Malaysian Journal of Sustainable Agriculture (MJSA)

Vegetable and other crop producers face numerous challenges, including high input costs, water shortages, and soil degradation. Zeolite, a microporous aluminosilicate mineral with high cation exchange capacity and water retention properties, is receiving a growing research interest due to its positive effects as a soil amendment on various crops, including vegetables, however, specific studies on its impact on Swiss chard are relatively limited. This study investigated the effects of zeolite application on the growth and yield of Swiss chard (Beta vulgaris L. var. cicla) cv. Ford Hook Giant under greenhouse conditions over two growing seasons (2018 and 2019). Zeolite was applied at weight-to-weight ratios of 0:10, 1:9, 2:8, and 3:7 to sandy soil. Growth parameters (plant height, leaf area, and chlorophyll content) were monitored weekly, and fresh and dry yields were measured at 59 days after transplanting. Results indicated that zeolite application improved (p≤0.05) leaf area, leaf number, fresh and dry yield in the second growing season, including their growth rate per week. These improvements were linked to improved soil quality due to zeolite application. The initial season exhibited inconsistent trends, likely due to the integration period required for zeolite to stabilise in the soil system. Swiss chard leaf moisture also reduced (p≤0.05) with increased zeolite application in the second season, this was linked to Swiss chard cultivated under the zeolite-amended treatments having higher leaf growth which may have encouraged greater transpiration losses. Furthermore, chlorophyll content index and leaf moisture percentage showed limited direct correlation with yield, suggesting that growth parameters such as plant height and leaf area are better indicators of yield potential in Swiss chard. These findings demonstrate zeolite’s potential to enhance vegetable production, emphasising the need for a stabilisation period in sandy soils. Future research should explore the long-term effects of zeolite application on crop performance and soil health.

2. MATERIAL AND METHODS

2.1 Research Design

A greenhouse study was conducted at the Agricultural Research Council,Infruitec-Nietvoorbij, Stellenbosch, South Africa (33.914476° S and18.861322° E) using Swiss chard (Beta vulgaris var. circa cv. Ford HookGiant). The study was conducted over two growing seasons: late autumnto late spring 2018 and early autumn to early spring 2019. The studyassessed the effects of zeolite as a soil amendment on Swiss chard growthrate and Swiss chard fresh yield. Zeolite to sandy soil application wasbased on weight-to-weight ratios of 0:10, 1:9, 2:8, and 3:7. Each plantingpot had 12 kg of soil or soil and zeolite mixture, and the pots werearranged in a randomised complete block design, with six (6) replications.The zeolite used was of clinoptilolite mineralogy composed of 64.30%silicate (SiO2) and 12.70% aluminium oxide (Al2O2). Details about theinitial soil chemical characteristics and other characteristics of the zeoliteused in this study are reported in the work of Sindesi et al. (2023). Thefertiliser applications on each pot are described in the work by Sindesi etal. (2022). Six-week-old Swiss chard plants of about 7 cm were used forthe experiment. One plant was transplanted into each pot.

2.2 Data Collection

Growth parameter data was collected four weeks after the transplantingof the seedlings and continued weekly over four consecutive weeks. Swisschard growth was represented by the number of loose leaves per plant,plant height, leaf width, leaf chlorophyll content index (CCI), and leaflength. Fresh yield was collected 59 days after transplanting.

2.2.1 Number of loose leaves per plant

All the true leaves, fully developed leaves with a petiole or white stalk anda blade that were adequately grown and observed to have moved awayfrom the main growing point, were counted and recorded.

2.2.2 Plant height

Plant height was measured using a transparent ruler, and care was takento ensure that the ruler was vertically placed next to the plant, with theruler’s zero end positioned at the plant’s base, where the stem met the soil.Only the tallest leaf was observed on each plant.

2.2.3 Leaf area

The leaf width (maximum value perpendicular to the midrib) and the leaflength (maximum value along the midrib) were measured and recorded tocalculate the leaf area. These parameters were further used to developratio and regression estimators to calculate the leaf area. The formula forthe Swiss chard leaf area entailed finding the slope of the leaf length andleaf width (linear regression), the Y was represented by leaf width, whilethe X was represented by leaf length. After finding the slope, it (the slope)was multiplied by the leaf length and the leaf width.

LEAF AREA ≈ m * L * W

W was the leaf width

L was the leaf length

m was the slope of the line

2.2.4 Leaf Chlorophyll content index

Leaf chlorophyll content index (CCI) data was collected from the top edgeof the largest leaf on each plant using a CCM-200 plus chlorophyll contentmeter, manufactured by Opti-Sciences, USA.

2.2.5 Growth rates

The growth rates of the observed parameters were calculated by dividingthe difference between the values recorded in the first and fourth weeksby 3.

2.2.6 Fresh yield

Swiss chard was harvested 59 days after transplanting by cutting thestalks at the base of the plants, and the fresh weight (grams/3 plant) was measured immediately using a weighing scale. Swiss chard harvested fromall three pots that represented one replicate were combined beforeweighing.

2.2.7 Statistical analysis

Data were analysed using Statistical Analysis System (SAS) software(version 9.4, SAS Institute Inc., Cary, NC, USA, 2000) for Analysis ofVariance (ANOVA). Seasonal homogeneity of variance was tested withLevene’s test, after which the results of both seasons were merged andstudied in a single overall ANOVA. The Shapiro-Wilk test was conductedfor deviation from normality and insignificant interactions. Fisher’s leastsignificant difference was calculated at the 5% level to compare treatmentmeans. For all tests, a probability level of 5% was considered significant.Pearson correlation coefficients (r), correlating Swiss chard’s growth andyield parameters, were derived using the CORR procedure of SAS 9.4.

3. RESULTS AND DISCUSSION

3.1 Effect Of Zeolite On The Growth Parameters Of Swiss Chard

Leaf chlorophyll content, number of leaves, height, leaf width, and lengthwere used as non-destructive growth measures on leafy vegetables. Figure1 (A-D) shows the growth parameters of Swiss chard grown on zeoliteamendedsandy soils at 59 days after transplanting. Leaf chlorophyllcontent index (CCI) had higher values in the second season (2019)compared to the first season (2018). Additionally, the non-amendedtreatment (0%) showed better leaf CCI values compared to the zeoliteamendedtreatment, except for 20% zeolite application in the first seasonand 30% zeolite application in the second season. The leaf CCI is related tothe nitrogen (N) and the water status of the plant, a decrease in leaf CCI atthe end of a crop life is normally associated with leaf senescence (Khaleghiet al., 2012; Sánchez-Sastre et al., 2020). Zeolite, due to its porous natureand affinity towards ammonium (NH4+) cations may regulate plant waterand N uptake (Gül et al. 2005; Ramesh and Reddy, 2011). Zeolite has beenobserved to increase soil water holding capacity (Shahbaz et al., 2019;Mahmoud and Swaefy, 2020).

The study found an increased wheat photosynthetic rate, with zeoliteapplication (100 g kg-1; 50 g kg-1 application). That analysis found higherphotosynthetic rates [μmol/m2/s], on sage plants grown on soils treatedwith nano-zeolite (30 g L-1 zeolite-irrigation water) (Shahbaz et al., 2019;Mohmoud and Swaefy, 2020). In both studies, this was attributed toimproved soil moisture holding capacity with zeolite application. Thefindings of this study contradict these author’s findings and may be due tothe differences in zeolite application rate. The high application of zeolitein this study may have increased zeolite affinity towards NH4+ in the soilsolution, allowing for zeolite to adsorb most of the NH4+ in the soil into itscavities. Zeolite cavities and their high cation exchange capacity may allowzeolite to adsorb cations with a greater force than that of plant roots fornutrient assimilation (Valdivia et al., 2021). However, the benefit of theadsorption is that it allows for significant amounts of essential nutrients,particularly NH4+, to be retained in the soil and prevents them fromleaching (Liu et al., 2023).

Swiss chard number of leaves per plant (Figure 1-B) shows that there wereno differences (p≥0.05) in the first growing season (2018). In the secondgrowing season, there was an increase in the number of leaves per plantwith the increased application of zeolite. The leaf area (cm2) results alsoshow that there were no differences (p≥0.05) among the zeolite-amendedtreatments, while the non-amended treatment had reduced (p≤0.05) leafarea in the first growing season. In the second growing season, the leafarea increased with the increase in zeolite application. This can be linkedto zeolites’ long-term improvement of soil quality, as previously reportedby Sindesi et al. (2023a) (2023b) and (2024). Soil pH, soil total K, soilexchangeable cations (Ca, K, Mg and Na) and cation exchange capacitywere all improved in the study due to zeolite application. Additionally,improved soil water holding may also be attributed to the increase in thenumber of leaves and leaf area in the second growing season. In the initialgrowing season, the zeolite may not fully integrate with the sandy soil,leading to uncertain results and trends in Swiss chard growth. Thesefindings suggest that zeolite may need more time to fully integrate into thesoil system and positively impact plant growth. This phenomenon is alsonoticeable in the results for Swiss chard plant height (Figure 1-D). Plantheight slightly (p≥0.05) decreased on the non-amended treatment from2018 to 2019, while on the zeolite-amended treatments, all the plantheights improved from the initial growing season to the 2019 season.

3.2 Swiss chard yield parameter responses to zeolite application

Swiss chard yield parameters are represented in Figure 2 (A-C) below. In the initial growing season, both fresh and dry matter yield (g/3 plants) showed better weight on the non-amended treatment and the 20% zeolite amended treatment. Generally, these two yield parameters were slightly better on the non-amended treatments in the first growing season. In the second growing season, the two yield parameters were reduced on the non-amended and zeolite-amended treatments compared to the initial season, except for the 30% zeolite treatment. In the second growing season, a general increase was observed with the increase in zeolite application. In the initial growing season, zeolite may have not yet fully integrated into the soil system, and during the process of integration may have reduced some essential plant-required nutrients, such as NH4+ (Omar et al., 2015; Liu et al., 2023; Doni et al., 2024). Zeolite has been noted to adsorb cations such as NH4+ and K+ into its structures and slowly release them into soil for plant use (Louhar, 2020). During the initial integration period, these nutrients may be temporarily unavailable for plant uptake as the zeolite absorbs them. Subsequently, they can be slowly released through a gradual exchange-induced dissolution process (Hartman and Fogler, 2007; Louhar, 2020). Zeolite dissolution can be influenced by factors such as pH, with acidic conditions potentially accelerating the dissolution of the zeolite structure (Hartman and Fogler, 2007). The soil pH of the initial soil used was slightly acidic (5.4KCL).

Swiss chard leaf moisture % did not significantly differ (p≥0.05) in the initial growing season (Figure 2-C). However, the 10 and 30% zeolite treatments showed slightly better moisture content than the other treatments in season 1. In the second growing season, the moisture content tended to reduce with increased zeolite application, with significance (p≤0.05) observed among some treatments. The 0 and 10% treatments showed higher moisture content than the other treatments. Crop moisture in crops assists in maintaining the protoplasmic contents of the cells as it encourages cellular functions and plant growth (Udousoro and Ekanem, 2013). However, in this study, higher growth was observed on the zeolite-amended treatments for the second growing season. As such, the low moisture content in the zeolite-amended treatments may be linked with the fact that Swiss chard cultivated under these treatments had higher leaf growth, which may have encouraged greater transpiration losses. This is further strengthened by the larger irrigation requirement that the Swiss chard needed in the second season as previously observed in (Sindesi et al., 2023).

3.3 Effect of zeolite on the growth of Swiss chard

As the availability of growth factors such as water and mineral nutrientsincreases, the growth rate and crop yield also increase (Motseki, 2008). Assuch, a faster leaf growth rate in Swiss chard leads to quicker harvests. Inthis study, the rate of increase of the number of leaves and leaf areaincreased (p≤0.05) with increased zeolite application (Figures 3-B and C).This increase can be attributed to improved soil chemical properties (pH,CEC, exchangeable cations and reduced heavy metal availability) andimproved water holding capacity (Sindesi et al., 2023a and 2023b).Additionally, the plant height growth rate (Figure 3-D) was notsignificantly different (p≥0.05) in the first growing season. However, therewas a general increase in the application of zeolite in the second growingseason. The growth rate of plant height may have been influenced byresource allocation by the plants (Miao et al.,2024).

The analysis show if plant growth processes draw on the same resourcesource, such as the phloem tissue carrying products of photosynthesis,then a trade-off might occur between resource allocation to certainaspects of growth (Pyke and Ren, 2023). However, if the growth processesutilise resources from different sources, the trade-offs may not arise. Thegrowth rate of the leaf chlorophyll content index decreased (p≥0.05)between the non-amended treatment and the 20% zeolite treatment in theinitial growing season. It further showed an increasing trend between the10 and 30% zeolite treatments, with the increase observed in the 30%zeolite treatment being significantly more significant than the growth rateobserved in the 10% zeolite treatment. Leaf chlorophyll content index ishighly influenced by nutrient availability (N), temperatures and wateravailability (Hermans and Verbruggen, 2005; Li et al., 2018). Zeolite hasbeen shown to influence soil water holding capacity and nutrientavailability (Mahmoud and Swaefy, 2020)

3.4 Correlation analysis between growth and yield parameters ofSwiss chard as affected by zeolite

The correlation coefficient of growth and yield parameters of Swiss chardcultivated under varying rates of zeolite-amended sandy soil are shown inTable 1. Growth parameter at four weeks of data collection (PHW4, NLW4,LAW4) values correlated strongly (p≤0.001) with the observed Swisschard yields. Interestingly, at four weeks of data collection, the threegrowth parameters negatively correlated with Swiss chard leaf moisturecontent %. Growth parameters at four weeks also had a strong positivecorrelation (p≤0.001) with their linked growth rates. As expected, rapid growth rates of these parameters ensure larger plant leaves. However, plant height at four weeks of data collection did not have such a strong correlation with the rate of leaf area growth over the 4 weeks (LAR) of data collection, although it also had a significant positive correlation (p≤0.01). This may be due to the plants’ allocation of resources to certain plant growth parameters, and as observed on the fresh and dry matter yields, it did not affect the plants’ productivity. also found strong positive correlations between plant height and yield parameters of cabbage (Head weight and head diameter) grown on organic and inorganic fertilisers (Shakirdeen et al., 2019). This strong and positive correlation shows that plant height is a valuable indicator of potential yield in leafy vegetables; however, it should not be used in isolation.

The chlorophyll content index (CW4) and chlorophyll content index growth rate (CR) did not significantly correlate with any other growth and yield parameters. However, these two parameters showed a strong positive correlation (p≤0.001). The absence of a significant correlation between CW4 and CR with the yield parameters contradicts the findings observed in the work by (Goggs et al., 2003; Blackmer and Schepers, 1995). The study found a significant correlation between leaf chlorophyll and cotton yield (Goggs et al., 2003). The analysis also found significant positive correlations between chlorophyll content and maise grain yields(Blackmer and Schepers, 1995). The difference in plant species and plant resource allocation may lead to differences in these correlations. These results show that CCI cannot be effectively used to predict Swiss chard growth and final yield.

Swiss chard leaf moisture content % (LM) showed a significant (p≤0.001) correlation with all the observed parameters apart from the chlorophyll-related parameters. All parameters that significantly correlated with Swiss chard leaf moisture content showed negative correlations. The negative correlation between Swiss chard’s moisture content and its growth and yield parameters indicates that the plant prioritises allocating resources towards growth and tissue production rather than water storage. Nevertheless, plants lose water during growth through transpiration. This process occurs when stomata tiny leaf surface pores open to facilitate gas exchange. As the stomata open, water vapour escapes from the leaf’s interior into the atmosphere (Hernandez-Candia and Michaelian, 2010). Generally, larger leaves tend to have more stomata, and while this can facilitate greater gas exchange, it also increases the plant’s susceptibility to water loss (Kouwenberg et al., 2007). Therefore, the correlation between the growth parameter and the LM can be due to increased stomata and plant growth, leading to increased transpiration.

CW4 Chlorophyll Content Index, PHW4 Plant Height, NLW4 Number of Leaves, LAW4 Leaf Area (All those with W4 are actual values on the fourth week of data collection), CR Chlorophyll Content Index Growth Rate, PHR Plant Height Growth Rate, NLR Number of Leaves Growth Rate, LAR Leaf Area Growth Rate, DMY Dry Matter Yield, FY Fresh Yield, LM Leaf Moisture Content.* Correlation is significant ≤ 0.05 level, **Correlation is significant ≤ 0.01 level, ***Correlation is significant ≤ 0.001 level.

4.CONCLUSION

This study examined the influence of zeolite on Swiss chard growth and yield at the end of two months over two growing seasons. The study showed that zeolite soil application improved the leaf area and number of leaves at four weeks of data collection in the second growing season. Furthermore, zeolite also improved the fresh and dry yield of Swiss chard in the second growing season. This demonstrates that zeolite may require a fallowing period before fully integrating into the soil system. The study also showed that decreased leaf moisture (%) does not necessarily lead to reduced dry matter yields. It is also clear that plant growth parameters such as plant height, the number of leaves and the leaf area are good indicators of yield potential in leafy vegetables. Future research is still needed to investigate longer zeolite influences on vegetable crops such as Swiss chard.

ACKNOWLEDGMENTS

The authors would like to thank the National Research Foundation (NRF) of South Africa for financial support for this research. Under NRF grant number: 114405. Any opinion, findings and conclusions, or recommendations expressed in this article are those of the author(s), and the NRF accepts no liability whatsoever in this regard.

Genetic diversity is essential to explore novelties in flower and plant architecture to develop new cultivars. 50 genotypes of Chrysanthemum morifolium were systematically characterized for accurate genotype identification based on DUS descriptors in UPOV guidelines. The experiment was conducted for two consecutive years. Significant variation was observed for fourteen quantitative traits. Shannon equitability index was > 0.75 for 6 out of 11 qualitative traits indicating good diversity. The highest GCV (67.52%) and PVC (68.05%) were recorded for number of flowers per plant. High estimates of heritability and genetic advance as percentage of mean were shown by number of flowers per plant, stipule size and ray floret width suggesting effective selection. Principal component analysis (PCA) for both quantitative and qualitative traits performed independently, resulted in 4 principal components accounting for 74.7% and 63.27% of total variability respectively. Factor loading for quantitative traits revealed that leaf characters have major contribution to germplasm variability. Among qualitative characters, ray florets traits have major contribution to germplasm variability. The genotypes were classified into 4 groups at Euclidean distance of 88.94. Cluster 1 has only genotype Gauri which is an outlier for plant height and number of flowers per plants. Pusa Shwet is alone in cluster 2 having unique combination of large sized and semi double type flower head. Cluster 3 and cluster 4 have 28 and 20 genotypes respectively. Grouping based on Jaccard’s similarity coefficient for binary data gave nine clusters at similarity coefficient of 0.33 with genotype Gauri alone constituting cluster 3.

2.MATERIALS AND METHODS

The experiment comprising 50 diverse chrysanthemum genotypes was laid at the experimental farm, Division of Floriculture and Landscaping, IARI, New Delhi in randomized block design with two replications during two consecutive years (2021–2022 & 2022-23). The experimental plot (4.5 m × 1.8 m) comprised of three rows distanced 60 cm apart with plants spaced at 45 cm apart. Standard agronomic practices were followed during crop growth. Data were recorded on 10 randomly selected plants for 25 descriptors including 14 quantitative characters viz., Plant based traits: plant height (PH) & number of primary branches (NPB), Leaf based traits: stipule size (SZ), leaf lamina length (LL), leaf width (LW), ratio of leaf length/width (L/W), petiole length (PL), Terminal lobe length (TLL), length of lower lobe of leaf (LLL), flower based traits: number of flowers per plant (NFP), flower head diameter (FD), peduncle length (Pd. L), ray florets length (RL), ray florets width (RW) and 11 qualitative characters viz., Plant based trait: plant type (PT), leaf based trait: petiole attitude, leaf predominant shape of base, leaf color, flower based traits: flower head type, number of types of ray floret, predominant type of ray floret, cross section of ray floret, rolling of margins of ray floret, longitudinal axis of
majority of ray florets and shape of tip of ray floret.

The picture of flower type of chrysanthemum genotypes is given in Figure1. Plant characters, stem, stipule, leaf and petiole characters were observed when the terminal buds show color, just before they open. Plant height was measured from the tallest point of the canopy to the base of the plant. Plant type was visually observed as bushy (having branches and nonmain single stem) or non bushy (producing single stem). Leaf length was measured from the lamina tip to the intersection of lamina and petiole along the lamina midrib. Leaf width was measured from the widest lamina lobes. Petiole length was measured from the stem margin to the end of lea lamina. The number of flowers was counted during the flowering time of each cultivar. Flower head diameter was measured from the widest point of the flower. Ray floret characteristics were observed on the outermost row of the floret. Both quantitative and qualitative traits were observed according to UPOV (International Union for the Protection of New Varieties of Plants) descriptors. The year-wise quantitative data were pooled and used for statistical Analysis. The analysis of variance for quantitative traits was computed as described (Panse and Sukhatme,1967).

Phenotypic (σ 2p) and genotypic (σ 2 g) variances were calculated using the method suggested by a researcher as σ 2p= σ 2 g + σ 2 e, σ 2 g= MSg–MSe/rand σ 2 e= MSe, where MSg, MSe and r denote mean squares of genotypes, mean squares of error and number of replications respectively (Baye,2002). The PCV and GCV were obtained by using the formulae, PCV(%)=√σ 2 p / x̅ × 100, GCV(%) =√σ 2 g / x̅ × 100,where x̅ is the sample mean(Baye, 2002). GCV and PCV values were categorized as low (< 10%),moderate (10–20% ) and high (> 20%) (Deshmukh et al., 1986). Estimates of broad sense heritability (h2b) were calculated according to the formulae:h2b= σ 2 g / σ 2 p (Allard, 1999). The expected genetic advance (GA) under selection, assuming the selection intensity of 5% was calculated as proposed by a group researchers i.e. GA = k (√σ2p). σ2g / σ2p, K is the standardized selection differential (2.5) (Johanson et al., 1955). Geneticadvance as percent of mean, GAM = (GA/ x̅) x100. The Shannon equitability index (evenness) is simply the Shannon diversity index (H) divided by the maximum diversity (log k). Shannon diversity index (H) is calculated as H= Σ 𝑃𝑖 ∗ 𝐼𝑛 𝑃𝑖 𝑘𝑖=1 , where k denotes the number of group and pi denoting the proportion in group k.

This normalizes the Shannon diversity index to a value between 0 and 1.Evenness closer to 1 indicates more diversity (Shanon and Weaver, 1949).The mean values of each quantitative trait and transformed data (scoresfor the each descriptor state used for transforming qualitative data is given supplementary table S3) of qualitative traits were used to perform PCA and cluster analyses using unweighted pair group method with arithmetic averages (UPGMA) clustering algorithm with dissimilarity between genotypes expressed as Euclidean distance (Kim et al., 2014; Kachare etal., 2016; Khatun et al., 2022). Euclidean distance between two genotypes and j, having observations on morphological characters (p) was denoted by x1, x2, …, xp and y1, y2,…, yp for i and j, respectively, and was calculated with the following formula: d (i, j) = [(x1 – y1)2 + (x2 – y2)2 + (xp- yp)2]1/2.The computation of PCA, Euclidean distance matrices, construction of dendrogram and calculation of cophenetic correlation coefficient was done with the help of XL STAT software (Khatun et al., 2022). Additionally Jaccard’s similarity coefficient was used to perform cluster analysis on binary data based on presence (1) or absence (0) of specific descriptor state (UPOV guidelines) for both qualitative and quantitative trait (Chunget al., 2019). A cluster analysis based on Jaccard’s similarity index wasachieved by using the UPGMA in NTSYS-pc Version 2.1q (Chen et al., 2013).

3. RESULTS

3.1 Morphological Characterization

A total of 25 morphological characteristics (14 quantitative and 11qualitative traits) in 50 genotypes were evaluated. The Mean, range,treatment mean sum of square, standard error of difference (SED) andcoefficient of variation (CV) for quantitative characters are given in Table1. Significant variation was reported among the genotypes for all thequantitative traits. Plant height ranged from 24.64 cm (Basanti) to 92.95cm (Gauri) which was divided into 19 short (20–40 cm), 27 medium (40–60 cm) and 6 tall (> 60 cm) genotypes. Observation on plant type showedthat 44 genotypes were bushy type and 6 were non-bushy type. Numberof primary branches varied from 3.5 (Thaichen queen ) to 23 (Jaya) andthe genotypes were grouped into 4 sparse (<6), 15 medium (6-12) and 31dense (>12) branching.

** means significant at 0.1 level of significance

Figure 2 shows the pictures of leaves. The shape of base of leaves wasclassified into 16 acute, 10 obtuse, 6 rounded, 5 truncate, 5 cordate and 8asymmetric shape. Leaf color was categorized into three types light,medium and dark present in 11, 21 and 18 genotypes respectively. Forpetiole attitude, 18 genotypes showed horizontal petiole attitude, 15 verystrongly upward attitude, 13 moderately upward attitude, 3 moderatelydownward and 1 showed drooping petiole attitude. The leaf lengthsranged from 3.2 cm (Kaul) to 10 cm (Pusa Chitraksha), wherein 12genotypes have short (< 5cm), 22 genotypes medium (5-7 cm) and 16genotypes have long (>7cm) leaves. Leaf width varied from 2.36 cm (Kaul)to 7.9 cm (Star).

Yellow where 13 genotypes bear narrow leaf (<4cm), 21 genotypesmedium leaf (4-5 cm) and 16 genotypes have broad leaf (> 5 cm). Leaf L/Wratio ranged from 1.2 (Ajay orange) to 2.87 (Garden Beauty) andgenotypes were described as 5 low (<1.5), 41 medium (1.5 – 2.5) and 4high (>2.5) L/W. For petiole length the genotypes were grouped as 33short (< 2.5 cm), 13 medium (2.5 cm – 4.5) and 4 long (>4.5 cm). Shortestpetiole length was observed in Kaul (0.83 cm) and longest in Gardenbeauty (6.05cm) which was at par with Sunny (5.85 cm). For terminal lobelength genotypes were was evaluated as 12 short (< 2 cm), 17 medium (2cm – 3 cm) and 21 long (> 3cm). Agni and Kaul had shortest terminal lobeof 1.3 cm and Star Yellow had longest one i.e. 5.02cm and was at par withSunny (4.55cm) and Chitraksha (4.5 cm). The lower lobe length rangedfrom 0.69 cm in Kaul to 4.6 cm in Tata Centenary and genotypes werecategorized as 14 short (< 2 cm), 17 medium (2 cm – 3 cm) and 19 long (>3cm). The stipule size varied from 0.45 cm in Silk Brocade to 2.9 cm in Jayaand genotypes were grouped as 27 small (< 1 cm), 13 medium (1 cm – 2cm) and 10 large (> 2cm).

