Integrated field and satellite based decision support system for climate-resilient and sustainable ranches and rangelands across California

Project Overview

SW22-933
Project Type: Research and Education
Funds awarded in 2022: $348,561.00
Projected End Date: 03/31/2025
Grant Recipient: University of California, Davis
Region: Western
State: California
Principal Investigator:
Yufang Jin
University of California, Davis
Co-Investigators:
Royce Larsen
University of California, ANR
Matthew Shapero
University of California Davis, ANR
Steven Steven Ostoja
USDA, Agricultural Research Service

Commodities

  • Animals: bovine

Practices

  • Animal Production: range improvement, rangeland/pasture management
  • Education and Training: decision support system, on-farm/ranch research, participatory research, workshop

    Proposal abstract:

    California’s 34 million acres of rangelands support diverse social-ecological resources, including some of the world’s major biodiversity hotspots, watersheds for nearly all of the State’s surface waters, and a >$3B annual livestock industry. Climate change and weather extremes threaten the economic viability and environmental sustainability of these highly valued systems. Rangeland management is also challenged by the characteristic climatic and biophysical diversity variability of these systems—resulting in high spatiotemporal variability in supply of ecosystem services, including forage productivity. If California rangeland managers had access to reliable data across these vast, dynamic landscapes, then they would be able to make cost-effective, climate-informed grazing decisions to sustainably enhance productivity and profitability.

    Our interdisciplinary team will build accessible decision-support tools based on high spatiotemporal resolution satellite remote sensing data to provide rangeland managers actionable information directly relevant to rangeland sustainability and ranch-level profitability. The proposed tools focus on tracking forage production—a top grazing management goal—and residual forage dry matter (RDM)—a critical rangeland health indicator for California’s annual grasslands. 

    Our research makes use of multi-scale, multi-source remote sensing imagery, augmented with on-ranch measurements, and provides more realistic range condition monitoring in near real-time. This effort capitalizes on our newly established, collaborative on-ranch monitoring sites across 32 counties. The field-based measurements will be used to further refine and calibrate an algorithm we previously developed for Landsat based forage production estimations. An automatic workflow will be developed to fuse Sentinel-2 10 m imagery every 5 days with Landsat imagery, augmented with higher resolution NAIP and Planet imagery. 

    We will also develop an innovative machine learning approach to estimate end-of-season RDM, by integrating improved growing season production time series, preceding weather, and soil properties, with concurrent Landsat/Sentinel imagery. The RDM estimation algorithm will be trained with field measured RDM with data augmentation from unique hyperspectral imagery. To reduce potential adoption barriers,  we will take advantage of the Google cloud platform and APIs to create accessible data visualization and decision-support tools.  

    We will engage ranchers and other rangeland stakeholders throughout the project via our project Advisory Group, on-ranch research, and in-person workshops. Extension education resources will be co-developed with our rangeland partners and will include online multimedia (decision-support apps, videos), fact sheets/policy briefs, newsletters, and open access journal publications. We will collect feedback on effectiveness of workshop content and decision-support tools via focus group discussions and on-site evaluations at each workshop. 

    Near real-time, scalable remote monitoring and decision-support tools, combined with effective and inclusive education, will help us broadly deliver information critical to rangeland agricultural sustainability. The data-driven tools will complement on-ranch monitoring, help ranchers accurately visualize rangeland health and thus optimize livestock grazing management to promote forage productivity and maintain beneficial RDM levels. Expected near- to mid- term outcomes include stakeholder adoption of decision-support tools and enhanced skills in integrating forage production and RDM monitoring into rangeland decision-making. Potential long-term impacts include enhanced sustainability and resilience of California’s rangelands and ranches through better resource management.

    Project objectives from proposal:

    Research Objectives 

    1. Collect and compile ground-truthed data in collaboration with UCCE and FSA to build an extensive field-based geospatial database for California.
    2. Develop a well-calibrated remote sensing algorithm scalable statewide for consistent forage production estimates in near real-time. 
    3. Develop a locally-optimized RDM estimation tool with multi-sensor imagery.
    4. Prototype an online interactive app to help stakeholders improve ranch management and risk mitigation decision-making.

    Education Objectives

    1. Assemble an Advisory Group of diverse rangeland stakeholders to provide immediately actionable, on-the-ground user input to developing accessible decision-support tools.
    2. Engage with ranching, policy, research, and management communities to extend research products and collect feedback on tool usability for rangeland decision-making.
    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.