ABSTRACT Field pea (_Pisum sativum_ L.) needs improvement to increase productivity due to its high price and demand. However, the incidence of powdery mildew (PM) disease limits its

production. This study aimed to analyze the diversity of qualitative and quantitative traits against powdery mildew resistance by utilizing cluster and principal component analysis to

explore PM resistance high-yield potential field peas. Shannon–Weaver's diversity index (Hʹ) displayed high intra-genotype diversity for quantitative and qualitative aspects.

Heterogeneity was identified for resistance against powdery mildew infections. Eighty-five genotypes were divided into five groups using Mohalanobis generalized distance (D2) statistics. The

were fairly susceptible, and 1 genotype was susceptible to powdery mildew disease. Among 29 resistant genotypes, BFP77, BFP74, BFP63, BFP62, BFP43, and BFP80 were high yielders and, could be

used directly and/or transferred through hybridization to high-yielding disease-susceptible genotypes. Among the 25 moderately resistant genotypes, BFP78, BFP45, BFP79, and BFP48 were found

to be high yielders. In principal component analysis (PCA), the first four PCs with Eigen values > 1 accounted for 88.4% variability for quantitative traits. Clustering sorted genotypes

into five groups, where groups 1 to 5 assembled 37, 28, 1, 8, and 11 genotypes, respectively. Genotypes of cluster 4 were identified as high yielders with its attributes. Pearson correlation

significantly and positively correlated across all traits except for PM. This variation suggested that there is a mechanism to select promising genotypes for field pea breeding. Considering

all features, BFP78, BFP77, BFP74, BFP63, BFP62, BFP45, BFP79, and BFP80 could be preferred as high yielders and PM resistance owing to longer pod lengths, seeds per pod and pods per plant.

SIMILAR CONTENT BEING VIEWED BY OTHERS ‘UNEMAT RUBI’, A NEW SPINELESS PINEAPPLE CULTIVAR AND RESISTANT TO FUSARIOSIS FOR THE INTERNATIONAL MARKET Article Open access 13 May 2025

COMPREHENSIVE EVALUATION OF SUGAR BEET VARIETIES BASED ON GENOTYPE × YIELD × TRAIT (GYT) IN DIFFERENT ENVIRONMENTS Article Open access 21 March 2025 GENETIC DIVERSITY, MORPHOLOGICAL AND

QUALITY TRAITS OF _MOMORDICA DIOICA_ Article Open access 04 December 2024 INTRODUCTION Field pea (_Pisum sativum_ L.) is one of the oldest crops in cultivation and the third-largest producer

of legumes worldwide1. It has chromosome number 2n = 14 and is a self-pollinated diploid. In tropical, subtropical, and temperate parts of the world, field pea is a significant cool-season

multifunctional crop that is widely farmed for grain and green pods2,3. It is recognized as one of the highly productive, nutritionally dense cool-season legume crops, which has great

promise for reducing protein deficiency in the resource-constrained society4. With a yield of over 16.20 metric tons and a global cultivation area of over 8.14 million hectares, it meets

consumer demand for food, feed, and fodder5. Due to numerous widespread biotic and abiotic stresses, of which powdery mildew, rust, and high temperatures are the main concerns6,7 the

productivity of dry peas in Bangladesh is very low and fluctuates between 1120 and 1338 kg/ha. According to Azam et al.8 and USDA9, the crop is mainly grown for human consumption, animal

feed, and as a source of income for farmers. For the human diet, it is a significant source of proteins and other nutrients10. Additionally, the crop is highly valuable economically since it

can grow in low-rainfall areas better than other pulses like faba beans and lentils11. However, the nation imports pulse waste at a cost of around 64512. Parihar et al.13 noted that pea

seeds have an average amount of iron, selenium, zinc, and molybdenum of around 97, 42, 41, and 12 ppm, respectively. They are also considered to have 21–33% protein and 56–74% carbohydrates.

Field peas are high in lysine and tryptophan amino acids and low in cysteine and methionine amino acids14,15 these are rich sources of protein (21–25%). Being a leguminous vegetable, it

occupies a prominent position among vegetables due to its high nutritional value, particularly proteins and a variety of other health-improving ingredients like carbohydrates, vitamin A,

vitamin C, calcium, phosphorus, and essential amino acids, particularly lysine16. As a result, it plays a crucial role in maintaining nutritional security for those who lack access to

resources in impoverished countries. By fostering a symbiotic relationship between nitrogen-fixing bacteria and plant root nodules, they can increase soil fertility17. In several locations

in Bangladesh, it is cultivated primarily as a relay crop alongside transplant Aman rice. After harvesting the pods, the plants are used as either feed or organic soil amendments, and the

green pods are sold as food. It is a member of the Fabaceae family, the largest family of flowering plants with 1200 species and 450 genera18. The cotyledons of pea grains come in a variety

of colors, including yellow, green, and orange. These grains are used in dal, stew, chhola, vegetables, snacks, soup, chat, and flour, whereas entire seeds are mostly used as animal

feed19,20. It is also helpful in food processing because it can be transformed into a variety of products, including biscuits, pasta, salad, noodles, cookies, and bread21. This makes it

extremely relevant economically. Smallholder farmers in rural areas of several developing nations, like Bangladesh, find it to be quite alluring8. Field pea serves as a unique food supply

during the lean season with minimal to no value addition, as well as function as feed for animals, green vegetables for humans, and an income source for both men and women7. Field pea seeds

are used as a raw material in a variety of industries to make goods with added value including _Bombay Chanachur_, _Ruchi,_ and _PRAN Dal Vaja_. It is extensively grown in a variety of

nations throughout the globe22. Since 2010, its global cultivable area has expanded from 6.58 to 8.09 million hectares, and its output has increased from 10.44 to 16.21 metric tons. The main

producers of peas are Canada, Russia, China, India, and the United States23 nevertheless; the United States produces the most peas overall 39.33%, followed by Europe (36.98%) and Asia

