Electric Fence Research Articles

DESIGN AND IMPLEMENTATION OF SMART ELECTRIC FENCE BUILT ON SOLAR WITH AN AUTOMATIC IRRIGATION SYSTEM

The agricultural sector faces significant challenges due to crop damage caused by animals, resulting in substantial financial losses and maintenance efforts for farmers. To mitigate this issue, a smart electric fence is proposed, energized upon animal approach and deactivated upon their retreat. The system administers minimal shock pulses to immobilize animals harmlessly. Additionally, an automated irrigation system is integrated, activating the pump during dry soil conditions and deactivating it when soil moisture is sufficient. Solar energy powers the entire system. The prototype features infrared sensors distributed across the fence, detecting approaching animals. A high-frequency switching transformer generates shock pulses, controlled by an IC 555 configured in an astable mode. The Arduino Nano serves as the main processing unit, displaying soil moisture and motor status on an LCD and transmitting data via Wi-Fi to a smartphone app. The system aims to provide efficient and sustainable solutions to agricultural challenges.

Animal Detection based Smart Farming in Animal Repellent Using AI and Deep Learning

The combination of artificial intelligence (AI) and deep learning in agriculture has ushered in a new era in agriculture with new solutions designed to solve many problems. This paper presents an animal killing system for smart agriculture that uses artificial intelligence and deep learning to reduce the growing problem of animal damage. As the world population continues to grow, increasing food availability is important; Therefore, it is critical to protect crops from wild animals and pests. Deforestation due to livestock farming has become one of the largest human-wildlife conflicts due to human interference with habitats and deforestation. Wild animals can kill farmers working in the fields, causing major crop losses. Farmers suffered huge crop losses due to wild animals such as elephants, wild boars and deer attacking agriculture. Protecting crops from wild animals is one of the biggest concerns of today's farmers. There are many ways to solve this problem, both lethal (such as shooting and trapping) and non-lethal. (such as railings, pesticides, organic matter, netting or electric fencing). The sensor rotates around the lens and uses DCNN software to detect the target. If an animal is found, it sends a message to the Animal Repellent Module with information on the type of ultrasound that should be produced based on the animal. The development of drones with controlled flight control, as well as lightweight and powerful hyperspectral snapshot cameras that can be used to calculate crop biomass growth and fertilization status, responds to complex agricultural management strategies. KEYWORDS: Animal detection, VGG-Net, Bi-LSTM, convolutional neural network, activity recognition, video surveillance, wild animal monitoring, alert system.

Structural Optimization of an Unmanned Ground Vehicle as Part of a Robotic Grazing System Design

Unmanned ground vehicles (UGVs) have gained increased attention in different fields of application; therefore, their optimization requires special attention. Lowering the mass of a UGV is especially important to increase its autonomy, agility, and payload capacity and to reduce dynamic forces. This contribution deals with optimizing a UGV unit prototype that, when connected with similar units, forms a moving electric fence for animal grazing. Together, these units form a robotic system that is intended to solve the critical problem of lack of human capacity in herding and grazing. This approach employs topology optimization (TO) and finite element analysis (FEA) to lower the mass of a UGV unit and validate the design of its structural components. To our knowledge, no optimization of this type of UGV has been reported in the literature. Here, we present the results of a case study in which a set of four load cases served as a basis for the optimization of the UGV frame. Response surface analysis (RSA) was used to identify the worst load cases, while substructuring was used to allow for more detailed meshing of the frame portion that was subjected to TO. Thereby, we demonstrate that the prototype of the UGV unit can be built using standard parts and that TO and FEA can be efficiently used to optimize the load-carrying structure of such a specific vehicle.

Designing online learning algorithms for electric fence placement in dockless bike-sharing system utilizing limited random parking strategies

Dockless shared bicycles, parked and frequently misplaced throughout the city, infringe upon urban public spaces and incur public costs. To address this issue, existing electric fence technology has been employed; however, compelling users to park their bicycles in static electric fences with fixed locations undermines the convenience offered by the dockless bike-sharing system's flexible parking. In this study, we develop a novel, implementable online learning algorithm that dynamically updates the electric fence siting scheme based on users' parking needs. We introduce the concept of a dynamic no-parking area, termed a ‘limited random parking’ strategy, which significantly reduces the average walking distance for users to between 9% and 40% of the original distance. The dynamic electric fence introduced in this research enhances Shannon entropy reduction by 5–19% compared to static electric fences. Moreover, we discovered that deploying merely approximately 20% of the total electric fences as dynamic ones suffices to further regulate parking without notably extending users' average walking distance, while the remaining 80% can remain static. Additionally, the marginal benefit of Shannon entropy sharply declines once the number of electric fences reaches a certain threshold. Building on these findings, we also offer guidelines for determining the optimal number of specific dynamic electric fences, suggesting that establishing approximately 500 dynamic electric fences in Nanjing represents an optimal strategy.

Farmland Intrusion Detection using Internet of Things and Computer Vision Techniques

Farmland security in Nigeria is still a major challenge and existing methods such as building brick fences around the farmland, installing electric fences, setting up deterrent plants with spikey branches or those that have displeasing scents are no longer suitable for farmland security. This paper presents an IoT based farmland intrusion detection model using sensors and computer vision techniques. Passive Infrared (PIR) sensors and camera sensors are mounted in strategic positions on the farm. The PIR sensor senses motion by the radiation of body heat and sends a message to the raspberry pi to trigger the camera to take a picture of the scene. An improved Faster Region Based Convolutional Neural Network is developed and used for object detection and One-shot learning algorithm for face recognition in the case of a person. At the end of the detection and recognition stage, details of intrusion are sent to the farm owner through text message and email notification. The raspberry pi also turns on the wade off system to divert an intruding animal away. The model achieved an improved accuracy of 92.5% compared to previous methods and effectively controlled illegal entry into a farmland.

Social-ecological predictors of spotted hyena navigation through a shared landscape.

Human-wildlife interactions are increasing in severity due to climate change and proliferating urbanization. Regions where human infrastructure and activity are rapidly densifying or newly appearing constitute novel environments in which wildlife must learn to coexist with people, thereby serving as ideal case studies with which to infer future human-wildlife interactions in shared landscapes. As a widely reviled and behaviorally plastic apex predator, the spotted hyena (Crocuta crocuta) is a model species for understanding how large carnivores navigate these human-caused 'landscapes of fear' in a changing world. Using high-resolution GPS collar data, we applied resource selection functions and step selection functions to assess spotted hyena landscape navigation and fine-scale movement decisions in relation to social-ecological features in a rapidly developing region comprising two protected areas: Lake Nakuru National Park and Soysambu Conservancy, Kenya. We then used camera trap imagery and Barrier Behavior Analysis (BaBA) to further examine hyena interactions with barriers. Our results show that environmental factors, linear infrastructure, human-carnivore conflict hotspots, and human tolerance were all important predictors for landscape-scale resource selection by hyenas, while human experience elements were less important for fine-scale hyena movement decisions. Hyena selection for these characteristics also changed seasonally and across land management types. Camera traps documented an exceptionally high number of individual spotted hyenas (234) approaching the national park fence at 16 sites during the study period, and BaBA results suggested that hyenas perceive protected area boundaries' semi-permeable electric fences as risky but may cross them out of necessity. Our findings highlight that the ability of carnivores to flexibly respond within human-caused landscapes of fear may be expressed differently depending on context, scale, and climatic factors. These results also point to the need to incorporate societal factors into multiscale analyses of wildlife movement to effectively plan for human-wildlife coexistence.

