Progress 06/15/22 to 06/14/23
Outputs
Target Audience:Farmers, industrial related to planting technology, academics, and students. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided? The project is currently training aPh.D. student (Jose). He is expected to defend his Dissertation in Spring 2025. Graduate student scholars are learning to work with farmers and collecting field data, while discussing ideas for improving current management. The student on the engineering side is leaning to develop and utilize data acquisition systems, selection of sensors and integration for field data collection, use of technology for post-planting data collection. Students are learning to develop research question, conduct on-farm experiments, and synthesize knowledge valuable for producers, industry professionals, and engineers for new product innovations. Additionally scholars trained: Two Postdoc Professionals (one resident of the Philippines and one on a short stay from Turkey). Threemaster's students (United States, Argentina and Nepal). Three Visiting scholars (one from Argentina and two from the Philippines). Three undergraduate students of Biological and Agricultural Engineering (from the United States). How have the results been disseminated to communities of interest?Results were disseminated through participation in departmental events, international conferences and one-to-one interaction with stake holders including meeting with Kansas Farm Bureau, industry leaders, and conferences including Cover Your Acres. Other avenues include other scientific meetings, social media posts, extension events (winter corn-soybean schools) and with a new publication (under review). What do you plan to do during the next reporting period to accomplish the goals?During the 2024 campaign, it is planned to carry out the following lines of research: Continue with adaptive field experimentation of novel commercial precision planting technologies to explore their efficacy and applicability in real-world settings. Implement real-time computer-vision and artificial intelligencetechniques to monitor seed placement and furrow closure, leading to the complete automation of the seeder of the future. Conduct a comprehensive field benchmarking study of furrow closure technologies in corn planting to evaluate their performance and identify opportunities for improvement. Enhance statistical prediction algorithms for precision crop planting systems to increase their accuracy and effectiveness in predicting crop yields and optimizing planting strategies.
Impacts
What was accomplished under these goals?
Four Key projects were imoplemented in 2022-2023. All of these projects involved 24-row John Deere Exact_Emerge planter with individual row hydraulic downforce control system. On-farm studies were implement on farmer cooperators farms, which included both irrigated and dryland operation. 1. Seed placement as response of downforce margin and speed:This study aims to understand the impact of ground speed and gauge wheel load on seed depth and emergence uniformity. The experiment was conducted using a John Deere 24-row planter with five different gauge wheel loads and two ground speeds. After planting, seed depth was measured using digital calipers and emergence speed was recorded every 12 hours. Preliminary results show that forward speed and downforce are directly correlated, and that seed depth and emergence speed are affected by the combination of ground speed and gauge wheel load. 2. Furrow closing devices benchmarking:This study aimed to compare the performance of aftermarket closing wheels with the standard round rubber wheels of the John Deere 1775Nt 24row30 planter in Northeast Kansas. The study found that twisted spikes wheels resulted in notably deeper seed depth compared to the other three treatments. Round rubber wheels had the highest emergence rate, while the Furrow Force treatment reached 100% emergence the fastest. Twisted spikes wheels had the lowest spacing coefficient of variation and standard deviation values. 3. Soybean planting with firming wheels and downforce set up:This study investigates the use of firming wheels in combination with downforce settings on soybean planting. The results show that firming wheels, in combination with active or fixed downforce settings, impact seed placement depth and emergence, but have negligible effects on yield. These findings provide valuable insights for soybean cultivation practices. 4. Influence of downforce margin and electroconductivity over corn yield:This study evaluated on-farm strip trials to help growers determine the ideal planter downforce required at planting. Results showed that fixed and active downforce systems with higher downforce margins (240 lbf) work best in moderately fine- to medium-textured, no-till fields, while a lower downforce margin (150 lbf) is ideal for poorly drained fine-textured soil. Soil ECa and GWL are correlated with yield variability, accounting for weak to moderate correlations. These parameters can be used by producers to develop strategies to maximize potential yield.
Publications
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2024
Citation:
Badua, S. A. & A. Sharda. (2024). Assessment of Corn Yield Across Different Planter Downforce Settings and Soil Electrical Conductivity Zones. In ASABE Annual International Meeting. July 28-31, 2024, Anaheim, CA
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2024
Citation:
Peiretti, J., S. A. Badua, & A. Sharda. (2024). Optimizng Corn Seed Placement: Impact of Downforce Margin, Planting Speed, and Row Unit Toolbar Location on Planting Accuracy. In 16th International Conference on Precision Agriculture, July 21-24, 2024, Manhattan, KS
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2024
Citation:
Peiretti, J., S. A. Badua, & A. Sharda. (2024). Optimizng Corn Seed Placement: Impact of Downforce Margin, Planting Speed, and Row Unit Toolbar Location on Planting Accuracy. In ASABE Annual International Meeting. July 28-31, 2024, Anaheim, CA
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2024
Citation:
Aryal, B. & A. Sharda. (2024). Assessing Plant Spacing Inequality and Its Impact on Crop Yield Using Lorenz Curves and Gini Index. In 16th International Conference on Precision Agriculture, July 21-24, 2024, Manhattan, KS
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2024
Citation:
Aryal, B. & A. Sharda. (2024). Assessing Plant Spacing Inequality and Its Impact on Crop Yield Using Lorenz Curves and Gini Index. In ASABE Annual International Meeting. July 28-31, 2024, Anaheim, CA
- Type:
Journal Articles
Status:
Under Review
Year Published:
2024
Citation:
Badua, S.A., A. Sharda, & B. Aryal. (2024). Quantifying Real-Time Opening Disk Load During Planting Operations to Assess Compaction and Potential for Planter Control. Precision Agriculture. Under review.
- Type:
Journal Articles
Status:
Under Review
Year Published:
2024
Citation:
Mishler, B., S. A. Badua, & A. Sharda. (2024). Benefits of Planter Turn Compensation on Irregular Shaped Fields. Applied Engg. in Agriculture. Under Review.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2024
Citation:
Badua, S. A. & A. Sharda. (2024). Assessment of Corn Yield Across Different Planter Downforce Settings and Soil Electrical Conductivity Zones. In 16th International Conference on Precision Agriculture, July 21-24, 2024, Manhattan, KS
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Progress 06/15/21 to 06/14/22
Outputs
Target Audience:The target audience was specifically farmers, industry professionals, service providers, manufacturers, graduate students, high school students and teachers, and faculty. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided? The project is currently training a MS student (Valentina Pereyra, target defense May 2023) and Ph.D. student (Jose). Graduate student scholars are learning to work with farmers and collecting field data, while discussing ideas for improving current management. This work is directly applied to help farmers across the state and in a regional scale. The student on the engineering side is learning to develop and utilize data acquisition systems, selection of sensors and integration for field data collection, use of technology for post-planting data collection. The student on the agronomy side is learning to develop efficient field protocols, improve data collection with new sensors, and the overall analysis/processing of data for presentation. Students are learning to develop research question, conduct on-farm experiments, and synthesize knowledge valuable for producers, industry professionals, and engineers for new product innovations. How have the results been disseminated to communities of interest?Results were disseminated through participation in departmental events, international conferences and one-to-one interaction with stake holders. Other avenues include scientific meetings, social media posts, extension events (winter soybean schools) and with a new publication (under review). What do you plan to do during the next reporting period to accomplish the goals?In the Year-3, the research plan includes Compare and contrast different closing wheel system to quantify impact on emergence, spacing and yields. Quantify impact of downforce selection, and speed on seeding depth, spacing and emergence uniformity, and potential of any compaction. Quantify the advantages gained on seed placement while using individual row hydraulic control (IRHC) compared to Set point comparison for down force management. Quantify impact of reducing soybean seeding rates to improve seed savings and farmer profitability.
Impacts
What was accomplished under these goals?
