Shrub Encroachment Early Detection System (SEEDS): a rangeland conservation tool

Progress report for GW19-196

Project Type: Graduate Student
Funds awarded in 2019: $24,994.00
Projected End Date: 07/31/2021
Grant Recipient: University of Arizona
Region: Western
State: Arizona
Graduate Student:
Major Professor:
Steven Archer
The University of Arizona
Major Professor:
Dr. Willem van Leeuwen
University of Arizona, Arizona Remote Sensing Center
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Project Information


Rangelands (e.g., grasslands, savanna, shrubland and woodland ecosystems) constitute ~50% of the Earth’s land surface and support the majority of the world’s livestock production. Rangelands also play integral roles in the global carbon, water, and nitrogen cycles and human health due to their extensive coverage. Many rangelands have undergone a shift from herbaceous plants to shrubs resulting in a decline of valuable livestock forage (e.g., perennial grasses). This phenomenon, known as “woody plant encroachment” or “shrub encroachment”, represents ecosystem degradation in arid and semi-arid regions and is typically accompanied by increases in bare soil and soil erosion. Efforts to limit shrub encroachment into rangelands include a variety of reactive brush management practices that remove or reduce the woody species via prescribed fire, mechanical, and/or herbicidal treatments. However, results of such actions are often short-lived and are seldom cost-effective or sustainable.

Instead of a reactive approach, preventative actions might be more ecologically sound and cost-effective for limiting shrub encroachment. I propose to develop SEEDS (Shrub Encroachment Early Detection System), a user-friendly tool (e.g., online application) that can be used as a first-step to promote preventative and proactive alternatives to reactive brush management. SEEDS will combine data from the literature and active research projects designed to understand how shrubs establish in grasslands with remote sensing and geospatial landscape-scale data sets to predict areas at risk for shrub encroachment. The goals for SEEDS are to (1) alert land managers, livestock producers, and citizen scientists to critical times and locations for inventory and monitoring for shrub invasion, (2) identify windows of opportunity for early intervention/brush management, and (3) assist in prioritization of shrub encroachment mitigation efforts at the scale of allotments/pastures.

Project Objectives:

The goal is to develop a SEEDS (Shrub Encroachment Early Detection System) framework for federal, state, and county land managers/rangeland specialists, ranchers, and the general public as a proactive and preventative measure to woody plant encroachment (WPE) to maximize the effectiveness of brush management strategies. Specific objectives are to:

(1) Compile published research on WPE to identify the primary drivers of and controls over woody plant establishment.

(2) Utilize existing remote sensing products and geospatial data (e.g., ecological site, soil type, depth to restrictive layer, precipitation, temperature, elevation, slope, etc.) to model WPE risk.

(3) Develop the SEEDS application.

  1. Combining models from (2), perform statistical analyses and validation of spatially explicit WPE risk.
  2. Create functions that enable spatially explicit user input (e.g., land use, stocking rates, management unit boundaries, etc.) within a GIS (Geographic Information System).
  3. Link Objectives 1-3 within the SEEDS framework to predict when, where, and under what conditions a given pasture/allotment will be at risk for WPE.

(4) Test and refine the SEEDS algorithms to maximize their accuracy for predicting WPE risk.

Areas identified as being at high risk for WPE could then be subject to monitoring and plans made for preventative measures (e.g., prescribed fire) to be used before encroachment progresses to the point where more expensive, less effective approaches are necessitated.


Click linked name(s) to expand
  • Dr. Larry Fisher - Technical Advisor (Researcher)
  • Dr. Larry Fisher - Technical Advisor (Researcher)
  • Dr. Elise Gornish - Technical Advisor (Educator and Researcher)
  • Dr. Elise Gornish - Technical Advisor (Educator and Researcher)
  • Dr. Elise Gornish - Technical Advisor (Educator and Researcher)
  • Patricia King - Technical Advisor - Producer
  • Patricia King - Technical Advisor - Producer
  • Dr. Willem van Leeuwen - Technical Advisor (Educator and Researcher)
  • Dr. Willem van Leeuwen - Technical Advisor (Educator and Researcher)
  • Dr. Willem van Leeuwen - Technical Advisor (Educator and Researcher)


Materials and methods:

Objective 1: Compile published research on Woody Plant Encroachment (WPE) to identify the primary drivers of and controls over WPE.

Published research was compiled using Clarivate Analytics Web of Science and Google Scholar. Search keywords included combinations of: “brush”, “shrub”, “manag*”, “encroach*”, “woody”, “control”, and “ecol*”, where information and variables were examined pertaining to setting (vegetation, soil, climate, topography), management history (AUMs, grazing rotation), focal species (native status, demographics, encroachment history), and brush management efforts (type, timing, success). Articles were stored and organized in a Mendeley Reference Manager database based on topic areas and brush management type. Thus far, ~1400 articles have been compiled and the leading biophysical drivers of and constraints to woody cover potential are as hypothesized to be climate (seasonal and annual mean precipitation/temperature), soils (clay content and depth to restrictive layer), topography (slope, aspect, elevation), and proximity to washes/arroyos. In addition to the literature searches, multiple discussions during Field Days and Tours (see Educational and Outreach Activities) with rancher/producers occurred regarding shrub encroachment patterns across their lands. Interesting patterns (as supported in our literature searches) emerged through these discussions on shrub cover/recruitment differences between ecological sites, soil conditions, wet/drought years, and grazing management history.

Objective 2: Utilize existing remote sensing products and geospatial data (e.g., ecological site, soil type, depth to restrictive layer, precipitation, temperature, elevation, slope, etc.) to model WPE risk.

Geospatial Data Compiling and Pre-processing

A total of 31 remote sensing and geospatial products relating to biophysical attributes (e.g., ecological sites, max clay percent, depth to max clay, seasonal precipitation and temperature, slope, etc.) have been compiled, georeferenced, and clipped to the ~21,000-hectare Santa Rita Experimental Range (SRER) using ESRI ArcGIS Pro (version 2.4.1). The compiled biophysical variables include those related to climate (temperature and precipitation), soils, topography, and hydrology. Given the large number of model variables, a select few are presented in Figs. 1-3 as examples. All variables were resampled to 30 meters to match Landsat 8 resolution for use in final SEEDS model development as independent variables.Annual Report Figure 1Figure 1. Percent clay (A) and depth to maximum clay percentage (B) rasters (30 m resolution) developed using the NRCS gSSURGO database for the Santa Rita Experimental Range. Areas in white are sites lacking clay (e.g., sandy wash beds and rock outcrops). Annual Report Figure 2Figure 2. Ecological sites present (30m) across the Santa Rita Experimental Range as designated by Breckenfeld and Robinett (1996).Annual Report Figure 3Figure 3. Distance from wash raster (30 m) calculated using the USGS National Hydrography Dataset for the Santa Rita Experimental Range. Distance calculations were based on Euclidean Distance from each wash/arroyo and rasterized.

Woody Cover Imagery Classification

Woody cover at a 30-meter resolution must be calculated for use in developing the relationship between Landsat 8 Vegetation Indices (VI) and woody cover. By relating woody cover to Landsat 8 VI, woody cover estimates can be calculated across time and space given the extensive history and area of Landsat imagery. Thus, Landsat-based woody cover values are used as the SEEDS model dependent variable. An Iso Cluster Unsupervised Classification (Memarsadeghi et al., 2007; Schowengerdt, 2007) was completed using USDA NAIP (National Agricultural Imagery Program) from July 2, 2015 with a spatial resolution of 1 m (Fig. 4A). The classification was performed using red, green, blue, and near infrared bands and a Canopy Height Model for shrub/woody plant detection. The classified image was evaluated using the true color NAIP imagery to detect the woody cover classes; remaining classes represented non-woody land cover. A majority filter was applied to the classified image to remove small and isolated pixels with a sensitivity of four neighboring cells. An accuracy assessment (Table 1) of the classified image was performed using independent orthorectified high-resolution (10 cm) imagery from the National Ecological Observation Network (NEON) Airborne Observation Platform (AOP) August 20-28, 2017 mission. A total of 200 points or 100 per class (e.g., woody and non-woody) were used for the accuracy assessment following protocol for a binomial classification (McCoy, 2005).

