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In the area of agriculture and livestock management, the integration of the Internet of Things (IoT) has emerged as a groundbreaking strategy to enhance operational efficiency and advance intelligent process management. However, this sector faces significant challenges, including ambiguity in product origins and limited regulatory oversight of IoT devices. This paper explores the innovative integration of blockchain technology within the agricultural and livestock IoT, highlighting how this convergence significantly enhances operational security and transparency. We provide an in-depth review of the latest applications and advancements of blockchain in these domains, offering a comprehensive analysis of the current state of technology and its implications. Furthermore, this paper discusses the potential future development trajectories in agricultural and livestock IoT, emphasizing blockchain’s role in addressing current challenges and shaping future innovations. The findings suggest that blockchain technology not only improves data security and trustworthiness but also opens new avenues for efficient and transparent management in agriculture and animal husbandry.

The impacts of climate change on agricultural production are becoming more severe, leading to increased food insecurity. Adopting more progressive methodologies, like smart farming instead of conventional methods, is essential for enhancing production. Consequently, livestock production is swiftly evolving towards smart farming systems, propelled by rapid advancements in technology such as cloud computing, the Internet of Things, big data, machine learning, augmented reality, and robotics. A Digital Twin (DT), an aspect of cutting-edge digital agriculture technology, represents a virtual replica or model of any physical entity (physical twin) linked through real-time data exchange. A DT conceptually mirrors the state of its physical counterpart in real time and vice versa. DT adoption in the livestock sector remains in its early stages, revealing a knowledge gap in fully implementing DTs within livestock systems. DTs in livestock hold considerable promise for improving animal health, welfare, and productivity. This research provides an overview of the current landscape of digital transformation in the livestock sector, emphasizing applications in animal monitoring, environmental management, precision agriculture, and supply chain optimization. Our findings highlight the need for high-quality data, comprehensive data privacy measures, and integration across varied data sources to ensure accurate and effective DT implementation. Similarly, the study outlines their possible applications and effects on livestock and the challenges and limitations, including concerns about data privacy, the necessity for high-quality data to ensure accurate simulations and predictions, and the intricacies involved in integrating various data sources. Finally, the paper delves into the possibilities of digital twins in livestock, emphasizing potential paths for future research and progress.

This study investigates gender-based differences in the gut microbiota of Simmental cattle, focusing on bacterial, archaeal, and fungal communities. Fecal samples were collected and analyzed using high-throughput sequencing, with taxonomic classification performed through the SILVA and UNITE databases. Alpha and beta diversity metrics were assessed, revealing significant differences in the diversity and composition of archaeal communities between males and females. Notably, females exhibited higher alpha diversity in archaea, while beta diversity analyses indicated distinct clustering of bacterial and archaeal communities by gender. The study also identified correlations between specific microbial taxa and hematological parameters, with Treponema and Methanosphaera showing gender-specific associations that may influence cattle health and productivity. These findings highlight the importance of considering gender in microbiota-related research and suggest that gender-specific management strategies could optimize livestock performance. Future research should explore the role of sex hormones in shaping these microbial differences.

This study explores the critical challenges the livestock sector faces, particularly those related to biosecurity, animal welfare, and antibiotic use restrictions. It highlights the need to implement advanced information and communication technologies to enhance operational sustainability and decision-making. We introduce the Biorisk® External platform, a cloud-based visit control system designed to optimize biosecurity management by accurately tracking visitor activity through QR codes and GPS geolocation. During a 6-month study period from July to December 2023, we analyzed visits to 142 different swine production sites and 30 vehicle movement patterns. The analysis revealed trends in visitation patterns and compliance with biosecurity SOPs. The software categorized visits as authorized (A), not authorized with access (NAWA), and not authorized without access (NAWOA), providing a framework to assess biosecurity risks. Additionally, network analysis identified interconnected farms, which were classified as ‘superspreaders’, highlighting their considerable risk of disease transmission. This study advocates for the integration of digital systems in livestock operations to improve biosecurity measures, facilitate real-time data input, and support informed decision-making. By enhancing biosecurity protocols through technology, the livestock industry can better safeguard animal health, increase operational efficiency, and reduce potential economic losses associated with disease outbreaks.

