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SCA Sampling Protocol Results: 2016

This study aims to develop a user-friendly sampling tool for sugarcane aphid in grain sorghum, based on distribution patterns and economic thresholds. The research involves analyzing variation within and between plants, cells, and fields, and classifying fields into five growth stages. The results indicate that sampling methods should be modified based on location, and further field sampling is needed for validation.

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SCA Sampling Protocol Results: 2016

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  1. SCA Sampling Protocol Results: 2016 Tom A. Royer Oklahoma State University Sorghum – Sugarcane Aphid Research Exchange Meeting Dallas, TX January 3 – 4, 2017

  2. Introduction • Goals: • Develop, validate and demonstrate a user-friendly, dynamic sampling tool based on seasonal and spatial distribution patterns and linked to research-based economic thresholds for sugarcane aphid in grain sorghum

  3. Materials & Methods • One sampling universe for every 80 acres • a field of 160 acres could be made into two fields • 48 of 54 samples per field taken from 2 fully expanded leaves, one on lower 1/3, and one upper 1/3 of plant • 6 plants were randomly selected for whole plant counts • Design allows for analysis of variation within and between plants, within and between cells, between fields, and between states • Fields classified into five growth stages • Vegetative (01) • Boot (02) • Flowering (03) • Milk (04) • Soft Dough to Maturity (05) • For data purposes use the closest of the five stages listed when collecting plant measure data

  4. Materials & Methods

  5. Data Slides • A nested ANOVA was conducted on data from125 fields, representing 6750 samples from TX and OK (fields from other states have not been included yet)

  6. Key Learnings • Mostof the variation in sampling is captured within plant samples or between the two sets of within cell samples • Sampling for SCA needs to be modified based on location • OK and North Texas distribution patterns saw no substantial difference in variation; data suggests that there is less need to consider edge when sampling • South Texas showed evidence of a slight edge effect due to a higher % of accounted variance in the “column” category when compared to N Texas and Oklahoma.

  7. Next Steps • Continued field sampling for increased robustness of data and for independent validation of sampling protocol • Evaluation of aphids counts on leaves within plant for most efficient estimation of aphid density

  8. Collaborators • Jessica Lindenmayer, Oklahoma State University*** • Kristopher L. Giles, Oklahoma State University • N.C. Elliott, USDA-ARS • Ali Zarrabi, Oklahoma State University • Mark Payton, Oklahoma State University • Allan Knutson, Texas A&M Agrilife • Xandra Morris, Texas A&M Agrilife • Robert Bowling, Texas A&M Agrilife • Michael Brewer, Texas A&M Agrilife • Nick Seiter, University of Arkansas • Sebe Brown, Louisiana State University • Brian McCornack, Kansas State University

  9. Discussion

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