A comparison between different chlorophyll content meters under nutrient deficiency conditions

Kalaji, Hazem M.; Dąbrowski, Piotr; Cetner, Magdalena D.; Samborska, Izabela A.; Lukasik, I; Brestič, Marián; Živčák, Marek; Horaczek, Tomasz; Mojski, Jacek; Kociel, Henryk; Panchal, Balaji

doi:10.1080/01904167.2016.1263323

Cited by 82 publications

(47 citation statements)

References 31 publications

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“…Studies have shown that due to high salt concentrations in the root zone, plant leaf micro-morphological and ultra-structural features are strongly altered in both halophytes and glycophytes [117,118]. A variable instrumental response to chlorophyll content has also been reported by Kalaji et al [119] under nutrient-deficient conditions. The ultra-structural alterations may be caused by specific ion toxicity and osmotic imbalance [120].…”

Section: Effect Of Stress On Spad-pigment Relationshipsmentioning

confidence: 78%

Response of Chlorophyll, Carotenoid and SPAD-502 Measurement to Salinity and Nutrient Stress in Wheat (Triticum aestivum L.)

2017

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Abiotic stress can alter key physiological constituents and functions in green plants. Improving the capacity to monitor this response in a non-destructive manner is of considerable interest, as it would offer a direct means of initiating timely corrective action. Given the vital role that plant pigments play in the photosynthetic process and general plant physiological condition, their accurate estimation would provide a means to monitor plant health and indirectly determine stress response. The aim of this work is to evaluate the response of leaf chlorophyll and carotenoid (C t ) content in wheat (Triticum aestivum L.) to changes in varying application levels of soil salinity and fertilizer applied over a complete growth cycle. The study also seeks to establish and analyze relationships between measurements from a SPAD-502 instrument and the leaf pigments, as extracted at the anthesis stage. A greenhouse pot experiment was conducted in triplicate by employing distinct treatments of both soil salinity and fertilizer dose at three levels. Results showed that higher doses of fertilizer increased the content of leaf pigments across all levels of soil salinity. Likewise, increasing the level of soil salinity significantly increased the chlorophyll and C t content per leaf area at all levels of applied fertilizer. However, as an adaptation process and defense mechanism under salinity stress, leaves were found to be thicker and narrower. Thus, on a per-plant basis, increasing salinity significantly reduced the chlorophyll (Chl t ) and C t produced under each fertilizer treatment. In addition, interaction effects of soil salinity and fertilizer application on the photosynthetic pigment content were found to be significant, as the higher amounts of fertilizer augmented the detrimental effects of salinity. A strong positive (R 2 = 0.93) and statistically significant (p < 0.001) relationship between SPAD-502 values and Chl t and between SPAD-502 values and C t content (R 2 = 0.85) was determined based on a large (n = 277) dataset. We demonstrate that the SPAD-502 readings and plant photosynthetic pigment content per-leaf area are profoundly affected by salinity and nutrient stress, but that the general form of their relationship remains largely unaffected by the stress. As such, a generalized regression model can be used for Chl t and C t estimation, even across a range of salinity and fertilizer gradients.

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Section: Effect Of Stress On Spad-pigment Relationshipsmentioning

confidence: 78%

Response of Chlorophyll, Carotenoid and SPAD-502 Measurement to Salinity and Nutrient Stress in Wheat (Triticum aestivum L.)

2017

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“…CC is closely associated with human perception of the green pigmentation of leaves (Barrett et al, 2010) and is also an important indicator of how plants respond to increasing LI. Both destructive and nondestructive methods are used to measure CC (Kalaji et al, 2016). Destructive measures usually report CC in terms of per weight (FW or DW) or per LA bases (Richardson et al, 2002), depending on the study's objectives.…”

Section: Measurementmentioning

confidence: 99%

“…However, because leaf morphology characteristics (e.g., SLA) are unknown, direct comparisons of their CC results are difficult. Furthermore, RCC indices are frequently presented as an alternative to destructive CC measurements due to ease of measurement (Kalaji et al, 2016;Richardson et al, 2002). However, RCC indices also do not account for varying leaf morphology (e.g., SLA) between treatments.…”

Section: Measurementmentioning

confidence: 99%

Different Microgreen Genotypes Have Unique Growth and Yield Responses to Intensity of Supplemental PAR from Light-emitting Diodes during Winter Greenhouse Production in Southern Ontario, Canada

Jones-Baumgardt¹,

Llewellyn²,

Zheng³

2020

horts

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Low natural daily light integrals (DLIs) are a major limiting factor for greenhouse production during darker months (e.g., October to February in Canada). Supplemental lighting (SL) is commonly used to maintain crop productivity and quality during these periods, particularly when the supply chain demands consistent production levels year-round. What remains to be determined are the optimum SL light intensities (LIs) for winter production of a myriad of different commodities. The present study investigated the growth and yield of sunflower (Helianthus annuus L., ‘Black oil’), kale (Brassica napus L., ‘Red Russian’), arugula (Eruca sativa L.), and mustard (Brassica juncea L., ‘Ruby Streaks’), grown as microgreens, in a greenhouse under SL light-emitting diode (LED) photosynthetic photon flux density (PPFD) levels ranging from 17.0 to 304 μmol·m−2·s−1 with a 16-hour photoperiod (i.e., supplemental DLIs from 1.0 to 17.5 mol·m−2·d−1). Crops were sown in a commercial greenhouse near Hamilton, ON, Canada (lat. 43°14′N, long. 80°07′W) on 1 Feb. 2018, and harvested after 8, 11, 12, and 12 days, resulting in average natural DLIs of 6.5, 5.9, 6.2, and 6.2 mol·m−2·d−1 for sunflower, kale, arugula, and mustard, respectively. Corresponding total light integrals (TLIs) ranged from 60 to 188 mol·m−2 for sunflower, 76 to 258 mol·m−2 for kale, 86 to 280 mol·m−2 for arugula, and 86 to 284 mol·m−2 for mustard. Fresh weight (i.e., marketable yield) increased asymptotically with increasing LI and leaf area increased linearly with increasing LI, in all genotypes. Hypocotyl length of mustard decreased and hypocotyl diameter of sunflower, arugula, and mustard increased with increasing LI. Dry weight, robust index, and relative chlorophyll content increased and specific leaf area decreased in kale, arugula, and mustard with increasing LI. Commercial microgreen greenhouse growers can use the light response models described herein to predict relevant production metrics according to the available (natural and supplemental) light levels to select the most appropriate SL LI to achieve the desired production goals as economically as possible.

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“…It is also not always possible to apply these methods in situ. Alternately, some portable chlorophyll content meters, such as CL-01, SPAD-502, Dualex, and CCM-200, have been used to estimate chlorophyll content [9]. However, light intensity also influences leaf thickness [10], and then it often makes the output of the meter obscure [11].…”

mentioning

confidence: 99%

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Sonobe

Hirono

2020

Plants

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Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms-random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)-in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 µg cm −2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves.Ultraviolet and visible light (UV-vis) spectroscopy and high-performance liquid chromatography (HPLC) techniques have previously been used to quantify chlorophyll content. These are destructive methods and may enforce a limited sample size because they are expensive and labour-intensive. It is also not always possible to apply these methods in situ. Alternately, some portable chlorophyll content meters, such as CL-01, SPAD-502, Dualex, and CCM-200, have been used to estimate chlorophyll content [9]. However, light intensity also influences leaf thickness [10], and then it often makes the output of the meter obscure [11]. In contrast, hyperspectral reflectance measurements may be used for detecting various responses from crops [12] or evaluating vegetation properties [13,14], and the use of reflectance for estimating chlorophyll content is being seriously considered. Some pigments, such as chlorophylls and carotenoids, absorb energy strongly in the ultraviolet, blue, and red regions, and then the reflectance and transmittance are weak [15]. The reflectance from vegetation is characterised by the low reflectance over the blue (400-500 nm) and red (650-690 nm) spectral regions due to strong absorption by chlorophylls plus carotenoids in the blue wavelengths and chlorophyll a in the red wavelengths, and the steep rise in reflectance between 680 and 750 nm is caused by the chlorophylls increasing to absorb at wavelengths beyond 700 nm [16]. Based on these features, some empirical approaches, such as vegetation indices, which are generally based on a few narrow or broad spectral bands, are convenient for use to estimate chlorophyll content. Most vegetation indices for chlorophyll content use wavelengths ranging from 400 to 860 nanometres [17][18][19] or the red edge (680 to 750 nm) [20,21]. However, some datasets, such as LOPEX, CALMIT, ANGERS, and HAWAII, that have been used for the development of vegetation indi...

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A comparison between different chlorophyll content meters under nutrient deficiency conditions

Cited by 82 publications

References 31 publications

Response of Chlorophyll, Carotenoid and SPAD-502 Measurement to Salinity and Nutrient Stress in Wheat (Triticum aestivum L.)

Response of Chlorophyll, Carotenoid and SPAD-502 Measurement to Salinity and Nutrient Stress in Wheat (Triticum aestivum L.)

Different Microgreen Genotypes Have Unique Growth and Yield Responses to Intensity of Supplemental PAR from Light-emitting Diodes during Winter Greenhouse Production in Southern Ontario, Canada

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

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