Way forward for sustainable livestock sector

Hoque, Mozammel; Akash, Akash; Mondal, Sukanta; Adusumilli, Satish

doi:10.1016/b978-0-12-822265-2.00016-8

Cited by 11 publications

(8 citation statements)

References 26 publications

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“…As a monogastric herbivore, rabbit can efficiently utilize plant fiber fractions that are indigestible to human, which means that raising rabbits for meat, fur and wool can be considered as an effective contribution to achieving global food security ( 46 ). In context of meat rabbits, the improved growth appearance is economically significant and could be achieved through genetic selection approaches.…”

Section: Discussionmentioning

confidence: 99%

Long-read sequencing-based transcriptomic landscape in longissimus dorsi and transcriptome-wide association studies for growth traits of meat rabbits

Jia,

Kang,

Wang

et al. 2024

Front. Vet. Sci.

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Rabbits are an attractive meat livestock species that can efficiently convert human-indigestible plant biomass, and have been commonly used in biological and medical researches. Yet, transcriptomic landscape in muscle tissue and association between gene expression level and growth traits have not been specially studied in meat rabbits. In this study Oxford Nanopore Technologies (ONT) long-read sequencing technology was used for comprehensively exploring transcriptomic landscape in Longissimus dorsi for 115 rabbits at 84 days of age, and transcriptome-wide association studies (TWAS) were performed for growth traits, including body weight at 84 days of age and average daily gain during three growth periods. The statistical analysis of TWAS was performed using a mixed linear model, in which polygenic effect was fitted as a random effect according to gene expression level-based relationships. A total of 18,842 genes and 42,010 transcripts were detected, among which 35% of genes and 47% of transcripts were novel in comparison with the reference genome annotation. Furthermore, 45% of genes were widely expressed among more than 90% of individuals. The proportions (±SE) of phenotype variance explained by genome-wide gene expression level ranged from 0.501 ± 0.216 to 0.956 ± 0.209, and the similar results were obtained when explained by transcript expression level. In contrast, neither gene nor transcript was detected by TWAS to be statistically significantly associated with these growth traits. In conclusion, these novel genes and transcripts that have been extensively profiled in a single muscle tissue using long-read sequencing technology will greatly improve our understanding on transcriptional diversity in rabbits. Our results with a relatively small sample size further revealed the important contribution of global gene expression to phenotypic variation on growth performance, but it seemed that no single gene has an outstanding effect; this knowledge is helpful to include intermediate omics data for implementing genetic evaluation of growth traits in meat rabbits.

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Section: Discussionmentioning

confidence: 99%

Long-read sequencing-based transcriptomic landscape in longissimus dorsi and transcriptome-wide association studies for growth traits of meat rabbits

Jia,

Kang,

Wang

et al. 2024

Front. Vet. Sci.

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“…However, our results are in accordance with the findings of [ 53 ], who identified the distance to water as a significant factor to be included in models, especially in warm seasons. Various authors [ 28 , 63 ] also identified climate conditions and topography as key factors. Furthermore, our models were able to identify hotspots of dung deposition, which was pointed out as very important by [ 2 , 57 , 60 ].…”

Section: Discussionmentioning

confidence: 99%

“…In summary, the findings of the present study are the first step in developing decision-making tools aimed at mitigating the environmental impact of livestock production, which has been a demand of farmers and other stakeholders for a long time [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

GPS Tracking to Monitor the Spatiotemporal Dynamics of Cattle Behavior and Their Relationship with Feces Distribution

Hassan-Vásquez

Maroto-Molina

Guerrero-Ginel

2022

Animals

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The sustainability of agrosilvopastoral systems, e.g., dehesas, is threatened. It is necessary to deepen the knowledge of grazing and its environmental impact. Precision livestock farming (PLF) technologies pose an opportunity to monitor production practices and their effects, improving decision-making to avoid or reduce environmental damage. The objective of this study was to evaluate the potential of the data provided by commercial GPS collars, together with information about farm characteristics and weather conditions, to characterize the distribution of cattle dung in paddocks, paying special attention to the identification of hotspots with an excessive nutrient load. Seven animals were monitored with smart collars on a dehesa farm located in Cordoba, Spain. Dung deposition was recorded weekly in 90 sampling plots (78.5 m2) distributed throughout the paddock. Grazing behavior and animal distribution were analyzed in relation to several factors, such as terrain slope, insolation or distance to water. Animal presence in sampling plots, expressed as fix, trajectory segment or time counting, was regressed with dung distribution. Cattle showed a preference for flat terrain and areas close to water, with selection indices of 0.30 and 0.46, respectively. The accumulated animal presence during the experimental period explained between 51.9 and 55.4% of the variance of dung distribution, depending on the indicator used, but other factors, such as distance to water, canopy cover or ambient temperature, also had a significant effect on the spatiotemporal dynamics of dung deposition. Regression models, including GPS data, showed determination coefficients up to 82.8% and were able to detect hotspots of dung deposition. These results are the first step in developing a decision support tool aimed at managing the distribution of dung in pastures and its environmental effects.

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“…If unresolved, these issues could limit productivity and sustainability. The advent of Artificial Intelligence (AI) and advanced sensor technology is instigating paradigm shifts in traditional sectors, including the dairy livestock export industry [ 2 ]. These technologies promise to redefine the sector’s landscape [ 3 ] by promoting humane, efficient, and sustainable practices.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation

Neethirajan

2023

Sensors

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This technical note critically evaluates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the swiftly evolving dairy livestock export industry. We focus on the novel application of the Internet of Things (IoT) in long-distance livestock transportation, particularly in livestock enumeration and identification for precise traceability. Technological advancements in identifying behavioral patterns in ‘shy feeder’ cows and real-time weight monitoring enhance the accuracy of long-haul livestock transportation. These innovations offer benefits such as improved animal welfare standards, reduced supply chain inaccuracies, and increased operational productivity, expanding market access and enhancing global competitiveness. However, these technologies present challenges, including individual animal customization, economic analysis, data security, privacy, technological adaptability, training, stakeholder engagement, and sustainability concerns. These challenges intertwine with broader ethical considerations around animal treatment, data misuse, and the environmental impacts. By providing a strategic framework for successful technology integration, we emphasize the importance of continuous adaptation and learning. This note underscores the potential of AI, IoT, and sensor technologies to shape the future of the dairy livestock export industry, contributing to a more sustainable and efficient global dairy sector.

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Way forward for sustainable livestock sector

Cited by 11 publications

References 26 publications

Long-read sequencing-based transcriptomic landscape in longissimus dorsi and transcriptome-wide association studies for growth traits of meat rabbits

Long-read sequencing-based transcriptomic landscape in longissimus dorsi and transcriptome-wide association studies for growth traits of meat rabbits

GPS Tracking to Monitor the Spatiotemporal Dynamics of Cattle Behavior and Their Relationship with Feces Distribution

Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation

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