Agro-morphological Characterization Assessment of Rice (Oryza Sativa L.) Varieties

doi:10.21203/rs.3.rs-5362300/v1

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Agro-morphological Characterization Assessment of Rice (Oryza Sativa L.) Varieties

https://doi.org/10.21203/rs.3.rs-5362300/v1

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Rice (Oryza sativa L.) is an essential constituent of the global food chain. The goals of this study are to assess the field performance, character association and variability of 30 rice varieties (Oryza sativa L.) accessions collected from various locations of Bangladesh. To evaluate the morphological variability, yield variability and correlation, the study employed a randomized complete block design (RCBD) with three replications. ACC04 exhibited the highest yield (186.19 g), total number of grains per panicle (197.33), panicle length (29.1 cm), the number of filled grain (188.6), grain length (9.86 mm), grain width (3.34 mm), defective grain length (7.63 mm), defective grain breadth (2.92 mm) and 1000 seed weight (31.26 g) and gave lowest the number of unfilled grain (8.6). It can be concluded that rice accessions exhibit a diverse array of qualitative and morphological traits. The varieties were divided into six groups based on cluster. In Cluster I and III, ACC4 gave the highest performance followed by ACC01 and ACC22 in grain length, grain breadth defective grain length, defective grain breadth and 1000 seed weight. Therefore, ACC04, ACC01 and ACC22 would be best and promising variety for rice production.

Rice (Oryza sativa L.) is a globally significant crop that supplies sustenance to the majority of the global population, which is approximately 50% of the total (Gaballah et al., 2022). Bangladesh has been the world's largest producer of rice for the past four years, and it is currently the third-largest producer globally (Parida et al., 2022). A total of 37.84 million metric tons of rice was produced in Bangladesh from 2021 to 2022 (Roy et al., 2022).

Most of the world's production and consumption of rice (Oryza sativa L.) occurs in South Asia. Since rice is the main food supply and essential component of countryside life in this area, increasing its production is crucial to handling issues associated with food availability. Significant production of rice is closely connected to a diversity of morphological parameters, such as the arrangement of plants, the quantity of tillers, leaf size, and the panicle structure, all of which affect the plant's ability to photosynthesize, implement nutrients, and evolve to a variety of environmental terms and conditions (Xie et al., 2024)

The quantity of plants and the yield of grains per unit area are influenced by the number of tillers, but the height of plants and the area of leaves are connected to the plant's capacity to absorb sunlight and conduct photosynthesis effectively. In order to achieve the greatest yield, grain cultivating and packing are greatly affected by panicle structure. Responding to geographical challenges including drought, flooding, and nutrient-poor soils all of which are prevalent in the South Asian region can be established more feasible with these additional features (Rahman et al., 2024). Although individual qualities are usually the primary concern of rice breeders, minimal has been discovered regarding how these traits incorporate and affect yield together. A greater tiller density could increase maximum yield, but provided the amount of foliage and nutritional requirements are not equally maintained, rivalry over resources could decrease total productivity. This is an example of how improving yields requires alternatives across features. In order to create rice varieties that are both high yielding and ideal for the agro-ecological requirements of South Asia, a comprehensive strategy that includes responsibility for the beneficial impacts of both features is crucial (Moe et al., 2019). This study evaluates the relationships between important morphological traits and rice yield, concentrating on conditions for cultivation in the region of South Asia.

The goal of the study is to assess which characteristics and arrangements enhance significant yield and adverse environmental resilience by considering these associations. In South Asia, these findings can direct focused development initiatives, resulting in rice cultivars that maximize resource utilization, sustain harsh environments, and maintain high yields. To enhance both food availability and stability in the economy, it is crucial to comprehend certain trait-yield associations in order to develop methods of reproduction that specifically deal with the difficulties that affect rice cultivators in that region. Moreover, the study's conclusions have significance beyond South Asia. Insights from the area may encourage larger initiatives to improve rice as scarcity of resources and climate change has a greater impact on agriculture globally. This study can contribute to the creation of globally adaptable rice varieties by discovering features that promote capacity and yield, establishing the cornerstone for environmentally friendly rice farming in areas dealing with comparable difficulties.

2.1 Experimental Site

The experiment took place in the field and laboratory as part of the research effort "Collection, Morphological Characterization, Rice (Oryza sativa L.) Variety" at Bangladesh Rice Research Institute, Gopalganj, located at located on the West –North corner of the Bihar State. Geographically it is located between 83.54° – 85.56° latitude and 26.12° – 26.39°North longitude (UNDP, 1988). The soil texture was silty loam with a pH 6.7. The trial's location was subtropical in nature. This research study occurred from July to December 2022. This period is characterized by no or mild rainfall, as well as low to normal temperatures. In this investigation, thirty rice varieties were assessed, with seeds received from diverse regions of Bangladesh through Bangladesh Rice Research Institute, Gopalganj. The collected varieties of rice (ACC) are categorized by their locations. In this investigation, Randomized Complete Block Design (RCBD) with three replications was followed, in addition, the experiment was conducted in plot. In a single plot, there were five plants of a single variety, and total plots were 90.

Table 1

Collecting the rice varieties from different locations.
Association	Variety	Location
ACC01	Kach Kolom	Alfadanga, Faridpur
ACC02	Shorna Dhan	Alfadanga, Faridpur
ACC03	Laxmi Digha	Alfadanga, Faridpur
ACC04	Kala Digha	Alfadanga, Faridpur
ACC05	Malabhog	Bhanga, Faridpur
ACC06	Shorna Dhan	Bhanga, Faridpur
ACC07	Sona Digha	Naria, Shariatpur
ACC08	Boro Digha	Madhukhali, Faridpur
ACC09	Arparina	Boalmari, Faridpur
ACC10	Debmani-1	Boalmari, Faridpur
ACC11	Boro Digha	Boalmari, Faridpur
ACC12	Ranga Digha	Alfadanga, Faridpur
ACC13	Nayanmani	Saltha, Faridpur
ACC14	Lal Shorna	Saltha, Faridpur
ACC15	Boro Digha	Sadarpur, Faridpur
ACC16	Nayanmani	Sadarpur, Faridpur
ACC17	Pari Dhan	Bhanga, Faridpur
ACC18	Sada Paijam	Bhanga, Faridpur
ACC19	Shorna	Bhanga, Faridpur
ACC20	Shialjota	Sadarpur, Faridpur
ACC21	Sissumoti	Sadarpur, Faridpur
ACC22	Dudkalom-1	Boalmari, Faridpur
ACC23	Kartiksail	Muksudpur, Faridpur
ACC24	Laxmi Digha	Muksudpur, Faridpur
ACC25	Kajol Shorna	Manda, Naogaon
ACC26	Basai Shorna	Manda, Naogaon
ACC27	Beerpala	Sadar, Gopalganj
ACC28	Betenga	Madhukhali, Faridpur
ACC29	Kartiksail	Kashiani, Faridpur
ACC30	Sissumoti	Kashiani, Faridpur

Thirty-eight-day-old seedlings with each entry were chosen to transplant maintaining one seedling per hill. Throughout the crop-growing season, necessary cultural activities were carried out. Urea, TSP, MOP, Gypsum, and ZnSO4 fertilizers were applied @260:100:120:110:10 kg/ha, respectively. The other fertilizers, with the exception of urea, were applied during the final land preparation. Three equally spaced applications of urea were made at 15 days after transplanting (DAT), 35 DAT, and right before blooming. When necessary, cross-cultural procedures and pest control measures were also carried out.

2.2 Collecting data

Data on these variables can be obtained from the examined plants during the experiment. The specifications of data recording are given below on an individual plant basis:

The amount of grain per panicle in rice was measured, begin by selecting a representative sample of panicles from the plot. Hand-thresh each panicle carefully to separate the grains, ensuring none are lost in the process. Then, count the grains from each panicle, using a seed counter if available for efficiency. Finally, calculate the average grain count per panicle by dividing the total number of grains by the number of panicles sampled.

To measure panicle length in rice, start by selecting a representative sample of panicles. Using a ruler scale, measure from the base (where the panicle attaches to the stem) to the tip of the panicle. Record the length of each panicle individually, and expressed in mm.

