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Article

Effect of Controlled Defoliant Application on Cotton Fiber Quality

by
Jyoti Neupane
1,
Joe Mari Maja
1,*,
Gilbert Miller
2,
Michael Marshall
3,
Matthew Cutulle
3 and
Jun Luo
4
1
Department of Agricultural Sciences, Clemson University, 240 McAdams Hall, Clemson, SC 29634, USA
2
Edisto Research and Education Center, Clemson University, 64 Research Road, Blackville, SC 29817, USA
3
Department of Plant and Environmental Sciences, Clemson University, 171 Poole Agricultural Center, Clemson, SC 29634, USA
4
School of Mathematical and Statistical Sciences, Clemson University, Martin O-15, Clemson, SC 29634, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(9), 5694; https://doi.org/10.3390/app13095694
Submission received: 24 March 2023 / Revised: 2 May 2023 / Accepted: 4 May 2023 / Published: 5 May 2023
Browse Figures
Versions Notes

Abstract

:
Cotton (Gossypium hirsutum L.) is an important industrial crop. It is a perennial crop and has indeterminate growth habit, however, in most parts of the United States, it is grown as an annual crop with the application of growth regulators. Defoliation is a major production practice influencing harvester efficiency, fiber trash content, cotton yield, and fiber quality. Currently, defoliant application is done by using a conventional boom sprayer and aerial spraying in which both systems spray chemicals horizontally downwards from the top of the canopy, which causes minimal chemical to reach at bottom canopy. However, for successful defoliation, each leaf must receive the defoliant as chemicals do not move within the plant. Thus, a new autonomous ground sprayer was developed using robotics and pulse width modulation, which can be run in between two rows covering the whole canopy of the plant. Field research was conducted to study the effect of three duty cycles (20%,40%, and 60%) on cotton fiber quality using autonomous spraying system. The result indicated that the defoliants could be applied safely at either of the three duty cycles without compromising most of the fiber quality parameters except for nep/gm, length (Ln), L (5%), SFC, trash content in field 1 and micronaire, nep size, length (Ln), L (5%), SFC, and fiber fineness in field 2 which were significant. However, application of 20% duty cycle will significantly reduce the chemical use therefore, 20% duty cycle could be a good choice for the autonomous cotton defoliation. The development of autonomous spraying technology would encourage the development of new sprayer industries and the unmanned ground vehicle industries, together with the need for the global development of an agricultural system with broad market prospects and brings about huge economic, social, and ecological benefits.

1. Introduction

Cotton (Gossypium hirsutum L.) is a very important industrial crop [1]. It is a perennial crop which has indeterminate growth habits. However, in most parts of the United States of America (USA), it is grown as an annual crop with the application of growth regulators [2]. It has a long history of cultivation and domestication [3]. Cotton has a huge role in the economy of the USA as the country is the third largest cotton producer and one of the leading cotton exporters in the world [4]. Cotton is grown primarily in 17 southern-States known as the “Cotton Belt.” Among them, Texas is the largest producer contributing approximately 40 percent of USA cotton, then followed by Georgia, Mississippi, and Arkansas. The USA produced approximately 20 million cotton bales contributing nearly 7 billion dollars to the USA economy during the marketing year (MY) 2019, i.e., August 2019–July 2020 [5]. Even though South Carolina is not a top cotton-producing state, cotton is one of the main cash crops of this state.
Defoliation is the falling of leaves mainly because of maturity, senescence, and injury. During defoliation, water-conducting tissues remain alive until the leaf drops–defoliation differs from desiccation. Defoliation is a physiological phenomenon in which an abscission layer develops between the petiole and stem. The enzymatic action of plant enzymes such as cellulase and pectinase then digest the cell wall and middle lamella of the abscission zone, which causes leaves to separate from the stem and drop from the plant. Plant hormones, such as abscisic acid and ethylene, play a major role in the development of abscission layers, whereas auxins discourage abscission layer development. [6]. Although defoliation is a natural phenomenon, untimely leaf fall requires the spraying of chemical defoliants to prepare the cotton plant for mechanical harvesting. Typically cotton defoliants are applied approximately two weeks prior to the expected harvest date. Appropriate and timely defoliation plays a major role in increasing harvesting efficiency, minimizing trash content, and improving the fiber quality [7].
Depending on the mode of action, defoliants are herbicidal or hormonal. Herbicidal defoliants injure the plant causing ethylene production which encourages leaf drop [8]. Some examples of herbicidal defoliants are Carfentrazone-ethyl, Thidiazuron, Diuron, and Tribufos. For the herbicidal defoliants, which do not move within the plant, adequate chemical penetration and the appropriate spraying pattern are necessary for adequate defoliation. Therefore, each leaf should receive the chemical for a successful leaf drop. However, hormonal defoliants encourage ethylene production and inhibit auxin transport in the plant, which encourages leaf abscission [9]. In the USA, chemical defoliants such as Tribufos, Dimethipin, and Ethephone are widely used [10,11]. The defoliants do not directly influence boll opening thus, they must be applied in combination with a boll opener, to provide satisfactory defoliation and boll opening [12,13]. Poor defoliation can lower the fiber quality while defoliating too early lowers yield and micronaire [14]. As leaves are the major source of carbohydrates production through photosynthesis, early defoliation can interfere the production and movement of energy from leaves to growing cotton bolls [15]. Therefore, cotton defoliation is often practiced when 60% of bolls are opened to avoid loss in yield and fiber quality [16]. However, Karademer et al. reported that defoliation is possible even when 40% of the cotton bolls were opened as they found insignificant results on seed cotton yield, ginning percentage, and fiber quality across different percentages of boll opening [14]. Although several factors affect cotton yield and fiber quality; timing of defoliation application [17], type of chemicals used [18], and spraying technologies are major factors [19].
With the recent advances in agriculture, new technologies have been adopted, such as the application of artificial intelligence [20], plant disease detection [21,22], soil, crop, weed, and disease management [23]. Cotton growers also adopt these technologies, especially for defoliation and mechanical harvesting. Conventional boom sprayers are widely used for defoliant applications [9]. The crop duster is another concept of agriculture spraying in which chemicals are applied in powdery form using aerial vehicles (unmanned or manned aircraft) [24,25] and most recently the use of unmanned aerial vehicles for cotton defoliant applications has also been increasing [26,27]. However, in all systems of chemical application, the spraying is done horizontally downward from the top, and a minimum of defoliant droplets reaches the lower canopy of the plant due to high wind turbulence (in the case of drone) and interlocking of branches and leaves on upper canopy of the plant. But for successful defoliation, each leaf must receive the defoliant as chemicals do not move within the plant [11]. Unfortunately, there are no study yet specific for cotton defoliation on the whole canopy. Therefore, to address this research gap, a new spraying system was developed using unmanned ground vehicles and pulse width modulation technology [28]. The development of autonomous spraying technology would encourage the development of new sprayer industries and the unmanned ground vehicle industries, together with the need for the global development of an agricultural system with broad market prospects and brings about huge economic, social, and ecological benefits.
Fiber quality is very important for cotton production. There are two methods mainly used for cotton fiber quality measurement: high volume instrument (HVI) and Advanced Fiber Information System (AFIS). The HVI system was developed with the aim of replacing manual fiber quality measurement methods by developing the instrumental fiber quality measurement method [29]. HVI measures fiber parameters such as upper half mean length (UHML), uniformity index (UI), strength, elongation, trash, reflectance, and yellowness (color grade). HVI is faster and more cost-effective than other methods; however, HVI doesn’t provide complete within-sample variation in fiber length compared to AFIS [29,30]. In AFIS, firstly, the fibers are individualized and then presented to an electrooptical sensor aerodynamically for the measurement of different fiber quality parameters [31]. In contrast, in the case of HVI, the fiber length measurement is based on the principle of fibro gram and based on a quick assessment of a bundle of fiber [32].
The use of robotics has been widely increasing in various sectors such as medical [33], business [34,35], and agriculture [36]. To our knowledge, no studies thus far have addressed the autonomous ground spraying platform for cotton defoliation covering the whole canopy of the plant. Thus, this study’s results concerning the effect of defoliant application through the autonomous robotic platform and pulse width modulation on yield and fiber quality parameters analyzed by HVI and AFIS separately and provide new references and bases for further improving the cotton defoliant spraying technique. Therefore, the study was conducted to evaluate the effect of different duty cycles (defoliant rate) on cotton fiber quality parameters.

2. Materials and Methods

2.1. The Unmanned Ground Vehicle (UGV) and Sprayer Unit

The autonomous platform (Husky A200, Clearpathrobotics, Kitchener, ON, Canada) at the Sensor and Automation Laboratory was used for this research (Figure 1). For the navigation purpose, the platform is equipped with an Inertial Measuring Unit (UM7, CH Robotics, Melbourne, Australia), Global Positioning System (Swiftnav, Swift Navigation, San Francisco, CA, USA), motors, encoders, and laser scanner (UST-10LX, Hokuyo, Osaka, Japan).
The sprayer unit (Model #1598042, County Line, USA) is a 94 L 2-nozzle trailer sprayer with a built-in 12V diaphragm 9.5 L/min pump. According to the specification, the rated pressure of the pump was 482 KPa. The sprayer was retrofitted with 6 nozzles (Model #625147-001, Capstan Ag Systems Inc., Topeka, KS, USA), and all valves, O-rings, flynut, and other sprayer parts were provided by Wilger Inc. (Wilger Industries, Saskatoon, SK, Canada). The nozzle from the bottom to the top was designated as the first, second, and third nozzles on both sides. An aluminum extrusion was used to hold the nozzles, and the third nozzle was positioned at an angle of 40 degrees. Three hex screws can adjust the extrusion holding the third nozzle. This is intended for crop height changes during the field test. The distance between the first and second nozzles was based on the spread of the tip used, while the third was based on the height of the crop. Some other characteristic parameters of UGV and the sprayer unit are presented below in Table 1 and Table 2, respectively.

2.2. Chemical Defoliants

A mixture of two defoliants and one boll opener was used for defoliation which are presented below (Table 3). All the chemicals applied, and compositions are commonly used by the Edisto Research and Education Center (EREC) farm crew for the defoliation of the station cotton.

2.3. Experimental Field, Cotton Cultivars, and Planting Schedule

The experiment was carried out in EREC research fields at Blackville SC, USA and field trials was conducted in two fields at the research farm (Field 1: 33.347, −81.319; Field 2: 33.353, −81.310). Cotton has been planted in the experimental fields for many years. Delta pine cotton cultivars (DP 2038B3XF and DP 2055) were planted in fields 1 and 2, respectively. In Field 1, cotton was planted in early May of 2022 in six rows for research treatment and two rows for control treatment and field was further divided into two plots. In Field 2, cotton was planted in late May of 2022. Similar to field 1, there were six rows for research treatment and two rows for control treatments, but field 2 has single plot and was smaller than Field 1. Thus, there were 16 and 8 cotton rows in Fields 1 and 2, respectively. The typical cotton row spacing in the research farm range was 97~114 cm. However, the research plot used in this research employed a skip row planting pattern to facilitate the movement of the autonomous sprayer. Thus, the actual row spacing for the research field was 193~229 cm, whereas, in the control plot, it was 97~114 cm. The management practices followed the South Carolina Cotton Grower’s Guide [11].

2.4. Experimental Design and Treatments

Completely randomized experimental design (CRD), with 4 replications in Field 1 and 2 replications in Field 2 was used. The sprayer system was set up at the Sensor and Automation Lab using the sprayer controller developed by the same lab. The volume output of each nozzle was tested at a different duty cycle (20%, 40%, 60%, 80, and 100%) by measuring the water output volume from each nozzle in 20 s. The duty cycle describes the amount of time a signal is in its on-state, and it is a concept of electronics used in pulse width modulation technology. In this research, the 20% duty cycle represents 20% of the nozzle orifice opening. Generally, spraying in the grower’s field is done without a spray controller where the nozzle orifice is 100% open during spraying. This preliminary laboratory test was made to study if each nozzle would generate the same volume with the same duty cycle settings. Based on the preliminary test result, three duty cycles were selected as a treatment for the research field (20%, 40%, and 60%), and a conventional tractor-mounted boom sprayer was a control treatment. The defoliant was applied on 10 October and 24 October 2022 in field 1 and field 2 respectively. The cotton bolls were harvested manually approximately 20 days after the treatment application i.e., 1st and 17th November 2022 in field 1 and field 2 respectively.

2.5. Data Collection and Analysis

A total of 120 plants (field 1 = 80 and field 2 = 40) were randomly selected. Before the treatment application, the selected plants were tagged with red thread to designate the same plant for multiple data collection. For the control treatment, the conventional tractor mounted sprayer was use for spraying chemical on the other side of the research field. Same chemical defoliant was used in research plot and control plot. The spraying was done approximately 20 days before the harvesting date in both fields.
To study the cotton yield and fiber quality, cotton bolls from 3 m (10 foot) length row were harvested manually, as shown in Figure 2A. Approximately 300 gm of seed cotton per sample was selected for ginning (Figure 2B), which was done in one of the laboratories at the research center. The 10 Saw Eagle Cotton Gin machine was used for ginning (Figure 3A). It is v-belt driven by a 1-1/2 horsepower electric motor and has 10-inch (254 mm) diameter saws with top and bottom mounted cast ribs. It consists of a rotating doffer brush which removes lint from the saw and delivers it to the lint storage section. The data on lint turnout and the seed was taken. The cotton fiber of approximately 65 gm per sample (Figure 3B) was sent to the Cotton Incorporated Laboratory for HVI and AFIS analysis (USTER AFIS PRO 2). One-way ANOVA and Tukey test were done to study treatment effects and mean separation using statistical software (R version 4.1.2 (1 November 2021), The R Foundation for Statistical Computing Platform, Vienna, AT).

3. Results and Discussion

3.1. Fiber Quality Analysis by HVI

The defoliation timing, strategy, and leaf pubescence characteristics can impact the efficacy of defoliation and the fiber quality parameters [37,38]. There were several studies on cotton fiber quality assessment [39,40,41,42]; however, no information is available on the effects of defoliant dosage with the application of cotton defoliation with side spraying covering the whole canopy of the plant (i.e., bottom, middle, and upper). This work focused on the cotton fiber quality assessment of the newly developed sprayer prototype which can autonomously run and sprays defoliants in between the cotton rows covering whole canopy of the plant. The study indicated that defoliants dosage (20%, 40%, and 60% duty cycle) had no significant effect on cotton fiber quality in both fields (Table 4) except the Micronaire in field 2 (Table 4) which has significant result. The treatments 20% and 60% duty cycle has statistically higher micronaire value in field 2 which means that the fiber is more matured and thicker as compared to other treatments in field 2. Therefore, this showed that the defoliants could be applied safely at any of the three duty cycles without affecting these cotton fiber quality parameters. However, applying a 20% duty cycle will significantly reduce the use of chemicals; therefore, it will be a good choice for cotton defoliation.

3.2. Quality Parameters Analyzes

Micronaire provides information on the thickness of the cotton fiber’s cell wall, which indicates fiber fineness and maturity. Low micronaire values indicate fine and/or immature fibers; high values indicate coarse and/or mature fibers. The fineness factor in micronaire is considered more important for spinning because it affects further processing of fiber, and fiber maturity is considered to have a significant effect on the dye-uptake process. Micronaire values between 3.8 and 4.2 were considered as desirable during early 2000 [43]. Similarly, the micronaire value of 3.5 to 4.9 is considered as MIC G5 which is standard range for good quality cotton [44]. The micronaire data from the research are within the range of good quality cotton, however, less than the overall average, and South Carolina’s average data on micronaire mentioned in “Quality summary of 2022 U.S. upland cotton—by state” reported by cotton incorporated, which are 4.35 and 4.39 respectively [45]. Many factors affect the fiber micronaire such as planting dates, cultivar, agronomy practice, crop load [46], and weather [47].
The upper half mean length (UHML) is average length of the upper half of the longest fibers which is equivalent to staple length. It is an important quality parameter as fiber fineness and fiber tensile strength are closely related to UHML. The longer staples are usually finer and stronger than the shorter staples. The fiber properties such as UHML are reported to be affected by compact yarn spinning processes [48].
The fiber strength in the HVI system is measured in terms of force in grams required to break one tex unit of fibers when clamping in between the two sets of jaws. The data on the strength is statistically insignificant across the treatments in both fields (Table 3 and Table 4). The fiber falls in the strong strength category (range is 29–30) except for the fiber from the control treatment in field 2 based on the cotton classification published by Cotton Incorporated and Cotton USA [49].
Fiber elongation is another fiber quality parameter. A positive correlation was reported between individual fiber elongation and tenacity [50]. In both Fields, the elongation of the fiber is statistically insignificant.
The reflectance (Rd) and yellowness (+b) determine the color grade of the cotton fiber. Reflectance denotes the brightness of the fiber, whereas yellowness indicates the degree of pigmentation. Planting time and harvesting time have a significant role in the fiber color grade [51]. Similarly, a significant effect of reflectance and yellowness was reported on some yarn properties [52]. The HVI color chart for American upland cotton ranges from 4 to 18, and the yellowness (or pigmentation) increases with an increase in value of +b. Likewise, the reflectance ranges from 40 to 90; the higher the value, the whiter (light) will be the fiber color.
Higher trash content in fiber negatively affects the fiber quality, and depending on the particle size, it can cause yarn breakage and may worsen the spinning stability. As in the delivered bale condition, the trash content of raw cotton is nearly in the range of 1–7% [53]. The trash in both fields is insignificant across the treatment. Therefore, we can apply any duty cycle safely for defoliation from the perspective of trash content.
Short fiber is defined as percent by weight of fibers which is 0.5 inches or less. The presence of excess amounts of short fibers in cotton is not good for the spinner and can cause many problems, such as excess waste, weaken yarn strength, and more yarn defects [54]. Both fields have an insignificant result on short fiber index within the fields. However, if we compare the data between the two fields, field 2 has a lower SFI value than field 1.
The ratio between the mean length of the fiber and the upper half mean length is known as the uniformity index of cotton fiber. In field 1, the value of UI is in the range of 80–82, which is defined as average UI, and in field 2, the range is 83–85, which is defined as high UI according to the US cotton fiber chart published by cotton incorporated [49]. The cultivar is an important determining factor for the uniformity index [55]. Besides cultivars, some production practices could also reduce uniformity, such as early defoliation and harvesting methods [55]. Similarly, post-harvest handling methods such as ginning also have a significant effect on uniformity index and other quality parameters [56]. In our research, harvesting was done manually in both fields however, the defoliation was done two weeks earlier in field 1, which might have cause for the lower uniformity compared to field 2. But at the same time, we need to consider the planting time which is almost two weeks late in field 2 as mentioned in the methodology section. Likewise, the cotton cultivar was different which could also be a major source for different UI in between the fields.

