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Common Indicators for Coffee Sustainability

Guidelines & Key Information

This project displays the initial basic common indicators for farm-level coffee sustainability. The common indicators are a result of a collective multi-stakeholder approach on defining key metrics for sustainability performance, based on the Sustainability Progress Framework elaborated under the joint facilitation of the Sustainable Coffee Challenge (SCC) and the Global Coffee Platform (GCP). The objective of this project was to define common denominators of indicators that are considered most important by a range of experts and practitioners. An additional criterion was the feasibility and complexity of the indicators. COSA--with feedback from the members of a carefully selected global expert committee -- have developed and synthesized practical metrics to operationalize the indicators so they can be functional across origins and comparable over time. The approach builds on extensive global experience with sustainability metrics and the expertise of the committee members.

Data Privacy

In the framework for this project, no data will be shared. However, it lays the ground for the potential to exchange data and making it easier to do so if needed, be it between business partners, or for sending information to a sustainability standard, etc. The project’s underlying philosophy is that every party has sovereignty over their own data and is not obligated to share it.

Impact vs. Monitoring Data

The following indicator approaches are built on a Monitoring methodology and not a Full Impact approach. The Monitoring approach generally relies on farmer recall of the most recent production year and reasonable local estimates that can provide good enough information in a simple way. This can facilitate wide adoption and use without the burden of full accounting which can be onerous for some organisations and farmers. Full Impact approaches can be used where desired and are in many cases compatible as it provides more accurate information but requires more investment and time in detailed record keeping, accounting, and data gathering skills.

Sustainability Monitoring (through farmer surveys) usually relies on a single farmer's response per household--usually the head of household. The head of the household can be any one person in the household but is generally the farm owner or main decision maker. To track activities or other engagements provided to farmers through programs or initiatives, an organisation may wish to capture additional information on multiple individuals in a household where relevant (e.g., all training or service recipients). COSA has a separate protocol for this type of producer and household identification and tracking and can provide that to interested organisations.

Producer Sampling Guidelines

  1. Representativeness: While sampling all farmers in a target group (census) is ideal, sampling a portion of farmers can be appropriate if the farmers selected for the survey are representative of the target population as a whole. Being aware of the homogeneity of the farmer population is important as well as individual farmer locations. The ideal approach would be a simple random sample where the appropriate number of farmers are randomly selected from a list and surveyors go to that list of farms to conduct the surveys. COSA has a Sample Size calculator built for Monitoring applications specifically.
  2. The accuracy of farmer recall (memory) diminishes significantly beyond one year, so try only to ask about the last production cycle. It is also optimal to visit farmers soon after the main harvest period (and ideally at approximately the same time each year). It is important to ask questions as close to the end of the last production year as possible to ensure that the full production and harvest cycle is included in the response. The production year refers to the end of the last harvest to the end of the corresponding harvest before that (12 month period).
  3. Try to talk to the head of the household for each farm (different people may give you different perspectives but typically the decision-makers will yield the most accurate results).
  4. Quality checks in the first week of a surveyor’s work can also make a big difference; make sure surveyors stick to the specific questions as written.

Certification & Audit Data

Some of the indicator data below may be covered in audits or through other compliance inquiries. If an entity wishes to use that data to report on the indicator framework, please be aware of the following:

  1. Compliance and audit data are usually collected on a much smaller sample of farmers than typical monitoring approaches (audit sampling typically relies on square root sampling instead of a large enough population to ensure statistically sound results). This means that audit data may not be representative of the whole population.
  2. Compliance data typically gives the user a binary result on a single topic, i.e., whether a certain condition was met or not. It does not usually convey the degree to which a certain condition was met, nor can it be used to see incremental change over time. Therefore, to achieve more control over the supply chain and improve the ability to remedy significant issues, it is strongly recommended to use the SMART indicator approaches detailed below (in fact, the approaches below could be built into an organisation's compliance assessment tools).

Guidance on Green Bean Equivalent calculation

For Green Bean Equivalent (GBE) calculations, one may follow the conversion rates as recommended by the International Coffee Organisation (ICO) and quoted in the Coffee Exporters Guide (http://www.thecoffeeguide.org/coffee-guide/world-coffee-trade/conversions-and-statistics/). However, for some locations, other conversion rates might be recommended. Please make sure these local differences are taken into account in the relevant cases.

The reference framework for first mile farm data

For its development, the Global Coffee Data Standard has taken the reference framework for first-mile-farm data as a starting point (https://farm-level-data-standard.readthedocs.io/en/latest/). In particular, the conceptual model has been embraced.

The first-mile reference framework has organised key data entities in the data structure, such as farmer groups, farmers, farms and plots in such a way that, many different concepts of agriculture can be incorporated, including family farms, sharecroppers, communal farms, industrial farms. In addition, the data structure is designed in such a way that it closely matches the way end-users think and talk about concepts in the real world.

It is therefore assumed that multiple organisations can easily develop a mapping from their own internal data structures onto such conceptual model because of the internal logic of the end-user community. The data model can therefore function as a neutral and organisation independent interface for the data from one database to another database. In analogy of the first-mile reference framework, each data element in the Global Coffee Data Standard is linked to the appropriate farmer, farm, plot level.

Also, the concept of a Global ID has been incorporated, allowing to trace each dataset back to its organisation of origin.

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