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PATRONS

EDITORIAL BOARDDr. Srinivasan Ancha, Principal Climate Change Specialist, Asian Development Bank, Manila, Philippines

Dr. M. Sankara Reddy, Professor, Dept. of Entomology and Plant Pathology, Auburn University, Alabama, U.S.A

Dr. A.T. Sadashiva, Principal Scientist & Head, Division of Vegetable Crops, Indian Institute of Horticultural Research, Bangalore

Dr. Meenu Srivastava, Professor, Dept. of Textiles and Apparel Designing, College of Home Science, Maharana Pratap University of Agriculture & Technology, Udaipur

Dr.S.R. Koteswara Rao, Dean of Student Affairs, ANGRAU, Guntur

Dr. T. Giridhar Krishna, Professor & Head, Dept. of Soil Science and Agricultural Chemistry, S.V. Agricultural College, ANGRAU, Tirupati

Dr. R.Sarada Jayalakshmi Devi, Professor & Head, Dept. of Plant Pathology, S.V. Agricultural College, ANGRAU, Tirupati

Dr. P. Sudhakar, Professor & Head, Dept. of Crop Physiology, S.V. Agricultural College, ANGRAU, Tirupati

Dr. Ch. V.V. Satyanarayana, University Head (Food Engineering), College of Food Science & Technology, ANGRAU, Bapatla

Dr. M.V. Ramana, Principal Scientist (Farm Mechanization), Regional Agricultural Research Station, ANGRAU, Tirupati

Dr. T. Neeraja, Professor & Head, Dept. of Resource Management and Consumer Sciences, College of Home Science, Guntur

Dr. K. Nirmal Ravi Kumar, Professor & Head, Dept. of Agricultural Economics, Agricultural College, ANGRAU, Mahanandi

ADVISORY BOARDDr. Suresh Babu, Head, Capacity Building, International Food Policy Research Institute, Washington, USA

Dr. Seri Intan Binti Mokthar, Director, Faculty of Agro- Based Industry, University of Malaysia, Kelantan

Dr. Ch. Srinivasa Rao, Director, National Academy of Agricultural Research Management, Hyderabad

Dr. Mahadev B. Chetti, Asst. Director General (HRD), Indian Council of Agricultural Research, New Delhi

Dr. Surinder Singh Kukal, Dean of Agriculture, Punjab Agricultural University, Ludhiana, Punjab

Dr. Y.G. Shadakshari, Director of Research, University of Agricultural Sciences, Bangalore

Dr. N. Trimurthulu, Principal Scientist (Bio-fertilizers) & Head, Agricultural Research Station, ANGRAU, Amaravati, Guntur

Dr. M.V. Ramana, Principal Scientist (Pulses), Regional Agricultural Research Station, ANGRAU, Guntur

Dr. K. Vijay Krishna Kumar, Senior Scientist (Pathology) & TS to Vice- Chancellor, Administrative Office, ANGRAU, Guntur

CHIEF PATRONDr. V. Damodara Naidu, Vice- Chancellor, ANGRAU, Guntur

Dr. J. Krishna Prasadji, Dean of Agriculture, ANGRAU, Guntur

Dr. D. Balaguravaiah, Dean of P.G. Studies, ANGRAU, Guntur

Dr. K. Yella Reddy, Dean of Agricultural Engineering and Technology, ANGRAU, G untur

Dr. L. Uma Devi, Dean of Home Science, ANGRAU, Guntur

Dr. N.V. Naidu, Director of Research, ANGRAU, Guntur

Dr. P. Rambabu, Director of Extension, ANGRAU, Guntur

CONTENTS

PART I: PLANT SCIENCES

Population fluctuation of natural enemies of stem borer, Chilo partellus (Swinhoe) in maize 1G.V. SUNEEL KUMAR, T. MADHUMATHI, D.V.SAIRAM KUMAR,V. MANOJ KUMAR and M. LAL AHAMAD

Effect of integrated nutrient management on nitrogen uptake in rice –no till 12jowar cropping sequenceB. MOUNIKA, CH. PULLA RAO, M. MARTIN LUTHER, P. R. K. PRASAD andY. ASHOKA RANI

Character association and path analysis for yield attributes in pigeonpea 20(Cajanus cajan (L.) Millsp.)V. GURUVENDRA REDDY, V. JAYALAKSHMI and T. SRINIVAS

Nutrient uptake and economics of cotton in high density planting system 26under varied plant spacing and nitrogen levelsB. DEVI, S. BHARATHI, M. SREE REKHA and K. JAYALALITHA

Impact of midseason drought on physiological and biochemical parameters in groundnut 30E. APARNA, Y. AMARAVATHI, R.P. VASANTHI, A.R. NIRMAL KUMAR andN.P. ESWARA REDDY

Performance of sorghum hybrids under different nitrogen levels in rice-fallow 40conditions of north coastal Andhra PradeshB. SRI SAI SIDDARTHA NAIK, K.V. RAMANA MURTHY, A.V. RAMANA andP. GURUMURTHY

Effect of spacing and nitrogen levels on productivity of pearlmillet in 48dryland regionsC. RADHA KUMARI, P.SHANTHI and B.SAHADEVA REDDY

Performance of maize-chickpea sequence at different sowing dates and 59nitrogen management practicesM.RATNAM, B.VENKATESWARLU, E.NARAYANA and A.LALITHA KUMARI

PART II: HORTICULTUREEffect of different shading intensities and genotypes on production of 67leafy coriander in off seasonC.SARADA, K.GIRIDHAR and L.NARAM NAIDU

PART III: SOCIAL SCIENCES

Adoption behaviour of groundnut farmers in Chittoor district of Andhra Pradesh 72P.BALAHUSSAIN REDDY, P.V.K. SASIDHAR and T.P.SASTRY

Impact of MGNREGA in terms of direct changes – A study in Srikakulam district 85of Andhra PradeshK.ARCHANA, P.RAMBABU, G. SIVA NARAYANA and D.V.S. RAO

Post Graduate Research vis-à-vis farmers’ needs – A study in the Farm University 92of Andhra PradeshT. SRINIVAS, T. V. SRIDHAR, P. PUNNA RAO, T.RAMESH BABU and P.SOWJANYA

PART IV: RESEARCH NOTES

Comparative economic efficiency of soybean processing units in Mandsaur 100district of Madhya PradeshSARMANLAL CHAUDARI, SEEMA and K.P. KULAKARNI

Effect of training programmes on knowledge levels of redgram and groundnut 105farmers in Prakasam district of Andhra PradeshO.SARADA

Information Communication Technology utilization pattern by university teachers 108ARPITA SHARMA

1

INTRODUCTION

Maize (Zea mays L.) is one of the mostimportant cereal crops next to wheat and rice.Theproduction of maize is constantly increasing becauseof the rising demand from the industries since maizeis used as raw material for extraction of edible oil,as feed for poultry birds and livestock, and also forstarch and glucose industry. Globally, India ranks5th in area, 4th in production and 3rd in productivity(Pal et al., 2009). Today, it is one of the importantcoarse cereal crops grown in different agro-climaticconditions in India. The multiple-pest complex ofmaize crop poses serious limitations in theintensification of maize cultivation in different agro-climatic regions of India. Although 139 insect pestscause varying degree of damage to maize crop, onlyabout a dozen of these are serious and require controlmeasures. Amongst maize stem borers, (Chilopartellus) (Swinhoe) occurs as serious pest in India.

POPULATION FLUCTUATION OF NATURAL ENEMIES OF STEM BORER,Chilo partellus IN MAIZE

G. V. SUNEEL KUMAR*, T. MADHUMATHI, D. V. SAIRAM KUMAR,V. MANOJ KUMAR and M. LAL AHAMAD

Agricultural Research Station, Darsi – 523 247

Date of Receipt: 18.12.2017 Date of Acceptance:22.01.2018

ABSTRACTExperiments were carried out during four seasons viz., Kharif, 2014, Rabi 2014-15, Kharif, 2015 and Rabi 2015-16 to

evaluate the population fluctuation of stem borer and natural enemies in maize crop. The highest stem borer infestation duringKharif season with mean population of 2.86 larvae / plant was recorded during 40th standard week coinciding with grain fillingstageof maize. While during Rabi season, peak larval population of C. partellus (2.42 larvae / plant) was noticed during 4th

standard week coinciding with flowering and fertilization (silking) stage of maize. Peak incidence of Coccinella transversalisFab. and Cheilomenus sexmaculata Fab. during Kharif season was noticed in 41st standard week (0.84 and 0.72 beetles perplant, respectively), while during Rabi, peak population of C. transversalis in 2nd and 3rd standard weeks (0.34 beetles per plant)and C. sexmaculata in 4th standard week (0.78 beetles / plant). Peak activity of spiders was found to follow the same trend duringkharif with 1.22 per plant in 41st standard week and 0.89 per plant during rabi in 4th standard week. Peak parasitism by Cotesiaflavipes was found during 40th standard week (25.12 %) in Kharif and 4th standard week (10.8%) in Rabi coinciding with peaklarval population of the maize stem borer. C. flavipes showed positive and significant correlation with larval population of C.partellus on average basis of both study years of Kharif (r = 0.940) and Rabi (r = 0.868) seasons.

*Correspondng Author E-mail: [email protected]; Part of Ph.D thesis submitted to ANGRAU, Guntur

J.Res. ANGRAU 46(1) 1-11, 2018

With the increasing resistance of many insectpest species to chemical insecticides and anincreasing organic food market, pest controlstrategies are slowly shifting towards moresustainable, ecologically sound and economicallyviable options. Biologically based pest managementstrategies present such opportunities throughpredation or parasitism of pests. Inevitably, theefficacy of biological control systems is highlydependent on natural enemy-prey interactions, whichwill likely be modified by changing climates.Environmental factors (e.g., temperature) directlyaffect the survival, development, reproduction anddispersal of pest insects and thus their potentialbiogeography and biotic potential (Southwood andHenderson, 2000). It is well known that temperaturefluctuations are the major factors affecting insectbiology, activity and distribution of natural enemiesin agro-ecosystems (Duale, 2005). Moreover, it isindicated that climate change affects several life

2

history parameters e.g., generation time, fecundity,sex ratios and lifespan of parasitoids and predators(Kalyebi et al., 2005). Weather factors may alsoinfluence parasitoid activity indirectly by inducingdiapause in the host larvae. Therefore, knowledge ofhow insect pests and their natural enemies respondto climate variation is of fundamental importance inunderstanding biological insect pest management.Keeping these in view, field and laboratoryexperiments were conducted to address theimportance and impact of weather on C. partellusincidence and interaction with natural enemies onmaize.

MATERIAL AND METHODS

The investigation was undertaken during fourseasons viz., kharif, 2014, rabi 2014-15, kharif, 2015and rabi 2015-16. The maize long duration hybrid,30V92 was sown with the spacing of 60 cm x 20 cmbetween row to row and plant to plant in the lastweek of July (30th standard week) during kharif , 2014and kharif , 2015 and in last week of November (48th

standard week) during rabi 2014-15 and 2015-16,respectively covering an area of 0.1 ha. All theagronomical practices were followed as per therecommendations of ANGRAU in raising the cropduring the experimental period.

The observations on population dynamics ofC. partellus larvae were recorded at weekly intervals.At each observation, 10 maize plants from each ofthe four corners leaving the boarder rows and another10 plants at the centre were chosen, uprooted anddissected to record the larval incidence ofC.partellus. Observations were taken from one weekafter seedling emergence i.e., 32nd standard weekduring kharif, 2014 and kharif ,2015 and continuedup to first week of November (45th standard week).During Rabi 2014-15 and Rabi 2015-16 observationsstarted from 50th standard week and was continuedtill the disappearance of the pest or harvest of thecrop in the field i.e., 11th standard week. Theobservations were also made for seasonal

occurrence of generalist predators in maizeecosystem viz., Coccinella transversalis Fabricius,Chilomenus sexmaculata Fabricius, and spiders,simultaneously.

The parasitic interactions with larvae ofC. partellus were also recorded at weekly intervals.To study the population of larval parasitoid, Cotesiaflavipes (Cameron) from larvae, the stem borerinfested maize plants were collected at weeklyintervals from the experimental field and then thelarvae were carefully isolated by dissecting the stems.The collected larvae were then reared on natural dietin the laboratory till adult emergence. The per centparasitism was worked out from the observationsmade on field collected larvae.

Correlation was worked out between larvalincidence of C. partellus and the parasitism byC. flavipes for four seasons of 2014-15 and 2015-16individually as well as on cumulative basis duringthe crop growth period from August to March. Theeffect of parasitism on the incidence of C. partelluson 30V92 maize hybrid was determined separatelyas well as on cumulative basis using multiple linearregression models equation of type 1 viz., Yi = a +b1x1 where incidence of C. partellus was taken asthe responsive variable (Y), 'a' is intercept, 'b' is slopeof regression and C. flavipes parasitism as predictorvariables to represent the equation.

RESULTS AND DISCUSSION

Larval incidence of stem borer, C. partellusin maize

The initial occurrence of C. partellus larvaeduring Kharif 2014 was noticed during 32nd standardweek (1st week of August, 2014) at 14 days aftercrop emergence (DAE) with 0.13 larvae / plant.Thereafter the larval population gradually increasedand reached the peak during 40th standard week (1st

week of October, 2014) at 70 DAE with a meannumber of 3.07 larvae / plant coinciding with grainfiling stag of maize.The prevailing maximum and

SUNEEL KUMAR et al.

3

minimum temperatures during the period were30.7 0 C and 22.5 0 C, while the average morning andevening relative humidty was 83.3 and 65.1 per cent,respectively. The peak larval incidence of C. partellusduring Kharif , 2015 was observed during 40th

standard week (1st week of October, 2015) at 70 DAEwith 2.64 larvae / plant (Table 1). During this periodthe average maximum and minimum temperatureswere 32.5 0 C and 23.9 0 C, while the averag morningand evening relative humidity was 80.6 % and66.9 %, respectively. The pooled data of Kharif, 2014and 2015 revealed that the peak stem borerinfestationwith mean population of 2.86 larvae/ plantwas recorded during 40th standard week (October1 st week). The larval incidence of C. partellus duringRabi 2014-15 reached to a peak by 5th standard week(1st week of February, 2015) at 63 DAE with a meannumber of 2.33 larvae / plant coinciding with floweringand fertilization (silking) stage of maize. During thisperiod the average maximum and minimumtemperatures were 29.80 C and 19.9 0 C, while theaveragde morning and evening relative humidity was74.4 and 43.7 percent, respectively. Thereafter theincidence has declined gradually to a minimum by11th standard week (2nd week of March, 2015). Thelarval population of C. partellus during Rabi 2015-16increased gradually and the peak incidence wasrecorded during 3rd standard week (3rd week ofJanuary, 2016) with a mean number of 2.73 larvae/plant coinciding with tasselling stage of maize (Table2).The prevailing conditions of this highest peak levelwere 31.80 C maximum, 18.4 0 C minimumtemperatures, 76.6 % morning and 45.2 % eveningrelative humidity and zero mm rainfall. Similar trendalso existed in pooled analysis of Rabi 2014-15 and2015-16, wherein peak larval population of C.partellus (2.42 larvae/ plant) was noticed during 4 thstandard week (January 4 th week) .

Population fluctuation of C. transversalis

The population of C. transversalis variedbetween 0.09 and 1.28 per plant with a peak

incidence during 37th standard week (2nd week ofSeptember) during Kharif, 2014. Further, thepopulation was higher during 38th to 41st and 36th

weeks compared to the population during rest of thestandard weeks. Densities of C. transversalisrecorded during Kharif, 2015 were relatively lowranged between 0.07 and 0.64 per plant with a peakincidence during 41st standard week (2nd week ofOctober, 2015). It was more during 33rd to 35th and37th to 40th weeks compared to the population duringrest of the standard weeks. The pooled abundanceof C. transversalis population varied from 0.08 to 0.84beetles per plant with peak incidence on 41st

standard week (Table 1).This can be attributed tothe availability of more prey during October. Further,the population was more during 35th to 40th weekscompared to rest of the standard weeks. Birader(2010) reported higher incidence of Coccinellids inlate sown maize crop and observed the populationfluctuation in relation to prey population.

Predatory activity of C. transversalis was firstfound at three weeks after crop emergence (52nd

standard week), but number was generally low earlyin the Rabi season of 2014-15. The peak occurrenceof beetles was recorded during 3rd standard weekand again on 8th standard week with 0.26 beetles /plant. During Rabi 2015-16, population ofC. transversalis was observed in the field from thefirst sampling date onwards with its population havingpeaked at 2nd standard week (0.5 beetles/plant). Thepooled population of C. transversalis during Rabi2014-15 and 2015-16 varied from 0.08 to 0.34 beetlesper plant with peak population on 2nd and 3rd standardweeks (Table 2).

Population fluctuation of C. sexmaculata

The variation of C. sexmaculata during Kharif2014 revealed that the predator beetle first observedin 34th standard week (3rd week of August, 2014) at28 DAE and was active up to 44th standard week (1st

week of November, 2014) with a peak population (0.46beetles / plant) noticed during 40th standard week

POPULATION FLUCTUATION OF NATURAL ENEMIES OF STEM BORER IN MAIZE

4

Tabl

e 1.

Inc

iden

ce o

f C. p

arte

llus

and

popu

latio

n of

pre

dato

rs a

nd p

aras

itoid

of C

. par

tellu

s in

mai

ze d

urin

g K

harif

201

4 an

d 20

15

3214

0.13

0.64

0.39

0.00

0.23

0.12

0.00

0.27

0.14

0.01

0.31

0.16

0.00

0.00

0.00

3321

0.80

0.76

0.78

0.49

0.34

0.42

0.00

0.41

0.21

0.24

0.41

0.33

0.00

0.00

0.00

3428

0.70

1.20

0.95

0.51

0.34

0.43

0.34

0.66

0.50

0.45

0.59

0.52

0.00

8.57

4.29

3535

1.27

1.16

1.22

0.91

0.40

0.66

0.21

0.53

0.37

0.98

0.60

0.79

10.7

110

.34

10.5

3

3642

1.37

1.36

1.37

1.13

0.29

0.71

0.31

0.60

0.46

0.94

0.83

0.89

17.2

44.

0810

.66

3749

1.13

1.57

1.35

1.28

0.31

0.80

0.34

0.53

0.44

0.85

0.84

0.85

20.5

97.

4114

.00

3856

1.43

1.84

1.64

1.12

0.33

0.73

0.26

0.80

0.53

0.84

0.79

0.82

24.0

07.

6915

.85

3963

2.40

2.36

2.38

1.06

0.40

0.73

0.18

0.81

0.50

0.75

0.71

0.73

26.9

212

.77

19.8

5

4070

3.07

2.64

2.86

1.16

0.41

0.79

0.46

0.56

0.51

0.99

0.91

0.95

36.6

713

.56

25.1

2

4177

2.83

2.24

2.54

1.03

0.64

0.84

0.43

1.01

0.72

1.13

1.30

1.22

32.4

311

.48

21.9

6

4284

2.47

1.68

2.08

0.72

0.16

0.44

0.41

0.41

0.41

0.81

0.61

0.71

28.1

39.

5218

.83

4391

1.67

2.04

1.86

0.55

0.19

0.37

0.29

0.37

0.33

0.64

0.39

0.52

20.0

012

.75

16.3

8

4498

1.33

1.76

1.55

0.32

0.16

0.24

0.05

0.19

0.12

0.49

0.24

0.37

17.9

514

.81

16.3

8

4510

50.

671.

701.

190.

090.

070.

080.

000.

070.

040.

180.

160.

1715

.18

11.1

113

.15

Mea

n1.

521.

641.

580.

740.

310.

530.

230.

520.

380.

660.

620.

6517

.84

8.86

13.3

6

SE0.

230.

160.

190.

110.

040.

070.

040.

070.

050.

090.

080.

083.

161.

252.

05

Kha

rif20

14K

harif

2015

Pool

edK

harif

2014

Kha

rif20

15Po

oled

Kha

rif20

14K

harif

2015

Pool

edK

harif

2014

Kha

rif20

15Po

oled

Kha

rif20

14K

harif

2015

Pool

ed

Day

s af

terc

rop

emer

genc

e(D

AE)

C. p

arte

llus

larv

ae(M

ean

No.

/ pl

ant)

Coc

cine

llatra

nsve

rsal

is(M

ean

No.

pla

nt-1)

Std.

Met

.W

eek

Chi

lom

enus

sexm

acul

ata

(Mea

nN

o. p

lant

-1)

Spid

ers

(Mea

n N

o. p

lant

-1)

% P

aras

itism

by

Cot

esia

flav

ipes

SUNEEL KUMAR et al.

5

(1st week of October, 2014). However, in Kharif 2015population of C. sexmaculata was noticed in the fieldright from the first observation at 14 DAE andcontinued throughout the season with a rangebetween 0.07 and 1.01 per plant. The initial peakpopulation was recorded in 39th standard week (4th

week of September, 2015) at 63 DAE with 0.81beetles per plant and the subsequent peak wasobserved in 41st standard week (2nd week of October,2015) with 1.01 beetles per plant. Pooled populationranged from 0.04 to 0.72 beetles per plant with peakincidence in 41st standard week (Table 1).

Population of C. sexmaculata was found fromthe first sampling date onwards, but their numberwas low (less than 0.2 per plant) early in the seasonduring Rabi 2014-15. Predator density increasedrapidly from 1st standard week (first week of January,2015) onwards, with a peak population of 0.55beetles/ plant recorded during 8th standard week (3rd

week of February, 2015) i.e. 84 DAE. During Rabi2015-16, a gradual increase in population ofC. sexmaculata was observed from first week ofobservation i.e. 50th standard week (2nd week ofDecember, 2015) to 3rd standard week (3rd week ofJanuary, 2016) and reached a peak population of 1.10per plant during 4th standard week (4th week ofJanuary, 2016). Thereafter, the population showed adeclining trend and reached to lowest by 11th

standard week (2nd week of March, 2016) with 0.06beetles per plant. The pooled abundance ofC. sexmaculata population was varied from 0.14 to0.78 beetles per plant with peak incidence on 4th

standard week. Further, the population was highestduring 1st to 3rd and 5th to 8th standard weeks comparedto the rest of the standard weeks (Table 2).

Population fluctuation of Spiders

Spiders were widespread in maizeexperimental fields and occurred throughout theseason. Identification up to genus level was doneand altogether three spider species viz., Araneussp., Neoscona sp., and Argiope sp. were found in

maize ecosystem at the experimental site, Araneussp. is the species being the most common. Althougha large number of spider species were found at thestudy site, the numbers of each species were verylow and hence they were recorded as single group.Average densities seldom surpassed 0.01 to 1.13spiders per plant during Kharif,2014 and maximumof 1.13 spiders per plant were found during 41st

standard week (2nd week of October, 2014). Theabundance of spiders increased in time during Kharif2015 and ranged from 0.31 to 0.84 per plant in thevegetative, 0.79 to 1.30 in the reproductive and 0.16to 0.61 in the mature plant growth stage with a peakactivity of 1.30 per plant during 41st standard week(2nd week of October, 2015). Pooled mean populationduring Kharif, 2014 and Kharif, 2015 ranged from0.16 to 1.22 spiders per plant with peak incidenceon 41st standard week (Table 1).

Average abundance of spiders varied from0.07 to 0.75 per plant, with a peak abundance during7th standard week (2nd week of February, 2015) duringrabi 2014-15. Spiders’ abundance increased with timefrom 14 to 56 DAE during rabi 2015-16 and washighest (1.12 per plant) in the 4th standard week (4th

week of January, 2016) during reproductive phase ofthe growing season when maize plants were attasselling stage. In pooled analysis of rabi 2014-15and 2015-16, spiders were active throughout theperiod, but peak activity was between 3rd to 7th

standard weeks with a range of 0.71 to 0.89 per plant(Table 2).

Predator communities were characterized bya rich variety of species, but predator numbers weregenerally low. The impact of predators was difficultto estimate in the study. Only few stem borer eggs,larvae or pupae were visibly preyed upon, butpredators may have had an impact by causingdisappearance. Disappearance was not estimatedin this study, but earlier workers showed that eggdisappearance ranged from 5 to 22% (Bonhof, 2000).The disappearance of young larvae can mainly be


6

Tabl

e 2.

Inci

denc

e of

C. p

arte

llus

and

popu

latio

n of

pre

dato

rs a

nd p

aras

itoid

of C

. par

tellu

s in

mai

ze d

urin

g R

abi 2

014-

15 a

nd 2

015-

16

5014

0.17

0.47

0.32

0.00

0.16

0.08

0.10

0.23

0.16

0.07

0.33

0.20

0.00

0.00

0.00

5121

0.47

0.57

0.52

0.00

0.41

0.21

0.16

0.51

0.34

0.20

0.63

0.41

0.00

0.00

0.00

5228

0.83

0.70

0.77

0.02

0.46

0.24

0.16

0.63

0.39

0.28

0.61

0.45

0.00

9.52

4.76

135

1.03

1.40

1.22

0.16

0.47

0.32

0.35

0.79

0.57

0.51

0.74

0.63

0.00

7.32

3.66

242

1.13

1.83

1.48

0.17

0.50

0.34

0.36

0.84

0.60

0.55

0.81

0.68

3.10

6.98

5.04

349

1.87

2.73

2.30

0.26

0.41

0.34

0.46

0.87

0.67

0.72

0.80

0.76

7.94

8.89

8.42

456

2.17

2.67

2.42

0.16

0.42

0.29

0.45

1.10

0.78

0.65

1.12

0.89

11.2

110

.42

10.8

2

563

2.33

2.30

2.31

0.20

0.31

0.26

0.44

0.84

0.64

0.69

0.95

0.82

11.9

28.

9310

.43

670

2.10

1.51

1.81

0.16

0.22

0.19

0.50

0.52

0.51

0.72

0.97

0.85

13.4

54.

298.

87

777

1.93

1.66

1.79

0.21

0.21

0.21

0.47

0.45

0.46

0.75

0.66

0.71

4.35

4.17

4.26

884

1.40

1.26

1.33

0.26

0.12

0.19

0.55

0.31

0.43

0.59

0.33

0.46

4.17

8.13

6.15

991

0.87

1.40

1.14

0.19

0.15

0.17

0.31

0.39

0.35

0.52

0.37

0.45

2.63

12.2

77.

45

1098

0.40

1.45

0.93

0.19

0.13

0.16

0.27

0.25

0.26

0.48

0.26

0.37

3.57

6.03

4.80

1110

51.

401.

341.

370.

170.

050.

110.

210.

060.

140.

390.

100.

255.

416.

255.

83

Mea

n1.

291.

521.

410.

150.

290.

220.

340.

560.

450.

510.

620.

574.

846.

665.

75

SE0.

190.

190.

180.

020.

040.

020.

040.

080.

050.

060.

080.

061.

240.

960.

89

Rab

i20

14R

abi

2015

Pool

edR

abi

2014

Rab

i20

15Po

oled

Rab

i20

14R

abi

2015

Pool

edR

abi

2014

Rab

i20

15Po

oled

Rab

i20

14R

abi

2015

Pool

ed

Day

s af

terc

rop

emer

genc

e(D

AE)

C. p

arte

llus

larv

ae(M

ean

No.

/ pl

ant)

Coc

cine

llatra

nsve

rsal

is(M

ean

No.

pla

nt-1)

Std.

Met

.W

eek

Chi

lom

enus

sexm

acul

ata

(Mea

n N

o. p

lant

-1)

Spid

ers

(Mea

n N

o. p

lant

-1)

% P

aras

itism

by

Cot

esia

flav

ipes

SUNEEL KUMAR et al.

7

Tabl

e 3.

Cor

rela

tion

betw

een

larv

al p

opul

atio

n of

C. p

arte

llus

and

para

sitis

m (%

) by

C. f

lavi

pes

Tabl

e 4.

Mul

tiple

line

ar re

gres

sion

equ

atio

ns b

etw

een

larv

al p

opul

atio

n of

C. p

arte

llus

and

para

sitis

m (%

) by

C. f

lavi

pes

Cor

rela

tion

Para

met

ers

Cor

rela

tion

coef

ficie

nt (r

) val

ues

Kha

rif 2

014

Kha

rif 2

015

Pool

edR

abi 2

014-

15R

abi 2

015-

16Po

oled

C. p

arte

llus

larv

ae v

s pa

rasi

tism

(%) b

y0.

907*

0.81

8*0.

940*

0.86

7*0.

568*

0.86

8*C

. fla

vipe

s

* S

igni

fican

t at P

< 0

.05

Year

Reg

ress

ion

Equa

tion

Stan

dard

Err

orC

oeffi

cien

t of

Det

erm

inat

ion

(R2 )

t- va

lue

f va

lue

Kha

rif 2

014

0.33

8 +

0.06

5*X 1

0.39

40.

813

7.18

251

.60

Kha

rif 2

015

0.74

3 +

0.10

7*X 1

0.35

00.

664

4.85

423

.55

Pool

ed0.

422

+ 0.

083*

X 10.

238

0.89

110

.110

102.

40

Rab

i 201

4-15

0.65

8 +

0.13

4* X

10.

378

0.74

65.

883

34.6

3

Rab

i 201

5-16

0.78

7 +

0.11

8* X

10.

591

0.32

62.

392

5.74

Pool

ed0.

401

+ 0.

179*

X1

0.34

10.

755

6.10

437

.30

X

1- P

aras

itism

(%) b

y C

. fla

vipe

s

*

= S

igni

fican

t at 5

% le

vel


8

attributed to dispersal, and subsequent mortality dueto desiccation, predation or inability to find a hostplant (Litsinger et al., 1991). Disappearance of lateinstar larvae is largely associated with dispersal andpredator activity and that of pupae with predatoractivity only (Bonhof, 2000).

Anecdotal observations and petri dishstudies indicated that coccinellids, earwigs,anthocorids, lacewings and ants were predators ofstem borer eggs. Spiders, earwigs, lacewings andants were also observed preying on small larvae(Dwumfour et al, 1991). Ants, earwigs and spidershave a high predation capacity (Bonhof, 2000) andare most important predator groups due to theirabundance in maize fields. Yogeswari (2015) reportedthe abundance of two species of coccinellids viz.,C.sexmaculata, Cycloneda sanguinea and fourspecies of spiders viz., Oxyopes spp., Argiopeanasuja (Thorell), Chrysilla sp., Oxyopes salticus insorghum and maize agroecosystem. Ants andspiders colonize maize fields early in the season,so they may have an impact on stem borerpopulations when plants are in their most vulnerablestage. Spiders, which feed almost exclusively oninsects, would have potential as biological controlagents, but little attention has been given to theirrole as pest regulators. Laboratory studies indicatedthat many spiders readily prey on small larvae offeredin petri dishes (Bonhof, 2000), and Tibellus sp.(Araneida: Philodromidae) also consumed eggs.Aphids (Rhopalosiphum maidis Fitch (Homoptera:Aphididae)) were common and present at the sametime as eggs and small larvae of C. partellus in thisstudy. The presence of alternative prey such asaphids may negatively influence predation of stemborers, especially when coccinellid predators preferthe more abundant and more accessible aphids thanthe more hidden eggs and small larvae of C. partellus.

Parasitism by Cotesia flavipes

Larval endoparasitoid, C. flavipes was moreactive (36.7% parasitization) during 40th standard

week followed by 41st standard week (32.4%) whileno activity was noticed from 32nd to 34th standardweeks during kharif, 2014. However, in kharif, 2015the maximum parasitic activity (14.8%) was noticedonly in 44th standard week. In pooled analysis ofkharif 2014 and 2015, C. flavipes activity was low inthe beginning of crop season at 14 to 28 DAE.Thereafter the parasitism was found to be increasedwith a peak activity recorded during the first (40th

standard week) and second (41st standard week)weeks of October (25.12 and 21.96%), respectivelycoinciding with peak larval population of the maizestem borer(Table 1).

Highest parasitization (13.5%) was recordedby the C. flavipes on C. partellus larvae during 6th

standard week followed by 5th standard week (11.9%)in Rabi 2014-15 on maize crop. However, in Rabi2015-16 season crop, peak parasitism of 10.4 percent was recorded initially in 4th standard week anda second peak was observed during the third weekof February, 2016 (12.3%) in 9th standard week. Inthe pooled analysis of both Rabi season study years,parasitization by C. flavipes was observed to be 10.8and 10.4 per cent during 4th and 5th standard weeks(Table 2).

Stem borer populations are also determinedby the mortality that occurs in the different life stages.In the study, parasitoids were also an importantmortality factor of stem borer larval stages. The resultof present study revealed that C. partellus populationwas relatively high in Kharif season than in Rabiseason crops. Hence, the parasitism was found tobe more in Kharif crop (25.1%) than Rabi seasoncrop (10.8%) and kept the pest infestations undercontrol which corroborates the findings with Jalaliand Singh (2002) who reported that larval populationand their natural enemies and pupal parasitism weresignificantly more in Kharif. Getu et al. (2003) reportedhigh percentage of parasitism of stem borers by C.flavipes in eastern Ethiopia than other surveyedregions, and the parasitism was significantly high.

SUNEEL KUMAR et al.

9

In India, Marulasiddesha (1999) reported that the percent parasitism of C. flavipes on the larvae of C.partellus is more in the month of October.Parasitization of C. partellus larvae by C. flavipesranged from 2.0 to 33.2 at Hissar and most activeduring third week of August to the first week ofSeptember (Mohan et al., 1991). Devi and Raj (1996)recorded 35 to 50 per cent parasitisation by Cotesiasp. on C. partellus larvae in maize ecosystem whichemerged up to the second week of October. Dualeand Nwanze (1997) also observed 13 per centparasitism by C. flavipes on C. partellus larvae.Highest parasitism (33.33%) by C. flavipes wasobserved on C. partellus larvae in sweet sorghum(Deepthi, 2007). Divya et al. (2009) also recordedmean parasitisation of C. partellus larvae (29%) byC. flavipes during Kharif season sorghum which isin close agreement with present study. Patel andPurohit (2012) reported that in Rabi season stemborer was parasitized by C. flavipes from third weekof November to fourth week of January withparasitism of 4.76 to 12.70 per cent. The larvaecollected from infested maize plants when reared inlaboratory, resulted in 5.0, 6.0, 23.0, 35.0 and 4.0per cent parasitization by C. flavipes at Ludhiana,Karnal, Hyderabad, Delhi and Kolhapur, respectively.The incidence of Cotesia was found to be minimum(3.03%) at 30 Days after germination (DAG) cropwhile maximum larvae were found parasitized(18.42%) at 50 DAG of maize crop (Yadav, 2015).

Relationship between larval population ofC. partellus and parasitism by C. flavipes

The results regarding correlation coefficientsbetween larval population of C. partellus andparasitism by its larval endoparasitoid, C. flavipeson maize crop are shown in Table 3. C. flavipesshowed positive and significant correlation with larvalpopulation of C. partellus during both study years ofkharif 2014 (r = 0.907); kharif 2015 (r = 0.818) aswell as on cumulative basis (r = 0.940) and rabi 2014-15 (r = 0.867), rabi 2015-16 (r = 0.568) and on

cumulative basis (r = 0.868). Patel et al. (2016) alsoreported that C. flavipes showed significant positivecorrelation with larval population of C. partellus inmaize during Kharif 2007 and 2008 in Gujarat.

The impact of parasitoid, C. flavipes on thelarvae of C. partellus by computing data into multiplelinear regression equations is given in Table 4. Themultiple linear regression equation for Kharif 2014was Y = 0.338 + 0.065*X1 indicating every unitincrease in C. partellus larval population increasedC. flavipes parasitism by 0.065 per cent with theinfluence to the extent of 81.3 per cent (R2 = 0.813).The multiple linear regression equation in Kharif 2015was Y = 0.743 + 0.107*X1 indicating an unit increasein C. partellus larval population, C. flavipesparasitism increased by 0.107 per cent, with thesignificant influence to the extent of 66.4 per cent(R2 = 0.664). The multiple linear regression equationfor the pooled data (Kharif 2014 and 2015) was Y =0.422 + 0.083*X1 which indicated an increase in C.partellus larval population, increased the C. flavipesparasitism by 0.083 per cent and the influence wasto an extent of 89.1 per cent (R2 = 0.891). DuringRabi 2014-15, the impact of parasitoid, C. flavipeson the larvae of C. partellus could be explained bymultiple linear regression equation Y = 0.658 + 0.134*X1 indicating every unit increase in C. partellus larvalpopulation increased C. flavipes parasitism by 0.134per cent with the influence to the extent of 74.6 percent (R2 = 0.746). The variability in C. partellus larvalpopulation due to parasitoid, C. flavipes during rabi2015-16 was best explained by multiple linearregression equation Y = 0.787 + 0.118*X1 indicatingan unit increase in C. partellus larval populationincreased C. flavipes parasitism by 0.118 per cent,with the significant influence to the extent of 32.6per cent (R2 = 0.326). The multiple linear regressionequation for the pooled data (rabi 2014-15 and 2015-16) was Y = 0.401 + 0.179*X1 which indicated anincrease in C. partellus larval population, increasedthe C. flavipes parasitism by 0.179 per cent and the


10

influence was to an extent of 75.5 per cent (R2 =0.755).

CONCLUSION

The study revealed the presence of diversifiedpredatory and parasitoid species associated withmaize crop. These natural enemies manifestedvariable seasonality trends which seem to beconnected with their preferable life stages of C.partellus abundance on the crop. The dominantoccurrence of C. flavipes all the year round maysuggests its potential role in combating C. partellus,and invites additional studies on this species to beexploited in proper way.

REFERENCES

Birader, S. R. 2010. Seasonal incidence andmanagement of insect pests in maize.M.Sc Thesis submitted to University ofAgricultural Sciences, Dharwad.

Bonhof, M. J. 2000. The impact of predators on maizestem borers in coastal Kenya. PhD Thesissubmitted to Wageningen University,Netherlands. pp. 181.

Deepthi, J. 2007. Studies on the management ofpests of sweet sorghum with specialreference to stem borer, Chilo partellus.M.Sc Thesis submitted to University ofAgricultural Sciences, Dharwad.

Devi, N and Raj, D. 1996. Extent of parasitization ofChilo partellus (Swinhoe) on maize byApanteles sp. in mid hill zone of HimachalPradesh (India). Journal of EntomologicalResearch. 30: 171-172.

Divya, K., Marulasiddesha, K.N., Krupanidhi, K andSankar, M. 2009. Population dynamics ofspotted stem borer, Chilo partellus(Swinhoe) and its interaction with naturalenemies in sorghum. Indian Journal ofScience and Technology. 3(1): 70-74.

Duale, A. H and Nwanze, K. F. 1997. Effect of plantresistance to insects on the effectiveness

of natural enemies. In: Plant resistance toinsects in sorghum (Sharma, H.C., FaujdarSingh and Nwanze, K.F., Eds.).International Crops Research Institute forthe Semi- Arid Tropics.Patancheru India.pp. 161-167.

Duale, A.H. 2005. Effect of temperature and relativehumidity on the biology of the stem borerparasitoid Pediobius furvus (Gahan)(Hymenoptera: Eulophidae) for themanagement of stem borers. EnvironmentalEntomology. 34: 1-5.

Dwumfour, E.F., Owino, J and Andere, M. 1991.Discovery capacity by parasitoids andpredators of Chilo partellus eggs. (ICIPE)19th Annual Report, 1991. ICIPE, Nairobi.pp. 23-24.

Getu, E., Overholt, W. A., Kairu, E and Omwega, C.O. 2003. Evidence of the establishment ofCotesia flavipes (Hymenoptera:Braconidae), a parasitoid of cerealstemborers, and its host range expansionin Ethiopia. Bulletin of EntomologicalResearch. 93: 125-129.

Jalali, S. K and Singh, S. P. 2002. Seasonal activityof stem borer and their natural enemies onfodder maize. Entomon. 27: 137-146.

Kalyebi, A., Sithanantham, S., Overholt, W.A.,Hassan, S.A and Mueke, J.M. 2005.Parasitism, longevity and progenyproduction of six indigenous Kenyantrichogrammatid egg parasitoids(Hymenoptera: Trichogrammatidae) atdifferent temperature and relative humidityregimes. Biocontrol Science andTechnology. 15: 255-270.

Litsinger, J. A., Hasse, V., Barrion, A.T andSchmutterer, H. 1991. Response of Ostriniafumacalis (Guenee) (Lepidoptera:Pyralidae) to intercropping. EnvironmentalEntomology. 20: 988-1004.

SUNEEL KUMAR et al.

11

Marulasiddesha, K. N. 1999. Bioecology of stemborer, Chilo partellus (swinhoe) and impactof its damage on juice quality of sweetsorghum. M. Sc. Thesis submitted toUniversity of Agricultural Sciences,Dharwad.

Mohan, B. R., Verma, A. N and Singh, S. P. 1991.Periodic parasitization of Chilo partellus(Swinhoe) larvae on forage sorghum inHaryana. Journal of Insect Science. 4: 162-169.

Pal, R., Singh, G., Prasad, C. S., Ali, N., Kumar, A.and Dhaka, S. S. 2009. Field evaluation ofbioagents against Chilo partellus (Swinhoe)in maize. Annual Review of Plant ProtectionSciences. 17 (2): 325-327.

Patel, D. R and Purohit, M. S. 2012. Seasonalabundance of parasitoids of stem borer,Chilo partellus on rabi sorghum. InsectEnvironment. 18 (1&2): 29-31.

Southwood, T.R.E and Henderson, P.A. 2000.Ecological Methods. Blackwell Science:Oxford, UK.pp.256-258.

Yadav, O. P. 2015. Director’s Review 2014-15, AICRPon Maize, IIMR, New Delhi .AnnualWorkshop held at PAU, Ludhiana, 4-6 April,2015.

Yogeswari, P. 2015. Performance of sorghumgenotypes in rice fallows under zero tillagecondition against shoot fly and stem borer.M.Sc Thesis submitted to Acharya N. G.Ranga Agricultural University, Guntur.


12

EFFECT OF INTEGRATED NUTRIENT MANAGEMENT ON NITROGEN UPTAKEIN RICE-NO TILL JOWAR CROPPING SEQUENCE

B. MOUNIKA*, CH. PULLA RAO, M. MARTIN LUTHER, P. R. K. PRASAD and Y. ASHOKA RANIDepartment of Agronomy, Agricultural College,

Acharya N.G. Ranga Agricultural University, Bapatla -522 101

Date of Receipt: 16.12.2017 Date of Acceptance: 19.01.2018

ABSTRACTThe experiment was conducted during Rabi for two consecutive years (2015-2016 and 2016-2017) on sandy clay

loam soils. The treatments consisted of different combinations of nitrogen during kharif preceding rice crop and three nitrogenlevels applied during rabi no till jowar crop. The experiment was laid out in a split plot design with thirteen main plots (precedingkharif rice) split into three sub plots (rabi no till jowar), thus, total 39 treatments and replicated thrice. Results indicated that all thecharacters studied were significantly higher with application of 100% RDN through inorganic fertilizer (T1) during kharif and 100%N level applied during rabi however it was on a par with that of application of 50% RDN+ 50 % Green manure (T12), 50% RDN +50% Poultry manure (T6) and 100 % N level during rabi in both the years of study.

J.Res. ANGRAU 46(1) 12-19, 2018

*Corresponding Author E-mail: [email protected]; Part of the Ph.D. Thesis submitted to ANGRAU, Guntur

INTRODUCTION

Jowar (Sorghum bicolor L. Moench) istraditionally grown as food crop in semi-arid tropicsof India which occupies an area of 6.07 M ha with aproductivity of 697 kg ha-1 and a total production of4.23 Mt (CMIE, 2016). In Andhra Pradesh, jowar isgrown in an area of 1.74 M ha with annual productionof 3.57 M t and productivity of 2051 kg ha-1 (CMIE,2016). In Krishna agroclimatic zone of AndhraPradesh, it is emerging as a potential alternate feed,fodder and bio-energy crop. Further, its tolerance tohigh temperature and drought makes it suitable fordry climatic condition. Previously, rice is succeededby blackgram which is slowly replaced by either jowaror maize due to severe Yellow Mosaic Virusinfestation to blackgram. Hence, farmers of thisregion are showing interest in jowar in view of its lowwater requirement and withstands to harsh climaticconditions. It is grown under rice-fallows covering anarea of 21,000 ha and yields 6.8 t ha-1 under zero tillconditions (Mishra et al., 2011).Zero tillage or reducedtillage is being followed widely for many crops aroundthe world and this technology has potential to allowsaving time, energy, water and labour in cropestablishment (Piggin et al., 2002). A no-till systemis a soil management technique that reduces soil

disturbance, increases soil organic matteraccumulation and can also increase crop yield(Bayer et al., 2000).


The experiment was conducted at AgriculturalCollege Farm, Bapatla which is situated at 150 54’ Nlatitude and 800 25’ E longitude, at an altitude of 5.49m above the MSL and is about 8 km away from theBay of Bengal. The chemical analysis of soil showedthat the soil is sandy clay loam in texture and is lowin available N, medium in P and high in OrganicCarbon and K. Average maximum and minimumtemperatures were 33.0°C and 22.1°C during 2015-16 and 33.2°C and 22.8°C during 2016-17,respectively.The weekly mean relative humidityranged from 71.1% to 82.9% during 2015-16 and69.5% to 79.3% during 2016-17, while the averagerelative humidity was 75.9% and 74.6% during 2015-16 and 2016-17, respectively. The crop was irrigatedas and when required. Overall, the weather conditionsprevailed during the growth period was normal andcongenial for optimum performance of sorghum crop.

The experiment was laid out during Rabi2015-16 and 2016-17 in split plot design with 13 maintreatments of different combinations of nitrogen during

13

Tabl

e 1.

Gra

in y

ield

(kg

ha-1) o

f no

till j

owar

as

influ

ence

d by

resi

dual

effe

ct o

f nitr

ogen

sou

rces

impo

sed

to k

harif

rice

in c

ombi

natio

n

w

ith n

itrog

en le

vels

app

lied

to ra

bi jo

war

RD

N: R

ecom

men

ded

dose

of N

itrog

en; F

YM

: Far

mya

rd M

anur

e; P

M: P

oultr

y M

anur

e; V

C: V

erm

i-com

post

; GM

: Gre

en m

anur

e

MOUNIKA et al.

T 1 :1

00%

RD

N25

9330

5935

9430

8226

6131

0039

9225

9326

2730

8037

9331

67

T 2 :7

5% R

DN

+25%

FY

M24

5528

4031

1428

0323

3227

3034

5724

5523

9427

8532

8628

22

T 3 :5

0% R

DN

+50%

FY

M23

3228

5332

3728

0823

7327

5733

6123

3223

5328

0532

9928

19

T 4 :2

5% R

DN

+75%

FY

M23

8725

5131

1426

8423

0526

6132

2423

8723

4626

0631

6927

07

T 5 :7

5% R

DN

+25%

PM

2236

2702

3251

2730

2250

2730

3443

2236

2243

2716

3347

2769

T 6 :5

0% R

DN

+50%

PM

2483

2990

3498

2990

2593

3059

3876

2483

2538

3025

3687

3105

T 7 :2

5% R

DN

+75%

PM

2332

2785

3224

2780

2401

2894

3512

2332

2367

2840

3368

2858

T 8 :75

% R

DN

+25%

VC

2277

2689

3169

2712

2346

2922

3402

2277

2312

2806

3286

2801

T 9 :5

0% R

DN

+50%

VC

2263

2785

3361

2803

2414

2785

3580

2263

2339

2785

3471

2865

T 10 :2

5% R

DN

+75%

VC

2401

2442

3196

2680

2414

2648

3251

2401

2408

2545

3224

2726

T 11 :7

5% R

DN

+25%

GM

2401

2538

3347

2762

2497

2894

3621

2401

2449

2716

3484

2883

T 12 :5

0% R

DN

+50%

GM

2538

2963

3539

3013

2620

3086

3978

2538

2579

3025

3759

3121

T 13 :2

5% R

DN

+75%

GM

2373

2648

3347

2789

2305

2840

3649

2373

2339

2744

3498

2860

Mea

n23

9027

5733

0724

2428

5435

7524

0728

0634

41

CDCD

CD(P

<0.0

5)CV

(%)

(P<0

.05)

CV (%

)(P

<0.0

5)CV

(%)

Trea

tmen

ts (T

)93

11.6

124

15.7

99.5

14.1

N le

vels

(N)

917.

810

89.

186

.410

.7

Nitr

ogen

sou

rces

2015

-16

Mea

n20

16-1

7

Mea

nN

itrog

en le

vel (

kg N

ha-1

)N

itrog

en le

vel (

kg N

ha-1

)0%

50%

100%

0%50

%10

0%0%

50%

100%

Pool

ed d

ata

Pool

edM

ean

14

kharif in a randomized block design and replicatedthrice (T1 :100 % RDN; T2 :75% RDN + 25% Farmyardmanure; T3 :50% RDN + 50% Farmyard manure; T4

:25% RDN + 75% Farmyard manure; T5 :75% RDN+ 25% Poultry manure; T6 :50% RDN + 50% Poultrymanure; T7 :25% RDN + 75% Poultry manure; T8

:75% RDN + 25% Vermicompost; T9 :50% RDN +50% Vermicompost; T10 :25% RDN + 75%Vermicompost; T11 :75% RDN + 25% Green manure;T12 :50% RDN + 50% Green manure and T13 :25%RDN + 75% Green manure) and three nitrogen levelsapplied during rabi no till jowar crop (0% N, 50% Nand 100% N). Recommended dose of Nitrogen (RDN)i.e. the inorganic nitrogen 120 kg N ha -1 was appliedthrough urea, one-third as basal at the time of sowing,remaining N is applied in two equal splits at activetillering and panicle initiation stages. The experimentwas repeated during 2nd year (2016-17) in anotherfield.

After harvesting the rice crop, next daybunds formation was done according to the rabilayout plan. Sowing of jowar was done manually byusing a seed rate of 10 kg ha-1 and adopting a spacingof 45 cm x 15 cm.Good and healthy seeds werehand dibbled into the soil at a depth of 2 cm- 3 cm toobtain optimum planting density. Nitrogen (100 kgha-1) was applied in the form of urea (46% N) in twoequal splits i.e., ½ at 10 DAS and remaining ½ at 30days after first application as per the treatments.Entire dose of phosphorus (60 kg ha-1) was appliedin form of Single Super Phosphate (16% P2O5) and40 kg K2O ha-1 was applied in the form of Murate ofPotash (60% K2O), at the time of 1st split applicationof urea. To maintain optimum plant population, gapfilling was done at 10 days after sowing (DAS) andthinning was done at 15 DAS. Timely weed control,irrigation and plant protection measures were takenup. The data on growth and yield attributes, grainand stover yield and nutrient uptake was analysedby adopting standard procedures.

RESULT AND DISCUSSION

Grain yield and Straw yield

Progressive and significant increase in grainyield (Table 1) was observed with each successiveincrement in nitrogen levels. Application of 100% Nlevel significantly enhanced the grain yield (3082 kgha-1, 3251 kg ha-1) over rest of the nitrogen levels(0% and 50%) in two successive years of study andpooled data analysis, as it might have promoted thegrowth of roots as well as functional activity resultingin higher extraction of nutrients from soil environmentto aerial plant parts. The improvement in yieldattributes with N consequently resulted in highergrain yield. The yield could be as a result of gooddry matter production for grain filling as a result ofgreater number of leaves. These results are inagreement with the findings of and Sareen andSharma (2010) who also reported similar increase ingrowth and yield of sorghum with increased nitrogenlevel. Increased stover yield was observed withincreasing levels of N, in both the years ofexperiment. Application of 100% N level (Table 2)significantly enhanced the stover yield (7293 kg ha-

1, 7645 kg ha-1) over rest of the nitrogen levels (0%and 50%). This could be ascribed to its positiveinfluence on both vegetative and reproductive growthof the crop which lead to increase in stover yield.Similar findings of response in rice fallow sorghumto higher nitrogen levels were reported by Sareenand Sharma (2010).

Nitrogen Uptake

During both the years of study and pooledanalysis, nitrogen (Table 3 and Table 4) uptake byno- till jowar followed the same trend as that of grainyield and significantly influenced by nitrogen sourcesand nitrogen levels during both the years of study,but there is a non- significant interaction.

Total nitrogen uptake by the crop recordedthe maximum content under 100% RDN (T1), which

EFFECT OF INM IN RICE-NO TILL JOWAR CROPPING SEQUENCE

15

Tabl

e 2.

Sto

ver y

ield

(kg

ha-1) o

f no

till j

owar

as

influ

ence

d by

resi

dual

effe

ct o

f nitr

ogen

sou

rces

impo

sed

to k

harif

rice

in

c

ombi

natio

n w

ith n

itrog

en le

vels

app

lied

to ra

bi jo

war

RD

N: R

ecom

men

ded

dose

of N

itrog

en; F

YM: F

arm

yard

Man

ure;

PM

: Pou

ltry

man

ure

VC: V

erm

icom

post

; GM

: Gre

en m

anur

e

MOUNIKA et al.

T 1 :1

00%

RD

N63

3773

5381

8972

9368

0476

6884

6476

4565

7175

1183

2774

69T 2

:75%

RD

N+2

5% F

YM

6077

6968

7942

6996

6310

7161

8148

7206

6194

7065

8045

7101

T 3 :5

0% R

DN

+50%

FY

M61

1871

3379

0170

5163

5172

1581

2172

2962

3571

7480

1171

40T 4

:25%

RD

N+7

5% F

YM

5926

7023

7984

6978

6173

7023

8038

7078

6050

7023

8011

7028

T 5 :7

5% R

DN

+25%

PM

6296

6941

7846

7028

6255

7394

8189

7279

6276

7168

8018

7154

T 6 :5

0% R

DN

+50%

PM

6228

7188

8052

7156

6667

7517

8313

7499

6448

7353

8183

7328

T 7 :2

5% R

DN

+75%

PM

6228

7147

7942

7106

6365

7353

8189

7302

6297

7250

8066

7204

T 8 :75

% R

DN

+25%

VC

6214

7257

7984

7151

6283

7449

8272

7334

6249

7353

8128

7243

T 9 :5

0% R

DN

+50%

VC

6091

7078

7956

7042

6365

7435

8134

7311

6228

7257

8045

7177

T 10 :2

5% R

DN

+75%

VC

6159

6831

7929

6973

6241

7119

8066

7142

6200

6975

7998

7058

T 11 :7

5% R

DN

+25%

GM

6145

6914

7956

7005

6324

7366

8231

7307

6235

7140

8094

7156

T 12 :5

0% R

DN

+50%

GM

6228

7421

8011

7220

6667

7613

8368

7549

6448

7517

8190

7385

T 13 :2

5% R

DN

+75%

GM

6063

6872

7970

6968

6447

7380

8148

7325

6255

7126

8059

7147

Mea

n61

6270

8779

7464

0473

6182

0662

8372

2480

90

CDCD

CD(P

<0.0

5)CV

(%)

(P<0

.05)

CV (%

)(P

<0.0

5)CV

(%)

Trea

tmen

ts (T

)13

910

.615

42.

714

6.5

6.65

N le

vels

(N)

158

5.3

573.

910

7.5

4.6

Nitr

ogen

sou

rces

2015

-16

Mea

n20

16-1

7

Mea

nN

itrog

en le

vel (

kg N

ha-1

)N

itrog

en le

vel (

kg N

ha-1

)0%

50%

100%

0%50

%10

0%0%

50%

100%

Pool

ed d

ata

Pool

edM

ean

16

Tab

le 3

. Nitr

ogen

upt

ake

(kg

ha-1) b

y gr

ain

of n

o til

l jow

ar a

t har

vest

as

influ

ence

d by

resi

dual

effe

ct o

f nitr

ogen

sou

rces

impo

sed

to

k

harif

ric

e in

com

bina

tion

with

nitr

ogen

leve

ls a

pplie

d to

rab

i jow

ar

RD

N: R

ecom

men

ded

dose

of N

itrog

en; F

YM: F

arm

yard

Man

ure;

PM

: Pou

ltry

man

ure

VC: V

erm

icom

post

; GM

: Gre

en m

anur

e


T 1 :1

00%

RD

N32

.341

.953

.242

.533

.144

.760

.446

.132

.743

.356

.844

.3

T 2 :7

5% R

DN

+25%

FY

M29

.636

.542

.736

.328

.737

.049

.438

.429

.236

.846

.137

.4

T 3 :5

0% R

DN

+50%

FY

M29

.036

.345

.036

.829

.337

.347

.438

.029

.236

.846

.237

.4

T 4 :2

5% R

DN

+75%

FY

M28

.532

.441

.034

.027

.835

.244

.535

.928

.233

.842

.835

.0

T 5 :7

5% R

DN

+25%

PM

27.0

34.7

44.0

35.2

27.5

37.3

47.6

37.5

27.3

36.0

45.8

36.4

T 6 :5

0% R

DN

+50%

PM

31.1

40.7

52.2

41.4

32.9

43.3

59.4

45.2

32.0

42.0

55.8

43.3

T 7 :2

5% R

DN

+75%

PM

27.8

35.4

43.4

35.6

29.5

38.9

49.0

39.1

28.7

37.2

46.2

37.4

T 8 :75

% R

DN

+25%

VC

27.3

34.3

43.7

35.1

28.9

39.6

47.9

38.8

28.1

37.0

45.8

37.0

T 9 :5

0% R

DN

+50%

VC

27.9

35.2

45.3

36.1

29.7

37.5

50.3

39.2

28.8

36.4

47.8

37.7

T 10 :2

5% R

DN

+75%

VC

28.9

30.9

42.7

34.2

29.4

35.1

44.6

36.4

29.2

33.0

43.7

35.3

T 11 :7

5% R

DN

+25%

GM

28.8

32.2

45.7

35.6

30.8

38.6

50.8

40.0

29.8

35.4

48.3

37.8

T 12 :5

0% R

DN

+50%

GM

32.0

39.9

51.4

41.1

33.4

42.9

59.0

45.1

32.7

41.4

55.2

43.1

T 13 :2

5% R

DN

+75%

GM

29.0

33.9

46.0

36.3

28.5

38.4

51.3

39.4

28.8

36.2

48.7

37.9

Mea

n29

.235

.745

.930

.038

.950

.929

.637

.348

.4

CDCV

(%)

CDCV

(%)

CDCV

(%)

(P<0

.05)

(P<0

.05)

(P<0

.05)

Trea

tmen

ts (T

)3.

26.

32.

27.

32.

76.

8

N le

vels

(N)

8.5

9.8

10.2

5.3

9.35

7.55

Nitr

ogen

sou

rces

2015

-16

Mea

n20

16-1

7

Mea

nN

itrog

en le

vel (

kg N

ha-1

)N

itrog

en le

vel (

kg N

ha-1

)0%

50%

100%

0%50

%10

0%0%

50%

100%

Pool

ed d

ata

Pool

edM

ean

17

Tabl

e 4.

Nitr

ogen

upt

ake

(kg

ha-1) b

y st

over

of n

o til

l jow

ar a

t har

vest

as

influ

ence

d by

resi

dual

effe

ct o

f nitr

ogen

sou

rces

impo

sed

to k

harif

rice

in c

ombi

natio

n w

ith n

itrog

en le

vels

app

lied

to ra

bi jo

war

RD

N: R

ecom

men

ded

dose

of N

itrog

en; F

YM: F

arm

yard

Man

ure;

PM

: Pou

ltry

man

ure

VC: V

erm

icom

post

; GM

: Gre

en m

anur

e

MOUNIKA et al.

T 1 :1

00%

RD

N19

.026

.435

.426

.922

.030

.838

.530

.420

.528

.637

.028

.7

T 2 :7

5% R

DN

+25%

FY

M14

.222

.232

.623

.015

.824

.235

.425

.115

.023

.234

.024

.1

T 3 :5

0% R

DN

+50%

FY

M14

.921

.332

.022

.716

.223

.134

.824

.715

.622

.233

.423

.7

T 4 :2

5% R

DN

+75%

FY

M12

.718

.829

.020

.214

.720

.131

.122

.013

.719

.530

.121

.1

T 5 :7

5% R

DN

+25%

PM

15.8

21.7

29.0

22.2

16.1

24.3

32.7

24.4

16.0

23.0

30.9

23.3

T 6 :5

0% R

DN

+50%

PM

18.1

25.0

34.0

25.7

20.0

27.1

36.9

28.0

19.1

26.1

35.5

26.9

T 7 :2

5% R

DN

+75%

PM

16.5

22.8

27.1

22.1

18.2

24.8

33.2

25.4

17.4

23.8

30.2

23.8

T 8 :75

% R

DN

+25%

VC

17.2

21.3

27.3

21.9

17.7

24.3

33.0

25.0

17.5

22.8

30.2

23.5

T 9 :5

0% R

DN

+50%

VC

15.7

22.0

30.2

22.6

17.3

24.7

32.7

24.9

16.5

23.4

31.5

23.8

T 10 :2

5% R

DN

+75%

VC

13.5

19.8

29.6

21.0

14.9

21.7

31.9

22.8

14.2

20.8

30.8

21.9

T 11 :7

5% R

DN

+25%

GM

15.4

22.9

29.5

22.6

17.3

25.7

33.5

25.5

16.4

24.3

31.5

24.1

T 12 :5

0% R

DN

+50%

GM

18.3

26.9

34.9

26.7

19.5

28.9

38.4

28.9

18.9

27.9

36.7

27.8

T 13 :2

5% R

DN

+75%

GM

14.4

21.1

29.7

21.7

16.8

23.7

32.6

24.4

15.6

22.4

31.2

23.1

Mea

n15

.822

.530

.817

.424

.934

.216

.623

.732

.5

CDCV

(%)

CDCV

(%)

CD(

CV (%

)(P

<0.0

5)(P

<0.0

5)P<

0.05

)

Trea

tmen

ts (T

)2.

94.

62.

38.

32.

656.

45

N le

vels

(N)

6.1

12.1

6.9

6.7

6.50

9.40

Nitr

ogen

sou

rces

2015

-16

Mea

n20

16-1

7

Mea

nN

itrog

en le

vel (

kg N

ha-1

)N

itrog

en le

vel (

kg N

ha-1

)0%

50%

100%

0%50

%10

0%0%

50%

100%

Pool

ed d

ata

Pool

edM

ean

18

was statistically on a par with 50% RDN+50% GM(T12) and 50% RDN+50% PM (T6), which might bedue to production of higher stover yield and highernutrient availability, residual effect of green manureand poultry manure during the next season. Thelowest N uptake was noticed in treatments 25%RDN+75% FYM (T4) and 25% RDN+75% VC (T10).Nitrogen uptake of a crop is the total of nitrogenabsorbed by grain and stover, which has increasedwith the age of the crop. The higher uptake at harvesteven with lower concentration of nitrogen in plantswas due to the substantial increase in biomassproduction. Adeniyanet al. (2011) reported thataddition of manures reduced the loss of nitratesthrough leaching from the soil by providing asignificant amount of plant nutrients which created abalancing effect on the supply of nitrogen.

Higher biomass production might be themost pertinent reason for the higher uptake ofnutrients in the treatments which received higherlevels of nitrogen. From 30 DAS, 60 DAS, 90 DASand at harvest (grain + straw), the highest nitrogenuptake was recorded with the application of highestlevel 100% N, which was significantly higher thanthat of 0% N and 50 % N level. These results are inclose conformity with the findings ofBalasubramanian and Ramamoorthy (1996) whonoticed in sweet sorghum that with increase innitrogen level from 0 kg N ha-1 to 120 kg N ha-1, grainyield and nitrogen uptake had increased and thehighest grain yield (3441 kg ha-1) was recorded at120 kg N ha-1 and Pushpendra Singh et al. (2012)observed that the Maximum grain yield (3915 kgha-1), straw yield ( 12,922 kg ha-1), biological yield(16,837 kg ha-1) and nitrogen uptake(89.3 kg ha-1) ofsorghum were recorded with the application of 120kg N ha-1 compared to lower levels (40 kg N ha-1 and80 kg N ha-1). Increased availability of nutrients alongwith drymatter might be the most pertinent reasonfor the increased uptake of nutrients in thetreatments with high levels of nitrogen. The inorganic

nitrogen not only provides an immediate source of Nfor plant growth but also enhances the mineralizationof applied as well as native organic matter by meetingthe N requirement of the decomposers. The resultsof the experiment support the findings of Jat et al.(2013) who reported that highest grain yield (4.18 tha-1) and stover yield (14.74 t ha-1) and nitrogenuptake (73.3 kg ha-1 ) was recorded by the treatmentthat received, 100% NPK.

CONCLUSION

Overall, the field studies conducted for twoconsecutive years clearly indicated that theapplication of 100% RDN through inorganic fertilizerwas remained on par with combined application ofinorganic and organic sources i.e. green manure andpoultry manure (@ 50% each) and these treatmentshad a significant influence in increasing productivity,profitability and high nutrient uptake in rice - no tilljowar sequence.

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Bayer, C.V., Mielniczuk, J., Amado, T. J .C., Neto,M. L and Fernandes S. V. 2000. Organicmatter storage in a sandy clay loam Acrisolaffected by tillage and cropping systemsin southern Brazil. Soil and TillageResearch. 54: 101–109.

Centre for Monitoring Indian Economy (CMIE). 2016.Commodities. Retrieved from website(www.http://commodities.cmie.com) on14.12.2017.

Jain, D., Rawat, A.K., Khare, A.K and Bhatnagar,R.K. 2003. Longterm effect of nutrientsources on Azotobacter, nitrifier populationand nitrification in Vertisols. Journal of the


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Indian Society of Soil Science. 51(1): 35–37.

Lingaraju, B.S., Marer, S.B and Chandra Sekhar, S.S.2010. Studies on intercropping of maize andpigeonpea under rainfed conditions inNorthern traditional zone of Karnataka.Karnataka Journal of Agricultural Sciences.21 (1): 1-3.

Mishra, J.S., Subbarayudu, B., Chapke, R.R andSeetharama, N. 2011. Evaluation ofSorghum (Sorghum bicolor) cultivars in rice(Oryza sativa) fallows under zero tillage.Indian Journal of Agricultural Sciences.81(3): 277-279.

Piggin, C.M., Gracia, C.O and Janiya, J.D. 2002.Establishment of irrigated rice under zeroand conventional tillage system in thePhilippines. In: Proceedings of International

Workshop on Herbicide ResistanceManagement and Zero Tillage in Rice-Wheat System held during March 4-6,2002,at Hisar, Haryana, India. pp.190-195.

Pushpendra Singh, Sumeriya, H.K and Solanki, N.S.2012. Effect of fertilizer on productivity andeconomics of elite sorghum genotypes.Madras Agricultural Journal. 99 (7-9): 567-569.

Sareen, H and Sharma, G.L. 2010. Effect of plantdensities and fertilizer levels on growth andN, P uptake by extra early sorghum. Annalsof Agricultural Research. 31 (1&2): 32-37.

Sumeriya. 2010. Influence of plant geometry andfertility levels on yield and nutrient uptakeand economics of sorghum. InternationalJournal of Tropical Agriculture. 28 (1-20):37-43.

MOUNIKA et al.

20

*Corresponding Author E-mail: [email protected]

INTRODUCTION

Pigeonpea [Cajanus cajan (L.) Millsp.] is animportant protein (20% - 22%) food legume of rainfedtropics and sub-tropics. In India, pigeonpea is secondmost important pulse crop.Pigeonpea has recordeda considerable increase in area and production,however, the crop productivity (average cropproductivity of 780 kg ha-1) needs furtherimprovement (Varshney et al., 2010). Improvementover existing varieties is a continuous process andthe increase in yield potential is the basic criteria inany crop improvement programme. Generally, directselection for yield is not aimed at, as it is a complexand quantitatively inherited character, highlyinfluenced by environment. As such, high genotypicand environmental interactions are likely to restrictthe improvement, if selection is based on yield as asimple trait. Therefore, correlation between seed yieldand yield components are of considerable importancein selection programmes(Gangadhar, 2013). Pathcoefficient analysis provides an effective means ofpartitioning the correlation into direct and indirecteffects (Renukadevi and Subbalakshmi, 2006).Correlation and path analysis thus help in identifyingsuitable selection criteria for improving the yield. Thestudy was undertaken in this context to elucidate

CHARACTER ASSOCIATION AND PATH ANALYSIS FOR YIELD ATTRIBUTES INPIGEONPEA (Cajanus cajan (L.) Millsp.)

V. GURUVENDRA REDDY, V. JAYALAKSHMI * and T. SRINIVASDepartment of Genetics and Plant Breeding, Agricultural College,Acharya N.G. Ranga Agricultural University, Mahanandi- 518 502


ABSTRACTCharacter associations and path coefficients among 47 pigeonpea genotypes were studied during kharif, 2013 for

seed yield and yield components.The results revealed that phenotypic and genotypic correlations are in similar direction andsignificant. Seed yield plant-1 was observed to be significantly and positively associated with days to maturity, plant height, no. ofprimary branches plant-1, no. of secondary branches plant-1 and no. of pods plant-1. The studies on path coefficients revealed highand positive direct effects of no. of pods plant-1, no. of secondary branches plant-1 and no. of primary branches plant-1 at bothphenotypic and genotypic levels on seed yield, indicating the importance of direct selection for these traits in pigeonpea yieldimprovement programmes.

J.Res. ANGRAU 46(1) 20-25, 2018

information on character association and pathcoefficient for seed yield and yield components inpigeonpea.


The study was conducted during kharif, 2013at Agricultural College Farm, Mahanandi which fallsunder the Scarce Rainfall Agro-Climatic Zone ofAndhra Pradesh situated at 15° 51’ N latitude, 78°61’ E longitude and altitude of 233.48m above MSL.Experimental material for present investigation wascomprised of 47 pigeonpea genotypes involving 22B-lines and 25 R-lines obtained from InternationalCrops Research Institute for the Semi-Arid Tropics(ICRISAT), Patancheru. The genotypes wereevaluated in a randomized complete block designwith two replications. Each genotype was raised infour-row plots of three m length with a spacing of120 cm x 30 cm. Border rows were also plantedaround the entries to increase the precision of studyand reduce border effect. All recommended packageof practices were followed for raising a healthy crop(ANGRAU, 2012). Observations were recorded onfive randomly selected plants in each replication forseed yield and yield components viz., plant height,no. of primary branches plant-1, no. of secondary

21

branches plant-1, no. of pods plant-1, no. of seedspod-1. Days to 50% flowering, days to maturity and100-seed weight were recorded on plot basis.Themean values were subjected to correlation analysisand path analysis (Dewey and Lu, 1959).


Correlations and path co-efficient analysisprovide a realistic basis for allocation of weightageto each of the contributing characters in decidingupon a suitable selection criterion for geneticimprovement of complex characters like yield. Hence,these parameters were studied in the investigationto assess the relationships among seed yield andits components for enhancing the usefulness ofselection.The results obtained on characterassociations for yield and yield components arepresented in Table 1. A perusal of the results revealedphenotypic and genotypic correlations to be in similardirection and significant in general. Further, thegenotypic correlations were noticed to be in generalhigher than phenotypic correlation values for almostall the characters, indicating the strong associationat genetic level and less influence by environment.

Seed yield plant-1 was observed to besignificantly and positively associated with days tomaturity (rp=0.2353* and rg=0.2827), plant height(rp=0.5069** and rg=0.5646), number of primarybranches plant-1 (rp=0.5366** and rg=0.5795), numberof secondary branches plant-1 (rp=0.6238** andrg=0.6627) and number of pods plant-1 (rp=0.7032**and rg=0.7097) indicating their importance asselection criteria in pigeonpea yield improvementprogrammes. The findings are in conformity with thereports of Techale et al. (2013) for seed yield plant-1

with days to 50 per cent flowering, days to maturity,pods plant-1, plant height; and Bhadru (2010) for seedyield plant-1 with no. of primary branches plant-1 andnumber of secondary branches plant-1. Associationof seed yield per plant with other characters, namely,days to 50 per cent flowering (rp= -0.0300 and rg= -0.0282), number of seeds per pod (rp=0.1144 and

rg=0.1015) and 100-seed weight (rp= -0.1210 and rg=-0.1504) was non-significant in the present study.These findings are in conformity with the reports ofSidhu et al. (1985) for days to 50 per cent flowering;Balyan and Sudhakar (1985) for number of seedspod-1; and Srinivas et al. (1999) for 100 seed weight.Studies on character associations among the yieldcomponents revealed significant and positiveassociation of days to 50 per cent flowering with daysto maturity (rp=0.5999** and rg=0.6354), plant height(rp=0.3367** and rg=0.3891), number of primarybranches plant-1 (rp=0.2931** and rg=0.3099) andnumber of secondary branches plant-1 (rp=0.2477**and rg=0.2784); days to maturity with plant height(rp=0.4235** and rg=0.4413), number of primarybranches plant-1 (rp=0.5248** and rg=0.5559), numberof secondary branches per plant (rp=0.5019**;rg=0.5442) and number of pods plant-1 (rp=0.3316**and rg=0.3588); plant height with number of primarybranches plant-1 (rp=0.5766** and rg=0.5852), numberof secondary branches plant-1 (rp=0.5732** andrg=0.6076) and number of pods plant-1 (rp=0.6536**and rg=0.6778); number of primary branches plant-

1with number of secondary branches plant-1

(rp=0.7859** and rg=0.8062) and number of podsplant-1 (rp=0.6097** and rg=0.6362); number ofsecondary branches plant-1with no. of pods plant-1

(rp=0.6832** and rg=0.7085); and number of seedspod-1 with 100 seed weight (rp=0.5467** and rg=0.5854)indicating the possibility of simultaneousimprovement of these characters through selection.However, negative inter-character associations wasobserved for number of primary branches with 100seed weight (rp= -0.2082* and rg= -0.2170); andnumber of pods plant-1 with 100 seed weight (rp= -0.3727** and rg= -0.3979), indicating competitionfor a common possibility, such as nutrient supplyand the need for balanced selection, while attemptingfor improvement of these traits. These results arealso in conformity with the findings of Salunke et al.(1995) for days to 50 per cent flowering with numberof secondary branches plant-1; Rama Devi (2012) for

GURUVENDRA REDDY et al.

22

Tabl

e 1

. Phe

noty

pic

and

geno

typi

c co

rrel

atio

ns fo

r yie

ld a

nd y

ield

com

pone

nts

in p

igeo

npea

* and

** S

igni

fican

t at 0

.05

and

0.01

leve

ls, r

espe

ctiv

ely;

Val

ues

in p

aren

thes

es a

re g

enot

ypic

cor

rela

tions

Day

s to

Day

s to

Plan

tN

o. o

fN

o. o

fN

o. o

fN

o. o

f10

0 se

ed50

%m

atur

ityhe

ight

prim

ary

seco

ndar

ypo

dsse

eds

wei

ght

flow

erin

g(c

m)

bran

ches

bran

ches

plan

t-1po

d-1(g

)pl

ant-1

pl

ant-1

Day

s to

50

% fl

ower

ing

0.59

99**

0.33

67**

0.29

31**

0.24

77*

0.03

420.

1864

0.15

25-0

.030

0(0

.635

4)(0

.389

1) (0

.309

9)(0

.278

4) (0

.057

4)(0

.218

0) (0

.133

8)(-0

.028

2)

Day

s to

mat

urity

0.42

35**

0.52

48**

0.50

19**

0.33

16**

-0.1

225

0.01

110.

2353

*(0

.441

3)(0

.555

9)(0

.544

2) (0

.358

8)(-0

.128

0) (0

.010

3) (0

.282

7)

Pla

nt h

eigh

t (cm

)0.

5766

**0.

5732

**0.

6536

**0.

1878

-0.0

622

0.50

69**

(0.5

852)

(0.6

076)

(0.6

772)

(0.1

834)

(-0.0

627)

(0.5

646)

No.

of p

rimar

y br

anch

es p

lant

-10.

7859

**0.

6097

**-0

.181

7-0

.208

2*0.

5366

**(0

.806

2) (0

.636

2)(-0

.217

1)(-0

.217

0)(0

.579

5)

No.

of s

econ

dary

bra

nche

s pl

ant-1

0.68

32**

-0.1

706

-0.1

798

0.62

38**

(0.7

085)

(-

0.20

30)

(-0.2

073)

(0.6

627)

No.

of p

ods

plan

t-1-0

.129

7-0

.372

7**

0.70

32**

(-0.1

508)

(-0.3

979)

(0.7

097)

No.

of s

eeds

pod

-10.

5467

**0.

1144

(0.5

854)

(0.1

015)

100

seed

wei

ght (

g)-0

.121

0(-0

.150

4)

Cha

ract

er

CHARACTER ASSOCIATION AND PATH ANALYSIS IN PIGEONPEA

23

days to 50 per cent flowering with plant height;Mahajan et al. (2007) for days to 50 per cent floweringwith primary branches plant-1; and Techale et al.(2013) for days to 50 per cent flowering with days tomaturity; and days to maturity with plant height,number of primary branches plant-1, number ofsecondary branches plant-1 and no. of pods plant-1;in addition to plant height with primary branchesplant-1, number of secondary branches plant-1andnumber of pods plant-1; Salunke et al. (1995) fornumber of primary branches plant-1 with number ofsecondary branches plant-1, and number of podsplant-1; Bhadru (2010) for number of secondarybranches plant-1 with number of pods plant-1; andSodavadiya et al. (2009) for number of seeds perpod-1 with 100 seed weight.

The Association of a character with yield orany other dependent variable may be found throughthe study of genetic correlations. Path coefficientpartitions the correlation into direct and indirecteffects. Hence, phenotypic and genotypic direct andindirect effects of yield components on seed yieldplant-1 for 47 pigeonpea genotypes in theinvestigation are arrived (Table 2). A perusal of theresults revealed genotypic and phenotypic path co-efficients are in similar direction and magnitude ingeneral. Further, the genotypic path co-efficients wereobserved to be of higher magnitude, compared tophenotypic path co-efficients.The results alsorevealed high residual effect for both phenotypic(0.6097) and genotypic (0.5499) path co-efficientsrespectively indicating that variables studied in thepresent investigation explained only about 40(phenotypic) and 45 (genotypic) per cent of thevariability in yield and therefore, other attributesbesides the characters studied are contributing forseed yield plant-1. A detailed analysis of the directand indirect effects also revealed high positive directeffect of number of pods plant-1 (PP= 0.4875 and Pg=0.2846) followed by number of secondary branchesplant-1 (Pp= 0.3115 and Pg= 0.3938) and number of

primary branches plant-1 (Pp= 0.1324 and Pg= 0.152).These traits, with the exception of number of seedsper pod had also exhibited highly significant andpositive associations (0.7032** and rg= 0.7097; rp =0.6238** and rg = 0.6627; and rp = 0.5366** and rg= 0.5795, respectively) with seed yield plant-1. Theseresults are in conformity with the findings ofSreelakshmi et al. (2011) for no. of pods plant-1andno. of secondary branches plant-1and Techale et al.(2013) for number of seeds pod -1. High direct effectsof these traits therefore appeared to be the mainfactor for their strong association with seed yield perplant. Hence, these traits should be considered asimportant selection criteria in all improvementprogrammes and direct selection for these traits isrecommended for yield improvement in pigeonpea.

High positive direct effect for number of seedspod -1 (Pp= 0.3182 and Pg= 0.4367) was also recordedin the investigation. However, its association withseed yield plant-1was noticed to be non-significant(0.1144 and rg= 0.1015), indicating the need foradoption of restricted simultaneous selection modelto nullify the undesirable indirect effects and makeuse of the direct effect. Negligible direct effects onseed yield plant-1 were recorded by days to maturity(Pp= 0.0562 and Pg= 0.1835) and plant height (Pp= -0.0722 and Pg= 0.0328). However, their associationwith seed yield plant-1 was observed to be significantand positive (rp= 0.2353* and rg= 0.2827; rp= 0.5069**and rg= 0.5646, respectively) indicating a major roleof indirect effects. In addition, days to 50 per centflowering (PP=-0.2314 and Pg=-0.4104) had recordedmore negative direct effects on seed yield plant-1.Association of this trait with seed yield plant-1 washowever, non-significant indicating the influence ofindirect effects of this trait, mostly through days tomaturity on seed yield plant-1. These results are inbroad agreement with the findings of Bhadru (2010)and Sreelakshmi et al. (2011) who reported thatmaximum direc effecs on seed yield was exhibitedby no. of primary branches plant-1 , days to 50%flowering and no. of pods plant-1 .


