Skip to main content
SpringerLink
Log in

Impacts of Improved Bean Varieties Adoption on Dietary Diversity and Food Security in Rwanda

  • Impact Evaluation Article
  • Published:
The European Journal of Development Research Aims and scope Submit manuscript

Abstract

Bean is a key staple crop in Rwanda; it is produced by nearly all rural households and consumed most days, making bean an important source of calorie, protein, and micronutrient. Consequently, substantial investment has been devoted to the development and promotion of improved bean varieties to enhance food security via increased productivity. The study evaluates the impact of improved bean variety adoption on household dietary diversity, food security, and food consumption groups among smallholder bean farmers. Improved bean varieties adoption has a positive impact on dietary diversity and reduces food insecurity, which likely occurs through the income effect resulting from the high yielding properties of these varieties. Increased dietary diversity among adopters is associated with a higher likelihood of consuming cereals, fish & seafood, and fats. These findings provide evidence that bean research and dissemination efforts can improve the food security of bean farmers in Rwanda.

Résumé

Les haricots sont une culture de base au Rwanda. Presque tous les foyers rurales les cultivent, et ils sont mangés la plupart des jours, ce qui rend les haricots une source importante de calories, protéines et micronutriments. Par conséquent, un investissement important a été dédié au développement et à la promotion de variantes améliorés d’haricots, afin d’assurer la sécurité alimentaire à travers d’une productivité plus élevée. Cet étude évalue l’impact de l’adoption des variétés améliorés d’haricots sur la diversité, sécurité, et consommation alimentaire des foyers des petits exploitants agriculteurs d’haricots. L’adoption des variétés améliorés d’haricots a un impact positif sur la diversité alimentaire, et réduit l’insécurité alimentaire, le plus probablement grâce à l’augmentation des revenus qui résultent du fort rendement de ces variétés. La diversité alimentaire des foyers qui adoptent ces variétés est associée à une propension accrue a la consommation de céréales, de poissons et crustacés, et de graisses. Ces résultats fournissent les preuves que les investigations sur les haricots, et les efforts de dissémination, peuvent améliorer la sécurité alimentaire des agriculteurs d’haricots au Rwanda.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. Available at: http://database.pabra-africa.org/.

  2. This study focuses on bean varieties released between 1998 and 2010 because the adoption and benefits of varieties released before 1998 are documented in Johnson et al. (2003). Data for this study were collected in 2011 and thus the reason for the 2010 upper bound.

  3. For the adoption part of the study, we determined that interviewing 18 households in 80 villages would allow to observe improved varieties adoption of 30% with a confidence level of 95% and a precision of 3%. The 30% expected adoption level was obtained from an expert panel.

  4. Bean production is higher in season A than season B. According to the 2008 National Agriculture Survey Report (National Institute of Statistics of Rwanda 2010), bean production was 23% higher in season A, while the 2013 Seasonal Agricultural Survey Report (National Institute of Statistics of Rwanda 2015) indicates that bean production was 67% higher in season A. No statistics were found for 2011.

  5. The consumption questionnaire took 30–40 minutes to complete, and the choice of only administering the questionnaire to half of the sample was based on time and resource constraints.

  6. A different sampling strategy was used to assess the impact of adoption on consumption expenditures. We determined that interviewing 700 households with at least 250 adopters and 250 non-adopters would allow us to detect at least 10% difference in the poverty level between adopters and non-adopters, with a confidence level of 95% and a precision of 3%.

  7. Out of the total sample size, about 1,300 households produced bean.

  8. Our adult equivalent scale is: 1 + 0.7 × (adult − 1) + 0.5 × children.

  9. The wealth index is estimated using Polychoric PCA and includes durable goods ownership, housing characteristics, and access to sanitation.

  10. TLU is a measure of livestock equivalent. Conversion factors are based on the FAO definition of TLU where the base camel = 1. The 250 kg live weight relevant conversion factors are cattle = 0.7, pig = 0.2, sheep=goat = 0.1 and poultry = 0.01.

