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Published in: BMC Public Health 1/2024

Open Access 01-12-2024 | Nutrition | Research

Key predictors of food security and nutrition in Africa: a spatio-temporal model-based study

Authors: Adusei Bofa, Temesgen Zewotir

Published in: BMC Public Health | Issue 1/2024

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Abstract

There is voluminous literature on Food Security in Africa. This study explicitly considers the spatio-temporal factors in addition to the usual FAO-based metrics in modeling and understanding the dynamics of food security and nutrition across the African continent. To better understand the complex trajectory and burden of food insecurity and nutrition in Africa, it is crucial to consider space-time factors when modeling and interpreting food security. The spatio-temporal anova model was found to be superior(employing statistical criteria) to the other three models from the spatio-temporal interaction domain models. The results of the study suggest that dietary supply adequacy, food stability, and consumption status are positively associated with severe food security, while average food supply and environmental factors have negative effects on Food Security and Nutrition. The findings also indicate that severe food insecurity and malnutrition are spatially and temporally correlated across the African continent. Spatio-temporal modeling and spatial mapping are essential components of a comprehensive practice to reduce the burden of severe food insecurity. likewise, any planning and intervention to improve the average food supply and environment to promote sustainable development should be regional instead of one size fit all.
Literature
1.
go back to reference Berkhout P. The impact of the war in Ukraine on food security. EuroChoices. 2022;21(2):50–1.CrossRef Berkhout P. The impact of the war in Ukraine on food security. EuroChoices. 2022;21(2):50–1.CrossRef
2.
go back to reference Din MSU, et al. World nations priorities on climate change and food security. Building climate resilience in agriculture: theory, practice and future perspective. 2022. pp. 365–384. Din MSU, et al. World nations priorities on climate change and food security. Building climate resilience in agriculture: theory, practice and future perspective. 2022. pp. 365–384.
3.
go back to reference Muroyiwa B. Agricultural Transformation in Africa: lessons learnt from the domestication of the comprehensive African Agriculture Development Programme (CAADP) processes in Lesotho. J Afr Union Stud. 2022;11(3):5–25.CrossRef Muroyiwa B. Agricultural Transformation in Africa: lessons learnt from the domestication of the comprehensive African Agriculture Development Programme (CAADP) processes in Lesotho. J Afr Union Stud. 2022;11(3):5–25.CrossRef
4.
go back to reference Cernev T, Fenner R. The importance of achieving foundational sustainable development goals in reducing global risk. Futures. 2020;115:102492.CrossRef Cernev T, Fenner R. The importance of achieving foundational sustainable development goals in reducing global risk. Futures. 2020;115:102492.CrossRef
5.
go back to reference Nicholson CF, et al. Food security outcomes in agricultural systems models: current status and recommended improvements. Agric Syst. 2021;188:103028.CrossRef Nicholson CF, et al. Food security outcomes in agricultural systems models: current status and recommended improvements. Agric Syst. 2021;188:103028.CrossRef
6.
go back to reference Li H, Zhang X. A spatial explicit assessment of food security in Africa based on simulated crop production and distribution. J Clean Prod. 2017;147:628–36.CrossRef Li H, Zhang X. A spatial explicit assessment of food security in Africa based on simulated crop production and distribution. J Clean Prod. 2017;147:628–36.CrossRef
7.
go back to reference Waha K, et al. Agricultural diversification as an important strategy for achieving food security in Africa. Glob Change Biol. 2018;24(8):3390–400.CrossRef Waha K, et al. Agricultural diversification as an important strategy for achieving food security in Africa. Glob Change Biol. 2018;24(8):3390–400.CrossRef
8.
go back to reference Wegenast T, Beck J. Mining, rural livelihoods and food security: a disaggregated analysis of sub-saharan Africa. World Dev. 2020;130:104921.CrossRef Wegenast T, Beck J. Mining, rural livelihoods and food security: a disaggregated analysis of sub-saharan Africa. World Dev. 2020;130:104921.CrossRef
9.
go back to reference Yuan Z, et al. Spatiotemporal change analysis of soil moisture based on downscaling technology in Africa. Water. 2022;14(1): 74.CrossRef Yuan Z, et al. Spatiotemporal change analysis of soil moisture based on downscaling technology in Africa. Water. 2022;14(1): 74.CrossRef
10.
go back to reference Cooper MW, et al. Text mining the food security literature reveals substantial spatial bias and thematic broadening over time. Global Food Secur. 2020;26:100392.CrossRef Cooper MW, et al. Text mining the food security literature reveals substantial spatial bias and thematic broadening over time. Global Food Secur. 2020;26:100392.CrossRef
11.
go back to reference Kassouri Y, Okunlola OA. Analysis of spatio-temporal drivers and convergence characteristics of urban development in Africa. Land Use Policy. 2022;112:105868.CrossRef Kassouri Y, Okunlola OA. Analysis of spatio-temporal drivers and convergence characteristics of urban development in Africa. Land Use Policy. 2022;112:105868.CrossRef
12.
go back to reference Calderazzo S, Wiesenfarth M, Kopp-Schneider A. A decision-theoretic approach to bayesian clinical trial design and evaluation of robustness to prior-data conflict. Biostatistics. 2022;23(1):328–44.CrossRefPubMed Calderazzo S, Wiesenfarth M, Kopp-Schneider A. A decision-theoretic approach to bayesian clinical trial design and evaluation of robustness to prior-data conflict. Biostatistics. 2022;23(1):328–44.CrossRefPubMed
13.
go back to reference Ver Hoef JM, et al. Spatial autoregressive models for statistical inference from ecological data. Ecol Monogr. 2018;88(1):36–59.CrossRef Ver Hoef JM, et al. Spatial autoregressive models for statistical inference from ecological data. Ecol Monogr. 2018;88(1):36–59.CrossRef
14.
go back to reference Bofa A, Zewotir T. Filling the gap in food and nutrition security data: what imputation method is best for Africa’s food and nutrition security? Lithuanian J Stat. 2022;61:16–31. Bofa A, Zewotir T. Filling the gap in food and nutrition security data: what imputation method is best for Africa’s food and nutrition security? Lithuanian J Stat. 2022;61:16–31.
15.
go back to reference Bofa A, Zewotir T. The determinants of severe food insecurity in africa using the longitudinal generalized poisson mixed model. 2023. School of Mathematics, Statistics & Computer Science,University of KwaZulu Natal. Bofa A, Zewotir T. The determinants of severe food insecurity in africa using the longitudinal generalized poisson mixed model. 2023. School of Mathematics, Statistics & Computer Science,University of KwaZulu Natal.
16.
go back to reference Afridi GS, et al. An analysis of food insecurity in Pakistan: prevalence of undernourishment (PoU) and Food Insecurity Experience Scale (FIES). J Appl Econ Bus Stud. 2021;5(1):175–90.CrossRef Afridi GS, et al. An analysis of food insecurity in Pakistan: prevalence of undernourishment (PoU) and Food Insecurity Experience Scale (FIES). J Appl Econ Bus Stud. 2021;5(1):175–90.CrossRef
17.
go back to reference FAO, ECA, AUC. Africa regional overview of food security and nutrition 2019. Accra: Food and Agriculture Organization; 2020. FAO, ECA, AUC. Africa regional overview of food security and nutrition 2019. Accra: Food and Agriculture Organization; 2020.
18.
go back to reference Allee A, Lynd LR, Vaze V. Cross-national analysis of food security drivers: comparing results based on the food insecurity experience scale and global food security index. Food Secur. 2021;13(5):1245–61.CrossRef Allee A, Lynd LR, Vaze V. Cross-national analysis of food security drivers: comparing results based on the food insecurity experience scale and global food security index. Food Secur. 2021;13(5):1245–61.CrossRef
19.
go back to reference Jalilian A, Mateu J. A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany. Stoch Env Res Risk Assess. 2021;35:797–812.CrossRef Jalilian A, Mateu J. A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany. Stoch Env Res Risk Assess. 2021;35:797–812.CrossRef
20.
go back to reference Knorr-Held L, Besag J. Modelling risk from a disease in time and space. Stat Med. 1998;17(18):2045–60.CrossRefPubMed Knorr-Held L, Besag J. Modelling risk from a disease in time and space. Stat Med. 1998;17(18):2045–60.CrossRefPubMed
21.
go back to reference Pandey R, Tolani H. Crime patterns in Delhi: a bayesian spatio-temporal assessment. Int J Syst Assur Eng Manage. 2022:1–10. Pandey R, Tolani H. Crime patterns in Delhi: a bayesian spatio-temporal assessment. Int J Syst Assur Eng Manage. 2022:1–10.
22.
23.
go back to reference Leroux BG, Lei X, Breslow N. EEstimation of disease rates in small areas: a new mixed model for spatial dependence. In: Statistical models in epidemiology, the environment, and clinical trials. New York: Springer; 2000. Leroux BG, Lei X, Breslow N. EEstimation of disease rates in small areas: a new mixed model for spatial dependence. In: Statistical models in epidemiology, the environment, and clinical trials. New York: Springer; 2000.
24.
go back to reference Fenta HM, Zewotir T, Muluneh EK. Space–time dynamics regression models to assess variations of composite index for anthropometric failure across the administrative zones in Ethiopia. BMC Public Health. 2022;22(1):1–11.CrossRef Fenta HM, Zewotir T, Muluneh EK. Space–time dynamics regression models to assess variations of composite index for anthropometric failure across the administrative zones in Ethiopia. BMC Public Health. 2022;22(1):1–11.CrossRef
25.
go back to reference Sahu SK. Bayesian modeling of spatio-temporal data with R. New York: Chapman and Hall/CRC; 2022. Sahu SK. Bayesian modeling of spatio-temporal data with R. New York: Chapman and Hall/CRC; 2022.
26.
go back to reference Knorr-Held L. Bayesian modelling of inseparable space‐time variation in disease risk. Stat Med. 2000;19(17–18):2555–67.CrossRefPubMed Knorr-Held L. Bayesian modelling of inseparable space‐time variation in disease risk. Stat Med. 2000;19(17–18):2555–67.CrossRefPubMed
27.
go back to reference Gummadi S, et al. Spatio-temporal variability and trends of precipitation and extreme rainfall events in Ethiopia in 1980–2010. Theoret Appl Climatol. 2018;134:1315–28.CrossRef Gummadi S, et al. Spatio-temporal variability and trends of precipitation and extreme rainfall events in Ethiopia in 1980–2010. Theoret Appl Climatol. 2018;134:1315–28.CrossRef
28.
go back to reference Deaton BJ, Deaton BJ. Food security and Canada’s agricultural system challenged by COVID-19. CJAE. 2020;68(2):143–9. Deaton BJ, Deaton BJ. Food security and Canada’s agricultural system challenged by COVID-19. CJAE. 2020;68(2):143–9.
29.
go back to reference Hasegawa T, et al. Extreme climate events increase risk of global food insecurity and adaptation needs. Nat Food. 2021;2(8):587–95.CrossRefPubMed Hasegawa T, et al. Extreme climate events increase risk of global food insecurity and adaptation needs. Nat Food. 2021;2(8):587–95.CrossRefPubMed
30.
go back to reference Grote U, et al. Food security and the dynamics of wheat and maize value chains in Africa and Asia. Front Sustainable Food Syst. 2021;4:617009.CrossRef Grote U, et al. Food security and the dynamics of wheat and maize value chains in Africa and Asia. Front Sustainable Food Syst. 2021;4:617009.CrossRef
31.
go back to reference Bonuedi I, Kamasa K, Opoku EEO. Enabling trade across borders and food security in Africa. Food Secur. 2020;12(5):1121–40.CrossRef Bonuedi I, Kamasa K, Opoku EEO. Enabling trade across borders and food security in Africa. Food Secur. 2020;12(5):1121–40.CrossRef
32.
go back to reference Cassimon D, Fadare O, Mavrotas G. The impact of food aid and governance on food and nutrition security in Sub-saharan Africa. Sustainability. 2023;15(2):1417.CrossRef Cassimon D, Fadare O, Mavrotas G. The impact of food aid and governance on food and nutrition security in Sub-saharan Africa. Sustainability. 2023;15(2):1417.CrossRef
33.
go back to reference Mughal M, Fontan Sers C. Cereal production, undernourishment, and food insecurity in South Asia. Rev Dev Econ. 2020;24(2):524–45.CrossRef Mughal M, Fontan Sers C. Cereal production, undernourishment, and food insecurity in South Asia. Rev Dev Econ. 2020;24(2):524–45.CrossRef
34.
go back to reference Mbogori T, et al. Nutrition transition and double burden of malnutrition in Africa: a case study of four selected countries with different social economic development. AIMS Public Health. 2020;7(3):425.CrossRefPubMedPubMedCentral Mbogori T, et al. Nutrition transition and double burden of malnutrition in Africa: a case study of four selected countries with different social economic development. AIMS Public Health. 2020;7(3):425.CrossRefPubMedPubMedCentral
35.
go back to reference Seferidi P, et al. Global inequalities in the double burden of malnutrition and associations with globalisation: a multilevel analysis of demographic and health surveys from 55 low-income and middle-income countries, 1992–2018. Lancet Global Health. 2022;10(4):e482-490.CrossRefPubMed Seferidi P, et al. Global inequalities in the double burden of malnutrition and associations with globalisation: a multilevel analysis of demographic and health surveys from 55 low-income and middle-income countries, 1992–2018. Lancet Global Health. 2022;10(4):e482-490.CrossRefPubMed
36.
go back to reference Morales DX, Morales SA, Beltran TF. Racial/ethnic disparities in household food insecurity during the COVID-19 pandemic: a nationally representative study. J Racial Ethnic Health Disparities. 2021;8(5):1300–14.CrossRef Morales DX, Morales SA, Beltran TF. Racial/ethnic disparities in household food insecurity during the COVID-19 pandemic: a nationally representative study. J Racial Ethnic Health Disparities. 2021;8(5):1300–14.CrossRef
37.
go back to reference Kookana RS, et al. Urbanisation and emerging economies: issues and potential solutions for water and food security. Sci Total Environ. 2020;732:139057.CrossRefPubMed Kookana RS, et al. Urbanisation and emerging economies: issues and potential solutions for water and food security. Sci Total Environ. 2020;732:139057.CrossRefPubMed
38.
go back to reference Nyiwul L. Climate change adaptation and inequality in Africa: case of water, energy and food insecurity. J Clean Prod. 2021;278:123393.CrossRef Nyiwul L. Climate change adaptation and inequality in Africa: case of water, energy and food insecurity. J Clean Prod. 2021;278:123393.CrossRef
Metadata
Title
Key predictors of food security and nutrition in Africa: a spatio-temporal model-based study
Authors
Adusei Bofa
Temesgen Zewotir
Publication date
01-12-2024
Publisher
BioMed Central
Keyword
Nutrition
Published in
BMC Public Health / Issue 1/2024
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-024-18368-2

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