Cover (July September 2017).cdr

INDIAN SOCIETY OF PULSES RESEARCH AND DEVELOPMENT (Regn. No. 877) The Indian Society of Pulses Research and Development (ISPRD) was founded in April 1987 with the following objectives:  To advance the cause of pulses research  To promote research and development, teaching and extension activities in pulses  To facilitate close association among pulse workers in India and abroad  To publish “Journal of Food Legumes” which is the official publication of the Society, published four times a year. Membership : Any person in India and abroad interested in pulses research and development shall be eligible for membership of the Society by becoming ordinary, life or corporate member by paying respective membership fee. Membership Fee Indian (Rs.) Foreign (US $) Ordinary (Annual) 500 40 Life Member 5000 400 Admission Fee 50 10 Library/ Institution 5000 400 Corporate Member 7500 -

The contribution to the Journal, except in case of invited articles, is open to the members of the Society only. Any non-member submitting a manuscript will be required to become annual member. Members will be entitled to receive the Journal and other communications issued by the Society. Renewal of subscription should be done in January each year. If the subscription is not received by February 15, the membership would stand cancelled. The membership can be revived by paying readmission fee of Rs. 50/-. Membership fee drawn in favour of Treasurer, Indian Society of Pulses Research and Development, through D.D. may be sent to the Treasurer, Indian Society of Pulses Research and Development, ICARIndian Institute of Pulses Research, Kanpur 208 024, India. In case of outstation cheques, an extra amount of Rs. 50/- may be paid as clearance charges.

EXECUTIVE COUNCIL : 2017-2020 Chief Patron Dr Trilochan Mohapatra

Patron Dr JS Sandhu Co-patron Dr NP Singh

President Dr NP Singh

Vice President Dr Guriqbal Singh Secretary Dr PK Katiyar

Joint Secretary Dr Jitendra Kumar

Treasurer Dr RK Mishra

Councillors Zone I Zone II Zone III Zone IV

: Dr Brij Nandan, SKUAST, Samba (J&K) : Dr C Bharadwaj, IARI, New Delhi : Dr Rajib Nath, BCKV, Kalyani : Dr Baldev Ram, AU, Kota

Zone V : Dr DK Patil, Badnapur Zone VI : Dr P Jagan Mohan Rao, RARS, Warangal Zone VII : Dr P Jayamani, TNAU, Coimbatore Zone VIII: Dr AK Parihar, ICAR-IIPR, Kanpur

Editor-in-Chief Dr CS Praharaj

Dr Puran Gaur, ICRISAT, Hyderabad Dr Shiv Kumar, ICARDA, Morocco Dr BB Singh, GBPUA&T, Pantnagar Dr DK Agarwal, ICAR-IISS, Mau Dr Sarvajeet Singh, PAU, Ludhiana Dr J Souframanian, BARC

Editors Dr Aditya Pratap, ICAR-IIPR, Kanpur Dr Narendra Kumar, ICAR-IIPR, Kanpur Dr Naimuddin, ICAR-IIPR, Kanpur Dr Meenaal Rathore, ICAR-IIPR, Kanpur Dr Archana Singh, ICAR-IIPR Regional Station, Bhopal Dr Abhishek Bohra, ICAR-IIPR, Kanpur

Journal of Food Legumes (Formerly Indian Journal of Pulses Research)

Vol. 30(3)

July-September 2017

CONTENTS RESEARCH PAPERS 1.

Genotype by trait (GxT) biplot analysis for trait relations and genotypes selection in mungbean [Vigna radiata (L.) Wilczek]

1

S Sofia, DM Reddy, M Shanti Priya and P Latha 2.

Agro-morphological diversity analysis in fieldpea (Pisum sativum L.) genotypes grown under protected irrigation at marginal soils of Manipur

7

Muniyandi Samuel Jeberson, K Sankarappa Shashidhar and Amit Kumar Singh 3.

Evaluation of chickpea (Cicer arietinum L.) germplasm in Jharkhand

11

Yogesh Kumar, J Ghosh, RK Mishra and SK Chaturvedi 4.

Influence of different spacing and fertilizer levels on yield, quality and economics of pea (Pisum sativum L.)

14

BM Kalalbandi, AS Lohakare and DB Kadre 5.

Effect of moisture content on physical properties of black gram (Vigna mungo L.) grains

17

J Jerish Joyner and BK Yadav 6.

Effect of potassium humate and bio-inoculants on nutrient content, uptake and quality of cowpea [Vigna unguiculata (L.) Walp]

23

Pradip Tripura, Sunil Kumar and Rajhans Verma 7.

Herbicide induced physiological changes in chickpea (Cicer arietinum L.) genotypes

26

Kawaljit Kaur, Jagmeet Kaur, Satvir Kaur Grewal, Sarvjeet Singh and Sukhpreet Kaur Sidhu 8.

Comparative efficacy of certain botanicals and bioagents against pod borer, Helicoverpa armigera on fieldbean [Lablab purpreus (L.)]

34

Apoorva Basavaraj, Hemant Lyall and Kamal Tanwar 9.

Histopathology of Burkholderia andropogonis in seeds of chickpea (Cicer arietinum L.)

36

Kailash Meena and Kailash Agrawal 10.

Management of whitefly, bemisia tabaci gennadius through seed treatment and insecticidal spray on urdbean

39

Reena Saini, Tarun Verma, Roshanlal and YPS Solanki 11.

Price forecasting of pulses: case of pigeonpea

42

Ashwini Darekar and A Amarender Reddy 12.

Nutritional quality of improved varieties of cowpea (Vigna unguiculata (L). Walp) Shweta Suri, Anuradha Dutta, YV Singh, RS Raghuvanshi and Sanjeev Agrawal

47

13.

Comparison of physico-chemical qualities of the small and large red kidney beans (Phaseolus vulgaris L.) flour

51

Neha Pathak and Kalpana Kulshrestha 14.

Significance and strategies of legume production for achieving nutritional security in North East Indian Himalayan Region

56

MA Ansari, PK Saraswat, SS Roy, SK Sharma, Punitha P, MH Ansari, N Prakash, RK Mishra, Niranjan Lal and Y Ramakrishna

SHORT COMMUNICATION 15.

Physico-chemical and sensory quality attributes of snacks prepared from different sources of soya protein

64

Gupta Prerna and Malik Anisa List of Referees for Vol. 30(3)

68

Journal of Food Legumes 30(3): 1-6, 2017

Genotype by trait (GxT) biplot analysis for trait relations and genotypes selection in mungbean [Vigna radiata (L.) Wilczek] S SOFIA, DM REDDY, M SHANTI PRIYA and P LATHA S.V. Agricultural College, Tirupati, Andhra Pradesh, India- 517 501; E-mail: [email protected] (Received : April 7, 2017 Accepted : July 28, 2017)

ABSTRACT Thirty five mungbean genotypes were used to study the relationships between yield and its component traits along with some physiological traits by using GT biplot technique. The GT biplot effectively revealed the interrelationships among the traits. Seed yield was found to be positively correlated with number of clusters per plant, number of pods per plant, days to maturity, number of pods per cluster, leaf area duration and chlorophyll content. Hence, effective selection criteria using these traits could be formulated in the mungbean breeding programme aimed to develop high yielding varieties. Among the genotypes studied LM 95 and GVIT 203 were identified as the ideal genotypes. The genotypes GVIT 203, LM 95, AKM 9904, KM 122 and WGG 2 showed superior performance for the traits such as seed yield per plant and number of clusters per plant indicating that these genotypes could be used as parents in the breeding programmes to develop high yielding genotypes in mungbean through selection focusing on more number of clusters per plant. Keywords : Biplot analysis, Mungbean

Mungbean is one of the most important leguminous crops in Asia. It belongs to the family Fabaceae. It occupies the third position after chickpea, redgram among legume crops. Mungbean is the cheap source of proteins (24%) and carbohydrates (38-50%) and among other pulses it is choosen first because of its easy digestibility. It is a short duration pulse crop grown mainly in kharif as well as summer seasons (about 60 days). It increases soil fertility due to its ability to fix nitrogen together with soil bacteria and it is also relatively tolerant to nutrient deficiency and drought. Though pulses play a pivotal role in the Indian dietary requirements, the per capita availability of pulses has declined from 60.7 g per day in 1951 to 46.4 g per day in 2014 (INDIASTAT, 2014-15) and is anticipated to fall further due to decline in their production and continuously rising prices. The low yields may be attributable to different factors such as lack of high yielding varieties suitable for different niches and susceptibility of existing local varieties to various biotic and abiotic stresses. Hence, there is a need to improve the pulse productivity in our country by developing high yielding genotypes through planned breeding programmes. The primary goal of any breeding program is to improve the yield. Since, yield is a complex trait, yield

improvement is difficult through direct selection. As a result, it has become a routine practice in any breeding trial, to gather data on multiple traits associated with grain yield. This is because a cultivar is more or less a complex biological system rather than a simple collection of independent traits, and an effective breeding programme requires the essential components of the system and the interrelationship among them (Yan and Kang, 2003). In general, most of the breeders depend on correlation and path analysis to understand various relationships among the traits and their direct and indirect effects. However, these methods could not reveal these complex relationships effectively among all the traits at a time and also difficult to compare the genotype performance on the basis of multiple traits. As, yield being a complex trait and it is the result of combined effect of several component characters and environment, there is a need of more efficient statistical tools to identify the relationships among the component traits and to identify the genotypes that are particularly good in certain combination of traits. The genotype by trait (GT) biplot analysis proposed by Yan and Kang (2003) is one of the powerful statistical tools for studying such relationship among traits, evaluating cultivars based on multiple traits and for identifying cultivars that are superior in certain traits. A GT biplot is an effective tool for exploring multitrait data. It graphically displays the genotype by trait table and allows the visualization of the associations among traits across the genotypes and of the trait profile of the genotypes (Yan and Kang, 2003). The genotype by trait biplot facilitates identification of traits that can be used in indirect selection for a target trait and those that may be redundantly measured (Yan and Rajcan, 2002 and Okoye et al. 2007). It can be used in independent culling based on multiple traits and in comparing selection strategies. Hence the present investigation was carried out to visualize the merits and shortcomings of mungbean genotypes, which are important for both cultivar evaluation and parental selection for development of cultivars with high yield. MATERIALS AND METHODS Field experiments and plant material : An experiment was carried out at Sri Venkateswara Agricultural College dry land farm, Tirupati. The experimental material consisted of 35 mungbean genotypes were evaluated in a randomized

