ABSTRACT
To verify the accuracy and adaptability of crop growth monitoring and diagnosis apparatus (CGMD) in monitoring nitrogen nutrition index of double cropping rice, we established a monitoring model of leaf nitrogen concentration (LNC) and leaf nitrogen accumulation (LNA) for double cropping rice based on CGMD. Eight early and late rice cultivars were selected and four nitrogen application rates were set up. The differential vegetation index (DVI), normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were collected using CGMD. Meanwhile, ASD FH2 high spectrometer was used to collect canopy spectral reflectance and calculated DVI, NDVI, and RVI. To verify the accuracy of CGMD, we compared the canopy vegetation indices change characteristics collected by CGMD and ASD FH2. The CGMD-based monitoring models of LNC and LNA were established, which was tested with independent field data. The results showed that LNC, LNA, DVI, NDVI and RVI of early and late rice increased with increasing nitrogen application rate, and increased first and then decreased with the advance of growth progress. The determination coefficient (R2) of fitting for DVI, NDVI and RVI from CGMD and ASD FH2 were 0.9350, 0.9436 and 0.9433, respectively. This result indicated that the measurement accuracy of CGMD was high, and that the CGMD could be used to replace ASD FH2 to measure canopy vegetation indices of early and late rice. Compared with the three canopy vegetation indices based on CGMD, the correlation between NDVICGMD and LNC and that between RVICGMD and LNA was the highest. The exponential model based on NDVICGMD could be used to accurate estimate LNC with the R2 in the range of 0.8581-0.9318, and the root mean square error (RMSE), relation root mean square error (RRMSE) and correlation coefficient (r) of model validation in the range of 0.1%-0.2%, 4.0%-8.5%, and 0.9041-0.9854, respectively. The power function model based on RVICGMD could be used to estimate LNA with the R2 in the range of 0.8684-0.9577, and the RMSE, RRMSE and r of model validation in the range of 0.37-0.89 g·m-2, 6.7%-20.4% and 0.9191-0.9851, respectively. Compared with the chemical testing method, using the CGMD could conveniently and accurately measure LNC and LNA of early and late rice, which had a potential to be widely applied for high yield and high efficiency cultivation and precise management of nitrogen fertilizer in double cropping rice production.
Subject(s)
Oryza , Fertilizers , Nitrogen , Plant LeavesABSTRACT
The spectrometer-based nitrogen (N) nutrition monitoring and diagnosis models for double-cropping rice in Jiangxi is important for recommending precise N topdressing rate, achieving high yield, improving grain quality and increasing economic efficiency. Field experiments were conducted in Jiangxi in 2016 and 2017, involving different early rice and late rice cultivars and N application rates. Plant N accumulation (PNA) and canopy spectral vegetation indices (VIs) were measured at tillering and jointing stages with two spectrometers, i.e., GreenSeeker (an active multispectral sensor containing 780 and 660 nm wavelengths) and crop growth monitoring and diagnosis apparatus (CGMD, a passive multispectral sensor containing 810 and 720 nm wavelengths). The VI-based models of PNA were established from a experimental dataset and then validated using an independent dataset. The N topdressing rates for tillering and jointing stages were calculated using the newly developed N spectral diagnosis model and higher yield cultivation experience of double-cropping rice. The results showed that the VIs from two spectrometers were strongly positively correlated with PNA at both growth stages, with the model performance for tillering or jointing stages was better than that for the early growth stages. The exponential equation of normalized difference vegetation index (NDVI(780,660)) from GreenSeeker could be used to estimate PNA with a determination coefficient (R2) in the range of 0.92-0.94, the root mean square error (RMSE), relative root mean square error (RRMSE) and correlation coefficient (r) of model validation in the range of 3.09-5.96 kg·hm-2, 5.8%-18.5% and 0.92-0.98, respectively. The linear equation of difference vegetation index (DVI(810,720)) from CGMD could be used to estimate PNA with a R2 in the range of 0.90-0.93, the RMSE, RRMSE and r of model validation in the range of 3.71-6.33 kg·hm-2, 11.7%-14.3% and 0.93-0.96, respectively. The recommended N topdressing rate with CGMD was higher than that with GreenSeeker. Compared with conventional farmer's plan, the precision N application plan reduced N fertilizer application rate by 5.5 kg·hm-2, while N agronomic efficiency and net income was improved by 0.8% and 128 yuan·hm-2, respectively. Application of the spectral monitoring and diagnosis method to guiding fertilization could reduce cost and increase grain yield and net income, and thus had great potential for guiding double-cropping rice production.
Subject(s)
Oryza , Agriculture , China , Edible Grain , Fertilizers , NitrogenABSTRACT
In this study, we developed a model for photosynthetic production in double cropping rice by integrating the advantages in current crop models (including the models of canopy structure, canopy light distribution, canopy photosynthesis and dry matter production). The canopy light distribution and dry matter accumulation were preliminarily validated with independent field experiment datasets. The distribution of direct radiation both on a level surface and on the leaf surface within canopy, the canopy daily photosynthate and its characteristics with varying leaf area index for three typical plant types (erect both upper and lower, upper erect and lower flat, and flat both upper and lower) were quantitatively analyzed by the model. The results indicated that there was a good agreement between the simulated and observed values. The root mean square error (RMSE), relative root mean square error (RRMSE) and correlation coefficient (r) of prediction of canopy light distribution in double cropping rice were 12.01 J ·m-2·s-1, 8.2% and 0.9929, respectively. Meanwhile, the RMSE, RRMSE and r of prediction of dry matter accumulation were 0.83 t·hm-2, 14.6% and 0.9772, respectively. It was indicated that the model had a performance. The upper erect and lower flat plant type had highest canopy daily photosynthate due to higher incident sun light received on the leaf surface, leaf photosynthetic efficiency and leaf area index.