Landsat 8 Vegetation Indices and Woody Cover Modeling

Percent woody cover was calculated using the classified 1m NAIP image with a moving window (30 X 30 pixel) average to achieve a 30 m resolution (Fig. 4B). This was completed to match the spatial resolution of the Landsat 8 imagery. The NAIP-derived 30-m raster was classified based on percent cover with each class in 10% increments. The relationship between woody cover and Landsat 8 VI was developed to investigate the specific biophysical variables most important for predicting a site-specific woody cover. The VIs chosen for analysis were MSAVI (Modified Soil-Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index). The percent woody cover (NAIP-derived) and each VI product was clipped to the SRER boundary and sampled (20 points per class for a total of 140 points) using an equally stratified random design. Point samples were split (i.e., 80% for training the models, 20% for testing and assessing model accuracy) in a cross-validation procedure. The relationship (e.g., linear, quadratic, polynomial, etc.) between NAIP-derived percent woody cover and Landsat 8 VI were explored for the best model fit (Fig. 5). The resulting best-fit relationship was used to calculate percent woody cover across the SRER (Fig. 6).Annual Report 2020 Figure 4Figure 4. Natural color 1m 2015 NAIP imagery (A) and resulting 30m woody cover percent (B) raster-based woody/non-woody classifications. The woody cover raster was used in conjunction with Landsat 8 vegetation indices (e.g., NDVI and MSAVI) to develop algorithms for quantifying woody cover on Landsat imagery.

Table 1. Error matrix to assess the accuracy of the classification performed on July 2, 2015 NAIP images of the Santa Rita Experimental Range. The reference data for the accuracy assessment was the 2017 NEON Airborne Observation Platform at a 10cm spatial resolution.Annual Report 2020 Table 1

Objective 3: Develop the SEEDS application.

  1. Combining models from (2), perform statistical analyses and validation of spatially explicit WPE risk.

The Landsat 8-based woody cover estimates (dependent variable) and the 31 biophysical rasters (independent variables) were sampled (n = 237,659 pixels) over the entire SRER. Stepwise regression, Support Vector Machine, and Random Forest modeling were used to examine independent variable importance and assess the model accuracy (e.g., Root Mean Square Error, R2, and Mean Absolute Error [excluding stepwise regression]) in predicting woody cover. Based on its superior accuracy, the Random Forest model-based woody cover results (Fig. 7C) will be used for calculating WPE risk (i.e., the ratio of current woody vegetation cover estimated by the Enhanced Vegetation Index to predicted woody cover). The WPE risk estimates are classified into 5 classes using Natural Breaks (Jenks, 1967), where areas with the lowest risk have a class of 1 and highest-risk areas are classified as 5. The WPE risk calculation and classification methodology finalizations are in progress at this writing.

  1. Create functions that enable spatially explicit user input (e.g., land use, stocking rates, management unit boundaries, etc.) within a GIS (Geographic Information System).

On schedule to begin in Year 2.

  1. Link Objectives 1-3 within the SEEDS framework to predict when, where, and under what conditions a given pasture/allotment will be at risk for WPE.

On schedule to begin in Year 2.

Objective 4: Test and refine the SEEDS algorithms to maximize their accuracy for predicting WPE risk.

The SEEDS (Shrub Encroachment Early Detection System) Model user functionality including the testing and refinement for predicting WPE risk are the principal goals for project Year 2. The current progress in Year 1 will allow for timely completion of Objectives 3 and 4 during the next project Year, and project progress is currently in line with our proposed timeline.

Research results and discussion:

Preliminary Results

Landsat 8 Vegetation Indices and Woody Cover Modeling

A total of 140 sample points across 7 classes in 10% woody cover increments (20 points per class) were used to investigate the relationship between NAIP-derived woody cover and Landsat 8 NDVI and MSAVI (Fig. 5). These two vegetation indices were chosen due to their documented performance and application within arid/semi-arid ecosystems including the SRER (Huete & Jackson, 1987; Hueng et al., 2018; Holifield-Collins et al., 2015). The best fit lines for both NDVI (RMSE = 7.89%, R2 = 0.8, p ≤ 0.001) and MSAVI (RMSE = 10.8%, R2 = 0.72, p ≤ 0.001) were quadratic, where woody cover becomes asymptotic at ~70%. The high cover class samples (e.g., 6 and 7) are concentrated in regions near washes and the mountain foothills with higher water availability. Landsat 8 NDVI was used for developing Landsat-based woody cover estimations for further modeling given a lower RMSE compared to MSAVI (Fig. 6). Furthermore, high-resolution NAIP imagery availability is limited in time and space with a relatively short collection history compared to the moderate-resolution Landsat, which has been collected every 8 or 16 days dating back to 1982. This flexibility in the Landsat dataset allows for broader applications of the final SEEDS model beyond the SRER and over longer time-periods. Provided the woody cover error rate of 7.9%, the Landsat-based woody cover estimates are elevated slightly compared to the classified imagery (Fig. 4B vs Fig. 6), but the high versus low woody cover areas are accurately modeled (e.g., high cover near washes and in higher elevation zones) using only the Landsat 8 NDVI values.   Annual Report 2020 Figure 5Figure 5. Classified USDA NAIP (National Agricultural Imagery Program) and Landsat 8 NDVI (Normalized Difference Vegetation Index) (A) and MSAVI (Modified Soil Adjusted Vegetation Index) quadratic regression. Points (n = 140) were equally stratified along 7 classes in 10% shrub cover increments for model development. This relationship was used for modeling woody cover across the Santa Rita Experimental Range (see Fig. 6).Annual Report Figure 6Figure 6. Classified woody cover in 2015 (9 cover classes in 10% increments) modeled using NAIP (National Agricultural Imagery Program) woody cover and Landsat 8 OLI NDVI (Normalized Difference Vegetation Index). The maximum and minimum percent woody cover values were 88% and 0%, respectively. The densely vegetated areas (e.g., washes and foothills) appear orange to dark red due to their high shrub cover. Blue areas are bare soil patches and/or Pleistocene aged soils with high clay content and low shrub cover (see also Fig. 1A).


