Optimizing the Beef Sector: Introducing Innovative Technologies for Farmers and Abattoirs

In this episode, representatives from a large consortium, including Agri-EPI, Ritchie, and SRUC, discuss the OptiBeef project, which aims to introduce new technologies to the beef sector to improve efficiency for farmers and abattoirs. The project started in 2019 and is set to wrap up in summer 2023. The OptiBeef project was inspired by the lack of available technology and inefficiency in the beef sector, which often leads to out-of-spec carcasses.

Currently, abattoirs in the UK mainly use a manual grading system, which depends on a trained individual visually grading carcasses for confirmation and fat. However, it is becoming harder to recruit and train individuals for this task. The OptiBeef project aims to automate this process and standardize it across abattoirs.

The project seeks to provide technology on farms to better manage the selection and processing of animals, as farmers often do not have accurate data until the animals have left the farm. OptiBeef aims to improve this by collecting data on the farm, such as daily weight measurements and grading information, to help farmers make more informed decisions on the optimal time to sell animals based on size and classification.

Additional technologies being developed by the consortium include the ability to monitor feed intake to calculate feed conversion efficiencies and better understand the value of inputs versus the value gained by the animals. The goal is to identify the most efficient point to sell the animals, reducing inefficiencies that arise from sending animals to abattoirs either too early or too late.

Transcript

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Hello and welcome to another episode of Seedling Sessions. Today we're speaking to a number of representatives from a very exciting project that Agri-EPI have also been involved in. It's an Innovate UK funded project, part of the Transforming Food Production bid. And it's a project that focused on exciting new technologies for the beef sector, working with both farmers and abattoirs. The three people that we've been speaking to are all representatives of the large consortium for this project. We've got Charlie Brown from Ritchie, Carol-Anne Duthie from SRUC. And Haley Gerry from Hallmark. Thank you very much for joining us to talk about this project. Obviously this started back in 2019 and with an extension due to COVID and things like that, we're looking at wrapping up in the summer of this year. But there's been obviously a lot of work that's happened, I guess, to kick us off. Carol-Anne would you mind maybe just telling us a little bit about the OptiBeef project and where the inspiration came for it and how it all kicked off back in 2019? Goodness, 2019 seems like such a long time ago now, but you know, this really kicked off, the concept kicked off a lot longer than that. It was probably 2017, 2018 we started working on this project and it was really inspired by the lack of available technologies for farmers and abattoirs and really focused on the inefficiency in the sector. We see lots of out of spec carcasses meeting the Abattoirs, and what we really want to do is provide a whole raft of different types of technologies that farmers can engage with, really to support that improved or optimize the management through the use of data. So the key thing about this project is using data that we can generate on farm and in the Abattoir to drive those improvements in efficiency. So I'm just thinking about the audience we've got here. Not everyone who listens to this is going to have a huge amount of knowledge around the beef sector and particularly the processing. Could someone just jump in quickly and give us a very top line what the kind of the current status quo is in measuring and grading of carcasses? So we got an idea of what happens at the moment and where this project was looking to build on that. I'll jump in there. At the moment we have in abattoirs, we have a manual grading system predominantly in the UK. And it is solely based on a person being trained to use their eye to grade the carcass for confirmation and fat. They are highly trained people. It is becoming harder and harder to recruit people that will be trained to the standard that they need to be trained. So if we can bring in the technologies that we have access to and we can pull together, then we could have an automated system that is standardized throughout. So it gives the farmers the knowledge that it doesn't matter whether they go to Abattoir A or Abattoir B, they will be graded in the same manner. Whereas if you've got person A and person B, you're open to slight human interpretation. Of course, everyone is trained to a high standard, but it's always going to be the case. So, yes, we are looking to improve technologies to allow a collaboration between farm and abattoir, to allow the farmers to know what they're putting through their system and what they're getting at the end of it. And I think an important point to mention here as well is that it's very difficult at the farm end to make sure that what you select and the time you select the animal to go through the processing factory is also down to that visual interpretation. And they don't often really get a true value of the animal until it's left the farm. And that's far too late for any optimized management or any changes to their decision making on farm. We really need to provide technology on farm that can give us that information at the point where it's most