No effects of macroconsumer exclusion on macroinvertebrate functional composition in Brazilian karst streams along a gradient of riparian forest loss

AbstractDegradation and loss of riparian vegetation are some of the main drivers of change in the trophic interactions among aquatic macroconsumers. These changes can often have major consequences for biodiversity and stream ecosystem functioning. Yet, many aspects of food‐web dynamics are still not well understood, including how food webs among tropical streams respond to riparian vegetation loss. We examined the role of macroconsumers on community structure of functional feeding groups of benthic macroinvertebrates along a forest cover gradient within the Bodoquena Plateau, Brazil. We compared the effect of excluding macroconsumers (using electric fences) on functional feeding groups to those in controls within seven karst streams along a gradient of riparian vegetation cover. Aquatic insect and gastropod communities were dominated by groups of collector‐gatherers, scrapers, and predators across all sites. The exclusion of macroconsumers did not lead to an increase in the abundance of macroinvertebrates and was not affected by the forest cover during the experiment. These findings demonstrated that the experimental exclusion of macroconsumers resulted in a lack of or negligible effect on functional feeding groups along the gradient of forest cover. It is important to consider the specificity of trophic relationships to fully understand the effects of vegetation loss on streams where complex groundwater hydrology can also affect food webs such as in karst terrain.Abstract in Portuguese is available with online material.

Farmer Friendly Solar Based Electric Fence for Rural Agriculture

Agriculture provides all the people’s food needsas well as a variety of raw materials for industry. However, there is a significant loss of crops due to animal intrusion in agricultural lands. Wild animals will wreak havoc on crops. As a result, it’s critical to keep an eye out for animals in the region. Then various devices should be activated to repel the dangerous animals. Operational amplifier circuits are put in place primarily for the purpose of detecting animal intrusion into the farm. The completed protection system is responsible for providing early warning of potential wild animal intrusion and harm. Key Words: agriculture operational amplifier circuits farm

32 Amazing Grazing webinar and workshop series improves producers understanding of pasture ecology

Abstract Grassland stewards are seeking a better understanding of the complex interactions occurring in their pastures. Information is readily available to assist producers but each farm provides its own unique challenges and opportunities. A diverse scope of knowledge and skills are required to build resilient grazing systems. To address this need Amazing Grazing conducted a webinar series (April 2023) followed by on-farm workshops in July/August 2023. Webinars were held on consecutive Wednesday evenings with 237 average number of registered attendees and 78 average number of actual participants. Topics covered during the first webinar were pasture ecology basics, plant growth principles, livestock forage demand and grazing management principles. The following week covered pasture layout and design, pasture renovation techniques and pasture soil health concepts. The final evening featured topics on electric fence basics, grazing systems for horses and small ruminants and a synopsis. NC Extension Livestock Agents assisted in the sight selection and execution of workshops (n = 8) that were held on working farms. These events provide the opportunity for hands-on instruction to demonstrate the concepts covered in the webinar series. Total workshop registration was 196, with 148 evaluations completed. Participants were 72% farmers, 1% extension agents, 10% Soil and Water Conservation District Employee, 7% NRCS employee and 10% other. The total number of pasture hectares grazed by participants was 3,217 with 1,257 ha as hay. Livestock inventories for attendees were 4,323 beef cattle, 1,366 sheep, 455 goats and 51 horses. Attendees were asked about their pre- and post- workshop knowledge (1 = Very Low; 5 = Very High) of topics presented. Participants indicated a knowledge gain (average pre = 2.78 and post = 3.91) in Concepts of Pastureland Ecology, Adaptive Management, Managing Nutrient Distribution, Pasture Renovation, Temporary Electric Fence, Troubleshooting Electric Fence and Pasture Plant Identification. Workshop impact was determined by asking attendees their post workshop intentions regarding management practices (1 = No, 2 = Maybe, 3 = Yes, 4 = Already Doing). Attendees plan to spending more time walking pasture to determine plant condition, learn more about soil, plant, animal and human interactions, increase use of temporary electric fence and adopt an adaptive management style (average score = 3.91). Continued workshop participation indicates the demand for hands-on education in these areas and that the on-farm format is an effective teaching environment.

