Maize production in Nigeria amounted to ***** million metric tons in 2021. This slightly increased from the previous year, when the volume reached **** million metric tons, the highest within the observed period. The quantity of maize produced in the country has generally increased since 2010.

Nigeria Agricultural Production: Maize data was reported at 18,570.260 Tonne th in 2017. This records an increase from the previous number of 18,097.500 Tonne th for 2016. Nigeria Agricultural Production: Maize data is updated yearly, averaging 11,087.360 Tonne th from Jun 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 18,570.260 Tonne th in 2017 and a record low of 4,547.660 Tonne th in 1997. Nigeria Agricultural Production: Maize 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.B003: Agricultural Production.

Forecast: Corn Production in Nigeria 2023 - 2027 Discover more data with ReportLinker!

Nigeria Agricultural Production: Guinea Corn data was reported at 17,600.830 Tonne th in 2017. This records an increase from the previous number of 17,109.000 Tonne th for 2016. Nigeria Agricultural Production: Guinea Corn data is updated yearly, averaging 11,234.780 Tonne th from Jun 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 17,600.830 Tonne th in 2017 and a record low of 5,096.150 Tonne th in 1998. Nigeria Agricultural Production: Guinea Corn 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.B003: Agricultural Production.

Forecast: Corn Yield in Nigeria 2022 - 2026 Discover more data with ReportLinker!

Statistics illustrates consumption, production, prices, and trade of Maize in Nigeria from 2007 to 2024.

This data comprises processed weather, soil, yield, and cultivation area for corn yield prediction in Sub-Sahara Africa, with emphasis on Nigeria. The data was collected to design a corn yield prediction model to help smallholder farmers make smart farming decisions. However, the data can serve several other purposes through analysis and interpretation.

The reference study region in Africa is Nigeria. The focuses on corn crop because there are over 211.4 million people, of which a large percentage of the population are smallholder farmers. Nigeria [9.0820° N, 8.6753° E] is within an arable land area of 34 million hectares located on the west coast of Africa. The region comprises of 36 states with the most and least number of districts being 214 and 10, respectively. For each state, the environment data are collected as follows.

Grid map climate data – This data spans spatial resolutions between ~1 km2 to ~340 km2 from the high spatial resolution WorldClim global climate database22. Each grid point on the map is monthly data from January to December between 1970 and 2000 years and records 8 climate variables. The variables are average temperature C0, minimum temperature C0, maximum temperature C0, precipitation (mm), solar radiation (kJ m^(-2) day(-1), wind speed (m s(-1)), and water vapor (kPa) taken at 30 seconds (s), 2.5 minutes m, 5 m, and 10 m.

Grid map soil data – This data is obtained from 250 minutes of spatial resolution AfSIS soil data23 from year 1960 to 2012. The variables are wet soil bulk density, dry bulk density (kg dm-3), clay percentage of plant available water content, hydraulic conductivity, the upper limit of plant available water content, the lower limit of, organic matter percentage, pH, sand percentage (g 100 g-1), silt percentage (g 100 g-1) and, clay percentage (g 100 g-1), and saturated volumetric water content variables measured at depths 0–5, 5–10, 10–15, 15–30, 30–45, 45–60, 60–80, 80–100, and 100–120 measured in centimeters (cm).

Corn yield data – This data is available on Kneoma Corporation website24. It ranged from years 1995 to 2006 and consisted of a corn yield of 1000 metric tonnes and a cultivation area of 1000 hectares.

Geolocation coordinates (latitude and longitude) – The geolocation of each of the 36 states with their districts is sampled from Google Maps. The output feds into the Esri-ArcGIS 2.5, a professional geographical software, for extracting the point-cloud values of each environmental variable (weather and soil) at specific geolocation of the 36 states of Nigeria.

Other Descriptions: Data type - Continous and Categorical Dataset Characteristics - Tabular Associated Tasks - Regression Feature Type - Real Number of Instances - 1828 Number of Features: 12

Forecast: Green Maize Yield in Nigeria 2023 - 2027 Discover more data with ReportLinker!

Africa Maize Market Report is Segmented by Geography (South Africa, Ethiopia, Nigeria, and More). The Report Includes Production Analysis (Volume), Consumption Analysis (Value and Volume), Export Analysis (Value and Volume), Import Analysis (Value and Volume), and Price Trend Analysis. The Market Forecasts are Provided in Terms of Value (USD) and Volume (Metric Tons).

Crop production in Nigeria has shown varied growth patterns in recent years. Between the first quarter of 2019 and the second quarter of 2023, the sector’s growth rate fluctuated significantly, reflecting the challenges and opportunities inherent in the country’s agricultural landscape. The quarter-on-quarter changes further depict the sector’s vulnerability to climatic variability, logistics, and policy directions. While some quarters witnessed positive growth, others experienced contractions at constant 2010 prices. These dynamics highlight the persistent need for strategic investment to stabilize and accelerate agricultural output. Land and labor Nigeria’s agricultural potential is anchored in its vast land resources. As of 2023, the country boasted around 69.4 million hectares of agricultural land, with approximately 36.9 million hectares classified as arable. This substantial land base is a key driver for major crops such as maize, cassava, and yam, securing rural livelihoods and contributing to food security. Despite rapid urban growth, agriculture remains a major employer in Nigeria. In 2023, the sector accounted for about 34.3 percent of all jobs nationwide, emphasizing its socio-economic relevance to the country. Export potential Agricultural exports, while notable, reveal untapped opportunities. In 2023, Nigeria exported agricultural products valued at 2.43 billion U.S. dollars, making up a modest portion of total national exports. This performance signals room for greater value addition and diversification, as well as the importance of policies that can transform raw production into higher foreign exchange earnings for the country.