(18.09%). The crop's average productivity is currently 2.0 t/ha worldwide, up about 36% over the past ten years (2007–2017), but it has the potential to produce up to 5.0 t/ha in many

nations, including the Netherlands, Denmark, Belgium, Germany, and Finland, where harvests range from 3.45 to 5.01 t/ha24. However, nations with productivity levels of less than 2.00 t/ha

include Australia, China, India, and Myanmar25. Peas have poor output and productivity due to their susceptibility to many fungal infections and parasites26. _Erysiphe polygoni_ a

filamentous fungus responsible for 25–70% of crop losses in pea production causes pea powdery mildew, an airborne fungal disease27,28. Nisar et al.29 found that powdery mildew disease

reduced the production potential of field pea germplasm grown in various regions of the globe by 86%. In the mid-altitudes and with moderate intensity, powdery mildew disease has been

observed to reduce field pea output by 20–30%. When the days are warm and dry and the nights are icy enough for dew to accumulate, powdery mildew is a bothersome illness. Generally, the

price of field peas is a little bit high in Bangladesh though it is affordable to consumers. However, sometimes yield loss by PM severely affects farmers' incomes, as the government

can’t support farmers with crop insurance. Furthermore, the loss of yield of field peas reduces the market supply, and prices of field peas hike at the consumer level. To prevent the

illness, farmers often employ chemical agents, which might pollute the environment30. Eklund et al.31 showed that spore discharge may potentially affect farm employees' respiration and

allergy responses. Due to the high expense of fungicides, adverse effects on the environment, social and health issues, and other factors, it is preferable to search out other alternative

ways of disease management. Crop breeding should prioritize genetically based resistance32. To enhance and maintain the output and productivity of field peas for small-scale farmers, it is

necessary to produce high-yielding and powdery mildew resistant cultivars33. The development of superior crop varieties depends heavily on their existing genetic variation and proper

selection against yield-attributed traits34,35,36. The degree of genetic diversity and variability37,38 and the degree of inheritance of required traits determine the achievement of up

gradation in breeding programs39. Approaches using multivariate analysis may be useful in identifying genetic diversity and classifying germplasm40. Principal component analysis (PCA), one

of these methods, uses statistics to group a large number of variables into fundamental, uncorrelated components. PCA may be used to determine germplasm characterization traits, show

individual differences and relationships, and evaluate each individual's contribution to overall variance41. In addition, germplasm may be categorized and organized using hierarchical

cluster analysis42,43,44. Selecting superior parents for hybridization will be aided by an evaluation of the kind and degree of variety45. Principal component analysis is a method used to

decrease the size of large datasets, improve interpretability, and limit information loss46. Designing breeding programs for novel objectives may be aided by estimates of the variety of

these characteristics, cluster analysis, and PCA47. For the preservation, breeding strategies, development, and commercialization of new varieties46,47, high-yielding phenotypic variability

provided by various morphological and agronomical characteristics, such as seed, leaf, plant, and fruit-related characteristics, is crucial to understand48,49,50,51. The economic yield, a

very complicated characteristic of crops, is influenced by local climate and genetic factors. The crop also experiences many biotic and abiotic stressors and exhibits poor response to

inputs52. To increase the genetic variety and revitalize breeding stocks, new germplasm must be introduced. So, reducing the present yield gap and creating high-yielding types depend on

productivity and growth crops. Because of their comparatively greater economic significance, early genotypes in particular still have poor productivity, mostly because there aren't many

kinds with stable, high-yielding potential and losses from a variety of biotic and abiotic stressors53. A comprehensive evaluation of the field pea's qualitative and quantitative

traits is however uncommon. To develop crop advancement programs that produce high-yielding progeny, it is thus necessary to investigate genetic variability54. The necessity for a varied

parent to produce better genotypes in segregating generations was discussed by many researchers1,55,56. Yield-related traits have been considered by many researchers in multivariate analysis

to categorize and estimate diversity in crops. The researchers have a great interest in developing new varieties with the improvement of qualitative57,58,59,60 and quantitative

traits61,62,63,64 based on stability65,66, variability, and diversity67,68,69,70. Pea is an important vegetable crop as well as legumes in Bangladesh. In the country, a limited number of

low-yielding pea genotypes are being used commercially which are severely infested by powdery mildew (PM) during the growing period resulting in reduced pea yield. Therefore, the current

study was carried out to assess the genetic divergence of significant morpho-agronomic traits and to evaluate the performance of various genotypes of field peas to identify desirable

high-yielding pea genotypes with tolerance to PM and find out the genotypes for desirable traits that could be used in pea breeding programs to broaden the gene pool of this important

vegetable crop. METHODS PLANT MATERIALS AND SITE This research employed a total of 85 different genotypes of field peas for characterization and assessment. The Plant Genetic Resources

Centre (PGRC), Bangladesh Agricultural Research Institute (BARI), provided seeds for 23 Indigenous types, 7 genotypes from various agro-ecological areas in Bangladesh, 32 exotic types for

exploration through Australia, 1 exotic types from Nepal, 5 released varieties from BARI, 1 released variety from BSMRAU, and 14 were collected from the Pulses Research Centre (PRC), BARI,

for these experiments. Released variety BFP84 (BARI Motor-2) was used as a check for powdery mildew scoring in studied genotypes. Table 1 gives the complete details of these genotypes. Pea

seeds used for the study comply with our institutional, national, and international guidelines and legislation. DESIGNING EXPERIMENTS AND CROP MANAGEMENT The field experiment was carried out

during the cropping season (November to February) of the years 2022 and 2023 at the field of the Pulse Breeding Division, PRC, BARI, Ishwardi, Pabna, Bangladesh, which is situated at a mean

height of 15–19 m above sea level and is located at 24.75°N latitude and 88.5°E longitude. A Randomized Complete Block Design (RCBD) with three replications was used to set up the research.

The unit plot for continuous line sowing was 4.0 m by 1 m (two lines). Plant-to-plant spacing was kept at 5–7 cm inside a row. The use of pre-sowing irrigation helped achieve optimal

germination. The experimental plots were well-prepared before planting, and we added Farm Yard Manure (FYM). Field pea cultivation was done using the recommended amounts of manure and

fertilizer71. The seeds were treated with Provex at 2.5 g/kg seed prior to sowing. The experiment was routinely weeded, and irrigation was given as needed. As and when required, other

cultural activities were carried out. DETERMINATION OF AGRO-MORPHOLOGICAL AND QUALITATIVE CHARACTERISTICS Ten competing plants were chosen at random from each entry in each replication, and

the observations were averaged by dividing each value by ten. Data were recorded on days to 80% flowering (DF), days to maturity (DM), plant height (PH), pod length (cm) (PL), seeds per pod

(SPP), pods per plant (PPP), hundred seed weight (HSW), and yield per plant (YPP). Data on grain yield and yield-related traits were collected on a plant basis. The days to flowering (DF)

and days to maturity (DM) were taken when each block reached 80% flowering and 90% physiological pod maturity and calculated from the time required to the date of sowing to the date of 80%

flowering and 90% physiological pod maturity, respectively. Plant height (PH), pod length (PL), seeds per pod (SPP), pods per plant (PPP), and yield per plant (YPP) were collected after

harvesting from ten randomly selected plants in each plot. Plant height was measured in cm from the base of the plant to the tip of the plant. Pod length was measured from ten randomly

selected pods using a scale in cm. The number of seeds in ten randomly selected pods was collected to measure number of seeds per pod. The number of pods in ten randomly selected plants was

calculated to measure pods per plant. A hundred dried and cleaned seeds were counted and weighed to measure hundred seed weight (HSW). Ten randomly selected plants were collected and the

seeds were separated, dried, and cleaned to measure yield per plant. A set of local descriptors that were produced with the help of several scientific descriptors from the European Union

descriptor (UPOV) and IBPGR were used to capture the qualitative characteristics72,73. DISEASE DATA SCORING Early stage, flowering stage, and pod setting stage disease reactions of powdery

mildew for various genotypes were documented on a full plot basis 60 days after planting at three times74 (Table 2). After seed germination, 10 plants were selected at random from each line

and scored for morphological data and powdery mildew. The development of the powdery mildew disease was started by natural infection, and the disease infection was created by spraying a

fungicide Benomyl at 2.5 kg/ha at a fixed spray interval of every 7, 14, and 21 days75. Using a knapsack sprayer with a 60.6 ml spray volume per 2.4 m2 plot, a fungicide was administered.

The application of fungicides began as soon as the first detectable disease symptom appeared. The disease scoring process used a scale of 1 to 9 are details presented in Table 2. According

to Singh76 the disease data collected using the aforementioned scale was translated to a percentage disease index (PDI). Percentage disease index (PDI) was calculated for each genotype using

the formula: $$\text{PDI }(\text{\%}) = \frac{\text{Sum of all ratings}}{\text{Maximum disease grade }\times \text{Total number of observed plants}} \times 100$$ STATISTICAL ANALYSIS Both

quantitative and qualitative characteristics were taken to evaluate the varieties. 11 qualitative and 8 quantitative measurements were combined. Five plants and five pods per plant per

accession were used to gather the observations. Each treatment from all the sample data of a trait was averaged to obtain a replication mean. Statistics and biometrics were used to assess

the average data of different quantitative traits. The ANOVA, descriptive statistics, and LSD were calculated by using Statistix 8. Microsoft Excel was used to assess the phenotypic

diversity for each qualitative feature using the Shannon–Weaver diversity index (Hʹ)77. Excel was used to generate the Standardized H' using the following formula77: $$\sum {\left[

{\left( {{\text{pi}}} \right) \times \log \left( {{\text{pi}}} \right)} \right]}$$ where Hʹ—Shannon diversity index, and n—Individuals of a certain kind or species, N- Total number of

individuals of a community. According to Eticha et al.78, the diversity index was categorized as low (0.10 ≤ H ≤ 0.40), intermediate (0.40 ≤ H ≤ 0.60), or high (H ≥ 0.60). Using the

doe-bioresearch packages, the findings for various agro-morphological parameters were evaluated. The means were separated using the least significant difference at a 5% level of

significance. Pooled data from two years (2022 and 2023) were used to perform all analyses. The tools R (v 4.0.5) and R Studio were used to conduct the multivariate analysis79. Principal

component analysis (PCA) was used to estimate the degree of connection between characteristics, and cluster analysis was used to group genotypes based on traits. Using R's Complex

Heatmap package, the two-way hierarchical clustering heatmap was created using the Ward D2 and Euclidean distance algorithms. The R packages ggplot2, Factoextra80, and FactomineR81, were

used to create the PCA-biplot. The R program corrplot was used to create the correlation matrix82 which was then arranged in hclust. Cluster analysis was used to determine the cluster means

and standard deviations for each characteristic. Cluster analysis assessed the cluster mean values, distance values, and Dendrogram; PCA calculated Eigenvalues, variability, cumulative

variability, and vector components. RESULTS QUALITATIVE TRAITS The qualitative traits of the tested field pea genotypes are presented in Table 3, and the conversion values of the individual

traits are exposed in the same Table 3. Flower color variation and variability of leaf and tendril characteristics are shown in Fig. 1. Our results showed significant variation among the

traits studied in the field pea genotypes. The leaves were green in 69 genotypes (76.47% of the total), and the remaining 12 genotypes had yellow-green leaves (14.12%). It was found that the

color of the tendrils of the collected genotypes was mainly green (56.47%), followed by pale green and purple green tendrils 36.47, and 7.06%, respectively. High leaf sizes were found in

medium (43.53%), small, large, very small, and very large (43.53%), 29.41, 16.47, and 3.53%, respectively. Most genotypes of flowers (59 of 85) were creamy white (69.41%). Great diversity in

immature pod color was identified. Green, light-green, slight dark green, and slight light green constituted the major types with 56.47, 16.47, 14.12, and 10.59% of the total variation,

while the remaining genotype had dark-green colored pods (2.35% of the total). Maximum twining tendrils showed intermediate type (60%) and the lowest found high twining tendrils (16.47%).

Two types of growth patterns were recognized in the evaluated genotypes: erect (74.12%) and flat (25.88%). Rough-type pod texture was recorded among the collected field pea genotypes,

followed by smooth and tuberculate types. The majority of genotypes had weakly curved pods (72.94%) and a little absent pod curvature (14.12%). Regarding seed size, three types, medium

(72.94%), small (20.00%), and large (7.06%)—were recorded from the field pea genotype. The seed colors of the collected genotype are mainly Whitish green (51.76%) and Cream (29.41%). In the

case of the seed wrinkle, two types of variation were noted. The frequency distribution of genotypes studied for quantitative traits is shown in Fig. 2. In the histogram, there was much

variation found in the evaluated genotypes among the tested traits (Fig. 2). GENOTYPIC VARIATIONS IN AGRO-MORPHOLOGICAL TRAITS The combined analysis of variance over two years data revealed

significant differences for studied traits among the tested field pea genotypes (Table 4). However, the analysis of variance for year showed non-significant differences among the tested

genotypes (Table 4). For this reason, the data of years 2022 and 2023 were pooled and the pooled data were analyzed for diversity study and other multivariate analysis. The results of the

wide range of parameters for mean performance indicated that significant variations were seen in all of the investigated features, particularly in yield, seed size, pod setting, and disease

response. Table 5 displays the average values for each characteristic in the 85 genotypes of field peas that were studied. For genotypes BFP30 and BFP56, the DF was found to have a mean of

54.88 days and a range of 25–91 days, respectively (Tables 5 and 6; Supplementary Tables S1 & S3). The DM values, which varied from 84 to 112 days (BFP84 to BFP57 and BFP61), had grand

averages of 103.14 (Tables 5 and 6). From the vegetative stage until maturity, the plant's height ranges from 11.94 to 219.62 cm, with a mean height of 113.68 cm (Tables 5 and 6;

Supplementary Tables S1 & S3). The PPP substantially distinguished across genotypes, with BFP65 (6.3) and BFP74 (47.78), respectively, having the highest and the lowest values, while the

mean was 21.13 (Tables 5 and 6; Supplementary Tables S1 & S3). The SPP and PM had grand means of 4.87 and 3.71, with a range of 5.32 to 6. BFP04 had the highest PL (6.14), followed by

BFP55, BFP65, and BFP14 (Tables 5 and 6; Supplementary Tables S1 & S3). Between genotypes, significant variance in HSW was displayed. The lowest HSW was shown by the BFP50, while the

highest HSW and mean were shown by the BFP44 (Tables 5 and 6; Supplementary Tables S1 & S3). Field pea genotypes' YPP varied from 0.75 to 26.16 g. BFP78 provided the greatest YPP,

followed by BFP77, BFP74, BFP72, BFP45, and BFP63. BFP77, BFP74, BFP72, and BFP63 were highly resistant to resistant and high yielding. The genotypes BFP78, BFP79, and BFP48, on the other

hand, were highly productive and only moderately resistant. BFP44, however, demonstrated high production potential and moderate susceptibility (Table 7 Supplementary Tables S2 & S4).

SHANNON WEAVER DIVERSITY ANALYSIS COMBINED WITH DESCRIPTIVE STATISTICS The diversity of the accessions on the quantitative characteristics shown in Table 6 was assessed using the descriptive

statistics (average, range, and standard deviation) and Hʹ. All of the genotypes' coefficients of variation fell into two categories: medium (between 10 and 20%), or low (below 10%).

The ninequantitative features' coefficients of variance varied from 1.04 to 12.15%. The YPP population had the highest CV (12.15%), followed by PPP (4.49), HSW (4.25), and PH (3.05).

The field pea genotype's high intra-varietal variability was demonstrated by the larger coefficients of variation for PH, PL, and DM. SPP (2.13–7.45) had the lowest diversity index

(0.80) across morphological traits while PH (11.94–219.62 cm) displayed the greatest diversity value (3.62). A high diversity index (H') was also seen in the yield-related

characteristics DF (1.00), DM (1.91), PPP (1.12), PL (1.93), HSW (0.88), and YPP (1.23). RESPONSE OF GENOTYPES TO THE ILLNESS OF POWDERY MILDEW The genotypes of field peas were tested in the

field at three development stages for natural infection with the powdery mildew disease caused by _Erysiphe polygoni_. From the early stages to flowering and pod setting, the disease's

severity increased (Table 7, Supplementary Tables S2 & S4, and Fig. 3). The responses of all genotypes examined to the illness caused by powdery mildew varied greatly. As a result,

among the genotype sources, 14.11 and 34.11% of the genotypes, respectively, were found to be highly resistant to _E. polygony_ infection. Hence forward, it was found that out of the total

85 field pea genotypes, twelve genotypes (BFP21, BFP29, BFP30, BFP31, BFP33, BFP34, BFP35, BFP52, BFP72, BFP73, BFP81 and BFP84) i.e. 14.11% were highly resistant (Disease Severity Scale 2)

