Next Article in Journal
Identifying a Detoxifying Uridine Diphosphate Glucosyltransferase (UGT), MdUGT83K2, Which Can Glycosylate the Aryloxyphenoxypropionate Herbicide
Previous Article in Journal
Anthropogenic Contribution and Migration of Soil Heavy Metals in the Vicinity of Typical Highways
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 13
Issue 2
10.3390/agronomy13020305
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Variation for Traits Related to Phosphorus Use Efficiency in Vigna Species

by
Deepali Kothari
1,2,
Nirmala Pargaien
2,
Lalit Mohan Tewari
3,
Harsh Kumar Dikshit
1,*,
Gyan Prakash Mishra
1,
Muraleedhar S. Aski
1,
Ruchi Bansal
4,
Sanjeev Gupta
5,
Shiv Kumar
6 and
Ramakrishnan Madhavan Nair
7
1
Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
2
Department of Botany, MBGPG College, Haldwani 263139, India
3
Department of Botany, D.S.B. Campus, Kumaun University, Nainital 263002, India
4
Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
5
Crop Science Division, Indian Council for Agricultural Research, New Delhi 110001, India
6
International Centre for Agricultural Research in the Dry Areas (ICARDA), New Delhi 110012, India
7
World Vegetable Centre, South Asia/Central Asia, Patancheru, Hyderabad 502324, India
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(2), 305; https://doi.org/10.3390/agronomy13020305
Submission received: 7 December 2022 / Revised: 4 January 2023 / Accepted: 9 January 2023 / Published: 19 January 2023
Browse Figures
Versions Notes

Abstract

:
Phosphorus (P) is a major limiting nutrient reducing crop yields especially in weathered soils of the subtropics and tropics. P exhibits poor mobility and availability to plants in soil. To overcome P deficiency in soil, phosphatic fertilizers are added. Global phosphate rock reserves are finite, and the addition of phosphatic fertilizers is not financially and ecologically sustainable. Mungbean (Vigna radiata (L.) Wilczek) is important grain legume for nutritional security. Attempts are being made to develop mungbean varieties with better P-use efficiency through enhanced P uptake and utilization. In the present study, 327 accessions of 18 Vigna species were examined for inter- and intra-specific variation for traits related to phosphorus uptake and utilization efficiency under hydroponic conditions at two levels of phosphorus. Significant species-specific variation was recorded for studied traits. Among the studied Vigna species, mungbean exhibited higher phosphorus use efficiency. Seven mungbean genotypes (IC 251950, IC 585931, V1002532AG, IC 371653, IC 331615, V1001400AG, and V1000532BG) were found to be promising for both PupE and PutiE. Using mean and standard deviation as criteria, mungbean genotypes identified with high phosphorus-use efficiency include IC 25950 and IC 583664. Mungbean genotypes KPS 1546, IC 277060, IC 697141, IC 343440, and Pusa 0831 were identified based on the stress tolerance index as genotypes that performed better under P stress. Cultivated species revealed higher PUE in comparison withwild forms. The most promising genotype identified from this study for PUE can be used as a parent for the development of a mapping population of mungbean for understanding genetics of PUE under a low-phosphorus environment.

1. Introduction

More than 100 species are included in the genus Vigna. These species are acclimatized to wetlands, sandy beaches, deserts, acid soils, and limestone rocks in subtropical and tropical regions of the world and are valuable reservoirs of genes for biotic and abiotic stresses [1,2]. Only seven Vigna species are domesticated. Among Vigna species, mungbeans (V. radiata (L.) Wilczek], urdbeans (V. mungo (L.) Hepper) and cowpeas (V. unguiculata (L.) Walp.) are staple crops and ricebeans (V. umbellate (Thunb.) Ohwi& Ohashi), adzuki beans (V. angularis (Willd.) Ohwi& Ohashi), moth beans (V. aconitifolia (Jacq.) Marechal) and bambara groundnuts (V. subterranean (L.) Verdn.) are economically important in several regions of world. Vigna species are grown for seeds and are also used as cover, forage, and green manure. Mungbean is endemic to India and Central Asia. Globally, mungbean is grown in about 7 million hectares with a 5-million-tonproduction [3]. Mungbean seeds are rich in easily digestible proteins [4], vitamins, minerals, dietary fibres, and bioactive compounds [5]. Mungbean incites low flatulence in comparison to other grain legumes [6]. In India, mungbean is grown on marginal soils. In marginal lands, nutrient management is important to realize the potential yield of crop variety. Nitrogen and phosphorus are the major macronutrients for mungbean. About 18 kg of nitrogen per hectare is applied through fertilizers as a basal dose and the rest is fixed by rhizobium. Around 50 kg of P2O5 is required to produce one ton of mungbean from one hectare of land. The required P is provided by fertilizer application. Improvement in phosphorus uptake and utilization efficiency is required to enhance productivity. Crop wild relatives (CWR) are the valuable reservoirs of genes for tolerance/resistance to biotic and abiotic stresses [7].
Phosphorus (P) is essential for functioning, growth, and reproduction. Phospholipids, nucleic acids, phosphorylated proteins, and P esters are main pools of P in plants. P plays a role in phosphorylation/dephosphorylation, photosynthesis, energy production, and redox reactions. Crops absorb 15–30% of applied P in the year of its application [8]. There is scope for improving P-uptake efficiency. Increased P uptake will benefit yields; however, regular inputs are required to level the total P imported from the field. Phosphorus exists in an insoluble form in soil. P chelates with metal oxides in acidic soil and in alkaline soil with carbonate compounds, and also subsists as organic P [9]. Phosphorus deficiency is routine in agricultural lands. The global phosphate reserves attribute for 80–90% of P fertilizer production. The global phosphate reserves are based in Morocco, Russia, USA, and China [10]. These are finite, priceless, and non-replenishable. Increasing fertilizer cost and water pollution from the use of fertilizers is alarming [11]. The predicted food production/demand and consumption pattern requires efficacy of P use.
Globally, around 5.7 billion hectares of land is P-deficient [12]. A P concentration of <10 µm in soil solution is considered as below the critical level for optimal crop yield [13]. Root architecture is crucial for P acquisition by plants. Root architecture is flexible and responds to low P in soil for facilitating the P acquisition. At low P, primary root growth is reduced and lateral root growth and root hair development is enhanced [12,14]. The root system alteration is influenced by variation in localized P concentration and sensitivity of growth regulators such as auxin, ethylene, sugar, reactive oxygen species, cytokinins, nitric oxide, and abscisic acid. The changes in phytohormone concentration and composition are controlled by the expression of several genes [12]. Cellular, molecular, and physiological processes are triggered due to the activation of genes. At low P, a plant responds by altering its root system for better exploration of soil P, secreting organic acids and protons that chelate Ca2+, Al3+, and Fe3+ and solubilise insoluble Pi forms [15], evolving Pi and adenylate-independent glycolytic bypass enzymes that allow glycolytic flux [15], remobilizing Pi in the plant to meet the requirements of growing sink tissue [16], and increasing anthocyanin pigment [17].
Genetic variation for root architectural traits within and between crossable species can be exploited to improve phosphorus-use efficiency. In mungbean, root traits such asbiomass, root carboxylate exudation capacity area, and volume were significantly higher in phosphorus-efficient genotypes [18]. In urdbean, important contributors to phosphorus uptake were the number of lateral roots, root surface area, root length, and root volume [19]. It was reported that lupin genotypes with deep lateral roots and a large root system were phosphorus-use efficient [20]. In response to phosphorus deficiency in lentils, total root length, primary root length, and the number of lateral roots increased [21]. In soybean and common beans, lateral and adventitious roots were associated with a higher P uptake [22,23]. Root hair density and length increased in rice [24] under phosphorus deficiency. Root biomass and root length were reported as important traits in wheat under phosphorus stress [25]. The study on phosphorus uptake and use efficiency in field crops revealed a high phosphorus concentration in grain legumes as compared withcereals. Higher phosphorus-use efficiency in cereals was reported in comparison withlegumes [26]. In legumes, limited efforts have been made to evaluate wild species for traits related to PUE. In response to P deficiency, QTLs for PUE traits have been reported in several crops, such as chickpea [27], mungbean [28], groundnut [29], common bean [30], soybean [31], wheat [32,33], rice [34,35], and maize [36,37].
In marginal lands, P deficiency is a limitation for crop productivity, and in high-input ecologies, intensive fertilization results in environmental pollution. Phosphorus uptake- and utilization-efficient crops are essential for improving food security and reducing environmental impacts. Improved phosphorus-use efficiency (PUE) implies a reduced P requirement for a higher yield under suboptimal P availability. The topsoil forging ideotype is efficient in P uptake. Breeding PUE mungbean cultivars with optimal root system architecture is essential for genetic improvement of the crop cultivated in marginal soil. Phosphorus-use efficiency can be defined as the amount of yield or biomass produced per unit of P uptake. Improving PUE means enhancing P acquisition from soil and enhancingthe use of P, resulting in higher growth and assignment of biomass to the economic part of plant. The present study was conducted to evaluate different Vigna species for phosphorus-use-efficiency-related traits and identify PUE-efficient genotype (s).