To measure filled grain per panicle and unfilled grain per panicle, start by randomly selecting a representative number of rice panicles from various points in the field. Carefully hand-thresh each selected panicle to separate the grains, ensuring that all grains are collected. Count the grains from each panicle individually, or use a seed counter if available. Then, calculate the field grain per panicle by averaging the grain count across all sampled panicles to get an accurate field estimate.

Place grains lengthwise on a flat surface and measure them individually using a digital caliper. Calculate the average grain length and breadth by measuring multiple grains and dividing by the total number measured.

Defective grain length and breadth were measured by using digital caliper and expressed in millimeter.

To measure the 1000-seed weight, first count out exactly 1000 seeds. Place them on a digital scale and record the total weight in grams to determine the 1000-seed weight. Yield per hill was measured by following formula and expressed in gm.

$$\:Yield\:per\:Hill=\:\frac{\:Total\:Grain\:Weight\:from\:Sampled\:Hills}{Number\:of\:Hills\:Sampled\:}$$

2.3 Statistical Analysis

The data were subjected to statistical investigation using STATISTIX version 10 software and SPSS version 30. The F test was used to do analyses of variance (ANOVA) on all of the variables. In their study, a group researcher used the LSD test to discern the statistical significance of the disparity between the means at the 5% and 1% levels of probability.

Phenotypic co-efficient of variation were calculated by the formula suggested by Genotypic co-efficient of variation

Similarly, the phenotypic co-efficient of variation was calculated from the following formula.

Phenotypic co-efficient variation (PCV) = $\:\sqrt{\frac{{\sigma\:}2\text{p}\text{h}}{x}}$ × 100

Phenotypic correlation (r_pxy) = $\:\frac{{\sigma\:}\text{p}\text{x}\text{y}\:}{\sqrt{{\sigma\:}2\:\text{p}\text{x},\:{\sigma\:}2\:\text{p}\text{y}\:}}$

Where, σ_gxy = Phenotypic co-variance between the traits x and y, σ²_px = Phenotypic variance of the trait x, σ²_py = Phenotypic variance of the trait y

Panicle Length

Statistically significant variance occurred in panicle length. The panicle length varied substantially among the accessions, with a mean value of 19.00 to 29.01 cm. The longest panicle length (29.1 cm) was observed in ASS04 followed by 27.6 in ACC18, while the shortest panicle length (19.0 cm) was discovered in ACC03 followed by 19.06 cm in ACC27 respectively (Table 2). Panicle length has very low correlation with yield per hill (Fig. 4).

Table 2

Variability and field performance of 30 rice varieties
Association	Panicle length (cm)	Field Grain/Panicle	Unfiled Grain/Panicle	Grain Length (mm)
1	21.8p	57.0u	26.0i	7.81i
2	23.8j	113.0k	37.0f	7.81e
3	19.0t	125.0i	51.0d	7.47ll
4	29.1a	188.6a	8.6q	9.86a
5	22.1o	174.0cd	10.0pq	7.74ij
6	23.8j	173.0d	17.0l	7.56jkl
7	21.8p	97.0o	14.3m	7.38lm
8	24.1i	111.0l	39.0e	7.69ijk
9	24.6h	137.0g	14.6m	7.90i
10	23.2l	81.0r	75.0a	8.97cd
11	21.1q	125.0i	17.0l	7.50kl
12	20.3r	89.0p	53.0c	7.83i
13	23.0m	110.0l	11.0op	7.84i
14	23.1lm	107.0m	60.0b	8.59efgh
15	22.6n	105.0n	23.0j	8.76cd
16	23.6k	90.0p	24.0j	8.46gh
17	26.5c	121.0j	20.3k	6.14o
18	27.6b	141.0f	17.0l	6.49n
19	22.0o	186.6b	32.0g	7.81i
20	25.4e	81.0r	24.0j	9.23b
21	26.5c	131.0h	33.0g	9.65a
22	25.2f	73.0s	18.3l	8.54fgh
23	25.0g	175.0c	11.0op	8.66efg
24	22.7n	83.0q	12.0no	8.70ef
25	24.0i	169.0e	29.0h	7.17m
26	24.0d	170.0e	50.0d	5.65p
27	19.6s	72.0s	32.0g	7.41ll
28	26.6c	52.0v	39.0e	7.70ijk
29	27.5b	140.0f	14.0m	8.43h
30	20.4r	68.0t	13.0mn	9.09bc
LSD_0.05	0.16	1.58	1.90	0.21
LSD_0.01	0.22	2.10	2.54	0.27
Level of Significance	**	**	**	**
CV%	0.43	0.82	4.25	1.60
Here, LS = Level of Significance, ** = 1% Level of significance and, * = 5% Level of significance
Total number of Grain per Panicle

