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Coconut based integrated farming: A climate-smart model for food security
and economic prosperity
B. Sudha, Jacob John, A.V. Meera, A. Sajeena, D. Jacob and J.S. Bindhu 104
Variability of exotic ginger (Zingiber officinale Rosc.) accessions for quality parameters
P. Anisha Babu, N.K. Leela, J. Venkatesh and D. Prasath 111
Performance of coconut (Cocos nucifera L.) hybrids for yield and quality in the
Utkal plain region of Odisha state, India
S.C. Sahoo, S. Sumitha, A.K. Karna, G. Mishra and H.P. Maheswarappa 121
Incorporation of coconut milk residue in pasta: Influence on cooking quality,
sensory and physical properties
P.P. Shameena Beegum, Monika Sharma, M.R. Manikantan, R. Pandiselvam and R.K. Gupta 128
Short Scientific Reports
Moisture content and water activity of arecanut samples:
A need to revisit storage guidelines
K.B. Hebbar, Sugatha Padmanabhan, S.V. Ramesh, S. Keshav Bhat,
P. P. Shameena Beegum, R. Pandiselvam, M.R. Manikantan and A.C. Mathew 136
Relationship between concentration of ATP in latex and yield potential in
seedlings of Hevea brasiliensis and its implications in breeding
C. Narayanan and S. Sreelatha 142
Soil organic carbon stocks (SOCS) in different land uses of Western Ghats,
Karnataka - A case study
B. Kalaiselvi, Rajendra Hegde, K.S. Anil Kumar, R. Vasundhara, S. Dharumarajan,
R. Srinivasan, M. Lalitha and S.K. Singh 146
Continued on back cover
Continued from front cover
Research Articles
Early evaluation of cocoa (Theobroma cacao L.) clones as intercrop
in coconut gardens under red river region of Assam state, India
R.M. Phukon, J.C. Nath, S. Sumitha, H.P. Maheswarappa and Elain Apshara 77
Soil fertility status of coconut and arecanut growing soils
R. Vasundhara, N.B. Prakash, K.S. Anil Kumar, Rajendra Hegde and S. Dharumarajan 83
Characterization and classification of major coconut growing soils
in South Eastern Ghats of Tamil Nadu, India
R. Srinivasan, K.S. Anil Kumar, M. Chandrakala, K.V. Niranjana, N. Maddileti and Rajendra Hegde 94
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Ravigadevi Sambanthamurthi
Malaysian Palm Oil Board, Malaysia
Lalith Perera
Coconut Research Institute, Sri Lanka
Meilina Ong Abdullah
Malaysian Palm Oil Board, Malaysia
Babu Valliyodan
University of Missouri, USA
Heru Suryaningtyas
Rubber Research Institute, Indonesia
Haeng-hoon Kim
Sunchon National University, Korea
Napawan Lekawipat
Rubber Research Institute, Thailand
ErIene Manohar
Philippine Coconut Authority, The Philippines
Kelvin Lamin
Malaysian Cocoa Board, Malaysia
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Sabah Agricultural Research Station, Malaysia
Do Kim Thanh
Rubber Research Institute, Vietnam
M. C. Ramon A. Castillo Gonzalez
INIFAP, Mexico
Smilja Lambert
Mars Australia, Australia
i
Research Articles
Early evaluation of cocoa (Theobroma cacao L.) clones as intercrop
in coconut gardens under red river region of Assam state, India
R.M. Phukon, J.C. Nath, S. Sumitha, H.P. Maheswarappa and Elain Apshara 77
Soil fertility status of coconut and arecanut growing soils
R. Vasundhara, N.B. Prakash, K.S. Anil Kumar, Rajendra Hegde and S. Dharumarajan 83
Characterization and classification of major coconut growing soils
in South Eastern Ghats of Tamil Nadu, India
R. Srinivasan, K.S. Anil Kumar, M. Chandrakala, K.V. Niranjana,
N. Maddileti and Rajendra Hegde 94
Coconut based integrated farming: A climate-smart model for food security
and economic prosperity
B. Sudha, Jacob John, A.V. Meera, A. Sajeena, D. Jacob and J.S. Bindhu 104
Variability of exotic ginger (Zingiber officinale Rosc.) accessions for quality parameters
P. Anisha Babu, N.K. Leela, J. Venkatesh and D. Prasath 111
Performance of coconut (Cocos nucifera L.) hybrids for yield and quality in the
Utkal plain region of Odisha state, India
S.C. Sahoo, S. Sumitha, A.K. Karna, G. Mishra and H.P. Maheswarappa 121
Incorporation of coconut milk residue in pasta: Influence on cooking quality,
sensory and physical properties
P.P. Shameena Beegum, Monika Sharma, M.R. Manikantan, R. Pandiselvam
and R.K. Gupta 128
Short Scientific Reports
Moisture content and water activity of arecanut samples:
A need to revisit storage guidelines
K.B. Hebbar, Sugatha Padmanabhan, S.V. Ramesh, S. Keshav Bhat,
P.P. Shameena Beegum, R. Pandiselvam, M.R. Manikantan and A.C. Mathew 136
Relationship between concentration of ATP in latex and yield potential in
seedlings of Hevea brasiliensis and its implications in breeding
C. Narayanan and S. Sreelatha 142
Soil organic carbon stocks (SOCS) in different land uses of Western Ghats,
Karnataka - A case study
B. Kalaiselvi, Rajendra Hegde, K.S. Anil Kumar, R. Vasundhara, S. Dharumarajan,
R. Srinivasan, M. Lalitha and S.K. Singh 146
Journal of Plantation Crops
Vol. 49 No. 2 August 2021
CONTENTS
77
Early evaluation of cocoa (Theobroma cacao L.) clones as
intercrop in coconut gardens under red river region of
Assam state, India
R. M. Phukon, J.C. Nath*, S. Sumitha1, H. P. Maheswarappa1 and Elain Apshara2
AICRP on Palms, HRS, Kahikuchi, Guwahati-781 017, Assam, India
1ICAR-Central Plantation Crops Research Institute, Kasaragod-671 124, Kerala, India
2ICAR-Central Plantation Crops Research Institute, Regional Station, Vittal-574 243, Karnataka, India
(Manuscript Received: 05-10-2020, Revised:19-04-2021, Accepted:28-05-2021)
Abstract
A field experiment was conducted at Horticultural Research Station (AICRP on Palms), Kahikuchi of Assam Agricultural University
for three consecutive years (2018-20) to study the initial performance of cocoa clones as intercrop in adult coconut (Cocos
nucifera L.) garden spaced at 7.5 m × 7.5 m taking 16 cocoa clones. Data on tree height, stem girth, number of pods, pod weight,
number of beans pod-1, dry bean weight tree-1 year-1 and single dry bean weight revealed significant variations among the 16 cocoa
clones. Dried beans are the prime economic produce of cocoa, and single dry bean weight ranged from 0.95 to 1.4 g. With respect
to dry bean yield tree-1, VTLC-20 recorded a significantly higher dry bean yield of 2.2 kg tree-1 followed by VTLC-18 (1.5 kg tree-1)
compared to other cocoa clones, and eight clones yielded more than 1.0 kg dry bean tree-1. Results showed that the number of
leaves on the crown, number of inflorescences per palm and nut yield of coconut were not negatively affected; rather, there was
an increase in the yield of coconut over the period.
Keywords: Assam, cocoa, coconut, dry bean, intercrop, VTLC clones
doi:10.25081/jpc.2021.v49.i2.6636
Journal of Plantation Crops, 2021, 49(2): 77-82
Research Article
*Corresponding Author: jogeshn2001@yahoo.co.in
Introduction
India’s share in global cocoa production is
negligible; however, the crop has tremendous
potential to develop as a monetary crop. The
increasing domestic demand for chocolate industries
should also drive for the area expansion and
production of cocoa. India produced 19,866 tonnes
of cocoa beans from 88,515 hectares with a
productivity of 580 kg ha-1 in the year 2020 (DCCD,
2020). Cocoa is grown primarily as an intercrop
(under palms) in Andhra Pradesh, Karnataka, Kerala
and Tamil Nadu, as the crop needs about 40-50 per
cent shade. More than two-thirds of cocoa are grown
in coconut (Thomas et al., 2010) and around a fifth
with arecanut (Sujatha et al., 2011), followed by
oil palm (Bhagya and Suresh, 2018) and rubber
(Rubber Board, 2011) plantations. Of late, Assam
has became the latest state in the country to start
cocoa cultivation, hitherto confined to southern
states, with the availability of around one lakh
hectares under coconut and arecanut gardens.
Brahmaputra valley or red river region of Assam,
has topographical proximity to the Equator, which
means it has quintessential climatic conditions for
cocoa cultivation. Cocoa entries screened for yield
and quality were planted across AICRP on Palms
centres to evaluate their performance under coconut
and oil palm-based cropping systems
(Maheswarappa et al., 2019). Coconut is an
important perennial oilseed crop of Assam, mostly
grown in the homestead garden as monocropping.
The structure and orientation of the adult coconut
78
canopy permit about 55 per cent active radiation to
penetrate down (Nelliat, 1979) and the gardens
spaced at 7.5 m × 7.5 m offer wide purview for
intercropping with suitable, biennial, seasonal and
perennial crops, including cocoa, leading to a
sustainable increase in the production and
productivity per unit area (Nath et al., 2019). The
increasing demand for cocoa in India has behoved
many high yielding hybrids/varieties with resistance
to biotic and abiotic stresses. Northeastern India is
now all set to embark on massive cocoa cultivation;
in Assam, evaluation of yield performance and yield
stability of the cocoa clones is yet to be undertaken.
Hence, a field experiment was carried out to identify
suitable cocoa clones for intercropping in coconut
gardens under Assam conditions.
Materials and methods
A field experiment was conducted in a 48-year-
old Assam Green Tall coconut garden at the
Horticultural Research Station (AICRP on Palms),
Kahikuchi, Guwahati, situated at 26.300 N latitude
91.70 E longitude with an altitude of 64 m above
MSL. The station enjoys a sub-tropical climate, with
an annual rainfall of about 1500 mm. The soil of
the experimental site was alluvial clay-loam with a
pH of 4.8, low in available nitrogen (231.2 kg ha-1),
medium in available phosphorus (23 kg ha-1) and
available potassium (160.4 kg ha-1) with an organic
carbon of 0.45 per cent. Sixteen cocoa clones viz.,
VTLC-13, VTLC-15, VTLC-17, VTLC-18, VTLC-20,
VTLC-23, VTLC-25, VTLC-28, VTLC-36, VTLC-38,
VTLC-39, VTLC-40, VTLC-128, VTLC-4A, EYT,
and VTLCH-1 (Hybrid) were received from ICAR-
Central Plantation Crops Research Institute,
Regional Station, Vittal during 2014-15 and planted
at a distance of 3.0 m x 7.5 m in a single hedge
system under 7.5 m x 7.5 m spaced coconut garden
with two replications in a randomized block design.
The package of practices recommended by ICAR-
CPCRI, Kasaragod were followed. Pruning was
regularly done in the clones wherein unproductive
chupons arising from the main trunk and fan
branches were removed before the monsoon onset.
Growth characters such as tree height, stem
girth, height at first branching and canopy volume
were measured from representative cocoa trees.
Table 1. Growth characters of VTLC clones of cocoa as intercrop in coconut garden
Cocoa clone Tree height Stem girth Height at first Canopy spread N-S Area πrl
(m) (cm) branching (cm) E-W (m) (m) (m2)
VTLC-13 1.95 ef 31.0cd 0.5 b1.7 fg 1.8 fgh 4.54 h
VTLC-15 2.17 bc 32.6b0.5 b2.2 b2.1 d6.51 e
VTLC-17 2.21 ab 33.0b0.4 c2.0 c2.1 d6.66 de
VTLC-18 2.24 ab 33.8b0.6 a2.3 b2.4 b7.37 b
VTLC-20 2.28 a36.0a0.6 a2.5 a2.6 a8.44 a
VTLC-23 2.06 cd 30.5cde 0.5 b1.7 ef 1.8 efg 5.50 fg
VTLC-25 2.08 de 29.6efg 0.4 c1.8 ef 1.9 ef 5.62 g
VTLC-28 1.78 g29.0fg 0.4 c1.6 gh 1.8 fgh 4.15 i
VTLC-36 1.75 g27.4gh 0.6 a1.7 ef 1.6 i4.30 hi
VTLC-38 1.89 f28.3fg 0.6 a1.8 de 1.9 e4.38 hi
VTLC-39 1.99 def 28.0fg 0.5 b2.2 b2.3 bc 6.98 cd
VTLC-40 1.96 def 31.6bc 0.4 c1.9 cd 2.2 cd 7.02 bc
VTLC-128 1.76 g27.0gh 0.6 a2.0 c2.1 d5.84 f
VTLC-4A 1.78 g27.8gh 0.5 b1.6 gh 1.7 hi 3.77 j
VTLCH-1 1.88 ef 29.0def 0.6 a1.7 ef 1.8 efg 4.10 hi
EYT 1.68 g25.7h0.5 b1.5 h1.7 ghi 3.42 j
CD(P=0.05) 0.12 1.82 NS 0.23 0.22 0.34
CV % 2.87 2.82 2.89 2.86 2.84 2.93
Values followed by the same alphabet in a column show non-significant differences at 0.05 level
Phukon et al.
79
The canopy area was calculated using the formula
πrl, where, r = (EW + NS)/4 and l = √r2 + h2, h =
canopy height.
The pod yield of individual trees in each clone
during each harvest was recorded and given as
average pod yield tree-1 year-1 for three years after
yield stabilization (2018-2020). Pod characteristics
such as individual pod weight and bean number per
pod were measured from five pods of each tree.
Beans were fermented, dried and single dry bean
weight (SBW) and dry bean yield (DBY) tree-1 year-1
was recorded. The data collected regarding various
parameters of growth and yield traits were analysed
statistically as described by Panse and Sukhatme
(1985).
Results and discussion
Tree characters
As cocoa has a cauliflorous bearing habit, the
tree height and stem girth are correlated with plant
vigour, which directly relates to bearing. In the present
study, clonal differences existed for tree height and
stem girth (Table 1). The trees of VTLC-128, VTLC-28,
VTLC-36, VTLC-4A and EYT showed short stature
with height ranging from 1.68 m to 1.78 m, while
the trees of VTLC-20, VTLC-17 and VTLC-18 had
a height of around 2.28 m. A few taller trees were
observed in eight other clones with more than 2 m in
height. The trunk circumference differed significantly
among the clones. In the case of height at first
branching, it was found to be non-significant among
the clones. Significantly the maximum canopy area
Table 2. Pod and yield characteristics of VTLC clones of
cocoa as intercrop in coconut garden
Cocoa Weight of Single dry Dry bean
clone pod bean weight yield
(g) (g) (kg tree-1)
VTLC-13 397.0 cde 1.1 c1.2 d
VTLC-15 391.4 cde 1.1 c1.1 e
VTLC-17 407.5 cd 1.2 b1.3 c
VTLC-18 462.0 b1.2 b1.5 b
VTLC-20 482.0 a1.4 a2.2 a
VTLC-23 376.0 ef 1.2 b1.0 f
VTLC-25 366.5 fg 1.1 c0.86h
VTLC-28 350.0 gh 1.1 c0.91gh
VTLC-36 370.4 ef 0.95d1.0 f
VTLC-38 330.0 hi 1.0 d0.87h
VTLC-39 315.0 i0.96d0.97fg
VTLC-40 387.0 de 0.97d1.0 f
VTLC128 412.0 c1.0 d0.97f
VTLC-4A 397.0 cde 1.1 c0.91gh
VTLCH-1 387.6 cde 1.1 c1.0 f
EYT 310.6 i0.98d0.58i
CD(P=0.05) 23.8 0.06 0.07
CV% 2.88 2.90 3.18
Values followed by the same alphabet in a column show non-
significant differences at 0.05 level
Fig. 1. Number of pods tree-1 and numbers of beans pod-1 of cocoa clones
Performance of cocoa clones in coconut garden
No of pods tree-1
No of beans pod-1
80
was recorded in cocoa clone VTLC-20 (8.44 m 2).
In general, a cocoa tree with good stem girth, more
numbers of fan branches and canopy spread reflects
the vigour of the trees, indirectly favouring higher
yield. The differences in the tree height and stem
girth of cocoa have been attributed to various factors
such as soil moisture, availability of nutrients,
environmental factors and genetic factors of the tree.
Similar differences in the tree height and stem girth
of clones and hybrids have been noted by Apshara
et al. (2009), Thondaiman et al. (2013), Sumitha
et al. (2018) and Bhalerao et al. (2018). A small
canopy with a 3.42 m2 spread was observed in the
clone EYT, whereas a voluminous canopy (8.44 m2)
was observed in the clone VTLC- 20. Pruning is an
important operation in cocoa, especially when it is
grown as an intercrop. Maintenance of optimum
canopy shape involves pruning certain branches as
it is known in cocoa that highly shaded leaves do
not show photosynthetic activity and penetration of
sunlight to stimulate cocoa flowering and fruit
setting (Uchoi et al., 2018).
Pod, bean and yield characters
In general, cocoa yield is determined by yield
contributing traits such as the number of pods, dry
bean yield tree-1 year-1 and pod value (Thondaiman
et al., 2013). The number of pods tree-1 year-1
showed significant variation among the cocoa clones
(Fig. 1), and VTLC-20 recorded the highest number
of pods tree-1 year-1 (38.0) followed by VTLC-40
(33.4), whereas the clone VTLCH-1 recorded the
lowest number of pods tree-1 year-1 (22.0). In respect
to pod characters, the pod weight and the number
of beans pod-1 were significantly different among the
cocoa clones with higher pod weight and the number
of beans pod-1 observed in VTLC-20 (482.0 g and
42.0 respectively) followed by VTLCH-18 (462.0 g
and 38.1 respectively) (Table 2 and Fig. 1). The
number of beans decides the total weight of beans
per pod. Glendinning (1963) observed that if a pod
was too small for the inherent number of beans to
be present at their inherent size, the number of beans
was reduced so that their inherent size was attained;
if the pod was larger than that required for the full
*Values followed by a same alphabet show non-significant differences between clones at 0.05 level
Fig. 2. Single dry bean weight (g) of cocoa clones
Table 3. Growth and yield of coconut in the experimental garden
Parameters Pre-treatment Experimental period
observations
(2010-11 to 2012-13) 2017-18 2018-19 2019-2020
No. of functional leaves 29.8 29.5 30.1 31.0
No. inflorescence palm-1 11.5 11.0 12.1 11.9
Nut yield palm-1 year-1 55.4 61.3 63.0 65.8
Nut yield ha-1 year-1 9709 10734 11025 11515
Phukon et al.
Single dry bean wt. (g)
81
number of beans to attain their inherent size, the
beans might increase in size above the inherent
value. It is essential to select cocoa genotypes with
less than 35 beans per pod to utilize the clones in
crop improvement or commercial plantations
(Sumitha et al., 2018).
As beans are the economic part of the cocoa
crop, in the present study, single dry bean weight
was observed from 0.95 g to 1.4 g (Fig. 2). Among
clones, VTLC-20 recorded a significantly higher dry
bean yield of 2.2 kg tree-1 followed by VTLC-18
(1.5 kg tree-1) than other cocoa clones. In total, eight
clones recorded more than 1 kg dry bean yield tree-1.
Enriquez and Soria (1968) studied the variability
in bean size, shell per cent and the number of beans
per pod in cocoa clones of different genetic origins
and found significant differences. The dry bean
recovery was significantly influenced by the number
of beans pod-1, bean weight (fresh & dry), single
bean weight (fresh & dry) and fermentation method.
A similar phenomenon was observed by
Thondaiman et al. (2013), Minimol et al. (2016),
Sajeevkumar et al. (2017) and Vithya et al. (2018).
Growth and yield of coconut
The mean data of three years from 2017-18 to
2019-20 showed that the number of leaves on the
crown, number of inflorescence and nut yield palm-1
did not differ significantly in the coconut
intercropped with cocoa clones (Table 3). This
indicated that cocoa clones could be grown as
intercrops in coconut without affecting the yield of
coconut. However, an increase in nut yield from
55 nuts palm-1 year-1 to 65 nuts palm-1 year-1 was
observed. The favorable microclimatic conditions
due to intercropping combined with improved
microbial activities and soil fertility status, which
in turn have increased the coconut yield. The
enhancement in nut yield of the coconut by
intercropping is also supported by the findings of
Bhalerao et al. (2018) and Nath et al. (2019).
Conclusion
Based on growth and yield parameters assessed
in the present study, it is indisputable that the
performance of all the clones of cocoa in terms of
yield is on par in the juvenile phase of the
establishment (six years after planting). However,
a continuous evaluation is required to assess their
yield potential and quality parameters when grown
as intercrop in coconut gardens under Assam.
Acknowledgement
The first and second authors are highly thankful
to ICAR-All India Coordinated Research Project
on Palms and Director of Research (Agriculture),
AAU, Jorhat, for the financial support provided
during the study period.
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Phukon et al.
83
Introduction
Arecanut (Areca catechu L.) and coconut
(Cocos nucifera L.) are predominant perennial
plantation crops in south India. Cultivation of these
palms in India also has a rich diversity and varied
history, with each crop having its own distinct
historical and economic context of development.
Karnataka is one of the major producers of
plantation crops. Coconut and arecanut are
cultivated in different agro-climatic regions in
Karnataka (Singh et al., 2013) under diverse climatic
conditions and soil types. Due to variation in
environmental conditions and management, the
productivity of these crops also varies under
different agro-climatic zones (ACZs). Soil fertility
is one factor that limits the productivity of these
crops, mainly influenced by the inherent capacity
of the soil. The factors which influence soil fertility
are mineral composition, soil pH, soil texture,
organic matter and cation exchange capacity (CEC).
To understand palm productivity in a particular
region or climate, an understanding of soil fertility
status is essential. Maintaining soil fertility is highly
important to sustain the yield of arecanut (Bhat and
Sujatha, 2014).
Knowing the inherent capacity of soils to
provide nutrients is an important crop nutrient
prerequisite to decide the extent of organic residues
required, i.e., the manures and fertilizers to be
applied per palm to obtain a better yield. Hence,
the present study was undertaken to understand the
fertility levels of soils in arecanut and coconut
growing areas under different agro-climatic
conditions of Karnataka to deliver soil quality
information to achieve sustainable yields. This
information will also guide the balanced use of
Soil fertility status of coconut and arecanut growing soils
R. Vasundhara*, N.B. Prakash1, K.S. Anil Kumar, Rajendra Hegde and S. Dharumarajan
ICAR-National Bureau of Soil Survey and Land Use Planning, Bengaluru-560 024, Karnataka, India
1University of Agricultural Sciences, Bengaluru-560 065, Karnataka, India
(Manuscript Received: 24-08-2020, Revised: 20-04-2021, Accepted: 28-05-2021)
Abstract
The present study was undertaken to assess the soil fertility status of major coconut and arecanut growing soils in different
agro-climatic conditions of Karnataka state, India. Based on the agro-climate variability, 30 typical soil pedons representing five
different agro-climatic zones (ACZs) of Karnataka, namely, eastern dry zone (EDZ), southern dry zone (SDZ), southern transitional
zone (STZ), hilly zone (HZ), and coastal zone (CZ), were studied for their physicochemical properties. The study revealed that
soils of semi-arid (EDZ and SDZ) and sub-humid (STZ) zones have near neutral to moderately alkaline reaction and humid
region soils (coastal and hilly zones) have high acidity. The soils are non-saline with low cation exchange capacity. Greater soil
organic carbon was recorded in arecanut soils than coconut under all ACZs except the coastal zone. The major nutrients status of
the soil samples indicated that the available nitrogen is low in all the pedons; the pooled data of available nitrogen content was
higher in arecanut (166.3 kg ha-1) than coconut (152.6 kg ha-1), and hilly zone soils recorded higher available nitrogen. A wide
range of available P2O5 was noticed in coconut and arecanut soils, ranging from 1.0 to 64.2 kg ha-1. The coconut soils (11.5 kg ha-1)
recorded higher available P2O5 than arecanut soils (9.62 kg ha-1) when data were pooled. The soils were low to medium in
available potassium, and a higher available K2O content was recorded in arecanut soils (151.7 kg ha-1) compared to coconut
(110.1 kg ha-1). The available K2O ranged from 66.8 to 511.7 kg ha-1 in the surface and 37.6 to 461.2 kg ha-1 in sub-surface soils.
Keywords: Arecanut, agro-climatic zone, coconut, major nutrients, pedons, soil fertility
doi:10.25081/jpc.2021.v49.i2.7254
Journal of Plantation Crops, 2021, 49(2): 83-93
Research Article
*Corresponding Author: vasundharagowda@gmail.com
84
fertilizer and implementation of cropping pattern
as a component crop in plantation-based systems
under varied climatic conditions.
Materials and methods
Soil profiles were excavated in coconut and
arecanut plantations covering five different agro-
climatic zones (ACZs), with varying rainfall,
topography, soil type and climatic characteristics,
including cropping patterns. The study location
represents five ACZs, covering major production
centres of arecanut and coconut plantations of
Karnataka. Three profiles from each of these five
ACZs were selected by recording GPS points (Fig. 1)
for the study, and the location details are mentioned
in Table 1. The soil profiles were dug to a depth of
120 cm. The site for sampling was representative
of the area. The soil profiles were examined by
demarcating different horizons based on colour,
texture and structure. The details such as depth,
colour, texture and structure were recorded in
standard proforma for soil profile description. The
list of profile samples collected from different ACZs
and the details are presented in Table 1.
One hundred seventy-six soil samples
representing 30 soil profiles were drawn at different
depths depending upon horizon distribution. A
portion of each of the soil samples was air-dried,
ground in a wooden pestle with mortar and passed
through a 2 mm stainless steel sieve for determining
various soil properties. Electrical conductivity and
pH were determined by standard methods (Jackson,
1973). Easily oxidizable organic carbon was
estimated according to the wet oxidation method
(Walkley and Black, 1934). Available nitrogen was
estimated by the alkaline permanganate method
(Subbiah and Asija, 1956). The soil samples were
both in the acidic and alkaline range. Hence both
Olsen’s reagent (for neutral and alkaline soils) and
Bray’s reagent (for acid soils) were used for
extraction. The phosphorus content in the soil
extract was determined by blue colour formed by
the ascorbic acid-molybdate complex, and the colour
intensity was read at 660 nm using a spectrophotometer
(Jackson, 1973). The exchangeable potassium was
extracted with neutral normal ammonium acetate
from a known quantity of soil. The filtered extract
was fed to a flame photometer for measuring
available potassium content (Page et al., 1982).
Site description
In Karnataka, these plantation crops are grown
in the eastern dry zone (EDZ), southern dry zone
(SDZ), southern transitional zone (STZ), hilly zone
(HZ), and coastal zone (CZ). EDZ and SDZ
experience hot moist, semi-arid climate, recording
average annual rainfall of 750 mm to 850 mm,
Table 1. Details of soil profile samples collected from arecanut and coconut plantations representing different agro-climatic
zones of Karnataka
Location (Coconut) Location (Arecanut)
Eastern dry zone Gubbi Hebbur, Tumkur
Kanakuppe, Tumkur Borogowdana Palya, Kunigal
Hottigana Hosahali Channapattana Hesaraghatta
Southern dry zone Thamadahalli, Chamarajanagar T. Narasipura
Kadaballi Nelamangala Cholanahalli Channarayapattana
V.C Farm, Mandya Channegowdana Doddi, Maddur
Southern transitional zone Bhadravathi Tarikere
Gurupura Hunusur H D Kote
Muddanahalli Village, Hassan Hosur Alur Hassan
Hilly zone Mavinakere, Kalasa Sukkamatti, Sringeri
Manase, Sringeri Bidharahalli, Mudigere
ARS Mudigere Sirsi
Coastal zone Udupi Mudalu, Karkala,
Mogeru, Belthangady Pillya, Belthangady
Bramhavar Kumata
Vasundhara et al.
