Genetic diversity among some traditional aromatic Rice

144 Pages·2015·3.56 MB·English

Journal of Progressive Agriculture Vol. 6, Issue-1, April, 2015

Genetic diversity among some traditional aromatic Rice (oryza sativa L.) land races
of Chhattisgarh

NIRMALA BHARTI PATEL, RAJEEV SHRIVASTAVA, VIKAS KUMAR AND NAMRATA DHIRHI
Department of Genetics and Plant Breeding, Indira Gandhi Agricultural University, Raipur(C.G.) India
Received: 26.07.14
Accepted: 02.11.14
ABSTRACT
The nature and magnitude of genetic diversity in thirty eight aromatic rice accessions collected
from different places of Chhattisgarh were evaluated for their performance. Data generated on
nine quantitative characters were subjected to D2 statistics and result indicated the presence of
appreciable amount of genetic diversity in the material. The thirty eight genotypes were grouped
into five clusters. Cluster II was the biggest one consisting of eleven genotypes viz., Shyamjeera,
Gopalbhog, Jawaphool, Kasturi, Vishnubhog-1, Dubraj-1, Kubrimohar-1, Jaigundhi, Shrikamal,
Tilkasturi, Vishnubhog-2 and cluster III, the smallest one consisting of four genotypes viz., Bisni,
Londhi, Anterved, Lallu-14. The intra-cluster distance varied from 1.828 for cluster III to 2.338 for
cluster I and the inter-cluster distance ranged from 2.534 between clusters IV and V to 4.765
between clusters III and V indicating closeness and diversity between the clusters respectively.
The highest inter-cluster distance between clusters III and V revealed highly divergent group.
Therefore, it is suggested that the parents should be chosen from clusters III and V for
hybridization to get high grain yield.

Key words: Aromatic rice, Cluster analysis, D2 analysis, Genetic divergence.

Rice (Oryza sativa L.) is one of the most initiating genetic improvement efforts of short
important staple food crops of Chhattisgarh. In grain aromatic rices, as meager attention has been
Chhattisgarh, it is mainly grown under rainfed paid for their improvement (Singh, 1996).
ecosystem, which covers about 74, 97 and 95 per Assessment of genetic diversity is an integral part
cent cropped area of Chhattisgarh plain, Bastar of crop improvement as exploitation of the genetic
plateau and Northern hill zones, respectively. The diversity helps the plant breeder to develop new
central plains of Chhattisgarh are known as rice varieties (Manonmani and Fazlullah Khan, 2003).
bowl of central India. Besides basmati types which Genetic diversity can be evaluated with
get high price in international market, the country morphological traits, seed proteins, isozymes and
also bounds with hundreds of indigenous short DNA markers. Conventionally, it is estimated by
grain aromatic cultivars and landraces grown in the D2 analysis, metroglyph and principle
pockets of different states. Almost every state of component analysis using morphological traits
India has its own collection of aromatic rice that (Manonmani and Fazlullah Khan, 2003).
performs well in native areas (Shobha Rani and For the assessment of variation on
Krishnaiah, 2001). These aromatic rice cultivars multivariate scale, Mahalanobis D2 statistics has
also possess exemplary quality traits like aroma, proved to be a powerful technique (Murty and
fluffiness and taste. However, the improvement of Arumachalam, 1966). Thus, keeping in view the
these rice varieties very much neglected as they above facts, present study was conducted to
lacked export value per se. The short and medium estimate the nature and magnitude of genetic
grained aromatic rice varieties are generally low divergence and characters contributing to the
yielders, susceptible to lodging, pest and diseases. genetic divergence in thirty eight selected aromatic
Some of these genotypes are being gradually rice landraces. This study will help in selection of
eroded from their respective place of origin and elite genotypes for crossing programme and also
are on the verge of becoming extinct due to they can be released as a variety.
competition from high yielding varieties (Ram et
MATERIALS AND METHODS
al. 2007). Hence, there is a need to catalogue,
characterize and conserve the non-basmati The experiment was conducted during the
traditional aromatic rice landraces, which are in kharif season of 2011 at the Research cum
extricably integrated with culture and traditional Instructional Farm of Indira Gandhi Krishi
knowledge of nation. Vishwavidyalay, Raipur. Thirty eight aromatic
Although genetic diversity analysis has been land races of rice collected from different places of
done by several workers, limited information is Chhattisgarh were selected for this study (Table 1).
available on traditional aromatic rices. Such Twenty one days old seedlings were subsequently
studies have especially become essential for transplanted in the field, in Randomized Block
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Journal of Progressive Agriculture Vol. 6, Issue-1, April, 2015

