Conferring Drought Tolerance in Rice Landraces Using Seedling Indices

doi:10.21203/rs.3.rs-5327215/v1

Landraces are a vital source of genetic diversity in crops, offering a rich pool of allelic variation that plant breeders can utilize for developing new cultivars. Although, Nepal has a huge diversity for rice landraces but their actual potentiality hasn’t been sufficiently explored yet. In this study, 25 rice landraces were tested to evaluate the drought tolerance level in completely randomized design (CRD) in 250 ml disposable cup at seedling stage. The rice landraces were tested under 60% FC, 100% FC, and saturated condition for ten quantitative traits. The analysis of variance suggested that there is a significant difference among the landraces for different root and shoot traits as well as in different moisture conditions. Correlation analysis showed that root length has significant positive correlation with shoot length, root: shoot ratio, and fresh root weight under drought condition with 60% FC. Similarly, negative correlation was observed between root: shoot ratio and root number under drought condition. However, germination percentage didn’t show discrimination over any moisture conditions. Principal component analysis showed positive connection of root length and root: shoot ratio towards 60% FC. While strong connection was observed between shoot length, fresh weight, dry weight and root number towards 100% FC and saturated condition. It was found that, Manamurey showed better performance under all studied traits but more insightful result can be obtained by further assessing at vegetative and reproductive stage respectively.

rice

landraces

drought stress

seedling stage

correlation

Rice is the major staple food of Nepal. Regarding acreage and yield, rice surpasses all other cereal crops in Nepal, ranking third globally after wheat and maize. The total area of land under rice cultivation is estimated to be 1,477,378 ha with annual production of 5,130,625 mt and productivity of 3.47 mt/ha (MOALD 2021). Rice contributes about 21% of AGDP in Nepal which is equivalent to almost 10% of national GDP (Aryal et al. 2022). Variations in altitude, geography, physical and climatic circumstances have enriched the country with immense genetic diversity in the form of landraces or traditional cultivars. Landraces are maintained and managed by the farmers in their fields for diverse purposes, including indigenous rites and beliefs, and their immense adaptability to changing conditions over time and across different environments (Bajracharya et al. 2010). Landraces are adopted to marginal to high fertile soil, drought to deep water, different planting seasons, different climatic conditions, pest and disease infestation. Districts such as Kaski, Lamjung, Tanahun, Bara and Sunsari are richer in rice diversity (Joshi 2017). Landraces possess specific traits to adapt for better local adaptation in their environment, various socio-economic and cultural values (Rayamajhi and Thakuri 2023).

From an irrigation perspective, out of the total agricultural land, 52% of the land remains rainfed, with only 48% of the land irrigated in which 39% of the land receives year-round irrigation (Irrigation Master Plan 2019). Lack of proper maintenance and associated regulatory mechanisms creates inadequate supply of irrigation in the irrigated areas of the country (Aryal et al. 2022). On average, 2500 liters of water is used, ranging from 800 liters to more than 5000 liters to produce 1 kg of rice which indicates water productivity of rice ranges from 0.6–1.6 kg/m³ in different parts of the world (Mboyerwa et al. 2021). Rice is highly sensitive to moisture stress during pre-flowering and grain-filling stages (Kandel et al. 2022). Drought stress at various growth stages may reduce the assimilates' ability to translocate to the grains, resulting in a drop in grain weight and an increase in empty grains (Moonmoon and Islam 2017). Under mild water stress, grain yield is reduced by 5–38%, and under severe water stress by 25–67% (Basha and A. 2017). Drought stress reduces plant development, affecting the root system architecture, leaf surface traits, normal plant senescence, and inhibition of stem reserves (Moonmoon and Islam 2017).

Nepal is considered one of the centers of diversity of rice (Ghimire et al. 2018). Landraces comprise a major component of Nepal's rice production system, accounting for about 70% of the country's total rice area (Amgai and Joshi 2004). Rice landraces, despite having low yield are believed to be adopted in the local environment (Rijal 2010), resistant to both biotic and abiotic stresses, and are considered reservoirs of genetic potential, whereas modern rice varieties don't hold such qualities (Tiwari et al. 2018). Preservation of local landraces helps in creating improved rice varieties and hybrids for drought tolerance. Therefore, the objective of this study is to identify local landraces adopted in drought conditions by analyzing and comparing root and shoot traits in seedling stage for its subsequent application in modern breeding programs.

Experimental site

The research was conducted in a greenhouse of the Institute of Agriculture and Animal Science, Lamjung Campus, Sundarbazar, Lamjung. The research site lies in the mid-hill region at an altitude of 610 masl, a latitude of 28.12° N, and a longitude of 84.41°E.

Climatic conditions and growing season

The research was conducted during July and August 2024.

Landraces tested

A total of 25 distinct rice landraces were tested in this experiment which were collected from the Purkot and Ghanpokhara Community Seed Bank of Tanahun and Lamjung respectively.

Experimental details

The experiment was conducted under a two-factor factorial completely randomized design.

