thesis of okra

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GENOTYPE X ENVIRONMENT INTERACTION IN OKRA [Abelmoschus esculentus (L.) Moench.], THESIS : DOCTOR OF PHILOSOPHY In AGRl. BOTANY (Genetics and Plant Breeding)

Thesis: Ph. D. (Agril. Botany-Plant Breeding & Genetics Sumitted to CCS Univ., Meerut

Related Papers

Ph.D. Thesis submitted to C C S Univ., Meerut

HortFlora Research Spectrum

Electronic Journal of Plant Breeding

Sateesh Adiger

The present study was undertaken on 163 genotypes including 43 parents and 120 crosses of okra to determine the genetic variability, nature of association among different yield attributes and their direct and indirect contribution towards yield. From the analysis of variance, it was observed that mean squares due to genotypes were significant for all the traits, indicating the presence of genetic variability in the experimental material. The values of PCV were higher than that of GCV values for all the ten characters indicating influence of environmental effects in the expression of these characters. The GCV, heritability and genetic advance as percentage of mean were higher for plant height, fruit yield per plant, fruit weight and days to 50 per cent flowering which might be attributed to additive gene action of inheritance. The Fruit yield has significantly positive correlation with plant height, number of branches per plant, inter nodal length, fruit length, fruit weight and numb...

Asian Journal of …

SHIVANANDA HONGAL

International Journal of Current Microbiology and Applied Sciences

Shahnaz Mufti

Spanish Journal of Agricultural Research

David K. Ojo

Prof. Dr. Ehab A. Ibrahim

The objectives of the study were to study the genetic behavior of some biological and economical traits of some okra families, resulting from applying two cycles of inbreeding with selection to fourteen okra populations collected from Dakahlia Governorate. Selection of individual plants based on earliness, high number of pods, and minimum neck/pod ratio was carried out in all generations. The results showed that the means and ranges of all studied traits for all families became smaller in the S2 generation than those in the S0 generation. Highly significant variations were observed among populations for all the studied traits. The mean performance showed a clear indication of agronomic superiority of some families over others. Family 9 followed by family 12 showed the earliest flowering plants and the highest yield per plant. Phenotypic variances were higher than the corresponding genotypic variances indicating predominance of environmental effects on the expression of these characters. The magnitude of phenotypic and genotypic coefficients of variation varied from trait to another. High broad sense heritability coupled with high genetic advance as percent of mean were shown by the different traits, especially, plant height, number of branches per plant, number of pods per plant, pod length, neck/pod ratio and plant yield. This provided that these parameters were under the control of additive genetic effects, and could be effectively improved through selection. Plant yield had positive and highly significant correlation at genotypic and phenotypic level with number of pods per plant, plant height and neck/pod ratio.

International Journal of Pure & Applied Bioscience

Prakash Kerure

Plant Breeding and Biotechnology

mohamed abed

Journal of Agricultural Science

Omolayo Johnson Ariyo

Acta Horticulturae et Regiotecturae

Adesola Nassir

A study was carried out at the Federal University of Agriculture Abeokuta, Nigeria to determine the gene action underlying the inheritance of important agronomic traits as well as the general combining ability (GCA) and specific combining ability (SCA) of the parents and hybrids, respectively. Ten hybrids were developed by crossing five lines to two testers. The hybrids and parents were evaluated on the field in a randomised complete block design replicated three times, and data were collected on days to 50% flowering, number of branches, stem diameter, plant height, pod length, pod width, pod weight, number of pods and pod yield. The data were subjected to line by tester analysis and results showed substantial variability among the genotypes for some of the characters measured. Days to 50% flowering, number of pods and pod yield were largely under additive gene action while non-additive gene action was more important in the inheritance of plant height. Favourable GCA and SCA effect...

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Evaluation of Varieties and Cultural Practices of Okra (Abelmoschus Esculentus) for Production in Massachusetts

Renato Mateus , University of Massachusetts Amherst Follow

Access Type

Open Access

Document Type

Degree program.

Plant & Soil Sciences

Degree Type

Master of Science (M.S.)

Year Degree Awarded

Month degree awarded.

