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United States Department of Agriculture (USDA) 2021

MANAGING FROM A DISTANCE : CONSERVATION OF SEMI-ARID GRASSLANDS THROUGH MACHINE LEARNING

Grasslands Conservation Semi-Arid

United States Department of Agriculture (USDA) National Institute of Food and Agriculture

Titre : MANAGING FROM A DISTANCE : CONSERVATION OF SEMI-ARID GRASSLANDS THROUGH MACHINE LEARNING

Identification : 1026393

Pays : Etats Unis

Durée : START : 15 JUN 2021 TERM : 14 JUN 2023

Résumé
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

Objectifs
The primary goal of this project is to develop a user-driven, user-friendly web-based resource for brush management (BM) and expand existing B M resources based on (i) current science and engagement with stakeholders, (ii) extension knowledge transfer strategies and (iii) learning technology. To achieve this end, four specific objectives will be addressed:Compile information from regional land manager and producer BM field days and Extension BM workshops to identify treatment successes and lessons learned.Perform a comparative analysis of machine learning techniques to predict a site’s shrub cover potential and efficacy of BM treatments.Expand an existing drought-based web application to address shrub encroachment/BM issues and thus add new functionality and integrated stakeholder network inputs.Develop and disseminate educational/outreach materials, and host local web application demonstrations for land managers, producers, and county Extension agents.

Financement total : $111,527

Performing Institution : UNIVERSITY OF ARIZONA
Investigator : Rutherford, W. A.

Présentation : USDA (NIFA)

Page publiée le 24 novembre 2021