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Water-induced variation in yield and quality can be explained by altered yield component contributions in field-grown cotton
Field Crops Research 224 (2018) 139–147
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Water-induced variation in yield and quality can be explained by altered yield component contributions in field-grown cotton
T
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Wei Hua,b, , John L. Sniderb, Haimiao Wanga,b, Zhiguo Zhoua, Daryl R. Chastainb, Jared Whitakerb, Calvin D. Perryc, Freddie M. Bourlandd a
College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu Province, 210095, PR China Department of Crop and Soil Sciences, University of Georgia, 115 Coastal Way, Tifton, GA, 31794, United States c C.M Stripling Irrigation Research Park, University of Georgia, Camilla, GA, 31730, United States d Northeast Research & Extension Center, University of Arkansas, Keiser, AR, 72351, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: Drought Cotton Yield Fiber quality Yield components
Novel yield component traits and fiber quality indices have been recently developed by breeders to screen for desirable cotton (Gossypium hirsutum L.) lines, but assessing these components in response to water-induced yield variability has received little attention. We investigated the hypothesis that differential sensitivities of wholecrop and intra-boll yield components to drought will largely explain water-induced yield and fiber quality variation in cotton. To test this hypothesis, two cotton cultivars were grown in the field under five contrasting irrigation regimes during the 2013 and 2014 growing seasons near Camilla, GA. Measurements included predawn leaf water potential (ΨPD) throughout the growing season and extensive yield component and fiber quality assessments at the end of the season. Water-induced yield variability was primarily associated with ΨPD at the flowering and boll development phase of crop growth. Boll density (bolls ha−1) was the dominant driver of drought-induced yield loss, but reduced boll mass and seed number per boll also contributed somewhat to yield loss. By comparison, increased drought severity decreased fiber density but increased individual fiber mass, producing a peak in total fiber weight per seed at a −0.7 MPa ΨPD irrigation threshold and increasing lint percentage in stressed treatments. Thus, the negative impacts of drought on overall boll mass and seed number per boll are partially offset by increased dry matter partitioning toward fiber growth. Fiber length declined with increased drought severity, whereas fiber micronaire increased in the most severely stressed treatments. This indicates that increasing drought severity during flowering and boll development decreases the number of individual fibers per seed and their final length, but their thickness is increased. The end result is a decline in the overall fiber quality index (Q-score) under drought stress.
1. Introduction Water deficit substantially limits lint yield and fiber quality in cotton (Gossypium hirsutum L.), even in production regions characterized by high annual rainfall (Chastain et al., 2014, 2016b; Pettigrew, 2004). This is especially true for cotton production regions in the Coastal Plain of the southeastern United States, where the coarse textured soils are characterized by limited water holding capacity, resulting in episodic drought events during the growing season (Ritchie et al., 2009). Drought limits yield by disrupting a number of underlying physiological processes. For example, soil water declines result in decreased cell turgor, which limits total source strength by inhibiting leaf area development and photosynthetic efficiency of the canopy (Chastain et al., 2014; Krieg and Sung, 1986; Pace et al., 1999;
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Pettigrew, 2004). This decline in source strength decreases the capacity to support a developing boll load, resulting in low fruit retention under drought stress (Krieg and Sung, 1986; Lokhande and Reddy, 2014; Snider and Oosterhuis, 2015). In agreement with these findings, some studies have shown that water-deficit significantly inhibited plant biomass production (Wang et al., 2016a; Zhang et al., 2017) and biomass accumulation of reproductive organs (Da Costa and Cothren, 2011). As a result, significant declines in both seedcotton yield and lint yield are observed under water-deficit conditions (Dağdelen et al., 2009; Wang et al., 2016b). Other scientists analyzed yield components to explain the underlying declines in cotton yield under drought stress and attributed them to lower individual boll weight, decreased boll numbers per plant, or lower lint percentage (Pettigrew, 2004; Sharma et al., 2015; Wang
Corresponding author at: College of Agriculture, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu, 210095, PR China. E-mail address: [email protected] (W. Hu).
https://doi.org/10.1016/j.fcr.2018.05.013 Received 18 March 2018; Received in revised form 16 May 2018; Accepted 17 May 2018 Available online 24 May 2018 0378-4290/ © 2018 Elsevier B.V. All rights reserved.
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overall fiber quality based on fiber length, strength and uniformity, and micronaire will be decreased by drought stress as a result of intra-boll yield component alterations. The objectives of the present study were to: 1) determine the effects of different degrees of drought stress on boll density, boll weight and lint percentage, and these novel yield component traits and to quantify the contributions of different yield components to yield loss; 2) estimate the effects of drought stress on fiber quality parameters, with a focus on the Q-score (obtained by normalizing HVI fiber length, strength, micronaire, and uniformity into an overall quality index).
et al., 2016b; Zhang et al., 2016; Zahoor et al., 2017). The product of the aforementioned components is lint yield. However, some breeders have utilized novel yield component traits that reflect fiber quantity, density, and weight to screen for high yielding cotton lines (Ali and Awan, 2009; Groves and Bourland, 2010). Because cotton fibers are unicellular trichromes protruding from the seed coat surface, Smith and Coyle (1997) reported that breeding for large seed surface area and number of seeds per boll could provide more surface area to increase lint yield (Culp and Harrell, 1973). Moreover, increased fiber density is a potentially useful selection criterion for improving fiber quantity per seed to increase lint yield (Groves et al., 2016). Cook (1908) suggested that lint index (lint mass per 100 seeds), could be used as a preferred selection tool for increased lint yield by increasing the total lint mass per seed (Groves et al., 2016), which is a function of average fiber number per seed and average individual fiber weight (Ali and Awan, 2009). Although improving these traits has been shown to increase lint yield through breeding efforts (Groves et al., 2016) and these yield component characteristics could be assessed to provide a detailed explanation of the underlying limitations to yield under water deficit, this information is extremely limited for field-grown cotton. Drought, depending upon severity, also negatively affects cotton fiber quality, including fiber length (Zheng et al., 2014), uniformity ratio (Niu et al., 2016), fiber strength (Dabbert et al., 2017) and micronaire (Wang et al., 2016b). However, different cotton fiber processing methods have different fiber quality requirements, and no single high volume instrument (HVI) parameter can be applied to all situations. Bourland et al. (2010) developed a fiber quality index called quality score (Q-score). Q-score is based on up to six HVI fiber parameters, and the user can apply specific weightings to each (Bourland et al., 2010). Since different drought levels can have different effects on fiber traits (Wang et al., 2016a), Q-score should be able to evaluate the effect of drought on overall fiber quality. The impact of drought on yield components and fiber quality in cotton differs from study to study (Snider and Oosterhuis, 2015), likely because irrigation treatments imposed under field conditions can vary greatly in drought severity due to environmental influence. Most studies define stress levels or irrigation treatments under field conditions using water balance approaches (Basal et al., 2009; Dağdelen et al., 2009). However, plant water status in the field can be affected by soil physical characteristics (Rab et al., 2009), atmospheric demand (Schulze et al., 1987; Jackson et al., 1981), and plant factors such as effective rooting depth and leaf area development (Snider and Chastain, 2016). Direct measures of leaf water potential integrate all of these factors and provide an accurate indicator of the need for irrigation (Grimes and Yamada, 1982). Predawn leaf potential (ΨPD) is a direct index of plant water status that has been strongly correlated with growth and physiological parameters such as leaf area, plant height, stomatal conductance, and photosynthetic rate (Jordan, 1970; McMichael et al., 1973; Turner et al., 1986; Jones, 2007; Snider et al., 2015; Chastain et al. 2016a). Previous work conducted in our laboratory quantified lint yield and water use efficiency (WUE) responses for cotton grown under five different irrigation treatments employing a combination of conventional and ΨPD-based irrigation scheduling thresholds during two growing seasons (Chastain et al., 2016b). Regardless of treatment, ΨPD was routinely measured for all irrigation treatments throughout the growing season, and ΨPD thresholds for optimal yield and WUE were identified. Because ΨPD was monitored near-continuously, the level of drought stress experienced by plants at key growth stages could be determined and combined with detailed yield component and fiber quality assessments to identify how underlying processes respond to increases in drought severity. We hypothesized that 1) apart from boll density, boll weight and lint percentage, the novel yield component traits mentioned above (seed index, seeds boll−1, seed surface area, lint weight seed−1, fibers seed−1, fiber density, weight fiber−1 etc.) also will be decreased by drought stress and contribute to yield loss under drought stress; 2)
2. Materials and methods 2.1. Plant material and study site To address the impact of crop water status on yield components and fiber quality in cotton, a field study was conducted at C.M. Stripling Irrigation Research Park (31°16′48″N, 84°17′29″W) near Camilla Georgia during the 2013 and 2014 growing seasons. The soil type at this site is a Lucy loamy sand (loamy, kaolinitic, thermic Arenic Kandiudults). Seeds of two commercial cotton varieties (PHY 499 WRF [Dow AgroSciences]) and FM 1944 GLB2 [Bayer CropScience]) were planted on May 6, 2013 and June 2, 2014. Practices such as tillage, row spacing, seeding rate, and planting depth were conducted according to University of Georgia Extension Service recommendations (Collins et al., 2014), and were described in detail by our previous paper (Chastain et al., 2016b). Individual plots were 6 rows wide × 40 m long with two buffer rows between adjacent plots. Immediately after planting, pre-emergence herbicides were applied to the soil surface, and irrigation was uniformly applied to the entire field using overhead sprinkler irrigation delivered via center-pivot to ensure herbicide activation and to prevent yield limitations due to poor stand establishment. Stand counts were conducted approximately two weeks after planting, and in-row plant densities were above levels needed to maximize yield (Collins et al., 2014). Supplemental irrigation was uniformly applied over the entire study until the first floral buds were visible to the naked eye (squaring; SQ). At this time, irrigation treatments (described below) were initiated. Crop management, including fertility and pest control, were conducted according to recommended practices (Collins et al., 2014). 2.2. Irrigation treatments The study was arranged as a split plot, randomized complete block design with four replications, where irrigation treatment was the whole plot factor and cultivar was the subplot factor. At squaring, five unique irrigation treatments were imposed. T1: Irrigation scheduled according to a well-established water balance approach referred to as the “checkbook” method (Collins et al., 2014), where supplemental irrigation is provided to meet crop growth stage-specific water requirements after accounting for weekly rainfall. T2-T4: Irrigation scheduled using predawn leaf water potential (ΨPD) values to trigger an irrigation event. Using this approach, ΨPD (measurements described in Section 2.3) was measured every two days during the irrigation treatment period, and when ΨPD was equal to or below predefined thresholds (T2 = −0.5 MPa; T3 = −0.7 MPa, and T4 = −0.9 MPa), water was delivered at 1/3 of total weekly checkbook recommended amounts since irrigation decisions were made three days per week. T5: No supplemental irrigation was provided during the irrigation treatment period. The ΨPD thresholds for T2–T4 were selected because these values correspond to plant water status levels shown previously to differentially impact net photosynthesis (Snider et al., 2015). Irrigation water was delivered using subsurface drip tape positioned at a 30 cm depth in alternating row middles. Weather data were obtained from an on-site weather station as part of the Georgia Automated Environmental Monitoring Network (www.georgiaweather.net). The minimum and 140
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Finally, contributions of lint weight per seed to yield loss (LLW) for any given treatment relative to the control were calculated as:
maximum daily temperature and precipitation patterns during the 2013 and 2014 growing seasons were reported previously in Chastain et al. (2016b). In 2013, total rainfall from planting to defoliation was 67 cm, ∼21 cm greater than what is recommended by the Checkbook approach (Collins et al., 2014). In contrast, the 2014 growing season total rainfall was 35 cm, ∼11 cm less than Checkbook total recommendations (Chastain et al., 2016b).
LLW = (YT1 – LBD – LSN) × (1 – LWT/LWT1)
(4)
Similarly, the lint yield could be considered as the product of boll density, seedcotton weight per boll, and lint percentage. Thus, the contribution of boll density, seedcotton weight per boll, and lint percentage were also calculated according to above path model. A 0.45 kg fiber sample was taken to the USDA Classing Office in Macon, GA for HVI assessments of fiber quality. In the current study, the fiber parameters of interest were HVI fiber length (upper half mean length), strength (g tex−1), micronaire, and uniformity (%). From the yield component data obtained above and HVI fiber quality parameters, the estimated average weight of an individual cotton fiber (Weight fiber−1) was calculated as HVI fiber length (in inches) × uniformity × (micronaire/1.0 × 106). Fibers Seed−1 was determined by dividing lint weight seed−1 by individual fiber weight, and fiber density was calculated by dividing the number of fibers per seed by SSA. Descriptions of the yield component estimates noted above can be found in Bourland (2013) and Groves et al. (2016). In addition to the individual fiber quality parameters noted previously, Q-Score was assigned to each plot according to Bourland et al. (2010), where the fiber quality parameters length, micronaire, uniformity, and strength were first normalized to values ranging from 0 to 1 and subsequently assigned weighted functions of 50 (length), 25 (micronaire), 15 (uniformity), and 10% (strength).
