In 2022, sugar cane was the most highly produced agricultural crop in South Africa, with about 18 million metric tons harvested. Maize followed closely with the production amounting to 16.14 million metric tons. Potatoes and wheat ranked next with around 2.5 million and 2.1 million metric tons, respectively.

Maize is the most extensively harvested agricultural product in South Africa. In 2022, some three million hectares of land with corn were harvested. Soybeans, sunflower seed, wheat, and sugar cane followed with around 925,300, 670,700, 566,800, and 258,400 hectares of harvested land, respectively.

South Africa ZA: Cereal Yield: per Hectare data was reported at 3,809.500 kg/ha in 2016. This records an increase from the previous number of 3,536.700 kg/ha for 2015. South Africa ZA: Cereal Yield: per Hectare data is updated yearly, averaging 1,876.750 kg/ha from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 4,894.000 kg/ha in 2014 and a record low of 911.400 kg/ha in 1965. South Africa ZA: Cereal Yield: per Hectare data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Agricultural Production and Consumption. Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.; ; Food and Agriculture Organization, electronic files and web site.; Weighted average;

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.

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Groundnut (Arachis hypogaea), also known as peanut, is grown around the world in a broad region between 40 degrees north and south latitude. Originally from South America, major producers of groundnut include China, India and the United States. Producing 30% of Africa"s total, Nigeria leads the continent"s production followed by Senegal, Sudan, Ghana, and Chad. Groundnut is a valuable source of protein and oil. It has the additional benefit of enriching depleted soils by converting nitrogen from the air into a form that is required by most plants. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofgroundnut harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of casava as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Groundnut (Peanut) Maize (Corn) Millet Potato Rice Sorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started follow these links: Landscape Layers - a reintroductionLiving Atlas Discussion Group

The South Africa Crop Type Competition is a dataset produced as part of the Radiant Earth Spot the Crop Challenge. This collection contains the field identification label which represents the area of crop fields and the corresponding crop type label collected via aerial and vehicle surveys.

As of 2020, nearly 2.6 thousand hectares of agricultural land in South Africa were used for organic cereal farming. The total organic area in the country was slightly over 40.9 thousand hectares. Moreover, there were 220 organic producers in the country in that year.

Forecast: Oil Crops Production in South Africa 2024 - 2028 Discover more data with ReportLinker!

South Africa ZA: Production Index: 2004-2006: Crop data was reported at 104.810 2004-2006=100 in 2016. This records a decrease from the previous number of 113.430 2004-2006=100 for 2015. South Africa ZA: Production Index: 2004-2006: Crop data is updated yearly, averaging 81.525 2004-2006=100 from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 123.420 2004-2006=100 in 2014 and a record low of 39.620 2004-2006=100 in 1961. South Africa ZA: Production Index: 2004-2006: Crop data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Agricultural Production Index. Crop production index shows agricultural production for each year relative to the base period 2004-2006. It includes all crops except fodder crops. Regional and income group aggregates for the FAO's production indexes are calculated from the underlying values in international dollars, normalized to the base period 2004-2006.; ; Food and Agriculture Organization, electronic files and web site.; Weighted average;

South Africa ZA: Land under Cereal Production data was reported at 2,668,905.000 ha in 2016. This records a decrease from the previous number of 3,372,124.000 ha for 2015. South Africa ZA: Land under Cereal Production data is updated yearly, averaging 6,170,465.500 ha from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 7,497,800.000 ha in 1972 and a record low of 2,668,905.000 ha in 2016. South Africa ZA: Land under Cereal Production data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Agricultural Production and Consumption. Land under cereal production refers to harvested area, although some countries report only sown or cultivated area. Cereals include wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

As of 2019, the majority of households in South Africa involved in agricultural activities owned the land they were using for crop production, amounting to approximately 1.47 million. Provinces with larger portions of rural areas, such as Limpopo, were more likely to share large numbers of households owning their land or occupying status of tribal authority. Just over 15 million households stated that they were not engaged in crop plantation at all.

South Africa: Crop production index (2004-2006 = 100): The latest value from 2022 is 124.1 index points, a decline from 125.2 index points in 2021. In comparison, the world average is 108.4 index points, based on data from 188 countries. Historically, the average for South Africa from 1961 to 2022 is 74.9 index points. The minimum value, 35 index points, was reached in 1961 while the maximum of 125.2 index points was recorded in 2021.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

Screening of South Africa BF:

(a) maize growers Location: Free State
Growers have to use pre-emergent and post-emergent herbicides
Growers have to use at least one fungicide and at least one insecticide

for maize 2 growers : Location: Mpumalanga

(b) potato growers Location: Limpopo
Growers have to use at least 4 insecticide applications and at least 4 fungicide applications

for potato 2 growers: Location: Free State

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing: Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group)

o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Abstract

In 2009, Statistics South Africa (Stats SA) conducted a study to evaluate the state of agricultural statistics in the country. The research sought to evaluate the quality, quantity (depth and breadth) and frequency of agricultural statistics as provided in the country at the time. The research revealed, among others, that agricultural statistics, at the time, fell short in terms of the specified aspects. Critically, regarding quantity, the country lacked information on smallholder and subsistence agriculture. In addition, the agricultural sector lacked a comprehensive frame (farmer list) that covered all agricultural activities in the country as the current census of commercial agriculture was partially covering the sector. A decision was reached in 2010 to include three questions related to agriculture in the Population Census 2011 (Census 2011) questionnaire. The main objective was to identify all households involved in agriculture in the country in order to plan a frame for a proper agricultural census. The list of households engaged in agriculture generated from the above exercise of Census 2011 will complement the current tax based frame sourced from the South African Revenue Service (SARS) to develop a complete frame of all agricultural activities in the country. The data presented in this report is obtained from Census 2011 and provide useful insights on the geographic sphere. Specifically, the information presented is a result of the three agricultural questions, which were included in the population questionnaire. This information is critical for the measurement of the food security of the country at both national and household levels.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the collection of census data was a "farming enterprise", defined as "a legal unit or a combination of legal units that includes and directly controls all functions necessary to carry out its production activities".

Kind of data

Sample survey data [ssd]

Sampling procedure

i. Methodological modality for conducting the census The CCA 2007 used the classical approach.

ii. Frame The main source of the frame was the business register, which contains all businesses undertaking agricultural activities registered for VAT with the South African Revenue Service (the Tax Office).

iii. Complete and/or sample enumeration methods The 2007 CCA was conducted on the basis of a complete enumeration of farming enterprises.

Mode of data collection

Face-to-face [f2f]

Research instrument

The CCA used a single questionnaire for census data collection on:

· Ownership of farm · Particulars of the farming unit · Land-use during the reporting period · Field crops, horticultural products and forestry · Animals, animal products · Other income · Employment · Current expenditure · Purchased livestock, poultry and additional products · Market value of assets and capital expenditure during the financial year · Losses and expenditure due to theft, disaster, accidents and violent crimes · Farming debt · Agricultural services · Inventory · Balance sheet · Ocean (marine) fishing

The census questionnaire covered 14 of the 16 core items recommended for the WCA 2010. Core items not covered were (i) "Household size"; and (ii) "Main purpose of production of the holding".

Cleaning operations

1. DATA PROCESSING AND ARCHIVING Manual data entry was used for the census questionnaires. Data entry application with consistency checks and skip patterns was applied. Ratio imputation was used for both item and unit non-response.

2. CENSUS DATA QUALITY Several steps were put in place to ensure the quality of census results, for example: careful design of the questionnaire and its testing in pilot studies; preparation of training manuals and training of enumerators; equipping the capturing system with warnings and consistency checks. Comparisons were made with the frame and with the estimates of the 2002 CoCA, and with the estimates from the annual agriculture and related services survey, as well as with various sources that reported on the sector.

Forecast: Sugar Crops Production in South Africa 2023 - 2027 Discover more data with ReportLinker!

In 2022/2023, the total production of maize in South Africa amounted to roughly 15.6 million metric tons. The Free State produced the most maize out of the nine provinces with approximately 44 percent of the overall production. The Western Cape produced the least amount with 35 thousand metric tons of maize.

Forecast: Primary Fibre Crops Production in South Africa 2024 - 2028 Discover more data with ReportLinker!

South Africa ZA: Cereal Production data was reported at 10,167,084.000 Metric Ton in 2016. This records a decrease from the previous number of 11,926,047.000 Metric Ton for 2015. South Africa ZA: Cereal Production data is updated yearly, averaging 11,726,961.000 Metric Ton from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 18,004,771.000 Metric Ton in 1981 and a record low of 5,056,344.000 Metric Ton in 1992. South Africa ZA: Cereal Production data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Agricultural Production and Consumption. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

South Africa ZA: GDP: Growth: Gross Value Added: Agriculture data was reported at 17.716 % in 2017. This records an increase from the previous number of -10.237 % for 2016. South Africa ZA: GDP: Growth: Gross Value Added: Agriculture data is updated yearly, averaging 2.810 % from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 30.376 % in 1974 and a record low of -27.260 % in 1992. South Africa ZA: 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 South Africa – Table ZA.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.

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Potato (Solanum tuberosum) a native of South America was firstdomesticatedbetween8000 and 5000 BC. In the middle of the 16th century it was introduced to Europe, Asia and Africa. Africa produces about 5% of the world"s potato crop. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofpotato harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of potato as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Cassava Groundnut (Peanut) Maize (Corn) Millet Rice Sorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started follow these links: Landscape Layers - a reintroductionLiving Atlas Discussion Group