As of 2018, most agricultural households in Ghana were located in the Ashanti region. This added up to slightly over 436,000, followed by the Brong Ahafo and Bono regions with nearly 320,000 households practicing agriculture. In total, the country had around 2.6 million agricultural households in the said year.

Employment in agriculture (% of total employment) (modeled ILO estimate) in Ghana was reported at 35.37 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Ghana - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Ghana: Employment in agriculture, % of total employment: The latest value from 2022 is 39.74 percent, a decline from 40.34 percent in 2021. In comparison, the world average is 23.00 percent, based on data from 179 countries. Historically, the average for Ghana from 1991 to 2022 is 49.73 percent. The minimum value, 35.18 percent, was reached in 2015 while the maximum of 63.44 percent was recorded in 1991.

**** percent of women in Ghana were employed in agriculture, forestry, and fishing in 2022. The share slightly decreased from the previous year. Generally, smallholder agriculture is the largest among rural communities in the country.

Abstract

The AGRIS Ghana Pilot test was implemented in 4 districts of the Ashanti Region (Ahafo Ano South, Asante Akim North, Ejura Sekye Dumase, and Sekyere Afram Plains) in February 2018, to collect information on: - Crop and livestock production as well as data on farm characteristics, diversification and structures; - Farm revenues and expenses; - Type of labour used by the agricultural holding; - Farming practices and their linkages with the natural environment; - Farm machinery, equipment and assets.

The general objective of the pilot was to customize AGRIS instruments and methodologies for adoption as a standard tool to efficiently gather relevant and reliable agricultural data for policy making and monitoring the Sustainable Development Goals (SDGs).

The specific objectives of the AGRIS Ghana pilot were as follows: - Elaborate the overall set up of AGRIS in Ghana; - Customize the content of the AGRIS questionnaire to the Ghanaian context; - Assess the overall efficiency of the customized, integrated questionnaires and their feasibility in terms of length, flow, use of Computer Assisted Personal Interviewing (CAPI), and integration of core and rotating modules; - Assess the difficulty and relevance of each question, each section and each generic questionnaire for different types of holdings; - Test the use of Survey Solutions software to implement CAPI data collection, and the current version of the CAPI questionnaires; - Assess the relevance of the training material developed to train survey enumerators and supervisors.

Geographic coverage

District level coverage. The 4 district covered by the survey were: - Ahafo Ano South (CORE+PME) - Asante Akim North (CORE+MEA) - Ejura Sekye Dumase (CORE+LABOUR) - Sekyere Afram Plains (CORE+ECO)

Analysis unit

Agricultural holdings in the household sector

Universe

All households, agricultural or not, in the 4 surveyed districts.

Kind of data

Sample survey data [ssd]

Sampling procedure

1. The Sampling Frame The initial plan for the pilot survey was to consider as statistical units, agricultural holdings covering both the household and the non-household sectors, as proposed in the AGRIS methodology. For holdings in the household sector, no updated list of agricultural households in the country was available, and therefore a sampling frame needed to be established. To do so, the 2010 Population and Housing Census (PHC) was used to build a frame of EAs which were the primary sampling units (PSUs) of the adopted sampling design. After selecting the sample of PSUs in the four districts of interest, a complete list of holdings in the selected EAs was built. All households, agricultural or not, present in the selected EAs were listed.

Holdings in the non-household sector are by definition, economic units such as commercial farms and government institutions engaged in agricultural production. GSS and MoFA provided a list of these holdings to be used as sampling frame. Therefore, the plan was to use as the overall sampling frame a multiple frame composed of the two lists described above (one for the household sector and one for the non-household sector). However, after further discussion and evaluation, it was determined that the list of holdings in the non-household sector could not be considered as a reliable sampling frame for the targeted units. As a consequence, the data collected for the 80 non-household units could not be analysed to represent holdings in the nonhousehold sector.

1. The Sampling design A stratified two-stage sampling design was used for the holdings in the household sector. The PSUs were the EAs and the secondary sampling units (SSU) were the agricultural households.

