Annual Agricultural Survey 2024–2025
Côte d'Ivoire, 2024 - 2025
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Reference ID
CIV_2024-2025_AAS_v01_M_v01_A_ESS
Producer(s)
Ministry of Agriculture, Rural Development and Food Production (MINADERPV, Ministère de l’Agriculture, du Développement Rural et des Productions Vivrières)
Metadata
Created on
Sep 29, 2026
Last modified
Sep 29, 2026
Identification
Survey ID number
CIV_2024-2025_AAS_v01_M_v01_A_ESS
Title
Annual Agricultural Survey 2024–2025
Abbreviation or Acronym
AAS/ EAA 2024-25
Country
| Name | Country code |
|---|---|
| Côte d'Ivoire | CIV |
Study type
Agricultural Survey [ag/oth]
Series Information
The Annual Agricultural Survey 2024–2025 is conducted as part of the implementation of the Supplementary Modules (SMs) of the 2015-2016 Census of Agricultural Holdings and Holders (REEA). This census was carried out in accordance with the World Programme for the Census of Agriculture 2010 (WCA 2010), using a modular approach. The core module of the REEA, conducted in 2015-2016, was based on a complete enumeration. It covered a limited range of essential data required for the formulation of national policies and the development of sampling frames. One or more Supplementary Modules were subsequently implemented through sample surveys in order to provide updated and detailed data.
These surveys form part of an integrated system of agricultural censuses and surveys (AGRIS), consistent with one of the two implementation modalities of the modular approach to agricultural censuses recommended by the FAO World Programme for the Census of Agriculture 2020 (WCA 2020).
These surveys form part of an integrated system of agricultural censuses and surveys (AGRIS), consistent with one of the two implementation modalities of the modular approach to agricultural censuses recommended by the FAO World Programme for the Census of Agriculture 2020 (WCA 2020).
Abstract
The Supplementary Modules of the 2015–2016 Census of Agricultural Holdings and Holders (MC-REE were planned under the 2015-16 Census. However, these were postponed till 2024-2025 and its implementation was therefore carried out as part of the Annual Agricultural Survey 2024–2025 (EAA 2024), integrated agricultural survey approach (AGRIS) promoted by the 50x2030 Initiative, for which FAO is responsible for implementation. Based on a representative sample drawn from the 2016 REEA and the 2021 Population and Housing Census (RGPH), the survey primarily covers crop and livestock production activities. For households engaged in these activities, information was also collected on fishing, aquaculture, and forestry activities.
The implementation of the EAA 2024 marks the effective launch of the agricultural statistics revitalization programme, as defined in the 1996-2000 Statistical Master Plan. This programme aims to establish an integrated and permanent system for the collection and processing of agricultural statistical data, thereby ensuring the availability of relevant and comparable information. The overall objective of the survey was to update and supplement the data from the REEA core module by collecting additional information from agricultural households covered by the supplementary module. The main findings are:
- low adoption of organic fertilizers and non-compliance with recommended application rates for major crops, limiting productivity gains;
- geographical concentration of agricultural production, exposing certain areas to increased risks of economic and climate vulnerability;
- limited diversification in the uses of agricultural production, with sales predominating at the expense of processing and storage;
- development of non-poultry livestock production, fishing, and aquaculture remains limited, despite their potential to contribute to food security and income generation;
- heavy reliance on forest resources for energy needs, increasing pressure on forest ecosystems.
The implementation of the EAA 2024 marks the effective launch of the agricultural statistics revitalization programme, as defined in the 1996-2000 Statistical Master Plan. This programme aims to establish an integrated and permanent system for the collection and processing of agricultural statistical data, thereby ensuring the availability of relevant and comparable information. The overall objective of the survey was to update and supplement the data from the REEA core module by collecting additional information from agricultural households covered by the supplementary module. The main findings are:
- low adoption of organic fertilizers and non-compliance with recommended application rates for major crops, limiting productivity gains;
- geographical concentration of agricultural production, exposing certain areas to increased risks of economic and climate vulnerability;
- limited diversification in the uses of agricultural production, with sales predominating at the expense of processing and storage;
- development of non-poultry livestock production, fishing, and aquaculture remains limited, despite their potential to contribute to food security and income generation;
- heavy reliance on forest resources for energy needs, increasing pressure on forest ecosystems.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Agricultural Households
Scope
Notes
The 2024-2025 Annual Agricultural Survey covers the main agricultural activities carried out by agricultural households, with a primary focus on crop and livestock production. It collects information on household characteristics and participation in agricultural activities, agricultural holdings, fields and plots, cultivated areas, crop production, livestock, agricultural inputs, production losses, income, and the use of agricultural production, including own consumption, sales, and storage. The survey also collects information on fishing, aquaculture, and forestry activities among households engaged in these activities.
