In the second quarter of 2023, the contribution of livestock production to Nigeria's GDP experienced an increase of 2.3 percent compared to the same period of the previous year. Agriculture contributes to a significant part of the country's GDP. It is a key activity for Nigeria's economy after oil. Nevertheless, agricultural activities provide a livelihood for many Nigerians, whereas the wealth generated by oil reaches a restricted share of people.

As of 2024, livestock in Ghana contributed around 5.4 billion Ghanaian cedis (GHS), roughly 348.4 million U.S. dollars, to the country's GDP. This represented nearly 13 percent of the contribution of agriculture to GDP in the country. In 2023, the added value of the livestock industry to GDP amounted to approximately 5.2 billion GHS (335.6 million U.S. dollars), following an upward trend observed since 2013. Chickens in Ghana are in the livestock lead Rural populations in Ghana are usually more engaged in farming and animal husbandry in comparison to their urban counterparts. In recent years, the country’s livestock production index has exceeded 100 points, showing an increased growth in livestock breeding. At the national level, chickens, goats, and sheep form the major species reared. As of 2022, the population of each animal amounted to over 88.6 million, 8.6 million, and 5.8 million, respectively. In fact, live chickens in Ghana considerably increased from around 47.8 million heads in 2010 to nearly 89 million heads in 2022. The situation in Africa is similar Likewise in Ghana, chickens, goats, and sheep are the main livestock reared in Africa. In 2022, the count of chicken heads reached over 2.4 billion on the continent. That of goats and sheep amounted to around 506 million and 419 million, respectively. Moreover, the majority of live chickens were found in Egypt. On the other hand, the largest goat and sheep population was found in Nigeria.

Nigeria NG: GDP: Growth: Gross Value Added: Agriculture, Forestry, and Fishing data was reported at 1.882 % in 2022. This records a decrease from the previous number of 2.127 % for 2021. Nigeria NG: GDP: Growth: Gross Value Added: Agriculture, Forestry, and Fishing data is updated yearly, averaging 3.926 % from Dec 1982 (Median) to 2022, with 41 observations. The data reached an all-time high of 55.578 % in 2002 and a record low of -4.382 % in 1984. Nigeria NG: GDP: Growth: Gross Value Added: Agriculture, Forestry, and Fishing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Gross Domestic Product: Annual Growth Rate. Annual growth rate for agricultural, forestry, and fishing value added based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Agriculture corresponds to ISIC divisions 01-03 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;Note: Data for OECD countries are based on ISIC, revision 4.

Nigeria GDP: Basic Prices: Agriculture: Livestock data was reported at 1,974,447.756 NGN mn in 2017. This records an increase from the previous number of 1,875,783.354 NGN mn for 2016. Nigeria GDP: Basic Prices: Agriculture: Livestock data is updated yearly, averaging 164,374.291 NGN mn from Dec 1981 (Median) to 2017, with 37 observations. The data reached an all-time high of 1,974,447.756 NGN mn in 2017 and a record low of 2,525.025 NGN mn in 1981. Nigeria GDP: Basic Prices: Agriculture: Livestock data remains active status in CEIC and is reported by National Bureau of Statistics of the Federal Republic of Nigeria. The data is categorized under Global Database’s Nigeria – Table NG.A007: GDP: by Industry: Current Price: Annual.

In the second quarter of 2023, the agricultural sector generated about ** percent of Nigeria's gross domestic product. The largest contribution was from crop production, which covered nearly ** percent of the GDP. Agriculture accounted for a significant portion of Nigeria's GDP as a key activity for the country's economy after oil. Nevertheless, agricultural activities provide a livelihood for many Nigerians, whereas the wealth generated by oil reaches a restricted share of people.

