IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i4p2059-d502432.html
   My bibliography  Save this article

Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador

Author

Listed:
  • Otilia Vanessa Cordero-Ahiman

    (Grupo de Investigación en Economía Regional (GIER), Facultad de Ciencias Económicas y Administrativas, Universidad de Cuenca, Cuenca 010107, Ecuador)

  • Jorge Leonardo Vanegas

    (Grupo de Investigación en Economía Regional (GIER), Facultad de Ciencias Económicas y Administrativas, Universidad de Cuenca, Cuenca 010107, Ecuador
    Grupo de Producción Animal e Industrialización (PROANIN), Facultad de Ingeniería, Universidad Nacional de Chimborazo, Riobamba 060103, Ecuador)

  • Christian Franco-Crespo

    (Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología, Campus Huachi, Universidad Técnica de Ambato, Ambato 180104, Ecuador)

  • Pablo Beltrán-Romero

    (Grupo de Investigación en Economía Regional (GIER), Facultad de Ciencias Económicas y Administrativas, Universidad de Cuenca, Cuenca 010107, Ecuador)

  • María Elena Quinde-Lituma

    (Grupo de Investigación en Economía Regional (GIER), Facultad de Ciencias Económicas y Administrativas, Universidad de Cuenca, Cuenca 010107, Ecuador)

Abstract

Inadequate food and nutrition affect human well-being, particularly for many poor subpopulations living in rural areas. The purpose of this research was to analyze the factors that determine the Household Dietary Diversity Score (HDDS) in the rural area of the Paute River Basin, Azuay Province, Ecuador. The sample size of 383 surveys was determined by a stratified random sampling method with proportional affixation. Dietary diversity was measured through the HDDS, with 12 food groups (cereals; roots and tubers; fruits; sugar/honey; meat and eggs; legumes or grains; vegetables; oils/fats; milk and dairy products; meats; miscellaneous; fish and shellfish) over a recall period of 7 days. A Poisson regression model was used to determine the relationship between the HDDS and sociodemographic variables. The results show that the average HDDS of food consumption is 10.89 foods. Of the analyzed food groups, the most consumed are cereals; roots and tubers; fruits; sugar/honey. In addition, the determinants that best explain the HDDS in the predictive model were housing size, household size, per capita food expenditure, area of cultivated land, level of education, and marital status of the head of household. The tools used in this research can be used to analyze food and nutrition security interventions. Furthermore, the results allow policymakers to identify applicable public policies in the fight against hunger.

