WEALTH FROM WATER PILOT PROGRAM

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2 WEALTH FROM WATER PILOT PROGRAM The Wealth from Water Pilot Program is part of Tasmania s Innovation Strategy (Innovation in Primary Production) A two-year, $2.15 million pilot program that commenced in November The program will provide farmers and agribusiness with the information needed to confidently invest in Tasmanian Irrigated Agriculture. Provides sound planning to address the current State expansion in irrigated agriculture ($400 million public-private schemes) Wealth from Water is a partnership between the: Department of Primary Industries, Parks, Water and Environment Department of Economic Development, Tourism and the Arts Tasmanian Institute of Agricultural Research

3 WEALTH FROM WATER AIMS: Classify Land (Tasmanian Irrigation Districts/ Schemes) according to its suitability for various agricultural enterprises (Approximately 70,000ha as a pilot) Provide comprehensive soil, climate, crop and enterprise data to potential investors and existing farmers. Aim to reduce risks associated with irrigation development (production/ financial/ environmental). Aid existing farmers move/ diversify to higher-valued enterprises, and/ or improve existing production.

4 WEALTH FROM WATER: Wealth from Water is currently in a Pilot Phase mapping land suitability for 7 different test enterprises in 70,000ha of newly established irrigation scheme areas. Areas: Enterprises: Meander Irrigation Scheme: 43,000ha Midlands (Tunbridge District): 27,000ha Poppies Carrots Blueberries Barley Hazelnuts Pyrethrum Commercial Hemp

5 WEALTH FROM WATER PROCESS: 1. Mapping/ Modeling Soil and Climate Data at a semicatchment scale 1:50, Applying the above mapped surfaces to Individual Enterprise Rules (developed by TIAR) 3. Produce a decision-based model to identify which areas are potentially suitable for each enterprise (its degree of suitability, and identification of the limiting factors) 4. Output suitability maps to The LIST (Land Information System, Tasmania) a land information internet portal

6 SUITABILITY MODELING REQUIREMENTS (SOILS): Existing soils data not at the scale or quality required for 1:50,000 land suitability mapping Identified need for soil attribute/ property mapping (rather than soil type) ph, EC, Drainage, Stoniness, Depth to Impeding Layer/ Sodic Layer, Clay % Need for quantitative surfaces (with a measure of certainty ), rather than traditional qualitative surfaces Need for multi-depth interrogation, surface generation, ie. 3-D models (eg. Depth to clay % > 35)

7 POTENTIAL SOLUTION: DIGITAL SOIL MAPPING (DSM) What is DSM? Digital Soil Mapping (or Predictive Soil Mapping) is a wellestablished (multi-)discipline using evolving computing power and latest (geo)-statistical techniques to predict soil and attribute patterns (has only become operationally possible over the last decade) It links (through statistics/ geo-statistics/ modeling) Existing (legacy) traditionally developed soils data (maps and sites, expert soils knowledge), Remote or proximally-sensed data (eg, satellite imagery, radiometrics, electromagnetics etc), Terrain analysis (DEM, TWI, Slope, Aspect etc), Existing spatial predictors eg. Geology, vegetation, land use, climate To predict spatial soil attributes or classes - as grid cells (raster data)

8 WHY DSM? a) Builds on existing soil data (incorporates soil surveyor knowledge) b) Statistically-sound (less subjective), more quantitative, legally defendable c) Produces raster surfaces, which can better represent Polygonal real-world, Mapping Raster Mapping continuous (and 3-Dimension) soil surfaces. d) Can produce both continuous attribute surfaces and discrete soil type/ class maps e) Dynamic surfaces can evolve with new data collection, statistical and modeling techniques f) Provide (un)certainty maps leading to targeting for future investment, or quantified risk assessments g) Is statistically validated (missing from most traditional surveys) h) Can require fewer sites than traditional mapping, while outputting better soil property surfaces

9 WHAT HAS BEEN DRIVING THE RECENT PUSH (NATIONAL & GLOBAL) IN DSM? CSIRO have identified five soil-based challenges for Australia 1. Improve efficiency of rain-fed and irrigated agriculture 2. Improve food security and human nutrition 3. Minimise exploitative land uses and soil degradation 4. Carefully plan land-use when responding to climate change 5. Understand carbon dynamics and biosequestration.dr Neil McKenzie, Chief, CSIRO Land & Water CSIRO Land & Water/ ACLEP, NCST and DAFF are behind the development and implementation of DSM throughout Australia to address a critical shortage in quality soils data Formation of Digital Soil Mapping Working Group (under the NCST) TERN Soils Facility, Global Soil Map, ACLEP state projects, Training etc

10 GLOBALSOILMAP.NET "Let there be no mistake about the significance of this wonderful project "Soil mapping is one of the pillars to the challenge of sustainable development" Kofi Annan Jeffrey Sachs 17th February 2009 Consortium of Global DSM to develop for the entire globe.. 1. Surfaces for 9 key soil attributes 2. 90m resolution 3. 6 fixed depths

11 WEALTH FROM WATER DSM PARTNERSHIPS: Three year Australian Research Council (ARC) Linkage Project with University of Sydney to develop and build capacity in DSM for Wealth from Water. (Supporting Facility, Oceania Node, GSM) Australian Collaborative Land Evaluation Program (ACLEP) project in 2011 with CSIRO Land & Water to map radiometrics and perform spectral scanning and calibration of soil samples. (Oceania Node Leader, GSM)

