Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Jin, Xiaoli; Shi, Chao; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J.

doi:10.3389/fpls.2017.00721

Cited by 97 publications

(58 citation statements)

References 29 publications

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“…Ten fresh leaves were weighed, and oven dried at 105 • C for 0.5 h and at 70 • C for 72 h or until reaching a constant weight, and then dry weight of leaves was recorded. The leaf water content (%) was calculated using the following Equation (Jin et al, 2017).…”

Section: Data Collection Methodsmentioning

confidence: 99%

Deficit Subsurface Drip Irrigation Improves Water Use Efficiency and Stabilizes Yield by Enhancing Subsoil Water Extraction in Winter Wheat

et al. 2020

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Understanding the temporal and spatial patterns of soil water extraction and their impacts on growth response of winter wheat to deficit subsurface drip irrigation (SDI) conditions is critical for managing water scarcity and stabilizing yield. A field experiment was conducted from 2016 to 2018 involving five SDI amounts: 0.25, 0.4, 0.6, 0.8, and 1.0 ETc, representing 25, 40, 60, 80, and 100% of crop evapotranspiration (ETc), respectively. The results showed that the 0.6 ETc treatment significantly increased soil water extraction from 40-80 and 80-140-cm from jointing to maturity as compared to the 1.0 ETc treatment. Whereas the 0.8 ETc treatment significantly increased soil water extraction from 80-140-cm deep soil from flowering to maturity in the first growing season. The crop was most water-stressed under the 0.25 and 0.4 ETc treatments, thus extracted more soil water from 0-140-cm soil profile. However, both treatments exhibited minimum plant tillers, lowest leaf water content, leaf area index (LAI), photosynthetic rate (P n), and transpiration rate (T r) as well as grain yield. All these parameters, except for leaf water content, P n after the flowering stage, and grain productivity, were also reduced in the 0.6 ETc treatment than the 1.0 ETc treatment. The differences between the 0.8 and 1.0 ETc treatments were minor in terms of plant height, LAI, spike number, P n and T r , but infertile tillers were fewer in the 0.8 ETc treatment. We obtained high yield from the 0.8 ETc treatment, and the 0.6ETc treatment resulted in the highest harvest index with improved WUE than other treatments. Integrating deficit irrigation into SDI can save water in winter wheat production in water-limited regions, which can not only enhance soil water extraction from deep soil layers, but also sustained yield by stimulating crop growth. Therefore, a deficit SDI system would be used to conserve water in water-limited regions.

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Section: Data Collection Methodsmentioning

confidence: 99%

Deficit Subsurface Drip Irrigation Improves Water Use Efficiency and Stabilizes Yield by Enhancing Subsoil Water Extraction in Winter Wheat

et al. 2020

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“…W had also been reported to be positively correlated with leaf mesophyll thickness (Garnier and Laurent 1994) and nitrogen content (Roderick et al 1999b), and had also been reported to be strongly associated with maximum photosynthetic efficiency and relative growth rate (Garnier and Laurent 1994, Smith et al 1997, Lusk and Reich 2000. W is also an indicator of tolerance to water deficit because low water content restricts transpiration and causes closure of stomata (Jin et al 2017). In the leaf, W is mainly represented by mesophyll tissue and epidermis (Roderick et al 1999a, Westoby et al 2002.…”

Section: Introductionmentioning

confidence: 98%

Increase in light interception cost and metabolic mass component of leaves are coupled for efficient resource use in the high altitude vegetation

Thakur

Rathore

Chawla

2018

Oikos

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Global syntheses of leaf trait scaling relationships report an increase in light interception costs or ‘diminishing returns’ with increase in leaf area. However, variation in light interception costs across ecological gradients and plant strategies to cope up with these costs are not adequately understood. We analyzed leaf area (A) – leaf dry mass (M), leaf water mass (W) – M and W – A scaling relationships in plants occurring in a high altitude region of western Himalaya across environmental gradients to understand changes in light interception cost and metabolic mass component. M represents light interception cost, whereas, W is considered as a proxy of metabolic mass component for liquid phase being the ultimate source of metabolic activity. Trait values were measured from 9278 leaves belonging to 136 dominant species occurring at different sites, slope aspects, elevations and habitat types. Overall, light interception cost increased with increasing A (scaling exponent (α) < 1 in A–M relationship) and metabolic mass component increased disproportionately high with increasing M and A. We found significant differences in scaling exponents of leaf trait relationship between sites, elevations, slope aspects and habitat types, indicating that increase in light interception cost was more evident at higher elevations, southern slopes and open habitats. Further, with increase in light interception cost, metabolic mass component also increased (α > 1 in W–M and W–A relationships). The changes in scaling exponents of various leaf trait relationships across ecological gradients indicated that vegetation of different regions have differences in light interception cost and metabolic mass component. Moreover, increasing light interception cost (increase in mechanical and hydraulic tissues) with increasing A and increasing metabolic mass (leaf thickness) with increasing A and M are favored in high altitude vegetation. This could be a key strategy of high altitude plants for efficient resource capture and use in harsh environments.

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“…where y is the actual LNC obtained by the experiment of laboratory measurement, y is the mean value of the actual LNC, andŷ is the predicted LNC obtained by multiple regression analysis using the least square method [33][34][35][36]. The predicted leaf nitrogen concentrations were validated by comparison with the actual leaf nitrogen concentrations.…”

Section: Resultsmentioning

confidence: 99%

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

2020

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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 (R 2 ) 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.Remote Sens. 2020, 12, 566 2 of 18 and hyperspectral imaging are mostly desirable as rapid, nondestructive, and noninvasive methods for predicting nitrogen in the leaves [4][5][6]. Leaf chlorophyll content is a key indicator of plant physiological status. It is correlated with leaf nitrogen concentration, although the correlation depends on soil condition, plant species, and the stage of growth [7][8][9][10][11][12]. The nitrogen status in the leaves can also be determined by analyzing the distribution of the color components of an image of a single leaf or group of plants.In one study, leaf nitrogen status was determined by hyperspectral indices which were based on various algorithms [4]. In other studies, hyperspectral remote sensing images and spectral indices were used to assess the leaf nitrogen status and reflectance spectra of a wheat and reed canopy, as shown by the variation in wavelengths [13][14][15]. Hyperspectral data, fluorescence, and near-infrared spectroscopy, detected via digital cameras and satellite-mounted hyperspectral sensors, have been developed and studie...

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Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Cited by 97 publications

References 29 publications

Deficit Subsurface Drip Irrigation Improves Water Use Efficiency and Stabilizes Yield by Enhancing Subsoil Water Extraction in Winter Wheat

Deficit Subsurface Drip Irrigation Improves Water Use Efficiency and Stabilizes Yield by Enhancing Subsoil Water Extraction in Winter Wheat

Increase in light interception cost and metabolic mass component of leaves are coupled for efficient resource use in the high altitude vegetation

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

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