How farmers are using artificial intelligence and what the future holds | Ep. 116

Artificial intelligence is suddenly everywhere — including on dairy farms.

In this episode, you’ll hear from DairyNZ senior scientist Dr Callum Eastwood and Perrin Ag senior consultant Rachel Durie, who have just completed a report on how dairy farmers are using generative artificial intelligence (GenAI).

They share what they found, including examples of how farmers are already using GenAI, what’s working well, the risks to watch for, and the opportunities ahead.

Find out more about the research into GenAI use on dairy farms & read the report.

How are you using GenAI tools on your farm? Take our short survey.

Ready to try it? Try DairyNZ's DAiSY tool on the homepage or find out more here.

Have feedback or ideas for future episodes? Email us at talkingdairy@dairynz.co.nz

Connect with DairyNZ

Stay up to date with advice, latest research, tools and resources. Read, browse, scroll, listen, or be there in person. Visit dairynz.co.nz/get-connected

Transcript

Introduction

SPEAKER_00 0:01

Kioda and welcome to Talking Dairy. I'm your host Jack McGowan from DairyNZ. It's great to have you with us. When you hear AI, you probably think of artificial insemination. And fair enough, it's been part of dairy farming for decades. But today we're talking about a different kind of AI, artificial intelligence. That's technology that's starting to show up on farms in ways you might not expect. More specifically, we're focusing on generative AI, tools that can create new content like text or images based on patterns and data. You've probably heard of ChatGPT, Copilot, Gemini or Claude. These are all examples of generative AI. DairyNZ recently commissioned a report to explore how farmers are starting to use AI, what opportunities are coming next, and how this technology could help you farm more efficiently. Some farmers are already experimenting with AI for things like feed planning, animal health checks, and even creating custom farm chat bots. Others are curious but cautious, and that's understandable. So today we're joined by Dari and Zenior Scientist Dr. Callum Eastwood, who leads the Workplace Productivity Programme, and Rachel Dury, senior consultant at Pear and Ag and author of the report. We'll talk about what farmers are doing now, what's coming next, and how DariNZ is helping farmers keep up with change and innovation. Let's get into it. Callum, let's start with you. Why did DariNZ commission this report in the first place? What were you hoping to learn for farmers?

SPEAKER_01 1:33

I think it mostly started with actually farmers asking us what's happening with AI, what is it, and what's the potential for use on farm? And that's probably about 12 months ago. We started thinking how we could build some work around this and thought the best place to start was just sort of have a look at what farmers are doing, understand more a bit about it, and sort of think about where it might head in the in the near term. So I guess that's what we did. And and I guess generally, I feel like you can't go a day without hearing something about AI. Just driving in this morning on the news, they were talking about AI use in schools or something, or it's sort of everywhere at the moment. So it was something we wanted to try and understand what's the potential use for farms. We've seen that it's being used in other workplaces, and people have probably heard about GPs using it. I have a GP friend who's using AI as part of their consults, you know, and the benefit being that they can concentrate on the patient while AI is essentially in the background recording the conversation and then spitting out notes, essentially improving their work that way. So the work that we're going to talk about today, yeah, the main aim was understanding how it could be used by farmers, how it is being used, and what might the future hold.

What AI Means In Dairying

SPEAKER_00 3:01

And when we talk about AI in this episode, what exactly do we mean? Can you give us a quick explanation of the different types of AI and why this report focused on generative AI?

SPEAKER_01 3:13

It's tricky in the dairy sector, isn't it? Because there's now two types of AI. So you still have to be a little bit specific. But artificial intelligence, AI, has been around actually since the 1950s. It's really got prominent, I guess, in the last really only three years with um the release of ChatGPT. Yeah, it's it's got various forms. And so you've got forms uh around machine learning, so actually sort of self-learning algorithms, the really sort of hardcore AI stuff. And that's often in the back end of technologies like Netflix or some of our animal monitoring. Yeah, there's machine learning, um, there's the computer vision AI, which is, you know, if you think self-driving cars, that's sort of computer vision version. There's natural language processing, which people might not realize, but when you're using Siri or, you know, your Google um voice in your car, that's actually AI-powered. But I guess what we're talking about today is the large language models, that's the generative AI side. So they've been trained on essentially, you know, all the stuff on the internet, and they can create text, you know, you can interact with them with text, create images, and also you can actually just chat to them with voice. Yeah.

