Så används statistiska metoder i jordbruksförsök

1. Så används statistiska metoder i jordbruksförsök Svenska statistikfrämjandets vårkonferens den 23 mars 2012 i Alnarp Johannes Forkman, Fältforsk, SLU

2. Agricultural field experiments Experimental treatments • Varieties • Weed control treatments • Plant protectiontreatments • Tillage methods • Fertilizers

3. Experimental design Allocate Treatments A and B to eight plots... Option 1: Option 2: Option 3:

4. Systematic error • The plots differ... • The treatments are not compared on equal terms. • There will be a systematic error in the comparison of A and B.

5. Randomise the treatments. This procedure transforms the systematic error into a random error. R. A. Fisher

6. Example The difference is 598

7. Randomisation test • The observed difference is 598 kg/ha. • There are 8!/(4! 4!) = 70 possible random arrangements. • The two most extreme differences are 598 and -598. • P-value = 2/70 = 0.029

8. t-test Compare with a t-distribution with 6 degrees of freedom P-value = 0.011

9. The randomisation model is the number of available plots.

10. The approximate model When is infinitely large . For statistical tests, we assume further that

11. A crucial assumption Unit-Treatment additivity: • Variances and covariances do not depend on treatment

12. Heterogeneity A B

13. Inference about what?? • Randomisation model: The average if the treatment was given to all plots of the experiment. • The approximate model: The average if the treatment was given to infinitely many plots? Sample Population

14. Variance in a difference When then . When then .

15. Independent errors • Randomisation gives approximately independent error terms • Information about plot position was ignored • This information can be utilized

16. Tobler’s law of geography “Everything is related to everything else, but near things are more related than distant things.” Waldo Tobler

17. Random fields The random function Z(s) is a • stochastic process if the plots belong to a space in one dimension • random field, if the plots belong to a space in two or more dimensions

19. Spatial modelling • Can improve precision. • Still rare in analysis of agricultural field experiments. • There are many possible spatial models and methods. • Can be used whether or not the treatments were randomized... • Which is the best design for spatial analysis?

20. Randomised block design

21. Strata • Replicates • Blocks • Plots Incomplete block design

22. Ofullständiga block

23. Split-plot design ReplicateIReplicate II 1 3 2 2 1 3 D D B C A A C B A D A B D C B B C A C B D D A C • Strata • Replicates • Plots • Subplots

24. ReplicateIReplicate II 1 3 2 2 1 3 Comparison 1a D D B C A A C B A D A B D C B B C A C B D D A C 1b D D B C A A C B A D A B D C B B C A C B D D A C 2a D D B C A A C B A D A B D C B B C A C B D D A C 2b D D B C A A C B A D A B D C B B C A C B D D A C 3 D D B C A A C B A D A B D C B B C A C B D D A C

25. A design with several strata Each replicate: sown conventionally sown with no tillage cultivar 2 cultivar 1 cultivar 3 Mo applied Mo applied Bailey, R. A. (2008). Design ofcomparative experiments. Cambridge University Press.

27. The linear mixed model y = Xb + Zu + e X: design matrix for fixed effects (treatments) Z: design matrix for random effects (strata) uis N(0, G)eis N(0, R)

28. “Those who long ago took courses in "analysis of variance" or "experimental design" that concentrated on designs for agricultural experimentswould have learned methods for estimating variance components based on observed and expected mean squares and methods of testing based on "error strata". (If you weren't forced to learn this, consider yourself lucky.) It is therefore natural to expect that the F statistics created from an lmer model (and also those created by SAS PROC MIXED) are based on error strata but that is not the case.” Bates about error strata

29. Approximate t and F-tests when L is one-dimensional, and otherwise. The number of degrees of freedom is an issue. SAS: the Satterthwaite or the Kenward & Roger method.

30. Likelihood ratio test Full model (FM): p parameters Reduced model (RM):q parameters is asymptotically c2with p – q degrees of freedom.

31. Bayesian analysis y = Xb + Zu + e uis N(0, G)eis N(0, R) G is diag(Φ) R is diag(σ2) Independent priori distributions: p(b), p(Φ) Sampling from the posterior distribution: p(b,Φ | y)

32. P-values in agricultural research • Only discuss statistically significant results • Do not discuss biologically insignificant results (although they are statistically significant). • “Limit statements about significance to those which have a direct bearing on the aims of the research”. (Onofri et al., Weed Science, 2009)

33. Shrinkage estimators Galwey (2006). Introduction to mixed modelling. Wiley.

34. Fixed or random varieties? Fixed varieties (BLUE) • Few varieties • Estimation of differences Random varieties (BLUP) • Many varieties • Ranking of varieties

35. Conclusions based on a simulation study • Modelling treatment as random is efficientfor small block experiments. • A model with normally distributed random effects performs well, even if the effects are not normally distributed. • Bayesian methods can be recommended for inference about treatment differences.

36. Summary • Fisher’s ideas about randomisation and blocking are still predominant. • Strong focus on p-values. • Linear mixed models are used extensively. • Spatial and Bayesian methods are used less often. • The question is what is random and fixed, and how to calculate p-values.

37. Tack för uppmärksamheten!