Proceedings
Authors
| Filter results5 paper(s) found. |
|---|
1. A New Version of the Nitrogen Trading Tool (NTT) To Assess Nitrogen Management across the USAA recent study from the USDA Economic Research Service (September 2011) reported that about one-third of U.S. cropland was found to meet the requirements for nutrient... J.A. Delgado, J.C. Ascough ii |
2. Integrated Approach to Site-specific Soil Fertility ManagementIn precision agriculture the lack of affordable methods for mapping relevant soil attributes is a fundamental problem. It restricts the development and application of advanced models and algorithms for decision making. The project “I4S - Integrated System for Site-Specific Soil Fertility Management” combines new sensing technologies with dynamic soil-crop models and decision support systems. Using sensors with different measurement principles improves the estimation of soil fertility... R. Gebbers, V. Dworak, B. Mahns, C. Weltzien, D. Büchele, I. Gornushkin, M. Mailwald, M. Ostermann, M. Rühlmann, T. Schmid, M. Maiwald, B. Sumpf, J. Rühlmann, M. Bourouah, H. Scheithauer, K. Heil, T. Heggemann, M. Leenen, S. Pätzold, G. Welp, T. Chudy, A. Mizgirev, P. Wagner, T. Beitz, M. Kumke, D. Riebe, C. Kersebaum, E. Wallor |
3. Effectiveness of UAV-Based Remote Sensing Techniques in Determining Lettuce Nitrogen and Water StressesThis paper presents the results of the investigation on the effectiveness of UAV-based remote sensing data in determining lettuce nitrogen and water stresses. Multispectral images of the experimental lettuce plot at Cal Poly Pomona’s Spadra farm were collected from a UAV. Different rows of the lettuce plot were subject to different level of water and nitrogen applications. The UAV data were used in the determination of various vegetation indices. Proximal sensors used for ground-truthing... S. Bhandari, A. Raheja, M.R. Chaichi, R.L. Green, D. Do, M. Ansari, J.G. Wolf, A. Espinas, F.H. Pham, T.M. Sherman |
4. Within Field Cotton Yield Prediction Using Temporal Satellite Imagery Combined with Deep LearningCrop yield prediction at the field scale plays a pivotal role in enhancing agricultural management, a vital component in addressing global food security challenges. Regional or county-level data, while valuable for broader agricultural planning, often lacks the precision required by farmers for effective and timely field management. The primary obstacle in utilizing satellite imagery to forecast crop yields at the field level lies in its low temporal and spatial resolutions. This study aims to... R. Karn, O. Adedeji, B.P. Ghimire, A. Abdalla, V. Sheng, G. Ritchie, W. Guo |
5. Simulating Climate Change Impacts on Cotton Yield in the Texas High PlainsCrop yield prediction enables stakeholders to plan farming practices and marketing. Crop models can predict crop yield based on cropping system and practices, soil, and other environmental factors. These models are being used for decision support in agriculture in a variety of ways. Cultivar selection, water and nutrient input optimization, planting date selection, climate change analysis and yield prediction are some of the promising area of applications of the models in field level farm management.... B. Ghimire, R. Karn, O. Adedeji, G. Ritchie, W. Guo |