  11. This is accomplished by subtracting three from all observed HDDS.

  12. The multivariate probit model is estimated using the Stata user-written command ‘cmp' (Roodman 2011).

  13. When differentiating between climbing and bush bean varieties, we find that 15% of households cultivate improved bush bean, 19% improved climbing bean, 46% local bush bean, and 43% local climbing bean. The sum is greater than 100% because households grow more than one variety.

  14. Tests for the validating of the IVs are performed using the Stata user-written command ‘ivreg2' (Baum et al. 2007).

  15. The deviance goodness-of-fit test and the Pearson goodness-of-fit test are not statistically significant, meaning that we cannot reject the hypothesis that the data are Poisson distributed (Manjón and Martínez 2014). The correlation coefficient (0.5) between the observed and predicted values also indicates a good fit. Last, the link test for model specification suggests that the dependent and independent variables are well specified since the predictions squared have no explanatory power (p-value = 0.465) (Cameron and Trivedi 2009).

References

  • Baum, C.F., M.E. Schaffer, and S. Stillman. 2007. Enhanced routines for instrumental variables/generalized method of moments estimation and testing. Stata Journal 7 (4): 465–506.

    Article  Google Scholar 

  • Cafiero, C., H.R. Melgar-Quiñonez, T.J. Ballard, and A.W. Kepple. 2014. Validity and reliability of food security measures. Annals of the New York Academy of Sciences 1331 (1): 230–248.

    Article  Google Scholar 

  • Cameron, A.C., and P.K. Trivedi. 2009. Microeconometrics using Stata. College Station, TX: Stata Press.

    Google Scholar 

  • CIAT. 2004. Enhancing farmers’ access to seed of improved bean varieties in Rwanda. Kampala, Uganda: CIAT.

    Google Scholar 

  • Coates, J. 2013. Build it back better: deconstructing food security for improved measurement and action. Global Food Security 2 (3): 188–194.

    Article  Google Scholar 

  • FAO. 2006. Food Security: FAO Policy Brief June 2006 Issue 2. Available at: http://www.fao.org/fileadmin/templates/faoitaly/documents/pdf/pdf_Food_Security_Cocept_Note.pdf (Accessed: July 10 2020).

  • Floro, V.O., IV., R.A. Labarta, L.A. Becerra López-Lavalle, J.M. Martinez, and T.M. Ovalle. 2018. Household determinants of the adoption of improved cassava varieties using DNA fingerprinting to identify varieties in farmer fields: A case study in Colombia. Journal of Agricultural Economics 69 (2): 518–536.

    Article  Google Scholar 

  • Fongar, A., T. Gödecke, A. Aseta, and M. Qaim. 2019. How well do different dietary and nutrition assessment tools match? Insights from rural Kenya. Public Health Nutrition 22 (3): 391–403.

    Article  Google Scholar 

  • Headey, D., and O. Ecker. 2013. Rethinking the measurement of food security: From first principles to best practice. Food Security 5 (3): 327–343.

    Article  Google Scholar 

  • Hoddinott, J., and Y. Yohannes. 2002. Dietary Diversity as a Food Security Indicator. Washington, DC: IFPRI. Available at: http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/81672/filename/81673.pdf.

  • INDDEX Project. 2018. Data4Diets: Building Blocks for Diet-related Food Security Analysis. Tufts University, Boston, MA. Available at: https://inddex.nutrition.tufts.edu/data4diets. (Accessed: 30 May 2020).

  • Islam, A.H.M.S., J. von Braun, A.L. Thorne-Lyman, and A.U. Ahmed. 2018. Farm diversification and food and nutrition security in Bangladesh: Empirical evidence from nationally representative household panel data. Food Security 10 (3): 701–720.

