Xtra, Xtra - read all about it, DiGGerXtra! Mario D Antuono, Neil Coombes and Dongwen Luo

Transcription

1 Xtra, Xtra - read all about it, DiGGerXtra! A sampling design question Mario D Antuono, Neil Coombes and Dongwen Luo Australasian Applied Statistics Conference November 28 - December 2, 2016 Barragga Bay, NSW

2 The question /17 Q: Want to take n (usually a small number) of soil samples per trial and from which plots? Wheat trials in GRDC national sodic soils project DiGGer designs (via the R package by Neil) Measure electro-magnetic response via an EM38 instrument per plot during the season Calibrate the EM38 data with some soil measurements

3 Methodology and research /17 What sampling design to use - a calibration problem? Darshan suggested a simple decile approach - choose 1 or 2 plots from each decile (O Leary, 2006) Amezketa (2007) used the USDA Salinity Research Laboratory approach - special software called ESAP-RSSD but limited to two sample sizes (12 or 20) - based upon response surface methodology ideas D-optimality suggests place points at the extreme?

4 One of the trials /17 Plot sizes small (6m x 1.8m centres with cone-seeder 7 rows per plot), harvest 5m x 5 rows per plot 240 plots = 12 RANGEs (banks or columns) with 20 plots (ROWs) in each bank 57 lines/varieties of wheat plus 3 plots of a control variety in each replicate block (making 60 plots per replicate block) 2-d balance with 4 ROW-replicates and 4 RANGE-replicates

5 Plan generated with DiGGer program /17

6 EM38 instrument /17 Easy and non-destructive method Can take 2 measurements flat (H) or on its side (V)

7 EM38 measurements /17 Taken from centre of the plot, as were the soil samples later Well-known that EM38 fn(clay, salinity, water) EM38 data collected over time such as winter and spring Darshan s hypothesis is that water uptake by plant is related to stress on the plant

8 USDA approach and software /17 Google search - Amezketa (2007) - used free USDA software from Salinity Research Laboratory (California, USA) Lesch et al. (1995) discuss the methodology Response surface methodology ideas for a regression model Fortran program for Windows - easy to use but is hands-on (time consuming) especially if more than one trial to do but limited to 2 sample sizes (12 or 20)

9 USDA approach with the selected plots /17 COLUMN-replicate numbers shown in the bi-variate plot The heatmap has the position of the 12 selected plots to sample the soil Consistent - i.e. the 12 samples selected are a subset of the 20 samples selected

10 DiGGerXtra (work in progress) /17 ASReml-R outputs of EM38V data σrow 2 = 30, γ = 0.28, σrange 2 = 33, γ = 0.31, σ2 residual = 107, γ = 1 \centering{

11 DiGGerXtra function /17 Constructed a samplediggerx function from Neil s R functions and his Fortran module (work in progress) The gammas from the variable with the most variation used in the function samplediggerx(designdigger, covariates=c(em38v,em38h), n=12)

12 SampleDiGGerX output /17 What is the best stopping rule?

13 Web-based DiGGer/DiGGerXtra /17 Dongwen presented at AASC2014 a web-based development of an R package To make life easier for many people (including ME!) Working with Dongwen to develop a web-based approach to doing DiGGer designs and soon DiGGerXtra

14 Snapshot of DiGGerShiny /17

15 In summary /17 The linear mixed model framework is very useful environment to explore spatial variability... See Lark, Cullis and Welham (2006) Making tools more efficient and easy to use for others is a noble art Thanks to my 2 amigos (Sir Neil and Sir Dongwen) for their help in trying to achieve this

16 References /17 1. Amezketa E. (2007) Soil salinity assessment using directed soil sampling from a geophysical survey with electromagnetic technology: a case study. Spanish Journal of Agricultural Research (1), (includes references to USDA software and manual) 2. Coombes, N.E. (2016) DiGGer version R package. neil.coombes@dpi.nsw.gov.au 3. O Leary, G. (2006) Standards for Electromagnetic Induction mapping in the grains industry in the GRDC Precision Agriculture Manual. 4. Lesch, S.M., D.J. Strauss and J.D. Rhoades. 1995b. Spatial prediction of soil salinity using electromagnetic induction techniques:2 An efficient spatial sampling algorithm suitable for multiple linear regression model identification and estimation. Water Resour. Res. 31: Lark RM, Cullis BR and Welham SJ (2006) Spatial prediction of soil properties in the presence of a spatial trend: The empirical best linear unbiased predictor (E-BLUP) with REML. European Journal of Soil Science 57: December 2006

17 Acknowledgements and more... Thanks to GRDC for their support, my colleagues Darshan Sharma, Rosemary Smith and Dana Mulvany at DAFWA for doing the experiments and collecting the valuable data, and their collaboration. Last modified: Wed Nov 30 08:09: