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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: [email protected]
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
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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: [email protected]
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: [email protected]
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.
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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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Ph.D. thesis submitted to CCSHAU, Hisar
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Cabello, M.J. (2003). Influence of nitrogen fertilizer
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Influence of planting patterns and integrated nutrient
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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: [email protected]
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.
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JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(2) 97
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Singh, R., Kumar, S. and Kumar, S. (2014). Performance and Preference of Broccoli Varieties
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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.
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genotypes for growth yield and quality. International
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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: [email protected]
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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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: [email protected]
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
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500
1000
1500
2000
2500
3000
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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).
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seed yield and economics of field pea (Pisum
sativum L.) and soil fertility changes. Journal of
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Kushwaha, B. L. (1994). Response of French bean
to nitrogen application in north Indian plains. Indian
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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
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Singh, G., Virk, H.K. and Khanna, V. (2017).
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Applied and Natural Science 9 (3): 1566-1572.
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growth and yield of lentil (Lens esculenta Moench).
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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,
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Yadav, R.B., Vivek, Singh, R.V. and Yadav, K.G.
(2013). Weed management in lentil. Indian Journal
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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: [email protected]
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: [email protected]
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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*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: [email protected]
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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