Bangladesh Rice Research Institute

Rice is a staple food for much of the global population and is essential for maintaining steady food production. However, in southern Bangladesh, soil conditions and several environmental factors present significant threats to achieving optimal productivity. This study aimed to evaluate the morphological differences among thirty local and modern rice genotypes in southern Bangladesh, concentrating on their adaptability, location, and performance in these challenging conditions. The experiment was conducted at the Bangladesh Rice Research Institute in Gopalganj, employing a randomized complete block design (RCBD) with three replications. Significant variation was observed in panicle length, filled grains, unfilled grains, grain length, grain breadth, 1000-grain weight, and grain yield per hill. ASS04 and ASS10 exhibited superior performance with longer panicles, higher 1000-grain weight, increased grain length, filled grains, and the highest grain yield per hill. On the other hand, Principal Component Analysis (PCA) and correlation analysis revealed that grain length, grain breadth, grain yield per hill, and 1000-grain weight were the major contributors to variability among the genotypes. In addition, cluster analysis identified cluster no. III, with the highest-performing cluster including genotypes ACC4, ACC29, ACC21, and ACC23. These findings highlight the importance of genetic diversity in rice improvement, particularly in southern environments. The identified high-performing genotypes would serve as valuable genetic resources for developing resilient and high-yielding rice varieties based on their agro-morphological traits.

Tropical monsoon regions, including Bangladesh, are becoming increasingly vulnerable to climate extremes driven by global climate change, which poses significant risks to water resources, agriculture, and public health. This study explores future climate extremes using outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble (MME). Key temperature and precipitation indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) were analyzed under three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the near (2015–2044), mid (2045–2074), and late (2075–2100) 21st century. Bias correction was performed using Simple Quantile Mapping (SQM), and results were validated against the ERA5 reanalysis dataset. Significant changes in climate extremes were identified relative to the baseline period (1985–2014) by using the Wilcoxon rank-sum test. Projections indicate substantial increases in temperature extremes, with maximum temperature (TXx) and minimum temperature (TNn) rising by 5.12°C and 5.49°C, respectively, under SSP5-8.5 by the late 21st century. Precipitation extremes, including one-day maximum rainfall (RX1day) and five-day cumulative rainfall (RX5day), are projected to increase by 94.03 mm and 103.04 mm, respectively, amplifying flood risks in northeastern and coastal regions of the country. Concurrently, the Diurnal Temperature Range (DTR) is expected to decline, whereas heat stress indicators, such as warm spell duration (WSDI), are projected to increase substantially. These findings underscore the intensification of climate risks in tropical monsoon regions. This study offers critical insights into regional climate dynamics, highlighting the need for targeted adaptation strategies, including resilient infrastructure, sustainable water management, and agricultural adaptation, while calling for future research on socio-economic and compound climate impacts. This study utilizes projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble (MME) to assess future climate extremes in Bangladesh. By analyzing temperature and precipitation-based indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), this study reveals a consistent intensification of climate extremes throughout the 21st century. Key indicators, such as the annual maximum temperature (TXx) and minimum temperature (TNn), show substantial increases under high-emission scenarios, such as SSP5-8.5, indicating a greater likelihood of prolonged heatwaves and elevated nighttime temperatures. Precipitation extremes, including one-day maximum rainfall (RX1day) and five-day cumulative rainfall (RX5day), are projected to rise significantly, amplifying flood risks, particularly in the northeastern and coastal regions of Bangladesh. Additionally, a notable decline in the Diurnal Temperature Range (DTR) suggests a reduction in day-night temperature variability, which could have implications for crop growth, human health, and energy demand. These projected changes highlight the pressing concerns related to heat stress, urban and rural infrastructure resilience, and water resource management in the region. The findings call for immediate and targeted adaptation strategies, including climate-resilient infrastructure, sustainable water management practices, and enhanced early warning systems to minimize future socio-economic and environmental risks in this highly vulnerable region.

