29 Hunt

Slide1: 

EPA Regional Science Workshop Animal Feeding Operations (AFOs) Science & Technical Support Needs Session IV – Risk Management & Water Quality: State of the Science Tom Hunt, Director of Research University of Wisconsin-Platteville Pioneer Farm Wednesday, December 8, 2004

Slide2: 

Science ? Is an open-ended (and open-minded) process! Embraces healthy skepticism! Always favors the null hypothesis! Never politicized!

Slide3: 

What is Risk? Risk exists if there is something you don’t want to happen – having a chance to happen!!! The probability that some event will cause an undesirable outcome to surface water and/or groundwater quality Bernhardt, 2002

Components of Risk: 

Components of Risk Vulnerable Location Bernhardt, 2002

Slide5: 

What is Risk Management? The Points of Vulnerability All The Potential Outcomes The Probability of Occurrence Cost of an Undesirable Outcome Tolerance Behavior and Values Assuring An Outcome by knowing, understanding, and communicating Bernhardt, 2002

Slide6: 

What is Risk Management? Knowledge Tools Actions Communication

Risk Management Communication: 

Risk Management Communication Enables effective policy implementation Reduces liability Reduces hostility Maintains management flexibility Reduces costs Focuses resources appropriately Proactive Trudell, 1997

Prioritizing Which Risks to Address First: 

Prioritizing Which Risks to Address First Probability of Happening Potential Impact Act if cost effective No action required Immediate action Action required

Slide9: 

Nowak, 2003 Log Normal Distribution High Vulnerability Bad Behavior Low Vulnerability Good Behavior Potential Environmental Degradation

Risk Management Tools: 

Risk Management Tools Imperfect predictors of ag impacts Models for variable scale & seasonality Models for utilization & efficacy Models for prevention & remediation Research increases accuracy, applicability, and practicality Monitor, calibrate, validate, & evaluate Adapt

Slide11: 

Implementation Spatial Scale Identify targeted watersheds/sub-watersheds (In Progress) Run SNAP+/Rusle2/P-Index on all agricultural fields in targeted watersheds/sub-watersheds. Rank exceedance of P/sediment thresholds (can be done with routine data) Evaluate management options with SNAP+/Rusle2/P-Index (can be done with routine data) Specification/installation of buffers (requires site-specific data) State-wide Watershed Farm Farm field Sub-field PALMS/Field-monitoring used for calibration of SNAP+/Rusle2/P-Index Setting P/sediment thresholds, science-based (“T” not relevant here) Research Evaluate DEM spec’s & costs vs field visit & traditional engineering design Wisconsin Experience: WBI Agricultural Activity 1 3 4 5 2 * Nowak, 2004

Slide12: 

Stream Health Model Nutrient/Sediment Model Lake Water Quality Model Low score = 10 (no ES, little Diversity) High score = 90 (Higgins eye Mussel’s last hide-out) Scoring: 1 factor (linear scale) Med. High Low Med. low low high high X Y X = nutrient loading (based on land use, soils, topography) Y = likelihood of buffer effectiveness (based on stream and landscape morphology) Scoring: 2 factors Scoring: 3 factors L MH H ML low low high high Y X X = existing lake water quality (low = eutrophic, high = oligotrophic) Y = stream nutrient loading to lake (low = insignificant, high = significant) Z = sensitivity to pollutants (based on water chemistry and flushing rate) low high Z ML MH MH = 10 (forest along a flat river) = 90 (row crops In headwaters) = 10 (a stinkin’ carp-filled runoff pond) = 90 (that secret stream-fed walleye hole) Nowak, 2004

Slide13: 

Implementation WBI Adaptive Management requires on-going data collection and data interpretation to update methods for predicting P/sediment loss (funding?) WBI Agricultural Adaptive Management Activity Data collection options for P, sediment & farming practices Options for Interpretation of collected data to improve SNAP+ Pioneer Farm Discovery Farms Arlington and other University farms Private targets of opportunity Expand stream monitoring & choose a few streams well suited to quantifying effects of farm management improvements Reproduce typical farm management practices at data collection sites Continue field studies of buffer design, installation & effectiveness Generalize results from data collection sites with process model like PALMS to update SNAP+/RUSLE2/P-Index Interpret stream monitoring data and consistency with SNAP+/RUSLE2/P-Index Periodically revisit science- based P and sediment thresholds for action BIG QUESTION: How will regulations be modified to reflect improvements from adaptive management? Nowak, 2004

N-CyCLE: Nutrient-Cycling Crops Livestock Environment: 

N-CyCLE: Nutrient-Cycling Crops Livestock Environment M. A. Wattiaux, University of Wisconsin-Madison

Slide15: 

Wisconsin Experience Phosphorus is building up on the landscape

Slide16: 

Wisconsin phosphorus management guidelines For planning manure applications: 1. Use soil test P thresholds – > 50 ppm – limit P applications to crop removal > 100 ppm – eliminate P applications if possible Or .. 2. Use the Wisconsin P Index The P Index calculates a gross estimate of total annual P delivery from a given field to the nearest surface water to allow assessment of the relative risk of P contamination from that field.

