|CONWAY, L - University Of Missouri|
|Sudduth, Kenneth - Ken|
|MYERS, D - Corteva Agriscience|
|LINDSEY, A - The Ohio State University|
|CARTER, P - Corteva Agriscience|
Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 5/28/2019
Publication Date: 5/28/2019
Citation: Conway, L.S., Kitchen, N.R., Sudduth, K.A., Myers, D.B., Lindsey, A.J., Carter, P.R. 2019. Planting depth and soil series effect on in-furrow soil sensor performance. In: Proceedings 5th Global Workshop on Proximal Soil Sensing, May 28-31, 2019, Columbia, Missouri. p. 47-57.
Interpretive Summary: Collecting information about soil properties during grain crop planting has the potential to improve planting equipment performance and overall management of grain crops. Recently, technologies have become commercially available that allow farmers to equip planters with sensors to collect data on several soil properties from within the planter seed trench. Evaluation of these sensors is needed to determine how they perform across a wide range of environments. Therefore, this research was conducted to determine how well commercial planter sensors (SmartFirmers) performed at estimating measured soil properties, and whether this information could be used to guide crop management decisions. The sensors slightly overestimated soil temperature, but were able to successfully detect relative differences across a range in temperatures. As planting depth increased, sensor accuracy increased. Average soil organic matter (OM) measured by the SmartFirmers slightly overestimated laboratory-measured OM, and the planter sensors did not work well to estimate extreme values of OM. An agronomic evaluation showed that soil moisture from the planter sensors was able to estimate corn emergence uniformity across sites, and sensed moisture and organic matter were useful for predicting yield potential at a subset of sites. This information will aid producers when interpreting measurements collected with SmartFirmers, and will help to improve at-planting decisions that have potential to increase grain-crop emergence uniformity and yield.
Technical Abstract: Integration of reflectance and temperature sensors into commercial planter components have allowed for a dense quantification of within-field spatial variability. This estimation of variability can now guide real-time management decisions, such as on-the-go variable rate applications (VRA) of seed and starter fertilizer. However, little is known about sensor performance across a range of environments. Therefore, a study was conducted in Missouri on multiple sites in 2018 and 2019 to determine (i) how well these sensors can estimate soil properties (i.e., moisture, temperature, and organic matter (OM)) and (ii) whether sensor output could be used to improve agronomic management. Research was performed with two planters across a range of planting depths, soil texture, and productivity. Data were collected in situ with soil reflectance (visible and near-infrared) and thermopile temperature sensors (Precision Planting SmartFirmer) on-the-go during corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) planting. Measured soil temperature was collected with a handheld thermocouple sensor and soil samples were collected for OM at a 0-15 cm depth. Results showed that at a constant planting depth, soil temperature and OM were slightly overestimated by the SmartFirmers when compared to measured data. Likewise, both furrow properties were affected by and were negatively correlated to planting depth. However, the decrease in temperature with each 25 mm increase in planting depth was overestimated by nearly 2°C by the SmartFirmers. An agronomic analysis found that SmartFirmer furrow moisture was an indicator of the evenness of corn emergence across sites. The most uniform emergence was observed when SmartFirmer moisture exceeded 40%. This would suggest that operators target 40%, rather than 20 or 30% recommended by the manufacturer, during corn planting to achieve optimal emergence uniformity. Results for OM did not indicate strong whole field relationships between SmartFirmer OM and yield potential. However, within-field correlations were observed. These preliminary results suggest that soil moisture and OM estimated by the SmartFirmer may be valuable tools for capturing soil spatial variability and guiding VRA, but that further evaluation is needed for proper implementation.