31 Pastureland Ecology 1 provides hands-on training in grazing management for USDA-NRCS professionals

Abstract Pastureland Ecology 1 is a 2-wk continuing education course taught by cooperating faculty at NC State University for USDA-NRCS Staff. The course was founded in the early 1990s and has been taught 23 times to over 500 participants from all 50 states. Participants primarily include grazing specialists, soil conservationists, and district conservationists. Instruction is split between classroom lectures and hands-on experience with grazing management. Lecture topics include; introduction to pasture ecology, principles of plant growth, determining animal forage needs, pasture design, principles of soil health, principles of grazing management, and grazing management systems for small ruminants, horses and cattle. Small groups of students work with groups of goats, sheep, horses, and beef cattle throughout the course, learning concepts of forage allocation, temporary electric fence, and adaptive management. Popular demonstrations developed over the years include; the reel race, dung beetle buckets, burying underwear to demonstrate soil health, exploring root diversity, and troubleshooting electric fence. Farm tours are conducted to show how concepts being taught are applied on commercial farms. In 2023 there were 29 students from 13 states including VA (4), MD (4), PA (3), OK (4), MI (1), UT (2), TN (3), WV (1), MA (1), OH (1), NM (1), WA (3), and RI (1). Participants had 8.4 ± 1.39 (AVG ± SEM) years of experience with NRCS with a range of 1 to 27 yr. Students self-assessed their understanding of major concepts before and after the course. On a 1 to 5 scale students increased their understanding by an average of 1.05 ± 0.129, averaged over all topics. Topics more familiar to participants had less increase including; Soil Health (0.38), Pasture Condition Evaluation (0.51), and Grazing Management for Beef Cattle (0.79). Less familiar topics had greater increases including Grazing Management for Horses (1.27) and Small Ruminants (1.27), and Temporary Electric Fence (1.43). Pastureland Ecology 1 remains a critical part of the Amazing Grazing Program at NC State University, impacting pasture-based livestock producers across the country. This educational model has proven to be an effective way of teaching the principles of pastureland ecology.

A Multiscale Approach for Free-Float Bike-Sharing Electronic Fence Location Planning: A Case Study of Shenzhen City

As an emerging technological means for managing free-float bike-sharing parking, electronic fences have attracted increasing attention in major cities as a solution to the challenges posed by disorderly parking of free-float bikes. Existing research has predominantly focused on employing clustering methods from the perspectives of free-float bike-sharing companies and users to plan and deploy electronic fences. However, the results often deviate significantly from the actual phenomenon. Therefore, scientific location selection is particularly important to fully harness the effectiveness of electronic fences. This paper proposes a multiscale clustering method based on free-float bike-sharing parking features to determine the optimal locations for electronic fences. A multiobjective mixed-integer programming model is established to address the location planning problem of electronic fences, determining the planning positions, quantities, and areas of electronic fences. A case study is conducted using a local area free-float bike-sharing dataset from Shenzhen city to validate the effectiveness of the proposed method. Comparative results with traditional approaches solely relying on K-means or DBSCAN methods demonstrate that the proposed approach achieves efficient location selection, through multiscale fusion site selection in the study area of 1.5∗1 km, and only 25 electronic fences need to be planned and deployed, covering a total area of 1691.88 square meters, which can provide rational placement solutions and better utilize the effectiveness of electronic fences. This method can thus offer decision-making support for the planning and location selection of electronic fences in free-float bike-sharing systems.

A low‐cost Raspberry Pi based time domain reflectometer for fault detection in electric fences

AbstractElectric fences used to create protected areas (PAs) are prone to faults that affect their operation. The conventional method of measuring the voltage of the fence periodically to detect faults and walking along the fence to locate the faults is inefficient and time consuming. This paper presents a low‐cost Raspberry Pi time domain reflectometer (TDR) for fault detection and localisation in electric fences. The system is designed using cheap off‐the‐shelf components. It uses time domain reflectometry to detect hard (open and short circuit) faults in electric fences. Time domain reflectometry is a method of detecting and locating faults in electrical cables. The Raspberry Pi TDR is evaluated and it has successfully detected and located open circuit and short circuit faults in electric fences with a mean absolute error of 1.52 m. The Raspberry Pi TDR offers the potential to remotely monitor electric fences autonomously, hence improving their effectiveness.

