Q&A: Methods for estimating genetic gain in sub-Saharan Africa and achieving improved gains

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 (). 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. Annual genetic gain assessment drives breeding progress and efficiency. Realized genetic gain is preferably estimated using historical data with at least two long-term checks. Realized genetic gain is estimated using the genotypes best linear unbiased estimators from the linear mixed model, regressed on their years of origin. State-of-the-art breeding techniques, especially genomic selection, improve genetic gains. Global hunger is a growing problem. Plant breeding can help produce better crops and higher yields. Monitoring breeding programs' successes by estimating genetic trends is therefore essential. Different methods of genetic trend estimation exist. To figure out how much genotypes have improved over time, we recommend a statistical model that looks at the genetic information of the plants. The model analyzes how the genotypes' performances relate to the years they were developed. This helps estimate the actual improvement in the genotypes over different years. Regular measurement of genetic trends allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. However, genetic progress is improved by adopting genomics and other methods to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing.