Yield Based Drought Tolerance Indices Used To Identify Drought Resilient Rice Genotypes In Gokuleshwor, Baitadi, Nepal

¹Department of Plant breeding, Tribhuvan University, Institute of Agriculture and Animal Science, Gokuleshwor Agriculture and Animal Science College, Baitadi, Nepal

Janak Adhikari^1*, Anka Kumari Limbu¹, Arati Dhami¹, Sangita Hamal¹, Arpana Joshi¹, Bishnu Prasad Kandel*²

²Department of Plant breeding and genetics, Tribhuvan University, Institute of Agriculture and Animal Science, Lamjung Campus, Lamjung, Nepal

*Correspondence: bkandel33@gmail.com

Abstract

Drought stress is a critical challenge to rice production, necessitating the development of drought-tolerant genotypes. This study aimed to evaluate 25 rice (Oryza sativa L.) genotypes for drought tolerance under rainfed and irrigated conditions at, Baitadi, Nepal during June–October 2022, using an alpha lattice design. The experiment aimed to identify drought-tolerant genotypes and effective drought tolerance indices based on grain yield. Drought stress, induced at the reproductive stage, reduced yields by an average of 24.2% (range: 9.6–53.3%). Mean Productivity (MP) and Geometric Mean Productivity (GMP) emerged as the most reliable indices, showing strong positive correlations with yield under both conditions. Principal Component Analysis (PCA) and drought tolerance indices (TOL, MP, GMP, STI, YSI, SSI) identified CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B as the most drought-tolerant genotypes, with high MP and GMP values and low stress susceptibility. These genotypes are potential candidates for breeding programs in the mid-hills of Nepal, supporting sustainable rice production under water-limited conditions.

Keywords:

Drought stress

stress-resilient

Introduction

Rice (Oryza sativa L.) is a staple crop for approximately one-third of the global population, with Asia accounting for around 90% of its production and consumption (Fukagawa & Ziska, 2019). However, climate change poses significant challenges to rice cultivation, particularly through altered precipitation patterns and increased drought frequency, which threaten water availability and yield stability. Rising temperatures, especially elevated night temperatures, exacerbate these impacts by shortening phenological stages and reducing grain yields (Peng et al., 2004). The extreme climate conditions model indicates that 1% rise in the occurrence of days with extreme variations in rainfall leads to a reduction of 0.28% in rice production (Rayamajhee et al., 2021). In rainfed systems, drought is the primary abiotic stress, causing yield reductions of 13–35% in moderate conditions (Tiwari et al., 2019; Kandel et al., 2022) and up to 65–85% in severe cases (Kumar et al., 2012). These losses are particularly critical in vulnerable regions like Nepal, which is ranked fourth globally in climate change vulnerability, and where only 28% of cultivated land benefits from year-round irrigation (Poudel et al., 2020).

In Nepal’s mid-hills, including Baitadi, rice farming relies heavily on monsoon rainfall, with terraced fields often lacking supplemental irrigation. This results in erratic water availability, leading to an average grain yield of 2.34 t ha⁻¹, below the national average of 2.71 t ha⁻¹ (Kandel et al., 2022). Drought stress, compounded by heat waves, increases pollen sterility and unfilled grains, further limiting productivity (Hussain et al., 2021). Previous studies in Nepal’s mid-hills have screened released rice varieties for drought tolerance using indices like Mean Productivity (MP) and Geometric Mean Productivity (GMP) (Adhikari et al., 2018; Kandel et al., 2022). However, evaluating unreleased pipeline varieties from the International Rice Research Institute (IRRI) in site-specific conditions, such as Baitadi, remains underexplored, presenting a critical gap for enhancing local adaptation. Tolerance level (TOL), mean productivity (MP), geometric mean productivity (GM), stress tolerance index (STI), and stress susceptibility index (SSI) are the various drought indices used to assess a cultivar's drought tolerance. In rice, cultivars with high STI values and low SSI and TOL are regarded as drought-tolerant (Adhikari et al. 2019). The best indices for assessing drought tolerance are those that have high MP, GMP, and STI (Hooshmandi 2019).

