3.1. EC and MC Measurement Results
Figure 9 shows the measurement results of the
EC and
MC of the field soil and stubble before spring sowing. It could be clearly seen that during this period, the
EC and
MC of soil and stubble had a significant difference. The average
MC of the soil was 15 ± 2.48%, and the average
MC of the stubble was 4.71 ± 2.4%. The maximum value of soil
MC was 20.59%, and the minimum value was 8.42%. The maximum value of stubble
MC was 12.39%, and the minimum value was 0.04%. The average
EC of the soil was 444.20 ± 32.34 μs/cm, and the average
EC of the stubble was 72.26 ± 25.54 μs/cm. The maximum value of soil
EC was 528.28 μs/cm, and the minimum value was 361.21 μs/cm. The maximum value of stubble
EC was 137.19 μs/cm, and the minimum value was 6.04 μs/cm.
Soil
EC was affected by
SMC [
39],
SC [
40] and organic matter content [
44]. The stubble returning to the field also contributed to storing soil moisture [
45]. On the other hand, the
MC of stubble covering the field was naturally relatively low even in the spring sowing period after being exposed to wind and sun.
3.2. Effect of Experiment Factors on STDE of Stubble Thickness
Since the stubble thickness was converted from the angle value determined by detecting the difference in
EC between the soil and stubble, the
STDE affected by the
EC could be equivalent to the
STDE affected by the stubble thickness.
Table 4 demonstrates the result of
SMC,
SC and
V on
STDE. The main effects and interactions were derived from ANOVA results (
Table 5 and
Table 6), and the regression model and optimized parameters were obtained. The regression model of
STDE was extremely significant (
p < 0.001), and the lack of fit of the model was not significant (
p > 0.05), indicating that the fitted regression equation was highly reliable and could accurately reflect the relationship among
SMC,
SC,
V and
STDE.
As shown in
Table 5, all experiment factors,
SMC,
SC, V and the interaction of
SC and
V, were significantly (
p < 0.001) related to
STDE. The interaction of
SMC and
SC, the interaction of
SMC and
V and the second-order terms of
SMC were significantly (
p < 0.01) related to
STDE. The interaction of
SMC and
V and the second-order terms of
V were significantly related to
STDE (
p < 0.05).
Table 6 shows that a “Pre R
2” of 0.8717 was in reasonable agreement with an “Adj R
2” of 0.9593. After removing insignificant terms, the final regression model was as given in Equation (5):
Figure 10 illustrates that
STDE decreased with the increase of
SMC and
SC and increased with the increase of
V. The reasons were analyzed as follows: when other factors remained unchanged, the increase of
SMC promoted the ion migration rate in the soil, and the
EC detection was more accurate, resulting in the reduction of
STDE. However, the influence was limited. When
SMC reached 30% [
46], the continuous increase of
SMC caused dilution and had a negative impact on
EC detection. Therefore, the range of
SMC in this research was set up at 10–30%. When other factors remained unchanged, the increasing
SC represented the increasing density of the soil so that the sensor probe was more fully in contact with the soil and the
STDE was reduced. When other factors remained unchanged, the increasing
V resulted in insufficient contact between the sensor probe and the soil, which in turn led to an increase in
STDE. Therefore, the stubble thickness detection device referred to a working condition with a low speed and a sufficient gap between
MC of soil and stubble, and
SMC should not exceed 30%.
3.3. Effect of Experiment Factors on SCR and WC
Table 7 shows the compared experiment with and without a stubble thickness detection device to adjust the pressure. Under the condition that
SCR remained unchanged, the
WC equipped with a stubble thickness detection device to adjust the pressure was reduced by 13.22%. However, it was undeniable that compared with the no-blocking effect of the ultra-high-pressure waterjet [
32], the waterjet pressure of 22–26 MPa had certain limitations. But it could still meet the requirements, and the slight blocking only appeared with the strongest stubble root parts.
Table 8 shows the effect of
P,
α and
V on
SCR and
WC. The main effects and interactions were derived from ANOVA results (
Table 9 and
Table 10). The regression models and optimized parameters were obtained. The regression model of
SCR was extremely significant (
p < 0.001), and the lack of fit of the model was not significant (
p > 0.05). The regression model of
WC was extremely significant (
p < 0.001), and the lack of fit of the model was not significant (
p > 0.05), indicating that the fitted regression equation had high reliability and could accurately reflect the relationship between
P,
α,
V and
SCR,
WC.
Table 9 demonstrates that
P,
α,
V and the second-order term of α were significantly (
p < 0.001) related to
SCR. The interaction term of
P and
α and the interaction term of α and
V were significantly (
p < 0.01) related to
SCR. The second-order term of
V was significantly (
p < 0.05) related to
SCR.
