Submitted to: Soil and Water Conservation Society
Publication Type: Abstract Only
Publication Acceptance Date: 2/18/2020
Publication Date: N/A
Interpretive Summary: Abstract only
Technical Abstract: Edge of field monitoring (EoF) has stimulated interest in water quality, the “productivity and environmental performance of agriculture systems, plant productivity and resistance to erosion relative to a native baseline” and “environmental quality, assessed in part via surface water quality” (Granatstein and Bezdicek; Doran, 1994). EoF initiatives are limited by the amount of water and soil measurements required, spatial variability and at times limited records or measurements. This research employs radiometry, reflectance of samples in the ~350 to 1200 nm region of the electromagnetic spectrum to reduce numbers of soil health measurements required for monitoring and to provide estimates of missing soil quality parameters using archived soils. Soils presented represent a portion of the spatial variability captured in our current parameterization experiment on the water resources and erosion watersheds experiment. The site located at the USDA-ARS Grazinglands Research Lab, El Reno, OK contains 1.6 ha watershed treatments, representing paddocks in southern tallgrass prairies or winter wheat (Triticum aestivum) in highly disturbed or minimal tillage management. This research builds upon earlier efforts to measure and link environmental quality to specific land management practices. Surface water quality data (runoff amount, suspended sediment, nitrogen and phosphorus) verified that reduced disturbance and retention of plant biomass significantly decreased soil and nutrient losses from 1977-99. Soil quality measurements were limited prior to 2018 when the inherent spatial variability of each watershed was parameterized and effects of land management determined using baseline soil quality parameters. Therefore, we illustrate how previous land management practices, measured surface water quality and limited soil measurements can be used to fill data gaps associated with historic soil quality parameters and their relationship to environmental performance past and current.