Introduction
Flue-cured tobacco (Nicotiana tabacum L.) is a special economic crop for leaf using. As an important agronomic
measure, controlling
N application rates and remained leaves numbers plays
an important role in the yield and quality of flue-cured tobacco leaves (FTLs)
(Chen et al. 2020). Controlling N application rates and remained
leaves numbers greatly change quality of field fresh tobacco leaves (FFTLs), while
the quality of FFTLs will further affect the curing characteristics and
ultimately determine the quality of FTLs (Finch et
al. 2019). As one of the three elements of
nutrition in plant growth,
N is not only the material basis of protein, nucleic acid and phosphoric acid, but also the
composition of key enzymes of carbon and N metabolism, hormones, and the photosynthetic-pigment chlorophyll (Lin et al. 2017; Wei
et al. 2017; Agami et al. 2018). The optimum N application is crucial to harvest
better yield and quality of FTLs and is second only to variety. Within a certain range, the yield and quality of
FTLs will be greatly improved with the increase of N application rates. However, excessive application of
nitrogenous fertilizers will cause soil acidification, the number of water stable aggregates and the
diversity of soil microorganisms decreased, and the yield and quality of FTLs decreased (Zou et al. 2018; Shen et al.
2020).
Increasing the N application rates
can improve the leaf area coefficient;
more leaf area results more dry matter of FTLs (Craftsbrandner et al. 1987). In
addition, as N
application rates exceed a certain standard, the agronomic traits of flue-cured tobacco increased
slowly (Ankica et al. 2019).
The chlorophyll will increase with the increase of N application
rates within a certain N application range, but too high N application rates will lead to
the decrease of photosynthesis (Charles and Tommy 1998). There is a positive
correlation between N application rates and N compounds in FTLs; especially nicotine is positively
regulated by N application rates (Lisuma et al. 2019; Chen
et al. 2019). It is worth mentioning that under the same N application, increasing remained leaves
numbers will reduce the nicotine and correspondingly increase the total and
reducing sugar contents (Mu et al. 2014).
The remained leaves numbers could
change the effective illumination of tobacco leaves,
and the difference of temperature and humidity between the
population, thus
affecting the yield and quality of tobacco leaves. With fewer remained leaves numbers, the single leaf can obtain
sufficient or even excessive nutrient absorption. Besides, fewer remained leaves numbers can also increase
the temperature difference between day and night among plant groups, thus leading to the
accumulation of dry matter. With the increase of remained leaves numbers, the plant height will increase, but the weight of single
leaf, leaf area
per plant, stem
circumference and pitch will decrease (King 1986). Meanwhile, more leaves will
cause high chlorophyll due to the reduction of light intensity (Zhou et al. 2016). Research shown that
the remained leaves numbers are negatively related to the total N, protein and nicotine; and are positively correlated with the
ratio of total sugar to reducing sugar, and sugar to nicotine in FTLs (Jiang et al. 2017).
Nitrogen application rates and
remained leaves numbers control many physiological and biochemical
characteristics of flue-cured tobacco, it will further affect the carbon and N metabolism process of tobacco
plants. At
present, there
are many studies on the influence of N application rates and remained leaves
numbers on the quality of flue-cured tobacco. However, there are few studies on the interaction between these two factors on
agronomic traits and chemical composition of FFTLs. Interactive effect of N application rates and
remained leaves numbers has great influence on agronomic traits and chemical
composition of FFTLs; and the quality index of FFTLs have influence on FTLs. Therefore, this study was conducted to
determine the optimum N application rates and the best remained leaves numbers
on yield and the coordinated chemical composition of FTLs.
Materials and Methods
Trial site and conditions
This two-year field trail was conducted during the 2017 and 2018 tobacco cropping seasons from middle of April
to late August at Majiazhuang village,
Jiuxi town, Jiangchuan district of Yuxi city in Yunan, China (elevation 1730 m, 102°38'13″ E, 24°18'14″ N), which is located in
the middle of Yunnan Province.
This region keeps a temperate climate, subtropical monsoon climate. Meteorological conditions
during the tobacco growing period were recorded using weather data collector (CR800, Campbell, U.S.A.)
installed in the field and given in Fig. 1. The former crop was Vicia sepium L. The
soil at the site is red earth,
medium fertility,
and the nutrient status before seeding consisted of pH 6.68, organic matter 30.2 g
kg-1, available N 101.60 mg kg-1, available K 147.9 mg
kg-1 and available P 24.2 mg kg-1.
Experiment details
Tobacco variety ‘K326’ (Zhongyan Tobacco Seed Co., Ltd,
China) was
selected as testing material,
which is a high-yield
and adequate-quality
variety grown widely in China.