Nigeria NG: GDP: Real: Gross Value Added at Basic Prices: Agriculture, Forestry, and Fishing data was reported at 19,306,490.277 NGN mn in 2023. This records an increase from the previous number of 19,091,072.816 NGN mn for 2022. Nigeria NG: GDP: Real: Gross Value Added at Basic Prices: Agriculture, Forestry, and Fishing data is updated yearly, averaging 7,817,084.495 NGN mn from Dec 1981 (Median) to 2023, with 43 observations. The data reached an all-time high of 19,306,490.277 NGN mn in 2023 and a record low of 2,303,505.416 NGN mn in 1984. Nigeria NG: GDP: Real: Gross Value Added at Basic Prices: Agriculture, Forestry, and Fishing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Gross Domestic Product: Real. Agriculture, forestry, and fishing corresponds to ISIC divisions 1-3 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4. Data are in constant local currency.;World Bank national accounts data, and OECD National Accounts data files.;;Note: Data for OECD countries are based on ISIC, revision 4.

Industry is the main economic sector in Nigeria. According to a forecast, the contribution of the industry sector to GDP in the country will grow by *** percent in 2025 compared to a *** percent growth in the previous year. On the other hand, the GDP contribution of agriculture is expected to grow by *** percent in 2025.

Abstract

NASS is designed to provide accurate and up-to-date agricultural statistics that allow policymakers, researchers, and development partners to make informed decisions that directly impact the well-being of farmers, rural communities, and the broader economy. These statistics are essential for enhancing food security, improving productivity, and addressing regional disparities in agricultural performance. Additionally, robust agricultural data are vital in supporting Nigeria’s efforts to diversify its economy from oil dependency. By identifying key areas for investment, such as crop production, livestock management, and agro-processing, data can guide both public and private sector investments to boost agricultural output and expand exports. Moreover, the survey contributes to tracking progress toward national goals while supporting Nigeria's efforts to meet global commitments like the Sustainable Development Goals (SDGs). Hence, NASS provides useful data for understanding the state of the agricultural sector and offers essential production and structural data to support evidence-based planning and implementation of agricultural programs vital for addressing current economic challenges and enhancing the livelihood of many Nigerians. This survey is also essential for monitoring and evaluating the effectiveness of existing agricultural programs and ensuring that resources are allocated efficiently. Capturing detailed data on agriculture practices, outputs, and challenges, the survey supports the planning and implementation of initiatives aimed at improving productivity, enhancing food security, and adapting to challenges like climate change and market fluctuations.

The objectives of the survey are to: i. provide data on agricultural production in 2022/2023 and the structure of the sector as a whole to assist the government in policy formulation and programme planning; ii. effectively and efficiently provide appropriate agricultural information to increase public awareness; and iii. provide data that could be used to compute agricultural sector contribution to the Gross Domestic Product (GDP).

Geographic coverage

The National Population Commission (NPC) provided the sampling frame of Enumeration Areas (EAs), newly demarcated for the proposed 2023 Housing and Population Census. This was used as the primary sampling frame. Although data were collected across the 36 states and the Federal Capital Territory (FCT), only 767 out of the 774 Local Government Areas (LGAs) were covered due to security challenges. The affected states/LGAs are Borno state (Monguno, Kukawa and Abadam LGAs) and Orlu, Orsu, Oru East, and Njaba LGAs in Imo state. The number of EAs covered varied from state to state depending on the number of Agricultural EAs and LGAs.

Analysis unit

Households

Universe

The final sampling units used were agricultural households involved in crop/ livestock farming, and fishery households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The final sampling units used were agricultural households involved in crop/ livestock farming, and fishery households. The sampling method of NASS-household is a stratified three-phased sampling as follows:

• First phase: Stratified Probability Proportional to Size (PPS) selection of 80 EAs per LGA
• Second phase: systematic sub-sampling of 40 EAs per LGA for the extended listing
• Third phase: two-stage sampling for NASS-household

Third-phase details: i. First stage: Stratification of EAs into agricultural and non-agricultural EAs drawn from the 40 EAs listed in each LGA ii. Second stage: Systematic sampling of ten farming households (crop/ livestock farming) and additional fishery-only households in fishery-intensive LGAs (18 in total) up to a maximum of 12 households per EA. This selection was stratified by sorting the listed farming households according to several agricultural characteristics, including type of farming activity, number of plots, livestock numbers (in tropical livestock units), and gender of household head.

Sample Size and Allocation

Nationally, a total of 15 591 EAs were selected across the 36 States of the Federation and FCT for the NASS household survey. The sample was distributed across LGAs based on the estimated total number of plots per LGA. Within each LGA, the sample was further allocated between urban and rural areas in proportion to the estimated agricultural population. In the selected EAs, 152 485 households were finally sampled.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASS household questionnaire served as a meticulously designed instrument administered within selected households to gather comprehensive data. The questionnaire was structured into the following sections:

0A. HOLDING IDENTIFICATION 0B. ROSTER OF HOUSEHOLD MEMBERS 0C. AGRICULTURAL ACTIVITIES 0D. AGRICULTURALACTIVITIES 2. PLOT ROSTER AND DETAILS 3. CROP ROSTER 1A: TEMPORARY (NON-VEGETABLE) CROP PRODUCTION 1H: TEMPORARY CROP PRODUCTION (VEGETABLE CROPS) 1B: TEMPORARY CROP DESTINATION 2A: PERMANENT CROP PRODUCTION 2B: PERMANENT CROP DESTINATION 4: SEED AND PLANT USE 3C: INPUT USE 2(DRY SEASON): PLOT ROSTER AND DETAILS 3(DRY SEASON): CROP ROSTER 1A(DRY SEASON): TEMPORARY (NON-VEGETABLE) CROP PRODUCTION - DRY SEASON 1H(DRY SEASON): TEMPORARY CROP PRODUCTION (VEGETABLE CROPS) - DRY SEASON 1B(DRY SEASON): TEMPORARY CROP DESTINATION - DRY SEASON 4(DRY SEASON): SEED AND PLANT USE - DRY SEASON 3C(DRY SEASON): INPUT USE - DRY SEASON 4A: LIVESTOCK IN STOCK 4B: CHANGE IN STOCK- LARGE AND MEDIUM-SIZED ANIMALS 4C: CHANGE IN STOCK-POULTRY 4G: MILKPRODUCTION 4H: EGG PRODUCTION 4I: OTHERLIVESTOCKPRODUCTS 4J:APIARYPRODUCTION (BEEKEEPING) 5A: FISH FARMING/AQUACULTUREPRODUCTION 6A: FISH HUNTING/CAPTURE 7A: FORESTRYPRODUCTION 9: LABOUR 2_GPS.PLOT GPS MEASUREMENT 99. END OFTHE SURVEY

Cleaning operations

Data processing and analysis involved data cleaning, data analysis, data verification/validation, and table generation. The World Food Programme (WFP), the Food and Agricultural Organization of the United Nations (FAO), and NBS carried out the data processing and analysis for both the household and corporate farms questionnaires. The corporate farm questionnaire involved manual editing as well as data entry.

STATISTICAL DISCLOSURE CONTROL

To safeguard the confidentiality of household information, rigorous anonymization techniques have been employed on the edited microdata. This process involved the removal of all direct identifiers, such as names, GPS locations, and specific addresses. Additionally, geographic information below the level of Local Government Area (LGA) has been excised to prevent any potential identification of individuals or households based on their location.

Furthermore, a masking technique (local suppression algorithms) has been implemented on the quasi-identifying variables using the R package sdcMicro. This ensures that even subtle patterns or combinations of variables that could potentially lead to re-identification are obfuscated, thereby enhancing the overall security and privacy of the dataset.

Sampling error estimates

Given the complexity of the sample design, sampling errors were estimated through resampling approaches (Bootstrap/Jackknife).

Nigeria NG: GDP: % of GDP: Gross Value Added: Agriculture data was reported at 20.845 % in 2017. This records a decrease from the previous number of 20.983 % for 2016. Nigeria NG: GDP: % of GDP: Gross Value Added: Agriculture data is updated yearly, averaging 32.271 % from Dec 1981 (Median) to 2017, with 37 observations. The data reached an all-time high of 47.096 % in 2002 and a record low of 19.990 % in 2014. Nigeria NG: GDP: % of GDP: Gross Value Added: Agriculture data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Gross Domestic Product: Share of GDP. Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted average; Note: Data for OECD countries are based on ISIC, revision 4.

Africa's live chicken population has seen steady growth over the past years, reaching 2.4 billion in 2023. This marks a significant increase from 1.66 billion in 2010, reflecting the growing importance of poultry farming across the continent. The rise in chicken stocks aligns with broader trends in African livestock production, where chickens, goats, and sheep form the backbone of animal husbandry.
Chickens dominate African livestock Chickens have emerged as the dominant livestock species in Africa, far outnumbering other animals. In 2022, the continent's chicken population soared to dwarf the goat and sheep populations. Egypt leads the continent in chicken farming, with a stock of 300 million birds, followed closely by Nigeria with 249 million. This prevalence of chickens is not limited to specific countries, but is a continent-wide phenomenon. Economic impact of livestock farming The growth in chicken populations has significant economic implications for African countries. In Ghana, for instance, the livestock sector contributed approximately 4.9 billion Ghanaian cedis (about 409.4 million U.S. dollars) to the country's GDP in 2022. This represents nearly 13 percent of the agricultural sector's total contribution to GDP. The number of chickens in Ghana has almost doubled since 2010, rising from 47.8 million to nearly 89 million in 2022. This trend mirrors the continent-wide growth in chicken farming and underscores the increasing economic importance of poultry in African agriculture.

Nigeria NG: GDP: Growth: Gross Value Added: Agriculture data was reported at 3.445 % in 2017. This records a decrease from the previous number of 4.107 % for 2016. Nigeria NG: GDP: Growth: Gross Value Added: Agriculture data is updated yearly, averaging 4.135 % from Dec 1982 (Median) to 2017, with 36 observations. The data reached an all-time high of 55.578 % in 2002 and a record low of -4.382 % in 1984. Nigeria NG: GDP: Growth: Gross Value Added: Agriculture data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Gross Domestic Product: Annual Growth Rate. Annual growth rate for agricultural value added based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

This data was collected by IFPRI as part of the World Bank-funded project (Fadama III–Additional Financing (AF II) phase II ) that was implemented in North-Eastern Nigeria. The Project was supporting the recovery of the agriculture sector in the North East (NE) of Nigeria in response to support the Government’s recovery and reconstruction initiative. The project sought to respond to the urgent food and livelihood needs of farming households who were affected by conflicts in the six North-East states in Nigeria—Borno, Yobe, Adamawa, Taraba, Bauchi, and Gombe. The North East States suffered huge losses and damage to property, economic infrastructure, and livelihoods because of the insurgency. Among the participating communities and households, the project was intended to improve nutritional security, food security, household incomes, boost job creation, improve infrastructure and increase access to market information as well as enhancing the managerial capacities of the local communities. The North-Eastern region of Nigeria was renowned for its large agricultural potential, with 80 percent of the population engaged in farming and contributing significantly to the regional and national GDP. Over the past two decades, however, the region had regressed with low education levels, limited access to healthcare and other basic amenities, and low GDP per capita. A once-promising zone now trails the other regions of Nigeria across all socio-economic indicators. The NE region in most recent times has also borne the brunt of human casualty, loss of properties, and diminished livelihoods emanating from the Boko Haram terrorist insurgency. Towards the end of the project activities in 2018, IFPRI was contracted by the National Fadama Coordination Office (NFCO) in Abuja Nigeria which was the project implementing agency on behalf of the Government of Nigeria and World Bank to conduct an endline survey to collect primary data that would be used in rigorous impact assessment hence this data set. The endline survey collected both the project endline data ( 2018 measurements) and the retrospective baseline data ( 2016 measurements). The sample household survey covered all the six states in North-Eastern Nigeria that received project financial support. A total of 1800 households were sampled in both project treatment communities and non-project control communities. The Survey data has information on insecurity conflicts and how these insecurity conflicts impacted on household migration and socio-economic conditions, humanitarian support received, value addition and agricultural processing, agricultural input aid received, demographic characteristics, crop production, livestock production, non-farm income, Fishing, and Aquaculture Income, beekeeping income, forestry and agroforestry income, wildlife income, food insecurity assessment, household dietary diversity, access to marketing infrastructure, productive assets, non-productive assets, access to credit, access to market information and extension.The data included is here for the Yobe state.

