As of 2023, Niger registered the agricultural sector's highest contribution to the GDP in Africa, at over ** percent. Comoros and Ethiopia followed, with agriculture, forestry, and fishing accounting for approximately ** percent and ** percent of the GDP, respectively. On the other hand, Botswana, Djibouti, Libya, Zambia, and South Africa were the African countries with the lowest percentage of the GDP generated by the agricultural sector. Agriculture remains a pillar of Africa’s economy Despite the significant variations across countries, agriculture is a key sector in Africa. In 2022, it represented around ** percent of Sub-Saharan Africa’s GDP, growing by over *** percentage points compared to 2011. The agricultural industry also strongly contributes to the continent’s job market. The number of people employed in the primary sector in Africa grew from around *** million in 2011 to *** million in 2021. In proportion, agriculture employed approximately ** percent of Africa’s working population in 2021. Agricultural activities attracted a large share of the labor force in Central, East, and West Africa, which registered percentages over the regional average. On the other hand, North Africa recorded the lowest share of employment in agriculture, as the regional economy relies significantly on the industrial and service sectors. Cereals are among the most produced crops Sudan and South Africa are the African countries with the largest agricultural areas. Respectively, they devote around *** million and **** million hectares of land to growing crops. Agricultural production varies significantly across African countries in terms of products and volume. Cereals such as rice, corn, and wheat are among the main crops on the continent, also representing a staple in most countries. The leading cereal producers are Ethiopia, Nigeria, Egypt, and South Africa. Together, they recorded a cereal output of almost *** million metric tons in 2021. Additionally, rice production was concentrated in Nigeria, Egypt, Madagascar, and Tanzania.

Africa had around 1,173 million hectares of agricultural land in 2022, which corresponded to nearly 40 percent of the continent's total land area. Since 2010, the area used for the cultivation of crops and the production of animals followed a slightly increasing trend on the continent.

This edition of the Abstract of Agricultural Statistics contains South African agricultural statistics of major importance that were available up to December 2017. The "Abstract" contains meaningful information on, inter alia, field crops, horticulture, livestock, important indicators and the contribution of agriculture.

The average for 2022 based on 47 countries was 48.19 percent. The highest value was in the Ivory Coast: 84.16 percent and the lowest value was in the Seychelles: 3.37 percent. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

As of 2022, some ** percent of total employment in Africa was in the agricultural sector. According to estimates, the share of people employed in agriculture has been slowly decreasing on the continent. In comparison, in 2010, **** percent of total employment in Africa was in the sector. On the other hand, employment in the service sector in Africa increased along the same period.

Agricultural Production In Africa Dataset

  Dataset Overview

Dataset Name: Cleaned Agricultural Production Data Source: Food and Agriculture organization (FAO) Dataset Type: Time-series agricultural statistics Number of Rows: ~790,714 Format: CSV Size: [File size]

  Dataset Description

This dataset contains comprehensive agricultural production statistics, including crop and livestock data across different regions and years. The data has been cleaned and… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Agricultural-Production-In-Africa-1961-to-2023.

HarvestStat Africa – Harmonized Subnational Crop Statistics for Sub-Saharan Africa

Authors:
D. Lee, W. Anderson, X. Chen, F. Davenport, S. Shukla, R. Sahajpal, M. Budde, J. Rowland, J. Verdin, L. You, M. Ahouangbenon, K. Davis, E. Kebede, S. Ehrmann, C. Justice, and C. Meyer

Publication:
Scientific Data (in revision); preprint available at EarthArXiv.

Author Information

Donghoon Lee
Department of Civil Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
Email: Donghoon.Lee@umanitoba.ca

Weston Anderson
Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
Email: Weston@umd.edu

Xuan Chen
Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
Email: X.Chen@cgiar.org

Frank Davenport
Climate Hazards Center, Depar...

Surveys for more than 9,500 households were conducted in the growing seasons 2002/2003 or 2003/2004 in eleven African countries: Burkina Faso, Cameroon, Ghana, Niger and Senegal in western Africa; Egypt in northern Africa; Ethiopia and Kenya in eastern Africa; South Africa, Zambia and Zimbabwe in southern Africa. Households were chosen randomly in districts that are representative for key agro-climatic zones and farming systems. The data set specifies farming systems characteristics that can help inform about the importance of each system for a country’s agricultural production and its ability to cope with short- and long-term climate changes or extreme weather events. Further it informs about the location of smallholders and systems at highest risk and permits benchmarking agricultural systems characteristics.