Topics
Keywords
Subsistence agriculture
Livestock
Agricultural crops
Household
Agricultural production
Coverage
Geographic Coverage
The 2024-2025 Integrated Annual Agricultural Survey covers the entire national territory, specifically, areas with agricultural activities. Consequently, the District of Abidjan, a predominantly densely urbanized area, is excluded from the geographic scope of the survey.
Universe
The survey covered households with at least one member engaged in an agricultural activity (crop production or livestock farming).
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Ministry of Agriculture, Rural Development and Food Production (MINADERPV, Ministère de l’Agriculture, du Développement Rural et des Productions Vivrières) | Republic of Côte d’Ivoire |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Ministry of Animal and Fisheries Resources (Ministère des Ressources Animales et Halieutiques) | Republic of Côte d’Ivoire | Collaborator |
| Ministry of Water and Forests (Ministère des Eaux et Forêts) | Republic of Côte d’Ivoire | Collaborator |
| Ministry of Environment and Ecological Transition (Ministère de l'Environnement et de la Transition Ecologique) | Republic of Côte d’Ivoire | Collaborator |
| National Statistics Agency (Agence Nationale de la Statistique) | Ministry of Planning and Development (Ministère du Plan et du Développement) | Collaborator |
| Food and Agriculture Organization of the United Nations | United Nations | Technical assistance |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Harmonizing and Improving Statistics in West Africa Project (Projet d'Harmonisation et d'Amélioration des Statistiques en Afrique de l'Ouest) | PHASAO | Funding through a credit from the International Development Association (IDA) |
| Government of Côte d’Ivoire | PHASAO | Co-financing |
Sampling
Sampling Procedure
The sampling frame was based on data from the 2015-2016 Census of Agricultural Holdings and Holders (REEA) and the 2021 Population and Housing Census (RGPH). The frame is maintained by the Ministry of Agriculture, Rural Development and Food Production and the Ministry of Planning and Development through the National Statistics Agency (ANStat).
The survey used a stratified two-stage sampling design. At the first stage, agricultural Enumeration Areas (EAs) were selected with probability proportional to the number of agricultural households in each EA. At the second stage, agricultural households were selected using simple random sampling within each sampled EA.
Before selecting households at the second stage, the list of agricultural households in the sampled EAs was updated through a listing operation. This involved enumerating all households within the selected agricultural EAs in each department in order to identify agricultural households, defined as households with at least one member engaged in an agricultural activity in the broad sense, including crop production, livestock farming, fishing, or aquaculture.
The sampling design relied on a complete sampling frame of agricultural EAs derived from the Enumeration Areas of the 2021 RGPH. Based on the sampling procedure and sample-size calculation, a total sample of 27 600 households was obtained, corresponding to 2 760 EAs, with a target of 10 households per EA, distributed across the country's 111 departments.
EAs were selected within each department to ensure that the sample was representative at the departmental level. Agricultural households were selected at the second stage after completion of the listing operation in the sampled EAs. Households eligible for second-stage selection were those whose agricultural holding was located in the household's department of residence.
Following the listing operation, 10 agricultural households were randomly selected from each sampled EA for the survey data collection. Where an EA contained fewer than 10 eligible agricultural households, all eligible households were included in the survey.
Unless otherwise specified in the individual dataset descriptions, the survey provides estimates at the national, district, regional, and departmental levels.
The survey used a stratified two-stage sampling design. At the first stage, agricultural Enumeration Areas (EAs) were selected with probability proportional to the number of agricultural households in each EA. At the second stage, agricultural households were selected using simple random sampling within each sampled EA.
Before selecting households at the second stage, the list of agricultural households in the sampled EAs was updated through a listing operation. This involved enumerating all households within the selected agricultural EAs in each department in order to identify agricultural households, defined as households with at least one member engaged in an agricultural activity in the broad sense, including crop production, livestock farming, fishing, or aquaculture.
The sampling design relied on a complete sampling frame of agricultural EAs derived from the Enumeration Areas of the 2021 RGPH. Based on the sampling procedure and sample-size calculation, a total sample of 27 600 households was obtained, corresponding to 2 760 EAs, with a target of 10 households per EA, distributed across the country's 111 departments.
EAs were selected within each department to ensure that the sample was representative at the departmental level. Agricultural households were selected at the second stage after completion of the listing operation in the sampled EAs. Households eligible for second-stage selection were those whose agricultural holding was located in the household's department of residence.
Following the listing operation, 10 agricultural households were randomly selected from each sampled EA for the survey data collection. Where an EA contained fewer than 10 eligible agricultural households, all eligible households were included in the survey.
Unless otherwise specified in the individual dataset descriptions, the survey provides estimates at the national, district, regional, and departmental levels.
Deviations from the Sample Design
Some households could not be interviewed for various reasons, including inaccessible Enumeration Areas (EAs), refusal to participate, prolonged absence, and other circumstances. However, these potential sources of non-response had already been taken into account when determining the sample size, using an anticipated non-response rate of 10 percent.
Weighting
Sampling weights were developed to allow survey results to be extrapolated to the departmental, regional, district, and national levels.
The sampling weight variable is named "WEIGHT".
The sampling weight variable is named "WEIGHT".
Survey instrument
Questionnaires
Data for the EAA 2024 were collected primarily using two questionnaires: the Phase 1 questionnaire and the Phase 2 questionnaire.
The Phase 1 questionnaire covered household size, agricultural activities carried out by each household member, the number of fields and plots, and the areas under cultivation.
The Phase 2 questionnaire covered crops grown and the production of various agricultural commodities across crop production, livestock farming, aquaculture, forestry, and other agricultural activities. It also collected information on income generated, the use of agricultural production (own consumption, sales, storage, etc.), agricultural inputs used (fertilizers and pesticides), as well as agricultural losses and their causes.
This survey is based on the integrated modular approach recommended by the 50x2030 Initiative. Accordingly, the questionnaires were developed with technical support from one of the Initiative’s partner organizations, the Food and Agriculture Organization of the United Nations (FAO).
In addition to the Phase 1 and Phase 2 survey questionnaires, the data collection instruments also included the Listing questionnaire, instruction manuals for interviewers and supervisors, and a methodological note on the estimation of agricultural production and cultivated areas.
The Phase 1 questionnaire covered household size, agricultural activities carried out by each household member, the number of fields and plots, and the areas under cultivation.
The Phase 2 questionnaire covered crops grown and the production of various agricultural commodities across crop production, livestock farming, aquaculture, forestry, and other agricultural activities. It also collected information on income generated, the use of agricultural production (own consumption, sales, storage, etc.), agricultural inputs used (fertilizers and pesticides), as well as agricultural losses and their causes.
This survey is based on the integrated modular approach recommended by the 50x2030 Initiative. Accordingly, the questionnaires were developed with technical support from one of the Initiative’s partner organizations, the Food and Agriculture Organization of the United Nations (FAO).
In addition to the Phase 1 and Phase 2 survey questionnaires, the data collection instruments also included the Listing questionnaire, instruction manuals for interviewers and supervisors, and a methodological note on the estimation of agricultural production and cultivated areas.
Data collection
Dates of Data Collection
| Start | End |
|---|---|
| 2024 | 2025 |
Time Method
Cross-sectional survey
Data Collection Mode
- Computer Assisted Personal Interview [capi]
Data Collection Notes
**Training of Interviewers and Supervisors**
As part of the preparation and implementation of the survey, a ten-day training workshop was organized to ensure a consistent understanding of the questionnaires among all interviewers and supervisors. The workshop aimed to ensure the quality of the data collected by harmonizing the methods and techniques used to administer the questionnaires.
Training sessions were held in several strategic locations: Dabou, Abengourou, Yamoussoukro, Bouaké, Korhogo, Daloa, Soubré, and Man. Each location hosted two groups of participants to maximize the effectiveness of the training and facilitate a better understanding of the topics covered. This approach encouraged dynamic interaction between trainers and participants and strengthened the acquisition of the skills required to conduct the survey rigorously and professionally.
Through these sessions, interviewers and supervisors were trained in the tools and techniques required to ensure reliable data collection in accordance with established methodological standards. This training constituted a key step in ensuring the successful implementation of the survey and the production of data suitable for analysis and decision-making.
**Quality Control Procedures**
To ensure the quality of the data collected in the field, several technical quality control mechanisms were implemented at different levels. These measures were designed to ensure data integrity, consistency, and completeness. The main quality control procedures included:
- Consistency checks embedded in the data entry forms: To reduce data entry errors, outliers, and inconsistencies, consistency checks were built directly into the questionnaire data entry forms. These checks ensured that entered data complied with predefined logical rules and expected relationships between fields. This made it possible to immediately identify erroneous or inconsistent data before they were submitted or recorded.
- Data consistency and integrity control program: This program was designed to perform thorough checks of the data collected to ensure their consistency and completeness. The checks were carried out by supervisors and members of the survey technical committee. Their role was to review the data collected in the field, identify errors or inconsistencies, and correct them prior to final data processing.
- Field team monitoring program: This program was designed to monitor the progress of field data collection. It helped ensure that field teams adhered to the data collection schedule, identify difficulties encountered during fieldwork, and verify that established procedures were correctly applied. The program provided regular reports on team progress, enabling proactive management and adjustments where necessary.
As part of the preparation and implementation of the survey, a ten-day training workshop was organized to ensure a consistent understanding of the questionnaires among all interviewers and supervisors. The workshop aimed to ensure the quality of the data collected by harmonizing the methods and techniques used to administer the questionnaires.
Training sessions were held in several strategic locations: Dabou, Abengourou, Yamoussoukro, Bouaké, Korhogo, Daloa, Soubré, and Man. Each location hosted two groups of participants to maximize the effectiveness of the training and facilitate a better understanding of the topics covered. This approach encouraged dynamic interaction between trainers and participants and strengthened the acquisition of the skills required to conduct the survey rigorously and professionally.
Through these sessions, interviewers and supervisors were trained in the tools and techniques required to ensure reliable data collection in accordance with established methodological standards. This training constituted a key step in ensuring the successful implementation of the survey and the production of data suitable for analysis and decision-making.
**Quality Control Procedures**
To ensure the quality of the data collected in the field, several technical quality control mechanisms were implemented at different levels. These measures were designed to ensure data integrity, consistency, and completeness. The main quality control procedures included:
- Consistency checks embedded in the data entry forms: To reduce data entry errors, outliers, and inconsistencies, consistency checks were built directly into the questionnaire data entry forms. These checks ensured that entered data complied with predefined logical rules and expected relationships between fields. This made it possible to immediately identify erroneous or inconsistent data before they were submitted or recorded.
- Data consistency and integrity control program: This program was designed to perform thorough checks of the data collected to ensure their consistency and completeness. The checks were carried out by supervisors and members of the survey technical committee. Their role was to review the data collected in the field, identify errors or inconsistencies, and correct them prior to final data processing.
- Field team monitoring program: This program was designed to monitor the progress of field data collection. It helped ensure that field teams adhered to the data collection schedule, identify difficulties encountered during fieldwork, and verify that established procedures were correctly applied. The program provided regular reports on team progress, enabling proactive management and adjustments where necessary.
Data processing
Data Editing
Data processing and editing were carried out in three main stages:
1. Preparation: This stage involved ensuring that all equipment and resources required for data processing were available and ready for use, including the necessary software and computer equipment.
2. Data cleaning: Data cleaning was conducted as an iterative process to ensure the completeness and consistency of the data. This included:
- Completeness checks: Identification and correction of missing or incomplete variables.
- Consistency checks: Verification that the recorded data complied with predefined logical rules and were internally consistent, including the identification of data errors and missing fields.
3. Data validation: Following the cleaning process, the data were validated to assess their quality and confirm that they were ready for statistical analysis.
Data processing, cleaning, and statistical analysis were performed using Stata, version 18.
**STATISTICAL DISCLOSURE CONTROL (SDC)**
The dataset was anonymized using statistical disclosure control methods implemented in R. As a first step, variables were classified into the following main categories: variables to be removed, quasi-identifiers, direct identifiers, and linked variables. All direct identifiers and unnecessary variables were removed.
Quasi-identifiers and their associated variables were then used to define potential disclosure scenarios. Disclosure risk was assessed primarily using k-anonymity and probabilistic risk measures.
Quasi-identifiers were anonymized using recoding, permutation, and local suppression techniques. These methods were applied to both quasi-identifiers and associated variables.
Information loss resulting from the anonymization process was assessed primarily on the basis of the number of missing values introduced during anonymization and the degree of association between categorical variables.
1. Preparation: This stage involved ensuring that all equipment and resources required for data processing were available and ready for use, including the necessary software and computer equipment.
2. Data cleaning: Data cleaning was conducted as an iterative process to ensure the completeness and consistency of the data. This included:
- Completeness checks: Identification and correction of missing or incomplete variables.
- Consistency checks: Verification that the recorded data complied with predefined logical rules and were internally consistent, including the identification of data errors and missing fields.
3. Data validation: Following the cleaning process, the data were validated to assess their quality and confirm that they were ready for statistical analysis.
Data processing, cleaning, and statistical analysis were performed using Stata, version 18.
**STATISTICAL DISCLOSURE CONTROL (SDC)**
The dataset was anonymized using statistical disclosure control methods implemented in R. As a first step, variables were classified into the following main categories: variables to be removed, quasi-identifiers, direct identifiers, and linked variables. All direct identifiers and unnecessary variables were removed.
Quasi-identifiers and their associated variables were then used to define potential disclosure scenarios. Disclosure risk was assessed primarily using k-anonymity and probabilistic risk measures.
Quasi-identifiers were anonymized using recoding, permutation, and local suppression techniques. These methods were applied to both quasi-identifiers and associated variables.
Information loss resulting from the anonymization process was assessed primarily on the basis of the number of missing values introduced during anonymization and the degree of association between categorical variables.
Data appraisal
Estimates of Sampling Error
To assess data quality, selected indicators were accompanied by estimates of sampling errors. In particular, coefficients of variation and standard errors were estimated for the purpose of publishing the data as open data.
access_policy
Access conditions
The datasets have been anonymized and are available as Public Use Files (PUFs). They contain individual-level (non-aggregated) data that have been processed to ensure strict confidentiality and prevent the direct or indirect identification of individuals or households. These confidentiality safeguards comply with applicable legislation.
In preparing these data files, direct identifiers were removed and characteristics that could potentially lead to the identification of individuals were treated to minimize disclosure risk. These data files may only be used for statistical analysis and reporting purposes.
Users must not attempt to use the data to identify individuals or establishments. If the identity of any individual or establishment is inadvertently discovered, the data producers must be notified. All users who obtain access to these data are required to comply with these restrictions.
In preparing these data files, direct identifiers were removed and characteristics that could potentially lead to the identification of individuals were treated to minimize disclosure risk. These data files may only be used for statistical analysis and reporting purposes.
Users must not attempt to use the data to identify individuals or establishments. If the identity of any individual or establishment is inadvertently discovered, the data producers must be notified. All users who obtain access to these data are required to comply with these restrictions.
Citation requirements
Users must provide appropriate attribution for any public use of the dataset or any work derived from it, crediting the author and/or copyright holder. Such attribution must include a link to the original data source. Users must also clearly indicate that the licensor does not endorse any work or product derived from the data.
Disclaimer and copyrights
Disclaimer
ANStat shall not be held liable to users for any loss or damage of any kind resulting from the use of its publications or datasets. ANStat shall also not be held responsible for the interpretation, usefulness, or availability of the data contained in the datasets. ANStat accepts no responsibility for the suitability of the information provided for users’ specific needs or, consequently, for any use they may make of such information.
The data producers and the funding agencies concerned assume no responsibility for the use of the data or for any interpretations or conclusions derived from such use.
The data producers and the funding agencies concerned assume no responsibility for the use of the data or for any interpretations or conclusions derived from such use.
Metadata production
DDI Document ID
DDI_CIV_2024-2025_AAS_v01_M_v01_A_ESS_FAO
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Directorate of Statistics, Documentation and Information Technology (Direction des Statistiques, de la Documentation et de l'Informatique) | DSDI | Ministry of Agriculture, Rural Development and Food Production (Ministère de l'Agriculture, du Développement Rural et des Productions Vivrières) | Metadata production |
| Statistics Division | ESS | Food and Agriculture Organization of the United Nations | Metadata preparation/ Metadata adapted for FAM |
| Development Data Group | DECDG | World Bank Group | Metadata adapted for World Bank Microdata Library |
Metadata version
DDI Document version
Identical to a metadata (CIV_2024_2025_EAA_v01_M_v01_A_ESS) published on FAO microdata repository (https://microdata.fao.org/index.php/catalog). Some of the metadata fields have been edited.