Abstract

The Ethiopian Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency (CSA) of Ethiopia and the World Bank Living Standards Measurement Study- Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic panel household level data with a special focus on improving agriculture statistics and the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

The specific objectives of the ESS are:

• Development of an innovative model for collecting agricultural data in conjunction with household data;
• Strengthening the capacity to generate a sustainable system for producing accurate and timely information on agricultural households in Ethiopia;
• Development of a model of inter-institutional collaboration between the CSA and relevant federal and local government agencies as well as national and international research and development partners; and
• Comprehensive analysis of household income, well-being, and socio-economic characteristics of households in rural areas and small towns.

The ESS contains several innovative features:

• Integration of household welfare data with agricultural data;
• Creation of a panel data set that can be used to study welfare dynamics, the role of agriculture in development and the changes over time in health, education and labor activities, inter alia;.
• Collection of information on the network of buyers and sellers of goods with which the household interacts;
• Expanding the use of GPS units for measuring agricultural land areas;
• Involvement of multiple actors in government, academia and the donor community in the development of the survey and its contents as well as its implementation and analysis;
• Creation of publicly available micro data sets for researchers and policy makers;

Geographic coverage

National Coverage.

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

ESS is designed to collect panel data in rural and urban areas on a range of household and community level characteristics linked to agricultural activities. The first wave was implemented in 2011-12 and the second wave is implemented in 2013-14. The first wave, ERSS, covered only rural and small town areas. The second wave, ESS, added samples from large town areas. The second wave is nationally representative. The existing panel data (2011/12-2013/14) is only for rural and small towns. Large towns were added during the second wave and, so far, there is only one round. The planned follow-up ESS surveys will continue to be nationally representative. The ESS sample size provides estimates at the national level for rural and small town households. At the regional level, it provides estimates for five regions including Addis Ababa, Amhara, Oromiya, SNNP, and Tigray.

The sample is a two-stage probability sample. The first stage of sampling entailed selecting primary sampling units, which are a sample of the CSA enumeration areas (EAs). A total of 433 EAs were selected based on probability proportional to size of the total EAs in each region. For the rural sample, 290 EAs were selected from the AgSS EAs. For small town EAs, a total of 43 EAs and for large towns 100 EAs were selected. In order to ensure sufficient sample in the most populous regions (Amhara, Oromiya, SNNP, and Tigray) and Addis Ababa, quotas were set for the number of EAs in each region. The sample is not representative for each of the small regions including Afar, Benshangul Gumuz, Dire Dawa, Gambella, Harari, and Somalie regions. However, estimates can be produced for a combination of all smaller regions as one "other region" category.

During the second wave 100 urban EAs were added. The addition also included one more region to the sample, Addis Ababa. In each EA 15 households were selected. The addition of urban EAs increased the sample size from 333 to 433 EAs or from about 3,969 to 5,469 households.

The second stage of sampling was the selection of households to be interviewed in each EA. For rural EAs, a total of 12 households are sampled in each EA. Of these, 10 households were randomly selected from the sample of 30 AgSS households. The AgSS households are households which are involved in farming or livestock activities. Another 2 households were randomly selected from all other non-agricultural households in the selected rural EA (those not involved in agriculture or livestock). In some EAs, there is only one or no such households, in which case, less than two non-agricultural households were surveyed and more agricultural households were interviewed instead so that the total number of households per EA remains the same.

In the small town EAs, 12 households are selected randomly from the listing of each EA, with no stratification as to whether the household is engaged in agriculture/livestock. The same procedure is followed in the large town EAs. However, 15 households were selected in each large town EA.

Households were not selected using replacement. Thus, the final number of household interviewed was slightly less than the 5,469 as planned in the design. A total of 3,776 panel households and 1,486 new households (total 5,262 households) were interviewed with a response rate of 96.2 percent.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

The interviews were carried out using paper and pen interviewing method. However, a concurrent data entry arrangement was introduced in this wave. In this arrangement, the enumerators did not wait until all the interviews were completed. Rather, once the enumerators completed some 3 to 4 questionnaires, the supervisors collected those completed interviews from the enumerators and brought them to the branch offices for data entry, while the enumerators are still conducting interviews with other households. Then questionnaires are keyed at the branch offices as soon as they are completed using CSPro data entry application software. The data from the completed questionnaires are then checked for any interview or data entry errors using a stata program. Data entry errors are checked with the data entry clerks and the interview errors are then sent to back to the field for correction and feedback to the ongoing interviews. Several rounds of this process were undertaken until the final data files are produced. In addition, after the fieldwork was completed the paper questionnaires were sent to the CSA headquarters in Addis Ababa for further checking. Additional cleaning was carried out, as needed, by checking the hard copies.

Response rate

Response rate was 96.2 percent.

Abstract

The production and utilization of food crops is a necessity to humanity. Agriculture, as a primary activity directly connected to food availability, plays a crucial role in responding to this necessity. Agriculture is presumed to be the engine for economic development in developing countries and more oriented to rural development to ensure the wellbeing of the population. Consequently the efforts of government and non - government organizations have been poured on to it besides the farmers' to realize food security. Adverse conditions emanating from natural disasters and man made problems such as the over exploitation of land generate shocks to agriculture that instigate crises related to food availability. These and other effects necessitate a priority in scrutinizing the performance of agriculture in order to combat food crises. Accurate and timely statistics are a requisite to check, appraise and gauge the agricultural situation. They are used to inform data users of the nature of agriculture and changes taking place in it and trigger policy intervention. To this end, the Central Statistical Agency (CSA) has been furnishing statistical information on the country's agriculture since 1980-1981. As part of this task the 2005-2006 (1998 E.C) Agricultural Sample Survey was conducted to provide data on crop area and production of crops within the private peasant holdings for Main (“Meher”) Season of the cited year.

The general objective of CSA's Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is essential for planning, policy formulation, monitoring and evaluation of mainly food security and other agricultural activities.

The specific objectives of Main (“Meher”) Season Post Harvest Survey are: - To estimate the total cultivated area, production and yield of crops and provide estimates of land use area and quantity of agricultural inputs. - To estimate the total volume of inputs used, inputs applied area and number of holders using inputs. - To estimate the total cultivated area and other forms of land use.

Geographic coverage

The 2005-2006 annual Agricultural Sample Survey covered the entire rural parts of the country except all zones of Gambella region, and the non-sedentary population of three zones of Afar and six zones of Somali regions.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: The list containing EAs of all regions and their respective agricultural households obtained from the 2001/02 Ethiopian Agricultural Sample Enumeration (EASE) was used as the sampling frame in order to select the primary sampling units (EAs). Consequently, all sample EAs were selected from this frame based on the design proposed for the survey. Resettlement localities, on the other hand, are sub-samples of the list of all resettlements localities obtained from each region. The second stage sampling units, households, were selected from a fresh list of households that were prepared for each EA/ resettlement localities at the beginning of the survey.

Sample Design: In order to select the sample a stratified two-stage cluster sample design was implemented. Enumeration areas (EAs) /resettlement locality were taken to be the primary sampling units (PSUs) and the secondary sampling units (SSUs) were agricultural households. In 2005-2006, unlike the years before, in order to obtain a fairly representative number of extension program participant households the CSA categorized the listed agricultural households in each EAs/resettlement area into two strata, i.e. households that are and that are not participants of extension program. The stratification was done on the basis of the six major crops where by the extension program is mostly exercised in the country. The crops are maize, teff, wheat, barley, sorghum and finger millet. The sample size for the 2005-2006 agricultural sample survey was determined by taking into account of both the required level of precision for the most important estimates within each domain and the amount of resources allocated to the survey. In order to reduce non-sampling errors manageability of the survey in terms of quality and operational control was also in addition considered. Except Harari, Addis Ababa and Dire Dawa, where each region as a whole was taken to be the domain of estimation; each zone of a region / special wereda was adopted as a stratum for which major findings of the survey are reported. Moreover, values about the 2005-2006 cultivated areas of crops and the expected amount of production for Gambella region is also provided. However, it is important to note that the values are not obtained from the survey but they are projections from the results of the 2003/04 annual Crop Production Forecast Sample Survey.

Selection Scheme: Enumeration areas/resettlement localities from each stratum were selected systematically using probability proportional to size sampling technique; size being number of agricultural households. The sizes for EAs were obtained from the 1994 Population & Housing Census and adjusted for the sub-sampling effect. Sizes for resettlement localities on the other hand were obtained from the 2004 listings of resettlement localities. From the fresh list of households prepared at the beginning of the survey 30 agricultural households within each sample EA/resettlement locality were selected systematically. Twenty of the households were selected from non extension participant agricultural households while the rest 10 households were selected from extension participant agricultural households.

Note: Distribution of sampling units planned and covered EAs and resettlement localities) by stratum is presented in Appendix III of 2005-2006 Agricultural Sample Survey, Volume I report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2005-2006 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaires: - AgSS Form 98/0: It contains forms that used to list all households in the sample areas. - AgSS Form 98/1: It contains forms that used to list selected households in the sample areas. - AgSS Form 98/2A: It contains forms that used to collect information about crops, results of area measurements covered by crops and other land uses. - AgSS Form 98/2B: It contains forms that used to collect information about miscellaneous questions for the holders. - AgSS Form 98/4: It contains forms that used to collect information about list of temporary crop fields for selecting crop cutting plots. - AgSS Form 98/5: It contains forms that used to collect information about list of temporary crop cutting results.

Note: The questionnaires are presented in the Appendix III of the 2005-2006 Agricultural Sample Survey report, Volume I which is provided as external resource.

Cleaning operations

Editing, Coding and Verification: Statistical data editing plays an important role in ensuring the quality of the collected survey data. It minimizes the effects of errors introduced while collecting data in the field, hence the need for data editing, coding and verification. Although coding and editing are done by the enumerators and supervisors in the field, respectively, verification of this task is done at the Head Office. An editing, coding and verification instruction manual was prepared and reproduced for this purpose. Then 55 editors-coders and verifiers were trained for two days in editing, coding and verification using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100 % basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires took 25 days.

Data Entry, Cleaning and Tabulation: Before data entry, the Natural Resources and Agricultural Statistics Department prepared edit specification for the survey for use on personal computers for data consistency checking purposes. The data on the edited and coded questionnaires were then entered into personal computers. The data were then checked and cleaned using the edit specifications prepared earlier for this purpose. The data entry operation involved about 80 data encoders and it took 60 days to finish the job. Finally, summarization of the data was done on personal computers to produce statistical tables as per the tabulation plan.

Response rate

A total of 2,024 enumeration areas (EAs) and 250 resettlement localities were selected to be covered in the survey. However, due to various reasons that are beyond control, in 12 EAs and 1 resettlement locality the survey could not be successful and hence interrupted. Thus, all in all the survey succeeded to cover 2,012 EAs and 249 resettlement localities (99.43 %) throughout the regions.

Sampling error estimates

Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix I and II of 2005-2006 Agricultural Sample Survey, Volume I report which is provided as external resource.

Abstract

The health and wealth of a nation and its potential to develop and grow depend on its ability to feed its people. To help ensure that food will remain available to those who need it, there is nothing more important to give priority to than agriculture. Accurate and timely statistics about the basic produce and supplies of agriculture are essential to assess the agricultural situation. To help policy maker's deal with the fundamental challenge they are faced within the agricultural sector of the economy and develop measures and policies to maintain food security, there should be a continuous provision of statistics. The collection of reliable, comprehensive and timely data on agriculture is thus required for the above purposes. In this perspective, the Central Statistical Agency (CSA) has endeavored to generate agricultural data for policy makers and other users. The general objective of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is considered essential for development planning, socio-economic policy formulation, food security, etc. The AgSS is composed of four components: Crop production forecast survey, Main (“Meher”) season survey, Livestock survey, and survey of the “Belg” season crop area and production.

The specific objectives of the Main (“Meher”) season area and production survey are: - To estimate the total cultivated land area, production and yield per hectare of major crops (temporary). - To estimate the total farm inputs applied area and quantity of inputs applied by type for major temporary and permanent crops.

Geographic coverage

The survey covered all sedentary rural agricultural population in all regions of the country except urban and nomadic areas which were not included in the survey.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2000/2001 (1993 E.C) Meher season agricultural sample survey covered the rural part of the country except three zones in Afar regional state and six zones in Somalie regional state that are predominantly nomadic. A two-stage stratified sample design was used to select the sample. Each zones/special wereda was adopted as stratum for which major findings of the survey are reported except the four regions; namely, Gambella, Harari, Addis Ababa and Dire Dawa which were considered as strata/reporting levels. The primary sampling units (PSUs) were enumeration areas (EAs) and agricultural households were the secondary sampling units. The survey questionnaires were administered to all agricultural holders within the sample households. A fixed number of sample EAs were determined for each stratum/reporting level based on precision of major estimates and cost considerations. Within each stratum EAs were selected using probability proportional to size systematic sampling; size being total number of agricultural households in the EAs as obtained from the 1994 population and housing census. From each sample EA, 40 agricultural households were systematically selected for the annual agricultural sample survey from a fresh list of households prepared at the beginning of the field work of the annual agricultural survey. Of the forty agricultural households, the first twenty-five were used for obtaining information on area under crops, Meher and Beleg season production of crops, land use, agricultural practices, crop damage, and quantity of agricultural households sampled in each of the selected EAs, data on crop cutting were collected for only the fifteen households (11th - 25th households selected). A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households.

Note: Distribution of the number of sampling units sampled and covered by strata is given in Appendix I of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000-2001 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. Lists of forms in the questionnaires: - AgSS Form 93/0: Used to list all households and agricultural holders in the sample enumeration areas. - AgSS Form 93/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 93/3A: Used to list fields and agricultural practices only pure stand temporary and permanent crops, list of fields and agricultural practices for mixed crops, other land use, quantity of improved and local seeds by type of crop and type and quantity of crop protection chemicals. - AgSS Form 93/4A: Used to collect results of area measurement. - AgSS Form 93/5: Used to list fields for selecting fields for crop cuttings and collect information about details of crop cutting.

Note: The questionnaires are presented in the Appendix IV of the 2000-2001 Agricultural Sample Survey Volume I report which is provided as external resource.

Cleaning operations

Editing, Coding and Verification: In order to insure the quality of the collected survey data an editing, coding and verification instruction manual was prepared and printed. Then 23 editors-coders and 22 verifiers were trained for two days in the editing, coding and verification operation using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100% basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires was completed in about 30 days.

Data Entry, Cleaning and Tabulation: Before starting data entry, professional staff of Agricultural Statistics Department prepared edit specifications to use on personal computers utilizing the Integrated Microcomputer Processing System (IMPS) software for data consistency checking purposes. The data on the coded questionnaires were then entered into personal computers using IMPS software. The data were then checked and cleaned using the edit specification prepared earlier for this purpose. The data entry operation involved about 31 data encoders and it took 28 days to complete the job. Finally, tabulation was done on personal computers to produce results as indicated in the tabulation plan.

Response rate

A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households. The response rate was found to be 99.14%.

Sampling error estimates

Estimation procedures of parameters of interest (total and ratio) and their sampling error is presented in Appendix II of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

Abstract

The general objective of CSA's Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is essential for planning, policy formulation, monitoring and evaluation of mainly food security and other agricultural activities. The AgSS is composed of four components: Crop Production Forecast Survey, Meher Season Post Harvest Survey (Area and production, land use, farm management and crop utilization), Livestock Survey and Belg Season Survey.

The specific objectives of Meher Season Post Harvest Survey are to estimate the total crop area, volume of crop production and yield of crops for Meher Season agriculture in Ethiopia.