In the fourth quarter of 2023, approximately 243,000 South Africans residing in the Western Cape were working in the agriculture industry, marking a year-on-year change increase of 11,000 people being employed. The KwaZulu-Natal and Limpopo provinces revealed high numbers of people being employed within the industry as well, at 153,000 and 129,000, respectively.

Agricultural land (% of land area) in South Africa was reported at 79.42 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Agricultural land (% of land area) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

The average for 2021 based on 53 countries was 212906 sq. km.. The highest value was in Sudan: 1126648 sq. km. and the lowest value was in the Seychelles: 16 sq. km.. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

Abstract

The Annual Agricultural Sample Survey (AASS) for the year 2022/23 aimed to enhance the understanding of agricultural activities across the United Republic of Tanzania by collecting comprehensive data on various aspects of the agricultural sector. This survey is crucial for policy formulation, development planning, and service delivery, providing reliable data to monitor and evaluate national and international development frameworks.

The 2022/23 survey is particularly significant as it informs the monitoring and evaluation of key agricultural development strategies and frameworks. The collected data will contribute to the Tanzania Development Vision 2025, Zanzibar Development Vision 2020, the Five-Year Development Plan 2021/22–2025/26, the National Strategy for Growth and Reduction of Poverty (NSGRP) known as MKUKUTA, and the Zanzibar Strategy for Growth and Reduction of Poverty (ZSGRP) known as MKUZA. The survey data also supports the evaluation of Sustainable Development Goals (SDGs) and Comprehensive Africa Agriculture Development Programme (CAADP). Key indicators for agricultural performance and poverty monitoring are directly measured from the survey data.

The 2022/23 AASS provides a detailed descriptive analysis and related tables on the main thematic areas. These areas include household members and holder identification, field roster, seasonal plot and crop rosters (Vuli, Masika, and Dry Season), permanent crop production, crop harvest use, seed and seedling acquisition, input use and acquisition (fertilizers and pesticides), livestock inventory and changes, livestock production costs, milk and eggs production, other livestock products, aquaculture production, and labor dynamics. The 2022/23 AASS offers an extensive dataset essential for understanding the current state of agriculture in Tanzania. The insights gained will support the development of policies and interventions aimed at enhancing agricultural productivity, sustainability, and the livelihoods of farming communities. This data is indispensable for stakeholders addressing challenges in the agricultural sector and promoting sustainable agricultural development.

Statistical Disclosure Control (SDC) methods have been applied to the microdata, to protect the confidentiality of the individual data collected. Users must be aware that these anonymization or SDC methods modify the data, including suppression of some data points. This affects the aggregated values derived from the anonymized microdata, and may have other unwanted consequences, such as sampling error and bias. Additional details about the SDC methods and data access conditions are provided in the data processing and data access conditions below.

Geographic coverage

National, Mainland Tanzania and Zanzibar, Regions

Analysis unit

Households for Smallholder Farmers and Farm for Large Scale Farms

Universe

The survey covered agricultural households and large-scale farms.

Agricultural households are those that meet one or more of the following two conditions: a) Have or operate at least 25 square meters of arable land, b) Own or keep at least one head of cattle or five goats/sheep/pigs or fifty chicken/ducks/turkeys during the agriculture year.

Large-scale farms are those farms with at least 20 hectares of cultivated land, or 50 herds of cattle, or 100 goats/sheep/pigs, or 1,000 chickens. In addition to this, they should fulfill all of the following four conditions: i) The greater part of the produce should go to the market, ii) Operation of farm should be continuous, iii) There should be application of machinery / implements on the farm, and iv) There should be at least one permanent employee.

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame used to extract the sample for the Annual Agricultural Sample Survey (AASS-2022/23) in Tanzania was derived from the 2022 Population and Housing Census (PHC-2022) Frame that lists all the Enumeration Areas (EAs/Hamlets) of the country. The AASS 2022/23 used a stratified two-stage sampling design which allows to produce reliable estimates at regional level for both Mainland Tanzania and Zanzibar.

