Page 264 - Special Topic Session (STS) - Volume 4

STS583 Yakob M. S.

Cost-effectiveness of remote sensing for
Agricultural Statistics in developing and
transition countries
Yakob Mudesir Seid
Statistician, Office of the Chief Statistician, FAO

Abstract
In broader terms, remote sensing enables improvements in the efficiency of
agricultural statistics methodology, generates and/or validates some
important agricultural related data, allows for more disaggregated data with
relative low cost, and provides early information on crop production
performance to engender early action. High-resolution optical and radar data
are becoming more readily available from approximately 200 earth-
observation satellites. However, their use in many countries is rather limited
due to mainly cost, data size and technological limitations to use Geographic
Information System (GIS) and image-processing software. Remote sensing use
for agricultural statistics is cost effective and relates to: i) the sustained decline
in image prices; ii) continued improvements in the quality of the available
remote sensing data; and iii) the GIS standardisation and image analysis of
open-source applications and cloud processing. This paper discusses how best
to use remote sensing to improve agricultural statistics by focusing on
methodological efficiency, generation and validation of data, disaggregation
and early information. Moreover, the costs and benefits of using remote
sensing is analysed and the cost effectiveness evaluated.

Keywords
Remote sensing, agricultural statistics, improved estimators, sensor suitability,
crop monitoring and yield forecasting

1. Introduction
Since the launch of Landsat series in July 1972, agriculture has been a
major beneficiary of satellite imagery. Despite some constraints posted by lack
of the required expertise in statistics, image software and budget availability,
remote sensing data has played a vital role in improving agricultural statistics
(Hanuschank and Delince, 2004; Taylor et al., 1997).
With spatial resolution brought down to 0.5 m (Marchisio, 2014), farmers’
declarations could be better-validated (Kay et al., 1997) and data precision on
farming (Schumpeter, 2014) would become feasible. On other scales, with
remote sensing data, generating land-cover mapping (Defourny et al., 2011;
Chen, 2014) and availing data for an early warning systems (Brown and
Brickley, 2012; Rembold et al., 2006 & 2013) become easier and efficient.
Footnote: Results from the research studies by the Global Strategy to Improve Agricultural and
Rural Statistics

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