A National Soil Outl tlook GW Leeper Mem emori rial Lec Lecture - - PowerPoint PPT Presentation

a national soil outl tlook
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A National Soil Outl tlook GW Leeper Mem emori rial Lec Lecture - - PowerPoint PPT Presentation

A National Soil Outl tlook GW Leeper Mem emori rial Lec Lecture re Mike Grundy 22 November 2019 Mike Grundy Leeper Lecture 2019 Mike Grundy Leeper Lecture 2019 Sustainable prosperity is possible, but not


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Mike Grundy Leeper Lecture 2019

A National Soil Outl tlook

GW Leeper Mem emori rial Lec Lecture re

Mike Grundy 22 November 2019

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Mike Grundy Leeper Lecture 2019

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Mike Grundy Leeper Lecture 2019

Sustainable prosperity is possible, but not predestined. Australia is free to choose.

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A business-led forum to shape Australia’s future

Mike Grundy Leeper Lecture 2019

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Mike Grundy Leeper Lecture 2019

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Mike Grundy Leeper Lecture 2019

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Mike Grundy Leeper Lecture 2019

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Mike Grundy Leeper Lecture 2019

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Mike Grundy Leeper Lecture 2019

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Mike Grundy Leeper Lecture 2019

$ returns Desired Outputs (e.g. Yield)

  • r quality)

Inputs (nutrients, water, labour, agro-chemicals, energy etc.)

Biological Optimum

Risk- adjusted Optimum

Economic Optimum

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Mike Grundy Leeper Lecture 2019

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The AgData Challenge

Dan Gladish Dan Pagendam Ross Searle Chris Sharman Ashley Sommer Matt Stenson Cameron Taylor Peter Taylor Jamie Vleeshouwer

The Team

Return Per Farm($ per Ha) $ Machine Usage (Ha)

Ha

Number of Herbicide Applications per Paddock per Year

Total Granular Fertiliser App Rates Per Aggregation

Return Per Aggregation ($ per Ha) $

Mike Grundy Leeper Lecture 2019

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Amount of Atrazine Applied (Ha) Proportion of Time Employee Spends on a Machine

From the management database we can also extract information about human resource management

Ha Sprayed

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Mark Branson - SA

Fertiliser Replacement Maps

https://view.knowledgevision.com/presentation/6bfd29c449bb4c82b564edf4204b738a

Mike Grundy Leeper Lecture 2019

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Return on Nitrogen Aggregating Data

  • Benchmarking – district, region, state, soil type
  • Improved soil attribute maps
  • Variety trials
  • Improved research targeting

Mike Grundy Leeper Lecture 2019

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EM Shallow EM Deep Radiometrics Th Radiometrics Total Slopes Ruggedness Flatness Elevation

Using statistical data mining techniques we can combine the topographic indices with the geophysics collected by LG and the soil test data, to generate soil property maps. Topographic Indices LG Geophysics Soil Test Data

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Organic Carbon Electrical Conductivity pH PBI

Soil property maps generated from digital soil modelling (DSM)

As we collect more soil test data we can potentially use this approach to make comparisons of changes

  • ver time.

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Actual Yields

(machine data)

Statistically Predicted Yields

Cond Model Variable 97 84 rain 72 81 elevation 30 50 N 22 71 DualEM.Shallow 21 58 Radiometrics.Potassium 15 84 Radiometrics.Total.Count 13 76 Radiometrics.Thorium 10 68 DualEM.Deep 7 18 gradient 6 55 TerrainSurfaceTexture 3 56 Radiometrics.Uranium

So using the models from the previous slide we can generate maps of predicted yields. The models can also potentially give us some insights into what is driving yield

  • utcomes.

The table below shows us the relative importance of the various input predictors in the overall predictions of yield – no surprises in the top predictors here!

Modelled Vs Predicted Yields

  • 20. Data Analysis – Yield Modelling

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Modelled Canola Yield 400mm Rain Modelled Canola Yield 450mm Rain Difference between Modelled Yields 450 - 400mm Rain

Canola Yield Vs Rainfall (Statistical Modelling)

Evaluating options . . .

Yield (T/Ha) Scenario Yield Differences (T/Ha)

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R2 0.91 LCCC 0.95 RMSE 2.61 ME 0.158 Proportion 0.99 Observed Average 35.69 Modelled Average 35.85

Modelled Vs Observed at Bolac

Mud Maps

Global Model Fit Stats

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Homi Kharas, Brookings Institute, 2017

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