Article Request Page ASABE Journal Article
Gin Saw Thickness Impact on Lint Turnout, Lint Value, and Seed Damage
Paul A. Funk1,*, Joseph W. Thomas2, Kathleen M. Yeater3, Neha Kothari4, Carlos B. Armijo1, Derek P. Whitelock1, John D. Wanjura5, Christopher Delhom2
Published in Applied Engineering in Agriculture 38(4): 645-650 (doi: 10.13031/aea.15171). 2022 American Society of Agricultural and Biological Engineers.
1 Southwestern Cotton Ginning Research Laboratory, USDA ARS, Las Cruces, New Mexico, USA.
2 US Cotton Ginning Research Unit, ARS USDA, Stoneville, Mississippi, USA.
3 Plains Area, USDA ARS, Fort Collins, Colorado, USA.
4 Cotton Incorporated, Cary, North Carolina, USA.
1 Southwestern Cotton Ginning Research Laboratory, USDA ARS, Las Cruces, New Mexico, USA.
6 Cotton Production and Processing Research Unit, USDA ARS, Lubbock, Texas, USA.
* Correspondence: email@example.com
The authors have paid for open access for this article. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License https://creative commons.org/licenses/by-nc-nd/4.0.
Submitted for review on 2 May 2022 as manuscript number MS 15171; approved for publication as a Research Article by Associate Editor Dr. Kevin McDonnell and Community Editor Dr. Heping Zhu of the Machinery Systems Community of ASABE on 23 June 2022.
This research was wholly supported by the U.S. Department of Agriculture, Agricultural Research Service. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. USDA is an equal opportunity provider and employer.
- We compared 0.91 to 1.14 mm thick gin saws by ginning seed cotton from three regions.
- The gap between the saw and rib was 0.914 mm for the thin saws and 0.800 mm for the thick ones.
- Response variables were tested using a sequentially reduced (backwards regression) model.
- Lint “turnout”, lint value, and seed damage did not appear to be significantly different due to saw thickness.
Abstract. Over 95% of U.S. cotton post-harvest processing is done using saw gins. Gin saws have long been supplied in three thicknesses. We quantified the effect of saw thickness on lint turnout, lint value, and seed damage, variables that determine producer returns. Saw cylinders stacked with 0.9144 and 1.143 mm (0.036 and 0.045 in.) thick saws, the thinnest and thickest available, were operated in laboratory conditions on three cotton growths (cultivars and production practices) from Mississippi, New Mexico, and Texas in an experiment with five replicates. Fiber quality from samples obtained after lint cleaning was measured using High Volume Instruments (HVI). HVI results were combined with Commodity Credit Corporation Marketing Assistance Loan premium and discount tables to calculate fiber value. Seed damage was estimated after germination using Association of Official Seed Analysts rules. A backwards regression approach in JMP reduced each response variable’s model until only significant controlled and uncontrolled variables remained. Tested variables included: growth and saw thickness and their interaction; processing rate; processing energy; test duration; foreign matter content; moisture content; and ambient humidity and temperature. There was no significant difference in fiber value due to saw thickness. Seed quality differences were insignificant. Differences in lint turnout due to saw thickness also were statistically insignificant. Saw thickness selection may be based on other considerations.
Keywords.Cotton gin, Cottonseed, Fiber quality, Gin saw, Saw gin
Cotton post-harvest processing service providers (gin facilities) optimize profitability and sustainability when they maximize processing rate and minimize energy consumption. Yet they must also preserve product and coproduct value to satisfy their customers. Thus, research attempting to reduce ginning cost and increase throughput must include quantification of value metrics to ensure that there are no unintended consequences that might negatively impact cotton producers.
Cotton gin saws are available in three thicknesses, 0.9144, 0.9398, and 1.143 mm (0.036, 0.037, and 0.045 in.). A recent laboratory study comparing the effect of gin saw thickness on energy use and processing rate (Funk et al., 2022) included fiber and seed sampling. This paper reports the effect that 0.9144 and 1.143 mm (0.036 and 0.045 in.) thick saws, the thinnest and thickest available, had on lint turnout, lint value, and seed damage – metrics that directly impact producer returns.