2. The Sampling Size For holdings in the household sector, the calculation of sample size was performed fixing the minimum degree of precision required for the final estimates of main variables of interest. The variable considered to determine the sample size was the area of the agricultural land owned by the households. This information had been collected during the 2012-2013 Ghana Living Standards Survey 6 (GLSS6). Therefore, data from this survey was used to estimate the coefficient of variation (CV) of the variable of interest in the chosen four districts.
   It should be noted that the estimation domain of the GLSS6 was the region. For that survey, a two-stage sampling design was used and the PSUs (EAs) were selected in each region with the probability proportional to size (PPS). The measure of size was given by the number of individuals in each region, provided for the chosen districts for the AGRIS-Ghana pilot survey by the GLSS6. For the estimation of the CV of the households' agricultural land, it was assumed that the EAs sampled in GLSS6 and located in the target districts were selected in these districts with the same method of selection (PPS). Thus, the households included in the sample were supposed to have been selected with a two-stage sampling design.

The formula for the computation of the sampling size can be consulted in the final report of the survey.

The number of households to be surveyed in each PSU is fixed to 10. Therefore, the size of the sample of PSU is the size of the sample of the households divided by 10.

Sampling deviation

As mentioned in the sampling procedure section, holdings in the non-household sector were not included in the survey, as per initial plan, due to a problem in the listing frame provided by the Ghana Statistical Service.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The AGRIS Core module integrated with the Economic module (Core+ Eco) collected information on household and holding characteristics, agricultural production and economic activities of agricultural holdings. A full appraisal of the contents of the questionnaires can be get by downloading the questionnaires in the documentation section.

Response rate

Out of 370 households planned for interview, 366 were interviewed (98.91% response rate).

In 2023, the employment in the agricultural sector as share of total employment in Ghana amounted to ***** percent. Between 1991 and 2023, the figure dropped by ***** percentage points, though the decline followed an uneven course rather than a steady trajectory.

Feed the Future (FTF) initiative in Ghana is a collaborative effort that supports country-owned processes and plans for improving food security and nutrition, particularly in the northern part of the country. These datasets cover the interim survey that took place in 2015 and was designed as a follow-up to the baseline survey that happened from 2012 to 2013. The survey covered a range of indicators organized around four groups: (1) economic well-being; (2) women and children anthropometry; (3) hunger and diet diversity; and (4) women's empowerment. The survey design involved two stages in which enumeration areas were selected followed by households. Data was collected in a face-to-face fashion using well-designed questionnaires and other study materials.

Web Map that contains over 30 data layers relevant to agricultural development in Ghana. Physical (soils, temperature, & rainfall), demographic (population, population density & unemployment rate), and agricultural (market location, irrigation sites, & farm locations) variables are included.

Ghana Agricultural Production: Primary Crops: Plantains data was reported at 4,278,830.000 Metric Ton in 2017. This records an increase from the previous number of 4,000,424.000 Metric Ton for 2016. Ghana Agricultural Production: Primary Crops: Plantains data is updated yearly, averaging 2,380,800.000 Metric Ton from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 4,278,830.000 Metric Ton in 2017 and a record low of 1,082,000.000 Metric Ton in 1992. Ghana Agricultural Production: Primary Crops: Plantains data remains active status in CEIC and is reported by Ministry of Food and Agriculture. The data is categorized under Global Database’s Ghana – Table GH.B002: Agricultural Production: Primary Crops.

Abstract

The objective of the GAPS is to strengthen the Multi-Round Annual Crop and Livestock Surveys (MRACLS) that the ministry implements through SRID. The MRACLS is the national agricultural survey on the basis of which SRID releases information on agricultural production and yields of important crops. The ultimate goal of GAPS is to provide more accurate and timely agricultural production estimates at the district, regional, and national levels. The survey is also to offer an opportunity for SRID to experiment with a number of potential improvements with a view to developing the required skills and competencies before scaling up, over time, to all the districts in the country.

As part of the terms of implementing GAPS, MoFA agreed to assign four Agriculture Extension Agents (AEAs) per district for data collection. The Agents were relieved from all extension duties. To distinguish these field data collection officers from other extension agents, they were referred to as District Agricultural Statistical Assistants (DASAs). One officer per district was designated as a District Management Information System (MIS) officer and was given additional responsibility as field supervisor and referred to as District Agricultural Statistical Officer (DASO). A total of 100 DASAs and DASOs were successfully trained and deployed to their districts for GAPS implementation and given the task of collecting and processing datafrom the field.