The experiment was arranged as randomized complete block design under
factorial arrangement with three replicates where the N
application rates had 4 levels [60 (N1), 90 (N2), 120 (N3) and 150 (N4) kg N ha-1] and the remained
leaves numbers had 2 levels
[18 (L1) and 22
(L2) leaves]. Each treatment was
repeated three times. Dimension
of each plot was 10 m
× 6.6 m, and the distance of row-row was 1.20 m and distance of plant to plant was 0.55 m which achieved the
population density of 16500 plant ha-1. In order to reduce
experimental error, guard
rows were reasonable set up.
Transplanting plantlets under mulch was adopted, and transplanting time was in April 20. The P application rate was 112.5 kg P2O2 ha-1, and the K application rate was 281.25 kg
K2O ha-1, and both remained
unchanged in different treatment. The fertilizer was strip applied; 50% fertilizer applied
before transplanting and the rest applied around 30 days after transplanting. Film-uncovering and earth-up were carried out at 35 days after transplantation. Topping at 65–70 days after
transplantation, and
chemical bud inhibitor was applied at the time. To determine the yield and quality of
flue-cured
tobacco, and
determine the chemical composition of flue-cured tobacco, the fresh flue-cured tobacco leaves at different N
application rates [60 (N1), 90 (N2), 120 (N3) and 150 (N4) kg N ha-1] and different
remained leaves numbers [18 (L1) and 22
(L2) leaves] were harvested and
cured using the local main technology.
%201423-1430_files/image002.png)
Fig. 1: Maximum temperature, minimum temperature (℃) and precipitation (mm) recorded during the
growing seasons in 2017
and 2018
Data recorded
Agronomic characters: At the mature stage of flue-cured tobacco, 10 tobacco
plants were randomly selected from each treatment in each plot, and the plant height, leaf area, fresh weight and dry
weights were measured. The agronomic characters were determined based on Standard of the
People’s Republic of China for Investigating
Methods of Agronomical Character of Tobacco (YC/142-1998). After decapitation, the height of plants from stem base on the
ground to the top of the stem was measured when the growth of stem top had been
stabilized. Three
replicates were measured. From 1 week to 10 days after topping stage, the length and width of the maximum leaf were
measured, and
the number of samples was no less than 10. The
product of length by width by correction factor (0.6345) was
used to represent the leaf area. The fresh weights of the middle leaves were determined immediately after harvest. The middle leaves of
tobacco plants were weighed after being killed out at 105°C and dried at 60°C until stabilized.
hlorophyll
contents
Chlorophyll contents were expressed by SPAD value.
At maturity, 10 tobacco plants were
randomly selected from each treatment in each plot, and leaf chlorophyll was estimated with a
chlorophyll meter (Minolta
SPAD-502 Konica Minolta, Osaka, Japan)
at topping (removal of flowers at the top of the plant). Measurements were made three times through
nipping the middle of leaves and these data were averaged.
Analyses and
methods for indices of economic traits
Based on Standard of the People’s Republic of China for Flue-cured Tobacco (GB2635-1992) and calculated in accordance with procurement price lists of flue-cured tobaccos in 2017 and 2018, the
proportions of superior and medium tobacco and average price were calculated. In accordance with
dry weight of tobacco leaves at each position, yield of tobacco leaves was
calculated.
Analyses of
conventional chemical indicators
Three kg of FTLs were random selected in each plot and treatment after
curing. And 20 pieces of middle FFTLs
were random selected in each plot and treatment at mature period. The contents of starch were
determined by spectrophotometry at 660
nm with HClO4 extraction (Kasheva et
al. 2018). Total sugar, reducing sugar and fructose were
determined by rapid colorimetric method with 3,
5-dinitrosalicylic acid (Chen et al.
2019). The contents of protein determined with
continuous flow analytical method (Wang et al. 2015). Total
N was determined with elemental analysis method (Haohui et al. 2013). Nicotine
determined with spectrophotometric method (Rai et al. 1994). Polyphenols contents and aroma precursors
determined with HPLC coupled with ESI-MS after solid-phase extraction (Li et al. 2013).
Statistical analysis
Statistical analysis was performed by two-way analyses of variation (ANOVAs) using
S.P.S.S.
22.0 (S.P.S.S. Institute Inc.) and Origin 8.0 (Origin Lab). The significance of
differences among treatments was tested using the least significant difference (LSD) method. The partial least squares path modeling (PLSPM) were
separately constructed by the indications
of agronomic characters, SPAD value, sugar-containing
compounds, N-containing compounds, aroma precursors in the
FFTLs and yield, yield components, quality parameter in the FTLs, which were used by R
software and Amap, Shape, Diagram
and software package. PLSPM was also used for analysis of the relationship of subgroup
variable data (Tao
et al. 2017).
