Accurate nitrogen (N) nutrition diagnosis is essential for improving N use efficiency in crop production. The widely used critical N (Nc) dilution curve traditionally depends solely on agronomic variables, neglecting crop water status. With three-year field experiments with winter wheat, encompassing two irrigation levels (rainfed and irrigation at jointing and anthesis) and three N levels (0, 180, and 270 kg ha⁻¹), this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration (T), providing a comprehensive analysis of the interaction between N and water availability. The Nc curves derived from both crop dry matter (DM) and T demonstrated N concentration dilution under different conditions with different parameters. The equation Nc = 6.43T^−0.24 established a consistent relationship across varying irrigation regimes. Independent test results indicated that the nitrogen nutrition index (NNI), calculated from this curve, effectively identifies and quantifies the two sources of N deficiency: insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root absorption. Additionally, the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit (Nand) and a positive correlation with relative grain yield (RGY). The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY, as indicated by larger R² values and smaller AIC. The novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status, predicting grain yield, and optimizing N fertilizer management across varying irrigation conditions. These findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models.

Extreme high-temperature stress (HTS) associated with climate change poses potential threats to wheat grain yield and quality. Wheat grain protein concentration (GPC) is a determinant of wheat quality for human nutrition and is often neglected in attempts to assess climate change impacts on wheat production. Crop models are useful tools for quantification of temperature impacts on grain yield and quality. Current crop models either cannot simulate or can simulate only partially the effects of HTS on crop N dynamics and grain N accumulation. There is a paucity of observational data on crop N and grain quality collected under systematic HTS scenarios to develop algorithms for model improvement as well as evaluate crop models. Two-year phytotron experiments were conducted with two wheat cultivars under HTS at anthesis, grain filling, and both stages. HTS significantly reduced total aboveground N and increased the rate of grain N accumulation, while total aboveground N and the rate of grain N accumulation were more sensitive to HTS at anthesis than at grain filling. The observed relationships between total aboveground N, rate of grain N accumulation, and HTS were quantified and incorporated into the WheatGrow model. The new HTS routines improved simulation of the dynamics of total aboveground N, grain N accumulation, and GPC by the model. The improved model provided better estimates of total aboveground N, grain N accumulation, and GPC under HTS (the normalized root mean square error was reduced by 40%, 85%, and 80%, respectively) than the original WheatGrow model. The improvements in the model enhance its applicability to the assessment of climate change effects on wheat grain quality by reducing the uncertainties of simulating N dynamics and grain quality under HTS.