Modeling Woody Cover Based on Biophysical Variable Importance

Although Landsat 8 NDVI/greenness values can be used for accurately determining shrub cover for the SRER, NDVI values alone do not provide insight into the specific biophysical/ecological variables that are most important for determining woody cover site potential. Additional modeling is needed to relate woody cover to biophysical variables. A total of 31 independent variables pertaining to seasonal and annual mean precipitation (PPT) and temperature (T), topography, soil characteristics, and ecological site were used in a stepwise regression, Support Vector Machine (SVM), and Random Forest (RF) modeling procedures for determining 1) the best/most accurate technique for modeling woody cover potential; 2) the most important of the 31 biophysical variables related to woody cover; 3) the final model to create SEEDS to calculate woody plant encroachment risk. A graphical comparison of the three models is shown in Fig. 7.Annual Report Figure 7Figure 7. Stepwise regression (A), Support Vector Machine (SVM; B), and Random Forest (C) woody cover model predictions versus 2015 Landsat NDVI-based woody cover for the Santa Rita Experimental Range. Per-pixel cross-validation (n = 45,839) was used for assessing each model’s prediction performance. Blue lines represent a 1:1 fit; red line is the linear regression of the actual fit.

The stepwise regression modeling (RMSE = 5.6%, R2 = 0.457) determined 12 variables to be the most important: aspect; depth to restrictive layer/bedrock; soil clay percent; depth to maximum clay percent; maximum 1980-2010 T distance to the nearest wash; mean winter PPT; minimum spring and fall T and maximum spring and summer T. Although the stepwise woody cover error is relatively low (RMSE = 5.6%), the prediction utility is poor (Fig. 7A) and the temperature variable/model variability is unconstrained as reflected in a low R2 value (R2 = 0.46). The SVM model for woody cover improved the overall model prediction errors (Mean Absolute Error [MAE] = 4.2%; RMSE = 5.5%) and captured more of the variation (R2 = 0.63) compared to the stepwise regression model. The best modeling technique was found to be the RF with the lowest modeling errors (MAE = 3.22%; RMSE = 4.2%), best fit (R2 = 0.70), and strongest prediction utility (Fig. 7C). The most important biophysical variables/predictors for determining woody cover potential, as determined via Gini Index of Impurity, are elevation, % soil clay, aspect, fall PPT, winter PPT, slope, and distance to the nearest wash (Fig. 8).

Annual Report Figure 8Figure 8. Woody cover model predictor variable importance as calculated by the Gini Index of Impurity for the Santa Rita Experimental Range.

The Random Forest model predictions for woody cover are being used in finalizing the SEEDS model for determining woody plant encroachment risk as the ratio of current woody vegetation cover estimated by the Enhanced Vegetation Index to a site’s estimated woody cover based on biophysical characteristics. The risk calculations, classifications, and validations are currently in progress to meet project Objectives 3.2, 3.3 and 4 in Year 2.


Participation Summary

This project does not contain a stakeholder academic interview component for use in analysis, but instead includes general participation to guide research applicability and development of the SEEDS tool. Ranchers/producers and agriculture professionals have participated in local meetings and field days/tours, where this project was highlighted and discussed, including the Altar Valley Conservation Alliance and Las Cienegas National Conservation Area Field Days (see Educational and Outreach Activities section). Targeted workshops of the SEEDS tool and brush management with quantifiable participation and surveys are projected for project Year 2 and will be conducted in collaboration with University of Arizona Cooperative Extension personnel.

