Proceedings
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| Filter results7 paper(s) found. |
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1. Response Of Rhodes Grass (Chloris Gayana Kunth) To Variable Rate Application Of Irrigation Water And Fertilizer NitrogenRhodes grass is cultivated extensively in Saudi Arabia under center pivot sprinkler irrigation system. The research work was carried out to optimize irrigation water and fertilizer nitrogen levels for the crop. The objectives of the study were: 1. To delineate the field in to management zones, 2. To study the effects of variable rate application (VRA) of irrigation water and fertilizer nitrogen on the yield of Rhodes grass. A field experiment was carried out from... V. Patil, R. Madugundu, E. Tola, S. Marey, D.J. Mulla, S.K. Upadhyaya, K.A. Al-gaadi |
2. Climate Sensitivity Analysis on Maize Yield on the Basis of Precision Crop ProductionIn this paper by prediction we have defined maize yield in precision plant production technologies according to five different climate change scenarios (Ensembles Project) until 2100 and in one scenario until 2075 using DSSAT v. 4.5.0. CERES-Maize decision support model. Sensitivity analyses were carried out. The novelty of the method presented here is that precision, variable rate technologies from relatively small areas (in our case 2500 m2) enable a large amount of data to be collected... A. Nyeki, G. Milics, A.J. Kovacs, M. Neményi, J. Kalmar |
3. Site-specific Irrigation of Peanuts on a Coastal Plain FieldIrrigator-Pro is an expert system that prescribes irrigation for corn (Zea mays L.), cotton (Gossypium hirsutum L.) and peanut (Arachis hypogaea). We conducted an experiment in 2007 to evaluate Irrigator-Pro as a tool for variable rate irrigation of peanut using a site-specific center pivot irrigation system. Treatments were irrigation of whole plots based on the expert system, irrigation of individual soils within plots based on the expert system, irrigation of individual... |
4. Variability in Corn Yield Response to Nitrogen Fertilizer in QuebecOptimizing nitrogen (N) fertilization is important to improve corn yield and to reduce N losses to the environment. The economic optimum nitrogen rate (EONR) is variable and depends on many factors, including weather conditions and crop management. The main objective of this study was to examine how grain corn yield response to N varies with planting date, soil texture and spring weather across sites and years in Monteregie, which is the most important with 64% of total area and 69%... L. Kablan, V. Chabot, A. Mailloux, M. Bouchard, D. Fontaine, T. Bruulsema |
5. Developing an Integrated Approach for Estimation of Soil Available Nutrient Content Using the Modified WOFOST Model and Time-Series Multispectral UAV ObservationsSoil available nutrient (SAN) plays an important role in crop growth, yield formation, and plant-soil-atmosphere system exchange. Nitrogen (N), phosphorus (P) and potassium (K) are recognized as three primary nutrients in crop production. Accurate and timely information on SAN conditions at key crop growth stages is important for developing beneficial management practices. While traditional field sampling can obtain reliable information for limited number of sites, it is infeasible for spatially... Z. Cheng, J. Meng, J. Shang, J. Liu, B. Qian, Q. Jing |
6. Accelerating Precision Agriculture to Decision Agriculture: Enabling Digital Agriculture in AustraliaFor more than two decades, the success of Australia’s agricultural and rural sectors has been supported by the work of the Rural Research and Development Corporations (RDCs). The RDCs are funded by industry and government. For the first time, all fifteen of Australia’s RDC’s have joined forces with the Australian government to design a solution for the use of big data in Australian agriculture. This is the first known example of a nationwide approach for the digital transformation... J. Trindall, R. Rainbow |
7. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and VisionAdvancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi |