Proceedings brand creation for a prescribed fire culture – utilizing key social media parameters. Lars Coleman*1, J. Kelly Hoffman1, Thomas McDaniel1, R. Patrick Bixler2, Urs P. Kreuter1, Morgan Russell3

Huisache (Acacia farnesiana [L.] Willd.), a pest tree species on rangelands throughout south Texas, resists control via herbicide, fire, or mechanical methods through vigorous resprouting and prolific seed production. Until now, however, it has been unknown when huisache seedlings develop the ability to resprout. We determined the age at which huisache seedlings’ cotyledon latent meristems are buried below the soil surface and plants attain resprouting ability. First, we grew 120 seedlings in the glasshouse and examined 10 every other month for 24 months to measure height of cotyledon latent meristem above (or below) soil level. Second, we grew 240 seedlings in the glasshouse, and cut 10 every month at the soil surface to determine resprouting ability. Seedling resprouting began at 6 months and increased to over 50% at 20 months.  Seedling cotyledon latent meristem height had a quadratic relationship with time, with average cotyledon latent meristem height at or below soil level at 22 months of age.  Seedling cotyledon latent meristems began to become buried at 7 months, with over 50% becoming buried at 21 months of age. We suggest that attempts at controlling huisache through top-removal methods might result in over 50% of plants resprouting when as young as 20 months of age.

ESTIMATION OF AVAILABLE FORAGE IN ASHE JUNIPER.

Ashe juniper (Juniperus ashei) occupies approximately 8 million acres of Texas rangeland and is one of three native junipers in the state. Considered an invasive, countless resources have been expended in efforts to manage and control its expansion. One biological method available is goat browsing. There is a need to better quantify available forage in junipers in order to inform goat stocking rate decisions. Our objective was to compare two methods of determining forage content in Ashe juniper: 1) allometric measurements (height, width, basal diameter) and 2) terrestrial Light Detection and Ranging (LiDar) scans. Nine Ashe juniper trees were selected at the Texas A&M Sonora Research Station; three < 0.9 m (small), three 0.9 to 1.8 m (medium) and three > 1.8 m (large). LiDar point clouds were collected prior to harvesting. Trees were hand cut and sorted into standard timelag fuel categories. Available forage was considered to be plant material (needles and stems) within the 1-hour category and that were less than 1.8 m from ground level. All harvested plant material was dried to a constant weight at 60o C. Subsets of the 1-hour category material was separated by hand into needle (~70%) and stem (~30%). Relationships between allometric measurements and area, volume, or weight were determined by linear regression. Available dry matter forage was 0.27± 0.14, 2.40 ± 1.17, and 13.02 ± 3.36 kgs for the small, medium, and large trees respectively. Basal diameter was the most effective single predictor of available forage (y = 0.8009x – 2.2757; R2 = 0.99; SE = 0.76; P < 0.01). LiDar prediction of available forage was also effective (y = 0.0026x+ 0.385; R2 = 0.95; SE = 1.57; P < 0.01). Data obtained by either method can be used to help inform stocking rate decisions for goats in pastures with Ashe juniper.

IMPROVING THE SEED GERMINATION OF LITTLE BLUESTEM WITH SELECTION

Rapid seed germination is an important characteristic when it comes to plant stand establishment under variable environmental conditions.  This research was designed to improve the seed germination of six experimental Syn-0 lines of little bluestem [Schizachyrium scoparium (Michx.) Nash].  Two cycle of recurrent selection were used to develop Syn-1 and Syn-2 lines from six experimental Syn-0 lines.  Cycle 1 consisted of germinating 3,500 open-pollinated seeds of each Syn-0 line in water of –0.8 MPa potential for seven days.  All seeds that germinated in seven days (approximately 250 for each of the six lines) were selected to create six Syn-1 lines.  Cycle 2 selection was similar to Cycle 1, except that Syn-1 lines were used to create six Syn-2 lines.  To test the effects of selection, four 50-sessile spikelet samples of the 18 lines were germinated in water of –0.8 MPa potential for seven days for seeds harvested in 2015 and 2016.  Germination of Syn-1 seeds was 3.7% higher than Syn-0 seeds, and Syn-2 seeds were 16.2% higher than Syn-1 seeds.  Selection for improved seed germination at a low water potential simultaneously increased the speed of seed germination as well as 50-sessile spikelet weight.  Thus, it was possible to increase the seed germination of the six little bluestem lines at the water potential used with recurrent selection with the added benefits of increased speed of seed germination and increased 50-sessile spikelet weight.

YEAR-TO-YEAR VARIATION IN POPULATION DENSITY OF DELPHINIUM ANDERSONII

LITTLE LOVE FOR FRACKING AHEAD OF EXPLORATORY DRILLING IN SOUTH AFRICAN GRASSLANDS. Devan A. McGranahan*¹, Kevin P. Kirkman²; ¹North Dakota State University, Fargo, ND, ²University of KwaZulu-Natal, Pietermaritzburg, South Africa

Applications for exploratory shale gas development via hydraulic fracturing, or fracking, have raised concern about energy development impacts to South African rangeland. Initially, fracking interest was focused in the arid, open Karoo, but applications for exploration have recently been handled for the grasslands of KwaZulu-Natal, a populous, agricultural province with high cultural, ecological, and economic diversity. We conducted four focus groups and an online survey to determine how residents of South Africa’s grassland districts perceived fracking. Focus group participants were unanimous in their opposition, primarily citing concerns over degraded water quality and rural way-of-life. Survey data collection is ongoing, but preliminary analysis indicates consistency with focus group responses. When asked which provinces might be directly affected by fracking, KwaZulu-Natal was ranked behind Northern Cape, Western Cape, and Eastern Cape, reflecting an awareness bias towards Karoo projects. The most frequently identified concerns regarding Agriculture and Natural Resources were Reduced quality of water, Negative impacts to ecosystems and natural biodiversity, Reduced quantity of water, and Pollution hazards. The most frequent concerns regarding Social, Cultural, and Local Community issues were Impacts to human health, Visual/aesthetic degradation of tourism areas, Degradation of local infrastructure, and Physical degradation of tourism sites.The majority of respondents were pessimistic about potential benefits of fracking to South Africa’s domestic energy supply and electricity production. The majority of respondents did not agree fracking would reduce negative impacts of coal mining or create jobs. Survey respondents showed strong pessimism about government’s preparedness for fracking and agreed fracking created opportunity for corruption. The majority of respondents agreed they would consider fracking when voting, and identified a need for more research on fracking in South Africa. Expressed research needs focused heavily on environmental impacts, especially water, in addition to the welfare of local citizens and their communities.  

WILD PIG DAMAGE ASSESMENT APP FOR APPLE AND ANDROID DEVICES. John M. Harper*¹, Roger Baldwin², Shane Feirer³, Elizabeth Hiroyasu⁴; ¹University of California, Ukiah, CA, ²University of California, Davis, CA, ³University of California, Hopland, CA, ⁴University of California, Santa Barbara, Santa Barbara, CA

ABSTRACT

The geographical extent of wild pig damage on rangeland in California and elsewhere is currently unknown, making it difficult to mitigate and manage losses, and estimate the economic impact on private landowners. Capturing the geographical extent of wild pig damage would allow land managers to more effectively mitigate wild pig damage by allowing identification of specific areas that are conducive to wild pigs.

Our team decided to take an innovative risk to capturing the data needed. We believed that through the development of a mobile app for use on Android and Apple devices (smartphones, tablets, etc.) that land managers could quickly assess the type, nature, and extent of wild pig damage occurring on their properties. The app allows the capacity to reach a variety of different types of land managers, including a diverse set of natural resource managers and agricultural producers who are affected by wild pig damage on their property. Results from this project could revolutionize wild pig management by allowing landowners to identify and target control efforts in habitats heavily used by pigs, or by allowing landowners to alter habitats to make them less suitable for this invasive species

Our team can use the data as a basis to estimate economic losses and impacts on natural resources on private or public property. The data is used to develop materials like newsletters, trade magazine articles, public brochures, presentation materials, and a website for land managers to learn more about impacts of wild pigs. Results create tools for managers to assess the relative costs and benefits of control actions for wild pig management.

The app was launched in September of 2016, based on a prior survey (See http://ucanr.edu/survey/survey.cfm?surveynumber=16522). It is available at the ITunes App Store and the Google Play site by searching for the term wild pig damage. It is free.

COMPARING THE EFFECTIVENESS OF OBJECT-BASED VERSUS SPECTRAL-BASED CLASSIFICATION IN DISTINGUISHING VEGETATION USING VERY HIGH-RESOLUTION IMAGERY ACQUIRED FROM SUAS (DRONES). . Steve Petersen, Teresa Gomez*, Ryan Jensen, Grayson Morgan; Brigham Young University, Provo, UT

Remote sensing has long been used to monitor vegetation health and to detect change in rangeland ecosystems. Recently, small unmanned aerial systems (sUAS) have provided a tremendous improvement in image resolution and the ability to discriminate surface characteristics.  The purpose of this research is to compare the effectiveness of object-based versus spectral-based classification in distinguishing vegetation (species, total cover), percent bare ground, litter, and rock using very high resolution imagery acquired from sUAS (drones). Images were obtained from sagebrush and annual grasslands in central Nevada (west of Elko). Flight missions were flown 100ft above ground level using automated flight paths, and individual images were processed into orthomosaics using Pix4D software. Features were classified using either spectral (supervised, maximum likelihood) classification or with eCognition (object based classification). Ground-based measurements were collected in the field to compare rangeland structure with output from either classification technique. Results indicate that very high resolution imagery can be effectively used to assess rangeland ecosystems that can aid in rangeland assessment and monitoring.

SCIENCE WITH A SELFIE STICK: PLANT BIOMASS ESTIMATION USING SMARTPHONE BASED &LSQUO;STRUCTURE FROM MOTION&RSQUO; PHOTOGRAMMETRY. Jonathan J. Maynard*¹, Jason W. Karl²; ¹USDA-ARS, Las Cruces, NM, ²University of Idaho, Moscow, ID

ABSTRACT

Significant advancements in photogrammetric Structure-from-Motion (SfM) software, coupled with improvements in the quality and resolution of smartphone cameras, has made it possible to create ultra-fine resolution three-dimensional models of physical objects using an ordinary smartphone. Here we present an open-source modeling framework for creating three-dimensional models of vegetation structure using smartphone video of a user-defined sampling space (e.g., 1 m2). Our main objective was to evaluate the accuracy of our SfM sampling method in predicting above-ground biomass relative to traditional estimation techniques. A series of permanent 1-m2 quadrat sampling sites at the Jornada Experimental Range in southern New Mexico were used to develop the SfM sampling method, with each quadrat differing in its proportions of shrub vs. herbaceous vegetation composition.  A smartphone mounted on a selfie stick was used to capture video of each 1-m2 quadrat by circumnavigating it while moving the smartphone up and down to capture a range of image angles. Images were extracted from video frames at sampling rate of 4 frames per second and used to create densely reconstructed point clouds using open-source SfM software. Point clouds were georeferenced using a set of 3 control points located on the ends of a 0.25-m3 PVC corner frame placed at each site prior to imaging. Canopy volume was calculated for each point cloud using a foliar canopy approach, and used to model above ground biomass using published linear regression models. These results were compared to traditional nondestructive biomass estimates that were calculated by measuring the dimensions (cover and height) of individual plants or plant parts within each quadrat and converted to aboveground biomass using allometric equations. Preliminary results show a strong correspondence between measured and SfM modeled biomass. Our presentation will describe the details of this method and discuss its potential utility in increasing the accuracy and repeatability of field-based biomass estimation.

THE LAND-POTENTIAL KNOWLEDGE SYSTEM (LANDPKS): MOBILE APPS FOR SOIL IDENTIFICATION AND RANGELAND INVENTORY AND MONITORING

UTILIZING REMOTE SENSING TO GENERATE DISTURBANCE RESPONSE GROUP EXTENT AND VEGETATIVE STATE MAPS IN MLRA 25

Restoration of disturbed landscapes involves many considerations, generally relating to utilizing tools appropriate to a given ecological site. Soil and plant community information was gathered from an area near Midas NV, where dominant ecological site and related soil differs from minor components in a binary fashion. Loamy and Claypan ecological sites respectively, vary along an elevation and precipitation gradient through 8”-10”, 10”-12” and 12”-14” precipitation zones. Spatial statistics from areas identified during field surveys with be utilized to build physical parameters for soil component extent at the site to determine soil component type. This information combined with ecological site change along aspect and elevation changes could provide spatial mapping of six ecological sites at landscape scale.

DEVELOPING A GRAZING STUDY VEGETATION MAP WITH REMOTE SENSING IN THE NORTHERN GREAT PLAINS

Accurate mapping of rangeland vegetation is critical in studies of livestock spatial distribution. Our objective was to develop a detailed vegetation map to help model the drivers of beef heifer grazing behavior on Northern Great Plains rangeland at the USDA-ARS Fort Keogh LARRL, Miles City, MT. Remote sensing technologies for mapping vegetation are less time-intensive than traditional on-the-ground techniques and cover larger areas. However, map classes should correspond with vegetation classes of interest, and the accuracy of a final map product should be known. Unsupervised classification of National Agricultural Imagery Program (NAIP) imagery guided ground data collection for training / testing supervised classification. Two line point transects were read at 15 sites within each class to determine plant cover and species composition. A 2 m belt transect was read along each line point transect to determine shrubs density. We discriminated four classes. Class 1: Badlands - sparsely vegetated hills; 57.7 ± 3.8 % bare ground (BG) and 998.7 ± 151.4 shrubs/ha. Class 2: Mixed grass prairie, dominated by cool season grasses; 13.7 ± 2.5 % BG and 227.5 ± 65.1 shrubs/ha. Class 3: Shrublands with mixed grass prairie understory; 27.1 ± 3.3 % BG and 765.1 ± 124.8 shrubs/ha. Class 4: Cool season grasses/legumes and shrubs; 8.5 ± 3.1 % BG and 1098.6 ± 268.4 shrubs/ha. Training data class separability was evaluated using Jeffries-Matusita and transformed divergence methods (ENVI software). Values of 1.6 – 1.9 indicated that classes were sufficiently separable. We used ENVI’s maximum likelihood algorithm to classify combined NAIP, Landsat 8 Operational Land Imager and digital elevation datasets (spatial resolution sampled to 15 m). Testing data were used to develop a confusion matrix and to calculate Kappa. Overall accuracy was 89.1 % with Kappa = 0.8. We conclude that these results are acceptable for the scope of our study.

ECOLOGICAL SITE GROUPS: DEVELOPMENT AND APPLICATION BY THE BLM MILES CITY FIELD OFFICE. Cynthia A. Tusler*¹, Joshua Buckmaster², Reyer J. Rens²; ¹USDI-BLM, Miles City, MT, Terry, MT, ²USDI-BLM, Miles City, MT, Miles City, MT

