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Sheep and Beef data

Sheep and Beef data. Project Team Femi Olubode (WRC) Blair Keenan (WRC) Jon Palmer (WRC) Rex Webby (Rural Consultant) Ian Jamieson (Independent Consultant). The research process Illustrative results Some policy implications Concluding remarks. Outline. Data collection Case studies

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Sheep and Beef data

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  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. The research processIllustrative resultsSome policy implicationsConcluding remarks Outline

  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

  4. 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

  5. Choice of mitigation options

  6. FARMAX modellingBiological feasibilityOVERSEER modellingNutrient budgets Scenario analysis…

  7. Illustrativecase study farms

  8. Case study II

  9. Case study II

  10. Case study IIIa

  11. Case study IIIa

  12. Highest mitigation level - catchment

  13. Policy implications…

  14. 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

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