Land measurement bias: Comparisons from global positioning system, self-reports, and satellite data - Andrew Dillon; Lakshman Nagraj Rao (Asian Development Bank (ADB), 2018)
Agricultural statistics derived from remote sensing data have been used primarily to compare land use information and changes over time. Nonclassical measurement error from farmer self-reports has been well documented in the survey design literature primarily in comparison to plots measured using Global Positioning System (GPS). In this paper, we investigate the reliability of remotely sensed satellite data on nonrandom measurement error and on agricultural relationships such as the inverse land size-productivity relationship and input demand functions. In our comparison of four Asian countries, we find significant differences between GPS and remotely sensed data only in Viet Nam, where plot sizes are small relative to the other countries. The magnitude of farmers' self-reporting bias relative to GPS measures is nonlinear and varies across countries, with the largest magnitude of selfreporting bias of 130% of a standard deviation (2.2-hectare bias) in the Lao People's Democratic Republic relative to Viet Nam, which has 13.3% of a standard deviation (.008-hectare bias). In all countries except Viet Nam, the inverse land size-productivity relationship is upwardly biased for lower land area self-reported measures relative to GPS measures. In Viet Nam, the intensive margin of organic fertilizer use is negatively biased by self-reported measurement error by 30.4 percentage points. As remotely sensed data becomes publicly available, it may become a less expensive alternative to link to survey data than rely on GPS measurement.
Keywords: O12; O13; Q12; Q15; 330; agriculture; land measurement; remote sensing; survey methods