Geographic coverage

The annual Agricultural Sample Survey (Meher season) covered the entire rural parts of the country except the non-sedentary population of three zones of Afar and six zones of Somali regions

Analysis unit

Agricultural household/ Holder/ Crop

Universe

The survey covered agricultural households in the sample selected regions.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The list containing EAs of all regions and their respective households obtained from the 2007 (1999 E.C) cartographic census frame was used as the sampling frame in order to select the primary sampling units (EAs). Consequently, all sample EAs were selected from this frame based on the design proposed for the survey. The second stage sampling units, households, were selected from a fresh list of households that were prepared for each EA at the beginning of the survey.

Sample Design In order to select the sample a stratified two-stage cluster sample design was implemented. Enumeration areas (EAs) were taken to be the primary sampling units (PSUs) and the secondary sampling units (SSUs) were agricultural households. The sample size for the 2010/11 agricultural sample survey was determined by taking into account of both the required level of precision for the most important estimates within each domain and the amount of resources allocated to the survey. In order to reduce non-sampling errors, manageability of the survey in terms of quality and operational control was also considered.

All regions were taken to be the domain of estimation for which major findings of the survey are reported.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2011-2012 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaires: - AgSS Form 2004/0: It contains forms that used to list all households in the sample areas. - AgSS Form 2004/1: It contains forms that used to list selected agricultural households and holders in the sample areas. - AgSS Form 2004/2A: It contains forms that used to collect information about crops, results of area measurements covered by crops and other land uses. - AgSS Form 2004/2B: It contains forms that used to collect information about miscellaneous questions for the holders. - AgSS Form 2004/4: It contains forms that used to collect information about list of temporary crop fields for selecting crop cutting plots. - AgSS Form 2004/5: It contains forms that used to collect information about list of temporary crop cutting results.

Cleaning operations

Editing, Coding and Verification Statistical data editing plays an important role in ensuring the quality of the collected survey data. It minimizes the effects of errors introduced while collecting data in the field, hence the need for data editing, coding and verification. Although coding and editing are done by the enumerators and supervisors in the field, respectively, verification of this task is done at the Head Office.

An editing, coding and verification instruction manual was prepared and reproduced for this purpose. Then 66 editors-coders and verifiers were trained for two days in editing, coding and verification using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100 % basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires took 18 days.

Data Entry, Cleaning and Tabulation Before data entry, the Agriculture, Natural Resources and Environment Statistics Directorate of the CSA prepared edit specification for the survey for use on personal computers for data consistency checking purposes. The data on the edited and coded questionnaires were then entered into personal computers. The data were then checked and cleaned using the edit specifications prepared earlier for this purpose. The data entry operation involved about 70 data encoders, 10 data encoder supervisors, 12 data cleaning operators and 55 personal computers. The data entered into the computers using the entry module of the CSPRO (Census and Survey Processing System) software, which is a software package developed by the United States Bureau of the Census. Following the data entry operations, the data was further reviewed for data inconsistencies, missing data … etc. by the regular professional staff from Agriculture, Natural Resources and Environment Statistics Directorate. The final stage of the data processing was to summarizing the cleaned data and produce statistical tables that present the results of the survey using the tabulation component of the PC based CSPRO software produced by professional staff from Agriculture, Natural Resources and Environment Statistics Directorate.

Response rate

A total of 2,290 Enumeration Areas (EAs) were selected. However, due to various reasons that are beyond control, in 17 EAs the survey could not be successful and hence interrupted. Thus, all in all the survey succeeded to cover 2,273 EAs (99.25 %) throughout the regions. The Annual Agricultural Sample survey (Meher season) was conducted on the basis of 20 agricultural households selected from each EA. Regarding the ultimate sampling units, it was intended to cover aa total of 47,080 gricultural households, however, 45,575 (98.9 %) were actually covered by the survey.

Sampling error estimates

Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix-I and II of the final report. Distribution of sampling units (sampled and covered EAs and households) by stratum is also presented in Appendix-III of the final report.

The Ethiopian Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study- Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic panel household level data with a special focus on improving agriculture statistics and the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. The specific objectives of the ESS are: Development of an innovative model for collecting agricultural data in conjunction with household data; Strengthening the capacity to generate a sustainable system for producing accurate and timely information on agricultural households in Ethiopia; Development of a model of inter-institutional collaboration between the CSA and relevant federal and local government agencies as well as national and international research and development partners; and Comprehensive analysis of household income, well-being, and socio-economic characteristics of households in rural areas and small towns. The ESS contains several innovative features: Integration of household welfare data with agricultural data; Creation of a panel data set that can be used to study welfare dynamics, the role of agriculture in development and the changes over time in health, education and labor activities, inter alia;. Collection of information on the network of buyers and sellers of goods with which the household interacts; Expanding the use of GPS units for measuring agricultural land areas; Involvement of multiple actors in government, academia and the donor community in the development of the survey and its contents as well as its implementation and analysis; Creation of publicly available micro data sets for researchers and policy makers

Abstract

Food security has become a burring issue in Ethiopia since it is an absolute prerequisite for political and social stability. It received national prominence in the aftermath of the recurring drought and famine and obviously became an immediate domestic policy concern. The gap between the dire need for food supply is compounded by rapidly increasing population, depletion of natural resources and the existing traditional way of farming. It even requires sacrifice to provide adequate supply of food in such a situation where natural and human factors have negatively impacted in the agricultural production and resulted in recurrent droughts and sometimes in catastrophe. Pressed by these problems and other economic factors, the Ethiopian government has centered its agricultural policy on ensuring food security by allocating more resources to increase agricultural production so as to ward off food shortage and ensure continuous adequate supply of food. To monitor and evaluate the performance of the policy and the trends in the charging patterns in agricultural, statistical information on agriculture is required as an input since agriculture is a primary activity connected with food availability. The Central Statistical Agency (CSA) has been generating statistical information used as inputs in the formulation of agricultural policies by collecting processing and summarizing reliable, comprehensive and timely data on the country's agriculture. As part of this mission the 2003-2004 (1996 E.C) Annual Agricultural Sample Survey was conducted to furnish data on cropland area and production of crops within the private peasant holdings for Main (“Meher”) season of the quoted year.

The general objective of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is essential for planning, policy formulation, food security, etc. The survey is composed of four components: Crop production forecast survey. Main (“Meher”) season survey, Livestock survey and “Belg” season survey.

The specific objectives of Main (“Meher”) season survey are: - To estimate the total cultivated area, production and yield of crops. - To estimate the total volume of inputs used, inputs applied area and number of holders using inputs. - To estimate the total cultivated area and other forms of land use.

Geographic coverage

The 2003-2004 annual Agricultural Sample Survey covered the entire rural parts of the country except all zones of Gambella region, and the non-sedentary population of three zones of Afar and six zones of Somali regions.

Note: The crop cutting exercise part of the survey from November 2003 up to January 2004 was not done in Gambela regional state, therefore no production estimates for the region was computed for Meher (main) season.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: The list containing EAs of all regions and their respective agricultural households obtained from the 2001/02 Ethiopian Agricultural Sample Enumeration (EASE) was used as the sampling frame in order to select the primary sampling units (EAs). Consequently, all sample EAs were selected from this frame based on the design proposed for the survey. Sample Design A stratified two-stage cluster sample design was used to select the sample. Enumeration Areas (EAs) were taken to be the primary sampling units (PSUs) and the secondary sampling units (SSUs) were agricultural households. Sample enumeration areas from each stratum were sub-samples of the 2001/02 (1994 E.C) Ethiopian Agricultural Sample Enumeration. They were selected using probability proportional to size systematic sampling; size being number of agricultural households obtained from the 1994 Population & Housing Census and adjusted for the sub-sampling effect. Within each sample EA a fresh list of households was prepared and 25 agricultural households from each sample EA were systematically selected at the second stage. The survey questionnaire was finally administered to the 25 agricultural households selected at the second stage. Information on area under crops and Meher season production of crops was obtained from the 25 households that were ultimately selected. It is important to note, however, that data on crop cutting were obtained only from fifteen sampled households (the 11th - 25th households selected).