Factor A: 25 rice landraces

Table 1 List of rice landraces tested in the experiment

Treatment	Landraces	Treatment	Landraces
T1	Kattikey	T14	Jarneli
T2	Rambilash	T15	Jungey
T3	Jhini	T16	Kamal
T4	Darmali	T17	Dalley Masino
T5	Basmati	T18	Gaurey
T6	Aangha	T19	Himali
T7	Mansara	T20	Jungey Kanchi
T8	Kalo Jhinuwa	T21	Manamurey
T9	Kalokattey	T22	Nouley Dalley
T10	Gurdo	T23	Pahele
T11	Pathijharey	T24	Biramphul
T12	Krishnabeli	T25	Seto Dalley
T13	Aapjhuttey

Factor B: Moisture condition

Table 2 Different moisture conditions tested in the experiment

S.N.	Treatment	Moisture Condition
1	D1	100% field capacity
2	D2	60% field capacity
3	S	Saturated

There were three replications of each factor. The experiment was conducted in the disposable cups of 250 ml.

Total landraces: 25

Moisture condition: 3

Total replication: 3

A total of 25×3×3 i.e. 225 disposable cups were used in the study containing two plants in each cup.

Cultivation practices

Planting media

The planting media was thoroughly prepared by mixing sand, soil (sandy loam texture), and FYM in the ratio of 1:2:1. Each cup was filled to its capacity weighing 200 g respectively.

Seeding

6 seeds were sown per cup. After 5 days of seedling establishment, only two seedlings were maintained per cup.

Water management

Three moisture conditions were maintained in the experiment (Table 2). To determine the field capacity, 200 g of soil was saturated in a cup and left to drain for 48 hours. The opening end of the cup was covered with plastic to prevent evaporation. The computation of water required for irrigation was performed in the following manner:

Weight of the disposable cups = 1.93 g

Weight of the planting media = 200 g

Weight of the oven-dried media at 72℃ for 24 hour = 159.97 g

In 200 g of planting media, the composition of soil solid was 159.97 g and that of soil water was 200-159.97 = 40.03 g

Weight of soil at FC after 48 hours = 233.92 g

Soil water at FC = 233.92 – 159.97= 73.95 g

Soil water at 100% FC = 100% of 73.95 = 73.95 g

Soil water at 60% FC = 60% of 73.95 = 44.37 g

So, the amount of water to be added at 100% FC = 73.95 – 40.03 = 33.92 g

And the amount of water to be added at 60% FC = 44.37 – 40.03 = 4.34 g

With the density of water being 1g/cc, 33.92 ml of water was added to the soil for 100% FC and 4.34 ml for 60% FC.

Each pot was watered every three days after the seedling establishment. Data collection was performed at 21 DAS with destructive sampling.

Data collection

All of the traits with their method of detection are expressed in (Table 3). Fresh plant samples were oven-dried at 70±5 for about 48 hours for the detection of dry weight (Badr et al. 2020). Germination percentage was calculated by determining the proportion of seeds that successfully germinated under controlled conditions. The following formula was used to calculate the germination percentage:

Germination Percentage (GP) = (Mamun et al. 2018).

Table 3 Observed traits description and method employed for data collection

S.N.	Parameters	Abbreviation	Detection Techniques
1	Germination percentage	GP	Recorded in percentage by using a mathematical formula.
2	Root length	RL	The distance from the base of the plant (where it connects to the plant) to the tip of the longest root and measured in centimeters using a measuring scale.
3	Shoot length	SL	The distance from the base of the plant (where it connects to the root) to the tip of the longest flag leaf and measured in centimeters using measuring the scale.
4	Root: shoot ratio	RL/SL	Dividing root length by shoot length.
5	Fresh root weight	FRW	Roots were placed on precision balance to measure their fresh weight in grams immediately after washing.
6	Fresh shoot weight	FSW	Shoots were placed on precision balance to measure their fresh weight in grams.
7	Total plant weight	TPW	FRW+FSW
8	Root dry weight	RDW	Fresh root samples were oven-dried at 70±5℃ for about 48 hours and measured their dry weight in grams.
9	Shoot dry weight	SDW	Fresh shoot samples were oven-dried at 70±5℃ for about 48 hours and measured their dry weight in grams.
10	Root number	RN	Washed roots were counted manually.

Data analysis

The observed data was entered in MS Excel (2021). Visualization of the interaction effect was done through MS Excel. Analysis of variance, mean separation, and F-test were performed at 5% level of significance. Boxplot, correlation, and principal component analysis were carried out in R (4.4.1).

Comparison of rice landraces for traits

Table 4 shows the analysis of variance for 10 quantitative traits among landraces, conditions, and the interaction between landraces and conditions at 21 days.

Table 4

Analysis of variance of studied traits
S.N.	Traits	Mean sum of square
		Landraces (DF = 24)	Condition (DF = 2)	Landraces*Condition (DF = 48)	Error (DF = 150)
1	Germination percentage	151.44^**	34.57^ns	120.73^*	81.84
2	Root length	12.16^***	203.47^***	4.68^ns	3.98
3	Shoot length	167.7^***	1299.4^***	31.3^***	12.8
4	Root: shoot ratio	0.0480^***	1.2540^***	0.0142^**	0.0078
5	Fresh root weight	0.00463^***	0.05342^***	0.00213^***	0.00042
6	Fresh shoot weight	0.01873^***	0.22429^***	0.00397^***	0.00097
7	Total plant weight	0.0378^***	0.4966^***	0.0088^***	0.0019
8	Root dry weight	0.0000640^***	0.0003217^***	0.0000242^***	0.0000111
9	Shoot dry weight	0.000358^***	0.003807^***	0.000163^***	0.000040
10	Root number	10.5^***	665.6^***	3.4^ns	2.2

*, **, and *** denote significance at 5%, 1%, and 0.1%, respectively, whereas ns denotes non-significant.

