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Effects of planting patterns on yield, quality, and defoliation in machine-harvested cotton
Journal of Integrative Agriculture 2019, 18(9): 2019–2028 Available online at www.sciencedirect.com
ScienceDirect
RESEARCH ARTICLE
Effects of planting patterns on yield, quality, and defoliation in machine-harvested cotton WANG Fang-yong1, HAN Huan-yong1, LIN Hai1, CHEN Bing1, KONG Xian-hui1, NING Xin-zhu1, WANG Xu-wen1, YU Yu1, LIU Jing-de2 1
Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science/Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding, Ministry of Agriculture and Rural Affairs, Shihezi 832000, P.R.China 2 Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, P.R.China
Abstract The aim of this study was to elucidate the effects of different machine-harvested cotton-planting patterns on defoliation, yield, and fiber quality in cotton and to provide support for improving the quality of machine-harvested cotton. In the 2015 and 2016 growing seasons, the Xinluzao 45 (XLZ45) and Xinluzao 62 (XLZ62) cultivars, which are primarily cultivated in northern Xinjiang, were used as study materials. Conventional wide-narrow row (WNR), wide and ultra-narrow row (UNR), wide-row spacing with high density (HWR), and wide-row spacing with low density (LWR) planting patterns were used to assess the effects of planting patterns on defoliation, yield, and fiber quality. Compared with WNR, the seed cotton yields were significantly decreased by 2.06–5.48% for UNR and by 2.50–6.99% for LWR, respectively. The main cause of reduced yield was a reduction in bolls per unit area. The variation in HWR yield was –1.07–1.07% with reduced bolls per unit area and increased boll weight, thus demonstrating stable production. In terms of fiber quality indicators, the planting patterns only showed significant effects on the micronaire value, with wide-row spacing patterns showing an increase in the micronaire values. The defoliation and boll-opening results showed that the number of leaves and dried leaves in HWR was the lowest among the four planting patterns. Prior to the application of defoliating agent and before machine-harvesting, the numbers of leaves per individual plant in HWR were decreased by 14.45 and 25.00% on average, respectively, compared with WNR, while the number of leaves per unit area was decreased by 27.44 and 36.21% on average, respectively. The rates of boll-opening and defoliation in HWR were the highest. Specifically, the boll-opening rate before defoliation and machine-harvesting in HWR was 44.54 and 5.94% higher on average than in WNR, while the defoliation rate prior to machine-harvesting was 3.45% higher on average than in WNR. The numbers of ineffective defoliated leaves and leaf trash in HWR were the lowest, decreased by 33.40 and 32.43%, respectively, compared with WNR. In conclusion, the HWR planting pattern is associated with a high and stable yield, does not affect fiber quality, promotes early maturation,
Received 13 September, 2018 Accepted 10 December, 2018 WANG Fang-yong, E-mail: [email protected]; Correspondence YU Yu, Tel: +86-993-6683745, E-mail: [email protected]; LIU Jing-de, E-mail: [email protected] © 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)62604-3
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and can effectively decrease the amount of leaf trash in machine-picked seed cotton, and thus its use is able to improve the quality of machine-harvested cotton. Keywords: machine-harvested cotton, planting patterns, defoliation, yield, quality
1. Introduction Xinjiang is a major cotton-production region in China and includes large area where the high-quality cultivation technique of “early maturing, high-density, short plant height, and plastic mulch”, which suits the local light and temperature resources, is applied (ICR and CAAS 2013). The production characteristics of Xinjiang include a large cultivation area, a high degree of mechanization, and high yield. In recent years, cotton harvesting in Xinjiang has become mechanized, which has decreased production costs, improved harvest yields, and promoted the development of complete mechanization in the cotton industry (Yu et al. 2015). Compared with major local and overseas cotton cultivation regions, the growth season in Xinjiang is short. In order to improve cotton boll-opening rates and decrease trash content in machine-harvested seed cotton during harvesting, defoliating, and ripening agents are applied during the boll-opening period to promote opening of green bolls and defoliation (Cathey et al. 1982; Tian et al. 2004). Currently, the conventional planting pattern adopted by Xinjiang is the wide-narrow row (66 cm+10 cm) high-density pattern, which is associated with high and stable yields within large areas. However, this pattern results in a higher content of trash in the machine-picked seed cotton, thereby increasing the ginning and processing steps. Increasing these steps causes poorer fiber quality in terms of fiber length, fiber strength, and the proportion of short fibers (Xu and Xia 2009; Zhang 2013), and insufficient market competitiveness, resulting in major economic losses. Therefore, under the premise of not affecting yield and quality, improving effective defoliation is crucial for improving the quality of machine-harvested cotton and ensuring the healthy and sustainable development of cotton-production region in Xinjiang. Weather conditions such as light, temperature, and rainfall shortly after defoliant application are key factors affecting defoliation (Snipes and Cathey 1992; Mccarty 1995). Light deficiency for a long time will reduce defoliation efficiency (Brown and Hyer 1954), while strong light intensity and high temperature are favorable for increasing defoliation rate. Usually, planting density is a key feature that determines the size of the population and the spatial distribution of plants,
which will affect individual growth, canopy structure, and field meteorological factors. A dense canopy will decrease light, temperature, and airflow beneath the canopy (Marois et al. 2004). High density conditions will result in a field microclimate that is not favorable for defoliation (Buxton et al. 1977). Previous studies have shown that cotton yields are stable (Buxton et al. 1977; Bednarz et al. 2000; Siebert et al. 2006; Dai et al. 2015) and fiber qualities are consistent (Buxton et al. 1977; Siebert et al. 2006; Ren et al. 2013; Zhang et al. 2016) when obtained within a certain planting density range. Conversely, high or low density will significantly decrease yield (Dong et al. 2012; Dai et al. 2015; Zhang et al. 2016). It is evident that in the optimization of planting patterns for machine harvested cotton, adjusting plant spacing layout and rationally reducing the density can improve the temperature and light conditions for the population. This will not only affect yield and quality but also increase defoliation. In addition, some leaves will remain attached to the cotton plants after defoliation, which is an important source of leaf trash in machine-harvested cotton. The study of Li et al. (2016) found that plant spacing has greater effects on undetached dried leaves after defoliant application. Some papers have reported the effects of conventional wide-narrow row (WNR), wide and ultra-narrow row (UNR), and wide-row spacing with low density (LWR) on growth, yield, and defoliation in machine-harvested cotton in Xinjiang (Li et al. 2016, 2017; Xu et al. 2017). Our research group proposed another pattern, the wide-row spacing with high density (HWR) pattern, for picker-harvested cotton by increasing the spacing distance and reducing the plant distance, thereby rationally reducing planting density. However, few studies have investigated the effects of this planting pattern on cotton yield, quality, and defoliation. In this study, field experiments were carried out to study yield, quality, and defoliation in two cotton cultivars under different machine-harvested cotton-planting patterns in order to provide technical support for the improvement of machineharvested cotton quality in Xinjiang.
2. Materials and methods 2.1. Overview of the study site The experiment was carried out in 2015–2016 at the Cotton Research Institute (85°59´E, 44°18´N) of the Xinjiang
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Academy of Agricultural and Reclamation Science, China. Cotton has been planted in the study site continuously for many years, and a north-south orientation is used for planting. Drip-film irrigation is used and cotton stalks are returned to the field. The soil used was loamy grey desert soil and had 35.7 g kg−1 organic matter, 66.5 mg kg−1 available nitrogen, 27.6 mg kg−1 available phosphorus, 272.3 mg kg−1 available potassium, and a pH of 7.8.
2.2. Experimental design A split-plot design was used with the main plots constituting the cotton cultivars. The test cultivars were the Xinluzao 45 (XLZ45) and Xinluzao 62 (XLZ62) cultivars, which are early maturity and primarily planted in northern Xinjiang. Different planting patterns were applied to the subplots, including WNR (10-cm plant distance, 66 cm+10 cm of wide-narrow rows, and a planting density of 263 000 plants ha−1), UNR (10-cm plant distance, 72 cm+4 cm of wide-narrow rows, and a planting density of 263 000 plants ha−1), HWR (6 cm plant distance, 76 cm row spacing, and a planting density of 219 000 plants ha−1), and LWR (10 cm plant distance, 76 cm row spacing, and a planting density of 13 2000 plants ha−1). Each subplot was about 41 m2 (6.8 m×6.0 m) in 2015 and 34 m2 (6.8 m×5.0 m) in 2016, and three replications were conducted in each season. Spot sowing on the film was carried out on 23 April 2015 and 22 April 2016, and the plots were watered on the following day. A total of 75 kg ha−1 urea (46%) and 225 kg ha−1 triple superphosphate (46% P2O5) were applied per hectare as base fertilizer. During the growth phase, an additional 600 kg ha−1 urea and 300 kg ha−1 ammonium potassium phosphate compound fertilizers (10% N, 30% P2O5, and 24% K2O) were added. Manual topping was carried out on 1 July in both years. Artificial spraying of thidiazuron (180 mL ha−1) and ethrel (1 200 mL ha−1) was carried out before and after 5 September for defoliation and ripening. Other field management procedures were the same as used in local large-field high-yield cotton plantings.