Geographic coverage

National Level Regions Districts

Analysis unit

Household

Universe

Agricultural household and holder

Kind of data

Census/enumeration data [cen]

Sampling procedure

The GAPS employed a three stage multi-sampling design in response to the Government of Ghana's requirement for reliable agricultural statistics at the national, regional and district levels.

· First Stage Sampling- Selection of 2 Districts from each of the 10 Regions. A total of 20 districts, 2 from each of the 10 regions were randomly selected with probability proportional to size, using districts' population in year 2000 as a measure of size.1. Eleven Metropolitan and Municipal Assemblies (Kumasi, Sunyani, Cape Coast, New Juaben, Accra, Tema, Tamale, Bolgatanga, Wa, Ho and Shama Ahanta East) were excluded from the study, given their urban predominance.

· Second Stage Sampling - Selection of 40 Enumeration Areas (EAs) from each of the 20 Districts. A total of 800 EAs was selected; 40 EAs were randomly selected with probability proportional to size in each district, using the list of EAs compiled by the 2010 Census as a sample frame, and projected total population as a measure of size.2 In the Kassena-Nankana East district, 53 of the 187 EAs compiled by the 2010 census were excluded from the study because of the land disputes prevalent in the area earlier in 2011.

· Third Stage Sampling - Selection of 5 holders At the third stage, five holders were randomly chosen in each EA, using as a sample frame; the full list of all holders, compiled from the Household and Holders Listing questionnaire. This provides a total sample of 4000 holders, consisting of 200 holders per district.

Sampling deviation

Not reported

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires used in the minor season survey include the followings:-

(a) The Household and Holding Inquiry - Pre-Harvest questionnaire, also known as the form 2a. This was used to make enquiries on the general characteristics of households and holdings for pre-harvest farming activities during the minor season. Information sought included changes in the household composition, detailed information on livestock, poultry and other animals owned by the selected holders, detailed information on tree crops grown by the selected holders, information on aquaculture practices, inputs, outputs and assets.

(b) The Household and Holding Inquiry - Post-Harvest questionnaire, also known as form 2b. This was used to make enquiries on field practices, inputs and outputs. The following information were sought: inventory of fields, inputs and expenses, Remaining major season production and marketing of crops, minor season crop production and marketing, holding information, shocks and adaptation to shocks, other income generating activities and household health status.

(c) The Household and Holding Inquiry - Pre-harvest field measurements questionnaire known as the form 3. This questionnaire was used to gather data on the nature and characteristics of crop fields and area measurements for individual crop fields for all selected holdings.

(d) Crop Yield Measurement questionnaire also known as the form 4. This was used to seek for data on the yields of food crops such as the cereals, root and tubers, plantain, legumes and nuts, and vegetables.

Cleaning operations

The set of questionnaires used in the minor season survey include:-

(a) The Household and Holding Inquiry – Pre-Harvest questionnaire, also known as the form 2a. This was used to make enquiries on the general characteristics of households and holdings for pre-harvest farming activities during the minor season. Information sought included changes in the household composition, detailed information on livestock, poultry and other animals owned by the selected holders, detailed information on tree crops grown by the selected holders, information on aquaculture practices, inputs, outputs and assets.

(b) The Household and Holding Inquiry – Post-Harvest questionnaire, also known as form 2b. This was used to make enquiries on field practices, inputs and outputs. The following information were sought: inventory of fields, inputs and expenses, Remaining major season production and marketing of crops, minor season crop production and marketing, holding information, shocks and adaptation to shocks, other income generating activities and household health status.

(c) The Household and Holding Inquiry – Pre-harvest field measurements questionnaire known as the form 3. This questionnaire was used to gather data on the nature and characteristics of crop fields and area measurements for individual crop fields for all selected holdings.

(d) Crop Yield Measurement questionnaire also known as the form 4. This was used to seek for data on the yields of food crops such as the cereals, root and tubers, plantain, legumes and nuts, and vegetables.

Response rate

The repond rate reported was 70%

Sampling error estimates

No estimates of sampling error given

Data appraisal

District information and communication infrastructure was upgraded in the 20 districts to improve data collection and management. Each office was provided with a computer, printer, voltage stabilizers, an internet modem, 5 GPS units, and other field equipment. Motorbikes were also provided to the DASAs to enhance mobility.

Similarly, the SRID head office was also upgraded with ICT equipment to facilitate work.To oversee the implementation of the pilot survey a cross-sectoral steering committee was established.

