Improved Livestock Management Research Articles

Prevalence and Associated Risk Factors of Brucella abortus and Brucella melitensis in Humans and Cattle Populations: A Comprehensive Study

A zoonotic disease unique to South Asia, brucellosis causes major public health and financial problems, notably in Pakistan. This research aimed to ascertain the molecular frequency of Brucella abortus and Brucella melitensis in humans and cattle and to pinpoint related risk factors in the Southern Khyber Pakhtunkhwa, Pakistan, districts. A total of 800 blood samples, 600 from cattle and 200 from humans, were gathered and examined using traditional PCR targeting the IS711 locus. In humans, the molecular frequency of Brucella melitensis and Brucella abortus was 16.5%; in cattle, it was 9.1%. Risk factor analysis in cattle revealed that seasonality, geographic location, sex, and insemination method significantly impacted brucellosis prevalence· Higher risk was observed in female cattle, those naturally inseminated, and during the summer months, particularly in regions such as Bannu· In humans, univariate logistic regression identified potential risk factors, including age (25–50 years), rural residency, lower socioeconomic status, female gender, direct contact with animals, and consumption of raw animal products. However, none of these factors reached statistical significance. The findings underscore the importance of continuous surveillance and targeted preventive measures to control brucellosis transmission between cattle and humans. Understanding the molecular prevalence and risk factors can guide public health strategies and improve livestock management practices to mitigate this zoonotic disease.

Sosialisasi Manajemen Kesehatan Kambing Perah serta Pengaruhnya terhadap Kualitas Susu pada Peternak Kambing Perah di Kecamatan Ampelgading dan Tirtoyudo Kabupaten Malang

Goat's milk is a food of animal origin that is easily damaged and requires handling after the milking process. Ampelgading and Tirtoyudo sub-districts are areas in Malang Regency where some of the population are dairy goat breeders. This community service aims to provide socialization and education on good animal health management so that it can improve the quality of goat milk. The activity began with collecting farmer data and analyzing the situation of dairy goat farming. The socialization method was carried out by conducting focus group discussions about knowledge of animal health management and checking the quality of goat milk. Milk composition testing uses Lactoscan, while organoleptic testing is based on Indonesian National Standard 01-2346-2006 concerning organoleptic and sensory testing instructions. The results of community service show that the quality of dairy goat milk in Ampelgading and Tirtoyudo Districts is premium based on the Thailand Agriculture Standard 2008. The results of the organoleptic test of goat milk have remained the same based on SNI 3141-1-2011 regarding the quality of fresh milk. The results of this goat milk quality test can be used as a basis for farmers to improve livestock management and milking. Through this activity, farmers can implement good livestock health management so that the quality of goat milk and the selling price increase. This activity will encourage young farmers to be more interested in becoming dairy goat breeders who have high economic prospects and contribute to increasing goat milk productivity. Apart from that, this activity can improve the welfare of farmers by improving the quality of goat milk with higher selling power.

Gastrointestinal helminths of Cattle slaughtered at Gwagwalada abattoir, federal capital territory, Nigeria

Gastrointestinal parasites are widespread in cattle worldwide, causing both clinical and sub-clinical infections that can lead to significant financial losses. This study investigated the prevalence of gastrointestinal helminths infection in cattle slaughtered in the abattoir at Gwagwalada area council, federal capital territory, Nigeria. A total of 120 faecal samples were collected and analysed by flotation and sedimentation methods. The results revealed an overall prevalence of 26.67%. The helminths identified were Toxocara sp (5.00%), Schistosoma sp (10.83%), and Fasciola sp (10.83%). These findings were compared to existing studies, with variations attributed to differences in climatic conditions, management practices, and seasonal factors. Age, breed, and sex-specific analyses revealed significant differences in infection rates. While no helminth infection was found in young adult cattle (1≤3 years), a 31.37% prevalence was recorded in older adult cattle (≥3 years). Among the breeds examined, Sokoto Gudali showed the highest prevalence (100%), followed by White Fulani (26.42%) and Red Fulani (16.67%). A significant breed-related difference (p = 0.0001) was recorded. Additionally, male cattle exhibited a higher prevalence (31.37%) compared to (0.00%) in females. The study highlighted the impact of geographical, ecological, and environmental factors on helminths infection, as well as the need for strategic anthelmintic treatment, improved livestock management, and educational programs to control these infections. Regular monitoring and selective breeding may further contribute to long-term infection control in cattle populations.

