Tillage practices have a significant effect on soil properties. Understanding the specific effects due to geometric and speed parameters of the tillage implement is the key to selecting a tillage practice that is best for a field. This is the first step of many towards optimizing an efficient tillage implement given initial field conditions and desired final conditions. Simple, small-scale tests were performed on idealized implement geometries as a proof-of-concept for future large-scale tests. The results of these tests are presented here and in-depth analysis will be presented in future work.
Tillage practices affect soil carbon, water pollution, and farmers’ energy and pesticide use, and therefore data on tillage can be valuable to understanding the practice’s role in reaching climate and other environmental goals. [
The negative effects of tillage have been a driving force behind the popularity of CSV (conservation tillage) and RT (reduced till). Modern NT (no-till) began to emerge with commercial use of synthetic herbicides [
A survey conducted at Oklahoma State University [
Impact | Conservation tillage | No-till |
---|---|---|
Aggregate stability | Organic matter concentrated near surface, encourages microbial growth, increasing aggregate stability. | Crop residue at surface prevents surface crusting, increasing aggregate stability. |
Compaction | Improves surface structure due to concentration of residues, decreasing compactibility; Can cause additional compaction in the untilled layer. | Dependent on soil type. |
Soil mineralization | Shallower depth with no soil inversion so releases less N for crop uptake than conventional tillage, which leads to less mineralization. | |
Emergence and root growth | Decreases soil temperature and increases residue, thus impeding crop emergence; Root growth depends on biological macropore ability to compensate for absence of mechanical macropores. | |
Soil water storage and infiltration | Increases C content in soil, which increases water storage capacity and water retention; Infiltration depends on soil type and biological porosity. | |
Weeds | Compared to conventional, more weeds in general. | |
Disease and pest control | Conventional tillage more effective for control of soil-bourne pathogens, but increased biological activity of conservation and no-till can form disease-suppressive soils. |
system have tried NT but then switched back to CNV (conventional tillage) within an average of 2.4 years. This switch may be due to a potential for crop yields to decrease in the first two to three years of NT. Additionally, the survey indicated that farmers may not feel informed enough about CSV to be comfortable with switching. The least important perceived benefit appeared to be increased yield, while the most important perceived benefit was reduced fuel costs. From the survey, CNV farmers thought there would be more problems with CSV than those farmers already employing it. The largest perceived problems, from those already using CSV, were lack of state and local research, equipment costs, and lack of knowledge. It is clear that farmers are interested in ways to be more efficient, increase yield, and protect the value of the crop land. However, there is a perceived lack in knowledge and research of tillage practices, and there are advantages and disadvantages to consider with each tillage practice.
There have been numerous experimental studies on the effects of tillage, but not relative to specific geometric parameters of tillage. The existing studies compare conventional tillage to varying types of conservation tillage, but do not look at modifications to the tillage implements. For example, Abdullah [
This work is the first of many steps towards optimizing tillage implements so that optimal field conditions can be obtained and maintained. As the first step in the process of optimizing tillage implements, a small-scale tillage test system was constructed. The goal of this system was to obtain preliminary data so that the experimental and theoretical modeling processes could be evaluated. Starting with small-scale allowed reduction of the complexity of implement geometry. “Scale models have many advantages, including lower cost of construction, greater flexibility in the range of parameters that can be investigated, and closer control of test conditions.” [
A variety of implement geometries were tested in a thorough test matrix, using a small-scale setup. The specifics of these are all discussed in the following sections.
The test bed had internal dimensions of 0.84 m (W) × 1.37 m (L) × 14.0 cm (D), with sand at a depth of 10.2 cm. Extruded aluminum rails constructed the frame of the setup, allowing translations in the x-, y- and z-directions and providing mounting for a camera. Driving motion for the implement was provided by a DC motor actuating a screw drive. Between each test the sand was leveled by sliding a depth-controlled board across the surface of the test bed. The setup is shown in
Data acquisition and motor control was run with a LabVIEW myRIO (Xilinx Z-7010 FPGA reconfigurable input/output device). A pulse-width modulation pulse from the myRIO controlled a Parallax HB-25 motor controller. Voltage supplied to the motor was measured by the myRIO. A strain gage on the implement was calibrated as a load cell via a P-3500 Strain Indicator module that output continuously to the myRIO. The wiring diagram for all control and acquisition is shown in
Additionally, a Logitech HD Webcam C525 was used to capture images of the ridge and furrow after each test. Spotlighting was used to highlight the features in the sand. Post-processing (calibration and measurements) of the images was performed with Image J [
Three basic geometries were developed for the test implements, shown in
For a single test, Input Voltage, Depth and Angle were user-specified to identify the test matrix parameters. The screw drive moved the implement through the sand while the VI recorded force continuously, via the calibrated strain gage. After the test was complete, the force was averaged over the range where till of the implement was fully developed (uniform ridge and furrow, constant force). Furrow and ridge measurements were obtained from images taken at the end of each test, using an average of three measurements for each.
ID | Angle [deg] | Input voltage [V] (speed [cm/s]) | Depth [cm] |
---|---|---|---|
1 | 0 | 6 (0.43) | 1.7 |
2 | 22.5 | 7.5 (0.56) | 3.0 |
3 | 45 | 9 (0.66) | |
4 | 67.5 |
Analysis of the results will be discussed in-depth in a later article to facilitate adequate coverage on both topics, and a basic statistical analysis will be presented here. Average and standard deviation values were calculated for each set of three repeated tests, and are also included in
Average values for each property are plotted in Figures 4-7 for all of the tools. The average values for the ridge and furrow follow a second order polynomial for the plate but a linear trend for the sphere and triangle. The force values behave exactly opposite. These behaviors are present because of the 3D geometry of the triangle and sphere, where the projected area engaging the sand is not decreasing as significantly as in the case of the flat plate. In the case of the triangle, the projected area actually begins to increase after some rotation.
Equations (1)-(4) give the linear fit for the furrow width (plate tool depth 1.7 cm and 3.0 cm, sphere tool depth 1.7 cm, triangular tool depth 1.7 cm, respectively), where d is the depth of the tool and θ is the angle of the tool. Values for R2 can be seen in Figures 4-7.
Average | Standard deviation | |||||||
---|---|---|---|---|---|---|---|---|
Input V | Depth [cm] | Angle [deg] | Force [g] | Furrow [cm] | Ridge [cm] | Force [g] | Furrow [cm] | Ridge [cm] |
6 | 1.7 | 0 | 34.3 | 4.93 | 13.5 | 1.07 | 0.04 | 0.03 |
7.5 | 1.7 | 0 | 40.2 | 4.86 | 13.5 | 5.00 | 0.14 | 0.13 |
9 | 1.7 | 0 | 44.0 | 4.88 | 13.5 | 2.26 | 0.23 | 0.10 |
6 | 1.7 | 22.5 | 30.5 | 4.94 | 12.9 | 2.01 | 0.21 | 0.01 |
7.5 | 1.7 | 22.5 | 29.0 | 4.83 | 12.9 | 1.24 | 0.13 | 0.21 |
9 | 1.7 | 22.5 | 28.5 | 4.94 | 12.7 | 0.53 | 0.03 | 0.01 |
6 | 1.7 | 45 | 15.7 | 3.62 | 10.5 | 0.23 | 0.01 | 0.05 |
7.5 | 1.7 | 45 | 14.5 | 3.66 | 10.5 | 0.75 | 0.07 | 0.04 |
9 | 1.7 | 45 | 15.1 | 3.73 | 10.6 | 0.73 | 0.03 | 0.09 |
6 | 1.7 | 67.5 | 4.9 | 1.51 | 6.6 | 0.29 | 0.06 | 0.10 |
7.5 | 1.7 | 67.5 | 4.7 | 1.49 | 6.7 | 0.11 | 0.01 | 0.00 |
9 | 1.7 | 67.5 | 4.7 | 1.54 | 6.7 | 0.63 | 0.06 | 0.01 |
7.5 | 3.0 | 0 | 101.0 | 2.14 | 15.7 | 16.85 | 0.04 | 0.05 |
9 | 3.0 | 0 | 101.0 | 2.65 | 15.6 | 17.84 | 0.04 | 0.13 |
7.5 | 3.0 | 22.5 | 72.2 | 2.07 | 14.8 | 0.03 | 0.24 | 0.11 |
9 | 3.0 | 22.5 | 71.8 | 2.09 | 14.9 | 1.64 | 0.10 | 0.07 |
7.5 | 3.0 | 45 | 38.9 | 0.73 | 12.7 | 2.69 | 0.12 | 0.07 |
9 | 3.0 | 45 | 37.0 | 0.70 | 12.7 | 2.06 | 0.08 | 0.07 |
7.5 | 3.0 | 67.5 | 13.3 | 0.00 | 8.8 | 1.19 | 0.00 | 0.07 |
9 | 3.0 | 67.5 | 14.4 | 0.00 | 8.8 | 1.70 | 0.00 | 0.08 |
Average | Standard deviation | |||||||
---|---|---|---|---|---|---|---|---|
Input V | Depth [cm] | Angle [deg] | Force [g] | Furrow [cm] | Ridge [cm] | Force [g] | Furrow [cm] | Ridge [cm] |
6 | 1.7 | 0 | 361 | 4.35 | 15.6 | 16.9 | 0.22 | 0.17 |
