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ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Publications at this Location » Publication #418472
Research Project: Enhancing Agronomic Traits, Fiber Quality, and Resistance to Environmental Stress, Nematodes, and Fungal Diseases in Cotton

Location: Genetics and Sustainable Agriculture Research

Title: Evaluation of moving grid adjustment (MGA) method in field variation control

Author
item Wu, Jixiang
item Jenkins, Johnie
item McCarty Jr, Jack

Submitted to: Open Journal of Statistics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/4/2024
Publication Date: 10/25/2024
Citation: Wu, J., Jenkins, J.N., Mccarty Jr, J.C. 2024. Evaluation of moving grid adjustment (MGA) method in field variation control. Open Journal of Statistics. https://doi.org/10.4236/ojs.2024.145019.
DOI: https://doi.org/10.4236/ojs.2024.145019

Interpretive Summary: A large field trial often encounters different degrees of spatial variation, which can result in biased estimation or prediction of treatment (i.e. genotype) values. An effective removal of spatial variation is needed to ensure unbiased estimation or prediction and thus increase the accuracy of field data evaluations. A moving grid adjustment (MGA) method, which was proposed by Technow, was evaluated using Monte Carlo simulation for its statistical properties regarding field spatial variation control. The method was applied to a large-scale cotton field trial. The simulation study and application to an actual cotton trial data set suggested that the MGA method can effectively separate spatial variation including blocking effects from random error variation and data analysis will be enhanced when a spatial pattern exists. Therefore, this MGA method can be a valuable addition to enhance a large-scale field trial data evaluation.

Technical Abstract: Spatial variation is often encountered when large scale field trials are conducted which can result in biased estimation or prediction of treatment (i.e. genotype) values. An effective removal of spatial variation is needed to ensure unbiased estimation or prediction and thus increases the accuracy of field data evaluation. A moving grid adjustment (MGA) method, which was proposed by Technow, was evaluated through Monte Carlo simulation for its statistical properties regarding field spatial variation control. Our simulation results showed that the MGA method can effectively account for field spatial variation if it does exist; however, this method will not change phenotype results if field spatial variation does not exist. The MGA method was applied to a large-scale cotton field trial data set with two representative agronomic traits: lint yield (strong field spatial pattern) and lint percentage (no field spatial pattern). The results suggested that the MGA method was able to effectively separate the spatial variation including blocking effects from random error variation for lint yield while the adjusted data remained almost identical to the original phenotypic data. With application of the MGA method, the estimated variance for residuals were significantly reduced (62.2% decrease) for lint yield while more genetic variation (29.7% increase) was detected compared to the original data analysis subject to the conventional randomized complete block design analysis. On the other hand, the results were almost identical for lint percentage with and without the application of the MGA method. Therefore, the MGA method can be a useful addition to enhance data analysis when field spatial pattern exists.