To create opportunities for more active stakeholder involvement, we collaborated with Dr. Elise Gornish, UA Cooperative Extension, on a USDA Renewable Resources Extension Act-National Focus Fund Project proposal, “An Online Toolkit for Managing Shrub Encroachment.” The proposal objectives are to compile local/regional stakeholder knowledge and input for use in decision support tool design, and broaden existing brush management online resources available to stakeholders via partnership with Rangelands Partnership, a long-term initiative among 19 western and Great Plains land-grant universities, which is a unique collaboration of rangeland specialists, librarians, and technology experts. Additionally, we will develop a framework for land managers and Extension educators to guide and enhance future tool functionality, relevance, and usability. The proposal was submitted on June 26th, 2020, and if funded will enable us to substantively expand the online presence of land grant university Extension, while promoting sustainable rangeland ecosystem management.

Participation Summary
2 Farmers participating in research

Educational & Outreach Activities

1 Curricula, factsheets or educational tools
1 Online trainings
2 Tours
7 Webinars / talks / presentations
2 Workshop field days
2 Served as Data Science Ambassador for University of Arizona College of Agriculture and Life Sciences during the 2019-2020 academic year. During my tenure, I presented twice to undergraduate and graduate students on the applicability of Data Science, Machine Learning, and R programing for big data analysis specifically in the field of Rangeland Ecology. This was well received and inspired students to pursue coursework related to Data Science.

Participation Summary

2 Farmers
20 Ag professionals participated
Education/outreach description:

Presentations, conferences, and workshops/field days during Year 1 (2019-2020) are listed below. In addition to these activities, an educational tool for the Natural Resource Conservation Service Soil Health Division learning module under the “Social and Economic Considerations” topic area was updated to target Arizona and Rangeland focused learners. Tours and Field Days with collaborators (typically attended by ~2-4 ranchers/produces and between 20-30 ag professionals) occurred at the Las Cienegas National Conservation Area, Altar Valley, and the Santa Rita Experimental Range in southern Arizona to discuss shrub encroachment concerns and brush management projects planned or proposed. Details and methods for this research was presented to mixed undergraduate and graduate University of Arizona College of Agriculture and Life Sciences students as part of Data Science and R-language programing community meetings. The presentations emphasized the utility and applicability of Big Data modeling practices to local and regional land management goals and research questions.

Webinars, talks, and presentations presented:

  • Rutherford, WA, SR Archer. (2020) Predicting Woody Plant Encroachment Risk on Sonoran Desert Rangelands. Poster Presentation; 73rd Annual Society for Range Management Meeting, Denver, CO.
  • Rutherford, WA. (2020) Altar Valley Conservation Alliance (AVCA) 2020 Update: Shrub Encroachment Early Detection System (SEEDS). Oral Presentation; AVCA Community Meeting, Virtual.
  • Rutherford, WA. (2020) Velvet Mesquite Establishment in Sonoran Grasslands. Oral Presentation; AZ Society for Rangeland Management Winter Meeting, Wickenburg, AZ.
  • Rutherford, WA, SR Archer. (2019/20) Predicting Woody Plant Encroachment Risk on Sonoran Desert Rangelands. Oral Presentations (2); University of Arizona R-programing Community Meetings, Tucson, AZ.
  • Rutherford, WA, SR Archer. (2019) Evaluating Shrub Encroachment Risk on Sonoran Desert Rangelands. Poster Presentation; Society for Ecological Restoration-Southwest, Tucson, AZ.
  • Jones, S, WA Rutherford, SR Archer. (2019) Evaluating Woody Plant Encroachment in Sonoran Grasslands for Brush Management Planning. Oral presentation (co-presented by Jones and Rutherford); Science on the Sonoita Plain Symposium, Sonoita, AZ.

