Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming.

Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of future rice genotypes in large-scale, multi-environment experiments may be challenging. A key component of the accurate assessment is the examination of stability in growth contexts and genotype-environment interaction. Using a split-plot design with three replications, the study was carried out in nine locations with five genotypes under continuous flooding (CF) and alternate wet and dry (AWD) conditions. Utilizing the web-based warehouse inventory search tool (WIST), the water status was determined. To evaluate yield performance for stability and adaptability, AMMI and GGE biplots were used. The genotypes clearly reacted inversely to the various environments, and substantial interactions were identified. Out of all the environments, G3 (BRRI dhan29) had the greatest grain production, whereas G2 (Binadhan-8) had the lowest. The range between the greatest and lowest mean values of rice grain output (4.95 to 4.62 t ha-1) was consistent across five distinct rice genotypes. The genotype means varied from 5.03 to 4.73 t ha-1 depending on the environment. In AWD, all genotypes out performed in the CF system. With just a little interaction effect, the score was almost zero for several genotypes (E1, E2, E6, and E7 for the AWD technique, and E5, E6, E8, and E9 for the CF method) because they performed better in particular settings. The GGE biplot provided more evidence in support of the AMMI study results. The study's findings made it clear that the AMMI model provides a substantial amount of information when evaluating varietal performance across many environments. Out of the five accessions that were analyzed, one was found to be top-ranking by the multi-trait genotype ideotype distance index, meaning that it may be investigated for validation stability measures. The study's findings provide helpful information on the variety selection for the settings in which BRRI dhan47 and BRRI dhan29, respectively, performed effectively in AWD and CF systems. Plant breeders might use this knowledge to choose newer kinds and to design breeding initiatives. In conclusion, intermittent irrigation could be an effective adaptation technique for simultaneously saving water and mitigating GHG while maintaining high rice grain yields in rice cultivation systems.

Potassium augments growth, yield, nutrient content, and drought tolerance in mung bean (Vigna radiata L. Wilczek.).

Uneven rainfall and high temperature cause drought in tropical and subtropical regions which is a major challenge to cultivating summer mung bean. Potassium (K), a major essential nutrient of plants can alleviate water stress (WS) tolerance in plants. A field trial was executed under a rainout shelter with additional K fertilization including recommended K fertilizer (RKF) for relieving the harmful impact of drought in response to water use efficiency (WUE), growth, yield attributes, nutrient content, and yield of mung bean at the Regional Agricultural Research Station, BARI, Ishwardi, Pabna in two successive summer season of 2018 and 2019. Drought-tolerant genotype BMX-08010-2 (G1) and drought-susceptible cultivar BARI Mung-1 (G2) were grown by applying seven K fertilizer levels (KL) using a split-plot design with three replications, where mung bean genotypes were allotted in the main plots, and KL were assigned randomly in the sub-plots. A considerable variation was observed in the measured variables. Depending on the different applied KL and seed yield of mung bean, the water use efficiency (WUE) varied from 4.73 to 8.14 kg ha-1 mm-1. The treatment applying 125% more K with RKF (KL7) under WS gave the maximum WUE (8.14 kg ha-1 mm-1) obtaining a seed yield of 1093.60 kg ha-1. The treatment receiving only RKF under WS (KL2) provided the minimum WUE (4.73 kg ha-1 mm-1) attaining a seed yield of 825.17 kg ha-1. Results showed that various characteristics including nutrients (N, P, K, and S) content in stover and seed, total dry matter (TDM) in different growth stages, leaf area index (LAI), crop growth rate (CGR), root volume (RV), root density (RD), plant height, pod plant-1, pod length, seeds pod-1, seed weight, and seed yield in all pickings increased with increasing K levels, particularly noted with KL7. The highest grain yield (32.52%) was also obtained from KL7 compared to lower K with RKF. Overall, yield varied from 1410.37 kg ha-1 using 281 mm water (KL1; well-watered condition with RKF) to 825.17 kg ha-1 using 175 mm water (KL2). The results exhibited that the application of additional K improves the performance of all traits under WS conditions. Therefore, mung beans cultivating under WS requires additional K to diminish the negative effect of drought, and adequate use of K contributes to accomplishing sustainable productivity.