Digital Twins And Custom Chatbots

Subscriptions, Benefits, And Trade‑Offs

Barriers: Accuracy, Trust, Time

Near‑Term Autonomous Decisions

SPEAKER_00 4:34

And Rachel, you spoke to farmers and industry experts for this report. What did you find? How are farmers actually using AI on farm right now? Yeah, so I guess it's still early days. Like adoption is not high at the moment, but we did find some farms that are employing generative AI in a multitude of ways, and largely they were using the likes of Chat GPT. And what we found is that we could really categorize the different ways that they were engaging with generative AI into sort of three main groups. So whether that was decision support, where they were using it to provide insights, or use the generative AI to provide insights for recommendations, or task enhancement, where they were using it to improve the efficiency of repetitive tasks, or then communication support where they were using it to enhance team coordination and communication. And it was certainly that decision support space where we found the most use cases of generative AI amongst farmers. So that might have been farmers that were using it in a more basic way in terms of using it in replacement for a Google search engine, because it could just so quickly filter, finding, filter, and summarise information. So it might be farmers that are asking it, you know, I'm thinking about summer crop this season, what should I use, or what minerals might I need to be giving to the herd at this time of the season, through to farmers that are actually uploading photos of weeds or a sick cow or a piece of machinery and trying to troubleshoot an issue. And then there was sort of the next step, and this is where a lot of the applications came from in terms of farmers that were really trying to ground the generative AI into their farm specific situation. So they were actually providing a whole lot of information about their farm to the generative AI and then using that to create really tailored insights. So a lot of examples of farmers using it to create feed budgets or spring rotation plans, optimise diets, identify mineral imbalances, and then through to farmers that are actually taking data out of their herb management program. So for instance, taking a CSV file of data out of MINDAR, uploading that into Chat GPT, and then very quickly interrogating that data using natural language. So, you know, asking what are the conception rates of my three-year-olds versus my five-year-old cows rather than having to manually create groups and filter information. Or actually pulling that information or that data from different software programs and pulling them together within ChatGPT to analyse potential patterns in the data. And then we had sort of a more novel application, so where a farmer had actually created a digital twin of their farm system. So uploading a lot of the information specific to their farm into the tool, into a custom GPT, and then interacting with it to understand in his case what might be the impact my business from differing weather patterns. So an El Nino weather pattern versus a La Nina, for instance. Can you just quickly explain what a digital twin is? Yeah, so I guess we have digital twins now. So you think of Farmax and Overseer, for instance, they are digital twins of the farm system. But in this case, it's creating a digital twin within Gernative AI. So it's like a text-based twin where you're uploading all this information. It might be farm policies, it might be weekly farm reports that are dating back many years of the farm. And the Gernative AI is accessing all this data to pull a sort of a virtual model of the farm together that a farmer can then interrogate just using natural language. And then some of the other applications that we found, I guess, were some of those more ones that are perhaps more mainstream that you hear about in terms of farmers that are using the likes of Microsoft Copilot to help draft emails or documents, generate standard operating procedures, or even draft those weekly or monthly farm reports. And then probably the last really innovative application that we came across was a farmer that had created a custom farm chatbot. So this was an operations manager. So they were managing multiple farms, multiple farm teams. And so what he'd done is created this custom GPT again, where he had uploaded a lot of his farm policies, particularly around grazing and supplement and pasture management, which were very well defined, and then also linking that custom GPT up to his trusted reference data. So the likes of Dairy and Z, Federated Farmers, Fonterra