2.3. Measurements Defoliation and boll-opening Representative sample points that exhibited uniform growth, had no missing seedlings, and were free from pathogens were selected from each subplot. At each point, five plants were continuously marked in each of three rows that were 76 cm apart, resulting in a total of 15 plants. The number of leaves per plant, total number of bolls, number of opened bolls, and number of ineffective defoliated leaves (leaves that did not fall to the ground as they remained attached to the cotton fiber or hanging on the stems and branches) before and after defoliant application
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were calculated. The plants were examined 1 day before application of defoliating and ripening agents and on days 7, 14, and 21 after application in 2015, and 1 day before application and days 10 and 20 after application in 2016. The boll-opening rate (proportion of opened bolls over total number of bolls), defoliation rate (percentage of fallen leaves over total number of leaves before defoliant application), number of leaves (for individual plants and per unit area), number of ineffective defoliated leaves per unit area, and the amount of leaf trash in the machine-harvested seed cotton (sum of number of leaves per unit area and number of ineffective defoliated leaves) were calculated. Yield and fiber quality Representative points within an area of 6.67 m2 were selected in each subplot prior to harvesting to investigate the number of bolls per unit area. Subsequently, cotton was manually harvested two times at the end of September and in mid-October. The opening bolls of the 15 cotton plants that were used for measuring defoliation and boll-opening status were collected and weighed before ginning. Following that, boll weight and lint percentage were determined. A sub-sample of lint was sent to assess the fiber quality using the high volume instrument (HVI) at the Supervision, Inspection and Test Center of Cotton Quality, Ministry of Agriculture and Rural Affairs, Anyang City, Henan Province, China.
2.4. Data statistics and analysis Microsoft Excel 2007 (Microsoft Corp., Albuquerque, NM, US) was used for data organization and the Data Processing System (DPS) 7.05 statistical package was used for analysis of variance (ANOVA) and multiple range tests.
3. Results 3.1. Effects of machine-harvested cotton planting patterns on cotton yield and yield components From Table 1, it is evident that the seed cotton yield trends in 2015 and 2016 were consistent. Compared with WNR, yield was significantly reduced in UNR and LWR, and averaged over all treatments showed yield reductions of 2.06–5.48% and 2.50–6.99%, respectively. The magnitude of reduction in XLZ45 was greater than that in XLZ62. The differences in yield between HWR and WNR were not significant, and the two cultivars showed reduced yields (XLZ45) and increased yields (XLZ62), with a variation in yield of –1.07 to 1.07%. The overall variation trends in lint yield and seed cotton yield were generally consistent. With regards to yield components (Table 1), the analysis results showed that compared with WNR, the number of bolls per unit area in UNR and HWR was reduced. There were
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no significant changes in boll weight or lint percentage in UNR, while boll weight was significantly increased in HWR. The number of bolls per unit area in LWR was significantly decreased in comparison to WNR, with a reduction of 9.63–14.68%, but boll weight was significantly increased by 6.16–9.19%. Lint percentage showed a decreasing trend in LWR. Overall, the number of bolls per unit area in XLZ45 was significantly higher than that in XLZ62, whereas boll weight and lint percentage were significantly lower than those in XLZ62. The ANOVA results (Table 1) showed that, with the exception of boll weight, which did not show any significant changes between the two years, the effects of cultivars and planting patterns on yield and yield components reached significant levels at P<0.05 and 0.01, and the effects of cultivars were greater than either year or planting patterns. The size of the interactions between year, cultivars, and planting patterns varied for different indicators.
and planting patterns did not significantly affect either cotton fiber length, fiber strength, length uniformity, or short fiber index, but did have a significant influence on micronaire values (Table 2). The micronaire value of XLZ45 was significantly lower than that of XLZ62. The micronaire values of HWR and LWR were significantly higher (P<0.01) than that in WNR, with an increase of 2.47–11.29% for LWR and an average increase of 4.82% for HWR. The ANOVA results (Table 2) showed that year, cultivars, planting patterns, or their interactions did not influence fiber length, and only year had significant (P<0.05) effects on fiber strength, length uniformity, and short fiber index. The effects of year, cultivars, and planting patterns on micronaire values were substantial, with the effects of year on cotton fiber quality being the greatest.
3.2. Effects of machine-harvested cotton planting patterns on cotton fiber quality indicators
The boll-opening rate in the various treatments increased with the number of days of defoliant and ripening agent application (Table 3). Planting pattern had a significant effect on the boll-opening rate. In the survey period before
Under the present experimental conditions, the cultivars
3.3. Effects of planting patterns on defoliation and boll-opening in cotton
Table 1 Effects of planting patterns on yield and yield components of machine-harvested cotton Year
Cultivar1)
Planting pattern2)
Boll no. m–2
2015
XLZ45
WNR UNR HWR LWR WNR UNR HWR LWR WNR UNR HWR LWR WNR UNR HWR LWR
123.8 a 117.1 b 116.5 b 108.4 c 101.8 d 98.6 d 99.4 d 92.0 e 151.9 a 145.8 b 142.1 c 129.6 d 119.8 e 115.8 f 117.9 ef 107.3 g
XLZ62
2016
XLZ45
XLZ62
F-value Year (Y) Cultivar (C) Planting pattern (P) Y×C Y×P C×P Y×C×P 1)
217.82** 1 553.33** 57.48** 55.59** 1.87 4.40* 0.63
Boll size (g boll–1) 4.56 f 4.61 f 4.80 e 4.91 de 5.03 cd 5.08 bc 5.16 b 5.34 a 4.46 e 4.40 e 4.72 d 4.87 c 5.25 b 5.28 b 5.38 b 5.72 a 2.51 585.41** 39.91** 50.75** 1.62 1.71 0.31
Lint percentage Seed cotton yield (%) (kg ha–1) 5 628.1 a 40.12 c 39.70 c 5 384.7 b 39.98 c 5 574.8 a 39.34 c 5 308.6 b 42.83 a 5 099.0 c 42.60 a 4 994.2 cd 42.65 a 5 114.7 c 41.69 b 4 893.7 d 41.54 b 6 774.5 a 41.37 bc 6 403.5 b 41.25 bc 6 701.8 a 40.71 c 6 301.2 bc 43.27 a 6 277.8 c 43.31 a 6 102.3 d 43.08 a 6 345.0 bc 43.04 a 6 120.6 d 40.87* 115.54** 4.36* 2.70 0.57 0.09 0.57
96.39* 367.92** 24.16** 7.92* 0.77 2.40 0.51
Lint yield (kg ha–1) 2 258.1 a 2 137.4 cd 2 229.0 ab 2 089.2 de 2 183.6 bc 2 126.9 cd 2 181.9 bc 2 039.1 e 2 813.8 a 2 649.6 c 2 765.0 ab 2 565.0 d 2 715.7 b 2 642.6 c 2 733.6 b 2 633.9 c 159.57** 63.05** 17.97** 99.41** 1.28 1.31 2.05
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. * and **, significances at 0.05 and 0.01 probability levels, respectively.
