Seeding depth and downforce study
The first study focused on planter downforce and seeding depth offered insight to how these two planter settings affect final seeding depth. It should be remembered that on traditional row-crop planters, these parameters are usually set once and not changed by the operator for an entire field or several fields. Typically these parameters are only changed during planting if field conditions significantly change. Table 2 provides a summary of overall mean soil moisture content, seeding depth and emergence for Fields 1 and 2. Statistical analysis indicated significant differences in soil moisture content (p < .0001), seeding depth (< .0001) and emergence (p = 0.0270) values between Fields 1 and 2. Differences in moisture content values for Fields 1and 2 indicated a considerable moisture variability between the two fields. Field 1 was relatively dry as exhibited by low soil moisture content than Field 2. Data showed that Field 1 tended to be planted little shallower than Field 2 as the seeding depth (39 mm) obtained in Field 2 was relatively deeper than seeding depth (35 mm) in Field 2. This could be attributed to the moisture variability between the two fields as a shallow seed depth can be expected in dry soil conditions with similar planter setup. Overall variation in seeding depth was relatively small across each field with standard deviations of 7 mm or less. The overall mean crop emergence between the two fields was significantly different, although Field 1 exhibited only 1% lower emergence than Field 2. The 95–96% crop emergence was considerably good for the soil types in these fields but lower than past studies focused on maize that mostly emerged between 96% and 99%. The low standard deviations between 4 and 5% for emergence in both fields indicated fairly uniform emergence across each field.
The results from ANOVA analysis for main effects of target depth, downforce, and depth × downforce interaction for Fields 1 and 2 are presented in Table 3. Statistical analysis showed that final planting depth was significantly affected (p < .0001) by the seed depth and downforce in both fields. Emergence was significantly different for the target seeding depths in Field 1 but not in Field 2. For both fields, downforce had no significant effect (p > 0.05) on emergence. The depth × downforce interaction was determined to be non-significant (p > 0.05) and did not affect final seeding depth and emergence in Fields 1 and 2. Table 4 presents the summary of mean final seeding depth and emergence values by each main treatment (target depth and downforce) separately for Fields 1 and 2. For both fields, the final seeding depth tended to be deeper for the target seeding depth of 25 mm and shallower for the target seeding depth of 51 mm. For Field 1, the emergence (93%) attained at 25 mm seeding depth was significantly lower than the emergence (97%) achieved at 51 mm seeding depth suggesting a decrease in crop emergence at shallower planting depth in this field. The mean emergence achieved in Field 2 was 96% for both seeding depths of 25 and 51 mm. For both fields, the final seeding depth tended to increase with applied downforce with deeper seeding depths attained at high downforce settings. However, statistical analysis showed significant differences in seeding depth existed only between none downforce treatment and all other downforce treatments. Although such a low or no downforce is typically not used by growers for planting maize in the Southeastern US, however the impact of downforce on planting depth especially at lower downforce values indicate a minimum downforce requirement (between zero and low) for planting maize where large variations in seeding depth can be expected in the field if not maintained above that minimum value. The final seeding depth was not significantly different between the downforce settings of low, medium and high suggesting no effect of downforce on seeding depth at these downforce treatments. For Field 1, the emergence seemed to increase with an increase in downforce but no significant differences existed between the emergence values at different downforce settings. The emergence differed between the low and high downforce for Field 2 with high downforce exhibiting higher overall emergence of 97%, though the emergence values were not statistically different from each other. This could be explained by the fact that higher downforce may have provided better seed to soil contact for the soil type and prevalent soil conditions in this field which favored higher seedling emergence.
In summary, both depth and downforce settings significantly influenced final planting depth but results from this study implied the difficulty of maintaining the target seeding depth in maize when using only one depth and down force setting. The results can further vary considerably depending on the amount of soil variability present within the field. An additional result from Field 1 and 2 data included that one of the primary factors driving emergence was soil moisture content. Further, the presence of large in-field soil moisture variability has the potential to affect crop emergence along with influence due to improper selection of depth and downforce during planting. This information reinforces the need to not only understand processes in play at planting time but develop technologies that would enable to better manage this variability and improve planting performance. The ability to place seeds at the target planting depth and at the correct population ensures that maximum or near maximum yield potential exists from day 1 when seeds are placed in the ground.