Results
Economic
characters and chemical composition coordination indexes of FTLs
The yield, average price and sugar-nicotine ratio of K326
flue-cured tobacco varieties under different years (Y), different remained
leaves numbers (L) and different N
application rates (N) showed significant
differences (P < 0.05), and its average price was also significantly affected by the interaction
between L ×
N and Y ×
L ×
N (Table 1). From 2017
to 2018, the yield and average price
index of flue-cured
tobacco were the best with 22 remained leaves numbers in plant. The yield of FTLs was the best when N
application rates were 150 kg N ha-1. However, the average price was
highest when N application rates were 120 kg N ha-1.
The highest treatment for sugar-nicotine ratio was Y2017 ×
L 2 2 × N60 and the lowest treatment was Y2017×L18×N150
(Table 1).
Agronomic traits and SPAD value in FFTLs at the maturation period
Conduct nonlinear regression fitting of the relationship between main
agronomic characters, SPAD value of FFTLs and N application rates under different remained
leaves numbers in 2017–2018 (Fig. 2), except fresh leaf
weight index (determination coefficient r <
0.8), the determination coefficients of other index models
are r²
≥ 0.9, which indicates that the established equation
had high fitting degree with measured values. The plant height index, leaf area per plant, dry leaf weight and SPAD index of FFTLs in these
two years was the highest by combining 22 remained leaves numbers with 150 kg N ha-1 N application rates.
Table 1: Economic characters and chemical
composition coordination indexes of FTLs under four NARs and two RLNs in 2017
and 2018
|
Year
(Y) |
Remained leaves numbers (L) |
Nitrogen rates (N) |
Yield (kg ha-1) |
Mid-to-high grade leaves (%) |
Average price (dollar kg-1) |
Sugar-nicotine ratio |
Nitrogen-nicotine
ratio |
|
2017 |
18 |
60 |
1901.34 ± 37.48c |
83.91 ±
0.47a |
1.73 ±
0.05c |
10.06 ±
0.80a |
0.63 ±
0.01a |
|
90 |
2091.64 ± 40.66bc |
87.39 ±
3.78a |
2.51 ±
0.16b |
7.89 ±
0.43ab |
0.58 ±
0.03a |
||
|
120 |
2280.86 ± 56.31ab |
81.01 ±
2.04a |
3.27 ±
0.17a |
8.17 ±
0.41ab |
0.64 ±
0.07a |
||
|
150 |
2420.16 ± 33.17a |
86.80 ±
1.97a |
2.86 ±
0.06ab |
5.74 ±
0.32b |
0.65 ±
0.04a |
||
|
22 |
60 |
2033.86 ± 35.53c |
86.27 ±
3.14a |
1.61 ±
0.12b |
11.74 ±
072a |
0.61 ±
0.05a |
|
|
90 |
2210.48±151.36bc |
90.54 ±
4.21a |
2.99 ±
0.2a |
9.98 ±
0.55ab |
0.59 ±
0.02a |
||
|
120 |
2414.64 ± 25.73ab |
87.74 ±
3.82a |
3.3 ±
0.12a |
8.28 ±
0.28bc |
0.61 ±
0.03a |
||
|
150 |
2506.91 ± 34.48a |
84.73 ±
2.17a |
2.8 ±
0.17a |
6.82 ±
0.30c |
0.68 ±
0.06a |
||
|
2018 |
18 |
60 |
1865.21 ± 76.06b |
82.74 ±
5.14a |
1.98 ±
0.05b |
11.50 ±
1.65a |
0.65 ±
0.06a |
|
90 |
2156.54 ± 96.19a |
81.29 ±
2.87a |
2.58 ±
0.14b |
9.03 ±
0.34ab |
0.60 ±
0.02a |
||
|
120 |
2217.33 ± 76.93a |
81.82 ±
1.69a |
3.42 ±
0.14a |
8.10 ±
0.88b |
0.68 ±
0.06a |
||
|
150 |
2326.58 ± 47.39a |
86.75 ±
3.75a |
2.51 ±
0.2b |
6.18 ±
0.33b |
0.62 ±
0.01a |
||
|
22 |
60 |
1996.16 ± 128.86b |
82.35 ±
3.72a |
1.76 ±
0.11c |
11.23 ±
2.55a |
0.62 ±
0.13a |
|
|
90 |
2149.72 ± 92.31b |
83.94 ±
3.45a |
3.34 ±
0.24ab |
11.42 ±
1.24a |
0.62 ±
0.08a |
||
|
120 |
2253.99 ± 144.36a |
86.61 ±
4.06a |
3.69 ±
0.16a |
9.22 ±
0.61b |
0.63 ±
0.01a |
||
|
150 |
2507.49 ± 41.51a |
83.06 ±
4.49a |
2.98 ±
0.13b |
8.40 ±
0.65b |
0.66 ±
0.07a |
||
|
|
Sources
of variation |
|
|||||
|
Y |
4.3* |
6.4* |
9.4* |
13.8* |
0.5ns |
||
|
L |
19.1*** |
3.0ns |
13.3** |
41.4*** |
0.1ns |
||
|
N |
80.5*** |
0.9ns |
247.5*** |
73.8*** |
2.1ns |
||
|
|
Y×
L |
0.5ns |
0.8ns |
2.7ns |
0.1ns |
0.0ns |
|
|
Y×
N |
1.0ns |
2.0ns |
2.8ns |
0.5ns |
0.6ns |
||
|
L×
N |
0.7ns |
3.4* |
16.5*** |
2.0ns |
1.0ns |
||
|
Y×
L×N |
1.1ns |
0.1ns |
3.5* |
0.0ns |
0.0ns |
||
Means
with different letters are statistically different from each other at P ≤ 0.05
*= Significant at P ≤ 0.05; *= Significant at P ≤ 0.01; ***= Significant at P ≤ 0.001; ns= Non-significant
Sugar-containing and N-containing compounds in FFTLs at maturity
Total sugar, reducing sugar, starch and amino acid indexes of FFTLs at mature stage are
significantly different (P
< 0.05) under different
treatments of remained leaves numbers; and total sugar, reducing sugar, starch,
total N, nicotine, protein, amino acid showed significant differences (P < 0.05) in different N
application rates (Table
2). Besides, total sugar, starch,
total N, and amino acid showed significant differences (P < 0.05) in different
remained leaves numbers and N application rates; and only protein showed
significant differences N application rates × remained leaves numbers × years, while other index have no significantly differences in different
treatment. Total
sugar and reducing sugar were highest during 2017 under L22 × N120 combination while the highest starch contents were in 2018 under L18 × N60 combination, and the lowest contents were during 2017 in L22 × N150 combination (Table 2). Protein and amino
acid indexes were highest during 2018 under L22 ×
N120 combination, and the lowest contents
were also in 2018 under L18×N90
combination (Table 2).
The aroma
precursors in FFTLs at maturity
Neochlorogenic acid, chlorogenic acid and rutin in FFTLs at mature stage showed significant
differences (P <
0.05) under different remained leaves numbers; and except the scopoletin other aroma precursors
in FFTLs showed significant differences (P < 0.05) under different N
application rates. Moreover, only the
neochlorogenic acid in FFTLs showed significant differences under different remained leaves numbers and N application rates. Similarly, chlorogenic acid was
significantly different (P <
0.05) in different L × N × years combinations while other aroma precursors
observed non-significant
affected under different treatments. Under the index of polyphenol compounds in flue-cured tobacco, the highest treatment of neochlorogenic acid was
Y2017 × L22 × N150 (2.86%), and the lowest was Y2017
× L22 × N60 (1.71%). The highest chlorogenic acid content was Y2017 × L22 × N120 (17.86%), and the lowest was Y2018
× L18 × N90 (9.81%). The highest rutin content was Y2018 × L22 × N120 (15.96%), and the lowest was Y2018
× L18 × N60 (9.30%). Under the index of plastid pigment compounds in flue-cured tobacco, the highest
treatment of lutein content was Y2018 × L22 × N120 (0.29%) and the lowest treatment was Y2017 × L18 × N60 (0.14%); the highest treatment of β-carotene content
was Y2018 × L22 × N150 (0.46%), The minimum is Y2017 × L18 × N90 (0.23%) (Table 3).
Table 2: Differences of sugar-containing compounds and nitrogen-containing compounds in FFTLs under different
NARs and RLNs at the maturation period
|
Year
(Y) |
Remaining leaves (R) |
Nitrogen fertilization (N) |
Total sugar (%) |
Reducing sugar (%) |
Starch (%) |
Fructose (%) |
Total nitrogen (%) |
Nicotine (%) |
Protein (%) |
Amino acid (%) |
|
2017 |
18 |
60 |
11.98 ± 0.95b |
8.91 ±
0.46b |
29 ±
0.38a |
1.85 ±
0.09a |
1.59 ±
0.08a |
2.15 ±
0.07a |
9.04 ±
0.80b |
18.63 ±
0.70ab |
|
90 |
11.06 ± 0.64b |
8.5 ±
0.95b |
28.09±0.53ab |
1.99 ±
0.15a |
1.82 ±
0.11a |
2.06 ±
0.17a |
10.43 ±
1.3ab |
17.73 ±
1.68b |
||
|
120 |
15.01 ± 0.85a |
11.04 ±
1.34a |
25.23 ±
0.61b |
2.15 ±
0.05a |
2.08 ±
0.02a |
2.47 ±
0.46a |
11.53 ±
1.04a |
20.25 ±
1.10a |
||
|
150 |
14.25 ± 0.92a |
10.24 ±
1.12a |
21.41 ±
1.40c |
2.10 ±
0.01a |
2.19 ±
0.07a |
2.53 ±
0.24a |
11.31 ±
0.74a |
19.48 ±
0.85ab |
||
|
22 |
60 |
10.17 ± 1.07b |
7.46 ±
1.77a |
31.64 ±
0.41a |
2.24 ±
0.11a |
1.76 ±
0.17a |
2.18 ±
0.17a |
9.14 ±
0.52a |
20.22 ±
1.48c |
|
|
90 |
13.17 ± 0.43a |
9.03 ±
1.10a |
27.95 ±
0.94b |
2.16 ±
0.17a |
1.95 ±
0.11a |
2.19 ±
0.05a |
9.70 ±
1.31a |
21.07 ±
0.40c |
||
|
120 |
13.37 ± 0.67a |
9.10 ±
0.64a |
23.64 ±
0.98c |
2.4 ±
0.12a |
1.92 ±
0.03a |
2.34 ±
0.27a |
9.08 ±
0.88a |
24.75 ±
1.12a |
||
|
150 |
11.33 ± 1.11b |
7.97 ±
0.63a |
24.81 ±
1.33c |
2.22 ±
0.07a |
2.07 ±
0.17a |
2.24 ±
0.10a |
11.12 ±
1.42a |
23.23 ±
1.03b |
||
|
2018 |
18 |
60 |
11.19 ± 1.08b |
9.27 ±
0.41a |
30.23 ±
1.22a |
1.61 ±
0.17a |
1.6 ±
0.13a |
2.18 ±
0.05a |
9.1 ±
1b |
21.59 ±
0.9ab |
|
90 |
10.74 ± 0.67b |
9 ±
0.37a |
26.93 ±
0.82c |
1.66 ±
0.53a |
1.8 ±
0.07a |
2.22 ±
0.23a |
8.71 ±
0.88b |
22.42 ±
1.27ab |
||
|
120 |
14.95 ± 1.12a |
10.95 ±
0.4a |
29.48 ±
1.6b |
1.99 ±
0.44a |
2 ±
0.11a |
2.62 ±
0.52a |
9.09 ±
1.55b |
23.79 ±
1.72a |
||
|
150 |
12.66±1.75ab |
8.94 ±
1.19a |
24.57 ±
2.28c |
1.65 ±
0.66a |
2.13 ±
0.08a |
2.25 ±
0.13a |
11.67 ±
0.94a |
19.91 ±
0.51b |
||
|
22 |
60 |
10.55 ± 0.89b |
6.98 ±
2.00a |
31.80 ±
1.25a |
1.85 ±
0.10a |
1.91 ±
0.17a |
1.94 ±
0.15a |
9.39 ±
1.39b |
20.26 ±
1.72b |
|
|
90 |
10.60 ± 2.48b |
7.84 ±
2.86a |
28.60 ±
0.61b |
1.86 ±
0.76a |
1.98 ±
0.12a |
2.15 ±
0.15a |
11.11±1.33ab |
23.78 ±
1.42ab |
||
|
120 |
12.12 ± 0.97a |
8.19 ±
0.64a |
29.13 ±
1.30b |
1.7 ±
0.50a |
1.87 ±
0.20a |
2.41 ±
0.25a |
12.22 ±
0.42a |
25.26 ±
1.56a |
||
|
150 |
11.67 ± 1.11a |
8.22 ±
1.09a |
25.06 ±
1.1c |
1.25 ±
0.26a |
2.12 ±
0.17a |
2.23 ±
0.12a |
11.67 ±
1.17a |
24.78 ±
1.26ab |
||
|
|
Source
of variation |
|
||||||||
|
Y |
4.92* |
0.98ns |
27.73*** |
1.4ns |
0.01ns |
0.09ns |
0.41ns |
32.99*** |
||
|
L |
11.25** |
5.47* |
8.33** |
0.72ns |
1.66ns |
2.18ns |
1.02ns |
46.71*** |
||
|
N |
15.27*** |
3.82* |
69.15*** |
1.12ns |
1.87ns |
1.48ns |
9.12*** |
15.21*** |
||
|
Y×L |
0.02ns |
0.39ns |
0.12ns |
2.16ns |
1.32ns |
0.27ns |
13.02** |
5.67* |
||
|
Y×N |
0.61ns |
0.09ns |
11.17*** |
0.86ns |
0.69ns |
0.84ns |
0.18ns |
2.7ns |
||
|
L×N |
4.88** |
1.45ns |
4.54** |
1.03ns |
1.37ns |
0.45ns |
0.38ns |
5.96** |
||
|
Y×L×N |
2.21ns |
0.93ns |
2.67ns |
0.49ns |
0.13ns |
0.78ns |
4.27* |
1.84ns |
||
Means with different
letters are statistically different from each other at P ≤ 0.05
*= Significant
at P ≤ 0.05; *= Significant at P ≤ 0.01; ***= Significant at P ≤ 0.001; ns= Non-significant
Construction of
PLSPM with quality indexes of FFTLs, and yield and
quality indexes of FTLs
PLSPM results are shown in Fig.
