EXECUTIVE COUNCIL : 2017-2020isprd.in/pdf/October-December2018_090719.pdf · Renu Verma, Naveen Kumar Arora, Annapragada Harika, Yaduvendra Singh Yadav and Murugesan Senthil Kumar

EXECUTIVE COUNCIL : 2017-2020

Zone I : Dr Brij Nandan, SKUAST, Samba (J&K)Zone II : Dr C Bharadwaj, IARI, New DelhiZone III : Dr Rajib Nath, BCKV, KalyaniZone IV : Dr Baldev Ram, AU, Kota

Councillors

Dr Puran Gaur, ICRISAT, HyderabadDr Shiv Kumar, ICARDA, MoroccoDr BB Singh, GBPUA&T, PantnagarDr DK Agarwal, ICAR-IISS, MauDr Sarvajeet Singh, PAU, LudhianaDr J Souframanian, BARC

Chief PatronDr Trilochan Mohapatra

PatronDr A K Singh

Co-patronDr NP Singh

Zone V : Dr DK Patil, BadnapurZone VI : Dr P Jagan Mohan Rao, RARS, WarangalZone VII : Dr P Jayamani, TNAU, CoimbatoreZone VIII: Dr AK Parihar, ICAR-IIPR, Kanpur

PresidentDr NP Singh

SecretaryDr PK Katiyar

Joint SecretaryDr Jitendra Kumar

TreasurerDr RK Mishra

Vice PresidentDr Guriqbal Singh

Editors

Editor-in-ChiefDr CS Praharaj

The Indian Society of Pulses Research andDevelopment (ISPRD) was founded in April 1987 with thefollowing 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 timesa year.

Membership : Any person in India and abroad interestedin pulses research and development shall be eligible formembership of the Society by becoming ordinary, life orcorporate member by paying respective membership fee.Membership Fee Indian (`) Foreign (US $)Ordinary (Annual) 500 40Life Member 5000 400Admission Fee 50 10Library/ Institution 5000 400Corporate Member 7500 -

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

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

Vol. 31 (4) October-December, 2018

CONTENTS

RESEARCH PAPERS

1. Honey bee mediated (Apis mellifera L.) hybrid pigeonpea seed production under net house condition 197

MI Vales, R Sultana, SB Patil, GV Ranga Rao, RV Kumar and KB Saxena

2. Growth environment effect on phenology, agroclimatic indices, symbiotic parameters and yield of kharifmungbean [Vigna radiata (L.) Wilczek] genotypes 205

Guriqbal Singh, Harpreet Kaur Virk, Navneet Aggarwal, Veena Khanna and KK Gill

3. Response of graded nitrogen doses on yield attributes of summer mungbean (Vigna radiata L.) 209

Rajesh Saha, Partha Sarathi Patra and Tarun Paul

4. Nutrient management with panchgavya in kharif clusterbean (Cyamopsis tetragonoloba L.) 212

JB Chaudhari, BJ Patel, KM Patel and GM Patel

5. Identification and characterization of root nodule associated bacteria from chickpea germplasm lines 215

Renu Verma, Naveen Kumar Arora, Annapragada Harika, Yaduvendra Singh Yadav andMurugesan Senthil Kumar

6. Cultural and morphological variability of Rhizoctonia solani causing web blight of mungbean in Jharkhandstate of India 221

Kanak Lata, HC Lal, Savita Ekka, CS Mahto, Niraj Kumar and Binay Kumar

7. Evaluation of mechanical harvesting efficiency in defoliated mungbean genotypes 226

Keerti, Ganajaxi Math and Raghuveer

8. Antagonistic potential of indigenous Trichoderma spp against Meloidogyne javanica 230

Devindrappa, Bansa Singh, Monika Mishra, RK Mishra and Krishna Kumar

9. Screening of resistant pigeonpea genotypes against pod infecting insects 234

Zadda Kavitha and C Vijayaraghavan

10. Investigating the change in pathogenesis related proteins (PR) in virus infected cowpea 241

NT Hirgal, SB Latake and AP Chavan

11. Assessing the potential of bio-agents and botanicals against chickpea wilt 244

Mohammad Faisal, Shashi Tiwari and Umesh Tiwari

12. Scaling mungbean production in rainfed agroecology of Rajasthan in India through frontline demonstrations 247

ML Meena

13. Estimation of production and yield of pulses using ARIMA-ARNN model 254

Puneet Dheer, Pradeep Yadav and PK Katiyar

SHORT COMMUNICATIONS

14. Correlation and path analysis studies in chickpea (Cicer arietinum L.) for seed yield and its attributes in theHimalayan region 258

Nitesh SD, Talwade AC and Gopal Katna

15. Genetic diversity studies in chickpea (Cicer arietinum L.) germplasm 261

Ambilwade Balasaheb Bapurao, Sanjay Kumar, Suresh BG and Anand Kumar

List of Referees for Vol. 31(4) 265

Journal of Food Legumes 31(4): 197-204, 2018

Honey bee mediated (Apis mellifera L.) hybrid pigeonpea seed production undernet house conditionMI VALES1, 2, R SULTANA1, 3, SB PATIL1, 4, GV RANGA RAO1, RV KUMAR1 and KB SAXENA1

1International Crops Research Institute for the Semi-arid Tropics, Patancheru, Telangana, India, 2Texas A & MUniversity, College Station, TX 77843, USA, 3Bihar Agricultural University, Sabour, Bhagalpur, Bihar, India,4Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, 12 Zhongguancun Nandajie, HaidianDistrict, Beijing, China; Email: [email protected](Received : July 17, 2018 ; Accepted : October 20, 2018)

ABSTRACT

The development of cytoplasmic nuclear male sterility(CMS) hybrid technology in pigeonpea was a significantbreakthrough. Hybrids produced yield 25% superior thanstandard varieties on farmers’ field. However, the expansionand acceptance of the CMS technology dependspredominantly on the efficiency of hybrid seed production.Hybrid seed is typically generated in isolated fields fromother pigeonpea production areas. Finding suitable isolatedfields is often difficult. Therefore, obtaining cross-pollinatedseed under enclosed conditions could be a logical alternative.This was tested by using male sterile (A) and maintainer (B)lines in a 3:1 ratio under captivity (net houses) containinghoneybees hives. The yield of the A line, obtained in nethouses containing honeybees, was significantly lower thanin open fields with natural pollinators. Intercroppingpigeonpea with sunflower or spraying a sugar solution didnot contribute to increase the yield of A line in the net houses.Sequential planting (weekly intervals) of A:B lines in opennatural field condition was beneficial to increase theproduction of the A line (1078.3 kg/ha) making yieldsequivalent to the B line (1047.2 kg/ha). Thus, natural fieldisolated plots combined with sequential planting of blocksof males and females is recommended for hybrid pigeonpeaseed production.

Key words: Artificial pollination, Cross-pollination,Cytoplasmic male sterility, Insects, Tur dal

Globally, pigeonpea (Cajanus cajan (L.) Millsp.) (alsoknown as tur dal or gandul) is planted in around 7 m ha,mainly in tropical and sub-tropical regions of the world.India is the undisputed leader, accounting for 80% of areaand 67% of the production (FAO, 2017). In India, thedomestic production of pigeonpea is inferior to its demandcreating a deficit of about 200,000 t per year which is metmainly with imports from Myanmar, Mozambique, KenyaTanzania and Malawi. In spite of serious breeding effortsover decades, pigeonpea yields have been historically lowwith a plateau around 700 kg/ha. In this context, recentefforts to enhance productivity through hybrids haveshown promise (Saxena et al. 2018) by increasing farm yieldsmore than 25% over commercial varieties. Technologically,the production of seed in this three-line hybrid system isdemanding and requires careful attention to preserve

genetic purity of parental lines and production of bona fidehybrid seed. The female parent, a male sterile (A) line, hasthe cytoplasm of a wild relative of pigeonpea, Cajanuscajanifolius, and the nuclear genome of the cultivated type(Cajanus cajan) (Saxena et al. 2005). The maintainer (B)line is iso-nuclear to the A line but has the cytoplasm of thecultivated pigeonpea. The cross of A x B reproduces the Aline. The R line, also called the fertility restorer, has boththe nuclear and cytoplasm of the cultivated pigeonpea withfertility restoring nuclear gene (s). In order to generate fertilehybrid seeds (both female and male), the A line is crossedwith the R line thanks to cross pollination facilitated byinsects. Many crops depend on insect pollinators tosuccessfully produce seed/fruits. Insect pollinator deficitis a very important factor in yield gap analysis (Garibaldi etal. 2016). Natural insect populations are subject to temporaland spatial fluctuations in quantity and type. This createsuncertainties about the final yield outcome each year. Inorder to overcome this situation, a number of approacheshave been explored to increase the number of pollinators(i.e. installing honey beehives in farmers’ fields) to attractpollinators to the crops and to increase pollinator activity(i.e. sugar solutions, pheromones, other plants withattractive flower color). In canola, seed yield was improvedby 46% by installing three honey beehives per hectare(Sabbahi et al. 2005). Some approaches show promise andcould be economically feasible. In the case of hybrid seedproduction using a male sterility system, there is anadditional challenge, having male sterile (no pollen) lines(used as females) together with male fertile lines. In onionCMS based hybrid seed production system, male sterilelines produce less nectar in addition to having no pollenwhich reduces pollinators activity (Soto et al. 2013 andWilkaniec et al. 2004). In pigeonpea, female lines also havea competitive disadvantage because they do not offer pollento the insects and thus are likely to receive fewer visits.This is a major concern in pigeonpea hybrid seedproduction.

In CMS hybrid technology, the large-scale seedproduction of the female parent and hybrid seed involvesmass transfer of pollen grains from B and R line on to A line,respectively. Since wind has no role in cross pollination inpigeonpea (Kumar and Saxena, 2001), this act is performed

mailto:[email protected]

198 Journal of Food Legumes 31(4), 2018

by insects such as Apis mellifera, A. dorsata, A indica(Pathak, 1970), Megachile spp. (Williams, 1977; Zeng-Honget al. 2011), and Xylocopa spp. (Onim, 1981). The extent ofnatural cross-pollination varies considerably in differentplaces (see review by Saxena et al. 2016); and variousbiological and ecological factors such as the genotypes,environment, and density of pollinating vectors determinethe extent of out-crossing at a particular location.

Howard et al. (1919) were the first to record 2-12%out-crossing in pigeonpea in Pusa (Bihar). Other locationshave reported significant level of natural cross-pollination(25-30%). Large-scale seed production of pigeonpea hybridsand their parents is typically done in physical isolationsfrom other pigeonpea growing fields. The minimum isolationdistance between pigeon fields for seed production is listedas 500 m in seed certification standards. This is always acumbersome and expensive exercise because it is difficultto find good isolation plots for a crop like pigeonpea, whichis extensively cultivated by farmers throughout India.Therefore, in order to overcome this constraint, weconducted studies to explore the possibility of producingcross-pollinated seed on the male sterile plants using honeybeehives enclosed in net houses.

Our goal was also to assess if using honeybees incaptivity (net houses) could be used as an effectivealternative to seed production in isolated fields. Hence afew options were considered to (i) compare seedproduction of A x B in net houses containing honey beehivesversus open conditions with natural pollinators; (ii) evaluateif intercropping with sunflower could increase seedproduction by helping the initial establishment ofhoneybees; (iii) assess if a sugar solution applied to the Aplants could attractant bees and stimulate cross-pollination;and (iv) determine if weekly plantings of blocks of A x Blines could contribute to yield increases by expanding thepollen availability period.

MATERIALS AND METHODS

Plant materials: The cytoplasmic nuclear male sterile lineused in the study was ICPA 2043. It contains A4 cytoplasmfrom Cajanus cajanifolius, accession (ICPW 29), a wildrelative of pigeonpea, and the nuclear genome of a cultivatedpigeonpea (Cajanus cajan) line ICPL 20176. The B line ofthis male sterile (A) line is ICPB 2043. Genetically, this lineis iso-nuclear to ICPA 2043 but with Cajanus cajancytoplasm. When ICPA 2043 is pollinated with ICPB 2043,it produces seed equivalent to ICPA 2043. Both lines (ICPA2043 and ICPB 2043) are of medium maturity with non-determinate growth habit. The male sterile line ICPA 2043has been used in the development of more than 40 hybrids,being ICPH 2671 and ICPH 2740 the most promising.Experimentation and data collection: In order tounderstand the processes of bee-aided cross-pollinationin pigeonpea, Experiments were conducted inside net

houses using honey beehives (see details about net houseconstruction and honeybee installation below) and undernatural open field conditions. The experiments wereconducted in 2010 and 2011 planting multiple sets of A:Bpigeonpea lines, 3:1 (3 rows of A females to 1 row of Bmale).

All the experiments were conducted at the researchfarm of ICRISAT, Patancheru, Telangana, India (17.53o N,78.27o E, 545 ml). The experiments were planted on July 20th

in 2010, and on July 7th, 15th and 21st in 2011 (sequential orstaggered planting). In each experiment, sowing was doneon ridges. The rows were 75 cm apart with intra-row spacingfor pigeonpea seed at 30 cm. The plots (both years) were 25m long under the net house and under open and underopen pollinated field conditions. We also compiled yielddata for additional seed produced under natural isolatedfields at ICRISAT (Patancheru, Telangana, India) andJabalpur (Madhya Pradesh, India)

In 2010, collected data from five sets of pigeonpea3:1 (A:B) of each treatment: inter-cropping with sunflower,sugar spray and untreated pure crop) in a net housecontaining honey beehives; and five sets of 3:1 (A:B) purecrop untreated under natural open field conditions. In thetests involving sunflower inter-cropping with pigeonpea,a single row of sunflower was sown in between the sets ofpigeonpea rows. A sunflower hybrid with slightly earlierflowering time than the pigeonpea lines was used. Thesowing of both pigeonpea and sunflower was done on thesame day. In the case of sunflower, the plant to plant spacingwas 20 cm. The idea behind using the inter-planting ofsunflower was to assess if inter-cropping would attract/assist more pollinators and thus contribute to increasecross-pollinated A x B seed yield. Similarly, the testsinvolving the spray of a sugar solution on the A lines wereintended to increase bee attraction. This operation(spraying sugar solution) had been used by farmers in someparts of India to attract pollinators. If found effective, itcould be used to increase productivity of cross-pollinatedseed for maintaining the male sterile A line (A x B) and alsoto produce hybrid seed (A x R). A 10% sugar solution wassprayed on 25th October and 9th November between 13:00to 14:00 h to the treated set (5 reps of 3:1 A:B) whereas theother set was left untreated.

In 2011, we conducted experiments in a net house(with honey beehives) and under natural open conditionsto figure out if the extend of flowering duration throughstaggered planting of A and B lines would improve pod seton the male sterile plants (2011). The male and female lineswere sown on three times, July 7, 15, and 21, withapproximately one week in between. We planted a centralblock of eight sets of 3A:1B followed by four sets of 3A:1Ba week later on both sides (four sets on the right and foursets on the left) and another planting of two weeks lateralso on both sides.

Vales et al. : Honey bee mediated hybrid pigeonpea seed production in net house 199

Standard agronomic practices were followed to raisea normal crop in both the years. These included basalirrigation to ensure good moisture for germination,application of 100 kg ha-1 of Di-ammonium phosphate, pre-emergence herbicide application of Stomp and Paraquat @2 and 4 L ha-1, respectively. In addition, one hand weedingwas also done at early vegetative stage. Irrigation wasprovided when necessary to ensure regular emergence ofnew flowers. To control the pod damage by Helicoverpaarmigera, a single insecticide (Spinosad @ 0.2 L ha-1) spraywas done one week before installing the honeybeehivesduring 2010, and twice in the 2011 experiment after 6 p.m. sothat the pollinators were not affected.

Yield and flowering data were recorded on a per plotbasis. In order to collect data on seeds/pod, seed yield perplant (g) and 100-seed weight (g), 10 randomly selectedindividual plants per row were used.Construction of net houses: The net houses for theexperiments were constructed using nylon black mosquitonets. The net houses were 3 m in height and covered anarea of 75 m x 25 m in both 2010 and 2011. To support thenets, 3 m tall iron poles were attached to the ground in thecorners and middle of the net. For providing lateral supportto net roofing, light-weight aluminum frames were used toreinforce support provided by iron poles. For entry intothe net house, a section with two mesh doors was erectedand attached to the main net house to control the migrationof bees.Bee management: The honeybee (Apis mellifera L.) frameswere obtained by Mr. Noorbasha Kaleshavali from AndhraPradesh (India) and multiplied by the ICRISAT IntegratedPest Management team. To acclimatize honeybees to nethouse environment, the bees were fed with 50% sugarsolution for the first couple of days. When pigeonpea wasat its peak flowering stage, beehives were installed in themiddle of the net house to improve the bee population andone brood frame was placed in the hives every week tocompensate the mortality of foraging bees. The 2010 trialwas partitioned into two portions (intercropping side, andpure crop side) and each part was provided with two seven-framed beehives with each frame housing approximately2000 bees. In 2011, the number of beehives was increasedto four with 7 frames each. The beehives were placed in thecenter of he netted area. As the bees having naturaltendency to move towards east, the hives were placed on awooden stool at two feet height with entrance facing east.To provide fresh water to the bees, water was filled daily tothe four metal trays placed below the stool and one in frontof the hives. To maintain the supply of healthy brood framesto the experimental area several beehives were maintainedon the farm. The beehives were removed after cessation offlowering, the first week of december.

The results were analyzed with the statistical packageJMP (JMP Pro 13, 2016) using the Standard Least Square

personality and the Effect Leverage emphasis option. Allfactors were considered fixed. The means of the A lineswere compared with the means of the B lines within andbetween treatments under different scenarios: Purepigeonpea crop 3:1 (A:B lines) under open field conditionswith natural pollinators; pure pigeonpea crop 3:1 (A:B) innet house with honeybees; sunflower intercropping in nethouse with honeybees, sugar spray in net house withhoneybees; and sequential planting in open pollinated fieldversus in net house with honeybees. For the meancomparison, Least Square means were generated andcompared using L S Means Student’s t. A significance levelof P<0.05 was used.

RESULTS AND DISCUSSION

Effect of net houses and honeybees on seed yield of thepigeonpea male sterile line: Pigeonpea flowers are proneto cross-pollination because they are not trulycleistogamous and the nectar produced by flowers attractsbees. Nectar production in flowers is regulated by a phyto-hormone called jasmonic acid that is produced in floralnectaries endogenously (Radhika et al. 2010). Kumar et al.(2009) observed that the nectar production in pigeonpeaflowers remains consistent throughout the day across theentire flowering duration. Besides nectar production, thereare factors such as extended stigma receptivity (Dalvi andSaxena, 2009) and competitive advantages of foreignpigeonpea pollen (Mazi et al. 2014) which also encouragecross-pollination in pigeonpea. Foreign pollen germinatesfaster (Reddy and Mishra, 1981) and the pollen tube growsat a faster pace than the self-pollen to affect cross-fertilization (Dutta and Deb, 1970).

For foraging, the pollinator bees extend theirproboscis to rob the nectar and during this process theybrush the anthers and a load of pollen gets stuck on variousbody parts such as hairs, silk, legs, mouthparts, thorax,and abdomen. Williams (1977) estimated that eachpollinating insect carried a load of about 5,000 to 90,000pollen grains on its body. The cross-pollination occurswhen the pollen-laden insects trip onto other flowers andrub their bodies on stigmatic surface. However, with respectto foraging period on pigeonpea flowers the reports arequite variable. Onim (1981) recorded that each insect visitto pigeonpea flower lasted for 15-55 seconds; while Pandoet al. 2011 recorded a high foraging speed of 10.3 flowers/min. Zeng-Hong et al. (2011) reported that the pollinatinginsects, on average, visited 4.8 flowers/10 minutes. Mazi etal. (2014) reported that bees, on average, sat on a flower for28 seconds to collect pollen, 43 seconds to collect nectar,and for 63 seconds to collect both nectar and pollen.

Using net houses to produce seed of the male sterileA line (ICPA 2043 x ICPB 2043) ensured isolation. Themosquito net did not allow insects (carrying pollen fromother fields) to come inside the net house. Honey beehivesinside the nets provided pollinators to move pollen between


plants. The effect of using a net house containinghoneybees versus natural open pollination was compared(Table 1). Flowering took place earlier in the net house thanin the open field, but no significant differences wereobserved between the A and B lines within each treatment.The A line had larger seeds (both under net and open field)but it was more evident in the net house, likely explainedby enhanced mobilization of nutrients to a small number ofseeds (Table 1). Open field conditions with natural insectpollinators were especially beneficial to increase the yieldof the A line (789.8 kg/ha), but honeybees inside the nethouse did not generate the anticipated benefits (the A lineonly produced 296.5 kg/ha in the net house). The totalyield of the A line under natural field conditions was almostthree times higher than inside the net house (Table 1). Theyield of the B line under natural field conditions (1,300.8kg/ha) was significantly higher than both the A and B linein the net house with honeybees (Table 1) and also A lineunder natural open field conditions. Despite being apromising idea, planting pigeonpea A:B lines in net houseswith honeybees is not recommended to produce seed ofthe male sterile A line because the yield of the A line wasvery low. According to the information published by Savoor(1998), the use of beehives started in 1892, for increasingthe efficiency of pollination and thereby enhancing cropyields, and by 1940 the pollination services werecommercialized in USA. These services are frequently usedglasshouse-grown tomatoes (Cribb et al. 1993). In Brassicanapus the hybrid program suffers due to ineffectivepollination. It happens because the pollinator bees (A.mellifera) confine on the fertile plants and seldom visit themale sterile plants. Rajkhowa and Deka (2016) studied theeffect of bee (A. cerana) population on pigeonpea pod setand yield and reported that by installing 5 beehives/ha inan open field, the pod set enhanced by 78% with a yield

advantage of 138% over the control treatment i.e. openfield crop without bee hive with cost benefit ratio of 1:1.49.Effect of sunflower intercrop on seed yield of thepigeonpea male sterile line: Inter-planting pigeonpea lineswith a sunflower cultivar was aimed to facilitate the initialestablishment of the bee population and to increase beemovement over pigeonpea plants to enhance pod set onthe pigeonpea male sterile plants. Sunflower plants floweredtwo days earlier than the pigeonpea lines ICPA 2043 andICPB 2043 (data not shown). Inside the net houses, bothpigeonpea lines (A and B) took similar time (about 98 days)to flower. Seed yield of the B line (on a per plant and perplot basis) was significantly higher than the yield of the Aline under both conditions, sole crop and inter-croppedwith sunflower (Table 2). The effect of inter crop was notvisible on the productivity on either of the lines. The malesterile lines possessed, in general, larger seed than theirmaintainer (B) counterparts; this could be due to enhancednutrient mobilization and allocation to a reduced number ofseeds. Ours observations showed the inter-plantingsunflower with pigeonpea could not attract more bees topollinate pigeonpea to influence pod set on the male sterileplants. Thus, this practice is not recommended. The resultsof the experiments we conducted in net houses usinghoneybees indicated that on average, the yield producedby the B line was much higher than the yield produced bythe A lines: 69.0% advantage in 2010 and 86.3% advantagein 2011 (sequential planting). Under open field conditions,the yield of the B line was superior to that of the A line in2010 (39.2% superiority), but the yields of the A and B lineswere equivalent under field conditions when sequentialplanting was used (2011) (no superiority of the B line). Ourdata also shows that the imposition of net a net house andhoneybees to the A line, resulted in yield losses instead ofincreases (62.5% yield loss in 2010 and 84.6% in 2011 with

Table 1. Effect of using net houses containing honeybeehives on flowering and yield of ICPA 2043 and ICPB 2043 (2010,ICRISAT, Telangana, India)

† Pure crop, no sugar spray.Values connected with the same letter within font type are not significantly different according to Student’s t test at probability 0.05.

Treatment A/B lines Days to flower 100-Seed weight (g) Yield/ plant (g) Grain yield (kg/ha) Under net† ICPA 2043 97.4 b 13.2 a 6.7 c 296.5 c

ICPB 2043 97.5 b 10.4 d 21.5 b 956.8 b Mean under net 97.4 B 11.8 A 14.1 B 626.7 B Open field ICPA 2043 104.0 a 11.5 b 17.8 b 789.8 b

ICPB 2043 102.9 a 11.1 c 29.3 a 1300.8 a Mean open field 103.4 A 11.3 B 23.5 A 1045.3 A

Table 2. Effect of inter-cropping pigeonpea with sunflower on flowering and yield of ICPA 2043 and ICPB 2043 in net housescontaining honey beehives (2010, ICRISAT, Telangana, India)

Values connected with the same letter within font type are not significantly different (according to Student’s t test at probability < 0.05).

Treatment A/B lines Days to flower 100-Seed weight (g) Yield/ plant (g) Grain yield (kg/ha) Pure crop ICPA 2043 97.4 a 13.2 a 6.7 b 296.5 b

ICPB 2043 97.5 a 10.4 b 21.5 a 956.8 a Mean pure crop 97.4 A 11.8 A 14.1 A 626.7 A Inter crop ICPA 2043 97.4 a 13.2 a 4.9 b 219.4 b

ICPB 2043 98.0 a 10.5 b 23.9 a 1060.6 a Mean inter crop 97.7 A 11.8 A 14.4 A 640.0 A


sequential planting). The yield of the B line was significantlyhigher in open fields than under net conditions in 2010, butthe advantage was only 26.4% (1,300 vs 956.8 kg/ha) (Table5). In 2011 (sequential planting) the yield of the B line wassuperior in net houses (Table 5). Based on the plot yielddata, the B line produced more than a ton per hectare yieldunder both net houses and open field (Table 5). Seedproduction data of A line in isolations fields at Jabalpur(781 kg/ha) and Patancheru (875 kg/ha) were comparablewith that of open field mean yield (789.8 kg/ha in 2010)using similar planting scheme (3:1 A:B and single planting).Based on this study, we would not recommend using A.mellifera in captivity to cross- pollinate the male sterileflowers of pigeonpea since the yields would be low.Producing seed in isolation fields is preferred. If isolationfields are an issue, it is possible to produce B line (orcommercial varieties) in captivity using net house andhoneybees in combination with sequential planting sincethe resulting yields would be equivalent or slightly higher(15%) than using natural open field conditions. A carefulevaluation of the pros and cons of open fields versus cageswith honeybees should be considered. In any case,production of seed in cages with honeybees is notrecommended for the production of the sterile A line (A x B)nor for the hybrid (A x R).Effect of spraying sugar solution on seed yield of thepigeonpea male sterile line: According to Jay (1986) andCurrie (1997) the spraying of specific substances on thetarget crops can potentially enhance cross-pollination byattracting insect pollinators. Spraying sugar solution wasconsidered as an option to attract honeybees and thusincrease seed production. Under captive conditions, thespraying of sugar solution on parental lines ICPA 2043 andICPB 2043 resulted in a delay in flowering (two days) butflowering time was not significantly different whencomparing the A and B lines within each treatment. Therewas also a reduction of plant yield when applying the sugarsolution, however it was not enough to declare significantdifferences. The yield reduction was more noticeable in thecase of the male sterile plants (ICPA 2043). In both situations(sprayed and unsprayed) ICPA 2043 produced lower yieldsthan ICPB 2043. ICPB 2043 had larger seeds independentlyof the sugar treatment (Table 3). Our results indicated thatspraying a sugar solution did not significantly increasepigeonpea seed production on the A line as initially

anticipated (Table 2, Table 3). By contrast, Currie et al.(1992) and Winston and Siessor (1993) reported thatspraying of synthetic pheromones increase honeybeeforaging activity and crop yield under a wide range ofconditions. Margalith et al. 1984 reported that spraying ofBeeline a food supplement, enhanced significantly the beeactivity as well as cross-pollination in cucurbits. Sagili etal. 2015 used honeybee brood hormone in hybrid carrotproduction and reported around 18% yield gain. Variationin nectar sugar concentration had little direct bearing onbee activity on different fruit crops (Abrol, 1993).Effect of staggered plantings on seed yield of thepigeonpea male sterile line: To assess the effects ofdifferent planting times on the efficiency of bees in cross-pollination, the set of A:B lines were sown on three dateseach separated by seven days. There were some differencesin flowering time between planting dates, but they wereconsider minor. In all cases, the A line flowered a little later(two to three days). Seed weight in the net house (2011sequential planting experiment) followed a similar patternthan that observed in 2010 (both in the net house and openfield): The A line had significantly larger seed than the Bline. However, seed weight was similar between the A andB lines (around 9.5 g) under natural field conditions whensequential planting was used. Under net conditions, the Aline had less seeds per pod than the B line. However, thenumber of seeds per pod was similar between the A and Blines under natural field conditions when sequentialplanting was used. Seeds per pod were not evaluated in2010, but visual observations indicated that the A lines hadless seeds per pod than the B line in all cases (open field,intercropping and sugar solution treatment). The yields ofA line under captive environment in the three sowing datesranged from 3.5 to 4.2 g/plant; while under the same net theB lines produced from 26.0 to 30.8 g/plant. The ratio of B/Afor plot yield was 6.1, 7.0 and 8.9 for dates 1, 2 and 3,respectively (inside net). The B line reached the maximumyield (1,368.9 kg/ha) when planted in date 3 (21 July) butthe yield of the A line was not affected by planting date inthe net house (Table 4). Under open field conditions, theyield data of the A and B lines was not significantly differentwithin each planting date. The productivity of B line wassimilar to the A line (almost 1:1) (Table 4). The first plantingdate resulted in the highest yield for both A and B line(1,200.8 and 1,212.6 kg/ha, respectively), probably

Table 3. Effect of spraying sugar solution on flowering and yield of ICPA 2043 and ICPB 2043 in net houses containinghoneybeehives (2010, ICRISAT, Telangana, India)

Values connected with the same letter within font type are not significantly different (according to Student’s t test at probability < 0.05).

Treatment A/B lines Days to flower 100-Seed weight (g) Yield/ plant (g) Grain yield (kg/ha) No spray ICPA 2043 97.4 a 13.2 a 6.7 b 296.5 b

ICPB 2043 97.5 a 10.4 b 21.5 a 956.8 a Mean no spray 97.4 B 11.8 A 14.1 A 626.7 A Sugar spray ICPA 2043 99.1 a 13.0 a 3.6 b 161.0 b

ICPB 2043 99.4 a 10.8 b 19.9 a 883.7 a Mean sugar spray 99.3 A 11.9 A 11.8 A 522.3 A


benefiting from exposure to extended pollen availability inthe environment (Table 4). Stagger planting (weeklyplantings for three weeks) under open field conditions withnatural insects contributed to increase the yield of the Aline, but sequential planting in the net house (withhoneybees) did not. Sequential planting in the net housecontaining honey beehives was effective, nevertheless, toproduce good yield of the B line (above 1,200 kg/ha).

In summary, based on the results of this study it isclear that the placement of honey beehives inside the nethouse environments, intended to increase seed yield ofmale sterile (A x B) lines and hybrid (A x R), is notrecommended. Honeybees were less attracted by male sterilelines, probably due to the lack of pollen, which resulted inlow yields. Intercropping with sunflower and spraying sugarsolution did not help increase yield of A lines. Sequentialplanting could be recommended but it does not solve theneed for isolation fields. We believe that sequential plantingincreased the pollen availability period and thus benefitedthe three planting dates since pigeonpea flowers wereavailable to be pollinated for an extended period of time.This, combined with the fact that natural insects were activeand ready to pollinate made the A x B production a success.The relative yield comparison of A vs B in 2010 versus 2011suggests that staggering was beneficial, to the point ofobtaining yield of A lines was equivalent to the yield of theB lines. Higher yields could be explained by the fact thatpollen was available for an extended period and to higherpollinator diversity under natural open field conditions.Thus, staggered planting is a recommended option to

increase yield obtained by cross-pollination under naturalfield conditions. This would need to take place in isolatedfields with presence of natural pollinators. There may be apossibility that, the natural pollinators (Apis cerena, Apisdorsata) in open field conditions are more active andefficient in pollination compared to the introduced Apismellifera in pigeonpea. Exploring the native bee speciesunder captive conditions is not possible due to theiraggressive nature. Under the captive conditions, thehoneybees are observed to be panicked and mostly foundtrying to escape the caged condition. More efforts shouldbe explored to make the honeybees more comfortable withthe captive conditions so that most of the energy investedin foraging ultimately increasing the seed yield. Further, itis advisable to explore the use of other natural pollinatinginsects (Greenleaf and Kremen, 2006; Pitts-Singer and Cane,2011). Among then, Megachile species would be a goodcandidates or trying the native species such as A. indica,which has a short distance foraging habit, for hybrid seedproduction in pigeonpea because they frequently visitpigeonpea flowers and, thus they should also be exploredfor the production of cross-pollinated seeds on the malesterile plants under captive conditions. In conclusion, werecommend the use of staggered/sequential weeklyplantings in isolated field for pigeonpea hybrid seedproduction, both A x B (production of the female A line)and A x R (hybrid seed). Production of clean pigeonpeaseed using net houses and honeybees, combined withsequential planting, is a feasible alternative to produce malefertile B lines or commercial varieties if isolation fields are

Table 4. Effect of three planting dates on flowering and yield of the male sterile line ICPA 2043 and its maintainer ICPB2043 line grown inside net houses with honeybeehives and in open field conditions with natural insects (2011,ICRISAT, Telangana, India)

.Values connected with the same letter within font type are not significantly different (according to Student’s t test at probability < 0.05).

Date of Sowing A/B lines Days to flower 100-Seed weight(g) Seeds/ pod Yield/ plant (g) Grain yield (kg/ha)Under net Date 1 (July 7) ICPA 2043 104.9 a 10.3 b 1.9 b 4.2 e 186.7 e

ICPB 2043 101.3 e 9.5 cd 3.3 a 26.0 bc 1155.2 bc Mean date 1 103.1 CD 9.9 B 2.6 B 15.1 DE 671.0 DE Date 2 (July 15) ICPA 2043 105.0 a 11.4 a 2.0 b 3.6 e 157.8 e ICPB 2043 102.5 cd 9.8 bcd 3.3 a 24.9 bc 1108.4 bc Mean date 2 103.8 AB 10.6 A 2.6 B 14.2 E 633.1 E Date 3 (July 21) ICPA 2043 105.0 a 11.7 a 2.0 b 3.5 e 154.0 e ICPB 2043 103.0 c 9.9 bc 3.3 a 30.8 a 1368.9 a Mean date 3 104.0 A 10.8 A 2.7 B 17.1 D 761.5 D

Mean ICPA 2043 105.0 A 11.1 A 2.0 C 4.2 C 166.2 C Mean ICPB 2043 102.3 C 9.8 B 3.3 B 27.2 A 1210.8 A

Open field Date 1 (July 7) ICPA 2043 103.2 c 9.5 cd 3.4 a 27.3 b 1212.6 b ICPB 2043 101.9 de 9.7 cd 3.6 a 27.0 ab 1200.8 ab Mean date 1 102.5 E 9.6 BC 3.5 B 27.2 A 1206.7 A Date 2 (July 15) ICPA 2043 104.6 a 9.5 cd 3.6 a 22.6 cd 1003.8 cd ICPB 2043 102.0 de 9.3 cd 3.5 a 19.3 d 857.2 d Mean date 2 103.3 BC 9.4 C 3.5 B 21.0 C 930.5 C Date 3 (July 21) ICPA 2043 103.9 b 9.4 cd 3.4 a 22.9 c 1018.5 c ICPB 2043 101.3 e 9.2 d 3.6 a 24.4 bc 1083.6 bc Mean date 3 102.6 DE 9.3 C 3.5 B 23.7 B 1051.0 B Mean ICPA 2043 103.9 B 9.5 B 3.5 AB 24.3 B 1078.3 B

Mean ICPB 2043 101.7 D 9.4 B 3.6 A 23.6 B 1047.2 B


not available but not for A line production (A x B) nor forhybrid seed production (A x R).

The authors would like to thank ICRISAT’s finantialsupport to conduct this project. We also would like toappreciate the involvement of ICRISAT’s multi-disciplinaryteam in various technical aspects of the project.

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Table 5. Summary of mean yields of A and B lines recorded at ICRISAT (Telangana, India) in captivity (net houses withhoneybees) and on open fields in isolation at Jabalpur (Madhya Pradesh, India) and Patancheru (Telangana, India)

†Sequential planting (July 7, July 15, July 21) ‡Isolation seed production plots

Plant yield (g/plant) Plot yield (kg/ha) Location Genotype Open field Under net Open field Under net % loss ICRISAT Sterile (A line) 17.8 6.7 789.8 296.5 62.5

Fertile (B line) 29.3 21.5 1300.0 956.8 26.4 % Diff. (B vs A) 39.2 69.0 -

ICRISAT† Sterile (A line) 24.3 4.2 1078.3 166.2 84.6

Fertile (B line) 23.6 27.2 1047.2 1210.8 -15.6 % Diff. (B vs A) -3.0 86.3 -

Jabalpur‡ Sterile (A–line) 781.0 Patancheru‡ Sterile (A–line) 875.0

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Growth environment effect on phenology, agroclimatic indices, symbioticparameters and yield of kharif mungbean [Vigna radiata (L.) Wilczek] genotypesGURIQBAL SINGH, HARPREET KAUR VIRK, NAVNEET AGGARWAL1, VEENA KHANNA andKK GILL

Punjab Agricultural University, Ludhiana 141004, Punjab, India, 1South Australian Research and DevelopmentInstitute, Clare, South Australia: E-mail: [email protected](Received : June 28. 2018 ; Accepted : August 12, 2018)

ABSTRACT

A field experiment was conducted during kharif (rainyseason) 2014 and 2015 at the research farm of PunjabAgricultural University, Ludhiana, Punjab to study the effectof growth environment in terms of five dates of sowing (1,10, 20, 30 July and 10 August) on phenology, agroclimaticindices, symbiotic parameters and yield of four genotypes(ML 818, PAU 911, ML 2037 and ML 2056) of mungbean. Thehighest grain yield was recorded under 20 July sowing, whichwas statistically at par with that in 30 July sowing butsignificantly higher than all other dates (1 July, 10 July and10 August sowings). Sowing around 20 July resulted inrealizing higher grain yield by 79.1, 43.1, 4.2 and 36.8 percent over that in 1 July, 10 July, 30 July and 10 Augustsowing in 2014 and 17.6, 13.8 and 4.7 per cent over 1 July, 10July and 30 July sowing in 2015, respectively. The plantheight decreased with delay in sowing. Mungbean sown on20 and 30 July recorded significantly higher number of pods/plant, seeds/pod and 100-seed weight in comparison to otherdates. Mungbean sown around 20 July provided the highestnodule number & nodule dry weight, gross, returns, netreturns and B:C ratio which was followed by 30 July sowing.Days to 50% flowering & maturity, accumulated growingdegree days and photothermal units were reduced with delayin sowing. The genotypes, ML 2037 and ML 2056, recordedhigher grain yield, which was significantly higher than ML818 and PAU 911. Genotype ML 818 took the highest numberof days for 50% flowering and maturity. It was concludedthat last one-third period of July 20-30 is the optimum sowingtime and mungbean genotypes viz., ML 2037 and ML 2056may be preferred under Punjab conditions.

Key words: Agroclimatic indices, Grain yield, Mungbean,Nodulation, Sowing date

Mungbean [Vigna radiata (L.) Wilczek], also knownas mungbean, is an important pulse crop of kharif seasonin India. It supplies a major share of protein requirement ofvegetarian population of the country and improves the soilfertility by fixing atmospheric nitrogen. It is a good sourceof protein and minerals. Early sowing or delay sowingreduces the grain yield of the crop drastically. Sowing dateis the major non-monetary input affecting growth, yieldand its contributing characters (Singh et al. 2016, Samantand Mohanty 2017, Singh et al. 2017). It is considered animportant factor to explore the maximum yield (Palsaniya etal. 2016). Earlier, the optimum time of sowing of mungbean

recommended in Punjab state was the first fortnight of July.However, with the changing climate, the optimum time ofsowing may vary. The different phenophases of crops havebeen attained according to crop duration and the variousphenological models have been developed by usinggrowing degree days (GDD), photothermal units (PTU) andHeliothermal units (HTU) (Esfandiary et al. 2009).

The proper growth of a genotype is determined bythe environment in which it grows. Thus there was a direneed to find out the optimum time of sowing of mungbeanfor obtaining high yield as well as timely maturity of thecrop. Different genotypes may perform differently underdiverse environments (Singh et al. 2013, Singh et al. 2016and Singh et al. 2017). Therefore, adaptability of a genotypeover diverse environments needs to be tested under whichit is to be grown.


A field study was conducted during kharif 2014 and2015 at the Research Farm of Punjab Agricultural University,Ludhiana (30°56’N, 72°52’E, altitude 247 m), Punjab, India.The soil of the experimental site was loamy sand in texture.The experiment was comprised of five dates of sowing (1July, 10 July, 20 July, 30 July and 10 August) in 2014 andfour dates of sowing (1 July, 10 July, 20 July and 30 July) in2015 in main plot and four genotypes (ML 818, PAU 911,ML 2037 and ML 2056) in sub plot in a split plot designwith four replications. Each plot measured 4.5 m × 2.7 m inboth years.

The nutrient dose of 12.5 kg/ha N and 40 kg/ha P2O5was applied through urea (46% N) and singlesuperphosphate (16% P2O5), respectively at the time ofsowing. The crop was sown in rows 30 cm apart by using aseed rate of 20 kg/ha. Weeds were controlled by usingpendimethalin @ 0.75 kg/ha as pre-emergence application.The crop was raised as per the recommended package ofpractices (PAU 2014). During the crop growing season,total rainfall of 394 mm and 555 mm was received in 2014and 2015, respectively. Weather data of crop season arepresented in Fig. 1.

Days taken to 50% flowering and maturity wererecorded for each genotype sown under different dates.Growing degree days were determined as per Nuttonson



(1955):

GDD = 2

Tmin Tmax – Tb

Where, Tmax, maximum temperature (oC) during a dayTmin, minimum temperature (oC) during a dayTb, base temperature of 10.0 oC

Heliothermal units (HTU), the product of GDD andcorresponding actual sunshine hours for that day, werecomputed on daily basis as follows:

HTU = GDD × Actual sunshine hoursPhotothermal units (PTU), the product of GDD and

corresponding day length for that day, were computed ondaily basis as follows:

PTU = GDD × Day lengthWhere, day length refers to maximum possible

sunshine hours.Heat use efficiency (HUE) was calculated as follows:

HUE = day) C(dheatunitsAccumulate

(kg/ha) Grainyield

Where, accumulated heat units refer to accumulatedgrowing degree days.

Growing degree days, heliothermal units (AHTU) andphotothermal (APHU) units were accumulated from the dateof sowing to 50% flowering and physiological maturity togive accumulated indices. Heat use efficiency was

calculated at physiological maturity of the crop.Data on phenology i.e. the number of days to 50%

flowering and physiological maturity were recorded. Dataon nodulation at 40 days after sowing (DAS), plant height,branches/plant and pods/plant at maturity were recordedfrom randomly selected five plants from each plot and seeds/pod were recorded from randomly selected 20 pods.Biological yield and grain yield was recorded plot wise andconverted into kg/ha. From the produce of each plot 100seeds were taken for 100-seed weight data. Harvest index(HI) was also calculated. Gross returns, net returns as wellas benefit: cost (B:C) ratio were worked out. Data weresubjected to analysis of variance (ANOVA) in a split plotdesign using CPCS1 statistical software.


Date of sowing: During both the years of study, the cropsown on 1 July recorded the highest number of days to50% flowering and physiological maturity and theseparameters decreased with delay in sowing (Table 1). Thesimilar trend was observed for accumulated growing degreedays, accumulated photo-thermal units and accumulatedhelio-thermal units in both years except accumulated helio-thermal units in 2014 at physiological maturity. In 2014, atphysiological maturity, less helio-thermal units wererecorded in 10 July sowing due to more number of cloudydays as compared to 20 and 30 July sowings. In 2015, at50% flowering, lesser accumulated heat units in 1 and 10July sowings than 20 and 30 July sowings can be due toless sunshine hours. Early sown crop acquired more heatunits due to more number of days taken to physiological

Table 1. Effect of dates of sowing and genotypes on different agroclimatic indices (GDD, HTU, PTU) and heat use efficiencyof kharif mungbean at 50% flowering and physiological maturity

50% flowering Physiological maturity Year Treatment DAS AGDD

(oC day) APTU

(oC day) AHTU

(oC day)

DAS AGDD

(oC day) APTU

(oC day) AHTU

(oC day) HUE

(kg ha-1 oC-1 day) Date of sowing 1 July 46 991 13446 6542 74 1525 20213 10070 0.53 10 July 45 962 12876 6474 71 1433 18719 9375 0.72 20 July 42 866 11421 5702 72 1405 17991 9440 1.04 30 July 40 797 10353 5331 68 1331 16726 9403 1.05 10 August 38 741 9416 5291 66 1229 15118 8891 0.87 Genotype ML 818 45 924 12175 6252 73 1434 18335 9789 0.76 PAU 911 41 851 11246 5715 70 1370 17588 9327 0.73 ML 2037 41 851 11246 5715 69 1367 17545 9313 0.91

2014

ML 2056 42 858 11342 5789 69 1367 17545 9313 0.97 Date of sowing 1 July 51 1034 13986 5602 72 1449 19201 9598 0.67 10 July 46 919 12282 5215 66 1331 17407 9205 0.75 20 July 43 876 11522 5829 64 1284 16541 8991 0.89 30 July 41 828 10717 5829 63 1222 15461 8686 0.89 Genotype ML 818 48 964 12758 6085 69 1370 17739 9489 0.70 PAU 911 46 924 12254 5696 67 1333 17291 9233 0.73 ML 2037 44 884 11747 5347 65 1292 16790 8879 0.94

2015

ML 2056 44 884 11747 5347 65 1292 16790 8879 0.84

Singh et al. : Growth environment effect on agroclimatic parameters in mungbean 207

maturity as compared to late sown crop. The low rainfalloccurrence during reproductive phase also contributed tohigher yield in 2014 as compared to 2015 (Figur 1).

Fig. 1. Weather data of crop season during 2014 and 2015

Table 2. Effect of sowing dates and genotypes on nodulation at 40 DAS and plant characters of kharif mungbeanTreatment Nodule number/plant Nodule dry weight/plant (mg) Plant height (cm) Branches/ plant

2014 2015 2014 2015 2014 2015 2014 2015 Date of sowing 1 July 20.5 21.7 40.0 49.8 71.3 77.7 4.9 4.4 10 July 21.5 21.8 48.6 48.0 66.8 73.0 4.9 4.3 20 July 27.8 19.6 62.2 45.6 64.5 69.9 5.6 4.6 30 July 26.9 21.1 58.5 43.5 64.1 59.2 5.9 4.3 10 August 24.7 - 50.0 - 61.4 - 4.9 - CD (P=0.05) 4.6 NS 7.2 NS 1.4 2.9 0.3 NS Genotype ML 818 21.7 18.2 47.2 43.8 64.3 70.6 4.9 4.2 PAU 911 25.4 18.4 56.2 40.5 66.2 70.3 5.3 4.2 ML 2037 24.3 20.5 50.2 48.0 66.5 70.0 5.3 4.7 ML 2056 25.7 21.3 53.8 50.1 65.5 68.9 5.5 4.5 CD (P=0.05) 2.9 NS 4.6 6.0 1.1 NS 0.3 0.3

Table 3. Effect of sowing dates on the yield attributes ofdifferent genotypes of kharif mungbean

Treatment Pods/plant Seeds/pod 100-seed weight (g) 2014 2015 2014 2015 2014 2015

Date of sowing 1 July 15.2 15.1 11.5 11.5 3.16 3.45 10 July 18.1 16.0 11.9 11.5 3.32 3.44 20 July 24.0 18.9 12.3 11.7 3.41 3.43 30 July 23.1 17.2 12.4 11.4 3.34 3.45 10 August 19.4 - 12.0 - 3.16 - CD (P=0.05) 1.3 1.5 0.3 NS 0.10 NS Genotype ML 818 18.0 14.9 11.7 11.3 3.26 3.29 PAU 911 15.1 15.7 11.8 11.4 3.12 3.30 ML 2037 22.5 19.0 12.2 11.7 3.61 3.59 ML 2056 24.3 17.7 12.3 11.6 3.41 3.61 CD (P=0.05) 0.8 0.9 0.2 0.2 0.10 0.12

Table 4. Effect of sowing dates on the biological yield,

grain yield and harvest index of differentgenotypes of kharif mungbean

Treatment Grain yield (kg/ha)

Biological yield (kg/ha)

Harvest index (%)

2014 2015 2014 2015 2014 2015 Date of sowing

1 July 814 969 5328 5886 15.3 16.5 10 July 1019 1001 5544 5158 18.4 19.4 20 July 1458 1140 6706 5780 21.7 19.7 30 July 1399 1088 5854 5297 23.9 20.5 10 August 1066 - 5315 - 20.1 - CD (P=0.05) 80 89 276 204 Genotype ML 818 1087 948 5594 5106 19.4 18.6 PAU 911 980 966 5197 5238 18.9 18.4 ML 2037 1230 1201 6044 5965 20.4 20.1 ML 2056 1308 1082 6162 5813 21.2 18.6 CD (P=0.05) 54 74 266 251

Number of nodules and their dry weight weresignificantly influenced by sowing date in 2014 (Table 2).The highest nodulation was recorded in 20 July sowing.Number of nodules/plant was recorded significantly higherin 20 July sowing than 1 and 10 July sowings which was,however, at par with 30 July and 10 August sowings. Dryweight of nodules/plant was significantly higher in 20 Julysowing than other sowing dates and at par with 30 Julysowing. Singh et al. (2010) reported that 15 July sowingand 10 August sowing of kharif mungbean recorded thehighest and lowest number of nodules and their dry weight/plant, respectively.

The plant height decreased with delay in sowing(Table 2). The crop sown on 1 July recorded significantlyhigher plant height than the other sowing dates. The highestnumber of branches/plant was recorded in 30 July sowingfollowed by 20 July sowing. Sowing on 20 and 30 Julyrecorded higher number of pods/plant, seeds/pod and 100-seed weight over other dates (Table 3).

The grain yield was significantly influenced bydifferent sowing dates (Table 4). There was a drasticreduction in grain yield in the early sowing (1 July) during

both the years. In 2014, the highest grain yield was recordedin 20 July sowing, which was statistically at par with 30July sowing but significantly higher than 1 July, 10 Julyand 10 August sowings. In 2015, the highest grain yieldwas recorded in 20 July sowing, which was statistically atpar with 10 and 30 July sowings but significantly higherthan 1 July sowing. The 20 July sowing recorded higher


grain yield by 79.1, 43.1, 4.2 and 36.8 per cent over 1 July, 10July, 30 July and 10 August sowing in 2014 and 17.6, 13.8and 4.7 per cent over 1 July, 10 July and 30 July sowing in2015, respectively. The highest grain yield in 20 July sowingcould be due to more number of pods/plant. Earlier, Singhet al. (2010) found that 5 to 25 July was the best sowingtime for kharif mungbean under Punjab conditions. Thebiological yield was the highest in 20 July sowing in 2014and 1 July sowing in 2015. The highest harvest index wasrecorded in 30 July sowing during both the years. The cropsown on 20 July recorded the highest gross returns, netreruns and B:C ratio during both the years (Table 5) due tohigher grain yield.Genotype: Among the genotypes, ML 818 took highestnumber of days to 50% flowering and physiological maturitywhereas genotypes ML 2037 and ML 2056 were early indays to 50% flowering and maturity (Table 1). Variations inflowering and maturity time among genotypes are governedby the genetic makeup of the genotype (Miah et al. 2009).The genotype ML 818 required the highest and ML 2037and ML 2056 required the lowest agro climatic indices(AGDD, AHTU and APTU) to attain physiological maturityduring both the years. During first year of study ML 2056and during second year ML 2037 performed better in termsof heat use efficiency for grain yield.

The genotypes ML 2056 and PAU 911 proved to besuperior for nodulation than all other genotypes (Table 2).Genotype ML 2037 was the tallest. Genotype ML 2056 in2014 and ML 2037 in 2015 recorded the highest branches/plant (Table 2). Genotype ML 2056 and ML 2037 recordedthe highest pods/plant and seeds/pod in 2014 and 2015,respectively (Table 3).

The genotypes ML 2037 and ML 2056 recordedsignificantly higher grain yield than ML 818 and PAU 911(Table 4). In 2014, the genotype ML 2056 recorded thehighest grain yield, which was significantly higher thanML 818, PAU 911 and ML 2037. In 2015, genotype ML 2037recorded the highest grain yield, which was significantlyhigher than ML 818, PAU 911 and ML 2056. Genotype ML2056 registered 20.3, 33.4 and 6.3 per cent increase in grainyield over ML 818, PAU 911 and ML 2037 in 2014 andgenotype ML 2037 registered 26.7, 24.3 and 11.0 per centincrease in grain yield over ML 818, PAU 911 and ML 2056in 2015. Other researchers (Miah et al. 2009, Singh et al.2010 and Rabbani et al. 2013) also reported genotypicvariation with respect to grain yield in mungbean. Theinteraction effect of sowing dates and genotypes withrespect to grain yield was non-significant during both theyears. Genotype ML 2056 and ML 2037 recorded the highestharvest index in 2014 and 2015, respectively which, mightbe due to more grain yield. The genotypes ML 2037 andML 2056 recorded higher gross returns, net returns andB:C ratio over other genotypes (Table 5) due to higher grainyields. It can be concluded that 20 to 30 July was the optimumsowing time and ML 2037 and ML 2056 the most promising

genotypes of mungbean under Punjab conditions.

REFERENCESEsfandiary F, Aghaie G and Mehr AD. 2009. Wheat yield prediction

through agro meteorological indices for Ardebil District.International Journal of Biological and Life Sciences 1: 48-51.

Miah MAK, Anwar MP, Begum M, Juraimi AS and Islam MA. 2009.Influence of sowing date on growth and yield of summermungbean varieties. Journal of Agricultural and Social Sciences5: 73-76.

Nuttonson MY. 1955. Wheat climatic relationship and use ofphenology in ascertaining the thermal and photo-thermalrequirements of wheat. American Institution of Crop Ecology,Washington DC.

Palsaniya S, Puniya R, Sharma A, Bazaya BR and Kachroo D. 2016.Effect of sowing dates and varieties on growth, yield and nutrientuptake of summer mungbean (Vigna radiata). Indian Journal ofAgronomy 61(2): 256-258.

PAU. 2014. Package of Practices for Kharif Crops of Punjab. PunjabAgricultural University, Ludhiana.

Rabbani MG, Chowdhary Akmsh, Bari MA and Salam MA. 2013.Effect of variety and sowing date on growth and yield of summermungbean [Vigna radiata (L.) Wilczek]. Journal of Agronomyfor Environment 7(1): 27-30.

Samant TK and Mohanty TR. 2017. Effect of sowing date and weedmanagement on productivity and economics of rainfed mungbean(Vigna radiata). Indian Journal of Agronomy 62(3): 332-337.

Singh G, Kaur H, Aggarwal N, Ram H, Gill KK and Khanna V. 2013.Symbiotic efficiency, thermal requirement and yield of blackgram(Vigna mungo) genotypes as influenced by sowing time. IndianJournal of Agricultural Sciences 83(9): 953-958.

Singh G, Kaur H, Aggarwal N, Ram H, Gill KK and Khanna V. 2016.Symbiotic characters, thermal requirement, growth, yield andeconomics of pigeonpea (Cajanus cajan) genotypes sown atdifferent dates under Punjab conditions. Journal of Applied andNatural Science 8: 381-385.

Singh G, Sekhon HS, Ram H, Gill KK and Sharma P. 2010. Effect ofdate of sowing on nodulation, growth, thermal requirement andgrain yield of kharif mungbean genotypes. Journal of FoodLegumes 23(2): 132-134.

Singh G, Virk HK, Singh S, Singh K, Singh S and Gill KK. 2017.Thermal requirements, growth and yield of pigeonpea [Cajanuscajan (L.) Millsp.] genotypes under different agroclimatic zonesof Punjab. Journal of Applied and Natural Science 9(4): 2377-2384.

Table 5. Effect of sowing dates on the economics ofdifferent genotypes of kharif mungbean

Treatment Gross returns (`/ha)

Net returns (`/ha)

B:C ratio

2014 2015 2014 2015 2014 2015 Date of sowing 1 July 37444 46997 11216 20769 1.43 1.79 10 July 46874 48549 20646 22321 1.79 1.85 20 July 67068 55290 40840 29062 2.56 2.11 30 July 64354 52768 38126 26540 2.45 2.01 10 August 49036 22808 - 1.87 - Genotype ML 818 50002 45978 23774 19750 1.91 1.75 PAU 911 45080 46851 18852 20623 1.72 1.79 ML 2037 56580 58249 30352 32021 2.16 2.22 ML 2056 60168 52477 33940 26249 2.29 2.00


ABSTRACT

The experiment was conducted during summer season of2016 and 2017 at the research farm of Uttar Banga KrishiViswavidyalaya, West Bengal in a Randomized block designwith nine treatment in three replication. Pooled data showedthat treatment (T6) receiving 25 kg nitrogen/ha recordedhighest growth and yield attributes namely, pod/plant (19.49),pod length (10.37 cm), no. of seed /pod (12.57) and 1000 seedweight (49.03), which led to more grain (921 kg/ha) and stoveryield (2200 kg/ha) followed by other higher doses of nitrogen.30 kg nitrogen/ ha (T7) and 35 kg nitrogen/ha (T8). Thetreatment receiving no nitrogen recorded significantlylowest values of all growth and yield attributes. Seed proteincontent (21.61%) and protein yield (199 g/ha) were also foundto be highest under T6 important for the nutritional aspectswhich might be due greater availability and uptake ofnitrogen (54.60 kg/ha).

Key words: Mungbean, Nitrogen uptake, Protein, Seed yield

Mungbean is self-pollinated legume crop which isgrown during summer (March-June) as well as in kharif(July-October) seasons in dry tracks of India. It is primarilya rainy season crop but with the development of earlymaturing varieties, it has also proved to be an ideal crop forspring and summer season. It is tolerant to drought andcan be grown successfully on drained loamy to sandy loamsoil in areas of erratic rainfall. Due to cheaper protein sourceit is designated as “poor man’s meat” (Aslam et al. 2010). Itis a short duration crop, fits well in various multiple andintercropping systems. After picking of pods, mungbeanplants may be used as green fodder or green manure. It isconsumed as a whole grains as well as dal being easilydigestible it is preferred by patients. It is valued for itsexcellent taste, flavour, high digestibility and free from the“flatulency effect” which is associated with other pulses.Besides these, the crop also improves soil by fixingatmospheric nitrogen.

Nitrogen is a crucial macronutrient needed by allplants to thrive. It is a central constituent of many structural,genetic and metabolic compounds in plant cells and one ofthe basic components of chlorophyll. Nitrogen is requiredfor growth and development. Therefore, nitrogen is appliedto the crop as per its need for better growth. It is establishedthat crop responds well with the addition of even smallamounts of nitrogen (Akram et al. 2004). Nitrogen isabundantly present but its availability is comparatively lessin the soil because only few plants of Fabaceae family candirectly harvest it from the atmosphere. Consequently the

Response of graded nitrogen doses on yield attributes of summer mungbean(Vigna radiata L.)RAJESH SAHA, PARTHA SARATHI PATRA and TARUN PAUL

Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal: Email: [email protected](Received : April 12, 2018 ; Accepted : September 10, 2018)

supply of available N often becomes inadequate especiallyduring the critical growing periods of plants. Hence, it hasbeen a long time challenge for agriculturalists to maintainsoil N at levels that are adequate for optimum cropproduction (Krishna et al. 2004). Applications of nitrogenincrease the source capacity, namely, leaf area, Leaf areaindex (LAI), early canopy closure and the rate ofphotosynthesis (Doughton et al. 1993). Though, mungbeancan trap atmospheric nitrogen in their root nodules, anapplication of 15 to 20 kg nitrogen per hectare as starterdose at sowing appeared to be optimum for the cropresponse. However, the degree of response depends oninherent soil fertility, soil moisture, temperature and thecropping patterns followed. Keeping the above facts inmind present experiment conducted to find out the optimumdose of nitrogen for better mungbean production.


The experiment was conducted during summerseasons of 2016 and 2017 at the Research Farm of UttarBanga Krishi Viswavidyalaya, Pundibari, Cooch Behar, WestBengal. Cooch Behar is situated in the terai agro climaticzone at 26019’86" N latitude and 89023’53" E longitude at anelevation of 43 meters above mean sea level. The soil of theexperimental site was sandy loam having pH 5.51, organiccarbon 0.74 %, available nitrogen 158.19 kg/ha, availablephosphorus 25.30 kg/ha and available potassium 112.20 kg/ha. The experiment was laid out in randomized completeblock design with three replications with an individual plotsize of 5 m × 4 m. The experiment comprised of the ninetreatments T1 = No nitrogen fertilizer (control); T2 = Nitrogen@ 5 kg/ ha; T3 = Nitrogen @ 10 kg/ha; T4 = Nitrogen @ 15kg/ha; T5 = Nitrogen @ 20 kg/ha; T6= Nitrogen @ 25 kg/ha;T7 = Nitrogen @ 30 kg/ha; T8 = Nitrogen @ 35 kg/ha and T9= Nitrogen @ 40 kg/ha. Mungbean variety Pusa Baishakiwas sown with a row to row spacing of 30 cm on 13th Apriland 14th March during 2016 and 2017, respectively. A plantto plant spacing of 10 cm was maintained. All otheragronomic practices such as weeding, hoeing, irrigation,plant protection measures etc. were kept normal and uniformfor all the plots. Nitrogen was applied as per treatments inthe form of urea (46 % N), phosphorus @ 50 kg/ ha in theform of single super phosphate (16 % P2O5) and potassium@ 30 kg/ha in the form of Muriate of potash (60 % K2O)were applied at the time of sowing of the crop. The cropwas harvested manually on 16th June and 15th July during2016 and 2017, respectively. The data on agronomicparameters were recorded during the course of investigation



using standard procedures. Total nitrogen in plant samplewas determined by distillation Kjeldhal method (Tandon,1993). The total uptake of nitrogen at harvest wasdetermined on dry weight basis multiplying the total drymatter of the crop with its corresponding content. Theprotein content in grain was obtained by multiplying thetotal nitrogen content by empirical factor 6.25 (Mckenzieand Wallace, 1954) based on assumption that averageprotein contains 16 per cent nitrogen by weight. Proteinyield of mungbean was calculated by multiplying the seedprotein content with seed yield and expressed in kg/ha.


All the growth attributes except number of nodules/plant recorded was found to be highest in treatment T6 i.e.nitrogen @ 25 kg/ha followed by T7 (Nitrogen @ 30 kg/ha),T8 (Nitrogen @ 35kg/ha), T5 (Nitrogen @ 20 kg/ha), T9(Nitrogen @ 40 kg /ha), T4 (Nitrogen @ 15 kg/ha), T3(Nitrogen @ 10 kg/ha) and T2 (Nitrogen @ 5 kg/ha), whileT1 recorded lower values all these growth attributes duringboth the years. Number of nodules/plant decreased withincreasing levels of nitrogen and was highest (32.43, 67.23and 44.83 at 30, 40 and 60 days after sowing) in control plot(T1). Treatment (T6) receiving 25 kg nitrogen/ ha recordedtallest plant (8.72, 16.86, 39.86 and 54.02 cm) higher drymatter accumulation (0.25, 2.63, 10.0, 18.10 g/ plant) andlarger leaf area (0.14, 0.85, 2.12) at 15, 30, 45 and 60 daysafter sowing respectively, which might be due to highernutrient availability, uptake and greater conversion

efficiency of applied nutrients by plants.Increased levels of nitrogen fertilizer upto 25 kg /ha

(T6) has pronounced effect on yield attributes of mungbeanand recorded significantly highest values of number of pod/plant (19.49), pod length (10.37 cm), no. of seed/pod (12.57)and 1000 seed weight (49.03 g), which ultimately leads tohigher seed (920.68 kg/ha) and stover yield (2200.37 kg/ha). Higher growth attributes and yield might be due tomore leaf area, which helps in accelerating photosynthesisprocess thus build up more dry matter. It is quite obviousthat treatment receiving no nitrogen fertilizer recordedlowest values of number of pod/plant (12.77), pod length(7.99 cm), no. of seed/pod (8.11) and 1000 seed weight (37.64g).Nitrogen content and uptake: Highest nitrogen contentsin grain and stover (3.46 and 1.64 %) were obtained with 25kg nitrogen/ha (T6) and T1 accounts for lowest nitrogencontent (2.56 and 1.16 %). The highest nitrogen contentmight be due to higher availability of soil nitrogen. Maximumuptake of nitrogen (54.60 kg/ha) was found in T6 (25 kgnitrogen/ ha) followed by T7 and T8. Increased nitrogenuptake might be due to consistent supply of nitrogenthroughout the crop growth period and reduced loss ofapplied nitrogen, which helps in producing higher grainand stover yield. The result of the present investigation isin conformity with the Yakadri et al. (2004), Athokpam et al.(2009), Agbenin et al. (1991), Dhage et al. (1987), Trug andYoshida (1983) and Patel R.D. (2012).

Table 1. Effect of varying level of nitrogen on growth attributes of mungbean (pooled data)Plant height

(cm) Nodules/plant Dry matter accumulation

(g/plant) Leaf area index

(LAI) Treatment

15 DAS

30 DAS

45 DAS

60 DAS

30 DAS

45 DAS

60 DAS

15 DAS

30 DAS

45 DAS

60 DAS

15 DAS

30 DAS

45 DAS

60 DAS

T1 7.29 14.52 33.97 41.69 32.43 67.23 44.83 0.14 1.88 7.01 10.48 0.10 0.52 1.53 0.96 T2 7.44 14.83 34.92 45.43 29.33 67.13 44.23 0.16 2.00 7.84 11.36 0.10 0.57 1.71 0.97 T3 7.63 15.19 36.18 46.08 25.07 58.67 43.14 0.18 2.10 8.04 11.56 0.11 0.61 1.75 1.06 T4 7.71 15.60 36.79 46.43 25.00 56.79 42.19 0.19 2.20 8.27 13.06 0.12 0.64 1.80 1.11 T5 7.89 16.07 38.07 47.99 23.83 56.70 36.08 0.21 2.41 9.14 15.35 0.12 0.71 1.85 1.18 T6 8.72 16.86 39.86 54.02 23.50 49.17 35.62 0.25 2.63 10.00 18.10 0.14 0.85 2.12 1.30 T7 8.15 16.66 39.14 52.52 22.00 39.00 35.19 0.23 2.46 9.56 16.35 0.13 0.83 1.92 1.26 T8 7.93 16.29 38.50 50.48 21.42 37.70 31.73 0.21 2.44 9.33 15.88 0.13 0.77 1.86 1.20 T9 7.76 15.84 37.46 47.44 19.17 28.25 27.78 0.20 2.36 8.86 14.61 0.12 0.69 1.78 1.14 S Em (±) 0.29 0.35 1.31 1.75 1.95 3.24 1.86 0.01 0.23 0.92 1.21 0.01 0.07 0.06 0.11 CD (p = 0.05) NS 1.06 NS 5.30 NS 9.78 5.64 NS NS NS 3.17 NS 0.21 0.17 NS

Table 2. Yield attributes and yields of mungbean as influenced by varying level of nitrogen (pooled data)

Treatment Pod/plant Pod length (cm)

Seed/pod 1000 seed weight (g)

Seed yield (kg/ha)

Stover yield (kg/ha)

Harvest Index (%)

T1 12.77 7.99 8.11 37.64 409.78 1524.17 22.56 T2 16.32 8.21 11.03 39.81 674.03 1757.45 31.76 T3 16.80 8.50 11.37 40.14 778.17 2098.23 30.74 T4 17.72 8.75 11.40 40.75 793.43 2164.35 31.00 T5 18.04 9.05 11.60 42.20 811.39 2065.11 32.71 T6 19.49 10.37 12.57 49.03 920.68 2200.37 32.96 T7 19.21 9.53 11.90 44.57 839.33 2135.92 31.65 T8 18.46 9.13 11.80 44.16 820.43 1983.61 33.95 T9 17.89 8.91 11.57 40.52 799.27 2068.90 31.55 S Em (±) 1.86 0.42 0.37 1.43 15.04 43.87 0.96 CD (p=0.05) NS 1.28 1.29 4.32 45.48 132.64 2.91

Saha et al. : Response of summer mungbean (Vigna radiata L.) to varying level of nitrogen 211

Table 3. Nitrogen content and its uptake, protein content and protein yield of mungbean as influenced by varying level ofnitrogen (pooled data)

Net return (×103 `/ha)

Benefit : cost ratio

Treatment Nitrogen content

(%) in seed

Nitrogen content (%) in stover

Nitrogen uptake (kg/ha)

Protein content

(%)

Protein yield (kg/ha)

2016 2017 2016 2017 T1 2.56 1.16 21.78 15.98 65.41 4 5 0.17 0.18 T2 2.79 1.36 33.75 17.46 117.26 21 22 0.87 0.89 T3 2.93 1.44 41.83 18.30 142.42 29 29 1.18 1.16 T4 3.12 1.48 44.87 19.52 154.81 30 29 1.24 1.18 T5 3.23 1.54 46.37 20.16 163.51 31 31 1.26 1.23 T6 3.46 1.64 54.60 21.61 199.00 38 38 1.54 1.52 T7 3.30 1.61 49.49 20.59 172.80 33 33 1.33 1.29 T8 3.25 1.58 46.71 20.31 166.69 32 31 1.28 1.21 T9 3.14 1.51 44.79 19.63 156.78 30 30 1.21 1.16

S Em (±) 0.05 0.05 1.03 0.32 3.86 - - - - CD (p=0.05) 0.15 0.15 3.11 0.97 11.69 - - - -

Protein content of mungbean seed improvedsignificantly with increasing the levels of nitrogen andreached highest at 25 kg nitrogen/ha (T6), thereafterdeclined. Pooled data revealed that comparatively higherprotein content (21.61%) was recorded with (T6) which wasfollowed by T7 (20.59%), T8 (20.31%) and T5 (20.16). Thisincrease in protein content due to application of 25 kgnitrogen/ ha was to the tune of 26.05 % over T1 (Nonitrogen). Similar findings have also been reported by Reddyand Ahlawat (1998) and Patel (2012). The increase in proteinharvest by application of 25 kg nitrogen/ha was to the tuneof 67.13 % over control (T1) which might be due to highernitrogen content and seed yield.Correlation: Correlation analysis revealed that all theparameters showed positive correlation at both 1 % and 5% level of significance (Table 4). Seed yield showed highercorrelation with pod/plant and seed/pod, respectively at1%level of significance. Seed yield and 1000 seed weightshowed higher correlation whereas stover yield and podlength showed lowest correlation at 5 % level ofsignificance.Economics: The net returns were less in first year ofexperiment as compared to the second year in almost all thetreatments simply due to lowers yield of mungbean in thefirst year (Table 3). Treatments receiving no nitrogen fertilizer(T1) recorded lowest net return (Rs. 4091.70 and 4470.80 perha) and benefit: cost ratio (0.17 and 0.18) during both theyears of experimentation. The results corroborate with the

earlier findings of Ambhore (2004); Patel, 2012 and Patel etal. (2004). It could be concluded that a good crop ofmungbean could be raised with 25 kg nitrogen/ha duringsummer season in Terai zone of West Bengal.