    Article  Google Scholar 

  • Johnson, N.L., D. Pachico, and C.S. Wortmann. 2003. The impact of CIAT’s genetic improvement research on beans. In Crop variety improvement and its effect on productivity: The impact of international agricultural research, ed. R.E. Evenson and D. Gollin, 257–274. Canbridge, MA: CABI Publishing.

    Chapter  Google Scholar 

  • Jones, A.D., A. Shrinivas, and R. Bezner-Kerr. 2014. Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data. Food Policy 46: 1–12.

    Article  Google Scholar 

  • Kalyebara, R., and R. Buruchara. 2008. Farm level impacts of improved bean varieties and agronomic technologies in Rwanda. Kampala, Uganda: CIAT.

    Google Scholar 

  • Katungi, E., C. Larochelle, J. Mugabo, and R. Buruchara. 2019. Climbing bean as a solution to increase productivity in land-constrained environments: Evidence from Rwanda. Outlook on Agriculture 48 (1): 28–36.

    Article  Google Scholar 

  • Katungi, E.M., C. Larochelle, J.R. Mugabo, and R. Buruchara. 2018. The effect of climbing bean adoption on the welfare of smallholder common bean growers in Rwanda. Food Security 10 (1): 61–79.

    Article  Google Scholar 

  • Kennedy, G., T. Ballard, M. C. Dop, and E. Union. 2011. Guidelines for Measuring Household and Individual Dietary Diversity. Food and Agriculture Organization of the United Nations.

  • Koppmair, S., M. Kassie, and M. Qaim. 2017. Farm production, market access and dietary diversity in Malawi. Public Health Nutrition 20 (2): 325–335.

    Article  Google Scholar 

  • Larochelle, C., J. Alwang, G. Norton, E. Katungi, and R. Labarta. 2015. Impacts of improved bean varieties on poverty and food security in Uganda and Rwanda. In Crop Improvement, adoption and impact of improved varieties in food Crops in Sub-Saharan Africa, ed. T.S. Walker and J. Alwang, 314–337. Boston, MA: CABI.

    Chapter  Google Scholar 

  • Manjón, M., and O. Martínez. 2014. The chi-squared goodness-of-fit test for count-data models. Stata Journal 14 (4): 798–816.

    Article  Google Scholar 

  • Maxwell, D., J. Coates, and B. Vaitla. 2013. How do different indicators of household food security compare? Empirical evidence from Tigray. Medford: Feinstein International Center, Tufts University.

    Google Scholar 

  • Ministry of Agriculture and Animal Resources. 2011. Master Plan for Fisheries and Fish Farming in Rwanda. Available at: http://extwprlegs1.fao.org/docs/pdf/rwa151563.pdf (Accessed: July 1, 2020).

  • National Institute of Statistics of Rwanda. 2010. National Agricultural Survey Report 2008. Available at: http://statistics.gov.rw/datasource/46 (Accessed: July 1, 2020).

  • National Institute of Statistics of Rwanda. 2015. Seasonal Agricultural Survey Report 2013. Available at: http://statistics.gov.rw/publication/seasonal-agricultural-survey-report-2013 (Accessed: July 1. 2020).

  • Pingali, P. 2015. Agricultural policy and nutrition outcomes—Getting beyond the preoccupation with staple grains. Food Security 7 (3): 583–591.

    Article  Google Scholar 

  • Pingali, P., A. Aiyar, M. Abraham, and A. Rahman. 2019. Diet diversity and the declining importance of staple grains. Transforming food systems for a rising India, 73–91. Cham: Springer International Publishing.

    Book  Google Scholar 

  • Pingali, P.L., and K.D. Ricketts. 2014. Mainstreaming nutrition metrics in household surveys—Toward a multidisciplinary convergence of data systems. Annals of the New York Academy of Sciences 1331 (1): 249–257.

    Article  Google Scholar 

  • RAB. 2012. Bean varieties information guide 2012. Kigali, Rwanda: Rwanda Agriculture Board.

    Google Scholar 

  • Romano, D., and S. Traverso. 2019. Disentangling the impact of international migration on food and nutrition security of left-behind households: Evidence from Bangladesh. The European Journal of Development Research: 1–29.

  • Roodman, D. 2011. Fitting fully observed recursive mixed-process models with cmp. The Stata Journal 11 (2): 159–206.

    Article  Google Scholar 

  • Ruel, M.T. 2003. Operationalizing dietary diversity: A review of measurement issues and research priorities. The Journal of Nutrition 133 (11): 3911S-3926S.

    Article  Google Scholar 

  • Schwei, R.J., H. Tesfay, F. Asfaw, W. Jogo, and H. Busse. 2017. Household dietary diversity, vitamin A consumption and food security in rural Tigray, Ethiopia. Public Health Nutrition 20 (9): 1540–1547.

    Article  Google Scholar 

  • Sibhatu, K.T., V.V. Krishna, and M. Qaim. 2015. Production diversity and dietary diversity in smallholder farm households. Proceedings of the National Academy of Sciences 112 (34): 10657–10662.

    Article  Google Scholar 

  • Smale, M., M. Moursi, and E. Birol. 2015. How does adopting hybrid maize affect dietary diversity on family farms? Micro-evidence from Zambia. Food Policy 52: 44–53.

    Article  Google Scholar 

  • Snapp, S.S., and M. Fisher. 2015. “Filling the maize basket” supports crop diversity and quality of household diet in Malawi. Food Security 7 (1): 83–96.

    Article  Google Scholar 

  • Swindale, A., and P. Bilinsky. (2006) Household Dietary Diversity Score (HDDS) for measurement of household food access: Indicator guide (v.2). Washington, DC: Food and Nutrition Technical Assistance Project, Academy for Educational Development.

  • Vaiknoras, K., C. Larochelle, E. Birol, D. Asare-Marfo, and C. Herrington. 2019. Promoting rapid and sustained adoption of biofortified crops: What we learned from iron-biofortified bean delivery approaches in Rwanda. Food Policy 83: 271–284.

    Article  Google Scholar 

  • Walker, T.S., and J. Alwang. 2015. Crop improvement, adoption and impact of improved varieties in food crops in Sub-Saharan Africa. Boston, MA: CABI.

    Book  Google Scholar 

  • Wooldridge, J.M. 2010. Econometric analysis of cross section and panel data, 2nd ed. Cambridge, MA: MIT Press.

    Google Scholar 

Download references

Acknowledgement

The authors are grateful to the Canadian International Development Research Center (IDRC) and the Bill and Melinda Gates Foundation (BMGF) for the financial support to the study. IDRC funded this research through the Pan African Bean Research alliance and BMGF through the Diffusion and Impact of Improved Varieties in Africa project. The authors are grateful to the Rwanda Agriculture Board, the International Center for Tropical Agriculture, and the International Potato Center for providing leadership in implementing household surveys. The authors would like to thank the two anonymous reviewers for their insightful comments and suggestions.

Author information

Authors and Affiliations

  1. Department of Agricultural and Applied Economics, Virginia Tech, 313 Hutcheson Hall, Blacksburg, VA, 24061, USA

    Catherine Larochelle

  2. Department of Agricultural and Applied Economics, Virginia Tech, 215-I Hutcheson Hall, Blacksburg, VA, 24061, USA

    Jeffrey Alwang

Authors
  1. Catherine Larochelle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeffrey Alwang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Catherine Larochelle.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Agricultural & Applied Economics Association’s 2014 AAEA Annual Meeting, Minneapolis, MN, July 27–29, 2014 available at http://www.ageconsearch.umn.edu, and online repository.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 17 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Larochelle, C., Alwang, J. Impacts of Improved Bean Varieties Adoption on Dietary Diversity and Food Security in Rwanda. Eur J Dev Res 34, 1144–1166 (2022). https://doi.org/10.1057/s41287-021-00376-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/s41287-021-00376-2

Keywords

Search

Navigation