Journal of Plant Development Sciences (An International Monthly Refereed Research Journal)

Volume 12 Number 2 February 2020

Contents

REVIEW ARTICLE

Multifarious scope of agro-forestry

—Vijay Upadhyay, Abhishek Raj, Neelu Jain and Brijesh Kumar Meena ----------------------------------- 59-63

RESEARCH ARTICLES

Do backward integration boost the technology adoption by Chilli farmers? The evidence from Andhra Pradesh,

India

—R. Asha and K. Umadevi--------------------------------------------------------------------------------------------- 65-72

Impact of tillage practices on physico-chemical and functional diversity in pearl millet-wheat cropping system

—Dhinu Yadav, Leela Wati, Dharam Bir Yadav and Ashok Kumar ----------------------------------------- 73-80

Comparative economic analysis of rice in kharif and rabi season in Guntur district of Andhra Pradesh

—Pradeep Kumar Patidar, R. Lakshmi Priyanka, N. Khan and Dharmendra ----------------------------- 81-85

Growth parameters and soil fertility status as influenced by nitrogen source in wheat

—Fazal Rabi, Meena Sewhag, Shweta, Parveen Kumar, Amit Kumar and Uma Devi -------------------- 87-92

Varietal performence of broccoli (Brassica Oleracea var. Italica) under northern hill zone of Chhattisgarh

—P.C. Chaurasiya and Sarswati Pandey ---------------------------------------------------------------------------- 93-97

Optimization of different propagating technique and time period to enhance higher success rate in propagation

of low chill peach cv. Shan-e-Punjab

—Rajat Sharma, P.N. Singh, D.C. Dimri, Shweta Uniyal, Vishal Nirgude and Manpreet Singh -------99-103

Effect of integrated crop management practices on growth, seed yield and economics of lentil (Lens culinaris

Medick.)

—S.K. Sharma, Rakesh Kumar and Parveen Kumar --------------------------------------------------------- 105-109

Effect of treatment imposed on total soluble protein content in wheat leaves infected by brown rust (Puccinia

recodita F.sp. Tritici rob. ex. Desm.) at Kanpur and Iari regional station Wellington (T.N.).

—Akash Tomar, Ved Ratan, Javed Bahar Khan, Dushiyant Kumar, Devesh Nagar and

Sonika Pandey --------------------------------------------------------------------------------------------------------- 111-114

Studies on the different species of insect pollinators/visitors visiting buckwheat flowers

—Jogindar Singh Manhare and G.P. Painkra ------------------------------------------------------------------ 115-118

SHORT COMMUNICATION

Survey of wheat crop for the prevailing brow rust (Puccinia recodita F.sp. Tritci rob. ex. Desm.) in

different region of Uttar Pradesh

—Akash Tomar, Ved Ratan , Javed Bahar Khan, Dushyant Kumar and Devesh Nagar -------------- 119-121

*Corresponding Author

________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 59-63. 2020

MULTIFARIOUS SCOPE OF AGRO-FORESTRY

Vijay Upadhyay, Abhishek Raj*, Neelu Jain and Brijesh Kumar Meena

Faculty of Agriculture and Veterinary Science, Mewar University, Chittorgarh-Rajsthan-312901 Email: ranger0392@gmail.com

Received-08.02.2020, Revised-26.02.2020 Abstract: Agroforestry is an ecologically sustainable land use system that maintains increase total yield by combining food crops (annuals) with tree crops (perennials) and/or livestock on the same unit of land. A large hectare is available in the form

of boundaries, bunds, wastelands where this system can be adopted. Farmers retain tree of acacia nilotica, acacia catechu, Dalbergia sissoo, Mangifera indica, Zizyphus mauritiana and Gmelina arborea etc in farm land. Agroforestry-the deliberate combination of woody perennials on the same piece of land with agricultural crops and/or animals, plays a crucial role in climate change mitigation especially due to its tree component. Trees accumulate CO2 (which is the most predominant GHG) in their biomass. Agroforestry not only helps in climate change mitigation but also climate change adaptation. It is an established fact that despite our present effort at climate changes mitigation (GHG reduction), there is a more pressing need to cope with the impact of climate change (adaptation). For instance, the trees in agroforests provide shade for both companion crops and the farmer against the rising temperatures, and also shelter the crops against the harmful effect of

raging storms. The presence of trees on the farms ensures income diversification through the provision of additional resources like fruits, nuts, timber, vegetables, fodder, etc. People should be aware about the scope and benefits of Agroforestry and they should participate in implementation and development of Agroforestry in India. Therefore, agroforestry system is economically and ecologically sound practices with enhancement of overall farm productivity, soil enrichment through litter fall, maintaining environmental services such as climate change mitigation (carbon sequestration), phytoremediation, watershed protection and biodiversity conservation.

Keywords: Agroforestry, Biodiversity, Bund, Climate change, Phytoremediation

INTRODUCTION

gro-forestry is not a new system or concept. The

practice is very old. Agro-forestry (AF) can be

defined as ―a collective name for land-use systems in

which woody perennials (trees, shrubs, etc.) are

grown in association with herbaceous plants (crops,

pastures) or livestock, in a spatial arrangement, a

rotation, or both; there are usually both ecological

and economic interactions between the trees and

other components of the system" (Lundgren, 1982). In simple terms, it consists of raising tree species and

agricultural crops on the same piece of land, resulting

in unique ecological interactions and maximized

economic returns (Young, 2002). These systems are

deliberately designed and managed to maximize

positive interactions between tree and non-tree

components and encompass a wide range of practices

like contour farming, intercropping, established

shelterbelts, riparian zones/buffer strips, etc. The

fundamental idea behind the practice of AF is that

trees are an essential part of natural ecosystems, and

their presence in agricultural systems provides a range of benefits to the soil, other plant species and

overall biodiversity. With threats that smallholder

farmers in the developing world face with predicted

impacts of climate variability and change, the scope

of AF systems to reduce vulnerability and adapt to

the conditions of a warmer, drier, more unpredictable

climate is now being recognized (McCabe, 2013).

Ecological sustainability and success of any

agroforestry system depends on the inter-play and

complementarily between negative & positive

interactions. It can yield positive results only if

positive interactions outweigh the negative

interactions (Singh et al., 2013). AF systems are also

being increasingly recognized as a tool for mitigating

climate change by reducing the overall volume of

greenhouse gases in the atmosphere and profiting the

economically weaker sections from emerging carbon

markets. Significant research on the types of AF

systems, their impacts on the environment, social and

economic aspects has been carried out over the years

at a range of spatial scales, right from local to regional and global scale. In this paper, the impacts

of AF systems on various aspects such as ecology

and environment, aesthetics and culture, social and

economic status of farmers practicing AF and finally,

climate change mitigation and adaptation is

discussed, based on a review of papers over the

temporal and spatial scale.

Constraint in Agro-Forestry systems (a) Agro-Forestry technology development and

transfer programmers are not adequately

incorporating farmers‘ relevant criteria to

evaluate the impact and implications of their work.

(b) Farmer participatory approaches are not being

exploited in the various phases of development

problem identification, programme design,

technology transfer etc.

Components in Agro-Forestry system

Trees are simultaneously planted in rows sparsely in

crop field and/or along the alies (bunds). These trees

provide food, timber, fuel, fodder, construction

materials, raw materials for forest-based small-scale

A

REVIEW ARTICLE

60 VIJAY UPADHYAY, ABHISHEK RAJ, NEELU JAIN AND BRIJESH KUMAR MEENA

enterprises and other cottage industries and in some

cases, enrich soil with essential nutrients (Ghosh et

al., 2011). Management practices for agro-forestry

are more complex because multiple species having

varied phonological, physiological and agronomic

requirements are involved (Manna et al., 2008). Woody perennial tress, herbaceous crops and

livestocks are the major components of Agro-

Forestry system which can control and governed by

village peoples and farmes that residing in/around the

village. Therefore, farmers play an inevitable role in

Agro-Forestry management and development in any

area. Land is another essential component which

affects existence of Agro-Forestry models as per

changing agroclimatic zones. In lieu of the above

components, farmers role in Agro-Forestry are

described below,

Farmer: For the purpose of this survey, a farmer is defined as ―a person who operates some land and

was engaged in Agro-Forestry. The poor, particularly

the rural poor, depend on nature for many elements

of their livelihoods, including food, fuel, shelter and

medicines (Jhariya and Raj, 2014). Agricultural

activities is meant the cultivation of field crops and

horticultural crops, growing of trees or plantations

(such as rubber, cashew, coconut, pepper, coffee, tea,

etc.), animal husbandry, poultry, fishery, bee-

keeping, vermiculture, sericulture, etc. Thus, a

person qualifies as a farmer if (i) he possessed some land (i.e. land, either

owned or leased in or otherwise possessed),

(ii) It may be noted that persons engaged in Agro-

Forestry / allied activities but not operating a

piece of land are not considered as farmers.

Similarly, agricultural labourers,

(iii) Coastal fishermen, rural artisans and persons

engaged in Agro-Forestry services are not

considered as farmers. It is also quite possible

such farmers are also excluded from the

coverage of the present Situation Assessment

Survey. Farmer household: A household having at least

one farmer as its member is regarded as a farmer

household in the context of the present survey. The

expenditure incurred by a household on domestic

consumption during the reference period is the

household's consumer expenditure. Household

consumer expenditure is the total of the monetary

values of consumption of various groups of items,

namely

(i) food, pan (betel leaves), tobacco, intoxicants and

fuel & light, (ii) clothing and footwear

(iii) miscellaneous goods and services and durable

Enhancing Soil Fertility and Water Use Efficiency This is a debatable concept today as soil is ―friends

or foe‖. Indeed, soil works as substratum which can

hold all the living and non-living substance. Soil

provides some essential nutrients to the tree and

crops by decomposition and decaying of plant

residues which can represented by leaf and liiter

shedding in frequent time interval in any agro

ecosystem models. Trees in Agro ecosystems can

enhance soil productivity through biological nitrogen

fixation, efficient nutrient cycling, and deep capture

of nutrients and water from soils. Even the trees that do not fix nitrogen can enhance physical, chemical

and biological properties of soils by adding

significant amount of above and belowground

organic matter as well as releasing and recycling

nutrients in tree bearing farmlands. In agroforestry

model, a suitable combination of nitrogen fixing and

multipurpose trees with field crops are played a

major role in enhancement of better yield

productivity, soil nutrient status and microbial

population dynamics which plays a major role in

nutrient cycling to maintain ecosystem (Raj et al.,

2014a). As per Raj et al. (2014b) the soil biological attributes are also responsible for determination &

maintenance of physical properties of soil.

Ecological intensification of cropping systems in

fluctuating environments often depends on reducing

the reliance on subsistence cereal production,

integration with livestock enterprises, greater crop

diversification, and Agro-forestry systems that

provide higher economic value and also foster soil

conservation. The next green revolution and

concurrent environmental protection will have to

double the food production. Agro-forestry may hold promise for regions where

success of green revolution is yet to be realized due

to lack of soil fertility. A useful path, complementary

to chemical fertilizers, to enhance soil fertility is

through Agro-forestry. Alternate land use systems

such as Agro-forestry, agro-horticultural, agro-

pastoral, and Agrosilvipasture are more effective for

soil organic matter restoration. Soil fertility can also

be regained in shifting cultivation areas with suitable

species.

Adaptation role of Agro-forestry

Agro-forestry systems can be useful in maintaining production during both wetter and drier years.

During the drought deep root systems of trees are

able to explore a larger soil volume for water and

nutrients, which will help during droughts.

Furthermore, increased soil porosity, reduced runoff

and increased soil cover lead to increased water

infiltration and retention in the soil profile which can

reduce moisture stress during low rainfall years.

Tree-based systems have higher evapotranspiration

rates and can thus maintain aerated soil conditions by

pumping excess water out of the soil profile more rapidly than other production systems. Finally, tree-

based production systems often produce crops of

higher value than row crops. In drought-prone

environments, such as Rajasthan, as a risk aversion

and coping strategy, farmers maintain Agro-forestry

systems to avoid long-term vulnerability by keeping

trees as an insurance against drought, insect pest

outbreaks and other threats, instead of a yield-

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 61

maximizing strategy aiming at short-term monetary

benefits. Numerous examples of traditional run-off

Agro-forestry discussed in this article and elsewhere

are other examples of adaptation to climate

variability.The role of Agro-forestry in reducing the

vulnerability of agro ecosystems—and the people that depend on them—to climate change and climate

variability needs to be understood more clearly

Analysis of Existing Land –Use System

Common factors usually noted with regard to the

analysis of an existing land use system are:

(a) Resource allocation at the community and

household levels with respect to land, labour and

inputs in alternative on farm and off farm

activities and resource with respect to land, tree,

animals and water are not well understood.

(b) Management levels associated with the various

production system of crop , livestock or tree are not well understood

(c) Performance (yield) in terms of meeting

socioeconomic priorities and criteria of the

household are not usually measured. Therefore

governmental projects should be analyzed to

identify the extent to which they are addressing

these socioeconomic factors in the analysis of

land –use systems.

Biodiversity Conservation Biodiversity is threatened worldwide, and despite

some local successes, the rate of biodiversity loss does not appear to be slowing. This can decrease

ecosystem functioning and services. Different

species promote ecosystem functioning during

different years, at different places, for different

functions and under different environmental change

scenarios. The species needed to provide one

function during multiple years are often not the same

as those needed to provide multiple functions within

one year. Therefore, precautionary investments are

required for managing biodiversity over the

landscape. Actions focused on enhancing and

restoring biodiversity are likely to support increased provision of ecosystem services.

Assessment of Agro-forestry Technologies

Common problems identified relating to assessment

of Agro-forestry technologies are planning of Agro-

forestry projects Is not appropriately addressing the

socioeconomic potentials, Impacts and implications

of improving or integrating new Agro-forestry

projects are not adequately and systematically

assessing the economic viability and social

acceptability of on farm research of extension work.

There is no training program to evaluate Agro-forestry technologies. Hence the following

socioeconomic criteria should be addressed in

technology assessment:

(a) Net returns to labourers and cash resources

(b) Compatibility with other on –farm and off farm

activities of the house-hold.

(c) Technology effects on the reduction / increase of

risk and uncertainty normally faced by farmers.

(d) Technology effects on the responsibilities of

household members with respect to resourse

allocation , implementing charges and receiving

the benefits

(e) Technology effects on the goals /objectives of

the household and their relations in the community.

Infrastructure and Support for Agro-forestry

It is generally noted that infrastructure and support

services for Agro-Forestry are inadequate because

Agro-Forestry. Information support (technical

communication, farmer, training, on- farm

demonstration research support etc.) does not exist in

most areas of the country. Credit is restricted by

conventional policies and markets for Agro-Forestry

products are not well developed and promoted and

multipurpose tree seeds, seedlings, and access to

nurseries and other sources of inputs may not be adequately developed. To be freed problems. The

following criteria should be considered.

(a) Government policy on rural service centres

should take into account the needs for Agro-

Forestry.

(b) Training of extention workers should aim at an

all-round extention worker who can handle the

multidisciplinary and multicommodity issues of

Agro-Forestry and land-use systems.

(c) Agro-Forestry development should be supported

with appropriate technology services at rural markets and growth centres.

(d) Project design should be such that adequate

technical and managerial skills are passed on so

that by the end of the project local households or

farmers themselves can take over the project

effectively.

Economic and Agriculture Development Policies

Operation and implementation of policies related to

Agro-Forestry development present an extremely

difficult task of co-ordination across government

must ministries and departments.

The fact remains that based on the socioeconomics system of a place appropriate technology needs to be

provided so that it becomes acceptable to the people

in the north east region where the jhum system is to

be followed the new system should not only make

good the return from jhum cultivation but should

give substantially higher returns with elimination of

jhum practices which are undesirable. Likewise, in

the arid region of Rajasthan, the economy of the

farmer is based on rain-fed agriculture and animal

husbandry, for which dry-land agriculture has been

adopted with scattered trees of Prosopis species, a multipurpose tree which provides fuel, fodder, food,

and timber and also enriches the soil through

nitrogen fixation. The system provided to such area

should be such the farmer could harvest better

through rain-fed agriculture and also grow trees in

the most efficient manner. Jatropha based

intercropping systems has potential to improve the

socioeconomic conditions in rural areas and to

62 VIJAY UPADHYAY, ABHISHEK RAJ, NEELU JAIN AND BRIJESH KUMAR MEENA

transform the National energy scenario and the

ecological landscape (Raj et al., 2016). Similarly,

gum production is a pillar of family economy and

considered as an income-generating source that

requires only a low input of work after the rainy

season (Raj et al., 2015; Raj, 2015a). As per Painkra et al. (2015) India is a rich diversity centre of

medicinal and aromatic plants and plays an important

role in supporting health care system in India. The

central India comprises, Madhya Pradesh,

Chhattisgarh, Andhra Pradesh, Orissa, Jharkhand and

Bihar and to some extent Gujarat and Rajasthan are

major source of commercially important gums in

good quantity and forms one of the major ecosystems

of the Indian subcontinent and constitutes a large

tract of tropical dry deciduous and tropical moist

deciduous forest type (Raj and Toppo, 2014; Toppo

et al., 2014). The tree characteristic that are particularly important to many local communities

include smokiness of fuelwood fodder, fodder and

flavours imparted by fuelwood and charcoal and

thorniness.

Accordingly, relevant technologies for different

situations should be made available to make this

land-use system a reality.

Agro-forestry Promotion

The World Congress on Agro-Forestry with the

theme ‗Trees for Life‘ was organized in February

2014 at New Delhi to have a forward outlook to any constraints that might restrict the adoption of Agro-

Forestry practices. Moreover, NAP, 2014 is a path-

breaker in making Agro-Forestry an instrument for

transforming the lives of the rural farming

population, protecting ecosystem and ensuring food

security through sustainable means. The major

highlights of the Policy are establishment of

institutional set-up at the national level to promote

Agro-Forestry under the mandate of the Ministry of

Agriculture GoI simplify regulations related to

harvesting, felling and transportation of trees grown

on farmlands; ensuring security of land tenure and creating a sound base of land records and data for

developing an market information system (MIS) for

Agro-Forestry. Investing in research, extension and

capacity building and related services; access to

quality planting material; institutional credit and

insurance cover to Agro-Forestry practitioners.

Increased participation of industries dealing with

Agro-Forestry produce, and strengthening marketing

information system for tree products. One of the

objectives of NAP, 2014 is to bring together various

programmes, schemes, missions among the elements of Agro-Forestry under one platform functioning in

various departments of agriculture, forestry and rural

sectors of the government. It is proposed to be

achieved through setting up of a National Agro-

Forestry Mission/ Board under the Department of

Agriculture and Co-operation (DAC), Ministry of

Agriculture, GoI and upgrading of NRCAF, Jhansi

(now CAFRI, Jhansi) as a nodal centre with agro-

ecology-based regional centres in different parts of

the country. This step will promote value chain,

climate-resilient technology development and pave

the way for region-based marketing linkages in

Agro-Forestry.

CONCLUSION

Agro-forestry is an interactive and sustainable

farming practice which not only maintains structure

and diversity but also helps in boosting income of

farmers by providing mulfarious products as timber

and NTFPs. The scope and potential of Agro-

Forestry should not be underestimated in term of

providing food and nutritional security,

phytoremediation, mitigating climate change,

effective bio-geochemical cycle, water and nutrient

management, watershed management and providing socio-economic security to farmers. Therefore, a

scientific oriented research should done under the

partnership of several government, non-govermental

institutions, university, NGOs etc for proper and

effective management of both traditional and new

age Agro-Forestry systems.

REFERENCES

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Rahman, G.M.M. (2011). Optimization of plant density of Akashmoni (Acacia auriculiformis) for

production of fuel wood in the bunds of crop land.

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management of Soil organic carbon in semiarid and

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farming. International Journal of Scientific

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Thesis, I.G.K.V., Raipur (C.G.), p 89.

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Readers Shelf, 10(3): 15-16.

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64 VIJAY UPADHYAY, ABHISHEK RAJ, NEELU JAIN AND BRIJESH KUMAR MEENA

*Corresponding Author ________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 65-72. 2020

DO BACKWARD INTEGRATION BOOST THE TECHNOLOGY ADOPTION BY

CHILLI FARMERS? THE EVIDENCE FROM ANDHRA PRADESH, INDIA

R. Asha* and K. Umadevi1

Agricultural Economics, Acharya N. G. Ranga Agricultural University Agriculture College, Bapatla 1Agricultural Economics, Institutional Development Plan, ANGRAU, Lam, Guntur

Email: asha.rallapalli06@gmail.com

Received-12.02.2020, Revised-27.02.2020

Abstract: The study intends to analyse the impact of backward integration on technology adaptation by chilli farmers. A sample of 128 farmers has been selected purposively from four mandals of Prakasam district in Andhra Pradesh. Technology adoption index, probit regression and poisson model with endogenous regression model used to analyse the impact backward integration on technologies adoption by chilli farmers. The findings show that majority (46.87%) of the chilli farmers who are following backward integration are adopting maximum technologies with technology adoption index 80-90 and the farmers who are not following backward integration (73.43%) are adopting less than four technologies with adoption index <50. The extension service (0.11) and backward integration (0.53) had a positive significant at 10 per cent and 5 per cent levels effect on adoption of technologies.

Keywords: Backward integration, Chilli farming, Technology adoption index, Probit regression, Poisson model

INTRODUCTION

arket liberalization and growth of international

trade have created export opportunities in

agricultural sector for many developing

countries. The traditional way for food production is

replaced by practicing more similar to manufacturing

processes, with greater co-ordination of farmers,

processors, retailers and other stakeholders in value

chain of agriculture. The agro-food sector can be

conceptualized as a system of vertically

intercorrelated stages. Vertical coordination is

harmonizing of vertical inter dependence of the

production and distribution of activities. Backward integration is a strategy under vertical integration

where a firm gains control over ownership or

increased control over its suppliers.

Agricultural processing gaining more importance as

export of agricultural commodity was increasing.

Spices has a major role in export. Chilli is the major

spice contributing 42-44 per cent by volume and 25-

28 per cent by value to total spices exported from

India (Spice Board of India, 2019). In India, Andhra

Pradesh ranked first in area and production of chilli,

accounting to 1.59 lakh hectares with a production of 6.3 lakh tonnes and productivity of 3,962 kg/ha

during 2018-19. Prakasam district in Andhra Pradesh

state occupied 2nd place with 0.58 lakh hectares area

and 1.50 lakh tonnes of production during 2018-19

(Agricultural Statistics at a Glance 2018-19).Wide

variation in yield levels leading to fluctuation in

chilli prices and farmers are facing problems like

high transportation cost, low productivity, viral

diseases, quality deterioration by contamination of

pesticides, industrial chemicals and aflatoxins. It is

vitally important to support the chilli farmers to

produce high quality, sustainable food safe spices to compete in the international market. The major

players like ITC, Synthite etc., are providing

customised solutions to diverse challenges of chilli farmers through backward integration.

The main objective of the study is to analyse the

impact of backward integration on adoption of

technologies in chilli farming.

METHODOLOGY

The decision to adopt technologies which improve

quantity and quality of the produce may be

determined by several characteristics of farmers, like

age, education, credit, extension visit, farming

experience, backward integration and to know the factor to intensify adoption of technologies count

data model were used by Isgin et al. (2008), Lohr

and Park (2002), Rahelizatovo and Gillespie (2004),

Ramirez and Shultz (2000), and Sharma et al. (2011)

employed count data models to explain intensity of

adoption of various technologies. A number of other

studies (Beshir, 2014; Caviglia-Harris, 2003;

Gebremedhin and Swinton, 2003) have considered

factors affecting both the decision to adopt and the

degree or intensity of adoption of technologies or

conservation practices using double hurdle models. These usually involve a first stage probit model and a

second stage poission model. Other studies (Mbaga-

Semgalawe and Folmer, 2000) use an integrated

socio-economic model of adoption to examine a first

stage perception of erosion, a second stage adoption

of improved soil and water conservation measures,

and then a Poisson regression model to analyse a

third stage adoption effort (or level of adoption) of

improved conservation measures in which selectivity

bias is accounted for using the Heckman two-stage

approach.

To assess the participation effect of farmers land tenure, activity in social, awareness of backward

M

RESEARCH ARTICLE

66 R. ASHA AND K. UMADEVI

integration, farm size and family size are major part.

Several other studies find that farmers land tenure,

farm size and family size are important in

participation (Baumgart Getz et al. 2012).

