Fences have recently been recognized as one of the most prominent linear infrastructures on earth. As animals traverse fenced landscapes, they adjust movement behaviors to optimize resource access while minimizing energetic costs of coping with fences. Examining individual responses is key for connecting localized fence effects with population dynamics. We investigated the multi-scale effects of fencing on animal movements, space use, and survival of 61 pronghorn and 96 mule deer on a gradient of fence density in Wyoming, USA.
Taking advantage of the recently developed Barrier Behavior Analysis, we classified individual movement responses upon encountering fences (i.e. barrier behaviors). We adopted the reaction norm framework to jointly quantify individual plasticity and behavioral types of barrier behaviors, as well as behavior syndromes between barrier behaviors and animal space use. We also assessed whether barrier behaviors affect individual survival.
Our results highlighted a high level individual plasticity encompassing differences in the degree and the direction of barrier behaviors for both pronghorn and mule deer. Additionally, these individual differences were greater at higher fence densities. For mule deer, fence density determined the correlation between barrier behaviors and space use, and was negatively associated with individual survival. Yet, these relationships were not statistically significant for pronghorn.
By integrating approaches from movement ecology and behavioral ecology with the emerging field of fence ecology, this study provides new evidence that an extraordinarily widespread linear infrastructure uniquely impacts animals at the individual level. Managing landscape for lower fence densities may help prevent irreversible behavioral shifts for wide-ranging animals in fenced landscapes.

Fencing is a major anthropogenic feature affecting human relationships, ecological processes, and wildlife distributions and movements, but its impacts are difficult to quantify due to a widespread lack of spatial data. We created a fence model and compared outputs to a fence mapping approach using satellite imagery in two counties in southwest Montana, USA to advance fence data development for use in research and management. The model incorporated road, land cover, ownership, and grazing boundary spatial layers to predict fence locations. We validated the model using data collected on randomized road transects (n = 330). The model predicted 34,706.4 km of fences with a mean fence density of 0.93 km/km² and a maximum density of 14.9 km/km². We also digitized fences using Google Earth Pro in a random subset of our study area in survey townships (n = 50). The Google Earth approach showed greater agreement (K = 0.76) with known samples than the fence model (K = 0.56) yet was unable to map fences in forests and was significantly more time intensive. We also compared fence attributes by land ownership and land cover variables to assess factors that may influence fence specifications (e.g., wire heights) and types (e.g., number of barbed wires). Private lands were more likely to have fences with lower bottom wires and higher top wires than those on public lands with sample means at 22 cm and 26.4 cm, and 115.2 cm and 110.97, respectively. Both bottom wire means were well below recommended heights for ungulates navigating underneath fencing (≥ 46 cm), while top wire means were closer to the 107 cm maximum fence height recommendation. We found that both fence type and land ownership were correlated (χ² = 45.52, df = 5, p = 0.001) as well as fence type and land cover type (χ² = 140.73, df = 15, p = 0.001). We provide tools for estimating fence locations, and our novel fence type assessment demonstrates an opportunity for updated policy to encourage the adoption of "wildlife-friendlier" fencing standards to facilitate wildlife movement in the western U.S. while supporting rural livelihoods.

This dataset contains all the raw data sets, processing code, and analysis for reproducing and replicating the analysis for the article: Virtual fencing in remote boreal forests: performance of commercially available GPS collars for free-ranging cattle. In total there are 21 files included, from which '01_Analysis.html' and '01_Analysis.pdf' describes the final output of all analysis and includes the figures as published in the article. '01_Analysis.qmd' is a quarto markdown file (Quarto is a multi-language, next generation version of R Markdown from Posit, see https://quarto.org/) which makes it possible to rerun the analysis. This file is dependent on the other files and the original folder structure. The dependent files include spatial information from the GPS collars ('collars.csv' and 'collars_new.csv'), measures from the differential GPS ('dGPS.csv' and 'dGPS_new.csv'), observations from field personnel ('kobo_forms.csv'), environmental information (all '.tiff' files), and other supporting information. Furthermore, data pre processing is conducted in the R-script '02_preparation_data.R' creating two output files ('processed_data_mob.txt' and 'processed_data_stat.txt'). This script can be optionally sourced from '01_Analysis.qmd'. Article abstract: Background The use of virtual fencing in cattle farming is beneficial due to its flexibility, not fragmenting the landscape or restricting access like physical fences. Using GPS technology, virtual fence units emit an audible signal and a low-energy electric shock when crossing a predefined border. However, animal welfare concerns arise from potential stress and confusion caused by GPS errors. Especially in large remote grazing areas and complex terrains, where the performance of the GPS units can be affected by landscape structure, errors can lead to unnecessary shocks to the animals. This study aimed to explore factors affecting the GPS performance of commercially available virtual fence collars for cattle (NoFence©), both using static tests and mobile tests, i.e. when deployed on free-ranging cattle. Results The static tests revealed generally high fix success rates (% successful positioning attempts), and a lower success rate at four of 30 test locations was most likely due to a lack in GSM coverage. On average the GPS precision and accuracy errors were 3.3 m ±2.5 SD and 4.6 m ±3.2 SD, respectively. We found strong evidence that the GPS precision and accuracy errors were affected by the canopy cover, with increased errors under closed canopies. We also found evidence for an effect of the sky-view on the GPS performance, although at a lesser extent than canopy. The direction of the accuracy error in the cartesian plane was not uniform, but biased, depending on the aspect of the test locations. With an average of 10.8 m ±6.8 SD, the accuracy error of the mobile tests was more than double that of the static tests. Furthermore, we found evidence that more rugged landscapes resulted in higher GPS accuracy errors. However, the error was not affected by canopy cover, sky-view, or behaviors during the mobile tests. Conclusions This study showed that GPS performance can be negatively affected by landscape complexity, such as increased ruggedness and covered habitats, resulting in reduced virtual fence effectiveness and potential welfare concerns for cattle. These issues can be mitigated through proper pasture planning, such as avoiding rugged areas for the virtual fence border.

United States Imports: 3-Digit: JP: Wire Products and Fencing Grills data was reported at 1.642 USD mn in May 2018. This records a decrease from the previous number of 2.004 USD mn for Apr 2018. United States Imports: 3-Digit: JP: Wire Products and Fencing Grills data is updated monthly, averaging 3.655 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 15.635 USD mn in Feb 1996 and a record low of 0.963 USD mn in Jan 2017. United States Imports: 3-Digit: JP: Wire Products and Fencing Grills data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA094: Trade Statistics: Japan: Imports: Customs: SITC.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

Net-wire fencing built to confine livestock is common on rangelands in the Southwestern USA, yet the impacts of livestock fencing on wildlife are largely unknown. Many wildlife species cross beneath fences at defined crossing locations because they prefer to crawl underneath rather than jump over fences. Animals occasionally become entangled jumping or climbing over fences, leading to injury or death. More commonly, repeated crossings under net-wire fencing by large animals lead to fence damage, though the damage is often tolerated by landowners until the openings affect the ability to enclose livestock. The usage, placement, characteristics, and passage rates of fence crossings beneath net-wire fencing are poorly understood. We monitored 20 randomly selected fence crossings on net-wire livestock fencing across two study sites on rangelands in South Texas, USA, from April 2018–March 2019. We assessed characteristics of fence-crossing locations (openings beneath the fence created by animals to aid in crossing) and quantified crossing rates and probability of crossing by all species of animals via trail cameras. We documented 10,889 attempted crossing events, with 58% (n = 6,271) successful. Overall, 15 species of medium- and large-size mammals and turkey (Meleagris gallopavo) contributed to crossing events. Crossing locations received 3–4 crossing attempts per day on average, but the number of attempts and probability of successful crossing varied by location and fence condition. Probability of crossing attempts was most consistently influenced by opening size of the crossing and season; as crossing size (opening) increased, the probability of successful crossing significantly increased for all species. Peaks in crossing activity corresponded with species’ daily and seasonal movements and activity. Density and size of fence-crossing locations were dependent on fence maintenance and not associated with vegetation communities or habitat variables. However, crossing locations were often re-established in the same locations after fence repairs. This is one of the few studies to monitor how all animal species present interacted with net-wire livestock fencing in rangelands. Our results will help land managers understand the impact of net-wire livestock fencing on animal movement. Methods Fence description and condition We surveyed boundary net-wire fence lines at both sites to verify the presence of intact, maintained fences, with ≤7 cm between the bottom fence wire and the ground. We randomly selected a 9,146-m boundary fence at El Sauz and a 2,174-m boundary fence at Santa Rosa; fence lengths differed because of the configuration of the property boundaries. Boundary fences were selected over interior fences because they often form long, linear features with no openings (e.g., gates). Therefore, animals must go under or over the wire to pass beyond the fence. Both fence lines were standard net-wire livestock fences 1.25 m in height. Both fences had an unpaved 2-track road on both sides, with mesquite and huisache woodlands beyond the roads, except for the exterior side of the fence at Santa Rosa which was grassland. We drove a utility vehicle along target fence lines at each study site to identify and record fence-crossing locations. At each identified crossing location, we recorded the maximum height of the bottom wire (m), and width (m) of each opening. We conducted these surveys of fence-crossing locations during Autumn (October – November) 2017, 2018, and Spring (April – early June) 2018, 2019. We then calculated the opening size of each crossing (m2) as the maximum height multiplied by width. When fence crossings become large enough for livestock to pass through, a common practice at these study sites is to patch the hole by securing a panel of net-wire livestock fence over the opening to discourage further crossings. Therefore, we also recorded fence-crossing locations in relation to previous repairs or patched locations. Landscape features Landscape features can influence wildlife habitat use (Van Dorp and Opdam 1987, Thogmartin 2001, Zemanova et al. 2017), and thus may influence where animals choose to cross fences. We quantified woody cover at fence-crossing locations using a spatial pattern analysis in ArcGIS ArcMap 10.5.1 (ESRI©, Redlands, CA) FRAGSTATS 4.2 (McGarigal et al. 2012) based on high-resolution (1-m) aerial multispectral images from the National Agriculture Imagery Program (NAIP) for 2016. We first classified imagery into 4 land cover types: woody cover, herbaceous, bare ground, and water using unsupervised image classification in ERDAS Imagine 2018 (Hexagon Geospatial; Norcross, GA; Xie et al. 2008). We conducted an accuracy assessment with 200 random points per image until ≥85% accuracy was achieved (Jensen 2016, Pulighe et al. 2016). We created 30-m buffers at fence-crossing locations and at an equal number of random locations on the same fence line at both sites. We focused on woody cover, as the most common cover types were woody and herbaceous; there was no permanent water near the boundary fencing, and bare ground was sporadic and ephemeral. At El Sauz, random locations were adjusted to not overlap other known or random crossing buffers. This approach was not feasible on Santa Rosa because crossings were relatively abundant. We clipped the imagery to the extent of the buffers to quantify the amount and spatial structure of woody cover within buffer areas. We characterized woody cover using 6 landscape metrics (McGarigal et al. 2012): patch density (PD, number of woody patches/100 ha), percentage of the landscape in woody cover (PLAND %), the mean area of woody patches (AREA_MN), the Euclidean nearest-neighbor distance between woody patches (ENN, m), the aggregation index (AI, frequency which like patches appear side by side, %) and edge density (ED, edge length of woody cover patches per unit area, m/ha). Crossing-site usage To assess the usage of each crossing location at each study site, we randomly assigned 10 camera traps to fence crossings identified through the fence surveys (Reconyx© HyperFire HC500 or XR6 UltraFire, Reconyx, Holmen, WI; Moultrie© A-5 Gen2 MCG-12688 Moultrie feeders, Alabaster, AL). We fastened cameras onto 1.5-m metal t-posts at a mean height (±SE) above ground of 0.54 ± 0.02 m (range 0.43–0.63) at El Sauz, and 0.66 ± 0.03 m (range 0.40–0.80) at Santa Rosa. The mean distance (±SE) from the t-post to crossing was 3.00 ± 0.12 m (range 2.40–3.58) at El Sauz and 1.72 ± 0.15 m (range 1.04–2.80) at Santa Rosa. The boundary fence at Santa Rosa often had an unpaved 2-track road close to the fence and we could not place cameras on the road; thus, the distance between the crossing and the site of camera placement was shorter than for El Sauz. We placed the cameras higher up to angle down at the crossings to address the reduced distances between cameras and fence crossings. The cameras were focused on crossing locations where wildlife crawled underneath fencing. Depressions on the top wire of these fences were rare, so we did not assess jumps over the fence by deer or nilgai. We first deployed cameras in January 2018 as a pilot study to assess camera placement and photo quality. During the pilot study, on March 28, 2018, two fence crossings were patched with a panel of livestock fencing at El Sauz. In response, we kept cameras at the two patched locations and added cameras to two active, un-patched fence crossings. These two patched crossings (ID: EF24 & EF25) provided an opportunity to assess wildlife response to blocking of well-established fence crossings. Both patched crossings were monitored from April 2018–March 2019. We checked cameras every two weeks to ensure functionality as extreme heat greatly reduced battery life, and frequent rubbing of the cameras by cattle increased camera failure. We programmed cameras to take a 3-photograph burst with a 10-s delay (Moultrie) or 15-s delay between triggers (Reconyx), with high motion detector sensitivity. The minimum delay interval for the Moultrie cameras was 10-s with 1-s between photo bursts. A no delay setting would minimize missed crossing attempts, but our delay was sufficient due to the open visibility on the opposite side of the fence and limited occurrences of large groups (besides turkeys) passing through the fences. During the camera checks we also measured the height (m) and width (m) of each fence-crossing location to record any changes during the study. Data analysis We used a Kolmogorov–Smirnov test to compare the distributions of each landscape metric between known fence-crossing locations and random locations along the fence line, implemented via the R programming language (R Core Team 2013). Known crossings included any crossing location that was recorded during the four surveys. Multiple factors likely influence the distribution of fence crossings, and certain landscape features might promote clusters of fence crossings in areas. To understand whether fence crossings were randomly spaced or clustered across the fence lines we conducted a Wilcoxon signed rank test to compare distances between known crossings sites and distances between random sites along the fence. We classified the first two weeks (336 hrs) of photographs each month per camera, from April 2018–March 2019. We classified all animal events by species, time of day, date, and outcome of each attempted crossing event as successful or unsuccessful. A successful crossing event was an attempted crossing event where the 3-photo burst showed an animal passing under the fence or had at least half of the body through the fence crossing. We classified “attempted crossing events” as animals in close proximity to the crossings, either between the camera and crossing (about 3 m), or on the opposite side of the fence that approached or came into contact with the fence. Attempted crossing events

United States Imports: 3-Digit: Wire Products and Fencing Grills data was reported at 9.526 USD mn in May 2018. This records an increase from the previous number of 7.133 USD mn for Apr 2018. United States Imports: 3-Digit: Wire Products and Fencing Grills data is updated monthly, averaging 10.036 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 18.589 USD mn in Apr 2007 and a record low of 5.706 USD mn in Feb 2017. United States Imports: 3-Digit: Wire Products and Fencing Grills data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA097: Trade Statistics: Korea: Imports: Customs: SITC.

Indonesia Iron and Steel: Production: Casting of Iron and Steel: Fence of Non Aluminium Metal data was reported at 0.231 IDR bn in 2011. This records a decrease from the previous number of 0.360 IDR bn for 2006. Indonesia Iron and Steel: Production: Casting of Iron and Steel: Fence of Non Aluminium Metal data is updated yearly, averaging 0.296 IDR bn from Dec 2006 (Median) to 2011, with 2 observations. The data reached an all-time high of 0.360 IDR bn in 2006 and a record low of 0.231 IDR bn in 2011. Indonesia Iron and Steel: Production: Casting of Iron and Steel: Fence of Non Aluminium Metal data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Iron and Steel Sector – Table ID.WAA007: Iron and Steel Production: Casting of Iron and Steel.

United States - Producer Price Index by Commodity: Metals and Metal Products: Steel and Aluminum Fences, Gates (Not Wire), and Railings and Window Guards was 310.51100 Index Dec 1997=100 in February of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Metals and Metal Products: Steel and Aluminum Fences, Gates (Not Wire), and Railings and Window Guards reached a record high of 310.51100 in February of 2025 and a record low of 99.70000 in March of 1998. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Metals and Metal Products: Steel and Aluminum Fences, Gates (Not Wire), and Railings and Window Guards - last updated from the United States Federal Reserve on March of 2025.