At the end of each phase of implementation, a team was put together to assess the institutional and financial feasibility of scaling up GAPS, and both assessment reports are available at SRID.

Chickens led the livestock population in Ghana in 2023, at roughly 93.7 million heads. Goats and sheep followed as major livestock species in the country, each with an approximate stock of 9.2 million heads and six million heads, respectively.

Abstract

Afrint intensification of food crops agriculture in sub-Saharan Africa Swedish-African Research Network Agricultural development and its relation to food security and poverty alleviation Primary research in nine sub-Saharan African countries. Afrint was in three phases 200I-2016. Afrint I - 2001-2005: The African Food Crisis, the Relevance of Asian Experiences. Afrint II - 2007-2010: The Millennium Development Goals and the African Food Crisis.

Gender gaps and pro-poor agricultural growth in Malawi and Zambia - (Sida). African Urban Agriculture - Kenya and Ghana (Sida, Formas).

Geographic coverage

Sub Saharan Africa, (Ethiopia,Ghana,Kenya,Malawi,Nigeria,Tanzania,Uganda,Zambia) Regions within selected countries

Analysis unit

Household

Universe

Farming Household

Kind of data

Aggregate data [agg]

Sampling procedure

Data collection for the first round of the Afrint project was made in 2002. The data collected as part of the second round are referred to as 2008 data, although in some cases collected in late 2007. From the outset the research team selected five case study countries: Ghana, Kenya,Malawi, Nigeria and Tanzania. Outside francophone Africa, these five countries were ideally suited, in the researchers view, to charting progress in intensification, induced from below by farmers themselves, or state induced, as in the Asian Green Revolution. At the insistence of Sida, to the original five countries, four more were added: Ethiopia, Mozambique, Uganda and Zambia. Unlioriginal five, the three last mentioned countries were deemed less constrained with respect to productive resources in agriculture. Ethiopia on the other hand is peculiar in an African context, with its long history of plough agriculture, and feudal-like social formation. In this project, the heterogeneous sample of countries has proved less cumbersome to work with than one might have expected.

Formally, the Afrint sample was drawn in four stages, of which the country selection described above was the first one. The next stage was regions within countries, followed by selection of villages within regions, and with selection of farm households as the last stage. All stages except the final one have been based on purposive sampling. Data collection was sought to be made at all four levels.

The households sampled within these countries were selected with respect to the agricultural potential of the areas in which they reside. The intention was to capture the dynamism in the areas that are 'above average' in terms of ecological and market (infrastructure) endowments but excluding the most extreme cases in this regard. For logistical reasons we could not aim for a sample which is representative in a statistical sense. Instead we aimed at a sample which is illustrative of conditions in the maize-cassava belt, excluding both lowpotential dry and remote areas and extreme outliers at the other end of the scale, i.e. privileged high-potential areas.

Thus we used a four-stage sample design, with purposive sampling at all stages, except the last one, where households were sampled after having made up household lists. When we compare point estimates from the sample with those from other sources, for examples yields for the various crops with FAO statistics, no apparent sample bias has been detected. In addition to household questionnaires we also used village questionnaires. Respondents to village interviews were key persons, like village leaders and extension agents. Investigators were also instructed to conduct focus group interviews with representatives for various segments of the village population, including women farmers.

When going for a second round and a panel in 2008, we went for a balanced panel design, i.e. constructing the 2008 sample so that in itself it would be representative of village populations in 2008. This also involved sampling descendants when a household had been partitioned since 2002. In case of sizeable in-migration to a village, we also provided for sampling from the newly arrived households. The 2002-2008 panel thus is a subset of the two cross sectional samples. In itself this subset is not statistically representative of the village population in any of the two years.

Sampling deviation

20.6 percent

Mode of data collection

Face-to-face [f2f]

Research instrument

Scope of Surey Round I (2001-2005)

Population size and land use Agricultural dynamism: agro-ecology Agricultural dynamism: infrastructure and markets State interventions Markets Farmer organisations Land and land tenure Credit History of intensification (indicators) Labour: Economic constraints and facilitating factors Gender aspects:

Scope of Survey Round II (2007-2010).