Technology Adoption Index

To measure the technology adoption of chilli farmers, technology adoption index was calculated

TAI = 𝐴𝑖

𝑀𝑖 * 100

Ai = Adoption score by the farmer

Mi = Maximum adoption score by the farmer

Poisson Model with Endogenous Treatment:

To estimate the impact of backward integration on adoption of technologies in chilli farming, poisson

model with endogenous treatment was used. A count

data model will be suitable for poisson model

(Greene, 1997). The method used by Greene, 1997 is

adopted, where endogenous regression model for

dependent variable i.e., number of technologies

adopted by farmer is specified. This specification

allows for well-defined correlation structure between

the unobservable variable that affects backward

integration as well as adoption. The interest model

equation was given by E (Yi/Xi, ci, ei) = exp (Xi b + δ ci + ei) …(1)

Xi is a vector of covariate that influences the level of

adoption. The probability density function for Yi is

conditional on the treatment ci, the covariates Xi and

error ei is given by (2)

E (Yi/Xi, ci, ei) =

𝑒𝑥𝑝 {( −exp X i b+δci+ei }{exp (X ib+δci+ei )}Y i

Yi ! …(2)

The treatment (backward integration) is determined

by (3)

ci = 1 𝑖𝑓 𝑤𝑖𝛾 + 𝑢𝑖 > 0

𝑜 𝑖𝑓 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 …(3)

The covariate vectors Xi and Wi are exogenous,

estimation of the parameters in such models may be

done by using maximum likelihood. The empirical model that assesses the participation

effect in integration on the adoption of technologies

is estimate by Probit regression model and Poisson

regression with endogenous model, given in two

equations below

Backward integration (c) = β0 + β1X1 + β2 X2 + β3 X3

+ β4 X4 + β5 X5 + β6 X6 + ui

X1 = Land tenure (1 if own land; 0 otherwise)

X2 = Membership of farmer based organisation (1 if

yes; 0 otherwise)

X3 = Awareness (1 if yes; 0 otherwise) X4 = Distance to market place (1 if <100km; 0

otherwise)

X5 = Farm size (ha)

X6 = Family size (number)

ui = Error term

Adoption (Y) = β0 + β7 X7 + β8 X8 + β9 X9 + β10 X10 +

β11 X11 + β12 X12 + ei

Where Y is a count variable ranging from 0 if a

farmer failed to adopt any of the technologies up to

8, the highest number of technologies. The

technologies identified in study area and taken for

the study is Soil testing, Selection of variety,

Agronomic practices, Pesticide and fertilizer

application, Utilization of green label/slightly toxic

chemicals, Integrated pest management, Integrated

crop management and Post-harvest handling. X7 = Age (number of years)

X8 = Education (1 if educated; 0 otherwise)

X9 = Credit (1 if available; 0 otherwise)

X10 = Extension visit (number of times visit per

month)

X11 = Farming experience (number of years)

X12 = Backward integration (1 if integrated farmers;

0 otherwise)

ei = Error term

Data and Sampling In 2018-2019 conducted a primary survey of

integrated farmers, non-integrated farmers and firms in four mandals of Praksam district in Andhra

Pradesh. Multistage random sampling technique was

adopted for selection of sample at different levels in

the present study. In Andhra Pradesh, Prakasam

district was selected purposively as the integrated

chilli farmers of both ITC and Synthite are present in

Prakasam district only. Prakasam district in Andhra

Pradesh state occupied 2nd place with 0.58 lakh

hectares area and 1.50 lakh tonnes of production

during 2017-18. The farmers who are adopting

backward integration are integration farmers. The farmers other than integrated farmers are mentioned

as non-integrated farmers. Four mandals and from

each mandal, two villages were selected based on the

highest number of integrated chilli farmers. By using

cocharn’s (1963) formula sample size was calculated.

From each village, 8 integrated farmers and 8 non-

integrated farmers were selected, making a total

sample of 128 farmers constituting 64 integrated and

64 non-integrated farmers. MS excel and software

STAT version 15, a trail version was used to analyse

technology adoption index, probit regression and

poisson model with endogenous regression model.

Characteristics of sample farmers

The data obtained through the primary survey

covered a wide range of information on age of the

farmers, education level, farming experience,

household size, farm size, distance to market,

backward integration, land tenure and membership of

a farmer based organization, among others. These

socioeconomic variables (e.g. Age, Education, etc.)

are relevant in the sense that it indicates whether a

farmer will take part in backward integration or

adopt improved farm technology. Chi-square test was done to know the presence of association between

variables and backward integration. The variables are

significant, means there is a significant association

between variables and backward integration.

The results from Table 3.1 indicate that majority of

the integrated farmers (67.19%) had formal

education while the rest (32.81%) had no formal

education, for non-integrated farmers 43.75 per cent

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 67

are educated and 56.25 per cent had no formal

education. From the total sample, majority (55.47%)

of them are educated. Educated farmers are able to

better process the information, allocate inputs more

efficiently, and more accurately assess the

profitability of new technology, compared to farmers with no education. 50 per cent of the total sample

farmers were under backward integration of some

sort while the rest were not. Land ownership is an

important factor in every production activity. A large

percentage (73.44%) of the integrated farmers and

34.38 per cent of non-integrated farmers owned their

land while the rest were tenants who paid some form

of rent to the land owners. From the total sample, owned land farmers found to be more than tenant

farmers.

Table 1. Categorical socioeconomic variables

Variable Integrated farmers

(n=64)

Non-integrated farmers

(n=64)

Per cent to total

(n=128) χ

2 test

Education

Educated 43 (67.19) 28 (43.75) 55.47 8.62**

Illiterate 21 (32.81) 36 (56.25) 44.53

Land tenure

Owned 47 (73.44) 22 (34.38) 53.91 19.65**

Rented 17 (26.56) 42 (65.63) 46.09

Member of FBO

Yes 19 (29.69) 10 (15.63) 22.66 3.61*

No 45 (70.31) 54 (84.38) 77.34

Awareness of backward integration

Aware 58 (90.63) 31 (48.44) 69.53 26.88***

Not aware 6 (9.38) 33 (51.56) 30.47

Note: figures in parenthesis indicate per cent to total, ***Significant at the 1 % level of significance,

**Significant at the 5 % level of significance, *Significant at the 10 % level of significance

The majority (70.31%) of the integrated farmers and non-integrated farmers (84.38%) were not members

of any of the farmers based organisation (FBO) while

the rest were members of FBO. From the total

sample, 77.34 per cent are not members of FBO.

90.63 per cent of integrated and 48.44 per cent of

non-integrated farmers were having knowledge about

backward integration farming and rest of them were

not aware of backward integration.

The other socio-economic variables like age,

experience, distance to market, farm size and

household size are presented in Table 3.2. Age of the respondents ranged between 24 to 65 years, with an

average of 41 years. A larger proportion (48.44%) of

the integrated respondents were aged between 41 to

60 years while non-integrated farmers had a larger

proportion (59.38%) of 21 to 40 years, which are the

most productive stages of their lives, all other things

being equal. Also, large percentages (45.31%) of the

integrated farmers were aged between 21 to 40 years

while 6.25 per cent were above 60 years. For non-

integrated farmers, 40.63 per cent belongs to the age

group of 41-60 years. From the total sample,

majority (52.34%) of the farmers belong to 21 to 40 years.

The average years of farming experience of the

respondents were 19 years, ranging from 4 to 40

years. A large number i.e., 42.19 per cent of the

integrated farmers and 50 per cent of non-integrated

farmers had farming experience between 11 and 20

years as shown in Table 3.2. The long years of

farming experience can increase farmers' confidence in adopting improved agricultural technologies.

10.94 per cent and 20.31 per cent from the total

sample of integrated and non-integrated farmers

respectively were having less than 10 years of

farming experience. Similarly, 37.50 per cent and

23.44 per cent of the total sample of integrated and

non-integrated farmers respectively were having less

21 to 30 years of farming experience. 9.38 per cent

and 6.25 per cent from the total sample of integrated

and non-integrated farmers respectively were having

31 to 40 years of farming experience. On the part of distance to market, the results show that a majority

(43.75%) of the non-integrated farmers travel a

distance of 101 kilometers to 150 kilometers. The

31.25 per cent of the non-integrated farmers travel a

distance of <100 kilometers to access a market.

Integrated farmers travelled less than 100 km to

market their products. The 78.13 per cent of the

integrated and 45.31 per cent of non-integrated

farmers cultivated a land size of >2 hectares. About

18.75 per cent of the integrated farmers and 39.06

per cent of non-integrated farmers cultivated a land

size of 1.01 to 2 hectares. 3.13 per cent of integrated farmers and 10.94 per cent of non-integrated farmers

cultivated a land size of 0.51 to 1.00 hectares. While

a small percentage (4.69%) of the non-integrated

farmers cultivated below 0.5 hectares. From the total

sample, majority (61.72%) of the farmers were large

farmers (>2 hectare).

68 R. ASHA AND K. UMADEVI

Table 2. Continuous socioeconomic variables

Variable Integrated

farmers (n=64)

Non-integrated

farmers (n=64)

Per cent to

total (n=128) χ

2 test

Age (years)

<20 0 (0.00) 0 (0.00) 0.00

5.64* 21-40 29 (45.31) 38 (59.38) 52.34

41-60 31 (48.44) 26 (40.63) 44.53

>60 4 (6.25) 0 (0.00) 3.13

Average 43 38 41

Experience in farming (years)

<10 7 (10.94) 13 (20.31) 15.63

7.60** 11-20 27 (42.19) 32 (50.00) 46.09

21-30 24 (37.50) 15 (23.44) 30.47

31-40 6 (9.38) 4 (6.25) 7.81

Average 20 18 19

Distance to market (Km)

<100 64 (100.00) 20 (31.25) 65.63

67.04*** 101-150 0 (0.00) 28 (43.75) 21.88

151-200 0 (0.00) 8 (12.50) 6.25

>200 0 (0.00) 8 (12.50) 6.25

Farm size (hectare)

Marginal (<0.50) 0 (0.00) 3 (4.69) 2.34

15.92*** Small (0.51-1.00) 2 (3.13) 7 (10.94) 7.03

Medium (1.01-2.00) 12 (18.75) 25 (39.06) 28.91

Large (>2.00) 50 (78.13) 29 (45.31) 61.72

Household size (No.)

1-3 22 (34.38) 26 (40.63) 37.50

3.86 4-7 41 (64.06) 33 (51.56) 57.81

>7 1 (1.56) 5 (7.81) 4.69

Note: figures in parenthesis indicate per cent to total, ***Significant at the 1 % level of significance,

*Significant at the 10 % level of significance

The average size of the households was 4 members.

A large percentage (64.06%) of the integrated

farmers and 51.56 per cent of non-integrated farmers

has household sizes that ranged between 4-7

members. From the total sample, majority (57.81%)

of the farmers were having 4-7 members family size.

A large household is an endowment and a reliable

source of labour if household members are available

to work on the farm as family labour, given the

labour-intensive nature of agricultural technologies.

RESULTS AND DISCUSSION

Technology adoption index

The technologies present in the study area in chilli

farming and frequency of farmers adopted was

showed in Table 4.1. The technologies are soil

testing, selection of variety, agronomic practices,

pesticide and fertilizer application, utilization of

green label/slightly toxic chemicals, integrated pest

management, integrated crop management and post-

harvest handling. 81.25 per cent of integrated farmers and 21.88 non-integrated farmers are following soil

testing technology. Synthite company is providing

soil testing for their integrated farmers and most of

the ITC farmers tested their soil in the soil

laboratory. The company extension agents

recommended fertilizer doses to the farmers

according to their soil testing results. Selection of

variety according to their climatic region and soil

health condition and production quantity was mostly

adopted 84.37 per cent of integrated farmers and

81.25 per cent of non-integrated farmers. 92.19 per cent of integrated farmers and 70.31 per cent of non-

integrated farmers are adopting agronomic practicing

technology like spacing and time of sowing. About

96.44 per cent of integrated farmers are adopting the

technology related to pesticide and fertilizer

application, i.e., the time schedule of application,

number of applications and quantity of application.

All these techniques are closely examined by the

company extension service agents. Only 29 out of 64

members of non-integrated farmers are following

these technologies because they don’t have

knowledge about number of applications and quantity of application of pesticides and fertilizers.

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 69

Table 3. Technology practice wise frequency distribution of integrated and non-integrated farmers

S. No. Technology Integrated farmers

(n=64)

Non-integrated

farmers (n=64)

1 Soil testing 52 (81.25) 14 (21.88)

2 Selection of variety 54 (84.37) 52 (81.25)

3 Agronomic practices 59 (92.19) 45 (70.31)

4 Time and number of Pesticide and fertilizer

application

62 (96.44) 29 (45.31)

5 Utilization of green label/slightly toxic

chemicals

64 (100.00) 3 (4.69)

6 Integrated pest management 64 (100.00) 19 (29.69)

7 Integrated crop management 49 (76.56) 19 (29.69)

8 Post-harvest handling 45 (70.31) 22 (34.38)

χ2 test: 52.64***

Note: values in parenthesis are per cent of the sample size, ***Significant at 1% level of significance

Source: Estimated by author

The farmers who are under backward integration of

Synthite company should strictly follow green label

chemicals and integrated pest management. ITC

company farmers should follow the technologies like

utilization of green label/slightly toxic chemicals,

integrated pest management and integrated crop

management. IPM is the core of food safety

strategies to ensure pesticide residue compliant

products for export companies. IPM model promotes

a corrective approach for pest management through a

combination of physical and cultural interventions to reduce agrochemicals consumption. IPM technology

transfer is assisting farmers to analyse pest

infestation to establish economic threshold levels to

optimise pesticide usage, improve productivity &

profitability. The integrated crop management is a

preventive approach to reduce pest incidence by

boosting plant immunity through agronomical

interventions. It helps to enhance productivity,

reduce cultivation costs and increase profitability.

Few percentages of non-integrated farmers are

following technologies like utilization of green label/slightly toxic chemicals (4.69%), integrated

pest management (29.69%) and integrated crop

management (29.69%). This is due to lack of

guidance and knowledge about them. 34.38 per cent

of the non-integrated farmers are following post

harvest handling technologies. About 70.31 per cent

of integrated farmers are following post harvest

handling like grading. Grading is most important

operation for integrated farmers and this operation is

followed under supervision of company agents. Top

graded chilli was purchased by company and least

graded produce sold in Guntur market.

Level of adoption of technologies was analyzed

through technology adoption index (TAI) and the

results are presented in Table 4.2. The TAI for each

farmer was computed by dividing the number of

practices adopted by farmers by total number of

practices selected and expressed as percentage. The

majority (46.87%) of the integrated chilli farmers

were adopted seven technologies with technology

adoption index of 80-90 and 12.50 per cent of the

integrated farmers adopted six technologies with

technology adoption index of 70-80. About 15.63 per cent farmers from total sample were adopted all

technologies. Most of the non-integrated farmers

(73.43%) are adopting less than four technologies

with adoption index of <50 and 9.37 per cent farmers

were adopted five technologies with technology

adoption index 60-70. Farmers who are under

backward integration are adopting more technologies

than others. Chi-square test was done for

understanding the association between backward

integration and technology adoption index. The test

was significant at 1 per cent level and it reveals that there is positive association between technology

adoption index and backward integration.

The integrated farmers were adopted technologies

like pesticide and fertilizer application, utilization of

green label/slightly toxic chemicals and integrated

pest management. Most of the non-integrated farmers

are following technologies like selection of variety,

agronomic practices, pesticide and fertilizer

application. Very few non-integrated farmers are

adopting technologies like soil testing, utilization of

green label/slightly toxic chemicals, post harvest

management i.e., grading like operations.

Table 4. Technology adoption index

Technology Adoption

Index

Number of

Technologies

Integrated Farmers

(n=64)

Non-Integrated

Farmers (n=64)

<50 3 0 (0.00) 47 (73.43)

50-60 4 0 (0.00) 7 (10.93)

60-70 5 6 (9.37) 6 (9.37)

70 R. ASHA AND K. UMADEVI

70-80 6 8 (12.50) 4 (6.25)

80-90 7 30 (46.87) 0 (0.00)

>90 8 20 (31.25) 0 (0.00)

χ2 test: 42.54***

Note: values in parenthesis are per cent of the total, ***Significant at 1% level of significance

Source: Estimated by author

Factors Influencing the Participation of Farmers

in Backward Integration

The probability of the model chi-square was found to

be 0.00 indicating that model was significant at 1 per

cent level and socioeconomic factors influence the

farmers to participate in backward integration. The coefficients of the probity regression only show the

direction of the effects that an explanatory variable

had on the dependent variable. The marginal effects

that shows the magnitude of the changes that occur

on the dependent variable when there are

corresponding changes in the independent variables

was also estimated. The results are presented in

Table 4.3.

The land tenure of the farmer had a positive

influence on farmers participation in backward

integration. The marginal effect indicates that when a

farmer had own land, the probability of taking part in

backward integration was 0.45 per cent greater than

tenant farmers. The secure land tenure will

encourage adoption decisions so, owned land farmers

were more likely to adopt the backward integration.

Membership of farmer-based organization (FBO) had no significant effect on the participation in backward

integration. Awareness had positive and 1 per cent

level of significant effect on the participation in

backward integration. The marginal effect indicates

that when a farmer had knowledge about backward

integration, the probability of taking part in

backward integration was 0.42 per cent greater than

others. Farmers who are aware of backward

integration technology are actively participating in

backward integration as they know the profitability

of that technology.

Table 5. Probit regression results of factors influencing participation of backward integration

Variable Coefficient Standard

Error

Marginal Effect Standard

Error

Land tenure 1.2129*** 0.3497 0.4491*** 0.1124

Membership of FBO 0.3350 0.4255 0.1330 0.1676

Awareness 1.155*** 0.4041 0.4156*** 0.1198

Distance to market place 1.8609*** 0.3973 0.6183*** 0.0912

Farm size 0.5844*** 0.1966 0.2318*** 0.0786

Family size 0.1900 0.1386 0.0753 0.0552

Constant -5.2258*** 1.0972

Prob > chi2 0.0000

Pseudo R2 0.5664

Log likelihood = -38.4685

Note: ***Significant at 1% level of significance

Distance to market place had a positive significant

effect on backward integration at 1 per cent level of

significance. The marginal effect indicates that for a

farmer having market at a distance less than 100 km

have probability of adopting backward integration was 0.62 per cent greater than others. Chilli market

for the farmers was nearly 200 km far way but the

company market place was very near to farmers, and

also companies bearing transportation expenses of

the farmer. Farm size had a significant effect on the

participation in backward integration. It was

positively significant at a level of 1 per cent. The

marginal effect indicates that when a farmer had

large farm size, the probability of taking part in

backward integration was 0.23 per cent greater than

others. This confirms the work of Rahman (2017)

who argue that land tenure (0.31%), awareness (0.28%) and farm size (0.04%) of the farmers had

positive influence to adopt the contract farming

technology.

Poisson Model with Endogenous Treatment

After looking at factors influencing the adoption of

backward integration, the effect of backward integration on the adoption of technologies was

analyzed by using poisson model with endogenous

treatment. As a result of possible sample selection

problem, there was an initial estimation of a selection

(backward integration) and substantive equations

(adoption of technologies) to correct for such

selection problem. The wald test of independent

equations shows a chi-square probability of 0.00

indicating that there is no selectivity bias problem in

the model. Table 4.4 shows the results from a

poisson estimation that indicates the factors

influencing the adoption of technologies.

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 71

Table 6. Maximum likelihood estimation of poisson model with endogenous treatment

Variable Coefficient Standard error Z-value P-value

Constant 1.3072*** 0.2409 5.43 0.000

Age -0.0063 0.0084 0.75 0.454

Education 0.1231 0.1050 1.17 0.241

Credit -0.0741 0.1157 0.64 0.521

Extension service 0.1075* 0.0611 1.76 0.079

Farming experience -0.0046 0.0100 0.46 0.645

Backward integration 0.5322** 0.2151 2.47 0.013

Prob > chi2 0.0000

Log likelihood -259.6293

Note: **Significant at 5% level of significance, *Significant at 10% level of significance

The poisson model is estimated using the maximum

likelihood method. The goodness of fit parameter of

the model indicates that the model adequately

predicted the determinants of adoption of

technologies. The chi-squared value significant at 1

per cent indicates that all the variables jointly

determined the dependent variable. The results

indicate that education, extension service and backward integration had a positive effect on

adoption of technologies. Extension service was

positive and significant at 10 per cent level of

significance. The farmers who have access to

extension services are more likely to adopt

technologies than farmers who have no access to

extension service. Reason for the access to extension

services are the means through which agricultural

technologies are transferred from researchers to

farmers by adopting techniques like training and

demonstrations. Therefore, access to the extension services facilitates uptake of technology. Farmers

who had contact with extension officers have 0.11

per cent greater probability of adoption. Studies by

Wanyoike et al. (2003) and Sall et al. (2000) had

shown the access to extension services as very

important factor in adoption decisions. Backward

integration was positive and had 5 per cent

significant level. This indicates that farmers who

were participating in backward integration are more

likely to adopt technologies than farmers who were

not under backward integration. Floyd et al. (2003)

observe a positive impact of farmers’ on extension service on the adoption of new technologies. Ransom

et al. (2003) find irrigated years of fertilizer use, off-

farm income and contact with extension as important

determinants for adoption of improved maize

varieties in Nepal. Rahman (2017) showed that

contract farming (0.25%) had a positive influence on

adopting improved farm technologies. Farmers who

are in backward integration have 0.53 per cent

greater probability of adoption. Backward integration

affords the farmers the opportunity to use modern

inputs, production methods and providing extension services to improve production and quality of output.

The use of such improved methods enhances farmer

flexibility or resilience to adoption.

CONCLUSION

In the total sample of integrated chilli farmers,

46.87 per cent of them are adopting seven

technologies and 73.43 per cent of the non-

integrated farmers are adopting less than four

technologies.

The land tenure, awareness, distance to market place and farm size of the farmer had positive

influence on participation in backward

integration. The marginal effect indicates that a

farmer with owned land, aware about Backward

integration, less market distance and more farm

size have a probability of adopting backward

integration greater than others.

Education, extension service and backward

integration had a positive effect on adoption of

technologies. Extension service and Backward

integration were positive and significant at 10 and 5 per cent levels respectively.

Policy implications

Backward integration technology increases

output and quality of the produce, so it should be

expanded by an assured alternative agency

(Government or co-operative) to increase

quantity and value of export of chilli.

Increase in extension service would create

knowledge about technologies in chilli farming

to farmers because most of the non-integrated

farmers are adopting less technologies than integrated farmers.

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*Corresponding Author

________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 73-80. 2020

IMPACT OF TILLAGE PRACTICES ON PHYSICO-CHEMICAL AND

FUNCTIONAL DIVERSITY IN PEARL MILLET-WHEAT CROPPING SYSTEM

Dhinu Yadav*, Leela Wati1, Dharam Bir Yadav

2 and Ashok Kumar

3

Department of Microbiology, CCS Haryana Agricultural University, Hisar 1Department of Microbiology, CCS Haryana Agricultural University, Hisar

2Department of Agronomy, CCS Haryana Agricultural University, Hisar

3Department of Agronomy, CCS Haryana Agricultural University, Hisar

Email: dhinuyadav737@gmail.com

Received-20.01.2020, Revised-17.02.2020

Abstract: Conservation agriculture based tillage practices mainly zero-tillage (ZT) considered as major component of sustainable agriculture that involves reducing the tillage operations retaining at coast 30% of plant parts/crop-residues at the soil surface and including crop-rotation in the existing cropping system. More research is needed for better understanding of tillage effects on soil physico-chemical and microbiological properties. Thus, the impact of two tillage systems: no-tillage (NT) and conventional tillage (CT) with different crop-rotations i.e. Conventional Tillage Wheat-Conventional Tillage

Pearlmillet (CTW-CTPM), Conventional Tillage Wheat-Zero Tillage Pearlmillet (CTW-ZTPM), Zero Tillage Wheat-Conventional Tillage Pearlmillet (ZTW-CTPM) and Zero Tillage Wheat-Zero Tillage Pearlmillet (ZTW-ZTPM) on physico-chemical and functional diversity of soil was evaluated in the present investigation at CCSHAU, Regional Research Station (RRS) at Bawal during 2014 year. After harvesting of wheat in 2017, triplicate soil samples from undisturbed and disturbed soil were obtained from two different depths (0-15 cm and 15-30 cm), for determination of CaCO3, Total N, P and K content and Functional diversity of microbes. Physico-chemical properties and functional diversity were recorded relatively higher under ZTW-ZTPM system at surface (0-15 cm) layer. SOC was recorded higher at surface layer under ZTW-ZTPM (0.29 %) as compared to CTW-CTPM (0.26 %) and the respective values at subsurface layer were 0.25 and 0.23%. In nutshell, NT

treatments promoted better physico-chemical and functional diversity of the soil relative to the CT treatment. Keywords: Functional diversity, Nutrient release pattern, Tillage systems

INTRODUCTION

illage is one of the fundamental agriculture

operation because it influences on crop growth, soil properties (physical, chemical and biological)

and environment and optimization of tillage practices

lead to improvement in soil health. Intensive

agricultural practices often lead to changes in soil

health governing properties like, soil structure,

aggregation, infiltration, bulk density, soil carbon

content, microbial biomass and their activities (Allen

et al., 2011). Soil with better health and quality will

be able to produce higher crop yield under favorable

as well as extreme climatic conditions (Congreves et

al., 2015), and soil health acts as a critical component for adaptation and mitigation of climate

change effects by the crops (Congreves et al.. 2015).

Therefore, it is important to apply appropriate tillage

practices that avoid the degradation of soil structure,

maintain crop yield as well as ecosystem stability.

Pearl millet–wheat has been most important cropping

system because it is a staple diet for the vast majority

of poor farmers and also forms an important fodder

crop for livestock. Resource degradation problems

are manifesting in the present-day agriculture,

necessitating for development of more innovative

conservation-based technologies in place of the conventional agriculture systems. In recent years,

interest of farmers in conservation agriculture (CA)

has increased because of escalation of capital and

production costs. Various on farm participatory trials

have revealed little or no difference in yields of crops

under zero-tillage system, compared with

conventional tillage (Krishna and Veettil 2014). The CA specifically aims to address the problems of soil

degradation due to water and wind erosion, depletion

of organic matter and nutrients from soil, runoff

losses of water, and, moreover, it purports to address

the negative consequences of climate change on

agricultural production. Relatively less attention has

been paid on the use of conservation agriculture in

the arid and semi-arid tropics, although a lot of

information is available from humid and sub-humid

regions globally (Jat et al., 2012). But region specific

CA options need to be identified for implementation by resource-poor farmers. Crop residues have

competing uses like fodder because of dominance of

livestock in these areas. Therefore, it is necessary

that suitable amount should be App. to improve crop

productivity and soil health in a cost-effective

manner. It is hypothesized that zero tillage with

residue retention improves soil physical, chemical

and biological properties compared to conventional

tillage in pearl millet – wheat cropping system.

MATERIALS AND METHODS

Study Site and Soil Sampling

The study site was located at CCSHAU, Regional

Research Station, Bawal, District- Rewari (Haryana)

T

RESEARCH ARTICLE

74 DHINU YADAV, LEELA WATI, DHARAM BIR YADAV AND ASHOK KUMAR

and no-tilled and conventionally tilled plots were

established in 2014. The soil samples collected

during 2017 after wheat harvest from surface and

subsurface soil profile from five random spots/tillage

plots were sieved through 2 mm sieve and stored at

4±1C. For determination of microbial activities, the soil was moistened to 60 % water holding capacity

(WHC) and incubated at 300 C for 10 days to permit uniform rewetting and allow microbial activity to

equilibrate after the initial disturbances. Sub-samples

were air-dried and ground for chemical analysis.

Characterization of Soil Physical and Chemical

Properties

CaCO3 and Soil organic carbon

Calcium carbonate content in different soil samples

was determined by the rapid titration method (Puri,

1949). The organic carbon content in different soil

samples was determined by the method of

Kalembassa and Jenkinson (1973).

Total N, P and K

Total nitrogen, phosphorous and potassium content

in different soil samples was estimated by Kjeldhal’s

method (Bremner and Mulvaney, 1982), John (1970)

and Knudsen et al., (1982).

Functional diversity of different microorganisms

using CLPP

Biolog microplate comprising of 22 different sugars

and 9 amino-acids as a substrate and a control well

without a carbon source was used to study functional

diversity of different microorganisms. Serial dilution

of each soil sample was made and 100 μl of diluted soil sample was added in a well of microtitre plate

having sugar basal medium and the plates were

incubated at 20±20 C in dark. Development of color

from blue to yellow was measured after every 24 h

for 5 days using an Elisa plate reader at 592 nm and

substrate utilization was calculated.

Statistical analysis

The significance of treatment effects was analyzed

using two factorial RBD analysis, using OP Stat

software, at CCS HAU, Hisar.

RESULTS AND DISCUSSION

CaCO3 and Soil organic carbon

Zero-tillage (ZT) affects the chemical properties of

the soil in entirely different patterns to as that of

what CT did. No-tillage can also lead to improvements in soil quality by improving soil

structure and enhancing soil biological activity,

nutrient cycling, soil water holding capacity, water

infiltration and water use efficiency (Hobbs et al.,

2008). The data on CaCO3 of soils under

conventional and zero-tillage systems under pearl

millet-wheat crop rotation presented in Fig. 1

indicated that on shifting from conventional to zero-

tillage, not many differences were observed in

CaCO3 content of the soil at different depths.

CaCO3 content of different soil samples varied

between 0.27-0.39 % at 0-15cm depth and 0.23-0.36 % at 15-30 cm depth under different tillage practices

whereas with the adoption of zero-tillage wheat

system, CaCO3 content increased to 0.39 % at surface

soil which decreased upto 0.36 % at subsurface soil

under ZTW-ZTPM system. Individually, CaCO3

content was significant with depth and interaction of

tillage and depth was also significant. The CaCO3

content of soil samples was affected by pearl millet-

wheat crop rotation under conventional and zero-

tillage to different extent, in present study and similar

findings have been reported in literature also. Neugschwandtner et al. (2014) reported increased

calcium carbonate at 30–40 cm depth because the

loss of CaCO3 was reduced by conversation tillage

due to greater retention of water in the soil profile.

Celik et al. (2017) observed that calcium carbonate

content of the soil was not significantly different

within 0-30 cm depth, might be due to the tillage

practices did not cause to accumulate calcium

carbonate content within 30 cm of the soil surface.

Reduction of Ca content in the tillage practices

reported by Nta et al. (2017) can be explained due to

the rapid breakdown and mineralization in soil organic carbon in mechanically tilled plot.

1= CTW-CTPM, 2= CTW-ZTPM, 3= ZTW-CTPM, 4= ZTW-ZTPM

Fig. 1: Effect of conventional and zero tillage on soil CaCO3

0.27

0.33 0.34

0.39

0.23

0.31 0.320.36

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 1 2 3 4 5

CaC

O3

(%)

Crop-rotation

Depth (cm) 0-15

Depth (cm) 15-30

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 75

Zero-tillage (ZT) affects the chemical properties of

the soil in an utterly diverse pattern to as that of what

conventional tillage did. Zero-tillage can also lead to

improvements in soil quality by improving soil

constitution and enhancing soil biological activity,

nutrient cycling, soil water holding capacity, water

infiltration and water use efficiency (Hobbs et al.,

2008).

Fig. 2: Effect of conventional and zero tillage on soil organic carbon

Locations Chemical properties

A (Tillage) B (Depth) A X B

C.D. at 5%

RRS, Bawal (Rewari) CaCO3 NS 0.013 0.019

SOC 0.008 0.008 0.012

Soil organic carbon is vital marker of soil health as it

affects almost all the physico-chemical properties.

The soil organic carbon in sandy soil was higher at

surface layer than subsurface layer with the values

0.26-0.29 % in surface layer and 0.23-0.25 % in

subsurface layer and organic carbon was relatively

higher with the adoption of ZTW-ZTPM (0.29%) at surface layer (Fig. 2). Individually, as well as

interaction of tillage and depth was significant under

pearl millet-wheat systems. Asenso et al. (2018)

reported highest organic C under ZT at 0–40 cm

depth that may be due to the undisturbed land

resulting an increased buildup of soil organic matter

which reflected a reduced rate of leaching in the soil

surface profile. The results are also supported by the

observations of other workers (Jat et al., 2018;

Kaushik et al., 2018; Kumar et al., 2018; Zuber et

al., 2018).

Total N, P and K Long-term field experiments are important for

explaining tillage and rotation effects on soil fertility

and to develop nutrient management strategies. Soil

total nitrogen (TN) is one of the main factors for

determining soil fertility. Traditional activities, such

as cropping methods and field management, play an

essential role in the accumulation of N in soil for

agricultural sustainability. Changes in total N, P and

K content of soils under different treatments are shown in Fig. 3-5. In general, the total N, P and K

content was higher in surface layer in CT as well as

ZT. The total N, P and K content of sandy soil was

relatively higher in ZTW-ZTPM (0.044, 0.24 and

0.36 %, respectively) at 0-15 cm depth and

corresponding values were 0.037, 0.18 and 0.34 % at

subsurface soil, while lowest total N, P and K content

were found under CTW-CTPM, with values 0.039, 0.16 and 0.31 %, respectively, at 0-15 cm depth and

respective values were 0.033, 0.13 and 0.28 %,

respectively, at 15-30 cm depth. Individually, total N,

P and K content was significant but the interaction of

tillage with depth was however, significant only for

total P content.

In present study, conservational tillage was found to

affect total N, P and K content under pearl millet-

wheat crop-rotation in sandy texture soil and higher

total N, P and K content was found under ZT system

at surface layer. Greater availability of total N, P and K content associated with the conservational tillage

at surface layer is closely related to SOM build up as

reported elsewhere. Dorr de Quadros et al. (2012)

reported significantly higher total N and P content in

the no- tillage system because of high microbial

diversity and high accumulation of soil organic

matter. In contrast to our findings, Islam et al. (2015)

reported non-significant interaction effect of tillage

on total N, P and K content at surface and subsurface

layer but relatively higher values under zero-tillage

treatment at surface layer than subsurface layer, might be due to increase in soil organic matter.

0%

20%

40%

60%

80%

100%

CTW-CTPM CTW-ZTPM ZTW-CTPM ZTW-ZTPM

0.26 0.27 0.26 0.29

0.23 0.24 0.24 0.25

Soil

organ

ic c

arb

on

(%

)

Crop-rotation

Depth (cm) 15-30

Depth (cm) 0-15

76 DHINU YADAV, LEELA WATI, DHARAM BIR YADAV AND ASHOK KUMAR

Similarly, in a comparative study of conventional

tillage and no-tillage carried out by Zuber et al.

(2015), higher total N under no-tillage was reported

compared to CT because losses of N in the form of

leaching of nitrates and denitrification gaseous losses

can offset the addition of N to the soil and the return

of greater crop residue is an important factor in the

greater total nitrogen under crop rotation that

incorporate these crops more frequently.

Fig. 3: Effect of conventional and zero tillage on total N content of soil

Fig. 4: Effect of conventional and zero tillage on total P content of soil

Fig. 5: Effect of conventional and zero tillage on total K content of soil

00.005

0.010.015

0.020.025

0.030.035

0.040.045 0.039 0.041 0.04

0.044

0.0330.037 0.036 0.037

Tota

l N

(%

)

Crop-rotation

Depth (cm) 0-15

Depth (cm) 15-30

0

0.05

0.1

0.15

0.2

0.25

0.3

0.16

0.26

0.20.24

0.13

0.210.18 0.18

Tota

l P

(%

)

Crop-rotation

Depth (cm) 0-15

Depth (cm) 15-30

00.05

0.10.15

0.20.25

0.30.35

0.40.31 0.33 0.33

0.36

0.280.31 0.32

0.34

Tota

l K

(%

)

Crop-rotation

Depth (cm) 0-15

Depth (cm) 15-30

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 77

Locations Chemical properties A (Tillage) B (Depth) A X B

C.D. at 5%

RRS, Bawal (Rewari)

Total N 0.002 0.002 NS

Total P 0.012 0.012 0.018

Total K 0.007 0.007 NS

Functional microbial diversity of soil under

conventional and zero-tillage practices

Community-level physiological profiling assesses the

microbial community on the basis of sugar and

amino acid utilization patterns and capacity to

metabolize specific sole carbon sources. EcoPlate™ method can be used to study the variability of the

community-level physiological profiling of

microorganisms. Functional microbial diversity in

different treatments was studied in terms of average

well color development, richness and diversity index.

Average well color development and Richness

The AWCD denotes the expression of different

microbial activities in the soil samples, which

integrates the microbial diversity and cell densities

with the substrate utilization patterns.

The results for AWCD of soil samples with pearl

millet-wheat crop-rotation shown in Fig. 6 revealed that the AWCD values significantly increased on

adopting zero-tillage practices. Maximum values of

AWCD i.e. 0.706 and 0.523 was observed under

pearl millet-wheat crop-rotation at 0-15 and 15-30

cm depth, respectively, under ZTW-ZTPM while

under CTW-CTPM corresponding values were 0.395

and 0.233.

Fig. 6: Effect of conventional and zero tillage on average well color development

R values represented the functional diversity

measured as the number of total C substrate utilized

and the maximum values was observed 19 and 14 at

surface and subsurface layer, respectively, under

ZTW-ZTPM while under CTW-CTPM,

corresponding values were 12 and 6 (Fig. 7).

Fig. 7: Effect of conventional and zero tillage on richness

0%

20%

40%

60%

80%

100%

0.395 0.483 0.503 0.706

0.233 0.282 0.455 0.523

Avera

ge w

ell

colo

r d

evelo

pm

en

t

Crop-rotation

Depth (cm) 15-30

Depth (cm) 0-15

0%

20%

40%

60%

80%

100%

12 16 17 19

6 12 15 14

Ric

hn

ess

Crop-rotation

Depth (cm) 15-30

Depth (cm) 0-15

78 DHINU YADAV, LEELA WATI, DHARAM BIR YADAV AND ASHOK KUMAR

Diversity Index

The diversity index of the microbial communities of

each sample was calculated as Shannon-Weiver (H)

and Simpson’s index (D) on the basis of sole carbon

and sole nitrogen source utilization (Fig. 8). The microbial diversity of the samples analyzed, as

Shannon-Weiver (H) was maximum at surface layer

(2.87) under ZTW-ZTPM and at subsurface layers

(2.8), while comparatively lower, respective values

were 2.657 and 2.524 under conventional tillage

system. The Simpson’s index (D) was observed

higher under ZTW-ZTPM at surface layer with the

value of 0.937 and 0.931 at subsurface layer while under conventional tillage respective values were

0.907 and 0.897.

Fig. 8: Effect of conventional and zero tillage on diversity Index

Principal Component Analysis

To determine how the different soil samples were

related with each on the basis of carbon source

utilization pattern, the absorbance values were subjected to Principal component analysis (PCA).

The scatter plot displayed the principal component 1st

and 2nd (PC1 and PC2) explaining % of variation in

the CLPP (Fig. 9). Pearl millet-wheat crop-rotation

was found different on correlating carbon source utilization pattern with PC1 and PC2 (R > 0.70).

Fig. 9(a): Principal component analysis of conventional tillage wheat for pearl millet- wheat crop rotation

Dot means CTW-CTPM 0-15 Plus CTW-CTPM 15-30

Square CTW-ZTPM 0-15 Fill square CTW-ZTPM 15-30

0%20%40%60%80%

100%

0-15 15-30 0-15 15-30

Depth (cm)

Shannon_H Simpson_1-D

2.657 2.524 0.907 0.897

2.866 2.737 0.936 0.923

2.727 2.579 0.917 0.9

2.87 2.8 0.937 0.931

Til

lage

Diversity Index

ZTW-ZTPM

ZTW-CTPM

CTW-ZTPM

CTW-CTPM

PC1 67.304

PC

2 2

2.6

7

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 79

Fig. 9(b): Principal component analysis of zero tillage wheat for pearl millet-wheat crop rotation

Dot means ZTW-CTPM 0-15 Plus ZTW-CTPM 15-30

Square ZTW-ZTPM 0-15 Fill square ZTW-ZTPM 15-30

During the present investigation, the results of

Biolog® Ecoplate™ from different tillage practices

with pearl millet-wheat crop-rotation revealed that

the microbial community was relatively higher at surface layer under ZT showing an expression of

different microbial activities in terms of AWCD,

total C substrate utilized (richness) and the number

of positively utilized substrates (Shannon-Weiver (H)

and Simpson’s index (D). Similar findings were

reported by Habig and Swanepoel, (2015) that

microbial diversity and activity were higher at

surface layer under no-till than conventional tillage

because the stimulation of soil microbial populations

in no-tillage, promoted the availability of carbon

sources for microbial utilization. Nivelle et al. (2016) found lowest AWCD and Shannon index under bare

fallow and highest under cover crop-NT plots might

be due to higher total nitrogen content and total

organic carbon content that led to increased the

diversity of substrate-richness and induced more

microbial enzymes because of greater metabolism of

phenolic compounds and carbohydrates (under no-

till) and polymers (under conventional till) as carbon

sources in plots under standard cover crop.

In contrary to our results, Janušauskaite et al. (2013)

found higher AWCD values under conventional

tillage than no-tillage because higher availability of hydrocarbon sources in conventional tillage could

promote microbial community’s diversity and

increased use of carbon sources. During present

investigation, different soil samples under pearl

millet-wheat crop-rotation were found related to each

other, based on C source utilization pattern on

principal component analysis. Gałązka et al. (2017)

observed that principal component analysis showed

strong correlation between soil quality parameters

and biodiversity indicators that explained 71.51 %

biological variability in no-tilled soils.

CONCLUSION

Zero-tillage practice resulted in relatively higher soil

organic carbon at the surface layer, as well as changes in the soil microbial community and the

tillage effect on microbial community varied by soil

depths. The use of community level physiological

profiling allows us to have better understanding

regarding the changes of the microbial community

under different management systems and might

provide insights into how conservation tillage

practice improves soil quality and sustainability.

ACKNOWLEDGMENTS The authors are grateful to the Chaudhary Charan

Singh Haryana Agricultural University, Hisar, for

providing research fund and infrastructural facility.

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*Corresponding Author

________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 81-85. 2020

COMPARATIVE ECONOMIC ANALYSIS OF RICE IN KHARIF AND RABI

SEASON IN GUNTUR DISTRICT OF ANDHRA PRADESH

Pradeep Kumar Patidar*, R. Lakshmi Priyanka, N. Khan and Dharmendra

JNKVV, College of Agriculture Rewa (M.P.)

Received-06.02.2020, Revised-24.02.2020 Abstract: Rice (Oryza sativa) is the second highest produced grain in the world after maize. World rice acreage is 161. 1 m

ha with world production volume of milled rice is 484.1mt during 2016-2017. The present investigation was conducted in

Guntur District of A.P. The study found that cost of cultivation of Kharif rice showed that on an average cost of cultivation

per hectare of Kharif rice crop on overall basis was found to be cost A1 that was paid out cost Rs.31473.56 followed by

Rs.32977.99 (cost B1), Rs.54791.26 (cost B2), Rs.35961.33 (cost C1), Rs.57774.59 (cost C2) and Rs.63552.43 (cost C3)

respectively. While the cost of cultivation of Rabi rice showed that on an average cost of cultivation per hectare of Rabi rice

crop was found to be Rs.28891.72 (cost A1) followed by Rs.30396.15 (cost B1), Rs.55213.45 (cost B2), Rs.32989.82 (cost

C1), Rs.57807.17 (cost C2) and Rs.63588.10 (cost C3). The average yield in Kharif and rabi season were found to be 66.22

quintal and 73.20 quintal per hectare of total grain yield and 23.98 quintal and 25.73 per hectare of by-product yield. Data

revealed that in kharif and rabi season the rice growers realized on an average of 1:2.05 and 1:2.30 as B.C. ratio in rice

production in Guntur district of Andhra Pradesh.

Keywords: Cost, Production, Income, Profitability, Rice

INTRODUCTION

ice (Oryza sativa) is the second highest

produced grain in the world after maize. World

rice acreage is 161. 1 m ha with world production

volume of milled rice is 484.1mt during 2016-2017.

The yield rate of paddy in Andhra Pradesh is higher

than the average yield rate for India at 2178 kg/ha

(Source:- Socio- Economic Survey of Andhra

Pradesh, 2015-2016). Rice is grown in both kharif &

rabi seasons in almost all the districts of Andhra

Pradesh. The area under paddy in Kharif 2015-16 is

estimated at 15.20 lakh hectares, the production of

paddy in Kharif 2016-17 is estimated at 79.04 lakh

tones. The estimated area under paddy in Rabi 2016-

17 is expected to be 6.20 lakh hectares and

production under paddy in Rabi 2016-17 is estimated

at 41.29 lakh tones. (Source:-Socio-Economic

Survey 2015-2016 & Agricultural Statistics at a

Glance 2015-2016).

Objectives

• To work out the cost and return of rice in both

seasons of study area.

• To identify the problems faced by rice growers in

both the seasons and to overcome them.

MATERIALS AND METHODS

Selection of the study area

Andhra Pradesh state has 13 districts. Out of 13

districts of Andhra Pradesh, Guntur district is rice

producing area. Therefore Guntur district was

selected for the present study.

Selection of the blocks

Guntur district has 6 blocks. Among them one block

that is Piduguralla was selected for the present study

on the basis of acreage under rice crop.

Selection of villages and respondents From selected block 3 villages were selected

randomly. viz., Brahmanapalli, Veerapuram and

Thummalacheruvu for the present study. From the

list of rice growers, 20 rice growers from each

village were selected randomly, thus total 60 rice

growers were selected for this purpose of the study.

RESULT AND DISCUSSION

Cost of cultivation of rice in both Kharif and Rabi

seasons The total cost of cultivation of Kharif rice of sample

farms has been observed on overall average basis as

Rs.57774.59 per hectare, total variable cost was

55.27% and the share of material input cost was

maximum and found to be 39.15% followed by labor

cost 16.11%, interest on working capital 1.93% and

fixed cost 44.73%.

R

RESEARCH ARTICLE

82 PRADEEP KUMAR PATIDAR, R. LAKSHMI PRIYANKA, N. KHAN AND DHARMENDRA

Table 1. Cost of cultivation of rice in Kharif season on different size of holding. (Rs./ha)

S.No. Particulars

Size group

Average

Small Medium Large

A. Operational cost

Human

labour

Family 2845

(5.07) 2610

(4.47) 3495 (5.92)

2983.33 (5.16)

Hired 1859.27

(3.31)

2051.82

(3.51)

1450.88

(2.46)

1787.32

(3.09)

Machine power +

Bullock power

4704.27

(8.39)

4439.83

(7.61)

4474.84

(7.58)

4539.64

(7.85)

Total labour cost

9408.54

(16.79)

9101.65

(15.60)

9420.72

(15.97)

9310.30

(16.11)

B. Material cost

Seed 2380

(4.24)

2390

(4.09)

2430

(4.12)

2400

(4.15)

Fertilizer and manures 7641.99

(13.64)

8064.71

(13.82)

8080.14

(13.70)

7928.94

(13.720

Plant protection measures 6569.05

(11.72)

6828.03

(11.70)

6903.91

(11.70)

6766.99

(11.71)

Irrigation charges 2921.15

(5.21)

2956.42

(5.06)

3070.10

(5.20)

2982.55

(5.15)

Other charges 2552.00

(4.55)

2614.00

(4.48)

2455.00

(4.16)

2540.33

(4.39)

Total material cost 22064.19

(39.38)

22853.16

(39.17)

22939.15

(38.90)

22618.83

(39.15)

Total operational cost 31472.73

(56.17)

31954.81

(54.77)

32359.87

(54.87)

31929.13

(55.27)

C. Fixed cost

Interest on working capital @10% 1101.54

(1.96)

1118.41

(1.91)

1132.59

(1.92)

1117.51

(1.93)

Rental value of land

(1/6th of gross income)

20224.50

(36.10)

22114.40

(37.90)

23100.90

(39.17)

21813.26

(37.72)

Depreciation 1485.00

(2.65)

1455.00

(2.49)

1200.73

(2.03)

1380.24

(2.39)

Revenue/ Tax 30

(0.05)

30

(0.05)

30

(0.05)

30

(0.05)

Interest on fixed cost @5% 1707.50

(3.04)

1662.60

(2.85)

1143.20

(1.93)

1504.43

(2.60)

Total fixed cost 24548.54

(43.82)

26380.41

(45.22)

26607.42

(45.12)

25845.46

(44.73)

Total cost 56021.27

(100)

58335.22

(100)

58967.29

(100)

57774.59

(100)

(Figures in parentheses show percentage to total

cost)

The data of the Table 1 revealed that the total cost of

cultivation of Rabi rice of sample farms has been

observed total variable cost was 49.34% and the

share of material input cost was maximum found to

be 32.42% followed by labour cost 16.92%, interest

on working capital 1.72% and fixed cost 50.65%.

Rental value of land 42.90% and interest on fixed

capital 2.6%, respectively.

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 83

Table 2. Cost of cultivation ofrice in Rabi season on different size of holding. (Rs./ha)

S.No. Particulars Size group

Average Small Medium Large

A. Operational cost

Human

labour

Family 3475

(6.29)

2239

(3.84)

2067

(3.44)

2593.66

(4.48)

Hired 1859.27

(3.31)

2051.82

(3.51)

1450.88

(2.46)

1787.32

(3.09)

Machine power +

Bullock power 1950.84

(3.35)

2084.77

(3.58)

3470.03

(5.77)

2501.88

(4.32)

Total labour cost

10434.32

(18.9)

8271.56

(14.2)

10648.14

(17.72)

9784.67

(16.92)

B. Material cost

Seed 2340

(4.24)

2365

(4.06)

2390

(3.97)

2365

(4.09)

Fertilizer and manures 6353.88

(11.51)

7363.90

(12.65)

7899.63

(13.15)

7205.80

(12.46)

Plant protection measures 5070.34

(9.18)

6205.35

(10.66)

7458.05

(12.40)

6244.58

(10.80)

Irrigation charges 3138.24

(5.68)

3301.40

(5.67)

2338.23

(3.89)

2925.95

(5.06)

Total material cost 16902.46

(30.63)

19235.65

(33.06)

20085.91

(33.44)

18741.34

(32.42)

Total operational cost 27336.78

(49.54)

27507.21

(47.27)

30734.05

(51.17)

28526.01

(49.34)

C. Fixed cost

Interest on working capital @ 10%

956.78

(1.73)

962.75

(1.65)

1075.69

(1.79)

998.40

(1.72)

Rental value of land (1/6th of gross

income)

22349.30

(40.5)

25455.10

(43.74)

26647.50

(44.36)

24817.30

(42.90)

Revenue/ Tax

30

(0.05)

30

(0.05)

30

(0.05)

30

(0.05)

Interest on fixed cost @5% 1707.50 (3.04)

1662.60 (2.85)

1143.20 (1.93)

1504.43 (2.60)

Total fixed cost 27840.29

(50.45)

30675.95

(52.72)

29327.07

(48.82)

29281.10

(50.65)

Total cost 55177.07

(100)

58183.16

(100)

60061.12

(100)

57807.12

(100)

(Figures in parentheses show percentage to total

cost)

Aggregate cost of Kharif rice cultivation:

From the Table 2 it has been observed that the cost of

cultivation of Kharif rice showed that on an average

cost of cultivation per hectare of Kharif rice crop on

overall basis was found to be cost A1 that was paid

out cost Rs.31473.56 followed by Rs.32977.99 (cost

B1), Rs.54791.26 (cost B2), Rs.35961.33 (cost C1),

Rs.57774.59 (cost C2) and Rs.63552.43 (cost C3)

respectively.