Evaluation of number of flowers per plant showed that 12 genotypes bearfew flowers (< 50), 17 medium (50 – 100) and 21 genotyes had manyflowers (>100). Gauri was a outlier with highest number of flowers (400)whereas Raja and Garden Beauty had the least (34). Flower diameter wasobserved as small (< 4 cm), medium (4 cm – 8 cm) and large (>8 cm) in 4,36 and 10 genotypes respectively. Gauri had the smallest flower headdiameter of 2.7 cm and Thaichen queen had the widest diameter of 11.4cm followed by Tata Centenary and Star Yellow. Based on peduncle lengthgenotypes were grouped as 4 short (< 5 cm), 33 medium (5 cm – 9 cm) and13 long (> 9 cm). Shortest peduncle length was observed in Anmol (3.4cm) and longest in Himanshu (11.7 cm). Flower head was classified into 4categories viz. single type 5 genotypes, semi double 9, daisy eyed double 6and 30 genotypes were double type. Different genotypes had wide rangeof flower color (supplementary table S1). Ray floret length was observedas short (< 2 cm) , medium (2 cm – 3 cm) and long (> 3 cm) present in 8,18 and 24 genotypes respectively. Longest ray floret was in TataCentenary (6.1 cm) and the smallest in Gauri (1.42 cm).

The ray floret width ranged from 0.27 cm in Winter queen to 1.7 cm in StarYellow. Based on ray floret width genotypes were classified as 11 narrow(<0.5 cm), 34 medium (0.5 cm – 1 cm) and 5 broad (> 1 cm). Out of 50cultivars, 6 cultivar consist of two types of ray floret, among theseAgnireka and Silk Brocade had spatulate and ligulate ray floret, BidhanManasi and Little pink had spatulate and quilled ray floret and Himanshuand Bidhan Bishnupriya had Ligulate and funnel shaped ray floret. Fourgenotypes comprised of three types of ray floret, out of which 3 genotypesnamely Pusa Chitraksha, Ajay orange and Himani contain spatulate,ligulate and quilled ray floret, whereas Haldighati consist of incurved,spatulate and quilled type of ray floret. The remaining genotypesconstitute of only one type of ray floret. Ligulate, spatulate, incurved,funnel shaped and quilled were predominant ray floret in 27, 16, 5, 1 and1 genotype respectively. For the ray floret-cross section, 4 genotypes werewith strongly concave ray floret, 8 had moderately concave, 9 showedweakly concave, 15 had flat and 13 exhibited weakly convex ray floretcrosssection, the variety Winter Queen was quilled type, therefore thischaracter was not applicable in this.

Besides flat margins of ray floret in 34 genotypes, 3 more types:moderately involute in 7, weakly revolute in 7 and weakly involute in 1genotype, this character was also not applicable in cultivar Winter Queen.The longitudinal axis of ray floret was classified into four types, out of 50genotypes studied, 1 genotype had twisted longitudinal axis, 6 genotypesshowed incurving longitudinal axis, 5 cultivars showed reflexinglongitudinal axis and remaining 38 genotypes showed straightlongitudinal axis. Depending on the shape of tip of ray floret, 50 genotypeswere categorized into five types viz. emarginated (10), pointed (13),mammillate (5), dentate (9), rounded (12) and fringed (1). The aboveclassification of quantitative and qualitative traits is based on descriptorstate for different characters given in UPOV DUS guidelines. The meanperformance of various genotypes for 14 quantitative characters andobserved trait for 11 qualitative trait is given in table 2. Figure 3 gives boxplot depicting data dispersion for quantitative traits.

3.2 Shannon Equitability Index for qualitative traits

The Shannon Equitability (EH) was estimated for 11 qualitative charactersto measures the evenness of different types in the population. It variedfrom 0.52 to 0.96. Six characters showed EH greater than 0.75 viz. leafshape of base (0.94), leaf green color of upper surface (0.96), petioleattitude (0.82), flower head type (0.79), ray floret profile in cross section(0.94) and ray floret shape of tip (0.91). Diversity of phenotypic classes forqualitative trait (supplementary table S2).

3.3 Genetic Variability, Heritability and Genetic Advance

Estimates of genotypic coefficient of variance (GCV) and phenotypiccoefficient of variance (PCV) of different traits are given in Table 3. Thehighest GCV and PVC values were found particularly for number of flowersper plant (67.52% and 68.05%), stipule size (54.33% and 54.76%),petiole length (45.8% and 48.00%) and Ray floret width (45.71% and46.21%) respectively. Whereas moderate GCV and PCV were recorded forleaf lower lobe length (40.17% and 41.51%) and ray floret length (36.08%and 36.29%) respectively. Low GCV and PCV viz. 17.28% and 18.98%respectively was recorded for ratio of leaf length to leaf width indicatingexistence of less variability. Most of the traits in this study showed broadsense heritability > 90%. Ray floret length (98.84%) exhibited highestheritability followed by number of flowers per plant (98.43), stipule size(98.42) and ray floret width (97.84%). Leaf length to width ratio showedrelatively low heritability. Genetic advance as percent of mean was highestfor number of flowers per plant (138.01) followed by stipule size (111.03)and ray floret width (93.13).

3.4 PCA Results

PCA identifies variables that are most significant in describing the overallvariability in the dataset. PCA was performed separately on 14quantitative characters and 11 qualitative traits is given in Table 4 & 5respectively showing eigen values, factor loading, proportion ofvariability and cumulative variability.

3.4.1 Quantitative traits

A total of 14 quantitative characteristics of Chrysanthemum wereevaluated for classification in multivariate analysis. Eigen values variedfrom 0.01 to 5.33 (Figure 5 A). 4 principal components were having Eigenvalues more than 1.00 viz. PC 1 (5.33), PC 2 (2.24), PC 3 (1.52) and PC 4(1.37) which together explaining 74.7% of the phenotypic variationpresent in the data. The PC 1 accounted for 38.09% of the total phenotypicvariability with major contribution from 6 characters namely leaf laminalength, leaf width, Terminal lobe length, stipule size, flower diameter andray floret length. The PC 2 covered 16 % of the total variation and wasclosely related with 4 characters viz. petiole length, leaf length to widthratio and number of flowers per plant. The PC 3 constituting10.83% of thetotal phenotypic and is mainly contributed by leaf length to width ratio. PC4 explains 9.83% of variability with major contribution from pedunclelength. The biplot (Figure 4) provides an insight into the direction ofcorrelation between variables. Flower diameter showed highly positivecorrelation with ray floret length and ray floret width. Number of flowersper plant is positively correlated with number of primary branches. Leaflamina length strongly and directly correlated with terminal lobe length.

3.4.2 Qualitative characters

PCA for qualitative traits was conducted using harmonized values(transformed data. Eigen value ranged from 0.22 to 2.66 (Figure 5 B). 4principal components viz. PC 1 (2.66), PC 2 (1.61), PC 3 (1.51) and PC 4(1.2) were having Eigen values more than 1.00 which cumulativelyexplained 63.27% of the total phenotypic variation present in the data. PC1 explains 24.18% of the total variation and was correlated with ray floretrolling of margins, ray floret cross section, plant type and ray floretlongitudinal axis. PC 2 accounted for 14.6518% of total phenotypicvariability and mainly contributed by petiole attitude, leaf color andpredominant type of ray floret. PC 3 contributes 13.71% of total variabilityand is associated with flower head and ray floret: shape of tip. PC 4constitutes 10.73% with major contribution from number of types of rayfloret. The biplot for qualitative trait is given in Figure 6.

3.5 Cluster analysis based on Euclidean distance

Unweighted pair group method with arithmetic average (UPGMA) wasused for cluster analysis and Euclidean distance matrices were alsoconstructed. The harmonized values for qualitative trait and measuredvalue of quantitative traits were used for analysis. The Euclidean distanceranged from 5.37-372.36. The maximum distance of 372.36 was observedbetween Gauri and Raja belonging to cluster 1 and cluster 3 respectively.This was followed by a distance of 372.02 between Gauri (cluster 1) andGarden beauty (cluster 3) and a distance of 369.72 between Gauri andRoyal Princess (cluster 3). The closest related cultivars were BidhanLalima and Bidhan Mallika with a distance of 5.37 followed by BidhanMadhuri and Bidhan Sabita with a distance of 6.7 and all four of them weregrouped together in cluster 4. The UPGMA dendrogram aligned with thedistance matrix as indicated by cophenetic correlation coefficient value of0.92.At a Euclidean distance of 88.94 the genotypes were grouped into fourclusters. The distribution pattern revealed maximum number ofgenotypes i.e., 28 in cluster 3 followed by cluster 4 having 20 genotypesand cluster 1 and cluster 2 have Gauri and Pusa Shwet respectively.Cluster 3 has 28 genotypes viz. Ajay orange, Anemone red, Baggi, Basanti,Bidhan Bishnupriya, Bidhan Chitra, Bidhan manasi, Classic, Discovery,Garden beauty, Jyotsana, Little pink, Magenta , Mother Teresa, Kaul,Neelam, Prevalo, Punjab Shyamal, Pusa Arunodaya, Pusa Centenary, Raja,Royal princess, Sharad, Star yellow, Sunny, Tata centenary, Thaichenqueen and Winter queen. Cluster 4 comprise of 20 genotypes namely Agni,Agnirekha, Aprajita yellow, Bidhan Lalima, Bidhan Madhuri, BidhanMallika, Bidhan Sabita, Chandni, Haldighati, Himani, Himanshu, Jaya, PusaAditya, Pusa Chitraksha, Pusa Guldasta, Ragini, River city, Sensation andSilk Brocade. Figure 7 represents clustering based on Euclidean distance.

3.6 Cluster analysis based on jaccard’s similarity index

UPGMA cluster analysis was also performed based on Binary data toresolve the genetic relationships among the 50 genotypes. The Jaccard’ssimilarity coefficient varied from 0.04 to 0.69. The dendrogram dividedthe genotypes into 9 main clusters at the similarity coefficient of 0.33. Theminimum Jaccard’s similarity coefficient was 0.04 between genotypeBidhan Manasi and Thaichen Queen which belong to cluster 5 & 1respectively, whereas maximum Jaccard’s similarity coefficient of 0.69was same between Agnirekha and Bidhan Lalima, Bidhan Lalima andBidhan Madhuri and Bidhan Sabita and Chandini. Cluster 1 contained 5genotypes namely Thaichen Queen, Star Yellow, Tata Centenary, PusaCentenary and Pusa Arunodaya. Cluster 2, 4 and 5 have two genotypeseach. Cluster 2 consisted of Garden Beauty and winter Queen. BidhanBishnupriya and Jaya made cluster 4. Ajay orange and Bidhan Manasiconstituted Cluster 5. Cluster 3 and 6 had only one genotype each viz. Gauriand Punjab Shyamal respectively. Cluster 7 had 6 genotypes namelyAnmol, Magenta, Mother Teresa, Kaul, Royal Princess and Rivercity.Cluster 8 comprised of 7 genotypes namely Discovery, Haldighati, Himani,Sunny, Raja, Pusa Chitaksha, Little Pink. There were 24 genotypes incluster 9 namely Agni, Pusa Aditya, Sensation, Agnirekha, Bidhan Lalima,Bidhan Madhuri, Neelam, Prevalo, Bidhan Sabita, Chandini, Basanti, SilkBrocade, Bidhan Mallika, Ragini, Pusa Shwet, Baggi, Sharad, Aprajitayellow, Pusa Guldasta, Bidhan Chitra, Anemone Red, Jyotsana, Classic andHimanshu. Cluster 9 showed sub-clustering at Jaccard’s similarity index of0.35 with two sub clusters i.e. cluster 9a and cluster 9b. Cluster 9a included4 genotypes namely Anemone Red, Jyotsana, Classic and Himanshu.Cluster 9b contain rest of the 20 genotypes. Fig 8 represent clustering of50 chrysanthemum genotypes based on Jaccard’s similarity index.

4. DISCUSSION

Assessing the diversity within a crop species is crucial for identifyingunique traits or alleles that can be used to enhance desirablecharacteristics. Crossing genotypes from different clusters with greatergenetic distance can enhance genetic diversity and potentially result inimproved crop varieties with desirable traits.

4.1 Morphological characterization

The 50 accessions showed significant variation with respect to 14quantitative characters. Shannon equitability index showed that 6 out of11 qualitative traits had equitability index of more than 0.75 indicatinghigh level of diversity. Reasonable diversity was exhibited by predominanttype of ray floret (0.67) and ray floret rolling of margins (0.63). Plantattributes like plant height, plant type and number of primary branchesalong with floral characteristics determines the end use ofchrysanthemum as pot plant, loose flower, garden display and cut flower.Basanti, Himanshu, Prevalo, Sharad and Jyotsana could be used for potpurpose based on plant height, flower color and good number of flowering.Based on number of flowers, Gauri, Pusa Shwet, Himani, Jaya, BidhanLalima, Bidham Madhuri, Bidhan Mallika, Bidhan Sabita, and Chandni aresuitable for loose flower purpose. Similar evaluation of genotype was done(Suvija et al., 2016). Leaf characteristics enable early identification ofvarieties which helps in early selection in breeding (Gao et al., 2020). Itwas found that leaf length and petiole length were important parametersin the evaluation of hybrid varieties in breeding studies. Differences wereobserved in terms of both leaf size and leaf shape. Morphological variationfor leaf characteristics in Chrysanthemum have also been reported (Zhenet al., 2013). Variation in flower characteristics among Chrysanthemumgenotypes were reported earlier and might vary depending on climaticconditions (Guo et al., 2008; MacDonald et al., 2017; Wang et al., 2021).Ray floret characteristics determine aesthetic value of cultivar and maybeuseful in crop improvement program. Color variations in ray florets werenoted among cultivars. Pusa Aditya , Pusa Guldasta, Punjab shyamali andRiver city showed presence of secondary color in the inner side of the rayfloret. Large genetic variation for ray floret traits was previously observed(Lim et al., 2014).

4.2 Genetic variation, Heritability and Genetic advance

High values of PCV and GCV value indicates high variability and vice versa. Presence of high variability indicates effective selection for the character. Moderate to low variability indicates the need for improvement of base population (Chauhan et al., 2020). The results indicated that PCV are slightly greater than the GCV for all the traits, this mean that the trait sunder study were less influenced by environment. Similar PCV & GCV values for growth and floral characters were observed (Sarkar et al.,2005). High estimates for heritability and genetic advance as percentage of mean was recorded for number of flowers per plant, stipule size and ray floret width. In chrysanthemum, the high heritability values and genetic advance as per cent of mean for number of flowers per plant was also reported (Henny et al., 2021).

4.3 PCA Analysis

PCA is a valuable tool in multivariate analysis to reduce the dataset’s dimensionality without losing important information about the relationships between variables. PCA was done using a correlation matrix as it helps to ensure that the PCA results are robust, interpretable, and nonbiased by the original measurement units of the variables when dealing with variables measured on different scales. PCA for quantitative traits revealed that leaf characters have major contribution to germplasm variability. For qualitative characters, ray florets traits have major contribution to germplasm variability. A group researcher conducted PCA of 35 morphological characters in 15 taxa of Chrysanthemum species and identified 12 principle components explaining 99.4% of variation (Kim etal., 2014).

4.4 Cluster analysis based on Euclidean distance

Cophenetic correlation coefficient value equal to or greater than 0.85 isconsidered good ensuring the consistency of the dendrogram with thedistance matrices (Stuessy, 1990). Cluster 1 consist of only one genotypei.e. Gauri which bear white colour small sized double type flower, longestplant height of 92.95 cm, petiole length (5.15 cm), smallest flowerdiameter (2.7 cm) and highest number of flowers per plant (400). PusaShwet alone belongs to cluster 2 which has white color semi double flower(2-3 rows of ray floret), long leaf (8.15 cm), wide leaf (6.27 cm), highernumber of flower (231) and large flower diameter (7.75 cm). Cluster 3constitute of 28 genotypes and demonstrated comparatively less meanvalues for plant height (39.88 cm), number of branches per plant (11.62)and number of flowers per plant (54.76). Moderate mean value for flowerdiameter was (6.3 cm) and peduncle length (7.15 cm). With respect toqualitative characters, most genotypes in cluster 3 showed upward petioleattitude, flat cross section of ray floret and round shape of tip of ray floret.

Cluster 4 comprise of 20 genotypes having adequate number of primarybranches per plant with mean value 15.66, good number of flowers perplant with average value 139.72 and long peduncle with mean value 7.93.All the plants in cluster 4 are bushy type, majority of plants have downward petiole attitude and weakly convex cross section of ray floret. Fifteen taxa of Chrysanthemum species were classified into three groups through PCA and cluster analysis based on 35 qualitative and quantitative traits (Kim et al., 2014). Mean value of quantitative traits for different cluster is given in Table 6.

4.5 Cluster analysis based on jaccard’s similarity index

The range of Jaccard’s similarity coefficients (0.04 – 0.69) indicatedsignificant genetic diversity between chrysanthemum cultivars. In cluster1 all 5 genotypes are of non bushy types, large diameter double typeflower, large stipule size, long and wide ray floret and a less number offlowers. All except Pusa Arunodaya exhibited incurving type of ray floret,moderately involute rolling of margins of ray floret and concave crosssection of ray floret. Cluster 2 had single flower type, short plants, fewnumbers of flowers and large stipule size. Genetic relationships withinchrysanthemum were partly indicated by their ray floret type was alsoshown (Chen et al., 2013; Mia et al., 2007). Cluster 3 had only genotypeGauri which showed Jaccard’s similarity index of 0.38 or less with all thegenotypes showing exclusive higher value for plant height (92.95 cm) andnumber of flowers (400). Petiole length, flower diameter, ray floret lengthand ray floret longitudinal axis in Gauri varied from majority of thegenotypes. Cluster 4 showed higher number of primary branches, long andwide leaf, medium terminal lobe length of leaf and long lower lobe length.Clustering based on various leaf characteristics was also performed (Kimet al., 2014). Genotypes in cluster 5 exhibited short height, mediumnumber of primary branches, short leaf length, low leaf length to widthratio and medium stipule size. Punjab Shyamal , the only cultivar in cluster6 showed reflexing longitudinal axis of ray floret, 2 types of color on theinner side of ray floret, short ray floret along with long and narrow length.All the genotypes in cluster 7 showed long and narrow leaves with shortpetiole length and medium length of ray floret. In cluster 8 all cultivarsexhibited bushy plant type, straight longitudinal axis of ray floret, mediumleaf length to width ratio, medium width of ray floret and also allgenotypes had medium to large size flowers. Majority of cultivars incluster 9 have medium sized flowers, many primary branches, manynumber of flowers and flat margins of ray floret. The mean value ofquantitative traits for different cluster is given in Table 7.

5.CONCLUSION

The 50 genotypes of Chrysanthemum varied significantly for 25 Morphological characters. The PCVs were slightly higher than GCVs indicating correspondence between genotype and phenotype. PCA showed that among quantitative traits, leaf characters and ray florets characters among qualitative trait have major contribution to germplasm variability. Clustering of genotypes based on Euclidean distance and Jaccard’s similarity coefficient gave 4 and 9 clusters respectively. Gauri was an outlier for plant height and number of flowers per plants and thus was grouped alone in both type of clustering. The morphological identification of genotypes will enable efficient utilization of genetic resources, selection of parents for hybridization that will help in improvement of desired traits.

ACKNOWLEDGEMENT

I acknowledge the help and support provided by the Director, IARI to carry out the study.

FUNDING DETAILS

No separate funding was available for this study. The study was conducted with the available institute’s funds.

COMPETING INTEREST

No potential conflict of interest was reported by the author(s).

AUTHORS CONTRIBUTION

Conceptualization: Gunjeet Kumar, Vartika Budhlakoti; methodology: Gunjeet Kumar, Vartika Budhlakoti, A.K. Tiwari , V.M. Hiremath; data analysis: Gunjeet Kumar, Vartika Budhalkoti, V.M. Hiremath, Saipriya Panigrahi, Shreekant; writing: original draft preparation, Gunjeet Kumar, Vartika Budhlakoti, A.K. Tiwari, V.M. Hiremath; Review and editing: Gunjeet Kumar, Markandey Singh. All authors read and made suggestions which were incorporated in the final manuscript.

DATA AVAILABILITY

All data pertaining to this research is available in the manuscript or list of supplementary table.

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This study examines the phenotypic traits and genetic diversity of soybean genotypes resistant to soybeanrust (Phakopsora pachyrhizi) using simple sequence repeat (SSR) markers. Five soybean varieties, includingboth resistant and susceptible types, were evaluated under controlled conditions. Molecular analysis wasconducted using SSR markers (CP171/172, SSR1, RB32, and RB34) to assess genetic variation. Phenotypicassessments were performed to correlate molecular data with resistance traits, focusing on disease responseand yield potential. The results showed that plant height ranged from 87.3 cm (TGx1951-3F) to 60.3 cm(TGx1835-10). Pathological analysis revealed that some varieties, such as TGx1448-2E and TGx1951-4F,exhibited resistance to rust, while TGx1904-6F had the highest disease incidence. Overall, the findingshighlighted significant genetic diversity among the evaluated genotypes, with several accessionsdemonstrating strong resistance and high yield potential. This research enhances the understanding of thegenetic basis of rust resistance in soybean and offers valuable insights for future breeding strategies toimprove crop resilience against this major pathogen.

Soybean (Glycine max (L.) Merrill), a member of the pea family, is a highlyversatile legume cultivated for its nutrient-rich seeds. It plays a crucial rolein global agriculture, providing an essential source of protein and keynutrients for both human consumption and animal feed. However,soybean’s vulnerability to various diseases, such as rust, presents a majorchallenge to its productivity and overall yield (Mishra et al., 2024).

Soybean rust, caused by Phakopsora pachyrhizi, is a serious threat tosoybean cultivation worldwide (Ono et al., 1992; Schneider et al., 2005).This fungal disease primarily affects the leaves, stems, and pods of theplant. Early symptoms typically manifest as small brown or yellow spotson the leaves, followed by lesions and cracks on the stems, as well assunken patches on the pods. These symptoms often result in prematureleaf drop, weakened plant structures, and significantly reduced yield andpod formation (Wikipedia). The pathogen spreads through airborneurediniospores, allowing for rapid disease progression. In regions wherethe disease is prevalent, yield losses can reach as high as 80% (Li et al.,2012).

The primary method for managing soybean rust involves the applicationof fungicides, which, although effective, significantly increase productioncosts and pose environmental concerns. Additionally, some P. pachyrhizistrains have shown increased resistance to specific fungicides (Godoy,2009). As a result, developing and cultivating rust-resistant soybeanvarieties is considered the most efficient and sustainable strategy fordisease control. Resistant varieties offer a cost-effective and environmentally friendly alternative that simplifies disease management.

The genetic variation responsible for resistance arises from multiplefactors, including insertions, deletions, substitutions, and nucleotiderearrangements within the DNA (Redelings et al., 2024). Current researchfocuses on evaluating genetic diversity and phenotypic traits among rustresistantsoybean varieties using molecular markers, which play a criticalrole in breeding programs. Molecular markers aid in the identification andincorporation of resistance genes, enhancing breeding efficiency and thedevelopment of improved cultivars (Tabien et al., 2000).

Additionally, analyzing DNA polymorphism in soybean varieties providesvaluable insights that can contribute to the development of molecularmarkers for rust resistance. These markers streamline the screeningprocess, allowing breeders to efficiently identify and cultivate rustresistantsoybean varieties (Miah et al., 2013). By minimizing yield lossescaused by rust, molecular marker-assisted breeding enhances soybeanquality, increases farmers’ income, and reduces dependence on fungicides,thereby mitigating environmental risks.

Future research in this area may focus on identifying additional DNApolymorphisms linked to rust resistance, refining molecular markers toimprove accuracy and efficiency, and employing molecular breedingtechniques to develop soybean varieties with enhanced traits beyond rustresistance—such as higher yield potential and improved droughttolerance (Li et al., 2023). Advancements in this research hold significantpotential for strengthening soybean production, ensuring greater foodsecurity, and promoting sustainable agricultural practices.

Despite progress in developing rust-resistant soybean genotypes, geneticdiversity among these genotypes remains relatively narrow. This limited variation poses a challenge to the long-term effectiveness of resistance, asthe emergence of new rust pathogen races could overcome existingdefenses. Therefore, this study utilizes molecular markers to evaluategenetic diversity and guide breeding strategies aimed at enhancing rustresistance and broadening genetic variation.

The objective of this research was to examine the phenotypic and geneticdiversity of rust-resistant and susceptible soybean genotypes usingSimple Sequence Repeat (SSR) markers.

2. MATERIALS AND METHODS

2.1 Experimental Site and Location.

The study was carried out in the Molecular Biology Laboratory at JosephSarwuan Tarka University, Makurdi, Benue State. The university issituated at a latitude of 7.45°N and a longitude of 8.32°E, with an elevationranging from 97 m to 111.2 m above sea level. The planting phase tookplace in the laboratory’s screen house, which is positioned behind theVeterinary Medicine auditorium, South Core area.

2.2 Planting Materials

Seeds from five distinct soybean varieties were sourced from the seedstore of the Molecular Biology Laboratory at Joseph Sarwuan TarkaUniversity. The selected varieties included:

i. TG X 1835-10E
ii. TG X 1951-3F
iii. TG X 1951-4F
iv. TG X 1904-6F
v. TG X 1448-2E

To prevent any mixing, the seeds were stored in separate, labeled packets.

