AMMIWins
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Introduction

The required input file for AMMIWINS is a machine-readable output file from MATMODEL, a program for Additive Main effects and Multiplicative Interaction (AMMI) analysis that is also available from Microcomputer Power.

Then this program, AMMIWINS, determines the winning genotype with the highest yield over every place in the parameter space for AMMI-1, AMMI-2, and higher-order AMMI models.

A major goal of agronomists and breeders is to maximize yield throughout a crop's heterogeneous growing region, despite differences in cultivar rankings from place to place due to genotype-environment interactions. Frequently, it is necessary to subdivide a growing region into several relatively homogeneous mega-environments and to breed and target adapted genotypes for each mega-environment. A mega-environment is defined as a broad, not necessarily contiguous geographic area with similar biotic and abiotic stresses, cropping system requirements, consumer preferences, and a volume of production sufficient to justify attention. Note that this definition encompasses environmental, genotypic, geographical, and even economic aspects of mega-environments.

The primary proposal adopted here for interlinking mega-environments and genotypes is to identify a mega-environment as a portion of a crop's overall growing region in which a particular genotype wins. A complementary proposal is to define mega-environments in terms of major environmental factors, such as irrigated lowlands or nonirrigated uplands. These genotype-based and environment-based proposals are interdependent. The genotype-based concept is relevant because planting the winning genotype in each mega-environment optimizes yield. And the environment-based concept is helpful for assigning individual farms to an appropriate mega-environment (and hence genotype recommendation), especially if the main environmental factor has an obvious and simple geographical distribution. Mega-environment differences exert their importance for plant breeders and farmers by changing genotype rankings from one mega-environment to another. Fortunately, a small and workable number of mega-environments often suffices to exploit interactions and increase yields.


Hugh G. Gauch, Jr.
AMMIWins
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