Abstract

NASC is an exercise designed to fill the existing data gap in the agricultural landscape in Nigeria. It is a comprehensive enumeration of all agricultural activities in the country, including crop production, fisheries, forestry, and livestock activities. The implementation of NASC was done in two phases, the first being the Listing Phase, and the second is the Sample Survey Phase. Under the first phase, enumerators visited all the selected Enumeration Areas (EAs) across the Local Government Areas (LGAs) and listed all the farming households in the selected enumeration areas and collected the required information. The scope of information collected under this phase includes demographic details of the holders, type of agricultural activity (crop production, fishery, poultry, or livestock), the type of produce or product (for example: rice, maize, sorghum, chicken, or cow), and the details of the contact persons. The listing exercise was conducted concurrently with the administration of a Community Questionnaire, to gather information about the general views of the communities on the agricultural and non-agricultural activities through focus group discussions.

The main objective of the listing exercise is to collect information on agricultural activities at household level in order to provide a comprehensive frame for agricultural surveys. The main objective of the community questionnaire is to obtain information about the perceptions of the community members on the agricultural and non-agricultural activities in the community.

Additional objectives of the overall NASC program include the following: · To provide data to help the government at different levels in formulating policies on agriculture aimed at attaining food security and poverty alleviation · To provide data for the proposed Gross Domestic Product (GDP) rebasing

Geographic coverage

Estimation domains are administrative areas from which reliable estimates are expected. The sample size planned for the extended listing operation allowed reporting key structural agricultural statistics at Local Government Area (LGA) level.

Analysis unit

Agricultural Households.

Universe

Population units of this operation are households with members practicing agricultural activities on their own account (farming households). However, all households in selected EAs were observed as much as possible to ensure a complete coverage of farming households.

Kind of data

Census/enumeration data [cen]

Sampling procedure

An advanced methodology was adopted in the conduct of the listing exercise. For the first time in Nigeria, the entire listing was conducted digitally. NBS secured newly demarcated digitized enumeration area (EA) maps from the National Population Commission (NPC) and utilized them for the listing exercise. This newly carved out maps served as a basis for the segmentation of the areas visited for listing exercise. With these maps, the process for identifying the boundaries of the enumeration areas by the enumerators was seamless.

The census was carried out in all the 36 States of the Federation and FCT. Forty (40) enumeration Areas (EAs) were selected to be canvassed in each LGA, the number of EAs covered varied by state, which is a function of the number of LGAs in the state. Both urban and rural EAs were canvassed. Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno States) were not covered due to insecurity (99% coverage). In all, thirty thousand, nine hundred and sixty (30,960) EAs were expected to be covered nationwide but 30,546 EAs were canvassed.

