The dataset shows the aggregate value of sales of agricultural produce from large and small farms for the last five years. Total value of sales increased by 8.2 per cent from KSh 413.2 billion in 2016 to KSh 446.9 billion in 2017. The value of output from small farms increased by 8.2 per cent from KSh 301.7 billion in 2016 to KSh 326.3 billion in 2017. Sales from large farms similarly increased from KSh 111.6 billion in 2016 to KSh 120.7 billion in 2017. The share of sales from small farms to total marketed production remained the same at 73.0 per cent in 2017.

Producer Prices in Kenya decreased 0.79 percent in September of 2025 over the same month in the previous year. This dataset provides - Kenya Producer Prices Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The dataset details the average gross commodity prices paid to farmers for various commodities for the period 2013 to 2017. Tea prices increased by 23.9 per cent from KSh 24,732.35 per 100 kilogram in 2016 to KSh 30,652.18 per 100 kilogram in 2017. Coffee prices paid to farmers improved by 16.5 per cent from KSh 40,815.54 per 100 kilogram in 2016 to KSh 47,547.71 per 100 kilogram in 2017. Favourable prices were also realized for maize, sugarcane, milk, beef and pork.

Graph and download economic data for Global price of Tea, Kenyan (PTEAUSDM) from Jan 1990 to Jun 2025 about tea, Kenya, World, and price.

Agriculture, Fisheries and Food Authority -This dataset reveals the Name of town, Commodity type, Price range, price, Quantity in Kgs, Grade(1 or 2), Sale type(Retail/Wholesale, Commodity state(Loose/Ground/Instant) and Date.

Tea fell to 188.90 INR/Kgs on November 22, 2025, down 0.58% from the previous day. Over the past month, Tea's price has fallen 6.55%, and is down 7.48% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Tea - values, historical data, forecasts and news - updated on December of 2025.

Kenya KE: Import Unit Value Index data was reported at 166.323 2000=100 in 2016. This records a decrease from the previous number of 173.422 2000=100 for 2015. Kenya KE: Import Unit Value Index data is updated yearly, averaging 132.000 2000=100 from Dec 1980 (Median) to 2016, with 37 observations. The data reached an all-time high of 209.209 2000=100 in 2011 and a record low of 78.000 2000=100 in 1993. Kenya KE: Import Unit Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Trade Index. Import unit value indices come from UNCTAD's trade database. Unit value indices are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, interna¬tional and national sources, and UNCTAD secretariat estimates.; ; United Nations Conference on Trade and Development, Handbook of Statistics and data files. (http://unctadstat.unctad.org); ;

Kenya KE: Tariff Rate: Applied: Simple Mean: Primary Products data was reported at 16.740 % in 2016. This records an increase from the previous number of 16.370 % for 2015. Kenya KE: Tariff Rate: Applied: Simple Mean: Primary Products data is updated yearly, averaging 16.810 % from Dec 1994 (Median) to 2016, with 18 observations. The data reached an all-time high of 32.160 % in 1994 and a record low of 15.240 % in 2007. Kenya KE: Tariff Rate: Applied: Simple Mean: Primary Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Trade Tariffs. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;

Kenya Import Volume Index data was reported at 98.425 2015=100 in 2021. This records an increase from the previous number of 93.063 2015=100 for 2020. Kenya Import Volume Index data is updated yearly, averaging 34.910 2015=100 from Dec 1980 (Median) to 2021, with 42 observations. The data reached an all-time high of 106.638 2015=100 in 2019 and a record low of 10.598 2015=100 in 1983. Kenya Import Volume Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Trade Index. Import volume indexes are derived from UNCTAD's volume index series and are the ratio of the import value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year’s trade values as weights. For economies for which UNCTAD does not publish data, the import volume indexes (lines 73) in the IMF's International Financial Statistics are used.;United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.;;

Time series data for the statistic Net barter terms of trade index (2000 = 100) and country Kenya. Indicator Definition:Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.The indicator "Net barter terms of trade index (2000 = 100)" stands at 91.90 as of 12/31/2023. Regarding the One-Year-Change of the series, the current value constitutes an increase of 6.24 percent compared to the value the year prior.The 1 year change in percent is 6.24.The 3 year change in percent is -6.61.The 5 year change in percent is -4.07.The 10 year change in percent is 3.61.The Serie's long term average value is 91.61. It's latest available value, on 12/31/2023, is 0.32 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1990, to it's latest available value, on 12/31/2023, is +30.45%.The Serie's change in percent from it's maximum value, on 12/31/1997, to it's latest available value, on 12/31/2023, is -19.74%.

