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The Cost of Misclassification of Seeds: Evidence from Improved Maize Variety Adopters in Ethiopia
Title statement The Cost of Misclassification of Seeds: Evidence from Improved Maize Variety Adopters in Ethiopia [rukopis] / Dibekulu Mulu Birhan Additional Variant Titles Improved seed adoption: misclassification and suboptimal decision Personal name Birhan, Dibekulu Mulu, (dissertant) Translated title Improved seed adoption: misclassification and suboptimal decision Issue data 2021 Phys.des. IX, 42 : mapy, grafy, schémata, tab. + 5 figures, 10 tables Note Ved. práce Maria Sassi Oponent Gopal Trital Another responsib. Sassi, Maria, (thesis advisor) Trital, Gopal, (opponent) Another responsib. Univerzita Palackého. Katedra rozvojových studií (degree grantor) Keywords Keywords: Misclassification * Improved seed * Propensity score matching * DNA fingerprinting * Self-reporting * False-negative * True-positive * Keywords: Misclassification * Improved seed * Propensity score matching * DNA fingerprinting * Self-reporting * False-negative * True-positive Form, Genre diplomové práce master's theses UDC (043)378.2 Country Česko Language angličtina Document kind PUBLIKAČNÍ ČINNOST Title Mgr. Degree program Navazující Degree program Geography Degreee discipline International Development Studies book
Kvalifikační práce Downloaded Size datum zpřístupnění 00274370-266773248.pdf 6 1.2 MB 31.05.2021 Posudek Typ posudku 00274370-ved-453916315.pdf Posudek vedoucího 00274370-opon-610228420.pdf Posudek oponenta
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.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.
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