China Import: Electric Fence Energizer data was reported at 0.790 USD th in Jun 2024. This records a decrease from the previous number of 4.547 USD th for May 2024. China Import: Electric Fence Energizer data is updated monthly, averaging 9.111 USD th from Jan 2003 (Median) to Jun 2024, with 250 observations. The data reached an all-time high of 191.418 USD th in Dec 2009 and a record low of 0.000 USD th in Feb 2018. China Import: Electric Fence Energizer data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s Electronic Sector – Table CN.RFB: Electronic Import.

Fire and grazing are common in grasslands world-wide to maintain grass cover and cattle production. The effects of fire, cattle grazing and riparian fencing efficacy on prairie stream ecology are not well characterized at catchment scales. We examined alterations to stream water quality and biology from patch-burn grazing (PBG) in tallgrass prairie during a five-year, replicated, catchment scale experiment that used a Before-After/Control-Impact (BACI) design and was analysed by mixed-effects models. Treatments included two patch-burned control catchments (fire but no grazers) and PBG in two riparian-fenced and two unfenced catchments. We assessed the effectiveness of riparian fencing for mitigating potential water quality impacts by monitoring water quality and riparian usage by cattle via Global Positioning System. Riparian fences effectively excluded cattle; however, in unfenced pastures, cattle aggregated along streams 10–20% of the grazing season. After initiation of PBG, we detected large increases in some nutrients, Escherichia coli, algal biomass, primary productivity and community respiration in all catchments with PBG. Some water quality variables, such as E. coli concentrations, recovered quickly after cattle were removed from pasture, which indicated resiliency. Riparian fencing moderately reduced the impacts to stream variables, indicating either overland flow and/or subsurface flow allowed nutrients and bacteria to enter the streams. Synthesis and applications. Patch-burn grazing is a measurable disturbance that can alter the ecological condition of streams. Riparian fencing lessened the degree of impact, yet some water quality variables still exceeded regional reference conditions. Managers will need to assess the costs of riparian fencing compared to the moderate benefits that fencing provides to water quality.

United States Imports: 3-Digit: MX: Wire Products and Fencing Grills data was reported at 22.803 USD mn in May 2018. This records an increase from the previous number of 19.742 USD mn for Apr 2018. United States Imports: 3-Digit: MX: Wire Products and Fencing Grills data is updated monthly, averaging 11.918 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 23.872 USD mn in May 2012 and a record low of 4.624 USD mn in Dec 1996. United States Imports: 3-Digit: MX: Wire Products and Fencing Grills data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA100: Trade Statistics: Mexico: Imports: Customs: SITC.

United States Exports: 3-Digit: UK: Wire Products and Fencing Grills data was reported at 1.285 USD mn in May 2018. This records an increase from the previous number of 0.751 USD mn for Apr 2018. United States Exports: 3-Digit: UK: Wire Products and Fencing Grills data is updated monthly, averaging 1.112 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 6.665 USD mn in Mar 2016 and a record low of 0.511 USD mn in Feb 2014. United States Exports: 3-Digit: UK: Wire Products and Fencing Grills data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA108: Trade Statistics: United Kingdom: Exports: FAS: SITC.

Indonesia Iron and Steel: Production: Pipes and Fittings of Iron and Steel: Fence of Non Aluminium Metal data was reported at 3.350 IDR bn in 2012. This records an increase from the previous number of 2.349 IDR bn for 2005. Indonesia Iron and Steel: Production: Pipes and Fittings of Iron and Steel: Fence of Non Aluminium Metal data is updated yearly, averaging 2.850 IDR bn from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 3.350 IDR bn in 2012 and a record low of 2.349 IDR bn in 2005. Indonesia Iron and Steel: Production: Pipes and Fittings of Iron and Steel: Fence of Non Aluminium Metal data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Iron and Steel Sector – Table ID.WAA005: Iron and Steel Production: Pipes and Fittings of Iron and Steel.