2. Materials and Methods

2.1. Plant Material and Growing Condition

Three hundred and twenty-seven Vigna genotypes, includingeighteen species/subspecies (Vigna radiata var. bourneae Gamble, V. pilosa (Willd.) Baker, V. minima (Roxb.) Ohwi & H.Ohashi, V. dalzelliana (Kuntze) Verdc., V. hainiana Babu, Gopin. & S.K.Sharma, V. mungo (L.) Hepper, V. mungo var. silvestris Lukoki, Marechal & Otoul V. trilobata (L.) Verdc., V. trinervia var. bourneae (Gamble) Tateishi & Maxted, V. membranaceae A. Rich., V. umbelleta (Thunb.) Ohwi & H.Ohashi, V. unguiculata (L.) Walp., V. unguiculata ssp. sesquipedalis (L.) Verdc., V. racemosa (G.Don) Hutch. & Dalziel ex Baker f., V. radiata var. sublobata (Roxb.) Verdc., V. radiata var. setulosa (Dalzell) Ohwi & H. Ohashi, V. radiata (L.) R. Wilczek and unknown V. species) were characterized for PUE-related traits under optimum-phosphorus (OP) and low-phosphorus (LP) conditions. The characterization was carried out at the Indian Agricultural Research Institute, New Delhi, India in the National Initiative on Climate Resilient Agriculture (NICRA)-controlled environment facility from June to November 2021. New Delhi is in the North-West Plain Zone; 28° 400″ N 77° 120″ E, 218 m above sea level [masl]. The day and night temperatures were maintained as 30 and 18 °C, respectively, the photoperiod duration was 12 h, and relative humidity was 90% in the greenhouse. The mungbean seeds were surface-sterilized with 0.1% (w/v) HgCl2. The seeds were immersed in HgCl2 for 3 min and rinsed twice with double-distilled water. The seeds of the wild Vigna species were scarified using a blade, to ensure seed germination. The seeds were wrapped in distilled water-soaked germination paper, placed in a plastic tray containing 1/10 volume water, and kept in the dark for seed germination. Upon the emergence of cotyledonary leaves (8 days after sowing), uniform seedlings were identified and transferred to Hoagland solution. The Hoagland solution included macronutrients K2SO4 (0.92 mM), Fe-EDTA (0.04 mM), MgSO4 (1 mM), CaCl2·2H2O (0.75 mM), Urea (5 mM)] and micronutrients [ZnSO4 (0.6 μM), H3BO3 (2.4 μM), CuSO4 (0.62μM), MnSO4 (0.9 μM), and Na2MoO4 (0.6 μM)] [38]. The studied genotypes were screened at two levels of phosphorus optimum P (250 μM) and low P (3 μM) using KH2PO4 as reported by Reddy et al. [39]. The pH of the solution was maintained at 6.0 with 1 M HCL or 1 M KOH. The Hoagland solution was aerated using an aquarium pump and replaced after 48 h. The seedlings were raised in plastic trays of size 30 cm × 45 cm × 15 cm with a capacity of 10 liters. A thermocol sheet of 2 thickness was used to cover the trays and support the seedlings by maintaining spacing of 5 cm × 5 cm. Randomized complete block design was followed with 3 replications.

2.2. Trait Measurement

On twenty-one-day-old seedlings, the root and shoot properties of the genotypes under study were evaluated. The roots and shoots were separated, and the root system was placed out on an acrylic tray for scanning. The Epson Perfect V700 Pro scanner was used to scan the roots (Seiko Epson, Suwa, Japan). The gray scale images in TIFF format were studied with WinRHIZO Pro 2016a software. The observations on the roots were based on image analysis (primary root length (PRL, cm), total root length (TRL, cm), total surface area (TSA, cm2), average root diameter (ARD, cm), total root volume (TRV, cm3), total root tips (TRT, cm), and root fork (RF). The clean root and shoot were dried in an oven for 48 h at 65 °C for the calculation of shoot dry weight (SDW, g per plant), root dry weight (RDW, g per plant), total dry weight (TDW, g per plant), and root-to-shoot ratio (RSR, g per g). 4.3.

2.3. Estimation of Phosphorus Concentration

The phosphorus was estimated following the method from Murphy and Reley [40]. Grounded samples (0.1 gm) of each of the 327 Vigna genotypes were digested with a 10 mL di-acid mixture (HNO3:HClO4, 9:4) and a volume that was made up to 50 mL. The digested samples were filtered using Whatman No. 42 filter paper. The supernatant samples were run on an inductively coupled plasma optical emission spectrometer (ICP-OES; model 5110, Agilent Technologies (Santa Clara, CA, USA) calibrated using the standard for measuring the absorbance of the blue-colored phosphor-molybdate complex at 660 nm [40]. The results of the four replications were averaged, and the P concentration was expressed as mg g−1 dry weight. The phosphorus uptake and utilization efficiency were calculated as per Irfan et al. and Neto et al. [41,42].
PupE (mg per plant) = P concentration (mg per mg) × dry matter (mg per plant)
PutiE (%) = TDW (LP)/TDW (SP) × 100

2.4. Estimation of Phosphorus-Use Efficiency

The PUE of the studied Vigna genotypes was measured using two different methods. In the first method, suggested by Osborne and Rengal and Aziz et al. [43,44], the population mean and standard deviation were used to characterize the genotypes as efficient (>µ + SD), medium (µ + SD to µ − SD) and inefficient (<µ − SD). The second method computes the PUE stress tolerance score (STC) for each genotype [45,46] using the formula:
(STC) = SSI + MPI + GMPI + HMI + STI + TI + SI
Here, the stress susceptibility index (SSI) = (1 − T/C)/(1 − xT/xC), mean productivity index (MPI) = (C + T)/2, geometric mean productivity index (GMPI) = √ C × T √ C, harmonic mean index (HMI) = 2 (C × T)/(C + T), stress tolerance index (STI) = (C × T)/(xC) 2, tolerance index (TI) = C − T, and stress index (SI) = T/C
Here, C represents the total dry mass under control conditions and T represents the total dry mass under treatment conditions. xC and xT represent the average total dry mass (TDM) of all studied Vigna genotypes under OP (control) and LP (treatment) conditions, respectively.

2.5. Statistical Analysis

The data obtained was subjected to analysis for mean, standard deviation, coefficient of variation, analysis of variance, and Pearson correlation coefficient susing the STAR (Statistical Tool for Agricultural Research) 2.1.0 software under both the OP and LP condition [47]. The broad-sense heritability under the OP and LP condition and combined and variance due to genotype × P level interaction were measured as the following [48]:
Broad-sense heritability H = σ2G/(σ2 G+σ2 e/r)
Combined heritability Hcom = σ2G/(σ2G+(σ2GE)/e) + (σ2e)/re))
where σ2G, σ2GE, and σ2 e are genotypic variance, variance due to genotype × P level interaction, and error variance, respectively; ‘r’ is the replication.
To find the common trends among the multidimensional data sets, principal component analysis was used. With the aid of the R software package “FactoMineR”, the principal component analysis (PCA) was carried out for several root and shoot attributes utilizing relative values under LP to identify the traits that contributed the most [49].

3. Results

3.1. Descriptive Statistics of Measured Traits

The descriptive statistics of 14measured traits related to PUE among the 327 studied Vigna genotypes grown in OP and LP conditions are presented in Table 1. The mean values of TRL, TSA, TRT, SDW, RF, TDW, PC, and PUpE were higher in the OP condition compared to the LP condition. PRL, ARD, TRV, RSR, and PUtiE were higher under LP conditions. The highest percentage reduction was noticed for PUpE (−78.62) followed by percentage P (−72.13) under the LP condition compared to OP condition. Whereas, the highest gain was noticed for PUtE (403.57) followed by RSR (50.00) under the LP condition. Among the studied genotypes, the PUpE ranged from 4.75 to 0.06 mg P/plant and 1.74–0.00 mg P/plant under OP and LP conditions, respectively, whereas PUtE ranged from 2.95 to 0.07 g dry mass/mg P and 31.25 to 0.20 g dry mass/mg P under OP and LP conditions, respectively, among the tested genotypes. The analysis of variance of the measured traits (except ARD under the OP condition) revealed significant variation among the genotypes studied in the experiment (Table 2). Combined analysis indicated a significant interaction between the genotype and P treatment. High broad-sense heritability was recorded for all the studied traits under the OP and LP condition (Table 2).

3.2. Variation of PUE Traits in Studied Vigna Species

A total of 327 accessions of 18 Vigna species were characterized for 14traits related to phosphorus-use efficiency (PUE). The trait-wise performance of different Vigna species is presented in Figure 1.
Primary root length: Eleven species/subspecies of Vigna exhibited an increase in primary root length in theLP condition (Figure 1). A high increase was recorded in Vigna unguiculata, Vigna unguiculata ssp. Sesquipedalis, Vigna racemosa, and wild Vigna. The maximum increase was recorded in Vigna unguiculata. The remaining seven species/subspecies exhibited a decline in primary root length in theLP condition. The maximum reduction in primary root length in theLP condition was recorded for Vigna pilosa.
Total root length: Twelve species/subspecies of Vigna exhibited an increase in total root length. The highest increase was recorded in Vigna unguiculata, followed by Vigna unguiculata ssp. sesquipedalis, Vigna racemosa, and wild Vigna. Vigna pilosa exhibited the maximum reduction in TRL in theLP conditions.
Total surface area: The maximum total surface area was recorded for Vigna unguiculata. This species exhibited an increase in TSA under the LP condition. Twelve species/subspecies exhibited an increase in TSA under the LP condition. Vigna radiata recorded a decrease in TSA in theLP condition and Vigna mungo revealed an increase in TSA in theLP condition.
Average root diameter: Only three studied species/subspecies, Vigna pilosa, Vigna radiata var. setulosa, and Vigna radiata, exhibited an increase in ARD under theLP condition. The remaining fifteen species/subspecies exhibited a reduction in ARD in theLP condition.
Total root volume: Three species/subspecies, Vigna pilosa, Vigna racemosa and unknown wild Vigna, exhibited a reduction in TRV under theLP condition. Fifteen species/subspecies revealed an increase in TRV in theLP condition. The maximum increase in TRV in theLP condition was recorded for Vigna unguiculata and Vigna unguiculata ssp. sesquipedalis.
Total root tips: The total root tips increased in 11 species/subspecies in response to low phosphorus. The maximum increase was recorded in Vigna unguiculata ssp. sesquipedalis, followed by Vigna unguiculata and Vigna membranaceae. Among the studied species, the highest TRT was recorded for Vigna membranaceae under both the LP and OP condition.
Root shoot ratio: Vigna pilosa, Vigna mungo var. silvestris, and Vigna radiata var. sublobata exhibited a decline in RSR under theLP condition. The otherfifteen species/subspecies showed enhanced RSR in theLP condition. Vigna mungo var. silvestris is a progenitor of cultivated urdbean, and Vigna radiata var. sublobata is a progenitor of cultivated mungbean.
Root forks: Root forks increased in theLP condition in Vigna pilosa, Vigna hainiana, Vigna mungo var. silvestris, Vigna trilobata, Vigna trinervia ssp. bourneae, Vigna unguiculata, Vigna unguiculata ssp. sesquipedalis, and Vigna racemosa. The maximum decline in RF was recorded for Vigna radiata var. setulosa in theLP condition.
Root dry weight: Ten Vigna species/subspecies exhibited a higher dry root weight in theLP condition. Avery high increase was found in Vigna unguiculata, Vigna unguiculata ssp. sesquipedalis, and wild Vigna.
Shoot dry weight: Vigna hainiana and Vigna unguiculata ssp. sesquipedalis exhibited a minor improvement in SDW under the LP condition. The remaining sixteen species revealed a decline in SDW in theLP condition.
Total dry weight: Sixteen species recorded a decrease in TDW under the LP condition. However, Vigna hainiana and Vigna unguiculata ssp. sesquipedalis exhibited an increase in TDW under the LP condition.
Phosphorus (%): Phosphorus (%) increased under LP conditions in Vigna mungo and Vigna trinervia ssp. bourneae and reduced significantly in the other sixteen studied species/subspecies.
Phosphorus uptake efficiency:Phosphorus uptake efficiency declined in fourteen species/subspecies in theLP condition. However, PUpE increased in the LP condition in Vigna pilosa, Vigna hainiana, Vigna mungo, and Vigna trinervia var. bourneae.
Phosphorus utilization efficiency: The maximum PUtE was recorded for Vigna mungo var. silvestris under both the conditions, followed by Vigna radiata var. bourneae. Nine species/subspecies revealed a decrease in PUtE in the OP/LP conditions.