(Table 8), twenty-nine (BFP04, BFP06, BFP13, BFP17, BFP18, BFP20, BFP23, BFP28, BFP32, BFP37, BFP38, BFP39, BFP40, BFP41, BFP42, BFP43, BFP55, BFP57, BFP59, BFP62, BFP63, BFP67, BFP68,

BFP69, BFP71, BFP74, BFP77, BFP80 and BFP82) i.e. 34.11% were resistant (Disease Severity Scale 3), twenty-five (BFP03, BFP10, BFP11, BFP12, BFP15, BFP19, BFP22, BFP24, BFP25, BFP26, BFP27,

BFP45, BFP46, BFP48, BFP50, BFP53, BFP54, BFP61, BFP70, BFP75, BFP76, BFP78, BFP79, BFP83 and BFP85) i.e. 29.41% were moderately resistance (Disease Severity Scale 4), eighteen (BFP01,

BFP02, BFP08, BFP14, BFP16, BFP36, BFP44, BFP58, BFP64, BFP65, BFP07, BFP09, BFP47, BFP49, BFP51, BFP56, BFP60 and BFP66) i.e. 21.76% were moderately susceptible (Disease Severity Scale 5

& 6) and three (BFP05) i.e. 1.17% were susceptible (Disease Severity Scale 7) (Table 8 and Fig. 3). The genotypes BFP21, BFP30, BFP31, BFP33, BFP34, BFP52, BFP72 were highly resistance

along with check variety BFP84 (BARI Motor-2). PRINCIPAL COMPONENT ANALYSIS (PCA) The results of PCA showed that only the first four principal components (PCs) had eigenvalues greater than

1.00 and that the highest variability across field pea genotypes for yield component attributes was around 88.4% (Fig. 4a,b). In field pea improvement programs, the traits corresponding to

these five PCs may be given the appropriate weight. Nine characteristics were used for the principal component analysis (Fig. 4b). 72.2% of the variance was explained by the first three

principal components (PC), which had values of 30, 23.9, and 18.3% for PC1, PC2, and PC3, respectively (Fig. 4a). Following the PC1 in terms of variety were the PC2, PC3, PC4, and PC5. The

earlier research by Hanci and Cebeci83 provided further support for the current investigation. The decision of how many variables to keep is aided by eigenvalues. According to Sharma54, the

number of variables is often equal to the sum of the eigenvalues. The first principal component (PC1) exhibited a variance of 30%, with the key positive contributions being YPP, HSW, PH, and

PPP, while the significant negative contributors were PL, DM, and SPP (Fig. 4c). The PC2 was mostly linked to yield parameters including DF, DM, and PPP, and accounted for 23.9% of the

overall variance (Fig. 4d). Figure 5 depicts the magnitude and direction of the quantitative features' contribution to the various main components. The PCA-biplot was created using the

first two PCs, which together accounted for 53.9% of the total variability (Fig. 5). The key yield and yield-attributing traits, however, grouped in trait clusters 2 and 3, such as HSW, PL,

and YPP, were mostly connected with and positively contributed to PC2 and PC3 (Figs. 4c,d, 5). Based on the features that contribute to yield, the genotypes in clusters 4 and 5 dominated

substantially and were favorably emphasized in both PC1 and PC2 (Fig. 5); in contrast, the genotypes in cluster 3 were strongly reflected by the traits DF, DM, and PL (Fig. 4). ANALYSIS OF

CLUSTER DISTANCES Based on D2 values, cluster analysis was used to divide the 85 genotypes into 5 distinctive groups. The number of genotypes in each cluster ranged from 1 to 37. Cluster 1

included the greatest number of genotypes (37) possible. Cluster 3 included the fewest number of genotypes only one. The genotypes in clusters 2, 4, and 5 were 28, 8, and 11, respectively

(Table 9 and Fig. 6). Prasad et al.1 and Kumar et al.84 noticed that great variety was present in the material under evaluation, as evidenced by the discrimination of genotyping lines into

so many distinct clusters. Table 9 and Fig. 6 provide estimates of intra- and inter-cluster distances for five clusters. Cluster 5 had the highest intra-cluster value (3.40), followed by

Cluster 2 (3.01), Cluster 1 (2.92), and Cluster 4 (2.06), indicating that these clusters' genotypes exhibit substantial genetic diversity (Table 9). Cluster 2 and Cluster 3 had the

greatest inter-cluster distance (11.89), followed by Cluster 1 and Cluster 3 (11.43) and Cluster 3 and Cluster 5 (5.35), indicating the greatest genotypic diversity in these clusters.

Therefore, it is suggested that improved segregants for high seed production and yield-contributing characteristics owing to non-allelic interactions are predicted if different genotypes

from these groups are employed in breeding programs along with other desired features. Cluster 3 and cluster 4 exhibited the least genetic diversity among their clusters, and they had the

same genetic architecture, as shown by the smallest inter-cluster difference between them (2.06), which was followed by cluster 4 and cluster 5 (3.40) (Table 9). In order to disrupt the

unfavorable relationship between yield and its associated qualities, such genotypes may also be employed in breeding programs to create bi-parental crosses between the most diversified and

close-proximity groups. GENOTYPES ARE GROUPED USING A HEATMAP-ORIENTED CLUSTERING PATTERN A heatmap is a two-dimensional data visualization technique that uses color to show the size of a

phenomenon. By examining color variation by intensity, the reader may observe how the phenomenon is categorized or varies through time. On a backdrop of mostly minor features, it depicts the

relative distribution of strongly expressed qualities (Figs. 6 and 7). The heatmap analysis produced two dendrograms as a consequence, one in the vertical direction representing the

germplasm accessions and the other in the horizontal direction reflecting the attributes that caused the diffusion. A heatmap is a two-dimensional data visualization technique that uses

