High-yielding Genotypes Research Articles

Characterization of high-yielding aman rice genotypes through genetic and agronomic analysis

ABSTRACT Rice (Oryza sativa L.) holds immense global significance, serving as a staple food crop that sustains billions of people and supports livelihoods across diverse cultures and economies, including Bangladesh. For food security in Bangladesh, there is a significant demand for high-yielding Aman rice varieties. We assessed genetic variability of 16 Aman rice genotypes, including 11 promising Aman lines and 5 established Aman cultivars in Bangladesh, with the aim of identifying high-yielding genotypes through a three-year field experiment. The examined genotypes displayed notable variations in both yield and agronomic traits contributing to yield, as evidenced by their greater range of coefficients of variance and genetic variability components. Traits such as effective tillers per hill (ET), filled grains per panicle (FG), plant height (PH) at 80 days after transplanting, and panicle length (PL) demonstrated notable correlations with grain yield (GY) throughout the three-year evaluation. Moreover, the higher broad-sense heritability (H 2) observed in ET, FG, PH, and PL traits suggested a substantial influence of genetic factors on the variability of the Aman rice genotypes. Principal component analysis based biplot and heatmap analyses revealed that the advanced lines BU acc4 and BU acc5 had higher harvest index and relatively greater GY compared to the other Aman rice varieties. These promising Aman lines could prove to be valuable materials for future multi-location yield trials or ongoing and forthcoming breeding programs aimed at enhancing high-yielding Aman rice cultivars.

Graphical Analysis of Multi-environmental Trials for Bread Wheat (Triticum aestivum L.) Grain Yield Based on GGE Bi-Plot Analysis

Bread wheat (Triticum aestivum L.) is a crucial crop in Ethiopia, and breeders test newly developed elite lines for superiority to existing cultivars to boost national productivity. The study was undertaken during the 2021–22 to 2022–23 cropping seasons at seven environments in optimum moisture areas of Ethiopian using 36 diverse and advanced bread wheat genotypes to evaluate the GEI by the graphical method of GGE biplot and to identify the genotypes with high mean yield performance and stability. Field experiments were conducted at the Adet, Asasa, Kulumsa, and Sinana research centers in Ethiopia. The experiments were planted in an alpha lattice design replicated three times in six rows of 2.5m long. Row-to-row distance and distance between blocks were 0.2m and 1.5m, respectively. The analysis of variance revealed that genotype, environment, and their interaction showed a highly significant effect on the yield as reflected in the GGE model and the GGE model indicated the suitability of the genotypes EBW202136 (33), Boru (1), and EBW202172 (12), with high mean yield and stability, whereas the genotypes EBW202185 (16) and Deka (36) produced high mean yield, but unstable. Likewise, the genotypes EBW202164 (27) and EBW202192 (29) produced low mean yield and unstable. The AMMI analysis of variance for grain yield across the environments showed that 17.26% of the total variation was attributed to genotypic effects, 64.03% to environmental effects, and 18.71% to GEI effects. Two mega environments were identified based on GGE biplot analysis and the which-won–model indicated the adaptation of genotypes Boru (1), EBW202159 (4), EBW202172 (12), EBW202171 (19), and EBW202136 (33) to first mega-environment and genotypes EBW202157 (3), EBW202166 (5), EBW202160 (6), EBW202162 (9), EBW202185 (16), Dursa (17) and Deka (36) in the second. These approaches allowed the identification of stable and high-yielding genotypes (EBW202136 (33) and EBW202172 (12)) which can be included in the national verification program, with a plan to release a new variety, and other genotypes with high yield could be utilized in breeding programs to further improve grain yield in bread wheat.

Evaluation of Kabuli Chickpea Genotypes for Tropical Adaptation in Northern Australia

Chickpea is one of the economically important legume crops adapted for winter season production in tropical climates. This study evaluated the physiological, morphological, and biochemical traits of eight Kabuli chickpea genotypes in an Australian tropical environment. The result revealed significant differences between genotypes for seed emergence, plant height, primary shoots, leaf number, leaf area index, gas-exchange parameters, seed yield, carbon discrimination (Δ13C), and natural abundance for nitrogen fixation. Among the tested genotypes, AVTCPK#6 and AVTCPK#19 exhibited late flowering (60–66 days) and late maturity (105–107 days), and had higher leaf photosynthetic rate (Asat) (28.4–31.2 µmol m−2 s−1), lower stomatal conductance (gsw) (516–756 mmol m−2 s−1), were associated with reduced transpiration rate (T) (12.3–14.5 mmol m−2 s−1), offered greater intrinsic water-use efficiency (iWUE) (2.1–2.3 µmol m−2 s−1/mmol m−2 s−1), and contributed a higher seed yield (626–746 g/m2) compared to other genotypes. However, a larger seed test weight (>60 g/100 seed) was observed for AVTCPK#24, AVTCPK#8, and AVTCPK#3. Similarly, a high proportion (45%) of larger seeds (>10–11 mm) was recorded for AVTCPK#24. Furthermore, a higher %Ndfa in AVTCPK#6 (71%) followed by AVTCPK#19 (63%) indicated greater symbiotic nitrogen fixation in high-yielding genotypes. Positive correlation was observed between %Ndfa and seed protein, as well as between seed yield and plant height, primary shoots, leaf count, leaf area index, leaf photosynthesis, stomatal conductance, transpiration rate at pod filling stage, biomass, and harvest index. An inverse correlation between (Δ13C) and iWUE, particularly in AVTCPK#6 and AVTCPK#19, indicates greater heat and drought tolerance, required for high-yielding Kabuli chickpea production in northern Australia.

Dependence of the yield in late-generation lines on the spike productivity in original segregating populations of spring bread wheat

Background. An ascertained possibility of culling segregating populations on the basis on their yield or grain weight per spike as a character closely correlated with yield can increase the efficiency of breeding practice with spring bread wheat. Materials and methods. Sixteen F2 –F4 segregating populations of spring bread wheat were assessed for their grain weight per spike, average grain weight per spike among the top 10%, coefficient of variation for grain weight per spike, and yield. The pedigree method was applied to select 2 to 3 F7 lines from each population. The obtained lines were tested for their yield in F8 and F9 . The data of grain weight per main spike in the parents that had been crossed to produce the studied segregating populations were retrospectively taken into account while the results were processed. Results Segregating populations persistently differed in their average grain weight per spike and the coefficient of its variation in different years of research. None of the 36 late-generation lines significantly surpassed the reference cultivar in yield. All the considered indicators of segregating populations had no significant correlations with the yield of late-generation lines. Average values of grain weight per main spike in the parent genotypes and the best parent as well as the differences between the parents in the average grain weight per main spike tended to have a negative correlation with the yield of late-generation lines (r up to –0.58). Conclusion. The culling of segregating populations based solely on the yield or grain weight per spike is associated with a risk of losing high-yielding genotypes. Late-generation lines with high yields occur in combinations where the grain weight per main spike in one parent or on average among the parents is not expressed to its highest level or when both parents differ only slightly from each other in the expression of this character.

Estimation of genetic variability for seed yield and yield related traits in mungbean [Vigna radiata (L.) Wilczek

Genetic variability is the most important factor for the success of any crop improvement program. Hence, the evaluation of genotypes has to be conducted as a preliminary step to study the extent of variability available in the genotypes and to identify suitable high-yielding genotypes that can be utilized in the crop improvement program. The present investigation was carried out to estimate genetic variability, heritability, and genetic advance for yield and yield contributing characters among 38 genotypes of mungbean for eleven quantitative traits. Significant differences were observed among genotypes for all eleven characters studied. The high degree of genetic variability along with high heritability and high genetic advance as percent of mean were recorded for seed yield per plant, harvest index, number of pods per plant, plant height, and the number of branches per plant which indicates that these characteristics were under the control of additive gene action and therefore, form the basis of selection for the mungbean improvement program.

Contrasted agronomical and physiological responses of five Coffea arabica genotypes under soil water deficit in field conditions.

Breeding programs have developed high-yielding Coffea arabica F1-hybrids as an adaptation against adverse conditions associated with climate change. However, theresponse to drought of coffee F1 hybrids has seldom been assessed. A trial was established with five C. arabica genotypes (2 pure lines: Catimor and Marsellesa and 3 F1 hybrids: Starmaya, Centroamericano and Mundo Maya) planted under the leguminous tree species Leuceana leucocephala. Coffee growth, yield and physiological responses were assessed under a rain-fed (control: CON) and a rainfall reduction treatment (RR) for 2 years. The RR treatment created a long-term rainfall deficit in a region with suboptimal temperature similar to those predicted by climate change scenarios. Moreover, the RR treatment reduced soil water content by 14% over 2 successive years of production and increased hydric stress of the three F1-hybrids (leaf water potentials averaged -0.8 MPa under RR compared with -0.4 MPa under CON). Under RR, coffee yields were reduced from 16 to 75% compared to CON. Mundo Maya F1 hybrid was the sole high-yielding genotype apable of sustaining its yield under RR conditions. Our results suggested that its significant increase in fine root density (CON = 300 and RR = 910 root.m-2) and its maintenance of photosynthetic rate (2.5 - 3.5 mmol CO2 m-2 s-1) at high evaporative demand might explain why this genotype maintained high yield under RR condition. This work highlights a possible drought tolerance mechanism in fruit bearing adult coffee trees where the plant fine root number increases to intake more water in order to preserve turgor and sustainphotosynthesis at high ETo and therefore conserves high yield in dry conditions.

Relationships between flowering behavior and seed yield in mungbean mutants

Flower is the prime sink that produces pods and yield depends on numbers and size of pods in legume crops. Flowering behavior affects the synchrony of pod maturity, which is crucial for efficient harvesing that regulates the yield. Thus, it is essential to study the relationship between flowering behavior and pod yield in mungbean mutants. This experiment was conducted using a Randomized Complete Block Design (RCBD) with three replications at the BINA headquarters physiology pot yard. The aim was to assess the relationship between flowering patterns, flowering duration, and seed yield in ten mungbean mutants/varieties. Results indicated that high-yielding mutant (MM-1) produced more opened flowers (38.0 flowers per plant) and had a longer flowering duration (12 days) compared to low-yielding varieties/mutants (13.1-18.0 flowers per plant and 7-9 days). Additionally, high-yielding varieties exhibited higher flower production rates over time, with peak flower production occurring within 5-8 days, while low-yielding varieties peaked within 3-4 days. High-yielding mungbean mutants/varieties exhibit lower pod set and reproductive efficiency (48.7-55.0%) compared to low-yielding ones (63.2-70.9%), indicating potential for yield improvement in high-yielding genotypes through enhanced pod set. Pod number, a key yield parameter, has a significant positive correlation with flower number (r = 0.56**), while pod number is also dependent on flowers per plant (r = 0.68**). Additionally, seed yield shows a strong positive relationship with the harvest index (r = 0.62**). These findings suggest that future breeding efforts should focus on synchronizing flower production to increase mungbean yield. Bangladesh J. Nuclear Agric, 38(1): 111-117, 2024

Genetic variability, character association and path coefficient analysis in okra

A study was conducted to investigate the genetic architecture of different lines for fruit yield and its associated traits in okra. Fourteen parents, consisting of twelve lines and two testers, were crossed using a Line × Tester mating design in the spring-summer season of 2019-20 at Vegetable Research Farm, CCSHAU, Hisar. The resulting twenty-four hybrids were cultivated in a randomized block design with three replications during the rainy season of 2020-21. Fruit yield/hectare showed a significant range from 87.86 to 150.18 quintals with an average of 117.73 quintals. The phenotypic coefficient of variation ranged from 3.91% (test weight) to 20.23% (Number of branches per plant), slightly surpassing the genotypic coefficient of variation which ranged from 1.98% (test weight) to 12.15% (fruit yield) with high estimate of PCV for the number of branches per plant (20.23%) and a moderate estimate of both PCV and GCV for fruit yield (14.67%, 12.15%), number of fruits per plant (14.34%, 12.06%), respectively. This suggests that the traits studied were more influenced by genetic factors than environmental factors. High heritability coupled with high genetic advance as percentage of mean for traits such as number of fruits/plant (70.74%, 20.89%) and fruit yield/hectare (68.65%, 20.74%) suggested that additive genes play a major role in determining these traits respectively. This indicates that it is possible to select high-yielding genotypes by selecting parents with desirable traits. Fruit yield/hectare displayed positive and significant associations with most of the traits like number of fruits per plant (0.95) and plant height (0.36) at phenotypic levels. The number of fruits per plant, 1000 seeds test weight, fruit diameter, and number of branches per plant had the most significant positive effect on fruit yield. Ultimately, such analysis has the potential to assist breeders in formulating their selection strategies aimed at enhancing fruit yield.

Image Analysis Artificial Intelligence Technologies for Plant Phenotyping: Current State of the Art

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended.

Genotype by environment interaction effect and fresh root yield stability of cassava genotypes under contrasting nitrogen regimes

Nitrogen (N) is an important nutrient element needed by cassava for optimum yield and it is a vital component of nucleotides (nucleic acids), enzymes, amino acids (proteins), chlorophyll molecules and hormones, among other essential compounds required for growth and development of cassava. Nitrogen stress is a major cassava production constraint, the study aimed to examine genotype by environment interaction (GEI) effects and fresh root yield stability of 203 diverse cassava clones to identify genotypes with stable performance under low and optimum nitrogen regimes across environments using AMMI and GGE biplot analysis. Experiments were conducted using an augmented block design with three replications for two years in three locations in Nigeria. There were significant differences (p < 0.001) in the genotype’s mean performances as well as significant differences (p < 0.001) in the environment’s mean performances for all the traits measured in both nitrogen regimes. The AMMI analysis of variance showed significant effects (p < 0.001) for genotypes, environments and the interactions for fresh root yield in both nitrogen regimes. The biplot analysis showed that for fresh root yield in the optimum nitrogen regime, the principal component accounted for 81.54% of the G + GE (Genotype plus and Genotype by Environment) variation. The G + GE for fresh root yield in the low nitrogen regime accounted for a total of 71.64% of the variation. Ten genotypes were identified as the best genotypes under the optimum nitrogen regime, while eleven genotypes were the best under the low nitrogen regime. Three genotypes under optimum nitrogen regimes were high-yielding. Still, they were unstable in their fresh root yield performance across the environments and can be recommended as specifically adapted to the environments they performed best. Three other genotypes were high-yielding genotypes under low nitrogen but were highly unstable in their fresh root yield mean performance across the environments. The environments Otobi_YR1, Igbariam_YR2, and Umudike_YR1 were identified as the most discriminatory among the test environments. The environments Umudike_YR2 and Igbariam_YR1 were identified as the most representative of the test environments and can represent a mega-environment. The best 21 genotypes that performed above the grand mean for fresh root yield in both nitrogen regimes can be further evaluated on the farmer’s field for possible advancement.

Leveraging genetic resource diversity and identification of trait-enriched superior genotypes for accelerated improvement in linseed (Linum usitatissimum L.)