Table 5

Mean separation table for quantitative traits in twenty-five rice landraces
Factor A: Landraces
Landraces	GP	RL	SL	RL/SL	FRW	FSW	TPW	RDW	SDW	RN
Aangha	94.444^c	13.678^fgh	34.450^bcd	0.406^kl	0.140^cdef	0.246^abcd	0.386^bcde	0.017^cdef	0.047^cdefg	9.333^f
Aapjhuttey	96.296^b	15.133^bcdefg	30.733^efghi	0.503^defghi	0.094^kl	0.177^efghi	0.271^jkl	0.012^hi	0.039^j	8.278^l
Basmati	88.889^f	13.506^gh	25.144^lmno	0.580^bc	0.121^ghi	0.151^ghi	0.272^jkl	0.018^bcd	0.043^fghij	8.944ⁱ
Biramphul	81.481^h	16.928^ab	30.056^fghij	0.576^bc	0.106^hijkl	0.203^defgh	0.309^ghijk	0.015^defg	0.041^ghij	8.389^k
Dalley Masino	96.296^b	15.017^cdefg	23.239^o	0.685^a	0.101^jkl	0.128ⁱ	0.229^l	0.010ⁱ	0.032^k	7.000^r
Darmali	98.148^a	15.344^bcdefg	35.628^abc	0.453^hijkl	0.143^cde	0.260^abcd	0.403^bcd	0.021^a	0.054^ab	9.056^h
Gaurey	94.444^c	13.800^efgh	31.639^defg	0.444^hijkl	0.142^cdef	0.208^cdefg	0.350^defg	0.016^cdefg	0.039^ij	9.833^d
Gurdo	94.444^c	13.556^fgh	31.061^efgh	0.458^hijkl	0.091^l	0.209^cdefg	0.306^ghijk	0.015^defg	0.042^ghij	8.222^l
Himali	94.444^c	15.144^bcdefg	29.639^ghijk	0.547^cde	0.131^defg	0.235^bcde	0.366^cdef	0.016^cdefg	0.042^ghij	9.222^g
Jarneli	96.296^b	14.117^defgh	37.694^ab	0.393^l	0.145^bcd	0.280^ab	0.425^b	0.018^bcd	0.054^ab	10.000^c
Jhini	98.148^a	14.372^defgh	24.728^mno	0.591^bc	0.117^ghij	0.212^cdefg	0.330^fghi	0.014^efgh	0.041^ghij	7.000^r
Jungey	94.444^c	13.861^efgh	26.944^jklmn	0.551^cb	0.126^efg	0.208^cdefg	0.334^efgh	0.013^ghi	0.042^ghij	7.556^o
Jungey Kanchi	90.741^e	15.400^abcdef	27.694^ijklm	0.587^bc	0.100^jkl	0.159^fghi	0.259^kl	0.015^defgh	0.039^j	7.444^p
Kalo Jhinuwa	96.296^b	14.522^defgh	26.594^klmn	0.557^bcd	0.113^ghijk	0.146^hi	0.258^kl	0.013^ghi	0.045^defgh	7.944^m
Kalokattey	94.444^c	14.239^defgh	27.789^hijklm	0.529^cdef	0.100^jkl	0.176^efghi	0.276^ijkl	0.015^defg	0.045^efghi	7.556^o
Kamal	94.444^c	16.906^ab	28.211^hijkl	0.621^ab	0.116^ghij	0.160^fghi	0.275^ijkl	0.014^fgh	0.040^hij	7.778ⁿ
Kattikey	98.148^a	14.361^defgh	33.383^cdef	0.438^ijkl	0.105^ijkl	0.218^bcdef	0.323^fghij	0.017^bcde	0.050^bcde	9.111^h
Krishnabeli	88.889^f	14.550^defgh	27.889^hijklm	0.527^cdefg	0.125^efgh	0.166^fghi	0.291^hijk	0.013^ghi	0.039^j	8.722^j
Manamurey	88.889^f	17.250^a	38.694^a	0.462^ghijk	0.179^a	0.303^a	0.483^a	0.020^ab	0.057^a	9.944^c
Mansara	94.444^c	14.283^defgh	35.200^bc	0.419^jkl	0.163^ab	0.266^abc	0.430^ab	0.015^defg	0.051^bc	10.611^a
Nouley Dalley	92.593^d	13.689^fgh	27.856^hijklm	0.525^cdefg	0.123^fghi	0.213^cdefg	0.336^efgh	0.013^ghi	0.039^hij	9.833^d
Pahele	96.296^b	15.633^abcde	34.511^bcd	0.471^fghijk	0.121^ghi	0.233^bcde	0.354^cdefg	0.019^abc	0.053^ab	10.333^b
Pathijharey	90.741^e	13.156^h	23.889^no	0.580^bc	0.118^ghij	0.182^efghi	0.300^ghijk	0.016^defg	0.039^j	7.333^q
Rambilash	92.593^d	16.572^abc	32.950^cdefg	0.506^defgh	0.149^bcd	0.258^abcd	0.407^bc	0.016^cdefg	0.049^bcdef	9.556^e
Seto Dalley	85.185^g	15.728^abcd	33.828^cde	0.48^efghij	0.156^bc	0.248^abcd	0.405^bcd	0.021^a	0.051^bcd	9.333^f
F-test	**	***	***	***	***	***	***	***	***	***
Mean	93.259	14.829	30.377	0.515	0.125	0.210	0.335	0.0157	0.0444	8.733
LSD	0.0417	1.858	3.332	0.0665	0.019	0.0624	0.0551	0.003	0.00589	0.0838
CV (%)	9.679	13.453	11.777	13.852	16.397	31.905	17.64	21.124	14.240	1.030
SEM	0.820	0.232	0.863	0.014	0.004	0.009	0.012	0.0005	0.0012	0.216
Factor B: Moisture condition
Conditions	GP	RL	SL	RL/SL	FRW	FSW	TPW	RDW	SDW	RN
D1	93.111^a	15.086^b	31.224^b	0.504^b	0.123^b	0.205^b	0.328^b	0.016^b	0.046^b	8.033^b
D2	94.000^a	16.333^a	25.857^c	0.649^a	0.099^c	0.157^c	0.257^c	0.013^c	0.036^c	6.166^c
S	92.666^a	13.069^c	34.052^a	0.391^c	0.152^a	0.267^a	0.419^a	0.017^a	0.049^a	12.00^a
F test	NS	***	***	***	***	***	***	***	***	***
Mean	93.259	14.829	30.377	0.515	0.124	0.210	0.335	0.157	0.04441	8.733
LSD	2.912	0.643	1.154	0.023	0.006	0.021	0.019	0.001	0.00204	0.0290
CV (%)	9.679	13.452	11.777	13.852	16.397	31.905	17.647	21.124	14.240	1.030
SEM	0.391	0.950	2.403	0.074	0.015	0.031	0.046	0.001	0.004	1.719
*Interaction (AB)**	*	NS	***	**	***	***	***	***	***	NS
LSD: Least Significant Difference; CV: Coefficient of Variation; SEM: Standard Error of Mean

