scholarly journals Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations

2021 ◽  
Vol 13 (19) ◽  
pp. 3991
Author(s):  
Raquel Peron-Danaher ◽  
Blake Russell ◽  
Lorenzo Cotrozzi ◽  
Mohsen Mohammadi ◽  
John Couture
Keyword(s):  
Winter Wheat ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Plant Nitrogen ◽  
Nitrogen Levels ◽  
Nitrogen Concentrations ◽  
Use Efficiency ◽  
Canopy Nitrogen ◽  
Wheat Lines

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.

Predicting Winter Wheat Grain Quality Using Hyperspectral Data Based on Plant Nitrogen Status

2012 ◽  
Vol 524-527 ◽  
pp. 2132-2138 ◽  
Author(s):  
Hui Fang Wang ◽  
Ji Hua Wang ◽  
Mei Chen Feng ◽  
Qian Wang ◽  
Wen Jiang Huang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Vegetation Index ◽  
Grain Quality ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Leaf Nitrogen Content ◽  
Wheat Grain ◽  
Quality Model ◽  
Plant Nitrogen

Quality of winter wheat from hyperspectral data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, two varieties winter wheat as the object, hyperspectral data were utilized to predict grain quality. Firstly, the leaf and stem nitrogen content at winter wheat anthesis stage was proved to be signification correctly with crude content and wet gluten. And the leaf relatedcoefficient more than stem at the anthesis. Then, spectral indices significantly correlated to plant nitrogen content at anthesis stage were potential indicators for grain qualities. The vegetation index, VI derived from the canopy spectral reflectance was signification correlated to the leaf nitrogen content at anthesis stage, and highly significantly correlated to the leaf nitrogen content. Based on above analysis, the predict grain quality model were build and the related coefficient were 0.86, 0.68, 0.84, 0.58 which were reached a very significant.The result demonstrated the model based on SIPI and RVI to predict different cultivars wheat grain quality were practical and feasible.

scholarly journals Estimation of leaf nitrogen levels in sugarcane using hyperspectral models

2022 ◽  
Vol 52 (7) ◽  
Author(s):  
Pedro Paulo da Silva Barros ◽  
Peterson Ricardo Fiorio ◽  
José Alexandre de Melo Demattê ◽  
Juliano Araújo Martins ◽  
Zaqueu Fernando Montezano ◽  
...  
Keyword(s):  
Block Design ◽  
Critical Role ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Nitrogen Levels ◽  
Nitrogen Concentrations ◽  
Santa Maria

ABSTRACT: Sugarcane is a good source of renewable energy and helps reduce the emission of greenhouse gases. Nitrogen has a critical role in plant growth; therefore,estimating nitrogen levels is essential, and remote sensing can improve fertilizer management. This field study selects wavelengths from hyperspectral data on a sugarcane canopy to generate models for estimating leaf nitrogen concentrations. The study was carried out in the municipalities of Piracicaba, Jaú, and Santa Maria da Serra, state of São Paulo, in the 2013/2014 growing season. The experiments were carried out using a completely randomized block design with split plots (three sugarcane varieties per plot [variety SP 81-3250 was common to all plots] and four nitrogen concentrations [0, 50, 100, and 150 kgha-1] per subplot) and four repetitions. The wavelengths that best correlated with leaf nitrogen were selected usingsparse partial least square regression. The wavelength regionswere combinedby stepwise multiple linear regression. Spectral bands in the visible (700-705 nm), red-edge (710-720 nm), near-infrared (725, 925, 955, and 980 nm), and short-wave infrared (1355, 1420, 1595, 1600, 1605, and 1610 nm) regions were identified. The R² and RMSE of the model were 0.50 and 1.67 g.kg-1, respectively. The adjusted R² and RMSE of the models for Piracicaba, Jaú, and Santa Maria were 0.31 (unreliable) and 1.30 g.kg-1, 0.53 and 1.96 g.kg-1, and 0.54 and 1.46 g.kg-1, respectively. Our results showed that canopy hyperspectral reflectance can estimate leaf nitrogen concentrations and manage nitrogen application in sugarcane.

Intercropping wheat and barley with N-fixing legume species: a method for improving ground cover, N-use efficiency and productivity in low input systems

1994 ◽  
Vol 123 (2) ◽  
pp. 175-183 ◽  
Author(s):  
M. P. Reynolds ◽  
K. D. Sayre ◽  
H. E. Vivar
Keyword(s):  
Cropping Systems ◽  
Ground Cover ◽  
Leaf Nitrogen ◽  
Wheat Crop ◽  
Total Biomass ◽  
Forage Crop ◽  
Nitrogen Levels ◽  
Biomass Yields ◽  
Use Efficiency ◽  
Efficiency And Productivity

SummaryTwo cereal cropping systems are described which, through the introduction of a leguminous intercrop, increased productivity, nitrogen output and ground cover of the systems in the absence of added nitrogen fertilizer. Nitrogen-fixing legumes were cultivated between rows of wheat or barley grown at low levels of soil nitrogen, and mostly under rainfed conditions, in Mexico between 1989 and 1992. None of the legumes tested reduced yields of the cereal crop in comparison to controls where cereal yields were in the range of 1–4 t/ha, while the extra total biomass from legumes in some cases more than doubled productivity. Different legume crops were tested to demonstrate the adaptability of the system to the varying needs of farmers. The intercropped legumes achieved dry biomass yields as high as 6·5 t/ha in the case of a sequentially cropped forage crop of hairy vetch, or 1·4 t/ha of dry beans plus 3·5 t/ha of green residue in the case of Vicia faba. Total biomass in the intercropped situation gave land equivalent ratios as high as 1·54. Light measurements inside the crop canopies indicated that the intercropped systems intercepted a higher proportion of the incident solar radiation than the cereal monocrop, presumably accounting for the large differences in total biomass produced. In addition, with leaf nitrogen levels of 3·8%, it is assumed that the intercropped legumes fixed considerably more nitrogen than was removed by the wheat crop. The potential of the system to stabilize erodible soils by increasing ground cover as well as by raising inputs of soil organic matteris discussed.

scholarly journals Determination of Leaf Nitrogen Concentrations Using Electrical Impedance Spectroscopy in Multiple Crops

2020 ◽  
Vol 12 (3) ◽  
pp. 566 ◽  
Author(s):  
Rinku Basak ◽  
Khan Wahid ◽  
Anh Dinh
Keyword(s):  
Impedance Spectroscopy ◽  
Correlation Coefficient ◽  
Regression Models ◽  
Electrical Impedance ◽  
Leaf Nitrogen ◽  
Electrical Impedance Spectroscopy ◽  
Coefficient Of Determination ◽  
Frequency Range ◽  
Nitrogen Concentrations

In this work, crop leaf nitrogen concentration (LNC) is predicted by leaf impedance measurements made by electrical impedance spectroscopy (EIS). This method uses portable equipment and is noninvasive, as are other available nondestructive methods, such as hyperspectral imaging, near-infrared spectroscopy, and soil-plant analyses development (SPAD). An EVAL-AD5933EBZ evaluation board is used to measure the impedances of four different crop leaves, i.e., canola, wheat, soybeans, and corn, in the frequency range of 5 to 15 kHz. Multiple linear regression using the least square method is employed to obtain a correlation between leaf nitrogen concentrations and leaf impedances. A strong correlation is found between nitrogen concentrations and measured impedances for multiple features using EIS. The results are obtained by PrimaXL Data Analysis ToolPak and validated by analysis of variance (ANOVA) tests. Optimized regression models are determined by selecting features using the backward elimination method. After a comparative analysis among the four different crops, the best multiple regression results are found for canola with an overall correlation coefficient (R) of 0.99, a coefficient of determination (R2) of 0.98, and root mean square (RMSE) of 0.54% in the frequency range of 8.7–12 kHz. The performance of EIS is also compared with an available SPAD reading which is moderately correlated with LNC. A high correlation coefficient of 0.94, a coefficient of determination of 0.89, and RMSE of 1.12% are obtained using EIS, whereas a maximum correlation coefficient of 0.72, a coefficient of determination of 0.53, and RMSE of 1.52% are obtained using SPAD for the same number of combined observations. The proposed multiple linear regression models based on EIS measurements sensitive to LNC can be used on a very local scale to develop a simple, rapid, inexpensive, and effective instrument for determining the leaf nitrogen concentrations in crops.

scholarly journals Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model

2018 ◽  
Vol 10 (9) ◽  
pp. 1463 ◽  
Author(s):  
Zhenhai Li ◽  
Xiuliang Jin ◽  
Guijun Yang ◽  
Jane Drummond ◽  
Hao Yang ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Spectral Response ◽  
Triticum Aestivum L ◽  
Hyperspectral Data ◽  
Area Index ◽  
Plant Nitrogen ◽  
Model Inversion ◽  
Leaf N Concentration ◽  
Canopy Nitrogen ◽  
Leaf N

Plant nitrogen (N) information has widely been estimated through empirical techniques using hyperspectral data. However, the physical model inversion approach on N spectral response has seldom developed and remains a challenge. In this study, an N-PROSAIL model based on the N-based PROSPECT model and the SAIL model canopy model was constructed and used for retrieving crop N status both at leaf and canopy scales. The results show that the third parameter (3rd-par) retrieving strategy (leaf area index (LAI) and leaf N density (LND) optimized where other parameters in the N-PROSAIL model are set at different values at each growth stage) exhibited the highest accuracy for LAI and LND estimation, which resulted in R2 and RMSE values of 0.80 and 0.69, and 0.46 and 21.18 µg·cm−2, respectively. It also showed good results with R2 and RMSE values of 0.75 and 0.38% for leaf N concentration (LNC) and 0.82 and 0.95 g·m−2 for canopy N density (CND), respectively. The N-PROSAIL model retrieving method performed better than the vegetation index regression model (LNC: RMSE = 0.48 − 0.64%; CND: RMSE = 1.26 − 1.78 g·m−2). This study indicates the potential of using the N-PROSAIL model for crop N diagnosis on leaf and canopy scales in wheat.

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