The sample size for the 2003-04 agricultural sample survey was determined by taking into account both the required level of precision for the most important estimates within each domain and the amount of resources allocated to the survey. In order to reduce non- sampling errors, manageability of the survey in terms of quality and operational capability was also considered. Except Harari, Addis Ababa and Dire Dawa, where each region as a whole was taken to be the domain of estimation; each zone of a region / special wereda was adopted as a stratum for which major findings of the survey are reported.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2003-2004 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaires: - AgSS Form 96/0: Used to list all households and agricultural holders in the sample enumeration areas. - AgSS Form 96/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 96/3A: Used to list fields under temporary crops and farm management practice. - AgSS Form 96/3B: Used to list fields under permanent crops and farm management practice. - AgSS Form 96/3C: Used to list fields under mixed crops and farm management practice. - AgSS Form 96/3D: Used to collect information about other land use type and area and other agricultural related questions. - AgSS Form 96/5: Used to list temporary crop fields for selecting crop fields for crop cutting. - AgSS Form 96/6: Used to collect information about temporary crop cutting results.

Cleaning operations

Editing, Coding and Verification: Statistical data editing plays an important role in ensuring the quality of the collected survey data. It minimizes the effects of errors introduced while collecting data in the field , hence the need for data editing, and verification. An editing, coding and verification instruction manual was perpared and reproduced. Then 65 editors-coders and verifiers were trained for two days in editing , coding and.verification using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 1OO % basis before the questioners were passed over to the data entry unit. The editlng, coding and verification exercise of all questionnaires took 40 days.

Data Entry, Cleaning and Tabulation: Before data entry, the Natural resource and Agricultural Statistics Department prepared edit specification for the survey for use on personal computers for data consistency checking purposes . The data on the edited and coded questionnaires were then entered into personal computers. The data were then checked and cleaned using the edit specification prepared earlier for this purpose. The data entry operation involved about 64 data encoders and it took 50 days to finsh the job. Finally, tabulation was done on personal computers to produce statistical tables as per the tabulation plan.

Response rate

A total of 2,072 enumeration areas were initially selected to be covered by the survey, however, due to various reasons 16 EA's were not covered and the survey was successfully carried out in 2,056 (99.23 %) EAs. As regards the ultimate sampling unit, it was planned to conduct the survey on 51,800 agricultural households and 51,300 (99.03 %) households were actually covered by the Meher season Agricultural Sample Survey.

Sampling error estimates

Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix I and II of 2003-2004 Agricultural Sample Survey, Volume I report.

Data appraisal

As it was explained in the response rate under sampling section, the non response rate was minimal. There is no testing for bias made in this survey.

Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the new ESS4 is based on the updated 2018 pre-census cartographic database of enumeration areas by CSA. The ESS4 sample is a two-stage stratified probability sample. The ESS4 EAs in rural areas are the subsample of the AgSS EA sample. That means, the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e. the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematically with PPS. This is designed in way that automatically results in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling for the ESS4 is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e. systematic random sampling. One important issue to note in ESS4 sampling is that the total number of agriculture households per EA remains 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA.

For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA. Table 3.2 presents the distribution of sample households for ESS4 by region, urban and rural stratum. A total of 7527 households are sampled for ESS4 based on the above sampling strategy.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey consisted of five questionnaires, similar with the questionnaires used during the previous rounds with revisions based on the results of the previous rounds as well as on identified areas of need for new data.

The household questionnaire was administered to all households in the sample; multiple modules in the household questionnaire were administered per eligible household members in the sample.

The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

Household questionnaire: The household questionnaire provides information on education; health (including anthropometric measurement for children); labor and time use; financial inclusion; assets ownership and user right; food and non-food expenditure; household nonfarm activities and entrepreneurship; food security and shocks; safety nets; housing conditions; physical and financial assets; credit; tax and transfer; and other sources of household income. Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; land use and agriculture income tax; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESS4 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). A total of 6770 households from 535 EAs were interviewed for both the agriculture and household modules. The household module was not implemented in 30 EAs due to security reasons (See the Basic Information Document for additional information on survey implementation).

Judicious use of pesticides in agriculture provides many important benefits and thus, they are used in the agricultural sector of Ethiopia. Trends in pesticide use between 2004/05 and 2019/20 ‘Meher’ crop seasons by smallholder farmers on field and horticultural crops at national and regional levels were assessed. For each cropping season and each crop type national and regional data on the total number of households; the number of households who applied pesticides; the total area sown; and the area treated with pesticide were obtained from the annual report on farm management practices by the Central Statistical Agency (CSA) of Ethiopia. For each crop the compounded annual growth rate (CAGR) of pesticide use was estimated by transforming the exponential trend model to semi-logarithm trend function. The results reveal that the CAGRs for the number of households who applied pesticides on each of the field and horticultural crop and the area of each particular crop sprayed with pesticides were positive at both national and regional levels, which indicate an increasing trend in pesticide use on field and horticultural crops. At the national level, depending up on the type of crop, pesticide applicator households increased at CAGR of 4.16 to 19.62%. Similarly, pesticide treated area increased at CAGR of 2.12 to 34.06%. The CAGR for pesticide applicator households and pesticide treated area was not evenly distributed among crops and regions; however, pesticides were applied nearly on all crop types at both national and regional levels. Generally the proportions of pesticide applicator households and the proportion of pesticide treated area were greater in Oromia region followed by Southern Nation Nationality and Peoples region, Tigray region, and Amhara region. The occurrence of several new invasive pests; inclusion of pesticides as parcel of crop production technology packages in extension program; increase in agrarian population and the expansion of cultivated land; and susceptibility of high yielding improved crop varieties are among the main reasons for increased trend in pesticide use in Ethiopia. The detailed reasons for increased use of pesticides and limitations of the CSA’s data are explained in the discussion part.

Abstract

The sound performance of agriculture warrants the availability of food crops. This accomplishment in agriculture does not only signify the adequate acquisition of food crops to attain food security, but also heralds a positive aspect of the economy. In regard to this, collective efforts are being geared to securing agricultural outputs of the desired level so that self reliance in food supply can be achieved and disaster caused food shortages be contained in the shortest possible time in Ethiopia. The prime role that agriculture plays in a country's political, economic and social stability makes measures of agricultural productions extremely sensitive. Statistics collected on agricultural productions are, therefore, fraught with questions of reliability by data users. To tackle these questions convincingly and dissipate the misgivings of users, information on agriculture has to be collected using standard procedures of data collection. Upholding this principle, the Central Statistical Agency (CSA) has been furnishing statistical information on the country's agriculture since 1980/81 to alert policy interventionists on the changes taking place in the agricultural sector. As part of this task the 2007-08 (2000E.C) Agricultural Sample Survey (AgSS) was conducted to provide data on crop area and production of crops within the private peasant holdings for Main (“Meher”) Season of the specified year.

The general objective of CSA's Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is essential for planning, policy formulation, monitoring and evaluation of mainly food security and other agricultural activities.

The specific objectives of Main (“Meher”) Season Post Harvest Survey are: - To estimate the total crop area, volume of crop production and yield of crops for Main (“Meher”) Season agriculture in Ethiopia. - To estimate the total volume of inputs used, inputs applied area and number of holders using inputs. - To estimate the total cultivated area and other forms of land use.

Geographic coverage

The 2007-08 (2000 E.C) annual Agricultural Sample Survey (Meher season) covered the entire rural parts of the country except the non-sedentary population of three zones of Afar and six zones of Somali regions. Accordingly, the survey took into account all parts of Harari, Dire Dawa, and 68 additional Zones / Special weredas (that are treated as zones) of other regions.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: The list containing EAs of all regions and their respective agricultural households obtained from the 2006/07 (1999 E.C) cartographic census frame was used as the sampling frame in order to select the primary sampling units (EAs). Consequently, all sample EAs were selected from this frame based on the design proposed for the 4 survey. The second stage sampling units, households, were selected from a fresh list of households that were prepared for each EA at the beginning of the survey.

