Isolating higher yielding and more stable rice genotypes in stress environments: fine-tuning a selection method using production and resilience score indices

doi:10.15302/J-FASE-2023521

2024, Vol. 11

Issue (1) : 169-185 https://doi.org/10.15302/J-FASE-2023521

Isolating higher yielding and more stable rice genotypes in stress environments: fine-tuning a selection method using production and resilience score indices

Arnauld THIRY(

), William J. DAVIES, Ian C. DODD

Lancaster Environment Centre, Lancaster University, Lancaster, Lancashire LA1 4YW, UK

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Abstract

● Score index methods readily discriminate genotypes adapted to a target environment.

● New quantitative method evaluated productivity and resilience of rice genotypes.

● Method identified A genotypes (high productivity and resilience) of Fernandez (1992).

● Method identified genotypes better adapted to reduced soil water conditions.

● Method can enhance rice sustainability (high productivity, low water use).

In Asia, the rice crop sustains millions of people. However, growing demand for this crop needs to be met while simultaneously reducing its water consumption to cope with the effects of climate change. Lowland cropping systems are the most common and productive but have particularly high water requirements. High-yielding rice genotypes adapted to drier environments (such as rainfed or aerobic rice ecosystems) are needed to increase the water use efficiency of cropping. Identifying these genotypes requires fast and more accurate selection methods. It is hypothesized that applying a new quantitative selection method (the score index selection method), can usefully compare rice yield responses over different years and stress intensities to select genotypes more rapidly and efficiently. Applying the score index to previously published rice yield data for 39 genotypes grown in no-stress and two stress environments, identified three genotypes (ARB 8, IR55419-04 and ARB 7) with higher and stable yield under moderate to severe stress conditions. These genotypes are postulated to be better adapted to stress environment such as upland and aerobic environments. Importantly, the score index selection method offers improved precision than the conventional breeding selection method in identifying genotypes that are well-suited to a range of stress levels within the target environment.

Keywords Aerobic rice breeding selection drought resilience production capacity index resilience capacity index stress score index upland

Corresponding Author(s): Arnauld THIRY

Just Accepted Date: 28 September 2023 Online First Date: 06 November 2023 Issue Date: 08 March 2024

Cite this article:

Arnauld THIRY,William J. DAVIES,Ian C. DODD. Isolating higher yielding and more stable rice genotypes in stress environments: fine-tuning a selection method using production and resilience score indices[J]. Front. Agr. Sci. Eng. , 2024, 11(1): 169-185.

URL:

https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2023521
https://academic.hep.com.cn/fase/EN/Y2024/V11/I1/169

Index name	Abbreviation	Formula
Stress susceptibility index	SSI^[²¹^]	$S S I = 1 − Y s Y p S I$
Stress intensity	SI^[²¹^]	$S I = [1 − Y s ¯ Y p ¯]$
Mean productivity	MP^[²⁷^]	$M P = Y s + Y p 2$
Stress tolerance index	TOL^[²⁷^]	$T O L = Y p − Y s$
Geometric mean productivity index	GMP^[²⁸^]	$G M P = Y s × Y p$
Stress tolerance index	STI^[²⁸^]	$S T I = Y p × Y s Y p ¯ 2$
Drought yield index	DYI^[²⁶^]	$D Y I = Y p Y S G Y p ¯ G Y s ¯$
Yield potential score index	YPSI^[²²^]	$Y P S I = ((M P s + S T I s) 2 − (S S I s + T O L s) 2)$
Yield stress score index	YSSI^[²²^]	$Y S S I = S S I s + S T I s 2$

Tab.1 List of the different indices, formulae and references

Tab.2 List of the 39 advanced rice breeding lines showing the yield value under irrigated conditions (Yp) and moderate stress (Yms) (data from Raman et al.^[²⁶^])

Tab.3 List of the 39 advanced rice breeding lines showing the yield value under irrigated conditions (Yp) and severe stress (Yss) (data from Raman et al.^[²⁶^])

Fig.1 Linear regression and the coefficient of determination of the yield potential scored index (YPSI) versus yield under no stress (a and b) and yield stress scored index (YSSI) versus yield under moderate (c) and severe (d) stress conditions. Calculation of YPSI and YSSI use yield data from rice advanced line published in Raman et al.^[²⁶^]. (a) and (c) are based on yield data from irrigated and moderate stress, when (b) and (d) on yield data from irrigated and severe stress environment. Each symbol is an individual genotype, yellow dot: IR64; orange dot: MTU 1010.

Tab.4 List of selected rice genotypes using YPSI and YSSI

Tab.5 List of the genotypes with a response superior to 80% of the population in moderate and severe stress environments in terms of production capacity index (PCI) and resilience capacity index (RCI)

Fig.2 Yield linear model of the reference cultivars (IR65, MTU 1010) and the six highlighted genotypes (IR74371-70-1-1, DGI 75, DGI 307, ARB 8, IR55419-04 and ARB 7) versus stress intensity.

Fig.3 Diagram of genotype distribution into four genotype classes (A, B, C and D)^[²⁸^] (a) as a function of yield under no stress (Yp) and stress (Ys) (b) as a function of the productivity capacity index (PCI) and the resilient capacity index (RCI). Modified from Thiry et al.^[²²^] under Creative Commons.

Genotype ID	MYI	Ranking	Moderate stress		Severe stress		Deviation from IR64 mean			Deviation from MTU 1010 mean
Genotype ID	MYI	Ranking	DYI	Ranking	DYI	Ranking	Control	Moderate	Severe	Control	Moderate	Severe
Annada	2.90	25	1.43	3	2.49	12	–0.83	0.73	0.64	–0.66	0.3	0.23
ARB 2	3.01	19	1.45	5	2.53	14	–0.64	0.83	0.69	–0.47	0.4	0.28
ARB 3	3.22	4	1.61	18	2.63	16	–0.15	0.84	0.8	0.02	0.41	0.4
ARB 4	2.95	22	1.57	11	2.3	7	–0.7	0.57	0.83	–0.52	0.14	0.43
ARB 5	2.99	21	1.44	4	2.22	5	–0.78	0.75	0.86	–0.61	0.32	0.46
ARB 6	3.02	17	1.72	25	2.69	18	–0.33	0.55	0.7	–0.16	0.12	0.3
ARB 7	3.10	11	1.41	2	2.07	3	–0.73	0.85	1.02	–0.56	0.42	0.62
ARB 8	3.34	1	1.33	1	2.05	1	–0.49	1.2	1.16	–0.32	0.77	0.76
Baranideep	3.05	14	1.6	16	2.78	21	–0.36	0.72	0.64	–0.18	0.29	0.23
CB 0-15-24	3.09	12	1.51	8	2.18	4	–0.59	0.74	0.99	–0.42	0.31	0.58
CB 2-458	2.82	26	1.94	32	3.32	27	–0.31	0.24	0.38	–0.14	–0.19	–0.03
DGI 237	2.75	31	1.6	17	3.33	28	–0.69	0.51	0.26	–0.51	0.08	–0.14
DGI 307	3.21	5	1.71	24	2.67	17	–0.06	0.72	0.81	0.12	0.29	0.41
DGI 75	3.25	3	1.86	31	2.76	20	0.16	0.61	0.84	0.34	0.18	0.43
DSL 104-1	3.11	10	1.66	20	3.31	26	–0.07	0.79	0.46	0.1	0.36	0.05
DSU 4-7	2.79	28	1.83	29	3.26	25	–0.45	0.31	0.36	–0.27	–0.12	–0.04
IR36	2.04	39	2.18	33	8.63	39	–1.08	−0.37	–0.57	–0.91	–0.81	–0.98
IR55419-04	3.17	7	1.49	7	2.05	2	–0.58	0.8	1.12	–0.4	0.37	0.72
IR64	2.72	34	2.31	35	4.85	33	0	0	0	0.17	–0.43	–0.4
IR66873-R-11-1	2.57	35	2.35	36	7.47	38	–0.03	–0.05	–0.36	0.14	–0.48	–0.77
IR67469-R-1-1	2.16	37	3.29	38	4.86	34	–0.68	–0.85	–0.14	–0.51	–1.28	–0.55
IR72667-16-1-B-B-3	3.00	20	1.57	12	2.4	11	–0.59	0.64	0.8	–0.41	0.2	0.39
IR74371-3-1-1	3.04	15	1.81	28	2.79	22	–0.19	0.48	0.69	–0.02	0.05	0.29
IR74371-46-1-1	3.05	13	1.77	27	2.55	15	–0.29	0.49	0.81	–0.12	0.06	0.41
IR74371-54-1-1	3.14	9	1.56	10	2.52	13	–0.34	0.81	0.81	–0.16	0.38	0.41
IR74371-70-1-1	3.30	2	1.75	26	2.72	19	0.13	0.77	0.85	0.31	0.34	0.44
IR74371-78-1-1	3.20	6	1.69	21	2.83	23	–0.03	0.76	0.72	0.14	0.33	0.32
Kallurundaikar	3.04	16	1.7	23	2.3	8	–0.46	0.5	0.94	–0.28	0.07	0.53
Khiradhan	2.72	33	2.18	34	6.65	37	0.12	0.18	–0.26	0.29	–0.25	–0.66
MTU 1010	2.94	23	1.85	30	3.36	29	–0.17	0.43	0.4	0	0	0
NDR 1098-6	2.77	29	1.45	6	2.95	24	–0.88	0.66	0.36	–0.7	0.23	–0.04
PM 1011	2.91	24	1.59	15	3.65	31	–0.39	0.73	0.23	–0.22	0.3	–0.17
PMK 1	2.23	36	4.04	39	5.96	36	–0.24	–0.98	–0.23	–0.06	–1.42	–0.63
PMK 2	2.12	38	3.09	37	5.45	35	–0.75	–0.79	–0.25	–0.58	–1.22	–0.65
Poornima	2.74	32	1.57	13	2.39	10	–0.97	0.39	0.65	–0.79	–0.04	0.25
R1027-2282-2-1	2.81	27	1.69	22	3.83	32	–0.42	0.53	0.16	–0.24	0.1	–0.24
RF 5329	3.01	18	1.52	9	2.33	9	–0.64	0.69	0.83	–0.47	0.26	0.43
RR 272-21	2.75	30	1.63	19	3.43	30	–0.64	0.5	0.24	–0.47	0.07	–0.17
Tripuradhan	3.15	8	1.58	14	2.24	6	–0.42	0.73	1.01	–0.24	0.3	0.61

Tab.6 Summary of the indices used by Raman et al.^[²⁶^] and their respective ranking value

Fig.4 Summary of the genotype selection as a function of each method presented and discussed in this paper.

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