1. Sheep and Beef data Project Team Femi Olubode (WRC) Blair Keenan (WRC)
Jon Palmer (WRC)
Rex Webby (Rural Consultant)
Ian Jamieson (Independent Consultant)

2. Outline
The research process Illustrative results Some policy implications Concluding remarks

3. Data collection Case studies
450 Farms – surveyed to study pugging/flooding mgt
170 Farms – allowed follow up questions
20 farms – spatially selected
13 farms – interviewed
12 farms – full data
Focus Group discuss/Workshop
Ian Jamieson

5. 5 farm types/systems...
1. Small lamb finishing farm 2. Traditional Hill country farm 3. Hill country/dairy support (maize silage cropping) 4. Hill country/dairy support (pasture silage) 5. Bull & prime beef finishing farm

6. Choice of mitigation options
Also available at

7. Scenario analysis…
FARMAX modelling Biological feasibility OVERSEER modelling Nutrient budgets

8. Illustrative case study farms

10. Case study II Description Mitigation option Scenarios
Ave. N used (kg/ha/yr)
Mitigation option
Scenarios
Traditional hill country
Steep slope - 10% of area
Large farm size: Ave. 475ha
High sheep ratio: 70%
Low stocking rate: 8.5SU/ha
0.5
Plant steep slope in trees
Baseline: 3%
20%
40%
60%
80%
100%

11. Case study II

12. Case study IIIa Description Mitigation option Scenarios
Ave. N used (kg/ha/yr)
Mitigation option
Scenarios
Hill country, dairy support on maize silage cropping
Cropping: 70% of eff. Area
High cattle ratio: 100%
High female cattle ratio: 80%
Low stocking rate: 8.6SU/ha
Beef breeding
125
Reduce are of maize silage cropping
Baseline:
70% of Area
Scenarios:
20%
40%
60%
80%
100%

13. Case study IIIa

14. Highest mitigation level - catchment

15. Proportional representation in the catchment model
Policy implications…
Proportional representation in the catchment model
Dry stock farming
Upper Waikato
Waipa
Central
Lower Waikato
Total
% Land area
8.2
9.3
0.7
8.7
26.8
% $ Contribution
5.3
5.1
0.4
5.2
16.1
% N leaching
6.1 6
0.5
5.7
18.4
% P loss
6.8
8.5
0.6
7.9
23.9
APS (Agricultural Production Survey) Hectares of land by farm type (ANZSIC code) by TA. Aggregated from primary farm use data (MPI, September 2012). Analysis completed 26/11/13. This data is subject to use restrictions and is not to be distributed any further, under the Biosecurity Act.
Agribase database

20. Concluding remarks
Expected results in terms of nutrient loss mitigation
Higher level of N loss mitigation
Lower cost per nutrient loss mitigation
Some win-win solutions
Its more expensive to mitigate P than N
The forestry option however similar (highest P loss mitigation)
Reducing stocking rate is the least popular
Increasing sheep to cattle ratio, ‘win-win’ outcome is most popular
The highest marginal cost indicates the highest cost to any farm – although not most equitable but most efficient