2)
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Table 2 Effects of planting patterns on the fiber quality of machine-harvested cotton Year
Cultivar1)
Planting pattern2)
2015
XLZ45
WNR UNR HWR LWR WNR UNR HWR LWR WNR UNR HWR LWR WNR UNR HWR LWR
XLZ62
2016
XLZ45
XLZ62
F-value Year (Y) Cultivar (C) Planting pattern (P) Y×C Y×P C×P Y×C×P
Fiber length (mm) 30.13 a 30.53 a 30.35 a 30.52 a 29.87 a 29.72 a 29.95 a 30.26 a 30.02 a 29.80 a 30.06 a 30.17 a 29.37 a 29.66 a 30.00 a 29.38 a
Strength (cN tex–1) 31.30 a 31.22 a 32.12 a 32.07 a 31.69 a 31.62 a 31.68 a 32.04 a 31.57 a 31.56 a 31.64 a 31.48 a 30.76 a 31.27 a 31.67 a 31.61 a
6.19 5.03 0.34 0.0022 0.24 0.10 0.49
22.65* 0.17 0.62 0.69 0.11 0.06 0.38
Micronaire 3.65 c 3.61 c 3.78 bc 3.74 bc 3.72 bc 3.86 b 4.05 a 4.14 a 3.85 d 3.82 d 4.05 c 4.23 ab 4.21 bc 4.15 bc 4.28 ab 4.38 a 38.09** 123.76** 9.07** 0.20 0.47 0.11 1.65
Uniformity (%) 84.79 a 84.99 a 84.91 a 85.44 a 85.03 a 84.96 a 85.23 a 84.86 a 86.82 a 86.42 a 86.41 a 86.66 a 87.13 a 87.31 a 87.09 a 87.01 a
Short fiber index (%) 7.27 a 7.01 a 6.95 a 7.01 a 7.02 a 7.13 a 6.93 a 7.08 a 6.83 a 6.81 a 6.82 a 6.74 a 6.73 a 6.68 a 6.76 a 6.89 a
26.87* 1.19 0.04 1.33 0.25 0.53 0.33
20.11* 0.09 0.36 0.01 0.46 0.65 0.39
1)
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. * and **, significances at 0.05 and 0.01 probability levels, respectively. 2)
Table 3 Dynamic changes in boll-opening rate under different planting patterns of cotton (%) Cultivar1) XLZ45
XLZ62
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
20153) 1 DBD 21.71 c 21.02 c 41.20 a 26.29 b 46.07 c 26.60 d 53.00 b 56.95 a
7 DAD 48.23 bc 43.80 c 63.44 a 51.99 b 67.75 b 53.02 c 74.52 a 76.48 a
14 DAD 68.40 b 70.41 b 75.41 a 69.02 b 86.42 b 84.90 b 92.51 a 86.89 ab
21 DAD 86.08 c 89.02 ab 90.96 a 87.86 bc 92.52 b 93.10 b 96.51 a 93.70 b
1 DBD 13.59 b 6.79 c 21.23 a 19.74 a 15.88 b 15.21 b 18.60 a 21.28 a
20163) 10 DAD 39.39 b 27.28 c 50.30 a 52.19 a 45.64 b 43.13 b 53.57 a 52.42 a
20 DAD 82.22 a 68.30 b 85.22 a 83.61 a 85.30 c 90.30 b 93.94 a 92.02 ab
1)
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DBD, days before defoliation application; DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
and after defoliant application, the boll-opening rate in the two equidistant row planting patterns (HWR and LWR) was both higher than that in WNR and UNR, respectively, and are thus favorable for promoting early maturation. This was particularly true for HWR, as it showed the best bollopening status. The boll-opening rate of HWR was on average 44.54 and 5.94% higher than that of WNR before defoliation and machine harvesting (around 20 days after defoliant application (DAD)), respectively. With regards
to cultivars, the boll-opening rates of XLZ62 in the same time period and same planting pattern were higher than those of XLZ45. The results of the direct effects of different planting patterns on cotton defoliation are indicated in Table 4. In the various survey periods in the two years, the defoliation rates in HWR were all higher and were consistent between both cultivars. In 2015, the defoliation rates in HWR on 7, 14, and 21 DAD were higher than those in WNR by
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1.74–2.62%, 3.14–3.20%, and 1.61–2.86%, respectively. In 2016, the defoliation rates in HWR on 10 and 20 DAD were higher than that in WNR by 7.31–9.92% and 1.72–5.31%, respectively; and higher by 3.45% on average than in WNR before machine harvesting. The defoliation rate of XLZ45 in LWR was lower than in WNR, while the opposite was observed in XLZ62. The number of leaves per plant and number of leaves per unit area before defoliant application represent the amount of leaves in individuals and populations. The number of dried leaves that do not fall off individuals and populations before harvesting constitutes an important source of leaf trash. The amount of leaves in individuals (Table 5) and population (Table 6) under different planting patterns differed significantly. In the various survey periods, the number of leaves in UNR was generally equivalent to that in WNR. The numbers of leaves and dried leaves in HWR were the lowest before defoliant spraying and before machine harvesting. Specifically, the number of leaves per plant decreased by 14.45 and 25.00% on average compared with WNR, respectively, while the number of leaves per unit area decreased by 27.44 and 36.21%, respectively. The number
of leaves in individual plants in LWR was the highest, and was significantly higher than the other patterns, while the number of leaves in the population was significantly lower than that in WNR and UNR. The variation trends of the two cultivars in the different planting patterns were consistent, in that as the defoliation period increased, the number of non-fallen leaves in individuals (Table 5) and populations (Table 6) gradually decreased. Ineffective defoliated leaves are another major source of leaf trash in machine-harvested cotton. As the duration of defoliant application increased, the number of leaves attached to the cotton plants gradually increased (Table 7). In the same survey period, the number of ineffective defoliated leaves per unit area in the two cultivars showed a trend of WNR>UNR>LWR>HWR. The number of ineffective defoliated leaves per unit area in HWR and LWR was significantly lower than that in WNR. Prior to machine picking, the number of ineffective defoliated leaves in HWR, which had the best defoliation results, was decreased by 33.40% on average in comparison to WNR. The sum of the number of leaves and ineffective defoliated leaves per unit area (i.e., non-defoliated leaves
Table 4 Changes in defoliation rate under different planting patterns of cotton (%) Cultivar1) XLZ45
XLZ62
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
20153) 14 DAD 83.57 b 81.57 c 86.77 a 84.15 b 78.34 b 78.02 b 81.48 a 82.65 a
7 DAD 60.78 ab 59.46 b 63.40 a 60.36 b 62.93 bc 61.11 c 64.67 ab 66.75 a
20163) 21 DAD 89.83 b 88.43 c 92.69 a 89.27 bc 88.87 b 89.83 ab 90.48 a 91.11 a
10 DAD 49.89 b 43.63 c 59.81 a 40.02 d 51.32 bc 49.52 c 58.63 a 53.10 b
20 DAD 80.21 ab 79.63 b 81.93 a 79.90 b 79.76 c 81.47 b 85.07 a 83.72 a
1)
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
Table 5 Changes in leaf number per plant under different planting patterns of cotton (leaves/plant) Cultivar1) XLZ45
XLZ62
1)
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
20153) 1 DBD 20.93 b 20.47 b 17.87 c 28.60 a 15.57 b 14.73 b 13.70 c 22.90 a
7 DAD 8.20 b 8.30 b 6.53 c 11.33 a 5.77 b 5.73 b 4.83 c 7.60 a
14 DAD 3.43 c 3.77 b 2.37 d 4.53 a 3.37 b 3.23 b 2.53 c 3.97 a
21 DAD 2.13 c 2.37 b 1.30 d 3.07 a 1.73 b 1.50 b 1.30 c 2.03 a
1 DBD 28.60 b 28.20 b 23.67 c 40.47 a 24.40 b 24.87 b 21.00 c 36.13 a
20163) 10 DAD 14.33 c 15.87 b 11.13 d 24.27 a 11.87 b 12.53 b 10.33 c 16.93 a
20 DAD 5.67 b 5.73 b 5.00 c 8.13 a 4.93 b 4.60 b 3.73 c 5.87 a
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DBD, days before defoliation application; DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
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and defoliated leaves that did not fall to the ground) can be used to measure the quantity of leaf trash in the various planting patterns. From Table 8, we can see that the amount of leaf trash per unit area in HWR and LWR was significantly lower than that in WNR, with leaf impurity decreasing by 32.43 and 29.56%, respectively, before machine harvesting. The two cultivars showed the same trend.
