Journal of Biosystems Engineering. September 2018. 161-172
https://doi.org/10.5307/JBE.2018.43.3.161


ABSTRACT


MAIN

  • Introduction

  • Materials and Methods

  •   Experimental site and test machine

  •   Experimental design

  •   Experimental procedure

  • Results and Discussion

  •   SIR

  •   SCB

  •   TCD

  • Conclusion

  • Conflict of Interest

Introduction

Tillage is an integral part of agricultural production and has been a prominent aspect of technological development in the evolution of agriculture. Gill and Vandenberg (1968) defined tillage as a process aimed at creating a desired final soil condition for seeds from some undesirable initial soil condition through the manipulation of agricultural soil. According to Abdalla (2014), soil tillage consists of breaking the compact surface of the earth to a certain depth and loosening the soil mass to obtain a good soil condition for plant growth. To ensure normal plant growth, the soil must be prepared in such conditions that plant roots have enough air, water, and nutrients (Gill and McCreery, 1960; Gill and Vandenberg, 1968; McKyes, 1985; Boydas and Turgut, 2007). Ati et al. (2015) explained that well-aggregated soil provides better moisture retention, adequate aeration, and easy penetration for plant roots. Tillage plays an important role in controlling weeds and managing crop residue through the turning or inversion of the soil (Raney and Zingg, 1957).

Numerous factors affect tillage operations. According to Naderloo et al. (2009), the tillage depth, working tool width, geometrical orientation of the implement, machine speed, et al are important parameters that affect the quality of soil during tillage. These factors were summarized by Olatunji (2007) as the power source, the soil physical properties, and the tillage implement used. To ensure a high-quality soil condition and tillage cost reduction, extensive knowledge of the impact of these factors on tillage operations is required. During tillage operations, the soil physical properties, especially the soil moisture content (SMC), significantly affect the dynamics of the tillage quality. Sherudin et al. (1988) noted that the power requirements and tillage quality were affected by the soil physical features, such as the soil type and its SMC. The SMC affects the forces that come into play in a tillage system, thereby affecting the effectiveness and cost of tillage operations. Ahmed and Haffar (1992) reported that, at a low SMC, the cohesion forces between the soil particles are very strong; hence, considerable energy is needed to overcome such forces during tillage operation. Hunt (1977) concluded that the quality of tillage operations and the draft force vary with respect to the SMC.

During tillage operations, varying some parameters of the implement may affect the tillage quality and the energy input. Al-Suhaibani and Al-Janobi (1997) evaluated the effects of the tillage depth and machine speed on the tillage quality of tillage implements for sandy loam soil. According to the results, the machine speed and TCD exhibited a good correlation with the quality of tillage. Smith et al. (1977) stipulated that increasing the machine speed increases the energy input. According to Sheruddin et al. (1996), changing the machine speed affected the soil aggregation, as a higher percentage of small soil aggregates was obtained at lower speeds in their study. Claude (1984) reported that in addition to the machine speed, the weight of some tillage implements is an important factor that affects the tillage depth.

According to all of these studies, it is prudent to further examine the effect of varying other parameters of different tillage implements to effectively manage the cost of tillage operations as well as achieve a good soil condition. There are several tillage implements used by farmers to achieve this goal (Al-Suhaibani and Ghaly, 2010). However the selection of tillage implements for seedbed preparation and weed control depends on the soil type and physical conditions, type of crop, previous soil treatments, crop residue, and weed types (Upadhyaya et al., 2009). The disc harrow as a tillage tool is typically used for secondary tillage operation through soil pulverization, soil clod breakage, and weed control. Prior knowledge of the effect of varying the disc-harrow parameters on the tillage quality is of significant help to the farmer. During secondary tillage operations, parameters such as the soil inversion, soil clod breakage, and TCD are varied depending on the conditions of the soil.

For a farmer to know the working efficiency of the disc harrow used for tillage when the SMC, machine speed, and disc spacing are varied, considerable knowledge regarding the effects of these parameters on tillage operations must be available. This will help the farmer in the selection of the optimum disc spacing, machine speed, and SMC to achieve high-quality tillage. However, there is little knowledge of the extent to which these variations affect the tillage quality.

The objective of this study was to ascertain the impact of varying the working machine parameters of a compact disc harrow on the tillage operation under various SMC conditions. This experiment gives an idea of how much soil is turned (inversion) and how much soil clods are broken during tillage operations in a given SMC medium as the disc parameters are varied. Additionally, the research will help to analyze the impact of the SMC on tillage operations using a compact disc harrow.

Materials and Methods

Experimental site and test machine

The research was conducted in a soil bin facility at Gyeongsang National University, South Korea. A rectangular soil bin with dimensions of 5 m (length) × 2 m (width) × 1 m (height) was used to produce a repeatable soil condition for the experiment. The soil bin had a test rig that provided variable machine speeds for the experiment. The test machine was a typical compact disc harrow (3001Terradisc, Poettinger Australia PTY Ltd., Campbell field, Australia) that was tilted at an angle of 30º with respect to the horizontal. Table 1 shows the soil physical properties used for the experiment. Figure 1 shows images of the soil bin with the test rig and mounted disc harrows, as well as the experimental methodology used in the research.

Table 1. Physical properties of the soil used for the experiment

Soil TypeSoil ConstituentsBulk density (mg/m3)
Sandy LoamSandClaySilt1.22-1.46
% constituents57.517.225.3

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F1.jpg
Figure 1.

Images of the working machine and experimental methodology used in this study.

In all the experiments, six disc harrows were used: three on each holding metal frame of the test rig. The United States Department of Agriculture (USDA) textural classification of the experimental soil was sandy loam. Table 2 shows the detailed specifications of the disc harrow used for the experiment.

Table 2. Detailed specifications of the disc harrow used for the experiment

SpecificationsValue
Disc diameter0.58 m
Maximum disc spacing0.125 m
Working width0.03 m
Disc thickness0.01 m
Frame height0.75 m
Power requirement70 kW / 95 Hp
Basic weight14.75 kg

Experimental design

In this experiment, three different SMCs were used; low, medium, and high. The SMC was tested and varied using the dry-oven method (Jung, 2017). A rainfall simulation exercise was performed to spread water evenly on the soil in the soil bin. After 24 h, soil samples were collected from various points at a depth of approximately 0.1 m using a tuber sampler. They were oven-dried at 105℃ over 24 h. The SMC was calculated using the following equation:

SMC%=wb-wawb×100,    (1)

where wb represents the weight of soil before drying, and wa represents the weight of soil after drying.

This technique was used to continuously check the SMC of the soil until the desired SMC was achieved. The low SMC was in the range of 15% to 18% on a dry basis. The medium SMC was in the range of 25% to 28% on a dry basis. The high SMC was in the range of 35% to 38% on a dry basis. The averages for the low, medium, and high SMCs were taken as 16.5%, 26.5%, and 36.5% respectively. In this study, the dependent variables were the tillage operational parameters, which included the soil inversion ratio (SIR), soil clod breakage ratio (SCB), and tillage cutting depth (TCD). The independent variables were SMC, disc spacing, and machine speed. The experiments were divided into three sections: experiments under a low SMC, medium SMC, and high SMC. A total of 27 experiments were performed. Each experiment was repeated three times, and the average value was taken. For each experiment, the disc spacing was varied from 0.2 to 0.4 m, and the machine speed was varied from 0.2 to 0.4 m/s.

Experimental procedure

SIR

The SIR is the ratio of the amount of top soil that is turned or inverted into the soil after a tillage operation. To determine the SIR, a whitish lime powder was spread on the soil surface in a given area depending on the disc spacing being used. For disc spacing of 0.2, 0.3, and 0.4 m, areas of 1.8 m (length) × 0.7 m (width), 1.8 m (length) × 0.9 m (width), and 1.8 m (length) × 1.3 m (width) were used, respectively. The white lime powder was applied to the soil surface three times to ensure that the targeted area was white. A point grey camera (FL2G-13S2M-C 1394, Point Grey Research, Inc. British Columbia, and Canada) was fixed on the test rig at a constant position and height. This was used to capture images on the lime-plated soil surface before tillage was performed. Table 3 shows the detailed specifications of the camera used to capture the images. After tillage, another image was captured using the same camera fixed at the same position and height. Figure 2 shows images of the white limed soil before and after tillage.

Table 3. Specifications of the camera used to capture images for analysis using the MATLAB R2018a software

DescriptionSpecifications
Model NameFL2G-13S2M-C 1394
CompanyPoint Grey Research, Inc.
Resolution1,288 × 964
Frame Rate30 FPS
Megapixels1.3 MP
ChromaMono
Sensor NameSony ICX445
Sensor TypeCCD
Pixel Size3.75 µm
Lens MountC-Mount
Grain Range0 to 24 db
Image ProcessingStandard, bulb, skip frames, overlap

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F2.jpg
Figure 2.

Images captured by the camera before and after tillage for MATLAB image processing.

The images were in Red, Green and Blue (RGB) format and were processed using the software MATLAB 2017Ra. First, all the images were resized to a 960 × 960 square matrix. Preprocessing techniques such as noise removal, image adjustment, and enhancement were performed to improve the quality of the image (Math Works, 2017). The RGB image was converted to grayscale using the rgb2gray( ) function. The imhist( ) function was used to display the histogram of the gray image. The threshold value was obtained from the histogram. The threshold values were selected according to the brightness values of the image, which ranged from 0–255. Next, the image was segmented using the Otsu method (Salem et al, 2010). The segmentation was performed to divide the image into two parts: white and black. The soil region without the white lime powder was black, whereas region with the powder was white. The areas of the white regions were determined using the regionprops( ) function in MATLAB (Math Works 2017Ra). The SIR was calculated as follows:

SIR=b-ab×100,    (2)

where b represents the area of white regions before tillage, and a represents the area of white regions after tillage.

Figure 3 shows the histogram of one of the images before and after tillage. The histogram shows the distribution of intensities of the grayscale image. The x-axis indicates the gray level (intensity or brightness) of the image. The intensity level ranged from 0–255, with 0 representing an extremely low intensity and 255 representing an extremely high intensity. The y-axis indicates how many pixels in the image had the gray or brightness level. The (graph) histogram helped in finding the threshold value. The threshold value was needed for binary segmentation of the image. Figure 3(a) shows the histogram of the soil surface before the tillage operation, and Figure 3(b) shows the histogram of the soil surface after the tillage operation. In Figure 3(a), the intensity of the white pixels is uniform, around the region of 100–160. However, after the tillage operations, the intensities varied from 0 to 255, as shown in Figure 3(b).

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Figure 3.

MATLAB histograms for images of the soil surface before and after tillage operations.

SCB

After each tillage operation, soil clods within the given area that was tilled were collected using a special auger. The soil clods were taken up to the depth tilled for each tillage operation. They were placed in a tray and air-dried for 21 d at temperatures ranging from 20–24 ºC. The dried soil (including clods) were sieved electrically in a sieve mesh with dimensions of 0.02 m × 0.02 m for 30 s. The equation for the SCB was obtained from the study of Lee et al. (2003):

SCB=WTS-WOSWTS×100,    (3)

where WTS is the weight of total soil per unit area, and WOS is the weight of soil units with a diameter of 0.02 m or more.

Figure 4 shows some of the soil clods that used were for the clod breakage analysis.

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F4.jpg
Figure 4.

Images of soil clods after tillage and after sieving with a 0.02-m sieve mesh.

TCD

The TCD was measured using a measuring tape, from the bottom of the furrow to the soil surface level, at randomly selected points. The depths of approximately five points in the given area where tillage was performed were measured. The TCD was taken as the mean of the measured values. Figure 5 shows images of the tillage depth being measured using the tape measure.

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F5.jpg
Figure 5.

Images showing the TCD being measured using the tape measure.

Results and Discussion

SIR

Effects of disc space, machine speed, and SMC on SIR

Figure 6 shows the effect of increasing the machine speed on the SIR at various disc spacing. Generally, the SIR decreased with the increase of the machine speed. However, there were a few exceptions. For instance, in the SMC range of 26.5% to 36.5%, the changes in the SIR were not prominent. The disc spacing of 0.2 m with 16.5% SMC and a machine speed of 0.2 m/s yielded the highest SIR, while the lowest SIR was obtained for 16.5% SMC with a 0.4 m/s machine speed and a disc spacing of 0.4 m. In all three situations, the machine speed of 0.4 m/s yielded the lowest soil inversion. As shown in Table 4, there was a good negative correlation between the SIR and the machine speed, with a coefficient of correlation of R = 0.69. The correlation between the machine speed and the soil inversion was significant, with a p-value of 0.0001.

Table 4. Statistical indices from the analysis of variance (ANOVA) and multiple regression analysis, showing the relationship among the disc spacing, machine speed, and SIR

Statistical Indicesp-valueRR2
Disc spacing0.0473-0.640.42
Machine speed0.0001-0.690.48
SMC0.00070.460.91

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F6.jpg
Figure 6.

SIR with increasing machine speed at various constant disc spacing.

Figure 7 shows the relationship between the disc spacing and the SIR at various machine speeds. In all three situations, 16.5% SMC with a disc spacing of 0.2 m yielded the highest SIR. Generally, increasing disc spacing decreased the SIR. There was also a good negative correlation, with a correlation coefficient of R = 0.64. Additionally, there was a significant relationship between the SIR and the machine speed, with a p-value of 0.0473, as shown in Table 4.

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Figure 7.

SIR with increasing disc space, at a constant machine speed.