3; the fitting degree of the model is 0.69, which showed that
properties and closeness of the relationship among latent variables have good reliability and high accuracy. As show in the model result diagram, the agronomic traits of
FFTLs including single leaf weight and leaf area had a significant positive
effect on tobacco economic characters (path coefficient =0.99, P ≤ 0.01) and tobacco chemical quality (path coefficient =0.90, P ≤ 0.01). Direct effects of sugars and N on tobacco
economic characters and tobacco chemical quality were positive and negative, respectively. The SPAD values had a
significant positive effect on tobacco economic characters (path coefficient =2.31, P ≤ 0.01) and tobacco chemical quality (path coefficient =1.40, P ≤ 0.01). Plastid pigments including lutein and beta-carotene content had a
negative effect on tobacco economic characters and tobacco chemical quality, but not significant. The indexes of agronomic
traits, photosynthetic
characteristics, sugar-containing
compounds, N-containing compounds and
plastid pigment can interact with each other, final jointly improve yield and promote the
formation of high-quality tobacco leaves.
Discussion
The interaction of N application rates and remained leaves numbers had
significant influence on FTLs’ mid-to-high
grade leaves, average
price, and the
content of total sugar, starch, amino
acid and neochlorogenic of FFTLs (Tables 1-3). With the decrease
of remained leaves numbers per plant, the palisade tissue of leaves will become stronger and
the mesophyll tissue ratio will increase, which is one of the main reasons for the increase of
single leaf weight and the higher SPAD value. Too many remained leaves will result in the
leaves be shielded from each other,
which will weaken photosynthesis and increase respiration, thus leading to a small
leaf area per plant and a lower leaf weight than those with smaller remained
leaves numbers. More
remained leaves numbers can weaken the activity of invertase and amylase during
carbon metabolism, which will result in higher total sugar content and lower starch
content in FFTLs (David and Ramsey 2014). Earlier study
showed that FTLs had the highest content of
petroleum ether extract, total phenol and chlorogenic acid under the same density, when the remained leaves
numbers were 22 (Zhao et al. 2008). The
reason is probably because that the photosynthetic characteristics of leaves and the acceptable range of
effective light radiation may be more in line with the demand of mature tobacco
leaves under the treatment of suitable remained leaves numbers, thus making a higher key
enzyme activity of polyphenol substances.
Many studies showed that the yield
and quality of FTLs could be greatly improved when the N application rates were
increased within a certain range (Dale and Gordon 1971). With the increase
of N application rates, the sugar-nicotine ratio will decrease as N is an important raw material for
nicotine synthesis. Increasing of N application rates will not only enhances the N
metabolism intensity of FTLs, but also weakens the carbohydrate transformation and accumulation in
the carbon metabolism process (Su et al. 2020). In the process of N metabolism, the activity of the key
enzyme nitrate reductase (NR) will
increase significantly with the increase of N application rates, resulting in the increasing
of protein and amino acid. However, too higher N application rates will reduce the reducing sugar and
starch in mature of FFTLs, and delay starch accumulation as well as weaken carbon accumulation and
metabolism (Ankica
et al. 2019). The
increase of N application rates will enhance the activity of phenylalanine
ammonia lyase (PAL), the key enzyme and rate limiting enzyme for
phenol metabolism. Therefore, N application can promote the synthesis of phenolic compounds. In a certain range, N application rates have a
positive correlation with the plant height, single leaf area, single leaf weight and SPAD value of FFTLs. Adequate or excessive
supply of N enables photosynthesis to proceed smoothly and to accumulate large
amounts of dry matter (Rosa et al. 2006).
