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 October 2009, Accra, Ghana.

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.

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

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.

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,

Historical Background of Agricultural 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.

Historical Background of Agricultural 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.

Historical Background of Agricultural 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.

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/ /08 was set up and implemented. On the other hand, a new National Statistical Development Strategy (NSDS) that covers the period 2009/ /14 has been established and endorsed by the National Statistical Council in May 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.

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.

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.

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.

Challenges of Forecasting Food Production in 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.

Challenges of Forecasting Food Production 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

15 Challenges of Forecasting Food Production in 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).

Advanced Methodology Implemented at 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

Advanced Methodology Implemented at CSA. 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. –.

Advanced Methodology Implemented at CSA. 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.

Advanced Methodology Implemented at CSA. 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.

Advanced Methodology Implemented at CSA. 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 Further examination is needed prior to dissemination.

Advanced Methodology Implemented at 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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