color to show the size of a phenomenon. Eighty-five field pea genotypes were used in the current research, and using heatmap-oriented cluster analysis, the genotypes were divided into five

groups based on the average values of all the analyzed variables. The distribution of genotypes in the clusters showed that cluster 1 had the highest number of genotypes (37) and cluster 3

had the lowest number of genotypes (1) (Table 10 and Figs. 6 and 7). Three other groups might be seen on other dendrograms. DF and DM are two characters connected to Group 1. Three

characters (YPP, HSW, and PL) are related to Group 3. Three characters (SPP, PH, and PPP) are allies of Group 3 (Fig. 7). ANALYSIS OF CLUSTERED MEANS To determine the acceptable genotypic

diversity present across all study groups, a dendrogram of 85 field pea genotypes was created using the Ward clustering technique85. The significant degree of variation in cluster means for

several traits (Table 10) further supported the variety. Eight yield and yield contributing characteristics, along with PM cluster means, were evaluated (Table 10). The average comparison of

the various characters revealed significant variations among the clusters for each character. Cluster 5 had the highest mean for DF (81.45), followed by Cluster 3 (57), while Cluster 2 had

the lowest mean for a DF (47.04). The mean values for Cluster 3 were the greatest for DM, HSW, and PM. In cluster 4, the greatest means for PH (190.05), PPP (38.03), and YPP (20.65) were

found. Twenty-eight genotypes constituted the Cluster 2, which had the second highest number of genotypes. This cluster had smaller seeds than the others, was more moderately vulnerable to

powdery mildew, and had a lower YPP than the others. In cluster 2, none of the characters had the highest mean value. These results showed that certain clusters performed better for various

character types. ANALYSIS OF THE TRAIT ASSOCIATIONS There were very strong relationships among the traits that were assessed (Fig. 8). The coefficient of correlation is the measurement of

the linear relationship between two variables. The correlation of nine parameters under field conditions is presented in Fig. 8. PPP had positive significantly correlation with PH (r = 0.65,

_p_ < 0.001), YPP (r = 0.46, _p_ < 0.001), whereas it had negative nonsignificant correlation with DF, PM and SPP under field condition. PH demonstrated positive significant

correlation with YPP (r = 0.34, _p_ < 0.01), and DM (r = 0.24, _p_ < 0.05) while expressed negligible negative correlation with DF, PM and SPP. YPP expressed positive highly

significant correlation with HSW (r = 0.85, _p_ < 0.001), while it showed negligible negative correlation with DF and PM. DF showed highly significant correlation with DM (r = 0.54, _p_ 

< 0.001). Powdery mildew (PM) disease severity had nonsignificant positive correlation with HSW (r = 0.03) and SPP (0.15). whereas rest of the traits had a nonsignificant negative

correlation with powdery mildew disease severity (Fig. 8). PL represented highly positive correlation with SPP (r = 0.80, _p_ < 0.001). DISCUSSION An established crop breeding technique

for managing and successfully using plant genetic resources is genotype evaluation and screening for desired traits86. The degree of genetic diversity in agro-morphological variables

associated with yield determines the breeding strategy. Research on multivariate analysis and genetic diversity is essential for an effective genotype assessment. Plant genotypes exhibit a

great degree of morphological variation. A great degree of diversity is used to create better cultivars of important crops. Crops that are less popular or aren’t used as much need to benefit

from this development as well since they have a high degree of variability both inside and between their accessions (intra-variation) and within (inter-variation). All studied field pea

genotypes showed a variation across several traits which define a wide array of variability among the traits. A similar kind of variability for traits in rice was recorded by several

researchers87,88. Breeders may choose better lines for future development by using morphological characterization in diverse areas89,90,91. Analyzing morphological traits is a common method

for determining genetic diversity for many crop species, including field peas. It is successfully used on a variety of crops, including _Pisum sativum_73, mungbean48,92, black gram93,

amaranth94,95,96,97,98, Maize99, and field pea17,22,23. The findings of this research support highly the Shannon–Weaver diversity indices across field pea populations for the qualitative

attributes of tendril color, tendril twinning, immature pod color, flower color, and seed size. Shubha et al.100 and Rosero-Lombana et al.101 both found similar findings for field pea. Table

3 displays the results of the Shannon–Weaver diversity study for qualitative traits. Indicators of variety varied from 0.36 for leaf color to 0.89 for pod curvature. Seven qualitative

traits in this research have a high variety index (H = 0.60). The results of this research showed that there was little variation in leaf color and pod curvature across field pea genotypes,

in contrast to studies73,102. For the majority of the characteristics listed in Tables 4 and 5, a broad range of differences were observed. The findings are validated by other

research17,73,103 and show that features like PH, HSW, DF, PPP, DM and YPP, showed a very wide variation in mean performance. There was a lot of phenotypic diversity in seed yield and

related traits. The great degree of diversity in yield and its associated traits was highlighted by the mean performance in this research (Table 3), suggesting that future breeding programs

will have more opportunities to make use of these traits104. In keeping with the findings of the current study, several researchers including those of our own earlier study16,17 showed

significant heterogeneity in field pea yield and its associated characteristics. Significant differences for studied traits among the tested field pea genotypes might be due to the

difference in genetic composition of the tested genotypes. This indicates that the tested genotypes have different potential for field pea crop production for studied characters which

corroborated with the results of Mogiso105 and Gurmu106 in field pea. Significant variations across genotypes were also reported in literatures107,108,109,110,111,112 which supported the

current findings. Crops that are affected by the powdery mildew disease have large yield losses74,113,114. Field pea breeding for resistance to powdery mildew needs effective disease

screening techniques. When field peas are produced for seed, powdery mildew losses are greater because the disease becomes more severe as the crop matures114. No matter where they came from,

