The Ghana Statistical Service (GSS), in collaboration with the Ministry of Food and Agriculture, has released three publications from the Non-Standard Units Survey (NSUS). 

Covering household, farmgate, and market measurements, the reports provide the first nationally representative evidence on how non-standard units—such as olonka tins, margarine tins, heaps, bundles, cups, bottles, and size-based classifications — can be reliably converted into standard metric measures.

Across Ghana, these non-standard units remain the dominant way agricultural commodities are measured, traded, and consumed. However, because the same unit often represents different quantities across commodities and regions, producing consistent and comparable agricultural statistics has long been a challenge. By establishing scientifically derived conversion factors calculated using the mean weight of sampled units, the NSUS provides a stronger foundation for measuring production, trade, consumption, food security, and major economic indicators such as Gross Domestic Product (GDP) and the Consumer Price Index (CPI), ultimately improving the quality of evidence that informs agricultural policy and national development.

Household measurements vary by region

The Household Report confirms that non-standard units remain the primary way households measure food across Ghana. Nationally, a small-sized cup of local rice weighs an average of 0.352 kg, while a medium-sized cup weighs 0.491 kg. However, the survey found notable regional differences. A small-sized cup of local rice ranges from 0.320 kg in the Savannah Region to 0.378 kg in the Greater Accra Region.

The variations extend beyond rice. A small-sized yam (Puna) weighs an average of 1.043 kg nationally but reaches 1.564 kg in the Bono Region. Similarly, the average weight of a medium-sized cup of gari varies from 0.267 kg in the Bono East Region to 0.292 kg in the Eastern Region.

Significant variations at the farmgate

The Farmgate Report reveals significant variations in the weight of agricultural commodities sold using the same non-standard units. For dried yellow maize, only seven regions predominantly use the cocoa (jute) sack as the standard transaction unit, yet the average weight ranges from 131.1 kg in the Greater Accra Region to 198.0 kg in the Bono East Region — a difference of nearly 67 kilograms.

The report also found variations in yams sold by count. Nationally, 100 large yam tubers weigh an average of 362.7 kg, with the Northern Region recording the highest average at 363.3 kg. Furthermore, the findings indicate that size-based classifications such as small, medium, and large are inconsistent indicators of weight. In contrast, container-based units used for oils, including bottles and gallons, show relatively greater consistency because of their fixed capacities.

Market measures show inconsistent weights

The Market Report finds that commonly used market measures represent different quantities across regions in Ghana. For example, a medium-sized margarine tin has a national average weight of 0.276 kg, but ranges from 0.228 kg in the Ahafo Region to 0.346 kg in the Ashanti Region.

The report also identified regional differences in yams sold by size. Nationally, medium-sized yams weigh an average of 1.772 kg, with weights ranging from 1.301 kg in the Ashanti Region to 2.042 kg in the Volta Region. These findings highlight the importance of using region-specific conversion factors to improve the accuracy of market statistics. As seen in the Farmgate Report, container-based units used for oils and farm animals showed relatively greater consistency than other non-standard measures.

A major step forward for agricultural data

Together, the three publications provide the first nationally representative conversion factors for non-standard units used across Ghana. By enabling commonly used measures to be accurately translated into standard measurements in kilograms and litres, the reports improve the accuracy, comparability, and reliability of agricultural statistics, equipping policymakers, researchers, development partners, and data users with stronger evidence for planning, monitoring, and decision-making.