Technology options for improving water quality while increasing land productivity

1. Technology options for improving water quality while increasing land productivity Compiled by: Liz Wedderburn (AgResearch) ,Clive Howard-Williams (NIWA), Peter Millard (Landcare Research) Together with input from: Scion Research, Plant and Food, GNS Science, ESR, University of Waikato, Lincoln Agritech, Massey University, Lincoln University, Aqualinc Research, Dairy New Zealand

2. Presentation format Key Messages Context (Definitions and variability) Technologies, impacts and costs Enabling tools Potential future technologies Case studies Adoption Key Messages

3. Take home messages There is no silver bullet but: • Technologies are currently available for a variety of circumstances from the paddock to catchment scale and future technologies are under development. • Effectively adopted technologies should be sufficient in most places (to meet desired standards and/or create headroom). • Land use change and/or restoratory approaches will be required in some vulnerable and already significantly impacted catchments and costs are likely to be significant in order to meet community outcomes. • Uptake of effective technology is dependent on the willingness and motivation of farmers at the source end, their skill base, effective adoption pathways and community understanding at the receptor end. • Science is also informing community processes at the catchment scale, and developing relevant networks to enable a cross sectoral/policy/science approach that will enable adoption.

4. Context definitions • What is Water Quality? • A variable, often defined by communities, but good quality may be water that is safely drinkable, swimmable and fishable, supports cultural values and healthy ecosystems. The three main issues affecting water quality in rural settings are: • Suspended sediments: that smother the beds of rivers and estuaries • Nutrients (nitrogen, phosphorus): that encourage excess plant growth, algal blooms • Faecal microbes: that affect human, and often animal, health. • 2. What is productivity? • “measure of the efficiency of production, defined as the ratio of output per unit of total input” • Increased volume and or increased value. 3. What is Technology? • An instrument (e.g. a sensor) • A system (e.g. climate forecasting) • A farm management practice (e.g. time of application of N fertiliser) • A catchment intervention (e.g. Riparian margins) • Infrastructure (e.g. Irrigation scheme) Included also are enablers of technologies such as decision support tools e.g. Crop calculator and adoption processes.

5. Co-benefits • Increasing efficiency in animal performance • Reducing GHG • Improving biodiversity on land and in water • Improving recreation use and landscape value • Integrating technologies that meet multiple outcomes has huge potential but interactions mean that the result may not be additive. • We can check for pollution swapping (e.g. decrease N to water, increase GHG), through application of tools such as Life Cycle Analysis.

6. Variability through space and timeConsequences for what and where • Understanding and managing variability, and uncertainty underpins our science and informs community decision making in an uncertain environment. • NZ’s complex landscapes and geology impart significant climatic, water resource and soil variability at regional and national scale. • Climate change will add further variability • Overlying this is variability that exists in farmer behaviour.

7. Variability in space Large and small scale variability in soils, vegetation, climate, water yield, water availability, etc. will affect ability to farm and maintain water quality. Low flow (L/s/km2) Runoff (mm/year)

8. Farm and catchment losses (1970 - present) Variability and land use types None Sheep Mixed Deer Dairy Arable Different land use types result in different contaminant losses adding further spatial variability The wide range of losses within a land use is due to: climate soil type topography management Infers much gain can be made

9. Variability in dairy production by region 2011-12

10. Variability in time (Waikakahi Catchment, Canterbury) Lag in response of suspended sediments (SS) and total phosphorus (TP) to improved stock exclusion and reduced pond discharge in 1996

11. Variability in performance Farm nutrient efficiency and N losses to waterways Room for improvement to low leaching levels Waikato

12. Variability in Farmer behaviour How farmers approach work is likely to depend on their personal preferences and likely to change through life

13. Process understanding to target technologies • Science has provided a considerable background understanding of sources, pathways and sinks of contaminants at farm and catchment scale • This along with understanding farm system behaviour has enabled identification of efficiency gains and system fit of technologies Some examples:

14. Science process understanding farm scale urine patch Drainage out of root zone (mm/month)

15. Science developed Technologies-farm scale Target: Reducing Nitrogen leaching in grazed pastures 3-5yrs >5yrs Now Now Now 3-5yrs 3-5yrs >5yrs >5yrs High cost Winter housing & manure management Tactical Restricted grazing Strategic Improve irrigation, farming practice Supplementary feeding, low N diet Soil processes, new products & formulations (commercial) Constructed and managed wetlands, denitrification systems Change Animal Type DCD North Island Transformational Greater root activity Duration control grazing DCD South Island Diuretic supplementation or N modifier Ryegrass N use efficiency Med cost High sugar grass Lipids or ionophores Environmental forecasting Match land to agricultural use Low N pasture High tannins BioChar Gain in nutrient efficiencies by nutrient management, farm systems approach, overseer, precision agriculture Strategic C addition Inhibitory root exudates Low cost Targeted mitigation high N,P areas Optimise timing of pasture grazing / feed to lower N in diet Optimal fertiliser management Effluent mgmt. Low Impact (0-10%) Medium Impact (10-30%) High Impact (>30%)

16. Technology pipeline Partnership with Sectors Proof of concept Plot/paddock testing On-farm testing System evaluation ACTION Fertiliser N efficiency Attenuation systems Reducing animal carriage rates Managing Critical Source Areas Split grass/clover Winter forage P sorbents Restricted grazing Portable standoff pads BMP toolbox Nitrification Inhibitors Diuretics Precision N fertiliser placement • Constructed wetlands • Bottom-of-catchment wetlands • P sorbers for wetlands • Grassed swales Legend: Microbes, phosphorus, nitrogen,combination

17. The effects of cumulative management changes on N leaching and farm profit: Southland dairy farm case study + Herd Shelters + wetlands Improved nutrient & effluent management; higher per cow + N inhibitor (DCD) Progressive implementation of measures:

18. Reducing P losses from southern dairy farms: Large reductions possible for relatively minor cost nil -1% Change in profit +1% ? Proposed target Natural baseline

19. Improve Production Efficiency: e.g. N Improved production efficiency of N can be achieved with higher genetic gain animals, better pastures and efficient use of artificial N, optimizing stocking rates and use of animal shelter e.g.:

20. Improve Production Efficiency: e.g. P Improved production efficiency of P can be achieved with: more genetically efficient plants and animals (inc. transgenic), manipulation of systems spatially and temporally e.g.: Have 10-15% of dairy farm occupied by: low P runoff areas near streams in ryegrass high P runoff areas in clover Modelling shows - profitability up $40-100/ha (10% more MS) - P losses to stream down by 40% Timeline: improved system within 3 years. c. 40% decrease

21. Mitigation Technologies: P Mean cost-benefit decreases away from source (or as scale inc.)

22. Science developed technologies - Catchment scale

23. Catchment –scale: Downstream technologies in receiving waters – Advantages may be rapid improvement in water quality but major risks are on-going operational costs

24. Enabling tools Overseer • Underpinned by 30 years of science • A farm-level nutrient management Decision Support System, freely available • Enables nutrient budgets to be constructed for many farm enterprises • Allows a wide range of management options and mitigation practices to be assessed • Enables flexibility in meeting water quality targets • The essential starting point for farm nutrient management plans • Links with other enabling tools, e.g. • Farmax – aids farm system design • CLUES – scales up to the catchment • Widely used throughout New Zealand by advisors, farmers, policy-makers Nutrient budget Nutrient management plan Whole farm plan

25. Testing farm feasibility under future scenarios

26. Enabling tools: CLUES (Catchment Land Use & Environmental Sustainability) • A nationally applicable, regional and catchment relevant GIS Decision Support Tool that uses OVERSEER and • assesses links between rural land-use, land use change, and catchment-level effects on water quality • allow users to make predictions of the effects of land use change on water quality & indicative socio-economics Land Use Water Quality N load Forest Dairy Other Pasture high low

27. Enabling tools: Other models for Simulating scenarios for evaluating future impact Models rely on good data

28. Next generation dairy farms: examples of regional responses • Waikato: • Higher genetic merit cows, lower stocking rate, lower replacement rate, off-paddock periods in autumn/winter, reduced N fertiliser inputs and improved dietary balance • Manawatu: • Off-paddock cow housing systems to control grazing periods and a greater area of summer forage crop to meet feed shortages

