The development of climate-smart varieties requires the identification of haplotypes influencing traits of agronomic interest with well-defined effects across environments. For that we need advanced crossing schemes, multi-environment characterization, and state of the art statistical methodology. In this contribution, we present a sorghum multi-reference back-cross nested association mapping population composed of 3901 lines produced by crossing 24 diverse parents to three elite parents from West and Central Africa (WCA-BCNAM). This population is potentially one of the most relevant sorghum resources for research and breeding purposes in that region. The population was characterized in environments contrasting for photoperiod, rainfall, temperature, and soil fertility. To analyse this multi-parental, multi- environment design, we developed a new statistical methodology for QTL detection and parental effect estimation. In addition, envirotyping data were integrated in our models to determine the influence of environmental covariables on the genetic effects. We used this strategy to analyse the genetic architecture of flowering time and plant height, which represent key adaptation mechanisms. Our results allowed a better characterisation of well-known genomic regions impacting 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. The strategy implemented in this work illustrates the need of synergy between genetics, breeding, and statistical genetics to harness the potential of sorghum in a changing climate. The resources and tools developed in this project should serve the whole sorghum community, for example by providing relevant elite parents for breeding programs.