Design (RBD) with three replications. Net plot The intra and inter-cluster values in terms of
size was 3 m x 15 m. Row to row distance was 25 D2 values among the five clusters are given in
cm and plant to plant distance was 25 cm. Table 3 and represented in Fig. 1.The inter-cluster
Observations were recorded on five plants for 9 D2 exhibited that cluster I (2.338) had the
morphological and quality characters viz., Days to maximum genetic diversity followed by cluster IV
50 % flowering, Days to maturity, Plant height, (2.200) and cluster II (2.138). The inter-cluster D2
Panicle length, Number of tillers per plant,
values of the five clusters that had the highest
Number of panicles per plant, Number of grains inter-cluster D2 value was between cluster III and
per plant, Grain yield per plants and 1000 seed
V (4.765) while, the lowest was between cluster
weight. The mean values over replications were
IV and V (2.534). From the above, it could be
used for statistical analysis. The analysis of genetic
concluded that, considerable diversity existed
divergence using Mahalanobis D2 statistics was
among genotypes included in the present study.
done as described by Rao (1952) and grouping of
This could be the result of selection in different
genotypes into a number of clusters as per the
direction by nature and human forces.
standard procedure described by Spark (1973).
The mean values of nine characters studied in
RESULTS AND DISCUSSION
thirty eight genotypes for five clusters are
One of the main objectives of any breeding presented in Table 4. The cluster III (91.92 days)
program is to produce high yielding and better had the early flowering genotypes whereas cluster
quality lines for release as cultivars to farmers. V (121.21days) had the late flowering genotypes.
The prerequisite to achieve this goal is the The genotypes of cluster II (115.25 cm) were
presence of sufficient amount of variability, in dwarfest while, genotypes of cluster IV
which desired lines are to be selected for further (146.72cm) were tallest compared to other
manipulation to achieve the target. For identifying clusters. Cluster II had the genotypes with lowest
such desired lines for crossing, by means of number of tillers per plant (14.88) and number of
Mahalanobis’s D2 statistic has been used in several panicles per plant (11.79) on contrary, the cluster
crops. It is a powerful tool used to quantify the V had the genotypes with highest number of tillers
genetic divergence between the genotypes and to per plant (16.58) and number of panicles per plant
relate clustering pattern. In the present (19.04). The genotypes lying in the cluster IV had
investigation, thirty eight genotypes of rice were shortest panicle length (24.56 cm) while, the
considered for assessment of nature of genetic genotypes in cluster I had longest panicle length
diversity by adopting Mahalanobis’s D2 statistic (32.78 cm). The number of grains per panicle was
(1936) considering nine characters. lowest in cluster I (108.07) and highest in cluster
V (186.50). The genotypes of cluster V had lowest
Thirty eight aromatic rice genotypes were
grain yield (1982.33 kg/ha) while, cluster V had
grouped into five clusters. Maximum divergence
highest grain yield (5730.33 kg/ha). The 1000 seed
observed between clusters III and V (4.765) while,
weight was lowest in cluster V (13.06 gm) and
minimum divergence was observed between
highest weight in cluster IV (19.44 gm).
clusters IV and V (2.534). Cluster II was the
biggest cluster consisting of eleven genotypes viz., The above grouping indicates the existence of
Shyamjeera, Gopalbhog, Jawaphool, Kasturi, broad genetic divergence among present
Vishnubhog-1, Dubraj-1, Kubrimohar-1, genotypes. Such high degree of divergence were
Jaigundhi, Shrikamal, Tilkasturi, and Vishnubhog- found in local collection by Gupta et al. (1999),
2 followed by cluster I, comprised of nine Sarawgi and Rastogi (2000) and Nayak et al.
genotypes viz., Kapoorsar, Kalikamod, Jeeradhan, (2004) as in International collections by Usha
Sukulphool, Elaychi, Tulsiprasad, Samudrafan, Kumari and Rangaswamy (1997).
Badhashahbhog and Kharigilas followed by cluster
These observations suggest that inter crossing
V, comprised of eight genotypes viz., Jeeraphool,
of genotypes from different cluster showing good
Chinnor-1, Tulsimanjari, Atmasheetal, Gangabaru,
mean performance may help in obtaining high
Keraghul, Jaophool and Katarnibhog followed by
yielding with good grain quality genotypes. The
cluster IV, comprised of six genotypes viz., Dujai,
genotypes Jeeraphool, Chinnor-1, Tulsimanjari,
Chinnor-2, Kubrimohar-2, Dubraj-2, Dubraj-3 and
Atmashital, Gangabaru, Keraghul, Jouphool, and
Maidubraj. The cluster III contained only four
Katarnibhog from cluster 5 and Dujai, Chinnor-2,
genotypes viz., Bisni, Londhi, Anterved and
Kubrimohar-2, Dubraj-2, Dubraj-3 and Maidubraj
Lalloo-14. The patterns of distribution of
from cluster 4 can be utilized as parents in
genotypes into various clusters are given in the
crossing programme to isolate desirable genotypes
Table 2.
for yield.
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The maximum per cent contribution towards Sarkar (2005), Anand Kumar and Indubala (2005)
genetic divergence was of grain yield followed by observed grain yield per plant to be main
number of grains per panicle and days to 50% contributor towards diversity. Shukla et al. (2006)
flowering. Roy and Panwar (1993), Sardana et al. observed biological yield and harvest index to be
(1997), Shanmugsundaram et al. (2000), Roy et main contributors towards genetic diversity.
al. (2002), Datta and Mani (2003), Senapati and
Table 1: Landraces used in study and places from where they are collected.

