Evaluating the effectiveness of agricultural management practices to

reduce nutrient loads from farms in PPWP

Port Phillip and Westernport Catchment

Project Manager: Anja George (DPI)

- Catchment and Agriculture Services -

Background

Deteriorating water quality is a major threat to the waterways and bays of PPWP

In 2004, only 25% of the waterways were in good or very good condition.

50% of the PPWP regions is utilised for agr. pursuits (4,500 enterprises, annual production value $1 billion ).

Agricultural land is a significant contributor of nutrients (nitrogen and phosphorus).

What we ALREADY know...Clear link between the way agricultural land is managed

and nutrient export.

Nutrient export from some agr. pursuits is controlled through licensing, reducing nutrients from majority of land uses relies on BMP’s.

Appropriate management of agr. land through the adoption of BMP can reduce nutrient exports and minimise water quality impacts.

Ability to reduce nutrient exports varies from farm to farm, catchment to catchment, industry to industry.

Practices that are successful in one area may not be suitable for all farms or land uses in catchment.

What we DON’T know...

To what extent can agri BMP’s be used to reduce TN and TP exports from farms to waterways in PPWP?

• specific land uses and characteristics of PPWP (soils, rainfall)

Traditionally difficult to measure benefit of individual BMP’s on water quality

Research into nutrient export from agricultural land has focused predominantly on the paddock scale (very few at farm scale) AND not in PPWP.

Specific information on effectiveness of BMP’s in reducing N and P exports in PPWP is limited.

What we NEED to know...

For the major agricultural land uses in PPWP:

What are the agricultural sources of nutrients?

Transport pathways of nutrients from farms to waterways?

Catchment and Environmental factors that influence export?

Which BMP’s? (one, all, point, diffuse sources?)

Which land uses ? (eg. dairy, beef)

How? (feasibility, cost and implementation mechanisms )

Project overview:

Aim: To evaluate the effectiveness of agricultural BMP’s to reduce nutrient (TN and TP) exports from farms to waterways.

• Two year project (June 2005- June 2007).

• Partnership between DPI CAS and PIRVic Soil and Water Platform

• Working group (9 members-inter-agency, technical expertise)

Information from this project will help land managers and catchment planners make informed decisions on management of agr. land for water quality protection.

Working Group:Name

Anja George (Project Manager)DPI CAS – Port Phillip and Westernport

Ruth DuncanDPI PIRVic, Tatura – Senior Hydrologist

QJ WangDPI PIRVic, Tatura– Principal Scientist, Soil and Water

David NashDPI PIRVic, Ellinbank– Statewide Leader – Soil Chemistry

Kirsten Barlow- Senior ScientistDPI PIRVic, Water Quality Project Manager

Murray McIntyreDSE, Manager, Water and Catchment Services

David McKenzieEPA-Gippsland

Hannah PextonMelbourne Water (and DSS Project Manager)

Mark Hincksman DPI, CASWhole Farm Planning (Horticlture)

Land uses Investigated Project focuses on catchments and land uses that have been identified as key sources of Nitrogen and Phosphorus in

PPWP:

Dairy (Westernport)Beef (Westernport)Strawberry (representative of annual horticulture) (PP-

Yarra)

Methodology

2 sections:• Bayesian Network Model development

• Model application and demonstration (Scenario testing)

Part 1: Bayesian Network ModelsDevelopment of 5 Bayesian Networks Models (TN and TP): 2 x Dairy 1 x Beef 2 x Annual horticulture (Strawberry)

Bayesian Network Models: Describe cause and effect of management decisions on outcomes Incorporate qualitative and quantitative information from all levels

(farmers, industry, agency, scientists etc..) thereby reducing uncertainty. Calculates consequence of agri. management practices by determining

probability (%) of small, medium and large TP/TN load under different management scenarios and landscape characteristics

Limitations (What it can’t do!): Give absolute numbers on nutrient export loads (ie. t/ha/yr). This is

presented in probability (%).

Model at farm scale (not catchment). Scenario are used to test and demonstrate wider industry/catchment /regional application.

