Selecting Tolerant Maize Hybrids Using Factor Analytic Models and Environmental Covariates as Drought Stress Indicators

<b>Background/Objectives</b>: A critical part of the maize life cycle takes place during the summer, and due to climate change, its growth and development are increasingly exposed to the irregular and unpredictable effects of drought stress. Developing and using new cultivars with increased drought tolerance for farmers is the easiest and cheapest solution. One of the concepts to screen for drought tolerance is to expose germplasm to various growth scenarios (environments), expecting that random drought will occur in some of them. <b>Methods</b>: In the present study, thirty-two maize hybrids belonging to four FAO maturity groups were tested for grain yield at six locations over two consecutive years. In parallel, data of the basic meteorological elements such as air temperature, relative humidity and precipitation were collected and used to compute two indices, scPDSI (Self-calibrating Palmer Drought Severity Index) and VPD (Vapor Pressure Deficit), that were assessed as indicators of drought (water deficit) severity during the vegetation period. Practical implementation of these indices was carried out indirectly by first analyzing yield data using a factor analytic model to detect latent environmental variables affecting yield and then correlating those latent variables with drought indices. <b>Results</b>: The first latent variable, which explained 47.97% of the total variability, was correlated with VPD (r = -0.58); the second latent variable explained 9.57% of the total variability and was correlated with scPDSI (r = -0.74). Furthermore, latent regression coefficients (i.e., genotypic sensitivities to latent environmental variables) were correlated with genotypic drought tolerance. <b>Conclusions</b>: This could be considered an indication that there were two different acting mechanisms in which drought affected yield.