No abstract

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.

No abstract

No abstract

No abstract