Dynamic repositioning in bike-sharing systems with uncertain demand: An improved rolling horizon framework

The bike-sharing system (BSS) serves as a vital shared mobility mechanism, providing first- and last-mile services in urban transportation. However, one ongoing challenge in the BSS is the spatiotemporal imbalance caused by fluctuating user demand. To address this issue, this paper studies a dynamic bike repositioning problem under scenario-based demand to achieve a balance in inventory levels for electronic fences. Specifically, a multi-period two-stage stochastic model is proposed to make the trade-off between operational costs and service quality. The model incorporates a vehicle no-return (VNR) strategy, which allows vehicles to remain at the last station at the end of each period, awaiting the next repositioning task. To solve the model, an improved rolling horizon framework is introduced to effectively handle the temporal dimension complexity of the multi-period problem. Then a customized hybrid heuristic algorithm based on parallel genetic algorithm and variable neighborhood search is developed to obtain high-quality solutions. To validate the effectiveness and applicability of the proposed method, small-scale and practical-scale numerical experiments are specifically conducted based on real-world BSS data from Beijing, China. The results demonstrate the proposed method (i) creates a substantial reduction in the number of unmet demands when multiple scenarios potentially occur compared to the determined model, (ii) yields an average reduction of 29.04% in operational costs compared to the scheme without the VNR strategy, (iii) overcomes the short-sightedness inherent in the conventional single-period rolling horizon approach, exhibiting closer to optimal performance, and (iv) is superior to baseline approaches in terms of solution quality and efficiency.

No benefit in using rubber‐coated wire to counter loss of voltage due to tall grass in large carnivore deterring fences

Electric wire enclosures to protect livestock represent one intervention to mitigate negative impacts of large carnivores. However, appropriate fence construction and voltage level in the live wires are crucial to deter carnivores. To ensure that the voltage level remains sufficiently high, maintenances and clearing the wires from tall or growing vegetation is regularly needed. In this study, we investigate the performance of a fence wire coated with conductive rubber, claimed by the supplier not to short circuit and drop in voltage due to contact with e.g. ground and vegetation. We examine the voltage of the rubber‐coated wire during contact with soil, ground, and wet vegetation and observed a voltage loss comparable to that of the standard metal fence wire. Our results imply that the rubber‐coated wire does not perform better than a conventional metal fence wire when in contact with growing grass and other elements that usually short circuit an electric fence.

Socioeconomic Sustainability of Chena Cultivation in the Forested Land of Hambantota District, Sri Lanka

Chena cultivation is one of the major agriculture systems and extensively practiced in rural areas of low country dry zone as their main livelihood. Generally, a slash-and-burn method is used to clear the land in the forested area where the land is prepared before the North-East monsoon starts. Short-duration, comparatively drought resistant crops such as grains, legumes, oil crops, and vegetables are commonly grown in Chena. However, scientific studies on Chena cultivation to assess socioeconomic status were limited in Sri Lanka. Therefore, this study attempted to assess the socioeconomic status of Chena farmers in the close proximity of forest lands in Southern part of Sri Lanka. Chena cultivation areas at Tissamaharama (8 Grama Niladhari divisions namely Gotabhayapura, Yodakandiya, Rathnelumwalayaya, Uddakandara, Viharamahadevipura, Ranakeliya, Wijayapura and Joolpallama) and Lunugamwehera (7 Grama Niladhari divisions namely Udamaththala, Padawgama, Pahalamaththala, Rabukwewa, new town-Lunugamwehera, new town-Weerawila and Keerthipura) Divisional Secretariats in Hambantota district was studied. 40 Chena farmers were purposively selected for the study. Primary data were collected from the farmers by using pre-tested structured questionnaire, personal interviews and field observations. Descriptive statistics and correlation tests were employed to analyse the data. Results revealed that farmers practiced four types of Chena such as Navadali Hena (clearing an untouched forest area), Ath Danduwa Hena (regrowth and cut), Mukulan Hena (consists of medium- sized trees) and Hen Kanaththa (land exist for survival). 98% of farmers are fully involved in chena cultivation in the study area. 32.5%, 22.5%, 20%, 12.5%, and 7.5% of farmers had 11-20, 21-30, 0-10, 31-40, and 41-50 years of experience, respectively in Chena cultivation. The result also revealed that 80% of the farmers were not a member of farmer`s societies and other organizations. A small number of farmers had received some relief from government such as fertilizer, agricultural extension services, 50% of subsidy seed, and 50% of subsidy loan. Chena farmers earned more than Rs.500,000.00 whereas the average income was Rs. 802,392.16 per annum. Further, 63% of the cost was contributed by labour followed next to land preparation (16%) and agrochemical (13%). Sweet melon, green gram, long beans, and tomato were recorded as major cultivated crops. Further, Chena areas were highly problematic by wildlife and cattle damage (87.5%), marketing issues (67.5%), legal (37.5%), drought (30%), and pest and diseases (27.5%). However, around 74% of respondents were willing to continue the Chena cultivation, because they have no other alternative livelihoods in this area. On the other hand, the majority (54%) were neutral or dissatisfied about their situation because they did not have land ownership. Introducing a registration procedure for land ownership, constructing the electrical fence to avoid wildlife damage, establishing a wildlife management unit to protect the wildlife habitat, and planning a proper irrigation method to secure the water supply throughout the year were the major suggestions to improve farming at a sustainable level of production and consumption.
 Keywords: Forest land use, Chena cultivation, Dry Zone, Hambantota

