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Spatial distribution of bolls affects yield formation in different genotypes of Bt cotton varieties
Journal of Integrative Agriculture 2019, 18(11): 2492–2504 Available online at www.sciencedirect.com
ScienceDirect
RESEARCH ARTICLE
Spatial distribution of bolls affects yield formation in different genotypes of Bt cotton varieties NIE Jun-jun1*, YUAN Yan-chao1*, QIN Du-lin1, 2, LIU Yan-hui1, 3, WANG Shuang-lei1, 4, LI Jin-pu1, 5, ZHANG Mei-ling1, 6, ZHAO Na1, GUO Wen-jun1, QI Jie1, MAO Li-li1, SONG Xian-liang1, SUN Xue-zhen1 1
State Key Laboratory of Crop Biology, Ministry of Science and Technology/College of Agronomy, Shandong Agricultural University, Tai’an 271018, P.R.China 2 Technical Guidance Station of Cotton Production in Shandong Province, Jinan 250013, P.R.China 3 Qufu Agricultural Bureau, Qufu 273100, P.R.China 4 Yantai Agricultural Technology Extension Service, Yantai 264000, P.R.China 5 Seed Management Department of Pingdingshan, Pingdingshan 467000, P.R.China 6 Shanghai Center for Plant Stress Biology, Chinese Academy of Sciences, Shanghai 201602, P.R.China
Abstract To optimize the spatial distribution of cotton bolls and to increase the yield, the relationship between yield components and boll spatial distribution was investigated among different Bt (Bacillus thuringensis) cotton varieties. A five-year field experiment was conducted to reveal the reasons for the differences in lint yield and fiber quality across three Bt cotton varieties with different yield formations from 2013 to 2017. The lint yield of Jiman 169 (the average yield from 2013–2017 was 42.2 g/ plant) was the highest, i.e., 16.3 and 36.9% higher than Lumianyan 21 (L21) and Daizimian 99B (99B), respectively. And the differences in boll weight among the three cultivars were similar to the lint yield, while the others yield components were not. So the increase in lint yield was mainly attributed to the enlargement in boll weight. However, the change in fiber quality was inconsistent with the lint yield, and the quality of L21 was significantly better than that of Jimian 169 (J169) and 99B, which was caused by the diversity of boll spatial distribution. Compared with 99B, the loose-type J169 had the highest number of large bolls in inner positions; the tight-type L21 had a few large bolls and the highest number of lower and middle bolls. And approximately 80.72% of the lint yield was concentrated on the inner nodes in Jiman 169, compared with 77.44% of L21 and 66.73% of 99B during the five-year experiment. Although lint yield was significantly affected by the interannual changes, the lint yield of J169 was the highest and the most stable, as well as its yield components. These observations demonstrated the increase in lint yield was due to the increase in boll weight, and the large bolls and high fiber quality were attributed to the optimal distribution of bolls within the canopies.
Received 23 July, 2018 Accepted 2 January, 2019 NIE Jun-jun, E-mail: [email protected]; YUAN Yan-chao, E-mail: [email protected]; Correspondence SUN Xue-zhen, Tel: +86-538-8242487, E-mail: [email protected]; SONG Xian-liang, Tel: +86-538-8242309, E-mail: [email protected] * These authors contributed equally to this study. © 2019, CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(19)62617-1
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Keywords: Bt cotton, yield formation, boll spatial distribution, lint yield, fiber quality
1. Introduction The lint yield of cotton is calculated by multiplying three yield components: the number of bolls per unit ground area, the average boll weight, and the lint percentage. Numerous studies have shown that the individual boll weight increased in the 1970s and 1980s, the boll number per plant and the lint percentage increased in the 1990s, and further improvement in the individual boll weight in this century resulted in an increase in cotton yield (Tang et al. 1993; Kong et al. 2000; Zhang et al. 2003; Mao 2010; Iqbal et al. 2013; Shakeel et al. 2015). Therefore, knowledge of how yield formation change is vital for high-yield and fine-quality cultivation and the selection of new varieties in cotton breeding. Cotton has an infinite flowering and boll-setting habit with a complex shape and living state of leaves, bolls, and branches, and the distribution of bolls in various positions and their share of economic production are intimately linked to the final yield (Constable 1991; Liu et al. 2015). Additionally, reproductive growth does not correspond with apical dominance in cotton, and the generative organs are distributed within the cotton canopy such that cotton plants have many reproductive growth centers distributed throughout the canopy. For this reason, cotton yield and quality are more susceptible to environmental conditions (e.g., light, temperature, rainfall, drought, and hail) than gramineous crops (Schurr et al. 2006). Simultaneously, lint yield and quality are a function of boll-setting, which is significantly affected by the boll-setting period, the boll spatial position, and the physiological state of the cotton plant. Previous research showed that the production of fruiting sites and fiber properties were spatially correlated (Wilkerson and Hart 1996; Johnson et al. 2002). These authors also noted that the micronaire exhibited a moderate degree of spatial variability, and strength showed the lowest degree of variability. Optimization of cotton fruiting can be realized through the formation of more bolls in the best boll-setting period and in the best spatial positions of a cotton plant with the healthiest physiological state (Dong et al. 2014). In general, the ideal spatial positions for the best cotton boll quality, the largest boll weight, and the finest fiber quality are the middle branches and inner node bolls (Tan 1992). Considerable research has shown that an increase in bolls on the middle fruit branches and internal parts resulted in increased cotton yield, but studies have consistently
focused on the variation of Bt and non-Bt cotton cultivars (Blaise 2006; Hofs et al. 2006) and the effect of cultural practices, e.g., plant density (Mao et al. 2015; Wang et al. 2016; Khan et al. 2017), water and fertilizer application (Read et al. 2006; Papastylianou and Argyrokastritis 2014; Dai et al. 2015; Zhang et al. 2015; Chen et al. 2016), mepiquat chloride utilization (Biles and Cothren 2001; Mao et al. 2015; Zhao et al. 2017), and sowing times (Liu et al. 2015; Lu et al. 2017). However, few studies have been conducted on the relationship between yield composition (boll number, boll weight, and lint percentage) and boll spatial distribution among different Bt cotton varieties since insect-resistant cotton breeding was initiated in the 1990s. Thus, three insect-resistant cotton varieties (Daizimian 99B (99B), Lumianyan 21 (L21), and Jimian 169 (J169)), which are local and prevalent cultivars that have been recommended by cotton breeders in the Yellow River region in China during different periods, were selected in this study. The objectives of this research were to investigate the range in agronomic characteristics and boll spatial distribution related to cotton yield and fiber quality among the different Bt cotton cultivars and to reveal the reasons for the differences in lint yield and quality.
2. Materials and methods 2.1. Experimental site and cultivars Field experiments were conducted from 2013 to 2017 at the State Key Laboratory of Crop Biology and the experimental farm of Shandong Agricultural University (36°10´N, 117°09´E, 158 m a.s.l.), Tai’an, Shandong Province, China. The experimental soil type is a brown loam. The concentrations of organic matter, total N, rapidly available phosphate, and rapidly available potassium in the upper 20 cm of soil were 16.64–18.33 mg kg−1, 1.07–1.21 mg kg−1, 37.47–42.63 mg kg−1, and 120.98–128.35 mg kg−1, respectively. The climate is temperate and monsoonal with an average annual temperature of 13°C, rainfall of 697 mm, sunshine duration of 2 627 h, and a frost-free period of 195 d. The average temperature (°C) and rainfall (mm) of the cotton growing seasons from 2013–2017 are shown in Fig. 1. The total rainfall was 591.7, 310.6, 343.1, 509.2, and 452.0 mm during the cotton growing seasons in the five respective years. Cotton is usually planted in late-April and harvested at the end of October, with a growth period of nearly six months. Three Bt (Bacillus thuringiensis) cotton varieties with
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100
2015
2014
80
30
60
20
40
10 0
20 0
5
40
6
7
9 8 Month
10
11 5
2016
6
7
8 Month
9
10
11 5
6
7
9 8 Month
10
11
100
2017
80
30
60
20
40
10 0
Rainfall (mm)
2013
Rainfall
20
Rainfall (mm)
Mean air temperature (°C)
Mean air temperature (°C)
Mean air temperature 40
0
5
6
7
8 9 Month
10
11 5
6
7
9 8 Month
10
11
Fig. 1 Daily mean air temperature (°C) and rainfall (mm) recorded during cotton growing seasons in 2013–2017.
different yield formation that were widely planted in the Yellow River region at different periods were selected in this experiment: 99B, a mid-season conventional variety with an insect-resistant gene, introduced from the United States with a growth period of approximately 130 days, boll weight of 4.9–5.5 g, and lint percentage of 36.0–38.8%; L21, China’s first-era of a mid-season conventional variety that is self-breeding and has a transgenic insect-resistant gene, with a growth period of approximately 133 days, boll weight of 5.8 g, and lint percentage of 41.6%; and J169, China’s second-era transgenic mid-season conventional variety with an insect-resistant gene, a growth period of 123 days, boll weight of 6.3 g, and lint percentage of 39.4%.
2.2. Experimental design and field management Cotton seeds were planted with manual hill-drop planting methods in late-April in a randomized complete block design with three biological replications. Each plot consisted of eight rows with wide-narrow row planting; the wide row was 100 cm, the narrow row was 60 cm, plant-to-plant distances were 25 cm, and the area of every plot was 51.2 m2 (8 m×6.4 m). Compound fertilizer (N:P:K=15:15:10) was used as the basal fertilizer, which was applied at 750 kg ha–1, with flowering stage topdressed urea (46%) at 210 kg ha–1. Other management practices, e.g., insect and weed control and chemical control with plant growth regulators, were conducted according to local
agronomic practices.
2.3. Data collection Twenty plants in the center rows of each plot were used to investigate the agronomic traits (i.e., plant height, first fruiting branch node and height, the number of fruiting branches and nodes, and the ratio of fruit nodes and branches) and boll spatial distribution in the boll opening stage. The fruits were vertically divided into the lower bolls (first to fourth sympodial branches), middle bolls (fifth to eighth sympodial branches), and upper bolls (ninth and beyond sympodial branches), and they were horizontally divided into inner bolls (first to second sections) and distal bolls (3rd section and beyond) according to the position of the bolls on the cotton plant (Hofs et al. 2006; Lv et al. 2013; Mao et al. 2015; Wang et al. 2016; Zhang et al. 2017). The opening bolls were manually harvested according to different spatial positions, and the number of harvested bolls in each position was recorded. Seed cotton was weighed after sun-drying, and then the individual boll weight was calculated. Cotton seed and fiber were separated using a roller ginning machine. Lint percentage was gin turnout, which was calculated as the weight of the fiber divided by the weight of the seed cotton. Lint yield was calculated by multiplying three yield components: the number of bolls, average boll weight, and lint percentage. Fiber quality (fiber length, fiber strength, micronaire, fiber uniformity, and fiber
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elongation) was assessed by the Supervision, Inspection, and Test Center of Cotton Quality, Ministry of Agriculture and Rural Affairs (formerly Ministry of Agriculture) in Anyang, Henan Province of China, using a high volume instrument.
2.4. Statistical analysis The means and standard errors were calculated for three replicates from each treatment with Microsoft Excel 2010. The variance analysis was performed using the General Linear Models procedure of SPSS 20 (IBM, USA). Least significant differences (LSD) were used to separate treatment means at the 5% level. In the five-year data analysis, variety and year were entered as fixed effects, while block (replicate) was entered as a random factor, the factor block was nested within year (Mao et al. 2015). All graphs were drawn using Sigma Plot 12.5 Software.
3. Results 3.1. Agronomic traits Plant height, first fruit branch node and height, the number of fruit branches and nodes, and the ratio of fruit nodes and branches were significantly affected by year, indicating that the agronomic traits were influenced by climate factors,
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e.g., rainfall and temperature (Table 1). Only the number of fruit branches was dramatically different, while the other characteristics did not display obvious differences among various cotton cultivars. The interaction between year and variety had a significant effect on the plant height and first fruit branch node and height. The coefficient of variation (CV) indicates the changes in agronomic traits in response to different climate conditions, such as temperature and rainfall over the five-year period. A lower CV indicates a lower effect of the climate and a more stable trait. The agronomic traits of the three varieties were inconsistent, but with respect to plant height, L21 was the most stable followed by J169 and 99B. The results of a comparison of plant height and first fruit branch node and height among the three varieties were not consistent from 2013 to 2017: J169 was the tallest, followed by 99B and L21; the first branch node of 99B was measured at the highest position, followed by J169 and L21; and the first branch height of L21 was the highest, followed by 99B and J169. No significant difference was observed in the number of fruit branches among the three varieties during the five-year experiment. The fruit node number of 99B was markedly higher than that of J169 and L21 in 2013, 2014, 2016, and 2017, while there were no obvious differences between J169 and L21 in those years. The ratio of fruit nodes and branches in L21 was
Table 1 Agronomic traits of different boll-weight Bacillus thuringensis (Bt) cotton varieties in 2013–2017 Year1)
Variety2)
2013
J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B Y V Y×V
2014
2015
2016
2017
CV (%)
P-value
1)
Plant height (cm) 124.73±1.19 a 107.70±0.72 c 114.24±0.93 b 92.24±0.82 a 88.27±0.54 a 91.18±0.64 a 103.38±0.87 a 97.39±0.52 b 101.33±1.01 ab 118.11±0.81 a 108.61±2.50 b 108.94±0.60 b 94.12±2.86 a 92.75±2.10 a 93.18±1.79 a 13.55 a 9.11 c 9.74 b 0 0 0.0265
First fruit branch nodes per plant 6.83±0.15 a 6.27±0.12 b 6.93±0.06 a 6.91±0.08 ab 6.70±0.26 b 7.20±0.10 a 5.67±0.06 a 5.33±0.06 b 5.83±0.06 a 5.44±0.12 a 4.53±0.06 b 5.47±0.06 a 5.34±0.12 a 4.43±0.06 b 5.37±0.06 a 12.81 c 18.60 a 13.81 b 0 0 0.0036
First fruit branch height (cm)
Fruit branch no. per plant
22.33±0.06 c 28.07±0.55 a 25.20±0.56 b 18.08±0.02 b 22.42±0.43 a 21.57±0.80 a 18.33±0.30 c 22.54±0.03 a 19.51±0.32 b 21.49±0.34 b 25.88±0.88 a 22.17±0.61 b 19.79±0.38 c 24.42±0.99 a 22.14±0.37 b 9.42 a 9.65 a 9.21 a 0 0 0.0268
13.73±0.57 a 13.85±0.15 a 13.47±0.47 a 13.83±0.23 a 13.91±0.37 a 13.61±0.23 a 14.59±0.30 a 14.56±0.38 a 14.02±0.32 a 13.15±0.33 a 12.59±0.08 a 12.99±0.10 a 12.95±0.28 a 12.54±0.31 a 13.08±0.26 a 4.70 b 6.57 a 3.08 c 0 0.2877 0.2513
Fruit node no. per plant 74.93±1.19 b 78.61±0.25 b 83.43±0.31 a 54.77±1.16 b 57.41±0.93 b 63.02±1.67 a 79.58±0.73 a 82.89±0.88 a 83.01±0.85 a 58.51±0.77 b 60.14±0.82 b 65.90±0.61 a 74.43±0.46 b 73.68±1.05 b 80.25±1.61 a 16.13 a 15.98 a 13.13 b 0 0 0.1192
Ratio of fruit nodes and branches 5.46±0.15 b 5.68±0.06 b 6.19±0.22 a 3.96±0.06 b 4.13±0.06 b 4.63±0.10 a 5.46±0.06 b 5.69±0.19 ab 5.92±0.17 a 4.29±0.08 c 4.78±0.03 b 5.07±0.05 a 5.75±0.15 b 5.88±0.22 ab 6.13±0.04 a 15.32 a 14.30 b 12.49 c 0 0 0.5487
CV, coefficient of variation. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Y and V mean year and variety, respectively. Values are mean±standard deviation. Values followed by a different small letter within same row are significantly different in a column at 0.05 probability level.
2)
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much higher than that in J169 but lower than that in 99B. These results indicate that the plant architecture of J169 had the loosest structure, followed by 99B, whereas that of L21 was the tightest.
3.2. Spatial distribution characteristics of cotton bolls The boll weight and lint percentage of every boll per plant were dramatically affected by year, variety, and their interaction. But the number of each spatial boll was only obviously affected by year; the variety only significantly affected the lower, upper, inner, and distal bolls; and the lower, upper, and inner boll numbers were markedly affected by the interaction between year and variety (Table 2). These results suggested that the characteristics of spatial bolls were obviously influenced by the changeful weather (especially the rainfall and temperature) and genetic properties. The number of bolls exhibited the greatest response to year, with a CV of 14.98–46.75%, indicating that it was the least stable yield component; while the other two components were more stable based on the smaller CV (3.00–9.11% in boll weight and 1.29–5.05% in lint percentage) (Table 3). A comparison of the CV of spatial bolls indicated inconsistencies among the three cultivars from 2013–2017; however, the boll number and weight of L21 and the lint percentage of J169 had the lowest stability. Boll number In the vertical distribution, the number of bolls in the middle parts was higher than that in the upper parts, but lower than that in the lower branch during 2014–2017 (Fig. 2). However, the upper bolls in J169 and 99B were higher than the middle bolls, which were caused by the highest rainfall (591.7 mm) in 2013. The significant differences in the number of lower bolls were found among the three varieties during 2013–2016. And the lower bolls of Table 2 ANOVA results for the effects of year (Y), variety (V), and their interaction on boll number, boll weight, and lint percentage for different spatial positions P-value Boll number
Boll weight
Lint percentage
Effect Y V Y×V Y V Y×V Y V Y×V
Vertical distribution Lower Middle Upper 0 0 0 0.0015 0.2887 0.0012 0.0007 0.4781 0.0049 0 0 0 0 0 0 0 0.0001 0 0 0 0 0 0.0013 0.0002 0.0001 0.0432 0.0002
Horizontal distribution Inner Distal 0 0 0 0.0004 0.0063 0.2193 0 0 0 0 0 0.0001 0 0 0 0 0 0.0013
L21 were higher than that of J169 and 99B in those years. The comparison between J169 and 99B showed that the number of lower bolls in 99B was obviously higher than that in J169 in 2013, 2014, and 2016. The differences in the number of middle bolls were inconsistent among the three cultivars during 2013–2017: the middle bolls in L21 were the highest, followed by 99B and J169 in 2013 and 2014; J169 and L21 had the higher number of middle bolls than that in 99B in 2015, while that trend was reversed in 2016 and 2017. The subtle differences were observed in the number of upper bolls for the three cultivars in 2014; while the number of upper bolls in J169 was higher than that in L21 and 99B in other years. In the horizontal distribution, the variations of the boll number in the three varieties were consistent in 2013–2017 (Fig. 3). The number of inner bolls in J169 and L21 was higher than that in 99B, but the opposite results were observed in the number of distal bolls. The comparison between J169 and L21 showed that J169 had the more inner bolls than that of L21 in 2015–2017, while it was adverse in the distal bolls in 2013–2015. Boll weight Boll weight of the vertical and horizontal bolls in J169 was the biggest, followed by L21 and 99B during 2013–2017 (Figs. 4 and 5). Lint percentage The lint percentage of every spatial boll in J169 was distinctly higher than that in 99B in 2013, and the lint percentage of the lower, middle, inner, and distal boll in J169 was also notably higher than that in 99B in 2015. But beyond that, no significant difference was observed (Figs. 6 and 7).
3.3. Yield and yield composition The boll number per plant, boll weight, and lint percentage were obviously affected by year, variety, and their interaction. Table 3 The coefficients of variation (CV) of boll number, boll weight, and lint percentage across different spatial positions (%) CV Boll number
Boll weight
Lint percentage 1)
Variety1) J169 L21 99B J169 L21 99B J169 L21 99B
Vertical distribution Lower Middle Upper 17.85 b 25.82 b 26.34 b 19.26 a 29.18 a 46.38 a 14.98 c 25.73 b 46.75 a 5.88 b 5.10 b 5.47 b 9.00 a 6.02 a 9.11 a 3.20 c 3.00 c 5.52 b 1.29 c 4.54 a 5.05 a 2.60 b 3.09 c 4.31 b 2.80 a 3.65 b 3.59 c
Horizontal distribution Inner Distal 22.06 b 20.34 c 20.50 c 42.11 a 30.61 a 28.69 b 4.44 b 6.31 b 8.66 a 7.62 a 3.20 c 3.82 c 3.15 a 3.98 a 2.42 c 3.50 b 2.74 b 3.63 b
J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Values followed by a different small letter within same row are significantly different in a column at 0.05 probability level.
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However, lint yield was only affected by the interaction
and 2015; that in L21 and 99B was higher than J169 in 2014
between year and variety (Table 4). Lint yield and the
and 2016; and that in J169 and 99B was much higher than
number of bolls per plant in J169, boll weight in 99B, and lint
L21 in 2017. Whereas, the variation of the other two yield
percentage in L21 was the most stable during the five-year
components (boll weight and lint percentage) was coincident
based on the coefficients of variation.
in the three cultivars during 2013–2017. The boll weight in
J169 exhibited the highest lint yield, followed by L21 and
J169 was the largest, followed by L21 and 99B. There was
99B. The variation of boll number per plant in the three
no significant difference in the lint percentage of the three
varieties was inconsistent during the five years: the number
varieties except that J169 exhibited a higher lint percentage
of bolls in J169 and L21 was higher than that in 99B in 2013
than 99B in 2013 and 2015. These results suggested that
J169 Boll no. per plant
10 8
99B 2015
2014
6 4 2 0 10
Boll no. per plant
2013
L21
8
Lower Middle Upper 2016
Lower Middle Upper
Lower Middle Upper
2017
6 4 2 0
Lower Middle Upper
Lower Middle Upper
Fig. 2 The number of vertical bolls in different boll weights of Bacillus thuringensis (Bt) cotton cultivars (lower branches, 1–4 fruit branches; middle branches, 5–8 fruit branches; upper branches, ≥9 fruit branches) in 2013–2017. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Bars mean the standard deviation, which was calculated from the three repeated values.
J169 Boll no. per plant
16
2013
99B
2014
2015
12 8 4 0 16
Boll no. per plant
L21
Inner
Distal
2016
Inner
Distal
Inner
Distal
Inner
Distal
2017
12 8 4 0
Inner
Distal
Fig. 3 The number of horizontal bolls in different boll weights of Bacillus thuringensis (Bt) cotton cultivars (inner nodes, 1–2 fruit nodes near the main stem; distal nodes, ≥3 fruit nodes) in 2013–2017. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Bars mean the standard deviation, which was calculated from the three repeated values.
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Boll weight (g)
10 8
2013
L21
99B
2014
2015
6 4 2 0 10
Boll weight (g)
J169
8
Lower Middle Upper 2016
Lower Middle Upper
Lower Middle Upper
2017
6 4 2 0
Lower Middle Upper
Lower Middle Upper
Fig. 4 The boll weight of vertical bolls in different genotypes of Bacillus thuringensis (Bt) cotton cultivars (lower branches, 1–4 fruit branches; middle branches, 5–8 fruit branches; upper branches, ≥9 fruit branches) in 2013–2017. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Bars mean the standard deviation, which was calculated from the three repeated values.
Boll weight (g)
10 8
J169 2013
99B
2014
2015
6 4 2 0 10
Boll weight (g)
L21
8
Inner
Distal
2016
Inner
Distal
Inner
Distal
Inner
Distal
2017
6 4 2 0
Inner
Distal
Fig. 5 The boll weight of horizontal bolls in different genotypes of Bacillus thuringensis (Bt) cotton cultivars (inner nodes, 1–2 fruit nodes near the main stem; distal nodes, ≥3 fruit nodes) in 2013–2017. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Bars mean the standard deviation, which was calculated from the three repeated values.
the increase in lint yield was mainly attributed to the increase in boll weight across the different cultivars.
3.4. The contribution rate of spatial bolls to the lint yield per plant The contribution rate of every position of boll to the lint yield per plant was notably affected by year and variety, but the interaction between year and variety only significantly influenced the contribution rate of lower bolls (Table 5). The contribution rate of upper and inner bolls in J169 and lower,
middle and distal bolls in 99B were the most stable during the five-year period based on the coefficients of variation. The correlation analysis between the spatial boll mass and the lint yield per plant showed that the correlation coefficient of the middle bolls was the highest, followed by the upper and the lower bolls in the vertical direction. And the correlation coefficient of the inner bolls was significantly higher than that of the distal ones in the horizontal distribution. The contribution rate of the lower bolls to the lint yield per plant was the highest, followed by the middle and the upper bolls in the vertical distribution during the five
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J169
Lint percentage (%)
48 2013
L21
2014
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99B 2015
44 40 36
Inner
Distal
Inner
Distal
Inner
Distal
Lint percentage (%)
48 2017
2016 44 40 36
Inner
Distal
Inner
Distal
Fig. 7 The lint percentage of horizontal bolls in different genotypes of Bacillus thuringensis (Bt) cotton cultivars (inner nodes, 1–2 fruit nodes near the main stem; distal nodes, ≥3 fruit nodes) in 2013–2017. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Bars mean the standard deviation, which was calculated from the three repeated values. Table 4 Yield and yield components among different boll-weight Bt cotton varieties in 2013–2017 Year1) 2013
2014
2015
2016
2017
CV (%)
P-value
Variety2) J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B Y V Y×V
Boll no./plant 12.80±0.20 b 13.54±0.06 a 11.85±0.25 c 18.75±0.26 b 21.13±0.36 a 20.99±0.36 a 16.25±0.16 a 15.22±0.29 b 13.40±0.45 c 16.76±0.14 b 18.06±0.29 a 18.37±0.14 a 13.08±0.16 a 11.51±0.18 b 13.42±0.14 a 16.38 b 23.83 a 24.89 a 0 0.0078 0.0246
Boll weight (g) 6.38±0.01 a 5.33±0.01 b 4.72±0.05 c 6.67±0.17 a 5.62±0.21 b 5.12±0.06 c 6.30±0.03 a 5.46±0.07 b 4.86±0.02 c 6.59±0.06 a 5.45±0.03 b 4.87±0.02 c 7.12±0.08 a 6.45±0.04 b 4.93±0.05 c 4.86 b 7.99 a 2.97 c 0 0 0
Lint percentage (%) 41.88±0.50 a 40.92±0.37 ab 39.18±0.18 b 42.43±0.02 a 41.81±0.03 a 41.42±0.14 a 41.38±0.01 a 40.06±0.26 ab 39.21±0.19 b 38.68±0.33 a 38.83±0.35 a 38.79±0.36 a 41.36±0.13 a 41.36±0.57 a 41.28±0.24 a 3.52 a 2.91 c 3.17 b 0 0 0.0001
Lint yield (g/plant) 34.20±0.63 a 29.53±0.20 b 21.91±0.74 c 53.06±1.88 a 49.65±1.32 b 44.51±0.76 c 42.36±1.62 a 33.29±1.16 b 25.54±0.89 c 42.72±0.29 a 38.22±0.83 b 34.70±0.35 c 38.52±0.83 a 30.71±1.05 b 27.31±0.36 c 16.62 c 22.59 b 29.14 a 0 0 0.2368
1)
CV, coefficient of variation. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Y and V mean year and variety, respectively. Values are means±standard deviation. Values followed by a different small letter within same row are significantly different in a column at 0.05 probability level.
2)
years. The variation of contribution rate of spatial bolls was inconsistent among the three varieties, which was caused by the changeable climate in those years. There were no significant differences in the contribution rate of the vertical bolls among the three cultivars in 2014, but of the lower and middle bolls in L21 and 99B were higher than that in J169 in
the other years, and the highest contribution rate of upper bolls was found in J169. In the horizontal distribution, the contribution rate of inner bolls was higher than that of distal bolls, while the opposite result was shown on 99B in 2013. The high contribution rate of distal bolls in 99B was mainly attributed to the highest
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Table 5 Comparison of the contribution rate of spatial bolls to the lint yield per plant among different boll-weight of Bacillus thuringensis (Bt) cotton in 2013–2017 Year1)
Variety2)
2013
J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B Y V Y×V
2014
2015
2016
2017
CV (%)
P-value
CC
Lower 37.76±1.32 b 37.19±0.75 b 49.74±1.15 a 39.51±4.20 a 39.19±1.98 a 40.62±3.16 a 46.48±1.13 b 51.25±1.28 a 50.07±0.72 a 37.89±1.31 c 47.62±1.24 a 40.33±1.18 b 56.27±2.23 b 62.07±2.22 a 53.46±1.80 b 18.22 b 21.12 a 12.80 c 0 0.0279 0.0042 0.730**
Vertical distribution Middle 27.97±0.98 c 32.09±0.31 b 35.07±0.39 a 35.22±3.16 a 35.92±2.98 a 35.31±2.90 a 34.76±0.28 b 35.49±0.31 b 37.83±0.95 a 35.13±1.15 b 31.45±1.42 c 38.52±1.24 a 26.93±0.35 c 29.37±0.73 b 34.32±1.26 a 13.06 a 8.48 b 5.10 c 0.0002 0.0006 0.1356 0.906**
Upper 34.27±0.31 a 30.72±0.95 b 15.19±0.17 c 25.27±0.66 a 24.89±1.81 a 24.07±2.98 a 18.76±0.61 a 13.26±0.22 b 12.10±0.16 b 26.98±0.23 a 20.93±0.96 b 21.16±1.10 b 16.80±0.66 a 8.56±0.55 c 12.23±0.72 b 28.54 c 45.15 a 31.95 b 0 0.0020 0.0818 0.822**
Horizontal distribution Inner Distal 69.38±0.81 a 30.62±0.67 b 67.61±1.16 a 32.39±0.91 b 49.77±0.41 b 50.23±0.45 a 83.34±3.19 a 16.66±1.62 c 73.70±2.71 b 26.30±3.33 b 67.32±1.07 c 32.68±1.90 a 89.07±0.57 a 10.93±0.25 c 87.48±0.85 a 12.52±0.29 b 78.00±1.17 b 22.00±0.39 a 78.35±3.06 a 21.65±2.01 b 71.17±2.83 b 28.83±3.66 a 68.28±2.27 b 31.72±1.52 a 83.46±2.39 a 16.54±0.26 b 87.26±0.62 a 12.74±1.30 c 70.29±2.25 b 29.71±2.48 a 9.15 c 38.32 b 12.03 b 41.31 a 15.54 a 31.17 c 0 0 0 0 0.1083 0.1083 0.904** 0.433**
1)
CV, coefficient of variation; CC, correlation coefficient. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Y and V mean year and variety, respectively. Values are mean±standard deviation. Values followed by a different small letter within same row are significantly different in a column at 0.05 probability level.
2)
rainfall in July 2013. The contribution rate of inner bolls in J169 and L21 was much higher than that in 99B from 2013 to 2017, while J169 was significant higher than L21 in 2014 and 2016. Contrary to the inner bolls, the contribution rate of distal bolls in 99B was the highest in the five years.
3.5. Fiber quality Fiber length, strength, uniformity, elongation, and micronaire were distinctly affected by year and cultivar, but their interaction only had a significant effect on fiber strength and elongation (Table 6). The respective CV of fiber length, strength, and uniformity were 2.36–2.60%, 2.77–3.88%, and 0.82–0.96%, while those of the micronaire and elongation were 7.88–12.23% and 12.83–13.45%, indicating that fiber length, strength, and uniformity were more stable than micronaire and elongation. The CV of micronaire in L21 (12.23%) was the highest, followed by 99B (8.94%) and J169 (7.88%). Nevertheless, no significant differences in the CV of fiber elongation were found in the three varieties. L21 exhibited the longest fiber length over the fiveyear period, and it was significantly longer than that of J169 from 2013–2016 and of 99B from 2013–2015, but
there was no obvious differences between J169 and 99B from 2013–2017. The fiber strength of L21 and 99B was markedly higher than that of J169 during the five years, but no prominent differences were observed between L21 and 99B except that the fiber strength of 99B was much higher than that of L21 in 2017. J169 exhibited the highest micronaire, followed by 99B and L21 from 2013–2017. Fiber uniformity changed slightly among the three varieties in these years. The fiber elongation of 99B was much higher than that of J169 and L21 from 2013–2015, but no significant differences were found among the three cultivars in the other years.
4. Discussion In agreement with other studies (Krishnarao and Mary 1996; Hofs et al. 2006; Salahuddin et al. 2010; Iqbal et al. 2013; Pujer et al. 2014; Shakeel et al. 2015), differences in lint yield and quality existed among the cotton varieties. Our experimental results clearly indicated that lint yield and quality differed considerably among the three cultivars studied. However, a high lint yield was not consistently associated with a high fiber quality. For example, J169 had the highest lint yield and L21 had the best fiber quality.
NIE Jun-jun et al. Journal of Integrative Agriculture 2019, 18(11): 2492–2504
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Table 6 Fiber quality across different boll-weight of Bacillus thuringensis (Bt) cotton varieties in 2013–2017 Year1) 2013
2014
2015
2016
2017
CV (%)
P-value
Variety2) J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B Y V Y×V
Fiber length (mm) 28.11±0.36 b 29.79±0.35 a 28.27±0.42 b 29.45±0.45 b 31.14±0.13 a 29.93±0.26 b 29.63±0.49 b 31.33±0.25 a 29.73±0.07 b 29.00±0.07 b 30.58±0.41 a 29.73±0.29 ab 28.27±0.04 a 29.63±0.28 a 28.59±0.37 a 2.36 b 2.52 a 2.60 a 0 0 0.5902
Fiber strength (cN tex–1) 29.40±0.29 b 31.65±0.55 a 32.92±0.34 a 29.51±0.05 b 32.14±0.29 a 32.41±0.52 a 29.63±0.32 b 32.22±0.33 a 32.04±0.47 a 28.59±0.42 b 31.54±0.79 a 30.97±0.55 a 27.53±0.21 c 29.25±0.38 b 30.93±0.41 a 3.05 b 3.88 a 2.77 c 0 0 0.0030
Micronaire 4.95±0.05 a 4.60±0.17 b 4.62±0.05 b 4.76±0.14 a 4.00±0.09 c 4.41±0.16 b 4.72±0.12 a 3.78±0.04 c 4.37±0.15 b 4.88±0.10 a 4.22±0.03 b 4.78±0.05 a 5.68±0.06 a 5.12±0.02 b 5.41±0.11 a 7.88 c 12.23 a 8.94 b 0 0 0.1857
Fiber uniformity (%) 83.14±0.16 a 84.85±0.39 a 83.98±0.33 a 84.68±0.15 a 85.64±0.56 a 85.29±0.22 a 84.67±0.15 a 85.28±0.61 a 85.21±0.31 a 85.26±0.47 a 86.37±0.22 a 86.24±0.16 a 84.08±0.45 a 84.57±0.08 a 84.83±0.79 a 0.95 a 0.82 b 0.96 a 0 0 0.2387
Fiber elongation (%) 4.80±0.11 b 5.04±0.11 ab 5.17±0.01 a 6.79±0.05 b 7.13±0.08 ab 7.27±0.13 a 6.76±0.10 b 7.06±0.01 ab 7.19±0.09 a 6.64±0.03 a 6.79±0.05 a 6.78±0.02 a 6.57±0.00 a 6.67±0.02 a 6.67±0.04 a 13.45 a 13.11 a 12.83 a 0 0 0.0006
1)
CV, coefficient of variation. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Y and V mean year and variety, respectively. Values are mean±standard deviation. Values followed by a different small letter within same row are significantly different in a column at 0.05 probability level.
2)
The boll weight of J169 was the largest, followed by L21 and 99B during the five-year period. The boll number per plant was not consistent among the three cultivars from 2013–2017. In addition, the lint percentage of the Bt cotton varieties was only slightly increased in different periods. These conclusions suggest that the increase in lint yield was primarily due to the increase in large bolls, a suggestion that is consistent with previous studies (Mao 2010; Iqbal et al. 2013; Shakeel et al. 2015).
4.1. Effects of plant-type difference on yield formation Plant type is the spatial arrangement and combination of morphological and functional attributes of all of the organs that affect the characteristics of light transmittance and the spatial distribution of bolls (Feng et al. 2016). In our study, J169 had much looser plant architecture and greater plant height than L21 and 99B; these properties were helpful in increasing the size of the vegetative organs and optimizing the light environment and consequently, promoting bollsetting and large boll formation. However, in the rainy year, this variety was more likely to overgrow, which led to the closed and poor light conditions that induced fruit-shedding from the lower and middle sympodial branches, similar to J169, which had the lowest number of middle bolls in the wet year (2013, rainfall was 591.7 mm during the cotton growing season). In contrast, the tight architecture of
99B, and especially L21, was slightly affected by the rainy conditions.
4.2. Contribution of within-plant distribution bolls to lint yield In addition to the influence of genetic characteristics, cotton yield and fiber quality are also affected by the spatial positions of bolls (Dong et al. 2014; Mao et al. 2015; Chen et al. 2016). In the previous studies, the fruit of the first node on the same sympodial branch contributed more to yield than the others (Jenkins et al. 1990; Heitholt et al. 1993; Liu et al. 2015). Wu et al. (2018) reported that the seedcotton weight of the first node in the first to third fruiting branches was the least affected by the light and temperature. The main reason of cotton yield reduction was that the number and weight of bolls at third fruiting branch decreased under late planting and shading (Zhao et al. 2018). The results of the current study indicated that the spatial variation of bolls differed significantly among the three varieties. Jiman 169 had the highest number of inner bolls and the largest boll weight with the loosest plant architecture; L21 had the highest number of bolls in the lower and middle positions and the second largest bolls with the tightest plant architecture; and 99B had the lowest number of bolls per plant, the highest number of distal bolls, and the smallest individual boll weight. Hofs et al. (2006) reported that a higher number of internal bolls
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increased the lint yield of Bt cotton varieties. The bolls located on the lower and middle fruit branches or the inner fruit positions contributed more to the yield than those located in the upper and outer fruit positions (Dong et al. 2010). A higher cotton yield was measured under a high boll load in the inner and middle fruit positions, and the boll weight was consistently higher at the same density (Jones and Wells 1998; Clawson et al. 2006; Ahmad et al. 2011; Zhi et al. 2016). Better boll retention in the first position on the first fruiting branch is an agronomic advantage, conferring a higher average boll weight and earlier production (Ungar et al. 1987). In our experiment, which was similar to the results of the above studies, the increased lint yield and boll weight were attributed mainly to the optimized boll setting among the different Bt cotton varieties. Previous studies have shown that 70–90% of total harvestable bolls come from the inner canopy (Jenkins et al. 1990; Wang et al. 2016). The results of our study were in line with those previous researches. The higher contribution rate of inner bolls in J169 and L21 was the main reason of the improvement of lint yield. The inconsistent contribution rate of spatial bolls in the five-year was attributed to the changeable weather. The contribution rate of spatial bolls to lint yield per plant was different from the spatial boll number, which might be related to the effect of boll weight on yield.
4.3. Effects of within-plant distribution bolls on fiber quality Cotton fiber quality is affected not only by genetic and environmental factors, but also by the positions and the resources captured by each fruit (Liakatas et al. 1998; Zhao and Oosterhuis 2000; Pettigrew 2001; Read et al. 2006; Richard et al. 2006; Yeates et al. 2010). The fiber quality of L21 was much better than that of Jiman 169 and 99B, which was chiefly caused by the increase in the number of bolls in lower and middle positions. This conclusion is in line with Mao et al. (2015) who found that an improvement in fiber strength under a high plant density and through the application of mepiquat chloride resulted from the increase in the number of bolls in the lower and middle positions. Similarly, Teague et al. (2011) reported that topping earlier than the reference time greatly increases fiber quality.
5. Conclusion The lint yield of J169 was the highest and the most stable, followed by L21 and 99B. Differences in the fiber quality of the three cultivars were inconsistent with lint yield, and the fiber quality of L21 was much better than that of J169
and 99B. Differences in lint yield and quality were caused by a higher number of large bolls in the inner position in the loosest-type of J169, fewer large bolls, and a higher number of bolls in the lower and middle positions in the tightest-type of L21, and the lowest number of large bolls but a high number of distal bolls in the looser-type of 99B. The differences in lint yield and its components during the five years were principally attributed to the climate conditions, such as temperature and rainfall. Our overall results support the finding that the increase in lint yield was due to the increase in boll weight, and the large bolls and high fiber quality were attributed to the optimal distribution of bolls within the canopies of the different Bt cotton varieties.
Acknowledgements This research was supported by the National Natural Science Foundation of China (31601253), the Modern Agroindustry Technology Research System, China (SDAIT-03), and the Natural Science Foundation of Shandong Province, China (ZR2016CQ20).