Variety Sources Variety Sources
Anterved Chhuriya/ Rajnandgaon Kapoorsar Jabera / Damoh
Atmasheetal Chhindgarh/ Bastar Kasturi Bakawand/ Bastar
Badshahbhog Jagadalpur Kharigilas Charama/ Bastar
Bisni Surajpur Katarnibhog Sabour (Bihar)
Chinnor –I Balaghat Kheraghul Gharghoda/ Raigarh
Chinnor-II Tilda / Raipur Kubrimohar-I Bemetra
Dubraj-I Nagari Kubrimohar-II Magarload / Raipur
Dubraj-II Balaodabazar/ Raipur Lalloo-14 Mandla
Dubraj-III Nagri Londhi Pendra
Dujai Pendra Mai Dubraj Bastar
Elaychi Phingeshwar/ Raipur Samudrafan Pandariya / Bilaspur
Gopalbhog Bagicha, Sarguja Shyamjeera Surajpur
Gangabaru Chhindgarh/Bastar Srikamal Raipur Naikin / Sidhi
Jaigundi Saraypali / Raipur Sukalaphool Jaijepur / Bilaspur
Javaphool Raigarh Tilkasturi Pithora / Raipur
Jeeradhan Tilda / Raipur Tulasiprasad Aarang / Raipur
Jeeraphool Bageecha, Sarguja Tulsimanjari Sabour / Bihar
Jaophool Lallunga / Raigarh Vishnubhog-I Pendra
Kalikamod Aarang / Raipur Vishnubhog-II Badrafnagar/ Sarguja

Table 2: Grouping of rice genotypes based on D2 analysis.

Number of
Cluster Entries numbers
entries
I 09 Kapoorsar (14), Kalikamod (15), Jiradhan (17), Sukulaphool (19), Elaychi (20),
Tulsiprasad (24), Samudrafan (27), Badhshahbhog (34), Karigilas (35).
II 11 Shyamjeera (1), Gopalbhog (6), Jawaphool (7), Kasturi (8), Vishnubhog-1 (9),
Dubraj-1 (10), Kubrimohar-1 (11), Jaigundhi (22), Shrikamal (36), Tilkasturi
(37), Vishnubhog-2 (38).
III 04 Bisni (2), Londhi (4), Anterved (23), Lallu-14 (32).
IV 06 Dujai (5), Chinnor-2 (18), Kubrimohar-2 (25), Dubraj-2 (29), Dubraj-3 (30),
Maidubraj (31).
V 08 Jeeraphool (3), Chinnor-1 (12), Tulsimanjari (13), Atmashital (16), Gangabaru
(21), Keraghul (26), Jouphool (28), Katarnibhog (33).

Table 3: Average intra and inter cluster D2 values.

I II III IV V
I 2.338
II 2.701 2.138
III 3.633 4.198 1.828
IV 3.251 3.000 4.268 2.200
V 3.297 2.849 4.765 2.534 1.951
*Bold values are intra-cluster values

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Table 4: The mean values of clusters for nine characters in thirty eight aromatic rice genotypes.

1000
Clusters Days to Plant Panicle Number of Grain
Days to Number of Number of seed
50% height length grains/panicle yield
maturity tillers/plant panicles/plant weight
Clusters flowering (cm) (cm) (Kg/ha)
(gm)
I 112.59 138.00 137.85 15.00 16.67 32.78 108.07 1982.33 19.25
II 116.36 140.18 115.25 14.88 11.79 26.88 137.52 3610.36 17.29
III 91.92 116.92 141.00 16.42 17.67 27.83 126.00 4115.42 17.23
IV 120.56 142.33 146.72 16.56 18.72 24.56 180.94 4788.33 19.44
V 121.21 146.08 123.00 16.58 19.04 29.75 186.50 5730.33 13.06

Fig. 1: D2 Diagrammetic representation of intra and inter cluster values.