Resources. And so then this chatbot was made available to his staff, and that they could query it, ask questions without having that delay in going and asking the operations manager and waiting for a response back. So the types of questions they might have been asking were I've got a down cow, I've tried X, Y, and Z, still not getting up. What else could I try? Or what is my round length supposed to be today? Or I've got a milk grade, what do I need to go and check? So those are the types of things that it was being used for. So yeah, a pretty cool application, pretty innovative application. And in that case, I think the farmer had taken him about he'd done that this year in February, didn't know what AI or generative AI was, and by August he was creating this chatbot. So yeah, pretty cool, pretty, pretty impressive. Yeah. And certainly farmers are engaging with generative AI in a range of ways too. So it's not just tech space. A lot of farmers are engaging using the voice functionality aspects and engaging with the on the go, or as I say, uploading photos and data as well. And so I guess the farmer who created a bespoke chatbot has invested money and time in that. The other farmers that you spoke with subscribing, or are they getting by on the free plans like I am? Yeah, a mixture. I mean, a lot are probably still using the free versions, but I think as farmers are starting to get towards those more advanced applications or wanting to do more with the Chat GPT and use more of the functionality that's available, they're starting to get into those subscription-based versions. And you mentioned that adoption is still low, but obviously there's some farmers that are really embracing it. What benefits are those farmers seeing? For those that are engaging with genitive AI, I think one of the key benefits that most farmers are valuing is the speed at which it can generate insights and provide information. So, you know, it might not be 100% accurate, but actually most of the farmers felt that the trade-off or speed was okay and they could actually manage any inaccuracies just by applying some critical thinking. And it was really pointed out that actually that's not really much different to the current situation where we might read information or hear information from someone. It doesn't mean it's right. We always have to pass that information through our own mental model and think, you know, is that applicable to me? Does it make sense? So it's not hugely different. And then the other points, too, I think, that were really motivating farmers to engage with generative AI was around reducing mental workload. So being able to accelerate that decision making, quickly access information and reduce the time they spent on some of the administrative or more repetitive tasks in their day, and actually be able to redirect that time to some of the more high value or strategic activities. And then, as I alluded to as well before, being able to generate new insights. So we've got all this data coming in from all these different places. It could be quite overwhelming. It's certainly time-consuming to go through and try to analyse it all. But now we can give that to generative AI and pull it together much quicker and uncover insights that perhaps we wouldn't have had the time to do before. On the flip side, what's holding farmers back? What concerns came up most often? Often it's around just the awareness and familiarity with the tool. So a lot, you know, are only just starting to realise what generative AI is. And many are still maybe they haven't downloaded it, they haven't had the time to engage with it, they don't know where it can fit within their system or what the practical tools or solutions it could provide or how they actually use it. So certainly awareness is a bit of a barrier. And then of course there is the concern around the reliability and accuracy of data, like how does it, how can it actually provide an insight that's very specific to my farm system? And then we have for some farms that are, you know, the operating well, they're they're happy with how the farm's running, they don't see the need or the desire to change. And probably one of the other interesting points that when we talk to farmers, you know, we hear generative AI being able to be used for data analysis, as I was just talking about. But some farmers actually really enjoy that piece. They really enjoy analysing their data. So why do they want to hand that over to the generative AI? Callum, the report mentions that AI could eventually make autonomous decisions in low-risk areas. How far away do you think that is and what needs to happen first?