Evaluation of yield attributes and bioactive phytochemicals of twenty amaranth genotypes of Bengal floodplain.

Twenty vegetable amaranth (VA) genotypes were evaluated to assess the variability, associations, path coefficient, and principal component analysis (PCA) of morpho-chemical traits. The genotypes exhibited adequate antioxidant colorants, phytochemicals, and antiradical capacity with significant variations across genotypes. Genetic parameters revealed selection criteria for the majority of the traits for improving amaranth foliage yield (FY). Based on correlation coefficient, stem weight, stem base diameter, root weight, plant height, and shoot weight for significant development of FY of VA. Observing significant genotypic correlation with high positive direct effects on FY, the path coefficient (PC) of root weight, stem base diameter, stem weight, and shoot weight could contribute to the noteworthy development of FY of VA. The genotypes AA5, AA6, AA8, AA10, AA11, AA19, and AA20 might be selected for high FY, antioxidant colorants, and antiradical phytochemicals to utilize as colorants and antiradical rich superior high yielding cultivars. The first PC accounted for 37.8% of the variances, which implies a larger proportion of variable information explained by PC1. The features that contributed more to PC1 were FY, SW, STW, RW, and PH, whereas Chl b, total Chl, and Chl a contributed to the second PC. This suggests that there are significant genetic differences between amaranths in terms of biochemical and agro-morphological characteristics. The findings of the current work support plant breeders to investigate the genetic potential of the amaranth germplasm, notably in biochemical parameters. High colorants enrich genotypes that can be selected for extracting natural colorants to use in food processing industries.

Genetic Analyses of Mungbean [Vigna radiata (L.) Wilczek] Breeding Traits for Selecting Superior Genotype(s) Using Multivariate and Multi-Traits Indexing Approaches.

Mungbean [Vigna radiata (L.) Wilczek] is an important food, feed, and cash crop in rice-based agricultural ecosystems in Southeast Asia and other continents. It has the potential to enhance livelihoods due to its palatability, nutritional content, and digestibility. We evaluated 166 diverse mungbean genotypes in two seasons using multivariate and multi-traits index approaches to identify superior genotypes. The total Shannon diversity index (SDI) for qualitative traits ranged from moderate for terminal leaflet shape (0.592) to high for seed colour (1.279). The analysis of variances (ANOVA) indicated a highly significant difference across the genotypes for most of the studied traits. Descriptive analyses showed high diversity among genotypes for all morphological traits. Six components with eigen values larger than one contributed 76.50% of the variability in the principal component analysis (PCA). The first three PCs accounted for the maximum 29.90%, 15.70%, and 11.20% of the total variances, respectively. Yield per plant, pod weight, hundred seed weight, pod length, days to maturity, pods per plant, harvest index, biological yield per plant, and pod per cluster contributed more to PC1 and PC2 and showed a positive association and positive direct effect on seed yield. The genotypes were grouped into seven clusters with the maximum in cluster II (34) and the minimum in cluster VII (10) along with a range of intra-cluster and inter-cluster distances of 5.15 (cluster II) to 3.60 (cluster VII) and 9.53 (between clusters II and VI) to 4.88 (clusters I and VII), suggesting extreme divergence and the possibility for use in hybridization and selection. Cluster III showed the highest yield and yield-related traits. Yield per plant positively and significantly correlated with pod traits and hundred seed weight. Depending on the multi-trait stability index (MTSI), clusters I, III, and VII might be utilized as parents in the hybridization program to generate high-yielding, disease-resistant, and small-seeded mungbean. Based on all multivariate-approaches, G45, G5, G22, G55, G143, G144, G87, G138, G110, G133, and G120 may be considered as the best parents for further breeding programs.