*, **, and *** denote significance at 5%, 1%, and 0.1%, respectively, whereas NS denotes non-significant.

^a−r means with the same set of letters are not significantly different.

Mean performance comparison of the study parameters

Germination percentage

Significant difference was observed between germination in 25 different rice landraces. Among tested landraces, the highest germination was achieved in Darmali, Jhini, and Kattikey with a germination percentage of 98.148. Whereas, the lowest germination was achieved in Biramphul (81.481%) followed by Seto Dalley (81.185%) respectively.

Root length

The interaction between landraces and moisture conditions exhibited statistical non-significance. Under the saturated condition, Biramphul displayed an extensive root length of 15.62 cm and was statistically similar to Himali (14.5), Seto Dalley (14.47), and Manamurey (14.43). Under 100% FC, Kamal exhibited a greater root length of 19.18 cm with statistical similarity to Biramphul (19.02), Manamurey (17.82), and Rambilash (17.07) respectively. Similarly, Kamal exhibited an extensive root length of 20.43 cm under drought conditions with 60% FC and was statistically consistent with the performance of Manamurey (19.5), Rambilash (18.48), and Jungey Kanchi (17.73). As for the main effects, the landraces showed highly significant differences in terms of root length. As illustrated in (Table 5), Manamurey exhibited the highest root length of 17.25 cm mean value which was statistically similar with Biramphul, Rambilash, Pahele, Kamal, Jungey Kanchi, and Seto Dalley. Substantial variability in the root length was evident between the three moisture conditions: S (13.069), D₁(15.086), and D₂(16.333). Greater root length under drought stress is similar with the findings of Kaysar et al. (2023).

Shoot length

Significant difference was observed between the shoot length in 25 different rice landraces. The interaction between landraces and moisture conditions exhibited statistical significance. Under saturated conditions, the most significant shoot length was observed in Manamurey with 43.28 cm followed by the Mansara (41.92), Jarneli (40.7), Seto Dalley (40.37), and Himali (39.25). Under 100% FC, Jarneli was found to incur the greatest shoot length with mean value of 42.65 cm which was statistically at par with Darmali (42.5), Manamurey (41.35) and Pahele (40.45). Under drought condition with 60% FC, Rambilash displayed extensive shoot length with a mean value of 32 cm which was statistically consistent with the performance of Manamurey (31.45), Mansara (30.48), and Aangha (30.42). As for the main effects, the landraces showed highly significant differences in terms of shoot length. As shown in (Table 5), shoot length was found to be highest in Manamurey with a mean value of 38.694 cm, which was statistically at par with Jarneli and Darmali. Similarly, the lowest shoot length was achieved in Dalley Masino with a mean value of 23.239 cm. Likewise, for the moisture condition, S, D_1, and D₂ exhibited highly significant differences with 34.052 cm, 31.224 cm, and 25.857 cm respectively. Patel et al. (2021) reported a decrease in shoot length in drought conditions.