29. Next generation dairy farms: Examples of regional responses • Canterbury: • Manipulation of crop management and animal diet, use of nitrification inhibitors • Southland & Otago: • Use of short rotation ryegrasses and whole crop cereal silage to increase feed availability, calving later, strategically grazing critical source areas to minimise farm runoff, using winter standoff pads or shelters

30. Precision Farming Technology Options and Timeline Tools developed that provide real-time monitoring to match the supply of water & nutrients with crop demand to maximise productivity: 2013 • Crop calculators • Water & irrigation management tools • Variable rate irrigation 2016 • New crop calculators developed (e.g. onions, kiwifruit) • Advanced climate and weather forecasting • Advanced irrigation scheduling

31. Scenarios for change 3 types: 1. Headroom, local/catchment Concentration target Intensify current landuse, no mitigation Current concentration Concentration With mitigation With mitigation 2. Hold the line With on-farm mitigation Concentration Time Concentration Current concentration With catchment-wide mitigation Time Time 3. Claw back Concentration target

32. Headroom - Hurunui: Estimates of N Losses to water

33. Holding the line - Waiokura 2008: • 50-60% riparian fencing and plants • Regional council riparian policy • Fewer pond discharges, more effluent irrigation • Sediment and P down by 30-40% • Faecal matter (E. coli) decreasing by 9% per year 2013: • Continuation of trends of reduced sediment, less P, less faecal pollution, clearer water • Mitigation not sufficient to hold the N line in this catchment due to intensification over the study period

34. Clawback - Rotorua: Target: To return water quality to that of 1960s;Large N and P loss reduction is needed. • In last 5-8 years a reduction of c. 15% in losses from dairy farms has occurred due to increased nutrient efficiency, BUT • this is insufficient and there will be a need for a combination of: • Increased N and P mitigations on farm, • Catchment interventions and management (e.g. alum dosing), some diversions, etc. and • Land use change e.g. dairy (23 farms) to sheep/beef or forestry

35. Science informing Community Choices Science centric management alone is not enough to address the issues • Role of Science is to provide the science based evidence for technologies and behaviour change in an integrative way • Science informed processes that allow: • inclusive and integrated community conversations • deliberation of the consequences of future land development while • making transparent the trade offs across diverse outcomes

36. A catchment will be allocated depending on the target set by the community Left-hand map is for N at a ‘Good-Fair’ (B-C) threshold for river algae growth, right-hand map is for N at ‘Fair-Poor’(C-D) threshold for river algae growth Purple = over allocation Green = under allocation

37. Technologies on their own will not achieve the change Must link to the motivation of the farmers/growers and place in a system context Build networks of farmers, educators, science and policy that link technologies and enabling tools to enable collective learning Klerkx et al (2012: 460)

38. Take home messages There is no silver bullet but: • Technologies are currently available for a variety of circumstances from the paddock to catchment scale and future technologies are under development. • Effectively adopted technologies should be sufficient in most places (to meet desired standards and/or create headroom). • Land use change and/or restoratory approaches will be required in some vulnerable and already significantly impacted catchments and costs are likely to be significant in order to meet community outcomes. • Uptake of effective technology is dependent on the willingness and motivation of farmers at the source end, their skill base, effective adoption pathways and community understanding at the receptor end. • Science is also informing community processes at the catchment scale, and developing relevant networks to enable a cross sectoral/policy/science approach that will enable adoption.

39. Acknowledgements The following scientists provided valuable background material and ideas for this presentation: AgResearch: Stewart Ledgard, Ross Monaghan, Mark Shepherd, Richard McDowell, Mike Freeman, Melissa Robson, Sue Peoples NIWA: Bob Wilcock, Bryce Cooper, Sandy Elliott Landcare Research: Peter Millard, Alison Collins, Suzi Greenhalgh Scion Research: Brian Richardson, Peter Clinton Plant and Food: Miriam Marshall, Derek Wilson, Brent Clothier GNS Science: Chris Daughney ESR: Murray Close University of Waikato: David Hamilton, DenizOzkundakci, Hannah Jones, Jonathan Abell, Dylan Clark and Chris McBride Lincoln Agritech: Hugh Canard, Peter Barrowclough Massey University: Mike Hedley Lincoln University : Leo Condron Aqualinc Research: John Bright Dairy New Zealand: Rick Pridmore, Bruce Thorrold