a new computer program for the practical application of ...a new computer program for the practical...
TRANSCRIPT
A new computer program forA new computer program forthe practical application ofthe practical application of
compatibility test to avoid heterogeneitycompatibility test to avoid heterogeneityof big compound seed lotsof big compound seed lots
Dr. Julianna Bányai
Corvinus University - Faculty of Horticultural Sciences Budapest, Hungary
Introduction
The development of seed storage and sampling made necessary unifying seed lots to big compound lot.
At unifying lots of different quality the main danger is the heterogeneity.
Statistical method to avoid heterogeneity
The elaborated compatibility test is based upon the
range heterogeneity test.
It is an invers heterogeneity test
Only those lots may be unified, from which the
R = Xmax – Xmin greatest difference of quality characteristic values
(purity, germination, other seed content) does not exceed the given limit.
Such lots could be considered as compatible.
The quality characteristic values of component lots can be read from their labels.
The „given limit” is the „critical range value” which can be found on B1, B2,B3 tables in ISTA Rules.
Multidimensional multiple comparison and grouping method
If the indicated N component lots are incompatible from viewpointof any quality characteristic, these lots cannot be unified.
In these cases fairly homogeneous groups can be formed by N lotson the basis of a multidimensional multiple comparison and of a grouping procedure.
A computer program for the practical use has been elaborated by thehelp of Ms Ágota Drégelyi-Kiss.
The language: Visual Basic for ExcelAdvantages: Easy to use
Easy to access for everyone
The computer compares data with tables and reveals which lots are compatible or incompatible and forms the homonegeous groups.
Neither computation nor use of tables are necessary. Both will be made by computer.
About the program
Program.xls
Example 1All of the 8 lots are miscible
Example 2
Two clusters
Example 2
Two clusters
Example 3
Example 3