Spatial Distribution of Fert.

poorfairgood

10.075.015.0

Fertiliser Application Rate

lowmediumhigh

10.060.030.0

Sub-Surface Flow (mm)

smallmediumlarge

43.832.323.9

38.3 ± 18

TP Load from Stock Access (kg/ha)

lowmediumhigh

50.042.57.50

0.45 ± 0.59

Surface TP Load Export (kg/ha)

lowmediumhigh

75.423.21.34

3.8 ± 2.8

Sub-Surface Transport Capacity

lowmediumhigh

43.832.323.9

38.3 ± 18

Surface Slope

lowhigh

80.020.0

Infiltration Capacity

lowmediumhigh

20.446.333.3

Fertility

lowmediumhigh

5.0050.045.0

Sub-Surface Soil Texture

lightmediumheavy

30.050.020.0

Surface Soil Texture

lightmediumheavy

70.030.0 0

Sub-Surface Drainage

noyes

90.010.0

Fert. Application Effectiveness

poorfairgood

22.026.851.2

Stocking Rate (cows/ha)

lightmediumheavy

65.020.015.0

2 ± 0.8

Phosphorus Balance

neutralpositivevery positive

9.8952.138.0

Nutrient Retention

smallmediumlargevery large

19.060.020.0 1.0

0.612 ± 0.17

Point TP Load (kg/ha)

smallmediumlarge

42.055.72.34

0.834 ± 0.74

Point Availability of TP (kg/ha)

lowmediumhigh

33.464.02.59

0.939 ± 0.8

Dairy point source (kg/ha)

lowmediumhigh

34.763.22.10

0.906 ± 0.75

Storage of Hay/Silage (kg/ha)

poorgood

30.070.0

0.015 ± 0.023

Rainfall Annual

lowmediumhigh

12.038.050.0

0.595 ± 0.17

TP from Erosion (kg/ha)

lowmediumhigh

58.319.122.5

0.0771 ± 0.13

Availability of TP Tunnel/Gully Erosion (...

lowmediumhigh

60.020.020.0

0.06 ± 0.11

Surface Flow (mm)

smallmediumlarge

5.7555.339.0

180 ± 61

Rainfall Annual

lowmediumhigh

12.038.050.0

1080 ± 150

Total Runoff (mm)

lowmediumhigh

12.038.050.0

223 ± 46

Timing of Application

poorfairgood

10.020.070.0

Distance of point source to Watercourse

closemediumfar

30.055.015.0

0.675 ± 0.24

Duration

dairy onlydairy feedpad

95.05.00

0.55 ± 0.22

Dairy/Feed Pad Effluent Mgmt

poorfairgood

60.030.010.0

0.315 ± 0.19

Track Design and Mmgt

poorfairgood

20.060.020.0

0.15 ± 0.14

Diffuse Availability of TP (mg/L)

lowmediumhigh

24.635.739.7

2.28 ± 0.71

Soil Mgmt

poorfairgood

50.030.020.0

Bought in Feed

lowmediumhigh

30.050.020.0

Sub-Surface Soil

peaty sandyother

5.0095.0

0.05 ± 0.22

Sub-Surface Drainage Capacity

lowmediumhigh

45.039.415.6

Stock Access to Watercourses

yesno

50.050.0

0.25 ± 0.25

Sub-Surface TP Load Export kg/ha)

smallmediumlarge

95.0.0534.95

0.0104 ± 0.052

TP Load from Dairy Farm (kg/ha)

smallmediumlarge

75.323.31.39

3.81 ± 2.8

Surface and Point TP Load (kg/ha)

lowmediumhigh

45.346.97.79

5.7 ± 3.6

Diffuse Surface TP Load (kg/ha)

smallmediumlarge

13.556.030.5

4.34 ± 2.4

HYDROLOGY

DIFFUSE SOURCES

POINT SOURCES

LOAD OUTPUTS

Probability of TP load from Dairy farm

Example: Diffuse TP load (Dairy)

Spatial Distribution of Fert.

poorfairgood

33.333.333.3

Fertiliser Application Rate

lowmediumhigh

33.333.333.3

Fertility

lowmediumhigh

33.333.333.3

Fert. Application Effectiveness

poorfairgood

36.726.736.7

Phosphorus Balance

neutralpositivevery positive

18.051.330.7

Timing of Application

poorfairgood

33.333.333.3

Diffuse Availability of TP (mg/L)

lowmediumhigh

26.733.539.8

2.44 ± 0.96

Bought in Feed

lowmediumhigh

33.333.333.3

Model Applications Scenario Testing:

To demonstrate how changes in climate, landscape factors (eg. soil types, rainfall, slope) and management practices (eg. effluent and fertiliser management) can influence TN and TP export.

