Throwing out plots
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THROWING OUT PLOTS. HOW DO YOU KNOW WHEN TO THROW OUT A PLOT?. MY APPROACH. OBVIOUS POOR STANDS WHEN UNSURE THEN NOTE PLOTS EXAMINE DATA AFTER HARVEST USE DIXON’S TEST FOR OUTLIERS. DIXON’S TEST FOR OUTLIERS.

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THROWING OUT PLOTS

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Throwing out plots

THROWING OUT PLOTS

HOW DO YOU KNOW WHEN TO THROW OUT A PLOT?


My approach

MY APPROACH

OBVIOUS POOR STANDS

WHEN UNSURE THEN NOTE PLOTS

EXAMINE DATA AFTER HARVEST

USE DIXON’S TEST FOR OUTLIERS


Dixon s test for outliers

DIXON’S TEST FOR OUTLIERS

SUBTRACT SUSPECT VALUE WITH NEXT HIGHEST OR LOWEST AND DIVIDE BY SUSPECT MINUS LOWEST OR HIGHEST.

GENERAL RULE OF THUMB- DIFFERENCE MUST BE 2 TIMES


Example of using dixon s test for outliers

EXAMPLE OF USING DIXON’S TEST FOR OUTLIERS

PLOT VALUES ARE 20 17 22 23 41

41 – 23 / 41 – 17 = .75

PROB. < 0.05

REF. W.J. DIXON, BIOMETRICS 9:89


Another example

ANOTHER EXAMPLE

PLOT VALUES ARE 20 7 22 23 19

19-7 / 23-7= 0.75

PROB. < 0.05


How many bad plots constitute a bad test

HOW MANY BAD PLOTS CONSTITUTE A BAD TEST?

MY RULE – IF YOU HAVE TO ADJUST 30% OF THE PLOTS OR MORE THEN THROW OUT THE ENTIRE TEST OR AT LEAST THAT REP


If you throw out a plot then how do you handle it

IF YOU THROW OUT A PLOT THEN HOW DO YOU HANDLE IT?

CALCULATE A MISSING PLOT VALUE? NOT LIKELY.

HOW DOES THIS AFFECT ANY SPATIAL ANALYSES LIKE NNA OR TREND?


So how do you know when to throw out an entire test

SO HOW DO YOU KNOW WHEN TO THROW OUT AN ENTIRE TEST?


1 when the cv is high 2 how high is too high 3 when yields are low how low

1. WHEN THE CV IS HIGH ?2. HOW HIGH IS TOO HIGH?3. WHEN YIELDS ARE LOW ? HOW LOW?


Throw out the test when the error variance is too high

THROW OUT THE TEST WHEN THE ERROR VARIANCE IS TOO HIGH

NEED HISTORICAL RECORD OF ERROR VARIANCES FOR EACH CROP

COMPARE SUSPECT TRIAL WITH POOLED ERROR VARIANCE

IF THE SUSPECT IS 2 TIMES THE POOLED ERROR VARIANCE THEN THROW THE BUM OUT

Ref. Bowman and Rawlings. 1995. Agronomy Journal 87:147-151.


What about when you have low yields

WHAT ABOUT WHEN YOU HAVE LOW YIELDS ?

STUDY IN NC SHOWED THAT DISCARDING LOW YIELDING TRIALS DID NOT IMPROVE PREDICTABILITY

TRUE THAT IT IS MORE DIFFICULT TO SEPARATE MEANS WHEN THEY ARE LOW

TRUE THAT LOW YIELDS CAUSE MORE QUESTIONS THAN ANSWERS

MY ANSWER- DON’T REPORT INDIVIDUAL LOCATION DATA AND INCLUDE IN ACROSS LOCATION MEANS


Bad data is worse than no data

BAD DATA IS WORSE THAN NO DATA


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