This document discusses different types of phosphorus (P) loss models and their appropriate uses. It summarizes that P models range from complex process-based models to simpler P index models. A new model called APLE aims to provide quantitative P loss estimates in a user-friendly format. The document also provides examples of APLE model inputs, outputs, and testing against field data. It demonstrates how the APLE model can be used to examine different management scenarios and their impacts on P loss and soil test P levels over multiple years.

1. Peter Vadas
USDA-ARS, Dairy Forage Research Center, Madison, WI
Current Models for Phosphorus
Loss from Agricultural Systems

2. Agricultural P Loss? It
Depends
How do we effectively integrate decades of data and
multiple processes into comprehensive method
producers, policy makers can use to make decisions?
Soil P quantity and chemistry
P Application rate and method
Manure and fertilizer chemistry
Runoff hydrology
Erosion
How much does agriculture contribute to P pollution?
What can producers do to decrease P loss? How do we
answer these questions?

3. The Case for Models
 Due to sheer number of important physical and
management interactions, impossible to meet demands
for information fast enough and cheap enough relying on
field research alone.
 Models are effective, efficient way to integrate variety of
field data to make decisions. Some scenarios (climate
change, system integration) impossible to address without
models.
 Model development forces us to formalize and test
understanding of natural processes, and thus identify
knowledge and data gaps.
 Models are simple representations of our understanding
of reality. Can’t capture all complexities, what we don’t
know.

4. Current P Model
Options
Complex Simple
SWAT
APEX
AnnAGNP
S
P Index
User friendly,
quantitative
APLE
WI SNAP+
OK PPM
TX TBET

5. Complex P Models
 Daily time-step, field to watershed scale,
quantitative predictions; For TMDL-type projects
 Process-based, spatially explicit simulations of
hydrology, multiple contaminant transport through
landscape
 Data intensive for inputs and testing, extensive
user experience and skill needed
 Should be calibrated
 Require dedicated support system for updating and
development

6. Simple P Indexes
 Annual time-step, field scale, relative ranking of
risk of P loss
 Good for producer/policy education
 Not data intensive, little user experience and
skill needed, no calibration needed
 Not process-based, calculations based on data
and professional judgment
 Generally had little field testing to verify
accuracy of predictions and recommendations

7. User-friendly,
Quantitative P Loss
Models Models that estimate (lb/ac) annual, field-scale,
P loss
 Moderate data requirements (mix of databases
and user-defined management)
 Require moderate user experience and skill, no
calibration needed
 Process-based equations based only on
experimental data, not spatially explicit
 Able to test with widely available P loss data

8. Excel spreadsheet model that estimates (lb/ac)
annual, field-scale, dissolved and sediment P loss in
surface runoff for given set of management, soil P,
erosion, runoff conditions.
Intended to be process-based like SWAT, APEX, but
user-friendly like P Index.

9. APLE Inputs

10. Grazing Animals
Milk
Cows Heifers
Dry
Cows Calves
Total Cow Days (# cows x # days) 0 0 0 0
Beef
Cows Calves
0 0
Solid Manure Applications Winter Spring Summer Fall
Manure Applications Manure Applied wet ton/acre 0 0 0 0
Manure Solids % 0 0 0 0
Manure Total P2O5 Content lbs/wet ton 0 0 0 0
Manure WEP/TP % 0 0 0 0
Manure Incorporated % 0 0 0 0
Depth of Incorporation inches 0 0 0 0
Liquid Manure Applications Winter Spring Summer Fall
Manure Applications Manure Applied gallons/acre 0 5000 0 5000
Manure Solids % 0 6 0 6
Manure Total P2O5 Content lbs/1000 gal. 0 6.1 0 6.1
Manure WEP/TP % 0 50 0 50
Manure Incorporated % 0 0 0 0
Depth of Incorporation inches 0 0 0 0
Fertilizer Applications
Fertilizer Application Fertilizer P Applied lb/ac 0
Fertilizer Incorporated % 0
Depth of Incorporation inches 0
Degree of Soil Mixing % 15
APLE Inputs

11. APLE Output
P Loss in Runoff
0.0
0.5
1.0
1.5
2.0
2.5
1 2 3 4 5 6 7 8 9 10
Year
RunoffPloss(lb/ac)
Soluble P from Fertilizer
Soluble P from Manure
Soluble P from Soil
Sediment P
Mehlich 3 Soil P
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10
Year
Mehlich3SoilP(ppm)
Top Layer
Bottom Layer
Whole Topsoil

12. APLE Testing
 P loss in runoff - Measured data from 28 crop
studies from 13 states, Australia, Ireland (Vadas et
al., JEQ 2009), 14 grazing studies from 5 states,
Australia, New Zealand (unpublished)
 Soil P dynamics - Measured data from 19
studies monitoring changes in soil P from 1 to 25
years (Vadas et al., JEQ 2012)
 Current updates include P loss from barnyards
and feedlots, uncertainty estimates

13. Case 1: 50 STP, 1 ton/ac erosion, 3 in runoff, 45 lb P/ac liquid on surface
Case 2: 50 STP, 3 ton/ac erosion, 6 in runoff, 45 lb P/ac liquid tilled
Case 3: 50 STP, 5 ton/ac erosion, 9 in runoff, 45 lb P/ac liquid tilled
Case 4: 100 STP, 1 ton/ac erosion, 3 in runoff, 45 lb P/ac liquid on surface
Case 5: 100 STP, 3 ton/ac erosion, 6 in runoff, 45 lb P/ac liquid tilled
Case 6: 100 STP, 5 ton/ac erosion, 9 in runoff, 45 lb P/ac liquid tilled
Keeping P Loss Low

14. 0.00
1.00
2.00
3.00
4.00
5.00
6.00
1 2 3 4 5 6 7 8 9 10
RunoffPloss(lb/ac)
Year
0.00
1.00
2.00
3.00
4.00
5.00
6.00
1 2 3 4 5 6 7 8 9 10
RunoffPloss(lb/ac)
Year
P Loss in Runoff
Soluble P from Fertilizer
Soluble P from Manure
Soluble P from Soil
Sediment P
0
20
40
60
80
100
120
140
1 2 3 4 5 6 7 8 9 10
Mehlich3SoilP(ppm)
Year
Mehlich 3 Soil P
Top Layer
Bottom Layer
Whole Topsoil
0
20
40
60
80
100
120
140
1 2 3 4 5 6 7 8 9 10
Mehlich3SoilP(ppm)
Year
Feeding Less P
0.5 % 0.3 %
3 ton/ac
erosion
6 in runoff
45 lb P/ac
liquid on
surface
9% less P
loss; 20%
less soil P

15. Soil P Buildup and Decline
1.5 ton/ac
erosion
5 in runoff
45 lb P/ac
No-till
Manure
applied (180
lb P/ac) and
tilled once
every 4 years

16. Summary
 Models are effective way to integrate years of P
research data to meet demand for management
and impact information fast and cheap.
 Models vary in complexity and appropriate uses,
not always easy to know which one to choose and
how to use output.
 New P models help capture current science,
balance versatility and complexity with user-
friendliness
 Models are indispensible; need to be well
developed and tested, have committed support
from policy makers, scientists