Get Full Government Meeting Transcripts, Videos, & Alerts Forever!

Panel weighs regional discount factors for organic amendments and modeling approaches

December 15, 2025 | State Water Resources Control Board, Agencies under Office of the Governor, Executive, California


This article was created by AI summarizing key points discussed. AI makes mistakes, so for full details and context, please refer to the video of the full meeting. Please report any errors so we can fix them. Report an error »

Panel weighs regional discount factors for organic amendments and modeling approaches
Panelists turned to Question 7 to evaluate whether discount factors (credits) for organic amendments and additional components of R (Rscavenge, Rtree, Rother) produce valid, comparable A over R and A minus R values across growers and regions.

Hannah Waterhouse summarized the technical definitions and constraints of discount factors (cover crop biomass thresholds, specific timing and biomass criteria for an Rscavenge credit) and asked whether the use of those incentives yields an accurate picture of nitrogen discharge. Daniel Geisler noted that Central Coast guidance gives explicit procedures for estimating available N from compost, while some Central Valley practice is limited to reporting "available N" without clear guidance on how growers should calculate it.

Thomas Harder and others noted that discount factors are empirically derived and regionally specific; the panel said the Central Coast's discount-factor tables are a defensible starting point where studies exist. Panelists also stressed the need for technical assistance and QA when growers or third parties calculate credits, and emphasized the role of in-season soil nitrate testing and improved reporting to reduce uncertainty.

Several panelists recommended the panel emphasize in its report the need for further region-specific research on long-term mineralization dynamics and comparability of discount-factor calculations across regions.

Key takeaway: discount factors can be defensible where rooted in local empirical data and implemented with QA and third-party calculation support; where data are weak, modeling (e.g., CV SWAT) or improved data collection should be used.

View the Full Meeting & All Its Details

This article offers just a summary. Unlock complete video, transcripts, and insights as a Founder Member.

Watch full, unedited meeting videos
Search every word spoken in unlimited transcripts
AI summaries & real-time alerts (all government levels)
Permanent access to expanding government content
Access Full Meeting

30-day money-back guarantee

Sponsors

Proudly supported by sponsors who keep California articles free in 2025

Scribe from Workplace AI
Scribe from Workplace AI
Family Portal
Family Portal