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produces adjusted means which often have predictive accuracy equivalent to unadjusted means based upon a few or several additional replicates (see inset). Therefore, AMMI analysis can often improve accuracy as much as doubling the data collection effort. Most strategies for improving accuracy require a particular experimental design, or else concomitant variables ranging from field maps locating each observation to additional data on soils, weather, or whatever. AMMI has no such requirements. AMMI is freely applicable to historical data, collected with any experimental design. Thus, AMMI is still useful when past data contribute to ongoing decisions. Moreover, the power of AMMI to extract additional information will often make hard-won historical data worthy of renewed attention. Version 2.0 represents a considerable improvement over earlier versions, allowing and imputing missing data. It also provides tools for optimal model selection from among the family of AMMI models, including assessment of predictive accuracy using data splitting and validation. Efficient memory utilization allows for processing about 30,000 observations, depending on system configuration and details of the analysis.

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