Proceedings
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| Filter results4 paper(s) found. |
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1. Variable Rate Application Of Nematicides On Cotton Fields: A Promising Site-specific Management StrategyThe impact of two nematicides [ 1,3 – Dichloropropene (Telone® II) and Aldicarb (Temik)] applied at two rates on RKN population density and cotton (Gossypium hirsutum L.) lint yield were compared across previously determined RKN management zones (MZ) in commercial fields between 2007 and 2009. The MZ were delineated using fuzzy clustering of various surrogate data for soil texture. All treatments were randomly allocated among... B. Ortiz, C. Perry, D.G. Sullivan, R.C. Kemerait, R.F. Davis, P. Lu, A. Smith |
2. Variable-Rate-Fertilization of Phosphorus and Lime – Economic Effects and Maximum Allowed Costs for Small-Scale Soil AnalysisThe pH values and macro nutrient contents are characterised by considerable variance within a field. A constant-rate-fertilization, which is practiced at most farms, does not reduce this effect, it may even boost variance. Besides the suboptimal nutrient supply, the site-specific yield potential is not exploited. Constant-rate-fertilization and liming results in an inefficient utilisation by over- and undersupply of most of the areas within a field. Fertilization with lime and phosphorus causes... S. Schulte-ostermann, P. Wagner |
3. A Growth Stage Centric Approach to Field Scale Corn Yield Estimation by Leveraging Machine Learning Methods from Multimodal DataField scale yield estimation is labor-intensive, typically limited to a few samples in a given field, and often happens too late to inform any in-season agronomic treatments. In this study, we used meteorological data including growing degree days (GDD), photosynthetic active radiation (PAR), and rolling average of rainfall combined with hybrid relative maturity, organic matter, and weekly growth stage information from three small-plot research locations... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi |
4. 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 |