Proceedings
Authors
| Filter results4 paper(s) found. |
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1. Winter Wheat Genotype Effect on Canopy Reflectance: Implications for Using NDVI for In-season Nitrogen Topdressing RecommendationsActive optical sensors (AOSs) measure crop reflectance at specific wavelengths and calculate vegetation indices (VIs) that are used to prescribe variable N fertilization. Visual observations of winter wheat (Triticum aestivum L.) plant greenness and density suggest that VI values may be genotype specific. Some sensor systems use correction coefficients to eliminate the effect of genotype on VI values. This study was conducted to assess the effects of winter wheat cultivars and growing conditions... O.S. Walsh, S.M. Samborski, M. Stępień, D. Gozdowski, D.W. Lamb, E.S. gacek, T. Drzazga |
2. On-Farm Evaluation of an Active Optical Sensor Performance for Variable Nitrogen Application in Winter WheatWinter wheat (Triticum aestivum L.) represents almost 50% of total cereal production in the European Union, accounting for approximately 25% of total mineral nitrogen (N) fertilizer applied to all crops. Currently, several active optical sensor (AOS) based systems for optimizing variable N fertilization are commercially available for a variety of crops, including wheat. To ensure successful adoption of these systems, definitive measurable benefits must be demonstrated. Nitrogen management strategies... O.S. Walsh, S.M. Samborski, D. Gozdowski, M. Stępień, E. Leszczyńska |
3. Spatial Decision Support System: Controlled Tile Drainage – Calculate Your BenefitsClimate projection studies suggest that extreme heat waves and floods will become more frequent, affecting future crop yields by 20%-30%, globally. Managing vulnerability and risk begins at the farm level where best management practices can reduce the impacts associated with extreme weather events. A practice that can assist in mitigating the impact of some extreme events is controlled tile drainage (CTD). With CTD, producers use water flow control structures to manage the drainage of water from... A. Kross, G. Kaur, D. Callegari, D. Lapen, M. Sunohara, H. Mcnairn, H. Rudy, L. Van vliet |
4. Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean YieldThe ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based... A. Kross, G. Kaur, E. Znoj, D. Callegari, M. Sunohara, H. Mcnairn, D. Lapen, H. Rudy, L. Van vliet |