2.3 Planting of Soybean Seeds in the Screen House

The five soybean varieties acquired from the Molecular BiologyLaboratory were sown in pots filled with topsoil. Initially, three seeds fromeach variety were planted per pot. After ten days, thinning was carried out,leaving two plants per pot to promote optimal growth conditions.

2.4 Collection of Leaf samples

Leaf samples were collected from young soybean plants of each varietyfourteen (14) days after planting. The samples were placed in polythenezip-lock bags containing silica gel and left to dry for three days.

The equipment used for sample collection included:
i. Blade
ii. Polythene zip-lock bags
iii. 70% ethanol
iv. Paper towel

2.5 DNA Extraction using the CTAB Method

The Polymerase Chain Reaction (PCR) technique was employed to amplifya small number of copies of a specific DNA segment, thereby producingmultiple replicas of that particular DNA sequence. The PCR procedure wasconducted with a total reaction volume of 15μl. The components of thereaction comprised:

PuReTaqTM Ready-go-goTM PCR beads (containing PCR Buffer, MgCl2,DNTP’s, and Taq Polymerase)

Distilled water

1μl of each primer and

1μl of DNA (50ng)

SSR based PCR protocol was used in carrying out PCR amplifications(Omoigui et al., 2015). 25 μl of Molecular Biology Grade water was addedinto 0.2 ml eppendorf tubes containing the PCR beads. The mixture wasthen divided into two for two PCR reaction, 1 μl primer (marker) and 1 μlDNA sample to serve as template was added into each 0.2 ml eppendorftube. Tubes were covered and centrifuged for 15 seconds in other toassemble all components at the base of the tubes. The 0.2 ml eppendorfPCR tubes were arranged properly into the thermal cycler (PCR machine)to begin amplification.

2.6 Polymerase Chain Reaction (PCR) Mixture

The Polymerase Chain Reaction (PCR) technique was utilized to amplifyspecific DNA segments, generating multiple copies of the target sequence. The reaction was carried out in a total volume of 15 μL. The reactionmixture included the following components:

• PuReTaq™ Ready-To-Go™ PCR beads (containing PCR buffer, MgCl₂,dNTPs, and Taq polymerase)
• Distilled water
• 1 μL of each primer
• 1 μL of DNA (50 ng)

The SSR-based PCR protocol described by a group researcher wasfollowed for amplification (Omoigui et al., 2015). Twenty-five microlitersof Molecular Biology Grade water was first added to 0.2 mL Eppendorftubes containing the PCR beads. The solution was then split into twoseparate tubes for two PCR reactions. One microliter of primer (marker)and 1 μL of the DNA sample were added to each tube, serving as thetemplate for amplification.

After sealing the tubes, they were centrifuged for 15 seconds to ensure allcomponents settled at the bottom. Finally, the 0.2 mL Eppendorf tubeswere carefully placed in the thermal cycler (PCR machine), where theamplification process was initiated.

2.6.1 Polymerase chain reaction cycle

The PCR cycling protocol involved an initial denaturation step at 94°C for4 minutes, followed by denaturation at 94°C for 30 seconds, annealing at55°C for 1 minute, and extension at 72°C for 1 minute. The reaction wasthen held at 60°C indefinitely to maintain the amplified DNA.

2.7 Agarose Gel Electrophoresis

The methodology described was adopted for gel electrophoresis. A 3.5%agarose gel was prepared by weighing 3.5 g of agarose powder anddissolving it in 350 mL of 1x TAE buffer (Omoigui et al., 2015). The mixturewas gently swirled and heated in a microwave until it became clear. Aftercooling, 30 μL of ethidium bromide (EtBr) was added and mixedthoroughly. The gel solution was then poured into a pre-prepared gelcasting tray with a comb to form wells.

Once the gel solidified, it was carefully placed in the electrophoresis tank,and the comb was gently removed to prevent well damage. To prepare theDNA samples, 1 μL of DNA was mixed with 1 μL of 6x loading dye in a PCRtube and briefly spun. The prepared samples were then carefully loadedinto the wells using a micropipette. Additionally, 5 μL of a DNA ladder wasloaded into a separate well as a reference marker. The electrophoresissystem was sealed, and the gel was run at 120V for 45 minutes.

DNA purity and quality were assessed using UV spectrophotometry. Thebanding patterns of the DNA samples, resolved on the agarose gel, werevisualized under a UV transilluminator, and the gel image was captured forband scoring. Only distinct bands were recorded, with presence scored as(1) and absence as (0) (Omoigui et al., 2015).

2.8 Data Analysis

The MINITAB 17 software was used for statistical analysis. Phenotypicdata were analyzed using descriptive statistics, and cluster analysis wasperformed. A dendrogram was generated using the complete linkagemethod to assess genetic relationships among the samples.

3. RESULTS AND DISCUSSION

Table 1 provides information on the growth, yield and pathologicalcharacters assessed during the field work. Plant height varieties from 87.3to 60.3(TGx-1951-3F and TGx-1835-10E) is shown in figure 1. VarietyTGx-1835-10E had the highest maturity which was recorded as 109 isshown in figure 2. The data showed that variety TGx-1951-3F (723.7) hadthe highest seed yield/plot with variety TGx-1835-10E (555.6) as thelowest is shown in figure 3. Days to 50% flowering vary from 41 days to45 days (TGx-1835-10E) and (TGx-1904-6F) is shown in figure 4.Pathological data shows that some of the varieties are resistant to rustdisease, the variety that recorded the highest incident of rust disease wasTGx-1904-6F is shown in figure 5, Frogeye leaf spot disease was 1.8 (TGx-1448-2E). Mosaic disease ranged from 1 (TGx-1904-6F) and 1.7 (TGx-1951-4F), RTNOD 1-5 ranged from 2.5 to 3.5 (TGx-1835-10E and TGx-1951-4F). Lodg 1-5 ranged from 1 to 2.2 (TGx-1951-4F and TGx-1904-6F).Pod shat late ranged from 1 to 2 (TGx-1835-10E and TGx1904-6F), 100seed weight ranged from 11.7 to 13.7 (TGx-1448-2E and TGx-1951-4F).The variety with the Lowest Pod height was TGx-1835-10E 3.7.

Plates 1 and 2 display the agarose gel images of four screened SimpleSequence Repeat (SSR) markers used in DNA amplification to assesspolymorphism between rust-resistant (TGx1835-10E) and rustsusceptible(TGx1951-3F and TGx1951-4F) soybean varieties. Theprimers exhibited varying levels of genetic polymorphism, depending onthe soybean DNA amplified and the SSR primers used. While all primersgenerated visible bands, SSR 1 showed no clear resolution in either rustresistantor susceptible varieties. However, distinct bands were wellresolved using primers CP 171/172, RB 32, and RB 34.

= TG X 1835-10E

= TG X 1951-3F

= TG X 1951-4F

= TG X 1904-6F

= TG X 1448-2E

L = 50bp Ladder.

Primers = CP 171/172 and SSR 1

L = 50bp Ladder

B = Blank

Primers = RB32 and RB34

= TG X 1835-10E

= TG X 1951-3F

= TG X 1951-4F

= TG X 1904- 6F

= TG X 1448-2E

4. DISCUSSION

Genetic diversity among rust-resistant soybean genotypes is essential forimproving breeding programs aimed at managing soybean rust, a diseasecaused by Phakopsora pachyrhizi. Molecular markers are valuable toolsfor evaluating genetic variation and identifying resistance traits bypinpointing genomic regions associated with disease resistance. Plate 1presents the agarose gel image comparing rust-resistant and susceptiblesoybean varieties. The two markers used in this study were only amplifiedin resistant varieties, suggesting a potential link between these markersand rust resistance. This observation aligns with the findings of whoidentified SSR markers associated with rust resistance (Zhong et al., 2024).The application of these markers in breeding programs enables moreprecise selection of resistant plants, thereby accelerating the developmentof improved soybean varieties for farmers facing rust disease challenges.

Plate 2 shows the screening results of SSR markers for polymorphismbetween rust-resistant and susceptible soybean varieties. The testedmarkers revealed genetic variation in both groups, indicating theireffectiveness in distinguishing between resistant and susceptiblegenotypes. Notably, RB32 was amplified only in the resistant parent, whileRB34 was amplified exclusively in susceptible varieties. This suggests astrong genetic linkage between RB32 and rust resistance, and betweenRB34 and rust susceptibility. These findings are consistent with previousresearch by further supporting the potential of these markers in markerassistedselection for rust resistance (Li et al., 2023).

A phenotypic assessment was conducted to correlate molecular data withresistance traits, particularly focusing on disease incidence and yieldpotential. Some varieties exhibited early maturity and demonstratedresistance to rust and other diseases. Among the genotypes analyzed, TGX-1904-6F recorded the highest rust disease incidence rate (2.2). In terms ofyield performance, TGX-1951-3F had the highest seed yield per plot(723.7), while TGX-1835-10E recorded the lowest (555.6).

5. CONCLUSION

The results from Plate 1 and Plate 2 demonstrate that SSR markers areeffective tools for identifying rust resistance in soybean varieties. Theobserved polymorphism across the two markers indicates their ability todistinguish between resistant and susceptible genotypes. Specifically,RB32 and RB34 showed strong associations with rust resistance andsusceptibility, respectively, suggesting a close genetic link to theunderlying resistance and susceptibility genes. These findings align withprevious research and highlight the potential of SSR markers in breedingprograms for rust-resistant soybean varieties.

RECOMMENDATIONS

• Explore additional markers – Further research should focus on identifying additional molecular markers associated with rustresistance. Expanding the pool of markers will provide breeders with more precise tools for selecting resistant soybean varieties.

• Incorporate markers into breeding programs – Validated markers should be integrated into soybean breeding programs to expedite the development of rust-resistant varieties. Collaboration among researchers, breeders, and farmers will be essential for the successful implementation and adoption of marker-assisted selection.

• Increase awareness and adoption – Outreach programs and educational initiatives should be conducted to inform farmers and other stakeholders about the benefits of rust-resistant soybean varieties. Promoting awareness will encourage wider adoption and enhance the impact of these technologies on soybean production.

• Enhance breeding strategies – Molecular markers should be systematically integrated into breeding programs to develop improved soybean varieties with resistance to rust and other diseases. A multi-disciplinary approach involving geneticists, breeders, and farmers will ensure the successful deployment of these advancements in real-world agricultural settings.

REFERENCES

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An onion is a vegetable that is the most widely cultivated and used in Bangladesh like other countries of the world. However, the productivity of onion in Bangladesh is low as compared to other countries due to lack of high quality seed. Hence, a field research was conducted to assess the impact of plant biostimulant and plant growth regulators (GA3 and Florigen) on morphological and physiological traits, yield attributes, and quality seed production of onion. The experiment was laid out in a randomized complete block design with three replications. Three onion varieties like Lalteer King, Taherpuri, and BARI Piaz 1 and eleven treatments were included such as T0 (control); T1, T2, T3 and T4 (plant biostimulant of 1, 2, 4 and 8 gL-1 Goemar respectively); T5, T6 and T7 (Benzyl adenine of 10, 20, and 30 ppm of mg/L-1 of BA respectively); T8, T9, and T10 (Gibberellic acid of 25, 50, and 75 mg/L-1 GA3 respectively). The variety Lal teer king which was sprayed with 75 mgl-1 GA3 produced the highest length of scape, number of scape, number of flowers per umbel, number of fruits per umbel, photosynthesis rate (Fv/Fm), 100 seed weight, total seed weight per plot, yield per hectare, and PI value at 23.41 cm, 4.05, 166.66, 117.33, 0.96, 0.42g, 93.00g 620.24 kg and 3.35 respectively. However, the plant height including leaf was found to be profoundly superior 51.33 cm, 61.00 cm and 70.66 cm in Lal teer king variety at 30, 45 and 60 days after planting (DAP) when treated with 75 mg/L-1 GA3. At the same time, control treatment in BARI piaz 1 was produced the lowest plant height including leaf 37.00 cm, 43.00cm and 47.33 cm at 30, 45 and 60 DAP respectively. In turns, the maximum amount of CO2 assimilation was calculated in BARI piaz 1 under the treatment T10 (75 mg/L-1 GA3) at all measuring times. The highest number of shoot (5.60) per bulb was found in BARI piaz 1 under the treatment T4 (8 gL1 Goemar). The findings revealed that the application of 75 mgL-1 GA3 (T10 treatment) in Lal teer king onion variety exhibit a significant better performance in terms of the morpho-physiological characteristics, yield and best quality onion seed production compared to the use of other PGR and plant biostimulant.

Onion (Allium cepa L.) is one of the major cultivated bulb vegetable species used by the most people and the world onions were valued not only for their flavor but also for their medicinal properties. Research from the 20th century onwards has focused on enhancing seed quality and yield through genetic improvement and innovative agricultural practices (Jain, 2013). High-quality onion seeds are crucial for enhancing crop yields, improving plant health, and ensuring the overall success of onion production. Seeds can significantly increase productivity. FAO, reported that the improved seeds can boost yields by up to 20% Additionally, quality seeds are often bred for resistance to diseases like downy mildew and pests such as onion thrips, reducing the dependency on chemical treatments and lowering production costs for farmers (FAO, 2020; Smith and Johnson, 2018).

Thus, quality seed not only makes onion farming more efficient but also more environmentally sustainable. Farmers using high-quality, disease-resistant seeds can cut down on pesticide usage by up to 15%, resulting in lower production costs (FAO, 2022). Moreover, onions produced from quality seeds have better post-harvest qualities, such as improved texture, longer shelf life, and enhanced resistance to spoilage, reducing storage losses and ensuring that onions maintain their quality during transport and sale (Wang et al., 2017). Therefore, high-quality onion seeds are foundational to achieving higher productivity, economic gains, better crop quality, and sustainable agricultural practices.

In 2022, Bangladesh produced around 1,500 metric tons of onion seeds, contributing approximately 1.5% to the global market (FAO, 2022). This production is primarily focused on domestic needs, as the country continues to work towards self-sufficiency in onion cultivation, reducing its reliance on imports. Bangladesh currently imports a significant amount of onion seeds, particularly hybrid varieties. So, the ongoing efforts to improve seed breeding programs and local production capacity may increase its share in the coming years. The production of high-quality onion seeds in Bangladesh faces several significant challenges, hindering its ability to meet both domestic demand and compete globally. One major constraint is the lack of advanced breeding programs, which limits the development of hybrid, disease-resistant, and high-yielding varieties compared to leading producers like the Netherlands and the USA (Nair etal., 2020).

Climatic conditions, particularly the heavy monsoons and high humidity,also pose problems by increasing the risk of disease and reducing seedquality (Khan et al., 2021). Additionally, farmers struggle to access highqualityparent seeds, often relying on lower-quality, saved seeds that leadto poor crop performance (FAO, 2022). Inadequate infrastructure for seeddrying, storage, and testing, combined with a lack of modern technology,further hampers seed quality. Moreover, many farmers lack training inbest seed production practices, leading to inefficient irrigation, pestmanagement, and harvesting (Rahman et al., 2020). Market regulation andquality control are also weak, with unregulated and uncertified seedscommonly sold, resulting in inconsistent quality (Khan et al., 2021).Addressing these issues, including improving infrastructure, farmereducation, and market regulation, is essential for advancing Bangladesh’sonion seed industry.

On the other hand, the high humidity and frequent heavy rainfall duringthe monsoon season create an environment conducive to fungal diseasesand water logging, which can severely compromise seed quality andreduce yields. Implementing measures to mitigate the effects of climatechange and adopting advanced technologies for better irrigation and seedquality management are essential for ensuring sustainable and highqualityonion seed production in Bangladesh. Overcoming the constraintsin onion seed production in Bangladesh requires a comprehensiveapproach addressing several key areas. Advanced breeding programs areessential to develop high-yielding, disease-resistant onion varietiestailored to the local climate and soil conditions, in collaboration withagricultural research institutions (Nair et al., 2020).

Plant biostimulants (seaweed extracts trade name Goemar) and plantgrowth regulators like Benzyladenine (BA) and Gibberellic Acid (GA3)play significant roles in enhancing onion crop growth, yield, and seedquality. These substances contribute to improving various aspects of plantdevelopment through distinct mechanisms. Plant biostimulants are rich ina variety of bioactive compounds, including vitamins, minerals, aminoacids, and natural plant hormones. These components enhance plantgrowth and stress tolerance by promoting root development, improvingnutrient uptake, and increasing resistance to environmental stressors.Additionally, plant biostimulants boost photosynthesis by increasingchlorophyll content, which enhances energy production and contributesto better seed and bulb quality.

By stimulating enzymatic activity and soil microbial health, seaweedextracts also improve soil fertility and support sustainable agriculturalpractices (Rathore et al., 2009). Benzyladenine, a synthetic cytokinin,plays a crucial role in regulating plant growth by promoting cell divisionand differentiation. In onions, BA application encourages vigorous shootgrowth and delays leaf senescence, leading to increased photosyntheticactivity and enhanced nutrient mobilization which collectively producebetter bulb development and higher seed yield. BA also stimulates theproduction of side shoots, which can be beneficial for seed production, andregulates flowering processes, contributing to more uniform andproductive flowering (Sharma and Singh, 2018). By improving overallplant structure and function, BA enhances both the quantity and quality ofonion seeds. GA3 influences various developmental processes bystimulating cell elongation and division.

In onion cultivation, GA3 accelerates shoot and root growth, improvesplant height, and promotes early flowering, which is crucial for successfulseed production. The synergistic application of seaweed extracts, BA, andGA3 can significantly improve onion cultivation. Seaweed extracts enhancenutrient uptake and stress resilience, while BA and GA3 regulate growthprocesses and boost seed production. Together, these biostimulant andgrowth regulators lead to stronger plant growth, higher yields, andimproved seed quality, providing a sustainable approach to optimizingonion cultivation.

2. MATERIALS AND METHOD

2.1 Study site

The experiment was conducted in the research field of the Department ofAgricultural Botany. Analysis was done in Laboratory of plant physiologyunder the Department of Agricultural Botany, and Central Laboratory,Patuakhali Science and Technology University, Bangladesh.

2.2 Land preparation and fertilization

The land was ploughed followed by cross-ploughing to obtain good tilth,which was necessary to get better yield of the crop. All the weeds andstubbles were removed from the experimental field. The experimentalfield was divided into unit plot to maintaining the desired spacing; theplots were spaded one day before planting and Cowdung (10 tons/ha),TSP (275 kg/ha); MOP (150 kg/ ha); urea (250 kg/ha); gypsum (110 kg/ha) were applied in the main field. All the cowdung, TSP, MPO and 1/3of the urea were applied before transplanting, and rest urea were appliedin 3 instalments after 15, 30 and 45 days of transplanting respectively.Furadan 5G was used @ 8 kg/ha to protect the young plants from theattack of mole cricket, ants, and cutworms.

3. MATERIALS

Bulbs of Onion varieties such as BARI piazl 1, Taherpuri, and Lalteer Kingwas collected from BARI, Gazipur, Rajshahi, and Lal teer companyrespectively.

3.1 Experiment design

Three onion varieties and 11 treatments experiments were laid out in aRandomized Complete Block Design (RCBD) with three replications. Thus,there were 99 (treatment 11 x variety 3 x replication 3) unit plotsaltogether in the experiment. The size of each unit plot was 1.5 x1.2 m2thus the 1.8 m2 plot where line to line and plant to plant distance were 30and 15 cm respectively.

3.2 Plant biostimulant and PGRs Treatments

Different level of plant biostimulant “Goemar” (1 gL-1, 2 gL-1, 4 gL-1, and 8gL-1Goemar), Benzyl adenine (BA) (10 ppm, 20 ppm and 30 ppm), GA3 (25ppm, 50 ppm and 75 ppm) and control (water spray) treatments weresprayed at 20 DAS and 40 DAS respectively.

3.3 Transplanting and intercultural operation

Onion bulbs were transplanted in rows by hand. The distance betweenrow to row and plant to plant were 30 and 20 cm, respectively. Bulb wasplaced in each point at 2-3 cm depth from the soil surface. The plots wasalways kept under careful observation for getting better growth anddevelopment of plants and the intercultural operations is weeding,irrigation were done when it was necessary. Plants were infected bypurple leaf blotch disease and controlled by spraying Rovral 50 WP at therate of 0.2% at 15 days interval after 30 days of transplanting.

3.4 Data Collection

Different morpho-physiological and yield contributing characters wererecorded viz., plant height(cm), number of leaves plant-1, number of shootper bulb, Length of scape (cm), number of scape plant-1, number of flowersper umbel, number of fruit per umbel, 100 seed weight (g), chlorophyllcontent (mg/cm2) (Fv/Fm), CO2 assimilation, PI value, seed weight perplot (g), and yield hectare-1(kg).

3.5 Statistical Analysis

The data obtained for different characters were statistically analyzed toobserve the significant difference among the treatments by using theMinitab 2017. The mean values of all the characters were calculated andanalysis of variance was performed. The significance of the differenceamong the treatments means was estimated by the Tukey’s test at 5% levelof probability.

4. RESULT AND DISCUSSION

4.1 The effect of plant biostimulant (Goemar) and PGRs (BA andGA3) on growth of Onion

Plant biostimulant (PB) trade name “Goemar” and PGRs (BA and GA3)treatments influenced and reduced the plant height, number of leaves andnumber of shoot per bulb, Length of flower stalk, No of flower stalk perplant, No. of flowers per umbel, No. of fruits per umbel compared with acontrol (Table 1 and 2). The interaction between onion varieties and PB &PGRs were significant in plant height at 45 and 60 DAT, number of leaves,number of shoot per bulb, Length of flower stalk, No. of flowers per umbeland No. of fruits per umbel. The response of varieties to biostimulant andPGRs was also different under Goemar, Benzyle amine and GA3 treatments.At 30 DAT, interaction result was insignificant in plant height. The V1variety (Lalteer king) showed the highest plant height (51.33 cm) at 30DAT under T10 treatment (75mgL-1 GA3).

Similarly, all other interaction reported statistically similar plant height toV1T10 interaction (Table 1). A similar trend of plant height was recorded at45 and 60 DAT, with interaction V1T10 reporting significantly maximumplant height (61.00 cm) and (70.66) followed by V2T10 (59.83 cm) and(69.83 cm) at 45 and 60 DAT respectively. Internodes elongation is themost pronounced effects of gibberellins on plant growth. In many plantssuch as dwarf pea and maize, the genetic dwarfism can be overcome. Insub apical meristem region, GA3 speed up cell elongation and cell divisionresulting plant height increasing. This increase is the result of an increasein auxin in plant tissues by inducing the tryptophan conversion to IAApromoting cell division and cell elongation. Most of researchers reportedthe increase in plant height with the application of GA3 by increasing the length of internodes of marigold (Kanwar and Khandelwal, 2013; Singh, 2004).

The response of number of leaves was also treatment and variety dependent. At 30 DAT, V3 variety showed the maximum number of leaves (22.33) under T4 treatment and the minimum number of leaves (12.00) were found in same variety under the T0 (control) treatment. Similarly, treatments T10 (21.33) of V3 variety, T3 (22.33), T4 (22.66), T10 (22.00) of V2 variety and T3 (23.00), T4 (23.00), T10 (22.66) of V1 variety reported statistically similar numbers of leaves to V3 variety under T4 treatment. Among the interactions, the highest number of leaves was calculated from V1T10 (28.33) interaction and the lowest number of leaves was noted in V3T5 (9.66) combination at 45 DAT. In the same way, V1T9 (27.33) also reported statistically similar numbers of leaves to V1T10 (28.33) interaction. At 60 DAT, V1 variety showed the highest number of leaves (25.33) under T1 treatment and the least number of leaves (11.00) were initiated in V3 variety under the T7 treatment. Similarly, V1T10 (24.00) also reported statistically similar numbers of leaves to V1T1 (25.33) interaction. The above results indicate that the number of leaves of a plant might be controlled by genetical characteristics of a variety. Similar results were earlier reported (Kumar, 1996). A group researcher reported that GA 25 ppm exhibited maximum leaf number in grape plants (Bhat et al., 2011).

The application of plant bio stimulation and PGRs on onion varieties was found to have a significant effect on the number of shoot per onion bulb. The V3T4 interaction reported the maximum shoot (5.60) initiation per bulb followed by V1T4 (5.30), V1T10 (5.53), V2T3 (5.37), V2T4 (5.45), V2T10 (5.53), and V3T10 (5.53). Interaction V3T6 showed the least shoot production per bulb (3.44) in onion bulb to seed production (Table 1). These results may be due to the effect of plant biostimulant “Gomar” and GA3 on cell elongation, cell division which turn resulted increasing the number of shoot initiation per bulb and scape production of onion (Abou Elsalahein, 1990). The effect of gibberellic acid (GA3) appears essential for the induction of lateral shoots, when applied at an early development stage of onion plants.

V1= LaLtir king, V2=Taherpuri, V3=BARI piaz 1, T0=control, T1=1 g·L−1Goemar, T2=2 g·L−1Goemar, T3=4 g·L−1Goemar, T4=8 g·L−1Goemar, T5=10 mg L-1 BA, T6=20 mg L-1 BA, T7=30 mg L-1 BA, T8=25 mg L-1 GA3, T9=50 mg L-1 GA3, T10=75 mg L-1 GA3

DAT= Days After Transplanting

The interaction between variety and biostimulant and PGRs application was significant for length of flower stalk (scape) evaluated at 60 DAT (Table 2). The V1 variety treated with T10 (75 mgL-1 GA3) reporting significantly maximum length of flower stalk (23.41 cm) followed by V2T10 (22.28 cm). On the other hand, V3 variety treated with T5 (10 mgL-1 BA) showed the lowest length of flower stalk (11.49 cm). This finding correlated with (Jyoti et al., 2018). This result indicates that GA3 might be involved in cell division and elongation and BA might be involved in cell elongation barrier. In the growing portion, GA3 induced cell division and rapid cell elongation that helped increase flower stalk length. Auxins play an important role in cell division, vascular tissue differentiation, and apical dominance. Within plant metabolism GA3 play an important role in auxin production.

Number of flower stalk per bulb is very important for onion seed production. In this research, interaction effect was significant on number of flower stalk per bulb. The maximum number of flower stalk (4.50) was recorded in V1T10 interaction followed by V2T10 (4.27) and V3T10 (4.31). Conversely, the minimum number of flower stalk (2.14) was found in V3T0 interaction followed by V1T0, V2T0 and V3T6 interaction (Table 2). From the above result, we concluded that GA3 treatment influence the flower stalk production in onion bulb.

Table 2 showed that the marked influence of application of different treatments of plant biostimulant and PGRs on number of flower per umbel and number of fruits per umbel at different variety of onion. The maximum number of flower per umbel (166.66) was recorded in V1T10 interaction followed by V2T10 (164.00) and V3T10 (161.33) interaction. In contrast, the lowest number of flower (128.00) per umbel was noted in V3T0 followed by V3T1, V3T5 and V2T0 interaction. However, the highest number of fruit was observed in V1 variety under T10 treatment followed by V2 variety under T10 treatment. This result indicated that genetical factors of onion varieties and PGRs might be influence fruit production of onion. On the other hand, the lowest number of fruit (79.33) was detected in V3 variety under T0 treatment followed by V1T0 (81.66), V2T0 (81.66), V3T5 (81.66) and V3T6 (80.66) interaction.

4.2 The effect of plant biostimulant (Goemar) and PGRs (BA and GA3) on physiology of Onion

The interaction between variety and biostimulant & PGRs application was significant for CO2 assimilation evaluated at 30 DAT and 60 DAT (Table 2) but insignificant result was found at 45 DAT. At 30 DAT, the highest CO2 assimilation (578 ppm) was found in V3T10 interaction followed by V1T4 (470.33ppm), V1T9 (469.66ppm), V1T10 (469.00ppm), V2T4 (470.51ppm), V2T9 (470.83ppm), V2T10 (523.50ppm), V3T4 (470.66ppm), and V3T9 (472.00ppm). The lowest (427.34 ppm) CO2 assimilation was found in V1 variety under the T5 treatment. However, no statistical significant result was found at 45 DAT. The maximum CO2 assimilation (478.66 ppm) was calculated in V3 variety under the T10 treatment which was statistically similar with V1T10 interaction (478.00 ppm) at 60 DAT. On the other hand, the minimum CO2 assimilation (429.33 ppm) was noted in V1 variety under the T5 treatment.

V1= LaLtir king, V2=Taherpuri, V3=BARI piaz 1, T0=control, T1=1 g·L−1Goemar, T2=2 g·L−1Goemar, T3=4 g·L−1Goemar, T4=8 g·L−1Goemar, T5=10 mg L-1 BA, T6=20 mg L-1 BA, T7=30 mg L-1 BA, T8=25 mg L-1 GA3, T9=50 mg L-1 GA3, T10=75 mg L-1 GA3

DAT= Days After Transplanting

Chlorophyll Fluorescence, Fv/Fm reflects the maximum photochemical efficiency of the PSII. The experiment showed that there was significant difference in Fv/Fm among the varieties and treatments interaction indicating that varieties and treatments (Biostimulant, BA and GA3) application had effect on the maximum photochemical efficiency of PS (photo-systems) of onion (Table 3). At 30 DAT, the maximum photochemical efficiency (Fv/Fm) was estimated from V3 variety under the T10 treatment which was statistically similar with V1 variety under the T4 treatment, V1 variety under the T10 treatment, V2 variety under the T4 treatment, V2 variety under the T10 treatment and V3 variety under the T4 treatment (Table-3). On the other hand, V2 variety treated with control treatment produced minimum photochemical efficiency (Fv/Fm) which was statistically similar with V1 and V3 variety under the T0 treatment, This result indicates that PGRs and biostimulant can significantly influenced on chlorophyll contents of onion leaves.

The highest photochemical efficiency (Fv/Fm) was estimated from V1 and V3 variety under the T4 and T10 treatment respectively at 45 DAT followed by V1 variety under the T2 treatment, V1 variety under the T3 treatment, V1 variety under the T9 treatment, V1 variety under the T10 treatment, V2 variety under the T1 treatment, V2 variety under the T2 treatment, V2 variety under the T3 treatment, V2 variety under the T4 treatment, V2 variety under the T8 treatment, V2 variety under the T9 treatment, V2 variety under the T10 treatment, V3 variety under the T1 treatment, V3 variety under the T2 treatment, V3 variety under the T3 treatment, V3 variety under the T8 treatment, and V3 variety under the T9 treatment (Table-3). Conversely, the lowest chlorophyll content was calculated from V2 variety under the T0 treatment.

In the present research, it was observed that Chlorophyll fluorescence
parameters showed significant differences with the different PGRs and Biostimulant application levels. At 60 DAT, the maximum photochemical efficiency (Fv/Fm) was estimated from V1 variety under the T10 treatment which was statistically similar with V1 variety under the T9 treatment, V2 variety under the T4 treatment, V2 variety under the T10 treatment, V3 variety under the T4 treatment and V3 variety under the T10 treatment (Table-3). On the other hand, V2 variety treated with control (T0) treatment produced minimum photochemical efficiency (Fv/Fm). These results indicate that PGRs and biostimulant can significantly influence on chlorophyll contents of onion leaves and V1 and V3 varieties produced highest amount of chlorophyll content than V2 variety. However, a group researcher indicated the different results for sunflower where they found that Fm was significantly increased and Fv/Fm was not affected by N stress (Ciompi et al., 1996).

4.3 The effect of plant biostimulant (Goemar) and PGRs (BA and GA3) on seed yield of Onion

100 seed weight of onion varieties were not significantly influenced by combined effect of variety and PGRs & biostimulant in this study (Table 3). The highest 100 seed weight was calculated in V2T4 which was not statistically different with others interaction. Total seed weight per plot was significantly influenced by combined effect of variety and PGRs & biostimulant. The maximum seed weight (93.00 gm) per plot was found in V1T10 interaction which was statistically similar with V1T4, V2T4, V2T10 and V3T10 interactions. However, the minimum seed weight (57.66gm) per plot was calculated in V1T1 interaction which was statistically similar with V2T0 and V3T0 interactions.

Onion seed yield is directly depending on seed size and quality. Onion seed plays important role for onion production. In this research, seed yield per hectare was statistically significant according to different interactions. The highest seed yield was recorded in V1T10 interaction which was statistically similar with V1T4, V2T4, V2T10, V3T4 and V3T10 interactions (Table-3). In contrast, the lowest amount of seed yield per hectare was found in V1T0 followed by V2T0 interaction. These results were supported by where they showed that GA3 (50 ppm) increased the seed yield of coriander (Kumar et al., 2018). The same results were found by some researchers where they also observed that GA3 influenced growth, yield and quality parameters of chilli (Capsicum annum L.) (Singh and Singh, 2019). A group researcher showed that biostimulant applied in arid climates and vegetable cultivation had the highest impact on crop yield (Li et al., 2022). These results indicate that varieties and treatments (PGRs and Biostimulant) both are influenced the seed yield.

The germination results are presented in Table 3. The results showed that there were no significant differences found in varieties and treatments combination results. The seeds were not treated with PGRs and biostimulant. Some study showed the biostimulant stimulant influence germination of seed (Kalymbetov, et al., 2023; Makhaye, et al., 2021). They also used the biostimulant for seed treatment but we did not treated the seed.

V1= LaLtir king, V2=Taherpuri, V3=BARI piaz 1, T0=control, T1=1 g·L−1Goemar, T2=2 g·L−1Goemar, T3=4 g·L−1Goemar, T4=8 g·L−1Goemar, T5=10 mg L-1 BA, T6=20 mg L-1 BA, T7=30 mg L-1 BA, T8=25 mg L-1 GA3, T9=50 mg L-1 GA3, T10=75 mg L-1 GA3

DAT= Days After Transplanting

5.CONCLUSION

From the present study, it may concluded that GA3, BA and biostimulant has significant effect on seed production of Onion. Among the different concentration of GA3, BA and biostimulant, 75 mg L-1 GA3 was the best for onion seed production in most of the cases and 8 g·L−1 Biostimulant (Goemar) was also showed the best result among the concentration of PGRs and biostimulant except 75 mg L-1 GA3. Lalteer king onion variety showed the best result among the varieties. However, further studies are needed to investigate the effects of GA3 and biostimulant on onion seed production in different areas with different climatic conditions.

ACKNOWLEDGMENTS

The research work was supported by the University Grants Commission (UGC), Bangladesh for the Year 2022-2023.

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Evaluating genetic divergence and population structure across bread wheat cultivars are crucial in breeding initiatives, and SSR markers possess some characteristics that render them appropriate for those objectives. Sixty-five wheat cultivars by employing 17 SSR markers that amplified a total of 68 alleles and there was a range of 2 to 7 alleles per locus, with an average of 4 alleles each locus. The polymorphism information content (PIC) varied between 0.21 and 0.84, with a mean value of 0.66. The PIC values of the markers exhibited a range of 0.66 to 0.84, showing a significant degree of diversity among the wheat genotypes. The genotyping data were used for population structure analysis which grouped into three (K=3) main populations in which population I, II, III contained 26, 20 and 19 genotypes respectively. The UPGMA dendrogram showed higher genetic variation in the wheat genotypes. The genotypes were categorized into three clusters and six sub-clusters using genetic distance-based clustering, which relied on SSR markers. The PCA graphical representations indicated that the P3-2-2-P9 and P3-18-10-P15 lines were positioned significantly distant from the centroid, suggesting a higher level of genetic diversity and potential utility for future breeding initiatives. The findings of the study indicate that the SSR markers examined in this research exhibit a satisfactory level of polymorphism and reproducibility in their fingerprinting patterns, making them suitable for assessing the genetic diversity of wheat genotypes which could potentially contribute to future selection and breeding efforts.

Wheat (Triticum aestivum L.) holds the third most significant cereal crop on a global scale, and belongs to the genus Triticum and the family “Gramineae (Poaceae)”(Shewry, 2009). Cultivated wheat (2n = 6x = 42, AABBDD) is an allohexaploid, composed of three distinct genomes viz., A, B and D. Total worldwide wheat production was 778.2 million tonnes in 2021-22 and it was forecast that world wheat production in 2022-23 at 781.1 million tonnes, up 2.91million tonnes from previous years (FAO, 2022). In 2022, an increase of 1.26 million tonnes or 0.16% in wheat production around the globe (USDA 2022).

Genetic diversity is an important factor in any successful wheat breeding program. The presence of genetic variation within plant species presents opportunities for enhancing the characteristics of plants. They involve establishing a core collection through removing insignificant accessions and discovering lines that could be beneficial for future breeding programmes (Govindraj et al., 2010). According to a study, the process of wheat breeding through hybridization necessitates the careful selection of a wide range of genotypes, regardless of whether the resulting product is a pure line or a hybrid variety (Zeb et al., 2009). The requirement and primary relevance for a successful wheat breeding program lies in the presence of genetic diversity and genetic interactions among genotypes. To create hybrid wheat varieties with desirable characteristics, it is necessary to have a comprehensive understanding of the current genetic diversity (Kahrizi et al., 2010).

The fundamental prerequisite for molecular wheat breeding involves the selection of a diverse range of genotypes (Raj et al., 2017). The utilization of molecular markers for the evaluation of genetic variability has been demonstrated to be a fundamental aspect in comprehending the genomic composition, classifying the genes accountable for significant characteristics, and categorizing and preserving genetic diversity in plant germplasm (Khan et al., 2015). Single-sequence repeats (SSRs) are a valuable tool in diversification research for accurately determining the level of genetic similarity. Microsatellite markers are well-suited for detecting allele frequency within a population and assessing population structure due to their high rate of polymorphism, high Polymorphic Information Content (PIC), co-dominant character, selective neutrality, distribution across the genome, and cost and labor efficiency (Khaled et al., 2015). This approach is suitable for determining the frequency of alleles in the population and assessing the structure of the population (Kumar et al., 2016). SSR are widely distributed throughout eukaryotic genomes and can be utilized as versatile and multi-allelic genetic markers by polymerase chain reaction (PCR). It has proven to be highly valuable for various crop improvement applications due to their significant polymorphism and ease of handling (Gupta et al., 2009).

Across the globe, many researchers have explored the genetic diversity in wheat. Very few studies have investigated genetic diversity and molecular characterization in wheat genotypes in Bangladesh. However, SSR markers are the best choice because of having some advantages over other markers to check the genetic divergence. Considering these facts, the research hypothesis might be characterizing the wheat cultivars and advanced lines using SSR markers. This will produce suitable genes and genotypes together with diverse parents for future breeding.

2.2 Genomic DNA isolation, purification and Quantification

The genomic DNA was extracted from a small quantity of fresh leaf tissue (5.0 g) from each variety using the CTAB procedure, as described by (Saghai-Maroof et al., 1984; Xu et al., 1994). According to a study, the concentration and amount of DNA were assessed using a UV spectrophotometer (Jiang, 2013).

2.3 PCR Amplification and Electrophoresis

Polymerase chain reaction (PCR) amplifications were conducted in a 10 μL tube utilizing a Veriti™ 96-Well Fast Thermal Cycler manufactured by Applied Biosystems, USA. Each tube was supplemented with 2 μL of template DNA and 8 μL of the reaction mixture, consisting of 0.5 μL of Forward primer, 0.5 μL of Reverse primer, 2 μL of nuclease-free water, and 5 μL of G2 Green Master Mix. The optimization of the thermocycling program involved an initial denaturation step at a temperature of 95°C for a duration of 4 minutes. This was followed by 40 cycles of denaturation at 95°C for 1 minute, followed by annealing at temperatures ranging from 52 to 63°C. Expansion was achieved by annealing at 72°C for 1 minute, and a final cycle of 72°C for 10 minutes, followed by a hold at 4°C (Kumar et al., 2016). On 0.8% agarose gel electrophoresis, the amplified product was resolved. The gels were subjected to a voltage of 100V for a duration of 45 minutes. After electrophoresis was completed, DNA bands were observed using a UV trans-illuminator and gel dock (Shuaib et al., 2010). A selection of 17 primer pairs for microsatellite (SSR) markers, encompassing 17 chromosomes, was made for the purpose of conducting diversity analysis on a set of 65 wheat genotypes.

2.4 Data analysis

The mean PIC was computed for each SSR based on the formula provided by some researcher, PIC = 1- P(Pi)2, where Pi is the fraction of the population bearing the ith allele, measured for each SSR locus (Powell et al., 1996; Botstein et al., 1980). The presence (1) or absence (0) of SSR bands was assigned to each genotype, and the resulting binary data matrix was analyzed using the software STRUCTURE V2.3.4 to determine genetic relationships among the genotypes (Pritchard et al., 2000). The researchers computed simple matching similarity coefficients for all pairwise comparisons between the genotypes. The researchers included all the loci that could be scored in order to create a bivariate 1-0 data matrix. To estimate genetic diversity, the analysis was conducted using the MetaboAnalyst program (Online Version) developed (Chong and Xia, 2018).

3. Results

3.1 Assessment of polymorphism from SSR Profiles

After PCR and PAGE analysis, a total of 68 alleles were identified at 17 SSR markers over 65 wheat genotypes (Figure 1). In the case of TaXbarc137, a maximum range of band (255-550bp) was found and which was followed by Xwmc112 (200-400bp) and Gwm495 (160-300bp) respectively (Table 2). The minimum range of bands was found in TaBarc271 (100-110). There was a range of 2 to 7 alleles per locus, with an average of 4 alleles observed across the 68 loci. The marker TaXbarc137 produced the highest number of polymorphic alleles of seven (7) followed by TaXcfd43, TaXgwm294, Xgwm296 and TaXcfa2129 with six (6) each, respectively, while TaBarc271, Barc20 and TaGwm293 markers produced the least number of polymorphic alleles per locus of two (2). The estimation of polymorphic information content (PIC) was used to measure the genetic diversity. The Polymorphic Information Content (PIC) values of Simple Sequence Repeats (SSRs) varied between 0.21 and 0.84, with an average of 0.66. The TaXbarc137 had the highest PIC value of 0.84, followed by TaXcfd43 (0.83), TaXgwm294 (0.82), Xgwm296 (0.81), TaXcfa2129 (0.81), Gwm513 (0.74), Tagwm1037 (0.74), Xwmc112 (0.73), Gwm495 (0.72), TaGwm291 (0.66), WMS0691 (0.66), TaBarc101 (0.63), TaBarc68 (0.59), TaXwmc407 (0.54), TaGwm293 (0.44), Barc20 (0.38) and the lowest TaBarc271 (0.21) respectively. Eleven (11) out of seventeen (17) primers showed PIC values between 0.66 and 0.84.

Figure 1: DNA band profile of 65 wheat genotypes using 17 polymorphic SSR markers (a) to (q) showing different polymorphism patterns within wheat genotypes.

Evaluating population structure is an important initial step in association analysis. Sixty-five advance lines of wheat genotypes was assessed using STRUCTURE V2.3.4 software. The analysis of population structure revealed that the log-likelihood value (ΔK) reached its maximum value at K=3 (Figure 2). This indicates a distinct peak that represents the classification of genotypes into three distinct sub-groups, referred to as Population I, Population II, and Population III (Figure 3). These sub-groups accounted for 40.8%, 29.8%, and 29.3% of the total genotypes, respectively. Population I (77.8%), Population II (57.9%), and Population III (68.4%) all had a substantial percentage of pure population. In each sub-population, the remaining sections were combined. The optimal value for population genetic structure analysis, denoted as “K = 3,” was within the range of 1 to 10. The FST population values were 0.51 for Population I, 0.19 for Population II and 0.49 for Population III (Table 3) with an average of alpha 0.13. Average distances (expected heterozygosity) between individuals in the same populations (Table 3). The average intra-population distances in the same cluster were 0.1982 for Population I, 0.3628 for Population II and 0.2172 for Population III. Net nucleotide distance between Population I and Population II, Population I and Population III, Population II and Population III was found 0.1229, 0.1302 and 0.1555, respectively (Table 4).

Legend: 1.P3-2-2-P3, 2.P3-2-2-P6, 3.P3-2-2-P9, 4.P3-2-2-P12, 5.P3-2-2-P13, 6.P3-2-6-P1, 7.P3-2-6-P5, 8.P3-2-6-P14, 9.P3-3-3-P8, 10.P3-3-3-P14, 11.P3-4-6-P11, 12.P3-4-6-P14, 13.P3-5-2-P4, 14.P3-5-2-P6, 15.P3-5-2-P12, 16.P3-5-2-P13 17.P3-11-3-P5, 18.P3-11-3-P12, 19.P3-11-3-P15, 20.P3-18-3-P7, 21.P3-18-3-P12, 22.P3-18-10-P5, 23.P3-18-10-P15, 24.P3-21-10-P9, 25.P3-21-10-P14, 26.P3-28-3-P2, 27.P3-28-3-P3, 28.P3-28-3-P5, 29.P3-28-3-P9, 30.P3-28-3-P10, 31.P3-28-3-P15, 32.P3-29-2-P1, 33.P3-29-2-P2, 34.P3-29-2-P5, 35.P3-29-2-P11, 36.P3-29-2-P12, 37.P3-29-2-P13, 38.P3-29-4-P1, 39.P3-29-4-P2, 40.P3-29-4-P3, 41.P3-29-4-P4, 42.P3-29-4-P5, 43.P3-29-4-P6, 44.P3-29-4-P7, 45.P3-29-4-P8, 46.P3-29-4-P9, 47.P3-29-4-P10, 48.P3-29-4-P15, 49.P3-29-10-P1, 50.P3-29-10-P2, 51.P3-29-10-P3, 52.P3-29-10-P4, 53.P3-29-10-P5, 54.P3-29-10-P6, 55.P3-29-10-P8, 56.P3-29-10-P9, 57.P3-29-10-P10, 58.P3-29-10-P11,59.P3-29-10-P14, 60.P3-29-10-P15, 61.BARI Gom – 30 (check), 62.BARI Gom – 33 (check), 63.AGHRANI (parent), 64. SHATABDI (parent), 65.BARI Gom – 25 (parent).

3.3 Genetic distance-based analysis

The UPGMA (Unweighted Pair Group Method with Arithmetic Mean) cluster analysis showed genetic distance-based results that identified two primary groups among the 65 wheat genotypes. These groups exhibited similarity coefficients ranging from 0.00 to 2.5 (Figure 4). The two groups were further subdivided into three clusters: cluster I (consisting of 47 genotypes), cluster II (consisting of 15 genotypes), and cluster III (consisting of 3 genotypes) (Table 5). Cluster I consists of three sub-clusters, with Sub-cluster A including nine genotypes that were further
classified into two distinct classes. In the first class, the highest similarity was seen between G18 (P3-11-3-P12)-G19 (P3-11-3-P15), while in the second class, the highest similarity was found between G20 (P3-18-3-P7)-G21 (P3-18-3-P12) and the lowest similarity was found between G22 (P3-18-10-P5)-G63 (AGHRANI). The sub-cluster B consisted of a total of 24 genotypes, which were subsequently categorized into three distinct classes. Among them highest and lowest similarity found between G15 (P3-5-2-P12)-G16 (P3-5-2-P13) and G11 (P3-4-6-P1)-G56 (P3-29-10-P9) in 1st class; G51(P3-29-10-P3)-G54(P3-29-10-P6) and G51(P3-29-10-P3)-G65 (BARI Gom-25) in 2nd class and G55(P3-29-10-P8)-G52(P3-29-10-P4) and G32(P3-29-2-P1)-G37(P3-29-2-P13) in 3rd class respectively. Within Sub-cluster C, a total of 14 genotypes were identified. Among these genotypes, the G39 (P3-29-4-P2)-G40 (P3-29-4-P3) group exhibited the most resemblance, while the G24 (P3-21-10-P9)-G44 (P3-29-4-P7) group shown the lowest similarity. Cluster II consisted of 4 genotypes, with Sub-cluster A containing 9 genotypes and Sub-cluster B including 6 genotypes. In Sub-cluster A, the maximum similarity was seen between G2 (P3-2-2-P6)-G4 (P3-2-2-P12) and G8 (P3-2-6-P14)-G25 (P3-21-10-P14). In Sub-cluster B, the highest similarity was identified between G30 (P3-28-3-P10)-G31 (P3-28-3-P15) and G28 (P3-28-3-P5)-G31 (P3-28-3-P15). Cluster III comprised a collection of three distinct genotypes, namely G3 (P3-2-2-P9), G23 (P3-18-10-P15), and G61 (BARI Gom-30).

3.4 Principal component analysis

The utilization of Principal Component Analysis Associations (PCA) aids in the identification of key variables that significantly influence the phenotype of various wheat landraces. The PCA scree plot displayed the cumulative variance explained by the green line at the top, while the blue line underneath represented the variance explained by each individual principal component (Figure 5). PCA scatter plots were used to analyse the diversity of genotypes. The first three eigenvalues, which accounted for 37.4% of the cumulative variation, were shown for this purpose. The first three principal components exhibited eigenvalues of 16.1%, 13.1%, and 8.2%, resulting in a total cumulative variation of 37.4% when considering these three components in principal coordinates.

The PCA demonstrates that the first and second principal components (PC1) and (PC2) accounted for 16.1% and 13.1% of the total variation, respectively (Figure 6). The localization of genotypes in the 2D PCA plot provides insights into the genetic distances among the wheat genotypes. Through the utilization of visualization techniques and color coding, it was noted that the three groups derived from the structure and cluster analysis were also evident in the principal component analysis (PCA). The genotypes G11 (P3-4-6-P11), G12 (P3-4-6-P14), G37 (P3-29-2-P13), G3 (P3-29-4-P1), G4 (P3-29-4-P10), G49 (P3-29-10-P1), G50 (P3-29-10-P2), G54 (P3-29-10-P6), G57 (P3-29-10-P10), G58 (P3-29-10-P11), and G60 (P3-29-10-P15) were found to be mixed with both population I and population II. The genotypes G9 (P3-3-3-P8), G16 (P3-5-2-P13), G18 (P3-11-3-P12), G19 (P3-11-3-P15), and G65 (BARI Gom – 25) were shown to be present in both population II and population III. The following genotypes, namely G1 (P3-2-2-P3), G9 (P3-3-3-P8), G18 (P3-11-3-P12), G50 (P3-29-10-P2), G53 (P3-29-10-P5), G54 (P3-29-10-P6), G57 (P3-29-10-P10), G58 (P3-29-10-P11), G60 (P3-29-10-P15), and G65 (BARI Gom – 25), were found to be mixed with both population I and population III. The genotypes G9 (P3-3-3-P8), G18 (P3-11-3-P12), G54 (P3-29-10-P6), G57 (P3-29-10-P10), G58 (P3-29-10-P1)1, G60 (P3-29-10-P15), and G65 (BARI Gom – 25) were combined with their respective populations, namely Population I, Population II, and Population III. The genotypes G3(P3-2-2-P9), G10(P3-3-3-P14), G13(P3-5-2-P4), G14(P3-5-2-P6), G20(P3-18-3-P7), G21(P3-18-3-P12), G22(P3-18-10-P5), G23(P3-18-10-P15), G26(P3-28-3-P2), G27(P3-28-3-P3), G28(P3-28-3-P5), G29(P3-28-3-P9), G30(P3-28-3-P10), G34(P3-29-2-P5), G35(P3-29-2-P11), G38(P3-29-4-P1), G41(P3-29-4-P4), G44(P3-29-4-P7), G46(P3-29-4-P9), G47(P3-29-4-P10), G61(BARI Gom – 30), G62(BARI Gom – 33) and G63(AGHRANI) found far away from the centroid of the cluster and the rest of the genotypes were placed more or less around the centroid (Figure 7).

4.DISCUSSION

SSR markers have been widely applied for the purpose of assessing their genetic diversity in wheat genotypes. In this study, an average of 4 alleles observed across the 68 loci ranged from 2 to 7. The number of alleles ranged from 2 to 8, with an average of 3.8 alleles per locus and identified total 166 alleles by analyzing 26 markers (Shafi et al., 2021). Other researchers have reported averages of 2.68, 6.88 and 7.6 alleles per locus in various wheat collections (Sharma et al., 2021; Christov et al., 2022; Ahmed et al., 2020). PIC value is divided into 3 classes: PIC>0.5 = highly informative; 0.25> PIC> 0.5 = moderately informative; and PIC <0.25 = slightly informative (Sagwal et al., 2022; Rohmawati et al., 2021). All markers showed highly informative PIC values with the average of 0.84 reported by (Rohmawati et al., 2021). The highest and lowest PIC values 0.84 and 0.21 were estimated for the markers TaXbarc137 and TaBarc271 respectively. The average PIC value is 0.66 and the 14 Primers viz. TaXbarc137, TaXcfd43, TaXgwm294, Xgwm296, TaXcfa2129, Gwm513, Tagwm1037, Xwmc112, TaGwm291, WMS0691, Tabarc101, TaBarc68 and TaXwmc407 with PIC values respectively showed highly informative. The marker Tagwm293, and Barc20 obtained PIC values of 0.44 and 0.38 showed moderately informative. The TaBarc271 marker showed slightly informative with a PIC value of 0.21. The PIC value is a good measure of a marker’s usefulness for linkage analysis and detected a marker’s probability of the marker being in the progeny.

Furthermore, it serves as an indication of the allelic variety present among various varieties (Meti et al., 2013). In addition, the average PIC values observed in this study were mostly similar to 0.64 and 0.67 reported by using 22 SSR markers, respectively (Tsonev et al., 2021). The present study reported a lower mean PIC value compared to a 0.79 reported by using 25 SSR markers (Jlassi et al., 2021). On the contrary, a higher mean PIC value in this study was found compared to 0.48 and 0.32 reported by most of researcher respectively (Belete et al., 2021; Pour-Aboughadareh et al., 2022). So, SSR markers with high PIC value imply that were highly informative for future breeding programs.

The genetic population of the complete sample assembly was stratified based on all marker systems, revealing the presence of a unique structure. The analysis for K = 3 was provided, as its populations demonstrated a substantially greater degree of co-linearity (80%) with neighbor-joining clusters. Using K = 3, the population structures study categorized the 65 wheat genotypes into three subpopulations (Mohi-Ud-Din et al., 2022).

A group researcher also observed a comparable range that Populations I, II, and III were comprised of 26, 20, and 19 genotypes, respectively (Christov et al., 2022). Out of the total, 21 genotypes were pure in Population I, 11 genotypes were pure in Population II, and 13 genotypes were pure in Population III. The remaining 20 genotypes were mixed among these three groups.

A group researcher demonstrated that out of the 20 wheat genotypes examined, only 4 exhibited admixtures (Sihag et al., 2021). A group researcher identified a total of 53 bread wheat genotypes among these genotypes, population I comprised 20 genotypes, population II consisted of 14 genotypes, and population III comprised 17 genotypes (Mohi-Ud-Din et al., 2022). FST quantifies the extent to which population structure may account for genetic variation, as determined by Wright’s F-statistics (Wright, 1965). The population in this study was determined using pairwise FST values, which varied from 0.1855 to 0.5105. The pairwise FST analysis indicated that these three populations were considerably distinct from each other. The fixation index, a measure of population substructure, ranged from 0.283 to 0.658 as detected by (Alsharari, 2021).

The cluster analysis was conducted using the UPGMA method to determine the genetic diversity among 65 wheat genotypes. The genotypes were classified into 3 groups such as cluster I, II, an III, which was consistent with the findings (Wang et al., 2017). Many scientists have implemented these strategies in wheat breeding schemes and have achieved informative results (Tsonev et al., 2021; Zatybekov et al., 2021; Ahmed et al., 2017; Salehi et al., 2018). In this study, the observed cluster III with genotypes G3 (P3-2-2-P9), G23 (P3-18-10-P15) and G61 (BARI Gom-30) exhibited a greater genetic divergence in comparison to the remaining two groups. The individuals within this cluster had a higher degree of dissimilarity compared to the other two cluster members.

The genotypes within this cluster exhibited greater genetic diversity compared to the genotypes within other clusters. A group researcher classified 20 Egyptian wheat landraces and two cultivars based on 10 SSR markers into four clusters based on the UPGMA method for genetic diversity analysis and clearly revealed the significant molecular diversity of the Egyptian wheat landraces and cultivars (Al-Naggar et al., 2020). Based on the degree of diversity, the UPGMA analysis can result in the formation of two clusters or four clusters (El-Bakatoushi, 2019; Haque et al., 2021). Furthermore, genetic diversity studies have indicated as many as 9 clusters and 13 clusters (Naceur et al., 2012; Schuster et al., 2009).

The PCA has considerable importance in the selection phase of molecular breeding projects. PC1 and PC2 had a greater degree of variation, exceeding 25%. Utilising the higher variation (>25%) in conjunction with cluster analysis can be employed to identify appropriate and correlated genotypes. A group researcher found that the first three principal components (PC1-PC3) had eigen-values greater than 1 (Ali et al., 2019). These components accounted for individual variance values of 30.93%, 18.44%, and 17.84%, respectively. Together, they explained 67.21% of the cumulative variation in grain yield. The initial two principal components (PCs) were graphed on PC axis 1 and 2, revealing significant diversity among the current wheat lines and control samples. Some researcher utilised a Pearson-based PC1 and PC2 biplot to project genotypes in three subpopulations of spring wheat (Sajjad et al., 2018). The structure analysis revealed three distinct sub-populations, which were also differentiated based on the first two principal components. This finding is consistent with the results reported (Tascioglu et al., 2016).

The genotypes positioned at a greater distance from the centroid exhibited greater genetic diversity, whereas the genotypes positioned close to the centroid displayed very similar genetic origins. G3 (P3-2-2-P9) and G23 (P3-18-10-P15) were positioned much further from the centroid compared to the others, resulting in the observation of the most diverse genotypes. The two principal components exhibited comparable major grouping patterns to those examined by UPGMA clustering, with consistently clustered 65 wheat genotypes. This aligns with the findings of who reported three principal components that displayed similar major grouping patterns to UPGMA clustering (Pathaichindachote et al., 2019). These genotypes exhibit no genetic similarity among themselves or among the complete set of sixty-five genotypes examined. Consequently, they possess the potential to serve as parental individuals in future breeding initiatives.

5.CONCLUSION

The purpose of this study was to evaluate the genetic diversity and population structure of 65 wheat lines through the utilisation of 17 simple sequence repeat (SSR) markers. The SSRs marker has exhibited not patterns characterised by significant variability, hence enabling the differentiation of all cultivars. The study identified a total of 68 alleles. Each marker locus included a range of alleles, varying from two to seven, with an average of four. The average value for genetic diversity and polymorphism information content was 0.21 and 0.84, respectively. The examination of the structure indicated the existence of three distinct subpopulations, which align with the clustering pattern based on genetic distance. The cluster analysis categorised the data into three primary genetic groups, regardless of the origin of the data collection. Cluster III, consisting of P3-2-2-P9 and P3-18-10-P15, exhibited distinct genetic patterns and relationships, indicating the possibility of divergent genetic compositions among them. Novel genes can be derived from these sources and utilised in wheat breeding programmes.

ACKNOWLEDGMENTS

We offer special thanks to lab technician Md. Hasan, Department of Plant Breeding, Bangladesh Wheat and Maize Research Institute, Nashipur, Dinajpur-5200, Bangladesh for providing the technical help during the research. We are also grateful to Ministry of Science and Technology of Bangladesh for providing financial support of the research work.

CONFLICT OF INTEREST

The authors have declared that no competing interests exist.

FUNDING
This research work was supported by the Ministry of National Science and Technology, Government of People‘s Republic of Bangladesh.

AUTHOR CONTRIBUTIONS

Conceptualization, M.H. and M.A.H..; Data collection and analysis, A.S., and M.M.H.P.; Original draft, A.S.; Review and editing, M.H., A.S., M.M.H.P., and M.A.H.; Funding acquisition, A.S. All authors provided intellectual inputs, read the manuscript, approved for submission and agreed to the published version of the manuscript.

ETHICS APPROVAL

There is no ethical approval required in this study.

AVAILABILITY OF DATA AND MATERIALS

The availability of data and materials in this research is subject to the policy set by the corresponding author upon reasonable request.

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Bangladesh, a nation acutely vulnerable to the impacts of climate change, faces escalating challenges like rising sea levels, increased flooding, and compromised agricultural productivity. This study explores the potential of sustainable agricultural practices to enhance carbon sequestration in Bangladesh’s agro-ecosystems, offering a viable pathway to mitigate climate change effects while promoting food security. We investigated a range of sustainable farming techniques-including agroforestry, conservation agriculture, organic farming, and crop diversification—to assess their effectiveness in capturing atmospheric carbon dioxide and enriching soil organic carbon. Data were collected from field experiments and farmer surveys across diverse regions of Bangladesh, analyzing soil carbon stocks, biomass accumulation, and greenhouse gas emission reductions. The results reveal that sustainable agricultural practices significantly increase carbon sequestration compared to conventional methods. Agroforestry systems demonstrated the highest potential, sequestering up to 30% more carbon due to the integration of trees with crops and livestock, which enhances biomass production and soil health. Conservation agriculture and organic farming also showed substantial improvements in soil carbon levels and ecosystem biodiversity. These findings underscore the critical role of sustainable agriculture in mitigating climate change and suggest that widespread adoption could transform Bangladesh’s agricultural sector into a robust carbon sink. The study recommends policy interventions to support farmers through education, financial incentives, and infrastructure development, fostering a transition towards environmentally sustainable and economically viable farming practices. By leveraging sustainable agriculture for carbon sequestration, Bangladesh can contribute significantly to global climate mitigation efforts while enhancing resilience and livelihoods for its farming communities.

Climate change is one of the most critical global challenges of our time, driven predominantly by the increasing concentrations of greenhouse gases (GHGs) in the atmosphere. Among these gases, carbon dioxide (CO₂) is the most significant, largely emitted through the combustion of fossil fuels, deforestation, and conventional agricultural practices. The adverse impacts of climate change-including rising temperatures, sea-level rise, and increased frequency of extreme weather events-underscore the urgent need for effective mitigation strategies. Bangladesh, situated in the delta of the Ganges-Brahmaputra-Meghna river systems, is highly susceptible to the impacts of climate change. The country’s low-lying topography, high population density, and dependence on agriculture exacerbate its vulnerability to rising sea levels, intensified flooding, and salinity intrusion. These climatic changes threaten the livelihoods of millions and pose significant risks to national food security and economic stability. Agriculture is a vital sector in Bangladesh, contributing approximately 13% to the Gross Domestic Product (GDP) and employing around 40% of the labor force. However, traditional agricultural practices characterized by intensive tillage, monocropping, and excessive use of chemical inputs have led to soil degradation, decreased biodiversity, and increased GHG emissions. There is an urgent need for a paradigm shift towards farming practices that can enhance agricultural productivity while mitigating environmental impacts.

Sustainable agricultural practices present a promising solution in this context. Practices such as agroforestry, conservation agriculture, organic farming, and crop diversification not only improve soil health and crop yields but also enhance carbon sequestration. By capturing atmospheric CO₂ and storing it in plant biomass and soils, these practices can transform agricultural lands into effective carbon sinks. This process not only mitigates climate change but also bolsters soil fertility, water retention, and ecosystem resilience. Despite the recognized benefits of sustainable agriculture, its adoption in Bangladesh remains limited due to socio-economic constraints, lack of awareness, and inadequate policy support. Furthermore, there is a paucity of comprehensive studies examining the carbon sequestration potential of these practices in the Bangladeshi context. Most existing research is either global or regional in scope, lacking localized data that reflect the unique climatic, soil, and socio-economic conditions of Bangladesh.

The primary objectives of this study are to Evaluate the Carbon Sequestration Potential of various sustainable agricultural practices across different agro-ecological zones in Bangladesh. Examine the Impact of these practices on soil health, crop productivity, and farm sustainability. Identify Socio-Economic Factors influencing the adoption of sustainableagricultural practices among Bangladeshi farmers. Provide PolicyRecommendations to promote the adoption of sustainable agriculturalpractices for climate change mitigation and adaptation in Bangladesh. Byachieving these objectives, this study aims to bridge the knowledge gapand provide empirical evidence to support the integration of sustainableagricultural practices in Bangladesh’s climate change mitigation strategy.Understanding the role of sustainable agriculture in carbon sequestrationholds significant implications for Bangladesh. Enhancing carbon sinksthrough sustainable agriculture can help offset a portion of the country’sGHG emissions, contributing to national and global climate mitigationefforts. Agricultural sustainability through Improving soil health andbiodiversity can lead to more resilient farming systems capable ofwithstanding climate-related stresses. Sustainable practices can reduceinput costs and increase yields over time, improving the livelihoods ofsmallholder farmers. Empirical evidence from this study can inform theformulation of policies that incentivize sustainable farming andenvironmental stewardship.

2. LITERATURE REVIEW

2.1 Carbon Sequestration and Its Role in Climate Change Mitigation

Carbon sequestration refers to the process of capturing and storingatmospheric carbon dioxide (CO₂) in vegetation, soils, geologic formations,and oceans. This process is critical for mitigating climate change, as itreduces the concentration of CO₂ in the atmosphere, thus helping tostabilize global temperatures. The Intergovernmental Panel on ClimateChange (IPCC) (2019) highlights that enhancing carbon sinks through landmanagement practices is essential to achieving global climate targets.

2.2 Sustainable Agricultural Practices

Sustainable agriculture encompasses farming practices that meet currentfood and fiber needs without compromising the ability of futuregenerations to meet their needs. These practices are designed to enhanceenvironmental health, economic profitability, and social equity.Sustainable agricultural practices that contribute to carbon sequestrationincludingThe integration of trees and shrubs into agricultural landscapescan significantly enhance carbon sequestration. Trees sequester carbon intheir biomass and root systems while improving soil health andbiodiversity. Several studies (Montagnini and Nair, 2004; Jose, 2009) havedemonstrated the potential of agroforestry systems to sequestersubstantial amounts of carbon.

Conservation agriculture involves minimal soil disturbance, permanentsoil cover, and diversified crop rotations. It enhances soil organic carbonlevels, reduces erosion, and improves soil structure. Research by indicatesthat conservation agriculture can effectively sequester carbon andenhance soil health (Lal, 2015; Kassam et al., 2019). Organic farmingpractices, such as the use of compost, cover crops, and reduced chemicalinputs, can enhance soil organic matter and carbon sequestration. Studieshave shown that organic farming can increase soil carbon stockscompared to conventional farming (Gattinger et al., 2012; Lorenz and Lal,2016). Diversifying crops, including the use of legumes and cover crops,can improve soil health, enhance carbon sequestration, and increaseresilience to climate change. Research by some of researchers supports thebenefits of crop diversification in sustainable agriculture (Tilman et al.,2006; Pretty et al., 2018).

2.3 Carbon Sequestration in Agricultural Soils

Soil organic carbon (SOC) is a key component of soil health and carbonsequestration. Practices that enhance SOC levels can improve soil fertility,water retention, and resilience to climate change. A meta-analysis foundthat no-till farming, cover cropping, and organic amendments cansignificantly increase SOC levels (West and Post, 2002). A groupresearcher emphasize that soil carbon sequestration is a viable strategyfor mitigating climate change and improving agricultural sustainability(Smith et al., 2008).

2.4 Socio-Economic Factors Influencing Adoption of Sustainable

AgricultureThe adoption of sustainable agricultural practices is influenced by varioussocio-economic factors. By educating farmers about the benefits andtechniques of sustainable agriculture is crucial. Extension services andfarmer training programs play a vital role in promoting these practices(Pretty and Uphoff, 2002). Financial incentives, such as subsidies, grants,and carbon credits, can encourage farmers to adopt sustainable practices.Economic analyses by suggest that well-designed incentives are effectivein promoting sustainable agriculture (Pannell et al., 2014). Governmentpolicies and institutional frameworks that support sustainable agricultureare essential for widespread adoption. Research by highlights the importance of policy support in facilitating the transition to sustainablefarming (Altieri, 2002; FAO, 2017).

2.5 Carbon Sequestration Potential in Bangladesh

Bangladesh’s unique climatic, soil, and socio-economic conditions presentboth challenges and opportunities for carbon sequestration throughsustainable agriculture. Studies specific to Bangladesh, such as thoseindicate that practices like agroforestry, conservation agriculture, andorganic farming hold significant potential for enhancing carbonsequestration and improving agricultural sustainability (Hossain et al.,2015; Sarker et al., 2018). Despite the potential, there is a need forlocalized research to provide empirical data on the carbon sequestrationcapacity of different sustainable practices in Bangladesh. This study aimsto address this gap by evaluating the carbon sequestration potential ofvarious sustainable agricultural practices across different agro-ecologicalzones in Bangladesh.

3. METHODOLOGY

3.1 Study Area

The study was conducted across various agro-ecological zones in Bangladesh to capture a diverse range of climatic, soil, and socio-economic conditions. These zones include the floodplains, coastal areas, and hill tracts, each representing distinct agricultural systems and challenges. The selected areas were chosen based on their relevance to different sustainable agricultural practices and their potential for carbon sequestration.

3.2 Research Design

This study employed a mixed-methods approach, combining quantitative and qualitative data collection and analysis. The research design included field experiments, farmer surveys, and soil sample analyses to assess the impact of sustainable agricultural practices on carbon sequestration and soil health.

3.3 Data Collection

3.3.1 Field Experiments

Field experiments were established in the selected study areas to compare conventional farming practices with various sustainable agricultural practices, including agroforestry, conservation agriculture, organic farming, and crop diversification. Each experimental plot was managed according to standard practices for a duration of two cropping seasons.

3.3.2 Soil and Biomass Sampling

Soil samples were collected from each experimental plot at the beginning and end of the study period. Samples were taken from the topsoil (0-30 cm depth) using a soil auger, ensuring a representative distribution across the plot. Biomass samples, including crop residues and tree litter, were alsocollected to assess above-ground carbon sequestration.

3.3.3 Farmer Surveys

Structured surveys were conducted with farmers practicing both conventional and sustainable agriculture in the study areas. The surveys aimed to gather information on farming practices, socio-economic factors, and perceived benefits and challenges of adopting sustainable practices.The sample size consisted of 150 farmers, selected through stratified random sampling to ensure representation across different regions and farming systems.

3.4 Data Analysis

3.4.1 Soil Carbon Analysis

Soil samples were analyzed for organic carbon content using the dry combustion method with a CHN analyzer. Soil bulk density was measured to calculate the total soil organic carbon stock. The change in soil carbon stocks over the study period was used to estimate the carbon sequestration potential of each agricultural practice.

3.4.2 Biomass Carbon Estimation

Above-ground biomass carbon was estimated by measuring the dry weight of crop residues and tree litter. The biomass samples were oven dried, and the carbon content was determined using the standard conversion factor (0.45). Total biomass carbon sequestration was calculated by summing the carbon content of all biomass components.

3.4.3 Statistical Analysis

Statistical analyses were performed using R software. Descriptive statistics, such as means and standard deviations, were calculated for soil and biomass carbon data. Analysis of variance (ANOVA) was used to compare carbon sequestration across different agricultural practices. Multiple regression analysis was conducted to identify the socio-economic factors influencing the adoption of sustainable agricultural practices.

3.5 Validation and Triangulation

To ensure the reliability and validity of the results, data triangulation was employed. The findings from soil and biomass analyses were cross validated with farmer survey responses and secondary data from relevant literature. Field observations and expert consultations were also conducted to corroborate the results.

3.6 Ethical Considerations

The study adhered to ethical guidelines for research involving human participants. Informed consent was obtained from all participating farmers, ensuring confidentiality and voluntary participation. The research design and methodology were approved by the relevant institutional review board.

4. RESULTS

4.1 Soil Carbon Sequestration

The study revealed significant differences in soil carbon sequestration among the various sustainable agricultural practices compared to conventional methods. Table 1 presents the changes in soil organic carbon(SOC) stocks over the two cropping seasons for each practice.

In Figure 1, Agroforestry systems exhibited the highest increase in SOC,sequestering an additional 11.8 Mg C/ha, representing a 38.7% increaseover the study period. Conservation agriculture and crop diversificationalso showed substantial improvements, with increases of 8.2 Mg C/ha(28.6%) and 7.0 Mg C/ha (25.8%) respectively. Organic farming practicesresulted in a 6.2 Mg C/ha (24.4%) increase in SOC. In contrast,conventional agriculture only showed a marginal increase of 1.5 Mg C/ha(5.1%).

4.2 Biomass Carbon Sequestration

The study also assessed above-ground biomass carbon sequestration. Table 2 presents the total biomass carbon sequestered by each practice.

Agroforestry again showed the highest increase, doubling the initialbiomass carbon to sequester an additional 12.3 Mg C/ha. Cropdiversification and conservation agriculture followed, with increases of7.2 Mg C/ha (65.5%) and 5.8 Mg C/ha (57.4%) respectively. Organicfarming resulted in a 5.1 Mg C/ha (54.3%) increase, while conventionalagriculture showed a minor increase of 0.8 Mg C/ha (7.5%) (Table 2,Figure 2).

4.3 Impact on Soil Health

Sustainable agricultural practices positively impacted soil health indicators, such as soil structure, nutrient content, and microbial activity. Agroforestry and conservation agriculture significantly improved soil structure, as evidenced by increased soil aggregate stability and reduced erosion. Organic farming enhanced nutrient content, particularly nitrogen and phosphorus, due to the application of organic amendments. Crop diversification increased microbial activity and biodiversity, as indicated by higher microbial biomass and diversity indices.

4.4 Socio-Economic Factors

The farmer surveys provided insights into the socio-economic factorsinfluencing the adoption of sustainable agricultural practices. Farmerswith higher awareness and access to training programs were more likelyto adopt sustainable practices. Financial incentives, such as subsidies andgrants, played a crucial role in encouraging adoption. Farmers reportedthat initial costs and perceived economic benefits were significantdeterminants. Strong policy and institutional support were identified ascritical for widespread adoption. Farmers highlighted the need forsupportive policies, extension services, and infrastructure development.

4.5 Comparative Analysis

A comparative analysis of the effectiveness of different practices inenhancing carbon sequestration and improving soil health is presented inTable 3.

Agroforestry and conservation agriculture emerged as the most effective practices in sequestering carbon and improving soil health (Figure 3). Organic farming and crop diversification also showed significant benefits, though their adoption was influenced by socio-economic factors such as education, costs, and incentives.

5.DISCUSSION

The results of this study demonstrate that sustainable agricultural practices have a significant positive impact on carbon sequestration and soil health in Bangladesh. Agroforestry emerged as the most effective practice, significantly enhancing both soil organic carbon (SOC) and above-ground biomass carbon. This can be attributed to the integration of trees within agricultural systems, which not only sequester carbon in their biomass but also contribute to improved soil structure and nutrient cycling through leaf litter and root biomass. Conservation agriculture also showed substantial benefits, primarily through reduced soil disturbance and continuous soil cover, which enhance SOC levels and prevent soil erosion. Organic farming practices improved SOC and biomass carbon, likely due to the application of organic amendments and crop rotations that enhance soil microbial activity and nutrient availability. Crop diversification, by increasing plant diversity and soil cover, contributed to higher SOC and biomass carbon compared to conventional monocropping systems. The marginal increase in SOC and biomass carbon under conventional agriculture highlights the limitations of traditional practices in mitigating climate change. Intensive tillage, monocropping, and excessive chemical use deplete soil organic matter and reduce soil health, underscoring the need for a transition to more sustainable farming systems.

The findings of this study have important implications for agricultural policy and practice in Bangladesh. Given its high potential for carbon sequestration and soil health improvement, agroforestry should be promoted through targeted policies and incentives. Training programs and extension services can help farmers integrate trees into their farming systems, providing technical support and financial assistance. Conservation agriculture practices should be encouraged through subsidies for no-till equipment, cover crops, and crop residues. Policies that support farmer-led research and demonstration projects can facilitate the adoption of these practices and highlight their benefits. Organic farming can be promoted through certification schemes, market development, and subsidies for organic inputs. Awareness campaigns and education programs can inform farmers about the long-term benefits of organic farming for soil health and productivity. Diversifying crops can enhance resilience and reduce risks associated with monocropping. Policies that support seed diversity, intercropping, and crop rotation can promote crop diversification. Extension services can provide farmers with information on suitable crop combinations and management practices.

The government should integrate climate change mitigation goals into agricultural policies, ensuring that sustainable practices are incentivized and supported. Policies should focus on research, development, and dissemination of climate-smart agricultural technologies. The adoption of sustainable agricultural practices is influenced by various socio-economic factors. This study identified key factors such as awareness, economic incentives, and policy support as critical determinants of adoption. Farmers who are aware of the benefits of sustainable practices and have access to training programs are more likely to adopt them. Financial incentives, such as subsidies and grants, play a crucial role in offsetting initial costs and encouraging adoption.

However, several barriers hinder the widespread adoption of sustainable practices. Many farmers are unaware of the benefits and techniques of sustainable agriculture. Expanding extension services and farmer training programs can address this gap. Initial costs of transitioning to sustainable practices can be high. Providing financial support and access to affordable inputs can help mitigate these constraints. Inconsistent and inadequate policy support can hinder adoption. Developing comprehensive policies that support sustainable agriculture and align with climate change mitigation goals is essential. Traditional farming practices and resistance to change can impede adoption. Promoting success stories and involving local communities in the decision-making process can foster acceptance.

6.LIMITATIONS AND FUTURE RESEARCH

While this study provides valuable insights into the potential of sustainable agricultural practices for carbon sequestration in Bangladesh, there are several limitations to consider: The study was conducted over two cropping seasons, which may not capture long-term trends and impacts. Long-term studies are needed to assess the sustained benefits of sustainable practices. The study focused on specific agro-ecological zones, and findings may not be generalizable to all regions of Bangladesh. Future research should include a broader geographic scope to validate the results. The study’s findings on socio-economic factors are based on a specific sample of farmers. Further research is needed to explore the variability in adoption factors across different socio-economic groups.

7.CONCLUSION

This study underscores the critical role of sustainable agricultural practices in enhancing carbon sequestration and mitigating climate change in Bangladesh. Agroforestry, conservation agriculture, organic farming, and crop diversification have demonstrated substantial potential for improving soil health and sequestering carbon. By promoting these practices through targeted policies and incentives, Bangladesh can enhance agricultural sustainability, contribute to global climate mitigation efforts, and improve the livelihoods of its farming communities.

Future research should focus on long-term impacts, broader geographic coverage, and a deeper understanding of socio-economic factors to develop comprehensive strategies for promoting sustainable agriculture in Bangladesh.

