The nitrogen (N) nutrition index (NNI) is a reliable indicator of crop N status and there is an urgent need to develop efficient technologies for non-destructive estimation of NNI to support the practical applications of precision N management strategies. The objectives of this study were to: (i) validate a newly established critical N dilution curve for spring maize in Northeast China; (ii) determine the potential of using the GreenSeeker active optical sensor to non-destructively estimate NNI; and (iii) evaluate the performance of different N status diagnostic approaches based on estimated NNI via the GreenSeeker sensor measurements. Four field experiments involving six N rates (0, 60, 120,180, 240, and 300 kg¨ha´1) were conducted in 2014 and 2015 in Lishu County, Jilin Province in Northeast China. The results indicated that the newly established critical N dilution curve was suitable for spring maize N status diagnosis in the study region. Across site-years and growth stages (V5-V10), GreenSeeker sensor-based vegetation indices (VIs) explained 87%-90%, 87%-89% and 83%-84% variability of leaf area index (LAI), aboveground biomass (AGB) and plant N uptake (PNU), respectively. However, normalized difference vegetation index (NDVI) became saturated when LAI > 2 m 2¨m´2 , AGB > 3 t¨ha´1 or PNU > 80 kg¨ha´1. The GreenSeeker-based VIs performed better for estimating LAI, AGB and PNU at V5-V6 and V7-V8 than the V9-V10 growth stages, but were very weakly related to plant N concentration. The response index calculated with GreenSeeker NDVI (RI-NDVI) and ratio vegetation index (R 2 = 0.56-0.68) performed consistently better than the original VIs (R 2 = 0.33-0.55) for estimating NNI. The N status diagnosis accuracy rate using RI-NDVI was 81% and 71% at V7-V8 and V9-V10 growth stages, respectively. We conclude that the response indices calculated with the GreenSeeker-based vegetation indices can be used to estimate spring maize NNI non-destructively and for in-season N status diagnosis between V7 and V10 growth stages under experimental conditions with variable N supplies. More studies are needed to further evaluate different approaches under diverse on-farm conditions and develop side-dressing N recommendation algorithms.

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Genetic improvement of grain yield is always an important objective in wheat breeding. Here, a genome-wide association study was conducted to parse the complex genetic composition of yield-related traits of 105 elite wheat varieties (lines) using the Wheat 90K Illumina iSelect SNP array. Nine yield-related traits, including maximum number of shoots per square meter (MSN), effective number of spikes per square meter (ESN), percentage of effective spike (PES), number of kernels per spike (KPS), thousand-kernel weight (TKW), the ratio of kernel length/kernel width (RLW), leaf-area index (LAI), plant height (PH), and grain yield (GY), were evaluated across four environments. Twenty four highly significant marker-trait associations (MTAs) (P < 0.001) were identified for nine yield-related traits on chromosomes 1A, 1D, 2A (2), 3B, 4A (2), 4B, 5A (4), 5B (4), 5D, 6B (2), 7A (2), and 7B (3), explaining 10.86–20.27% of the phenotypic variations. Of these, four major loci were identified in more than three environments, including one locus for RLW (6B), one locus for TKW (7A), and two loci for PH (7B). A cleaved amplified polymorphic sequence (CAPS) marker Td99211 for TKW on chromosome 5A was developed and validated in both a natural population composed of 372 wheat varieties (lines) and a RIL population derived from the cross of Yangxiaomai × Zhongyou 9507. The CAPS marker developed can be directly used for marker-assisted selection in wheat breeding, and the major MTAs identified can provide useful information for fine-mapping of the target genes in future studies.

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