85
length of dry period >150 days. STZ has a sub-
humid climate receiving 950 mm rainfall, length of
dry period-120 days. Hilly and coastal zone have
hot, humid climate receiving an annual average
rainfall of >1500 mm and 4500 mm, respectively,
with 90 days of dry period. The soil temperature
regime is isohyperthermic for all the studied soils
as they have a difference of less than 5 °C between
mean summer temperature and mean winter
temperatures at a depth of 50 cm and a mean annual
soil temperature of 22 °C or higher.
Results and discussion
Soil reaction and electrical conductivity
Soil nutrient availability was directly
influenced by soil pH. Results of soil pH revealed
that the soil pH was higher in all the agroclimatic
zones except in HZ and CZ of the study area. (Table 2
and Fig. 2).
Soil pH ranged from 5.13-8.34 in coconut to
4.99-8.32 in arecanut soils. The pH of EDZ, SDZ,
STZ, HZ and CZ varied from 7.46-7.79, 8.05-8.34,
6.79-7.58, 5.15-6.12 and 5.13-5.25, respectively, in
coconut soils, whereas in arecanut, the soil pH
varied from 7.55-7.91, 8.12- 8.32, 7.72-7.85, 4.99-
5.34 and 5.43-5.65, respectively.
Irrespective of crop, a very low pH range was
recorded in HZ and CZ soil pedons, while high pH
was observed in SDZ soil pedons (Fig. 2a & b).
Electrical conductivity in EDZ, SDZ, STZ, HZ and
CZ ranged from 0.155-0.212, 0.115-0.18, 0.135-
0.15, 0.043-0.05, and 0.03-0.232 dS m-1 in coconut
soils. In arecanut soils, EC ranged from 0.185-0.253,
0.151-0.207, 0.155-0.251, 0.03-0.0.52 and 0.022-0.031
dS m-1, respectively.
Fig. 1. Location map of the study area
Soil fertility status of coconut and arecanut growing soils
86
Analysis of coconut and arecanut soils
representing different agro-climatic zones of
Karnataka revealed that EDZ, SDZ, and STZ soils
were slight to moderately alkaline, HZ and CZ were
acidic, and STZ were neutral in pH (Fig. 2 a and b).
Electrical conductivity values indicated the non-
saline nature of the soils of the study area.
Moderate to slightly acidic soil reaction in HZ
and CZ soils are mainly due to the heavy rainfall,
which causes leaching of bases, thereby reducing
the soil pH. Badrinath et al. (1995) reported that
southern parts (CZ and HZ) of Karnataka were
distributed with acidic soil, affecting crop yields.
Slightly to moderately alkaline soil reaction in other
areas was due to accumulation of basic salts from
the weathered parent material.
Soil organic carbon and cation exchange
capacity (CEC)
Soils of arecanut plantations recorded higher
organic carbon content compared to coconut. An
increase in organic carbon content noticed in
arecanut soils may be due to well managed mixed
farming system, which produces higher recyclable
biomass from component crops, and higher biomass
turnover in arecanut enhances the carbon in soil
compared to coconut. Arecanut has a higher
recycling potential of organic wastes (Sujatha et al.,
2015). Arecanut also equally contributes greater
Fig. 2. Soil reaction-in different depths of coconut and arecanut plantations across different agro-climatic zones
Fig. 3. CEC in different depths of coconut and arecanut plantations across different agro-climatic zones
Vasundhara et al.
87
Table 2. Per cent clay, pH, EC and CEC of soil pedons of coconut and arecanut plantations across different agro-climatic zones (ACZ) of Karnataka
Coconut Arecanut
ACZ Depth Clay pH EC CEC cmol Clay pH EC CEC cmol
(cm) (%) (dS m-1) (p+) kg-1 (%) (dS m-1) (p+) kg-1
Eastern dry zone 0-30 31.2(±10.8) 7.8(±0.6) 0.2 (±0.2) 11.7(±3.2) 28.1(±12.8) 7.7(±0.24) 0.253(±0.09) 11.39(±2.71)
30-60 32.4(±9.2) 7.6(±0.4) 0.2 (±0.1) 12.1(±2.6) 33.9(±9.4) 7.9(±0.36) 0.187(±0.05) 10.71(±3.22)
60-90 39.9(±3.1) 7.7(±0.2) 0.2(±0.2) 14.8(±4.6) 44.5(±3.1) 7.6(±0.43) 0.194(±0.01) 14.58(±5.22)
90-120 41.1(±4.8) 7.5(±0.9) 0.2(±0.3) 17.5(±3.0) 43.6(±3.4) 7.7(±0.22) 0.185(±0.03) 13.58(±4.41)
Southern dry zone 0-30 27.4(±12.3) 8.05(±0.3) 0.12(±0.06) 11.69(±3.88) 32.4(±5.6) 8.12(±0.3) 0.207(±0.08) 12.56(±1.56)
30-60 37.3(±9.9) 8.34(±0.61) 0.116(±0.06) 11.7(±3.96) 38.7(±4.2) 8.19(±0.28) 0.151(±0.04) 12.97(±2.35)
60-90 38.1(±3.6) 8.26(±0.41) 0.115(±0.09) 11.69(±4.95) 43.1(±4.8) 8.25(±0.34) 0.165(±0.07) 19.81(±1.09)
90-120 27.2(±7.7) 8.25(±0.4) 0.182(±0.04) 9.28(±6.21) 44.4(±3.3) 8.32(±0.44) 0.18(±0.07) 20.32(±2.31)
Southern transitional zone 0-30 25.6(±3.1) 7.58(±0.61) 0.15(±0.05) 9.32(±1.71) 38.1(±10.1) 7.72(±0.46) 0.223(±0.03) 21.62(±9.78)
30-60 37.5(±7) 6.84(±0.7) 0.142(±0.07) 11.02(±3.12) 43.3(±7.4) 7.85(±0.46) 0.251(±0.12) 26.0(±13.84)
60-90 36.8(±6.4) 6.79(±0.8) 0.135(±0.08) 10.99(±2.5) 42.6(±5) 7.78(±0.54) 0.199(±0.14) 23.95(±13.24)
90-120 36.5(±7.6) 6.84(±0.88) 0.135(±0.08) 10.44(±3.13) 46.2(±2.5) 7.78(±0.6) 0.155(±0.11) 22.28(±11.36)
Hilly zone 0-30 28.8(±8.8) 6.12(±1.87) 0.05(±0.0) 8.70(±2.88) 45.4(±3.6) 5.31(±0.44) 0.05(±0.04) 10.08(±4.88)
30-60 29.5(±7.5) 5.15(±0.31) 0.05(±0.0) 6.61(±3.17) 47.2(±6) 4.99(±0.52) 0.052(±0.05) 9.31(±7.09)
60-90 34.6(±10.2) 5.18(±0.4) 0.043(±0.01) 6.77(±3.74) 46.5(±6.8) 5.24(±0.36) 0.037(±0.03) 9.13(±6.55)
90-120 33.3(±13.4) 5.25(±0.32) 0.043(±0.01) 6.87(±3.82) 45.1(±7.7) 5.34(±0.33) 0.03(±0.02) 8.85(±6.15)
Coastal zone 0-30 30.3(±2.8) 5.25(±0.38) 0.232(±0.24) 8.70(±2.49) 32.4(±4.5) 5.65(±0.39) 0.031(±0.01) 7.45(±2.11)
30-60 34.2(±6.4) 5.25(±0.27) 0.035(±0.02) 6.9(±3.37) 39.3(±6.7) 5.52(±0.39) 0.023(±0.01) 7.29(±1.98)
60-90 33.7(±5.3) 5.13(±0.24) 0.03(±0.02) 6.97(±3.21) 40.5(±5) 5.48(±0.41) 0.025(±0.02) 8.0(±3.14)
90-120 33.3(±4.9) 5.17(±0.28) 0.036(±0.02) 6.51(±2.85) 39.6(±5.4) 5.43(±0.36) 0.022(±0.01) 7.66(±3.82)
Soil fertility status of coconut and arecanut growing soils
88
biomass (8.5-18 t ha-1 depending on the age of
arecanut crop) like other palms such as coconut
(12-18 t ha-1) and oil palm (14-15 t ha-1) owing to
high population density per unit area. Among the
ACZs, HZ soils recorded the highest organic carbon
content, which might be due to clay mineralogy,
clay fraction and biochemical environment of the
soil such as pH, higher rainfall, its intensity and
distribution in a year, which favours higher
production of biomass. It is very much evidenced
(Table 4) that the clay content of the hilly zone was
higher compared to other ACZ pedons. Higher
accumulation of soil organic carbon in hilly and
Table 3. Available nitrogen (kg ha-1) of soil pedons of coconut and arecanut plantations in different agro-climatic zones
(ACZ) of Karnataka
Depth (cm) EDZ SDZ STZ HZ CZ Crop × depth
mean
Coconut
0-30 192.9 (±83.4)a164.5 (±41.2) 134.8 (±15.4) 179.5 (±68.6) 239.7 (±28.4) 182.3
30-60 168.9 (±51.2)ab 179.3 (±110.6) 175.8 (±39.6) 149.1 (±88.2) 213.9 (±51.3) 177.4
60-90 110.9 (±35.1)bc 98.1 (±33.6) 141.5 (±43.7) 123.9 (±119.8) 164.7 (±91.2) 127.8
90-120 102.3 (±62.6)c100.9 (±92.2) 115 (±50.4) 125.4 (±122.1) 170.0 (±82.5) 122.7
Coconut×zone mean 143.8 135.7 141.8 144.5 197.1 152.6
SEm± 17.2 34.7 17.8 16.6 20.1
CD at 0.05 59.4 NS 54.0 50.7 61.0
Arecanut
0-30 158.4 (±64.4) 236.6 (±70.2) 223.7 (±18)a263.6 (±38.8) 189.9 (±86.1) 214.4
30-60 160.8 (±28.3) 185.0 (±33) 188.4 (±52.2)ab 235.3 (±135.9) 158.5 (±42.4) 185.6
60-90 141.1 (±68.9) 139.9 (±97) 143.7 (±32.3)bc 207.3 (±112.1) 131.2 (±39.5) 152.7
90-120 116.2 (±24.6) 86.2 (±51.4) 118.8 (±25.8)c168.6 (±112.2) 73.1 (±50.6) 112.6
Arecanut×zone mean 144.1 161.9 168.7 218.7 138.2 166.3
SEm± 28.4 29.9 16.8 30.0 33.5
CD at 0.05 NS 90.1 58.2 NS 99.6
Grand zone mean 143.9 148.8 155.2 181.6 167.6 Depth mean
Interactions C Z D C×Z C×D Z×D C×Z×D
SEm± 9.1 14.3 12.8 20.2 18.1 28.6 40.5
CD at 0.05 NS NS 36.1 57.1 NS NS NS
The results are shown as the mean (±SD). Values with the same letters within columns (soil depths) are not significantly different
at P<0.05. C:Crop, Z:Zone, D:Depth, EDZ:eastern dry zone, SDZ:southern dry zone, STZ:southern transition zone, HZ:hilly
zone and CZ:coastal zone
mountain areas can be attributed to favourable
soil-climate environment (Fig. 4).
The results revealed that the CEC of 176 soils
representing 30 soil pedons ranged from 2.53 to 38.12
cmol (p+) kg-1. Lower CEC was recorded in CZ, while
higher CEC was observed in STZ soil pedons
(Fig. 3). Soils collected from coconut plantations
recorded low CEC, ranging from 6.51 to 17.45 cmol
(p+) kg-1. In arecanut soils, CEC ranged from 7.29 to
26.0 cmol (p+) kg-1 (Table 2 and Fig. 3).
CEC was higher in STZ ranging from 9.32 to
26.0 cmol (p+) kg-1 followed by SDZ (9.28 to 21.32
cmol (p+) kg-1) and EDZ (10.71 to 17.45 cmol (p+) kg-1).
Vasundhara et al.
89
Lower CEC was observed in HZ and CZ, ranging
from 6.61 to 10.08 cmol (p+) kg-1 and 6.51 to 8.7
cmol (p+) kg-1, respectively. Low CEC in HZ and CZ
due to the presence of kaolinite minerals. Heavy
rainfall in these regions enhanced K leaching, leading
to lesser fixation of K in sites. Normally, kaolinite
minerals soils hold a lesser amount of available and
exchangeable K (Martin and Sparks, 1985). Organic
carbon and clay content play a major role in
influencing CEC. Organic carbon content decreased
with depth in all soils; still, CEC showed an increasing
trend indicating the mineralogy of the soils, which
probably had a larger role in regulating CEC in these
soils than the organic matter. Saikh et al. (1998)
observed a poor correlation between CEC and organic
carbon in ferruginous soils under deciduous forest
and attributed this change in CEC to mineralogy.
Available major nutrients status in coconut
and arecanut plantations
Available nitrogen
The average content of available nitrogen in
coconut and arecanut soils varied from 98.1 to 239.7
kg ha-1 and 73.1 to 263.6 kg ha-1, respectively. The
mean available nitrogen content was higher in
arecanut (166.3 kg ha-1) than coconut (152.6 kg ha-1).
In coconut, higher available nitrogen was observed
in CZ (197.1 kg ha-1) followed by HZ (144.5 kg ha-1)
and in arecanut, maximum available nitrogen was
observed in HZ (218.7 kg ha-1) followed by STZ
(168.7 kg ha-1), SDZ (161.9 kg ha-1), CZ (138.2 kg ha-1)
and EDZ (144.1 kg ha-1). Among the ACZs the
available nitrogen content followed the order of HZ
(181.6 kg ha-1) >CZ (167.6 kg ha-1) >STZ (155.2
kg ha-1)> SDZ (148.8 kg ha-1) >EDZ (143.9 kg ha-1).
Table 4. Available P2O5 (kg ha-1) in soil pedons of coconut and arecanut plantations in different agro-climatic zones (ACZ)
of Karnataka
Depth (cm) EDZ SDZ STZ HZ CZ Crop × depth mean
Coconut
0-30 16.5 (±11.1) 8.3 (±5.3) 64.3 (±35.7)a4.1 (±3.6) 15.9 (±20.0) 21.8
30-60 12.1 (±9.4) 7.9 (±4.2) 15.0 (±9.4)b1.4 (±0.5) 12.8 (±16.1) 9.8
60-90 9.2 (±10.6) 4.2 (±2.8) 11.9 (±6.6) b 1.3 (±0.6) 10.4 (±13.6) 7.4
90-120 6.2 (±5.1) 4.7 (±2.8) 11.5 (±5.6) b 1.0 (±0.0) 10.4 (±13.6) 6.8
Coconut×zone mean 11.0 6.3 25.7 2.0 12.4 11.5
SEm± 2.3 1.1 8.7 1.0 1.8
CD at 0.05 7.1 3.4 30.2 NS NS
Arecanut
0-30 17.4 (±6.87)a33.1 (±28.09) 20.9 (±7.75) a 11.5 (±8.38) 5.9 (±7.5) 17.8
30-60 5.7 (±2.13)b19.5 (±16.77) 3.8 (±1.99)b8.0 (±9.17) 3.0 (±0.6) 8.0
60-90 7.2 (±4.05) b 10.9 (±11.31) 3.5 (±2.3)b7.5 (±8.29) 3.8 (±0.1) 6.6
90-120 6.1 (±2.52) b 8.3 (±4.7) 2.8 (±1.65)b8.9 (±10.75) 4.6 (±1.2) 6.1
Arecanut×zone mean 9.1 18.0 7.8 9.0 4.3 9.6
SEm± 2.1 4.8 2.1 4.2 2.2
CD at 0.05 7.3 15.2 7.1 NS NS
Grand zone mean 10.1 bc 12.1 ab 16.7 a5.5 c8.3 bDepth mean
Interactions C Z D C×Z C×D Z×D C×Z×D
SEm± 9.1 14.3 12.8 20.2 18.1 28.6 40.5
CD at 0.05 NS NS 36.1 57.1 NS NS NS
The results are shown as the mean (±SD). Values with the same letters within columns (soil depths) are not significantly different
at P<0.05. C:Crop, Z:Zone,D:Depth, EDZ: eastern dry zone, SDZ: southern dry zone, STZ: southern transition zone, HZ: hilly
zone and CZ: coastal zone
Soil fertility status of coconut and arecanut growing soils
90
Available P2O5
The available P2O5 status in coconut and
arecanut soils varied from 1.0 to 64.2 kg ha-1 and
2.9 to 47.8 kg ha-1, respectively. The coconut soils
(11.5 kg ha-1) recorded higher available P2O5 when
compared to arecanut soils (9.62 kg ha-1); however,
no significant difference was noticed (Fig. 4b). The
low status of available P2O5 in arecanut soils is
attributed to higher removal than replenishment due
to high nutrient demand from the main crop and
component crop (Table 4).
In coconut, higher available P2O5 was
observed in STZ (25.66 kg ha-1) followed by CZ
(12.37 kg ha-1), EDZ (11.0 kg ha-1), SDZ (6.27
kg ha-1) and HZ (1.96 kg ha-1). In arecanut
growing soil, available P2O5 followed the trend
SDZ (17.95 kg ha-1) > EDZ (9.1kg ha-1) > HZ
(8.97 kg ha-1) ≥ STZ (7.75 kg ha-1) > CZ (4.31kg ha-1).
The available nitrogen status in coconut and
arecanut soils in different ACZs are presented in
Figure 4a and Table 3. Higher available nitrogen
was noticed in the surface (0-30 cm) and decreased
with an increase in depth; a significant difference
was noticed at all depths. Higher available nitrogen
noticed in 0-60 cm depth might be due to applying
chemical fertilizers and FYM, high turnover of crop
residues. Bhat and Sujatha (2007) observed
significantly higher mineral N in the
arecanut+pepper crop-based system (0-30 cm depth)
than in cocoa and clove. Organic matter recycling
(OMR) was significantly higher in arecanut based
cropping systems than other plantations. Higher
nitrogen content in HZ might be due to higher
biomass returns to soil (Achalu et al., 2012).
Table 5. Available K2O (kg ha-1) in soil pedons of coconut and arecanut plantations in different agro-climatic zones (ACZ)
of Karnataka
Depth (cm) EDZ SDZ STZ HZ CZ Crop ×depth mean
Coconut
0-30 233.6 (±275.0) 105.3 (±75.6) 198.7 (±98.2) 79.4 (±9.8)a125.6 (±68.1)a148.5
30-60 461.2 (±696.0) 54.2 (±8.2) 115.9 (±44.1) 37.3 (±5.2)b75.5 (±37.4)ab 148.8
60-90 126.8 (±105.4) 50.7 (±19.0) 114.3 (±35.6) 40.3 (±3) b 60.4 (±35.4)b78.5
90-120 88.2 (±25.0) 43.7 (±38.4) 101.2 (±35.3) 37.6 (±3.8) b 51.5 (±34.2)b64.4
Coconut×zone mean 227.4 63.5 132.5 48.7 78.3 110.1
SEm± 175.0 17.4 21.4 3.7 15.0
CD at 0.05 NS 53.4 65.4 12.9 51.7
Arecanut
0-30 119.2 (±62.4) 250.0 (±101.9)a511.7 (±533.2) 188.8 (±135.3) 66.4 (±31.7) 227.2
30-60 70.1 (±28.4) 136.7 (±77.4)c308.5 (±252.9) 118.3 (±96.7) 56.8 (±32.3) 138.1
60-90 76.9 (±27.3) 177.1 (±91.8)b262.7 (±134.5) 69.9 (±49.9) 58.1 (±34.3) 128.9
90-120 66.0 (±20.9) 178.7 (±94.6)b212.1 (±85) 48.4 (±32.3) 57.2 (±39.7) 112.5
Arecanut×zone mean 83.1 185.6 323.7 106.3 59.6 151.7
SEm± 24.4 15.0 131.7 30.1 12.2
CD at 0.05 NS 35.9 NS 91.4 NS
Grand zone mean 155.3 ab 124.5 b228.1 a77.5 b69.0 bDepth mean
Interactions C Z D C×Z C×D Z×D C×Z×D
SEm± 20.9 3.1 29.6 46.8 41.9 66.2 93.6
CD at 0.05 NS 90.6 81.1 128.2 NS NS NS
The results are shown as the mean (±SD). Values with the same letters within columns (soil depths) are not significantly different
at P<0.05. C:Crop, Z:Zone,D:Depth, EDZ:eastern dry zone, SDZ:southern dry zone, STZ:southern transition zone, HZ:hilly
zone and CZ:coastal zone
Vasundhara et al.
91
The pooled data of available P2O5 content in
different ACZs were 16.70, 12.11, 10.05, 8.34 and
5.47 kg ha-1 for STZ, SDZ, EDZ, CZ and HZ,
respectively.
Lower available P2O5 content was observed in
HZ, and CZ attributed to the lower application of
fertilizers and fixation of P in acidic soils of hilly
and coastal zones. The low status of P in these
Fig. 4a. Distribution of available nitrogen (kg ha-1) in (a) coconut and arecanut plantations (b) different agro-climatic
zones (c) different depths of soil pedons
Fig. 4c. Distribution of available K2O (kg ha-1) in (a) coconut and arecanut plantations (b) different agro-climatic zones
(c) different depths of soil pedons
Fig. 4b. Distribution of available P2O5 (kg ha-1) in (a) coconut and arecanut plantations (b) different agro-climatic zones
(c) different depths of soil pedons
regions is due to the soils rich in hydrated and
amorphous oxides of Fe and Al, which are potential
phosphorus fixers (Perur, 1996: West et al., 1997).
SDZ and EDZ zones recorded lower P2O5 than STZ
due to the higher soil pH that cause P fixation in
soil. The higher amount of available P2O5 in STZ
might be due to the application of FYM and
recommended dose of fertilizer, which enhances the
Soil fertility status of coconut and arecanut growing soils
Available nitrogen (kg ha-1)
300
200
100
0
Coconut Arecanut
Available nitrogen (kg ha-1)
Available nitrogen (kg ha-1)
300
200
100
0
300
200
100
0
EDZ SDZ STZ HZ CZ
Agro-climatic zones
Agro-climatic zones
0-30 30-60 60-90 90-120
Depth (cm)
Available P2O5 (kg ha-1)
100
80
60
40
20
0
Coconut Arecanut EDZ SDZ STZ HZ CZ
Agro-climatic zones
0-30 30-60 60-90 90-150
Depth (cm)
100
80
60
40
20
0
Available P2O5 (kg ha-1)
Available P2O5 (kg ha-1)
100
80
60
40
20
0
Available K2O (kg ha-1)
1250
1000
750
500
250
0
Coconut Arecanut EDZ SDZ STZ HZ CZ
Agro-climatic zones
0-30 30-60 60-90 90-150
Depth (cm)
Available K2O (kg ha-1)
1250
1000
750
500
250
0
Available K2O (kg ha-1)
1250
1000
750
500
250
0
92
availability in soil, and the neutral pH of these soils.
An increase in P status was mainly attributed to the
addition of FYM and chemical fertilizers in coconut
plots (Maheswarappa et al., 2008). During
decomposition of organic manure, various organic
acids will be produced, which solubilize phosphates
and other phosphate bearing minerals, thereby
lowering the phosphate fixation and increasing
availability. Manna et al. (2006) reported that
available phosphorus content increased due to the
addition of FYM over initial and control. The
organic matter also forms a cover on sesquioxides.
It makes them inactive and thus reduces the
phosphate fixing capacity of the soil, which
ultimately helps in the release of an ample quantity
of phosphorus (Bhardwaj et al., 2010).
Higher available P2O5 content was observed in
surface (0-30 cm) in coconut soils and ranged from
4.1 to 64.4 kg ha-1 with a mean of 21.82 kg ha-1.
In arecanut soils, available P2O5 at the surface
(0-30 cm depth) varied from 5.9 to 33.1 kg ha-1 with
a mean of 17.71 kg ha-1. Available P2O5 was the
least in the depth of 90-120 cm in both arecanut
and coconut soils in ACZs. The declining trend in
available P2O5 with depth was observed in soils of
all zones. There was a significant difference in
available P2O5 among depths and zone, crops and
zone in the soil.
Available K2O
The maximum amount of available K2O content
was recorded in arecanut (151.71 kg ha-1) compared
to coconut (110.1 kg ha-1). In arecanut soils,
available K2O content ranged from 66.8 to 511.7
kg ha-1 in the surface and 48.4 to 308.5 kg ha-1 in the
sub-surface. Available K2O content in coconut soils
varied from 79.4 to 233.6 kg ha-1 and 37.6 to 461.2
kg ha-1 in surface and subsurface, respectively
(Table 5).
Available K2O levels in coconut and arecanut
plantations were in the order of STZ > EDZ > CZ >
SDZ > HZ and STZ > SDZ > HZ > EDZ > CZ,
respectively. Among the zones, irrespective of crops,
STZ recorded higher available K2O (228.1 kg ha-1)
followed by SDZ (124.5 kg ha-1), EDZ (155.3 kg ha-1),
HZ (77.5 kg ha-1) and CZ (69.0 kg ha-1). Available
K2O was the lowest in coastal zone (laterite) soils
due to heavy rainfall (>3000 mm) and topography,
which accentuate the process of leaching of nutrients
resulting in poor nutrient retention capacity, and
these soils are poor in bases of native soil fertility
with abundant sesquioxides (Babu,1981). The
deficiency of nitrogen and potassium is due to the
dominant clay mineral kaolinite, which has a low
K fixation capacity (Badrinath et al., 1998).
Available nitrogen and potassium were significantly
(P < 0-05) lower in soil under coconut plantations
compared with double fruit crops (Manna and
Singh, 2001).
In general, higher available K2O was observed
in surface (0-30 cm), and with increasing depth to
the extent of 187.9, 143.5, 103.7 and 88.5 kg ha-1
for 0-30 cm, 30-60 cm, 60-90 cm and 90-120 cm,
respectively. There was a significant difference
between depths, zone and crop zone for available
K2O. At the same time, the interaction effect for
crop depth and zone depth and crop×zone×depth
was found to be non-significant.
Conclusion
The fertility status of coconut and arecanut
growing soils of different agro-climatic zones of
Karnataka indicated that soils are low to medium in
available N and K in surface and sub-surface. The
soils were low in available phosphorus due to high
P fixing capacity and also removal than
replenishment. Higher available N and K2O were
observed in arecanut soils than coconut soils.
However, hilly and coastal zone have higher
nitrogen than other zones due to higher biomass
addition. Soils from STZ have higher inherent
available K2O related to mixed clay mineralogy.
References
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Soil fertility status of coconut and arecanut growing soils
94
Characterization and classification of major coconut growing
soils in South Eastern Ghats of Tamil Nadu, India
R. Srinivasan*, K.S. Anil Kumar, M. Chandrakala, K.V. Niranjana, N. Maddileti
and Rajendra Hegde
ICAR- National Bureau of Soil Survey & Land Use Planning, Regional Centre, Hebbal, Bengaluru-560 024,
Karnataka, India
(Manuscript Received: 07-12-2020, Revised: 02-04-2021, Accepted: 14-05-2021)
Abstract
Six soil series representing major coconut growing soils of the Eastern Ghats in Krishnagiri district of Tamil Nadu state, India,
were evolved from granite gneiss and alluvium parent materials. Characterization of different soil properties was done using
a detailed soil survey at 1:10000 scale. The soils were neutral to moderately alkaline in reaction (7.31 to 9.19), non-saline,
poor to moderately well-drained and moderately shallow (<75 cm) to very deep (>150 cm) in depth. The soils were sandy to
clay in texture, sub-angular blocky to crumb in structure, dark reddish-brown to brown, very low to high in OC content (0.06
to 2.70%), low to medium in AWC (3.44 to 22.39%), low to high in CEC (4.70 to 54.0 cmol (p+) kg-1) and having high base
saturation (77 to 100%). The soils also had sizable amounts of exchangeable sodium (4.29 to 33.46%), which was maximum
in P5, P6 and P1, and high clay content in P5 and P2. The distribution of CaCO3 in different depths was found to be maximum
in P4 and P1. The soil orders identified in the coconut area were Inceptisols, Entisols, Alfisols and Vertisols. Assessment of soil
resources and identification of yield-limiting soils factors on coconut could enable of better management and improved
productivity.
Keywords: Classification, coconut, Eastern Ghats, soil characterization, sustainability
doi:10.25081/jpc.2021.v49.i2.7255
Journal of Plantation Crops, 2021, 49(2): 94-103
Research Article
*Corresponding Author: srinivasan.surya@gmail.com
Introduction
Soil is a vital natural resource for the survival
on the earth, and its assessment is a prerequisite for
determining soil productivity and the sustainability
of the ecosystem. Precise scientific information on
characteristics, potentials, limitations, and
management of different soils are indispensible for
planning and developing soil resources and
maintaining the productivity of different plantation
crops like coconut (Lal, 2013).
In India, coconut cultivation is an indivisible
implant of socio-historical culture as well as an
ethnic identity. The livelihood and food security of
the major population in Asia and other coastal
countries depends mostly on coconut palm (Raghavi
et al., 2019). It is estimated that about 12 million
people in India are dependent on the coconut sector
through cultivation, processing and trading
activities. With an annual production of around
17,000 million nuts, the contribution of coconut to
the Indian GDP is about ` 15,000 crores and a major
share of the world’s total production is from India,
the Philippines and Indonesia (APCC, 2017).
In India, coconut is mainly cultivated in Kerala,
Tamil Nadu, West Bengal, Odisha, Karnataka,
Maharashtra, and Pondicherry. Of late, coconut
cultivation has been introduced to suitable locations
in non-traditional states, including Assam, Gujarat,
Madhya Pradesh, Rajasthan, Bihar, Tripura,
Manipur, and Arunachal Pradesh and the hinterland
regions of the coconut growing states. In India,
Tamil Nadu tops the list in the productivity of
coconut, but production is high in Karnataka and
Kerala tops in the area. Tamil Nadu has the highest
95
yield among the major coconut growing states with
14,873 nuts, followed by Andhra Pradesh with
13,808 nuts (Singh, 2016).
Coconut is a tropical crop and grows well in
hot climates. The palm adapts almost all types of
well-drained soils, such as alluvial, red sandy loam,
coastal sandy with slightly acidic to neutral soil
reaction (Kannan et al., 2017). A decline in soil
qualities is primarily responsible for coconut palm’s
declining health and productivity in the different
states (Nair et al., 2018). Major soil parameters like
poor drainage, depth, nutrients status and organic
carbon had a role in deciding coconut growth and
yield in Eastern Ghats. Root penetration and
development, important for water uptake, depends
on soil physical properties (Avinash et al., 2019).
In Tamil Nadu, it is impossible to increase area-
wise production in coconut because of water deficit,
high labour cost, and utilization of agricultural land
for other purposes. Therefore yield has to be
increased within the area by using site-specific land
resources information and adopting a scientific
method of cultivation. A proper understanding of
the soil characteristics of the coconut growing soils
in Eastern Ghats will be a key indicator for
sustainable productivity. Therefore, a case study was
undertaken in the Eastern Ghats in Kaveripattinam
block, Krishnagiri district of Tamil Nadu, to evaluate
the soil properties for better management and
productivity.
Materials and methods
Study area
The Eastern Ghats located along peninsular
India extends over 1750 km between 77°22’ to
85°20’ E longitude and 9°95’ to 20°74’ N latitude
covering states of Tamil Nadu, Karnataka Andhra
Pradesh and Odisha. The study was confined to the
southern portion of the Eastern Ghats located in
Tamil Nadu state. The area lies between 11°47' &
12°33' N latitude and 77°27' & 78°38' E longitude
covering Kaveripattinam block of Krishnagiri
district (Fig. 1), which belongs to agro-
ecological sub-region (AESR) of 8.2 and covers
29,839 ha. Landscape and soil characteristics are
given in Table 1.
Field studies
A detailed soil survey was carried out on a
1:10,000 scale during 2017-2019. The base map was
prepared using a survey of India toposheet (1:50000
scale) and IRS-P6 LISS IV remote sensing image
merged with village cadastral map. Soil survey
fieldwork was carried out based on imagery
characteristics, geology, landform, slope and
vegetation. Based on the slope variability, different
transect and random observation were studied.
Three hundred and fifteen soil profiles were studied
in the Kaveripattinam block. Among them, six
representative soils series (P1-Nedungal (NDG),
P2- Pannanthur (PNT), P3- Papparapatti (PPT),
P4- Arasampatti (ASP), P5- Puliyur (PLR) and
P6- Kottapatti (KTP) were identified from a coconut
growing soils. Soil pits/profiles were excavated on
each landform for describing morphological
characteristics (Soil Survey Staff, 2003).
Climatic condition
The study area receives rainfall under the
influence of both southwest and northeast monsoons.
The mean annual rainfall varied from 750 to 900 mm,
and more than 60 to 70 per cent is received during
the northeast monsoon (October-December).
Table 1. Site characteristics of study area
Pedons Series Area Landform MSL Slope Runoff Drainage Nuts Management
covering (%) tree-1
(ha) yr-1
P1 Nedungal (NDG) 1125 Valley floor 538 0-1 Slow Poor 160 Good
P2 Pannanthur (PNT) 1768 Valley floor 431 1-3 Medium Somewhat poorly 140 Moderate
P3 Papparapatti (PPT) 1323 Lower pediment 429 1-3 Medium Moderate 165 Good
P4 Arasampatti (ASP) 814 Valley floor 417 0-1 Slow Somewhat poorly 150 Good
P5 Puliyur (PLR) 342 Valley floor 418 0-1 Very slow Poor 185 Very good
P6 Kottapatti (KTP) 3213 Valley floor 410 0-1 Slow Somewhat poorly 150 Good
Coconut growing soils in South Eastern Ghats of Tamil Nadu
96
The mean maximum summer temperature was
37°C, and the mean minimum temperature was
25.5°C. The soil temperature class was hyperthermic
and moisture regime, “ustic”, which is Deccan
Plateau, hot semiarid eco-region with mixed red
loamy soils and LGP 150 to 180 days.
Soil analysis
The soil samples were collected from major
coconut growing soils from different depths marked
as the horizon and kept for air drying. Subsequently,
samples were powdered and sieved using a 2 mm
sieve to determine soil physical and chemical
characteristics. Soil texture was determined by using
the International Pipette Method (Day, 1965). While
soil pH and EC were determined according to
Jackson (1973) and Page et al. (1982), soil organic
carbon (SOC) was estimated by the wet oxidation
method (Walkley and Black, 1934). Available water
capacity (AWC) was calculated as the water retained
between suction 0.03 and 1.5 MPa using pressure
plate apparatus (Klute, 1986). Cation exchange
capacity (CEC) was determined using 1 N
ammonium acetate at pH 7.0, whereas base
saturation was calculated as the sum of bases divided
by CEC and multiplied by 100. Exchangeable
sodium percentage (ESP) is the sodium adsorbed
on soil particles, and this was calculated as ESP =
(exchangeable Na × 100) / cation exchange capacity.
Land capability classes (LCC) and land irrigability
classes (LIC) developed for coconut-based soil and
site variability (AIS & LUS, 1970). The soils were
classified as per the guidelines given in Keys to Soil
Taxonomy (Soil Survey Staff, 2014).
Results and discussion
Soil morphology
Detailed morphological characteristics of
different soil series are given in Table 2. The soil
depth varied from moderately shallow (P4) to very
deep (P5). The variations in topography and slope
gradient have resulted in the variation in different
depths (Srinivasan et al., 2011). The soils are poorly
to moderately drained. The surface soil matrix
colour varied from dark brown (7.5YR 3/2) to very
Fig. 1. Location map of the Study area
Srinivasan et al.
97
Table 2. Morphological and physical characteristics of the coconut growing soils
Depth Horizon Colour Sand Silt Clay Texture Structure Consistence
(cm) (moist) (%)
Pedon 1: Nedungal series DMW
0-18 Ap 10 YR 3/1 54.45 18.87 26.68 scl m 2 sbk sh fr ms/mp
18-29 Bw1 10 YR 3/2 50.23 20.26 29.51 scl m 1 sbk - fr ms/mp
29-42 Bw2 10 YR 3/2 59.09 17.91 23.00 scl m 1 sbk - fr ms/mp
42-91 Bw3 10 YR 5/2 64.41 12.27 23.31 scl f 1 sbk - fr ms/mp
Pedon 2: Pannanthur series
0-18 Ap 7.5 YR 4/2 42.58 21.48 35.94 cl f 1 sbk s vfr s/p
18-32 A2 5 YR 4/3 61.76 15.60 22.64 scl f 2 sbk sh fr ms/mp
32-50 Bt1 2.5 YR 3/4 53.37 13.95 32.68 scl m 1 sbk - fr ms/mp
50-80 BC 2.5 YR 3/6 74.54 9.52 15.94 sl m 1 sbk - fr so/po
Pedon 3: Papparapatti series
0-18 Ap 7.5 YR3/2 54.69 21.95 23.36 scl f 2 sbk s vfr ss/sp
18-42 A1 7.5 YR 3/4 68.27 17.60 14.12 sl f 2 sbk - vfr so/po
42-75 AC 7.5 YR 4/3 83.40 7.87 8.73 ls f 1 cr - l so/po
75-105 CA 7.5 YR 4/3 89.06 3.37 7.57 s f 1 cr - l so/po
Pedon 4: Arasampatti series
0-17 Ap 10 YR 3/3 63.51 19.15 17.33 sl f 2 sbk l vfr so/po
17-42 Bw1 10 YR 3/2 43.07 32.35 24.58 sil f 2 sbk - fr so/po
42-73 Bk1 10 YR 6/1 40.23 29.57 30.20 cl f 2 sbk - fr ms/mp
Pedon 5: Puliyur series
0-19 Ap 10YR 3/1 28.67 29.18 42.15 c f 1 sbk l l vs/vp
19-49 Bss1 10YR 3/1 27.98 25.24 46.77 c f 2 sbk - vfr vs/vp
49-75 Bss2 10YR 3/1 24.21 26.47 49.32 c f 2 sbk - vfr vs/vp
75-110 Bss3 10YR 2/1 17.57 25.25 57.18 c f 2 sbk - vfr vs/vp
110-158 Bss4 10YR 2/1 16.74 25.00 58.26 c f 2 sbk - vfr vs/vp
Pedon 6: Kottapatti series
0-16 Ap 10 YR 4/2 38.85 41.38 19.77 sil f 1 sbk l vfr ss/sp
16-38 Bw1 10 YR 5/4 34.68 43.30 22.02 sil f 2 sbk - fr ss/sp
38-75 Bw2 10 YR 5/3 38.39 40.25 21.36 sil f 2 sbk - fr ss/sp
75-102 BC 10 YR 5/3 56.23 27.19 16.58 sl f 1 sbk - fr so/po
102-130 CB 10 YR 4/3 83.42 7.81 8.77 ls f 1 sbk - vfr so/po
Texture: s-sand, ls- loamy sandy, sl- sandy loam, sil- silt loam, scl - sandy clay loam; cl-clay loam, c-clay.
Structure: Size (S) - vf - very fine, f - fine, m - medium, c - coarse; Grade (G) - 0 - structure less, 1- weak, 2 - moderate, 3 - strong;
Type (T) cr - crumb, sg - single grain, abk - angular blocky, sbk - sub-angular blocky; Consistence: Dry: s - soft, 1- loose,
sh - slightly hard, h - hard; Moist: 1- loose, vfr- very friable, fr - friable, fi - firm, vfi - very firm; Wet: so - non-sticky, ss - slightly
sticky, ms - moderately sticky, vs - very sticky; po - non-plastic, ps - slightly plastic, mp - moderately plastic, vp - very plastic
dark gray (10YR 3/1) and dark grayish brown
(10YR 4/2), whereas subsoils were reddish-brown
to dark red in P2, others were brown (7.5YR 4/3)
and black (10YR 2/1). Reddish colour (P2) was
attributed to the differential degrees of erosion,
lesser content of organic matter and iron oxide
content (Patil and Dasog, 1999) and intense leaching
of bases resulting in sesquioxides at the surface. The
dark colour of P1, P3, P4, P5 and P6 soils were due
to different drainage systems, and influences of
clay-humus complex and status of organic matter
were responsible for colour change in deeper layers
Coconut growing soils in South Eastern Ghats of Tamil Nadu
98
Table 3. Physico-chemical characteristics of the coconut growing soils
Depth pH EC AWC OC CEC CEC/ BS ESP CaCO3
(cm) (1:2.5) (dS m-1) (%) (%) cmol Clay (%) Eq
H2O (p
+) kg-1 ratio
Pedon 1: Nedungal series
0-18 7.31 1.38 12.05 2.70 13.59 0.51 100 10.68 6.74
18-29 8.60 0.55 12.89 0.34 20.97 0.71 100 9.32 7.10
29-42 8.75 0.50 11.94 0.18 16.35 0.71 100 13.96 6.37
42-91 8.73 0.56 12.70 0.08 16.37 0.70 100 9.25 6.37
Pedon 2: Pannanthur series
0-18 8.35 0.41 10.96 0.87 21.07 0.59 100 4.83 4.56
18-32 8.31 0.49 9.04 0.56 14.70 0.65 100 5.58 4.08
32-50 7.91 0.66 8.03 0.45 14.11 0.43 100 5.72 1.20
50-80 8.03 0.53 6.74 0.21 11.47 0.72 100 8.23 0.84
Pedon 3: Papparapatti series
0-18 7.86 0.47 10.55 0.87 15.09 0.65 100 4.29 3.12
18-42 7.56 0.10 9.40 0.56 9.80 0.69 100 8.47 0.84
42-75 7.54 0.77 5.10 0.16 6.96 0.80 100 7.40 0.72
75-105 7.55 0.37 3.44 0.06 4.70 0.62 100 5.41 0.48
Pedon 4: Arasampatti series
0-17 8.42 0.88 10.24 1.08 12.84 0.74 100 4.84 9.84
17-42 8.54 0.65 10.59 0.21 21.85 0.89 100 7.53 4.20
42-73 8.74 0.46 11.56 0.25 22.15 0.73 100 6.35 24.60
Pedon 5: Puliyur series
0-19 8.01 1.64 7.84 1.49 43.12 1.02 100 15.64 5.28
19-49 8.36 1.10 11.44 0.41 43.22 0.92 100 16.24 5.28
49-75 8.87 1.04 9.92 0.35 49.20 1.00 100 21.79 6.60
75-110 9.10 1.34 18.33 0.29 51.55 0.90 100 27.67 7.20
110-158 9.19 0.53 22.39 0.29 54.00 0.93 100 33.46 5.88
Pedon 6: Kottapatti series
0-16 8.16 0.715 12.04 0.17 13.52 0.68 100 14.23 2.88
16-38 7.85 0.495 12.67 0.23 14.70 0.67 89 8.73 0.00
38-75 7.82 0.462 12.11 0.19 13.72 0.64 77 8.79 0.00
75-102 7.92 0.350 9.89 0.21 11.96 0.72 89 9.53 0.00
102-130 8.17 0.198 7.28 0.14 5.78 0.66 91 9.14 0.00
(Rao et al., 1995). The different soil colour
variations were attributed to different chemical
properties (Fe and Mn), minerals influences, and
textural makeup of soils conditioned by slope
position and water movement (Walia and Rao, 1997;
Somasundaram et al., 2010). The texture of the soils
varied from sandy to clay. This might be due to
different combinations of weathering process and
soil-forming factors (climate, parent material,
topography, biota and vegetation) and processes
caused at different times (Srinivasan et al., 2016).
The structure of soils was subangular blocky to
crumb type. Different blocky structure formation is
due to different clay content (Sharma et al., 2004).
The dry consistency varied from soft to slightly
hard, moist consistency varied from friable to very
friable and wet consistency varied from non-sticky/
plastic to very sticky to very plastic. The presence
of various structures and consistency of the soils is
because of influences of clay fraction and clay
Srinivasan et al.
99
minerals (Thangasamy et al., 2005). Soil structure
was better in all the coconut growing soils except
the lower layer of P3, which are better aggregated
due to higher organic carbon and relatively lower
compaction.
Physical characteristics
The comprehensive soil physical properties are
given in Tables 2 and 3. Particle-size distribution
data revealed that the clay content varied from 7.57
to 58.26 per cent. Higher clay content was observed
in subsoils of P5 and P1. Silt content in all the
pedons showed irregular tendency with different
depth, except P2 and P3, which might be due to
variation of soil-forming process and action of
different weathering process and highest silt content
was recorded in P6 and P4 soils, which may be due
to higher water fluctuations from different sources
(canal, river and well) at different intervals
(Srinivasan et al., 2015). Sand distribution was
maximum in P3 and P6 soils, ranging from 34.68
to 89.06 per cent sand contributing maximum in
texture classes, which could be attributed to the
siliceous nature of granite-gneiss parent material.
Sandy soils exhibit limitations to coconut
production due to poor nutrient retention and water
holding capacity, limiting moisture availability to
the palm (Arachchi and Somasiri, 1997). These soils
have to be properly managed to improve coconut
production. Water holding capacity (WHC) of
different pedons varied from 3.44 to 22.39 per cent.
These variations were due to the difference in depth,
clay, silt and organic carbon content. Low WHC in
coarse texture (P2 and P3) soils was due to high
sand content. The irregular trend of WHC with depth
was due to the clay and water movement from the
surface to different lower horizons (Fig. 2). Pedons
5, 1 and 6 have a higher water holding capacity in
the sub-surface horizon than in surface horizons,
which might be due to excess clay content and
organic matter in the soils (Satish et al., 2018).
Chemical characteristics
All the series were neutral to moderately
alkaline, with pH varying from 7.31 to 9.19. This
extensive variation was attributed to the nature of
the parent material, leaching, presence of CaCO3
and exchangeable Na. Higher pH in soils of P4 and
P5 were attributed to alkaline hydrolysis of
carbonates and bicarbonates in the presence of a
high amount of exchangeable Na (Srinivasan et al.,
2019). The presence of exchangeable bases brought
by runoff water in surface horizons to subsurface
and alternate wet and dry situations results in the
Fig. 2. Depth wise distribution of clay, AWC and OC contents
Coconut growing soils in South Eastern Ghats of Tamil Nadu
100
deposition of soluble salts in surface soils (Ram
et al., 2010). All the soil series had shown low EC
values varying from 0.10 to 1.64 dS m-1, indicating
non-saline nature. The low EC may be due to excess
water movement and frequently leaching of base
cations by percolating water. This could also
indicate the low status of cations and anions, which
are important in coconut nutrition (Nair et al., 2018).
The organic carbon content of the soils was
found low to high on the surface (0.17 to 2.70%)
and low to medium in subsoils (0.06 to 0.56%).
Surface soils recorded higher organic carbon content
than sub-surface soils due to increased litter, crop
residues, and the addition of manure and fertilizers
to the surface soils. Organic carbon content
decreased with depth in all the pedons (Fig. 2).
Brown to dark brown surface colour in all these
series compared to sub-surface horizons was due to
deposition of organic matter. Further, the organic C
was leached to lower layers and percolating water
leading to its loss from the surface soils (Leelavathi
et al., 2009). Cation exchange capacity (CEC) and
base saturation (BS) varied from 4.70 to 54.0 cmol
(p+) kg-1 and 77 to 100 per cent, respectively, which
corresponded to the clay content, organic C content
and also type of clay mineral present in the different
horizons of soils. The higher base saturation
observed in almost all pedons might be due to the
higher amount of Ca2+ occupying exchange sites on
the colloidal complex. The differences in base
saturation indicate the degree of leaching (Sharma
et al., 2011). CEC/clay ratio varied from 0.43 to
1.02, which indicated the nature of clay minerals in
different pedons. The exchangeable sodium
percentage (ESP) ranged from 4.29 to 33.46 per
cent, with high ESP in lower layers of P5 and P1.
This may be due to the occurrence of sodium ions
Table 4. Classification of major coconut growing soils in the study area
Pedons Series Soil taxonomy
Order Sub order Great group Sub group Family
P1 Nedungal Inceptisols Aquepts Endoaquepts Typic Endoaquepts Fine-loamy
P2 Pannanthur Alfisols Ustalfs Rhodustalfs Typic Rhodustalfs Fine-loamy
P3 Papparapatti Entisols Orthents Ustorthents Typic Ustorthents Sandy
P4 Arasampatti Inceptisols Ustepts Calciustepts Aquic Calciustepts Fine-loamy
P5 Puliyur Vertisols Aquerts Natraquerts Typic Natraquerts Fine
P6 Selakuttapatti Inceptisols Ustepts Haplustepts Fluventic Haplustepts Fine-silty
enriched through runoff water and parent materials.
The high CaCO3 may be due to the climate
responsible for the pedogenic processes resulting
in the depletion of Ca2+ ions from the soil solution
in CaCO3 with the concomitant increase in ESP with
different depths of soils. Free CaCO3 percentage
ranged from 0.0 to 24.6 per cent, and the highest
CaCO3 content was noticed in P4 soil, which might
be due to river flow accumulation and climate
change effect on pedogenic processes resulting in
the depletion of Ca2+ ions from calcite parent
materials (Khanday et al., 2017).
Soil classification
Based on the variation in soil development and
soil characteristics, different soil series were
identified, and family level classification was carried
out as per the keys to soil taxonomy (Table 4). These
soils were classified in the order Entisols (P3),
Inceptisols (P1, P4 and P6), Alfisols (P2) and
Vertisols (P5). Pedon 3, without any diagnostic
horizon, was classified as Entisols. Pedons 1, 4 and
6 have cambic (Bw) subsurface diagnostic horizons
and were classified under Inceptisols. The P2 series
having an argillic horizon with ochric epipedon and
were classified under Alfisols. Pedons 5 was placed
under Typic Natraquerts at sub-group level due to
the presence of sodic horizon (ESP > 15) and
showed intersecting slickensides, wedge-shaped
aggregates, more than 30% clay in all the horizons
and cracks (2-5 cm wide) in the B horizon resulting
in the development of Bss horizon, were classified
under Vertisols. The development of different kinds
of soils in a single region may be influenced by
variation in climatic condition, geology, sloping
position, water movement and vegetation over time
(Srinivasan et al., 2013).
Srinivasan et al.
101
Table 5. Evaluations and management of coconut growing soils
Pedons Series LCC LIC Major limitations Suitable management
1. Nedungal IIws 4ws Leveled low land with moderately Adopting suitable drainage system and
deep, slight erosion, slow runoff, deep pit plantation will be more sustainable.
poor drainage and low soil nutrients Addition of appropriate quantity of
status and high sodium content. manures and fertilizers will get more yields.
2. Pannanthur IIse 2sw Very gently slope with moderately Improving soil quality by addition different
deep depth, slight erosion, medium manures and management systems.
runoff and low AWC & poor Adopting appropriate soil and water
nutrients status. conservation measures and nutrient
management techniques.
3. Papparapatti IIIse 2st Very gently slope with deep depth, Adopting appropriate soil and water
slight erosion, medium runoff and conservation measures. Application of
low AWC & poor nutrients status. organic manures and adopting integrated
nutrient management (INM).
4. Arasampatti IIs 2w Leveled low land with moderately Adopting suitable drainage system and deep
shallow, slow runoff, somewhat plantation techniques. Application of
poorly drainage and low soil optimum dose of manures and fertilizers.
nutrients status and high sodium
content.
5. Puliyur IIsw 3wt Leveled low land with very deep, High clay rich soils should add excess
slow runoff, poor drainage and low organic manures and suitable drainage
soil nutrients status and high system. Application of different organic
sodium content. manures and remediation of deficient
nutrients.
6. Kottapatti IIsw 2wt Leveled low land with very Addition of excess organic measures (FYM,
deep depth, slight erosion, vermicompost, coir pith and compost etc)
slow runoff, somewhat poorly improving the soil quality and fruiting
drainage and low soil nutrients yield. Application of balanced fertilizer in
status. different age of the coconut system.
Interpretative groupings
Soils were interpreted and evaluated for land
capability and irrigability for coconut plantation
considering climatic conditions, soil depth, texture,
drainage, slope, AWC, CaCO3 and exchangeable
sodium percent. The categorization of soils into
capability classes and subclasses were done mainly
based on the number and severity of several
limitations viz., erosion risk (e), wetness (w), rooting
zone (soils) limitations (s) and climatic limitations
(c). The major coconut growing soils placed in the
land capability class of II, except P3, was grouped
in IIIse (Table 5). Land irrigability assessment
categorized different coconut growing soils into
2 to 4 with different subclasses wetness, topography
and soil limitations. Major constraints encountered
for coconut cultivation are shallow depth, poor
drainage, high sodium salts, low AWC and poor soil
fertility. Appropriate site-specific soil and water
conservation measures and the addition of organic
manures and fertilizers will overcome the limitations
and improve the productivity of coconut plantations
in the Eastern Ghat regions.
Conclusion
Extensive low soil depth, poor drainage, low
soil organic matter, high sodium level and
widespread nutrient deficiencies were major
Coconut growing soils in South Eastern Ghats of Tamil Nadu
102
limitations of coconut production in the Eastern
Ghat regions. Therefore, adopting appropriate site-
specific technologies will be achieving sustainable
productivity. The study will help identify potential
areas and yield-limiting soil parameters for the
scientific cultivation of coconut and ensure
sustainable production in this region.
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Coconut growing soils in South Eastern Ghats of Tamil Nadu
104
Coconut based integrated farming: A climate-smart model for
food security and economic prosperity
B. Sudha*, Jacob John, A.V. Meera, A. Sajeena1, D. Jacob1 and J.S. Bindhu
Integrated Farming System Research Station, Kerala Agricultural University, Karamana,
Thiruvananthapuram-695 002, Kerala, India
1On Farm Research Centre, AICRP on IFS, Kerala Agricultural University, Vellayani,
Thiruvananthapuram-695 522, Kerala, India
(Manuscript Received: 16-04-2020, Revised:07-06-2021, Accepted: 30-06-2021)
Abstract
A coconut-based integrated farming system (IFS) model suited for lowlands was developed at the Integrated Farming System
Research Station (IFSRS), Karamana, Kerala state, India, under Kerala Agricultural University. The area of the model was
decided as 0.2 ha, matching the average per capita land availability of a marginal farmer in the state. Apart from the major crop
coconut, intercrops, such as vegetables, fruit crops, spices, fodder and tuber crops were included in the model. The allied enterprises
integrated were livestock, azolla, and agroforestry. Tree components of the model comprised of teak, jack, breadfruit, garcinia
and mango. Research data for five years revealed that the model generated food products above the requirement of a four-member
family, and the surplus production could contribute to farmer’s income. The productivity under the IFS model was enhanced ten-
folds compared to that under the sole crop of coconut for the same area. Plant nutrients were generated within the farm through
organic recycling, which contributed to the substantial saving of chemical fertilizers. The system was found climate-smart because
of reduced use of chemical fertilizers and net negative emission of greenhouse gases mostly achieved through agroforestry. This
IFS model could also ensure considerable employment generation. The model could be adopted by farmers of lowland tracts of
Kerala having similar agro-climatic features for better economic returns and environmental benefits.
Keywords: Benefit:cost ratio, carbon sequestration potential, coconut equivalent yield, diversity index, greenhouse gas emission,
integrated farming systems, nutrient recycling, sustainability
doi:10.25081/jpc.2021.v49.i2.7256
Journal of Plantation Crops, 2021, 49(2): 104-110
Research Article
*Corresponding Author: sudha.b@kau.in
Introduction
Coconut has the status of a plantation crop
worldwide. Among the main coconut producing
states in the country, Kerala ranks first in area and
production. Coconut is raised in 7.61 lakh ha and
occupies 29.6 per cent of the gross cropped area.
