Introgression Progress for Phenotypic Traits and Parent-progeny Diversity at Advanced Segregation Population From Oryza Barthii and Oryza Glaberrima/oryza Sativa Crosses

Rice is a cereal staple of global fame and importance. Oryza barthii, a wild species holds significant traits and its utilization in rice breeding is rare. This study traced introgression trend of heritable traits in the offspring of O. barthii with an Africa-Asian progenitor to F8 and assessed diversity between the parents and the F8 population. Significant (P<0.05) genotypic variation existed for all the traits except tiller number, panicle/meter squared, grains/panicle and 1000 grain weight. Grains/panicle and days to 50% flowering had respective least (3.34%) and highest (96.32%) broad sense heritabilities. All traits had lower GCV compared to PCV. The least (5.28% and 8.05%) and the highest (90.8% and 98.1%) GCV and PCV were respectively from grains/panicle and tiller number. Clear variations on the panicles and grains include: variations in sizes, shapes, colours, presence or absence of awns. The total variance explained by five principal component axes was 80.1%. Plant height at maturity was the only trait with significant (p ≤ 0.01) correlation and regression between F6 and F7. Progenies resemblance to Parent 1(IRGC 104084) retrogressively declined but parent-offspring to parent 2 (TGS 25) progressively increased from F6 to F8. Three visible groups of rice type in this study were: the O. barthii (11%), O. sativa (67%) and the intermediate group (22%). This research has added to rice genetic resources; an investigation of the nutritional status of the progenies would be an interesting research.

Stomata Detector: High-throughput automation of stomata counting in a population of African rice (Oryza glaberrima) using transfer learning

Stomata are dynamic structures that control the gaseous exchange of CO2 from the external to internal environment and water loss through transpiration. The density and morphology of stomata have important consequences in crop productivity and water use efficiency, both are integral considerations when breeding climate change resilient crops. The phenotyping of stomata is a slow manual process and provides a substantial bottleneck when characterising phenotypic and genetic variation for crop improvement. There are currently no open-source methods to automate stomatal counting. We used 380 human annotated micrographs of O. glaberrima and O. sativa at x20 and x40 objectives for testing and training. Training was completed using the transfer learning for deep neural networks method and R-CNN object detection model. At a x40 objective our method was able to accurately detect stomata (n = 540, r = 0.94, p<0.0001), with an overall similarity of 99% between human and automated counting methods. Our method can batch process large files of images. As proof of concept, characterised the stomatal density in a population of 155 O. glaberrima accessions, using 13,100 micrographs. Here, we present developed Stomata Detector; an open source, sophisticated piece of software for the plant science community that can accurately identify stomata in Oryza spp., and potentially other monocot species.

High-throughput phenotyping reveals a link between transpiration efficiency and transpiration restriction under high evaporative demand and new loci controlling water use-related traits in African rice, Oryza glaberrima Steud.

Because water availability is the most important environmental factor limiting crop production, improving water use efficiency, the amount of carbon fixed per water used, is a major target for crop improvement. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand, remain unknown. These traits were measured in a panel of 147 African rice Oryza glaberrima genotypes, known as potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly differentiated genotypes confirmed these associations and suggested that the root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency and its residuals, that links to genes involved in water transport and cell wall patterning. Our data suggest that root shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.

Assessment of Genetic Diversity Among Intra- and Interspecific Lowland Rice using Morpho-agronomic Traits and SSR Markers

Rice is staple food in many countries of Africa and a major part of the diet in many others. However, Africa’s demand for rice exceeds production with the deficit of 40% being imported. One way to improve Africa’s rice production is through breeding high yielding varieties suitable for the different environment conditions. This study was conducted to assess the genetic variability and stability performance of 48 lowland rice genotypes including 37 interspecific (Oryza glaberrima × Oryza sativa ssp. indica) and 11 intraspecific (O. sativa ssp. indica × O. sativa ssp. indica) in 12 environments in Nigeria, Benin Republic and Togo using Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype+ Genotype x Environment (GGE) biplot models. The combined analysis of variance revealed significant differences (P<0.01) among the genotypes, environments, and genotypes x environment interaction. Both the AMMI and GGE models identified NERICA-L8 and NERICA-LI2 as the best genotypes for cultivation across environments. Ouedeme environments in Benin Republic were the most stable and ideal for rice cultivation, while Ibadan sites were the most unstable. TOG 5681 had the least yield and was the most unstable across seasons. Genetic diversity was analyzed using 22 important morpho-agronomic traits and 50 simple sequence repeat (SSR) markers and the results were subjected to principal components analysis (PCA). The results revealed that the first eight PC axes (PC1–8) accounted for 75.13% of the total variation, while PC1–4 accounted for 50.39% of the total variation among rice genotypes. However, 10 of the 50 SSR markers were polymorphic and generated 49 alleles (average = 4.9 alleles per locus), suggesting moderate to substantial genetic diversity among the rice genotypes. The polymorphic information content (PIC) ranged from 0.24 to 0.65, with an average PIC value of 0.45. Two structured populations were observed which clustered into five heterotic groups and an outgroup, respectively. This suggests that heterosis could be exploited in the next hybridization program by crossing one of the genotypes in any SSR marker-defined cluster, with the rice accession TOG 5681 in cluster I. The results of this study suggest that morpho-agronomic traits should be used to compliment SSR data in rice diversity studies, especially if a few polymorphic SSR markers are to be used.

Field performance of heat tolerant mutant rice lines generated from Oryza sativa and Oryza glaberrima

This study evaluated mutant lines developed from two cultivated species of upland rice, Oryza sativa, and Oryza glaberrima, in field experiments conducted during the hot and dry seasons of 2014/2015 in Morogoro, Tanzania. The growth yield and yield components of 34 and 14 Gamma induced mutant upland rice lines developed from O. sativa (Kihogo red) and O. glaberrima were evaluated, respectively. The mutant lines were selected based on variable expression of heat shock protein genes (HSPs) in previously conducted heat tolerance studies. The minimum and maximum temperatures and rainfall during the field performance experiment were measured between 20 oC and 35 oC, and 32.7 mm and 155.5 mm, respectively. The data for 12 yield and yield component parameters such as days to early and 50% flowering, days to physical maturity, plant height, number of tillers, number of panicles, spikelets, filled grains, unfilled grains and 1,000 grain weights were collected and analysed using ANOVA and Principal Component Analysis. Significant differences (P≤0.05) were obtained among the mutant lines in terms of grain yield, spikelet sterility and other variables, which were further used as criteria for selection of heat and drought tolerant rice lines. Eight heat and drought tolerant mutant rice lines with high yields (over 3.5 ton/ha) and low spikelet sterility were selected for further advancement in breeding programmes