3.1). Figure 3.1. G*Power output of sample size required for multiple regression The sample size of 68 is, however, not attainable, as only data from 2015-2020 will be analysed, which six cases. Thus, non-parametric bootstrapping will be employed due to this low sample size. The bootstrap provides an opportunity to use statistics to draw a conclusion about a population from a small sample (Mooney & Duval, 1993). Sources of Data As mentioned earlier, archival data from various internet sources such as the Global Hunger Index (https://www.globalhungerindex.org) and the World Health Organization (https://www.who.int/). Additionally, other possible sources include the reports of Food and Agricultural Organization of United Nations, Agriculture Organization (FAO), the International Fund for Agricultural Development (IFAD), the United Nations Children’s Fund (UNICEF), the World Food Programme (WFP), and the World Health Organization (WHO) and any other global agencies fighting food insecurity in the African continent. Validity Validity consists of two types: external and internal validity. External validity refers to the degree in which the results of the study can be generalized to the population. Studies utilizing purposive sampling, such as this one, present challenges to external validity (Etikan, 2016). Studies that involve purposive samples may have issues with the generalizability of the study findings to broader populations of interest (Etikan, 2016). Internal validity refers to the validity of the findings within the research study. Testing hypotheses can involve threats to the validity of interpretation for quantitative researchers. Quantitative research may involve rejecting null hypotheses or failing to reject null hypotheses (Martin & Bridgmon, 2012). Consequently, threats to conclusive findings occur when quantitative researchers encounter a Type I error, which involves rejecting a valid null hypothesis (Ibrahim, Ghani, & Embat, 2013). Data Collection and Management To gain access of the data, the researcher will access the websites globalhungerindex.org and who.int. On the global hunger index site, data from 2005 – 2020 will be obtained. The researcher will select the specific countries pertinent to this study. The data will be downloaded as an Excel file. Likewise, poverty data will be obtained from WHO website. Data on poverty levels may be obtained by navigating to the “poverty by country” link. There, the researcher will download poverty data ranging from 2015-2020 for the specific countries. Data Analysis Procedures Analysis of the resulting quantitative data will be conducted using the statistical software suite Statistical Package for the Social Sciences (SPSS) version 23. The data will be cleaned by examining the dataset for missing data (Field, 2013). If a value is missing, the entire case will be removed from the analysis (listwise deletion). In listwise deletion, a case is dropped from an analysis because it has a missing value in at least one of the specified variables. The analysis is only run on cases which have a complete set of data. Categorical variables (i.e., nominal variables) will be dummy coded for the purpose of regression (Field, 2013). As an example, the nominal variable time period has six categories: 2015, 2016, 2017, 2018, 2019, and 2020. Thus, this variable will be dummy coded into five “dummy” variables where a value of 1 indicated inclusion and 0 otherwise. Specifically, 2015 will be coded as “1” indicating that case if for 2015 and 0 otherwise. This will be repeated for the years 2016, 2017, 2018, and 2019. The year 2020 will not be dummy coded, as it will serve as the reference time period. Descriptive statistics of the data for the predictor and dependent variables will be reported. Frequency and percentages summary will be obtained for categorical variables while the measure of central tendencies of means and standard deviations and minimum and maximum values will be conducted for continuous variables, such as GHI and poverty. Multiple regression will be conducted in order to measure the relationships between the study variables GHI, poverty level, and time period. Prior to conducting multiple regression, the parametric assumptions must be tested. Parametric assumptions are statistical tests conducted to determine when normality or homogeneity of variance assumptions are met or satisfied (Mertler & Vannatta, 2013). Mertler and Vannatta (2013) said that the parametric assumptions for multiple regression analysis includes linearity, normality, homoscedasticity, multicollinearity, and outlier detection (Mertler & Vannatta, 2013). Plots of the