Section I Village identification Summary on agro-ecological potential Section II General village characteristics Population size and land use Infrastructure land and land tenure Agricultural dynamism: agro-ecology and environmental problems Cattle Section III General village characteristics Credit Contract farming (commercial) Section IV Staple crops: availability and access to varieties Fertilizer Fertilizer access Agricultural techniques Extension Food security indicators

Section V General village characteristics Population size and land use

Land and land tenure Rural-urban linkages Gender dynamics in relation to crops Food security indicators

Cleaning operations

No editing specification given.

Response rate

79.4 percent

Sampling error estimates

No sampling error estimates given.

Data appraisal

No other forms of appraisal given.

The Ghanaian population is aware of the increasing health challenges in our health facilities and the need to consume more vegetables to improve their health status. This, coupled with population growth and changing consumer patterns has led to an increasing demand for vegetable products in Ghana. Smallholder farmers in the country have thus intensified the production of vegetables during the dry season to meet consumers’ demand and to generate income. However, their outputs have been lower than the country’s potential, so the research was conducted to identify the causes and determinants of the low yields. A total of 322 dry-season vegetable farmers in seven (7) districts in twenty-four (24) communities were selected from the Upper East Region of Ghana using a purposive random sampling technique. The Kumbhakar model was employed to compute the production risk, technical inefficiency and determinants of vegetable production in the region. The study reveals that the input variables: labour, seed, fertilizer, agrochemical and irrigation costs positively are related to the output value of vegetables with an increasing return to scale. In addition, labour, seed and agrochemical costs show a significant production risk-decreasing effect while the risk of vegetable production is reduced with fertilizer and irrigation costs. The study further depicts that extension visits, experience, water pumps and gravity-fed irrigation systems positively affect the technical efficiency of dry-season vegetable production. Again, given the current state of technology and resources available to the farmers, enhancing the vegetable outputs could be achieved by reducing the technical inefficiencies by 27% while considering the effects of production risk. The study concludes that the farmers can improve the output of the vegetable farms for higher income by adopting the best vegetable production practices such as efficient water-saving irrigation technologies and fertilizer usage while adopting the knowledge from the extension training to improve their technical efficiency.

In 2023, the share of value added by the agriculture, forestry and fishing sector to the gross domestic product in Ghana stood at 21.1 percent. Between 1960 and 2023, the figure dropped by 19.75 percentage points, though the decline followed an uneven course rather than a steady trajectory.

Description of inefficiency variables that influence vegetable production.

The Ghanaian population is aware of the increasing health challenges in our health facilities and the need to consume more vegetables to improve their health status. This, coupled with population growth and changing consumer patterns has led to an increasing demand for vegetable products in Ghana. Smallholder farmers in the country have thus intensified the production of vegetables during the dry season to meet consumers’ demand and to generate income. However, their outputs have been lower than the country’s potential, so the research was conducted to identify the causes and determinants of the low yields. A total of 322 dry-season vegetable farmers in seven (7) districts in twenty-four (24) communities were selected from the Upper East Region of Ghana using a purposive random sampling technique. The Kumbhakar model was employed to compute the production risk, technical inefficiency and determinants of vegetable production in the region. The study reveals that the input variables: labour, seed, fertilizer, agrochemical and irrigation costs positively are related to the output value of vegetables with an increasing return to scale. In addition, labour, seed and agrochemical costs show a significant production risk-decreasing effect while the risk of vegetable production is reduced with fertilizer and irrigation costs. The study further depicts that extension visits, experience, water pumps and gravity-fed irrigation systems positively affect the technical efficiency of dry-season vegetable production. Again, given the current state of technology and resources available to the farmers, enhancing the vegetable outputs could be achieved by reducing the technical inefficiencies by 27% while considering the effects of production risk. The study concludes that the farmers can improve the output of the vegetable farms for higher income by adopting the best vegetable production practices such as efficient water-saving irrigation technologies and fertilizer usage while adopting the knowledge from the extension training to improve their technical efficiency.

Sample size distribution of dry-season vegetable farmers.

In 2022, the number of cattle in Ghana reached a total of 2.24 million, which was a slight increase from the previous year, when 2.17 million heads were counted. Overall, the number of cattle bred in the country increased progressively between 2009 and 2022. To rear and keep cattle in Ghana, farmers usually use the free-range system.

Description of variables used in the production risk function.

This scatter chart displays electricity production from natural gas sources (% of total) against rural population (people) in Ghana. The data is about countries per year.