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Multi‐trait selection for mean performance and stability in maize
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Multi‐trait selection for mean performance and stability in maize
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Journal Article
Multi‐trait selection for mean performance and stability in maize
2021
Overview
There is a need to strengthen maize (Zea mays L.) breeding strategies based on multivariate selection to obtain high‐yielding hybrids that are more stable and resilient to contrasting environmental conditions. Here, we show how the multi‐trait stability index (MTSI) can be used to select maize hybrids for mean performance and stability of multiple traits. A set of 10 traits, including grain yield (GY), yield components, and plant‐related traits with negative and positive desired selection gains (SGs), were accessed in 90 F1 hybrids conducted in multi‐environment trials. Hybrid and hybrid × location interaction effects were significant (p ≤ .001) for all analyzed traits. The MTSI provided positive gains for all the four traits that were wanted to increase (2.52% ≤ SG ≤ 4.86; mean, 3.28%), including GY (SG, 4.86%), and negative gains for all the six traits that were wanted to decrease (–20.28% ≤ SG ≤ –0.09%; mean, –6.70%), including tassel branch number (SG, –20.28%) and plant height (SG, –1.2%). We also observed desired gains for the stability of all traits. Direct and univariate selection for GY solely was not efficient to provide desired gains for all traits. The MTSI provides a unique, robust, and easy‐to‐handle selection process that allows identifying the strengths and weaknesses of hybrids. The index was found to be a powerful tool to develop better selection strategies, optimizing the use of resources and time, thus contributing to the sustainability of maize breeding programs worldwide. Core Ideas The selection of maize hybrids in multi‐environment trials was performed using the multi‐trait stability index (MTSI). Direct and univariate selection for grain yield is not efficient. The MTSI provided desired gains for both mean performance and stability of all studied traits. The MTSI allows identifying the strengths and weaknesses of hybrids graphically.
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