standardized residuals and the standardized predicted values will be examined to assess linearity and homoscedasticity. If the plots are not curvilinear, there is no violations of the assumption of linearity (Field, 2013; Tabachnick & Fidell, 2012). Additionally, if the plots form a rectangular pattern, there is no violation of the assumption of homoscedasticity (Field, 2013; Tabachnick & Fidell, 2012). A Shapiro-Wilk test of normality will be used to determine if the data were normally distributed (Field, 2013; Tabachnick & Fidell, 2012). Kurtosis and skewness statistics will also be generated to further assess normality. The variable inflation factor (VIF) will be calculated for each variable to determine if there was a violation in multicollinearity between any two variables (Mertler & Vannatta, 2013). If the VIF scores fall below 10, there is no violation of the assumption of multicollinearity (Field, 2013; Tabachnick & Fidell, 2012). Outlier detection will be assessed by the calculation of standardized values. The following regression models will be tested in SPSS: GHI = bo + b1 2015 Time_2015 + b2 Time_2016 + b3 Time_2017 + b4 Time_2018 + b5 Time_2019GHI = b0 + b1 PovertyGHI = = bo + b1 2015 Time_2015 + b2 Time_2016 + b3 Time_2017 + b4 Time_2018 + b5 Time_2019 + b6 Poverty Ethical Considerations Because existing datasets will be used, this study will not require informed consent procedures. Data will be retrieved from the publiclyavailable websites mentioned previously. Because there are no data at the individual level, only collective, participants are not identifiable in the data, thus no special precautions will be required to safeguard anonymity of participants. Limitations A limitation of the study is due to the use of purposive sampling which limits the generalizability of study findings relative to probabilistic, or random, sampling techniques. Additionally, correlational design cannot deduce any cause-and-effect relationships between the study variables, as there will be no manipulation of independent variables. This study is delimited to the countries Sudan, Zimbabwe, Cameroon, Burkina Faso, Nigeria, Ethiopia, Somalia, Mozambique, Sierra Leone, Mali, Niger, Central African Republic, and the Democratic Republic of Congo. Data will be collected from 2015- 2020. Summary This chapter provided a comprehensive description of the quantitative correlational research design used for this study. The results and findings from the data analysis will be presented in Chapter 4, along with the tables and graphics providing the descriptive results and inferences regarding the underlying connection between the study variables. Following, the interpretations of the findings are provided in Chapter 5, along with the study’s limitations, recommendations for future studies, and implications for positive social change. References Ashe, M. O. (2019). International agencies and the quest for food security in Nigeria, 1970- 2015. Ubuntu: Journal of Conflict and Social Transformation, 8(Special Issue 1), 251-274. Retrieved on 10th October, 2020, Retrieved from:https://www.researchgate.net/profile/Muesiri_Ashe/publication/332925170_International_agencies_and_the_quest_for_food_security_in_Nigeria_1970-2015/links/5dbc0070a6fdcc2128f6520a/International-agencies-and-the-quest-for-food-security-in-Nigeria-1970-2015.pdf Ayed, I., Ghazel, A., Jaume-i-Capó, A., Moyà-Alcover, G., Varona, J. and Martínez-Bueso, P., (2019). Vision-based serious games and virtual reality systems for motor rehabilitation: A review geared toward a research methodology. International journal of medical informatics, 131, p.103909. Berger, J., Bayarri, M. J., & Pericchi, L. R. (2013). The effective sample size. Economic Reviews, 33(1-4), 197-217. doi:10.1080/07474938.2013.807157 Bresalier, M. (2018). From Healthy Cows to Healthy Humans: Integrated Approaches to World Hunger, c. 1930–1965. In Animals and the Shaping of Modern Medicine (pp. 119-160). Palgrave Macmillan, Cham. Retrieved from: http://library.oapen.org/bitstream/handle/20.500.12657/28420/Bookshelf_NBK481745.pdf?sequence=1#page=131 Caiado, R. G. G., Leal Filho, W., Quelhas, O. L. G., de Mattos Nascimento, D. L., & Ávila, L. V. (2018). A literature-based review on potentials and constraints in the implementation of the sustainable development goals. Journal of cleaner production, 198, 1276-1288. Retrieved on 11th October, 2020, Retrieved from https://www.researchgate.net/profile/Md_Washim_Akram/post/How_to_implement_the_weak_constraints/attachment/5bd757cc3843b0067541649f/AS%3A687144280543233%401540839371933/download/caiado2018.pdf Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillside, NJ: Lawrence Erlbaum Associates. Coleman-Jenson, et. al. (2017). Food insecurity is associated with subjective well-being among individuals from 138 countries in the 2014 Gallup World Poll. The Journal of Nutrition, 147(4), 680-687. Creswell, J. (2020). ‘I Just Need the Comfort’: Processed Foods Make a Pandemic Comeback. Retrieved from https://www.nytimes.com/2020/04/07/business/coronavirus-processed- foods.html Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five traditions (4th ed.). Thousand Oaks, CA: Sage Publications Dodds, S. and Hess, A.C., (2020). Adapting research methodology during COVID-19: lessons for transformative service research. Journal of Service Management. Dos Santos, M. J. P. L. (2020, January). Free trade and implications for hunger. In 1st International Conference on Business & Technological Trends. RTS-Research Training Solutions. Retrieved from https://repositorio.ipl.pt/bitstream/10400.21/11416/1/Maria%20Dos-Santos_UNSDG%204-20.pdf Fao.org, (2018). Achieving Zero Hunger in Africa by 2025. Food and Agricultural Organisation of the United Nations. Retrieved from: http://www.fao.org/3/i8624en/I8624EN.pdf Fao.org, (2019). New UN report reveals that hunger in Africa continues to rise. Food and Agricultural Organisation of the United Nations. Retrieved from: http://www.fao.org/news/story/en/item/1180443/icode/ Field, A. (2013). Discovering statistics using IBM SPSS statistics. SAGE Publications. Food and Agriculture Organization of the United Nations. (2019). The state of food security and nutrition in the world: Safeguarding against economic slowdowns and downturns. Food Engineering. (2019). Top 100 Food & Beverage Companies. Retrieved from https://www.foodengineeringmag.com/ext/resources/Issues/2019/09-September/Top-100- page-29.pdf. Gundersen C, Kreider B. Bounding the effects of food insecurity on children’s health outcomes. J Health Econ. 2009;28:971–83. Haas, J. P. (2012). Sample size and power. American Journal of Infection Control, 40(8), 766-767. Retrieved from doi:10.1016/j.ajic.2012.05.020 Hirtzer, M. and Durisin, M. (2020). U.S. Crop Report Signals Worsening Global Food-Insecurity Crisis. Bloomberg Economic Report. Retrieved from https://www.bloomberg.com/news/articles/2020-10-09/u-s-crop-report-signals-worsening- global-food-insecurity-crisis INDDEX Project (2018), Data4Diets: Building Blocks for Diet-related Food Security Analysis. Tufts University, Boston, MA. Retrieved from https://inddex.nutrition.tufts.edu/data4diets. Iacobucci, D. (2017). Marketing Models: Multivariate Statistics and Marketing Analytics 4th ed. Jones, A. D. (2017). Food insecurity and mental health status: A global analysis of 149 countries. American Journal of Preventive Medicine, 53(2), 264-273. Kursmark M, Weitzman M. (2009). Recent findings concerning childhood food insecurity. Curr Opin Clin Nutr Metab Care. 12:310–6. Learner, M. (n.d.). What is Hunger? Bread for the World Institute. Retrieved from https://www.bread.org/what-hunger Mayo Clinic Staff (2020). Breast-feeding vs. formula-feeding: What’s best? Retrieved from https://www.mayoclinic.org/healthy-lifestyle/infant-and-toddler-health/in-depth/breast- feeding/art20047898#:~:text=Commercial%20infant%20formulas%20don’t, who%20have%20typical%20dietary%20needs.ing. McCusker, K., & Gunaydin, S. (2015). Research using qualitative, quantitative or mixed methods and choice based on the research. Perfusion, 30(7), 537-542. doi:10.1177/0267659114559116 Mertler, C. A. (2013). Advanced and multivariate statistical methods: Practical application and interpretation. Mooney, C. Z., & Duval, R. D. (1993). Sage university papers series. Quantitative applications in the social sciences, No. 95.Bootstrapping: A nonparametric approach to statistical inference. Sage Publications, Inc. https://doi.org/10.4135/9781412983532 Ndzovu, H. J. (2020). Sacralization of the Humanitarian Space: Faith Based Organizations, Mission-Aid and Development in Africa. Religion and development in Africa, 125. Retrieved from: https://repository.maseno.ac.ke/bitstream/handle/123456789/2299/Religion_and_Civic_Participation_in_Post.pdf?sequence=1#page=126 Orr, D. (2015). UN agencies expand operations in southern Africa as poor harvests deepen food insecurity. WFP. Retrieved from https://news.un.org/en/story/2015/10/512992-un- agencies-expand-operations-southern-africa-poor-harvests-deepen-food Oosthuizen, J. H., Usher, J. V., & Nukunah, C. T. E. (2018). Principles of responsible management education: an assessment of South African business schools. Journal of Contemporary Management, 15 (Special Edition 1), 37-56. Retrieved from: https://journals.co.za/content/journal/10520/EJC-12121ab40c?crawler=true&mimetype=application/pdf Oxfarm Briefing Paper, (2019). GENDER INEQUALITIES AND FOOD INSECURITY. Retrieved from https://reliefweb.int/sites/reliefweb.int/files/resources/bp-gender-inequalities-food-insecurity-150719-en.pdf Panting, J., (2015). Approaching International Hunger. E-International Relations. Retrieved from: https://www.e-ir.info/2012/12/12/approaching-international-hunger/ Rampa, F., Dekeyser, K., Alders, R., & Dar, O. (2019). The global institutional landscape of food and agriculture. Retrieved on 12th October, 2020, Retrieved from: https://euagenda.eu/upload/publications/the-global-institutional-landscape-of-food-and-agriculture-how-to-achieve-sdg-2.pdf Rampa, F., Dekeyser, K., Alders, R., & Dar, O. (2019). The global institutional landscape of food and agriculture. Retrieved on 9th October, 2020, Retrieved from: https://euagenda.eu/upload/publications/the-global-institutional-landscape-of-food-and-agriculture-how-to-achieve-sdg-2.pdf Sadza, H. C., Nherera, C. M., Nhenga-Chakarisa, T., Tagwireyi, M. J., & Munyuki-Hungwe, M. ZIMBABWE ZERO HUNGER STRATEGIC REVIEW. 2020. Retrieved from: https://documents.wfp.org/stellent/groups/public/documents/communications/wfp290422.pdf Sayed, D. (2015). Food Insecurity in Pakistan and the scope for regional cooperation. Retrieved from https://www.unescap.org/sites/default/files/South%20Asia%20Policy%20 Dialogue%20on%20Regional%20Cooperation%20for%20Food%20Security%20- %20Ms.%20Duaa%20S.%20Sayed.pdf Seligman, H. K. Laraia, B.A. and Kushel, M. B. (2009). Food Insecurity Is Associated with Chronic Disease among Low-Income NHANES Participants. The Journal of Nutrition, Volume 140, Issue 2, February 2010, Pages 304–310, https://doi.org/10.3945/jn.109.112573 Shahar, S.M., Ma’arif, M.Y., Yusof, M.F.H. and Satar, N.S.M., (2019), September. Research Methodology Trending in Evolutionary Computing. In International Conference on Computational Collective Intelligence (pp. 474-485). Springer, Cham. Slack KS, Yoo J. (2005). Food hardship and child behavior problems among low-income children. Soc Serv Rev. 79:511–36. Smith, M. D., Rabbitt, M. P., & Coleman-Jensen, A. (2017). Who are the world’s food insecure? New evidence from the Food and Agriculture Organization’s Food Insecurity Experience Scale. World Development, 93, 402-412. Snyder, H., 2019. Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, pp.333-339. Sullivan, G. M., & Feinn, R. (2012). Using Effect Size-or Why the P Value Is Not Enough. Journal of graduate medical education, 4(3), 279-82. Tabachnick, B.G. and Fidell, L.S. (2012) Using Multivariate Statistics. 6h Edition, Person Education, Boston. Tan, H. (2020). Global food prices have been rising during the coronavirus pandemic, hitting food security. Retrieved from https://www.cnbc.com/2020/09/09/global-food-prices-have-been-rising-during-pandemic-hitting-food-security.html United Nations (n.d.) Food. Retrieved from www.unitednations.org United Nations (2019). Overview. https://www.wfp.org/overview Un.org, (2018). Feeding the Hungry in Africa: Not All Is Lost. UN Chronicle. Retrieved from: https://www.un.org/en/chronicle/article/feeding-hungry-africa-not-all-lost CGTN Africa (2020). UN says 3.5 million people face acute food insecurity in Somalia. Retrieved from https://africa.cgtn.com/2020/07/06/un-says-3-5-million-people-face-acute-food-insecurity-in-somalia/ Weinreb L, Wehler C, Perloff J, Scott R, Hosmer D, Sagor L, Gundersen C. (2002). Hunger: its impact on children’s health and mental health. Pediatrics. 110:e41. Whitaker RC, Phillips SM, Orzol SM. (2006). Food insecurity and the risks of depression and anxiety in mothers and behavior problems in their preschool-aged children. Pediatrics. 118:e859–68. World Bank. (2018). Poverty and shared prosperity 2018: Piecing together the poverty puzzle. Washington, DC. World Bank, (2020). Food Security and COVID-19. Retrieved from https://www.worldbank.org/en/topic/agriculture/brief/food-security-and-covid-19 World Bank (2020). How nutrition can protect people’s health during COVID-19 MUHAMMAD ALI PATEMARTIEN VAN NIEUWKOOP|MAY 13, 2020. Retrieved from https://blogs.worldbank.org/voices/how-nutrition-can-protect-peoples-health-during-covid-19 Worldhunger.org, (2018). AFRICA HUNGER AND POVERTY FACTS. Hunger Notes. Retrieved from: https://www.worldhunger.org/africa-hunger-poverty-facts-2018/#:~:text=Global%20estimates%20of%20undernourishment%20rose,of%20the%20population%20is%20undernourished. Source: African Studies and African Country Resources @ Pitt: Central African Countries

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