Optimization of carbon and nitrogen partitioning in DP202216 maize hybrids

Context The photoassimilates and nitrogen (N) allocation in grain crops can be studied via the harvest index (HI) and the N harvest index (NHI). These two variables should be considered in breeding programs seeking more effective use of resources in maize (Zea mays L.) and other grain crops. Molecular biotechnology has emerged as a complement to improve not only maize grain yield but also resource utilization efficiency. A recent study reported a beneficial impact of increasing and extending the expression of a maize MADS-box transcription factor (zmm28) on the NHI. Objective This research aimed to further identify enhancements of C-N partitioning (HI, NHI) in DP202216 maize hybrids recognizing the main physiological and morphological traits associated to the C-N dynamics via in-season intra-canopy measurements. Methods Two DP202216 elite hybrids were evaluated with their respective wild-type (WT) controls during two field growing seasons under two N regimes. The N nutrition index (NNI), as crop N status, was quantified to avoid misinterpretations of the results. Results The DP202216 trait showed, on average, a ∼6% increase in the HI with a concomitant positive impact of ∼9% in the NHI across hybrids, N regimes, and seasons. Those increases were consistent at different crop NNI. After accounting for the NNI, it was shown that the increases in both HI and NHI were supported by the accumulation of more N in leaves (mainly in both lower and middle sections of the canopy vertical profile) and more water-soluble carbohydrates (WSC) in stems before flowering, combined with more aggressive post-anthesis remobilization. Conclusion In this study, it was shown an increase in the efficient use of crop resources in DP202216 hybrids with respect to their isogenic WT controls at similar NNI via increments in C and N partitioning (HI and NHI). Implications This physiological and morphological understanding of C- and N-related traits addressed the challenge of optimizing the effective use of resources in maize breeding programs. To better understand traits by management interactions with yield and yield stability, DP202216 hybrid evaluations across a broad range of commercially relevant N scenarios would be valuable.