Abelmoschus Esculentus, Okra, Varieties, Densities

Okra ( Abelmoschus esculentus ) is a traditional crop commercially cultivated in many parts of the world. Fresh okra has a high nutritional value and grows very quickly with high temperatures, which lends its production to more tropical areas. This study was implemented to evaluate different varieties of okra and determine the optimum density for production in Massachusetts. Two experiments were carried out between May and September of 2009 and 2010 at the UMass Research Farm in South Deerfield, MA. For the variety trial in 2009: Annie Oakley, Baby Bubba, Cajun Delight, Chifre de Veado, Clemson Spineless, Millionaire, North & South and Santa Cruz 47. The immature pods were harvested when reached 70 mm in length (size desired by the market in the USA) and in another plot for Chifre de Veado and Santa Cruz 47 the pods were harvested when reached 100 mm (market in Brazil). The density trial was set in a randomized complete block design with seven different plant spacings (7.5, 15.0, 22.5, 30.0, 38.5, 45.0 and 52.5 cm) in double row of plants of Cajun Delight. The pods were harvested three times a week, counted and weighted. Analyses of variance were performed by SAS, and means were compared using Duncan’s new multiple range test ( P = 0.05) and orthogonal polynomial comparisons. In 2010, Santa Cruz 47 harvested based on Brazilian market size had the best performance over the season with the yield of 17.86 ton.ha -1 and similar statistic results comparing to North and South (15.99 ton.ha -1 ) and Annie Oakley (15.24 ton.ha -1 ). The differences among the plant spacings in 2010, were represented by a quadratic relationship, where the greater plant spacing for yield was ‘52.5 cm’ with the total yield of 14.91 ton.ha -1 . Both trials in 2009 were negatively affected by the soil-borne fungus Verticillium spp., which, combined with the cold and wet weather, became very aggressive, especially in the end of the season. The results show that the varieties: North and South, Annie Oakley, Cajun Delight, Millionaire, Clemson Spineless, Santa Cruz 47 can be commercially grown in Massachusetts and the recommended plant spacing of okra is 52.5 cm.

https://doi.org/10.7275/2220859

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Frank Mangan

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Wesley Autio

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• Published: 20 May 2021

Review on the “Biological Applications of Okra Polysaccharides and Prospective Research”

• Ali A. A. Al-Shawi   ORCID: orcid.org/0000-0002-0690-4612 1 ,
• Mustafa F. Hameed 2 ,
• Kawkab A. Hussein   ORCID: orcid.org/0000-0001-9796-0929 1 &
• Haneen K. Thawini   ORCID: orcid.org/0000-0002-8709-4198 1 , 3  

Future Journal of Pharmaceutical Sciences volume  7 , Article number:  102 ( 2021 ) Cite this article

4779 Accesses

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Vegetables with edible parts like flowers, fruits, stems, leaves, fibers, roots, and seeds are rich sources of essential vitamins, minerals, and trace elements with various medical functions. Many diseases such as osteoporosis, diabetes, high cholesterol, obesity, heart diseases, and stroke are caused by poor, healthy lifestyle or nutrition. Therefore, generation of new biological functions from vegetables will increase the interests of scientific research and applications.

Okra is an edible vegetable which contains vitamins, fiber, carbohydrates, protein, and minerals. The bioactive compounds of okra possess various biological activities such as anti-inflammation, antibacterial, anticancer, and antifungal. Polysaccharides from vegetables or medicinal plants are important large molecules with various biological applications. In this review, we will focus on the biological properties and nanoparticle uses of polysaccharides isolated from okra and the extraction methods of polysaccharides.

This review will enhance the scientific research findings of okra polysaccharides and recommend future prospective of polysaccharides for biological uses.

Edible plants are one of the important sources of proteins, carbohydrates, vitamins, amino acids, minerals, and lipids that enhance the immune system, bones, muscles, and other parts of the human body to fight diseases [ 1 , 2 ]. Edible vegetables have common benefits for the human body and animals due to the chemical components of primary metabolism, which depends on the type of soil, used water, and environment changes [ 3 , 4 ].

Okra is one of the delicious edible vegetable in North America, West Africa, South Asia, and Arab countries; it has few common names like lady fingers (English-speaking countries), Bamya (common name in Iraq), and father of musk (some Arabic countries) [ 5 ]. Okra belongs to the Malvaceae family, genus Abelmoschus, species Esculentus and contains edible green seeds, pods, and fibers (Fig. 1 ) [ 6 ].

Okra vegetable

Fresh okra contains energy, 90% water, 7% carbohydrates, 2% protein, fibers (contains alpha-cellulose, hemicellulose, lignin, pectin, fat, and wax matter), some important soluble vitamins in water and fat, and minerals like calcium, iron, magnesium, phosphorus, potassium, and zinc [ 7 , 8 ]. Therefore, okra is an important edible vegetable for human health. Okra mucilage is used in industrial as turbidity from wastewater [ 9 , 10 ], and also under investigations as biodegradable food packaging [ 11 , 12 ]. The biological studies of okra bioactive compounds were investigated as antioxidant, neuroprotective, anti-diabetic, anti-hyperlipidemia, and anti-fatigue activities [ 13 ]. Okra polysaccharides have not yet pharmacology extensively been investigated. In this review, we will present the extraction methods, chemical structure, nanoparticles, and the biological activities of polysaccharides extracted from okra vegetable, which presents a wide understanding of okra polysaccharides’ importance and further uses.

Okra polysaccharide (OP)

Isolation and purification of polysaccharides from plants depend on the extraction methods and purification solutions, which may keep or break down the structure of polysaccharides and may reduce the biological properties. Okra polysaccharides have been isolated and identified. Liu et al. used hot water method for extraction and isolation of okra polysaccharides; it consisted of the main four monosaccharides (arabinose, galactose, rhamnose, and galacturonic acid). The isolated polysaccharides showed good hyperglycemia activity (Fig. 2 ) [ 14 ].