Extrinsic and intrinsic drivers of prevalence and abundance of hard-bodied ticks (Acari: Ixodidae) in one-humped camel (Camelus dromedarius)

BackgroundTicks are ectoparasites and can be vectors of a wide range of pathogens, posing significant health risks to livestock. In the Sahara Desert of Algeria, particularly among one-humped camels (Camelus dromedarius), there is a need to better understand the factors influencing tick infestation patterns to improve livestock management and health outcomes. ObjectivesThis study aimed to investigate the prevalence, intensity, and abundance of hard-bodied ticks (Acari: Ixodidae) among dromedaries, examining both intrinsic factors (sex, age, coat color) and extrinsic variables (farming systems, vegetation types, climate zones, and elevation) that might influence tick infestation in this region. MethodsTicks were collected from 286 dromedaries across nine sites in the pre-Saharan regions of Algeria, with elevations ranging from 736 m to 980 m. The sampled camels, which ranged in age from 6 days to 21 years, were examined for tick infestations. The ticks were identified through macroscopic and microscopic methods, and their abundance was analyzed in relation to the camels' characteristics and environmental factors. Three breeding systems were recognized: extensive, intensive, and mixed. ResultsA total of 980 ticks were collected, with Hyalomma dromedarii Koch, 1844 being the most abundant species (553 specimens), followed by Hyalomma impeltatum Schulze & Schlottke, 1930 (393 specimens), and Hyalomma excavatum Koch, 1844 (34 specimens). H. dromedarii showed a preference for parasitizing brown-coated dromedaries and exhibited significantly higher infestation levels during spring (p < 0.001). No significant association was observed between tick infestation and the camels' age or sex (p > 0.05). However, the farming system had a significant impact on tick abundance, with extensive and mixed systems showing higher tick burdens compared to intensive systems (p < 0.01). Additionally, the vegetation type, climate zone, and foraging habitat elevation were found to significantly influence tick densities and prevalence. ConclusionThis study provides essential insights into the tick infestation dynamics in dromedaries in drylands of Algeria. It highlights the influence of coat color, seasonality, and farming practices on tick burden, with brown-coated camels being more susceptible during the spring. The findings underline the importance of considering both intrinsic and extrinsic factors when developing effective tick control strategies, especially for camels raised in extensive or mixed farming systems in diverse arid rangelands. Future research should expand the scope to cover other arid regions in North Africa for a comprehensive understanding of tick-host dynamics.

A comprehensive review of livestock development: insights into domestication, phylogenetics, diversity, and genomic advances.

Livestock plays an essential role in sustaining human livelihoods, offering a diverse range of species integral to food security, economic stability, and cultural traditions. The domestication of livestock, which began over 10,000years ago, has driven significant genetic changes in species such as cattle, buffaloes, sheep, goats, and pigs. Recent advancements in genomic technologies, including next-generation sequencing (NGS), genome-wide association studies (GWAS), and genomic selection, have dramatically enhanced our understanding of these genetic developments. This review brings together key research on the domestication process, phylogenetics, genetic diversity, and selection signatures within major livestock species. It emphasizes the importance of admixture studies and evolutionary forces like natural selection, genetic drift, and gene flow in shaping livestock populations. Additionally, the integration of machine learning with genomic data offers new perspectives on the functional roles of genes in adaptation and evolution. By exploring these genomic advancements, this review provides insights into genetic variation and evolutionary processes that could inform future approaches to improving livestock management and adaptation to environmental challenges, including climate change.

459 A new approach to measuring dry matter intake, from drylot to pasture: Activities of the alliance for regenerative livestock