Numerous prior studies: Griffin Jr and McCaskill (1969); Childers and Baker (1978); Griffin Jr. (1979); Anthony (1985); Mangialardi Jr. et al. (1988); Mangialardi Jr. and Anthony (2005); Byler (2006); Holt and Laird (2008); Armijo et al. (2013); Hughs and Armijo (2013); among others, have examined the fiber quality impact of factors such as: tooth design, gin saw diameter, saw spacing, rotational speed, ginning capacity or processing rate, seed cotton foreign matter and moisture content, and seed roll: size; shape; agitation; and density. Though cotton gin saws have been produced in multiple thicknesses for many decades, as far as we know this is the first study to report fiber quality as a function of gin saw thickness. Antecedent research, including Watson and Helmer (1964) and Armijo et al. (2006), looked at cottonseed quality in the context of some of the above-mentioned variables, but as far as we know this also is the first study to investigate gin saw thickness impact on cottonseed mechanical damage.
Materials and Methods
The experiment was conducted at the USDA-ARS Southwestern Cotton Ginning Research Laboratory in Las Cruces, New Mexico (SWCGRL) using a Continental Double Eagle gin stand with 406 mm (16 in.) diameter saws (Continental Gin Company, Prattville, Ala.) modified for research to a width of 46 saws or 781 mm (30.75 in.). On a per-saw basis, the designed ginning or processing rate was approximately 5.0 lint g saw-1 s-1. For this experiment the maximum processing rate averaged 5.4 lint g saw-1 s-1. Table 1 lists the response, controlled, and random variables that were used in a backwards regression statistical analysis. Detailed descriptions of controlled and response variables follow. More detail is available in Funk et al. (2022).
Table 1. Response, independent, and random variables of gin saw thickness on cotton lint and seed quality that were quantified for analysis. Variables Response Lint “turnout” (lint portion of pre-cleaned seed cotton; %) Lint value (product of classing data and loan charts; $ kg-1) Seed damage (viable portion of total germinated, binomial) Controlled
Saw thickness (0.914 & 1.143 mm) (0.036 & 0.045 in.) Cotton growth (Mississippi, New Mexico, & Texas) >91 kg (200 lb) per test Processing rate (avg 4.5 & 5.4 g saw-1 s-1 at normal & max rate) Random Net ginning energy (avg. 20.5 & 25.8 W-h kg-1 at normal & max rate) Test duration (averaged 179 & 150 s at normal & max rate) Seed cotton moisture content (range of 5.2 to 9.0% d.b.) (table 2) Total foreign matter content (3.19% to 4.75%) (table 3) Ambient relative humidity (14% to 37%) Ambient temperature (11°C to 22°C) (52°F to 72°F) Seed roll status (full or empty) Date and time of test
Two gin saw mandrels were each professionally stacked (Precision Gin Works, Lubbock, Texas) with new 406 mm (16 in.) diameter saws (Phoenix Gin Saws, Hartsville, S.C.) that were robotically trained (McSami Saw Trainer, McCleskey Saw and Machine, Bronwood, Ga.) within 0.127 mm (0.005 in.) of the plane perpendicular to their axis of rotation (U.S. Patent No. 8,590,109; Schramm, 2013). One saw cylinder was a complete set of thin (original equipment thickness) saws [0.9144 mm (0.036 in.)], the other consisted of thicker saws [1.143 mm (0.045 in.)].
Typically, the gap between ribs is three times the saw thickness, so the space for fiber to pass through on each side is equal to the saw thickness. However, we did not replace the ribs to account for the thicker saws, so the gap where fiber could pass through was 0.9144 mm (0.0360 in.) for the thin saws, and 0.8001 mm (0.0315 in.) on each side for the thicker ones. For reference, our laboratory gin had 20% less clearance than a commercial gin stand sold with 1.143 mm (0.045 in.) saws (Consolidated Cotton Gin Co, Inc., Lubbock, Tex.), as it was designed to have 1.003 mm (0.0395 in.) clearance on each side of the saws. We do not know what impact this had on results (see Conclusion).
