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