REFERENCES
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Ananda Kumar, C.R. and Indu Bala, K. 2005. Genetic
diversity in rice genotypes under humid tropics of
diversity studies in drought resistant cultures of rice.
Andaman based on grain yield and seed characters.
Ann. Agric. Res. New. Series, 26(3): 395-397.
Indian J. Agri. Sci. 72(2): 84 -87.
Datta, S. and Mani, S.C. 2003. Genetic divergence in elite
Sarawgi, A.K. and Rastogi, N.K. 2000. Genetic diversity
genotypes of basmati rice (Oryza sativa L.). Indian J.
in traditional aromatic rice accessions from Madhya
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Pradesh. Indian J. Plant Genet. Res., 13: 138-146.
Gupta, K.R., Panwar, D.V.S. and Kumar, R. 1999.
Sardana, S., Borthakur, D.N. and Lakhanpal, T.N.
Studies on quality status of indigenous upland rice (
1997.Genetic divergence in rice germplasm of
Oryza sativa L.). Indian J. Genet., 47: 145-151.
Tripura. Oryza, 34(3): 201-208
Mahalanobis, P.C. 1928. On the generalized distance in
Shanmugasundaram, P., Souframanien, S. and
statistics. Natl. Inst. Sci., 2: 49-55.
Sadasivum, S. 2000. Genetic divergence among rice
Manonmani, S. and Khan F.A.K. 2003. Analysis of varieties released from paddy breeding station,
genetic diversity for selection of parents in rice. Coimbatore, India. Oryza, 37(3): 225-228.
Oryza, 40: 54-56.
Shukla, V., Singh, S., Singh, H. and Pradhan, S.K. 2006.
Murty, B.R. and Arunachalam, V. 1966. The nature of Multivariate analysis in tropical japonica “New plant
genetic divergence in relation to breeding system in type rice (Oryza sativa L.)”. Oryza, 43(3): 203-207.
crop plants. Indian J. Genet., 26: 188- 198.
Singh, A.K. 1996. Genetic divergence in scented and fine
Shobha Rani, N. and Krishnaiah, K. Science Publishers, genotypes of rice (Oryza sativa L.). Ann. Agric. Res.,
Inc., USA. 2001, 49-78. 17(2): 163-166.
Nayak AR, Chaudhury D, Reddy JN. 2004. Genetic Spark, D.N. 1973. Euclidean Cluster Analysis. Algorithm
Divergence in scented rice. Oryza, 41: 79-82. As. 58. Applied Statistics, 22: 126-130.
Rao, C.R. 1952. Advanced Statistical Methods in Usha Kumari, R. and Rangaswamy, P. 1997. Studies on
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rice. Oryza, 31: 97-101.

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Journal of Progressive Agriculture Vol. 6, Issue-1, April, 2015

Effect of weather factors on Chilli production and strategies for improvement of
productivity

C.SARADA, L. NARAM NAIDU AND C VENKATA RAMANA
Horticultural research Station( Dr YSRHU) Lam, India
Received: 23.07.14
Accepted: 14.12.14
ABSTRACT
Over many centuries, agricultural production has been reported to be extremely influenced by
changes in climate. The environment may affect the availability of nutrients to the plant, crop
growth and response of plants to biotic and abiotic stresses. Chilli, a well known commercial
annual crop is used as a condiment, culinary supplement and also as a vegetable. Among the
spices, dry chilli ranks first in per capita consumption in the country. Though India leads the world
in production, consumption and export of chilli, the productivity levels are reported to be far below
the actual potential of this crop. Of the many reasons attributed for low yields in chilli, vagaries of
weather, pest and disease incidence are the most important. In Andhra Pradesh, the leading
producer of the crop Guntur district ranks first in production of chilli. A study was conducted for
assessing the influence of weather parameters on the chilli production and productivity in Guntur
district. The study revealed that, the mean minimum temperature and annual rainfall had
significant positive correlation whereas the variables mean maximum temperature and mean sun
shine hours had negative correlation with production and productivity of chilli. Unfavourable
climate conditions like excess/low rainfall, high humidity coupled with wide diurnal variations in
temperature predispose the crop for pest and disease incidence. Hence, there is need to work out
the strategies to sustain the productivity of the crop. Development of prediction models based on
weather conditions for forecasting pest and disease incidence will go a long way in helping the
farmers to protect their crop. Further there is need to motivate the farmers on adoption of
improved package of practices such as IPM/IDM, INM,IWM technologies to minimize
indiscriminate use of chemicals and maximize the efficient utilization of available resources
leading to increased benefit cost ratio. Strategies to improve quality and minimize post harvest
losses are also to be adopted to get premium price.

Key words: Chilli, Climate, INM, IDM, IPM and IWM.

Climate is the primary determinant of in area, production and productivity of chilli
agricultural production and productivity. Adverse contributing 30% of the total area and 51% of total
climate effects influence crop production and production. In AP, chilli is majorly grown in
invariably on human livelihood. The negative Guntur, Prakasam, Krishna, khamamm, Warangal,
impacts of climate change are stunted growth, easy Srikakulam and vizianagaram districts. In Andhra
spread of pests and diseases, drying of seedlings Pradesh, Guntur ranks first in area, production and
after germination/transplanting, low yield resulting productivity of chilli. The crop is sensitive to
in crop failure. Climate change elicits a significant weather, soil moisture as well as management
change in agricultural production both in terms of practices. Uncertainties in rainfall and other
the quantum of products as well as the location of environmental hazards like temperature and
production (Oladipo, 2010). The focus on climate sunshine hours cause large year-to-year
change issue largely center nowadays on fluctuations in production and productivity. The
enhancing the capacity of developing nations to fluctuations in chilli production are due to biotic
adapt to the climate change impacts (Jones, and abiotic stresses. According to an estimate,
2010).Crop yields are directly affected by changes crop yields less than 25 percent of the potential
in climatic factors such as temperature, yield are due to the adverse environmental
precipitation and the frequency and severity of conditions and low water availability as much as
extreme events like droughts, floods, and wind all the other environmental factors combined
storms (Richard et al., 1998). Climate models (Boyer, 1982; Boyer and Westgate, 2004). Among
indicate that warming over will take place in the the abiotic stresses, excess/deficiency of soil
next several decades irrespective of any action moisture constitutes a primary limitation to crop
taken today. productivity (Turner, 1997; Sinclair, 2005). Chilli
is sensitive to excess moisture and heavy rains
Chilli (Capsicum annum Linn) is an
during crop maturity and harvesting time damage
important commercial cash crop grown as spice
the crop. The present study was intended to know
and vegetable in India. Andhra Pradesh ranks first
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Journal of Progressive Agriculture Vol. 6, Issue-1, April, 2015

the behavior of weather parameters on chilli weather parameters rainfall is important factor
production and productivity in Guntur district of affecting production. Hence in the present study
Andhra Pradesh so as to know the impact of for correlation, the weather data during this period
weather on yield of chilli and also adoption of was categorized into three groups based on annual
various extension strategies to minimize the losses rainfall (the years where the annual rainfall is
due to adverse weather conditions. <750mm, 750-1000mm, >1000mm). The weather
variables, maximum and minimum temperature,
MATERIALS AND METHODS
annual rainfall, seasonal rainfall and sun shine
The weather data of 14 years (1999-2013) hours for past 13 years (1999-2012) were
was correlated with production and productivity of correlated with production and productivity of
chilli in Guntur district. Among the various chilli in the Guntur district of Andhra Pradesh.

Table1: weather data, production and productivity of chilli during the 1999-2013.

Mean max Mean min
Total Rainfall RH Morning RH Afternoon Production “000” Productivity
Years temp temp
( 0C) ( 0C) (mm) (%) (%) tons (Kg/ha)
1999-00 34.6 22.7 914.7 86.03 58.18 86 1879
2000-01 34.3 22.4 997.6 83.47 57.25 148 2942
2001-02 34.4 23 1019.8 86.34 69.45 204 3995
2002-03 34.7 22.7 534.1 81.08 61.75 119 2022
2003-04 34.2 23.3 1271.2 82.22 56.54 339 5042
2004-05 33.4 22.8 746.9 81.58 54.90 273 4911
2005-06 33.9 23.2 1046.5 81.56 57.42 194 4706
2006-07 31.8 21.8 744 82.94 56.36 286 4855
2007-08 33.4 22.7 1224.2 81.03 54.21 312 4926
2008-09 35.6 22.7 893.9 82.09 51.73 306 4996
2009-10 33.1 22.5 746.1 84.88 53.72 341 5520
2010-11 32.29 22.31 1367.9 84.26 57.69 206 3346
2011-12 34.9 23.2 874 79.98 51.67 332 4467
2012-13 33.2 22.5 1172 89.8 56.6 320 5000
Mean 33.84 22.70 968.06 83.37 56.96 247.57 4186.21
Range 3.80 1.50 833.80 9.79 17.78 255.00 3641.00
Minimum 31.80 21.80 534.10 79.98 51.67 86.00 1879.00
Maximum 35.60 23.30 1367.90 89.77 69.45 341.00 5520.00

Table 2: Descriptive statistics of weather parameters in Guntur district during 1999-2013.

Mean Mean
Total RH
max min RH Afternoon Production Productivity
Parameter Rainfall Morning
temp temp (%) “000” tons (Kg/ha)
(mm) (%)
( 0C) ( 0C)
Mean 33.84 22.70 968.06 83.37 56.96 247.57 4186.21
Range 3.80 1.50 833.80 9.79 17.78 255.00 3641.00
Minimum 31.80 21.80 534.10 79.98 51.67 86.00 1879.00
Maximum 35.60 23.30 1367.90 89.77 69.45 341.00 5520.00

Table 3: Correlation coefficients of weather parameters with production and productivity of chilli in
different rainfall situations.