Sample Design: In order to select the sample a stratified two-stage cluster sample design was implemented. Enumeration areas (EAs) were taken to be the primary sampling units (PSUs) and the secondary sampling units (SSUs) were agricultural households. The sample size for the 2007/08 (2000 E.C) agricultural sample survey was determined by taking into account both the required level of precision for the most important estimates within each domain and the amount of resources allocated to the survey. In order to reduce non-sampling errors, manageability of the survey in terms of quality and operational control was also considered. Except Harari and Dire Dawa, where each region as a whole was taken to be the domain of estimation; each zone of a region / special wereda was adopted as a stratum for which major findings of the survey are reported.

Selection Scheme: Enumeration areas from each stratum were selected systematically using probability proportional to size sampling technique; size being number of agricultural households. The sizes for EAs were obtained from the 2006/07 (1999 E.C) cartographic census frame. From the fresh list of households prepared at the beginning of the survey 20 agricultural households within each sample EA were selected systematically. Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix I and II respectively.

Note: Distribution of sampling units (sampled and covered EAs) by stratum is also presented in Appendix III of 2007-2008 Agricultural Sample Survey, Volume I report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2007-2008 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaires: - AgSS Form 2000/0: It contains forms that used to list all households in the sample areas. - AgSS Form 2000/1: It contains forms that used to list selected agricultural households and holders in the sample areas. - AgSS Form 2000/2A: It contains forms that used to collect information about crops, results of area measurements covered by crops and other land uses. - AgSS Form 2000/2B: It contains forms that used to collect information about miscellaneous questions for the holders. - AgSS Form 2000/4: It contains forms that used to collect information about list of temporary crop fields for selecting crop cutting plots. - AgSS Form 2000/5: It contains forms that used to collect information about list of temporary crop cutting results.

Note: The questionnaires are presented in the Appendix IV of the 2007-2008 Agricultural Sample Survey report Volume I.

Cleaning operations

a) Editing, Coding and Verification: Statistical data editing plays an important role in ensuring the quality of the collected survey data. It minimizes the effects of errors introduced while collecting data in the field, hence the need for data editing, coding and verification. Although coding and editing are done by the enumerators and supervisors in the field, respectively, verification of this task is done at the Head Office. An editing, coding and verification instruction manual was prepared and reproduced for this purpose. Then 34 editors-coders and verifiers were trained for two days in editing, coding and verification using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100 % basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires took 35 days.

b) Data Entry, Cleaning and Tabulation: Before data entry, the Natural Resources and Agricultural Statistics Department of the CSA prepared edit specification for the survey for use on personal computers for data consistency checking purposes. The data on the edited and coded questionnaires were then entered into personal computers. The data were then checked and cleaned using the edit specifications prepared earlier for this purpose. The data entry operation involved about 97 data encoders, 4 data encoder supervisors, 8 data cleaning operators and 57 personal computers. The data entered into the computers using the entry module of the CSPRO (Census and Survey Processing System) software, which is a software package developed by the United States Bureau of the Census. Following the data entry operations, the data was further reviewed for data inconsistencies, missing data … etc. by the regular professional staff from Natural Resources and Agricultural Statistics Department. The final stage of the data processing was to summarizing the cleaned data and produce statistical tables that present the results of the survey using the tabulation component of the PC based CSPRO software produced by professional staff from Data processing Department.

Response rate

To be covered by the survey, a total of 2,200 enumeration areas (EAs) were selected. However, due to various reasons that are beyond control, in 75 EAs the survey could not be successful and hence interrupted. Thus, all in all the survey succeeded to cover 2,125 EAs (96.59%) throughout the regions. The Annual Agricultural Sample survey (Meher season) was conducted on the basis of 20 agricultural households selected from each EA. Regarding the ultimate sampling units, it was intended to cover a total of 44,200 agricultural households, however, 42,523 (96.21%) were actually covered by the survey.

Sampling error estimates

Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix I and II respectively of 2007-2008 Agricultural Sample Survey, Volume I report.

This statistic shows the share of economic sectors in the gross domestic product (GDP) in Ethiopia from 2013 to 2023. In 2023, the share of agriculture in Ethiopia's gross domestic product was 35.79 percent, industry contributed approximately 24.48 percent and the services sector contributed about 36.98 percent.

Abstract

The Land and Soil Experimental Research 2013, was conducted as a joint collaboration with The World Bank (LSMS Team), the Central Statistical Agency of Ethiopia (CSA) and the World Agroforestry Center (ICRAF) in an effort to improve the quality of agricultural data, particularly with respect to land area and soil fertility measurements in Ethiopia. The aim of the LASER study was to assess the data quality associated with a number of possible measurement methodologies associated with land area, soil quality, and crop production while piloting the use of each method and assessing the feasibility of implementation in national household surveys. Accurate and timely crop production statistics are critical to adequate government policy responses and the availability of accurate measures are pivotal to establishing credible performance evaluation systems. However, agricultural statistics are often marred by controversy over methods and overall quality, leading to inertia at best, or entirely incorrect policy actions. Major advances in recent years in technologies and practices offer an opportunity to improve on some of the indicators commonly used to measure agricultural performance.

Considerable efforts were made in the 1960s and 1970s, primarily by the Food and Agriculture Organization (FAO), to build a body of knowledge on agricultural statistics based on sound research which, over the years, has proven invaluable to researchers and practitioners in the field of agriculture. However, little new knowledge has been generated over the past few decades and much of the available methodological outputs are now obsolete in view of the changing structure of the sector, driven by global and local trends in both the agronomics of farming and the environment. Measuring land area and soil quality was essential in properly estimating the factors that both promoted and hindered agricultural productivity. It is also critical to assess the accuracy of the key output variable, crop production, in order to validate the methodologies used to collect harvest data as well as analyse the impact of various input measurements on yield estimates. By measuring these components using a variety of methods it was possible to identify the implications of using each and move forward with the superior methods in future household surveys. LASER was implemented across three administrative zones of the Oromia region, namely: East Wellega, West Arsi, and Borena. In total, 1018 households were interviewed, with nearly 1800 agricultural fields selected for objective land area and soil fertility measurement.

Geographic coverage

Regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The objectives of the sample design for the Land and Soil Experimental Research study were multifaceted and included indicators related to soil properties, crop type, and socio-economic characteristics, among others. Because there were multiple indicators, calculating the sample size based on the variance of a single indicator was not the preferred approach. Instead, practical sampling allocation with implicit stratification was used. Three administrative zones of the Oromia region were selected based primarily on agroecology and geographic diversity. Secondary consideration was made for the availability of local soil research centers that were used for soil processing. The three selected zones were East Wellega, West Arsi and Borena. Using the Central Statistical Agency of Ethiopia (CSA) and the Agricultural Sample Survey (AgSS) as the sampling frame, a total of 85 Enumeration Areas (EAs) were selected.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Data collection for the study was completed via Computer-Assisted Personal-Interview (CAPI). Each enumerator and supervisor had a personal laptop computer equipped with the Census and Survey Processing System (CSPro), based CAPI application for the Post-Planting, Crop-Cutting, and Post-Harvest questionnaires. Each team was provided with a flash drive, to share data from enumerator to supervisor, and a wireless router, to share consolidated team data with the World Bank project manager. Supervisors were instructed to share data at the close of EA, and only after reviewing all completed questionnaires. Data review and cleaning took place via supervisor review, periodic error reports generated by the World Bank project manager, unplanned CSA supervisor household visits to cross-check responses, and ultimately data review and standard checks (possible value ranges, outliers, etc.).

Abstract

The Ethiopia Socioeconomic Panel Survey (ESPS) is a collaborative project between the Ethiopian Statistical Service (ESS) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. ESPS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households on agriculture activities in the country. The ESPS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, and access to services and resources. The ability to follow the same households over time makes the ESPS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESPS is the first-panel survey to be carried out by the Ethiopian Statistical Service that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the second phase ESPS panel survey is based on the updated 2018 pre-census cartographic database of enumeration areas by the Ethiopian Statistical Service (ESS). The sample is a two-stage stratified probability sample. The ESPS EAs in rural areas are the subsample of the AgSS EA sample. That means the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e., the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematic PPS. This is designed to automatically result in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS, and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e., systematic random sampling. One important issue to note in ESPS sampling is that the total number of agriculture households per EA remains at 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA. For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA.

The ESPS-5 kept all the ESPS-4 samples except for those in the Tigray region and a few other places. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The ESPS-5 survey consisted of four questionnaires (household, community, post-planting, and post-harvest questionnaires), similar to those used in previous waves but revised based on the results of those waves and on the need for new data they revealed. The following new topics are included in ESPS-5:

a. Dietary Quality: This module collected information on the household’s consumption of specified food items.

b. Food Insecurity Experience Scale (FIES): In this round the survey has implemented FIES. The scale is based on the eight food insecurity experience questions on the Food Insecurity Experience Scale | Voices of the Hungry | Food and Agriculture Organization of the United Nations (fao.org).

c. Basic Agriculture Information: This module is designed to collect minimal agriculture information from households. It is primarily for urban households. However, it was also used for a few rural households where it was not possible to implement the full agriculture module due to security reasons and administered for urban households. It asked whether they had undertaken any agricultural activity, such as crop farming and tending livestock) in the last 12 months. For crop farming, the questions were on land tenure, crop type, input use, and production. For livestock there were also questions on their size and type, livestock products, and income from sales of livestock or livestock products.

d. Climate Risk Perception: This module was intended to elicit both rural and urban households perceptions, beliefs, and attitudes about different climate-related risks. It also asked where and how households were obtaining information on climate and weather-related events.

e. Agriculture Mechanization and Video-Based Agricultural Extension: The rural area community questionnaire covered these areas rural areas. On mechanization the questions related to the penetration, availability and accessibility of agricultural machinery. Communities were also asked if they had received video-based extension services.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESPS-5 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). However, due to the security situation in northern Ethiopia and to a lesser extent in the western part of the country, only a total of 4999 households from 438 EAs were interviewed for both the agriculture and household modules. The security situation in northern parts of Ethiopia meant that, in Tigray, ESPS-5 did not cover any of the EAs and households previously sampled. In Afar, while 275 households in 44 EAs had been covered by both the ESPS-4 agriculture and household modules, in ESPS-5 only 252 households in 22 EAs were covered by both modules. During the fifth wave, security was also a problem in both the Amhara and Oromia regions, so there was a comparable reduction in the number of households and EAs covered there.

More detailed information is available in the BID.

This dataset contains the files produced in the implementation of the “Integrated Monitoring Framework for Climate-Smart Agriculture” in the Basona Werana Climate Smart Village (Ethiopia) in February 2021. This monitoring framework developed by CCAFS is meant to be deployed annually across the global network of Climate-Smart Villages to gather field-based evidence by tracking the progress on: adoption of CSA practices and technologies, as well as access to climate information services and their related impacts at household level and farm level The CSA framework allows to address three key research questions: Who within each CSV community adopts which CSA technologies and practices and which are their motivations, enabling factors? To which extent farmers access and use climate information services? Which are the gender-disaggregated perceived effects of CSA options on farmers’ livelihood, agricultural, food security and adaptive capacity, and on key gender dimensions (participation in decision making, participation in CSA implementation and dis-adoption, control and access over resources and labour). Which are the CSA performance, synergies and trade-offs found at farm level? (Note that this 3d. question was not addressed in this specific Basona Werana 2021 monitoring, as farm level data were not collected) The CSA framework proposes a small set of standard Core Indicators linked to the research questions, and Extended indicators covering aspects related to the enabling environment. At household level (17 Core indicators): 7 Core Uptake indicators (they track CSA Implementation and adoption drivers; CSA dis-adoption and drivers; Access to climate information services and agro-advisories, Capacity to use them and constraining factors). 10 Core Outcome indicators (they track farmers perceptions on the effects of CSA practices on their Livelihoods, Food Security and Adaptive Capacity and on Gender dimensions. Those include namely: CSA effect on yield/production, on Income, on Improved Food Access and Food Diversity, on Vulnerability to weather related shocks and on Changes in agricultural activities induced by access to climate information. Four are Gender related Outcome indicators (Decision-making on CSA implementation or dis-adoption, Participation in CSA implementation, CSA effect on labor, Decision making and control on CSA generated income). An additional set of complementary Extended indicators allows to determine and track changes in enabling conditions and farmers characteristics such as: Livelihood security, Financial enablers, Food security, Frecuency of climate events, Coping strategies, Risk Mitigation Actions, Access to financial services and Training, CSA Knowledge and Learning. At farm level, 7 CORE indicators 7 Core indicators are used to determine the CSA performance of the farms as well as synergies and trade-offs among the three pillars (productivity, adaptation and mitigation, via farm model analysis). This integrated framework is associated with a cost-effective data collection App (Geofarmer) that allowed capturing information in almost real time. The survey questionnaire is structured around different thematic modules. For the implementation in the context of the EU-IFAD/CCAFS project, some slight changes were made to the questionnaire in order to focus the data collection on tackling: The impacts of Climate events Farmers’ access and use of CIS Farmer’s implementation of CSA practices, and the perceived household level outcomes related to the implementation of CSA practices in the two targetted Ethiopian CSVs. The adjusted survey questionnaire includes the following thematic modules: -M1A Demographic (few additional questions not included in the “MASTER” CSA monitoring questionnaire were added coming from Rhomis) -M1D Financial services (reduced set of original questions from the Financial Master) -M2, Climate events (no changes made) -M3, Climate Information Services -M5, CSA practices (no changes made) ** The Module Food Security from the Master questionnaire of the CSA framework was not included. Information on Food security captured using RhOMIS.

In this digital world, the most important component is availability of quality data that is accessible for those who need it the most. In developing regions, data on agriculture will be the most needed because the sector is the basis of livelihoods and national economy. Unfortunately, agriculture is among the least digitalized of all sectors, even in the developed world. There is thus mismatch in availing a technology to those who need it the most. However, the recent surge in digital technologies is transforming agriculture in many places that will facilitate its diffusion and scaling towards the developing regions. Ethiopia is one of the proponents of digital economy reflected through its “Digital Ethiopia 2025” national strategy, which is set to transform the country's national economy through four major sectoral pathways including agriculture, manufacturing, IT-enabled services and the tourism. There is tremendous effort to not only digitalize sectors and systems but also to benefit from advanced analytics to guide the countries various growth and development initiatives. The recent effort in the development of the “National Digital Agricultural Extension and Advisory Services Roadmap to 2030” is a clear indication of the country’s commitment to transform its agriculture through digital solutions. However, the expected rapid transformation of the Ethiopian agriculture guided by digital solutions can be challenged. As witnessed during various workshops and based on a recent assessment by the Alliance of Bioversity and CIAT and partners in Ethiopia, there is a serious challenge related to the availability of quality and standard data in an organized manner. Because of this gap, there is duplication of effort and wastage of time and resources. In addition, bringing different datasets into an integrated system is complex because of different standards and formats used and in most cases data are not georeferenced. For instance, various organizations and stakeholders in the country have collected voluminous data related to agriculture, environment, and infrastructure. The Agricultural Transformation Agency (ATA) has collected data on soils. The Ethiopian Institute of Agricultural Research (EIAR) has been collecting data related agricultural experiment since the 1960s. The Ministry of Agriculture (MoA) has been collecting data on natural resources and agriculture for more than 50 years. The Central statistical Agency (CSA) has been collecting census data on crop yield and other aspects for many decades. The National Meteorological Authority is the custodian of time-series and predicted climate/weather data. The Geospatial Information Institute (GII) holds tremendous aerial photograph and satellite data. However, these data are not integrated, are not available in a standardized form to share and even most are not accessible due to different reasons. The seriousness of the matter is that even Directorates within one Ministry do not know what each other has that precludes the possibility to share. For instance, the National Soil Information System (NSIS) at the Soil Resource Information and Mapping Directorate and the National Rural Land Administration Information System (NRLAIS) at the Rural Land Administration and Use Directorate (both at the MoA) are not yet integrated and cannot share dataset efficiently. Various efforts have been made to harmonize datasets and facilitate storage and data sharing. However, some of them have already failed while some others are still struggling to succeed due to different reasons including lack of champion institution and limited buy-in and capacity of the government to support initiatives, limited cooperation between sectors and among Directorates within sectors, low technological readiness, governance barriers, and data incompatibility. Against the above background, the Accelerating Impacts of CGIAR Climate Research in Africa (AICCRA) project considered development of integrated ag- data hub as one of its key interventions. The project is expected to co-develop a dedicated, and publicly owned and operated - one-stop-shopping national ag-data hub for Ethiopian government policymakers, agrometeorology experts, ag-extension officers, farmers, value chain actors, and other end users (through consolidation, integration and upgrading of the existing systems) that provides all relevant data, insights and analytics needed to make fact-based decisions when conducting agricultural operations.