An example of the chemical structure of polysaccharides isolated from okra vegetable via hot water extraction method

Chandra et al. extracted polysaccharides from the okra head waste which contains a high ratio of mucilage and found it to lower thermal degradation properties [ 15 ]. Kunli et al. used ultrasound-assisted extraction method to extract polysaccharides from okra vegetable which contains monosaccharides (glucose, mannose, galactose, arabinose, xylose, fructose, and rhamnose). It exhibited high antioxidant activity versus superoxide radicals and DPPH, and weak antioxidant activity versus hydroxyl radicals was revealed [ 16 ]. Xi et al. used hot water method to isolate polysaccharides from various five cultivated okra in China. The polysaccharide structure consists of similar monosaccharides (rhamnose, galacturonic acid, galactose, and arabinose); they suggested that the identified polysaccharides could be used as functional food ingredients for industrial application prospects [ 17 ]. Huricha et al. used macerated method and identified three fractions from okra polysaccharides with different molecular weight (600, 990, and 1300 kDa) and two groups of monosaccharide composition (group 1 galactose, rhamnose, galacturonic acid, and glucuronic acid; and group 2, galactose, rhamnose, galacturonic acid, glucuronic acid, and glucose). They found that those okra polysaccharides may potentially serve as novel immunomodulators supported by future studies [ 18 ]. Qin et al. used three extraction methods to evaluate the efficiency of okra polysaccharide extract; the three extraction methods were hot water extraction (HWE), pressurized water extraction (PWE), and microwave-assisted extraction (MAE). They found that the method PWE was a good extraction technique for okra polysaccharide with high biological activity for industrial applications [ 19 ]. Xi et al. used an ultrasonic method to extract okra polysaccharides (obtained pectic polysaccharides, composed of rhamnose, galacturonic acid, and galactose) which promised to be a potential functional food and pharmaceutical industries [ 20 ].

Table 1 showed the type of extraction method and structure analysis of okra polysaccharides. Ultrasound extraction method of okra polysaccharides showed only two similar monosaccharides (galactose and rhamnose). These differences in monosaccharide types cause variations in the biological activities, therefore, needed extensive applications to compare and target the function of structure on the efficiency of okra polysaccharides.

Antioxidant and biological activities of OP

Natural antioxidant plays a role in our life because it can keep and protect the human health rather than an industrial antioxidant. Several studies showed the antioxidant activity of okra chemical components and related to the phenolic and flavonoid contents in okra seeds, flowers, and fruits [ 21 , 22 , 23 , 24 ]. Gemede et al. found that okra pod mucilage is a good source as a natural antioxidant [ 25 ]. Okra polysaccharides have been investigated for its antioxidant activity; Kunli et al. found that okra polysaccharides extracted by the ultrasound method exhibited significant in vitro antioxidant activity [ 16 ]. Weijie et al. extracted polysaccharides from okra flowers using the hot water extraction method. The composition of isolated polysaccharides was [2)-α-D-Rhap-(1 → 4)-α-D-GalpA-(1 → 2,4)-α-D-Rhap-(1 → 4)-α-D-GalpA-(1] with a branch of terminal T-α-D-Galp pointed at C4 of 1,2,4-α-D-Rhap), and it was found that it exhibited a significant antioxidant activity and could be used in nutritional food and material application [ 26 ].

There are several biological applications of okra polysaccharides. Wang et al. found that those okra polysaccharides extracted by the cold water extraction method exhibited antioxidant, α-amylase, and α-glucosidase inhibitory activities in vitro [ 27 ]. Li et al. found that the neutral saccharide side chains of the OP could induce different secondary conformation change of gelatin during complex coacervation [ 28 ]. Gao et al. used fractions of okra polysaccharides, as anti-fatigue and observed it may be the main anti-fatigue remedy among A. esculentus substances [ 29 ]. Wahyuningsih et al. found that crude okra polysaccharides could play a role in enhancing the immune response, including phagocytic activity, spleen index, splenocytes proliferation, and control immune responses through cytokine production [ 30 ]. Liu et al. found that polysaccharides isolated from okra named (rhamnogalacturonan) possess hypoglycemic activity and are responsible for the hypoglycemia function in OP [ 31 ]. Fan et al. those okra polysaccharides possess therapeutic functions on metabolic diseases by the inhibition of LXR and PPAR signaling [ 32 ]. Deters et al. found that pectin-like polysaccharides reduced the proliferation significantly, but improved the cell viability [ 33 ]. Table 2 summarized the historical research of OP.

Anticancer properties of OP

Anticancer properties of okra extracts have been little investigated [ 35 , 36 ]. Thus, the anticancer activity of polysaccharides isolated from okra has not yet been reported, and this point will enhance to explore the anticancer properties of okra polysaccharides using different extraction methods.