4. Discussion 4.1. Effects of planting patterns on defoliation and boll-opening in cotton Machine-harvested cotton in Xinjiang requires defoliant and ripening agent application during the boll-opening
Table 6 Changes in leaf number per unit soil area under different planting patterns of cotton (leaves m−2) Cultivar1) XLZ45
XLZ62
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
1 DBD 440.51 a 427.93 a 316.74 b 322.95 b 327.65 a 298.92 b 232.49 d 257.14 c
20153) 7 DAD 14 DAD 172.57 a 72.25 a 173.35 a 78.83 a 115.73 c 41.88 c 127.94 b 51.19 b 121.34 a 70.86 a 116.30 a 65.59 a 82.14 b 43.00 b 85.30 b 44.53 b
21 DAD 44.89 b 49.47 a 23.02 d 34.62 c 36.50 a 30.42 b 22.13 c 22.82 c
1 DBD 526.71 a 501.32 b 380.05 d 400.32 c 446.52 b 479.61 a 335.96 d 368.06 c
20163) 10 DAD 263.65 b 282.48 a 178.64 d 240.04 c 216.97 b 241.97 a 165.39 c 172.54 c
20 DAD 104.09 a 102.08 a 80.23 b 80.42 b 90.21 a 88.76 a 59.71 b 59.84 b
1)
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DBD, days before defoliation application; DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
Table 7 Effects of planting patterns on the number of non-effective defoliated leaves of cotton (leaves m−2) Cultivar1) XLZ45
XLZ62
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
7 DAD 79.96 a 78.11 a 58.53 b 53.85 b 75.82 a 71.03 a 46.35 c 55.42 b
20153) 14 DAD 87.68 a 87.03 a 62.59 b 62.50 b 89.11 a 77.78 b 57.73 c 58.03 c
20163) 21 DAD 101.71 a 98.99 a 77.41 b 77.48 b 93.30 a 90.64 a 71.38 b 64.75 c
10 DAD 85.83 a 71.20 b 42.82 d 48.76 c 63.35 a 60.41 a 40.51 c 47.54 b
20 DAD 89.59 a 78.16 b 46.00 d 52.74 c 68.23 a 63.07 b 42.61 d 49.62 c
1)
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
Table 8 Effects of planting patterns on the amount of leaf trash of machine-harvested cotton (leaves m−2) Cultivar1) XLZ45
XLZ62
1)
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
7 DAD 252.53 a 251.47 a 174.26 b 181.79 b 197.16 a 187.33 a 128.49 c 140.72 b
20153) 14 DAD 159.93 a 165.85 a 104.47 c 113.69 b 159.97 a 143.37 b 100.74 c 102.56 c
20163) 21 DAD 146.60 a 148.46 a 100.43 c 112.10 b 129.80 a 121.06 a 93.51 b 87.57 b
10 DAD 349.47 a 353.68 a 221.46 c 288.80 b 280.31 b 302.37 a 205.89 c 220.08 c
20 DAD 193.68 a 180.24 b 126.23 d 133.16 c 158.45 a 151.83 a 102.32 c 109.46 b
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
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phase. The boll-opening rate is an important indicator for determining when chemicals should be applied (Williamson and Riley 1961), as chemical application under high bollopening conditions has the lowest effect on fiber maturation in the upper cotton balls, which is favorable for increasing the yield (Faircloth et al. 2004) and the quality of machineharvested cotton (Snipes and Baskin 1994). Li et al. (2016) found that plant spacing has strong effects on boll-opening, as the boll-opening rate in equidistant row spacing and low density, and equidistant row spacing and high density, before chemical application were significantly higher than that in WNR pattern. The results of this study showed that the boll-opening rates of two equidistant row-spacing patterns (HWR and LWR) were significantly higher than that in WNR (Table 3) prior to defoliant application, indicating that equidistant row-spacing patterns promote cotton growth and development and thus cotton yield. This has importance in promoting early maturation in cotton production in Xinjiang. Improving defoliation results and reducing the quantity of trash in machine-harvested seed cotton (Xu and Xia 2009; Zhang 2013) are important measures for improving the quality of machine-harvested cotton. Decreasing the amount of trash can reduce cleaning and processing steps and decrease damage to cotton fibers (Xu and Xia 2009; Xu et al. 2015). Leaf trash constitutes a major component of the trash in machine-harvested cotton (Xie et al. 2014), and thus reducing the quantity of leaf trash in machineharvested seed cotton is extremely important. The major sources of leaf trash are non-defoliated dried leaves and defoliated leaves that are attached to the cotton plants (ineffective defoliated leaves). Our study found that the number of leaves per individual plant and per unit area in HWR was the lowest, i.e., “smaller individual, smaller population”. This pattern was associated with the highest defoliation rate and the lowest amount of dried leaves and ineffective defoliated leaves, as well as the lowest amount of leaf trash. The number of leaves per individual in LWR was the highest, while the number of leaves per population was significantly lower than that in WNR, i.e., “larger individual, smaller population”. The defoliation rates of the two cultivars in LWR showed an opposite trend to that of WNR and had lower amounts of ineffective defoliated leaves and leaf trash. Previous studies have also noted that in equidistant row-spacing planting patterns, the defoliation rate is higher, while the amount of hanging and dried leaves is lower (Li et al. 2016). Therefore, optimizing row spacing and rationally decreasing planting density can improve the field microclimate and create a canopy structure that is suitable for defoliation, thereby increasing defoliation while concurrently decreasing the amount of leaf trash in machineharvested cotton. This allows for a reduction in cleaning and
processing steps and decreased damage to cotton fibers.
4.2. Effects of planting patterns on cotton yield and quality The number of bolls per unit area, boll weight, and lint percentage are three important yield components of cotton. Among these three components, lint percentage is mainly determined by the genetic characteristics of the cultivar; boll weight is affected by the dual effects of cultivar genetic characteristics and environment; while the number of bolls per unit area, which has the highest plasticity, is the key to obtaining high and stable yield in planting techniques in which the relationship between populations and individuals are adjusted. Previous studies have found that boll weight decreases with increasing density (Baker 1976; Buxton et al. 1977; Bednarz et al. 2000). Yield stability was achieved across a range of plant densities by regulating boll density and boll weight (Buxton et al. 1977; Bednarz et al. 2000; Siebert et al. 2006; Dai et al. 2015). Brodrick et al. (2010) found that, compared with conventional wide spacing planting patterns, boll weight is reduced in ultra-narrow row system in which density is increased, but the increase in boll number per unit area leads to increased yield. Our study indicated that HWR and WNR showed similar yields with good yield stability, while UNR and LWR showed significantly reduced yields (Table 1). With regards to yield components, it is clear that stable yield was achieved in HWR through increased boll weight, while the number of bolls in the population was decreased. The decrease of boll number was attributed to the rationally reduced planting density, and the increase of boll weight might be due to the optimized field microclimate (e.g., temperature and light) and the improved boll-opening rate before defoliant and ripening agent application. This shows that an adjustment in row spacing and rational reduction in density will not affect yield. The reduced yield in UNR is due to the reduction in the number of bolls per unit area, and it is obvious that this pattern is not suitable for high-density cultivation. For the LWR pattern, although boll weight was the highest, the number of bolls per unit area was the lowest, resulting in the greatest reduction in yield. This indicates that drastic reductions in density will significantly reduce yield (Table 1). Previous studies have found that planting density within a certain range does not have any significant effects on cotton fiber quality (Brodrick et al. 2010; Lou et al. 2010; Liu et al. 2016; Zhang et al. 2016), while other researchers found that micronaire values will decrease at high planting densities (Baker 1976; Bednarz et al. 2005). The results of this study showed that fiber quality indicators were not affected by planting patterns, with the exception of micronaire value
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(Table 2). The micronaire values of the two equidistant row-spacing patterns in which planting density was reduced were significantly increased compared with WNR pattern.
5. Conclusion This study showed that row spacing adjustment and appropriate reduction in density could be used to optimize the planting patterns for machine-harvested cotton. HWR achieved high and stable yields by increasing boll weight under conditions in which the number of bolls per unit area was reduced, and had no negative effects on cotton fiber quality. With regard to defoliation and boll-opening, this pattern can result in significantly earlier boll-opening and is favorable for promoting ripening, has good defoliation results, and has fewer dried leaves and ineffective defoliated leaves. This can effectively reduce the source of leaf trash in machine-harvested cotton and has an important role in improving the quality of machine-picked cotton.
Acknowledgements This work was supported by the National Natural Science Foundation of China (31560342), the Major Science and Technology Projects of Xinjiang Production and Construction Corps, China (2016AA001-2), and the National Key Research and Development Program of China (2017YFD0201900).
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Dong H Z. 2015. Manipulation of dry matter accumulation and partitioning with plant density in relation to yield stability of cotton under intensive management. Field Crops Research, 180, 207–215. Dong H Z, Li W J, Eneji A E, Zhang D M. 2012. Nitrogen rate and plant density effects on yield and late-season leaf senescence of cotton raised on a saline field. Field Crops Research, 126, 137–144. Faircloth J C, Edmisten K L, Wells R, Stewart A M. 2004. Timing defoliation applications for maximum yields and optimum quality in cotton containing a fruiting gap. Crop Science, 44, 158–164. ICR, CAAS (Institute of Cotton Research, Chinese Academy of Agricultural Sciences). 2013. Cultivation of Cotton in China. Shanghai Scientific and Technical Publishers, Shanghai. p. 884. (in Chinese) Li J F, Liang F B, Chen H C, Wang C, Zhang W F, Kang P. 2016. Effect of plant and row spacing on agronomic characters and yield of machine-picked cotton. Xinjiang Agricultural Sciences, 53, 1390–1396. (in Chinese) Li J F, Wang C, Liang F B, Chen H C, Tian J S, Kang P, Zhang W F. 2017. Row spacing and planting density affect canopy structure and yield in machine-picked cotton in Xinjiang. Cotton Science, 29, 157–165. (in Chinese) Liu S H, Peng Y, Peng X F, Luo Z, Dong H Z. 2016. Effects of regulated deficit irrigation and plant density on plant growth and yield and fiber quality of cotton in dry land area. Cotton Science, 28, 184–188. (in Chinese) Lou S W, Zhao Q, Gao Y G, Guo R S, Abuli K M, Zhang J S. 2010. The effect of different density to canopy-microclimate and quality of cotton. Cotton Science, 22, 260–266. (in Chinese) Marois J J, Wright D L, Wiatrak P J, Vargas M A. 2004. Effect of row width and nitrogen on cotton morphology and canopy microclimate. Crop Science, 44, 870–877. Mccarty W. 1995. Defoliation, the art and the science. Mid-South Farmer, 2, 12–14. Ren X M, Zhang L Z, Du M W, Evers J B, Werf W V D, Tian X L, Li Z H. 2013. Managing mepiquat chloride and plant density for optimal yield and quality of cotton. Field Crops Research, 149, 1–10. Siebert J D, Stewart A M, Leonard B R. 2006. Comparative growth and yield of cotton planted at various densities and configurations. Agronomy Journal, 98, 562–568. Snipes C E, Baskin C C. 1994. Influence of early defoliation on cotton yield, seed quality, and fiber properties. Field Crops Research, 37, 137–143. Snipes C E, Cathey G W. 1992. Evaluation of defoliant mixtures in cotton. Field Crops Research, 28, 327–334. Tian X L, Duan L S, Li Z H, Wang B M, He Z P. 2004. Physiological bases of chemical accelerated boll maturation and defoliation in cotton. Plant Physiology Communications, 40, 758–762. (in Chinese) Williamson E B, Riley J A. 1961. Interrelated effects of defoliation, weather and mechanical picking on cotton quality. Transactions of the ASAE, 4, 164–165, 169.
