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Measuring agricultural knowledge and adoption

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  • Florence Kondylis
  • Valerie Mueller
  • S. Zhu

Abstract

Understanding the tradeoffs in improving the precision of agricultural measures through survey design is crucial. Yet, standard indicators used to determine program effectiveness may be flawed, and at a differential rate for men and women. We use a household survey from Mozambique to estimate the measurement error from male and female self-reports of their adoption and knowledge of three practices: intercropping, mulching, and strip tillage. Despite clear differences in human and physical capital, there are no obvious differences in the knowledge, adoption, and error in self-reporting between men and women. Having received training unanimously lowers knowledge misreports and increases adoption misreports. Other determinants of reporting error differ by gender. Misreporting is positively associated with a greater number of plots for men. Recall decay on measures of knowledge appears prominent among men but not women. Findings from regression and cost-effectiveness analyses always favor the collection of objective measures of knowledge. Given the lowest rate of accuracy for adoption was around 80%, costlier objective adoption measures are recommended for a subsample in regions with heterogeneous farm sizes.

Suggested Citation

  • Florence Kondylis & Valerie Mueller & S. Zhu, 2015. "Measuring agricultural knowledge and adoption," Agricultural Economics, International Association of Agricultural Economists, vol. 46(3), pages 449-462, May.
    • Handle: RePEc:bla:agecon:v:46:y:2015:i:3:p:449-462
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    1. Uaiene, Rafael N. & Arndt, Channing, 2009. "Farm Household Efficiency In Mozambique," 2009 Conference, August 16-22, 2009, Beijing, China 51438, International Association of Agricultural Economists.
    2. Ragasa, Catherine, 2012. "Gender and Institutional Dimensions of Agricultural Technology Adoption: A Review of Literature and Synthesis of 35 Case Studies," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126747, International Association of Agricultural Economists.
    3. Johnston, David W. & Propper, Carol & Shields, Michael A., 2009. "Comparing subjective and objective measures of health: Evidence from hypertension for the income/health gradient," Journal of Health Economics, Elsevier, vol. 28(3), pages 540-552, May.
    4. Beegle, Kathleen & Carletto, Calogero & Himelein, Kristen, 2012. "Reliability of recall in agricultural data," Journal of Development Economics, Elsevier, vol. 98(1), pages 34-41.
    5. Butler, J S, 1987. "Measurement Error in Self-reported Health Variables," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 644-650, November.
    6. Kondylis, Florence & Mueller, Valerie & Zhu, Jessica, 2017. "Seeing is believing? Evidence from an extension network experiment," Journal of Development Economics, Elsevier, vol. 125(C), pages 1-20.
    7. Kilic, Talip & Zezza, Alberto & Carletto, Calogero & Savastano, Sara, 2017. "Missing(ness) in Action: Selectivity Bias in GPS-Based Land Area Measurements," World Development, Elsevier, vol. 92(C), pages 143-157.
    8. de Nicola, Francesca & Giné, Xavier, 2014. "How accurate are recall data? Evidence from coastal India," Journal of Development Economics, Elsevier, vol. 106(C), pages 52-65.
    9. Deininger, Klaus & Carletto, Calogero & Savastano, Sara & Muwonge, James, 2012. "Can diaries help in improving agricultural production statistics? Evidence from Uganda," Journal of Development Economics, Elsevier, vol. 98(1), pages 42-50.
    10. Andre Croppenstedt & Markus Goldstein & Nina Rosas, 2013. "Gender and Agriculture: Inefficiencies, Segregation, and Low Productivity Traps," The World Bank Research Observer, World Bank, vol. 28(1), pages 79-109, February.
    11. House, Lisa & Lusk, Jayson L. & Jaeger, Sara & Traill, W. Bruce & Moore, Melissa & Valli, Carlotta & Morrow, Bert & Yee, Wallace M.S., 2004. "Objective And Subjective Knowledge: Impacts On Consumer Demand For Genetically Modified Foods In The United States And The European Union," 2004 Annual meeting, August 1-4, Denver, CO 20125, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. Michael Baker & Mark Stabile & Catherine Deri, 2004. "What Do Self-Reported, Objective, Measures of Health Measure?," Journal of Human Resources, University of Wisconsin Press, vol. 39(4).
    13. F. Bailey Norwood & Jayson L. Lusk, 2011. "Social Desirability Bias in Real, Hypothetical, and Inferred Valuation Experiments," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(2), pages 528-534.
    14. Pender, John L. & Nkonya, Ephraim M. & Kato, Edward & Kaizzi, Crammer & Ssali, Henry, 2009. "Impacts of Cash Crop Production on Land Management and Land Degradation: The Case of Coffee and Cotton in Uganda," 2009 Conference, August 16-22, 2009, Beijing, China 50760, International Association of Agricultural Economists.
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    2. Shikuku, Kelvin Mashisia & Okello, Julius Juma & Wambugu, Stella & Sindi, Kirimi & Low, Jan W. & McEwan, Margaret, 2019. "Nutrition and food security impacts of quality seeds of biofortified orange-fleshed sweetpotato: Quasi-experimental evidence from Tanzania," World Development, Elsevier, vol. 124(C), pages 1-1.
    3. Denise Hörner & Adrien Bouguen & Markus Frölich & Meike Wollni, 2019. "The Effects of Decentralized and Video-based Extension on the Adoption of Integrated Soil Fertility Management – Experimental Evidence from Ethiopia," NBER Working Papers 26052, National Bureau of Economic Research, Inc.
    4. Khushbu Mishra & Abdoul G. Sam & Gracious M. Diiro & Mario J. Miranda, 2020. "Gender and the dynamics of technology adoption: Empirical evidence from a household‐level panel data," Agricultural Economics, International Association of Agricultural Economists, vol. 51(6), pages 857-870, November.
    5. Annemie Maertens & Hope Michelson & Vesall Nourani, 2021. "How Do Farmers Learn from Extension Services? Evidence from Malawi," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 569-595, March.
    6. Kondylis, Florence & Mueller, Valerie & Zhu, Jessica, 2017. "Seeing is believing? Evidence from an extension network experiment," Journal of Development Economics, Elsevier, vol. 125(C), pages 1-20.
    7. Alix-Garcia, Jennifer M. & Sims, Katharine R.E. & Costica, Laura, 2021. "Better to be indirect? Testing the accuracy and cost-savings of indirect surveys," World Development, Elsevier, vol. 142(C).
    8. Tewodaj Mogues & Valerie Mueller & Florence Kondylis, 2019. "Cost-effectiveness of community-based gendered advisory services to farmers: Analysis in Mozambique and Tanzania," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-32, March.
    9. Okello, Julius & Shikuku, Kelvin Mashisia & Lagerkvist, Carl Johan & Rommel, Jens & Jogo, Wellington & Ojwang, Sylvester & Namanda, Sam & Elungat, James, 2023. "Social incentives as nudges for agricultural knowledge diffusion and willingness to pay for certified seeds: Experimental evidence from Uganda," Food Policy, Elsevier, vol. 120(C).
    10. Shikuku, K.M., 2018. "Information exchange links, knowledge exposure, and adoption of agricultural technologies in Northern Uganda," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275974, International Association of Agricultural Economists.

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