Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data generated by hyperspectral cameras are richer than what can be summarized in a VI. Therefore, in this study, we examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs. For hyperspectral prediction equations, we considered three estimation methods: ordinary least squares, partial least squares (a dimension reduction method), and a Bayesian shrinkage and variable selection procedure. We also examined the benefits of combining reflectance data collected at different time points. Data were generated by CIMMYT in 11 maize (Zea mays L.) yield trials conducted in 2014 under heat and drought stress. Our results indicate that using data from 62 bands leads to higher prediction accuracy than what can be achieved using individual VIs. Overall, the shrinkage and variable selection method was the best‐performing one. Among the models using data from a single time point, the one using reflectance collected at 28 d after flowering gave the highest prediction accuracy. Combining image data collected at multiple time points led to an increase in prediction accuracy compared with using single‐time‐point data.

Multienvironment trials (METs) are conducted to evaluate cultivars across locations and years with often incomplete data structure due to annual cultivar replacements. The imbalance could cause biased variance component (VC) estimates depending on data dimension, proportion of missing values, and the cultivar dropout mechanism. The objective of this study was to quantify the bias of VC estimates obtained from imbalanced datasets. We performed simulations of METs with different data dimensions (number of cultivars, locations, and years) using VC parameters taken from real wheat (Triticum aestivum L.) METs. The missing values were generated by annually dropping and replacing cultivars. The genotypic variance estimates obtained from analyses of 2 yr of METs, and >40% missing values, were overestimated in all simulated scenarios. The percentage of bias was highly influenced by the number of years considered for analysis. Variance component estimates from simulations with more years of METs were less biased: 8‐yr analyses produced <5% bias in the genotypic variance and its interactions, even in highly imbalanced datasets. Increasing the number of annually tested cultivars or the number of locations was less beneficial in terms of decreasing bias than increasing the number of years. Cultivar‐mean repeatability was considerably affected by increases in the percentage of missing values, which caused reductions of up to 60% with few years of METs. Results showed that, even with cultivar replacement, linear mixed models can estimate VCs with <5% bias when there are four or more years of METs, with or without imbalance (up to 40%).

clear tendencies with TT. Nevertheless, and whatever the stage of fruit development, secoiridoids were the major phenolic components. Results suggest greater sensitivity of fatty acid metabolism to temperature in cv. Arbequina. This fact points out the necessity of appropriate evaluation of the ambient thermal characteristics before introducing this cultivar into new growing environments.