Agriculture, Fisheries and Food Authority - This dataset from AFFA, reveals the average monthly prices of Food Crops in the market. It covers a variety of Cereals, Legumes, Roots, Tubers, and horticultural produce.The Full list includes, Dry Maize Green Maize, Finger Millet, Sorghum, Wheat, Beans Canadian, Beans Rosecoco, Beans Mwitemania, Mwezi Moja, Dolichos (Njahi), Green Gram, Cowpeas, Fresh Peas, Groundnuts, Red Irish Potatoes, White Irish Potatoes, Cassava Fresh, Sweet Potatoes, Cabbages, Cooking Bananas, Ripe Bananas, Carrots, Tomatoes, Onions Dry, Spring Onions, Chillies, Cucumber, Capsicums, Brinjals, Cauliflower, Lettuce, Passion Fruits, Oranges, Lemons, Mangoes Local, Mangoes Ngowe, Limes, Pineapples, Pawpaw, Avocado and Kales

Credit report of Yash Commodities Kenya Ltd. contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

This data set comprises a market survey of foods and beverages and their packaging as sold at a sample of retail outlets in urban Greater Accra, Ghana and Kisumu, Kenya. It also comprises responses to a short shopper questionnaire and all data were collected in areas classified as slums. The data set was collected with the aim of quantifying packaging waste associated with food and beverage purchases. It also aimed to develop and evaluate a low-cost method for identifying plastic resin types in resource-poor settings lacking laboratory facilities. At each sampled retail outlet, observations were made of vendor or consumer packaging behaviours when food or beverage purchases took place. Short interviews were conducted with shoppers. Where foods or beverages were wrapped in plastics, packaging samples were collected and further characterised in a basic laboratory. Alongside recording labelling and other packaging characteristics, the laboratory protocol also completed a simple observation checklist (e.g. testing if packaging floated; how it behaved on cutting), so as to infer the packaging's plastic resin type. The data set should thus be of interest to researchers wishing to understand waste generation and packaging in urban Africa.

According to WHO/UNICEF, whilst 91.8% of urban households in Sub-Saharan Africa (SSA) had access to piped or protected groundwater sources in 2015, only 46.2% had safely managed water available when needed. Vendors provide a key role in supplying urban off-grid populations, with consumption of bottled or bagged water (sachets, water sold in 500ml plastic bags) growing in SSA. Whilst several studies show bottles and bags are usually free from faecal contamination, given that many off-grid urban populations lack solid waste disposal services, when people drink such water, there can be problems disposing of the plastic bags and bottles afterwards. This project aims to deliver evidence on the different ways that people sell water to off-grid populations and what this means for plastic waste management. We plan to do this in Ghana, where most urban household now drink bagged water, and by way of contrast, Kenya, where the government has banned plastic bags. In this way, we want to widen access to safe water and waste management services among urban off-grid populations, by supporting water-sellers and waste collectors to fill the gaps in municipal services. Both countries (and many others elsewhere) already have nationwide household surveys that collect data on the food and goods people consume and the services they have. However, as yet, these surveys have not been connected to the problem of waste management. We plan to visit marketplaces, buying foods and then recording packaging and organic waste. By combining this information with the household survey data, we can work out how much domestic waste like plastics gets collected and how much is discarded or burned, ultimately entering the atmosphere or oceans. In Ghana, we will also survey informal waste collectors in urban Greater Accra. We want to find out how much these small businesses support waste collection and recycling across this urban region (particularly plastic from bagged water), so we can help government identify gaps in waste collection coverage. We also believe highlighting the important role of small waste collectors could lead to greater business support for such collectors. We will also evaluate whether community education campaigns to encourage domestic waste recycling reduce the amount of waste and plastic observed in the local environment. Such campaigns are currently pursued by several local charities with support from the Plastic Waste Management Project. In Kenya, where water is usually sold in jerrycans rather than bagged, the jerrycan water often gets contaminated. We plan to find out whether this jerrycan water is safer under an arrangement known as delegated management. This involves a water utility passing on management of the piped network to a local business in slum areas, so as to reduce vandalism of pipes and bring water closer to slum-dwellers. We will compare water quality in areas with and without this arrangement to see if it makes the water sold safer. We also plan to bring water-sellers and consumers together to find and test ways of reducing contamination of water between a jerry-can being filled and water being drunk at home. Rather than imposing a solution, we want to work together with vendors and consumers on this issue, but there are for example containers designed to keep water cleaner that we could explore. Through these activities, we thus plan to develop evidence on different strategies for water-sellers to deliver safer water to people lacking piped connections, whilst managing plastic waste at the same time. In Ghana, this involves trying to increase recycling and waste collection for bagged water, which is relatively safe. In Kenya, this involves trying to reduce...

Jeay Latif Commodities Limited Import Export Turnover 6.56 and 0 USD Million during August 2024 to July 2025. Also check supply chain analytics, top import and export commodities with price, buyers, suppliers, main competitors of Jeay Latif Commodities Limited in Kenya.

Kenya KE: Import Volume Index data was reported at 273.100 2000=100 in 2016. This records a decrease from the previous number of 298.821 2000=100 for 2015. Kenya KE: Import Volume Index data is updated yearly, averaging 97.893 2000=100 from Dec 1980 (Median) to 2016, with 37 observations. The data reached an all-time high of 298.821 2000=100 in 2015 and a record low of 31.345 2000=100 in 1983. Kenya KE: Import Volume Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Trade Index. Import volume indexes are derived from UNCTAD's volume index series and are the ratio of the import value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year’s trade values as weights. For economies for which UNCTAD does not publish data, the import volume indexes (lines 73) in the IMF's International Financial Statistics are used.; ; United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.; ;

Kenya KE: Tariff Rate: Applied: Simple Mean: Manufactured Products data was reported at 11.500 % in 2016. This records an increase from the previous number of 11.400 % for 2015. Kenya KE: Tariff Rate: Applied: Simple Mean: Manufactured Products data is updated yearly, averaging 11.950 % from Dec 1994 (Median) to 2016, with 18 observations. The data reached an all-time high of 31.150 % in 1994 and a record low of 11.080 % in 2013. Kenya KE: Tariff Rate: Applied: Simple Mean: Manufactured Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: Trade Tariffs. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;

Kenya Net Barter Terms of Trade Index data was reported at 92.155 2015=100 in 2021. This records a decrease from the previous number of 99.322 2015=100 for 2020. Kenya Net Barter Terms of Trade Index data is updated yearly, averaging 91.564 2015=100 from Dec 1980 (Median) to 2021, with 42 observations. The data reached an all-time high of 114.504 2015=100 in 1997 and a record low of 70.448 2015=100 in 1990. Kenya Net Barter Terms of Trade Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Trade Index. Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.;United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.;;

Kenya KE: Net Barter Terms of Trade Index data was reported at 102.224 2000=100 in 2016. This records a decrease from the previous number of 102.535 2000=100 for 2015. Kenya KE: Net Barter Terms of Trade Index data is updated yearly, averaging 90.490 2000=100 from Dec 1980 (Median) to 2016, with 37 observations. The data reached an all-time high of 114.019 2000=100 in 1997 and a record low of 70.149 2000=100 in 1990. Kenya KE: Net Barter Terms of Trade Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Trade Index. Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.; ; United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.; ;

Kenya KE: Export Volume Index data was reported at 193.163 2000=100 in 2016. This records an increase from the previous number of 191.564 2000=100 for 2015. Kenya KE: Export Volume Index data is updated yearly, averaging 102.832 2000=100 from Dec 1980 (Median) to 2016, with 37 observations. The data reached an all-time high of 199.958 2000=100 in 2014 and a record low of 47.289 2000=100 in 1983. Kenya KE: Export Volume Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Trade Index. Export volume indexes are derived from UNCTAD's volume index series and are the ratio of the export value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year’s trade values as weights. For economies for which UNCTAD does not publish data, the export volume indexes (lines 72) in the IMF's International Financial Statistics are used.; ; United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.; ;

Njema Multiple Elite Company Limited Import Export Turnover 0.10 and 0 USD Million during February 2023 to January 2024. Also check supply chain analytics, top import and export commodities with price, buyers, suppliers, main competitors of Njema Multiple Elite Company Limited in Kenya.