Genetic improvements in maize (Zea mays L.) have contributed significantly to increases in grain yield over time. Previous studies have investigated changes in gross morphological traits associated with increased plant productivity; however, less is known about how dynamic traits uniquely change throughout a growing season in new compared with old hybrids. In light of this, the objective of this study was to monitor seasonal patterns of leaf chlorophyll concentration and relative water content in a set of era hybrids. Nondestructive, hyperspectral reflectance data were gathered on 34 era hybrids throughout the growing season. The 760/730 reflectance index correlated best to leaf chlorophyll concentration, and the photochemical reflectance index (PRI) was most strongly correlated to relative water content. These vegetative indices were therefore used as proxies for these traits. Genetic gain for grain yield was estimated to be 65 kg ha−1 yr−1, which is very similar to previously reported estimates. Chlorophyll concentration measured during the late vegetative and midreproductive stages increased at a rate of 0.06 mg mL−1 yr−1. Using the 760/730 index, newer hybrids maintained elevated chlorophyll concentrations at every developmental stage sampled except R5 and R6 and displayed the greatest changes through time at the R1 stage. Newer hybrids displayed greater PRI values at the V16 and R5 stages, but differences were not observed between hybrid eras for all other sampling dates. Results from this study illuminate the developmental stages most influenced by selection for grain yield and could inform future studies aimed at dissecting the physiology of grain yield.
|Original language||English (US)|
|Number of pages||13|
|State||Published - Mar 1 2018|
Bibliographical noteFunding Information:
We are grateful to DuPont Pioneer for providing many era hybrids used in this study. Timothy Arkebauer and Thomas Hoegemeyer provided excellent advice and support. We received technological support from scientists and staff at the Center for Advanced Land Management Information Technologies (CALMIT) at the University of Nebraska. We would also like to thank Lorenz laboratory members Amritpal Singh, Nonoy Ban-dillo, Dnyaneshwar C. Kadam, and Erin Gilbert for their help in collecting reflectance and agronomic trait data. Financial support for this study was provided by the Nebraska Corn Board.
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