The Soils and Range disciplines of BLM’s Miles City Field Office (MCFO) developed Ecological Site groups independently and almost simultaneously. Each identified the need for a landscape unit useful at the Field Office and project management levels in response to the 2015 MCFO’s Approved Resource Management Plan (ARMP). The ARMP directed using the Assessment, Inventory and Monitoring (AIM) Strategy for Land Use Plan (LUP) effectiveness monitoring. In addition, the ARMP integrated using data collected under the AIM Strategy to inform the Habitat Assessment Framework (HAF) for greater sage grouse. To implement ARMP requirements, the MCFO needed to develop a LUP monitoring design based on meaningful sample strata that fit the AIM Strategy and integrated many years of legacy monitoring data, some of which has been regularly collected since the 1960’s. Data collected would inform the HAF, land health assessments, and reclamation success of surface disturbing projects across the MCFO. Grouping Ecological Sites by common soil properties and plant species proved to be the most ecologically relevant strata useful at different management scales and needs. The cooperative result was six strata representing the soils and vegetation across the MCFO’s 2.7 million acres.

ECOLOGICAL STATE MAPS FOR THE SANTA RITA EXPERIMENTAL RANGE AND WALNUT GULCH EXPERIMENTAL WATERSHED. Wilma Renken*¹, Dan Robinett²; ¹USDA-NRCS, Tucson, AZ, ²Robinett Rangeland Resources LLC, Elgin, AZ

NRCS and Robinett Rangeland Resources LLC used existing soil survey and published Ecological Site Descriptions to map ecological states on the University of Arizona Santa Rita Experimental Range (SRER, 5500 acres) and the USDA ARS Walnut Gulch Experimental Watershed (7000 acres) in 2016-17. We developed protocols a priori for validating ecological sites and states as a basis for developing maps. Classification criteria for shrubs and exotic grass species were derived from long-term vegetation monitoring on the SRER and expert judgement. Other indicators for classification criteria were developed in the field and documented. From these protocols, we produced high resolution maps, revised state-and-transition models (STMs) with field keys. In addition, we identified research questions and made recommendations for additional monitoring to help NRCS and other land management agencies refine and develop STMs. In this presentation, we will illustrate maps of ecological states on Limy Slopes 12-16 inch pz ecological site (with suggested revisions). We used the best available information from the published literature and expert consensus to define states, disturbance regimes, transitions and threshold values. Topographic and climatic gradients as well as a recent history of land treatments (brush management) and plant invasions (exotic grass species) on the Limy Slopes ES offer abundant research opportunities for students and researchers at UA and ARS.

USING ESRI&RSQUO;S ARCGIS ONLINE AND COLLECTOR APP FOR TEACHING STUDENTS FIELD DATA COLLECTION AND MANAGEMENT. Marc R. Horney*; California Polytechnic State University, San Luis Obispo, CA

ABSTRACT

A number of apps have been developed recently that facilitate developing and deploying highly customizable data collection tools for use on cellphones and tablet computers. When networked, they allow teams of people to simultaneously and independently collect field data, which then can be uploaded to a common GIS repository. When managed well, this can help speed the collection, processing, and analysis of field data. This poster presentation shows how the Collector app for ESRI’s ArcGIS Online web-based GIS is being used in an introductory principles of range management course at the California Polytechnic State University at San Luis Obispo to teach methods of field data collection and principles for organizing and managing field work. In this course, Collector and ArcGIS Online have (1) replaced Garmin GPS receivers and paper datasheets with students’ smartphones for field data collection exercises in lab activities, (2) reduced the time required for processing results, and (3) created opportunities for learning about the design and management of data collection processes.

USING REMOTE-SENSING CAMERAS (RSC) AND GEOGRAPHIC INFORMATION SYSTEMS (GIS) TO PREVENT LIVESTOCK-CARNIVORE CONFLICT. Korinna Domingo¹, Diana Lakeland², Denise M. Peterson*³, Veronica Yovovich⁴; ¹Humboldt State University, Arcata, CA, ²Mountain Lion Foundation, Sacramento, CA, ³Mountain Lion Foundation, Salt Lake City, UT, ⁴Mountain Lion Foundation, Berkeley, CA