SPEAKER_01 13:08

Just reflecting on what Rachel was talking about, you know, there really is a range of sort of types of AI technology at the moment. You know, we sort of say adoption is low, but actually nowadays if you do a Google search, you get that AI summary at the top. Um so that's almost ubiquitous, I think.

SPEAKER_00 13:28

So perhaps intentional adoption is still low, but Yeah.

SPEAKER_01 13:33

Like any technology, you know, there's just differences in its capability. And so yeah, it's amazing that, you know, some of those farmers that Rachel talked to have self essentially self-taught and and built their own GPT sort of chatbot or a digital twin kind of thing. But a lot of us are probably using it and not really knowing we're using it, actually. And I guess to your question around the autonomous decisions. Yeah, we see that there are sort of very near-term opportunities and companies are already working on these things around grazing management, let's say. If you've got the right data and decision rules, essentially an AI system could make those decisions for you.

SPEAKER_00 14:19

Aaron Ross Powell So what kind of decisions?

Data Quality And AI‑Ready Datasets

SPEAKER_01 14:22

Like where to graze, which paddocks to graze next and allocation, and even through to virtual fencing where that virtual fence could be set automatically. So I guess that's kind of the autonomous decisions on sort of part of the farm decision making. And mating may be another one where if you've got wearable data, you know the mating decisions you want to make around your genetics. That kind of thing could be automated about which cow gets what and when. And it was interesting, we'll talk about it in a bit, but um, part of the work we're doing is working with farmers to understand what the what the future use might be. And one of the farmers in a recent workshop talked about the decisions or the process they have to make every morning during mating and checking their wearable data, lining it up with other things and making a decision about, I guess, what the artificial insemination tech will do that day. And their question was, couldn't I just automate this workflow with AI? And I think it's those things are entirely possible. And it'll be pretty near-term. I guess what needs to happen to for those things is and Rachel kind of touched on it, data availability and data quality. Essentially, AI works on digital data. It needs access to data in whatever form. It can be text, it can be um numbers. You know, we think in the dairy sector we've got quite a lot of data, but actually a lot of it is not well organized or not easily accessible. So I guess that's one thing uh we need to work on. And and at a sector level, you know, we talk about AI-ready data sets. So sort of preparing our data for AI to be able to access, you know, in the near future. Yeah.

SPEAKER_00 16:10

Is that something farmers need to do or I think they can.

Interoperability And The Next 3–5 Years

SPEAKER_01 16:15

And definitely, you know, if you look at things like wearables, it's the standard stuff of making sure the data you're putting into those systems is is correct. Your EIDs are right. You've actually carved your cows in the system, all the basic data quality. Because it's said very often, but the the rubbish-in, rubbish out thing. And if you're using farm data in in an AI and Chat GPT or something, then it's only as good as the data that you put in. That can be a focus for farmers, but also the sector with our sector data sets, but also how we link data together. What else needs to happen or we that we need to be aware of is using the right data. So these systems, ChatGBT and others, use the Internet essentially. If you don't tell them not to. And that goes to what Rachel was saying about there's good and bad information on the Internet. And so you can actually ring fence and tell them just to use certain data sets. And I guess DNZ's daisy AI platform is an example. It only uses DNZ information. So farmers can do that too. And they are doing that around this feed in certain data and say, just use this data, don't go and use something from North America or something when you're giving feedback. For us to sort of trust these systems to do autonomous decisions, we kind of just need to get started and work out what their capability is. And we being probably organizations like Dairy and Ted on behalf of farmers and build the understanding, build the trust and build the knowledge on how to use these things.

