Proceedings
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| Filter results4 paper(s) found. |
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1. Supporting and Analysing On-Farm Nitrogen Tramline Trials So Farmers, Industry, Agronomists and Scientists Can LearN TogetherNitrogen fertilizer decisions are considered important for the agronomic, economic and environmental performance of cereal crop production. Despite good recommendation systems large unpredicted variation exists in measured N requirements. There may be fields and farms that are consistently receiving too much or too little N fertilizer, therefore losing substantial profit from wasted fertilizer or lost yield. Precision farming technologies can enable farmers (& researchers) to test appropriate... D. Kindred, R. Sylvester-bradley, S. Clarke, S. Roques, D. Hatley, B. Marchant |
2. Nitrogen Placement Considerations for Maize Production in the Eastern US CornbeltProper fertilizer placement is essential to optimize crop performance and amount of applied nitrogen (N) along with crop yield potential. There exists several practices currently used in both research within farming operations on how and when to apply N to maize (Zea mays L). Split applications of N in Ohio is popular with farmers and provides an economic benefit but more recently some farmers have been using mid- and late-season N fertilizer applications for their maize production. ... J.P. Fulton, E. Hawkins, S. Shearer, A. Klopfenstein, J. Hartschuh, S. Custer |
3. 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 |
4. Early Forecasting of Maize Lodging Risk Through Multi-period and Multi-source Data IntegrationLodging is a critical constraint on global maize (Zea mays L.) productivity, primarily through detrimental effects on both grain yield and quality. However, reliable methods to predict maize lodging risk early in the growing season are lacking, which hinders timely implementation of effective agronomic management interventions to increase crop lodging resistance and reduce corresponding yield losses. This work aimed to develop a feasible early season maize lodging risk prediction method... L. Dong, Y. Miao, X. Wang, P. Berry, D. Hatley, K. Kusnierek |