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Science Week 2016
This is a special issue of The Mysore Journal of Agricultural Sciences brought out coinciding with the Science Week 2016. This issue comprises papers of Ph.D. Scholars from four major divisions – Crop Production, Crop Improvement, Plant Protection and Social Sciences. Sixty nine original papers are published in this issue, which is the outcome of original research work carried out by Ph.D. scholars of different departments of the University. University of Agricultural Sciences, Bengaluru, has pioneered the organization of Science Week as a platform for showcasing the research outputs of Post Graduate Students. Science Week 2016 is being celebrated in UAS, Bengaluru, for the third consecutive year making it a hat-rick event. Master’s students are given an opportunity to present the salient outcome of their research work in the form of a Poster, while the Doctoral students make an oral presentation of a paper out of their thesis research as part of the Science Week. An interesting outcome of the Science Week is the positive interaction amongst the disciplines, encouraging divergent ideas and constructive criticism leading to new vistas in research. Students and Faculty are becoming aware of the research work being carried out in other Departments, widening their horizons. This interaction opens up avenues leading to inter-disciplinary research programmes and co-operation. These young authors of the papers published in this issue are the custodians of Agricultural Research and Education of tomorrow. We wish all the very best for these youngsters who have done a marvelous job, and hope they maintain the high standards of research and education in their career, which is the hall-mark of UAS, Bengaluru. Editors
Mysore J. Agric. Sci., 50 (2) : 193-197, 2016
An Economic Study of Factors Influencing Emergence of Allied and Non-Farm Activities among Farm Households in Karnataka GAYATHRI MOHAN AND B. V. CHINNAPPA REDDY Department of Agricultural Economics, College of Agriculture, UAS, GKVK, Bengaluru-56 0065
ABSTRACT A study was undertaken to identify the factors influencing the emergence of Allied Activities (AA) and Non-Farm Activities (NFA) among farm households of Karnataka. The sample respondents were selected using a multistage random sampling technique from four districts of Karnataka representing four different agro-climatic zones. The Correspondence Analysis was employed to identify the principal factors influencing emergence of Allied Activities and Non-Farm Activities (AA & NFA). The primary factors contributing to emergence of AA & NFA among farm households were agricultural growth-driven in the case of Mandya and Hassan districts while in Kolar and Chitradurga districts agricultural resource constraint factors were conducive.
THE agriculture sector has served as a cynosure in achieving inclusiveness in economic growth in India. However, literature have shown that the overdependence on agriculture as a sole source of livelihood in rural areas has resulted in performance deficiency posing a threat to the rural livelihoods leading to the emergence of alternate sources of livelihood and employment strategies. Typical regional contrast is seen in the emergence of alternate sources of livelihood across all states. Contemplating these aspects, an attempt was made to identify factors responsible for the emergence of agricultural Allied Activities (AA) and rural Non-Farm Activities (NFA) in the agrarian economy of Karnataka.
300 sample respondents. Data were collected through interviews using a well-structured pretested schedule. The sample respondents were asked to score the various factors responsible for the emergence of AA and NFA, identified through preliminary survey and literature.
The study was carried out in four districts of Karnataka viz., Kolar, Mandya, Hassan and Chitradurga representing the four agro-climatic zones i.e., Eastern Dry Zone, Southern Dry Zone, Southern Transition Zone and Central Dry Zone, respectively. A multi-random sampling procedure was adopted to select 75 sample farm households from each district engaged in different kinds of Allied Activities and NonFarm Activities (AA & NFA) constituting a total of
The proportion of respondents carrying out different activities in the four districts is revealed in Fig. 1. Mandya (49.33 %) had the highest proportion of farm households involved in both AA & NFA while the farm households with highest proportion of participation in NFA alone was in Chitradurga (69.33 %) followed by Hassan (54.66 %) and Kolar (40 %). Thus the factors influencing the emergence of AA & NFA in these districts may also vary.
The factors were scored from one to four (4 High, 3 - Medium, 2 - Low, 1 - No contribution) by the respondents based on the intensity of factors leading to the emergence of AA & NFA. The scores assigned were analysed using Simple Correspondence Analysis (Reddy, 2007 and Suneetha, 2004) to identify the region specific factors responsible for the emergence of the AA & NFA.
194
GAYATHRI MOHAN AND B. V. CHINNAPPA REDDY
Kolar
Mandya
Hassan
Chitradurga
Fig. 1: District-wise percentage of households engaged in different activities Note: Allied activities: dairy, goat and sheep rearing, poultry, piggery unit and sericulture Non-farm activities: Trade and commerce, service and repair, processing and manufacturing and salaried and wage employment.
The graphical representation of the variability between the districts corresponding to different factors is displayed in Fig. 2. The inertia value gives an account of the percentage variation explained sby the different components (similar to the R2 value in regression analysis). The number of components to be extracted for explaining the condition is decided based on the cumulative inertia value. In the present study, the cumulative inertia explained by the first two components is 89 per cent. The component 1 axis relates to availability of agricultural resource endowments while the component 2 axis displays the institutional and
infrastructural related factors of the district like transportation facility for agricultural marketing, labour productivity in AA & NFA, food security through PDS and proximity to towns and urban hubs. Kolar displayed emergence of AA & NFA as agricultural resource constraint driven with inadequate irrigation water availability and inadequate electricity facility being the prime factors. In Mandya, other than agricultural labour scarcity, the AA & NFA pull factors predominated in the emergence of AA & NFA. More comprehensively it can be summarized that the emergence of AA and Legend Distic ts
:
1‐K olar, 2‐Mandya , 3 ‐Hassan, 4‐ Chitradurga Factors: 1‐ Risk in agric ulture 2‐ Agricultural Labour scarcity 3‐ L ow agricultural profitability 4‐ Inadequate agricultural institutional support/subsidy 5‐ Inadequate Transportation facility for agricultural marketing 6‐ Inadequate electricity facility 7‐ Inadequate irrigation water availability 8‐ Inadequate agricultural marketing facilities 9 Low risk in AA & NFA
Fig. 2.
Factors Classification or clustering of different districts based on the reasons identified for the emergence of allied and non farm activities
Note: Analysis of Contingency Table Axis Inertia Proportion Cumulative Histogram 1 0.0117 0.6908 0.6908 ****************************** 2 0.0034 0.2010 0.8919 ******** 3 0.0018 0.1081 1.0000 **** Total 0.0170
AN ECONOMIC STUDY OF FACTORS INFLUENCING EMERGENCE OF ALLIED AND NON-FARM ACTIVITIES
High AA & NFA profitability, low risk in AA & NFA, high AA & NFA labour productivity, adequate food security through PDS, proximity to towns and urban hubs were the reasons that contributed in Hassan. Chitradurga displayed risk in agriculture, AA & NFA promoting institutional support and younger labour force as the major factors contributing to emergence of AA & NFA activities. The factors, low agricultural profitability and inadequate agricultural institutional support, were lying close to the centre, indicating not much contribution to the factor loading (Table I). Thus it can be said that in Kolar and Chitradurga, which represent dry zone regions of Karnataka, resource crunch in the availability of irrigation water and electricity, thereby a higher risk in agriculture emerged as the main factors and can be categorized as agriculture constraint driven factors. In Mandya and Hassan, which represent relatively water abundant zones, agricultural development and allied and nonfarm pull factors were the major reasons contributing to emergence of allied and non-farm activities. The results are in accordance with the studies of Rao and Chandrashekar (2012) and Udmale et al (2014). Kolar and Chitradurga have shown strong tendency towards
195
AA & NFA due to constraints in agricultural development. Further to analyse whether the investment made on AA & NFA is from surplus income generated from agriculture sector, the investment made across the four regions were tested using Chi-square test. It was observed that investment in AA & NFA in Mandya and Hassan districts were more as they are bestowed with abundant water supply compared to Kolar and Chitradurga. The Chi-square test was used to analyse the hypothesis that investment made in AA & NFA are same irrespective of zonal water resource endowment. The results showed that there exists significant difference in the level of investment made across the districts (Table II and Table III). Mandya and Hassan districts showed a higher level of investment in both AA & NFA indicating the agricultural income surplus contributed to higher investment supporting the findings in Table I. While in Kolar and Chitradurga the investment in AA & NFA was attributed to agricultural resource constraint as observed earlier. The districts of Mandya and Hassan have a strong multiplier effect on the emergence of AA & NFA.
TABLE I District-wise clustering of factors District Kolar Mandya
Component1 (Resource Endowments)
Hassan
Irrigation water and electricity availability Agricultural labour scarcity, high AA & NFA profitability and better education level High AA & NFA profitability
Chitradurga
Risk in agriculture
Component 2 (Infrastructural and Institutional factors)
Proximity to towns and urban hubs, AA & NFA Profitability, Adequate food security through PDS Age of labour force, AA & NFA promoting institutional support
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GAYATHRI MOHAN AND B. V. CHINNAPPA REDDY
TABLE II District- wise investment made on allied activities Kolar Investment (Rs.)
Number of house holds
Mandya
Hassan
Chitradurga
Per cent
Number of house holds
Per cent
Number of house holds
4
7.14
3
4.48
0
0.00
1000000
56
67
Mean Investment (Rs.)
64
97328
69
230449
126550
34807
Chi square value = 0.034805; significant at 5%
TABLE III District- wise investment made on non-farm activities Kolar Investment (Rs.)
Number of house holds
Mandya Per cent
Number of house holds
Hassan Per cent
Number of house holds
Chitradurga Per cent
Per cent
Number of house holds
1000000
0
0.00
1
2.22
0
0.00
0
0.00
45
100
45
100
34
100
23
100
Mean Investment (Rs)
389278
Chi square value = 0.006636; significant at 1%
420027
432338
257636
AN ECONOMIC STUDY OF FACTORS INFLUENCING EMERGENCE OF ALLIED AND NON-FARM ACTIVITIES
This needs to be further strengthened by encouraging value addition in agriculture and promotion of farm machinery custom hiring activities which could further expand the AA & NFA. However, in Kolar and Chitradurga, emergence of AA & NFA is due to resource constraint in Agriculture. Therefore, to promote AA & NFA, assistance in the form of capital and incentives has to be provided, as it is not possible to address the resource constraint in agriculture within a short-term to sustain the rural livelihoods. REFERENCES RAO GOPINATH M. AND CHANDRASHEKAR, H., 2012, Role and contribution of irrigation to rural non-farm activity: A case of Cauvery command in Karnataka. Project
(Received : May, 2016
197
Report, Ministry of Statistics and Programme Implementation, GOI. REDDY MANJUNATHA, H. N., 2007, Export competitiveness and performance of selected fresh fruits and vegetables from India: An economic analysis. M.Sc. (Agri.)Thesis, Univ. Agric. Sci. Bangalore. SUNEETHA, M. S., 2004, Economic valuation of medicinal plants in the context of the convention on biodiversity and intellectual property rights in India. Ph.D. Thesis, Univ. Agric. Sci. Bangalore. UDMALE, P., ICHIKAWA, Y., MANANDHAR, S., ISHIDAIRA, H. AND KIEM S.A., 2014, Farmers’ perception of drought impacts, local adaptation and administrative mitigation measures in Maharashtra State, India. Int. J. Disaster Mngt.,10 : 250-269.
Accepted : June, 2016)
Mysore J. Agric. Sci., 50 (2) : 198-201, 2016
Trade Creation and Trade Diversion Effects of India-Japan Comprehensive Economic Partnership Agreement (IJCEPA) on Agricultural and Allied Sector SHAIKH MOHD MOUZAM AND G. S. MAHADEVAIAH Department of Agricultural Economics, College of Agriculture, UAS, GKVK, Bengaluru - 560 065
ABSTRACT India and Japan signed a Comprehensive Economic Partnership Agreement (IJCEPA) in 2011. The signing of this agreement has resulted in different opinions when it comes to India’s Agricultural exports to Japan. The present study focused on trade creation and diversion effect of India-Japan free trade agreement on agricultural and allied sector. This study is based on secondary data and was performed using an ex-ante partial equilibrium model i.e., Software for Market Analysis and Restrictive Trade (SMART) model. The results revealed that the IJCEPA will lead to considerable increase in exports of agricultural products and this increase in exports is mostly driven by trade diversion (0.012) rather than trade creation (0.010) replacing efficient non-partner exporters of Japan. On imports side, tariff preferences offered from India’s side creates very little scope for Japan to expand their shares in Indian market. The danger of cheap imports replacing the domestic products in the Indian markets therefore, is not much. The results indicated that, IJCEPA will have a favourable effect on Indian agricultural trade.
INDIA and Japan signed a Comprehensive Economic Partnership Agreement (IJCEPA) in 2011. According to official notifications, the trade in goods agreement (Chapter 2) which focuses on tariff liberalization on mutually agreed tariff lines on both the sides and is targeted to eliminate about 94 per cent of the tariffs between India and Japan over a period of 10 years (i.e., 2021). The agreement on trade in goods, proposes to gradually reduction of tariffs for over 4500 tariff lines (HS-6-digit level) by 2021 on both the sides. The major items of India’s export include marine products, spices, fruits such as mangoes, lemons, etc.
The signing of this agreement had resulted in different opinions, while Geethanjali and Ashwani (2014) in their study concluded that reduction of trade barriers as a result of signing FTA with Japan boosted India’s Agricultural, Pharmaceutical and Textiles exports but contrary to this, the analytical report of APEDA (2013) states that since the signing of IJCEPA in 2011, there is a decrease in share of agricultural and marine exports to Japan in India’s total goods export. It is therefore, of great interest to analyse whether Indian agricultural sector gains or losses and whether this IJCEPA will lead to trade creation or diversion in agricultural trade.