However, with respect to productivity, it is fifth,
next to Andhra Pradesh, Tamil Nadu, West Bengal
and Karnataka (CDB, 2017-18). Unlike several
countries where coconut is grown in large gardens,
Kerala has a unique feature of coconut-based home
gardens that have evolved in response to the pressure
of shrinking land resources coupled with high
population density. Enhancement of productivity
from coconut gardens is extremely important, and
a switch to integrated farming becomes increasingly
important in this context.
Integrated farming is currently recognized as the
most viable strategy in enhancing agricultural
production and farm income. Optimum utilization of
farm resources through judicious recycling, on-farm
generation of organic manures to substitute chemical
fertilizers and generation of employment opportunities
around the year are the other major benefits of
integrated farming. Coconut, the major crop of coastal
tracts of India when grown as a monoculture, often
leads farmers to distress mainly due to crop loss
associated with the pest and disease incidences and
market price fluctuations. However, it has been
well established that resorting to coconut-based
105
integrated farming can enhance the yield from the
unit area and bring about a considerable hike in
farm income, apart from environment-friendly
effects resulting from saving fertilizers. The social
relevance of such systems also is quite promising,
as evidenced by the generation of more employment
opportunities.
Materials and methods
The experiment was carried out at the Integrated
Farming System Research Station (IFSRS),
functioning under Kerala Agricultural University at
Thiruvananthapuram in Kerala, India, a network
centre of the All India Co-ordinated Research
Project (AICRP) on Integrated Farming Systems,
the apex body coordinating research on integrated
farming at national level in India. The experimental
site was a lowland (5 m above mean sea level) and
was located at 8°28'25" N Latitude and 76°57'32"E
Longitude. The soil type was riverine alluvium of
sandy clay loam texture.
The station has developed and validated
integrated farming models matching Kerala’s major
crops/cropping situations. A highly successful
coconut-based integrated farming system model of
0.2 hectares has been developed by the station,
which especially suits the land area and other
resources of marginal farmers of the state. The
establishment of the IFS model was initiated in the
year 2011-12.
Research work carried out for five years suggests
that the model is quite successful in generating higher
yields, better income, and eco-friendly in terms of
on-farm generation of nutrients and providing a
negative balance of greenhouse emissions. The
various components included in the model are
detailed in Table 1.
Coconut trees (West Coast Tall) of 15 years of
age, established on bunds raised on the lowland, at a
spacing of 7.5 x 7.5 m, were selected. Water channels
of about 2 m depth were naturally formed between
the bunds due to soil excavation from the low lying
tract. These channels were utilized in raising fish
species viz., Genetically Improved Farm Tilapia
(GIFT). Different intercrops comprising of fruit trees
(papaya, rose apple), vegetables (vegetable cowpea,
ash gourd, amaranthus), spice crops (ginger, turmeric,
garcinia, cinnamon), fodder crops (guinea grass, para
grass), beverage crops (cocoa), tuber crops (cassava,
coleus) etc., were planted in the interspaces of
coconut. All along the boundaries of the model,
miscellaneous trees were planted, like breadfruit,
mango, jack etc. Azolla was raised in two separate
pits of 2 m2 each, and it served as fish feed. A dairy
unit comprising a cow and a calf was introduced as
an integral component of the model. A bee hive that
reared stingless honey bees helped in the pollination
of vegetables raised in the model. Once the model
was well established and yields stabilized by 2014-15,
research data were recorded.
On-farm generation of plant nutrients obtained
from the byproducts (crop residues, dung, cow’s
urine, stall wash and nutrient-rich silt from fish
pond) were quantified for five years starting from
2014-15. The fertilizer equivalent of plant nutrients
generated and the energy equivalent of fertilizers
saved was worked out for all these years.
The various products from the model were equated
as coconut equivalent yield (CEY) and worked out
for five years. The economics of the model in terms
Table 1. Coconut based IFS model (0.2 ha)
Components Net area Gross area
(m2)(m
2)
Coconut on bunds and adjoining area (30 nos.) 1480 1480
Teak trees along the border (15 nos.) 400 400
Multitier cropping (in interspaces of coconut) papaya (6), clove (1), nutmeg (1), cocoa (1),
rose apple (1), mango (1), jack (1), bread fruit (1), spices (ginger + turmeric), tuber crops,
fodder crops and azolla (in a shallow pit of size 2 x 1 x 0.2 m3, lined with silpaulin) 20 620
Cow unit: Cross bred (1+1) 100 100
Freshwater fish (GIFT: Genetically Improved Farm Tilapia) reared in trenches dug between
the bunds planted with coconut 0 400
Total 2000 3000
A climate-smart model for coconut farming
106
of gross returns, net returns and benefit: cost ratio
were also worked out. Greenhouse gas emission is
a measure of the carbon sequestration potential of
any IFS model, and the net emission of greenhouse
gases from the model was worked out for the years
2017-18 and 2018-19 as per the guidelines
suggested by the Inter-Governmental Panel on
Climate Change. The various sources for the release
of greenhouse gas were worked out based on farm
activities like the use of inorganic fertilizers, plant
protection chemicals, use of energy (operation of
farm machinery and consumption of fuel) etc.
Activities like crop residue recycling, the addition
of organic manures and sequestration of carbon in
soil and plant biomass, contributed mostly through
agroforestry, were accounted as sinks for
greenhouse gases. Carbon sequestered in the soil
and plant biomass was estimated as per the life cycle
approach suggested by Yadav et al. (2017). Also,
the employment generation potential of the system,
which is much related to the IFS model’s social
relevance, was worked out for all these years.
The sustainability yield index (SYI) for each
treatment was computed as suggested by Singh
et al. (1990).
SYI = (A-SD)/Ymax, where, A = average yield
over the years for a particular treatment, SD =
standard deviation for the treatment and Ymax =
maximum yield (average) obtained in any of the
treatments over the years. The diversity index was
calculated by computing the reciprocal sum of
squares of the share of the gross revenue received
from each individual farm enterprise in a single year.
Results and discussion
Nutrient generation from the model
Data on the generation of major plant nutrients
viz., nitrogen, phosphorus and potassium from the
coconut-based IFS model in different years under
the study are presented in Table 2. The major sources
of plant nutrients were crop residues, dairy outputs
including cow dung, cow’s urine, stall wash etc.
Nutrient-rich pond silt, which was excavated from
the fish rearing channels in alternate years, was also
a source of plant nutrients. The nutrient content of
these resources was estimated every year, and the
plant nutrient generation was quantified by
multiplying the nutrient content by the quantity of
each resource produced every year.
On an average, plant nutrients to the tune of
177 kg nitrogen, 89 kg phosphorus and 98 kg
potassium were generated annually from the model.
These nutrients were recycled into the system itself,
thereby saving considerable quantities of chemical
fertilizers. The fertilizers equivalent of the nutrients
generated is presented in Table 3. Chemical
fertilizers to the tune of 386 kg urea, 446 kg rock
phosphate and 163 kg muriate of potash could be
saved as a result of on farm generation of organic
manures. This way, an approximate amount of
`8236/- could be saved annually on fertilizers. Such
a considerable saving of fertilizers is definite to
impact the environment positively. Several
researchers have pointed out the need to shift from
fertilizer intensive nutrient management to an
integrated nutrient management strategy. Ghosh
(2004) opined that intensive use of fertilizers could
certainly damage soil quality in the long run. A
judicious blend of chemical fertilizers with organic
manures is always the right option for plant
nutrition. One of the harmful effects of chemical
fertilizers, as pointed out by Chandini Kumar et al.
(2019), include the release of toxins during the
manufacturing process, which can cause air
pollution. When the wastes of chemical fertilizers
are disposed of untreated in nearby water bodies, it
can cause more harmful effects such as water
pollution. In these ways, fertilizers as crop
production input are depleting our environment and
ecosystem. Any reduction of chemical fertilizers in
crop production by resorting to integrated nutrient
management is always appreciable.
Table 2. On-farm generation of major plant nutrients from the 0.2 ha model (kg)
Nutrients (kg) Period under study
2014-15 2015-16 2016-17 2017-18 2018-19 Average
Nitrogen 173 127 199 222 166 177
Phosphorus 68 69 105 117 87 89
Potassium 71 76 113 125 103 98
Sudha et al.
107
The energy equivalent of fertilizers saved was
worked out for individual years, and the average value
arrived is presented in Table 3. On an average, 12573
Mega Joules of energy could be saved every year by
saving fertilizers. Reports suggest that fertilizer
manufacture is an energy-intensive industry. It has
been estimated that fertilizer production accounts for
approximately 1.2 per cent of the world’s energy, of
which about 93 per cent is consumed by nitrogen-
based fertilizers (Yuan, 2014). To optimize energy
inputs and to reduce greenhouse gas emissions from
agricultural fields, a reduction in the quantity of
chemical fertilizers is much essential (Woods et al.,
2010). This can be attained by the on-farm generation
of plant nutrients through organic recycling, thereby
reducing the purchase of chemical fertilizers. In this
context, saving fertilizers, thereby bringing down the
energy expenditure on the same, as successfully
achieved in this coconut-based integrated farming
system model, is of considerable significance in
environmental benefits.
Carbon sequestration potential of the IFS model
The net emission of greenhouse gases from any
farming system model is a good indicator of the
carbon sequestration potential of the model.
Greenhouse gas emission from the model was
worked out for two consecutive years viz., 2017-18
and 2018-19 and the data is presented in Table 4.
The net negative emission of greenhouse gases
could be well related to the farming activities and
component selection followed in the model. The
model can be rated as climate-smart. Crop residues
and dung and urine obtained from livestock
components could generate considerable quantities
of plant nutrients, thereby reducing the quantity of
chemical fertilizers. The animal component was the
major source for greenhouse gas emissions,
contributing 91 and 79 per cent during 2017-18 and
2018-19, respectively. The role of livestock in GHG
emission is noticed through the release of methane
and nitrous oxide (Moran and Wall, 2011; Grossi
et al., 2019)
The inclusion of tree components in the system,
like teak, mango, jack, garcinia, rose apple,
breadfruit etc., along with the main crop coconut
(Table 1), contributed to the buildup of above and
below-ground plant biomass, which could sequester
much carbon. Agroforestry/ tree component
contributed over 90 per cent carbon sequestration
Table 3. Fertilizer equivalent of nutrients generated; price and energy equivalent of fertilizers saved
Year Fertilizers (kg) Energy equivalent Price equivalent
Urea Rock Muriate of fertilizers saved of fertilizer
phosphate of potash (mega joules) (` )
2014-15 376 340 119 12034 6721
2015-16 276 345 127 8971 6244
2016-17 432 525 189 13973 9522
2017-18 482 585 209 16152 10594
2018-19 360 435 172 11735 8097
Average 386 446 163 12573 8236
Table 4. Greenhouse gas emissions from the IFS model
Carbon emissions/ Components Period
sequestration
Source 2017-18 2018-19
kg CO2 equivalent
Crop (cropping systems/ horticultural crops / fodder) 141 462
Animals (livestock/ poultry/ fish) 1451 1747
Total 1592 2209
Sink Agroforestry 18313 18394
Biomass / compost incorporated 1576 1249
Total 19889 19643
Net emission -18297 -17434
A climate-smart model for coconut farming
108
during both the years - 18313 and 18394 kg CO2
equivalent in 2017-18 and 2018-19, respectively.
This justifies the negative value for greenhouse gas
emission from the model. Trees belonging to
Tectonia (Teak) species have very higher carbon
sequestration potential and thereby offer much to
ecosystem services (Pichhode and Nikhil, 2017).
The inclusion of 15 teak trees in this IFS model as
boundary planting justifies the higher C sequestration.
Carbon sequestration by trees belonging to 45 species
was estimated by Prasadan and Jithila (2018), and they
noticed higher sequestration rates with tree species
Artocarpus heterophyllus. The IFS model under study
included tree components belonging to Artocarpus
species viz., A. heterophyllus (breadfruit) and A. integrifolia
(jack). This also justifies the net negative emission of
greenhouse gases from the model.
More use of nitrogenous fertilizers results in
the release of important greenhouse gas nitrous
oxide. Integrated farming systems can help mitigate
the emission of greenhouse gases through the on-
farm generation of manures, thereby reducing the
reliance on nitrogenous fertilizers, as reported by
Pathak et al. (2014). This observation is in
agreement with the present study where on-farm
generation of plant nutrients largely saved external
purchase of chemical fertilizers.
Yield, economics and employment generation
potential of the model
Data generated on yield and economics of the
model are presented in Table 5. From the table, it is
inferred that the average value for coconut
equivalent yield is 13227 coconuts from an area of
0.2 ha. As the average value for coconut
productivity from 0.2 hectares in the state is only
1378 nuts, it is evident that a switch to the integrated
farming model can very well enhance production
up to even tenfold. This is of very high significance
as far as a marginal farmer is concerned. By shifting
to an integrated farming approach, the farmer could
well enhance production from the limited available
land area and could satisfy most of the nutritional
demands of the farmer’s household. The diverse
production from the 0.2 ha IFS model for the period
under study is detailed in Table 6. The annual
requirement for an average four-member family
including four adults under low-cost diet
management (Swaminathan, 2010) is presented.
Table 5. Yield, economics and employment generation from the IFS model
Year Coconut equivalent yield Gross returns Net returns B:C ratio Employment generation
(nuts 0.2 ha-1) (` ) (`) (man days per year)
2014-15 12213 183201 52781 1.40 79.5
2015-16 10776 183191 56922 1.45 69.0
2016-17 13704 232962 52595 1.28 101.0
2017-18 14274 242663 69055 1.4 90.0
2018-19 15170 257891 60919 1.31 87.0
Average 13227 219982 58454 1.37 85.3
Table 6. Diverse food products obtained from the model
Year Food products
Coconut oil (kg) Milk (litre) Fruits (kg) Vegetables (kg) Fish (kg)
2014-15 137 2434 105 558 133
2015-16 95 2884 35 322 81
2016-17 170 2030 445 1205 * 225
2017-18 95 2884 88 597 155
2018-19 85 3127 38 756 106
Average 116 2672 142 688 140
Annual requirement for
a four-member family (adults)
under a low-cost diet plan 66 183 44 429 120
(Swaminathan, 2010 )
*Cassava and ash gourd accounted as vegetables justify the enhanced vegetable production during 2016-17
Sudha et al.
109
The production from the model is higher than the
requirement, and surplus food items could be
marketed to generate income. The gross returns from
the model were estimated to be ` 2,19,982. The
average net income from the model was worked out
as ` 58,454. The benefit: cost ratio of the model
was 1.37 and remained higher than 1.0 all these
years. This indicates that the model is consistent in
generating better profits. The potential of an
integrated farming approach in enhancing farm
income and contributing to family nutrition, as
noticed in the present study, has been reported in
many studies. Swarnam et al. (2016) reported that
the inclusion of livestock components along with
diverse crops in an integrated farming system model
of 0.75 hectares could significantly enhance the food
diversity and help meet the dietary requirement of
a family of five members. Gurjar and Swami (2019)
reported integrated farming as the most viable
approach for attaining food and nutritional security.
Behera and Mahapatra (1999) reported that IFS
could enhance the income of small and marginal
farmers of India. In a study conducted in the semi-
arid tracts of the Telangana region, India, Ramana
and Sridevi (2017) reported that, with the adoption
of integrated farming (horticultural crops +
buffalo + goat), the net farm income enhanced
up to ` 1.5 lakh per hectare. In contrast, the average
income of farmers of the region was only ` 54,000
per hectare.
The social relevance of the model in terms of
employment generation potential is detailed in Table 5.
On an average, 85 person-days could be generated
from the model annually. Khan and Parashari (2018)
reported that integrated farming systems offer
employment opportunities during off-farm seasons,
especially for the care and management of livestock.
In a study conducted at Bulandshahr in U.P,
employment opportunities and income generation
were seen much enhanced for women labourers with
the adoption of the IFS approach.
Sustainability and diversity indices of the
model
From the study, the dairy component was
identified as the most sustainable (0.715). When
combined with other enterprises on scientific lines,
the dairy enterprise offers great opportunities for
increasing farm income, as reported by
Vinodakumar and Desai (2017). The diversity index
(value) was worked out for all the years under study.
From the second year onwards, there was a
considerable enhancement in DI, peaking in the third
year of study (3.316), attributed to the enhanced
Table 7. Sustainability index of the model
Enterprise Yield data (2014-2019) Mean Max. Standard Standard Sustainability
14-15 15-16 16-17 17-18 18-19 yield yield Error Deviation Index
(Ymax)
Main crop
(Coconut) 2170 1583 2267 1357 1133 1702.0 2267 223.07 498.80 0.531
Intercrops 663 357 1650 685 794 829.8 1650 217.53 486.31 0.208
Dairy 2434 2884 2030 2884 3127 2671.8 3127 195.67 437.53 0.715
Fisheries 2434 2884 2030 2884 3127 140.0 225 24.63 55.08 0.377
Table 8. Diversity index of the model
Enterprises 2014-15 2015-16 2016-17 2017-18 2018-19
Main crop (Coconut) 26030 19000 34000 19000 17000
Intercrops 7741 10111 34022 38731 58995
Dairy 109530 129780 97440 138432 150096
Fisheries 39900 24300 67500 46500 31800
Gross returns 183201 183191 232962 242663 257891
Mean 45800 45798 58241 60666 64473
SE 22240.47 28146.69 15265.63 26560.3497 29836.383
SD 44480.945 56293.384 30531.278 53120.699 59672.767
Diversity index 0.845 1.875 3.316 2.540 2.435
A climate-smart model for coconut farming
110
income from diverse components of the model. In
support of this, Swarnam et al. (2016) reported that
the inclusion of different components with diversified
species of crops in an integrated farming system could
contribute to income generation, thereby improving
the value diversity index of the system
Conclusion
Based on the results, it could be concluded that
the components were carefully selected in the
coconut-based integrated farming system model and
were found complementary. Resource recycling is
very well practised within the system. The model is
essentially climate-smart because of its less reliance
on chemical fertilizers, more carbon sequestration
and net negative emission of greenhouse gases. The
data suggest that diverse food production contributes
to family nutrition, and the surplus could be
marketed in generating income. Enhancement of
income from diverse components could also
contribute to a higher diversity index of the model.
The model is consistent in yield performance and
is profitable over the years. Lowland farmers of a
similar agro-climatic tract could well adopt this
model for food security and economic and
environmental benefits.
Acknowledgement
The authors are grateful to the Indian Institute
of Farming Systems Research (IIFSR-ICAR) and
Kerala Agricultural University for the financial
assistance and support for the project.
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111
Introduction
Ginger (Zingiber officinale Rosc.), 2n=(2x)=22,
a monocotyledon belonging to the family
Zingiberaceae, is a major spice and medicinal plant
that originated in South-East Asia and was later
introduced to many parts of the globe (Purseglove
et al., 1981; Burkill, 1996; Park and Pizutto, 2002;
Parthasarathy et al., 2011). It is a perennial
herbaceous but grown as an annual, erect, having
many fibrous roots, aerial shoots with leaves and
modified underground stem (Ravindran et al., 2005).
Ginger has been used as a medicine in Indian,
Chinese, and Arabic herbal traditions since ancient
times (Kizhakkayil and Sasikumar, 2011). In
addition, it possesses the potential to prevent and
manage many diseases due to the bioactive
compounds viz., gingerols and shogaols (Mao et al.,
2019). Moreover, it imparts flavour and pungency
to food and beverages. The characteristic
organoleptic properties of ginger are contributed by
the volatile oil and non-volatile compounds. The
essential oils and oleoresin have great importance
in export markets. Ginger rhizomes contain essential
oil (1 to 2.7%), oleoresin (3.9 to 9.3 %), crude fibre
(4.8 to 9.8%), and starch (40.4 to 59%) (Natarajan
et al., 1972) in addition to protein and minerals.
The hydro-distilled volatile oil of ginger is a
combination of monoterpenes, oxygenated
monoterpenes and sesquiterpenes; their relative
proportions depend on the genotype and are
influenced by geographical locations (Sharma et al.,
2002; Raina et al., 2005).
Variability of exotic ginger (Zingiber officinale Rosc.)
accessions for quality parameters
P. Anisha Babu*, N.K. Leela1, J. Venkatesh and D. Prasath1
College of Horticulture, University of Horticultural Sciences, Udyanagiri, Bagalkot-587 104, Karnataka, India
1 ICAR-Indian Institute of Spices Research, Kozhikode-673 012, Kerala, India
(Manuscript Received: 08-03-2021, Revised:12-04-2021, Accepted: 07-05-2021)
Abstract
Ginger (Zingiber officinale Rosc.) is one of the important and widely used spices throughout the world in fresh and dried forms.
The study on quality characterization and essential oil profiling of 13 ginger genotypes was conducted at ICAR-Indian Institute
of Spices Research, Kozhikode, Kerala, during 2019-2020. The genotypes included eight promising exotic accessions (Acc. 393,
Acc. 607, Acc. 736, Acc. 833, Acc. 869, Acc. 872, Acc. 873 and Acc. 874), four popular cultivars (Nadan, Himachal, Maran, Rio-
de-Janeiro) and a released variety IISR Varada. Significant differences among the genotypes were recorded for various quality
parameters such as oleoresin, essential oil and crude fibre content. Among the exotic genotypes, Acc. 869 recorded the highest
essential oil content (2.44%), followed by Acc. 393 (2.42%), Acc. 833 and Acc. 873 (2.10%). The accessions, Acc. 869, Acc. 874,
Acc. 873 and Acc. 393 recorded higher oleoresin content of 5.88 per cent, 5.63 per cent, 5.34 per cent and 5.28 per cent,
respectively. Considering essential oil and oleoresin contents, the exotic accessions, Acc. 873, Acc. 393 and Acc. 869 were
identified as promising genotypes. Among the other genotypes, Rio-de-Janeiro recorded the highest essential oil (2.76%) and
oleoresin content (6.69%). The exotic genotypes, viz., Acc. 607, Acc. 736 and Acc. 393 recorded crude fibre content of less than
5 per cent whereas, Acc. 869 recorded the maximum of 7.85 per cent. Fifty compounds were identified through essential oil
profiling, and the major classes were sesquiterpene hydrocarbons followed by monoterpene hydrocarbons. The major compound
identified was α-zingiberene and was highest in Acc. 393 (30.49%), followed by Maran (30.32%).
Keywords: Crude fibre, GC-MS analysis, ginger, oil profile, oleoresin, zingiberene
doi:10.25081/jpc.2021.v49.i2.7257
Journal of Plantation Crops, 2021, 49(2): 111-120
Research Article
*Corresponding Author: anishababup95@gmail.com
112
Ginger is multiplied vegetatively and thus have
limited chances for crop improvement through
conventional breeding. In such a species, the extent
of diversity will be too narrow unless the sample is
drawn from diverse agro-climatic conditions
(Ravindran et al., 2005). There are numerous studies
on the quality attributes and oil profiling of ginger.
Still, most of them are restricted to the assessment
and selection of naturally occurring clonal variations
of indigenous types. Pandotra et al. (2015) reported
that different genotypes from different regions were
distinct genetically and chemically. Hence, the study
was undertaken to identify the variability of quality
parameters among the promising exotic genotypes,
which is vital to the ginger improvement program.
Materials and methods
Plant material /genotypes
The experimental materials were drawn from
different agroclimatic zones, conserved at the
National Active Germplasm Site (NAGS) of ICAR-
Indian Institute of Spices Research (ICAR-IISR),
Kozhikode, Kerala state, India. Initially, 19 exotic
accessions viz., Acc. 17 (Jamaica), Acc. 130 (Nepal),
Acc. 393 (Taiwan), Acc. 428 (Jamaica), Acc. 430
(Fiji), Acc. 431 (Fiji), Acc. 578 (Nepal), Acc. 607
(Bhutan), Acc. 736 (Brazil), Acc. 833 (China), Acc.
850 (Malaysia), Acc. 866 (USA), Acc. 867 (USA),
Acc. 869 (USA), Acc. 870 (USA), Acc. 871
(Australia), Acc. 872 (Australia), Acc. 873
(Uganda), Acc. 874 (Vietnam), four popular
cultivars of ginger in India viz., Nadan, Himachal,
Maran and Rio-de-Janeiro, and a released variety
IISR Varada were characterized for morphological
and yield parameters. Based on the yield, eight high
yielding (≥18 t ha-1) accessions were selected and
characterized along with the popular cultivars for
quality attributes (dry recovery, essential oil,
oleoresin and crude fibre levels) and essential oil
profile (Table 1).
Experimental site and field management
The study was conducted at IISR, Kozhikode,
during 2019-2020 in randomized complete block
design (RCBD) with two replications. The
experimental field was located at 11°36'34" North
latitude and 75°49'12" East longitude and 60 m MSL.
The area falls under a warm, humid climate with an
annual rainfall of 4889.6 mm spread over 155 rainy
days. The mean monthly maximum and minimum
temperatures during the cropping period were
32.9°C and 23.8°C, respectively. For the same
duration, the maximum and minimum relative
humidity recorded were 94.5 per cent (January
2020) and 56.9 per cent (May 2019), respectively.
The maximum sunshine hours was recorded during
January 2020 (7 h), while the minimum was
recorded during August 2019 (0.9 h). The location
soil type was Ustic humitropept with clay loam
texture, acidic (pH 4.4) having an organic carbon
content 2.3 per cent, phosphorous 12 kg ha-1,
potassium 338 kg ha-1, calcium 719 kg ha-1,
magnesium 162 kg ha-1 and zinc 0.6 mg kg-1.
Field planting was carried out during May 2019
with the recommended package of practices
(Jayashree et al., 2015). The crop was cultivated as
a rainfed crop and harvested after seven months of
planting for quality characterization.
Quality parameters
The quality characterization among the
genotypes was undertaken with major biochemical
constituents viz., oleoresin, essential oil and crude
Table 1. List of ginger accessions used for quality analysis
Genotypes Country of cultivation
Exotic genotypes
Acc. 393 Taiwan
Acc. 607 Bhutan
Acc. 736 Brazil
Acc. 833 China
Acc. 869 USA
Acc. 872 Australia
Acc. 873 Uganda
Acc. 874 Vietnam
Popular cultivars in India (Check varieties)
Nadan (Acc. 21) Kottagiri, Tamil Nadu
Himachal Landrace from Himachal Pradesh, India
Maran Landrace from Assam, India
Rio-de-Janeiro Introduction from Brazil, popular ginger
cultivar in South India
Released variety
IISR Varada clonal selection, released from ICAR-IISR,
Kozhikode, India
Babu et al.
113
fibre contents. The essential oil profiling was
undertaken using GC-MS analysis (Shimadzu
QP-2010 gas chromatograph).
Dry recovery
The dry recovery was determined from peeled
sundried (10-12 days) rhizome samples after they
attained a moisture level of 10-11 per cent
(PPV&FRA, 2007). The dry recovery per cent was
calculated by noting the differences between fresh
and dry weight.
Oleoresin
Dried and powdered rhizome samples were
used for oleoresin extraction. Ten grams of sample
were transferred to a glass column (18 × 500 mm)
with a stop cock after packing the column with a
cotton plug. For extraction, 50 mL of acetone was
added to the column and allowed to stand overnight.
The filtrate was drained into a pre-weighed beaker.
After that, 30 mL of acetone was added to the same
column and drained for an hour. The acetone was
evaporated at a uniform temperature till the weight
of residue became constant. The quantity of
oleoresin was estimated gravimetrically (ASTA,
1997).
Oleoresin (%) = [weight of residue (g) /weight of
sample (g)] x 100
Crude fibre
Crude fibre content was estimated from dried
crushed ginger samples using Fibra plus FES 6 from
Pelican equipment. One gram of coarsely ground
sample was taken to subsequent digestion with acid
(1.25% H2SO4) and alkali (1.25% NaOH). Initially,
the sample was kept at 500 oC until it started to
boil; boiling was continued for 45 minutes at 400 oC
during both acid and alkaline digestion. Then the
digested samples were washed with distilled water.