12 DSM BASICS Predicted soil surfaces based on Soil Data & Covariates (or predictors) the SCORPAN approach.. S p / S c = ( S, C, O, R, P, A, N ) Soil Data (point and/ or polygon) Climate (rainfall, temp) Organisms (vegetation, land use) Relief (DEM terrain analysis) Parent Material (geological maps) Age (age of material, temporal components) N (spatial coordinates, spatial variability)..mcbratney et al 2003

13 BASIC DSM SYSTEM. Courtesy Budiman Minansy et al 2010, (USyd)

14 MEANDER SPATIAL COVARIATATES : Used to spatially predict changes in soil attributes between sample points: Existing 1:100,000 soil maps 30m SRTM DEM and Derivatives (SAGA GIS) Elevation, Topographic Wetness Index, Curvature, Slope, Valley Depth, MrVBF (Multi-Resolution Valley Bottom Flatness Index), MrRTF (Multi-Resolution Ridge Top Flatness) Radiometric Potassium, Thorium, Uranium Geology SPOT and Landsat NDVI, FVC

15 22 MEANDER COVARIATES TRAINING SITES + 60 VALIDATION SITES Meander East SRTM Sample/ DEM Validation Sites Topographic Radioactive Potassium Wetness Index Fractional Veg Cover Points Combined with All Covariates Products Based on 30m SRTM DEM sample density, modeling and outputs produced at 30m resolution ie. 30 x 30m pixels

16 SAMPLE DESIGN: Sample + Covariate Distribution Need to ensure that the full range (population distribution) of all covariates (predictors) are sampled (to enable best possible predictions). Used a random-stratified approach to ensure and test this requirement. Covariate Distribution Methods: Conditioned Latin Hypercubes (McBratney et al), and validated by a cluster approach based on fuzzy k- means (more practicality in the field).

17 COMBINED SAMPLES WITH COVARIATES Samples Value. Eg. EC combined with all covariates these are used to determine a relationship between value at each depth, and predictor variables CUBIST Decision Tree Model Artificial Neural Networks

18 SOIL SURFACE OUTPUTS FOR SUITABILITY MODEL Climate Surfaces + Temperature Loggers Soil ph Stone Drainage Content Index Legend Legend Legend Chill Hours ph Stone apr-aug % Value Value Value High : High : High : 51.9 Low : Low : Low : 0 Validation sites are applied to all surfaces to test for model(s) fit, (aiming for minimum 60%). Uncertainty values can be generated for each pixel to highlight areas requiring extra sampling or methodology tweaks

19 COMBINE SOIL SURFACES WITH LAND SUITABILITY RULES* * Rules derived by TIAR from a combination of research, literature, industry workshops and expert opinion

20 LAND SUITABILITY MODEL Land Suitability Model applies rules for each enterprise to soil and climate surfaces results in a Land Suitability Rating plus the limitations for each 30m pixel

21 ENTERPRISE SUITABILITY MAPS (DRAFT) Legend Carrots Hazelnuts Barley Industrial Hemp Overall suitability rating Well Suitable suited Suitable Marginally suitable Marginally Unsuitable suitable Unsuitable

22 DIGITAL SOIL MAPPING CONCLUSIONS: DSM benefits from soil expert inputs (soil-landscape models) Is a tool to enhance existing and future soil survey work DSM is under-pinned by field sampling and validation, it does not replace the need to measure real field data.

23 PLANNING/ FINAL OUTPUTS: If pilot program deemed successful/ funding available, potential to role out across all Tasmanian Irrigation Areas (approx 300,000ha) Derive Suitability Surfaces for 20 different enterprises Allow public access to Land Suitability mapping through the LIST

24 FUTURE: Emerging opportunities over the next several years to: 1) Enhance Tasmanian Irrigated Agricultural expansion, diversification and intensification and the State's economy through the Wealth from Water Program 2) Provide better chance of sustainable expansion 3) Addresses more efficient water usage and food security 4) Provide a mechanism to collect invaluable land resource information (high resolution soils and climate data) to address a range of other demands; Eg. Carbon accounting, climate change, land degradation risk mapping, natural values modeling, ecosystem monitoring etc 5) Link to the TERN and Global Soil Map undertakings 6) Produce better and more efficient spatial data products as geo-statistical, remote and proximal sensing techniques evolve.

25 CONCLUSIONS: Examples such as Murray-Darling Basin Irrigation, and the environmental and productive shortcomings highlight the dangers of irrigation expansion without adequate suitability assessment, and the spatial tools to deliver this. A failure to invest in the collection of good quality spatial land resource and climate information could lead to ill-planned or un-controlled agricultural development in the State, with associated economic and environmental risks. The fact that old State soil maps (up to 70 years old) are still being used on a regular basis highlights the long-term cost-benefits of the collection of spatial data, but also raises the concerns of basing current planning on out-dated or inappropriately-scaled products.

26 ACKNOWLEDGEMENTS: DSM/ Suitability: Chris Grose 1, Rob Moreton 1, Mathew Webb 1, Zhuo Wang 1, Regan Parkinson 1, Rhys Stickler 1, Brendan Malone 2, Alex McBratney 2, Budiman Minasny 2, Raphael Viscarra Rossel 3, Bill Cotching 4, Leigh Sparrow 4, Rowan Smith 4, Fiona Kerslake 4 1 Department of Primary Industries, Parks, Water & Environment, Tasmania, 7250, Australia 2 University of Sydney, Faculty of Agriculture, Food and Natural Resources 3 CSIRO Land & Water 4 Tasmanian Institute of Agricultural Research (TIAR)