Suggested Citation

  • Otilia Vanessa Cordero-Ahiman & Jorge Leonardo Vanegas & Christian Franco-Crespo & Pablo Beltrán-Romero & María Elena Quinde-Lituma, 2021. "Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:2059-:d:502432
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/4/2059/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/4/2059/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chegere, Martin J. & Stage, Jesper, 2020. "Agricultural production diversity, dietary diversity and nutritional status: Panel data evidence from Tanzania," World Development, Elsevier, vol. 129(C).
    2. Otilia Vanessa Cordero-Ahiman & Jorge Leonardo Vanegas & Pablo Beltrán-Romero & María Elena Quinde-Lituma, 2020. "Determinants of Food Insecurity in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador," Sustainability, MDPI, vol. 12(3), pages 1-18, January.
    3. Kibrom T. Sibhatu & Matin Qaim, 2018. "Farm production diversity and dietary quality: linkages and measurement issues," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(1), pages 47-59, February.
    4. Joselin Segovia & Mercy Orellana & Juan Pablo Sarmiento & Darwin Carchi, 2020. "The effects of taxing sugar-sweetened beverages in Ecuador: An analysis across different income and consumption groups," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-18, October.
    5. Alain Janvry & Elisabeth Sadoulet, 2006. "Progress in the Modeling of Rural Households’ Behavior under Market Failures," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Alain Janvry & Ravi Kanbur (ed.), Poverty, Inequality and Development, chapter 0, pages 155-181, Springer.
    6. I-Hsin Lin & Tuyen Van Duong & Shih-Wei Nien & I-Hsin Tseng & Hsu-Han Wang & Yang-Jen Chiang & Chia-Yen Chen & Te-Chih Wong, 2020. "Dietary Diversity Score: Implications for Obesity Prevention and Nutrient Adequacy in Renal Transplant Recipients," IJERPH, MDPI, vol. 17(14), pages 1-11, July.
    7. Ashraful Alam & Morseda Chowdhury & Michael J. Dibley & Camille Raynes-Greenow, 2020. "How Can We Improve the Consumption of a Nutritionally Balanced Maternal Diet in Rural Bangladesh? The Key Elements of the “Balanced Plate” Intervention," IJERPH, MDPI, vol. 17(17), pages 1-12, August.
    8. Muthini, Davis & Nzuma, Jonathan & Qaim, Matin, 2020. "Subsistence production, markets, and dietary diversity in the Kenyan small farm sector," Food Policy, Elsevier, vol. 97(C).
    9. Shinoj Parappurathu & Anjani Kumar & M. Bantilan & P. Joshi, 2015. "Food consumption patterns and dietary diversity in eastern India: evidence from village level studies (VLS)," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 7(5), pages 1031-1042, October.
    10. Fiese, Barbara H. & Gundersen, Craig & Koester, Brenda & Jones, Blake, 2016. "Family chaos and lack of mealtime planning is associated with food insecurity in low income households," Economics & Human Biology, Elsevier, vol. 21(C), pages 147-155.
    11. repec:bla:devpol:v:26:y:2008:i:6:p:657-692 is not listed on IDEAS
    12. Jones, Andrew D. & Shrinivas, Aditya & Bezner-Kerr, Rachel, 2014. "Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data," Food Policy, Elsevier, vol. 46(C), pages 1-12.
    13. Takahashi, Akihito & Kurosawa, Takeshi, 2016. "Regression correlation coefficient for a Poisson regression model," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 71-78.
    14. Jitendra Kumar Singh & Dilaram Acharya & Salila Gautam & Mandira Adhikari & Ji-Hyuk Park & Seok-Ju Yoo & Kwan Lee, 2019. "Socio-Demographic and Diet-Related Factors Associated with Insufficient Fruit and Vegetable Consumption among Adolescent Girls in Rural Communities of Southern Nepal," IJERPH, MDPI, vol. 16(12), pages 1-11, June.
    15. Vaitla, Bapu & Coates, Jennifer & Glaeser, Laura & Hillbruner, Christopher & Biswal, Preetish & Maxwell, Daniel, 2017. "The measurement of household food security: Correlation and latent variable analysis of alternative indicators in a large multi-country dataset," Food Policy, Elsevier, vol. 68(C), pages 193-205.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nonkululeko Thandeka Brightness Zondi & Mjabuliseni Simon Cloapas Ngidi & Temitope Oluwaseun Ojo & Simphiwe Innocentia Hlatshwayo, 2022. "Impact of Market Participation of Indigenous Crops on Household Food Security of Smallholder Farmers of South Africa," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    2. Sienso, Gifty & Lyford, Conrad & Oldewage-Theron, Wilna, 2022. "Using instrumental variables to establish the relationship between household production diversity and household dietary diversity in northern Ghana," African Journal of Food, Agriculture, Nutrition and Development (AJFAND), African Journal of Food, Agriculture, Nutrition and Development (AJFAND), vol. 22(07).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bai, Yunli & Zeng, Xuanye & Zhang, Linxiu & Song, Yiching & Zeng, Xuanye, 2021. "Domestic decision-making, crop diversity, and household dietary diversity: Evidence from five developing countries in Asia," 2021 Conference, August 17-31, 2021, Virtual 315393, International Association of Agricultural Economists.
