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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #427881
Research Project: Knowledge Systems and Tools to Increase the Resilience and Sustainability of Western Rangeland Agriculture

Location: Range Management Research

Title: Sensitivity of ecological site identification to soil and geomorphology observations in Nevada (USA)

Author
item MARTINEZ, PEDRO - University Of California, Riverside
item McCord, Sarah

Submitted to: Soil Science Society of America Annual Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 8/1/2025
Publication Date: 8/22/2025
Citation: Martinez, P., McCord, S.E. 2025. Sensitivity of ecological site identification to soil and geomorphology observations in Nevada (USA). Soil Science Society of America Annual Meeting. Abstract.

Interpretive Summary:

Technical Abstract: Ecological site identification is important to decide where and when to apply land management practices (e.g., livestock grazing). However, uncertainties in soil and geomorphology observations may lead to errors in ecological site identification. Here, we determine variations in ecological site identification by comparing field-collected data, expert review, and national soil databases returned with the Land Potential Knowledge System (LandPKS) algorithm at 524 monitoring plots across all Major Land Resource Areas in Nevada state. The plots contain information on ecological site ID, soil series, soil texture, plant ID, and landscape type collected by a national vegetation and soils monitoring dataset. The monitoring dataset underwent a Quality Control (QC) review by an expert specialist. QC modified the ecological site IDs in 176 plots (33% of plots), of which 78 and 31% had changes in soil series and landscape type, respectively. Field-ascribed ecological sites agreed with the top-ranked soil component from LandPKS in 48% of the plots, while 84% matched at least one of the 12 soil components predicted for a given plot location. Our results indicate a need for improvements in training field data collectors regarding soil and landscape characterization. We suggest quantifying the limitations of algorithms through probabilistic measures or ranking systems, as done by LandPKS, when using cloud-based data to verify ecological sites.