# Assessing the future uptake of digital tools and technologies by sheep and goat farmers in the meat and dairy industries

Ann Mc Laren, Lise Grøva, Anne de Boer, T.W. Keady, Brid Mc Clearn, Valeria Giovanetti, Marco Acciaro, Laurence Depuille, Renáta Klein, Assaf Godo, et al.

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## To cite this version:

Ann Mc Laren, Lise Grøva, Anne de Boer, T.W. Keady, Brid Mc Clearn, et al.. Assessing the future uptake of digital tools and technologies by sheep and goat farmers in the meat and dairy industries. 75. Annual Meeting of the European Federation of Animal Science (EAAP 2024), 75th European Federation of Animal Production Annual Meeting (EAAP), Sep 2024, Firenze, Italy. pp.285, 10.5281/zenodo.14245533. hal-05059900

HAL Id: hal-05059900

https://hal.inrae.fr/hal-05059900v1

Submitted on 7 May 2025

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# Book of Abstracts
of the 75th Annual Meeting
of the European Federation of Animal Science

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Book of Abstracts No. 34 (2024)

Florence, Italy

1-5 September, 2024
# Book of Abstracts of the
75th Annual Meeting of the
European Federation of
Animal Science

Florence, Italy, 1st – 5th September, 2024

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## EAAP Scientific Committee:

F. Miglior

L. Pinotti

L. Boyle

D. Kenny

M. Lee

M. De Marchi

G. Hadjipavlou

S. Millet

R. Evans

L. Gasco

J. Maselyne

G. Pollott (secretary)

H. Spoolder (chair)
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned. Nothing from this publication may be translated, reproduced, stored in a computerised system or published in any form or in any manner, including electronic, mechanical, reprographic or photographic, without prior written permission from the publisher:

EAAP
Via G. Tomassetti 3 A/1
Rome (Italy)
www.eaap.org
eaap@eaap.org

ISBN: 979-12-210-6769-9

First published, 2024

© EAAP, 2024

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EAAP
European Federation
of Animal Science

The individual contributions in this publication and any liabilities arising from them remain the responsibility of the authors.

The designations employed and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of the European Federation of Animal Science concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

The publisher is not responsible for possible damages, which could be a result of content derived from this publication.
Optimizing Disease Risk Classification: A Data-Integrative Approach

C. Matzhold¹, K. Schodl¹, C. Egger-Danner¹

¹ ZuchtData EDV-Dienstleistungen GmbH, Dresdner Straße 89/B1/18, 1200 Vienna, Austria

In this study we apply a data integrative modelling approach and machine learning (ML) techniques to advance the classification of dairy cows at health risk. Central to our approach is the optimization of the balance between static and dynamic information and an evaluation of the interplay between model inputs and disease classification performance. By synthesizing insights from previous research on farm and livestock risk models, we minimize reliance on static traits by highlighting the most important traits, improving the application of an integrative approach to disease risk modeling. Analyzing data from 342 farms and 24,663 dairy cows, our study integrates over 350 static features, including farm management strategies and genetic information, alongside dynamic features such as DHI milking records and AMS data, and health diagnostic outcomes. A variety of machine learning techniques, such as XGBoost and RF, are applied to identify the best classification model. Preliminary findings demonstrate the efficacy of the XGBoost model in predicting lameness, achieving a high degree of sensitivity (0.81) and specificity (0.94), culminating in an F1 score of 0.78. This underscores the potential of employing advanced ML techniques in the precise identification and management of health risks in dairy cows. The study represents a further step towards the development of a data-integrative approach to disease risk modeling that supports farmers in their decision-making process.

Assessing the future uptake of digital tools and technologies by sheep and goat farmers in the meat and dairy industries

A. Mclaren¹, L. Grøva², A. De Boer², T. W. Keady³, B. Mcclearn³, G. Valeria⁴, M. Acciaro⁴, L. Depuille⁵, R. Klein⁶, A. Godo⁷, P. Piirsalu⁸, F. Kenyon⁹, I. Halachmi⁷, I. Llach¹⁰, C. Morgan-Davies¹

¹ SRUC, Hill and Mountain Research, West Mains Road, EH9 3JG Edinburgh, United Kingdom, ² NIBIO, 6630 Tingvoll, Norway, ³ Teagasc, Athenry, Galway, Ireland, ⁴ AGRIS Sardegna, S.S.291 Km 18,600, 07100 Sassari, Italy, ⁵ IDELE, Campus INRAe, 31321 Castanet Tolosan, France, ⁶ UNIDEB, Egyetem Ter 1, 4032 Debrecen, Hungary, ⁷ ARO, The Volcani Centre, 7505101 Rishon LeTsiyon, Israel, ⁸ EULS, Fr.R. Kreutzwaldi 1, 51006 Tartu, Estonia, ⁹ MRI, Pentlands Science Park, Penicuik EH26 0PZ, United Kingdom, ¹⁰ INRAE, 2 Place Pierre Viala, 34000 Montpellier Montpellier, France

Sm@RT, an EU Horizon 2020 funded project, aims to improve the understanding, awareness and uptake of digital tools and technologies available to small ruminant farmers. To assess factors influencing the rate and peak level of uptake, stakeholder groups in the 8 Sm@RT countries (Estonia, France, Hungary, Ireland, Israel, Italy, Norway, UK) used the Adoption and Diffusions Outcome Prediction Tool (ADOPT) (https://adopt.csiro.au/home.aspx). Questions were in 4 categories; characteristics of the tool/technology; characteristics of the farming population; advantage of using the tool/technology; and learnability. To date, sessions have been completed on 20 different tools/technologies presented by the project as possible solutions to needs identified previously. For example, predicted rate and peak level of uptake for an EID stick reader (across 4 different countries) ranged from 9-24 years and 72-97% of the population. Overall results indicate that answers given relating to what proportion of the population will need new skills/knowledge, and the proportion of farms that could benefit from the tool/technology, are influential in terms of rate and peak level of uptake respectively.
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