Abstract

Traditional sampling strategies for paddy rice statistics rely on outdated list frames, incomplete holding information, or administrative data that are prone to numerous biases. The objective of this study is to test the utility of an area frame developed using remote sensing data in three pilot provinces— Savannakhet (Lao People’s Democratic Republic), Ang Thong (Thailand), and Thai Binh (Viet Nam). Direct estimates of total paddy rice area and production are calculated from area frame using two methods––one involving measurement of plot size using a Global Positioning System instrument and the other utilizing a digitized map of farmer-identified plot boundaries on a high-resolution Google Earth image. A third method involving the calculation of ratio estimates using independent mesh-level measures is compared with the first two methods involving direct estimates, and with the estimates generated from administrative data from the countries. Our study finds that ratio estimation significantly improves the level of precision of paddy rice statistics. Substantial deviations are also observed between official statistics and the statistics generated through direct estimation.

Highlights

  • And reliable agricultural statistics are critical for monitoring government agricultural development plans and mitigating the effects of extreme weather and climate change

  • These weights were used to produce alternative direct estimates of the total area planted in rice alongside calculating corresponding standard error (SE), 95% confidence intervals and design effects

  • Traditional sampling strategies commonly employed in Asia and the Pacific for paddy rice statistics on outdated list frames, incomplete holding information, or administrative data that are prone to numerous biases

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Summary

Introduction

And reliable agricultural statistics are critical for monitoring government agricultural development plans and mitigating the effects of extreme weather and climate change They are useful in gaining a better understanding of people’s well-being through timely and effective policy interventions. In the case of administrative data, the starting point in most cases is a government agricultural officer who determines crop area and production in his or her assigned locality by observing harvests and interviewing experts such as village heads, farmers, and traders. These estimates are reported to the level of bureaucracy until the summary statistics reach the national government (ADB 2016). Data collection officers and others involved in the process may have vested interests to either support their claims of accomplishment that influence the estimation process upward or showcase a downward trend in expectations of subsidies or other government amenities (Carfagna and Carfagna 2010)

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