REFERENCESAslam M, Hussain N, Zubair M, Hussain SB and Baloch MS. 2010.

Integration of organic & inorganic sources of phosphorus forincreased productivity of mungbean (Vigna radiata L.). PakistanJournal of Agriculture Science 47(2): 111-114.

Akram HM, Iqbal MS, Muhammad S, Yar A and Abbas A. 2004. Impactof fertilizer on the yields of chickpea genotypes. InternationalJournal of Agriculture and Biology 6(1): 108-109.

Doughton JA, Vallis I and Saffigna PG. 1993. Nitrogen fixation in chickpea,Influence of prior cropping or fallow, nitrogen fertilizer and tillage.Australian Journal of Agricultural Research 44: 1403-1413.

Krishna S, Sharma AP and Bhushan C. 2004. Nitrogen and sulphurnutrition of chickpea grown under semi arid conditions of CentralUttar Pradesh. Legume Research 27(2): 146-148.

Mckenzie HH and Wallace HS. 1954. The Kjeldahl determinationof nitrogen: a critical digestion conditions-temperature, catalystand oxidizing agent. Australian Journal of Chemistry 7: 55-70.

Tandon HLS. 1993. Methods of Analysis of Soil, Plants, Waters andFertilizers. Fertilizer Development and ConsultationOrganization p 54-61.

Yakadri M, Tahatikunta R and Latchanna A. 2004. Dry matterproduction and nutrient uptake of mungbean (Vigna radiata L.)as influenced by nitrogen and phosphorus during wet season.Legume Research 27(1): 58-61.

Athokpam HS, Nandini, Chongtham, Singh RKK, Singh NG andSingh NB. 2009. Effect of nitrogen, phosphorus and potassiumon growth, yield and nutrient uptake by blackgram (Vigna mungoL.). Environment and Ecology 27(2): 682-684.

Agbenin JO, Lombin G and Owonubi JJ. 1991. Direct and interactiveeffect of boron and nitrogen on selected agronomic parametersand nutrient uptake by mungbean (Vigna radiata L.) under glasshouse conditions. Tropical Agriculture 68(4): 352-362.

Patel RD. 2012. Response of different cultivar of mungbean (Vignaradiata L.) to integrated nutrient management under south Gujaratcondition. M.Sc. (Agri.) Thesis submitted to Navsari AgriculturalUniversity, Navsari.

Reddy NRN and Ahlawat IPS. 1998. Response of chickpea (Cicerarietinum) genotypes to irrigation and fertilizers under late-sown conditions. Indian Journal of Agronomy 43(1): 95-101.

Trung BC and Yoshida S. 1983. Significance and nitrogen nutritionon the productivity of mungbean (Vigna radiata L. Wilczek).Japanese Journal of Crop Science 52(4): 493-499.

Table 4. Correlation analysis among different parametersof mungbean as influenced by varying level ofnitrogen

**Correlation is significant at the 0.01 level (2-tailed); *Correlationis significant at the 0.05 level (2-tailed).

Parameter Pod/ plant

Pod length (cm)

Seed/pod 1000 seed

weight (g)

Seed yield

(kg/ha)

Stover yield

(kg/ha)

Pod length .840** Seed/pod .970** .759* 1000 seed weight (g) .812** .971** .749* Seed yield (kg/ha) .979** .803** .987** .771* Stover yield (kg/ha) .894** .730* .889** .644 .939** Harvest Index (%) .901** .604 .951** .634 .911** .749*


ABSTRACT

A field was conducted at Agronomy Instructional Farm, S.D.Agricultural University, Sardarkrushinagar during kharif2016 to study the Nutrient management with panchgavya inkharif clusterbean (Cyamopsis tetragonoloba L.). Two levelswith FYM (0 and 5 tonnes/ha) and six fertilizers levels withpanchgavya (50 %, 75 %, 100 % RDF with panchgavya spray@ 3 % and 6 % at branching + flowering RDF with 25:50:00NPK kg/ha were compared. Application of 5 tonnes FYM/hasignificantly increased plant height at harvest, number ofbranches/plant, number of pod/plant, length of pod, 1000seed weight, seed yield and stover yield over the control,whereas number of seeds/pod remained unaffected. Thehigher value of all above parameters (except length of podand number of seeds/pod) were recorded significantly withapplication of 100% FYM + panchgavya spray @ 3% atbranching + flowering which ultimately reflected in higherseed yield (879 kg/ha) and stover yield (1699 kg/ha). However,it was remained at par with application of 100% RDF+panchgavya spray @ 6% at branching + flowering. The highernet return and benefit: cost ratio can be achieved byfertilizing kharif clusterbean with the application of 5 tonnesFYM/ha along with 100 % RDF (25:50:00 NPK kg/ha) withpanchgavya spray @ 3 % at branching and flowering stages.

Key words: Foliar application, Farm yard manure, Growth,Panchgavya

Clusterbean (Cyamopsis tetragonoloba L.) ispopularly known as guar and has been recognized as oneof the most important commercial crop of arid and semi aridregions. It is a good source of carbohydrates, protein, fiberand minerals like calcium, phosphorus and iron and containsappreciable amount of vitamin ‘C.’ In India, clusterbean iscultivated in 56.03 lakh hectares with production andproductivity of 27.15 lakh tonnes and 485 kg/ha, respectivelyduring 2013-14 (Annual Report, 2014). In Gujarat, it iscultivated in 2.79 lakh hectares with a production of 1.68lakh tonnes and productivity of 604 kg/ha, respectivelyduring 2014-15 (DOA, 2015). Conventional agriculture hasmade an adverse impact on soil and plant health.

To achieve soil fertility and crop productivity in asustainable manner, the role of organic manures and nutrientmanagement with fermented liquid organic fertilizer(panchgavya) are very vital. It has in addition to nutrients,plant growth promoting substances and microbial load/count which help in stimulating plant growth, yield,metabolic activity and resistance to pest and diseases.

Nutrient management with panchgavya in kharif clusterbean (Cyamopsistetragonoloba L.)JB CHAUDHARI, BJ PATEL, KM PATEL and GM PATEL

Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar, Gujarat, India; email:(Received : May 13, 2018 ; Accepted : July 15, 2018)

Keeping this in views, present investigation was undertakenby to find out effect of FYM of INM on productivity andnutrient uptake in kharif cluster bean.


A field experiment was conducted during kharif 2016at Agronomy Instructional Farm, C. P. College of Agriculture,S. D. Agricultural University, Sardarkrushinagar, Gujarat.The soil was loamy sand in texture, low in organic carbon(0.17 %) and available nitrogen (161 kg/ha), medium inavailable phosphorus (39 kg/ha) and potash (159 kg/ha)with pH 7.20.

The treatments consisted of two levels with FYM (0and 5 tonnes/ha, six fertilizers levels with panchgavya(50 %, 75 %, 100 % RDF with panchgavya spray @ 3 %and 6 % at branching + flowering RDF (25 : 50 : 00 NPK kg/ha). these treatments were evaluated in randomized blockdesign with factorial concept. Cluster bean variety GujaratGuar was sown by keeping 45 cm row spacing using a seedrate of 15 kg/ha on July 25, 2016. Application of fertilizerswas applied basal as per treatment. FYM was mixed in thesoil before 2 weeks of field preparation as per treatments.The procedure followed for preparation as per treatments.The procedure followed for preparation of panchgavya isas under (Natarajan, 2002).Preparation of Panchgavya: Panchgavya is a specialpreparation made from five products of cow along withcertain other ingredients incubated for specific duration inan earthen or wide plastic container. Ingredients forpreparation of Panchgavya are :

Fresh cow dung : 7 kg.Cow urine : 10 lit.Cow milk : 3 lit.Cow curd : 2 lit.Cow ghee : 1 kg.Jaggery : 3 kg.Tender coconut water : 3 lit.Ripe banana : 2 kg.Water : 10 lit.

Panchgavya solution was prepared by thoroughmixing of above ingredients at different time in an open(broad) mouth plastic container. The procedure followedfor preparation of Panchgavya is as under (Natarajan, 2002).

Chaudhari et al. : Nutrient management with panchgavya in kharif clusterbean (Cyamopsis tetragonoloba L.) 213

Step-I: On the first day, 7 kg fresh cow dung was mixed with1 kg cow ghee in a 25.0 lit. plastic barrel and kept for 3 days(72 hrs.) with intermittent stirring to exhaust methane gas.Step-II: On the fourth day, add 10 lit. cow urine and 10 lit.water in above mixture. The mixture was stirred twice in aday and allowed to ferment for 15 days. The mouth of thecontainer was covered with a thin cloth and kept in openshade.Step-III: On 19th day, 3.0 lit. cow milk, 2.0 lit. cow curd, 3.0 kgjaggery, 2.0 kg ripe banana and 3.0 lit. tender coconut waterwere added in the mixture and allowed to ferment for sevendays while stirring twice a day, both in the morning andevening to facilitate aerobic microbial activity. Addition ofjaggery, tender coconut water and ripe banana can help toaccelerate the fermentation process. The mixture was filteredthrough a muslin cloth. The Panchgavya stock solutionwas ready for use after 25 days. It was applied as foliarspray by sprayer in a standing crop as per treatments.

Spraying of Panchgavya sprayed @ 3 % and 6 % atbranching and flowering stages was done as per treatments.Crop was harvested on November 19, 2016. Total amountof rainfall during the crop life period was 572.2 mm in 28rainy days. The weather conditions were normal, but therewas a long dryspell of rainfall during September, 2016.Therefore a protective irrigation was applied in crop. Growthand yield parameters were recorded at harvest of the crop.Seed and stover yields/ha were worked out based on yieldsrecord in each plot.


Effect of FYM: Significant effect of FYM was observed onplant height, number of branches/plant, number of pods/plant, length of pods and 1000-seed weight, whereasnumber of seeds/pod remain unaffected (Table 1). Anapplication of FYM 5 t/ha increased seed and stover yieldsby 16.93 and 10.92 %, respectively over control (Table 3).The marked increase in various yield components withaddition of FYM of assimilates per nutrients, but also to itspivotal role in enhancing physicochemical and biologicalproperties of the soil. In the recent years with increasingevidences on potential role of growth hormones in yieldformation, it has been advocated that balanced hormonalpattern in plant system exert profound influence on properdevelopment of growth and reproductive structuresultimately leads to productivity of the crop. Singh et al.(2010) and Dutt et al. (2013), also similar finding in terms ofcrop yield performances.Effect of fertility levels and panchgavya spray: Fertilitylevels and panchgavya spray had significant effect ongrowth parameters viz,: plant height and number ofbranches/plant. Application of 100% RDF with panchgavyaspray @3% at branching and flowering attainedsignificantly higher plant height (98.0 cm) and number ofbranches per plant (9.30) over RDF. However, they were atpar with application of 100 % RDF with panchgavya spray@ 6 % at branching and flowering. Panchgavya is a provenbiofertilizers, viz., Azospirilum, azotobactor, phosphobator,

Table 1. Effect of different treatments on growth and yield parameters of clusterbean

Treatments Plant height (cm) at harvest

Number of branches per

plant

Number of pods per

plant

length of pod (cm)

Number of seeds per

pod

1000-seed weight

(g) Levels of organic manure (M) : M1 : No FYM 89.25 7.67 72.35 5.33 8.52 31.99 M1 : With FYM (5 t/ha) 94.15 8.30 77.24 5.85 8.72 32.82 S.Em. ± 1.46 0.16 1.66 0.10 0.08 0.20 C. D. (P = 0.05) 4.23 0.48 4.82 0.30 NS 0.59 Levels of fertilizer application and panchgavya spray (N) : N1 : 50% RDF + panchgavya spray @ 3% at branching + flowering

88.52 7.77 73.77 5.40 8.53 32.21

N2 : 50% RDF + panchgavya spray @ 6% at branching + flowering

87.60 7.23 67.88 5.30 8.27 31.26


93.35 8.17 76.77 5.75 8.80 32.99


91.17 7.62 71.63 5.62 8.68 32.54


98.00 9.30 82.93 6.07 8.87 33.62


96.35 8.73 79.90 5.73 8.83 33.22

N7 : RDF (25:50:00 NPK kg/ha) 86.93 7.10 70.68 5.28 7.37 31.00 S.Em. ± 2.73 0.31 3.10 0.19 0.15 0.38 C. D. (P = 0.05) 7.92 0.89 9.02 NS NS 1.11 Interaction (M x N) : S.Em. ± 3.85 0.43 4.39 0.27 0.22 0.54 C. D. (P = 0.05) NS NS NS NS NS NS C.V. % 7.28 9.38 10.16 8.49 4.31 2.87


pseudomonas that play an important role in stimulates ofplant growth by secreting IAA and GA (Kumar et al. 2011).

Yield attributes viz., number of pod per plant and1000-seed weight and seed and stover yields recordedsignificantly the higher values with application of 100 %RDF with panchgavya spray @3% at branching andflowering. This treatments increased number of pods/plantby 17.00 %, 1000-seed weight by 8.45 %, seed yield by32.38 % and stover yield by 16.05 % over RDF, However,this treatment was at par with application of 100 % RDFwith panchgavya spray @6% at branching and flowering.Length of pod and number of seeds per pod remainedunaffected.Economic: The net return and benefit:cost ratio of 1:16were also the higher with 5 t/ha. Application of 100 % RDFwith panchgavya spray @3% at branching + flowering wasmore economical with higher net return as well as B;C ratiorather than their lower net return and BCR. Combined FYMand application of 100% RDF with panchgavya spray @3%at branching + flowering recorded the higher net returnand BCR. The trend in economic return is mainly owing tothe treatment effects on the seed and stover yields. It couldbe concluded that application of FYM along with 100 %RDF + panchgavya spray@ 3 % at branching + floweringimproved yield and net profit in clusterbean under NorthGujarat agro climatic condition.

Table 2. Economics of kharif cluster bean as influenced by different treatmentsTreatments Yield (kg/ha) Gross return

(`/ha) Total cost

(`/ha) Net return

(`/ha) BCR Seed Stover Levels of organic manure (M) : M1 : No FYM 691 1475 27135 23941 3194 1.13 M2 : With FYM (5 t /ha) 808 1636 31552 27201 4351 1.16 S.Em. ± 16.63 39.85 C. D. (P = 0.05) 48.35 115.85 Levels of fertilizer application and panchgavya spray (N) : N1 : 50% RDF + panchgavya spray @ 3% at branching + flowering 698cd 1481ab 27392 24260 3132 1.13

N2 : 50% RDF + panchgavya spray @ 6% at branching + flowering 659d 1439b 25943 25010 933 1.04

N3 : 75% RDF + panchgavya spray @ 3% at branching + flowering 768bc 1580ab 30040 25220 4820 1.19

N4 : 75% RDF + panchgavya spray @ 6% at branching + flowering 727cd 1527ab 28499 25970 2529 1.10

N5 : 100% RDF + panchgavya spray @ 3% at branching + flowering 879a 1699a 34163 26180 7983 1.30

N6 : 100% RDF + panchgavya spray @ 6% at branching + flowering 851ab 1699a 33183 26930 6253 1.23

N7 : RDF (25:50:00 NPK kg/ha) 664d 1464ab 26168 25430 738 1.03 S.Em. ± 31.12 74.56 Interaction (M x N) : SEm +/- 44.00 105.44 CV (%) 10.17 11.74 Selling price of produce : Seed : ` 35/kg and Stover : ` 2/kg.

REFERENCES

Annual Report. 2014. NIAM for USDA on an Analysis of Guar cropin India (2013-14), New Delhi.

Datt N, Dubey YP and Chaudhary R. 2013. Studies on impact oforganic, inorganic and integrated use of nutrients on symbioticparameters, yield, quality of Frenchbean (Phaseolus vulgarisL.) vis-a-vis soil properties of an acid Alfisol. African Journal ofAgricultural Research 8(22): 2645-2654.

DOA. 2015. Districtwise area, production and productivity ofimportant crops of Gujarat (2014-15) Directorate of Agriculture,Gujarat State, Krishi Bhavan, Sector 10-A, Gandhinagar.

Jat RA, Arvadia MK, Tandel B, Patel TU and Mehta RS. 2012.Response of saline water irrigated mungbean (Vigna radiata) toland configuration, fertilizers and farmyard manure in Tapicommand area of south Gujarat. Indian Journal of Agronomy57(3): 270-274.

Kumar RS, Ganesh P, Tharmaraj K and Saranraj P. 2011. Growthand development of blackgram [Vigna mungo (L.)] under foliarapplication of Panchgavya as organic source of nutrient. CurrentBotany 2(3): 9-11.

Natarajan K. 2002. Panchgavya-A manual. other India press, mapusa,Goa. pp. 13-27.

Singh G, Sekhon HS, Ram H and Sharma P. 2010. Effect of farmyardmanure, phosphorus and phosphate solubilising bacteria onnodulation, growth and yield of kabuli chickpea. Journal ofFood Legumes 23(3&4): 226-229.


Identification and characterization of root nodule associated bacteria fromchickpea germplasm linesRENU VERMA1, NAVEEN KUMAR ARORA1, ANNAPRAGADA HARIKA, YADUVENDRA SINGHYADAV and MURUGESAN SENTHIL KUMAR

ICAR-Indian Institute of Pulses Research, Kanpur Uttar Pradesh, India. 1Babasaheb Bhimrao AmbedkarUniversity, Lucknow, Uttar Pradesh, India; Email: [email protected](Received : May 11, 2018 ; Accepted : August 15, 2018)

ABSTRACT

Present study explored the genetic variability of chickpeaminicore lines to understand the diversity of endophyticbacteria. A total of 395 nodule associated bacteria fromhealthy root nodules of chickpea and 30 bacterial isolateswere identified based on 16SrDNA sequence similarity andcharacterized for plant growth promoting traits. BLASTanalysis indicated that 16.67% of tested isolates showed only68-96% sequence similarity with available dataset at NCBI.In total, 22 bacterial isolates belong to Gram negative Genusvia. Enterobacter, Rhizobium, Stenotrophomonas, Pseudomonas,and Burkholderia while 8 are Gram positive (Bacillus, andBrevibacillus). Bacterial isolate No. 329B recorded very low16S rDNA sequence similarity (69%) to Pseudomonasrequired further analysis for identifying its appropriatetaxonomy. He was detected in isolate 281 while nodAB wasdetected in isolate 365 and 369 of Burkholderia. All 30bacterial isolates gave negative results for mineral phosphatesolubilization as well as HCN production, while 9 isolatesproduced siderophore. Two bacterial isolates (251A, 265A)were identified for IAA production. Further research onrevealing endophytic bacterial diversity and their plantbeneficial traits may lead to development andcommercialization of microbial formulations to enhancechickpea yield under rainfed condition.

Key words: Chickpea, Endophytic bacteria, Nodule, Plantgrowth promotion

Grain legumes are the major vegetarian sources ofprotein for billions of people in the globe. Among a dozenof grain legumes, chickpea (Cicer arietinum L.) is the mostimportant food legume, generally grown on marginal landunder rainfed condition. India contributed nearly 61.49%of global chickpea production with the productivity of 0.951t/ha (FAOSTAT 2018). Chickpea contains high protein (20-22%), fibber, minerals (phosphorus, calcium, magnesium,iron and zinc), ß-carotene, and unsaturated fatty acids (Gaur2010). Despite its nutritional value, chickpea productivityis stagnating (<1000 kg/ha) due to several biotic and abioticstresses. Insects particularly, gram pod borer (Helicoverpaarmigera Hubner) is the major devastating pest causesyield loss up to 20-30% annually. Terminal drought alongwith heat stress results further yield losses. Though severalagro-techniques were developed to minimize the impact ofbiotic and abiotic stresses on chickpea yield, microbial

formulations are considered as eco-friendly solution toenhance chickpea production under rainfed condition(Choudhary et al. 2016; Santoyoa et al. 2016). Severalstudies have revealed that endophytic bacteria particularlywith plant growth promoting (PGP) traits increasednodulation, growth and yield of legume varieties (Bai et al.2000; Zhao et al. 2018). They ubiquitously colonize theinternal tissues of plants and promote seedling emergenceand plant establishment under adverse conditions.Moreover bacterial endophytes are able to communicateand interact with the host plant more efficiently thanrhizospheric bacteria under different environmentalconditions and promote plant growth either by direct orindirect mechanisms (Ali et al. 2012; Coutinho et al. 2015).Piriformospora indica induced salt tolerance in barley byincreasing the levels of antioxidants (Baltruschat et al. 2008)while Phoma glomerata and Penicillium significantlyincreased plant biomass, assimilation of essential nutrients,and reduced the sodium toxicity in cucumber under salinityand drought stress (Waqas et al. 2012).

Plant growth promoting rhizobacteria with amino-cyclo propane carboxylate deaminase ameliorate plantstress caused by water logging, nutritional shortage,drought, high salts and the presence of pollutants(Senthilkumar et al. 2016). Achromobacter piechaudiiARV8, which produces 1-aminocyclo-propane- 1-carboxylate (ACC) deaminase, conferred IST to droughtstress in pepper (Capsicum annuum L.) and tomato(Solanum lycopersicum L.) plants. Pseudomonas putidastrain GAP-P45 imparted drought tolerance to sunflowerby forming biofilm on the root surface (Sandhya et al. 2009).Bacillus spp. isolated from soils of different arid and semiaridregions, could grow at minimal water potential (-0.73 MPa)and possessed plant growth promoting properties like IAA,GA, cytokinin, P-solubilization, production of siderophores,HCN, and ammonia.

Plant genotype and soil moisture status determinethe carbon allocation to root zone and influence thequantitative and qualitative nature of root exudates. Itincludes changes in the availability of chemo-attractantsor signal compounds as well as a different C/N ratio ornutrient availability (Kandeler et al. 2006) that exerts strongselection pressure on the composition, abundance oractivity of microflora in rhizosphere as well as root or nodule



tissues. Nodules of symbiotic N2 fixing legumes are rich innutrients as compared to roots. The root and nodule tissueof different legume plant possess both non-rhizobial andrhizobial endophytic bacteria including Agrobacterium,Phyllobacterium, Stenotrophomonas, Enterobacter,Bacillus, Bordetella, Burkholderia, Erwinia, Devosia,Rhizobium, Ensifer, Curtobacterium, Sphingomonas,Mesorhizobium, and Pseudomonas (Dudeja et al. 2012).Diversity of these endophytes has been widely studiedusing legume cultivars. As plant genotype exerts selectionpressure to shape root/nodule endophytic microbialcommunity, chickpea minicore lines were used in the presentinvestigation to understand the diversity of noduleassociated bacteria and characterize them for plant growthpromoting traits.


Isolation of root nodule associated bacteria: Chickpeaminicore lines (214 genotypes) were grown on B-16 filed,New Research Campus, Indian Institute of Pulses Research,Kanpur during Rabi-2017-2018 under augmented designwith RSG-888 and DCP-92-3 as check cultivars. Chickpeagenotypes were randomly selected and healthy plants wereuprooted for isolation of endophytic bacteria. Root systemwas washed with water to remove soil particle and healthypink colour nodules were detached and surface sterilizedwith mercuric chloride 0.01% for 3-5 min and 70% ethanolfor 30 seconds. Treated nodulated were washed with steriledistilled water for about 5-7 times to eliminate traces ofethanol and mercuric chloride. Each 100 µl of water fromfinal wash was spread plated on nutrient agar for sterilitycheck. Surface sterilized nodules were mashed with knownvolume of sterile water in mortar pestle. Nodule cell maceratewas serially diluted up to 10-4 dilution with sterilized distilledwater, spread plated on Yeast Extract Mannitol Agar andincubated at 28p C for 7 days. Individual colonies appearedon YEMA plates were streak plated frequently for obtainingpure culture and then stored as 25% glycerol stocks in -20°C.Identification and phylogenetic analysis of noduleassociated bacteria: Nodule associated bacteria weretaxonomically classified based on 16S rDNA sequencesimilarity. 16S rDNA was amplified through colony PCRusing universal primers PA(5’-AGAGTTTGATCCTGGCTCAG-3’) and PH (5’-AAGGAGGTGATCCAGCCGCA-3’) PCR program includedinitial denaturation (3 min at 94 °C), 35 cycles of denaturation(1 min at 94 °C), annealing (1 min at 52 °C), extension (1 minat 72 °C), and final extension for 5 min at 72 °C using VeritiThermal Cycler (Edwards et al. 1989). Each 5ìL of the PCRproduct was electrophoresed in 1.2% agarose gel with 1XTAE buffer and observed under UV transilluminator. PCRamplicons of 16S rDNA were sequenced by outsourcingwith Chromous Biotech Pvt. Ltd. Bangalore, India andfurther subjected to BLAST analysis using NCBI Database.

The evolutionary history was inferred using the Neighbor-Joining method (Saitou and Nei, 1987). The bootstrapconsensus tree inferred from 1000 replicates is taken torepresent the evolutionary history of the taxa analyzed.Branches corresponding to partitions reproduced in lessthan 50% bootstrap replicates are collapsed. The percentageof replicate trees in which the associated taxa clusteredtogether in bootstrap test (1000 replicates) is shown nextto the branches (Felsenstein, 1985). The evolutionarydistances were computed using the p-distance method (Neiand Kumar, 2000) and are in the units of the number of basedifferences per site. Evolutionary analyses were conductedin MEGA7 (Kumar et al. 2016).Mineral phosphate solubilization and HCN production:Fresh cultures (24h grown) of nodule associated bacteriawere spot inoculated on Pikovskya Agar (PVK) containingtri-calcium phosphate (Ca3 (PO4)2) and incubated at 28p Cfor 5-7days. P-solubilizing ability of bacterial isolates interms of clear zone was observed. Fresh cultures (24hgrown) of bacterial isolates were streak inoculated on King’smedium B amended with 4.4g/L glycine. Filter paper strips(Whatman No.1) soaked in 0.5% picric acid in 0.2% sodiumcarbonate were placed inside the lid of inoculated petriplate,sealed with parafilm and then incubated at 28p C for 5-7days. Un-inoculated control with treated filter paper wasused as control. Plates were observed for change of filterpaper colour from yellow to orange brown to dark brown(Baker and Schippers 1987).PCR amplification of nif and nod genes: Presence of nifH was detected by using primers 19F (5’-GCIWTYTAYGGIAARGGIGG -3’) and 407R (5’-AAICCRCCRCAIACIACRTC-3’). PCR program includedinitial denaturation (4 min at 94 °C), 35 cycles of denaturation(30 sec at 94 °C), annealing (1 min at 50 °C), extension (30sec at 72 °C), and final extension for 5 min at 72 °C usingVeriti Thermal Cycler (Ueda et al. 1995). Presence of nodABwas determined using primers nodA-1 (5-TGCRGTGGAARNTRNNCTGGGAAA-3‘) and nodA-2 (5‘-GGNCCGTCRTCRAAWGTCARGTA-3‘). PCR programincluded initial denaturation (4 min at 94 °C), 35 cycles ofdenaturation (45 sec at 94 °C), annealing (1 min at 49 °C),extension (1 min at 72 °C), and final extension for 5 min at 72°C using Veriti Thermal Cycler (Haukka et al. 1998).Siderophore production by Chrome-Azurol-S (CAS) plateassay: Chrome Azurol S (CAS) agar plate assay was usedto test siderophore production. CAS dye was prepared (60.5mg CAS/50ml distilled water) along with 5ml iron solution(1mM FeCl3.6H2O) and 5ml 10mM HCl. After sterilization,50 ml of prepared CAS dye was poured into 500 ml sterilizednutrient agar and then plated. Fresh cultures (24h grown)of the bacterial isolates were spot inoculated on CAS agarplates and incubated for 5-7days at 28p C. Production ofsiderophore was indicated by the formation of bright zonewith a yellowish (hydroxamate), pinkish (catecholate) andwhitish (carboxylate) colour surrounding the bacterial

Verma et al. : Identification and characterization of root nodule associated bacteria from chickpea germplasm lines 217

colonies on dark CAS-blue agar medium (Schwyn andNeilands 1987).Salt and pH tolerance of nodule associated bacteria:Freshly grown bacterial isolates were streak inoculated onNutrient agar containing different concentrations of NaCl(2–10% wD v) and incubated at 28p C for 48h. Appearanceand growth of bacterial colonies after incubation wererecorded. For testing pH tolerance, bacterial isolates wereinoculated in Yeast Extract Mannitol broth with pH adjustedto various levels (pH 4, pH 5, pH 8, and pH 9) and incubatedat 28p C for 48h. Bacterial growth at different pH levels wasevaluated at optical density OD540 nm usingspectrophotometer.


Host genotype and its physiological stage determinethe diversity of associated endophytic microbiome.Endophytic communities of rice indica seeds are shapedby the hosts’ genotype, their physiological adaptation tosalt stress and phylogenetic relatedness (Walitang et al.2018). Bacterial endophytes are fulfilling importantfunctions for its host including the promotion of plantgrowth, protection against biotic and abiotic stress as wellas the production of essential secondary metabolites (Chenget al. 2019, Muller et al. 2015, Alavi et al. 2013).Understanding the bacterial diversity is the first towards

exploring beneficial plant–bacteria interactions. Hence, thepresent investigation explored the diverse nature ofchickpea minicore lines and associated endophytic bacterialcommunity. A total of 395 bacterial isolates were obtainedfrom active root nodules of chickpea. Absence of bacterialcolonies on nutrient agar plates served as sterility checkconfirmed proper sterilization of root nodule surface andhence isolated bacteria are putative endophytes in nature.Size of bacterial colonies appeared on YEMA varied frompin point to maximum diameter of ~5mm after 48hincubation. Colonies were circular to ovoid in shape withsmooth or irregular margin. Diverse morphological natureof bacterial colonies on YEMA indicated that root nodulesof chickpea minicore lines were colonized by severalbacterial genera/species apart from Mesorhizobium. Nearfull length of 16S rDNA, approximately 1500bp were amplifiedwith universal primer set through PCR and sequenced.BLAST analysis of 16S rDNA sequences indicated that 25bacterial isolates belong to 7 different genera with 98-100%sequence similarity while remaining 5 isolates shared 68-96% sequence similarity (Table 1). Among the bacterialisolates, eleven (249A, 251A, 270, 272C, 329A, 335A, 350,356A, 358A, 373A, 378) belong to Enterobacter, 3 isolates(281, 365, 369) belong to Burkholderia, 4 isolates (251B,358B, 283A, 373B) belong to Pseudomonas, and isolate335b belongs to Stenotrophomonas. Two bacterial isolatesvia 275A, 275B are identified as Rhizobium with 99%

Table 1. 16S rDNA sequence based identification of chickpea nodule associated bacteriaS.

No. Identity number for

bacterial isolates Accession number of

chickpea Query length of 16S rDNA

sequence (bp) % Query Coverage

% Similarity of 16S rDNA

Genus level identification

1. 335A ICC11121 680 100% 99% Enterobacter 2. 335B ICC11121 366 100% 99% Stenotrophomonas 3. 251A ICC3218 745 99% 98% Enterobacter 4. 251B ICC3218 786 99% 99% Pseudomonas 5. 373A ICC15518 762 100% 99% Enterobacter 6. 373B ICC15518 765 100% 99% Pseudomonas 7. 275A ICC4814 733 99% 99% Rhizobium 8. 275B ICC4814 725 99% 99% Rhizobium 9. 358A ICC12537 757 83% 99% Enterobacter 10. 358B ICC12537 743 100% 99% Pseudomonas 11. 249A ICC2990 716 98% 95% Enterobacter 12. 283A ICC5504 727 100% 98% Pseudomonas 13. 329A ICC9942 723 100% 99% Enterobacter 14. 329B ICC9942 713 96% 68% Pseudomonas 15. 369 ICC14815 518 100% 100% Burkholderia 16. 350 ICC11944 659 100% 100% Enterobacter 17. 270 ICC4463 699 100% 99% Enterobacter 18. 365 ICC12928 568 87% 99% Burkholderia 19. 356A ICC12328 703 99% 99% Enterobacter 20. 272C ICC4567 356 93% 91% Enterobacter 21. 281 ICC5383 724 100% 100% Burkholderia 22. 378 ICC16487 686 89% 96% Enterobacter 23. 279B ICC4872 716 100% 99% Brevibacillus 24. 326B ICC9848 425 100% 100% Bacillus 25. 265A ICC3761 734 99% 99% Bacillus 26. 265B ICC3761 318 98% 93% Bacillus 27. 338A ICC11168 746 100% 100% Bacillus 28. 267B ICC3946 679 100% 99% Bacillus 29. 297A ICC8195 350 100% 99% Bacillus 30. 297B ICC8195 742 100% 100% Bacillus


sequence similarity (Fig. 2). Isolate no. 272C and 249Aclustered with Enterobacter with 91% and 95% sequencesimilarity respectively. Seven bacterial isolates (265A, 265B,267B, 283A, 297A, 326B, 338A) belong to Bacillus while279B, showed 99% sequence similarity to Brevibacillus. Intotal, 22 bacterial isolates are Gram negative (Enterobacter,Rhizobium, Stenotrophomonas, Pseudomonas, andBurkholderia) while 8 are Gram positive (Bacillus, andBrevibacillus) (Fig. 3). Bacterial isolate No. 329b recordedvery low 16S rDNA sequence similarity (69%) toPseudomonas required further analysis for identifying itsappropriate taxonomy.

Bacterial isolates were tested for the presence of nifHand nodAB genes. PCR fragments (390 bp) of nifH wereamplified in isolate 281 (Burkholderia) but not in Rhizobiumisolates via 275A and 275B (Fig. 1b). Forward primer nodA1starts from base 14 in nodA, and reverse primer nodA2ends in nodB at 88 bases (Sinorhizobium meliloti) afterthe end of nodA. DNA fragment (666 bp) corresponding tonodAB was amplified in isolate 365 and 369 of Burkholderia(Fig. 1c). Nodule associated bacteria were tested for theirtolerance level against different concentrations of NaCl andpH. More than 65% of tested isolates have the potential togrow at 10% NaCl concentration while only two isolates ofBurkholderia (281 and 365) failed to grow at 2% saltconcentration (Fig. 4). None of the bacterial isolates weregrown at pH 4.0. Many of the selected endophytic bacteriawere grown at high pH 9. Deora and Singhal (2010) havereported that slight variation in the pH of growth mediummight have an enormous effect on Rhizobium growth.Rhizobial isolates were observed to be more sensitive tolow pH than their host and this affects the establishment of

the symbiosis, limiting the survival and persistence of therhizobia. Since microbial siderophores increases the plantavailable iron by forming Fe-siderophore complex atrhizosphere, bacterial isolates were also tested forsiderophore production and found that only 6 isolates(329A, 329B, 265A, 265B, 338A, 267B) produced

Fig. 1. PCR amplification of a) 16S rDNA, b) nifH and c) nodCgenes

Fig. 2. Phylogeny of Gram positive bacteria isolated from chickpearoot nodules

Fig. 3. Phylogeny of Gram negative bacteria isolated from chickpearoot nodules

Verma et al. : Identification and characterization of root nodule associated bacteria from chickpea germplasm lines 219

siderophore. Siderophores stimulate plant growth bydirectly increasing iron availability at rhizosphere andindirectly by competitively inhibiting the growth of plantpathogens with less efficient iron uptake system. Testedbacterial isolates were found to neither solubilize mineralphosphate nor produce HCN for biological control ofphyto-pathogens. Two bacterial isolates (251A, 265A)produced IAA in the presence of tryptophan and have thepotential to promote chickpea growth under rainfedconditions. IAA produced by the rhizobacteria loosensthe plant cell walls thereby facilitating root exudation.Bacterial IAA production by rhizobacteria has been reportedto increase ACC synthase expression in plants and give acompetitive advantage for ACC deaminase producingbacteria over other soil microorganisms. Physiologicallyactive IAA, produced by rhizobacteria can havepronounced effects on plant growth. It promotes rootgrowth directly by stimulating root elongation or theformation of lateral and adventitious roots or indirectly byinfluencing bacterial ACC deaminase activity (Zazueta etal. 2013). Identification of such beneficial bacteria byexploring the diversity of chickpea minicore associatedbacteria may results on development and commercializationof microbial formulations to enhance chickpea yield underrainfed condition.

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ABSTRACT

Web blight of mungbean is one of the major diseases whichincur huge loss and holds back the mung bean production. Acomprehensive study was made during Kharif 2012 on thecharacterization of R. solani on the basis of morphology,virulence, reaction of pathogen on differential host and ecofriendly management of the disease. The surveillance maderevealed that the blight of mung bean was prevalent in allthe places visited. The disease incidence varied from 33.10per cent to 53.10 per cent in different localities. The pathogenR. solani was isolated from infected plants obtained fromdifferent locations and designated as RS 1 to RS 9. In themorphological study, all the isolates of R. solani shared typicalcharacters like right angle branching near the distal septumof the young vegetative hyphe, formation of dolipore septum,no clamp connection and no conidium except moniliod cells.All the nine isolates grown on PDA showed differences inmycelial growth. RS 8 isolate showed highest growth rateafter 72 hours of observation. Among nine isolates, fiveexhibited aerial growth of their colonies, four isolates RS 4,RS6, RS 7 and RS 9 produced sub-aerial colonies. The colonycolours of RS 2, RS 6 and RS 8 isolates were brown whilethat of isolates RS 3, RS 5, RS 7 and Rs 9 were white. RS 1,RS 2, RS 3, RS 5, RS 6 and RS 8 isolates grew fast as well asRS 4, RS7, RS 9 grew moderately. Morphological studies ofR.solani on four solid and liquid broth medium was takenPDA is the best solid medium for R. solani followed byCzapek’s medium while in case of Czapek’s broth is bestfor growth of R. solani. Sclerotial charcters indicated that in3 isolates sclerotia were located on surface of colony, sub-surface location of sclerotia was observed in RS 6 and RS 9isolates and in RS 1, RS 3, Rs 4, RS 7 sclerotia were embeddedinside medium. Sclerotial size was invariably macro exceptRS 2, RS 6 and RS 9. The maximum sclerotia size 2.960 mmwas observed in PDA medium.Out of 15 mungbean varieties,one HUM-1 showed resistant reaction, one PDM 54 showedmoderately resistant reaction, six variety showed susceptiblereaction and seven variety showed highly susceptiblereaction.

Key words: Cultural, Morphological, Mungbean, Rhizoctoniasolani, Variability

Thanatephorus cucumber is (Fr.) Donk(=Rhizoctonia solani Kuhn), is the most destructive andwidely distributed soil borne pathogen and most studiedfungal species causing diseases in many plant speciesworld-wide. The pathogen is cosmopolitan with a very widehost range and attacks large number of host plants andweeds (Sharma and Singh, 2003). Mungbean is attacked by

Cultural and morphological variability of Rhizoctonia solani causing web blightof mungbean in Jharkhand state of IndiaKANAK LATA, HC LAL, SAVITA EKKA, CS MAHTO, NIRAJ KUMAR and BINAY KUMAR

Birsa Agricultural University, Kanke, Ranchi, Jharkhand; Email: [email protected](Received : July 26, 2018 ; Accepted : September 15, 2018)

many diseases caused by Fungi, bacteria and viruses.Among these, web blight caused by Rhizoctonia solaniKuhn is widely distributed causing considerable losses ingrain yield. Disease was observed in a very severe form (5-100%) at Kanke itself (Dubey and Mishra, 1991). The diseaseis characterized by the presence of spider web like myceliumand small brown sclerotia on the infected plants.Rhizoctonia solani Kuhn causes pre- and post- emergencerot in this crop resulting in maximum mortality of seedlings.Rhizoctonia solani is highly diverse consisting ofgenetically distinct groups often varying in their cultural,morphological, physiological pathological characters(Ogoshi, 1996). The complexities and variations inRhizoctonia solani Kuhn have been reported from differentparts of the world that can affect management ofdisease(Basu et al. 2004).Hence present study was carriedout to investigate the cultural and morphological variabilityamong the Rhizoctonia solani isolates collected fromdifferent mungbean growing location of Jharkhand.


The affected parts i.e., leaves, stems, petioles, twigsand pods of mungbean showing typical and characteristicsymptoms of web blight disease were collected fromdifferent growing areas during the crop season. Thesesamples were brought to laboratory in clean paper bags.These samples were washed thoroughly with tap waterand then dried between folds of the filter paper. The sampleswere the kept in paper bags in laboratory for further studies.The fungus was isolated from the diseased sample onpotato dextrose agar (PDA) plates following followingstandard isolation procedures and incubated at 26±10C inBOD incubator .The cultures of the pathogen were purifiedthrough single hyphal tip method (Rangaswami andMahadevan, 2004), maintained on PDA slants and storedin refrigerator at 40 C for further studies. The cultures weredesignated as RS1, RS2, RS3, RS4, RS5, RS6, RS7, RS8 andRS9 based on locality from where these isolates wereisolated. To study the morphological and culturalcharacteristics, the isolates of Rhizoctonia solani weregrown on PDA and four different semi solid and liquid brothmedium, in Petri plates and conical flasks at 26±10C. Afterthat, colony and sclerotial characters were studied followedby their categorization based on sclerotial and colonycharacteristics. The parameters of morphological andcultural characteristics included colony diameter ofmycelium, mycelium weight, abundance of mycelium,



location, size and distribution pattern of sclerotia etc. Theobservation on colony colour and texture were recordedafter incubation for 5 days. Sclerotial character of eachisolate was observed after 15 days of incubation. Thesclerotial intensity, pattern of production, location ofsclerotia was recorded.Estimation of dry mycelial weight: After the lapse ofrequired incubation period, the mycelial mats were harvestedfrom the liquid media and were filtered through previouslyweighed whatman’s filter paper No. 42 with the precautionto remove all the sticking mycelial growth in the flask. Themycelia mats along with filter papers were dried in hot airoven at 800C for 24 hours and subsequently cooled indesiccators with CaCl2 at the bottom for 4-6 hours, till theconstant weight was obtained (Lily and Barnett, 1951).Pathogenicity test: Pathogenicity was tested with the testculture on a variety SML 668. Inoculations were made withR. solani for establishing pathogenicity on 20 days oldplants in 10 inches pots. These pots were earlier filled withsterilized soil. Soil was sterilized for two consecutive daysin an autoclave at 15 lb pressure p.s.i. for 20 minutes. Potswere filled up with the sterilized soil and covered withpolythene sheets to prevent external contamination. Themungbean plants were inoculated by applying inoculumsin the whorl of the plant through pre-colonized sorghumgrain preferably in the evening hours. Adequate moisturewas maintained after inoculation. A set of un-inoculatedplants served as control. The observation on diseaseseverity was recorded.


Morphological/Cultural Characteristics: Theobservations pertaining to cultural and morphologicalcharacteristics such as abundance of mycelium, colonycolour, and growth rate and linear growth, sclerotialdistribution and size as well as location of sclerotia of the 9isolates of R. solani grown on PDA revealed that all the 9isolates of R. solani shared typical characteristics like rightangle branching near the distal septum of the cells in youngvegetative hyphae, formation of a dolipore septum in branchnear the point of origin, construction of the branch near

the point of origin, no clamp connection, no conidiumexcept moniliod cells (Table 1). RS 8 isolate showed highestgrowth rate after 72 hours of observation. The least growthwas observed in the isolate RS4. Out of 9 isolates, 5 isolates( RS1, RS2, RS3, RS5, RS8) exhibited aerial growth of theircolonies on the plates. 4 isolates namely RS4, RS6, RS7 andRS9 produced sub-aerial colonies. The colony colours ofRS2, RS6 and RS8 isolates were brown while that of isolatesRS3, RS5, RS7 and RS9 were white. Of the 9 isolates, sixisolates viz., RS1, RS2, RS3, RS5, RS6 and RS8 isolateswere placed under fast growth rate category. Three isolatesviz., RS4, RS7 and RS9 grew moderate. On PDA the radialgrowth of RS8 ioslate was significantly superior (87.74 mm)over the remaining 8 isolates. The radial growth of RS4isolate was distinctly least as compared to other 8 isolates.Morphological characteristics of all nine isolates of thepathogen such as abundance of mycelium, colony colour,growth rate, sclerotial distribution and size as well aslocation of sclerotia confirm identification of R.solani.Similar observations were also reported by Parmeter andWhetney (1970), Tiwari and Khare (1998), Sunder et al.(2003) and Upmanyu et al. (2005).Effect of solid and liquid media on mycelial growth andweight of different isolates of R. solani: All the nineisolates grown on four semi solid medium viz.; Richard’s,PDA, Martin’s and Czapek’s medium indicated that PDAsupported the maximum colony diameter of RS 8 (87.33 mm)followed by RS 1 (86.33 mm) and was significantly at par(Table 2). There was slow to moderate growth of all theisolates of R. solani in Richard’s medium except RS 1 whichshowed 72.66 mm colony diameter. Colony diameter rangedfrom 37.66 to 57.66 mm in Richard’s medium for other isolates.In Martin’s medium also, there was slow to moderate growthrates in all the isolates and colony diameter ranged from36.0 to 61.66 mm. Czapek’s medium supported the maximumgrowth in colony diameter in all the nine isolates of R.solani.Maximum colony diameter of 87.0 mm was recorded in RS 4and RS 5 followed by RS 3 (85.33 mm). Growth of theseisolates in Czapek’s medium was significantly at par. Growthof other isolates ranged from 70.0 mm to 83.66 mm. All thenine isolates were grown on four liquid broth medium viz.,Richard’s, PDA, Martin’s and Czapek’s to determine theweight of different isolates of R. solani. Czapek’s mediumsupported highest mycelial weight in most of the isolates.The highest mycelial weight of 1.917 g was recored by RS 4and RS 5 followed by RS 3 (1.737 g), RS 8 (1.483 g) and RS2 (1.460 g), respectively. Mycelial weight in other isolatesranged from 0.903 to 1.210 g. Mycelial weight in Richard’smedium ranged from 0.97 g to 1.19 g in all the nine isolates.Highest mycelial weight of 1.747 g was recorded by RS 5 inPDA medium followed by RS 4 (1.433 g), RS 6 (1.183 g) andRS 3 (1.177), respectively. Mycelial weight of other isolatesin PDA ranged from 0.970 g to 1.157 g. Mycelial weight wasless in Richard’s medium for all the isolates and it rangedfrom 0.970 g to 1.190 g. Bateman (1962) while working on

Table 1. Morphological characteristics of different isolatesof R. solani

Isolates Colony characteristics of different isolates

Abundance Colour Type Growth Diameter (mm)

RS 1 Moderate Dark brown Aerial Fast 81.80 RS 2 Abundant Brown Aerial Fast 76.46 RS 3 Moderate White Aerial Fast 82.20 RS 4 Slight Dark brown Sub-aerial Moderate 60.24 RS 5 Moderate White Aerial Fast 84.79 RS 6 Abundant Brown Sub-aerial Fast 78.89 RS 7 Slight White Sub-aerial Moderate 61.32 RS 8 Abundant Brown Aerial Fast 87.74 RS 9 Slight White Sub-aerial Moderate 61.40

Lata et al. : Cultural and morphological variability of Rhizoctonia solani causing web blight of mungbaen in Jharkhand 223

physiology of Thiellaviopsis basicola and R. solani foundpotato dextrose broth and PDA were found to be the bestmedia for growth of the fungus. Azam and Khan (1973)found that growth and sclerotial formation of R. solaniwere best on potato dextrose agar among the solid media,whereas in the case of liquid media, Richard’s medium wasfound to be the best for growth as well as sclerotialformation. In the present investigation also, similar resultswere noticed with some variation among the isolates.Madhusadan et al. (1977) stated that the growth of R. solaniwas comparatively higher on Czapek’s broth than Richard’sbroth.Variation in sclerotial characters: All the isolates grownon PDA were investigated for morphological variation insclerotia such as size, density, and colour in addition tolocation, distribution and time taken for sclerotial formation(Table 3). Sclerotia were located on surface of colony in 3isolates. Sub- surface location of sclerotia was observed inRS 6 and RS 9 isolates and in isolates RS 1, RS 3, RS 4 andRS 7 sclerotia were embedded inside medium. distributionpattern of sclerotia was ‘through out plate ’Type, in 5isolates while, ‘near inoculation point’ distribution patternwas recorded in RS 1, RS 3, RS 4 and RS 7 isolate. Sclerotialsize invariably was macro. In RS 2, RS 6 and RS 9, microsizesclerotia were recorded. Results further revealed that allthe isolates of R. solani took variable time in producingsclerotia. Isolate RS 3 and RS 9 took minimum time (12 days)while RS 4 and RS 6 took maximum time (21 days) to producesclerotia. RS 8 and RS 7 produced the sclerotia in 12 days

while 17 days taken to produced sclerotia in RS 1 and RS 2.Azam and Khan (1973) found that growth and sclerotialformation of R. solani were best on potato dextrose agaramong the solid media, whereas in the case of liquid media,Richard’s medium was found to be the best for growth aswell as sclerotial formation. In the present investigationalso, similar results were noticed with some variation amongthe isolates. Madhusadan et al. (1977) stated that thegrowth of R. solani was comparatively higher on Czapek’sbroth than Richard’s broth. ,Variation in sclerotial size on different media: All theisolates of R. solani were grown on four different mediaviz., Richard’s, PDA, Martin’s and Czapek’s medium forestimation of sclerotial size (Table 4). Maximum sclerotialdiameter of 2.960 mm was recorded in RS 1 grown on PDAfollowed by RS 7 (2.830 mm), RS 6 (2.430 mm), RS 8 (2.297mm), RS 9 (2.213 mm) and RS 5 (2.080 mm), respectively.Sclerotial size of other isolates in PDA ranged from 1.840mm to 1.927 mm. Maximum sclerotial size of 1.727 mm in RS9 was reported in Richard’s medium followed by RS 8 (1.707mm), RS 4(1.605 mm) and RS 3 (1.510 mm), respectively.Sclerotial size of other isolates in Richard’s medium rangedfrom 0.86 mm to 1.34 mm. In Martin’s medium, maximumsclerotial size of 1.608 mm was recorded for RS 3 followedby RS 5 (1.581 mm) and was significantly at par. RS 4recorded 1.478 mm size of sclerotia. Sclerotial diameter in

Table 2. Mycelial growth and weight of different isolates of R. solani in solid and liquid media

* Average of 3 replications.

Treatments Isolates

Colony diameter (mm)* Mycelial wt. (g)* Richard’s PDA Martins’s Czapek’s Richard’s PDA Martins’s Czapek’s

RS 1 72.66 86.33 52.00 75.33 1.067 1.103 1.577 1.210 RS 2 48.00 79.33 61.00 83.66 1.163 1.157 1.177 1.460 RS 3 55.66 78.33 57.00 85.33 1.077 1.177 1.310 1.737 RS 4 37.66 77.33 53.00 87.00 1.043 1.433 0.923 1.917 RS 5 61.00 75.66 61.00 87.00 0.970 1.747 1.077 1.917 RS 6 56.00 82.66 40.66 76.00 1.157 1.183 1.000 1.007 RS 7 50.00 72.66 48.33 70.00 1.180 1.153 1.203 0.940 RS 8 39.66 87.33 36.00 77.66 1.160 0.987 1.473 1.483 RS 9 57.66 73.66 61.66 70.66 1.190 0.970 0.983 0.903 SEm± 0.78 0.96 0.81 0.83 0.026 0.029 0.026 0.025 CD (p=0.05) 2.35 2.44 2.44 2.51 0.076 0.086 0.078 0.076 CV (%) 2.56 2.10 2.70 1.84 3.974 4.085 3.778 3.129

Table 3. Sclerotial morphology of different isolates of R.solani

Isolate Sclerotial characters of different isolates Location Distribution Size IP (Day)

RS 1 Embedded Near inoculation point Macro 17 RS 2 Surface Through out plate Micro 17 RS 3 Embedded Near inoculation point Macro 12 RS 4 Embedded Near inoculation point Macro 21 RS 5 Surface Through out plate Macro 20 RS 6 Subsurface Through out plate Micro 21 RS 7 Embedded Near inoculation point Macro 15 RS 8 Surface Through out plate Macro 15 RS 9 Subsurface Through out plate Micro 12

Table 4. Sclerotial size of different isolates of R. solani indifferent media

* Average of 3 replications.

Isolates Sclerotial diameter (mm)* Richard’s PDA Martin’s Czapek’s

RS 1 1.34 2.960 1.394 1.155 RS 2 1.24 1.927 1.230 1.329 RS 3 1.51 1.847 1.608 0.883 RS 4 1.605 1.840 1.478 0.769 RS 5 0.88 2.080 1.581 0.849 RS 6 1.06 2.430 0.869 0.830 RS 7 0.86 2.830 0.849 0.619 RS 8 1.707 2.297 1.00 0.966 RS 9 1.727 2.213 0.980 0.863 SEm± 0.026 0.047 0.026 0.019 CD (p=0.05) 0.078 0.141 0.076 0.056 CV (%) 3.382 3.593 3.624 3.528


other isolates ranged from 0.849 mm to 1.394 mm. Thesclerotial size in Czapek’s medium was found to be minimumin all the isolates. Maximum sclerotial diameter of 1.329 mmwas recorded in RS 2 followed by 1.155 mm in RS 1. Thesclerotial size of other isolates in Czapek’s medium rangedfrom 0.619 mm to 0.966 mm. Dutta et al. (2013) studiedmorpho-cultural and pathogenic variations among 15isolates of Rhizoctonia solani obtained from sheath blightinfected rice plants, 5 each from Jammu, Kathua andUdhampur areas of Jammu division, were studied during2006-07. The isolates differed in respect of mycelia colour,hyphal width, radial growth and in size and shape andcolour of sclerotia. Despite morphological variation, theisolates showed positive hyphal fusion and thus suggestedthe presence of a single anastomosis group. Gurav et al.(2017) worked on cultural morphological variability andanastomosis behaviour of R. solani isolates causing sheathblight of rice. Based on radial colony growth rate, all isolateswere categorized into three groups as slow, medium andfast growing. Hyphal width in all the isolates rangedbetween 6to 10.5um. most isolates produced raised or fluffyand pale yellow to very pale brown mycelium with variedpatterns of sclerotial formation. Isolates produced brownto dark brown sclerotia which were either present in theform of concentric ring at the centre or periphery or scatteredthroughout the colony. Isolates were highly variable bothin mycelia and sclerotial parameters with no consistentcharacters related geographic origin.

R. solani isolated from diseased plants was foundpathogenic to mungbean plants. The isolated pathogenproduces similar symptoms as observed in nature on bothinjured and un-injured plants (Table 5). Susceptible reactionwas observed in RS 1 (45%), RS 2 (40%) and RS 4 (50%).Rest all the isolates were highly susceptible showing 55 to75 per cent disease severity. The uninoculated cheek couldproduce only 15 per cent disease severity. The diseaseseverity was quite higher on injured plants than un-injuredones. The present findings confirmed the observation ofDubey and Mishra (1995) and Gokulapalan et al. (2000).Racial characterization through host differentialreactions: Pot experiments were conducted to screen 15mungbean varieties in sick soil and pre and post-emergence

Table 5. Pathogenicity of R. solani on mungbean cultivarSML 668

Pathogen Disease Severity (%) RS 1 45 RS 2 40 RS 3 55 RS 4 50 RS 5 60 RS 6 70 RS 7 60 RS 8 65 RS 9 75

Control 15

Table 6. Screening of different varieties of mungbeanagainst Rhizoctonia solani

Sl. No. Varieties

Mortality (%) Reduction

(%) ReactionPre emergence

(%)

Post emergence

(%) 1 Barabanki 20.00 30.00 50.00 S 2 C 5 70.00 10.00 80.00 HS 3 C 6 50.00 5.00 55.00 HS 4 CGG 973 35.00 40.00 75.00 HS 5 HUM-1 10.00 0.00 10.00 R 6 K 851 36.67 10.00 46.67 S 7 Kopargaon 45.00 5.00 50.00 S 8 KUG 531 40.00 0.00 40.00 S 9 Meha 33.33 0.00 33.33 S 10 PDM 54 13.33 6.67 20.00 MR 11 Pusa 971 70.00 0.00 70.00 HS 12 Pusa 972 33.33 33.33 66.66 HS 13 Pusa Baisakhi 65.00 15.00 80.00 HS 14 Pusa Vishal 65.00 0.00 65.00 HS 15 Samrat 20.00 25.00 45.00 S

mortality percentage revealed that mungbean entriesdiffered in their susceptibility to the pathogen (Table 6).Among which C 5, C 6, CGG 973, Pusa 971, Pusa 972, PusaBaisakhi and Pusa vishal were highly susceptible againstthe pathogen. 6 varieties viz., Barabanki, K 851, Kopargaon,KUG 531, Meha and Samrat showed susceptible reaction. 1variety HUM-1 showed resistant reaction against thepathogen. The present finding finds similarities with thefinding of Dubey et al. (2012) and Lal et al. (2012).

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ABSTRACT

The field experiment were carried out to study the effect ofparaquat on efficiency of mechanical and manual harvestingof mungbean (Vigna radiata L. Wilczek) genotypes at MainAgricultural Research Station, University of AgriculturalSciences, Dharwad. The field experiment was laid out insplit- split plot design with two main plots (methods ofharvesting), three sub plots (genotypes) and two sub-sub plots(paraquat spray and control). The methods of harvestingand genotypes did not record significant difference withrespect to yield but spraying of paraquat recordedsignificantly higher seed yield (1,269 kg ha-1) compared tocontrol. Among the interactions, mechanical harvesting ofall the three genotypes with paraquat recorded significantlyhigher seed yield (1,304-1,245 kg ha-1), field efficiency (91.79- 90.45 %), harvest efficiency (521-498 kg ha-1). Whereasmechanical harvesting of genotypes without paraquat sprayrecorded significantly higher threshing loss (5.90 - 5.19 %).Mechanical harvesting of mungbean aimed at to getting thebenefit from the lower cost of labour required for timelyharvesting of mungbean.

Key words: Harvesting efficiency, Mechanical harvesting,Mungbean, Paraquat

There is less scope for production of mungbeanbecause of many production constraints like non availabilityof improved quality seeds, long statured plants and shortduration varieties. Shattering of pod, rains during later stagedeteriorate quality and create problem in harvesting of crop.In recent years, large number of labours migrated from ruralto urban area due to rapid industrialization, which created aproblem of scarcity of labour during harvesting. Toovercome these problems we need to go for mechanicalharvesting in mungbean. The mechanical harvesting is doneby combine harvester, which was introduced in the early1990s. The combine could harvest 2.4 to 3.0 acres in onehour. Cutting height during combine harvesting is oftenhigher than with other harvesting methods. Combine is anefficient, economical and less labour and time consumingmachine, in addition 2 to 3 weeks of saving in harvestingtime (Upasana et al. 2015).

Further, suitability of variety for mechanicalharvesting, greenish nature of leaves even after maturationof pod, the indeterminate flowering habit of mungbean andhigh moisture of stalk could affect working efficiency ofmachine, which led to increased harvesting loss and storagedifficulty (Abdul et al. 2003). Paraquat application (4ml/l)

Evaluation of mechanical harvesting efficiency in defoliated mungbean genotypesKEERTI, GANAJAXI MATH and RAGHUVEER

University of Agricultural Sciences, Dharwad, Karnataka; Email: [email protected](Received : January 21, 2018 ; Accepted : May 20, 2018)

at physiological maturity (70 DAS) result into completedefoliation takes place, within 3-4 days. Main objective ofdefoliation is to get a uniform drying of the crop, facilitateshedding of leaves before harvesting at an appropriate timeand ensure clean and fast picking of pods and reducelosses.


The field experiment was conducted at MainAgricultural Research Station, Dharwad, kharif 2015.Thefield experiment was laid out in split- split plot design withtwo main plots (methods of harvesting), three sub-plots(genotypes) and two sub sub-plots (paraquat spray andcontrol). The soil was medium deep black soil with pH 7.10.The available N, P2O5 and K2O contents were 240.5, 23.5and 354.6 kg ha-1, respectively. FYM (5 t ha-1) was applied15 days before sowing of the crop. For sowing, two seedsper hill were dibbled 5 cm deep in furrows at a spacing of 30cm x 10 cm. Recommended dose of N and P2O5 were appliedas basal at the time of sowing. The crops were harvested attheir physiological maturity. The data was analysedstatistically based on mean values obtained. The level ofsignificance used in ‘F’ and ‘T’ test was P = 0.05 (Gomezand Gomez, 1984).


Seed yield: Seed yield of mungbean did not differsignificantly due to the methods of harvesting. The cropharvested by manual method evidenced higher seed yield(1219 kg ha-1) compared to mechanical method of harvesting(1110 kg ha-1). There was no significant difference in yieldof different genotypes. Among paraquat sprayedtreatments significantly higher seed yield (1269 kg ha-1) thancontrol (1061 kg ha-1).

Among the interactions of harvesting methods,genotypes and paraquat spray (H×G×D), mechanicallyharvested genotypes sprayed with paraquat recordedsignificantly higher yield (1245-1304 kg ha-1) over all thegenotypes harvested mechanically without spraying ofparaquat (911-990 kg ha-1) and interaction of manualharvesting, genotypes, with paraquat spray did not showany significant difference among them (Table 1). Becausethe control plot recorded higher harvest losses likethreshing loss of about 56.4%, damaged grains about44.68%, unthreshed pods about 55.29 % compared toparaquat sprayed plots. The similar results recorded byThakar and Brar (2000) and Keith (2000). Interaction of


Keerti et al. : Evaluation of mechanical harvesting efficiency in defoliated mungbean genotypes 227

manual harvesting, varieties, with paraquat spray did notshow any significant effect on yield. The result indicatedthat the paraquat spray did not have any vital role on manualharvesting.Threshing loss, damaged grains and unthreshed pods:Methods of harvesting had significant effect on threshingloss, damaged grains and unthreshed pods. Mechanicalharvesting showed significantly higher threshing loss(4.21%), damaged grains (0.65 %) and unthreshed pods(3.37 %) than manual method of harvesting (2.73 %, 0.50 %and 2.16 %, respectively). In mechanical method ofharvesting the harvest loss was mainly attributed to feedrate, cylinder speed and screen size. The results are inconformity with the findings of Saxena et al. (1987); Latheret al. (2000); Turnar et al. (2001); Rahim zadeh et al. (2006)and Upasana (2015). Paraquat spray recorded significantlylower threshing loss, damaged grains and unthreshed pods(2.71 %, 0.47 % and 2.17 %, respectively) compared to non-sprayed treatment (4.24 %, 0.47 % and 3.37 %, respectively).Mungbean genotypes did not influence significantly thethreshing loss, damaged grains and unthreshed pods.Among the interactions mechanically harvested genotypeswithout paraquat recorded significantly higher threshingloss (5.19%-5.90%), damaged grains (0.79-0.86%) andunthreshed pods (4.14-4.70%) over all the genotypesharvested mechanically with paraquat and interaction ofmanual harvesting, genotypes, with paraquat spray did notshow any significant difference among them (Table 1 and2).

Field efficiency and Harvest efficiency: Methods ofharvesting had significant effect on field efficiency andharvest efficiency. Mechanical harvesting showedsignificantly higher field efficiency and harvest efficiency(444 kg h-1 and 86.14%) than manual method of harvesting(8 kg h-1 and 72.64 %). This is the situation because inmechanical harvesting saving of time (productive time washigher in mechanical harvesting and in short period of timeit harvested large area) was more compared to manualmethod of harvesting. Similar results were observed byOzcan and Zeren (1987); Kalsirislip and Singh (1999);Padmanathan et al. (2006); Zhang et al. (2012) andSomanagouda (2013). Paraquat spray recorded significantlyrecorded significantly higher harvest efficiency and fieldefficiency (260 kg h-1 and 82.43%) compared to non sprayedtreatment (8 kg h-1and 76.35 %). Mungbean genotypes didnot influence significantly the threshing loss, damagedgrains, and unthreshed pods. Among the interactions ofmethods of harvesting, genotypes and paraquat spray(H×G×D), mechanical harvested genotypes with paraquatspray recorded significantly higher field efficiency andharvest efficiency (90.45%-91.79% and 498 kg h-1-521 kg h-

1) than all other interactions. (Table 3)Harvest per cent and Grain purity: Methods of harvestinghad significant effect on harvest per cent and grain purity.Mechanical harvesting showed significantly lesser harvestper cent and grain purity (96.6% and 96.0%) than manualharvesting (97.8 % and 97.3 %). This was attributed tohigher number of damaged grains (0.65 %) and unthreshed

Table 1. Seed yield (kg ha-1) and threshing loss (%) of mungbean as influenced by method of harvesting, paraquat spray andgenotype

Main plot- Methods of harvesting (H) Sub plot - Genotype (G) Sub sub plot- Defoliator chemical (D) H1: Mechanical harvesting G1: DGGV-2 D1: Paraquat @ 4ml l-1

H2: Manual harvesting G2: DGG-1 D2: Control G3: Nirmal (popular local variety)

Seed yield (kg ha-1) Threshing loss (%) Treatment Spray

Harvesting Genotypes D1 D2 Mean D1 D2 Mean H1 G1 1245 990 1117 2.83 5.19 4.01 G2 1304 920 1112 2.89 5.90 4.39 G3 1290 911 1101 2.87 5.59 4.23 Mean 1280 940 1110 2.87 5.56 4.21 H2 G1 1224 1165 1195 2.52 2.94 2.73 G2 1294 1208 1251 2.61 2.83 2.72 G3 1256 1169 1213 2.51 2.98 2.75 Mean of H Mean 1258 1181 1219 2.55 2.92 2.73

G1 1234 1078 1156 2.68 4.07 3.37 G2 1299 1064 1181 2.75 4.36 3.56 G3 1273 1040 1157 2.69 4.28 3.49

Mean 1269 1061 2.71 4.24 For comparison of Means S.Em+ CD at 5% S.Em+ CD at 5% H 21 NS 0.08 0.51 G 18 NS 0.10 NS D 23 72 0.09 0.29 H x G 25 81 0.14 0.47 H x D 33 101 0.13 0.41 G x D 40 124 0.16 0.50 H x G x D 57 176 0.23 0.71


cent and grain purity. Paraquat spray recorded significantlyrecorded significantly higher harvest per cent and grainpurity (97.8 % and 97.4 %) compared to non sprayedtreatment (96.6 % and 96.0 %). In interactions of methodsof harvesting, genotypes and paraquat spray (H×G×D),

Table 2. Damaged grains (%) and unthreshed pods (%) of mungbean as influenced by method of harvesting, paraquat sprayand genotype



Damaged grains (%) Unthreshed pods (%) Treatment Spray

Harvesting Genotypes D1 D2 Mean D1 D2 Mean H1 G1 0.46 0.79 0.63 2.30 4.14 3.22 G2 0.52 0.86 0.69 2.29 4.70 3.50 G3 0.48 0.81 0.64 2.32 4.48 3.40 Mean 0.48 0.82 0.65 2.30 4.44 3.37 H2 G1 0.46 0.54 0.50 2.00 2.32 2.16 G2 0.44 0.54 0.49 2.10 2.20 2.15 G3 0.46 0.54 0.50 2.00 2.36 2.18 Mean of H Mean 0.45 0.54 0.50 2.03 2.29 2.16

G1 0.46 0.67 0.56 2.15 3.23 2.69 G2 0.48 0.70 0.59 2.19 3.45 2.82 G3 0.47 0.68 0.57 2.16 3.42 2.79

Mean 0.47 0.68 2.17 3.37 For comparison of Means S.Em+ CD at 5% S.Em+ CD at 5% H 0.007 0.041 0.07 0.43 G 0.014 NS 0.08 NS D 0.012 0.038 0.07 0.23 H x G 0.020 0.067 0.11 0.36 H x D 0.018 0.054 0.10 0.32 G x D 0.022 0.066 0.13 0.39 H x G x D 0.030 0.094 0.18 0.55

Table 3. Field efficiency (%) and harvest efficiency (kg ha-1) of mungbean as influenced by method of harvesting, paraquatspray and genotype


H2: Manual harvesting G2: DGG-1 D2: Contro G3: Nirmal (popular local variety)

Field efficiency (%) Harvest efficiency (kg h-1) Treatment Spray

Harvesting Genotypes D1 D2 Mean D1 D2 Mean H1 G1 90.45 80.40 85.43 498 396 447 G2 91.79 82.54 87.16 521 368 445 G3 90.85 80.80 85.83 516 364 440 Mean 91.03 81.25 86.14 512 376 444 H2 G1 74.25 71.30 72.77 9 8 8 G2 73.34 71.30 72.32 9 8 9 G3 73.91 71.76 72.83 9 8 8 Mean of H Mean 73.83 71.45 72.64 9 8 8

G1 82.35 75.85 79.10 253 202 227 G2 82.56 76.92 79.74 265 188 227 G3 82.38 76.28 79.33 262 186 224

Mean 82.43 76.35 260 192 For comparison of Means S.Em+ CD at 5% S.Em+ CD at 5% H 1.03 6.26 3 19 G 0.21 NS 6 NS D 0.24 0.75 8 24 H x G 0.29 0.95 9 30 H x D 0.34 1.06 11 34 G x D 0.42 1.29 14 42 H x G x D 0.59 1.83 19 59

pods (3.37 %) in mechanical harvesting compared to manualharvesting 0.50 % and 2.16 %, respectively. These resultsare in line with those of Mohammad et al. (2013) andSomanagouda (2013). There was no significant differencerecorded between genotypes with respect to harvest per

Keerti et al. : Evaluation of mechanical harvesting efficiency in defoliated mungbean genotypes 229

mechanical harvesting of mechanically harvested genotypeswithout paraquat recorded significantly lower harvestpercent and grain purity (95.3%-95.8% and 94.4 %-95.1 %)over all other interactions. (Table 4).

Main aim of the mechanical harvesting is to harvestat the right time and at optimum moisture content. Comparedto manual harvesting, mechanical harvesting will reducethe labour requirement and cost of cultivation. Applicationof paraquat @ 4 ml l-1, three to four days before harvestreduced the moisture levels in the stalk and leaves to greaterextent, which led to increase in working efficiency and alsograin recovery in combined harvester. The paraquat spraydid not have any vital role on manual harvesting.

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Table 4. Harvest percent (%) and grain purity (%) of mungbean as influenced by method of harvesting, paraquat spray andgenotype



Harvest percent (%) Grain purity (%) Treatment Spray

Harvesting Genotypes D1 D2 Mean D1 D2 Mean H1 G1 97.7 95.8 96.8 97.2 95.1 96.2 G2 97.7 95.3 96.5 97.2 94.4 95.8 G3 97.7 95.5 96.6 97.2 94.7 96.0 Mean 97.7 95.5 96.6 97.2 94.7 96.0 H2 G1 98.0 97.7 97.8 97.5 97.1 97.3 G2 97.9 97.8 97.8 97.5 97.3 97.4 G3 98.0 97.6 97.8 97.5 97.1 97.3 Mean of H Mean 98.0 97.7 97.8 97.5 97.2 97.3

G1 97.8 96.8 97.3 97.4 96.1 96.7 G2 97.8 96.5 97.2 97.3 95.8 96.6 G3 97.8 96.6 97.2 97.4 95.9 96.6

Mean 97.8 96.6 97.4 96.0 For comparison of Means S.Em+ CD at 5% S.Em+ CD at 5% H 0.07 0.44 0.08 0.47 G 0.08 NS 0.09 NS D 0.07 0.23 0.09 0.26 H x G 0.11 0.36 0.13 0.42 H x D 0.11 0.32 0.12 0.37 G x D 0.13 0.40 0.15 0.46 H x G x D 0.18 0.56 0.21 0.64

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ABSTRACT

Root-knot nematodes are sedentary endoparasites and mostwidely distributed in sandy loamy soils. Meloidogyneincognita (Kofoid and White) and M. javanica (Treub) areaffecting food legumes in tropical and subtropical regions.These nematodes have wide host ranges. Because of non-availability of nematicides, research of alternative methodsagainst these enemies is necessary. Beneficial fungi in thegenus Trichoderma have been known since 1920s as a biocontrol agents against several plant pathogens and nematodepests. The resident isolates of Trichoderma harzianum(IIPRTh-1, Th-2, Th-4, Th-25 & Th-26), T. asperellum(IIPRTas-21, Tas-22, Tas-23 & Tas-24) tested againstMeloidogyne javanica showed a nematicide effect in eggparasitism and egg hatching. Those effectiveness varied withspecies and isolates under laboratory conditions. Culture ofT. harzianum (IIPRTh-4) was most effective against theMeloidogyne javanica in regards to egg parasitism (50%)and egg hatching inhibition (86%) compared to controls.Thus, T. harzianum (IIPRTh-4) showed good bio controlpotential against root-knot nematode, M. javanica under invitro conditions can further be evaluated for its commercialuse as bio-control agent against root knot nematodes.

Key words: Bio-control, Meloidogyne javanica, Pulse crops,Trichoderma spp.

Management of any soil inhabitant pathogens in acrop is very difficult and root knot disease caused byMeloidogyne spp is no exception. Chemical nematicidesmay play a major role in crop protection. In recent years theban on certain nematicides due to health hazards andenvironmental concerns has prompted the search foralternatives to manage the nematode damage. However,for managing the root-knot nematodes, various approachescould be adopted; biological control is one of the choicesamong the plant protection scientist. The rhizospheres aremore intensified zone of microbial activity and populationof fungi, bacteria, nematodes and other group of organisms(Katznelson, 1965). The association of microorganisms inthe rhizosphere involves symbiosis, antagonism orantibiosis etc. Plant parasitic nematodes destroying severalfungi are widespread in the soil. Beneficial Fungi in thegenus Trichoderma have been known since 1920s for theirability to act as bio-control agents against several plantpathogens and nematode pests. Trichoderma species aresometimes found associated with Meloidogyne spp. infields. Trichoderma can penetrate their eggs and females,their successful deployment as a bio-control agent against

Antagonistic potential of indigenous Trichoderma spp against MeloidogynejavanicaDEVINDRAPPA, BANSA SINGH, MONIKA MISHRA, RK MISHRA and KRISHNA KUMAR

ICAR-Indian Institute of Pulses Research, Kanpur, India ; Email: [email protected](Received : July 05, 2018 ; Accepted : September 11, 2018)

nematodes may depend on a thorough understanding ofthis fungus. Compatibility between the fungal isolate, hostcultivar and soil substrate may, therefore, play an importantrole in the proliferation and persistence of Trichodermaspp. in soil. Bio-control of root-knot nematodes(Meloidogyne spp.) by different species of Trichodermahas been reported by several scientists (Mascarin et al.,2012; Naserinasab et al. 2011; Rao et al. 1998; Spiegel et al.2007; Al-Shammari et al. 2013). Different mechanisms suchas antibiosis, competition, mycoparasitism, and enzymatichydrolysis have been suggested for the bio-control activityof Trichoderma spp. against phytopathogenic fungi (Elad,1995; Sivan and Chet, 1992). Enzymes such as chitinases,glucanases, and proteases seem to be very important inthe mycoparasitic process (Hasan et al. 1996). All thesemechanisms, except competition, can potentially beinvolved in the nematode bio-control process (Sharon etal. 2001).The usage of chemicals is limited owing to theirtemporary effect, high cost and non-availability, resistancedevelopment in nematodes, health and environmentalhazards, residual toxicity and adverse effects on thebeneficial micro flora and fauna in the soil besides effect onthe crop. Biological control of plant parasitic nematodesthrough microorganisms offers an alternative orsupplemental management tools to replace chemicalmethods (Siddiqui and Mahmood, 1999). Among the bio-control agents, Trichoderma spp. has been widely usedagainst several plant pathogens worldwide (Harman et al.2004, Mukherjee et al. 2013, Mishra et al. 2018). It isimportant that bio-control isolates are able to compete andpersist in the environment, rapidly colonize and efficientlyproliferate on newly formed roots (Sariah et al. 2005) andprovide continued benefits over the duration of annualcrops (Harman, 2000). Under the present study, local isolatesof Trichoderma harzianum (IIPRTh-1, Th-2, Th-4, Th-25 &Th-26) and T. asperellum (IIPRTas-21, Tas-22, Tas-23 &Tas-24) were evaluated in vitro for their antagonisticpotential against Meloidogyne javanica.


Isolation and Maintenance of Root knot Nematodeculture: In order to maintain sufficient number of root knotnematode culture for the experiment, Culture ofMeliodogyne javanica was collected from infested field atNew Research Centre and maintained at greenhouse,Division of crop protection, ICAR-Indian Institute of PulsesResearch, Kanpur. The population of the test nematode


Devindrappa et al. : Antagonistic potential of indigenous Trichoderma spp against Meloidogyne javanica 231

was developed from single egg mass on brinjal plants grownin cemented pots, well ahead of the beginning of theexperiment. After two months of inoculation, the egg massesof Meliodogyne javanica from infected roots of brinjal werehandpicked with the help of sterilized forceps and afterwashing in distilled water were placed in sieve with a layerof double tissue paper. The required numbers of fresh eggmasses were collected from culture as per the requirement.Isolation of Indigenous Trichoderma spp. from pulsesrhizospheres: Twenty random rhizospheres soil sampleswere collected from healthy pigeonpea, chickpea, lentil andfieldpea from Kanpur Nagar, Kanpur Dehat, Fatehpur,Auriya and four district of Bundelkhand (Hamirpur, Jalaun,Banda, Jhansi) and stored in sterile plastic bags. The soilsamples were air dried. Isolation was done by using serialdilution technique on Trichoderma Selective Medium (Eladand Chet, 1983; Morton and Stroube, 1955). Cultures werepurified by single spore culture technique on PDA plates(point inoculation) and incubated at 27°C for 24-48 hr. Theywere identified on the basis of their cultural andmorphological characters (Rifai, 1969). Morphological andxcultural identification was done based on colony colour,nature of sporulation and their growth, conidia andconidiophores characteristics through microscopicobservation (Plate.1). Isolates of Trichoderma harzianum(IIPRTh-1, Th-2, Th-4, Th-25 & Th-26) and T. asperellum(IIPRTas-21, Tas-22, Tas-23 & Tas-24) were taken for thisstudy.Studies on egg parasitism and inhibition of hatching:The effect of different isolates of indigenous Trichodermaspp viz., Trichoderma asperellum, Trichoderma harzianumand untreated control (distilled water) on egg parasitismand egg hatching inhibition were studied under in vitroconditions.Layout and design of experiments: The collected eggmasses were transferred to a sterile beaker and surfacesterilized with 0.1 per cent sodium hypochlorite (NaCl) for10 seconds followed by five washings with sterile water.Trichoderma culture were taken and inoculated on the PDAat the centre of the petri plate. Four egg masses of M.javanica were placed around the fungal bit. In control, e\ggmasses were placed on the PDA without Trichodermaculture, each isolate was replicated thrice. Patriplates wereincubated at 25OC in BOD. After one week, egg masseswere checked for parasitism and leftover egg masses wereplaced in distilled water for hatching. The plates wereexamined after 24, 48 and 72 hours for hatching of juvenilesand the number of juveniles hatched was counted at eachinterval.


Studies on the comparative efficacy of indigenousisolates of Trichoderma harzianum and T. asperellumagainst Meliodogyne javanica egg parasitism and hatching

under in vitro condition was carried out.Studies on egg parasitism and hatching inhibition: Resultsindicated that, isolates of both species of Trichodermaparasitized the egg masses (Plates 2) and reduced thehatching (Fig. 2) compared to control. Parasitization of eggsand inhibition of hatching was more by isolates of T.harzianum compared to isolates of T. asperellum. However,the parasitism and egg hatching inhibition varied amongthe isolates of both the species of Trichoderma. Maximumegg parasitism (50%) and inhibition of hatching (86%) wasobserved in isolate IIPR T4 of T. harzianum compared toother isolates of H. harzianum and T. asperellum (Fig. 1&2)

Several studies have shown that among a wide rangeof commercially important fungi, Trichoderma species arehighly efficient in biological control (Whipps and Lumsden,2001). Sharon et al. (2007) reported that Trichodermaparasitized on egg masses, their derived eggs and the

Plate 1: Potential indigenous Trichoderma isolates from pulsesrhizospheres

Plate 2: Trichoderma parasitized Meloidogyne javanica egg mass,eggs and hatched juveniles

Fig.1. Effect of Trichodermaspp on egg parasitism Meloidogynejavanica

Fig.2. Effect of Trichodermaspp on hatching of Meloidogynejavanica


second stage juveniles (J2). When the egg masses weredestroyed, there was reduction in number of the infectivejuveniles (IJs) thereby reducing the overall population ofthe nematode. Our results corroborate with these results.In our findings egg parasitism was recorded 50% and egghatching inhibition were 86% in Trichoderma harzianum(IIPR T4) indicating effective bio-control potential. Theseresults are similar with the findings of Sahebani and Hadavi(2008) T. harzianum BI was able to penetrate nematode eggmass matrix and significantly decreased nematode egghatching level.

It has been demonstrated that bio-agents producedifferent metabolites and antibiotics which directly orindirectly stimulate plant growth (Kloepper et al. 1991).Trichoderma spp. is utilized in the production of a numberof antibiotics, such as trichoderin, trichodermol A andharzianolide. They result in genral plant growth and yieldof crops due to their nematicidal effect. This is also in linewith the findings of Nawar (2007) who reported the inhibitionof most fungal phytopathogens by compounds producedby Trichoderma spp.

The involvement of lytic enzymes has long beensuggested and demonstrated in Meloidogyne parasitism(.Spiegel et al. 2005). Besides direct antagonism, othermechanism involved in Meloidogyne control byTrichoderma spp. includes production of fungal metabolitesand induced resistance (Samuels, 1996 & Goswami et al.2008). Trichoderma harzianum has been found to be aneffective bio-control agent for the management of root-knot and other nematodes (Casas-Flores et al. 2007 &Moosavi, 2012). In accordance to these studies, our resultsof T. harzianum (IIPR T4) showed a remarkable level ofantagonistic characteristic against root-knot nematode.Hence, the present study clearly indicated that, the potentialof indigenous T. harzianum (IIPR T4) differ in its bio-control ability which represents a safer, more natural andenvironmental-friendly approach to overcome the problemscaused by synthetic nematicides against the plant parasiticnematodes in pulses. Therefore, the identified potentialisolates IIPR T4 of T. harzianum can be used for makingformulations as well as consortia for their furtherexploitation in pulses for management of root knotnematode.

From this study we can conclude that, Trichodermaharzianum (Th-24) native strain can be effectively utilisedas one of the biological control agents for the managementof Meloidogyne javanica in the pulse crops. Moreover,enhancement of the naturally existing T. harzianum speciesin soil is expected to be highly beneficial to man and hiscrops.

The authors are very grateful to Director, ICAR-Indian institute of Pulses Research, Kanpur, for providingthe research facilities and the Indian Council of AgriculturalResearch New Delhi for financial support.

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Screening of resistant pigeonpea genotypes against pod infecting insectsZADDA KAVITHA and C VIJAYARAGHAVAN1

National Pulses Research Centre, Vamban, Pudukottai, Tamil Nadu; 1Tamil Nadu Agricultural University,Tamil Nadu; Email: [email protected](Received : May 13, 2018 ; Accepted : July 05, 2018)

ABSTRACT

Pigeonpea is being attacked by several pod borers from theinitiation of f lowering to harvesting. During poddevelopment and maturity, pod sucking bugs and pod flycause much damage and in turn severe economic losses.Identification of tolerant or resistant cultivars to these insectpests is of utmost important. So, a three year screeningstudy was conducted from 2013 to 2015 during which 145nos. of pigeonpea germplasm were screened and entries whichshowed resistance to a particular insect in all the three yearswere identified as the resistant entries. For plume moth,twenty entries showed stable resistance levels during allthe three years. Among them, ICPL 8719 and H 23 recordedminimum PSI of 1.7. Eight entries i.e., CORG 9900134, H23, V 127, ICP 11293, ICP 49114, ICP 11957, SMR 1693158and ICPL 8719 showed constant resistance levels to bluebutterfly. Among them, minimum PSI of 1.3 was recorded inICPL 8719. Four entries i.e., H 23, GR 28, ICP 49114 andSMR 1693158 were found to be resistant to pod bug complex.SMR 1693158 with the PSI of 2.7 was found to be promisingamong them. For pod fly, five entries were constantlyresistant (PLS 476 A, V 127, BAHAR, ICP 8863 and WRG157) among which V 127 recorded less PSI (1.7).

Key words: Blue butterfly, Pigeonpea, Plume moth, Pod bugcomplex, Pod fly, Screening

Pigeonpea, Cajanus cajan (L.) Millspaugh, is animportant pulse crop of rainfed agriculture in the Indiantropics and subtropics. In India, pigeonpea is the secondmost important pulse crop after chickpea (Nene et al. 1990).Besides being highly nutritive, it is a crop that restores thesoil fertility. Its deep root system makes it an ideal crop forcultivation in rainfed situations. Intercropping withpigeonpea is a common practice among the farmers andreduces crop loss risk to farmers. Inspite of all these reasons,in recent years, area under pigeonpea cultivation wasdeclined due to several constraints. Among them, damageinflicted by insect pests and in turn yield reduction are themajor ones. More than 300 species of insect pests attackpigeonpea (Prasad and Singh, 2004). The insects that attackpigeonpea during the flowering stage are more destructiveand cause severe economic losses by eating away thereproductive structures of the flower. The pod borer, plumemoth, Exelastis atomosa (Walsingham) (Lepidoptera:Pterophoridae) is very active throughout the year. As itslarval stage lasts for 30 days, it remains in the field for a

long time and damages the seeds. Plume moth damage startsfrom flowering and their population was observed tillharvest. Reed et al. 1989 reported pod borer Helicoverpaarmigera Hubner, blue butterfly, Lampides boeticusLinnaeus and plume moth, Exelastis atomosa (Walsingham)as the most economical pests that attack at flowering andpodding stages of the pigeonpea crop. Blue butterfly causesdamage to buds, flowers and tender pods. During poddevelopment and maturity, pod sucking bug complex i.e.,Riptortus pedestris, Clavigrella gibbosa and Nezaraviridula cause much damage resulting in shrivelled podsand seeds. Dolling, 1973 reported total loss of pigeonpeacrop when 10 C. gibbosa adults are present per plant. Onan average, 25.20% pod and 28.38% grain damage by podsucking bugs was reported by Veda, 1993. Among the poddamaging insects, the most devastating insect pest is podfly, Melanogromyza obtusa (Malloch) (Diptera:Agromyzidae). In a survey conducted by ICRISAT, M.obtusa is reported to cause 22.5 per cent damage topigeonpea pods in north India, 21 per cent in central Indiaand 13.2 per cent in south India (Lateef and Reed, 1981).Management of this insect becomes most complicated dueto it’s concealed mode of habitat. Use of tolerant or resistantcultivars to manage the insect pests particularly that havecryptic habitat nature like pod fly is of utmost important.Identification of resistant/tolerant donors to evolvevarieties least susceptible to insect pests is the need of thehour. Hence, a three year screening study was conductedat National Pulses Research Centre, Vamban from 2013 to2015 to identify the resistant / tolerant pigeonpea sourcesto pigeonpea pod damaging insects.


Resistance Screening Study: During kharif 2013, onehundred and forty five (145 nos.) pigeonpea germplasmwere screened for resistance or tolerance to pigeonpea podborers i.e., plume moth, Exelastis atomosa, blue butterfly,Lampides boeticus, pod bug complex and pod fly,Melanagromyza obtusa. Among them, forty one (41 nos.)pigeonpea germplasm that exhibited various resistancelevels to any one of the above insects were selected andscreened during kharif 2014 and 2015 for confirmation oftheir resistance. Pigeonpea entries which showed resistancein all the three years were selected as the resistant entries.Screening Method: Three hundred (300) pods were


Kavitha et al. : Screening of resistant pigeonpea genotypes against pod infecting insects 235

collected from each entry at harvest and observations onpod damage due to plume moth, blue butterfly, pod bugcomplex and pod fly were taken separately for each insectand entry. Based on the pod damage by individual insect inthe entries and local check (VBN 3), pest susceptibility percent (PSP) and pest susceptibility index (PSI) were calculatedfor each entry. Based on the PSI, category of resistancewas given for each entry for each insect.

Pest susceptibility per cent (PSP) was calculated bythe following formula

PSP = per cent damage in check – per cent damage in entry

× 100 –––––––––––––––––––––––––––––––––––––––––– Per cent damage in check

Following scale was followed for categorizing theresistance in various germplasm entries.

PSP PSI Category of resistance 100 1 Highly Resistant

75 to 99.9 2 Resistant 50 to 74.9 3 Moderately Resistant 25 to 49.9 4 Moderately Resistant 10 to 24.9 5 Moderately Susceptible

(-10) to (9.9) 6 Moderately Susceptible (-25) to (-9.9) 7 Susceptible

(-50) to (-24.9) 8 Highly Susceptible Less than -50 9 Highly Susceptible

Table 1. Per cent damage of plume moth, blue butterfly, pod bug complex and pod fly in the selected pigeonpea entries(kharif 2013)

Per cent damage S. No. Name of the Entry Plume moth Blue butterfly Pod bug Pod fly

1 ICP11007 1.0 1.0 16.0 15.0 2 CORG 9900134 1.0 1.0 9.0 12.0 3 VRG 11 0.0 1.0 18.0 14.0 4 ICP 14505 4.0 1.0 16.0 5.0 5 ICP 763 C 8.0 2.0 23.0 11.0 6 P 11 2001 A 0.0 2.0 11.0 8.0 7 H 23 0.0 1.0 5.0 14.0 8 JKM 209 3.0 0.0 8.0 16.0 9 PLS 476 A 1.0 0.0 18.0 5.0 10 V 127 1.0 0.0 26.0 1.0 11 ICP 11293 3.0 1.0 15.0 6.0 12 ICP 10175 4.0 2.0 9.0 1.0 13 ICP 763-C 2.0 0.0 15.0 10.0 14 BAHAR 2.0 1.0 13.0 2.0 15 NRG 101 1.0 1.0 8.0 9.0 16 CO 6 11.0 2.0 21.0 18.0 17 V 87 3.0 4.0 10.0 5.0 18 V 521 3.0 2.0 4.0 16.0 19 ICP 7624 2.0 1.0 11.0 11.0 20 ICP 8863 1.0 2.0 18.0 3.0 21 DA 322 2.0 2.0 12.0 12.0 22 VRG 05-011 0.0 0.0 30.0 3.0 23 CORG 990014 1.0 2.0 9.0 12.0 24 JKE 110 4.0 2.0 7.0 16.0 25 GR 28 2.0 3.0 8.0 9.0 26 WRG 42 2.0 5.0 2.0 19.0 27 ICP 11174 2.0 2.0 15.0 9.0 28 ICP 49114 12.0 4.0 15.0 20.0 29 ICP 11957 1.0 1.0 10.0 16.0 30 SMR 1693158 2.0 1.0 8.0 9.0 31 ICP 14505 9.0 4.0 18.0 18.0 32 ICP 139184 2.0 1.0 16.0 15.0 33 ICPL 8719 0.0 0.0 23.0 4.0 34 RVKT 261 4.0 6.0 12.0 3.0 35 BRG-10-02 3.0 6.0 14.0 4.0 36 BRG-11-01 3.0 7.0 9.0 7.0 37 CORG 7 3.0 2.0 3.0 3.0 38 WRG157 3.0 10.0 0.0 0.0 39 WRP 1 7.0 6.0 12.0 1.0 40 PA 409 4.0 3.0 7.0 6.0 41 PT-04-307 3.0 6.0 7.0 4.0 VBN 3 5.0 2.0 17.0 8.0


First year screening study (kharif 2013): A total numberof 145 pigeonpea entries were screened during kharif 2013against Plume moth, Blue butterfly, Pod bug, Pod fly. Damageper cent of plume moth, blue butterfly, pod bug complexand pod fly ranged from 0.0 to 21.0, 0.0 to 11.0, 4.0 to 36.0


Table 2. Per cent damage of plume moth, blue butterfly, pod bug complex and pod fly in the selected pigeonpea entries(kharif 2014)

Per cent damage S. No. Name of the Entry Blue butterfly Plume moth Pod bug Pod fly

1 ICP11007 4.0 11.0 12.0 7.0 2 CORG 9900134 1.0 11.0 7.0 7.0 3 H 23 2.0 3.0 4.0 8.0 4 PLS 476 A 7.0 9.0 19.0 2.0 5 V 127 4.0 11.0 16.0 0.0 6 ICP 11293 0.0 9.0 12.0 6.0 7 ICP 10175 19.0 7.0 16.0 2.0 8 BAHAR 10.0 14.0 14.0 7.0 9 V 87 4.0 12.0 13.0 9.0 10 ICP 8863 7.0 9.0 5.0 1.0 11 DA 322 4.0 8.0 5.0 10.0 12 GR 28 1.0 14.0 4.0 11.0 13 WRG 42 7.0 10.0 6.0 18.0 14 ICP 11174 4.0 6.0 15.0 3.0 15 ICP 49114 3.0 7.0 4.0 12.0 16 ICP 11957 4.0 7.0 21.0 9.0 17 SMR 1693158 4.0 5.0 16.0 6.0 18 ICP 139184 6.0 7.0 13.0 5.0 19 ICPL 8719 1.0 1.0 9.0 3.0 20 RVKT 261 2.0 11.0 6.0 10.0 21 BRG-10-02 4.0 1.0 13.0 4.0 22 WRG157 3.0 7.0 4.0 7.0 23 WRP 1 3.0 4.0 3.0 1.0 24 PA 409 6.0 11.0 4.0 2.0 25 PT-04-307 7.0 22.0 15.0 16.0 VBN 3 6.0 20.5 8.0 16.0

Name of the Pest Entries PSI Category of resistance VRG 11, P 11 2001 A, H 23, VRG 05-011 and ICPL 8719, 1 Highly resistant ICP 11007, CORG 9900134, PLS 476 A, V 127, NRG 101, ICP 8863, CORG 990014 and ICP 11957 2 Resistant

ICP 49114, CO 6, ICP 763-C, BAHAR, ICP 7624, DA 322, GR 28, WRG 42, ICP 11174, SMR 1693158 and ICP 139184 3 Moderately Resistant

Plume moth, Exelastis atomosa

JKM 209, ICP 11293, V 87, V 521, BRG 10-02, BRG 11-01, CORG 7, WRG 157 and PT-04-307 4 Moderately Resistant

JKM 209, PLS 476 A, V 127, ICP 763-C, VRG 05-011 and ICPL 8719 1 Highly resistant Blue butterfly, Lampides boeticus ICP 49114, ICP 11007, CORG 9900134, VRG 11, CO 6, ICP 14505, H 23, ICP 11293, BAHAR, NRG 101, ICP 7624, ICP 11957, SMR 1693158 and ICP 139184

3 Moderately Resistant

WRG 157 1 Highly resistant V 521, WRG 42, V 127, Bahar and CORG 7 2 Resistant ICP 49114, CO 6, H 23, JKM 209, NRG 101, JKE 110, GR 28, SMR 1693158, PA 409 and PT-04-307 3 Moderately Resistant

Pod bug complex

CORG 9900134, P 11 2001 A, ICP 10175, V 87, ICP 7624, DA 322, CORG 990014, ICP 11957, RVK 261, BRG 11-01 and WRP 1. 4 Moderately Resistant

WRG 157 1 Highly resistant ICP 10175 and WRP 1 2 Resistant VRG 05-011, ICPL 8719, RVKT 261, BRG 10-02, CORG 7and PT 04-307 3 Moderately Resistant

Pod fly, Melanagromyza obtusa

ICP 14505, PLS 476 A, ICP 11293, V 87 and PA 409 4 Moderately Resistant

and 3.0 to 38.0 per cent respectively during the study period.Among the 145 entries, forty one (41 nos.) entries exhibitedvarious resistance levels to different insect pests ofpigeonpea and those entries were selected for furtherconformational screening.

The following entries were found to be promisingagainst plume moth, blue butterfly, pod bug complex andpod fly during kharif 2013.

Second year screening study (kharif 2014): During kharif2014, the selected 41 pigeonpea entries were again screenedfor their resistance. Among them, damage per cent of plumemoth, blue butterfly, pod bug complex and pod fly rangedfrom 1.0 to 24.0, 0.0 to 19.0, 3.0 to 30.0 and 1.0 to 23.0 percent respectively (Table 2). Among the 41 entries, twentyfive (25 nos.) entries were found to exhibit consistentresistant reaction and they were selected for third yearscreening study.


Name of the Pest Entries PSI Category of resistance H 23, ICPL 8719, BRG-10-02 and WRP 1 2 Resistant PLS 476 A, ICP 1129, ICP 10175, ICP 8863, DA 322, WRG 42, ICP 11174, ICP 49114, ICP 49114, ICP 11957, ICP 139184 and WRG 157 3 Moderately Resistant


ICP 11007, CORG 9900134, V 127, BAHAR, V 87, GR 28, SMR1693158, RVK 261and PA 409 4 Moderately Resistant

ICP 1129 1 Highly resistant CORG 9900134, GR 28 and ICPL 8719 2 Resistant H 23, ICP 49114, SMR 1693158, RVK 261, WRG 157 and WRP 1 3 Moderately Resistant

Blue butterfly, Lampides boeticus

ICP11007, V 127, V 87, DA 322, ICP 11174, ICP 11957, BRG-10-02 and PT-04-307 4 Moderately Resistant

H 23, WRG 157, WRP 1 and PA 409 3 Moderately Resistant Pod bug complex ICP 8863, DA 322, WRG 42, ICP 49114, GR 28, SMR 1693158 and RVK 261 4 Moderately Resistant

V 127 1 Highly resistant ICP 10175, ICP 8863, ICP 11174, ICPL 8719, WRP 1 and PA 409 2 Resistant ICP 11007, CORG 9900134, ICP 11293, BAHAR, DA 322, SMR 1693158, ICP 139184, BRG-10-02 and WRG 157 3 Moderately Resistant


H 23, PLS 476 A, V 87, DA 322, GR 28, ICP 49114, ICP 11957 and RVKT 261 4 Moderately Resistant

Table 3. Per cent damage of different pod damaging insects in the selected pigeonpea entries (kharif 2015)Per cent damage S.No. Name of the Entry

Blue butterfly Plume moth Pod bug Pod fly 1 ICP11007 5.0 3.0 4.0 6.0 2 CORG 9900134 1.0 2.0 7.0 5.0 3 H 23 1.0 2.0 4.0 6.0 4 PLS 476 A 1.0 0.0 17.0 10.0 5 V 127 2.0 1.0 9.0 4.0 6 ICP 11293 3.0 4.0 8.0 14.0 7 ICP 10175 1.0 2.0 3.0 7.0 8 BAHAR 2.0 4.0 15.0 9.0 9 V 87 2.0 3.0 22.0 5.0 10 ICP 8863 2.0 1.0 5.0 8.0 11 DA322 3.0 1.0 6.0 4.0 12 JKE 110 2.0 2.0 6.0 15.0 13 GR 28 1.0 5.0 5.0 9.0 14 WRG 42 1.0 3.0 4.0 11.0 15 ICP 11174 1.0 8.0 12.0 9.0 16 ICP 49114 2.0 4.0 2.0 10.0 17 ICP 11957 2.0 2.0 2.0 5.0 18 SMR1693158 2.0 4.0 1.0 8.0 19 ICP 139184 1.0 0.0 3.0 3.0 20 ICPL 8719 0.0 1.0 3.0 10.0 21 RVKT 261 2.0 2.0 2.0 15.0 22 BRG-10-02 1.0 7.0 4.0 6.0 23 WRG 157 3.0 0.0 13.0 11.0 24 WRP 1 7.0 0.0 5.0 12.0 25 PA409 5.0 6.0 9.0 33.0 26 VBN 3 6.0 20.0 8.0 16.0

Based on the pest susceptibility index (PSI),

pigeonpea entries found to be promising against variouspod damaging insects of pigionpea during kharif 2014 areas following.Third year screening study (kharif 2015): Among thetwenty five (25 nos.) pigeonpea entries screened duringkharif 2015, damage per cent of plume moth, blue butterfly,pod bug complex and pod fly ranged from 1.0 to 8.0, 0.0 to7.0, 1.0 to 22.0 and 2.0 to 15.0 per cent respectively (Table3).

Based on the pest susceptibility index (PSI), entriesfound to be promising against various pod damaging insect

pests of pigeonpea are as following.Among the 25 germplasm which exhibited resistance

to plume moth during kharif 2013 and 2014, twenty entriesshowed stable resistance levels during the third year studyalso (Table 4). ICPL 8719 and H 23 with minimum PSI of 1.7were found to be the ideal donors for plume moth resistance.ICPL 8719 and H 23 recorded the mean per cent incidenceof 0.7 and 1.7 respectively as against 15.2 in the local check,VBN 3 (Fig 1). Next to these were PLS 476 A (PSI-2.0) andICP 139184, ICP 11957 and ICP 8863 (PSI-2.3). During thisthree year screening study, eight entries i.e., CORG 9900134,H 23, V 127, ICP 11293, ICP 49114, ICP 11957, SMR 1693158


Name of the Pest Entries PSI Category of resistance PLS 476 A, ICP 139184, WRG 157 and WRP 1 1 Highly resistant ICP 11007, CORG 9900134, H 23, V 127, ICP 1129, ICP 10175, BAHAR, V 87, ICP 8863, DA 322, JKE 110, WRG 42, ICP 49114, ICP 11957, SMR 1693158, ICPL 8719 and RVKT 261

2 Resistant


GR 28, ICP 11174, BRG-10-02 and PA 409 3 Moderately Resistant ICPL 8719 1 Highly resistant CORG 9900134, H 23, PLS 476 A, ICP 10175, GR 28, WRG 42, ICP 11174, ICP 139184 and BRG-10-02 2 Resistant

Blue butterfly, Lampides boeticus

V 127, ICP 1129, BAHAR, V 87, ICP 8863, DA322, JKE 110, ICP 49114, ICP 11957, SMR1693158, RVKT 261 and WRG 157 3 Moderately Resistant

ICP 49114, ICP 11957, SMR1693158 and RVKT 261 2 Resistant ICP11007, H 23, ICP 10175, WRG 42, ICP 139184, ICPL 8719 and BRG-10-02 3 Moderately Resistant

Pod bug complex

ICP 8863, DA 322, JKE 110, GR 28 and WRP 1 4 Moderately Resistant V 127, ICP 8863, DA 322 and ICP 139184 2 Resistant ICP 11007, CORG 9900134, H 23, V 127, ICP 10175, V 87, ICP 11957, SMR1693158 and WRG 157 3 Moderately Resistant


PLS 476 A, BAHAR, GR 28, WRG 42, ICP 11174, ICP 49114 and ICPL 8719 4 Moderately Resistant

Table 4. Resistance categories and mean PSI of the selected pigeonpea entries to plume moth and blue butterfly during

three years of screening (kharif 2013 to 2015)

MR 3 – Moderately resistant with PSI 3 MR 4 – Moderately resistant with PSI 4

Category of Resistance Plume moth Blue butterfly S.

No. Name of the Entry I year II year III year

Mean PSI I year II year III year

Mean PSI

1 ICP11007 R MR4 R 2.7 -- -- -- -- 2 CORG 9900134 R MR4 R 2.7 MR3 R R 2.3 3 H 23 HR R R 1.7 MR3 MR3 R 2.7 4 PLS 476 A R MR3 HR 2.0 -- -- -- -- 5 V 127 R MR4 R 2.7 HR MR4 MR3 2.7 6 ICP 11293 MR4 MR3 R 3.0 MR3 HR MR3 2.3 7 BAHAR MR3 MR4 R 3.0 -- -- -- -- 8 V 87 MR4 MR4 R 3.3 -- -- -- -- 9 ICP 8863 R MR3 R 2.3 -- -- -- --

10 DA322 MR3 MR3 R 2.7 -- -- -- -- 11 GR 28 MR3 MR4 MR3 3.3 -- -- -- -- 12 WRG 42 MR3 MR3 R 2.7 -- -- -- -- 13 ICP 11174 MR3 MR3 MR3 3.0 -- -- -- -- 14 ICP 49114 MR3 MR3 R 2.7 MR3 MR3 MR3 3.0 15 ICP 11957 R MR3 R 2.3 MR3 MR4 MR3 3.3 16 SMR1693158 MR3 MR4 R 3.0 MR3 MR3 MR3 3.0 17 ICP 139184 MR3 MR3 HR 2.3 -- -- -- -- 18 ICPL 8719 HR R R 1.7 HR R HR 1.3 19 BRG-10-02 MR4 R MR3 3.0 -- -- -- -- 20 WRG 157 MR4 MR3 HR 2.7 -- -- -- --

Table 5. Resistance categories and mean PSI of the selected pigeonpea entries to pod bug complex and pod fly during threeyears of screening (kharif 2013 to 2015)

MR 3 – Moderately resistant with PSI 3 MR 4 – Moderately resistant with PSI 4

Category of Resistance Pod bug complex Pod fly S.

No. Name of the Entry I year II year III year

Mean PSI I year II year III year

Mean PSI

1 H 23 MR3 MR3 MR3 3.0 -- -- -- -- 2 PLS 476 A -- -- -- -- MR4 MR4 MR4 4.0 3 V 127 -- -- -- -- R HR R 1.7 4 BAHAR -- -- -- -- R R MR3 2.3 5 ICP 8863 -- -- -- -- MR4 MR4 MR3 3.7 6 GR 28 MR4 MR4 MR4 4.0 -- -- -- -- 7 ICP 49114 R MR4 MR3 3.0 -- -- -- -- 8 SMR1693158 MR3 MR3 R 2.7 -- -- -- -- 9 WRG 157 -- -- -- -- MR3 R MR4 3.0


Table 6. Pigeonpea entries identified with multipleresistances

Resistant to S. No.

Name of the Entry Plume

moth Blue

butterfly Pod bug complex Pod fly

1 CORG 9900134 2 H 23 3 PLS 476 A 4 V 127 5 ICP 1129 6 V 87 7 DA322 8 WRG 42 9 ICP 49114

10 ICP 11957 11 SMR1693158 12 ICPL 8719

Fig. 1. Mean % infestation of plume moth in the selection resistantgermplasm in comparison with the local check, VBN(Rg)3.

Fig. 2. Mean % infestation of blue butterfly in the selection resistantgermplasm in comparison with the local check, VBN(Rg)3.

Fig. 3. Mean % infestation of pod bug complex in the selectionresistant germplasm in comparison with the local check,VBN(Rg)3.

Fig. 4. Mean % infestation of pod fly in the selection resistantgermplasm in comparison with the local check, VBN(Rg)3.

and ICPL 8719 exhibited constant resistance to bluebutterfly. Among them, ICPL 8719 has recorded minimumPSI of 1.3. This entry has recorded the mean per centincidence of 0.3 as against the 4.7 per cent in the localcheck, VBN 3 (Fig 2). Ghetiya and Mehta, 2014 reportedthat the pigeonpea varieties, ICPL 87119, GAUT 2001-10,GAUT 97-33, AAUT 2005-7, GAUT 2002-16 and AAUT 2005-8 exhibited tolerant reactions against blue butterfly basedon larval population.

Among the 145 nos. of germplasm screened, threeentries i.e., H 23, GR 28 and ICP 49114 were found to beresistant to pod bug complex during all the three years(Table 5). H 23, recorded mean per cent incidence of 4.3while the check variety, VBN 3 has recorded 8.3 per centmean incidence of pod bug complex (Fig 3). For pod fly,five entries were constantly resistant (PLS 476 A, V 127,BAHAR, ICP 8863 and WRG 157). V 127 with less PSI to

pod fly may be used in the resistance breeding programme.In V 127, mean per cent incidence of pod fly was 1.7 and inVBN 3, the local check it was 13.3 per cent. Manzoor HussainDar et al. 2010 reported that pigeonpea cultivars resistantto pod fly exhibited resistance by affecting the biology ofpod fly. Moudgal, RK et al. 2009 evaluated 78 pigeonpeaaccessions and reported that GP 75, GP118, GP 233 andGP253 were found to be highly promising against pod flyas they recorded low mean pod and grain damage. ManeeshKumar Singh et al. 2017 reported tolerance mechanism ofresistance in the pigeonpea entries IVT-520, IVT-509 andAVT-603 against pod fly damage.

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Veda OP. 1993. Effect of weather factors on the incidence ofpod bug, Clavigralla gibbosa Spinola (Hemiptera: Coreidae)in pigeonpea. Indian Journal of Entomology 55(4): 351-354.


ABSTRACT

Infection due to any pathogen triggers a series of reaction inplant which alters the levels of various pathogenesis related(PR) proteins governing the degree of resistance of that plant.Amongst the various plant viruses infecting cowpea crop,Blackeye cowpea mosaic virus (BICMV) is one of theimportant and widespread one responsible for reduction inyield of the crop. Changes in the PR proteins viz. -1, 3glucanase, Phenylalanine ammonia lyase (PAL), peroxidaseand polyphenol oxidase were estimated from leaves ofcowpea genotypes inoculated with BICMV. The resultsindicated that resistant varieties had higher levels of PRproteins than moderately resistant. In susceptible varietydecreasing trend in PR proteins over the period was recordedindicating that more induction of PR proteins in cowpea ispositively correlated with resistance to BICMV.

Key words: BICMV, Cowpea, Proteins, Reiststance

Diseases are prominent biotic stresses known toaffect productivity of pulses and particularly that of cowpeacrop. Amongst various biotic stresses of the crop, virusesconstitute the major group of pathogens (Mali andThottapilly, 1986). In fact, viral diseases are significantlycontributing to the reduced yield of cowpea in Asia, Africaand Latin America. The effect of viruses could bedevastating and a major constraint to the production ofcowpea. The crop is infected by about 40 viruses worldwide(Hughes and Shoyinka, 2003). Blackeye cowpea mosaicvirus (BICMV) belonging to potyvirus group is thedominant and important one infecting cowpea crop inMaharashtra state (Mali and Kulthe, 1980). The virus isreported to cause varying degrees of symptoms rangingfrom mild mosaic, mottling, yellowing, leaf distortion tostunting in cowpea. Infection by pathogen triggers adefense mechanism in the plant due to which the levels ofvarious biochemical parameters either increases ordecreases. These biochemical synthesized in plant afterpathogen infection plays a crucial role in governingresistance of plant to the pathogen. In fact, these areimportant markers in deciding resistance or susceptibilityof the plant. Therefore, during present investigation, levelsof various pathogenesis related (PR) proteins in cowpeacultivars in response to BICMV infection were estimated.


The virus infected cowpea leaves typical symptoms

Investigating the change in pathogenesis related proteins (PR) in virus infectedcowpeaNT HIRGAL, SB LATAKE and AP CHAVAN

Mahatma Phule Krishi Vidyapeeth, Rahuri, Distt. Ahmednagar, Maharashtra; E-mail : [email protected](Received : April 15, 2018 ; Accepted : June 21, 2018)

of BICMV were collected from farmer ’s field andexperimental farm of Pulses Improvement Project, MPKV,Rahuri. The virus was maintained under glasshouseconditions on susceptible cowpea variety VCM-8 as asystemic host. The identity of the virus was confirmed asBICMV by electron microscopy and host range studies.Twenty five (25) promising cowpea genotypes obtainedfrom Pulses Improvement Project, MPKV, Rahuri werescreened under glasshouse conditions against BICMV byartificial sap inoculation method. The seedlings were raisedin earthen pots filled with sterilized soil and were inoculatedwith the virus sap at vegetative growth stage. The plantswere observed regularly for virus symptoms and per centdisease incidence was worked out. Based on diseaseincidence, genotypes were categorized as resistant (< 10%),moderately resistant (10-30 %) and susceptible (> 30%).Genotypes showing resistant, moderately resistant andsusceptible reaction were selected for biochemical analysis.For this, seeds of theses genotypes were sown underglasshouse conditions and the plants were inoculated withthe virus sap. The changes in the levels of various PRproteins viz., -1, 3 glucanase, Phenylalanine ammonialyase (PAL), peroxidase and polyphenol oxidase from theleaves of the plants by adopting standard procedure 15,30, 45 and 60 days after sowing of the seeds. Firstly, solubleprotein content was determined by method of Lowry et al.(1951) while reducing sugar by Nelson Somogyi’s method(Somogyi, 1952). The assay of -1, 3 glucanase was carriedout as per method described by Rakshit et al. (2000), PALactivity by method of Compas et al. ( 2004) and peroxidaseand polyphenol oxidase was determined by method givenby Kumar and Khan (1982).


Upon artificial inoculation, the virus producedsymptoms viz., chlorosis, mosaic, mottling and stuntingetc. on the susceptible cultivar VCM-8 within 20-22 daysunder glasshouse conditions. The electron microscopy ofthe virus was carried out using Transmission ElectronMicroscope (TEM) available with Department of PlantPathology and Agril. Microbiology, MPKV Rahuri. Themicrophotographs revealed the presence of flexous rodshaped particles of the virus typical to potyvirus group.The host range was typical to that of BICMV. Thus basedon symptoms, particle morphology and host range the viruswas identified as BICMV which is regarded as strain ofBean common mosaic virus.



Biochemical changes in cowpea genotypes due toBICMV: On the basis of disease reaction data of the 25genotypes screened against BICMV by mechanical sapinoculation, two resistant (Phule vithai and PCP 090210),two moderately resistant (PCP 09024 and Phule Pandhari)and one susceptible (PCP 09037) genotype were analyzedfor levels of various biochemical parameters.1. -1,3 glucanase: The data obtained on activity of -1,3 glucanase from inoculated leaves of cowpea cultivars ispresented in Table 1. It was observed that the -1, 3glucanase activity was markedly increased in resistantvarieties viz., Phule Vithai and PCP 090210 (1.95 to 8.41,1.94 to 9.05 mg of glucose released mg-1 protein hr-1,respectively) than moderately resistant varieties viz., PCP-09024 and Phule Pandhari (1.49 to 5.60 and 1.95 to 6.09 mgof glucose released mg-1 protein hr-1, respectively) from15to 60 DAS. However the susceptible variety (PCP 09037)showed lowest activity of -1, 3 glucanase. Thus,comparatively higher -1, 3 glucanase activity was recordedin resistant cultivars. These results are in agreement withRakshit et al. (2000) who reported that –1, 3 glucanaseactivity in powdery mildew resistant lines (1.87 ± 0.20 ìmole glucose eq min-1mg-1 protein) was 2.03 times morethan powdery mildew susceptible lines (0.92 ± 0.20 ì moleglucose eq min-1 mg-1 protein). Similarly, maximumenhancement of –1, 3 glucanase activity in pea cultivarsresistant to Erysiphi polygoni than susceptible cultivarswas observed by Katoch et al. (2004).2. Phenylalanine ammonia lyase: A significant increasein PAL activity was observed in resistant variety thansusceptible. The increasing trend of PAL was noticed in

varieties viz., Phule Vithai and PCP 090210 at 15, 30, 45 and60 DAS than moderately resistant varieties viz., PCP-09024and Phule Pandhari. The susceptible variety PCP-09037 hadlowest PAL activity.

Similar results were reported by many researchers.Kale and Choudhary (2001) investigated expression of PALactivity in groundnut cultivar in response to biotic stressCercoporidum personatum. Maximum activity wasobserved in resistant cultivars as compared to susceptiblecultivar. However, increased level of PAL enzyme in resistantand susceptible cotton plants after inoculation withVerticillium dahlia were recorded by Xu et al.(2011), butthe increase was greater in the resistant lines as comparedto control. Patel et al. (2013) investigated the biochemicalchanges in mungbean induced by MYMV and reportedthat PAL was found in decreasing trend in the susceptibleleaves as compared to resistant.3. Peroxidase and polyphenol oxidase: Similar trend asthat –1, 3 glucanase activity was also observed in case ofperoxidase and polyphenol oxidase activity. Higherperoxidase and polypenol oxidase activity was recorded inresistant cultivars than susceptible. The two resistancevarieties viz., Phule Vithai and PCP-090210 showedincreasing peroxidase and ployphenol oxidase activity at15, 30, 45 and 60 DAS. However, lowest activity wasrecorded from the susceptible variety (PCP-09037)indicating its susceptibility to the virus.

Marked increase in the peroxidase activity in resistantgenotypes had been reported earlier by various workersviz., infection of sterility mosaic in pigeonpea (Bhite et al.1997), wilt in chickpea (Singh et al. 2003) and Rhizoctoniain Norway sprus (Nagy et al. 2004). Similarly, Anuradha et

Table 3. Peroxidase activity from the leaves of cowpeagenotypes

(A 340 min-1 mg-1 protein)

Genotype 15 DAS 30 DAS 45 DAS 60 DAS PCP 09024 (MR) 0.80 1.95 2.63 3.41 Phule Pandhari (MR) 0.84 2.08 2.70 3.53 PCP 09037 (S) 0.30 0.41 0.55 0.37 Phule Vithai (R) 1.38 3.22 4.58 5.99 PCP 090210 (R) 1.41 3.32 4.85 6.81

Mean 0.95 2.19 3.06 4.02 SE (±) 0.009 0.010 0.01 0.07

CD at 5 % 0.026 0.027 0.025 0.020


Mean 1.68 3.07 5.23 6.09 SE (±) 0.011 0.010 0.010 0.012

CD at 5 % 0.033 0.032 0.031 0.038

Table 1. -1,3 glucanase activity from the leaves of cowpeagenotypes

MR-Moderately resistant, S-Susceptible, R-Resistant (mg of glucosereleased mg-1 protein hr-1)

Table 2. Phenylanine ammonia lyase activity from theleaves of cowpea genotypes

(µ moles of cinnamic acid mg-1 protein hr-1)


Mean 1.09 2.32 3.84 4.44 SE (±) 0.099 0.010 0.011 0.010

CD at 5 % 0.260 0.031 0.032 0.030

Table 4. Polyphenol oxidase activity from the leaves ofcowpea genotypes

(A 340 min-1 mg-1 protein)


Mean 0.32 0.98 1.53 2.12 SE (±) 0.062 0.008 0.067 0.08

CD at 5 % 0.152 0.023 0.193 0.024

Hirgal et al. : Studies on biochemical changes in cowpea in response to virus infection 243

al. (2015) studied the biochemical changes in Banana dueto Banana Bunchy Top Virus and found that the amountof peroxidase was significantly higher in resistant plant.As regards polyphenol oxidase activity Arora et al. (2009)inoculated resistant and susceptible genotypes ofmothbean with yellow mosaic virus and found that theactivity of polyphenol oxidase showed an increasing trendin the inoculated leaves of resistant genotypes.

Thus, in general, it was noticed that the levels ofvarious PR proteins showed an increasing trend in resistantand moderately resistant cultivars and the increase wasmore in resistant cultivars than the moderately resistant. Incase of the susceptible cultivar the PR proteins increasedupto 45 DAS and thereafter decreased at 60 DAS. Thisclearly indicated that more induction of PR proteins incowpea plants is positively related with resistance toBlackeye cowpea mosaic virus suggesting that various PRproteins plays a role in governing resistant/susceptibilityof cowpea genotypes against BICMV. The findings ofpresent investigation will be helpful in breeding programmefor evolving disease resistant varieties as well as formanagement of the virus.

REFERENCES

Anuradha C, Selvarajan R, Vasantha S and Suresha GS. 2015.Biochemical characterization of compatible plant virusinteraction: A case study with bunchy top virus host pathosystem.Journal of Plant Pathology 14(4): 212-222.

Arora R, Joshi U, Gupta P and Singh J. 2009. Effect of yellowmosaic virus on pathogenesis related enzymes and chlorophyllcontent in mothbean. Acta Phytopathology EntomologyHungarica 44(1): 49-60.

Bhite BR, Chavan JK and Kachare DP. 1997. A biochemical markerfor resistance to sterility mosaic in pigeonpea. Journal ofMahatma phule Agriculture University 22(3): 340-344.

Campos R, Nonogaki H, Suslow T and Saltveit MS. 2004. Isolationand characterization of a wound inducible phenylalanineammonia lyase gene from romaine lettuce leaves. PhysiologiaPlantarum 121(3): 429-438.

Hughes JA and shoyinka SA. 2003. Overview of viruses if legumesother than groundnut in Africa. In : Plant virology in sub-saharnAfrica. Hughes, J. A. and J. Odu (Eds.). International Institute of

tropical Agriculture, Ibadan, Nigeria. Pp: 553-568.

Kale MC and Choudhary AD. 2001. Induction of phenylalanineammonia lyase in groundnut cultivars in response to biotic andabiotic stress. Indain Phytopathology 54(3): 288-292.

Katoch R, Mann ADS, Sohal BS and Munshi GD. 2004. Effect ofelicitor spray and Erysiphe polygoni inoculation on -1, 3glucanase activity in pea cultivars resistant and susceptible topowdery mildew. Indian Journal Plant Physiology 9(3): 316-319

Kumar KB and Khan PA. 1982. Peroxidase and polyphenol oxidasein excised ragi (Eleusine corocana cv. PR-202) leaves duringsenescence. Indain Journal of Experimental Biology 20: 412:416.

Lowry OH, Rosenbrough NJ, farr AL and Randall RJ. 1951. Proteinmeasurement with Folin Phenol reagent. Journal of BiologicalChemistry 193(1): 265-275.

Mali VR and Kulthe KS. 1980. A seedborne potyvirus causing mosaicdisease of cowpea in India. Plant Disease 64(10): 925-928.

Mali VR and Thottappilly G. 1986. Virus diseases of cowpea in thetropics. Review of Tropical Plant Pathology. Vol. 3 (edited byRaychaudhari, S. P. and Verma, J.P.) New Delhi, India; Todayand Tomorrow’s printer and Publishers

Nagy NE, Fossdal CG, Dalen LS, Lonneborg A, Heldal I and JohsenO. 2004. Effect of Rhizoctonia infection and drought onperoxidase and chitinase activity in Norway spruce (Picea abies).Physiologia Plantarum 120(3): 465-473.

Patel H, Kalaria R, Mahatma M and Chauhan DA. 2013.Physiological and biochemical changes induced by Mungbeanyellow mosaic virus (MYMV) in mungbean (Vigna radiate L.).Journal Cell tissue Research 1: 34.

Rakshit S, Mishra SK, Dasgupta SK and Sharma B. 2000. Dynamicsof â-1, 3 glucanase activity in powdery mildew resistant andsusceptible lines of pea. Journal of Plant Biochemistry andBiotechnology 9(2): 95-98.

Singh G, Singh K, Kapoor SS, Verma MM, Bhan LK and Sidhu PS.1987. Pigeonpea lines resistant to sterility mosaic. IndianPhytopathology 40: 18-21.

Somogyi M. 1952. Notes on sugarcane determination. Journal ofBiology and Chemistry 195(1): 19-23.

Xu L, Zhu L, Tu L, Liu L, Yuan D, Jin L, Long, L and Zhang X.2011. Lignin metabolism has a central role in the resistance ofcotton to wilt fungus Verticillium dahlia as revealed RNA-seq-dependent transcriptional analysis and biochemistry. Journal ofExperimental Botany 62(15): 5607-5621.


Assessing the potential of bio-agents and botanicals against chickpea wiltMOHAMMAD FAISAL, SHASHI TIWARI and UMESH TIWARI

Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, Allahabad Uttar Pradesh;E-mail: [email protected](Received : July 15, 2018 ; Accepted : August 08, 2018)

ABSTRACT

Protein Sources are large in numbers but vegetarians havelimited choice of milk and pulses. In pulses, chickpea (Cicerarietinum L.) is one of the important and rich sources ofprotein but its production is constrained by various diseasesin worldwide. Among diseases, wilt caused by Fusariumoxysporum f. sp. ciceri (Padwick) is the most important threatwhere annual losses range from 10 to 90 %. Although, variousfungicidal treatments have been recommended but harsheffects of chemicals has raised human requirement to findan alternative to manage the disease. Hence, a field studywas carried out using bio-agents and botanical viz., T1 (T.viride + P. flourescens + NSKE) @ 5%, T2 (T. viride + P.flourescens) @ 5%, T3 (P. flourescens + NSKE) @ 5%, T4 (T.viride + NSKE) @ 5%, T5 (Trichoderma viride @ 5%), T6(Pseudomonas flourescens @ 5%), T7 (Neem seed kernelextract @ 5%) and T0 (Control). Results revealed thatchickpea wilt incidence was successively reduced (13.89) andgrowth parameters such as plant height (26.62), number ofbranche/plant (15.13) and yield (12.44 q/hec.) were higher incombination treatment T1 (T. viride + P. flourescens + NSKE@ 5%) followed by T4 (T. viride + NSKE @ 5%).

Key words: Chemicals, Chickpea, Fusarium oxysporum, Pulse,Wilt

Pulse production in India directly indicates the needof protein content for vegetarians. Although many sourcesexist in nature but adequate requirement can only bemanaged by the consumption of pulses in diet. There islarge scale pulse production in India where some are limitedto certain areas while pulse like chickpea is grown in a largescale. Chickpea (Cicer arietinum L.) is important and richsource of protein in India. Primarily, it is consumed as pulse,for preparation of various kinds of foods, sweetswhereasfresh green chickpea or the plants are commonlyused as vegetable. Chickpea contains protein (17-22%),carbohydrate (60-64%), vitamins and minerals with low fatcontent (3-4%). The world has come to an edge urginghuge requirements of food and nutrients for the increasingpopulation, among which protein is one of the importantnutrient. Part of a population can depend on meat, eggsand even they can use pulses and other sources but thevegetarians are completely dependent on pulses, milk etc.for their protein source. Hence, the pulse production needsa keen attention to fulfill the requirement. From the totalpulse production, chickpea shares 40% of the area and

47% production. The production of chickpea is threatenedby various diseases worldwide which have lead averageannual production to be dropped down of 7.59 million tonsfrom an area of 9.19 million hectares with a productivity of825 kg hec-1 during the year 2014-15 in India (Anonymous,2014).

The chickpea crop is attacked by several fungi amongwhich wilt caused by Fusarium oxysporum f.sp. ciceri(Padwick) is most threatening and serious disease whereannual losses may range from 10 to 90 % (Singh and Dahiya,1973). Abiotic and biotic stresses in combination areresponsible for infection and severity of diseases. Fusariumoxysporum f. sp. ciceri is a fungal pathogen whichcolonizes the xylem vessels resulting in complete blockagedue to tylose formation leading to plant wilt. It is soil, seedborne facultative saprophyte which can survive in soil upto six years in the absence of susceptible host (Haware etal. 1978).

Various fungicides are recommended to manage thedisease low ever alternative eco-friendly management ofthe disease is also a researchable issue presently. Biologicalcontrol is a potential alternative to chemical fungicides andhence, improves plant nutritional status without any harsheffects. The present study is designed to find out biologicalcontrol agents mediated management of Fuserium wilt ischickpea.


Collection of infected wilt chickpea plants was donealong with its rhizosphere region. Isolation was carried outfrom the rhizosphere region and infected stem. Soon, afterthe fungal growth was observed, the culture was purifiedto obtain the pure culture. Koch’s postulates were followedthere on to test the Pathogencity. Further, massmultiplication of Fusarium oxysporum f. sp. ciceris wasdone on the overnight soaked substrate, sterilized sorghumseeds. The pathogen was allowed to proliferate on thesubstrate keeping it in the incubator at 25oC. After fifteendays, when the substrate was completely covered with thefungal growth, the culture was mixed in the plot to make awilt sick plot.

The field experiment was conducted during rabiseason at Central Research Farm, Department of PlantPathology, Naini Agricultural Institute, Sam Higginbottom


Faisal et al. : Assessing the potential of bio-agents and botanicals against chickpea wilt 245

University of Agriculture, Technology and Sciences,Allahabad. The experiment was conducted in a randomizedblock design of plot size 2 x 2 m2 having seven treatmentsof bio-agents, botanicals alone and in differentcombinations as seed treatments with one as control check.The treatments were: T1 (T. viride + P. flourescens + NSKE)@ 5%, T2 (T. viride + P. flourescens) @ 5%, T3 (P.flourescens + NSKE) @ 5%, T4 (T. viride + NSKE) @ 5%, T5(Trichoderma viride @ 5%), T6 (Pseudomonas flourescens@ 5%), T7 (Neem seed kernel extract @ 5%) and T0 (Control).Two trials in the successive rabi season were carried outand the observation on plant height, number of branches,disease incidence and yield were recorded at 30, 45 and 60DAS.


Observations were recorded at 30, 45 and 60 days fordisease incidence, growth parameters and yield per plotwere found significant. The data of 30 DAS revealed thatall the treatments were better than the control but treatmentT1 (0.56) followed by T4 (2.78) were the most efficienttreatments managing the disease incidence in comparisonto other treatments T5 (3.89), T2 (3.89), T3 (4.44), T6 (6.67), T7(13.89) and Control T0 (17.22). Similarly, at 45 DAS patternT1 (8.89) followed by T4 (10.56) were the most effective inmanaging the disease incidence as compared to othertreatments T5 (13.33), T2 (14.44), T3 (17.22), T6 (18.89), T7(27.78) and Control T0 (31.67). Also at 60 DAS the treatmentT1 (13.89) followed by T4 (15.56) maintained the consistencyto manage the disease incidence followed by othertreatments T5 (20.00), T2 (26.11), T3 (30.00), T6 (31.67), T7

(39.44) and Control T0 (45.56) [Table 2 and fig. 2] (d).On the other hand it is clear from the observations

recorded for plant height at 30, 45 and 60 DAS was foundsignificant. Observations recorded at 30 DAS best plantheight was attained by the treatment T1 (15.28) followed byT4 (14.00). Similarly, at 45 DAS in treatment T1 (23.71)followed by T4 (21.71) and same consistency was seen at60 DAS in treatment T1 (26.62) and T4 (25.88) whereas itwas lowest in control (20.45) [Table 3 and fig. 3].

Treatment Detail 30 DAS

45 DAS

60 DAS

T0 Control 17.22 31.67 45.55

T1 (T. viride + P. flourescens + NSKE) @ 5% 0.56 8.89 13.89

T2 (T. viride + P. flourescens) @ 5% 3.89 14.44 26.11 T3 (P. flourescens + NSKE) @ 5% 4.44 17.22 30.00 T4 (T. viride + NSKE) @ 5% 2.78 10.56 15.56 T5 Trichoderma viride @ 5% 3.89 13.33 20.00 T6 Pseudomonas flourescens @ 5% 6.67 18.89 31.67 T7 Neem seed Kernel Extract 13.89 27.78 39.44

C.D. (0.05) 6.326 7.104 7.280

Table 1. Disease Incidence at 30, 45 and 60 DAS

Table 2. Pooled Plant Heights at 30, 45 and 60 DAS


45 DAS

60 DAS

T0 Control 11.470 16.356 20.453


T2 (T. viride + P. flourescens) @ 5% 12.796 19.406 23.713

T3 (P. flourescens + NSKE) @ 5% 12.655 18.416 22.926 T4 (T. viride + NSKE) @ 5% 14.003 21.705 25.880 T5 Trichoderma viride @ 5% 13.196 20.406 24.470 T6 Pseudomonas flourescens @ 5% 12.455 17.913 22.195 T7 Neem seed Kernel Extract 12.040 17.436 21.993

C.D. (0.05)

Table 3. Pooled Branches per Plant at 30, 45 and 60 DAS


45 DAS

60 DAS

T0 Control 2.43 9.47 11.93


T2 (T. viride + P. flourescens) @ 5% 3.77 11.93 13.83 T3 (P. flourescens + NSKE) @ 5% 3.50 11.13 13.77 T4 (T. viride + NSKE) @ 5% 4.13 12.47 14.3 T5 Trichoderma viride @ 5% 3.93 11.97 13.97 T6 Pseudomonas flourescens @ 5% 3.30 10.07 12.40 T7 Neem seed Kernel Extract 3.07 9.93 12.13

C.D. (0.05) 0.937 1.499 1.420

Table 4. Pooled Yield per Plot (gms.) at 30, 45 and 60 DAS

Treatment Detail Yield / Plot

Yield (Q/ha.)

T0 Control 346.67 8.67

T1 (T. viride + P. flourescens + NSKE) @ 5% 497.50 12.44

T2 (T. viride + P. flourescens) @ 5% 454.17 11.35 T3 (P. flourescens + NSKE) @ 5% 451.67 11.29 T4 (T. viride + NSKE) @ 5% 475.17 11.88 T5 Trichoderma viride @ 5% 471.33 11.78 T6 Pseudomonas flourescens @ 5% 425.00 10.63 T7 Neem seed Kernel Extract 378.33 9.46

C.D. (0.05) 23.841 --

Fig. 1. Disease Incidence at 30, 45 and 60 DAS

Fig. 2. Pooled Plant Heights at 30, 45 and 60 DAS


Observations recorded for number of branches thesesthat the treatments were significant at 30, 45 and 60 DAS.The data shows that maximum number of branches obtainedin treatment T1 (15.13) followed by T4 (14.37) which was atpar with Control T0 (11.93). The yield per plot was also bestfor treatment T1 (497.5) followed by T4 and lowest obtainedin T0 Control (475.2). Hence, overall studies clearly indicatesthat disease incidence was best minimized in T1 (T. viride +P. flourescens + NSKE @ 5%) (13.89) with the highest plantheight (26.62), maximum number of branching (15.13) andhighest yield (497.5).

It is clear from the above studies that combined seedtreatment with Trichoderma viride + Pseudomonasflourescens + Neem Seed Kernel Extract @ 5% was mostefficient treatment in reducing wilt incidence therebyboosting excellent plant growth parameters viz., plant height(26.62), number of branches (15.13) and yield (12.44 q/ha).Similar type of findings were recorded bringing bio-agentsand botanicals to mange wilt incidence in chickpea by Nikam

Fig. 3. Pooled Branches per Plant at 30, 45 and 60 DAS

Fig. 4. Pooled Yield per Plot (gms.) at 30, 45 and 60 DAS

et al. (2007) and Vats et al. (2016), Khan et al. (2004) alsofound that combination of bio-agents were effective forthe management of Fusarium oxysporum f. sp. ciceri byreducing the spore population.

On the basis of the studies, it can be recommendedfor chickpea growing farmers to treat the seeds withTrichoderma viride + Pseudomonas flourescens + NeemSeed Kernel Extract @ 5% as it’s an easiest method forthem to understand and apply because they are aware ofthe consequences and use of chemicals but alternatemethods are hard for them to understand . Precisely, wecan also make them understand that use of botanicals andbio-agents not only help in managing the wilt disease butalso have no harmful effect on human health andenvironment. Beside, the emerging population demandsfor the food organically produced with use of any claimsand the findings in this study are an answer.

REFERENCES

Anonymous. 2014. Integrated pest management package forChickpea. National Centre for Integrated Pest ManagementPg.1

Haware MP, Nene YL, Raje Haware MP and Rajeshwari R. 1978.Eradication of Fusarium oxysporum f. sp. ciceri transmitted inchickpea seed. Phytopathology 68: 1364-1367.

Khan MR, Khan SM and Mohiddin FA. 2004. Biological control ofFusarium wilt of chickpea through seed treatment with thecommercial formulation of Trichoderma harzianum and/orPseudomonas fluorescens. Phytopathology Mediterrdian 43:20-25.

Nikam PS, Jagtap GP and Sontakke PL. 2007. Management ofchickpea wilt caused by Fusarium oxysporum f. sp. ciceri. AfricanJournal of Agricultural Research 12(2): 692-697

Singh KB and Dahiya BS. 1973. Breeding for wilt resistance inchickpea. Resistance in Bengal Gram. I.A.R.I. New Delhi, India.Pg. 13-14

Vats AS, Singh AK and Pandey MK. 2016. Field evaluation ofTrichderma viride for wilt management in chickpea crop.International Journal of plant Science 2(11): 233-236.


ABSTRACT

This study assessed the impact of frontline demonstrations(FLDs) on mungbean during Rabi 2010 to 2016 in the Palidistrict of western Rajasthan, in which 120 participantfarmers and 120 non-participant farmers were selectedthrough stratified random sampling. Results revealed thatthe yield advantage up to 66.30% with incremental benefit:cost ratio (IBCR) of 3.56 with increased knowledge level tothe extent of 48.31% in case of participant farmers togetherwith high (74.16%) to medium (21.27%) to low (04.27%)adoption (against low grain yield, low return, low knowledgelevel and medium (18.35%) to low (79.17%) extent ofadoption in case of non-participating farmers). The resultsof regression analysis revealed that level of knowledge withsome socio-personal, psychological and communicationvariables among respondent farmers indeed helped incontributing to extent of adoption of improved technologyamongst farming community. This could be seen as positiveindicators for formulating and disseminating, more extensive,technology specific and farmer centric FLD programme toimprove overall knowledge and adoption amongst farmersin the region to boost pulse production.

Key words: Adoption, Economic, Frontline demonstrations,Mungbean, Yield gap analysis

Pulses are important food crops for humanconsumption and animal feed. Being leguminous in nature,they are considered to be important components ofcropping systems because of their viability to fixatmospheric nitrogen, add substantial amounts of organicmatter to the soil and produce reasonable yields with lowinputs under harsh climatic and soil conditions. Moong-wheat cropping system is predominant and is continuouslypracticed by the farmers in the arid zone of Rajasthan. Thereis evidence of system productivity stagnation, nutrientwater imbalances and increased insect-pest and diseasesincidence due to prolonged use of this cereal dominatedsystem source. Mungbean (Vigna radiate L. Wilczek.) isan important multi-season pulse crop of India. It is a tropicaland sub-tropical grain legume, adapted to different typesof soil conditions and environments. It has strong rootsystem and capacity to fix the atmospheric nitrogen intothe soil and improves soil health and contributessignificantly to enhancing the yield of subsequent crops(Meena and Singh 2017). However the production andproductivity is very low in mungbean mainly due ofcultivation in resource poor lands with minimum inputs,

Scaling mungbean production in rainfed agroecology of Rajasthan in Indiathrough frontline demonstrationsML MEENA

ICAR-CAZRI, Krishi Vigyan Kendra, Pali-Marwar, Rajasthan, India; Email: [email protected](Received : May 11, 2017 ; Accepted : November 20, 2017)

non-synchronous maturity and indeterminate growth habit.Mungbean yield is also affected by insect-pests anddiseases, especially by mungbean yellow mosaic virus andCercospora leaf spot (CLS). There is a strong need todevelop the lines/varieties which give outstanding andconsistent performance in kharif season over diverseenvironment. Development of varieties with high yield andstable performance is a prime target of all mungbeanimprovement programmes. The total production of pulsesin the world was 14.76 billion tones from the area of 14.25billion hectares in the year 2015-16 while in India total pulsesproduction was 19.78 million tons from the area of 23.63million hectares in the year 2015-16. Whereas in Rajasthan,the total pulses production was 1.55 million tons from thearea of 3.78 million hectares. The mungbean productionamong pulses was 3.73 lacs tons from the area of 8.85hectares in Rajasthan in the year 20015-16. The majorcultivation of mungbean is based upon rainfed conditions.Pali district stands first in term of area and production ofmungbean in the state. In this district, the mungbean cropis grown in an area of 2.46 lacs ha with an annual productionof over 1.30 tones (GOR, 2015-16).

The Front Line Demonstration is an important methodof transferring the latest package of practices to farmers.Form which, farmers learn latest technologies of cropproduction under real farming situation at his own field,which may lead to higher adoption. Further, thesedemonstrations are designed carefully with provisions ofspeedy dissemination of demonstrated technology amongfarming community through organization of othersupportive extension activities, such as field days andfarmers convention. The main objective of the Front LineDemonstration is to demonstrate newly released cropproduction, protection and management technologies atthe farmers’ field under different agro-climatic regions andfarming situations. While demonstrating the technologiesat the farmer’s field, the scientists are required to study, thefactors contributing to higher crop production, fieldconstraints of production and thereby generatingproduction factor and feed-back information. Front LineDemonstrations are conducted in a block of two to fourhectares of land in order to have better impact of thedemonstrated technology on the farmers and field levelextension functionaries with full package of practices.Keeping in view the present study was done to assessmentof frontline demonstrations on mungbean cultivation.




This study was conducted in Pali district of westernRajasthan state. The district were selected to cover highaltitude; hot agro-ecological situation where the mungbeancultivation is most suitable. Geographically the district is1233079 hectare, lying between 240-45’ latitude and 740-20’longitude and ranging between 212 to 225 MSL. The districtis having arid climatic conditions and temperature rangingfrom 36-460C with annual precipitation of about 309.9 mm.Soils are mainly sandy loam and sandy having potassiumand phosphorus deficiency. The district is manly un-irrigated, predominantly rural (77.15%) and the people ofthe districts are mostly engaged in agriculture (80.55%).Principal crops being moth bean, cluster bean, cumin,mustard, wheat, chickpea and barley cultivated on an areaof absent 43590 ha. The FLDs on mungbean wereconducted during Kharif 2011 to 2016 by ICAR incollaboration with ATARI and KVK in entire northern Indianarid region state of Rajasthan but due to paucity of timeand proximity, study was confined to the Pali districtpurposively as it has vast and diverse pulses cultivationresources ideally suited for taking up improved mungbeanproduction technologies. Eight representative villages wereselected randomly in each district and within each village,15 farmer (respondents) were selected following stratifiedrandom sampling method to form a sampling size of 120participant farmers, who participated in FLD programmeand 120 non-participant farmers who did not participate inFLD programme. The data on Frontline Demonstrationsconducted under pulse crops for 7 years were used forcalculation of grain yield, gap analysis and economics usingdifferent parameters as suggested by Yadav et al. (2004).Knowledge level of respondent farmers was calculatedbased on Client Satisfaction Index developed by Kumaranand Vijayaragavan, (2005). The dependent variable adoptionof mungbean improved production practices was quantifiedby using adoption quotient developed by Sengupta (1967).Based on a thorough review of relevant literature anddiscussion with the experts in the subjects 16 independentvariables comprising of socio-personal, socio-economic,psychological and communication variables, having somebearing on the dependent variables were identified forinclusion in the study. These independent variablesrepresented age, education, family size, land size, farmimplements used, socio economic status, occupation,achievement motivation, innovative proneness,

cosmopolitenes, scientific orientation, social participation,extension contact, exposure to media, extension contact,and mass media exposure and were empirically measuredby procedures evolved for the purpose, using suitablescales and scoring procedures developed by earlierresearchers. The data was collected through personalinterview and analyzed using R Software.


Grain yield: The increase in grain yield underdemonstration varied from 27.07 to 54.60% than farmersown practices. The difference in grain yield during differentyears could be due to more feasibility of recommendedtechnologies and variability in climatic conditions. On thebasis of seven years, average demonstration yield was1005.86 kg/ha as compared to 747.29 kg/ha in case of farmerstraditional practice thereby recording yield advantage of34.22% under demonstrations carried out with improvedpractices of mungbean. Similar yield enhancement indifferent crops in front line demonstration have beendocumented in previous studies (Kumar et al. 2010).Gap analysis: An extension gap of 280 to 530 kg per hectarewas found between demonstrated technology and farmer’sown practices during seven years and on average basisthe extension gap was 425 kg/ha (Table 1). The extensiongap was lowest (280 kg/ha) during 2010 and was highest(530 kg/ha) during 2014. Such gap might be attributed toadoption of improved technology in demonstrations whichresulted in higher grain yield than the traditional farmer’spractices. Wide technology gap were observed duringdifferent years and this was lowest (250 kg/ha) during 2013and was highest (390 kg/ha) during 2013. On seven yearsaverage basis the technology gap of total 43 demonstrationswas found as 338 kg/ha. The difference in technology gapduring different years could be due to more feasibility ofrecommended technologies and variability in climaticconditions. Similarly, the technology index for all thedemonstrations during different years were in accordancewith technology gap. Higher technology index reflectedthe insufficient extension services for transfer oftechnology. The results of yield gaps were in conformancewith the earlier studies Chandra (2010), Gauttam et al. (2012),Lothwal (2010) and Math et al. (2014).

Different variables like seed, fertilizers, bio fertilizersand pesticides were considered as cash inputs for the

Table 1. Grain yield extension gap, technological gap and technology index of FLD on mungbean (N=240)Year Variety No of FLDs Potential yield DY (kg/ha) Yield FP (kg/ha) % increase EG (kg/ha) TG (kg/ha) TI (%) 2010 GM 4 30 1260 930 650 43.08 280 330 26.19 2011 GM 4 25 1260 880 540 62.96 340 380 30.16 2012 GM 4 40 1260 950 520 82.69 430 310 24.60 2013 GM 4 45 1260 1010 615 64.23 395 250 19.84 2014 IPM 02-3 50 1450 1060 730 72.60 530 390 26.89 2015 IPM 02-3 60 1450 1090 690 72.75 502 360 24.83 2016 IPM 02-3 55 1450 1105 760 65.79 500 345 23.79 Overall - 43.6 1341 1004 644 66.30 425 338 25.19

Meena et al. : Scaling mungbean production in rainfed agroecology of Rajasthan in India through frontline demonstrations 249

demonstrations as well as farmer’s practice and on anaverage an additional investment of Rs.1246 per ha wasmade under demonstrations. Economic returns as a functionof grain yield and Maximum Sale Price of mungbean variedduring different years. Maximum returns of Rs. 5340 /hawas obtained during the year 2010 due to very high grainyield and higher MSP as declared by Government of India.The higher additional returns and effective gain obtainedunder demonstrations could be due to improvedtechnology, non-monetary factors, timely operations of cropcultivation and scientific monitoring. The lowest andhighest incremental benefit: cost ratio (IBCR) was 2.88 and3.62 in 2015 and 2011, respectively (Table 2). Overall averageIBCR was found as 3.56. The results confirm the findingsof FLDs on oilseed and pulse crops (Dayanand et al. 2012;Meena and Singh 2016; Meena et al. 2012; Poonia andPithia 2011; Ra et al. 2013, Rajni et al. 2014; Singh andMeena 2011; Sachin et al. 2009; Meena and Singh 2012;Meena and Singh 2017 and Yadav et al. (2007).Knowledge about improved mungbean productionpractices: Knowledge level of respondent farmers onvarious aspects of improved mungbean productiontechnologies was measured and compared by applyingdependent ‘t’ test. It could be seen from the Table 3 thatfarmers mean knowledge score had increased by 48.31 inparticipating farmers. The results were at par with the earlierstudies (Malik et al. 2005 and Singh et al. 2007). In otherwords there was significant increase in knowledge level ofthe farmers due to frontline demonstration. This showspositive impact of frontline demonstration on knowledgeof the farmers due to the concentrated efforts made by theKVK scientists.Extent of adoption of improved mungbean productionpractices: The distribution of respondents based on theirlevel of adoption towards improved practices of mungbean

cultivation revealed that in case of participating farmers,majority of respondents (74.16%) belonged to highadoption category, 21.17% belong to medium category andthe remaining 4.17% to low adoption category as comparedto 02.50, 18.33 and 79.17% in case of non-participatingfarmers respectively (Table 4). Thus, it implied that majorityof the participating farmers were belonging to high-mediumadoption category as compared to medium adoption amongnon-participating farmers, which might have been due tothe fact that most of the participating farmers had correctinformation and knowledge about improved technologies(Singh et al. 2010).Extent of adoption of components of improved mungbeanproduction technology: An attempt was made here toanalyze the level of adoption of individual components ofimproved mungbean production technology by the farmerrespondents. Some of the individual practices were adoptedin full, or not adopted at all, while among some practices,partial adoption was recorded. The results are presented inTable 5.Land preparation: Mungbean crop requires fine, firm andmoist seed-bed to ensure adequate moisture for germinationand young seedlings. To achieve this, the field is supposeddeep ploughing soon after the Rabi crop in the March toApril. Improved land preparation practices were adoptedby 91.17% participant farmers. Nearly 8.33% of them didnot adopt this practice at all. Among non-participant farmers40.83% were adopting the practice while 59.17% do notadopt this practice at all.Use of high yielding varieties: Quality seed and nature ofvariety plays primary role for harvesting a good yield.Among the participant respondents, nearly 87.50% adopteduse of high yielding varieties of mungbean. About 08.33%of them adopted this practice partially and about 04.17%did not use them in their fields. Among non-participant

Table 2. Economic analysis of frontline demonstrations on mungbeanYear Cost of cash input

(`/ha) Additional cost in demonstrations

(`/ha)

Sale price of grain (MSP)

Total returns (`)

Additional returns

(`)

Effective gain (`)

IBCR

DP FP DP FP 2010 2450 1250 1200 3670 36700 31360 5340 4140 4.45 2011 2660 1370 1290 3590 39390 34720 4670 3300 3.62 2012 2770 1450 1320 3850 38500 33800 4700 3380 3.56 2013 2850 1680 1170 4250 51000 47000 4000 2830 3.42 2014 3080 1750 1330 4060 48712 43480 5232 3902 3.93 2015 3240 2060 1180 4350 52200 48800 3400 2220 2.88 2016 3360 2130 1230 4450 53400 49600 3800 2570 3.09 Average 2916 1670 1246 4031 39143 41251 4449 3192 3.56

Table 3. Comparison between knowledge levels of therespondent farmers about improved farmingpractices of mungbean (N=240)

Significant at 0.01 level of probability

Mean knowledge score (N=240) Mean difference

Calculated ‘t’ value Participants

farmers (N=120)

Non participant farmers (N=120)

79.67 31.36 48.31 4.678*

Table 4. Distribution of respondents on extent of adoptionof improved practices of mungbean (N=240)

Parameter Participant farmers (N=120)

Non-participant farmers (N=120)

F % F % Low (<mean-SD) 05 04.17 95 79.17 Medium (Between mean) 26 21.67 22 18.33 High (> Mean + SD) 89 74.16 03 02.50 Total 120 100.00 120 100.00


farmers 31.67% had adopted this component of thetechnology while as 14.17% adopted this practice partiallyand 54.17% have not adopted this practice.Sowing time: Sowing time is crucial for every crop asappropriate sowing time results in healthy and good crop.For mungbean July (on set of monsoon) has beenrecommended practice for sowing. Majority (85.50 %) ofparticipant farmers had adopted the right time for sowingmungbean in their fields and rest (14.17 %) did not adoptthis practice. This may be due to non-availability of moisturecontent or lack of water for pre-sowing irrigation. Amongnon-participant farmers majority (71.67%) did not adoptthis practice due to unawareness about the practice.Sowing methods: Line sowing with 30 and 10 cm gapbetween line-to-line and plant respectively is recommendedpractice for mungbean. Majority 79.17% of participantfarmer respondents had adopted the recommended linesowing method, while majority of them (20.83%) were usingbroadcast method for sowing mungbean. Out of non-participants farmers majority (92.50%) did not practice thiscomponent. This may be due to the involvement ofadditional labour and apprehension of more cost associatedwith the practice.Plant density: Maintenance of optimum plant populationplays a bid role for good harvests. In order to maintainplant density in mungbean, thinning after 15 to 20 days ofsowing has been recommended. Among participant farmers70.00% were “sing hand thinning methods wherevernecessary to keep plant density while 15.83% were usingthis practice partially. Among non-participant farmersmajority (52.50%) did not maintaining planting density atall.Manure and fertilizers: Mungbean respond well both toorganic and inorganic manures. At the time of fieldpreparation 15 to 20 tones/ha of FYM or compost isrecommended practice besides N (60 kg/ha), P (30 kg/ha),K (20 kg/ha) and sulphur 20 kg/ha. Majority (59.17%) wereusing appropriate dozes of manures and fertilizers in their

fields. But 40.83% of them were not using appropriate timefor its application. Amongst non-participant farmers 82.50%also did not adopt the practice.Insect and pest management: Pod borer have beenobserved as common insect pest of mungbean. Seed isrecommended to be treated with thiram or captan at 2.5 g/kg of seed before sowing to keep crop away from any seedborne disease. Majority of participant farmers (45.00%) andnon-participating farmers (73.33%) did not apply anyinsecticides or pesticides. This may be due to higher costassociated with the adoption of practice.Weed management: Uncontrolled weeds in mungbean cropmay cause 20 to 70% reduction in yield due to crop weedcompetition. Weeding is recommended soon after thinning.About 45.83% among participant farmers and 26.67% amongnon-participant farmers were using weed managementpractices in their fields. The lukewarm response towardsthe practice may be due to demand of more labour.Post-harvest management: Mungbean crop isrecommended to be harvested once the pods turn blackand moisture content of the seed is around 20%. Harvestingis done preferably in the morning hours, when the pods areslightly damp with night dew to minimize the shatteringlosses. Bundles of the harvested plants are staked anddried in the sun for a few days and threshed by mechanicalmethods of threshing the dried plants. Moisture content ofthe seed must be less than 8% at the storage time. Majorityof the respondent farmers irrespective of their participationin FLD programme farmers were using post-harvestmanagement practices, respectively.Drainage facility: Drainage facility in mungbean fields’remains important practice particularly in Rajasthan as rainwater logged, posing a great threat to the emerged crop.Majority of the respondent farmers are not making drainagefacility in their field irrespective of their participation. Thismay be due to utilization of more labour.Correlates of extent of adoption of improved practices ofmungbean: A correlation analysis was done using

Table 5. Extent of various components of improved practices of mungbean cultivation by farmers (N=240)

F= Frequency; %=Percentage; N= Number of farmers

S. No.

Improved practices Participants farmers (N=120) Non-participant farmers (N=120) Fully

adopted Partially adopted

Not adopted

Fully adopted

Partially adopted

Not adopted

F % F % F % F % F % F % 1 Land preparation 110 91.67 - - 10 08.33 49 40.83 - - 71 59.17 2 Use of HYV 105 87.50 10 08.33 05 04.17 38 31.67 17 14.17 65 54.17 3 Sowing time 103 85.58 - - 17 14.17 34 28.33 - - 86 71.67 4 Sowing method 95 79.17 - - 25 20.83 09 07.75 - - 111 92.50 5 Plant density 84 70.00 19 15.83 17 14.17 29 24.17 28 23.33 63 52.50 6 Application of manures and fertilizers (Doses) 71 59.17 - - 49 40.83 21 17.50 - - 99 82.50 7 Application of manures and fertilizers (Time) 82 68.33 - - 38 31.67 36 30.00 - - 84 70.00 8 Insect and pest and diseases management 54 45.00 - - 66 55.00 32 26.67 - - 88 73.33 9 Weed management 55 45.83 - - 65 54.17 11 09.17 - - 109 90.83 10 Drainage availability 88 73.33 - - 32 26.67 06 05.00 - - 114 95.00 11 Post harvest management 114 95.00 - - 06 05.00 56 46.67 - - 64 53.33


statistical package (R Software) to identify the factors whichare related to extent of adoption of improved productionpractices of mungbean among the respondents (Table 6). Acursory look at the correlation results revealed that all setsof independent variables except annual income included inthe study were significantly associated with adoption ofFLD mungbean. Among participant farmers education, landsize, farm power, socio-economic status, achievementmotivation, scientific orientation, innovative proneness,extension contact, media exposure and level of knowledgeabout improved methods of mungbean cultivation werehaving positive and high significant correlation with thedependent variable. However, a strong positive correlationwas found among non-participating farmers with land sizeand family size. This may be due to good family size (average8 members) to support labour component and also landholding support (average 0.64 ha) amongst non-participating farmers. Also a negative correlation was foundin respect of non-participating farmers with the variables

age, social participation and media exposure. This may bedue to the fact that majority of non-participating farmerswere illiterate and middle to old aged, comprehendingknowledge aspects differently from media and tend toparticipate in social meetings for entertainment and personalgains.Regression analysis of extent of adoption of improvedmungbean production practices by farmers: In order toassess the contribution of various independent variablesto the variation in the extent of adoption of improvedmungbean production practices by respondent farmers aregression model was used (Table 7). A perusal of the resultspresented in Table 7 indicates that 75 and 29% variationbetween participant farmers and non-participant farmersrespectively exists with respect to extent of adoption ofimproved mungbean production practices, which wereexplained by the independent variables included in theregression equation. F value at 14 and 105 degrees offreedom was 28.43 which is significant 0.05 level ofprobability. This indicated that the independent includedin the study were appropriate as they could explain largevariance in the dependent variable.

Also a cursory look at the table reveals that onlyfour variables viz., education, innovative proneness,extension contact and level of knowledge of improvedmungbean production practices could contributesignificantly to the variance and predicting extent ofadoption of improved mungbean production practicesamongst participant farmers. Moreover, two variables familysize, land size, could also contribute positively andsignificantly to the variance and predicating extent ofadoption of improved mungbean production practicesamongst non-participant farmers. However, socialparticipation and exposure to media could contributenegatively and significantly to the variance and predicatingextent of adoption of improved production practices

Table 6. Correlation coefficient of extent of adoption ofimproved practices of mungbean

*, ** Significant at 0.05 and 0.01 level of probability, respectively

Independent variables Correlation co-efficient (r) Participant

farmers Non-participant

farmers Age -0.0845 -0.1954* Education 0.5689** 0.0765 Family size -0.0879 0.5467** Land size 0.4236** 0.5567** Farm power 0.5966** 0.0156 Socio-economic status 0.6433** 0.1843* Achievement motivation 0.5678** -0.0803 Innovative proneness 0.6325** -0.0765 Scientific orientation 0.3456** -0.0876 Annual income 0.0642 -0.0678 Social participation -0.0654 0.0060 Extension contact 0.9422** -0.2870* Media exposure 0.5482** 0.0341 Level of knowledge about greengram cultivation

0.4835** -0.0343

Table 7. Regression coefficient of extent of adoption of improved practices of mungbean

*, ** Significant at 0.05 and 0.01 level of probability, respectively

Independent Variable Participants farmers Non-participant farmers Partial ‘b’ T Sig. Partial ‘b’ T Sig.

Age 2.87612 1.643 0.1413 6.07534 1.643 0.2342 Education 0.14567 0.532 0.7541 -1.03745 -1.765 0.2975 Family size 0.58226 2.478 0.0321* 0.33372 0.877 0.4378 Land size -0.02323 -0.086 0.8792 0.54388 2.785 0.0148* Farm power 0.34452 0.784 0.6548 1.57346 2.644 0.0258 Socio-economic status 0.02317 0.066 0.3348 -0.26371 -0.743 0.6743 Achievement motivation 0.33278 1.249 87660 0.35604 0.554 0.8546 Innovative proneness 0.03432 0.533 0.0065* -0.04017 -0437 0.8432 Scientific orientation 0.87734 2.877 0.7548 -0.44017 -0.678 0.6554 Annual income 0.17567 0.504 0.2186 -0.45780 -0.784 0.4578 Social participation 0.64389 1.783 0.4453 0.57843 1.045 0.0678* Extension contact 0.44578 0.865 8.4030 -1.2547 -1.876 0.5785 Exposure to media -0.02116 8.054 0.7590 0.28653 0.765 0.0567** Level of knowledge of greengram cultivation 0.43667 -0.032 0.0776 0.06652 -1.403 0.3267 R 0.864

0.789 28.43*

0.1765 0.345 4.321

R2 F(14, 105)


amongst non-participant farmers. This may be due to goodland holding, contribution of family labour in joint familysystem, useless social contact and good materialpossession like radio, TV, computer, internet and mobilephones for personal entertainment in non-participantfarmers. The results are in agreement with Patel et al. (2009).Thus it can be concluded that FLD on mungbean has greatinfluenced on the adoption level of the participant farmersthan non-participant farmers.

Although mungbean FLDs has been seeninstrumental in increasing yield up to 66.30% with ICBR of3.56 but extension and technology gap was still 425 and338 kg/ha, respectively with technology index of 25.19.Knowledge dissemination through FLD programme hasalso increased level of knowledge among participant farmersas compared to non-participant farmers. Among participantfarmers mean knowledge score was 79.67% as compared to31.36% among non-participating farmers with meandifference of 48.31. Despite of the fact that mean score ofadoption about improved practices amongst participantfarmers was high (74.16%) only 04.17% of them had lowlevel of adoption, nearly 21.67% of them had moderate levelof extent of adoption, thereby indicating a overall medium-to-high level of adoption. Meanwhile the mean score ofextent of adoption of improved practices amongst non-participant farmers was low level adoption 79.17%, only02.50% of them had high level of adoption, nearly 18.33%of them had moderate and lower extent of adoption,respectively, thereby indicating a general low to moderatelevel of adoption among them. Majority of the participantfarmers had fully adopted only a few components ofimproved practices such as land preparation, use of highyielding varieties, sowing time, pre-sowing irrigation andtime of application of manures and fertilizers. However, jointfamilies with sizable number of farm workforce and goodland holding have contributing towards adoption ofmaximum improved practices. The results of regressionanalysis revealed that FLD programme had helped incontributing to the extent of adoption of improvedMungbean production practices viz-a-viz productionenhancement and yield gap minimization. This can be seenas a positive indicator for formulating an adoption orientedand extensive FLD programme to educate farmers aboutimproved mungbean production practices and enrich theirlevel of knowledge through ‘working by doing’ and ‘doingby learning’ for ensuring higher mungbean production inthis temperate region.

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ABSTRACT

Several time series generated from agriculture can beeffectively modelled using various time-series modellingtechniques such as ARIMA (Box-Jenkins) modellingtechnique, State-Space modelling technique, StructuralTime Series modelling and other time series modellingdepend on the properties of the given time series. Modellingand related forecasting for thetime serieswere performedusing Autoregressive Moving Average (ARIMA),Autoregressive Neural Network (ARNN) and ARIMA-ARNNhybrid models. First, to maintain the stationarity propertyof the data (1950-51 to 2017-18)as a necessary step, thedatasetwas tested,and thefirst order difference series wereconsidered for modelling using the Box-Jenkins approach.ARIMA (0,1,1) were found suitable for the production andyield databased on the least value of Schwarz-BayesianCriterion (SBC). Secondly, Autoregressive Neural Network(ARNN) of orderARNN (2,2) wasselected for both the dataset.Lastly, ARIMA (0,1,1) - ARNN (4,6) for both production andyield were found suitable. All the three models were testedfor their forecast accuracy using Root Mean Square Error(RMSE) and Mean Absolute Percentage Error (MAPE).Accordingly, the ARIMA-ARNN hybrid model was found tobe best as compared to the individual ARIMA and ARNNmodel. Based on the ARIMA-ARNN model, the forecastingof the production and yield for the year 2050 was found to be35.84 million tonnes and 1062.01 kg/ha, respectively of pulsesin India.

Key words: ARIMA, ARIMA-ARNN, Pulses

Indian agriculture has made substantial progress,particularly with respect to food crops like wheat, rice cerealsand pulses. But, the variability in projection of estimatesfor a particular year has been observed in the case of croplike pulses. On the global perspectives, India is the biggestproducer of pulses in the world. In spite of that, as per datasubmitted by the agriculture ministry, pulses import stoodat 50.8 lakh tonnes during April-December period of 2017-18fiscal. During 2016-17 fiscal, 66.08 million tonnes pulseswere imported, while imports stood at 57.97 lakh tonnes in2015-16 and 45.8 lakh tonnes in 2014-15.

However, several studies have been reported for theestimation of demand of pulses with considerabledifferences in projections due to methodology andassumptions followed there in. Country would require inyear 2021, around 19.1, 42.5, 19.5, 18.4 million tonnes ofpulses according to Chand (2007), Mittal (2008), Kumar etal. (2009) and Ganesh kumar et al. (2012), respectively.

Estimation of production and yield of pulses using ARIMA-ARNN modelPUNEET DHEER1, PRADEEP YADAV2 and PK KATIYAR2

1SRM Institute of Science and Technology, Kattankulathur-603203, India; 2ICAR-Indian Institute of PulsesResearch, Kanpur-208024, India. E-mail: [email protected](Received : July 20, 2018 ; Accepted : September 28, 2018)

Narayanmoorthy (2000) projected the total demand ofpulses will be at 27.45 million tonnes in the year 2030.TheIndian Institute of Pulses Research, Kanpur has projectedthe demand of pulses at 39 million tonnes by 2050, whichwill require the production to grow at an annual rate of2.2% (IIPR, 2015). Nevertheless, the projection of productionand yield of pulses is lacking.

Various time series modelling techniques weredeveloped for analyzingand forecasting the series, dependsupon the characteristics of the time series. If the time seriesis linear, then Auto-Regressive Integrated Moving Average(ARIMA) can be employed most of the times. ARIMAmodels (Box et al. 1994) have been utilized for crop yield orany other agricultural production. Sarika et al. (2011)applied ARIMA model for modelling and forecasting India’spigeon pea production data. Suresh et al. (2011) appliedARIMA model for forecasting sugarcane area, productionand productivity of Tamil Nadu state of India. Earlier findingreported that combining different models enhance theaccuracy of forecasting as compared to individual model.The hybrid methodology given by Zhang (2003) is one ofthe most applied hybrid techniques which combine ARIMAandARNN models. Naveena et al. (2017) utilized ARIMA-ARNN hybrid model to forecast the price of washedcoffee.Keeping these in view, three models viz., ARIMA,ARNN and ARIMA-ARNN were tested for their forecastaccuracy and subsequently forecasting of production andyield accommodating 68 years data (1950-51 to 2017-18) ofpulses in India.


In this study, the time series data of production andyield of pulses for the period of 1950-51 to 2017-18 wereanalyzed. Out of the 68 years data, for training the model-first 63 years data were used and for model validation-thelast 5 years data are used. The data were obtained from theAnnual Report (2017-2018) All India Coordinated Projecton Chickpea, ICAR-IIPR, Kanpur.Box-Jenkins ARIMA Model: The most common time seriesmodel used in order to predict future outcomes based on alinear function of past data points and past errors terms isthe Autoregressive Integrated Moving Average (ARIMA)also known as Box–Jenkins model. In theory and practice,ARIMA model is commonly utilized for forecasting a timeseries, either to predict future points or to get a better insightabout the data. To satisfy the ARIMA assumptions, a


Dheer et al. : Estimation of production and yield of pulses using ARIMA-ARNN model 255

sequence of steps is performed on the raw data in order tomaintain a statistical stationarity property such as mean,variance and autocorrelation do not change over time. Thestationarity property of a time series can be confirmed byusing unit root test such as Augmented Dickey-Fuller test(ADF) and stationarity test such as Kwiatkowski–Phillips–Schmidt–Shin test (KPSS). If a series found to be a non-stationary based on these tests, differencing is performeduntil the data are finally made stationary. In general, anARIMA model represented as ARIMA(p,d,q), consists ofthree parameters: (I) p, the order of Auto-Regression (AR),(II) d, the order of integration (differencing) to achievestationarity, and (III) q, the order of Moving Average (MA).

X(t) = 0 + 1 X(t – 1) + ... + p X(t – p) + (t)

+ 1 (t – 1) + ... + q (t – q) (1)Where X(t) and (t) represent the actual value and

random error at time period t respectively, i (i=1, 2, ..., p)and j (j=1, 2, ..., q) are model parameters, and p and q arelagged values. Random errors (t), are assumed to beindependently and identically distributed with a mean zeroand a constant variance, 2.

After satisfying the stationarity property of the timeseries, the Box-Jenkins approach follows four steps:(i) Model identification: Examine the data by ACF (MA

(q) term) and PACF (AR (p) term) to identify thepotential models.

(ii) Parameter estimation: Estimate the parameters usingleast square for potential models and select the bestmodel using Akaike Information Criterion (AIC) orSchwarz- Bayesian Criterion (SBC).

(iii) Diagnostic checking: Check the ACF/PACF andLjung Box Test of residuals. Do the residuals followsrandom distribution? If yes go to (iv), otherwise goto (i) and repeat the same.

(iv) Final model: Generate the required forecasts byusing the selected model.

Artificial Neural Network Approach for Time SeriesModelling: On the other hand, Artificial Neural Networks(ANNs), is a family of statistical learning algorithms inspiredfrom biological neural networks of the brain. An ANNs isgenerally represented from finite numbers of artificialneurons that are associated with weights, which leads tothe neural architecture and are organized in layers (input,hidden and output layer). ANNs are advantageouscompared with ARIMA in many applications because ANNsdo not assume linearity. ANN is a non-linear mathematicalmodel and its ability to model a complex non-linear processthat build a relationship between inputs and outputs of asystem. The Autoregressive Neural Network (ARNN) modelperforms a nonlinear functional mapping from the pastobservations [X(t – 1), X(t – 2), ..., X(t – p)] to the future value X(t),i.e.,

X(t) = f (g (X(t – 1), X(t – 2), ..., X(t – p), w)) + (t) (2)Where‘f ’ is a non-linear activation function

determined by the network structure (such as sigmoid, TanH,ReLU, etc.), ‘g’ is linear function, p is the lagged value, ‘w’is a vector of connection weights with bias and (t) is anoise or error terms. Thus, the ANN is equivalent to anonlinear autoregressive model (ARNN).

The important task of ARNN (p,q) modelling for atime series is to select an appropriate number of hiddennodes q, as well as to select the dimension of input vector(aka, the lagged observations), p. It is difficult to determinep and q values atfirst place. Hence, in practice, experimentsare often conducted to select the appropriate values for pand q.Hybrid Based Model on Autoregressive IntegratedMoving Average and Artificial Neural Network: ARIMA–ARNN hybrid model was proposed (Zhang, 2003) for timeseries forecasting. Any time series sequence is assumed tobe the sum of two components, linear and nonlinear.

X(t) = L(t) + N(t) (3)where L(t) and N(t) denote the linear and non-linear

components, respectively.First, an ARIMA model is fit to the given time series

sequence. Then the error sequence from ARIMA is assumedto be the nonlinear component and is modeled using anARNN. The predictions obtained from both the ARIMAmodel and the ARNN model are combined to obtain thefinal forecast.

Let (t) denote the residual at time t from the linear

model, then X(t) is actual value and (t)L̂ is forecast value:

(t) = X(t) – (t)L̂ (4)

By modeling residuals (t) series using ARNNs,nonlinear relationships can be discovered. With n inputnodes, the ARNN model for the residuals will be:

E(t) = f ((t – 1), (t – 2), ..., (t – n)) + e(t) (5)where f is a nonlinear function determined by the

neural network and e(t) is the random error. Let’s denote the

E(t) as (t)N̂ , the combined forecast will be:

(t)(t)(t) N̂ L̂ X (6)

Forecast Evaluation Criteria:There are many measurements to evaluate the

residuals. We used the Root Mean Square Error (RMSE)and Mean Absolute Percentage error (MAPE).

RMSE =

n

1 t

2(t)(t) )X̂– (X

n1


MAPE = 100 * X

)X̂– (X

n1 n

1 t (t)

(t)(t)

where n is the number of data points, X(t) is actual

value at time t and (t)X̂ is predicted value. The lesser valueof RMSE and MAPE, makes the better model for forecasting.


The data of production and yield of Pulses were usedin order to forecast forthe year 2050 using the models asdescribed earlier.Stage 1: First 63 year of data from production and yielddataset as a training set are used to analyze the time seriesregarding its stationarity property and model building with

an objective to forecast. Fig. 1 shows the time series of all68 data points for both the dataset. In order to apply theARIMA model on the given training set, it is necessary tocheck the stationarity property by investigating the ACFplots in Fig. 2 and p-value of ADF test. It was observedthat the dataset is non-stationary because the autocorrelation is decreasing very slowly and remains well abovethe significance level. This is indicative of a non-stationaryseries and confirmed by ADF test with p-value > 0.05supporting the null hypothesis that the series is non-stationary. The time series was differentiatedand againperformed the ADF test and investigated the ACF plots inFig 3 shows no significant autocorrelation. ADF test p-value <= 0.05 confirms the alternative hypothesis aboutthe time series is stationary.

Fig 2. Autocorrelation and partial autocorrelation plot Fig 3. Autocorrelation and partial autocorrelation plot ofdifferentiated time series (order of 1)

Fig 1. Time series of production and yield in pulses

Dheer et al. : Estimation of production and yield of pulses using ARIMA-ARNN model 257

Models Production Yield RMSE MAPE RMSE MAPE

ARIMA (0,1,1) 1.37 9.13 45.28 6.77 ARNN (2,2) 1.24 8.25 39.39 5.91 ARIMA-ARNN (4,6) 0.41 2.49 12.05 1.78

Table 1. Forecast evaluation of models on training data

Table 2. Forecast evaluation of models on testing dataModels Production Yield

RMSE MAPE RMSE MAPE ARIMA (0,1,1) 4.26 14.87 76.75 8.95 ARNN (2,2) 4.06 15.05 101.75 11.19 ARIMA-ARNN (4,6) 3.91 14.59 65.25 7.49

Stage 2: After satisfying the stationarity property forARIMA, the potential model is selected based on the AICor SBC criterion. After running the experiments, it wasfoundthat AR (p) and MA (q) order identified by least SBC criterionare 0 and 1 respectively. It is partially confirmed that ARIMA(0, 1, 1) may be the best suited model for both theproduction and yield dataset. To get the final confirmation,diagnostic checking by Ljung box test was conducted onfitted residuals of ARIMA (0,1,1) for both the productionand yield with p-value > 0.05 supporting the null hypothesisthat the residuals follow the white noise. Subsequently,both the time series were modelled using Auto RegressiveNeural Networks. The ARNN (2, 2) model was found to bebest for modelling both the production and yield.Stage 3: As discussed earlier, ARIMA only capture thelinear combination. To capture the linear and non-linearityin the time series, hybrid ARIMA-ARNN was selected forforecasting. The residuals of fitted data obtained fromARIMA once again fitted using ARNN. Later, outputs fromboth the components of the hybrid modelare combined forfinal forecasting.

The performance of all aforecited 3 models wereevaluated based on evaluation criteria. Table 1 and Table 2show that the ARIMA-ARNN model with least values ofRMSE and MAPE was appeared to be the best model forforecasting under reference. Thus, finally ARIMA-ARNN

model was selected and used for forecasting. On the basisof this model, it was concluded that the production andyield of the pulses would be around 35.84 million tonnesand 1062.01 kg/ha for the year 2050 keeping in view thetrend of production and yield of pulses in India.

REFERENCES

Box GEP, Jenkins GM and Reinsel GC. 1994. Time Series Analysis,Forecasting and Control. 3rd edition, Prentice Hall, EnglewoodClifs.

Chand Ramesh. 2007. Demand for foodgrains. Economic and PoliticalWeekly 42(52): 10-13.

Ganeshkumar A, Mehta R, Pullabhotla H, Prasad SK, Ganguli K andGulati A. 2012. Demand and supply of cereals in India: 2010-2025, IFPRI Discussion Paper 01158, International Food PolicyResearch Institute, New Delhi office, pp.1-54

IIPR. 2015. Vision 2050. Indian Institute of Pulses Research, Kanpur,Utttar Pradesh.pp.1-50

Kumar P, Joshi PK and Birthal PS. 2009. Demand projections forfoodgrains in India. Agricultural Economics Research Review22(2): 237-243

Mittal Surbhi. 2008. Demand supply trends and projections of foodin India, Working Paper No. 209, Indian Council for Researchon International Economic Relations, pp.1-20

Narayanmoorthy A. 2000. Demand and supply position of pulses: Amacro level analysis. Productivity 41(2): 327-337

Naveena K, Singh Subedar, Rathod Santosha and Singh Abhishek.2017. Hybrid ARIMA-ANN modelling for forecasting the priceof Robusta coffee in India. International Journal of CurrentMicrobiology and Applied Sciences 6(7): 1721-1726.

Sarika, Iquebal MA and Chattopadhyay. 2011. Modelling andforecasting of pigeonpea (Cajanus cajan) production usingautoregressive integrated moving average methodology. IndianJournal of Agricultural Sciences 81(6): 520-523

Suresh KK and Krishna Priya SR. 2011. Forecasting sugarcane yieldof Tamil Nadu using ARIMA Models. Sugar Technology 13(1):23-26

Zhang G. 2003. Time series forecasting using a hybrid ARIMA andneural network model. Neurocomputing 50: 159-175


ABSTRACT

Correlation and path coefficient analysis was done with 25chickpea accessions to find out association among charactersand to assess the direct and indirect effects of elevencharacters on seed yield. The study was conducted duringrabi 2016-17. Correlation studies indicated that seed yieldper plant exhibited a significant positive association withplant height, number of secondary branches, number of podper plant, biomass yield and 100-seed weight. Path analysisrevealed that the trait number of pod per plant had highestpositive direct effect followed by 100-seed weight, days to75% flowering, biomass yield and number of seeds per pod.Traits like number of secondary branches per plant, plantheight and number of seed per pod contributed to seed yieldmainly through indirect effects via the trait number of podsper plant. Hence, selection for high number of pods per plantand 100-seed weight would lead to high seed yield. Selectionfor number of secondary branches per plant, plant heightand number of seed per pod would facilitate selectinggenotypes for the high number of pods per plant.

Key words: Chickpea, Correlation, Himalayan region, PathAnalysis

Chickpea (Cicer arietinum L. 2n=2x=16) belongs togenus Cicer, family Fabaceae, and sub family papilionacea.It is an annual, self-pollinating, diploid pulse crop with agenome size of 750Mbp. It is an important winter pulsecrop of India and has a significant contribution to pulseeconomy. This crop occupies an indispensable place inour daily diet as a very good source of protein, fits well incropping system and tolerates drought. Seed yield beingthe most important and polygenically controlled complexcharacter, hence is not an efficient character for selection.The correlation coefficient between various dependent andindependent variables helps to obtain best combinationsof attributes in chickpea crop for crop improvementprogrammes. Correlation studies do not clearly reveal suchsort of information and knowledge about interrelationshipsof heritable traits that may lead to negative results. On theother hand, partitioning of total correlation into direct andindirect effects by path analysis helps in making theselection more effective. Path analysis provides the directand indirect effects of different yield component characterson seed yield thus aids in getting high selection response

Short Communication

Correlation and path analysis studies in chickpea (Cicer arietinum L.) for seedyield and its attributes in the Himalayan regionNITESH SD, TALWADE AC and GOPAL KATNA1

Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, Uttar Pradesh, 1CSK HimachalPradesh Agriculture University Palampur, Himachal Pradesh; Email: [email protected](Received : August 15, 2018 ; Accepted : September 08, 2018)

simultaneously for several characters. Hence the presentinvestigation was done to study the association betweenthe characters and the direct and indirect effects of yieldcomponents on seed yield per plant in chickpea

The present experimental material consists of twentyfive genotypes of chickpea accession collected fromICRISAT and Department of Crop Improvement, CSK HPKV,Palampur. The experiment was laid out in Randomizedcompletely block design (RCBD) with three replicationduring rabi 2016-17 at Crop Improvement Experiment Farm,CSK HPKV, Palampur. Each genotype was sown in tworows of ridges and furrows. Row-to-row and plant-to-plantspacing was maintained at 30 and 10cm respectively. Datawas generated on five randomly picked competitive plantsand observations were recorded on agronomic traits likedays to 50% flowering, days to 75% flowering, Plant height(cm), number of primary branches, number of secondarybranches, number of nodes per plant, number of pod perplant, number of seed per pod, biomass yield (g), harvestindex, 100-seed weight, and seed yield per plant. Thecoefficient of correlations was computed as per the methodsuggested by Al-Jibourie et al. (1958) and path coefficientwas analyzed by employing the method suggested byDewey and Lu (1959).

In the present investigation, the traits like plant height(0.323), number of secondary branches (0.587), number ofpods per plant (0.835), biomass yield (0.320) and 100-seedweight (0.352) recorded significant positive correlation withseed yield per plant. These characters can be givenimportance during selection to improve the yield potentialof chickpea (Table 1). This was in accordance with thefindings of Sharma et al. 1999 and Jeena and Arora 2001.Among these characters; number of pods per plant (0.835)had the highest positive correlation with seed yield perplant, indicating the fact that selection of genotype for thistrait would also target genotypes with high seed yieldcapacity. Harvest index (-0.398) revealed significantnegative correlation indicating no association. Similarfindings were reported by Arshad et al. 2004 in chickpea.

Days to 50% flowering exhibited a positiveassociation with Days to 75% flowering (0.982). Plant heighthad a significant positive association with number of nodesper plant (0.802) and number of pods per plant (0.356).


Nitesh et al. : Correlation and path analysis studies in chickpea (Cicer arietinum L.) for seed yield and its attributes 259

Number of primary branches had a positive associationwith number of secondary branches (0.433) and 100-seedweight (0.545). Number of secondary branches showed apositive association with a number of pod per plant (0.714).Number of nodes per plant exhibited a positive associationwith yield biomass (0.470). Harvest index had a positiveand significant association with a number of nodes perplant (0.405), number of seeds per pod (0.697) and yieldbiomass (0.419). The results revealed a strong inter traitcorrelation, which paves way for improvement of thesecharacters through simple selection techniques.

The significant negative correlation was revealed by100-seed weight with days to 50% flowering (-0.305), daysto 75% flowering (-0.426), number of nodes per plants(-0.405), number of seeds per pod (-0.695) and harvest index(-0.507). Harvest index had a negative association withnumber of secondary branches (-0.490) and number of podsper plant (-0.252). Yield biomass had a negative associationwith days to 75% flowering (-0.227). Number of primarybranches recorded negative association with a number ofseeds per pod (-0.251), days to 50% flowering (-0.357) anddays to 75% flowering (-0.362). In this case, there was anegative association between agronomically importanttraits. This hinders the progress of improvement to isolategenotypes with an optimum expression of aforesaidcharacters.

Path coefficient analysis provides informationregarding direct and indirect effects caused by independentvariables having positive correlation with dependentvariable like yield. (Table 2). A combination of direct andindirect selection will be effective to get high selectionresponse. The positive direct effect on seed yield wasrevealed by days to 75% flowering (0.3658), plant height(0.1131), number of primary branches (0.1208), number ofpods per plant (1.0033), number of seeds per pod (0.2197),yield biomass (0.3371) and 100-seed weight (0.5577) whichdepicted a true character association and the selection based

on these traits would be highly desirable. Among thesetraits number of pod per plant (1.0000) exerted a highestpositive direct effect. While selecting for high seed yieldper plant, the main emphasis should be given to thesecharacters. A similar finding was reported by Tyagi et al.1982, Bhambota 1994, Alam et al. 2005 and Renukadevi andSubbalaxmi, 2006 in chickpea.

Negative direct effect on seed yield was observedfor days to 50% flowering (-0.1816), number of secondarybranches (-0.3664), number of nodes per plant (-0.2659) andharvest index (-0.2084). These negative direct effects werecompensated by other traits with positive indirect effects.For instance, number of secondary branches (-0.3664)which had the highest direct negative effect wascompensated by the positive indirect effect of plant height(0.0104), number of nodes per plant (0.0540), number ofseeds per pod (0.0353), yield biomass (0.0425) and harvestindex (0.1795).

Positive indirect effect on seed yield per plant isnumber of secondary branches (0.7161) via number of podsper plants followed by days to 50% flowering (0.3593) viadays to 75% flowering. Plant height (0.357) had a positiveindirect effect via number of pods per plant, whereas forthe trait number of primary branches (0.3040) it was via 100-seed weight. Number of seeds per pod (0.2834) had apositive indirect effect via number of pods per plant.

The significant positive association and higher directeffect on seed yield per plant suggest that selection shouldbe oriented towards the traits number of pod per plant,number of secondary branches, 100-seed weight, days to75% flowering and days to 50% flowering. These traits hadhighest positive direct and indirect effects via other traitson the seed yield per plant. Highly significant and positiveassociation among the various yield-attributing traitsindicate immense scope for the seed yield improvement inchickpea.

Character D50 D75 PH NPB NSB NNP NPP NSP BY HI 100SW SYP D50 D75 0.982** PH -0.078NS -0.119NS

NPB -0.357** -0.362** 0.065NS NSB 0.151NS 0.117NS -0.028NS 0.433** NNP -0.040NS -0.005NS 0.802** -0.251* -0.147NS NPP 0.136NS 0.099NS 0.356** -0.001NS 0.714** 0.274* NSP 0.211NS 0.208NS 0.282* -0.429** -0.096NS 0.094NS 0.282* BY -0.211NS -0.227* 0.241* -0.127NS -0.116NS 0.470** 0.197NS 0.218NS HI -0.135NS -0.077NS -0.038NS -0.109NS -0.490** 0.405** -0.252* 0.697** 0.419**

100SW -0.305** -0.426** -0.133NS 0.545** 0.165NS -0.405** -0.119NS -0.695** -0.108NS -0.507** SYP 0.051NS -0.064NS 0.323** 0.157NS 0.587** -0.002NS 0.835** 0.072NS 0.320** -0.398** 0.352**

Table 1. Correlation coefficients among the twelve characters in chickpea

** Significance at 1% and * Significance at 5%Abbreviation: D50=Days to 50% flowering, D75=Days to 75% flowering, PH=Plant height (cm), NPB=Number of Primary branches,NSB=Number of Secondary branches, NNP=Number of nodes per plant, NPP=Number of pod per plant, NSP=Number of seed per pod,BY=Biomass Yield, HI=Harvest Index, 100SW=100-Seed weight (g), SYP=Seed yield per plant


I hereby duly acknowledge for providing the fundingsupport by ICAR, New Delhi and Department of CropImprovement, CSK HPKV, Palampur for the institutionalsupport for this study.

REFERENCES

Alam SS, Haq MA, Atta BM, Shah TM, Mahmudul H and Hina S.2005. Correlation and path coefficient studies in induced mutantsof chickpea (Cicer arietinum L.). Pakistan Journal of Botany37: 293-298

Al-Jibourie HA, Miller PA and Robinson HF. 1958. Genotypic andenvironmental variances and co-variances in upland cotton crossof inter specific origin. Agronomy Journal 50: 633-636

Arshad Muhammad, Bakhsh A and Abdul Ghafoor. 2004. PathCoefficient analysis in chickpea (Cicer arietinum L.) underrainfed conditions. Pakistan Journal of Botany 36: 75-81

Bhambota SK, Sood BC and Garton SL. 1994. Contribution of differentcharacters towards seed yield in chickpea. Indian Journal ofGenetics 54: 381-388

Dewey DR and Lu K. 1959. A correlation and path coefficientanalysis of components of crested wheat grain seed production.Agronomy Journal 51: 515-520

Jeena AS and Arora PP. 2001. Correlation between yield and itscomponents in chickpea. Legume research 24: 63-64

Renukadevi P and Subbalaxmi B. 2006. Correlation and path analysisin chickpea. Legume research 29: 201-204

Sharma SK, Dua RP and Dharamendra Singh. 1999. Selection criteriafor yield in chickpea under sodic stress condition. Indian Journalof Pulses Research 12: 247-250

Tyagi PS, Singh BD, Jaisutal HK, Annigere A and Singh RM. 1982.Path analysis of yield and protein content in chickpea. IndianJournal of Agricultural Sciences 52: 81-85

Table 2. Path analysis depicting direct and indirect effects of eleven characters on seed yield per plant

Residual effects = -0.04893.Abbreviation: D50=Days to 50% flowering, D75=Days to 75% flowering, PH=Plant height (cm), NPB=Number of Primary branches,NSB=Number of Secondary branches, NNP=Number of nodes per plant, NPP=Number of pod per plant, NSP=Number of seed per pod,BY=Biomass Yield, HI=Harvest Index, 100SW=100-Seed weight (g), SYP=Seed yield per plant

Character D50 D75 PH NPB NSB NNP NPP NSP BY HI 100SW Correlation with SYP

D50 -0.1816 0.3593 -0.0088 -0.0431 -0.0553 0.0105 0.1367 0.0463 -0.0710 0.0281 -0.1701 0.0512 D75 -0.1784 0.3658 -0.0135 -0.0437 -0.0429 0.0013 0.0995 0.0458 -0.0764 0.0161 -0.2378 -0.0642 PH 0.0141 -0.0436 0.1131 0.0079 0.0104 -0.2133 0.3570 0.0621 0.0811 0.0079 -0.0739 0.3227

NPB 0.0648 -0.1324 0.0074 0.1208 -0.1588 0.0666 -0.0010 -0.0942 -0.0427 0.0226 0.3040 0.1572 NSB -0.0274 0.0428 -0.0032 0.0523 -0.3664 0.0392 0.7161 -0.0212 -0.0391 0.1021 0.0920 0.5872 NNP 0.0072 -0.0018 0.0907 -0.0303 0.0540 -0.2659 0.2749 0.0207 0.1585 -0.0843 -0.2258 -0.0020 NPP -0.0247 0.0363 0.0402 -0.0001 -0.2615 -0.0729 1.0000 0.0621 0.0664 0.0525 -0.0664 0.8351 NSP -0.0383 0.0762 0.0319 -0.0518 0.0353 -0.0250 0.2834 0.2197 0.0737 -0.1453 -0.3878 0.0720 BY 0.0382 -0.0829 0.0272 -0.0153 0.0425 -0.1250 0.1976 0.0480 0.3371 -0.0874 -0.0602 0.3199 HI 0.0245 -0.0282 -0.0043 -0.0131 0.1795 -0.1076 -0.2527 0.1532 0.1413 -0.2084 -0.2826 -0.3984

100SW 0.0554 -0.1560 -0.0150 0.0659 -0.0605 0.1076 -0.1195 -0.1527 -0.0364 0.1056 0.5577 0.3521


ABSTRACT

The present experiment was carried out with thirty fivegenotypes of Chickpea in Rabi 2017-18 at fieldexperimentation centre of the Genetics and Plant Breeding,Naini Agricultural Institute, Sam Higginbottom Universityof Agriculture, Technology and Sciences, Allahabad to assessthe nature and magnitude of genetic divergence usingMahalanobis’s D2 Statistics. Analysis of variance revealedhighly significant differences among the thirty two genotypesfor fifteen characters studied indicating that significantamount of genetic variability present in the material. Seedyield per plant had maximum Phenotypic and GenotypicCoefficient of Variation (PCV and GCV), followed by, 100-seed weight and number of pods per plant. High magnitudeof heritability (broad sense) was recorded for seed yield perplant, number of pods per plant and harvest index. Highheritability coupled with high genetic advance was observedfor seed yield per plant, number of pods per plant, harvestindex and biological yield per plant suggesting that, the roleof additive gene effect and possibilities of achieving highgenetic progress through selection. The twenty five Chickpeagenotypes were grouped into six clusters clusters suggestingconsiderable amount of genetic diversity in the material.The cluster VI had maximum ten genotypes followed bycluster I (08 genotypes), cluster II and V having fivegenotypes, while cluster IV had four genotypes and clusterIII having three genotypes, respectively. The intra-clusterD2 value ranged from 11.17 to 49.53 while, inter-cluster D2

value ranged from 32.01 to 152.13. The maximum intracluster distance was exhibited by cluster IV followed bycluster V and cluster VI. The maximum inter-clusterdistance was observed between cluster II and V(152.13),followed by cluster V and VI(146.93) and cluster II and VI(143.60) suggesting that the genetic architecture of thegenotypes in one cluster differ entirely from those includedin other clusters. Cluster IV had exhibited highest clustermean value for seed yield per plant and seed index. ClusterV has highest mean value for number of pods per plant,biological yield per plant and days to maturity. Cluster VIshowed high mean value number of primary branches perplant and harvest index. Cluster III exhibited highest meanvalue for number of secondary branches per plant and lowestmean value for plant height and days to maturity. Maximumcontribution toward the total divergence was exhibited by100-seed weight followed by seed yield per plant, biologicalyield, number of pods per plant and harvest index. Thegenotypes BCG101, BCG708, PBC37, IC275313, Phule GVikram ICC144, IC275326, ICC3812, ICC303 and PKV4 wereidentified as genetically diverse parents, which can beutilized for future crop improvement programme in

Short Communication

Genetic diversity studies in chickpea (Cicer arietinum L.) germplasmAMBILWADE BALASAHEB BAPURAO, SANJAY KUMAR1, SURESH BG and ANAND KUMAR1

Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad, Uttar Pradesh; 1BiharAgricultural University, Sabour, Bhagalpur, Bihar; Email: [email protected](Received : April 08, 2018 ; Accepted : August 21, 2018)

Chickpea. The above results indicated that these genotypeshave maximum genetic diversity and useful for developing alarge number of segregants through crossing programme byusing maximum diverse genotypes.

Key words: Chickpea, Cluster analysis, D2 statistics, Geneticvariability

Chickpea is one of the most important food legumesin the world. Chickpea is the only cultivated species underthe genus ‘Cicer’, and has 2n= 16 chromosome withrelatively small genome size of 738.09 Mbp (Varshney et al.2011). Globally, it is cultivated in more than 57 countriesand rank second in acreage after dry bean. However, itstands 3rd in production following dry bean and peas withthe productivity of about 913 kg/ha (FAO, 2017). Southand South-East Asian countries account for more than twothird of the total chickpea production. India remains a netimporter of chickpea despite contributing to more than 60%to global chickpea in area and production. To meet thedemands of increasing population, there is need to develophigh yielding varieties. Chickpea breeding strategiesinvolves assembling or generating variable germplasm andselection of superior genotype from the germplasm forutilizing them in hybridization programme to develop asuperior variety. In all these stages, estimation of geneticvariability, heritability and genetic advance is necessary.Seed yield is the most important economic character and isa very complex character in nature. It is governed by thepolygenes and greatly influenced by the environmentalfactors. The progress due to selection in nature, inquantitative traits depends on the nature and magnitude ofvariability present in the populations to be improved.(Vaghela et al. 2009). Genetic diversity among parents, whichis heritable, is a pre-requisite for any successful breedingprogramme. The proper choice of parents in the breedingprogramme is of paramount importance. Genetic divergenceamong the parents plays a vital role in cultivar improvementbecause crosses involving genetically diverse parents arelikely to produce high heterotic effects and also morevariability in segregating generations, which can beexploited for desired improvement. Therefore, there is aneed to select diverse parents with desirable characters forfurther hybridization programme for chickpea yieldimprovement. D2 statistical analysis is a powerful tool inquantifying the degree of divergence among the population.Murthy and Arunachalam (1966) stated that multivariate



analysis with “ Mahalanobis D2 statistics” is a powerfultool to know the clustering pattern to establish therelationship between genetic and geographic divergenceand to determine the role of different quantitative characterstowards the maximum divergence. In view of the abovefacts, the present investigation was undertaken to studythe genetic variability and genetic diversity among chickpeagenotypes.

The experimental material consisted of thirty fivegenotypes of chickpea grown in Rabi 2017-18 at fieldexperimentation centre of the Genetics and Plant Breeding,Naini Agricultural Institute, Sam Higginbottom Universityof Agriculture, Technology and Sciences, Allahabad. Theexperiment was laid out in a randomized complete blockdesign with three replications during Rabi 2017-18. Theplot size was 4.8 m2, with 1 row of 4.0 m length. Inter rowspacing distance was kept 30 cm and plant to plant spacingwas 30 x 10 cm. The recommended packages of practiceswere followed to raise a healthy crop. Data were recordedon ten quantitative traits viz. days to 50% flowering, daysto maturity, plant height (cm), number of primary branchesper plant, number of secondary branches per plant, numberof pods per plant, 100 seed weight (g), biological yield (g)and seed yield per plant (g).The days to 50% flowering,days to maturity, and seed yield per plant were recorded ona plot basis and plant height, number of primary branchesper plant, number of secondary branches per plant, number

of pods per plant, 100 seed weight (g), biological yield andharvest index were recorded from a random sample of fiveplants in each plot. Genetic divergence was estimated byusing D2 statistics of Mahalanobis (1936) and clustering ofgenotypes was done according to Tocher’s method. Theper cent contribution of characters towards geneticdivergence was calculated according to Singh andChaudhary (1985).

The analysis of variance revealed significantdifferences for all the ten characters studied (Table 1)indicating that significant amount of genetic variabilitypresent in the material. Estimates of genetic parameters ofvariability are presented in Table 2. Coefficient of variationat phenotypic and genotypic levels was observed relativelyhigh in for seed yield per plant, biological yield, Number ofpods per plant and secondary branches per plant indicatingthe presence of high amount of variation in these traits.The magnitude of PVC was higher than GVC for all thecharacters indicating the influence of environment of thesetraits. The highest heritability was observed in seeds perpod followed by pod length, days to 50% flowering, daysto first flowering, seed yield per plant and dry matters ofplant. These traits showed high habitability indicatingadditive gene effect. The genetic advance under selectionwas low. The present study for high habitability for thesecharacters was conformed to those observed by Kumar etal. (2017), Chandra (1968), Joshi (1972) and Indu (1985) indifferent chickpea trials. The highest heritability wasobserved for seed yield per plant, number of pods per plantand harvest index. High genetic advance was recorded bybiological yield. High heritability coupled with high geneticadvance were recorded for seed yield per plant, biologicalyield per plant, harvest index and number of pods per plantindicating that these characters were governed largelythrough the additive gene effect as reported by Kumar etal. (2017), Parshuram et al. (2003), Chavan (1994), Joshi(1972) and Chandra (1968). Asawa et al. (1977) also observedhigh genetic coefficient of variation in chickpea, which wasin conformity with the present study.

All the genotypes were grouped into six clusters asper Tocher’s method, with cluster VI containing themaximum of 10 genotypes followed by eight genotypes in

Table 1. Analysis of variance for ten quantitativecharacters in chickpea

*, ** = Significant at 5% and 1% levels of significance, respectively.

S. No.

Characters Mean sum of squares Replications

(df= 2) Treatments

(df= 34) Error

(df= 68) 1 Days to 50% flowering 1.03 19.14** 8.18 2 Days to maturity 0.88 19.57** 6.89 3 Plant height (cm) 1.18 187.27** 47.09 4 No. of primary

branches/plant 0.016 0.83** 0.23

5 No. of secondary branches/plant

1.39 13.69** 3.02

6 No. of pods/plant 103.50 350.00** 33.72 7 100-seed weight (g) 7.65 19.03** 9.31 8 Biological yield (g) 713.71 3022.48** 381.08 9 Seed yield per plant(g) 148.30 964.75** 65.25 10 Harvest Index (%) 97.64 358.35** 35.71

Table 2. Genetic parameters of variability in thirty five genotypes of chickpeaS. No.

Characters Mean Range σ²g σ²p GCV PCV Heritability (h2bs)

Genetic advance

GA as % of mean

1 Days to 50% flowering 71.15 66.00-75.66 3.65 11.84 2.69 4.84 31.00 2.19 3.07 2 Days to maturity 120.48 115.33-125.33 4.23 11.12 1.71 2.77 38.00 2.61 2.71 3 Plant height (cm) 52.34 34.46-68.46 46.73 93.82 13.06 18.50 50.00 9.94 18.99 4 No. of primary branches/plant 2.46 1.26-3.60 0.20 0.43 18.07 26.75 46.00 0.62 25.42 5 No. of secondary branches/plant 8.56 5.00- 13.26 3.56 6.58 22.2 35.08 54.00 2.86 33.50 6 No. of pods/plant 44.18 25.93- 66.66 105.43 139.15 23.24 26.69 76.00 18.41 41.66 7 100-seed weight (g) 20.42 17.33-27.66 3.24 12.55 8.81 17.34 26.00 1.88 9.22 8 Biological yield (g) 123.38 76.00-212.33 880.47 1261.55 23.99 28.72 70.00 51.07 41.29 9 Seed yield per plant (g) 62.97 36.33-125.33 299.83 365.09 27.50 30.43 82.00 32.33 51.33 10 Harvest Index (%) 53.99 32.81-76.47 107.54 143.26 19.21 22.17 75.00 18.51 34.28

Bapurao et al. : Genetic diversity studies in chickpea (Cicer arietinum L.) germplasm 263

cluster I, five genotypes in cluster II and V, 4 genotypes incluster IV and 3 genotypes in cluster III. (Table 3). It meansthe overall genetic similarity was found in the germplasmwere presented within the cluster and the pattern ofdistribution of genotypes in different clusters exhibited thatgeographical diversity was not related to genetic diversityas genotypes of same geographical region were groupedinto different cluster and vice-versa, as supported by earlierfinding of Lal et al. (2001), Raval et al. (2004),Paramesharappa et al. (2011), Parashi et al. (2013) andAgrawal et al. (2018). The possible reason for grouping ofgenotypes of different places into one cluster could be freeexchange of germplasm among the breeder of differentregion or unidirectional selection practiced by breeder intailoring the promising cultivar for selection of differentregion.

The intra-cluster D2 value ranged from 11.17 to 49.53while, inter-cluster D2 value ranged from 32.01 to 152.13(Table 4) in Tocher’s method. The highest intra-clusterdistance was exhibited by cluster IV (49.53) followed bycluster V (9.14) and cluster VI (28.29). The intra clusterdistance was maximum in cluster IV followed by cluster Vwhich indicated that hybridization involving genotypeswithin the same clusters may result in cross combinations.The highest inter-cluster distance was observed betweencluster II and V (152.13), followed by cluster IV and VI(146.93), cluster II and VI (143.60) and cluster III and V(139.92) suggesting that the genetic architecture of thegenotypes in one cluster differ entirely from those included

in other clusters. These lines may be utilized in furtherbreeding programme for the exploitation of hybrid vigourand suggesting wide diversity between them andgenotypes in these clusters could be used as parents inhybridization programme to develop desirable type becausecrosses between genetically divergent lines will generateheterotic segregants. As heterosis can be best exploitedand chances of getting transgressive segregants aremaximum when generating diverse lines are crossed (Kumaret al. 2018). Therefore, crosses between the members ofclusters separated by inter-cluster distances are likelyseemed to be beneficial for further improvement. Significantdifferences among the genotypes for different charactersindicated variations among the genotypes favorable fortheir use in the breeding programs. Crosses betweenparents with maximum divergence would be moreresponsive to improvement since they are likely to producehigher heterosis and desirable genetic recombination.

Cluster IV had exhibited highest cluster mean valuefor seed yield per plant and seed index. Cluster V has highestmean value for number of pods per plant, biological yieldper plant and days to maturity (Table 5). Cluster VI showedhigh mean value number of primary branches per plant andharvest index. Cluster III exhibited highest mean value fornumber of secondary branches per plant and lowest meanvalue for plant height and days to maturity. Among the tentraits studied, maximum contribution was made by 100-seedweight (51.18 %) (Table 6), followed by seed yield per plant(9.75%), biological yield (9.45%), number of pods per plant(9.24%) and harvest index (8.07%). These findings are inaccordance with the results of Kumar et al. (2018) andAgrawal et al. (2018). Therefore, these characters may begiven importance during hybridization programme. Thegenotypes BCG101, BCG708, PBC37, IC275313, Phule GVikram ICC144, IC275326, ICC3812, ICC303 and PKV4 wereidentified as genetically diverse parents, which can beutilized for future crop improvement programme in Chickpea.

Table 3. Distribution of thirty five chickpea genotypes in various clustersCluster No. of Genotypes Name of Genotypes

I 08 ICC807, PG739, BCG944, PG12310. BCH902, BDNGK798, VIRAT, PKV2 II 05 BCG101, BCG708, PBC37, IC275312, PhuleG VIKRAM III 03 IC275347, JAKI9218, SAKI9516 IV 04 KRIPA, VIJAY, IC275321, IC275322 V 05 ICC144, IC275326, ICC3812, ICC303, PKV 4 VI 10 PBC1103, IC275341, Digvijay, Vishal, IC275323, IC275340, IC275339, BDNG797, IC275329, IC275338

Table 4. Average intra and inter cluster D2 values amongsix clusters for thirty five genotypes of chickpea

Clusters I II III IV V VI I 27.84 32.01 44.05 79.60 120.50 115.95 II 11.17 48.56 129.53 152.13 143.60 III 11.32 123.26 139.92 124.91 IV 49.53 132.10 146.93 V 29.14 44.30 VI 28.29

Table 5. Mean values of clusters of different characters towards genetic divergence in thirty five chickpea genotypesCluster Days to 50%

flowering Days to

maturity Plant

Height (cm)

No. of primary

branches / plant

No. of secondary branches /

plant

No. of pods per

plant

100- seed weight

(g)

Biological yield/plant

(g)

Harvest index (%)

Seed yield per plant

(g)

I 70.62 120.95 51.42 2.48 7.78 46.34 21.41 116.45 54.13 60.66 II 69.13 120.40 63.33 1.98 6.28 42.38 19.33 103.20 49.55 50.86 II 72.66 118.22 40.00 2.73 11.44 33.31 18.77 111.55 39.89 40.22 IV 72.83 119.91 53.28 2.43 9.91 53.58 23.00 192.50 54.06 98.33 V 73.40 121.53 51.68 2.10 7.96 53.85 18.66 135.33 52.18 65.53 VI 70.33 120.53 51.47 2.80 9.11 37.04 20.63 110.00 61.02 62.26


Table 6. Contribution of different characters towardsgenetic divergence of thirty five chickpeagenotypes

SI.No. Source Contribution % 1 Days to 50% Flowering 1.21 2 Days to maturity 0.84 3 Plant height (cm) 2.02 4 No. of primary branches/plant 3.03 5 No. of secondary branches/plant 5.21 6 Number of pods/ plant 9.24 7 100 -seed weight (g) 51.18 8 Biological yield (g) 9.45 9 Harvest index (%) 8.07

10 Seed yield per plant (g) 9.75

The above results indicate that these genotypes havemaximum genetic diversity and useful for developing a largenumber of segregants through crossing programme byusing maximum diverse genotypes.

The genotypes BCG101, BCG708, PBC37, IC275313,Phule G Vikram ICC144, IC275326, ICC3812, ICC303 andPKV4 were identified as genetically diverse parents, whichcan be utilized for future crop improvement programme inChickpea. The above results indicate that these genotypeshave maximum genetic diversity and useful for developinga large number of segregants through crossing programmeby using maximum diverse genotypes.

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Journal of Food Legumes 31(4): 265, 2018

List of Referees for Vol. 31(4)

The Editorial Board gratefully acknowledges the help rendered by following referees in reviewing manuscripts for theVol. 31(4): 2018.

Dr. D. K. Agarawal, ICAR-IISS, Mau

Dr. C. S. Praharaj, ICAR-IIPR, Kanpur

Dr. A. K. Singh, ICAR-IIPR, Kanpur

Dr. Narendra Kumar, ICAR-IIPR, Kanpur

Dr. R.D.S. Yadav, NDUA&T, Ayodhya

Dr. R. K. Mishra, ICAR-IIPR, Kanpur

Dr. Lalit Kumar, ICAR-IIPR, Kanpur

Dr. C. L. Maurya, CSAUA&T, Kanpur

Dr. Vijay Laxmi, ICAR-IIPR, Kanpur

Dr. Yogesh Kumar, ICAR-IIPR, Kanpur

Dr. Abhishek Bohra, ICAR-IIPR, Kanpur

Dr. A. K. Parihar, ICAR-IIPR, Kanpur

Dr. Amrit Lamichaney, ICAR-IIPR, Kanpur

Dr. Vabhav Kumar, ICAR-IIPR, Kanpur

Dr. Shripad Bath, ICAR-IIPR, Kanpur

Dr. Debjyoti Sen Gupta, ICAR-IIPR, Kanpur

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