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.

Effects of dense planting patterns on photosynthetic traits of different vertical layers and yield of wheat under different nitrogen rates

A two-year field experiment was conducted to measure the effects of densification methods on photosynthesis and yield of densely planted wheat.Inter-plant and inter-row distances were used to define ratefixed pattern(RR)and row-fixed pattern(RS)density treatments.Meanwhile,four nitrogen(N)rates(0,144,192,and 240 kg N ha-1,termed N0,N144,N192,and N240)were applied with three densities(225,292.5,and 360×10^(4)plants ha^(-1),termed D225,D292.5,and D360).The wheat canopy was clipped into three equal vertical layers(top,middle,and bottom layers),and their chlorophyll density(Ch D)and photosynthetically active radiation interception(FIPAR)were measured.Results showed that the response of Ch D and FIPAR to N rate,density,and pattern varied with different layers.N rate,density,and pattern had significant interaction effects on Ch D.The maximum values of whole-canopy Ch D in the two seasons appeared in N240 combined with D292.5 and D360 under RR,respectively.Across two growing seasons,FIPAR values of RR were higher than those of RS by 29.37%for the top layer and 5.68%for the middle layer,while lower than those of RS by 20.62%for the bottom layer on average.With a low N supply(N0),grain yield was not significantly affected by density for both patterns.At N240,increasing density significantly increased yield under RR,but D360 of RS significantly decreased yield by 3.72%and 9.00%versus D225 in two seasons,respectively.With an appropriate and sufficient N application,RR increased the yield of densely planted wheat more than RS.Additionally,the maximum yield in two seasons appeared in the combination of D360 with N144 or N192 rather than of D225 with N240 under both patterns,suggesting that dense planting combined with an appropriate N-reduction application is feasible to increase photosynthesis capacity and yield.

Modeling the effects of extreme high-temperature stress at anthesis and grain filling on grain protein in winter wheat

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.

Key variable for simulating critical nitrogen dilution curve of wheat:Leaf area ratio-driven approach

Nitrogen(N)dilution curves,a pivotal tool for N nutrition diagnosis,have been developed using different winter wheat(Triticum aestivum L.)tissues.However,few studies have attempted to establish critical nitrogen(N_(c))dilution curves based on the leaf area ratio(LAR)to improve the monitoring accuracy of N status.In this study,three field experiments using eight N treatments and four wheat varieties were conducted in Jiangsu Province of China from 2013 to 2016.The empirical relationship of LAR with shoot biomass(expressed as dry matter)was developed under different N conditions.The results showed that LAR was a reliable index,which reduced the effects of wheat varieties and years compared with the traditional indicators.The N nutrition index(NNI)based on the LAR approach(NNI-LAR)produced equivalent results to that based on shoot biomass.Moreover,the NNI-LAR better predicted accumulated N deficit and best estimated the relative yield compared with the other two indicator-based NNI models.Therefore,the LAR-based approach improved the prediction accuracy of N_(c),accumulated N deficit,and relative yield,and it would be an optimal choice to conveniently diagnose the N status of winter wheat under field conditions.

SPSI:A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery

Rapid and accurate estimation of panicle number per unit ground area(PNPA)in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final yield.The accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were ignored.This study proposed a spectral-textural PNPA sensitive index(SPSI)from unmanned aerial vehicle(UAV)multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before heading.The effect of background materials on PNPA estimated by textural indices(TIs)was examined,and the composite index SPSI was constructed by integrating the optimal spectral index(SI)and TI.Subsequently,the performance of SPSI was evaluated in comparison with other indices(SI and TIs).The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI_([HOM]),TI_([ENT]),and TI_([SEM])among all indices from 8 types of textural features.SPSI,which was calculated by the formula DATT_([850,730,675])+NDTICOR_([850,730]),exhibited the highest overall accuracies for any date in any dataset in comparison with DATT_([850,730,675]),TINDRE_([MEA]),and NDTICOR_([850,730]).For the unified models assembling 2 experimental datasets,the RV^(2) values of SPSI increased by 0.11 to 0.23,and both RMSE and RRMSE decreased by 16.43%to 38.79%as compared to the suboptimal index on each date.These findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.