2

Journal of Food Legumes 30(3): 2017

block design with three replications during kharif 2015. Each genotype was sown in three rows of 3m length with a spacing of 30 cm between rows and 10 cm between plants within rows. Observations were recorded on twelve yield and physiological characters viz., plant height, number of clusters per plant, number of pods per cluster, number of pods per plant, hundred seed weight, harvest index, seed yield per plant, net assimilation rate, leaf area duration, SPAD Chlorophyll meter reading (SCMR) and chlorophyll content. The character days to maturity were recorded on per plot basis. Five random plants were selected at random from each experimental plot in each replication. Data were recorded from the middle row of each plot. Statistical analysis : The data were subjected to analysis of variance (ANOVA) using a randomized block design. The GT biplot method was employed to display the genotype by trait two-way data. It is based on the following formula:

−

2

=

2

ƞ

∗

+ Ɛ =

=1

ƞ∗ + Ɛ

=1

Where, áij = The mean value of genotype i for trait j; âj = The mean value of all genotypes for trait j; íj = The standard deviation of trait j among genotype means; = The singular value for Principal Component (PCn); The PCn score for genotype i; = The PCn score for trait j; ij = The residual associated with genotype i in trait j. In the GT biplot, a vector is drawn from the biplot origin to each marker of the traits to facilitate visualization of the relationship between and among the traits. A Genotype by Trait (GT) biplot is constructed using ‘‘R’’ packages by plotting PC1 scores against PC2 scores for each genotype and each trait. RESULTS AND DISCUSSION Results of the analysis of variance showed highly significant differences among the genotypes for all the characters studied. This indicates the presence of considerable amount of genetic variation among 35 genotypes for all the traits. The significant genotypic effect for all the measured traits justified the use of GT biplot for the genotype-by-trait analysis. Trait relationships : The graphical interrelationship among twelve traits that showed high variability viz., days to maturity, plant height, number of clusters per plant, number of pods per cluster, number of pods per plant, 100 seed weight, harvest index, seed yield per plant, net assimilation rate, Leaf Area Duration, SPAD based on thirty five mungbean genotypes were presented in Fig 1. Principal components PC1 and PC2 explained 52.65% of the total variation observed among the cultivars based on all the traits. In GT biplot, a vector drawn from origin to each trait facilitates the visualization of the relationships among the traits. The line between marked point of any trait and origin of a biplot is termed as traits vector and cosine angle between trait vectors determine interrelationship among

Fig. 1. Vector view of the genotype-by-trait biplot showing interrelationships among various morpho-physiological traits of thirty five mungbean genotypes. PH = Plant height DM = Days to maturity SW = Seed weight HI = Harvset index NAR = Net assimilation rate CC = Chlorophyll content

CP = Clusters per plant PP = Pods per plant SY = Seed yield PC = Pods per cluster LAD = Leaf area duration SCMR = SPAD Chlorophyll meter reading

the traits. Two traits are positively correlated if the angle among their vectors is an acute angle (< 90°) and negatively correlated if the angle among trait vectors is an obtuse angle (> 90°). While, trait vectors that are approximately at right angle (= 90°) are not closely related i.e. independent and traits that are at angle 180o (directly opposite) are strongly negatively correlated (Yan and Kang, 2003 and Yan et al, 2007). Among the traits tested, seed yield was highly and positively correlated with number of clusters per plant. It was also positively correlated with chlorophyll content, number of pods per plant, days to maturity, number of pods per cluster and leaf area duration. However, seed yield was negatively correlated with SCMR, net assimilation rate and 100 seed weight. While, this trait showed independent association with harvest index and plant height indicting that zero contribution of these traits in manifestation of seed yield. Similar results were also reported by Oladejo et al. 2011 in cowpea where seed yield was positively correlated with all the morphological traits and Paramesh, 2014 in mungbean where seed yield was positively correlated with all the morphological traits except 100 seed weight. Similarly, Singh et al. 2014 used GT biplot in mungbean and observed that seed yield was highly and positively correlated with number of pods per cluster and number of clusters per plant. Based on these trait interrelationships biplot, seed yield was positively correlated

Sofia et al. : Genotype by trait (GxT) biplot analysis for trait relations and genotypes selection in mungbean

3

with most of the morphological traits except 100 seed weight, which had negative correlation with it. For the physiological traits, a positive association was observed between NAR, CC and SCMR. Similarly, positive association was also observed between leaf area duration and chlorophyll content. Hence, it could be suggested that the traits number of clusters per plant, number of pods per cluster, chlorophyll content, number of pods per plant, days to maturity and leaf area duration could be considered in the breeding programmes that aim to develop high yielding genotypes. Genotype comparisons : The evaluation and identification of best genotypes for multiple morphological traits was done using biplot analysis. The polygon view of a GT biplot is the best way to visualize the interaction patterns between genotypes and traits (Yan and Rajcan, 2002). Fig. 2 is a GT biplot with a polygon view that represents the data of thirty five genotypes with twelve characters. A polygon is drawn on genotype located away from the biplot origin such that all other genotypes are contained within the polygon. The perpendicular lines to the polygon sides facilitate comparison between neighbouring vertex genotypes (Yan and Tinker, 2006). The genotype which occupied vertex position in the biplot is known as vertex genotype. The vertex genotype for each sector had the greatest values for all traits falling within that sector and such could be ideal candidates as parents in greengram breeding. The mungbean genotypes generated a biplot with LM 95, GVIT 203, WGG 42, EC 396117, VG 7098A, MGG 295, LGG 528, JBT 37/150, KM 8651 and WGG 37 at the vertex of polygon (Fig. 2). These vertex genotypes exhibited superior performance for the traits allocated within the sector. Among these vertex genotypes LM 95 and GVIT 203 exhibited superior performance for the characters seed yield per plant, number of clusters per plant and chlorophyll content indicating that these genotypes could be exploited in breeding programme for the development of variety and populations that are outstanding in these traits. The genotypes WGG 42 and EC 396117 exhibited better performance for the trait 100-seed weight, net assimilation rate, SCMR and harvest index, while the genotype KM 8651 exhibited superior performance for number of pods per cluster, plant height. The genotype WGG 37 exhibited better performance for the trait number of pods per plant, days to maturity and leaf area duration. The genotypes VG 7098A, MGG 295, LGG 528 and JBT 37/150 were also vertex genotypes but no trait was found in their respective sector, an indication that they are not outstanding for any of the morphological traits. These results are in conformity with Oladejo et al. 2011 in cowpea, Safari et al. 2013 in maize and Odewale et al. 2013 in coconut while identifying the better genotypes in the respective crops.

Fig.2. A “which won where/what” or “which wins where” of genotype-by-trait biplot of twelve morpho-physiological traits for thirty five mungbean genotypes. PH = Plant height DM = Days to maturity SW = Seed weight HI = Harvset index NAR = Net assimilation rate CC = Chlorophyll content


Among these vertex genotypes GVIT 203 and LM 95 exhibited superior performance for the characters seed yield per plant, number of clusters per plant and chlorophyll content. The genotypes WGG 42 and EC 396117 exhibited better performance for the trait 100-seed weight, net assimilation rate, SCMR and harvest index. Hence, the crosses viz., GVIT 203 X WGG 42, GVIT 203 X EC 396117, LM 95 X WGG 42 and LM 95 X EC 396117 could be suggested for improving high yield coupled with bold seeded grains. The genotype WGG 37 exhibited better performance for the traits number of pods per plant and leaf area duration. Similarly the crosses viz., GVIT 203 X WGG 37 and GVIT 203 X WGG 37 could be suggested for improving high yield coupled with number of pods per plant. Ranking of genotypes: In genotype-by-trait analysis, an ideal cultivar has been defined as the cultivar that combines several good traits in its genetic composition (Apraku et al. 2010). The GT biplot can also be used in ranking of the ideal cultivars based on the mean performance over the multiple traits. Genotype-by-trait biplot representing the ranking of thirty five mungbean genotypes on the basis of their mean performance across the selected traits is presented in Fig 3. Evaluation of the genotypes based on average trait is achieved by drawing an Average Tester Coordinate (ATC) on the biplot (Yan and Kang, 2003). The line that passes through the biplot origin and the average trait called the average trait axis is the abscissa of the ATC.

4


usefulness of GT biplot technique in ranking and identifying the best genotypes.

Fig. 3. A vector view of genotype-by-trait biplot showing the ranking of thirty five mungbean genotypes for various morpho-physiological traits. PH = Plant height DM = Days to maturity SW = Seed weight HI = Harvset index NAR = Net assimilation rate CC = Chlorophyll content


Selection of Genotypes for Seed Yield and Yield Related Traits : The GT biplot can be used to aid genotype selection on the basis of individual traits in addition to selection based on multiple traits Fig. 4. A illustrated the selection of the genotypes based on seed yield by culling genotypes that yielded below average. This was done by drawing a line that passes through the biplot origin and the marker of seed yield per plant, followed by drawing a line that passes through the biplot origin and is perpendicular to the seed yield line. In the biplot display, the projections onto the ATC abscissa (single-arrowed horizontal line) approximated the performance of the genotypes based on seed yield, while the ATC ordinate (vertical line) divides the abscissa into two i.e. entries with low performance (below average on the right side) and entries with high performance (above average on the left side). In the present biplot, the principal components PC1 and PC2 explained 52.65% of the total variation observed among the cultivars based on all the traits.

The cultivars are ranked along the ATC abscissa, with the arrow pointing to higher mean performance. The small circle in Fig. 3, which is located on the ATC abscissa and with an arrow pointing to it, represents the ideal cultivar. The concentric circles, taking the ideal cultivar as the center, help in visualizing the distance between all cultivars and the ideal cultivar. The ideal genotype is described as the entry with the longest projection onto ATC abscissa and positioned closest to the ideal entry. The biplot accounted for 52.65% of the total variation among the varieties for the measured traits. Based on the selected traits, LM 95 and GVIT 203 was identified as the ideal genotypes. Based on their performance, the genotypes may be ranked as follows; LM 95 GVIT 203 > AKM 9904 KM 122 > WGG 37 > ML 145 > MGG 350 > WGG 2 PM 115 IPM 02-03 TLM 7 PM 110 ASHA PUSA 9531 » PUSA VISHAL RM 112 > MGG 347 » TM 96-2 » VG 6197A » MGG 380 » ML 267 » LGG 460 » MGG 295 RMG 9912 > SML 1023 » LGG 407 » IPM 02-14 » LGG 450 » JBT37/150 > MGG 295 » VG 7098A » LGG 528 » IPM 02-19 » WGG 42 > EC 396117. Among all the genotypes LM 95, GVIT 203 followed by AKM 9904, KM 122 and WGG 37 were identified as the ideal cultivars. Similarly, researchers in other crops such as peanut (Safari et al. 2013), wheat (Farshadfar et al. 2015) and maize (Apraku et al. 2010) demonstrated the

Fig. 4A. Mungbean genotype selection by genotype-by-trait biplot on the basis of seed yield per plant.

