Phenology, sterility and inheritance of two environment genic male sterile (EGMS) lines for hybrid rice

Abstract

BackgroundThere is still limited quantitative understanding of how environmental factors affect sterility of Environment-conditioned genic male sterility (EGMS) lines. A model was developed for this purpose and tested based on experimental data from Ndiaye (Senegal) in 2013-2015. For the two EGMS lines tested here, it was not clear if one or more recessive gene(s) were causing male sterility. This was tested by studying sterility segregation of the F2 populations. ResultsDaylength (photoperiod) and minimum temperatures during the period from panicle initiation to flowering had significant effects on male sterility. Results clearly showed that only one recessive gene was involved in causing male sterility. The model was applied to determine the set of sowing dates of two different EGMS lines such that both would flower at the same time the pollen would be completely sterile. In the same time the local popular variety (Sahel 108, the male pollen donor) being sufficiently fertile to produce the hybrid seeds. The model was applied to investigate the viability of the two line breeding system in the same location with climate change (+2oC) and in two other potential locations: in M’Be in Ivory Coast and in the Nile delta in Egypt.ConclusionsApart from giving new insights in the relation between environment and EGMS, this study shows that these insights can be used to assess safe sowing windows and assess the suitability of sterility and fertility period of different environments for a two line hybrid rice production system.

A Large-Scale Genome-Wide Association Analyses of Ethiopian Sorghum Landrace Collection Reveal Loci Associated With Important Traits

“…For example, strong association was found between male sterility and disease susceptibility in hybrids produced using cytoplasmic-genic male sterility system and attributed to the same gene in maize (Levings, 1990). A significant effect of photoperiod and minimum temperatures on male sterility during the period from panicle initiation to flowering were also reported in hybrid rice (El-Namaky and van Oort, 2017). In addition, the strongest SNP on chromosome 4 (S4_1379552) landed near the transcription factor gene ABORTED MICROSPORES (AMS) , underlying male sterility in Maize (Liu et al, 2017), Melon ( Cucumis melo L.) (Sheng et al, 2017), and Arabidopsis (Sorensen et al, 2003).…”

Harnessing heterosis and male sterility in soybean [Glycine max (L.) Merrill]: A critical revisit

“…Male sterility in plants refers to a phenomenon where the reproductive structures either fail to develop or show reduced growth. These can be either of nuclear origin (nuclear/genetic; NMS) or mitochondrial genes (cytoplasmic; CMS) along with another MS system known as photoperiod/temperature-sensitive genic MS (PTGMS) which is also referred to as environment-sensitive genic MS (EGMS) ( El-Namaky and van Oort, 2017 ; Li et al, 2019 ; Nadeem et al, 2021 ). There are various demerits associated with these systems of sterility.…”

Role of Modelling in International Crop Research: Overview and Some Case Studies

“…Potato disease modelling, foresight, further model development Kroschel et al, 2013 [167]; Sporleder et al, 2013 [168]; Condori et al, 2014 [169]; Carli et al, 2014 [170]; Kleinwechter et al, 2016 [171]; Kroschel et al, 2017 [172]; Fleisher et al, 2017 [173]; Raymundo et al, 2017 [174]; Raymundo et al, 2017 [175]; Quiroz et al, 2017 [176]; Ramirez et al, 2017 [177]; Mujica et al, 2017 [178]; Scott and Kleinwechter, 2017 [179]; Petsakos et al, 2018 [180] AfricaRice: Model improvement, yield gap analysis, genotype × environment interactions, impact of climate change van Oort et al, 2014 [181]; van Oort et al, 2015 [182]; van Oort et al, 2015 [183]; Dingkuhn et al, 2015 [184]; van Oort et al, 2016 [185]; El-Namaky and van Oort, 2017 [186]; van Oort et al, 2017 [187]; Dingkuhn et al, 2017 [104,105]; van Oort and Zwart, 2018 [188]; van Oort, 2018 [189], Duku et al, 2018 [190] ICRAF: Agroforestry and intercropping modelling Africa Luedeling et al, 2014 [191]; Araya et al, 2015 [192]; Luedeling et al, 2016 [193]; Smethurst et al, 2017 [194], Masikati et al, 2017 [195] ILRI: crop-livestock-farm interactions Van Wijk et al, 2014 [196]; Herrero et al, 2014 [197] IITA: Modelling on Yams in West Africa Marcos et al, 2011 [198]; Cornet et al, 2015 [199]; Cornet et al, 2016 [200] ICARDA: Climate variability and change impact studies, foresight, conservation agriculture impact, genotype × environment interactions Sommer et al, 2013 [201]; Bobojonov and Aw-Hassan, 2014…”