This dataset contains the files produced in the implementation of the “Integrated Monitoring Framework for Climate-Smart Agriculture” in the Dogoyena Climate Smart Village (Ethiopia) in December 2020. This monitoring framework developed by CCAFS is meant to be deployed annually across the global network of Climate-Smart Villages to gather field-based evidence by tracking the progress on: adoption of CSA practices and technologies, as well as access to climate information services and their related impacts at household level and farm level The CSA framework allows to address three key research questions: Who within each CSV community adopts which CSA technologies and practices and which are their motivations, enabling factors? To which extent farmers access and use climate information services? Which are the gender-disaggregated perceived effects of CSA options on farmers’ livelihood, agricultural, food security and adaptive capacity, and on key gender dimensions (participation in decision making, participation in CSA implementation and dis-adoption, control and access over resources and labour). Which are the CSA performance, synergies and trade-offs found at farm level? (Note that this 3d. question was not addressed in this specific Doyogena 2020 monitoring, as farm level data were not collected) The CSA framework proposes a small set of standard Core Indicators linked to the research questions, and Extended indicators covering aspects related to the enabling environment. At household level (17 Core indicators): 7 Core Uptake indicators (they track CSA Implementation and adoption drivers; CSA dis-adoption and drivers; Access to climate information services and agro-advisories, Capacity to use them and constraining factors). 10 Core Outcome indicators (they track farmers perceptions on the effects of CSA practices on their Livelihoods, Food Security and Adaptive Capacity and on Gender dimensions. Those include namely: CSA effect on yield/production, on Income, on Improved Food Access and Food Diversity, on Vulnerability to weather related shocks and on Changes in agricultural activities induced by access to climate information. Four are Gender related Outcome indicators (Decision-making on CSA implementation or dis-adoption, Participation in CSA implementation, CSA effect on labor, Decision making and control on CSA generated income). An additional set of complementary Extended indicators allows to determine and track changes in enabling conditions and farmers characteristics such as: Livelihood security, Financial enablers, Food security, Frecuency of climate events, Coping strategies, Risk Mitigation Actions, Access to financial services and Training, CSA Knowledge and Learning. At farm level, 7 CORE indicators 7 Core indicators are used to determine the CSA performance of the farms as well as synergies and trade-offs among the three pillars (productivity, adaptation and mitigation, via farm model analysis). This integrated framework is associated with a cost-effective data collection App (Geofarmer) that allowed capturing information in almost real time. The survey questionnaire is structured around different thematic modules. For the implementation in the context of the EU-IFAD/CCAFS project, some slight changes were made to the questionnaire in order to focus the data collection on tackling: The impacts of Climate events Farmers’ access and use of CIS Farmer’s implementation of CSA practices, and the perceived household level outcomes related to the implementation of CSA practices in the two targetted Ethiopian CSVs. The adjusted survey questionnaire includes the following thematic modules: -M1A Demographic (few additional questions not included in the “MASTER” CSA monitoring questionnaire were added coming from Rhomis) -M1D Financial services (reduced set of original questions from the Financial Master) -M2, Climate events (no changes made) -M3, Climate Information Services -M5, CSA practices (no changes made) ** The Module Food Security from the Master questionnaire of the CSA framework was not included. Information on Food security captured using RhOMIS.

This dataset contains the files produced in the implementation of the “Integrated Monitoring Framework for Climate-Smart Agriculture” in the Doyogena Climate Smart Village (Ethiopia) in October 2019. This monitoring framework developed by CCAFS is meant to be deployed annually across the global network of Climate-Smart Villages to gather field-based evidence by tracking the progress on: adoption of CSA practices and technologies, as well as access to climate information services and their related impacts at household level and farm level This framework proposes standard Descriptive Indicators to track changes in: 5 enabling dimensions that might affect adoption patterns, a set of 5 CORE indicators at Household level to assess perceived effects of CSA practices on Food Security, Productivity, Income and Climate vulnerability and 4 CORE indicators on Gender aspects (Participation in decision-making, Participation in implementation, Access/control over Resources and work time). At farm level, 7 CORE indicators are suggested to determine farms CSA performance, as well as synergies and trade-offs among the three pillars. This integrated framework is associated with a cost-effective data collection App (Geofarmer) that allowed capturing information in almost real time. The survey questionnaire is structured around different thematic modules (Demographic, Livelihoods, Food Security, Climate events, Climate Services, CSA practices, Financial Services) connected to standard CSA metrics and the specific indicators. The framework responds to three main research questions: Within each CSV community, who adopts which CSA technologies and practices and what are their motivations, enabling/constraining factors? What are the gender-disaggregated perceived effects of CSA options on farmers’ livelihood (agricultural production, income, food security, food diversity and adaptive capacity) and on key gender dimensions (participation in decision-making, participation in CSA implementation and dis-adoption, control and access over resources and labour)? How does CSA perform at farm level, and what synergies and trade-offs exist (whole farm model analysis)? NOTE: In the case of the 2019 Implementation in Doyogena only questions 1 and 2 where addressed (The “Calculator Modules” of the survey allowing assessing farm level effects of CSA practice on performance were not applied).

Climate-smart agriculture (CSA)-based management practices are getting popular across South-Asia as an alternative to the conventional system for particular weed suppression, resources conservation and environmental quality. An 8-year study (2012–2013 to 2019–2020) was conducted to understand the shift in weed density and diversity under different CSA-based management practices called scenarios (Sc). These Sc involved: Sc1, conventional tillage (CT)-based rice–wheat system with flood irrigation (farmers’ practice); Sc2, CT-rice, zero tillage (ZT)-wheat–mungbean with flood irrigation (partial CA-based); Sc3, ZT rice–wheat–mungbean with flood irrigation (partial CSA-based rice); Sc4, ZT maize–wheat–mungbean with flood irrigation (partial CSA-based maize); Sc5, ZT rice–wheat– mungbean with subsurface drip irrigation (full CSA-based rice); and Sc6, ZT maize–wheat–mungbean with subsurface drip irrigation (full CSA-based maize). The most abundant weed species were P. minor > A. arvensis > M. indicus > C. album and were favored by farmers’ practice. However, CSA-based management practices suppressed these species and favored S. nigrum and R. dentatus and the effect of CSAPs was more evident in the long-term. Maximum total weed density was observed for Sc1, while minimum value was recorded under full CSA-based maize systems, where seven weed-species vanished, and P. minor density declined to 0.33 instead of 25.93 plant m− 2 after 8-years of continuous cultivation. Full CSA-based maize–wheat system could be a promising alternative for the conveniently managed rice–wheat system in weed suppression in north-west India.

The report presents an unprecedented stocktaking of all CGIAR-related innovations in a given country as well as new estimates of adoption of those innovations from a nationally representative dataset generated through a partnership among the Ethiopian Central Statistics Agency (CSA), the World Bank Living Standards Measurement Study (LSMS) team, and the CGIAR Standing Panel on Impact Assessment (SPIA). Ethiopia was chosen for this exercise because it is a hotspot of CGIAR research, with almost all the CGIAR centers represented in Addis Ababa.