The Sampling method adopted involved three levels of stratification. The objective of this was to provide representative data on every Local Government Area (LGA) in Nigeria. Thus, the LGA became the primary reporting domain for the NASC and the first level of stratification. Within each LGA, eighty (80) EAs were systematically selected and stratified into urban and rural EAs, which then formed the second level of stratification, with the 80 EAs proportionally allocated to urban and rural according to the total share of urban/rural EAs within the LGA. These 80 EAs formed the master sample from which the main NASC sample was selected. From the 80 EAs selected across all the LGAs, 40 EAs were systematically selected per LGA to be canvassed. This additional level selection of EAs was again stratified across urban and rural areas with a target allocation of 30 rural and 10 urban EAs in each LGA. The remaining 40 EAs in each LGA from the master sample were set aside for replacement purposes in case there would be need for any inaccessible EA to be replaced.

Details of sampling procedure implemented in the NASC (LISTING COMPONENT). A stratified two-phase cluster sampling method was used. The sampling frame was stratified by urban/rural criteria in each LGA (estimation domain/analytical stratum).

First phase: in each LGA, a total sample of 80 EAs were allocated in each strata (urban/rural) proportionally to their number of EAs with reallocations as need be. In each stratum, the sample was selected with a Pareto probability proportional to size considering the number of households as measure of size.

Second phase: systematic subsampling of 40 EAs was done (10 in Urban and 30 in Rural with reallocations as needed, if there were fewer than 10 Urban or 30 Rural EAs in an LGA). This phase was implicitly stratified through sorting the first phase sample by geography.

With a total of 773 LGAs covered in the frame, the total planned sample size was 30920 EAs. However, during fieldwork 2 LGAs were unable to be covered due to insecurity and additional 4 LGAs were suspended early due to insecurity. For the same reason, replacements of some sampled EAs were needed in many LGAs. The teams were advised to select replacement units where possible considering appurtenance to the same stratum and similarity including in terms of population size. However about 609 EAs replacement units were selected from a different stratum and were discarded from data processing and reporting.

Sampling deviation

Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno states) were not covered due to insecurity (99% coverage).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASC household listing questionnaire served as a meticulously designed instrument administered within every household to gather comprehensive data. It encompassed various aspects such as household demographics, agricultural activities including crops, livestock (including poultry), fisheries, and ownership of agricultural/non-agricultural enterprises.

The questionnaire was structured into the following sections:

Section 0: ADMINISTRATIVE IDENTIFICATION Section 1: BUILDING LISTING Section 2: HOUSEHOLD LISTING (Administered to the Head of Household or any knowledgeable adult member aged 15 years and above).

Cleaning operations

Data processing of the NASC household listing survey included checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning was carried out electronically using the Stata software package. In some cases where data inconsistencies were found a call back to the household was carried out. A pre-analysis tabulation plan was developed and the final tables for publication were created using the Stata software package.

Sampling error estimates

Given the complexity of the sample design, sampling errors were estimated through re-sampling approaches (Bootstrap/Jackknife)

Nigeria NG: GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data was reported at 23.357 % in 2021. This records a decrease from the previous number of 24.143 % for 2020. Nigeria NG: GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data is updated yearly, averaging 22.235 % from Dec 1981 (Median) to 2021, with 41 observations. The data reached an all-time high of 36.965 % in 2002 and a record low of 12.240 % in 1981. Nigeria NG: GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Gross Domestic Product: Share of GDP. Agriculture, forestry, and fishing corresponds to ISIC divisions 1-3 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4. Note: For VAB countries, gross value added at factor cost is used as the denominator.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;Note: Data for OECD countries are based on ISIC, revision 4.