3.3. Fiber Quality Analyze by Advance Fiber Information System (AFIS)

The AFIS generates data on 20 different variables which are categorized on different categories and discussed below.
A.
Information on Neps Parameter
Neps are the entangled and knotted fibers that are formed during cotton harvesting or the ginning process. Approximately 5–20 fibers found to be knotted together to form a single Neps. Harvesting methods have a significant role in Neps; manually harvesting cotton results in fewer neps than mechanically harvested cotton. The existence of Neps in a cotton bale is unavoidable. A cotton bale with about 100 to 200 fiber Neps per gram is considered the best-case scenario, whereas 200 to 350 Neps/gm considers the range of a manageable level [57]. The cotton in each field was manually harvested. Nep size in field 1, and Nep/gm in field 2 has an insignificant result (Table 5). Similarly, the control has statistically higher Nep/gm in field 1, and higher Nep size in field 2. In both field, Nep/gm is within the best to manageable range. Similarly, AFIS also provides the information on Seed Coat Neps (SCN). The seed coats that remain with the fiber are seed coat neps. In both field the SCN is statistically insignificant.
  • B. Information on length parameter
AFIS is a count-based system, and values given on a number basis are actual measurements, whereas values given on a weight basis are calculated. In the table (n) represents the number basis, and (w) represents weight basis measurements. Fiber length is critical as it greatly influences the end use of fiber and the process needed for fiber transformation [58]. Also, the AFIS analysis for cotton fiber length and diameter provides a close estimation of fiber behavior in the spinning process [59]. In both fields, the fiber length (number basis) is statistically similar between the treatments (Table 6), however, the control showed statistically significant result and has lowest mean length in both fields. Likewise, coefficient of variation of fiber length (Ln CV%) showed statistically insignificant result on field 2 whereas it was significant on field 1 and 40% duty cycle and control had statistically higher coefficient of variation of fiber length. Another length parameter is L5% i.e., 5% span length and had statistically significant effect on both fields. In field 1, 40% duty cycle and in field 2, 20% duty cycle had higher 5% span length. Different result is observed on the weight basis measurement where 60% duty cycle has the lowest mean length in field 1, and no difference in length was observed in field 2. Some literature mentioned weight basis measurement as “length-biased distribution” [58]. Therefore, the fiber length property in this paper will be concluded according to number-based results. Zurek et al. (1999) mentioned that the selection of improved fiber length distribution was accomplished by AFIS method for their cotton breeding research, and the range of fiber length in their research was 18.8 mm to 24.6 mm, which is close is the length observed in our research [60].
The SFC was found to influence most of the yarn properties, such as yarn strength, irregularity, and frequency of thick and thin defects. This is justified by the strong correlation with each of the three measures of short fiber content [61]. In field 1, a 60% duty cycle was found to have lower SFC as compared to other treatments. Similarly, in field 2, 60% and 40% duty cycles were found to have lower SFC values. Kelly et al. (2012) reported a mean SFCn of 21% to 30% based on AFIS analysis done for plant breeding purposes, whereas the SFCn value ranges from 20.56–25.60% in our research. The lower the SFC, the better the fiber quality.
  • C. Information on trash parameter
Trash measures the amount of non-lint material in cotton, such as leaves and bark. The major causes of trash are crop management, harvest, and post-harvest processing such as ginning. The presence of trash in cotton fiber quality was reported to degrade the quality of HVI and AFIS length measurements [62]. The total (cnt/g), trash size, dust, trash (cnt/g), and VFM %variables are statistically insignificant across the treatments in both fields (Table 7).
  • D. Information on fiber fineness, maturity ratio, and immature fiber content (IFC)
The fiber fineness has a great role in determining the stiffness or softness of fabric, twisting property during yarn formation, strength, and uniformity of a yarn and neps formation [63]. Likewise, fiber maturity is determined by fiber wall thickness, and immature fibers have a minimum wall thickness, possibly due to interruption or retardation of secondary cell wall cellulose biosynthesis during cotton fiber development. The higher proportion of immature fiber deteriorates the fiber quality by decreasing breaking strength and increasing neps [64]. Similarly, the fiber maturity ratio is very crucial property and is directly proportional to the degree of wall thickening. Thus, the immature fibers have a very small maturity ratio with little or no secondary wall thickening [65]. In field 1, there is no significant effect of treatment on the fineness, IFC, and mat ratio whereas in field 2, treatments have no effect on IFC and maturity ratio, however, fineness is significant, and 60% duty cycle has higher fiber fineness as compared to other treatments (Table 8).