With a range of 81.0 to 220.0 grains, the total amount of grain per panicle varied significantly. The maximum total amount of grain per panicle (220.0 grains) was noticed in ACC26 followed by 218.67 grains in ACC19 whereas, the minimum total amount of grain per panicle (81.0 grains) was revealed in ACC30 followed by 83.0 grains in ACC01 sequentially (Fig. 1). There is very low correlation between total number grain per panicle and yield per hill (Fig. 5)

Field Grain per Panicle

Significant variance was additionally observed in the field grain per panicle, which ranged from 52.0 to 188.6 grains. The largest number of field grains (188.6 grains) was noticed in ACC04 followed by 186.6 field grains per panicle in ACC19, meanwhile the lowest number of field grains (52.0) was observed in ACC28 followed by 57.0 field grains per panicle in ACC01 respectively (Table 2).

Unfilled Grain per Panicle

With a range of 8.6 to 75.0 grains, the unfilled grain per panicle also varied significantly. The least number of unfilled grains (8.6 grains) was noted in ASS04 followed by 10.0 unfilled grains per panicle in ACC05 as well the maximum number of unfilled grains (75.0 grains) was recorded in ACC10 followed by 60.0 grains in ACC14 (Table 2).

Grain Length

The accessions ranged in grain length from 5.65 mm to 9.86 mm. The most extended grain length (9.86 mm) was found in ACC04 followed by 9.65 mm in ACC21 and the most concise grain length (5.65 mm) was exposed in ACC26 followed by 6.14 mm in ACC17 correspondingly (Table 2).

Grain Breadth (mm)

The thicknesses of grain breadth ranged from 1.91 mm to 3.34 mm, indicating substantial variations among the accessions. The broadest grain (3.34 mm) was identified in ACC04 and ACC08 followed by 3.21 mm in ACC01, whereas the narrowest grain (1.91 mm) was detected in ACC26 followed by 2.00 mm in ACC18 sequentially (Table 3).

Table 3

Variability and field performance of 30 rice varieties
Association	Grain breadth (mm)	Defective Grain Length (mm)	Defective Grain Breadth (mm)
1	3.21b	5.80hi	2.63e
2	2.79i	6.98b	2.37l
3	3.18c	5.55jkl	2.50h
4	3.34a	7.63a	2.92a
5	2.91f	5.53jklm	2.60f
6	2.73k	5.22n	2.49hi
7	3.12d	5.26n	2.68c
8	3.34a	5.38lmn	2.65d
9	2.32q	5.76hij	2.11r
10	3.09e	6.31def	2.75b
11	2.53o	5.37lmn	2.25p
12	2.82h	5.95gh	2.57g
13	2.71l	5.70ijk	2.41k
14	2.60n	6.14efg	2.28o
15	2.73k	6.33de	2.30n
16	2.63m	6.68c	2.34m
17	2.04t	4.45o	1.65w
18	2.00u	3.88p	1.80u
19	2.30r	5.70ijk	2.09s
20	3.18c	6.53cd	2.74b
21	2.60n	6.71c	2.45j
22	2.76j	6.08fg	1.66w
23	2.62m	6.33de	2.50h
24	2.15s	6.30def	2.02t
25	2.77j	5.31mn	2.40k
26	1.91v	4.28o	1.76v
27	2.88g	5.54jklm	2.65d
28	2.45p	5.50klm	2.21q
29	3.10e	6.33de	2.66d
30	2.83h	6.68c	2.48i
LSD_0.05	0.01	0.23	0.01
LSD_0.01	0.02	0.31	0.02
Level of Significance	**	**	**
CV%	0.41	2.51	0.44
Here, LS = Level of Significance, ** = 1% Level of significance and, * = 5% Level of significance
Defective Grain Length (mm)

Additionally, there was substantial variation in defective grain length, with values ranging from 3.88 mm to 7.63 mm. The greatest defective grain length (7.63 mm) was monitored in ACC04 followed by 6.98 mm in ACC02, despite the fewest defective grain length (3.88 mm) was revealed in ACC18 followed by 4.28 mm in ACC26 individually (Table 3).

Defective Grain Width

The range of the defective grain width was 1.65 mm to 2.92 mm. The largest width of defective grain (2.92 mm) was observed in ACC04 followed by 2.75 mm in ACC10, in which the shortest grain width (1.65 mm) was discovered in ACC17 followed by 1.66 mm in ACC22 in corresponding order (Table 3).

Weight of 1000 Grains

The weight of 1000 grains varied from 9.5 g to 31.26 g. The heaviest grain (31.26 g) was recorded in ACC04 followed by 30.75 g in ACC24. In contrast, the lightest grain (9.5 g) was observed in ACC26 followed by 9.7 g in ACC17 correspondingly (Fig. 2).

Yield per hill

The yield assessment reveals notable variation among the various association, regarding values varying from 53.64 g to 187.19 g per hill. The highest yield (187.19 g) was discovered in ACC10 followed by 186.2 g in ACC12. Conversely, the lowest yield (53.64 g) was found in ACC08 followed by 53.65 g in ACC07 individually (Fig. 3).

3.1 Cluster analysis

The 30 rice varieties were grouped into six clusters to identify the most homogenous groups with the best performance (Table 4). Cluster analysis revealed that Cluster III exhibited the best overall results for rice production, followed closely by Cluster I. In contrast, Cluster VI, which included ACC10 and ACC12, showed comparatively lower performance than Clusters III and I (Fig. 7). Cluster II displayed the lowest performance among all clusters, with minimal variation in morphological traits (Fig. 6), indicating limited potential for yield improvement.

Table 4

Six cluster of rice varieties
Cluster	Association
I	ACC01, ACC22
II	ACC02, ACC03, ACC08, ACC09, ACC11, ACC14, ACC17, ACC18, ACC21, ACC29,
III	ACC04,
IV	ACC05, ACC06, ACC19, ACC23, ACC25, ACC26,
V	ACC07, ACC13, ACC15, ACC16, ACC20, ACC24, ACC27, ACC28, ACC30
VI	ACC10, ACC12,

Figures 6 and 7 show varying coefficients of variation among rice varieties, with ACC22-ACC28 displaying the highest coefficients, indicating greater variability in their trait values.Figure 6. Coefficient variation in rice varieties

3.2 PCA analysis

PCA analysis revealed that grain length, grain breadth, defective grain length, defective grain breadth, and 1000-seed weight had the strongest significant correlations among all parameters (Table 6). Consequently, Clusters I and III demonstrated the highest performance in these traits, including grain length, grain breadth, defective grain length, defective grain breadth, and 1000-seed weight (Table 5).

Table 5

Coefficient variation among parameters
Components	Initial Eigenvalues			Extraction Sums of Squared Loadings
Components	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
Panicle length	3.811	38.106	38.106	3.811	38.106	38.106
Filled grain per panicle	2.253	22.531	60.637	2.253	22.531	60.637
Unfilled grain per panicle	1.427	14.267	74.904	1.427	14.267	74.904
Grain length	0.912	9.118	84.021
Grain breadth	0.625	6.252	90.273
Defective Grain Length	0.514	5.139	95.412
Defective Grain Breadth	0.250	2.505	97.917
1000 seed Weight	0.121	1.212	99.129
Yield per hill	0.087	0.871	100.00
Total number of grains per panicle	1.50	1.50	100.00

The study of rice accessions revealed significant diversity in their morphological variation, and the parameter are varied significantly, there is a few correlations between the number of panicles per grain and yield panicle in our investigation. Hannan et al. (2020) claimed that a significance will remain between yield and total number of panicles. In our investigation, panicle length varied significantly among accessions, with the longest at 29.1 cm in ACC04 and the shortest at 19.0 cm in ACC03, and Mulsanti et al. (2020) told that the relationship between panicle length and yield is typically weak or inconsistent. While longer panicles might hold more grains, studies often show a low or negligible correlation between panicle length and yield per hill. This suggests that factors beyond panicle length, such as grain filling, density, and environmental conditions, play more significant roles in determining overall yield. The total grain per panicle varied significantly, ranging from 81.0 grains in ACC30 to 220.0 grains in ACC26 (Fig. 1). There was a very low correlation between the total grains per panicle and yield per hill is similar value with Siddique et al. (2016). Akter et al. 2019 claimed that the correlation between the total amount of grain per panicle and yield per hill was very low, indicating that a higher grain count per panicle does not strongly influence overall yield per hill. This suggests that factors other than grain count may play a more significant role in determining yield. Field grain per panicle varied significantly, ranging from 52.0 grains in ACC28 to 188.6 grains in ACC04. The filled grain count per panicle is positively correlated with yield, as more filled grains typically contribute to a higher yield. However, this relationship can vary depending on other factors like panicle number, grain size, and plant health, which also impact overall yield (Chowhan et al., 2021). Grain length among accessions varied from 5.65 mm in ACC26 to 9.86 mm in ACC04, in addition, ACC21 had the second-longest grains at 9.65 mm, while ACC17 had the second-shortest at 6.14 mm. Grain length has a weak or inconsistent correlation with yield, as longer grains do not necessarily increase yield per hill. Yield is influenced more by factors like filled grain count, panicle number, and grain weight rather than grain length alone (Ilieva et al., 2019). The weight of 1000 grains ranged from 9.5 g in ACC26 to 31.26 g in ACC04. ACC24 had the second heaviest grains at 30.75 g, while ACC17 had the second lightest at 9.7 g. Grain weight is positively correlated with yield, as heavier grains typically contribute to higher overall yield per hill. Additionally, grain weight can relate to panicle length and grain length; longer panicles may produce heavier grains due to increased nutrient allocation, while longer grains can also be heavier if they are filled adequately. However, the strength of these relationships can vary depending on environmental factors and genetic traits (Chowhan et al., 2021).

The study showed the significant morphological variability and yield potential of the 30 rice accessions that were assessed. In terms of yield and critical agronomic traits, including panicle length, total grain count per panicle, filled grain count, grain length and breadth, and 1000-seed weight, ACC04 exhibited the highest overall performance. Additionally, it had the fewest unfilled grains. The grain and yield-related characteristics of ACC04, ACC01, and ACC22 were the most promising, as evidenced by the cluster analysis that classified these accessions into six distinct clusters. These results suggest that these accessions have the potential to improve rice production in Bangladesh. Therefore, ACC04, ACC01, and ACC22 are emerging as highly prospective varieties for future rice cultivation and improvement endeavors.

CONFLICTS OF INTEREST

The authors declare they have no conflicts of interest.

Author Contribution

Nayan Chandra Howlader- Conceptualization, Data analysis, data carnation, manuscript writing and editing Mohammad Tanvir Ahmed Bulbul- Data collection Easir Arafat- Data Collection Md. Sohel Rana- Manuscript writing, editing and reviewing Md. Ziaul Hasan- Data collection and review

ACKNOWLEDGEMENTS

The authors would like to express their gratitude to the concerned authority, Bangladesh Rice Research Institute for their financial and technical assistance in conducting the research work.

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No competing interests reported.

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Agro-morphological Characterization Assessment of Rice (Oryza Sativa L.) Varieties

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Abstract

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1. Introduction

2. Materials and Methods

2.1 Experimental Site

2.2 Collecting data

2.3 Statistical Analysis

3. Result

3.1 Cluster analysis

3.2 PCA analysis

4. Discussions

5. Conclusion

Declarations

CONFLICTS OF INTEREST

Author Contribution

ACKNOWLEDGEMENTS

References

Additional Declarations

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