Smap a new soil database for New Zealand

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S-map a new soil database for New Zealand

• Allan Hewitt

Landcare Research, New Zealand
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The situation

• Increased demand for soil information to underpin
  sustainable developmentand environmental
  management
• But current data is patchy

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• Soil maps
• and reports
• Patchy distribution
• Varying age / quality
• Limited soil attributes

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Vision for S-map efficient access to relevant
soil information

• Consistent soil map coverage of NZ
• Key soil attributes mapped
• Data transformed into useful information for all
  soils
• Ease of linkage to management packages
• Web access

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Vision for S-map efficient access to relevant
soil information

• Centralised curation and quality assurance
• Ease of integration of soil with other data sets
• (e-Government/ e-Science)
• Ease of regional and national scale analyses
• Achieved through cooperation

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Principles and design

• Building on best of the old filling gaps
• Consistent national coverage new data
• All digital escape the constraints of paper
• Incorporates a database (data information)
• National soil correlation
• Base for efficient modelling
• Expression of uncertainty
• Base for good public access to soil information
• Scale 150 000 or better

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Uses the NZSCto group similar soil series into
soil families
NZSC Parent Mat. Rock class Texture
group Permeability
A
Soil Family
Siblings
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2
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4
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Depth Drainage Texture Stones Misc.
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Functional horizons
T3
T9
Horizons Based on Soil physical functions
S13
S6
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Base Properties Example
1st Functional horizon Thickness (cm)
1st Functional horizon Stones ()
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25
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40
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Probability distribution functions to expression
variability and uncertainty
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Uncertainty in S-map

• Knowledge of soil variability and uncertainty
  recorded where possible
• How reliable is this?
• What else might it be?
• What is the likely range of soil property values?
  (and how confident are we of this range)
• How good (or bad) are the models?

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Map scale

• The map scale 150 000
• But better resolution than 150 000 scale paper
  maps.
• Growing desire for finer scale farm maps
• growing interest in precision agriculture
• Finer scale farm mapping will be supported by the
  S-map, by
• Identifying probable soils
• Indicating the fine scale pattern of soil
  variation

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Mapping procedure

• Mixture of conventional soil survey and modelling
• to new S-map standard
• Correlating to NZ legend
• New mapping to fill in the gaps
• Lowlands conventional soil survey techniques
• Uplands model the landscape using a DEM

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Uplands

Lowlands
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Modelling in hilly mountainous land

• Within land systems - use a DEM to divide a
  landscape into land elements
• Use field work and expert knowledge to develop
  soil-landscape models to predict soils within the
  land elements
• Models mainly in the form of rules that relate
  land elements to soil classes and attributes.

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Apply soil-landscape model
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Derived data
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1. Correlation
3. Derived
2. Base
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Fact sheetsfor easy access to information

• Southland fact sheets as document files
  http//map.es.govt.nz/Departments/LandSustainabili
  ty/soilmaps.aspx
• Otago fact sheets as reports from a soil
  information database (ORC)
• Linked to digital soil maps
• Customised fact sheets as on-the-fly reports
• Fact sheets automatically updated with growth of
  database

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Fact sheets
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Current work

• FRST
• Database design Linda Lilburne and Co
• Eastern dry greywacke mountains Ian Lynn
• Canterbury Plains - Trevor Webb
• Motueka catchment Allan Hewitt,
• Wairarapa Hugh Wilde, Murray Jessen
• Northland Malcolm Mcleod
• Masters at Lincoln Sam Carrick
• ORC and FRST
• Otago regional soil information system fact
  sheets

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An S-map cooperative?

• We cannot do this alone
• We can do it if we cooperate
• Regional Councils, District councils
• Other CRIs
• Farm servicing agencies
• Universities
• Room for consultancies to do farm mapping linked
  to S-map
• Make S-map a national effort

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Land Evaluationsystems to assist decision making
in land use managementAllan Hewitt
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What is Land Evaluation All About ?

• Inappropriate land use can lead to
• inefficiency and degradation
• and social and economic problems

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The goal good marriages between use and land
capability
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Land evaluation

• Exploring productive opportunities
• What will grow well?
• Identify the management constraints
• No surprises development
• Evaluate the environmental risks
• will it harm the environment?
• Future research

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1. Exploring productive opportunities

• Land use suitability
• Crop matching
• Plantgro crop-matching software

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Land Evaluation PLANTGRO
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Dry Beans(Phaseolus vulgaris)

• 15C at sowing
• Frost free growing season
• lt 35C during flowering
• 650 GDD (base 10)
• 120 days sowing to harvest
• Stone free soil
• Free draining silt loam
• Irrigation essential

Currently grown in Marlborough Will they grow in
North Canterbury ?
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Ginseng suitability Tairei Plains (Plantgro)
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2. Identify the management constraints No
surprises development

• Advance warning of constraints
• such as
• Wet areas
• Stony areas
• Shallow hard pan
• Acidity
• Water holding capacity
• Natural nutrients

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No surprises - NZLRI
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No surprises Irrigation Suitability
classification
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3. Evaluate the Environmental risks

• E.g.
• Nitrate leaching risk
• Structural decline risk
• Water logging risk
• Erosion risk
• Risk is higher under intensive land use
• If you know the risk you can avoid it

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Output National scale N leaching risk map
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Potential N leaching index
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Land use pressure
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N Leaching Risk
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3. Environmental risk
Risk of nitrate leaching to ground-water
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3. Environmental risk
Risk of soil pugging under heavy stock
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New researchdeveloping a framework to design
use to match capability of the land

• Consider
• Functions of the soil
• (e.g. toxin absorbance, water storage)
• Productive opportunities
• Soil ecosystem services
• Integrate and develop a range of land use
  scenarios
• Consider risks associated with each scenario
• Provide scenario visualising tools
• to help communities of interest to make choices