Post on 07-Dec-2014
description
J A M E S A . K L A N G , P EA N D R E W F A N G , P E
K I E S E R & A S S O C I A T E S , L L C5 3 6 E . M I C H I G A N A V E . , S T E . 3 0 0
K A L A M A Z O O , M I 4 9 0 0 7J K L A N G @ K I E S E R - A S S O C I A T E S . C O M
Does Precision Agriculture Result in Consistent and Predictable
Nutrient Loading Reductions?
Natural Resources Conservation ServiceConservation Innovation Grant Project
Project Lead:American Farmland TrustProject Title: Coupling Precision Agriculture with Water Quality Credit TradingProject Objectives: Create, test, and define a Water Quality Credit Trading credit estimator to incorporate Variable Rate Technology-based nutrient management crediting into wastewater treatment plant trading programs.Project Area: Within Ohio, Kentucky, Indiana, and/or Illinois
This material is based upon work supported by the Natural Resources Conservation Service, U.S. Department of Agriculture, under number 69-3A75-12-177. Any opinions,
findings, conclusions, or recommendations expressed in the this presentation are those of the authors and do not necessarily reflect the views of the U.S. Department of Agriculture.
Project Team Collaborators
American Farmland Trust Indiana State Department of Agriculture John Deere Kentucky Division of Conservation Kieser & Associates, LLC Ohio Department of Natural Resources Ohio Farm Bureau Ohio State University Purdue University Trimble USDA Natural Resource Conservation Service University of Kentucky
Overview
What is Water Quality Credit Trading? Trading credit characteristics and requirements Project approach Precision Ag technologies Limiting factors for nutrient management credit
generation Assessment methodology Project status Next steps
What is Water Quality Credit Trading?
Water Quality Credit Trading (WQCT) is a flexible U.S. EPA National Pollutant Discharge Elimination System permit compliance option
Allows a new effluent limit to be met by purchasing credits from other locations with equal or greater reductions
Trading options:Point source to point source tradingPoint source to nonpoint source tradingNonpoint source to nonpoint source trading
Water Quality Credit TradingWater Quality Credit Trading
Trading uses a Watershed Approach Treatment plants treat to a baseline level before
being allowed to trade Trade for a specific parameter, plant treats all others WQCT allows flexibility and cost savings WQCT provides greater protection of the ecosystem
than conventional treatment
Past Uncertainties for Trading Credits Generated by Nutrient Management
Establishing a baseline (e.g., field history, county averages, or comprehensive nutrient management plans?)
Weather variability introduced uncertainty Differences in crop uptake over time Yearly yield increases Changes in crop rotation
Different application rates and timing from Equipment upgrades Fertilizer purchases Manure management systems
Adequate data and record storage Leakage (e.g., manure management must be for whole farm) Drainage (numerous complications when present)
Midwest Nutrient Estimation Method;Fields Experiencing Sheet & Rill Erosion
Region V model (a.k.a. STEPL) Explanation in Michigan DEQ “Pollutants Controlled
Calculation and Documentation for Section 319 Watersheds Training Manual” Revised Universal Soil Loss Equation (RUSLE) Chemicals, Runoff and Erosion from Agricultural Management
Systems (CREAMS) nutrient enrichment algorithm Default nutrient concentration values applied
Nutrient Enrichment
Particle size distribution changes during transport
Upland particles Edge-of-field particles
Sand, silt and clay bind phosphorus at different rates
Midwest Sheet and Rill Erosion Method
CREAMS enrichment algorithm: For sediment-attached nutrients (includes
organically bound nutrients) Estimates increase in soil nutrient concentrations
due to redeposition of coarse materials Inputs: erosion rate, delivery ratio, and upland
nutrient concentration Soil nutrient concentration default values: Sand: 0.85 pounds of P per ton of sediment Silt: 1.0 pound of P per ton of sediment Clay: 1.15 pounds of P per ton of sediment
Precision Ag Technologies
Many different forms of Precision Ag exist:● VRT nutrient applications
● On-the-go● Zone mapping
● GPS tractor guidance systems● VRT pesticide applications● VRT seeding● VRT irrigation controls
Project Focus is nutrient controls; credit estimator development:● VRT nutrient applications
● Zone mapping● GPS guidance systems
Project Approach
Collect data from operators that have a long-term VRT history with records
Preference for sites with edge-of-field water quality monitoring (difficulty finding VRT fields with monitoring)
Select a field-scale watershed model:
Considers: Provides field or edge-of-field:● Agricultural inputs ● Yield response● Soil characteristics ● NPS volume of runoff ● Crop dynamics ● NPS sediment loading● Climate variability ● NPS nutrient loading
Modeling Approach
Use model to create multiple scenarios: Vary weather patterns Simulate different VRT and uniform application
nutrient rates Perform a sensitivity analysis to identify which
inputs the model is most responsive to Create a multiple linear regression equation based
on field-modeled estimates of NPS loading to create an edge-of-field phosphorus and nitrogen credit estimator
Model Selection Criteria
Primary project needs: Appropriate for the Ag setting (e.g., considers
timing of equipment passes, application rates, crop rotations,…)
Edge-of-field nonpoint source loadings for sediment, nitrogen, and phosphorus
Additional desired attributes: Robust crop yield estimates Ability to model under extended weather datasets
The “4 R’s” of Nutrient Management
4 R’s for Nutrient Management Right Source (balanced nutrients in management plan) Right Rate (for N & P applied, based on crop needs) Right Time (placed when the crop needs it) Right Place (applied where the plant uptake occurs)
VRT crediting focus on changing the rate: Assumed producer uses the right balance of all nutrients Illustrated load reductions from timing and placement
The KY Farm Site (124 acres)
No-till over a decade; VRT phosphorus application in 2010.
