Evaluation of Morpho-Physiological and Yield-Associated Traits of Rice (Oryza sativa L.) Landraces Combined with Marker-Assisted Selection under High-Temperature Stress and Elevated Atmospheric CO2 Levels

Abstract

Rice (Oryza sativa L.) is an important cereal crop worldwide due to its long domestication history. North-Eastern India (NEI) is one of the origins of indica rice and contains various native landraces that can withstand climatic changes. The present study compared NEI rice landraces to a check variety for phenological, morpho-physiological, and yield-associated traits under high temperatures (HTs) and elevated CO₂ (eCO₂) levels using molecular markers. The first experiment tested 75 rice landraces for HT tolerance. Seven better-performing landraces and the check variety (N22) were evaluated for the above traits in bioreactors for two years (2019 and 2020) under control (T1) and two stress treatments [mild stress or T2 (eCO₂ 550 ppm + 4 °C more than ambient temperature) and severe stress or T3 (eCO₂ 750 ppm + 6 °C more than ambient temperature)]. The findings showed that moderate stress (T2) improved plant height (PH), leaf number (LN), leaf area (LA), spikelets panicle^-1 (S/P), thousand-grain weight (TGW), harvest index (HI), and grain production. HT and eCO₂ in T3 significantly decreased all genotypes' metrics, including grain yield (GY). Pollen traits are strongly and positively associated with spikelet fertility at maturity and GY under stress conditions. Shoot biomass positively affected yield-associated traits including S/P, TGW, HI, and GY. This study recorded an average reduction of 8.09% GY across two seasons in response to the conditions simulated in T3. Overall, two landraces-Kohima special and Lisem-were found to be more responsive compared to other the landraces as well as N22 under stress conditions, with a higher yield and biomass increment. SCoT-marker-assisted genotyping amplified 77 alleles, 55 of which were polymorphic, with polymorphism information content (PIC) values from 0.22 to 0.67. The study reveals genetic variation among the rice lines and supports Kohima Special and Lisem's close relationship. These two better-performing rice landraces are useful pre-breeding resources for future rice-breeding programs to increase stress tolerance, especially to HT and high eCO₂ levels under changing climatic situations.

Keywords: North-East India; SCoT marker; climate change; elevated CO2; grain yield; high temperature; rice landrace.

Figure 1

Representation of experimental setup in this study. (A) Bioreactor where the rice plants were grown in pots, (B) control condition (T1), (C) treatment 2 (T2), and (D) treatment 3 (T3).

Figure 2

Relationship of pollen traits, biomass, and yield-associated parameters in studied rice genotypes under control and stress treatments (mild and severe). ‘*’, ‘**’, and ‘***’ denote the significance level at p ≤ 0.05, p ≤ 0.01, and p ≤ 0.001, respectively. ‘ns’ = Non-significant.

Figure 3

Relationship of grain yield (GY m⁻²) with the stress-responsive physiological parameters: canopy temperature (A), SPAD (B), NDVI (C), root length (D), relative leaf water content (RLWC) (E), and leaf area (cm²⁾ (F) under control and two different stress conditions (mild and severe). ‘*’, ‘**’, and ‘***’ denote the significance level at p ≤ 0.05, p ≤ 0.01, and p ≤ 0.001, respectively. ‘ns’ = Non-significant.

Figure 4

Correlation matrix in the form of corrplot using the studied parameters of rice genotypes of the control set in both year-1 (A) and year-2 (B). The range of correlation coefficient (r) is shown in the horizontal index bar. The cross mark (X) denotes a non-significant correlation (p = 0.05). DtoF—Days to flowering, DtoPM—days to physiological maturity, AnL—anther length, P/A—pollens per anther, CT—canopy temperature, SPAD—Soil–Plant Analysis Development, NDVI—normalized difference vegetation index, RL—root length, RV—root volume, RLWC—relative leaf water content, TL/P—tillers per plant, LN—leaf number, LA—leaf area, SB—shoot biomass, PH—plant height, PL—panicle length, SP/P—spikelets per panicle, G/P—grains per panicle, GY—grain yield, HI—harvest index, TGW—thousand-grain weight, SF%—spikelet fertility%.

Figure 5

Principal component analysis (PCA) for the studied parameters of yield and non-yield components of eight rice genotypes for year-1 (season 1) under control and two treatment (treatment-2 and treatment-3) conditions. (A) represents the genotypic and treatment interactions, and (B) represents the variable vectors. Control (ambient temperature and CO₂), treatment-2 (ambient temperature + 4 °C, CO₂ 550 ± 20 ppm), and treatment-3 (ambient temperature + 6 °C, CO₂ 750 ± 20 ppm). The studied traits are canopy temperature (CT), days to flowering (DtoF), days to physiological maturity (DtoPM), plant height (PH), NDVI, tiller plant⁻¹ (TL/P), leaf number (LN), root length (RL), relative leaf water content (RLWC), panicle length (PL), grains panicle⁻¹ (G/P), spikelets panicle⁻¹ (S/P), spikelet fertility% (SF%), grain yield (GY), thousand-grain weight (TGW), and harvest index (HI).

Figure 6

Principal component analysis (PCA) for the studied parameters of yield and non-yield components of eight rice genotypes for year-2 (season 2) under control and two treatment (treatment-2 and treatment-3) conditions. (A) represents the genotypic and treatment interactions, and (B) represents the variable vectors. Control (ambient temperature and CO₂), treatment-2 (ambient temperature + 4 °C, CO₂ 550 ± 20 ppm), and treatment-3 (ambient temperature + 6 °C, CO₂ 750 ± 20 ppm). The studied traits are canopy temperature (CT), days to flowering (DtoF), days to physiological maturity (DtoPM), plant height (PH), NDVI, tiller plant⁻¹ (TL/P), leaf number (LN), root length (RL), relative leaf water content (RLWC), panicle length (PL), grains panicle⁻¹ (G/P), spikelets panicle⁻¹ (S/P), spikelet fertility% (SF%), grain yield (GY), thousand-grain weight (TGW), and harvest index (HI).

Figure 7

Radar charts showing the mean variability of major yield-associated traits—days to flowering (A), shoot biomass m⁻² (B), rice-grains panicle⁻¹ (C), spikelet fertility% (D), grain yield m⁻² (E) and thousand-grain weight (F)—of studied rice genotypes across two years for the genotypic selection under different high temperature and eCO₂ level. Genotypes are Tatza (G1), Kohima special (G2), Kaladhan (G3), Laldhan (G4), Tzumma (G5), Lisem (G6), Mapok Temeseng (G7), and N22 (G8).

Figure 8

Dendrogram showing the relationship between studied rice genotypes using SCoT-marker-based genotyping data.