2021
DOI: 10.1002/csc2.20481
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Kernel weight responses to the photothermal environment in maize dent × flint and flint × flint hybrids

Abstract: Maize (Zea mays L.) grain yield is assumed to be source limited during the flowering period but sink limited during grain growth; however, environmental restrictions during active grain filling may strongly affect final kernel weight (KW). In this study, we evaluated the effect of natural changes in photothermal conditions during lag phase (LP) and effective grain‐filling period (EGFP) on KW, its physiological determinants, and the post‐flowering source–sink relationships of flint and semident germplasm. F1 hy… Show more

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Cited by 7 publications
(3 citation statements)
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“…Limited environmental conditions during grain filling can strongly affect yield. Photothermal conditions are important, especially seasons with photothermal imbalances that affect kernel weight and yield (Hisse et al, 2021).…”
Section: Resultsmentioning
confidence: 99%
“…Limited environmental conditions during grain filling can strongly affect yield. Photothermal conditions are important, especially seasons with photothermal imbalances that affect kernel weight and yield (Hisse et al, 2021).…”
Section: Resultsmentioning
confidence: 99%
“…The F 1 × E component corresponding to each N level was entirely represented by the F 1 × Y interaction. In this sense, both experimental years differed notably in the photothermal conditions during grain filling (Hisse et al., 2021), affecting, to a greater extent, the potential condition corresponding to the N200 treatment. For this N level, a higher response degree to the changes in the photothermal environment is expected, with the subsequent increase in the F 1 × E component (Table S1), and hence, in the reduction of heritability (Figure 2).…”
Section: Discussionmentioning
confidence: 99%
“…KW, KGR, and durations of the effective grain‐filling (EGFD) and lag ‐phase (LPD) periods were estimated by fitting a bilinear model relating kernel dry weight and TT after silking (Borrás & Otegui, 2001). Maximum kernel volume (KV MAX ) was estimated by fitting a bilinear model relating kernel volume and TT after silking (Hisse et al., 2021). Maximum kernel water content (KWC MAX ) was computed by fitting a trilinear model relating kernel water content and TT after silking (Gambín et al., 2007).…”
Section: Methodsmentioning
confidence: 99%