Temporal Dynamics and the Contribution of Plant Organs in a Phenotypically Diverse Population of High-Yielding Winter Wheat: Evaluating Concepts for Disentangling Yield Formation and Nitrogen Use Efficiency

Post-anthesis dry matter and nitrogen accumulation, partitioning, and translocation in maize under different nitrate-ammonium ratios in Northwestern China

Introduction

An appropriate supply of ammonium (NH4+) in addition to nitrate (NO3-) can greatly improve plant growth and promote maize productivity. However, knowledge gaps exist regarding the mechanisms by which different nitrogen (N) fertilizer sources affect the enzymatic activity of nitrogen metabolism and non-structural carbohydrates during the post-anthesis period.

Methods

A field experiment across 3-year was carried out to explore the effects of four nitrateammonium ratio (NO3-/NH4+ = 1:0 (N1), 1:1 (N2), 1:3 (N3), and 3:1 (N4)) on postanthesis dry matter (DM) and N accumulation, partitioning, transportation, and grain yield in maize.

Results

NO3-/NH4+ ratio with 3:1 improved the enzymatic activity of N metabolism and non-structural carbohydrate accumulation, which strongly promoted the transfer of DM and N in vegetative organs to reproductive organs and improved the pre-anthesis DM and nitrogen translocation efficiency. The enzymatic activities of nitrate reductase, nitrite reductase, glutamine synthetase, glutamine oxoglutarate aminotransferase, and non-structural carbohydrate accumulation under N4 treatment were increased by 9.30%-32.82%, 13.19%-37.94%, 4.11%-16.00%, 11.19%-30.82%, and 14.89%-31.71% compared with the other treatments. Mixed NO3--N and NH4+-N increased the total DM accumulation at the anthesis and maturity stages, simultaneously decreasing the DM partitioning of stem, increasing total DM, DM translocation efficiency (DMtE), and contribution of pre-anthesis assimilates to the grain (CAPG) in 2015 and 2017, promoting the transfer of DM from stem to grain. Furthermore, the grain yield increased by 3.31%-9.94% (2015), 68.6%-26.30% (2016), and 8.292%-36.08% (2017) under the N4 treatment compared to the N1, N2, and N3 treatments.

Conclusion

The study showed that a NO3-/NH4+ ratio of 3:1 is recommended for high-yield and sustainable maize management strategies in Northwestern China.

Post-anthesis Relationships Between Nitrogen Isotope Discrimination and Yield of Spring Wheat Under Different Nitrogen Levels

Wheat grain yield and nitrogen (N) content are influenced by the amount of N remobilized to the grain, together with pre-anthesis and post-anthesis N uptake. Isotopic techniques in farmed areas may provide insight into the mechanism underlying the N cycle. 15N-labeled urea was applied to microplots within five different fertilized treatments 0 kg ha-1 (N1), 52.5 kg ha-1 (N2), 105 kg ha-1 (N3), 157.5 kg ha-1 (N4), and 210 kg ha-1 (N5) of a long-term field trial (2003-2021) in a rainfed wheat field in the semi-arid loess Plateau, China, to determine post-anthesis N uptake and remobilization into the grain, as well as the variability of 15N enrichment in aboveground parts. Total N uptake was between 7.88 and 29.27 kg ha-1 for straw and 41.85 and 95.27 kg ha-1 for grain. In comparison to N1, N fertilization increased straw and grain N uptake by 73.1 and 56.1%, respectively. Nitrogen use efficiency (NUE) and harvest index were altered by N application rates. The average NUE at maturity was 19.9% in 2020 and 20.01% in 2021; however, it was usually higher under the control and low N conditions. The amount of 15N excess increased as the N rate increased: N5 had the highest 15N excess at the maturity stage in the upper (2.28 ± 0.36%), the middle (1.77 ± 0.28%), and the lower portion (1.68 ± 1.01%). Compared to N1, N fertilization (N2-N5) increased 15N excess in the various shoot portions by 50, 38, and 35% at maturity for upper, middle, and lower portions, respectively. At maturity, the 15N excess remobilized to the grain under N1-N5 was between 5 and 8%. Our findings revealed that N had a significant impact on yield and N isotope discrimination in spring wheat that these two parameters can interact, and that future research on the relationship between yield and N isotope discrimination in spring wheat should take these factors into account.

