Nitrogen Nutrition Index and Its Relationship with N Use Efficiency, Tuber Yield, Radiation Use Effi ciency, and Leaf Parameters in Potatoes

Knowledge about crop growth processes in relation to N limitation is necessary to optimize N management in farming system. Plant-based diagnostic method, for instance nitrogen nutrition index （NNI） were used to determine the crop nitrogen status. This study determines the relationship of NNI with agronomic nitrogen use efficiency （AEN）, tuber yield, radiation use efficiency （RUE） and leaf parameters including leaf area index （LAI）, areal leaf N content （NJ and leaf N concentration （N0. Potatoes were grown in field at three N levels： no N （N 1）, 150 kg N ha^-1 （N2）, 300 kg N ha^-1 （N3）. N deficiency was quantified by NNI and RUE was generally calculated by estimating of the light absorbance on leaf area. NNI was used to evaluate the N effect on tuber yield, RUE, LAI, NAL, and NL. The results showed that NNI was negatively correlated with AEN, N deficiencies （NNI〈 1） which occurred for N 1 and N2 significantly reduced LAI, NL and tuber yield; whereas the N deficiencies had a relative small effect on NAL and RUE. To remove any effect other than N on these parameters, the actual ratio to maximum values were calculated for each developmental linear relationships were obtained between NNI and tuber RUE to NNI. stage of potatoes. When the NNI ranged from 0.4 to 1, positive yield, LAI, NL, while a nonlinear regression fitted the response of

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.

An optimized strategy of nitrogen-split application based on the leaf positional differences in chlorophyll meter readings

Modern rice production faces the dual challenges of increasing grain yields while reducing inputs of chemical fertilizer.However,the disequilibrium between the nitrogen(N)supplement from the soil and the demand for N of plants is a serious obstacle to achieving these goals.Plant-based diagnosis can help farmers make better choices regarding the timing and amount of topdressing N fertilizer.Our objective was to evaluate a non-destructive assessment of rice N demands based on the relative SPAD value(RSPAD)due to leaf positional differences.In this study,two field experiments were conducted,including a field experiment of different N rates(Exp.I)and an experiment to evaluate the new strategy of nitrogen-split application based on RSPAD(Exp.II).The results showed that higher N inputs significantly increased grain yield in modern high yielding super rice,but at the expense of lower nitrogen use efficiency(NUE).The N nutrition index(NNI)can adequately differentiate situations of excessive,optimal,and insufficient N nutrition in rice,and the optimal N rate for modern high yielding rice is higher than conventional cultivars.The RSPAD is calculated as the SPAD value of the top fully expanded leaf vs.the value of the third leaf,which takes into account the non-uniform N distribution within a canopy.The RSPAD can be used as an indicator for higher yield and NUE,and guide better management of N fertilizer application.Furthermore,we developed a new strategy of nitrogen-split application based on RSPAD,in which the N rate was reduced by 18.7%,yield was increased by 1.7%,and the agronomic N use efficiency was increased by 27.8%,when compared with standard farmers'practices.This strategy of N fertilization shows great potential for ensuring high yielding and improving NUE at lower N inputs.

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.

Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images

Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner.The nitrogen nutrition index(NNI)can quantitatively describe the nitrogen status of crops.Nevertheless,the NNI diagnosis for cotton with unmanned aerial vehicle(UAV)multispectral images has not been evaluated yet.This study aimed to evaluate the performance of three machine learning models,i.e.,support vector machine(SVM),back propagation neural network(BPNN),and extreme gradient boosting(XGB)for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images.The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI.The XGB model performed the best among the three models in predicting nitrogen weight.The prediction accuracy of nitrogen weight at the upper half-leaf level(R^(2)=0.89,RMSE=0.68 g m^(-2),RE=14.62%for calibration and R^(2)=0.83,RMSE=1.08 g m^(-2),RE=19.71%for validation)was much better than that at the all-leaf level(R^(2)=0.73,RMSE=2.20 g m^(-2),RE=26.70%for calibration and R^(2)=0.70,RMSE=2.48 g m^(-2),RE=31.49%for validation)and at the plant level(R^(2)=0.66,RMSE=4.46 g m^(-2),RE=30.96%for calibration and R^(2)=0.63,RMSE=3.69 g m^(-2),RE=24.81%for validation).Similarly,the XGB model(R^(2)=0.65,RMSE=0.09,RE=8.59%for calibration and R^(2)=0.63,RMSE=0.09,RE=8.87%for validation)also outperformed the SVM model(R^(2)=0.62,RMSE=0.10,RE=7.92%for calibration and R^(2)=0.60,RMSE=0.09,RE=8.03%for validation)and BPNN model(R^(2)=0.64,RMSE=0.09,RE=9.24%for calibration and R^(2)=0.62,RMSE=0.09,RE=8.38%for validation)in predicting NNI.The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields,which can help farmers implement precise cotton nitrogen management in a timely and accurate manner.

A New Critical Nitrogen Dilution Curve for Rice Nitrogen Status Diagnosis in Northeast China

In-season diagnosis of crop nitrogen(N) status is crucial for precision N management. Critical N(N_c) dilution curve and N nutrition index(NNI) have been proposed as effective methods to diagnose N status of different crops. The N_c dilution curves have been developed for indica rice in the tropical and temperate zones and japonica rice in the subtropical-temperate zone, but they have not been evaluated for short-season japonica rice in Northeast China. The objectives of this study were to evaluate the previously developed N_c dilution curves for rice in Northeast China and to develop a more suitable N_c dilution curve in this region. A total of17 N rate experiments were conducted in Sanjiang Plain, Heilongjiang Province in Northeast China from 2008 to 2013. The results indicated that none of the two previously developed N_c dilution curves was suitable to diagnose N status of the short-season japonica rice in Northeast China. A new N_c dilution curve was developed and can be described by the equation N_c = 27.7 W^(-0.34) if W ≥ 1 Mg dry matter(DM) ha^(-1) or N_c = 27.7 g kg^(-1) DM if W < 1 Mg DM ha^(-1), where W is the aboveground biomass. This new curve was lower than the previous curves. It was validated using a separate dataset, and it could discriminate non-N-limiting and N-limiting nutritional conditions. Additional studies are needed to further evaluate it for diagnosing N status of different rice cultivars in Northeast China and develop efficient non-destructive methods to estimate NNI for practical applications.

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.