This study examines the relationship between integrated reporting quality (IRQ) disclosures, corporate governance quality (CGQ), and the implied cost of equity capital (ICC) in developed markets, focusing on Australia and New Zealand. The increasing adoption of integrated reporting and its potential implications for firms’ ICC motivates this research. Moreover, the study highlights the role of IRQ in mitigating information asymmetry between firms and investors, emphasizing the need for high-quality disclosures. Using a quantitative approach with panel data analysis, the research analyzes a sample of the top 174 companies by Standard and Poor’s market capitalization in Australia and New Zealand from 2018 to 2022, encompassing 870 observations post-IRQ implementation. Statistical methods, including fixed-effects, IV2SLS, two-step system-GMM, pooled OLS, and medium quantile regression, were applied to ensure robust findings. The results reveal a significant negative relationship between IRQ disclosure and ICC, with CGQ playing a moderating role in strengthening this association. Consistent with agency theory, the findings suggest that to reduce information asymmetry, firms issue more information which allows to reduce the cost of capital. Therefore, a more comprehensive firms’ reporting, including information about their strategy and risks, increases investors’ confidence, hence it may reduce the cost of capital. This study provides valuable insights for regulators and policymakers by emphasizing the importance of integrated reporting frameworks and robust corporate governance practices to promote transparency, reduce information asymmetry, and optimize capital allocation efficiency in developed markets.

This study examined 48 Aman rice landraces in Bangladesh for genetic variability, trait correlation, and direct and indirect trait impacts on grain yield. All fourteen (14) agro-morphological traits tested exhibited significant differences among the 48 Landraces. Flag leaf area, filled grain number per panicle, thousand-grain weight, grain length, grain breadth, and grain yield were recorded as highly heritable along with high genetic advance and genetic advance as percent of mean. Days to first flowering, filled grain number per panicle, thousand-grain weight, grain length, and kernel length demonstrated a strong favorable effects and associations with yield of grain at the genotypic as well as the phenotypic levels. We have used 69 SSR markers which are dispersed over 12 rice chromosomes to clarify the landraces’ genetic divergence, with 328 alleles detected in the allelic diversity profile; 41 unique alleles were identified by 31 SSRs with RM303 producing 5 unique alleles solely. It was identified that, RM206 was found as the most effective marker as it has the highest PIC value. Additionally, three (3) clusters were spotted in the neighboring tree that joins, and 4 major groupings were observed in the dendrogram with a coefficient value of 0.77. Among 48 landraces, 40 were pure and 8 were admixed, with the L30 (Chapail), L32 (Kasra), L36 (Dudhshor), and L40 (Dadkhani) landraces the most diversified among of those studied. Data from this study provides agro-morphological and molecular traits insights with high breeding value to improve the yield of grain and other traits which contributes to the yield.

Salinity stress significantly impacts global food production by hindering crop growth and reducing cultivable land. Efforts to develop salinity-tolerant rice varieties have faced challenges due to the complexity of salinity tolerance traits and a lack of suitable genetic donors. One complexity of salinity stress is that fluctuating degrees of severity often occur over a rice growing season, which may require plants to recover quickly as salinity levels temporarily decrease. This study evaluated salinity recovery in 256 diverse rice accessions, including 230 from the 3K Rice Genomes Project. Key physiological traits were measured, indicating accessions that outperformed the salinity-tolerant variety FL478, three of which were common in both the field and in hydroponics: BRRI dhan 47, Kalar Kar and WAS 170-B-B-1-1. Genome-wide association mapping identified significant single nucleotide polymorphisms linked to salinity tolerance and recovery-related traits, with four genes (LOC_Os01g71350, LOC_Os02g56510, LOC_Os03g53150 and LOC_Os04g40410) consistently identified in both the field and greenhouse. Based on colocating loci, a favourable haplotype for salinity recovery was identified on chr 3. The accessions with salinity tolerance and good recovery and the genes/loci identified here will provide useful information for future studies on genetics and breeding of salt tolerant/resilient rice. This article is part of the theme issue ‘Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the ‘Resilience Revolution’?’.

Breeding in the Consultative Group on International Agricultural Research (CGIAR) system is an intricate process that integrates the contributions of market research, pre‐breeding, breeding, breeding operations, and seed systems. Therefore, a well‐defined framework is critical for the effective and efficient operation of a breeding program. The OneRice Breeding Framework developed at the International Rice Research Institute (IRRI) integrates these components, from initial market research to establish breeding goals, creating breeding strategies for improved product design and development, and swiftly testing and replacing products through effective seed systems. The framework represents a cutting‐edge breeding approach that offers comprehensive guidance on harnessing modern tools and technologies, including genomic selection, speed breeding, sparse testing, and so on. Additionally, the framework outlines strategies for systematically integrating novel genetic variation into elite breeding programs through pre‐breeding efforts. It is adaptable across different crops and is dynamic, allowing adjustments in the breeding program based on target objectives, resource availability, and tools. The OneRice Breeding Framework is a comprehensive end‐to‐end framework that integrates all the components to enhance genetic gains and develop and disseminate better products faster to address food, nutrition, and income security. Consequently, the OneRice Breeding Framework is the fundamental blueprint for modern rice (Oryza sativa) crop breeding.

Global climate models (GCMs) are essential for projecting future climate changes, yet their ability to accurately simulate extreme precipitation, particularly in South Asia, remains a major challenge. This study assessed the performance of GCMs from CMIP5 and CMIP6 in replicating 11 extreme precipitation indices, using ERA5 data from 1975 to 2005. The results revealed substantial variability across individual models, with CMIP6 generally outperforming CMIP5, though certain inconsistencies persisted. Both CMIP5 and CMIP6 multi‐model ensemble means (MMEs) exhibited higher root mean square error (RMSE) than the best individual models, highlighting the need for further improvements in model accuracy. On average, CMIP6 models achieved a Kling–Gupta efficiency (KGE) of 0.42, outperforming CMIP5's 0.38, and demonstrated better agreement in Taylor diagrams, with an average r² of 0.65 compared to 0.59 for CMIP5. CMIP6 also showed reduced uncertainty in interannual monthly precipitation variability projections. EC‐Earth3 (CMIP6) and EC‐Earth (CMIP5) consistently correlated well with various indices, while MIROC‐ESM was also a strong performer in both generations. The CMIP6 MME performed better overall, with a KGE of 0.48 and r² of 0.71, surpassing CMIP5 MME's 0.44 and 0.67. Future projections indicate significant changes in precipitation extremes under different emission scenarios for the 2040s and 2080s. While CMIP6 shows clear advancements over CMIP5, continued model refinement is essential to more accurately simulate extreme precipitation events.

Zinc deficiency poses a significant health challenge in Bangladesh, particularly among pre-school-aged children and non-pregnant, non-lactating women. To address this issue, the government of Bangladesh has made efforts to promote the cultivation and consumption of zinc-biofortified rice and wheat, considering rice’s significant role as a staple food in the country. This study aimed to assess the awareness and knowledge of rice farmers about zinc-biofortified rice in Bangladesh. By understanding the perceptions of farmers, the research sought to identify potential pathways for the successful adoption and integration of this critical agricultural innovation. Data was collected from 1301 rice farmers across seven districts from January to March 2022. The data collection process involved surveys and interviews to evaluate farmers’ familiarity with and understanding of zinc-biofortified rice, as well as their experiences and practices related to its cultivation. The study revealed that a significant portion of the surveyed farmers, approximately 56.2%, had heard about zinc-biofortified rice and wheat. However, only less than 30% of these farmers demonstrated a comprehensive understanding of the substantial benefits associated with the consumption of zinc-biofortified rice. Additionally, although a considerable percentage (48%) of the sampled farmers cultivated zinc-biofortified rice during the 2021–22 dry season, a majority (77%) of them relied on free seeds provided by research organizations and NGOs. The study highlights the need for comprehensive information dissemination on zinc deficiency and the benefits of zinc-biofortified crops among farmers. The study recommends the implementation of targeted awareness programs and initiatives to facilitate the widespread adoption and consumption of zinc-biofortified rice and wheat, thereby potentially addressing the pervasive zinc deficiency issue in the country. By financing research and awareness programs, international donor agencies can play an important role in fighting zinc deficiency in Bangladesh.

Timely monitoring and precise estimation of physicochemical properties, such as pH, total soluble solids (TSS), and firmness, are crucial for assessing the quality and ripeness of strawberries. Therefore, this study examined the application of convolutional neural network (CNN)-regression models for predicting pH, TSS, and firmness of strawberries based on image data captured by RGB camera. Three CNN architectures, namely a typical single branch convolutional neural network (CNNtl), a parallel convolutional neural network (CNNpl), and a series convolutional neural network (CNNsl) architectures were developed, and their performance were compared. To develop these models, 600 fruits in six different ripening stages were collected and indexed for enabling the measurement of pH, TSS, and firmness levels, as well as the acquisition of images. Through statistical analysis, significant correlations were obtained among pH, TSS, and firmness in strawberries, suggesting valuable insights into the physicochemical changes that occurred during the ripening process. The pH and TSS levels exhibited a continuous increase from the early to late ripening stages, while fruit firmness significantly decreased throughout the ripening process. Among the tested models, CNNsl outperformed CNNtl and CNNpl in predicting the physicochemical properties of strawberries, which precisely explained the relationship between the image data and the targeted properties. For pH prediction, CNNsl achieved an R2 greater than 0.74 and an RMSE below 0.20. The CNNsl model demonstrated better performance in predicting TSS, with a 9.65% increase in R2 and reductions of 14.34% and 14.51% in RMSE and MAE, respectively, compared to the CNNtl model. Furthermore, the CNNsl architecture achieved the best results for firmness prediction, with an increase inss R2 of 2.74% and 6.92%, and reductions of 9.13% and 16.38% in RMSE, and 8.34% and 15.33% in MAE, compared to the CNNpl and CNNtl models, respectively. The consistency assessment of these models indicated that CNNsl exhibited the highest consistency among the tested models with minimal decreases in R2 and small increases in RMSE and MAE, followed by CNNpl and CNNtl. However, in terms of detection speeds, CNNtl required the shortest prediction time compared to CNNpl and CNNsl. Overall, this study demonstrated the potential of CNN-regression models in precisely predicting the physicochemical properties of strawberries based on image data. The findings may contribute valuable insights in determining physicochemical characteristics of strawberries, emphasizing the importance of advanced deep learning techniques in agricultural applications.

Developing high‐yielding, flood‐tolerant rice (Oryza sativa L.) varieties is essential for enhancing productivity and livelihoods in flood‐prone ecologies. We explored genetic avenues beyond the well‐known SUB1A gene to improve flood resilience in rice. We screened a collection of 6274 elite genotypes from IRRI's germplasm repository for submergence and stagnant flooding tolerance over multiple seasons and years. This rigorous screening identified 89 outstanding elite genotypes, among which 37 exhibited high submergence tolerance, surpassing the survival rate of SUB1A introgression genotypes by 40%–50%. Thirty‐five genotypes showed significant tolerance to stagnant flooding, and 17 demonstrated dual tolerance capabilities, highlighting their adaptability to varying flood conditions. The genotypes identified have a broader genetic diversity and harbor 86 key quantitative trait loci (QTLs) and genes related to traits such as grain quality, grain yield, herbicide resistance, and various biotic and abiotic traits, highlighting the richness of the identified elite collection. Besides germplasm, we introduce an innovative breeding approach called “Transition from Trait to Environment” (TTE). TTE leverages a parental pool of high‐performing genotypes with complete submergence tolerance to drive population improvement and enable genomic selection in the flood breeding program. Our approach of TTE achieved a remarkable 65% increase in genetic gain for submergence tolerance, with the resulting fixed breeding genotypes demonstrating exceptional performance in flood‐prone environments of India and Bangladesh. The elite genotypes identified herein represent invaluable genetic resources for the global rice research community. By adopting the TTE approach, which is trait agonistic, we establish a robust framework for developing more resilient genotypes using advanced breeding tools.

Salinity stress affects rice growth and yield by impairing physiological processes in coastal and irrigated areas. This study investigates on physiological and agronomical parameters in two rice varieties BRRI dhan97 (salt-tolerant) and BRRI dhan100 (salt-sensitive) differing in their salt tolerance to 80 mM NaCl solution. The results revealed that the dry weight of BRRI dhan 97 was not significantly altered by salt stress, whereas salt-sensitive BRRI dhan100 significantly decreased. Regarding photosynthetic pigments, salt-sensitive BRRI dhan100 degraded more Chl a (55.55%), Chl b (44.84%), Chl a+b (52.16%) and carotenoid (17.15%) than the BRRI dhan97 under salt stress. Salinity significantly reduced relative water content in BRRI dhan100 but did not affect in BRRI dhan97. The leaf proline concentration is higher in BRRI dhan100 (86.82µg g-1 FW) than BRRI dhan97 (82.01µg g-1 FW) in saline condition. The shoot Na+ concentration in BRRI dhan97 (760 ppm) was less than that of BRRI dhan100 (960 ppm), whereas Na+/K+ ratio was significantly higher in BRRI dhan100 than in BRRI dhan97. The findings of this research primarily highlight the physiological differences, including photosynthetic efficiency, osmotic activity, and ionic stress resistance between two cultivars. This comprehensive study might help farming communities in determining salt-resistant cultivars at salinity-affected areas.