%201423-1430_files/image004.png)
Fig.2: Variation trend of agronomic traits
and SPAD in FFTL sunder different N application rates and remained leaves
numbers
In the relationship between FTLs and FFTLs, the quality index of FFTLs, including agronomic traits, photosynthetic
characteristics, sugars and N have a direct impact on the yield and quality of FTLs. The content of total sugar
and reducing sugar of FFTLs have significant correlation with the yield and quality
of FTLs. Different
remained leaves numbers change the quality index of FFTLs by affecting the
population distribution, plant shape and photosynthetic utilization efficiency of tobacco plants, thus affecting the yield
and quality of FTLs directly or indirectly. There is a very significant negative correlation
between sugar metabolism indexes and N metabolism indexes. Meanwhile, there is a very significant positive correlation
between sugar metabolism indexes of FFTLs and chemical composition coordination
of FTLs, while N
metabolism indexes are opposite.
Previous studies found that carbon and N metabolisms are two
interdependent transformation processes, N metabolism requires carbon metabolism to
provide carbon source and energy,
while carbon metabolism requires N metabolism to provide enzyme
protein and photosynthetic pigment (Erdal and Turk 2016; Duan et al.
2018). The coordination degree of carbon and N
metabolism plays an important role in the formation of yield and transformation
of chemical components,
which is directly or indirectly related to yield and quality of
FTLs. Production
of high-quality
tobacco should make tobacco leaves changed from N metabolism to carbon
accumulation metabolism at proper time (Zhang et al.
2016). Photosynthetic indexes are
positively correlated with plastid pigment and coordination of chemical
components, indicating
that photosynthesis plays a key role in determining the quality of FTLs. Improvement of
photosynthesis is beneficial to growth and development of tobacco plants, accretion and effective
transformation of plastid pigment,
thereby promoting FTLs quality (Yang et al.
2018).
Conclusion
Results of this two-year field study unveiled that application of 120 kg N ha-1
and 22 remained
leaves numbers are the optimal combination to improve yield and quality of
field fresh tobacco leaves, economic returns and chemical composition of flue-cured tobacco leaves. Moreover, agronomic traits, photosynthetic characteristics, sugars and nitrogen of
fresh tobacco leaves had a direct impact on the yield and quality of flue-cured tobacco leaves.
Author Contributions
Yi Chen, Qing Yang, Xu Wang and Congming Zou conceived the original research plans. Shubin Sun and Wentao Zhao designed the experiments. Chenggang He and Yun Tang performed the experiments. Junying Li and Ying Lin analyzed the data. Ke Ren, Rui Yang and Conglian He wrote the manuscript. All authors
reviewed and approved the final manuscript.
Table 3:
Differences of aroma precursors in FFTLs under different NARs and RLNs at the
maturation period
|
Year
(Y) |
Remaining leaves (R) |
Nitrogen fertilization (N) |
Neochlorogenic acid (mg/g) |
Chlorogenic acid (mg/g) |
Caffeic acid (mg/g) |
Scopoletin (mg/g) |
Rutin (mg/g) |
Kaempferol-3-O-rutinoside (mg/g) |
Lutein (mg/g) |
β-carotene (mg/g) |
|
2017 |
18 |
60 |
1.57±0.02a |
11.69±0.93c |
0.22±0.04a |
0.18±0.02a |
10.1±0.86b |
0.13±0.02a |
0.16±0.02b |
0.28±0.01a |
|
90 |
2.19±0.1a |
14.04±0.42b |
0.25±0.03a |
0.19±0.02a |
11.83±1.34ab |
0.15±0.01a |
0.18±0.03ab |
0.33±0.03a |
||
|
120 |
2.33±0.12a |
16.81±0.96a |
0.23±0.02a |
0.21±0.04a |
14.01±1.15a |
0.17±0.02a |
0.23±0.03ab |
0.34±0.04a |
||
|
150 |
2.38±0.14a |
13.72±1.69b |
0.24±0.03a |
0.23±0.02a |
13.84±0.82ab |
0.16±0.02a |
0.24±0.03a |
0.3±0.01a |
||
|
22 |
60 |
2.01±0.03c |
12.72±1.17b |
0.2±0.02a |
0.2±0.02a |
9.38±1.19c |
0.15±0.02a |
0.23±0.02a |
0.31±0.02a |
|
|
90 |
2.29±0.09b |
11.92±0.41b |
0.22±0.03a |
0.21±0.04a |
12.89±1.01b |
0.16±0.02a |
0.24±0.06a |
0.31±0.01a |
||
|
120 |
2.65±0.19a |
15.86±0.73a |
0.29±0.03a |
0.24±0.05a |
15.25±0.5a |
0.17±0.02a |
0.23±0.02a |
0.38±0.03a |
||
|
150 |
2.5±0.16ab |
16.78±1.11a |
0.21±0.03a |
0.23±0.02a |
14.25±1.07ab |
0.15±0.02a |
0.22±0.01a |
0.32±0.03a |
||
|
2018 |
18 |
60 |
2.08±0.08a |
11.31±1.08b |
0.21±0.04a |
0.23±0.03a |
9.3±1.16b |
0.16±0.02a |
0.21±0.06a |
0.34±0.03b |
|
90 |
2.32±0.15a |
9.81±0.9b |
0.2±0.03a |
0.22±0.02a |
12.22±0.97ab |
0.19±0.01a |
0.17±0.09a |
0.29±0.04b |
||
|
120 |
2.34±0.14a |
15.57±0.87a |
0.25±0.01a |
0.21±0.02a |
12.42±1.44a |
0.2±0.01a |
0.3±0.03a |
0.37±0.05a |
||
|
150 |
2.28±0.14a |
14.97±1.11a |
0.26±0.01a |
0.18±0.02a |
13.28±1.12ab |
0.15±0.01a |
0.23±0.05a |
0.35±0.09ab |
||
|
22 |
60 |
2.24±0.09a |
13.4±0.63c |
0.19±0.03a |
0.2±0.04a |
10.81±1.24b |
0.16±0.02a |
0.22±0.05a |
0.3±0.03b |
|
|
90 |
2.14±0.13b |
13.91±1.3c |
0.23±0.02a |
0.17±0.01a |
11.68±0.97b |
0.16±0.02a |
0.23±0.07a |
0.34±0.04ab |
||
|
120 |
2.22±0.12a |
16.36±0.5a |
0.25±0.02a |
0.2±0.03a |
15.96±1.66a |
0.18±0.02a |
0.25±0.03a |
0.35±0.08ab |
||
|
150 |
2.42±0.17a |
14.93±1.1b |
0.24±0.02a |
0.22±0.04a |
13.57±1.42ab |
0.14±0.01a |
0.2±0.03a |
0.41±0.03a |
||
|
|
Source
of variation |
|
|
|
|
|
|
|
|
|
|
Y |
0.17ns |
2.06ns |
0.23ns |
0.77ns |
0.75ns |
6.12* |
0.6ns |
3.48ns |
||
|
L |
11.46*** |
12.16*** |
0.23ns |
0.09ns |
6.52* |
0.98ns |
0.94ns |
1.55ns |
||
|
N |
29.28*** |
46.21*** |
1.27ns |
1.09ns |
36.35*** |
8.08*** |
3.02* |
4.04* |
||
|
Y×L |
11.46*** |
6.73* |
0.03ns |
3.09ns |
1.12ns |
3.92ns |
1.35ns |
0.04ns |
||
|
Y×N |
11.98*** |
0.85ns |
1.6ns |
1.97ns |
0.39ns |
2.2ns |
1.18ns |
2.02ns |
||
|
L×N |
3.72* |
1.78ns |
2.56ns |
0.83ns |
2.39ns |
0.98ns |
2.92ns |
0.60ns |
||
|
Y×L×N |
1.76ns |
11.18*** |
2.49ns |
2.00ns |
2.05ns |
0.65ns |
0.33ns |
2.14ns |
||
Means with different
letters are statistically different from each other at p≤0.05
*= Significant at p≤0.05; *= Significant at p≤0.01; ***= Significant at p≤0.001; ns=
Non-significant
%201423-1430_files/image006.jpg)
Fig. 3: Partial least squares path modeling
of the association of agronomic characters, SPAD value, sugar-containing compounds, nitrogen-containing compounds, aroma precursors in the FFTLs and yield, yield components, quality parameter in the FTLs. Goodness of fit of
the modeling is 0.6932. Blue
and red arrows indicate positive and negative path coefficient, respectively, while solid and dashed lines indicate significant and non-significant path
coefficients, respectively. * and ** indicate significance at the level of P＜ 0.05 and 0.01, respectively
Acknowledgement
We wish to thank the National
Natural Science Foundation of China (Grant nos. 41601330), China National Tobacco Corporation (110202001015
(XX-11), and Yunnan Tobacco Company
of China National Tobacco Corporation (nos.
2020530000241004) for valuable help in
funding.
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