2.3. Water potential measurements Predawn water potential was measured three days per week from the start of squaring/irrigation treatment initiation until the first open boll was observed in the latest maturing treatment. Measurements were conducted between 04:00 and 06:00 h using a Scholander pressure chamber by severing the petiole of the uppermost fully expanded mainstem leaf (fourth unfurled leaf node below the plant terminal), sealing the petiole in a compression gasket in the chamber head, sealing the leaf blade in the chamber, and pressurizing at a rate of 0.1 MPa s−1 until xylem sap was initially visible at the cut surface of the stem. Water potential values are recorded in negative MPa. To quantify the impact of irrigation treatment on plant water status at key growth stages, ΨPD was averaged for each plot for three different phenological stages of crop development. 1) ΨSQ: average predawn water potential from the start of squaring until first flower. 2) ΨEF: average predawn water potential during the first two weeks of flowering. 3) ΨBD: average predawn leaf water potential from peak bloom (∼2 weeks after first flower) until irrigation termination (first open boll). 2.4. Yield, yield components, and fiber quality
2.5. Statistical analysis
While the impact of plant-based irrigation scheduling on yield and water use efficiency for mechanically harvested plots was reported previously (Chastain et al., 2016b), the current study sought to define the impact of crop water status at key growth stages on yield components and fiber quality. To this end, at crop maturity and following defoliation, all the harvestable bolls within either a one (2013) or two (2014) meter length of row were counted and hand harvested for each plot. Subsequently, samples were ginned to obtain lint and seed weight and the mass of 500 fuzzy seed was determined to calculate average seed mass. From these data, the following parameters were estimated: lint yield (kg ha−1), bolls ha−1, seedcotton weight boll−1 (g), lint percentage, seed index (g per 100 seed), and seed number boll−1. Using the relationship defined in Groves and Bourland (2010), average seed surface area (SSA) was estimated from seed index of fuzzy cotton seed as SSA = 35.74 + 6.59 × seed index. To attribute yield losses (relative to the control treatment; T1) to particular yield components, a simple path model comparable to the approach utilized by Earl and Davis (2003) was employed. We first considered lint yield a product of boll density, seed number per boll, and lint weight per seed according to the following equation.
Because irrigation events were applied to entire treatments (i.e. ΨPD thresholds were triggered based on treatment averages), rather than to individual plots or to individual cultivars separately within a treatment, the average ΨPD for each growth stage and treatment is provided in
Y = BD × SN × LW
Table 1 Average predawn leaf water potential values for three different phenological stages of crop development for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons under five different irrigation treatments. Values are means (n = 8) and those sharing a common letter within the same column were not significantly different (P < 0.05). Year
(1)
ΨEF
ΨBD
2013
T1 T2 T3 T4 T5
2014
T1 T2 T3 T4 T5
—— −0.45a −0.44a −0.42a −0.44a P = 0.258 −0.40a −0.42ab −0.43bc −0.41ab −0.45c P = 0.009
−0.35a −0.35a −0.35a −0.35a −0.35a P = 0.992 −0.55a −0.56a −0.72b −0.82c −0.81c P < 0.001
−0.44a −0.45a −0.46a −0.48a −0.47a P = 0.328 −0.49a −0.52a −0.64b −0.68b −0.82c P < 0.001
ΨSQ, average predawn water potential from the start of squaring until first flower; ΨEF, average predawn water potential during the first two weeks of flowering; ΨBD, average predawn leaf water potential from peak bloom (∼2 weeks after first flower) until irrigation termination (first open boll). T1: Irrigation scheduled according to a well-established water balance approach referred to as the “checkbook” method (Collins et al. 2014). T2-T4: Irrigation scheduled using predawn leaf water potential (ΨPD) values to trigger an irrigation event. When ΨPD was equal to or below predefined thresholds (T2 = −0.5 MPa; T3 = −0.7 MPa, and T4 = −0.9 MPa), water was delivered at 1/3 of total weekly checkbook recommended amounts. T5: No supplemental irrigation was provided during the irrigation treatment period. —— means no data for this growth stage and treatment.
(2)
Yield losses accounted for by seed number per boll (LSN) for each treatment relative to the control were calculated as: LSN = (YT1 – LBD) × (1 - SNT/SNT1)
ΨSQ (MPa)
where Y is lint yield, and BD, SN and LW represent boll density, seed number per boll, and lint weight per seed, respectively. We assume that boll density is affected by drought first, followed by seed number per boll, and lint weight per seed based on the results that boll number is the major determinant for lint yield under different irrigation levels reported by Sharma et al. (2015). Yield losses attributable to boll density (LBD) for any given treatment (T) relative to the control (T1) can be calculated according to the following equation. LBD = YT1 × (1 - BDT/BDT1)
Treatment
(3) 141
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Table 1. The effect of irrigation treatment within a given year was quantified using a one-way analysis of variance (ANOVA) with block considered a random effect and irrigation treatment a fixed effect. The effects of irrigation treatment, cultivar, and irrigation treatment × cultivar interaction on all other parameters of interest within a given year were assessed using a mixed effects two-way ANOVA and restricted maximum likelihood (REML) method with means separation performed using LSD post hoc analysis at 0.05 alpha level. While irrigation treatment and cultivar main effects were observed for many different parameters of interest, interactions between these two effects were rarely observed, and will not be discussed further. To quantify associations between yield, water status at key growth stages, yield components, and fiber quality parameters, all parameters of interest were subjected to pairwise correlation analysis to determine the correlation coefficient and probability level for each pair of dependent variables. Since 2013 was a wet year due to high rainfall (Chastain et al., 2016b), no significant differences were observed for ΨPD among all treatments for all growth stages. In addition, interactions of cultivar and irrigation treatment were rarely observed for measured parameters. Thus, correlation analysis only evaluated effects of irrigation treatments in 2014. For brevity, only parameters significantly (P < 0.05) correlated with lint yield were presented in the table showing the results of correlation analysis.
Table 2 Two-way ANOVA results for irrigation treatment, cultivar, and irrigation treatment × cultivar interaction on yield and yield components for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Significant main effects or interaction effects are indicated in bold. Year
2013
2014
3. Results 3.1. Growth stage-dependent variation in plant water status
Parameter
Lint yield Bolls ha−1 Seedcotton weight boll−1 Lint percentage Seed index Seeds boll−1 Seed surface area Lint weight seed−1 Fibers seed−1 Fiber density Weight fiber−1 Lint yield Bolls ha−1 Seedcotton weight boll−1 Lint percentage Seed index Seeds boll−1 Seed surface area Lint weight seed−1 Fibers seed−1 Fiber density Weight fiber−1
P Value Irrigation
Cultivar
Irrigation × cultivar
0.0450* 0.0446* 0.4614 0.7742 0.1091 0.8158 0.1091 0.1861 0.0254 0.0665 0.0303* < .0001 < .0001 0.0016 < .0001 < .0001 0.0055 < .0001 0.0052 0.0006 0.0062 < .0001
0.2692 0.3366 0.0125* < .0001 < .0001 0.2485 < .0001 0.0501 0.0621 < .0001 0.8713 0.2722 0.7277 < .0001 < .0001 < .0001 0.2598 < .0001 0.0130 0.0758 < .0001 < .0001
0.5884 0.5743 0.5801 0.4556 0.8944 0.3547 0.8944 0.2918 0.3283 0.4494 0.2372 0.4595 0.4585 0.6664 0.2949 0.5243 0.7501 0.5243 0.9646 0.1107 0.0371 0.0569
* Although the P value for this effect is less than 0.05, it should be noted that the P value for the whole-model F test was > 0.05 for this particular parameter, preventing post-hoc analysis of this main effect.
In 2013, no significant irrigation treatment differences were observed for average predawn leaf water potential (ΨPD) at any of the growth stages (Table 1). Average predawn leaf water potential exceeded -0.5 MPa in each treatment. In 2014, ΨSQ, ΨEF and ΨBD were significantly affected by irrigation treatments. During squaring, predawn water potential ranged from −0.40 for T1 to −0.45 MPa for T5. For all growth stages, T1 and T2 were similar and had the highest water potentials relative to the other treatments. ΨEF values of treatments T3, T4 and T5 were 30%, 48% and 46% lower than ΨEF of T1. ΨBD values for treatments T3, T4 and T5 were 31%, 39% and 67% lower than ΨBD of T1.
first and then decreased, with T3 treatment having the greatest value. A significant cultivar effect was observed for lint percentage, seed index, SSA, and fiber density with P values < 0.0001 during the 2013 growing season (Table 2). Similarly, there was a significant cultivar effect for seedcotton weight per boll, lint percentage, seed index, SSA, lint weight per seed, fiber density, and weight per fiber during the 2014 growing season, with P values ranging from < 0.0001 to 0.0130. Even though lint yield did not vary significantly between the two cultivars in this study, lint percentage and fiber density were significantly higher for PHY499 than FM1944 (Tables 5 and 6). In contrast, seed index and SSA were markedly lower for cultivar PHY499 than cultivar FM1944. Interestingly, just during the 2014 growing season, cultivar PHY499 had 9.5%, 2.6%, and 6.8% lower seedcotton weight per boll, lint weight per seed, and weight per fiber than cultivar FM1944.
3.2. Yield components Irrigation treatment × cultivar interaction did not affect any yield component in either year, except fiber density during the 2014 growing season (Table 2). Lint yield and all yield components except fibers seed−1 did not respond significantly to irrigation treatments in 2013, primarily because the whole-model F statistic was not significant in most instances. However, irrigation treatments significantly affected lint yield and all yield component parameters with P values ranging from < 0.0001 to 0.0062 during the 2014 growing season. T1 and T2 treatments had similar lint yield in 2014, but lint yield for treatments T3, T4 and T5 were 12%, 22% and 44% lower than that of the T1 treatment (Table 3). Regarding yield components, treatments T1, T2 and T3 had similar values for bolls per hectare, seeds per boll and fibers per seed in 2014 (except seeds per boll in T3, Tables 3 and 4), but bolls per hectare, seeds per boll and fibers per seed were significantly lower in T4 and T5 treatments than the T1 treatment. For seedcotton weight per boll, seed index and SSA, no differences were measured between T1, T2, T3 and T4 treatments, but T5 treatment had the lowest values relative to other treatments during the 2014 growing season, with declines of 10.8%, 4.8% and 3.1% relative to the T1 treatment, respectively. However, lint percentage was obviously increased for the most severely stressed treatments (T4 and T5) relative to the T1 treatment, and individual fiber weight was pronouncedly increased for treatments T3, T4 and T5 relative to treatments T1 and T2 in 2014. In addition, as the ΨPD decreased, the total lint weight per seed increased
3.3. Fiber quality For all of the fiber quality parameters, irrigation treatment × cultivar interaction was only significant for micronaire in 2014 (Table 7). No irrigation treatment effect was observed for any fiber quality parameter in 2013 (P > 0.05). However, during the 2014 growing season, a significant irrigation treatment effect was observed for fiber length, micronaire and Q-score. During the 2014 growing season, treatments T1, T2 and T3 had similar values in fiber length and Q-score, which were significantly higher than those in T4 and T5 treatments (Table 8). For micronaire, there was no significant difference between T1 and T2 treatments during the 2014 growing season. Treatments T3, T4 and T5 had higher micronaire values compared to the T1 treatment, where increases of 12.0%, 22.0% and 23.0% were detected for T3, T4 and T5, respectively. There was a significant cultivar effect for all fiber qualities in 2013 and for length and micronaire in 2014 (Table 7). Generally, FM1944 had greater fiber length, fiber strength, and Q-score than PHY499 (no significance for fiber strength and Q-score in 2014, Table 9). In 142
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Table 3 The effects of irrigation treatment on yield and yield components for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons under five different irrigation treatments. Values are means (n = 8) and those sharing a common letter are significantly different (LSD; P < 0.05). Treatments are defined in the caption of Table 1 as well as in the materials and methods. Year
Treatment
Lint yield (kg ha−1)
Bolls ha−1 (no.)
Seedcotton weight boll−1 (g)
Lint percentage (%)
Seed index (g)
2013
T1 T2 T3 T4 T5 T1 T2 T3 T4 T5
1937a 1588a 2274a 1797a 2278a 1598a 1673a 1405ab 1234b 894c
1000785a 766816a 1109467a 855875a 1082297a 789458ab 837006a 692096bc 621151c 476241d
4.49a 4.72a 4.80a 4.87a 4.87a 4.84a 4.86a 4.81a 4.64ab 4.32b
43.44a 43.82a 42.97a 43.30a 43.61a 41.61c 41.00c 42.21bc 43.16ab 43.62a
9.49a 9.99a 9.98a 9.87a 10.01a 10.37ab 10.92a 10.96a 10.37ab 9.88b
2014
yield in all irrigation treatments except for the control, peaking at 104 kg ha−1 for T3. Additionally, the results in Fig. 1B showed that boll mass declines under extreme drought also contribute approximately 60 kg ha−1 (T4) to 104 kg ha−1 (T5) to lint yield loss. In contrast, drought stress has a slight, positive impact on lint percentage, thereby partially offsetting yield losses by 44 (T4) and 33 kg ha−1 (T5) under severe drought stress.
contrast, micronaire and uniformity values were lower in cultivar FM1944 relative to cultivar PHY499 (no significant difference in uniformity in 2014). 3.4. Relationships among ΨPD, yield, yield components and fiber quality Although a significant cultivar effect was observed for some yield components and fiber qualities, irrigation treatment × cultivar interaction did not affect any yield component or fiber quality parameter in either year (except fiber density and micronaire during the 2014 growing season). Thus, correlation analyses among water status at key growth stages, yield, yield components, and fiber quality parameters across both cultivars and all irrigation treatments for 2014 only since this is the year that irrigation treatment effects were observed. Both ΨEF and ΨBD were strongly associated with lint yield along with some yield components and fiber quality parameters (Table 10).The yield components except seed index had significant correlations with ΨEF and ΨBD. Fiber quality parameters, including fiber length, micronaire and Qscore had extremely strong relationships with both ΨEF and ΨBD (P < 0.05). Interestingly, micronaire had a highly significant negative relationship with both ΨEF and ΨBD (P < 0.001). Lint yield was most strongly and positively associated with boll number per hectare (r = 0.96, Table 10). To a lesser extent, lint yield was also significantly associated with total seedcotton weight per boll, seed number per boll, seed index, weight fiber−1, fibers seed−1 and lint percentage. In addition, yield component contributions to yield loss are shown in Fig. 1. Boll density represented the greatest contributor to yield loss, where losses ranged from 197 kg ha−1 in T3 to 634 kg ha−1 in the most severely stressed treatment (T5) relative to the control (Fig. 1A). Seed number per boll accounted for a much smaller and more stable portion of yield loss at 90 to 92 kg ha−1 lint yield loss in T3 through T5. By comparison, lint mass per seed had a positive impact on
4. Discussion The main goals of this study were to assess the water-induced changes in yield components contributing to yield variability, and to evaluate the effect of plant water status at key developmental stages on fiber quality for field-grown cotton. ΨPD was monitored from floral bud initiation until irrigation termination (first open boll) under five irrigation scheduling systems. High rainfall in 2013 prevented variation in ΨPD among all treatments for all growth stages (Chastain et al., 2016b), hence 2013 can be considered a wet year. In fact, ΨPD never averaged less than −0.5 MPa for any given growth stage or irrigation treatment (Table 1). This indicates that ΨPD was consistently above values shown previously to limit physiological processes and lint yield (Chastain et al., 2014, 2016a, 2016b; McMichael et al., 1973; Snider et al., 2015). Not surprisingly, almost none of the yield components, lint yield and fiber quality parameters were affected by irrigation treatment in 2013. Moreover, although the two cultivars had different yield and fiber quality characteristics (Tables 2,5,6,7 and 9), the interaction effect of irrigation × cultivar was not significant for any yield components or fiber quality parameters in either year (except fiber density and micronaire in 2014), indicating that irrigation treatment trends were similar for the two cultivars. Hence we have focused on the effects of different water-deficit levels on yield components and fiber qualities, especially in the 2014 season.
Table 4 The effects of irrigation treatment on yield components for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Values sharing a common letter within the same column were not significantly different (P < 0.05). Data are means (n = 8). Treatments are defined in the caption of Table 1 as well as in the materials and methods. Year
Treatment
Seeds boll−1 (no.)
SSA
Lint weight seed−1 (g)
Fibers seed−1 (no.)
Fiber density (no./mm2)
Weight fiber−1 (μg)
2013
T1 T2 T3 T4 T5 T1 T2 T3 T4 T5
25.71a 25.62a 26.33a 27.01a 26.60a 27.24a 26.27ab 25.39bc 25.40bc 24.71c
98.25a 101.59a 101.52a 100.81a 101.72a 104.10ab 107.67a 107.95a 104.09ab 100.84b
0.076a 0.081a 0.078a 0.078a 0.080a 0.074c 0.076bc 0.080a 0.079ab 0.076bc
15192b 16808a 15630b 15359b 15952ab 17719a 18085a 17192ab 16144bc 16011c
154.97ab 165.59a 154.37ab 152.56b 157.04ab 170.28a 168.76ab 159.58ab 155.30b 159.21ab
4.99a 4.83a 5.01a 5.09a 5.00a 4.18b 4.20b 4.66a 4.88a 4.77a
2014
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Table 5 The effects of cultivar on yield and yield components for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Values sharing a common letter within the same column were not significantly different (P < 0.05). Data are means (n = 20). Year
Cultivar
Lint yield (kg ha−1)
Bolls ha−1 (no.)
Seedcotton weight boll−1 (g)
Lint percentage (%)
Seed index (g)
2013
PHY 499 FM 1944 PHY 499 FM 1944
1884a 2066a 1312a 1409a
924405a 1001691a 676247a 690134a
4.55a 4.94a 4.46b 4.93a
45.11a 41.74b 43.34a 41.30b
9.39b 10.35a 9.87b 11.12a
2014
Table 6 The effects of cultivar on the rest of the yield components for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Values sharing a common letter within the same column were not significantly different (P < 0.05). Data are means (n = 20). Year
Cultivar
Seeds boll−1 (no.)
SSA
Lint weight seed−1 (g)
Fibers seed−1 (no.)
Fiber density (no./ mm2)
Weight fiber−1 (μg)
2013
PHY 499 FM 1944 PHY 499 FM 1944
25.76a 26.75a 25.57a 26.04a
97.64b 103.92a 100.81b 109.05a
0.080a 0.077a 0.076b 0.078a
16096a 15481a 17326a 16734a
164.83a 148.99b 171.78a 153.47b
4.98a 4.99a 4.38b 4.70a
2014
Table 9 The effects of cultivar on fiber quality for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Values sharing a common letter within the same column were not significantly different (P < 0.05). Data are means ± SE (n = 20).
Parameter
2013
Length Strength Micronaire Uniformity Q-Score Length Strength Micronaire Uniformity Q-Score
2014
Cultivar
Irrigation × cultivar
0.6974 0.1780 0.0931 0.0921 0.2021 < 0.0001 0.0142* < 0.0001 0.7557 < 0.0001
< 0.0001 0.0003 < 0.0001 0.0008 0.0002 < 0.0001 0.4860 0.0072 0.0022* 0.0632
0.6974 0.4544 0.1317 0.9612 0.2021 0.9259 0.7923 0.0130 0.8789 0.3289
Table 8 The effects of irrigation treatment on fiber quality for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Values sharing a common letter within the same column were not significantly different (P < 0.05). Data are means (n = 8). Treatments are defined in the caption of Table 1 as well as in the materials and methods. Treatment
Fiber length (cm)
Fiber strength (g/tex)
Micronaire
Uniformity (%)
Q-Score
2013
T1 T2 T3 T4 T5 T1 T2 T3 T4 T5
2.98a 2.98a 2.98a 3.01a 2.99a 3.03a 3.07a 3.02a 2.90b 2.82b
32.09a 31.33ab 31.14ab 31.53ab 30.85b 33.66a 34.11a 35.25a 34.93a 34.23a
5.09ab 4.96b 5.10ab 5.16a 5.06ab 4.18c 4.14c 4.68b 5.10a 5.14a
83.66ab 83.04b 83.74ab 83.20ab 83.91a 83.88a 84.00a 83.76a 83.74a 83.66a
60.25a 60.63a 59.38a 59.00a 59.63a 66.13a 66.25a 65.13a 60.00b 59.25b
2014
Fiber strength (g/tex)
Micronaire
Uniformity (%)
Q-Score
2013
PHY 499 FM 1944 PHY 499 FM 1944
2.90b 3.08a 2.89b 3.04a
30.74b 32.03a 34.33a 34.54a
5.20a 4.96b 4.58b 4.71a
83.93a 83.09b 84.10a 83.52a
58.80b 60.75a 63.70a 63.00a
Many studies have documented that drought stress limits lint yield in upland cotton (Pettigrew, 2004; Basal et al., 2009; Chastain et al., 2014; Wang et al., 2016b; Luo et al., 2018), and the current study is no exception. While significant differences in ΨSQ were observed during the 2014 season, average values were well above the −0.5 MPa threshold shown to produce maximal lint yields previously (Chastain et al., 2016b), indicating that the crop was not under drought stress during this time period for any irrigation treatment (Table 1). ΨPD during flowering and boll development in 2014 ranged from a maximum of −0.49 MPa for T1 to −0.82 MPa for T5, and lint yield was strongly associated with plant water status during these stages of development (Table 10). The results illustrated that yield variability in the current study was largely water induced due to the exceptionally dry growing season (Chastain et al., 2016b). Boll density, seedcotton weight per boll, and lint percentage ultimately determine lint yield and are considered sensitive to water deficit stress (Pettigrew, 2004). In 2014, where significantly negative effects of drought on lint yield were observed, and boll number ha−1 trends essentially matched lint yield trends, suggesting that boll density contributed to yield loss under water deficit stress. This is primarily because drought decreases total source strength (leaf area × average photosynthetic rate per unit leaf area) and the capacity to support a developing boll load (Krieg and Sung, 1986). By comparison, seedcotton weight per boll was only negatively impacted by the most extremely drought stressed treatment (T5) in 2014. All other irrigation treatments produced similar seedcotton weight per boll. These results are similar to previous observations, where boll weight was more stable than boll number in response to water deficit stress (Krieg and Sung, 1986; Lokhande and Reddy, 2014; Pettigrew, 2004; Wang et al. 2016b). Drought stress has been shown to significantly accelerate crop plant development and shorten the duration of reproductive growth (Yang and Zhang, 2006), resulting in less biomass accumulation in bolls under water deficit (Yang et al., 2014), which likely explained the lower seedcotton weight per boll in the most extremely stressed treatment (Table 3). In contrast with lint yield responses, lint percentage increased as drought stress severity increased, suggesting that drought stress favored dry matter partitioning toward fiber instead of seed. Although the response of the aforementioned yield components
* Although the P value for this effect is less than 0.05, it should be noted that the P value for the whole-model F test was > 0.05 for this particular parameter, preventing post-hoc analysis of this main effect.
Year
Fiber length (cm)
4.1. Effect of plant water status on lint yield and yield components of fieldgrown cotton
P Value Irrigation
Cultivar
2014
Table 7 Two-way ANOVA analysis of irrigation treatment, cultivar, and irrigation treatment × cultivar interaction on fiber quality for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Significant main effects or interaction effects are indicated in bold. Year
Year
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0.62*** 0.49** 0.72*** ——
0.48** 0.39* 0.80*** 0.20 ——
−0.38* −0.41** −0.05NS −0.41** 0.21NS ——
0.38* 0.32* 0.32* 0.31 NS 0.13 NS −0.80*** ——
−0.60*** −0.60*** −0.65*** −0.41** −0.77*** 0.11 NS −0.04 NS ——
0.61*** 0.58*** 0.69*** 0.36* 0.80*** −0.14 NS 0.21 NS −0.89*** ——
−0.58*** −0.60*** −0.33* −0.49** −0.16 NS 0.90*** −0.77*** 0.46** −0.55*** ——
0.63*** 0.63*** 0.42** 0.45** 0.30 NS −0.74*** 0.67*** −0.49** 0.61*** −0.89*** ——
0.65*** 0.69*** 0.32* 0.47** 0.20 NS −0.67*** 0.45** −0.56*** 0.57*** −0.80*** 0.78*** ——
0.69*** 0.71*** 0.39* 0.43** 0.30 NS −0.62*** 0.54*** −0.51*** 0.62*** −0.80*** 0.81*** 0.83*** ——
have been extensively studied (Pettigrew, 2004; Basal et al., 2009; Yang et al., 2014; Chastain et al., 2014; Lokhande and Reddy, 2014; Wang et al., 2016b), the response of more detailed intra-boll yield components (Groves et al., 2016; Ali and Awan, 2009) to drought have been largely unexplored. Seedcotton weight per boll is the sum of fiber weight per boll and seed weight per boll. Meanwhile, seed weight per boll is a function of seed index and seeds per boll (Ali and Awan, 2009). Results in Tables 3 and 4 showed that seed index in the T5 treatment and seeds per boll in T3, T4 and T5 treatments were significant lower than these in the T1 treatment, which would result in lower total seed weight per boll. On the other hand, fiber weight per boll is a function of seeds per boll, fibers per seed and individual fiber weight. Drought stress increased individual fiber weight in the low ΨPD treatments (T3, T4 and T5) relative to the T1 treatment (Table 4). In contrast, fewer fibers per seed were measured in the low ΨPD treatments (T4 and T5) relative to the T1 treatment. Thus, increased drought severity during flowering and boll development seems to limit the total number of fibers produced per seed, but increases individual fiber mass, with a peak in total lint weight per seed observed in T3 (the −0.7 MPa ΨPD treatment). Lint weight per seed was positively correlated with lint percentage, and breeding for high lint weight per seed will likely increase seed size since larger seed have greater surface area to produce fibers (Groves et al., 2016). In the present study, the change in SSA could not explain the changes in lint percentage, since lint percentage increased with the decreased ΨPD (Table 4), but SSA increased first then declined with decreasing of ΨPD (Table 3). Lint percentage measures the relative proportion of lint and seed in seedcotton (Groves et al., 2016), and in the present study, it could be a function of seed index and lint weight per seed. Thus, larger lint percentage under T3 and T4 treatments relative to the T1 treatment should be attributed to higher lint weight per seed (Table 4), but larger lint percentage in the T5 treatment relative to the T1 treatment was likely due to lower seed index. The results in Table 10 showed that boll number ha−1 was more strongly correlated with ΨEF and ΨBD, and lint yield than any other yield component, indicating that boll density was the most sensitive yield component to water deficit stress and had the most important impact on yield variability under different irrigation levels. Lint yield losses attributed to boll ha−1, seed number boll−1, lint mass seed−1 were provided in Fig. 1A, which showed that lint mass seed−1 had a positive impact on yield in all irrigation treatments except for the control. This indicates that increased lint mass seed−1 can partially offset the negative impacts of drought stress on other yield components. Boll ha−1 represented the greater contributor to yield loss than seed number boll−1 in T3 through T5. Additionally, lint yield loss attributable to boll density, individual boll weight, and lint percentage and was estimated in Fig. 1B, which indicated that boll number also is the dominant contributor to yield loss among the three yield components. This is consistent with a report by Sharma et al. (2015) that boll density was the major driver of water induced yield variability in field-grown cotton but boll mass also contributed a small percentage to final lint yield response. In contrast, drought stress has a slight, positive impact on lint percentage, thereby partially offsetting yield loss under drought stress. Therefore, based on the aforementioned observations, we conclude that yield limiting drought stress decreases boll size due to a decline in seed number per boll but that these declines are partially offset by increases in lint percentage and lint mass per seed. As a result, total yield loss under drought is nearly indistinguishable from yield loss driven by a reduction in boll density alone (Fig. 1A).
* = P < 0.05. ** = P < 0.01. *** = P < 0.001, NS = not significant.
0.97*** —— Lint yield Boll ha−1 Seedcotton weight boll−1 Seeds boll−1 Seed index Weight fiber−1 Fibers seed−1 Lint percentage Fiber length Micronaire Q-score ΨEF ΨBD
——
0.66*** 0.49** ——
ΨBD ΨEF Q-score Micronaire Fiber length Lint percentage Fibers seed−1 Weight fiber−1 Seed index Seeds boll−1 Seedcotton weight boll−1 Boll ha−1 Lint yield
Table 10 Pairwise correlation coefficients for predawn leaf water potential, yield components, and fiber quality for both cultivars grown under five different irrigation treatments near Camilla, GA, for the two cultivars PHY 499 and FM 1944 during the 2014 growing season. For brevity, only parameters significantly (P < 0.05) correlated with lint yield were presented.
W. Hu et al.
4.2. Effect of plant water status on fiber quality of field-grown cotton Fiber strength did not differ among irrigation treatments (Tables 7 and 8). Similarly, Enciso et al. (2003) also reported that there was no significant effect of drought on fiber strength, whereas Wang et al. (2016b) and Dağdelen et al. (2009) found lower fiber strength when 145
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Fig. 1. Yield loss contributions attributed to individual yield components for five different irrigation treatments during the 2014 cotton growing season at a field site near Camilla, Georgia. Scenario (A) considered yield a product of boll density, seed number per boll, and lint weight per seed, whereas scenario (B) considered yield the product of boll density, individual boll weight, and lint percentage. Values are means ± standard error (n = 8).
Moreover, among these yield component traits, water-induced yield loss was most strongly associated with boll density, because seed number and boll mass-induced yield losses were partially offset by increased dry matter partitioning to fiber growth. Fiber strength and length uniformity did not differ among irrigation treatments. Fiber length declined with increased drought severity, whereas fiber micronaire increased in the most severely stressed treatments. This indicates that increasing drought severity during flowering and boll development decreases the number of individual fibers per seed and their final length, but their thickness is increased. The end result is a general decline in the overall fiber quality assessment (Q-score).
soil moisture deficits were imposed. In addition, length uniformity was not significantly altered by water deficits, which is similar to the results reported by Pettigrew (2004). Consistent with previous studies (Lokhande and Reddy, 2014; Wang et al., 2016b), fiber length decreased under the most severe water deficit treatments (T4 and T5) compared with the well-watered (T1) treatment (Table 8). Compared with fiber length, micronaire is considered more sensitive to environmental changes (Wang et al., 2016b), but micronaire has been shown to increase (Pettigrew, 2004) and decrease (Marani and Amirav, 1971) under drought conditions in prior studies. Wang et al. (2016b) stated that the effects on micronaire primarily depended on the intensity of drought. In support of their conclusion, our results showed that micronaire was not significantly different between the highest yielding treatments (T1 and T2), but micronaire increased with increasing drought severity in 2014 (Table 8). Mechanistically, it appears that severe drought decreases the number of fibers produced per seed and individual fiber length, but individual fibers are thicker and heavier than those produced by well-watered plants. A challenge for cotton breeding efforts and fiber processing is to determine which fiber quality or group of parameters should be given priority. Hence Q-score could be used to provide an overall assessment of fiber quality by normalizing fiber quality parameters and differentially weighting fiber length, strength, micronaire, and uniformity (Bourland et al., 2010), and has been used in cotton breeding programs to facilitate the identification of lines having the superior fiber quality (Bourland and Jones, 2012; Campbell et al., 2015; Bourland and Jones, 2018). The Q-score was significantly decreased in the T4 and T5 treatments compared with the T1 treatment (Table 8), suggesting that overall fiber quality was not affected by mild water deficit but was decreased by severe water deficit.
Acknowledgments The authors thank the Georgia Cotton Commission for funding this research and the University of Georgia for providing facilities to carry out this work. We also thank Lola Sexton, Dudley Cook, Calvin Perry, Will Vance, Jenna Pitts, Tyler Beasley, and Keri Dixon for their assistance in the field. References Ali, M.A., Awan, S.I., 2009. Inheritance pattern of seed and lint traits in cotton (Gossypium hirsutum). Int. J. Agric. Biol. 11, 44–48. Basal, H., Dagdelen, N., Unay, A., Yilmaz, E., 2009. Effects of deficit drip irrigation ratios on cotton (Gossypium hirsutum L.) yield and fibre quality. J. Agron. Crop Sci. 195, 19–29. Bourland, F., 2013. Novel approaches used in the university of Arkansas cotton breeding program. In: San Antonio, TX. Proc. Beltwide Cotton Prod. Res. Conf.. pp. 7–10. Bourland, F.M., Hogan, R., Jones, D.C., Barnes, E., 2010. Development and utility of Qscore for characterizing cotton fiber quality. J. Cotton Sci. 14, 53–63. Bourland, F.M., Jones, D.C., 2012. Registration of ‘UA222’cotton cultivar. J. Plant Reg. 6, 259–262. Bourland, F.M., Jones, D.C., 2018. Registration of Arkot 0705 and Arkot 0711c germplasm lines. J. Plant Reg. 12, 246–250. Campbell, B., Jones, M., Greene, J., Jones, D., 2015. Registration of PD 06001 and PD 06078 germplasm lines of cotton. J. Plant Reg. 9, 363–366. Chastain, D.R., Snider, J.L., Choinski, J.S., Collins, G.D., Perry, C.D., Whitaker, J., Grey, T.L., Sorensen, R.B., van Iersel, M., Byrd, S.A., 2016a. Leaf ontogeny strongly influences photosynthetic tolerance to drought and high temperature in Gossypium hirsutum. J. Plant Physiol. 199, 18–28. Chastain, D.R., Snider, J.L., Collins, G.D., Perry, C.D., Whitaker, J., Byrd, S.A., 2014. Water deficit in field-grown Gossypium hirsutum primarily limits net photosynthesis by decreasing stomatal conductance, increasing photorespiration, and increasing the ratio of dark respiration to gross photosynthesis. J. Plant Physiol. 171, 1576–1585. Chastain, D.R., Snider, J.L., Collins, G.D., Perry, C.D., Whitaker, J., Byrd, S.A., Oosterhuis,
5. Conclusions In the current study, water-induced yield variability was primarily correlated with crop water status at the flowering and boll development phase of crop growth. Moderate to severe drought significantly decreased boll density (bolls ha−1) and decreased individual boll mass by decreasing seed index, seeds per boll, and fibers per seed. Increasing severity of drought stress decreased fiber density but increased individual fiber weight, producing a peak in total fiber weight per seed in the treatment irrigated according to a ΨPD threshold of −0.7 MPa. 146
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D.M., Porter, W.M., 2016b. Irrigation scheduling using predawn leaf water potential improves water productivity in drip-irrigated cotton. Crop Sci. 56, 3185–3195. Collins, G., Whitaker, J., Culpepper, S., Harris, G., Kemerait, B., Perry, C., Roberts, P., Shurley, D., Smith, A., 2014. Georgia Cotton Production Guide. Publication No. CSS13-01. University of Georgia Cooperative Extension Service, Tifton, GA. Cook, O.F., 1908. Danger in judging cotton varieties by lint percentages. U.S.D.A Bur. Plant Ind., Cir. pp. 11. Culp, T., Harrell, D., 1973. Breeding methods for improving yield and fiber quality of upland cotton (Gossypium hirsutum L.). Crop Sci. 13, 686–689. Da Costa, V.A., Cothren, J.T., 2011. Drought effects on gas exchange, chlorophyll, and plant growth of 1-methylcyclopropene treated cotton. Agron. J. 103, 1230–1241. Dabbert, T.A., Pauli, D., Sheetz, R., Gore, M.A., 2017. Influences of the combination of high temperature and water deficit on the heritabilities and correlations of agronomic and fiber quality traits in upland cotton. Euphytica 213, 6. Dağdelen, N., Başal, H., Yılmaz, E., Gürbüz, T., Akcay, S., 2009. Different drip irrigation regimes affect cotton yield, water use efficiency and fiber quality in western Turkey. Agric. Water Manage. 96, 111–120. Earl, H.J., Davis, R.F., 2003. Effect of drought stress on leaf and whole canopy radiation use efficiency and yield of maize. Agron. J. 95, 688–696. Enciso, J., Unruh, B., Colaizzi, P., Multer, W., 2003. Cotton response to subsurface drip irrigation frequency under deficit irrigation. Appl. Eng. Agric. 19, 555. Grimes, D., Yamada, H., 1982. Relation of cotton growth and yield to minimum leaf water potential. Crop Sci. 22, 134–139. Groves, F.E., Bourland, F.M., 2010. Estimating seed surface area of cottonseed. J. Cotton Sci. 14, 74–81. Groves, F.E., Bourland, F.M., Jones, D.C., 2016. Relationships of yield component variables to yield and fiber quality parameters. J. Cotton Sci. 20, 320–329. Jackson, R.D., Idso, S., Reginato, R., Pinter, P., 1981. Canopy temperature as a crop water stress indicator. Water Resour. Res. 17, 1133–1138. Jones, H.G., 2007. Monitoring plant and soil water status: established and novel methods revisited and their relevance to studies of drought tolerance. J. Exp. Bot. 58, 119–130. Jordan, W.R., 1970. Growth of cotton seedlings in relation to maximum daily plant water potential. Agron. J. 62, 699–701. Krieg, D.R., Sung, J.F.M., 1986. Source-sink relations as affected by water stress during boll development. In: Mauney, J.R., Stewart, J.M. (Eds.), Cotton Physiology. The Cotton Foundation, Memphis, TN p 73–78. Lokhande, S., Reddy, K.R., 2014. Reproductive and fiber quality responses of upland cotton to moisture deficiency. Agron. J. 106, 1060–1069. Luo, Z., Liu, H., Li, W., Zhao, Q., Dai, J., Tian, L., Dong, H., 2018. Effects of reduced nitrogen rate on cotton yield and nitrogen use efficiency as mediated by application mode or plant density. Field Crop. Res. 218, 150–157. Marani, A., Amirav, A., 1971. Effects of soil moisture stress on two varieties of upland cotton in Israel I. The coastal plain region. Exp. Agric. 7, 213–224. McMichael, B., Jordan, W., Powell, R., 1973. Abscission processes in cotton: induction by plant water deficit. Agron. J. 65, 202–204. Niu, J., Zhang, L., Zhang, S., Liu, S., Ma, H., Zhao, X., 2016. Studies on yield and fiber quality changes in cotton under drought stress after re-watering during the flowering and boll-forming stages. Cotton Sci. 28, 584–593. Pace, P., Cralle, H.T., El-Halawany, S.H.M., Cothren, J.T., Senseman, S.A., 1999. Droughtinduced changes in shoot and root growth of young cotton plants. J. Cotton Sci. 3, 183–187.
