2 Use cases

This chapter provides a nine of use cases describing where the reference framework and its data model applies.

2.1 Use cases for farmer groups

Narrative 1: to support the design of member management systems. Smallholder farmers in many sectors and countries are organized into cooperatives or farmer groups. Farmer groups often use management information system to store and retrieve information about their members. The collection and storage of this information happens more and more by digital means, also when smallholder farmers are involved. Farmer organisations use this information for decision making and to provide services to the farmers. In the management information system many different data elements may be stored including: personal information, demographics of the farmer group, expected and previous harvest results, the use or need for inputs, information on pest and diseases, soil quality etc. The data model as provided in this document provides a means to: 1 structure data in such a management information system, 2 exchange data between systems and 3 provide recommendations on how data can be best collected in the field.

Narrative 2: to facilitate data exchange between member management systems. Farmer groups are dynamic. Farmers often have different options to join a farmer group in their region. When a farmer switches group, it would be beneficial if the 2 farmer groups involved can easily exchange their data. This includes personal information, as well as the location of the farm description and the historical farm performance. The reference framework provides a format that facilitates the exchange of data between organisations if both organisations comply to the format or map their data infrastructure upon the proposed data format.

2.2 Use cases for voluntary standards systems

Narrative 3: to facilitate farm data exchange. Voluntary standards systems generally work with many different farmer groups in different commodities around the world. As a result of the digital revolution in agriculture, more and more first mile data about the farms is being shared between the farmer groups and the voluntary standards system. The complexity of re-using farmer field data within the standards organisation will reduce if the farmer groups are aligned in the way they digitally collect and store their farmer information using this framework.

Narrative 4: to support development of innovative ways of compliance evaluation. Voluntary standards systems are experimenting with new ways to evaluate the compliance to their standards. Examples include the application of earth observation data and (geospatial) data analysis. Accurate and standardized spatial data on field locations as well as ground observations are required to do this analysis efficiently and to develop automated methodologies.

Narrative 5: to support the multiple certification. If the required data collection by farmer groups, companies and auditors between different standard organisations would be aligned, switching between standards or adopting multiple standards would become easier and cheaper.

Narrative 6:to help avoiding double selling of multiple certified produce. Double selling is the practice of a farmer to sell the same quantity of produce twice with different certification labels. This may happen if an amount of produce is certified for 2 or more standards. On the first sale the farmer may sell all farm produced certified product as label A. As a result the farmer can no longer sell produce labelled A, because of registration at voluntary standards system A. However voluntary standards system B may not be aware of the sale of the produce. In priniple, the farmer could buy the same amount of uncertified produce on the market and sell this as B certified produce. A data standard facilitating easy exchange of farm data between different voluntary standards systems helps to finish this kind of practices.

2.3 Use cases for certification bodies

Narrative 7: to ease audits. The role of a control body is to inspect if all practices at a farm or farmers group are being performed conform certification requirements and to inform the voluntary standards system about the results of the inspection. If the auditor, the person who actually performs the inspection, could receive farm data in advance, he could make a pre analysis which farms he likes to visit and can use this information as a guidance in the field to focus on specific farmers or topics. The data can also be helpful to validate some audit points in advance before the field visit or to pre fill some of the data points that need to be collected in the field, for example it is less work to check a field boundary then to measure it, saving time and money. The reuses of existing data will be facilitated if all organisation use the same data formats and use the same data collection methods, or if the could map their data on a standard data exchange format.

2.4 Use cases for technology providers

Narrative 8: to align the technology demand from clients. If the clients are more aligned in what data need to be collected, how data is collected and how data is stored, it will become more easy to develop the specific tools. This applies to data collection tools, management information systems, data analytics, visualization and data exchange via an API.

2.5 Use cases for monitoring and evaluation

Narrative 9: re-use of data and meta analysis. Numerous projects are run by NGO’s, companies, voluntary standards systems to improve the life of small holder farmers. It is often challenging to assess the impact on the different farmer communities. This reference framework will ease the exchange of farm data between organisations and project. Researchers could reuse the data making a more thorough analysis of impact and to compare between projects, regions or even in time, leading to more effective interventions.