The Cost of Misclassification of Seeds: Evidence from Improved Maize Variety Adopters in Ethiopia

Dibekulu Mulu BIRHAN

Master's thesis

Improved seed adoption: misclassification and suboptimal decision

Abstract:

Improved seed adopters in developing countries are likely to misclassify the adopted variety as a landrace due to challenges such as dominant informal seed market, poor seed certification system, and widespread seed recycling practice that surround the agriculture sector. Improved seeds need differential treatment in terms of the supply of farm inputs for optimal farm production. Misclassification error could lead to suboptimal allocation of the inputs and thus cause a loss of potential yield. The purpose of this study is to estimate the loss of the yield attributable to the misclassification measurement error. Employing a combination of DNA fingerprinting and self-reporting cross-sectional plot-level data, the study analyzed the yield loss in the 2018/19 cropping season by Ethiopian farmers who adopted improved maize varieties using the propensity score matching technique. The seed adopted on 61% of the plots is found misclassified (false-negative adopters). The misclassification borne yield loss found is considerable. The average maize yield from false-negative plots is less in the range of 609 to 776 kgs per hectare than from true-positive counterfactuals. Although this study sheds light on the causal impact of misclassification on yield, given it is the first empirical investigation on the topic, further studies are needed to verify the robustness and generalizability of the findings.

Abstract:

Improved seed adopters in developing countries are likely to misclassify the adopted variety as a landrace due to challenges such as dominant informal seed market, poor seed certification system, and widespread seed recycling practice that surround the agriculture sector. Improved seeds need differential treatment in terms of the supply of farm inputs for optimal farm production. Misclassification error could lead to suboptimal allocation of the inputs and thus cause a loss of potential yield. The purpose of this study is to estimate the loss of the yield attributable to the misclassification measurement error. Employing a combination of DNA fingerprinting and self-reporting cross-sectional plot-level data, the study analyzed the yield loss in the 2018/19 cropping season by Ethiopian farmers who adopted improved maize varieties using the propensity score matching technique. The seed adopted on 61% of the plots is found misclassified (false-negative adopters). The misclassification borne yield loss found is considerable. The average maize yield from false-negative plots is less in the range of 609 to 776 kgs per hectare than from true-positive counterfactuals. Although this study sheds light on the causal impact of misclassification on yield, given it is the first empirical investigation on the topic, further studies are needed to verify the robustness and generalizability of the findings.

Keywords

Keywords, Misclassification, Improved seed, Propensity score matching, DNA fingerprinting, Self-reporting, False-negative, True-positive

Language used: English

Date on which the thesis was submitted / produced: 31. 5. 2021

Thesis defence

• Supervisor: prof. Maria Sassi

Palacký University Olomouc

Faculty of Science

Master programme / field:
Geography / International Development Studies