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Xie Z L, Xu H, Cao J Q. 2014. Effect of ginned cotton cleaning times on machine picked cotton spinning property. Cotton Textile Technology, 42, 17–20. (in Chinese) Xu H, Li Y, Du W D, Xie Z L, Cao J Q, Lei J X, Jia D, Zhong M. 2015. Questions and suggestions of machine picked cotton. Shanghai Textile Science & Technology, 43, 65–68. (in Chinese) Xu H, Xia X. 2009. Property comparison between machine picked cotton and hand picked cotton. Journal of Textile Research, 30, 5–10. (in Chinese) Xu X X, Lei J F, Gao L L, Zheng H, Li G, Wang L H, Suo Z C, Li J, Zhang J S. 2017. Effects of different row spacing patterns on growth and photosynthetic production of machine-
harvested cotton. Agricultural Research in the Arid Areas, 35, 51–56. (in Chinese) Yu S X, Zhou Y L, He L. 2015. Development of mechanization of cotton production in the Xinjiang Production and Construction Corps. China Cotton, 42, 1–4, 7. (in Chinese) Zhang D M, Luo Z, Liu S H, Li W J, Tang W, Dong H Z. 2016. Effects of deficit irrigation and plant density on the growth, yield and fiber quality of irrigated cotton. Field Crops Research, 197, 1–9. Zhang L J. 2013. Quality analysis between mechanical harvesting and hand harvesting cotton. China Cotton, 40, 16–19. (in Chinese)
Executive Editor-in-Chief LI Shao-kun Managing editor WANG Ning
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RESEARCH ARTICLE
Effects of planting patterns on yield, quality, and defoliation in machine-harvested cotton WANG Fang-yong1, HAN Huan-yong1, LIN Hai1, CHEN Bing1, KONG Xian-hui1, NING Xin-zhu1, WANG Xu-wen1, YU Yu1, LIU Jing-de2 1
Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science/Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding, Ministry of Agriculture and Rural Affairs, Shihezi 832000, P.R.China 2 Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, P.R.China
Abstract The aim of this study was to elucidate the effects of different machine-harvested cotton-planting patterns on defoliation, yield, and fiber quality in cotton and to provide support for improving the quality of machine-harvested cotton. In the 2015 and 2016 growing seasons, the Xinluzao 45 (XLZ45) and Xinluzao 62 (XLZ62) cultivars, which are primarily cultivated in northern Xinjiang, were used as study materials. Conventional wide-narrow row (WNR), wide and ultra-narrow row (UNR), wide-row spacing with high density (HWR), and wide-row spacing with low density (LWR) planting patterns were used to assess the effects of planting patterns on defoliation, yield, and fiber quality. Compared with WNR, the seed cotton yields were significantly decreased by 2.06–5.48% for UNR and by 2.50–6.99% for LWR, respectively. The main cause of reduced yield was a reduction in bolls per unit area. The variation in HWR yield was –1.07–1.07% with reduced bolls per unit area and increased boll weight, thus demonstrating stable production. In terms of fiber quality indicators, the planting patterns only showed significant effects on the micronaire value, with wide-row spacing patterns showing an increase in the micronaire values. The defoliation and boll-opening results showed that the number of leaves and dried leaves in HWR was the lowest among the four planting patterns. Prior to the application of defoliating agent and before machine-harvesting, the numbers of leaves per individual plant in HWR were decreased by 14.45 and 25.00% on average, respectively, compared with WNR, while the number of leaves per unit area was decreased by 27.44 and 36.21% on average, respectively. The rates of boll-opening and defoliation in HWR were the highest. Specifically, the boll-opening rate before defoliation and machine-harvesting in HWR was 44.54 and 5.94% higher on average than in WNR, while the defoliation rate prior to machine-harvesting was 3.45% higher on average than in WNR. The numbers of ineffective defoliated leaves and leaf trash in HWR were the lowest, decreased by 33.40 and 32.43%, respectively, compared with WNR. In conclusion, the HWR planting pattern is associated with a high and stable yield, does not affect fiber quality, promotes early maturation,
Received 13 September, 2018 Accepted 10 December, 2018 WANG Fang-yong, E-mail: [email protected]; Correspondence YU Yu, Tel: +86-993-6683745, E-mail: [email protected]; LIU Jing-de, E-mail: [email protected] © 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)62604-3
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and can effectively decrease the amount of leaf trash in machine-picked seed cotton, and thus its use is able to improve the quality of machine-harvested cotton. Keywords: machine-harvested cotton, planting patterns, defoliation, yield, quality
1. Introduction Xinjiang is a major cotton-production region in China and includes large area where the high-quality cultivation technique of “early maturing, high-density, short plant height, and plastic mulch”, which suits the local light and temperature resources, is applied (ICR and CAAS 2013). The production characteristics of Xinjiang include a large cultivation area, a high degree of mechanization, and high yield. In recent years, cotton harvesting in Xinjiang has become mechanized, which has decreased production costs, improved harvest yields, and promoted the development of complete mechanization in the cotton industry (Yu et al. 2015). Compared with major local and overseas cotton cultivation regions, the growth season in Xinjiang is short. In order to improve cotton boll-opening rates and decrease trash content in machine-harvested seed cotton during harvesting, defoliating, and ripening agents are applied during the boll-opening period to promote opening of green bolls and defoliation (Cathey et al. 1982; Tian et al. 2004). Currently, the conventional planting pattern adopted by Xinjiang is the wide-narrow row (66 cm+10 cm) high-density pattern, which is associated with high and stable yields within large areas. However, this pattern results in a higher content of trash in the machine-picked seed cotton, thereby increasing the ginning and processing steps. Increasing these steps causes poorer fiber quality in terms of fiber length, fiber strength, and the proportion of short fibers (Xu and Xia 2009; Zhang 2013), and insufficient market competitiveness, resulting in major economic losses. Therefore, under the premise of not affecting yield and quality, improving effective defoliation is crucial for improving the quality of machine-harvested cotton and ensuring the healthy and sustainable development of cotton-production region in Xinjiang. Weather conditions such as light, temperature, and rainfall shortly after defoliant application are key factors affecting defoliation (Snipes and Cathey 1992; Mccarty 1995). Light deficiency for a long time will reduce defoliation efficiency (Brown and Hyer 1954), while strong light intensity and high temperature are favorable for increasing defoliation rate. Usually, planting density is a key feature that determines the size of the population and the spatial distribution of plants,
which will affect individual growth, canopy structure, and field meteorological factors. A dense canopy will decrease light, temperature, and airflow beneath the canopy (Marois et al. 2004). High density conditions will result in a field microclimate that is not favorable for defoliation (Buxton et al. 1977). Previous studies have shown that cotton yields are stable (Buxton et al. 1977; Bednarz et al. 2000; Siebert et al. 2006; Dai et al. 2015) and fiber qualities are consistent (Buxton et al. 1977; Siebert et al. 2006; Ren et al. 2013; Zhang et al. 2016) when obtained within a certain planting density range. Conversely, high or low density will significantly decrease yield (Dong et al. 2012; Dai et al. 2015; Zhang et al. 2016). It is evident that in the optimization of planting patterns for machine harvested cotton, adjusting plant spacing layout and rationally reducing the density can improve the temperature and light conditions for the population. This will not only affect yield and quality but also increase defoliation. In addition, some leaves will remain attached to the cotton plants after defoliation, which is an important source of leaf trash in machine-harvested cotton. The study of Li et al. (2016) found that plant spacing has greater effects on undetached dried leaves after defoliant application. Some papers have reported the effects of conventional wide-narrow row (WNR), wide and ultra-narrow row (UNR), and wide-row spacing with low density (LWR) on growth, yield, and defoliation in machine-harvested cotton in Xinjiang (Li et al. 2016, 2017; Xu et al. 2017). Our research group proposed another pattern, the wide-row spacing with high density (HWR) pattern, for picker-harvested cotton by increasing the spacing distance and reducing the plant distance, thereby rationally reducing planting density. However, few studies have investigated the effects of this planting pattern on cotton yield, quality, and defoliation. In this study, field experiments were carried out to study yield, quality, and defoliation in two cotton cultivars under different machine-harvested cotton-planting patterns in order to provide technical support for the improvement of machineharvested cotton quality in Xinjiang.
2. Materials and methods 2.1. Overview of the study site The experiment was carried out in 2015–2016 at the Cotton Research Institute (85°59´E, 44°18´N) of the Xinjiang
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Academy of Agricultural and Reclamation Science, China. Cotton has been planted in the study site continuously for many years, and a north-south orientation is used for planting. Drip-film irrigation is used and cotton stalks are returned to the field. The soil used was loamy grey desert soil and had 35.7 g kg−1 organic matter, 66.5 mg kg−1 available nitrogen, 27.6 mg kg−1 available phosphorus, 272.3 mg kg−1 available potassium, and a pH of 7.8.
2.2. Experimental design A split-plot design was used with the main plots constituting the cotton cultivars. The test cultivars were the Xinluzao 45 (XLZ45) and Xinluzao 62 (XLZ62) cultivars, which are early maturity and primarily planted in northern Xinjiang. Different planting patterns were applied to the subplots, including WNR (10-cm plant distance, 66 cm+10 cm of wide-narrow rows, and a planting density of 263 000 plants ha−1), UNR (10-cm plant distance, 72 cm+4 cm of wide-narrow rows, and a planting density of 263 000 plants ha−1), HWR (6 cm plant distance, 76 cm row spacing, and a planting density of 219 000 plants ha−1), and LWR (10 cm plant distance, 76 cm row spacing, and a planting density of 13 2000 plants ha−1). Each subplot was about 41 m2 (6.8 m×6.0 m) in 2015 and 34 m2 (6.8 m×5.0 m) in 2016, and three replications were conducted in each season. Spot sowing on the film was carried out on 23 April 2015 and 22 April 2016, and the plots were watered on the following day. A total of 75 kg ha−1 urea (46%) and 225 kg ha−1 triple superphosphate (46% P2O5) were applied per hectare as base fertilizer. During the growth phase, an additional 600 kg ha−1 urea and 300 kg ha−1 ammonium potassium phosphate compound fertilizers (10% N, 30% P2O5, and 24% K2O) were added. Manual topping was carried out on 1 July in both years. Artificial spraying of thidiazuron (180 mL ha−1) and ethrel (1 200 mL ha−1) was carried out before and after 5 September for defoliation and ripening. Other field management procedures were the same as used in local large-field high-yield cotton plantings.
2.3. Measurements Defoliation and boll-opening Representative sample points that exhibited uniform growth, had no missing seedlings, and were free from pathogens were selected from each subplot. At each point, five plants were continuously marked in each of three rows that were 76 cm apart, resulting in a total of 15 plants. The number of leaves per plant, total number of bolls, number of opened bolls, and number of ineffective defoliated leaves (leaves that did not fall to the ground as they remained attached to the cotton fiber or hanging on the stems and branches) before and after defoliant application
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were calculated. The plants were examined 1 day before application of defoliating and ripening agents and on days 7, 14, and 21 after application in 2015, and 1 day before application and days 10 and 20 after application in 2016. The boll-opening rate (proportion of opened bolls over total number of bolls), defoliation rate (percentage of fallen leaves over total number of leaves before defoliant application), number of leaves (for individual plants and per unit area), number of ineffective defoliated leaves per unit area, and the amount of leaf trash in the machine-harvested seed cotton (sum of number of leaves per unit area and number of ineffective defoliated leaves) were calculated. Yield and fiber quality Representative points within an area of 6.67 m2 were selected in each subplot prior to harvesting to investigate the number of bolls per unit area. Subsequently, cotton was manually harvested two times at the end of September and in mid-October. The opening bolls of the 15 cotton plants that were used for measuring defoliation and boll-opening status were collected and weighed before ginning. Following that, boll weight and lint percentage were determined. A sub-sample of lint was sent to assess the fiber quality using the high volume instrument (HVI) at the Supervision, Inspection and Test Center of Cotton Quality, Ministry of Agriculture and Rural Affairs, Anyang City, Henan Province, China.
2.4. Data statistics and analysis Microsoft Excel 2007 (Microsoft Corp., Albuquerque, NM, US) was used for data organization and the Data Processing System (DPS) 7.05 statistical package was used for analysis of variance (ANOVA) and multiple range tests.
3. Results 3.1. Effects of machine-harvested cotton planting patterns on cotton yield and yield components From Table 1, it is evident that the seed cotton yield trends in 2015 and 2016 were consistent. Compared with WNR, yield was significantly reduced in UNR and LWR, and averaged over all treatments showed yield reductions of 2.06–5.48% and 2.50–6.99%, respectively. The magnitude of reduction in XLZ45 was greater than that in XLZ62. The differences in yield between HWR and WNR were not significant, and the two cultivars showed reduced yields (XLZ45) and increased yields (XLZ62), with a variation in yield of –1.07 to 1.07%. The overall variation trends in lint yield and seed cotton yield were generally consistent. With regards to yield components (Table 1), the analysis results showed that compared with WNR, the number of bolls per unit area in UNR and HWR was reduced. There were
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no significant changes in boll weight or lint percentage in UNR, while boll weight was significantly increased in HWR. The number of bolls per unit area in LWR was significantly decreased in comparison to WNR, with a reduction of 9.63–14.68%, but boll weight was significantly increased by 6.16–9.19%. Lint percentage showed a decreasing trend in LWR. Overall, the number of bolls per unit area in XLZ45 was significantly higher than that in XLZ62, whereas boll weight and lint percentage were significantly lower than those in XLZ62. The ANOVA results (Table 1) showed that, with the exception of boll weight, which did not show any significant changes between the two years, the effects of cultivars and planting patterns on yield and yield components reached significant levels at P<0.05 and 0.01, and the effects of cultivars were greater than either year or planting patterns. The size of the interactions between year, cultivars, and planting patterns varied for different indicators.
and planting patterns did not significantly affect either cotton fiber length, fiber strength, length uniformity, or short fiber index, but did have a significant influence on micronaire values (Table 2). The micronaire value of XLZ45 was significantly lower than that of XLZ62. The micronaire values of HWR and LWR were significantly higher (P<0.01) than that in WNR, with an increase of 2.47–11.29% for LWR and an average increase of 4.82% for HWR. The ANOVA results (Table 2) showed that year, cultivars, planting patterns, or their interactions did not influence fiber length, and only year had significant (P<0.05) effects on fiber strength, length uniformity, and short fiber index. The effects of year, cultivars, and planting patterns on micronaire values were substantial, with the effects of year on cotton fiber quality being the greatest.
3.2. Effects of machine-harvested cotton planting patterns on cotton fiber quality indicators
The boll-opening rate in the various treatments increased with the number of days of defoliant and ripening agent application (Table 3). Planting pattern had a significant effect on the boll-opening rate. In the survey period before
Under the present experimental conditions, the cultivars
3.3. Effects of planting patterns on defoliation and boll-opening in cotton
Table 1 Effects of planting patterns on yield and yield components of machine-harvested cotton Year
Cultivar1)
Planting pattern2)
Boll no. m–2
2015
XLZ45
WNR UNR HWR LWR WNR UNR HWR LWR WNR UNR HWR LWR WNR UNR HWR LWR
123.8 a 117.1 b 116.5 b 108.4 c 101.8 d 98.6 d 99.4 d 92.0 e 151.9 a 145.8 b 142.1 c 129.6 d 119.8 e 115.8 f 117.9 ef 107.3 g
XLZ62
2016
XLZ45
XLZ62
F-value Year (Y) Cultivar (C) Planting pattern (P) Y×C Y×P C×P Y×C×P 1)
217.82** 1 553.33** 57.48** 55.59** 1.87 4.40* 0.63
Boll size (g boll–1) 4.56 f 4.61 f 4.80 e 4.91 de 5.03 cd 5.08 bc 5.16 b 5.34 a 4.46 e 4.40 e 4.72 d 4.87 c 5.25 b 5.28 b 5.38 b 5.72 a 2.51 585.41** 39.91** 50.75** 1.62 1.71 0.31
Lint percentage Seed cotton yield (%) (kg ha–1) 5 628.1 a 40.12 c 39.70 c 5 384.7 b 39.98 c 5 574.8 a 39.34 c 5 308.6 b 42.83 a 5 099.0 c 42.60 a 4 994.2 cd 42.65 a 5 114.7 c 41.69 b 4 893.7 d 41.54 b 6 774.5 a 41.37 bc 6 403.5 b 41.25 bc 6 701.8 a 40.71 c 6 301.2 bc 43.27 a 6 277.8 c 43.31 a 6 102.3 d 43.08 a 6 345.0 bc 43.04 a 6 120.6 d 40.87* 115.54** 4.36* 2.70 0.57 0.09 0.57
96.39* 367.92** 24.16** 7.92* 0.77 2.40 0.51
Lint yield (kg ha–1) 2 258.1 a 2 137.4 cd 2 229.0 ab 2 089.2 de 2 183.6 bc 2 126.9 cd 2 181.9 bc 2 039.1 e 2 813.8 a 2 649.6 c 2 765.0 ab 2 565.0 d 2 715.7 b 2 642.6 c 2 733.6 b 2 633.9 c 159.57** 63.05** 17.97** 99.41** 1.28 1.31 2.05
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. * and **, significances at 0.05 and 0.01 probability levels, respectively.
