Data standards

How a “common language” is making agriculture smarter.

Data standards as a "common language"

Imagine you have an international team of farmers, agronomists and technicians. They all speak different languages, which makes communication difficult. Everyone understands things differently and misunderstandings happen, costing valuable time and resources. It is similar when different machines, software tools and systems in agriculture talk to each other. Tractors, sensors, apps and data systems collect information – about the soil, the harvest, the weather, the animals – but they often cannot share it directly with each other because the formats are not compatible. They speak “different languages”. Data standards are like a “common language” for all machines and programs in agriculture. People agree on a language or jointly construct a new language. But what does that mean in concrete terms? A data standard defines what type of data is collected, how it is structured and in what format it is transmitted.

Let’s take a closer look at data attributes, for example

Attributes are the properties of an object that are defined. For a tractor, for example, this could be Speed, engine power, GPS coordinates, production date, consumption, etc. For a weather station: temperature, humidity, amount of precipitation, air pressure, etc. For an animal: genus, species, date of birth, etc.

Additional attributes are also defined in the data standard. For example, units such as temperature (°C), weight (kg) and length (m) are defined for measured values. For calendar data, the notation for the day, month and year is also defined.

Attributes are therefore an important component of data standards. An attribute describes a specific property that is stored in a standardized format. In agriculture, this could be, for example:

  • Soil moisture
    Measured as a percentage (%) to assess the condition of the soil for irrigation.
  • Harvest volume
    In kilograms per hectare to assess the productivity of an area.
  • Machine data
    Such as engine power (in kilowatts) or fuel consumption (in liters per 100 km) to monitor the efficiency of the machines.

Thanks to standardized attributes, different systems can immediately recognize what is meant and how the data is to be interpreted.

A comparison for illustration

It’s as if all the devices on the farm speak the same language and the words have the same meaning. They “understand” each other straight away because they know what data is being transmitted and what it means – whether it’s the temperature of a sensor or the location of a tractor. There is no confusion about what units are being used or whether a value is “temperature” or “humidity”.

Everyday benefits for data producers and data recipients using the example of animal owners

Imagine a pet owner being able to record their animals directly in a single app. He enters the type of animal (e.g. a sheep), date of birth, sex, color, etc. into the app. The animal owner acts as a data producer. Thanks to the data standard, the livestock owner’s app can now exchange data with other systems clearly, unambiguously and completely. If other connected systems now have access to the transmitted data, they can also understand this information thanks to the data standard. For example, a system that only requires the type of animal can read this information from the transmitted data. Another system reads out the sex of the animal. Therefore, it is not the transmitted data that is important for the development of systems, but the common definition of a language that can be understood by all, i.e. the data standard that is generally valid over a longer period of time.

With data standards, the agriculture of tomorrow can begin today. They enable all systems – from the machines in the field to the apps in the office – to work together seamlessly, paving the way for innovation and sustainable success.

The first results of the eCH expert group on agricultural data

At the end of 2021, the Agricultural Data Expert Group was founded on the initiative of the Federal Office for Agriculture (FOAG). It is made up of various stakeholders from the agricultural and food sector. After around two years of work, the first data standards were published 2024.

These data standards are harmonized with eCH-0108 Company Master Data and Company Register Version 6.0 of 4 April 2024. Further data standards are being developed and will most likely follow.

Continue to the eCH association

The eCH association was founded on December 13, 2002 in Bern as a non-profit organization on the initiative of the Federal IT Strategy Unit (FSU).
It develops standards in the field of eGovernment – for efficient digital collaboration between authorities, companies and private individuals.
In addition to the Confederation, all cantons and various municipalities, over 100 companies, universities of applied sciences, associations and individuals are members of eCH.
The president is Peppino Giarritta, delegate of the Confederation and the cantons for the Swiss Digital Administration (DVS), and the managing director is Lorenz Frey-Eigenmann.
The office supports 23 specialist groups in their work.
These ensure that the standards are developed and maintained to a high quality and free from vested interests.
The twelve-member expert committee controls the standardization process and decides on the approval of the data standards submitted by the expert groups.