Tariff Reduction Schedules and Category: According to the trade in goods agreement, the tariff lines (HS 8-digit) subject to tariff reduction or elimination are classified into six broad categories viz., category A, B5, 7, 10, 15 and X.
The analysis of customs union dates back to Viner (1950), who introduced the terms “trade creation” versus “trade diversion”. Trade creation refers to a situation where in displacement of less efficient national production takes place in favour of more efficient partner country production. Trade diversion occurs when displacement of more efficient non partner imports in favour of less efficient partner country sourced imports.
India kept 35 per cent of its total agricultural products, out of tariff liberalization schedule (i.e., in category X) Japan as opposite to non-agricultural market access (NAMA) products kept its 50 per cent of its total agricultural product lines in exclusion list and about 35 per cent of agricultural product lines given immediate zero duty status which will help India’s agricultural traders.
For this study we used the 6-digit (Nomenclature HS 2007) trade and tariff data of HS 1 to 24 tariff lines except fishery (and relevant tariff lines of HS 29, 33, 35, 38, 41, 43, 50, 51, 52 & 53)
EFFECTS OF INDIA - JAPAN COMPREHENSIVE ECONOMIC PARTNERSHIP AGREEMENT
collected from the World Integrated Trade Solution (WITS) and Commodity Trade Statistics (COMTRADE) databases. The other data source like Ministry of Commerce and industries, export-import data bank. The SMART Model: This study employs the partial equilibrium Software for Market Analysis and Restrictive Trade (SMART1) model to simulate the tariff effect of a single market on disaggregated product lines to estimate the trade creation and trade diversion effects. This model was developed by United Nation Conference on Trade and Development (UNCTAD) and World Bank in the 1980’s with theoretical background borrowed from Laird and Yeats (1986). The SMART contains incorporated analytical modules that carry trade policy analysis, such as the effects of tariff cuts, preferential trade liberalization and ad hoc tariff changes. Here it considers only single import market and its export partner or partners and analyses the impact of a tariff reduction or elimination scenarios by estimating new values for a set of variable.
199
(Scenario-II) of agricultural products from Japan. Drawing from the tariff reduction schedule of both the countries as per the IJCEPA agreement, the impact of tariff change in 2021(i.e., termination year) compared to base year tariff in 2013 were simulated for the relevant product lines of agricultural products at HS 6-digit level, later results were aggregated and presented at sectoral level. Scenario 1: India as exporter of Agricultural products to Japan The simulation results for Agricultural and allied sector products showing gains to India 2021 (terminal year for phased tariff reduction as per IJCEPA) as compared to the base year 2013 are presented in Table I.
The underlying assumptions in this model are: import demand elasticity is based on Armington assumption, which implies that similar goods from different countries are imperfect substitutes. The values of this elasticity are provided by SMART module is used. Import substitution elasticity is assumed at 1.5 for each good. Export supply elasticity is assumed as infinite, which implies that an increase in demand for a particular good will always be matched by the producers and exporters of the good, without any influence on the price of the good.
The total value of Indian Agricultural exports in the base year from all the categories (i.e., X, A, B7, B10 and B15) was about US$ 61006.67 million of which 45 per cent is from X category only. If Japan reduces tariffs to zero duty or dismantles the tariffs imposed on Indian imports, trade worth US$ 13.98 million would be increased in favour of India in 2021. Overall, the results suggest that the total increase in exports is mostly driven by trade diversion (Table III). Trade diversion signifies the level of trade that is replaced by Indian producers which was earlier exported by rest of world to Japan due to tariff preference given to Indian agricultural exports. As a result, many countries lost their market in Japan. Overall, the Indian agricultural exporters will benefit from this agreement.
India exports to as well as imports agricultural and allied sector products from Japan, with a positive net trade balance. Hence, the tariff reduction commitments under IJCEPA would affect both the Indian exports as well as imports of agricultural and allied products. Therefore, to quantify the effect of the tariff reduction commitments made under the agreement, simulations were carried out in two different scenarios as mentioned earlier.
At the category level, the gains are particularly noteworthy in A category as its share in total increase in export value is about 92 per cent in 2021 (Table I). Almost 70 per cent of the total trade diversion towards Japan would be attributable to 5 countries; they are Chile, Finland, Switzerland, Paraguay and South Africa, implying that these countries will lose their market share of agricultural products in Japan while India will be the gainer.
The simulation modelling was carried out with India as an exporter (Scenario-I) as well as importer
Scenario II: India as importer of Agricultural products from Japan
200
SHAIKH MOHD MOUZAM AND G. S. MAHADEVAIAH
TABLE I Impact of IJCEPA on Indian Agricultural exports to Japan by 2021 Category
Base year Exports ( in Million US$)
B7
392.89
B15 B10
Total change in Exports ( in Million US$)
Trade Creation Effect (%)
Trade Diversion Effect (%)
00.003 (0.0007)
0.0003
0.0004
1265.90
00.005 (0.0004)
0.0001
0.0003
4299.37
00.018 (0.0004)
0.0003
0.0001
A
29754.02
12.988 (0.0436)
0.0206
0.0231
X
25294.49
00.969 (0.0038)
0.0020
0.0019
Total
61006.67
13.983 (0.0229)
0.0109
0.0120
Note: Figure in parentheses indicate percentage change in imports to base year’s imports
Under IJCEPA agreement, like Japan, India has also committed to reduce tariff on agricultural products under A and B10 category. The reduction of tariff by a country have two effects on its economy, one it may lead to loss in tariff revenue and another it can increase consumer surplus due to access to cheaper imports from Japan. Due to this tariff reduction commitment, there would be an increase in India’s import value of agricultural products from Japan by US$ 3.35 million by 2021. Overall, the simulation results reveal that trade creation outweighs the trade diversion in total as well as in each category (Table II). This additional trade would benefit the Indian consumers in the sense
that more efficient Japanese producers and exporters will supplant the inefficient producers in India. The level of welfare gain depends mainly on the level of trade creation. Weighed against the revenue loss, the trade creation effect and positive welfare effect changes in the terminal year of tariff reduction present IJCEPA as potentially beneficial arrangement for India (Table III). However, these are static results and welfare results do not represent the producer surplus loss that will occur due to replacement of domestic producers of India by the Japanese producers. The results obtained from the study suggest that the IJCEPA will lead to considerable increase in exports
TABLE II Impact of IJCEPA on Indian Agricultural imports from Japan by 2021 Base year (2013) Imports (in Million US$)
Total change in Imports (in Million US$)
4351.14
2.64 (0.0606)
0.0052
0.0008
A
840.73
0.000006 (0.0000)
0.0000
0.0000
X
11906.93
0.72 (0.0060)
0.0431
0.0175
Total
17098.82
3.35 (0.0196)
0.0146
0.0050
Category
B10
Trade Creation Effect (%)
Trade Diversion Effect (%)
EFFECTS OF INDIA - JAPAN COMPREHENSIVE ECONOMIC PARTNERSHIP AGREEMENT
201
TABLE III Revenue and Welfare effects of IJCEPA on Indian economy Category B10 A
Revenue shortfall in Million US$
Total welfare in Million US$
0.120
0.913
-0.000005
0.000001
X
-1.620
1.235
Total
-1.499
2.148
of agricultural and allied products as per the SMART analysis; however, these would have to be analysed from the point of view of the SPS and TBT measures also to come to any conclusion. But in SMART model quality parameters are not taken into consideration and therefore these results are based on new tariff allocations only. The increase in exports is mostly driven by trade diversion rather than trade creation replacing efficient exporters of Japan like Chile, Finland, Switzerland, Paraguay and South Africa. In future, the inefficient agricultural and allied producers will become efficient due to achievement in economies of scale. On imports side, tariff preferences offered from India’s side creates very little scope for Japan to expand their shares in Indian market. The danger of cheap imports supplanting the domestic products in the Indian markets is not much. However, clear directives and necessary assistance should be provided to the domestic agricultural and allied commodities producers to counter the competition. (Received : May, 2016
REFERENCES CHAUFFOUR, J. P. AND MAUR, J. C., 2011, Preferential Trade Agreement Policies for Development: A Handbook. World Bank Publications: 144-145. GEETHANJALI NATARAJ AND ASHWANI, 2014, India-Japan Economic Partnership Agreement: Gains And Future Prospects, Occasional Paper No. 50, Observer Research Foundation (Orf), New Delhi. LAIRD, S. AND YEATS, A., 1986, The Unctad Trade Policy Simulation Model. Discussion Papers, Unctad, Geneva 10, Switzerland. RAGHAVAN, B. S., 2011, India-Japan CEPA holds great promise. The Bussiness Line. retrieved from http:// www.thehindubusinessline.com/opinion/columns/bs-raghavan/indiajapan-cepa-holds-great-promise/ article2430062.ece. VINER, J., 1950. The Customs Union. New York.
Accepted : June, 2016)
Mysore J. Agric. Sci., 50 (2) : 202-206, 2016
Consumer Preference for Organic Food Products in Southern Karnataka: An Analysis of Socio-economic Factors K. P. NAVEENA,
AND Y.
S. ARUNKUMAR
Department of Agricultural Economics, College of Agriculture, UAS, GKVK, Bengaluru-560 065
ABSTRACT The demand for eco-friendly products such as organic foods has significantly increased due to increasing awareness on health, food safety and environmental concerns. The present study was an attempt to assess consumer preference for organic food products in South Karnataka,through personal survey carried out in some organic outlets found in Bengaluru, Mysuru and Mandya. A logitfunction was used to analyse the sociodemographic features and preference to purchase organic food products. Safety, eco-friendly and taste were the driving forces to prefer organic food products which are influenced positively by age, purchase frequency and experience and negatively by increase in the family size of the respondents.
H OW safe is the food we are eating…??!! If onerealizes the truth he will be horrified. The farm products we use in preparing the food are being produced with the use of high levels of chemical inputsespecially indiscriminate and heavy doses of pesticides including banned as well as systemic pesticides which have long residual effect. In fact, someof the fruits and vegetables are dipped in pesticides to prevent post-harvest losses.It has become a common practice to ripen fruits using chemicals. Processed foods are added with preservatives many of which have dangerous side-effects.Many of the processing locations and processing procedures are done in highly unhygienic conditions. All these factors contribute to the food we consume unsafe that affects not only human health but also environmental safety. In recent years, increasing concern about food safety, health hazards and environmental damage both among consumers and producers is leading to increased adoption of organic farming and production of organic foods. In India, there are over 5, 97,873 certified organic producers and the number is growing fast over the years (IFOAM, 2015).Earlier days, organic foods were produced and majorly exported to Europe and the United States as most of the Indian consumers were price conscious and not quality conscious. However, the scenario has changed in recent years,domestic demand for organic food products in India has picked upsubstantially and registereda growth
of 25-30 per cent during the last couple of years particularly in metros such as Delhi, Mumbai and Bengaluru. However, the consumer awareness about organic food productsand their availability is still low in India. Creation of consumer awareness is an important factor to augment demand fororganic food. This needs understanding the factors that motivate the consumers to choose organic food products as well as the socio-economic factors which determine such concern. An attempt was made to analyse the consumer preference and the motivating factors to choose organic food products and its relationship with socio-economic characteristics of the consumers. The results are expected to help administrators and policy makers to design strategies and policy as well as identify the target group for consumer education on the benefits of opting for organic food. The study was based on primary data collected in Bengaluru, Mysuru and Mandya districts representing the South Karnataka from 60 consumers available in selected retail outlets during the random visits of the researcher in 2015-16.The information collected was tabulated and analyzed using simple averages and percentages. Garrett’sranking technique was employed to analyze the motivating factors influencing consumers to prefer organic food products. As per the Garrett’s ranking technique, the percentage positions was calculated by the following formula:
CONSUMER PREFERENCE FOR ORGANIC FOOD PRODUCTS IN SOUTHERN KARNATAKA
203
respondents bought organic food products on monthly basis, while, 25 per cent were occasional buyers. Most of the consumers (50 %) were aware of organic certification and logos and buying the products for more than one year in organic outlets. Further,eight per cent of the respondents were growing organic food cropson terraces for their requirements.