Voice Tools, Multilingual SOPs

Risks

SPEAKER_00 17:59

And Rachel, where do you see AI heading in the next three to five years for dairy farming? Give us a picture of what a typical day could look like. Looking into the future is always a challenging thing, particularly in the world of AI where it's evolving so rapidly. We did undertake a process through this project to have a look and see where it could go. And for me, I think the real key to unlocking the potential of generative AI is being able to have that really strong data integration and system interoperability between all the various programs that we interact with as farmers. So, you know, we've got herd management programs, we've got sensor portals, wearable portals, production dashboards, counting models, weather forecasts. And I think if we really want to unlock the potential germative AI, we need to be able to have access to all of those and then have those systems talking to each other and have the germative AI being able to access them quite seamlessly. If we can't unlock that system interoperability challenge, then I think we'll probably be limited to generative AI acting as a digital assistant rather than that digital partner, I guess. But that in itself still has a lot of benefits. So, for instance, if we think about pasture management in the future, and Callum sort of alluded to this, if we we can imagine a space where generative AI is now embedded into the pasture management software that we have available now. And so a farmer could go in there and interact with the germitive AI and model the impact of different grazing decisions and then use the insights that the generative AI provides to actually determine what is the best decision to implement without actually having to go and trial that in the paddock and wait for the feedback loops. And then if we think, if we take the next step forward and we think, okay, now we do have that system interoperability piece unlocked, well now generative AI could actually integrate with the likes of virtual fencing, like Callum mentioned, could integrate with sensors around the farm and actually start to autonomously manage that grazing round, unlock some agentic capabilities, still within human defined parameters, but it can use because it has this live access to the data, it can use the information. And can monitor animal performance and pasture regrowth, for instance, and use that to inform and improve future decision making. I think, regardless of whether we have that system interoperability piece resolved or not, I think we can expect to see gerative AI functionality embedded within all the different apps and platforms and software programs that we see. I think we can expect to see that in the future. And because a lot of those platforms are familiar with farmers, they kind of have a greater trust acceptance, I think, when that gerative AI functionality becomes available. So I would imagine that would receive quite high uptake amongst farmers. But really the opportunities for AI, I think, are endless. And it really depends how far an individual farmer wants to go with it, how far they want to experiment, how much time they want to put towards creating these more advanced tools. There is, I think, for the majority a tension between being a hands-on decision maker and being involved in the practical day-to-day realities of farming. So I think it's going to be about generative AI tools that seamlessly integrate within the business, provide a proven benefit, whether that be time-saving or performance benefits, and don't require significant time at the computer. I think it has to be about enhancing the potential of the farmer and not replacing or taking out the enjoyment of what they do. Callum, looking ahead, what excites you most about AI for dairy farming?

SPEAKER_01 21:24

The first thing is, I mean, just how quickly it's it's come on us. The Daisy platform on our website has only been live since November. You know, farmers are already using that, asking, you know, questions around feeding decisions for cows, how they set up their farm system, you know, that kind of you know, quite basic functionality is here now. So that's quite exciting. And from Rachel's work, some really interesting use cases that have only developed in the last year. So it's moving really fast. I guess compared to other technologies, it's a really low-cost or no-cost entry. Like you can use the free version. And even if you go to a subscription version of a lot of these tools, it's something like$40 a month. So there's not many other things on farm you'd pay that little for. And you get much more functionality and control over the subscription versions. What's most exciting to me is actually the voice function. Through some of the work we've been doing in the last couple of months, we've been, you know, looking at how you use these tools and running sort of some scenarios. And we've done some work with the voice functionality. And it it really is amazing how it seems like you're talking to another human. The AI system will talk back to you, you know, a bit like your car navigation in an accent of your choice, you know, that kind of thing.

SPEAKER_00 22:47

I notice a little bit more friendly and um encouraging than the car navigation, though.

Getting Started And Building Skill

SPEAKER_01 22:52

Yeah, that's right. We might talk about sort of cautions and considerations in a minute, but that is one thing to be aware of. They it will be very supportive. I mean, they're designed that way, and that can be great. I think the farmer said last week that that feels really good, that they say, oh, that's a great question. I guess you've got to be careful that that is not actually a human. But the car navigation systems have changed because I gave some feedback to our car navigation system the other day and it told me I wasn't very nice. I never said that before when we weren't going the way I wanted to. So they are getting quite interactive. But I think the voice functionality we see for how farmers do their jobs, you know, on the go. You know, it's been the problem with other computer-based systems, is you need to go to the office or whatever, you need to type in on your phone when you're in the field. So being able to talk to the system and you know, maybe input data when you're out in the paddock and it gets captured or ask questions when you're out actually doing whatever you need the answer to. And at the recent workshop we did with farmers, one of the farmers said for the times that they're on the tractor or something like that, they'd actually just really like to engage in a conversation, like strategic kind of conversation with an AI system. And they have tried it, where they're sort of asking about their system setup or their carving date and just bouncing ideas back and forth. And I think that's only going to get better, especially if you feed it the right data and it knows a bit of information about your farm, then it is like your your business partner or digital assistant. So you can imagine just um exploring ideas using these tools. Yeah.

Comparing ChatGPT, Claude, And Copilot

SPEAKER_00 24:36

We're recording this podcast in English, but that's not the first language for all of our farmers. Can these tools work in other languages?

SPEAKER_01 24:45

Yep. And I'm pretty sure you know the Daisy system on with DerenZ has can give you responses back in different languages. But definitely all these other systems can. And yeah, that's a massive win because if you're even just creating SOPs, the standard operating procedures using the Gen AI, to be able to do it in different languages that match who's on your farm, that would have taken a lot more money and work a few years ago if you wanted to do that.

SPEAKER_00 25:14

And not just in different languages too, but also being able to provide it in different formats that's best suited to those individual people on farm. You know, everyone learns in a different way. So whether it's an audio or a short video or written, you know, that can do all of that. Callum, you mentioned risks. What are some considerations farmers should keep in mind with AI use?

What DairyNZ Is Doing Next

Resources, Links, And Closing

SPEAKER_01 25:34

Yeah, I mean, the first one uh is generative AI will always give you an answer. It may not be a correct answer. They pretty much won't say, I don't know. So if you ask something, just take it with a grain of salt or be healthy skepticism, maybe, of what is coming out. And I think one of the farmers that Rachel talked to said that that's what they told their team. You know, use this tool, but yeah, have some skepticism, run your own mental ruler across it. And that will depend on the information it's drawn on. So if you can limit that information to trusted sources and say only go to these sites or something, you'll limit that. You'll hear of hallucination that these systems hallucinate, essentially make stuff up. So that's one thing, I guess, to be aware of. Like I said before, there's systems like Daisy that are actually ring-fenced to more credible data, more credible information. So that's a sometimes safer systems to use. There's a lot of potential questions around not knowing how responses are come up with. So this idea of explainability, a little bit of a black box. You can ask the systems to sort of say where they got the information from. That's sort of one thing people can do. You can get biased responses based on the information they're using. And be aware of data privacy. You're putting information into a system that that information is essentially stored on some server somewhere in the world and maybe used to further train that algorithm. So, yeah, these are things I think we need to get our heads around about what we're comfortable with. And then there's sort of larger questions around carbon footprints, energy consumption, the influence of tech companies in our future of farming. And you see the sort of battles that are happening at the moment with the large AI companies investing, investing to try and get ahead of each other. And what does it mean for our farm systems around changing roles and skills on farm? I think we're in a transition period with skills due to a lot of different technologies like wearables and things. But yeah, what do we actually think about AI use? Rachel mentioned it that farmers farm for a certain reason. And so we've we have to kind of mesh together the sort of joy of farming with these new tools and make sure we don't fully automate decisions, and then people are like, no, this is not why I went farming. So there's some of those bigger issues I think that we need to be aware of. Yeah.

SPEAKER_00 28:16

Rachel, do you have anything to add there? I think that was pretty well covered. I think, you know, Callum talked around the healthy scepticism, and I guess the the part I'd add there is, you know, durative AI, it works off the digital information that's available online. So it doesn't, it can't replicate the tacit knowledge that's held by farmers. You know, the knowledge that they've built up over years of experience and observation. That's all undigitized and and it's not even easily articulated in a lot of cases by farmers. So it's just important to be aware of that when interpreting the responses and understanding, you know, what information does it have available to generate that response? It's really always going to struggle to have that really nuanced contextual awareness that experienced farmers have. So yeah, just applying that critical thinking, human oversight interpretation, that's all going to remain quite critical. If it doesn't sound right, it's it's probably not the right. If a farmer listening today is curious but hasn't knowingly tried AI yet, what's your advice for them getting started?

SPEAKER_01 29:18

I uh yeah, I sound sound just plugging Daisy. That is a good place to start. Go to the DRNZ website and and try out Daisy, ask some questions, and other tools like ChatGPT. You can do the same thing. Yeah, there's some tips and tricks. I think we'll be working over the next sort of six months to try and develop some better tips and tricks for farmers about how to get the most out of these tools. But one thing I have heard is just treat some of these tools like a person that you'd be asking questions of. I think we we treat it like a search tool, but it's more than that. So you can kind of have a conversation by text or voice with it, but also with that healthy skepticism in mind, treat it like a very intelligent new staff member, someone who knows a lot of stuff but might not know that much about your farm or the context. So you may need to ask it very specific questions to get the answer that you want. Yeah, I guess that's some ways to get started. And yeah, check out our website where we'll have some guidance on that.

SPEAKER_00 30:20

Yeah, Daisy's are so great because you know it's credible information. What do you think, Rachel? How would you suggest they get started? Just have a go. Like if you haven't played around Geriative AI before, just have a go, maybe download one of the apps on your phone, the Chat GPT, or another one on your phone. You don't need to start with anything technical or complex, just ask some basic questions or ask us to do some basic tasks, like summarise an article you haven't had time to read, or you know, explain something you've always wondered about, or build a dinner recipe with the ingredients in your fridge. Just start with some of those small things and get a gauge on how it works. Yeah, don't take everything as fact. It is just a probability model. But over time, as you gain experience with it, you you start to understand how it works, what it's good at, what it's not so good at, how best to ask it questions and engage with it. And then, yeah, with that, you can naturally build up to the more complex tasks and applications and naturally allow those guardrails to go up and start looking at subscriptions and more of the advanced applications. But just have a go. Yeah. We mentioned Chat GPT a few times because it's almost like the word Google as a stand-in, but there's other similar models like Claude and Co-Pilot. Are they all the same or? Yeah, play around with the different ones because they are all slightly different in the amount of creativity they apply and how they answer questions or or follow tasks. The co-pilot, for instance, is quite a good one in terms of if you've got it using Microsoft on your laptop, it's sort of already, assuming your laptop's up to date, it's probably already there, sitting in the background, ready to go. Like a co-pilot, for instance, can often already interact with different Microsoft programs on your computer, whether that be Outlook or your calendar or something, so you can actually use it to go, oh, what have I missed in my emails over the last week? You know, how you prioritise what I need to do. All right, Callum, what's next in this line of research at DairyNZ? How will this work help farmers feel confident about using these tools?

SPEAKER_01 32:16

I guess next steps for us, we're getting alongside farmers, I guess, to learn. We're learning. So are farmers and other parts of the sector. So, you know, we're currently running some workshops with some farmers, rural professionals to understand what's the opportunities, where what's the needs here for farmers. And then we'll we'll be looking to partner with some farms that are using these tools and essentially, I guess, show other farmers what the potential is, but also what things to look out for in their use. Yeah, we're really interested to see what farmers are doing at the moment. And and Rachel's uncovered some of those things, but there will be a link in the show notes where you can add some of the add some of the things that you're doing if you are using it, because um we're just yeah, interested in essentially inspiration from the farming community. And we'll be summarizing this sort of report on the website so you can check out some of the insights on the website and over time adding more guidance for farmers about how to get the most out of these tools. And yeah, or you can just ask Daisy how to get the most out of the tools because they will they will actually reply to you if you say, What should I do?

SPEAKER_00 33:28

All right, and that's it for this episode of Talking Dairy. A huge thank you to Dr. Callum Eastwood and Rachel Dury for sharing those insights with us. If you'd like to know more, you can read more about this report and research at dairynz.co.nz forward slash AI dash report. This is an exciting space, and DariNZ is working hard to make sure you have the tools and knowledge to stay ahead of the curve. Thanks for listening and we'll catch you next time. Matewa. If you'd like to get connected with DariNZ's latest advice, research, tools, and resources, whether it's reading, scrolling, listening, or in person, you can visit DariNZ.co.nz forward slash get connected, and don't forget to hit follow to keep up to date with our latest episodes. As always, if you have any feedback on this podcast or have some ideas for future topics or guests, please email us at talkingdairy at dairynz.co.nz. Thanks for listening and we'll catch you next time on Talking Dairy.