Slide17: 

P Delivery to Stream (P Index) Annual edge-of-field runoff losses (Annual sediment-bound P + Annual dissolved P + Losses from surface manure/fertilizer P applications) Total P Delivery Ratio x = Stream Framework Equation WardGood, 2004

Slide18: 

25 tons per acre dairy manure applied in fall (04) , winter (05) or spring (06) 2007: alfalfa/brome 2008: alfalfa/brome SNAP-Plus Check the P Index website: http://wpindex.soils.wisc.edu

Slide19: 

Combine models of vertical transport in soil and canopy with overland flow models Precision Agricultural-Landscape Modeling System, PALMS (Molling et al., 2003) PALMS: Norman, 2004 Wisconsin Experience

Slide20: 

Biophysical Processes Simulated in PALMS With IBIS ©CCMolling, 2002

Slide21: 

Distributed Runoff (Routing) Model Diffusive Wave Model Schematic for Water Balance at Raster (i,j) q(1) (i-1,j) q(2) (i,j+1) q(3) (i+1,j) q(4) (i,j-1) Ppt • Surface Routing • Tillage Effects • Row Direction effects on Surface Roughness • Surface Sealing • Run-on and Closed Depressions Norman, 2004

Slide22: 

Physical equations describing water flow and sediment generation: Detachment/deposition: Water Erosion Prediction Project, WEPP (Nearing et al., 1989) Numerical methods in a two dimensional grid Grid Erosion Model Subroutine (GEMS) Photo courtesy of USDA NRCS.

Slide23: 

Application to Bragger Farm (WI) 2.7 ha (6.8 acre) 14% slope Silt loam 90 mm (3.5 in) Norman, 2004

Slide24: 

(m3) > 400 m3 Outflow from field occurs primarily in concentrated flow 50 m3 Norman, 2004

Rill width is wider where more water flows and bluewould be gullies for this 90 mm rain event on bare soil: 

Rill width is wider where more water flows and blue would be gullies for this 90 mm rain event on bare soil (Rill Width) STREAM Where would You put the BUFFER? Road Norman, 2004

Slide26: 

Farm B North Basin

Wisconsin Experience: Pioneer Farm: 

Wisconsin Experience: Pioneer Farm 5 miles south of Platteville, WI. Located in MLRA 105 – Northern Mississippi Loess Hills

Pioneer Farm Soil Tests1968-2003: 

Pioneer Farm Soil Tests 1968-2003

Slide29: 

Runoff & Stream Sampling Sites on DEM (1ft CI)  stream  1  2  3  6 5   4  8  7

Slide30: 

Typical Pioneer Farm Monitoring Station Raingage Solar Panel Gaging station Shaft-encoder stage sensor Plywood wingwall H-Flume - flow measurement

Sediment Load vs. Total P Load: 

Sediment Load vs. Total P Load

Effect of Cropping System on Total P Load: 

Effect of Cropping System on Total P Load

Snowmelt: Total and Dissolved P: 

Snowmelt: Total and Dissolved P

Impact of Winter Manure Applications: 

Impact of Winter Manure Applications 2004 Winter Runoff –3 events Received winter manure

Calibrating the P Index: Why use Pioneer Farm?: 

Calibrating the P Index: Why use Pioneer Farm? PI has been determined for all fields Single-crop subwatersheds provide ideal conditions for measuring edge-of-field losses Have flexibility in management to test assumptions of the PI

Results of PI and Annual Loads : 

Results of PI and Annual Loads **** Provisional Data **** channelized flow & gully erosion

Is the PI a better predictor of runoff losses than Soil Test P?: 

Is the PI a better predictor of runoff losses than Soil Test P? *** Provisional data and Site 2 – 2004 removed Baxter, 2004

Ongoing Research on the relationship between STP and runoff P losses: 

Ongoing Research on the relationship between STP and runoff P losses Watershed Scale Small Plot Scale Simulated Runoff Natural Runoff

Evaluating Acute Losses: 

Evaluating Acute Losses Fall / Winter 2004-2005: Applications of solid and liquid dairy manure Beef lot runoff

What is the state of the science?Are we making a difference?: 

What is the state of the science? Are we making a difference?