The report documents the reach of CGIAR-related agricultural innovations in a comprehensive manner across the core domains of CGIAR research activity: animal agriculture; crop germplasm improvement; natural resource management; and policy research. In order to identify the right innovations to collect data on, SPIA conducted more than 90 interviews with CGIAR research leaders, scientists, government officials, and colleagues from the Ethiopian Institute for Agricultural Research (EIAR), all the while compiling documented evidence to support claims made by these key informants. The output of that work is a stocktaking of 52 agricultural innovations and 26 claims of policy influence.

Quantitative evidence on the adoption of 18 of these innovations was obtained through the incorporation of measurements of the reach of these innovations in the Ethiopian Socioeconomic Survey (ESS), a regionally and nationally representative panel survey of households. We report some data from the third wave (ESS3, carried out in 2015/16), but our major focus is on ESS4 (2018/19). The 2018/19 ESS (ESS4) datasets can be downloaded at: https://microdata.worldbank.org/index.php/catalog/3823">https://microdata.worldbank.org/index.php/catalog/3823

Degree of socio-economic marginality in areas with capabilitiy gaps Socio-economic marginality: Socio-economic marginality in Ethiopia was defined by the following economic, health and educational conditional indicators: 1. Economy: 1.1 Regional poverty headcount indices (% of population whose income/consumption is below the poverty line = 3781 birr) 1.2 Food poverty headcount indices (% of population whose income/consumption for food is below the cost of 2.200 kcal/day per adult food consumption) 1.3 Wealth index (% of population being part of the lowest/2.lowest wealth quintile) 2. Health: 2.1 Child mortality rate (no. of deaths out of 1000 live births <5 years) 2.2 Nutritional status of children (% of children <5 years being stunted) 2.3 Nutritional status of adults (% of men/women age 15-49 with BMI <18.5 = acute under nutrition) 3. Education: 3.1 Illiteracy rate (% of population not being able to read/write in their native language) 3.2 Net enrolment ratio primary school 3.3 Net enrolement ratio high school Data source: 1.1/1.2: Ministry of Finance and Economy Development (2012): Ethiopia‘s Progress Towards Eradicating Poverty: An Interim Report on Poverty Analysis Study (2010/11). Addis Ababa, Ethiopia 1.3/2.1/2.2/2.3: Central Statistical Agency(CSA), ICF International (2012): Ethiopia Demographic and Health Survey 2011. Addis Ababa, Ethiopia; Calverton, USA Capability gap: Areas with good agro-ecological suitability, but limited socio-economic capabilities of farmers to make use of this suitability. Agro-ecological suitability in Ethiopia was defined from the raster data set of agro-ecological suitability for rainfed crops (Fischer et al. 2002) Data source: Fischer et al. (2002): Global Agro-ecological Assessment for Agriculture in the 21st Century: Methodology and Results. International Institute for Applied Systems Analysis, Laxenburg, Austria The socio-economic capabilities of farmers were defined by the following indicators: 1. Access to technology (% of holders applying inorganic fertilizer to any crop during Meher season) 2. Access to credit (% of holders utilizing credit services) 3. Access to knowledge (% of holders utilizing advisory services) Data source: Central Statistical Agency (CSA) (2002): Ethiopian Agricultural Sample Enumeration. Addis Ababa, Ethiopia

Abstract

Agriculture is the major contributor to the Ethiopian economy. A majority of the Ethiopian populations are engaged in agriculture to earn their livelihood and most of the nation's exports are made up of agriculture produces. The collection of reliable, comprehensive and timely data on agriculture is, thus, essential for policy formulation, decision making and other uses. In this regard the Central Statistical Agency(CSA) has exerted effort to provide users and policy makers with reliable and timely agriculture data.

The general objectives of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is considered essential for development planning, socio-economic policy formulation, food assistance, etc.

The specific objectives of the Main (“Meher”) season area and production survey are: - To estimate the total cultivated land area, production and yield per hectare of major crops (temporary). - To estimate the total farm inputs applied area and quantity of inputs applied by type for major temporary and permanent crops.

Geographic coverage

The survey covered all sedentary rural agricultural population in all regions of the country except urban and nomadic areas which were not included in the survey.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 1998-1999 (1991 E.C) Main ("Mehere") season agricultural survey covered the rural part of the country except two zones in Afar region and six zones in Somalie region that are predominantly nomadic. A two-stage stratified sample design was used to select the sample EAs and the agricultural households. Each zone/ special wereda in the sampled population of Tigray, Afar, Amhara, Oromiya, Somalie, Benishangul_Gumuz, SNNP regions was adopted as stratum for which major finings of the survey are reported. But each of the four regions, namely; Gambela, Harari, Addis Ababa and Dire Dawa were considered as reporting levels. The primary sampling units (PSUs) were enumeration areas (EAs) and agricultural households were the secondary sampling units. The survey questionnaires were administered to all agricultural holders within the sample households. A fixed number of sample EAs was determined fro each stratum/reporting level based on precision of major estimates and cost considerations. Within each stratum EAs were selected using probability proportional to size; size being total number of households in the EAs as obtained form the 1994 population and housing census. From each sample EA, 25 agricultural households were systematically selected for the 'Meher" season survey from a fresh list of households prepared at the beginning of the fieldwork of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1998-1999 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaire: - AgSS Form 91/0: Used to list all agricultural households and holders in the sample enumeration areas. - AgSS Form 91/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 91/2: Used to collect information about crop condition. - AgSS Form 91/3A: Used to list fields and agricultural practices only pure stand temporary and permanent crops, list of fields and agricultural practices for mixed crops, other land use, quantity of improved and local seeds by type of crop and type and quantity of crop protection chemicals. - AgSS Form 91/3B: Used to collect information about quantity of production of crops. - AgSS Form 91/4A: Used to collect information about results of area measurement and field area measurement. - AgSS Form 91/4B: Used to collect information about results of area measurement and field area measurement. - AgSS Form 91/5: Used to list fields for selecting fields for crop cuttings and collect information about details of crop cutting. - AgSS Form 91/6: Used to collect information about cattle by sex, age and purpose.

Note: The questionnaires are provided as external resource.

Cleaning operations

Editing, Coding and Verification: In order to insure the quality of collected survey data an editing, coding and verification instruction manual was prepared and fifty editors/coders and ten verifiers were trained for two days to edit, code and verify the data using the aforementioned manual as a reference and teaching aid. The filled-in questionnaires were edited, coded and later verified by supervisors on a 100% basis before the questionnaires were sent to the data processing unit for data entry. The editing, coding and verification of all questionnaires was completed in fourty days.

Data Entry, Cleaning and Tabulation: Before starting data entry professional staffs of Agricultural Statistics Department of Central Statistical Authority prepared edit specification that used to developed data entry and cleaning computer programs by data processing staffs using Integrated Microcomputer Processing System (IMPS). The edited and coded questionnaires were captured into computers and later cleaned using cleaning program that was developed for this purpose earlier. Fifty data encoders were involved in this process and it took thirty-five days to complete the job. Finally, using tabulations format provided by the subject matter specialist computer program was developed and survey results were produced accordingly.

Response rate

A total of 1,450 EAs (2.9 % of total EAs in the rural areas of the country) were selected for the survey. However, 22 EAs were not covered by the survey due to various reasons that are beyond the control of the Agency. Thus, the survey succeeded in covering 1428 (98.48%) EAs. With respect to ultimate sampling units, it was planned to cover a total of 36,250 agricultural households for area measurement and 21,750 agricultural households for crop cutting (see Appendix III in the report which is provided as external resource). The response rate was found to be 98.94 % for area measurement and 95.50 % for crop cutting.

Sampling error estimates

Estimation procedures of parameters of interest (total and ratio) and their sampling error is presented in Appendix II of the 1998-1999 annual Agricultural Sample Survey, Volume I report which is provided in this documentation.