Linseed or flaxseed, native to the Indian subcontinent, had undergone domestication, edaphic selection and evolutionary processes that may have resulted in huge genetic variability in Indian genotypes. To understand the hitherto unexplored genetic diversity for sustainable flaxseed production amid challenges of climate fluctuation and identify trait-specific high-yielding genotypes, 2576 unique linseed accessions were comprehensively evaluated for 36 traits for up to six environments representing two major agroecological zones in India. A wide range of variability was recorded for days to initiation of flowering (42.86–114.99), plant height (43.31–122.88 cm), capsules/plant (64.62–375.87), seed size (6.06–14.44 cm2), thousand seed weight (2.80–11.86 g), seed yield (2.93–17.28 g/plant), oil content (30.14–45.96%) and fatty acid profile especially the key constituent omega-3 fatty acid (25.4–65.88%). Most of the traits such as plant height, flowering time, seed yield, seed and capsule size showed a high or moderately high level of variance coupled with high broad sense heritability indicating precise capturing of less heritable quantitative traits. The infraspecific classification of the tested collection revealed the seed/oil type (2498 accessions) as the dominant morphotype over dual-purpose/fiber flax (78 accessions) in the conserved collection. Correlation analysis indicated a significant positive association between flowering time, plant height, days to maturity and oil content. Trait-specific superior genotypes for earliness (50% flowering in < 60 days, maturity in < 122 days), bold seeds with high thousand seed weight (> 11 g), capsules/plant (> 350), oil content (> 45%) and fatty acid composition (> 65% alpha-linolenic acid) were identified to aid genetic improvement of linseed and to broaden the narrow genetic base.

Exploring genotype by environment interaction in sunflower using genotype plus genotype by environment interaction (GGE) and best linear unbiased prediction (BLUP) approaches

Sunflower is important for food, feed and ornamentals, faces rising demand for quality oil. Identifying resilient high-yielding genotypes is essential. Multi-environmental trials and integrated approaches to evaluate genotype by environment interactions (GEI) enhance accuracy and comprehensiveness of breeding programs. This study assess the representativeness and discriminative power of tested environments; classify mega-environments for genotype recommendations and evaluate how GGE and BLUP methods identify stable and superior sunflower genotypes. The experiment used a randomized complete block design with three replications across six locations for two seasons. The pooled analysis of variance for GEI on seed yield showed significant variability (p < 0.05) with mean values from 1171 to 2564 kg ha−1, indicating inconsistent genotypes performance across environments. The study identified E11, E2, and E6 as key discriminating and representative environments and grouped test locations into four mega-environments. Genotypes G6, G1, and G4 are the most desirable based on stability and seed yield. E11 and G6 are identified as ideal environment and genotype, respectively. GGE biplot and BLUP methods consistently identify stable and ideal sunflower genotypes. However, GGE stability indices differ somewhat in assessing genotypes stability possibly due to low number of genotypes, testing environment, and year in the dataset. The study confirmed that simultaneous consideration of GGE and BLUP models for GEI analysis helps to select more stable and adaptable sunflower genotypes across environments. It also clarifies GEI pattern, identifying mega-environments, and aiding genotype selection in sunflower production. However, validating these mega-environments and exploring the genetic basis of adaptability and stability remains essential.

Genotype-Specific Activation of Autophagy during Heat Wave in Wheat.

Recycling of unnecessary or dysfunctional cellular structures through autophagy plays a critical role in cellular homeostasis and environmental resilience. Therefore, the autophagy trait may have been unintentionally selected in wheat breeding programs for higher yields in arid climates. This hypothesis was tested by measuring the response of three common autophagy markers, ATG7, ATG8, and NBR1, to a heat wave under reduced soil moisture content in 16 genetically diverse spring wheat landraces originating from different geographical locations. We observed in the greenhouse trials that ATG8 and NBR1 exhibited genotype-specific responses to a 1 h, 40 °C heat wave, while ATG7 did not show a consistent response. Three genotypes from Uruguay, Mozambique, and Afghanistan showed a pattern consistent with higher autophagic activity: decreased or stable abundance of both ATG8 and NBR1 proteins, coupled with increased transcription of ATG8 and NBR1. In contrast, three genotypes from Pakistan, Ethiopia, and Egypt exhibited elevated ATG8 protein levels alongside reduced or unaltered ATG8 transcript levels, indicating a potential suppression or no change in autophagic activity. Principal component analysis demonstrated a correlation between lower abundance of ATG8 and NBR1 proteins and higher yield in the field trials. We found that (i) the combination of heat and drought activated autophagy only in several genotypes, suggesting that despite being a resilience mechanism, autophagy is a heat-sensitive process; (ii) higher autophagic activity correlates positively with greater yield; (iii) the lack of autophagic activity in some high-yielding genotypes suggests contribution of alternative stress-resilient mechanisms; and (iv) enhanced autophagic activity in response to heat and drought was independently selected by wheat breeding programs in different geographic locations.

Assessing the impact of early and terminal droughts on root growth, grain yield and yield stability in old and modern wheat cultivars on the Loess Plateau

Understanding changes in root traits of high-yielding genotypes that consistently perform during different drought periods is crucial for improving wheat production and yield stability. A 2-year field trial and a pot experiment were conducted to determine the response of root growth and grain yield to early and terminal droughts in old and modern cultivars (FC3: 1960s and CH1: 2010s) under rainfed and irrigated conditions in the field. Rainfed wheat faced terminal drought in 2015–2016 and early drought in 2016–2017. Plants in the pot experiment were exposed to terminal drought, early drought, and well-watered. Both field and pot experiments showed higher grain yield in modern cultivar CH1 compared to FC3. Early and terminal droughts reduced grain yield in both cultivars, with a greater reduction in FC3. CH1 exhibited higher yield stability under drought conditions and experienced less reduction in early drought. Compared to FC3, CH1 produced more root biomass in the early growth stage. At anthesis, CH1 had significantly lower root biomass and root length density in the topsoil layer (00.2 m) and higher in the subsoil layer (0.21.0 m) than FC3, respectively. At maturity, the retention rate of subsoil root biomass in CH1 was higher, especially under early drought (80.2 % in field and 93.9 % in pot experiments). Both cultivars consumed similar seasonal water use, but CH1 reduced pre-anthesis water use and consumed more water after anthesis. This study indicates that modern cultivar had vigorous root growth at early stage. A higher proportion of root biomass preserved in deep soil after anthesis enhanced post-anthesis water use and photosynthetic rate, improved post-anthesis dry matter accumulation, and thus resulted in higher thousand kernel weight. These factors ensure higher yield and yield stability under both early and terminal drought stress.

Restricted Irrigation Regimes and Rapeseed High-Yielding Genotypes Can Be Applied to Cope With the Water Shortage Crisis and More Stable Oil Production

Restricted Irrigation Regimes and Rapeseed High-Yielding Genotypes Can Be Applied to Cope With the Water Shortage Crisis and More Stable Oil Production

Genetic Variability Studies for Identification of High Yielding Genotypes with High Protein Content in Grain Cowpea [Vigna unguiculata (L.) Walp.

Background: Cowpea is a major legume crop which has high protein content and provides a powerful tool to combat malnutrition due to its highly nutritional value. Identification of superior genotypes will aid in cowpea breeding program. Genetic variability is the foremost requirement in a breeding population for the selection of high yielding genotypes. To develop a new high yielding variety, it is important to study the genetic variability present in it so that proper and efficient selection could be done and most promising genotypes could be identified. Methods: Keeping this in mind, studies on genetic variability in grain cowpea was done utilizing 40 diverse exotic and indigenous cowpea genotypes which were evaluated for ten quantitative characters for two consecutive years 2020 and 2021. The experiment was planted in randomized block design with 3 replications at Pantnagar. Data was recorded on ten characters. Result: Mean squares were found to be highly significant for all the characters. Cluster analysis revealed five genetically diverse clusters. Cluster I had 8 genotypes, cluster II had 9 genotypes, cluster III consisted of 12 genotypes, cluster IV had 6 genotypes and Cluster V consisted 5 genotypes. Cluster I had the greatest intra-cluster distance (2.500) whereas cluster V had the smallest intra-cluster distance (1.701). In the cluster V, one of the genotype Pant Lobia-2 exhibited the highest mean values for all the characters. The highest GCV and PCV was recorded by number of pods per plant (28.87, 29.19) followed by yield (q/ha) (28.72, 29.04). The higher value of phenotypic component compared to the corresponding genotypic component suggest that there was an environmental influence on the performance of the genotypes. Correlation studies revealed positive correlation between pod length, pods per cluster, seeds per pod and yield quintal per hectare. Path coefficient analysis showed the significant positive direct relationship between plant height, peduncle length, pod length, number of pods per cluster, number of pods per plant and number of seeds per pod with yield quintals per hectare. The results of the study showed that, out of the 40 genotypes evaluated, Pant Lobia-2 exhibited the best performance. It has attractive red bold seeds with 31% protein content. Therefore it can be used in grain cowpea breeding program.

Performance evaluation of advanced durum wheat genotypes under irrigated condition at Bhairahawa, Nepal

A field research was carried out at the National Wheat Research Program (NWRP) in Bhairahawa, Nepal in 2022 to investigate elite durum wheat genotypes and key traits contributing to grain yield. The experiment was performed in an alpha lattice design with two replications. Thirty distinct durum wheat genotypes were assessed, focusing on fourteen quantitative traits including days to booting, days to heading, days to maturity, plant height, spike length, peduncle length, number of tillers per square meter, number of spikes per square meter, number of grains per spike, grain weight per spike, thousand kernel weight, grain yield, biomass yield, chlorophyll content. The studied genotypes were grown under irrigated condition. Genotype NL1779 attained the highest grain yield of 3828 kg/ha, followed by NL1769 (3784 kg/ha), NL1772 (3726 kg/ha), NL1789 (3640 kg/ha) and NL1784 (3570 kg/ha). Principal components analysis revealed that eight traits were the major loadings on the first two principal components that describe 53.4% of the total morphological variance at irrigated condition. Cluster analysis grouped the different genotypes into four clusters, with each cluster showing variation in performance for different traits under irrigated conditions. Cluster III is characterized by genotypes exhibiting the highest grain yield, biomass yield, spike length, number of grains per spike, and number of spikes per square meter. Notably, the high-yielding genotypes NL1779, NL1769, NL1772, NL1789, NL1784, and NL1773 identified within this cluster could serve as potential candidates for inclusion in the national breeding program. These superior genotypes could be recommended for irrigated environment after further evaluation. Integrating them into national breeding programs offers an opportunity for genetic improvement, contributing to establishing a robust durum wheat production system in Nepal, meeting the growing demand for durum wheat products while promoting dietary diversity and sustainable agriculture.

Evaluation of source–sink manipulation through defoliation treatments in promising bread wheat lines under optimal irrigation and rainfed conditions

The source–sink (S-S) ratio during the grain-filling period is crucial for wheat crop yield. The aim of this study was to investigate the relative sensitivity of grain yield in response to treatments of S-S ratio changes to determine the extent of S-S limitation during grain filling in modern wheat genotypes. The S-S manipulation treatments included four levels: check (CH), removal of flag leaf (RFL), removal of all leaves (RAL), and removal of the upper half of the spikes (RHS). The results showed significant differences between genotypes (pb&amp;lt; 0.001%) in all traits. Drought stress decreased grain weight per spike (GWS) (g) and grain yield (GY) (kg/ha) by 18% and 25%, respectively. The average reduction in GWS under irrigation and rainfed conditions was 8.25% and 6.71% for RFL and 12.25% and 11.15% for RAL, respectively. By RFL and RAL, increasing the remobilization from the stem and spike straw helped to reduce the effects of source limitation. Also, by RHS, the reduction in photosynthetic materials production in both conditions was only equivalent to 38% and 29% of the expected values, respectively, which shows the presence of strong sinks in vegetative organs (stem and spike) compared to grains. Vegetative organs seem to have a larger sink for the uptake of photosynthetic materials than grains when the source–sink ratio increases. However, high-yield genotypes showed more severe source limitation, while low-yield genotypes showed more relative sink limitation. Overall, to increase the yield potential in high-yielding genotypes, photosynthetic sources and sinks in low-yielding genotypes should be improved.