Based on the biplot, GVIT 203 followed by LM 95, AKM 9904, KM 122 and WGG 37 were found as ideal cultivars for seed yield per plant (Fig 4A). Similarly, the genotypes WGG 42, EC 396117, IPM 02-19, RMG 9912 and VG 7098A for NAR (Fig 4B) and the genotypes WGG 42, EC 396117, IPM 02-19, RMG 9912 and VG 7098A for SCMR (Fig 4C) were identified as the best cultivars. For days to maturity the genotypes viz., LM 95, GVIT 203, WGG 37, KM 8651 and AKM 9904 located in the left part of the ATC ordinate could be considered for late maturity. However, the genotypes WGG 42, EC 396117, IPM 02-19, VG 7098A and RMG 9912 appeared in the right part of the ATC ordinate could be considered for early maturity (Fig. 4D).

Sofia et al. : Genotype by trait (GxT) biplot analysis for trait relations and genotypes selection in mungbean

Fig. 4B. Mungbean genotype selection by genotype-by-trait biplot on the basis of net assimilation rate.

5

Fig. 4D. Mungbean genotype selection by genotype-by-trait biplot on the basis of days to maturity.

yield per plant. Hence, these genotypes could be recommended for commercial exploitation. Similarly, the genotypes EC 396117 and IPM 02-19 could be used for the development of bold seeded types in mungbean. REFERENCES Apraku BB, Akinwale RO and Fakorede MAB. 2010. Selection of early maturing maize inbred lines for hybrid production using multiple traits under striga-infested and striga-free environments. Mayaica. 55: 261-274. Farshadfar E, Kianifar S and Chaghakabodi R. 2015. GT biplot analysis of genetic diversity in bread wheat using in vitro indicators of drought tolerance. Biological Forum–An International Journal 7(1): 1439-1447. Indiastat 2014-15. stats.aspx.

Fig. 4C. Mungbean genotype selection by genotype-by-trait biplot on the basis of SPAD Chlorophyll meter reading (SCMR).

CONCLUSION This study demonstrated the utility of GT biplot as an excellent tool for visualizing genotype by trait data. The GT biplot clearly revealed complex relationships between traits and genotypes. The traits viz., number of clusters per plant, number of pods per plant, days to maturity, number of pods per cluster, leaf area duration and chlorophyll content had positive correlation with seed yield per plant. Therefore, these traits could be considered as major yield contributing characters in mungbean and emphasis should be made on these traits in the selection programme to evolve high yielding genotypes in mungbean. The genotypes LM 95 and GVIT 203 showed superior performance for seed

https://www.indiastat.com/agriculture/2/

Odewale JO, Agho C, Ataga CD, Okolo EC, Ikuenobe CE and Ahanon MJ. 2013. Genotype by trait relations of yield and other physilogical traits of coconut (Cocos nucifera L.) hybrids based on GT biplot. Merit Research Journal of Agricultural Science and Soil Sciences 1(3): 042-049. Okoye N, Okwuagwu CO, Uguru MI, Ataga CD and Okolo EC. 2007. Genotype by trait relations of oil yield in oil palm (Elaeis guineesis Jacq.) based on GT biplot. African Crop Science Society 8: 72372 8. Oladejo AS, Akinwale RO and Obisesan IO. 2011. Interrelationships between grain yield and other physiological traits of cowpea cultivars. African Crop Science Journal 19(3): 189–200. Paramesh M. 2014. Studies on genetic diversity and genotype by trait biplot analysis for yield and drought related traits in mungbean (Vigna radiata (L.) Wilczek). M.Sc. (Ag.) Thesis. Acharya N.G. Ranga Agricultural University, Hyderabad. Safari P, Honarnejad R and Esfahani M. 2013. Indirect selection for increased oil yield in peanut: comparison selection indices and biplot analysis for simultaneous improvement multiple traits. International Journal of Biosciences. 3(8): 87-96.

6


Singh CM, Mishra SB and Pandey A. 2014. Pattern of agromorphological trait relationship and genetic divergence in greengram (Vigna radiata (L.) Wilczek). Electronic Journal of Plant Breeding. 5(1): 97-106. Yan W and Kang MS. 2003. GGEBiplot Analysis: A Graphical tool for Breeders, Geneticists and Agronomists. CRC Press. ISBN: 08493-1338, 4: 63-88.

Yan W and Rajcan I. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Science 42: 11-20. Yan W and Tinker NA. 2006. Biplot analysis of multi-environment trail data: principles and applications. Canadian Journal of Plant science 86: 623-645. Yan W, Kang MS, Ma B, Woods S and Cornelius PL. 2007. GGEBiplot vs. AMMI Analysis of genotype-by-environment data. Crop Science. 47: 643-655.


Agro-morphological diversity analysis in fieldpea (Pisum sativum L.) genotypes grown under protected irrigation at marginal soils of Manipur MUNIYANDI SAMUEL JEBERSON, K SANKARAPPA SHASHIDHAR and AMIT KUMAR SINGH Directorate of Research, Central Agricultural University, Imphal, Manipur; E-mail: [email protected] (Received : May 23, 2017, Accepted : July 28, 2017) ABSTRACT The investigation was undertaken to examine the genetic variability present in the 17 fieldpea genotypes, grown under protected irrigation at the marginal soils of foothills of Manipur. The significant variability was recorded for traits except days to 50% flowering. In PCA, there are two eigen values greater than 1 implied the choice of the two principal components (PCs) and the first and second PCs had 60% and 18% of the total variance, respectively. The 1 st PC was strongly influenced by characters like plant height, days taken to attain 50% flowering, maturity and pods/plant, whereas 2nd PC was largely influenced by seed yield and 100 seed weight. Genotype “HFP 9426” was found superior based on both PC1 and PC2. The genotypes arranged in bipolar plane clearly exhibited that pods/plant, days to 50% flowering and maturity were with greatest length and directly helped to determine the level of Agro-morphological diversity. Cluster analysis generated three distinct groups of the genotypes studied here. Key words: Correlation, Cluster analysis, Fieldpea, Genetic Variability, Principal component analysis

Globally, fieldpea (Pisum sativum L. 2n=14, Fabaceae) is one of the most important food legume crops grown in winter season and it covers 6.27 million ha of acreage and produces 11.16 million tons of food grains (FAOSTAT, 2015). In India however, it has an area of 3.05 lakh ha with a production of 1.9 million tons of food grains in general and 30 thousand ha of area, 28 thousand tones of production with a productivity of 937 kg/ha only (Anon. 2015) in Manipur in particular. Fieldpea grains are primarily used for human consumption or as livestock feed. The grains are nutritionally rich in proteins, carbohydrates, vitamins and essential minerals etc. Unfortunately, the productivity of fieldpea is very low in India when compared to the world’s productivity of 1.7 tons/ha. This seems due to the narrow genetic base and limited variability used to improve the local varieties (Kumar et al. 2004). Therefore, path-breaking breeding approaches are required not only to improve the quality but also to increase the production of fieldpea by doubling the productivity using diverse and foreign sources. Improvement of crop greatly depends on the accessibility of diverse materials and their efficient utilization. Therefore, present investigation was formulated to evaluate the genetic diversity and characteristic associations in different

set of fieldpea genotypes for their location specific utilization in crop improvement programs. MATERIALS AND METHODS The field study was undertaken at Research Farm of Central Agricultural University Imphal, Manipur during 2014-15. Experimental units situated at 94° 0.03463 E longitude; 24° 0.45893 N latitude and an altitude of 875.0 meter above sea level and fall under the “Eastern Himalayan Region (II)” and the agro climatic zone “Sub-Tropical Zone (NEH-4)”. A brief detail of experimental unit is presented in Table-1. Table 1.

Soil Conditions of Experimental Field (top 30cm depth)

Particulars Soil texture pH

Values/Status Clay loam 5.60 (Acidic)

EC

0.065 dSm-1

Avg. Annual Rainfall

1212 mm

Particulars Organic Carbon Available Nitrogen Available Phosphorus Available Potassium

Values/Status 0.93% (high) 292.18 kg/ha (medium) 18.0 kg/ha (low) 304 kg/ha (medium)

The experiment was laid out under randomized completely block design (RCBD) with 17 fieldpea genotypes collected from across the India and 3 replications. The experimental field prepared to obtain desired tilth and compact seedbed for good and uniform germination, optimum growth & better development of crop through tillage operations. Later on, desired plots having size of 7.2 m2 (1.8 m x 4.0 m) were made. Subsequently, sowing was done @ 70 kg seed/ha at a spacing of 30 cm in row at a depth of 4-5 cm only. Recommended package of practices were adapted towards crop raised. Five plants from each plot were randomly selected and used to estimate yield components such as number of days to 50% flowering and days to maturity, plant height, pods/plant, 100 seed weight, pod length and seed yield. However, all the data pertaining to the present investigation were subjected to statistical analysis by SAS version-9.3. Analysis of variance (ANOVA) for both principal component analysis and cluster analysis was computed out to assess the response of treatment variables. RESULTS AND DISCUSSION Scrutiny of the data slough Table-2 revealed that a high degree of variability was observed for all the

8

Table 2.


Mean, standard error, range, mean sum of squares and coefficient of variation for six characters in 17 tall Indian fieldpea genotypes

Traits Days taken to attain 50% flowering Days taken to attain maturity Plant Height Pods/plant 100 seed weight (g) Yield (g/plot)

Mean 78.08 111.22 37.07 6.73 19.01 553.04

Standard Error 1.66 1.88 0.63 2.40 2.12 147.15

morphological characters except days taken to attain 50% flowering, this observation corroborated with the report of Khan et al. (2013), Wani et al. (2013), Gixhari et al. (2014), Ouafi et al. (2016) and Georgieva et al. (2016). The simple correlation matrix shows that the days to attain 50% flowering positively influenced the days to maturity, plant height and pods/plant (Table 3). Similarly, days to maturity were significantly influenced the plant height and pods/ plant and plant height also influenced the pods/plant. All the first four characters were negatively influenced the 100seed weight. The seed yield was not significantly influenced by all the characters. These results are in close conformity Table 3. Traits DFF DM Pht Pods/plant sw

Table 4.

Correlation coefficients among six quantitative characters in 17 Tall Indian fieldpea genotypes DFF DM Pht Pods/plant sw 1 0.7823 0.7609 0.7313 -0.4316 1 0.8597 0.7285 -0.3702 1 0.7445 -0.4649 1 -0.2303 1

yd 0.0218 -0.152 0.0877 0.0448 0.1442

Range 75.0-80.67 108.67-115.0 36.44-38.33 3.17-10.83 15.88-22.22 366.67-791.67

Mean sum of squares 8.23ns 10.58* 538.25** 17.35* 13.53** 64960.66**

CV (%) 3.87 2.06 17.91 14.62 7.79 7.74

with the results obtained by Jeberson et al. (2016) in fieldpea. Eigen values of six principal components have been shown in the scree plot (Figure 1). Principal component analysis (PCA) was done using of six characters (Table 4). The first principal component was absolutely linked to days to 50% flowering, days to maturity, plant height, pods/plant. However, 100-seed weight and seed yield were negatively correlated. The second principal component was negatively related to days to maturity and plant height, while rest of the characters were positively correlated. The third principal component was partially influenced by 100-seed weight. Analysis of genetic distances and the most important morphological characters using principal co-ordinates was showing which is the most prominent traits influencing the seed yield. All the six quantitative variables contribute in the total source 100% of variance. The first three principal components accounted for 90% (59.76%, 18.04% and 13.19%, respectively) of the variability present in the material (Table 4). The first principal component is the major source

Principal components (PCs) for six quantitative characters in Indian tall fieldpea

Eigen Value Proportion σ2 Cumulative σ2 Characters Days taken to attain 50% flowering Days taken to attain maturity Plant height Pods/plant 100 seed weight Yield

PC1 3.5298 58.83 58.83 Eigen Vectors 0.4793 0.4893 0.4952 0.4510 -0.2864 -0.0164

PC2 1.0825 18.30 77.13

PC3 0.7912 12.74 89.87

0.0596 -0.0770 0.1086 0.1825 0.3924 0.8896

0.0132 0.2246 -0.0493 0.3085 0.8295 -0.4046

Fig. 1. Principal component analysis for different quantitative characters of Indian tall fieldpea genotypes; a.) Scree plot, b.) component pattern of PC1 and PC2 c.) component scores of different genotypes

Jeberson et al. : Agro-morphological diversity analysis in fieldpea (Pisum sativum L.) genotypes grown under protected

Table 5.

Three clusters grouping fieldpea genotypes based on six quantitative characters

Cluster I

Frequency 6

II

7

III

4

Cluster Memberships Pant P 286, RFPG 78, VL 63, IPF 15-21, RFP 2011-3, HFP 9426 Pant P 302, KPMR 939, RFP 11-2, Prakash, VL 62, IPF 15-13, Pant P 42 HFP 1024, RFPG 78, RFPG 85, Rachna

Fig. 2. Dendrogram of Indian tall fieldpea genotypes

of the variation that account for the greatest possible variance. The proportion of total variation more than 75% is acceptable in this kind of studies (Cadima and Jolliffe, 2001 and Jolliffee, 2002). Rahim et al. (2008) got more than 71.48% of the variability amongst 34 genotypes evaluated for 8 traits. Siddika et al. (2014) reported that first principal component alone showed the variation of 91.42% while studying with twenty five advanced breeding lines of vegetable pea. Ouafi et al. (2016) also reported that the principal component analysis revealed more than 85% of variation in fieldpea. Esposito et al. (2007) also got similar results of 81 % variation in fieldpea genotypes studies using PCA analysis and concluded that this much variability in the material is sufficient for generating new gene combination for further yield improvement. Habtamu and Million (2013) were studied principal component analysis in fieldpea and found out that among 12 PCs, four were accounted for more than 89% of the total variation, in that first PC alone contributed 40.26% of the total variation. From the location of the genotypes in bipolar graph, it was found that pod/plant and days to 50% flowering had the greatest length and determining the level of variability (Figure 1. b, c). Genotypes HFP 9426 and Pant P 286 characterized by positive values of both the PC1 and PC2. The second quadrant containing RFPG 85, Rachna, Prakash and RFP 11-2, while the genotypes, RFPG 78, VL 62, KPMR 939 and Pant P 302 were situated in the third quadrant. The fourth quadrant harboured the genotypes RFPG 95, HFP 1024 and RFP 2011-3 and IPF 15-21. Genotypes placed in the first, second, third and fourth quadrants revealed that

9

the genotypes were phenotypically dissimilar for all the characters (Figure 1.c). Genotypes Pant P 42 and VL 63 were placed in the middle line show that they are not useful for further improvement. The characters viz., pods/plant, plant height and days to 50% flowering were placed in the first quadrant and exhibited their influence in the seed yield. Similar results were obtained by Georgieva et al. (2016) in fieldpea. Cluster analysis of 17 genotypes and relationship among them are illustrated in Figure 2. All the genotypes were categorized into three clusters. Cluster I, II and III included six genotypes, seven genotypes and four genotypes, respectively. The grouping of clustering is clearly showing yielding pattern. The cluster I, II and III were having high yielding, low yielding and medium yielding genotypes, respectively and detail is provided in Table. 4. Similar results were obtained in cluster analysis while studying Albanian pea genotypes. The results in this study show agreement with the results of Gixhari et al. (2014) in Albanian pea genotypes; Rahim et al. (2008) in mungbean; Georgieva et al. (2016) in fieldpea; Gatti et al. (2011) in fieldpea; Zubair et al. (2007) in mungbean and Jeberson et al. (2017) in fieldpea. The genotypes from cluster I and III can be utilized for future breeding to generate the superior genotypes. From the present investigation, it may be concluded that Indian tall fieldpea genotypes shown a wide range of variability for most of the traits studied except days taken to attain 50% flowering. This will empower the breeder to identify, select and unite the genotypes to get important characters in one genotype with wide genetic base. The clusters made the genotype in to groups revealed that only a small portion of variability has been utilized for fieldpea improvement. From this clustering pattern of the genotypes, we could select the diverse advanced breeding lines for further utilizing to generate the different gene combinations for genetic improvement of fieldpea. ACKNOWLEDGEMENTS The authors are duly acknowledged for providing, the funding support by ICAR-IIPR, Kanpur, Uttar Pradesh, and institutional support by the Directorate of Research, CAU, Imphal for this study. REFERENCES Cadima JF and Jolliffe IT. 2001. Variable selection and the interpretation of principal subspaces. Journal of Agricultural, Biological, and Environmental Statistics (6): 62-79. Esposito MA, Milanesi LA, Martin EA, Cravero VP, Anido FSL and Cointry EL. 2007. Principal component analysis based on morphological characters in fieldpea (Pisum sativum). International Journal of Plant Breeding 1(2): 135-137. FAOSTAT. 2015. Food and Agriculture organization of the United Nations, Statistics Division. available oline: http://faostat.fao.org /August 15, 2016

10


Gatti I, Esposito MA, Almira P, Cravero VP and Cointry EL. 2011. Diversity of pea (Pisum sativum L.) accessions based on morphological data for suitable fieldpea in Argentina. Genetics and Molecular Research 10(4): 3403-3410.

Khan TN, Ramzan A, Jillani G, Mehmood T. 2013. Morphological performance of peas (Pisum sativum L) genotypes under rainfed conditions of potowar region. Journal Agriculture Research 51(1): 51-60.

Georgieva N, Nikolova I and Kosev V. 2016. Evaluation of genetic divergence and heritability in pea (Pisum sativum L.). Journal of BioScience and Biotechnology 5(1): 61-67.

Kumar S, Gupta S, Chandra S and Singh BB. 2004. How wide is the genetic base of pulse crops. In: Masood Ali, B.B.Singh, Shiv Kumar and Vishwa Dhar (eds), Pulses in new perspective. Indian Society of Pulses Research and Development, IIPR, Kanpur, India. pp. 211-221.

Gixhari B, Vrapi H and Hobdari V. 2014. Morphological characterization of pea (Pisum sativum L.) genotypes stored in Albanian gene bank. Albanian. Journal of Science (Spcial Edition): 169-173. Habtamu S and Million F. 2013. Multivariate analysis of some Ethiopian fieldpea (Pisum sativum L.) genotypes. International Journal of Genetics and Molecular Biology 5(6): 78-87. Jeberson MS, Manish Kumar, Shashidhar KS and Ranjit Sharma. 2017. Multivariate analysis of Indian Fieldpea (Pisum sativum L.). National Seminar on Emerging crops of North East India suitable for horticultural based integrated farming system held at COA, CAU, Imphal. Pp-308. from Feb. 7-9, 2017, Pp-308. Jeberson MS, Shashidhar KS and Iyanar K. 2016. Estimation of genetic variability, expected genetic advance, correlation and path analysis in field pea (Pisum sativum L.). Electronic Journal of Plant Breeding 7(4): 1074-1078. Jollifee IT. (2002). Principal component analysis, second edition, p. cm. springer series in stastistics, UAS.

Quafi L, Alane F, Bouziane RH and Abdelguerfi A. 2016. Agromorphological diversity within fieldpea (Pisum sativum L.) genotypes. African Journal of Agricultural Research 11(40): 4039-4047. Rahim MA, Mia AA, Mahmud and Afrin KS. 2008. Multivariate analysis in some mungbean (Vigna radiate L. Wilczek) accessions on the basis of agronomic traits. American-Eurasian. Journal of Scientific Research 3(2): 217-221. Siddikka A, Aminul Islam AKM, Rasul MG, Mian MAK and Ahmed JU. 2014. Genetic diversity in advanced generation. Bangladesh Journal of Plant Breeding and Genetics 27(1): 9-16. Wani G, Mir B and Shah M. 2013. Evaluation of diversity in pea (Pisum sativum L.) genotypes usingagro-morphological characters and RAPD analysis. International Journal of Current Research and Review 5(10): 17-25. Zubair M, Ajmal SU, Anwar M and Haqqani AM. 2007. Multivariate analysis for quantitative traits in mungbean (Vigna radiata L. Wilczek). Pakistan Journal of Botany 39(1): 103-113.


Evaluation of chickpea (Cicer arietinum L.) germplasm in Jharkhand YOGESH KUMAR1, J GHOSH2, RK MISHRA3 and SK CHATURVEDI4 1

Central Rainfed Upland Rice Research Station, Hazaribag-825301, Jharkhand, 2ICAR-Indian Institute of Natural Resins & Gums, Ranchi-834010, Jharkhand, 3&4ICAR-Indian Institute of Pulses Research, Kalyanpur-208024, Kanpur, Utter Pradesh, E-mail: [email protected] (Received : November 2016 Accepted: March 13, 2017) ABSTRACT Pulses remain stagnant inspite of production potential of 2-3 tonnes per hectare of almost all the new released cultivars. Production constraints in pulses include lack of high yielding varieties adapted to diverse growing condition, large area under rainfed cultivation (88%), biotic and abiotic stresses (up to 30% losses), poor plant stand, poor response to high input conditions and better management, moisture stress at terminal growth stage, inadequate seed replacement rate, emerging deficiencies of secondary and micronutrients, low risk bearing capacity, resource poor farmers, poor crop management, moisture stress at terminal growth stage, inadequate seed replacement rate, emerging deficiencies of secondary and micronutrients, low risk bearing capacity, resource poor farmers and poor crop management. A total of 782 germplasm were sown in an augmented complete block design with three checks (BGD 72, BG 362 and BG 1053) for yield and other quantitative traits. Consequently the germplasm accession ICC 492 (3270 kg/ha), ICC 483 (2298 kg/ha) and ICC 5681/38 (1882 kg/ha) having higher grain yield along with other yield contributing factors will be utilized in breeding programme for stability performance in Jharkhand. Key words: Chickpea, Evaluation, Germplasm, Quantitative traits

Chickpea (Cicer arictinum L.) is an important winterseason food legume and it is protein rich supplement to cereal-based diets, especially to the agrarian population of the country and cheaper source of protein in developing countries. India is the largest producer of chickpea accounting for 64% of global chickpea production. In the post-green revolution period, the per capita availability of pulses has declined sharply in the country, mainly due to mismatch in population and production growth. In spite of having the largest area under chickpea, pigeonpea, lentil, dry beans and total pulses in the world, India’s position in average production of these pulses has not been decent (FAO 2001). Despite the importance of pulses in our daily diet and agricultural production, the production of pulses has not yet increased proportionately compared to the increase in the cereal production. In India, chickpea is being grown in an area of about 6.7 m ha from 320 N in northern India with cooler long season environment to 100 N in southern India with warmer short season environment. The productivity of chickpea in this region is constrained mainly by terminal drought because it is traditionally cultivated as

a winter crop using conserved soil moisture. Large number of important high yielding varieties of chickpea has been evolved, but yield of those varieties were not stable over environments, which was one of the reasons for poor adaptation. Access to genetic variability is a prerequisite for any crop improvement program. Because of the increasing recognition, evaluation and characterization of chickpea germplasm has received the attention of plant breeders. Utilization of exotic and genetically diverse germplasm is needed to develop stable and high-yielding cultivars with a broad genetic base. Genetically diverse lines provide ample opportunity to create favorable gene combinations and the probability of producing a unique genotype increases in proportion to the number of genes by which the parents differ. However, it is a difficult task to select a few probable parental lines from a huge germplasm collection. Present investigation on germplasm evaluation will provide the user genetically and ecologically distinct accessions. MATERIALS AND METHODS Considering above facts, chickpea germplasm were screened and evaluated at Birsa Agricultural University, Ranchi, Jharkhand from 2011-12 to 2012-13 to assess relative superiority of the lines for their further use in breeding programme. The farm is situated at 23019’E latitude, 85031’ longitude and 625 meters altitude. The chickpea germplasm including chickpea elite lines were evaluated at Birsa Agricultural University, Ranchi in 2010-11. Selected lines (985) were again evaluated in 2011-12. Finally on the basis of yield and morphological traits 782 germplasm were sown during 2012-13 in an augmented complete block design (Federer, 1956) with three checks (BGD 72, BG 362 and BG 1053) to know potential yield and other quantitative traits. Field lay out were prepared by SPAD software developed by Indian Agricultural Statistical Research Institute (IASRI), New Delhi. These test entries were sown in fourteen blocks, each block having 56 test entries along with three checks replicated twice in each block i.e., 62 plots. The last block has 60 entries only. Each germplasm and checks were sown in paired row of 4 m with row spacing of 45 cm and the plant spacing was 15 cm. Two irrigations were provided after sowing and during pod formation. The zone has a subtropical semi-arid climate with dry summer and cold winter. Recommended package of practices were

12


Table 1. Mean performance of ten good germplasm of chickpea and checks Sl. Germplasm No. 1 2 3 4 5 6 7 8 9 10

ICC 492 ICC 483 ICC 568138 ICC-849 ICC-6029 ICC-516 ICC-5708 IC-327423 ICC-501 IC-327425 Check 1 (BGD 72) Check 2 (Pusa 362) Check 3 (Pusa 1053) General Mean SE (m) CD (Check) at 5% CD germplasm at 5% (same block) CD germplasm at 5% (different block) CD check vs germplasm at 5% CV %

Days to flowering 59.55 68.55 82.55 73.55 96.21 73.55 46.55 83.55 85.55 83.55 81.07 73.50 75.07 66.91 4.83 7.51 28.10 32.44 23.34 14.44

Primary branches 3.64 3.94 1.64 2.04 4.04 3.04 1.84 1.38 3.04 1.38 1.47 1.80 1.66 1.67 0.19 0.29 1.10 1.27 0.92 22.75

Plant height

Pods per plant

Days to maturity

100 seed weight

44.20 61.10 48.10 44.90 37.90 36.30 39.70 47.36 53.10 41.36 39.70 44.04 38.14 39.49 2.90 4.59 17.17 19.83 14.27 14.96

106.21 99.21 42.41 51.21 62.88 45.61 37.81 30.35 58.01 35.55 19.91 26.36 16.17 19.85 4.94 6.36 23.81 27.50 19.79 41.26

122.38 124.38 132.38 125.38 145.71 125.38 92.38 138.71 129.38 134.71 130.00 126.93 127.21 118.14 4.54 7.15 26.73 30.87 22.21 7.78

16.93 12.63 12.63 13.63 25.47 11.43 14.43 13.87 19.83 13.27 26.34 25.40 26.26 17.75 1.71 2.67 9.99 11.53 8.30 19.35

Yield (kh/ha) 3269.90 2297.90 1881.90 1603.90 1465.24 1464.90 1464.90 1418.90 1325.90 1279.90 615.93 626.00 497.79 408.27 132.35 205.70 769.65 888.71 639.54 64.83

Table 2. Mean sum of square of quantitative traits of chickpea germplasm Source of variation

df

Block (adjusted) Germplasm (adjusted) Among checks Among germplasm Check vs germplasm Error

13 784 2 781 1 26

Days to flowering 90.90* 177.03** 223.53* 171.91* 4117.95** 93.37

Primary branches 0.24* 0.28* 0.38 0.28* 0.03 0.14

followed to raise good crop. Data were recorded for grain yield and other yield contributing traits like days to flowering, days to physiological maturity, plant height, number of pods per plant and 100 seed weight etc. RESULTS AND DISCUSSION Significant variation was observed among treatments and among germplasm for all the seven traits under investigation i.e., days to flowering, primary branches, plant height, pods per plant, days to maturity, 100 seed weight and grain yield. Checks were also significant in almost all traits except days to maturity, primary branches per plant and 100-seed weight. Chaudhary et al. (2009) evaluated 88 chickpea genotypes for 12 qualitative and quantitative traits. They observed significant variation with respect to days to first flower, 50% flowering, days to first pod set, physiological maturity, leaflet per leaf, pods per plant, 100 seed weight and seed yield per plant. Significant difference between check vs germplasm reflected contrasts belovian of germplasm and checks for all traits. Accessions ICC 492, ICC 5687 and ICC 483 have higher number of pods per plant (106, 102 and 99, respectively) and accessions ICC 11155, ICC 5337 and IC 327400 have higher 100 seed weight (i.e., 38.3, 36.1 and 34.4

Plant height 49.60* 43.44* 130.89** 43.20* 59.17* 34.88

Pods per plant 77.32* 103.17** 317.63** 102.56** 42.25* 67.06

Days to maturity 50.40 176.45** 40.31 171.51 4350.12 84.54

100 seed weight 35.10** 15.57* 3.81 11.76* 3011.85** 11.79

Yield (k/ha) 86623.36* 71103.12* 232796.89* 70697.36 65552.04 70065.60

gram, respectively). Earlier Bahl et al. (1991) evaluated 329 chickpea lines, comprising 130 kabuli and 199 desi types originated from six regions to know genetic variation and adaptability in chickpea. They observed that the degree of expression of certain characters could be ascribed to specific areas, which eventually led to area specific adaptations. Similarly, Upadhyaya et al. (2006) evaluated 1956 accessions of chickpea core collection for 14 agronomic traits at ICRISAT to identify diverse lines for agronomic traits for use in crop improvement. The authors suggested that core collections represent an important means to identify useful parents for crop improvement programme. Naghavi and Jahansouz (2005) evaluated 362 chickpea accessions, collected from the major chickpea growing areas of Iran for variation in the agronomic and morphological traits of iranian chickpea accessions. High coefficients of variation (CVs) were recorded in pods per branch, seeds per pod, yield per plant, seeds per plant, pods per plant and branches per plant. Using principal component analysis (PCA), the first four PCs with eigen values more than 1 accosted for 84.10% of the total variability among accessions, whereas PC5 to PC10 were less than unity. PC1 was positively related to days to first maturity, days to 50% flowering and days to 50% maturity. The characters with the greatest weight on PC2 were seeds per plant and yield per plant, whereas PC3

Kumar et al. : Evaluation of chickpea (Cicer arietinum L.) germplasm in Jharkhand

was mainly related to pods per plant, seeds per pod and 100-seed weight, and PC4 was positively related to pods per branch and negatively to branches/plant. The germplasm was grouped into four clusters using cluster analysis. Each cluster had some specific characteristics of its own and the cluster I was clearly separated from clusters II, III and IV. These accessions are an important resource for the establishment of a core collection of chickpeas in the world. For grain yield, maximum variation was observed ranging between 376 kg/ha in ICC 10992 to 3270 kg/ha in ICC 492. More than 20 germplasm liner showed significantly better performance than best check (BGD 72) for grain yield. The germplasm accession, namely ICC 492 (3270 kg/ha), ICC 483 (2298 kg/ha) and ICC 5681/38 (1882 kg/ha) having higher grain yield along with other yield contributing factors will be utilized in breeding programme for stability performance in Jharkhand state.

13

REFERENCES Bahl PN, Kuamr J and Raju DB. 1991. Genetic variations and adaptations in chickpea, Plant Breeding 106(2): 164-167. Chaturvedi SK, Mishra DK and Kumar H. 2009. Evaluation of donors for various quantitative and yield traits in chickpea (Cicer arietinum L.). Trends in Biosciences 2(1): 53-55. Federer WT. 1956. Augmented design. Hawain Planneters Record 40: 191-207. Meena HP, Kumar J, Upadhyaya HD, Bharadwaj C, Chauhan SK, Verma AK and Rizvi AH. 2010. Chickpea mini core germplasm collection as rich sources of diversity for crop improvement, SAT e-Journal, www.ejournal.icrisat.org 8: 1-4. Naghavi Md Reza and Jahansouz Md Reza. 2005. Variation in the Agronomic and Morphological Traits of Iranian Chickpea Accessions. Journal of Integrative Plant Biology 47(3): 37537 9. Upadhyaya HD, Dwivedi SL, Gowda CLL and Singh S. 2007. Identification of diverse germplasm for agronomic traits in a chickpea (Cicer arietinum L.) core collection for use in crop improvement, Field Crops Research 100: 320-326.


