Improving prediction of genotypic values in historical crop trial data via stepwise adjustment method

Authors: Wu, Jixiang; Zeng, Linghe; Jenkins, Johnie; McCarty Jr, Jack

Submitted to: Open Journal of Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/15/2025
Publication Date: 6/18/2025
Citation: Wu, J., Zeng, L., Jenkins, J.N., McCarty Jr, J.C. 2025. Improving prediction of genotypic values in historical crop trial data via stepwise adjustment method. Agronomy Journal. 15:47-61. https://doi.org/10.4236/ojgen.2025.152005.
DOI: https://doi.org/10.4236/ojgen.2025.152005

Interpretive Summary: In order to enhance the utilization of the long-term historical trial data for both genetic study and crop improvement, improving prediction of genotypic values from the data has been desired by researchers for years. However, because many long-term trial data are highly unbalanced due to the frequent changes in test entries and locations, it is statistically challenging to analyze the long-term historical data simultaneously without proper adjustment. In this study, we proposed a recursive method that can be used to adjust the differences caused by environmental conditions among years. Our simulation study showed that this recursive adjustment method can use overlapped entries between every consecutive years to adjust the difference caused by environmental conditions among years thus improve prediction of genotypic values across the trial years. Its efficiency is related to the number of overlapped entries between every two consecutive years and year variance. The adjustment method applied to a 16-year soybean trial data set in South Dakota showed that model fitness for the genetic gain model over years was improved compared to unadjusted data (0.85 vs 0.48). The annual genetic gain estimated from unadjusted data was 1.35 bushel/ac while the annual genetic gain for adjusted data was 0.72 bushel/ac. It is likely that favorable environmental conditions in September from 2013-2016 contributed the inflated annual genetic gain if data were not adjusted.

Technical Abstract: Improving prediction of genotypic values from long-term historical crop trial data will enhance the utilization of the data for both genetic study and crop improvement. However, because many long-term historical crop trial data are highly unbalanced due to the frequent changes in test entries and locations, it is statistically challenging to analyze the long-term historical data simultaneously without proper adjustment. In this study, we proposed a recursive method that can be used to adjust the differences caused by environmental conditions among years. Our simulation study showed that this recursive adjustment method can help adjust the difference caused by environmental conditions among years. Its efficiency is related to the number of overlapped entries between every two consecutive years and year variance. High genotype x environment interaction will decrease the adjustment efficiency. The adjustment applied to a 16-year soybean trial data set in South Dakota showed that model fitness for genetic gain modeling over these 16 years was improved compared to unadjusted data (0.85 vs 0.48). The annual genetic gain estimated from unadjusted data was 1.35 bushel/ac while the annual genetic gain for adjusted data was 0.72 bushel/ac. It is likely that favorable environmental conditions in September from 2013-2016 caused the inflated annual genetic gain if data were not adjusted.