Previous research has identified several drought-tolerant rice genotypes.Kandel et al. (2022) evaluated 14 rice genotypes were evaluated under both drought stress and non-stress and identified NR 119 is followed by Chaite 5 and Chaite 4 as a drought-tolerant genotype. There are some drought tolerant rice varieties (DTR) such as Sukhadhan-1, Sukhadhan-2 and Sukhadhan-3 released in 2011 and Sukhadhan-4, Sukhadhan-5 and Sukhadhan-6 were released in 2014. These were released for targeting rainfed low land condition from terai to lower hills upto 1000 masl (Dhakal et al., 2020). Rai et al. (2023) thirty drought-tolerant spring rice (Oryza sativa L.) genotypes using various drought tolerant indices at inner plain region and reported that genotype IRE16L1661 is stable under drought conditions based on drought tolerance indices. Previous studies have focused on released cultivars and mostly evaluated in inner plain area

In order to address this, the current research accessed pipelines rice genotypes originated from IRRI and applying multiple drought tolerance indices, we sought to identify drought resilience rice genotypes for mid-hill growing condition at Baitadai, thereby contributing to sustainable rice production in drought-prone regions.

Materials and Methods

Research site and rice genotypes

An experiment was carried out at the agronomy farm of Gokuleshwor, Baitadi, having 29⁰ 58′ 25” N latitude, 80⁰ 31′ 43” E longitude, at a height of 850 masl, from 27th of June to 12th of October, 2022. Agroclimatic details of the experiment site are shown in (Fig. 1). The experiment consists of twenty-five genotypes, among which twenty-three were pipeline varieties that originated from IRRI, Philippines and two were check varieties. The rice genotypes utilized in the experiment, along with their details, have been provided in table (1).

Fig. 1

Climatic details of research area during the crop period (Jun to Oct) in 2022

Table 1
List of rice cultivars used in research.
S.N.	Genotypes	Remarks
1	CVT BOROSVIN-312	Pipeline cultivar
2	IR 129077-2-1-21-8-B	Pipeline cultivar
3	RAVI DROUGHT FR 129077-1-1-12-7-B	Pipeline cultivar
4	Rato Basmati	Released Variety
5	RAVI-DROUGHT IR 80991-B-330-0-1	Pipeline cultivar
6	Juwa Basmati	Released Variety
7	CVT SPRING IR 86515-1-19-2-1-1-1	Pipeline cultivar
8	Sukha Dhan-3**	Released Variety
9	CVT PIGMENTED-121-121	Pipeline cultivar
10	RAVI-N IR 96321-1447-651-B-1-1-2	Pipeline cultivar
11	CVT BORO-IR16L1726	Pipeline cultivar
12	RFFT-N NR 2184	Pipeline cultivar
13	Ravi control IR 129077123 1-1-55-8-B 83	Pipeline cultivar
14	RAVI DROUGHT IR 129077-2-1-42-5-B	Pipeline cultivar
15	RAVI TARAHARA-105	Pipeline cultivar
16	CFFT-FAR IVR 21841871	Pipeline cultivar
17	RC IR 1611795	Pipeline cultivar
18	RAVI DROUGHT IR 129077-2-1-7-8B	Pipeline cultivar
19	IR 129077:2-1-36-8-B	Pipeline cultivar
20	CVT BORO IR 1621226	Pipeline cultivar
21	CVT BORO 1621004	Pipeline cultivar
22	CFFT-SPRING IR 10L192	Pipeline cultivar
23	CVT SPRING IR 17L1317	Pipeline cultivar
24	Kaljadey*	Local landraces
25	Hardinath-1	Released variety

*,** Local check and standard check respectively (Kaljadey is a popular landraces in the research area, whereas Sukha Dhan-3 is one of the promising drought-tolerant variety released by NARC and recommended for drought condition).

Experimental field layout and crop management

The field experiment was laid out in alpha-lattice design with 10 blocks and two replications (two environmental conditions each) (5 genotypes in each block). Nursery seeding took place on May 24. 24 hours before sowing, the seeds were initially treated with Bavistin at a rate of 2g kg^− 1 of seeds. Rice seedlings were cultivated in a dry seedbed for 25 days before being transplanted into well-puddled soil with a 20cm x 20cm spacing and a 3m× 2m plot size. A total of 6 tons ha^− 1 of farmyard manure (FYM) and 120:40:40 kg NPK ha^− 1 of chemical fertilizer were applied to both irrigation and drought-prone regions. Half of the nitrogen dosage was given during field preparation, along with full phosphorus, potassium, and FYM doses. The other half of the dosage was divided between the tillering and panicle initiation stages. After transplantation, water was left on the field for a month to allow healthy establishment of the crop during the vegetative stage, and no further irrigation was given. At the start of the reproductive stage, extra water was removed from the field by implementing a drainage system, and drought conditions were maintained. This was done in a rainfed field maintained as a drought-stressed field.

Drought tolerance indices and statistical analysis

To distinguish genotypes based on drought response in terms of grain yield, drought tolerant indices based on grain yield tons ha^− 1 for irrigated (Yp) and drought stress (Ys) conditions for each genotype were computed using the formula presented in table (2). MS Excel (version 19) was used to record the data, and R (4.0.5) was utilized to analyze it. R Studio was used to determine the association between drought indices and yield under stress and non-stress conditions. Principal components analysis examined the statistical relationship between genotypes under stress using R Studio.

Table 2
Drought tolerance indices used in the study
Indices	Formula	References
Tolerance index (TOL)	TOL = Yp-Ys	(Rosielle & Hamblin, 1981)
Mean productivity index (MP)	MP=(Yp + Ys)/2	(Adhikari et al., 2019; Kandel et al., 2022)
Geometrical mean productivity (GMP)	GMP= (Yp*Ys )1/2	(Adhikari et al., 2019)
Stress tolerance index (STI)	STI = Yp × Ys/(Yp)²	(Fernandez 1992; Anwaar et al. 2020)
Stress susceptibility index (SSI)	SSI = 1-(Ys/Yp)/SI while SI = 1 − (Ys/Yp)	(Fischer & Maurer, 1978)

Results and Discussion

Drought tolerant indices and grain yield under stress and normal conditions

In table (3), all six drought tolerance indices were analyzed and presented. The tolerance indices MP and GMP were noted highest on genotype CFFT-SPRING IR 10L192 (3.44, and 19.11 respectively) with the highest yield under both environmental conditions Yp and Ys. In TOL, the highest was recorded in genotype Ravi control IR 129077123 1-1-55-8-B 83 (1.78) so this leads to deterioration in the yield of rice. Whereas the STI and YSI were found maximum on genotype CVT SPRING IR 17L1317 and Sukha Dhan-3 (0.90). The highest SSI (2.08) and maximum yield loss (50.66%) were obtained on genotype Ravi control IR 129077123 1-1-55-8-B 83. In contrast, the grand mean yield under stress and non-stress conditions were discovered to be, respectively, 2.3 and 3 tonsha^− 1. The yield loss has an average of 24.2% and ranges from 9.6% to 53.3%. Our findings are by those of(Kandel et al., 2022), drought conditions reduced yield by an average of 27%. In drought-affected conditions, 12–46% of grain yield was lost (Ouk et al., 2006); and the yield reduced by 9–51%(Pantuwan et al., 2004).

