Enhancing Genomic Prediction Models for Forecasting Days to Maturity in Soybean Genotypes Using Site-Specific and Cumulative Photoperiod Data
نویسندگان
چکیده
Genomic selection (GS) has revolutionized breeding strategies by predicting the rank performance of post-harvest traits via implementing genomic prediction (GP) models. However, pre-harvest in unobserved environments might produce serious biases. In soybean, days to maturity (DTM) represents a crucial stage with significant impact on yield potential; thus, genotypes must be carefully selected ensure latitudinal adaptation this photoperiod-sensitive crop species. This research assessed use daylength for DTM (CV00). A soybean dataset comprising 367 spanning nine families Soybean Nested Association Mapping Panel (SoyNAM) and tested 11 (year-by-location combinations) was considered study. The proposed method (CB) returned root-mean-square error (RMSE) 5.2 days, Pearson correlation (PC) 0.66, predicted vs. observed difference environmental means (PODEM) ranged from ?3.3 4.5 days; however, absence data, conventional GP implementation produced an RMSE 9 PC PODEM range ?14.7 7.9 days. These results highlight importance dissecting phenotypic variability (G × E) based photoperiod data non-predictable stimuli improving predictive ability accuracy soybeans.
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ژورنال
عنوان ژورنال: Agriculture
سال: 2022
ISSN: ['2077-0472']
DOI: https://doi.org/10.3390/agriculture12040545