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Water-induced variation in yield and quality can be explained by altered yield component contributions in field-grown cotton
T
⁎
Wei Hua,b, , John L. Sniderb, Haimiao Wanga,b, Zhiguo Zhoua, Daryl R. Chastainb, Jared Whitakerb, Calvin D. Perryc, Freddie M. Bourlandd a
College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu Province, 210095, PR China Department of Crop and Soil Sciences, University of Georgia, 115 Coastal Way, Tifton, GA, 31794, United States c C.M Stripling Irrigation Research Park, University of Georgia, Camilla, GA, 31730, United States d Northeast Research & Extension Center, University of Arkansas, Keiser, AR, 72351, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: Drought Cotton Yield Fiber quality Yield components
Novel yield component traits and fiber quality indices have been recently developed by breeders to screen for desirable cotton (Gossypium hirsutum L.) lines, but assessing these components in response to water-induced yield variability has received little attention. We investigated the hypothesis that differential sensitivities of wholecrop and intra-boll yield components to drought will largely explain water-induced yield and fiber quality variation in cotton. To test this hypothesis, two cotton cultivars were grown in the field under five contrasting irrigation regimes during the 2013 and 2014 growing seasons near Camilla, GA. Measurements included predawn leaf water potential (ΨPD) throughout the growing season and extensive yield component and fiber quality assessments at the end of the season. Water-induced yield variability was primarily associated with ΨPD at the flowering and boll development phase of crop growth. Boll density (bolls ha−1) was the dominant driver of drought-induced yield loss, but reduced boll mass and seed number per boll also contributed somewhat to yield loss. By comparison, increased drought severity decreased fiber density but increased individual fiber mass, producing a peak in total fiber weight per seed at a −0.7 MPa ΨPD irrigation threshold and increasing lint percentage in stressed treatments. Thus, the negative impacts of drought on overall boll mass and seed number per boll are partially offset by increased dry matter partitioning toward fiber growth. Fiber length declined with increased drought severity, whereas fiber micronaire increased in the most severely stressed treatments. This indicates that increasing drought severity during flowering and boll development decreases the number of individual fibers per seed and their final length, but their thickness is increased. The end result is a decline in the overall fiber quality index (Q-score) under drought stress.
1. Introduction Water deficit substantially limits lint yield and fiber quality in cotton (Gossypium hirsutum L.), even in production regions characterized by high annual rainfall (Chastain et al., 2014, 2016b; Pettigrew, 2004). This is especially true for cotton production regions in the Coastal Plain of the southeastern United States, where the coarse textured soils are characterized by limited water holding capacity, resulting in episodic drought events during the growing season (Ritchie et al., 2009). Drought limits yield by disrupting a number of underlying physiological processes. For example, soil water declines result in decreased cell turgor, which limits total source strength by inhibiting leaf area development and photosynthetic efficiency of the canopy (Chastain et al., 2014; Krieg and Sung, 1986; Pace et al., 1999;
⁎
Pettigrew, 2004). This decline in source strength decreases the capacity to support a developing boll load, resulting in low fruit retention under drought stress (Krieg and Sung, 1986; Lokhande and Reddy, 2014; Snider and Oosterhuis, 2015). In agreement with these findings, some studies have shown that water-deficit significantly inhibited plant biomass production (Wang et al., 2016a; Zhang et al., 2017) and biomass accumulation of reproductive organs (Da Costa and Cothren, 2011). As a result, significant declines in both seedcotton yield and lint yield are observed under water-deficit conditions (Dağdelen et al., 2009; Wang et al., 2016b). Other scientists analyzed yield components to explain the underlying declines in cotton yield under drought stress and attributed them to lower individual boll weight, decreased boll numbers per plant, or lower lint percentage (Pettigrew, 2004; Sharma et al., 2015; Wang
Corresponding author at: College of Agriculture, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu, 210095, PR China. E-mail address: [email protected] (W. Hu).
https://doi.org/10.1016/j.fcr.2018.05.013 Received 18 March 2018; Received in revised form 16 May 2018; Accepted 17 May 2018 Available online 24 May 2018 0378-4290/ © 2018 Elsevier B.V. All rights reserved.
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overall fiber quality based on fiber length, strength and uniformity, and micronaire will be decreased by drought stress as a result of intra-boll yield component alterations. The objectives of the present study were to: 1) determine the effects of different degrees of drought stress on boll density, boll weight and lint percentage, and these novel yield component traits and to quantify the contributions of different yield components to yield loss; 2) estimate the effects of drought stress on fiber quality parameters, with a focus on the Q-score (obtained by normalizing HVI fiber length, strength, micronaire, and uniformity into an overall quality index).
et al., 2016b; Zhang et al., 2016; Zahoor et al., 2017). The product of the aforementioned components is lint yield. However, some breeders have utilized novel yield component traits that reflect fiber quantity, density, and weight to screen for high yielding cotton lines (Ali and Awan, 2009; Groves and Bourland, 2010). Because cotton fibers are unicellular trichromes protruding from the seed coat surface, Smith and Coyle (1997) reported that breeding for large seed surface area and number of seeds per boll could provide more surface area to increase lint yield (Culp and Harrell, 1973). Moreover, increased fiber density is a potentially useful selection criterion for improving fiber quantity per seed to increase lint yield (Groves et al., 2016). Cook (1908) suggested that lint index (lint mass per 100 seeds), could be used as a preferred selection tool for increased lint yield by increasing the total lint mass per seed (Groves et al., 2016), which is a function of average fiber number per seed and average individual fiber weight (Ali and Awan, 2009). Although improving these traits has been shown to increase lint yield through breeding efforts (Groves et al., 2016) and these yield component characteristics could be assessed to provide a detailed explanation of the underlying limitations to yield under water deficit, this information is extremely limited for field-grown cotton. Drought, depending upon severity, also negatively affects cotton fiber quality, including fiber length (Zheng et al., 2014), uniformity ratio (Niu et al., 2016), fiber strength (Dabbert et al., 2017) and micronaire (Wang et al., 2016b). However, different cotton fiber processing methods have different fiber quality requirements, and no single high volume instrument (HVI) parameter can be applied to all situations. Bourland et al. (2010) developed a fiber quality index called quality score (Q-score). Q-score is based on up to six HVI fiber parameters, and the user can apply specific weightings to each (Bourland et al., 2010). Since different drought levels can have different effects on fiber traits (Wang et al., 2016a), Q-score should be able to evaluate the effect of drought on overall fiber quality. The impact of drought on yield components and fiber quality in cotton differs from study to study (Snider and Oosterhuis, 2015), likely because irrigation treatments imposed under field conditions can vary greatly in drought severity due to environmental influence. Most studies define stress levels or irrigation treatments under field conditions using water balance approaches (Basal et al., 2009; Dağdelen et al., 2009). However, plant water status in the field can be affected by soil physical characteristics (Rab et al., 2009), atmospheric demand (Schulze et al., 1987; Jackson et al., 1981), and plant factors such as effective rooting depth and leaf area development (Snider and Chastain, 2016). Direct measures of leaf water potential integrate all of these factors and provide an accurate indicator of the need for irrigation (Grimes and Yamada, 1982). Predawn leaf potential (ΨPD) is a direct index of plant water status that has been strongly correlated with growth and physiological parameters such as leaf area, plant height, stomatal conductance, and photosynthetic rate (Jordan, 1970; McMichael et al., 1973; Turner et al., 1986; Jones, 2007; Snider et al., 2015; Chastain et al. 2016a). Previous work conducted in our laboratory quantified lint yield and water use efficiency (WUE) responses for cotton grown under five different irrigation treatments employing a combination of conventional and ΨPD-based irrigation scheduling thresholds during two growing seasons (Chastain et al., 2016b). Regardless of treatment, ΨPD was routinely measured for all irrigation treatments throughout the growing season, and ΨPD thresholds for optimal yield and WUE were identified. Because ΨPD was monitored near-continuously, the level of drought stress experienced by plants at key growth stages could be determined and combined with detailed yield component and fiber quality assessments to identify how underlying processes respond to increases in drought severity. We hypothesized that 1) apart from boll density, boll weight and lint percentage, the novel yield component traits mentioned above (seed index, seeds boll−1, seed surface area, lint weight seed−1, fibers seed−1, fiber density, weight fiber−1 etc.) also will be decreased by drought stress and contribute to yield loss under drought stress; 2)
2. Materials and methods 2.1. Plant material and study site To address the impact of crop water status on yield components and fiber quality in cotton, a field study was conducted at C.M. Stripling Irrigation Research Park (31°16′48″N, 84°17′29″W) near Camilla Georgia during the 2013 and 2014 growing seasons. The soil type at this site is a Lucy loamy sand (loamy, kaolinitic, thermic Arenic Kandiudults). Seeds of two commercial cotton varieties (PHY 499 WRF [Dow AgroSciences]) and FM 1944 GLB2 [Bayer CropScience]) were planted on May 6, 2013 and June 2, 2014. Practices such as tillage, row spacing, seeding rate, and planting depth were conducted according to University of Georgia Extension Service recommendations (Collins et al., 2014), and were described in detail by our previous paper (Chastain et al., 2016b). Individual plots were 6 rows wide × 40 m long with two buffer rows between adjacent plots. Immediately after planting, pre-emergence herbicides were applied to the soil surface, and irrigation was uniformly applied to the entire field using overhead sprinkler irrigation delivered via center-pivot to ensure herbicide activation and to prevent yield limitations due to poor stand establishment. Stand counts were conducted approximately two weeks after planting, and in-row plant densities were above levels needed to maximize yield (Collins et al., 2014). Supplemental irrigation was uniformly applied over the entire study until the first floral buds were visible to the naked eye (squaring; SQ). At this time, irrigation treatments (described below) were initiated. Crop management, including fertility and pest control, were conducted according to recommended practices (Collins et al., 2014). 2.2. Irrigation treatments The study was arranged as a split plot, randomized complete block design with four replications, where irrigation treatment was the whole plot factor and cultivar was the subplot factor. At squaring, five unique irrigation treatments were imposed. T1: Irrigation scheduled according to a well-established water balance approach referred to as the “checkbook” method (Collins et al., 2014), where supplemental irrigation is provided to meet crop growth stage-specific water requirements after accounting for weekly rainfall. T2-T4: Irrigation scheduled using predawn leaf water potential (ΨPD) values to trigger an irrigation event. Using this approach, ΨPD (measurements described in Section 2.3) was measured every two days during the irrigation treatment period, and when ΨPD was equal to or below predefined thresholds (T2 = −0.5 MPa; T3 = −0.7 MPa, and T4 = −0.9 MPa), water was delivered at 1/3 of total weekly checkbook recommended amounts since irrigation decisions were made three days per week. T5: No supplemental irrigation was provided during the irrigation treatment period. The ΨPD thresholds for T2–T4 were selected because these values correspond to plant water status levels shown previously to differentially impact net photosynthesis (Snider et al., 2015). Irrigation water was delivered using subsurface drip tape positioned at a 30 cm depth in alternating row middles. Weather data were obtained from an on-site weather station as part of the Georgia Automated Environmental Monitoring Network (www.georgiaweather.net). The minimum and 140
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Finally, contributions of lint weight per seed to yield loss (LLW) for any given treatment relative to the control were calculated as:
maximum daily temperature and precipitation patterns during the 2013 and 2014 growing seasons were reported previously in Chastain et al. (2016b). In 2013, total rainfall from planting to defoliation was 67 cm, ∼21 cm greater than what is recommended by the Checkbook approach (Collins et al., 2014). In contrast, the 2014 growing season total rainfall was 35 cm, ∼11 cm less than Checkbook total recommendations (Chastain et al., 2016b).
LLW = (YT1 – LBD – LSN) × (1 – LWT/LWT1)
(4)
Similarly, the lint yield could be considered as the product of boll density, seedcotton weight per boll, and lint percentage. Thus, the contribution of boll density, seedcotton weight per boll, and lint percentage were also calculated according to above path model. A 0.45 kg fiber sample was taken to the USDA Classing Office in Macon, GA for HVI assessments of fiber quality. In the current study, the fiber parameters of interest were HVI fiber length (upper half mean length), strength (g tex−1), micronaire, and uniformity (%). From the yield component data obtained above and HVI fiber quality parameters, the estimated average weight of an individual cotton fiber (Weight fiber−1) was calculated as HVI fiber length (in inches) × uniformity × (micronaire/1.0 × 106). Fibers Seed−1 was determined by dividing lint weight seed−1 by individual fiber weight, and fiber density was calculated by dividing the number of fibers per seed by SSA. Descriptions of the yield component estimates noted above can be found in Bourland (2013) and Groves et al. (2016). In addition to the individual fiber quality parameters noted previously, Q-Score was assigned to each plot according to Bourland et al. (2010), where the fiber quality parameters length, micronaire, uniformity, and strength were first normalized to values ranging from 0 to 1 and subsequently assigned weighted functions of 50 (length), 25 (micronaire), 15 (uniformity), and 10% (strength).
2.3. Water potential measurements Predawn water potential was measured three days per week from the start of squaring/irrigation treatment initiation until the first open boll was observed in the latest maturing treatment. Measurements were conducted between 04:00 and 06:00 h using a Scholander pressure chamber by severing the petiole of the uppermost fully expanded mainstem leaf (fourth unfurled leaf node below the plant terminal), sealing the petiole in a compression gasket in the chamber head, sealing the leaf blade in the chamber, and pressurizing at a rate of 0.1 MPa s−1 until xylem sap was initially visible at the cut surface of the stem. Water potential values are recorded in negative MPa. To quantify the impact of irrigation treatment on plant water status at key growth stages, ΨPD was averaged for each plot for three different phenological stages of crop development. 1) ΨSQ: average predawn water potential from the start of squaring until first flower. 2) ΨEF: average predawn water potential during the first two weeks of flowering. 3) ΨBD: average predawn leaf water potential from peak bloom (∼2 weeks after first flower) until irrigation termination (first open boll). 2.4. Yield, yield components, and fiber quality
2.5. Statistical analysis
While the impact of plant-based irrigation scheduling on yield and water use efficiency for mechanically harvested plots was reported previously (Chastain et al., 2016b), the current study sought to define the impact of crop water status at key growth stages on yield components and fiber quality. To this end, at crop maturity and following defoliation, all the harvestable bolls within either a one (2013) or two (2014) meter length of row were counted and hand harvested for each plot. Subsequently, samples were ginned to obtain lint and seed weight and the mass of 500 fuzzy seed was determined to calculate average seed mass. From these data, the following parameters were estimated: lint yield (kg ha−1), bolls ha−1, seedcotton weight boll−1 (g), lint percentage, seed index (g per 100 seed), and seed number boll−1. Using the relationship defined in Groves and Bourland (2010), average seed surface area (SSA) was estimated from seed index of fuzzy cotton seed as SSA = 35.74 + 6.59 × seed index. To attribute yield losses (relative to the control treatment; T1) to particular yield components, a simple path model comparable to the approach utilized by Earl and Davis (2003) was employed. We first considered lint yield a product of boll density, seed number per boll, and lint weight per seed according to the following equation.
Because irrigation events were applied to entire treatments (i.e. ΨPD thresholds were triggered based on treatment averages), rather than to individual plots or to individual cultivars separately within a treatment, the average ΨPD for each growth stage and treatment is provided in
Y = BD × SN × LW
Table 1 Average predawn leaf water potential values for three different phenological stages of crop development for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons under five different irrigation treatments. Values are means (n = 8) and those sharing a common letter within the same column were not significantly different (P < 0.05). Year
(1)
ΨEF
ΨBD
2013
T1 T2 T3 T4 T5
2014
T1 T2 T3 T4 T5
—— −0.45a −0.44a −0.42a −0.44a P = 0.258 −0.40a −0.42ab −0.43bc −0.41ab −0.45c P = 0.009
−0.35a −0.35a −0.35a −0.35a −0.35a P = 0.992 −0.55a −0.56a −0.72b −0.82c −0.81c P < 0.001
−0.44a −0.45a −0.46a −0.48a −0.47a P = 0.328 −0.49a −0.52a −0.64b −0.68b −0.82c P < 0.001
ΨSQ, average predawn water potential from the start of squaring until first flower; ΨEF, average predawn water potential during the first two weeks of flowering; ΨBD, average predawn leaf water potential from peak bloom (∼2 weeks after first flower) until irrigation termination (first open boll). T1: Irrigation scheduled according to a well-established water balance approach referred to as the “checkbook” method (Collins et al. 2014). T2-T4: Irrigation scheduled using predawn leaf water potential (ΨPD) values to trigger an irrigation event. When ΨPD was equal to or below predefined thresholds (T2 = −0.5 MPa; T3 = −0.7 MPa, and T4 = −0.9 MPa), water was delivered at 1/3 of total weekly checkbook recommended amounts. T5: No supplemental irrigation was provided during the irrigation treatment period. —— means no data for this growth stage and treatment.
(2)
Yield losses accounted for by seed number per boll (LSN) for each treatment relative to the control were calculated as: LSN = (YT1 – LBD) × (1 - SNT/SNT1)
ΨSQ (MPa)
where Y is lint yield, and BD, SN and LW represent boll density, seed number per boll, and lint weight per seed, respectively. We assume that boll density is affected by drought first, followed by seed number per boll, and lint weight per seed based on the results that boll number is the major determinant for lint yield under different irrigation levels reported by Sharma et al. (2015). Yield losses attributable to boll density (LBD) for any given treatment (T) relative to the control (T1) can be calculated according to the following equation. LBD = YT1 × (1 - BDT/BDT1)
Treatment
(3) 141
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Table 1. The effect of irrigation treatment within a given year was quantified using a one-way analysis of variance (ANOVA) with block considered a random effect and irrigation treatment a fixed effect. The effects of irrigation treatment, cultivar, and irrigation treatment × cultivar interaction on all other parameters of interest within a given year were assessed using a mixed effects two-way ANOVA and restricted maximum likelihood (REML) method with means separation performed using LSD post hoc analysis at 0.05 alpha level. While irrigation treatment and cultivar main effects were observed for many different parameters of interest, interactions between these two effects were rarely observed, and will not be discussed further. To quantify associations between yield, water status at key growth stages, yield components, and fiber quality parameters, all parameters of interest were subjected to pairwise correlation analysis to determine the correlation coefficient and probability level for each pair of dependent variables. Since 2013 was a wet year due to high rainfall (Chastain et al., 2016b), no significant differences were observed for ΨPD among all treatments for all growth stages. In addition, interactions of cultivar and irrigation treatment were rarely observed for measured parameters. Thus, correlation analysis only evaluated effects of irrigation treatments in 2014. For brevity, only parameters significantly (P < 0.05) correlated with lint yield were presented in the table showing the results of correlation analysis.
Table 2 Two-way ANOVA results for irrigation treatment, cultivar, and irrigation treatment × cultivar interaction on yield and yield components for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Significant main effects or interaction effects are indicated in bold. Year
2013
2014
3. Results 3.1. Growth stage-dependent variation in plant water status
Parameter
Lint yield Bolls ha−1 Seedcotton weight boll−1 Lint percentage Seed index Seeds boll−1 Seed surface area Lint weight seed−1 Fibers seed−1 Fiber density Weight fiber−1 Lint yield Bolls ha−1 Seedcotton weight boll−1 Lint percentage Seed index Seeds boll−1 Seed surface area Lint weight seed−1 Fibers seed−1 Fiber density Weight fiber−1
P Value Irrigation
Cultivar
Irrigation × cultivar
0.0450* 0.0446* 0.4614 0.7742 0.1091 0.8158 0.1091 0.1861 0.0254 0.0665 0.0303* < .0001 < .0001 0.0016 < .0001 < .0001 0.0055 < .0001 0.0052 0.0006 0.0062 < .0001
0.2692 0.3366 0.0125* < .0001 < .0001 0.2485 < .0001 0.0501 0.0621 < .0001 0.8713 0.2722 0.7277 < .0001 < .0001 < .0001 0.2598 < .0001 0.0130 0.0758 < .0001 < .0001
0.5884 0.5743 0.5801 0.4556 0.8944 0.3547 0.8944 0.2918 0.3283 0.4494 0.2372 0.4595 0.4585 0.6664 0.2949 0.5243 0.7501 0.5243 0.9646 0.1107 0.0371 0.0569
* Although the P value for this effect is less than 0.05, it should be noted that the P value for the whole-model F test was > 0.05 for this particular parameter, preventing post-hoc analysis of this main effect.
In 2013, no significant irrigation treatment differences were observed for average predawn leaf water potential (ΨPD) at any of the growth stages (Table 1). Average predawn leaf water potential exceeded -0.5 MPa in each treatment. In 2014, ΨSQ, ΨEF and ΨBD were significantly affected by irrigation treatments. During squaring, predawn water potential ranged from −0.40 for T1 to −0.45 MPa for T5. For all growth stages, T1 and T2 were similar and had the highest water potentials relative to the other treatments. ΨEF values of treatments T3, T4 and T5 were 30%, 48% and 46% lower than ΨEF of T1. ΨBD values for treatments T3, T4 and T5 were 31%, 39% and 67% lower than ΨBD of T1.
first and then decreased, with T3 treatment having the greatest value. A significant cultivar effect was observed for lint percentage, seed index, SSA, and fiber density with P values < 0.0001 during the 2013 growing season (Table 2). Similarly, there was a significant cultivar effect for seedcotton weight per boll, lint percentage, seed index, SSA, lint weight per seed, fiber density, and weight per fiber during the 2014 growing season, with P values ranging from < 0.0001 to 0.0130. Even though lint yield did not vary significantly between the two cultivars in this study, lint percentage and fiber density were significantly higher for PHY499 than FM1944 (Tables 5 and 6). In contrast, seed index and SSA were markedly lower for cultivar PHY499 than cultivar FM1944. Interestingly, just during the 2014 growing season, cultivar PHY499 had 9.5%, 2.6%, and 6.8% lower seedcotton weight per boll, lint weight per seed, and weight per fiber than cultivar FM1944.
