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Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation
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.
Journal Article
Reducing greenhouse gas emissions from livestock production
Reducing greenhouse gas emissions from livestock production provides authoraitative reviews on measure GHG emissions from livestock as well as the range of methods that can be applied to reduce emissions, ranging from breeding to animal health and manure management. The collection also reviews nutritional approaches such as improving forage quality and the use of plant bioactive compounds and other feed supplements to limit emissions by modifying the rumen environment.
Book
Silage preparation and sustainable livestock production of natural woody plant
As the global population increases and the economy grows rapidly, the demand for livestock products such as meat, egg and milk continue to increase. The shortage of feed in livestock production is a worldwide problem restricting the development of the animal industry. Natural woody plants are widely distributed and have a huge biomass yield. The fresh leaves and branches of some woody plants are rich in nutrients such as proteins, amino acids, vitamins and minerals and can be used to produce storage feed such as silage for livestock. Therefore, the development and utilization of natural woody plants for clean fermented feed is important for the sustainable production of livestock product. This paper presents a comprehensive review of the research progress, current status and development prospects of forageable natural woody plant feed resources. The nutritional composition and uses of natural woody plants, the main factors affecting the fermentation of woody plant silage and the interaction mechanism between microbial co-occurrence network and secondary metabolite are reviewed. Various preparation technologies for clean fermentation of woody plant silage were summarized comprehensively, which provided a sustainable production mode for improving the production efficiency of livestock and producing high-quality livestock product. Therefore, woody plants play an increasingly important role as a potential natural feed resource in alleviating feed shortage and promoting sustainable development of livestock product.
Journal Article
Sustainability Indicators of Different Production Systems of a Greek Local Sheep Breed
This study develops a toolkit of sustainability indicators to analyze the economic, environmental, and social performance of various pasture-based production systems rearing Karagkouniko sheep (both specialized and mixed), and compares it with the intensive Lacaune production system in the same region. The analysis showed that despite the lower milk productivity, the group of specialized livestock Karagkouniko farms was more profitable compared to the Lacaune (35% higher net profit) production system, mainly due to savings in purchased feedstuff (64% lower expenses). This implies that grazing—if properly managed—can indeed enhance the profitability of farms. The group of mixed Karagkouniko farms—cultivating crops for both feedstuff and markets—was the least profitable group (−144.76 per ewe) as well as the least efficient in terms of use of energy (EUR 4.66 of output per EUR 1 of energy cost) and agrochemical inputs (537.2 kg of fertilizers and 3.3 liters of pesticides per ha). This suggests that strong organizational skills are required to effectively manage both crop and livestock production. Trade-offs were also observed between the sustainability dimensions. To address these trade-offs and ensure a transition to more sustainable agriculture, a comprehensive framework should be developed, integrating a mix of socioeconomic and agro-environmental schemes.
Journal Article
Computational Architectures for Precision Dairy Nutrition Digital Twins: A Technical Review and Implementation Framework
Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010–2025, this systematic review reveals how DT architectures for dairy cattle are conceptualized, validated, and deployed. We introduce a novel five-dimensional classification framework—spanning application domain, modeling paradigms, computational topology, validation protocols, and implementation maturity—to provide a coherent comparative lens across diverse DT implementations. Hybrid edge–cloud architectures emerge as optimal solutions, with lightweight CNN-LSTM models embedded in collar or rumen-bolus microcontrollers achieving over 90% accuracy in recognizing feeding and rumination behaviors. Simultaneously, remote cloud systems harness mechanistic fermentation simulations and multi-objective genetic algorithms to optimize feed composition, minimize greenhouse gas emissions, and balance amino acid nutrition. Field-tested prototypes indicate significant agronomic benefits, including 15–20% enhancements in feed conversion efficiency and water use reductions of up to 40%. Nevertheless, critical challenges remain: effectively fusing heterogeneous sensor data amid high barn noise, ensuring millisecond-level synchronization across unreliable rural networks, and rigorously verifying AI-generated nutritional recommendations across varying genotypes, lactation phases, and climates. Overcoming these gaps necessitates integrating explainable AI with biologically grounded digestion models, federated learning protocols for data privacy, and standardized PRISMA-based validation approaches. The distilled implementation roadmap offers actionable guidelines for sensor selection, middleware integration, and model lifecycle management, enabling proactive rather than reactive dairy management—an essential leap toward climate-smart, welfare-oriented, and economically resilient dairy farming.