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Hossain, M.A., 2015. Carbon sequestration potential of agroforestry systems in Bangladesh. Journal of Environmental Management, 150, Pp. 67-76.

Hossain, M.A., 2015. Carbon sequestration potential of agroforestry systems in Bangladesh. Journal of Environmental Management, 150, Pp. 67-76.

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The study examined the effects of Magnesium Oxide (MgO) nanoparticles on the biochemical andphysiological yield of mung beans. Two varieties, IC39328 and IC39500, were obtained from the seed storesof the Botany department of Joseph Sarwuan Tarka University, located in Makurdi, Benue state, Nigeria. Theseeds were then treated with MgO nanoparticles concentrations (20, 40, 60, 80, and 100 ppm), salt, and NPKfertilizer. For the seed germination test, results for both IC39328 on day 7 showed that percentage survival,average length of plantlet, were improved at 10 ppm (100%), 50 ppm (18.6 cm), and 50 ppm (5.8 cm), whileplant vigour was maintained (4). However, plant vigour was reduced on day 20 at 100 ppm (2.8) butimproved on day 30 at 100 ppm (5). For IC39500, percentage survival, average length of plantlet, and averageroot length were improved at 10 ppm (100%), 50 ppm (18.6 cm), and 50 ppm (5.7 cm). However, plant vigour(5) was reduced on day 80 (3.5) and on day 30 at 20 ppm, 80 ppm, and 100 ppm (4). Effect on plant biomassand moisture showed that wet biomass and dry mass were significantly improved at 40 ppm (9.09 g) and 40ppm (5.98 g) compared to salt (10.55 g and 5.89 g) and NPK fertilizer (7.66 g and 4.61 g), respectively. Whilepercentage moisture was reduced at 100ppm (42.06%) compared to salt (40.59%) and NPK fertilizer(47.99%) respectively. Results for leaf chlorophyll and protein content showed that leaf chlorophyll and leafprotein content were not significantly reduced by the nano treatment at 40 ppm (4.87%) and 40 ppm (3.54%)compared to salt (4.09% and 3.15%) and NPK fertilizer (1.17%), respectively. Result for biochemicalparameters showed that sugar and lipid content were not significantly reduced at 20ppm (45.075%) and60ppm (81.0%) compared to salt (29.55% and 45.50%) and NPK fertilizer (24.43% and 56.0%) respectively.However, fiber content was maintained at 60ppm (147.6%) compared to salt (146.5%) and NPK fertilizer(145.0%). From the result, MgO nano-treatment improved seed germination, biochemical, and physiologicalparameters in both mung bean varieties compared to salt and NPK fertilizers. MgO nanoparticles arerecommended for effective seed germination and yield in mung bean cultivation.

The introduction of magnesium oxide nanoparticles (MgO NPs) intoagriculture has attracted considerable attention due to their potentialeffects on crop physiology and biochemistry (Barela et al., 2022).Nanotechnology, with its capacity to revolutionize agricultural practices,plays a crucial role in enhancing food production (Ebert, 2014). Over thepast decade, numerous patents and products incorporating engineerednanoparticles (NPs) have been developed for agricultural applications,such as nano-pesticides, nano-fertilizers, and nano-sensors, with thecollective goal of increasing precision, reducing inputs, and boosting farmincome compared to traditional methods (Mahakham et al., 2016). Recentyears have seen the application of various metal-based nanoparticles,including MgO NPs, Ag NPs, Au NPs, Cu NPs, Fe NPs, FeS2 NPs, TiO2 NPs,Zn NPs, and ZnO NPs, as seed pre-treatment agents to promote seedgermination, seedling growth, and stress tolerance in certain crops(Mohamed, 2017; Panyuta et al., 2016; Srivastava et al., 2014; Srivastavaet al., 2014; Latef et al., 2017; Latef et al., 2017).

Mung bean (Vigna radiata L.), a vital leguminous crop, is essential forglobal food security, making it imperative to explore innovative methodsfor improving its yield and quality. Magnesium oxide nanoparticles,recognized for their unique physicochemical properties, have beenstudied across various fields, including agriculture, to understand theireffects on plant growth and development. With characteristics such ashigh surface area, reactivity, and biocompatibility, MgO NPs show promisein agricultural applications. Their small size enhances nutrient uptake andinteraction with plant systems, potentially influencing physiologicalprocesses. Given the rising demand for mung beans, optimizing their yieldis crucial. Investigating novel approaches, such as the application of MgONPs, could contribute to sustainable agriculture and meet the growingdemand for this essential crop.

Previous studies have reported that MgO NPs can enhance plantphysiology by improving photosynthesis, nutrient uptake, and water useefficiency (Faizan et al., 2022). These nanoparticles may function as nanocarriers,delivering magnesium ions directly to plant cells and influencingkey physiological processes. Understanding the biochemical changesinduced by MgO NPs is crucial. Research has shown that MgO NPs can modulate antioxidant enzyme activities, aiding the plant’s defence againstoxidative stress (Mittal et al., 2020). Furthermore, alterations in secondarymetabolites and nutrient content have been observed, indicating acomplex impact on plant biochemistry.

Despite numerous studies on the effects of nanoparticles on plant growth,a significant research gap exists concerning the specific impact of MgOnanoparticles on the biochemical and physiological yield of mung beans(Vigna radiata). While various studies have explored the influence ofdifferent nanoparticles on plant growth, the specific interactions andmechanisms involving MgO nanoparticles and mung beans remainunderexplored. Understanding the complex relationship between MgOnanoparticles and the biochemical and physiological processes in mungbeans is essential for maximizing agricultural productivity and ensuringsustainable food production. Future research should focus on elucidatingthe underlying molecular and physiological mechanisms that govern theseobserved effects, thereby contributing to a more comprehensiveunderstanding of nanomaterial-plant interactions.

One of the primary challenges in mung bean production is low yield.Conventional fertilizers have been used to improve yields, but they can beharmful to both animal and plant health. Nanoparticles may accumulate inthe edible parts of crops, posing potential health risks. This researchfocuses on mung bean cultivation to provide insights into the potentialharmful effects of magnesium nanoparticles on other crops by studyingbiochemical and physiological parameters. These nanoparticles can alsobe detrimental to soil health and microorganisms. Additionally, currentliterature lacks comprehensive insight into the physiological andbiochemical responses of mung beans to magnesium oxide nanoparticles,which is crucial for maximizing the potential benefits of MgOnanoparticles in optimizing mung bean growth and development.Therefore, this study is highly significant.

Nanotechnology holds significant potential for transforming agriculturalpractices, as emphasized by (Saritha et al., 2022). Exploring the specificeffects of MgO nanoparticles on mung bean cultivation could introducenovel and sustainable approaches to agriculture. Mung bean, as a globallyimportant staple crop, not only provides essential nutrients but alsoserves as a key component of agricultural systems. Understanding thenuanced interactions between MgO nanoparticles and mung beanphysiology is particularly important for ensuring food security, especiallyin regions where mung bean plays a vital role in agriculture. The uniquebiological characteristics of mung bean distinguish it from other plantspecies, highlighting the need for targeted research into species-specificresponses to MgO nanoparticles. The findings from this study willcontribute to a deeper understanding of the complex interplay betweenMgO nanoparticles and mung bean physiology, enabling more precise andeffective agricultural interventions. By identifying optimal applicationmethods, the study aims to inform guidelines for farmers on theresponsible and efficient use of nanotechnology in agriculture. Theprimary objective of this research is to thoroughly investigate the impactof MgO NPs on the physiological yield and biochemical properties of mungbean. By clarifying these effects, the study seeks to provide valuableinsights that can enhance mung bean cultivation practices.

2. MATERIALS AND METHODS

2.1 Study Area

This research was conducted in the Department of Botany at JosephSarwuan Tarka University, Makurdi, Benue State. Makurdi is locatedwithin the Guinea savannah vegetation zone, with geographicalcoordinates of 8° 53′ 00″ N latitude and 7° 73′ 00″ E longitude. The regionexperiences a minimum temperature range of 21.71°C ± 3.4°C and amaximum temperature range of 32.98°C ± 2.43°C. The area receives anaverage annual precipitation of 134.92 mm (5.31 inches) and has relativehumidity levels ranging from 39.5% ± 2.20% to 64.0% ± 4.80%. The cityis situated at an elevation of 84 meters above sea level and covers alandmass of 804 km² within a 16 km radius, with an estimated populationof 500,797.

Makurdi is positioned in the Lower Benue Valley, where the relief isgenerally low, with elevations ranging from 73 to 167 meters above sealevel. The soils in this region are predominantly highly ferruginoustropical soils. Climatically, Makurdi falls within a tropical, sub-humidclimate with distinct wet and dry seasons. The wet season spans fromApril to October, while the dry season occurs from November to March.Rainfall in Makurdi LGA varies between 775 mm and 1,792 mm, with anaverage annual total of 1,190 mm. The mean monthly relative humidityranges from 43% in January to 81% during the July-August period(Barizomdu et al., 2019).

2.2 Materials Used

Seeds of mung beans, Synthesized MgO nanoparticles, Double distilledwater, Mesh screen, Petri dishes, Agar powder (Bacteriological),Micropipette Potting, soil, Polythene leather, Watering can, Weighingscale, oven, Meter rule, Notebooks, Gloves and Laboratory coat. NPKFertilizer.

2.2.1 Collection of Plant Materials (Mung beans)

Mung bean leaves were harvested from a local farm in the Makurdi LocalGovernment Area of Benue State and identified at the Department ofBotany, Joseph Sarwuan Tarka University, Makurdi. The fresh leaves weresorted, washed with clean water to remove dirt and other unwantedmaterials, and then air-dried before being taken to the laboratory foranalysis.

2.2.2 Preparation of Plant Materials (Mung beans)

Mung bean leaves were thoroughly washed with clean water to removeany dirt and unwanted materials. After washing, the leaves were air-driedfor 3 to 4 days at room temperature. Once dried, the leaves were groundusing an electric blender and stored in a clean container. A 6g portion ofthe ground leaves was then mixed with 100mL of double-distilled waterin a beaker and heated at 80°C for 1 hour.

2.2.6 Experimental Design

A completely randomized design with five replicates was employed toassess the growth responses of mung beans. Treatments were randomlyallocated to different groups to ensure unbiased comparisons andaccurate evaluation of growth rates. Various treatment levels of 20, 40, 60,80, and 100 ppm were applied.2.2.7 PlantingOn September 1, 2023, four seeds were manually sown at a depth of 3 cmin each pot. After the seedlings were established, they were thinned tothree per pot.

2.2.8 Seed Germination Test on two varieties of Groundnut (IC39328and IC39500)

The impact of MgO nanoparticles on the percentage of seed germinationin two groundnut varieties was assessed by germinating the seeds onsterilized agar solution, supplemented with varying concentrations ofMgO nanoparticles (0, 10, 25, 50, and 100 ppm). The germinationpercentage was calculated by dividing the number of germinated seeds bythe total number of seeds inoculated, then expressing this as a percentage.

2.2 Determination of Biochemical Yield Parameters

2.2.1 Protein content determination

The micro-Kjeldahl method, as described was employed to determine theprotein content of the groundnut powder (AOAC International, 2005).Precisely 2 grams of the sample was mixed with 10 mL of concentratedsulfuric acid (H₂SO₄) in a Kjeldahl digestion flask. A selenium catalyst tablet was added, and the mixture was heated under a fume hood. Theresulting digest was transferred into a 100 mL volumetric flask and dilutedwith distilled water. An aliquot of 10 mL from the digest was thencombined with an equal volume of 45% sodium hydroxide (NaOH)solution and introduced into a Kjeldahl distillation apparatus. The mixturewas distilled, and the distillate was collected into a solution of 4% boricacid containing 3 drops of Zuazaga indicator (a mixture of methyl red andbromocresol green), bringing the total volume to 50 mL. The distillate wassubsequently titrated with 0.02N sulfuric acid (H₂SO₄) solution. Thetitration was conducted until the color changed from green to a deep redor pink endpoint. The total nitrogen content was calculated and thenmultiplied by a factor of 6.25 to determine the protein content.

N = Normality of filtrate ((H2S04) = 0.02N

VF = Total volume of the digest = 100ml

VA = Volume of the digest distilled

2.2.2 Fat content determination

The mung bean seeds were ground to increase surface area and achievehomogeneity. A 1g sample was accurately weighed using an analyticalbalance. The sample was then placed into a glass thimble. The Soxhletextraction apparatus was set up, with the thimble positioned in theextraction chamber. Approximately 10 mL of hexane was added to theround-bottom flask at the base of the apparatus. The extraction processcommenced, allowing the solvent to circulate through the sample andextract the lipids. The Soxhlet apparatus was run for 4 hours to ensurethorough lipid extraction. Following extraction, the solvent containing thelipids was collected in the round-bottom flask. The solvent was thenevaporated using a rotary evaporator to isolate the lipids. The extractedlipids were further dried to eliminate any residual solvent by placing thesample in an oven at a low temperature until a constant weight wasachieved. Finally, the dried lipids were weighed using an analyticalbalance.

Formula for the Calculation:

Where:

W = weight of the sample

W1 weight of empty extraction flask

W2 = weight of flask and oil extract

2.2.3 Fibre content determination

The determination was carried out using the Weende method as describedby (AOAC International, 2005). Approximately 2g of each sample, afterdefatting during fat analysis, was treated with 200ml of 1.2% H₂SO₄ andboiled under reflux for 30 minutes. The resulting mixture was then filteredand washed multiple times with hot water using a two-fold muslin cloth totrap any remaining particles. The washed samples were transferred to abeaker and boiled for another 30 minutes with 200ml of 1.25M NaOHsolution. The digested sample was washed thoroughly with hot water,carefully scraped into a weighed porcelain crucible, and dried in an ovenat 150°C for 3 hours. After drying, the sample was cooled in a desiccatorand weighed. The sample was then ashed in a muffle furnace at 550°C for2 hours, cooled again in a desiccator, and reweighed.

The fibre content was calculated using the formula:

W1 = weight of crucible sample after washing and drying in oven

W2 = weight of crucible + sample ash

2.2.4 Sugar content determination

Mung beans were finely ground to ensure uniformity. A 1g sample of the ground mung beans was mixed with 5ml of a distilled water and ethanolsolution to extract the soluble sugars. The mixture was left to stand tofacilitate the extraction process. After extraction, the mixture was filteredto remove solid particles, yielding a clear solution containing the extractedsugars. For calibration, standard solutions with known sugarconcentrations were prepared. To both the filtered extract and thestandard solutions, 2ml of phenol-sulfuric acid reagent was added inprecise proportions to initiate a colorimetric reaction. The reactionmixtures were then incubated in a water bath at a controlled temperaturefor a specified period to allow for colour development. Absorbancereadings of the coloured solutions were taken using a spectrophotometerat a specific wavelength, with a blank solution (containing all reagentsexcept the sample or standard solution) measured as a control. Thismethod of sugar determination aligns with the procedure outlined in(AOAC International, 2005).

The sugar content was determined using the formular:

2.3 Determination of Physiological Yield Parameters

2.3.1 Moisture content determination

Moisture content was determined using the air oven method (AOACInternational 2005), as outlined by (Ahn et al., 2014). Crucibles wereinitially washed and dried in an oven, then allowed to cool in a desiccatorbefore their weights were recorded. Subsequently, 5 grams of each samplewere placed in the crucibles and dried at a temperature between 103°Cand 105°C for 2 hours. After drying, the crucibles were removed, cooled ina desiccator, and weighed. This process of heating, cooling, and weighingwas repeated until a constant weight was achieved. The moisture contentwas calculated using the following formula:

2.3.2 Chlorophyll content determination

0.1 g of fresh mung bean leaves was collected and placed in a test tubecontaining 10 ml of acetone. The mixture was incubated in a dark room at4°C for 24 hours to obtain a green extract. The extract was thentransferred to a cuvette for spectrophotometric analysis, where theabsorbance of the chlorophyll was measured at 663 nm for chlorophyll aand 645 nm for chlorophyll b.

The Chlorophyll content was determined using the formular:

Total Chlorophyll Content: Total Chl (mg/g) = (8.2 × A663) + (20.2 × A645)       (6)

2.3.3 Statistical Analysis

Minitab 16.0 was used for data analysis. The following tools were applied:descriptive statistics (mean, standard error), one-way ANOVA, andPearson’s correlation. Tukey’s method was employed for mean separationat a 95% confidence level (p-value = 0.05).

3. RESULTS AND DISCUSSION

3.1 Effect of Magnesium oxide (MgO) nano treatment on twovarieties of mung beans (IC39328 and IC39500)

3.1.1 Effect on seed germination of IC39328

From Table 1, the effect of MgO nanoparticle treatment on seedgermination parameters at day 7 indicated that the percentage survival,average plantlet length, and average root length were improved at 10 ppm(100%), 50 ppm (18.6 cm), and 50 ppm (5.8 cm), respectively, comparedto the control (75%, 6.55 cm, and 1.55 cm). Plant vigour, however,remained at 4 across all treatment concentrations. Results at day 20, asshown in the box plot in Figure 1, revealed that plant vigour decreased at100 ppm (2.8), and by day 30, plant vigour was still reduced at 100 ppm(5).

3.1.2 Effect on the seed germination of IC39500

From Table 2, the effect of MgO nanoparticle treatment on seedgermination parameters demonstrated that the percentage survival,average plantlet length, and average root length were enhanced at 56 ppm(100%), 50 ppm (18.6 cm), and 50 ppm (5.7 cm), respectively, comparedto the control (74%, 6.55 cm, and 1.55 cm). Plant vigour, however, wasconsistently maintained at a level of 5 across all treatment concentrations.According to the box plot in Figure 2, at day 20, plant vigour decreased at80 ppm (3.5), and by day 30, it was reduced at 20 ppm, 80 ppm, and 100ppm (4).

3.1.3 Effect on the plant biomass and moisture content

From Table 3, the effect of MgO nanoparticle treatment on plant biomassand moisture is as follows: Wet biomass significantly increased at 40 ppm(9.09 g) and 60 ppm (8.50 g) compared to the fertilizer treatment (7.66 g).However, the greatest improvement was observed with the salt treatment(10.55 g). The treatment effect on wet biomass was significant (F = 72),with variety having a minimal contribution (T = 0.54) to the observeddifferences. Dry mass was notably higher at 40 ppm (5.98 g) compared toboth the salt treatment (5.89 g) and the fertilizer (4.61 g). The treatmenteffect on dry mass was significant (F = 76), while the relatively low T-valueof 2.04 indicates that variety had a minor impact on the differencesobserved in dry mass. Percentage moisture did not significantly decreasewith the nano treatment at 100 ppm (42.06%) compared to the salttreatment (40.59%). The fertilizer treatment had a higher moisturecontent (47.99%). A significant treatment effect was observed (F = 77),and the T-value of 3.70 suggests a notable impact of variety on thedifferences in moisture content.

Means not sharing the same letters are significantly different at P ≤ 0.05

a = Not significantly different, b = Not significantly different, c = Notsignificantly different, ab = Significantly different, ac = Significantlydifferent, bc = Significantly different, abc = Significantly different

3.1.4 Effect on leaf chlorophyll and protein contentFrom Table 4, the leaf chlorophyll content was not significantly reducedby treatments at 40 ppm (4.87%), with salt (4.09%), or with NPK fertilizer (1.60%). The analysis revealed a significant treatment effect (F = 24),while the T-value of 0.32 indicates that the variety had only a minor impacton the observed differences in chlorophyll levels. Similarly, leaf proteincontent was not significantly reduced by treatments at 40 ppm (3.54%),salt (3.15%), or NPK fertilizer (1.17%). A significant treatment effect wasobserved (F = 22), with a low T-value of 0.18 suggesting minimal influenceof the variety on the observed differences in protein content.

Means not sharing the same letters are significantly different at P ≤ 0.05

a = Not significantly different, b = Not significantly different, ab =Significantly different

3.1.5 Effect on sugar, fiber and lipid content

From Table 5, the sugar content was not significantly different at 20 ppm(45.075%) and 100 ppm (42.88%) compared to salt (29.55%) and NPKfertilizer (24.43%). A significant treatment effect was observed (F = 29),with a high T-value of 5.61 indicating a substantial impact of the varietyon the differences in sugar levels. Fiber content was effectively maintainedby the nano treatment at 60 ppm (147.6%), but was not significantlydifferent from the levels observed with salt (146.5%) and NPK fertilizer(145.0%). The treatment effect was significant (F = 29), and the high Tvalueof 155.43 suggests a substantial impact of the variety on theobserved differences in fiber levels. Lipid content showed a significantimprovement at 60 ppm (81.0%) compared to salt (45.5%) and fertilizer(56.0%). A significant treatment effect was noted (F = 22), with a T-valueof 3.47 indicating a notable impact of the variety on the observeddifferences in lipid levels.

Means not sharing the same letters are significantly different at P ≤ 0.05

a = Not significantly different, b = Not significantly different, ab = significantly different

4. DISCUSSION

From Tables 1 and 2, the improved seed germination parameters (percentage survival, average plantlet length, and average root length) in both varieties (IC39328 and IC39500) of mung beans treated with MgO nanoparticles suggest a positive influence on biochemical and physiological yield, potentially enhancing overall plant growth and development. This finding aligns with who demonstrated the beneficial effects of MgO nanoparticles on leguminous crops, including mung beans (Nwachukwu et al., 2020). However, a group researcher suggest that MgO nano treatment may not consistently enhance biochemical and physiological yields in leguminous plants, indicating a need for further investigation into the variability of outcomes (Adeyemi et al., 2019). Possible reasons for the disparity in results could include variations in experimental conditions, plant varieties, and the characteristics of the MgO nanoparticles, underscoring the importance of considering these factors in nano-agriculture research.

The consistent plant vigour observed in IC39328 (4) and IC39500 (5) across all treatment concentrations suggests that MgO nanoparticles may contribute to maintaining the physiological stability of the plants. A study by supports this observation, reporting stable plant vigour across different concentrations of MgO nanoparticles in a similar crop species (Akinbode et al., 2021). However, the observed reduction in plant vigour after 20 days of MgO nanoparticle treatment in both varieties implies a potential negative impact on the physiological well-being of the plants at higher concentrations. This finding is consistent with who reported a dose-dependent decrease in plant vigour in response to high concentrations of MgO nanoparticles in a similar plant species (Ogunbanwo et al., 2019). Conversely, a group researcher reported an increase in plant vigour, highlighting the need for further exploration of the factors that influence plant responses to nanomaterials (Adegbola et al., 2020). Differences in plant varieties, experimental conditions, and the specific physiological mechanisms influenced by MgO nanoparticles may account for these conflicting results. The observed improvement in plant vigour at day 30 in the IC39500 variety suggests a potential long-term positive impact of MgO nanoparticles on the physiological development of the plants.

From Table 3, the significant improvement in wet biomass at 40 ppm following MgO nano treatment suggests a positive influence on overall plant growth and water content. This result aligns with a study by which similarly reported an enhancement in wet biomass in response to MgO nanoparticles in a related plant species (Adebayo et al., 2021). However, a group researchers reported no significant improvement in wet biomass with MgO nano treatment, indicating a need for further investigation into the factors influencing biomass response (Lawal et al., 2018). Discrepancies may be due to variations in plant species, experimental conditions, and the specific physiological processes affected by MgO nanoparticles.

The significant improvement in dry mass at 40 ppm following MgO nano treatment indicates a positive impact on biomass accumulation after accounting for moisture content. This finding aligns with who also reported enhanced dry mass in response to MgO nanoparticles (Adekoya et al., 2023). However, a group researcher found no significant improvement in dry mass with MgO nano treatment, emphasizing the complexity of plant responses to nanomaterials (Ojo et al., 2020). Differences in plant varieties, experimental conditions, and the specific mechanisms through which MgO nanoparticles influence dry mass accumulation may explain these disparities.

The lack of a significant reduction in percentage moisture at 100 ppm following MgO nano treatment suggests that, at this concentration, the nanoparticles did not substantially decrease the water content of the plants. This finding is consistent with who similarly observed no significant reduction in moisture content with MgO nano treatment in a related plant species (Adewale et al., 2019). However, a group researchers reported a significant decrease in moisture content at 100 ppm, highlighting the need for further exploration of the factors influencing nanomaterial interactions with plant water dynamics (Yusuf et al., 2021). Variations in plant physiological responses, nanoparticle characteristics, and experimental conditions may account for these discrepancies. From Table 4, the lack of a significant reduction in leaf chlorophyll and protein content at 40 ppm following MgO nano treatment suggests that the nanoparticles did not negatively impact these levels at this concentration. This observation is consistent with studies by some researchers which similarly found no significant reduction in chlorophyll and protein content with MgO nano treatment in comparable plant species (Adeleke et al., 2023). In contrast, in other study reported a significant decrease in leaf chlorophyll content at 40 ppm, underscoring the need for further investigation into the specific factors influencing nanomaterial interactions with chlorophyll metabolism (Ahmed et al., 2018). Variations in plant varieties, experimental conditions, and the physiological pathways affected by MgO nanoparticles could explain these differences.

From Table 5, the lack of significant improvement in sugar and lipid content following MgO nano treatment suggests that, at the concentrations tested, the nanoparticles did not exert a noticeable positive effect on sugar levels in the plants. This finding is consistent with who similarly observed no significant improvement in sugar and lipid content with MgO nano treatment in a related plant species (Oluwaseun et al., 2022). However, a group researcher reported a significant increase in sugar content at 20 ppm, indicating the need for further exploration of the factors influencing nanomaterial interactions with sugar metabolism (Adeyemi et al., 2019). Discrepancies may arise from variations in plant varieties, experimental conditions, and the specific biochemical pathways influenced by MgO nanoparticles.

The maintenance of fiber content at 60 ppm following MgO nano treatment suggests that the nanoparticles did not significantly alter fiber levels in the plant at this concentration. This observation is consistent with who similarly reported no significant change in fiber content with MgO nano treatment in a related plant species (Olajumoke et al., 2022). However, a group researcher reported a significant increase in fiber content at 60 ppm, emphasizing the need for further investigation into the factors influencing nanomaterial interactions with fiber metabolism (Adewumi et al., 2017). Variations in plant varieties, experimental conditions, and the biochemical pathways affected by MgO nanoparticles may contribute to these differing results.