Weather correlation during study correlation when Rainfall correlation when Rainfall correlation when Rainfall
Parameters period <750mm 750mm -1000mm >1000mm
Production Productivity Production Productivity Production Productivity Production Productivity
“000” tons (Kg/ha) “000” tons (Kg/ha) “000” tons (Kg/ha) “000” tons (Kg/ha)
Mean max
temp ( 0C) -0.21 -0.28 -0.74 -0.75 0.72 0.79 0.08 0.46
Mean min
temp ( 0C) 0.09 0.10 -0.34 -0.31 0.65 0.45 0.05 0.48
Total Rainfall
(mm) 0.19 0.18 0.95 0.98 -0.63 -0.51 0.37 -0.22
RH Morning
(%) -0.10 -0.06 0.81 0.73 -0.94 -0.87 0.06 -0.12
RH Afternoon -0.56 -0.45 -0.97 -0.99 -0.99 -0.97 -0.54 -0.46

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RESULTS AND DISCUSSION indicating that high RH is favorable for disease
Seasonal rainfall: incidence and ultimately resulting in low yields.
The weather data during 1999-2013 and Correlation of production and productivity of
descriptive statistics (Table-1) during the period chilli when rainfall is >1000mm: The correlation
under study revealed that the mean annual rainfall of weather parameters when annual rainfall is
is 968.04 mm. There was excess rainfall in seven >1000mm indicated that among the various
years, normal rainfall in three years and deficit weather variables, only relative humidity in the
rainfall in four years. The maximum and minimum afternoon hours had significant negative influence
temperatures ranged from 31.8 to 35.6 o C and 21.8 while the other variables had no significant effect
to 23.3 o C respectively (table 2). The correlation on chilli on production and productivity.
of various weather variables during study period The correlation of weather parameters based
viz 1999-2013 has no significant influence on on rainfall pattern indicated that production was
production and productivity of chilli. However the maximum when rainfall was <750 mm, while the
study period was categorized into 3 parameters higher rainfall had negative and no impact on
based on annual rainfall and correlation was done chilli. The study indicates distribution of rainfall is
to study the Influence of individual weather playing a key role than quantum of rainfall on
parameters on chilli production and productivity. chilli production.
Influence of weather parameters on chilli Chilli is sensitive to excess moisture
production and productivity: conditions resulting in low oxygen in root zone.
Correlation of production and productivity of Excess moisture due to heavy rains not only result
chilli when rainfall is >750mm: in flower drop and also favours incidence of
The study indicated that significant negative diseases like wilt and blight The chilli crop under
correlation was observed with relative humidity in rain fed area also suffers drought stress resulting in
the afternoon, maximum and minimum yield loss up to 50-60%where as severe drought
temperatures when the rainfall was <750mm. conditions also affect the crop badly leading to
Where as, rainfall and relative humidity in the poor growth and severe incidence of sucking pest
evening hours had significant positive correlation complex resulting in virus. The farmers are
with production and productivity of chilli. The incurring huge amounts on pesticides in chilli due
present study indicated that even at low rainfall to heavy load of pest complex which is often
conditions also rainfall had significant positive correlated with temperature and humidity.
correlation indicating that the distribution of Role of Extension in Adaptation and Mitigation
rainfall is important rather than amount of rainfall. to Changing Climate:
In chilli several studies indicated the importance of Agricultural extension can be defined as the
optimum time of sowing (Hosamani 1982, entire set of organizations that support people
Nagaraja et al 2007, Gayathri and Giraddi engaged in agricultural production and facilitate
2009,Islam et al.,2010, Hamma et al., 2012, as it their efforts to solve problems and obtain
results in maximum yield with less incidence of information, skills, and technologies to improve
pest incidence. The lower incidence of thrips their livelihoods. Extension has proven itself to be
ultimately resulted in lower incidence of leaf curl a cost-effective means of bringing about greater
virus with higher yield when crop was planted economic returns for farmers with significant and
early in the season (Kempegowda 1980, Bagle positive effects on knowledge, adoption, and
1993, Mallapur et al.1987). productivity. Now days the extension strategies are
Correlation of production and productivity of going beyond of technology transfer to facilitation,
chilli when rainfall is 750mm-1000mm: beyond training to learning, and includes helping
The correlation of weather parameters when farmers to form groups, dealing marketing issues,
annual rainfall is 750mm-1000mm indicated that and broad range of service providers. Hence
significant positive correlation with maximum and extension is a cost-effective tool that can play an
minimum temperatures. Rahman and Inden (2012) important role in dealing with changing climate
suggested that relatively higher temperature is while at the same time helping to increase
better for sweet pepper production. The rainfall productivity and reduce poverty (Kristen, 2009).
and relative humidity had significant negative Extension can help farmers prepare for greater
correlation with production and productivity of climate variability and uncertainty, create
chilli. The study indicates that chilli is sensitive to contingency measures to deal with exponentially
excess moisture and the high relative humidity in increasing risk, and alleviate the consequences of
this present study has negative role on productivity changing climate by providing advice on how to
deal with droughts, floods, and so forth. Extension
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Journal of Progressive Agriculture Vol. 6, Issue-1, April, 2015