P and
V were significantly (
p < 0.001) related to
WC. The interaction of
P and
V was significantly (
p < 0.01) related to
WC. The second-order term of
P was significantly (
p < 0.05) related to
WC.
Table 10 shows that the “Pre R
2” of 0.9698 and the “Adj R
2” of 0.9850 related to
SCR, and the “Pre R
2” of 0.9086 and the “Adj R
2” of 0.9631 related to
WC were reasonable. After removing insignificant terms, the final regression models were as given in Equations (6) and (7):
Figure 11a shows the effect of
P and α on
SCR when
V was 4 km/h.
SCR increased with the increase of
P and increased and then decreased with the increase of
α. When
P was 18 MPa, as α increased from 75° to 105°,
SCR increased from 71.67% to the highest point and then decreased to 85.57%. When
P was 26 MPa, as α increased from 75° to 105°,
SCR increased from 82.5% to the highest point and then decreased to 86.37%.
Figure 11b illustrates the effect of α and
V on
SCR when
P was 22 MPa. When
α was 75°, as
V increased from 3 km/h to 5 km/h,
SCR decreased from 80.75% to 72.45%. When
α was 105°, as
V increased from 3 km/h to 5 km/h,
SCR decreased from 85.1% to 83.2%.
Figure 11c demonstrated the effect of
P and
V on
WC when
α was 90°.
WC increased with the increase of
P and decreased with the increase of
V. When
P was 18 MPa, as
V increased from 3 km/h to 5 km/h,
WC decreased from 43.56 L/ha to 28.35 L/ha. When
P was 26 MPa, as
V increased from 3 km/h to 5 km/h,
WC decreased from 51.66 L/ha to 45.35 L/ha. The change in
WC caused by the increase of
P was greater than that caused by the increase of
V.
The higher P produced a faster waterjet stream, which meant higher momentum and more destruction. The waterjet with higher impact energy was able to cut stubble deeper, with the benefit of increasing SCR. Increasing P also improved the cutting efficiency. Conversely, a faster V caused less exposure time, which decreased the impact energy of the waterjet on stubble. Aiming at increasing the efficiency of agricultural production, which meant a faster V, demanding a higher P to compensate for the less exposure time caused by the faster V. As for WC, it had nothing to do with α; it was only related to P and V. Under the condition that V remained unchanged, the greater the P, the greater the WC. Under the condition that P remained unchanged, the faster the V, the smaller the WC.
Compared with the research by Hu et al. [
3], this research found that when
P and
V remained constant, the optimal
α did not always appear in the vertical direction of 90°. As shown in
Figure 7, it could be seen that the optimal
α was in the range of 90–95°. The reason might be caused by the difference in
P. The equipment designed by Hu [
3] adopted the range of pressure factor to be 240–280 MPa, which had stronger kinetic energy in the vertical direction and was not easily affected by the horizontal movement of the machine. However, the
P range in this study was 18–26 MPa. When the cutting angle was 90–95°, the horizontal movement of the machine helped correct the horizontal force component of the waterjet [
46], which was more effective for stubble cutting.
3.4. Optimization Analysis and Verification Experiment
In order to obtain the optimal parameters for the stubble-cutting device, the Design Expert V12 was used to optimize the solution of the constrained objective according to the above experiment results and the regression equations. In order to ensure the minimum
STDE, the
SCR over 95% and the minimum
WC, the objective equations and constraints were set as follows:
The optimal parameters for the STDE were: SMC was 29.91%, SC was 0.84 MPa and V was 3.01 km/h. Under these conditions, the STDE was 3.023%. The optimal parameters for SCR and WC were P, 26 MPa; α, 90.45°; and V, 3.36 km/h. Under these conditions, SCR was 95.49% and WC was 49.98 L/ha.
The two factors (
SMC and
SC) in the
STDE experiment were not controllable in the field and were not applied in the field verification experiment. The forward speed was set the same as the
SCR and
WC experiment. According to the optimization analysis results,
P was set to 26 MPa,
α was set to 90.45° and the forward speed was set to 3.36 km/h in the field verification experiment.
Table 11 shows the results of the verification experiment, and the work performance and optimal parameters are shown in
Figure 12. The relative error of
STDE between the measured value of the verification experiment and the predicted value of the regression model was 4.27%. The relative error of
SCR was 4.07%. The relative error of
WC was 1.82%, which was basically consistent with the theoretical optimization results, indicating that the experimental regression model was reasonable.