Bi-environmental evaluation of oat (Avena sativa L.) genotypes for yield and nutritional traits under cold stress conditions using multivariate analysis

AbstractOat being a rabi/winter crop in Kashmir, experiences extremely low temperatures which has detrimental effects on its growth and development. Therefore, this study was designed to evaluate a set of 130 oat genotypes in multi-location trials across temperate conditions of Kashmir valley from 2018 to 2022. From the preliminary data of 56 genotypes, including five checks, were selected and evaluated for nutritional and yield attributing traits under cold stress conditions at two locations. The results demonstrated significant genetic variation and high heritability for majority of traits, except for days to 50% flowering, days to maturity and dry fodder. Positive correlations were observed between green fodder yield and other traits, indicating their potential for enhancing yield. Principal component analysis identified four principal components that accounted for 69.87% of the total variation. Cluster analysis categorized the genotypes into two main clusters and six sub-clusters. Frost damage assessment was conducted at tillering stage after the snow melted in late January 2021 and 2022 using cold tolerance rating scale and subsequently tested for chilling injury through an electrolyte leakage test. From field and lab data analysis, five most promising cold tolerant, nutritious and high-yielding genotypes were identified. These genotypes have significant potential for utilization in future breeding programmes to improve cold tolerance in cultivated oats within the Kashmir valley thus promoting agricultural productivity and sustainability. The outcomes also provide valuable insights into the genetic variation, heritability, genotype-by-environment interactions, correlations and cold tolerance of oat genotypes in Kashmir.

Genotype-by-environment interaction and stability analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes using AMMI and GGE biplot analyses

Bread wheat is a vital staple crop worldwide; including in Ethiopia, but its production is prone to various environmental constraints and yield reduction associated with adaptation. To identify adaptable genotypes, a total of 12 bread wheat genotypes (G1 to G12) were evaluated for their genotype-environment interaction (GEI) and stability across three different environments for two years using Additive Main Effect and Multiplicative Interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplots analysis. GEI is a common phenomenon in crop improvement and is of significant importance in genotype assessment and recommendation. According to combined analysis of variance, grain yield was considerably impacted by environments, genotypes, and GEI. AMMI and GGE biplots analysis also provided insights into the performance and stability of the genotypes across diverse environmental conditions. Among the 12 genotypes, G6 was selected by AMMI biplot analysis as adaptive and high-yielding genotype; G5 and G7 demonstrated high stability and minimal interaction with the environment, as evidenced by their IPCA1 values. G7 was identified as the most stable and high-yielding genotype. The GGE biplot's polygon view revealed that the highest grain yield was obtained from G6 in environment three (E3). E3 was selected as the ideal environment by the GGE biplot. The top three stable genotypes identified by AMMI stability value (ASV) were G5, G7, and G10, while the most stable genotype determined by Genotype Selection Index (GSI) was G7. Even though G6 was a high yielder, it was found to be unstable according to ASV and ranked third in stability according to GSI. Based on the study's findings, the GGE biplot genotype view for grain yield identified Tay genotype (G6) to be the most ideal genotype due to its high grain yield and stability in diverse environments. G7 showed similar characteristics and was also stable. These findings provide valuable insights to breeders and researchers for selecting high-yielding and stable, as well as high-yielding specifically adapted genotypes.