Per cent position = where, Rij= Rank given for the ith item by the jth individual, and Nj= Number of items ranked by the jthindividual Further, logitfunction was used to assessthe impact of socio-economic characteristics on the motivating factor for the consumer to choose organic food products rather than conventional food. The model was:
The organic food products that the consumers regularly purchased from selected organic outlets are shown in Fig. 1. Jaggery powder was the most common item purchased by the sample customers (75 %). More than 60 per cent the consumers bought fruits and finger millet. Organic foxtail millet, honey, milk and milk
Motivation for purchase of organic food product (yes/xi) = á + ÓâiiXii+ e where, Yi= ‘1’ if consumer respond positive answer to particular variable, otherwise ‘0’ á = Constant term Xi = Independent variable (socio-demographic factors)
n
∑ [(Rij − 0.5) / Nj] *100 j =1
âi = Logistic coefficients for the ith independent variable e = Error term Fig. 1 :
The socio- demographic feature of the respondents consuming organic food products are listed in Table I. The respondents selected for under standing the consumer preference to buy organic food products were mostly females(72 %).Usually, females tend to have more responsibility and concern about food safety and family health than males. Youngerconsumers in the age group of 36 - 45 years (Avg. age 41 years) werethe major buyers(45 %) of organic food products.It is mostly the elite population that buys organic food products and seems to be following consumers of the developed countries.Approximately 82 per cent of the respondents were college educated and the average family size was four members. As for as income level of the consumers was concerned, majority (68 %) of the respondents belong to higher income group (>Rs. 50001). About 62 per cent of
Major organic food products preferred by the respondents
products were also on the list and got significant appreciation from the consumers (> 40 %). Among the most relevant marketable organic items, rice and leafy vegetables were bought by 32 per cent of the respondents,while, pulses, sugar, vegetables and coconut accounted to 28, 27, 25 and 18 per cent, respectively. These findings are similar to that of Idda et al. (2008) who studied motivational profile of organic food consumers in Italy and opined that more than 60 per cent of the interviewed consumers bought rice and durum wheat pasta. The motivating factors influencing consumers to prefer organic food productsare delineated in Table II. It was found that, food safety and eco-friendly
204
K. P. NAVEENA AND Y. S. ARUNKUMAR
TABLE I Socio- demographic profile of the respondents and description of independent variables n = 60 Variable
Notation
Description
Gender
X1
0 if female 1 if male
Age
X2
1= 55 Average age
Education
X3
1= Primary school 2= Secondary school 3= PUC 4= Degree and above
Family size
X4
Income
X5
Purchase experience
X6
Frequency of purchase
Place of purchase
Number
Per cent
17 43
28.33 71.67
2 14 27 12 5 40.82
3.33 23.33 45.00 20.00 8.33
0 0 11 49
0.00 0.00 18.33 81.67
1= Single 2= 2 people 3= 3 people 4= 4 people 5= 5 or more people Average family size
4 16 11 22 7 3.55
6.67 26.67 18.33 36.67 11.67
1= 100000 1= < 1month 2= 1-6months 3= 6-12 months 4= 1to 2 years 5= > 2years
0 19 32 9 6 13 11 22 8
0.00 31.67 53.33 15.00 10.00 21.67 18.33 36.67 13.33
X7
1= occasionally 2= weekly 3= fortnightly 4= monthly
15 6 2 37
25.00 10.00 3.33 61.67
X8
1=Organic outlets 2=Organic outlets and super bazaars 3=Organic outlets and own
60 9 5
100.00 15.00 8.33
205
CONSUMER PREFERENCE FOR ORGANIC FOOD PRODUCTS IN SOUTHERN KARNATAKA
TABLE II
Factors influencing consumers’ preference for organic food products Reasons
Garrett’s mean score
Food safety Eco-friendly Tasty Nutrition Increased shelf-life
76.13 69.01 56.34 48.62 38.55
Rank I II III IV V
reasons were the major concerns for purchase of organic food products with Garrett mean score of 76.13 and 69.01, respectively. In addition to the above, the other major reasons were taste (56.34), nutrition (48.62) and increased shelf-life (38.55) as indicated by the Garrett’s ranking. The results indicated that the factors for purchase of organic food products in Southern Karnataka were similar to the findings from Huang (1996). The relationship between consumer preference to buy organic food products and socio-demographic
TABLE III A. Food safety Variables constant Age Gender Purchase experience
Estimated parameters of logit model Co efficient
Odds ratio
P value
2.110** 0.950 ** “0.659 1.010***
8.248 2.586 1.934 2.747
0.0160 0.0263 0.7859 0.0024
Log-likelihood Log likelihood ratio Test Akaike criterion Schwarz criterion Correct prediction (%)
-39.45 62.19 86.91 75.29 90.00
Note: ** and *** represent 5 % and 1 % significant levels, respectively
B. Eco-friendly Variables constant Age Education Family size Income Frequency Purchase experience
Co efficient 3.591 ** “0.327 1.224 “0.518** 0.164 0.641*** 0.440**
Log-likelihood Log likelihood ratio test Akaike criterion Schwarz criterion Correct prediction (%) Note: ** and *** represent5 % and 1 % significant levelsrespectively
Odds ratio 36.23 1.39 3.40 1.68 1.18 1.90 1.55 -29.74 35.34 71.57 84.13 76.70
P value 0.028 0.783 0.191 0.027 0.778 0.008 0.010
206
K. P. NAVEENA AND Y. S. ARUNKUMAR
C. Taste Variables
Co efficient
Odds ratio
constant
1.359 **
3.892
0.028
Age
0.729 **
2.073
0.011
“0.459
1.582
0.057
Frequency
0.218
1.244
0.249
Purchase experience
0.724**
2.063
0.013
Gender
Log-likelihood Log likelihood ratio test
-30.42 57.59
Akaike criterion
70.84
Schwarz criterion
81.31
Correct prediction (%)
78.30
P value
Note: ** represent 5% and 1% significant level
variables are analyzed through step-wise logit model (Table III). Safety model was built by taking into the regular organic customers and their age, as they are true organic consumers mainly associated to health.It could be seen from the safety model that, a unit change in purchase experience leads to significant increase in odds ratio in favour of preferring organic food products as health consciousness by 27 per cent. This clearly indicates that the people who are using organic food products have been really convinced about health benefits of these products. The eco-friendly model confirms the reasons for purchase of organic food products. The results revealed that one who has low household size rather than large families confirms their purchasing behaviour of organic foods as eco-friendly. This may be because smaller families have more disposable income and can afford to purchase organic food products which are relatively costly. Similarly, purchase frequency also contributed significantly to prefer organic food products as eco-friendly nature. Further, the taste model confirms that only experienced consumers who evidently enjoy personal satisfaction (taste) when eating organic food products. These findings corroborate with the study conducted by Chandrashekar (2014) in Mysuru district of Karnataka. In conclusion, safety, eco-friendly and taste conform to be the most important driving factors for (Received : May, 2016
organic food customers. It is not surprising to notice that the small family size prefer to buy organic food products than large families. This indicates, as the organic food products are bit costly and it is difficult to purchase for large families. More over, some promotional strategies need to implemented to ensure general health and environmental safety of the consumers. REFERENCES BEHARRELL, B. AND MACFIE, J. H., 1991, Consumer attitudes to organic foods. British Food Journal, 93(2): 25-30. CHANDRASHEKAR, H. M., 2014, Consumers perception towards organic products - A study in Mysore city. International Journal of Research in Business Studies and Management, 1(1): 52-67. HUANG, C. L., 1996, Consumer preferences and attitudes towards organically grown produce.European Review of Agricultural Economics, 23 (3): 331-342. IDDA, L., MADAU, F. A. AND PULINA, P., 2008, Themotivational profile of organic food consumers: A survey of specialized stores customers in Italy. 12th Congress of the European Association of Agricultural Economists, 1-10.
www.ifoam.com Accepted : June, 2016)
Mysore J. Agric. Sci., 50 (2) : 207-212, 2016
Impact of Climate Variability on Cropping Pattern in Chitradurga District, Karnataka: An Economic Analysis M. SAGAR AND G. S. MAHADEVAIAH Department of Agricultural Economics, College of Agriculture, UAS, GKVK, Bengaluru-560 065
ABSTRACT Agriculture is highly dependent on rainfall. Any irregularities in climate variables impact the production, cropping pattern and farm income of the rural households. Analysis of rainfall data from 1900 to 2015, temperature data from 1971 to 2014 of Chitradurga district showed that there were break points in annual rainfall, pre-monsoon rainfall, monsoon rainfall and minimum and maximum temperature. Results of Markov chain analysis revealed that rice, arecanut, groundnut, maize and ragiwere the stable crops in the districts as more than 75 per cent of their previous share in area of these crops were retained.
THE climate variability is one of the serious challenges faced by Indian agriculture. It is observed from the literature that climate change is an ongoing process with respect to the temperature, annual rainfall, rainfall distribution and number of rainy days. The key findings from the research at macro level may not hold good for micro level such as individual districts, since the agro-climatic conditions, land pattern, cropping systems, cropping pattern and resource availability vary over space (Chand et al., 2011 and Jangra, 2011). Therefore, there is a need to carry out studies on climate parameters and their impact on agriculture at regional level. The normal agricultural and allied activities largely depend on rainfall and number of rainy days in the year. Any deviation in climatic parameters from their normal mean acts as stress to the rural livelihood and rural economy. The agrarian crisis in the state has increased over years due to the distress conditions in agriculture. With this background, the present study attempted to analyse the changes in monthly rainfall, annual rainfall and temperature and their impact on cropping pattern in Chitradurga District, Karnataka State. Chitradurga district is located at latitude 14° 14' N and longitude 76° 26' E in central part of Karnataka State. The annual average rainfall is about 514 mm. The south west monsoon plays the major role in the agriculture and its activities in the district.The economy
of Chitradurga district was mainly driven by agriculture as it contributed 19.39 percent to the district’s GDP which was about ¹ 87,727 lakhs at constant prices of 2004-05. Chitradurga district stood 21st in the State according to the Gross District Domestic Product criterion.The study aims to find out the influence of climate variables on cropping pattern changes in Chitradurga district. The data regarding climate parameters (rainfall and temperature) were collected from India Meteorological Department and Karnataka State Natural Disaster Monitoring Cell. The secondary data regarding area, production and productivity were collected from Directorate of Economics and Statistics, Bengaluru. a. Identification of structural break in the time series: Homogeneity testwas carried out to examine the exact shift or break in the time series data.Pettitt’s test, Standard normal homogeneity test (SNHT) and Buishand’s test were used to check the homogeneity with null and alternate hypothesis given below. H0: Data are homogeneous Ha: There is a break in the series of data related to rainfall and temperature b. Markov Chain Analysis: The Markov Chain Analysis was carried out to examine shifts in cropping
208
M. SAGAR AND G. S. MAHADEVAIAH
pattern as influenced by climate variables for the period 1956-57 to 2011-12.The current study aims to identify the changes in cropping pattern due to climate parameters for the study period.
The results of homogeneis1ty testfor climate parameters is indicated below. a) Rainfall : The rainfall data for the Chitradurga district was analysed for the period 1900 to 2015. The
TABLE I Homogeneity tests for climate parameters (rainfall & temperature) in Chitradurga district Pettitt’s test
Standard normal homogeneity test
Annual rainfall 940.00 K t 1985 p-value (Two-tailed) 0.047 Pre-Monsoon rainfall K 1020.00 t 2003 p-value (Two-tailed) 0.025 Monsoon rainfall K 1244.00 t 1973 p-value (Two-tailed) 0.002 Annual Maximum temperature K 8574082.00 t 02/02/1995 p-value (Two-tailed) < 0.0001 Annual Minimum temperature K 9863383.00 t 13/03/1997 p-value (Two-tailed) < 0.0001 August K t p-value (Two-tailed) September K t p-value (Two-tailed) June K t p-value (Two-tailed) July K t p-value (Two-tailed) August K t p-value (Two-tailed)
Buishand’s test
T0 t p-value (Two-tailed)
33.80 2005 0.021
Q t p-value (Two-tailed)
18.28 2004 0.001
T0 t p-value (Two-tailed)
27.42 2003 0.001
Q t p-value (Two-tailed)
17.24 2003 0.005
T0 t p-value (Two-tailed)
31.12 2005 0.022
Q t p-value (Two-tailed)
17.35 2004 0.002
T0 t p-value (Two-tailed)
255.47 10/02/2010 < 0.0001
Q t p-value (Two-tailed)
779.85 21/01/2002 < 0.0001
T0 465.19 t 16/02/2011 p-value (Two-tailed) < 0.0001 Minimum temperature
Q t p-value (Two-tailed)
1037.92 12/03/1997 < 0.0001
253.00 1997 0.011
T0 t p-value (Two-tailed)
10.47 2011 0.015
Q t p-value (Two-tailed)
8.59 1997 0.036
249.00 1997 0.013
T0 21.98 t 2010 p-value (Two-tailed) 0.000 Maximum temperature
Q t p-value (Two-tailed)
10.07 1997 0.006
240.00 1994 0.019
T0 t p-value (Two-tailed)
13.64 2008 0.001
Q t p-value (Two-tailed)
9.92 1994 0.011
279.00 1995 0.004
T0 t p-value (Two-tailed)
19.24 2009 0.000
Q t p-value (Two-tailed)
11.49 2001 0.001
328.00 1992 0.000
T0 t p-value (Two-tailed)
23.75 2008 < 0.0001
Q t p-value (Two-tailed)
12.32 1994 0.000
IMPACT OF CLIMATE VARIABILITY ON CROPPING PATTERN IN CHITRADURGA DISTRICT
total rainfall was analysed according to year, months and different rainfall seasons. Significant structural breaksoccurredin the total annual rainfallseries from 524.49 mm to 764.60 mm, 528.36 mm to 1204.00 mm and 525.65 mm to 1168.00 mm during the years 1985, 2005 and 2004, respectively (Table I).In the case of pre-monsoon rainfall, shift was observed from 98.98 mm to 193.26 mm during 2003 (Table I). The monsoon rainfall was analysed for the study period which revealed that significant breaks in terms of increase in rainfall from 257.08 mm to 424.08 mm, 269.14 mm to 831.92 mm and 267.43 mm to 796.87 mm2 during the years 1973, 2005 and 2004, respectively (Table I).
b) Temperature: The temperature was analysed for the period 1971 to 2014 by employing homogeneity test to identify the shift. The results revealed that temperature (maximum & minimum) was increased during the study period (Table I). The maximum temperature was increased from 30.19°C to 30.79°C, 30.33°C to 31.57°C and 30.25°C to 30.97°C during 02.02.1995, 10.02.2010 and 21.01.2002, respectively whereas the minimum temperature was also increased from 18.80°C to 19.45°C, 18.93°C to 20.39°C and 18.80°C to 19.45°C during 13.03.1997, 16.02.2011 and 12.03.1997, respectively. Temperature was analysed for monsoon season (June to September) and the results revealed that the minimum temperature for August month increased from 19.84°C to 20.23°C, 19.93°C to 20.84°C and 19.84°C to 20.22°Cduring 1997, 2011and 1997. The minimum temperature for September month was increased from 19.65°C to 20.21°C, 19.73°C to 21.19°C and 19.65°C to 20.21°C during 1997, 2010 and 1997 (Table I).
The results of the structural break analysis for maximum temperature in the monsoon season revealed that June month recorded significant shift (increase) in temperature from 29.35°C to 30.40°C, 29.57°C to 31.46°C and 29.35°C to 30.40°C during 1994, 2008 and 1994, respectively whereas in July month there was a significant shift (increase) from 27.60°C to 28.55°C, 27.79°C to 29.77°C and 27.67°C to 28.85°C during 1995, 2001 and 2009, respectively. Shift in maximum temperature was recorded during August
209
month from 27.22°C to 28.17°C, 27.44°C to 29.32°C and 27.25°C to 28.23°Cin the years 1992, 1994 and 2008, respectively (Table I). Markov Chain analysis: The dynamics of change in area under different crops in Chitradurga district were analyzed using the Markov transitional probability matrix (Table II). The row elements in the transitional probability matrix indicate the extent of loss (decrease) in the area on account of competing crops. The column elements indicate the probability gains in previous share of area from other competing crops and the diagonal elements indicate probability of retention of the previous share in area by the respective crop in the current year. The study revealed that the maize, groundnut, arecanut, ragi and rice are the stable crops cultivated in the district as they retained more than 75 per cent of their previous share in area. Rice gained its previous share in area from cotton (3.6%) and ragi (3.7%), whereas, it lost its previous share in area to coconut (2.5 %), ragi (2.4%) and aware (1.0%), respectively. While maize gained its previous share in area from sunflower (7.0%) and ground nut (4.0%) whereas maize lost its previous share in area to ragi (5.0%), coconut (2.9%), ground nut (2.4%), sunflower (2.4%) and arecanut (2.0%), respectively. Groundnut gained its previous share in area from green gram (30.7%), onion (29.6%), sunflower (22.2%), coconut (9.7%), maize (2.4%) and ragi (2.4%). Groundnut lost its previous share in area to coconut (4.1%), maize (4.0%) and sunflower (3.2%) respectively. Arecanut gained its previous share in area from maize (2.0%) while it lost its previous share to bengal gram (6.3%) and save (5.1%). Ragi gained its previous share in area from sesamum (99.4%), aware (22.3%), greengram (14.0%), safflower (12.2%), onion (6.8%), maize (5.0%), rice (2.4%) and horesegram (2.03%) respectively. Ragi lost its previous share in area to sesamum (7.2%), jowar (6.6%), rice (3.6%), groundnut (2.4%) and horsegram (2.4%), respectively. The study found significant structural breaks (increase) were found in annual rainfall, pre-monsoon rainfall and monsoon rainfall with respect to rainfall analysis. Significant structural breaks (increase) were
Rice
0.929
0.000
0.000
0.000
0.000
0.000
0.036
0.000
0.000
0.000
0.037
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Crops
Rice
Jowar
Maize
Bengalgram
Ground nut
Sunflower
Cotton
Sugarcane
Arecanut
Coconut
Ragi
Navane
Tur
Bajra
Onion
Save
Haraka
Horse gram
Green gram
Sesamum
Castor
Nigerseed
Safflower
Tobacco
Dry chillies
Avare
Others
0.000
0.000
0.000
0.000
0.000
0.000
0.251
0.000
0.000
0.388
0.673
0.000
0.000
0.000
0.537
0.000
0.066
0.000
0.000
0.000
0.246
0.000
0.000
0.000
0.000
0.558
0.000
Jowar
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.070
0.041
0.000
0.837
0.000
0.000
Maize
0.034
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.014
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.011
0.064
0.000
0.000
0.000
0.005
0.693
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.307
0.000
0.000
0.000
0.296
0.000
0.000
0.000
0.024
0.097
0.000
0.000
0.000
0.223
0.858
0.000
0.024
0.000
0.000
0.000
0.006
0.000
0.000
0.103
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.261
0.000
0.703
0.032
0.000
0.025
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.017
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.641
0.000
0.000
0.000
0.000
0.123
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.740
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.885
0.000
0.000
0.000
0.005
0.000
0.021
0.000
0.000
Bengalgram Ground nut Sunflower Cotton Sugarcane Arecanut
0.000
0.000
0.052
0.000
0.263
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.195
0.000
0.000
0.000
0.006
0.706
0.000
0.000
0.000
0.000
0.041
0.000
0.029
0.000
0.026
Coconut
Transition Probability Matrix of the crops cultivated in Chitradurga district (1956-57 to 2011-12)
TABLE II
0.000
0.223
0.000
0.000
0.122
0.000
0.000
0.994
0.140
0.204
0.000
0.000
0.068
0.000
0.000
0.000
0.765
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.051
0.000
0.024
Ragi
0.259
0.000
0.000
0.000
0.157
0.000
0.228
0.000
0.032
0.000
0.000
0.000
0.000
0.000
0.292
0.000
0.000
0.000
0.000
0.000
0.047
0.000
0.000
0.000
0.000
0.068
0.000
Others Tar
Table II contd.
0.000
0.000
0.000
0.746
0.000
0.000
0.000
0.000
0.000
0.045
0.000
0.404
0.000
0.115
0.000
0.623
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.037
0.000
Avare Navane
210 M. SAGAR AND G. S. MAHADEVAIAH
Bajra
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.176 0.000 0.431 0.000 0.062 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.431 0.000 0.062 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Crops
Rice Jowar Maize Bengalgram Ground nut Sunflower Cotton Sugarcane Arecanut Coconut Ragi Navane Tur Bajra Onion Save Haraka Horse gram Green gram Sesamum Castor Nigerseed Safflower Tobacco Dry chillies Avare Others Bajra Onion Save Haraka Horse gram Green gram Sesamum Castor Nigerseed Safflower Tobacco Dry chillies Avare Others
0.000 0.077 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.164 0.001 0.000 0.000 0.000 0.441 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.033 0.000 0.105 0.000 0.000 0.000 0.441 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.033 0.000 0.105 0.000 0.000
Onion
0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.052 0.000 0.000 0.000 0.000 0.000 0.000 0.180 0.000 0.048 0.000 0.000 0.000 0.000 0.000 0.000 0.299 0.000 0.042 0.000 0.000 0.180 0.000 0.048 0.000 0.000 0.000 0.000 0.000 0.000 0.299 0.000 0.042
Save 0.000 0.016 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.071 0.000 0.059 0.000 0.000 0.327 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.059 0.000 0.000 0.327 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.022 0.000 0.000 0.000 0.024 0.130 0.000 0.377 0.000 0.328 0.000 0.244 0.081 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.377 0.000 0.328 0.000 0.244 0.081 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.089 0.000 0.040 0.007 0.000 0.000 0.000 0.000 0.012 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.265 0.000 0.000 0.000 0.099 0.000 0.000 0.000 0.089 0.000 0.000 0.000 0.000 0.000 0.265 0.000 0.000 0.000 0.099 0.000 0.000 0.000 0.089
0.000 0.005 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.072 0.000 0.000 0.017 0.000 0.000 0.000 0.029 0.000 0.000 0.000 0.372 0.000 0.000 0.000 0.000 0.000 0.017 0.000 0.000 0.000 0.029 0.000 0.000 0.000 0.372 0.000 0.000 0.000 0.000 0.000
Marka Horsegram Greengram Sesamum 0.000 0.000 0.000 0.014 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.056 0.000 0.000 0.008 0.000 0.005 0.014 0.000 0.521 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.008 0.000 0.005 0.014 0.000 0.521 0.000 0.000 0.000 0.000 0.000 0.000
0.004 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.000 0.628 0.000 0.254 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.000 0.628 0.000 0.254 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.010 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.217 0.000 0.000 0.044 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.217 0.000 0.000 0.044 0.000
Castor Nigerseed Safflower 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.000 0.019 0.000 0.011 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.019 0.000 0.011 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.008 0.001 0.000 0.000 0.000 0.000 0.006 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.010 0.000 0.000 0.000 0.000 0.000 0.000 0.544 0.000 0.000 0.000 0.000 0.000 0.000 0.010 0.000 0.000 0.000 0.000 0.000 0.000 0.544 0.000 0.000
Tobacco Dry chillies
Transition Probability Matrix of the crops cultivated in Chitradurga district (1956-57 to 2011-12)
TABLE II Contd.
0.010 0.021 0.007 0.000 0.000 0.000 0.000 0.000 0.000 0.010 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.000 0.000 0.728 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.000 0.000 0.728 0.000
0.000 0.000 0.007 0.253 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.116 0.000 0.000 0.000 0.000 0.000 0.147 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.577 0.000 0.000 0.000 0.000 0.000 0.147 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.577
IMPACT OF CLIMATE VARIABILITY ON CROPPING PATTERN IN CHITRADURGA DISTRICT
211
212
M. SAGAR AND G. S. MAHADEVAIAH
observed in annual maximum and minimum temperature, August and September months for minimum temperature, whereas, June, July and August for maximum temperature, respectively. The Markov chain analysis revealed that rice, arecanut, groundnut, maize and ragi were the stable crops in the districtsas more than 75 per cent of their previous share in area of these crops were retainedin Chitradurga. (Received : May, 2016
REFERENCES CHAND, R., SINGH, U. P., SINGH, Y. P., SIDDIQUE, L. A. AND KORE, P. A., 2011, Analysis of weekly rainfall of different period during rainy season over Safdarjung airport of Delhi for 20th century – A study on trend, decile and decadal analysis.Mausam, 62, 2, 197-204. JANGRA, S. AND SINGH, M., 2011.Analysis of rainfall and temperatures for climatic trend in Kullu valley.Mausam, 62, 1, 77-84.
Accepted : June, 2016)
Mysore J. Agric. Sci., 50 (2) : 213-217, 2016
Growth, Instability and Total Factor Productivity of Ragi (Finger Millet) in Karnataka VEERABHADRAPPA BELLUNDAGI AND K. B. UMESH Department of Agricultural Economics, College of Agriculture, UAS, GKVK, Bengaluru - 560 065
ABSTRACT The study was undertaken to analyze the growth, instability and total factor productivity (TFP) of ragi in Karnataka. The required data was obtained from the Directorate of Economics and Statistics, Ministry of Agriculture, Karnataka from 1984-85 to 2013-14. Exponential growth model was used to compute growth rates, Thornqvist Theil index was used to calculate total factor productivity and Regression analysis was used to decompose the factors influencing TFP growth. The results revealed that, the structural break for production and productivity in 1993-94 and 1991-92 coincides with liberalization and the release of MR-1 variety. Hence, these break periods were taken as base to compute the growth rates. The results of growth rate indicated that, there was decrease in production of ragi in Karnataka and India and it was mainly due to decrease in area, though there was a significant growth in productivity during overall period (1984-85 to 2013-14) due to introduction of drought resistant and high yielding varieties. In Karnataka ragi production exhibited higher degree of instability in period II (1994-95 to 2013-14). The results of TFP indicated that, the average TFP index for 25 years was 1.30. Public research has significantly contributed to TFP growth in ragi. Hence there is a need for more technological breakthrough to enhance the production.
RAGI (Eleusine coracana) (Finger millet) is a widely cultivated crop of the tropical and subtropical regions of the world. In India, ragi is one of the important cereals which occupies the highest area under cultivation among the small millets. Ragi was cultivated over an area of 1.19 million hectares with a production of 1.98 million tonnes with an average productivity of 1661 kg per ha. Karnataka (56.21 %), Maharashtra (10.56 %), Tamil Nadu (9.94 %), Uttarakhand (9.40 %), Orissa (4.74 %) and Andhra Pradesh (3.69 %) are the major ragi growing states with respect to per cent share of area in India. Karnataka is the largest producer (59.52 %) of ragi in the country and main staple food consumed by majority of the population in South Karnataka (www.indiastat.com, 2013-14). In Karnataka, Tumakuru district accounts for larger area (22.7 %) and production (18.6 %) of ragi followed by Hassan and Ramanagara districts (www.eands.dacnet.nic.in). Ragi is gaining importance in recent years due to its medicinal and nutritive value. The value addition brings more returns to the farmers and enhances nutritional status of their family members. Ragi is mainly consumed as mudde (dumpling), dosa, roti,
vermicelli and value added products like biscuits, malt and mixture in Southern states of India. However, there appear to be deceleration of area though several improved ragi varieties are evolved over time. In this context, the present study has been taken up to analyze the growth, instability and total factor productivity of ragi in Karnataka. Growth in area, production and productivity of ragi : Growth in area, production and productivity of ragi was estimated by using the exponential function. Bai-Perron test (Bai and Perron, 2003) was used to find out the structural break in the area, production and productivity of ragi during 1984-85 to 2013-14. Structural break appears in the data when there is abrupt shift in the data there by helping us to know when there is a significant change in the data. In case of area, production and productivity of ragi in India the study period (1984-85 to 2013-14) was divided into two sub periods based on the breaks, viz, area - I (1984-85 to 1993-94) and II (1994-95 to 201314) period this break coincided with liberalization; The liberalization might have contributed for the decrease in area under ragi due to shift in area from ragi to
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214
maize. For production - period I (1984-85 to 2001-02) and period II (2002-03 to 2013-14) and for productivity period I (1984-85 to 1990-91) and period II (1991-92 to 2013-14) were considered in Karnataka. These structural breaks coincide with the release of MR-1 variety (1990) which contributed for significant increase in yield and the economic liberalization and drought situation prevailed in the state as well as in the country. Growth rates for area, production and productivity of ragi in Karnataka and India ; The compound growth rates (CGR) were calculated for Karnataka and India. The results revealed that, the growth rates of area for period I (1984-85 to 2005-06) and overall period (1984-85 to 2013-14) for Karnataka
were found to be significant and negative (1.20 and 1.81 % respectively) (Table I). For the overall period, area under ragi for Karnataka (-1.81 %) was declining at a slower rate compared to India (-2.59 %). With respect to production of ragi in Karnataka, there was a significant and positive growth during period I (1.64 %) and for India, during overall period the growth was found to be significant and negative (1.27 %). The analysis revealed that, the growth in productivity of ragi during overall period for both Karnataka (1.60 %) as well as India (1.38 %) indicated a positive and significant growth. Though there was deceleration both in area and production there was significant growth in productivity during overall period due to introduction of drought resistant and high yielding varieties.