natural sources of resistance with varying disease responses to powdery mildew in field peas have been found in the germplasm32. Crops suffering from powdery mildew disease experience

significant losses of yield114. It is necessary to use the right disease-screening techniques while breeding field peas to resist powdery mildew. According to Teshome75, the powdery mildew

disease significantly reduces the yield potential of field pea germplasm grown throughout the world by generating an 86% loss. Therefore, the greatest alternative for crop breeding is

genetically based resistance to harmful diseases32,75,115. In order to choose powdery mildew-resistant lines for documentation and identification of top lines for widespread cultivation, the

chosen pea lines were also assessed for the powdery mildew disease. Researchers have used a variety of approaches to screen for powdery mildew29,75,115, but artificial inoculation has been

found to be the most reliable and effective29,105,115,116. In this study, the same eighty-five germplasms were tested in a field setting for resistance to powdery mildew. Out of the 85

germplasms listed in Tables 6 and 7, none were found to be extremely resistant (immune). However, 29 germplasms were observed to be resistant and 12 to be highly resistant. Eighteen

genotypes demonstrated moderately susceptible, while the remaining twenty-five germplasms exhibited moderate resistance. The BFP05 genotype showed susceptible to PM disease. In order to

better understand the level of genetic diversity and agronomic performance of resistant germplasm accessions, more information should be gathered29,117,118. The germplasm accessions, which

have high levels of resistance and agronomic superiority, may minimize the time necessary to eradicate the undesired genes via repeated backcrossing by plant breeders. Three basic selection

strategies including tandem selection, independent culling levels and index selection can be utilized for improvement of plants species in breeding programs. Tandem selection attempts to

improve a breeding germplasm for several traits by selecting one trait at one time for several generations, then another trait is focused on for next breeding cycle119. Identification of

diversity serves two roles: it helps to organize the identity and integrity of an ex-situ collection and it also provides a structure accessing this diversity for breeding efforts120. PCA is

a useful method for locating significant characteristics that have a bigger influence on the total variables, and each vector's coefficient indicates the percentage contribution of

each original variable that each principal component is linked to Sanni et al_._121. According to studies, the first three main components are often the most significant in showing the

patterns of variation among the various genotypes and the traits linked to genotype differentiation. Raji122 asserted that traits with coefficients greater than 0.3 (regardless of whether

they are positive or negative) are important, while traits with coefficients less than 0.3 are thought to have the least impact on the overall variation seen. This methodology was used in

the current study123. Based on the dendrogram produced by cluster analysis and PCA, the 85 genotypes of field pea were divided into five groups (Tables 9 and 10 and Fig. 6). Sharma103

determined a similar clustering pattern using PCA and hierarchical cluster analysis for 22 field pea genotypes. According to Euclidean distance, clusters mostly emerge following the origin

of genotypes or geographical locations. Most genotypes with the same geographic origin are clustered together, although a smaller number of genotypes with different origins are also included

in the same cluster. Understanding the link between variables may be aided by the use of multivariate statistical analysis, such as PCA. These might help determine the traits nature and

simplify data collection124. The first four principal components in this study's PCA analysis of the nine quantitative characteristics explained 88.4% of all variance, indicating a very

significant link between the features under investigation (Fig. 4a). The first PC was the most important, accounting for 30% of the variance on its own. Because of their significant

loadings, the features of YPP, HSW, PH, and PPP were crucial in the first PC for distinguishing the genotypes (Fig. 4c). Similar findings were found in a field pea crop by Chowdhury and

Mian125. A method of multivariate analysis known as PCA biplot combines characteristics and genotypes in two dimensions while eliminating overlapping variations from large, complicated data

sets, making it easier to identify key figures for selection (Fig. 5). As a result, PCA revealed distinct trait differentiation as well as significant variation across the five groups of the

85 field pea genotypes (Fig. 5). The traits PPP, HSW, PH, and YPP significantly contributed to describing the variations among the BFP74, BFP78, BFP76, BFP80, BFP75, BFP45, and BFP73

genotypes, and as a result, it will be possible to improve the genetic diversity of field pea genotypes through selection using these traits. Singh et al.113 claimed that while choosing the

kind of cluster to utilize for further selection and the pattern to use for hybridization, greater focus should be placed on the characteristic that provides the greatest divergence. The

correlation matrix of the observed qualities in Fig. 5 also confirmed the variability of traits determined by PCA biplot and cluster analysis. One of the widely used statistical methods for

categorizing items into groups that have a lot in common with other groups of objects is cluster analysis. The clusters will be useful for upcoming heterotic breeding since different sets of

alleles may affect their traits and performance125. According to earlier publications by Prasad et al.1 and Kumar et al.126, great variety was present in the material under evaluation, as

evidenced by the discrimination of genotyping lines into so many distinct clusters. Table 9 displays the estimated intra and inter-cluster distances for five clusters. Cluster 5 had the

largest intra-cluster distance, which was followed by clusters 2, 1, and 4, whereas clusters 2 and 3 had the greatest inter-cluster distance, which was followed by clusters 1 and 3 (Table