can also help with mitigation of changing climate  Planting of different crops/ crop rotation
by providing links to new markets, new  Different planting dates;
government priorities and policies. Mitigation  Planting of different varieties
strategies are procedures or activities that help  Early planting
prevent or minimize the process of climate change  Early harvesting under drought conditions.
(Nyong et al., 2007).Mitigation and adaptation are  Frequent application of inputs in smaller
complementary rather than mutually exclusive doses f (fertilizers, agro-chemicals);
(Fussel 2007),. The capacity of farmers to cope  Adoption of Seed treatment practices to
with such different forms of risk will become ever reduce pesticide usage
more crucial, and extension efforts must pay 3. Protection Measure:
special attention to educating farmers about their  Adoption of INM practices to increase quality
options to enhance resilience and response of produce besides reducing cost of
capacity. Increasing the availability of extension cultivation.
officers is critical to transfer the knowledge  Adoption of IWM to lessen the weed density
necessary for farmers to respond to changing and ultimately
climate. Training is required in crop production  Application of soil amendments e.g. farm yard
technologies and innovations appropriate to the manure, vermicompost,
changing climate, such as new, early maturing  Use of green manures, Trico derma viridi and
seed varieties. Extension workers also need to be bio fertilizers.
made aware of climate variability and trained on  Foliar nutrition in case of rained chilli.
how to communicate it and what to do about it. 4. Post harvest operations:
Extension can make a significant contribution  Use of solar/poly house dries for quick drying
through enhanced farmer decision making in the to escape from unfavourable weather
light of changing climate. condition, results in uniform drying of
Adoption strategies has to be followed to produce in short time and also increases
overcome the unfavourable vagaries of climate quality of produce.
which not only results in pest load on crop but also  Conversion of raw produce in to value added
results in crop loss up to 60-70%. (Ayanwuyi et products like chilli powders, capsacin and
al., 2010). oleresins.
Besides these adoption strategies, prediction
Adaptation Strategies to Climate Change models have to be developed based on collected
1. Adaptation: climate data and forecast of weather parameters.
 Water conservation techniques (precision There by farmers can be advised well in advance
farming,). about the forecast of pest and disease incidence
 Soil conservation through frequent and it will help in reducing the risk of crop
intercultivation. failure. The decision making support and early
 Shading and shelter/mulching. warning system provides the farmer some advice
 Planting of early varieties like LCA-353 to reduce the crop failure risks due to climate
2. Farming Operations: change.

REFERENCES

Ayanwuyi, E., Kuponiyi, E., Ogunlade, F.A. approaches, and key lessons. Sustainability
and Oyetoro, J.O. (2010). Farmers,Perception Science, 2:265-275.
of Impact of Climate Changes on Crop Hamma, I. L., Ibrahim, U. and Haruna, M.
Production in OgbomoshoAgricultural Zone of (2012). Effect of planting date and spacing on
Oyo State, Nigeria. Global Journal of Human the growth and yield of sweet pepper
Social Science, 10(7): 33-39. (capsicum annuum l.) in samaru area of zaria in
Boyer, J.S. (1982). Plant productivity and nigeria effect of planting date and spacing on
environment Science (Washington, DC). 218: the growthand yield of sweet pepper (capsicum
443–448 annuum l.) in samaru area of zaria in Nigeria.
Nigerian Journal of Agriculture, Food and
Boyer, J.S. and Westgate, M.E. (2004). Grain
Environment. 8(1):63- 66
yield with limited water. Journal of
Experimental Botany. 55: 2385-2394. Islam, M. Saha, S. Akand, H. and Rahim, A.
(2010). Efffect of sowing date on the growth
Fussel, H.M. 2007. Adaptation planning for
and yield of sweet pepper (Capsicum annuum
climate change: concepts, assessment
L.) Agronomski Glasnik 1/2010.
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Jones, L. (2010). Overcoming Social Barriers to the Country’s State of Preparedness for
Adaptation. Overseas Development Institute Climate Change Adaptation.Henrich Boll
(ODI) Background Note, Foundation, Nigeria.
www.odi.org.uk/50years. Rahman, M.J. and Inden, H. 2012. Effect of
Mallapur, C.P., Kubasad, V.S. and Raju, S.G., nutrient solution and temperature on capsaicin
(1987). Inference of Influence of nutrient content and yield contributing characteristics in
management on chilli pests. Karnataka six sweet pepper (Capsicum annuum L.)
J.Agric. Sci., 10: 67-69. cultivars. Journal of Food, Agriculture &
Nagaraja, T., Sreenivas, A.G., Patil, B.V. and Environment, 10(1): 524-529.
Naganagoud, A. (2007). Influence of Different Richard, M., Adams1, Brian H. Hurd,
Dates of Sowing on Thrips and Mites in Chilli Stephanie Lenhart, Neil Leary (1998).
under Irrigated Ecosystem. Karnataka J. Agric. Effects of global climate change on agriculture
Sci., 21(3): 450-45. :an interpretative review climate research. 11:
Nyong, A., Adesina, F. and Elasha, B.O. (2007). 19–30.
The value of indigenous knowledge in climate Sinclair, T.R. (2005). Theoretical analysis of soil
change mitigation and adaptation strategies in and plant traits influencing daily plant water
the African Sahel. Mitigation and Adaptation flux on drying soils. Agron. J. 97: 1148-1152.
Strategies in Global Change, 12:787-797.
Turner, N.C. (1997). Further progress in crop
Oladipo, E. (2010). Towards Enhancing the
water relations Adv. Agron., 58: 293-338.
Adaptative Capacity Of Nigeria: A Review of

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Journal of Progressive Agriculture Vol. 6, Issue-1, April, 2015

Genetic variability in indigenous collection of chickpea (Cicer arietinum L.)
genotypes for seed yield and quality traits

KAMLA DESAI, C.J.TANK, 1R.A. GAMI, A.M.PATEL AND R.M.CHAUHAN
1Assistant Professor, Department of Genetics and Plant Breeding, C.P.College of Agriculture,
Assistant Research Scientist, Center of Excellence for Research on Pulses, S.D. Agricultural
University, Sardarkrushinagar, Ta: Dantiwada, Dist: Banaskantha, Pin: 385 506 (Gujarat)
Received: 30.07.14
Accepted: 03.03.15
ABSTRACT
Present study was carried out to ascertain the extent of genetic variability in the population of
48 different chickpea genotypes with respect to yield, nutritional quality and other agronomic
characters. The highest genotypic coefficient of variation was observed for 100-seed weight
followed by methionine content, number of pods per plant, seed yield per plant and number of
seeds per pod. High heritability coupled with high genetic advance was observed for methionine
content, 100-seed weight, pods per plant and days to flowering indicating the predominance of
additive gene action. The results demonstrated ample scope for improving seed yield in chickpea
through selection of 100-seed weight, pods per plant and days to flowering. The protein quality can
also be improved through changing the methionine content through selection.

Key words: Cicer arietinum L., genetic advance, genetic variability, heritability and methionine
content.

Chickpea (Cicer arietinum L.) commonly also genetic variability with respect to grain yield,
known as gram, Chana, Bengal gram and Garbanzo nutritional quality and other agronomic characters.
beans is the most important pulse crop of arid and
MATERIALS AND METHODS
semi-arid regions.Chickpea is native of south-
eastern Turkey and Syria (Saxena and Singh, The experiment was conducted to evaluate 48
1987). Among the food crops grown in India, different genotypes including three cultivated
chickpea is fifth in acreage and sixth in production, varieties (Table 1) at S. D. Agricultural University,
while total area under its cultivation was about 7.37 Sardarkrushinagar during rabi season of 2011-12.
million hectares with a total production of 5.89 The observation on five randomly selected plants
million tons and productivity of 799.19 kg/ha were recorded for days to flowering, days to
(Anon., 2011). Chickpea is the only domesticated maturity, plant height, number of effective
species under the genus Cicer.The genus Cicer branches per plant, number of pods per plant,
containing 41 species belongs to the tribe Cicereae number of seeds per pod, seed yield per plant, 100-
of the family Fabaceae(Van der Maesen, 1973, seed weight, harvest index (%), protein content
ILDIS, 2013).Chickpea seed contains about 17 to (determined by using Near Infrared Ray (NIR)
21 per cent protein, 56.5 per cent carbohydrates, spectrophotometer) and methionine content,
0.49 per cent essential amino acid lysinebesides whichwas estimated by colorimetric method as per
ash, calcium, phosphorus and iron (Thakur, 1980). McCarthy and Paille (1959).Analysis of variance
Assessment of genetic variability in the base was performedas per the method suggested by
population is the first step in any breeding Panse and Sukhatme, 1978. The Genotypic and
programme. It should be determined with the help Phenotypic coefficient of variation (PCV and
of parameters, such as genotypic and phenotypic GCV) were estimated as per Burton,
co-efficient of variation, heritability and expected 1953.Heritability in broad sense was estimated as
genetic advance under selection. Present suggested by Allard, 1960whileexpected genetic
investigation was undertaken in the population48 advance (in terms of percentage of mean) for each
genotypes of chickpea to ascertain the extent of character was computed following Johnson et al.,
1955.

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