THE GOAL OF THIS PREDOCTORAL FELLOWSHIP IS TO USE PIONEERING COMPUTATIONAL (E.G., MACHINE LEARNING) TECHNIQUES TO DEVELOP A DEPENDABLE FRAMEWORK FOR MANAGING SHRUB ENCROACHMENT TO ENSURE THE PRODUCTIVITY OF SEMI-ARID GRASSLANDS VIA AN ONLINE, OPEN-SOURCE PLATFORM AND OUTREACH. THIS INTEGRATED PROJECT ALIGNS AFRI FARM BILL "BIOENERGY, NATURAL RESOURCES, AND ENVIRONMENT," AND "AGRICULTURAL SYSTEMS AND TECHNOLOGY," PRIORITY FOCUS AREAS WITH FUNCTIONAL AND CAREER GOALS OF COUPLING RESEARCH AND EXTENSION. LAND MANAGERS ARE CHALLENGED WITH BALANCING NUMEROUS GRASSLAND ECOSYSTEM SERVICES WHILE PROMOTING SUSTAINABLE LIVESTOCK PRODUCTION. BOTH ARE THREATENED BY SHRUB ENCROACHMENT AND REQUIRE A VARIETY OF 'BRUSH MANAGEMENT' ACTIVITIES. THERE IS LITTLE CONSENSUS ON THE ECOLOGICAL SITE RESPONSES AND BIOPHYSICAL VARIABLES RELEVANT FOR PLANNING AND GUIDING THE DECISIONS REGARDING THE TYPE (MECHANICAL, HERBICIDAL, PRESCRIBED BURNING) AND TIMING OF TREATMENTS. MODERN DATA SCIENCE METHODS AND THE GROWING DISCIPLINE OF MACHINE LEARNING CAN PROVIDE PRECISE RESEARCH-BASED INFORMATION WITH APPLICABILITY TO RANGELAND ECOLOGY AND MANAGEMENT CONCERNS. RECENT WEB APP DEVELOPMENTS RELY ON A SINGLE MACHINE LEARNING TECHNIQUE (E.G., RANDOM FORESTS), BUT A WIDER VARIETY OF POTENTIALLY USEFUL METHODS ARE AVAILABLE (E.G., SUPPORT VECTOR MACHINES, NEURAL NETWORKS, ETC.). A COMPARATIVE ANALYSIS OF THESE METHODS VERSUS TRADITIONAL TECHNIQUES (E.G., LINEAR AND/OR STEPWISE REGRESSION) WILL PROVIDE A BASIS FOR DEVELOPING, TESTING, AND SELECTING A MACHINE LEARNING ALGORITHM. ANALYSIS OF THESE ALGORITHMS FOR A TARGETED APPLICATION (E.G., BRUSH MANAGEMENT) WILL BE FOLLOWED BY QUALITATIVE ASSESSMENTS INVOLVING STAKEHOLDER/RANGELAND COMMUNITY WORKSHOPS AND TUTORIALS. THE PROPOSED ACTIVITY WILL LEVERAGE EXISTING KNOWLEDGE TO MAKE TIMELY MANAGEMENT DECISIONS WITHOUT THE NECESSITY FOR EXTENSIVE AND EXPENSIVE FIELD CAMPAIGNS.