Impact of heat stress during late grain filling on head rice yield in rice: prioritizing milling yield traits using a bioeconomic quantitative genetic model

Abstract Rice production is globally significant, yet the sustainability of milling yield traits has received limited attention. To address this gap, the present study presents a decision-support tool, the bioeconomic quantitative genetic model (BQGM), to rank elite rice genotypes for enhanced head rice yield percentage (HRY%). A set of 282 recombinant inbred lines (RILs; F 7 generation) from a biparental mapping population developed by crossing two medium grain rice genotypes, ‘M2O5’ and ‘Baru’. Heat stress was imposed (day/night; 31/23°C; 12 h/12 h) during late grain filling (10–20 days after anthesis, DAA) under a controlled environment. The population exhibited substantial genetic control for head rice yield percentage (HRY%), with additive genetic variance (σ²ₐ) accounting for up to 37%. Narrow-sense heritability was high (h²ₙ > 40%), supporting strong potential for selection. The estimated genetic advance exceeded 4% per generation, with a predicted genetic gain of up to 7%. Measured traits showed moderate to strong genotypic correlations (r g ranging from + 0.3 to − 0.7; P < 0.05), indicating both associations and trade-offs among key quality parameters. Genomic prediction models incorporating bioeconomic adjustments were applied to derive genomic estimated breeding values. This approach enabled the identification of 19 superior genotypes combining high HRY% with reduced associated losses (husk loss, broken brown rice loss, and broken white rice loss). The present study highlights the first application of a bioeconomic quantitative genetic model to tease apart the genetic effects on milling yield traits under elevated-temperature conditions in rice. This approach enables breeders to efficiently select genotypes with improved milling performance under elevated-temperature conditions.