Abstract The current state of feed intake measurement for cattle in confinement relies on expensive monitoring systems. There is no effective method for measuring feed intake in grazing animals. To address these limitations, our research aims to tap into the potential of passively collected data, such as data from the Internet of Things (IoT), to address practical challenges in animal agriculture. This includes improving livestock management, optimizing resource allocation, and reducing ecological footprints. The benefits of our research extend beyond improved livestock management to include reducing ecological footprints, such as water, carbon, and environmental footprints. Grazing livestock have a crucial role in nutrient cycling in grasslands and advancing more sustainable livestock production systems. This requires innovation of research methods for quantifying intakes of animals housed in a variety of systems including confinement facilities that lack expensive feed intake systems and for grazing animals. Currently, the industry standard for determining feed intake are equations published in the Nutrient Requirements of Beef Cattle (NASEM, 2016). Our recent efforts using machine learning approaches (Blake et al., 2023) dramatically outperforms the equations. Using the same dataset, the NASEM (2016) approach overestimates intake by 34%. We have developed a predictive algorithm utilizing data collected from animals in confinement where individual feed intake (ground truth) is known. We have developed machine learning models such as Repeated Measures Random Forests (RMRF) and Extreme Gradient Boosting (XGB) to better predict feed intake in confined cattle. We have also used these predictive algorithms to determine feed intake of grazing animals and animals in confinement without feed intake systems and validated the predictive value of the approach.

A Novel Active RFID and TinyML based system for livestock Localization in Pakistan

Localization of livestock is a vital component of good livestock management in Pakistan. This abstract describes a unique method for livestock localization in Pakistan that makes use of Active RFID technology and Tiny Machine Learning (TinyML) approaches. The incorporation of Active RFID technology allows for precise and long-range livestock tracking, while TinyML provides on-device analysis and decision-making. This method has a number of advantages, including high precision, real-time localization, and less reliance on external infrastructure. Accurate triangulation-based localization is obtained by putting Active RFID tags on cattle and carefully positioning Active RFID anchors in specific regions. TinyML integration on resource-constrained microcontrollers within Active RFID tags allows for efficient on-device analysis of Active RFID signals. The suggested system has the potential to significantly improve livestock management practices in Pakistan, including animal tracking and monitoring, behavior analysis, and increased animal welfare. To realize the full potential of this unique Active RFID and TinyML-based livestock localization system in Pakistan, further research should focus on optimizing localization algorithms, enhancing TinyML models, and exploring interaction with upcoming technologies

IoT-based livestock tracking: Addressing challenges in Somali livestock farming

Livestock plays a vital role in Somalia's economy, contributing more than 60% of the country's gross domestic product. However, livestock production in Somalia faces many challenges, including conflict, insecurity, climate change and environmental degradation. These challenges can lead to livestock losses, which can significantly affect the livelihoods of livestock owners. This paper proposes an Internet of Things (IoT)-based livestock tracking system to help farmers locate their lost livestock. The system uses GPS and GSM/GPRS technology to track the location of livestock in real-time. The system also includes a boundary restriction feature that can be used to ensure that livestock remains within a designated area. The IoT-based livestock tracking system has the potential to address a number of challenges facing livestock production in Somalia. The system can help reduce livestock losses, improve livestock management practices, and increase productivity. The system is currently being field-tested in Somalia. The system successfully detects livestock crossing the border and transmits the livestock's location in real-time. Field test results show successful real-time tracking of livestock. The test data will be used to improve the system and assess its effectiveness in helping farmers locate their lost livestock.

OPTIMIZATION OF LIVESTOCK MONITORING SYSTEM IN OUTDOOR BASED ON INTERNET OF THINGS (IOT)

Livestock businesses are often underestimated by the public because they are associated with less hygienic working environments. However, the demand for livestock products such as meat and milk is increasing, providing significant business opportunities. Several obstacles, such as livestock loss and the capital required for cage construction, are barriers to starting a livestock business. Livestock losses, especially in outdoor farms, often occur because of the lack of proper monitoring and data collection. Therefore, technology is required to overcome this problem. The application of IoT technology is an effective solution for overcoming this problem. By utilizing sensors, such as GPS, temperature, and heart rate, farmers can monitor farm animals remotely using Android applications. In this study, a U-blox Neo6m GPS sensor was used to track the location of farm animals, a temperature sensor was used to monitor the temperature conditions of farm animals, and a heart rate sensor was used to determine the health of farm animals that had been tested. The use of a 1500 mAh LI-ION LITHIUM battery as a power source proved to be sufficient for 7 h. The results showed that this IoT-based Outdoor Livestock Monitoring System can provide information on the last location of livestock as well as real-time heart rate and temperature data in the database. This innovation opens opportunities for farmers to improve livestock management and monitoring efficiently, minimize losses, and increase the productivity of their livestock business

Nourishing the Future: AI-Driven Optimization of Farm-to-Consumer Food Supply Chain for Enhanced Business Performance

Efficiency and sustainability are upending the global food supply system. The current study examined the relationship between Artificial Intelligence (AI) and agriculture to optimize food supply chain from farm to consumer business model. The study examined how AI-driven solutions may boost efficiency, reduce waste, and promote environmental responsibility with an emphasis on sustainability. The food supply system faces resource depletion, climate change, and growing global food consumption. AI technologies, such as automation, data analytics, and machine learning may solve these issues. AI systems use real-time data, predictive analytics, and intelligent logistics to improve production, distribution, and consumption. This reduces food production and transportation of carbon emissions along with improving resource allocation. AI-powered precision agriculture helps the farmers to increase crop yields while lowering fertilizer and pesticide use along with supporting sustainable farming as well. IoT devices and sensor networks are helpful to improve livestock management and crop monitoring, enabling data-driven agriculture. The current study highlighted how AI ensures food quality and safety across supply chain. By identifying impurities, monitoring storage conditions, and forecasting shelf life, AI-powered quality control systems may decrease food wastage and ensure safe, high-quality goods. In conclusion, AI-agriculture integration is an innovative way to increase farm-to-consumer food supply chain efficiency and sustainability. AI technology may help the supply chain stakeholders to create resource-efficient, environmentally friendly food production that meets the needs of a growing population. The current study discussed food industry sustainability and AI uses in agriculture, as well as future possibilities and challenges.

Epidemiological Studies of Subclinical Mastitis in Dairy Goats in Lumajang Regency, East Java, Indonesia

Subclinical mastitis is an economically important disease in dairy goat farming. Lumajang is a center for dairy goats in East Java. However, routine examination of subclinical mastitis for screening tests has no data. This study aimed to provide prevalence data and identification factors that influenced subclinical mastitis. Therefore, this study was carried out as part of the national surveillance program. The research design is a cross-sectional study with simple random sampling. Samples came from 19 farms with a total individual sample of 224 dairy goats. Detection of subclinical mastitis using an undirect test with the California Mastitis Test (CMT). Identification of management factors and characteristics that can influence subclinical mastitis in dairy goat farms using a structured questionnaire. The results showed that the prevalence of subclinical mastitis in Lumajang District was 36.2%. There was a significant difference in subclinical mastitis in each village (p&lt;0.05). The highest prevalence of subclinical mastitis was in Kandangtepus village. A farmer's characteristic factor that significantly influences subclinical mastitis is the farmer's experience. The aspect of livestock management that significantly affects subclinical mastitis is structured work management. Subclinical mastitis was prevalent in dairy goats in Lumajang District and requires prevention efforts. Prevention approach through education for farmers to increase knowledge, attitude, and socialization to improve livestock management.

66 Emerging Precision Ranching Technology Is Enabling the Development of a “smart” Biome

Abstract Precision ranching technology is revolutionizing remote data collection from livestock and the land base they depend on, greatly improving our ability to assess the entire biome. Remotely Piloted Aircraft System (RPAS or drone) based remote sensing to date has been used primarily to assess intensive cropping; but RPAS systems equipped with interchangeable cameras: high-resolution visual, thermal, LiDAR, and multi/hyperspectral imagers are now being deployed on rangelands, to monitor subtle changes in the visible, near infrared and infrared spectrums (radiation) that both plants and animals reflect. A single RPAS system can now be used simultaneously for high-quality vegetation mapping and behavioral analysis of animals, providing a suite of comprehensive data-collection tools for studying livestock in a variety of landscapes and rugged terrain. For example, we have used RPAS systems to measure behavioral and physiological indicators of cattle heat stress in feedlots and on pasture (Mufford et al., 2021). These same drones can be utilized for RPAS collection of high-resolution imagery (using multi/hyperspectral imagers and LiDAR) for vegetative and topographical mapping, greatly improving our ability to visualize and characterize habitat attributes such as forage availability, quality (stage of growth) and accessibility. Equipping cattle with new tracking technology, including "smart" GPS ear tags, rumen boluses, and wireless fencing collars (based on low-earth orbit satellites and cellular networks) enable animals to be readily identified and tracked on the landscape, while providing invaluable physiological data, such as internal body temperature to monitor estrous, heat stress, or the onset of disease (Figure 1). Additionally, rapid advances in wireless fencing systems have the potential to improve livestock management through autonomous mustering or the implementation of advanced rotational grazing for use in regenerative agriculture efforts. Wireless fencing may decrease production (infrastructure) costs, while also providing a strategy to better align forage demands with use, and thereby enhance pasture condition at the habitat and landscape level. Finally, non-invasive pasture weighing systems, enabled with or without RPAS technologies, are capable of monitoring key livestock production metrics year-round. Together, these technological advances enabled by this new "smart biome" will improve animal health and welfare while ultimately enhancing the sustainability of extensive livestock production systems.

A teacher-to-student information recovery method toward energy-efficient animal activity recognition at low sampling rates

Automated animal activity recognition (AAR) has advanced greatly through recent advances in sensing technologies and deep learning, and improved livestock management efficiency, animal health, and welfare monitoring. In practical automated AAR systems where animals need to be monitored over a long period, the sampling rate dramatically affects the energy consumption and battery life of sensing devices due to continuous data collection and transmission. Considering real-world benefits, existing works have often lowered the sampling rate to reduce energy costs. However, when the sampling rate falls below a threshold, the AAR performance degrades rapidly due to many relevant signals being missed. Therefore, this study proposed a novel method, dubbed teacher-to-student information recovery (T2S-IR), to improve the performance of AAR at low sampling rates. This approach effectively leverages the knowledge obtained from high-sampling-rate data, to assist in recovering the missing information in features extracted by the classification network trained on low-sampling-rate data. The workflow of the T2S-IR contains two main steps. (1) we utilize high-sampling-rate data for training teacher classification and reconstruction networks sequentially. (2) Then, we train a student classification network using low-sampling-rate data, while promoting its performance by exploiting the knowledge learned by trained teacher networks via two novel modules, namely the reconstruction-based information recovery (RIR) module and the correlation-distillation-based information recovery (CDIR) module. Specifically, the RIR module employs the pretrained teacher reconstruction network to enforce the student classification network to learn complete and descriptive features. The CDIR module enforces the feature maps of student network to mimic internal correlations within feature maps of pretrained teacher classification network along temporal and sensor axes directions. To validate our proposed T2S-IR, we conducted experiments on two public datasets acquired for horses and goats using triaxial accelerometers and gyroscopes with an initial sampling rate of 100 Hz. Data having low sampling rates were obtained by downsampling the original data at different frequencies (i.e., 50, 25, 12.5, 10, 5, and 2 Hz). The results demonstrated that our method remarkably boosted the classification network trained on low-sampling-rate data (e.g., percentage-point increments in the precision, recall, F1-score, and accuracy of 3.33%, 3.58%, 3.45%, and 2.19%, respectively, for the 12.5-Hz horse data and 7.6%, 4.44%, 6.9%, and 0.79%, respectively, for the 5-Hz goat data) while outperforming existing knowledge distillation methods. The enhanced classification network can be directly applied in practical AAR tasks with low sampling rates, significantly beneficial for scenarios with constrained energy sources for wearable devices.

Adoption of improved management practices of livestock: Case of small-scale farmers in rural Bangladesh

This study examines the drivers of improved livestock management practices (ILMP) adoption in rural Bangladesh using data from the International Food Policy Research Institute's (IFPRI) Bangladesh Integrated Household Survey (BIHS). This study investigated four improved management practices: artificial insemination, concentrate feed, vaccination, and deworming. The binary logit and multivariate probit (MVP) models were used to analyse the data. According to the findings, approximately 65% of farmers practiced deworming, but only about 20% of farmers vaccinated their livestock. Logit regression analysis suggested that rural households with mobile phones and televisions were 5.2% and 3.8% more likely to adopt. Furthermore, compared to their peers, farmers who have maintained contact with livestock extension agents are 11% more likely to adopt. The MVP model indicated that likelihood of using concentrate feed increases with income, while artificial insemination is more prevalent among farmers who raise livestock for sale. Policy implication included the need for more extension agent-delivered awareness development programmes to educate livestock farmers on the benefits of ILMP. Scale-appropriate management practices can also play an important role. Farmers should be guaranteed of the availability of livestock feed and veterinary services at reasonable prices to promote adoption.

Twenty years of lion conservation in a commercial rangeland

Lion populations in Africa declined precipitously in the twentieth century outside of protected areas but the commercial cattle ranches of Laikipia County, Kenya, have been a unique exception, with low livestock stocking rates, careful range management, large populations of wild ungulates, and a full complement of large carnivores, including lions at a stable density of about 6/100 km2. The Laikipia Predator Project, later Living With Lions, started in 1997 with two primary objectives: improving livestock husbandry to reduce lion predation losses and subsequent killing of lions, and studying behavioral adaptations of lions to human activities and persecution. We initially interviewed ranch owners and managers on lion numbers, losses to all mortality factors, husbandry methods, costs, and lion control measures. Studies of husbandry and control methods led to improved livestock management, which gradually reduced losses and retaliatory lion killing. Persecuted lions are secretive and nocturnal, so behavioral research was dependent upon radio collaring, requiring development an effective capture technique. Collars introduced ranchers to their lions as individuals, decreasing their propensity to shoot them after livestock predation. The most important breakthrough was the development of “lion-proof” mobile bomas (corrals) which dramatically reduced night time losses and retaliatory killing. Global positioning system (GPS)-Iridium collars for research into lion movements, allowed development of a Lion Early Warning System to inform ranchers of morning lion locations, allowing them to avoid lions during day time grazing. These measures reduced retaliatory lion killing by 90% between 1998 and 2017. Development of simple and inexpensive hyena-proof bomas for traditional pastoralists dramatically reduced their losses and motivation to poison predators. Studies of lion movements and ecological energetics in relation to human activities have revealed patterns of diel avoidance of humans/livestock by day and predation of wild prey near bomas at night, showing that lions partition their activities temporally in order to utilize high quality hunting habitat while minimizing risk of encountering humans. Studies of predation ecology suggest that lion predation does not have a significant impact on Laikipia’s important population of endangered Grevy’s zebra. Several other carnivore research and conservation projects in Kenya arose out of the initial work in Laikipia.

Elevation gradient drives distribution of soil carbon in a semiarid grassland of British Columbia

A sequence of Brown, Dark Brown and Black Chernozems spanning a 600 m elevation gradient in a semiarid bunchgrass ecosystem (Lac du Bois Grassland) near Kamloops, British Columbia was first described in 1961. More soil organic carbon (SOC) at higher elevations along the sequence was attributed to increasing effective precipitation with increasing elevation. Since the 1961 study, plant community composition has shifted toward the desired climax community due to improved livestock management instituted in the 1970s; however, changes in soil carbon stocks remain unknown. The objective of this study was to quantify SOC and soil inorganic carbon (SIC) stocks using the same site selection criteria as used in 1961. SOC stocks (kg m−2 ± SD; 0–60 cm) were similar for Brown (5.73 ± 1.7) and Dark Brown Chernozems (5.87 ± 0.76) but increased sharply (10.11 ± 2.5) for the higher elevation Black Chernozems. SIC increased with depth in all three soil zones, representing 33%–50% of total C from the 30–60 cm soil depth. To evaluate changes in SOC (0–20 cm) from the 1961 measurements, three different approaches for calculating SOC stocks were used based on the inclusion or exclusion of coarse fragments. Results varied across the three soil zones from no change to a 20% increase in the Brown, an increase of 7% to a reduction of 26% in the Dark Brown, and a decrease of 12% to 35% in the Black soil zone. Information about soil coarse fragments and the distribution of SOC and SIC stocks within the soil profile is crucial for accurate comparisons across studies or resampling events.

Global investment gap in agricultural research and innovation to meet Sustainable Development Goals for hunger and Paris Agreement climate change mitigation

This paper provides estimates of the global investment gap in agricultural research and development (R&amp;amp;D) and innovation. The investment gap is defined as the additional annual investments required to end hunger in 2030 (Sustainable Development Goal SDG2) and to put agriculture on the pathway to the Paris Agreement target for 1.5°C increase over pre-industrial temperature levels. The investment gap is projected relative to a reference scenario with projections to 2030 using an integrated economic-biophysical model of the global agri-food system. In addition to showing the impacts on hunger, the modeling results are used to simulate the effect of the gap-closing investments on greenhouse gas (GHG) emissions from agriculture. In addition to projecting the impacts of overall investment in agricultural R&amp;amp;D on productivity and environmental outcomes, the analysis assesses the contributions of different types of innovative technologies and farming systems to the environmental outcomes, especially technologies that contribute to sustainability outcomes. Sustainability-oriented technologies and management practices examined include conservation tillage, nitrogen-use efficiency, improved livestock management, and other climate-smart technologies. The projected results show that additional agricultural R&amp;amp;D investments of USD 4 billion per year above baseline investments together with USD 6.5 billion per year invested in technical climate-smart options, can reduce hunger to 5% globally and achieve 2030 GHG emission reductions consistent with the Paris Agreement 2°C and 1.5°C pathways to 2030.

