Estimation of Phosphorus Loss From Agricultural Land in the Southern Region of the USA Using the APEX, TBET, and

APLE Models

Deanna Osmond, NC State UniversityAdam Forsberg and David Radcliffe, University of Georgia

John Ramirez, Mississippi State UniversityDan Storm and Aaron Mittelstet, Oklahoma State University

Carl Bolster, ARS

Waste to Worth ConferenceSeattle, WA

March 30 – April 2, 2015

Comparing Ratings of the Southern P Indices: Prior Work

TX

OK

FL

AL GA

AR

LA

NC

MS

TN

KY

SC

²0 250 500125 Miles

Albers Equal-Area Conic

Southern States Involved in the USDA-NRCS Funded Conservation

Innovation Grant (CIG)

Southern CIG: Objectives

1. Determine pre-existing watershed or plot-scale (11) sites where accuracy of P Indices to estimate site P loss potential can be evaluated.

2. Compare predictions of P-Indices to water quality data from benchmark sites.

3. Compare fate and transport models (APEX, TBET, APLE) against water quality data. Use water quality data (monitored or predicted by model) to guide refinement of P Indices.

4. Compare predictions of P Indices against fate and transport water quality models (APEX, TBET, APLE) for calibrated and uncalibrated models.

5. Refine P Indices to ensure better consistency in ratings across state boundaries and within physiographic provinces.

Locations of Data Sets

TX

OK

FL

AL GA

AR

LA

NC

MS

TN

KY

SC

²0 250 500125 Miles

Albers Equal-Area Conic

Southern Field SitesState # Plots Date range Site-years Crop STP range (ppm)

1 2 3 4AR 7 2009 – 2011 21 Pasture 81 - 183 Captina (C)GA 6 1995 – 1998 24 Pasture 14 - 142 Cecil (B) Altavista (C) Sedgefield (C) Helena (C)

NC 5 2011-2013 15Corn with wheat

cover44-121 Delanco (C)

MS 2 1996-1999 8Cotton or soybens with wheat cover

37-79 Dubbs (B) Tensas (D) Alligator (D) Dundee (C)

OK 1 1972-1976 4 Cotton 20 McLain (C) Reinach (C)OK 1 2006-2007 1.17 Pasture 50 Clarksville (B)OK 1 1977-1992 16 Native grass 15 Bethany (C)OK 1 1980-1985 6 Wheat 35 Norge (B)TX 1 1998-2001 4 Hay 435 Duffau (B)TX 1 2005-2008 4 Sorghum/Oats 34 Topsey (C) Brackett (C) Krum (D)TX 1 2005-2008 4 Native grass 10 Nuff (C)

TX 1 2001-2008 7Corn with wheat

cover51 Houston Black (D)

Soil Series (hydro group)

Texas BMP Evaluation Tool (TBET)

Climate • Daily rainfall &

temperature

Soils• Up to 3 series

Land use• Crop system

Topography• Field area• Field slope

Soil Test P• Mehlich III

Fertilization

TBET Model ProcessCalibrated

Single year simulations run on a daily time-step• (1/1/YYYY – 12/31/YYYY)

2 years of warm-up• Initialize soil-moisture profile and nutrient pools

Compared model predictions to measured values on an event-basis• Events within each year were summed for annual comparisons

Runoff events greater than 0.1 mm were compared• If event-basis runoff spanned more than one day, total runoff for the entire

storm (up to three days) was lumped for analysis

Model evaluation• Slope, intercept, R-squared• Nash-Sutcliffe Efficiency• Percent Bias

TBET Baseline Results: RunoffOverall annual observed vs predicted runoff

0 100 200 300 400 500 600 700 800 900 10000

200

400

600

800

1000f(x) = 1.12036092139429 x + 39.5352427294702R² = 0.709923934579262

GANCMSTX/OK

Observed runoff (mm/yr)

Sim

ulat

ed ru

noff

(mm

/yr)

SiteLinear Relationship

NSE PBIASIntercept Slope R2

Overall 40 1.1 0.7 0.3 34AR 31 2.2 0.9 -5.1 164GA 120 0.5 0.5 0.1 30NC 104 1.1 0.7 0.0 46MS 39 1.3 0.9 0.1 45TX/OK -18 0.8 0.8 0.7 -30

TBET Baseline Results: Sediment

Overall annual observed vs predicted sediment

0 10 20 30 40 50 60 70 80 90 1000

20

40

60

80

100

f(x) = 2.63294238712031 x + 4.65572569362076R² = 0.106239571073184

NCMSTX/OKAR

Observed SS (ton/ha/yr)

Sim

ulat

ed S

S (t

on/h

a/yr

)

Site Linear Relationship NSE PBIASIntercept Slope R2

Overall 4.7 2.6 0.1 -67.6 488AR 0.0 3.8 0.3 -66.1 378GA -- -- -- -- --NC 20.8 7.1 0.4 -304.6 1698MS 0.3 1.3 0.8 0.4 43TX/OK 0.4 0.4 0.3 0.1 -40

TBET Baseline Results: Total POverall annual observed vs predicted total P

Site Linear Relationship NSE PBIASIntercept Slope R2

Overall 3.5 1.7 0.1 -26.6 166

AR 0.7 0.8 0.5 0.1 39

GA 2.1 0.3 0.1 -0.4 -43

NC 21.7 5.9 0.5 -158.3 961

MS 1.0 0.4 0.4 0.4 -16

TX/OK 0.6 0.4 0.2 0.1 -36

0 50 100 1500

50

100

150

f(x) = 1.69410424381338 x + 3.52490790442359R² = 0.102774162089251

GANCMSTX/OK

Observed TP (kg/ha/yr)

Sim

ulat

ed T

P (k

g/ha

/yr)

TBET Baseline Results: Dissolved P

Overall annual observed vs predicted dissolved P

0 2 4 6 8 10 12 14 16 18 200

2

4

6

8

10

12

14

16

18

20

f(x) = 0.469034620588692 x + 0.273556242110901R² = 0.485190550099921

GANCMSTX/OKAR

Observed DP (kg/ha/yr)

Sim

ulat

ed D

P (k

g/ha

/yr)

SiteLinear Relationship

NSE PBIASIntercept

Slope R2

Overall 0.3 0.5 0.5 0.4 -41AR 0.4 0.5 0.4 0.4 -10GA 1.6 0.3 0.2 -0.2 -40NC 0.2 0.3 0.5 0.4 -36MS 0.0 0.3 0.9 -0.7 -72TX/OK 0.0 0.2 0.2 -0.5 -77

TBET Preliminary ConclusionsTBET was used after being calibrated• Runoff predictions are satisfactory

with slight overprediction• Sediment for AR & NC is

overpredicted• Total P is affected by

overprediction of sediment and underprediction of dissolved P, which is systematically underpredicted

• Modeling TBET was very time consuming with uncertain outcomes thus it may not be an appropriate field-based tool for predicting P loss especially if it is uncalibrated

R2 NSE PBIASRunoff 0.74 0.42 22Sediment 0.07 -77.61 489Total P 0.08 -30.53 176Dissolved P 0.49 0.40 -44

Agricultural Policy/Environmental eXtender

(APEX)

• Underpredicted estimations when annual Q< 100mm and overpredicted when Q > 100mm

• Poor correlation and performance

Line 1:1Line 1:1

Line 1:1

APEX: Georgia Results (Flow, TP, and DP)

Line 1:1

Line 1:1

Line 1:1

Line 1:1

APEX: North Carolina Results (Flow, Soil Loss, TP, and DP)

APEX Preliminary Conclusion (Uncalibrated)

• Acceptable model performance predicting runoff• Very inaccurate predictions for phosphorus losses• Inaccurate soil erosion prediction in small plots • Tillage practices appears to be a factor that determines

model performance (e.g. overprediction or underprediction)

• Model setup required additional information that was not available in the southern databases and most producers would not have this information either

• Modeling APEX was very time consuming with uncertain outcomes thus it may not be an appropriate field-based tool for predicting P loss especially if it is uncalibrated

Annual P Loss Estimator (APLE)

•User-friendly spreadsheet•Annual time step•Requires runoff and erosion as inputs•Does not require calibration•Has most up-to-date fertilizer and manure application algorithm

APLE Model ProcessUncalibrated

Runoff and erosion values obtained from TBET model simulations

Does not require warm-up

Compared model predictions to measured values on an annual basis

• Events within each year were summed for annual comparisons

Model evaluation• Slope, intercept, R2

• Nash-Sutcliffe Efficiency (NSE)• Percent Bias (PBIAS)

APLE Results: Total POverall annual observed vs predicted total P

SiteLinear Relationship

NSE PBIASIntercept

Slope R2

Overall 3.4 1.5 0.2 -18 -140AR 0.7 1.0 0.6 0.3 -64GA 1.5 0.4 0.3 0.1 37NC 15 4.4 0.4 -3 -670MS -0.4 1.4 0.8 -18 -24TX/OK 0.3 0.2 0.4 -0.1 66

Measured TP loss (kg/ha)

0 5 10 15 20 25

Pre

dicte

d TP

loss (kg/ha

)

0

20

40

60

80

100

120

NCMSGAARTX/OK

APLE Results: Dissolved POverall annual observed vs predicted dissolved P

SiteLinear Relationship

NSE PBIASIntercept

Slope R2

Overall 0.99 0.6 0.5 0.5 4.3AR 0.5 1.0 0.7 0.3 -51GA 1.2 0.5 0.4 0.1 28NC 1.6 0.5 0.1 -3 -180MS 0.3 2.0 0.3 -18 -160TX/OK 0.3 -0.1 0.03 -1.4 49

Measured DRP loss (kg/ha)

0 5 10 15

Pre

dicte

d DR

P lo

ss (kg/ha

)

0

2

4

6

8

10

12

14

16

NCMSGAARTX/OK

APLE Preliminary Conclusions

• APLE is uncalibrated• APLE uses modeled runoff and

erosion• Dissolved P is better than total P

R2 NSE PBIASRunoff -- -- --Sediment -- --- --Total P 0.2 -18 -140Dissolved P 0.5 0.5 4.3

Conclusions

• Flow generally predicted better than sediment, TP or DP

• Modeling was very time consuming with uncertain outcomes thus it may not be an appropriate field-based tool for predicting P loss

Questions

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