The agricultural sector is responsible for a significant proportion of global anthropogenic methane (CH4) emissions, with enteric CH4 produced from ruminant livestock representing approximately 28% of the total. The development of effective mitigation strategies necessitates the accurate and actionable monitoring of CH4 emissions. However, a considerable research gap remains concerning real-time monitoring techniques capable of supporting on-farm enteric CH4 mitigation strategies. To bridge this research gap, this study explores the current status of real-time enteric CH4 emission monitoring techniques and technologies for ruminants. The study achieves this by reviewing key biomarkers and proxies for ruminant emissions, examining established animal-based measurement techniques, exploring emerging technologies, and critically assessing technological limitations and opportunities. By shedding light on this research area, this study aims to assist stakeholders in developing a viable pathway for on-farm emission monitoring, with the hope of facilitating a meaningful reduction in GHG emissions from the livestock sector.

Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive detection, but existing methodologies often rely on simplistic temperature differentials, potentially leading to inaccurate assessments. This study proposes an advanced algorithmic approach integrating thermal imaging processing with statistical texture analysis and t-distributed stochastic neighbor embedding (t-SNE). Our method achieves a high classification accuracy of 84% using the support vector machines (SVM) algorithm. Furthermore, we introduce another commonly employed evaluation metric, correlating thermal images with commercial California mastitis test (CMT) results after establishing threshold conditions on statistical features, yielding a sensitivity (the true positive rate) of 80% and a specificity (the true negative rate) of 92.5%. The evaluation metrics underscore the efficacy of our approach in detecting subclinical mastitis in dairy sheep, offering a robust tool for improved management practices.

This research article focuses on the evolution of a Mediterranean landscapes and the intricate interplay between natural and human-induced processes in the context of the Italian Anti-Apennine mountains. The study employs a multi-temporal approach to analyze changes in land use and landscape ecology, livestock activities, and agro-pastoral practices over seven decades. We noted a 18% decrease in animal units, particularly in goat and pig farming, accompanied by a 10% reduction in horse populations. Farmers’ adaptation strategies involve increasing animals per farm, aligning with broader agricultural trends toward intensification and specialization. In parallel, we observed a 22% reduction in grassland surfaces juxtaposed with an overall 15% increase in woodlands and shrublands, a 13% decreasing trend in habitat edge, and an overall 18% increase in patches aggregation at the landscape scale. The decline in anthropogenic pressures linked to depopulation triggered secondary successions, resulting in a 25% increase in homogeneous closed woodlands. These landscape alterations contribute to a 19% decrease in ecosystem heterogeneity and complexity, favoring ecological connectivity for forest-linked species but posing challenges for open meadow species. This, coupled with the loss of biocultural heritage, including traditional settlements dating back to the Bronze Age and Roman times, as well as pastoral traditions and knowledge, underscores the need to rethink future development strategies with a focus on retaining younger generations and preventing the loss of crucial ecosystem services.

The functions of non-agricultural crops unearthed from archaeological sites mainly pertain to their usage as livestock feed. However, the studies of livestock feed have predominantly relied on qualitative analysis, which often lacks descriptive objectivity and relies heavily on subjective feelings and experiences. In this paper, we aim to address this gap by focusing on quantitative analysis, utilizing macro-plant remains from the data of seventy-five archaeological settlements and one archaeological investigation in northern China spanning the Neolithic Age to the Bronze Age, as well as stable isotope analyses of carbon and nitrogen. This research delves into various aspects, including the exploration of the plant resources and livestock farming and the categorization of feed types for cattle and pigs in captivity. By employing quantitative analysis, we can gain a more comprehensive and objective understanding of these subjects. This approach aligns with studies on ancient livestock management and feed diversity. In essence, the discussion of civilization development and social changes during the Pre-Qin period holds significant value when considering forage analysis, just as crop analysis has proven insightful. By focusing on the quantitative analysis of non-agricultural crops and their role as livestock feed, we can shed light on important aspects of ancient societies and their agricultural practices.

