Effects of leaf N concentration and leaf area index on determining rice tillering

Relative tillering rate(RTR)increased linear-ly with the increasing of leaf N concentration(NLV)has been already reported.To testwhether this relationship could be used toquantitatively explain the difference in tilleringamong a wide range of N application,field ex- periments were conducted at the IRRI farm,Los Banos,Laguna,the Philippines.Two in- dica cultivars,IR 72 and IR68284H wereused.For each cultivar,12 treatments includ- ing 4 N levels(0,60,120,and 180kgN·ha)and 3 transplanting spacing(30×20,20×20,and 10×20cm)were arranged in a ran-domized split-plot design with 4 replications.The N treatments were designated as mainplots and spacings as subplots.Fourteen-day-old seedlings were transplanted with 3seedlings per hill.The subplot area was 20m~2.Nitrogen fertilizer was applied as basal,atmidtillering,and at panicle initiation in threeequal splits.P,K,and Zn were applied asbasal at normal dosage.The field was flooded.Plant samples were taken every 7-14 d from 14d after transplanting to flower

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.