Table 3. Aggregate cost of rice in Kharif season on different size of holdings (Rs./ha)

S.No. Particulars Size group

Average Small Medium Large

1 Cost A1 31244.27 31948.22 31228.19 31473.56

2 Cost B1 32951.77 33610.82 32371.39 32977.99

3 Cost B2 53176.27 55725.22 55472.29 54791.26

4 Cost C1 35796.77 36220.82 35866.39 35961.33

5 Cost C2 56021.27 58335.22 58967.29 57774.59

6 Cost C3 61623.95 64169.23 64864.11 63552.43

84 PRADEEP KUMAR PATIDAR, R. LAKSHMI PRIYANKA, N. KHAN AND DHARMENDRA

Aggregate cost of Rabi Rice cultivation:

From the Table 3 it is revealed that the cost of

cultivation of Rabi rice showed that on an average

cost of cultivation per hectare of Rabi rice crop was

found to be Rs.28891.72 (cost A1) followed by

Rs.30396.15 (cost B1), Rs.55213.45 (cost B2),

Rs.32989.82 (cost C1), Rs.57807.17 (cost C2) and

Rs.63588.10 (cost C3).

Table 4. Aggregate cost of rice in Rabi season on different size of holdings (Rs./ha)

S.No. Particulars Size group

Average Small Medium Large

1 Cost A1 27645.27 28826.46 30203.42 28891.72

2 Cost B1 29352.77 30489.06 31346.62 30396.15

3 Cost B2 51702.07 55944.16 57994.12 55213.45

4 Cost C1 32827.77 32728.06 33413.62 32989.82

5 Cost C2 55177.07 58183.16 60061.12 57807.12

6 Cost C3 60694.91 64002.15 66067.25 63588.10

Productivity of rice production in Kharif and Rabi seasons:

Table 5. Productivity of rice in Kharif season on different size of holding (q/ha)

S.No. Particulars Size group

Average Small Medium Large

1 Total grain yield (q/ha.) Kharif 62.48 66.45 69.73 66.22

2 Total by-product yield (q/ha.) Kharif 23.47 23.93 24.56 23.98

3 Total grain yield (q/ha.) Rabi 71.5 72.81 75.3 73.203

4 Total by-product yield (q/ha.) Rabi 23.09 26.85 27.25 25.73

From the Table 5 it has been observed that the

average yield in Kharif season was found to be 66.22

quintal per hectare of total grain yield and 23.98

quintal per hectare of by-product yield. The average

yield in rabi season was found to be 73.20 quintal per

hectare of total grain yield and 25.73 quintal per

hectare of by-product yield.

Profitability from rice cultivation in Kharif and

Rabi seasons:

Table 6. Profitability of rice in Kharif season on different size of holding. (Rs./ha.)

S.No. Particulars Size group

Average Small Medium Large

Kharif season

1 Gross income 121347 132686.50 138605.63 130879.43

2 Net income 59723.05 68517.27 73741.52 67327.28

3 Family labour income 68170.72 76961.27 83133.33 76088.44

4 Farm business income 90102.72 100738.27 107377..43 99406.14

5 Input-output ratio 1:1.96 1:2.06 1:2.13 1:2.05

Rabi season

1 Gross income 134096.00 152731.00 159884.76 148904

2 Net income 73401.10 88728.80 93817.46 85316

3 Family labour income 82393.93 96786.84 101890.64 93690

4 Farm business income 106450.73 123904.54 129681.34 120012

5 Input-output ratio 1:2.20 1:2.38 1:2.42 1:2.33

The overall gross income per hectare of Kharif and

rabi rice were found to be Rs.130879.71 and Rs.

148904 per hectare. The net income were found to be

an average of Rs.67327.28 and Rs. 85316 per

hectare. The trend of net income revealed that it was

increased with increasing size of holding. The other

profitability measures reveal that on an average the

rice growers realized that Rs.76088.44 and Rs. 93690

per farm as family labour income Rs.99406.14 and

Rs. 120012 per farm as farm business income in

Kharif and rabi season respectively. The B.C. ratio

determines the return per rupee investment found in

kharif and rabi season the rice growers realized on

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 85

an average of 1:2.05 and 1;2.33 as B.C. ratio in rice

production respectively.

Comparative productivity and profitability of rice

in Kharif and Rabi seasons:

The main aim of the study is to measure the relative

productivity and profitability of rice cultivation on

per hectare basis under Kharif and Rabi seasons.

Table 7. Average productivity and profitability of rice crop in both Kharif and Rabi seasons: (Rs./ha.)

S.No. Particulars Kharif Rabi Increased over kharif

1 Total grain yield (q/ha.) 66.22 73.20 6.98

2 Total by-product yield q/ha.) 23.98 25.73 1.75

3 Gross income 130879.70 148904.00 18024.29

4 Net income 67327.28 85316.00 17988.72

5 Family labour income 76088.44 93690.00 17601.56

6 Farm business income 99406.14 120012.00 20605.86

Input-output ratio 1:2.05 1:2.33 1:0.28

Study revealed that the average rice growers realized

additional total grain yield 6.98 quintal per hectare

with total by-product yield 1.75 quintal per hectare in

rabi over kharif season due to less cultural practices

like, weeding, plant protection chemicals, fertilizers

and manures in Rabi over Kharif season. On the other hand, the rice growers also realized additional

net income of RS.17988.72 per hectare in rabi over

kharif season. Although, the return over rupee

investment was higher in rabi but it was very

nominal. Hence, the study revealed that the rice

growers could realize higher productivity and higher

profitability in rabi over kharif season.

CONCLUSION

The cost of cultivation of kharif and rabi rice showed that on an average cost of cultivation per

hectare of kharif rice crop on overall basis were

found to be total Rs.63552.43 (cost C3) and

Rs.63588.10 (cost C3) respectively. The overall

gross income per hectare of kharif and rabi rice were

found to be Rs.130879.71 and Rs. 1308789.71 per

hectare. The net income was found to be an average

of Rs.67327.28 and Rs. 67327.28 per hectare. The

B.C. ratio determines the return per rupee investment

found in kharif and rabi season the rice growers

realized on an average of 1:2.05 and 1:2.33 as B.C.

ratio in rice production.

REFFERNCES

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and Constraint in Dhamtari District of Chhattisgarh,

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651-656.

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Resource Use Efficiency of Low Land Rice

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Veterinary Science, Volume 7, Issue 6, June 2014,

pp. 35-39.

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Cultivation of Major Crops in Malappuram District.

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Ukoha, A. (2014). Economic Analysis of Paddy Rice

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Resource Use Efficiency in Rice Production under

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Crop and Weed. 12(1):36-39.

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Resource Use Efficiency of Paddy Cultivation in

Peechi Command Area of Thrissur District of

Kerala.Agricultural Economics Research

Review.Volume 19, pp.159- 171.

86 PRADEEP KUMAR PATIDAR, R. LAKSHMI PRIYANKA, N. KHAN AND DHARMENDRA

*Corresponding Author

________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 87-92. 2020

GROWTH PARAMETERS AND SOIL FERTILITY STATUS AS INFLUENCED BY

NITROGEN SOURCE IN WHEAT

Fazal Rabi, Meena Sewhag, Shweta, Parveen Kumar, Amit Kumar* and Uma Devi

Department of Agronomy,

CCS Haryana Agricultural University, Hisar, Haryana, India

Received-07.02.2020, Revised-27.02.2020

Abstract: In order to study morphological response of wheat to different nitrogen sources a field experiment was conducted during the rabi season of 2017-2018 at the Agronomy Research Farm of Chaudhary Charan Singh Haryana Agricultural University, Hisar .The soil of the experimental field is slightly alkaline in reaction, sandy loam in texture, low in organic carbon and nitrogen, medium in available phosphorus and potassium. The experiment was laid out in Randomized Block

Design replicated thrice with ten treatments viz. T1 (Control) , T2 (Vermicompost @ 6 t ha-1) , T3 (Azotobacter + Vermicompost @ 6 t ha-1), T4 (30 kg N ha-1 + Vermicompost @ 3 t ha-1), T5 (40 kg N ha-1 + Vermicompost @ 2 t ha-1), T6 (50 kg N ha-1 + Vermicompost @ 1 t ha-1), T7 (30 kg N ha-1 + Azotobacter + Vermicompost @ 3 t ha-1), T8 (40 kg N ha-1 + Azotobacter + Vermicompost @ 2 t ha-1), T9 (50 kg N ha-1 + Azotobacter + Vermicompost @ 1 t ha-1) and T10 (60 kg N ha-1). The results of the experiment indicated that no variations in plant population at 15 DAS and N, P and K status of soil after harvesting of wheat crop was observed due to application of various combinations of nitrogen fertilizer, vermicompost and Azotobacter. Among various treatments of nitrogen fertilizer, vermicompost and Azotobacter T10 was at par with T8 and T9 for plant height at all the stages of crop growth. Treatment T10 at all the stages of crop growth resulted in highest dry matter accumulation. Treatment T10 (100 % RDN) being at par with treatment T9 and T8 required significantly

higher number of days to attain physiological maturity than all other treatments. Treatment T10 resulted in highest grain yield which was at par with treatments T8 and T9 and significantly higher than all other treatments. Straw yield obtained with treatment T10, was significantly higher than all other treatments except T9. Highest biological yield was recorded with treatment T10 which was at par with treatments T8 and T9.

Keywords: Growth parameters, Nitrogen, Soil, Wheat

INTRODUCTION

heat popularly known as “Staff of life or king

of cereals” has been described as a strategic

cereal crop for the majority of the world’s population

which is rich in carbohydrates and protein so it has

its own outstanding importance as a human food.

Wheat is cultivated in at least 43 countries of the

world. The leading countries in wheat cultivation are China, India, Thailand, Indonesia and U.S.A. and

total production of wheat was 647 million tonnes

under area of 218million hectares with a productivity

of 2960 kg/ha (FAO, 2012).The continuous use of

chemical fertilizers in indiscriminate manner has

developed many problems like decline of soil organic

matter, increase in salinity and sodicity, deterioration

in the quality of crop produce, increase in hazardous

pests and diseases and increase in soil pollutants

(Chakarborti and Singh, 2004). On account of

continuing energy crisis in the world and spiraling

price of fertilizer, the use of organic manure as a renewable source of plant nutrients is gaining

importance. In this endeavor proper combination of

inorganic and organic fertilizer is important not only

for increasing crop yield but also for sustaining soil

health (Weber et al., 2007 and Pullicinoa et al.,

2009). The vermicomposting is bio- oxidation and

stabilization of organic material involving the joint

action of earthworm and microorganisms. Although,

microbes are responsible for the biological

degradation of the organic matter, earthworms are

the important drivers of the process, conditioning the

substrate and altering biological activity (Aira et al.,

2002).The use of organics largely excludes the use of

synthetic fertilizers, pesticides, growth regulators and

livestock feed additives, enriches the soil, encourages

bio-diversity, reduce the toxic bodies, improves

water quality, creates a safe environment for people

and wild life, produces nutritious food of high

quality, supply micronutrients in soil and maintains soil fertility and crop productivity (Sawrup, 2010).

Wheat requires a good supply of nutrients especially

nitrogen for its growth (Mandal et al., 1992).

Keeping the above aspects in view, the present

investigation “Morphological response of wheat to

different nitrogen sources in semi arid climate of

Haryana” has been planned with the objective to

study the effect of vermicompost and Azotobacter on

growth characters of desi wheat.

MATERIALS AND METHODS

Field experiment was conducted during rabi 2017-

2018 at the Agronomy Research Farm of Chaudhary

Charan Singh Haryana Agricultural University, Hisar

which is situated at latitude of 29°10’ North,

longitude of 75°46’ East and elevation of 215.2 m

above mean sea level in the semi-arid, subtropical

climate zone of India. The experiment was laid out in

Randomized Block on sandy loam (63.5% sand,

17.3% silt and 19.2% clay) soil which is slightly

alkaline in reaction, low in organic carbon and

W

RESEARCH ARTICLE

88 FAZAL RABI, MEENA SEWHAG, SHWETA, PARVEEN KUMAR, AMIT KUMAR AND UMA DEVI

nitrogen, medium in available phosphorus and

potassium. The treatment were comprised of ten

treatments viz. T1 (Control) , T2 (Vermicompost @ 6

t ha-1) , T3 (Azotobacter + Vermicompost @ 6 t ha-

1), T4 (30 kg N ha

-1 + Vermicompost @ 3 t ha

-1), T5

(40 kg N ha-1 + Vermicompost @ 2 t ha-1), T6 (50 kg N ha-1 + Vermicompost @ 1 t ha-1), T7 (30 kg N

ha-1 + Azotobacter + Vermicompost @ 3 t ha-1) , T8

(40 kg N ha-1 + Azotobacter + Vermicompost @ 2 t

ha-1), T9 (50 kg N ha-1 + Azotobacter +

Vermicompost @ 1 t ha-1) and T10 (60 kg N ha-1).

Azotobacter was. Prior to sowing, the seed pertaining

to inoculated plots was treated with Azotobacter

culture obtained from Department of Microbiology,

CCS Haryana Agricultural University, Hisar, as per

treatment. The seed was wetted with sugar solution

and 50 ml of bio inoculants was used as per the

recommendation. The treated seed was kept in shade for the completion of inoculation. Both treated and

untreated seeds were sown as per the treatments.

Sowing of Desi wheat C 306 was done on 10th

November 2017 at about 5.0 cm depth by drilling in

rows using 120 kg seed ha-1and spacing of 20 cm

between rows.Pre-sown irrigation of 5 cm depth

was applied on 3 th November 2017. Three post

sown irrigations were applied on 04.12.2017,

27.02.2018 and 13.03.2018. Harvesting was done

with the help of sickles manually by cutting the

plants from the net area of each plot separately on 11th April 2018. Full dose of phosphorus (62.5 kg

P2O5 ha-1) and half nitrogen as per treatments were

applied at the time of sowing and remaining half of

the nitrogen was top dressed at 23 DAS.

Full dose of P and half dose of N as per treatments

were applied to the field before sowing and rest of N

was top dressed after first irrigation. Urea (46%),

Diammonium phosphate (18% N, 46% P2O5), and

Azotobacter were used as source of N and P.

Physiological maturity was determined by pressing

the grain between thumb and index finger. At this

stage, the material inside the grain is solid and hard and does not yield to mild pressure. Five

representative plants from each plot were selected

randomly and tagged for recording the effect of

different treatments on yield attributes. Plant height

of five randomly tagged plants was recorded at 30,

60, 90 DAS and at maturity. The height of each plant

was measured with the help of wooden scale from

the soil surface to fully opened top leaf of the plant

before ear emergence and up to the ear head after

heading stage. Plants were harvested from 50 cm row

length from two places in the second row on either side in each plot at 30, 60, 90 DAS and at harvest.

These harvested plants (above ground parts) were

sun dried first and then oven dried at 60oC till a

constant weight was obtained at each stage and

weighed. All yield attributing characters were

recorded periodically on these randomly selected and

tagged plants.

RESULTS AND DISCUSSION

Data related to plant population at 15 DAS of desi

wheat are presented in Table 3 indicated that various

combinations of nitrogen fertilizer, vermicompost

and Azotobacter did not affect the plant population at 15 DAS of desi wheat. The plant population of desi

wheat varied from 37.9 to 39.4. Treatment T10 (100

% RDN) being at par with treatment T9 for

physiological maturity required significantly higher

number days to attain physiological maturity as

compared to other treatments. Days taken to

physiological maturity were reduced by nine days

under treatment T1 (Control) as compared to T10 (100

% RDN).Data presented in Table1 indicated that

among various combinations of nitrogen fertilizer,

Azotobacter and vermicompost treatment T10 (100 %

RDN) was at par with treatment T8 and T9 at all the stages of crop growth and resulted in significantly

taller plants than other treatments. However, plant

height at maturity in treatment T8 and T9 were

recorded at par with each other. The plant height at

maturity was 36 cm, 32 cm and 27 cm more in

treatment T10, T9 and T6 treatments compared to the

control (110 cm), respectively. The magnitude of

plant height recorded under various treatments varied

from 110 cm under control (T1) to 146 cm under

treatment T10 at harvest. Lowest plant height was

recorded in treatment T1 at all the stages of crop growth. This might be due to nitrogen concentration

in plant resulting in higher photosynthetic activity

and thereby rapid cell division and cell elongation

and consequently taller plant. Improved growth and

yield attributes increased with increased dose of N,

may be due to fact that N being an important

constituent of nucleotides, proteins, chlorophyll and

enzymes involves in various metabolic process

which has a direct impact on vegetative and

reproductive phase of plants. Results reported by

Rathore et al. (2003), and Shirinzadeh et al. (2013)

reported similar results. Taller plants in treatment containing vermicompost may be owing to increased

supply of multi-nutrients, plant growth regulators and

beneficial microflora released from vermicompost in

addition to the most favourable conditions with

respect to physico-chemical and biological properties

of the soil. At higher level of nitrogen, crop

absorbed sufficient amount of N, resulting in better

growth parameters such as plant height, dry matter

accumulation, number of tillers. Nitrogen application

increased plant height (Moreno et al., 2003; Meena

et al., 2012) and tillering (Birch and Long, 1990), which ultimately led to higher dry matter production.

Irrespective of the treatments, dry matter

accumulation at various growth stages of desi wheat

increased progressively from vegetative to maturity

stage (Table 2). The rate of dry matter accumulation

per mrl was slow up to initial 30 days and highest

between 60 to 90 DAS and thereafter the increase

was at a decreasing rate up to maturity. Among

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 89

various combinations of nitrogen fertilizer,

vermicompost and Azotobacter treatment, application

of 100% RDN at all the stages of crop growth

resulted in significantly higher dry matter

accumulation. This might be due to combined effect

of nitrogen fertilizer, vermicompost and Azotobacter in balanced proportion played a very crucial role in

decomposition and easy release of different nutrients

and their uptake by wheat crop which led to higher

dry matter production and its translocation in

different plant parts of growth and yield parameters,

which in turn resulted into higher yield. These results

are in complete agreement with those of Ram and

Mir (2006) and Kakraliya (2017).

A thorough look on data indicated that grain yield of

desi wheat was significantly higher in treatment T10

(100% RDN). But, the differences in grain yield in

treatments T10 (28.2 q ha-1), T9 (27.8 q ha-1) and T8 (26.3 q ha-1) were not significant. This might be due

to combined effect of fertilizer and vermicompost

might have resulted in easy release of different

nutrients and their uptake by wheat crop which led to

higher better growth and higher yield parameters,

which in turn resulted into higher grain yield. These

results are in complete agreement with those of Ram

and Mir (2006) and Kakraliya et al., (2017). Straw

yield was highest in treatment T10 (76.4 q ha-1), being

significantly higher than other treatments but

statically at par with treatment T9 (74.6 q ha-1).The straw yield in treatment T5 (68.3 q ha-1) and T6 (70.6

q ha-1), T7 (68.7 q ha-1) and T8 (71.2 q ha-1) were also

at par with each other. Biological yield was recorded

highest with treatment T10 with biological yield of

104.60 q ha-1. But, the difference in biological yield

in treatment T8, T9 and T10 were not significant.

Significantly lower value for biological yield was

recorded in treatment T1 (53.60 q ha-1) which was statistically lower than rest of the treatments. The

biological yield in treatment T4 (84.90 q ha-1) and T5

(92.73 q ha-1) was statistically at par with each other.

Similarly, the difference in biological yield in

treatment T5 (92.73 q ha-1), T6 (96.10 q ha-1), T7

(93.30 q ha-1) and T8 (97.50 q ha-1) were also not

significant. Improvement in yield of wheat might

have resulted from favourable influence of fertilizers,

Azotobacter and vermicompost on the growth

attributes and efficient and greater partitioning of

metabolites and adequate translocation of

photosynthates and nutrients to developing reproductive structures. These results confirm the

findings of Singh and Kumar (2010).

The influence of various treatments on available N, P

and K content in soil was recorded after harvest of

wheat crop. Data for same have been given in Table

4. A close perusal of the data on nutrient status of

soil revealed that there was no significant difference

resulted due to application of various combinations

of nitrogen fertilizer, vermicompost and Azotobacter

on N, P and K status of soil after harvesting of wheat.

The range of soil N status varies from 140.8 (T1) to 165.4 (T3).

Table 1. Plant height (cm) of desi wheat as influenced by various combinations of nitrogen fertilizer,

vermicompost and Azotobacter

Treatments Plant height (cm)

30

DAS

60

DAS

90

DAS

At

Maturity

T1 : Control 19 46 81 110

T2 : Vermicompost @ 6 t/ha 22 49 96 117

T3 : Azotobacter + Vermicompost @ 6 t/ha 23 50 104 125

T4 : 30 kg N /ha + Vermicompost @ 3 t/ha 22 49 106 131

T5 : 40 kg N /ha + Vermicompost @ 2 t/ha 23 51 107 134

T6 : 50 kg N /ha + Vermicompost @ 1 t/ha 23 52 109 137

T7 : 30 kg N /ha + Azotobacter + Vermicompost @ 3 t/ha 26 51 111 136

T8 : 40 kg N /ha + Azotobacter+ Vermicompost @ 2 t/ha 25 52 113 139

T9 : 50 kg N /ha + Azotobacter+ Vermicompost @ 1 t/ha 26 54 115 142

T10 : RDN (60 kg N ha-1) 27 57 120 146

SEm ± 0.4 0.5 0.5 1.6

CD at 5 % 1.3 1.6 1.5 4.6

90 FAZAL RABI, MEENA SEWHAG, SHWETA, PARVEEN KUMAR, AMIT KUMAR AND UMA DEVI

Table 2. Dry matter accumulation (g/mrl) of desi wheat as influenced by various combinations of nitrogen

fertilizer, vermicompost and Azotobacter

Treatments Dry matter accumulation (g/mrl)

30

DAS

60 DAS 90

DAS

At

Maturity

T1 : Control 16 34 94.5 140.8

T2 : Vermicompost @ 6 t/ha 18 39 106.2 157.2

T3 : Azotobacter + Vermicompost @ 6 t/ha 19 41 109.5 162.0

T4 : 30 kg N /ha + Vermicompost @ 3 t/ha 18 40 106.8 158.4

T5 : 40 kg N /ha + Vermicompost @ 2 t/ha 19 41 111.9 165.6

T6 : 50 kg N /ha + Vermicompost @ 1 t/ha 19 42 114.9 169.2

T7 : 30 kg N /ha + Azotobacter + Vermicompost @ 3 t/ha 22 42 112.8 167.2

T8 : 40 kg N /ha + Azotobacter+ Vermicompost @ 2 t/ha 21 43 114.3 169.2

T9 : 50 kg N /ha + Azotobacter+ Vermicompost @ 1 t/ha 22 45 119.1 176.4

T10 : RDN (60 kg N ha-1) 23 47 127.8 188.4

SEm ± 0.6 1.1 2.7 6.9

CD at 5 % 1.9 3.5 8.2 20.7

Table 3. Grain yield, straw yield, biological yield and harvest index of desi wheat as influenced by various

combinations of nitrogen fertilizer, vermicompost and Azotobacter

Treatments Plant

population

at 15 DAS

Days to

physiological

maturity

Grain

yield

(q/ha)

Straw

yield

(q/ha)

T1 : Control 38.3 131

15.1 38.5

T2 : Vermicompost @ 6 t/ha 39.0 133 20.8 50.3

T3 : Azotobacter + Vermicompost @ 6 t/ha 39.4 134

21.3 52.5

T4 : 30 kg N /ha + Vermicompost @ 3 t/ha 37.9 135 22.7 62.2

T5 : 40 kg N /ha + Vermicompost @ 2 t/ha 38.3 136

24.4 68.3

T6 : 50 kg N /ha + Vermicompost @ 1 t/ha 39.0 137

25.5 70.6

T7 : 30 kg N /ha + Azotobacter + Vermicompost

@ 3 t/ha 39.4 137

24.6 68.7

T8 : 40 kg N /ha + Azotobacter+ Vermicompost

@ 2 t/ha 38.3 137

26.3 71.2

T9 : 50 kg N /ha + Azotobacter+ Vermicompost

@ 1 t/ha 39.0 138

27.8 74.6

T10 : RDN (60 kg N ha-1) 39.4 140

28.2 76.4

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 91

SEm ± 1.02 0.9

0.74 1.31

CD at 5 % N.S. 2.7 2.23 4.01

Table 4. Effect of various combinations of nitrogen fertilizer, vermicompost and Azotobacter on NPK status of

soil

Treatments N

(kg ha-1

)

P2O5

( kg ha-1

)

K2O

( kg ha-1

)

T1 : Control 140.80 15.80 110.02

T2 : Vermicompost @ 6 t/ha 163.60 18.30 119.07

T3 : Azotobacter + Vermicompost @ 6 t/ha 165.40 18.00 120.69

T4 : 30 kg N /ha + Vermicompost @ 3 t/ha 158.30 18.40 124.20

T5 : 40 kg N /ha + Vermicompost @ 2 t/ha 153.60 18.80 121.05

T6 : 50 kg N /ha + Vermicompost @ 1 t/ha 148.40 19.10 121.14

T7 : 30 kg N /ha + Azotobacter + Vermicompost @ 3 t/ha 164.30 19.80 127.99

T8 : 40 kg N /ha + Azotobacter+ Vermicompost @ 2 t/ha 160.70 19.50 129.16

T9 : 50 kg N /ha + Azotobacter+ Vermicompost @ 1 t/ha 157.40 19.70 130.71

T10 : RDN (60 kg N /ha ) 151.20 16.30 121.16

Figure 1: Grain, straw and biological yield (kg ha-1) of desi wheat as influenced by various treatments

CONCLUSION

Among various combinations of nitrogen fertilizer, vermicompost and Azotobacter T10 recorded

significantly higher growth parameters viz. [plant

height (cm) and dry matter accumulation/plant],

grain straw and biological yield of desi wheat. But

various treatments failed to produce any significant variation in plant population and soil nutrient status.

0

20

40

60

80

100

120

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10

Grain yield (q/ha) Straw yield (q/ha) Biological yield (q/ha)

Yie

ld

(q h

a-1

)

92 FAZAL RABI, MEENA SEWHAG, SHWETA, PARVEEN KUMAR, AMIT KUMAR AND UMA DEVI

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nitrogen on the growth, yield and grain protein

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Publication, FAO, Rome

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variability in climate smart villages of Haryana.

Ph.D. thesis submitted to CCSHAU, Hisar

Meena, L. R., Mann, J. S. and Meena, S. L. (2012). Effect of levels and mode of nitrogen

application on dual purpose barley (Hordeum

vulgare L.) under semi-arid condition. Indian

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Moreno, A., Moreno, M.M., Ribas, F. and

Cabello, M.J. (2003). Influence of nitrogen fertilizer

on grain yield of barley (Hordeum vulgare L.) under irrigated conditions. Spanish Journal of Agricultural

Research, 1(1), 91–100.

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R. and Gigliottia, G. (2009). Organic matter

dynamics in a compost-amended anthropogenic

landfill capping-soil. European J. Soil Sci., 61: 35-

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nutrient management on yield and attributing

characters of wheat (Triticum aestivum L.). Indian Journal of Agronomy, 51 (3), 189-192.

Rathore, V.S., Singh, P. and Gautam, R.C. (2003).

Influence of planting patterns and integrated nutrient

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rainfed pearl millet. Annals of Agriculture Research,

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*Corresponding Author

________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 93-97. 2020

VARIETAL PERFORMENCE OF BROCCOLI (BRASSICA OLERACEA VAR.

ITALICA) UNDER NORTHERN HILL ZONE OF CHHATTISGARH

P.C. Chaurasiya* and Sarswati Pandey1

1RMDCARS, Ambikapur, IGKV-College of Agriculture & Research Station, Mahasamund (C.G.)

Email: pcchaurasiaigkv@gmail.com

Received-05.02.2020, Revised-26.02.2020

Abstract: Broccoli (Brassica oleracea var. italica. L.) is one of the most prominent vegetable grown all over the world and is an important fancy and highly nutritive exotic vegetable. Vegetables play a very important role in our daily diet. As an unconventional vegetable “Broccoli” is yet to gain the desired popularity in our country. It is very rich source of various anti-cancer agents as well as Vitamin C and dietary fibre. However, considerable attention is being given on the production technology of Broccoli which is rich in nutrient content and greater yield potential. But yet, no systematic work has been done on evaluation and commercialization of high value nutrient rich this Cole crops. Therefore, the present study were

carried out at Potato & Temperate Fruit Research Station, Mainpat, Surguja, Chhattisgarh under Indira Gandhi Krishi Vishwavidyalaya during the year 2017-2018 in Rabi season with objectives to varietal performance of Broccoli and to standardize the production technology of sprouting broccoli in northern hill zone of Chhattisgarh. Cultivation of these value added vegetables can boost the income of farmers due to very high market price and export demand. The investigations were followed in Randomized Block Design with three replications. Nine varieties of Broccoli viz. Palam Samridhi, Green Giant, Green Speed, KTS-1, Puspa, Palam Haritika, Priya, Aiswarya and Prema were evaluated for best performance. In general, the performances of this crop with different varieties proved that there is good scope to grow broccoli vegetable due to prevailing suitable agro-climatic condition as well as the gaining importance as potential vegetable for export. Among all the

varieties of Broccoli Palam Samridhi was found superior, which gave higher yield (184.5q/ha) followed by Green Speed (173.74q/ha), Green Giant (156.23q/ha) and Palam Haritika (144.84q/ha) respectively in combination with best head formation.

Keywords: Performance, Broccoli, Varieties, Quality and yield

INTRODUCTION

roccoli is an important vegetable among the

Cole crops. It is a rich source of Vitamins and

minerals. In fact, it contains more vitamin A than

cabbage and cauliflower and the highest amount of

proteins among the Cole crops. It also contains anti-cancerous compounds and antioxidants. India is

endowed with a wide range of tropical, sub-tropical

and temperate vegetable crops. But still there are

some vegetables which are lesser known or rare to

most of our growers and con- summers. Our farmers

can earn a lot of profit by growing this rare or

unusual high value Cole vegetables nearby big cities

(periurban areas) and towns as they attract very high

prices in cosmopolitan markets, star hotels and

places of tourists’ interest. They can also be exported

to foreign especially European countries where their cultivation is not possible throughout the year in

open field conditions. But due to lack of information

about their cultural practices for our conditions the

production or availability of these vegetables is still

meager. Chinese cabbage, Sprouting broccoli, Red

cabbage and Brussels sprouts, etc. have opened up

new opportunities for vegetable growers of our

country for diversification and off-season production

for high market in metropolis. But due to lack of

preference in food among Indians some of the

introduced vegetables could not get popularity

though they are rich in protein, carbohydrates, minerals, vitamins and fibers etc. However, with the

growing tourist industry and nutritional awareness

among people, these vegetables are gaining popular.

Among the Cole crops broccoli is more nutritious

than other Cole crops, such as cabbage, cauliflower

and kohlrabi. It is fairly rich in carotene and ascorbic

acid and contains appreciate quantities of thiamin,

riboflavin, niacin and iron. Realizing the tremendous potential of sprouting broccoli in domestic and

foreign market, the Kharif season potato growers of

Northern Hill Zone of Chhattisgarh are gradually

adopting the broccoli cultivation. To popularize this

high value Cole crops and its variety among the

marginal and small farmers, proper demonstration

should be adopted through personal contact

approach, monitoring, motivation and awareness

creation about benefits. However, State is facilitated

with good and congenial agro-climatic condition for

cultivation of these crops. Therefore, present studies were aimed at promotion of high value Cole

vegetables by identifying new promising varieties

with high productivity under wide range of

environmental conditions, better horticultural

characteristics and market opportunities.

MATERIALS AND METHODS

The present studies were carried out at Potato &

Temperate Fruit Research Station, Mainpat, Surguja,

Chhattisgarh under Indira Gandhi Krishi

Vishwavidyalaya during Rabi season (2017-2018) with the principle objective to standardize the

B

RESEARCH ARTICLE

94 P.C. CHAURASIYA AND SARSWATI PANDEY

production technology of sprouting broccoli. The

investigation details are as follows: Broccoli seed

were sown in nursery beds. At four leaf stage the

seedlings were transplanted in the main field in a plot

size 2.5 x 4m. The design of experimental site was

Randomized Block Design replicated thrice utilizing nine genotypes showing diverse features. Genotypes

taken under observations were Palam Samridhi,

Green Giant, Green Speed, KTS-1, Puspa, Palam

Haritika, Priya, Aiswarya and Prema. The

transplanting of seedlings was accomplished on first

week of November with the spacing of 60cm x

45cm. Applied fertilizer doses are in NPK ratio of

[120:80:100] kg per hectare. Nitrogen was applied in

the form of urea in two split doses. The half dose of

nitrogen was applied along with full dose of

phosphate and potassium. P and K were applied in

the form of diammonium phosphate and muriate of potash respectively at the time of transplanting. The

remaining dose of nitrogen was applied 30 days after

transplanting Regular cultural practices, crop

protection measures were adopted as per the

requirements of crop. Observations were taken under

physical, yield and quality attributing parameters.

Mean value of randomized data were analysed by

following standard statistical technique (Panse and

Sukhatme 1985).

RESULTS AND DISCUSSION

The nine different varieties of Broccoli were varied

significantly. The days taken for germination was

varied from 4.2 (Palam Samridhi) to 5.79 (Green

Giant). The minimum germination days taken by

variety Palam Samridhi (4.2) followed by KTS-1

(4.41), Palam Haritika (4.60) and Puspa (4.96) while,

variety Green Giant (5.79) have taken maximum

days for germination of seed. The yield and yield

attributing characters due to different varieties

showed a significant differences effect. In respect of

earliness of head initiation and days required to harvesting, the cultivars under study were found

significant. The average number of days to head

initiation varied from (55.50 to 63.50). The cultivar

Palam Samridhi (55.50), Prema (57.50), Puspa

(58.48) and (60.97) found earlier and KTS-1 and

Priya was found very late in respect of head

initiation. The average period required days to

harvesting varied from (72.67 to 93.74). The cultivar

Palam Samridhi (72.67), Puspa (78.07), Prema

(80.13) and Palam Haritika (82.23) found earlier and

Green Giant found very late (93.74). The height of the plants varied from (33.07 to 56.95 cm). From the

data it revealed that the variety, Palam Samridhi

recorded significantly maximum plant height (56.95

cm) while Puspa variety recorded the minimum

(30.78 cm). The lowest plant height observed in

some other varieties might be due to its inherent

genotypic characteristics or for the variations in agro-

climatic condition. The number of leaves per plant is

an important character that might influence the yield.

The cultivars included in the study produced an

average variation of (13. 22 to 16.53) leaves per

plant. The maximum number of leaves per plant was

recorded as (16.53) in variety Palam Samridhi,

followed by Green Speed (16.50), Palam Haritika (16.25), and KTS-1 (15.56). The lowest number of

leaves was noticed in the variety Puspa (13.22),

Prema (14.30), Green Giant (15.06) and Aiswarya

(15.13). Lower number of leaves in some cultivars

was probably due to slow rate in leaf initiation which

would be an inherent character of the cultivars. This

wide variation in vegetative growth of the different

varieties was also recorded by earlier investigators

(Abou El-Magd et al. 2005, 2006; El-Helaly 2006).

Similar results were also recorded by Damato (2000),

Damato and Trotta (2000), Sharma (2003), Siomos et

al. (2004) and Singh et al. (2014) and Renbomo and Biswas (2014). More number of leaves might have

reduced the head size and total head weight due to

more nutrient absorption by the leaves. This is in

agreement with previous investigation in which some

of the cultivars were included. In this investigation,

plant spread in each cultivar were recorded and

found significant differences. The range of head

diameter was (13.10 to 20.5cm). It has been found

from the experiment results, the maximum head

diameter (20.57cm) was obtained with variety Prema

followed by Puspa (20.17cm), Priya (20.14cm) and Palam Haritika (16.17cm). The minimum head

diameter of (13.10cm), with variety Green Speed was

recorded. It has been found from the experimental

results that the highest stem diameter was measured

in variety Palam Samridhi (4.52 cm) followed by

Green Giant (4.24 cm). Similarly the higher site in

diameter of stem was observed with variety Green

Speed (3.79 cm), Palam Haritika (3.75 cm), and

Prema (3.70 cm). From the [Table-1] it is clear that

among the above mentioned varieties there were

significant differences among themselves. Rest of the

other varieties different significantly from the above mentioned one. However, the lowest diameter of

stem was obtained with variety KTS-1 (3.39cm).

This similarity and dissimilarity among the varieties

in stem diameter may be attributed to the variability

in their genetic configuration. The maximum head

weight of (410.23gm) was found with Palam

Samridhi, variety. The varieties which produced

comparatively more head weight are namely Green

Speed (372.43g), Aishwarya (366.83g) and Palam

Haritika (309.07g). The highest head weight might

be due to resulted from the highest head diameter and number of sub sprout in the respective varieties. The

minimum head weight of (260.20.g) was obtained

with Priya variety. In respect of the stem length,

statistically parity was observed. Among the nine

varieties the minimum stem length (21.77 cm) was

observed in variety Puspa and maximum stem length

observed in variety of Palam Samridhi (28.43cm).

This showed that the cultivars represent a good range

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 95

of genetic diversity in response of stem length. The

tabulated data (Table-1. showed clearly that the best

quality of more number of sprout (spears) was

recorded from the variety Palam Samridhi (6.87)

followed by Priya (6.60) and Green Giant (5.59). The

lowest numbers of sprout were observed from Prema (4.30) variety. The differences in number of sprout

among these varieties may be due to their own

genetic characters. Results obtained in (Table-1)

reflect significant differences in the sprout weight of

the different varieties. The highest sprout weight was

obtained from Palam Samridhi (40.73g) followed by

Palam Haritika (37.70g) and KTS-1 (36.03g) while,

minimum sprout weight found in Prema (28.17g). The

highest sprout yield per plot was obtained from

Palam Samridhi (6.5kg) followed by Green Giant

(5.26kg) and Aiswarya (4.7kg) while, minimum yield

per plant was observed in Prema (2.40kg). The highest yield per plant was obtained from Palam Samridhi

(323.33g) followed by Green Giant (318.00g) and

KTS-1 (300.00g) while, minimum head yield per plant

found in Puspa (253.48g). The highest yield per plot

was obtained from Palam Samridhi (14.34kg)

followed by Green Giant (12.8kg) and Palam Haritika

(12.2kg) while, minimum head yield per plant found

in Prema (9.50kg). There was a significant and

positive effect of different varieties on head yield

(q/ha) Palam Samridhi performed the highest results

in head yield (184.0q/ha) and the other two varieties

showed statistically similar results Green Speed

(173.7q/ha), Green Giant (156.2q/ha) and Palam

Haritika (144.8q/ha) Table 2. This wide variation in

yield of the different varieties was also recorded by earlier investigators (Abou El-Magd et al. 2005,

2006; El-Helaly 2006). Similar results were also

recorded by Damato (2000), Damato and Trotta

(2000), Sharma (2003), Siomos et al. (2004), Singh

et al. (2014) and Renbomo and Biswas (2014). It

indicates that next to Palam Samridhi, there three

varieties, Green Speed, Green Giant and Palam

Haritika have ability to produced good head yield.

The present experiment revealed that the yield and

yield attributing characters significantly differed

within the different varieties. On the basis of

performance of varieties related to head yield and concerning yield attributing characters, Palam

Samridhi performed the highest head yield and other

two varieties Green Speed and Palam Haritika are

also considered suitable for positive response for

boosting higher yield. The variety of Broccoli Palam

Samridhi was very significantly quantitative

character and this was good for cultivation northern

hill zone of Chhattisgarh.

Table 1. Performance of Broccoli in northern hill zone of Chhattisgarh S. No

Varieties Days

taken

for

germina

tion

Days to

Head

Initiation

(Days)

Days to

Harvest

(Days)

Plant

height

(cm)

No of

Leaves/

plant

Head

diameter

(cm)

Head

weight

(g)

Stem

diameter

(cm)

1. Palam Samridhi

4.28 55.50 72.67 56.95 16.53 16.67 410.23 4.52

2. Green giant

5.79 62.50 93.74 48.21 15.06 14.60 293.67 4.24

3. Green speed

5.00 62.20 84.48 47.79 16.50 13.10 372.43 3.79

4. KTS-1

4.41 63.60 85.22 48.53 15.56 14.51 285.00 3.39

5. Puspa

4.96 58.48 78.07 43.07 13.22 20.17 290.5 3.50

6 Palam Haritika

4.60 60.97 82.23 46.33 16.25 15.13 309.07 3.75

7. Priya

5.61 63.05 82.41 46.61 15.28 20.14 260.2 3.61

8. Aishwarya

4.66 61.92 83.67 51.67 15.13 13.88 366.83 3.46

9. Prema

5.45 57.50 80.13 45.20 14.30 20.57 270.2 3.70

S. Em 0.38 1.35 1.54 1.28 0.41 0.66 6.20 0.10

CD 5% 1.14 4.06 4.63 4.04 1.24 2.10 12.55 0.30

96 P.C. CHAURASIYA AND SARSWATI PANDEY

Photographs: Research work done at IGKV-Potato & Temperate Fruit Research Station, Mainpat, Surguja

(C.G.)

Table 2. Performance of Broccoli in northern hill zone of Chhattisgarh

S. No

Varieties Stem length

(cm)

Yield/ plant

(g)

Yield

(kg/plot)

No of

Sprout

Sprout

weight (g)

Sprout

yield /plot

(kg)

Yield

(q/ha)

1. Palam Samridhi

28.43 323.33 14.34 6.87 40.73 6.50 184.00

2. Green giant

27.70 318.00 12.80 5.59 35.26 5.26 156.23

3. Green speed

27.12 276.49 8.96 5.36 34.12 4.48 173.74

4. KTS-1

28.05 300.00 10.72 4.51 36.03 3.73 134.36

5. Puspa

21.77 253.48 11.30 4.62 32.34 2.52 107.92

6 Palam Haritika

26.38 283.40 12.20 4.31 37.70 3.88 144.84

7. Priya

26.03 269.67 9.50 6.60 35.17 3.60 120.17

8. Aishwarya

26.33 290.00 9.61 4.57 33.59 4.70 137.71

9. Prema

23.31 285.10 12.8 4.30 28.17 2.40 140.50

S. Em 0.73 6.93 0.38 0.13 1.08 0.14 4.42

CD 5% 2.20 10.77 1.22 0.39 3.26 0.42 11.27

CONCLUSIONS

The present study revealed that the growth, yield and

yield attributing characters significantly differed

within the different varieties. On the basis of

performance of varieties related to head yield and

other yield attributing characters Palam Samridhi proved to be the best suited and other three varieties

namely Green Speed, Green Giant and Palam

Haritika are also suitable for growing by the farmers

in the region.

REFERENCES

Abou El-Magd, M.M., El-Bassiony, A.M. and Fawazy, Z.F. (2006). Effect of organic manure with

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 97

or without chemical fertilizers on growth, yield and

quality of some varieties of broccoli plants. Journal

of Applied Sciences Research 2(10).

Damato, G. (2000). Late sowing dates and high

plant density in six cultivars of broccoli for

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transplant age, cultivars and yield in broccoli. Acta

Horticulturae 533: 153-160.

El-Helaly, M.A. (2006). Studies on growth and

development of broccoli. PhD thesis, Faculty of

Agriculture, Cairo University, Egypt. 791-798.

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New Delhi,347.

Renbomo, N. and Biswas, P. K. (2014).

Performance of different varieties of broccoli under

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Sharma, D.K. (2003). Studies on evaluation and

commercialization of exotic vegetables for

sustainable agriculture production in Himachal

Pradesh. Haryana J. Hort. Sciences 32(1/2): 130-

133.

Singh, R., Kumar, S. and Kumar, S. (2014). Performance and Preference of Broccoli Varieties

Grown under Low Hill Conditions of Himachal

Pradesh. Indian Res. J. Ext. Edu. 14 (1):112-114.

Siomos, A.K., Papadopoulou, P.P. and Dogras,

C.C. (2004). Compositional differences of stem and

floral portions of broccoli heads. Journal of

Vegetable Crop Production 10(2): 107-118.

Thapa, U. and Rai, R. (2012). Evaluation of

Sprouting Broccoli (Brassica oleracea var. Italica)

genotypes for growth yield and quality. International

J. Ag. Science. Vol. 4(7):284-286.

98 P.C. CHAURASIYA AND SARSWATI PANDEY

*Corresponding Author

________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 99-103. 2020

OPTIMIZATION OF DIFFERENT PROPAGATING TECHNIQUE AND TIME

PERIOD TO ENHANCE HIGHER SUCCESS RATE IN PROPAGATION OF LOW

CHILL PEACH CV. SHAN-E-PUNJAB

Rajat Sharma*, P.N. Singh, D.C. Dimri, Shweta Uniyal, Vishal Nirgude and Manpreet Singh

Department of Horticulture, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar 263 145, Uttarakhand

Email: rajathorti10@gmail.com

Received-07.02.2020, Revised-26.02.2020

Abstract: An experiment was conducted to study the propagation of low-chill peaches in Tarai region of Uttarakhand.

Three different methods of propagation viz., chip budding, T-budding and tongue grafting were practiced during period of

experiment. Growth parameters and economic study was made in peach cv. Shan-e-Punjab. The results of the experiment

revealed that treatment tongue grafting practiced on 20th January was found superior for almost all the parameter studied

except for days taken for sprouting initiation, which was least (6.00 days) with grafting on 20th February. The parameters

such as graft diameter, number of branches, plant height, saleable plants, number of leaves, leaf area, number of primary and

secondary roots, fresh weight of roots and shoots and root to shoot ratio were found to be maximum in case of tongue

grafting followed by chip budding. However, economics of experiment as benefit cost ratio was found higher (2.08) in chip

budded plant as compared to tongue grafting (1.78) and T-budding (0.81).

Keywords: Peach, Propagation, Tongue grafting, T-budding, Chip budding

INTRODUCTION

each Prunus persica (L.) Batsch is an important

fruit crop of temperate climate of the world but it

can be grown quite successfully in the sub-tropical condition using suitable low-chill cultivars. In India,

peaches are being cultivated over an area of 18000

hectares with the production of 107,000 MT

(Anonymous, 2018), whereas, Uttarakhand leads in

the peach production with an area of 78.55 thousand

hectare and an annual production of 57.93 thousand

MT (Anonymous, 2017). The successful introduction

of high-quality low chilling peach cultivars in India

have created a tremendous scope for its cultivation in

north western plains (Nijjar and Khajuria, 1979). The

varieties like Florida Prince, Early Grande, Partap, Sharbati and Shan-e-Punjab have become very

popular and are grown commercially in N.I. plains

and valley area of Uttarakhand. However, the limited

availability of sufficient planting material is a major

constraint for the slow pace of area coverage under

peach in this region. Whereas, to meet the increasing

requirement of quality planting material of low chill

peaches, standardization of suitable propagation

methods for plains and Tarai region is essential.

Worldwide, peaches are still principally propagated

by either grafting or budding (Rom and Carlson,

1987). Success of budding/grafting does not only depend upon the choice of appropriate method but on

time of operation also. Considering all these factors,

the present experiment carried out to optimize the

method and time of propagation in subtropical

peaches at Tarai region of Uttarakhand.

MATERIALS AND METHODS

The investigation was carried out at Horticultural

Research Centre, GBPUA&T, Pantnagar on peach

cv. Shan-e-Punjab during 2016-2018. The treatment consists of three method of propagation (tongue

grafting, chip budding and T-budding) performed at

different months i.e. T1-Tongue grafting (20th

January, 2017), T2-Tongue grafting (10th February,

2017), T3-Tongue grafting (20th February 2017), T4 -

Chip budding (20th January, 2017), T5-Chip budding

(10th February, 2017), T6 -Chip budding (20th

February, 2017), T7-Chip budding (1st June, 2017),

T8-Chip budding (20th June, 2017), T9-Chip budding

(10thJuly, 2017), T10-Chip budding (10th August,

2017), T11-Chip budding (30th August, 2017), T12-T-budding (10th August, 2017) and T13 -T-budding (30th

August,2017). Thus, there were total 13 treatments

which were replicated thrice in a Randomized Block

Design (RBD) with 15 grafts per treatment. One-year

old uniform seedlings of wild peach having pencil

thickness were used as rootstock. For grafting

purpose, 10 cm long scion wood of peach cv. Shan-e-

Punjab having more than 3 buds from the previous

season growth was collected and used. For chip

budding, scion with mature bud was selected and a

chip was taken out from the scion wood and placed

on rootstock followed by tying with alkathene tape in order to avoid desiccation of graft union. In case of

T-budding, T-shaped incision was given on stock and

bark was removed, then a chip of scion was placed in

incision. Regular pinching was done to control the

unwanted growth of shoot from the seedlings, below

graft union. Uniform cultural treatments were given

P

RESEARCH ARTICLE

100 RAJAT SHARMA, P.N. SINGH, D.C. DIMRI, SHWETA UNIYAL, VISHAL NIRGUDE AND MANPREET SINGH

to all plants during the course of investigation.

Observation on days taken for sprouting initiation,

sprout percentage, per cent success, plant growth

parameters and cost benefit ratio were recorded. For

calculating the economics of the experiment, the

gross income (Table 2) was worked out after selling the obtained saleable plants at prevailing market

price (Rs. 50 per plant), subsequently, the net income

(Table 2) was calculated by subtracting the total

expenditure from the gross return. Finally, the return

per rupees invested i.e., benefit: cost ratio was

calculated for the entire propagation method viz.

tongue grafting, chip budding and shield/T-budding.

The data obtained were analysed using standard

statistical procedure (Cochran and Snedecor, 1987).

RESULTS AND DISCUSSION

The data pertaining to days to sprouting, sprouting

percentage, success and plant growth parameters are

presented in Table 1. Different methods of grafting

and budding at different time intervals show the

significant effect on days taken for initiation of

sprouting. Treatment T2 (Tongue grafting on 10th

February) took minimum number of days (6.00 days)

taken for initiation of sprouting which was

statistically at par with T3 (Tongue grafting on 20th

February), T5 (Chip budding on 10th February) and T6

(Chip budding on 20th February) in which sprout initiation took place in 6.33 days, 7.33 days and 8.00

days respectively. Initiation of sprouting is different

among the other methods succeeded by plants of T9

i.e. chip budding on 10th July (9 days). However,

treatment T4 (chip budding on 20th January) took

maximum days (24.33 days) to initiate sprouting. T-

budded plants reached the sprout initiation stage

ranging in 15.33-15.66 days depending upon the time

of operation. Tongue grafted plants on 10th February

sprouted earlier because the graft union formation

was faster (Skene et al., 1983) and the basic

physiological flowering and leaf sprout initiation process in grafted plants compared to the other

methods i.e., chip and T-budded plants. This

observed variation of days in reference to the

propagation techniques at one or another date might

be due to the fact that the growth of the plants occurs

at faster pace due to breaking of dormancy after

completion of the chilling requirement and remained

into quiescence stage for shorter time period

(Lockwood and Coston, 2005). Temperature, soil and

air moisture played an important role in faster graft

union formation due to the cell sap flow in both scion and rootstocks. Complete lacking of cell sap

movement in plant may lead to the drying of cell sap

and necrosis of cell. Similar findings have been

reported in peach (Bohra, 2008; Chakraborty and

Singh, 2011) and apple (Dimri et al., 2009). The time

and method of propagation have profound impact on

sprouting percentage of plants, where, highest

percent of sprouted plants (97.78%) were recorded

on T1 treatment (tongue grafting on 20th January)

which was at par (86.66%) with the treatment T3 i.e.

plants grafted on 20th February. While, the minimum

sprouting percentage (10.24%) was observed in T11

treatment (chip budding on 30th

August) followed by

T13 i.e., T-budding on 30th August. Maximum sprouting percentage in plants Tongue grafted on 20th

January might be attributed to the availability of

ample moisture in soil and air. The less success with

T-budding might be due to the less and erratic

rainfall and higher temperature at time when rain

water was essentially required for successful

operation. These results were found in harmony with

finding of Bohra, (2008) in peach and Celik et al.,

(2006) in kiwifruit, who recorded higher sprouting in

chip budding than T-budding. Further, the efficacy of

any propagation depends upon salability of the

plants, i.e. plants gain enough height and girth to reach the saleable stage. Maximum number of

saleable plants (97.28 per cent) were obtained when

plants were propagated by tongue grafting carried out

on 20th January (T1), followed by T2 (tongue grafting

on 10th February) viz. 86.67 per cent whereas,

minimum (13.44 per cent) was record with tongue

grafting on 20th February succeeded by T-budding on

10th August (T11) viz. 17.07 per cent. Amongst the

methods, T-budded plants got minimum number of

saleable plants than other two method utilized for

propagation. More number of saleable plants obtained in tongue grafting might be because of

proper and quick union formation, early bud

sprouting and longer time period available for

growth. Upadhyay (2016) also obtained maximum

number of saleable plants with tongue grafting.

Graft diameter is one of the indicator criteria for

standardization of propagation technique. The plant

is considered as marketable when they acquire the

optimum girth. In present experiment, the girth was

measured from three positions from the shoot i.e., 5

cm above union, at union and 5 cm below the union,

respecttively. In relation to the method and time of propagation, tongue grafted plant produced

maximum stem diameter and least was noticed in

chip budded plant on 30th August (T11). Tongue

grafted plants on 20th January (T1) produced the

diameter of 18.44 mm, 21.49 mm, 19.47 mm,

respectively, followed by chip budding on 20th

January (T4) which obtained a girth of 15.13 mm,

18.88 mm and 16.68 mm, while, minimum was

observed in T11 with 5.90 mm, 9.71 mm and 7.77

mm succeeded by T9 i.e., chip budding on 10th July

(5.24 mm, 14.47 mm and 9.48 mm) respectively. The tongue grafted plants grafted earlier produced girth

higher than later grafted plants because of the better

graft union of cambium layers of both stock and

scion, more surface contact of scion with stock,

optimum humidity and temperature, early initiation

of bud sprouting. Similarly plant height was recorded

maximum (128.67 cm) when plants tongue grafted on

20th January which was statistically at par with T4

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 101

(123.20 cm) when chip budding practiced on 20th

January followed by T3 (115.83 cm) when plants

tongue grafted on 20th February. Minimum plant

height (18.20 cm) was recorded in T11 treatment (chip

budding on 30th

August) which was at par with T13

(T-budding on 30th August) and T10 (Chip budding on 10th August) producing plant of height 19.83 cm and

21.57 cm respectively. These results were in

conformity with results of Awasthi and Negi, (2016).

The maximum plant height in tongue grafted plants

may be attributed to favorable climatic conditions,

presence of greater number of leaves that might have

raised the rate of photosynthesis and hence increased

carbohydrate formation. In the similar pattern,

maximum number of branches (11.63) were obtained

when tongue grafting was practiced on 20th January

(T1) followed by T4 (9.82) when chip budding was

carried out on 20th January and minimum branches were produced when T-budding was done on 30th

August (T13) i.e., 1.50 followed by T5 and T9 viz.,

chip budding on 10th of February and 10th of July

both obtained 1.63 branches. The number of

branches obtained was maximum under T1 that might

be due to the production of more number of primary

and secondary numbers of roots. These finding are in

lined with the results of Bohra (2008) and Ahmad,

(2012).

Maximum number of leaves (96.33) were obtained

when tongue grafting was carried out on 20th January (T1) followed by T4 (72.33) when chip budding was

carried out on 20th January and minimum number of

leaves were obtained when chip budding was done

on 1st June (T7) i.e., 16.80 which was at par with T8,

T9 and T11 viz., chip budding on 20th June, 10th July

and 30th August obtained 17.35, 18.05 and 19.33

leaves respectively. The maximum number of leaves

on plants grafted on 20th January might be due to

higher shoot length attained and number of branches

produced by such plants. Similarly, the maximum leaf

area (23.98 cm2) was recorded on T1 treatment (tongue

grafting on 20th January), followed by treatment T4

i.e. 21.76 cm2 (chip budding on 20th January),

whereas, minimum leaf area (12.16 cm2) was

recorded in T8 treatment (chip budding on 20th June),

which was found to be statistically at par with T7 and

T9, when plants chip budded on 1st June and 10th July

where leaf area was 12.28 cm2 and 12.76 cm2

respectively. The maximum leaf area may be due to

prevailing moisture and temperature availability

during course of experiment. The above result was

found in harmony with finding of Chakraborty and

Singh (2011) and Gill et al., (2014) in peach. On the other hand, the method of propagation had

profound and significant influence on number of

primary and secondary roots as shown in Table 1.

The maximum number of primary and secondary

roots was produced in case of tongue grafted plants.

The tongue grafting on 20th January (T1) produced

14.60 and 20.33 primary and secondary roots,

respectively. In case of various chip budding dates,

20th January (T4) produced 13.33 and 15.00 primary

and secondary roots respectively. While lowest

values for primary (4.83) and secondary roots (4.50)

were observed when chip budding was done on 20th

June (T8) and 10th

February (T8). More number of

primary and secondary roots was noticed with tongue grafted plants might be attributed to early

establishment of grafted plant which resulted in more

transport of nutrients from roots. Similarly, tongue

grafted plants on 20th January (T1) obtained

maximum fresh root (107.06g) and shoot (122.33g)

weight followed by chip budding on 20th January

(T4) i.e., 104.17g and 106.83g, respectively. While,

the fresh weight of root was observed to be minimum

(27.54g) in T10 and shoot weight (26.33g) in T11

treatment. Variation in fresh weight of root and shoot

may be due to the variation in the length of shoot and

number of roots which might have absorbed more nutrient and water (Deshmukh et al., 2017), beside

this, higher accumulation of carbohydrates in plant

body might contributed to the gain in fresh weight.

The maximum root to shoot ratio (2.39) on fresh

weight basis was recorded when chip budding of

plants done on 10th February (T5) followed by chip

budding on 30th August (1.58), i.e. T11, whereas, the

minimum value (0.73) was noted when plants were

tongue grafted on 20th February (T2), which might be

attributed to optimum weather condition of the

rootstock and propagation methods coincide with synthesis of required quantities of secondary

metabolites like phenolic and alkaloid compounds

which were needed for the protection of the

rootstocks with less root attack by the soil-borne

pathogens and insect-pests (El-motty et al., 2010).

The economics of any experiment is an important

aspect as farmers are convinced considering input-

output ratio of cropping. The careful scrutiny of data

indicates that total expenditure was found highest

being Rs. 27,250.00 in tongue grafting method

followed by Rs. 20,750 in chip budding method,

whereas, lowest expenditure (Rs. 18500.00) was calculated in control shield budding. Similarly, the

maximum number of saleable plants (972) was

recorded in tongue grafting followed by (866) in chip

budding, however, the minimum number of saleable

plants (243) was recorded in under shield budding.

Therefore, based on saleable plants obtained in

individual methods, the highest gross income (Rs.

48,600.00) was recorded in tongue grafting, followed

by Rs. 43,300.00 in chip budding, whereas, lowest

gross income (Rs. 15,000.00) was calculated in

shield budding. Further, after deducting the total expenditure from the gross income of corresponding

methods of propagation, the highest net income (Rs.

22,550.00) was calculated in chip budding, whereas

it was found lowest (Rs. 12,150) under shield

budding. The finding of experiment revealed that

chip budding (2.08) followed by tongue grafting

(1.78) were found higher in their benefit-cost ratio.

On contrary, T-budding recorded minimum success

102 RAJAT SHARMA, P.N. SINGH, D.C. DIMRI, SHWETA UNIYAL, VISHAL NIRGUDE AND MANPREET SINGH

and minimum number of saleable plants, therefore,

its benefit-cost ratio is minimum (0.81) among

various techniques (Table 2). The chip budding was

found economical superior because of budding,

wherein, one need only single bud, whereas, in

grafting, the scion wood must contain two or three dormant buds (Misra et al., 2017). Therefore, more

amounts were spent on procuring scion wood for

tongue grafting compared to chip budding.

Therefore, based on results obtained, it can be

concluded that, among all the propagation techniques

studied, tongue grafting and/followed by chip

budding on 20th January was found to be superior

having all desirable growth characters, whereas, from

economic point of view, chip budding followed by tongue grafting and T-budding produced higher

profitable values.

Table 1. Effect of different method and time of propagation on growth attributes of peach cv. Shan-e-Punjab. Treatments Days

taken for

sprouting

initiation

Sprouting

percentage

Saleable

plants

(%)

Graft diameter (mm) Number

of

branches

Plant

height

(cm)

Number

of

leaves

Leaf

area

(cm2)

Number

of

primary

roots

Number

of

secondary

roots

Fresh

weight

of root

(g)

Fresh

weight

of

shoots

(g)

Root to

shoot

ratio on

fresh

weight

basis

5cm

above

union

At

union

5cm

below

union

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

T11

T12

T13

23.33

06.00

06.33

24.33

07.33

08.00

11.66

10.66

09.00

11.66

17.33

15.33

15.66

97.78

(76.06)*

55.72 (51.58)

86.66 (54.73)

61.08 (49.46)

59.67 (43.07)

36.60 (34.01)

29.51 (40.94)

47.52 (48.88)

53.33 (30.58)

21.84 (23.63)

10.24 (5.20)

22.94 (24.53)

14.66 (07.51)

97.28

(84.82)*

13.44

(21.47)

86.67

(69.01)

56.96

(49.01)

52.74

(46.58)

39.99

(39.20)

29.78

(33.04)

43.70

(41.38)

44.44

(41.75)

19.98

(26.35)

25.00

(30.00)

17.07

(24.33)

25.55

(30.37)

18.44

9.97

11.50

15.13

9.41

8.40

10.24

8.55

5.24

7.20

5.90

9.24

7.82

21.49

16.43

15.13

18.83

14.84

13.80

14.13

12.59

14.47

11.04

9.71

14.41

12.31

19.47

11.41

11.73

16.68

10.19

9.62

10.26

10.59

9.48

8.30

7.77

10.43

10.40

11.63

4.00

6.83

9.82

1.63

4.48

2.50

3.00

1.63

2.23

1.83

2.30

1.50

128.67

95.07

115.83

123.20

52.90

54.53

40.13

39.00

29.30

21.57

18.20

27.07

19.83

96.33

55.67

66.00

72.33

53.37

35.31

16.80

17.35

18.05

21.44

19.33

27.83

22.33

23.98

15.05

18.09

21.76

18.03

15.67

12.28

12.16

12.76

17.24

16.54

18.05

17.16

14.60

7.33

4.67

13.33

7.17

8.83

5.33

4.83

6.50

6.17

6.33

7.17

4.00

20.33

4.50

5.00

15.00

4.50

6.67

4.63

6.67

4.92

5.33

5.10

4.50

4.50

107.06

52.00

77.50

104.17

67.83

54.83

46.27

34.17

31.36

27.54

41.67

33.08

29.25

122.33

71.00

100.67

106.83

28.42

51.83

32.8

30.20

29.33

32.97

26.33

35.83

26.67

0.88

0.73

0.77

0.98

2.39

1.06

1.41

1.13

1.07

0.84

1.58

0.92

1.10

S.Em.± 0.79 (61.21) (17.25) 0.60 0.88 0.59 0.15 2.04 1.22 0.50 0.63 0.68 2.12 2.0 0.10

C.D. at 5% 2.33 (13.18)

(7.00) 1.74 2.58 1.73 0.45 5.99 3.60 1.45 1.87 1.98 6.24 5.87 0.29

*The figure under the parentheses are the angular transformed values.

Table 2. Economics of experiments using different method and time of propagation in peach cv. Shan-e-Punjab.

Treatments Rootstock

procuring

Bed

preparation

and

transplanting

Scion cost Propagatio

n cost

Irrigation

cost

Weeding

and

hoeing

cost

Uprooting

& grading

cost

Total

expenditure

(rupees)

Gross income Net

income

(Rupees)

Cost :

Benefit

ratio Number of

saleable

plants @

50/-

Total return

Tongue

grafting

3500.0 250*4 =

1000.0

1000*10 =

10000.0

250*4 =

1000.0

150*50 =

7500.0

250*15 =

3750.0

250*2 =

500.0 27250.0 972.0 48600.0 21350.0 1:1.78

Chip

budding

3500.0 250*4 =

1000.0

350*10 =

3500.0

250*4 =

1000.0

150*50 =

7500.0

250*15 =

3750.0

250*2 =

500.0 20750.0 866.0 43300.0 22550.0 1:2.08

T-budding 3500.0 250*4 =

1000.0

350*10 =

3500.0

250*4 =

1000.0

150*50 =

7500.0

250*6 =

1500.0

250*2 =

500 18500.0 243.0 15000.0 12150.0 1:0.81

ACKNOWLEDGEMENT

Authors are thankful to G.B. Pant University of Agriculture and Technology for providing every

support and facility during course of experiment.

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Khan, M.A. and Wasila, H. (2012). Effect of

different time of budding on the bud take success of

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Welfare (DAC&FW), Ministry of Agriculture and

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in Applied Natural and Social Science, 4(11):25-30.

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methods in peach cv. Sharbati under Tarai condition

of Uttarakhand. Thesis, M.Sc. Ag. (Horticulture),

department of Horticulture, G.B.P.U.A&T,

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manual grafting unit on kiwifruit propagation in the

field. Hort Science, 33(2): 57–60.

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P., Jha, A.K. and Malhotra, S.K. (2017). Influence

of rootstock age and propagation methods on scion

physiology and root morphology of Khasi mandarin

(Citrus reticulata). Indian Journal of Agricultural Sciences 87 (2): 203–209.

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104 RAJAT SHARMA, P.N. SINGH, D.C. DIMRI, SHWETA UNIYAL, VISHAL NIRGUDE AND MANPREET SINGH

*Corresponding Author

________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 105-109. 2020

EFFECT OF INTEGRATED CROP MANAGEMENT PRACTICES ON GROWTH,

SEED YIELD AND ECONOMICS OF LENTIL (LENS CULINARIS MEDICK.)

S.K. Sharma*, Rakesh Kumar and Parveen Kumar

Department of Agronomy, Chaudhary Charan Singh Haryana Agricultural University,

Hisar-125004, Haryana, India

Email: sksharma67@rediffmail.com

Received-02.02.2020, Revised-21.02.2020 Abstract: A field experiment was carried out during rabi season of 2013-14 to 2015-16 at Research Farm of Pulse Section, Hisar, to study the effect of different crop management practices on growth, yield and economics of lentil. Different treatments were included in the experiment viz. control, NM (Nutrient Management): RDF (20:40 kg NP ha-1), WM (Weed Management): Pendimethalin @ 1.0 kg ha-1 + one hand weeding at 30 DAS), PM (Pest Management): spray of quinalphos 25 EC one litre per ha in 250-300 litres of water as and when required, NM + WM, NM + PM, WM + PM, NM + WM + PM laid out in randomized block design and replicated thrice. Results revealed that significantly higher plant height, number of branches plant-1, number of pods plant-1, number of seeds pod-1, seed and straw yield were achieved in treatment having integration of NM +WM + PM being at par with that of integration of NM + WM over rest of the treatments. Integration of

NM + WM + PM recorded lower weeds dry weight (31.1 kg ha-1) and higher weed control efficiency (94.18%) compared to all other treatments. The practice of integration of NM + WM + PM also produced higher net returns (Rs13190/ha) and BC ratio (1.53) compared to other crop management practices.

Keywords: BC ratio, Lentil, Nutrient management, Pest management, Seed yield, Weed management, Yield attributes

INTRODUCTION

ulses contribute about 10 per cent of the daily

protein intake and 5 per cent of energy intake and

hence are of particular importance for sustainable

food security in the country. India is the world’s largest grower, producer and consumer of pulses

accounting 34 per cent of total acreage, 26 per cent

of total production and about 30 per cent (23-24

million tonnes) of the total consumption in the world.

In India, the area under pulses was >29 million ha

with the total production of 25.23 million tonnes at a

productivity of 841 kg ha-1during 2017-18

(Anonymous, 2018). Lentil (Lens culinaris Medick.)

is one of the important rabi pulse crops of India next

to chickpea. The nutrient value of lentil composed of

60% of carbohydrates, 26% of proteins, 7.5% of iron,

2% of sugars and 0.87% of thiamine vitamin B1

(Sharara et al., 2011). It is the richest source of

important amino acids (lysine, arginine, leucine and

other S-containing amino acids) among all the winter

season legumes. The low yield of lentil is mainly

attributed to its cultivation on poor and marginal

soils declined soil fertility and unpredictable

environment conditions arisen due to intensive use of

lands without proper replenishment of plant nutrients

especially where high yielding varieties of cereals are

being cultivated using unbalanced doses of mineral

fertilizers. The successful cultivation of lentil is feasible only with the espousal of appropriate

nutrient management practices to alleviate the

gruelling conditions of farmers.

Despite the use of herbicidal weed control in

conventional production, similar weed control

problems are being faced due to increased presence

of herbicide resistant weeds. As a result sustainable

weed management strategies must be developed

(Mortensen et al., 2012).

In recent years due to increased labour cost and their

non-availability for weeding, insect pest and disease

management at peak requirement, the use of integrated crop management is indispensable.

Integrated crop management is a pragmatic approach

to the production of crops. Yield of lentil can be

increased by adopting improved varieties, fertilizer

management, weed management and pest

management practices (Singh and Singh, 2014). In

present study, different crop management practices

either singly or in combinations were tested in lentil

crop. The experiment was under taken during rabi

season of 2013-14 to 2015-16 at the Research Farm

of Pulses Section, Department of Genetics and Plant

Breeding, CCS Haryana Agricultural University, Hisar, with the objective to study the effect of

different crop management practices on growth, seed

yield and economics of lentil crop.

MATERIALS AND METHODS

The present investigation entitled “Effect of

integrated crop management practices on growth,

seed yield and economics of lentil (Lens culinaris

Medick.)” was conducted during 2013-14 to 2015-16

at Pulse Research Farm, Department of Genetics and Plant Breeding, Chaudhary Charan Singh Haryana

Agricultural University, Hisar at 20o-10′N latitude,

75o-46′E longitude and at altitude of 215.2 m above

mean sea level. A pre-sowing irrigation was given in

the first week of November during respective years

to facilitate proper ploughing and to ensure adequate

P

RESEARCH ARTICLE

106 S.K. SHARMA, RAKESH KUMAR AND PARVEEN KUMAR

soil moisture for seed germination, establishment and

subsequent plant growth. The total rainfall received

during the crop growing season was 70 mm, 148.7

mm and 30.5 mm during 2013-14, 2014-15 and

2015-16, respectively. The sandy loam soil of the

experimental field was low in organic carbon (0.36%), available N (131 kg ha-1), medium in

available P (13.8 kg ha-1) and high in available K

(408 kg ha-1) with pH 8.2. The experiment consisted

of eight crop management practices viz. control, NM

(Nutrient Management): RDF (20:40 kg NP ha-1),

WM (Weed Management): Pendimethalin @ 1.0 kg

ha-1 + one hand weeding at 30 DAS), PM (Pest

Management): spray of quinalphos 25 EC one litre

per ha in 250-300 litres of water as and when

required, NM + WM, NM + PM, WM + PM, NM +

WM + PM was laid out in randomized block design

with three replications. Gross and net plot sizes were 4.5 m x 4.0 m and 3.9 m x 3.0 m, respectively. HM 1

variety of lentil was sown during the third week of

November and harvested in second week of April

during the respective years. The seeds were sown in

lines at 22.5 cm apart with recommended seed rate of

35 kg ha-1. Nutrient management, weed management

and pest management were done as per treatments

and irrigation was applied as per requirement of the

crop. The data on growth and yield attributes viz.,

plant height, number of branches, pods plant-1 and

yields were recorded at maturity. Weeds dry weight was recorded at the time of harvest of the crop. Since

similar trend was noticed during all the years, the

data pertaining to all the three years were pooled.

The economics of the treatments was worked out

considering the prevailing cost of inputs and outputs.

All the results were then analyzed statistically for

drawing conclusion using Analysis of Variance

(ANOVA) procedure.

RESULTS AND DISCUSSION

Growth and yield attributes All the crop management practices either singly or in

combinations had a significant effect on number of

branches plant-1, number of pods plant-1 and number

of seeds pod- compared to control while the effects

were non-significant on 100 seed weight (Table 1).

The plant height of lentil was significantly higher in

treatments having combination of NM + WM, NM +

PM, WM + PM and NM + WM + PM compared to

control. Integration of NM + WM + PM practices

being at par with that of NM + WM recorded

significantly higher plant height, number of branches plant-1, number of pods plant-1 and number of seeds

pod-1of lentil than other crop management practices.

Higher values of growth and yield parameters in the

treatment having integration of NM + WM + PM

were the result of better supply of all the essential

nutrients in a balanced amount that resulted in better

crop growth and development (Fatima et al., 2013).

The lowest values of these attributes were, however,

recorded under control owing to inadequate nutrient

supply. The number of pods plant-1 is very important

and key factor in determining the yield performance

of leguminous crops. The number of pods plant-1

ranged from 61.5 in control plot to 83.8 in treatment

having integration of NM + WM + PM practices. This may be attributed to better crop growth

environment along with less crop weed competition

in these treatments than control. The results confirm

the findings of Aggarwal and Ram (2011), Singh and

Singh (2014) and Singh et al. (2016).

Number of seeds pod-1 is another important factor

that is directly related in determining the yield of

leguminous crops. Basically this is a genetic

character but may also be affected by the

environmental conditions and agronomics practices.

The data regarding number of seeds pod-1 is given in

table 1 showed that all the crop management practices had significant effect on number of seeds

pod-1. The number of seeds pod-1 varied from 1.6 in

control plot to 2.1 in treatment having integration of

NM + WM + PM practices. The results are in

conformity with those obtained by Singh et al., 2017.

Weeds dry weight

Different treatments viz. WM, NM + WM, WM +

PM and NM + WM + PM had significant effect on

weeds dry weight compared to other crop

management treatments (Table 1). Weeds always

compete with crop for nutrient, water and light which significantly affect the growth and development of

crops and ultimately reduced the yield depending

upon the severity of the weeds. The treatment having

integration of NM + WM + PM being at par with

WM, NM + WM and WM + PM recorded least and

significantly lower weeds dry weight (31.1 kg ha-1)

over rest of the treatments. Highest weed control

efficiency (WCE) of 94.18% was recorded in

treatment having integration of NM + WM + PM

followed by WM + PM treatment. Herbicides

showed significant reduction in weed growth thereby

facilitated vigorous crop growth, increased photosynthesis and biomass accumulation and

ultimately helped to smother weeds resulted in higher

weed control efficiency (Awal and Roy, 2015).

Seed yield

Different crop management practices significantly

influenced the seed and straw yield of lentil (Table 2

& Fig.1). Integration of NM +WM + PM practices

being at par with that of NM + WM produced

significantly higher seed and straw yield compared to

all other treatments. The trend observed for yield

attributes perpetuated to build up the final outcome in terms of seed yield. Further, the nutrient

management also facilitated a greater economic sink

capacity as the yield had a highly significant

correlation with yield attributes (Kushwaha 1994). In

lentil, seed yield was most affected by nutrient

management (NM) treatment as a single factor

followed by weed management (WM) and pest

management (PM). The increase in seed yield due to

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 107

NM, WM and PM was recorded 28.71, 18.82 and

14.15 per cent over control (728 kg ha-1), while the

decrease in seed yield was 17.88, 24.19 and 27.17

per cent over full package (NM + WM + PM) i.e.

1141 kg ha-1

, respectively. Among the single

management practices, nutrient management (NM) recorded 28.71, 8.32 and 12.75 per cent improvement

in seed yield of lentil over control, weed

management (WM) and pest management (PM)

treatments, respectively. Among the combined

application of two treatments, NM + WM produced

8.4 and 19.6 % more seed yield over NM + PM and

WM + PM treatments, respectively. The increase

might be due to improved photosynthetic efficiency,

plant properties and better utilization of nutrients,

moisture, light and space (Kumari et al., 2012). The

increase in seed and straw yield due to integration is

a clear reflection of increase in growth and yield attributes as the integrated crop management helps in

better dry matter partitioning, increase net

photosynthetic and nitrate reductase activity. The

results are in conformity with the findings of Suresh

(2015). Integration of NM + WM + PM practices

recorded significantly 56.7 and 38.9 % higher seed

and straw yield over control. Crop performance was

poor in control plot thus the yield recorded per

hectare was lower than that obtained in other

treatments. All the crop management practices had

higher harvest index of lentil compared to control

plot. However, highest harvest index was recorded in treatment having integration of NM + WM + PM

followed by NM + WM practices. Similar results

were also reported by Yadav et al., 2013.

Economics

The economic studies of data revealed that

integration of NM + WM + PM practices produced

higher net returns (Rs13190/ha) and BC ratio (1.53)

over other crop management practices and control.

This is in conformity with the results obtained by

Singh et al., 2018. Thus, crop management practice

of involving NM + WM + PM was the most

remunerative for lentil. Among the single factor of production, NM (Nutrient Management): RDF

(20:40 kg NP ha-1) produced higher net returns

(Rs10700/ha) and BC ratio (1.51) over other single

crop management practices. Minimum net returns

(Rs5420/ha) and BC ratio (1.28) was recorded under

control due to poor crop yield.

Table 1. Growth, yield attributes and weeds dry weight of lentil as influenced by different crop management

practices Treatments Plant height

(cm)

Number of

branches

plant-1

Number of

pods

plant-1

Number of

seeds

pod-1

100 seed

weight (g)

Weeds dry

weight

(kg ha-1

)

Weed

control

efficiency

(%)

Control 46.6 4.2 61.5 1.6 1.8 534.9 -

Nutrient management (NM) 51.3 4.7 74.0 1.9 1.9 294.1 45.01

Weed management (WM) 47.3 4.6 70.8 1.8 1.9 51.9 90.29

Pest management (PM) 50.9 4.5 66.0 1.9 1.8 312.4 41.60

NM + WM 53.3 4.9 81.1 2.0 1.9 50.5 87.25

NM + PM 52.7 4.8 78.0 1.9 1.9 301.3 43.67

WM + PM 51.9 4.7 71.2 1.8 1.9 68.2 90.55

NM + WM + PM 54.2 5.1 83.8 2.1 1.9 31.1 94.18

CD (0.05)

5.1 0.5 8.5 0.2 NS 43.2

NM (Nutrient Management): RDF (20:40 kg NP ha-1), WM (Weed Management): Pendimethalin @ 1.0 kg ha-1

+ one hand weeding at 30 DAS), PM (Pest Management): spray of quinalphos 25 EC one litre per ha in 250-300

litres of water as and when required

108 S.K. SHARMA, RAKESH KUMAR AND PARVEEN KUMAR

Fig. 1: Effect of different treatments on seed and straw yield of lentil

Table 2. Seed, straw yield and economics of lentil as influenced by different crop management practices Treatments Seed

yield

(kg ha-1

)

Straw

yield

(kg ha-1

)

Seed yield

% increase

over control

Seed yield

%

decrease

over full

package

Harvest

index (%)

Cost of

cultivation

(Rs ha-1

)

Net

returns

(Rs ha-1

)

BC

ratio

Control 728 1954 - 36.20 27.14 19110 5420 1.28

Nutrient management

(NM)

937 2385 28.71 17.88 28.20 20690 10700 1.51

Weed management

(WM)

865 2219 18.82 24.19 28.04 21850 7190 1.33

Pest management

(PM)

831 2194 14.15 27.17 27.47 19960 7990 1.40

NM + WM 1087 2641 49.31 4.73 29.15 24170 12090 1.50

NM + PM 1003 2497 37.77 12.09 28.65 22910 10600 1.46

WM + PM 909 2316 24.86 20.33 28.18 23660 6790 1.28

NM + WM + PM 1141 2715 56.73 - 29.59 24810 13190 1.53

CD (0.05) 72 165

NM (Nutrient Management): RDF (20:40 kg NP ha-1), WM (Weed Management): Pendimethalin @ 1.0 kg ha-1

+ one hand weeding at 30 DAS), PM (Pest Management): spray of quinalphos 25 EC one litre per ha in 250-300

litres of water as and when required

CONCLUSION

It can be concluded that integration of NM (Nutrient

Management): RDF (20:40 kg NP ha-1) + WM

(Weed Management): Pendimethalin @ 1.0 kg ha-1 +

one hand weeding at 30 DAS) + PM (Pest

Management): spray of quinalphos 25 EC one litre

per ha in 250-300 litres of water as and when

required is beneficial in terms of crop productivity of

lentil.

REFERENCES

Aggrawal, N. and Ram, H. (2011). Effect of

nutrients and weed management on productivity of

lentil (Lens culinaris L.). Journal of Crop and Weed

7(2): 191-194.

Anonymous (2018). Source agricoop.nic.in.

Department of Agriculture, Cooperation and Farmer

Welfare, Government of India.

Awal, M.A. and Roy, A. (2015). Effect of weeding

on the growth and yield of three varieties of lentil

0

500

1000

1500

2000

2500

3000

3500

4000

4500

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seed yield

Treatments

Yie

ld (

kg

ha

-1)

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(Lens culinaris L.). American Journal of Food

Science and Nutrition Research 2(2): 26-31.

Fatima, K., Hussain, N., Pir, F.A. and Mehdi, M. (2013). Effect of nitrogen and phosphorus on growth

and yield of lentil (Lens culinaris). Applied Botany

57: 14323-14325. Kumari, A., Singh, O.N. and Kumar, R. (2012).

Effect of integrated nutrient management on growth,

seed yield and economics of field pea (Pisum

sativum L.) and soil fertility changes. Journal of

Food Legumes 25 (2): 121-124.

Kushwaha, B. L. (1994). Response of French bean

to nitrogen application in north Indian plains. Indian

Journal of Agronomy 39: 34-37.

Mortensen, D.A., Egan, J.F., Maxwell, B.D., Ryan,

M.R. and Smith, R.G. (2012). Navigating a critical

juncture for sustainable weed management.

Biological Science 62(1): 75-84.

Sharara, F., El-Shahawy, T. and El-Rokiek, K. (2011). Effect of benzoic acid combination on weeds,

seed yield and yield components of lentil (Lens

culinaris L.). Electronic Journal of Polish

Agricultural Universities 14: 1-2.

Singh, Charan, Singh, Virendra, Singh,

Satyabhan and Singh, Jodh Pal (2016). Effect of

integrated weed management in lentil (Lens culinaris

medikus) under irrigated conditions of Western Uttar

Pradesh. 4th International Agronomy Congress, Nov.

22-26, 2016: 382-384

Singh, D. and Singh, R. P. (2014). Effect of

integrated nutrient management on growth,

physiological parameters and productivity of lentil (Lens culinaris Medik.). International Journal of

Agricultural Science 10(1): 175-178.

Singh, G., Virk, H.K. and Khanna, V. (2017).

Integrated nutrient management for high productivity

and net returns in lentil (Lens culinaris). Journal of

Applied and Natural Science 9 (3): 1566-1572.

Singh, K.M., Kumar, M. and Choudhary, S.K. (2018). Effect of weed management practices on

growth and yield of lentil (Lens esculenta Moench).

International Journal of Current Microbiology and

Applied Sciences 7: 3290-3295.

Suresh (2015). Influence of integrated crop management practices on the performance of field

pea (Pisum sativum L.). M.Sc. Thesis submitted to G.

B. Pant University of Agriculture and Technology,

Pantnagar.

Yadav, R.B., Vivek, Singh, R.V. and Yadav, K.G.

(2013). Weed management in lentil. Indian Journal

of Weed Science 45(2): 113-115.

110 S.K. SHARMA, RAKESH KUMAR AND PARVEEN KUMAR

*Corresponding Author ________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 111-114. 2020

EFFECT OF TREATMENT IMPOSED ON TOTAL SOLUBLE PROTEIN

CONTENT IN WHEAT LEAVES INFECTED BY BROWN RUST (PUCCINIA

RECODITA F.SP. TRITICI ROB. EX. DESM.) AT KANPUR AND IARI REGIONAL

STATION WELLINGTON (T.N.).

Akash Tomar*, Ved Ratan , Javed Bahar Khan, Dushiyant Kumar, Devesh Nagar

and Sonika Pandey

Department of Plant Pathology, Chandra Shekhar Azad University of Agriculture & Technology Kanpur 208002 (U.P.) India

Email: atakshay343@gmail.com

Received-03.02.2020, Revised-23.02.2020

Abstract: In India, wheat (Triticum aestivum L.) is a staple food. Rust caused by. Puccinia Recondita f. sp. tritici Rob. ex. Desm. (Brown rust) is the most destructive and one of the most common diseases of wheat worldwide. It probably results in higher total annual losses worldwide because of its more frequent and widely distributed diseases of wheat in India and

elsewhere that affects its yield potential. Although, chemical control of these diseases is known but is not economic and environmental friendly to be used on a large scale. . The chemical changes in leaves due to infection of brown rust protein quantification were done by Lowry method. The soluble protein contents in treatment T16 (Soil treatment with Mycorrhiza (VAM) @ 5 gm / plot + Soil treatment with Trichoderma harzianum @ 5 gm / plot + Three spray with Propiconazole @ 25 EC 0.1 %) treated leaves were 0.37 mg/ml, followed by T1 (0.32 mg/ml) and T3 (0.28 mg/ml) which is the highest among all the treatments. Keywords: Soluble protein, Treatment, Brown rust, Wheat

INTRODUCTION

heat (Triticum aestivum L.) is one of the most

important food crops and is a staple food for

over one-third of the world's population. In Pre-

historic times, it was grown in ancient Persia, Egypt,

Greece, and Europe as early as 10,000 to 15,000 B.C.

and in China about 3000 B.C. From all possible

records, it seems that its center of origin in South-

Western Asia. It is believed that Aryans brought

wheat grains to India, and since then it has been

cultivated in India. The pieces of evidence from the ancient sites of

Jarmo in Eastern Iraq and the excavations of

Mohenjo-Daro in the Indian subcontinent indicate

that wheat was cultivated in India more than 5,000

years ago. Specific references are made to wheat in

"Atharva Veda" which is believed to have been

written around 15000 to 5,000 B.C. More of the

earth's surface is covered by wheat than with any

other food crop. Wheat is the third most-produced

cereal after maize and rice, but in terms of dietary

intake, it is currently second to rice as the main food crop, given the more extensive use of maize as an

animal feed. As a hardy crop, which can grow in a

wide range of environmental conditions and that

permits large-scale cultivation as well as long-term

storage of food, wheat has been key to the emergence

of city-based societies for millennia. India is the

second-largest producer of the wheat in the world

and is outranked only by China. But in terms of

productivity, India ranks 38 in the world. In India,

Wheat is the second most important cereal crop

occupying 52.8 percent of the total Rabi food grains,

and it ranks second in production and area after rice.

It covered about 30.50 million hectares area during

2016-17 with a record production of 98.38 million

tones with the productivity of 3216 kg/hectare.

MATERIALS AND METHODS

Total Soluble Protein Extraction

Total Soluble protein in wheat leaves infected by

brown rust was extracted by using method developed

by Goggin et al., (2011). Leaves from treated wheat

plants (approx. 500mg) were frozen by liquid nitrogen, grinding to a fine powder using mortar and

pestle then transferred to a fresh centrifuge tube. Two

ml of extraction buffer (Tris-HCl 1M, pH 8, EDTA,

0.25), SDS, 10%, glycerol, 50%) was added and

mixed well. The content of the tubes were centrifuge

at 12000 rpm for 20 min at 4°C. After centrifugation

process supernatant was discarded. Mixed the pellets

with 1ml of sample buffer (80% Acetone, 0.07% β-

mercaptoethanol and 2mM EDTA) and centrifuged

at 12000 rpm for 5 minutes. The process was

repeated until all chlorophyll removed. Mixed clear pellet with milli Q water and stored at -20°C. Protein

concentration of all the samples was determined

using Lowry et al., (1951) and Yurganova et al.,

(1989).

Protein Quantification

For quantification of protein content 1mg/ml of BSA

standard was used. Different dilutions of the standard

were made. To each tube of standard and sample 2ml

of complex forming reagent was added and kept for

10 minutes at room temperature. After 10 minutes of

incubation period, 0.2ml of Folin-Ciocalteu reagent

W

RESEARCH ARTICLE

112 AKASH TOMAR, VED RATAN , JAVED BAHAR KHAN, DUSHIYANT KUMAR, DEVESH NAGAR

AND SONIKA PANDEY

solution was added to each tube and incubated for

20-30 minutes at room temperature in dark. After

incubation period sample absorbance was taken at 660nm by using spectrophotometer (Bio-Rad).

Calibration curve was constructed by plotting

absorbance reading on Y axis against standard

protein concentration (mg/ml) on X axis. Sample

concentration was calculated using standard graph as

a reference.

Observations on total protein content in wheat plants treated with different concentrations was revealed

that treatment was found best field conditions at

Kanpur and IARI Regional Station

Wellington (T.N.), yielded highest protein

respectively.

Value of concentration and Absorbance with slandered graph.

Table 1. Total treatment with different combination

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 0.5 1

Ab

sorb

ance

at

660

Protein conc

Absorbance

concentration Absorbance

0

0

0.1

0.17

0.2

0.3

0.4

0.64

0.6

0.89

0.8

1.24

T1 Seed treatment with carbendazim @ 2 gm/ kg seed

T2 Seed treatment with Carbendazim @ 2 gm/ kg seed + Soil treatment with Trichoderma harzianum @ 5 gm

/ plot

T3 Seed treatment with Carbendazim @ 2 gm/ kg seed + Soil treatment with Mycorrhiza (VAM) @ 5 gm /

plot

T4 Seed treatment with Carbendazim @ 2 gm/ kg seed + Three sprey with Propiconazole @ 25 EC 0.1 %

T5 Seed treatment with Carbendazim @ 2 gm/ kg seed + Three sprey with Triadimefon @ 25 EC 0.1 %

T6 Seed treatment with Carbendazim @ 2 gm/ kg seed + Three sprey with Hexaconazole @ 25 EC 0.1

%

T7 Soil treatment with Trichoderma harzianum @ 5 gm / plot

T8 Soil treatment with Trichoderma harzianum @ 5 gm / plot + Three sprey with Propiconazole @ 25 EC

0.1 %

T9 Soil treatment with Trichoderma harzianum @ 5 gm / plot + Three sprey with Triadimefon @ 25

EC 0.1 %

T10 Soil treatment with Trichoderma harzianum @ 5 gm / plot Three sprey + with Hexaconazole @ 25

EC 0.1 %

T11 Soil treatment with Mycorrhiza (VAM) @ 5 gm / plot

T12 Soil treatment with Mycorrhiza (VAM) @ 5 gm / plot + Three sprey with Propiconazole @ 25 EC 0.1

%

T13 Soil treatment with Mycorrhiza (VAM) @ 5 gm / plot + Three sprey with Triadimefon @ 25 EC 0.1

%

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 113

RESULTS

Soluble Average protein concentration

The data presented in Table showed that the

soluble protein contents in treatment T16 treated

leaves were 0.37 mg/ml, followed by T1 (0.32

mg/ml) and T3 (0.28 mg/ml), T2 and T5 (0.25

mg/ml) which is the highest among all the

treatments. The soluble average protein contents of

control T0 was 0.06 mg/ml. The decrease protein in

infected leaves with comparison to treatment

imposed may be due to utilization of some protein by

the pathogen.

Table 2. Effect of Treatment Impose on total soluble protein content in Wheat leaves after Eighth week of

disease observation of Brown Rust in Kanpur and IARI regional station Wellington.

S.No. Treatments Protein concentration

mg/ml

Average

Kanpur Wellington

1. T1 0.30 0.34 0.32

2. T2 0.25 0.26 0.25

3. T3 0.30 0.27 0.28

4. T4 0.18 0.26 0.22

5. T5 0.21 0.29 0.25

6. T6 0.12 0.18 0.15

7. T7 0.11 0.32 0.21

8. T8 0.23 0.19 0.21

9. T9 0.16 0.20 0.18

10. T10 0.14 0.18 0.16

11. T11 0.20 0.23 0.21

12. T12 0.19 0.25 0.22

T14 Soil treatment with Mycorrhiza (VAM) @ 5 gm / plot + Three sprey with Hexaconazole @ 25 EC 0.1

%

T15 Soil treatment with Mycorrhiza (VAM) @ 5 gm / plot + Soil treatment with Trichoderma harzianum @ 5

gm / plot

T16 Soil treatment with Mycorrhiza (VAM) @ 5 gm / plot + Soil treatment with Trichoderma harzianum @

5 gm / plot + Three sprey with Propiconazole @ 25 EC 0.1 %

T17 Soil treatment with Mycorrhiza (VAM) @ 5 gm / plot + Soil treatment with Trichoderma harzianum @

5 gm / plot + Three sprey with Triadimefon @ 25 EC 0.1 %

T18 Soil treatment with Mycorrhiza (VAM) @ 5 gm / plot+ Soil treatment with Trichoderma harzianum @

5 gm/ plot + Three sprey with Hexaconazole @ 25 EC 0.1 %

T19 Three sprey with Propiconazole @ 25 EC 0.1 %

T20 Three sprey with Triadimefon @ 25 EC 0.1 %

T21 Three sprey with Hexaconazole @ 25 EC 0.1 %

T22 Three sprey with Propiconazole @ 25 EC 0.1 % + (F.Sp.) with Triadimefon @ 25 EC 0.1 % + (F.Sp.)

with Hexaconazole @ 25 EC 0.1 %

T0 Control

114 AKASH TOMAR, VED RATAN , JAVED BAHAR KHAN, DUSHIYANT KUMAR, DEVESH NAGAR

AND SONIKA PANDEY

13. T13 0.12 0.29 0.20

14. T14 0.15 0.17 0.16

15. T15 0.26 0.21 0.23

16. T16 0.30 0.44 0.37

17. T17 0.09 0.16 0.12

18. T18 0.10 0.16 0.13

19 T19 0.15 0.20 0.17

20 T20 0.14 0.20 0.17

21 T21 0.16 0.23 0.19

22 T22 0.17 0.26 0.21

23 T0 control 0.07 0.05 0.06

CD at 5% 0.026

DISCUSSION

The data presented in Table showed that the

soluble protein contents in treatment T16 treated

leaves were 0.37 mg/ml, followed by T1 (0.32

mg/ml) and T3 (0.28 mg/ml), T2 and T5 (0.25

mg/ml) which is the highest among all the treatments. The soluble average protein contents of

control T0 was 0.06 mg/ml. The decrease protein in

infected leaves with comparison to treatment

imposed may be due to utilization of some proteins.

The reduced disease incidence indicates that some

protein must be associated with induction of

resistance against the pathogen. Antoniew et al.

(1980) considered that pathogen related proteins (PR

protein) are involved in plant defense response to

pathogens. Boller (1985) was also of the opinion that

proteins are associated with defense of plants against

fungi and bacteria by their action on cell walls of invading pathogen. Most of antifungal proteins are in

the form of chitinase, PR-1, peroxides, -glycosidase etc. In the presence of defense response, synthesis of

protein related enzymes are enhanced and

accumulation of these antifungal elements causes

lysis of the cell wall of pathogens. Such results are in

agreement with Vannacci, G. et. al.(1987),

Vidhyasekaran, P. (1974).

REFERENCES

Antoniew, J.F., Ritter, E.F., Pierpoint, W.S. and

Van Loon, L.E. (1980). Comparison of three

pathogenesis-related proteins from plants of two

cultivars of tobacco infected with TMV. 1. General

Virology 47: 79-87.

Boller, T. (1985). 'Induction of hydrolases as a

defense reaction against pathogens. In: Cellular and

Molecular Biology of Plant stress. (Eds.). Key, J.L.

and Kosuge, T., UCLA Sym. on Molecular and Cellular Biology, New Series, Volume 22, Alan R.

Liss. Inc., New York. pp. 247- 262.

Goggin, D.E., Powel, S.B. and Steadman, K.J.

(2011). Selection for low or high primary dormancy

in Lolium rigidium gaud seeds results in constitutive

differences in stress protein expression and

peroxidase activity. Journal of Experimental

Botany. 62:1037-1047.

Lowry, O.H., Rosebrough, N.J., Farr, A.L. and

Randall, R.J. (1951). Protein measurement with the

folin phenol reagent. Journal of Biological

Chemistry. 193: 265-275. Vannacci, G. and Harman, G.E. (1987). Biocontrol

ofseed borne Alternaria rapani and A. brassicicola.

Can. 1. Microbiol. 33: 850-856.

Vidhyasekaran, P. (1974). Role of phenolics in leaf

spot incidence in ragi incited by Helminthosporium

tetramera. Indian Phytopath. 27: 583-586.

Yurganova, LA., Nogaideli, D.E., Chalova, L.I.,

Chalenko, G.I. and Ozeretskovskaya, O.L.(1989).

Activity of lipoxygenase in potato tubers after

immunization. Mikol. Fitopatolol. 23: 73-79.

*Corresponding Author

________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 115-118. 2020

STUDIES ON THE DIFFERENT SPECIES OF INSECT POLLINATORS/VISITORS

VISITING BUCKWHEAT FLOWERS

Jogindar Singh Manhare* and G.P. Painkra

*Department of Entomology, IGKV, RajMohini Devi College of Agriculture

Research Station, Ambikapur, Surguja 497001 Chhattisgarh, India Email: manharejogindar@gmail.com

Received-03.02.2020, Revised-23.02.2020

Abstract: Studies on the succession of various species of insect pollinators/visitor visiting on buckwheat flowers was undertaken at Research cum Instructional Farm of RMD CARS, Ajirma, Ambikapur (C.G.) of Indira Gandhi Krishi

Vishwavidyalaya Raipur during year 2016-2017. Total 10 species of insect pollinators/ visitors were found visiting on buckwheat flowers. Amongst the pollinators/visitors, Apis cerana indica appeared first on buckwheat flower followed by Apis florea, Danaus chrysippus, Eristalis sp., Apis dorsata, Musca domestica, Dysdercus cingulatus, Amata passelis, Chrysomya bezziana, Coccinella septumpunctata and Vespa cincta. They were found visiting on buckwheat flower throughout the bloomimg period.

Keywords: Buckwheat, Succession of insect pollinator/visitors

INTRODUCTION

uckwheat is the most important crop of the

mountain regions both for grain and greens. It

occupies about 90% of cultivated lands in the higher

Himalayas with a solid stand. It is a short duration

crop (2-3 months) and fits well in the high Himalayas

where a crops growing season is of limited period

because of early winter and snow fall. In the higher

Himalayas, up to 4500m, this is the only crop grown (Joshi and Paroda, 1991).

Buckwheat, Fygopyrum esculentum L. is an

important pseudocereal crop grown extensively in

the hilly areas of Northern Hill Zone of Chhattisgarh

specially at Mainpath block in Surguja district in

approximately 10-15 ha. Area is by the” Tibbati”

refuge people in the past 7-8 year. It is herbaceous

plant, grows upon a height of 3-4 meter. The

buckwheat plant is complete its life cycle in 90-115

days. The white flower heads of 2-3 cm develop in

the leaf axil. Buckwheat is cross pollinated and an entomophilic

plant. Honey bees are the major pollinators. The

cultivation of buckwheat along with bee keeping may

produce 40 to 60 kg of honey per hectare, due to

itsextended flowering period for more than 30 days

(Rajbhandari, 2010).

MATERIALS AND METHODS

The experiment was conducted at Research cum

Instructional Farm of RMD CARS, Ajirma,

Ambikapur of Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.) during rabi season

in year 2016-17. It was upland single plot keeping

plot size 10x10m, variety- Local spacing 20x10cm.

When the buckwheat crop started flowering different

honey bee spices were recorded starting from

0600hrs to 1800hrs at two hours intervals one square

meter area within five minutes early as well as peak

flowering period of crop.

RESULTS AND DISCUSSION

The finding of the present study of various insect

pollinators/visitors visiting buckwheat flower under

the following heads:

Indian honey bee (Apis cerana indica) The visit of Indian honey bee (Apis cerana indica) was observed from 4th week of November 2016 to

2nd week of January 2017. Their occurance was

gradually increased from 1st week of December 2016

(48.00 bees/5min/m2), 2nd week of December 2016

(57.14 bees/5min/m2) and it was reached its peak

population during 3rd week of December 2016 (70.14

bees/5min/m2),thereafter, its population was

decreased during 4th week of December 2016 (59.85

bees/5min/m2), 5th week of December 2016 (31.00

bees/5min/m2), and 1

st week of January 2017 (14.57

bees/5min/m2) , its population was again decreased during full flowering period (14.57 bees/5min/m2)

and last 2nd week of January 2017 population was

declined (8.14 bees/5min/m2). The mean population

was 40.82 bees/5min/m2.

These findings are in close agreement with earlier

reports of Neves (2008) he found missing out from

6.00 to 9.00 AM a period when the flower had 100%

visible pollen grains and 100% stigmatic

respectively. Ahmad and Srivastava (2002) reported

that among the eleven species of Hymenoptera as

pollen/nectar collectors, Apis cerana indica was

found most predominant pollen/nectar collectors on pigeon pea followed by A. dorsata, A. florea, A.

mellifera, Xylocopa fenestrata, Halictus viridisima,

Megachile femorata, Cressoniella relata,

Cressoniella carbonaria, Cressoniella anthracina

and Chalicodoma lanatum

B

RESEARCH ARTICLE

116 JOGINDAR SINGH MANHARE AND G.P. PAINKRA

Rock bee (Apis dorsata) The Rock bee ( Apis dorsata ) was observed during

4th week of November 2016 (27.42 bees/5min/m2) to

2nd week of January 2017 (5.57 bees/5min/m2) and

gradually increased during 1st week of December

2016 (35.85bees/5min/m2), 2nd week of December 2016 (39.71 bees/5min/m2) and then reached its peak

population during 3rd week of December 2016

(52.42bees/5min/m2), therefore, its population was

decreased during 4th week of December 2016 (46.00

bees/5min/m2), 5th week of December 2016 (9.14

bees/5min/m2), and 1st week of January 2017 its

population was more decreased during last flowering

period (7.71 bees/5min/m2) and last 2nd week of

January 2017 population was declined (5.57

bees/5min/m2). The mean population was 27.28

bees/5min/m2. These results are in close related with

that of Jadhav et al. (2010) recorded Apis dorsata as more frequent insects pollinators in hybrid sunflower

followed by Trigona iridipenis and Apis cerana

indica whereas Mohapatra et al. (2011) recorded on

mustard.

Little bee (Apis florea) The activity of little from 4th week of November

2016 (0.57 bees/5min/m2) to 2nd week of January

2017 (0.57 bees/5min/m2). There was first

appearance on 4th week of November 2016 (0.57

bees/5min/m2). The activity was increased during

starting week of December 2016 (1.42 bees/5min/m2) and 2nd week of December 2016 (1.42

bees/5min/m2). The maximum activity was recorded

during 3rd week of December 2016 (1.85

bees/5min/m2) and again increased during 5th week

of December 2016 (1.14 bees/5min/m2) and 1st week

of January 2017 the population was recorded 1.28

bees/5min/m2. The decreased activity was recorded

during the 4th Week of December 2016 (1.00

bees/5min/m2) and was very gradually decreased

during the 2nd week of January 2017(0.57

bees/5min/m2). The mean population was 1.15

bees/5min/m2. The finding are in close agreements with Mohapatra

et al. (2011) recorded that Apis cerana indica, Apis

dorsta and Apis florea, trigona iridipenis and

Bombus sp. on Indian mustard flowers. Nidagundi

and sattagi (2005) on bitter gourd and Rashmi et al.

(2010) recorded the Apis florea on pigeonpea.

Syrphid fly (Eristalis sp.) The major activity period of Eristslis sp. was

recorded during 4th week of November 2016 (5.85

syrphid fly/5min/m2) and then population was

decreased during 1st week of December 2016 (5.28 syrphid fly/5min/m2). Its peak activity was recorded

during 2nd week of December 2016 (6.57 syrphid

fly/5min/m2). The increased activity period was 3rd

week of December 2016 (5.57 syrphid fly/5min/m2)

and 5th week of December 2016 (5.57 syrphid

fly/5min/m2). The decreased activity was period of

4th week of December 2016 (5.57 syrphid

fly/5min/m2) and the last activity was recorded

during 2st week of January 2017 0.71 syrphid

fly/5min/m2. The mean population of syrphid fly was

4.69 syrphid fly/5min/m2.

Miller et al. (2013) who recorded the various

flowering plants have been shown to attract and

sustain populations of aphidophagous syrphidae in agriculture. Thapa (2006) recorded the syrphi fly on

broccoli, buckwheat, squash, sesamum, red gram,

rapeseed, radish, okra, mango and litchi. Phartiyal et

al. (2012) observed in citrus the syrphid flies were

the most frequency visitors including Syrphus

corolla, Episyrphus balteatus, Spherophoria spp. and

Melanostoma spp.

House fly (Musca domestics) The population of Musca domestica was noticed

from 4th week of November 2016 (3.00 house

flies/5min/m2) to 2nd week of January 2017 (0.42

house flies/5min/m2). The highest population was recorded during the period of 2nd December 2016

(2.85 house flies/5min/m2) and later the peak activity

period was recorded during the period of 5th week of

December 2016 (3.14 house flies/5min/m2).

Thereafter started declined during 1st week of

January 2016 (1.57 house flies/5min/m2) and lowest

activity was 2nd week of January 2017 (0.42 house

flies/5min/m2). The slightly increased during 1st

week of December 2016 (2.42 house flies/5min/m2)

and the slightly decreased during 3rd week of

December 2016 (2.14 house flies/5min/m2). The mean population of house flies ware 2.22 house

flies/5min/m2.

These are finding in closely related with on wahab et

al. (2011) who reported the house fly belonging to

order Diptera represented a higher number of insects

pollinators at 12 noon during the daily activity of

seed setting and yield production of black cumin.

Tiger moth (Amata passelis)

The population of tiger moth, Amata passelis was

recorded from 4th week of November 2016 (1.14

tiger moth/5min/m2). The peak activity was recorded

during 4th week of December 2016 (1.71 tiger moth/5min/m2) followed by 1st week of December

2016 (1.00 tiger moth/5min/m2) and 2nd week of

December 2016 (1.00 tiger moth/5min/m2) and then

increased activity during 3rd week of December 2016

(1.42 tiger moth/5min/m2). The population was

decreased during 5th week of December 2016 (0.71

tiger moth/5min/m2) and 2nd January 2017 (0.71 tiger

moth/5min/m2) and lower population was recorded

during 1st week of January 2017 (0.42 tiger

moth/5min/m2). The mean population was recorded

in (1.01 tiger moth/5min/m2) in weekly. Present results endorse the finding of Painkra et al.

(2015) recorded the Apis florea, Danaus chrysippus,

Eristalis sp., Pelopidas mathias, Apis dorsata, Musa

domestica, visited on niger crop.

Monarch butterfly (Danaus chrysippus) The activity period of monarch butterfly, Danaus

chrysippus was recorded during 4th week of

November 2016 (1.42 monarch butterfly/5min/m2)

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 117

and population was increased during 2nd week of

December 2016 (1.28 monarch butterfly/5min/m2),

1st week of January 2017 (1.42 monarch

butterfly/5min/m2). Its similar activity was recorded

during 4th

week of December 2016 (1.14 monarch

butterfly/5min/m2), 5th week of December 2016 (1.14 monarch butterfly/5min/m2) and the decreased

activity period was 1st week of December 2016 (0.85

monarch butterfly/5min/m2). The minimum activity

was recorded in 2nd week of January 2017 (0.57

monarch butterfly/5min/m2). The mean population of

was recorded 1.19 monarch butterfly/5min/m2 in

weekly.

The present results more or less similar with Dhakal

and Pandev (2003), who reported the butter flies,

visited the niger flowers throughout the flowering

span. Thakur and Mattu (2010) also reported as a

flower visitors and pollinators in Shiwalik Hills of Western Himalayas.

Red cotton bug (Dysdercus cingulatus) The population of Dysdercus cingulatus was

observed during 4th week of November 2016 (0.71

red cotton bug/5min/m2) and the peak activity was

recorded in 1st week of December 2016 (1.14 red

cotton bug/5min/m2). Whereas, during 2nd week of

December 2016 (1.00 red cotton bug/5min/m2), 3rd

week of December 2016 (1.00 red cotton

bug/5min/m2) and 1st week of January 2017 (1.00 red

cotton bug/5min/m2) in similarly. The activity was decreased during 4th week of December 2016 (0.71

red cotton bug/5min/m2) and 5th week of December

2016 (0.57 red cotton bug/5min/m2). Finally, it was

not appeared during 2nd week of January 2017 (0.00

red cotton bug/5min/m2). The mean weekly activity

period of red cotton bug was recorded 0.76 red

cotton bug/5min/m2. They early worker Thapa

(2006) had observed and reported that the red cotton

bug was visiting on radish flowers.

Lady bird beetle (Coccinella septumpunctata) The maximum activity of Coccinella septumpunctata

was recorded during the 4th week of November 2016 (2.57 lady bird beetle/5min/m2), similar activity was

recorded during 1st week of December 2016 (2.57

lady bird beetle/5min/m2). The peak activity was

appeared during 2nd week of December 2016 (3.85

lady bird beetle/5min/m2) and its decreased activity

was recorded during 3rd week of December 2016

(2.85 lady bird beetle/5min/m2) and 4th week of

December 2016 (2.57 lady bird beetle/5min/m2). Its

again increased activity was last week of December

2016 (3.28 lady bird beetle/5min/m2). Further, the activity was decreased during 1st week of January

2017 (2.28 lady bird beetle/5min/m2) and the finally,

the activity decreased during 2nd week of January

2017 (1.57 lady bird beetle/5min/m2). The weekly

mean activity of lady bird beetle was 2.69 lady bird

beetle/5min/m2.

Earlier reports support the observation by Viraktmath

et al. (2001) who recorded the relative abundance of

pollinator fauna of sesame during two successive

seasons. Mahfouz et al. studied on the total number

of pollinators was highest at 9-11am followed by that

at 11-1 pm, 1-3 pm and 3-5 pm. Sajjanaret et al. (2004) observed coccinella spp. visited more active

during morning hours when flower well opening.

Wahab et al. (2011) who also reported the lady bird

beetle as a visitor of black cumin.

Wasp (Vespa cincta) The population of Vespa cinta was observed from 4th

week of November 2016 (1.00 wasps/5min/m2) to 2nd

week of January 2017 (0.71 wasps/5min/m2). The

activity of Vespa cinta was recorded during 1st week

of December 2016 (1.42 wasps/5min/m2) and its

activity period was 2nd week of December 2016 (1.00 wasps/5min/m2). The population of wasps was

recorded from 3rd week of December 2016 (1.42

wasps/5min/m2) and then again it was decreased

during 4th week of December 2016 (1.28

wasps/5min/m2) and 5th week of December 2016

(1.14 wasps/5min/m2). The maximum activity was

found during 1st January 2017 (1.85 wasps/5min/m2)

and its last activity was recorded during 2nd week of

January 2017(0.71 wasps/5min/m2). The weekly

mean activity of wasp was recorded 1.23

wasps/5min/m2.

The present results are in line with findings of Jadhav et al. (2010), who recorded the wasp on sunflower, a

good visitor for nectar. Rashmi et al. (2010) who was

also observed the wasp on pigeon pea as a nectar

forager.

Table 1. The succession of various insect pollinators/visitors on buckwheat flowers during year 2016-17 S. No. Pollintors/

visitors

Scientific

name

Order Family I II III IV V VI VII VIII Mean

1. Indian honey

bee

Apis cerana

indica

Hymenoptera Apidae (37.71)

1st appear.

48.00 57.14 (70.14)

Peak

activity

59.85 31.00 14.57 8.14 40.82

2 Rock bee Apis dorsta Hymenoptera Apidae (27.42)

1st appear.

35.85 39.71 (52.42)

Peak

activity

46.00 9.14 7.71 5.57 27.28

3 Little bee Apis florae Hymenoptera Apidae (0.57)

1st appear

1.42 1.42 (1.85)

Peak

activity

1.00 1.14 1.28 0.57 1.15

4 Syrphid fly Eristalis sp. Diptera Syrphidae (5.85)

1st appear

5.28 (6.57)

Peak

activity

5.85 5.57 5.85 1.85 0.71 4.69

5 House fly Musca domestica Diptera Muscidae (3.14)

1st appear

peak

activity

2.42 2.85 2.14 2.28 (3.00)

1.57 0.42 2.22

118 JOGINDAR SINGH MANHARE AND G.P. PAINKRA

6 Tiger moth Amata passelis Lepidoptera Amatidae (1.14)

1st appear

1.00 1.00 1.42 (1.71)

Peak

activity

0.71 0.42 0.71 1.01

7 Monarch

butterfly

Danaus

chrysippus

Lepidoptera Danaidae (1.42)

1st appear

0.85 1.28 (1.71)

Peak

activity

1.14 1.14 1.42 0.57 1.19

8. Red cotton

bug

Dysdercus

cingulatus

Hemiptera Pyrrhocorid

ae

(0.71)

1st appear.

(1.14)

Peak

activity

1.00 1.00 0.71 0.57 1.00 0.00 0.76

9. Lady bird

beetle

Coccinalla

septumpunctata

Hemiptera Coccinellid

ae

(2.57)

1st appear.

2.57 (3.85)

Peak

activity

2.85 2.57 3.28 2.28 1.57 2.69

10. wasp Vespa cincta Hymenoptera Vespidae (1.00)

1st appear.

1.42 1.00 1.42 1.28 1.14 (1.85)

Peak

activity

0.71 0.85

CONCLUSION

It is concluded that different species of insect

pollinators/visitors visiting on buckwheat flower was

worked out. Total 10 species of pollinators/visitors

were recorded. Honey bee, Apis cerana indica and Apis dorsata is the most dominant among all the

pollinators/visitors. Other than pollinators/visitors

like Eristalis sp., Musca domestica, Amata passelis,

Danaus chrysippus, Dysdercus cingulatus,

Coccinalla septumpunctata and vespa cincta were

also found visiting on buckwheat flowers. The

activity of various insect pollinator/visitors on

buckwheat flowers are conducted during 0600, 0800,

1000, 1200, 1400 and 1800 hrs. during interval of

every two hours in experiment.

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Pollinator fauna and foraging activity of bees in

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Gupta, R. (2015). Succession of various

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G.T. (2010). Pollinators diversity with special

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Prabhuswamy, H.P. (2004). Insect visitors of

cucumber (Cucumis sativus L.) and the role of honey

bee Apis cerana F. in its pollination. Pest

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pollinators of cultivated plants. A Review, J. Inst.

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Guruprasad, G.S. (2001). Relative abundance of

pollinator fauna of cross-pollinated oilseed crops at

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*Corresponding Author ________________________________________________ Journal of Plant Development Sciences Vol. 12(2) : 119-121. 2020

SURVEY OF WHEAT CROP FOR THE PREVAILING BROW RUST (PUCCINIA

RECODITA F.SP. TRITCI ROB. EX. DESM.) IN DIFFERENT REGION OF

UTTAR PRADESH

Akash Tomar*, Ved Ratan , Javed Bahar Khan, Dushyant Kumar and Devesh Nagar

Department of Plant Pathology, Chandra Shekhar Azad University of Agriculture & Technology

Kanpur 208002 (U.P.) India Email: atakshay343@gmail.com

Received-03.02.2020, Revised-25.02.2020

Abstract: Uttar Pradesh is considered to be hot spot area for the development of leaf rust omplex. Thus, this study was carried out to investigate the distribution and intensity of wheat leaf rust, and to detect the virulence spectrum of Puccinia recondita f. sp. tritici Rob. ex. Desm during cropping season 2012-13. Survey programme were conducted in different wheat

growing area of Utter Pradesh and covers four regions basically Eastern U.P., Central U.P., Bundelkhand region and Western U.P. region. The data was collected on the basis of Global Cereal Rust Monitoring Form provided by BGRI (borlauge global rust initiative). In East U.P. region, district Lakhimpurkhiri brown rust traces were observed in village Katania (8-10 plants, severity upto 20S) on the cv. Sonalika. However in Paliakalannon brown rust were observed on date. At Golagokharnath leaf rust were recorded on cv. Lalbahadur with severity 10S. In the village Akbarpur of Kanpur Dehat (Central U.P. region) brown rust were observed on variety C-306, LOK1 at the disease severity of 30S. the brown rust were observed in farmer field Uin village in district Lucknow on variety Agra local, HD 2189 , rust severity from 20S -80S were recorded. Area near Unnao at village Atarsa brown rust observed on variety HD 3095, and farmer local varieties, severity

20S- 40S were recorded. In Jhansi, the district of Bundelkhand region only trace of Brown rust were observed in Agra local , C-306 and lok1 at farmer field villages Badanpur , Babina and Amarpur. Survey at Lalitpur area, variety Agra local, Lalbhadur and Lok 1 shows 30S-40S severity. Area near Banda district shows 40S-60S severity at farmer local variety. Survey during West U.P.region in the district Meerut, Muzaffarnagar, and Bijnor brown rust found in very low severity with very low incidence. In district Meerut, village Mihiwa, Mator and Kashampur shows 10S-20S severity on variety PBW 343, PBW 550, W -75. In district Muzaffarnagar variety PBW343, PBW 550 and PBW 373 shows 20S- 40S severity in village Hashampur, Bhuma, Ghatayan. In district Bijnor, village Kasopur, Khaikheda, and Salimpur shows symptoms of brown rust of wheat with severity 10S -20S. Key words: Brown rust, Puccinia recondita f. sp. tritici, Uttar Pradesh, Disease severity,

Disease incidence. Keywords: Survey, Crop, Brown rust, Wheat

INTRODUCTION

heat (Triticum aestvium L.) is among the

major cereal crops cultivated in Ethiopia. Ethiopia is the second largest producer of wheat in

sub-Saharan Africa. It was cultivated in about 1.5

million hectares of land with productivity of 1.75

tons per hectare (CSA, 2008/09). However, the

productivity is by far below the world’s average

yield/ha which is about 3.3 tones/ha. This low yield

is attributed to multi-faced abiotic and biotic factors

such as cultivation of unimproved low yielding

varieties, low and uneven distribution of rainfall,

poor agronomic practices, insect pests and serious

disease like rusts (Derje and Yaynu, 2000).

Rust fungal pathogens are among the major stresses that cause high yield losses in wheat crop. Over 30

fungal wheat diseases are identified in Ethiopia, stem

rust caused by Puccinia graminis f.sp. tritici (Pgt) is

one of the major production constraints in most

wheat growing areas of the country; causing yield

losses of up to 100% during epidemic years

(Belayneh and Emebet, 2005). Usually, new virulent

races of rust are considered to be found in East

Africa. Races prevalent in the central highlands of

Ethiopia are among the most virulent in the world

(Van Ginkel et al., 1989). Studies showed that most

of the previously identified races were virulent on

most of varieties grown in the country (Belayenh and

Embet, 2005). Hence, continuous surveying, and examining the

virulence composition and dynamics of the races in

the pathogen population is paramount important for

improvement of wheat (Admassu et al., 2009). This

study was, therefore, carried out to investigate the

distribution and intensity of wheat stem rust, and to

detect the virulence spectrum of P. graminis f.sp.

tritici in wheat growing areas of Eastern Showa of

Central Ethiopia.

MATERIALS AND METHODS

Survey, collection, isolation and identification of

pathogen (Puccinia recondite f.sp. tritici )

Survey and collection of desired materials

To find out the prevalence and severity of the brown

rust of wheat during the crop season 2012-2013. An

extensive survey for the occurrence and severity of

the disease was carried out in major wheat growing

areas of Uttar Pradesh and also in adjoining areas.

The survey was covers four region of Uttar Pradesh

namely Eastern U.P. region, Central U.P. region,

W

SHORT COMMUNICATION

120 AKASH TOMAR, VED RATAN , JAVED BAHAR KHAN, DUSHYANT KUMAR AND DEVESH NAGAR

Bundelkhand region and Western U.P. region

Naturally infected leaves of wheat showing the

characteristic symptoms of the brown rust were

collected and critically brought to the laboratory. All

the specimens were properly preserved, labeled and

kept at 10 C for further studies and records.

Procedure for recording diseased intensity

Fifty leaves were randomly picked up from infected

plant of different location during the survey. These

leaves were arranged in different categories on the

basis of leaf area infected. To calculate the average

percentage leaf area infected by the disease, using

modified cob scale method for diseased intensity in

percent. In order to determine the intensity of disease

in different fields a large number of leaves with

varying degree of infection were collected from

severely infected fields. The area of the leaf as well

as its total diseased area was determined with the represented in term of percentage of leaf area

infected. The disease severity was recorded on the

basis of modified cob scale method.

Disease severity scale

Visual percentage

(%)

Actual percentage (%)

5 1.85

10 3.70

20 7.40

40 14.80

60 22.20

100 37.00

Symptoms of the disease under natural conditions To study the symptoms of disease appeared on the

leaves, of naturally infected plants, were critically

examined and the size, shape and color of the postule

were noted along with the visual presence of the pathogenic structure. Symptoms produced Brown

rust of wheat was studied.

Leaves

The brown rust disease frequently occurred on wheat

every year in the vicinity of Uttar Pradesh. Diseased

foliage from different localities and varieties were

collected and studied for the association of the

fungus.

RESULTS

An extensive survey for the occurrence and severity

of the disease was carried out in major wheat

growing areas of Uttar Pradesh and also in adjoining

areas during the crop season 2012-2013. The survey

covered four region of Uttar Pradesh namely viz.,

Eastern U.P., Central U.P., Bundelkhand and

Western U.P. The survey data was collected on the

basis of Global Cereal Rust Monitoring Form

provided by BGRI (Borlaug Global Rust Initiative

and giving below in table.

Eastern U.P (Lakhimpur, Palia Kalan, Gola

Gokharnath )

During extensive survey in the district Lakhimpur

Kheri disease severity of brown rust was observed

traces to 20S in the village Katania (severity upto

20S) on the variety Sonalika. However in Palia Kalan

no brown rust was observed. During the survey in

Gola Gokarnath disease severity of leaf rust was recorded 10S on the variety Lal Bahadur.

Central U.P (Kanpur, Lucknow, Unnao).

During survey in Kanpur Dehat brown rust was

observed in the village Akbarpur on the verity C-306,

the severity was observed 30S to 40S. During the

survey in district Lucknow brown rust was observed

in the Uin village severity was ranges 20S -40S.

Adjoining areas near Unnao specially in Atarsa

village brown rust was observed on the variety HD

3095, with severity 20S-40S.

Bundelkhand ( Jhansi, Lalitpur, Banda)

During survey in Jhansi only trace of Brown rust were observed at farmers field in the villages

Badanpur, Babina and Amarpur. Survey near

Lalitpur area disease severity showed 30S-40S. Area

near Banda village showed 10S-20S severity at

farmers field.

Western U.P (Meerut, Muzaffarnagar, Bijnor)

Survey during west U.P area nearby Meerut,

Muzaffarnagar and Bijnor district brown rust was

found in very low severity. Survey during district

Meerut in the village Mihiwa, Mator and Kashampur

shows 10S-20S severity on variety PBW 343. Survey during district Muzaffarnagar variety PBW343, and

PBW 373 shows 20S- 40S severity in the village

Hashampur, Bhuma and Ghatayan. Survey during

district Bijnor in the village Kasopur, Khaikheda and

Salimpur brown rust of wheat was observed with

severity 10S -20S.

DISCUSSION

Uttar Pradesh is considered to be hot spot area for the

development of leaf rust complex. Thus, this study

was carried out to investigate the distribution and intensity of wheat leaf rust, and to detect the

virulence spectrum of Puccinia recondita f. sp. tritici

Rob. ex. Desm during cropping season 2012-13.

Survey programme were conducted in different

wheat growing area of Utter Pradesh and covers four

regions basically Eastern U.P., Central U.P.,

Bundelkhand region and Western U.P. region.. The

data were collected on the basis of Global Cereal

Rust Monitoring Form provided by BGRI (Borlauge

Global Rust Initiative). In East U.P. region, district

Lakhimpurkhiri brown rust traces were observed in village Katania (8-10 plants, severity upto 20S) on

the cv. Sonalika. However in Paliakalan brown rust

were observed on date. At Golagokharnath leaf rust

were recorded on cv. Lalbahadur with severity 10S.

In the village Akbarpur of Kanpur Dehat (Central

U.P. region) brown rust were observed on variety C-

306, LOK1 at the disease severity of 30S. the brown

rust were observed in farmers field Uin village in

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 121

district Lucknow on variety Agra local, HD 2189 ,

rust severity from 20S -80S were recorded. Area near

Unnao at village Atarsa brown rust observed on

variety HD 3095, and farmers local varieties, severity

20S-40S were recorded. In Jhansi, the district of

Bundelkhand region only trace of Brown rust were observed in Agra local , C-306 and lok1 at farmers

field villages Badanpur, Babina and Amarpur.

Survey at Lalitpur area, variety Agra local,

Lalbhadur and Lok 1 shows 30S-40S severity. A

similar finding was also given by (Nagarajan and

Joshi, 1975). Lemma, et al. (2014) were carried out

the survey and found similar results showed that 30

(38.9%) of the fields were affected with stem rust.

Area near Banda district shows 40S-60S severity at

farmers local variety. Survey during West U.P.

region in the district Meerut, Muzaffarnagar, and

Bijnor brown rust found in very low severity with very low incidence. In district Meerut, village

Mihiwa, Mator and Kashampur shows 10S-20S

severity on variety PBW 343, PBW 550, W -75. In

district Muzaffarnagar variety PBW343, PBW 550

and PBW 373 shows 20S- 40S severity in village

Hashampur, Bhuma, Ghatayan. In district Bijnor,

village Kasopur, Khaikheda, and Salimpur shows

symptoms of brown rust of wheat with severity 10S -

20S. A similar result was also reported by (Saari and

Wilcoxson, 1974) and (Mehta, 1940; Joshi et al.,

1972).

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122 AKASH TOMAR, VED RATAN , JAVED BAHAR KHAN, DUSHYANT KUMAR AND DEVESH NAGAR