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1. Soil and Site Factors Responsible for Yield Variations in Two Southern Illinois Farm FieldsYield variability within a field is largely influenced by the soil physical. chemical. and topographic features of that field. On-farm field research studies were conducted from 1997 through 1999 on two southern Illinois farm fields (one in Jefferson County and the other in Pope County) with varying soil physical, chemical, and topographic features to determine which factors most influenced crop yield variability. Each location was GPS-gridded utilizing a grid cell size of 0.45 acres. Detailed topographic... |
2. The Influence of Variably Applied Potassium Fertilizer on Yield Variations in Two Southern Illinois Farm FieldsThe basis of Variable Rate Technology (VRT) is to apply nutrients where they are needed and to reduce the application of nutrients where they are not needed. However, there is considerable uncertainty as to whether the variable application of nutrients to farmers fields should be on the basis of soil test levels or yield potentials. A field study was conducted at two locations in southern Illinois from 1997 to 1999 to evaluate VRT as a management tool for corn and soybean production on fields with... |
3. Swine Manure Applications for Soybean Production - Environnmental and Pathological ImplicationsSoybeans are leguminous plant sp ecies capable of fixing nitrogen (N) from the atmosphere to support their growth and development. Despite th e fact that soybeans can fix their own N, in a soil environment rich in inorgani c N they will scavenge for available N decreasing the level of root nodulation (Schmidt et al., 2000; Hester man and Isleib, 1991). Studies conducted to measure the impact of N fertilization of soybean s reveals that crop response to commercial N fertilizer does not result in increased... |
4. Evaluation of Nitrate Leaching Potential from Slow and Controlled Release Nitrogen Fertilizer Application to Irrigated CornThe use of slow and controlled release nitrogen (N) fertilizers as a practice to reduce nitrate leaching from irrigated corn was evaluated on coar se-textured soils in the Central Platte River Valley of Nebraska in 2007 & 2008. Slow-releas e methylene urea (MU) and polymer-coated urea (PCU) were compared to a standard fertil izer of urea ammonium nitrate (UAN) solution. Flooding in 2007 severely impacted the study site, and results are not reported here. Saturated soils were common at the site in... |
5. Response Surface Models Of Subsoil K Concentration For Loess Over Till Soils In MissouriCrop uptake of potassium (K) has demonstrated sensitivity to subsoil variation in K content. This fact has not been sufficiently considered in K management strategies in part due to logistical difficulties in sampling spatially variable subsoil K. We propose a simplified soil factorial model, a response surface, to enable site-specific accounting of whole root zone K supply for loess over till soils. We compared the performance of two peak functions and a non-parametric local regression procedure... |
6. Integrating Management Zones and Canopy Sensing for Improved Nitrogen Recommendation AlgorithmsActive crop canopy sensors have been studied as a tool to direct spatially variable nitrogen (N) fertilizer applications in maize, with the goal of increasing the synchrony between N supply and crop demand and thus improving N use efficiency (NUE). However, N recommendation algorithms have often proven inaccurate in certain subfield regions due to local spatial variability. Modifying these algorithms by integrating soil-based management zones (MZ) may improve their accuracy... J. Crowther, J. Parrish, R. Ferguson, J. Luck, K. Glewen, T. Shaver, D. Krull, L. Thompson, N. Mueller, B. Krienke, T. Mieno, T. Ingram |
7. Comparison of Ground-Based Active Crop Canopy Sensor and Aerial Passive Crop Canopy Sensor for In-Season Nitrogen ManagementCrop canopy sensors represent one tool available to help calculate a reactive in-season nitrogen (N) application rate in corn. When utilizing such systems, corn growers must decide between using active versus passive crop canopy sensors. The objectives of this study was to 1) determine the correlation between N management by remote sensing using a passive sensor and N management using proximal sensing with an active sensors. Treatments were arranged as field length strips in a randomized complete... J. Parrish, R. Ferguson, J. Luck, K. Glewen, L. Thompson, B. Krienke, N. Mueller, T. Ingram, D. Krull, J. Crowther, T. Shaver, T. Mieno |