3.3. Correlation Coefficients between Measured Traits

The correlation coefficients among the studied traits are presented in Table 3. Under low-P conditions, primary root length was correlated with total root length, total surface area, total root volume, total root tips, root forks, root dry weight, root-to-shoot ratio, P (%) and phosphorus uptake efficiency. Total root volume and total root tips were correlated with each other and correlated to root forks, shoot dry weight, root dry weight, total dry weight, root-to-shoot ratio, P (%) and phosphorus uptake efficiency. Total root length was correlated with total surface area, total root volume, total root tips, root forks, root dry weight, root-to-shoot ratio, P (%) and phosphorus uptake efficiency. Total surface area was positively correlated with total root volume, total root tips, root forks, root dry weight, root-to-shoot ratio, P (%) and phosphorus uptake efficiency. Average root diameter was correlated to total root volume, root forks, shoot dry weight, root dry weight, and phosphorus uptake efficiency. Root forks were correlated to shoot dry weight, root dry weight, total dry weight, and phosphorus uptake efficiency. Seed dry weight was correlated to root dry weight, total dry weight, and phosphorus uptake efficiency. Root dry weight was correlated to total dry weight, root-to-shoot ratio, phosphorus utilization efficiency, and phosphorus uptake efficiency. Total dry weight was correlated to phosphorus uptake efficiency and phosphorus utilization efficiency. Root-to-shoot ratio was correlated to P (%) and phosphorus uptake efficiency.
Under optimum-P conditions, phosphorus utilization efficiency was correlated to average root diameter, total root volume, root forks, shoot dry weight, root dry weight and root-to-shoot ratio. Similarly, phosphorus uptake efficiency was correlated to primary root length, total root length, total surface area, total root volume, total root tips, root forks, shoot dry weight, total dry weight, root dry weight, and P (%). P (%) was correlated to primary root length, total root length, total surface area and total root tips. Root-to-shoot ratio was correlated to primary root length, total root length, total surface area, and root dry weight. Total dry weight was correlated to total root length, total surface area, average root diameter, total root volume, total root tips, root forks, seed dry weight, and root dry weight. Root dry weight was correlated to total root length, total surface area, average root diameter, total root volume, total root tips, root forks, and shoot dry weight. Shoot dry weight was correlated to total root length, total surface area, average root diameter, total root volume, total root tips and root forks. Root forks were correlated to primary root length, total root length, total surface area, total root volume, and total root tips. Total root tips were correlated to primary root length, total root length, total surface area, and total root volume. Total root volume was correlated to primary root length, total root length, total surface area, and average root diameter. Total surface area was correlated to primary root length and total root length.Total root length was correlated to primary root length.
The PCA for all 14 root traits recorded 59% of the variation in the first two PCA axes, with the first component capturing 33.9% of the variation under the LP treatment conditions (Figure 2),while the treatment OP contributed 65.9% of the total variation through the first two components, and the first PCA contributed 37.5% of the total variation. The graph above also shows the relationships between all 14 variables at once. Variables that contribute similar information are grouped together or correlated. RDW and TRV are two variables that are positively correlated when subjected to the LP treatment. The same pattern can be seen with TRT, TRL, and TSA in the OP treatment. The trait P uptake was constant and did not change its position under both the treatments, while ARD and P utilization pattern differed in the LP and OP treatments.
In the case of the LP treatment, the scree plot indicated that 4major PCs explained about 83% of the total variation. Figure 3 the remaining minor PCs explained about 17% of the variation. However, under the OP treatment, the first 3major PCs explained about 76.5% of the total variation. The remaining minor PCs explained about 23.5% of the variation.

3.4. Identification of Superior Genotypes for Important PUE Traits in Vigna Species

A total of 10superior genotypes were identified for key traits contributing to PUE in Vigna species, based on the characterization of 327 Vigna accessions (Table 4). A total of 9genotypes, IC 343440, V1002190BG, V1001162AG, V1003959BG, V1002872BG, V1001066BG, V1002672AG, V1001698BG, and V1000470AG, were in the top 10 genotypes for TDW and SDW. Among these genotypes, V1002190BG, IC 343440,and IC 273244 possessed a higher RDW. Seven genotypes (IC 251950, IC 585931, V1002532AG, IC 371653, IC 331615, V1001400AG, and V1000532BG) exhibited both high PupE and PutiE.

3.5. Categorization of Vigna Genotypes for PUE

Based on the standard deviation and mean, the studied genotypes were characterized as efficient, medium, or inefficient [42,43]. The 327 studied Vigna genotypes exhibited differences in the studied traits in both low-phosphorus and optimum-phosphorus conditions (Table 4). Under the LP condition, genotype V1003948B-BR (V. radiata) recorded the highest score (13/15), and IC 25950 (V. radiate bourneae), IC 58367 (V. dazelliana), and V 1001066BG (V. radiata) recorded the highest score (13/15) in the OP condition. The lowest score of 8 was recorded by 27 genotypes in the LP condition and 9 by 40genotypes inthe OP condition. The overall performance of the studied Vigna genotypes identified IC 25950 and IC 583664 as the best genotypes with the highest score of 25/30 obtained by summing up the score at both P levels (Table 5). A total of 23 genotypes (IC 251950, IC 277000, IC 305153, IC 202643, IC 259502, IC 371653, IC 248206, IC 585931, IC 251419, IC 583670, IC 583662, IC 331450, IC 331615, IC 331452, IC 583665, V1001339AG, V1000532BG, V1001400AG, V1004734AG, V1001806BG, andV1002532AG) exhibited an overall score of 24 (efficient) based on RDW, SDW, RSR, TPU, and PUtE. The poor genotypic performance of the genotypes is due to low P uptake resulting in low root and shoot biomass production.
In the second method [45,46], the stress tolerance score STS was calculated using seven P deficiency tolerance indices of 327 genotypes (Table 6). Among the studied genotypes, the highest STS score was recorded by EESM 18-163 (18.85), followed by Pusa 1431 (17.703) and SM 18–21 (14.28).

