Transcript Agriculture Powerpoint Template

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 Duncan
DPI PIRVic, Tatura – Senior Hydrologist
QJ Wang
DPI PIRVic, Tatura– Principal Scientist, Soil and Water
David Nash
DPI PIRVic, Ellinbank– Statewide Leader – Soil Chemistry
Kirsten Barlow- Senior Scientist
DPI PIRVic, Water Quality Project Manager
Murray McIntyre
DSE, Manager, Water and Catchment Services
David McKenzie
EPA-Gippsland
Hannah Pexton
Melbourne Water (and DSS Project Manager)
Mark Hincksman
DPI, CAS
Whole 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 Models
Development 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.
Surface Soil Texture
Rainfall Annual
low
medium
high
light
medium
heavy
12.0
38.0
50.0
HYDROLOGY
Sub-Surface Soil Texture
70.0
30.0
0
light
medium
heavy
30.0
50.0
20.0
Surface Slope
low
high
80.0
20.0
1080 ± 150
Soil Mgmt
poor
fair
good
Infiltration Capacity
50.0
30.0
20.0
low
medium
high
Sub-Surface Drainage Capacity
20.4
46.3
33.3
low
medium
high
DIFFUSE SOURCES
Sub-Surface Drainage
45.0
39.4
15.6
no
yes
90.0
10.0
Total Runoff (mm)
low
medium
high
12.0
38.0
50.0
POINT SOURCES
Sub-Surface Transport Capacity
low
medium
high
223 ± 46
43.8
32.3
23.9
38.3 ± 18
Surface Flow (mm)
small
medium
large
Duration
Sub-Surface Flow (mm)
5.75
55.3
39.0
small
medium
large
180 ± 61
Spatial Distribution of Fert.
Fertiliser Application Rate
poor
fair
good
low
medium
high
10.0
75.0
15.0
Timing of Application
poor
fair
good
LOAD OUTPUTS
low
medium
high
30.0
50.0
20.0
Fert. Application Effectiveness
poor
fair
good
neutral
positive
very positive
Diffuse Availability of TP (mg/L)
24.6
35.7
39.7
2.28 ± 0.71
Dairy/Feed Pad Effluent Mgmt
Track Design and Mmgt
poor
fair
good
poor
fair
good
0.315 ± 0.19
9.89
52.1
38.0
Stocking Rate (cows/ha)
Dairy point source (kg/ha)
Storage of Hay/Silage (kg/ha)
low
medium
high
poor
good
65.0
20.0
15.0
34.7
63.2
2.10
Point Availability of TP (kg/ha)
Sub-Surface Soil
Stock Access to Watercourses
5.00
95.0
yes
no
0.05 ± 0.22
low
medium
high
50.0
50.0
0.25 ± 0.25
Sub-Surface TP Load Export kg/ha)
TP Load from Stock Access (kg/ha)
low
medium
high
4.34 ± 2.4
0.0104 ± 0.052
50.0
42.5
7.50
0.45 ± 0.59
0.675 ± 0.24
Point TP Load (kg/ha)
small
medium
large
30.0
55.0
15.0
Rainfall Annual
42.0
55.7
2.34
low
medium
high
0.834 ± 0.74
12.0
38.0
50.0
0.595 ± 0.17
Surface and Point TP Load (kg/ha)
TP from Erosion (kg/ha)
Availability of TP Tunnel/Gully Erosion (...
low
medium
high
low
medium
high
45.3
46.9
7.79
TP Load from Dairy Farm (kg/ha)
Surface TP Load Export (kg/ha)
small
medium
large
low
medium
high
3.81 ± 2.8
Distance of point source to Watercourse
close
medium
far
low
medium
high
5.7 ± 3.6
75.3
23.3
1.39
33.4
64.0
2.59
0.939 ± 0.8
small
medium
large
95.0
.053
4.95
30.0
70.0
0.015 ± 0.023
0.906 ± 0.75
Diffuse Surface TP Load (kg/ha)
Probability of TP
load from Dairy
farm
0.15 ± 0.14
2 ± 0.8
peaty sandy
other
20.0
60.0
20.0
light
medium
heavy
small
medium
large
13.5
56.0
30.5
60.0
30.0
10.0
5.00
50.0
45.0
Phosphorus Balance
22.0
26.8
51.2
low
medium
high
38.3 ± 18
Fertility
low
medium
high
95.0
5.00
0.55 ± 0.22
10.0
60.0
30.0
Bought in Feed
10.0
20.0
70.0
dairy only
dairy feedpad
43.8
32.3
23.9
75.4
23.2
1.34
3.8 ± 2.8
58.3
19.1
22.5
0.0771 ± 0.13
Nutrient Retention
small
medium
large
very large
19.0
60.0
20.0
1.0
0.612 ± 0.17
60.0
20.0
20.0
0.06 ± 0.11
Example: Diffuse TP load (Dairy)
Spatial Distribution of Fert.
poor
33.3
fair
33.3
good
33.3
Timing
poor
fair
good
of Application
33.3
33.3
33.3
Fertiliser Application Rate
low
33.3
medium
33.3
high
33.3
Bought in Feed
low
33.3
medium 33.3
high
33.3
Fert. Application Effectiveness
poor
36.7
fair
26.7
good
36.7
Fertility
low
33.3
medium
33.3
high
33.3
Phosphorus Balance
neutral
18.0
positive
51.3
very positive 30.7
Diffuse Availability of TP (mg/L)
low
26.7
medium
33.5
high
39.8
2.44 ± 0.96
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
Poor Management
Description
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
Variables
Poor
Practices
Scenario 2
Current
Management
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
Scenario 3
Farmers
Planned
Scenario 4
Greatest
Nutrient
Reduction
(costeffective)
Greatest
Nutrient
Reduction
(Not costeffective)
Scenario 5
Best
Practice
(costeffective)
Best
Practice
(Not costeffective)
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
Yes
Yes 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
Probability of
nutrient loads
Poor/Past
Practices
Scenario 2
Current
Management
Scenario 3
Farmers
Planned
Scenario 4
Greatest
Nutrient
Reduction
(costeffective)
Scenario 5
Greatest
Nutrient
Reduction
(Not costeffective)
Best
Practice
(costeffective)
Best
Practice
(Not costeffective)
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%
0.13
0.72
1.03
0.85
1.03
Large
Very Large
Very Large
Very
Large
Very Large
0.09
0.22
0.81
1.13
0.94
1.13
Small
Large
Very Large
Very Large
Very
Large
Very Large
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%
0.06
0.40
0.59
0.56
0.68
Small
Very Large
0.12
0.18
0.52
0.71
0.69
0.80
Large
Large
Very Large
Very Large
Very
Large
Very Large
Change in Phosphorus Load
Improvement in TP load
Direction
compared to current management
Change in Phosphorus Load
Improvement in TP load
compared to poor management
and
magnitude of
change in nutrient
load to compare
scenarios
Probability of SMALL TN load
Change in Nitrogen Load
Improvement in TN load
compared to current management
Change in Nitrogen Load
Improvement in TN load
compared to poor management
Very Large
Very
Large
Very Large
Where to from here?
Assessment of results
What do these results mean for:
a)
b)
c)
d)
e)
f)
Farmers?
Land use and agri industry (ie. dairy)?
Management of agricultural land in catchment?
Broader application/PPWP/BBW Strategy?
Future Implementation mechanisms?
Knowledge and research gaps (R and D requirements)?
 Final Project report due: June 2007.
Thank You
Anja George
Department of Primary Industries
Woori Yallock
Ph: (03) 5954 4001
[email protected]