Influence of different spacing and fertilizer levels on yield, quality and economics of pea (Pisum sativum L.) BM KALALBANDI, AS LOHAKARE and DB KADRE College of Horticulture, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani 431402, Maharashtra India; E-mail: [email protected] (Received: June 12, 2017, Accepted: March 21, 2017)

ABSTRACT

MATERIALS AND METHODS

The experiment was laid out in a factorial randomized block design with three replications comprising three level of spacing (30X10 cm, 30X15 cm and 30X20 cm) and three fertilizer levels (20:40:40, 30:60:60 and 40:80:80 NPK kg/ ha) thereby involving nine treatment combinations on pea cv. ‘Arkel’. Adoption of closer spacing (30X10 cm) recorded higher yield attributes of pea, while 30X20 cm spacing level recorded higher quality attributes. As regard to fertilizer levels 40:80:80 NPK kg/ha found to be beneficial in producing higher yield and improved its quality as compared to rest of treatments under study. The maximum yield and B:C ratio was found in interaction effect of treatment combination S1F 2 (30x10 cm + 40:80:80 NPK kg/ha) while for quality attributes S2F2 (30x20 cm + 40:80:80 NPK kg/ha) was found significant.

A field experiment entitled ‘Influence of different spacing and fertilizer levels on yield, quality and economics of pea (Pisum sativum L.) cv. ‘Arkel’ was conducted at Department of Horticulture, Vasantrao Naik Marathwada Keisha Vidyapeeth, Parbhani during winter season of 20142015. In the present investigation, three level of fertilizers viz., nitrogen, phosphorus and potash combined with spacing variation viz. S0 - 30x 5 cm (RS), S1-30 x 10 cm and S2-30 x 20 cm and F0- 30:60:60 NPK kg/ha (RDF), F1-20:40:40 NPK kg/ha and F2-40:80:80 NPK kg/ha were tried involving 09 treatment combinations on the gross plot 3.0 x 2.4 m and net plot 2.7 x 2.2 m size, respectively. The experiment was laid out in Factorial randomized block design with three replications. The yield attributes viz., pod yield per plant (g) and pod yield per plot (kg) was measured by selecting five plants randomly while above parameters were calculated for pod yield per hectare (q) basis. Regarding, quality parameters viz., Total soluble solids (0Brix), shelling percentage (%), protein content (%) and strength (Kgf) were recorded. Data obtained on above various variables were analyzed by analysis of variance method suggested by Panse and Sukhatme (1967). The gross monetary returns (`/ha) obtained due to different treatments in the present study, were worked out by considering market prices of economic product, by product and crop residues during the experimental year. However, the net monetary returns (`/ha) of each treatment were worked out by deducting the mean cost of cultivation (`/ha) of each treatment from the gross monetary returns (`/ha) gained from the respective treatments. The cost of cultivation (`/ha) of each treatment was worked out by considering the price of inputs, charges for cultivation, labour and other charges. Subsequently, the benefit: cost ratio of each treatment was calculated by dividing the gross monetary returns by the means cost of cultivation.

Key words: B: C ratio, Fertilizer levels, Pod yield, Quality, Spacing

Garden pea (Pisum sativum L.) is an important vegetable crop, has acquired a place of prominence not only in sumptuous banquets but in diets of the ordinary and poor class people also. It is being recognized as an important protein supplement. Garden pea is rich in digestible protein, vitamin A and C. It is also rich in minerals like calcium, potassium, iron and phosphorus. Fresh green pea is excellent food for human consumption taken as vegetable or in soup. Large proportion of pea is processed canned, frozen, and dehydrated. Several high yielding varieties are cultivated but average productivity of pea is quite low (6 MT/ha) in marathwada region due to several production constraints. The average productivity of pea crop can be enhanced by standardizing the production technique to be followed in field. Maximum yield can be obtained mainly by providing the most optimum plant population per unit area and supply of adequate andbalanced nutrients under field conditions. Since these two factors not only enhance the productivity of crop but also decide the commercial success of pod vegetable crop like pea. Attar et al. (2013) recorded significant increase in pod yield and quality attributing characters when the seed crops were grown under the most optimum plant density per unit area in pea. However, such systematic works on spacing and fertilizer doses are inadequate and inconclusive in pea for marathwada region. Realizing the need, the present investigation was undertaken.

RESULTS AND DISCUSSION Yield attributes: The perusal of data presented in (Table 1) regarding yield and quality of pea as influenced by spacing and fertilizer levels recorded significant differences. Results regarding effect of spacing level on pea revealed that the maximum pod yield/plant i.e. 116.39 g plant/yieldwas recorded in S2 (30 x 20 cm) followed by S0 (30 x 15 cm) and

Kalalbandi et al. : Influence of different spacing and fertilizer levels on yield, quality and economics of pea (Pisum sativum L.)

minimum yield (88.01 g/plant) was observed in S1 (30 x 10 cm). This might be due to that closely spaced plants had a very little space for their lateral development as compared to widely spaced plants. This again can be explained on the basis of efficient photosynthetic activity, uptake of nutrients and better translocation of food material from sources to sink. This result was in confirmation with Attar et al. (2013) in pea. The maximum pod yield per plant (113.79 g) of pea was obtained in fertility level F2 (40.80:80 NPK kg/ ha) followed by treatment level F0 (105.28 g) where fertilizer dose was applied at 40:80:80 NPK kg/ha. The minimum pod yield per plant (95.04 g) was observed in treatment F1 where lowest fertilizer dose was applied at 20:40:40 NPK kg/ha. The present findings are in accordance with results of Bahadur et al. (2006) in pea. Data revealed that interaction effect between spacing and fertilizer levels was found significantly influenced in pea. The maximum pod yield per plant (125.12 g) was observed in treatment combination S2F2 (30x20 cm + 40:80:80 NPK kg/ha). Whereas, minimum yield per plant (78.22 g) was observed in S1F1 (30 x10 cm + 20:40:40 NPK kg/ha). The highest yield per plot was observed in treatment S1 (6.86 kg/plot) at closer spacing (30x10 cm) followed by recommended spacing (30x15 cm) whereas, lowest pod yield (5.03 kg/plot) was observed in wider spacing 30x20 cm. This may be due to maintaining higher plant population in per unit area which might led to produce maximum pod yield/plot. Higher yield at closer spacing was also reported by Shrikanth et al. (2008). The maximum pod yield/plot was recorded in fertilizer level F2 (5.79 kg/plot) where fertilizer level of (40:80:80 NPK kg/ha) was applied followed by F0 (5.32 kg/plot). Whereas, minimum pod yield (4.86 kg/plot) was observed in lowest fertilizer dose applied at (20:40:40 NPK kg/ha). This result was in confirmation with Gul et al. (2006). Results pertaining to interaction effect of spacing and fertilizer level in pea on yield/plot revealed that treatment combination of S1 F2 (30x10 cm + 40:80:80 NPK kg/ha) showed highest pod yield/ plot (7.46 kg) followed by closer spacing and RDF (30x10 cm + 30:60:60 NPK kg/ha) whereas, minimum pod yield/plot (3.81 kg) in wider spacing and lowest fertilizer dose. Present findings are in confirmation with findings of those reported by Attar et al. (2013).The maximum (118.94 q) yield/ha was observed in S1 (30x10 cm) whereas, minimum yield (74.48 q) was observed in S2 (30x20 cm). Significantly, Maximum yield/ ha (101.07 q) recorded in fertilizer level F2 (40:80:80 NPK kg/ ha) followed by F0 (93.84 q) where recommended fertilizer dose was applied. The minimum yield/ha was recorded in fertilizer level F1 (86.29 q) where lowest fertilizer dose i.e. 20:40:40 NPK kg/ha was applied. Data regarding interaction effect of spacing and fertilizer levels on yield per hectare of pea showed significant differences. The maximum pod yield/ ha (128.63 q) was recorded in treatment combination S1F2 (30x10 cm + 40:80:80 NPK kg/ha) whereas, minimum pod yield/ha (69.69 q) in treatment combination S2F1 (30x20 cm + 20:40:40 NPK kg/ha). Similar result was recorded by Dass et al. (2005) in pea.

15

Quality attributes: The data presented in (Table 1) showed that the TSS was significantly affected by different levels of spacing. The more total soluble solids (15.60 0B) were recorded in the wider spacing S2 (30x20 cm). Whereas, less (13.410B) in closer spacing S1 (30x10 cm). Similar results were reported by Sharma et al. (2007) in garden pea. The maximum TSS (16.200B) was recorded with application of highest fertilizer level F2 (40:80:80 NPK kg/ha) followed by F0 (14.770B) while, less TSS was observed in F1 (13.52 0B) i.e. 20:40:40 NPK kg/ha. The interaction effect of spacing and fertilizer level on TSS of pea was found maximum (17.54 0 B) in treatment combination S2F2 (30x20 cm + 40:80:80 NPK kg/ha), which was followed by S0F2 (16.380B). The minimum total soluble solids (12.200B) were observed in S1F1 where lowest fertilizer dose and closer spacing was adopted. The maximum protein content (18.64%) was recorded in wider spacing S2 (30x20 cm) which was at par with S0 (30x15 cm) spacing (18.62%). Whereas, minimum protein content (17.21%) was obtained in the treatment S1 i.e. closer spacing 30x10 cm. Similar result was given by Sharma et al. (2007) in pea. Protein content was significantly influenced by different fertilizer levels. Maximum protein content (19.58%) was recorded in F2 (40:80:80 NPK kg/ha) which was at par with F0 (18.40%) whereas, minimum protein content (16.50%) was observed in treatment F1 (20:40:40 NPK kg/ha). Promotive effect of higher levels of protein content in higher level of fertilizer might be due to increase in phosphorous levels. The interaction between effect of spacing and fertilizer levels found significant in protein percent of pea. The maximum protein content (20.53%) was found in S2F2 (30x20 cm + 40:80:80 NPK kg/ha) which was at par with S0F2 (20.17%) due to application of (30x15 cm + 30:60:60 NPK kg/ha). Minimum protein content was found in S1F1 (16.12%) where (30x10 cm + 20:40:40 NPK kg/ha) was applied. Regarding the highest shelling percentage (53.44%) was observed in spacing S2 (30x20cm) followed by S0 i.e. 30x15 cm (51.77%) whereas, less shelling percentage (49.84%) was recorded in closer spacing i.e. 30x10 cm. Similarly the maximum shelling percentage was recorded in fertilizer level F2 (55.68%) followed by treatment F0 (52.97%). Whereas, the minimums helling percentage (47.20%) was recorded in fertilizer level F1(20:40:40 NPK kg/ha). Data regarding interaction between spacing and fertilizer levels on shelling percentage of pea showed significant effect. The maximum shelling percentage (58.11%) was observed in S2F2 treatment combination (30x20 cm + 40:80:80 NPK kg/ha) whereas, minimum (45.47%) was observed in S1F1 (30x10 cm + 20:40:40 NPK kg/ha). These results are on similar track with Rana et al. (2009). The influence of spacing and fertilizer levels on pea showed non-significant differences on strength or firmness of pea. The interaction effect of spacing and fertilizer levels of pea strength was also found to be nonsignificant and indicated in Table 1. Economics: The perusal of data presented in Table 2 revealed that influence of spacing on benefit cost ratio showed that it was increasing with decreasing in spacing

16


Table 1. Influence of different spacing and fertilizer levels on yield and quality attributes of pea. Treatment Yield/ plant (g) Yield/ plot (kg) Spacing levels (cm) S1-30x10 88.01 6.86 S2-30x20 116.38 4.08 S0-30x15 109.71 5.03 SE± 1.244 0.181 CD at 5% 3.728 0.544 Fertilizer levels (NPK kg/ha) F1-20:40:40 95.04 4.86 F2-40:80:80 113.79 5.79 F0-30:60:60 105.28 5.32 SE± 1.244 0.181 CD at 5% 3.728 0.544 Interaction effect (SxF) S1F1 78.22 6.25 S1F2 96.03 7.46 S1F0 89.79 6.87 S2F1 106.02 3.81 S2F2 125.12 4.42 S2F0 118.01 4.02 S0F1 100.87 4.53 S0F2 120.21 5.49 S0F0 108.04 5.07 SE± 2.154 0.314 CD at 5% 6.458 0.942 G.M. 104.7 5.32

Yield/ ha (q) TSS (0Brix) Protein (%) Shelling (%) Strength/Firmness (kgf) 118.94 74.48 87.78 1.066 3.194

13.41 15.60 15.49 0.14 0.43

17.21 18.64 18.62 0.11 0.34

49.71 53.44 52.70 0.71 2.14

4.26 4.25 4.27 0.02 NS

86.29 101.07 93.84 1.066 3.194

13.52 16.2 14.77 0.14 0.43

16.5 19.58 18.4 0.11 0.34

47.20 55.68 52.97 0.71 2.14

4.26 4.27 4.26 0.02 NS

110.14 128.63 118.05 69.69 79.72 74.03 79.03 94.86 89.44 1.846 5.532 93.73

12.20 14.67 13.36 13.46 17.54 15.79 14.90 16.38 15.17 0.25 0.75 14.83

16.12 18.04 17.47 16.78 20.53 18.63 16.61 20.17 19.10 0.20 0.59 18.10

45.09 52.74 51.31 49.03 58.11 53.16 47.47 56.18 54.45 1.23 3.71 51.95

4.26 4.27 4.26 4.26 4.25 4.24 4.26 4.27 4.28 0.02 NS 4.26

Table 2. Benefit cost ratio (B:C) of pea as influenced by spacing and fertilizer levels. Treatment

Spacing

S1F1 S1F2 S1F0 S2F1 S2F2 S2F0 S0F1 S0F2 S0F0

30x10 30x20 30x15 30x10 30x20 30x15 30x10 30x20 30x15

N:P:K kg/ha Treatment cost Common Cost Total Cost (Rs.) (Rs.) (Rs.) 20:40:40 2739.93 48160 50900 40:80:80 5359.86 48160 53520 30:60:60 4049.91 48160 52210 20:40:40 2739.93 40900 43640 40:80:80 5359.86 40900 46261 30:60:60 4049.91 40900 44950 20:40:40 2739.93 44530 47270 40:80:80 5359.86 44530 49909 30:60:60 4049.91 44530 48580

level. More B:C ratio (3.4) was obtained in spacing S1 (30x10 cm) as compared to S2 and S0. Less B:C ratio (2.5) was obtained in S2 (30x20 cm). These results are in accordance with findings of Dubey et al. (2012) and Rajput and Rawat (2014) in pea. Whereas, the maximum benefit cost ratio (3.0) was recorded in F2when 40:80:80 NPK kg/ha fertilizer was applied. The minimum (2.7) was obtained in fertilizer level F1, where fertilizer dose at 20:40:40 NPK kg/ha. The treatment combination consigning of S1F2 (3.6) recorded maximum B:C ratio while minimum in S2F1 (2.4) over control and rest of treatment combinations under study. REFERENCES Attar AV, Patil BT, Bhalekar MN and Shinde KG. 2013. Effect of spacing and fertilizer levels on growth, yield and quality of garden pea (Pisum sativum L.) cv. Phule Priya. Bioinfolet 10(4B):1240-1242. Bahadur Anant, Jagdish Singh, Singh KP and Mathura Rai. 2006. Plant growth, yield andquality attributes of garden pea as influenced by organic amendments and biofertilizers. Indian Journal of Horticulture 63(4): 464-466. Dass A, Patnaik US and Sudhishri S. 2005. Response of vegetable pea (Pisum sativum L.) to sowing date andphosphorus under

Gross Monetary Returns (Rs.) 165210 192945 177075 104535 119580 111045 118545 142290 134160

Net Monetary Returns (INR) 114310 139425 124865 60895 73320 66095 71275 92400 85880

B:C ratio 3.24 3.60 3.39 2.39 2.58 2.47 2.50 2.85 2.76

on-farm conditions. Indian Journal of Agronomy 50(1): 64-66. Dubey Dharmendra Kumar, Singh SS, Verma RS and Singh PK. 2012. Integrated nutrient management in garden pea (Pisum sativum var. hortense). Horticulture Flora Research Spectrum 1(3): 244-247. Gul Nazia Irum, Muhammad Saleem Jilai and Kashif Waseem. 2006. Effect of split application of nitrogen levels onthe quality and quality parameters of pea (Pisum sativum L.) International Journal of Agriculture and Biology 8(2): 226-230. Panse VG and Sukhatme PV. 1967. Statistical methods for agricultural workers ICAR, New Delhi. Rana MC, Sharma GD, Bindra AD and Angiras NN. 2009. Effect of farmyard manure, fertilizer levels and plant density on the performance of garden pea (Pisum sativum L.) inhigh hill dry temperate conditions. Himachal Journal of Agriculture Research 35(1): 21-23. Sharma Akhilesh, Sood Meenakshi, Rana Ashwini and Singh Yudhvir. 2007. Genetic variability and association studies for green pod yield and component horticultural traits in garden pea under high hill dry temperate conditions. Indian Journal of Horticulture 64(4): 410-414. Shrikanth MN, Merwade Channaveerswami AS, Shantappa T, Mallapur CP and Hosamani RM. 2008. Effect of spacing and fertilizer levels on crop growth and seed yield in lablab bean (Lablab purpureus L.) Karnataka Journal of Agriculture Science 21(3): 440-443.


Effect of moisture content on physical properties of black gram (Vigna mungo L.) grains J JERISH JOYNER1 and BK YADAV2 1

Department of Food Engineering, College of Food and Dairy Technology, (Tamil Nadu Veterinary and Animal Sciences University), Koduvalli, Alamathi (Po), Chennai 2 Department of Food Packaging and System Development, Indian Institute of Crop Processing Technology, Pudukkottai Road, Thanjavur–613005, Tamilnadu, India; E-mail: [email protected] (Received : May 23 2017 Accepted: June 28, 2017) ABSTRACT This study was carried out to evaluate the effect of moisture content on selected physical properties of black gram grains. The physical properties were studied at six levels of moisture content ranging from 10.26 to 20.32% (w.b.). As the moisture content increased, the diameter of arithmetic mean and geometric mean of the grains increased from 3.830 to 4.152 mm and 3.787 to 4.108 mm respectively, while the sphericity decreased from 83.74 to 82.92%. The surface area increased from 45.02 to 52.98 mm2, 1000 grains mass increased from 42.73 to 50.03g and volume increased from 32.28 to 41.93 mm3. The bulk density and true density decreased from 799.17 to 785.96 kg/m3 and 1204.70 to 1191.73 kg/m3 respectively, whereas porosity and angle of repose increased from 33.66 to 34.05% and 22.45 to 27.39°, respectively. The static coefficient of friction on various surfaces also increased linearly in the specified range. The various properties measured would serve as a useful tool in process and equipment design. Key words: Black gram, Equipment design, Moisture content, Physical properties

Black gram (Vigna mungo L.) is a food legume grown prevalently in Indian subcontinent with a long history of use in culinary and medicinal applications. Black gram seed/ grain, commonly known as urad in India, is cooked and eaten as whole or splits. The flour of the seed is largely used in south Indian foods, such as dosa, idli, vada and papadum. Black gramis known to be a hypolipidemic pulse (Indira and Kurup 2003) and is recommended for diabetics. Hence, black gram is an important and highly prized pulse in India from the industry and export perspective. After harvest, the black gram grains need to be processed sufficiently before being consumed, through various unit operations such as threshing, conveying, cleaning, grading, storing, drying, milling and packing. To develop appropriate equipment for the above post harvest processing operations, information of the physical properties of black gram their variations with respect to moisture content need to be known. The physical properties of agricultural materials are moisture dependent. Principal dimensions are very important parameters in the design of sizing, cleaning and

grading machines whereas bulk density and porosity are vital in the design of drying and storage systems (Dursun and Dursun 2005). The angle of repsose is important in designing of storage and transporting structures. The static coefficient of friction of the grain against the various surfaces is necessary in designing of conveying, storing structures and it also plays an important role in transporting (load and unload) of grains. These data on physical properties would be of value not only to engineers but also to food scientists and processors who may exploit these properties and find new uses (Mohsenin, 1986). The effect of moisture content on the physical properties of various grains such as pigeon pea (Shepherd and Bhardwaj1986), chickpea (Konak et al. 2002), locust bean seed (Olajide and Ade-Omowaye 1999), lentil (Amin et al. 2004), soybean (Tavakoli et al. 2009)and faba bean (Altuntas and Yildiz 2007) have been studied. Siebenmorgen and Jindal (1987) determined the resistance to airflow in a common variety of long-grain rough rice as affected by the moisture content and bulk density. Similarly, Kenghe et al (2013) determined the pressure drop at higher airflow rates through clean grain beds of lathyrus at different levels of moisture content, bulk density, grain size and bed depth. Razavi and Farahmandfar (2008) evaluated some physical properties of three rice varieties in different stages of the rice processing like hulling, milling in order to provide data for designing equipment for the same. Kiliçkan and Güner (2010) determined thephysical properties of chickpeas and the air velocity, pressure drop and power requirement in relation to pneumatic conveying characteristics. Detailed information on the physical properties of black gram and their variations with respect to moisture contents has not been reported. Such a study would help in developing processing equipment providing better handling and less mechanical damage to the grains. Therefore this study was undertaken with an objective of investigating some moisture-dependent physical properties of black gram grains, namely, principal dimensions, sphericity, surface area, 1000 grain mass, volume, bulk density, true density and porosity, angle of repose and static coefficient of friction on various surfaces in the moisture content range from 10.26 to 20.32% (w.b.).

18


MATERIALS AND METHODS Grains of black gram variety ADT 5 was used in this study, obtained from the Soil and Water Management Research Institute (Tamil Nadu Agricultural University), Kattuthottam, India. The grains were first cleaned manually and later mechanically to remove all dirt, foreign matters, broken and immature grains. The initial moisture content of the grains was determined by oven-drying at 130°C for 18h (ASAE 2003). Physical properties for black gram were determined at the six moisture levels, viz., 10.26, 12.43, 14.17, 16.54, 18.01 and 20.32% (w.b.), since harvesting, transportation, storage and dehulling operations of black gram are performed in this moisture range. The grain samples of the desired moisture levels were prepared by adding calculated amounts of distilled water, thorough mixing and then sealing in separate polyethylenebags (AACC 1995). The samples were kept at 5°C (± 1°C) for 7 days in a refrigerator to allow the moisture to distribute uniformly throughout the sample. Before each experiment, samples were taken out of the refrigeration and equilibrated at room temperature (30 ± 2°C) for 2 h and the moisture was checked in triplicates using the standard oven-dry method (Carman 1996; Deshpande et al.1993; Ogut 1998). All tests were conducted in the laboratory at an ambient temperature of about 30±2°C and relative humidity of 55–65%.Ten replications of each test were made at each moisture level. Principal dimensions of the black gram were determined by randomly selecting hundred grains from each moisture content and measuring the length (L), width (W) and thickness (T) using verniercaliper of 0.01mm precision. From the values of the axial dimensions, the average diameters namely arithmetic mean diameter and geometric mean diameter were found using the following equations given by (Mohsenin 1986). Da=

L+W +T 3

....................................................................... (1)

Dg= (LWT) ........................................................................ (2) Where Da is the arithmetic mean diameter in mm; Dg is the geometric mean diameter in mm; L is the length in mm; W is the width in mm; T is the thickness in mm. The sphericity of black gram was determined using the data on geometric mean diameter and the major axis (L) i.e. length, as given in Eq. 3 (Mohsenin 1986). (LWT )1/3 L

S = (Dg) 2................................................................ (4) Where S is the surface area in mm2 and Dg is the geometric mean diameter in mm. To obtain the unit mass of the grain, 1000 grains mass were measured. To evaluate 1000 grains mass, 100 randomly selected grains from the bulk were weighed by an electronic balance (Model: BL-2200H, Shimadzu, Kyoto, Japan) to an accuracy of 0.01 g and averaged. The volume of black gram grains was determined from the following relationship (Eq. 5) given by Özarslan (2002). V=(m/ t) × 106............................................................... (5) Where V is the volume (V) of black gram grains in mm3, m is the unit mass of the seed in g and t is the true density in kg/m3. The bulk density is defined as the ratio of a sample mass of the grain to its total volume. Bulk density was determined using the standard test weight procedure (Singh and Goswami 1996), filling a container of 500 ml with the grain from a height of 150 mm at a constant rate and weighing the content (Özarslan 2002). The true density of a grain is defined as the ratio of the mass of the grainto the true volume of the grain. True density was determined by the toluene (C7 H8) displacement method. Toluene was used in place of water because it is absorbed by the grains to a lesser extent. The volume of toluene displaced was found by immersing a weighted quantity of grains in the toluene (Mohsenin 1986; Sacilik et al. 2003). The porosity ( ) is the ratio of free space between grains to the total of bulk grains. It was determined by the relationship (Eq. 6) given by Mohsenin (1986). ρ

ε = 1 − ρb × 100 ...................................................... (6) t

1/3

Φ= {

The surface area of black gram was found by analogy with a sphere of same geometric mean diameter, using expression (Eq. 4) cited by Olajide and Ade-Omowaye (1999) and Sacilik et al. (2003).

} × 100 ....................................................... (3)

Where is the sphericity in % and Lis the length, W is the width and T is the thickness, all in mm.

Where ñbis the bulk density and all in kilogram per cubic metre.

t

is the true density,

The angle of repose was determined using the apparatus described by Sreenarayanan et al. (1988). It was determined from the height and diameter of the naturally formed heap of grains on a circular plate. The coefficient of static friction against was determined using the apparatus that consisted of a frictionless pulley fitted on a frame, a cylindrical container of negligible weight with both ends opened, loading pan and test surfaces (Visvanathan et al. 1996). The container, placed on a test surface was filled with a known quantity of material and weights were added to the loading pan until the container began to slide. The coefficient of static friction

Joyner & Yadav : Effect of moisture content on physical properties of black gram (Vigna mungo L.) grains

was calculated as the ratio of weights added (frictional force, F) and material mass (normal force, N) as given below (Eq. 7). µ = F/ N......................................................................... (7)

19

The linear relationship between moisture content (Mc) and the axial dimensions length (L), width (W) and thickness (T) can be given by Eq. 8, 9 & 10 respectively.

Where µ is the coefficient of static friction (dimensionless); F is the frictional force for static friction (g) and N is the normal force for static friction (g).

L = 4.075 + 0.043Mc

The experiment was conducted at different moisture contents of black gram grains using test surfaces of glass, plywood and mild steel. For each experiment, the container was emptied and refilled with a different sample at the same moisture content (Sacilik et al. 2003).

The arithmetic and geometric mean diameters increased from 3.830 to 4.152 mm and 3.787 to 4.108 mm respectively, as the moisture content increased from 10.26 to 20.32%. The average diameters calculated by the arithmetic mean and geometric mean also increased linearly with the increase in moisture content.

The results obtained were subjected to analysis of variance (ANOVA) followed by Duncan’s test using SPSS18.0 (SPSS Inc. 2009) software and analysis of regression using Microsoft Excel 2007 (Microsoft Corp., USA).

(R² = 0.998) ................................... (8)

W = 3.620 + 0.014Mc (R² = 0.961) ................................... (9) T = 2.779 + 0.040Mc

(R²=0.992) ................................... (10)

The linear relationship between the moisture content (Mc) and arithmetic mean diameter (Da) and geometric mean diameter (Dg) can be given by Eq. 11 & 12 respectively. Da = 3.491+ 0.033Mc (R² = 0.998) .......................... (11)

RESULTS AND DISCUSSION

Dg = 3.449 + 0.033Mc (R² = 0.997) ........................ (12)

Principal dimensions: The experimental data showing the moisture dependent physical properties of black gram are shown in Table 1. The principal three axial dimensions viz., length, width and thickness increased with increasing moisture content. As the moisture content increased from 10.26 to 20.32%, the length, width and thickness of grains increased from 4.522 to 4.954 mm, 3.772 to 3.901 mm and 3.196 to 3.602 mm respectively.The increase in the dimensions can be attributed to expansion or swelling due to moisture absorption in the intracellular spaces within the black gram grains. Each axial dimension appeared to be linearly dependent on the moisture content.

The linear increase in dimensions was also observed by Amin et al. (2004) and Kiani DehKiani et al. (2008), Tavakoli et al. (2009) for lentil seed, red bean grain and soybean respectively. Sphericity: The sphericity ( ) was found to decrease from 83.74 to 82.92% with increase in moisture content from 10.26 to 20.32% (Figure.1) The relationship between moisture content (Mc) and sphericity ( ) appears linear and can be represented by the following equation (Eq. 13). = 84.467 - 0.077Mc (R² = 0.977) ....……………. (13)

Table 1. Physical properties of black gram at different moisture contents 2 3 3 3 Moisture L (mm) W (mm) T (mm) Da (mm) Dg (mm) Ф (%) S (mm ) M1000 (g) V (mm ) ρb (kg/m ) ρt (kg/m ) content (% w.b.)

10.26 12.43 14.17 16.54 18.01 20.32

4.522a (0.001) 4.602b (0.011) 4.700c (0.008) 4.789d (0.003) 4.856e (0.009) 4.954f (0.012)

3.772a (0.014) 3.792a,b (0.016) 3.808b (0.012) 3.851c (0.011) 3.892d (0.015) 3.901d (0.021)

3.196a (0.012) 3.262a (0.016) 3.369b (0.013) 3.447b,c (0.018) 3.492c (0.014) 3.602d (0.130)

3.830a (0.008) 3.885b (0.002) 3.959c (0.005) 4.029d (0.003) 4.080e (0.009) 4.152f (0.007)

3.787a (0.002) 3.842b (0.003) 3.916c (0.007) 3.986d (0.008) 4.036e (0.002) 4.108f (0.009)

83.74f (0.09) 83.48e (0.08) 83.32d (0.05) 83.22b,c (0.05) 83.10b (0.08) 82.92a (0.06)

45.02a (0.58) 46.34b (0.35) 48.15c (0.51) 49.88d (0.49) 51.14e (0.95) 52.98f (0.83)

42.73a (0.016) 44.91b (0.023) 46.18c (0.013) 47.11d (0.033) 47.85e (0.027) 50.03f (0.018)

32.28a (0.36) 34.97b (0.39) 37.89c (0.45) 38.36c (0.96) 40.02d (0.82) 41.93e (0.73)

799.17e (1.65) 796.20d,e (2.75) 794.20c,d (2.60) 791.12b,c (2.51) 789.00a,b (1.93) 785.96a (1.51)

1204.70e (1.05) 1202.19d (1.09) 1200.77d (1.13) 1197.89c (1.05) 1195.45b (1.06) 1191.73a (0.75)

ε (%) θ (degree)

33.66a (0.25) 33.77a (0.21) 33.86a (0.37) 33.96a (0.21) 34.00a (0.19) 34.05a (0.36)

22.45a (1.03) 23.01a,b (0.93) 23.64a,b (0.42) 24.51c,d (0.92) 25.83d,e (1.07) 27.39f (1.13)*

*Values in parentheses represent standard deviation. Values in the same columns followed by different superscripts are significantly different (P