Root: shoot ratio

The interaction between landraces and moisture conditions exhibited statistical significance. As demonstrated in (Fig. 4), in saturated condition Pathijharey exhibited the highest ratio with mean value of 0.502 which was statistically at par with Dalley Masino (0.488) and Biramphul (0.47). Under 100% FC, Kamal showed the highest ratio with mean value of 0.687 statistically similar with Dalley Masino (0.673), Kalo Jhinuwa (0.658), and Biramphul (0.637). Likewise, Dalley Masino exhibited the highest ratio under 60% FC with mean value of 0.893. Significant variances were observed among the landraces concerning the root: shoot ratio. The highest root: shoot ratio was recorded in Dalley Masino (0.685) whereas the lowest was in Jarneli with 0.393 respectively. Similarly, Substantial variability in the ratio was evident between the three watering conditions: S (0.391), D₁ (0.504), and D₂ (0.649). These results indicated that drought stress condition induces longer roots in the rice landraces which ultimately increase the root shoot ratio. These findings are consistent with the study of Hou et al. (2022).

Fresh root weight

Noticeable variations were identified among the different landraces concerning the fresh root weight as presented in (Table 5). The interaction between landraces and moisture conditions also exhibited statistical significance. Under saturated conditions, Seto Dalley showed the highest weight with mean value of 0.218 g which was statistically similar with Manamurey (0.208), Himali (0.206), Rambilash (0.196), and Jarneli (0.19). Manamurey exhibited the highest fresh root weight under 100% FC, with mean value of 0.201 g which was statistically at par with Darmali (0.178) and Mansara (0.173). Under drought conditions with 60% FC, Mansara showed the highest weight with mean value of 0.156 g which was statistically consistent with Kamal (0.131) and Manamurey (0.128). As illustrated in (Table 5), Manamurey showed the highest fresh root weight with 0.179 g and was statistically at par with Mansara (0.163). Likewise, for the moisture condition, S, D_1, and D₂ exhibited highly significant differences with 0.152 g, 0.123 g, and 0.099 g respectively. The findings of this experiment are consistent with Dien et al. (2017), which has indicated that plants grown in saturated conditions have more fresh root weight in contrast to drought conditions.

Fresh shoot weight

The interaction between landraces and moisture conditions exhibited statistical significance. Under saturated conditions, Manamurey demonstrated the highest fresh shoot weight with mean value of 0.396 g which was statistically significant with Himali (0.385) and Jarneli (0.37). Under 100% FC, Darmali was found to possess highest fresh shoot weight with mean value of 0.341 g which was statistically consistent with Manamurey (0.32). Similarly, at drought conditions with 60% FC, Aangha exhibited highest weight with a mean value of 0.221 g which was statistically consistent with Rambilash (0.211) and Mansara (0.203) respectively. As for the main effects, the landraces showed highly significant differences in terms of fresh shoot weight. As illustrated in (Table 5), Manamurey showed highest fresh shoot weight with 0.303 g and was statistically at par with Jarneli (0.280), Mansara (0.266), Darmali (0.260), Rambilash (0.258), Seto Dalley (0.248) and Aangha (0.246). Likewise, for the moisture condition, S, D_1, and D₂ exhibited highly significant differences with 0.267 g, 0.205 g, and 0.157 g respectively. Similarly, Saha et al. (2019) reported decrease in fresh weight of shoots in drought conditions.

Total plant weight

The interaction between landraces and moisture conditions exhibited statistical significance. Under saturated conditions, Manamurey exhibited highest total plant weight with mean value of 0.605 g which was statistically similar with Himali (0.591), Jarneli (0.56), and Seto Dalley (0.553). Under 100% FC, Manamurey demonstrated highest plant weight with a mean value of 0.521 g and was statistically consistent with Darmali (0.52). At drought conditions with 60% FC, Mansara was found to incur the highest plant weight with mean value of 0.36 g which was statistically similar with Rambilash (0.333), Aangha (0.331), and Manamurey (0.323). Regarding main effects, the landraces showed highly significant differences in terms of total plant weight, and highest plant weight was achieved in Manamurey with a mean value of 0.483 g. Substantial variability in the weight was evident between the three moisture conditions: S (0.419), D₁(0.328), and D₂ (0.257).

Root dry weight

A significant difference was observed in the root dry weight among the 25 different rice landraces. Landraces showed significant impact with the moisture conditions, as shown in (Fig. 8). Under saturated condition, Kalokattey showed highest root dry weight with mean value of 0.0218 g which was statistically similar with Seto Dalley (0.0215), Manamurey (0.0214), and Kattikey (0.0211). Manamurey exhibited highest root dry weight under 100% FC with mean value of 0.0249 g which was statistically consistent with Seto Dalley (0.0241) and Darmali (0.0215). Under drought condition at 60% FC, highest root dry weight was recorded for Pahele with mean value of 0.0205 g. As illustrated in the (Table 5), Seto Dalley and Darmali exhibited highest root dry weight with 0.021 g which was statistically similar with Manamurey (0.02) and Pahele (0.019). For the moisture condition, S, D_1, and D₂ exhibited highly significant differences with 0.017 g, 0.016 g, and 0.013 g respectively. Decline in the root dry weight with the increase in water stress is in agreement with the findings of Patel et al. (2021).

Shoot dry weight

Noticeable variations were identified among the different landraces concerning the shoot dry weight as presented in (Table 5). The interaction between landraces and moisture conditions is also statistically significant. As demonstrated in (Fig. 9), under saturated condition, Jarneli showed highest shoot dry weight with mean value of 0.0636 g which was statistically similar with Kalokattey (0.0632) and Rambilash (0.0622). Under 100% FC, Manamurey exhibited highest dry weight with mean value of 0.0713 g and was statistically similar with Darmali (0.0647). Under drought condition at 60% FC, highest shoot dry weight was recorded for Pahele with a mean value of 0.0503 g. Regarding main effects, the landraces showed highly significant differences in terms of shoot dry weight. As illustrated in (Table 5), shoot dry weight was found to be highest for the landrace Manamurey with 0.057 g and was statistically at par with Darmali (0.054), Jarneli (0.054) and Pahele (0.053). For the moisture condition, S, D_1, and D₂ exhibited highly significant differences with 0.049 g, 0.046 g, and 0.036 g respectively. Saha et al. (2019), Patel et al. (2021) reported a significant reduction in the dry matter of shoots when exposed to stress conditions.

Root number

A significant difference was observed in the root number among the 25 different rice landraces. Landraces showed a significant impact with the moisture conditions, as shown in (Fig. 10). The most significant root number was observed in Aangha with mean value of 14.33 in saturated condition, Mansara with 10.83 in 100% FC, and Pahele with 8.83 in drought condition with 60% FC. Among the 25 rice landraces, highest root number was observed in Mansara with 10.611 and was significantly different from other landraces. On the contrary, lowest root number was observed in Dalley Masino and Jhini with mean value of 7.00. Substantial variability in the ratio was evident between the three watering conditions: S (12.00), D₁(8.033), and D₂ (6.166).Kaysar et al. (2023) reported prominent number of roots in rice landraces under saturated conditions.

Correlation between the traits

Correlation (r-value) was determined using a correlation matrix to identify the inter-relationship of studied traits. The significant r values among different traits varied from 32, 29, and 25 under Saturated, 100% FC, and 60% FC respectively.

Root length showed a significant positive correlation with root: shoot ratio (0.49, 0.54) under saturated and 100% FC while, a positive correlation was observed with shoot length (0.32), root dry weight (0.29), and root number (0.33) under saturated condition and fresh shoot weight (0.25) and root dry weight (0.32) in 100% FC. Under 60% FC, it showed a positive correlation with shoot length (0.36), root: shoot ratio (0.31), fresh root weight (0.36), and total plant weight (0.26). However, no significant correlation was observed for root length and root number in 100 and 60% FC. Notably, shoot length scored a highly significant positive correlation with all the traits in a saturated condition, except fresh root weight in 100% FC and root dry weight in 60% FC. Likewise, root: shoot ratio was negatively correlated with shoot length under all conditions (-0.65, -0.77, -0.75). There was a significant correlation observed between root: shoot ratio with fresh shoot weight (-0.27, -0.47, -0.45) including shoot dry weight (-0.40) under saturated condition, total plant weight (-0.42) under 100% FC and root number (-0.38) in case of 60% FC. However, a negative correlation was observed with fresh root weight (-0.27), total plant weight (-0.36), root dry weight (-0.27, -0.25), and root number (-0.28, -0.30) under saturated condition and 100% FC while, total plant weight (-0.34) and shoot dry weight (-0.27, -0.32) under 60% FC and 100% FC respectively. Fresh root weight was significantly correlated with all the traits except root length in saturated condition and root length, shoot length, and root: shoot ratio in 100% FC along with root dry weight and root: shoot ratio under 60% FC condition. Fresh shoot weight showed significant positive correlation with total plant weight (0.98, 0.91, 0.91), root dry weight (0.47, 0.57), shoot dry weight (0.46, 0.58, 0.37), and root number (0.58) under saturated, 100% FC and 60% FC respectively. There was a positive correlation of total plant weight with root dry weight (0.48, 0.51), shoot dry weight (0.45, 0.58, 0.38), and root number (0.59, 0.23, 0.24). Root dry weight showed a highly significant correlation with shoot dry weight (0.65, 0.75, 0.67) under all conditions while a positive correlation was observed with root number (0.43, 0.24) under saturated and 60% FC conditions. Shoot dry weight positively correlated with root number (0.26, 0.24, 0.27) at all moisture conditions.

Principal Component Analysis

25 rice landraces were studied through principal component analysis (PCA) biplot, where the landraces and traits are mapped based on their relationships across the first two principal components (PC1, PC2). Under the 60% FC, both axes of components (PC1 and PC2) explain a significant portion of the variance in the dataset with an eigenvalue greater than one. PC1 accounted for 46.7% while, PC2 accounted for 17.4%, totaling 64.1% of the variability (Supplementary Table 1). Furthermore, traits like RL, SL, FRW, FSW, TPW, and RN are highly associated with PC1 whereas, RDW and SDW are highly associated with PC2 (Supplementary Table 4). Thus, interpreting (Fig. 18), Jungey Kanchi, Basmati, and Kamal are landraces located far from the origin, representing extreme or distinct characteristics compared to other landraces. Likewise, traits like FRW, TPW, FSW, RN, and SL are closely aligned, indicating that these traits are strongly and positively correlated.

Similarly, under 100% FC and Saturated conditions, both components explain a total of 67.7% and 70.9% of the variability in the dataset with an eigenvalue greater than one (Supplementary Table 1). Under 100% FC, traits RL, SL, FRW, FSW, TPW, RDW, and SDW are highly associated with PC1 whereas, GP and RN are highly associated with PC2 (Supplementary Table 3). Likewise, under saturated condition, all traits except GP and SDW are associated with PC2 (Supplementary Table 2). Thus, interpreting (Fig. 16,17) traits like RDW, SDW, FRW, FSW, TPW, RN, and SL are aligned closely suggesting a significant positive correlation. This implies that landraces with higher shoot and root weight also tend to have higher root numbers and shoot length. Under all conditions, RL/SL have arrows pointing opposite to FSW, TPW, and SL, indicating a negative correlation which means that landraces with higher shoot biomass (FSW, TPW, SL) tend to have lower root-to-shoot ratios. Interestingly, RL and RN have almost perpendicular arrows, indicating little to no correlation between these two traits (Fig. 17).

Similarly, the PCA-Biplot shown in (Fig. 19), indicates the contribution of traits to overall variation in the dataset. PC1 explains more than half (53.8%) of the variance in the data while PC2 explains a smaller portion of the variance (12.7%) in the dataset. Together, PC1 and PC2 account for 66.5% of the total variation. Unlike the above PCA Biplots, traits like RDW, SDW, FRW, FSW, TPW, SL, and RN suggest a strong correlation between them. Likewise, RL/SL and RN were directed towards opposite directions, indicating a negative correlation between them. Traits with an arrow directed toward the environment indicate a strong association with that tested environment. Therefore, RL and RL/SL are directed towards the D2 condition (60% FC), suggesting these traits have a positive association with 60% FC. Traits like: RDW, SDW, FRW, TPW, FSW, SL, and RN are directed towards D1 (100% FC) and saturated condition suggesting these traits have a positive association with this moisture condition. However, GP falls in all moisture conditions which indicates that GP doesn’t have discrimination over any moisture conditions.

The performance of rice landraces for drought-tolerant traits may be best assessed by analyzing the presence of variability among the tested landraces with the evaluated traits. Numerous experts across the globe have conducted similar studies on rice landraces and have found agro-morphological diversification linked to vegetative traits (Mishra et al. 2018; Ndikuryayo et al. 2023). In nearly every trait under study, the ANOVA findings showed significant differences between the genotypes under S, D1, and D2 conditions that suggest evident indication of genetic variability among the landraces under study.

Under saturated conditions, highest shoot biomass (FSW, TPW, SL) can be attributed to the ability of plants to uptake more nutrients as they are more readily available in moist conditions, allowing for optimal growth of shoot (Ros et al. 2003). According to (Fig. 9), the findings indicated that under saturated condition, Manamurey incurred highest values of shoot fresh weight than other landraces. The observed outcome may derive from the enhanced capacity of the rice landrace to absorb water and nutrients, as well as their increased stomatal conductance, which in turn leads to enhanced photosynthesis (Kamarudin et al. 2018). However, it has been demonstrated that in drought condition, chemical and hydraulic signals transmitted from the drying roots to the shoots undertake to regulate stomatal closure, resulting in lower CO2 assimilation and net photosynthetic rates (Hasanuzzaman et al. 2013). This could be attributed for the reduction in shoot biomass in drought condition. The results were similarly consistent with those of (Zubaer et al. 2007), who noted that rice shoot dry matter of Aman rice genotypes decreased as water stress increased.

Different rice genotypes exhibit varying responses to drought stress, with some showing enhanced root growth as a strategy to avoid drought. Water stress influences roots to grow toward areas with higher water content inducing to form a deeper and thinner root system and increasing the total absorption surface area favoring the uptake of water and nutrients (Kou et al. 2022). Hassan et al. (2023) concluded that extreme drought can limit secondary root growth and cause primary roots to become thicker but less branched, resulting in fewer overall roots. Deeper root system is facilitated with the rise in abscisic acid concentration in the roots (Panda et al. 2021). Signaling of ABA during water stress leads to the auxin biosynthesis modifying root morphology and root system architecture ensuring water uptake (Kalra et al. 2024). Drought stress condition may induce longer roots in the rice landraces which ultimately increase the root shoot ratio (Hou et al. 2022).

Studies have shown that different rice genotypes exhibit varying levels of DRO1 expression that plays significant role in down streaming of auxin signaling, which is crucial for root development and gravitropic responses when subjected to drought stress (Uga et al. 2015; Zubaer et al. 2007). The increase in root length under drought conditions may be attributed to the enhanced expression and functional variations of the DRO1 gene (Uga et al. 2013).

Root: shoot ratio (RL/SL) is an important indicator of drought tolerance (Xu et al. 2015). A higher root: shoot ratio indicates that a plant allocates more resources to its root system to exploit available moisture. When drought stress occurs, plants often reduce shoot growth to conserve resources for root development (Takahashi et al. 2020). This mechanism can help to maintain water uptake efficiency, as roots continue to grow and explore for moisture even when above-ground growth slows down (Kou et al. 2022). As per (Fig. 4), the highest value of root: shoot ratio was observed in Dalley Masino at 60% FC. Varieties with a favorable root: shoot ratio generally exhibit better yield performance and maintain physiological stability under drought conditions (Hassan et al. 2023). Ultimately, optimizing the RL/SL can be a crucial strategy for breeding programs aimed at enhancing drought resilience in various crop species.

Plants may initially increase root biomass to seek moisture; however, as they adapt to prolonged stress, they may allocate more resources toward maintaining existing shoots rather than expanding roots further (Sainju et al. 2017). This shift can lead to an overall increase in shoot biomass with a corresponding decrease in the root biomass. This adaptive strategy highlights the complex balance must be maintained between root and shoot growth in response to environmental stresses (Numajiri et al. 2024).

A negative correlation was observed between shoot biomass (FSW, TPW, SL) and root: shoot ratio in the landraces under study. This could be reasoned with altered carbohydrate partitioning exhibited by rice seedlings, favoring either root growth over shoot growth or vice-versa (Bui et al. 2019). It is reported that drought stress may lead to a significant increase in the proportion of soluble sugars and starch in roots, while the amount in stems decreased proportionally as a result of an increase in the activity of root invertase and leaf sucrose-phosphate synthase (Xu et al. 2015). This trade-off of resources may favor roots to improve water uptake efficiency and enhance root: shoot ratio.

The results concluded that certain cultivars develop elongated roots without a corresponding increase in root number, thus demonstrating a negative correlation. This elongation occurred at the expense of root number, as resources are diverted towards extending existing roots rather than producing new ones which could be depicted as the adaptive mechanism by certain genotypes under drought conditions (Yang et al. 2022). Deeper roots can penetrate lower soil layers where moisture may still be available, thus optimizing water absorption and enhancing drought resistance (Huang et al. 2019; Shafi et al. 2023).

Several QTLs were identified as common between root and shoot traits, indicating a genetic basis for their interdependence, particularly suggesting the correlation between root and shoot weight for two subgroups indica and japonica (Zhao et al. 2019). The presence of pleiotropic QTLs suggests that certain genetic factors regulate the growth of both root and shoot systems simultaneously, promoting coordinated biomass accumulation. These QTLs may explain the positive correlation exhibited by root and shoot weight by different landraces under study.

For assessing drought-tolerant landraces, longer root length (Zhao et al. 2019; Kim et al. 2020), less reduction in shoot length (Islam et al. 2022; Das et al. 2024) and higher root: shoot ratio (Hussain et al. 2022) suggest that rice is capable of thriving in water-scarce conditions. Taking into account root and shoot traits, Manamurey emerges as a robust competitor under drought conditions.

Given that this study focuses primarily on the seedling stage, it is also essential to place considerable emphasis on reproductive traits. Furthermore, the selection of drought-tolerant genotypes should consider various other factors, such as biochemical responses, environmental conditions, anatomical changes, extensive field trials, and overall plant performance. While Manamurey demonstrates great potential, more thorough testing under real-world conditions is necessary. Additionally, future research should concentrate on its ability to withstand reproductive drought, which would be valuable for advancing its use in breeding programs aimed at enhancing drought resistance

Drought stress greatly affects the growth and development of rice roots and shoots, both of which play a vital role in the plant's ability to adapt drought conditions. Therefore, it’s necessary to explore the inherent genetic potential of existing germplasm which shows promising performance under drought conditions in seedling stage. Assessing the performance of landraces under the seedling stage is one of the primary steps in developing drought-resistant cultivar after evaluation in vegetative and reproductive stage successively. This study shows a significant difference among the landraces for different root and shoot traits. Similarly, significant difference was observed among the performance of landraces in different moisture conditions. Among studied traits, the mean value of root length and root: shoot ratio was observed highest in drought condition with 60% FC. However, other traits like shoot length, fresh weight, dry weight and root number were highest mean value under saturated condition. Likewise, drought condition with 60% FC, showed positive correlation between root length and shoot length. While 100% FC and saturated condition showed positive correlation between shoot length, fresh root weight and root number. Notably, negative correlation was observed between root: shoot ratio and root number under drought condition. Furthermore, principal component analysis showed that root length and root: shoot ratio showed strong connection towards drought condition with 60% FC while shoot length, fresh weight, dry weight and root number showed strong connection towards 100%FC and saturated condition. Interestingly, germination percentage didn’t show discrimination over any moisture conditions. These findings can be used as selection criteria for drought stress conditions to develop better drought tolerant cultivar however, further study on their tolerance ability in vegetative and reproductive stage would be more fruitful.

Author Contribution

All the authors contributed equally in conception and design of experiment, data collection, data analysis, and manuscript writing. All authors read, proofread, and approved the submitted version and agreed to publish the final version of the manuscript.

Funding

No funding or financial support was received during the experiment and preparation of this manuscript.

Acknowledgment

The authors are grateful towards Purkot and Ghanpokhara community seed bank for providing the rice landraces for research. In addition, deeply indebted towards Institute of Agriculture and Animal Science, Lamjung Campus for providing research materials during research period.

Conflict of interest

The author declares no conflict of interest.

Data Availability

The datasets generated and analyzed during the current study are not publicly available due to privacy reasons but are available from the corresponding author on reasonable request.

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No competing interests reported.

SupplementaryTable.docx

Conferring Drought Tolerance in Rice Landraces Using Seedling Indices

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Version 1

Abstract

Figures

Introduction

Materials and methods