5. CONCLUSION

In conclusion, the investigation into the effects of magnesium oxide (MgO) nanoparticles on the biochemical and physiological yield of mung beans (Vigna radiata L.) revealed significant positive impacts on various growth parameters. The application of MgO nanoparticles led to enhanced yield by improving key biochemical and physiological metrics in mung beans. Notably, the IC39328 variety exhibited better germination performance compared to the IC39500 variety. The nanoparticles appeared to play a crucial role in improving nutrient uptake, photosynthetic efficiency, and overall plant health. Therefore, incorporating MgO nanoparticles into mung bean cultivation practices could be a promising strategy for optimizing crop productivity.

RECOMMENDATIONS

The following recommendations are made based on the results of this study:

• Further research should be conducted to explore the effects of higher concentrations of MgO nanoparticles, aiming to clearly establish the relationship between nanoparticle activity and concentration.

• Mung bean farmers should consider using MgO nanoparticles to enhance the biochemical and physiological yield of their crops, as itmay be a more effective alternative to traditional salt or NPK fertilizers.

• The active ingredients responsible for the beneficial effects observed with MgO nanoparticles should be further investigated to better understand their role in crop improvement.

• A comparative analysis of other nanoparticles should be conducted to assess their potential effectiveness and determine their applications in mung bean cultivation.

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The increasing use of inorganic fertilizers is one of the significant causes of environmental pollution. The dependence upon fertilizers can be noticeably reduced with the elevated use of compost prepared from organic residues. Such practice improves the nutrient status of soil as well as saves our environment by managing waste. A study was carried out with nine treatments to show the effects of poultry litter and kitchen waste on soil properties and the growth of spinach plants. Clay loam soil was treated with different combinations of compost made with only poultry litter (PL) and co-compost (CC) prepared poultry litter mixed with kitchen waste by an aerobic process. The results showed that applying CC at 25% to the soil resulted in the highest spinach yield, leaf length, and number, fresh weight, and biological yield. Soil pH was found highest in CC50%, EC was high in PL50%, CEC was high in PL50%, and OC was high in PL25%. The study also showed that the available N of soil was higher by applying CC50%, and the available P, K, and S of soil was higher by applying CC25%. The nutrient content of spinach plants showed noticeable variation due to compost application. Using inorganic fertilizer as control (R)50% showed the highest N, K content of leaves and P content was high by applying CC25% and S content was high by applying PL50%. The findings indicated that co-composting of poultry litter and kitchen waste can be a useful method for boosting plant production and nutrient availability in soil.

Agriculture production in the last 1–2 decades has increased significantly with the help of recent technologies and farm mechanization. However, extensive use of fertilizers, insecticides, and many other pesticides had a negative impact on human health, and environmental pollution and raised the cost of crop cultivation. Pesticide residues in food chains have a well-documented harmful effect and have put the world’s life support systems at peril (Ali et al., 2021). In 1989, Japanese researchers provided evidence that compost is the end product of aerobic decomposition of organic matter. In addition, Lal claimed in a review book of Agronomy for Sustainable Development in 2008 that it is frequently considered as a balanced fertilizer in agricultural crop fields and is commonly used as a soil conditioner. The end product of the aerobic decomposition of organic compounds using multiple feedstocks is called co-compost (Giagnoni et al., 2020). Litter, plant remains, and animal manure are the most common ways that soil nutrients are recycled back into the soil. The management of kitchen waste produced by the majority of hotels, motels, restaurants, and households is a challenge that requires urgent attention. Our environment suffers from improper waste management. Since a greater percentage of these wastes are organic, an alternate supply of biodegradable materials that could improve soil might become alluring. Compostable kitchen waste is a sustainable recycling method that decreases landfill disposal when used as a soil additive in crop development (Petersen et al., 2003; Chen and Jiang 2014). One of the largest and fastest-expanding agro-based industries in the world is the poultry sector. In Bangladesh, the commercial poultry industry has expanded quickly. There are many distinct types of waste produced by poultry farms. Poultry litter is a mixture of animal bedding materials like rice or peanut hulls and wood shavings (FSA, 2007). According to Feedlot Services Australia in 2007, a 35 and 49-day-old bird is thought to excrete 0.34 and 0.63 kg of solids, respectively, based on the diet’s dry matter digestibility (87.5%). The environment-unfriendly methods of disposing of poultry waste include burning, burying, incineration, and rendering. To lessen their hazard to the environment, cost-effective and environmentally friendly poultry waste treatment techniques are required.

The present research was therefore carried out to investigate the co-composting of poultry litter with kitchen wastes in different combinations to convert these biological wastes into useful nutrient-rich composts for enhancing plant growth. In addition to providing mature compost with the best possible nutrient supply for developing plants, co-composting offers safe disposal. The aim of the research work was the characterization of poultry litter and kitchen waste compost and their effect on soil properties and nutrient content and selective agronomic parameters of Spinach (Spinacia oleracea locally known as Palong Shak) by applying these composts.

2.MATERIALS AND METHODS

2.1 Compost Preparation

Poultry litter was collected from a nearby chicken shop, situated at Jashore railway station and kitchen waste was collected from a middle-class family home. Composts were prepared under aerobic conditions. Three fresh plastic drums (10 L) were collected and then those drums were washed with water. Collected poultry litter (PL) was crashed with a hammer and poured 10 kg of it into one dram. The co-compost was prepared by mixing kitchen waste and poultry litter in 1:1 ratio. 5 kg of kitchen waste, after removing plastic bags and other non-biodegradable things, was mixed with 5 kg of poultry litter. The mixer poured in the other two drams. The drams were sealed with lids for 120 days but opened every 3–5 days to control the temperature, aeration, and moisture. After composting, the total amount of materials in each drum was mixed thoroughly by rolling the drum. Then, the materials were passed through the sun-drying condition for 7 days. The compost was crashed before their addition to experimental pots. The chemical properties of the compost are given in Table 3.

2.2 Experimental Setup

A pot experiment was conducted at the field laboratory of Soil, Water and Environment Discipline, Khulna University. The soil sample was collected from behind the central library of Khulna University. The GPS reading of the location is 22.8031 ̊ North latitude and 89.5323 ̊ East longitude. The experimental field lies in the Ganges Tidal Flood Plain (AEZ-13) (SRDI, 2008). The texture of the experimental field is clay loam.

The soil sample was air-dried and pulverized to pass through a 2 mm sieve. Selected basic properties of the experimental soil are presented in Table 2. Nine treatments as listed in Table 1 were replicated thrice in the experiment. Each pot (25 × 22 cm) was filled with 3 kg air-dried soil mixed with the required volume of organic materials.

PL=Poultry litter, CC=Co-compost (poultry litter+kitchen waste), R=Recommended dose of inorganic fertilizer

Spinach (Spinacia oleracea) was used as a test crop. Seeds were sown on November 2021 after soaking overnight in water. 5 to 10 seeds were sown in every pot with 1.2–2 cm depth and then arranged the pots according to CRD. After germination, only 5 plants were kept in each pot. The intercultural operations such as irrigation, weeding, and pest control, were done as needed during the growing period. After 50 days, when the leaves of spinach grow to the consumable stage, the plants were harvested. Before harvesting, selective agronomical parameters were measured. Plant height was measured in cm from the ground level to the tip of the uppermost awn. Leaf length was measured in cm by keeping the leaves flat. The length of each leaf is measured from the pointy part at one end of the leaf to the point where the leaf joins the stalk at the other end. After harvesting, using an electrical balance, weigh three plants from each pot. Calculate the mean of three plants and keep it notated. The unit will then be g plant-1. The biological weight of the whole plant was measured gravimetrically after 24 hours of oven drying at (60 ̊ ─ 65 ̊)

2.3 Laboratory Analysis

The particle size analysis of the initial soil was done by the hydrometer method as described by Gee and Bauder in 1979. The textural class was determined by Marshall’s Triangular coordinate system. The total nitrogen content in the initial soil, plant, and compost was determined by Micro-Kjeldahl following colorimetric measurement as described (Bremner and Mulvaney, 1982). Plants and compost were digested with nitric-perchloric acid (2:1) as described by (Piper, 1966). Total phosphorus in the digested plant, compost samples, and initial soil were determined by the vanadomolybdophosphoric yellow color method by using a UV spectrophotometer (470 nm) (Jackson, 1973). Total K was determined by the Flame Emission Spectroscopic (FES) method as described by Jackson in 1967. The turbidimetric method described by Jackson was used for the determination of the total sulfur of initial soil, digested plant, and compost samples (Jackson, 1973). Organic carbon (OC) content was determined by using the Walkley and Black wet oxidation method (Jackson, 1973). The CEC of the soil samples was measured by the flame photometric method (Chapman, 1965). The pH of both soil samples and compost was determined electrochemically with the help of a glass electrode pH meter (Jackson, 1962). To conduct this soil samples were mixed with distilled water in a 1:2.5 ratio, and compost samples were in a 1:5 ratio. (Jackson, 1962) The electrical conductivity of both soil samples and compost was measured at a soil-water ratio of 1:5 and it was converted into a 1:1 ratio as described by U.S. Salinity Laboratory Staff in 1954 with the help of an EC meter. The soil available N was extracted using 1 N KCl solution and estimated by the alkali distillation method outlined by (Chapman, 1965). Available phosphorus in soil (soil: Olsen extractant = 1:20) (Olsen, 1954) at pH 8.5 was determined by the Molybdophosphoric blue color method by using a UV spectrophotometer (882 nm) described by (Murphy-Riley, 1962). Available K in soils was extracted (NH4OAc) in a 1:10 soil-to-extractant ratio (Jackson, 1967). Soil available sulfur (soil: extractant = 1:8) was determined by the turbidimetric method (Jackson, 1973).

2.4 Statistical Analysis

Data were analysed statistically by one-way ANOVA Gomez and Gomez in 1984 to examine whether treatment effects were significant or not. Mean values were compared by R programming software. Moreover, graphs were prepared by using R programming language. In the graph, the different letter indicates significant differences at 0.05% level and the same letter is not significantly different at p<0.05 according to the Tukey test. The error bar indicates the standard deviation.

3.RESULTS AND DISCUSSION

Some basic properties of the experimental soil, which have a great influence on plant growth, are presented in the following Table 2.

Compost PL and co-compost PL+KW were produced in aerobic conditions; the chemical properties of compost have been tabulated in Table 3.

3.1 Agronomic Parameter

3.1.1 Biological Yield

The biological yield was remarkably influenced by applying various treatments. This yield was noticeably different from the recommended dose of inorganic fertilizer but non-significant by applying the recommended dose of 50% and more significant in the recommended dose (R) of 100% (Figure 1). Again, the biological yield was considerably different by applying PL compost but non-significant by applying PL 25%. The biological yield of plants is also significantly different by applying co-compost of kitchen waste and poultry litter, and the most significant is applying 25% co-compost. The biological yield was found best by applying CC at 25% and the highest yield value was 4.37 g plant-1. The lowest yield was found in control of R and CC 0% (2.05 g plant-1). Organic fertilizer can produce organic acids that can mobilize insoluble P from the soil to the soil solution in a labile form where P nutrient is often a problem in inhibiting plant growth due to its low availability in the soil (Dotaniya et al., 2016; Hammond et al., 2009).

The different letters indicate a significant difference at 0.05% level.

3.1.2 Fresh Weight

Fresh yield per plant ranged from 12.31 g to 32.78 g and the highest fresh weight of the plant was found by applying CC 25% (32.78 g) (Figure 2). The fresh weight of the plant was considerably influenced by various treatments with rates. Fresh weight was not markedly different by applying a 0% rate of all fertilizers, but it was noticeably different by 25% and 50%. Again, the fresh weight of plants was most significant by applying 25% CC. The application of compost showed a significant increase in nutrition minerals in the soil such as N, P, Ca, Mg, and other minerals needed for plant growth (Meunchang et al., 2005).

3.1.3 Plant Height

Plant height was noticeably influenced by different treatments irrespective of their application rate (Figure 3). Co-composting produced plants with a maximum height (25.5 cm) compared with both control and compost addition in soil. The influence was observed noticeably higher at a 25% rate of application (Figure 3). The plant height of spinach plants in the co-compost of used coffee grounds and cat manure as amended soil was much higher than that of spinach grown in the compost-amended soil containing chicken manure (Keeflee et al., 2020). Researchers showed the effects of different rates of composted KW (kitchen waste) and PM (poultry manure) amendments on the growth and yield of another leafy plant (Corchorus olitorius) at a rate of 15 t ha-1, the mean plant height was higher with PM than with KW, and it was 24.96 cm (Oladele et al., 2018). This could also be attributed to the large quantities of available phosphorus and available potassium contained in the chicken manure. A study indicated that the soil could be enhanced with the application of organic material which tends to decompose and release relatively large amounts of nitrogen into the soil before planting each fresh crop to boost yield (Rao, 1991).

3.1.4 Number of Leaves

In most cases, the leaf number of plants was noticeably affected by different treatments. Again, the leaf number of plants was most significant by applying co-compost of kitchen waste and poultry litter 25%. The lowest plant height was found in control of the recommended dose 0% (15.5 cm) (Figure 4). This observation could be attributed to a better supply of nitrogen mineral elements during the composting process which enhanced leaf growth conditions for spinach (Ryckeboer et al., 2003). A study showed that poultry manure significantly increased the number of leaves in Corchorus olitorius compared to KW. The different rates of amendment applied increased the number of leaves. The Corchorus grown in soil without amendment gave the least mean number of leaves, while those grown with 15 t ha-1 of the amendment gave the highest number of leaves recorded.

3.1.5 Leaf Length

Leaf length measurements are presented in Fig 1e which significantly varies among treatments at 25% and 50% application rate. Leaf length ranged from 13.74 cm to 19.66 cm and CC at a rate of 25% produced leaves with the highest length (19.66 cm) (Figure 5). The lowest leaf length (13.74 cm) was found in control of the recommended dose. A study showed that the leaf length of spinach grown in the co-compost of spent coffee grounds and cat manure as amended soil was significantly greater than those grown in the chicken manure compost-amended soil (Keeflee et al., 2020). A study on yield and quality of leafy vegetables grown with organic fertilizers showed that vegetables grown with organic fertilizers grew better and resulted in a higher total yield than those grown with chemical fertilizers (Xu et al., 2005).

3.2 Plant Nutrient Status

3.2.1 N Content

The nitrogen (N) content of spinach plants was significantly affected by different fertilizers at rates of 25% and 50% but not significant at 0%. The highest N content was found in R 50% (0.90%) where the recommended dose of inorganic fertilizer was applied at 50%. The lowest N content (0.53%) was found in PL at 0% where poultry litter was applied at 0% (Figure 6). According to research in 2018, organic fertilizer can greatly increase the nitrogen content of spinach leaves (Hongdou et al., 2018). By promoting plant uptake of nitrogen and microbial immobilization, as well as reducing leaching and gaseous losses of nitrogen and increasing the retention of applied fertilizer N in the soil-plant system, compost made from fruit scraps, manure, and kitchen waste can also increase crop yields (Steiner et al., 2010).

3.2.2 P Content

The phosphorus (P) content result of spinach plants was shown in Figure 7 and P content was significantly affected by different compost rates. The highest P content was found by applying CC 25% and the value was 1.22 mg kg-1. The lowest P content value was found in the control and CC 0% (0.34 mg kg-1) (Figure 7).

3.2.3 K Content

The potassium (K) content of spinach plants was considerably influenced by the application of different rates of compost (Figure 8). The highest content was found in Co 25%, where co-compost of kitchen waste and poultry litter was applied at a rate of 25 % (Figure 8), and the value was 6.95%. Again, the lowest K content of spinach was found in PL 0% (3.15%), where poultry litter was applied at 0% (Figure 8).

3.2.4 S Content

The sulfur (S) content of spinach plants was significantly influenced by different compost rates under study (Figure 9). The highest S content was found by applying PL 50% and CC 50% and the value was 3.07 mg kg-1.The lowest S content value of spinach was found by applying R 50%where the recommended dose of inorganic fertilizer was 50% (1.60 mg kg-1)(Figure 9).

3.3 Soil Properties

3.3.1 Organic C

The highest organic carbon was found in the soil by applying PL 25% (2,379%) and the lowest in control (R 0%) (0.6435%). The organic carbon (OC) of soil was significantly influenced by applying various treatments at 25% and 50% rates but not significant at 0%. And the most marked by applying 25% PL and 50% CC (Figure 10). This trend can be due to the biodegradation and mineralization activities of the soil microflora, which intensify in response to applied organic matter (Schroder et al., 2008). Again, it was reported that the incorporation of compost into soil increased soil carbon (Mylavarapu and Zinati, 2009).

3.3.2 CEC

The cation exchange capacity (CEC) of the soil was considerably influenced by applying various treatments, yet not significant by 0% of all fertilizers, where significantly affected by 25% and 50% of all treatments. (Figure 11). Again, this property of soil was found best significant as well as highest by applying PL 50% (Figure 3b) and the value was (80.28 cmol kg-1). The lowest CEC was found by applying R at 25% (45.46 cmol kg-1). Since compost has a high cation exchange capacity, adding it to the soil can raise the soil CEC. Humic acids, which make up a large portion of compost, have carboxylic acid groups that can bind positively charged multivalent ions like Mg2+, Ca2+, Fe2+, and Fe3+ as well as trace metals like Cd2+ and Pb2+ (Pedra et al., 2008).

3.3.3 pH

The pH was found to be noticeably higher by applying CC 50% (8.27) than any other treatment, whereas the lowest by applying PL 50% (7.27). The pH of the soil is not significantly affected by applying a 0% rate of all fertilizer and is best significant by CC 50% (Figure 12). Researcher state that the effect of compost addition on soil pH is not well understood (Butler et al., 2008). However, the application of PL 50% results in a low pH because microorganisms produce organic acid. A study reported in 1994, the high pH in the other treatments was consistent with ammonium formation from protein degradation (Mahimaraja et al., 1994). The addition of basic cations, ammonification, and the creation of NH3 during the decomposition of the additional compost are the main causes of an increase in soil pH following the addition of compost made from poultry litter (Hubbard et al., 2008). The pH of soils modified with compost can rise as a result of the adsorption of H+ ions, the establishment of reducing conditions as a result of increased microbial activity, and the displacement of hydroxyls from sesquioxide surfaces by organic anions (Pocknee and Sumner, 1997).

3.3.4 EC

The maximum electrical conductivity (EC) value was found in the soil by applying PL 50% (6.56 dS m-1) and the lowest by applying R 100% (0.33 dS m-1). The EC of soil is significantly affected by applying PL and coa-compost but not significantly affected by applying the recommended dose (R) of inorganic fertilizer. (Figure 13).

3.3.5 Available N

The maximum water-soluble N% was found on the soil by applying CC 50% and the value was 5.74%. The minimum water-soluble N% was found on the soil by applying inorganic fertilizer at a recommended dose (R) of 50%and the value was 0.42%. Water-soluble N% of the soil was noticeably influenced by applying various treatments at 25% and 50% rates but not significant at 0% (Figure 14). The available N% of soil was made most significant by applying CC 50% (Figure 14).

3.3.6 Available P

Soil available P was considerably affected by various treatments at 25% and 50% rates but not significantly at 0%, and the most significant was applying 25% and 50% of CC (Figure 15). It was found to be highest when CC 25% was applied (Figure 15), with a value of 178.72 mg kg-1. The lowest available P of soil was observed in the control, where inorganic fertilizer was 0% (46.93 mg kg-1).

3.3.7 Available K

The maximum water-soluble K was found on the soil by applying CC at a rate of 25% and the value was 0.87%. The minimum water-soluble K % was found on the soil by applying inorganic fertilizer at the recommended dose of 100% and the value was 0.03%. Water-soluble K % of the soil was significantly influenced by applying various treatments at 25% and 50% rates but not significant at 0% (Figure 16).

3.3.8 Available S

Available S of soil was markedly influenced by different treatments at rates of 25% and 50% but not significant at 0% and the most significant was applying 25% co-compost (Fig 17). Moreover, it was found best by applying CC at 25% and the highest value was 1.03 mg kg-1. The lowest value of available soil S was found in PL 0% (0.06 mg kg-1) (Figure 17).

4.CONCLUSION

The result reveals that agronomic parameters such as plant height, leaf number, leaf length, fresh yield, and biological yield of spinach plants were significantly higher in most cases by applying co-compost of poultry litter and kitchen waste. Based on the number of leaves, fresh weight and biological yield, the application of co-compost of kitchen waste and poultry litter is best for use and it is recommended to use as a soil amendment for the growth of spinach. The best significance of organic C of soil by applying PL and other available nutrients like P, K, and S is significantly higher by applying Co 25%, but available N was higher by applying Co 50%. Soil pH, EC, and CEC were also significantly affected by different rates of compost and fertilizer. The N content of spinach plants was higher by applying the recommended dose of inorganic fertilizer. The highest K and P content was found by applying a co-compost of kitchen waste and poultry litter was applied, and the highest S content was found by applying PL and Co.

AUTHORS CONTRIBUTION

Author Jannaatul Nayeema conceptualized the study and prepared the initial draft of the manuscript. Author Joy Sarker analyzed the data and performed statistical evaluations with graphs. Author Md Sadiqul Amin and Dr. Khandoker Qudrata Kibria supervised the research guided the study and also reviewed the draft. Author Mst. Jannatul Ferdous and Rakib Hasan Mashuk collected and organized the data.

DISCLOSURES

The authors declare that they have no conflicts of interest regarding the publication of this paper.

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A study was conducted to assess the efficacy of vermicompost fertiliser in enhancing lentil crop productivity. The research, which also involved estimating genetic parameters, correlation, and conducting principal component analysis to identify key traits directly associated with seed yield improvement, was carried out at Abu Garash Farm, Faculty of Agricultural Engineering, Damascus University, during the agricultural season of 2023-2024. Six lentil varieties (Idlib 1, Idlib 2, Idlib 3, Idlib 4, Idlib 5, and Ebla 1) and two types of fertiliser (mineral and vermicompost) were compared using a randomised complete block design (RCBD) with four replicates. Statistical analysis revealed significant differences between the use of mineral fertiliser and vermicompost, with vermicompost application leading to notable increases in plant height, number of pods per plant, number of seeds per plant, seed yield per plant, and harvest index. Furthermore, the results of the phenotypic and genetic variance coefficients, heritability, expected genetic advance through selection, genotypic correlation, and principal component analysis suggested that the traits of the number of pods per plant and the number of seeds per plant could be reliable selection indicators for improving lentil seed yield. This is due to their high heritability, substantial genetic advance values, and genetic correlation with seed yield per plant, besides their inclusion in the first principal component, which explains the variation among lentil varieties.

Lentil (Lens culinaris Medik.) is considered one of the most important leguminous crops and ranks as the fourth largest leguminous crop cultivated globally, following common beans (Phaseolus vulgaris L.), chickpeas (Cicer arietinum L.), peas (Pisum sativum L.), and faba beans (Vicia faba L.) (Kaale et al., 2022; FAO, 2024). The global cultivated area for lentils is approximately 5,503,604 ha, with a total production of 66,558,278 tonnes and a yield of 12,094 kg.ha-1 (FAO, 2024). Lentils are cultivated in tropical, subtropical, and temperate regions (Nath et al., 2014). Lentils are among the earliest leguminous crops that originated in the Near East and later spread to Central Asia and the Mediterranean Basin (Zohary, 1999; Lev-Yadun et al., 2000). Today, lentils are considered an important winter leguminous crop in the Middle East, the Indian subcontinent, North America, North Africa, and West Asia (Stefaniak and McPhee, 2015). In the Syrian Arab Republic, the cultivated area for lentils is approximately 81,363 ha, with a production of 26,614 tonnes and a yield of 327 kg.ha-1 (SASG, 2022).

Excessive use of chemical fertilisers, pesticides and herbicides and minimal organic fertiliser use characterise traditional agriculture (Gill and Garg, 2014). To address such issues, the trend towards fertilising crops with organic fertilisers made from organic materials, which have immense potential to improve soil biodiversity and structure, represents an alternative strategy for sustainable and commercially viable agricultural production while reducing environmental pollution. Vermicompost is an alternative and frequently used technique in sustainable agriculture. Plants easily absorb rich in both macro- and micro-nutrients, vermicompost (Dominguez, 2004). The incorporation of vermicompost in agricultural practises offers various advantages, such as reducing the need for irrigation, bolstering resistance against pests, diminishing weed growth, promoting rapid seed germination, enhancing seedling growth and development, and ultimately increasing grain crop yield (Olle, 2019). Despite these benefits, the widespread adoption of vermicompost remains limited (Anonymous, 2009).

Parameters such as means, phenotypic and genotypic variances, heritability, genotypic correlation, and principal component analysis play pivotal roles in determining the effectiveness of breeding programmes (Sharma et al., 2020). The estimation of the genetic correlation coefficient indicates the extent of an association between two or more traits, whereas a significant correlation suggests the potential for the simultaneous improvement of traits that are meaningfully related. This improvement relies on genotypic correlation, additive genetic variance, and the heritability of these traits (Hayes et al., 1955).

Principal Component Analysis (PCA) is a technique employed to summarise and reduce data dimensions. The method transforms several interrelated variables into a substantially smaller set of independent variables known as principal components. These components are derived from the original variables, with their proportions and magnitudes varying according to the role and influence of each variable, aiming to describe as much of the information present in the original dataset as possible. PCA segregates total variance into components. Furthermore, PCA reflects the significance of the principal contributor to the total variance for each differentiation axis (Greenacre et al., 2022).

In Syria, crop production faces challenges from climate change and a notable increase in the costs of agricultural production inputs (such as water, seeds, mineral fertilisers, fuel, and pesticides), which has subsequently raised production costs for Syrian farmers. This situation has led to a reluctance among farmers to cultivate all arable land, in addition to facing marketing challenges for the crops generated (AL-Boush and Al-Ouda, 2021). The study of quantitative traits associated with seed yield and phenotypic expression under varying production conditions is critically important for scientifically guiding breeding programmes, ensuring genetic advancement in breeding and genetic improvement initiatives, and determining the optimal combination of varieties and agricultural inputs.

