• VIWA - Animal Welfare pillar (Niclas / Lena & Gaby)

• Digi - social (Gaby/Lena), economic pillar (Haseeb) Environment??? - do we need to make a case or fill this gap?

for technical companies to address when finding or tuning technical solutions with

DIGI - MAP / RANKING OF SOLUTIONS BEST SUITED FOR FARMERS IN SWEDEN (FOCUS)

Based on the survey, modelling, and interviews, indicate what tools they use more and why they find them useful.

• 1) Relevance of data (animal-based measures) to achieve enhanced animal protection and welfare

• 2) Inaccurate predictions and decisions due to poor external validation

• 3) Technical failures, harmful effects of exposure

• 4) Adaptation or wearing of hardware/software components,

• 5) Lack of uptake of the most meaningful indicators for animal welfare (i.e. remove disease-centred focus in the development of PLF functionality or cost-benefit case)

• 6) Under- or Over-reliant on PLF technology causing:

  • Reducing quality time with the animals or environment

  • Loses animal-oriented husbandry skills (djüroga).

  • Compromise the interests of the environment, animals or their caretakers by creating transformations in dairy farming so that the housing and management are adapted to optimize PLF performance rather than PLF working for the animals the farmer.

  • Further negative impact on the moral status of farm animals in society by creating false expectations

• 7) Creation of a false sense of sustainability and good animal welfare by increasing production (intensification)

1. How useful is the information we get from the cows (e.g., activity monitors and milk sensors) in helping us make better decisions for their health and welfare?

   • how valuable is the information for you

   • for the cows

   • for the future of the farm or long-term goals

   • For better management of resources (this one leading to the environment)

   • In terms of food safety

2. Have you experienced situations where technology gave you predictions or advice that turned out wrong? / Are there times when the data doesn't match what we see with our own eyes?

   • How did you notice the mistake, and what did you do about it?

3. How much do you run into technical issues, and how do these problems impact your cows or daily work?

4. How easy or hard is it to maintain equipment on the farm and the cows (e.g., software updates, parts, collars, tags, or sensors)?

5. From 1 to 10, how much would you like your system to focus on other than the detection of problem cows (i.e. sick cows

   • What kinds of information would you find helpful?

   • What kinds of information would you like to combine to aid you further in your on-farm decision-making?

6. Have you noticed changes in how you or your staff connect with the cows or the farm environment? What has influenced how you prioritize your work?

7. Do you think the system has modified your djuröga skillset? Can you imagine any examples where the system's recommendations didn’t align with your djuröga?

8. How can we ensure the data reflects the animals' true well-being and the farm's sustainability?

• These are old stories related to the integration of new technology. If is repeating this means keeps being an issue... Why so?

• Ripple effect how this affects the animals, the environment and the farmers.

• What comes first the happy cow = happy farmer and visversa

• Tension on repacking data / data ownership

• Power dynamics, if something does not work farmers are in problem.

• Farm blindness and the animal eye. How is expertise defined, how this expertise is shifted to embody predictions to data predictions

• Desire of calf and heifers information. The explainable AI only kicks in unique aspects of the cycle. (Dorota reaction - Is not super popular in the industry, cause you do not see the profit directly as with the cows. / Gaby - this means that other stakeholders are perpetuating the lack of care in others here: the calves and the heifers ... Something needs to start) / Dorota the calves are often housed in loose environments with no individual id and thus adds a cost to create a system that oversees a group.

• *Gaby the dangers of only taking opportunities based on economic returns.

• Karin: Her group requested sensors for neglected cows. Terms matter, use terms of late gestations rather than dry cows maybe they will increase the VIP status of these animals. / Questions that it might not be to difficult to have individual following up. So to show the value and make a case.

Liselle:

• The difficulties of making your own system. To customize to fit their needs.

• They feel they are not trusted. When you make entry errors, there is a big loop in making the changes (contact the manager), yet they feel they are the data owners; they pay for it.

• Tools are a co-worker and thus they should not rely on as will be 24/7. This also relates with a sense of trust, Tension of the sensitivity. Some examples are in heat detection and time to inseminate.

• They want to switch how sensitive the system is.

Lisa:

• Different purposes and clear differences for your everyday work

• Diferentiation from their data.

• What is a digital tool

• Pros and Cons to whom?

• They agreed on what was important to the farms.

• They try to get as much as possible in one tool to define what is more important. *They think the more, the merrier.

• Difficult to change the focus on to what information is needed.

• I agree that the activity meter is the most important. Thus, I wanted the rumination also included there, but by discussing it, they see no point in doing so.

• The exercise worked in that they did not see neutral points

Ninas group (Advisors and Technical advisors)

• There is an overload of signals.

• Reduced milk is enough.

• Most of the positives are related to the life-work of the farmers.

Tech companies store as much information as possible in one place. On the other hand, we have the conversation of

Fragmented data and the need to have it all in one place... Fragmentation in different ways within the system, between systems and in future solutions

Again, others confirmed that most of the notes and posts were about societal values - tech/advisors.

But in other groups with farmers they connected with economics and the environment, some.

Ideas around economics - environment: Battery life, renting vs. buying, how they see the pay off (at times, this is not in money but in other aspects)

Stewardship is not in climate impact per se.

Liselle Group

• Here, the focus is not on farmers as for Lisa's groups

• Identification as the second best tool here / Niclas group also reach into that... he points out that RFDI is not bulletproof and has many negative consequences around that.

Feedintake is not yet in place.

What are the positive, and in what areas

What are the negative,e and in what areas

In the discussion, we must separate gaps from what is in place. So state the gaps in terms

- Reproduction

- Sick animals

- Management in relation to the tools and the social aspects of the farm

- Impact on sustainability: impacts resources on the farm and the management of their animals. It is about internal resources, but little can be said about the external environment.