Q&A: Methods for estimating genetic gain in sub-Saharan Africa and achieving improved gains
2024
Dieng, Ibnou | Gardunia, Brian | Covarrubias‐Pazaran, Giovanny | Gemenet, Dorcus C. | Trognitz, Bodo | Ofodile, Sam | Fowobaje, Kayode | Ntukidem, Solomon | Shah, Trushar | Imoro, Simon | Tripathi, L. | Mushoriwa, Hapson | Mbabazi, Ruth | Salvo, Stella | Derera, John
Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi‐environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics‐IITA/Estimating‐Realized‐Genetic‐Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains.
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