The identification of traits associated with drought tolerance in popcorn is a contribution to support selection of superior plants under soil water deficit. The objective of this study was to choose morphological traits and the leaf greenness index, measured on different dates, to estimate grain yield (GY) and popping expansion (PE), evaluated in a set of 20 popcorn lines with different genealogies, estimated by multiple regression models. The variables were divided into three groups: morpho-agronomic traits-100-grain weight (GW), prolificacy (PR), tassel length (TL), number of tassel branches, anthesis-silking interval, leaf angle (FA) and leaf rolling (FB); variables related to the intensity of leaf greenness during the grain-filling period, at the leaf level, measured by a portable chlorophyll meter (SPAD) and at the canopy level, calculated as the normalized difference vegetation index (NDVI). The inbred lines were cultivated under two water conditions: well-watered (WW), maintained at field capacity, and water stress (WS), for which irrigation was stopped before male flowering. The traits GY (55%) and PE (28%) were most affected by water restriction. Among the morpho-agronomic traits, GW and PR were markedly reduced (>10%). Under dry conditions, the FA in relation to the plant stalk tended to be wider, the FB curvature greater and leaf senescence accelerated (>15% at 22 days after male flowering). The use of multiple regression for the selection of predictive traits proved to be a useful tool for the identification of groups of adequate traits to efficiently predict the economically most important features of popcorn (GY and PE). The SPAD index measured 17 days after male flowering proved useful to select indirectly for GY, while, among the morphological traits, TL stood out for the same purpose. Of all traits, PR was most strongly related with PE under WS, indicating its use in breeding programs. The exploitation of these traits by indirect selection is expected to induce increments in GY and PE.
Keywords: SPAD; drought; leaf greenness index; multiple regressions; normalized difference vegetation index.