valuable. Yeah, fascinating. I can see that. And so this is a kind of move towards automation and also kind of more accurate data for all those people involved in that food value chain. Now we've got a bit of an understanding of kind of what the status quo is. What is the idea, how is this OptiBeef system, in theory supposed to work, and what kind of technologies have you bought in? I start at the farm end, we're looking to collect data basically on the farm. So, as Carol-Anne mentioned, rather than grading animals by eye, we're weighing them on a daily basis so we can monitor their weight. And not only can you choose the optimum time to sell them on size, the cameras are also providing information on the grades of the animals, confirmations, so we can also select the optimal time for getting the best classification so we have less animals out of spec. So, again, it's always interesting for me to try and understand kind of how things previously worked. So is that to suggest that previously it would have been using just standardized weight scales? And you guys at Richie and part of the consortium have sort of added technologies that kind of improve on just taking weights. Yeah. So rather than taking our weight every few weeks, we can monitor the weight daily without having to run animals through any races. We're also able to monitor the feed intake units, how much the animals are eaten, so we can get feed conversion efficiencies, and we can understand how much money we're spending on inputs and how much the increase in value of the animals is. So if the animal is putting on less value than you're putting in as an input, then it's the optimum time to sell the animal. So we're basically looking for the most efficient point to sell the animal. Correct me if I'm wrong, aren't we historically within the UK, certainly. Don't we historically send them like two weeks too late to the abattoir? So you've got an inefficiency, potentially, of sending animals slightly past or slightly under their optimal weight? Yeah, the picture hasn't changed for a few years or certainly for a long time. If you look at the sort of statistics of animals reaching these processing plants, I think we're looking at just short of 50% being out of spec. So the inefficiencies are huge and that's a combination of animals that are not quite ready for finishing and those that have been on the farm too long. And all of this has greenhouse gas impacts, it has financial impacts, et cetera. So there's a lot we can do to improve the efficiency of the sector and that's really why we're working on this whole raft of different technologies. And what Charlie mentioned is really interesting, and the critical thing here is that through all of the technology that's being developed on Farm by Ritchie and Innovant, we're now able to get a lot of information about the individual animal, not a group. So when he's talking about feed intake, we're looking at the individual animal. And that allows us to get a much better picture of what's really happening on these commercial farms. So that's the technologies and a raft of them that are being employed on farm. What does it look like then, once we get to the abattoirs? So in the abattoir, we have, through the project, installed 3D cameras. They are able to do the classification for confirmation and for fat. Obviously, everything needs refining, that's the point of these projects. But what Carol-Anne is able to do is transfer the data into such a manner that they can build an algorithm which then will allow these 3D cameras, which are relatively small, so they're not going to require a massive infrastructure change in the abattoir. They're able to be put into place. I mean, you're talking potentially 30 cm square. They're not massive. Of course, they do need to be put on some railings and some guide rails, but essentially it is affordable, it is doable. The technology in theory is going to bring forward an awful lot for both the producer and the processor. Especially by marrying the information that comes from farm, I.E. you've got your daily live weight gains of that particular animal. You know the efficiencies of it because you know the feed intake, the water intake, the weight and the image that's being taken of it. So the farmers being told, right now is the time for it to go to the abattoir. And then that camera, which is essentially the same as what is on farm, will say, yes, that's right, it is the grade that the farm camera told you it was going to be. And so the farmer knows all the way through what his animal is going to produce. For the processors, the other end equally it gives the processors a head start because they know what they've got to produce for their contracts. So it certainly allows them to fulfill their contracts as much as the farmer to become efficient, his end as well. So it's an end-to-end thing, really. So we obviously we heard there a lot about how this project is very much focused and seems to be quite successful in terms of efficiencies and potential profitability across the food chain. Carol-Anne, you'd hinted at some potentials for this kind of work to also have a knock on effect around sustainability. I appreciate the project wasn't focused on that, but could you talk in more general terms, potentially, about this possible sustainability impacts? Yeah, the project really is focused around driving improvements across the whole sector, focusing on beef. Now, what we know is that we're under significant pressures to reduce our greenhouse gas emissions, really to support the delivery of government net zero targets. Greenhouse gas emissions are very topical and really important. So the only way really to do this is to engage with novel innovations, develop new technologies. And technologies like this, although focused on specific aspects of production, can have a huge impact on how greenhouse gas emissions per kilo product produced. If we're driving down for improvements in efficiency, this is really, really critical for us as a sector. Thank you. Thank you. So, obviously we said at the start that this project kicked off back in 2019. Now, I'm sure we're all aware nothing particularly challenging has happened both globally or in the agricultural sector since then. I'm sure there have been some challenges in a big project like this. Would you like to just have a chat about any of those and how they kind of managed to overcome them? Obviously you've got the obvious, haven't you? You've got the Brexit and you've got the COVID Each have had a massive impact on this project, as you have alluded to, but also within the project, it is an innovative project. The technologies that we are utilizing are new technologies. They are things that have not been used before, so we have to take lots of different things into consideration. And atmospheres are one of those things that we have had to consider throughout this project as we move forwards with it. So, yeah, the challenges are all there with technologies as well as compounded by the obvious Brexit and Covid and staffing issues and whatnot as well. So, yeah, there certainly have been challenges, but as a consortium we're really lucky to have there's ten partners in the consortium and we're able to, for want of a better terminology, cover each other's back really. We're all trying to get to the same end game, so we've all pulled out the stops, really, to get to where we want to be through the challenges that have been presented. Thank you. So something we often talk about on this podcast around agtech in general is I think we've got quite good at collecting lots and lots of data. Sometimes the area that we aren't quite so good at is then collating that correctly and putting it into actionable insights that can be used either by the farmer or other individuals and organizations up the food chain, whether it's vets or processors or retailers. Obviously this project hasn't necessarily focused on that entirely, but where have you kind of got to with that and how have you kind of overcome the challenges of producing actionable insights? Charlie? Richie are working on a user platform on Farm, so it comes in the form of a website. So all the weight data and feed intake, water intake data gets fed into a database and then algorithms will work through the data and give us some figures that we can represent in graphical or numbers on a website. So then the farmer can easily see which animals are performing which ones are coming in spec. They can be grouped on the website and we're also looking to make selection on the website as well. So from the animals we want to move on to slaughter, then they will be put in a group and they can be selected with an automatic drafting gate so the farmer doesn't have to go in, stir up all the animals, get them all stressed. There's just a separate pen beside the pen they're in and as they drink, they will then be drafted, if they're in that selection group, into the next pen. So it's a much less stressful operation and automation and efficiencies are driven forward with that. I'll maybe jump in a little bit here as well, really on the scale of data. So we've been talking about all these different technologies. We're working with farms, we're working with abattoirs, but I can't really underestimate how big a challenge it's been for us to basically combine all of that data and generate something useful. We're working in pretty harsh environments on farm, in abattoirs as well, and three dimensional image parameters are quite heavy in terms of load. So just to give you a feel, we're talking at millions of rows of data in a database that we have to do something sensible with. So it's a huge challenge for us and I think we've come a long way with this project and it's really down to the fact that we've got such a good diverse consortium. It's been really helpful in pulling all that together in a sensible way. Yeah, it's good to hear some acknowledgment about how difficult these environments are to work in and therefore why these projects with these large consortiums are so important for us to move things forward. It all sounds very easy in theory, but in reality it's quite a challenge. Charlie, just to follow up for you, I think, because obviously there's been processors and producers involved in this project. What's been the response from people that have been involved in this and what you've been trying to achieve? I think the farmers are keen to move forward with technology. We have inquiries about feed intake units already, we have inquiries about automatic drafting. So this is the sort of technology that farmers are looking for already. It's not something that's going to be decades in the future. So I can see this making a difference to the industry quite quickly and I think that leads into a wider question, which is kind of open to all three of you around the importance of collaboration and partnership in agriculture, particularly for these kind of larger types of projects. It obviously sounds like it's worked quite well with this. How do you see, are there ways for it to continue to evolve, not just on this project, but in general? Maybe I can jump in there. First of all, we are really lucky. We've got such a good group of individuals and everyone brings their own technologies or their own experience and their own knowledge of the supply chain. We've got people who are real technology developers with almost no background in agriculture and then we've got the real experts around nutrition or meat processing. So we have a whole raft of individuals involved in this project and without that collaboration we wouldn't have managed to get this far. It does need all of those people working together to build something sensible for the sector. So in that case, collaboration is key. Do you have any advice? I think people come into these projects sometimes and the consortiums are all built together. But you've had a number of years working on this particular project and also experience on other projects. I know - Carol-Anne. Do you have any advice for people who might be listening to us and thinking about putting together a consortium about how to make sure that collaboration and partnership works? Yeah. Oh, goodness. I mean, we have been doing this for a long time now, and I guess the key thing is to be really clear about what it is you're trying to achieve in these projects and that will almost define what your collaboration needs to be and work with experts that can really help you build that collaboration. And the right one is important. Not having partners for partner's sake, I'm really not into that. Everyone has to play a key role and to be on board the concept. Work with people like KTN, the Knowledge Transfer Network. Their goal, really, in helping you build that collaboration and putting you in contact with individuals that you might not have met before. So attending networking events, et cetera, these are all really, really key things to do to build a successful project. That's really helpful. So we mentioned already that the project officially comes to a close in July of this year. And I appreciate it might be a bit early to ask me things, but do you have any concept of what the next steps or how it might evolve outside of this project? All the work that's gone into this? Yeah, certainly the abattoir side of it. There's many countries that use the same systems that we use for grading at the moment. So we have a potential of going global with any product that we're able to commercialize for an abattoir. And I don't see any reason why the farm side of it couldn't do the same. It's an open space for people to be able to go global with really. You need it licensed. So we need to start in this country because we need to get a licensing through Defra, which is by no means a small task. So that's the first thing that will need to happen come the end of the project. When we have a product to commercialize, we need to get it licensed. When it's licensed in this country, then there's no reason why we couldn't go global with it. There are, like I say, multiple countries that use the same grading systems as us that are open to these technologies. Why not? They're movable. But yes, so the farm as well, I guess. Charlie it could go global for Richie, if possible. Richie have customers using precision technology in other countries as it is. And like you say, if you extend the abattoir end of the system to other countries, then there's no reason why their own farm stuff can't go that way. As well, maybe jump in from a scientist point of view. I'm a scientist, so I'm always looking for the next thing and the new innovation. And I think what we've proven in this project is that the technology can do what we want it to do. And Haley mentions all the licensing that needs to happen to get that to commercial product. But I'm really keen to see how we can use this technology. Look at novel features of the carcass. Can we start to measure things that are moving away from the current classification system, potentially towards yield based measurements, looking at specific aspects of quality, for example, integrating new sensor systems into the technology that we've already developed on this project. So, for me, it's all about what's the next innovation? Yeah, very exciting. Well, look, I think that's been a very interesting conversation. It's obviously a long, complex and large project that's had some really incredible results. Is there anything else any of you'd like to add about how people potentially can get involved at the back end of this project or if they want to find out more, who's the best person to contact? I think we're really keen to talk to anyone who's got any interest in these technologies or the developments, et cetera. And I think, really the best way to contact us is probably through the Agri-EPI Center. There'll be a link on the website and there'll be project details on the website and there'll be a key contact there, so that's easier or equally. If anyone wants to contact any of us directly, please feel free to do so. Is this at a stage where outside of tech developers, but also would you be interested to speak to farmers or processors? Definitely. These are the key end users of our technology, is abattoirs and farmers. And of course, we'd be delighted to talk to anyone with an interest in the project. Wonderful. Well, as you said, people can get in touch with us directly at the Agri-EPI Center, at the email team@agri-EPIcenter.com or via the website, and we'd be happy to get you to the relevant parties on this one. But thank you, all three of you very much for taking the time to have a chat about the project. Thank you. Thank you. Thanks. Thank you.