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 dete...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展开更多
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 t...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.展开更多
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 nitrog...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.展开更多
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)...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.展开更多
基金supported by the National Key Technology R&D Program (2011BAD12B03)
文摘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
基金supported by the earmarked fund for China Agriculture Research System(CARS-03)the National Key Research and Development Program of China(2017YFD0201501 and 2016YFD020060306)the National Natural Science Foundation of China(41701375 and 61661136003)。
文摘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.
基金funded by the National Key Research and Development Program of China(2022YFD1900401)the Chinese Universities Scientific Fund(2452020018)。
文摘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.
基金supported by the National Natural Science Foundation of China(No.32071903)the Earmarked Fund for Jiangsu Agricultural Industry Technology System,China(Nos.JATS(2020)415 and JATS(2020)135)+1 种基金the Fund of Jiangsu Agricultural Science and Technology Innovation,China(No.CX(20)3072)the Jiangsu Provincial Key Technologies R&D Program of China(No.BE2019386)。
文摘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.