Rice (Oryza sativa L.) is the major food crop of nearly half of the world’s population. It is the staple food of nearly one-half  of   the   world’s   population.  The  rising  World population is increasing rapidly; rice production needs to be increased to meet its demands in the coming years in order  to  keep  pace  with increasing population mainly in Asia and Sub-Saharan Africa (Anyaoha et al., 2018). It is estimated that a 50% increase in rice grain yield may be required by 2050 to keep hunger away (Pang et al., 2017). This important cereal is cultivated and consumed in White Nile State in Sudan but its production is characterized by poor yields resulting from the use of low farm inputs and cultivation of unimproved cultivars with poor yield potentials (Osman et al., 2012). Hence, improving rice yield potential or its yield under various conditions is the foremost task for rice breeders (Ali et al., 2017). In West Africa, the average annual production is 10.1 million tons (milled equivalent, 2009 to 2019 period and the average annual growth of production (2009 to 2019) is higher than 10%. However, it still does not meet the demand, with imports representing 46% of rice consumption on average annually over the 2009 to 2019 period (Soullier et al., 2020).

NERICA (New Rice for Africa) is a group of interspecific hybrid rice varieties and lines between Asian rice varieties (Oryza Sativa L.) with high yield potential and African rice varieties (Oryza Glaberrima L.) with resistant to main constraint to rice production such as major African insect pests and disease across ecologies in sub-Sahara Africa (SSA) (Africa Rice Center (WARDA)/FAO/ SAA, 2010). There are 18 upland NERICA varieties. NERICA-4 is the most widely adopted upland variety, grown in more than 10 countries in sub-Saharan Africa. Upland NERICA varieties give good yields, are early-maturing (in 75  to 100 days)  and  are  tolerant  to  major local stresses, Early maturity of upland NERICAs is much appreciated by farmers, especially women farmers as it allows them to have food during the ‘hunger period’ while waiting for the harvest of other crops (Africa Rice Center (WARDA)/FAO/SAA, 2017). 

Some NERICA varieties have about 25% higher protein than imported Asian varieties. The genetic variability provides useful information with regard to the possibility and extent of improvement that may be expected in the characters through breeding and selection. Breeders are interested in evaluating genetic diversity based on morphological characteristics as they are inexpensive, rapid, and simple to score. Moreover, this evaluation could be useful in developing reliable selection indices for important agronomic traits in rice. Therefore, as a result of the above facts, the present research study was undertaken to estimate genetic variability, heritability and genetic advances among yield and yield contributing traits for rice genotypes that can establish relationship in yields and their components and utilization of the available population in future rice breeding programs.

Study site and experimental design

The experimental materials were 18 upland rice genotypes with two checks namely PAKIST and KOSTI-1 (Table 1) grown for two consecutive  seasons  (2017  and  2018)  during  the  rainy  season under irrigated condition; at White Nile Research Station Farm (latitude 14?24'N and longitude 33?22'E), Kosti of the Agricultural Research Corporation (ARC) Sudan. The soil of the experimental plots was classified as vertisol with high clay content (40 to 65%), less than 1% organic carbon, low in available nitrogen (0.03% total nitrogen) and medium in available P₂O₅ (406 to 700 ppm total phosphorus), pH values is slightly alkaline which ranging from 7 to 8.2. The climate was semi-arid (Table 2). The experimental plots were laid out in a complete randomized block design (CRBD) with three replications. In both seasons, deep ploughing, harrowing and leveling were practiced to prepare the experimental area. The seed were drilled on July 7^th and 10^th of 2017 and 2018 in turn; using a seed rate of 80 kg/ha. The plot size was six rows of 6 m length with 0.2 m row spacing giving a total area of 7.2 m². Thinning was preformed after two weeks from sowing. Nitrogen and phosphorus fertilizers application at the rate of 129 and 43 kg ha^-1 as source of N and P, respectively. Phosphorus fertilizer in form of triple super phosphate (P₂O₅) was applied as basal dose during the final land preparation. Nitrogen, in the form of urea (46% N) was top dressed in two equal split doses at 21 days after sowing and the other before panicle initiation. Hand weeding was performed four times per season. All plots were irrigated at sowing and then at weekly intervals until it reached the flowering stage and then every 3 days as needed. Generally, the crop was established well with no incidence of pests and diseases.

Data collection

Different quantitative traits at appropriate growth stages of rice plant were collected according to the standard evaluation system (SES) for rice 5^th edition (IRRI, 2014). Data on days to flowering (DF) (days from sowing to time when 50% of the plant in plot started to shed pollen), days to maturity (DM) (days from sowing up to when 80% of the panicles reached full maturity), then at harvest, plant height (cm), panicle length (cm), number of tillers/plant, number of filled grain/panicle, percentage of unfilled grain/panicle, number panicles per m², 1000 grain weight (g) and grain yield (t/ha) based on grain yield per plot were recorded. Ten randomly selected plants in the net harvested area of each plot were used for data collection.

Data analysis

Analysis of variance (ANOVA) was carried out on the data to assess the genotypic effects and their interaction using the general linear  model  (GLM)  procedure   for  randomized  complete  blocks design in SAS (version 8) (SAS Institute, 2010). Then, the combined analysis of variance was done for traits in which the mean squares were homogenous. The phenotypic and genotypic variances and their coefficients, heritability in the broad sense and genetic advance were computed according to the formula described by Singh and Chaudhary (1985). Combined over seasons were used to compute simple linear correlation coefficients between grain yields and 12 other traits.

Variation in growth, yield and yield component among genotypes

The analysis of variance showed the presence of significant differences among the tested genotypes for all characters. This gives an opportunity for rice breeders to improve those traits through selection and hybridization to improve the desired traits. The range and mean of genotypes for all studied traits also indicated wide ranges of variation which also revealed possible amount of variability among the genotypes (Table 3). The genetic analysis of quantitative traits is a prerequisite for plant breeding programs, which can lead to a systemic method of design and to the appropriate planning of plant breeding strategies.