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Executive Editor-in-Chief LI Shao-kun Managing editor WANG Ning
ScienceDirect
RESEARCH ARTICLE
Spatial distribution of bolls affects yield formation in different genotypes of Bt cotton varieties NIE Jun-jun1*, YUAN Yan-chao1*, QIN Du-lin1, 2, LIU Yan-hui1, 3, WANG Shuang-lei1, 4, LI Jin-pu1, 5, ZHANG Mei-ling1, 6, ZHAO Na1, GUO Wen-jun1, QI Jie1, MAO Li-li1, SONG Xian-liang1, SUN Xue-zhen1 1
State Key Laboratory of Crop Biology, Ministry of Science and Technology/College of Agronomy, Shandong Agricultural University, Tai’an 271018, P.R.China 2 Technical Guidance Station of Cotton Production in Shandong Province, Jinan 250013, P.R.China 3 Qufu Agricultural Bureau, Qufu 273100, P.R.China 4 Yantai Agricultural Technology Extension Service, Yantai 264000, P.R.China 5 Seed Management Department of Pingdingshan, Pingdingshan 467000, P.R.China 6 Shanghai Center for Plant Stress Biology, Chinese Academy of Sciences, Shanghai 201602, P.R.China
Abstract To optimize the spatial distribution of cotton bolls and to increase the yield, the relationship between yield components and boll spatial distribution was investigated among different Bt (Bacillus thuringensis) cotton varieties. A five-year field experiment was conducted to reveal the reasons for the differences in lint yield and fiber quality across three Bt cotton varieties with different yield formations from 2013 to 2017. The lint yield of Jiman 169 (the average yield from 2013–2017 was 42.2 g/ plant) was the highest, i.e., 16.3 and 36.9% higher than Lumianyan 21 (L21) and Daizimian 99B (99B), respectively. And the differences in boll weight among the three cultivars were similar to the lint yield, while the others yield components were not. So the increase in lint yield was mainly attributed to the enlargement in boll weight. However, the change in fiber quality was inconsistent with the lint yield, and the quality of L21 was significantly better than that of Jimian 169 (J169) and 99B, which was caused by the diversity of boll spatial distribution. Compared with 99B, the loose-type J169 had the highest number of large bolls in inner positions; the tight-type L21 had a few large bolls and the highest number of lower and middle bolls. And approximately 80.72% of the lint yield was concentrated on the inner nodes in Jiman 169, compared with 77.44% of L21 and 66.73% of 99B during the five-year experiment. Although lint yield was significantly affected by the interannual changes, the lint yield of J169 was the highest and the most stable, as well as its yield components. These observations demonstrated the increase in lint yield was due to the increase in boll weight, and the large bolls and high fiber quality were attributed to the optimal distribution of bolls within the canopies.
Received 23 July, 2018 Accepted 2 January, 2019 NIE Jun-jun, E-mail: [email protected]; YUAN Yan-chao, E-mail: [email protected]; Correspondence SUN Xue-zhen, Tel: +86-538-8242487, E-mail: [email protected]; SONG Xian-liang, Tel: +86-538-8242309, E-mail: [email protected] * These authors contributed equally to this study. © 2019, CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(19)62617-1
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Keywords: Bt cotton, yield formation, boll spatial distribution, lint yield, fiber quality
1. Introduction The lint yield of cotton is calculated by multiplying three yield components: the number of bolls per unit ground area, the average boll weight, and the lint percentage. Numerous studies have shown that the individual boll weight increased in the 1970s and 1980s, the boll number per plant and the lint percentage increased in the 1990s, and further improvement in the individual boll weight in this century resulted in an increase in cotton yield (Tang et al. 1993; Kong et al. 2000; Zhang et al. 2003; Mao 2010; Iqbal et al. 2013; Shakeel et al. 2015). Therefore, knowledge of how yield formation change is vital for high-yield and fine-quality cultivation and the selection of new varieties in cotton breeding. Cotton has an infinite flowering and boll-setting habit with a complex shape and living state of leaves, bolls, and branches, and the distribution of bolls in various positions and their share of economic production are intimately linked to the final yield (Constable 1991; Liu et al. 2015). Additionally, reproductive growth does not correspond with apical dominance in cotton, and the generative organs are distributed within the cotton canopy such that cotton plants have many reproductive growth centers distributed throughout the canopy. For this reason, cotton yield and quality are more susceptible to environmental conditions (e.g., light, temperature, rainfall, drought, and hail) than gramineous crops (Schurr et al. 2006). Simultaneously, lint yield and quality are a function of boll-setting, which is significantly affected by the boll-setting period, the boll spatial position, and the physiological state of the cotton plant. Previous research showed that the production of fruiting sites and fiber properties were spatially correlated (Wilkerson and Hart 1996; Johnson et al. 2002). These authors also noted that the micronaire exhibited a moderate degree of spatial variability, and strength showed the lowest degree of variability. Optimization of cotton fruiting can be realized through the formation of more bolls in the best boll-setting period and in the best spatial positions of a cotton plant with the healthiest physiological state (Dong et al. 2014). In general, the ideal spatial positions for the best cotton boll quality, the largest boll weight, and the finest fiber quality are the middle branches and inner node bolls (Tan 1992). Considerable research has shown that an increase in bolls on the middle fruit branches and internal parts resulted in increased cotton yield, but studies have consistently
focused on the variation of Bt and non-Bt cotton cultivars (Blaise 2006; Hofs et al. 2006) and the effect of cultural practices, e.g., plant density (Mao et al. 2015; Wang et al. 2016; Khan et al. 2017), water and fertilizer application (Read et al. 2006; Papastylianou and Argyrokastritis 2014; Dai et al. 2015; Zhang et al. 2015; Chen et al. 2016), mepiquat chloride utilization (Biles and Cothren 2001; Mao et al. 2015; Zhao et al. 2017), and sowing times (Liu et al. 2015; Lu et al. 2017). However, few studies have been conducted on the relationship between yield composition (boll number, boll weight, and lint percentage) and boll spatial distribution among different Bt cotton varieties since insect-resistant cotton breeding was initiated in the 1990s. Thus, three insect-resistant cotton varieties (Daizimian 99B (99B), Lumianyan 21 (L21), and Jimian 169 (J169)), which are local and prevalent cultivars that have been recommended by cotton breeders in the Yellow River region in China during different periods, were selected in this study. The objectives of this research were to investigate the range in agronomic characteristics and boll spatial distribution related to cotton yield and fiber quality among the different Bt cotton cultivars and to reveal the reasons for the differences in lint yield and quality.
2. Materials and methods 2.1. Experimental site and cultivars Field experiments were conducted from 2013 to 2017 at the State Key Laboratory of Crop Biology and the experimental farm of Shandong Agricultural University (36°10´N, 117°09´E, 158 m a.s.l.), Tai’an, Shandong Province, China. The experimental soil type is a brown loam. The concentrations of organic matter, total N, rapidly available phosphate, and rapidly available potassium in the upper 20 cm of soil were 16.64–18.33 mg kg−1, 1.07–1.21 mg kg−1, 37.47–42.63 mg kg−1, and 120.98–128.35 mg kg−1, respectively. The climate is temperate and monsoonal with an average annual temperature of 13°C, rainfall of 697 mm, sunshine duration of 2 627 h, and a frost-free period of 195 d. The average temperature (°C) and rainfall (mm) of the cotton growing seasons from 2013–2017 are shown in Fig. 1. The total rainfall was 591.7, 310.6, 343.1, 509.2, and 452.0 mm during the cotton growing seasons in the five respective years. Cotton is usually planted in late-April and harvested at the end of October, with a growth period of nearly six months. Three Bt (Bacillus thuringiensis) cotton varieties with
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100
2015
2014
80
30
60
20
40
10 0
20 0
5
40
6
7
9 8 Month
10
11 5
2016
6
7
8 Month
9
10
11 5
6
7
9 8 Month
10
11
100
2017
80
30
60
20
40
10 0
Rainfall (mm)
2013
Rainfall
20
Rainfall (mm)
Mean air temperature (°C)
Mean air temperature (°C)
Mean air temperature 40
0
5
6
7
8 9 Month
10
11 5
6
7
9 8 Month
10
11
Fig. 1 Daily mean air temperature (°C) and rainfall (mm) recorded during cotton growing seasons in 2013–2017.
different yield formation that were widely planted in the Yellow River region at different periods were selected in this experiment: 99B, a mid-season conventional variety with an insect-resistant gene, introduced from the United States with a growth period of approximately 130 days, boll weight of 4.9–5.5 g, and lint percentage of 36.0–38.8%; L21, China’s first-era of a mid-season conventional variety that is self-breeding and has a transgenic insect-resistant gene, with a growth period of approximately 133 days, boll weight of 5.8 g, and lint percentage of 41.6%; and J169, China’s second-era transgenic mid-season conventional variety with an insect-resistant gene, a growth period of 123 days, boll weight of 6.3 g, and lint percentage of 39.4%.
2.2. Experimental design and field management Cotton seeds were planted with manual hill-drop planting methods in late-April in a randomized complete block design with three biological replications. Each plot consisted of eight rows with wide-narrow row planting; the wide row was 100 cm, the narrow row was 60 cm, plant-to-plant distances were 25 cm, and the area of every plot was 51.2 m2 (8 m×6.4 m). Compound fertilizer (N:P:K=15:15:10) was used as the basal fertilizer, which was applied at 750 kg ha–1, with flowering stage topdressed urea (46%) at 210 kg ha–1. Other management practices, e.g., insect and weed control and chemical control with plant growth regulators, were conducted according to local
agronomic practices.
2.3. Data collection Twenty plants in the center rows of each plot were used to investigate the agronomic traits (i.e., plant height, first fruiting branch node and height, the number of fruiting branches and nodes, and the ratio of fruit nodes and branches) and boll spatial distribution in the boll opening stage. The fruits were vertically divided into the lower bolls (first to fourth sympodial branches), middle bolls (fifth to eighth sympodial branches), and upper bolls (ninth and beyond sympodial branches), and they were horizontally divided into inner bolls (first to second sections) and distal bolls (3rd section and beyond) according to the position of the bolls on the cotton plant (Hofs et al. 2006; Lv et al. 2013; Mao et al. 2015; Wang et al. 2016; Zhang et al. 2017). The opening bolls were manually harvested according to different spatial positions, and the number of harvested bolls in each position was recorded. Seed cotton was weighed after sun-drying, and then the individual boll weight was calculated. Cotton seed and fiber were separated using a roller ginning machine. Lint percentage was gin turnout, which was calculated as the weight of the fiber divided by the weight of the seed cotton. Lint yield was calculated by multiplying three yield components: the number of bolls, average boll weight, and lint percentage. Fiber quality (fiber length, fiber strength, micronaire, fiber uniformity, and fiber
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elongation) was assessed by the Supervision, Inspection, and Test Center of Cotton Quality, Ministry of Agriculture and Rural Affairs (formerly Ministry of Agriculture) in Anyang, Henan Province of China, using a high volume instrument.
2.4. Statistical analysis The means and standard errors were calculated for three replicates from each treatment with Microsoft Excel 2010. The variance analysis was performed using the General Linear Models procedure of SPSS 20 (IBM, USA). Least significant differences (LSD) were used to separate treatment means at the 5% level. In the five-year data analysis, variety and year were entered as fixed effects, while block (replicate) was entered as a random factor, the factor block was nested within year (Mao et al. 2015). All graphs were drawn using Sigma Plot 12.5 Software.
3. Results 3.1. Agronomic traits Plant height, first fruit branch node and height, the number of fruit branches and nodes, and the ratio of fruit nodes and branches were significantly affected by year, indicating that the agronomic traits were influenced by climate factors,
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e.g., rainfall and temperature (Table 1). Only the number of fruit branches was dramatically different, while the other characteristics did not display obvious differences among various cotton cultivars. The interaction between year and variety had a significant effect on the plant height and first fruit branch node and height. The coefficient of variation (CV) indicates the changes in agronomic traits in response to different climate conditions, such as temperature and rainfall over the five-year period. A lower CV indicates a lower effect of the climate and a more stable trait. The agronomic traits of the three varieties were inconsistent, but with respect to plant height, L21 was the most stable followed by J169 and 99B. The results of a comparison of plant height and first fruit branch node and height among the three varieties were not consistent from 2013 to 2017: J169 was the tallest, followed by 99B and L21; the first branch node of 99B was measured at the highest position, followed by J169 and L21; and the first branch height of L21 was the highest, followed by 99B and J169. No significant difference was observed in the number of fruit branches among the three varieties during the five-year experiment. The fruit node number of 99B was markedly higher than that of J169 and L21 in 2013, 2014, 2016, and 2017, while there were no obvious differences between J169 and L21 in those years. The ratio of fruit nodes and branches in L21 was
Table 1 Agronomic traits of different boll-weight Bacillus thuringensis (Bt) cotton varieties in 2013–2017 Year1)
Variety2)
2013
J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B Y V Y×V
2014
2015
2016
2017
CV (%)
P-value
1)
Plant height (cm) 124.73±1.19 a 107.70±0.72 c 114.24±0.93 b 92.24±0.82 a 88.27±0.54 a 91.18±0.64 a 103.38±0.87 a 97.39±0.52 b 101.33±1.01 ab 118.11±0.81 a 108.61±2.50 b 108.94±0.60 b 94.12±2.86 a 92.75±2.10 a 93.18±1.79 a 13.55 a 9.11 c 9.74 b 0 0 0.0265
First fruit branch nodes per plant 6.83±0.15 a 6.27±0.12 b 6.93±0.06 a 6.91±0.08 ab 6.70±0.26 b 7.20±0.10 a 5.67±0.06 a 5.33±0.06 b 5.83±0.06 a 5.44±0.12 a 4.53±0.06 b 5.47±0.06 a 5.34±0.12 a 4.43±0.06 b 5.37±0.06 a 12.81 c 18.60 a 13.81 b 0 0 0.0036
First fruit branch height (cm)
Fruit branch no. per plant
22.33±0.06 c 28.07±0.55 a 25.20±0.56 b 18.08±0.02 b 22.42±0.43 a 21.57±0.80 a 18.33±0.30 c 22.54±0.03 a 19.51±0.32 b 21.49±0.34 b 25.88±0.88 a 22.17±0.61 b 19.79±0.38 c 24.42±0.99 a 22.14±0.37 b 9.42 a 9.65 a 9.21 a 0 0 0.0268
13.73±0.57 a 13.85±0.15 a 13.47±0.47 a 13.83±0.23 a 13.91±0.37 a 13.61±0.23 a 14.59±0.30 a 14.56±0.38 a 14.02±0.32 a 13.15±0.33 a 12.59±0.08 a 12.99±0.10 a 12.95±0.28 a 12.54±0.31 a 13.08±0.26 a 4.70 b 6.57 a 3.08 c 0 0.2877 0.2513
Fruit node no. per plant 74.93±1.19 b 78.61±0.25 b 83.43±0.31 a 54.77±1.16 b 57.41±0.93 b 63.02±1.67 a 79.58±0.73 a 82.89±0.88 a 83.01±0.85 a 58.51±0.77 b 60.14±0.82 b 65.90±0.61 a 74.43±0.46 b 73.68±1.05 b 80.25±1.61 a 16.13 a 15.98 a 13.13 b 0 0 0.1192
Ratio of fruit nodes and branches 5.46±0.15 b 5.68±0.06 b 6.19±0.22 a 3.96±0.06 b 4.13±0.06 b 4.63±0.10 a 5.46±0.06 b 5.69±0.19 ab 5.92±0.17 a 4.29±0.08 c 4.78±0.03 b 5.07±0.05 a 5.75±0.15 b 5.88±0.22 ab 6.13±0.04 a 15.32 a 14.30 b 12.49 c 0 0 0.5487
CV, coefficient of variation. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Y and V mean year and variety, respectively. Values are mean±standard deviation. Values followed by a different small letter within same row are significantly different in a column at 0.05 probability level.
2)
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much higher than that in J169 but lower than that in 99B. These results indicate that the plant architecture of J169 had the loosest structure, followed by 99B, whereas that of L21 was the tightest.
3.2. Spatial distribution characteristics of cotton bolls The boll weight and lint percentage of every boll per plant were dramatically affected by year, variety, and their interaction. But the number of each spatial boll was only obviously affected by year; the variety only significantly affected the lower, upper, inner, and distal bolls; and the lower, upper, and inner boll numbers were markedly affected by the interaction between year and variety (Table 2). These results suggested that the characteristics of spatial bolls were obviously influenced by the changeful weather (especially the rainfall and temperature) and genetic properties. The number of bolls exhibited the greatest response to year, with a CV of 14.98–46.75%, indicating that it was the least stable yield component; while the other two components were more stable based on the smaller CV (3.00–9.11% in boll weight and 1.29–5.05% in lint percentage) (Table 3). A comparison of the CV of spatial bolls indicated inconsistencies among the three cultivars from 2013–2017; however, the boll number and weight of L21 and the lint percentage of J169 had the lowest stability. Boll number In the vertical distribution, the number of bolls in the middle parts was higher than that in the upper parts, but lower than that in the lower branch during 2014–2017 (Fig. 2). However, the upper bolls in J169 and 99B were higher than the middle bolls, which were caused by the highest rainfall (591.7 mm) in 2013. The significant differences in the number of lower bolls were found among the three varieties during 2013–2016. And the lower bolls of Table 2 ANOVA results for the effects of year (Y), variety (V), and their interaction on boll number, boll weight, and lint percentage for different spatial positions P-value Boll number
Boll weight
Lint percentage
Effect Y V Y×V Y V Y×V Y V Y×V
Vertical distribution Lower Middle Upper 0 0 0 0.0015 0.2887 0.0012 0.0007 0.4781 0.0049 0 0 0 0 0 0 0 0.0001 0 0 0 0 0 0.0013 0.0002 0.0001 0.0432 0.0002
Horizontal distribution Inner Distal 0 0 0 0.0004 0.0063 0.2193 0 0 0 0 0 0.0001 0 0 0 0 0 0.0013
L21 were higher than that of J169 and 99B in those years. The comparison between J169 and 99B showed that the number of lower bolls in 99B was obviously higher than that in J169 in 2013, 2014, and 2016. The differences in the number of middle bolls were inconsistent among the three cultivars during 2013–2017: the middle bolls in L21 were the highest, followed by 99B and J169 in 2013 and 2014; J169 and L21 had the higher number of middle bolls than that in 99B in 2015, while that trend was reversed in 2016 and 2017. The subtle differences were observed in the number of upper bolls for the three cultivars in 2014; while the number of upper bolls in J169 was higher than that in L21 and 99B in other years. In the horizontal distribution, the variations of the boll number in the three varieties were consistent in 2013–2017 (Fig. 3). The number of inner bolls in J169 and L21 was higher than that in 99B, but the opposite results were observed in the number of distal bolls. The comparison between J169 and L21 showed that J169 had the more inner bolls than that of L21 in 2015–2017, while it was adverse in the distal bolls in 2013–2015. Boll weight Boll weight of the vertical and horizontal bolls in J169 was the biggest, followed by L21 and 99B during 2013–2017 (Figs. 4 and 5). Lint percentage The lint percentage of every spatial boll in J169 was distinctly higher than that in 99B in 2013, and the lint percentage of the lower, middle, inner, and distal boll in J169 was also notably higher than that in 99B in 2015. But beyond that, no significant difference was observed (Figs. 6 and 7).
3.3. Yield and yield composition The boll number per plant, boll weight, and lint percentage were obviously affected by year, variety, and their interaction. Table 3 The coefficients of variation (CV) of boll number, boll weight, and lint percentage across different spatial positions (%) CV Boll number
Boll weight
Lint percentage 1)
Variety1) J169 L21 99B J169 L21 99B J169 L21 99B
Vertical distribution Lower Middle Upper 17.85 b 25.82 b 26.34 b 19.26 a 29.18 a 46.38 a 14.98 c 25.73 b 46.75 a 5.88 b 5.10 b 5.47 b 9.00 a 6.02 a 9.11 a 3.20 c 3.00 c 5.52 b 1.29 c 4.54 a 5.05 a 2.60 b 3.09 c 4.31 b 2.80 a 3.65 b 3.59 c
Horizontal distribution Inner Distal 22.06 b 20.34 c 20.50 c 42.11 a 30.61 a 28.69 b 4.44 b 6.31 b 8.66 a 7.62 a 3.20 c 3.82 c 3.15 a 3.98 a 2.42 c 3.50 b 2.74 b 3.63 b
J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Values followed by a different small letter within same row are significantly different in a column at 0.05 probability level.
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However, lint yield was only affected by the interaction
and 2015; that in L21 and 99B was higher than J169 in 2014
between year and variety (Table 4). Lint yield and the
and 2016; and that in J169 and 99B was much higher than
number of bolls per plant in J169, boll weight in 99B, and lint
L21 in 2017. Whereas, the variation of the other two yield
percentage in L21 was the most stable during the five-year
components (boll weight and lint percentage) was coincident
based on the coefficients of variation.
in the three cultivars during 2013–2017. The boll weight in
J169 exhibited the highest lint yield, followed by L21 and
J169 was the largest, followed by L21 and 99B. There was
99B. The variation of boll number per plant in the three
no significant difference in the lint percentage of the three
varieties was inconsistent during the five years: the number
varieties except that J169 exhibited a higher lint percentage
of bolls in J169 and L21 was higher than that in 99B in 2013
than 99B in 2013 and 2015. These results suggested that
J169 Boll no. per plant
10 8
99B 2015
2014
6 4 2 0 10
Boll no. per plant
2013
L21
8
Lower Middle Upper 2016
Lower Middle Upper
Lower Middle Upper
2017
6 4 2 0
Lower Middle Upper
Lower Middle Upper
Fig. 2 The number of vertical bolls in different boll weights of Bacillus thuringensis (Bt) cotton cultivars (lower branches, 1–4 fruit branches; middle branches, 5–8 fruit branches; upper branches, ≥9 fruit branches) in 2013–2017. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Bars mean the standard deviation, which was calculated from the three repeated values.
J169 Boll no. per plant
16
2013
99B
2014
2015
12 8 4 0 16
Boll no. per plant
L21
Inner
Distal
2016
Inner
Distal
Inner
Distal
Inner
Distal
2017
12 8 4 0
Inner
Distal
Fig. 3 The number of horizontal bolls in different boll weights of Bacillus thuringensis (Bt) cotton cultivars (inner nodes, 1–2 fruit nodes near the main stem; distal nodes, ≥3 fruit nodes) in 2013–2017. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Bars mean the standard deviation, which was calculated from the three repeated values.
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Boll weight (g)
10 8
2013
L21
99B
2014
2015
6 4 2 0 10
Boll weight (g)
J169
8
Lower Middle Upper 2016
Lower Middle Upper
Lower Middle Upper
2017
6 4 2 0
Lower Middle Upper
Lower Middle Upper
Fig. 4 The boll weight of vertical bolls in different genotypes of Bacillus thuringensis (Bt) cotton cultivars (lower branches, 1–4 fruit branches; middle branches, 5–8 fruit branches; upper branches, ≥9 fruit branches) in 2013–2017. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Bars mean the standard deviation, which was calculated from the three repeated values.
Boll weight (g)
10 8
J169 2013
99B
2014
2015
6 4 2 0 10
Boll weight (g)
L21
8
Inner
Distal
2016
Inner
Distal
Inner
Distal
Inner
Distal
2017
6 4 2 0
Inner
Distal
Fig. 5 The boll weight of horizontal bolls in different genotypes of Bacillus thuringensis (Bt) cotton cultivars (inner nodes, 1–2 fruit nodes near the main stem; distal nodes, ≥3 fruit nodes) in 2013–2017. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Bars mean the standard deviation, which was calculated from the three repeated values.
the increase in lint yield was mainly attributed to the increase in boll weight across the different cultivars.
3.4. The contribution rate of spatial bolls to the lint yield per plant The contribution rate of every position of boll to the lint yield per plant was notably affected by year and variety, but the interaction between year and variety only significantly influenced the contribution rate of lower bolls (Table 5). The contribution rate of upper and inner bolls in J169 and lower,
middle and distal bolls in 99B were the most stable during the five-year period based on the coefficients of variation. The correlation analysis between the spatial boll mass and the lint yield per plant showed that the correlation coefficient of the middle bolls was the highest, followed by the upper and the lower bolls in the vertical direction. And the correlation coefficient of the inner bolls was significantly higher than that of the distal ones in the horizontal distribution. The contribution rate of the lower bolls to the lint yield per plant was the highest, followed by the middle and the upper bolls in the vertical distribution during the five
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J169
Lint percentage (%)
48 2013
L21
2014
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99B 2015
44 40 36
Inner
Distal
Inner
Distal
Inner
Distal
Lint percentage (%)
48 2017
2016 44 40 36
Inner
Distal
Inner
Distal
Fig. 7 The lint percentage of horizontal bolls in different genotypes of Bacillus thuringensis (Bt) cotton cultivars (inner nodes, 1–2 fruit nodes near the main stem; distal nodes, ≥3 fruit nodes) in 2013–2017. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Bars mean the standard deviation, which was calculated from the three repeated values. Table 4 Yield and yield components among different boll-weight Bt cotton varieties in 2013–2017 Year1) 2013
2014
2015
2016
2017
CV (%)
P-value
Variety2) J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B Y V Y×V
Boll no./plant 12.80±0.20 b 13.54±0.06 a 11.85±0.25 c 18.75±0.26 b 21.13±0.36 a 20.99±0.36 a 16.25±0.16 a 15.22±0.29 b 13.40±0.45 c 16.76±0.14 b 18.06±0.29 a 18.37±0.14 a 13.08±0.16 a 11.51±0.18 b 13.42±0.14 a 16.38 b 23.83 a 24.89 a 0 0.0078 0.0246
Boll weight (g) 6.38±0.01 a 5.33±0.01 b 4.72±0.05 c 6.67±0.17 a 5.62±0.21 b 5.12±0.06 c 6.30±0.03 a 5.46±0.07 b 4.86±0.02 c 6.59±0.06 a 5.45±0.03 b 4.87±0.02 c 7.12±0.08 a 6.45±0.04 b 4.93±0.05 c 4.86 b 7.99 a 2.97 c 0 0 0
Lint percentage (%) 41.88±0.50 a 40.92±0.37 ab 39.18±0.18 b 42.43±0.02 a 41.81±0.03 a 41.42±0.14 a 41.38±0.01 a 40.06±0.26 ab 39.21±0.19 b 38.68±0.33 a 38.83±0.35 a 38.79±0.36 a 41.36±0.13 a 41.36±0.57 a 41.28±0.24 a 3.52 a 2.91 c 3.17 b 0 0 0.0001
Lint yield (g/plant) 34.20±0.63 a 29.53±0.20 b 21.91±0.74 c 53.06±1.88 a 49.65±1.32 b 44.51±0.76 c 42.36±1.62 a 33.29±1.16 b 25.54±0.89 c 42.72±0.29 a 38.22±0.83 b 34.70±0.35 c 38.52±0.83 a 30.71±1.05 b 27.31±0.36 c 16.62 c 22.59 b 29.14 a 0 0 0.2368
1)
CV, coefficient of variation. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Y and V mean year and variety, respectively. Values are means±standard deviation. Values followed by a different small letter within same row are significantly different in a column at 0.05 probability level.
2)
years. The variation of contribution rate of spatial bolls was inconsistent among the three varieties, which was caused by the changeable climate in those years. There were no significant differences in the contribution rate of the vertical bolls among the three cultivars in 2014, but of the lower and middle bolls in L21 and 99B were higher than that in J169 in
the other years, and the highest contribution rate of upper bolls was found in J169. In the horizontal distribution, the contribution rate of inner bolls was higher than that of distal bolls, while the opposite result was shown on 99B in 2013. The high contribution rate of distal bolls in 99B was mainly attributed to the highest
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Table 5 Comparison of the contribution rate of spatial bolls to the lint yield per plant among different boll-weight of Bacillus thuringensis (Bt) cotton in 2013–2017 Year1)
Variety2)
2013
J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B Y V Y×V
2014
2015
2016
2017
CV (%)
P-value
CC
Lower 37.76±1.32 b 37.19±0.75 b 49.74±1.15 a 39.51±4.20 a 39.19±1.98 a 40.62±3.16 a 46.48±1.13 b 51.25±1.28 a 50.07±0.72 a 37.89±1.31 c 47.62±1.24 a 40.33±1.18 b 56.27±2.23 b 62.07±2.22 a 53.46±1.80 b 18.22 b 21.12 a 12.80 c 0 0.0279 0.0042 0.730**
Vertical distribution Middle 27.97±0.98 c 32.09±0.31 b 35.07±0.39 a 35.22±3.16 a 35.92±2.98 a 35.31±2.90 a 34.76±0.28 b 35.49±0.31 b 37.83±0.95 a 35.13±1.15 b 31.45±1.42 c 38.52±1.24 a 26.93±0.35 c 29.37±0.73 b 34.32±1.26 a 13.06 a 8.48 b 5.10 c 0.0002 0.0006 0.1356 0.906**
Upper 34.27±0.31 a 30.72±0.95 b 15.19±0.17 c 25.27±0.66 a 24.89±1.81 a 24.07±2.98 a 18.76±0.61 a 13.26±0.22 b 12.10±0.16 b 26.98±0.23 a 20.93±0.96 b 21.16±1.10 b 16.80±0.66 a 8.56±0.55 c 12.23±0.72 b 28.54 c 45.15 a 31.95 b 0 0.0020 0.0818 0.822**
Horizontal distribution Inner Distal 69.38±0.81 a 30.62±0.67 b 67.61±1.16 a 32.39±0.91 b 49.77±0.41 b 50.23±0.45 a 83.34±3.19 a 16.66±1.62 c 73.70±2.71 b 26.30±3.33 b 67.32±1.07 c 32.68±1.90 a 89.07±0.57 a 10.93±0.25 c 87.48±0.85 a 12.52±0.29 b 78.00±1.17 b 22.00±0.39 a 78.35±3.06 a 21.65±2.01 b 71.17±2.83 b 28.83±3.66 a 68.28±2.27 b 31.72±1.52 a 83.46±2.39 a 16.54±0.26 b 87.26±0.62 a 12.74±1.30 c 70.29±2.25 b 29.71±2.48 a 9.15 c 38.32 b 12.03 b 41.31 a 15.54 a 31.17 c 0 0 0 0 0.1083 0.1083 0.904** 0.433**
1)
CV, coefficient of variation; CC, correlation coefficient. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Y and V mean year and variety, respectively. Values are mean±standard deviation. Values followed by a different small letter within same row are significantly different in a column at 0.05 probability level.
2)
rainfall in July 2013. The contribution rate of inner bolls in J169 and L21 was much higher than that in 99B from 2013 to 2017, while J169 was significant higher than L21 in 2014 and 2016. Contrary to the inner bolls, the contribution rate of distal bolls in 99B was the highest in the five years.
3.5. Fiber quality Fiber length, strength, uniformity, elongation, and micronaire were distinctly affected by year and cultivar, but their interaction only had a significant effect on fiber strength and elongation (Table 6). The respective CV of fiber length, strength, and uniformity were 2.36–2.60%, 2.77–3.88%, and 0.82–0.96%, while those of the micronaire and elongation were 7.88–12.23% and 12.83–13.45%, indicating that fiber length, strength, and uniformity were more stable than micronaire and elongation. The CV of micronaire in L21 (12.23%) was the highest, followed by 99B (8.94%) and J169 (7.88%). Nevertheless, no significant differences in the CV of fiber elongation were found in the three varieties. L21 exhibited the longest fiber length over the fiveyear period, and it was significantly longer than that of J169 from 2013–2016 and of 99B from 2013–2015, but
there was no obvious differences between J169 and 99B from 2013–2017. The fiber strength of L21 and 99B was markedly higher than that of J169 during the five years, but no prominent differences were observed between L21 and 99B except that the fiber strength of 99B was much higher than that of L21 in 2017. J169 exhibited the highest micronaire, followed by 99B and L21 from 2013–2017. Fiber uniformity changed slightly among the three varieties in these years. The fiber elongation of 99B was much higher than that of J169 and L21 from 2013–2015, but no significant differences were found among the three cultivars in the other years.
4. Discussion In agreement with other studies (Krishnarao and Mary 1996; Hofs et al. 2006; Salahuddin et al. 2010; Iqbal et al. 2013; Pujer et al. 2014; Shakeel et al. 2015), differences in lint yield and quality existed among the cotton varieties. Our experimental results clearly indicated that lint yield and quality differed considerably among the three cultivars studied. However, a high lint yield was not consistently associated with a high fiber quality. For example, J169 had the highest lint yield and L21 had the best fiber quality.
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Table 6 Fiber quality across different boll-weight of Bacillus thuringensis (Bt) cotton varieties in 2013–2017 Year1) 2013
2014
2015
2016
2017
CV (%)
P-value
Variety2) J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B J169 L21 99B Y V Y×V
Fiber length (mm) 28.11±0.36 b 29.79±0.35 a 28.27±0.42 b 29.45±0.45 b 31.14±0.13 a 29.93±0.26 b 29.63±0.49 b 31.33±0.25 a 29.73±0.07 b 29.00±0.07 b 30.58±0.41 a 29.73±0.29 ab 28.27±0.04 a 29.63±0.28 a 28.59±0.37 a 2.36 b 2.52 a 2.60 a 0 0 0.5902
Fiber strength (cN tex–1) 29.40±0.29 b 31.65±0.55 a 32.92±0.34 a 29.51±0.05 b 32.14±0.29 a 32.41±0.52 a 29.63±0.32 b 32.22±0.33 a 32.04±0.47 a 28.59±0.42 b 31.54±0.79 a 30.97±0.55 a 27.53±0.21 c 29.25±0.38 b 30.93±0.41 a 3.05 b 3.88 a 2.77 c 0 0 0.0030
Micronaire 4.95±0.05 a 4.60±0.17 b 4.62±0.05 b 4.76±0.14 a 4.00±0.09 c 4.41±0.16 b 4.72±0.12 a 3.78±0.04 c 4.37±0.15 b 4.88±0.10 a 4.22±0.03 b 4.78±0.05 a 5.68±0.06 a 5.12±0.02 b 5.41±0.11 a 7.88 c 12.23 a 8.94 b 0 0 0.1857
Fiber uniformity (%) 83.14±0.16 a 84.85±0.39 a 83.98±0.33 a 84.68±0.15 a 85.64±0.56 a 85.29±0.22 a 84.67±0.15 a 85.28±0.61 a 85.21±0.31 a 85.26±0.47 a 86.37±0.22 a 86.24±0.16 a 84.08±0.45 a 84.57±0.08 a 84.83±0.79 a 0.95 a 0.82 b 0.96 a 0 0 0.2387
Fiber elongation (%) 4.80±0.11 b 5.04±0.11 ab 5.17±0.01 a 6.79±0.05 b 7.13±0.08 ab 7.27±0.13 a 6.76±0.10 b 7.06±0.01 ab 7.19±0.09 a 6.64±0.03 a 6.79±0.05 a 6.78±0.02 a 6.57±0.00 a 6.67±0.02 a 6.67±0.04 a 13.45 a 13.11 a 12.83 a 0 0 0.0006
1)
CV, coefficient of variation. J169, Jimian 169; L21, Lumianyan 21; 99B, Daizimian 99B. Y and V mean year and variety, respectively. Values are mean±standard deviation. Values followed by a different small letter within same row are significantly different in a column at 0.05 probability level.
2)
The boll weight of J169 was the largest, followed by L21 and 99B during the five-year period. The boll number per plant was not consistent among the three cultivars from 2013–2017. In addition, the lint percentage of the Bt cotton varieties was only slightly increased in different periods. These conclusions suggest that the increase in lint yield was primarily due to the increase in large bolls, a suggestion that is consistent with previous studies (Mao 2010; Iqbal et al. 2013; Shakeel et al. 2015).
4.1. Effects of plant-type difference on yield formation Plant type is the spatial arrangement and combination of morphological and functional attributes of all of the organs that affect the characteristics of light transmittance and the spatial distribution of bolls (Feng et al. 2016). In our study, J169 had much looser plant architecture and greater plant height than L21 and 99B; these properties were helpful in increasing the size of the vegetative organs and optimizing the light environment and consequently, promoting bollsetting and large boll formation. However, in the rainy year, this variety was more likely to overgrow, which led to the closed and poor light conditions that induced fruit-shedding from the lower and middle sympodial branches, similar to J169, which had the lowest number of middle bolls in the wet year (2013, rainfall was 591.7 mm during the cotton growing season). In contrast, the tight architecture of
99B, and especially L21, was slightly affected by the rainy conditions.
4.2. Contribution of within-plant distribution bolls to lint yield In addition to the influence of genetic characteristics, cotton yield and fiber quality are also affected by the spatial positions of bolls (Dong et al. 2014; Mao et al. 2015; Chen et al. 2016). In the previous studies, the fruit of the first node on the same sympodial branch contributed more to yield than the others (Jenkins et al. 1990; Heitholt et al. 1993; Liu et al. 2015). Wu et al. (2018) reported that the seedcotton weight of the first node in the first to third fruiting branches was the least affected by the light and temperature. The main reason of cotton yield reduction was that the number and weight of bolls at third fruiting branch decreased under late planting and shading (Zhao et al. 2018). The results of the current study indicated that the spatial variation of bolls differed significantly among the three varieties. Jiman 169 had the highest number of inner bolls and the largest boll weight with the loosest plant architecture; L21 had the highest number of bolls in the lower and middle positions and the second largest bolls with the tightest plant architecture; and 99B had the lowest number of bolls per plant, the highest number of distal bolls, and the smallest individual boll weight. Hofs et al. (2006) reported that a higher number of internal bolls
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increased the lint yield of Bt cotton varieties. The bolls located on the lower and middle fruit branches or the inner fruit positions contributed more to the yield than those located in the upper and outer fruit positions (Dong et al. 2010). A higher cotton yield was measured under a high boll load in the inner and middle fruit positions, and the boll weight was consistently higher at the same density (Jones and Wells 1998; Clawson et al. 2006; Ahmad et al. 2011; Zhi et al. 2016). Better boll retention in the first position on the first fruiting branch is an agronomic advantage, conferring a higher average boll weight and earlier production (Ungar et al. 1987). In our experiment, which was similar to the results of the above studies, the increased lint yield and boll weight were attributed mainly to the optimized boll setting among the different Bt cotton varieties. Previous studies have shown that 70–90% of total harvestable bolls come from the inner canopy (Jenkins et al. 1990; Wang et al. 2016). The results of our study were in line with those previous researches. The higher contribution rate of inner bolls in J169 and L21 was the main reason of the improvement of lint yield. The inconsistent contribution rate of spatial bolls in the five-year was attributed to the changeable weather. The contribution rate of spatial bolls to lint yield per plant was different from the spatial boll number, which might be related to the effect of boll weight on yield.
4.3. Effects of within-plant distribution bolls on fiber quality Cotton fiber quality is affected not only by genetic and environmental factors, but also by the positions and the resources captured by each fruit (Liakatas et al. 1998; Zhao and Oosterhuis 2000; Pettigrew 2001; Read et al. 2006; Richard et al. 2006; Yeates et al. 2010). The fiber quality of L21 was much better than that of Jiman 169 and 99B, which was chiefly caused by the increase in the number of bolls in lower and middle positions. This conclusion is in line with Mao et al. (2015) who found that an improvement in fiber strength under a high plant density and through the application of mepiquat chloride resulted from the increase in the number of bolls in the lower and middle positions. Similarly, Teague et al. (2011) reported that topping earlier than the reference time greatly increases fiber quality.
5. Conclusion The lint yield of J169 was the highest and the most stable, followed by L21 and 99B. Differences in the fiber quality of the three cultivars were inconsistent with lint yield, and the fiber quality of L21 was much better than that of J169
and 99B. Differences in lint yield and quality were caused by a higher number of large bolls in the inner position in the loosest-type of J169, fewer large bolls, and a higher number of bolls in the lower and middle positions in the tightest-type of L21, and the lowest number of large bolls but a high number of distal bolls in the looser-type of 99B. The differences in lint yield and its components during the five years were principally attributed to the climate conditions, such as temperature and rainfall. Our overall results support the finding that the increase in lint yield was due to the increase in boll weight, and the large bolls and high fiber quality were attributed to the optimal distribution of bolls within the canopies of the different Bt cotton varieties.
Acknowledgements This research was supported by the National Natural Science Foundation of China (31601253), the Modern Agroindustry Technology Research System, China (SDAIT-03), and the Natural Science Foundation of Shandong Province, China (ZR2016CQ20).
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