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The impact of grain yield on post-silking N uptake (PostN) in maize has been a major focus of previous studies, although results are mixed as to the direction and magnitude of the relationship between these two variables. The objective of this study was to understand how grain yield and other plant traits interact with exogenous N and water availability to regulate PostN in maize. In a greenhouse experiment, maize was subjected to high or low levels of N and water supply pre-silking during vegetative growth, which created large variations in source and sink components such as ear size and leaf area. Notably, these large differences in source and sink components were generated not by cutting off plant organs but instead by relying on maize response to vegetative-stage N and water stress. These plants were then subject to high and low levels of N and water supply post-silking, and the relationship between plant traits and PostN was characterized. Final grain yield was irrevocably reduced in the treatments where pre-silking water stress occurred compared to the well-watered pre-silking treatments (30 g plant vs. 106 g plant). Because of the reduced ear sink strength in the treatments experiencing pre-silking water stress, post-silking biomass (PostBM) and PostN accumulated in vegetative organs. This resulted in greater PostN at maturity in the lower yielding treatments when post-silking water and/or N stress occurred (1.1 vs. 0.6 g N plant). Due to the shift in assimilate and N partitioning towards vegetative organs, leaf CER and root dry weight during grain-fill were better maintained in the lower yielding treatments. We conclude that while biomass accumulation (PostBM) regulates PostN, under post-silking N or water stress, shifting sink organs from the grain to vegetative structures increases PostN by improving vegetative organ function and enhancing post-silking source-sink ratios.
This article was published in the following journal.
Name: Journal of plant physiology
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A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.
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