Scenarios DescriptionPoor Management Worst or poor management practices

Current Management Management of farms at time of investigation

Farmers Future Plans Landholder selected management practices they are

planning to implement within the next 5-10 years Greatest Nutrient Reduction

(A = feasible, B =not feasible)

Management practice with greatest capacity for reducing TN and TP export from farms as informed by models (top 3). Feasibility (cost effectiveness) is also investigated

Best Practice(A = feasible, B =not

feasible)

All best management practices as informed by industry guidelines.

  Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5

VariablesPoor

PracticesCurrent

ManagementFarmers Planned

Greatest Nutrient

Reduction (cost-

effective)

Greatest Nutrient

Reduction (Not cost-effective)

Best Practice

(cost-effective)

Best Practice (Not

cost-effective)

Annual rainfall   High

         

Surface soil texture  Heavy 30%, Medium 70%

Sub-surface soil texture   Heavy

Surface slope   High

Sub-surface soil*   Other

Fertility*   High

Distance to waterways   Close

Soil management Poor Fair Fair Fair Fair Good Good

Sub-surface drainage No No No No No No Yes

Timing of fertiliser application Poor Fair Fair Good Good Good Good

Spatial distribution of fertiliser Poor Poor Poor Poor Poor Good Good

Fertiliser application rate High High High Low Low Low Low

Bought in feed Low Low Low Low Low Low Low

Stocking rate Light Light Light Light Light Light Light

Effluent Management Poor Poor Good Poor Poor Good Good

Track design and management Poor Fair Fair Fair Fair Good Good

Storage of silage Poor Good Good Good Good Good Good

Stock access to watercourses YesYes 50% No 50%

Yes 50% No 50%

No No No No

Tunnel/Gully erosion* High High High High High Medium Low

Nutrient retention Small Small Small Medium Very Large Medium Very Large

  Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5

 Poor/PastPractices

Current Management

Farmers Planned

Greatest Nutrient

Reduction (cost-

effective)

Greatest Nutrient

Reduction (Not cost-effective)

Best Practice

(cost-effective)

Best Practice (Not cost-

effective)

Probability of SMALL TP load 15% 19% 24% 69% 100% 82% 100%

Probability of MEDIUM TP load 56% 59% 62% 31% 0% 19% 0%

Probability of LARGE TP load 28% 22% 14% 0% 0% 0% 0%

Change in Phosphorus Load     0.13 0.72 1.03 0.85 1.03

Improvement in TP load compared to current management

    Large Very Large Very LargeVery

LargeVery Large

Change in Phosphorus Load   0.09 0.22 0.81 1.13 0.94 1.13

Improvement in TP load compared to poor management

  Small Large Very Large Very LargeVery

LargeVery Large

Probability of SMALL TN load 1% 1% 4% 12% 28% 19% 31%

Probability of MEDIUM TN load 42% 54% 55% 72% 59% 73% 62%

Probability of LARGE TN load 57% 45% 42% 16% 13% 6% 7%

Change in Nitrogen Load     0.06 0.40 0.59 0.56 0.68

Improvement in TN load compared to current management

  Small Very Large Very LargeVery Large

Very Large

Change in Nitrogen Load   0.12 0.18 0.52 0.71 0.69 0.80

Improvement in TN load compared to poor management

  Large Large Very Large Very LargeVery

LargeVery Large

Probability of nutrient loads

Direction and magnitude of change in nutrient load to compare scenarios

Where to from here?

Assessment of results

What do these results mean for:a) Farmers?b) Land use and agri industry (ie. dairy)?c) Management of agricultural land in catchment?d) Broader application/PPWP/BBW Strategy?e) Future Implementation mechanisms?f) Knowledge and research gaps (R and D requirements)?

Final Project report due: June 2007.

Thank You

Anja GeorgeDepartment of Primary Industries

Woori YallockPh: (03) 5954 4001

anja.george@dpi.vic.gov.au