Bacterial leaf blight (BLB) and blast are the two major threats that remarkably reduce rice yield. This study was undertaken to enhance the resistance of BRRI dhan81 against these diseases. Five resistance (R) genes that conferred resistance against these diseases were introgressed in BRRI dhan81. BLB resistance genes Xa21 , xa13 and Xa4 were detected using markers MP1/MP2, xa13‐pro and pTA248, respectively, while blast resistance genes Pi9 and Pb1 were identified using markers NMSMPi9‐US2 and RM206 in the breeding population of BRRI dhan81. The F 1 to BC 3 F 6 populations were developed by successive backcrossing, selfing and foreground selection. The segregation analysis of 400 BC 3 F 2 populations confirmed the single Mendelian inheritance pattern for BLB and blast resistances. Among the final selected 30 advanced pyramided lines, 21 contained all five R genes. The BLB and blast disease scores of these lines varied from 0 to 5. Analysis of marker trait association revealed that molecular markers were negatively associated with these diseases. In multilocation trials under four agroecological zones, G13 (BR(Path)13811‐ BC 3‐17‐13‐7) exhibited the highest mean yield (8.17 t ha ⁻¹ ) across all the locations, followed by G12 (8.00 t ha ⁻¹ ) and G23 (7.72 t ha ⁻¹ ). These developed advanced pyramided lines hold the potential to be released as a resistant variety to BLB and blast diseases or may be used as a potential gene stock to incorporate with desired genes to improve the fitness and quality.

The dissipation pattern and mobility of applied pesticides in the soil represent a crucial process for pesticide safety and subsequent groundwater contamination. In this study, two distinct experiments were conducted to explore the environmental fate, dissipation, and mobility of two pesticides, phorate and boscalid, in greenhouse conditions and laboratory soil column studies, respectively. The role of organic matter and growing conditions was evaluated during dissipation and mobility studies. In the first study, commercial formulations of phorate (10 G) and boscalid (20% SC) were sprayed in the designated greenhouse for Korean cabbage following the recommended dosage. A sequential collection of plant samples (e.g., 0, 7, 14, 21 days) was performed. On the other hand, three sets of packing columns were prepared (control, biochar-amended, and H2O2 treated). The effect of organic matter addition or removal during the leaching of pesticides was explored. A 14-day interval after the last spray was suggested for safe spraying. After 30 days of leachate collection, no pesticide residue was detected in the leaching water, indicating the immobility of the studied pesticides. However, the metabolic transformation of phorate was evident during this column study, with slight mobility within soil columns. In particular, phorate sulfoxide and sulfone were mostly detected in the top soil layer (vadose zone) of the soil column. In summary, phorate and boscalid were considered immobile pesticides with moderate persistence in the soils. The safe pre-harvest interval should be maintained to reduce the health risk of pesticides.

Fertilizer application to supply essential plant nutrients is a widely recognized approach to better rice production. Since long-term chemical fertilizer use leads to decreased soil fertility, applying organic fertilizer is irrefutable to maintain soil health. A field experiment was conducted during 2017-2019 at Bangladesh Rice Research Institute, Gazipur, aiming to evaluate the suitability of different sources of organic materials and chemical fertilizers for the Boro-Fallow-T. Aman rice cropping pattern. The experiment involved a randomized complete block design with three replications. Five treatments, with organic materials and chemical fertilizers, have been imposed. The treatments are (i) BRRI recommended chemical fertilizer doses, (ii) Kitchen waste @ 6 t ha −1 , (iii) Cowdung bio-slurry @ 6 t ha −1 , (iv) Poultry manure 6 t ha −1 , and (v) control (no nutrient supply). Applying chemical fertilizer, poultry manure, kitchen waste, and cowdung bio-slurry produced the highest plant height, tiller, and panicle number per, spikelet per panicle over control in both season and year. Among organic materials, the poultry manure (6 t ha −1) effect was statistically similar to chemical fertilizer in the case of plant height, tiller, and panicle number. Maximum grain (6.3-4.9 t ha −1) and straw (6.7-4.9 t ha −1) yield were observed in chemical fertilizer and poultry manure-treated plots in both season and year. Chemical fertilizer and all organic materials treated plots produced the maximum grain yield over control in both seasons and years. There was a slight increase in soil organic matter, pH, total N (%), available P, S, and exchangeable K from initial in 2 years. The study's overall findings indicate that the huge number of organic materials, especially poultry manure, is suitable for sustainable crop yield in a rice-rice cropping pattern.

Key message Calmodulin binding domain truncation from OsGAD1 and OsGAD3 resulted in enhanced GABA accumulation, upregulated stress related genes, and improved tolerance to multiple abiotic stresses. Abstract Rice (Oryza sativa L.), a critical crop for global food security, faces significant challenges from abiotic stresses. Gamma-aminobutyric acid (GABA), synthesized by glutamate decarboxylase (GAD), plays a vital role in stress tolerance. Truncating the calmodulin-binding domain (CaMBD) in GAD enzymes enhances GAD activity and GABA production. In this study, we developed a hybrid line, Hybrid #78, by crossing two genome-edited lines, OsGAD1ΔC #5 and OsGAD3ΔC #8, with truncated CaMBD in OsGAD1 and OsGAD3, respectively. Hybrid #78 demonstrated significantly improved survival rates in cold (25%), salinity (33%), flooding (83%), and drought (83%) stress conditions, compared with wild-type Nipponbare (0–33%), OsGAD1∆C #5 (0–66%), and OsGAD3∆C #8 (0–50%). Hybrid #78 showed the highest GABA levels during stress, with increases of 3.5-fold (cold), 3.9-fold (salinity), 5-fold (flooding), and 5-fold (drought) relative to wild-type Nipponbare and up to 2-fold higher than that of the parent lines. RNA-seq analysis from shoot tissues in control conditions identified 975 differentially expressed genes between Hybrid #78 and wild-type Nipponbare, with 450 genes uniquely expressed in the hybrid. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment revealed that upregulation in nitrogen metabolism pathways likely contributes to enhanced GABA synthesis via increased glutamate production. Hybrid #78 also showed broader gene expression variability, suggesting enhanced adaptability to stress, especially upregulation of stress-related genes, such as OsDREB, OsHSP70, and OsNAC3. These findings highlight the potential of CaMBD truncation in OsGAD1 and OsGAD3 to develop rice lines with increased GABA accumulation and resilience to multiple abiotic stresses.

This study aims to enhance the precision of climate simulations by optimizing a multi-model ensemble of General Circulation Models (GCMs) for simulating precipitation, maximum temperature (Tmax), and minimum temperature (Tmin). Bangladesh, with its susceptibility to rapid seasonal shifts and various forms of flooding, is the focal point of this research. Historical simulations of 19 CMIP6 GCMs are meticulously compared with ERA5 data for 1986–2014. The bilinear interpolation technique is used to harmonize the resolution of GCM data with the observed grid points. Seven distinct error metrics, including Kling-Gupta Efficiency and normalized root mean squared error, quantify the grid-to-grid agreement between GCMs and ERA5 data. The metrics are integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for seasonal and annual rankings of GCMs. Finally, the ensemble means of top-performing models are estimated using Bayesian Model Averaging (BMA) and Arithmetic Mean (AM) for relative comparison. The outcomes of this study underscore the variability in GCM performance across different seasons, necessitating the development of an overarching ranking system. Results reveal ACCESS.CM2 is the preeminent GCM for precipitation, with an overall rating matric of 0.99, while INM.CM4.8 and UKESM1.0.LL excel in replicating Tmax and Tmin, with rating matrices of 1.0 and 0.88. In contrast, FGOALS.g3, KACE.1.0.G, and CanESM5 are the most underperformed models in estimating precipitation, Tmx, and Tmn, respectively. Overall, there are five models, ACCESS.ESM1.5, ACCESS.CM2, UKESM1.0.LL, MRI.ESM2.0, EC.Earth3 performed best in simulating both precipitation and temperature. The relative comparison of the ensemble means of the top five models revealed that the accuracy of BMA with Kling Gupta Efficiency (KGE) of 0.82, 0.65, and 0.82 surpasses AM with KGE of 0.59, 0.28, and 0.45 in capturing the spatial pattern of precipitation, Tmax and Tmin, respectively. This study offers invaluable insights into the selection of GCMs and ensemble methodologies for climate simulations in Bangladesh. Improving the accuracy of climate projections in this region can contribute significantly to climate science. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-96446-0.

Parboiling is a hydrothermal processing of paddy grain applied to 20% of all rice consumed. Parboiling can alter rice biochemical composition, starch properties, and subsequent starch digestibility. In this experiment, glucose release on in vitro digestion of nonparboiled and parboiled rice was examined, and the protein and lipid content of rice was analyzed. Glucose release on in vitro digestion was decreased in nine parboiled rice samples but increased in six samples. There was little change in lipid and total protein content in parboiled rice samples. Extractable globulin, glutelin, and prolamin proteins were significantly reduced in all parboiled rice may be due to a stronger protein–starch matrix induced by parboiling. Acetic acid extracted globulin had a negative correlation with glucose release upon in vitro digestion in parboiled rice samples, and correlation was significant at 120 min digestion ( r = −0.67, p < 0.01). Changes in glucose release upon in vitro digestion due to parboiling had weak negative correlations with changes in lysophospholipid content ( r = −0.69) and lipid‐bound amylose content ( r = −0.57). In conclusion, parboiling has little effects on the total lipid and total protein content, however, can change the interaction between starch and nonstarch components (protein and lipid) in rice impacting glucose release upon in vitro rice digestion.

The adoption of newly released rice varieties in Bangladesh remains slow, particularly in coastal ecosystems, where multiple stressors reduce productivity. Limited knowledge transfer on climate‐resilient varieties has led farmers to favor traditional cultivars over newer ones. Head‐to‐Head Adaptive Trials (HHATs) were introduced to promote the dissemination of improved varieties, but their effectiveness has not been fully assessed. This study evaluates farmers' trait preferences, varietal selection criteria, adoption patterns, key determinants, and the impact of HHATs on varietal adoption in coastal Bangladesh. HHATs were conducted in 2021–2022 and 2022–2023, with data collected from April to June 2023. Using purposive sampling, 50 participant farmers were selected, while 150 neighboring farmers were systematically sampled based on geographic proximity. Findings indicate that yield, taste, and resilience to salinity and drought were the most important traits influencing varietal selection. While farmers valued the superior grain quality and resilience of newer varieties, concerns over yield consistency and climate adaptability led many to continue adopting older varieties. HHATs created spillover effects, encouraging broader adoption among neighboring farmers. Education, farming as a primary occupation, income, commercial farming, extension services, training, social networks, seed access, grain quality, varietal resilience, and market price significantly influenced adoption, while age, low soil fertility, high input costs, and large landholdings were barriers. Propensity score matching analysis confirmed that HHATs increased adoption rates by 11.25%–17.71%, though limited seed distribution hindered widespread adoption. The study highlights the need for targeted policy measures to enhance seed access, extension services, and farmer support to scale up the adoption of climate‐resilient rice varieties.

Blast and bacterial blight (BB) are the most dangerous rice diseases. We used marker‐assisted backcross breeding (MABB) to pyramid the candidate resistant genes against these diseases, specifically Pi9 and Pb1 for blast, and Xa4 , xa13, and Xa21 for BB within the genetic background of BRRI dhan89. We used Pi9 ‐US2 as the donor parent for Pi9 , Pb1‐US2 for Pb1 and IRBB58 for the Xa4 , xa13 and Xa21 resistant genes. For this, triple crosses were made, and F 1 to BC 3 F 5 was produced by subsequent backcrossing, selfing and foreground selection. The chi‐square evaluation of the phenotyping and genotyping of 300 BC 3 F 2 individuals revealed the heredity of blast and BB resistance inherited by the single‐gene principle of Mendelian genetics. Finally, we selected the best 23 fixed advance lines (ALs). Among them, 12 lines possessed all these 5 genes ( Pi9 , Pb1 , Xa4 , xa13 and Xa21 ), while 9 ALs consisted of 4 genes. The disease rating of 23 ALs varied from 0 to 3 for both blast and BB diseases, while BRRI dhan89 had a disease rating ranging from 7 to 9. The G5, G6, G19, G4, G2, G11 and G10 ALs exhibited the higher yield ha ⁻¹ (8.19, 8.16, 8.14, 8.14, 8.02, 8.02 and 8.01 ton ha ⁻¹ , respectively) compared to BRRI dhan89 (7.26 ton ha ⁻¹ ). Therefore, gene introgression by MABB could effectively identify and functionally validate the candidate genes with high accuracy to develop a durable, resistant variety in rice breeding programs.