24

Tabl

e 2.

Phe

noty

pic

and

geno

typi

c pa

th c

oeffi

cien

ts fo

r yie

ld a

nd y

ield

com

pone

nts

in p

igeo

npea

* and

** S

igni

fican

t at 0

.05

and

0.01

leve

ls, r

espe

ctiv

ely;

Res

idua

l effe

ct=

0.60

97 (P

heno

typi

c ) a

nd 0

.549

9 (G

enot

ypic

) ; D

iago

nal v

alue

s= d

irect

effe

cts;

Off-

diag

onal

val

ues=

indi

rect

effe

cts;

Val

ues

in p

aran

thes

es a

re g

enot

ypic

effe

cts

Day

s to

Day

s to

Plan

tN

o. o

fN

o. o

fN

o. o

fN

o. o

f10

0 se

edSe

ed50

%m

atur

ityhe

ight

prim

ary

seco

ndar

ypo

dsse

eds

wei

ght

yiel

dflo

wer

ing

(cm

)br

anch

esbr

anch

espl

ant-1

pod-1

(g)

plan

t-1 (g

)pl

ant-1

pla

nt-1

Day

s to

50

%-0

.231

4-0

.138

8-0

.077

9-0

.067

8-0

.057

3-0

.007

9-0

.043

1-0

.035

3-0

.030

0flo

wer

ing

(-0.4

104)

(-0.2

608)

(-0.1

597)

(-0.1

272)

(-0.1

143)

(-0.0

235)

(-

0.08

95)

(-0.0

549)

(-0.0

282)

Day

s to

mat

urity

0.03

370.

0562

0.02

380.

0295

0.02

820.

0186

-0.0

069

0.00

060.

2353

*(0

.116

6) (0

.183

5) (0

.081

0)(0

.102

0) (0

.099

8) (0

.065

8)(-0

.023

5)(0

.001

9)(0

.282

7*)

Pla

nt h

eigh

t (cm

)-0

.024

3-0

.030

6-0

.072

2-0

.041

6-0

.041

4-0

.047

2-0

.013

60.

0045

0.50

69**

(0.0

128)

(0.0

145)

(0.0

328)

(0.0

192)

(0.0

199)

(0.0

222)

(0.0

060)

(-0.0

021)

(0.5

646*

*)

No.

of p

rimar

y0.

0388

0.06

950.

0764

0.13

240.

1041

0.08

07-0

.024

1-0

.027

60.

5366

**br

anch

es p

lant

-1(0

.048

1) (0

.086

3) (0

.090

8) (0

.155

2) (0

.125

1) (0

.098

7)(-0

.033

7)(-0

.033

7)(0

.579

5**)

No.

of s

econ

dary

0.07

720.

1563

0.17

860.

2448

0.31

150.

2128

-0.0

531

-0.0

560

0.62

38**

bran

ches

pla

nt-1

(0.1

096)

(0.2

143)

(0.2

392)

(0.3

175)

(0.3

938)

(0.2

790)

(-0.0

799)

(-0.0

816)

(0.6

627*

*)

No.

of p

ods

plan

t-10.

0167

0.16

170.

3186

0.29

720.

3331

0.48

75-0

.063

2-0

.181

70.

7032

**(0

.016

3)(0

.102

1)(0

.192

8)(0

.181

1)(0

.201

7)(0

.284

6)(-0

.042

9)(-0

.113

3)(0

.709

7**)

No.

of s

eeds

pod

-10.

0593

-0.0

390

0.05

97-0

.057

8-0

.054

3-0

.041

30.

3182

0.17

390.

1144

(0.0

952)

(-0.0

559)

(0.0

801)

(-0.0

948)

(-0.0

887)

(-0.0

659)

(0.4

367)

(0.2

556)

(0.1

015)

100

seed

wei

ght (

g)0.

0001

0.00

000.

0000

-0.0

001

-0.0

001

-0.0

002

0.00

030.

0005

-0.1

210

(-0.0

164)

(-0.0

013)

(0.0

077)

(0.0

266)

(0.0

254)

(0.0

487)

(-0.0

717)

(-0.1

224)

(-0.1

504)

Cha

ract

er

CHARACTER ASSOCIATION AND PATH ANALYSIS IN PIGEONPEA

25

CONCLUSION

The study revealed that direct selection forno. of pods plant-1 and number of branches plant-1 iseffective for yield improvement in pigeonpea.

REFERENCES

ANGRAU. 2012. Pigeonpea. In: Proceedings of ZonalResearch and Extension AdvisoryCouncil(ZREAC) Meeting of scarce rainfallzone held at RARS, Nandyal on April 3rd-4th, 2012.pp.28-31.

Balyan, H.S and Sudhakar, M.V. 1985. Variability,character association and path coefficientstudies on genotypes of early maturity groupin pigeonpea (Cajanus cajan (L.)Millsp.).Madras Agricultural Journal.72:168-12.

Bhadru, D. 2010. Studies on genetic parameters andinterrelationships among yield and yieldcontributing traits in pigeonpea (Cajanuscajan (L.) Millsp). Legume Research.33(1):23-27.

Dewey, D.R and Lu, K.H. 1959. A correlation andpath- coefficient analysis of components ofcrested wheatgrass seed production.Agronomy Joural. 51:515-518.

Gangadhar, D.S. 2013. Genetic variability, correlationand diversity analysis in pigeonpea. M.ScThesis submitted to Mahatma Phule KrishiVidyapeeth, Rahuri.

Mahajan, V., Shukla, S.K., Tiwari, V., Prasad, S.V.Sand Gupta, H.S. 2007. Path analysis inpigeonpea (Cajanus cajan (L.) Millsp) in midaltitudes of North-Western Himalayas. CropImprovement. 34 (1): 56-58.

Rama Devi, S., Prasanthi, L., Hari Prasad Reddy, Kand Bhaskara Reddy, B.V. 2012. Studies oninter-relationships of yield and its attributes

and path analysis in pigeonpea (Cajanuscajan (L.) Millsp.). Legume Research. 35(3):207-213.

Renukadevi, P and Subbalakshmi, B.2006.Correlations and path coefficient analysis inchickpea. Legume Research. 29(3): 201-204.

Salunke, J.S., Aher, R.P., Shinde, G.C and Kute,N.S. 1995. Correlation and path coefficientanalysis in early pigeonpea. LegumeResearch. 18 (3/4):162-166.

Sidhu, P.S., Verma, M.M., Cheema, H.S and Sra,S.S. 1985. Genetic relationships among yieldcomponents in pigeonpea. Indian Journal ofAgricultural Sciences. 55(4): 232-235.

Sodavadiya, P.R., Pithia, M.S., Savaliya, J.J.,Pansuriya, A.G and Korat, V.P. 2009. Studieson characters association and path analysisfor seed yield and its components inpigeonpea (Cajanus cajan (L.) Millsp).Legume Research. 32 (3): 203-205.

Sreelakshmi, Ch., Sameer Kumar, C.V and Shivani,D. 2011. Genetic analysis for yield and itscomponents in hybrid pigeonpea. ElectronicJournal of Plant Breeding. 2(3): 413-416.

Srinivas, T., Jain, K.C., Reddy, M.V and Reddy,M.S.S. 1999. Genetic relationships amongyield components in pigeonpea. IndianJournal of Pulses Research. 12(2): 180-186.

Techale, B., Habtamu, Z and Amsalu, A. 2013.Correlation and path analysis in pigeonpea(Cajanus cajan (L.) Millsp.). Indian Journalof Agricultural Research. 47(5):441-444.

Varshney, R.K., Penmetsa, R.V., Dutta, S., Kulwal,P.L., Saxena, R.K and Datta,S.2010.Pigeonpea genomics initiative: aninternational effort to improve crop productivityof pigeonpea. Molecular Breeding.26(3):393-408.


26

INTRODUCTION

Cotton crop is an important commercial cropgrown in vertisols under rainfed conditions of coastalAndhra Pradesh. It occupies an area of about 4.41lakh ha with an annual production of 13.1 lakh balesand productivity of 719 kg lint ha-1 (AICCIP, 2017).Cotton production is labour intensive in almost allthe developing countries. A novel way to avoid labourproblem is to go for mechanical harvesting. Cottonbeing indeterminate in growth habit, it is difficult toharvest the seed cotton in one time. However,research results suggest that by manipulating thecrop geometry especially by providing closer spacingone time harvest is possible and the yield reductionmay be compensated by increasing the plantpopulation by way of High Density Planting System(HDPS). The HDPS is now being conceived as analternate production system having a potential forimproving the productivity and profitability, enhancinginput use efficiency, reducing input costs andminimizing the risks associated with the currentcotton production system in India.Fertilizer,particularly nitrogen requirement is most likely to behigher under HDPS (Jost and Cothren, 2000).

Nitrogen is required for all stages of plantgrowth and development because it is the essential

NUTRIENT UPTAKE AND ECONOMICS OF COTTON IN HIGH DENSITYPLANTING SYSTEM UNDER VARIED PLANT SPACING AND NITROGEN LEVELS

B. DEVI, S. BHARATHI*, M. SREE REKHA and K. JAYALALITHADepartment of Agronomy, Agricultural College,

Acharya N.G. Ranga Agricultural University, Bapatla – 522 101


ABSTRACTThe experiment was conducted during Kharif, 2016 - 2017 to determine the optimum spacing and nitrogen in cotton

under high density planting system. A closer spacing of 60 cm × 10 cm gave significantly higher seed cotton yield (1666 kg ha-1)and maximum uptake of NPK and higher gross returns, net returns and benefit cost ratio (BCR) than the other spacings tested andit was on par with 75 cm × 10 cm. The highest seed cotton yield and maximum uptake of NPK and higher gross returns, net returnsand benefit- cost ratio was recorded with a nitrogen level of 150 kg N ha-1 (1620 kg ha-1) than other nitrogen levels tested.

J.Res. ANGRAU 46(1) 26-29, 2018


element of both structural and non structuralcomponents of the plant (Majid et al., 2011). Theinvestigation was therefore undertaken to study thenutrient uptake and economics of cotton under HDPSwith different spacing and nitrogen levels.


The experiment was conducted at AgriculturalCollege, Bapatla, during Kharif 2016 – 17. The soilof experimental field was black clay in texture,slightly alkaline in reaction (7.62), low in organiccarbon (0.4 %), low in available nitrogen (200.3 kgha-1), medium in available phosphorus (25 kg ha-1),medium in available potassium (250 kg ha-1). Theexperiment was laid out in split plot design andreplicated thrice with sixteen treatments comprisingof four spacings 60 cm × 10 cm (S1), 75 cm × 10 cm(S2), 90 cm × 10 cm (S3), 90 cm × 45 cm (S4) asmain plot treatments and four nitrogen levels 60 kgN ha-1 (N1), 90 kg N ha-1 (N2), 120 kg N ha-1 (N3), 150kg N ha-1 (N4) as sub plot treatments. Cotton varietySuraj was sown on 07.8.2016. Nitrogen was applied(in the form of urea) as per the treatments in threeequal splits at 30 DAS, 60 DAS and 90 DAS alongwith recommended dose of potassium and entirequantity of phosphorus was applied basally.Recommended cultural practices and plant protection

27

Tabl

e 1.

Nut

rient

con

tent

, nut

rient

upt

ake

and

econ

omic

s of

cot

ton

unde

r hig

h de

nsity

pla

ntin

g sy

stem

as

influ

ence

d by

var

ied

spa

cing

and

leve

ls o

f nitr

ogen

DEVI et al.

Seed

Stal

kco

tton

yiel

dyi

eld

(kg

ha-1)

(kg

ha-1)

Spac

ing

(cm

)N

PK

NP

K

S1-

60

cm ×

10

cm16

6671

180.

720.

220.

6265

.820

.056

.683

306

4975

11.

48

S2 –

75 c

m ×

10

cm15

5065

510.

710.

20.

660

.917

.952

.177

522

4409

91.

31

S3 –

90

cm ×

10

cm13

5659

140.

70.

190.

5952

.915

.044

.667

817

3448

61.

03

S4 –

90

cm ×

45

cm11

7354

540.

690.

180.

5749

.013

.040

.958

665

2567

80.

77

CD

@ 5

%17

4.0

420.

6N

SN

SN

S3.

51.

92.

6

N

itrog

en le

vels

(k

g ha

-1)

N1 -

60

kg N

ha-1

1253

5107

0.68

0.17

0.58

45.7

11.6

38.9

6267

029

894

0.91

N2 –

90

kg N

ha-1

1372

5674

0.7

0.19

0.59

52.8

14.7

44.7

6862

535

484

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6270

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28

measures were followed throughout the crop growingseason. Seed cotton yield and stalk yield andnutrient content in the plants, nutrient uptake by theplants and gross returns, net returns and benefit-cost ratio (BCR) were recorded.


The plant height, dry matter accumulationand number of bolls per square meter were maximumat closer spacing of 60 cm X 10 cm. Similarly,superior growth and yield attributes were recordedat 150 kg N ha-1. The seed cotton yield wassignificantly higher with closer spacing of 60 cm ×10 cm than other spacings tested due to morenumber of bolls per unit area and the stalk yield alsosignificantly higher with 60 cm × 10 cm due to moredry matter accumulation.Similarly,Venugopalan et al.(2014) reported that genotypes suraj, PKV 081, ADB39 and ADB 281 were promising interms of yiledmorphological features, earliness and nutrient useefficiency under HDPS(45 cm x 15 cm) under verticinceptisols of Nagpur. Regarding nitrogen,significantly superior seed cotton yield was recordedat 150 kg N ha-1 than other levels of nitrogen testeddue to its favourable effect on plant growth anddevelopment, which resulted in increased dry matteraccumulation and associated betterment in yieldattributing characters. Highest stalk yield wasrecorded at 150 kg N ha-1 (Table 1).

The nitrogen, phosphorus and potassium (N,P and K) content in the plants were not significantlyaffected by plant spacing and levels of nitrogen.Similar results were reported by Babaria et al. (2010)in Bt cotton with different fertilizer levels in southsourastra region. However, uptake of nutrients by theplants at harvest was influenced by varied spacingand levels of nitrogen tested, but their interactionswere non-significant. The maximum N, P and Kuptake by plants at harvest was recorded with closerspacing of 60 cm × 10 cm than other spacings tested.This might be due to higher dry matter accumulationper unit area which is a result of more number of

plants accommodated per unit area. Similarly,Devaraj et al. (2011) reported that maximum drymatter accumulation was recorded at coser spacingof 67.5 cm x 60 cm than wider spacing of 100 cm x45 cm and 100 cm x 60 cm at both thelocations(Sirsa and Hisar). The N, P and K uptakeby the plants was significantly higher with applicationof 150 kg N ha-1, may be due to favourable effect ofnitrogen on the growth and development of the plants(Table 1). Similar results of higher nutrient uptakeunder narrow geometry than wide geometry wasobserved by Dhillon et al.(2006) and Jithendra Singhet al.(2016). Higher gross returns, net returns andbenefit cost ratio (BCR) (1.48) was recorded at closerspacing of 60 cm × 10 cm, regarding nitrogen levelsthe gross returns, net returns and benefit-cost ratio(BCR) was significantly higher with application of 150kg N ha-1, similar results was observed by Divyaet al.(2016).

CONCLUSION

Seed cotton yield, stalk yield and nutrientuptake, gross returns, net returns and benefit-costratio was significantly higher at closer spacing of60 cm × 10 cm with application of 150 kg N ha-1.

REFERENCES

AICCIP.2017. Annual Report 2016-17. All IndiaCoordinated Cotton Improvement Project,Central Institute for Cotton Research,Nagpur.

Babaria , N.B., Shalini Kumari, Rajani, A.V andSakarvadia, H.L. 2010. Effect of balancedfertilization on yield, nutrient content anduptake in Bt cotton (Gossypium hirsutumL.) on south Saurashtra Region. In:Agriculture - Towards a New Paradigm ofSustainability (Mishra, G.C., Editor).Excellent Publishing House, New Delhi. pp.238 – 244.

Devaraj, Bhatto, M.S., Duhan,B.S., Promila Kumariand Jain, P.P. 2011. Effect of crop geometry

NUTRIENT UPTAKE AND ECONOMICS OF COTTON IN HIGH DENSITY PLANTING SYSTEM

29

and fertilizer levels on seed cotton yield andnutrient uptake of Bt cotton under irrigatedconditions. Journal of Cotton Research andDevelopment. 25(2): 176 – 180.

Dhillon, G.S., Chhabra, K.L and Punia, S.S. 2006.Effect of crop geometry and integratednutrient management on fibre quality andnutrient uptake by cotton crop. Journal ofCotton Research and Development. 20(2):221 – 223.

Divya, S., Saravanan, P., Kathiravan, J andVenkatesan, P. 2016. Effect of plantspacing on yield and economics of extralong staple (ELS) Bt cotton hybrids. Journalof Cotton Research and Development.30(2): 214 – 217.

Jitendra Singh, Patel, A.M., Rathore, B.S., ShaukatAli and Yadav, B.L. 2016. Nutrient uptakeand physico- chemical properties of soil asinfluenced by Bt cotton (Gossypium

hirsutum L.) based cropping systems underdifferent spacings. Advance ResearchJournal of Crop Improvement. 7(2): 180 –186.

Jost, P.H and Cothren, J.T. 2000. Growth and yieldcomparisions of cotton planted inconventional and ultra narrow row spacings.Crop Science. 40: 430 – 435.

Majid, R., Mohsen, S and Mohammad, G. 2011.Response of yield and yield componentsand fibre properties of cotton to differentapplication rates of nitrogen and boron.American – Eurasian Journal of Agricultureand Environmental Science. 10(4): 525 –531.

Venugopalan, M.V., Kranthi, K.R., Blaise, D.,Shubhangi Lakde and Sankaranarayana, K.2014. High density planting system incotton – The Brazil experience and Indianinitiatives. Cotton Research Journal. 5(2):172 – 185.

DEVI et al.

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INTRODUCTION

Groundnut (Arachis hypogaea L.) is animportant oilseed legume in the world. The leadinggroundnut producing countries are China, India,Nigeria, USA and Myanmar. India has beencontributing a significant share occupying first placewith a cultivated area of 5.8 million hectares andproduction of 6.8 million tonnes in India (FAO, 2016)and second place next to China in production (Janilaet al., 2016). Drought is one of the major abioticstress and groundnut yields suffers due to low, erraticand unassured rainfall. Yield losses due to moisturestress depend on crop growth stage, drought intensityand duration (Nigam et al., 2005). In the changingglobal climatic scenario, the yield losses can be morenoticeable in future due to vagaries of monsoons andfrequent occurrence of dry spells in crop growthperiod. Prolonged dry spell at critical crop growthstages such as flowering, peg formation/penetrationand pod filling stage commences in the mid-seasonfrom 50 to 80 DAS. Terminal drought occurred duringseed filling and maturity that affect seed quality, 100

IMPACT OF MIDSEASON DROUGHT ON PHYSIOLOGICAL AND BIOCHEMICALPARAMETERS IN GROUNDNUT

E. APARNA, Y. AMARAVATHI*, R. P. VASANTHI, A. R. NIRMAL KUMAR and N. P. ESWARA REDDYDepartment of Molecular Biology and Biotechnology, S. V. Agricultural College,

Acharya N.G. Ranga Agricultural University, Tirupati – 517 502


ABSTRACTDrought is one of the important abiotic stresses restricting the productivity of groundnut in rainfed situation by altering

physiological and biochemical parameters. The effect of transient water deficit stress on physiological and biochemical parameterslike SPAD Chlorophyll Meter Reading (SCMR), the Specific Leaf Area (SLA), proline content, catalase and peroxidase enzymeswas analysed during Kharif, 2016 at pegging and pod filling stages in four groundnut genotypes with different levels of droughttolerance under two water regimes viz., field capacity and mid-season drought. The results indicated that SCMR, proline content,catalase and peroxidase levels were increased and SLA decreased significantly in all genotypes at different levels of moisturestress condition when compared to control. Under prolonged drought stress, the increase in proline content is more predominantin drought sensitive genotypes like Kadiri6 and Narayani and is twice as that of drought tolerant genotypes TCGS 1157 and MLTG4. In contrast, catalase and peroxidase enzymes activity was found to be significantly high in drought tolerant genotypes than indrought sensitive genotypes.The genotypes with high SCMR, peroxidase and catalase activity and low SLA and proline contentcan tolerate moisture stress and can be used as a basis in development of groudnut genotypes for rainfed condition.

J.Res. ANGRAU 46(1) 30-39, 2018

seed weight, and increased levels of aflatoxincontamination (Girdthai et al., 2010).

To attain sustainable groundnut yields,development of genotypes with high water useefficiency and drought tolerance are highly essential.The genotypes with the ability to increase their rootlength and volume significantly contribute to droughtstress alleviation (Songsri et al., 2009). In additionto high level of drought tolerance, the genotypesshould maintain sustainable pod yield under moisturestress. SLA and SCMR have been used as surrogatetraits for water use efficiency (Sheshshayee et al.,2005) and are relatively stable across environments.Nigam et al. (2005) suggested that rapid progress indrought tolerance breeding can be achieved by usingphysiological traits such as SCMR and SLA.

At cellular level, a series of enzymatic andnon-enzymatic antioxidant systems get activated tocope with drought stress. Moisture stress induceschanges in osmolytes viz., proline accumulation andincreased expression of specific enzymes such ascatalase and peroxidase to avoid oxidative stress.


31

Proline accumulation is a common metabolicresponse of higher plants under water deficits (Leighet al., 1981). Tardieu (2014) reported that undermoisture stress situation, groundnut inducesstomatal closure as mechanism to reduce water loss.Stomatal closure decreases CO2 assimilation resultsin increased rate of photorespiration which inturn lossof chlorophyll and thereby resulted in decreasedphotosynthetic activity, which results in increasedrate of photorespiration and reactive oxygen species(ROS) were generated. The ROS were detoxified byincreased levels of catalase and peroxidase activity.Therefore, an understanding of traits associated withdrought tolerance such as large root system forincreased water uptake, maintain highphotosynthesis capacity, maintain plant waterpotential and to protect the cells from oxidative stressgreatly contribute to crop yield under water stress.The main objective of the experiment was to assessthe physiological and biochemical traits contributingto mid-season drought stress tolerance in groundnut.


The experiment was conducted using 11groundnut genotypes namely, TCGS 1073, TCGS1157, TCGS 1173, TCGS 1398, MLTG 4, TPT 1,Narayani, Kadiri 6, Kadiri 9, ICGV 0707 and ICGV07132 were screened for moisture stress toleranceunder two water regimes viz., field capacity andmoisture stress occured during Kharif, 2016. After50 DAS, water level at field capacity was maintainedthroughout the experiment for control, whereas, inmoisture stress imposition treatment, water waswithheld from 50 DAS (Puangbut et al., 2009). Amongthe 11 genotypes, MLTG 4 and TCGS 1157 weretolerant to moisture stress and in contrast Narayaniand Kadiri 6 were highly sensitive to moisture stress.The field experiments were carried out with thosefour genotypes at Regional Agricultural ResearchStation (RARS), Tirupati during Rabi 2016-17 (DOS:9.12.16) in randomized block design. There were twomain treatments viz., control (watered to fieldcapacity) and mid-season moisture stress (50-80

DAS).Control plot was irrigated to field capacity andin case of mid-season moisture stress imposed plot,irrigation was withheld from 50 DAS to 80 DAS andirrigation is resumed after 80 DAS till harvest.

Analysis of physiological and biochemicalparametersRoot length and root to shoot ratio

The root length and root to shoot ratio were recordedin both watered and respective drought stressimposed genotypes at the time of final harvestaccording to the method of Bohm (1979). Formeasurement of total root length, the roots weretraced up to the bottom by using a backhoe.Groundnut plants were dug out along with huge massof soil and washed with tap. The root system wasplaced on a clear plastic sheet and the total rootlength was determined. To determine root/ shootratio, root dry matter obtained was divided by theabove ground dry matter.

Specific Leaf Area (SLA)

Specific leaf area was calculated by dividingtotal leaf area by total leaf weight according to theprocedure of Kalariya et al. (2015). Three tetra foliateleaves (3rd fully expanded leaf from the top on themain axis for uniformity) were collected for calculatingthe SLA for each replication in the morning hours(9:00 to 9:30 am) at 60 DAS, 70 DAS and 80 DAS inboth stressed and respective controls. These leaveswere cleaned and their leaf area was estimated usinga leaf area meter (LI 3100C Area meter, LI COR.,USA). The samples were dried in a hot air oven at80oC untill constant weight was achieved and leafdry weight was determined by using electronicallyoperated analytical weighing balance (Shimadzu,Japan) with accuracy of 0.01 mg.

SPAD Chlorophyll Meter Reading (SCMR)

The SCMR was measured according to theprocedure of Kalariya et al. (2015). From eachgenotype, third leaf from the terminal bud of mainaxis (with four leaflets) was used for the measurement

APARNA et al.

32

of SCMR in three replications by handheld portableMinolta SCMR meter (SPAD-502 Minolta, Tokyo,Japan).

Estimation of Leaf Proline Content

Proline content of the leaf was estimatedby modified method of Bates et al. (1973) at 10, 20and 30 days after stress imposition. Proline contentwas estimated by OD value at 520 nm using UV2450 visible spectrophotometer. The standard graphwas prepared by using different concentrations ofproline. The Leaf proline content was calculated usingthe following formula and expressed as µg g-1 of leafsample.

Estimation of Peroxidase activity

Peroxidase activity was estimated byfollowing the procedure of Angelini et al. (1990) withslight modifications. Sample absorbances wererecorded at 436 nm in a time difference of one minute

using UV 2450 visible spectrophotometer. ThePeroxidase activity was calculated in min-1 g-1 usingthe following formula

Peroxidase activity = change in OD value/minute/gram of leaf sample

Estimation of Catalase activity

Catalase activity was assayed by followingthe procedure of Aebi (1974) with slight modificationsat 10, 20 and 30 days after stress imposition. Sampleabsorbances were recorded at 240 nm using UV 2450visible spectrophotometer. The Catalase activity wascalculated in units min-1 g-1 using the following formulaCatalase activity = change in OD value/minute/gramof leaf sample

Statistical analyses

All the mean values of the physiologicalparameters and biochemical parameters wereanalysed using SPSS trial version 2.0.


Root length varied among the genotypes andincreased under various moisture regimes (Table 1).The drought sensitive genotypes (Kadiri 6 andNarayani) have short root length than the droughttolerant genotypes (MMLTG 4 and TCGS 1157). Theaverage root length and root: shoot ratio of groundnutgenotypes were increased after 30 days of waterstress (50-80 DAS) in all the genotypes whencompared to control. Drought tolerant genotypes likeTCGS1157 (16.0 cm), MLTG4 (16.0 cm), displayedbetter ability to expand their roots than droughtsusceptible genotypes Kadiri6 (14.8cm) andNarayani (15.2cm) (Table 1). These genotypes alsoshowed increase in total root length in drought regime

as compared to non-stressed control, however, thisincrease in root length was non-significant. The extentand the pattern of root development were closelyrelated to the ability of the plants to absorb water,enhancement of root growth under drought conditionsallows the plant to extract more water from deeperzones may contribute to the drought tolerance ingroundnut (Madhusudhan and Sudhakar, 2014).Higher root/shoot ratios were obtained in moisturestress regime and increased with the increase inthe intensity of drought stress when compared tocontrol. Among genotypes, TCGS 1157 showedhighest root/shoot ratio followed by MLTG 4 (Table2). Paramasivam et al. (2014) stated that theimposition of drought stress in sunflower genotypesinhibited the shoot growth significantly but the rootlength increased to a larger extent.

tissproline/g of molesµ

Where, 115.5 is the molecular weight of proline

IMPACT OF MIDSEASON DROUGHT IN GROUNDNUT

33

Table 1. Root length of groundnut genotypes estimated in well watered (Control) and respective moisture stress imposed condition at the time of harvest

Root length

S.No Genotypes Control (cm) Moisture Stress (cm)

Mean CV Mean CV

1 MLTG 4 13.5±1.1 8.1 16.0±1.5 9.3

2 TCGS 1157 13.4±1.2 8.9 16.0±2.5 15.6

3 Narayani 12.0±0.8 6.9 15.2±1.1 7.2

4 Kadiri 6 12.6±1.1 8.7 14.8±1.3 8.7

Table 2. Root shoot ratio of groundnut genotypes estimated in well watered (Control) andrespective, moisture stress imposed condition at the time of harvest

S.No Genotypes Root shoot ratio

Control Stressed

1 MLTG 4 0.34 0.61

2 TCGS 1157 0.33 0.67

3 Narayani 0.36 0.55

4 Kadiri 6 0.38 0.50

APARNA et al.

Table 3. SPAD chlorophyll meter reading of groundnut genotypes estimated in well watered (Control) and respective moisture stress imposed condition at 60 DAS, 70 DAS and 80 DAS

SPAD at 50DAS (before SPAD at 60 DAS SPAD at 70 DAS SPAD at 80 DAS

S.No Genotypes impositionof moisture

stress Control Stressed Control Stressed Control Stressed

1 MLTG 4 41.2±0.4 46.5±1.1 48.2±1.6 47.7±0.5 49.1±1.5 49.2±0.1 50.1±1.4

2 TCGS 1157 44.0±2.3 44.6±0.7 41.8±1.9 45.3±1.4 41.8±0.9 47.1±0.8 45.9±1.5

3 Narayani 28.5±1.8 38.0±3.0 40.8±0.5 40.9±0.6 40.8±0.5 40.4±1.3 42.2±0.1

4 Kadiri 6 33.1±0.7 38.2±1.9 41.8±1.0 37.6±0.8 38.4±0.7 39.8±0.9 39.2±0.6

Mean 36.7 41.79 43.13** 42.89 42.52** 44.125* 45.35**

C.V 2.54 9.457 14.04

** Significant at 1% level of significance

34

The groundnut genotypes displayedsignificant differences in SCMR over different periodsof moisture stress. Before moisture stress impositionthe SCMR values were regarded in drought tolerantgenotypes like MLTG4 (41.2) and TCGS1157 (44.0)and in drought susceptible genotypes Kadiri 6 (28.5)and Narayani (43.3) (Table 3). The SCMR values havecontinuously increased upto 80 DAS after stressimposition regarded 50.1 and 45.9 (MLTG4 andTCGS1157) in drought tolerant genotypes and 39.2and 42.2 in drought susceptible genotypes (Kadiri 6and Narayani).Similar to these results,results, Jongrungklang et al. (2008) found thatdrought significantly increased SCMR value. Leafphotosynthesis is generally correlated withchlorophyll content per unit leaf area and hence theSPAD chlorophyll meter can provide a useful tool toscreen for genotypic variation in potentialphotosynthetic capacity under drought conditions(Nageswara Rao et al., 2001; Songsri et al., 2009).At cellular level, the increase in SCMR might be dueto reduction SLA and RWC and thereby thechloroplast density per unit leaf area may beincreased under moisture stress conditions. Atphysiological level, high SCMR under stress is dueto less reduction in leaf water potential (Talwar etal., 2004).

Low SLA is preferable as it indicates higherdrought resistance (Chakraborty et al., 2015).Drought stress significantly reduced speciûc leaf area(SLA) in all the groundnut genotypes under moisturestress at different time intervals ranging from 60 DASto 80 DAS, whereas, a continuous increase in SLAunder control condition. At 30 days after stressimposition 19% reduction in SLA values wereregarded in drought stress imposed genotypes. Incase of TGCS 1157 and MLTG 4 genotypes recorded23% decreased in SLA at 30 days after stressimposition when compared with respective control.Drought stress significantly reduced speciûc leaf area(SLA) in all the groundnut genotypes under moisturestress at different time intervals ranging from 60 DAS

to 80 DAS, whereas, a continuous increase in SLAunder controlled condition (Table 4). Based on theSLA values of the study, drought tolerant groundnutgenotypes (TCGS 1157 and MLTG 4) can be clearlydistinguished from the drought susceptible genotypes(Kadiri 6 and Narayani). Low SLA indicated thickerleaves and could be used as an economicallysurrogate trait for drought resistance. Similar resultswere reported by Chakraborty et al. (2015) whoobserved that the imposition of water stressdecreased SLA signiûcantly at both pegging and poddevelopment stages and also the drought tolerantgenotypes displayed more reduction in SLA thandrought susceptible genotypes. Under rainfedsituation, genotypes with low SLA and high SCMRvalues can be suggested without much reduction inyield even though the crop experienced dry spellsduring crop growth period. In addition, SCMR can beused as non-destructive measure to estimatechlorophyll density while screening groundnutgenotypes for drought tolerance (Arunyanark et al.,2009).However, Nageswara Rao et al. (2001)suggested that if SLA is to be used as a screeningtool, then sampling should be performed on clear(full sunlight) days. Under high-radiation condition,variation in SLA should be largely driven byphotosynthetic capacity. Thus, genotypic differencesin SLA as a consequence of photosynthetic capacitymay be better expressed on days with high radiation.It could be hypothesized that peanut genotypes withlow SLA have more photosynthetic machinery perunit leaf area and hence potential for greaterassimilation under drought stress because thickerleaves usually have a greater photosynthetic capacitycompared with thinner leaves.

In drought tolerant genotypes viz., MLTG 4and TCGS 1175, at 10 days after moisture stressimposition the proline content was recorded as 136.3µg g-1and 168.3 µg g-1, respectively. In droughtsusceptible genotypes viz., Kadiri 6 and Narayani,at 10 days after stress imposition the accumulationof proline content was 134 µg g-1 and 211µg g-1,


35

Table 4. Specific Leaf Area (cm2 g-1) of groundnut genotypes estimated in Control and respective moisture stress imposed condition at 60 DAS, 70 DAS and 80 DAS

SPAD at 50DAS (before SPAD at 60 DAS SPAD at 70 DAS SPAD at 80 DAS

S.No Genotypes impositionof moisture

stress Control Stressed Control Stressed Control Stressed

1 MLTG 4 118.3±4.1 128.3±0.3 113.8±11.8 129.4±0.4 114.7±3.3 151.1±1.7 123.9±2.0

2 TCGS 1157 107.9±1.9 118.3±3.1 117.9±1.4 138.7±3.1 128.6±2.9 161.7±1.4 133.1±2.2

3 Narayani 138.4±4.4 120.0±4.0 120.0±1.1 135.7±4.2 128.2±0.4 144.8±3.3 136.5±0.4

4 Kadiri 6 139.4±9.4 130.3±2.0 125.0±0.2 140.7±1.9 137.6±5.4 146.6±0.3 133.2±1.5

Mean 126.62 124.17 119.17** 136.24 127.25** 151.05 131.67**

C.V 3.36 3.02 3.17


Table 5. Proline content (µg g-1) of groundnut genotypes estimated in Control and respective moisture stress imposed condition at 60 DAS, 70 DAS and 80 DAS

50DAS (before Proline content Proline content Proline content

S.No Genotypes imposition at 60 DAS at 70 DAS at 80 DASof moisture

stress) Control Stressed Control Stressed Control Stressed

1 MLTG 4 10±1.6 8.5±0.6 136.6±1.6 25.5±2.5 179.0±2.5 37.3±1.5 262.7±1.8

2 TCGS 1157 7.1±1.1 16.1±1.9 168.3±1.6 26.1±2.5 241.1±12.0 39.9±1.8 247.2±5.3

3 Narayani 1.7±0.2 3.8±1.0 211.0±2.0 18.0±3.3 322.8±2.0 69.0±1.7 432.7±10.8

4 Kadiri 6 3.4±0.9 5.5±0.9 134.9±3.4 15.7±0.4 288.3±5.3 32.0±0.2 409.6±3.2

Mean 5.41 8.27 162.45** 21.24 257.63** 44.40** 337.85**

C.V 38.72 32.54 22.6

** Significanct at 1% level of significance

APARNA et al.

36

respectively. The accumulation of proline hasincreased significantly in MLTG 4 and TCGS 1175genotypes at 20 days (176 and 241µg g-1) and 30days (247 and 262 µg g-1) after moisture stressimposition. The proline content has increased evenmore in Kadiri 6 and Narayani than drought tolerantgenotypes at 20 days (288 and 322 µg g-1) and 30days (409 and 432 µg g-1) after stress imposition.The proline accumulation in drought susceptiblegenotypes viz., Kadiri 6 and Narayani has increasedtwo folds than the drought tolerant genotypes viz.,TCGS 1157 and MLTG 4 at prolonged moisture stressof 20-30 days (Table 5). In response to droughtstress, proline accumulation normally occurs in thecytosol where it contributes substantially to thecytoplasmic osmotic adjustment (Leigh et al., 1981).In addition to proline, accumulation of soluble sugarsand aminoacids such as glutamate, glutamine andasparagin were found in tissues as a generalresponse to drought stress. The accumulated prolinemay facilitate the survival of cells under severemoisture stress by stabilization of macromolecules,a sink for excess reductant, ROS and a store ofcarbon and nitrogen for use after relief of water deficit(Maiti et al., 2000). Naidu et al. (2001) reported thatthe accumulation of leaf proline content in green gramhas increased due to drought stress in all the greengram genotypes, among the ten genotypes studiedK 851 and LGG 407 accumulated more proline underdrought stress. This accumulated proline possiblycontributed towards osmotic adjustment which playsa major role in maintaining turgor over fluctuating soilwater potential.

Under prolonged moisture stress conditionthe catalase activity increased continuously in allgroundnut genotypes. Catalase activity at 30 daysafter stress imposition has recorded two fold increasein drought tolerant genotypes viz., MLTG4 (5.8 Ug-1)and TCGS 1157 (4.7 Ug-1) under stress condition whencompared with control. Lowest catalase was noticedin drought susceptible genotypes viz., Narayani (3.3unit enzyme g-1 fresh weight) and Kadiri 6 (2.6 unit

enzyme g-1 fresh weight) at 80 DAS (Table 6). Theperoxidase activity was recorded as 0.39 Ug-1,3.71 Ug-1, 0.39 Ug-1 and 0.79 Ug-1 in MLTG 4, TCGS1157, Kadiri 6 and Narayani at 50 days after sowing.Highest peroxidase activity recorded in TCGS 1157and Narayani groundnut genotypes (30.0 Ug-1 and32.4 Ug-1, respectively) than Kadiri 6 and MLTG 4(13.9 Ug-1 and 18 U g-1) at 30 days after moisturestress. Similar results were reported by Chakrabarthyet al. (2015) who found that the total chlorophyllcontent of the leaf increased in most of the cultivarswhen drought was imposed at the initial stage.Prolonged drought stress of 35 days (45 DAS to 80DAS) induces stomatal closure and therebydecreases CO2 assimilation. It resulted in increasedrate of photorespiration which inturn destroy thechlorophyll and induces senescence. Moisture stressincreases production of ROS that inactivateenzymes, damage cellular components and diminishthe defense capacity of plants. Plants possessantioxidant defense system against ROS, operativethrough the changes in activities of catalase andperoxidase (Chakraborty et al., 2015). In this study,oxidative stress (H2O2 production) varied in thegroundnut cultivars facing water deûcit stress at 10,20 and 30 days of moisture stress; however, theextent varied with the cultivars. Activity of catalaseand peroxidase was noticed in tolerant genotypeswas relatively higher than in the susceptible onesdue to better balance maintained between ROSproduction and the capacity of the defensemechanisms to protect the plant under severe stress.Tardieu (2012) also reported similar kind of resultsin groundnut that moisture stress situation inducesstomatal closure as mechanism to reduce water lossand thereby resulted in decreased photosyntheticactivity.

CONCLUSION

Under moisture stress condition, droughttolerant genotypes MLTG4 and TCGS1157 showedhighest SCMR at different moisture stress levels thandrought susceptible genotypes Kadiri 6 and Narayani


37

Table 6. Catalase activity (units min-1 g-1) of groundnut genotypes estimated in Control and respective moisture stress imposed condition at 60 DAS, 70 and 80 DAS

50DAS (before Catalase activity Catalase activity Catalase activity



1 MLTG 4 0.5±0.1 0.9±0.1 1.8±0.2 0.9±0.1 2.9±0.7 1.9±0.6 5.8±1.7

2 TCGS 1157 0.6±0.4 1.3±0.1 2.1±0.1 1.4±0.2 2.5±1.0 1.7±0.4 4.7±0.1

3 Narayani 0.2±0.0 0.5±0.2 0.6±0.0 0.4±0.2 2.3±1.4 0.3±0.1 3.3±0.6

4 Kadiri 6 0.3±0.1 0.3±0.9 0.7±0.1 0.4±0.1 1.7±1.0 0.5±0.3 2.6±0.5

Mean 0.4 0.75 1.1 0.77 2.35 1.1 4.1

C.V 54.32 49.76 33.18


Table 7. Peroxidase activity (units min-1 g-1) of groundnut genotypes estimated in control and respective moisture stress imposed condition at 60 DAS, 70 and 80 DAS

50DAS (before Peroxidase activity Peroxidase activity Peroxidase activity



1 MLTG 4 0.39±0.3 0.59±0.6 2.00±1.2 1.19±0.1 5.80±1.2 7.20±0.2 18.00±1.1

2 TCGS 1157 0.71±0.6 1.19±0.7 1.90±0.8 3.00±0.3 6.50±0.5 6.50±0.5 30.00±0.5

3 Narayani 0.39±1.2 0.39±0.5 1.20±0.5 2.00±0.5 2.90±0.1 3.70±1.2 32.40±2.0

4 Kadiri 6 0.79±0.5 0.79±1.2 2.00±1.0 0.87±0.1 2.00±0.1 4.30±0.9 13.90±2.3

Mean 0.56 0.73 1.84 1.76 4.45 5.42 17.10

C.V 101.75 115.93 39.18

APARNA et al.

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and SCMR can be used as non-destructive measureto estimate chlorophyll density while screeninggroundnut genotypes for drought tolerance. SLAdecreased significantly in drought tolerant genotypesthan in drought susceptible genotypes. Theincreased levels proline under drought stress can bebetter considered as drought stress indicator ingroundnut. The accumulation of proline was more indrought susceptible (Kadiri 6 and Narayani)genotypes than drought tolerant (TCGS 1157 andMLTG 4) genotypes, both catalase and peroxidaseactivities have increased in all the genotypessubmitted to prolonged moisture stress to protectthe cell from ROS generated in photorespiration.Under prolonged drought stress, the increase inproline content is predominant in drought sensitivegenotypes viz., Kadiri 6 and Narayani and is twiceas that of drought tolerant genotypes TCGS 1157and MLTG 4. In contrast, catalase and peroxidaseenzymes activity was found to be significantly highin drought tolerant genotypes than in droughtsensitive genotypes. The genotypes with high SCMR,peroxidase and catalase activity and low SLA andproline content that tolerate moisture stress can beused as a basis for development of groudnutgenotypes for rainfed condition.

REFERENCES

Aebi, H. 1974. Catalase. In: Methods of EnzymaticAnalysis. 2nd English Edition, AcademicPress Inc., New York and London. pp. 673-684.

Angelini, R., Manes, F and Federico, R. 1990. Spatialand functional correlation between diamineoxidase and peroxidase activities and theirdependence upon etiolation and woundingin chick pea stem. Planta.182: 89-96.

Anjum, S. A., Xie, X., Wang, L., Saleem, M. F. Man,C and Lei, W. 2011. Morphological,physiological, and biochemical responses

of plants to drought stress. African Journalof Agricultural Research. 6(9):2026-2032.

Arunyanark, A., Jogloy, S., Wongkaew, S.,Akkasaeng, C., Vorasoot, N., Wright, G.C.,Rachaputi Rao, C.N and Patanothai, A.2009. Association between aflatoxincontamination and drought tolerance traitsin peanut. Field Crops Research. 114:14-22.

Bates, L.S., Waldrew, R.R and Teare, I.D. 1973.Rapid determination of free proline for waterstress studies. Plant Soil. 39:205-207.

Chakraborty, K., Singh, M.L., Kalariya, K.A.,Goswami. N and Zala, P.V. 2015.Physiological responses of peanut (Arachishypogaea L.) cultivars to water deficit stressstatus of oxidative stress and antioxidantenzyme activities. Acta Botanica Croatica.74 (1):123-142.

Food and Agriculture Organization. 2014. StatisticalDatabases 2014. Retrieved from websitefrom (http:// www .fao.org) on 3.1.2018.

Girdthai, T., Jogloy, S., Vorasoot, N., Akkasaeng,C., Wongkaew, S., Holbrook, C.C andPatanothai, A. 2010. Associations betweenphysiological traits for drought toleranceand aflatoxin contamination in peanutgenotypes under terminal drought. PlantBreeding. 129:693-699.

Janila, P., Murali, T., Pandey, M.K., Desmae, H.,Motagi, N.B., Okori, P., Manohar, S.S.,Rathnakumar, A. L., Radhakrishnan, T.,Liao, B and Varshney, R.K. 2016. Genomictools in groundnut breeding program: statusand perspectives. Frontiers in PlantSciences. 7(289): 1-16.

Jongrungklang, N., Toomsan, B., Varsoot, N., Jogloy,S., Kesmala, T and Patanothai, A. 2008.


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Identification of peanut genotypes with highwater use efficiency under drought stressconditions from peanut germplasm ofdiverse origins. Asian Journal of PlantSciences. 21: 1682-3974.

Kalariya, T., Koilkonda, P., Sato, S.,Tabata, S.,Shirasawa, K., Hirakawa, H and Sakai, H.2015. Large-scale development ofexpressed sequence tag-derived simplesequence repeat markers and diversityanalysis in Arachis spp. MolecularBreeding. 30: 125-138.

Leigh, R.A., Ahmad, N and Wyn Jones, R.G., 1981.Asessment of glycine betaine and prolinecompartmentation by analysis isolated beetvacuoles. Planta. 153: 34-41.

Madhusudhan, K.V and Sudhakar, C. 2014.Morphological responses of a high yieldinggroundnut cultivar (Arachis hypogaea L. cv.K-134 ) under water stress. Indian Journalof Pharmaceutical and BiologicalResearch. 2 (1): 35-38.

Maiti, R.K., Moreno-Limon, S and Wesche-Ebeling,P. 2000. Responses of groundnut crop tovarious abiotic stress factors and itsphysiological and biochemical basis ofresistances. Agricultural Reviews. 21:155-167.

Nageswara Rao, R.C and Wright, G.C. 1994. Stabilityof the relationship between specific leafarea and carbon isotope discriminationacross environments in peanut. CropSciences. 34: 98-103.

Naidu, T.C.M., Raju, N and Narayanan, A. 2001.Screening of drought tolerance ingreengram (Vigna radiata L. Wilczek)genotypes under receding soil moisture.Indian Journal of Plant Physiology. 6: 197-201.

Nigam, S. N., Chandra, S., Shridevi, K. R., Bhukta,M and Reddy, A. G. S. 2005. Efficiency ofphysiological traits-based and empiricalselection approaches for drought tolerantin groundnut. Annals of Applied Biology.146: 433-439.

Paramasivam, M., Robert, G.A., Rajasekar, M andSomasundaram, R. 2014. Drought stress-induced modification on growth andpigments composition in different genotypesof Helianthus annuus. Current Botany. 5:7-13.

Puangbut, D., Jogloy, S., Vorasoot, N., Akkasaeng,C., Kesmala, T and Patanothai, A. 2009.Variability in yield responses of peanut(Arachis hypogaea L.) genotypes underearly season drought. Asian Journal PlantScience. 8: 254-264.

Sheshshayee, M.S., Bindumadhava, H., Ramesh,R., Prasad, T.G., Lakshminarayana, M.Rand Udayakumar, M. 2005. Oxygen isotopeenrichment (Delta18O) as a measure oftime-averaged transpiration rate. Journal ofExperimental Botany. 56: 3033-3039.

Songsri, P., Jogloy, S., Vorasoot, N., Akkasaeng,C., Patanothai, A and Holbrook, C.C. 2008.Root distribution of drought-resistant peanutgenotypes in response to drought. Journalof Agronomy and Crop Science. 194(2):92-103.

Talwar, H.S., Nageswara Rao, R., Nigam, S.N andWright, G.C. 2004. Leaf anatomicalcharacteristics associated with water useefficiency in groundnut (Arachis hypogaeaL.). In: Proceedings of the National Seminarheld at NRCG, Junagadh during January 11-13, 2004.

Tardieu, F. 2012. Any trait or trait-related allele canconfer drought tolerance: just design theright drought scenario. Journal ofExperimental Botany. 63:25-31.

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INTRODUCTION

Sorghum (Sorghum bicolor L. Moench) is animportant crop for millions of farmers in the semi-arid tropics in India. It is re-emerging as a potentialalternative food, feed, fodder and bioenergy crop.However, part of the sorghum crop area has now beenreplaced by soybean, cotton and maize and is shiftedto marginal lands. In rice-fallows of Coastal AndhraPradesh, sorghum cultivation is gaining popularityamong farmers due to its high productivity and lowwater requirement (Mishra et al., 2011). It is nowgrown in more than 24,000 ha area in rice-fallowswith an average productivity of 6.5 t ha-1in A.P., whichis the highest in the country.

Usually, farmers grow pulses (greengram andblackgram) in rice-fallows in the Krishna-Godavarizone of Andhra Pradesh as utera cropping(broadcasting of seeds in standing crop of rice).However, in the recent times, the area under pulseshas declined due to late planting of rice and severeattack of viral diseases and parasitic weed Cuscuta.Farmers of the region are now growing maize (inassured irrigated areas) and sorghum (in less

PERFORMANCE OF SORGHUM HYBRIDS UNDER DIFFERENT NITROGENLEVELS IN RICE-FALLOW CONDITIONS OF NORTH COASTAL

ANDHRA PRADESH

B. SRI SAI SIDDARTHA NAIK*, K.V.RAMANA MURTHY, A. V. RAMANA and P. GURUMURTHYDepartment of Agronomy, Agricultural College,

Acharya N.G. Ranga Agricultural University, Naira - 532 185

Date of Receipt:15.12.2017 Date of Acceptance:31.01.2018

ABSTRACTThe field experiment was conducted on sandy loam soil during Rabi 2016-17. The treatments comprised combination of

four sorghum hybrids and four nitrogen levels laid out in split plot design with three replications. Among the hybrids, CSH 25 wasrecorded with highest yield (6841 kg ha-1) and panicle length (28.25 cm).With the different nitrogen levels tested, application of 120kg N ha-1(7491 kg ha-1)was recorded with highest yield, panicle length (29.90 cm) and test weight (3.97). However, stover yieldwas highest with hybrid CSH 15R and with 120 kg N ha-1. Gross returns, net return and B:C ratio were also highest with CSH 25and with highest N level (120 kg ha-1). Hence, the study revealed that for rice fallow situation sorghum can be successfully grownwith hybrid CSH-25 and application of 120 kg N ha-1which were found promising for North Coastal Zone of Andhra Pradesh.

J.Res. ANGRAU 46(1) 40-47, 2018

*Corresponding Author E-mail:: [email protected]

irrigated areas) in rice-fallows as alternate crops topulses. It resultsin manyeconomic and environmentalbenefits over conventional tillage, such as lower labourand fuel needs, reduced soil erosion, reduced runoff,increased soil organic Carbon content, and increasedsoil biological activity (West and Post, 2002).Schlegelet al. (2007) recorded 25% higher grain yieldin no tillage than reduced tillage and 98% greaterthan conventional tillage.

Farmers of the area are using the fertilizersand pesticides indiscriminately (Chapke et al., 2011).The input use by sorghum may vary with differentcultivars depending upon their growth behaviour androoting pattern. Nitrogen(N)fertilization is becomingincreasingly important in gauging the economic andenvironmental viability of agroecosystems andexploiting genotypic differences in N demand andefficiency have been proposed as possiblealternatives for reducing the cost and reliance uponfertilizer N (Gardner etal., 1994). Many promisingsorghum hybrids have been evolved for traditionalsorghum growing areas, making it essential toinvestigate the differential response of promising

41

hybrids to nitrogen in non-traditional areas such asrice-fallows. The investigation was thereforeundertaken to find out the relative response ofsorghum hybrids to different nitrogen levels in rice-fallows of North Coastal Zone of Andhra Pradesh.


The field experiment was conducted duringRabi, 2016-17 at the Agricultural College, Naira. Theexperiment soil was sandy loam in texture with a pHof 7.42 and EC of 0.064 dSm-1,medium in organiccarbon (0.56%), low in available nitrogen (96 kgha-1), low in available phosphorus (12.4 kg ha-1) andmedium in available potassium (151 kg ha-1). Theexperiment was laid out in split plot design with threereplications. The treatments comprised combinationof four sorghum hybrids viz., V1- CSH 15R, V2- CSH16, V3- CSH 25and V4- MLSH 296 and four nitrogenlevels N1: 0 kg N ha-1, N2: 80 kg N ha-1, N3: 100 kg Nha-1 and N4: 120 kg N ha-1. The total rainfall of 8.0mm was received in two rainy days during the growthperiod of rice fallow sorghum. A recommended doseof 80 kg P2O5 and 80 kg K2O ha-1 was applied asbasal and nitrogen was applied in the form of urea inthree splits at 15 DAS, 60 DAS and at flowering.Data on growth parameters viz., days to 50% panicleemergence, days to maturity, yield attributes viz.,panicle length and test weight, yield parameters suchas grain yield and stover yield was recorded. Grossreturns, net returns and B:C ratio was calculated.Statistical analysis of the data collected was carriedout following the analysis of variance technique forsplit plot design.


Growth parameters

The results showed that 50 percent paniclesemerged significantly early with the application of120 kg N ha-1 (N4) gradually followed by the 100 kg Nha-1 (N3), 80 kg N ha-1 (N2) and no application ofnitrogen (0 kg N ha-1 ) with clear statistical disparitybetween each of them (Table 1). Non application of

nitrogen (N1) resulted in a delay by about 12 days inthe emergence of 50 per cent panicles compared toN4.Similarly, N4 recorded significantly early maturitygradually followed by the other N levels with cleardisparity between each of them. Control treatment(N1) recorded late maturity by nearly three dayscompared to N4. Higher concentration and uptake ofnitrogen by plants with sufficient fertilization mighthave resulted into greater synthesis of protein andearlier flower primordial development which ultimatelyresulted into earlier flowering and maturity. Similarly,Patidar and Mali (2004) reported that there wassignificant difference in number of days taken for 50per cent flowering with increasing levels of nitrogenfrom 0 to 120 kg N ha-1 and control (75) tookmaximum number days for 50 per cent flowering.Mishra et al. (2014) also similarly reported thatnumber of days taken for 50 per cent floweringdecreased with 80 kg N ha-1 and control tookmaximum number of days to 50 per cent flowering inkharif grain sorghum.

Application of nitrogen at higher levels mighthave helped in enhanced reproductive growth anddevelopment resulting in early appearance ofreproductive structures. On an average, fertilizedplants were found to flower five days earlier thanthose that were not fertilized. These results aresimilar to that of Buah and Mwinkaara (2009) whoreported that the application of N significantly affecteddays to flowering, plant height and leaf area index insorghum crop in Guinea Savanna Zone of Africa.

Yield attributing characters

Panicle length in CSH 16(V2) wassignificantly superior among the hybrids. CSH 25(V3) (29.82 Cm) was superior to MLSH 296 (V4) andCSH 15R(V1) and was inferior to CSH 16 (V2). CSH15R (V1) (21.90 Cm) was significantly inferior to allthe sorghum hybrids taken for trial (Table 2). Paniclelength at the highest nitrogen level (N4) (29.90 Cm)was significantly superior as compared to all the otherthe levels of nitrogen tried. Regarding, interaction,

SRI SAI SIDDARTHA NAIK et al.

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Table 1. Effect of different hybrids and nitrogen levels on growth parameters of rice fallow sorghum

Days to 50% Days toTreatments Plant Population per m2 panicle maturity

emergence

Hybrids Intial Final

CSH 15R 12.7 11.9 70 105

CSH 16 12.9 12.5 70 107

CSH 25 12.9 12.5 70 106

MLSH 296 12.9 12.7 70 106

CD @ 5% NS NS NS NS

CV% 2.75 1.03 1.87 1.89

N-levels (kg ha-1)

0 12.5 12.0 77 107

80 12.9 12.0 71 107

100 13.0 12.4 67 106

120 13.0 13.2 65 104

CD @ 5% NS NS 1.0 1.0

CV% 2.67 3.12 2.27 2.29

H at N

CD @ 5% NS NS NS NS

N at H

CD @ 5% NS NS NS NS

panicle length was the highest with the hybrid CSH25 at 120 kg N ha-1 (V3N4) and the lowest paniclelength was recorded by CSH 15R at 0 kg N ha- 1

(V1N1).The differentiation in the panicle length canbe attributed to the variation in the geneticconstitution of different hybrids. These results are inconformity with Mishra et al. (2011) who reported thatsorghum hybrid CSH 16 recorded significantlyhighest panicle length (41.4cm) over the other hybridsand varieties under rice fallow conditions of Gunturdistrict. Chapke et al. (2014) recorded significantly

highest panicle length with hybrid 3660A x CB35 (35cm) under rice-fallow conditions of Guntur district.Marked differences on panicle length were recordedwith each increment in nitrogen levels. Greaterpanicle length due to 120 kg N ha -1 could be attributedto its favourable effect on cell enlargement,production of larger leaves and improvesphotosynthetic efficiency of plants. The lowestpanicle length was observed in 0 kg N ha-1. Similarly,Mishra et al. (2013) reported that the hybrids CSH

PERFORMANCE OF SORGHUM HYBRIDS UNDER DIFFERENT NITROGEN LEVELS

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Table 2. Effect of different hybrids and nitrogen levels on yield attributes and yield of rice fallow sorghum

Panicle length Test weight Grain StoverTreatments (cm) (100- seed yield yield

weight) (kg ha-1) (kg ha-1)

Hybrids

CSH 15R 21.90 3.35 5023 15477

CSH 16 29.82 3.50 6068 12873

CSH 25 28.25 3.35 6841 13536

MLSH 296 27.72 3.46 6044 9222

CD @ 5% 0.88 NS 1037 598

CV% 3.22 4.9 14 4.7

N-levels (kg ha-1)

0 22.80 2.93 3436 8872

80 26.61 3.19 6296 11992

100 28.39 3.57 6751 14043

120 29.90 3.97 7491 16201

CD @ 5% 0.48 0.11 593 407

CV% 2.12 3.9 13 8.2

H at N

CD @ 5% 1.2 0.26 NS 920

N at H

CD @ 5% 1.0 0.24 NS 868

16 recorded highest panicle length (28.19 cm)significantly at the level of 125 kg N ha-1 of sorghumunder rice fallow conditions. Besides, Patil (2013)reported that panicle length of sorghum was higherwith application of 80 kg N ha-1 (16.6cm) ascompared with absolute control (14.4cm) in deepblack soils of Bellary. Goutami et al. (2015) reportedthat highest panicle length was obtained withapplication of 150 kg N ha-1 (22.8) but was on a par

with 90 kg N ha-1 (22.1cm) and superior over absolutecontrol (20.9 cm) in rice fallow conditions sorghum.

Test weight of rice fallow sorghum hybridsdid not differ with each other. Results showed thattest weight at highest nitrogen level (N4) (3.97), wassignificantly superior as compared to all other levelsof nitrogen tried. Test weight obtained with theapplication of 100 kg ha-1 (N3) was the next besttreatment, but was, however, significant with the


44

application of 80 kg ha-1 (N2). Test weight with thecombination of CSH 15R at 120 kg N ha-1(V1N4)(4.13)was the highest which and on a par with MLSH296 at 120 kg N ha-1 (V4N4) (3.96) while the lowesttest weight was recorded by CSH 15R at 0 kg Nha-1(V1N1) (2.70).Increase in test weight at higherlevel of nitrogen might be due to greater assimilatingsurface at reproductive development resulted in bettergrain formation because adequate production ofmetabolites and their translocation towards grainwhich shows improvement in nutrient concentrationand up take. This might have resulted in increasedweight of individual grain expressed in terms of testweight. Similar findings were reported by PushpendraSingh et al. (2012) who observed that highestthousand seed weight (27.39) in sorghum (CSV 15)with the application of 120 kg N ha-1 as comparedwith other levels of nitrogen during kharif season atUdaipur, Rajasthan.

Grain and Stover yield

Grain yield of sorghum indicated that yieldobtained with CSH 25 (V3) (6841 kg ha-1) wassignificantly higher than all the other hybrids exceptCSH 16 (V2) (6068 kg ha-1) with which it wasstatistically comparable (Table 2). Grain yieldrecorded with MLSH 296 (V4) (6044 kg ha-1) was ona par with all the hybrids except CSH 25 (V3). Thelowest grain yield was recorded with CSH 15R (V1)(5023 kg ha-1) among all hybrids taken for study.Grain yield obtained at highest nitrogen level (N4)(7491 kg ha-1) was significantly superior as comparedto all the nitrogen levels tried. Yield obtained withthe application of 100 kg ha-1 (N3) was next besttreatment but was, however, comparable with theapplication of 80 kg ha-1 (N2). Both these nitrogenlevels were significantly superior to N4 andsignificantly superior to no application of nitrogen (N1)(3436 kg ha-1), which recorded the significantly lowestgrain yield among all the four levels of nitrogen testedin this experiment.

The superiority of hybrid CSH 25 (V3) in termsof yield under rice fallow conditions can be attributedto its higher number of grains per panicle, dry matteraccumulation at harvest as compared to other threehybrids. It has also the ability to put up the growthunder low temperature conditions at early stages.Similar observations were reported by Chapke et al.(2014) who recorded highest grain yield with hybrid456A x CB134 (9.05t ha-1) which was comparablewith hybrid CSH 16 (8.62 t ha-1) under rice fallowconditions of Guntur district. The increase in the grainyields with enhanced N application could be ascribedto better plant growth and dry matter production dueto higher photosynthetic area. This was furthersupported by the fact that soil of the experimentalfield was low in nitrogen (96 kg ha-1). These resultsare in corroboration with Mishra et al.(2014) whoreported higher grain yield and it was on par with 60kg N ha-1 (31.4 q ha-1) and this was significantlyhigher than control. Similarly, Vara Prasad et al.(2014) reported that grain yield of sorghum increasedlinearly with increasing nitrogen fertilizer regimes.Maximum grain yield was obtained at 90 kg Nha-1(46.4 q ha-1) followed by 45 kg N ha-1 (35.4 qha-1 ) and the lowest yield was obtained with 0 kg Nha-1 (31.4 q ha-1).

Stover yield obtained with CSH 15R (V1)(15477 kg ha-1)was significantly superior to all thehybrids. Yield of stover with CSH 25 (V3) was foundto be superior to all other hybrids except V1, whileyield with CSH 16 (V2) was significantly superior toMLSH 296 (V4) (9222 kg ha-1). Stover yield at thehighest nitrogen level (N4) (16201 kg ha-1) wassignificantly superior as compared to all the otherthe levels of nitrogen levels tried. Stover yield obtainedwith the application of 100 kg ha-1 (N3) was the nextbest treatment but was, however, significantlysuperior to 80 kg ha-1(N2). No application of nitrogen(N1) (8872 kg ha-1) recorded the significantly thelowest yield among all the four levels of nitro gen


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Table 3. Effect of different hybrids and nitrogen levels on economics of rice fallow sorghum

Treatments Gross return (Rs. ha-1) Net return (Rs. ha-1) B:C ratio

Hybrids

CSH 15R 97053 55456 1.33

CSH 16 110159 68520 1.64

CSH 25 124696 83057 2.00

MLSH 296 114781 73141 1.75

CD @ 5% 13882 13866 0.34

CV% 12 10 4

N-levels (kg ha-1)

0 64684 23044 0.55

80 114305 72666 1.73

100 122868 81270 1.95

120 144833 103194 2.47

CD @ 5% 13512 13511 0.32

CV% 13 11 5

H at N

CD @ 5% NS NS NS

N at H

CD @ 5% NS NS NS

tested in this experiment. Stover yield was the highestwith the hybrid CSH 15R at 120 kgha-1 (V1N4) (18900kg ha-1)which was superior over other interactioncombinations. The lowest stover yield was recordedby MLSH 296 at 0 kg N ha-1(V4N1) (5522 kg ha-1).Higher stover yield with CSH 15R (V1) might be owingto its tall growing nature as reflected by its highestplant height and also dry matter production. Similarobservations were noticed by Patil (2007) whoreported that Maximum stover yield was recorded insorghum hybrid CSH 15R (2.20 t ha-1) as compared

with other sorghum hybrids during rabi in vertisols ofBellary. Highest stover yield recorded with theapplication of 120 kg N ha-1 might be due to the factthat nitrogen application increases the activity ofcytokinin in plant which leads to the increased celldivision and elongation. Madhukumar et al. (2013)also reported that significantly higher stover yield(89.6 q ha-1) was recorded with the application of180 kg N ha-1 and application of 90 kg N ha-1

produced significantly lower yield (79. q ha-1) duringpost rainy season.


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Economics

Economics (Gross reurns and net returns)registered with CSH 25 (V3) was significantly higherthan all the other hybrids except MLSH 296 (V4) withwhich it was statistically comparable. Economicsrecorded with CSH 16 (V2) was on par with all thehybrids except CSH 25 (V3) 9 (Table 3). The lowestreturns were recorded with CSH 15R (V1). B:C ratioof CSH 25 (V3) (2.00)was significantly superior to allthe hybrids. B:C ratio with MLSH 296 (V4) was foundto be superior to all other hybrids except V3, whileB:C ratio with CSH 16 (V2) was significantly superiorto CSH 15R (V1)(1.33). Significantly lowest B:C ratiowas recorded with CSH 15R (V1) and was thus inferiorto all the sorghum hybrids under trial.

Higher gross, net returns and B:C ratio wereobtained at the highest nitrogen level (N4) (2.47)andwas superior to all the nitrogen levels tried (Table3). Benefit-cost ratio obtained with the application of100 kg ha-1 (N3) was comparable with the applicationof 80 kg ha-1 (N2). Both these nitrogen levels wereinferior to N4 and significantly superior to noapplication of nitrogen (N1) (0.55), which recordedthe significantly lowest of these parameters.Interactions of hybrids and N levels for gross, netreturns and benefit cost ratio were found to be non-significant. Nitrogen fertilization markedly influencedthe B:C ratio. The ratio increased with increasedlevels of nitrogen. Higher B: C ratio was recordedwith the application of 120 kg ha-1 (N4). This higherB:C ratio might be due to more economic yieldobtained at higher levels of nitrogen than lower levelof nitrogen. Mishra et al. (2015) also reported similarfindings and observed that the highest net returns( Rs.25,970 ha-1) and benefit - cost ratio (1.79) wereobtained with application of 150% RDF (120 kg Nha-1) which were on par with 100 % RDF, butsignificantly superior over control .

CONCLUSION

Sorghum can be successfully grown bychoosing hybrid CSH 25 (V3) and with application of120 kg N ha-1 (N4) for obtaining the highest yieldmaking it technically feasible and economicallyprofitable proposition under the resource constrainedrice fallow conditions of North Coastal Zone of AndhraPradesh.

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Buah, S. S. J and Mwinkaara, M. 2009. Responseof sorghum to nitrogen fertilizer and plantdensity in the Guinea Savanna zone.Journal of Agronomy. 8 (4)124-130.

Chapke, R.R., Mishra, J.S., Subbarayudu, B.,Hariprasanna, K and Patil, J.V. 2011.Sorghum cultivation in rice-fallows: Aparadigm shift. Bulletin, Directorate ofSorghum Research, Hyderabad, India.pp. 31.

Chapke, R.R., Mishra, J.S., Babu, S., Aruna, C andPatil, J.V. 2014. On farm evaluation ofadvanced sorghum hybrids in rice fallowsunder zero tillage. Current Advances inAgricultural Sciences. 6(2): 180-182.

Gardner, J. C., Maranville, J .W and Paparozzi, E.T.1994. Nitrogen-use efficiency among diversesorghum cultivars. Crop Science. 34:728–33.

Goutami, N., Prasuna Rani, P., Ravindra Babu, Pand Lakshmi Pathy, R. 2015. Performanceof rice fallow sorghum as influenced bynutrient management. The AndhraAgricultural Journal. 62(2):334-338.

Madhukumar, M., Munirathnam, P and SrinivasaReddy, M. 2013. Studies on the effect ofnitrogen fertilization on growth and yield ofsorghum (Sorghum bicolor) varieties duringpost rainy (maghi) season. The AndhraAgricultural Journal. 60 (4): 760-763.


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Mishra, J. S., Subbarayudu, B., Chapke, R.R andSeetharama, N. 2011. Evaluation ofsorghum (Sorghum bicolor) cultivars in rice(Oryza sativa)-fallows under zero tillage.Indian Journal of Agricultural Sciences. 81(3): 277-279.

Mishra, J.S., Chapke, R.R., Subbarayudu, B.,Hariprasanna, K and Patil, J.V. 2013.Response of sorghum hybrids to nitrogenunder zero tillage in rice fallows of CoastalAndhra Pradesh. Indian Journal ofAgricultural Sciences. 83(3): 359-361.

Mishra, J.S., Thakur, N. S., Pushpendra Singh,Kubsad,V.S., Kalpana, R., Alse, U. N andNemade, S. M. 2014. Tillage and integratednutrient management in rainy- season grainsorghum (Sorghum bicolor). Indian Journalof Agronomy. 59(4): 619-623.

Mishra, J. S., Thakur, N. S., Pushpendra Singh,Kubsad,V. S., Kalpana, R., Alse, U. N andSujathamma, P. 2015. Productivity, nutrient-use efficiency and economics of rainy-season grain sorghum (Sorghum bicolor)as influenced by fertility levels and cultivars.Indian Journal of Agronomy .60(1): 76-81.

Patidar, M and Mali, A.L. 2004. Effect of farmyardmanure, fertility levels and bio-fertilizers ongrowth, yield and quality of sorghum(Sorghum bicolor). Indian Journal ofAgronomy. 49(2):117-120.

Patil, S.L.2013. Productivity of winter sorghum andchickpea as influenced by integratednutrient management in deep black soilsof Bellary region, India. Indian Journal ofSoil Conservation. 41(1): 52-63.

Pushpendra Singh, Sumeriya, H. K and Solanki, N.S. 2012. Effect of fertilizer levels onproductivity and economics of elite sorghum(Sorghum bicolor (L.) Moench) genotypes.Madras Agricultural Journal. 99(7-9): 567-569.

Schlegel, A., Stone, L., Dumler, T and Thompson,C. 2007. Long-term no-till improves soilproperties and increases grain yield. Paperpresented during Annual Meeting of the Soiland Water Conservation Society, SaddleBrook Resort, Tampa, Florida, July 21,2007.

Vara prasad, P.V., George Mahama, David B. Mengeland Tesfaye T. Tesso. 2014. Influence ofnitrogen fertilizer on growth and yield of grainsorghum hybrids and inbred lines.Agronomy Journal.106 (5):1623-1630.

West, T. O and Post, W. M. 2002. Soil organic carbonsequestration rates by tillage and croprotation: A global data analysis. Soil ScienceSociety of America Journal. 66:1930–1946.


48

INTRODUCTION

Pearlmillet, the world’s hardiest warm seasoncereal crop (Reddy et al., 2013), is an indispensablearid and semiarid crop of India (Ramesh et al., 2006)cultivated as dual purpose (food and feed) crop inover 8.3 mha ranking fourth among total cereals.Further, the nutritional value of pearlmillet offers muchscope to development of value added products innew health conscious consumer segments (Yadavet al., 2011) as it contains more fibre and is good fordiabetic and heart patients. It is the richest sourcesof nutrition, especially iron, calcium and zinc amongcereals and hence can provide all the nutrients atthe least cost compared to wheat and rice(Parthasarathy et al., 2006). The application ofbalance nutrients and their better utilization undermoisture condition for enhancing growth, yield andyield attributing parameters of crops is importantfactor under rainfed condition.

Fertilization of crop enhance water useefficiency, controlling soil erosion by promoting rapidand vigorous growth of crop to check runoff andincreases the water holding capacity of soil.Application of nitrogen helps in better vegetativegrowth of plants, phosphorous stress condition.

EFFECT OF SPACING AND NITROGEN LEVELS ON PRODUCTIVITY OFPEARLMILLET IN DRYLAND REGIONS

C. RADHA KUMARI*, P. SHANTHI and B. SAHADEVA REDDYAgricultural Research Station, Acharya N.G. Ranga Agricultural University, Anantapuram -515 001


ABSTRACTStudies on response of pearlmillet (ABH-1) to spacing and nitrogen levels in alfisols of scarce rainfall zone under rainfed

conditions revealed that plant height, DMP, number of panicles plant-1, panicle length, threshing percentage, grain yield, net returnsand B:C ratio did not differ significantly due to spacing, however, pearlmillet sown at 60 cm x 15 cm produced higher growth, yieldattributes, yield and economics. Application of 80 kg N ha-1 resulted in tallest plants and 60 kg N ha-1 produced higher drymatterproduction. Application of 100 kg N ha-1 produced higher leaf area and leaf area index, yield attributes, whereas, grain yield washigher with application of 60 kg N/ha and straw yield was higher with application of 80 kg N ha-1. Nitrogen levels did not influencepanicle length and threshing percentage.

J.Res. ANGRAU 46(1) 48-58, 2018


Potassium increases the potential and improving thequality of grains.

Pearlmillet is an exhaustive crop that requiresmore nitrogen. The productivity of the crop is verylow (25-26 q ha-1) due to imbalance application offertilizers, adopting improper intrarow spacing,uncertain and erratic distribution of rainfall. Nitrogenis the major nutrient required by pearlmillet and hasshown variable growth and yield response to Napplication (Gascho et al., 1995). Generally,pearlmillet is known for growing under low Nmanagement and several studies showed that Napplication can increase pearlmillet productionefficiency (Singh et al., 2010). Nitrogen use efficiency(NUE) of pearlmillet is higher than many other crops,increasing the rate of N fertilization does not alwaysaccompany a corresponding increase in grain yield(Muchow, 1988).

Ananthapuram district is the second mostdrought - affected district of India. It receives around500 mm rainfall annually. In this region pearlmillet isan important crop produces food and fodder within ashort period of 85 to 90 days for resource poorfarmers. Quantity of nitrogen requirement dependson the inherent fertility status of the soil, season

49

and planting pattern besides several other factors.Agronomic variations among pearl millet genotypeshave been reported earlier by Khairwal et al. (2007).Hence, there is a need to improve yields of pearlmilletby refining the existing agronomic practices.Pertinent information regarding optimum nitrogendose and spacing for pearlmillet during kharif seasonis meagre. Keeping these in view, the experimentwas conducted to study the response of pearlmilletto spacing and nitrogen levels under rainfed condition.


The field experiment was conducted inalfisols of scarce rainfall zone under rainfed conditionsduring kharif, 2015-16 at Agricultural ResearchStation, Ananthapuramu of Andhra Pradesh. The soilwas red sandy loam with shallow depth, low in organiccarbon (0.34%) and low in available nitrogen (138 kgha-1), medium in available phosphorous (28 kg ha-1)and potassium (215 kg ha-1).The experimentconsisted of three spacings (S1: 30 cm x 15 cm; S2:45 cm x 15 cm and S3: 60 cm x 15 cm) and fivenitrogen level treatments viz., N1: Control (No N); N2:40 kg ha-1; N3: 60 kg ha-1; N4: 80 kg ha-1 and N5: 100kg ha-1. and was laid out in Factorial RBD with threereplications. The experimental field was prepared byworking with a tractor drawn disc plough and thentractor drawn cultivator was drawn along the field.The individual plots were laid out according to the

layout plan. The seeds of ABH – 1 were sown bydibbling method in furrows at a depth of 5 cm. Asper the treatments half of the N, entire P2 O5 and K2Okg ha-1 were applied at the time of sowing in the formof urea, single super phosphate and muriate ofpotash, respectively. Remaining half N was appliedat 30 DAS depending on rainfall event. Thinning andgap filling was done for maintaining optimum plantdensity. Weeding and hoeing were taken up at criticalstages of crop weed competition. Standard culturalpractices were followed and is uniform for alltreatments (ANGRAU, 2014). At harvest, five plantswere randomly selected from each treatment forrecording growth and yield parameters. The crop washarvested from a uniform net plot (5 m x 5 m) in alltreatments for recording grain and straw yields. Costof cultivation was worked out based on labour chargesan input costs. Gross returns were calculated basedon local market price of pearlmillet and net returnsby subtracting the total cost of cultivation from grossreturns.


Rainfall and Crop performance

It is observed that the rainfall during the yearof experiment is within the limits of normal and thecrop has also received normal rainfall of 248 mm in21 rainy days during its growth period of 83 days.The details of temperature and rainfall distribution

Table 1. Rainfall and rainy days during crop growth period

Date of sowing 16.7.2015

Date of harvesting 6.10.2015

Crop duration (days) 83

Normal annual rainfall (mm) 590.6

Actual annual rainfall (mm) 641

Rainfall during crop period (mm) 248

Number of rainy days during the year 44

Number of rainy days during crop period 21

RADHA KUMARI et al.

50

revealed that the temperatures (maximum andminimum) were within the normal range during allthe growth stages (Table 2). The distribution of rainfallwas optimum during different phenophases exceptduring the period from panicle emergence to flowering.There was a well distribution of rainfall of 170.4 mmduring the critical periods from flowering to maturity,which helped the crop to give better yields.

Response of pearlmillet to adopted spacing andapplied nitrogen level

Growth

Drymatter production of pearlmillet at harvestdid not differ significantly due to spacing (Table 3).However, larger drymatter production was recordedwith 60 cm x15 cm spacing. Drymatter productionmeasured at harvest was significantly influenced bynitrogen levels. Highest drymatter production wasrecorded with 60 kg N ha-1 which was comparablewith 80 and 100 kg N ha-1. Nitrogen is the maincomponent of the protoplasm that involves in variousmetabolic processes viz. photosynthesis, stimulationof cell division and elongation (Ali, 2010). Interactioneffect of spacing and nitrogen levels on drymatterproduction was found non-significant.

Leaf area index was significantly influencedby spacing at 60 DAS (Table 3). The crop spaced at30 cm x 15 cm recorded significantly highest leafarea index compared to the crop spaced at 45 cm x

15 cm and 60 cm x 15 cm. Leaf area index wassignificantly influenced by nitrogen levels. Amongdifferent nitrogen levels, 100 kg N ha-1 and 80 kg Nha-1 found significantly superior to all othertreatments which were comparable with each other.The interaction effect was found non- significant.Higher stature of growth attributes viz., dry matterproduction and leaf area index was observed withthe application of higher nitrogen levels. While allthese parameters were at their lowest value with nonitrogen application. It could be attributed to the factthat higher nitrogen levels might have acceleratedthe synthesis of more chlorophyll and amino acidsand stimulated the cellular activity, which is usefulfor the process of cell division, meristematic growthcoupled with cell enlargement, resulting in productionof larger leaves which ultimately leads to higherdrymatter accumulation.

Yield attributes

Varied spacings had not exerted anysignificant influence on yield attributes of pearlmillet(Table 3). However, highest number of panicles perplant and panicle length recorded with 60 cm x 15cm and the lowest was recorded with 30 cm x 15cm spacing. Varied spacings had not shownsignificant influence on threshing percentage.However, highest threshing percentage was recordedwith 30 cm x 15 cm and the lowest was recordedwith 45 cm x 15 cm.

Table 2. Weather during different phenophases of pearlmillet

Phenophases Days Maximum Minimum Rainfall Rainytaken Temperature (o C)Temperature (o C) (mm) days

Sowing to Emergence 7 33.9 25.2 10.6 1

Emergence to panicle emergence 42 35.1 24.6 67.0 8

Panicle emergence to flowering 51 34.7 24.8 0.0 0

Flowering to grain formation 61 33.7 24.2 116.0 6

grain formation to maturity 83 33.2 24.1 54.4 6

Total/Mean 83 34.1 24.6 248 21

EFFECT OF SPACING AND NITROGEN LEVELS ON PEARLMILLET

51

RADHA KUMARI et al.

Tabl

e 3.

Effe

ct o

f spa

cing

and

nitr

ogen

leve

ls o

n pr

oduc

tivity

of p

earlm

illet

in d

ryla

nd re

gion

s

Spac

ing

S1:

30 x

15

cm98

.253

.21.

041.

5618

.868

.127

2030

7828

573

2.34

S2: 4

5 x

15 c

m97

.956

.70.

791.

5618

.765

.128

2830

1930

203

2.46

S3:

60 x

15

cm98

.560

.10.

731.

7519

.465

.429

8833

4432

595

2.67

CD

at 5

%N

SN

S0.

12N

SN

SN

SN

S24

3N

SN

S

Nitr

ogen

leve

ls

N1:

Con

trol (

No

N)

90.3

53.9

0.62

1.42

18.9

62.9

2431

3034

2496

82.

17

N2:

40 k

g/ha

101.

553

.40.

781.

5818

.765

.427

9031

4234

233

2.85

N3:

60

kg/h

a94

.860

.20.

871.

6219

.267

.130

8330

4829

581

2.41

N4:

80 k

g/ha

102.

859

.30.

931.

6919

.266

.229

6433

5431

924

2.54

N5:

100

kg/h

a10

1.7

57.4

1.06

1.87

18.9

69.5

2958

3157

3157

82.

47

CD

at 5

%6.

13.

10.

150.

35N

SN

S36

626

854

94N

S

Inte

ract

ion

(S X

N)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Trea

tmen

tsD

MPa

tha

rves

t (g/

plan

t)

LAI

at 6

0D

AS

Num

ber o

fpa

nicl

espl

ant-1

Pani

cle

leng

th (c

m)

Thre

shin

gPe

rcen

tage

Gra

in y

ield

(kg

ha-1)

Stra

w y

ield

(kg

ha-1)

Net

Ret

urns

(Rs.

ha-1

)B

: C ratio

Plan

the

ight

at

har

vest

(cm

)

52

Tabl

e 4.

Cor

rela

tion

coef

ficie

nt b

etw

een

yiel

d at

trib

utes

and

yie

ld o

f pea

rlmill

et a

s in

fluen

ced

by s

paci

ng

Num

ber o

f till

ers

pla

nt-1

1

Num

ber o

f pan

icle

s pl

ant-1

0.99

9**

1

Pan

icle

leng

th (c

m)

0.98

3**

0.99

1**

1

Sin

gle

pani

cle

wei

ght

(g)

0.99

6**

0.99

9**

0.99

5**

1

Gra

in w

eigh

t per

pan

icle

(g)

0.86

8**

0.89

3**

0.94

5**

0.90

8**

1

Thre

shin

g Pe

rcen

tage

(%)

-0.4

68-0

.419

-0.2

96-0

.388

0.03

31

Test

wei

ght (

g)0.

962*

*0.

975*

*0.

996*

*0.

982*

*0.

971*

*-0

.208

1

Har

vest

Inde

x0.

731*

*0.

693*

0.59

20.

669*

0.29

6-0

.945

0.5

171

Gra

in Y

ield

(kg

ha-1)

0.93

7**

0.91

6**

0.85

5**

0.90

2**

0.63

9*-0

.748

0.80

5**

0.92

4*1

Stra

w y

ield

(kg

ha-1)

0.97

5**

0.98

5**

0.99

9**

0.99

1**

0.95

7**

-0.2

590.

999*

*0.

560

0.83

5**

1

*

= S

igni

fican

t at 5

% le

vel

; *

* =

Sig

nific

ant a

t 1 %

leve

l

Para

met

er

Num

ber o

ftil

lers

plan

t-1

Num

ber o

fpa

nicl

espl

ant-1

Pani

cle

leng

th(c

m)

Sing

lepa

nicl

ew

eigh

t (g)

Gra

inw

eigh

tpa

nicl

e-1

(g)

Thre

shin

gPe

rcen

tage

(%)

Test

wei

ght (

g)H

arve

stIn

dex

Gra

inYi

eld

(kg

ha-1

)

Stra

wyi

eld


53

Number of panicles plant-1 was higher withapplication of 100 kg N ha-1 which was comparablewith 80 kg ha-1, 60 kg ha-1and 40 kg ha-1 withsignificant parity between treatments andsignificantly superior over control which recordedlowest number of panicles plant-1. These results werecontradictory to Eric Obeng et al. (2012) who reportedthat nitrogen rates 0, 40 kg N ha-1, 80 kg N ha-1 and120 kg N ha-1 did not show any significant differencefor number of panicles.

Panicle length was not significantlyinfluenced by different nitrogen levels. However,maximum panicle length was recorded with 60 kg Nha-1 and 80 kg N ha-1. The least panicle length wasobserved with application of 40 kg N ha-1. Thesefindings are in contrast to the studies of Maas andHanna (2006) who reported that an increase in rateof nitrogen up to 112 kg ha-1 led to a boost in headlength. Ahmed et al. (2015) observed that from 0 –80 kg N ha-1 panicle length increases with increasein N application but further increase of N from 80-120 kg N ha-1 has not resulted in further increase inpanicle length. These results are in conformity withthe findings of Pandey and Sinha (2010).

Threshing percentage was not significantlyinfluenced by nitrogen level. However, maximumthreshing percent was recorded with application of100 kg N ha-1 compared to other levels of Nitrogen.The control treatment has registered least value. Testweight was also not significantly influenced bydifferent nitrogen levels. However, application of 80kg N ha-1 recorded highest test weight. The lowesttest weight was recorded with control treatment.These results are in accordance with findings ofMustapha et al. (1997) who reported that Napplication to the crop produced heavier seeds thanthe control.

Grain and straw yield

Grain yield was not significantly influencedby variable spacing (Table 3). However, 60 cm x 15

cm spacing recorded maximum grain yield comparedto 45 cm x 15 cm and 30 cm x 15 cm. Variation ingrain yield was remarkable due to applied level ofnitrogen. Maximum grain yield was recorded with 60kg N ha-1 compared to lower level of 40 kg N ha-1 andhigher level of 80 kg N ha-1 and 100 kg N ha-1 withsignificant parity between treatments. The lowestgrain yield was registered with control treatment. Itwas observed that grain yield increased with increasein nitrogen level from 0 to 60 kg ha-1 but furtherincrease of N until 100 kg ha-1 did not result in furtherincrease in grain yield. Similar results were reportedby Hassan and Bibinu (2010), Jat et al. (1994) andSharma et al. (1999) who observed that grain yieldof pearlmillet had increased with increased levels ofnitrogen. The interaction effect due to adoptedspacing and applied levels of nitrogen on grain yieldwas found non-significant.

Straw yield was significantly influenced bythe adopted spacing (Table 3). The crop spaced atthe spacing of 60 cm x 15 cm registered higheststraw yield compared to 30 cm x 15 cm and 45 cmx 15 cm. Among different levels of nitrogen applied,highest straw yield was recorded with 80 kg N ha-1

compared to 40 and 100 kg N ha-1 with significantparity. The lowest straw yield was recorded withcontrol. Among different levels of nitrogen, 80 kgha-1 was optimum as it gave significantly higher grainand straw yield compared to 40 kg N ha-1, 60 kg Nha-1 and on par yield at 100 kg N ha-1. The increasedyield response upto a dose of 80 kg N ha-1 comparedto a higher dose of 100 kg N ha-1 can be attributed toincreased uptake of nitrogen. As the soil wasdeficient in available nitrogen, the crop absorbed thisnutrient efficiently and thereby enhanced the drymatter production. This might have resulted in morenumber of panicles per sq m that supported highergrain and straw yield (Yakadri and Pratap KumarReddy, 2009). The interaction effect was found to benon-significant with varied spacing and levels ofnitrogen.

RADHA KUMARI et al.

54

Tabl

e 5.

Cor

rela

tion

coef

ficie

nt b

etw

een

yiel

d at

trib

utes

and

yie

ld o

f pea

rlmill

et a

s in

fluen

ced

by n

itrog

en le

vels

Num

ber o

f till

ers

pla

nt-1

1

Num

ber o

f pan

icle

s p

lant

-10.

960*

*1

Pan

icle

leng

th (c

m)

0.31

00.

159

1

Sin

gle

pani

cle

wei

ght

(g)

0.98

8**

0.97

1**

0.21

71

Gra

in w

eigh

t per

pan

icle

(g)

0.85

7**

0.87

2**

0.13

00.

921*

*1

Thre

shin

g Pe

rcen

tage

%0.

992*

*0.

958*

*0.

202

0.98

7**

0.85

0**

1

Test

wei

ght (

g)0.

851*

*0.

854*

*0.

608*

0.82

7**

0.74

5**

0.78

6**

1

Har

vest

Inde

x0.

561

0.54

0-0

.198

0.50

20.

177

0.62

7*0.

216

1

Gra

in Y

ield

(kg

ha-1)

0.83

9**

0.72

9**

0.53

60.

745*

*0.

449

0.81

6**

0.74

5**

0.69

4*1

Stra

w y

ield

(kg

ha-1)

0.31

60.

474

0.30

10.

280

0.15

50.

260

0.64

6*0.

164

0.37

91

*

= S

igni

fican

t at 5

% le

vel ;

** =

Sig

nific

ant a

t 1 %

leve

l

Para

met

er

Num

ber o

ftil

lers

plan

t-1

Num

ber o

fpa

nicl

espl

ant-1

Pani

cle

leng

th(c

m)

Sing

lepa

nicl

ew

eigh

t (g)

Gra

inw

eigh

t per

pani

cle

(g)Th

resh

ing

Perc

enta

ge(%

)

Test

wei

ght (

g)H

arve

stIn

dex

Gra

inYi

eld

(kg

ha-1

)

Stra

wyi

eld

(kg

ha-1

)


55

Tab

le 6

. C

orre

latio

n co

effic

ient

bet

wee

n yi

eld

attr

ibut

es a

nd y

ield

of p

earlm

illet

as

influ

ence

d by

inte

ract

ion

betw

een

sp

acin

g an

d n

itrog

en le

vels

No.

of t

iller

s pl

ant-1

1

No.

of p

anic

les

plan

t-1

0.8

50**

1

Pan

icle

leng

th (c

m)

0.19

50.

333

1

Sin

gle

pani

cle

wei

ght

(g)

0.36

40.

116

0.45

51

Gra

in w

eigh

t per

pan

icle

(g)

0.29

20.

045

0.33

50.

889*

*1

Thre

shin

g %

0.00

0-0

.076

-0.0

950.

163

0.58

6*1

1000

see

d w

eigh

t (g)

0.06

4-0

.194

-0.0

730.

306

0.31

90.

154

1

Har

vest

Inde

x0.

444

0.28

6-0

.236

0.01

50.

119

0.20

00.

337

1

Gra

in y

ield

(kg

ha-1

)0.

326

0.21

7-0

.213

0.11

80.

260

0.37

30.

600*

0.78

1**

1

Stra

w y

ield

(kg

ha-1

)-0

.208

-0.1

090.

061

0.14

10.

226

0.32

20.

355

-0.3

550.

303

1

*

= S

igni

fican

t at 5

% le

vel

;

** =

Sig

nific

ant a

t 1 %

leve

l

Para

met

er

Num

ber o

ftil

lers

plan

t-1

Num

ber o

fpa

nicl

espl

ant-1

Pani

cle

leng

th(c

m)

Sing

lepa

nicl

ew

eigh

t (g)

Gra

inw

eigh

t per

pani

cle

(g)Th

resh

ing

Perc

enta

ge(%

)

1000

see

dw

eigh

t (g)

Har

vest

Inde

x

Gra

inYi

eld

(kg

ha-1

)

Stra

wyi

eld

(kg

ha-1

)

RADHA KUMARI et al.

56

Economics

Net returns and B:C ratio were notsignificantly influenced by varied spacings andnitrogen levels (Table 3). However, highest net returnsand B:C ratio was recorded with 60 cm x 15 cm andthe lowest net returns and B:C ratio was recordedwith 30 cm x 15 cm. Among nitrogen levels, highernet returns and B:C ratio was recorded withapplication of 40 kg N ha-1 compared to 60 kg Nha-1, 80 kg N ha-1 and 100 kg N ha-1 which inturn arecomparable to each other. The higher economicreturns might be due to higher grain and straw yieldsregistered under higher nitrogen levels. The lowestnet returns and B:C ratio was recorded with control.The interaction effect was found to be non-significantwith different levels of nitrogen and spacing.

Correlation between yield attributes and yieldas influenced by spacing

There was positive and significant correlationbetween number of tillers and panicles plant-1 (Table4). Panicle length was positively and significantlycorrelated with number of tillers and panicles plant-1.Single panicle weight, grain weight panicle-1, testweight, harvest index, grain yield, straw yield werepositively and significantly correlated with number oftillers and panicles plant-1. Panicle length waspositively and significantly correlated with singlepanicle weight. Panicle length and single panicleweight were positively and significantly correlatedwith grain weight panicle-1, test weight, grain andstraw yield. There was positive and significantcorrelation between single panicle weight and harvestindex. Grain weight panicle-1 was positively andsignificantly correlated with test weight, grain andstraw yield. Test weight was positively andsignificantly correlated with grain and straw yield.There was positive and significant correlation betweengrain yield and harvest index, grain yield and strawyield.

Correlation between yield attributes and yieldas influenced by nitrogen levels

Single panicle weight, grain weightpanicle-1, threshing percentage, test weight, grainyield was positively and significantly correlated withnumber of tillers and panicles plant-1 (Table 5).Therewas positive and significant correlation betweennumber of tillers plant-1 and number of panicles plant-1, panicle length and test weight, harvest index andgrain yield. Single panicle weight was positively andsignificantly correlated with grain weight panicle-1,threshing percentage, test weight and grain yield.Grain weight panicle-1 was positively and significantlycorrelated with threshing percentage and test weight.Threshing percentage was positively and significantlycorrelated with test weight, harvest index and grainyield. Test weight was positively and significantlycorrelated with grain and straw yield.

Correlation between yield attributes and yieldof pearlmillet as influenced by interactionbetween spacing and nitrogen levels

Number of panicles plant-1 was positively andsignificantly correlated with number of tillers (Table6).There was positive and significant correlationbetween grain weight panicle-1 and single panicleweight.Threshing percentage was positive andsignificantly correlated with grain weight panicle-1.Grain yield was positive and significantly correlatedtest weight and harvest index.

CONCLUSION

ABH-1 can be grown at inter row spacing of45 cm or 60 cm to get higher grain yield and netreturns under rainfed conditions. Though, applicationof 60 kg N ha-1 and 80 kg N ha-1 resulted in highergrain and straw yield, application of 40 kg N ha-1 isoptimum because of higher net returns and B:C ratio.


57

REFERENCES

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Ali, E. A. 2010. Grain yield and nitrogen use efficiencyof pearl millet as affected by plant density,nitrogen rate and splitting in sandy soil.American-Eurasian Journal of Agricultureand Environmental Sciences. 7(3): 327-355.

ANGRAU.2014. Bajra. In: Proceedins of ZonalResearch and Extension AdvisoryCouncil(ZREAC) Meeting of scarce rainfallzone held at RARS, Nandyal on April 6th - 7th, 2015. pp.31-33.

Ayub, M., Nadeem, M. A., Tahir, M., Ibrahim, M andAslam, M. N. 2009. Effect of nitrogenapplication and harvesting intervals onforage yield and quality of pearl millet(Pennisetum americanum L.). PakistanJournal of Life and Social Sciences. 7: 185-189.

Eric Obeng1, Ernst Cebert, Bharat P. Singh, RufinaWard, Leopold M. Nyochembeng1 andDavid A. Mays 2012. Growth and grain yieldof pearlmillet (Pennisetum glaucum)genotypes at different levels of nitrogenfertilization in the southeastern UnitedStates. Journal of Agricultural Sciences.4(12):155-163.

Gascho, G. J., Menezes R. S. C., Hanna, W. W.,Hubbed R. K and Wilson J. P. 1995. Nutrientrequirements of pearl millet. In: Proceedingsof National Grain Pearlmillet Symposium.University of Georgia, Tifton. pp. 92-97.

Hassan, A.M and Bibinu, A.T.S. 2010. Effect ofNitrogen on the growth and yield of millet

in north-eastern Nigeria, Maiduguri.Continental Journal of Agronomy. 4:10-14.

Jat, Rameshwar, Mali, A.L and Chaudhary, G.R.1994. Studies on integrated nutrientmanagement in pearlmillet (Pennisetumamericamum L.) R. Br.) In: Proceedings ofXIII National Symposium on IntegratedNutrient Management for Efficient CropProduction held at Coimbatore, 22nd-25th

February, 1994. pp.145.

Khairwal, I. S., Yadav, S. K., Rai, K. N., Upadhyaya,H. D., Kachhawa1, D., Nirwan, B andSrikant 2007. Evaluation and identificationof promising pearl millet genotype for grainand fodder traits. SAT e-Journal. 5(1):33-37.

Maas, A. L and Hanna, W. W. 2006. Cover cropaffects nitrogen response of pearlmillet ina strip-till system grain production. pp.124. ISMN 4.

Muchow, R. C. 1988. Effect of nitrogen supply onthe comparative productivity of maize andsorghum in a semi-arid tropical environmentleaf growth and leaf nitrogen. Field CropsResearch. 18(1): 131-43.

Mustapha, S.N., Victor, J.H., Kaltungo and Singh,L. 1997. Effect of nitrogen and phosphatefertilizer on performance of wheat (Triticumaestivum L.) varieties in Adama Land. In:Management of Marginal Lands in Nigeria,Singh B.R. (Editor). Proceedings of the 23rd

Annual Conference of the Soil Society ofNigeria held at Usmanu DanfodiyoUniversity, Sokoto during 2-5 March, 1997.pp. 153-164.

Pandey, S.N and Sinha, B.K. 2010. PlantPhysiology. Vikas Publishing House (4th

Edition). pp. 682.

Parthasarathy, R. P., Birthal, P. S., Reddy, B. V. S.,Rai, K. N and Ramesh, S. 2006.

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Diagnostics of sorghum and pearl milletgrains based nutrition in India. InternationalSorghum Millets Newsletter (ISMN). 47: 93-96.

Ramesh, S., Santhi, P and Ponnuswamy, K. 2006.Photosynthetic attributes and grain yieldof pearl millet (Pennisetum glaucum (L.)R.Br.) as influenced by the application ofcomposted coir pith under rainfedconditions. Acta Agronimica Hungarica.54(1) : 83-92.

Reddy, A. A., Rao, P. P., Yadav, O. P., Singh, I .P.,Ardeshna, N. J., Kundu, K. K., Gupta, S.K., Sharma, R,, Sawargaonkar, G., Malik,D. P., Shyam, D. M and Reddy, K. S. 2013.Prospects for kharif (rainy season) andsummer pearl millet in western India.Working paper series no. 36. Patancheru.pp. 302- 324.

Sharma, P.K., Yadav G.L., Fageria V.D., SudeshKumar and Sharma, B.L. 1999. Responseof pearlmillet (Pennisetum glaucum (L.) R.Br.) varieties to different levels of nitrogenunder late-sown rainfed conditions. IndianJournal of Agronomy. 44 (4): 765-767.

Shekhawat, G.S and Bhari, N.R and Chundawat, G.S.1972. Note on effect of NPK and plantspacings on yield of hybrid pearlmillet(Pennisetum typhoides) on grey brownalluvial soil of arid Rajasthan. Indian Journalof Agronomy. 17 (4): 348 -349.

Singh, R. K., Chakraborty, D., Garg, R. N., Sharmay,P. K and Sharma, U. C. 2010. Effect ofdifferent water regimes and nitrogenapplication on growth, yield, water use andnitrogen uptake by pearl millet (Pennisetumglaucum). Indian Journal of AgriculturalSciences. 80: 213-216.

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59

INTRODUCTION

Improving and maintaining soil health forenhancing and sustaining agricultural production isof utmost importance for India’s food and nutritionalsecurity. Though India is a food surplus nation withabout 231.5 million tonnes food grain production perannum, it will require about 4-5 million tonnesadditional food grain each year if the present trendin rising population persists (CMIE, 2010). Due toincrease in population pressure, the demand for food,feed, fodder, fiber, fuel, pulses and oil seed productionis rapidly increasing. To meet the future demand, wewould need better planning and management andas well as intensification of crop production. It isanticipated in India by 2025, total food grain demandwill reach 291 million tonnes comprising 109 mt ofrice, 91 mt of wheat, 73 mt of coarse grains and 25mt of pulses in spite of the limitation for theexpansion of cultivable land area (Kumar and Shiva,2010). One of the alternatives to achieve this goal isto raise the crop productivity through improvedvarieties and the matching production technologiesto sustain soil fertility, cropping systems and cropproductivity in future. Intensive cultivation through themultiple cropping with proper planning in asustainable way will help in increasing the food grain

PERFORMANCE OF MAIZE-CHICKPEA SEQUENCE AT DIFFERENT SOWINGDATES AND NITROGEN MANAGEMENT PRACTICES

M. RATNAM*, B. VENKATESWARLU, E. NARAYANA and A. LALITHA KUMARIRegional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Guntur- 522 034


ABSTRACTStudies were conducted on clay soils for two years to find out the influence of sowing time and nitrogen levels on the

yield performance and yield attributing characters in maize-chickpea sequence. Sowing dates followed and nitrogen levelsapplied to maize and chickpea significantly influenced the yield attributes, yield and monetary returns of both the crops during boththe years of the experiment. Highest grain and straw yield, higher amount of N uptake by kernel and stover and higher B:C Ratiowere recorded by both the crops when maize was sown on the 1st FN of July with 200% RDN and 100% RDN applied tosucceeding chickpea during Kharif and Rabi of 2013-14 and 2014-15.

J.Res. ANGRAU 46(1) 59-66, 2018

production for meeting the future demand andrequirement.

Maize in kharif and chickpea in rabi is oneof the crop sequence in India in both irrigated andrainfed areas. This sequence occupies 5.4 lakhhectraes and contributes 0.65 % of total food grainproduction of the country (Anonymous, 1996). In theMalaprabha Command area of Karnataka, maize inkharif and chickpea in rabi is the most profitablecropping system occupying nearly one lakh ha(Anonymous, 2006). Of these two crops, maizeremoves lot of nutrients (148 kg N, 62 kg P2O5 and133 kg K2O ha-1) and addition of nutrients to soil isoften less than the removal (Anonymous, 2004). Thesucceeding chickpea builds up the soil nitrogensymbiotically and its leaf senescence character alsoimproves the soil organic matter. The yield of maizeand chickpea mainly depends on the majoragronomic practices i.e., time of sowing and nitrogensupply to these crops ( Jaliya et al., 2008 and Gauret al., 1992). The productivity of chickpea was alsosignificantly influenced by different dates of sowings(Harpreet et al., 2005) and nitrogen levels from 0 to45 kg ha-1 (Shrikrishna et al., 2004). Maize has wideadaptability and compatibility under various soil andclimatic conditions; hence it is cultivated in sequence

*sCorresponding Author E-mail: [email protected]; Part of the PhD Thesis submitted to ANGRAU,

Guntur

60

with different crops under various agro ecologicalconditions of the country (Vidyavathi et al., 2011).Hence, the maize-chickpea sequence was studiedwith an objective to examine the agro-economicfeasibility of maize-chickpea sequence for rainfedareas of Krishna agro climatic zone of AndhraPradesh.


The field experiment was conducted atRegional Agricultural Research Station, Lam locatedat Guntur (Latitude:160181, Longitude: 800291,Altitude:33 above MSL). The climate is sub-tropicalwith average annual rainfall of 950 mm. The soil ofexperimental field was clay loam in texture, neutralto slightly alkaline in reaction (pH 7.8 to 8.2), low inavailable N (204 kg ha-1), high in P2O5 (96.5 kg ha-1)and K2O (886.5 kg ha-1) and medium in organic carbon(0.51%), respectively. The experiment wasconducted for two successive kharif and rabi seasonsof 2013-14 and 2014-15 in Krishna agro-climatic zoneof Andhra Pradesh. The experiment consisting ofthree sowing dates as main plots treatments viz.,2nd FN of June, 1st FN of July and 2nd FN of July, threenitrogen levels as sub-plot treatments viz., 100 %,150 % and 200 % RDN (RDN=200 kg N ha-1) appliedto preceding maize and four N levels as sub-sub plottreatments viz., 0, 50 %, 75 % and 100 % RDN(RDN=20 kg N ha-1) to succeeding chickpea. Alltreatments were randomly allocated and replicatedthrice in a split plot design for kharif crop and split-split designs for rabi crop during 2013-14 and 2014-15 of the experimentation. Each main plot was dividedinto required size of three sub plots and each sub-plot is again divided into four sub-sub plots of requiredsize. Recommended dose of N for maize was appliedin three splits (1/2 at sowing, ¼ at knee high stageand ¼ at taselling stage, respectively) to precedingmaize and entire dose of N was applied at the timeof sowing to succeeding chickpea. A popular andnon-lodging medium duration maize variety P-3396and popular desi chickpea JG-11 were used in both

the years of study. The data pertaining to soil, weatherand yield attributes and yield was collected duringcrop growth period. Statistical analysis pertaining toyield and yield attributes was done following theanalysis of variance technique for split and split- splitdesign. Statistical significance was tested byapplying F-test at 0.05 level of probability and criticaldifference (CD) were calculated for those parameters.


Weather prevailing during crop growth

On season basis, kharif maize crop receivedmean maximum and minimum temperatures of32.20C and 22.2 0C, sunshine hours 4.6 hrs day-1

and total rainfall of 799 mm in 49 rainy days. Rabichickpea received mean maximum and minimumtemperature of 31.0 0C and 19.4 0C, sunshine hours5.7 hrs day-1 and total rainfall of 276 mm in 11 rainydays, respectively. The above weather data was theaverage of both the years of the experiment.

Response of maize to different sowing dates andnitrogen levels under maize-chickpea sequence

The rate of increase in plant height of maizewas faster up to 60 days and thereafter it sloweddown. Among the three sowing dates; taller plants,more drymatter accumulation, cob length, numberof kernel rows cob-1, cob diameter, cob weight andnumber of kernels cob-1 were recorded with thesowing on 2nd FN of June and was on a par with 1st

FN of July. Sowing either during 1st FN or 2nd FN ofJuly gave statistically comparable plant height duringboth years of the study. The nitrogen levels tried werefound to be significant w.r.t plant height, drymatteraccumulation and above yield attributes. Among thethree nitrogen levels, nitrogen applied at 200 % RDNrecorded significantly taller plant, more drymatter andhigher yield attributes over 100 % RDN but it was ona par with 150 % RDN during both the years of study.The interaction between sowing dates and nitrogenlevels was found to be non -significant. This increasein growth and yield attribute at higher levels of

RATNAM et al.

61

Table 1. Growth response of maize at different sowing dates and N levels under maize-chickpea sequence (mean of two years)

Plant height (cm) Dry matter accumulation (gm-2)

Treatments 30 DAS 60 DAS 90 DAS Harvest 30 DAS 60 DAS 90 DAS Harvest

Main plots : Sowing dates

2nd FN of June 86.83 237.39 241.43 107.6 67.0 1001.7 1994.1 21.9

1st FN of July 86.32 217.58 222.09 100.6 32.6 793.8 1820.9 21.1

2nd FN of July 82.37 201.95 209.71 91.6 30.8 510.7 1537.7 19.4

CD (P<0.05) NS 28.57 30.03 8.7 7.0 85.4 207.3 2.1

CV (%) 12.61 9.89 10.04 8.5 12.4 8.5 8.9 6.3

Sub plots: N Levels

100 % RDN 79.48 204.49 203.22 97.5 34.7 575.6 1506.8 19.2

150 % RDN 86.48 218.81 222.40 103.5 44.2 756.7 1836.1 20.8

200 % RDN 89.57 233.61 247.61 109.1 51.5 974.0 2009.8 22.5

CD (P<0.05) NS 25.93 31.36 7.3 5.7 76.4 209.7 1.9

CV (%) 10.96 11.41 13.53 6.8 12.8 9.7 11.5 8.2

Interaction NS NS NS NS NS NS NS NS

nitrogen might be due to better availability andutilization of nitrogen resulting in improvedassimilation of nitrogen at increasing levels ofnitrogen resulting in increased plant height and celldivision and cell elongation as promoted by nitrogen(Table 1 and Table 2).

Kernel and stover yield of maize wassignificantly affected by sowing dates and nitrogenlevels during the both the years of experiment.However, the interaction of these factors was foundto be non-significant. Among the different sowingdates, the higher kernel and stover yield was recordedwith 2nd FN of June and was significantly superiorover 2nd FN of July sowing and on a par with the 1st

FN of July sowing during the first and second yearsof the study. The increased kernel and stover yieldin first sowing window might be due to the cumulative

effect of substantial improvement in growth characterslike plant height, drymatter accumulation and higheryield attributes viz., increased size of cob, morenumber of filled kernels. Increase in the kernel yieldwith increase in the nitrogen level was observed. Asignificantly increased kernel yield was registeredby the application of the 200 % RDN which wasfollowed by 150 % RDN. Applying 200 % RDN and150 % RDN were statistically comparable with eachother. Improvement in kernel and stover yield ofmaize with increase in the level of nitrogen might bedue to the manifestation of elevated level of nitrogenon growth and yield parameters resulting in thesuperior performance of maize over the lower levels.Similar results were also reported by Maryam et al.(2013) who recorded that if consumption of nitrogenincreases, the plant will have more use possibility

PERFORMANCE OF MAIZE-CHICKPEA SEQUENCE

62

Tabl

e 2.

Yie

ld a

nd y

ield

attr

ibut

es o

f mai

ze a

s in

fluen

ced

by s

owin

g d

ates

and

N le

vels

und

er m

aize

-chi

ckpe

a se

quen

ce (m

ean

of

two

year

s

RATNAM et al.

Trea

tmen

tsco

bN

o. o

fco

bco

bN

o. o

f10

0K

erne

lst

over

Ava

ille

ngth

kern

eldi

amet

erw

eigh

tke

rnel

ske

rnel

yiel

dyi

eld

-abl

e(c

m)

row

s(c

m)

(g)

cob-1

wt (

g) (q

ha-1

)(q

ha-1

)so

il N

cob-1

Ker

nel

stov

er

Mai

n pl

ots

: Sow

ing

dat

es

2nd F

N o

f Jun

e21

.915

.02.

426

3.6

535.

225

.711

1.9

107.

695

.212

.310

7.5

1st F

N o

f Jul

y21

.114

.52.

322

9.9

522.

525

.110

2.4

100.

693

.211

.198

.3

2nd F

N o

f Jul

y19

.413

.72.

320

9.3

462.

224

.595

.891

.677

.410

.585

.9

CD

(P<

0.05

)2.

11.

3N

S33

.556

.00.

011

.514

.012

.51.

716

.3

CV (%

)6.

36.

610

.311

.18.

48.

68.

59.

910

.811

.39.

0

Sub

plot

s: N

Lev

els

2nd F

N o

f Jun

e19

.213

.62.

321

2.2

481.

624

.497

.594

.981

.112

.392

.5

1st F

N o

f Jul

y20

.814

.72.

323

4.7

509.

025

.110

3.5

100.

091

.011

.198

.4

2nd F

N o

f Jul

y22

.515

.82.

325

5.9

529.

325

.710

9.1

104.

993

.610

.510

4.3

CD

(P<

0.05

)1.

90.

8N

S29

.633

.20.

07.

310

.28.

11.

210

.4

CV %

8.2

5.7

10.4

12.1

6.4

7.3

6.8

8.7

8.9

10.0

7.3

N u

p ta

ke(k

g ha

-1)

63

Tabl

e 3.

Gro

wth

resp

onse

of c

hick

pea

at d

iffer

ent s

owin

g da

tes

and

N le

vels

und

er m

aize

-chi

ckpe

a se

quen

ce (m

ean

of tw

o ye

ars)


30 D

AS

60 D

AS

HARV

EST

30 D

AS

60 D

AS

HARV

EST

by s

eed

by s

tove

r

Mai

n pl

ots

: Mai

ze s

owin

g da

tes

2nd F

N o

f Jun

e14

.11

28.2

839

.90

8.07

116.

1118

8.72

60.1

451

.22

141.

06

1st F

N o

f Jul

y17

.66

41.9

847

.28

9.61

125.

1129

1.28

60.0

851

.59

142.

54

2nd F

N o

f Jul

y15

.95

33.5

445

.16

8.53

121.

9126

8.60

59.4

551

.45

140.

52

CD

(P<0

.05)

0.97

1.99

1.24

0.70

5.01

36.3

5N

SN

SN

S

CV (%

)9.

088.

794.

3112

.22

6.38

12.8

75.

505.

507.

47

Sub-

plot

: N

app

lied

to M

aize

100%

RD

N15

.63

33.1

243

.27

8.46

114.

9219

6.89

59.0

850

.76

140.

40

150%

RD

N15

.86

34.8

443

.79

8.72

118.

2022

7.45

59.8

151

.16

140.

85

200%

RD

N16

.23

35.8

345

.27

9.01

129.

3932

4.25

60.7

752

.34

142.

86

CD

(P<

0.05

)0.

470.

811.

180.

463.

2942

.89

1.28

1.46

NS

CV (%

)5.

714.

585.

2210

.34

5.29

10.3

75.

635.

637.

89

Sub-

sub

plot

: N

app

lied

chic

kpea

0 %

RD

N15

.67

16.3

341

.65

16.3

316

.33

220.

2459

.11

50.7

713

8.25

50 %

RD

N15

.72

16.8

143

.15

16.8

116

.81

245.

2259

.72

51.2

413

9.73

75 %

RD

N15

.92

17.2

745

.07

17.2

717

.27

261.

8560

.12

51.2

914

2.28

100

% R

DN

16.3

317

.90

46.5

917

.90

17.9

027

0.82

60.5

952

.39

145.

23

CD

(P<0

.05)

0.56

0.82

1.70

0.59

2.88

43.0

61.

241.

504.

05

CV (%

)6.

424.

377.

0712

.50

4.38

6.83

5.43

5.43

4.49

Inte

ract

ion

AxB

NS

NS

NS

NS

NS

NS

NS

NS

Ava

ilabl

e so

il N

(kg

ha-1) a

fter h

arve

stTr

eatm

ents

Plan

t hei

ght a

t har

vest

(cm

)D

M a

ccum

ulat

ion

at h

arve

st(g

m-2)

Nitr

ogen

upt

ake

(kg

ha-1)

64

Tabl

e 4.

Yie

ld a

nd y

ield

attr

ibut

es o

f chi

ckpe

a as

influ

ence

d by

sow

ing

date

s a

nd N

leve

ls u

nder

mai

ze-c

hick

pea

sequ

ence

(mea

n o

f tw

o ye

ars)

RATNAM et al.

No.

of

No.

of

No.

of

1000

Har

vest

Seed

yie

ldSt

over

Ava

ilabl

eTr

eatm

ents

bran

ches

pods

seed

sgr

ain

i

ndex

(%)

(q h

a-1)

yiel

dso

il N

(kg

ha-1)

plan

t-1 p

lant

-1 p

od-1

wei

ght (

g)(t

ha-1)

afte

r har

vest

Mai

n Pl

ots:

Mai

ze s

owin

g d

ates

2nd F

N o

f Jun

e3.

2178

.30

1.19

237.

0238

.27

13.3

21.

6314

1.06

1st F

N o

f Jul

y5.

3011

0.39

1.20

247.

1938

.71

17.4

32.

9314

2.54

2nd F

N o

f Jul

y5.

0696

.25

1.20

243.

2037

.47

15.4

52.

6914

0.52

CD

(P<0

.05)

0.49

6.36

NS

NS

NS

0.31

0.17

NS

CV (%

)11

.82

14.0

217

.42

3.21

14.9

43.

0310

.30

7.47

Sub-

plot

: N

app

lied

to M

aize

100%

RD

N4.

3082

.98

1.19

238.

4537

.43

14.0

21.

9414

0.40

150%

RD

N4.

4894

.05

1.20

242.

7238

.05

14.7

52.

3714

0.85

200%

RD

N4.

7910

7.91

1.20

246.

3539

.03

17.4

23.

2314

2.86

CD

(P<0

.05)

0.20

6.78

NS

NS

NS

0.57

0.11

NS

CV (%

)8.

1514

.69

13.4

66.

9911

.67

7.25

8.50

7.89

Sub-

sub

plot

: N

app

lied

to c

hick

pea

0 %

RD

N4.

0279

.05

1.19

236.

7937

.02

618.

102.

2113

8.25

50 %

RD

N4.

3390

.88

1.19

242.

3337

.82

738.

242.

4713

9.73

75 %

RD

N4.

6398

.06

1.20

244.

7738

.39

826.

652.

6614

2.28

100

% R

DN

5.12

111.

921.

2024

6.13

39.4

293

5.23

2.72

145.

23

CD

(P<0

.05)

0.34

7.14

NS

NS

NS

0.66

0.13

4.05

CV (%

)16

.96

15.2

415

.92

7.77

14.1

37.

8612

.40

4.49

Inte

ract

ion

NS

NS

NS

NS

NS

NS

NS

NS

65

and by more nitrogen absorbing by the roots andtransferring to reproductive organs, harvest index willincrease.

Response of chickpea to sowing dates andnitrogen levels under maize-chickpea sequence

Plant height, drymatter accumulation, yieldattributes and yield of rabi chickpea was significantlyinfluenced by maize sowing dates and N levels andN application to succeeding rabi chickpea. Maximumplant height, drymatter accumulation, number ofbranches, number of pods plant -1 in chickpea wasrecorded when the preceding maize was sown on 1st

FN of July at 200 % RDN and 100 % RDN applied tosucceeding chickpea. Early sowing, adequatenitrogen and soil moisture that makes higheravailability of nutrients in the soil, longer sunshinehours per day resulting in more photosyntheticactivity, efficient translocation of photosynthates tosink might have resulted in increased drymatterproduction might be due to the fact that nitrogenfertilization made the plants more efficient inphotosynthetic activity, enhancing the carbohydratemetabolism and ultimately increasing in drymatteraccumulation finally resulted in higher yield.

All the sowing dates and nitrogen levels triedin preceding maize and also N levels tried insucceeding chickpea differed significantly on grainyield and stover yield of chickpea with one anotherduring both the years of the study. Significantlyhigher grain yield and stover yield of chickpea wasrecorded when the preceding maize was sown on 1st

FN of July and applying 200 % RDN and giving 100% RDN to succeeding chickpea in both the years ofthe study gave higher yields. Nitrogen levels tried inpreceding maize and N levels tried in succeedingchickpea only differed significantly with N up takeduring both the years of the study. Significantly higherN uptake of succeeding chickpea was recorded whenthe preceding maize was applied with 200 % RDNand 100 % RDN applied to succeeding chickpea inboth the years of the study. Similar results were

reported by Sreerekha et al. ( 2015). From themaize- chickpea sequence,high gross returns(Rs. 2,18,429 /-), net returns (Rs.1,38,555/-) and B:CRatio (3.8) were registered when maize was sownon 1st FN July applied with 200 % RDN and whenchickpea was applied with 100 % RDN. These resultsare in accordance with the findings of Lingaraju etal. (2010); Jnanesha and Alagundagi (2012);Vidyavathi et al. (2012) and Mohankumar andHiremath (2015) who reported significantly higher netreturns and benefit- cost ratio in maize- chick peasequence.

CONCLUSION

There is scope to adopt maize-chickpeacropping sequence in rainfed areas of Krishna zoneas evident from the better yield levels of both thecrops in both the years of study. Sowing maizeduring 1st FN of July, applying 200 % RDN to maizeand applying 100 % RDN to succeeding chickpeawere found to be the best agronomic managementpractices for maize-chickpea sequence in rainfedareas of Krishna zone.

REFERENCES

Anonymous. 2004. Annual Progress Report 2003-2004. Water Management Centre,Belvatagi under University of AgriculturalSciences, Dharwad.

Anonymous. 2006. Annual Progress Report 2005-2006. Water Management Centre,Belvatagi, University of AgriculturalSciences, Dharwad.

CMIE. 2010. Monthly review of the Indian economy.Economic Intelligence Service. August,2010. Centre for Monitoring IndianEconomy, Mumbai. Retrieved from website(https://www.cmie.com) on 6.1.2018.

Directorate of Cropping Systems Research. 1996.Annual Progress Report 1995-1996.Directorate of Cropping Systems Research,Meerut, Uttar Pradesh.


66

Gaur, B.L., Mansion, P.R and Gupta, D.C. 1992.Effect of nitrogen levels and their splits onyield of winter maize (Zea mays L.). IndianJournal of Agronomy. 34 (4): 816-817.

Harpreet Kaur Virk, Guriqbal Singh and Sekhon, H.N. 2005. Effect of time of sowing on theproductivity of chickpea (Cicer arietinumL.)varieties. Journal of Research, PunjabAgricultural University. 42 (2): 148-149.

Jaliya, M. M., Falaki, A . M., Mahmud, M and Sani,Y. A. 2008. Effect of sowing date and NPKfertilizer rate on yield and yield componentsof quality protein maize (Zea mays L.).African Journal of Agricultural and BiologicalScience. 3 (2): 23-29.

Jnanesha, A.C and Alagundagi, S.C. 2012. Integratednutrient management practices in maize-chickpea sequence cropping under broadbed and furrow in model watershedDharwad. Karnataka Journal of AgriculturalSciences. 25 (4): 154-157.

Krishna, S., Sharma, A.P and Bhushan Chandra.2004. Nitrogen and sulphur nutrition ofchickpea (Cicer arietinum L.) grown undersemi-arid conditions of Uttar Pradesh.Legume Research. 27 (2): 146-148.

Kumar, V and Shiva, Y.S. 2010. Integrated nutrientmanagement: An ideal approach forenhancing agricultural production andproductivity. Indian Journal of Fertilizers. 6(5):41-57.

Lingaraju, B. S., Parameshwarappa, U. K., Hulihalliand Basavaraju, B. 2010. Effect of organicson production and economic feasibility inmaize-chickpea sequence Indian Journal ofAgricultural Sciences. 44 (3): 211-215.

Maryam Jasemi, Fereshteh Darabi and RahimNaseri. 2013. Effect of Planting Date andNitrogen Fertilizer Application on Grain Yieldand Yield Components in Maize (SC 704).American-Eurasian Journal of Agriculturaland Environmental Sciences. 13 (7): 914-919.

Mohankumar, R and Hiremath, S. M. 2015. Residualeffect of maize hybrids, plant population andfertility levels on performance of chickpeain maize-chickpea sequence. KarnatakaJournal of Agricultural Sciences. 28 (4): 482-485.

Sreerekha, M., Subbaiah, G., Veeraraghavaiah, R.,Ashokarani, Y and Prasunarani, P. 2015.Influence of rabi legumes and nitrogen levelson growth and yield of summer maize. TheAndhra Agricultural Journal. 62 (3): 518-522.

Vidyavathi, G. S., Dasogh, B., Babalad, N .S.,Hebsur, S. K., Gali, Patil, S.G andAlagawadi, A.R. 2011. Influence of nutrientmanagement practices on crop responseand economics in different croppingsystems in vertisols. Karnataka Journal ofAgricultural Sciences. 24 (4): 455-460.

RATNAM et al.

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INTRODUCTION

Coriander (Coriandrum sativum) leaf is richin Vitamin A and has lot of medicinal values.(Balakrishna and Pushpa Kumari, 2001; Murthy andSridhar, 2001).There is a lot of demand for corianderleaf throughout the world for its fragrance andmedicinal values. Coriander under irrigated conditionsis grown through out the year for leaf purpose.However, cultivation of this crop is restricted to onlycooler parts of the regions in summer season as thecrop can’t withstand high temperatures. This offersthe growers high remunerative price (Rs.100/- to150/-per kg) for the crop. There is a great demandfor leaf during summer months from March to June,as production is limited to only a few areas of thecountry, where summer temperatures are low. Isbell(2009) reported that coriander can be grown as shortduration crop and has potential to rotate as a secondcrop following winter crop and can get remunerativeincome in a short span of time. However, hightemperature is considered as the major limiting factorfor germination and growth of coriander duringsummer. Bomme et al. (2007) studied the spring

EFFECT OF DIFFERENT SHADING INTENSITIES AND GENOTYPES ONPRODUCTION OF LEAFY CORIANDER IN OFF SEASON

C.SARADA*, K.GIRIDHAR and L.NARAM NAIDUHorticultural Research Station, Dr. YSR Horticultural University, Guntur -522 034


ABSTRACTThe investigation was under taken to study the effect of different intensities of shade as one factor and promising

genotypes as another factor in Factorial Randomized Block Design with three replications during the years 2009-10 and 2010-11.Among the treatments, days taken for germination was observed significantly less under 50% shade (9.88 days) followed by75% shade (11.04 days). With regard to genotypes, Sadhana germinated earlier (11.20 days) being on par with other genotypes.Number of leaves was recorded significantly more under 50% and 75% shade (6.11) than control (4.41). Among the genotypessignificantly more number of leaves were recorded in LCC- 244 (6.43) being on par with LCC- 234 (6.12) than Sadhana (4.81) i.e.check. Significantly maximum yield was recorded with 50% shade (3.59 t ha-1) followed by 35% shade (3.01 t ha-1) than control(0.51 t ha-1). Among the genotypes, significantly maximum yield was recorded in LCC- 244 (3.18 t ha-1) being on par with LCC-234(2.89 t ha-1) than check (1.87 t ha-1).

J.Res. ANGRAU 46(1) 67-71, 2018


culivation of coriander for leaf and suggested thatthe coriander variety ‘Santos’ can be grown forhighest leaf yield. There is lack of informationregarding the technology for summer production ofleafy coriander and suitability of genotypes for leaf.Protected cultivation provides the best way toincrease the productivity and quality of vegetablesas well as to tolerate biotic and abiotic stressescompared to open field condition (Singh et al.,2007).Greenhouse cultivation showed superior yieldand yield attributing characters as compared to openfield condition of cucurbits (Dixit et al., 2005). Tomake the conditions congenial, some low coststructures (50% agro shadenet as roofing material)may be used especially during off-season. However,information is not available on the technology forcultivation of coriander in off season and the potentialgenotypes for leafy coriander. Hence, theinvestigation was under taken with an objective tostudy the effect of different shade intensities andgenotypes suitable for leaf purpose in coriander duringsummer season.

68


The investigation was carried out for a periodof two years during 2009-10 and 2010-11 atHorticultural Research Station, Lam, Guntur. Theexperiment was conducted in Factorial RandomisedBlock Design(RBD) with three replications consistingof different intensities of shade as one factor andgenotypes as second factor. Six promisinggenotypes viz., LCC- 231, LCC- 234, LCC- 242, LCC-244 and Sadhana (check) were evaluated under open,35%, 50% and 75% shade intensties during summerseason in Factorial RBD with three replications. Theexperimental soil was slightly alkaline (pH 7.7), lowin organic carbon (0.43%), medium in availablenitrogen (168 kg ha-1), low in available phosphorus(17.4 kg ha-1) and high in exchangeable potassium(954 kg ha-1). At the time of sowing, organic manurein the form of FYM was added as basal dose inaddition to 100 gm of urea/ 30 m2/ plot. Sowing wasdone at 2cm - 5 cm depth and paddy straw wasused as mulch upto germination. Necessary culturaloperations were taken up periodically for theexperimental field. The crop was sown during secondfortnight of May in both the seasons. As the cropwas sown for leaf purpose harvesting was done at45 days after sowing. Data on days to germination,number of leaves, yield and essential oil content wasrecorded during the crop growth period. Pooledanalysis was done as per the method suggested byJawahar (2006).


The pooled analysis of the data indicated thatthe genotypes and different intensities of shade underevaluation exhibited significant differences for all thecharacters under study.

Number of days to germination

Results of the investigation revealed thatnumber of days to germination was significantlyinfluenced by the varieties and intensities of shade.(Table 1). Number of days taken for germination was

more under open conditions (14.2 days) than undershade net. Among the treatments, days taken forgermination was significantly less under 50% shade(9.88 days) followed by 75% shade (11.04 days).Early germination in shade net may be due to microclimatic conditions resulting in low temperature andhigh humidity under shade net. Better germinationand growth under shading as compared to controlwas observed by Guha et al. (2013) in coriander. Theoptimum temperature range for coriander seedgermination is 24°C-25°C (Allahmoradi et al., 2013).The low percentage of germination as well as delayedgermination in open (control) plot may be attributedto higher air temperatures. In case of 50% shadingand 75% shading plots, the air temperatures (at 2.30pm) were 3°C to 5 °C less than that of control plot.Shading might have created the optimum micro-climatic condition by reducing the soil temperatureand increasing the available soil moisture (Saradaet al., 2011). The reduced soil temperature along withenhanced humidity might have activated the embryosand thereby reflecting earlier and higher percentageof germination under shade net condition. With regardto genotypes, Sadhana germinated earlier (11.20days) being on a par with other genotypes. Thevariability observed with regard to germination amongcultivars may be due to variability in genotypes.

Number of leaves

Number of leaves was significantly influencedby the varieties and shade intensity (Table 1).Number of leaves was significantly higher under 50%shade and 75% shade (6.11) than open conditions(4.41). Among the genotypes, significantly morenumber of leaves were recorded in LCC- 244 (6.43)being on a par with LCC – 234 (6.12) than check(4.81). Similar results of variation in performance ofgenotypes were reported by Uliana and Timoth (2007)in summer coriander. This might be due to variabilityin performance of genotypes under beneficial micro-climate in the shade net house. Similar results ofbetter leaf growth was observed in protected

SARADA et al.

69

Table 1. Effect of shade intensities and genotypes on no. of days to germination and no. of leaves in coriander

Open 35% 50% 75% Mean Open 35% 50% 75% Mean

LCC-231 14.8 13.3 10.0 9.7 11.9 3.5 5.0 5.9 5.7 5.1

LCC-233 15.3 12.7 10.1 10.9 12.3 3.8 5.2 6.4 6.0 5.4

LCC-234 14.0 13.1 10.8 11.9 12.4 5.3 6.2 6.7 6.3 6.1

LCC-242 13.3 12.8 9.5 12.0 11.9 4.8 5.4 5.7 6.1 5.5

LCC-244 13.4 12.1 9.8 12.3 11.9 5.3 6.7 6.7 7.0 6.4

Sadhana 14.3 11.9 9.1 9.5 11.2 3.7 4.8 5.3 5.5 4.8

Mean 14.2 12.7 9.9 11.0 12.0 4.4 5.6 6.1 6.1

CD CV CD CV (P<0.05) (%) (P<0.05) (%)

Treatments 1.83 23.1 0.40 11.0

Cultivars 2.25 0.5

VxT 4.5 0.99

Table 2. Effect of shade intensities and genotypes on leaf yield and essential oil % in coriander

LCC-231 0.23 2.45 2.46 2.67 1.95 0.10 0.15 0.22 0.15 0.16

LCC-233 0.23 2.7 3.57 2.63 2.28 0.12 0.24 0.20 0.19 0.19

LCC-234 0.29 3.55 4.54 3.18 2.89 0.10 0.19 0.21 0.23 0.18

LCC-242 0.24 3.09 4.13 3.06 2.63 0.10 0.13 0.20 0.20 0.16

LCC-244 0.32 3.86 5.03 3.52 3.18 0.19 0.33 0.25 0.22 0.25

Sadhana 0.19 2.39 1.81 1.57 1.49 0.12 0.15 0.20 0.17 0.16

Mean 0.25 3.01 3.59 2.77 0.12 0.20 0.21 0.19

CD CV CD CV(P<0.05) (%) (P<0.05) (%)

Treatments 1.18 22.5 NS 23.5

Cultivars 1.45 0.07

VxT 2.9 NS

No. of days to germination Number of leaves

EFFECT OF DIFFERENT SHADING INTENSITIES AND GENOTYPES ON CORIANDER

Leaf Yield (t ha-1) Essential Oil (%)

Open 35% 50% 75% Mean Open 35% 50% 75% MeanShade

intensity

Entry

Shadeintensity

Entry

70

cultivation in coriander by Guha et al. (2016);vegetables by Rajasekhar et al. (2013) and Singhet al. (2007) which may be due to less biotic andabiotic stress conditions compared to open field. Thereduction in the average maximum air temperatureand light intensity by shading might have created anenvironment congenial for growth of plant therebyreflecting in higher foliage growth, while theconsistently higher temperature in the open plotmight have resulted in poor growth of coriander duringsummer season. The early germination under shadenet might have also resulted in more vegetative growthof plant. The seeds germinated earlier had morenumber of leaves and resulted in vigorous growthduring the later period also.

Yield

Results of the investigation revealed that leafyield (t ha1) was significantly influenced by thevarieties and shade intensities (Table 2). Among thegenotypes maximum yield was recorded in LCC- 244(3.18 t ha1) being on par with LCC-234 (2.89 t ha1)than check (1.87 t ha1). Similar results of variation inperformance of coriander genotypes in shadenetsduring off season was reported by Bomme et al.(2007), Tehlan and Malik (2010). Among the shadeintensities, maximum yield was recorded with 50%shade (3.59 t ha1) followed by 35% shade(3.01 t ha1)than open conditions (0.51 t ha1).This indicated thatthe yield of leafy coriander during the summer seasonwas significantly influenced by different shadingtreatments. The various shading treatmentssignificantly increased the yield to an extent of about6-7 times as compared to the control. Similar resultsof increase in yield of leafy coriander due to differentintensities of shade were reported by Dixit (2007);shade intensities and variety was reported by Tehlanand Malik (2010). In case of coriander, as entire plantis used as a vegetable, increase in the growthcharacters, early germination, more number ofleaves, etc. might have directly resulted in increasedyield. Therefore, better yields in shading treatments

might be attributed to better growth of plants due tofavourable range of temperatures and light intensitythat prevailed in shading treatments which in turnenhanced the rate of photosynthesis and therebymore carbohydrate accumulation. (Kotadia et al.,2012).Higher germination percentage in shadedtreatments might have resulted in increased plantpopulation and subsequently increased yield.

Essential Oil (%)

The data pertaining to essential oilpercentage revealed that there was significantdifference with respect to genotypes and shadeintensities (Table 2). Among the genotypes, essentialoil percentage was significantly maximum in LCC-244 (0.25%) being on a par with LCC-233 (0.19%).The variability observed in essential oil content mightbe due to variability in performance of genotypes.However, the essential oil percentage among thedifferent shade intensities was not significant.

CONCLUSION

Among the different shaded treatments, 50%shade was ideal for coriander cultivation as itrecorded early germination, more number of leavesand higher yield. With regard to genotypes, LCC-244 and LCC-234 gave more number of leaves andyield than Sadhana (check). Hence, the studyindicated that during off season (summer) coriandercan be cultivated under 50% shade net conditionswith LCC -244 and LCC- 234 genotypes.

REFERENCES

Allahmoradi Pezhman, Mokhtar Ghobadi, andShayesteh Taherabadi. 2013. Assessingcardinal temperature for germination incoriander (Coriandrum sativum), Sainfoin(Onobrychis vicifolia) and Bitter Vetch (Viciaervilia). Annual Review and Research inBiology. 3(4): 881-887.

Balakrishna, K. U and Pushpakumari, K. N. 2001.Extracts of seed spices. In: Seed Spices

SARADA et al.

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Production Quality and Export. Agarwal, S.,Sastry, E.V.D and Sharma, R. K. (Editors).Pointer Publishers, Jaipur. pp. 195-229.

Bomme, U., Blum, H and Rinder, R. 2007. Springcultivation of coriander (Coriandrum sativumL.) for leaf use - results from several yearsof variety testing. Zeitschrift fur Arznei- andGewurzpflanzen. 12 (1): 24-29.

Dixit, A., Agrawal, N., Sharma, H.G and Dubey, P.2005.Performance study of leafy vegetablesunder protected and open field conditions.Haryana Journal of Horticultural Sciences.34 (1-2): 196.

Dixit, A. 2007. Performance of leafy vegetables underprotected environment and open fieldcondition. Asian Journal of Horticulture. 2:197-200.

Guha, S., Debnath, S and Sharangi, A.B.2016.Influence of growing conditions onyield and essential oil of coriander duringyear-round cultivation international journalof agriculture sciences. 8 (5):1021-1026.

Guha, S., Sharangi, A.B and Debnath, S. 2013. Effectof different sowing times and cuttingmanagement on phenology and yield of offseason coriander under protectedcultivation. Trends in Horticulture Research.3(1):27-32.

Isbell, T. 2009. Effort in the development of new crops(coriander, cuphea, lesquerella andpennycress). Proceedings of the JournéesChevreul. 16(4):205-210.

Jawahar R Sharma. 2006. Experimental field esignfor plant breeding. Statistical andBiometrical techniques in Plant Breeding.NewAge Intenational (P) LimitedPublishers, New Delhi. pp.11-16.

Kotadia, H.R., Patil, S.J., Bhalerao, P.P., Gaikwad,S.S and Mahanthi, H.D. 2012. Influence ofdifferent growing conditions on yield of leafyvegetables during summer season. TheAsian Journal of Horticulture. 7(2):300-302.

Murthy, A. R and Sridhar, V. 2001. Seed spices andAyurveda. In: Seed Spices ProductionQuality and Export. Agarwal, S., Sastry,E.V.D and Sharma, R. K. (Editors). PointerPublishers, Jaipur. pp. 290-302.

Rajasekar, M., Arumugam, T and Ramesh Kumar,S. 2013. Influence of weather and growingenvironment on vegetable growth and yieldJournal of Horticulture and forestry.5(10):160-167.

Sarada, C., Giridhar, K., Yellaman, R andVenkatareddy, P. 2011. Weathermodification for off season production ofCoriander (Coriandrum sativum L.) for leaf.Journal of Agro. Meteorology. 13: 54-57.

Singh, B., Kumar, M and Sirohi, N.P.S. 2007.Protected cultivation of cucurbits under lowcost protected structures- A sustainabletechnology for peri urban areas of NorthernIndia. Acta Horticulture. 731: 267-272.

Tehlan, S.K and Malik, T. P. 2010. Influence ofdifferent shade intensities and varieties onleaf yield of coriander during summer.National Seminar on Recent Trends inHorticulture Crops- Issues and Strategiesfor Research and development (AbstractBook) held on 22nd-24th March, 2010 at CCSHaryana Agricultural University, Hisar,Haryana. pp. 123.

Uliana and Timoth. 2007. Diversity and physiologicalplasticity of vegetable genotypes ofcoriander improves herb yield, habit andharvesting window in any season.Euphytica.180(3):369-384.

EFFECT OF DIFFERENT SHADING INTENSITIES AND GENOTYPES ON CORIANDER

72

INTRODUCTION

The Indian Council of Agricultural Research(ICAR) has a network of 681 Krishi Vigyan Kendras(KVKs) in India (ICAR, 2017). Ever since theirestablishment, KVKs have played effective role oftechnology backstopping to extension personneland subsequently to farmers so as to enable themto augment their productivity and profitability(Kokate, 2010).The KVKs are playing the role ofintermediary institutions to fine tune the researchconducted,often under controlled conditions,before its adoption in farmer’s field.An investigationwas conducted to study the adoption behaviour ofrespondents from adopted villages of the KVK withrespect to the recommended Groundnut productiontechnologies in Chittoor district of Andhra Pradesh.Wilkening (1950) stated that the farmer’s decisionfor adoption of improved farm practices may beconsidered as a process in which he/she (a) hearsabout the practice, (b) discusses its advantagesand disadvantages with other farmers or withexperts, (c) makes the decision to adopt thepractice and obtains the specific informationnecessary to carry out the practice. This processmay occur over a period of time.

ADOPTION BEHAVIOUR OF GROUNDNUT FARMERS IN CHITTOOR DISTRICTOF ANDHRA PRADESH

P. BALAHUSSAIN REDDY*, P.V.K. SASIDHAR and T.P.SASTRYSchool of Extension and Development Studies, Indira Gandhi National Open University,

New Delhi- 110 068


ABSTRACTAdoption behaviour of 180 farmer beneficiaries from eight adopted villages of RAAS Krishi Vigyan Kendra (KVK),

Chittoor with respect to the recommended Groundnut production technologies was studied duing the year 2012.Four types ofbehaviour viz., full adoption, partial adoption, discontinuation and non-adoption for fourteen recommended package of practicesin Groundnut was analysed.The results revealed that overall there was a positive trend (greater than 50% full adoption for tenrecommended practices) in the adoption behaviour of the beneficiaries of KVK. However, KVKs need to put efforts to influence thefarmers (through different interventions) for complete adoption of the recommended technologies for improving the productivity andprofits of the farmers.

J.Res. ANGRAU 46(1) 72-84, 2018

*Corresponding Author E-mail: [email protected]; * Part of Ph.D thesis submitted to IGNOU, New Delhi

Degree of adoption of any item of packageof practice may be of complete or full, partial, non-adoption and discontinuation. A proper feedbackfrom the farmers will certainly provide an insight tothe scientists for further research or modification ofthe new farm technology. In the study, adoptionbehaviour of farmers from adopted villages of KVKwith respect to recommended product iontechnologies of Groundnut crop was examined.


RAAS Krishi Vigyan Kendra of ChittoorDistrict, Andhra Pradesh was purposively selectedfor the study (2012-13). Out of the 20 mandalsadopted by the KVK, five Mandals viz.,Yerpedu,Chandragiri, Ramachandrapuram, Narayanavanamand Karvetinagaram were selected randomly forthe study. Of the 23 villages adopted by the KVKin these five mandals, eight villages were selectedrandomly.About 180 KVK beneficiaries (farmers)who are involved in KVK programmes such asTrainings, OFTs, FLDs, Field days, etc., wereselected from these five villages proportionatelyfor the study. The adoption behaviour of the farmerswas studied using an interview schedule with respect

73

to recommended package of practices of Groundnut(ANGRAU,2012) in terms of four types of adoptionbehaviour viz., full adoption, partial adoption,discontinuation and non adoption with the scores 3,2, 1 and 0, respectively. The reasons associated witheach type of behaviour were extracted from therespondents and presented as frequencies andpercentages.


The adoption behaviour of the respondentstowards recommended package of practices ispresented in Table 1.

About 39.03 per cent of the respondentshave not adopted the recommended groundnutvarieties while 29.31 per cent of respondents havefully adopted, 25.70 per cent have partially adoptedand 5.97 per cent have discontinued the practices.The extent of adoption of the respondents with respectto Farmer’s seed multiplication was 31.11 per centfull adoption, 38.89 per cent partial adoption, 11.11per cent discontinued and 18.89 per cent non-adoption. Respondents, by and large, had fullyadopted (97.22%) recommended land preparationpractices for Groundnut cultivation. With respect torecommended sowing season, the extent of adoptionwas 71.67 per cent full adoption, 17.78 per cent partialadoption, 2.05 per cent discontinuation and 8.06 percent non-adoption. From Table 1 it is observed that65 per cent of the respondents fully adopted therecommended seed rate, 25.56 per cent partiallyadopted, 2.22 per cent discontinued and 7.22 percent had not adopted at all. Regarding recommendedseed treatment, the adoption behaviour was 44.33per cent full adoption, 15.56 per cent partial adoption,7.56 per cent discontinuation and 32.55 per cent non-adoption.

Full adoption of the recommended sowingmethod was only 28.33 per cent, while 42.22 per centhave not adopted this practice. 20.56 per cent partially

adopted and 8.89 per cent discontinued this practice.With respect to recommended spacing, 77.22 per centof the respondents fully adopted this practice, 16.11per cent partially adopted, and 6.67 per cent had notadopted. None of the farmers had discontinued thispractice. The adoption behaviour of the respondentswith respect to water management practices was56.11 per cent full adoption, 16.11 per cent partialadoption, 0.85 per cent discontinuation and 26.95per cent non-adoption. The distribution of respondentswith respect to the adoption of fertilizer managementpractices was 71.25 per cent full adoption, 15.14per cent partial adoption, 2.50 per centdiscontinuation and 11.11 per cent non-adoption.

More than half of the respondents (68.70%)fully adopted recommended weed managementpractices followed by 16.48 per cent partial adoption,13.70 per cent non-adoption and 1.11 per centdiscontinuation. With regard to recommended culturalpractices such as trap crops, border crops, birdperches, etc., the adoption behaviour as noticed fromthe Table 1 was 63.62 per cent full adoption, 24.17per cent partial adoption, 1.39 per centdiscontinuation and 10.84 per cent non-adoption.About 56.11 per cent of respondents fully adoptedpest management practices as perrecommendations, 13 per cent partially adopted, 7.22per cent discontinued and 23.67 per cent had notfollowed pest management practices as perrecommendations suitable for the area. The adoptionbehaviour with respect to recommended harvestingpractices of Groundnut revealed 83.33 per cent fulladoption followed by 7.22 per cent of partial adoption,5 per cent of non-adoption and 4.45 per cent ofdiscontinuation (Table 1).

Reasons for different adoption behaviour asexpressed by the respondents

Various reasons expressed for different typesof adoption behaviour of the respondents is presentedin Table 2.

BALA HUSSAIN REDDY et al.

74

Tabl

e 1.

Ado

ptio

n be

havi

our o

f far

mer

s to

war

ds re

com

men

ded

pack

age

of p

ract

ices

in G

roun

dnut

1R

ecom

men

ded

impr

oved

var

ietie

s53

29.3

146

25.7

011

5.97

7039

.03

2Fa

rmer

’s s

eed

mul

tiplic

atio

n56

31.1

170

38.8

920

11.1

134

18.8

9

3La

nd p

repa

ratio

n17

597

.22

31.

671

0.56

10.

56

4So

win

g se

ason

reco

mm

ende

dfo

r the

are

a12

971

.67

3217

.78

52.

5014

8.06

5R

ecom

men

ded

Seed

rate

117

6546

25.5

64

2.22

137.

22

6S

eed

treat

men

t80

44.3

328

15.5

614

7.56

5832

.88

7M

etho

d of

sow

ing

5128

.33

3720

.56

168.

8976

42.2

2

8R

ecom

men

ded

spac

ing

139

77.2

229

16.1

10

012

6.67

9W

ater

man

agem

ent

101

56.1

129

16.1

12

0.83

4826

.95

10Fe

rtiliz

er m

anag

emen

t12

871

.25

2715

.14

52.

5020

11.1

1

11W

eed

man

agem

ent

124

68.7

029

16.4

82

1.11

2513

.70

12R

ecom

men

ded

Cul

tura

l pra

ctic

es11

363

.62

4424

.17

31.

3920

10.8

4

13Pe

st m

anag

emen

t10

156

.11

2313

.00

137.

2243

23.6

7

14R

ecom

men

ded

harv

estin

g pr

actic

es15

083

.33

137.

228

4.45

95.

00

Rec

omm

ende

dpa

ckag

e of

pra

ctic

es

n=18

0

Freq

uenc

y(N

)%

Freq

uenc

y(N

)%

Freq

uenc

y(N

)%

Freq

uenc

y(N

)%

S.N

o

Ado

ptio

n be

havi

our

Full

adop

tion

Part

ial a

dopt

ion

Dis

cont

inue

dN

on-a

dopt

ion

ADOPTION BEHAVIOUR OF GROUNDNUT FARMERS IN CHITTOOR DISTRICT

75

Tabl

e 2.

Rea

sons

exp

ress

ed b

y th

e fa

rmer

s fo

r diff

eren

t ado

ptio

n be

havi

our o

f the

tech

nolo

gies

Rec

omm

ende

dpa

ckag

e of

prac

tices

S.N

oR

easo

ns (i

n or

der o

f far

mer

s re

spon

se)

n=1

80

Full

adop

tion

Part

ial a

dopt

ion

Dis

cont

inue

dN

on-a

dopt

ion

Rec

omm

ende

dim

prov

edva

rietie

s

Hig

h yi

eldi

ng th

an a

nyot

her v

arie

ties

(43.

4%)·

Shor

t dur

atio

n va

riety

and

henc

e su

itabl

e fo

rou

r far

min

g si

tuat

ion

(28.

3%)·

Uni

form

peg

ging

and

mat

urity

(20.

75%

)·

Sui

tabl

e to

rabi

sea

son

(7.5

5%)

Rec

omm

ende

d se

edra

tesp

ecifi

ed fo

r the

varie

ty is

not

follo

wed

(63.

04%

)·

Sow

ing

is n

ot d

one

inth

e sp

ecifi

ed s

easo

n(3

6.96

%)

Low

she

lling

perc

enta

ge (7

2.73

%)·

Yie

lds

not s

atis

fact

ory

(27.

27%

)·

Una

war

e of

the

new

varie

ties

(65.

71%

)·

Non

ava

ilabi

lity

ofqu

ality

see

d of

late

stva

rietie

s (3

4.29

%)

Farm

er’s

see

dm

ultip

licat

ion

1 2R

isk

of p

urch

ase

ofse

ed a

t hig

her c

ost i

sav

oide

d (3

7.5%

)·

See

d pu

rity

ism

aint

aine

d (2

8.57

%)·

Tim

ely

sow

ing

ispo

ssib

le (1

9.64

%)·

Qua

lity

of s

eed

&ge

rmin

atio

n is

goo

d(8

.93%

)·

Hig

h yi

eld

is a

chie

vabl

e(5

.36%

)

Ow

n se

ed is

dev

elop

edon

ly fr

om ra

bi s

easo

n’s

crop

(72.

86%

)·

Rec

omm

ende

d m

oist

ure

for s

tora

ge is

not

mai

ntai

ned

(27.

14%

)

Labo

ur s

horta

ge (4

0%)·

Qua

lity

seed

is n

otpr

oduc

ed fr

om m

y fie

ld(3

5%)·

Han

d pi

ckin

g is

labo

rious

and

cos

tly(1

5%)·

Coi

ncid

ence

of r

ains

durin

g ha

rves

ting

(10%

)

See

d pr

oduc

ed in

my

field

will

not g

ive

high

yiel

ds if

use

d fo

r nex

tse

ason

(44.

12%

)·

Lack

of s

eed

stor

age

faci

litie

s (2

6.47

%)·

Gro

undn

ut is

cul

tivat

edon

ly o

ne s

easo

n in

aye

ar (2

0.59

%)·

Forc

ed to

sal

e fo

rim

med

iate

requ

irem

ent

of c

ash

(8.8

9%)


76

Red

uced

pes

tin

cide

nce

(58.

86%

)·W

eed

infe

stat

ion

isre

duce

d (2

8%)·

Impr

oves

the

yiel

d(1

3.14

%)

Plo

ughi

ng is

don

e w

ithni

ne ty

ne c

ultiv

ator

(100

%)

Inte

nsiv

e cr

oppi

ngsy

stem

- th

ree

crop

s a

year

(100

%)

No

addi

tiona

l ben

efit

from

dee

p pl

ough

ing

(100

%)

Sow

ing

seas

onre

com

men

ded

for

the

area

4H

igh

yiel

ds a

reob

tain

ed (5

9.69

%)·

Pes

t and

dis

ease

sin

cide

nce

is n

ot h

igh

ifso

win

gs a

re d

one

timel

y (2

9.46

%)·

Nor

th e

ast m

onso

onhe

lps

in g

ettin

g go

odcr

op d

urin

g ra

bi s

easo

n(1

0.85

%)

Very

ear

ly s

owin

gs a

rehe

lpin

g to

ach

ieve

hig

hyi

elds

(59.

38%

)·

Sow

ing

date

is p

repo

ned

due

to c

limat

e ch

ange

inkh

arif

(40.

63%

)

Har

vest

ing

is d

iffic

ult

due

to c

oinc

iden

ce o

fra

infa

ll (10

0%)

·

Del

ay in

rain

fall i

nkh

arif

seas

on(4

2.86

%)·

Non

ava

ilabi

lity

ofbu

llock

dra

wn

plou

ghs

(21.

43%

)·S

eed

is n

ot a

vaila

ble

timel

y (2

1.43

%)·

Gro

undn

ut c

ultiv

atio

n is

done

onl

y in

rabi

seas

on &

no

khar

ifcr

op (1

4.29

%)

Rec

omm

ende

dse

ed ra

te5

Opt

imum

pla

ntpo

pula

tion

give

s hi

ghyi

elds

(79.

49%

)·Fi

eld

cove

rage

with

crop

can

opy

redu

ces

the

suck

ing

pest

com

plex

on

the

crop

(20.

51%

)

Mor

e se

ed ra

te is

requ

ired

for h

igh

yiel

ds(4

5.65

%)·

Cos

t of s

eed

is h

igh

(39.

13%

)·

Rai

nfed

cro

p in

kha

rifse

ason

(15.

22%

)

Som

e pl

ants

die

in th

efie

ld a

nd h

ence

mor

ese

ed ra

te is

use

d(1

00%

)

Not

aw

are

of th

ere

com

men

ded

seed

rate

(61.

54%

)·

Not

pos

sibl

e du

e to

man

ual s

owin

g(3

8.46

%)

Land

pre

para

tion

3 6S

eed

treat

men

tC

heap

est a

nd e

asie

stm

etho

d of

pes

tco

ntro

lling

prac

tice

(42.

5%)·

·

See

d tre

atm

ent i

s do

new

ith fu

ngic

ide

alon

e(6

0.71

%)·

Che

mic

al is

not

app

lied

as p

er th

e re

com

men

ded

dose

(25%

)·

Labo

rious

pro

cess

(50%

)·

Tric

hode

rma

virid

i is n

otav

aila

ble

in th

e op

enm

arke

t (35

.71%

)·

Una

war

e of

Tric

hode

rma

virid

i see

d tre

atm

ent

(32.

76%

)·N

ot a

war

e of

trea

tmen

tw

ith Im

idac

lopr

id(3

1.03

%)·


77

7M

etho

d of

sow

ing

Very

effe

ctiv

e ag

ains

tso

il bor

ne &

see

d bo

rne

path

ogen

s (3

5%)·

Cro

p is

pro

tect

edag

ains

t pes

ts a

nddi

seas

es u

p to

30

days

afte

r sow

ing

(16.

25%

)·

Prev

ents

root

gru

bda

mag

e (6

.25%

)·

See

d tre

atm

ent i

s do

neju

st b

efor

e so

win

g(1

4.29

%)

Not

so

bene

ficia

l(1

4.29

%)

Lack

of s

uffic

ient

tim

efo

r see

d tre

atm

ent

(22.

41%

)·

Chl

orpy

ripho

s se

edtre

atm

ent i

s no

tne

cess

ary

for o

ur a

rea

as ro

ot g

rub

is n

otob

serv

ed (1

3.79

%)

Sow

ing

oper

atio

n is

com

plet

ed q

uick

ly th

antra

ditio

nal m

etho

d(3

7.25

%)·

Cos

t effe

ctiv

e co

mpa

red

to tr

aditi

onal

sow

ing

(21.

57%

)·S

eed

rate

is le

ss th

antra

ditio

nal s

owin

gm

etho

d (1

7.65

%)·

Sow

ing

is d

one

timel

y(1

7.65

%)·

Trad

ition

al b

ullo

ckdr

awn

impl

emen

ts a

reno

t ava

ilabl

e (5

.88%

)

See

d ra

te u

sed

is n

ot a

spe

r rec

omm

enda

tion

(62.

16%

)·

Ferti

lizer

is n

ot a

pplie

dth

roug

h tra

ctor

dra

wn

ferti

cum

see

d dr

ill(3

8.84

%)

Ger

min

atio

n is

affe

cted

(43.

75%

)·

Skill

invo

lvem

ent

requ

ired

for s

owin

g w

ithfe

rti c

um s

eed

drill

sow

ing

(37.

5%)·

Yie

lds

wer

e no

tsa

tisfa

ctor

y (1

8.75

%)

Non

- ava

ilabi

lity

of th

eim

plem

ent i

n ou

r villa

ge(7

5%)·

Not

aw

are

of fe

rti -

cum

-se

ed d

rill s

owin

g (2

5%)

8R

ecom

men

ded

spac

ing

Hig

h pl

ant p

opul

atio

n is

mai

ntai

ned

(75.

86%

)·

Due

to p

est a

nd d

isea

sein

cide

nce

afte

r sow

ing

(24.

14%

)

Not

pos

sibl

e w

ithtra

ditio

nal s

owin

gm

etho

d (7

5%)·

Not

aw

are

of th

ere

com

men

ded

spac

ing

(25%

)

Hig

h yi

eldi

ng is

achi

evab

le (6

6.91

%)·

Cov

erag

e of

fiel

d w

ithcr

op c

anop

y re

duce

sw

eed

infe

stat

ion

(33.

09%

)


78

9W

ater

man

agem

ent

Uni

form

and

pro

fuse

flow

erin

g (4

0.59

%)·

Hig

h yi

elds

are

achi

evab

le d

ue to

low

pest

inci

denc

e(3

2.67

%)·

Judi

ciou

s us

e of

irrig

atio

n w

ater

thro

ugh

sprin

kler

sys

tem

(20.

79%

)·

Wee

d in

fest

atio

n is

redu

ced

(5.9

4%)

Dur

ing

khar

if, d

iffic

ult t

ow

ithho

ld ir

rigat

ion

up to

25 d

ays

afte

r sow

ing

due

to c

oinc

iden

ce o

f rai

nsdu

ring

sow

ing

time

(100

%)

Lack

of f

unds

to g

et n

ewsp

rinkl

er s

et (1

00%

)C

rop

is u

nder

rain

fed

situ

atio

n (4

5.83

%)·

Poor

gro

undw

ater

sou

rce

(31.

25%

)·

Inst

alla

tion

of s

prin

kler

syst

em is

cos

tly(2

2.92

%)

10Fe

rtiliz

erm

anag

emen

tO

il con

tent

and

she

lling

perc

enta

ge in

crea

sed

due

to G

ypsu

m(5

5.47

%)·

Pes

t pro

blem

isre

duce

d (2

7.34

%)·

Req

uire

d nu

trien

ts c

anbe

sup

plie

d th

roug

h so

ilte

st re

sults

(12.

5%)·

Man

agem

ent o

f Iro

nde

ficie

ncy

incr

ease

dyi

elds

(4.6

9%)

Cos

t of f

ertil

izer

s is

incr

ease

d (4

4.44

%)·

Com

plex

ferti

lizer

s ar

eus

ed (3

3.33

)·

Ava

ilabi

lity

of S

SP

ispo

or (2

2.22

)

Exp

ecte

d yi

elds

are

not

obta

ined

(100

%)

Una

war

e of

reco

mm

ende

d fe

rtiliz

erdo

ses

(40%

)·

Una

war

e of

iron

defic

ienc

y (3

0%)·

Gyp

sum

is n

ot ti

mel

yav

aila

ble

(30%

)

11W

eed

man

agem

ent

Cos

t of w

eed

man

agem

ent i

sre

duce

d (3

7.1%

)·

Cro

p lo

ss is

redu

ced

and

yiel

ds c

an b

eim

prov

ed (3

3.87

%)·

Wee

dici

de d

oses

are

not

as p

er re

com

men

datio

n(5

5.17

%)·

App

licat

ion

ofw

eedi

cide

s is

not

tim

ely

(44.

83%

)

Par

then

ium

is n

otm

anag

eabl

e an

d to

be

erad

icat

ed c

ompl

etel

y(1

00%

)

Una

war

e of

wee

dici

des

(44%

)·W

eedi

cide

s da

mag

e th

em

ain

crop

(36%

)·A

ble

to m

anag

e w

eeds

thro

ugh

man

ual w

eedi

ng(2

0%)


79

12C

ultu

ral p

ract

ices

Inci

denc

e of

Spo

dopt

era

is n

otic

edin

the

egg

stag

e its

elf

and

cont

rolle

d at

that

stag

e be

fore

itda

mag

es th

e cr

op(3

7.17

%)·

Win

d bo

rne

pest

infe

stat

ion

is re

duce

d(2

6.55

%)·

Bud

nec

rosi

s in

cide

nce

is re

duce

d du

e to

cont

rol o

f thr

ips

(23.

89%

)·

Vira

l dis

ease

s ar

eco

ntro

llabl

e. (1

2.39

%)

Trap

cro

ps a

re n

ot s

own

as p

er th

ere

com

men

datio

n(6

3.64

%)·R

ando

mso

win

g of

Jow

ar/M

aize

alon

g th

e bo

rder

(36.

36%

)

Labo

rious

proc

ess(

100%

)a

Lack

of t

ime

(45%

)·

a N

ot p

ract

icab

le u

nder

rain

fed

situ

atio

n (3

5%)·

a N

on a

vaila

bilit

y of

Cas

tor

/ Sun

flow

er s

eed

in ti

me

(20%

)

Wee

ds a

re b

ette

rco

ntro

lled

usin

g pr

e-em

erge

nce

wee

dici

de(2

1.77

%) ·

Pes

t and

dis

ease

prob

lem

is re

duce

d(7

.26%

)

13P

est

man

agem

ent

Pre

caut

iona

ry s

pray

at

15 d

ays

redu

ces

suck

ing

pest

inci

denc

e(3

2.67

%)·

Des

troyi

ng p

est a

t egg

stag

e is

ver

yec

onom

ical

(26.

73%

)·

Pes

ticid

es /

fung

icid

esar

e no

t app

lied

as p

erth

e re

com

men

ded

dose

s(7

3.91

%)·

App

lyin

g ne

em o

ilpu

rcha

sed

from

mar

ket

at u

nspe

cifie

d do

ses

(26.

09%

)

Non

-ava

ilabi

lity

of N

PVin

tim

e in

mar

ket

(61.

54%

)·

Nec

essi

ty o

f poi

son

bait

for S

podo

pter

a di

dn’t

aris

e as

it w

as m

anag

edth

roug

h ch

emic

als

(38.

46%

)

Una

war

e of

the

reco

mm

ende

dch

emic

als

and

dose

s(2

7.91

%)·

Una

war

e of

NPV

conc

ept (

25.5

8%)·


80

14R

ecom

men

ded

harv

estin

gpr

actic

es

Cos

t of s

eed

inpu

t for

next

cro

p is

redu

ced

(40.

67%

)·

Addi

tiona

l pric

e be

nefit

(34.

67%

)·

Qua

lity

of s

eed

ism

aint

aine

d if

pods

are

man

ually

sep

arat

edfro

m p

lant

s (1

9.33

%)·

See

d is

not

bla

cken

eddu

e to

han

dpic

king

(5.3

3%)

Pod

s ar

e no

t tre

ated

with

che

mic

als

befo

rest

orin

g (6

9.23

%)·

Rec

omm

ende

d m

oist

ure

perc

enta

ge is

not

mai

ntai

ned

in th

e po

ds(3

0.77

%)

Shor

tage

of m

anua

lla

bour

(62.

5%)·

Qua

lity

of s

eed

was

dam

aged

dur

ing

stor

age

in p

revi

ous

seas

on(3

7.5%

)

Cos

tly p

roce

ss to

ado

ptha

nd p

icki

ng o

f see

d(6

6.67

%)·

Mar

ketin

g th

roug

hm

iddl

e m

en a

nd h

ence

care

is n

ot ta

ken

(33.

33%

)

Tikk

a le

af s

pots

are

effe

ctiv

ely

cont

rolle

d(2

1.78

%)·

Poi

son

bait

is c

ost

effe

ctiv

e an

d be

stm

etho

d fo

r con

trollin

gSp

odop

tera

(10.

89%

)·

Opt

imum

pla

ntpo

pula

tion

ism

aint

aine

d (7

.92%

)

Spo

dopt

era

is n

ot a

serio

us p

est i

n ra

infe

dkh

arif

situ

atio

n (1

8.6%

)·

Nee

m s

eed

is n

otav

aila

ble

in o

ur a

rea

(16.

28%

)·

Labo

rious

pro

cess

toco

llect

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s af

fect

edw

orm

s an

d pr

epar

e N

PVso

lutio

n (1

1.63

%)


81

Recommended Improved Varieties: The reasonsfor full adoption were ‘High yielding than any othervarieties’, ‘Short duration variety and hence suitablefor our farming situation’, ‘Uniform pegging andmaturity’ and ‘suitable to rabi season’.‘Recommended seed rate specified for the variety isnot followed’ and ‘sowing is not done in the specifiedseason’ were the reasons given by farmers for partialadoption behaviour. ‘Low shelling percentage’ and‘Yields not satisfactory’ were the reasons behinddiscontinuation and ‘Unaware of the new varieties’and ‘Non availability of quality seed of latest varieties’were the reasons for non-adoption.TAG-24 was thepopular variety in the area due to high yielding natureand short duration, hence it was adopted. The newvarieties released are to be introduced among thefarming community duly emphasizing the advantagesover the existing popular varieties.

Farmer’s seed multiplication: ‘Risk of purchaseof seed at higher cost is avoided’, ‘Seed purity ismaintained’, ‘Timely sowing is possible’, ‘Quality ofseed and germination will be good’ and ‘High yield isachievable’ were the reasons expressed by groundnutfarmers for full adoption of this practice. ‘Own seedis developed only from rabi season’s crop’ and‘Recommended moisture for storage is notmaintained’ were the reasons for partial adoption.Reasons for discontinuation given by the respondentswere ‘Labour shortage’, ‘Quality seed is not producedfrom my field’, ‘Hand-picking is laborious and costly’and ‘Coincidence of rains during harvesting’. ‘Seedproduced in my field will not give high yields if usedfor next season’, ‘Lack of seed storage facilities’,‘Groundnut is cultivated only one season’ and‘Immediate requirement of cash’ were the reasonsfor non-adoption. Misconception among the farmersregarding own seed usage for next season might bethe reason for non-adoption of own seed production.Introduction of mahinery especially harvesting andthreshing equipment for Groundnut need to beintroduced and awareness shall be created amongthe farmers.

Land preparation: The reasons for full adoptionwere ‘Reduces pest incidence’, ‘Weed infestation isreduced’ and ‘improves the yield’. Reason for partialadoption was ‘Ploughing is done with nine tynecultivator’. ‘Intensive cropping system - three cropsa year’ was the reason given for discontinuation ofrecommended land preparation practice. ‘Noadditional benefit from deep ploughing’ was the reasonfor non-adoption of this practice. The probable reasonfor discontinuation and non-adoption of recommended

practice might be due to land preparation practiceusing nine tyne cultivator involves less drudgery thanMB Plough. Awareness on benefits of recommendedland preparation practices needs to be createdamong the farming community for full adoption ofthe practice.

Sowing season recommended for the area: Therespondents expressed ‘High yields are obtained’,‘Pest and diseases incidence is not high if sowingsare done timely’ and ‘North- East monsoon helps ingetting good crop during rabi season’ as the reasonsfor high adoption of sowing in recommended timeline.‘Very early sowings are helping to achieve high yields’and ‘Sowing date is preponed due to climate changein Kharif’ were the reasons for partial adoption.‘Harvesting is difficult due to coincidence of rainfall’was the reason for discontinuation while ‘Delay inrainfall in Kharif season’, ‘Non availability of bullockdrawn ploughs’, ‘Seed was not available timely’ and‘Groundnut cultivation is done only in rabi seasonand no kharif crop’ were the reasons for non-adoptionof this practice (Table 2).

Recommended seed rate: ‘Optimum plantpopulation gives high yields’ and ‘Field coverage withcrop canopy reduces the sucking pest complex onthe crop’ were the reasons behind full adoption ofrecommended seed rate practice by the respondents.‘More seed rate is required for high yields’, ‘Cost ofseed is high’ and ‘Rainfed crop in kharif season’ werethe reasons given for partial adoption. The reason fordiscontinuation was ‘some plants wither in the field


82

and hence more seed rate is used’. ‘Not aware ofthe recommended seed rate’ and ‘Not possible dueto manual sowing’ were the reasons for non-adoption.Awareness programmes on optimum seed rate andsowing using tractor drawn seed drill have to beconducted so that farmers adopt recommended seedrate.

Seed treatment: ‘Cheapest and easiest method ofpest controlling practice’, ‘Very effective against soiland seed borne pathogens’, ‘Crop is protected againstpests and diseases up to 30 days after sowing’ and‘Prevents root grub damage’ were the reasons forhigh adoption. ‘Seed treatment is done with fungicidealone’, ‘Chemical is not applied as per therecommended dose’ and ‘Seed treatment is donejust before sowing’ were the reasons for partialadoption. ‘Laboriuos process’, ‘Trichoderma viridi isnot available in the open market’ and ‘Not sobeneficial’ were the reasons for discontinuation. Therespondents who had not adopted this practiceexpressed reasons such as ‘Unaware of Trichodermaviridi seed treatment’, ‘Not aware of treatment withImidacloprid’, ‘Lack of sufficient time for seedtreatment’ and ‘Chlorpyriphos seed treatment is notnecessary for our area as root grub is not observed’.Majority of the farmers were practicing seed treatmentusing Mancozeb,however, to achieve full benefit ofseed treatment farmers need to be sensitized ontreatment with insecticide viz., Imidacloprid formanagement of Collor rot, Peanut Bud necrosis,Peanut Stem necrosis, etc.

Method of sowing: ‘Sowing operation is completedquickly than traditional method’, ‘Cost effectivecompared to traditional sowing’, ‘Seed rate is lessthan traditional sowing method’, ‘Sowing is donetimely’ and ‘Traditional bullock drawn implements arenot available’ were the reasons given for full adoptionbehaviour.‘Seed rate used is not as perrecommendation’ and ‘Fertilizer is not applied throughtractor drawn ferti cum seed drill’ were the reasonsfor partial adoption. The reasons for discontinuation

are ‘Germination is affected’, ‘Skill involvement forsowing with ferti cum seed drill sowing’ and ‘Yieldswere not satisfactory’. ‘Unavailability of theimplements in our village’ and ‘Not aware of ferti -cum - seed drill sowing’ were the reasons for non-adoption of this practice. This trend might be due tonon-availability of tractor drawn seed drill and skilledlabour to operate the implement ensuring goodgermination, low seed rate, spacing, etc.

Recommended spacing: Respondents expressedthat ‘High yielding is achievable’ and ‘Coverage offield with crop canopy reduces weed infestation’ werethe reasons for full adoption. The reason for partialadoption was ‘High plant population is maintained’.Reasons for non-adoption were ‘Not possible withtraditional sowing method’ and ‘Not aware of therecommended spacing’. This trend is noticed due tonon-adoption of tractor drawn seed drill and farmersare still sowing behind plough using labour. Availabilityof tractor drawn seed drills at village level increasesadoption of recommended sowing method by thefarmers (Table 2).

Water management: ‘Uniform and profuseflowering’, ‘High yields are achievable due to low pestincidence’, ‘Judicious use of irrigation water throughsprinkler system’ and ‘Weed infestation is reduced’were the reasons stated by the respondents for highadoption behaviour.The reason for partial adoptionwas ‘During kharif difficult to withheld irrigation upto25 DAS due to coincidence of rains during sowingtime’. ‘Lack of funds to get new sprinkler set’ wasthe reason stated for discontinuation. The reasonsfor non-adoption were ‘Crop is under rainfed situation’,‘Poor groundwater source’ and ‘Installation ofsprinkler system is costly’. Majority of the farmersare still practicing flood irrigation and they need tobe sensitized on the advantages of Sprinkler irrigationon Groundnut growth and yielding. Subsidycomponent on the micro irrigation system needs tobe enhanced for the benefit of all the categories offarmers.


83

Fertilizer management: ‘Oil content and shellingpercentage increased due to Gypsum’, ‘Pestproblem is reduced’, ‘Required nutrients can besupplied through soil test results’ and ‘Managementof Iron deficiency increased yields’ were the reasonsfor full adoption of fertilizer management practices.‘Cost of fertilizers is increased’, ‘Complex fertilizersare used’ and ‘Availability of SSP is poor’ were thereasons for partial adoption. The main reason fordiscontinuation was ‘Expected yields are notobtained’. Non-adoption behaviour was due toreasons such as ‘Unaware of recommended fertilizerdoses’, ‘Unaware of iron deficiency’ and ‘Gypsum isnot timely available’. Timely availability of Gypsumto the farmers and training programmes onmanagement of iron deficiency helps in adoption ofrecommended fertilizer management practices.

Weed management: ‘Cost of weed managementis reduced’, ‘Crop loss is reduced and yields can beimproved’, ‘Weeds are better controlled using pre-emergence weedicide’ and ‘Pest and diseaseproblem is reduced’ were the reasons for full adoptionof recommended weed management practices.Reasons for partial adoption behaviour were‘Weedicide doses are not as per recommendation’and ‘Application of weedicides is not timely’. Reasonfor discontinuation was ‘Parthenium is notmanageable and to be eradicated completely’.‘Unaware of weedicides’, ‘Weedicides damage themain crop’ and ‘Able to manage weeds throughmanual weeding’ were the reasons for non-adoption.Awareness on pre and post emergence herbicides,their mode of application, cost effectiveness overmanual weeding need to be created among farmersfor adoption of effective weed management practices.

Cultural practices: The respondents stated that‘Incidence of Spodoptera is noticed in the egg stageitself and controlled at that stage before it damagesthe crop’, ‘Wind borne pest infestation is reduced’, ‘Budnecrosis incidence is reduced due to control of thrips’and ‘Viral diseases are controllable’ as the reasons for

full adoption of the cultural practices. ‘Trap crops arenot sown as per the recommendation’ and ‘Randomsowing of Jowar/Maize along the border’ were thereasons for partial adoption. ‘Laborious process’ wasthe reason for discontinuation of cultural practices. Thereasons for non-adoption behaviour were ‘Lack of time’,‘Not practicable under rainfed situation’ and ‘Nonavailability of Castor / Sunflower seed in time’. The trendobserved emphasizes the need of imparting knowledgeto the farmers about border cropping, trap cropping,arrangement of bird perches, physical collection ofcaterpillars, etc., for effective management of pests anddiseases in groundnut crop (Table 2).

Pest management: ‘Precautionary spray at 15 daysreduces sucking pest incidence’, ‘Destroying pestat egg stage is very economical’, ‘Tikka leaf spotdisease is effectively controlled’, ‘Poison bait is costeffective and best method for controlling Spodoptera’and ‘Optimum plant population is maintained’ werethe reasons for full adoption of recommended pestmanagement practices. ‘Pesticides / fungicides arenot applied as per the recommended doses’ and‘Applying neem oil purchased from market atunspecified doses’ were the reasons for partialadoption behaviour. The reasons for discontinuationwere ‘Non-availability of NPV in market’ and‘Necessity of poison bait for Spodoptera didn’t ariseas it was managed through chemicals’. ‘Unaware ofthe recommended chemicals and doses’, ‘Unawareof NPV concept’, ‘Spodoptera is not a serious pestin rainfed Kharif situation’, ‘Neem seed is notavailable in our area’ and ‘Laborious process tocollect virus affected worms and prepare NPVsolution’ were the reasons expressed by therespondents. This trend of non-adoption ofrecommended pest management practicesespecially Integrated Pest Management practicesindicates the necessity of arranging trainingprogrammes to the farmers on advantages of IPMmeasures and market availability of Neem Oil, NPV,etc.


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Recommended Harvesting Practices: Therespondents expressed that ‘Cost of seed for nextcrop is reduced’, ‘Additional price benefit’, ‘Qualityof seed is maintained if pods are manually separatedfrom plants’ and ‘Seed is not blackened due tohandpicking’ as the reasons for full adoptionbehaviour. ‘Pods are not treated with chemicalsbefore storing’ and ‘Recommended moisturepercentage is not maintained in the pods’ were thereasons for partial adoption behaviour. The reasonsfor discontinuation were ‘Shortage of manual labour’and ‘Quality of seed was damaged during storage’.‘Costly process to adopt hand picking of seed’ and‘Marketing tthrough middle men’ were the reasonsfor non-adoption behaviour of the respondents. Theabove trend indicated that still there is a section offarming community who were not adoptingrecommended harvesting practices for production ofgood quality seed such as hand picking, shadedrying, etc. Farmers were also not aware that seedproduction has high market price than selling entireproduce (Table 2).

CONCLUSION

The investigation revealed that majority ofthe farmers (78%) are adopting (full adoption andpartial adoption) one or the other recommendedpackage of practices in Groundnut crop in adopted

villages of KVK, while 22% of the farmers fell undernon adoption category (discontinuation or non-adoption). Timely dissemination of the technologythrough various extension methods resulted inimproved adoption of recommended technologies byGroundnut farmers. However, KVKs need to put effortsto influence the farmers (through differentinterventions) for complete adoption of therecommended technologies for improving theproductivity and profits of the farmers.

REFERENCES

ANGRAU.2012. Groundnut. In: Proceedins of ZonalResearch and Extension AdvisoryCouncil(ZREAC) Meeting of southern zoneheld at RARS, Tirupati on April 7th - 8 th, 2012.pp.34-37.

ICAR. 2017. Krishi Vigyan Kendras. Retrieved fromhttp://www.icar.org.in/content/krishi-vigyan-kendra on 09.2.2018.

Kokate, K. D. 2010.Farm innovations for agripreneurs.Proceedings of Fifth National Conferenceon KVK held from 22-24, December, 2010at Maharana Pratap University of Agricultureand Technology, Udaipur.

Wilkening, E.A. 1950. Sources of information forimproved farm practices. Rural Society.15:1.


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INTRODUCTION

The Government of India launched theNational Rural Employment Guarantee Scheme, oneof the largest rural development programmes, inFebruary, 2006 and it was renamed as MahatmaGandhi National Rural Employment Guarantee Act(MGNREGA) on 2nd October, 2009. MGNREGAincludes activities under nine different heads.MGNREGA aims at enhancing livelihood security ofhouseholds in rural areas by providing 100 days ofwage employment to every household whose adultmembers volunteer to do manual work, which isprimarily for natural resource management offeringgender neutral wages. The fact that it is not simply awork creation programme but derives its legitimacyfrom being an asset creation programme is oftenoverlooked. When it is not, there is a widespreadbelief that assets created under MGNREGA are ofdubious usefulness. Recently a few researchers havebegun to assess the impact of MGNREGA especiallyfocusing on migration,gender issues, agricultural andlivelihood vulnerabiliy reduction and environmentalimplications. Such efforts are still relatively infrequentcompared with those that focus on labour andwages,implementation, etc.

IMPACT OF MGNREGA IN TERMS OF DIRECT CHANGES- A STUDY INSRIKAKULAM DISTRICT OF ANDHRA PRADESH

K. ARCHANA*, P. RAMBABU, G. SIVANARAYANA and D. V. S. RAODepartment of Agricultural Extension, Agricultural College,

Acharya N.G. Ranga Agricultural University, Bapatla- 522 101

Date of Receipt:09.01.2018 Date of Acceptance:12.02.2018

ABSTRACTThe study examined the impact of MGNREGA in terms of direct changes experienced by the beneficiaries during the

year 2015. One hundred and twenty(120) respondents were selected randomly for the study. The results revealed that majorityof the MGNREGA beneficiaries’ experienced medium direct changes followed by high and low direct changes. Direct changesobserved due to implementation of MGNREGA were increased employment generation (286 days/ year), increased number ofemployed persons in the family (3 or more), increased daily working hours (> 7 hours), increased daily wage rates (360/- Rs.),increased income generation, increased community and individual assets creation and reduced migration (47 days/ year).

J.Res. ANGRAU 46(1) 85-91, 2018


Hence, there is a paramount need to findout the impact of MNREGA experienced by thebeneficiaries in terms of direct changes. With thisback drop, the study was conducted to know theimpact occurred due to implementation ofMGNREGA in terms of direct changes in srikakulamdistrict of Andhra Pradesh.


The study was conducted during the year2015 with ex-post facto research design inSrikakulam district of Andhra Pradesh. MGNREGAis carried in a big way in all the 40 mandals ofsrikakulam distrcit. Based on the criteria of maximumwage employment generation, out of 40 mandals inthe district, three mandals namely Seethampeta,Ranasthalam and Polaki and four villages from eachmandal (a total of 12 villages) were selectedpurposively. From each village, 10 beneficiaries wereselected randomly, thus, constituting a total of 120respondents. The data was collected from the sampleof MGNREGA beneficiaries through personalinterview method. Statistical tools viz., Frequency,Percentage, Mean and Standard Deviation wereapplied to analyse the data. Z test was applied tostudy the significance of difference with respect to

86

the Direct changes occurred before and afterimplementation of MGNREGA.


Direct changes occurred experienced byMGNREGA beneficiaries

Direct changes were measured in terms ofemployment generation, income generation,reduction in migration and community & individualassets creation, which were experienced by the

beneficiaries as a result of implementation ofMGNREGA in Srikakulam district.The direct changesbefore implementation of MGNREGA serves as abench mark, whereas, the estimate of direct changesafter implementation of MGNREGA minus beforeMGNREGA will be the impact of direct changesthrough MGNREGA.The beneficiaries werecategorised into three groups on the basis of meanand SD.

Table 1. Distribution of the selected MGNREGA beneficiaries according to the direct changes (n=120)

S. No. Direct changes MGNREGA Beneficiaries

Frequency Percentage

1 Low direct changes ( > 40.38) 15 12.50

2 Medium direct changes (36.72- 40.38) 67 55.83

3 High direct changes (< 36.72) 38 31.67

Total 120 100.00

Mean: 38.55 SD: 1.83

Majority (55.83%) of MGNREGAbeneficiaries experienced medium direct changesfollowed by high (31.67%) and low (12.50%) directchanges (Table 1). The direct changes experiencedby the beneficiaries were assessed in the categoriesof

1. Employment generation

It was evident from Table 2 that beforeintroduction of MGNREGA, majority (68.33%) ofMGNREGA beneficiaries had medium employmentgeneration followed by high (19.17%) and low(12.50%) employment generation. After introductionof MGNREGA, majority (75.84%) of MGNREGAbeneficiaries had medium employment generationand the rest of all belonged to the category of high(12.55%) followed by low (11.61%) employmentgeneration.In order to find significance of differencein employment generation of beneficiaries before and

after MGNREGA, the data was subjected to ‘Z’ testand the ‘Z’ value (10.73**) obtained was foundsignificant at 1 per cent level of significance indicatingthat there existed a significant difference inemployment generation of MGNREGA beneficiariesbefore and after MGNREGA. The mean employmentavailable for MGNREGA beneficiaries beforeintroduction of MGNREGA was 218.50 days and afterintroduction it was 286.25 days. This increase inemployment days might be due to policy initiativeand enhanced fund allocation by the CentralGovernment and the scheme is accessible toeveryone at their door step. These findings were inagreement with the findings of Jeyshree et al. (2010),Kantharaju (2011), Dadhabahu and Gopikrishna(2013) and Sitarambabu et al. (2013) who observedthat MGNREGA is providing employment.Employment generation was also assessed in termsof:

ARCHANA et al.

87

a. Number of employed persons in the family

The beneficiaries were categorized into threegroups based on number of persons employed inthe family as one person, 2 persons, 3 and morepersons. It is evident from Table 2 that beforeintroduction of MGNREGA, majority (54.17%) ofMGNREGA beneficiary families had one employedperson followed by two (34.16%) and more than threepersons(11.67%) in the family. After introduction ofMGNREGA, majority (84.17%) of families had threeand more persons employed followed by two(14.16%) persons and one (1.67%) person employedin the family. Calculated ‘Z’ value (14.09**) of Table 2was found significant at 1% level of significanceindicating that there existed a significant differencein employed persons in MGNREGA beneficiaryfamilies before and after MGNREGA implementation.This might be due to 33.00 per cent reservation forwomen under MGNREGA. As the works undergoingwere within 5 KM radius of their residence, everymember within the family was willing to participateto gain employment. Similar results were reportedby Argade (2010).

b. Daily working hours

The daily working hours of MGNREGA workswas categorized into three groups <5 hours, 5-7 hoursand >7 hours. Results furnished in Table 2 clearlyexhibit that majority (46.67%) of MGNREGAbeneficiaries daily working hours were 5-7 hoursbefore MGNREGA introduction. After implementationof the scheme 76.70 per cent of MGNREGAbeneficiaries were having more than 7 daily workinghours. Calculated ‘Z’ value (8.06**) of Table 2 wasfound significant at 1% level of significance indicatedthat there existed a significant difference in dailyworking hours of MGNREGA beneficiaries before andafter MGNREGA. This might be due to theMGNREGA working hours besides regular andadditional working hours for the eligible familymembers. These findings were in disagreement withthe findings of Argade (2010).

c. Daily wage rates

The wage rates for MGNREGA works wascategorized into three groups upto Rs.200, Rs.200-Rs.300 and >Rs.300. It is clear from the Table 2 thatmajority (67.50%) of beneficiaries were having dailywage rates in between Rs.200 to Rs.300 beforeimplementation of the scheme. About 76.66 per centof beneficiaries were getting more than Rs.300 asdaily wage rate after implementation of thescheme.Calculated ‘Z’ value (19.69**) of Table 2 wasfound significant at 1 per cent level of significance. Itindicated that there existed a significant differencein wage rates of MGNREGA beneficiaries before andafter MGNREGA. Overall, the mean daily wage rateincreased to Rs.360.50 from Rs.267.08 due to theintroduction of MGNREGA. The daily wage rates ofthe beneficiaries were automatically increased byMGNREGA works besides the regular agriculturalwages and other allied activities. These findings werein agreement with the results of Pyditalli (2015) whoreported that NREGA is able to provide a higher rateof employment to the rural poor households with ahigh average wage rate per day per person since itsinception in the Srikakulam district of AndhraPradesh.

2. Income generation

It is evident from Table 2 that beforeintroduction of MGNREGA, majority (47.50%) of thebeneficiaries belonged to medium income generationcategory followed by high (39.17%) and low (13.33%)categories. After introduction of MGNREGA, majority(88.33%) of the beneficiaries belonged to high incomegeneration category followed by medium (8.33%) andlow (3.34%) income generation categories.Calculated‘Z’ value (12.70**) of Table 2 was found significant at1% level of significance. It indicated that there existeda significant difference in income generation ofbeneficiaries before and after MGNREGA. The meanincome of MGNREGA beneficiaries beforeintroduction of MGNREGA was Rs.21,284/- and afterintroduction of MGNREGA the mean income was

IMPACT OF MGNREGA IN TERMS OF DIRECT CHANGES- A STUDY

88

Tabl

e 2.

Com

para

tive

dist

ribut

ion

of s

elec

ted

bene

ficia

ries

and

sign

ifica

nce

of d

iffer

ence

in d

irect

cha

nges

exp

erie

nced

by

ben

efic

iarie

s be

fore

and

afte

r MG

NR

EGA

N=12

0

ARCHANA et al.

89

Tabl

e 3.

Per

cent

age

incr

ease

d by

in c

omm

unity

and

indi

vidu

al a

sset

s cr

eate

d be

fore

and

Afte

r MG

NR

EGA

S. N

oTy

pe o

f ass

ets

Bef

ore

MG

NR

EGA

Afte

r MG

NR

EGA

Perc

enta

ge in

crea

sed

by

1.C

omm

unity

ass

ets

a. N

umbe

r of w

ells

con

stru

cted

8715

072

.00

b. N

umbe

r of w

ells

rech

arge

d59

137

132.

00

c. N

umbe

r of f

arm

pon

ds c

onst

ruct

ed52

100

92.0

0

d. N

umbe

r of p

lant

atio

n w

orks

take

n19

028

851

.00

e. R

ural

con

nect

ivity

(roa

ds la

id)

4995

93.0

0

f. N

umbe

r of w

ater

shed

wor

ks62

102

64.0

0

g. S

ocia

l for

estry

158

282

78.0

0

h. D

rinki

ng w

ater

tank

s co

nstru

cted

6810

452

.00

i. D

e-si

lting

of d

rinki

ng w

ater

tank

s52

9786

.00

Tota

l ass

ets

crea

ted

777

1355

74.0

0

2.In

divi

dual

ass

ets

a. F

arm

pon

ds c

onst

ruct

ed24

545

184

.00

b. E

arth

en fi

eld

bund

s, s

tone

, bun

ding

of f

ield

s25

941

660

.00

c. P

lant

atio

n w

orks

316

499

57.0

0

d. W

ells

rech

arge

d21

842

092

.00

e. W

ells

con

stru

cted

173

307

77.0

0

Tota

l ass

ets

crea

ted

1211

2093

72.0

0


90

Rs.42,979/-. It meant that MGNREGA helped thebeneficiaries to double their income. This shift frommedium income generation to high incomegeneration after introduction of MGNREGA might bedue to the increase in number of days ofemployment, wage rates and also due to the numberof persons employed in a family. Similar trend wasreported by Ramesh and Krishna Kumar (2009),Dadabahu and Gopikrishna (2013) and Adeppa(2014).

3. Migration

It is evident from Table 2 that beforeintroduction of MGNREGA, majority (60.84%) ofMGNREGA beneficiaries had high migration followedby low (22.50%) and medium (16.66%) migration.After introduction of MGNREGA, more than half(55.84%) of beneficiaries were found to be havingmedium migration followed by low (31.84%) and high(12.32%) migration.Calculated ‘Z’ value (14.94**)was found significant at 1% level of significance.Itindicated that there existed a significant differencein migration of MGNREGA beneficiaries before andafter MGNREGA. This shift from high migration tomedium migration might be due to additionalemployment opportunities provided to MGNREGAbeneficiaries thereby increasing their man-days ofemployment. Generally, after kharif season peopleused to migrate to urban areas for nearly about 97days as it is off season for agriculture activities.However, after introduction of MGNREGA, theirmigration was reduced to upto 47 days due toavailability of employment for them in their ownvillages.These results are in agreement with thefindings of Naidu et al. (2010), Dadhabahu andGopikrishna (2013), Sitarambabu et al. (2013) andAdeppa (2014). These results are in contradictionwith the fndings of Ahuja et al.(2011) who inferredthat in agriculturally developed area of HaryanaMGNREGA did not check the migration as the peoplewere earning more income from migration.

4. Community and Individual assets creation

Overall there was an increase to the extentof 74.00 per cent in community assets created as aresult of MGNREGA (Table 3). An orderlyarrangement has shown that percentage increase inwells recharged (132.00%) and rural connectivity(93.00%) was more followed by farm pondsconstructed (92.00%), de-silting of drinking watertanks (86.00%), social forestry (78.00%), wellsconstructed (72.00%), watershed works (64.00%),drinking water tanks constructed (52.00%) andplantation works taken up (51.00%).

Table 3 clearly indicates that the overallincrease to extent of 72.00 per cent in individualassets created as a result of MGNREGA wasobserved. About 92.00 percentage of increase inrecharge of wells was identified followed byconstruction of farm ponds (84.00%), constructionof wells (77.00%), earthen and stone field bunding(60.00%) and plantation works (57.00%). This trendmight be due to the policy initiative of MGNREGA totake up labour intensive activities which may providesteady employment in agricultural slack season,facilitate in engaging more labour as well as increation of community and individual assets. Thesefindings were in agreement with the findings of Deepakand Mohanty (2009), Chhabra and Sharma (2010),Kantharaju (2011) and Dadhabahu and Gopikrishna(2013) who studied implementation of NREGA withemphasis on coverage of households, employmentguaranteed, works undertaken, strengths,bottlenecks and strategies for further strengtheningthe programme.

CONCLUSION

MGNREGA is a labour intensiveprogramme, which is providing employment to therural people and improves their livelihood. Overall,majority of the beneficiaries experienced mediumdirect changes (55.83%) due to the implementationof MGNREGA. The direct changes observed were

ARCHANA et al.

91

increased employment generation (286 days peryear) as the scheme provides opportunities for everyrural household. Subsequently, income generationwas also prominent. It is also succesful in terms ofassets creation and reduction in migration

REFERENCES

Adeppa, D. 2014. Implementation and impact ofMGNREGA: A Study in Anantapuramdistrict of Andhra Pradesh. Galaxy:International Multidisciplinary ResearchJournal. 3 (2): 1-17.

Ahuja,U.R., Tyagi, D., Chauhan, S and Chaudary,K.R.2011. Impact of MGNREGA on ruralemploymet and migration: A study inagriculturally- backward and agriculturally-advanced districts of Haryana. AgriculturalEconomics Research Review.24:495-502.

Argade, S.D. 2010. A Study on National RuralEmployment Gaurantee Scheme in Thanedistrict of Maharashtra. M. Sc. Thesissubmitted to Acharya N G RangaAgricultural University, Hyderabad.

Chhabra, S and Sharma, G.L. 2010. National ruralemployment guarantee scheme (NREGS):Realities and challenges. LBS Journal ofManagement and Research. 2 (6): 64-72.

Dadabahu, A.S and Gopikrishna,T. 2013. Sustainablerural livelihoods for small and marginalfarmers through employment generation inMaharashtra.International Journal ofScientific Research. 2 (5): 581-583.

Deepak, S and Mohanty, S. 2009. Implementationof NREGA during eleventh plan inMaharashtra- Experience, challenges andways forward. Indian Journal of AgriculturalEconomics. 65 (3): 540-551.

Jeyshree, P., Subramanian, K., Murali, N and Peter,M.J. 2010.Economic analysis of MahatmaGandhi NREGS - A study. SouthernEconomist. 49: 13-16.

Kantharaju, C.N. 2011. Impact of MGNREGA onemployment generation and assetscreation in Tumkur district of Karnatakastate. M.Sc. Thesis submitted to Universityof Agricultural Sciences, Bengaluru.

Naidu, G.V., Gopal, T and Nagabhushan. 2010.Impact of MGNREGA on the living conditionof rural poor. Southern Economist. 49: 17-20.

Pyditalli, D.2015.Implementation of MGNREGA andperformance in Srikakulam district.International Journal of MultidisciplinaryAdvanced Research Trends.2(1):2015.pp.62-67.

Ramesh, G and Krishnakumar, T. 2009. A study inKarimnagar district of Andhra Pradesh(MGNREGA). Kurukshetra. 58 (2): 29-30.

Sitarambabu, V., Rao, D.V.S., Reddy, G.R.,Vijayabhinandana, B and Rao, V.S.2013.Socio-economic impact analysis ofMahatma Gandhi National RuralEmployment Guarantee Scheme in AndhraPradesh.International Journal ofDevelopment Research. 3 (10): 76-86.


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INTRODUCTION

To improve the agricultural production in thefarm, an appropriate technology is necessary.Appropriate technology is defined as the latestscientific and technological development that havebeen adjusted to suit the local conditions to thehighest possible degree (FAO, 1996). It is knownthat State Agricultural Universities (SAUs) are involvedin developing many technologies through researchand Post Graduate(PG) Research is one which isnoticeable. In all the SAUs in the country, the existingPost Graduate system is course work for twosemesters followed by research work for twosemesters and at Doctoral level course work for twosemesters followed by research work for foursemesters. Accordingly, a lot of data is beinggenerated at SAUs from the research workconducted by the students. Studies should beconducted to find out whether the outcome generatedthrough PG research is meeting the needs of farmersdirectly or indirectly, so that the efforts are meaningful.Sandra et al. (1989) noted that the goal of agriculturalresearch is the development of stable technologies,the production system changes, a new constraint

POST GRADUATE RESEARCH VIS-À-VIS FARMERS’ NEEDS – A STUDYIN THE FARM UNIVERSITY OF ANDHRA PRADESH

T. SRINIVAS*, T. V. SRIDHAR, P. PUNNA RAO, T. RAMESH BABU and P. SOWJANYAAcharya N.G. Ranga Agricultural University, Lam, Guntur- 522 034


ABSTRACTThe study conducted on the relevance of Post Graduate research in Acharya N.G. Ranga Agricultural University and

Farmers Preference in Andhra Pradesh revealed that the PG Research is relevant to the farmers needs. However, the relevancewas more in research pertaining to crop improvement and less in crop protection. In Crop Production, priority of the farmers washigh on nutrient management (19.22 %), weed management (16.99 %) and soil biology & fertility management (12.26 %) andaccordingly research was conducted.Research in organic agriculture, cropping & farming systems, soil and water pollution wasnot conducted as preferred by farmers. Research on interaction between biotic organisms, compatibility among the pesticide,fertilizers, herbicides need to be focused in view of farmers’ demand. Eventhough close relevance was observed betweenfarmers needs and PG research in crop improvement, enough research in molecular biology & biotechnology was not conducted.Thedemand for research on training needs (19.67%) was more by the farmers while research is being conducted on productioneconomics and farm management (25.00%).

J.Res. ANGRAU 46(1) 92-99, 2018


becomes the most limiting and new technology mustbe developed or adjusted to suit farmers needs.Keeping these in view,a study was conducted withan objective to know the relevance of Post Graduateresearch conducted in ANGRAU and preference offarmers of Andhra Pradesh.


The study was conducted in the Acharya N.G.Ranga Agricultural University (ANGRAU), AndhraPradesh, covering the two colleges offering PostGraduate courses i.e., Agricultural College, Bapatlaand S.V. Agricultural College, Tirupati. Post Graduateresearch work conducted during 2011-2016 wascollected department-wise from the selectedcolleges. The data was tabulated, research area wiseand compared with the preferred areas of researchby the farmers.The data collected was categorizedduly identifying the major areas of research (Table 1-4) to compare the farmers’ preference and PGresearch conducted in the state of AndhraPradesh.The data pertaining to the PG Research wascollected with a total sample of 194 in cropproduction, 129 in crop protection, 116 in cropimprovement and 108 in social sciences.

93

The technological, socio-economic and otherresearch areas preferred by the farmers was collectedfrom the Zonal Research and Extension AdvisoryCouncil (ZREAC) reports, District Level CoordinationCommittee (DLCC) reports of District AgriculturalAdvisory and Transfer of Technology Centres(DAATTCs) and Scientific Advisory Committee (SAC)meeting reports of KrishiVigyanKendras etc., andalso through the questionnaire developed in telugulanguage. A total of 1950 questionnaires were sentto 13 districts of Andhra Pradesh i.e. 150questionnaires to each district (75 for DAATTCs and75 for KVKs) for date collection. The data wascollected pertaining to the research areas preferredby the farmers with a total sample of 359 in cropproduction, 313 in crop protection, 225 in cropimprovement and 183 in social sciences.Comparision was made between the research areaspreferred by the farmers and also PG research workconducted in the departments to find out therelevance of the post graduate research to the needsof the farmers. The data was computed using rankorder correlation method.


The analysed data related to category wiseareas of PG Research conducted in ANGRAU andFarmers Preference for research areas in the stateof Andhra Pradesh are presented hereunder.

Crop Production

Nutrient management (19.22 %), weedmanagement (16.99 %), integrated cropmanagement (13.93 %), soil biology and fertilitymanagement (12.26 %) and cropping and farmingsystems (10.03 %)were the most preferredresearchable areas as perceived by the farmers,whereas, the PG research was concentrating onnutrient management (23.20 %), followed by weedmanagement (15.46 %) and soil fertility and fertilitymanagement (10.82%).This clearly showed thatrelevance of research was observed in the areas of

soil fertility and fertility management, nutrient andweed management.Studies were also conducted onsoil health management (7.73%), even thoughfarmers’preference is very low (1.11%). Priority in PGresearch was given to performance of cultivars(7.22%) and soil survey and classification (8.76%)even though farmers are preferring organic agriculture(Table 1). Alex et al. (2010) also observed that theUniversity Research Policy, Strategy andOrganization should research the clients throughpublication of research results.

It could be inferred from the results that eventhough organic agriculture,integrated cropmanagement and cropping and farming systems wererecognised as the preferred areas of research by thefarmers, much importance was not given in PGresearch. Water management, which is of topicalimportance in the present context of climatevariability, was equally recognised by farmers andpost graduate researchers. Research was alsoconducted on importance of resource managementeven though farmers have not recognised (Table 1).

Moreover, studies on climate resilientagriculture was not perceived as significantresearchable area by the both categories ofrespondents. Hence, awareness on the climatevariability and impact on the crops has to be createdamong the farmers and the research focus also needto be shifted to this area. In the context ofenvironmental pollution and stagnation of theproductivity in crops, soil health management andresource management plays a vital role to sustainagriculture. Hence, awareness has to be createdamong the farmers and also research has to beconcentrated in these areas.

Crop Protection

Management studies of insect pests anddiseases were given top priority in farmers’ preferenceas well as PG research (41.53% and 39.53%,respectively)followed by studies on insect pests and

SRINIVAS et al.

94

diseases(14.73%)and Insecticide Residence (10.85%) in PG research and Insecticide Resistance(12.46%)and studies on insect pests anddiseases(14.73%) in Farmers Preference. Thisclearly depicts that close relevance was observed inchoosing crop protection in post graduate research.It could be illustrated from the above results thatmanagement studies and insect pest and diseasestudies were most preferable areas by the farmersas pest management is the primary concern to nullifythe damage caused by insect pests and diseasesto crops (Table 2). Studies on insect pest anddisease development factors were equallyrecognised by both farmers and PG researchers.

Table 1. Comparision of farmer’s preferences and PG research conducted in Crop Production

S.No Research areas Farmers PG researchpreference conducted

(n=359) (n=194)

F (%) F (%)

1 Integrated crop management 50 13.93 9 4.64

2 Nutrient management 69 19.22 45 23.20

3 Weed management 61 16.99 30 15.46

4 Water management 21 5.85 10 5.15

5 Organic agriculture 32 8.91 8 4.12

6 Cropping and farming systems 36 10.03 11 5.67

7 Performance of cultivars 13 3.62 14 7.22

8 Studies on climate resilientagriculture 1 0.28 2 1.03

9 Soil survey and classification 16 4.46 17 8.76

10 Soil health management 4 1.11 15 7.73

11 Soil biology and fertility management 44 12.26 21 10.82

12 Resource management 0 0 10 5.15

13 Remote sensing & GIS 1 0.28 1 0.52

14 Soil & water pollution studies 4 1.11 1 0.52

15 Others (Soil transformation, Soil delineation, etc.) 7 1.95 0 0

r value 0.628

Studies on compatibility of chemicals andalso interaction between biotic organisms is not beingconducted by researchers, however farmers haverecognised their importance. Even though the farmershave not recognised the importance of studies oninsect and disease development factors and biologicalcontrol, but recognising their importance enoughfocus is being given by researchers. These findingsfurther indicated that farmers are facing problems inidentifying the casual organism of a particular pestand their management. In some cases, they weregetting confused in differentiating the pest incidenceand deficiency symptoms. The PG research is alsobeing concentrated on these aspects indicating the

POST GRADUATE RESEARCH VIS-À-VIS FARMERS’ NEEDS – A STUDY

95

importance in having clarity regarding the diseasecausing organisms and their management.Simultaneously it was indicated that research shouldalso take care of making the farmers to understandand differentiate disease symptoms from deficiencysymptoms.

Crop Improvement

It is clearly evident from the results (Table 3)that there was matching in farmer’s preference andPG research that is being carried out in cropimprovement with respect to research areas viz.,genetic divergence (22.22% and 29.32 %), heterosisand combining ability (13.33% and 17.24%),character association and selection indices (11.11%

and 11.21%) and stress management (14.22% and11.21%).

The results also indicated that both the farmerand PG researchers were not interested in bio-technology aspects, however, it is need of the hourto address different problems in pest managementand increase the yield by using bio-technologicaltools.In the present scenario of climate resilientagriculture, suitable varieties have to be developedby taking consideration of farmer’s preferences using

different molecular breeding techniques and bio-technological tools.

Comparision of farmer’s preference with thePG research indicated that majority of the farmerswere preferring research on nutritional aspects(15.56%) but slightly less percent (7.76%) of studieshad been carried out in this aspect which is to beconsidered while formulating the PG research. Theimportant area where PG research concentratesmore was on characterization of genotypes (11.21%),

Table 2. Comparision of farmers’ preferences and PG research conducted in Crop Protection


(n=225) (n = 116)

F (%) F (%)

1 Management studies on InsectPests and Diseases 130 41.53 51 39.53

2 Taxonomic Studies of Insect Pests 8 2.56 2 1.55

3 Insecticide Resistance 39 12.46 14 10.85

4 Isolation/Characterization of insectpests and Diseases 27 8.63 19 14.73

5 Biological control of insect pests 10 3.19 13 10.08

6 Insect and Disease Development factors 31 9.90 12 9.30

7 Grain storage pests and their management 13 4.15 4 3.10

8 Post harvest technologies to minimize theyield losses by insect pests 0 0.00 1 0.78

9 Disease causing organisms 25 7.99 13 10.08

10 Others ( Interaction among biotic pathogens,compatability studies, etc.) 30 9.58 0 0.00

r value 0.533

SRINIVAS et al.

96

however, farmers were not preferring this aspect(6.67%).

With regard to studies on plant growthregulators, research was taken up to a considerablepercentage (4.31%) while farmers have not preferred.In spite of non-preference of farmers studies mustbe conducted as it is an important area which couldgive key information to study management aspects.Research on seed physiology was not conductedeven though farmers were showing preference tocertain extent (3.56%). Crop growth models werenot preferred as researchable area by farmers andeven by the researchers. However, there is need tocarry out research in this aspect and also shouldbring awareness among the farmers in this aspect.In

terms of determining adoption of new practices byfarmers, it clearly goes beyond just agronomicevaluation. Rather, it was essential to not only identifythe costs and benefits of each introducedtechnology,but also conduct impact assessmentsto determine farmers’ actual use and managementof current and potential technologies, the perceivedbenefits of the technologies to farmers, and theirideas and perceptions of the technology (Adesinaand Baidu,1995; Alphonce, 1997; Antle and Diagana,2003)

Social Sciences

Training needs area was insisted by thefarmers (19.67%), whereas, preference was very lowby the PG researchers(2.78%) which has to be taken

Table 3. Comparision of farmers’ preferences and PG research conducted in Crop improvement


(n = 225) (n = 116)

F (%) F (%)

1 Genetic Divergence 50 22.22 34 29.31

2 Heterosis & Combining Ability 30 13.33 20 17.24

3 Stability Analysis 22 9.78 9 7.76

4 Path Analysis 1 0.44 0 0.00

5 Character association & Selection studies 25 11.11 13 11.21

6 Bio technology/ Genetically modified crops 0 0.00 0 0.00

7 Characterization of genotypes 15 6.67 13 11.21

8 Plant growth regulators 0 0.00 5 4.31

9 Stress management 32 14.22 13 11.21

10 Nutritional studies 35 15.56 9 7.76

11 Seed Physiology 8 3.56 0 0.00

12 Others (genetic variability, detection, mappingand gene pyramiding studies etc) 7 3.11 0 0.00

r value 0.730


97

care. More or less similar preference was observedon diffusion and adoption studies by both thecategories of respondents. The strategies suggestedin diffusion and adoption studies need to be followedby the extension personnel to transfer thetechnologies to the farmers’ fields.

Studies on entrepreneurship were havingclose relevance (9.26%) with the farmers’ preferences(9.84%). Similar trend was observed in case ofinformation and communication technologies (ICT’s),impact studies and indigenous technical knowledge(Table 4). Hence, there is a need to focus on themodern aspects viz., information and communicationtechnologies and developmental programmes so thatwe can transfer the latest technologies to the farmersfields easily in an understandable way without anybarriers.

Crop insurance and agricultural policy whichwas an important issue in the present day scenariowas paid little attention by the farmers and the PostGraduate researchers. Hence, academic researchershave to recognize the importance of crop insuranceand agricultural policies and reach the farmers tominimize the economic losses in cost of productionof crops and should also bring awareness with regardto this aspect.Even though farmers have encounteredconstraints regarding agricultural marketing andfinance & credit, research has been given lessimportance to these aspects. Hence, researchershave to study these aspects and make sure thatfarmers are not being exploited by middlemen inmarketing their produce. There is a need to createawareness on the market prices, market places tothe farmers to fetch remunerative prices to theirproduce.

Table 4. Comparision of the farmers’ preferences and PG research conducted in Social Sciences

Farmers PG researchS.No Research areas preference conducted

(n = 183) (n = 108)

F (%) F (%)

1 Diffusion & adoption studies 20 10.93 13 12.042 Entrepreneurship 18 9.84 10 9.263 Developmental programmes 10 5.46 9 8.334 Organizational studies (Training Needs) 36 19.67 3 2.785 Impact studies 13 7.10 8 7.416 Information and Communication Technologies(ICT’s) 5 2.73 3 2.787 Indigenous Technical Knowledge (ITK’s) 2 1.09 1 0.938 Agricultural policy 6 3.28 5 4.639 Agricultural marketing 15 8.20 6 5.5610 Crop insurance 4 2.19 2 1.8511 Agricultural finance & credit 14 7.65 12 11.1112 Production economics & farm management 20 10.93 27 25.0013 Impact assessment & evaluation studies 9 4.92 9 8.3314 Others (Psychological studies, studies on

rural development,Economic modeling, etc) 11 6.01 0 0.00

r value 0.608

SRINIVAS et al.

98

As research and extension projects andgovernment intervention often come with limitedmonetary funds and timelines,integrating farmers’preference into the initial project planning phaseusing the analytical hierarchy process will be moreefficient and beneficial for future outcomes (SMARTS,2009).The correlation studies have indicated thathighest relevance between farmers’ preference andPG research was observed in Crop Improvement(0.730) followed by Crop Production (0.628) and CropProtection (0.533).

CONCLUSIONS

Close relevance was observed in farmers’preferences and PG Research being conducted inthe state of Andhra Pradesh in majority of theresearchable areas except cropping and farmingsystems, integrated crop management, organicagriculture, character association and selectionstudies, genetic divergence, management studiesof pests and insects, impact evaluation studies andstudies on agricultural finance and credit. Studieson climate resilient agriculture need to be focussedby the researchers and awareness on the climatevariability and impact on the crops has to be createdamong the farmers. Studies on soil healthmanagement and resource management should bestrengthened as they play a vital role in sustainableagriculture in the context of environmental pollutionand stagnation of the productivity in crops. Farmersmainly resort to chemical spraying for themanagement of pests and diseases and hence,studies should be more concentrated on thecompatibility of chemicals used for the managementof pests and diseases. Post-harvest pest anddisease management and grain storage managementstudies should also be given priority. In order to makefarming profitable and sustainable, crop insurance,marketing and policies on agricultural credit need tobe studied and solutions to the problems need to beidentified to implement these components effectively.

Research studies on nutritional managementneed to be prioritised in addition to studies on stressmanagement and characterization of genotypes.Research studies on rural development andpsychology of the farmers need to be initiated andthe results need to be utilised in framing suitablepolicies. Studies on crop growth models, genemapping and pyramiding are some of the importantand novel areas where research studies should beconcentrated to solve some of the persistentproblems. The study gave a clear picture about theareas where research has to be strengthened. It hadalso given the relevance of farmers’ problems withthe research being attempted by the PG researchersand serves as a guide for planning the research areasin future.

Acknowledgement

This article was based on the study fundedunder ICAR Extramural Research Project entitled“Impact of PG Research in Higher AgriculturalEducation in Southern India”. The authors are highlythankful to the Indian Council of AgriculturalResearch, New Delhi.

REFERENCES

Adesina, A and Baidu, F. J. 1995. Farmers’perceptions and adoption of new agriculturaltechnology: Evidence from analysis inBurkina Faso and Guinea,West Africa.Agricultural Economics. 13: 1-9.

Alex, G., Derek, B., Solomon, B., Ed, Q.I., Serejskiand Willem, Z. 2010. Investing in Integrationof Universities into National AgriculturalResearch and Extension Systems. (Draft).Washington DC: World Bank.

Alphonce, C. 1997. Application of the analytichierarchy process in agriculture indeveloping countries. Agricultural Systems.53: 97-112.


99

Antle, Jand Diagana, B. 2003. Creating incentivesfor the adoption of sustainable agriculturalpractices in developing countries: the roleof soil carbonsequestration. AmericanJournal of Agricultural Economics. 85: 1178-1184.

Food and Agriculture Organisation (FAO). 1996. Foodfor all. Report of the World Food Summit.13-17 November, 1996.pp. 32-39.

Sandra, R., Bremer, Fox, J., Poats, S and Graig. L.1989. Gender variable in agriculturalresearch. A Report prepared for the Womenin Development Office, United Statespp.3-4.

Sustainable Management of AgroecologicalResources for Tribal Societies (SMARTS).2009. SMARTS Proposal: Response toEEP comments. University of Hawaii atManoa, Honolulu, Hawaii.

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In India, Soybean production is mainlyconfined to Madhya Pradesh, Maharashtra,Rajasthan, Andhra Pradesh, Karnataka, UttarPradesh and Chhattisgarh. Madhya Pradesh hasemerged as the Soy state of the country with a shareof over 55.63 per cent in area and 53.17 per cent inproduction. The area under crop in Madhya Pradeshduring 2012-13 was 60.32 lakh hectares and theproduction was 78 lakh tones (Sen, 2013). It hasbeen estimated that nearly 85 per cent of soybeanproduced in the country is processed and nearly 40per cent is processed in Madhya Pradesh state alone(Jaiswal, 2009).

Present utilization pattern of soybean in Indiais that 80 per cent is used for oil extraction, 10 percent for seed and only 10 per cent for food and feedbut it contributes about 12% to the domestic edibleoil pool and country earns substantial foreignexchange through export of soy-meal (Kulkarni,1999). Soybean processing units has capabilities toprocess soybean for food, feed, pharmaceutical andindustrial applications. Soybean can be processedin a variety of ways. Common forms of processedsoybean include soy meal, soy flour, soy milk, tofu,soy lecithin, soybean oil and textured vegetableprotein (TVP). Three types i.e. large, medium andsmall scale soybean processing unit have been takeninto consideration. It is noticed that most of the largesoy processing units are involved in soya oil andsoymeal processing and medium scale unit areinvolved into production of soya nuggets and soy flour

COMPARATIVE ECONOMIC EFFICIENCY OF SOYBEAN PROCESSING UNITS INMANDSAUR DISTRICT OF MADHYA PRADESHSARMAN LAL CHAUDHARI*, SEEMA and K.P. KULKARNI School of Agribusiness Management, College of Agriculture,

Acharya N.G. Ranga Agricultural University, Hyderabad- 500 030


Research NoteJ.Res. ANGRAU 46(1) 100-104, 2018

whereas small scale is producing mainly soymilkand soy paneer (Tofu). Therefore, an attempt wasmade to analyse and understand the economicaspects of soybean processing units in Mandsaurdistrict of Madhya Pradesh as the crop is grown bylarge number of farmers in an area of 2.7 lakh ha andhas scope for soybean processing.

Mandsaur district was purposively selectedas it is one of the major producers of soybean in theMadhya Pradesh and many of the soybeanprocessing units are located in this district. The studywas conducted during the year 2013- 14 and twolarge, five medium and eight small scale processingunits involved in producing various processedproducts were purposively selected. Thus, a totalsample size of 15 processing units formed the basisof the study. The primary data was collected fromthe processors of the selected units with the help ofa pretested schedule.

Table 1 indicated the total processing costsof soybean with respect to particular category ofprocessing unit. The processing costs of large scaleunit were calculated on yearly basis. Out of totalprocessing costs, (Rs. 38,489/- per tonne) the majorshare was spent on purchase of raw material (Rs.26,021/- per tonne) which accounted for 67.61 percent of the total processing cost. Other major costwas interest on working capital (9.84 per cent),salaries to permanent employees (3.72 per cent),depreciation on machinery and vehicles (3.70 percent), electricity and fuel charges (2.83 percent) and


101

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SARMAN LAL CHAUDHARI et al.

102

wages for daily labour (2.58 per cent), etc. Theprocessing costs of medium scale soybeanprocessing units was estimated as Rs. 54,123/- pertonne. The major share was occupied by cost of rawmaterial (Rs.35,410/- per tonne) which accountedfor 65.42 percent. Other major costs were intereston working capital (9.75 per cent), wages topermanent employees (4.11 per cent), depreciationon machinery and vehicles (3.81 per cent) andelectricity and fuel charges (3.53 per cent), etc.Similarly, for the small scale unit, the total cost ofprocessing was found to be Rs. 2,07,308/- per tonneper year. Out of total processing costs, the majorshare was towards raw material and chemicals (Rs.4,51,333/- per tonne) which accounted for 21.77 percent. The other major components are cost ofpacking material (12.38 %), electricity and fuelcharges (10.64 %), interest on working capital (9.07per cent), transportation cost (8.14 %), storagecharges (6.38 %) and depreciation on machinery andvehicles (5.97 %), etc.

It was noticed that the gross return (Rs.59,616/- per tonne) was more than two times of netreturns (Rs. 21,127/- per tonne) for the large scaleunit and returns per rupee of expenditure wasestimated as 1.55 for the unit processing about 1028tonnes per annum soybean to produce soy oil andsoy meal (Table 2). Similarly, Table 3 shows thereturns of medium scale unit engaged in productionof soy nuggets and soy flour from the processingof 440 tonnes per annum. The gross return (Rs.82,045/- per tonne) was approximately three timesof net returns (Rs.27,922/- per tonne) and theestimated returns per rupee of expenditure was 1.52.Whereas, the gross return (Rs.3,64,800/- per tonne)was more than two times of net returns (Rs. 1,57,492/- per tonne) fr small scale unit. Returns per rupee ofexpenditure was 1.76 fo processing about 15 tonnesof soybean to produce soy milk and tofu, respectively(Table 4).

Table 2. Returns from processing of soybean into soya oil and soy meal of large scale unit

S.No. Particulars Soyoil Soymeal Total

1 Quantity processed (Tonne) 195 833 1028

2 Quantity produced (kg) 1,75,000 8,12,000

3 Price (Rs./kg) 95 55

4 Total cost (Rs./year) 3,95,66,782

5 Total cost( Rs./tonne) 38,489

6 Gross return (Rs./year) 1,66,25,000 4,46,60,000 6,12,85,000

7 Gross return (Rs./tonne) 85,256.41 53,613.44 59,616

8 Net return (Rs./year) 2,17,18,218

9 Net return (Rs./ tonne) 21126.67

10 Returns per rupee of expenditure (Rs.) 1.55

COMPARATIVE ECONOMIC EFFICIENCY OF SOYBEAN PROCESSING UNITS

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Table 3. Returns from processing of soybean into soy nuggets and soy flour of medium scale unit

S.No. Particulars Soy nuggets Soy flour Total

1 Quantity processed (Tonne) 280 160 440

2 Quantity produced (kg) 2,66,000 1,52,000

3 Price (Rs./kg) 90 80

4 Total cost (Rs./year) 2,38,14,323

5 Total cost (Rs./tonne) 54,123

6 Gross return (Rs./year) 2,39,40,000 1,21,60,000 3,61,00,000

7 Gross return (Rs/tonne) 85,500 76,000 82045

8 Net return (Rs./year) 1,22,85,677

9 Net return (Rs./tonne) 27921.99

10 Returns per rupee ofexpenditure(Rs.) 1.52

Table 4. Returns from processing of soybean into soy milk and tofu of small scale unit

S. No. Particulars Soy milk Unit Tofu Total

1 Quantity processed(Tonne) 9 Tonne 6 15

2 Quantity produced (L) 75,600 Kilogram 7800

3 Price (Rs./litre) 60 Rs./kg 120

4 Total cost (Rs./year) 31,09,624

5 Total cost (Rs./tonne) 2,07,308

6 Gross return (Rs./year) 45,36,000 Rs./year 9,36,000 54,72,000

7 Gross return (Rs/tonne) 5,04,000 Rs./tonne 1,56,000 3,64,800

8 Net return (Rs./year) 23,62,376

9 Net return (Rs./tonne) 157491.73

10 Returns per rupee ofexpenditure (Rs.) 1.76

It has been found that there is differenceamong the processing costs of large (Rs. 38,489/-per tonne), medium (Rs.54,123/- per tonne) and smallscale (Rs.2,07,308/- per tonne) of processing unitsas the capital requirement is different to produce

different products. The cost of raw material hassignificantly influenced the total costs of all categoriesof soybean processing units as supported by findingsof Banafar et al. (2004). The study shows that theannual net return was positive for all the units with

SARMAN LAL CHAUDHARI et al.

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returns per rupee of expenditure of more than onerupee indicating the profitability by producing varioussoybean processed products.

REFERENCES

Banafar, K.N.S., Gauraha, A.K., Singh, G.K and Jain,B.C. 2004. Constraints in soybeanproduction, marketing and processing inSehore district of Madhya Pradesh.Agricultural Marketing. 47 (1): 54-56.

Jaiswal, A. 2009. Economics of production and valueaddition to soybean in Madhya Pradesh.

M.Sc. Thesis submitted to University ofAgricultural Sciences, Bangalore, India.

Kulkarni, B.S. 1999. Production, marketing andprocessing of soybean in Belgaum districtof Karnataka – An Economic Analysis.M.Sc. Thesis submitted to University ofAgricultural Sciences, Dharwad.

Sen, P.K. 2013. Profitability of different value addedproducts of Soybean: A case study of Kotadistrict in Rajasthan. M.B.A. (A.B.M.)Thesis submitted to Acharya N.G. RangaAgricultural University, Hyderabad.

COMPARATIVE ECONOMIC EFFICIENCY OF SOYBEAN PROCESSING UNITS

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Human resource is the most preciousresource for any country. It is, however, not thenumerical but the qualitative strength of the peoplewhich forges a country towards progress andprosperity. It is basically the development of humanresources that brings about socio-economic orpolitical-cultural transformation of any society. Avariety of extension programmes are implementedfor creating awareness, educating and motivating thefarmers, farm women and rural youth to adopt andmanage the new agricultural technologies in the field.This is one of the major contributing factors formaking India a food surplus country (Samanta andGowda, 2003). Training is an empowerment processof creating awareness, imparting knowledge andcapacity building leading

to greater participation for greater decisionmaking (Punia et al., 2007). Training programmescan bring about tremendous desirable change inknowledge, skill, attitudes and behavior of thefarmers enabling them not only to become wellacquainted with the recent technologies but alsoenhance their skills, competencies and efficiency ina desired manner. The advantage of assessing aknowledge, attitude and practice after training offarmers on a given agro technology is one of thetools for information on the effectiveness of training(Adhikarya,1996).With this background the pre- andpost-evaluation has been conducted to know theknowledge of the farmers/trainees before and after

EFFECT OF TRAINING PROGRAMMES ON KNOWLEDGE LEVELS OFREDGRAM AND GROUNDNUT FARMERS IN PRAKASAM DISTRICT OF

ANDHRA PRADESHO. SARADA*

District Agricultural Advisory and Transfer of Technology Centre,Acharya N.G. Ranga Agricultural University, Ongole - 523 262

Date of Receipt: 29.12.2017 Date of Acceptance: 30.01.2018



the training to understand how the trainingprogrammes have changed the knowledge of thefarmers/trainees.

The evaluation study was conducted atDAATTC, Ongole, Prakasam district of AndhraPradesh during the year 2015-16. The major cropsof the district include rice, redgram (55,891 ha),bengalgram and groundnut (8,222 ha). Two trainingprogrammes (for two batches of 30 redgram farmersand 32 groundnut farmers) were organized byDAATTC, Ongole. To understand the level ofknowledge of the participants on different aspects ofredgram and groundnut improved agriculturaltechnologies before and after the training programmea pretested questionnaire was used. The scoresobtained by the participants in both the tests wererecorded and analyzed to evaluate the knowledgegain. Correlation analysis was carried out to assessthe relationship between profile characteristics offarmers and their knowledge gain through training.

Knowledge levels of the redgram farmersbefore and after conducting trainingprogramme

The effect of training programme on theknowledge level of respondents about the redgramproduction technology in pre- test revealed that morethan fifty per cent (53.33%) redgram farmers werewith medium knowledge followed by low knowledgegroup (40.00%) and very low per cent (6.67%) of

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trainees in high knowledge group. Almost equal percent (46.67% and 43.33 %) of the trainees hadmedium and high knowledge levels followed by verymeager (3.613%) in low knowledge category in post-test. Regarding knowledge gain, half of the traineeswere with medium knowledge gain followed by highknowledge gain category (50.00%). Very negligible(3.33%) per cent of trainees were with low knowledgegain. It is therefore, concluded that redgram farmershad high level of knowledge after getting trained.These findings were in conformity with the results ofAkhilesh Kumar and Srivastava (2007) and Rathoreand Dhakar (2012). The knowledge gain clearlyindicates the contribution of the training programmetowards their increased knowledge levels on recentresearch developments viz., recently developed shortduration varieties, seed treatment and pest anddisease management in redgram production.

The pre-evaluation test majority of theredgram farmers had correct knowledge levels onsowing time (60.00%) and seed rate (53.33%) butthey had incorrect knowledge on short durationvarieties (90.00%), seed treatment (83.33%), fertilizermanagement (76.67%), disease management(73.33%) and pest management (63.33%). Aftergetting trained great majority of the farmers hadcorrect knowledge on sowing time (86.67%), seedtreatment (76.67%), seed rate (73.33%), pestmanagement (70.00%), fertilizer management(66.67%), disease management (63.33%) and shortduration varieties (56.67%). This might be becauseof their non-exposure to recent research findingsparticularly recently developed short durationvarieties to mitigate the moisture stress situation atpod development stage, seed treatment withTrichoderma viridi for wilt management and integratedpest and disease management.

Knowledge levels of the groundnut farmersbefore and after conducting trainingprogramme

It could be inferred that before training almostfifty (46.87%) per cent of the groundnut farmers had

medium knowledge followed in low (40.63%) and high(12.00%) knowledge groups. After participating intraining programme fifty six per cent farmers were inhigh knowledge group followed by medium (37.50%)and in low (6.25 %) knowledge groups. With respectto knowledge gain fifty per cent were in mediumcategory followed by high (40.625%) and low (9.00%)knowledge gain categories.

Component-wise knowledge analysisrevealed that in pre test great majority of thegroundnut farmers had incorrect knowledge ongypsum management (81.25%), seed rate (75.00%),weed management (71.87%), seed treatment(68.75%), micro nutrient management (65.62%),fertilizer management and disease management(62.50%) and pest management (56.25%). Whereasin post test majority of them had correct knowledgeon weed management (80.00%), seed treatment(75.00%), fertil izer management (71.87%),micronutrient management (68.75%), seed rate anddisease management (65.63%), pest management(62.50%) and gypsum management (59.38%). Theprobable reason for increased knowledge on weedmanagement was previously they don’t know the postemergence herbicide and its use. Increasedknowledge on seed treatment, seed rate and pestand disease management was due to the awarenesscreated in the training programme on seed treatmentto maintain optimum plant stand with recommendedseed rate leading to decreased pest and diseaseincidence as a part of Integrated Crop Management.

Relationship between profile characteristicsof redgram farmers and their knowledge gainthrough training programme

It can be concluded that extension contacthad significant positive relation at 5% level,innovativeness, trainings undergone and mass mediause were having highly significant relation withknowledge gain at 1% level of significance. This mightbe because these were the major factors facilitatingfarmers to gain knowledge on redgram production

SARADA

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technology. Age, education, farming experience andfarm size had no significant relation with knowledgegain. Parvinder Sharma et al. (2013) reported similarfindings in their study.

Relationship between profile characteristics ofgroundnut farmers and their knowledge gainthrough training programme

It is evident that education was thevariable which had positive relation with knowledgegain at 5% level of significance. The plausibleexplanation for this result may be that educationmight have provided a platform to people to learnmore and gain more information in a given learningsituation and because of the easy understanding ofthe training content by the farmers with their higheducation. Innovativeness, trainings undergone andmass media exposure were the variables havinghighly significant relation at 1% level of significancewith knowledge gain of the groundnut farmers.Innovativeness was the major factor motivating famersto get acquainted with recent technologies. Morenumber of trainings participated leads to higherknowledge levels of the farmers. Mass media usewas another factor contributing for update of thefarmers’ knowledge on production technologies. Age,farming experience, farm size and extension contactwere found to have no significant relationship withknowledge gain. Similar results were reported bySarma et al. (2013).

Knowledge levels of the farmers after gettingtrained in both training programmes indicated theirknowledge gain through trainings. Therefore, thereis a need to give due importance to organize needbased training programmes to farmers on major cropproduction technologies. To be fruitful, the trainingprogrammes should be designed based on actualtraining needs and socio-economic profile of potentialtrainees.

REFERENCES

Adhikarya, R. 1996. Strategic extension campaign:Increasing cost - effectiveness and farmers’

participation in applying agriculturaltechnologies. Retrieved from website (http://www.fao.org/sd/EXdirect/EXan 0003.htm)on 27.12.2017.

Akhilesh Kumar, D and Srivastava, J. P. 2007. Effectof training programme on knowledge andadoption behaviour of farmers on wheatproduction technologies. Indian ResearchJournal of Extension Education. 7 (2&3):41-43.

Parvinder Sharma, Singh, G.P and Jha, S.K.2013.Impact of training programme on knowledgeand adoption of preservation technologiesamong farm women: A comparative study.Indian Research Journal of ExtensionEducation. 13 (1):96-100.

Punia, R.K., Singh, S and Malik, J.S. 2007.Empowering rural women through dairyhusbandry trainings. In: Proceedings ofNational Symposium on ‘Recent trends inpolicy initiatives and technologicalinterventions for rural prosperity in smallholder livestock production systems’heldfrom June 20-22, 2007 at Sri VenkateswaraVeterinary University, Tirupati. pp. 267.

Rathore, R.S and Dhakar, S.D. 2012. Impact of KVKtraining programme on knowledge andadoption of guava crop technologies inChittorgarh district of Rajasthan. IndianResearch Journal of Extension Education.Special Issue (2):123-124.

Samanta, R and Gowda, M. 2003. Krishi VigyanKendra: The capacity builder of farmers.Kisan World. 4: 41-43.

Sarma, H., Talukdar R.K and Mishra, P. 2013. Impactof training on knowledge level of integratedrice-fish farming practices. Indian ResearchJournal of Extension Education.13 (1): 35-38.

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Information and communication technology(ICT) is an inclusive term and has positive impact onthe development of a nation. ICT is defined as thecombination of hardware and software for efficientmanagement of information, that is storage, retrieval,processing and communication, for social, economicand cultural development. (Bisht et al., 2010).According to Mudrak (2004), common ICTs are:computer, LCD projectors, internet, camera, televisionand cell phone. ICT tools are necessary to maketeaching and learning process more effective. TodayICTs has changed the relationship between teachersand students. Adequate technologies are necessaryto enhance effective teaching and learning process.Drucker (2006) reported that the use of adequateand appropriate communication and information toolscan lead to effective teaching and learning. Informationand communication technology (ICTs) is the modernscience of gathering, storing, manipulating,processing and communicating desired type ofinformation in a specific environment. Computertechnology and communication technology are themain supporting pillars of ICTs and the impact of thesetwo, in the information storage and dissemination isvital (Mahajan, 2002). G.B. Pant University ofAgriculture & Technology, Pantnagar is having fullyoperational computer labs, smart classrooms in allthe colleges. Although scientists of this Universityare using ICTs, however, researchable questions were[1] What types of ICTs they are using? [2] To whatextent they are using Information CommunicationTechnologies for improving their professional

INFORMATION COMMUNICATION TECHNOLOGY (ICT) UTILIZATION PATTERNBY UNIVERSITY TEACHERS

ARPITA SHARMA*Assistant Professor, Department of Agricultural Communication,

College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar- 263 145



efficiency?. Hence, The study was undertakenamong the Agricultural faculty of GBPUA&T,Pantnagar with the objective of studying the utilizationpattern of Information Communication Technologiesamong the agricultural scientists.

A survey was carried out in June, 2017 andJuly, 2017 on University teachers of GBPUA&T,Pantnagar. Simple random sampling was used forthe study. A total of 40 University teachers wereselected for the study. A semi-structured interviewschedule was developed to study the utilizationpattern of ICTs. Data revealed that 42.5 per cent ofrespondents were Professors followed by AssociateProfessors (30 per cent) and Assistant Professors(27.5 per cent).

Mobile Phone Utilization Pattern:

Mobile Ownership: All scientists ownedmobile phone (100%). This is supported by thefindings of Singh and Singh (2017) that majority offaculty members owned mobile phone.

Brand of mobile owned: Thirty per centscientists have owned Sony brand mobile phonefollowed by Lenovo (27.5 %). Vivo phone is ownedby 22.5 % of faculty members.Fifteen per centscientists have Motorola phone followed by Samsung(12.5 per cent) and Nokia/Micromax (7.5 per cent).Only 5 per cent respondents have Apple and RedmiMobile phone.

Type of Sim: More than three- fourth (92.5%)of the respondents have double sim followed by singlesim (5% ).

*Corresponding Author E-mail:[email protected]

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Mobile services being used: A total of 87.5per cent respondents were using Idea servicesfollowed by Jio (42.5 %).A total of 35 % respondentswere using Airtel mobile service followed by Vodafoneservices (30%).

Approximate monthly expenditure onmobile phone: Nearly 75 per cent respondents werespending less than Rs. 500/- as monthly expenditureon mobile phone followed by 22.5 per cent spendingRs.500/- to Rs.1000/- and only 2.5 % are spendingabove Rs.1000/-.

Internet Utilization Pattern

Frequency of Use of ICTs: More than halfper cent of the respondents (62.5 per cent) were usinginternet daily followed by weekly(30 per cent) andoccasionally (5 per cent). It is fairly clear from thefindings that the use of ICT in updating knowledgelevel and teaching skills by the University teacherswas to a great extent. Moreover, ICT is helpingteachers in setting innovative projects for studentsand generating computer online quiz, online test,online discussion forum.Table 1. Purposes for using social media by university teachers (Multi response Table)

S. No. Statement SA A NeitherAgree NorDisagree D SD

1) I use social media for communicating 5 (12.5) 26 (65) 2 (5) 4 (10) 3 (7.5)and interacting with friends.

2) I use social media for online learning. 32 (80) 7 (17.5) 1 (2.5) 0 0

3) I use social media for academicpurposes. 23 (57.5) 11 (27.5) 5 (12.5) 0 0

4) I use social media for official 5 (12.5) 12 (30) 10 (25) 9 (22.5) 4 (10)communication, mobilizing andorganizing National or Internationallevel conferences, Seminars, etc.

5) I use social media for updatingprofile information. 15 (37.5) 14 (35) 7 (17.5) 4 (10) 0

[SA=Strongly agree, A=Agree, D=Disagree SD= Strongly Disagree]; figurs in parntheses include percentges

ARPITA SHARMA

Extent of use of ICT for various purposes:Total 97.5 per cent respondents were using ICT fordata analysis and treatment and communicating withstudents followed by 92.5 per cent in writing researchand conceptual papers. Most of the respondents (95%) respondents were using Internet for acquiring andupdating knowledge in the area of specializationfollowed by self improvement (90 per cent). More thanhalf of the respondents (87.5 per cent) used Internetfor teaching/professional competence followed byparticipation in seminars, workshop and conferences(72.5 %). Only 30 per cent respondents were usingInternet for sharing professional information andfindings.A total of 47.5 per cent respondents wereusing Internet for Research project work. Researchfindings are supported by Devendra Kumar (2010)who reported that majority of faculty of Sardar VallabhBhai University of Agriculture & Technology usedinternet particularly e–mail and world widewibe(WWW). Nearly all college faculty memberssurveyed reported using the Internet to communicatewith their students (98%).

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ICT UTILIZATION PATTERN BY UNIVERSITY TEACHERS

Use of Social Media: Majority ofrespondents (87.5 per cent) were using WhatsappPurpose of using social media: A total of 80 percent respondents were in strong agreement with thestatement that “I use social media for onlinelearning”. A total of 65 per cent respondents were inagreement with the statement that “ I use socialmedia for communicating and interacting with friends”followed by the statement “ I use social media foracademic purposes” (57.5 per cent). A total of 42.5per cent respondents were in agreement that theyuse social media for communicating, mobilizing andorganizing national or international level conferences,seminars, etc (Table 1).

It can be concluded that all the scientists(100 %) were using ICts to update academic notes,writing research proposals, conceptual papers,gather and spread information about seminars,workshops and conferences, for acquiring andupdating knowledge in the area of their specialization,etc. University teachers were also using social mediasuch as Whatsapp (87.5%), Facebook (30%) andLinked-in (27.5%), etc.

REFERENCES

Bisht, S., Mishra, Y. D., Bharadwaj, N and Mishra,R. 2010. Utilization pattern of InformationCommunication Technology (ICT) amongagricultural scientists. Journal ofCommunity Mobilization and SustainableDevelopment. 5 (1): 90-95.

Devendra Kumar. 2010. Faculty use of internetservices at a university of agriculture andtechnology.Library Philosophy and Practice.Retrieved from website(www.digitalcommons.unl.edu/libphilprac/323) on 05.1.2018.

Drucker, P.F. 2006. Innovation and Entrepreneurship.Collins: Newyork.pp.15-17.

Mudrak, P. 2004. Information Communicationtechnologies used by farmers.Communication Today. 5(1): 23-24.

Mahajan, K.2002. History of community radio in India.Journal of Science and Technology. 2(3):9-10.

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Edited BookBreckler, S.J and Wiggins, E.C.1992. On defining attitude and attitude theory: Once more with feeling. In:Attitude Structure and Function. Pratkins, A.R., Breckler, S.J and Greenwald, A.G.(Eds). Hillsdale, NJ:Lawrence Erlbaum Associates. pp. 407-427.ThesisIbrahim, F. 2007. Genetic variability for resistance to sorghum aphid (Melanaphis sacchari, Zentner) insorghum. Ph.D. Thesis submitted to Acharya N.G. Ranga Agricultural University, Hyderabad.Seminars / Symposia / WorkshopsNaveen Kumar, P.G and Shaik Mohammad. 2007. Farming Systems approach – A way towards organicfarming. Paper presented at the National symposium on integrated farming systems and its role towardslivelihood improvement. Jaipur, 26th – 28th October, 2007. pp. 43-46.Proceedings of Seminars / SymposiaBind, M and Howden, M. 2004. Challenges and opportunities for cropping systems in a changing climate.Proceedings of International crop science congress. Brisbane –Australia. 26th September – 1st October,2004. pp. 52-54.WebsiteCotton Corporation of India. 2017. Area, production and productivity of cotton in India. Rtreived from website(www.cotcorp.gov.in/statistics.aspx) on 21.9.2017.Annual ReportAICCIP. 2017. Annual Report 2016-17. All India Coordinated Cotton Improvement Project. Coimbatore,Tamilnadu. pp. 26-28.Manuscripts and communication

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ANGRAU

THE JOURNAL OFRESEARCHANGRAU

The J. Res. A

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U, Vol. XLV I N

o. (1), pp. 1-120, January-March, 2018

Indexed by CAB International (CABI)www.cabi.org and www.angrau.ac.in

The J. Res. ANGRAU, Vol. XLVI No. (1), pp. 1-120, January-March, 2018

THE JOURNAL OF RESEARCH ANGRAUangrau.ac.in/angrau/pdf/Vol 46(1), 2018, The JoR ANGRAU.pdf · 2018. 12. 28. · The Journal of Research ANGRAU, Acharya N.G. Ranga Agricultural University,

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