Deep Phenotyping of Yield-Related Traits in Wheat

The complex formation of grain yield (GY) is related to multiple dry matter (DM) traits; however, due to their time-consuming determination, they are not readily accessible. In winter wheat (Triticum aestivum L.), both agronomic treatments and genotypic variation influence GY in interaction with the environment. Spectral proximal sensing is promising for high-throughput non-destructive phenotyping but was rarely evaluated systematically for dissecting yield-related variation in DM traits. Aiming at a temporal, spectral and organ-level optimization, 48 vegetation indices were evaluated in a high-yielding environment in 10 growth stages for the estimation of 31 previously compared traits related to GY formation—influenced by sowing time, fungicide, N fertilization, and cultivar. A quantitative index ranking was evaluated to assess the stage-independent index suitability. GY showed close linear relationships with spectral vegetation indices across and within agronomic treatments (R2 = 0.47–0.67 ***). Water band indices, followed by red edge-based indices, best used at milk or early dough ripeness, were better suited than the widely used normalized difference vegetation index (NDVI). Index rankings for many organ-level DM traits were comparable, but the relationships were often less close. Among yield components, grain number per spike (R2 = 0.24–0.34 ***) and spike density (R2 = 0.23–0.46 ***) were moderately estimated. GY was mainly estimated by detecting total DM rather than the harvest index. Across agronomic treatments and cultivars, seasonal index rankings were the most stable for GY and total DM, whereas traits related to DM allocation and translocation demanded specific index selection. The results suggest using indices with water bands, near infrared/red edge and visible light bands to increase the accuracy of in-season spectral phenotyping for GY, contributing organ-level traits, and yield components, respectively.

High-Throughput Field Phenotyping Traits of Grain Yield Formation and Nitrogen Use Efficiency: Optimizing the Selection of Vegetation Indices and Growth Stages

High-throughput, non-invasive phenotyping is promising for evaluating crop nitrogen (N) use efficiency (NUE) and grain yield (GY) formation under field conditions, but its application for genotypes differing in morphology and phenology is still rarely addressed. This study therefore evaluates the spectral estimation of various dry matter (DM) and N traits, related to GY and grain N uptake (Nup) in high-yielding winter wheat breeding lines. From 2015 to 2017, hyperspectral canopy measurements were acquired on 26 measurement dates during vegetative and reproductive growth, and 48 vegetation indices from the visible (VIS), red edge (RE) and near-infrared (NIR) spectrum were tested in linear regression for assessing the influence of measurement stage and index selection. For most traits including GY and grain Nup, measurements at milk ripeness were the most reliable. Coefficients of determination (R²) were generally higher for traits related to maturity than for those related to anthesis canopy status. For GY (R² = 0.26-0.51 in the three years, p < 0.001), and most DM traits, indices related to the water absorption band at 970 nm provided better relationships than the NIR/VIS indices, including the normalized difference vegetation index (NDVI), and the VIS indices. In addition, most indices including RE bands, notably NIR/RE combinations, ranked above the NIR/VIS group. Due to index saturation, the index differentiation was most apparent in the highest-yielding year. For grain Nup and total Nup, the RE/VIS index MSR_705_445 and the simple ratio R780_R740 ranked highest, followed by other RE indices. Among the vegetative organs, R² values were mostly highest and lowest for leaf and spike traits, respectively. For each trait, index and partial least squares regression (PLSR) models were validated across years at milk ripeness, confirming the suitability of optimized index selection. PLSR improved the prediction errors of some traits but not consistently the R² values. The results suggest the use of sensor-based phenotyping as a useful support tool for screening of yield potential and NUE and for identifying contributing plant traits-which, due to their expensive and cumbersome destructive determination are otherwise not readily available. Water band and RE indices should be preferred over NIR/VIS indices for DM traits and N-related traits, respectively, and milk ripeness is suggested as the most reliable stage.