The current study suggests that the PCV was higher than the GCV for all traits. This was also the case for all the traits observed in a previous study (Osman et al., 2012) which reported that the environmental effect on any trait is indicated by the magnitude of the differences between the genotypic and phenotypic coefficients of variation; large differences reflect a large environmental effect, whereas small differences reveal a high genetic influence. In this study, phenotypic coefficients of variation were slightly higher than the genotypic coefficients of variation for all the studied traits (Table 4). This indicated the presence of environmental influence to some degree in the phenotypic expression of the characters;  this  results similar  to  the study of Idris et al. (2012). The small differences between the PCV and GCV for most of the traits, such as days to 50% flowing, panicle length (cm), number of filled grain/panicle and 1000 grain weight represented some degree of environmental influence on the phenotypic expression of these characters. It also suggests that selection based on these characters would be effective for future crossing programs. The other traits, which showed a higher difference between PCV and GCV, indicated that the environmental effect on the expression of those traits is higher and that selection based on these characters independently is not effective for further yield improvement. The highest PCV was recorded for number of tiller/plant (20.315 to 21.01), number of unfilled grains/panicle (30.02-45.94) and grain yield (22.95-15.70 t/ha) in both seasons revealed that the genotypes have a broad base genetic background so  that  they  can respond positively to selection; this results similar to the study of Mulugeta et al. (2011) and Abebe et al. (2017). The broad sense heritability is the relative magnitude of genotypic and phenotypic variances for the traits and has a predictive role in selection procedures. This gives an idea of the total variation ascribable to genotypic effects, which are an exploitable portion of the variation. The characters that showed relatively high heritability estimates (≥ 60%) were days to 50% flowering (91.18 and 76.7%), grain yield (86.97 and 56.00%), plant height (79.17 and 71.50%), 1000 grain weight (76.57 and 59.4%), biomass yield (75.81 and 30.6%), days to maturity (73.75 and 62.9%) and number of filled grain/panicle (71.28 and 40.3%) in both seasons respectively (Table 4). High broad sense heritability estimates would enable plant breeders to base their selection on  the  phenotypic   performance   indicating  that their improvement could be achieved by mass selection. Similar results were reported by Manjunatha et al. (2018), Limbani et al. (2017) and Behera et al. (2019). Since high heritability does not always indicate high genetic gain, high to medium heritability and genetic advance were recorded for the number of tillers/plant, number of unfilled grains/panicle, biomass yield and grain yield. This suggests that theme traits are primarily under genetic control and selection for them can be achieved through their phenotypic performance. High heritability estimates with low genetic advance observed for grain length (mm), grain width (mm) and grain yield t/ha indicated non-additive type of gene action and that genotype × environment interaction played a significant role in the expression of the traits. The genetic analysis of quantitative traits is a prerequisite for plant breeding programs, which  can  lead to a systemic method of design and to the appropriate planning of plant breeding strategies. The results further revealed that most of the traits exhibited wide range of variability (Table 5a and b). 

Interrelationship

Yield is a complex product being influenced by several independent quantitative characters. Breeders always look for variation among traits to select desirable types. Some of these characters are highly associated among themselves and with grain yield. The analysis of the relationships among these characters and their associations with grain yield is essential to establish selection criteria. When more characters are involved in correlation study it becomes difficult to ascertain the characters which really contribute toward yield. The relationships existing between 13 quantitative traits represented as simple correlation coefficients are presented in Table 6. Correlation of yield and other traits is important in indirect selection for high yield improvement in crop genotypes (Mulugeta et al., 2011; Sadia et al.,   2020).   Grain    yield    was    positively   and significantly correlated with all the measured traits, except number of unfilled grains per panicle. Increase in the percent of unfilled grain per panicle decreased grain yield. The results of this present study were in agreement with the findings of Mulugeta et al. (2011) and Sravan et al. (2012). There was a highly significant and positive correlation of grain yield with biological yield (0.764), number of tillers per plant (0.439) and number of filled grain per panicle (0.423). The significant and positive correlation with grain yield are a strongly indication that these traits are major factors in improving grain yield; also suggests that selection directed towards these character will be effective in ensuring high grain yield of rice under White Nile State condition. These results collaborate with the finding of Ogunbayo et al. (2014) who observed a positive and significant correlation between grain yield and biological yield, number  of  tiller  per  plant  and  number  of field grain per panicle. Also, these results are in agreement with that reported of Jambhulkar and Bose (2014) and Abdulfiyaz et al. (2011).

It could be concluded from this study that there is adequate genetic variability among 20 tested genotypes. Hence, the information generated from this study can be exploited by rice breeder for future rice breeding program. The study was also carried out for one sit and two seasons. These qualified them to be used as selected specie, the following had the highest yield NERICA3 (5.2t/ha), NERICA4 (5.1t/ha), NERICA6 (4.9 t/ha), NERICA12 (4.6 t/ha) and PACIST-ck (4.7 t/ha) these were needed to check the adaptability, stability and to test major rice- growing  areas  to  make sound recommendations for release.

The authors have not declared any conflict of interests.

The authors gratefully acknowledge the University of Gezira Collage of Agricultural Sciences. Authors very gratefully acknowledge the Africa Rice Center Gen Bank, Benin for providing research material, White Nile Research Station staff for their help during the fieldwork. The authors are also grateful to Prof. Abu Elhassan I. Salih and Khalid A. Osman for giving me the opportunity to use this research. The study was funded by Agricultural Research Corporation (ARC), WNRS, Kosti, Sudan.