In the first stage, the EAs (primary sampling units) were stratified into 2-3 strata within each region and then selected by using a systematic sampling procedure with probability proportional to size (PPS), where the measure of size is the number of agricultural households in the EA. Before the selection, within each stratum and domain (region), the Enumeration Areas (EAs) were ordered according to the codes of District and Council which reflect the geographical proximity, and then ordered according to the codes of Constituency, Division, Wards, and Village. An implicit stratification was also performed, ordering by Urban/Rural type at Ward level.

In the second stage, a simple random sampling selection was conducted . In hamlets with more than 200 households, twelve (12) agricultural households were drawn from the PHC 2022 list with a simple random sampling without replacement procedure in each sampled hamlet. In hamlets with 200 households or less, a listing exercise was carried out in each sampled hamlet, and twelve (12) agricultural households were selected with a simple random sampling without replacement procedure. A total of 1,352 PSUs were selected from the 2022 Population and Housing Census frame, of which 1,234 PSUs were from Mainland Tanzania and 118 from Zanzibar. A total number of 16,224 agricultural households were sampled (14,808 households from Mainland Tanzania and 1,416 from Zanzibar).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2022/23 Annual Agricultural Survey used two main questionnaires consolidated into a single questionnaire within the CAPIthe CAPI System, Smallholder Farmers and Large-Scale Farms Questionnaire. Smallholder Farmers questionnaire captured information at household level while Large Scale Farms questionnaire captured information at establishment/holding level. These questionnaires were used for data collection that covered core agricultural activities (crops, livestock, and fish farming) in both short and long rainy seasons. The 2022/23 AASS questionnaire covered 23 sections which are:

1. COVER; The cover page included the title of the survey, survey year (2022/23), general instructions for both the interviewers and respondents. It sets the context for the survey and also it shows the survey covers the United Republic of Tanzania.

2. SCREENING: Included preliminary questions designed to determine if the respondent or household is eligible to participate in the survey. It checks for core criteria such as involvement in agricultural activities.

3. START INTERVIEW: The introductory section where basic details about the interview are recorded, such as the date, location, and interviewer’s information. This helped in the identification and tracking of the interview process.

4. HOUSEHOLD MEMBERS AND HOLDER IDENTIFICATION: Collected information about all household members, including age, gender, relationship to the household head, and the identification of the main agricultural holder. This section helped in understanding the demographic composition of the agriculture household.

5. FIELD ROSTER: Provided the details of the various agricultural fields operated by the agriculture household. Information includes the size, location, and identification of each field. This section provided a comprehensive overview of the land resources available to the household.

6. VULI PLOT ROSTER: Focused on plots used during the Vuli season (short rainy season). It includes details on the crops planted, plot sizes, and any specific characteristics of these plots. This helps in assessing seasonal agricultural activities.

7. VULI CROP ROSTER: Provided detailed information on the types of crops grown during the Vuli season, including quantities produced and intended use (e.g., consumption, sale, storage). This section captures the output of short rainy season farming.

8. MASIKA PLOT ROSTER: Similar to Section 4 but focuses on the Masika season (long rainy season). It collects data on plot usage, crop types, and sizes. This helps in understanding the agricultural practices during the primary growing season.

9. MASIKA CROP ROSTER: Provided detailed information on crops grown during the Masika season, including production quantities and uses. This section captures the output from the main agricultural season.

10. PERMANENT CROP PRODUCTION: Focuses on perennial or permanent crops (e.g., fruit trees, tea, coffee). It includes data on the types of permanent crops, area under cultivation, production volumes, and uses. This section tracks long-term agricultural investments.

11. CROP HARVEST USE: In this, provided the details how harvested crops are utilized within the household. Categories included consumption, sale, storage, and other uses. This section helps in understanding food security and market engagement.

12. SEED AND SEEDLINGS ACQUISITION: Collected information on how the agriculture household acquires seeds and seedlings, including sources (e.g., purchased, saved, gifted) and types (local, improved, etc). This section provided insights into input supply chains and planting decisions based on the households, or head.

13. INPUT USE AND ACQUISITION (FERTILIZERS AND PESTICIDES): It provided the details of the use and acquisition of agricultural inputs such as fertilizers and pesticides. It included information on quantities used, sources, and types of inputs. This section assessed the input dependency and agricultural practices.

14. LIVESTOCK IN STOCK AND CHANGE IN STOCK: The

The GROW-Africa (Groundtruthing Remote-sensing for Optimizing Yield in Africa) Database includes n = 535,844 georeferenced observations of crop yields across Africa for the period 1960-2023. The vast majority of the observations span the period 2000-2023. The database includes 25 key crops, including maize, sorghum, cassava, groundnuts, cowpeas, rice, yams, and millet. The database assimilates observations from a range of spatial scales, from regional government statistics, to household farmer surveys, to plot-level crop cuts. The GROW-Africa database is intended to provide a platform for performing data-driven analyses of historical yield trends, as well as for training algorithms to quantify crop yields from Earth Observation (satellite) data.

The database is described in the publication:

Geyman, E.C., Ferris, A., Sahajpal, R., Anderson, W., Lee, D., and Hausmann, N. An Africa-wide agricultural production database to support policy and satellite-based measurement systems. Scientific Data (in review). 2025.

The Agriculture in South Africa Market Report is Segmented by Commodity Type (Cereals and Grains, Pulses and Oilseeds, and More). The Report Includes Production Analysis (Volume), Consumption Analysis (Value and Volume), Import Analysis (Value and Volume), Export Analysis (Value and Volume), and Price Trend Analysis. The Market Forecasts are Provided in Terms of Value (USD) and Volume (Metric Tons).

CA has been promoted as a technology for tackling southern Africa’s smallholder farmers’ economic and agricultural challenges such as decreasing food production, climate change and variability, and soil nutrient depletion for more than a decade. Despite the investment, empirical evidence suggests variable and often partial adoption rates. In Zambia, for example, wide discrepancies exist in CA adoption numbers and area reported in both peer-reviewed literature and unpublished project reports (Whitfield et al 2015 and Twomlow et al., 2008). Thus, lack of clarity on what constitutes CA adoption represents a huge challenge in the accurate calculation of adoption rates in southern Africa and the whole African continent at large. Knowledge of the geographical spread and the number of CA adopters is useful planning tool for policy and evaluating economic and poverty impact at both country and regional level. The practice of CA does not necessarily make a farmer an “adopter”. Understanding the different kinds of possible CA ‘adoptions’ is important in defining and contextualizing indicators that relate to the adoption process. Measuring adoption for complex technology packages such as CA, that farmers disentangle into smaller parts and only adopt components they perceive to fit their farming systems requires a two tier process. First, measuring whether or not the technology has been adopted at all. Secondly, analyzing which components are adopted in what combination by which types of farmers across time and space. In this brief, we establish and verify the extent CA adoption in Mozambique and Zambia based on regionally accepted key minimums for CA.

As part of the US government’s Feed the Future initiative that aims to address global hunger and food security issues in sub-Saharan Africa, the US Agency for International Development is supporting multi-stakeholder agricultural research projects under Africa Research In Sustainable Intensification for the Next Generation (Africa RISING - AR) program. The overall aim of the program is to transform agricultural systems through sustainable intensification projects in Ghana, Ethiopia, Tanzania, Malawi, Mali, and (potentially) Zambia. In Ethiopia, the project, led by the International Livestock Research Institute (ILRI), will be supporting crop-livestock farming systems. Multiple participatory and adaptive agricultural interventions are currently taking place in eight kebeles (Goshe Bado, Gudo Beret, Salka, Ilu-Sanbitu, Jawe, Upper Gana, Emba Hasti and Tsibet) in four regions (Amhara, Oromia, Southern Nations Nationalities and Peoples (SNNP), and Tigray) in Ethiopia, led by researchers from the ILRI. Experts from ILRI have supported or introduced intercropping, new crop varieties, water conservation practices, and integrated tree cropping. The International Food Policy Research Institute (IFPRI) leads the monitoring and evaluation (M&E) activities of the AR program. As part of the M&E activities in Ethiopia, IFPRI contracted BDS Center for Development Research to conduct baseline household and community surveys in Amhara, Oromia, SNNP, and Tigray regions. The main objective of this survey is to collect high-quality baseline household data to support the M&E activities of the AR Program in Ethiopia. More specifically, the survey aims to collect detailed information on the composition of the household, employment, health, agriculture, income and expenditures, credit, assets, subjective welfare and food security, shocks, and the anthropometric status of children and women.