Nanoparticles of OP

Gold nanoparticles of aqueous extract of okra have been synthesized and exhibited antibacterial activity against Bacillus subtilis , Bacillus cereus , E. coli , Micrococcus luteus , and P. aeruginosa and act as an effective antifungal agent [ 37 , 38 ] . Silver nanoparticles of the okra aqueous extract have been synthesized by Jassim et al. and showed different antibacterial and enzyme activities [ 39 ]. Hamid et al. synthesized ZnO nanostructure film that contains okra mucilage that showed high antibacterial activity against S. aureus than E. coli [ 34 ]. Agi et al. used cost-effective and easier method to synthesize cellulose nanoparticles from okra mucilage using an ultrasonic method [ 40 ]. Bhavani et al. used okra extract to synthesize ZnAl 2 O 4 nano-catalysts and found that microwave method is better than conventional heating in conversion of alcohol to carbonyl group [ 41 ]. Thus, further investigation of gold or silver or other metal nanoparticles of okra polysaccharides is of importance in discovering new biological functions and mechanism of actions.

Okra is an important vegetable for human health because of its functional bioactive compounds as antioxidant. A polysaccharide of okra had some biological functions such as anti-fatigue, hypoglycemia, and phagocytic activities. Therefore, needed extensive studies of the biological research to identify the anticancer and antimicrobial properties of okra polysaccharide and nanoparticles forms to target the main purposes of polysaccharide uses, and develop its functions in the medical applications.

Availability of data and materials

Data and material are available upon request.

Abbreviations

Phosphoinositide 3-kinase

Protein kinase B

Glycogen synthase kinase 3 beta

Nuclear factor erythroid 2-related factor 2

Liver X receptor

Peroxisome proliferator-activated receptors

2,2-diphenyl-1-picrylhydrazyl

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This review is supported by the Chemistry Department, College of Education for Pure Sciences, University of Basrah, Basrah, Iraq.

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Al-Shawi, A.A.A., Hameed, M.F., Hussein, K.A. et al. Review on the “Biological Applications of Okra Polysaccharides and Prospective Research”. Futur J Pharm Sci 7 , 102 (2021). https://doi.org/10.1186/s43094-021-00244-0

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Phenotypic Diversity Assessment of Okra ( Abelmoschus Esculentus (L.) Moench) Genotypes in Ethiopia Using Multivariate Analysis

Jemal mohammed.

1 Crop and Horticulture Biodiversity Directorate, Ethiopian Biodiversity Institute, Addis Ababa, Ethiopia

Wassu Mohammed

2 School of Plant Science, Haramaya University, Dire Dawa, Ethiopia

Eleni Shiferaw

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Okra is a minor crop that has not gained research attention in Ethiopia. Characterization of such underutilized crops has important implications for their utilization. Thus, this study was conducted to assess the genetic diversity of okra genotypes in Ethiopia using agromorphological and biochemical markers. Thirty-six okra genotypes were evaluated for 29 agromorphological and biochemical traits. The results of the analysis of variance showed significant differences among genotypes for most of the traits, except for the number of flower epicalyx and fruit diameter. Results of the principal component analysis indicated that the first eight principal component axes accounted for 3.83 to 30.54% and 82.44% of the total variability. Genetic distances estimated by Euclidean distances from 27 traits ranged from 3.55 to 14.49. The 36 genotypes were grouped into four distinct clusters from the Euclidean distance matrix using the unweighted pair group method with arithmetic mean (UPGMA). The first cluster contained 24 (66.66%) genotypes, and the second cluster contained 10 (27.77%) of the genotypes. This study showed the presence of considerable genetic variation among the genotypes for most of the traits, including fruit yield, seed yield, and nutrient content of seeds, indicating the possibility of using these genotypes to develop okra varieties with high fruit-yielding and good nutritional content.

1. Introduction

Today, the world's food supply is based on a small number of crop species, mostly major cereals (wheat, rice, and maize) leaving an abundance of genetic resources and potentially beneficial traits neglected [ 1 ]. In the face of climate change, utilizing the vast pool of minor and underutilized crop species would provide a more varied agricultural system and food sources, ensuring food and nutrition security problems. Underutilized crops play a significant role in ensuring food security, nutrition, and income generation for resource-poor farmers and consumers, especially in the developing world [ 2 ].

Climate change may increase the relevance of plant species that were previously underutilized or thought to be of minor importance [ 1 ]. One approach to maintaining a good fit between crops and environmental challenges because of climate change is to use underutilized (minor, orphan, or neglected) crops and their wild relatives. Okra is among the most underutilized crops cultivated in the southwestern and western parts of Ethiopia [ 3 – 5 ]. The characterization of okra genotypes existing in the country would contribute to developing varieties that could thrive in extreme climatic conditions and would allow further utilization of the crop for enhancing food security.

The value of germplasm collections depends on their diversity, and crop improvement prominently relies on existing genetic variation [ 6 , 7 ]. Shujaat et al. [ 8 ] suggested that genetic variation is an important feature to achieve the diversified goals of plant breeding, including higher and quality yield, resistance to diseases, and wider adaptations.