3.2. Yield components Irrigation treatment × cultivar interaction did not affect any yield component in either year, except fiber density during the 2014 growing season (Table 2). Lint yield and all yield components except fibers seed−1 did not respond significantly to irrigation treatments in 2013, primarily because the whole-model F statistic was not significant in most instances. However, irrigation treatments significantly affected lint yield and all yield component parameters with P values ranging from < 0.0001 to 0.0062 during the 2014 growing season. T1 and T2 treatments had similar lint yield in 2014, but lint yield for treatments T3, T4 and T5 were 12%, 22% and 44% lower than that of the T1 treatment (Table 3). Regarding yield components, treatments T1, T2 and T3 had similar values for bolls per hectare, seeds per boll and fibers per seed in 2014 (except seeds per boll in T3, Tables 3 and 4), but bolls per hectare, seeds per boll and fibers per seed were significantly lower in T4 and T5 treatments than the T1 treatment. For seedcotton weight per boll, seed index and SSA, no differences were measured between T1, T2, T3 and T4 treatments, but T5 treatment had the lowest values relative to other treatments during the 2014 growing season, with declines of 10.8%, 4.8% and 3.1% relative to the T1 treatment, respectively. However, lint percentage was obviously increased for the most severely stressed treatments (T4 and T5) relative to the T1 treatment, and individual fiber weight was pronouncedly increased for treatments T3, T4 and T5 relative to treatments T1 and T2 in 2014. In addition, as the ΨPD decreased, the total lint weight per seed increased
3.3. Fiber quality For all of the fiber quality parameters, irrigation treatment × cultivar interaction was only significant for micronaire in 2014 (Table 7). No irrigation treatment effect was observed for any fiber quality parameter in 2013 (P > 0.05). However, during the 2014 growing season, a significant irrigation treatment effect was observed for fiber length, micronaire and Q-score. During the 2014 growing season, treatments T1, T2 and T3 had similar values in fiber length and Q-score, which were significantly higher than those in T4 and T5 treatments (Table 8). For micronaire, there was no significant difference between T1 and T2 treatments during the 2014 growing season. Treatments T3, T4 and T5 had higher micronaire values compared to the T1 treatment, where increases of 12.0%, 22.0% and 23.0% were detected for T3, T4 and T5, respectively. There was a significant cultivar effect for all fiber qualities in 2013 and for length and micronaire in 2014 (Table 7). Generally, FM1944 had greater fiber length, fiber strength, and Q-score than PHY499 (no significance for fiber strength and Q-score in 2014, Table 9). In 142
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Table 3 The effects of irrigation treatment on yield and yield components for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons under five different irrigation treatments. Values are means (n = 8) and those sharing a common letter are significantly different (LSD; P < 0.05). Treatments are defined in the caption of Table 1 as well as in the materials and methods. Year
Treatment
Lint yield (kg ha−1)
Bolls ha−1 (no.)
Seedcotton weight boll−1 (g)
Lint percentage (%)
Seed index (g)
2013
T1 T2 T3 T4 T5 T1 T2 T3 T4 T5
1937a 1588a 2274a 1797a 2278a 1598a 1673a 1405ab 1234b 894c
1000785a 766816a 1109467a 855875a 1082297a 789458ab 837006a 692096bc 621151c 476241d
4.49a 4.72a 4.80a 4.87a 4.87a 4.84a 4.86a 4.81a 4.64ab 4.32b
43.44a 43.82a 42.97a 43.30a 43.61a 41.61c 41.00c 42.21bc 43.16ab 43.62a
9.49a 9.99a 9.98a 9.87a 10.01a 10.37ab 10.92a 10.96a 10.37ab 9.88b
2014
yield in all irrigation treatments except for the control, peaking at 104 kg ha−1 for T3. Additionally, the results in Fig. 1B showed that boll mass declines under extreme drought also contribute approximately 60 kg ha−1 (T4) to 104 kg ha−1 (T5) to lint yield loss. In contrast, drought stress has a slight, positive impact on lint percentage, thereby partially offsetting yield losses by 44 (T4) and 33 kg ha−1 (T5) under severe drought stress.
contrast, micronaire and uniformity values were lower in cultivar FM1944 relative to cultivar PHY499 (no significant difference in uniformity in 2014). 3.4. Relationships among ΨPD, yield, yield components and fiber quality Although a significant cultivar effect was observed for some yield components and fiber qualities, irrigation treatment × cultivar interaction did not affect any yield component or fiber quality parameter in either year (except fiber density and micronaire during the 2014 growing season). Thus, correlation analyses among water status at key growth stages, yield, yield components, and fiber quality parameters across both cultivars and all irrigation treatments for 2014 only since this is the year that irrigation treatment effects were observed. Both ΨEF and ΨBD were strongly associated with lint yield along with some yield components and fiber quality parameters (Table 10).The yield components except seed index had significant correlations with ΨEF and ΨBD. Fiber quality parameters, including fiber length, micronaire and Qscore had extremely strong relationships with both ΨEF and ΨBD (P < 0.05). Interestingly, micronaire had a highly significant negative relationship with both ΨEF and ΨBD (P < 0.001). Lint yield was most strongly and positively associated with boll number per hectare (r = 0.96, Table 10). To a lesser extent, lint yield was also significantly associated with total seedcotton weight per boll, seed number per boll, seed index, weight fiber−1, fibers seed−1 and lint percentage. In addition, yield component contributions to yield loss are shown in Fig. 1. Boll density represented the greatest contributor to yield loss, where losses ranged from 197 kg ha−1 in T3 to 634 kg ha−1 in the most severely stressed treatment (T5) relative to the control (Fig. 1A). Seed number per boll accounted for a much smaller and more stable portion of yield loss at 90 to 92 kg ha−1 lint yield loss in T3 through T5. By comparison, lint mass per seed had a positive impact on
4. Discussion The main goals of this study were to assess the water-induced changes in yield components contributing to yield variability, and to evaluate the effect of plant water status at key developmental stages on fiber quality for field-grown cotton. ΨPD was monitored from floral bud initiation until irrigation termination (first open boll) under five irrigation scheduling systems. High rainfall in 2013 prevented variation in ΨPD among all treatments for all growth stages (Chastain et al., 2016b), hence 2013 can be considered a wet year. In fact, ΨPD never averaged less than −0.5 MPa for any given growth stage or irrigation treatment (Table 1). This indicates that ΨPD was consistently above values shown previously to limit physiological processes and lint yield (Chastain et al., 2014, 2016a, 2016b; McMichael et al., 1973; Snider et al., 2015). Not surprisingly, almost none of the yield components, lint yield and fiber quality parameters were affected by irrigation treatment in 2013. Moreover, although the two cultivars had different yield and fiber quality characteristics (Tables 2,5,6,7 and 9), the interaction effect of irrigation × cultivar was not significant for any yield components or fiber quality parameters in either year (except fiber density and micronaire in 2014), indicating that irrigation treatment trends were similar for the two cultivars. Hence we have focused on the effects of different water-deficit levels on yield components and fiber qualities, especially in the 2014 season.
Table 4 The effects of irrigation treatment on yield components for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Values sharing a common letter within the same column were not significantly different (P < 0.05). Data are means (n = 8). Treatments are defined in the caption of Table 1 as well as in the materials and methods. Year
Treatment
Seeds boll−1 (no.)
SSA
Lint weight seed−1 (g)
Fibers seed−1 (no.)
Fiber density (no./mm2)
Weight fiber−1 (μg)
2013
T1 T2 T3 T4 T5 T1 T2 T3 T4 T5
25.71a 25.62a 26.33a 27.01a 26.60a 27.24a 26.27ab 25.39bc 25.40bc 24.71c
98.25a 101.59a 101.52a 100.81a 101.72a 104.10ab 107.67a 107.95a 104.09ab 100.84b
0.076a 0.081a 0.078a 0.078a 0.080a 0.074c 0.076bc 0.080a 0.079ab 0.076bc
15192b 16808a 15630b 15359b 15952ab 17719a 18085a 17192ab 16144bc 16011c
154.97ab 165.59a 154.37ab 152.56b 157.04ab 170.28a 168.76ab 159.58ab 155.30b 159.21ab
4.99a 4.83a 5.01a 5.09a 5.00a 4.18b 4.20b 4.66a 4.88a 4.77a
2014
143
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Table 5 The effects of cultivar on yield and yield components for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Values sharing a common letter within the same column were not significantly different (P < 0.05). Data are means (n = 20). Year
Cultivar
Lint yield (kg ha−1)
Bolls ha−1 (no.)
Seedcotton weight boll−1 (g)
Lint percentage (%)
Seed index (g)
2013
PHY 499 FM 1944 PHY 499 FM 1944
1884a 2066a 1312a 1409a
924405a 1001691a 676247a 690134a
4.55a 4.94a 4.46b 4.93a
45.11a 41.74b 43.34a 41.30b
9.39b 10.35a 9.87b 11.12a
2014
Table 6 The effects of cultivar on the rest of the yield components for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Values sharing a common letter within the same column were not significantly different (P < 0.05). Data are means (n = 20). Year
Cultivar
Seeds boll−1 (no.)
SSA
Lint weight seed−1 (g)
Fibers seed−1 (no.)
Fiber density (no./ mm2)
Weight fiber−1 (μg)
2013
PHY 499 FM 1944 PHY 499 FM 1944
25.76a 26.75a 25.57a 26.04a
97.64b 103.92a 100.81b 109.05a
0.080a 0.077a 0.076b 0.078a
16096a 15481a 17326a 16734a
164.83a 148.99b 171.78a 153.47b
4.98a 4.99a 4.38b 4.70a
2014
Table 9 The effects of cultivar on fiber quality for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Values sharing a common letter within the same column were not significantly different (P < 0.05). Data are means ± SE (n = 20).
Parameter
2013
Length Strength Micronaire Uniformity Q-Score Length Strength Micronaire Uniformity Q-Score
2014
Cultivar
Irrigation × cultivar
0.6974 0.1780 0.0931 0.0921 0.2021 < 0.0001 0.0142* < 0.0001 0.7557 < 0.0001
< 0.0001 0.0003 < 0.0001 0.0008 0.0002 < 0.0001 0.4860 0.0072 0.0022* 0.0632
0.6974 0.4544 0.1317 0.9612 0.2021 0.9259 0.7923 0.0130 0.8789 0.3289
Table 8 The effects of irrigation treatment on fiber quality for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Values sharing a common letter within the same column were not significantly different (P < 0.05). Data are means (n = 8). Treatments are defined in the caption of Table 1 as well as in the materials and methods. Treatment
Fiber length (cm)
Fiber strength (g/tex)
Micronaire
Uniformity (%)
Q-Score
2013
T1 T2 T3 T4 T5 T1 T2 T3 T4 T5
2.98a 2.98a 2.98a 3.01a 2.99a 3.03a 3.07a 3.02a 2.90b 2.82b
32.09a 31.33ab 31.14ab 31.53ab 30.85b 33.66a 34.11a 35.25a 34.93a 34.23a
5.09ab 4.96b 5.10ab 5.16a 5.06ab 4.18c 4.14c 4.68b 5.10a 5.14a
83.66ab 83.04b 83.74ab 83.20ab 83.91a 83.88a 84.00a 83.76a 83.74a 83.66a
60.25a 60.63a 59.38a 59.00a 59.63a 66.13a 66.25a 65.13a 60.00b 59.25b
2014
Fiber strength (g/tex)
Micronaire
Uniformity (%)
Q-Score
2013
PHY 499 FM 1944 PHY 499 FM 1944
2.90b 3.08a 2.89b 3.04a
30.74b 32.03a 34.33a 34.54a
5.20a 4.96b 4.58b 4.71a
83.93a 83.09b 84.10a 83.52a
58.80b 60.75a 63.70a 63.00a
Many studies have documented that drought stress limits lint yield in upland cotton (Pettigrew, 2004; Basal et al., 2009; Chastain et al., 2014; Wang et al., 2016b; Luo et al., 2018), and the current study is no exception. While significant differences in ΨSQ were observed during the 2014 season, average values were well above the −0.5 MPa threshold shown to produce maximal lint yields previously (Chastain et al., 2016b), indicating that the crop was not under drought stress during this time period for any irrigation treatment (Table 1). ΨPD during flowering and boll development in 2014 ranged from a maximum of −0.49 MPa for T1 to −0.82 MPa for T5, and lint yield was strongly associated with plant water status during these stages of development (Table 10). The results illustrated that yield variability in the current study was largely water induced due to the exceptionally dry growing season (Chastain et al., 2016b). Boll density, seedcotton weight per boll, and lint percentage ultimately determine lint yield and are considered sensitive to water deficit stress (Pettigrew, 2004). In 2014, where significantly negative effects of drought on lint yield were observed, and boll number ha−1 trends essentially matched lint yield trends, suggesting that boll density contributed to yield loss under water deficit stress. This is primarily because drought decreases total source strength (leaf area × average photosynthetic rate per unit leaf area) and the capacity to support a developing boll load (Krieg and Sung, 1986). By comparison, seedcotton weight per boll was only negatively impacted by the most extremely drought stressed treatment (T5) in 2014. All other irrigation treatments produced similar seedcotton weight per boll. These results are similar to previous observations, where boll weight was more stable than boll number in response to water deficit stress (Krieg and Sung, 1986; Lokhande and Reddy, 2014; Pettigrew, 2004; Wang et al. 2016b). Drought stress has been shown to significantly accelerate crop plant development and shorten the duration of reproductive growth (Yang and Zhang, 2006), resulting in less biomass accumulation in bolls under water deficit (Yang et al., 2014), which likely explained the lower seedcotton weight per boll in the most extremely stressed treatment (Table 3). In contrast with lint yield responses, lint percentage increased as drought stress severity increased, suggesting that drought stress favored dry matter partitioning toward fiber instead of seed. Although the response of the aforementioned yield components
* Although the P value for this effect is less than 0.05, it should be noted that the P value for the whole-model F test was > 0.05 for this particular parameter, preventing post-hoc analysis of this main effect.
Year
Fiber length (cm)
4.1. Effect of plant water status on lint yield and yield components of fieldgrown cotton
P Value Irrigation
Cultivar
2014
Table 7 Two-way ANOVA analysis of irrigation treatment, cultivar, and irrigation treatment × cultivar interaction on fiber quality for cotton grown near Camilla, GA, during the 2013 and 2014 growing seasons. Significant main effects or interaction effects are indicated in bold. Year
Year
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0.62*** 0.49** 0.72*** ——
0.48** 0.39* 0.80*** 0.20 ——
−0.38* −0.41** −0.05NS −0.41** 0.21NS ——
0.38* 0.32* 0.32* 0.31 NS 0.13 NS −0.80*** ——
−0.60*** −0.60*** −0.65*** −0.41** −0.77*** 0.11 NS −0.04 NS ——
0.61*** 0.58*** 0.69*** 0.36* 0.80*** −0.14 NS 0.21 NS −0.89*** ——
−0.58*** −0.60*** −0.33* −0.49** −0.16 NS 0.90*** −0.77*** 0.46** −0.55*** ——
0.63*** 0.63*** 0.42** 0.45** 0.30 NS −0.74*** 0.67*** −0.49** 0.61*** −0.89*** ——
0.65*** 0.69*** 0.32* 0.47** 0.20 NS −0.67*** 0.45** −0.56*** 0.57*** −0.80*** 0.78*** ——
0.69*** 0.71*** 0.39* 0.43** 0.30 NS −0.62*** 0.54*** −0.51*** 0.62*** −0.80*** 0.81*** 0.83*** ——
have been extensively studied (Pettigrew, 2004; Basal et al., 2009; Yang et al., 2014; Chastain et al., 2014; Lokhande and Reddy, 2014; Wang et al., 2016b), the response of more detailed intra-boll yield components (Groves et al., 2016; Ali and Awan, 2009) to drought have been largely unexplored. Seedcotton weight per boll is the sum of fiber weight per boll and seed weight per boll. Meanwhile, seed weight per boll is a function of seed index and seeds per boll (Ali and Awan, 2009). Results in Tables 3 and 4 showed that seed index in the T5 treatment and seeds per boll in T3, T4 and T5 treatments were significant lower than these in the T1 treatment, which would result in lower total seed weight per boll. On the other hand, fiber weight per boll is a function of seeds per boll, fibers per seed and individual fiber weight. Drought stress increased individual fiber weight in the low ΨPD treatments (T3, T4 and T5) relative to the T1 treatment (Table 4). In contrast, fewer fibers per seed were measured in the low ΨPD treatments (T4 and T5) relative to the T1 treatment. Thus, increased drought severity during flowering and boll development seems to limit the total number of fibers produced per seed, but increases individual fiber mass, with a peak in total lint weight per seed observed in T3 (the −0.7 MPa ΨPD treatment). Lint weight per seed was positively correlated with lint percentage, and breeding for high lint weight per seed will likely increase seed size since larger seed have greater surface area to produce fibers (Groves et al., 2016). In the present study, the change in SSA could not explain the changes in lint percentage, since lint percentage increased with the decreased ΨPD (Table 4), but SSA increased first then declined with decreasing of ΨPD (Table 3). Lint percentage measures the relative proportion of lint and seed in seedcotton (Groves et al., 2016), and in the present study, it could be a function of seed index and lint weight per seed. Thus, larger lint percentage under T3 and T4 treatments relative to the T1 treatment should be attributed to higher lint weight per seed (Table 4), but larger lint percentage in the T5 treatment relative to the T1 treatment was likely due to lower seed index. The results in Table 10 showed that boll number ha−1 was more strongly correlated with ΨEF and ΨBD, and lint yield than any other yield component, indicating that boll density was the most sensitive yield component to water deficit stress and had the most important impact on yield variability under different irrigation levels. Lint yield losses attributed to boll ha−1, seed number boll−1, lint mass seed−1 were provided in Fig. 1A, which showed that lint mass seed−1 had a positive impact on yield in all irrigation treatments except for the control. This indicates that increased lint mass seed−1 can partially offset the negative impacts of drought stress on other yield components. Boll ha−1 represented the greater contributor to yield loss than seed number boll−1 in T3 through T5. Additionally, lint yield loss attributable to boll density, individual boll weight, and lint percentage and was estimated in Fig. 1B, which indicated that boll number also is the dominant contributor to yield loss among the three yield components. This is consistent with a report by Sharma et al. (2015) that boll density was the major driver of water induced yield variability in field-grown cotton but boll mass also contributed a small percentage to final lint yield response. In contrast, drought stress has a slight, positive impact on lint percentage, thereby partially offsetting yield loss under drought stress. Therefore, based on the aforementioned observations, we conclude that yield limiting drought stress decreases boll size due to a decline in seed number per boll but that these declines are partially offset by increases in lint percentage and lint mass per seed. As a result, total yield loss under drought is nearly indistinguishable from yield loss driven by a reduction in boll density alone (Fig. 1A).
* = P < 0.05. ** = P < 0.01. *** = P < 0.001, NS = not significant.
0.97*** —— Lint yield Boll ha−1 Seedcotton weight boll−1 Seeds boll−1 Seed index Weight fiber−1 Fibers seed−1 Lint percentage Fiber length Micronaire Q-score ΨEF ΨBD
——
0.66*** 0.49** ——
ΨBD ΨEF Q-score Micronaire Fiber length Lint percentage Fibers seed−1 Weight fiber−1 Seed index Seeds boll−1 Seedcotton weight boll−1 Boll ha−1 Lint yield
Table 10 Pairwise correlation coefficients for predawn leaf water potential, yield components, and fiber quality for both cultivars grown under five different irrigation treatments near Camilla, GA, for the two cultivars PHY 499 and FM 1944 during the 2014 growing season. For brevity, only parameters significantly (P < 0.05) correlated with lint yield were presented.
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4.2. Effect of plant water status on fiber quality of field-grown cotton Fiber strength did not differ among irrigation treatments (Tables 7 and 8). Similarly, Enciso et al. (2003) also reported that there was no significant effect of drought on fiber strength, whereas Wang et al. (2016b) and Dağdelen et al. (2009) found lower fiber strength when 145
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Fig. 1. Yield loss contributions attributed to individual yield components for five different irrigation treatments during the 2014 cotton growing season at a field site near Camilla, Georgia. Scenario (A) considered yield a product of boll density, seed number per boll, and lint weight per seed, whereas scenario (B) considered yield the product of boll density, individual boll weight, and lint percentage. Values are means ± standard error (n = 8).
Moreover, among these yield component traits, water-induced yield loss was most strongly associated with boll density, because seed number and boll mass-induced yield losses were partially offset by increased dry matter partitioning to fiber growth. Fiber strength and length uniformity did not differ among irrigation treatments. Fiber length declined with increased drought severity, whereas fiber micronaire increased in the most severely stressed treatments. This indicates that increasing drought severity during flowering and boll development decreases the number of individual fibers per seed and their final length, but their thickness is increased. The end result is a general decline in the overall fiber quality assessment (Q-score).
soil moisture deficits were imposed. In addition, length uniformity was not significantly altered by water deficits, which is similar to the results reported by Pettigrew (2004). Consistent with previous studies (Lokhande and Reddy, 2014; Wang et al., 2016b), fiber length decreased under the most severe water deficit treatments (T4 and T5) compared with the well-watered (T1) treatment (Table 8). Compared with fiber length, micronaire is considered more sensitive to environmental changes (Wang et al., 2016b), but micronaire has been shown to increase (Pettigrew, 2004) and decrease (Marani and Amirav, 1971) under drought conditions in prior studies. Wang et al. (2016b) stated that the effects on micronaire primarily depended on the intensity of drought. In support of their conclusion, our results showed that micronaire was not significantly different between the highest yielding treatments (T1 and T2), but micronaire increased with increasing drought severity in 2014 (Table 8). Mechanistically, it appears that severe drought decreases the number of fibers produced per seed and individual fiber length, but individual fibers are thicker and heavier than those produced by well-watered plants. A challenge for cotton breeding efforts and fiber processing is to determine which fiber quality or group of parameters should be given priority. Hence Q-score could be used to provide an overall assessment of fiber quality by normalizing fiber quality parameters and differentially weighting fiber length, strength, micronaire, and uniformity (Bourland et al., 2010), and has been used in cotton breeding programs to facilitate the identification of lines having the superior fiber quality (Bourland and Jones, 2012; Campbell et al., 2015; Bourland and Jones, 2018). The Q-score was significantly decreased in the T4 and T5 treatments compared with the T1 treatment (Table 8), suggesting that overall fiber quality was not affected by mild water deficit but was decreased by severe water deficit.
Acknowledgments The authors thank the Georgia Cotton Commission for funding this research and the University of Georgia for providing facilities to carry out this work. We also thank Lola Sexton, Dudley Cook, Calvin Perry, Will Vance, Jenna Pitts, Tyler Beasley, and Keri Dixon for their assistance in the field. References Ali, M.A., Awan, S.I., 2009. Inheritance pattern of seed and lint traits in cotton (Gossypium hirsutum). Int. J. Agric. Biol. 11, 44–48. Basal, H., Dagdelen, N., Unay, A., Yilmaz, E., 2009. Effects of deficit drip irrigation ratios on cotton (Gossypium hirsutum L.) yield and fibre quality. J. Agron. Crop Sci. 195, 19–29. Bourland, F., 2013. Novel approaches used in the university of Arkansas cotton breeding program. In: San Antonio, TX. Proc. Beltwide Cotton Prod. Res. Conf.. pp. 7–10. Bourland, F.M., Hogan, R., Jones, D.C., Barnes, E., 2010. Development and utility of Qscore for characterizing cotton fiber quality. J. Cotton Sci. 14, 53–63. Bourland, F.M., Jones, D.C., 2012. Registration of ‘UA222’cotton cultivar. J. Plant Reg. 6, 259–262. Bourland, F.M., Jones, D.C., 2018. Registration of Arkot 0705 and Arkot 0711c germplasm lines. J. Plant Reg. 12, 246–250. Campbell, B., Jones, M., Greene, J., Jones, D., 2015. Registration of PD 06001 and PD 06078 germplasm lines of cotton. J. Plant Reg. 9, 363–366. Chastain, D.R., Snider, J.L., Choinski, J.S., Collins, G.D., Perry, C.D., Whitaker, J., Grey, T.L., Sorensen, R.B., van Iersel, M., Byrd, S.A., 2016a. Leaf ontogeny strongly influences photosynthetic tolerance to drought and high temperature in Gossypium hirsutum. J. Plant Physiol. 199, 18–28. Chastain, D.R., Snider, J.L., Collins, G.D., Perry, C.D., Whitaker, J., Byrd, S.A., 2014. Water deficit in field-grown Gossypium hirsutum primarily limits net photosynthesis by decreasing stomatal conductance, increasing photorespiration, and increasing the ratio of dark respiration to gross photosynthesis. J. Plant Physiol. 171, 1576–1585. Chastain, D.R., Snider, J.L., Collins, G.D., Perry, C.D., Whitaker, J., Byrd, S.A., Oosterhuis,
5. Conclusions In the current study, water-induced yield variability was primarily correlated with crop water status at the flowering and boll development phase of crop growth. Moderate to severe drought significantly decreased boll density (bolls ha−1) and decreased individual boll mass by decreasing seed index, seeds per boll, and fibers per seed. Increasing severity of drought stress decreased fiber density but increased individual fiber weight, producing a peak in total fiber weight per seed in the treatment irrigated according to a ΨPD threshold of −0.7 MPa. 146
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