Conferences/Workshops/Field Days Attended (2019):

  • Science on the Sonoita Plain June 22nd, 2019, Elgin, Arizona. Organized by Cienega Watershed Partnership. Approximately 50 participants.
  • Research Insights in Semiarid Ecosystems (RISE)October 26th, 2019, Tucson, Arizona. Organized by Agricultural Research Services and College of Agriculture and Life Sciences University of Arizona. Approximately 60 participants.
  • Southwestern Grasslands Research & Management Workshop November 7th, 2019, Tucson, Arizona. Organized by Southwest Division Resources/Society for Ecological Restoration. Approximately 100 participants.
  • Society for Ecological Restoration Southwest (SER SW)November 8th-10th, 2019, Tucson, Arizona. Approximately 500 participants.
  • Altar Valley Conservation Alliance (AVCA) Vegetation Watershed Working Group (bi-monthly from 2019-Current)
  • AVCA Field Day November 1st, 2019, Altar Valley, AZ.
  • Las Cienegas National Conservation Area (LCNCA) Winter Planning Meeting/Field Day December 12th, 2019, Sonoita, AZ. Organized by Bureau of Land Management and Southwest Division Resources. Approximately 50 participants.

Conferences/Workshops/Field Days Attended (2020):

  • Society for Range Management Annual Meeting February 16th-20th, 2020. Denver, CO.
  • Arizona Society for Range Management Winter Meeting January 22nd – 24th, 2020. Wickenburg, AZ.
  • AVCA Community Virtual Meeting May 20th, 2020.

Upcoming and planned Summer/Fall 2020 meetings and workshops (e.g., AZ SRM Summer Meeting, Society for Ecological Restoration Southwest, AVCA Watershed Working Group, and LCNCA Fall Planning Meeting/Field Day) are either delayed or cancelled due to the COVID-19 pandemic. I am presenting this research at the virtual Ecological Society of America 2020 Annual Meeting, and plan to attend/present at the Research Insights in Semi-arid Ecosystems 2020 and Society for Rangeland Management 2021 virtual annual meetings.

Upcoming Ecological Society of America 2020 Presentation:

  • Rutherford, WA and SR Archer. (2020) Evaluating shrub proliferation risk on Sonoran Desert rangelands. Invited Oral/Inspire presentation; Ecological Society of America Annual Meeting, Virtual Meeting.

My dissertation research projects are being compiled and updated to be presented online via As results from this project become available the site will be updated. This site will also nest other products of this research such as fact sheets, published manuscripts, and other outreach materials.

Project Outcomes

Did this project contribute to a larger project?:
2 New working collaborations
Project outcomes:

This project is approaching the end of its first year, so the full outcomes have yet to be fully realized. Objectives 1 and 2, which are either met or being finalized, have solidified the necessary biophysical variables most applicable for determining woody cover and predicting site risk for woody plant encroachment in Sonoran Desert grasslands. Objectives 3 and 4 are focused on community/producer participation with validation of the utility of this project and the SEEDS (Shrub Encroachment Early Detection System) Model. This will be a key focus in Year 2.

The project data-driven decision-support framework will streamline the management planning process for meeting conservation and livestock production goals. The SEEDS tool will increase the effectiveness and reduce the cost (money and time) of rangeland monitoring by directing it to landscapes most at-risk for shrub encroachment while simultaneously facilitating planning efforts for the location, type, and timing of brush management. An additional focus of this project is to reduce, limit, and concentrate future brush management treatments to minimize adverse environmental impacts while concurrently enhancing ecosystem quality for livestock production and native wildlife.

Knowledge Gained:

A primary focus of my efforts, outside of compiling research articles and performing geospatial analyses, has been making local and regional connections with ranchers and land management practitioners. Some of these connections have been formed by attending and presenting at local meetings (e.g., AZ Society for Rangeland Management and Research Insights in Semi-arid Ecosystems). Many attendees have expressed interest and have highlighted the utility of a tool like SEEDS for their specific brush management planning needs. Despite this success, participating in field days and tours has been much more fruitful with the benefit of speaking more informally and discussing issues/concerns in more detail while on the land. These multiple opportunities have greatly improved my outreach and Extension skills and has inspired Extension as a career goal into the future. I plan to continue my outreach efforts into project Year 2 to the best of my abilities given the current difficulties stemming from the COVID-19 guidelines and limitations. Professional society meetings are moving online, but most of my efforts will be given to capitalizing on smaller, more targeted local and regional meetings/symposia as they hopefully will continue as currently planned through project Year 2.

Analysis for this project is still underway, but in Year 1 we have gleaned interesting results on the most important biophysical variables as well as methodologies for understanding woody cover and shrub encroachment potentials in southern Arizona. We have also employed innovative modeling techniques in geospatial analysis in Year 1 (and continuing in Year 2) for the specific purpose of sustainable livestock production including the field of rangeland ecology and management. This project will provide the required accuracy and information for brush management planning and decision making by our rancher/producer and land management stakeholders.

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.