Multienvironment trials are important in agronomy because the effects of ag
ronomic treatments can change differentially in relation to environmental c
hanges, producing a treatment X environment interaction (T X E), The aim of
this study was to find a parsimonious description of the T X E existing in
the 24 agronomic treatments evaluated during 10 consecutive years by (i) i
nvestigating the factorial structure of the treatments to reduce the number
of treatment terms in the interaction and (ii) using quantitative year cov
ariables to replace the qualitative variable year. Multiple factorial regre
ssion (MFR) for specific T X E terms was performed using standard forward s
election procedures for finding year covariables that could replace the fac
tor gear in those T X E terms, Subsequently, we compared the results of the
final MFR with those of a partial least squares based analysis to achieve
extra insight in both the T X E and final MFR model, The MFR model with a s
tepwise procedure used in this study for describing the T X E showed that t
he most important interaction with year was that due to different N fertili
zer levels and the most important environmental variables that explained ye
ar X N interaction were minimum temperatures in January, February, and Marc
h and maximum temperature in April. Evaporation in December and April were
important covariables for describing year X tillage and year X summer crop
interactions, whereas precipitation in December and sun hours in February w
ere important for explaining the year X manure interaction, We also discuss
the parallels with extended additive main effect and multiplicative intera
ction analysis. Biological interpretation of the results are provided.