Journal Article
Prediction of voluntary intake and enteric methane emission by dairy heifers in integrated systems
To compare the predictions of voluntary intake and enteric methane (CH4) emissions in integrated crop-livestock (ICL) and integrated crop-livestock-forestry (ICLF) systems, a 2 × 2 crossover trial was carried out with eight Girolando heifers divided into two groups according to their live weight (LW) and age. The daily means of total dry matter intake (9.66 and 8.44 kg day-1) and total enteric CH4 emission (9.99 and 8.79 MJ day-1; and 186.68 and 164.30 g day-1) were similar between ICL and ICLF, respectively. The CH4 emission expressed per unit of crude protein intake (CPI) was lower (P
Journal Article
Integrated Model for Intelligent Monitoring and Diagnostics of Animal Health Based on IoT Technology for the Digital Farm
The object of the research is the process of intelligent monitoring and diagnosis of animal health using IoT technology in the context of a digital farm. The problem lies in the absence of an integrated approach that can provide near-real-time assessment of an animal’s physiological and behavioral state, predict potential health risks, and adapt decision-making algorithms to specific species and environmental conditions. Traditional monitoring methods rely heavily on periodic manual inspection and limited sensor data, which reduces the timeliness and accuracy of diagnostics, especially for large-scale farms. To address this issue, a comprehensive model is proposed that integrates an IoT-based tag device for livestock, a data collection and transmission system, and an intelligent analysis module. The system utilizes statistical profiling to create baseline health parameters for each animal, applies anomaly detection methods to identify deviations, and leverages machine learning algorithms to predict health deterioration. The novelty of the approach lies in the combination of individualized baseline modeling, continuous sensor-based monitoring, and adaptive decision-making for early intervention. The approach scales across farm sizes and multi-sensor setups, making it practical for precision livestock farming. From a sustainability perspective, the approach enables earlier and more targeted interventions that can reduce unnecessary treatments, avoid preventable productivity losses, and support animal welfare. The design uses energy-aware IoT practices (on-device 60 s aggregation with one-minute uplinks) and lightweight analytics to limit device power use and network load, aligning the system with resource-efficient livestock operations.
Journal Article
Sustainability and Brazilian Agricultural Production: A Bibliometric Analysis
Agriculture is one of the most important industries in the world. In this context, the importance of Brazil as a strategic country to meet a range of SDG’s targets linked to food security, fighting against hunger, and poverty reduction is undeniable. This study aimed to highlight the production and dissemination of scientific research developed by Brazilian institutions, and to identify prominent authors and institutions based on articles related to sustainability, agriculture, livestock, and agribusiness. A bibliometric analysis was developed based on a sample of 3139 documents published between 2000 and 2022, comprising 21,380 authors that were then analyzed using the Biblioshiny package. As result, the term “sustainability” showed growth as it branched out to semantically similar terms, such as “sustainable agriculture” and “sustainable intensification”; and “crop–livestock integration” and “agroforestry” were highlighted as important in the development of future research. The majority of documents were produced by the University of São Paulo (~33%), the State University of São Paulo (~15%), and the Federal University of Rio Grande do Sul (~11%), suggesting that their researchers could act as coordinators in future research through the formation of multi-collaborative groups to jointly lead to the participatory elaboration of public policies that promote more sustainable paths for agricultural production.
Journal Article