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A Case of Mistaken Identity? Measuring Rates of Improved Seed Adoption in Tanzania Using DNA Fingerprinting

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  • Ayala Wineman
  • Timothy Njagi
  • C. Leigh Anderson
  • Travis W. Reynolds
  • Didier Yélognissè Alia
  • Priscilla Wainaina
  • Eric Njue
  • Pierre Biscaye
  • Miltone W. Ayieko

Abstract

Studies of improved seed adoption in developing countries are almost always based on household surveys and are premised on the assumption that farmers can accurately self‐report their use of improved seed varieties. However, recent studies suggest that farmers’ reports of seed varieties planted, or even whether the seed is local or improved, are sometimes inconsistent with the DNA fingerprinting results of those crops. We use household survey data from Tanzania to test the alignment between farmer‐reported and DNA‐identified maize seed types planted. In the sample, 70% of maize seed observations are correctly reported as local or improved, while 16% are type I errors (falsely reported as improved) and 14% are type II errors (falsely reported as local). Type I errors are more likely to have been sourced from other farmers, rather than formal channels. An analysis of input use, including seed, fertiliser, and labour allocations, reveals that farmers tend to treat improved maize differently, depending on whether they correctly perceive it as improved. This suggests that errors in farmers’ seed type awareness may translate into suboptimal management practices. The average yield of seed that is correctly identified as improved is almost 700 kg per hectare greater than that of type I errors. This indicates that investments in farmers’ access to information, seed labelling, and seed system oversight are needed to complement investments in seed variety development.

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  • Ayala Wineman & Timothy Njagi & C. Leigh Anderson & Travis W. Reynolds & Didier Yélognissè Alia & Priscilla Wainaina & Eric Njue & Pierre Biscaye & Miltone W. Ayieko, 2020. "A Case of Mistaken Identity? Measuring Rates of Improved Seed Adoption in Tanzania Using DNA Fingerprinting," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 719-741, September.
    • Handle: RePEc:bla:jageco:v:71:y:2020:i:3:p:719-741
    DOI: 10.1111/1477-9552.12368
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    4. Ayala Wineman & C. Leigh Anderson & Travis W. Reynolds & Pierre Biscaye, 2019. "Methods of crop yield measurement on multi-cropped plots: Examples from Tanzania," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 11(6), pages 1257-1273, December.
    5. Ndhlovu, Obrian & Muchapondwa, Edwin, 2021. "The Impact of Farmer Input Support Programme Reform on Crop Diversification and Rotation in Zambia," EfD Discussion Paper 21-10, Environment for Development, University of Gothenburg.
    6. Justice A. Tambo & Mathews Matimelo, 2022. "An act of defiance? Measuring farmer deviation from personalised extension recommendations in Zambia," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(2), pages 396-413, June.
    7. Aihounton, Ghislain & Christiaensen, Luc, 2024. "Does agricultural intensification pay in the context of structural transformation?," Food Policy, Elsevier, vol. 122(C).
    8. Felister Y. Tibamanya & Mursali A. Milanzi & Arne Henningsen, 2021. "Drivers of and Barriers to Adoption of Improved Sun- flower Varieties amongst Smallholder Farmers in Singida, Tanzania: the Double-Hurdle Approach," IFRO Working Paper 2021/03, University of Copenhagen, Department of Food and Resource Economics.
    9. Abay, Kibrom A. & Barrett, Christopher B. & Kilic, Talip & Moylan, Heather & Ilukor, John & Vundru, Wilbert Drazi, 2023. "Nonclassical measurement error and farmers’ response to information treatment," Journal of Development Economics, Elsevier, vol. 164(C).
    10. Frederic Kosmowski & Alemayehu Ambel & Asmelash H. Tsegay & Alemayehu Teressa Negawo & Jason Carling & Andrzej Kilian & The Central Statistics Agency, 2021. "A Large-Scale Dataset of Barley, Maize and Sorghum Variety Identification Using DNA Fingerprinting in Ethiopia," Data, MDPI, vol. 6(6), pages 1-7, June.
    11. Rosina Wanyama & Pepijn Schreinemachers & Justus Ochieng’ & Omary Bwambo & Roselyne Alphonce & Fekadu Fufa Dinssa & Ya-ping Lin & Roland Schafleitner, 2023. "Adoption and impact of improved amaranth cultivars in Tanzania using DNA fingerprinting," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 15(5), pages 1185-1196, October.
    12. Kosmowski, Frederic & Chamberlin, Jordan & Ayalew, Hailemariam & Sida, Tesfaye & Abay, Kibrom & Craufurd, Peter, 2021. "How accurate are yield estimates from crop cuts? Evidence from smallholder maize farms in Ethiopia," Food Policy, Elsevier, vol. 102(C).
    13. Wossen, Tesfamicheal & Abay, Kibrom A. & Abdoulaye, Tahirou, 2022. "Misperceiving and misreporting input quality: Implications for input use and productivity," Journal of Development Economics, Elsevier, vol. 157(C).
    14. Jourdain C. Lokossou & Hippolyte D. Affognon & Alphonse Singbo & Michel B. Vabi & Ayoni Ogunbayo & Paul Tanzubil & Alcade C. Segnon & Geoffrey Muricho & Haile Desmae & Hakeem Ajeigbe, 2022. "Welfare impacts of improved groundnut varieties adoption and food security implications in the semi-arid areas of West Africa," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(3), pages 709-728, June.

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