Resource Selection of Domestic Sheep on Mountainous Summer Habitat in Utah, United States

Past research has documented livestock grazing patterns and dynamics across pastoral landscapes. We used spatial locations of domestic range sheep (Ovis aries) obtained from Global Positioning System collars to study habitat selection on summer mountain habitat North of Scofield Reservoir Utah, United States. Data were collected between the months of July to September 2020. We developed a resource selection function to determine the influence of slope, distance to water, aspect, ruggedness, elevation, and vegetation types on habitat selection by sheep while grazing on summer mountain habitat. We found sheep selected for sites closer to water, with more gentle terrain, higher in elevation, and north-facing slopes. Vegetation types were less predictive, ostensibly due to the lack of species composition information in available geographic information system layers and the possibility of sheep being herded to avoid areas of overuse. Although it is often assumed that sheep use slopes more than their heavier and larger cattle counterparts, overall they tended to avoid steep slopes compared with all other predictor variables. This information on selection for higher elevations and avoidance of steep slopes can be used to improve livestock management practices including flock management that increases sheep foraging patterns and energy efficiency.

Measuring the Economic Value of the Negative Externality of Livestock Malodor in South Korea

The South Korean livestock industry has increased in scale and production, generating positive impacts on the national economy. However, livestock externalities, primarily malodor, have subsequently led to increased conflicts between producers and affected communities. This study estimated Korean households’ willingness-to-pay (WTP) for government subsidies to help address livestock malodor using a contingent valuation method (CVM) derived from a double-bounded dichotomous choice model. The annual average household WTP was estimated at 29,206 Korean won (KRW) (USD 25). This was slightly higher than the respondents’ self-reported average amount of KRW 25,457 (USD 22). The estimated economic value nationally is KRW 628 billion (USD 546 million) annually, for a total of KRW 3.14 trillion (USD 2.73 billion) over a proposed five-year period. The public’s estimated WTP can be leveraged to improve livestock management practices, more efficient waste disposal techniques, and improved husbandry methods to address conflicts between producers and surrounding communities.

Technology, infrastructure and enterprise trade-off: Strengthening smallholder farming systems in Tamil Nadu State of India for sustainable income and food security

The complexities of smallholder farming systems pose a challenge in demonstrating the potential benefits or risks of new technologies and policies. Using Integrated Analysis Tool, a rule-based dynamic simulation model, this study tried to improve the performance of major farming systems in the Tamil Nadu State of India. Amongst the four major farming systems viz. Black gram-based (BFS), Paddy-based (PFS), and Integrated Farming Systems (IFS) in Villupuram district and Dryland Farming System (DFS) in Virudhunagar district, IFS was found to be the most profitable and resilient based on their performance simulated for a 3-year rotation. Setting IFS as a benchmark, potential interventions were evaluated under other farming systems to improve their relative performance. The analysis allowed understanding the interactions in smallholder farming systems and the potential impact of interventions in a whole farm way considering the cash flows, cost intensity, and input-output trade-offs. While multi-bloom technology in black gram increased the net profit of BFS without much stress on input and labour, area expansion under rainfed groundnut incurred high expenditure. Trading-off paddy with maize and groundnut significantly increased the net profit of PFS but replacing sugarcane with tapioca and turmeric was not remunerative. Improved livestock management practices have substantially increased the net profit of DFS wherein crop yield could not be enhanced substantially without the prospects of good irrigation infrastructure. The irrigation endowed PFS has achieved 90% performance, whereas the water-starved BFS and DFS could achieve only 65% performance of IFS. We conclude that agricultural policy must not only focus on potential interventions that are profitable but also consider what is acceptable to the farmer, considering synergies and trade-offs between competing resources at the farm level.