Panel weighs regional discount factors for organic amendments and modeling approaches

State Water Resources Control Board expert panel (second statewide agricultural expert panel) · December 15, 2025

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Summary

Panelists compared Region 3's empirically derived discount-factor approach for compost/organic fertilizers with Region 5's CV SWAT model-based method, stressing regional sensitivity of mineralization rates and calling for more targeted research and QA for reporting.

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.