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Research Article Open access CC BY 4.0

Studies to Estimate Expected Genetic Advance in Seven Rice (Oryza sativa L.) Crosses

Elias Jeke, James Bokosi, Rosemary Murori, Maxwell Darko Asante, Kingsley Masamba

Journal of Experimental Agriculture International · pp. 373–386 · Published 26 Sep 2026

10.9734/jeai/2026/v48i104534

Abstract

Rice (Oryza sativa L.) is a staple food for more than half of the world’s population, and enhancing its yield, grain quality, and adaptability remains a central goal in most breeding programs. Genetic advance is the expected improvement in the mean performance of a population from one generation to the next as a result of selection and is a fundamental indicator of breeding progress. This study was conducted to estimate expected genetic advance in four key rice traits from backcross populations, together with F3 progenies and their parents. The experiment was conducted at Lifuwu Agricultural Research Station, which is located 8.5 km off Senga Bay Road and lies at an altitude of 500 m above sea level (masl). A total of 22 plant materials were evaluated during the 2025/2026 rainy season, comprising seven BC1F2 populations, eight F3 progenies, and seven parental lines. The backcross populations were developed during the 2024/2025 rainy season by crossing F2 populations with the recurrent parents, producing BC1F1 generations. These were selfed and multiplied during the 2025 irrigated season to generate BC1F2 populations, which were used in the current study. Trials were established to assess genetic advance in yield, plant height, days to maturity, and grain quality. The experiment was conducted using a Randomised Complete Block Design (RCBD) with three replications. Each plot measured 5 m × 0.4 m, with a row spacing of 20 cm and a plant-to-plant spacing of 20 cm within the rows. The study provides insights into the effectiveness of backcrossing for accelerating trait improvement in rice breeding programmes in Malawi.

Rice breeding broad-sense heritability genetic advance backcrossing grain yield trait correlation principal component analysis cluster analysis

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