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Book
Year: 2017 Publisher: Washington, D.C. : The World Bank,
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Based on a two-round household panel survey conducted in Eastern Uganda, this study shows that the analysis of the inverse scale-productivity relationship is highly sensitive to how plot-level maize production, hence yield (production divided by GPS-based plot area), is measured. Although farmer-reported production-based plot-level maize yield regressions consistently lend support to the inverse scale-productivity relationship, the comparable regressions estimated with maize yields based on sub-plot crop cutting, full-plot crop cutting, and remote sensing point toward constant returns to scale, at the mean as well as throughout the distributions of objective measures of maize yield. In deriving the much-debated coefficient for GPS-based plot area, the maize yield regressions control for objective measures of soil fertility, maize genetic heterogeneity, and edge effects at the plot level; a rich set of plot, household, and plot manager attributes; as well as time-invariant household- and parcel-level unobserved heterogeneity in select specifications that exploit the panel nature of the data. The core finding is driven by persistent overestimation of farmer-reported maize production and yield vis-a-vis their crop cutting-based counterparts, particularly in the lower half of the plot area distribution. Although the results contribute to a larger, and renewed, body of literature questioning the inverse scale-productivity relationship based on omitted explanatory variables or alternative formulations of the agricultural productivity measure, the paper is among the first documenting how the inverse relationship could be a statistical artifact, driven by errors in farmer-reported survey data on crop production.
Crop Cutting --- Household Surveys --- Inverse Scale-Productivity Relationship --- Maize --- Plot Area Measurement --- Remote Sensing --- Yield Measurement
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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