The techniques of agriculture and animal husbandry at Tell Humeida, a Middle Uruk Period (Late Chalcolithic) site on the middle Syrian Euphrates, were studied using stable isotopes of bone collagen of domestic and wild mammals and from cereal and ruderal plant seeds. Two archaeological campaigns in 2009 and 2011 yielded a small collection of bones, most of which were taxonomically indeterminable. The work had to be interrupted due to the political conflict. The faunal study comprised collagen peptide fingerprinting for taxonomic identification, followed by isotopic analysis. Multiple 14C dating were performed to date the infill to around 3600 cal BC. An isotopic analysis of the sparse plant remains suggested that irrigation and manuring were common practices. Sheep and equids predominated in the faunal assemblage. Sheep grazed on manured soils, and their diet could include millet or another C4 plant, of which, however, no carpological remains were found. The diet of equids differed from that of sheep but also that of other wild ungulates (cervids/gazelles). Their isotopic signatures indicated that they grazed in humid areas, near the watercourse. These finds indicated a settlement that was closely linked to the availability of water, which made it possible to grow crops in an almost desert-like area, and the rearing of sheep.

The transformation of forests into agricultural and livestock systems negatively affects the ecological dynamics and the ecosystem services provided by different groups of insects, including dung beetles, which stand out for their importance in recycling livestock dung. Since the 1980s, farmers in different regions of the world have been using Ivermectin to control parasites that affect cattle. The main route of elimination of this molecule and its metabolites is through manure, which affects the richness, abundance, and biomass of dung beetles when they use dung from treated animals. To quantify this effect, we carried out an experimental design in the field in the Colombian Caribbean, where nine cattle farms were evaluated, of which three were taken for each of the different cattle management practices most used in the region: (i) Ivermectin not applied, (ii) two doses of Ivermectin at 1% applied per year and (iii) two doses of Ivermectin at 3.15% applied per year. To assess the richness, abundance, biomass, and functional groups of dung beetles, during the dry and wet seasons, 30 pitfall traps were baited on each farm with fresh cattle manure with the same management doses described above. A total of 25,441 individuals belonging to 19 genera and 30 species were collected. The richness, abundance, and biomass of beetle assemblages decreased along the gradient represented by management without using Ivermectin and management where Ivermectin was used. Paracoprid beetles were the functional group that was most negatively affected in cattle farms with Ivermectin use. In cattle farms where Ivermectin was not used, there was a greater diversity and higher functional structure of dung beetle assemblages than in those where this veterinary medicinal product was used. Using Ivermectin generates short- and long-term effects on the richness, abundance, biomass, and functional groups of dung beetles in livestock systems in the Colombian Caribbean. Therefore, we suggest using integrated treatment management to prevent the recycling fauna from being affected.

Images taken by drones often must be preprocessed and stitched together due to the inherent noise, narrow imaging breadth, flying height, and angle of view. Conventional UAV feature-based image stitching techniques significantly rely on the quality of feature identification, made possible by image pixels, which frequently fail to stitch together images with few features or low resolution. Furthermore, later approaches were developed to eliminate the issues with conventional methods by using the deep learning-based stitching technique to collect the general attributes of remote sensing images before they were stitched. However, since the images have empty backgrounds classified as stitched points, it is challenging to distinguish livestock in a grazing area. Consequently, less information can be inferred from the surveillance data. This study provides a four-stage object-based image stitching technique that, before stitching, removes the background’s space and classifies images in the grazing field. In the first stage, the drone-based image sequence of the livestock on the grazing field is preprocessed. In the second stage, the images of the cattle on the grazing field are classified to eliminate the empty spaces or backgrounds. The third stage uses the improved SIFT to detect the feature points of the classified images to o8btain the feature point descriptor. Lastly, the stitching area is computed using the image projection transformation.

Livestock management is an emerging area of application of the quadrotor, especially for monitoring, counting, detecting, recognizing, and tracking animals through image or video footage. The autonomous operation of the quadrotor requires the development of an obstacle avoidance scheme to avoid collisions. This research develops an obstacle avoidance-based autonomous navigation of a quadrotor suitable for outdoor applications in livestock management. A Simulink model of the UAV is developed to achieve this, and its transient and steady-state performances are measured. Two genetic algorithm-based PID controllers for the quadrotor altitude and attitude control were designed, and an obstacle avoidance algorithm was applied to ensure the autonomous navigation of the quadrotor. The simulation results show that the quadrotor flies to the desired altitude with a settling time of 6.51 s, an overshoot of 2.65%, and a steady-state error of 0.0011 m. At the same time, the attitude controller records a settling time of 0.43 s, an overshoot of 2.50%, and a zero steady-state error. The implementation of the obstacle avoidance scheme shows that the distance threshold of 1 m is sufficient for the autonomous navigation of the quadrotor. Hence, the developed method is suitable for managing livestock with the average size of an adult sheep.

In recent times, designated grazing areas/fields or routes for livestock grazing are usually defined. Hence, their herding activities’ success relies on data extracted from aerial photographs. As such, a direct and cost-effective way of monitoring livestock for perimeter coverage and in other natural situations is required. This paper presents a coverage solution involving multiple interacting unmanned aerial vehicles. The presented approach is built on a graph, with geographic coordinates set such that several Unmanned Aerial Vehicles (UAVs) can successfully cover the area. The maximum flying time determines the number of UAVs employed for coverage. The proposed solution is thought to solve some practical problems encountered during the execution of the task with actual UAVs. It is suitable for long-term monitoring of animal behavior under various weather conditions and observing the relationship between livestock distribution and available resources on a grazing field. The simulation was carried out using MATLAB and aerial images from Google Earth.

This paper examines the problem of food security in the Republic of Kazakhstan over the past 10 years. Based on statistical data, an assessment was made of the prevalence of malnutrition among the population of the country, including children under 5 years of age. There has been a trend towards for an improvement in the nutrition of the population for a few indicators; however, further optimization of food security indicators is required to achieve the goals of sustainable development (SDGs) of the FAO WHO Agenda for the period up to 2050 in Kazakhstan and in its individual regions. The paper reflects data on demographic changes over the past 10 years and its self-sufficiency in basic foods for 2019. A high degree of self-sufficiency in meat products (117.6%) is revealed in the population of the Republic of Kazakhstan. However, self-sufficiency in dairy products is at an extremely low level (0.1%). Camel breeding has been successfully developing in the country over the past 10 years. However, the number of camels in the country is still at a low level. Camel milk can be considered as a great source of macronutrients, its daily consumption partially facilitates the problem of Food Security in Kazakhstan.

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

Agricultural grasslands provide vital habitats for many species. Yet despite representing a significant proportion of European land use, they are disproportionately understudied compared to arable systems. Increases in productivity and intensification have led to changes in management practices, which are likely to affect grassland habitats and the ecological communities that they support. This study simultaneously monitored three trophic levels to assess the impacts of permanent versus temporary pasture (leys) on vegetation composition, carabid and dung beetle abundance, and the activity of beetle-feeding bats. Leys had lower abundances of soil-inhabiting dung beetles, which may be explained by the more recent exposure to tilling compared with permanent pasture. Beetle-feeding bat activity was also greater in leys, with positive relationships between E. serotinus activity and Onthophagus abundance across both pasture types. However, the lack of any positive relationships between beetle-feeding bat activity and Aphodius, a well-known prey genus, suggests that other key prey orders (Lepidoptera) may be of more influence on bat activity. As well as the management of pasture, differences in cattle management between pasture types can have a significant impact on vegetation and soil structure, which influence invertebrate communities and potentially dictate habitat suitability for bats.

Ghana is predominantly an agricultural country with a clear majority of its population depending partly or fully on agriculture for their livelihoods. Pork consumption in Ghana is on the increase and the livestock industry is a key sector of alternative agri-business for farmers looking for profitable business ventures and household income. The growth of Ghana’s domestic piggery industry has been impeded by several constraints such as inadequate information, lack of improved breeding stock, land limitations, rising feed cost and water availability. In this research, systems models provide an understanding of the interconnectedness and relationships present within the piggery industry of Ghana. The Industry’s problems were pooled together and key factors identified, whose behavior over time causes problems by developing various individual two-loop system archetypes coupled with a list of sustainable management strategies. Also, the individual variables affecting the industry were consolidated to obtain a causal loop structure and behavior-over-time graph of the piggery industry. The system archetype developed here is generic enough to explain any productivity decline and business rivalry behavior of any agribusiness industry. The proposed archetype can also help farm managers and producers to make various decisions. The system archetypes contribute effectively to understanding the root cause of challenges rather than providing quick fixes ‘now’ which gives rise to a much bigger problem to fix ‘later’. Application of CLDs models and system archetypes can help farmers and policy makers understand the behaviour of the entire complex piggery systems that will aid in more clarity and consistency in decisions and policy objectives. This will close the gap of inadequate information both to farmers and policy makers and governance. Through the archetypes and the CLDs, it has been identified that using a systemic approach in addressing the problems could lead to the provision of adequate data and management strategies for effective decision making for improving the piggery/livestock industry.

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.