The pattern and level of genetic diversity in a given gene pool can be measured in terms of genetic distance, which is a measurement of average genetic divergence between genotypes or populations [ 9 ]. Regardless of the dataset (morphological, biochemical, or molecular marker data), multivariate analytical procedures that simultaneously make several measurements on each individual under examination are frequently utilized in genetic diversity studies [ 10 ]. This study aimed to assess the phenotypic and biochemical diversity of Ethiopian landrace okra genotypes along with exotic commercial varieties using multivariate analysis for further utilization of the crop and contribute to ensuring food security and alleviating malnutrition.

2. Materials and Methods

2.1. description of the study site.

The field experiment was conducted at the Melkassa Agricultural Research Center, Ethiopia, during the 2018 main cropping season (rainy season). Melkassa is located at 8°24′59.20″ N latitude and 39°19′15.19″ E longitude, with an altitude of 1,548 m above sea level [ 11 ]. The biochemical contents were determined at the Ethiopian Biodiversity Institute Nutrition Laboratory (total ash and crude fat), the Debre Ziet Agricultural Research Center (crude fiber), and the Melkassa Agricultural Research Center (total protein).

2.2. Experimental Materials and Design

Thirty-six okra genotypes of 24 landrace accessions (collected by the Ethiopian Biodiversity Institute from different okra growing regions of Ethiopia), three genotypes (from the Humera Agricultural Research Center), and nine exotic commercial varieties (eight from India and one from the USA) were used in this study. The 36 genotypes were planted in a 6 × 6 simple lattice design. Three seeds per hill were sown and thinned to one plant per hill when plants reached the 3–4 leaf stage.

2.3. Data Collection

Data were collected for phenology traits (days to 50% emergence, days to first flowering, days to 50% flowering, and days to 90% maturity), growth and yield-related traits (plant height, stem diameter, number of primary branches per stem, number of internodes, internodes length, leaf length, leaf width, number flower of epicalyxes, peduncle length, fruit length, fruit diameter, average fruit weight, number of tender fruits per plant, number of mature pod per plant, number of ridges on fruit, fruit yield per plant, fruit yield per hectare, number of seeds per pod, hundred seed weight, seed yield per plant, and seed yield per hectare), and biochemical content of the seed (total ash, total fat, crude fiber, and total protein). Phenology and growth-related traits' data were recorded according to the IPGRI [ 12 ] descriptor list developed for okra.

2.3.1. Total Ash

Total ash was determined following the method of AOAC [ 13 ] using the gravimetric method. Crucible was cleaned, dried, and ignited at 550°C for 1 hour and weighed (m1). The flour sample (3 g) weighed (m2) and dried at 120°C for 1 hour. Then, the dried sample was carbonized over a blue flame and ignited in a muffle furnace at 550°C until ashing was complete (over 12 hrs). After being ignited, the sample was cooled to ambient temperature and was weighed (m3). Finally, the total ash content was calculated as follows:

where m1 is the mass of crucible (g), m2 is sample mass with crucible (g), and m3 is the final mass of sample with crucible (g).

2.3.2. Crude Fat

The crude fat content of okra seed was determined by the Soxhlet extraction method according to the AOAC [ 13 ]. The flour sample (3 g) was weighed and added into a thimble. The thimble with the sample was placed in a 50 ml beaker and dried in an oven for 2 hours at 110°C. A 150–250 ml dried beaker was weighed and rinsed several times with petroleum ether. The sample contained in the thimble was extracted with petroleum ether in a Soxhlet extraction apparatus for 6–8 hours. After extraction is completed, the extracted fat was transferred into a preweighed beaker ( M i ). The beaker with the extracted fat was placed in a fume hood to evaporate the solvent on a steam bath unit no odor of the solvent is detectable. Then, the beaker with contents was removed, cooled in a desiccator, and weighed ( M f ). The amount of fat in flour was calculated by using the following formula:

where M f is the dried mass of the fat with beaker (g), M i is the mass of beaker (g), and M is the sample mass (g).

2.3.3. Crude Fiber

The crude fiber was determined according to the AOAC [ 13 ]. Ground sample (3 g) was weighed ( m 1 ), placed in 500 ml beaker, digested with 1.25% sulfuric acid, and washed with water and was further digested with 1.25% sodium hydroxide and filtered in course porous (75  μ m) crucible in apparatus at a vacuum of about 25 mm. The residual left after refluxing was washed again with 1.25% sulfuric acid at near boiling point. Then, the residual was dried at 110°C overnight, cooled in a desiccator, and weighed ( m 2 ). After being dried, the sample was ashed at 550°C until the ashing was complete, cooled in a desiccator, and weighed again (m3). The total crude fiber was expressed in percentages as follows:

where m1 is a mass of sample (g), m2 is mass of sample with crucible before ashing (g), and m3 is mass of sample with crucible after ashing (g).