In the Year-2 of the project, following projects were undertaken: In the Year-2, the research projects includes Quantify impact of downforce selection, and speed on seeding depth, spacing and emergence uniformity, and potential of any compaction. Quantify the impact of row spacing and variety on plant uniformity and yields for soybeans. Our goal will be to collect a last year of data working with farmers on this critical soybean topic to understand how we can help with the management of seeding rates to compensate potential effects of lack of uniformity and considering to include a simple economic analysis to facilitate the evaluation of risk of this activity. Project-1: Quantify impact of downforce selection, and speed on seeding depth, spacing and emergence uniformity, and potential of any compaction. Two planters were used for this study, a John Deere Exact Emerge Planter (referred henceforth as Planter-A) and a Case IH Early Riser Planter (referred henceforth as Planter-B), both with 16 row units spaced at 76.2 centimeters apart. Both planters were equipped with electric seed meters, mechanical seed delivery tubes/brush belt, and individual row hydraulic downforce control systems. Planter-A utilized ExactEmerge® technology; and John Deere IHRC, controllers and Deere field computer (Gen4, Deere and Company, Moline, IL, USA); and Planter-B had Precision Planting Vset® seed meters with vDrive® electric motors, mechanical seed delivery tubes, and Deltaforce® IHRC (Precision Planting - AGCO, Tremont, IL, USA), which was implemented utilizing CaseIH field computer (AFS Pro 700, CNH Industrial, Burr Ridge, IL, USA). Both the planters were programmed to plant using two different active downforce settings, 54.4 kgf (120 lbs) and 99.8 kgf (220 lbs), and three speeds, 9.7 kph (6 mph), 12.1 kph (7.5 mph), and 16.1 kph (10 mph). Both planters utilized central commodity systems (CCS). On Planter-A, each odd numbered row was equipped with a hydraulic pressure transducer to measure the real-time hydraulic oil pressure applied. Hydraulic oil pressure readings were utilized to deduce actual real-time downforce applied by the hydraulic system on row units to implement desired gauge wheel load target during planting. These rows were also equipped with proprietary loadcells, which measured the real-time gauge wheel load (GWL) during planting. The actual real-time GWL data from loadcells was collected using the CAN Bus built into the tractor and planter. On both planters, accelerometers were mounted on the odd numbered rows to measure vertical acceleration of the row units during planting in real-time. Location and travel speed were measured using a sub-inch accuracy GPS unit. Load cells, hydraulic pressure transducers, accelerometers, CAN Bus data, and GPS signals were recorded using laptop computer and a NI cRIO chassis via C Series modules at 100 Hz sampling frequency. Results: The results of plant spacing for both planters are shown in Table 1. The average plant spacing for all treatments was 21.3 cm for Planter-A and 21.7 cm for Planter-B respectively. The standard deviation indicated that plant spacing was within 7.4 cm of target spacing. Statistical analysis showed no significant treatment effects on spacing for Planter B, however Planter A showed significant effects on spacing bias from the interaction between Row Type and Downforce as well as speed. ANOVA results indicated that the Planter A had no significant treatment effects on plant emergence. However, Planter B shows a significant interaction effect between Row Type and Downforce on emergence. In summary, none of the treatment factors in this study showed a significant effect on emergence for Planter A. In Planter B there were no significant effects observed due to speed. However, the interaction between row type and downforce showed a significant effect on emergence with Track rows achieving 21% higher emergence by day 3 at 54.4 kgf compared to 99.8 kgf. The average seeding depth for all treatments was 4.4 cm for Planter-A and 5.2 cm for Planter-B respectively. The standard deviation indicated that seeding depth was within 0.9 cm of the target depth. Statistical analysis showed no statistical treatment effects on seeding depth for Planter B, however Planter A showed significant treatment effects on seeding depth both from Speed (precision), interaction between Row Type and Speed (for bias) and the interaction between Row Type and Downforce (for accuracy). The summary of the yield data collected is shown in Table 8 for both Planter A and Planter B. The average yield for all treatments was 13,913 kg/ha for Planter-A and 14,330 kg/ha for Planter-B respectively. Statistical analysis showed that there was no significant difference in yield between any of the treatments for either planter. Also, small variations in yield cannot reliably be attributed to planter performance due to the numerous factors that can affect yield. Project-2: Studying the contribution of branching for old versus new soybean varieties From the perspective a protocol, the main factors investigated were: i)row spacing (7.5, 15, 30, 60 inches), ii) seeding rate (60,000 and 180,000 plants ac-1) and iii) variety (P3981-1980 vs P39T67R-2013). The study was designed in a randomize complete block design with 3 blocks with a split-plot design. Each plot has a size of 10*20 ft= 200ft2. For plant sampling, to fix the sampling area, a length of 21 inches will be delimited with steaks/flags in the middle of the plot (5ft approx.) following the next instructions according to each row spacing. Several determinations were collected from soil moisture, crop phenology, canopy coverage, light interception, allometric models for crop biomass. Results: The main result clearly shows more yield benefit for modern variety under narrow row spacing for 2021 growing season (with greater attainable yields) relative to the 2020 season (more drought year). The contribution of branches under narrow rows (<15 inches) did help to express more yield for modern soybean genotypes. From a perspective of singulation and farmer standpoint, decreasing seeding rates (improving seed savings) can be further explored by exploiting the capacity of soybeans to produce more branches under good environmental conditions. This could be translated into potential improvement on farming profit by reducing the overall seed costs. As a result of this second project, an abstract for presentation was submitted to the annual agronomy conference (see below). In addition, results from this project and the singulation study were presented in six winter soybean schools across the state of Kansas, during Janurary-Februray 2022.
Publications
- Type:
Journal Articles
Status:
Submitted
Year Published:
2022
Citation:
Mishler, B., S. A. Badua, I. A. Ciampitti, E. Brokesh, and A. Sharda. (2022). Turn Compensation Feature Utilization and Effectiveness of planters with electric seed meters. Submitted to Applied Engineering in Agriculture.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2021
Citation:
Badua, S., B. Mishler, A. Sharda, and D. Flippo. 2021. Quantifying Real-Time Opening Disk Load to Assess Compaction and Potential for Planter Control 2101180. ASABE-AIM, July 12-16th, 2021
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2021
Citation:
Badua, S., A. Sharda, D. Flippo, and I. Ciampitti. 2021. Quantifying Extent of Turn Compensation Technology Utilization on Planters for Corn Planting 2101174. ASABE-AIM, July 12-16th, 2021.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2021
Citation:
Pereyra, M.V., A. Sharda, and I. Ciampitti. 2021. Revisiting the importance of spatial and temporal uniformity on soybean yield. America Society of Agronomy, International Annual Meetings, Nov. 2021.
- Type:
Journal Articles
Status:
Submitted
Year Published:
2022
Citation:
Pereyra, V., A. Sharda, E. Brokesh, I.A. Ciampitti. (2022). The importance of branching and row spacing for reducing the dependency on seeding rates for modern soybean varieties (Submitted to Agronomy Journal).
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Progress 06/15/20 to 06/14/21
Outputs
Target Audience:The target audience was specifically farmers, industry professionals, service providers, manufacturers, graduate students, high school students and teachers, and faculty. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The professional development activities include: The project is currently training a MS student (Valentina Pereyra, target defense May 2023) and Ph.D. student (Jose). Graduate student scholars are learning to work with farmers and collecting field data, while discussing ideas for improving current management. The student on the engineering side is leaning to develop and utilize data acquisition systems, selection of sensors and integration for field data collection, use of technology for post-planting data collection. Students are learning to develop research question, conduct on-farm epxeriments, and synthesize knowledge valuable for producers, industry professionals, and engineers for new product innovations. How have the results been disseminated to communities of interest?Results were disseminated through participation in departmental events, international conferences and one-to-one interaction with stake holders. Other avenues include scientific meetings, social media posts, extension events (winter soybean schools) and with a new publication (under review). What do you plan to do during the next reporting period to accomplish the goals?In the Year-2, the research plan includes Quantify impact of downforce selection, and speed on seeding depth, spacing and emergence uniformity, and potential of any compaction. Quantify the advantages gained on seed placement while using individual row hydraulic control (IRHC) compared to Set point comparison for down force management. Quantify the impact of row spacing and variety on plant uniformity and yields for soybeans. Our goal will be to collect a last year of data working with farmers on this critical soybean topic to understand how we can help with the management of seeding rates to compensate potential effects of lack of uniformity and considering to include a simple economic analysis to facilitate the evaluation of risk of this activity.
Impacts
What was accomplished under these goals?
During 2022, the following key project were undertaken: Understand the advantages of Turn Compensation feature within planting systems Quantify disc load to assess soil strength, potential compaction and need for down force system Quantify the effect of uniformity on soybeans and to implement new management across their fields The project were implemented on producer collaborator farms. Eight no-till fields for project-1 and one porject-2 were selected for these tasks. Field sizes ranged from 40 to 135 acres. A 16-row Exact-Emerge planter with IRHC, electric seed meters and pneumatically controlled row cleaners and closing wheels was utilized for on-farm experiments. High frequency sensors were mounted to measure hydraulic pressure, acceleration, two GNSS GPS units (one of left end and one at center of the toolbar), and gauge wheel load over planter CAN bus. A custom data acquisition system was developed to record data er at 100 Hz to collect high frequency data for analyis and mapping . Soil EC measurements across the fields were obtained using the Veris mobile sensor platform (MSP). Recorded shallow-soil EC data were used to develop soil EC maps with three zones of low, medium, and high EC using the natural breaks classification in ArcGIS. For turn compensation study, machine as-applied data was collected from eight different field locations which included GPS coordinates, speed, heading across the field and other operating parameters. Various field shapes and typical field sizes in Kansas were discussed with different producers. Based on the field size and shape observations, field were selected with varying sizes and irregular boundary shapes (Table 1). For each field, the areas planted with active turn compensation were calculated using Microsoft Excel and ArcMap in ArcGIS (Esri, Redlands, CA, USA). Soil electrical conductivity (EC) correlates very strongly with particle size and soil texture, soil EC measurements across the fields were obtained using the Veris mobile sensor platform (MSP). Recorded shallow-soil EC data were used to develop soil EC maps with three zones of low, medium, and high EC using the natural breaks classification in ArcGIS. Actual opening disc load on each row unit was calculated using the following formula: LOD = (LHP + LRU) − (LGW + LCW) Where LOD is the actual load for the opening disc for soil penetration, LHP is the load derived from the recorded hydraulic pressure readings, LRU is the predetermined weight of the row unit, LGW is the real-time gauge wheel load (GWL), and LCW is the predetermined load exerted by the closing wheels. The potential of opening disc to reduce soil compaction was analyzed using real-time GWL and hydraulic pressure readings while creating two scenarios with low and high downforce implementation. Those instances where actual opening disc load was less than the available opening disc load was determined and for these situations, we say we need uplift because of the excess load available on the opening disc that might lead to sidewall compaction. The two scenarios are as follows: Scenario 1: GWL was set at 100 lbs Scenario 2: GWL was set at 200 lbs To illustrate, for scenario 1, if the weight of row unit is 270 lbs and load on closing wheels is 50 lbs then there is 120 lbs left for the opening discs to use for creating the furrow. If the actual opening disc load used at a certain instance in creating the seed furrow was 100 lbs, then at this situation we need uplift because the opening disc is carrying an excess load of 20 lbs which could result to sidewall compaction. Likewise, the GWL within each soil EC was obtained using various toolsets in ArcGIS to understand the correlation between soil EC and GWL. Results: Turn Compensation: When examining the results from the 8 fields in this study, it is evident that there was a significant benefit to having a turn compensation enabled planter. Across the 8 fields in this study, turn compensation was used to some extent 7.04% of the time, though it was used up to 12.01% of the time on one of the fields and as little as 4.64% on another. The effect of using turn compensation on these fields could have a significant impact on yield and input costs when added up across an entire crop. To confirm the effectiveness of using a turn compensation system, 5 different turns in two of the fields were analyzed, which showed that the turn compensation system was effective in correcting the planting rate to the desired value for both extreme and gradual turns. Result shows that the seeding rate is accurately adjusted to the desired value even on small radii turns which can improve yields and reduce inputs on fields with many turns. Disc Load Quantification: In a 100 lbs GWL scenario, it can be observed that on average about 13% of the total planting uplift was needed. Wing and track sections showed higher and lower instances of requiring uplift, respectively. Increasing the GWL to 200 lbs reduced the frequency of needing uplift by about 6%. On the other hand, instances of needing uplift increased when planting on a tilled field, where on average 18% of planted area required uplift under the 100 lbs GWL scenario while those instances were reduced to about 8% under 200 lbs GWL scenario. Overall, it was observed that uplift situations are fewer with only about 5% of the time under the 100 lbs GWL scenario and almost insignificant under the 200 GWL scenario. Similar results indicated that soil conditions of the fields (heavier soil texture) required higher opening disc load which resulted in fewer instances of uplift even at 100 lbs GWL scenario. Such result indicates the importance of selecting the proper level of downforce based on soil condition at planting to reduce the incidence of soil compaction that could potentially impact plant growth and development. Project 3: Quantify the effect of uniformity on soybeans and to implement new management across their fields. Increased soybean (Glycine max L. Merril) seed costs have motivated interest in reduced seeding rates to improve profitability while maintaining or increasing yield. However, little is known about the effect of early-season plant-to-plant spatial uniformity on the yield of modern soybean varieties planted at reduced seeding rates. Two years were used to investigate the effect of i) traditional and devise new metrics for characterizing early-season plant-to-plant spatial uniformity, ii) identify the best metrics correlating plant-to-plant spatial uniformity and soybean yield, and iii) evaluate those metrics at different seeding rate (and achieved plant density) levels and yield environments. Soybean trials planted in 2019 and 2020 compared seeding rates of 160, 215, 270, and 321 thousand seeds/ha planted. All site-years were separated into low- (2.7 Mg ha-1), medium- (3 Mg ha-1), and high- (4.3 Mg ha-1) yielding environments, and the tested seeding rates were separated into low (<200 seeds m-2), medium (200-300 seeds m-2), and high (>300 seeds m-2) levels. Out of the 13 metrics of spatial uniformity, standard deviation (sd) of spacing and of achieved versus targeted evenness index (herein termed as ATEI, observed to theoretical ratio of plant spacing) showed the greatest correlation with soybean yield in US trials (R2 = 0.26 and 0.32, respectively). The effect of spatial uniformity (ATEI sd) on soybean yield differed by yield environment. Increases in ATEI sd (values > 1) negatively impacted soybean yields in both low- and medium-yield environments, and in achieved plant densities below 200 thousand plants/ha. This study provides new insights into the effect of early-season plant-to-plant spatial uniformity on soybean yields, as influenced by yield environments and reduced plant densities.
Publications
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2020
Citation:
Mishler, B., S. Badua, A. Sharda, and I. Ciampitti. (2020). Influence of Ground Speed and Planter Downforce on Uniformity of Seeding Depth, Spacing and Emergence when Utilizing Individual Row Hydraulic Downforce System. Paper No. 2001381. Virtual ASABE-AIM, July 13-15th 2020.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2020
Citation:
Mishler, B., S. Badua, and A. Sharda. (2020). Quantifying Planter Row Unit Opening Disk Load to Assess Spatial Soil Strength and Compaction. Paper No. 2001548. Virtual ASABE-AIM, July 13-15th 2020.
- Type:
Journal Articles
Status:
Submitted
Year Published:
2022
Citation:
Valentina M. Pereyra, Andr� Froes de Borja Reis, Leonardo M. Bastos, Ricardo J.M. Melchiori, Nicolas E. Maltese, Stefania C. Appelhans, P.V. Vara Prasad, Yancy Wright, Edwin Brokesh, Ajay Sharda, and Ignacio A. Ciampitti. 2022). Early-season spatial plant-to-plant uniformity can affect soybean yields. (submitted to Nature Scientific Reports/Frontiers).
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