In Sub-Saharan Africa, Genotype-Environment interaction plays a key role in formulating strategies for crop improvement. Multi-location trials have created enabling structure to determine varieties yield performance and stability. Crop modeling led to prediction of long-term and spatial effects of climate variability. Three improved varieties were compared to three landraces. Optimum cultivation areas minimizing the risk of crop failure were delineated by comparing predicted flowering dates and end of rainy seasons. Agronomic values were determined in trials from three climatically different zones in 27 farms. Yield stability was determined using linear regression depending on each environmental mean and the AMMI model. Photoperiod sensitive varieties have wider optimal cultivation areas whereas early-maturing varieties (photoperiod insensitive) are subjected to strong constraints on sowing date. In low productivity conditions, landraces and improved varieties are not distinct. As the environmental cropping conditions increase, improved lines become significantly superior to landraces. Photoperiod insensitive landrace is subservient to climate conditions of its area of origin and its productivity drops sharply when moved to a wetter area. Varieties studied combined productivity and stability traits. These findings are important steps toward breeding climate resilient varieties for meeting the challenges of climate smart agriculture and sustainable intensification.
The identification of haplotypes influencing traits of agronomic interest, with well-defined effects across environments, is of key importance to develop varieties adapted to their context of use. It requires advanced crossing schemes, multi-environment characterization and relevant statistical tools. Here we present a sorghum multi-reference back-cross nested association mapping (BCNAM) population composed of 3901 lines produced by crossing 24 diverse parents to three elite parents from West and Central Africa (WCA-BCNAM). The population was characterized in environments contrasting for photoperiod, rainfall, temperature, and soil fertility. To analyse this multi-parental and multi-environment design, we developed a new methodology for QTL detection and parental effect estimation. In addition, envirotyping data were mobilized to determine the influence of specific environmental covariables on the genetic effects, which allowed spatial projections of the QTL effects. We mobilized this strategy to analyse the genetic architecture of flowering time and plant height, which represent key adaptation mechanisms in environments like West Africa. Our results allowed a better characterisation of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod sensitive and candidate gene Elf3 being insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the WCA-BCNAM constitutes a key genetic resource to feed breeding programs in relevant elite parental lines and develop climate-smart varieties.
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