1,720,991 research outputs found
Spring Mix—An Emerging Crop for Florida
No full textSL323, a 3-page illustrated fact sheet by Santiago Rosaro, Alan L. Wright, David D. Sui, Nikol Havranek, and Yigang Luo, describes the components of spring mix, explains the reasons why consumers are looking for these mixes, and why growers in the Everglades Agricultural Area of Florida are producing them. Published by the UF Department of Soil and Water Science, June 2010.
SL323/SS535: Spring Mix—An Emerging Crop for Florida (ufl.edu
Spring Mix—An Emerging Crop for Florida
Get PDFSL323, a 3-page illustrated fact sheet by Santiago Rosaro, Alan L. Wright, David D. Sui, Nikol Havranek, and Yigang Luo, describes the components of spring mix, explains the reasons why consumers are looking for these mixes, and why growers in the Everglades Agricultural Area of Florida are producing them. Published by the UF Department of Soil and Water Science, June 2010.
SL323/SS535: Spring Mix—An Emerging Crop for Florida (ufl.edu
Spring Mix—An Emerging Crop for Florida
No full textSL323, a 3-page illustrated fact sheet by Santiago Rosaro, Alan L. Wright, David D. Sui, Nikol Havranek, and Yigang Luo, describes the components of spring mix, explains the reasons why consumers are looking for these mixes, and why growers in the Everglades Agricultural Area of Florida are producing them. Published by the UF Department of Soil and Water Science, June 2010.
SL323/SS535: Spring Mix—An Emerging Crop for Florida (ufl.edu
sj-docx-2-tct-10.1177_15330338211050767 - Supplemental material for Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
No full textSupplemental material, sj-docx-2-tct-10.1177_15330338211050767 for Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm by Zarrukh Baig, Nawaf Abu-Omar, Rayyan Khan, Carlos Verdiales, Ryan Frehlick, John Shaw, Fang-Xiang Wu and Yigang Luo in Technology in Cancer Research & Treatment</p
sj-docx-1-tct-10.1177_15330338211050767 - Supplemental material for Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
No full textSupplemental material, sj-docx-1-tct-10.1177_15330338211050767 for Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm by Zarrukh Baig, Nawaf Abu-Omar, Rayyan Khan, Carlos Verdiales, Ryan Frehlick, John Shaw, Fang-Xiang Wu and Yigang Luo in Technology in Cancer Research & Treatment</p
sj-pdf-3-tct-10.1177_15330338211050767 - Supplemental material for Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
No full textSupplemental material, sj-pdf-3-tct-10.1177_15330338211050767 for Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm by Zarrukh Baig, Nawaf Abu-Omar, Rayyan Khan, Carlos Verdiales, Ryan Frehlick, John Shaw, Fang-Xiang Wu and Yigang Luo in Technology in Cancer Research & Treatment</p
sj-docx-5-tct-10.1177_15330338211050767 - Supplemental material for Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
No full textSupplemental material, sj-docx-5-tct-10.1177_15330338211050767 for Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm by Zarrukh Baig, Nawaf Abu-Omar, Rayyan Khan, Carlos Verdiales, Ryan Frehlick, John Shaw, Fang-Xiang Wu and Yigang Luo in Technology in Cancer Research & Treatment</p
Nitrogen Cycling and Management for Romaine and Crisphead Lettuce Grown on Organic Soils
No full textLettuce has rapid growth and reaches harvest in 60–70 days in South Florida. This leads to high demands for nitrogen fertilization during the short growing season. A good N fertilization scheme should reduce costs, conserve natural resources, and minimize negative environmental impacts. The keys to using fertilizer efficiently are understanding the crop nutrient requirements to predict fertilizer needs and management as well as knowing the appropriate amounts to apply. Lettuce growers also need to know the nutritional status of the crop through soil and plant tissue testing. This 4-page fact sheet was written by Luis Santos, Alan L. Wright, Yigang Luo, Huangjun Lu, and D. Calvin Odero, and published by the UF Department of Soil and Water Science, September 2013.
http://edis.ifas.ufl.edu/ss58
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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