4. Discussion

The present study characterized a panel of 327 Vigna genotypes from 18 Vigna species for different root and shoot traits at contrasting P levels under controlled conditions. The purpose of this study was to identify superior Vigna species/genotypes with a significant P-use efficiency. Phosphorus-use efficiency (PUE) can be enhanced by improving both uptake and utilization efficiencies of crop plants [50]. Root architectural traits are critical for nutrient and water uptake from the soil. In mungbean, limited studies have been conductedon root traits [28]. Studies on root traits of other Vigna species are lacking. Variation inroot traits is essential for enhancing PUE. Under LP conditions, the genotypes with a higher root length and density exhibit enhanced P uptake [51]. In the present study, 327 genotypes from 18 Vigna species were screened for 14 traits related to phosphorus-use efficiency in Vigna species. Significant variationsinstudied traits with high heritability and significant correlations were recorded at different P levels. These variations can be attributed to the genetic variations among the studied Vigna species for PUE traits. Similar results have been reported in mungbean [39], lentil [52], common beans [52], maize [53], and Brassica [54].
For the variables under study, a substantial interaction between genotypes and P treatment was noted. The study also showed substantial differences between the several Vigna species under LP conditions for traits with strong heritability, suggesting potential genetic gain through selection. In comparison to the OP condition, the mean values of PRL, ARD, TRV, RSR, and PUtiE were greater under LP conditions. According to earlier reports by [55] for rice and [33] for wheat, PUtE was greater under the LP condition.
The higher biomass produced per unit of P uptake and P harvest index contribute to the higher PUtE [56]. Shoot dry mass drops while RSR increases under LP conditions. Under LP conditions, plants’ adaptive strategy is to increase RSR. The results were in agreement with earlier research by [57].
Higher TRV, RSA, and carbon exudation efficiency were found in mungbean P-efficient genotypes in a prior study [58]. In the case of rice, a rise in RSR and RDW was correlated with a reduction in SDW [59].Between PRL and TRL, TSA, TRV, TRT, RF, RDW, RSR, P, and PUpE, there is a strong positive association. Under LP conditions, TRL was favourably connected with TRV and TSA in maize [60], and with TRT and RF in mungbean [19]. RF, SDW, RDW, TWD, and PUpE were all linked with ARD in this study. Nevertheless, a prior study on chickpea [61] found no significant association between ARD and the majority of characteristics. Under both the LP and OP conditions, SDW was associated with RDW and TDW.
Under the OP condition, RDW was positively correlated to TDW, and in the LP condition, RDW was correlated to TDW and SDW. PCA revealed that TSA, TRL, and TRT contributed the most for total variation. In a previous study on mungbean, it was reported that the LP condition alters TSA, TRT, TRL, and SDW [39], and TSA and TRL in common beans [52].
Positive correlations between PUpE and biomass attributes and TPU and root traits, PRL, TRL, TSA, TRV, and RSR were seen under LP circumstances. RDW and TDW had a favourable correlation with PUtE. The study found a substantial and favourable association between biomass attributes and TPU. This characteristic may result from increased cytokinin production, which would increase biomass partitioning [62]. Total P uptake in cotton showed a favourable and substantial connection with plant roots and shoots dry mass [63]. Additionally, P uptake is benefiting leaf photosynthetic and transpiration rates [64]. In cotton, Singh et al. (2013) [65] observed that P deficiency drastically impairs photosynthetic characteristics and reduces plant development. To understand how leaf photosynthetic features affect P transport to the root surface and, ultimately, P uptake, it is possible to study their effects under low-P conditions. The success of plant breeding programmes mostly depends on the selection of genotypes that perform well under both stressful and non-stressful conditions.
The enhancement of PUE in upcoming breeding projects requires further genotype categorization. For the classification and selection of effective genotypes for PUE in wheat [66,67,68], a variety of techniques and attributes were applied. Two procedures were used in this investigation to choose P-efficient genotype(s). In the first procedure, the genotypes were divided into efficient, medium, and inefficient kinds based on the attributes RDM, SDM, RSR, TPU, and PUtE. In order to find the efficient genotypes in wheat [69] and brassica [70], a similar classification method was applied. However, under sufficient P conditions, the efficient genotype under LP conditions was unable to achieve the same type of performance [71,72]. The genotypes that perform better at various P levels are able to thrive in soils with a range of P conditions [51]. Therefore, it is necessary to classify genotypes under both LP and OP situations. Both LP and OP conditions were used to categorize genotypes, and the scores for each genotype were added up to obtain the overall genotype score. The genotype IC 25950 and IC 583664 showed a higher root, shoot biomass, and P uptake with a cumulative score of 25 out of 30, combining both control and stress conditions. Whereas, the genotypes IC 282094 and M1443 recorded the lowest score of 15 out of 30, combining both P conditions. The low root, shoot biomass output, and total P uptake of genotypes under normal and low P circumstances are the key causes of the genotypes’ poor performance. The performance of genotypes supported the claim that biomass and P absorption efficiency features have a linear relationship.
Based on P efficiency and responsiveness, the mungbean genotypes were divided into four categories, namely efficient responsive(ER),efficient non-responsive (ENR), inefficient responsive (IR), and inefficient non-responsive (INR), in accordance with the categorization approach suggested by [69,73]. Under both P circumstances, the most productive genotypes, PUSA 1333 and Pusa Ratna, fell into the ENR group. The genotypes ML 1666 and V6183, which were put in the ER category under the LP condition, were moved to the INR condition. This supports the idea that classification is important both at normal and low P levels [39]. The genotypes categorized under the ER category had good soil P-level adaptation. However, cultivars belonging to the ENR group could thrive in soils with low levels of P. The crossing programme may use the genotypes of the IR category to incorporate P-responsive characteristics. However, the PUE improvement programme does not significantly depend on the genotypes of the INR group [74]. This categorization method makes it possible to choose genotypes that are suitable for a variety of cultivation at different P levels [41]. However, the basic foundation of this strategy is the population mean. It therefore has a fairly limited range between types that are responsive or nonresponsive as well as efficient or wasteful [75]. For instance, the genotypes M 512, ML 818, Muskan, and RMG 1028 under the NP condition, and M 1316, M 961, MH 810, Muskan, and PUSA 1132 under the LP condition, were located along the boundary between the efficient and inefficient groups [39]. As a result, it is challenging to categorize genotypes as efficient or inefficient and responsive or nonresponsive given their low divergence from the population mean. Therefore, this method is not appropriate for investigating and classifying genotypes on a broad scale [41,69].
In order to categorize genotypes [46,76], stress tolerance indices were calculated for the dry mass of genotypes under control and stress circumstances. The P deficiency tolerance indices of all genotypes were determined in the current investigation using TDM under both OP and LP circumstances. The susceptibility indices SSI, TI, and SI tend to distinguish between genotypes that are stress-tolerant and susceptible to stress, and have a negative connection with yield and biomass [77]. While the indices MPI, GMPI, and STI can be used to identify genotypes with a high average yield/biomass and stress tolerance and demonstrate a positive connection with yield/biomass [78]. Based on PCA analysis, it is evident from the current study that two indices, MPI and SSI, were able to account for the majority of the variation among the analyzed indices. According to [79], the most effective indices for locating stress-tolerant genotypes under both control and stressful circumstances are the GMPI, MPI, and STI. The stress-tolerant genotypes with high yields/biomass under both control and stress conditions, however, were not detectable by any of these tolerance and susceptible indices [80]. In order to find the genotypes that are more tolerant to stress, [76] propose combining the susceptibility and tolerance indices as a relevant criterion. In the prior investigations, the genotype PUSA 1333 had the highest STS score, demonstrating its strong efficacy under various P circumstances. In the present study, genotype EESM 18-163 had the highest STC score.
Drought-tolerant genotypes of wheat [46], pearl millet [45], and sorghum were chosen using the classification of genotypes based on stress tolerance indices. A robust criterion for classifying genotypes with high productivity, resilience, and a clear visualization of contrast in genotypes for biomass under stress conditions is provided by this new selection technique. This research offers fresh perspectives on genotype selection and a deeper comprehension of genotype behaviour under phosphorus stress.

5. Conclusions

The identification and creation of more P-efficient genotypes in the mungbean breeding programme would depend on the categorization of the existing germplasm. Under optimum- and low-P conditions, the study found a significant variation in genotypes for the attributes RDM, SDM, TDM, PC, TPU, and PUtE. To create P-efficient genotypes, it is possible to take advantage of the strong interactions that exist between genotypes and P treatments. The characteristics and techniques used for classifying genotypes under optimum- and low-phosphorus conditions influence the P efficiency of the genotypes in different ways. The classification of 327 Vigna genotypes using the study’s methodology showed that the optimum technique for analyzing the slight variation in genotype P efficiency is to first divide them based on efficiency, then distribute them into different groups. The best way to depict the contrast in genotypes in terms of biomass output and resistance under low-P conditions is to categorise them based on their score for stress tolerance. Under optimum and low-P conditions, seven genotypes (IC 251950, IC 585931, V1002532AG, IC 371653, IC 331615, V1001400AG, and V1000532BG) of Vigna radiata (L.) Wilczek were found promising for both PupE and PutiE. Using the mean and standard deviation as criteria, Vigna radiata (L.) Wilczek genotypes with high phosphorus utilization efficiency were found as IC 25950 and IC 583664. KPS 1546, IC 277060, IC 697141, IC 343440, and Pusa 0831 were selected by characterization based on the stress tolerance index as genotypes that performed better under P stress. The effective genotype IC 343440 performed better in terms of dry mass production and P absorption efficiency. Additionally, the characteristics RDM, SDM, TDM, TPU, and PUtE played a significant role in classifying the genotypes for PUE in mungbean. The tested genotypes should, nonetheless, undergo additional field testing for adult-stage features. The study’s overall findings could be applied to the enhancement of genotypes with P efficiency, which will increase P uptake and utilization.

Author Contributions

D.K.: data curation, methodology, formal analysis, investigation, methodology, writing—original draft; N.P.: conceptualization, supervision, visualization; L.M.T.: conceptualization, supervision, visualization; H.K.D.: fund acquisition, conceptualization, project administration, methodology, resources, writing—review and editing; G.P.M.: fund acquisition, conceptualization, resources, writing—original draft; M.S.A.: formal analysis, software; R.B.: methodology, visualization; S.G.: conceptualization, writing—review and editing; S.K.: writing—review and editing; R.M.N.: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

The funds for this study were provided by the Department of Science and Technology, Ministry of Science and Technology, Government of India (grant number CRG/2018/002642) to Harsh Kumar Dikshit. The partial funding for the study was provided by the Australian Centre for International Agricultural Research (ACIAR) for the International Mungbean Improvement Network project (grant number CROP/2019/144).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors are thankful to the Director, ICAR-Indian Agricultural Research Institute, New Delhi, India for providing the resources and facilities to carry out this work.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Maxted, N. African Vigna: An Ecogeographic Study; IPGRI: Rome, Italy, 2004; Volume 11, ISBN 9290436379. [Google Scholar]
  2. Tomooka, N.; Kaga, A.; Isemura, T.; Vaughan, D. Vigna. In Wild Crop Relatives: Genomic and Breeding Resources; Springer: Berlin/Heidelberg, Germany, 2011; pp. 291–311. [Google Scholar]
  3. Nair, R.M.; Pandey, A.K.; War, A.R.; Hanumantharao, B.; Shwe, T.; Alam, A.; Pratap, A.; Malik, S.R.; Karimi, R.; Mbeyagala, E.K. Biotic and Abiotic Constraints in Mungbean Production—Progress in Genetic Improvement. Front. Plant Sci. 2019, 10, 1340. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Sandarani, M.; Kulathunga, K. A brief review: Lectins, protease inhibitors and saponins in cereals and legumes. Asian Food Sci. J. 2019, 10, 1–4. [Google Scholar] [CrossRef] [Green Version]
  5. Kaur, S.; Kumari, A.; Singh, P.; Kaur, L.; Sharma, N.; Garg, M. Biofortification in pulses. In Advances in Agri-Food Biotechnology; Tilak Raj, S., Rupesh, D., Humira, S., Eds.; Springer: New York, NY, USA, 2020; pp. 85–103. [Google Scholar]
  6. Dahiya, P.K.; Linnemann, A.R.; van Boekel, M.; Khetarpaul, N.; Grewal, R.B.; Nout, M.J.R. Mung Bean: Technological and Nutritional Potential. Crit. Rev. Food Sci. Nutr. 2015, 55, 670–688. [Google Scholar] [CrossRef] [PubMed]
  7. Warschefsky, E.J.; Rieseberg, L.H. Laying the Groundwork for Crop Wild Relative Conservation in the United States. Proc. Natl. Acad. Sci. USA 2021, 118, e2024375118. [Google Scholar] [CrossRef] [PubMed]
  8. Syers, J.K.; Johnston, A.E.; Curtin, D. Efficiency of Soil and Fertilizer Phosphorus Use. In FAO Fertilizer and Plant Nutrition Bulletin; FAO: Rome, Italy, 2008; Volume 18. [Google Scholar]
  9. Heuer, S.; Gaxiola, R.; Schilling, R.; Herrera-Estrella, L.; López-Arredondo, D.; Wissuwa, M.; Delhaize, E.; Rouached, H. Improving Phosphorus Use Efficiency: A Complex Trait with Emerging Opportunities. Plant J. 2017, 90, 868–885. [Google Scholar] [CrossRef] [Green Version]
  10. van Kauwenbergh, S.J.; Stewart, M.; Mikkelsen, R. World Reserves of Phosphate Rock a Dynamic and Unfolding Story. Better Crops Plant Food 2013, 97, 18–20. [Google Scholar]
  11. Ulrich, A.E.; Frossard, E. On the History of a Reoccurring Concept: Phosphorus Scarcity. Sci. Total Environ. 2014, 490, 694–707. [Google Scholar] [CrossRef]
  12. Niu, Y.F.; Chai, R.S.; Jin, G.L.; Wang, H.; Tang, C.X.; Zhang, Y.S. Responses of Root Architecture Development to Low Phosphorus Availability: A Review. Ann. Bot. 2013, 112, 391–408. [Google Scholar] [CrossRef]
  13. Batjes, N.H. A World Dataset of Derived Soil Properties by FAO–UNESCO Soil Unit for Global Modelling. Soil Use Manag. 1997, 13, 9–16. [Google Scholar] [CrossRef]
  14. Hammond, J.P.; Broadley, M.R.; White, P.J.; King, G.J.; Bowen, H.C.; Hayden, R.; Meacham, M.C.; Mead, A.; Overs, T.; Spracklen, W.P. Shoot Yield Drives Phosphorus Use Efficiency in Brassica Oleracea and Correlates with Root Architecture Traits. J. Exp. Bot. 2009, 60, 1953–1968. [Google Scholar] [CrossRef] [Green Version]
  15. Plaxton, W.C.; Tran, H.T. Metabolic Adaptations of Phosphate-Starved Plants. Plant Physiol. 2011, 156, 1006–1015. [Google Scholar] [CrossRef]
  16. Ramaekers, L.; Remans, R.; Rao, I.M.; Blair, M.W.; Vanderleyden, J. Strategies for Improving Phosphorus Acquisition Efficiency of Crop Plants. Field Crops Res. 2010, 117, 169–176. [Google Scholar] [CrossRef]
  17. Pant, B.; Erban, A.; Huhman, D.; Kopka, J.; Scheible, W. Identification of Primary and Secondary Metabolites with Phosphorus Status-dependent Abundance in A Rabidopsis, and of the Transcription Factor PHR 1 as a Major Regulator of Metabolic Changes during Phosphorus Limitation. Plant Cell Environ. 2015, 38, 172–187. [Google Scholar] [CrossRef]
  18. Pandey, R.; Meena, S.K.; Krishnapriya, V.; Ahmad, A.; Kishora, N. Root Carboxylate Exudation Capacity under Phosphorus Stress Does Not Improve Grain Yield in Green Gram. Plant Cell Rep. 2014, 33, 919–928. [Google Scholar] [CrossRef]
  19. Jakkeral, S.A.; Kajjidoni, S.T.; Koti, R. v Genotypic Variation for Root Traits to Phosphorus Deficiency in Blackgram (Vigna mungo L. Hepper). Karnataka J. Agric. Sci. 2009, 22, 946–950. [Google Scholar]
  20. Chen, Y.L.; Dunbabin, V.M.; Diggle, A.J.; Siddique, K.H.M.; Rengel, Z. Phosphorus Starvation Boosts Carboxylate Secretion in P-Deficient Genotypes of Lupinus Angustifolius with Contrasting Root Structure. Crop Pasture Sci. 2013, 64, 588–599. [Google Scholar] [CrossRef]
  21. Sarker, B.C.; Karmoker, J.L. Effects of Phosphorus Deficiency on the Root Growth of Lentil Seedlings Grown in Rhizobox. Bangladesh J. Bot. 2009, 38, 215–218. [Google Scholar] [CrossRef]
  22. He, J.; Jin, Y.; Du, Y.-L.; Wang, T.; Turner, N.C.; Yang, R.-P.; Siddique, K.H.M.; Li, F.-M. Genotypic Variation in Yield, Yield Components, Root Morphology and Architecture, in Soybean in Relation to Water and Phosphorus Supply. Front. Plant Sci. 2017, 8, 1499. [Google Scholar] [CrossRef] [Green Version]
  23. Borch, K.; Bouma, T.J.; Lynch, J.P.; Brown, K.M. Ethylene: A Regulator of Root Architectural Responses to Soil Phosphorus Availability. Plant Cell Environ. 1999, 22, 425–431. [Google Scholar] [CrossRef]
  24. Vejchasarn, P.; Lynch, J.P.; Brown, K.M. Genetic Variability in Phosphorus Responses of Rice Root Phenotypes. Rice 2016, 9, 29. [Google Scholar] [CrossRef] [Green Version]
  25. Shen, Q.; Wen, Z.; Dong, Y.; Li, H.; Miao, Y.; Shen, J. The Responses of Root Morphology and Phosphorus-Mobilizing Exudations in Wheat to Increasing Shoot Phosphorus Concentration. AoB Plants 2018, 10, ply054. [Google Scholar] [CrossRef] [PubMed]
  26. Fageria, N.K.; Moreira, A.; dos Santos, A.B. Phosphorus Uptake and Use Efficiency in Field Crops. J. Plant Nutr. 2013, 36, 2013–2022. [Google Scholar] [CrossRef]
  27. Thudi, M.; Chen, Y.; Pang, J.; Kalavikatte, D.; Bajaj, P.; Roorkiwal, M.; Chitikineni, A.; Ryan, M.H.; Lambers, H.; Siddique, K.H.M. Novel Genes and Genetic Loci Associated with Root Morphological Traits, Phosphorus-Acquisition Efficiency and Phosphorus-Use Efficiency in Chickpea. Front. Plant Sci. 2021, 12, 636973. [Google Scholar] [CrossRef] [PubMed]
  28. Reddy, V.R.P.; Das, S.; Dikshit, H.K.; Mishra, G.P.; Aski, M.; Meena, S.K.; Singh, A.; Pandey, R.; Singh, M.P.; Tripathi, K. Genome-Wide Association Analysis for Phosphorus Use Efficiency Traits in Mungbean (Vigna radiata L. Wilczek) Using Genotyping by Sequencing Approach. Front. Plant Sci. 2020, 11, 537766. [Google Scholar] [CrossRef] [PubMed]
  29. Kadirimangalam, S.R.; Jadhav, Y.; Nagamadhuri, K.V.; Putta, L.; Murugesan, T.; Variath, M.T.; Vemula, A.K.; Manohar, S.S.; Chaudhari, S.; Choudhary, S. Genetic Approaches for Assessment of Phosphorus Use Efficiency in Groundnut (Arachis hypogaea L.). Sci. Rep. 2022, 12, 21552. [Google Scholar] [CrossRef]
  30. Yan, X.; Liao, H.; Beebe, S.E.; Blair, M.W.; Lynch, J.P. QTL Mapping of Root Hair and Acid Exudation Traits and Their Relationship to Phosphorus Uptake in Common Bean. Plant Soil 2004, 265, 17–29. [Google Scholar] [CrossRef]
  31. Liang, Q.; Cheng, X.; Mei, M.; Yan, X.; Liao, H. QTL Analysis of Root Traits as Related to Phosphorus Efficiency in Soybean. Ann. Bot. 2010, 106, 223–234. [Google Scholar] [CrossRef] [Green Version]
  32. Su, J.-Y.; Zheng, Q.; Li, H.-W.; Li, B.; Jing, R.-L.; Tong, Y.-P.; Li, Z.-S. Detection of QTLs for Phosphorus Use Efficiency in Relation to Agronomic Performance of Wheat Grown under Phosphorus Sufficient and Limited Conditions. Plant Sci. 2009, 176, 824–836. [Google Scholar] [CrossRef]
  33. Yuan, Y.; Gao, M.; Zhang, M.; Zheng, H.; Zhou, X.; Guo, Y.; Zhao, Y.; Kong, F.; Li, S. QTL Mapping for Phosphorus Efficiency and Morphological Traits at Seedling and Maturity Stages in Wheat. Front. Plant Sci. 2017, 8, 614. [Google Scholar] [CrossRef] [Green Version]
  34. Luo, N.; Li, X.; Chen, A.Y.; Zhang, L.J.; Zhao, H.M.; Xiang, L.; Cai, Q.Y.; Mo, C.H.; Wong, M.H.; Li, H. Does Arbuscular Mycorrhizal Fungus Affect Cadmium Uptake and Chemical Forms in Rice at Different Growth Stages? Sci. Total Environ. 2017, 599, 1564–1572. [Google Scholar] [CrossRef]
  35. Mahender, A.; Anandan, A.; Pradhan, S.K.; Singh, O.N. Traits-Related QTLs and Genes and Their Potential Applications in Rice Improvement under Low Phosphorus Condition. Arch. Agron. Soil Sci. 2018, 64, 449–464. [Google Scholar] [CrossRef]
  36. Zhu, J.; Kaeppler, S.M.; Lynch, J.P. Mapping of QTLs for Lateral Root Branching and Length in Maize (Zea mays L.) under Differential Phosphorus Supply. Theoretical and Applied Genetics 2005, 111, 688–695. [Google Scholar] [CrossRef]
  37. Chen, J.; Xu, L.; Cai, Y.; Xu, J. QTL Mapping of Phosphorus Efficiency and Relative Biologic Characteristics in Maize (Zea mays L.) at Two Sites. Plant Soil 2008, 313, 251–266. [Google Scholar] [CrossRef]
  38. Sivasakthi, K.; Tharanya, M.; Kholová, J.; Wangari Muriuki, R.; Thirunalasundari, T.; Vadez, V. Chickpea Genotypes Contrasting for Vigor and Canopy Conductance Also Differ in Their Dependence on Different Water Transport Pathways. Front. Plant Sci. 2017, 8, 1663. [Google Scholar] [CrossRef] [Green Version]
  39. Reddy, V.R.P.; Aski, M.S.; Mishra, G.P.; Dikshit, H.K.; Singh, A.; Pandey, R.; Singh, M.P.; Ramtekey, V.; Rai, N.; Nair, R.M. Genetic Variation for Root Architectural Traits in Response to Phosphorus Deficiency in Mungbean at the Seedling Stage. PLoS ONE 2020, 15, e0221008. [Google Scholar] [CrossRef]
  40. Murphy, J.; Riley, J.P. A Modified Single Solution Method for the Determination of Phosphate in Natural Waters. Anal. Chim. Acta 1962, 27, 31–36. [Google Scholar] [CrossRef]
  41. Irfan, M.; Abbas, M.; Shah, J.A.; Akram, M.A.; Depar, N.; Memon, M.Y. Categorization and Identification of Brassica Genotypes for Phosphorus Utilization Efficiency. Int. J. Agric. Biol. 2020, 23, 227–234. [Google Scholar]
  42. Neto, A.P.; Favarin, J.L.; Hammond, J.P.; Tezotto, T.; Couto, H.T.Z. Analysis of Phosphorus Use Efficiency Traits in Coffea Genotypes Reveals Coffea Arabica and Coffea Canephora Have Contrasting Phosphorus Uptake and Utilization Efficiencies. Front. Plant Sci. 2016, 7, 408. [Google Scholar] [CrossRef] [Green Version]
  43. Osborne, L.D.; Rengel, Z. Screening Cereals for Genotypic Variation in Efficiency of Phosphorus Uptake and Utilisation. Aust. J. Agric. Res. 2002, 53, 295–303. [Google Scholar] [CrossRef]
  44. Aziz, T.; Finnegan, P.M.; Lambers, H.; Jost, R. Organ-specific Phosphorus-allocation Patterns and Transcript Profiles Linked to Phosphorus Efficiency in Two Contrasting Wheat Genotypes. Plant Cell Environ. 2014, 37, 943–960. [Google Scholar] [CrossRef] [Green Version]
  45. Negarestani, M.; Tohidi-Nejad, E.; Khajoei-Nejad, G.; Nakhoda, B.; Mohammadi-Nejad, G. Comparison of Different Multivariate Statistical Methods for Screening the Drought Tolerant Genotypes of Pearl Millet (Pennisetum americanum L.) and Sorghum (Sorghum bicolor L.). Agronomy 2019, 9, 645. [Google Scholar] [CrossRef] [Green Version]
  46. Grzesiak, S.; Hordyńska, N.; Szczyrek, P.; Grzesiak, M.T.; Noga, A.; Szechyńska-Hebda, M. Variation among Wheat (Triticum easativum L.) Genotypes in Response to the Drought Stress: I–Selection Approaches. J. Plant Interact. 2019, 14, 30–44. [Google Scholar] [CrossRef] [Green Version]
  47. Gulles, A.A.; Bartolome, V.I.; Morantte, R.; Nora, L.A.; Relente, C.E.N.; Talay, D.T.; Caneda, A.A.; Ye, G. Randomization and Analysis of Data Using STAR [Statistical Tool for Agricultural Research]. Philipp. J. Crop Sci. 2014, 39, 137. [Google Scholar]
  48. Hallauer, A.R.; Carena, M.J.; de Miranda Filho, J.B. Quantitative Genetics in Maize Breeding; Springer Science & Business Media: New York, NY, USA, 2010; Volume 6, ISBN 1441907661. [Google Scholar]
  49. Lê, S.; Josse, J.; Husson, F. FactoMineR: An R Package for Multivariate Analysis. J. Stat. Softw. 2008, 25, 1–18. [Google Scholar] [CrossRef]
  50. Weih, M.; Hamnér, K.; Pourazari, F. Analyzing Plant Nutrient Uptake and Utilization Efficiencies: Comparison between Crops and Approaches. Plant Soil 2018, 430, 7–21. [Google Scholar] [CrossRef] [Green Version]
  51. Liu, Z.; Gao, K.; Shan, S.; Gu, R.; Wang, Z.; Craft, E.J.; Mi, G.; Yuan, L.; Chen, F. Comparative Analysis of Root Traits and the Associated QTLs for Maize Seedlings Grown in Paper Roll, Hydroponics and Vermiculite Culture System. Front. Plant Sci. 2017, 8, 436. [Google Scholar] [CrossRef] [Green Version]
  52. da Silva, D.A.; de Esteves, J.A.F.; Gonçalves, J.G.R.; Azevedo, C.V.G.; Ribeiro, T.; Chiorato, A.F.; Carbonell, S.A.M. Evaluation of Common Bean Genotypes for Phosphorus Use Efficiency in Eutrophic Oxisol. Bragantia 2016, 75, 152–163. [Google Scholar] [CrossRef] [Green Version]
  53. Wang, Q.; Yuan, Y.; Liao, Z.; Jiang, Y.; Wang, Q.; Zhang, L.; Gao, S.; Wu, F.; Li, M.; Xie, W. Genome-Wide Association Study of 13 Traits in Maize Seedlings under Low Phosphorus Stress. Plant Genome 2019, 12, 190039. [Google Scholar] [CrossRef] [Green Version]
  54. Akhtar, M.S.; Oki, Y.; Adachi, T.; Murata, Y.; Khan, M.H.R. Relative Phosphorus Utilization Efficiency, Growth Response, and Phosphorus Uptake Kinetics of Brassica Cultivars under a Phosphorus Stress Environment. Commun. Soil Sci. Plant Anal. 2007, 38, 1061–1085. [Google Scholar] [CrossRef]
  55. Wissuwa, M.; Kondo, K.; Fukuda, T.; Mori, A.; Rose, M.T.; Pariasca-Tanaka, J.; Kretzschmar, T.; Haefele, S.M.; Rose, T.J. Unmasking Novel Loci for Internal Phosphorus Utilization Efficiency in Rice Germplasm through Genome-Wide Association Analysis. PLoS ONE 2015, 10, e0124215. [Google Scholar] [CrossRef] [Green Version]
  56. Baligar, V.C. Nutrient Use Efficiency in Acid Soils: Nutrient Management and Plant Use Efficiency. In Plant-Soil Interactions at Low pH; Brazilian Soil Science Society: Viçosa, Brazil, 1997; pp. 75–95. [Google Scholar]
  57. Kim, H.-J.; Li, X. Effects of Phosphorus on Shoot and Root Growth, Partitioning, and Phosphorus Utilization Efficiency in Lantana. HortScience 2016, 51, 1001–1009. [Google Scholar] [CrossRef] [Green Version]
  58. Ramtekey, V.; Bansal, R.; Aski, M.S.; Kothari, D.; Singh, A.; Pandey, R.; Tripathi, K.; Mishra, G.P.; Kumar, S.; Dikshit, H.K. Genetic Variation for Traits Related to Phosphorus Use Efficiency in Lens Species at the Seedling Stage. Plants 2021, 10, 2711. [Google Scholar] [CrossRef]
  59. Faez, A.; Adam, P.; David, J. Root Architecture and Genetic Variations Associated with Phosphorus Uptake in Rice. Int. J. Appl. Agric. Sci. 2015, 1, 1–10. [Google Scholar] [CrossRef] [Green Version]
  60. Li, P.; Chen, F.; Cai, H.; Liu, J.; Pan, Q.; Liu, Z.; Gu, R.; Mi, G.; Zhang, F.; Yuan, L. A Genetic Relationship between Nitrogen Use Efficiency and Seedling Root Traits in Maize as Revealed by QTL Analysis. J. Exp. Bot. 2015, 66, 3175–3188. [Google Scholar] [CrossRef]
  61. Pang, J.; Bansal, R.; Zhao, H.; Bohuon, E.; Lambers, H.; Ryan, M.H.; Ranathunge, K.; Siddique, K.H.M. The Carboxylate-releasing Phosphorus-mobilizing Strategy Can Be Proxied by Foliar Manganese Concentration in a Large Set of Chickpea Germplasm under Low Phosphorus Supply. New Phytol. 2018, 219, 518–529. [Google Scholar] [CrossRef] [Green Version]
  62. Blum, A.; Brumbarova, T.; Bauer, P.; Ivanov, R. Hormone Influence on the Spatial Regulation of IRT1 Expression in Iron-Deficient Arabidopsis Thaliana Roots. Plant Signal Behav. 2014, 9, e28787. [Google Scholar] [CrossRef] [Green Version]
  63. Gill, M.A.; Sabir, M.; Ashraf, S.; Rahmatullah, A.T.; Aziz, T. Effect of P-Stress on Growth, Phosphorus Uptake and Utilization Efficiency of Different Cotton Cultivars. Pak. J. Agric. Sci. 2005, 42, 42–47. [Google Scholar]
  64. Thuynsma, R.; Kleinert, A.; Kossmann, J.; Valentine, A.J.; Hills, P.N. The Effects of Limiting Phosphate on Photosynthesis and Growth of Lotus Japonicus. S. Afr. J. Bot. 2016, 104, 244–248. [Google Scholar] [CrossRef]
  65. Singh, S.K.; Badgujar, G.B.; Reddy, V.R.; Fleisher, D.H.; Timlin, D.J. Effect of Phosphorus Nutrition on Growth and Physiology of Cotton under Ambient and Elevated Carbon Dioxide. J. Agron. Crop Sci. 2013, 199, 436–448. [Google Scholar] [CrossRef] [Green Version]
  66. Gill, H.S.; Singh, A.; Sethi, S.K.; Behl, R.K. Phosphorus Uptake and Use Efficiency in Different Varieties of Bread Wheat (Triticum aestivum L.). Arch. Agron. Soil Sci. 2004, 50, 563–572. [Google Scholar] [CrossRef]
  67. Kosar, H.S.; Gill, M.A.; Aziz, T.; Tahir, M.A. Relative Phosphorus Utilization Efficiency of Wheat Genotypes in Hydroponics. Pak. J. Agric. Sci. 2003, 40, 28–32. [Google Scholar]
  68. Gunes, A.; Inal, A.; Alpaslan, M.; Çakmak, İ. Genotypic Variation in Phosphorus Efficiency between Wheat Cultivars Grown under Greenhouse and Field Conditions. Soil Sci. Plant Nutr. 2006, 52, 470–478. [Google Scholar] [CrossRef] [Green Version]
  69. Bilal, H.M.; Aziz, T.; Maqsood, M.A.; Farooq, M.; Yan, G. Categorization of Wheat Genotypes for Phosphorus Efficiency. PLoS ONE 2018, 13, e0205471. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Aziz, T.; Rahmatullah; Maqsood, M.A.; Sabir, M.; Kanwal, S. Categorization of Brassica Cultivars for Phosphorus Acquisition from Phosphate Rock on Basis of Growth and Ionic Parameters. J. Plant Nutr. 2011, 34, 522–533. [Google Scholar] [CrossRef]
  71. Manske, G.G.B.; Ortiz-Monasterio, J.I.; van Ginkel, M.; Gonzalez, R.M.; Rajaram, S.; Molina, E.; Vlek, P.L.G. Traits Associated with Improved P-Uptake Efficiency in CIMMYT’s Semidwarf Spring Bread Wheat Grown on an Acid Andisol in Mexico. Plant Soil 2000, 221, 189–204. [Google Scholar] [CrossRef]
  72. Hammond, J.P.; White, P.J. Sucrose Transport in the Phloem: Integrating Root Responses to Phosphorus Starvation. J. Exp. Bot. 2008, 59, 93–109. [Google Scholar] [CrossRef]
  73. Fageria, N.K.; Baligar, V.C. Screening Crop Genotypes for Mineral Stresses; Workshop on Adaption of Plants to Soil Stresses; University of Nebrasca: Lincoln, NE, USA, 1993. [Google Scholar]
  74. Abbas, M.; Irfan, M.; Shah, J.A.; Memon, M.Y. AJAB. Asian J. Agric. Biol. 2018, 6, 35–45. [Google Scholar]
  75. Abbas, M.; Aslam, M.; Shah, J.A.; Depar, N.; Memon, M.Y. Relative Growth Response of Hydroponically Grown Wheat Genotypes to Deficient and Adequate Phosphorus Levels. Pak. J. Agric. Agric. Eng. Vet. Sci. 2016, 32, 169–181. [Google Scholar]
  76. Thiry, A.A.; Chavez Dulanto, P.N.; Reynolds, M.P.; Davies, W.J. How Can We Improve Crop Genotypes to Increase Stress Resilience and Productivity in a Future Climate? A New Crop Screening Method Based on Productivity and Resistance to Abiotic Stress. J. Exp. Bot. 2016, 67, 5593–5603. [Google Scholar] [CrossRef]
  77. Sareen, S.; Tyagi, B.S.; Tiwari, V.; Sharma, I. Response Estimation of Wheat Synthetic Lines to Terminal Heat Stress Using Stress Indices. J. Agric. Sci. 2012, 4, 97. [Google Scholar] [CrossRef]
  78. Khodarahmpour, Z.; Choukan, R.; Bihamta, M.R.; MAJIDI, H.E. Determination of the Best Heat Stress Tolerance Indices in Maize (Zea mays L.) Inbred Lines and Hybrids under Khuzestan Province Conditions. J. Agric. Sci. Technol. 2011, 13, 111–121. [Google Scholar]
  79. Mohammadi, M.; Karimizadeh, R.; Abdipour, M. Evaluation of Drought Tolerance in Bread Wheat Genotypes under Dryland and Supplemental Irrigation Conditions. Aust. J. Crop Sci. 2011, 5, 487–493. [Google Scholar]
  80. Khayatnezhad, M.; Zaeifizadeh, M.; Gholamin, R. Investigation and Selection Index for Drought Stress. Aust. J. Basic Appl. Sci. 2010, 4, 4815–4822. [Google Scholar]
Agronomy 13 00305 g001a 550Agronomy 13 00305 g001b 550
Figure 1. Performance of Vigna spp. under optimum-phosphorus (OP) and low-phosphorus (LP) conditions. (I) Primary root length (cm), (II) total root length (cm), (III) total surface area (cm2), (IV) average root diameter (mm), (V) total root volume (cm3), (VI) total root tips, (VII) root forks, (VIII) shoot dry weight (g per plant), (IX) root dry wight (g per plant), (X) total dry weight (g per plant), (XI) P (%), (XII) P uptake efficiency (g per plant), (XIII) P utilization efficiency, (XIV) root-to-shoot ratio.(A) Vigna radiata var. bourneae, (B) Vigna pilosa, (C) Vigna minima, (D) Vigna dalzelliana, (E) V. hainiana, (F) Vigna mungo, (G) Vigna mungo var. silvestris, (H) Vigna trilobata, (I) Vigna trinervia var. bourneae, (J) Vigna membranaceae, (K) Vigna umbelleta, (L) Vigna unguiculata, (M) Vigna unguiculata ssp. sesquipedalis,(N) Vigna racemosa, (O) Vigna radiata var. sublobata, (P) Vigna radiata var. setulosa, (Q) Vigna radiata, (R) Vigna species (unknown).
Figure 1. Performance of Vigna spp. under optimum-phosphorus (OP) and low-phosphorus (LP) conditions. (I) Primary root length (cm), (II) total root length (cm), (III) total surface area (cm2), (IV) average root diameter (mm), (V) total root volume (cm3), (VI) total root tips, (VII) root forks, (VIII) shoot dry weight (g per plant), (IX) root dry wight (g per plant), (X) total dry weight (g per plant), (XI) P (%), (XII) P uptake efficiency (g per plant), (XIII) P utilization efficiency, (XIV) root-to-shoot ratio.(A) Vigna radiata var. bourneae, (B) Vigna pilosa, (C) Vigna minima, (D) Vigna dalzelliana, (E) V. hainiana, (F) Vigna mungo, (G) Vigna mungo var. silvestris, (H) Vigna trilobata, (I) Vigna trinervia var. bourneae, (J) Vigna membranaceae, (K) Vigna umbelleta, (L) Vigna unguiculata, (M) Vigna unguiculata ssp. sesquipedalis,(N) Vigna racemosa, (O) Vigna radiata var. sublobata, (P) Vigna radiata var. setulosa, (Q) Vigna radiata, (R) Vigna species (unknown).
Agronomy 13 00305 g001aAgronomy 13 00305 g001b
Agronomy 13 00305 g002 550
Figure 2. (A) Biplot PupE and (B) Biplot PutiE using the relative values of tested root and shoot traits of Vigna species under LP conditions (PupE, phosphorus uptake efficiency; PutiE, phosphorus utilization efficiency; LP, low phosphorus).
Figure 2. (A) Biplot PupE and (B) Biplot PutiE using the relative values of tested root and shoot traits of Vigna species under LP conditions (PupE, phosphorus uptake efficiency; PutiE, phosphorus utilization efficiency; LP, low phosphorus).
Agronomy 13 00305 g002
Agronomy 13 00305 g003 550
Figure 3. Scree plot under LP and OP conditions.
Figure 3. Scree plot under LP and OP conditions.
Agronomy 13 00305 g003
Table
Table 1. Descriptive statistics of the measured traits in Vigna genotypes under optimum- and low-phosphorus conditions.
Table 1. Descriptive statistics of the measured traits in Vigna genotypes under optimum- and low-phosphorus conditions.
TraitsOptimum PhosphorusLow Phosphorus% Reduction of the Mean
Max.Min.MeanCV(%)SEMax.Min.MeanCV(%)SE
PRL55.7010.130.479.740.2560.2010.1032.889.020.317.91
TRL1857.5853.0644707.8411.2812.542071.4239.66634.0812.1611.31−10.42
TSA249.429.956873.5912.001.44234.526.9166.8512.591.27−9.16
ARD0.690.25670.356.870.000.590.240.376.340.005.71
TRV2.890.050.6711.280.012.900.050.7411.070.0110.45
TRT2495.0065765.0611.5312.352986.0019.00685.4612.0911.20−10.40
RF9790.00782474.449.5239.698724.00122.002005.4010.9936.53−18.96
SDW1.100.0740.258.410.010.550.050.178.470.00−32.00
RDW0.330.0050.04810.010.000.230.010.04811.910.000.00
TDW1.360.0980.307.690.010.690.080.217.590.00−30.00
RSR1.180.0241450.2212.100.001.050.040.3311.550.0150.00
% P12.960.3395.744.830.084.950.031.608.490.03−72.13
PUpE4.750.0676891.459.940.021.740.000.3111.650.01−78.62
PUtiE2.950.077160.287.140.0131.250.201.4113.630.08403.57
Table
Table 2. Estimates of variance components and broad-sense heritability (H) for root and shoot traits under optimum- and low-phosphorus conditions.
Table 2. Estimates of variance components and broad-sense heritability (H) for root and shoot traits under optimum- and low-phosphorus conditions.
TraitOptimum PhosphorusLow PhosphorusCombined AnalysisHcom
GenotypesHGenotypesHGenotypesGenotypes × P levels
PRL173.32 **0.86259.32 **0.90326.23 **106.41 **0.67
TRL450,566.87 **0.96360,584.61 **0.95740,774.95 **70,375.99 **0.90
TSA5960.86 **0.964611.21 **0.969708.32 **863.76 **0.91
ARD0.0076 ns0.800.0089 **0.850.0137 **0.0027 **0.80
TRV0.4106 **0.960.356 **0.940.6040 **0.1626 **0.73
TRT434,595.05 **0.95347,816.83 **0.94613,095.14 **169,316.74 **0.72
RF4,517,922.02 **0.963,691,107.09 **0.965,761,460.68 **2,447,568.43 **0.58
SDW0.0966 **0.990.0249 **0.980.0861 **0.0354 **0.59
RDW0.0040 **1.000.0027 **1.000.0051 **0.0017 **0.67
TDW0.1301 **0.990.0328 **0.970.1190 **0.0440 **0.63
RSR0.0466 **0.960.0756 **0.940.0761 **0.0462 **0.39
% P17.11 **0.992.2658 **0.9814.6616 **4.7055 **0.68
PUpE1.3064 **0.950.1116 **0.970.9491 **0.4688 **0.51
PUtiE0.3141 **1.0017.1371 **0.9910.8076 **6.6428 **0.39
** Significance at p < 0.05 and ns not significant.
Table
Table 3. Pearson’s correlation analysis for the studied root and shoot traits under optimum-phosphorus (upper diagonal) conditions and low-phosphorus (lower diagonal) conditions.
Table 3. Pearson’s correlation analysis for the studied root and shoot traits under optimum-phosphorus (upper diagonal) conditions and low-phosphorus (lower diagonal) conditions.
PRLTRLTSAARDTRVTRTRFSDWRDWTDWRSRPPupEPutiE
PRL1.000.58 **0.49 **−0.40 **0.30 **0.63 **0.14 **−0.13 **0.03 ns−0.10 **0.17 **0.47 **0.33 **−0.34 **
TRL0.74 **1.000.95 **−0.33 **0.62 **0.86 **0.46 **0.26 **0.42 **0.30 **0.16 **0.34 **0.39 **−0.11 **
TSA0.74 **0.97 **1.00−0.15 **0.70 **0.79 **0.50 **0.37 **0.53 **0.41 **0.13 **0.23 **0.38 **−0.04 ns
ARD−0.33−0.43−0.351.000.22 **−0.37 **0.05 ns0.42 **0.36 **0.43 **−0.05 ns−0.53 **−0.07 ns0.27 **
TRV0.54 **0.59 **0.62 **0.12 **1.000.57 **0.63 **0.67 **0.70 **0.70 **0.05 ns−0.12 **0.39 **0.15 **
TRT0.56 **0.71 **0.68 **−0.280.59 **1.000.44 **0.16 **0.28 **0.19 **0.12 **0.39 **0.44 **−0.21 **
RF0.28 **0.31 **0.29 **0.15 **0.53 **0.37 **1.000.52 **0.41 **0.52 **−0.17−0.130.26 **0.14 **
SDW−0.17−0.20−0.180.67 **0.34 **0.02 **0.45 **1.000.75 **0.99 **−0.25−0.520.22 **0.40 **
RDW0.33 **0.41 **0.50 **0.21 **0.65 **0.38 **0.34 **0.32 **1.000.82 **0.34 **−0.400.13 **0.39 **
TDW−0.05−0.05−0.010.64 **0.48 **0.12 **0.49 **0.96 **0.57 **1.00−0.15−0.520.21 **0.41 **
RSR0.42 **0.47 **0.53 **−0.24 **0.36 **0.30 **−0.03 ns−0.390.65 **−0.151.000.08−0.110.06 **
% P0.21 **0.22 **0.21 **−0.180.05 **0.13 **−0.20−0.30−0.09−0.290.21 **1.000.57 **−0.77
PupE0.06 **0.05 **0.07 **0.30 **0.29 **0.08 **0.07 **0.37 **0.26 **0.40 **0.07 **0.681.00−0.49
PutiE−0.05 ns0.01 ns0.03 ns−0.04 ns0.03 ns0.003 ns0.11 ns0.06 ns0.15 **0.10 **0.02 ns−0.60 ns−0.49 ns1.00
** Significance at p < 0.05 and ns not significant.
Table
Table 4. Top 10 genotypes for traits contributing to PUE in 327 accessions of studied Vigna species.
Table 4. Top 10 genotypes for traits contributing to PUE in 327 accessions of studied Vigna species.
GenotypesTDW
(g)		GenotypesSDW
(g)		GenotypesRDW
(g)		GenotypesPupE
(g plant−1)		GenotypesPutiE (g DW/mg P)
IC 3434400.669V1003959BG0.516V1002190BG0.220IC 25195018.21IC 251950167.0
V1002190BG0.652V1001162AG0.505IC 3503470.210IC 58593115.07IC 585931140.7
V1001162AG0.598V1001066BG0.497IC 3434400.207IC 25950213.05V1002532AG137.7
V1003959BG0.544V1000470AG0.476IC 3434360.170V1002532AG12.17IC 259502114.6
V1002872BG0.537V1001698BG0.470IC 1462390.167IC 37165312.15V1001400AG97.4
V1001066BG0.535V1002872BG0.468KPS 15460.160IC 33161511.30IC 37165397.0
V1002672AG0.534V1000380AG0.465SM 18-990.137V1001400AG11.06IC 33161588.1
V1001698BG0.524V1002672AG0.465IC 2732440.120V1000532BG9.45V1000532BG75.0
V1000470AG0.522IC 3434400.462V1002195AG0.119V10002647AG9.07IC 58366666.5
IC 2732440.502V1002190BG0.432IPM 02-30.113V1004734AG9.01IC 58366565.2
TDW, total dry weight; SDW, shoot dry weight; RDW, root dry weight; PupE, phosphorus uptake efficiency; PutiE, phosphorus utilization efficiency.
Table
Table 5. Efficient genotypes identified based on population mean and standard deviation under low-phosphorus and optimum-phosphorus conditions [43,44].
Table 5. Efficient genotypes identified based on population mean and standard deviation under low-phosphorus and optimum-phosphorus conditions [43,44].
S.No.GenotypeSpeciesLow PhosphorusOptimum PhosphorusTotal Score
/Out of 30
RDWSDWRSRTPUPUtETotal Score
/Out of 15		RDWSDWRSRTPUPUtETotal Score
/Out of 15
1IC 251950V.radiata var. bourneaeMMMEE12MMMEE1224
2IC 277000V.radiata var. bourneaeMMMEE12MMMEE1224
3IC 25950V.radiata var. bourneaeMMMEE12MMEEE1325
4IC 305153V.radiata var. bourneaeMMMEE12MMMEE1224
5IC 202643Vigna radiata var. sublobataMMMEE12MMMEE1224
6IC 259502Vigna radiata var. sublobataMMMEE12MMMEE1224
7IC 371653Vigna radiata var. sublobataMMMEE12MMMEE1224
8IC 248206Vigna mungo var. silvestrisMMMEE12MMMEE1224
9IC 585931Vigna mungo var. silvestrisMMMEE12MMMEE1224
10IC 251419Vigna radiata var. setulosaMMMEE12MMMEE1224
11IC 583670Vigna dalzellianaMMMEE12MMEEE1224
12IC 583662Vigna dalzellianaMMMEE12MMMEE1224
13IC 583689Vigna dalzellianaMMMEE12MMMEE1224
14IC 331450Vigna hainianaMMMEE12MMMEE1224
15IC 331615Vigna hainianaMMMEE12MMMEE1224
16IC 331452Vigna hainianaMMMEE12MMMEE1224
17IC 583665Vigna trinervia var. bourneaeMMMEE12MMMEE1224
18IC 583664Vigna trinervia var. bourneaeMMMEE12MMEEE1325
19V1001339AGVigna radiataMEMEM12MEMEM1224
20V10002647AGVigna radiataMMMEE12MMMEE1224
21V1000532BGVigna radiataMMMEE12MMMEE1224
22V1001400AGVigna radiataMMMEE12MMMEE1224
23V1004734AGVigna radiataMMMEE12MMMEE1224
24V1001806BGVigna radiataMMMEE12MMMEE1224
25V1002532AGVigna radiataMMMEE12MMMEE1224
RDW, root dry weight; SDW, shoot dry weight; RSR, root-to-shoot ratio; TPU, total phosphorus uptake; PUtE, phosphorus utilization efficiency.
Table
Table 6. Phosphorus deficiency tolerance indices of promising genotypes identified under optimum- and low-phosphorus conditions [45,46].
Table 6. Phosphorus deficiency tolerance indices of promising genotypes identified under optimum- and low-phosphorus conditions [45,46].
S.No.Genotype SpeciesSSIMPIGMPIHMISTITISISTC
1IC 277060V. minima5.6128860.3070.2547430.2113810.7716280.340.2836247.78
2IC 697141Vigna membranaceae4.9560350.490.416370.3538042.0614020.520.3095779.10
3IC 343440Vigna unguiculata0.3223660.7171670.7155250.7138876.0877020.100.8733139.53
4KPS 1546Vigna radiata5.0265490.8026670.6802920.5765755.5029460.850.30656513.75
5Pusa 0831Vigna radiata4.531590.6866670.5927340.511654.1775660.690.32903211.52
6Pusa 0871Vigna radiata4.2135640.50.4367940.3815782.2685960.490.3452918.63
7Pusa 1371Vigna radiata7.8529410.6271670.4826360.3714132.7697710.800.22056413.13
8Pusa 1431Vigna radiata11.65950.7488330.5165330.3562963.1724931.080.16008317.70
9Pusa 1641Vigna radiata5.5748790.51850.4308250.3579752.2070150.580.2850069.95
10M 1319Vigna radiata2.6842680.540.5002550.4634362.975690.410.4529158.02
11M 209Vigna radiata5.3037040.6580.5520870.4632223.6242570.720.29527611.61
12V 6173Vigna radiata7.3218390.5118330.4006630.3136391.9088120.640.23283811.33
13EESM 18-163Vigna radiata14.087960.6671670.4334670.2816292.2341661.010.13624818.85
14SM 18-93Vigna radiata6.6971080.610.4874990.3895992.8258690.730.24914711.99
15SM 18-94Vigna radiata4.9608760.5008330.42550.3614982.1527940.530.3093689.24
16SM 18-96Vigna radiata8.5462960.4676670.3522120.265261.4750690.620.20636311.93
17SM 18-98Vigna radiata3.9835390.5120.4511980.3976172.420690.480.358098.61
18SM 18-99Vigna radiata5.004630.6803330.5770730.4894863.959730.720.30749511.74
19SM 18-101Vigna radiata2.5353540.5758330.5365010.4998553.4225130.420.4670918.46
20SM 18-102Vigna radiata6.2297980.70450.5720680.4645313.8913460.820.26292212.95
21SM 18-106Vigna radiata4.6666670.5671670.4871220.4183742.8214960.580.3225819.86
22SM 18-9Vigna radiata4.7460320.5790.4958230.4245942.9231870.600.31890710.09
23SM 18-21Vigna radiata6.9513110.7606670.6027580.4776314.3200690.930.24224314.28
24IPM 02-3Vigna radiata2.8404840.5518330.5081570.4679373.0704320.430.438948.31
SSI, stress susceptibility index; MPI, mean productivity index; GMPI, geometric mean productivity index; HI, harmonic men index; STI, stress tolerance index; TI, tolerance index; SI, stress index; STC, stress tolerance index.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kothari, D.; Pargaien, N.; Tewari, L.M.; Dikshit, H.K.; Mishra, G.P.; Aski, M.S.; Bansal, R.; Gupta, S.; Kumar, S.; Nair, R.M. Genetic Variation for Traits Related to Phosphorus Use Efficiency in Vigna Species. Agronomy 2023, 13, 305. https://doi.org/10.3390/agronomy13020305

AMA Style

Kothari D, Pargaien N, Tewari LM, Dikshit HK, Mishra GP, Aski MS, Bansal R, Gupta S, Kumar S, Nair RM. Genetic Variation for Traits Related to Phosphorus Use Efficiency in Vigna Species. Agronomy. 2023; 13(2):305. https://doi.org/10.3390/agronomy13020305

Chicago/Turabian Style

Kothari, Deepali, Nirmala Pargaien, Lalit Mohan Tewari, Harsh Kumar Dikshit, Gyan Prakash Mishra, Muraleedhar S. Aski, Ruchi Bansal, Sanjeev Gupta, Shiv Kumar, and Ramakrishnan Madhavan Nair. 2023. "Genetic Variation for Traits Related to Phosphorus Use Efficiency in Vigna Species" Agronomy 13, no. 2: 305. https://doi.org/10.3390/agronomy13020305

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop