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 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.

HYDROLOGY DIFFUSE SOURCES POINT SOURCES LOAD OUTPUTS Probability of TP load from Dairy farm

Example: Diffuse TP load (Dairy)

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. ScenariosDescription Poor 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 1Scenario 2Scenario 3Scenario 4Scenario 5 Variables Poor Practices Current Management Farmers 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 managementPoorFair Good Sub-surface drainageNo Yes Timing of fertiliser applicationPoorFair Good Spatial distribution of fertiliserPoor Good Fertiliser application rateHigh Low Bought in feedLow Stocking rateLight Effluent ManagementPoor GoodPoor Good Track design and managementPoorFair Good Storage of silagePoorGood Stock access to watercoursesYes Yes 50% No 50% No Tunnel/Gully erosion*High MediumLow Nutrient retentionSmall MediumVery LargeMediumVery Large

Scenario 1Scenario 2Scenario 3Scenario 4Scenario 5 Poor/Past Practices 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% Change in Phosphorus Load Improvement in TP load compared to current management LargeVery Large Very Large Very Large Change in Phosphorus Load Improvement in TP load compared to poor management SmallLargeVery Large Very Large Very Large Probability of SMALL TN load 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 Improvement in TN load compared to current management SmallVery Large Very Large Very Large Change in Nitrogen Load Improvement in TN load compared to poor management Large Very Large Very Large Very 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 George Department of Primary Industries Woori Yallock Ph: (03)