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21st Session of the African Commission on
Agricultural Statistics
Experience of Ethiopia in Implementing an Integrated
Survey Program and Advanced Technology
Samia Zekaria Gutu
Director General
Central Statistical Agency of Ethiopia
28-31 October 2009, Accra, Ghana.
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Outline of the Presentation
• Introduction
• Importance of Agricultural Statistics
• Historical Background of Agricultural Statistics in Ethiopia
• Establishment of National Statistics System
• Advantages of integrating Ag. Statistics in the National Statistics
System
• Master Sample Frame for Agricultural Statistics
• Challenges of Forecasting Food Production in Ethiopia
• Advanced Methodology Implemented at the CSA.
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Introduction
• According to the World Development Report (WDI),
three-fourths of the poor people in developing
countries live in rural areas and most depend on
agriculture for their livelihood.
• The importance of agriculture in the effort to reduce
poverty places agriculture at the centre of the
development agenda.
• This increases the need for monitoring and evaluation
tools to learn what does and does not work. These
tools require much of the basic data, but are often not
available
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Importance of Agricultural Statistics
• Agriculture produces the food to feed a country’s
population, and fulfills other requirements such as the
production of fiber, fuel, ingredients for manufacturing,
it has remained the dominant sector in most Ethiopian
economy.
• The traditional view of agriculture was that one mainly
needed to know the amount of crops that were
produced.
• Current global food crisis calls for much closer
scrutiny and monitoring of the input to agriculture as
well as its production processes.
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Importance of Agricultural Statistics
(cont’d)
• Food security is an important issue in an agricultural
economy like Ethiopia. It has important policy
implication and data needs.
• One of the major food security indicators for Ethiopia
is the level of annual crop production.
• Inaccurate estimates and trends will impact decisions
concerning agricultural strategies, policies and food
aid allocations and distributions.
• Realizing the importance of having accurate and
reliable statistical information the 2005 decree gave a
mandate to the CSA to play its leading role in the
Development of National Statistical System of the
country,
S Historical Background of Agricultural
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Statistics in Ethiopia
Five distinct periods of agricultural statistical
developments can be characterized as follows:
– Prior to 1974: “Ad-hoc surveys”
– 1974 – 1979: “Annual Agricultural Sample Surveys”
– 1980 – 1992: “Rural Integrated Household Survey Program,”
“Integrated System of Food and Agricultural Statistics
Program,”
– 1992 – 2008: “National Integrated Household Survey
Program”
– 2008 and on: Introduction of Area Frame sampling on pilot
basis with advanced technologies.
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Statistics in Ethiopia
• Prior to 1974: Statistical services in general and
agricultural statistics in particular were not
adequate with respect to coverage, timeliness and
reliability of data.
• Between 1974 to 1979: The MoA continued to
obtain assistance from FAO and conducted six
Annual Agricultural Sample Surveys that included
a 1976/77 small-scale agricultural sample census.
• Between 1980 to1992 under Rural Integrated
Household Survey Program (RIHSP) the CSO
carried out 13 Annual Agricultural Sample Surveys.
•
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Statistics in Ethiopia (cont’d)
• The National Integrated Household Survey
Program (NIHSP) that was established from 1992
onwards enabled the CSA to run a number of
annual socioeconomic and demographic surveys.
• In general, the integrated systems mentioned
above enabled the CSA to fully utilize its available
infrastructure, field staff (enumerators, supervisors,
and drivers), logistics support, and data processing
facilities very efficiently and cost effectively for the
past three decades.
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Establishment of National Statistical System
(NSS)
• To address the problem of the national statistical
system through a more comprehensive approach, the
Medium Term National Statistical Program (MTNSP)
from 2003/04- 2007/08 was set up and implemented.
• On the other hand, a new National Statistical
Development Strategy (NSDS) that covers the period
2009/10-2003/14 has been established and endorsed
by the National Statistical Council in May 2009.
• This statistical strategy differs in content, scope and
coverage from the already completed MTNSP. It
provides the country with a strategy of strengthening
statistical capacity across the entire National Statistical
System.
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Advantages of integrating Ag. Statistics in
the NSS
• Agricultural Statistics has been integrated in the
MTNSP as well as in NSDS. The advantages
are:
– Concepts, definitions and classifications become
standardized allowing better collection of data
across sources.
– Ethiopia could maximize these advantages
especially in agricultural statistics and the CSA will
play its leading role once its NSDS is fully
implemented.
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Master Sample Frame for Agricultural
Statistics
• The CSA has developed its master sample frame
basically from its Population censuses.
• As a result, all socioeconomic and demographic
surveys including agricultural census and annual
agricultural sample surveys are based on sample
units that have been selected from its master
sample frame.
• The Master Sample Frame that of NIHSP forms
the foundation for the integrated survey framework,
which also considers the linkage between different
data items.
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Master Sample Frame for Agricultural
Statistics (cont’d)
• The master sample frame obtained from the
population census helps the CSA to identify
agricultural households. It has provided a
linkage between households from the
population census to those with agricultural
holdings.
• The use of a master sample frame has
ensured the sampling and reporting units of the
CSA to be consistently classified across
different socio-economic and demographic
survey results.
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Ethiopia
• Several organizations are active in the field of
predicting crop production and monitoring food
security situation in Ethiopia.
• These include non-government and international
organizations; Regional Bureau of Agriculture and
Rural Development, the Central Statistical Agency,
and the National Meteorological Agency.
• Monitoring activities of international agencies are
based on remote sensing, while BoARD and CSA
yield forecasting exclusively on field assessments
and surveys, respectively.
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in Ethiopia (cont’d)
• Remote-sensing based approaches provide quick results
early in the season, but are not able to generate useable
quantitative estimates below the level of administrative
regions.
• CSA generates sampling-based yield forecasts for all
important crops down to sub-national administrative levels
mostly during Dec./January of each year.
• BoARD through regional offices and extension agents
posted in each administrative unit across the country
provides crop production estimates.
• These estimates are founded mostly on the visual
appreciation of conditions by contact farmers and
development agents
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Ethiopia (cont’d)
• The discrepancy between the CSA’s and BoARD’s
annual crop production forecast data has been a
challenge for the data users so far thus creating
confusion.
• However, an initiative has been put in place to resolve
the mentioned discrepancy by exploiting the strength of
each institution through cooperation and collaborative
efforts.
• In the past two years, assessments of crop production
was carried out jointly, by MoARD /BoARD, CSA and
FAO. As a result the problem of conflicting report of
crop production forecast could be minimized at Federal
level.
• This momentum is expected to be more effective as the
CSA is in the process of developing and implementing
the National Strategy for Statistical Development
(NSDS).
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CSA.
• CSA is implementing and integrating several
advanced technologies. These changes will
improve the data by providing that are more
accurate, timely and credible. These are:
– Stratification- Land Cover
– Area frame methodology
– Multiple frame methodology
– Small area estimates methodology
– Other methodological improvements
•
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• Improved Stratification: Land-cover
– Land cover land use classification is undergoing at
the CSA with the support of FAO/EC’s financial and
technical support using the satellite imagery
– The land cover is used for stratification and
precedes the area frame construction process.
– This stratification system is useful since each
stratum has tremendous diversity in land use.
– The new survey design has deeper types of
stratification in order for the sampling to be more
efficient.
–.
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Area Frame Methodology
• An Area Frame (AF) is a special case of cluster sampling
where farm fields are the clusters. “The concepts of AF
sampling are simple: i.e. divide the total land area to be
surveyed into N small parcels of land, without overlap or
omission; select a random sample of n parcels”. The final
sampling units are called segments.
•
Data collection of the segment is simple because data must
be collected from all reporting units inside the segment
boundaries without error. This method can better control field
data collection both in the field and office.
• At the CSA, in 2008 pilot of AF in one zone was carried out in
about 40 EAs. Currently, the pilot is expanded into one of the
big regions in about 215 EAs.
•
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• Multiple Frame Methodology
Multiple frame sampling is an advanced type of
technology that should always be used with area frame
methods.
Multiple frame (MF) sampling employs the use of two or
more sampling frames conjointly. Collectively, frames
should include all farms in Ethiopia.
One frame is a general purpose frame such as an AF in
which all farms are included. The second frame is a list
of farming households that are important farms for a
specific item of interest.
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• Small Area Estimates Methodology
– The CSA could generate small area (woreda/ district
level) data for agriculture using small area model.
– Three different types of data have been used as an
input for the small area model.
– These are data from the 2001 agricultural census,
wereda level data from the MoARD and the direct
estimates from the annual agricultural sample survey
of the CSA.
– Evaluations at different levels have been conducted
to examine the validity of the data obtained from small
area model for 2007. Further examination is needed
prior to dissemination.
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CSA.
• Other methodological improvements
– CSA has implemented the latest technology such as
global positioning system (GPS) for its area
measurement.
– Reduced the size of crop cutting plots to improve the
estimates of crop yield.
– On the other hand, the CSA is in the process of
establishing the Ethiopian Data Quality Assessment
Framework with the financial and technical assistance
of the World Bank.
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Thank you for your attention