Because both saw cylinders were new, when a saw cylinder was first installed it was operated at a reduced processing rate for approximately 2 h (three bales) to break in the saws. This was done using a fourth cultivar, which had been spindle harvested; the break-in lint was not used in the analysis.
Since the most time-consuming variable to change was the thickness of the gin saws, requiring about two days to disassemble and reassemble the gin stand, randomized saw thickness was blocked within each of five replicates. Blocked within each saw thickness were three randomized cotton growths, and blocked within each growth were two randomized processing rates. Growth here means the combination of cultivar, production practices, and harvest method unique to each region. The three growths, identified by state of origin, were:
(MS): a spindle harvested upland (ST 4848GLT, BASF, Ludwigshafen, Germany) from Stoneville, Mississippi (Mississippi);
(NM): a spindle harvested upland (NG 4545 B2XF, Americot, Lubbock, Tex.) from Las Cruces, New Mexico (New Mexico);
(TX): a stripper harvested upland (DP 1549 B2XF, Bayer CropScience, Leverkusen, Germany), harvested using a stripper equipped with a field cleaner, from Lubbock, Tex. (Texas).
The third variable was lint processing rate. Two target levels, normal and maximum, were assigned randomly under each cotton growth. Processing rates averaged 4.51 and 5.40 lint g saw-1 s-1 (0.597 and 0.714 lint lb min-1 saw-1) for “normal” and “maximum,” respectively. With five replicates, the planned experiment was for 60 runs. Twelve runs were repeated to recover lost electrical data and four additional runs tested very low processing rate yielding a total of 76 runs with fiber and seed samples.
Seed cotton from all three regions was shipped to SWCGRL. It was pre-cleaned at SWCGRL in advance of the test to reduce contamination between growths. Precleaned seed cotton was stored in trailers. The two spindle-harvested growths were pre-cleaned using an inclined cylinder cleaner, a stick machine, and a second inclined cylinder cleaner. The stripper-harvested growth was pre-cleaned using an inclined cylinder cleaner, two stick machines, and a second inclined cylinder cleaner. These pre-cleaning sequences are considered common practice for each harvest method (Anthony and Mayfield, 1994). The moisture content of each seed cotton growth, from 5.6% to 8.0%, was low enough to not require drying. Pre-cleaning machinery was thoroughly cleaned between each growth to prevent cross-contamination.
Once the appropriate saw cylinder was installed, a trailer containing the growth of pre-cleaned seed cotton called for by the experiment design was parked for unloading on the SWCGRL 22,680 kg (50,000 lb) capacity truck scale; resolution 2.3 kg (5 lb). A test lot of the seed cotton weighing from 91 to 102 kg (200 to 225 lb) was transferred via suction pipe to the saw gin overflow hopper to be automatically fed to the gin stand during the test run. The gin stand ginning rate selector knob was set for the target processing rate, and the overflow feeder was set to supply the required amount of seed cotton for that processing rate. If empty, the seed roll was packed with 0.9 kg (2 lb) of seed cotton (less than 1% of the lot weight) to more quickly reach steady-state conditions. Then the gin machinery was activated and the test was run. These steps were then repeated for the second processing rate on the current growth. Once both processing rates had been run for the current growth the seed cotton trailer was removed and a trailer with the next growth was brought into the facility. At the same time, the seed roll was emptied and the gin machinery was cleaned to reduce contamination between growths. The preceding steps were then repeated for the second and third growths. Once all three growths had been run, the gin stand was disassembled and the cylinder with saws of the other thickness was installed. The above was repeated for the next thickness, and the full procedure was repeated a total of five times.
To ensure a fair test, ambient temperature and relative humidity were recorded during each run and moisture and trash levels were measured using seed cotton sampled before ginning. Seed cotton samples (150 g each) collected from the overflow hopper were placed in plastic bags for foreign matter analysis by fractionation (Shepherd, 1972). Additional seed cotton samples (50 g each) were also collected from the overflow hopper and placed in air-tight containers for moisture content determination by oven method (Funk et al., 2018).
The mass ratio of lint ending up in the bale to seed cotton entering the process is called turnout. In this study the ratio of lint to seed cotton is based on seed cotton that had been pre-cleaned before the tests, which differs from the method used to calculate turnout in a commercial facility, hence the quotation marks around the word. Also, one growth (TX) was pre-cleaned using more machinery than the other two, so growths cannot be compared using lint “turnout” as defined here; it is intended only to compare saw thicknesses. The lint produced during each run was kept separate and later weighed on our bale scale [Model 7332F, Hobart, Troy, Ohio, resolution 0.23 kg (0.5 lb)]. The “turnout” value was calculated by dividing the lint mass processed by the mass of pre-cleaned seed cotton used for that run.
To assess fiber quality, two lint sub-samples per run (30 g each) were collected both before and after lint cleaning (Continental/Moss-Gordon Lodestar, Continental Gin Company, Prattville, Ala.), and sent to Cotton Incorporated (Cary, N.C.) for high volume instrument (HVI, Uster Technologies AG, Uster, Switzerland) measurements, providing length uniformity, micronaire, strength, upper half mean length, elongation, brightness, color, short fiber index, foreign matter content (count and area), color grade, leaf grade, and staple length. We used the after-lint-cleaning results.
To convert fiber quality to value we used the loan charts. USDA’s Commodity Credit Corporation marketing assistance loans (MAL) help growers by providing cash flow at the time of harvest without growers having to sell their commodity when the price is at its lowest. The program also results in more orderly marketing of commodities throughout the year. The MAL value published annually by FSA for upland cotton includes adjustments for color, trash content, and length, with additional premiums and discounts applied to correct for uniformity, micronaire (an estimate of maturity based on diameter), and fiber strength. We calculated the monetary value of each sample by combining the base price (FSA, 2021a) with adjustments made using HVI fiber properties data and MAL premiums and discounts tables (FSA, 2021b).
Seed samples (300 g each) were collected from the seed belt under the gin stand, placed in gallon zipper freezer bags, and sent to the Louisiana Department of Agriculture and Forestry State Seed Testing Laboratory (Baton Rouge, Louisiana) to assess seed quality by germination. The analysis was based on root viability instead of visible mechanical damage because roots can be quantified more objectively; damaged seeds have less primary and secondary root mass, providing a visual means to quantify seed damage that is more than superficial, with levels defined by the Association of Official Seed Analysts (AOSA) (fig. 1). We considered undamaged (2a) and moderate damage (2b) to be good seed, and germinated seed classed as 2c and 2d to be unacceptable, presumably due to gin saw damage. Because germination rates differed widely by growth, analysis of covariance indicated that we could not include the Mississippi growth in the seed quality analysis (germination was impacted by events that took place before ginning and was only 5%). For the New Mexico and Texas growths we used the binomial proportion of good seed to total germinated seed as our response variable.
Statistical analyses were conducted using the Fit Model specification in the JMP statistical package (Version 13.2, SAS Institute Inc., Cary, N.C.). The null hypothesis was that there is no difference in lint “turnout,” seed damage, or fiber value (response variables) between gin saws 0.914 and 1.14 mm thick (the controlled or independent variable). Other variables that were explored for potential influence on lint “turnout,” lint value, and seed damage included: growth; growth and saw thickness interaction; processing rate; processing energy; test duration; seed cotton foreign matter and moisture content; ambient temperature and relative humidity; if the seed roll was empty or full at the start of the test; and the date and time of the test. A backwards regression approach was used to sequentially eliminate variables with the highest p-value, as those had the least influence on the model.
Figure 1. AOSA rules for reporting seed damage based on germinated seeds.
Results and Discussion
Seed Cotton Moisture Content
Table 2 presents seed cotton moisture content information from samples taken at the start of each test. The ideal moisture content for ginning is 6.4% to 7.5% dry basis (6% to 7% wet basis) (Hughs et al., 1994). That our tests were not always in the ideal range may have affected fiber quality results (Hardin IV et al., 2018). However, moisture content values were within the range found in the context of commercial cotton ginning, and were representative of what might be typical for their respective regions. Additionally, the scope of these trials became broader because the three seed cotton growths had distinct moisture levels. More importantly, the average moisture level of seed cotton before ginning was not statistically different between the saw thickness. Therefore, lint turnout and fiber quality results as a function of saw thickness were not confounded by initial seed cotton moisture content.