2.3.4. Total Protein

A dried and grounded sample was taken (0.5 g) and added into a Kjeldahl digestion flask. One gram of catalyst (Na 2 SO 4 mixed with anhydrous CuSO 4 in a ratio of 10 : 1) and 5 ml of concentrated H 2 SO 4 were added into the digestion flask. Then, using a digester, the mixed sample was digested at 350°C for about two hours until the sample was completely digested. Then, the flask was removed from the digester and allowed to cool and the digested sample was diluted by adding 30 ml of distilled water. Then, 25 ml concentrated 40% NaOH was added into the digestion flask to neutralize the acid and make the solution slightly alkaline. The contents were immediately distilled by inserting the digestion tube line into the receiver flask that contained 25 ml of 4% boric acid solution and about 150 ml of distillate collected. Then, the distillate was titrated by a standard acid (0.1 N HCl). The percentage of crude protein was calculated by multiplying the nitrogen percentage by the conversion factor (6.25) [ 13 ].

where V  = volume of standard acid used for titration of sample (A) and blank sample (B), N  = normality of standard acid used for titration (0.1 N HCl), 0.014 is the molecular weight of nitrogen, and W  = weight sample taken for digestion, on a dry basis.

2.4. Analysis of Variance

The quantitative field data were subjected to analysis of variance (ANOVA) and computed with R statistical software agricolae package [ 14 ]. The biochemical traits were analyzed following the CRD (completely randomized design) procedure. The traits that exhibited significant mean squares in ANOVA were further subjected to multivariate analysis.

2.5. Principal Component Analysis

Principal component analysis (PCA) was computed to find out the traits, which accounted more for the total variation. The data were standardized to mean zero and variance of one before computing principal component analysis to avoid differences in measurement scales. The principal component based on the correlation matrix was calculated using the R statistical software FactoMineR package [ 15 ].

2.6. Euclidean Distance and Clustering of Genotypes

Euclidean distance (ED) was computed from quantitative after subtracting the mean value and dividing it by the standard deviation as established by Sneath and Sokal [ 16 ]. R statistical software factoextra package [ 17 ] was used for the analysis of distance matrix and constructing dendrogram. The dendrogram was constructed based on the unweighted pair group method with arithmetic mean (UPGMA) from the distance matrix of phenotypic traits.

3. Results and Discussion

3.1. analysis of variance.

The results of the analysis of variance for phenology, growth, yield-related traits, and biochemical traits showed a significant ( P < 0.05) difference. However, the genotypes exhibited a nonsignificant difference for the number of flower epicalyx and fruit diameter (Tables ​ (Tables1 1 and ​ and2 2 ).

Mean square from analysis variance following simple lattice design for 25 quantitative traits of 36 okra genotypes evaluated at Melkassa in 2018.

∗∗, ∗ and ns, significant at P < 0.01, P < 0.05, and nonsignificant, respectively. Rep = replication, Adj = adjusted, Uadj = unadjusted, CV (%) = coefficient of variation in percent. The number in parenthesis in each source of variation represents the degree of freedom.

Mean square from analysis variance for four seed biochemical contents of 36 okra genotypes evaluated at Melkassa in 2018.

∗∗ , significant at P<0.01, DF = degree of freedom, and CV (%) = coefficient of variation in percent.

3.2. Principal Component Analysis

The result of principal component analysis for 27 quantitative traits is presented in Table 3 . With eigenvalues ranging from 1.033 to 8.247, the principal component analysis resulted in eight principal components (PC1 to PC8). The eight principal components each accounted for a different percentage of the total variance, ranging from 3.83 to 30.54%, for a total variance of 82.44%. The PCs with an eigenvalue of <1 were ignored due to Gutten's lower bound principle that eigenvalues <1 should be ignored. The first principal component (PC1) contributed to most of the variation (30.54%), followed by PC2, PC3, and PC4, which contributed 14.11%, 10.87%, and 6.98%, of the variation respectively, and the first four PCs accounted for 62.51% of the total variation.

Factor loading of the first eight principal components for 27 quantitative traits of 36 okra genotypes.

PC1 to PC8 represent the first principle component to the eighth principal component.

A similar result on okra was reported by Muluken et al. [ 18 ] in which the first three principal components PC1, PC2, and PC3, with values of 32.4%, 16.7%, and 8.2%, respectively, contributed more to the total of 57.3% variation. Amoatey et al. [ 19 ] reported the first, second, and third principal components with values of 32.44%, 19.78%, and 9.68% of the total genetic variation, respectively. Ahiakpa [ 20 ] also reported that the first principal component (PC1) was (32.44%) the major contributor for variance in okra genotypes.

Within the PC1, traits with the largest values closer to one influence the cluster more than traits with lower absolute values closer to zero [ 21 ]. Hence, the differentiation of the genotypes into different clusters was because of the cumulative effect of several traits rather than the large contribution of a few traits. In this regard, stem diameter (7.41%), fruit yield per hectare (8.03%), leaf width (7.56%), fruit yield per plant (7.87%), leaf length (7.11%), peduncle length (6.75), seed yield per plant (6.75%), and seed yield per hectare (6.75%) had relatively higher contributions to PC1. This indicates that these traits were responsible for the differentiation of the clusters and had a greater contribution to the total diversity. In PC2, days to 50% flowering (18.58%), days to first flowering (16.87%), date of maturity (14.76%), and the number of mature pods (8.12%) had more contribution, whereas fruit length, fresh fruit weight, and ash content had relatively more contribution in PC3 ( Table 3 ).

A biplot was performed based on the first two PCs ( Figure 1 ). The genotypes and quantitative traits were shown on a biplot to visualize their associations. The first and the second PC biplots explained 44.66% of the total variability among the genotypes, displaying that stem diameter, leaf length, and leaf width, fruit yield per plant, fruit yield per hectare, seed yield per plant, and seed yield per hectare were considered the most discriminating traits.

Biplot (axes PC1 and PC2) of 27 quantitative traits of 36 okra genotypes.

The genotypes positioned on the right top quadrant were characterized by late maturity, high fresh fruit weight, much fruit ridges, and high stem diameter. The genotypes depicted in the bottom right quadrant had the highest seed yield, number of fruits per plant, number of mature pods, and longest, and widest leaf. The genotypes distributed around the origin had similar genetic characteristics, while the genotypes that were found far from the origin are considered unrelated genotypes ( Figure 1 ). Therefore, these divergent genotypes could be used as potential parents for successful hybridization to develop heterotic groups in the okra-breeding program.

PC3 and PC4 biplots are presented in Figure 2 . These two PCs accounted for 17.85% of the total variability among genotypes, showing that ash content, and total protein content, crude fiber content, fruit length, and fresh fruit weight were the most contributing traits. PC3 and PC4 biplots provided information regarding the similarities and the pattern of differences among the okra genotypes and the association between traits. Genotypes were distributed in all four quadrants on the axes, indicating the presence of wide genetic variability for the traits studied. Overlapped accessions and accessions closer to each other on the axes had similar genetic makeup. However, genotypes that are apart from each other could be considered genetically distinct. Genotypes positioned in the top right quadrant were characterized by high-seed protein and fiber content. The top left quadrant consists of the okra genotypes that are closely related and have a high number of internode and a high number of mature pods. Genotypes found on the right bottom quadrant exhibited the highest seed ash content.

Biplot (axes PC3 and PC4) of 27 quantitative traits of 36 okra genotypes.

3.3. Cluster Analysis

The optimum number of clusters was determined by the total within sum of square (WSS) (elbow method) using R statistical software version 3.6.3 ( Figure 3 ). A dendrogram was constructed based on the unweighted pair group method with arithmetic mean (average) from the distance matrix of phenotypic traits.

Determination of the optimum number of clusters K using total within sum of square (WSS) method.

The distances of all possible pairs of the 36 okra genotypes from 27 quantitative traits were estimated by Euclidean distance. The distances between okra genotypes ranged from 3.55 to 14.49 with a mean, standard deviation, and coefficient of variation of 7.12, 1.80, and 25.25%, respectively. The highest genetic distance (Euclidean distance) was computed between 29407 and Humera 2 (14.49), whereas the lowest genetic distance was estimated between Dhenu and Mithra (3.55) ( Figure 4 ).

Scatter plot constructed based on PC1 and PC2 for 27 quantitative traits.

Based on PC axes 1 and 2, a scatter plot was constructed for four clusters ( Figure 4 ). The plot showed that the genotypes that have similar genetic makeup were grouped in a cluster (near to overlap), and those genotypes that have different genetics were positioned in the opposite corner of the scatter plot.

Generally, the Euclidean distances measured among the introduced varieties were lower than the genetic distances among genotypes collected from Ethiopia. This showed that there is a higher chance of improving fruit yield and seed-related traits through the selection and/or hybridization of okra genotypes collected from different okra growing regions of Ethiopia.

By characterizing 24 Ethiopian okra genotypes, Fozia [ 22 ] reported Euclidean distance that ranged from 1.96 to 11.36 with a mean, standard deviation, and coefficient of variation of 5.85, 1.97, and 33.75%, respectively. The same study also reported that introduced (Indian) varieties had lower (1.96 to 10.01) genetic distances than Ethiopia's okra collection, which ranged from (2.07 to 11.36). Muluken et al. [ 18 ] reported that Ethiopian okra collections exhibited a wider genetic distance than exotic varieties. Anteneh [ 23 ] estimated the genetic distances of all possible pairs of 25 okra genotypes and reported that the highest genetic distances were observed between okra collections from Ethiopia and introduced commercial varieties from other countries, while the lowest genetic distance was estimated between introduced commercial varieties.

The extent of diversity present between genotypes determines the extent of improvement gained through selection and hybridization. The more distant the two genotypes are, the greater the probability of improvement through selection and hybridization. Mihretu et al. [ 3 ] also reported the presence of considerable genetic distance among okra collections from Gambela regional state, which is one of the okra-growing regions in Ethiopia.

Clustering of genotypes based on Euclidean distances revealed four major clusters. The number and names of genotypes in each cluster along with their collection origin are presented in Table 4 . Cluster I consists of the majority of the genotypes, which accounted for 24 (66.66%) of the genotypes. Cluster II contains 10 (27.77%) genotypes, while clusters III and IV each contain only single genotypes ( Table 4 , Figure 5 ).

Dendrogram illustrating dissimilarity of 36 okra genotypes by an average method of clustering method from Euclidean distance matrix based on 27 traits.

Clusters, number of accessions, name of genotypes, and collection origin of 36 okra genotypes.

Genotypes clustered in cluster I and cluster II were early maturing, while the two genotypes positioned in cluster III and cluster IV were late-maturing genotypes ( Table 5 ). Therefore, genotypes found in cluster I and cluster II could be used for okra production in areas characterized by the low amount of rainfall. These genotypes could also be used by breeders for developing varieties suitable for drought-prone areas. On the contrary, the two genotypes found in clusters III and IV could be used for areas that have a long rainfall season. The highest mean of fruit yield was measured from the genotype (29407), which is found in cluster IV. This genotype also had the highest seed protein content. This genotype could be used for further evaluation to identify genotypes with high fruit yield and desirable nutrient contents.

Cluster mean value for 27 quantitative traits of 36 okra genotypes.

4. Conclusions

The results showed the presence of considerable genetic diversity for the studied morphological and biochemical traits. This variation could be exploited to develop varieties with different desirable agronomic traits like early maturing, high yield, and good nutrient content through either selection and/or hybridization using the okra genotypes collected and conserved in Ethiopia.

In addition, the study revealed the potential of the landrace okra genotypes as sources of nutrients. This indicates the importance of neglected crops that could be utilized for ensuring food security and alleviating malnutrition in developing countries like Ethiopia, where malnutrition is a widespread problem. It is also recommended to extend the research in okra to include micronutrient content analysis, molecular diversity study, and sequencing okra genotypes to identify important agronomic and biochemical traits and to characterize the genes responsible for the traits.

Acknowledgments

The authors are highly grateful to the Africa Center of Excellence for Climate Smart Agriculture and Biodiversity Conservation and the Haramaya University for funding this research.

Data Availability

Conflicts of interest.

The authors have not declared any conflicts of interest.

Evaluation of okra genotypes against yellow vein mosaic disease (YVMD)

• Research Article
• Published: 16 January 2022
• Volume 75 , pages 549–557, ( 2022 )

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• Alphy Mathew 1 ,
• T. Pradeepkumar 1 ,
• J. S. Minimol 2 ,
• K. Anita Cherian 3 &
• M. Sangeeta Kutty 1  

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Okra is one of the most important vegetable crops grown all over the world. The production of okra is constrained by a viral disease, yellow vein mosaic disease (YVMD) which causes huge losses to okra cultivators. Even though many resistant varieties were released in the past, they had become susceptible to the disease. Thirty four genotypes of okra were evaluated under open field conditions for the selection of resistant or tolerant ones among them. The disease incidence in genotypes were recorded using the parameters viz., percent disease incidence, percent disease severity and coefficient of infection. Six genotypes were categorised as highly susceptible, 27 genotypes as susceptible and Susthira was the only resistant genotype obtained after field screening. The resistance of Susthira was further confirmed under protected conditions using whitefly mediated artificial inoculation of yellow vein mosaic virus. Studies on trichome density revealed higher number of trichomes in the stem, leaf and fruit of Susthira. The resistance of Susthira could be used for developing YVMD resistant varieties. Correlation studies between different plant growth, yield and disease parameters were carried out and it was found that disease incidence lead to the reduction in growth and yield of okra. The characters viz., average fruit weight and number of fruits per plant had positive correlation and high positive direct effect on yield. Selection of genotypes based on these characters along with coefficient of infection of YVMD is useful for further crop improvement.

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Acknowledgements

The authors acknowledge the NBPGR (National Bureau of Plant Genetic Resources) regional station, Akola for the provision of okra germplasm used in this study. The authors are also grateful to Kerala Agricultural University for providing the funding needed for the research.

Research grant for MSc. Research provided by Kerala Agricultural University (R7/62047/19 dtd. 04.05.2019).

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Department of Vegetable Science, College of Agriculture, Kerala Agricultural University, Thrissur, Kerala, India

Alphy Mathew, T. Pradeepkumar & M. Sangeeta Kutty

Cocoa Research Centre, Vellanikkara, Thrissur, Kerala, India

J. S. Minimol

Department of Plant Pathology, College of Agriculture, Vellanikkara, Thrissur, Kerala, India

K. Anita Cherian

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Mathew, A., Pradeepkumar, T., Minimol, J.S. et al. Evaluation of okra genotypes against yellow vein mosaic disease (YVMD). Indian Phytopathology 75 , 549–557 (2022). https://doi.org/10.1007/s42360-021-00457-6

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Received : 02 September 2021

Revised : 11 December 2021

Accepted : 27 December 2021

Published : 16 January 2022

Issue Date : June 2022

DOI : https://doi.org/10.1007/s42360-021-00457-6

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