Proceedings
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| Filter results17 paper(s) found. |
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1. Impact of Nitrogen Application Timing on Corn ProductionWater quality issues have renewed interest in timing of nitrogen (N) application as a means to improve use efficiency in corn and reduce losses. Improved economic return is also desired as N fertilization is one of the most costly inputs to corn production. Time of fertilizer application is a component of the site-specific 4R nutrient management stewardship programs. In Iowa, the Nutrient Reduction Strategy has a 7% (37% std. dev.) nitrate-N reduction with a 0% (3% std. dev.) corn yield change... J. Sawyer, D. Barker, J. Lundvall |
2. Roto and Shoot Biomass and Nutrient Composition in a Winter Rye Cover CropNitrogen loss from applied fertilizer can be a significant environmental quality issue if NO 3 moves to surface or ground water. The Iowa nutrient reduction strategy science assessment identified winter cereal rye (Secale cereal L.) cover crop as a practice that can significantly reduce N and P loss (41% NO 3-N and 21% P reduction) from corn (Zea mays L.) and soybean [Glycine max. (L.) Merr.] fields. Cereal rye, when used as a cover crop, through its fibrous root system is able to explore the soil... |
3. Influence of Potassium, Sulfur, and Zinc Fertilizer on Corn and Soybean Grown on High Tetsing SoilThe influence of potassium, sulfur and zinc fertilizer on corn and soybean yields grown on high testing soils was studied at two locations in Eastern South Dakota over a period of six years. Treatments were 50 Ibla K,O, 25 Ibla sulfur and 5 Ib/a zinc. The treatments were applied to the same plots at each location every year. Soil test levels were in the high range for all elements in question and additional nutrients would not have been recommended by the South Dakota State University Soil Testing... |
4. Remote Sensing of Corn Canopy Dynamics and Biophysical Variables Estimation in MichiganThis study was initiated to evaluate sensor-based nitrogen and water application for corn (Zea mays L.) in Michigan. The specific objectives of this study were: 1) to identi@ wavelengths that are more sensitive to N deficiency in corn, 2) to determine when to predict corn grain yield fiom spectral remote sensing data, and 3) to estimate biophysical variables ofcorn such as leafareaindex (LAI) and fractional cover (Fc) fiom spectral vegetation indices (SVI) obtained fiom radiometric measurements over... |
5. Sulfur Influence on Corn and Soybean Yields in Eastern South DakotaClean air legislation, the increasing use of conservation tillage, and the manufacture of phosphorus fertilizers without sulfur have all contributed to lowering soil sulfur (S) availability to crops. Soil S availability has been affected to some extent by all three issues in eastern South Dakota. Hilltop erosion has exposed subsoil in which the pH is higher and organic matter content is lower than at lower landscape positions. In some eroded shoulder positions of the landscape in no-till fields.... |
6. Relationship Between Response Indices Measured In-Season and at Harvest in Winter WheatCurrent methods for making nitrogen recommendations in winter wheat (Triticum aestivurn L.) do not adjust for in-season temporal variability of plant available non-fertilizer nitrogen (N) sources. The purpose of this study was to compare the use of different nitrogen response indices determined in-season @INDVI and RIPLANTHEIGkm) to the nitrogen response index measured at harvest (RIHARvEST). In addition, this study evaluated the use of the in-season response indices for determining topdress nitrogen... |
7. The Analysis of Nitrogen and Plant Population InteractionsNitrogen and plant population are significant factors for corn production. Accurate nitrogen fertilizer and seeding rate recommendations are essential for optimizing profitability for the fmer and minimizing nitrogen losses. Research and development of yield response curves provide important information that can be used to understand the relationships between these inputs (nitrogen fertilizer and corn seeds) and output (grain yield). Over the varying levels of inputs. yield response functions can... |
8. Influence of Soil Test Phosphorus on Phosphorus Runoff Losses from South Dakota SoilsApplications of manure and fertilizer phosphorus (P) to soil in excess of optimal crop requirements leads to a buildup of soil test phosphorus (STP) and increases the risk of offsite transfer of P during heavy precipitation events. The first step to developing effective manure and fertilizer P application strategies for South Dakota is to evaluate the relationship that exists between soil and runoff P. The objectives of this study were to: 1) determine the relationship between STP and runoff P concentrations... |
9. A Novel Use of Data Translation Allows 3D Prediction of Soil Fertility LandscapesSoil fertility managers need better estimates of the subsoil contribution to the nutrient pool. Thls need could be achieved through 3-D predictions of subsoil fertility using a novel method of soil- profile data translation in relation to a controlling genetic horizon. For this translation, the depth of a controlling pedogenic feature is used as the origin and the rest of the profile is linearly scaled to it. When applied to a group of soils, from across a local or regional landscape, with varying... |
10. Cropping Systems Management Effects on Soil N Mineralization DynamicsChanges in soil organic matter (SOM) content due to cultivation also impacts the amount of indigenous soil nitrogen (IN) supply. Crop management practices designed to achieve high yields also result in high residue inputs, which can contribute to SOM build up and enhanced indigenous N supply. The objective of this study was to evaluate the long term effect of crop rotation and nutrient management in conventional and intensive maize based systems on the change in soil N supply. Soil samples fiotn... |
11. Should We Abandon Soil Testing and Yield Goals in Estimating Nitrogen Rates for CornIf the prices of corn and fertilizer-N and the shape of the N response function relating crop yield to the amount of fertilizer used are known, calculating an economically optimal N rate (EONR) for maximizing the net return to applied N is straightforward: the EONR is the N rate at which no firher increase in net return occurs. In most cropping systems and under common price scenarios, crop yield at the EONR is within 95 to 99% of the maximum yield obtained for the specific management package. In... |
12. Management Zone Delineation Techniques to Aid In-Season Sensor Based Nitrogen ApplicationThe increased efficiency of nitr ogen fertilizer (N) use has been a long-term goal in reduction of nitrate contamination in the stat e of Nebraska. Preliminary rese arch has shown sensor based in- season application of nitrogen has the ability to be economic and environmentally viable. Although benefits have been published there is an opportunity for increased accuracy of N application through the integration of preprocesse d georeferenced management zones. In-season sensor based N application relies... |
13. Evaluation of Sulfur Fertilizers In Corn ProductionSulfur deficiencies and corn yield increases from S fertilization have been documented in Iowa since the mid-2000’s. Therefore, S fertilization management is an important component of high yield corn production. Many S fertilizers are available for use in correcting deficiencies. However, specific product evaluations have not been widely conducted in Iowa. In addition, a new S fertilizer has recently become available in the Midwest U.S., the mineral polyhalite. The research objectives were... J. Sawyer, M. Castellano, A. Sassman, J. Lundvall |
14. Winter Cereal Rye Nitrogen Response and Fertilization RequirementWinter cereal rye (Secale cereale L.) has been a common cover crop choice due to seed cost, winter hardiness, and rapid spring growth. It could also be an alternative grain crop to include in a corn-soybean rotation. Also, a rye crop would provide soil coverage during the springtime when corn and soybean do not. No known research has investigated the N fertilization requirement for winter cereal rye seed production in Iowa. The objectives of this study were to evaluate N response in cereal... C. Martins, J. Sawyer, J. Lundvall |
15. 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 |
16. 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 |
17. Grid Soil Sample Interpolation Using Geographicaly Weighted Regression and Random ForestSoil sampling is useful in agriculture for setting fertilizer application rates. High density soil samples can also be used for variable rate seeding and other precision agriculture applications. Half-acre grid soil samples were collected from 6 soybean fields, and phosphorous (P), potassium (K), and organic matter (OM) were measured. Each soil parameter was interpolated for each field, with terrain attributes as covariates, using two different methods: geographically weighted regression (GWR)... E. Matcham, S. Subburayalu, J. Fulton, E. Hawkins, P. Paul, L. Lindsey |