Plant nitrogen status at phenological stages can well estimate wheat yield and its components.

Yield components play a decisive role in regulating the potential crop grain yields, and their formation is greatly influenced by in-season nitrogen (N) management, as N is essential of optimizing crop growth. To test the applicability of critical N dilution curve based instantaneous N nutrition index (NNI) and integrated NNI (NNI inte) in-season prediction of grain yield and its components in wheat. This study evaluated the applicability of the critical N dilution curve for predicting grain yield and yield components in wheat by establishing the relationships of NNI and NNI inte with relative grain yield (RY), dry matter (RDM), number of spikes (RPN), number of grains per spike (RGN), and grain weight at different growth stages of wheat. Data were acquired from six multi-N rates field experiments conducted in eastern and central China. The RY, RPN, and RDM were estimated by NNI and accumulated NNI inte during different growth stages of wheat. The higher R2 values of relationships observed during the middle to later growth stages compared with early growth stages indicated that the early growth stages might not be suitable for estimating wheat grain yield and yield components. The NNI inte specifically during booting to anthesis stage showed improved accuracy estimating the number of grains per spike compared to other stages. Overall, the estimation performance based on accumulated NNI inte was superior compared to NNI or NNI inte during the single growth stage of wheat. The above models provided comprehensive and accurate estimates dry matter at maturity, grain yield and yield components as early as the anthesis stage. The findings provided valuable insights for predicting grain yield and yield components in intensive wheat cropping systems, and also offer practical guidance for precisely scheduling N management. • Wheat grain yield and yield components were estimated by NNI and integrated NNI (NNI inte) during different growth stages. • Single-stage NNI (booting) allows earlier estimates of dry matter at maturity and spike number. • Single-stage NNI inte (anthesis) only improved the estimation performance of grain number. • NNI inte over multiple consecutive stages could better estimate dry matter at maturity, grain yield, and spike number. • The models can comprehensively and accurately estimate grain yield and yield components as early as the anthesis stage. [ABSTRACT FROM AUTHOR]