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

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 time Consequences 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.
Runoff (mm/year)
Low flow (L/s/km2)

8. Variability and land use types
Farm and catchment losses ( present)
None
Sheep
Mixed
Deer
Dairy
Arable
Variability and land use types
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
Production (Million kg)
Export Revenue $7.44/kg
Northland
125
929
Waikato
509
3,789
Bay of Plenty 68
505
Taranaki
173
1,290
Lower North Island
152
1,129
North Island
1,027
7,642
West Coast - Tasman 50
369
Marlborough - Canterbury
328
2,439
Otago - Southland
280
2,085
South Island
658
4,893
TOTAL
1,685
12,535
The average annual increase in milksolids production per herd since the season has been 4,721 kilograms or a least squares annual
growth rate of 4.5%. Contributing to this has been:
• More hectares – annual growth in milking area of 3.8 ha (+3.1% per year);
• More cows – annual growth of 12.8 cows per herd (+3.9% per year);
• Higher stocking rate – annual growth of 0.02 cows per ha (+0.8% per year);
• More milksolids per cow – annual growth of 1.7 kg (+0.5% per year); and
• More milksolids per hectare – annual growth of 11.8 kg (+1.4% per year).
In summary, the annual growth in cows per farm (+3.9%) has been slightly faster than the growth in milking hectares per farm (+3.1%), and
therefore stocking rate has increased slightly (+0.8%) over the last 10 years. This increase in stocking rate coupled with the small growth in
milksolids per cow (+0.5%) resulted in an annual increase in milksolids per milking hectare of 1.4%

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
Now
3-5yrs
>5yrs
Now
3-5yrs
>5yrs
Now
3-5yrs
>5yrs
High cost
Winter housing & manure management
Tactical
Restricted grazing
Strategic
Supplementary feeding, low N diet
Improve irrigation, farming practice
DCD
North Island
Soil processes, new products & formulations (commercial)
Constructed and managed wetlands, denitrification systems
Change Animal Type
Transformational
Greater root activity
Duration control grazing
Med cost
Ryegrass N use efficiency
DCD
South Island
Diuretic supplementation or N modifier
High sugar grass
Lipids or ionophores
Environmental forecasting
Match land to agricultural use
High tannins
Low N pasture
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. Partnership with Sectors
Technology pipeline
Partnership with Sectors
Proof of concept
Plot/paddock testing
On-farm testing
System evaluation
ACTION
Reducing animal carriage rates
Managing Critical
Source Areas
Split grass/clover
Winter forage
P sorbents
Fertiliser N efficiency
Attenuation systems
Restricted grazing
Portable standoff pads
BMP toolbox
Nitrification Inhibitors
Applied end of development pipe-line. I.e. no underpinning research which is coming from other programmes such as SWQ and Taupo.
Increased interaction with extension agencies (particularly Dexcel, but also MWNZ) at right hand side of pipeline.
Diuretics
Precision N fertiliser placement
Constructed wetlands
Bottom-of-catchment wetlands
P sorbers for wetlands
Grassed swales
Legend: Microbes, phosphorus, nitrogen, combination

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

18. Reducing P losses from southern dairy farms:
Large reductions possible for relatively minor cost
nil
-1%
+1% ?
Change in profit
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.:
Scenario
Profit
($/ha)
Production
(kg MS/ha
N loss
(kg N/ha)
Baseline Canterbury dairy farm (modelled)
2000
1500 40
Current breeding worth & better N management
2150
1600 35
High breeding worth, low stocking rate & better N management
2450
1750 20
High breeding worth, high stocking rate & better N management
2500

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
23/05/10
Strategy
Effectiveness
(%)
Cost
($/kg P conserved)
Effluent pond storage / low rate application
Source management
7-10
20-25
Optimum soil test P
15-45
0-6
Low water soluble P
5-48
1-9
Restricted grazing
30-50
10-100
Tile drain
In-field amendment
15-50
41-54
Aluminium sulphate to pasture / cropland
10-40
Buffer strips
Edge of field
0-10
>200
Stream fencing
14-50
19-27
Sorbents in and near streams
30-80
59-91
Irrigation water use and recycling
10-80
-80 - >400
Natural and constructed wetlands
-3-20
Mean cost-benefit decreases away from source (or as scale inc.)

22. Science developed technologies - Catchment scale
Technology/model (example)
+ve Impact on water quality
Relative Cost
Environmental forecasting, detailed time and space (EcoConnect)
High
Low
Critical source area mapping and delineation
Catchment-wide riparian management
Medium
Storm-flow mitigation
Riparian management guidelines
Decision support systems: EFSAP, NZ Farm (economic modelling), LUMASS (land optimisation tool)
Improved waste water treatment technologies
Constructed wetlands and managed natural wetlands
Microbial source tracking

23. Catchment –scale: Downstream technologies in receiving waters – Advantages may be rapid improvement in water quality but major risks are on-going operational costs
Intervention
Sediment capping
Lake Okaro (30 ha) modified zeolite application c. $75,000 p.a. over 3 years
Phosphorus inactivation
Lake Rotorua alum dosing $1M p.a.
Dredging
Expensive although costs will vary considerably depending on circumstances
Oxygenation/destratification
Destratification trial in lake Rotoehu (790 ha):
$524,000
Hypolimnetic withdrawal
Limited application so far in NZ but proven to be “low cost” in Europe and USA.
Weed harvesting
Hornwort harvesting in Lake Rotoehu (790 ha):
$52,800 p.a.
$22/kg N and $165/kg P
Diversions
Ohau Channel wall in Lake Rotoiti (124 ha):
$10 million

24. Enabling tools 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
Overseer

25. Testing farm feasibility under future scenarios
Dairy in the Manawatu out to 2020.
Milk solids from 950 to 1230 /ha. Very much business as usual N leaching losses also increase . Will this be permitted ?
Large absolute differences in N loss between sheep and beef and dairy 4-8 fold, in addition to trajectories .

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
Model
User
Use
Scale
SPASMO
Research, Policy
Soil, plant and atmosphere
Farm
NZ-Farm
Economic modelling of policy impacts
APSIM
Research
Agricultural Production systems simulator – farm management
MyLAND
Policy
Economic and environment impacts of land use and land management
Farm/catchment
LUMASS
Land Use management support system
Farm/Catchment
ROTAN
Nitrogen transport model – GIS based
Catchment
NZEEM
Erosion and sediment generation/transport
NZ-SEDNET
Sediment sources and transport – GIS based
TOPNET
Surface water flows – NZ national stream flow model
MODFLOW/Mt3D
Groundwater flow and transport model
Aquifersim
Groundwater flow model

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

32. Headroom - Hurunui: Estimates of N Losses to water
Reducing dairy losses:
Low-hanging fruit:
10-20%
Efficiency gains (animals & irrigation)
Maximum technically possible:
up to 50%
Multiple measures;
greater complexity; lower profit but more development

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. Purple = over allocation Green = under allocation
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
Klerkx et al (2012: 460)
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
Under delivery of potential impacts of new technologies
Calls from ministers for CRIs to participate more in extension
Everyone is struggling to implement on the ground

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, Deniz Ozkundakci, 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