4. Conclusions

In conclusion, the results from this study indicated that the autonomous ground sprayer with any duty cycles (i.e., 20%, 40%, or 60%) could be used safely for cotton defoliation without affecting the fiber quality. The defoliant dosages did not significantly affect most of the fiber quality parameters. Meanwhile, some parameters showed significant results. In field 1; nep/gm, length (Ln), L (5%), SFC, trash content, and in field 2; micronaire, nep size, length (Ln), L (5%), SFC, and fiber fineness were significant. In the big picture of the research, while studying the result closely, the parameter with the significant result has a better result on our newly developed sprayer (i.e., on the research plot) compared to the control plot where spraying was done by conventional tractor mounted boom sprayer. Thus, the combined application of an unmanned ground vehicle and a controlled sprayer has a great potential to contribute to the advancement of precision technology in the agricultural sector. Moreover, the research results could guide further study of autonomous ground sprayers, and this technology is useful not only for cotton, but also for any other agricultural crops.

Author Contributions

Conceptualization, J.M.M., J.N. and G.M.; methodology, J.M.M., J.N., J.L., G.M., M.M. and M.C.; software, J.N. and J.M.M.; validation, J.N. and J.L.; formal analysis, J.N. and J.L.; investigation, J.N., J.M.M., G.M., M.M., M.C. and J.L.; resources, J.M.M., G.M., M.M. and M.C.; data curation, J.N.; writing—original draft preparation, J.N. and J.M.M.; writing—review and editing, J.N., J.M.M., G.M., M.M., M.C. and J.L.; visualization, J.M.M.; supervision, J.M.M., G.M., M.M., M.C. and J.L.; project administration, J.M.M.; funding acquisition, J.M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was partially supported by a grant from Cotton Inc., Project No. 17-209 and is based on work supported by NIFA/USDA under project number SC-1700611. The authors thank Clemson University Professional Internship and Co-op Program (UPIC). Student engagement is one of Clemson University’s areas of investment. To meet this goal, Clemson developed an on-campus internship and co-op program (UPIC) in 2012 to allow students to work closely with a member or members of Clemson’s faculty or administration in an on-campus or Clemson-affiliated position.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank Edward Barnes of Cotton Inc. for funding our project. We would also like to thank Wilger Inc. for providing sprayer materials and the Edisto-REC Shop (Tim Still and Kim Still) for the help with the spraying equipment. Likewise, the authors would like to extend our gratitude to Andi Koretsky and the staff of the Cotton Inc. Product Evaluation Laboratory for processing the cotton samples.

Conflicts of Interest

The authors declare no conflict of interest.

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Applsci 13 05694 g001 550
Figure 1. UGV equipped with spraying unit.
Figure 1. UGV equipped with spraying unit.
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Figure 2. Manual cotton harvesting (A) and sampling cotton for ginning (B).
Figure 2. Manual cotton harvesting (A) and sampling cotton for ginning (B).
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Figure 3. Ginning of cotton (A) and sampling cotton lint for quality analysis (B).
Figure 3. Ginning of cotton (A) and sampling cotton lint for quality analysis (B).
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Table
Table 1. UGV Parameters used in this work.
Table 1. UGV Parameters used in this work.
Parameter (UGA)
External dimensions (mm)990 × 670 × 390
Internal dimensions (mm)296 × 411 × 155
Weight (kg)50
Max payload (kg)75
Speed (m/s)1
Table
Table 2. Spraying unit parameters used in this work.
Table 2. Spraying unit parameters used in this work.
Parameter (Spraying unit)
Nozzle typesHollow conical nozzle
Nozzle numbersix
Pressure (kpa)414
Nozzle height from ground (cm)38, 84, and 145
Flowrate0–100%
Tank capacity (L)94
Table
Table 3. Information on chemicals used for the research.
Table 3. Information on chemicals used for the research.
Product
Formulation		Active IngredientRate (per 38 L)Remarks
Folex 6 ECTribufos 719 g/L454 gCotton defoliant
Free fall SCThidiazuron 42.4%91 gCotton defoliant
Super bollEthephon 719 g/L907 gBoll opener
Table
Table 4. Effect of defoliant dosage on fiber quality of field 1 and field 2 cotton analyzed by high volume instrument (HVI). (Values not sharing a common letter within treatments are significantly different (p ≤ 0.05)).
Table 4. Effect of defoliant dosage on fiber quality of field 1 and field 2 cotton analyzed by high volume instrument (HVI). (Values not sharing a common letter within treatments are significantly different (p ≤ 0.05)).
Field#TreatmentMicUHML
(Inch)		UI
(%)		SFI
(%)		Str
(g tex−1)		Elo
(%)		Rd+bTrash
(Count)
120%4.19 a1.12 a82.32 a8.75 a29.92 a6.83 a83.38 a7.28 a5.25 a
40%4.30 a1.13 a82.31 a8.93 a30.05 a6.76 a83.35 a7.11 a8.37 a
60%4.30 a1.12 a82.06 a8.46 a30.00 a6.60 a83.16 a7.35 a5.37 a
Control3.96 a1.13 a81.92 a9.42 a29.95 a6.62 a84.00 a6.97 a10.75 a
220%4.05 a1.26 a83.56 a6.75 a30.06 a8.13 a78.51 a7.60 a9.16 a
40%3.83 ab1.26 a83.81 a6.50 a30.25 a8.33 a78.35 a7.43 a8.50 a
60%4.09 a1.25 a84.30 a6.75 a30.98 a8.21 a77.80 a7.55 a11.66 a
Control3.65 b1.23 a83.30 a7.71 a31.26 a7.76 a80.75 a7.36 a8.00 a
Legend: Mic—Micronaire; UHML—Upper half mean lengths; Str—Strength; Elo—Elongation; Rd—Reflectance; +b—Yellowness; SFI—Short fiber index; UI—Uniformity %.
Table
Table 5. Effect of defoliant dosage on fiber quality cotton analyze by AFIS (Neps parameter). (Values not sharing a common letter within treatments are significantly different (p ≤ 0.05)).
Table 5. Effect of defoliant dosage on fiber quality cotton analyze by AFIS (Neps parameter). (Values not sharing a common letter within treatments are significantly different (p ≤ 0.05)).
TreatmentsField 1Field 2
Nep SizeNep/gmSCN (cnt/g)SCN SizeNep SizeNep/gm SCN (cnt/g)SCN Size
20%654.87 a163.62 b9.00 a1132.12 a637.16 b178.66 a5.16 a1124.66 a
40%666.12 a160.62 b10.37 a1252.12 a646.83 ab192.66 a6.33 a1110.33 a
