Development of a novel critical nitrogen concentration-cumulative transpiration curve for optimizing nitrogen management under varying irrigation conditions in winter wheat

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 ha1),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=6.43T0.24 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.

An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat

The nitrogen nutrition index(NNI)is a reliable indicator for diagnosing crop nitrogen(N)status.However,there is currently no specific vegetation index for the NNI inversion across multiple growth periods.To overcome the limitations of the traditional direct NNI inversion method(NNI_(T1))of the vegetation index and traditional indirect NNI inversion method(NNI_(T2))by inverting intermediate variables including the aboveground dry biomass(AGB)and plant N concentration(PNC),this study proposed a new NNI remote sensing index(NNI_(RS)).A remote-sensing-based critical N dilution curve(Nc_(_RS))was set up directly from two vegetation indices and then used to calculate NNI_(RS).Field data including AGB,PNC,and canopy hyperspectral data were collected over four growing seasons(2012–2013(Exp.1),2013–2014(Exp.2),2014–2015(Exp.3),2015–2016(Exp.4))in Beijing,China.All experimental datasets were cross-validated to each of the NNI models(NNI_(T1),NNI_(T2)and NNI_(RS)).The results showed that:(1)the NNI_(RS)models were represented by the standardized leaf area index determining index(sLAIDI)and the red-edge chlorophyll index(CI_(red edge))in the form of NNI_(RS)=CI_(red edge)/(a×sLAIDI~b),where"a"equals 2.06,2.10,2.08 and 2.02 and"b"equals 0.66,0.73,0.67 and 0.62 when the modeling set data came from Exp.1/2/4,Exp.1/2/3,Exp.1/3/4,and Exp.2/3/4,respectively;(2)the NNI_(RS)models achieved better performance than the other two NNI revised methods,and the ranges of R2 and RMSE were 0.50–0.82 and 0.12–0.14,respectively;(3)when the remaining data were used for verification,the NNI_(RS)models also showed good stability,with RMSE values of 0.09,0.18,0.13 and 0.10,respectively.Therefore,it is concluded that the NNI_(RS)method is promising for the remote assessment of crop N status.