An internet of things based smart agriculture monitoring system using convolution neural network algorithm

Farming is a crucial vocation for survival on this planet because it meets the majority of people's necessities to live. However, as technology developed and the Internet of Things was created, automation (smarter technologies) began to replace old approaches, leading to a broad improvement in all fields. Currently in an automated condition where newer, smarter technologies are being upgraded daily throughout a wide range of industries, including smart homes, waste management, automobiles, industries, farming, health, grids, and more. Farmers go through significant losses as a result of the regular crop destruction caused by local animals like buffaloes, cows, goats, elephants, and others. To protect their fields, farmers have been using animal traps or electric fences. Both animals and humans perish as a result of these countless deaths. Many individuals are giving up farming because of the serious harm that animals inflict on crops. The systems now in use make it challenging to identify the animal species. Consequently, animal detection is made simple and effective by employing the Artificial Intelligence based Convolution Neural Network method. The concept of playing animal-specific sounds is by far the most accurate execution. Rotating cameras are put to good use. The percentage of animals detected by this technique has grown from 55% to 79%.

Examining human-wildlife conflict and management strategies in Indian protected areas: evidence from Jim Corbett Tiger Reserve

ABSTRACT The study examines the trends and patterns of human-wildlife conflict and explores stakeholders’ perceptions toward management strategies in the Jim Corbett Tiger Reserve, India. The study used a mixed “methods” research approach. Human-wildlife conflict quantitative data was analyzed using a trend line approach. Grounded Theory and thematic analysis were used to analyze local households (n = 150), park employees (n = 20), and eco-development committees (n = 10) interviews content. The results revealed a significant decline in human casualties, cattle depredation, and crop damage, possibly attributed to strategies like awareness programs, electric fencing, and alternative livelihoods. The study found no significant evidence of retaliatory wildlife killing by locals. Although human-wildlife conflict has declined significantly due to existing management strategies, the thematic analysis highlights serious challenges, including inadequate funding, insufficient compensation, and limited local participation in alternative livelihoods. These challenges need to be addressed urgently for the sustainable management of conflict.

Stress indicators in dairy cows adapting to virtual fencing.

Virtual fencing (VF) enables livestock grazing without physical fences by conditioning animals to a virtual boundary delimited with an audio tone (AT) and an electric pulse (EP). The present study followed the adaptation process of lactating dairy cows to a VF system with changing virtual boundaries and investigated its impact on animal welfare. Twenty cows were divided into stratified groups (2× VF; 2× electric fencing, EF) of five individuals. Each group grazed half-days in a separate EF paddock of comparable size during 3d of acclimation (P0), followed by 21, 14, 14, and 7d of experimental treatment (P1 to P4). At the start of the trial, all cows were equipped with an IceQube pedometer (Peacock Technology Ltd, Stirling, UK) and a VF collar (Nofence AS, Batnfjordsøra, Norway). During P0, cows were accustomed to their first paddock with a deactivated virtual boundary and wearing the sensors. In P1 to P4, an active virtual boundary for the VF groups, and a second EF for the EF groups was set up parallel to an outer EF within their paddock. Throughout the trial, the sensors continuously tracked cow positions and activity behavior at 15-min intervals. From P1 onwards, the VF collars additionally recorded each AT and EP per cow with a georeferenced time stamp. During P0 to P4, daily feed intake, body weight, and milk yield were recorded in the barn. A total of 26 milk samples were collected per cow to determine milk cortisol levels. Behavioral observations were conducted for 2h on day 23 to record agonistic behaviors, vocalizations, and excretions. The total number of stimuli per cow ranged from 37 to 225 ATs (mean ± SD: 1.9 ± 3.3 per day) and 3 to 11 EPs (mean ± SD: 0.1 ± 0.7 per day) throughout the trial. The maximum number of EPs per day was 8 for an individual cow and occurred once on D1. Mean EP/AT decreased by 55% during the first three half-days of grazing and with each paddock change from 0.2 EP/AT in week 1 to 0.03, 0.02, and 0 EP/AT in weeks 4, 6, and 8, respectively. Linear and generalized mixed effects models revealed that milk yield and cortisol, feed intake, body weight, and activity and lying behavior did not significantly differ between VF and EF groups. A higher number of agonistic behaviors were observed in the VF groups when the VF system was activated. However, due to the short observation periods only few contacts were observed in total. Overall, all cows adapted to the VF system without evidence of lasting adverse effects on animal welfare.

Comparative Study on Wildlife Impact in Electric Fenced and Unfenced Communities in Dungmin Geog, Pemagatshel, Bhutan

Human wildlife conflicts are one of the major challenges for wildlife conservation throughout the globe, and electric fencing (EF) has been used as one of the means to address those challenges. However, the comprehensive study to assess its effectiveness is lacking in many parts. Thus, this study attempted to understand the impact on crops and crop guarding time before and after fencing in communities having both fenced and unfenced households in three Chiwogs under Pemagatshel district. The data were collected through total enumeration using semi-structured interviews from 80 households. Wilcoxon’s signed-rank test showed that the electric fence (EF) has a significant difference (p=0.00, M=50) to farmers in reducing their common crop loss to wildlife and protected staple crop damages (maize). However, bitter buckwheat and radish continued attacking by barking deer. EF reduced both day and night guarding time significantly to all common crops. On the contrary, the unfenced farmers have suffered from increasing pressure from the wildlife after the establishment of EF in their community. Maize and potato damage have significantly increased after EF. There was a significant difference in maize and potato's day and night guarding time before and after EF. Only night guarding time has a significant difference between sweet buckwheat and bitter buckwheat before EF and after EF. Therefore, it emphasizes the need to cover the whole community by EF promoting crop production and reducing crop guarding against wildlife to avert similar issues in other communities.

Predator-friendly ranching, use of electric fences, and creole cattle in the Colombian savannas

Livestock ranching overlaps extensively with jaguar and puma distribution in the Casanare Department of east-central Colombia. Conservation programs prioritize large carnivores in the region. Ranchers retaliate by killing them in response to economic losses or fear related to their presence. Reducing retaliatory hunting is a top priority in the conservation of feline species. Predation mitigation methods (PMMs) are necessary to reduce depredation and increase tolerance for large felines. In a prospective cohort study, 16 ranches between 2017 and 2019 used electric fences (n = 14) and introduced creole cattle (n = 2) as PMMs. There was a significant difference in the risk and odds ratios between the control and treatment groups. Livestock depredation was 14.78 times higher outside PMM areas (OR, 14.78; RR, 0.069; p < 0.001) than inside such areas. The losses caused by depredation were much higher than the investments made in PMMs.