7.5 | 1.7 | 0 | 367 | 4.45 | 15.4 | 4.4 | 0.07 | 0.08 |
9 | 1.7 | 0 | 376 | 4.56 | 15.4 | 18.6 | 0.03 | 0.04 |
6 | 1.7 | 22.5 | 334 | 4.19 | 14.9 | 22.2 | 0.35 | 0.15 |
7.5 | 1.7 | 22.5 | 360 | 4.46 | 14.8 | 24.7 | 0.03 | 0.14 |
9 | 1.7 | 22.5 | 341 | 4.39 | 14.8 | 20.5 | 0.07 | 0.06 |
6 | 1.7 | 45 | 196 | 3.88 | 13.5 | 27.1 | 0.12 | 0.11 |
7.5 | 1.7 | 45 | 195 | 3.81 | 13.5 | 4.1 | 0.05 | 0.06 |
9 | 1.7 | 45 | 202 | 3.94 | 13.5 | 6.2 | 0.04 | 0.10 |
6 | 1.7 | 67.5 | 61 | 2.71 | 11.3 | 4.6 | 0.07 | 0.09 |
7.5 | 1.7 | 67.5 | 66 | 2.80 | 11.4 | 4.0 | 0.05 | 0.04 |
9 | 1.7 | 67.5 | 60 | 2.79 | 11.3 | 1.9 | 0.10 | 0.04 |
Average | Standard deviation | |||||||
---|---|---|---|---|---|---|---|---|
Input V | Depth [cm] | Angle [deg] | Force [g] | Furrow [cm] | Ridge [cm] | Force [g] | Furrow [cm] | Ridge [cm] |
6 | 1.7 | 0 | 0.80 | 2.47 | 13.1 | 0.02 | 0.21 | 0.12 |
7.5 | 1.7 | 0 | 0.84 | 2.69 | 13.1 | 0.02 | 0.06 | 0.04 |
9 | 1.7 | 0 | 0.86 | 2.26 | 13.3 | 0.03 | 0.70 | 0.24 |
6 | 1.7 | 22.5 | 0.74 | 2.44 | 12.2 | 0.01 | 0.23 | 0.12 |
7.5 | 1.7 | 22.5 | 0.82 | 2.47 | 12.1 | 0.01 | 0.07 | 0.09 |
9 | 1.7 | 22.5 | 0.79 | 2.58 | 12.1 | 0.01 | 0.05 | 0.07 |
6 | 1.7 | 45 | 0.44 | 3.53 | 12.2 | 0.02 | 0.13 | 0.22 |
7.5 | 1.7 | 45 | 0.42 | 3.49 | 12.1 | 0.02 | 0.09 | 0.13 |
9 | 1.7 | 45 | 0.42 | 3.48 | 12.2 | 0.01 | 0.04 | 0.07 |
6 | 1.7 | 67.5 | 0.17 | 4.08 | 12.7 | 0.01 | 0.11 | 0.12 |
7.5 | 1.7 | 67.5 | 0.16 | 4.19 | 12.7 | 0.00 | 0.06 | 0.02 |
9 | 1.7 | 67.5 | 0.15 | 4.26 | 12.6 | 0.01 | 0.07 | 0.12 |
Force [g] | Furrow [cm] | Ridge [cm] | ||
---|---|---|---|---|
Plate (60 tests) | Max | 18 | 0.25 | 0.20 |
Min | 0.0 | 0.03 | 0.00 | |
Avg | 4.5 | 0.08 | 0.08 | |
Sphere (35 tests) | Max | 27 | 0.36 | 0.18 |
Min | 0.0 | 0.03 | 0.05 | |
Avg | 14 | 0.10 | 0.10 | |
Triangle (36 tests) | Max | 14 | 0.69 | 0.23 |
Min | 0.0 | 0.05 | 0.03 | |
Avg | 4.5 | 0.15 | 0.13 |
Equations (5)-(8) give the fits for the ridge width (plate tool depth 1.7 cm and 3.0 cm, sphere tool depth 1.7 cm, triangular tool depth 1.7 cm, respectively). Values for R2 are in Figures 4-7.
Equations (9) and (10) give the fits for the plate tool force for depths 1.7 cm and 3.0 cm, respectively. Equation (11) gives the fit for the sphere tool force and Equation (12) gives the fit for the triangular tool force. Values for R2 are shown in Figures 4-7.
A small-scale test system with a sand bed was successfully implemented. Data for several implement geometries were obtained, following a thorough test matrix for each implement. There results will be used as the foundation for developing a system to optimize a tillage implement. The need clearly exists for improved understanding of tillage. Useful data were obtained and will be analyzed in-depth in future work.
ElizabethFrink,DanielFlippo,AjaySharda, (2016) Primitive Geometry Tillage Modeling. Open Journal of Soil Science,06,34-43. doi: 10.4236/ojss.2016.62004