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 predestined. Australia is free to choose. Mike Grundy Leeper Lecture 2019
A business- led forum to shape Australia’s future Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
Biological Optimum Economic Optimum Risk- adjusted Optimum Desired Outputs $ returns (e.g. Yield) or quality) Inputs (nutrients, water, labour, agro-chemicals, energy etc.) Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
The AgData Return Per Farm($ per Ha) Challenge Machine Usage (Ha) Ha $ The Team Dan Gladish Dan Pagendam Ross Searle Chris Sharman Ashley Sommer Matt Stenson Number of Herbicide Applications per Paddock per Year Cameron Taylor Peter Taylor Return Per Aggregation Jamie Vleeshouwer ($ per Ha) $ Total Granular Fertiliser App Rates Per Aggregation Mike Grundy Leeper Lecture 2019
From the management database we can also extract information about human resource management Amount of Atrazine Applied (Ha) Proportion of Time Employee Spends on a Machine Ha Sprayed Mike Grundy Leeper Lecture 2019
Fertiliser Replacement Maps Mark Branson - SA https://view.knowledgevision.com/presentation/6bfd29c449bb4c82b564edf4204b738a Mike Grundy Leeper Lecture 2019
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
Topographic Indices Ruggedness Slopes Flatness Elevation Using statistical data mining techniques we can combine the Soil Test topographic indices with the geophysics collected by LG and the soil Data test data, to generate soil property maps. LG Geophysics EM Shallow EM Deep Radiometrics Total Radiometrics Th Mike Grundy Leeper Lecture 2019
Organic Electrical Carbon Conductivity 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 over time. PBI pH Mike Grundy Leeper Lecture 2019
Modelled Vs Predicted Yields Actual Yields Statistically (machine data) Predicted Yields 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 outcomes. 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! 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 20. Data Analysis – Yield Modelling Mike Grundy Leeper Lecture 2019
Canola Yield Vs Rainfall Modelled Canola Yield Modelled Canola Yield (Statistical Modelling) 400mm Rain 450mm Rain Yield (T/Ha) Scenario Yield Differences (T/Ha) Evaluating options . . . Difference between Modelled Yields 450 - 400mm Rain Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
Mud Maps Global Model Fit Stats 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 Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
Homi Kharas, Brookings Institute, 2017 Mike Grundy Leeper Lecture 2019
Mike Grundy Leeper Lecture 2019
Recommend
More recommend