Weight of residue was noted after it was free from
moisture CWBA (W1). After ashing the dried residue
in the muffle furnace at 500 oC, weight was noted
until a constant value CWAA (W2). Weight loss
during combustion in a muffle furnace represents
the crude fibre content (ASTA, 1997).
Crude fibre (%) = [W3 /W] x 100
[W3 (crude fibre) = W1 – W2; W= weight of sample
taken for estimation (1g)]
Essential oil
The essential oil was extracted by using
Clevenger-type apparatus (ASTA, 1997). Twenty
five grams of powdered sample was allowed to
continuously boil for 3 hours in a 1000 mL round
bottom flask with 500 mL of water. The extracted
oil was allowed to stand overnight to get clear
oil. Next day readings were noted, and the
collected oil was stored at -20 oC after adding a
pinch of anhydrous Na2SO4 to remove the residual
moisture for GC-MS analysis.
Essential oil (%) = [quantity of oil
collected (mL) /weight of sample (g)] x
100
The essential oil yield (L ha-1) and oleoresin
yield (kg ha-1) was calculated by multiplying the
dry yield per hectare of varieties with their
respective essential oil and oleoresin contents.
Gas chromatography-mass spectrometry
analysis
GC/MS analysis of oil samples was carried
out using Shimadzu GCMS QP 2010 Gas
chromatograph equipped with capillary column
RtX-5 (0.25 μm × 0.32 mm × 30 m). Helium was
used as the carrier gas at the flow rate of 1 mL
min-1. The injection port temperature was 250 oC
and the detector temperature 220 oC. The oven
was programmed: at 60 oC for 5 min; up to 110 oC at
5 oC min-1, then up to 200 oC at 3 oC min-1, again
up to 220 oC at 5 oC min-1; and finally at 220 oC for
5 min. (Ionization energy: 70 eV; Mass range:
60-450 amu; Split ratio - 1:40).
The constituents of the oil were identified by
comparison of retention indices with those
reported in Nist 08 library (Stein, 2008) and Wiley
library and Mass Spectra (Adams, 2007). Area
per cent was accounted as per cent composition
of respective compound.
Statistical analysis
Analysis of variance among the genotypes
for different quality parameters such as dry
recovery, essential oil, oleoresin and fibre content
was done according to Panse and Sukhatme
(1989), followed by a post hoc comparison of
means using the least significant difference (LSD)
test.
Variability of exotic ginger
114
Results and discussion
Eight high yielding (≥18 t ha-1) exotic
accessions were selected and compared with four
local cultivars and a released variety for various
quality parameters. The genotypes differed
significantly for important quality traits such as
essential oil, oleoresin and crude fibre except for
dry recovery (Table 2).
Oleoresin
Among the exotic accessions, higher oleoresin
content was recorded in Acc. 869 (5.88%) followed
by Acc. 874 (5.63%), Acc. 873 (5.34%) and
Sasikumar (2009) reported a lower oleoresin content
in improved varieties compared to primitive types
or landraces.
Oleoresin content mainly depends on the
genotype, solvent extraction conditions, state of
rhizomes (fresh or dried), the country of origin,
agroclimatic regions, harvest season and cultivation
practices (Ratnambal et al., 1987; Vernin and
Parkanyi, 2005; Connel, 1969). In the present study,
all the genotypes were cultivated in a uniform
environment; the variability observed among the
genotypes for oleoresin might be due to the effect
of genotypes, the country of origin or their place of
Table 2. Variability in quality parameters of 13 ginger genotypes
Genotypes Dry recovery Dry yield Essential oil Oleoresin Crude fibre Essential oil Oleoresin
(%) (t ha-1) (%) (%) (%) (L ha-1) (kg ha-1)
Acc. 393 21.56 5.24 2.42 5.28 4.25 126.88 1285.26
Acc. 607 17.89 5.58 1.62 3.65 3.50 90.05 1137.28
Acc. 736 20.92 3.76 1.62 3.40 3.95 60.99 612.04
Acc. 833 19.21 4.59 2.10 5.15 5.25 96.24 1229.80
Acc. 869 21.21 5.18 2.44 5.88 7.85 126.24 1433.68
Acc. 872 17.77 3.34 1.62 4.61 5.05 54.07 866.72
Acc. 873 18.52 6.91 2.10 5.34 5.45 144.65 1995.54
Acc. 874 16.95 5.43 1.64 5.63 4.85 89.65 1824.24
Nadan (Acc. 21) 19.96 3.08 1.62 3.52 4.15 49.88 543.54
Himachal 19.42 2.79 2.02 4.45 4.90 56.45 639.86
Maran 22.80 3.64 2.42 6.03 6.05 88.00 961.78
Rio-de-Janeiro 18.57 2.03 2.76 6.69 6.25 56.25 727.22
IISR Varada 20.33 2.94 1.62 4.01 5.05 47.68 580.39
Mean 19.62 4.19 2.00 4.89 5.12 1087.03 13837.35
CD @ 5% NS 1.31 0.15 0.09 0.55 23.47 112.29
CV (%) NS 14.31 3.34 0.89 4.90 12.89 4.842
NS: Non-significant
Acc. 393 (5.28 %), which is significantly higher
than the released variety IISR Varada (4.01%) and
the lowest in Acc. 736 (3.4%). The health benefits
of ginger are mainly due to the phenolic compounds
such as gingerols and shogoals (Mao et al., 2019).
Among 13 genotypes, the oleoresin content ranged
from 3.4 per cent (Acc. 736) to 6.69 per cent (Rio-
de-Janeiro) (Table 2). The highest oleoresin content
in Rio-de-Janeiro was also reported by Kallappa et al.
(2015) and Akshitha et al. (2020). Kizhakkayil and
collection. Therefore the high yielding exotic
accessions Acc. 873, Acc. 874, Acc. 869, Acc. 393
and Acc. 833 can be used as potential alternatives
for the commercial exploitation of oleoresin.
Crude fibre
Crude fibre content ranged from 3.5 per cent
in Acc. 607 to 7.85 per cent in Acc. 869 (Table 2).
Low fibre content varieties are preferred to produce
syrup, candy, and other confectionaries. However,
Babu et al.
115
high fibre content varieties can be used as dry ginger.
Latona et al. (2012) observed that high fibre was
coupled with therapeutic value in ginger. Among
the genotypes, low crude fibre was recorded in
exotic accessions such as Acc. 607 (3.5%), Acc. 736
(3.95%), Acc. 393 (4.25%) and in indigenous
genotype, Himachal. High crude fibre content
(>7%) was recorded in exotic type Acc. 869. All
other varieties recorded medium fibre content of 5 to
7 per cent. The crude fibre content of dry ginger
ranged from 4.8 to 9.0 per cent, as reported by
Natarajan et al. (1972). It also varies with variety,
stage of harvesting etc. (Ratnambal et al., 1987;
Vernin and Parkanyi, 2005).
Essential oil
Essential oil content in different genotypes in
the present study ranged from 1.62-2.44 per cent
(Table 2). Higher oil content of 2.76 per cent was
recorded in Rio-de-Janeiro, followed by exotic
accessions Acc. 869 (2.44%) and Acc. 393 (2.42%).
Indigenous cultivar Maran also showed higher oil
content of 2.42 per cent. Among the genotypes, lower
essential oil content of 1.62 per cent was reported in
other genotypes. Earlier reports also recorded a lower
essential oil content in IISR Varada (Kizhakkayil and
Sasikumar, 2009; Akshitha et al., 2020). Steam
distillation of ginger yields 0.2 to 3.0 per cent of
essential oil according to the origin and the state of
the rhizome (fresh or dried) (Van Beek et al., 1987;
Ekundayo et al., 1988). Therefore, high yielding
exotic accessions, Acc. 873, Acc. 393 and Acc. 869
can be of much use in the commercial exploitation
of essential oil for food, nutraceutical, cosmetic,
pharmaceutical and perfume industries.
Gas chromatography-mass spectrometry
analysis
The chemical composition of 13 ginger
genotypes was identified and quantified by GC/MS
analysis (Table 3 & Fig.1). Total 50 compounds
were identified in oil profiling of the present
investigation and grouped into different classes of
organic compounds: monoterpene and sesquiterpene
hydrocarbons, monoterpenols, sesquiterpenols,
esters, aldehydes and ketones. Sesquiterpene
hydrocarbons followed by monoterpene
hydrocarbons accounted for the major part of
essential oils.
α-zingiberene was the major sesquiterpene,
identified in the essential oil of all 13 genotypes.
The zingiberene content among the genotypes varied
from 19.28 per cent (Himachal) to 30.49 per cent
(Acc. 393). The higher zingiberene content was
reported in Acc. 393 (30.49%) followed by Maran
(30.32%) and Acc. 869 (28.92%). Zingiberene was
the major compound in fresh and dry ginger oil
(Sasidharan and Menon, 2010). The current results
are also in agreement with previous studies where
zingiberene was the principal component identified
from dry ginger oils (Robbers et al., 1996;
Kizhakkayil and Sasikumar, 2011; Choudhari and
Kareppa, 2013). The literature revealed that the
different genotypes from different regions were
distinct both genetically and chemically (Pandotra
et al., 2015). It might be the cause of variability for
zingiberene among the ginger accessions.
β-sesquiphellandrene and α-farnesene was the
second highest compound in most of the genotypes
(Table 3). In the present study, β-sesquiphellandrene
content was highest in Acc. 393 (15.39%) followed
by Acc. 869 (14.47%) and Maran (14.17%). The
lowest sesquiphellandrene content was recorded in
Acc. 872 (11.70%). α-Farnesene content varied
widely among the genotypes, and the highest was
observed in Acc. 833 (15.17%) followed by Nadan
and Himachal (14.52 per cent and 14.05 per cent),
respectively. The very low α-farnesene content of
4.17 per cent and 4.61 per cent was recorded in
Maran and Rio-de-Janeiro, respectively. The higher
per cent composition of the same was also reported
by Nigam et al. (1964) and Hassanpouraghdam
et al. (2011). Ar-Curcumene content among the
genotypes varied from 6.61 per cent (Acc. 393) to
9.03 per cent (Rio-de-Janeiro). A high amount of
α-curcumene and β-sesquiphellandrene were also
detected in all the studied ginger accessions
(Kizhakkayil and Sasikumar, 2011). The other
major sesquiterpene hydrocarbon compound was
β-bisabolene, which ranged from 2.09 per cent
(Acc. 872) to 6.07 per cent (Maran), and most of
them showed 2-3 per cent.
Among monoterpene hydrocarbons, camphene
content among the genotypes varied from 0.81
per cent (Acc. 874) to 3.42 per cent (Acc. 873);
β-phellandrene (1.68-3.14 %); β-myrcene and
α-pinene were also detected in notable amounts.
Variability of exotic ginger
116
The 1,8-cineole content ranged from 1.68 per
cent (Maran) to 4.2 per cent (Acc. 873). Trans-
nerolidol content in the present study also ranged
from 1.89 per cent (Acc. 872) to 3.31 per cent (Acc.
736), which was absent in exotic genotype Acc. 874;
neral (0.47-4.14%) and geranial (0.74-5.85%),
collectively referred to as citral, accounted for a
notable proportion in the current study and is
responsible for the lemon aroma in ginger oil
(Onyenekwe and Hashimoto, 1999). The geraniol
content varied from 0.53 per cent (Acc. 833) to 2.51
per cent (Acc. 873), which was absent in Acc. 393,
Fig. 1. GC/MS chromatograms of ginger accessions: (A) Acc. 393 (B) Acc. 869;
(C) Maran
Babu et al.
117
Table 3. Essential oil profile of 13 accession through GC/MS analysis
Compound Accessions
AI Acc. Acc. Acc. Acc. Acc. Acc. Acc. Acc. Nadan Himachal Maran Rio-de- IISR
value 393 607 736 833 869 866 868 867 (Acc. 21) Janeiro Varada
HYDROCARBONS
Monoterpenes
α-Pinene 932 1.05 1.02 0.71 0.70 0.85 0.41 1.30 0.24 0.35 0.90 0.74 1.08 0.64
Camphene 946 3.34 2.91 2.16 2.25 2.84 1.17 3.42 0.81 1.17 2.86 2.46 3.19 2.13
β-Pinene 974 0.19 0.16 0.11 0.09 0.15 0.11 0.30 0.05 0.05 0.11 0.07 0.16 0.08
β-Myrcene 988 0.63 0.98 0.74 0.68 0.53 0.61 1.21 0.33 0.48 0.8 0.68 0.77 0.64
α-Phellandrene 1002 0.09 0.09 0.06 0.08 0.07 0.05 0.15 0.03 0.40 0.10 0.14 0.12 0.07
β-Phellandrene 1025 2.01 2.40 1.79 2.02 1.68 1.73 3.02 1.31 1.37 2.33 3.14 2.51 1.7
α –Terpinolene 1086 0.16 0.18 0.13 0.14 0.13 0.10 0.24 0.11 0.11 0.17 0.12 0.17 0.15
Sesquiterpenes
Cyclosativene 1369 0.18 0.23 0.23 0.25 0.17 0.16 0.26 0.35 0.24 0.25 0.16 0.22 0.23
α –Copaene 1374 0.47 0.44 0.45 0.49 0.47 0.46 0.55 0.78 0.44 0.50 0.46 0.54 0.46
β-Elemene 1389 0.68 0.75 0.80 0.76 0.63 0.75 0.89 1.19 0.70 0.72 0.69 0.76 0.72
α-Bergamotene 1432 0.23 0.24 0.24 0.23 0.23 0.20 0.21 0.28 0.22 0.21 0.23 0.23 0.23
(Z)-β- Farnesene 1440 1.21 1.07 0.64 1.03 1.18 0.91 0.94 1.17 1.12 1.02 0.94 1.09 1.11
Alloaromadendrene 1458 0.31 0.24 0.24 0.23 0.45 0.53 0.37 0.40 0.25 0.24 0.33 0.35 0.25
Ar-Curcumene 1479 6.61 7.23 8.09 8.43 6.75 8.89 7.47 8.86 8.21 8.09 9.00 9.03 8.40
β- Selinene 1489 - 0.24 0.25 - 0.20 0.24 0.25 - 0.23 0.26 0.22 0.24 -
α –Zingiberene 1493 30.49 24.35 25.74 24.77 28.92 20.59 20.56 24.65 23.51 19.28 30.32 26.21 23.61
α- Murrolene 1500 1.53 - - - 2.19 - 1.17 1.53 - - - 0.99 -
α- Farnesene 1505 9.89 13.21 12.64 15.15 11.13 13.51 10.15 11.77 14.52 14.05 4.17 4.61 13.80
β-Bisabolene 1505 3.91 2.66 2.90 2.32 3.42 2.09 2.31 2.78 2.36 2.38 6.07 5.33 2.59
γ- Cadinene 1513 0.57 0.41 0.40 0.46 0.76 0.82 0.60 0.67 0.44 0.43 0.61 0.58 0.46
β-Sesquiphellandrene 1521 15.39 13.15 13.84 13.72 14.47 11.70 11.18 13.64 13.51 12.92 14.17 13.94 13.29
ALCOHOLS
Monoterpenes
β-Linalool 1095 0.41 1.00 0.74 1.05 0.53 1.08 0.99 1.06 0.98 1.23 1.99 1.52 1.08
Borneol 1165 1.30 1.17 1.09 1.42 1.28 0.73 1.14 1.06 1.19 1.74 1.07 1.20 1.58
Terpinen-4-ol 1174 0.10 0.11 0.10 0.11 0.08 0.09 0.15 0.12 0.11 0.15 0.08 0.13 0.12
α -Terpineol 1186 0.67 0.67 0.61 0.59 0.69 1.10 1.27 0.99 0.65 0.64 0.49 0.74 0.64
β-Citronellol 1223 0.10 0.45 0.47 0.36 0.15 1.09 0.75 0.58 0.49 0.68 0.39 0.20 0.48
Geraniol 1249 - 2.09 1.75 0.53 - 1.01 2.51 1.42 1.79 0.65 - - 0.72
Variability of exotic ginger
118
Sesquiterpenes
Elemol 1548 1.02 1.07 1.08 0.92 1.02 1.11 1.22 1.37 1.04 0.77 0.81 0.81 0.97
α-Bisabolol 1685 1.19 1.09 1.20 1.29 1.30 1.14 1.30 1.30 1.48 1.19 1.26 1.13 1.40
Zingiberenol (I) 1593 1.30 1.25 1.31 1.38 1.36 1.37 1.32 1.40 1.34 1.27 1.27 1.20 1.42
Zingiberenol (II) 1635 1.03 1.06 1.16 1.12 1.10 1.26 1.49 1.37 1.22 1.04 1.40 1.39 1.14
Sesquisabinene hydrate 1577 0.62 0.65 0.64 0.60 0.67 0.61 0.60 0.72 0.67 0.55 0.60 0.54 0.65
γ- Eudesmol 1630 0.32 0.39 0.37 0.30 0.39 0.45 0.40 0.42 0.35 0.24 0.31 0.29 0.31
β-Eudesmol 1649 0.85 0.91 0.98 0.77 0.93 0.93 0.81 0.91 0.97 0.65 0.64 0.74 0.82
Trans Nerolidol 1561 1.97 2.95 3.31 2.60 2.04 1.89 1.99 -3.20 2.18 2.02 2.20 2.54
Aliphatics
2- Heptanol 894 - 0.11 0.02 0.09 0.05 0.25 0.12 0.23 0.11 0.14 0.13 0.08 0.08
ALDEHYDES
Monoterpenes
Citronellal 1148 - - 0.08 0.07 0.06 0.14 0.12 - 0.08 0.18 0.26 0.22 -
Neral 1235 0.72 0.78 1.01 0.47 0.72 1.34 1.24 0.47 1.51 4.14 1.69 2.95 0.99
Geranial 1264 1.12 1.25 1.61 0.74 1.09 2.04 1.93 0.76 2.30 5.85 2.38 4.13 1.51
KETONES
Monoterpenes
Camphor 1141 0.05 0.04 - 0.05 0.05 0.05 0.08 0.1 0.03 0.05 0.05 0.08 0.04
Aliphatics
6-Mehtyl-5-hepten-2-one 986 0.19 0.44 0.42 0.21 0.19 0.54 0.47 0.19 0.40 0.13 - 0.08 0.18
2-Nonanone 1087 - 0.07 - 0.06 - 0.09 0.04 0.09 0.05 0.10 0.18 - 0.10
2-Undecanone 1293 - 0.12 - 0.20 - 0.28 0.09 0.44 0.14 0.32 0.44 0.09 0.23
Sesquiterpenes
Ar-Turmerone 1668 - - - 1.35 0.35 - - - - 0.31 0.59 - 1.30
Curlone (β Turmerone) 1705 - - - 0.94 - 0.15 - - 0.10 0.12 0.26 - 1.09
ESTERS
Monoterpene esters
Geranyl acetate 1379 0.05 1.31 0.93 0.05 - 0.41 1.02 0.74 0.91 0.23 - - 0.11
Bornyl acetate 1287 0.17 0.27 0.24 0.10 0.11 0.04 0.16 0.15 0.27 0.26 - - 0.18
β-Citronellyl acetate 1350 - 0.14 0.12 - - 0.16 0.17 0.12 0.11 0.09 - - 0.04
Miscellaneous
1,8 - Cineole 1026 2.86 2.33 1.94 1.95 2.72 3.05 4.20 2.03 1.57 2.00 1.68 2.61 1.73
Hexadecanoic Acid 1959 - - - - - - - 0.13 - - - - -
AI: Retention index
Babu et al.
119
Acc. 833, Maran and Rio-de-Janeiro. Other
oxygenated components identified in trace amounts
in ginger essential oil were: borneol (0.73-1.74%);
zingiberenol (I) and zingiberenol (II) (1.03-1.49%);
α-bisabolol (1.09-1.48%); elemol (0.77-1.37%),
β-linalool (0.41-1.99%), α-terpineol (0.49-1.27%),
terpinen-4-ol, citronellal, β-eudesmol, 2-heptanol,
γ-eudesmol, sesquisebanine hydrate and 6-methyl-
5-heptene-2-one. Vernin and Parkanyi (1994) had
reported the presence of zingiberenol (I),
zingiberenol (II) and 6-methyl-5-heptene-2-one
from the essential oil of ginger samples while
comparing the chemical composition of commercial
ginger oils from India and China.
The compounds cyclosativene and
alloaromadendrene were observed in very low
amounts, i.e., <0.5 per cent in all genotypes.
Similarly, these compounds were also reported from
the hydro distilled essential oil of air-dried ginger
from the Iranian herb market (Hassanpouraghdam
et al., 2011). The presence of compounds such as;
Ar- turmerone was observed in the essential oils of
Acc. 833, Acc. 869, Himachal, Maran, IISR Varada
and curlone from Acc. 833, Acc. 872, Nadan,
Himachal, Maran and IISR Varada.
Conclusion
The present study identified a considerable
extent of variability for various quality parameters
among 13 genotypes. The essential oil and oleoresin
harvest per hectare were significantly high for some
exotic accessions that can be commercially
exploited after the multi-location trials at different
agroclimatic conditions. The exotic genotypes with
lower crude fibre (<5%) are suitable for ginger
processing industries, and those with higher crude
fibre can be useful for dry ginger. Essential oil
profiling of the genotypes revealed that
sesquiterpene hydrocarbons were the major class
of compounds identified, followed by monoterpene
hydrocarbons.
Acknowledgement
The authors thank Director, ICAR-IISR,
Kozhikode, Kerala for providing the facilities for
carrying out the research work.
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Performance of coconut (Cocos nucifera L.) hybrids for yield
and quality in the Utkal plain region of Odisha state, India
S.C. Sahoo, S. Sumitha1*, A.K. Karna, G. Mishra and H.P. Maheswarappa1
AICRP on Palms, College of Agriculture (OUAT), Bhubaneswar-751 003, Odisha, India
1ICAR-Central Plantation Crops Research Institute, Kasaragod-671 124, Kerala, India
(Manuscript Received: 05-10-2020, Revised: 07-06-2021, Accepted: 30-06-2021)
Abstract
Cocos nucifera L. is a perennial oil yielding crop with a long productive life span (>60 years); thus, identifying a suitable high
yielding hybrid to a particular agro-climatic region plays a prime role in achieving sustainable coconut yield. In this context, an
evaluation trial with varietal cross combinations involving Tall × Dwarf (six crosses) and Dwarf × Tall (two crosses) was conducted
at All India Coordinated Research Project (AICRP) on Palms, Bhubaneshwar Centre, Odisha, for 15 years. The experiment was
laid out in randomized block design with four replications maintaining six palms per replication. Observations on yield and yield
attributing characters during 2018 to 2020 revealed the superior performance of ECT × GBGD (99.1 nuts), which was followed
by ECT × MYD (86.9 nuts) over the local check (ECT) by recording higher nut yield. Copra output per palm was significantly the
highest under ECT × GBGD (20.6 kg palm-1), followed by LCT × COD (18.6 kg palm-1). Hybrids possessed a higher quantity of
organoleptically ‘good’ tender nut water (270.3 to 354.1 mL) with TSS of 5.8 to 6.9 °Brix, 25.4 to 34.0 ppm of sodium and 2065.9
to 2885.0 ppm of potassium.
Keywords: Coastal, coconut, hybrids, tender nut water, yield
doi:10.25081/jpc.2021.v49.i2.7258
Journal of Plantation Crops, 2021, 49(2): 121-127
Research Article
*Corresponding Author: sumithasundaram12@gmail.com
Introduction
Coconut (Cocos nucifera L.) is one of the
important plantation crops in Odisha state, India.
The major area is confined to five districts, i.e., Puri
(9468 ha and 837 lakh nuts ha-1), Cuttack (5563 ha
and 360 lakh nuts ha-1), Ganjam (5163 ha and 267
lakh nuts ha-1), Nayagarh (4853 ha and 361 lakh
nuts ha-1) and Khurda (3638 ha and 255 lakh nuts
ha-1) (CDB, 2020). East Coast Tall (ECT) is the
dominant variety under cultivation in the state, with
an average yield of 80 nuts palm-1 year-1. Tall
cultivars are mainly grown for copra and oil
purposes, while dwarfs are preferred for tender nuts.
The coconut hybridization programmes aim to
develop heterosis for traits such as early flowering
and bearing, more nuts with high copra content and
wider adaptability to different agro-climatic regions.
So far, hybrid evaluation trials conducted involve
inter-varietal crosses of Tall × Dwarf (T × D) and
Dwarf × Tall (D × T) types. The superiority of
hybrids over local tall cultivars in terms of early
flowering, number of nuts per ha and copra per nut
were established (Satyabalan and Vijayakumar,
1982; de Taffin et al., 1991). The expression of
hybrid vigour is influenced by environmental factors
(Chapman et al., 2000). Hence, the evaluation of
hybrids in different locations is necessary to
ascertain their suitability to particular vicinity. A
good number of T × D, D × T and Tall × Tall (T × T)
hybrid combinations have been developed and
evaluated over the years in different locations viz.,
Kerala (Jerard et al., 2015), Assam (Nath et al.,
2017), Coastal Andhra Pradesh (Ramanandam et al.,
2017), Tamil Nadu (Vijay et al., 2017), Maharashtra
(Shinde et al., 2018) and Karnataka (Sumitha et al.,
2020) and released (Table 1). The productivity level
in the coconut growing regions of Odisha is very
low compared to the national average because of
the pervasiveness of local tall variety coupled with
122
non-adoption of scientific production technologies.
Identifying suitable coconut hybrids to a particular
agro-climatic region plays an important role in
achieving higher and sustained yield. As the
development of new varieties is a long-term process,
the immediate step is to evaluate already released
hybrids to assess their suitability for yield and tender
nut water. Keeping these points in view, the present
investigation was carried out for identifying better
performing coconut hybrids (T × D and D × T) for
cultivation in Uktal Plains of Odisha.
Materials and methods
A set of eight hybrids maintained at the All India
Coordinated Research Project on Palms, at
Bhubaneswar, situated at 20.15° N latitude, 85. 5°E
longitude and at an altitude of 25.9 m above MSL.
The soils are the loamy sand (textural class) type
with a pH of 5.8 and impeded drainage. The mean
maximum temperature ranges from 28.1 to 38.8°C,
and the minimum temperature ranges from 11.9 to
27.2°C. The average rainfall during experimentation
was 1450 mm, of which about 80 per cent was
received during the monsoon season from June to
October. The hybrid cross combinations included
released hybrids viz., VHC1 (ECT × MGD), VCH 2
(ECT × MYD), Godavari Ganga (ECT × GBGD),
Chandra Laksha (LCT × COD), Laksha Ganga
(LCT × GBGD), Konkan Bhatye Coconut hybrid 1
(GBGD × ECT), Kera Sree (WCT × MYD), GBGD
× PHOT, which was received from ICAR-CPCRI,
Kasaragod and AICRP on Palms, Veppankulam
Centre along with East coast Tall (ECT) as local
check planted during 2005 and evaluated for yield
performance till 2020. The details of parents are
presented in Table 1. The hybrids and check were
planted with a 7.5 x 7.5 m (with a density of 175
palms ha-1) in a randomized block design with four
replications @ 6 palms per replication under
irrigated conditions.
Morphological characters related to leaf,
inflorescence, fruit and fruit characters, tender nut
quality, viz. volume of water (mL), TSS (o Brix),
total sugars (g 100 mL-1), sodium (ppm) and
potassium (ppm) were recorded in the adult palms
during 2018-19 at the age of 15 years. Data
pertaining to nut production and estimated copra
out turn recorded from 2014 to 2020 (six years)
was used for assessing the performance of the
hybrids. The yield (nuts palm–1 year–1) was
recorded periodically during each harvest from
July to June and pooled to get the yield palm–1 year–1 .