Table 2. Average seed cotton moisture content dry basis for each growth and saw thickness. Average p-value Growth[a] MS (Mississippi)a 8.07% <0.0001 NM (New Mexico)b 5.56% TX (Texas)c 6.48% Saw thickness 0.914 mm (0.036 in.) 6.77% 0.45235 1.143 mm (0.045 in.) 6.57%
[a] Growths with different letters had different moisture content at the 0.05 level of significance.
Seed Cotton Foreign Matter
All three growths had been cleaned in advance using the machinery combination typical to their harvest method. Foreign matter content of the Mississippi, New Mexico, and Texas growths were 3.6%, 3.2%, and 4.8%, respectively, before ginning. Differences between the three growths were statistically significant (the one-way ANOVA p-value was less than 0.05); differences between the two saw thicknesses were not (table 3). Since there were no significant differences in foreign matter content between the saw thicknesses tested, lint turnout and fiber quality results as a function of saw thickness were not confounded by initial seed cotton foreign matter content.
The response lint “turnout” was modeled using all controlled and random variables, and the model was reduced sequentially until the only variables remaining were net ginning energy, processing rate, saw thickness, and its interaction with growth. The reason lint “turnout” correlated with the confounded variables energy and processing rate was because clean cotton could be ginned faster, and ginning faster takes more energy. Table 4 presents lint “turnout” experiment means and model least squares means by growth and saw thickness. Model least squares means more accurately estimate reality because the model accounts for covariates, interactions, and unbalanced data. For this test, lint “turnout” least squares means were 0.3940, 0.3894, and 0.3952 for the MS, NM, and TX growths, respectively, with a p-value of 0.7401, indicating statistical insignificance for growth. They were 0.3882 and 0.3976 for the 0.914- and 1.14-mm thick saws, respectively, and the p-value was 0.2525, indicating that thickness did not have a statistically significant effect on lint “turnout” at the 0.05 level.
Table 3. Average foreign matter content in total and by type for each growth and saw thickness. Hulls Sticks Motes Leaf Missing or Dust Total Foreign
p-value Growth[a] MSa 0.93% 0.23% 1.03% 0.58% 0.82% 3.59% <0.0001 NMb 0.99% 0.25% 1.06% 0.55% 0.33% 3.19% TXc 0.64% 0.51% 2.23% 0.82% 0.56% 4.75% Saw Thickness 0.914 0.81% 0.33% 1.44% 0.66% 0.60% 3.84% 0.92332 1.413 0.90% 0.33% 1.43% 0.64% 0.56% 3.86%
[a] Growths with different letters had different trash content at the 0.05 level of significance.
Table 4. Lint “turnout” by saw thicknesses for the experimental data (experiment means) and the least squares means from the statistical model (model means) with P-value. Experiment
p-value Growth MS 0.3902 0.3940 0.7401 NM 0.4026 0.3894 TX 0.3825 0.3952 Saw Thickness 0.914 mm 0.3983 0.3882 0.25252 1.143 mm 0.3858 0.3976
Fiber quality information from each sample combined with 2020 MAL values published by FSA provided a monetary value for each lint sample. Differences between growths were as much as $0.33 kg-1 ($0.15 lb-1). Lint cleaning improved lint value by $0.057 kg-1 ($0.026 lb-1). The lint value response model was reduced sequentially, removing variables with the highest p-value first. Saw thickness least squares means were $1.070 kg-1 and $1.109 kg-1 ($0.485 lb-1 and $0.503 lb-1) for the 0.914 and 1.14 mm thick saws, respectively, and the 0.064 p-value indicated insignificance (table 5).
Table 5. Lint value after lint cleaning ($ kg-1) by growths and saw thicknesses for the experimental data (experiment means) and the least squares means from the statistical model (model means) with P-values. Experiment Means Model Means p-value Growth[a] MS a 1.150 1.127 <0.0001 NM b 1.238 1.142 TX c 0.904 0.999 Saw Thickness 0.914 mm 1.092 1.070 0.0641 1.143 mm 1.102 1.109
[a] Growths with different letters had different fiber value at the 0.05 level of significance.
Fiber quality information from each HVI sample was averaged by growth and saw thickness (table 6). Differences between each growth due to genetics and climate were significant. Differences between average HVI properties for the two saw thicknesses were not significant, as expected, due to the above results for lint value.
Table 6. Average HVI data by growths and saw thicknesses.[a] n UI Mic Str UHM Elo Rd +b Shor Fiber Index Trash Count Trash Area% Staple Growth MS 56 81.8 4.9 31.6 1.1 5.8 72.2 7.4 8.7 31.0 0.27 36.0 NM 48 82.0 4.8 27.8 1.1 7.9 77.6 8.2 8.4 20.9 0.17 36.7 TX 48 76.7 4.8 25.4 0.9 5.9 71.3 10.3 13.7 35.5 0.27 29.1 Saw Thickness 0.914 86 80.2 4.9 28.3 1.1 6.5 73.5 8.6 10.3 29.8 0.25 33.9 1.143 66 80.3 4.8 28.5 1.1 6.5 73.7 8.5 10.1 28.5 0.23 34.1
[a] n = number of samples, UI = Length Uniformity Index (%), Mic = Micronaire, Str = Strength (g tex-1), UHM = Upper Half Mean Length (in.), Elo = Elongation, Rd and +b = brightness and yellowness, Staple = Thirty-seconds of an inch.
Using AOSA rules for reporting seed damage based on germinated seeds had a limitation in this case. One growth had a low germination rate because that seed cotton was stored for over a year in warm, humid conditions. All growths had lower than normal germination rates because the seed was not cleaned (which normally separates lightweight non-viable seed as well as other foreign matter). Therefore, we analyzed the number of damaged seedlings as a percent of the total that germinated. There was no statistical significance in the differences between thick and thin saws, table 7, but because of abnormally high variability it would be difficult to assert conclusively that this was because there was no difference in the effect of saw thickness.
Table 7. Fraction of damaged germinated seed to all germinated seed by growths and saw thicknesses for the experimental data. Saw Thickness 0.9144 1.143 Avg. MS 0.121 0.017 0.069 NM 0.032 0.022 0.028 TX 0.315 0.306 0.310 AVG. 0.156 0.115
When making any change to the cotton ginning process, it is important to consider how unintended consequences might impact product and co-product value. No part of the ginning system has greater potential to impact cottonseed and lint turnout and value than the gin saw, the agent of seed-fiber separation. Seed damage was challenging to quantify, but differences due to saw thickness appeared to be insignificant. Lint “turnout” was not affected in a significant way by saw thickness. Comparing 0.914 mm thick saws to 1.143 mm thick ones resulted in no significant difference in value based on fiber quality using 2020 CCC loan charts. However, the data was normally distributed, so a larger sample size from additional replicates theoretically could increase precision to the point that the p-value for saw thickness could become less than 0.05, the threshold we selected for statistical significance. It was unfortunate that seed germination had degraded before the test was run, making it harder to claim that we did not see statistically significant differences in seed quality as measured by root viability after germination. We recommend another trial using test material with viable seed to conclusively quantify the effect of saw thickness on seed quality (although energy consumption and processing rates results reported earlier did not recommend the use of thicker saws). Not changing the ginning ribs when changing the saw cylinder resulted in a smaller rib-to-saw gap for the thicker saws. Additional research is needed to investigate the effect of this variable. As seed size has decreased from breeding for higher yields, investigating the effects of the rib-to-saw gap is clearly justified.
The authors are grateful for the assistance of Cotton Incorporated Product Evaluation Laboratory Manager Suzanne Holmes and Laboratory Technician Roberta Smith-Uhl, and Louisiana Department of Agricultura and Forestry State Seed Laboratory Seed Programs Coordinator and President of the Association of Official Seed Analysts David M. Johnston. The authors express appreciation for the contribution of lab personnel Kirk Zivkovich, Cassy Salvatti, Branyan Sanxter, Hannah Podruchny, Tye Lightfoot, Ernest Herrera, Juan Gomez, Arnold Gomez, Russel Gardner, and Paul Delgado; they stayed healthy and accomplished much under pandemic guidelines.
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