Table 3
Mean Performance of twenty-five rice genotypes using various drought tolerant indices
Genotype	Ys(tons ha^− 1)	Yp(tons ha^− 1)	MP	TOL	GMP	STI	YSI	SSI	Yield loss (%)
CFFT-FAR IVR 21841871	2.20	2.60	2.40	0.40	2.39	0.64	0.85	0.66	13.33
CFFT-SPRING IR 10L192	4.10	4.80	4.45	0.70	4.44	2.19	0.85	0.63	23.33
CVT BORO 1621004	2.10	2.60	2.35	0.50	2.34	0.61	0.81	0.82	16.67
CVT BORO IR 1621226	3.30	4.00	3.65	0.70	3.63	1.47	0.83	0.75	23.33
CVT BORO-IR16L1726	1.70	2.20	1.95	0.50	1.93	0.42	0.77	0.97	16.67
CVT BOROSVIN-312	1.90	2.30	2.10	0.40	2.09	0.49	0.83	0.75	13.33
CVT PIGMENTED-121-121	1.40	1.70	1.55	0.30	1.54	0.26	0.82	0.76	10.00
CVT SPRING IR 17L1317	2.80	3.10	2.95	0.30	2.95	0.96	0.90	0.41	10.00
CVT SPRING IR 86515-1-19-2-1-1-1	2.30	3.10	2.70	0.80	2.67	0.79	0.74	1.11	26.67
Hardinath-1	2.10	3.10	2.60	1.00	2.55	0.72	0.68	1.38	33.33
IR 129077-2-1-21-8-B	1.20	2.70	1.95	1.50	1.80	0.36	0.44	2.38	50.00
IR 129077:2-1-36-8-B	1.90	2.60	2.25	0.70	2.22	0.55	0.73	1.15	23.33
Juwa Basmati	2.20	2.90	2.55	0.70	2.53	0.71	0.76	1.03	23.33
Kaljadey (Local)	2.50	3.70	3.10	1.20	3.04	1.03	0.68	1.39	40.00
Rato Basmati	3.00	3.90	3.45	0.90	3.42	1.30	0.77	0.99	30.00
Ravi control IR 129077123 1-1-55-8-B 83	1.70	3.40	2.55	1.70	2.40	0.64	0.50	2.14	56.67
RAVI DROUGHT FR 129077-1-1-12-7-B	3.10	3.70	3.40	0.60	3.39	1.27	0.84	0.70	20.00
RAVI DROUGHT IR 129077-2-1-42-5-B	2.70	3.60	3.15	0.90	3.12	1.08	0.75	1.07	30.00
RAVI DROUGHT IR 129077-2-1-7-8B	1.50	1.70	1.60	0.20	1.60	0.28	0.88	0.50	6.67
RAVI TARAHARA-105	2.10	2.30	2.20	0.20	2.20	0.54	0.91	0.37	6.67
RAVI-DROUGHT IR 80991-B-330-0-1	3.20	3.90	3.55	0.70	3.53	1.39	0.82	0.77	23.33
RAVI-N IR 96321-1447-651-B-1-1-2	1.20	2.10	1.65	0.90	1.59	0.28	0.57	1.84	30.00
RC IR 1611795	1.80	3.10	2.45	1.30	2.36	0.62	0.58	1.80	43.33
RFFT-N NR 2184	2.00	3.00	2.50	1.00	2.45	0.67	0.67	1.43	33.33
Sukha Dhan- 3 (std check)	3.00	3.40	3.20	0.40	3.19	1.13	0.88	0.50	13.33
CV (%)
F-test
Grand Mean	2.30	3.00	2.65	0.74	2.61	0.82	0.75	1.05	24.67

*Significant at P-value ≤ 0.05, **Significant at 0.01 level, Ys: yield under stress, Yp: yield under non-stress, MP: mean productivity, TOL: Tolerance index, GM: geometric mean productivity, STI: stress tolerance index, YSI: yield stability index, SSI: stress susceptibility index.

Correlation of drought indices

A correlation study of grain yield and various drought tolerance indices can be useful criteria for choosing the optimal genotypes and stress tolerance indices shown in figure (2). Ypwas found to be positive and significant association with Ys, MP, and GMP. The study ofKandel et al.(2022) has also found a similar result. In earlier studies Mousavi et al. (2008);Dadbakhsh et al. (2011)and Bennani et al. (2017), reported that Yp and Ys were positively correlated. The association of Yp was non-significant and negative with YSI and STI. Ys showed a positive and statistically significant relationship with STI and YSI. Adhikari et al. (2019) reported similar findings. The SSI and YL have shown significant and negative correlation with Ys whereas, in the case of Yp, there was a non-significant and positive correlation with Yp. This suggested that the selection based on SSI and YL increased yield under Yp. Due to the positive correlation of Yp with SSI and TOL,the selection based on SSI and TOL will result in increased yield under Yp(Sio-Se Mardeh et al., 2006). The MP was found to be highly positive correlation with the yield at both conditions. A similar result was obtained byKandel et al. (2022), that the GMP, MP, and SSI were positive and significantly correlated with yield. A positive correlation between yield under stressed and non-stressed conditions has also been reported in earlier studies (Saeed et al., 2008; Ahmadizadeh et al., 2011; Bennani et al., 2017). The YSI and STI have high significance levels and negative correlations with TOL, SSI, and YL. In our experiment, MP and GMP were identified as pertinent indices to select stress tolerance genotypes as these indices haveshowna highly positive correlationwith grain yield (Rahimi et al., 2013; Bhandari et al., 2020)under both drought and irrigated conditions. So, to select high-yielding lines, MP and GMP can be used in functional rice breeding programs.

Fig. 3

Correlations between drought indices of rice

Principal Component Analysis

The cumulative variance of the first two main components exceeded 99%, with an eigenvalue larger than one. The first principal component analysis (PCA) accounted for 59.6% of the total variance in Yp, Ys, STI, MP, GMP, and STI. Thus, the first component might be labeled yield potential and stress tolerance, whilst the second PCA accounted for 39.9% of total variability. Drought tolerance indicators and genotype response were studied using principal component analysis. MP and GMP exhibited significantly positive association with the yield of grain under Yp and Ys, implying that selecting based on these indices will result in higher grain yield in both situations.

Similarly, STI and YSI were the two indices that were highly positive with Ys, hence, the increasing grain yield in condition will result based on the selection of these indices. The SSI and YL were highly correlated in a positive direction, showing a close association aligned with the genotypes. The SSI, YL, and TOL indices were positively associated with Yp and Ys. The genotypes CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B were located between Yp and Ys and the indices between MP and GMP. The CVT SPRING IR 17L1317, CVT BORO 1621004, CFFT-FAR IVR 21841871, CVT BOROSVIN-312, RAVI TARAHARA-105, CVT PIGMENTED-121-121 and RAVI DROUGHT IR 129077-2-1-7-8B had considerable correlation with STI, YSI, YL, SSI and TOL figure (3). While, the genotypes Juwa Basmati, IR 129077:2-1-36-8-B, RAVI-N IR 96321-1447-651-B-1-1-2, and CVT BORO-IR16L1726 by MP, and GMP as well as the genotypes Ravi control IR 129077123 1-1-55-8-B 83, Kaljadey, RC IR 1611795, Hardinath-1, IR 129077-2-1-21-8-B, CVT SPRING IR 86515-1-19-2-1-1-1, and RFFT-N NR 2184 by SSI, YL, TOL, STI and YSI were identified as responsive to drought. In contrast, the rest genotypes were recognized as genotypes with moderate drought tolerance. While screening drought-tolerant genotypes using the drought tolerance indices and multivariate analysis, CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B genotypes were genotypes with the highest drought tolerance.

Similar findings were reported by Jeyaprakash et al. (2016) and Rahimi et al. (2013)in drought tolerance of rice, among all other indices, YI, GMP, MP, STI, MPI, and HM emerged as the top stress indicators for identifying drought-tolerant genotypes demonstrated by biplot graph.

Fig. 4P

CA biplot showing drought tolerant indices and rice genotypes

Conclusion

According to the study's findings, water stress dramatically lowers rice output across all cultivars throughout the critical growth period of the crop. It also suggests that selecting cultivars with better drought tolerance and greater production potential and stability can be done effectively with the use of drought tolerance index selection. The CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B rice genotypes were found to have a high level of drought tolerance based on drought tolerance indices. These genotypes also displayed low SSI and TOL with high STI values. These genotypes fared better against drought stress and produced the maximum yield in irrigated situations. Consequently, these rice varieties resistant to drought can be better substitutes for drought-prone belts for rice cultivation.

Acknowledgments

The authors were thankful to the Directorate of Agricultural Research, Tarahara, Koshi Province, Sunsari for providing genetic materials used in the study.

Consent to participate

Not applicable

Author Contribution

Janak Adhikari: Conceptualization, proposal development, methodology, analysis, resources, writing-original draft.Anka Kumari Limbu: Data curation, investigation, review, writing-original draft.Arati Dhami: Methodology, analysis, writing-original draft.Sangita Hamal: Data collection, review and editingArpana Joshi: Review and editing, analysis.Bishnu Prasad Kandel: Supervision, data management, statically analysis and reviewing editing.

Anka Kumari Limbu: Data curation, investigation, review, writing-original draft.

Arati Dhami: Methodology, analysis, writing-original draft.

Sangita Hamal: Data collection, review and editing

Arpana Joshi: Review and editing, analysis.

Bishnu Prasad Kandel: Supervision, data management, statically analysis and reviewing editing.

Competing statement

The authors state that they do not have any competing interests.

Ethical statement

This article does not include any studies involving human or animal subjects conducted by any of the authors.

Funding

No funding was received.

Consent to Publish

declaration

Not applicable

Data Availability

All data generated or analysed during this study are included in this published article

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Yes