Proceedings
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1. Revising Nitrogen Recommendations For Wheat In Response To The Need For Support Of Variable-rate Nitrogen ApplicationSampling studies in North Dakota conducted from 1994 to 2003 showed that variable-rate N application could be practically directed with zone soil sampling. Results from variable-rate N studies using zone soil sampling were often less than rewarding due in part to the use of a whole-field predicted yield-based formula for developing the N recommendation in each zone. Nitrogen rate studies on spring wheat and durum were established in 2005 through 2009 to reexamine N recommendations. The results... D. Franzen, G. Endres, R. Ashley, J. Staricka, J. Lukach, K. Mckay |
2. Using A Surface Energy Model (reset) To Determine The Spatial Variability Of ET Within And Between Agricultural FieldsRemote sensing algorithms are currently being used to estimate regional surface fluxes (e.g. evapotranspiration (ET)). Many of these surface energy balance models use information derived from satellite imagery such as aircraft, Landsat, AVHRR, ASTER, and MODIS to estimate ET. The remote sensing approach to estimating ET provides advantages over traditional methods. One of the most important advantages is that it can provide estimates of actual ET for each pixel in the image. Most conventional... L. Garcia, A. Elhaddad |
3. Use of Corn Height to Improve the Relationship Between Active Optical Sensor Readings and Yield EstimatesPre-season and early in-season loss of N continues to be a problem in corn. One method to improve nitrogen use efficiency is to fertilize based on in-season crop foliage sensors. The objective of this study was to evaluate two different ground-based, active-optical sensors and explore the use of corn height with sensor readings for improved relationship with corn yield. Two different ground-based active-optical sensors (GreenseekerTM and... L. Sharma, D.W. Franzen |
4. Use of Zone or Grid Soil Nutrient Management as Part of an Integrated Site-specific Nutrient StrategyZone and grid sampling are used as a basis for fertilizing with nutrients site-specifically. Use of sensors to assist in-season management of nitrogen is also gaining momentum. The presentation will suggest when grid or zone sampling for preplant nutrients might be utilized and how these recommendations would be used in an integrated approach of preplant plus in-season nutrient management. ... D. Franzen |
5. Active Optical Sensor Algorithms For Corn Yield Prediction And In-Season N Application In North DakotaA recent series of seventy seven field N rate experiments with corn (Zea mays, L.) in North Dakota was conducted. Multiple regression analysis of the characteristics of the data set indicated that segregating the data into those with high clay soils and those with medium textures increased the relationship between N rate and corn yield. However, the nearly linear positive slope relationship in high clay soils and coarser texture soils with lower yield productivity indicated... L. Sharma, H. Bu, R. Ashley, G. Endres, J. Teboh, D.W. Franzen |
6. In-Season Nitrogen Requirement For Maize Using Model And Sensor-Based Recommendation ApproachesNitrogen (N), an essential element, is often limiting to plant growth. There is great value in determining the optimum quantity and timing of N application to meet crop needs while minimizing losses. Low nitrogen use efficiency (NUE) has been attributed to several factors including poor synchrony between N fertilizer and crop demand, unaccounted for spatial variability resulting in varying crop N needs, and temporal variances in crop N needs. Applying a portion... L.J. Stevens, R.B. Ferguson, D.W. Franzen, N.R. Kitchen |
7. Use Of Quality And Quantity Information Towards Evaluating The Importance Of Independent Variables In Yield PredictionYield predictions based on remotely sensed data are not always accurate. Adding meteorological and other data can help, but may also result in over-fitting. Working with American Crystal Sugar, we were able to demonstrate that the relevance of independent variables can be tested much more reliably when not only yield but also quality attributes are known, such as the sugar content and the sugar... E. Momsen, J. Xu, D.W. Franzen, J.F. Nowatzki, K. Farahmand, A.M. Denton |
8. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
9. Modifying the University of Missouri Corn Canopy Sensor Algorithm Using Soil and Weather InformationCorn production across the U.S. Corn belt can be often limited by the loss of nitrogen (N) due to leaching, volatilization and denitrification. The use of canopy sensors for making in-season N fertilizer applications has been proven effective in matching plant N requirements with periods of rapid N uptake (V7-V11), reducing the amount of N lost to these processes. However, N recommendation algorithms used in conjunction with canopy sensor measurements have not proven accurate in making N recommendations... G. Bean, N.R. Kitchen, D.W. Franzen, R.J. Miles, C. Ransom, P. Scharf, J. Camberato, P. Carter, R.B. Ferguson, F. Fernandez, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan |
10. Window-based Regression Analysis of Field DataHigh-resolution satellite and areal imagery enables multi-scale analysis that has previously been impossible. We consider the task of localized linear regression and show that window-based techniques can return results at different length scales with very high efficiency. The ability of inspecting multiple length scales is important for distinguishing factors that vary over different length scales. For example, variations in fertilization are expected to occur on shorter length... A.M. Denton, H. Chavan, D.W. Franzen, J.F. Nowatzki |
11. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
12. On Farm Studies to Determine Seeding Rate in CornSeeding rate (SDR) is one of the most critical production practices impacting productivity and economic return for corn (Zea mays L.) By changing SDRs in different zones within a field, herein termed as site-specific management, better economic results can be produced as the outcome of reducing SDRs in low productivity areas and increasing SDRs under high-yielding environments, relative to the uniform SDR management performed by the producer. The aim of this study was to analyze yield responses... G. Balboa, S. Varela, I. Ciampitti, S. Duncan, T. Maxwell, D. Shoups, A. Sharda |
13. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor AlgorithmNitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan |
14. Terrain Modeling to Improve Soil Survey in North DakotaUsers of site-specific technologies would prefer to use digitized soil survey boundaries to help in delineating management zones for nutrient application. However, the present scale of soil type does not allow meaningful zone delineation. A project was conducted to use terrain modeling and other site- specific tools to delineate smaller-scale soil type boundaries that would be more useful for directing within-field nutrient management. Topography, soil EC, yield mapping and satellite imagery were... D.W. Franzen, J.L. Boettinger |
15. Regional Usefulness of Nitrogen Management Zone Delineation ToolsIn the Northern Plains of Montana, North Dakota and Minnesota, a number of site-specific tools have been used to delineate nitrogen management zones. A three-year study was conducted using yield mapping, elevation measurements, satellite imagery, aerial Ektochrome® photography, and soil EC to delineate nitrogen management zones and compare these zones to residual fall soil nitrate. At most of the sites, variable-rate N was applied and compared with uniform N application. The site-specific... D. Franzen, F. Casey, J. Staricka, D. Long, J. Lamb, A. Sims, M. Halvorson, V. Hofman |
16. Summary of Forty Years of Grid Sampling ResearchBetween the years of 1961 and 2001, two 12.5-ha fields in Illinois were sampled for soil pH, and available P and K in a 24.3-m grid. One field was sampled beginning in 1961 while the other field was sampled from 1982. At each sampling, the samples were obtained in the same grid. This resulted in the ability not only to compare grid sample density to delineate fertility patterns within the fields, but also to determine the rate of soil test change with P and K applications, the change in fertility... D.W. Franzen |
17. Practical and Affordable Technologies for Precision Agriculture in Small Fields: Present Status and Scope in IndiaThe objective of this review paper is to find out practical and affordable precision agriculture(PA) technologies present status and scope in India that are suitable for small fields. The judicious use of inputs like water, fertilizers, herbicides, pesticides and better management of farm equipments will increase the net profit for farmers. The important components of PA in India which are being used for small lands are Geographic Information System(GIS), laser land leveler, leaf color chart,... S. Kumar, M. Singh, H. Mirzakhaninafchi, R.U. Modi, M. Ali, M. Bhardwaj, R. Soni |
18. Improving Corn Nitrogen Rate Recommendations Through Tool FusionImproving corn (Zea maysL,) nitrogen (N) fertilizer rate recommendation tools can improve farmer’s profits and help mitigate N pollution. One way to improve N recommendation methods is to not rely on a single tool, but to employ two or more tools. Thiscould be thoughtof as “tool fusion”.The objective of this analysis was to improve N management by combining N recommendation tools used for guiding rates for an in-seasonN application. This evaluation was... C.J. Ransom, N.R. Kitchen, J.J. Camberato, P.R. Carter, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J. Shanahan, J.E. Sawyer |
19. Constraint of Data Availability on the Predictive Ability of Crop Response Models Developed from On-farm ExperimentationDue to the variability between fields and across years, on-farm experimentation combined with crop response modeling are crucial aspects of decision support systems to make accurate predictions of yield and grain protein content in upcoming years for a given field. To maximize accuracy of models, models fit using environmental covariate and experimental data gathered up to the point that crop responses (yield/grain protein) are fit repeatedly over time until the model can predict future crop responses... P. Hegedus, B. Maxwell |
20. The Effect of Slope Gradient on the Modelling of Soil Carbon Dioxide Emissions in Different Tillage Systems at a Farm Using Precision Tillage Technology in HungaryUnderstanding the role of natural drivers in greenhouse gas (GHG) emitted by agricultural soils is crucial because it contributes to selecting and adapting acceptable eco-friendly farming practices. Hence, Syngenta Ltd. collaborating with researchers, aimed to investigate the effect of two tillage treatments, conventional-tillage (CT) and minimum-tillage (MT) on soil carbon dioxide (CO2) emissions. The research field is in Hungary. Soil columns were derived from different tillage systems... I.M. Kulmany, S. Benke, L. Bede, R. Pecze, V. Vona |
21. Generation of Site-specific Nitrogen Response Curves for Winter Wheat Using Deep LearningNitrogen response (N-response) curves are tools used to support farm management decisions. Conventionally, the N-response curve is modeled as an exponential function that aims to identify an important threshold for a given field: the economic optimum point. This is useful to determine the nitrogen rate beyond which there is no actual profit for the farmers. In this work, we show that N-response curves are not only field-specific but also site-specific and, as such, economic optimum points should... G. Morales, J.W. Sheppard, A. Peerlinck, P. Hegedus, B. Maxwell |
22. Optimizing Nitrogen Application to Maximize Yield and Reduce Environmental Impact in Winter Wheat ProductionField-specific fertilizer rate optimization is known to be beneficial for improving farming profit, and profits can be further improved by dividing the field into smaller plots and applying site-specific rates across the field. Finding optimal rates for these plots is often based on data gathered from said plots, which is used to determine a yield response curve, telling us how much fertilizer needs to be applied to maximize yield. In related work, we use a Convolutional Neural Network, known... A. Peerlinck, J. Sheppard, G.L. Morales luna, P. Hegedus, B. Maxwell |