The SMC had an effect on the SIR as the machine speed and disc spacing were varied. As the SMC increased, the SIR decreased. This phenomenon was probably due to the high water content of the soil, which impeded the easy movement of the disc harrow. In all situations, when the SMC changed from 26.5% to 36.5%, the SIR did not change significantly. The means of the SMC were significant, with a p-value of 0.0007, as shown in Table 4.

Figure 8 illustrates the trend of the SIR as the SMC, disc spacing, and machine speed were varied. At all three machine speeds and disc spacing, the maximum SIR occurred with 16.5% SMC. This maximum SIR (40.1%) occurred when the disc was spaced at 0.2 m with a machine speed of 0.2 m/s. The lowest SIR (19.9%) occurred with 36.5% SMC at a 0.4-m disc spacing and 0.4-m/s machine speed. Generally, the 16.5% SMC treatment with a disc spacing of 0.4 m yielded the sharpest change in the SIR as the machine speed increased. With a machine speed of 0.2 m/s, the SIR was 37.1%. However, as the machine speed increased to 0.3 m/s, the SIR decreased sharply to 20.6%, representing a 44.4% reduction in value. It further decreased to 18.7%, representing a 9.22% reduction in value. The SIR with 36.5% SMC at a machine speed of 0.4 m/s had the lowest value as the disc spacing increased. Comparatively, as the disc spacing increased from 0.2 to 0.3 m, there was only a small decline in the SIR—from 22.9% to 22.6%, representing a 1.31% reduction. There was a further decline of approximately 11.9% as the disc spacing increased from 0.3 to 0.4 m.

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F8.jpg
Figure 8.

Graph showing the trend of the SIR as the disc spacing, machine speed, and SMC were varied.

SCB

Effects of disc space, machine speed, and SMC on SCB

Figure 9 shows the effect of the machine speed on the SCB. As the machine speed increased from 0.2 to 0.4 m/s, the SCB also increased. This may be due to the loosened bond of the soil particles resulting from the increase of the SMC. The highest SCB occurred with 36.5% SMC at a machine speed of 0.4 m/s and disc spacing of 0.3 m, and the lowest occurred with 26.5% SMC at a machine speed of 0.2 m/s and disc spacing of 0.4 m. As shown in Table 5, there was a good positive correlation between the machine speed and the SCB, with a correlation coefficient of R = 0.8105. The correlation was significant, with a p-value of 0.0002 and a coefficient of determination of R2 = 0.6750.

Table 5. Statistical indices from the ANOVA and multiple regression analysis, showing the relationship among the disc spacing, machine speed, and SCB

Statistical Indicesp-valueRR2
Disc spacing0.00020.81050.6570
Machine speed0.00010.96340.9282
SMC0.0080.89450.8003

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F9.jpg
Figure 9.

SCB with increasing machine speed at various constant disc spacing.

Figure 10 shows the relationship between the disc space and the SCB. Increasing the disc spacing caused slight changes in the SCB, and these changes were dependent on the SMC. At 16.5% SMC and a machine speed of 0.2 m/s, an increase in the disc spacing from 0.2 to 0.3 m caused a slight increase in the SCB. However, the SCB decreased as the disc spacing further decreased to 0.4 m. A similar phenomenon was observed at machine speeds of 0.3 and 0.4 m/s. Generally, the SCB was highest with the 0.3-m disc spacing and lowest with the 0.4-m/s disc spacing. There was a very good positive correlation between the SCB and the disc space, with a correlation coefficient of R = 0.9634 and a coefficient of determination of R2 = 0.9282, as shown in Table 5.

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F10.jpg
Figure 10.

SCB with increasing disc spacing at various constant machine speeds.

As the SMC increased from 16.5% to 26.5%, increasing the disc spacing during the tillage operation increased the SCB. However, further increasing the SMC from 25.6% to 36.5% caused the SCB to decrease. Generally, 26.5% SMC yielded the highest SCB, except at a machine speed of 0.4 m/s with a disc space of 0.4 m, which yielded the second-highest SCB. Additionally, 36.5% SMC yielded the lowest SCB in all three situations. Generally, as the SMC increased, the means of the various SMCs differed significantly, with a p-value of 0.008 and an R2 value of 0.8003, as shown in Table 5.

The maximum SCB (66.4%) occurred with 26.5% SMC, whereas the minimum SCB (36.0%) occurred with 36.5% SMC. Generally, as the machine speed increased from 0.2 to 0.3 m/s, the SCB also increased, as shown in Figure 11. However, as the machine speed increased from 0.3 to 0.4 m/s, the SCB decreased. In the SMC range of 16.5% to 36.5%, the SCB increased within a range of 1.5% to 9.2%. However, the SCB decreased within a range of 0.9% to 13% as the machine speed increased from 0.3 to 0.4 m/s. Hence, in all SMC conditions, to achieve a good SCB, the machine speed should be maintained at 0.3 m/s.

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F11.jpg
Figure 11.

SMC, SCB, disc spacing, and machine speed trend analysis, showing their numerical values.

TCD

Effects of disc space, machine speed, and SMC on TCD

Figure 12 shows the trend of the TCD with an increasing machine speed at various disc spacing. Generally, there was an irregular pattern in the correlation as the machine speed increased from 0.2 to 0.4 m/s for each disc spacing. The highest TCD was achieved at a machine speed of 0.2 m/s and a disc spacing of 0.2 m with 16.5% SMC, and the lowest occurred at a 0.4-m/s machine speed and a disc spacing of 0.2 m with 26.5% SMC. The correlation between the machine speed and the TCD was poor, with a correlation coefficient of R = 0.15 and a coefficient of determination of R2 = 0.08, as shown in Table 6. A similar trend was observed in the analysis of the disc spacing and TCD. The correlation coefficient was 0.34, with an R2 value of 0.11.

Table 6. Statistical indices from the ANOVA and multiple regression analysis, showing the relationship among the disc spacing, machine speed, and TCDA

Statistical Indicesp-value RR2
Disc spacing0.08710.2430.1486
Machine speed0.05000.1410.0196
SMC0.05880.7810.6111

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F12.jpg
Figure 12.

TCD with increasing machine speed at various constant disc spaces.

Figure 13 shows the trend of the TCD with increasing disc spacing at constant machine speeds. Generally, increasing the machine speed or disc spacing did not significantly affect the TCD of the disc harrow during the tillage operations. The TCD was highest at a disc spacing of 0.2 m and a machine speed of 0.2 m/s. Apart from this phenomenon, the TCD did not change significantly as the disc spacing increased. This is evident from the low R and R2 values of 0.1486 and 0.0196, respectively, shown in Table 6.

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F13.jpg
Figure 13.

TCD with increasing disc spacing at various constant machine speeds.

The SMC did not significantly affect the TCD (p-value of 0.0588), as shown in Table 6. Generally, 16.5% SMC yielded the highest TCD. The TCD corresponding to 26.5% and 36.5% SMC were not distinctively different, with the largest difference occurring at 0.4 m/s with a disc spacing of 0.2 m.

This phenomenon could probably be due to the low speed of the disc. As the disc moved slowly, the penetration power of the disc harrow increased. Additionally, as the machine speed increased, the TCD decreased.

With a low machine speed of 0.2 m/s, the highest TCD values were achieved, regardless of the disc spacing used. The maximum TCD occurred with a value of 0.095 m at a disc spacing of 0.2 m and a machine speed of 0.2 m/s, as shown in Figure 14. The lowest (0.05 m) occurred with 16.5% and 26.5% SMC at a disc spacing of 0.4 m and a machine speed of 0.4 and 0.2 m/s, respectively. Similar to the machine speed and disc spacing, the SMC did not significantly affect the TCD. This is evident from the p-value of 0.0588 shown in Table 6.

http://static.apub.kr/journalsite/sites/ksam/2018-043-03/N0770430301/images/ksam_43_03_01_F14.jpg
Figure 14.

Graph showing the trends of the SMC, disc spacing, and TCD with their numerical values.

Conclusion

Experiments were conducted to ascertain the effects of varying the machine operational parameters with variable SMC on tillage operations. As tillage parameters, the SIR, SCB, and TCD were measured. As working machine parameters, the disc spacing and machine speed were varied. The experiments were performed with three different SMCs: low (16.5%), medium (26.5%), and high (36.5%).

The results showed that varying the machine speed significantly affected the SIR. Increasing the disc spacing and machine speed caused a decrease in the SIR. Additionally, the disc spacing and machine speed significantly affected the SCB. Generally, as the machine speed and disc spacing increased, the SCB decreased. Increasing the machine speed increased the SCB. Finally, although there were changes in the TCD as the disc spacing and machine speed were varied, these changes were not significant. According to the results, the optimum soil condition for a high SIR is 16.5% SMC with a disc spacing of 0.2 m and a machine speed of 0.2 m/s. The highest SCB was achieved with 26.5% SMC at a disc spacing of 0.2 m and a machine speed of 0.2 m/s. Hence, to obtain a high SIR, SCB, and TCD, the SMC range of 15% to 27% is preferable.

Conflict of Interest

The authors have no conflicting interests, financial or otherwise.

Acknowledgements

This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) through the Advanced Production Technology Development project funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (716001-7).

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