According to a survey conducted in 2019, about 50 percent of farming households in Nigeria were growing maize crops, the most common crop in the country. Cassava crops followed directly, with some 46 percent of households growing this root. In addition, other widespread crops were Guinea corn, yam, and beans, with 20 percent to 30 percent of surveyed households cultivating them.

Statistics illustrates consumption, production, prices, and trade of Preserved Sweet Corn in Nigeria from 2007 to 2024.

Statistics illustrates consumption, production, prices, and trade of Maize (Corn) Starch in Nigeria from 2007 to 2024.

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

Communities (in Enumerated Areas).

Analysis unit

Community

Universe

The population units are communities encompassing the designated enumeration areas, where household listing was performed.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Focus group interviews were performed in communities overlapping with in the EAs selected for the extended listing operation. Accordingly, a focus group discussion in a total of 26,555 communities were undertaken to administer the community level questionnaire. It is important to note here that the results from the community survey are unweighted results and all the tables produced from the community level data are only from the 26,555 communities interviewed.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASC community listing questionnaire served as a meticulously designed instrument administered within every community selected to gather comprehensive data. It encompassed various aspects such as agricultural activities in the community, infrastructures, disaster, etc. The questionnaire was structured into the following sections:

• Identification of the community • Respondent Characteristics (Name, Sex, age) • Agricultural Activities in the Community • Disasters and Shocks • Community Infrastructure and Transportation • Community Organizations • Community Resources Management • Land Prices and Credit • Community Key Events • Labour

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.

The provided dataset was collected using a specially designed solar-powered Arduino IoT sensor device that features cloud API functionalities, facilitating continuous data logging and physical parameter measurements. This data collection process spanned an entire planting season within an experimental farm. The primary objective of the experimental farm was to study three distinct maize varieties, namely V1 (DMR-ESRY), V2 (BR9928 DMRSR), and V3 (ART-98-SW-1), which correspond respectively to DMR-ESRY (NCRI/IITA, 1991), BR9928 DMRSR (IITA, 2009), and ART-98-SW-1 (I.A.R. & T., 2001). These maize varieties were subjected to three different soil treatments across fields denoted as F1, F2, and F3.Field F1 was designated as the control field, representing the natural soil of the experimental farmland without any modifications. Field F2 encompassed areas treated with poultry manure, applied a week before planting at a rate of fifteen (15) tons per hectare (ha), equivalent to eighty (80) kilograms (kg) of Nitrogen (N) per hectare (ha), with careful consideration of pre-application manure analysis. Field F3 consisted of sections treated with NPK fertilizer, applied according to the NPK formula (400 kg NPK 20-10-10 per hectare [ha]), translating to eighty kilograms (80kg) of Nitrogen (N), forty kilograms (40kg) of phosphorous (P), and forty kilograms (40kg) of potassium (K).To manage variations stemming from diverse treatments and replicates, the chosen experimental design was the randomized complete block design (RCBD), as outlined by Anderson & McLean (2019) and Grant (2010). This approach ensured effective control over experimental factors. An available collection of weekly images captured during the experiment can be accessed at the provided link (https://doi.org/10.6084/m9.figshare.23972252.v2).The dataset includes several components:Soil-Environmental-Data: Data collected and stored in the c2snet cloud (https://iot.c2snet.org/data/data.php) via the specially designed solar-powered Arduino IoT sensor device throughout the experimental period.Physical Data: Daily measurements of maize growth physical parameters obtained from the experimental farm.Yield-Data: Measured and computed post-harvest yield data.MergedData2: A unified dataset obtained through the application of an inner merging technique to the three initial datasets.Furthermore, accompanying the dataset are the Python notebook code used for data merging and visual images depicting portions of the dataset. References:Anderson, V. L., & McLean, R. A. (2019). Randomized Complete Block Design (RCBD). In Design ofGrant, T. (2010). The Randomized Complete Block Design (RCBD). Crop Science, 1–12.I.A.R. & T. (2001). ART-98-SW-1 Maize (Zea mays) - Nigerian Seed Portal Initiative. https://www.seedportal.org.ng/variety.php?keyword=&category=&varid=203&cropid=7&task=viewIITA. (2009). BR9928 DMRSR - Maize (Zea mays) - Nigerian Seed Portal Initiative. https://www.seedportal.org.ng/variety.php?keyword=&category=&varid=218&cropid=7&task=viewNCRI/IITA. (1991). DMR-ESRY Maize (Zea mays) - Nigerian Seed Portal Initiative. https://www.seedportal.org.ng/variety.php?keyword=&category=&varid=191&cropid=7&task=viewOlayinka, Akinola Samson; Olayinka, Tosin Comfort; Adetunmbi, Adebayo Olusola; Obe, Olayinka Olumide; Ibam, Emmanuel Onwuka; Ogedegbe, Sunday; et al. (2023). Weekly Progression Images of Maize Growth in an Experimental Field in Benin City, Nigeria. figshare. Media. https://doi.org/10.6084/m9.figshare.23972252.v2

Statistics illustrates production of maize in Nigeria from 2007 to 2024.

534.1 (1000 metric tons) in 2006.

Statistics illustrates consumption, production, prices, and trade of Cereal Grain Products (Including Corn Flakes) in Nigeria from 2007 to 2024.

Production of Maize in Nigeria - 2025. Find the latest marketing data on the IndexBox platform.

95.1 (1000 metric tons) in 2006.