Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

Abstract

Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.

Plant phenotyping relevance

ドローン画像由来の時系列植生指数をFPCAで要約し、穀粒収量・草丈予測へ統合するフェノタイピング手法と予測モデルを複数年・環境で評価しており、表現型取得・抽出と技術的評価が中心である。

Code and data availability

The authors deposited a public figshare archive ('Data.zip') containing the paper's phenomic FPCA result files, plant height and grain yield BLUEs, genomic numerical files, and the R prediction/FPCA code needed to reproduce the analysis.