2)
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Table 2 Effects of planting patterns on the fiber quality of machine-harvested cotton Year
Cultivar1)
Planting pattern2)
2015
XLZ45
WNR UNR HWR LWR WNR UNR HWR LWR WNR UNR HWR LWR WNR UNR HWR LWR
XLZ62
2016
XLZ45
XLZ62
F-value Year (Y) Cultivar (C) Planting pattern (P) Y×C Y×P C×P Y×C×P
Fiber length (mm) 30.13 a 30.53 a 30.35 a 30.52 a 29.87 a 29.72 a 29.95 a 30.26 a 30.02 a 29.80 a 30.06 a 30.17 a 29.37 a 29.66 a 30.00 a 29.38 a
Strength (cN tex–1) 31.30 a 31.22 a 32.12 a 32.07 a 31.69 a 31.62 a 31.68 a 32.04 a 31.57 a 31.56 a 31.64 a 31.48 a 30.76 a 31.27 a 31.67 a 31.61 a
6.19 5.03 0.34 0.0022 0.24 0.10 0.49
22.65* 0.17 0.62 0.69 0.11 0.06 0.38
Micronaire 3.65 c 3.61 c 3.78 bc 3.74 bc 3.72 bc 3.86 b 4.05 a 4.14 a 3.85 d 3.82 d 4.05 c 4.23 ab 4.21 bc 4.15 bc 4.28 ab 4.38 a 38.09** 123.76** 9.07** 0.20 0.47 0.11 1.65
Uniformity (%) 84.79 a 84.99 a 84.91 a 85.44 a 85.03 a 84.96 a 85.23 a 84.86 a 86.82 a 86.42 a 86.41 a 86.66 a 87.13 a 87.31 a 87.09 a 87.01 a
Short fiber index (%) 7.27 a 7.01 a 6.95 a 7.01 a 7.02 a 7.13 a 6.93 a 7.08 a 6.83 a 6.81 a 6.82 a 6.74 a 6.73 a 6.68 a 6.76 a 6.89 a
26.87* 1.19 0.04 1.33 0.25 0.53 0.33
20.11* 0.09 0.36 0.01 0.46 0.65 0.39
1)
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. * and **, significances at 0.05 and 0.01 probability levels, respectively. 2)
Table 3 Dynamic changes in boll-opening rate under different planting patterns of cotton (%) Cultivar1) XLZ45
XLZ62
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
20153) 1 DBD 21.71 c 21.02 c 41.20 a 26.29 b 46.07 c 26.60 d 53.00 b 56.95 a
7 DAD 48.23 bc 43.80 c 63.44 a 51.99 b 67.75 b 53.02 c 74.52 a 76.48 a
14 DAD 68.40 b 70.41 b 75.41 a 69.02 b 86.42 b 84.90 b 92.51 a 86.89 ab
21 DAD 86.08 c 89.02 ab 90.96 a 87.86 bc 92.52 b 93.10 b 96.51 a 93.70 b
1 DBD 13.59 b 6.79 c 21.23 a 19.74 a 15.88 b 15.21 b 18.60 a 21.28 a
20163) 10 DAD 39.39 b 27.28 c 50.30 a 52.19 a 45.64 b 43.13 b 53.57 a 52.42 a
20 DAD 82.22 a 68.30 b 85.22 a 83.61 a 85.30 c 90.30 b 93.94 a 92.02 ab
1)
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DBD, days before defoliation application; DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
and after defoliant application, the boll-opening rate in the two equidistant row planting patterns (HWR and LWR) was both higher than that in WNR and UNR, respectively, and are thus favorable for promoting early maturation. This was particularly true for HWR, as it showed the best bollopening status. The boll-opening rate of HWR was on average 44.54 and 5.94% higher than that of WNR before defoliation and machine harvesting (around 20 days after defoliant application (DAD)), respectively. With regards
to cultivars, the boll-opening rates of XLZ62 in the same time period and same planting pattern were higher than those of XLZ45. The results of the direct effects of different planting patterns on cotton defoliation are indicated in Table 4. In the various survey periods in the two years, the defoliation rates in HWR were all higher and were consistent between both cultivars. In 2015, the defoliation rates in HWR on 7, 14, and 21 DAD were higher than those in WNR by
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1.74–2.62%, 3.14–3.20%, and 1.61–2.86%, respectively. In 2016, the defoliation rates in HWR on 10 and 20 DAD were higher than that in WNR by 7.31–9.92% and 1.72–5.31%, respectively; and higher by 3.45% on average than in WNR before machine harvesting. The defoliation rate of XLZ45 in LWR was lower than in WNR, while the opposite was observed in XLZ62. The number of leaves per plant and number of leaves per unit area before defoliant application represent the amount of leaves in individuals and populations. The number of dried leaves that do not fall off individuals and populations before harvesting constitutes an important source of leaf trash. The amount of leaves in individuals (Table 5) and population (Table 6) under different planting patterns differed significantly. In the various survey periods, the number of leaves in UNR was generally equivalent to that in WNR. The numbers of leaves and dried leaves in HWR were the lowest before defoliant spraying and before machine harvesting. Specifically, the number of leaves per plant decreased by 14.45 and 25.00% on average compared with WNR, respectively, while the number of leaves per unit area decreased by 27.44 and 36.21%, respectively. The number
of leaves in individual plants in LWR was the highest, and was significantly higher than the other patterns, while the number of leaves in the population was significantly lower than that in WNR and UNR. The variation trends of the two cultivars in the different planting patterns were consistent, in that as the defoliation period increased, the number of non-fallen leaves in individuals (Table 5) and populations (Table 6) gradually decreased. Ineffective defoliated leaves are another major source of leaf trash in machine-harvested cotton. As the duration of defoliant application increased, the number of leaves attached to the cotton plants gradually increased (Table 7). In the same survey period, the number of ineffective defoliated leaves per unit area in the two cultivars showed a trend of WNR>UNR>LWR>HWR. The number of ineffective defoliated leaves per unit area in HWR and LWR was significantly lower than that in WNR. Prior to machine picking, the number of ineffective defoliated leaves in HWR, which had the best defoliation results, was decreased by 33.40% on average in comparison to WNR. The sum of the number of leaves and ineffective defoliated leaves per unit area (i.e., non-defoliated leaves
Table 4 Changes in defoliation rate under different planting patterns of cotton (%) Cultivar1) XLZ45
XLZ62
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
20153) 14 DAD 83.57 b 81.57 c 86.77 a 84.15 b 78.34 b 78.02 b 81.48 a 82.65 a
7 DAD 60.78 ab 59.46 b 63.40 a 60.36 b 62.93 bc 61.11 c 64.67 ab 66.75 a
20163) 21 DAD 89.83 b 88.43 c 92.69 a 89.27 bc 88.87 b 89.83 ab 90.48 a 91.11 a
10 DAD 49.89 b 43.63 c 59.81 a 40.02 d 51.32 bc 49.52 c 58.63 a 53.10 b
20 DAD 80.21 ab 79.63 b 81.93 a 79.90 b 79.76 c 81.47 b 85.07 a 83.72 a
1)
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
Table 5 Changes in leaf number per plant under different planting patterns of cotton (leaves/plant) Cultivar1) XLZ45
XLZ62
1)
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
20153) 1 DBD 20.93 b 20.47 b 17.87 c 28.60 a 15.57 b 14.73 b 13.70 c 22.90 a
7 DAD 8.20 b 8.30 b 6.53 c 11.33 a 5.77 b 5.73 b 4.83 c 7.60 a
14 DAD 3.43 c 3.77 b 2.37 d 4.53 a 3.37 b 3.23 b 2.53 c 3.97 a
21 DAD 2.13 c 2.37 b 1.30 d 3.07 a 1.73 b 1.50 b 1.30 c 2.03 a
1 DBD 28.60 b 28.20 b 23.67 c 40.47 a 24.40 b 24.87 b 21.00 c 36.13 a
20163) 10 DAD 14.33 c 15.87 b 11.13 d 24.27 a 11.87 b 12.53 b 10.33 c 16.93 a
20 DAD 5.67 b 5.73 b 5.00 c 8.13 a 4.93 b 4.60 b 3.73 c 5.87 a
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DBD, days before defoliation application; DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
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and defoliated leaves that did not fall to the ground) can be used to measure the quantity of leaf trash in the various planting patterns. From Table 8, we can see that the amount of leaf trash per unit area in HWR and LWR was significantly lower than that in WNR, with leaf impurity decreasing by 32.43 and 29.56%, respectively, before machine harvesting. The two cultivars showed the same trend.