This data was collected by IFPRI as part of the World Bank-funded project (Fadama III–Additional Financing (AF II) phase II ) that was implemented in North-Eastern Nigeria. The Project was supporting the recovery of the agriculture sector in the North East (NE) of Nigeria in response to support the Government’s recovery and reconstruction initiative. The project sought to respond to the urgent food and livelihood needs of farming households who were affected by conflicts in the six North-East states in Nigeria—Borno, Yobe, Adamawa, Taraba, Bauchi, and Gombe. The North East States suffered huge losses and damage to property, economic infrastructure, and livelihoods because of the insurgency. Among the participating communities and households, the project was intended to improve nutritional security, food security, household incomes, boost job creation, improve infrastructure and increase access to market information as well as enhancing the managerial capacities of the local communities. The North-Eastern region of Nigeria was renowned for its large agricultural potential, with 80 percent of the population engaged in farming and contributing significantly to the regional and national GDP. Over the past two decades, however, the region had regressed with low education levels, limited access to healthcare and other basic amenities, and low GDP per capita. A once-promising zone now trails the other regions of Nigeria across all socio-economic indicators. The NE region in most recent times has also borne the brunt of human casualty, loss of properties, and diminished livelihoods emanating from the Boko Haram terrorist insurgency. Towards the end of the project activities in 2018, IFPRI was contracted by the National Fadama Coordination Office (NFCO) in Abuja Nigeria which was the project implementing agency on behalf of the Government of Nigeria and World Bank to conduct an endline survey to collect primary data that would be used in rigorous impact assessment hence this data set. The endline survey collected both the project endline data ( 2018 measurements) and the retrospective baseline data ( 2016 measurements). The sample household survey covered all the six states in North-Eastern Nigeria that received project financial support. A total of 1800 households were sampled in both project treatment communities and non-project control communities. The Survey data has information on insecurity conflicts and how these insecurity conflicts impacted on household migration and socio-economic conditions, humanitarian support received, value addition and agricultural processing, agricultural input aid received, demographic characteristics, crop production, livestock production, non-farm income, Fishing, and Aquaculture Income, beekeeping income, forestry and agroforestry income, wildlife income, food insecurity assessment, household dietary diversity, access to marketing infrastructure, productive assets, non-productive assets, access to credit, access to market information and extension.The data included is here for theTaraba state.

Nigeria NG: GDP: Gross Value Added at Basic Prices: Agriculture, Forestry, and Fishing data was reported at 53,273,143.682 NGN mn in 2023. This records an increase from the previous number of 47,944,062.797 NGN mn for 2022. Nigeria NG: GDP: Gross Value Added at Basic Prices: Agriculture, Forestry, and Fishing data is updated yearly, averaging 4,251,520.648 NGN mn from Dec 1981 (Median) to 2023, with 43 observations. The data reached an all-time high of 53,273,143.682 NGN mn in 2023 and a record low of 17,052.176 NGN mn in 1981. Nigeria NG: GDP: Gross Value Added at Basic Prices: Agriculture, Forestry, and Fishing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Gross Domestic Product: Nominal. Agriculture, forestry, and fishing corresponds to ISIC divisions 1-3 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4. Data are in current local currency.;World Bank national accounts data, and OECD National Accounts data files.;;Note: Data for OECD countries are based on ISIC, revision 4.

The 2006 Nigeria SAM is a comprehensive, economy-wide data framework, representing the structure of the Nigerian economy; the links among production activities, income distribution, consumption of goods/services, savings and investment, and foreign trade of the economic agents in year 2006. This 2006 Nigeria SAM is a 61 sector square matrix table with the column and row beginning with activities account, followed by commodities account and thereafter accounts for the economic agent in the Nigerian economy. Each cell in the matrix represents the flow of economic activities in monetary terms from a column account (expenditure or outflow) to a row account (income or inflow). Also, each activity and commodity account begins with letter 'a ' and 'œc' respectively. This 2006 SAM was built for the dynamic CGE (DCGE) model that examined the growth and investment options available in the agricultural sector for reducing poverty in Nigeria, and was an integral part of the Agricultural Policy Support Facilites activities for strengthening evidence-based policymaking in Nigeria. Given the agricultural policy analysis focus of the SAM and DCGE model, 34 sector of the SAM are under agriculture and included key cash and food crops as well as livestock sub-sector. The 2006 Nigeria SAM also includes 12 manufacturing (such as beef, textiles, and wood products); 2 mining sector (including crude petroleum and natural gas); and 13 service sectors (such as building and construction, electricity and water, and hotels and restaurants). While the total number of sector for the SAM is 61, the commodities account is 62 as fertilizer was treated as commodity rather than activity. The 2006 SAM data files comprise two worksheets; one for the SAM data and the other containing legend to the SAM data. The value for each of the cell entries is reported in naira million (2006 prices). The data used for building this SAM were obtained from various sources including but not limited to publications of the National Bureau of Statistics (NBS), the Central Bank of Nigeria (CBN), and the Federal Ministry of Agriculture and Water Resources (FMAWR). Data from an earlier SAM of the country developed by United Nations Development Programme (UNDP), 1995 are also used, and was balanced using the cross entropy estimation method. The SAM was built following the International Food Policy Research Institute (IFPRI) standard format (Lofgren et al. 2001).

Nigeria NG: GDP: Gross Value Added at Factor Cost: Agriculture data was reported at 23,952,554.203 NGN mn in 2017. This records an increase from the previous number of 21,523,512.499 NGN mn for 2016. Nigeria NG: GDP: Gross Value Added at Factor Cost: Agriculture data is updated yearly, averaging 1,127,693.120 NGN mn from Dec 1981 (Median) to 2017, with 37 observations. The data reached an all-time high of 23,952,554.203 NGN mn in 2017 and a record low of 13,580.320 NGN mn in 1981. Nigeria NG: GDP: Gross Value Added at Factor Cost: Agriculture data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Gross Domestic Product: Nominal. Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4. Data are in current local currency.; ; World Bank national accounts data, and OECD National Accounts data files.; ; Note: Data for OECD countries are based on ISIC, revision 4.

Nigeria NG: GDP: Real: Gross Value Added at Factor Cost: Agriculture data was reported at 17,179,495.287 NGN mn in 2017. This records an increase from the previous number of 16,607,337.334 NGN mn for 2016. Nigeria NG: GDP: Real: Gross Value Added at Factor Cost: Agriculture data is updated yearly, averaging 4,703,643.682 NGN mn from Dec 1981 (Median) to 2017, with 37 observations. The data reached an all-time high of 17,179,495.287 NGN mn in 2017 and a record low of 2,303,505.416 NGN mn in 1984. Nigeria NG: GDP: Real: Gross Value Added at Factor Cost: Agriculture data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Gross Domestic Product: Real. Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4. Data are in constant local currency.; ; World Bank national accounts data, and OECD National Accounts data files.; ; Note: Data for OECD countries are based on ISIC, revision 4.

Nigeria NG: GDP: 2010 Price: USD: Gross Value Added Per Worker: Agriculture data was reported at 0.006 USD mn in 2017. This records a decrease from the previous number of 0.006 USD mn for 2016. Nigeria NG: GDP: 2010 Price: USD: Gross Value Added Per Worker: Agriculture data is updated yearly, averaging 0.003 USD mn from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 0.006 USD mn in 2010 and a record low of 0.001 USD mn in 1994. Nigeria NG: GDP: 2010 Price: USD: Gross Value Added Per Worker: Agriculture data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Gross Domestic Product: Real. Value added per worker is a measure of labor productivity—value added per unit of input. Value added denotes the net output of a sector after adding up all outputs and subtracting intermediate inputs. Data are in constant 2010 U.S. dollars. Agriculture corresponds to the International Standard Industrial Classification (ISIC) tabulation categories A and B (revision 3) or tabulation category A (revision 4), and includes forestry, hunting, and fishing as well as cultivation of crops and livestock production.; ; Derived using World Bank national accounts data and OECD National Accounts data files, and employment data from International Labour Organization, ILOSTAT database.; Weighted Average;