United States PPI: Mfg: WP: OW: MW: CS: PP: Sawn Wood Fence, Wood Lath & Contract data was reported at 213.544 Dec2003=100 in Jan 2025. This stayed constant from the previous number of 213.544 Dec2003=100 for Dec 2024. United States PPI: Mfg: WP: OW: MW: CS: PP: Sawn Wood Fence, Wood Lath & Contract data is updated monthly, averaging 117.000 Dec2003=100 from Dec 2003 (Median) to Jan 2025, with 225 observations. The data reached an all-time high of 213.544 Dec2003=100 in Jan 2025 and a record low of 88.700 Dec2003=100 in Jun 2014. United States PPI: Mfg: WP: OW: MW: CS: PP: Sawn Wood Fence, Wood Lath & Contract data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I081: Producer Price Index: by Industry: Manufacturing: Wood, Paper and Printing Activities.

Japan IME: Shi: LE: Housing: MR: Other: External Wall & Fence data was reported at 1,614.000 JPY in May 2018. This records a decrease from the previous number of 1,803.000 JPY for Apr 2018. Japan IME: Shi: LE: Housing: MR: Other: External Wall & Fence data is updated monthly, averaging 614.000 JPY from Jan 2005 (Median) to May 2018, with 161 observations. The data reached an all-time high of 31,861.000 JPY in May 2005 and a record low of 0.000 JPY in Jan 2018. Japan IME: Shi: LE: Housing: MR: Other: External Wall & Fence data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.H050: Income and Expenditure Survey: Include Agriculture, Forestry & Fisheries: By District: Shikoku.

United States Exports: 3-Digit: FR: Wire Products and Fencing Grills data was reported at 0.162 USD mn in May 2018. This records a decrease from the previous number of 0.457 USD mn for Apr 2018. United States Exports: 3-Digit: FR: Wire Products and Fencing Grills data is updated monthly, averaging 0.451 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 7.247 USD mn in Jun 2013 and a record low of 0.108 USD mn in Jul 2012. United States Exports: 3-Digit: FR: Wire Products and Fencing Grills data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA082: Trade Statistics: France: Exports: FAS: SITC.

Canada Domestic Export Val: HS: AIS: Wire, barbed, twisted hoop, single flat or twisted double of iron/steel, for fencing data was reported at 63.198 CAD th in Oct 2021. This records a decrease from the previous number of 103.789 CAD th for Sep 2021. Canada Domestic Export Val: HS: AIS: Wire, barbed, twisted hoop, single flat or twisted double of iron/steel, for fencing data is updated monthly, averaging 12.967 CAD th from Oct 2009 (Median) to Oct 2021, with 145 observations. The data reached an all-time high of 250.790 CAD th in Nov 2009 and a record low of 0.000 CAD th in Jul 2021. Canada Domestic Export Val: HS: AIS: Wire, barbed, twisted hoop, single flat or twisted double of iron/steel, for fencing data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.WA010: Domestic Exports Value: by Harmonized System 6 Digits (Discontinued).

Japan IME: Hoku: LE: Housing: MR: Other: External Wall & Fence data was reported at 434.000 JPY in May 2018. This records an increase from the previous number of 225.000 JPY for Apr 2018. Japan IME: Hoku: LE: Housing: MR: Other: External Wall & Fence data is updated monthly, averaging 725.000 JPY from Jan 2005 (Median) to May 2018, with 161 observations. The data reached an all-time high of 13,844.000 JPY in May 2015 and a record low of 0.000 JPY in Jan 2018. Japan IME: Hoku: LE: Housing: MR: Other: External Wall & Fence data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.H046: Income and Expenditure Survey: Include Agriculture, Forestry & Fisheries: By District: Hokuriku.

Canada Import Val: HS: AIS: Wire, barbed, twisted hoop, single flat or twisted double of iron/steel, f fencing data was reported at 305.516 CAD th in Jan 2025. This records a decrease from the previous number of 475.971 CAD th for Dec 2024. Canada Import Val: HS: AIS: Wire, barbed, twisted hoop, single flat or twisted double of iron/steel, f fencing data is updated monthly, averaging 208.628 CAD th from Jan 1988 (Median) to Jan 2025, with 445 observations. The data reached an all-time high of 1,808.580 CAD th in Oct 2008 and a record low of 4.104 CAD th in Dec 1989. Canada Import Val: HS: AIS: Wire, barbed, twisted hoop, single flat or twisted double of iron/steel, f fencing data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.WA006: Imports Value: by Harmonized System 6 Digits.