TABLE I Growth in area, production and productivity of ragi in Karnataka and India Year Karnataka
I (1984-85 to 2005-06) II (2006-07 to 2013-14) Overall (1984-85 to 2013-14)
CGR (%) Area (000 ha)
Year India
CGR (%)
-1.199***
I (1984-85 to 1993-94)
-2.695***
-1.387
II (1994-95 to 2013-14)
-2.442***
-1.811***
Overall (1984-85 to 2013-14)
-2.594***
1.642***
I (1984-85 to 2001-02)
-0.327
1.058
II (2002-03 to 2013-14)
0.453
-0.238
Overall period (1984-85 to 2013-14)
-1.274***
Production (000 tonne) I (1984-85 to 1993-94) II (1994-95 to 2013-14) Overall period(1984-85 to 2013-14)
Productivity (kg/ha) I (1984-85 to 1991-92)
0.236
I (1984-85 to 1990-91)
0.746
II (1992-93 to 2013-14)
0.930
II (1991-92 to 2013-14)
0.922**
1.602***
Overall period (1984-85 to 2013-14)
1.379***
Overall period(1984-85 to 2013-14)
Note: ***, ** indicates Significant at 1, 5 per cent, respectively
GROWTH INSTABILITY AND TOTAL FACTOR PRODUCTIVITY OF RAGI ( FINGER MILLET) IN KARNATAKA
District wise growth in area, production and productivity of ragi in Karnataka For the ragi growing districts of Karnataka, the compound growth rates were worked out for 30 years (1984-85 to 2013-14) and the same was worked out for 15 years (1998-99 to 2013-14) for Chamarajanagar, Davanagere, Haveri and Gadag. For Ramanagara and Chikkaballapura districts growth rates were worked out for seven years (2007-08 to 2013-14) considering the formation of districts. It is evident from the analysis that, there was decrease in area and production across all ragi growing districts of Karnataka (Fig 1). The results revealed that, among the major ragi growing districts of Karnataka the area and production showed negative trend, but with respect to production, Tumakuru and Ramanagara districts showed positive trend. There was significant and positive growth in productivity in Bengaluru Urban (2.56 %), Bengaluru Rural (1.96 %), Mysuru (1.93 %), Mandya (1.53 %), Kolar (1.49 %), Uttara Kannada (1.22 %) and Tumakuru (1.12 %). Although Tumakuru stands first in area and production, it stands seventh position with respect to productivity. It is interesting to observe that the productivity has been showing positive trend in all the major ragi growing districts. The decrease in area can be attributed to shift in area from ragi to maize. The share of ragi has decreased from 17.77 per cent during 200001 to 13.66 per cent in 2013-14, whereas the share of maize has increased from 11.62 per cent during 200001 to 28.02 per cent in 2013-14.
Fig 1.: District wise growth rate (%) in area, production and productivity of ragi in Karnataka during 1984-85 to 2013-14
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Instability in area, production and productivity: Variations in area, production and productivity is a cause for concern, hence, it is important to know the extent of variability in area, production and productivity. The variability in area, production and productivity was analyzed using the following instability index. Instability Index =
Standard Deviation Mean
× 100 × √ 1-R2
Where, R2 = Coefficient of determination. Ragi production exhibited higher degree of instability during period II (1994-95 to 2013-14) and overall period (1984-85 to 2013-14) at 45.99 per cent and 19.69 per cent, respectively in Karnataka (Table II). Similar trend was observed at all India level (17.71 % and 12.78 %). The high instability in the production of ragi may be attributed to increase in productivity. The instability in area during period I (6.0 % and 3.9 %) was less both in Karnataka and India and for the overall period. These findings are in confirmatory with the results of Divya (2011). Total factor productivity of ragi : The total factor productivity (TFP) was computed using Thornqvist Theil index. The TFP was estimated taking into account two outputs and five inputs. From Fig 2 it was observed that, the TFP for ragi increased from 1.07 in 1991 to 1.39 in 2014. The TFP fell to 0.78 in 2000 and 0.99 in 2005 due to drought during that period. The highest TFP index was observed in 2003 (2.01). The average TFP index for 25 years was 1.30. The output index of ragi increased from 1.33 in 1991 to 1.40 in 2014. The highest output index was observed in 2008 (2.15). The average output index for 25 years was 1.47. In the case of input index, there were marginal fluctuations, decreasing from 1.25 in 1991 to 1.01 in 2014. The average input index of ragi was 1.15 for 25 years (Fig. 2). Similar findings have been reported by Kumar et al. (2008). Sources of total factor productivity growth in ragi : To quantify the contributions of various factors to TFP growth, the variables considered were research
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TABLE II Instability in area, production and productivity of ragi in Karnataka and India Karnataka
India
Instability index %
Instability index (%)
Area I (1984-85 to 2005-06)
6.00
I (1984-85 to 1993-94)
3.86
II (2006-07 to 2013-14)
11.81
II (1994-95 to 2013-14)
6.78
Overall (1984-85 to 2013-14)
8.44
Overall (1984-85 to 2013-14)
5.75
Production I (1984-85 to 1993-94)
11.98
I (1984-85 to 2001-02)
6.94
II (1994-95 to 2003-04)
45.99
II (2002-03 to 2013-14)
17.71
(1984-85 to 2013-14)
19.69
Overall period (1984-85 to 2013-14)
I (1984-85 to 1991-92)
9.81
I (1984-85 to 1990-91)
5.05
II (1992-93 to 2013-14)
15.51
II (1991-92 to 2013-14)
11.38
(1984-85 to 2013-14)
15.66
Overall period (1984-85 to 2013-14)
11.36
Overall period
12.78
Productivity
Overall period
expenditure, rural literacy and kharif rainfall (Table III). The results indicate that public research (0.132) and rural literacy (0.682) significantly contributed to TFP growth in ragi. The rainfall is a crucial determinant of TFP in ragi. The estimated R square value was 0.60 indicating that 60 per cent of variation in TFP explained by the factors included in the model and F value was statistically significant (3.534) indicating a good fit of the model. Hence, the public research is key and significant source of TFP growth in staple food crop like ragi. These findings are in conformity with the results of Suresh and Chandrakanth (2015). Ragi has outstanding properties as a subsistence food crop. Ragi is not a season bound crop and hence
Fig 2.: Trend in total input, total output and TFP index of ragi in Karnataka
GROWTH INSTABILITY AND TOTAL FACTOR PRODUCTIVITY OF RAGI ( FINGER MILLET) IN KARNATAKA
TABLE III Estimated parameters of TFP decomposition for ragi from 1990 to 2014 Variables
Coefficients
Standard Error
Intercept
-5.874**
2.313
Research
0.132*
0.095
0.682**
0.343
0.039
0.098
Rural literacy Kharif Rainfall R2
0.60
F-value
3.534**
Note: ** and * indicate significance at 5 and 10 per cent levels, respectively
it can be cultivated throughout the year. The growth and instability results shows that there is a scope to cultivate more ragi in Karnataka and India. After the implementation of Anna Bhagya Yojana in Karnataka State, there is increased demand for ragi. Hence, there is a need for more technological breakthrough to enhance the production. Therefore similar kind of programmes have to be implemented in other states (Received : May, 2016
217
also to increase the demand for ragi as it is considered to be one of the highly nutritive crop. Sources of TFP growth revealed that, public research was significant source of TFP growth. Hence the Government should allocate substantial funds to public research mainly to enhance the productivity of ragi.
REFERENCES A NONYMOUS, 2014, www.eands.dacnet.nic.in. Official website of the Government of Karnataka. ANONYMOUS, 2014, www.Indiastat.com: Official website of the Government of India. BAI, J. AND PERRON, 2003, Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18: 1–22. DIVYA, G. M., 2011, Growth and instability analysis of finger millet crop in Karnataka. M.Sc. (Agri.) Thesis (Unpublished), Uni. of Agril. Sci., Bengaluru. KUMAR PRADUMAN, A., MITTAL S. AND HOSSAIN M., 2008, Agricultural growth accounting and total factor productivity in South Asia: A review and 21 policy implications. Agricultural Economics Research Review, 21 (2): 145-172.s SURESH, K. AND CHANDRAKANTH, M. G., 2015, Total Factor Productivity and returns to investment in Ragi (finger millet) crop research in Karnataka state, India. Indian J. Econ. and Development, 3 (3): 199-205.
Accepted : June, 2016)
Mysore J. Agric. Sci., 50 (2) : 218-222, 2015
Post Abdominal Structures, A New Facet in Tephritid Taxonomic Research: A Case Study of The Genus Dacus Fabricius (Diptera: Tephritidae: Dacinae: Dacini) K. J. DAVID, S. RAMANI AND PRASANTH MOHANRAJ Department of Agricultural Entomology, College of Agriculture, UAS, GKVK, Bengaluru-560 065
ABSTRACT Post abdominal structures of seven species of Dacus Fabricius were examined, illustrated and described. Phylogenetic analysis revealed genus Dacus to be a monophyletic clade though the subgeneric branching was not in concordance with the present sub-generic classification.
WITH more than 4500 described species, Tephritidae represent one of the most diverse acalyptrate dipterans of superfamily Tephritoidea (Freidberg, 2006). Tribe Dacini, predominantly frugivorous, belongs to subfamily Dacinae with three genera Bactrocera Macquart, Dacus Fabricius and Monacrostichus Bezzi. Dacus is primarily African, represented by 195 species from Africa (White, 2006; White & Goodger, 2009) and 75 species from Indo-Australasian region (Drew & Romig, 2013). Ten species of Dacus are known from India (Agarwal & Sueyoshi, 2005; David & Ramani, 2011). Most of the taxonomic studies on tribe Dacini and genus Dacus in particular were purely based on external morphology without much attention to genitalia characters. An attempt is made here to study seven species of Dacus viz., D. (Callantra) longicornis Wiedemann, D. (Didacus) ciliatus Loew, D. (Leptoxyda) persicus Hendel, D. (Mellesis) crabroniformis (Bezzi), D. (M.) discophorus (Hering), D. (M.) insulosus Drew & Hancock and D. (M.) ramanii Drew & Hancock based on post abdominal structures.
abdominal structures (epandrium, lateral surstylus, medial surstylus, prensisetae, proctiger, aedeagus, glans) of six species and female genital characters (aculeus shape, shape of spicules of eversible membrane, number and shape of spermatheca) of four species were studied and illustrated. Phylogenetic analysis of all the ten species of Dacus along with four species of Bactrocera Macquart, two each from subgenus groups viz., Bactrocera group [B. dorsalis (Hendel)] and B. correcta (Bezzi)) and Zeugodacus group (B. tau (Walker) and B. cucurbitae (Coquillett)) were performed by selecting Gastrozona fasciventris Macquart (Tribe: Gastrozonini) as the outgroup taxon. Though specimens of D. (M.) icariiformis (Enderlein), D. (M.) polistiformis (Senior-White) and D. (Neodacus) sphaeroidalis (Bezzi) were not available for the present study, they were scored based on the original descriptions and examination of digital images of the types. Morphological matrix generated from 18 characters of 15 taxa were analysed using TNT software (Tree Analysis Using New Technology) (Goloboff et al., 2008).
Specimens deposited in following museums were examined for the present study: ICAR-NBAIRNational Bureau of Agricultural Insect Resources, Bengaluru and Department of Agricultural Entomology, University of Agricultural Sciences, Bengaluru; images of genitalia were acquired using a Leica DFC 425 camera mounted on a Leica DMLB 1000S microscope; the images were stacked and combined to a single image using Combine ZP (Hadley, 2011). Measurements of male and female genitalia were taken using a calibrated ocular micrometer fixed on a Leica DM1000 microscope. The terminology adopted here follows White et al. (1999). Male post
Post abdominal structures: Comparative analysis of the genitalia characters of six species for male (Table I) and four species for female (Table II) of Dacus studied is presented below: Strict consensus tree for tribe Dacini with mapped synapomorphies and bootstrap values (Fig. 1). Phylogenetic analysis: Analysis revealed fourteen most parsimonious trees of 28 steps. Consensus tree (Figure 11) generated, revealed Dacus to be a monophyletic clade (bootstrap value=99) with following synapomorphies viz., fused abdominal
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TABLE I Comparative analysis of male genitalia of Dacus Fabricius lonngicornis (Figs 1a.b.c)
ciliatus (Figs 2a.b.c)
Constriction between epandrium and surstylus (posterior view)
prominent
prominent
Posterior lobe of lateral surstylus
Not longer than anterior lobe
2-3 times longer 2-3 times longer than anterior than anterior
4-5 times longer 4-5 times longer 4-5 times longer than anterior lobe than anterior than anterior lobe
Apex of posterior lobe of surslus
Broad, pointed
Hook shaped
Narrow, angular
Broad, angular
Broad, blunt
Broad, angular
Proctiger shape
quadrate
triangular
quadrate
quadrate
quadrate
quadrate
Acrophallus pattern polygonal
polygonal
elongate
polygonal
polygonal
polygonal
Aedeagal length
2.3"2.5 mm
2 mm
4.8"5 mm
2.4 mm
1.6 mm
2.12 mm
Praeputium
absent
present
absent
present
present
present
Characters
persictus (Figs 1a.b.c)
discophorus (Figs 4a.b.c)
prominent
not prominent
crabroniformis (Figs 5a.b.c)
ramanii (Figs 6a.b.c)
prominent
prominent
TABLE II Comparative analysis of female geminalla of Dacus Fabricius Characters
lonngicornis (Figs 7a.b.c)
ciliatus (Figs 8a.b.c)
persictus (Figs 9a.b.c)
discophorus (Figs 10a.b.c)
Oviscape shape
Conical, dorsoventrally flattened
Conical, dorsoventrally flattened
Bottle shaped, not dorsoventrally flattened
Conical, dorsoventrally flattened
Oviscape length
1.7 mm
1.21 mm
2.3-2.7 mm
1.87 mm
Eversible membrane length
4.3 mm
2.1 mm
5 mm
2.11 mm
Spicules at distal end of eversible membrane
5-6 pointed projections, tapering laterally
4-5 projections of equal height
18-20 sharp projections
8-10 sharp projections, the medial one broad 5-6 times higher than
basal ones Aculeus length
2.4 mm
1.43 mm
3.6"3.8 mm
1.72 mm
Aculeus tip
Acute, needle shaped
Acute, needle shaped
Broad, rounded
Broad, angulate
Preapical setae
three
three
three
Three
Spermatheca
Convoluted, curved
Bunch shaped
`Club shaped, with transverse striations
Club shaped, mooth texture
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K. J. DAVID
tergites, weak frontal setae and aculeus with three pairs of preapical setae. Bactrocera (Zeugodacus) group and Bactrocera (Bactrocera) group branched separately as monophyletic clades with bootstrap values, 73 and 99, respectively.
Fig. 11 : Strict consensus tree for tribe Dacini with mapped synapomorphies and bootstrap values
et al. Studies revealed the presence of patterned acrophallus of glans, sphaeropedunculate epandrium in posterior view, broad lateral surstylus shorter than epandrium in posterior view and hyaline proctiger smaller than epandrium. Aculeus tip of females of four species examined were acute with three pairs of preapical setae, spicules of eversible membrane with spine like projections and two spermathecae. Branching of the cladogram was not in concordance with the present system of subgeneric classification of Dacus as the present one is exclusively based on the permutations and combinations of several homplasious characters like shape of posterior margin of sternite 5 of males, shape of abdomen and scape length and presence or absence of postsutural supraalar seta. Inclusion of more number of species from other regions like the Australasian and African regions could have produced a more comprehensive tree.
Figure 1-6 : Male genitalia of Dacus Fabricius ; a, epandrium, proctiger and surstyli (lateral view); b, epandrium and surstyli (posterior view); c, glans of phallus (lateral view); 1, D. langicornis (Wiedemann); 2. D. ciliatus (Loes); 3, D. persicus Hendel; 4, D. discophorus (Hering); 5, D. crabroniformis Bezzi; 6. D. ramanii Drew & Hancock
POST ABDOMINAL STRUCTURES, A NEW FACETIN TAPHRITID TAXONOMIC RESEARCH
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Figure 7-10 : Female genitalia of Dacus Fabricius ; a, aculeus tip; b, spicules at distal end of eversible membrane; c, spermatheca; 7, D. longicornis Wiedemann; 8, D. ciliatus (Loew); D. persicus Hendel; 10, D. insulosus Drew & Hancock 5, D. crabroniformis Bezzi; 10. D. ramanii Drew & Hancock
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et al.
REFERENCES
GOLOBOFF, P. A., FARRIS, J. S. AND NIXON, K. C., 2008. TNT, a free program for phylogenetic analysis. Cladistics, 24, 1–13
AGARWAL, M. L. AND SUEYOSHI, M., 2005, Catalogue of Indian Fruit flies (Diptera: Tephritidae). Oriental Insects, 39: 371–433.
H ADLEY , A., 2011, Combine ZP. http:// www.hadleyweb.pwp.blueyonder.co.uk/ (accessed 22 February 2016)
DAVID, K. J. AND RAMANI, S., 2011, An illustrated key to fruit flies (Diptera: Tephritidae) from Peninsular India and the Andaman and Nicobar Islands. Zootaxa, 3021: 1–31.
WHITE, I. M., 1999, Morphological Features of the Tribe Dacini (Dacinae): Their Significance to Behavior and Classification. In: Aluja, M. and Norrbom, A.L. (Eds.), Fruit flies (Tephritidae): phylogeny and evolution of behavior. CRC Press, Boca Raton, pp. 505–534.
DREW, R. A .I AND ROMIG, M. C., 2013, Tropical fruit flies (Tephritidae: Dacinae) of South-East Asia. CAB International, Wallingford, 653 pp. FREIDBERG, A. (Ed.), 2006, Biotaxonomy of Tephritoidea. Israel Journal of Entomology, 35–36: 1–597.
WHITE, I. M., 2006, Taxonomy of the Dacina (Diptera: Tephritidae) of Africa and the Middle East. African Entomology Memoir 2, pp. 156. WHITE, I. M. AND GOODGER, K. F. M., 2009, African Dacus (Diptera: Tephritidae); New Species and Data, with Particular Reference to the Tel Aviv University Collection. Zootaxa, 2127, 1–49.
(Received : May, 2016 Accepted : June, 2016)
Mysore J. Agric. Sci., 50 (2) : 223-228, 2015
Morphological Adaptations of Forelegs Associated with Prey Capture in Assassin Bugs (Reduviidae: Heteroptera) S. N. BHAGYASREE AND H. KHADER KHAN Department of Agricultural Entomology, College of Agriculture, UAS, GKVK, Bengaluru-560 065
ABSTRACT Assassin bugs are one of the most diverse lineage of predatory bugs and have evolved with diversified forelegs for capturing the prey. Preliminary attempts were made to document the morphological diversity of forelegs and to construct the phylogenetic relationship to reveal the patterns of leg evolution across major leg types, based on the morphology of forelegs and prey preference across 46 genera representing 13 subfamilies present in south India. Phylogenetic analysis revealed that, tibiaroliate legs are plesiomorphic, present in common ancestors of Reduviidae. Alternative prey capturing structures were evolved with multiple independent loss of fossula spongiosa for efficient prey capturing.
A SSASSIN bugs are one of the largest and morphologically diverse group of Heteroptera, with 7000 described species worldwide and 448 species in India. They are predatory bugs, whose prey consumption ranges from euryphagy to stenophagy. Evolution of diversified and novel prey capturing strategies in relation to their modified forelegs is one of the key features for their success (in the 178 million years of diversification), in addition to venomous saliva. Their modified forelegs apparently reflect the correlation between the leg structure with respect to prey type and strategies involved in prey capture. Based on forelegs, assassin bugs are classified into two groups, i.e., tibiaroliate and non-tibiaroliate, based on the presence or absence of ‘fossula spongiosa’. A term referred to the adhesive and cushion-like hairs embedded on the ventroapical surface of the fore tibia and sometimes on midtibia, that helps in grasping the prey during predation. Tibiaroliate legs are shown to be part of ancestral raptorial legs of assassin bugs, which were lost multiple times during the course of evolution. Subsequent to loss of fossula spongiosa, non-tibiaroliate lineages evolved alternative method for capturing different type of prey (Zang et al., 2016). In south India, six subfamilies are known to have tibiaroliate legs and seven are non tibiaroliate having some degree of specialization with respect to their prey during the development of wide repository of novel prey capturing strategies. In this study, morphological differences
associated with the forelegs, their functional dynamics with respect to prey type and their phylogenetic relationships are discussed for south Indian species of the family. Tibiaroliate group of reduviids : Examples : Reduviinae, Peiratinae, Ectrichodinae, Salyavatinae, Centrocneminae and Triatominae. Members of this group are generalist predators feeding on wide range of prey, except Ectrichodinae (feeding on millipedes), Salyavatinae (feeding on termites) and Triatominae (feeding on vertebrate blood). This generalism is related to the ancestral condition of the tibiaroliate Reduviidae. Prey specialization has evolved independently in Ectrichodinae, Salyavatinae and Triatominae (Hwang and Weirauch, 2012). Tibiaroliate legs are plesiomorphic and less specialized compared to non tibiaroliate legs, which are almost similar, with slight modification in the length, width, density of hairs and shape of fossula spongiosa. Species of Reduviinae (Fig. 1B), Peiratinae (Fig. 1E) and Salyavatinae (Fig. 1C) are quite active, provided with short and stout powerful forelegs with elliptical fossula spongiosa at the apex of tibia. They prey on medium sized prey like termites, caterpillars, bees, bugs and ants. After finding the prey, they quickly approach and grab it with their forelegs and kill by injecting toxic saliva.
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S. N. BHAGYASHREE AND H. KHADER KHAN
In the case of Ectrichodinae (Fig. 1A), fore and mid femur are short and powerful with oval shaped fossula spongiosa at the apex of tibia to firmly grip the smooth and shining exoskeleton of millipedes. In blood feeding Triatominae (Fig. 1D), fossula spongiosa is restricted to the apex of fore tibia. It is not involved in prey capturing, but helps in smooth climbing on the vertebrate body. Non-tibiaroliate group of reduviids: Example: Harpactorinae, Holoptilinae, Stenopodainae, Tribelocephalinae, Emesinae, Saicinae and Phymatinae. Non tibiaroliate vary in morphology and prey capturing strategies. Non tibiaroliate bugs have long and slender forelegs with poor mechanical support, because of this constraint they have evolved various novel strategies to capture prey, which can be grouped into five major types, which includes 1. Sticky trap or fly trap (Weirauch, 2006), 2. Chelated / subchelated (Schuh and Slater, 1995), 3. Raptorial legs (Wignall and Taylor, 2011), 4. Feather legged bugs (Jacobson, 1911) and 5. Legs with spines. Morphological modifications Sticky trap or fly trap: Examples: Harpactorinaes. These are generalist predators, feeding on wide variety of insects, they are armed with small bristles and setae on fore, mid and hind legs, which are denser especially on forelegs (Fig. 1G). Females collect resin with forelegs, and transfer to the apex of meso tibia and then to meta tibia, finally transfer to gonocoxae and gonapophysis of genitalic sclerites. Eggs get coated with resin when passing from genital opening to avoid predation by other insects, after hatching nymphs coat the same resin to their fore legs to trap the prey, hence their behaviour is called as sticky trap. Once the bug moults they collect back the resin which is present in their cast skin. Chelated / subchelated: Example: Phymatinae. These are Ambush bugs and generalist predators. They have enlarged and thicker forefemur and fused tarsus, tend to be more stockier than other reduviids often mimic the flowers (Fig. 1J). Bugs sit motionless on flowers and wait for their prey, once prey lands on flowers, bugs pounce quickly and grab their prey with chelated fore legs.
Raptorial legs: Example: Emesinae and Saicinae. Emesinae are also called as thread legged bugs, look similar to Hydrometridae, Culicidae and Berytidae. Their front legs similar to those of mantids. These bugs are specialized to feed on spiders and psocids and quite small, slender and light enough to walk on spider webs with the help of forelegs by exhibiting aggressive mimicry without entangling. Compared to mid and hind legs their fore legs are stouter and ventrally spinous, coxa are more than three times longer than broad, femora stout and invariably bear two series of ventral spines or denticles and fore tibia are shorter than femora (Fig. 1I). Bugs invade the web and pluck the silk, plucking behaviour of the bugs mimics the vibrations generated by the spiders prey struggling in the web, their by spiders are misled about the presence of bugs. Feather legged bugs: Examples: Holoptilinae. Feather legged bugs are more unusual in prey choice and prey capturing strategies. They are specialized to feed on ants and posses specialized trichomes on the abdominal venter for secreting honey dew (Fig. 1H). The position of predator when an ant appears itself is unique, when ant appears it raises its body displays abdomen with ant attracting sugar which has paralysing effect. Ants get attracted, feed on honey dew and become motion less. Once the ant gets paralysed, bugs enjoy their meal. Legs with spines: Example: Stenopodainae. These bugs are generalist predators, having stockier forelegs which gives strong mechanical support for hunting the prey. Structure of forelegs and prey capturing behaviour is similar to that of tibiaroliate group of reduviids but they lack fossula spongiosa rather their forelegs are armed with spines and denticles to grip the prey (Fig. 1F). Phylogenetic analysis using leg morphology: Preliminary attempts were made to construct the phylogenetic tree to reveal the patterns of leg evolution across major leg types based on the morphology of forelegs and prey preference using binary character across 46 genera representing 13 subfamilies and by fixing Nabidae as out group taxa. Parsimony was used as optimal criterion, all the searches were completed in NONA spawned from WINCLADA for bootstrap
MORPHOLOGICAL ADAPTATIONS OF FORELEGS ASSOCIATED WITH PREY CAPTURE IN ASSASSIN BUGS
TABLE I Leg pattern observed in different south Indian genera of Reduviidae Leg type
Subfamily
Centrocnemidinae Ectrichodinae A. Thibiaroliate leg
Peiratinae Reduvinae sp. Salyavatinae Triatominae
B. Non-tibiaroliate leg Sticky trap type
Harpactorinae
Chelated/subchelated Raptorial type
Phymatinae Emesinae
Feathery leg Legs with spines
Saicinae Holoptilinae Stenopodinae
Genera
Centrocnemis sp. and Paracentrocnemis sp. Eriximachus sp., Haematorrhophus sp., Scadra sp. and Vilius sp. Androclus sp., Catamiarus sp., Ectomocoris sp., Pirates sp. and Sirthenea sp., Acanthaspis sp., Edocla sp., Reduvius sp. and Pa Lisarda sp. and Petalocheirus sp. Linshcosteus sp. and Triatoma sp. Brassoivola sp., Coranus sp., Endochus sp., Epidaus sp., Euagorus sp., Isyndus sp., Neonagusta sp., Rhynocoris sp., Sphedanolestes sp., Sycanus sp. and Vesbius sp. Carcinocoris sp., Amblythyreus sp. and Cnizocoris sp. Stenolemus sp., Bagauda sp., Schidium sp. and Empicoris sp. Gallobelgicus sp. and Polytoxus sp. Holoptilus sp. and Ptilocerus sp. Aulacogeniasp., Canthesaneus sp., Oncocephalus sp., Pygolampis sp., Staccia sp. and Thodelmus sp.
TABLE II Characters and character states Character states Charcaters
Fore tibial fossula spongiosa Chelated or subchelated fore legs Combs on forelegs Spines or any armatures on forelegs Resin on abdomen Raptorial legs Feathers on legs Prey preference Shape of fore femur Curvatures on forelegs Subrectal glands in female genitalia
0
absent absent absent absent absent absent absent generalists short and stout absent absent
1
present present present present present present present specialists long and rounded present present
225
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S. N. BHAGYASHREE AND H. KHADER KHAN
Fig. 1 : Strict consensus tree for tribe Dacini with mapped synapomorphies and bootstrap values
MORPHOLOGICAL ADAPTATIONS OF FORELEGS ASSOCIATED WITH PREY CAPTURE IN ASSASSIN BUGS
227
Fig. 2 : Strict consensus clodogram of mostparunonious tree showing patterns of leg evolution in Assassin bugs. Number aboveand respectively, (values below 0 % not shown).Consistency Index ; 69 ; Retention Index ; 94
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S. N. BHAGYASHREE AND H. KHADER KHAN
and Jack knife value. All the cladograms were generated using WINCLADA using the default settings. Phylogenetic reconstruction of the leg pattern in assassin bugs revealed that, tibiaroliate legs are found to be the plesiomorphic, present in common ancestors of Reduviidae (Fig. 2). Fossula spongiosa was lost multiple times independently in different lineage, such as feathery legs in Holoptilinae, chelated in Phymatinae, sticky trap in Harpactorinae, legs with armature in Stenopodainae to enhance prey capture. Zhang et al. (2016) also reconstructed the phylogeny with the same patterns of evolution. They reported that salivary toxicity may be another factor influencing the evolution of fossula spongiosa. Saliva with higher toxicity may paralyse and kill prey faster and could remove constrains on raptorial legs. Studies have shown that bugs without fossula spongiosa immobilise the prey faster than those with fossula spongiosa (Ambrose, 1999). Therefore, conclude that these alternative raptorial traits likely to have evolved independently from the loss of fossula spongiosa to enhance their prey capture. So understanding morphological adaptation to prey capture in assassin bugs will benefit from further research to show complex inter-relationships involved in prey specialization, predatory behaviour and toxicity of venomous saliva.
REFERENCES AMBROSE, D. P., 1999, Assassin bugs. Science Publishers, Inc. HWANG, W. S. AND WEIRAUCH, C., 2012, Evolutionary history of assassin bugs (Insecta: Hemiptera: Reduviidae): Insights from divergence dating and ancestral state reconstruction. PLoS One, 7: 45523. JACOBSON, E., 1911, Biological notes on the hemipteron Ptilocerus ochraceus. Tijdschr. Voor. Entomol. 54: 175–179. SCHUH, R. T. AND SLATER, J. A., 1995, True bugs of the world (Hemiptera: Heteroptera) Classification and natural history. Cornell Univ. Press, Ithaca and London, 12 + 337 pp. WEIRAUCH, C., 2006, Observations on the sticky trap predator Zelus luridus Stål (Heteroptera: Reduviidae: Harpactorinae), with the description of a novel gland associated with the female genitalia. Denisia, 50: 1169–1180. WIGNALL, A. E. AND TAYLOR, P. W., 2011, Assassin bug uses aggressive mimicry to lure spider prey. Proc. Biol. Sci., 278: 1427–1433. ZHANG, J., GORDON, E. R. L., FORTHMAN, M., HWANG, W. S., WALDEN, K., SWANSON, D. R., JOHNSON, K. P., MEIER, R. AND WEIRAUCH, C., 2016, Evolution of the assassin’s arms: insights from a phylogeny of combined transcriptomic and ribosomal DNA data (Heteroptera: Reduvioidea). Sci. Rep., 6: 22177.
(Received : May, 2016 Accepted : June, 2016)
Mysore J. Agric. Sci., 50 (2) : 229-233, 2015
The Shape Matters: Morphology of Male Genital Variations in the Large Carpenter Bees (Hymenoptera:Apidae) of Karnataka C. PRASHANTHA AND V. V. BELAVADI Department of Agricultural Entomology, College of Agriculture, UAS, GKVK, Bengaluru - 560 065
ABSTRACT Large carpenter bees (genus Xylocopa) are prominent members of the Indian bee fauna. The genus is represents 45 species in the Indian region. The study was undertaken to know the male genital variations among nine species of Xylocopa recognized from Karnataka including X. (Biluna) nasalis, X.(Ctenoxylocopa) fenestrata, X.(Koptortosoma) hafizii, X.(Koptortosoma) pubescens, X.(Koptortosoma) ruficornis, X. (Mesotrichia) latipes, X.(Nodula) amethystina, X. (Nyctomelitta) tranquebarica and a probable new species X.(Zonohirsuta) sp.1.
BEES are a large group of insects that are specialized for feeding at flowers and gathering nectar and pollen. They play an important role in the life of flowering plants by participating in their sexual reproduction. For some crops, wild bees are more effective pollinators on a per visit basis than honey bees and functionally complement the dominant visitor. There are 16,325 species of bees in the world, grouped under 425 genera, in the division Apiformes under the super family Apoidea (Michener, 2007). Species composition amongst different families of bees, Apidae constitutes 57 per cent. The Xylocopini is one of four tribes in the subfamily Xylocopinae, the other three - Allodapini, Ceratinini, and Manueliini are mostly smaller than the Xylocopini. Hurd and Moure (1963) in their revision of the tribe Xylocopini recognized three genera: Lestis Lepeletier & Serville, Proxylocopa Hedicke, and Xylocopa Latreille. In a cladistic analysis of the genera and subgenera based on parsimony analyses of morphological characters, Minckley (1998) concluded that the genera Lestis and Proxylocopa should be considered as subgenera of Xylocopa to avoid paraphyly of this genus. The large carpenter bees belong to genus Xylocopa and are cosmopolitan in distribution. It consists of 469 species in 51 subgenera in the world (Michener, 2007). These are prominent members of the Indian bee fauna. Gupta and Yanega (2003) recorded 45 species and subspecies under 11 subgenera in the Indian region. These are robust, fastflying, some of which are among the largest of all the bees. As their name implies, carpenter bees excavate nest galleries in woody plant material, including dead
branches, stalks, stumps, and structural timbers of buildings, hollow culms of bamboo or pithy stems. Members of subgenus Proxylocopa construct their nests underground. (Hurd and Moure, 1963; Gerling et al., 1989). The nesting behavior ranges widely from solitary to social, which makes them of interest to behavioural ecologists because of their utility for studies of social evolution (Gerling et al., 1989). Carpenter bees are also important for investigating the evolution of mating systems, because a variety of mating strategies have been found in the group. Some particular mating strategies seem to be correlated with morphological adaptations and are found in a limited number of subgenera (Leys, 2000). Male genitalia are widely recognized as being the most variable and divergent of all morphological structures. Male genital structures are considered as important diagnostic traits in insect systematics, and there are entire groups the classification of which is based solely on the structure of male genitalia. This study represents the first description of male genital structures of the Indian large carpenter bees, to understand the genital variations among different species of Xylocopa. Cladistic analysis of the phylogenetic relationships by using genital characters coded for nine species of the ingroup, and to identify traits that could be used in the systematics of the genus Xylocopa were also attempted. Specimen collections were made from different parts of Karnataka. Male genitalia were dissected by treating with 10 per cent KOH for 24 hours and were
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transferred to test tubes containing 70 per cent ethyl alcohol. The different skeletal parts were separated under a stereo-binocular microscope and placed on a cavity block with glycerol. Genitalia of each species were stored in small eppendorf tube containing a few drops of glycerol for subsequent studies. Illustrations of genitalia were photographed using Leica 205A microscope mounted with camera Leica DFC450, plates were prepared in Adobe Photoshop CS5.The terminology used in this work is that proposed by Maa (1938), Hurd and Moure (1963), Minckley (1998) and Michener (2007). The various parts and morphological terms of male genitalia were illustrated in fig. 1. Preliminary attempts were made to construct a phylogenetic tree to reveal the patterns of genitalia evolution across the species based on the morphology of genitalia using binary character across nine species of the genus Xylocopa and by fixing the small carpenter bee, Ceratina (Pithitis) binghami as out group taxon. In a large group, such as the Xylocopini, it seemed reasonable to expect that some characters homoplasious within some clades will serve as synapomorphies of other clades. Parsimony was used as the optimality criterion. All searches were completed in NONA (Goloboff, 1999) spawned from Winclada (Nixon, 2002). All the cladograms were generated using Winclada. For this analysis the matrix developed using character states (Table. I and II) of 9 species and 12 characters of Xylocopa were processed using Winclada. A heuristic search was carried out holding
Fig. 1 : Structure male genitalia of large carpenter bee (Xylocopa (Koptortosoma) ruficornis - Dorsal vies)
a maximum of 1000 trees in memory, with 1000 replications and 10 trees to hold per replication, in random addition sequence. The strict consensus tree of large carpenter bees had 36 steps, a consistency index of 0.54 and a retention index of 0.60. The analysis of taxa of the carpenter bees produced seven equally parsimonious trees. Only those branches with Bootstrap and Jack-knife values above 50 were considered and are indicated above the branches or nodes. Phylogenetic reconstruction of the genital variations in large carpenter bees (Fig. 2) revealed that using Ceratina binghami as the outgroup, Xylocopa proved to be monophyletic. Subgenera Ctenoxylocopa and Mesotrichia are monophyletic and the synapomorphies defining the nodes are inner margin of the gonocoxite nearly parallel and hairs on base of the penis valve absent. Ctenoxylocopa (Fig. 5-6) is separated from all other subgenera by genital capsule wider than long. Subgenus Koptortosoma is monophyletic with three species X. hafizii, X. pubescens, X. ruficornis. The synapomorphies that unite these three species are genital capsule narrower at base and gonostylus with slender lobe like projection. X. ruficornis (Fig. 15-16) is separated from X. hafizii and X. pubescens by median lobe on gonostylus arising from base. X. pubescens (Fig. 13-14) is separate from X. hafizii (Fig. 7-8) by presence of setae on apex of gonostylus and ventro apical plate of gonocoxite being weakly carinate. Subgenus Nyctomelitta has only one taxon X. tranquibarica (Fig. 17-18) with genital capsule shape almost subequal and gonostylus with sharp spine like projection. It gets separated from other groups by the ventroapical plate of gonocoxite strongly carinate and presence of hairs on base of the penis valve. The subgenus Zonohirsuta (Fig.19-20) is separated from the Biluna and Nodula by the penis valve being slender and parallel. Biluna (Fig. 11-12) is separated from Nodula (Fig. 3-4) by absence of short spine like projection on gonostylus and genital capsule shape narrower at base. This study strongly demonstrates that the male genital characters of Xylocopa species can be considered species specific since the combination of the shape and size variations and the differences in individual genital components is unique for each species.
THE SHAPE MATTERS
: MORPHOLOGY OF MALE GENITAL VARIATIONS IN THE LARGE CARPENTER BEES
231
TABLE I Characters and character states Character states Characters
0
1
2
Genital capsule size
Longer than wide
Wider than long
Nearly subequal
Genital capsule shape
Narrower at base
Nearly subequal
-
Gonostylus at apex of gonocoxite Slender
Rounded or conical
-
Gonostylus with projection
Absent
Present(short spine like)
Present (slender lobe like)
Setae on apex of gonostylus
Absent or very few
Present (less dense)
Present (more dense)
Median lobe on gonostylus
Absent
Present arising from apex
Present arising from base
Inner margin of gonocoxite
Diverging from the base
Weakly parallel
-
Penis
Entirely membranous
Basal half sclerotized
-
Ventoapical plate of gonocoxite
Absent
PresentWeakly carinate
Present strongly carinate
Penis valve
Apically expanded
Slender and parallel
-
Lateral edge on genital capsule
Absent
Weakly present
Strongly present
Hairs on base of the penis valve
Absent
Present
-
Fig. 2 : Strict consensus caldogram of most parsimonious tree showing patterns of genital variations in large carpenter beed. Filled circles represent non-homoplastic characters ; open circles, homoplastic characters. Numbers above and below) are bootstrap and Jack-Knife support values respectively. (values below 50% not shown) CI - 54, RI-60
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C. PRASHANTHA AND V. V. BELAVADI
TABLE II Data matrix used in the phylogenetic analyses Species
1
2
3
4
5
6
7
8
9
10
11
12
Ceratina (Pithitis) binghami
0
1
0
0
0
0
0
0
0
1
0
1
X. (Biluna) nasalis
0
0
1
0
2
0
0
0
1
0
2
0
X.(Ctenoxylocopa) fenestrata
1
1
0
0
0
0
1
0
1
1
0
0
X.(Koptortosoma) hafizii
0
0
0
2
0
1
1
0
2
0
2
1
X.(Koptortosoma) pubescens
0
0
0
1
1
1
0
0
1
1
0
1
X.(Koptortosoma) ruficornis
0
0
0
2
0
2
1
0
2
0
2
1
X. (Mesotrichia) latipes
2
0
0
0
0
0
1
1
0
1
2
0
X.(Nodula) amethystina
0
1
1
1
2
1
0
0
1
0
1
0
X. (Nyctomelitta) tranquebarica
0
1
0
1
1
1
0
0
2
1
1
1
X.(Zonohirsuta) sp.1
2
1
0
1
1
1
0
0
1
1
1
0
Fig. 3-20 : Genital capsule of Xylocopa species (dorsal and ventral view respectively) 3-4: X. (Nodula) amethystina, 5-6 (Ctenoxylocapa) fenestrata, 7-8 X (Kotortosoma) hafrzii, 9-10. X. (Mesotrichia) latipes, 11-12. X. (Biluna) nasalis, 13-14. X. (Koptortosoma) pubescens, 15-16. X. (Koptorlosoma) ruficornis, 17-18 X. (Nyctomelitta) tranquebarica, 19-20. X (Zonohirsata) sp.1
THE SHAPE MATTERS
: MORPHOLOGY OF MALE GENITAL VARIATIONS IN THE LARGE CARPENTER BEES
REFERENCES GERLING, D.W., VELTHUIS, H.D. AND HEFETZ, A., 1989, Bionomics of the large carpenter bee of the genus Xylocopa. Annu. Rev. Entomol., 34: 163–190. GOLOBOFF, P., 1999, Nono (No Name) Ver 2. Published By The Author, Tucumán, Argentina. GUPTA, R. K. AND YANEGA, D., 2003, A taxonomic overview of the carpenter bees of the Indian region (Hymenoptera, Apoidea, Apidae, Xylocopinae, Xylocopini, Xylocopa (Latreille). Pp. 79-100. In : R. K. Gupta (ed.), Advancements in Insect Biodiversity, Publication AgroBios (India). HURD, P. D. JR. AND MOURE, J. S., 1963, A classification on the large carpenter bees (Xylocopini) (Hymenoptera: Apoidea). University of California Publications in Entomology, 29: 1–365.
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LEYS, R., 2000, A revision of the Australian carpenter bees, genus Xylocopa Latreille, subgenera Koptortosoma Gribodo and Lestis Lepeletier & Serville (Hymenoptera: Apidae). Inver. Syst., 14(1): 115–136. MAA, T. C., 1938, The Indian species of the genus Xylocopa Latr. (Hymenoptera). Rec. Ind. MICHENER, C. D., 2007, The bees of the world. The Johns Hopkins University Press, Baltimore, London, 913 pp. MINCKLEY, R. L., 1998, A cladistic analysis and classification of the subgenera and genera of the large carpenter bees, tribe Xylocopini (Hymenoptera: Apidae). Scientific Papers, Natural History Museum, The University of Kansas. 9: 1–47. NIXON, K. C., 2002, WinClada ver. 1.0000. Published by The Author, Ithaca, New York.
(Received : May, 2016 Accepted : June, 2016)
Mysore J. Agric. Sci., 50 (2) : 234-237, 2015
Behavioral and Electrophysiological Responses of the Banana Pseudostem Weevil Odoiporous longicollis Olivier to Host Plant Volatiles A. T. RANI, K. R. M. BHANU AND A. K. CHAKRAVARTHY Department of Agricultural Entomology, College of Agriculture, UAS, GKVK, Bengaluru - 560 065
ABSTRACT Electroantennography (EAG) and behavioral bioassays were conducted to determine the effective hostplant volatile(s) for the attraction of Odoiporus longicollis adults. Volatiles from healthy and mechanically damaged pseudostem extract elicited significantly higher antennal response in male (1.187±0.102mV) and female weevils (0.942±0.116mV), respectively. The Y tube olfactometer study revealed that significantly more males were attracted towards the healthy (66.67%) and mechanically damaged pseudostem extract (61.90%), while females showed more attraction towards BSW damaged (57.14%)) and decaying pseudostem extract (57.14%) in addition to healthy plant volatile (52.38%). Hence, these plant volatiles may be used as attractants for the management of the pest.
BANANA (Musa spp., Musaceae) is one of the most important fruit crops of South East Asian region. India is the largest producer of banana in the world with 822 thousand ha and 29,221 thousand tons production (NHB, 2014). Banana is infested with nineteen insect pests from planting to harvesting in India (Padmanaban et al., 2001). Of these, the banana pseudostem weevil (BSW) Odoiporus longicollis Olivier (Coleoptera: Curculionidae) is gaining importance as a serious pest causing heavy losses to the growers. Now-a-days, this weevil poses a serious threat to the banana cultivation in the banana growing belts of India (Ravi and Palaniswami, 2002). Banana pseudostem weevil is a monophagous pest. Adult females of O. longicollis lay eggs in the outermost leaf sheath of banana. Larvae hatched from the eggs bore into the living tissue, producing frassfilled tunnels that weaken the affected parts of the host plant and permit invasion of fungal and bacterial pathogens. Mature larvae pupate in cocoons made from plant fibers close to the exit holes. The severity of the loss is greater when infestation occurs at the early vegetative stage (5 months old). It is estimated that banana pseudostem weevil incurs 10-90 per cent yield loss depending on the crop growth stage and management practice (Padmanaban and Sathiamoorthy, 2001). At present, banana pseudostem trapping is the only method available to monitor this pest. The
endophytic behavior of the larvae and long life span of adults complicates the management of this pest as the insecticides were ineffective. Hence, there is an urgent need to explore an alternative ecofriendly practical approach. Use of kairomones is a recent trend in developing semiochemical-based pest monitoring and management, which is one of the viable technologies to combat this pest. Owing to its restricted feeding habit and monophagy, O. longicollis may use specific host plant volatiles to find its host for feeding and oviposition. Exploiting these behaviorally active plant volatiles as a potential tool for monitoring and mass trapping purposes offers an ecofriendly management option. In the present study, electroantennographic (EAG) technique was employed to screen different extracts from the banana pseudostem for detecting volatiles with possible semiochemical property. O. longicollis larvae were collected from damaged banana plants and reared on banana pseudostem pieces under laboratory conditions at 12L: 12D at 25 ± 20 C, 70 ± 10 per cent RH at Biocontrol Research laboratories (BCRL), Pest Control (India) Pvt. Ltd. (PCI), Bengaluru. Emerged adults were separated into males and females by rostrum characteristics. They were maintained in separate plastic containers (29cm x 17cm x 33cm). Weevils were provided with freshly cut pseudostem pieces as food, replaced every 5 days. Solvent extraction method was followed for volatile collection. Pseudostem pieces of 5gm each from
BEHAVIORAL AND ELECTROPHYSIOLOGICAL RESPONSES OF THE BANANA PSEUDOSTEM WEEVIL
healthy plant, BSW damaged plant, mechanically damaged plant and decaying plant were collected and immersed in 10 ml dichloromethane (CH2Cl2) (HPLC grade) at 25 ± 20 C for 3 days. The solvents were filtered and were concentrated under a gentle stream of nitrogen. On condensing, the sample was stored at -200 C until used for bioassay. Electroantennography (EAG) (Syntech, The Netherlands) bioassay was carried out at BCRL to assess the olfactory sensitivity of both male and female O. longicollis adults to different pseudostem extracts. Ten μl of aliquot placed on a filter paper strip (60mm long, 5mm wide Whatman No. 1) inside a glass Pasteur pipette (Dimensions – 5.75, Length – Overall 145.0 mm; tip-47.0 mm) was used for stimulus delivery. This was connected to the stimulus controller by silicone rubber tubing. After 10 seconds, the solvent was blown out with first puff. Another 60 seconds later, the stimulus was puffed on to the excised antenna by injecting the vapour phase of the chemical stimuli through a polysterene tube along with a continuous air stream (pulse rate 0.5 s, continuous flow 25 ml s-1, pulse flow 21 ml s-1) to the antenna. Five samples were used, for each sample 5 replicates were performed per sex and each replicate represented one antenna. The behavioral responses of O. longicollis males and females to different pseudostem extracts after EAG screening were further tested in a dual choice Y-tube olfactometer at Insect Behavior Testing Lab (IBTL) of BCRL. The olfactometer consisted of Y-shaped acrylic tube of 6 cm dia. The main tube (stem) of the olfactometer and the two arms were each 30 cm in length at 900. The air-delivery unit (model) was connected to the two arms of the Y-tube to draw purified air to pass through the odor sources in the Y-tube. Airflow through each of the olfactometer arms was maintained at 0.5 L min-1. The olfactometer study was carried out in a room separated from O. longicollis culture. The bioassay was carried out between 11:00 and 1600 h during photophase, which corresponded with the peak mating behavior of O. longicollis. Fixed number of pseudostem extracts (40 ìl) were loaded in Whatman filter paper strips of 1 x 3 cm size and were placed in one of the Y-tube chambers and the other chamber served as control
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(equal volume of HPLC-grade DCM). New filter papers with the extracts and DCM were used for each trial (for every 3 weevils). The position of treatments was alternated after each trial, to avoid directional bias. A group of 3 BSWs were introduced into the base tube of the olfactometer, and the behavior was observed for 15 m. when a weevil crossed the choice line 10 cm after the division of the base tube and remained there for at least 20 s, it was recorded as a choice for the odor source in that arm. If the weevils stayed in the common tube or at the junction of the two arms and did not make a choice during this time were considered a non-responding individual and were excluded from the statistical analysis. Age and mating status of the weevils were not controlled during the bioassays because of the difficulty of rearing these insects under laboratory conditions. All the extracts elicited antennal response in both male and female BSW. Among different extracts, healthy pseudostem and BSW damaged pseudostem extract elicited significantly higher response (F4, =4.652; P=0.008) in male antennae than control. 20 While extracts of decaying pseudostem and mechanically damaged pseudostem were found on par. When female antenna was exposed to above mentioned extracts, mechanically damaged pseudostem extract elicited significantly higher response (F4, 20= 6.256; P=0.001) than other extracts and control (Table I). Dual choice Y-tube olfactometer studies were conducted to determine the behavioral responses of male and female O. longicollis adults to different fractions of banana pseudostem extracts. The results indicated that, significantly higher number of males were attracted towards the healthy pseudostem (÷2=7.118, p=0.008) and mechanically damaged pseudostem exracts (÷2=8.067, p=0.005) compared to control. The male responses towards BSW damaged (÷2=0.333, p=0.564) and decaying pseudostem extracts (÷2=3.267, p=0.071) were not statistically different from respective control (Table 2). Similarly, male weevil attraction towards healthy (÷2=6.231, p=0.013), BSW damaged (÷2=7.143, p=0.008) and decaying pseudostem extracts (÷2=5.400, p=0.020) were significantly higher than the control. Response of males toward mechanically damaged pseudostem extract
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et al.
TABLE I EAG response (Mean (±SEM)) of BSW male and female antennae against banana pseudostem extracts EAG Response (mV) (Mean±SEM)
Treatments Male Healthy pseudostem extract BSW damaged pseudostem extract Decaying pseudostem extract Mechanically damaged pseudostem extract Control (DCM) DF F test P value
Female
1.187±0.102a 1.274±0.102a 1.001±0.092ab 1.058±0.119ab 0.730±0.056b 4, 20 4.652 0.008**
0.578±0.070abc 0.867±0.080ab 0.528±0.096bc 0.942±0.116a 0.377±0.081c 4, 20 6.256 0.001**
Note :Figures within a column followed by a common letter are not significantly different by Tukey post hoc test (p Mg2+> Na +> K+ on the exchange complex. The soils in Bettadapura micro watershed of chamarajanagaradistrict were shallow to very deep in depth, neutral to slightly alkaline in reaction, non-saline and low to medium in organic carbon and the exchangeable complex was dominated by Ca 2+ followed by Mg2+, Na+ and K+.
Pedon 1: Ap Bwss1 Bwss2 Bwss3 Bwss4 Bck1 Bck2 C Pedon 2: Ap Bw1 Bw2 Bw3 C Pedon 3: Ap Bw1 Bw2 Bw3 Bw4 Bw5 Bck1 Bck2 Bck3 C Pedon 4: Ap Bt1 Bt2 Bt3 Bt4 Bt5 C Pedon 5: Ap Bw C. Pedon 6: Ap Bw1 Bw2 Bw3
Horizon
7.5YR 2.5/2 7.5YR 2.5/2 5YR 2.5/2 5YR3/3 7.5YR3/2 5YR3/2
7.5YR 2.5/2 10YR3/2
0-25 25-40 40-56 56-71 71-82 82-108 Weathered parent material
0-17 17-36 Weathered parent material 0-20 20-38 38-63 63-92
10YR 4/2 10YR3/2 10YR4/2 10YR4/2 10YR3/2 10YR4/2 10YR3/2 10YR4/2 10YR3/2
0-16 16-29 29-55 55-72 72-93 93-113 113-133 133-145 145-190 Weathered parent material
10YR3/2 10YR3/1 10YR3/1 10YR3/1
3/1 3/1 2/1 2/1
10 10 10 10
0-18 18-32 32-60 60-90 Weathered parent material
YR YR YR YR
10YR2/1 10YR2/1 10YR3/1 10YR3/1 10YR3/1 -
Colour (moist)
0-23 23-47 47-70 70-100 100-125 125-157 157-180 Weathered parent material
Depth (cm)
scl sc c c
sc c
sc sc sc sc c scl
c c c c c c c c c
c c c c
c c c c c c c
Texture
10 5