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Book
Year: 2016 Publisher: Washington, D.C. : The World Bank,
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Sweet potato (Ipomoea batatas) varieties have important nutritional differences and there is strong interest to identify nutritionally superior varieties for dissemination. In agricultural household surveys, this information is often collected based on the farmer's self-report. However, recent evidence has demonstrated the inherent difficulties in correctly identifying varieties from self-report information. This study examines the accuracy of self-report information on varietal identification from a data capture experiment on sweet potato varieties in southern Ethiopia. Three household-based methods of identifying varietal adoption are tested against the benchmark of DNA fingerprinting: (A) elicitation from farmers with basic questions for the most widely planted variety; (B) farmer elicitation on five sweet potato phenotypic attributes by showing a visual-aid protocol; and (C) enumerator recording observations on five sweet potato phenotypic attributes using a visual-aid protocol and visiting the field. The reference being the DNA fingerprinting, about 30 percent of improved varieties were identified as local or non-improved, and 20 percent of farmers identified a variety as local when it was in fact improved. The variety names given by farmers delivered inconsistent and fuzzy varietal identities. The visual-aid protocols employed in methods B and C were better than method A, but still way below the adoption estimates given by the DNA fingerprinting method. The findings suggest that estimating the adoption of improved varieties with methods based on farmer self-reports is questionable, and point toward a wider use of DNA fingerprinting, likely to become the gold standard for crop varietal identification.
Agricultural Survey --- Agriculture --- Measurement Error --- Sweet Potato --- Varietal Identification
Book
Year: 2018 Publisher: Washington, D.C. : The World Bank,
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To document the relative accuracy of methods for microdata collection on root and tuber crop production, an experiment was implemented in Malawi over a 12-month period, randomly assigning cassava-producing households to one of four approaches: daily diary-keeping, with semi-weekly supervision visits; daily diary-keeping, with semi-weekly supervisory phone calls; two six-month recall interviews, with six months in between; and a single 12-month recall interview. Lapses in diary-keeping can underestimate true production, albeit to a lesser degree compared to recall. And the comparisons between the diary variants and the variation in underestimation by recall period are unclear ex ante. The analysis reveals that compared to traditional diary-keeping, the household-level annual cassava production is 295 kilograms higher, on average, (and assumed as closer to the truth) under diary-keeping with phone calls. This effect corresponds to 28 percent of the average traditional diary-keeping production estimate. Although the difference between the estimates based on six-month recall and traditional diary-keeping is statistically insignificant, 12-month recall underestimates annual production, on average, by 516 kilograms and 221 kilograms, respectively, compared to diary-keeping with phone calls and traditional diary-keeping. While the recall-based approaches both underestimate true production, six-month recall does so to a lesser extent. The evidence additionally demonstrates likely gross overestimation in international and ministerial statistics on cassava yields in Malawi. For improved microdata on root and tuber crop production, the adoption of (i) diary-keeping with phone calls (particularly if deployed in a broader mobile phone-based survey) or (ii) six-month recall, as a second-best alternative, is recommended.
Cassava --- Climate Change And Agriculture --- Crop Cutting --- Crops & Crop Management Systems --- Extended-Harvest Crops --- Food Security --- Harvest Diaries --- Health, Nutrition And Population --- Household Surveys --- Industry --- Information And Communication Technologies --- Nutrition --- Primary Metals --- Production Measurement --- Recall --- Root Crops --- Tree Crops --- Tuber Crops --- Yield Measurement
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