David Ritchie (Implements) Limited Public Funding

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One of the most essential tasks involved in the production of lamb is the weighing of growing lambs in order to monitor their liveweight gain and suitability for sale. However, this is a time-consuming process and can be stressful to the lambs being gathered and weighed (Dwyer and Gauci, 2004) leading to considerable weighing intervals, limiting the use of weighing to a means of drafting lambs for sale as opposed to gathering extensive datasets to build in-depth pictures of individual and flock growth rates. Such data would allow lamb producers to monitor growth rates, enabling advanced and accurate prediction of lamb sales as well as providing information with which to alter feeding, grazing and breeding management. In order to turn lamb weighing into a less stressful and time-intensive activity, this project will design, prototype and validate on-farm an automated, in-field lamb weighing solution. Three different designs will be evaluated, with a walk-over weigher, creep-feed attractant and platform (designed to utilise a lamb's play instinct) designs, with a data-handling app being developed alongside which will work with all designs. The project will also develop a creep feed meter to monitor whole-flock and individual animal feed intake, in order to provide DMI data to relate to lamb DLWG in creep-feeding settings. Using one of Ritchie's trial farms and three of AEC's satellite farms, Ritchie will construct initial prototypes for each design of weigher and commission them on-farm for validation. Farmers will continue to weigh their lambs as they currently do, with data gathered from this and the auto-weighers being compared to gain insights into the reliability and value of the data gathered from the auto-weigher versus conventional weighing practice by AEC's data and automation team. Lamb weight data will also be carried through to Ritchie's existing auto-drafting equipment to allow for lamb with sufficient weight data to be drafted automatically, reducing lamb stress and operator time at drafting. Lifecycle assessment will be used to examine the differences in carbon intensity between lamb systems with and without auto-weigher solutions to quantify the difference high-resolutions DLWG monitoring may bring. Given the time between weighing intervals and The project will be managed by AEC's PRINCE2-qualified project management team and dissemination will be carried out by AEC's marketing and communications department via their existing multimedia dissemination channels and in conjunction with the National Sheep Association utilising their dissemination channels to broaden the dissemination audience.

The proposed project focusses on developing an **AUTOMATED** data-driven **INNOVATION** for **NON-INVASIVE** and **REAL-TIME** monitoring of respiratory disease in dairy-bred calves. Bovine respiratory disease (BRD) is the most serious issue facing beef and dairy farmers, costing the UK sector alone around £80M per annum (Zoetis 2022) through mortality, veterinary costs, increased labour and reduced animal productivity at the point of infection, but also in later life. The **AMBITION** of **DETECT** is to utilise state-of-the-art technology, alongside commercially relevant lower-cost sensors, to characterise the volatile metabolites found on the breath of healthy cattle, and of those diagnosed with respiratory infection. The system will be designed with flexibility in mind -- in that it will be able to integrate into any calf shed/equipment to provide **ADAPTABLE**, **AUTOMATED** and **REAL-TIME** monitoring of disease at the individual animal level. The **KEY OBJECTIVES** of **DETECT**: 1.Develop an automated disease-detection system, based on passive measurement of breath from individual calves. 2.Identify differences in the breath of healthy and BRD-diagnosed calves, using these findings to optimise a low-cost automated real-time sensor system. 3.Apply advanced data analytics to distinguish between breath and cattle shed background, and to develop models for disease detection. 4.Demonstrate the ability of the tool to diagnose (sub-) clinical instances of BRD across a range of farm management and feeding systems. 5.Develop a user-friendly decision-support platform to aid early health and management decisions by co-designing the platform with end-users and understanding opportunities and challenges. The project will develop the first passive breath monitoring system designed specifically for calves for the early detection of BRD. This will facilitate early intervention and optimise treatment of individual animals. Reducing the impact of disease in younger calves will improve production efficiency and reduce greenhouse gas (GHG) emissions/kg product (through reduced losses associated with mortality and poor performance). Optimised disease management offers aligned societal benefits (improved welfare, optimised antimicrobial use) and will support increased resilience of the UK's beef and dairy sectors.

"There is currently extensive inefficiency in the UK beef sector. Producers routinely assess the performance of their animals by eye and frequently retain them on farm too long, resulting in animals becoming too fat. This leads to increased variable farm costs, reduced annual capacity of beef finishing units and sub-optimal price paid for carcasses -- for a finishing unit producing 300 animals per year this equates to a cost of £11,400\. Over-fat animals also increase the primary processing costs for abattoirs and have a higher environmental impact per kg of product produced. The price paid to the producer for a beef carcass is also predominantly assessed subjectively by eye. Lack of confidence in the reliability of carcass evaluation makes it difficult to agree quality-based payments that reflect the true value of carcasses. This project aims to develop on-farm and in abattoir technologies to automate and optimise on-farm selection of animals for slaughter and carcass evaluation. The project will integrate automated data gathered across the whole life of individual beef animals (from calf to carcass) to create an enhanced decision support platform to modernise and drive efficiency improvements across the UK beef supply chain."

Tail biting in growing pigs is affected by many risk factors, but an outbreak can start without warning or obvious cause. This unpredictable tail biting results in pain and sickness for bitten pigs and severe economic losses for farmers: infection through tail wounds results in abattoir condemnation of meat. Tail docking of piglets is partly effective at reducing tail biting in later life, but is seen as an undesirable mutilation and its routine use is banned in the EU. Our innovative new solution to this long-standing problem begins with the observation that pigs hold their tails down before a damaging tail biting outbreak starts. In an earlier project, we used 3D cameras and developed machine vision software that automatically detects these changes in tail posture. In this project we will build on our promising early feasibility results to develop a prototype decision support system to give farmers early warning of tail biting. Testing it on diverse pig farm types in the UK with both tail docked and undocked pigs, we will assess its welfare and economic benefits for pig producers and breeders. There is considerable domestic demand and export potential for TailTech for use in pig production systems globally. Tackling tail biting and reducing tail docking involves a multi-disciplinary farm to fork approach which is reflected in our project team of Agri-tech engineers, animal scientists, veterinarians and pork supply chain partners.

This project aims to develop a non-intrusive system which will allow beef finishing units to identify the optimal time to market finished cattle. The system will combine innovative; animal handling; automated weighing; image capture; and analysis software to provide realtime objective feedback on animal condition, market value and optimal time to take to market.Optimising cattle finishing times allows farmers to achieve maximum marketable yield and profit, by a reduction in variable costs such as feeding and bedding, and by improving the efficiency of capital resources such as animal housing. Further benefits accrue by reducing the requirement for farm visits by supply chain customers. Environmental impact will be from a reduction in animal greenhouse gas (GHG) emissions through faster finishing times and reducing resources used and requirements for farm visits by abattoirs. The consortium is David Ritchie (Implements) Ltd, The Harbro Group Ltd, Innovent Uk Ltd, Wm Morrison Supermarkets Ltd, Scotbeef Ltd and SRUC.

To establish an internal data bank and knowledge base for implementation in design of industrial gas or offshore equipment based on relevant engineering standards and/or regulations.