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Jason Osborne and Eric Simonne

The challenges encountered and discussions generated during the review process of the manuscripts submitted to the Variety Trials category of HortTechnology have revealed the need to review issues encountered during manuscript preparation and to provide flexible guidelines for authors and reviewers. Using a question/answer format, this manuscript discusses issues related to data collection and statistical methods available to compare varieties. Clear objectives and conclusions, adequate plot size, careful selection of entries, and sound statistical procedures are considered essential. Several additional factors (following standard production practices, using multiple seed sources, reporting analysis of variance table and mean square error, reporting multiyear/multilocation trials) are regarded as desirable, with different degrees of desirability, depending on the crop. These flexible guidelines should be viewed as recommendations for authors and reviewers rather than requirements. While defining the state-of-the-art in variety trialing is of interest to all those involved, it may be difficult to achieve when resources are limiting. It is ultimately the prerogative and responsibility of the author(s) to ensure that the work is scientifically sound.

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G.K. Panicker, G.A. Weesies, A.H. Al-Humadi, C. Sims, L.C. Huam, J. Harness, J. Bunch and T.E. Collins

Even though research and education systems have transformed agriculture from a traditional to a high-technology sector, soil erosion still remains as a major universal problem to agricultural productivity. The Universal Soil Loss Equation (USLE) and its replacement, the Revised Universal Soil Loss Equation (RUSLE) are the most widely used of all soil erosion prediction models. Of the five factors in RUSLE, the cover and management (C) factor is the most important one from the standpoint of conservation planning because land use changes meant to reduce erosion are represented here. Even though the RUSLE is based on the USLE, this modern erosion prediction model is highly improved and updated. Alcorn State Univ. entered into a cooperative agreement with the NRCS of the USDA in 1988 to conduct C-factor research on vegetable and fruit crops. The main objective of this research is to collect plant growth and residue data that are used to populated databases needed to develop C-factors in RUSLE, and used in databases for other erosion prediction and natural resource models. The enormous data collected on leaf area index (LAI), canopy cover, lower and upper biomass, rate of residue decomposition, C:N ratio of samples of residues and destructive harvest and other gorwth parameters of canopy and rhizosphere made the project the largest data bank on horticultural crops. The philosophy and methodology of data collection will be presented.

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Arthur Villordon, Jason Franklin and Don LaBonte

Handheld computing devices, such as personal digital assistants (PDAs), can potentially reduce repetitive tasks that pervade data collection activities in horticultural research. PDA-collected records are electronically transferred to a desktop computer, eliminating manual reentry as well as the need of reviewing for incorrect data entries. In addition, PDAs can be enclosed in protective cases, enabling data collection in inclement weather. Visual CE-generated database forms installed on PDAs were used to electronically collect data from research trials conducted in 2003. The records were subsequently transferred to Microsoft Access desktop database tables for archiving and subsequent statistical analyses. Data for certain trials were also manually collected using paper forms to facilitate comparison between manual and PDA-assisted data collection methods under controlled conditions. Using paired samples analysis, we determined that electronic transfer of records reduced the time required to store the records into desktop computer files. Manual and PDA-based recording methods did not vary in the time required to enter numerical measurements. Our experience demonstrates that off-the-shelf software and consumer PDA devices are viable options for data collection in research. PDA-assisted data collection is potentially useful in situations where remote, site-specific records need to be merged into a central database and where standardized measurements and observations are essential for performing analysis.

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Matthew H. Kramer, Ellen T. Paparozzi and Walter W. Stroup

Design, Data Collection, and Analysis Pointers for Writing about Statistics for the Horticultural Sciences Literature Cited and Selected References Section 1: When Are Statistics Needed and What Is the Purpose of Statistics in a Research Paper? The scope

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Christopher J. Currey and Roberto G. Lopez

make decisions to consistently produce high-quality greenhouse crops can be challenging for students. TCM is a holistic approach to containerized crop production based on integrating data and data collection with critical evaluation ( Faust et al., 2000

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Phil McInnis Jr. and Bruce I. Reisch

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W.R. Okie and E.G. Okie

Check digit technology is frequently used in commercial applications such as shipping labels and credit cards to flag errors in numbers as they are used. Most systems use modular arithmetic to calculate a check digit from the digits in the identification number. Check digits are little used in horticultural research because the guidelines for implementing them are neither well known nor readily accessible. The USDA–ARS stone fruit breeding program at Byron, Ga., plants thousands of trees annually, which are identified using a 2-digit year prefix followed by a sequential number that identifies the tree location in the rows. Various records are taken over the life of the tree including bloom and fruit characteristics. Selected trees are propagated and tested further. To improve the accuracy of our records we have implemented a system which uses a check number which is calculated from the identification number and then converted to a letter that is added onto the end of the identification number. The check letter is calculated by summing the products of each of the digits in the number multiplied by sequential integers, dividing this sum by 23, and converting the remainder into a letter. Adding a single letter suffix is a small change and does not add much complexity to existing data collection. The types of errors caught by this system are discussed, along with those caught by other common check digit systems. Check digit terminology and theory are also covered.

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D. Bassi, E. Muzzi, P. Negri and R. Selli

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Mark E. Uchanski, Kulbhushan Grover, Dawn VanLeeuwen and Ryan Goss

construction activity to allow for data collection and reflection. The hoop houses used in this project were adapted from Jimenez et al. (2005) , and were oriented with the long side running north to south based on magnetic north. The frame of the tunnel was

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Gregory E. Bell, Dennis L. Martin, Kyungjoon Koh and Holly R. Han

sensor scanned an area that was 2 ft wide perpendicular to the direction of travel and 0.375 inches long in the direction of travel. Reflectance data collection was by pulse rather than continuous scanning with samples taken at up to 900 samples per