Putting Smart into Nutrition

Kyriazakis, Ι.

doi:10.1007/978-3-031-22584-0_1

Cited by 3 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A frequent stumbling block for estimating emissions during animal health challenges has been the measurement of feed intake, required by both Tier 2 and 3 calculations, especially given the fact that several challenges lead to a reduction in feed intake [45]. However, recent advances in precision farming allow increasingly accurate measurement of feed intake of livestock kept both indoors and outdoors (grazing) [69]. These estimates at group or individual animal level can provide granularity consistent with what is aimed to be achieved [70].…”

Section: If We Build It Data Will Comementioning

confidence: 99%

Improve animal health to reduce livestock emissions: quantifying an open goal

Kyriazakis,

Arndt,

Aubry

et al. 2024

Proc. R. Soc. B.

View full text Add to dashboard Cite

Greenhouse gas (GHG) emissions from livestock production must be urgently tackled to substantially reduce their contribution to global warming. Simply reducing livestock numbers to this end risks impacting negatively on food security, rural livelihoods and climate change adaptation. We argue that significant mitigation of livestock emissions can be delivered immediately by improving animal health and hence production efficiency, but this route is not prioritized because its benefits, although intuitive, are poorly quantified. Rigorous methodology must be developed to estimate emissions from animal disease and hence achievable benefits from improved health through interventions. If, as expected, climate change is to affect the distribution and severity of health conditions, such quantification becomes of even greater importance. We have therefore developed a framework and identified data sources for robust quantification of the relationship between animal health and greenhouse gas emissions, which could be applied to drive and account for positive action. This will not only help mitigate climate change but at the same time promote cost-effective food production and enhanced animal welfare, a rare win–win in the search for a sustainable planetary future.

show abstract

Section: If We Build It Data Will Comementioning

confidence: 99%

Improve animal health to reduce livestock emissions: quantifying an open goal

Kyriazakis,

Arndt,

Aubry

et al. 2024

Proc. R. Soc. B.

View full text Add to dashboard Cite

show abstract

“…Nutrition: according to Kyriazakis (2023) , the term Smart Nutrition describes the method of feeding animals with the help of smart devices (such as cameras and sensors), that through communication with other devices or networks and/or the use of data analytics and AI, are able to capture information automatically and generate outputs that can be used for the management of livestock systems. The use of these technologies are already described in cattle (Gonzáles et al, 2023; Kyriazakis, 2023 ), poultry ( Zuidhof et al, 2023 ), pigs ( Brossard et al, 2023 ), fish ( Fore et al, 2023 ) and other animals, offering solutions that encompass automated individual feed intake quantification ( Bezen et al, 2020 ), feeding behavior ( Bloch et al, 2023 ), rumination pattern tracking ( Kliemann et al, 2023 ), pasture management and biomass calculation ( Castro et al, 2020 ), among others.…”

Section: Applicationsmentioning

confidence: 99%

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Curti,

Selli,

Pinto

et al. 2023

Anim. Reprod.

View full text Add to dashboard Cite

Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.

show abstract