Increasing photosynthetic performance and post-silking N uptake by moderate decreasing leaf source of maize under high planting density

To date,little attention has been paid to the effects of leaf source reduction on photosynthetic matter production,root function and post-silking N uptake characteristics at different planting densities.In a 2-year field experiment,Xianyu 335,a widely released hybrid in China,was planted at 60 000 plants ha^(–1 )(conventional planting density,CD) and 90 000 plants ha^(–1) (high planting density,HD),respectively.Until all the filaments protruded from the ear,at which point the plants were subjected to the removal of 1/2 (T1),1/3 (T2) and 1/4 (T3) each leaf length per plant,no leaf removal served as the control(CK).We evaluated the leaf source reduction on canopy photosynthetic matter production and N accumulation of different planting densities.Under CD,decreasing leaf source markedly decreased photosynthetic rate (P_(n)),effective quantum yield of photosystem II (ΦPSII) and the maximal efficiency of photosystem II photochemistry (F_(v)/F_(m)) at grain filling stage,reduced post-silking dry matter accumulation,harvest index (HI),and the yield.Compared with the CK,the 2-year average yields of T1,T2 and T3 treatments decreased by 35.4,23.8 and 8.3%,respectively.Meanwhile,decreasing leaf source reduced the root bleeding sap intensity,the content of soluble sugar in the bleeding sap,post-silking N uptake,and N accumulation in grain.The grain N accumulation in T1,T2 and T3 decreased by 26.7,16.5 and 12.8% compared with CK,respectively.Under HD,compared to other treatments,excising T3 markedly improved the leaf P_(n),ΦPSII and F_(v)/F_(m) at late-grain filling stage,increased the post-silking dry matter accumulation,HI and the grain yield.The yield of T3 was 9.2,35.7 and 20.1% higher than that of CK,T1 and T2 on average,respectively.The T3 treatment also increased the root bleeding sap intensity,the content of soluble sugar in the bleeding sap and post-silking N uptake and N accumulation in grain.Compared with CK,T1 and T2 treatments,the grain N accumulation in T3 increased by 13.1,40.9 and 25.2% on average,respectively.In addition,under the same source reduction treatment,the maize yield of HD was significantly higher than that of CD.Therefore,planting density should be increased in maize production for higher grain yield.Under HD,moderate decreasing leaf source improved photosynthetic performance and increased the post-silking dry matter accumulation and HI,and thus the grain yield.In addition,the improvement of photosynthetic performance improved the root function and promoted postsilking N uptake,which led to the increase of N accumulation in grain.

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.