Variable-rate seeding study
The ANOVA analysis results for VR seeding study indicated that plant spacing varied significantly (p < .0001) for the different meter speeds for both John Deere Standard and Precision Planting eSet meter (Table 5). This result was expected as plant spacing was influenced by seeding rate which impacts meter speed. For PP eSet meter, CV was significantly (p = 0.0021) affected by the meter speed whereas meter speed did not influence CV for JD Standard meter. Statistical analysis also indicated no significant differences (p > 0.05) in crop emergence for different meter speeds indicating no effect of meter speed on emergence for both seed meters. Table 6 presents the summary of mean plant spacing, CV, and emergence for different meter speeds attained during the field tests. The measured plant spacing was within 2–13 mm of the target spacing for the JD Standard meter, and within 2–11 mm for the Precision Planting eSet seed meter. The measured plant spacing differed between the meter speeds due to the fact that meter speed is a function of seeding rate and ground speed, and any changes in seeding rate affects the target seed spacing at the given ground speed. For both seed meters, the overall trend observed was that the CV values increased with an increase in meter speed, however no significant differences existed between the CV values at different meter speeds for the John Deere Standard meter. The computed CV values for Precision Planting eSet were, on average, lower than the CV values for the John Deere Standard. The CV values were 31.2% or higher for meter speeds above 25 rpm for John Deere Standard meter setup with a maximum of 36.4% above this rpm. The CV for the eSet meters was only higher than 31% for meter speeds above 28.5 rpm with a maximum of 33.8%. For Precision Planting eSet meter, the CV values observed at meter speeds of 27.7, 37.2, 38.2, and 43.0 rpm were significantly differently from the CV attained at lowest meter speed of 15.4 rpm. This result indicated that the plant spacing uniformity degraded with an in increase in meter speed for the selected meter speeds. Lab testing of these meter units on a meter test stand prior to planting demonstrated that meter performance can degrade sharply at higher meter speeds (> 38 rpm) for both meter setups. The overall crop emergence for both seed meters ranged from 93% to 99% with some of the lowest emergence values (93–95%) observed at the meter speeds of 32.1, 37.2 and 43.0 rpm for both seed meters. Both seed meters provided very comparable emergence with no particular trend observed in emergence values with an increase in meter speed. Results indicated that the plant spacing uniformity and emergence was not affected by meter speed for both seed meters with the exception of CV values at the lowest meter speed (15.4) and three high meter speeds (37.2, 38.2 and 43.0) for Precision Planting eSet meter.
The VRS results in Field 3 revealed the distance to make a seeding rate change (e.g. transition distance) was 2.0 m or less regardless of the magnitude in the rate change. Converting the distance values to seconds indicated the response time for the variable-rate system for making seeding rate transitions was close to 1.0 s or less irrespective of the ground speed. No significant difference was found between the rate transition times for each ground speed (Table 7). The only small trend observed was that the rate transition time decreased at higher ground speeds which makes sense since the distance measured in the field for a rate transition was consistent among different ground speed treatments. Observing the magnitude of rate increments or decrements at the management zone boundaries (Table 8), the transition time was very consistent (0.7–1.0 s) irrespective of the rate change magnitude. Data showed no effect of ground speed and the magnitude of the rate transition (whether increasing or decreasing) on the transition time. This indicated that the VRS technology used in the study was considered quick and consistent. This feature is highly desirable in a VR planter since a quick response time minimizes rate change errors between management zones.
For as-planted data comparison, two figures were generated to point out differences between actual planted data versus prescription map (Fig. 3) and the as-planted map generated by the VR display (Fig. 4). One note of the actual as-planted data (Figs. 3b, 4b) is that no VR seeding was performed during first pass due to operator error, therefore the data from first pass was omitted for this analysis. Comparison of the as-planted data in Fig. 3 revealed that a delay existed between when a rate transition occurred and the boundary of the management zone. The direction of travel, East-to-West versus West-to-East, generated different delay distances with the West-to-East being about half. The average delay distance West-to-East passes was 3.8 m with a maximum of 5.5 m at one transition while the East-to-West was on average 7.7 m and a maximum of 13 m. During an individual pass, the delay distance was consistent. This delay can be corrected with the look-ahead feature within the display but must be known to the operator in order to set it up precisely. The as-planted map (Fig. 4b) generated from data acquisition supports the above results in a quick rate transition (abrupt color changes) versus the display generated map (Fig. 4a). Comparison between the Rx and Actual as-planted map (Fig. 3) indicates that once a target population was achieved by the VRS technology, performance was good for at least meeting the target population until the next rate transition occurred. Differences also existed between the estimated applied or planted population in some areas. However, while global trends existed between the actual and display as-planted maps (similar color regions between the two maps) as observed in Fig. 4, illustrated differences between these layers indicated that the polygon representations were averaged values. This is a concern because while global trends (highs and lows) in estimated planted population tended to exist, the map does not provide true spatial detail on population of planter performance as reflected in the actual as-planted map. It was noted that neither of the as-planted maps (Figs. 3b, 4b) provided in-depth details on when rate transitions actually occurred indicating that these maps were not reflective of the actual planting (rates) in the field making it difficult to make any setup or on-the-go adjustments within the VRS technology. Therefore, disparity existed between display feedback and the resulting as-planted data which most likely was due to the averaging routines and spatial representation (e.g. polygon) within the VRS technology. Further investigation is needed to understand the quality of as-planted data generated by other displays but these results emphasize that more detailed high-resolution maps are required to implement and support VRS. Quality as-planted data reflecting notable details of planter performance will be most needed on the planting equipment for VR seeding applications, especially in areas where in-field soil variability can be considerable.
In summary, while these experiments only represent one growing season, they highlight the impact of adjustable planter parameters that influence final population, plant spacing, and ultimately plant emergence. These parameters need to be carefully considered within a VRS program since planter performance is important to implement this type of seeding strategy. Results suggested the difficulty in using only one planter setup across varying soil types to maintain a target seeding depth in maize, and resulted in seeding depth variations within the same field. Actual planting depth was affected by the planter depth setting and downforce which makes sense but difficult to manage by operators when moving between fields or especially within an individual field. The absence of detail to these parameters can cause seeding rate errors, variations in seeding depth and deviation in seed spacing within seeding zones thereby negating the purpose of VRS and ability to properly evaluate a VRS program. Incorporation of variable seed depth technology on newer planting machinery in near future necessitates that desired seeding depth is maintained with none to minimum variations by ensuring correct depth and downforce setting and by performing field verification of these settings. Variable-rate technology has improved over the years with technology available to implement quick rate changes today. This study indicated quick response time of the rate controller for making rate transitions whereas display as-applied map showed a delayed transitions at the management zone boundaries which was different depending upon the direction of travel. This difference can be corrected through use of the look-ahead feature within the in-cab display setup in order to shift the rate change to the management zone boundary. Although planter performance through current PA displays providing real-time population, singulation and other planting parameters, has helped to improve the quality of planting in the Southeast US. However, PA practitioners and seed companies providing VRS services must be aware of the correct planter and technology setup, and most importantly its limitations, to ensure success of implementation but also proper evaluation. The quality of as-planted data is vital as the Big Data evolution develops in agriculture. Results of this study highlight the need for improvement in as-planted data layers so they accurately reflect in-field seeding parameters such as final population. It may be necessary that as-planted data provide more information than just population to support VRS in maize here in the Southeast US due to high in-field variability. Quality of as-planted data is needed as farmers rely on data management services to help drive decisions about input and machine management. Accurately documenting factors which influence emergence such as seeding rate, seed spacing and depth through as-planted data would help to ensure that proper decisions are made when evaluating VRS or other on-farm trials related to maize.