4. Discussion 4.1. Effects of planting patterns on defoliation and boll-opening in cotton Machine-harvested cotton in Xinjiang requires defoliant and ripening agent application during the boll-opening
Table 6 Changes in leaf number per unit soil area under different planting patterns of cotton (leaves m−2) Cultivar1) XLZ45
XLZ62
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
1 DBD 440.51 a 427.93 a 316.74 b 322.95 b 327.65 a 298.92 b 232.49 d 257.14 c
20153) 7 DAD 14 DAD 172.57 a 72.25 a 173.35 a 78.83 a 115.73 c 41.88 c 127.94 b 51.19 b 121.34 a 70.86 a 116.30 a 65.59 a 82.14 b 43.00 b 85.30 b 44.53 b
21 DAD 44.89 b 49.47 a 23.02 d 34.62 c 36.50 a 30.42 b 22.13 c 22.82 c
1 DBD 526.71 a 501.32 b 380.05 d 400.32 c 446.52 b 479.61 a 335.96 d 368.06 c
20163) 10 DAD 263.65 b 282.48 a 178.64 d 240.04 c 216.97 b 241.97 a 165.39 c 172.54 c
20 DAD 104.09 a 102.08 a 80.23 b 80.42 b 90.21 a 88.76 a 59.71 b 59.84 b
1)
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DBD, days before defoliation application; DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
Table 7 Effects of planting patterns on the number of non-effective defoliated leaves of cotton (leaves m−2) Cultivar1) XLZ45
XLZ62
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
7 DAD 79.96 a 78.11 a 58.53 b 53.85 b 75.82 a 71.03 a 46.35 c 55.42 b
20153) 14 DAD 87.68 a 87.03 a 62.59 b 62.50 b 89.11 a 77.78 b 57.73 c 58.03 c
20163) 21 DAD 101.71 a 98.99 a 77.41 b 77.48 b 93.30 a 90.64 a 71.38 b 64.75 c
10 DAD 85.83 a 71.20 b 42.82 d 48.76 c 63.35 a 60.41 a 40.51 c 47.54 b
20 DAD 89.59 a 78.16 b 46.00 d 52.74 c 68.23 a 63.07 b 42.61 d 49.62 c
1)
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
Table 8 Effects of planting patterns on the amount of leaf trash of machine-harvested cotton (leaves m−2) Cultivar1) XLZ45
XLZ62
1)
Planting pattern2) WNR UNR HWR LWR WNR UNR HWR LWR
7 DAD 252.53 a 251.47 a 174.26 b 181.79 b 197.16 a 187.33 a 128.49 c 140.72 b
20153) 14 DAD 159.93 a 165.85 a 104.47 c 113.69 b 159.97 a 143.37 b 100.74 c 102.56 c
20163) 21 DAD 146.60 a 148.46 a 100.43 c 112.10 b 129.80 a 121.06 a 93.51 b 87.57 b
10 DAD 349.47 a 353.68 a 221.46 c 288.80 b 280.31 b 302.37 a 205.89 c 220.08 c
20 DAD 193.68 a 180.24 b 126.23 d 133.16 c 158.45 a 151.83 a 102.32 c 109.46 b
XLZ45, Xinluzao 45; XLZ62, Xinluzao 62. WNR, conventional wide-narrow row planting pattern; UNR, wide and ultra-narrow row planting pattern; HWR, wide row planting pattern with high density; LWR, wide row planting pattern with low density. 3) DAD, days after defoliation application. Means within a column followed by different letters are significantly different at the 0.05 probability level in the same year. 2)
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phase. The boll-opening rate is an important indicator for determining when chemicals should be applied (Williamson and Riley 1961), as chemical application under high bollopening conditions has the lowest effect on fiber maturation in the upper cotton balls, which is favorable for increasing the yield (Faircloth et al. 2004) and the quality of machineharvested cotton (Snipes and Baskin 1994). Li et al. (2016) found that plant spacing has strong effects on boll-opening, as the boll-opening rate in equidistant row spacing and low density, and equidistant row spacing and high density, before chemical application were significantly higher than that in WNR pattern. The results of this study showed that the boll-opening rates of two equidistant row-spacing patterns (HWR and LWR) were significantly higher than that in WNR (Table 3) prior to defoliant application, indicating that equidistant row-spacing patterns promote cotton growth and development and thus cotton yield. This has importance in promoting early maturation in cotton production in Xinjiang. Improving defoliation results and reducing the quantity of trash in machine-harvested seed cotton (Xu and Xia 2009; Zhang 2013) are important measures for improving the quality of machine-harvested cotton. Decreasing the amount of trash can reduce cleaning and processing steps and decrease damage to cotton fibers (Xu and Xia 2009; Xu et al. 2015). Leaf trash constitutes a major component of the trash in machine-harvested cotton (Xie et al. 2014), and thus reducing the quantity of leaf trash in machineharvested seed cotton is extremely important. The major sources of leaf trash are non-defoliated dried leaves and defoliated leaves that are attached to the cotton plants (ineffective defoliated leaves). Our study found that the number of leaves per individual plant and per unit area in HWR was the lowest, i.e., “smaller individual, smaller population”. This pattern was associated with the highest defoliation rate and the lowest amount of dried leaves and ineffective defoliated leaves, as well as the lowest amount of leaf trash. The number of leaves per individual in LWR was the highest, while the number of leaves per population was significantly lower than that in WNR, i.e., “larger individual, smaller population”. The defoliation rates of the two cultivars in LWR showed an opposite trend to that of WNR and had lower amounts of ineffective defoliated leaves and leaf trash. Previous studies have also noted that in equidistant row-spacing planting patterns, the defoliation rate is higher, while the amount of hanging and dried leaves is lower (Li et al. 2016). Therefore, optimizing row spacing and rationally decreasing planting density can improve the field microclimate and create a canopy structure that is suitable for defoliation, thereby increasing defoliation while concurrently decreasing the amount of leaf trash in machineharvested cotton. This allows for a reduction in cleaning and
processing steps and decreased damage to cotton fibers.
4.2. Effects of planting patterns on cotton yield and quality The number of bolls per unit area, boll weight, and lint percentage are three important yield components of cotton. Among these three components, lint percentage is mainly determined by the genetic characteristics of the cultivar; boll weight is affected by the dual effects of cultivar genetic characteristics and environment; while the number of bolls per unit area, which has the highest plasticity, is the key to obtaining high and stable yield in planting techniques in which the relationship between populations and individuals are adjusted. Previous studies have found that boll weight decreases with increasing density (Baker 1976; Buxton et al. 1977; Bednarz et al. 2000). Yield stability was achieved across a range of plant densities by regulating boll density and boll weight (Buxton et al. 1977; Bednarz et al. 2000; Siebert et al. 2006; Dai et al. 2015). Brodrick et al. (2010) found that, compared with conventional wide spacing planting patterns, boll weight is reduced in ultra-narrow row system in which density is increased, but the increase in boll number per unit area leads to increased yield. Our study indicated that HWR and WNR showed similar yields with good yield stability, while UNR and LWR showed significantly reduced yields (Table 1). With regards to yield components, it is clear that stable yield was achieved in HWR through increased boll weight, while the number of bolls in the population was decreased. The decrease of boll number was attributed to the rationally reduced planting density, and the increase of boll weight might be due to the optimized field microclimate (e.g., temperature and light) and the improved boll-opening rate before defoliant and ripening agent application. This shows that an adjustment in row spacing and rational reduction in density will not affect yield. The reduced yield in UNR is due to the reduction in the number of bolls per unit area, and it is obvious that this pattern is not suitable for high-density cultivation. For the LWR pattern, although boll weight was the highest, the number of bolls per unit area was the lowest, resulting in the greatest reduction in yield. This indicates that drastic reductions in density will significantly reduce yield (Table 1). Previous studies have found that planting density within a certain range does not have any significant effects on cotton fiber quality (Brodrick et al. 2010; Lou et al. 2010; Liu et al. 2016; Zhang et al. 2016), while other researchers found that micronaire values will decrease at high planting densities (Baker 1976; Bednarz et al. 2005). The results of this study showed that fiber quality indicators were not affected by planting patterns, with the exception of micronaire value
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(Table 2). The micronaire values of the two equidistant row-spacing patterns in which planting density was reduced were significantly increased compared with WNR pattern.
5. Conclusion This study showed that row spacing adjustment and appropriate reduction in density could be used to optimize the planting patterns for machine-harvested cotton. HWR achieved high and stable yields by increasing boll weight under conditions in which the number of bolls per unit area was reduced, and had no negative effects on cotton fiber quality. With regard to defoliation and boll-opening, this pattern can result in significantly earlier boll-opening and is favorable for promoting ripening, has good defoliation results, and has fewer dried leaves and ineffective defoliated leaves. This can effectively reduce the source of leaf trash in machine-harvested cotton and has an important role in improving the quality of machine-picked cotton.
Acknowledgements This work was supported by the National Natural Science Foundation of China (31560342), the Major Science and Technology Projects of Xinjiang Production and Construction Corps, China (2016AA001-2), and the National Key Research and Development Program of China (2017YFD0201900).
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Executive Editor-in-Chief LI Shao-kun Managing editor WANG Ning