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Enhancing Genomic Prediction Models for Forecasting Days to Maturity in Soybean Genotypes Using Site-Specific and Cumulative Photoperiod Data

Author

Listed:
  • Reyna Persa

    (Agronomy Department, University of Florida, Gainesville, FL 32611, USA)

  • George L. Graef

    (Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • James E. Specht

    (Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Esteban Rios

    (Agronomy Department, University of Florida, Gainesville, FL 32611, USA)

  • Charlie D. Messina

    (Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA)

  • Diego Jarquin

    (Agronomy Department, University of Florida, Gainesville, FL 32611, USA)

Abstract

Genomic selection (GS) has revolutionized breeding strategies by predicting the rank performance of post-harvest traits via implementing genomic prediction (GP) models. However, predicting pre-harvest traits in unobserved environments might produce serious biases. In soybean, days to maturity (DTM) represents a crucial stage with a significant impact on yield potential; thus, genotypes must be carefully selected to ensure latitudinal adaptation in this photoperiod-sensitive crop species. This research assessed the use of daylength for predicting DTM in unobserved environments (CV00). A soybean dataset comprising 367 genotypes spanning nine families of the Soybean Nested Association Mapping Panel (SoyNAM) and tested in 11 environments (year-by-location combinations) was considered in this study. The proposed method (CB) returned a root-mean-square error (RMSE) of 5.2 days, a Pearson correlation (PC) of 0.66, and the predicted vs. observed difference in the environmental means (PODEM) ranged from −3.3 to 4.5 days; however, in the absence of daylength data, the conventional GP implementation produced an RMSE of 9 days, a PC of 0.66, and a PODEM range from −14.7 to 7.9 days. These results highlight the importance of dissecting phenotypic variability (G × E) based on photoperiod data and non-predictable environmental stimuli for improving the predictive ability and accuracy of DTM in soybeans.

Suggested Citation

  • Reyna Persa & George L. Graef & James E. Specht & Esteban Rios & Charlie D. Messina & Diego Jarquin, 2022. "Enhancing Genomic Prediction Models for Forecasting Days to Maturity in Soybean Genotypes Using Site-Specific and Cumulative Photoperiod Data," Agriculture, MDPI, vol. 12(4), pages 1-18, April.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:4:p:545-:d:791545
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    References listed on IDEAS

    as
    1. Xinyue Zhang & Tingting Wu & Huiwen Wen & Wenwen Song & Cailong Xu & Tianfu Han & Shi Sun & Cunxiang Wu, 2021. "Allelic Variation of Soybean Maturity Genes E1 – E4 in the Huang-Huai-Hai River Valley and the Northwest China," Agriculture, MDPI, vol. 11(6), pages 1-9, May.
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