    2. Sikhulumile Sinyolo & Catherine Ndinda & Conrad Murendo & Sithembile A. Sinyolo & Mudzunga Neluheni, 2020. "Access to Information Technologies and Consumption of Fruits and Vegetables in South Africa: Evidence from Nationally Representative Data," IJERPH, MDPI, vol. 17(13), pages 1-17, July.
    3. Eric O. Verger & Cédric Gaillard & Andrew D. Jones & Roseline Remans & Gina Kennedy, 2021. "Construction and Interpretation of Production and Market Metrics Used to Understand Relationships with Dietary Diversity of Rural Smallholder Farming Households," Agriculture, MDPI, vol. 11(8), pages 1-21, August.
    4. Orkhan Sariyev & Tim K. Loos & Ling Yee Khor, 2021. "Intra-household decision-making, production diversity, and dietary quality: a panel data analysis of Ethiopian rural households," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(1), pages 181-197, February.
    5. Keenan, Michael & Karanja, Stanley & Pamuk, Haki & Ruben, Ruerd, 2021. "Smallholder Farming Households' Make-or-Buy Decisions: Linking Market Access, Production Risks, and Production Diversity to Dietary Diversity," 2021 Conference, August 17-31, 2021, Virtual 315349, International Association of Agricultural Economists.
    6. Sayla Khandoker & Alka Singh & Shivendra Kumar Srivastava, 2022. "Leveraging farm production diversity for dietary diversity: evidence from national level panel data," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-20, December.
    7. Romaza Khanum & Petra Schneider & Muhammad Salim Al Mahadi & Mohammad Mojibul Hoque Mozumder & Md. Mostafa Shamsuzzaman, 2022. "Does Fish Farming Improve Household Nutritional Status? Evidence from Bangladesh," IJERPH, MDPI, vol. 19(2), pages 1-16, January.
    8. Julius Chegere, Martin & Sebastian Kauky, Monica, 2022. "Agriculture commercialisation, household dietary diversity and nutrition in Tanzania," Food Policy, Elsevier, vol. 113(C).
    9. Chegere, Martin J. & Stage, Jesper, 2020. "Agricultural production diversity, dietary diversity and nutritional status: Panel data evidence from Tanzania," World Development, Elsevier, vol. 129(C).
    10. Wanglin Ma & Puneet Vatsa & Hongyun Zheng & Yanzhi Guo, 2022. "Does online food shopping boost dietary diversity? Application of an endogenous switching model with a count outcome variable," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-19, December.
    11. Olabisi, Michael & Obekpa, Hephzibah Onyeje & Liverpool-Tasie, Lenis Saweda O., 2021. "Is growing your own food necessary for dietary diversity? Evidence from Nigeria," Food Policy, Elsevier, vol. 104(C).
    12. Sikhulumile Sinyolo & Conrad Murendo & Admire Mutsa Nyamwanza & Sithembile Amanda Sinyolo & Catherine Ndinda & Chijioke Osinachi Nwosu, 2021. "Farm Production Diversification and Dietary Diversity among Subsistence Farming Households: Panel Data Evidence from South Africa," Sustainability, MDPI, vol. 13(18), pages 1-14, September.
    13. Tesfaye, Wondimagegn & Tirivayi, Nyasha, 2020. "Crop diversity, household welfare and consumption smoothing under risk: Evidence from rural Uganda," World Development, Elsevier, vol. 125(C).
    14. Sékou Amadou Traoré & Christoph Reiber & Bekele Megersa & Anne Valle Zárate, 2018. "Contribution of cattle of different breeds to household food security in southern Mali," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(3), pages 549-560, June.
    15. Del Prete, Davide & Ghins, Léopold & Magrini, Emiliano & Pauw, Karl, 2019. "Land consolidation, specialization and household diets: Evidence from Rwanda," Food Policy, Elsevier, vol. 83(C), pages 139-149.
    16. Thottappilly, Anna, 2021. "Identifying the Income Effect on Nutrition for Agricultural Households: Separability of Production and Consumption," 2021 Conference, August 17-31, 2021, Virtual 315335, International Association of Agricultural Economists.
    17. Chrisendo, Daniel & Krishna, Vijesh V. & Siregar, Hermanto & Qaim, Matin, 2020. "Land-use change, nutrition, and gender roles in Indonesian farm households," Forest Policy and Economics, Elsevier, vol. 118(C).
    18. Martin C. Parlasca & Oliver Mußhoff & Matin Qaim, 2020. "Can mobile phones improve nutrition among pastoral communities? Panel data evidence from Northern Kenya," Agricultural Economics, International Association of Agricultural Economists, vol. 51(3), pages 475-488, May.
    19. Venkatesh, P. & Sangeetha, V. & Singh, P., 2016. "Relationship between Food Production and Consumption Diversity in India – Empirical Evidences from Cross Section Analysis," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 29(Conferenc).
    20. Vu, Khoa & Vuong, Nguyen Dinh Tuan & Vu-Thanh, Tu-Anh & Nguyen, Anh Ngoc, 2022. "Income shock and food insecurity prediction Vietnam under the pandemic," World Development, Elsevier, vol. 153(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:2059-:d:502432. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.