Restricted accessResearch articleFirst published online 2025-7
Land Measurement Bias: Evidence Based on the Validation Exercise of the Area Under Paddy Cultivation in Bhutan Using Three Different Measurement Techniques Vis-à-Vis Farmer Self-report (FSR),Global Positioning System (GPS) and Google Estimate
We investigate how land measurements vary across three distinct land measurement techniques—farmers’ self-reported data, Global Positioning System (GPS) measurements, and Google estimates from remotely sensed satellite data—in Bhutan. The findings reveal a lack of significant differences between GPS and Google measurements across five key paddy-growing districts. However, the study identifies a noteworthy bias in farmers’ self-reported data relative to GPS measures. This bias is evident in both the levels and logarithmic representations of land area, with farmers tending to over-report for smaller-sized plots and underreport for larger-sized plots.
CarlettoC., GourlayS., MurrayS., & ZezzaA. (2020). Land area measurement in household surveys: Empirical evidence and practical guidance for effective data collection. The World Bank.
2.
CarlettoC., GourlayS., & WintersP. (2015). From guesstimates to GPStimates: Land area measurement and implications for agricultural analysis. Journal of African Economies, 24(5), 593–628.
3.
CarlettoC., SavastanoS., & ZezzaA. (2013). Fact or artifact: The impact of measurement errors on the farm size–productivity relationship. Journal of Development Economics, 103, 254–261.
4.
De WeerdtJ., GibsonJ., & BeegleK. (2020). What can we learn from experimenting with survey methods? Annual Review of Resource Economics, 12, 431–447.
5.
DillonA., GourlayS., McGeeK., & OseniG. (2019). Land measurement bias and its empirical implications: Evidence from a validation exercise. Economic Development and Cultural Change, 67(3), 595–624.
6.
DillonA., & RaoL. N. (2021). Land measurement bias: Comparisons from Global Positioning System, self-reports, and remote sensing data[Global poverty research lab working paper 21-102]. Asian Development Bank.
7.
DivisionA. O. (1982). Estimation of crop areas and yields in agricultural statistics (Issue 22). Food & Agriculture Org.
8.
GoldsteinM., & UdryC. (1999). Agricultural innovation and resource management in Ghana. In Final report to IFPRI under MP17. Mimeo, Yale University.
9.
NSB. (2022a). 2022 integrated agriculture and livestock census of Bhutan. NSB. https://www.nsb.gov.bt/integrated-agriculture-and-livestock-census-of-bhutan-2022/
10.
NSB. (2022b). Labour Force Survey Report 2021. NSB. https://www.nsb.gov.bt/publications/labour-force-survey-report/
11.
NSB. (2022c). National Accounts Statistics 2021. NSB. https://www.nsb.gov.bt/publications/labour-force-survey-report/
12.
SchøningP., ApuuliJ. B. M., MenyhaE., & Muwanga-ZakeE. S. (2005). Handheld GPS equipment for agricultural statistics surveys: Experiments on area-measurements done during fieldwork for the Uganda Pilot Census of Agriculture, 2003. Statistics Norway’s Reports.
13.
SimaikaJ. B. (1982). Estimation of crop areas and yields in agricultural statistics. Agricultural and Food Sciences.
14.
SudU. C., AhmadT., GuptaV. K., ChandraH., SahooP. M., AdityaK., SinghM., & BiswasA. (2017). Methodology for estimation of crop area and crop yield under mixed and continuous cropping. FAO, Rome Publication.
15.
VermaV., MarchantT., & ScottC. (1988). Evaluation of crop-cut methods and farmer reports for estimating crop production: Results of a methodological study in five African countries[Working or discussion paper]. Agricultural and Food Sciences.
