Submitted to: Plant Breeding
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 6, 2007
Publication Date: November 26, 2007
Repository URL: http://hdl.handle.net/10113/19347
Citation: Campbell, B.T., Bauer, P.J. 2007. Improving the precision of cotton performance trials conducted on highly variable soils of the southeastern USA Coastal Plain. Plant Breeding 126:622-627. Interpretive Summary: Reliable yield and fiber quality data generated in Upland cotton (Gossypium hirsutum L.) cultivar performance trials are highly valuable. These data are highly valuable because they provide an estimation of the yield and fiber quality potential of specific cultivars. Precise estimation of yield and fiber quality potential allows for selection of the best performing cultivars. The precision of yield and fiber quality estimations are impacted by soil variability and/or soil fertility trends. Highly variable soils are known to be present in southeastern USA production fields. The objective of this study was to evaluate the efficiency of statistical procedures to increase the precision of yield and fiber quality estimates in Upland cotton performance trials. We evaluated the efficiency of the randomized complete block (RCB) design and the nearest neighbor adjustment (NNA) for their ability to increase the precision of six cotton performance trials conducted from 2000-2005. Overall, the RCB design and NNA both increased precision. In comparison to the RCB, relative efficiency of the NNA procedure varied among traits and trials.
Technical Abstract: Reliable agronomic and fiber quality data collected from Upland cotton (Gossypium hirsutum L.) cultivar performance trials are highly valuable. A common strategy to insure reliable performance trial data uses experimental design to minimize experimental error resulting from spatial variability. An alternative strategy uses a posteriori statistical procedure to account for spatial variability. In this study, the efficiency of the randomized complete block (RCB) design and nearest neighbor adjustment (NNA) were compared in a series of cotton performance trials conducted in the southeastern USA to identify the efficiency of each in minimizing experimental error for agronomic and fiber quality data. In comparison to the RCB, relative efficiency of the NNA procedure varied among traits and trials. Results also show that experimental analyses, depending on the trait and selection intensity employed, can affect cultivar or experimental line selections. We recommend researchers conducting cotton performance trials on variable soils employ a two-step strategy to minimize experimental error. The first step identifies if spatial variability exists and the second step adjusts cultivar means using NNA or other spatial methods.