Pearl millet phenology assessment: An integration of field, a review, and in silico approach

Abstract

Pearl millet [Pennisetum glaucum (L.) R.Br.] is an essential subsistence cereal forfood security in dryland farming systems of the semiarid tropics (e.g., in sub-SaharanAfrica) and has improved tolerance to drought, heat, and salinity stress compared toother domesticated cereals. Assessing the variation on phenology is critical towarddevising effective adaptative management strategies for crop adaptation to currentand future climate change. In this context, pearl millet presents a vast genetic diversity, exhibiting sensitivity to temperature and photoperiod. Hence, this study aimsto describe the genotypic variability in the phenological responses of pearl millet totemperature and photoperiod, particularly affecting leaf number with implicationson the overall total time to flowering. The dataset encompassed 21 publications fromseven countries, with experiments conducted from 1965 to 2023, including three fieldstudies from the United States. Broad variability has been reported for phyllochronvalues ranging from 45 to 111˚Cd leaf−1, with a mean value of 67˚Cd leaf−1. Thermaltime to panicle initiation ranged from 340 to 594˚C, but no response to photoperiodduration was found due to the nature of dataset. Maximum number of leaves pershoot ranged from 11 to 25, showing response (1.55–2.15 leaf h−1) to photoperioddue to variations in thermal time to flowering (from 875 to 1346˚Cd). Thermal timeto flowering increased ca. 323˚Cd h−1 under day durations longer than 13.3 h, belowwhich basic vegetative phase duration was close to 1033˚Cd. Based on the Agricultural Production Systems sIMulator simulations, different combinations of the aboveresponses (in silico cultivars) generated a great range of times to flowering (44–120days) for locations in Senegal, Brazil, India, and United States. The findings of thisstudy can help breeders to explore the phenological genetic variability of pearl milletand provide inputs for crop growth models to evaluate future in silico scenarios.