Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events.Leaf water content(LWC)is an important waterlogging indicator,and hyperspectral ...Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events.Leaf water content(LWC)is an important waterlogging indicator,and hyperspectral remote sensing provides a non-destructive,real-time and reliable method to determine LWC.Thus,based on a pot experiment,winter wheat was subjected to different gradients of waterlogging stress at the jointing stage.Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature.Combined with methods such as vegetation index construction,correlation analysis,regression analysis,BP neural network(BPNN),etc.,we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC.LWC decreased faster under severe stress than under slight stress,but the effect of long-term slight stress was greater than that of short-term severe stress.The sensitive spectral bands of LWC were located in the visible(VIS,400–780 nm)and short-wave infrared(SWIR,1400–2500 nm)regions.The BPNN Model with the original spectrum at 648 nm,the first derivative spectrum at 500 nm,the red edge position(λr),the new vegetation index RVI(437,466),NDVI(437,466)and NDVI´(747,1956)as independent variables was the best model for inverting the LWC of waterlogging in winter wheat(modeling set:R^(2)=0.889,RMSE=0.138;validation set:R^(2)=0.891,RMSE=0.518).These results have important theoretical significance and practical application value for the precise control of waterlogging stress.展开更多
Crop modelling can facilitate researchers＇ ability to understand and interpret experimental results, and to diagnose yield gaps. In this paper, the Decision Support Systems for Agrotechnology Transfer 4.6 （DSSAT） m...Crop modelling can facilitate researchers＇ ability to understand and interpret experimental results, and to diagnose yield gaps. In this paper, the Decision Support Systems for Agrotechnology Transfer 4.6 （DSSAT） model together with the CENTURT soil model were employed to investigate the effect of low nitrogen （N） input on wheat （Triticum aestivum L.） yield, grain N concentration and soil organic carbon （SOC） in a long-term experiment （19 years） under a wheat-maize （Zea mays L.） rotation at Changping, Beijing, China. There were two treatments including NO （no N application） and N150 （150 kg N ha-1） before wheat and maize planting, with phosphorus （P） and potassium （K） basal fertilizers applied as 75 kg P205 ha-1 and 37.5 kg K^O ha-~, respectively. The DSSAT-CENTURY model was able to satisfactorily simulate measured wheat grain yield and grain N concentration at NO, but could not simulate these parameters at N150, or SOC in either N treatment, Model simulation and field measurement showed that N application （N150） increased wheat yield compared to no N application （NO）. The results indicated that inorganic fertilizer application at the rates used did not maintain crop yield and SOC levels. It is suggested that if the DSSAT is calibrated carefully, it can be a useful tool for assessing and predicting wheat yield, grain N concentration, and SOC trends under wheat-maize cropping systems.展开更多
基金This work was supported by the National Key Research and Development Program of China(2016YFD0200600,2016YFD0200601)the Key Research and Development Program of Hebei Province,China(19227407D)+1 种基金the Central Public-interest Scientific Institution Basal Research Fund(JBYW-AII-2020-29,JBYW-AII-2020-30)the Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2020-AII).
文摘Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events.Leaf water content(LWC)is an important waterlogging indicator,and hyperspectral remote sensing provides a non-destructive,real-time and reliable method to determine LWC.Thus,based on a pot experiment,winter wheat was subjected to different gradients of waterlogging stress at the jointing stage.Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature.Combined with methods such as vegetation index construction,correlation analysis,regression analysis,BP neural network(BPNN),etc.,we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC.LWC decreased faster under severe stress than under slight stress,but the effect of long-term slight stress was greater than that of short-term severe stress.The sensitive spectral bands of LWC were located in the visible(VIS,400–780 nm)and short-wave infrared(SWIR,1400–2500 nm)regions.The BPNN Model with the original spectrum at 648 nm,the first derivative spectrum at 500 nm,the red edge position(λr),the new vegetation index RVI(437,466),NDVI(437,466)and NDVI´(747,1956)as independent variables was the best model for inverting the LWC of waterlogging in winter wheat(modeling set:R^(2)=0.889,RMSE=0.138;validation set:R^(2)=0.891,RMSE=0.518).These results have important theoretical significance and practical application value for the precise control of waterlogging stress.
基金funded by the National Natural Science Foundation of China (41471285)the Agricultural Science and Technology Innovation Program (ASTIP) of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016AII)+2 种基金the Key Laboratory of Nonpoint Source Pollution Control,Ministry of Agriculture,China (2014-37)the Newton Fund,United Kingdom (BB/N013484/1)the National Key Research and Development Program of China (2016YFD0200601)
文摘Crop modelling can facilitate researchers＇ ability to understand and interpret experimental results, and to diagnose yield gaps. In this paper, the Decision Support Systems for Agrotechnology Transfer 4.6 （DSSAT） model together with the CENTURT soil model were employed to investigate the effect of low nitrogen （N） input on wheat （Triticum aestivum L.） yield, grain N concentration and soil organic carbon （SOC） in a long-term experiment （19 years） under a wheat-maize （Zea mays L.） rotation at Changping, Beijing, China. There were two treatments including NO （no N application） and N150 （150 kg N ha-1） before wheat and maize planting, with phosphorus （P） and potassium （K） basal fertilizers applied as 75 kg P205 ha-1 and 37.5 kg K^O ha-~, respectively. The DSSAT-CENTURY model was able to satisfactorily simulate measured wheat grain yield and grain N concentration at NO, but could not simulate these parameters at N150, or SOC in either N treatment, Model simulation and field measurement showed that N application （N150） increased wheat yield compared to no N application （NO）. The results indicated that inorganic fertilizer application at the rates used did not maintain crop yield and SOC levels. It is suggested that if the DSSAT is calibrated carefully, it can be a useful tool for assessing and predicting wheat yield, grain N concentration, and SOC trends under wheat-maize cropping systems.