Hydrologic Response Unit #851 is a Lowel Silt Loam with 5 to 10 % slopes; low STP with higher application rates
Hydrologic Response Unit #1933 is a Nicholson silt loam with 2 to 5 % slopes, high STP with low application rates
4 R’s as Seen Through SWAT
Testing of 4 R’s Develop equation using multiple linear regression Sensitivity of nonpoint source edge-of-field loading
to changes 4 R input scenarios Check for input statistical significance Check multicollinearity of inputs Estimate equation’s ability to
explain edge-of-field loading
SWAT Estimated Reduction for Right Rate (Averaged Across Entire Field)
SWAT Scenarios 2010 & 2011 Cropping Years
Right RateP2O5 (lbs/ac)
Based on farm records includes VRT rates 59
Increased VRT rate to county average 99
2010 loading difference (Corn) 0.7(+12.1%)
2011 loading difference (Beans) 1.1 (+8.9%)
SWAT Scenarios 2010 & 2011 Cropping Years Right Time
Based on farm records, applications in the spring
Spring2010
Switched nutrient application to fall of prior year
Fall2009
2009 fall application; Increase in 2009
0.036 lb P/ac+0.7%
2010 loading difference (Corn) 0.0084 lb P/ac(+0.2%)
Increase 2010
2011 loading difference (Beans) -0.2488 lb P/ac(-2.0%)
SWAT Estimated Reduction for Right Time (Averaged Across Entire Field)
SWAT Scenarios 2010 & 2011 Cropping Years
Right Place(Magic -
Incorporation into no-till!)
Based on farm records includes VRT rates Broadcast
Increased VRT rate to county average Incorporation
2010 loading difference (Corn) -0.8 lbs P/ac(-13.7%)
2011 loading difference (Beans) -1.4 lbs P/ac(-10.9%)
SWAT Estimated Reduction for Right Place (Averaged Across Entire Field)
Modeled Field Characteristics
Calibrated on yield Sediment roughly calibrated to 2.6 tons/acre/yr SWAT algorithms used to estimate water quality
results at edge-of-field Highly SEDP and ORGP dominated NPS loading
(e.g., average for all corn years: 38% SEDP, 53 % ORGP, 9% SOLP)
Silt loams modeled with 2 to 10 percent slopes Phosphorus depletion driven by both erosion and
crop uptake
Expanded List of Scenarios; Focus on Two Different Zones
Varied all hydrologic resource units to experience: 1. An initial available soil P at the 2007 soil test
value 2. An initial available soil P at the highest 2007
soil test result 3. An initial soil soluble P level at the lowest 2007
soil test result 4. A one-year precipitation and temperature shift 5. A VRT rate reduced by 5% 6. A two-week shift of precipitation and
temperature plus a VRT rate increase by 10%
Fluctuations Observed During a 40-Year Weather Simulation
Years
Erosion Rates
(tons/acre)
Mehlich 3 STP Test
Estimation Results
Application Rates
(lbs P2O5)
NPS TP Edge-of-field
Loading (lbs. TP/ acre)
1995 versus 1998 1.2 & 1.8 Low & Low 52 & 31 5.5 & 4.1
1973 +1975 + 1978 versus same 9 & 9 105 & 71
(Averages)Same acrossall six years 26.1 & 22.5
Supports long-term NPS loading reductions occur when practicing 4 R’s. Therefore, any confusion occurs within the crediting constraints.
1975 versus 1975(Two Scenarios) 2.7 & 2.7 105 & 72 31 & 31 10.4 & 9.1
1975 versus 1975(Two Scenarios)
3.2 & 1.7 (Est. by 1-yr weather shift)
68 & 64 52 & 52 8.9 & 2.1
The edge-of-field loading is dominated by SEDP and ORGP. Therefore, variability in erosion create larger variability in loading compared to variability of STP.
SWAT OutputsSWAT Outputs Available Field EstimatorsAvailable Field Estimators
NPS Edge-of-field Sediment Yield Sediment Phosphorus Organic Phosphorus Soluble Phosphorus
USLE, RUSLE, RUSLE2
STEPL, Region V (CREAMS model nutrient enrichment estimate added to USLE family estimates)
SWAT-Based Multiple Linear Regression
SWAT OutputsSWAT Outputs Available Field EstimatesAvailable Field Estimates
Cropping Crop yield Plant uptake Fresh organic to mineral
P Organic P to labile P Labile to active P Active to stable P
Application rate Average yield Soil test phosphorus Estimates of average P
uptake per bushel
SWAT-Based Multiple Linear Regression
Validation of Selected Equation
Multiple linear regression equation developed on one HRU and tested on a second
Setup on HRU #851, has higher slopes and lower STP initial values
Validation on HRU #1933 with lower slopes and higher initial STP values
Both Loam soils Equation developed on High Mehlich 3 STP results
TPeof = 1.608 – 0.03 (STP) + 2.81 (SED)
Regression Statistics: R2 = 0.84, F = 77.4, Significance F = 2.35 E-12Independent Variable Statistics: STP P-value = 0.0008
SED P-value = 1.35 E-12
Validation of High STP Based Equation
Validation Site
Erosion Rate Range (tons/acre)
Mehlic3 STP test Estimate Results
Multiple Linear Regression
Equation ResultRanges
(lbs TP/ acre)
SWAT Result
Ranges for Same Years
Average Error Across Ten
Corn Years (%)0.6 to 3.3
(Average 2.3) Very High 1.3 to 9.9(Average 6)
1.6 to 11 (Average 7.1)
17% Under Estimated
1.5 to 4.1 (Average 2.5) Very Low 5.6 to 13
(Average 8.8) 2 to 6
(Average 3.8)131% Over Estimated
TPeof = 1.608 – 0.03 (STP) + 2.81 (SED)
Compared to SWAT model HRU #1933 results
Comparison Findings for VRT Based STP Management
Long-term VRT applications reduce long-term nutrient loading but not always yearly loading
Lowering STP results takes time (Randle, 1997), (Mallarino, et al., 2011), (Hanson et al., 2002)
Field erosion rate has the greatest influence on NPS nutrient edge-of-field loading
Application rate increases show up for two years Prediction equations like Region V model need
calibration
Implications for Trading
Current use of long-term erosion averages is appropriate
Verification of credits can be done by STP measurements
Use of default inputs introduce higher uncertainty Nutrient management practices and VRT based on
zone mapping can be credited (STP part of the mgt) On-the-go VRT application rates need to be
associated with erosion predictions if trading
GPS Applicator Guidance Controls for Section Boom; Courtesy of John Deere
NH3 Swath Control Pro
Purpose Develop multi-section on/off system to allow for greater
control of NH3 and High Speed Low Draft machineGoals Increased application precision Product savings Reduced operator strain
Retain Original distribution accuracy Instant on/Instant off (eliminate gassing on ends) Develop monitoring system
Swath Control System Overview
Each opener has an additional on / off valve 9 section system on 15 opener bar Six two-opener sections on outsides Three single opener sections in middle
Swath Control System Overview
• On/off valve every opener• Pressurized NH3 hose from
manifold to valve
Swath Control System Results
Illustrated Overlap Reductions
$325 Product Savings or $3.25 / Acre
(5 acres of overlap removed; 1000 lbs less NH3 applied)
Swath Control System Results
What does this mean? 100 acre actual field size 201 lbs NH3 (165 units) $650 / ton NH3
Without Swath Pro
105.5 applied acres = 10.60 ton10.60 x $650/ton = $6890.00
With Swath Pro
100.5 applied acres = 10.10 ton10.10 x $650/ton = $6565.00
Next Steps
Add a scenario that balances STP results across the 40-year weather simulation
Complete data gathering from a field in Illinois VRT practiced on N and P Long-term record with tile water monitoring Setup and calibrate SWAT Test Kentucky equations on the Illinois field
Develop crediting protocol for GPS guidance systems Make recommendations for calibration of multiple linear
regression equation and Region V model Develop crediting protocol for zone mapping VRT