The zip.file contains the Farm DESIGN model (software) as well as the input data. A user manual of the software is available online. The zipped Farm DESIGN model contains the whole-farm representation of nine farms in Northern Ghana. A low, medium and a high resource endowed farm per site, namely in Duko (Northern Region), Nyangua (Upper East) and Zanko (Upper West). There are several models per farm: 1. The current/ actual farm configuration 2. The baseline (reset to a situation with traditional (no Africa RISING) farming practices) 3. The farm, where Africa RISING Package 1 (Maize) is implemented 4. The farm, where Africa RISING Package 2 (Cowpea) is implemented 5. The farm, where Africa RISING Package 3 (Soybean) is implemented 6. The farm, where Africa RISING Package 4 (Maize-Legume Rotation) is implemented 7. The farm, where Africa RISING Package 5 (Maize-Legume Strip Crop) is implemented 8. A farm-model that is ready for an exploration (containing additional options the model may choose)

The overall project goal is to improve food security and farming system resilience of smallholder mixed crop-livestock farmers in East Africa while mitigating climate change through wide-scale adoption of climate-smart agriculture (CSA). The project integrates interdisciplinary approaches, including participatory research, integrating a meta-analysis of CSA practices, real-time land and soil health assessments, crop suitability modelling, socio-economic appraisals and multi-dimensional trade-off analyses, as well as on-farm participatory evaluations of CSA to identify, test, implement, and outscale locally appropriate CSA practices.

The Africa Agricultural Machinery Market Report is Segmented Into Type (Tractors, Plowing and Cultivating Machinery, Planting and Fertilizing Machinery, Harvesting Machinery, Haying and Forage Machinery, Irrigation Machinery, and Other Product Types) and Geography (South Africa and the Rest of Africa). The Report Offers Market Size and Forecasts in Terms of Value (USD) for all the Above Segments.

The African Crop Protection Chemicals market is experiencing robust growth, driven by increasing agricultural production to meet the demands of a burgeoning population and rising incomes. The market, segmented by function (fungicides, herbicides, insecticides, molluscicides, nematicides), application mode (chemigation, foliar, fumigation, seed treatment, soil treatment), and crop type (commercial crops, fruits & vegetables, grains & cereals, pulses & oilseeds, turf & ornamental), shows significant potential across diverse agricultural sectors. While precise market size figures for 2019-2024 are unavailable, a reasonable estimation based on global trends and regional agricultural data suggests a substantial market value, likely in the hundreds of millions of dollars, exhibiting a Compound Annual Growth Rate (CAGR) reflecting the expanding agricultural sector and intensifying pest and disease pressures. Key growth drivers include rising investments in agricultural infrastructure, the adoption of improved farming techniques, and government initiatives promoting food security. However, challenges remain, including the limited access to advanced crop protection technologies in certain regions, affordability constraints for smallholder farmers, and environmental concerns related to pesticide use. The forecast period (2025-2033) anticipates continued expansion of the African Crop Protection Chemicals market, spurred by ongoing agricultural modernization, climate change adaptation strategies, and increased awareness of pest management best practices. Major players such as Adama Agricultural Solutions, BASF, Bayer, Corteva, FMC, Nufarm, Sumitomo Chemical, Syngenta, UPL, and Wynca Group are actively involved, competing through product innovation, strategic partnerships, and distribution network expansion. The market’s future trajectory will be shaped by factors including the development of sustainable and environmentally friendly crop protection solutions, the integration of precision agriculture technologies, and regulatory changes related to pesticide use. Growth will likely be strongest in regions with high agricultural potential and supportive government policies. The increasing adoption of integrated pest management (IPM) strategies is also expected to influence the market's structure and product demand. Recent developments include: January 2023: Bayer formed a new partnership with Oerth Bio to enhance crop protection technology and create more eco-friendly crop protection solutions.November 2022: Corteva Agriscience introduced Zorvec Encantia, a fungicide that targets late blight, a detrimental pathogen inhibiting potato growth. The product is based on Zorvec Active and is the first of a novel family of fungicides that uses a distinct biochemical mode of action and does not cross-resist other fungicides.August 2022: BASF and Corteva Agriscience collaborated to provide soybean farmers with the weed control of the future. By working together, BASF and Corteva aim to satisfy farmers' demand for specialized weed control solutions that are distinct from those that are currently available or being developed.. Notable trends are: The market is growing due to the rising consumption of pesticides to protect crops from pests and weeds.