9). In cluster 4 with 8 genotypes, we observed that variables that contribute to PPP, PL, PH, and YPP were substantially higher (Table 10). The findings of this research showed that

yield-related positive and significant traits had the ability to enhance seed production. Since they demonstrated a favorable and statistically significant link with grain production, these

traits were taken into account throughout the selection process. Thus, the field pea hybridization program for a variety of applications might employ these genotypes of field pea as parental

sources. In conclusions, both multivariate statistical analysis tools showed the existence of wide genetic diversity among the landraces in the study for qualitative and quantitative

characteristics during the years 2022 and 2023. The results of the current research showed that there was enough genetic variation within and between genotypes, suggesting the prospect of

further genetic improvement in field pea yield and traits associated with yield. The studied qualitative and quantitative traits both revealed connections between one another. The findings

of the current study showed that there was significant variation in both seed yield and its related traits and resistance to powdery mildew diseases, suggesting the possibility of selecting

promising gene pools that could be used as direct sources or transferred through hybridization to genotypes with high yields but disease-prone traits. The BFP77, BFP74, BFP63, BFP62, BFP43,

and BFP80 genotypes were also high-yielding and resistant to PM, while check variety (BFP84) was highly resistance but lower yield; they could be selected as elite genotypes to pass to the

next yield trial or for crossing purposes. BFP78, BFP72, BFP79, and BFP48 genotypes were found to be high-yielding and moderately resistant to PM. For breeding reasons, elite genotypes might

be chosen from high-yielding and resistant gene pools (BFP77, BFP74, BFP63, and BFP62) and low-yielding and resistant gene pools (BFP50, BFP69, BFP53, BFP30, BFP57, and BFP65). The 85

genotypes were clearly divided into five groups of clusters using the dendrogram and heatmap-oriented cluster analysis, and there was substantial genetic variation. The genotypes BFP78,

BFP77, BFP74, BFP45, BFP79, and BFP80 were identified as promising for PPP and PL in relation to yield features. All four PCs had eigen values greater than one and displayed roughly 88.4%

variability. Therefore, these PCs received the attention they deserved for the following explanation. The PC1 displayed 30% of the overall variation, followed by the second and third

components, which accounted for 23.9 and 18.3% of the genotype-specific total variation, respectively. While YPP, HSW, PH, and PPP were associated with PC1, DF and DM predominated in PC2.

All potential pairs of clusters' genetic divergence were extremely significant. The selection of these features could be utilized as selection criteria to increase the seed yield of

field pea genotypes because PH, PPP, and HSW demonstrated a substantial positive connection with YPP. The examined gene pools were extremely varied, as evidenced by the inter-cluster D2

value, which ranged from 2.06 to 11.89, while cluster 5 had the greatest intra-cluster distance. The mean values for PH, PPP, PL, and YPP were the highest in Cluster 4 whereas SPP had the

lowest value (3.79) found in cluster 5. Maximum DM and HSW mean values were observed in clusters 5 and 3, respectively. Furthermore, it might be deduced that offspring from different

crossings should exhibit a broad range of genetic diversity and had a greater capacity to identify transgressive segregants in subsequent generations. The genetic variation of the genotypes

of field peas that were evaluated in this study might provide helpful information for further trait specific breeding initiatives. It is concluded that the germplasm tested in this

experiment had a high degree of resistance to powdery mildew, combined with enough genetic diversity and agronomic superiority, making it suitable for use in breeding field pea varieties

with both high yield and resistance to powdery mildew. This work used phenotypic selection for agronomic performance within field pea genotypes that would produce many useful field pea

breeding lines for the future. In the current investigation, six genotypes BFP77, BFP74, BFP63, BFP62, BFP43, and BFP80 compared to check BFP84 were found to be highly resistant with a

higher yield. These genotypes can be used to select field pea genotypes for the creation of molecular markers, mapping populations for molecular breeding, QTL identification for PM

resistance, and the development of PM-resistant varieties. DATA AVAILABILITY All data generated or analyzed during this study are included in this published article and its supplementary

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Curr. Microbiol. Appl. Sci._ 8, 3032–3038 (2019). Article  Google Scholar  Download references ACKNOWLEDGEMENTS The authors sincerely thank Bangladesh Agricultural Research Institute (BARI)

for providing research facilities and financial support for this study. They also thank pulses research team members of Pulses Research Centre, Ishurdi, for their kind assistance in the

proper execution of these trials at their fields and for recording observations on field pea reaction meticulously. The authors would like to extend their sincere appreciation to the

Researchers Supporting Project Number (RSP2024R194), King Saud University, Riyadh, Saudi Arabia. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Pulses Research Centre, Bangladesh Agricultural

Research Institute, Ishurdi, Pabna, 6620, Bangladesh Mohammad Golam Azam * Department of Genetics and Plant Breeding, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural

University, Gazipur, 1706, Bangladesh Umakanta Sarker * Department of Genetics and Plant Breeding, Faculty of Agriculture, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh

Mohammad Amir Hossain * Pulses Research Sub-Station, Bangladesh Agricultural Research Institute, Gazipur, 1701, Bangladesh A. K. M. Mahabubul Alam * Agronomy Division, Regional Agricultural

Research Station, Bangladesh Agricultural Research Institute, Pabna, 6620, Bangladesh Mohammad Rafiqul Islam * Department of Agronomy, Iowa State University, Ames, IA, 50010, USA Nazmul

Hossain * Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia Saud Alamri Authors * Mohammad Golam Azam View author publications You can

also search for this author inPubMed Google Scholar * Umakanta Sarker View author publications You can also search for this author inPubMed Google Scholar * Mohammad Amir Hossain View author

publications You can also search for this author inPubMed Google Scholar * A. K. M. Mahabubul Alam View author publications You can also search for this author inPubMed Google Scholar *

Mohammad Rafiqul Islam View author publications You can also search for this author inPubMed Google Scholar * Nazmul Hossain View author publications You can also search for this author

inPubMed Google Scholar * Saud Alamri View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS M.G.A. and U.S. initiated the research work and

conceived the study; M.G.A. and U.S. performed the experiments; M.G.A. and U.S. performed statistical analysis; M.G.A., U.S., M.A.H., A.K.M.M., M.R.I., N.H., and S.A. drafted, edited,

interpreted data, and prepared the manuscript. U.S., N.H., and S.A. contributed to revising the manuscript and English editing the revised manuscript. CORRESPONDING AUTHOR Correspondence to

Umakanta Sarker. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE Springer Nature remains neutral with regard

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