Identifying climate-smart sorghum lines for Mali
I am a PhD student from the Universite des Sciences, des Techniques et des Technologies de Bamako, Mali, working on the integration of crop modelling and crop screening methods to better identify drought-tolerant traits in Malian sorghum genotypes. This will help crop improvement programs develop progenies with highest value in terms of productivity and yield stability in the face of dwindling resources, especially water.
To do so, different but complementary objectives were set. As a first step, a crop simulation modelling approach is being used to characterize the sorghum production environment in Mali to identify the major types of stress patterns and the frequency of their occurrence, experienced by two representative genotypes (CSM335 and CSM63E). The model is being allowed to identify the most favorable sowing date based on rainfall information, in order to set up a baseline of stress scenarios. Once the baseline is set up, the effect on yield of modifying some of these criteria (e.g. sowing date, fertilizer application date) based on expert knowledge and data of actual farmer practices will be tested. It is planned to generalize this characterization approach to the Sudano-Sahelian region of West Africa using the ICRAF-ISRIC VNIR Soil Database and gridded meteorological data generated by the Marksim software under current conditions and future climate scenarios.
In addition to the environment characterization, selected sorghum parental lines are being screened in phenotyping platforms and other research facilities available at ICRISAT (India and Mali) to identify plant adaptive traits that best match these drought stress patterns. These results will be used as inputs in the APSIM modelling platform developed in Australia to analyze and assess the value of physiological traits identified for crop improvement programs.
We will also phenotype specific populations obtained from these parental lines under different phenotypic platforms and undertake the genetic analysis to dissect genomic regions involved in the traits of interest.
Figure 2. Four environment types were identified by cluster analysis of simulated water stress index across the sorghum production belt of Mali for the genotype CSM335. This daily index corresponding to the ratio between water supply and demand, was centered around flowering (represented by the vertical line) and averaged every 100°Cd from emergence to 400°Cd after flowering. A ratio of 1 indicates no water stress, while a ratio of 0 corresponds to a full stress.
This research is being conducted under Dr. Vincent Vadez and Dr. Jana Kholova’s supervision in ICRISAT, India and Dr. Myriam Adam in ICRISAT, Mali.
Why this study is important
Sorghum (Sorghum bicolor) is one of the most important food for many rural communities in the drier regions of West Africa. In Mali, sorghum is the fourth important cereal crop after rice, maize and pearl millet. Mali is the second largest producer in Africa after Nigeria. Although numerous Malian sorghum landraces are well adapted to biotic and abiotic stresses due to their photoperiod sensitivity and good grain quality, their production remains low compared to the potential yield. We interpret this is due to the difficulty of breeding sorghum cultivars that can deal with complex abiotic stress, such as drought prevailing in the region, complex interactions among genotypes (G), management (M), and environments (E), the temporal and spatial variability in rainfall and the low-input farming systems.
This integration of crop modelling, phenotyping and genotyping will enable a better understanding of the complex Genotypes x Environments x Management (G×M×E) interactions that condition crop performance, and then to guide breeding and agronomic efforts towards the optimal combinations of genetics and agronomy to maximize system productivity and reduce cropping risks.
This knowledge will help Malian breeders to more efficiently develop climate-smart breeding lines that better cope with specific stress patterns. Part of this effort currently implies improving the model’s capacity to accurately predict photoperiod responses which is a key trait for adaptation in the Malian genotypes.
About the author:
Madina Diancoumba, is a PhD student from Universite des Sciences, des Techniques et des Technologies de Bamako, Mali, currently in ICRISAT-Patancheru.