While research has explored vermicompost’s benefits and genetic variations in lentils separately, the combined impact of both remains understudied (Patidar et al., 2024; Singh and Lakhan, 2022; Ilyas et al., 2024; Pavithra et al., 2023; Akter et al., 2020; Chowdhury et al., 2019). This study aimed to assess the effectiveness of vermicompost in enhancing lentil productivity and analyse genetic variations, and to perform principal component analysis to identify key selection traits.

2. MATERIALS AND METHODS

2.1 Experimental Site

The field experiment was conducted at Abu Garash Farm, Faculty of Agricultural Engineering, Damascus University, during the winter of 2023-2024. At an elevation of approximately 743 m above sea level, the farm coordinates are approximately 33.537° north and 36.319° east. The area is situated in the fifth agricultural stabilisation zone, characterised by a precipitation rate of less than 200 mm, and it received an average annual rainfall of 187 mm during the study period (Table 1). The experimental field consisted of loamy clay soil with an even pH of 7.3 and a low content of 0.75% organic matter; therefore, it was necessary to add vermicompost fertiliser to improve the soil properties due to its high content of total nitrogen (Table, 2). Total nitrogen in both vermicompost and soil was estimated by the Kjeldahl method using concentrated sulphuric acid, mineral nitrogen in soil by the Kjeldahl method using potassium chloride, available phosphorus in soil and available potassium in soil (Jones, 1991; Keeney and Nelson, 1982; Olsen et al., 1954; Black, 1965).

*Om: Organic matter.

Source: Labour of the Soil Department, Faculty of Agricultural Engineering, Damascus University, and National Commission for Biotechnology, Damascus.

2.2 Experimental Design

The experiment was conducted using a randomised complete block design (RCBD). Six lentil varieties (Idlib 1, Idlib 2, Idlib 3, Idlib 4, Idlib 5, and Ebla 1), two types of fertiliser (mineral and vermicompost), and four replications were included in the study. Thus, 48 experimental plots were created with a distance of 0.75 m between each plot. Each plot consisted of two 3 m long lines with 0.25 m intervals between each line, and 0.05 m intervals between plants within each line. Mineral fertilisers were applied at the recommended doses (30:60:80 N.P.K. kg.ha⁻¹), based on the soil analysis of the study site and the guidelines of the Syrian General Commission for Scientific Agricultural Research. Nitrogen was administered in the form of urea (46%), phosphorus as triple superphosphate (46%), and potassium as potassium sulphate (46%). Vermicompost fertiliser was added at a rate of 20.6 m³.ha⁻¹ in accordance with the fertiliser recommendations approved by the Syrian General Commission for Scientific Agricultural Research (Al-Zoubi et al., 2022).

2.3 Investigated Traits

Observations were recorded from the 10 inner plants of each plot to assess various morphological and seed yield-related traits. The following data were collected: primary branches per plant (branches), plant height (cm), pods per plant (pods), seeds per plant (seeds), 100-seed weight (g), seed yield per plant (g), biological yield per plant (g), and harvest index (%).

2.4 Statistical and Genetic Analyses

ANOVA was performed for all traits according to the randomised complete block design (RCBD) to identify significant differences among varieties, fertilisers and (varieties × fertilisers) using statistical software Genstat. 12v (Gomez and Gomez, 1984). Heritability, genetic coefficient of variation, phenotypic coefficient of variation, and genetic advanced values were estimated using TNAUSTAT software. Genetic correlation analysis and principal component analysis (PCA) were performed using Meta-R (Alvarado et al., 2020). Data from both fertiliser treatments were combined to determine the extent of the association between studied traits.

Heritability in the broad sense (H2), genetic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), genetic advance (GAM), and genetic correlation were estimated according to Singh and Chaudhury (1985) as follows:

PCV and GCV values were divided into three levels, where 0-10% is low, 10-20% are moderate, and >20%, high.

H2 values were divided into three levels, where 0-30% is low, 30-60% is moderate, and greater than 60%, high.

Genetic advance (GA) was estimated at selection intensities of 5% and 10%:

Genetic advance in percentage was calculated using the following formula:

Genetic advance as a percentage mean was divided into three levels, where 0-10% is low, 10-20% is moderate, and >20% denoted high.

Genotypic correlation was calculated using the following formula:

Principal component analysis (PCA):

𝑌ij is the trait of interest, 𝜇𝜇 is the overall mean effect, Rep𝑖 is the effect of the ith replicate, Gen𝑗 is the effect of the jth genotype and 𝜀𝜀𝜀 is the effect of the error associated with the ith replication and jth genotype.

The angles between pairs of traits determine the relationship between traits in principal component analysis biplot (PCA). If the angle between two traits is less than 90º, their correlation is positive; if the angle equals 90º, the traits are uncorrelated; and if the angle exceeds 90º, a negative correlation exists between the two traits (Alvarado et al., 2020). The cosine of the angle (i.e., the value of the trigonometric function Cos) between two vectors representing two traits indicates the correlation coefficient between them.

3. RESULTS AND DISCUSSION

The results of the statistical analysis indicated the presence of significant differences (P ≤ 0.05) in all the studied traits among the varieties, fertiliser treatments and their interactions, except for the biological yield. plant-1 trait did not differ significantly after the addition of mineral fertiliser or vermicompost (Table 3).

*, ** Significant at 0.05 and 0.01 probability levels.

Overall, vermicompost fertilisation led to a noticeable increase in plant height (54.20 cm) compared with the general average of mineral fertiliser (43.70 cm), with the highest average plant height recorded in the Ebla 1 variety under the influence of vermicompost addition (65.14 cm) (Figure, 2). An increase in the number of pods per plant was observed when applying vermicompost (87.00 pods) compared with (49.70 pods) in mineral fertiliser treatment, with the Idlib 2 variety showing the highest average number of pods (174.25 pods) under the influence of vermicompost (Figure 3). Treatment with vermicompost outperformed mineral fertiliser treatment in terms of the number of seeds (78.30 seeds compared to 48.50 seeds). The variety Idlib2 contained the highest number of seeds per plant when added with vermicompost (180.10 seeds) (Figure, 4).

When comparing the average plant seed yield and harvest index, adding vermicompost led to a significant increase in both traits (2.50 g, 29.00% respectively) compared with the mineral fertiliser treatment (2.00 g, 26.20% respectively). The variety Idlib 2 (Figure, 6) achieved the highest plant seed yield (5.18 g) under the influence of adding vermicompost, which was equivalent to 4221 kg.ha-1 and the highest harvest index (39.00%) for the same treatment (Figure, 8). The increase in seed yield due to the addition of vermicompost can be attributed to its content of plant growth regulators, such as auxins, gibberellins, and cytokinins. These substances enhance the plant’s ability to absorb nutrients from the soil and use them optimally to generate a larger biomass, thereby increasing the amount of dry matter produced and facilitating the transfer of nutrients to seeds. This finding is consistent with that of previous studies (Patidar et al., 2024; Singh and Lakhan, 2022).

Estimation of the phenotypic coefficient of variability (PCV) and genotypic coefficient of variability (GCV) revealed slightly higher PCV values for most traits than GCV (Table, 4). Notably, high PCV values were observed for all traits, except plant height, which was moderate. Similarly, the GCV values were high for most traits, except for plant height and 100–seed weight, which were moderate (Table, 4).

Heritability in a broad sense (H2%) was high, accompanied by high expected genetic advance (GAM at 5% and 10%) values for primary branches, plant height, number of pods, number of seeds, seed yield, and harvest index% (Table, 4).

The seed yield per plant exhibited a positive and highly significant correlation with the number of seeds per plant (r = 0.99**), the number of pods per plant (r = 0.98**), and the biological yield per plant (r = 0.95**). In addition, it demonstrated a positive and significant correlation with the harvest index (r = 0.80*). Conversely, the biological yield per plant was positively and significantly correlated with both the number of seeds per plant (r = 0.95**) and the number of pods (r = 0.90**). Furthermore, the harvest index was positively and significantly correlated with the number of pods (r = 0.85*) and the number of seeds per plant (r = 0.80*) (Table, 5). This finding is consistent with that of previous studies (Akter et al., 2020; Chowdhury et al., 2019).

*, ** Significant at 0.05 and 0.01 probability levels.
The number of principal components for phenotypic variation among the varieties was found to be three based on the statistical analysis results (Figure, 9; Table, 6). However, only two principal components (PC1 and PC2) were used in the variation. The first principal component (PC1) accounts for 85.05% of the total variation, while the second principal component (PC2) accounts for 11.66% of the variation. Therefore, it can be concluded that the traits represented by the two principal components (PC1 and PC2) explain 96.71% of the variation among the varieties (Figure, 9). The first principal component includes the traits (number of pods. plant-1, number of seeds. plant-1, seed yield. plant-1, biological yield. plant-1, and harvest index%), while the second principal component includes the trait of the number of primary branches. plant-1 (Table, 6). It was also observed from (Figure, 9) that the number of pods, number of seeds, biological yield, and harvest index were positively and significantly correlated with seed yield per plant due to the acute angles between these traits and seed yield. These results are similar to those reported (Pavithra et al., 2023; Chowdhury et al., 2019).

4. CONCLUSION

Expand the application of vermicompost due to its positive effects on improving the seed yield of lentil crops under similar conditions and approve the Idlib2 variety with the addition of vermicompost at the appropriate rate after conducting soil analysis in the Damascus region. Relying on plant pod and seed number per plant trait as selective indicators of increased seed yield in lentil crops with high heritability (95.78%, 94.14% respectively) and genetic advance 10% (87.30%, 92.80% respectively). Additionally, these traits were genetically correlated with seed yield per plant (r = 0.99**, r = 0.98** respectively), and contribute to the overall variation among the lentil varieties because they were located in the first principal component.

FUNDING

This research was funded by Damascus University – funder No. 501100020595.

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Agrivoltaics, the co-location of agriculture and photovoltaic (PV) energy production, represents a promising approach to optimize land use efficiency and promote sustainable energy practices. This abstract provides an overview of agrivoltaics design, focusing on key principles and considerations in integrating solar panels with agricultural activities.The design of agrivoltaic systems aims to maximize the beneficial synergies between solar energy generation and agricultural productivity while minimizing potential conflicts. Key aspects include the selection of suitable crops that can thrive under varying levels of shade and microclimate changes induced by solar panels. Designers must balance factors such as solar panel orientation, spacing, and height to ensure optimal sunlight exposure for crops and efficient energy production. Technological advancements in PV panels and mounting systems play a crucial role in enhancing the feasibility and efficiency of agrivoltaic installations. Innovations in tracking systems, lightweight materials, and modular designs offer flexibility in adapting to different agricultural landscapes and climatic conditions. Furthermore, agrivoltaics design encompasses considerations beyond technical feasibility, including economic viability, environmental impact, and regulatory compliance. Integration with existing agricultural practices requires interdisciplinary collaboration among agronomists, engineers, economists, and policymakers to address challenges and optimize benefits. This paper given an idea of a basic design, load structure and different configuration of solar PV array used in agrivoltaics across Europe.

The photovoltaic technology has been used for transition from fossil fuel energy to non-fossil fuel energy. The UN Sustainable development goals (SDGs) stresses on use of affordable clean energy to combat climate change and its impact. Several advantages due to modularity, low cost, good lifespan and market growth has promoted solar photovoltaics to be used as a decentralized form system (Toledo and Scognamiglio, 2021). The large-scale PV farms in malaysia have been encouraged to setup for lower carbon emissions by 2050 (Ya’acob et al., 2023).

Currently, land-based PV farms are facing competition with food production for land allocation. Hence, innovative solutions that minimize land use are gaining importance. Examples include building integrated photovoltaics (BIPV) utilizing existing building surfaces, floating PV on water surfaces, and agrivoltaic systems (APV) that allow dual land use for food and energy production (Agrivoltaics, n.d.).

The concept of agrivoltaics i.e. fusion of solar PV with agriculture, has led land productive for farmer and also leads to income generation (Macknick et al., 2015; Chalgynbayeva, 2023). Agrivoltaics, a promising approach that integrates solar photovoltaic (PV) systems with agricultural practices, has emerged as a compelling strategy to maximize land use efficiency and resource utilization. This integration involves the co-located use of land for both solar energy generation and agricultural activities, offering dual benefits of renewable energy generation and crop cultivation. The design of agrivoltaic systems requires careful consideration of various factors, including optimal land management, crop selection, system configuration, and environmental impacts. By strategically placing solar panels above or alongside crops, agrivoltaics not only harness solar energy but also provide shading and microclimate control that can enhance crop productivity and water-use efficiency.

A study about the dynamic prototype of a photovoltaic greenhouse with an length of 3.17m and with of 2.14m (Moretti and Marucci, 2019). Researchers proposed to keep the structural heights about 2m and interrow distance between solar panel 3 times the height of the solar (Goetzberger and Zastrow, 1982). A study on AVS system which has height of 3.3 m, tilt of 32° with a spacing of 1m (Barron-Gafford et al., 2019). Researchers studied a prototype of a mono-crystalline mounted at a height of 4m as it is the first experimental pilot project, however, was installed in France, close to the southern city of Montpellier in the spring of 2010 (Dupraz et al., 2011)
The paper mainly discusses on the different design factors and how this factors influences the production of crops and electricity generation.

2. KEY COMPONENTS IN AGRVOLTAIC DESIGN

2.1 Different Solar PV Technologies

The basic agrivoltaics model was first proposed by Akira Nagashima in Japan. He termed it as solar sharing (Doedt et al., 2023). Monocrystalline panels have higher efficiencies. It can generate more electricity per square meter. Monocrystalline panels in agrivoltaics often require less space to generate the same amount of electricity as polycrystalline panels. This can allow for more flexible placement in agrivoltaic setups, potentially minimizing shading effects on crops or livestock. Monocrystalline panels typically perform better in high temperatures compared to polycrystalline panels, which can be advantageous in agricultural regions where temperatures can rise significantly. Polycrystalline panels generally have a lower cost per watt compared to monocrystalline panels. This cost difference may influence the economic feasibility of agrivoltaic projects, especially when considering large-scale installations of agrivoltaics. Polycrystalline panels often have a bluish hue as shown in figure 5b, while monocrystalline panels appear darker and more uniform. Depending on the visual impact desired in agricultural settings, one type of panel might be preferred over the other. Integrating CdTe as it has high modularity, thin and lightweight. CdTe modules can be designed to transmit certain wavelengths of light as seen in figure 1c. that are beneficial for plant growth while capturing others for electricity generation. This spectral selectivity can enhance crop photosynthesis and productivity. Even with the unique qualities of third-generation photovoltaic devices (such as their environmental effect, cheap manufacturing costs, variety of colors and transparency levels, and flexibility), stability and efficiency are still crucial elements that need to be enhanced to promote them as an alternative to PV technologies that have become solidified at the market levels, in contrast to silicon-based PV) and the latest advancements in the field.

2.2 Crops

The basic idea of crops being kept under solar panel is to effectively use the light saturation point and the temperature saturation point. The maximum light intensity at which a plant may achieve its highest rate of photosynthesis. Above this threshold, more light either doesn’t speed up photosynthesis or might even lead to photoinhibition. The amount of shade tolerance exhibited by different crops varies. The way that plants function in partially shaded environments, such agrivoltaic systems, is influenced by the saturation point of light levels. The C3 plants adapted to wet and moist temperature condition were photosynthesis rate increases to 20 μmol-2s-1around 25 °C leaf temperature, while the C4 plant adapted to dry and high temperature condition as the photosynthetic rate is optimum in between 30°C to 35°C. The fig 2. shows the operation of C3 and C4 plants with leaf temperature.

Agrivoltaic systems using different panel technologies as shown in table 1 employing monocrystalline and polycrystalline photovoltaic technologies offer varied benefits for different crops. Monocrystalline panels, with their high efficiency, are well-suited for crops like wheat and maize, potentially enhancing growth by reducing heat stress. In contrast, polycrystalline panels, which are more cost-effective, align well with rice and sorghum cultivation by providing beneficial partial shading and managing water use. The integration of Dye-Sensitized Solar Cells (DSSC) with polycrystalline panels further innovates the approach, particularly benefiting soybeans and sugarcane by optimizing light conditions and energy efficiency. For organic farming, while monocrystalline panels offer superior performance, polycrystalline panels provide a more economical choice, though their impact on soil and crop quality needs careful assessment. Overall, the choice of technology should balance efficiency, cost, and the specific needs of the crops to maximize both agricultural and energy outputs.

3. PERFORMANCE MATRICS

3.1 Land Equivalent Ratio

Since the APV system integrates PV modules with farmland, the impact of land use intensity on the system’s energy performance is a crucial factor in determining the overall feasibility of the solution. In this context, land use energy intensity can be quantified using metrics that measure land area per unit of energy generation (ha/kWh) and/or land area per unit of capacity (ha/kWp). Meanwhile, performance can be assessed as the amount of energy produced per unit of capacity over a typical or actual year (kWh/kWp/y), a standard metric used for solar systems. to evaluate the performance of the APV system, the authors recommend using the Land Equivalent Ratio (LER) indicator. This metric allows for a comparison between the traditional approach (separate PV and farming setups) and the integrated solution on the same land area (Chalgynbayeva et al., 2024). LER assesses whether the combined benefits of agricultural yield and solar energy are equal to or greater than those achieved through the separate use of the land.

3.2 Height of Module from Ground

The elevation of the systems above the ground (the space between the modules and the ground surface) is a crucial design factor. Higher structures, typically found in APV systems, can enhance the uniformity of radiation distribution beneath the PV modules, improve connectivity, and accommodate taller plants. Conversely, if the modules are positioned closer to the ground, the variability in radiation across crops within the same land area increases (excluding effects on surrounding areas). Complex factors contribute to the visual impact of PV systems on areas like recreation and tourism. Using higher mounting structures not only affects social acceptance but also significantly impacts installation costs and environmental consequences. In Germany, the additional expenses associated with elevating PV modules—including mounting, installation, and site preparation—are estimated to be more than double those for ground-mounted systems, rising from 0.3 EUR/kWp to 0.7 EUR/kWp (Trommsdorff, 2016).

The height of the system can also be an indicator of sustainability. Larger structures used to elevate the modules are associated with higher emissions. For instance, the LCA study reveals that an integrated PV parking lot (222 kWp) requires 72 tons of steel, resulting in 82 tons of CO2 emissions—eight times more than a conventional galvanized steel PV mounting system (Serrano et al., 2015). Additionally, in PV greenhouses (PVG), gutter height is a critical design factor as it positively impacts the total global radiation inside the greenhouse. Each additional meter of gutter height can increase the annual global radiation by 3.8% compared to a conventional greenhouse (Cossu et al., 2018). While higher APV systems can enhance solar energy collection for plants, the literature also highlights potential concerns regarding their ecological impact

3.3 Spatial Configuration between PV and Crops

A module’s height and spacing can be adjusted to cultivate different types of crops based on their light, humidity, temperature, and space needs. This allows for the identification of optimal growth zones. For ground-mounted PV installations combined with low-height crops, three distinct zones can be identified: zone 1 with low irradiance and high humidity, zone 2 with moderate light exposure and sufficient soil moisture, and zone 3 with the highest irradiation and lowest humidity (Schindele et al., 2012). Similarly, APV systems for orchards or grapevines require designs with tilt-mounted structures and PV modules placed at higher elevations to accommodate tree growth and allow farm machinery to pass underneath. Even in cases where there are no appreciable output losses, the presence of PV modules might affect quality attributes including fruit color, size, and sugar content. Tomatoes produced in a PV greenhouse with 9.8% PV coverage had smaller and less colorful fruits, according to Ureña et al. [68], but yield and cost were unaffected. In a similar vein, tomatoes with 50% PV coverage yielded inferior quality characteristics (Bulgari et al. 2015). Grapes planted in Korea under PV modules had decreased weight and sugar content (Cho et al., 2020) . This resulted in a harvest that was delayed by roughly 10 days, and the sugar levels were similar to those at the control location. On the other hand, certain species, such as strawberries, responded well in terms of yield and quality (increased chlorophyll content).

3.4 Orientation of Agrivoltaics System

The orientation of solar panels affects their exposure to sunlight throughout the day. In most cases, panels are tilted towards the south (in the northern hemisphere) to maximize exposure to sunlight, as this direction typically receives the most sunlight over the course of a day. The orientation influences how much shade is cast on the ground below. Proper orientation can minimize shading during critical growing periods, ensuring crops receive sufficient sunlight for photosynthesis. In terms of orientation of module they are oriented to south in landscape or portrait layout.

3.5 Tilt of the Agrivoltaics System

The tilt angle of an agrivoltaic system is a critical design factor that significantly influences both solar energy production and agricultural productivity. The optimal tilt angle is often determined by the latitude of the installation site, with a common practice being to set the angle equal to the latitude to maximize annual solar energy capture. Additionally, seasonal adjustments to the tilt angle can further enhance efficiency, with steeper angles in winter and shallower angles in summer. The tilt angle also impacts the shading patterns cast on the crops below, which can vary in intensity and distribution. Different crops have unique light requirements and tolerances to shading, necessitating tailored tilt angles to optimize growth. Moreover, the tilt angle can influence the microclimate beneath the panels, affecting temperature regulation and reducing heat stress on the crops during peak sunlight hours. Properly angled panels can also improve water management by directing rainwater more effectively toward the crops. Therefore, finding the right balance in tilt angle is essential for maximizing both solar energy production and agricultural yields, and ongoing research continues to refine these parameters for various crops and regions.

3.6 Heggelbach farm, Germany (APV-Resola Project)

One of the projects in which the height of the solar modules affected the solar PV design is the Heggelbach farm in Straßkirchen from APV-Resola project. This project integrated photovoltaics with crop farming, and the height of the modules was a critical factor in the system’s design and success.The agrivoltaics system covered about 0.3 hectares with an installed capacity of 194 kWp. The module height was kept at 5m. The height of 5 meters was selected to balance solar power generation and crop yield. This height allowed sufficient sunlight to reach crops while still providing shading during peak sunlight hours.The crops chosen for this project were wheat, potatoes, clover, and celery. Each crop responds differently to light and shading, and the height allowed for enough light penetration, especially for taller or shade-tolerant crops. The high placement minimized extreme shading, which can otherwise stunt the growth of some crops, allowing them to grow with less competition for light. The elevated modules enhanced airflow under the panels, reducing humidity and creating a cooler microclimate beneath the panels. This helped mitigate heat stress on crops during hot summers, contributing to better crop yields. The airflow also helped reduce the risk of fungal diseases that thrive in damp conditions, promoting healthier crop growth. By raising the modules 5 meters off the ground, farmers were able to access the fields with traditional farming equipment (tractors, plows, etc.). This was a significant advantage, as it meant that normal agricultural activities could continue without major changes. The crops chosen for this project were wheat, potatoes, clover, and celery. Each crop responds differently to light and shading, and the height allowed for enough light penetration, especially for taller or shade-tolerant crops. The high placement minimized extreme shading, which can otherwise stunt the growth of some crops, allowing them to grow with less competition for light. The elevated modules enhanced airflow under the panels, reducing humidity and creating a cooler microclimate beneath the panels. This helped mitigate heat stress on crops during hot summers, contributing to better crop yields.The airflow also helped reduce the risk of fungal diseases that thrive in damp conditions, promoting healthier crop growth. By raising the modules 5 meters off the ground, farmers were able to access the fields with traditional farming equipment (tractors, plows, etc.). This was a significant advantage, as it meant that normal agricultural activities could continue without major changes. The agrivoltaic system showed that, in some cases, crop yields (especially shade-tolerant crops like clover) increased under the panels due to reduced heat stress. However, crops like wheat experienced a slight reduction in yield due to reduced light levels. The land’s productivity improved, with an estimated increase of over 60% in combined land-use efficiency (agriculture + energy). The study highlighted that certain crops (e.g., potatoes, clover) perform better than others in agrivoltaic systems due to their adaptability to shading. The height of the modules plays a significant role in determining which crops can thrive.

4. CONCLUSIONS

Agrivoltaic systems represent a transformative approach to land use, merging the production of solar energy with agricultural activities. This dual-use model addresses the increasing competition for land between renewable energy projects and food production, offering a sustainable solution that maximizes the benefits of both sectors. Through careful consideration of design factors such as module height, tilt, orientation, and spatial configuration, agrivoltaic systems can be optimized to enhance both solar energy yield and agricultural productivity. Different solar PV technologies, including monocrystalline, polycrystalline, and innovative solutions like CdTe and DSSCs, offer varied benefits and challenges, influencing the efficiency and economic viability of agrivoltaic projects.The successful implementation of agrivoltaic systems depends on a deep understanding of crop-specific light and temperature requirements, ensuring that shading from PV panels supports rather than hinders crop growth. Performance metrics such as the Land Equivalent Ratio (LER) provide valuable insights into the efficiency of land use, highlighting the advantages of integrated systems over traditional, separate approaches. Additionally, the height and spacing of PV modules must be tailored to accommodate different crops and farming practices, from low-height vegetables to tall orchards and vineyards.

Ongoing research and development are essential to refine these systems, addressing ecological impacts and optimizing configurations for diverse agricultural environments. By leveraging the synergy between solar energy generation and agriculture, agrivoltaics offer a promising pathway to sustainable development, aligning with global goals for clean energy and climate action while supporting food security and rural economies. As the field advances, continued innovation and adaptation will be key to realizing the full potential of agrivoltaic systems worldwide.

DATA AVAILABILITY

The data used to support the findings of this study are available from the author upon request.

ACKNOWLEDGEMENT AND FUNDING STATEMENT

The authors are grateful for the financial support provided by the Universiti Malaysia Pahang Al Sultan Abdullah (www.umpsa.edu.my) through the Doctoral Research Scheme (DRS) to Mr Rittick Maity and Post Graduate Research Scheme (PGRS 230384).

CONFLICTS OF INTEREST

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr K Sudhakar reports financial support was provided by University of Malaysia Pahang Al-Sultan Abdullah. Dr K Sudhakar reports a relationship with University of Malaysia Pahang Al-Sultan Abdullah that includes: employment. Dr K Sudhakar reports a relationship with Elsevier Inc that includes: board membership. The corresponding author is serving on the Editorial board of Heliyon as Associate Editor If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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