60%634.25 a138.50 b5.87 a1154.00 a650.83 ab212.66 a6.50 a1152.66 a
Control647.00 a236.25 a9.50 a1066.75 a670.33 a229.00 a9.83 a1246.66 a
Table
Table 6. Effect of defoliant dosage on fiber quality cotton analyze by AFIS (Length parameter). (Values not sharing a common letter within treatments are significantly different (p ≤ 0.05)).
Table 6. Effect of defoliant dosage on fiber quality cotton analyze by AFIS (Length parameter). (Values not sharing a common letter within treatments are significantly different (p ≤ 0.05)).
Field#TreatmentL(n)L(n) CV%SFC (n)L (5%)L(w)L(w) CV%UQL (w)SFC (W)
120%0.81 ab43.33 ab21.76 ab1.34 ab0.96 ab37.17 a1.17 a8.81 ab
40%0.82 a44.87 a22.47 ab1.37 a0.97 a35.48 ab1.20 a8.98 ab
60%0.81 a42.31 ab20.56 b1.34 ab0.96 ab33.81 b1.16 ab8.32 a
Control0.77 b45.37 a25.00 ab1.33 ab0.93 ab36.22 a1.15 b10.65 a
220%0.86 a47.00 a22.13 b1.51 a1.05 a36.48 a1.32 a8.51 b
40%0.84 ab47.40 a23.13 ab1.49 ab1.03 ab36.90 a1.30 ab9.05 ab
60%0.86 a46.58 a22.40 b1.49 ab1.04 a35.95 a1.30 ab8.71 b
Control0.81 b48.45 a25.60 a1.45 b1.00 b37.55 a1.27 b10.31 a
Table
Table 7. Effect of defoliant dosage on fiber quality cotton analyzed by AFIS (Trash parameter). (Values not sharing a common letter within treatments are significantly different (p ≤ 0.05)).
Table 7. Effect of defoliant dosage on fiber quality cotton analyzed by AFIS (Trash parameter). (Values not sharing a common letter within treatments are significantly different (p ≤ 0.05)).
Field#TreatmentsTotal (cnt/g)Trash SizeDustTrash (cnt/g)VFM (%)
120%82.62 a315.75 a70.25 a12.37 ab0.28 a
40%129.20 a328.87 a108.37 a20.87 a0.48 a
60%75.50 a322.50 a63.25 a12.25 ab0.27 a
Control109.75 a373.50 a88.00 a22.00 a0.51 a
220%149.66 a317.00 a126.83 a22.50 a0.44 a
40%151.66 a308.83 a129.66 a21.66 a0.43 a
60%132.00 a306.00 a113.16 a18.66 a0.38 a
Control117.83 a356.33 a95.66 a22.16 a0.46 a
Table
Table 8. Effect of defoliant dosage on fiber maturity ratio, fineness, and immature fiber content. (Values not sharing a common letter within treatments are significantly different (p ≤ 0.05)).
Table 8. Effect of defoliant dosage on fiber maturity ratio, fineness, and immature fiber content. (Values not sharing a common letter within treatments are significantly different (p ≤ 0.05)).
TreatmentsField 1Field 2
Fine (m tex)IFC (%)Mat RatioFine (m tex)IFC (%)Mat Ratio
20%165.87 a6.31 a0.86 a158.66 ab7.38 a0.84 a
40%160.37 a6.51 a0.86 a158.00 ab7.43 a0.83 a
60%167.37 a5.83 a0.88 a161.83 a7.50 a0.84 a
Control163.00 a6.97 a0.84 a156.66 ab7.25 a0.83 a
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Neupane, J.; Maja, J.M.; Miller, G.; Marshall, M.; Cutulle, M.; Luo, J. Effect of Controlled Defoliant Application on Cotton Fiber Quality. Appl. Sci. 2023, 13, 5694. https://doi.org/10.3390/app13095694

AMA Style

Neupane J, Maja JM, Miller G, Marshall M, Cutulle M, Luo J. Effect of Controlled Defoliant Application on Cotton Fiber Quality. Applied Sciences. 2023; 13(9):5694. https://doi.org/10.3390/app13095694

Chicago/Turabian Style

Neupane, Jyoti, Joe Mari Maja, Gilbert Miller, Michael Marshall, Matthew Cutulle, and Jun Luo. 2023. "Effect of Controlled Defoliant Application on Cotton Fiber Quality" Applied Sciences 13, no. 9: 5694. https://doi.org/10.3390/app13095694

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