Table 1. Details of parental palms used in hybridization programme
Hybrid cross Parental information Hybrid and
combination year of release
ECT × MGD Selection from East Coast Tall as female parent and Malayan Green Dwarf VHC 1 (1982)
as male parent (T × D cross)
ECT × MYD Selection from East Coast Tall as female parent and Malayan Yellow Dwarf VHC 2 (1988)
as male parent (T × D cross)
LCT × COD Selection from Laccadive Ordinary as female parent and Chowghat Dwarf Chandra Laksha (1985)
Orange as male parent (T × D cross)
LCT × GBGD Selection from Laccadive Ordinary as female parent and Gangabondam Laksha Ganga (1989)
as male parent (T × D cross)
WCT × MYD Selection from West Coast Tall as female parent and Malayan Yellow Kera Sree (1992)
Dwarf as male parent (T × D cross)
ECT × GBGD Selection from East Coast Tall as female parent and Gangabondam Godavari Ganga (1992)
as male parent (T × D cross)
GBGD × ECT Selection from Gangabondam as female parent and East Coast Tall as Konkan Bhatye Coconut
male parent (D × T cross) Hybrid 1(2007)
GBGD × PHOT Selection from Gangabondam as female parent and Philippines Ordinary Vasista Ganga (2014)
Tall as male parent (D × T cross)
ECT Local check ECT
Sahoo et al.
123
Fruit component analysis was carried out by
selecting mature nuts (12-months-old) of each hybrid
crosses by following the method prescribed by
Ratnambal et al. (2000). Copra yield per palm was
calculated based on the copra content per nut,
expressed as kg palm-1. The data on different
characters were subjected to statistical analysis as per
the standard procedures (Panse and Sukhatme, 1985).
Results and discussion
Growth and reproductive characters
The palm height and stem girth at the age of 15
years varied significantly among different coconut
hybrids (Table 2), and the cross combination, ECT
× GBGD and GBGD × ECT, recorded the lowest
palm height (4.2 m), while ECT × MYD recorded
the highest palm height (4.9 m). The girth at one
metre height was the highest in ECT × MYD and
LCT × GBGD (100 cm). The girth was significantly
lower in GBGD × ECT and GBGD × PHOT hybrids
(89.2 and 87.2 cm), which might be due to dwarf
female parents. Similar findings were also reported
by Nagwekar et al. (2002) and Ramanandam et al.
(2017). Higher the girth of the palm favours more
accumulation of biomass and hence increases the
carbon sequestration potential of the palms (Ghavale
et al., 2020). With respect to the number of
Table 2. Performance of coconut hybrids for growth parameters
Hybrid cross Palm height Girth at 1 m No. of functional Annual leaf Petiole length
combination (m) height leaves production (cm)
(cm) (crown) (Nos.)
ECT × MGD 4.4 90.9 33.0 12.7 150.6
ECT × MYD 4.9 100.4 32.0 12.7 153.0
LCT × COD 4.3 86.9 31.5 12.8 153.4
LCT × GBGD 4.3 100.5 31.9 12.5 152.9
WCT × MYD 4.3 90.5 32.4 12.8 151.1
ECT × GBGD 4.1 90.5 33.6 12.7 148.3
GBGD × ECT 4.2 87.9 31.1 12.8 161.6
GBGD × PHOT 4.5 89.2 32.6 12.7 155.9
ECT 4.6 94.6 31.9 12.7 151.6
SEm ± 0.07 1.59 0.55 0.21 2.65
CD (P=0.05) 0.22 4.80 NS NS NS
Table 3. Reproductive characters of coconut hybrids
Hybrid cross Age at first No. of No. of Fruit set Nut yield
combination flowering inflorescences female flowers percentage palm–1 year–1
(months) palm-1 palm-1 ( 2019 - 20)
ECT × MGD 69 12.6 271.9 32.4 88.1
ECT × MYD 70 12.7 283.6 32.8 93.2
LCT × COD 69 12.5 271.6 33.6 91.3
LCT × GBGD 64 12.5 281.0 33.3 90.3
WCT × MYD 69 12.7 279.9 31.5 87.9
ECT × GBGD 56 12.6 286.5 38.7 108.5
GBGD × ECT 50 12.7 285.7 31.6 90.1
GBGD × PHOT 52 12.6 271.9 33.5 90.9
ECT 72 12.5 231.2 34.6 80.1
SEm± 1.09 0.21 4.80 0.57 1.57
CD (P=0.05) 3.31 NS 14.53 1.73 4.77
Evaluation of coconut hybrid in Odisha condition
124
functional leaves, ECT × GBGD recorded the
maximum number of functional leaves on the crown
(33.6) and was on par with other hybrids except for
GBGD × ECT. Nampoothiri et al. (1975) reported
that the number of leaves present on the crown was
positively correlated with yield in coconut.
However, annual leaf production and petiole length
did not differ significantly among the hybrids.
Regarding age (month) at first flowering, the
hybrids showed significant differences for the trait
(Table 3). The earliest to flower was GBGD × ECT
cross combination (50 months after planting), and
it was on par with GBGD × PHOT (52 months)
followed by ECT × GBGD (56 months) with regular
bearing habit. The earliest flowering was noticed
in cross combinations where GBGD (dwarf cultivar)
was involved as the female parent; these results are
in line with the early reports by Ramanandam et al.
(2017) and Sumitha et al. (2020). The total number
of female flowers per palm exhibited significant
differences among the hybrids evaluated. The
highest numbers of female flowers per palm were
observed in ECT × GBGD (286.5) and on par with
GBGD × ECT (285.7), GBGD × PHOT (271.9) and
ECT × MYD (283.6). Nut yield in coconut palm
can be increased by increasing the number of female
flowers per inflorescence, and it is the most
important criterion for yield. Hence, improved nut
yield (108 nut palm-1) observed during the present
study could be justified. In their study, Kannan and
Nambiar (1974) indicated that high yielding hybrids
produced a higher number of female flowers. Fruit
setting percentage of coconut is an important trait
Table 4. Nut yield of coconut hybrids over a period of six
years (Nut yield palm–1 year–1)
Hybrid cross 2014-16 2016-18 2018-20
combination
ECT × MGD 22.6 56.1 82.1
ECT × MYD 25.5 58.7 86.9
LCT × COD 19.4 54.1 85.5
LCT × GBGD 25.7 56.1 82.5
WCT × MYD 28.4 57.5 81.9
ECT × GBGD 25.4 63.0 99.1
GBGD × ECT 23.2 56.2 84.1
GBGD × PHOT 18.9 54.1 83.9
ECT 22.8 52.5 73.2
Fig. 1. Nut yield of coconut hybrids over a period of six years
Sahoo et al.
125
influencing the nut yield, and in the present study,
it was within the range from 31.5 to 38.7, and the
maximum fruit set was obtained in ECT × GBGD
and the lowest in WCT × MYD. However,
variations in the fruit set percentage of different
cross combinations in coconut were also observed
(Thomas et al., 2012; Nath et al., 2017; Sumitha et al.,
2020). In coconut, inter-spadix overlapping of
female and male phases is important for fruit set
and cross-pollination from nearby palms
(Henderson, 1988).
Yield and yield attributing characters
A wide deviation was observed for nut yield in
coconut hybrids (Table 4, Fig. 1) from 2014 to 2020.
From 2014 to 2016, the average biennial yield was
low due to waterlogging conditions in the field. It
was rectified by providing adequate drainage and
improved management practices. Among the
different hybrid combinations, the mean nut yield
per palm during 2018-2020 was significantly higher
in ECT × GBGD (99.1 nuts), which was followed
by ECT × MYD (86.9 nuts) and on par with LCT ×
COD, GBGD × PHOT, GBGD × ECT and LCT ×
GBGD. The ECT (local check) recorded 80 nuts
palm-1 year-1. Hybrids under favourable weather
conditions and high input management practices
have performed better than the local Tall. The high
yield potential of the hybrids is mainly due to
precocity conferred on the hybrids by their dwarf
parents (Bourdeix, 1999; Ohler and Magat, 2001).
The number of nuts harvested to the number of
female flowers produced is the most important
yardstick for consideration. The study indicated that
the nut yield in coconut (Table 3) could be increased
by increasing the production of the female flower
per inflorescence (Patel, 1938). Similar results of
higher nut yield per palm in hybrids were reported
by Sumitha et al. (2020), Shinde et al. (2018),
Ramanandam et al. (2017), Nath et al. (2017) and
Basavaraju et al. (2011).
The variations obtained in fruit component
traits are given in Table 5. The fruit length and fruit
breadth were significant among the hybrids, and the
highest fruit length was recorded in ECT × GBGD
(24.2 cm) and was on par with LCT × COD and
GBGD × PHOT and the lowest was recorded in ECT
(20.1 cm). The fruit breadth ranged from 14.2 to
16.1 cm. Fruit length and breadth are generally
greater in the tall palms than dwarfs (Ratnambal et al.,
2000). The fruit weight was significantly higher in
LCT × COD (1398.5 g) which was on par with ECT
× GBGD (1365.0 g). The cross combination LCT ×
COD recorded significantly higher copra content
(210.5 g nut-1), followed by LCT × GBGD (195.3 g
nut-1) and ECT × GBGD (190 g nut-1). Copra output
per palm was significantly higher in ECT × GBGD
(20.6 kg palm-1) followed by LCT × COD (18.6 kg
palm-1), while it was significantly low in ECT
(12.0 kg palm-1). Similar observations were also
reported by Ramanandam et al. (2018) that higher
copra output in ECT × GBGD could be ascribed to
higher nut yield and copra content.
Table 5. Performance of coconut hybrids for fruit components
Hybrid cross Fruit length Fruit breadth Whole nut weight Copra weight Copra output
combination (cm) (cm) (g) (g nut-1) (kg palm-1)
ECT × MGD 22.9 16.1 1105.3 180.7 15.9
ECT × MYD 22.8 14.5 1186.0 168.7 15.7
LCT × COD 23.6 16.2 1398.5 210.5 18.6
LCT × GBGD 21.9 14.9 1073.0 195.3 17.6
WCT × MYD 22.6 15.1 1095.0 184.6 16.2
ECT × GBGD 24.2 14.9 1365.0 190.0 20.6
GBGD × ECT 22.1 13.5 1040.0 161.0 14.5
GBGD × PHOT 23.3 15.5 1303.7 182.3 16.6
ECT 20.1 14.2 913.5 149.5 12.0
SEm± 0.39 0.25 19.96 3.16 0.28
CD (P=0.05) 1.18 0.78 60.35 9.57 0.87
Evaluation of coconut hybrid in Odisha condition
126
Tender nut quality parameters
The quality and acceptability of tender nut
water are influenced by the harvesting time of nut,
variety, agro-climatic conditions and management
practices. Tender nut water content was significantly
higher in GBGD × PHOT (362.0 mL nut-1), which
was on par with ECT × GBGD (354.0 mL nut-1),
LCT × GBGD (352.4 mL nut-1) and was
significantly less in ECT (257.7 mL nut-1). The TSS
was maximum in ECT × GBGD and LCT × GBGD
(6.9o Brix) (Table 6). The sodium and potassium
contents of 34 ppm, 2885 ppm and 29.7 ppm and
2602.5 ppm, respectively, were recorded in ECT ×
GBGD and LCT × GBGD cross combinations. The
significant difference in quality-related traits might
be due to the utilization of GBGD as one of the
parents (hybrid combinations). These findings are
in concurrence with the previous report from
Karnataka by Sumitha et al. (2020), who observed
that GBGD × PHOT and GBGD × LCT and GBGD
× FJT were better performing hybrids for tender nut
purposes as they recorded higher tender nut water,
TSS and optimal levels of sodium and potassium.
Coconut improvement through the production
of hybrids is a tedious and time-consuming process
mainly because of its long gestation period, larger
area, and complex resources required for
experimentation, and the immediate step is
evaluation of already released hybrids to assess their
suitability to Odisha conditions. With all the above
said quantitative and qualitative characteristics, the
hybrids ECT × GBGD and GBGD × PHOT released
from Andhra Pradesh proved to be the best not only
for yield but also for tender nut purposes for the
Utkal plain region of Odisha.
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ECT × MGD 270.3 6.7 5.0 28.7 2480.8
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Evaluation of coconut hybrid in Odisha condition
128
Introduction
Coconut milk residue (CMR) is one of the by-
products obtained during the extraction of coconut
milk during the production of packaged milk or
coconut milk powder, flavoured coconut milk or
virgin coconut oil (VCO). The processing of 500
coconuts generates approximately 25 kg of coconut
milk residue (Beegum et al., 2016). Per cent
recovery of coconut milk residue, based on
pulverized gratings, generally ranges from 38.5 to
55.6 (Manikantan et al., 2016). Dried and powdered
coconut milk residue with a moisture content of 2.86
per cent consists of 5.29 per cent crude protein,
49.24 per cent crude fat, 25.51 per cent crude fibre
with 46.50 per cent dietary fibre and 0.93 per cent
ash content (Manikantan et al., 2015). The fibre
content present in coconut milk residue is higher
than cereals, including rice bran, oats, and barley
bran; in fact, it is double than wheat bran (Lalitha,
2014). Further, Sindurani and Rajamohan (1998)
stated that neutral detergent fibre extracted from
coconut kernel effectively reduced serum total
cholesterol, LDL cholesterol and triglycerides
concentrations in rats. Characterization of CMR
revealed that the soluble, insoluble and total dietary
fibre content in CMR is 2.7 per cent, 28.4 per cent
and 31.1 per cent, respectively, which was more than
those present in fruits such as orange, peach and
pear (Ng et al., 2010; Gunathilake et al., 2009).
Incorporation of coconut milk residue in pasta: Influence on
cooking quality, sensory and physical properties
P.P. Shameena Beegum*, Monika Sharma1, M.R. Manikantan*, R. Pandiselvam
and R.K. Gupta2
ICAR-Central Plantation Crops Research Institute, Kasaragod-671 124, Kerala, India
1ICAR-National Dairy Research Institute, Southern Regional Station, Adugodi, Bengaluru-560 030,
Karnataka, India
2ICAR-Central Institute of Post Harvest Engineering and Technology, Ludhiana-141 004, Punjab, India
(Manuscript Received: 21-01-2021 , Revised:23-04-2021, Accepted:28-05-2021)
Abstract
A study was conducted to explore the potentiality of coconut milk residue (CMR) for cold extrusion (pasta preparation). Proximate
analysis revealed that coconut milk residue is a rich source of crude fibre (24.03%) in addition to crude fat (41.55%), crude
protein (5%), total carbohydrates (26.24%) and ash content (0.97%) at 2.23 per cent moisture. The effect of coconut milk residue
upon replacing durum wheat semolina on cooking qualities, colour parameters, textural property and overall sensory acceptability
of pasta samples were evaluated. Incorporation of coconut milk residue significantly influenced the observed parameters (P<0.01).
Cooking time was unaffected by incorporating milk residue up to 10 per cent (P<0.05). Though the addition of residue increased
the gruel loss (0.84 to 1.34%), the per cent loss was below the technologically acceptable limit (<8%). A similar effect was
visualized in water absorption. Conversely, the firmness gets reduced with an increased concentration of coconut milk residue
beyond 10 per cent. Pasta with 5 per cent and 10 per cent coconut milk residue were accepted as that of control by the sensory
panel. Free fatty acid content was not affected by the period of storage (P>0.05). Thus, the study recommends incorporating 10
per cent coconut milk residue in durum wheat semolina for pasta preparation. Moreover, the entrepreneurs engaged in the
coconut milk/milk powder and virgin coconut oil industry would be benefitted by adopting this venture, wherein they would
be able to fetch huge additional income by placing their residue product on an upgraded fast-moving consumer good (FMCG)
value chain.
doi:10.25081/jpc.2021.v49.i2.7259
Journal of Plantation Crops, 2021, 49(2): 128-135
Research Article
*Corresponding Authors:shameena.pht@gmail.com, manicpcri@gmail.com
129
CMR is effective against diabetes, colon cancer,
and coronary heart diseases (Trinidad et al., 2003).
Despite being an excellent source of nutrients, its
use is limited to only a few baking industries and is
mainly utilized for animal feed or haphazardly
thrown as waste (Trinidad et al., 2003; Manikantan
et al., 2016). CMR was explored for protein and
dietary fibre enrichment in wheat bread with up to
20 per cent substitution of wheat flour (Gunathilake
et al., 2009). Manikantan et al. (2015) attempted
the feasibility of CMR in hot extrusion and found
that it could be very well utilized in extrusion.
However, limited studies have been undertaken to
explore the feasibility of CMR for cold extrusion
processing.
Pasta is a convenient food prepared through the
cold extrusion method. It is the most common
processed cereal product next to bread, which people
of all ages relish. Pasta products are healthy,
delicious and convenient, especially for working
women. It is one of the fastest-growing categories
in the packaged food market in our country,
particularly in urban clusters. Wheat is usually used
for pasta preparation because of its gluten content.
The addition of water causes the plasticization of
proteins, thereby shaping the wheat flour or
semolina during extrusion. Due to the properties of
gluten and the natural yellow pigment, durum wheat
semolina is preferred for pasta making (Padmaja et
al., 2015). With the backdrop of several health
benefits associated with coconut milk residue,
especially as a source of good fibres and further its
limited utilization in food products, the present study
has been planned to explore the potential of fibre
and other components present in the CMR to make
pasta. Utilizing CMR in pasta making would serve
as a practical solution for residue management and
provide an additional source of income for the
stakeholders involved in coconut processing.
Materials and methods
Raw materials
Semolina was purchased (‘Rajdhani’ brand) from
the local market. CMR obtained after extracting
coconut milk was dried at 60-65°C in a tray dryer
up to 2.5 per cent moisture level and sieved
(60 BSS 0.251 mm) by a standard sieve shaker.
Pasta preparation
Preliminary standardization revealed that
replacement of semolina with more than 20 per cent
affects the shape and texture of pasta during
cooking. Hence, semolina was supplemented with
CMR up to 20 per cent (0, 5, 10, 15 and 20%). The
mixing chamber of the cold extruder was filled with
the prepared flour (Model: Dolly, La Monferina,
Asti, Italy). Further, water was added and mixed
uniformly for 10 min. A metal extruder attachment
was placed in the machine fitted with a spiral-shaped
die. The length of each pasta was fixed to 5cm.
Samples were then dried at 50 ±5oC for 5 h in a hot
air oven till they attained 6-7 per cent moisture. It
was packed in 100μ thick polyethylene bags without
any preservative under ambient conditions
(32+ 3°C) till further use.
Cooking time and cooking quality evaluation
Pasta samples were cooked as per the method
followed by Yadav et al. (2014). Pasta sample (25 g)
was added to 250 mL boiling water (in a 500 mL
glass beaker) and cooked until the disappearance
of the hard central core. Time taken for cooking
was noted.
Solids lost in cooking water (gruel loss) and
water absorption was found using the following
formula (AACC method 66-50). Cooking loss was
determined in water collected from each sample
after cooking by evaporation to constant weight in
a hot air oven at 105°C.
Weight of dry residue
Gruel loss (%) = x 100 (1)
Initial weight of raw pasta
Weight of cooked pasta-weight of raw pasta
Water absorption (%) = x 100 (2)
Weight of raw pasta
Colour
The colour of the pasta samples (raw and
cooked) was measured using a Hunter Lab
colorimeter (Mini Scan XE Plus). Since the pasta
samples were light yellow, L (lightness) and b
(yellow-blue) values were observed. L represents
black to white (0-100), +b is yellow and -b is blue.
The measurements were performed in two
replications and repeated 3 times per replicate.
Incorporation of coconut milk residue in pasta
130
Textural analysis
The texture of the cooked pasta samples was
measured using a Texture Analyzer (TA-HDi,
Stable Micro Systems Ltd., Surrey, UK). The
settings were followed, as stated by Yadav et al.
(2014). Maximum force in the force-time graph
was taken as firmness. Six measurements were
taken for each sample.
Sensory acceptability
A panel of 10 semi-trained judges evaluated
the sensory acceptability of cooked pasta samples.
100 g sample was cooked in 500 mL water added
with 3 g table salt. After completion of cooking,
excess water was drained and 25 g of cooked pasta
from each treatment were served to the panel for
comparing evaluating the sensory attributes such
as appearance, flavour, taste, texture, mouthfeel, and
overall acceptability (OA) using a nine-point
hedonic scale. The overall acceptability was
considered for the statistical analysis.
Storage studies
The optimized pasta sample containing CMR
packaged in low-density polyethylene (LDPE)
pouches and stored at ambient temperature (33±3oC)
was analysed for changes in free fatty acid content
during the three months storage at 30 days interval.
To estimate FFA, crude fat was extracted using
soxhlet apparatus at every 30 days interval. Fat (1 g)
was mixed with 10 mL neutral solvent mixture
(Diethyl ether and ethanol as 1:1) and titrated against
0.01 N KOH with phenolphthalein as indicator. The
persistence of pink colour for 15 seconds was taken
as the endpoint.
Statistical analysis
The data obtained were analyzed with the
analysis of variance (ANOVA) using a completely
randomized design (CRD), and the significance
among the mean values was tested using the least
significant difference (LSD) method
Results and discussion
The proximate composition of the raw materials
is given in Table 1, which is in accordance with the
earlier reports on the composition of semolina and
coconut milk residue, respectively (Kaur et al.,
2012; Manikantan et al., 2015). Crude fibre obtained
from CMR was 24.03 g 100 g-1, indicating the
feasibility to enrich the fibre content in pasta. The
crude fibre content in rice bran and barley bran are
reported to be 11.5 per cent and 14.9 per cent,
respectively (Kaur et al., 2012). The fibre content in
whole oats is 12.45 per cent (Usman et al., 2010).
Thus, it is evident that coconut milk residue is a rich
source of crude fibre compared to all these cereals.
Similarly, the protein content obtained from CMR
(5%) was comparable with that of maize (5.5%), as
reported by Hager et al. (2012). CMR is also rich in
crude fat (41.55%), especially with medium-chain
fatty acids that give immense health benefits. The
addition of fat improves the pasta texture and makes
dough that easily extrudes from the machine apart
from enhancing flavour (Fuad and Prabhasankar,
2010). Trinidad et al. (2006) mentioned the relevance
of CMR substituted foods with respect to their healthy
fatty acids, low glycemic index and weight
management. In addition, a good amount of minerals
(in terms of ash content) is present in CMR (0.97%),
which is higher than that of semolina. It contains only
26.24 per cent carbohydrates indicating its role as a
low carbohydrate product than wheat, rice and other
cereals.
Preparation of pasta
Preliminary experiments revealed that
replacement of semolina with more than 20 per cent
CMR resulted in complete disruption of the shape
during cooking. Hence the level of incorporation
Table 1. Proximate analysis of the ingredients - Average composition (g 100 g-1) ± SD
Moisture Crude protein Crude fat Total carbohydrates Crude fibre Ash
CMR 2.23±0.31 5.00±0.02 41.55±0.13 26.24±0.67 24.03±2.2 0.97±0.02
Wheat semolina 13.5±0.14 11.67±0.15 1.79±0.03 71.07±0.24 1.42± 0.03 0.5±0.02
Values represented as mean of three replications with standard deviation
Beegum et al.
131
of CMR in pasta was fixed at 0, 10, 15, and 20
per cent.
Cooking quality
Pasta quality is primarily decided by the
cooking performance, which is the ultimate test of
acceptability of pasta. During cooking, volume
increases and dry matter loss occurs while
maintaining the shape without any disintegration
(Cleary and Brennan, 2006). The structural changes
include starch gelatinization and protein
coagulation.
The average time taken for cooking in CMR-
supplemented samples varied from 6.5 to 8.0 min
(Table 2), which was lesser than the cooking time
of traditional durum wheat pasta, i.e. 7.0 to 9.0 min
(Petitot et al., 2010). Jalgaonkar et al. (2019)
reported a cooking time of 5.15 min as one of the
desirable traits of pasta. Substitution up to 10 per
cent CMR did not show any significant effect on
cooking time (P<0.05). As the concentration
increased further, it took more time to cook, which
might be due to the fibres present in CMR, and it
took more time for the disappearance of the centre
core of pasta. Semolina pasta supplemented with
mushroom powder (0-12%) and defatted soy flour
(0-15%) also lead to an increase in cooking time
(Kaur et al., 2013). In addition, enrichment of pasta
with protein through CMR might also result in
increased cooking time.
Gruel loss
Gruel loss is the total solids leached out in gruel
during the cooking of pasta, or it is the resistance of
pasta against disintegration. The lesser the amount
of solids in the cooking water, better is the quality
of pasta (Pagani et al., 2007). The gruel loss in the
pasta samples was varied from 0.84 to 1.34 per cent
(P<0.05) (Table 2). The lowest gruel loss was
observed for the control pasta, whereas the highest
was for pasta with 20 g CMR 100 g-1 semolina. The
addition of CMR with comparatively larger particle
size may increase the cooking loss by loosening the
compact structure of pasta (Padmaja et al., 2015).
Nevertheless, per cent gruel loss was lesser than
previous reports (Eman et al., 2012, Yadav et al.,
2014). Pasta samples containing 5 per cent and 10
per cent CMR showed no significant difference in
the gruel loss during cooking (P>0.05). The addition
of non-gluten additives causes more disruption to
the structure resulting in higher cooking losses
(Piwinska et al., 2015). Also, the weaker starch-
protein interaction might get disrupted during
cooking. Gunathilake and Abeyrathane (2008)
observed an increasing tendency for breakage of
noodles at a higher level (more than 20%) of coconut
flour addition due to lowering the gluten content.
Nonetheless, the cooking loss in all the samples was
below the technologically acceptable limit as per BIS
standard (≤8%) (Jalgaonkar et al., 2019). As a thumb
rule, the residue shall not surpass 7-8 per cent of the
dry weight of pasta (Gull et al., 2015). The addition
of stabilizers such as carboxymethyl cellulose could
be suggested for more than 10 per cent CMR levels
so that the residual loss during cooking can be
avoided.
Water absorption
It is the absorption of water gram-1 of dry pasta
and a test for the nature and type of ingredients and
its capability to absorb and hold water. Significant
variation was observed (P<0.05) among the samples
(Table 2). As the level of incorporation of CMR
Table 2. Effect of CMR on cooking quality and textural parameter of pasta
Level of CMR Cooking time Gruel loss Water absorption Firmness
(g 100 g-1) (min) ** (%) ** (%) ** (N) **
0 6.53±0.03c0.84 ±0.15c114.52 ±0.31e24.43 ±0.37a
5 6.58±0.01 c 0.95 ±0.07bc 131.66 ±2.09d21.78±0.74b
10 6.71 ±0.25c0.96 ±0.05bc 145.72 ±1.37c21.90±0.06b
15 7.03±0.06b1.10±0.14b150.03 ±0.22b18.78 ±0.22c
20 8.03±0.06 a1.34±0.15a182.44 ±0.58a16.54±0.38d
**Significant at 1%, Mean value with different letters differ significantly
Values are mean replications ± standard deviation
Incorporation of coconut milk residue in pasta
132
increased, the water absorption was also increased
(114.52 to 182.44% for 0 to 20% CMR
incorporation). This increase in water absorption
was due to the weaker gluten network. Besides, the
high fibre content present in CMR allowed easier
water penetration, which resulted in a substantial
increase in water absorption. The strong binding
ability of fibre can disrupt the gluten matrix (Chillo
et al., 2008; Chen et al., 1988). Coconut fibre has
the highest water holding, swelling and water
retention capacity (Raghavarao et al., 2008).
Colour
It is an imperative parameter influencing the
visual quality of pasta. L and b values are considered
important colour attributes in pasta made from
semolina (Rayas-Duarte et al., 1996). Colour
parameters L (white to black), b (yellow to blue) of
raw and cooked pasta are revealed in Table 3.
L value of raw pasta ranged from 66.76 to 75.19,
while after cooking, there was an increase from
77.60 to 80.57. Control pasta showed the maximum
value for lightness followed by 20 per cent CMR,
15 per cent CMR, 10 per cent CMR and 5 per cent
CMR incorporated sample, respectively (P<0.001).
An increase in L value indicates loss of colour during
cooking. Nonetheless, samples with 10 per cent and
15 per cent CMR did not show any significant
difference (P>0.05). Similarly, the highest value for
b was obtained for the control pasta. Though the
samples behaved similarly after cooking to that of
raw samples, significant differences were observed
among each other (P<0.05). It was evident that to
get a significant change in the yellowness of cooked
semolina pasta, at least 10 per cent CMR
incorporation was needed. Pasta with 15 per cent
and 20 per cent CMR showed a similar b value.
Table 3. Colour attributes of CMR incorporated pasta
Treatments L value b value
Raw ** Cooked * Raw ** Cooked **
0 75.19a80.57a17.82a12.74a
5 66.76d77.60c13.75b12.58ab
10 70.15c78.47bc 13.13b12.50b
15 72.26b77.86bc 13.30b12.13c
20 72.33b79.67ab 13.58b12.12c
** and * Significant at 1% and 5% respectively.
Mean value with different letters differ significantly
Values are mean replications ± standard deviation
Texture
The texture is one of the most significant quality
attributes of cooked pasta. A significant effect was
shown by different pasta samples on the firmness
after cooking. Table 2 shows that firmness value
reduced significantly (P< 0.05) from 24.43 N to
16.54 N, which is in accordance with Marti and
Pagani (2013) in gluten-free pasta. Increasing non-
gluten proteins weaken the gluten strength and
overall structure of the pasta (Rayas-Duarte et al.,
1996). Due to the weakening in gluten strength,
there was a loss in firmness in cooked pasta (Kaur
et al., 2012). However, the semolina pasta replaced
with 5 per cent and 10 per cent CMR could retain a
similar firmness as that of the control (P>0.05). This
clearly indicated that more than 10 per cent
replacement of semolina with CMR would affect
the quality of cooked pasta.
Overall acceptability
The sensory parameters such as appearance,
colour, texture, flavour and taste of cooked pasta
are essential for acceptability. The overall
acceptability (including all sensory parameters) was
maximum for control pasta with a mean value of
8.46 (liked very much) and the minimum for pasta
containing 20 per cent CMR with 3.7 (Dislike
moderately) (Fig.1). The non-significant effect
between pasta with 5 per cent and 10 per cent CMR
indicates its applicability up to 10 per cent
replacement level in pasta. Gunathilake et al. (2009)
also mentioned the declining effect of sensory
parameters on increasing coconut flour
concentration in bread. Bread and noodles with 20
per cent substitution with CMR showed maximum
sensory acceptability (Gunathilake and Abeyrathne,
2008).
Free fatty acid content during storage
FFA content in the control and pasta with 10
per cent CMR incorporation ranged from 0.55 to
0.65 per cent and 0.56 to 0.84 per cent, respectively.
Fig. 2 represents the trend in FFA over 90 days of
storage period. Though there was a gradual increase
in FFA during storage, the level was not enough to
give noticeable changes in the product, which was
in accordance with the earlier reports (Yadav et al.,
2014; Manthey et al., 2008; Kaur et al., 2012).
Beegum et al.
133
Conclusion
The study exploited the potential of coconut
milk residue for the preparation of pasta. The
developed pasta had good structural integrity,
as evident from the low gruel loss. However,
firmness value of the cooked pasta was lower
for the fortified sample as compared to control.
Results also revealed that coconut milk residue
could be incorporated into durum wheat
semolina at 10 per cent level as a source of fibre
due to its high fibre content (24%). Recovery of
CMR during the extraction of coconut milk
ranged from 38.5 to 55.6 per cent. If a coconut
processing firm processes coconut milk with 500
nuts per day capacity, then approximately 25 kg
milk residue is generated. The selling price of
CMR is `10-15 kg-1. If it is incorporated for
pasta making along with durum wheat semolina,
a firm can sell the product at a better price of
`300-700 kg-1. The fortified pasta can provide
more than 50 per cent of recommended dietary
allowance of fibre in addition to enrichment of
pasta with protein. Thus, fortifying semolina
pasta with CMR is encouraging due to its
nutritional, textural and sensory quality
attributes.
Fig. 2. Effect of incorporation of CMR on the free fatty acid content of pasta during storage
Fig.1. Sensory scores of cooked pasta
Storage days
Free fatty acid (%)
Overall acceptability
CMR based pasta formulations (g/100 g batch)
Sensory score
Incorporation of coconut milk residue in pasta
134
Acknowledgement
The authors are grateful to the Project Co-
ordinator, AICRP on Post-harvest Engineering and
Technology, ICAR-CIPHET, and the Director,
ICAR-CPCRI, for the support. The corresponding
author is thankful to Dr. S. Jayasekhar for providing
valuable suggestions.
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Publications Journal 4: 312-319.
Yadav, D.N., Sharma, M., Chikara, N., Anand, T. and Bansal,
S. 2014. Quality characteristics of vegetable-blended
wheat–pearl millet composite pasta. Agricultural
Research 3: 263-270.
Incorporation of coconut milk residue in pasta
136
Arecanut (Areca catechu L.), also called betel
nut, is an economically important palm species
(Jaiswal et al., 2011). The palm is cultivated in most
South Asian countries, including its major
producers, India and China and other countries like
Bangladesh, Japan, Malaysia, The Philippines, and
Sri Lanka (Ramesh and Mohanraju, 2017). India is
the largest producer of arecanut, accounting for
49.74 per cent of the world’s output, and the crop is
grown in an area of 4.95 lakh ha with a production
of 8.09 lakh tonnes of the dry kernel (http://
www.fao.org/faostat/en/#data/QC). The economically
important component of the palm, the fruit, goes
for chewing as “supari” owing to its stimulant
effects and a minor fraction of the production for
other commercial purposes. Thus, the edible
endosperm is widely used as a masticatory in India
and South East Asia, either alone or as a constituent
of the quid along with betel leaves, lime and tobacco.
Depending upon the nature of end products, the fruit
is harvested at different stages of maturity for
processing. The most popular traded form of
arecanut is the whole dried nut known as chali or
kottapak. The fruit of the palm is dried under direct
sunlight for around three weeks or even more to
reduce the moisture content to around 12-15 per cent
(d.b.) (Kulanthaisami et al., 2007; Kalamullah and
Gunasekar, 2002). Subsequently, they are stored in
gunny bags or wooden boxes as whole nuts or
dehusked nuts until they are sold (Mathew et al.,
Moisture content and water activity of arecanut samples:
A need to revisit storage guidelines
K.B. Hebbar*, Sugatha Padmanabhan, S.V. Ramesh, S. Keshav Bhat1, P. P. Shameena
Beegum, R. Pandiselvam, M.R. Manikantan and A.C. Mathew
ICAR-Central Plantation Crops Research Institute, Kasaragod-671 124, Kerala, India
1Arecanut Research and Development Foundation, Mangaluru-575 001, Karnataka, India
(Manuscript Received: 21-01-2021, Revised: 30-06-2021, Accepted: 02-07-2021)
Keywords: Arecanut, food standards, moisture content, storage, water activity
doi:10.25081/jpc.2021.v49.i2.7260
Journal of Plantation Crops, 2021, 49(2):136-141
Short Scientific Report
*Corresponding Author: hebbar.kb@icar.gov.in
1963). They are available in different grades like
new rashi, old rashi, supari, patora, ulligadde etc.
Another important form of traded arecanut is
processed semi-mature varieties (such as bette,
choor, rashi, and saraku), also known as red type,
wherein tender nuts of 6-7 months maturity are
dehusked, cut into pieces, boiled with water or
diluted extract from a previous boiling, followed
by tannin coating and drying. The number of cuts
and dimensions of the cut pieces is categorized into
api, bette, hasa, gorabulu etc. Dried nuts of both
chali and red type are stored for marketing. Improper
storage leads to spoilage of nuts and reduction in
the market value rendering it unsuitable as a
masticatory (Koti and Nambiar, 1979). One-year-
old nuts during storage showed a significant decline
in protein, fat, phenols, sugars and arecoline content
(Chempakam and Saraswathy, 1985).
Moisture content (MC) of agricultural produce
is an important physiological factor as it has an
inverse relationship with the storage period. The
high MC (>14% w.b.) during the storage increases
the respiration rate of agricultural products, resulting
in mould/fungus infection (Aspergillus spp., which
produces aflatoxins) and insect infestation. Further
discolouration (due to heat buildup) and a severe
loss in the fresh odour and sensory characteristics
of products were observed. Further, the rate of
physiological and biochemical changes in products
137
aggravates with an increase in MC. Water activity
(aw), a measure of the energy status of water, is
defined as a ratio of the vapour pressure of water in
a sample to the vapour pressure of pure water at
identical temperature and environmental conditions.
A decrease in aw level below 0.7 prevents the
spoilage caused by microorganisms (Labuza et al.,
1972). To extend the storage life of food and
agricultural products, the water activity has to be in
a lower range where the deterioration rate is
minimised (Ayub et al., 2003).
In arecanut, studies on the moisture content of
processed nuts are scarce, and there are hardly any
reports on the water activity of arecanuts. The
permissible and critical limits of MC fixed for dried
mature nut chali (13.1 and 15.2%, respectively) and
processed tender nut (11.0 and 12.32 per cent,
respectively) seemed to be very high to prevent it
from deterioration (Shivashankar and Govindarajan,
1963; Viraktamath 1963; Nambiar and
Radhakrishnan Nair, 1970). On the other hand, the
maximum standard moisture level of 7 per cent fixed
by the Food Safety and Standards Authority of India
(FSSAI) is too low to attain by the farmers (https:/
/www.thehindu.com/news/cities/Mangalore/re-fix-
standard-moisture-level-in-arecanut-to-14-
stakeholders/article31035233.ece). Therefore, there
is a need to optimise the moisture content of
processed arecanut for its safe storage and ideal for
mastication. In this context, the goal of the present
study was to analyse the MC and aw of arecanut
samples (red and chali) collected from different
parts of arecanut growing regions of Karnataka,
India and to identify suitable moisture content and
water activity parameters so that it could be
recommended for compliance by farmer growers
and merchants during storage.
Arecanut samples were sourced from different
places of Karnataka during dry and rainy seasons,
including tender processed (red) and mature dried
chali and their subtypes. Subtypes under red
included api (4), bette (17), rashi (4), hasa (10),
gorabalu (4). On the other hand, white 1 (4), new
rashi (3), old rashi (3) patora (3) and ulligadde (1)
are included under chali. A total of 53 samples were
collected for the analysis. The samples (~250 g nuts)
were drawn from collection centres of ‘The Malnad
Areca Marketing Co-operative Society
(MAMCOS)’ of Koppa, Shringeri, Bhadravathi,
Hosnagara, Shivamogga, and Thirthahalli located
in Karnataka, India, and private vendors and farmers
from Sirsi, Sagar, and Shivamogga. These samples
were placed in sealed polythene bags for further
analysis.
Prior to the analysis, the arecanut samples were
broken into pieces using a supari cutter and ground
Fig. 1. Relationship between moisture content (MC) of arecanut samples (n=16)
measured by gravimetric method and using infrared moisture metre
MC based on gravimetric method
MC based on infra red
moisture analyser
15
14
13
12
11
10
9
8
8 10 12 14 16
R3 - 0.838
(p<0.001)
Arecanut moisture content and water activity
138
into a fine powder using a laboratory blender. MC
of the samples was estimated using moisture
analyser A&D MX-50 (Oxfordshire, UK). In few
samples (n=16), the MC of the arecanut samples
were further estimated using a gravimetric method
to calibrate the infrared moisture analyser for
arecanut samples. The aw of the samples were
measured using Novasina-Lab Master-aw: bench
top metre (Novasina AG Neuheimstrasse 12 CH-
8853 Lachen). Each experiment was conducted in
three replications. The data were analysed using one
way ANOVA, and the treatment means were
compared by Duncan multiple range test. The single
factor ANOVA and MC and aw frequency
distribution pattern were analysed using SAS
software.
Table 1. Water activity (aw) and moisture content (MC) of arecanut samples collected from different places of Karnataka,
India
Sample Types and sub types Water activity (aw) Moisture content (%)
Across all samples (n=159) 0.689 10.21
Type Red (n=117) 0.689 10.16
chali (n=42) 0.676 9.54
SEm 0.013 0.62
Sub types: Red Api 0.696 10.43
Bette 0.682 9.96
Rashi 0.705 11.22
Hasa 0.689 10.21
Gorabalu 0.695 9.69
SEm NS NS
Chali White 1 0.658 b9.07 b
New Rashi 0.679 b9.43 b
Old Rashi 0.690 ab 10.08 ab
Patora 0.666 b9.22 b
Ulligadde 0.727 a11.11 a
SEm 0.039 1.32
Collection season Summer 0.670 9.09 c
Monsoon 0.684 10.17 b
SEm NS 0.92
Collection from different locations Shivamogga 0.709 10.91
Sagar 0.701 10.48
Koppa 0.686 9.74
Shringeri 0.661 8.96
Thirthahalli 0.681 9.73
Sirsi 0.673 9.75
Kumta 0.707 10.26
Thrissur 0.686 9.6
Puttur 0.684 10.63
Others 0.691 10.32
SEm NS NS
[There were totally 53 samples of which 39 are red [Api(4); Bette (17);Rashi (4); Hasa (10); Gorabalu (4)] and 14 are chali
[White (4); New Rashi (3); Old Rashi (3); Patora (3); Ulligadde (1)] types. Data are mean value of three replicates. Analysis of
variance was calculated across types (red and chali), sub types, summer and monsoon collections and collection from different
locations. Mean with same superscript letter represents non significance while different letter represents significance. Significance
level of each factor is indicated by p-values and standard error of mean (SEm)]
Hebbar et al.
139
MC, determined by infra-red moisture analyser,
had a strong positive correlation with the moisture
content estimated by the gravimetric method
(R2(16)=0.84, p<0.001) (Fig.1). It suggests that
powdered arecanut samples could be directly
measured for MC using an instant infra-red-based
moisture analyser rather than following a relatively
cumbersome and time-consuming gravimetric
method. Hence, the arecanut growers and processing
industries may use an infra-red-based moisture
analyser to estimate the moisture content of different
types of arecanut.
Table 2 depicts the MC and aw values across
all the arecanut samples. The MC and aw values
ranged from 8.33 to 14.46 per cent (w.b.) and 0.63
to 0.75, with a mean of 10.21 per cent and 0.689,
respectively (Table 1). The red type arecanut
samples had significantly high mean MC and aw
(10.16% and 0.689) compared to chali samples
(9.54% and 0.676). However, the MC values of both
the types were lower than the earlier suggested
permissible limits for chali (13.1%) and processed
tender nut (https://www.thehindu.com/news/cities/
Mangalore/re-fix-standard-moisture-level-in-
arecanut-to-14-stakeholders/article31035233.ece).
Analysis of MC and aw of various subtypes of red
and chali arecanut samples revealed that there were
no significant differences among the subtypes of
red samples. Nevertheless, the subtypes of chali
samples showed significant differences for MC and
aw (p-value 0.05). Significantly high MC and aw
were documented in ulligadde sub-samples
followed by old rashi, whereas the least MC and aw
was observed in white-I subsamples (Table 1).
Expectedly, the samples collected during the rainy
season (monsoon) had significantly (p≤0.01) more
moisture (10.17%) than the samples obtained during
the summer season (9.09%). However, there was
no significant difference in their aw. The MC (%)
and aw of the samples collected from different
locations were analysed, and it was found that the
place of the collection did not cause any significant
change in both the parameters suggesting the
robustness of this method for various agro-
ecological zones of the country.
The frequency distribution charts of MC and
aw of arecanut samples are depicted in Figure 3. It
is apparent that 44.2 per cent (23 out of 52) of the
Fig. 2. Correlation between moisture content (%) and water activity (aw)
of arecanut samples (n=53). Samples included dried red and chali
and their sub types collected from different locations during summer
and monsoon seasons
Moisture content (MC) %
Water activity (aw)
0.8
0.76
0.72
0.68
0.64
0.6
8 10 12 14 16
Arecanut moisture content and water activity
140
samples recorded a MC of 9.1 to 10.19 per cent
(w.b.). Whereas 40.4 per cent of samples exhibited
aw of 0.685-0.709 and 36.5 per cent samples
recorded aw of 0.66-0.684. Overall, more than 90
per cent of the samples recorded the MC and aw of
<11.3 per cent and <0.71, respectively. The
minimum aw required for the growth of bacteria and
fungi is around 0.8 and 0.75, respectively.
Viraktamath (1963) reported the loss of flavour,
crispness, development of stale odour and mould
formation on nuts stored at a moisture content of
13-14 per cent while Nambiar and Radhakrishnan
Nair (1970) observed an increase in fungal infection
of nuts stored in jute bags from 16 to 32.3 per cent
with an increase in moisture content from 9.5 to
15.6 per cent. Aspergillus flavus is the main fungi
producing aflatoxin in arecanut. The fresh nuts
having high MC are therefore easily contaminated
with Aspergillus flavus. These studies indicated that
Aspergillus flavus requires minimum water activity
of 0.78 at 33 °C, 0.81 at 30 °C and 0.80 at 37°C
(Pitt and Hocking, 2009). Astoreca et al. (2012)
reported that the storage of corn at <0.77 water
activity would be safe regardless of the storage
temperature. As gunny bags are used to store the
dehusked nuts, the fresh chali nuts are more
Fig. 3. Frequency distribution of (a) moisture content (MC) and (b) water activity (aw) of arecanut samples.
X-axis represents the frequency distribution class and the values above the bar indicate count against each
class from n=53
8-9.09 9.1-10.19 10.2-11.29 11.3-12-39 12.4-13.49 13.5-14.59
0.61-0.63 0.635-0.659 0.66-0.685 0.685-0.709 0.71-0.735 0.735-0.759
(a)
(b)
11
23
13
2
0
2
2
2
4
5
19
21
Hebbar et al.
141
susceptible to insect infestation and moulds or
fungal infections with high moisture content (Thube
et al., 2017).
In this study, arecanut samples sourced from
different places during dry and rainy seasons,
processed (red) and chali and their sub-types had a
mean aw of <0.71 (corresponding moisture content
of <11.3%), which is safe and unlikely to cause nut
deterioration during storage. This study had not
evaluated the plausibility of fungal or bacterial
contamination and deterioration in other physical
attributes depending on aw. Nevertheless, prior
investigations in various food products (Barbosa-
Cánovas et al., 2003; Erkmen and Bozoglu, 2016,
Asagbara et al., 2019) suggest that aw values <0.71
is safe. Therefore, mycotoxin accumulation and
deterioration of quality of nuts seem improbable at aw
values <0.71. Hence, the arecanut growers, processing
industries and quality standard organization’s like
FSSAI must ensure aw of <0.71 for dried arecanut
samples for its safe storage and mastication.
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In Hevea brasiliensis, the Para rubber tree, as
in many other perennial trees, the breeding
programme is a long-term strategy. It takes almost
23 years before any superior clone is released for
commercial planting (Simmonds, 1989; Mydin,
2014). Utility of several physiological, molecular
and biochemical parameters influencing rubber
yield as early screening tools were investigated, and
concentration of adenosine triphosphate (ATP) in
latex was found as a potential marker for high yield
considering its central regulatory role in energy
metabolism-related to rubber biosynthesis
(Sreelatha et al., 2004). Tapping or extraction of
latex through the wounding of bark involves intense
metabolic processes. These dynamic metabolic
processes include rubber biosynthesis and
reconstitution of sub-cellular components during
latex removal by tapping, which involves a
constant supply of energy in the form of ATP. The
supply of sucrose and the availability of ATP plays
a significant role in the latex regeneration
processes. ATP is a source of energy for the
mobilization of different solutes across lutoid
membrane (Marin et al., 1981). Thus, high latex
[ATP] could be an indicator for high rubber yield
in Hevea (Sreelatha et al., 2004).
Previous studies using clones of Hevea have
already demonstrated that high yielding clones
possessed high latex [ATP]. Using clones with
variable rubber yields, it was demonstrated that
high-yielding clones RRII 105 and RRIM 600 also
Relationship between concentration of ATP in latex
and yield potential in seedlings of Hevea brasiliensis
and its implications in breeding
C. Narayanan* and S. Sreelatha
Rubber Research Institute of India, Kottayam-686 009, Kerala, India.
(Manuscript Received: 23-04-2021, Revised: 28-06-2021, Accepted: 02-07-2021)
Keywords: Full-sibs, half-sibs, Hevea, juvenile yield, latex [ATP], polycross progenies, seedling nursery
doi:10.25081/jpc.2021.v49.i2.7261
Journal of Plantation Crops, 2021, 49(2): 142-145
Short Scientific Report
*Corresponding Author: cnarayanan@rubberboard.org.in
possessed high [ATP] (Sreelatha et al., 2004). The
above study also showed that low yielding clones
had very low [ATP]. In another study using
immature plants and trees of ten clones with variable
rubber yield, it was shown that high yielding clones
like RRII 105, RRIM 600, PB 217 and PB 235
displayed consistently high [ATP]. In the same
study, low yielding clones like RRII 33 and RRII
38 also had low [ATP], thus strengthening the view
that high latex [ATP] could be an indicator of high
rubber yield in Hevea. In a recent study, latex [ATP]
was shown to have a significant positive correlation
with crop efficiency of the polyclonal population
(r=0.76) and selected genotypes (r=0.61) (Dey et al.,
2018). However, so far, no studies have been carried
out to assess the relationship between latex [ATP]
and corresponding juvenile yield potential using
seedlings.
In the Hevea breeding programme, ‘test-tap’ or
juvenile yield has been used as an estimator of yield
in young plants of H. brasiliensis (Varghese et al.,
1989). Test tapping is carried out in two to three-
year-old plants in a nursery, and the selections are
evaluated in mature clonal trials after vegetative
multiplication through budding (Mydin and
Saraswathyamma, 2005). Previous studies have used
mature trees of clonal and polyclonal origin for
studying the relationship between yield based on
normal tapping and latex [ATP] (Sreelatha et al., 2014;
Dey et al., 2018). In the present study, we
investigated the relationship between test-tap yield
143
potential and corresponding latex [ATP] using a
seedling population in a nursery evaluation trial.
The experimental population comprised of
seedling progenies (full-sibs, half-sibs and open-
pollinated polycross progenies), which were
evaluated in a nursery trial in the farm of Rubber
Research Institute of India (Kottayam, Kerala state,
India). Details of the progenies and their pedigree
are given in Table 1. Standard procedures were
followed for assessing the juvenile yield through
test-tapping (Mydin and Saraswathyamma, 2005).
The progenies were planted at a spacing of 60 cm x
60 cm, and test-tapping was carried out at a height of
20 cm from ground level. After discarding latex from
the first five test-tappings, latex from the subsequent
15 test tappings were collected, air-dried and weighed
to compute mean test-tap yield as grams per tree per
tap (g tree-1 tap-1). Test tapping was initiated from the
third year of planting, and the yield recording was
continued for two more years. ATP concentration in
latex was determined in the third year of test-tapping
(Amalou et al., 1992; Sreelatha et al., 2014). Data
on test-tap yield and latex [ATP] of progenies were
regressed to find the correlation.
Mean test-tap yield and latex [ATP] of the
progenies are given in Table 2. Test-tap yield ranged
from 0.3 to 31.5 g tree-1 tap-1, indicating very high
variability in the experimental population (Table 2).
Among full-sibs, 14/C33 (RRII 414 x 90/21),
followed by 14/A216 (RRII 414 x RO 230),
recorded a high mean yield of 31.5 g tree-1 tap-1 and
19.1 g tree-1 tap-1, respectively. Among hybrids of
RRII 105, one hybrid (14/A143) gave a mean yield
of 4.8 g tree-1 tap-1. The remaining two hybrids,
14/A240 and 14/A236 recorded very low mean
yields ranging from 0.3 to 1.0 g tree-1 tap-1.
Half-sibs of Fx 516 had a mean yield ranging
from 9.5 in 14/HS/Fx516/D5 to 28.2 g tree-1 tap-1 in
14/HS/Fx516/D3. Similarly, there was a wide
variation in yield among half-sibs of RRII 430 and
RRII 414. The mean yield of half-sibs of RRII 414
ranged from 6.6 in 14/HS/RRII414/D205 to 13.6 g
tree-1 tap-1 in 14/HS/RRII414/D97. Half-sibs of RRII
430 had a mean yield ranging from 5.9 in 14/HS/
RRII430/D83 to 29.9 g tree-1 tap-1 in 14/HS/RRII430/
C64. The half-sib of H. spruceana (14/HS/HSP/
A154) gave the lowest yield of 0.4 g tree-1 tap-1.
Progenies exhibited very high variation for
latex [ATP] (Table 2). Maximum ATP concentration
Table 1. Details of progenies and their parentage
Progeny identity Parentage
14/HS/Fx516/D3 HS of Fx 516 (F 4542 x AVROS 363)
14/HS/Fx516/A248 HS of Fx 516
14/HS/Fx516/A95 HS of Fx 516
14/HS/Fx516/D5 HS of Fx 516
14/HS/HSP/A154 HS of H. spruceana
14/HS/RRII414/C3 HS of RRII 414 (RRII 105 x RRIC 100)
14/HS/RRII414/D97 HS of RRII 414
14/HS/RRII414/D205 HS of RRII 414
14/HS/RRII414/D214 HS of RRII 414
14/HS/RRII414/D209 HS of RRII 414
14/HS/RRII414/C5 HS of RRII 414
14/HS/RRII414/D215 HS of RRII 414
14/HS/RRII430/C48 HS of RRII 430 (RRII 105 x RRIC 100)
14/HS/RRII430/D248 HS of RRII 430
14/HS/RRII430/C64 HS of RRII 430
14/HS/RRII430/D142 HS of RRII 430
14/HS/RRII430/D231 HS of RRII 430
14/HS/RRII430/D226 HS of RRII 430
14/HS/RRII430/D19 HS of RRII 430
14/HS/RRII430/D227 HS of RRII 430
14/HS/RRII430/D26 HS of RRII 430
14/HS/RRII430/D78 HS of RRII 430
14/HS/RRII430/D225 HS of RRII 430
14/HS/RRII430/D66 HS of RRII 430
14/HS/RRII430/D83 HS of RRII 430
14/HS/RRII430/D250 HS of RRII 430
14/HS/RRII430/D71 HS of RRII 430
14/HS/RRII430/D138 HS of RRII 430
14/OP/OPCES/A197 Polycross progeny
14/A143 RRII 105 (Tjir 1 x Gl 1) x Fx 516
14/A240 RRII 105 x RO 230
14/A236 RRII 105 x RO 230
14/C33 RRII 414 x 90/21 (RRII 105 x RO 142)
14/A216 RRII 414 x RO 230
Latex [ATP] and juvenile yield in Hevea
(375.5 μM) was detected in 14/C33, which also
possessed the maximum rubber yield (31.5 g tree-1
tap-1). Very low [ATP] (46 μM) was recorded in 14/
A236 (RRII 105 x RO 230), which also had a very
low rubber yield of 1.0 g tree-1 tap-1. Very low
yielding progenies 14/A240 (RRII 105 x RO 230;
0.3 g tree-1 tap-1) and 14/HS/HSP/A154 (half-sib of
H. spruceana; 0.5 g tree-1 tap-1) also had very low
[ATP] of 80 and 53 μM, respectively. Overall, high-
144
yielding half-sib selections of RRII 414, RRII 430
and Fx 516 also had very high latex [ATP] of more
than 300 μM.
Regression analysis revealed a direct
relationship (R2=0.66) between latex [ATP] and test-
tap yield of the progenies (Fig. 1), which conformed
with the earlier findings from investigations with
clones (Sreelatha et al., 2014; Dey et al., 2018). In
general, high-yielding progenies had more latex
[ATP] than low-yielders and vice versa. ATP is a
direct source of energy for the conversion of sucrose
to poly-isoprene molecules (rubber). It indirectly
affects rubber yield, mediated through lutoid
membrane ATPase activity (Sreelatha et al., 2014).
Hence, higher levels of ATP in latex of high yielding
progenies indicated that ATP could be involved in
increasing the efficiency of rubber biosynthesis, as
reported earlier (Sreelatha et al., 2014).
Presently, ‘test tapping’ or juvenile tapping of
seedlings at the age of two to three years in the
nursery stage is the only recommended strategy for
early screening of large populations for assessing
their yield potential and shortlisting the selections
before proceeding to further clonal evaluation (Ho,
1976; Tan, 1987). Only a very low percentage of
selections (based on test-tap yield) from seedling
nurseries turned into high yielders. Test-tapping is
mandatory and cannot be dispensed with until more
reliable methods for phenotype selection are evolved
(Mydin, 2012). Nevertheless, the correlations
between nursery yield (based on test-tapping) and
mature yield though significant, are still not very
strong enough (r = 0.22 to 0.26) to make juvenile
yield evaluation by test-tapping alone a completely
reliable method for early selection as only 25 per
cent of selections from seedlings gave significantly
higher yield at mature stages (Tan, 1998; Mydin,
2012). Thus, more detailed information on major
yield components and stable sub-components,
including biochemical components in the immature
phase, could help evolve more reliable parameters
for early prediction of yield with more precision
(Licy et al., 1998; Prabhakaran Nair, 2010).
Although the high correlation between latex
[ATP] and mature yield has already been established
using clones of various ages and mature polycross
population (Sreelatha et al., 2004, Sreelatha et al.
2014, Dey et al., 2018), so far, no such studies have
been carried out using young seedlings. The present
study conducted using nursery seedlings of a
heterogeneous population consisting of half-sib, full-
sib and polycross progenies similarly demonstrated
Table 2. Juvenile test-tap yield and latex [ATP] of
progenies.
Progeny Test-tap yield Latex
(g tree-1 tap-1)*,# [ATP](μM)
14/C33 31.5 375.7
14/HS/RRII430/C64 29.9 309.9
14/HS/Fx516/D3 28.2 315.3
14/HS/RRII430/C48 28.0 355.2
14/A216 19.1 268.1
14/HS/Fx516/A248 19.0 305.9
14/HS/RRII430/D227 18.1 206.1
14/HS/RRII430/D142 17.3 266.8
14/HS/RRII430/D19 16.6 206.3
14/HS/RRII430/D26 15.0 202.9
14/HS/RRII430/D78 14.9 193.0
14/HS/RRII430/D248 14.8 339.0
14/HS/RRII430/D66 14.1 171.0
14/HS/RRII414/D97 13.6 261.7
14/OP/OPCES/A197 13.0 241.3
14/HS/RRII414/C3 12.8 359.4
14/HS/RRII430/D231 12.4 240.2
14/HS/Fx516/A95 12.2 273.4
14/HS/RRII430/D71 11.6 141.7
14/HS/RRII430/D225 11.4 173.4
14/HS/RRII414/D209 11.2 145.4
14/HS/RRII430/D138 11.2 122.6
14/HS/RRII414/D215 10.8 122.3
14/HS/RRII430/D226 10.2 215.6
14/HS/Fx516/D5 9.5 134.0
14/HS/RRII414/C5 8.7 124.4
14/HS/RRII414/D214 7.0 171.8
14/HS/RRII430/D250 7.0 161.6
14/HS/RRII414/D205 6.6 182.3
14/HS/RRII430/D83 5.9 164.0
14/A143 4.8 124.3
14/A236 1.0 46.1
14/HS/HSP/A154 0.4 52.9
14/A240 0.3 79.9
Mean 13.2 207.5
Range 0.3-31.5 46.1-375.7
S.E. 1.3 15.1
*Mean over three years; #Progenies are listed in descending
order, based on their yield performance.
Narayanan and Sreelatha
145
a very strong relationship between juvenile test-tap
yield and latex [ATP] irrespective of sibling
composition. Thus, only seedlings with high test-tap
yield and high latex [ATP] should be selected in order
to ensure more precision in the recovery of high
yielding genotypes to forward them to subsequent
stages of clonal evaluation in larger field trials.
Acknowledgement
The authors are thankful to Dr. James Jacob
for his critical review and valuable suggestions for
improving the manuscript.
References
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(Ethylene releaser) induced increase in the adenylate pool
and transtonoplast pH within Hevea latex cells. Plant
Physiology 98: 1270-1276.
Varghese, Y. A., Licy, J., John, A. and Panikkar, A. O. N. 1989.
An incision method for early selection of Hevea
seedlings. Indian Journal of Natural Rubber Research
2: 112-117.
Dey, S. K., Antony, P. D. and Mehra, B. K. 2018. [ATP] content
of latex as a marker for high rubber yield in clones grown
in north-eastern region of India. Rubber Science 31:
22-30.
Ho, C. Y. 1976. Clonal characters determining the yield of
Hevea brasiliensis. In: Proceedings of international
rubber conference. Kuala Lumpur: Rubber Research
Institute of Malaysia, Kuala Lumpur, pp. 17-38.
Licy, J., Thomas, M., Saraswathyamma, C. K. and Sethuraj,
M. R. 1998. Studies on biochemical subcomponenets of
latex for crop improvement in Hevea. 1998. In: National
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Plant Biochemistry, Department of Plant Science,
University of Hyderabad, India. p. 161.
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difference across the vacuo-lysosomal membrane of
Hevea brasiliensis (Rubber tree) and its modification by
a membrane bound adenosine tri phosphatase.
Biochemical Journal 198: 365-372.
Mydin, K. K. 2012. Juvenile-mature correlations and
associations among rubber yield and yield attributes in
Hevea brasiliensis. Natural Rubber Research 25: 1-12.
Mydin, K. K. 2014. Genetic improvement of Hevea
brasiliensis: sixty years of breeding efforts in India.
Rubber Science 27: 153-181.
Mydin, K. K. and Saraswathyamma, C. K. 2005. Hevea
Breeding Manual. Rubber Research Institute of India,
Kottayam, India, 97 p.
Prabhakaran Nair, K. P. 2010. Rubber (Hevea brasiliensis).
In: The Agronomy and Economy of Important Tree Crops
of the Developing World, (Ed.) Prabhakaran Nair, K. P.
London: Elsevier Press, pp. 237-273.
Simmonds, N. W. 1989. Rubber breeding. In Rubber, (Ed.)
Webster, C. C. and Baulkwill, W. J. USA: Longman
scientific and technical, pp. 85-124.
Sreelatha, S., Simon, S.P. and Jacob, J. 2004. On the possibility
of using latex ATP concentration as an indicator of high
yield in Hevea. Journal of Rubber Research 7: 71-78.
Sreelatha, S., Jacob, J., Mercykutty, V. C., Simon, S. P.,
Krishnakumar, R. and Annamalainathan, K. 2014. ATP
concentration in latex as an indicator for early evaluation
of yield in Hevea brasiliensis. Journal of Plantation
Crops 42: 48-53.
Tan, H. 1987. Strategies in rubber tree breeding. In: Improving
Vegetatively Propagated Crops, (Ed.) Abbott, A.J. and
Atkim, R.K. London: Academic Press, pp. 28-62.
Tan, H. 1998. A study on nursery selection in Hevea breeding.
Journal of Rubber Research 1: 253-262.
Fig. 1. Relationship between test-tap yield and latex [ATP]
(g tree-1 tap-1)
M
Latex [ATP] and juvenile yield in Hevea
146
Soil organic carbon stocks (SOCS) in different land
uses of Western Ghats, Karnataka - A case study
B. Kalaiselvi*, Rajendra Hegde, K.S. Anil Kumar, R. Vasundhara, S. Dharumarajan,
R. Srinivasan, M. Lalitha and S.K. Singh1
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Bangalore-560 024,
Karnataka, India
1ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur-440 033, Maharashtra, India
(Manuscript Received: 09-07-2020, Revised: 30-06-2021, Accepted: 02-07-2021)
Keywords: Carbon sequestration, SOC stocks, pedons, Western Ghats
doi:10.25081/jpc.2021.v49.i2.7262
Journal of Plantation Crops, 2021, 49(2): 146-150
Short Scientific Report
*Corresponding Author: kalaimitra15@gmail.com
Soil organic carbon is the prime indicator of
soil quality and environmental sustainability.
Sequestering organic carbon in the terrestrial pool
helps to reduce the atmospheric CO2 concentration,
improves soil quality and prevents soil from further
degradation. Soil is the largest organic carbon pool
with 1115 to 2200 Pg globally (Batjes, 1996) and
9 Pg in India (Bhattacharyya et al., 2000), which
interacts strongly with the atmospheric
concentration of CO2 and other greenhouse gases.
Overall, in India, the organic carbon content is low
when compared to world scenario owing to the
influence of arid to semi-arid, and sub-humid to per
humid climate, which is the major factor for poor
productivity of tropical soils (Syers et al., 1996;
Katyal et al., 2001). Maintaining organic C levels
in tropical soils is more difficult because of the rapid
oxidation of organic matter under prevailing high
temperatures as the need arises to increase organic
carbon content (Lal, 2002). Soil organic carbon
stocks and fluxes are strongly influenced by changes
in climate and land cover or land use (Yigini and
Panagos, 2016). Land use has a great influence on
soil physical, chemical and biological properties
since it affects the amount and quality of litter input,
litter decomposition rates and processes of organic
matter stabilization in the soils (Murty et al., 2002).
Finding the carbon sequestration potential of
different land uses is of paramount importance to
recommend appropriate land use to enhance SOC
stocks as it gives voluminous benefits. With this
view, a study was conducted in Bilalgodu micro-
watershed, Mudigere Taluk, Chikkamagaluru
district to compare soil organic carbon stocks of
plantation systems compared to forest and
agricultural systems.
Bilalgodu microwatershed is located in Kalasa
Hobli, representing the eastern side of (Central
Sahyadris) Western Ghat and experiencing humid
tropical climate. The micro-watershed with a total
area of 710 hectares lies between 13°11'54" and
13°13'49" N latitudes and 75°17'54" and 75°20’03" E
longitudes. The general elevation of the area ranges
from 700 to 1100 m above mean sea level (MSL).
It comes under agro-ecological sub-region (AESR)
of 19.2- hot, moist, sub-humid to humid with the
annual precipitation is 2000-3000 mm. The average
annual temperature is 22.5 °C, and the length of growing
period (LGP) ranges from 210-270 days. Hence, these
areas are predominant with deep, well-drained, acidic,
red and lateritic soils. Physiographically, it ranges from
steep hills of Western Ghats to nearly level lower
lateritic terraces with all slope classes ranging from
1 to 33 per cent. Terracing is the common practice
for field crops such as paddy in the lower sector,
whereas, in the summit and side slopes, coffee,
arecanut, coconut and forest are the major land-use
types.
147
Remote sensing data products from IRS-P6-
LISS IV and Cartosat-1 (ortho-corrected) at the
same scale were used in conjunction with the
cadastral maps to identify the landforms and other
surface features. These base maps and imageries
helped in the identification and delineation of
different land uses. Based on the physiography and
land uses, 21 soil profiles were located and opened
up to 2 meters or shallower if the depth is limited
by a rock or hard substratum. Profiles were studied
in detail for all their morphological and physical
characteristics (Soil Survey Staff, 2003), and
horizon wise samples were collected for laboratory
analysis. The horizon-wise collected soil samples
were, air-dried, powdered and sieved using a 2 mm
sieve. Particle size distribution was determined by
the International pipette method (Piper, 1966), and
organic carbon was estimated by Walkley and Black
(1934) method. The bulk density of the samples was
determined by the field moist method using core
samples (diameter 50 mm) of known volume
(100 cubic cm) (Jackson, 1973). Range in soil
characteristics of forest, plantation and agricultural
systems was used to estimate the SOC stocks of
different land-use systems. SOC stocks were
calculated for soil horizons using the Grossman
equation (Grossman et al., 2001), and the weighted
average of SOC was calculated for top 25 cm,
25-50 cm, 50-100 cm, 100-150 cm and 0-50 cm,
0-100 cm and 0-150 cm layers. For each of the soil
profiles, SOC stocks were calculated for 150 cm by
summing the stocks of different layers in the
proportion of their occurrence within this reference
thickness. The total organic carbon stock in kg m-2
soil for each pedon was estimated using the general
equation presented below (Grossman et al., 2001).
Fig.1. Depth wise soil organic carbon stock (kg m-2) under different land uses
Where SOC = soil organic carbon in kg m-2
n is the number of soil layers in the soil profile,
θ
i% represents the volumetric percentage of
gravel (>2 mm) content,
ρ
i is the soil bulk density (g per cm3),
Ci is the organic carbon content (C g kg-1), and
Ti represents the thickness (cm) of layer i.
The soil properties of representative pedons
under different land uses are presented in Table.1.
The soils were extremely acid (pH-4.39) to slightly
acid (pH-6.01), which did not exhibit any significant
difference between the systems. The acidic pH of
the soils is attributed to the leaching of the bases
and acidic parent materials (Srinivasan et al., 2013).
Invariably, the study area soils are non-saline due
SOC stocks = [(1–θi%) × ρi × Ci × Ti/100]
Σ
n
i=1
SOCS in different land uses in Karnataka
SOC Stock (kg m-2)
Depth (cm)
Forest Plantation Agriculture
0
25
50
75
100
125
150
175
0 1 2 3 4 5 6 7 8 9 10 11 12
148
Table 1. Soil properties of representative pedons of forest, plantation and agricultural land use systems
Forest land use systems
Depth Horizon Sand Silt Clay Coarse Textural pH EC OC BD CEC CEC/ Base SOCS
(cm) (2.0-0.05) (0.05-0.002) (<0.002) fragments class (1: 2.5) (1:2.5) (%) (Mg m-3) (cmol kg-1) clay saturation (kg m-2)
v/v(%) (USDA) Water dS m-1 (%)
0-16 O 55.44 12.07 32.49 12 scl 6.01 0.038 2.84 1.46 9.28 0.29 63 7.543
16-37 Bt1 44.63 15.42 39.96 30 cl 5.89 0.02 2.05 1.57 7.24 0.18 48 7.189
37-64 Bt2 43.42 11.68 44.89 55 c 5.73 0.018 1.26 1.54 3.67 0.08 87 5.988
64-101 Bt3 62.22 8.94 22.84 65 scl 5.52 0.019 0.39 1.64 3.37 0.15 43 5.364
101-140 Bt4 69.83 4.36 25.81 60 scl 5.62 0.017 0.47 1.68 3.06 0.12 79 3.079
140-172 Bt5 61.26 18.33 20.41 - scl 5.79 0.012 0.27 1.75 2.35 0.12 80 1.540
Plantation land use systems
0-24 A 54.68 15.35 29.97 - scl 5.43 0.047 1.97 1.54 9.28 0.31 40 7.281
24-55 Bt1 52.68 15.20 32.12 - scl 5.2 0.012 1.04 1.59 8.16 0.25 9 5.674
55-80 Bt2 50.05 17.83 32.12 - scl 5.3 0.02 0.76 1.69 7.34 0.23 15 3.667
80-109 Bt3 55.96 16.63 27.41 - scl 5.25 0.011 0.72 1.68 6.02 0.22 22 3.560
109-141 Bt4 63.47 14.23 22.30 - scl 5.57 0.013 0.52 1.76 3.57 0.16 31 3.145
141-170 Bt5 63.86 17.46 18.68 - sl 5.43 0.013 0.24 1.71 2.75 0.15 38 1.121
Agricultural land use systems
0-19 AP 39.44 30.20 30.36 - scl 4.39 0.151 1.81 1.27 9.59 0.32 21 4.883
19-44 Bw1 36.39 30.03 32.58 - scl 5.75 0.038 1.32 1.62 6.83 0.20 59 5.346
44-75 Bw2 41.32 24.33 33.89 - c 5.89 0.02 0.76 1.64 5.81 0.19 47 3.864
75-110 Bw3 37.84 27.20 34.96 - sc 5.5 0.02 0.58 1.65 5.30 0.15 60 3.613
110-150 Bw4 38.24 26.31 35.45 - sc 5.79 0.017 0.56 1.72 3.6 0.10 90 3.920
150+ Saturated water layer
Kalaiselvi et al.
149
to leaching losses of the bases by high rainfall. The
clay content increases with the depth due to
illuviation (32-44%) between 20 cm and 75 cm
depth attributed to downward translocation of finer
particles from the surface layers (Bhaskar and
Subbaiah, 1995). The subsurface clay content of the
agricultural system was registered comparatively
higher, indicating the continuous deposition of finer
soil particles in the low-lying areas from the higher
elevation. Sand content of the plantation systems
recorded high (50-64 %), which might be due to
high erosional loss of finer particles as the plantation
crops are being preferably cultivated in highly
sloping landscapes to maintain water drainage. In
contrast, the agricultural systems recorded low sand
content (36-41%). The forest soils are very gravelly
(35-60 %), which might be due to high erosional
soil losses from steep sloping undulating terrain.
The SOC of the study area, which was high
(>0.75%), could be attributed to the density of the
vegetation and conducive climatic conditions for
the accumulation of organic matter. The organic
carbon content of the soils is high in surface soils
(1.04-3.07%) and tend to decrease with increasing
depth, which might be due to poor accumulation of
organic matter in the subsurface. Among the
different land-use systems, the forest system has
recorded the highest organic carbon content (2.84%)
in the surface layer with mean SOC content of 1.21
per cent, followed by the plantation system (1.97%).
In contrast, the agricultural land use system recorded
comparatively lesser OC content ranged between
0.56 and 1.81 per cent, with a mean of 1.11 per cent.
From the depth-wise distribution of soil organic
carbon, it was evident that the high organic carbon
content of forest land-use systems than others are
attributed to maximum litter fall and plant residues
associated with microbial activities (Iqbal and
Tiwari, 2016). Irrespective of the land use, surface
soils found to be high in SOC content might be due
to a high rate of biomass deposition than removal
and slow decomposition rate (Kharche et al., 1999).
While comparing the bulk density of the surface
soils amidst the systems, low lying agricultural land
uses recorded low (1.27 Mg m-3), but throughout
the depth, the forest land uses recorded the lower
bulk density might be because of high litter fall and
organic residues on the surface (Lal and Kimble,
1997). Regardless of the land uses, the bulk density
increased with depth, which might be due to
compaction and dominance of finer particles in
deeper layers (Thangasamy et al., 2004). In this study
area, the agricultural land use system soils had a water
saturated layer beneath 150 cm depth attributed to
the narrow valley of the high rainfall region.
Soil organic carbon stocks of the soil horizons
were calculated using weighted averages for variable
depths viz., 0-25 cm, 25-50 cm, 50-100 cm, 100-150
cm using SOC content, soil depth and bulk density.
The surface soil organic carbon stock of the forest
system ranged between 5.7-8.2 kg m-2, followed by
plantation land use ranging from 3.6 to 7.3 kg m-2.
The computed SOC stock for different depth levels
0-25 cm, 25-50 cm, 50-100 cm and 100-150 cm
registered that the forest system had higher SOC
stocks viz., 10.62 kg m-2, 6.99 kg m-2, 8.32 kg m-2
and 3.71 kg m-2, respectively (Fig. 1). It might be
attributed to the greater canopy, higher litter
deposition and reduced soil disturbance of forest land
use (Gupta and Sharma, 2011). SOC stocks of
agricultural systems were also found on par with
plantations because of better management practices
and the addition of biomass (Bhattacharyya et al.,
2007).
The study revealed that land use and its
management influence soil organic carbon stocks by
biomass accumulation and level of decomposition at
high temperatures, its microclimate and control on
soil erosion. Forest land-use system, associated with
good ground and canopy cover, significantly
increased the soil organic carbon content and stock
compared to plantation and agricultural land-use
systems. But in the current situation of increasing
food demand and reduction in cultivable areas,
recommending forest crops will not be functional and
fruitful. Still, the areas must be protected and
preserved against land degradation and wildlife
habitation apart from above-ground biomass creation.
Forest, plantations, agro-forestry systems, high
carbon accumulating grass and multistoried cropping
options can be recommended in per-humid sub-
humid areas of India to make the soil and environment
ecologically sustainable. Liming of soils can be
advocated to agricultural and plantation land use
systems for enhancement of mineralization and
biomass production as the soils are acidic; this
ensures the rejuvenation of microbes and betterment
of C-sequestration.
SOCS in different land uses in Karnataka
150
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Statement of ownership and other particulars about
Journal of Plantation Crops
1. Place of publication : Kasaragod-671 124, Kerala
2. Periodicity of its publication : Three issues per year (April, August, December)
3. Printers name : Dr. S. Jayasekhar
Nationality : Indian
Address : Secretary,
Indian Society for Plantation Crops,
ICAR-Central Plantation Crops Research
Institute, Kasaragod-671 124
Kerala, India
4. Publisher’s name : Dr. S. Jayasekhar
Nationality : Indian
Address : Secretary,
Indian Society for Plantation Crops,
ICAR-Central Plantation Crops Research
Institute, Kasaragod-671 124
Kerala, India
5. Editor-in-Chief : Dr. R. Krishnakumar
Nationality : Indian
Address : Indian Society for Plantation Crops,
ICAR-Central Plantation Crops Research
Institute, Kasaragod-671 124
Kerala, India
6. Ownership : Indian Society for Plantation Crops,
ICAR-Central Plantation Crops Research
Institute, Kasaragod-671 124
Kerala, India
7. Name of the Printing press : Alois Graphics, Kottayam, Kerala, India
I, Dr. S. Jayasekhar, hereby declare that the particulars given above are true to the best of
my knowledge and belief.
Sd/-
Date: 23-08-2021 (Dr. S. Jayasekhar)
Journal of Plantation Crops
GUIDELINES TO AUTHORS
General
Original research papers and short communications based on research on all aspects of plantation crops
(coconut, arecanut, cocoa, cashew, oil palm, coffee, tea, rubber including plantation based cropping system)
and spices (black pepper, cardamom, ginger, turmeric, tree spices) are accepted for publication in the Journal.
Review articles that summarize, analyze and synthesize the existing state of knowledge on an aspect of
plantation crops/spices are generally published by invitation only. Original papers including Tables,
Illustrations and References may not exceed 4000 words. Short communications should not exceed 1300
words. All the authors should be members of the Indian Society for Plantation Crops for publication of their
paper in the Journal of Plantation Crops. The manuscripts and correspondence concerning editorial matters
may be addressed to the Editor, Journal of Plantation Crops, Central Plantation Crops Research Institute,
Kasaragod-671 124, Kerala, India.
Manuscripts
Articles published or submitted elsewhere for publication should not be submitted for publication in the
Journal. An undertaking to this extent needs to be taken by the corresponding author before accepting an
article for publication in the Journal. Manuscripts should conform to the Journal format. It must be typed in
double-space on one side of A4 size paper with a margin of 4 cm each on the left-hand side and top, and 3
cm each on right-hand side and bottom and numbered serially. Manuscripts should be submitted through
Online Submission Portal. Corresponding/submitting author must register via https://updatepublishing.com/
journal/index.php/JPC/user/register and will then receive an e-mail containing a Username and Password
that will allow access for manuscript submission. Authors who have already registered can submit their
manuscripts via https://updatepublishing.com/journal/index.php/JPC/login by the login.
The manuscript is to be arranged in the following order:
Title page: Provide a separate Title page with the following items.
Title of the paper should be informative and concise.
By-line should contain the name(s) of author(s) and footnote symbols to indicate corresponding author and
address, including e-mail.
From-line should contain the name and address of the institution where the research work was carried out.
Keywords: A maximum of six keywords suitable for indexing may be given in alphabetical order.
A short running title not exceeding 30 characters should be provided
Abstract: This should contain a brief (not exceeding 250 words) and accurate summary of the objectives,
contents and conclusions of the paper. It should be intelligible without reference to the full paper.
Abbreviations should be avoided in the Abstract.
Introduction: It may explain the scope of research work. Only the most relevant and recent references of
literature may be included.
Material and Methods: It should be brief, but sufficiently informative enough to be able to repeat the
work. The data should be subjected to statistical analysis.
Results and Discussion: These may be combined. The significance of the results obtained is to be highlighted
in relation to other relevant published works.
In the case Short Communication, the above section headings may be avoided.
Acknowledgments: This may be provided wherever needed and be specific.
References: This should include only publications cited in the text. References to unpublished data, private
communications and documents with limited circulation may be avoided to the possible extent. An article
should not be referred to as “in press” unless it has been accepted for publication. The name of the Journal
in which such an article has been accepted should be given in the cited reference. The references may be
cited in alphabetical order of the first authors name followed by the co-authors. The following examples
may be adopted.
Articles from Journals: Name(s) and initial(s) of author(s); year of publication (further distinguished by
the addition of small letters a, b, c etc., where there are citations to more than one paper published by the
same author(s) in one year), title of the paper, name of the journal in italics abbreviated as per the “World
list of periodicals”, volume number (in bold) followed by page numbers.
E.g.: Wigley, T.M., Briffa, K.R. and Jones, P.D.1984. Predicting plant productivity and water resources.
Nature (London) 312:102-103.
Articles from Symposia proceedings or Seminars: Name(s) and initial(s) of author(s), year of publication,
title of the article, page numbers followed by In: Title of volume in italics, name(s) of editor(s) proceeded
by Ed(s). name (s) of publisher and place of publication.
E.g.: Sreenath, H.L., Muniswamy, B., Naidu, M.M., Dharmaraj, P.S. and Ramaiah, P.K.1992.Immature
embryo culture in coffee from three interspecific crosses. In: PLACROSYM IX. (Eds). Nambiar, K.K.N.,
Iyer, R.D., Rao, E.V.B.B, Rao, W.K., Premkumar, T. and Hegde, M.R., Indian Society for Plantation Crops,
Kasaragod. pp. 23-36.
Chapter in a Book: e.g.: Baldesdent, J. and Marotti, A. 1996. Measurement of soil, organic matter using
13C natural abundance. In: Mass Spectroscopy of Soil. (Eds.) Bouten, S. and Yamasaki, S. Marcel Dekker,
New York. pp. 83-111.
Book: Name(s) of the author(s) followed by their initial(s), year of publication, complete Title of the book
in italics, Edition (if applicable), total number of pages, name of publisher and place of publication.
E.g.: Giller, K.E. and Wilson, K.J. 1991. Nitrogen Fixation in Tropical Cropping Systems. CAB International,
Wallingford, UK. 313 p.
Tables: The tables may be typed on separate pages, numbered serially in Arabic numerals and carry
appropriate titles in bold type. The preferred position of the Table may be indicated in the manuscript.
Presenting Tables that are too long to print across the page should be avoided. Vertical rules should not be
marked within the table.
Illustrations: Figures may be serially numbered in the order of their first citation in the text. Send only
sharp, glossy, black and white/colour photographic prints, not larger than 20 x 25 cm. High quality laser
prints and graphs are also accepted. Letters, numbers and symbols should be clear and even, throughout
and of sufficient size so that all the items will still be legible when reduced for publication. Titles and
detailed explanation should be given separately in the legends for figures. Every figure should have a label
on its back indicating the number of the figure and author’s name. The preferred position of the figure may
be indicated in the manuscript. Presentation of same data in both table and figures/graphs should be avoided.
All articles submitted to JPC will be subjected to peer reviewing. They will be sent back to the corresponding
author along with the Referee’s comments for revision, if necessary. The revised article should be returned
in electronic form (MS WORD), in addition to a hard copy along with the original and the Referee’s
comments. The electronic manuscript should be in a CD, labelled properly, giving details of file name,
operating system and software used.
Proof: The proof in PDF will be sent to the corresponding author for correction, if found necessary. They
should be returned to the Editor within a period of two weeks. Alterations in proof other than printing errors
may be avoided. Excessive corrections are chargeable to the Author.
