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ФЕДЕРАЛЬНАЯ СЛУЖБА ПО НАДЗОРУ В СФЕРЕ СВЯЗИ, ИНФОРМАЦИОННЫХТЕХНОЛОГИЙ И МАССОВЫХ КОММУНИКАЦИЙ (РОСКОМНАДЗОР)РОССИЙСКИЙ ЖУРНАЛ СЕЛЬСКОХОЗЯЙСТВЕННЫХ И СОЦИАЛЬНО-ЭКОНОМИЧЕСКИХ НАУКRUSSIAN-ENGLISH JOURNALRussian Journalof Agricultural and Socio-EconomicSciences№1(13), January 2013ISSN 2226-1184, http://www.rjoas.com

СОДЕРЖАНИЕРоссийский журналсельскохозяйственных и социальноэкономическихнаукCONTENTRussian Journalof Agricultural and Socio-EconomicSciencesвыпуск 1(13) issueянварь 2013 JanuaryМ.С. РахманSOCIO-ECONOMIC DETERMINANTS OFOFF-FARM ACTIVITY PARTICIPATIONIN BANGLADESHБ. Цазовачии, Ц. Мутами, Д. БовораCOMMUNITY GARDENS AND FOODSECURITY IN RURAL LIVELIHOODDEVELOPMENT: THE CASEOF ENTREPRENEURIAL AND MARKETGARDENS IN MBERENGWA, ZIMBABWEМ. МкпадоSERVICE TRADE AND NON-OIL EXPORTIN NIGERIAК.М. Куворну, М. Сулейман, П.К. АмегашиANALYSIS OF FOOD SECURITY STATUSOF FARMING HOUSEHOLDS IN THEFOREST BELT OF THE CENTRAL REGIONOF GHANAГ.Д. АкквахASYMMETRIC PRICE TRANSMISSIONMODELING: THE IMPORTANCE OF MODELCOMPLEXITY AND THE PERFORMANCEOF THE SELECTION CRITERIAХ. Кумар, Р. СингхECONOMIC ANALYSIS OF FRESHWATERAQUACULTURE PRODUCTION:A COMPARATIVE ANALYSIS OFDIFFERENT PRODUCTION SYSTEMSН.А. ЖаттоASSESSING THE RETURNS TO SCALE:EVIDENCE FROM FISH FARMERS INILORIN, KWARA STATEА.М. ВакилиECONOMIC ANALYSIS OF COWPEAPRODUCTION IN NIGERIAГ. Раджович, Ж. БулатовичMOVEMENT POPULATION IN THE SECONDOF XX AND BEGINNING OF XXICENTURYN: THE CASE NORTHEASTERNMONTENEGRO3 M.S. RahmanSOCIO-ECONOMIC DETERMINANTS OFOFF-FARM ACTIVITY PARTICIPATIONIN BANGLADESH8 B. Chazovachii, C. Mutami, J. BoworaCOMMUNITY GARDENS AND FOODSECURITY IN RURAL LIVELIHOODDEVELOPMENT: THE CASEOF ENTREPRENEURIAL AND MARKETGARDENS IN MBERENGWA, ZIMBABWE18 M. MkpadoSERVICE TRADE AND NON-OIL EXPORTIN NIGERIA26 K.M. Kuwornu, M. Suleyman,P.K. AmegashieANALYSIS OF FOOD SECURITY STATUSOF FARMING HOUSEHOLDS IN THEFOREST BELT OF THE CENTRAL REGIONOF GHANA43 H. de-Graft AcquahASYMMETRIC PRICE TRANSMISSIONMODELING: THE IMPORTANCE OF MODELCOMPLEXITY AND THE PERFORMANCEOF THE SELECTION CRITERIA49 H. Kumar, R. SinghECONOMIC ANALYSIS OF FRESHWATERAQUACULTURE PRODUCTION:A COMPARATIVE ANALYSIS OFDIFFERENT PRODUCTION SYSTEMS56 N.A. JattoASSESSING THE RETURNS TO SCALE:EVIDENCE FROM FISH FARMERS INILORIN, KWARA STATE60 A.M. WakiliECONOMIC ANALYSIS OF COWPEAPRODUCTION IN NIGERIA66 G. Rajović, J. BulatovićMOVEMENT POPULATION IN THE SECONDOF XX AND BEGINNING OF XXICENTURYN: THE CASE NORTHEASTERNMONTENEGRO

The AGRIS – International Information System for the Agricultural Sciences andTechnology (http://agris.fao.org) initiative was set up by the FAO – Food andAgriculture Organization of the United Nations (http://www.fao.org) in the 70sand created a worldwide cooperation for sharing access to agricultural science andtechnology information. Based on available technologies, AGRIS was initiallycollecting bibliographic references for a central database. However, since theadvent of the Internet in the late 90s AGRIS has become the brand name for anetwork of centres, which are promoting the exchange of agricultural science andtechnology information through the use of common standards and methodologies.The AGRIS open archives and bibliographical databases cover the many aspects ofagriculture, including forestry, animal husbandry, aquatic sciences and fisheries,and human nutrition, extension literature from over 100 participating countries.Material includes unique grey literature such as unpublished scientific andtechnical reports, theses, conference papers, government publications, and more.AGRIS today is part of the CIARD (Coherence in Information for AgriculturalResearch for Development) initiative, in which the CGIAR (http://www.cgiar.org),Global Forum on Agricultural Research (http://www.egfar.org and FAO collaborateto create a community for efficient knowledge sharing in agricultural research anddevelopment.CIARD RING – Routemap to Information Nodes and Gateways(http://ring.ciard.net) is a global registry of web-based services that give access toany kind of information pertaining to agricultural research for development (ARD).It is the principal tool created through the CIARD initiative to allow informationproviders to register their services in various categories and so facilitate thediscovery of sources of agriculture-related information across the world. The RINGaims to provide an infrastructure to improve the accessibility of the outputs ofagricultural research and of information relevant to ARD management.AIMS – Agricultural Information Management Standards (http://aims.fao.org)is a web portal managed by the FAO. It disseminates standards and good practicesin information management for the support of the right to food, sustainableagriculture and rural development. AIMS underpins CIARD the internationalinitiative which seeks to improve information access and coherence in andbetween organizations.AIMS supports the implementation of structured and linked information andknowledge by fostering a community of practice centered on the themes ofinteroperability, reusability and cooperation. It shares vocabularies,methodologies, tools and services in order to promote coherence in agriculturalinformation.DOAJ – Directory of Open Access Journals (http://www.doaj.org) is a websitemaintained by Lund University which lists open access journals. The project definesopen access journals as scientific and scholarly journals that meet high qualitystandards by exercising peer review or editorial quality control and use a fundingmodel that does not charge readers or their institutions for access. As of January2013, the database contained 8532 journals. The aim of DOAJ is to increase thevisibility and ease of use of open access scientific and scholarly journals therebypromoting their increased usage and impact.EPPO – European and Mediterranean Plant Protection Organization(http://www.eppo.int) is an intergovernmental organization responsible forEuropean cooperation in plant health. Founded in 1951 by 15 European countries,EPPO now has 50 members, covering almost all countries of the European andMediterranean region. Its objectives are to protect plants, to develop internationalstrategies against the introduction and spread of dangerous pests and to promotesafe and effective control methods.As a Regional Plant Protection Organization, EPPO also participates in globaldiscussions on plant health organized by F.A.O. and the International PlantProtection Convention Secretariat. Finally, EPPO has produced a large number ofstandards and publications on plant pests, phytosanitary regulations, and plantprotection products.

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)SOCIO-ECONOMIC DETERMINANTS OF OFF-FARM ACTIVITY PARTICIPATIONIN BANGLADESHM.S. Rahman, Assistant ProfessorDepartment of Management and Finance, Faculty of Agribusiness ManagementSher-e Bangla Agricultural University, Dhaka, BangladeshE-mail: saadrhmn@yahoo.comABSTRACTThe study was conducted in two districts of Bangladesh to determine the factors affecting theparticipation in off-farm activity. A total of 150 sample farmers were selected for interviewthrough random sampling technique. The results showed that the average annual incomewas higher for service holders (Tk.1,83,696) compared to business (Tk. 1,69,215) and offfarmlabour activities (Tk.1,09,373). Participations in activities like business and serviceswere positively influenced by the farm size and education respectively. On the other hand,farm size and education were inversely related with participation in off-farm labour activities.Farmers in the study areas mentioned low income from agriculture as a reason forparticipating in off-farm activity.KEY WORDSHousehold income; Off-farm income; Service; Off-farm labour; Determinants.Due to economic pressure, many households often search for alternative means likeoff-farm activities to cope with the problem of income variability. Off-farm activities havebecome an important component of livelihood strategies among rural households in ourcountry. Several studies have reported a substantial and increasing share of off-farm incomein total household income (Haggblade et al, 2007). The role of off-farm activities in promotinggrowth of rural economy and reducing poverty is well documented (Child and Kaneda, 1975;Islam, 1984; Ranis and Stewart, 1993; Reardon, 1997; Weijland, 1999; Lanjouw, 2001).Rural off-farm sector encompasses generally all non crop activities that are not directlyrelated to crop and non crop production operations but are carried out as backward-forwardlinkages to the various enterprises with in the rural areas proper and also in the small urbanand peri urban areas of large metropolitan (Mandal, 2002).Diversifying one’s sources of income has become a major challenge in Bangladesh inrecent years. Compared to the agricultural sector, employment opportunities in the off-farmsector have been increasing rapidly since the early nineties (The Financial Express, 2012).The Government of Bangladesh in its national poverty reduction strategy paper identified theoff-farm sector as a “leading sector” in the rural economy (GOB, 2005). There are severalstudies (Islam, 1984; Hossain et al, 1994; Bhattacharya, 1996; Khandker, 1996; Hossain,2005) reviewed the off-farm sector in Bangladesh. The purpose of this paper is to provideadditional information on off-farm activity in rural areas of Bangladesh for assessing therecent status of off-farm activity. Keeping these factors in consideration the present studywas undertaken with the following specific objectives.Specific Objectives:1. To examine the structure of rural household incomes in the study areas;2. To find out factors affecting household participation in off-farm activities; and3. To find out the reasons for participating in off-farm activities.METHODOLOGYThe study was conducted in two districts namely Jessore and Rangpur due to highconcentration of off-farm activities. Rural off-farm activities in the study areas were classifiedinto three categories; i) Business enterprises such as shop keeping, petty trading, contractorservices etc; ii) Services such as salaried service in public and private sector institutions,teachers, lawyer, village doctors etc and iii) Off-farm labour such as mechanics, wageemployment in rural business, transport operations, construction labour etc. A total of 1503

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)samples taking 25 from each group and 75 from each district were selected randomly for thisstudy. The study was mainly based on primary data collected through face to face interviewduring the month of March to May, 2011. The collected data were then edited and processedto fulfill the objectives of the study.Analytical technique. Descriptive statistics were used to analyze the annualhousehold income of the sample farmers. The probit model was used to identify the factorsinfluencing the participation in off-farm activities; a binary choice model based on the methodof maximum likelihood is specified. The dependent variable of these models wasparticipation in off-farm activities. Since the dependent variable was dichotomous, OLScannot be used. Therefore, the following type of probit model was used for this study.Y i * = β X i + u i , where u i ~ N(0, 1), i = 1,…..nY = 1 {Y*>0} = 1 if Y* > 00 OtherwiseWhere, Y i = Farmers participating in the off-farm activities (if participate = 1; other wise = 0),X i = Independent variables. Three separate models for this purpose were run for threecategories of off-farm activity like (i) Business activities (ii) Services, and (iii) Off-farm labour.Variable used in the probit model and their measurement:Age (X 1 ): Respondent’s age in year was directly inserted in the model. This variablecould have a positive or negative effect on the respondent’s decision to participate in the offfarmactivities.Farm size (X 2 ): Farm size is an indicator of social status of the respondents. It wascalculated on per hectare basis for each respondent.Household workers (X 3 ): It was measured on the basis of number of earning membersin the family.Dependency ratio (X 4 ): It is the ratio of total number of family members and earningmembers of the family.Organizational participation (X 5 ): It was measured on basis of participation in thedifferent organization. A respondent was given a score of one if he is a member of anyorganization, otherwise 0.Infrastructure development (X 6 ): In this study development of road and highways wasconsidered as a proxy of infrastructure development. A score of 1 is given if the respondentshave the facilities to use the roads and highways, otherwise 0.Education (X 7 ): Education of the respondent was measured on the basis of totalschooling years.RESULTS AND DISCUSSIONAverage annual income of the respondents. Average annual income was foundhigher for service holders (Tk. 183696) than that of business (Tk. 169215) and off-farmlabour (Tk. 109373). Among the service holders higher income was found for therespondents of Rangpur compared to Jessore. Out of the total income, highest portion of theincome comes from off-farm income activities compared to agricultural income for allcategories of respondents. Service holders of Jessore received highest 68% of their totalincome from off-farm activities compared to the service holders of Rangpur (see Table 1).Factors affecting participation in off-farm activities. The parameters of the Probitmodel estimated to identify the factor influencing participation in off-farm activities arepresented in Table 2, 3 and 4.The intensity of participation in business activities is positively related with farm size,organization participation and infrastructure development. If the farm size increases by 1%,keeping other factors constant, the probability of participating in business activities wouldincrease by 0.60%. This may be for the fact that if farm size increases respondents may earnmore money by producing more crops in the field. As a result they can invest this extramoney in their business activities. Similarly, if the respondents can avail developed4

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)infrastructure like road and highways they can easily communicate with other areas andincrease their volume of business (see Table 2).Table 1. Average annual income of different categories of respondentsSourcesBusiness Service Off-farmJ R All J R All J R AllA. Agricultural income (Tk)Crop sector 54162 77943 66052 40997 76962 58980 36132 41213 38672Livestock 9605 9988 9796 4620 11596 8108 10795 8991 9893Poultry 665 428 547 596 304 450 852 490 671Fisheries 1260 3140 2200 1060 1280 1170 800 600 700Others 3880 - 1940 8780 980 4880 1600 28 814Sub Total69572 91499 80535 56053 91122 73588 50179 51322 50750(50) (46) (48) (32) (47) (40) (45) (47) (46)B. Off-farm income (Tk)Business 68880 108480 88680 - - - - - -Service - - - 118248 101969 110108 - - -Off-farmlabourSub TotalGrand total(A+B)- - - - - - 60222 57024 5862368880(50)138452(100)108480(54)199979(100)88680(52)169215(100)118248(68)174301(100)101969(53)193091(100)110108(60)183696(100)60222(55)110401(100)Note: J= Jessore, R= Rangpur, Figures in the parentheses indicates percentage of grand totalTable 2. Factors affecting participation in business activities:estimates of a probit model57024(53)108346(100)Factors Coefficients Standard error z-value Marginal effectEducation 0.004 0.03 0.17 0.0016Age 0.004 0.01 0.37 0.0014Farm size 0.601** 0.23 2.50 0.2027**Household workers 0.004 0.11 0.03 0.0013Dependency ratio 0.19 0.14 1.53 0.0652Organizational participation 0.75** 0.27 2.92 0.2401***Infrastructure development 1.27*** 0.35 3.71 0.3392***Constant -1.93** 0.85 -2.39 -Log likelihood function -75.16LR chi2 40.62Prod>chi2 0.000Pseudo R 2 0.21Observations (n) 15058623(54)109373(100)In the case of service, education plays a positive and significant role. The respondentshaving higher education are encouraged to participate in services. If the education isincreased by 1%, keeping other factors constant, the probability of participation in serviceswould increase by 0.20%. Dependency ratio also positively associated with the participationin services. On the other hand, organizational participation is negatively related with servicesdue to the fact that organizational participation requires additional time which restricts theservice holders to take part in this kind of activity (see Table 3).Most of the factors included in the model are negatively associated with theparticipation in off-farm labour activities. The negative association with age indicates thepreference of the younger generation for off-farm jobs over agricultural wage labour.Negative association with farm size indicates that if the respondents have more land theycan produce more crop and earn money from selling this crops. Organizational participationand infrastructure development were also negatively associated with off-farm labouractivities. Negative association of education indicates educated persons are morecomfortable with service sector compared to off-farm labour activities (see Table 4).5

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Table 3. Factors affecting participation in different services:estimates of a probit modelFactors Coefficients Standard error z-value Marginal effectEducation 0.207*** 0.04 5.72 0.0704***Age 0.005 0.01 0.50 0.0020Farm size -0.150 0.23 -0.68 -0.0513Household workers 0.148 0.15 1.13 0.0504Dependency ratio 0.288** 0.14 2.32 0.0981**Organizational participation -0.419* 0.27 -1.63 -0.145*Infrastructure development -0.386 0.29 -1.38 -0.136Constant -2.90*** 0.77 -3.55 -Log likelihood function -69.40LR chi2 52.15Prod>chi2 0.000Pseudo R 2 0.27Observations (n) 150Table 4. Factors affecting participation in off-farm labour activities:estimates of a probit modelFactors Coefficients Standard error z-value Marginal effectEducation -0.23*** 0.04 -5.49 -0.060***Age -0.22* 0.01 -1.60 -0.005*Farm size -1.43** 0.61 -2.34 -0.374**Household workers 0.18 0.15 1.14 0.047Dependency ratio 0.21 0.15 1.41 0.055Organizational participation -0.33 0.29 -1.12 -0.088Infrastructure development -0.93** 0.35 -2.64 -0.284**Constant 4.40*** 1.10 4.05 -Log likelihood function -52.61LR chi2 88.03Prod>chi2 0.000Pseudo R 2 0.46Observations (n) 150Reasons of participation. According to the Table 5 the majority of the respondents(79%) mentioned that low income from agriculture is the major reason for participating in offfarmactivity in the study areas. Burden of maintaining large family was ranked second mostimportant reason for participating in off-farm activity followed by availability of off-farm workopportunity.Table 5. Reasons for participating in off-farm activitiesReasons% of farmersJessore Rangpur All areasBurden of large family 80 77 77Low income from agriculture 76 83 79Available opportunities 67 53 60CONCLUSIONThe findings of the study reveal that on an average service holders received higherannual income compared to other categories of respondents. Farm size, infrastructuredevelopment and education had significant contribution in promoting off-farm activities likebusiness and service whereas these factors are inversely related with off-farm labour activities.Low income and large family were the reasons for participating in off-farm activities in the studyareas. Government and concerned authority should provide efficient support services to thefarmers and build roads and highway to ensure participation in off-farm activities. By promotingthis sector, farmers will be able to get sufficient amount of income which in turn may be usedfor investment in the farm practices. Off-farm activities may be used as a means of incomediversification which will help to reduce poverty and boost the rural economy as a whole.6

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)REFERENCES[1] Bhattacharya, D. 1996. The emerging pattern of rural non-farm sector in Bangladesh:A Review of Micro Evidence. The Bangladesh Development Studies, 24 (3-4), 143-180.[2] Child, F.C., and H. Kaneda. 1975. Links to the green revolution: A study of smallscaleagriculturally related industry in the Pakistan Punjab. Economic Developmentand Cultural Change, 23 (2): 249-275.[3] Haggblade, S., Hazell, P.B., Reardon, T. 2007. Transforming the Rural Off-farmEconomy, Johns Hopkins University Press, Baltimore, MD.[4] Hossain, M., 2005. Growth of the rural non-farm economy in Bangladesh:determinants and impact on poverty reduction. In: Proceedings of Internationalconference 'Rice is life: scientific perspectives for the 21st century', 436-439.[5] Hossain, M. Rahman, M. Bayes A. 1994. Rural off-farm Economy in Bangladesh: aDynamic Sector or a Sponge of Absorbing Surplus Labor? SAAT Working Paper,International Labor Organization, New Delhi.[6] Islam, R. 1984. Off-farm Employment in Rural Asia: Dynamic Growth orProletarization? Journal of Contemporary Asia. Vol. 14: 306-324.[7] Khandker, Shahidur R.1996. Role of targeted credit in rural non-farm growth. TheDevelopment Studies, 24(3-4), 181-193.[8] Lanjouw, P. 2001. Rural non-agricultural sector and poverty in El Salvador. WorldDevelopment, 29(3): 529-527.[9] Mandal, M. A. Sattar and M. Assaduzzaman. 2002. Rural Off-farm Economy inBangladesh: Characteristics, Issues and Livelihood Strategies for the Poor, FarmEconomy, Vol. 12: 43-61.[10] Ranis, G., and F. Stewart. 1993. Rural non-agricultural activities in development:Theory and application. Journal of Development Economics 40: 75-201.[11] Reardon T. 1997. Using evidence of household income diversification to inform studyof the rural nonfarm labor market in Africa. World Development, 25 (5) 735-748.[12] The Financial Express. 2012. Non farm Employment in Rural Bangladesh, March 31,Bangladesh.[13] Weijland H. 1999. Microenterprise clusters in rural Indonesia: Industrial seedbed andpolicy target. World Development, 27 (9): 1515-1530.7

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)COMMUNITY GARDENS AND FOOD SECURITY IN RURAL LIVELIHOODDEVELOPMENT: THE CASE OF ENTREPRENEURIAL AND MARKET GARDENSIN MBERENGWA, ZIMBABWEBernard Chazovachii, Cephas Mutami, LecturersDepartment of Rural and Urban Development, Great Zimbabwe University, ZimbabweE-mail: bchazovachii@gmail.com, cmutami@gmail.comJohn Bowora, LecturerDepartment of Rural and Urban Planning, University of Zimbabwe, ZimbabweE-mail: johnbowora@gmail.comABSTRACTThis paper seeks to assess the contribution of community gardens on food security in rurallivelihoods development in Mberengwa ward 27. Despite the introduction of communitygardens in ward 27, poverty persisted amongst the vulnerable groups in the district. Bothqualitative and quantitative methods were used in collection of data through questionnaires,interviews and focused group discussions (FGDs). Analysis was done using descriptivestatistics and content analysis. This study revealed that the vulnerable people of Mberengwaderived income, basic horticultural skills, enriching their garden soils and food commoditiesfrom the Imbahuru community garden. Factors like all year-round production of crops,intensiveness of the activity, monitoring and evaluation by extension workers, field days in allseasons and demand of the crop varieties grown influence food security in the district.However challenges persisted due to their seclusion of these gardeners from creditfacilicities, lack of irrigation equipment, unstable power relations among leaders and theproject was associated with the weak in society. The research concludes that the gardeningproject should be done not in isolation with the Zimbabwe’s agrarian reform programmewhich would provide all forms of capital which capacitated the vulnerable rural dwellers.KEY WORDSCommunity gardens; Vulnerability; Livelihoods; Food security; Sustainable ruraldevelopment.Community gardens were initiated back from the eighteenth and nineteenth centurieswhere tropical veg culture survived in remote areas and mixed gardens in south East Asia(Grigg, 1974). According to Taylor and Francis (2009) community gardens in Africa involvedirrigation in home gardens since prehistoric time with the provision of vegetables forhousehold consumption. The goal of community gardens was to increase household andintra household food security throughout the year. Community gardens provide marketingopportunities to rural people and built a base for food production for the vulnerable. Recentlymass establishment of community gardens was done by non-governmental organisationsnamely Action Faim and CARE Zimbabwe in a bid to maintain sustainable rural livelihoodsamong the rural households.Communities have been upgrading communal gardens by selling the surplusproduction to obtain household income. Auret (1990) revealed that NGOs assist inestablishing small irrigated vegetable gardens as they are a major component for the dailyfood consumption. In 2006, CARE Zimbabwe assisted in establishing community gardenprojects in Mberengwa ward 27, Imbahuru Community Garden to accommodate thevulnerable groups to alleviate rural poverty. Community gardens promoted food security aschildren and elderly participate in this field agriculture,(World Bank, 2007).Since the inceptionof community garden projects in Mberengwa ward 27 there is persistent food insecurityderailing sustainability of other livelihood activities. The problem this study seeks to addressis the increasing in poverty among the vulnerable groups despite, the introduction ofcommunity gardens in Mberengwa ward 27 by CARE to reduce poverty. The garden projectswere supposed to offer food security among the households but the real poor are still in8

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)shocks and stress. The goal of sustainable rural livelihoods remained elusive due to inherentfactors challenging community gardens as a rural livelihood strategy. It is the aim of thisproject to unearth why poverty is growing despite the introduction of community gardens andexamine the coping strategies used as solutions to the challenges faced in communitygarden projects.Community Gardens. According to Middleton (2009), community gardens are a placeto grow food crops, flowers and herbs in the company of friends and neighbours. It may alsobe a place to reconnect with nature or get physical exercise. Basing on this definitioncommunity gardens have attracted different meaning, uses, and purposes to differentsocieties and communities. As a result some use community gardens because they lackadequate space at their homes to have a garden and to build a sense of community amongneighbours (Middleton, 2009). In rural areas, community gardens takes different shapes,forms and sizes and purposes that make them differ from each other and from place toplace. Community gardens are innumerable i.e. neighborhood community gardens, YouthCommunal gardens and School gardens, Nutritional gardens, Entrepreneurial and Marketgardens, Home gardens, Therapy gardens and Demonstration gardens.Neighborhood community gardens as eluded by Middleton, (2009) are located on landthat is divided into different plots for individuals or families. These gardens have leaders,committee for management and can be found at churches. In Zimbabwe they are found inwetlands as dambos distributed by headmen for each household (Leach, 1990). Crops suchas maize, sorghum, vegetables and bananas are found in these gardens. Youth and Schoolgardens are found in schools for educational purposes to young people. They are located ata community centre for the unemployed youths to earn a living. At schools they provideclassroom lessons in different subjects such as agriculture and science subjects. Speciessuch as vegetables, groundnuts, beans, maize, and tomatoes are found in these gardens.Nutritional gardens are funded by NGOs through the ministry of health and land isallocated to the vulnerable groups in order to offer supplementary diets to everyday meals.Green vegetables, onion and carrots are mainly found and especially medicinal plants,(Moyo and Tevera, 2000).There are also Entrepreneurial and Market gardens which specialized to improvemarket and are established by non profit organization to teach business and job skills toyouth and vulnerable groups. The participants are paid by money after sales, (Middleton,2009). Crops mainly found in these gardens are vegetables, fruit trees like oranges andavocados and also cash crops which are coconuts and sugarcane.Home gardens take different size and activities. They are called small gardens orkitchen gardens located near the homestead specifically for vegetables and water forirrigation can be obtained from dish washing and bathing. They are mostly found in arid andsemi arid areas in sub Saharan Africa. They may also take gardens plot and may becommunally owned, (Taylor and Francis, 2009). Home gardens can take a form of nurseriesto provide seedlings, floriculture with ornamental plant located in peri-urban for market andthey can also be home market gardens. According to Rangasamy et al (2002) these may becalled forest gardens found in the humid tropics integrating poultry, vegetables and fruit treeshence mixed gardens.Communal gardens are located in a communal centre, organized and managed by acommunity group to share work and rewards. They own the land collectively and share theproceeds among members. They can donate to food pantry what they harvest at theirpleasure. Communal gardens can accommodate at least thirty people up to more than twohundred people. Different types of crops are grown such as cereals that are maize, sorghum,cash crops, timber, forage, fruit trees and different types of vegetables.The food bank normally dominated these gardens and located at a food pantry. Foodbank and pantry are for storage facilities and volunteers are participants or food pantryclients who grow and donate to the food pantry. Mainly cereals such as maize, nutritionalcrops like beans and peas are grown in these gardens.Therapy gardens are mainly for horticultural therapy to hospital patients, located athospitals, prisons and senior caters. A horticulture therapist leads programs and activities.9

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Horticultural plants are cultivated in these gardens such as ornamental crops, herbs,medicinal plants, flowers, garlic and different types of vegetables.Lastly, Demonstration gardens located at working community gardens managed andmaintained by the public while led by extension master gardeners. Community members aretrained as volunteers to educate the public about gardening. Different crops of interests maybe chosen for example a cereal, vegetable, fish, fruit tree or horticultural products on how it iscultivated. This study would focus on Entrepreneurial and Market gardens with the use ofImbahuru community garden in Mberengwa district. Imbahuru community garden has provedto be a livelihood which is sustainable, cope with and recover from stress and shocks,maintain or enhance its capabilities and assets, and provide sustainable livelihoodopportunities for the next generation, and which contributes net benefits to other livelihoodsat the local and global levels in the long and short term, (Ellis, 1998). A method of selectingbeneficiaries in community gardens is limited to social and technical criteria. Localcommunity based organizations play an important role in selecting beneficiaries. Accordingto Eshtayeh and Earis (2006) women are invited to a meeting in which information iscollected from the community based organizations (CBOs) representing the CBOs but alsofrom the public to foster transparency. Moyo and Tevera (2000) noted that in nationalgardens which are funded by Non governmental organizations (NGOs) through the ministryof health, the councillors who are recognized political leaders allocated land for gardens .Thiscreated conflicts with traditional leaders.Nutritional gardens normally favor the sick to get balanced diet mostly those withchronic diseases like tuberculosis, AIDS and others .NGOs collect information about theliving standards, family size, assets and choose according to the vulnerability context of thatcommunal area.Community gardens have important resources with socio-economic reproduction rolesfor the communal people (Moyo and Tevera, 2000).Some villagers have resorted togardening while waiting for the rain season and they make profits using them for accessinginputs during the main season of farming (New farmer, 2004).Some A1 resettled farmers inShamva who have no adequate irrigation facilities have opted for gardening instead ofirrigation schemes because of their huge profits. Huge profits are being made from gardeningby selling their crops to Mbare musika and along Shamva road (New farmer, 2004).Scoones (2010) postulated that gardens have benefited women through specializationand they obtained vegetables, groundnuts and Bambara nuts for the household foodconsumption. .Community have benefited from participation in those gardens where theyderive their income. .Community run schemes have performed better than governmentmanaged schemes because of their flexibiliity, lower cost of operation and participation ofwomen (Rukuni et al 2006). Community gardens in rural areas utilized wetlands as source ofwater to irrigate their crops and vegetables. These wetlands existed together with communitygardens for many years and proved to be highly productive as they contribute to social andeconomic welfare of many rural families, (Rukuni, et al 2006).The use of wetlands tovegetable gardening is increasing in small holder farmers.More so, community gardens contribute to the affected and vulnerable household’sfood security. Implementing organizations are helping promoting vegetable gardens to helpvulnerable groups and affected households get access to vegetables to ensure food andnutrition security (FAO, 2002).These nutritional gardens have benefited households andchronically ill people with herbs and vegetables as they improve their nutrition throughout theyear. These are also activities for women where income generation becomes easy for them.Medical plants found in these community gardens such as garlic and onions have role oftreating HIV related symptoms, improving digestion and stimulating appetite (FAO, 2002).Gardens are for income generation and food producing activities. These are necessary forthe contribution to food security and safety. Over 2, 8 million dollars worth of food wasproduced from the subsistence gardens during the depression end by the time of secondwar, and the food administration set up a nutritional victory garden programme which sawhuge benefits, (FAO, 2002).10

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Although other community gardens are illegally located along river banks they havesupported families through income and food throughout the year, (Scoones, 2010). Microirrigationhas been also more successful with gardens being the source of year aroundvegetables and maize. These are found in resettlements and are an important basis forlivelihood strategies (Scoones, 2010).Communities have upgraded gardens and individuals from these gardens sell surplusproduce to obtain household incomes which in turn cater for household food security, basicand other emergencies. Community gardens benefited the communities to build socialnetworks through sharing gardening activities .According to Moyo and Tevera (2000) familyand kinship act as the distributive mechanisms as well as promoting interpersonal relationsand social identity of individual members .This mainly happen in sharing dambo gardensamong the families who participate .Gardens have promoted intermarriages between thefamilies and thereby building networks of kinship, (Moyo and Tevera, 2000).Community gardens act as a survival strategy for the poor in many communities toshare resources together in order to meet their daily basic needs and mutual obligations.Preservation of aesthetic and cultural values is demonstrated in Asian gardens. Homegardens were also useful in the slavery stage and were influenced by African slaves inCaribbean as food used by poor people as a strategy against food inflation resulting fromheavy reliance on imported food.Challenges in Community Gardens. Community gardens face many challenges thatlimit their production and interaction between members. Lack of irrigation equipmentundermined the ability of poor households to raise their agricultural incomes and made themeven more vulnerable to frequent droughts. Power relations are an impediment to thesuccess of gardens. These relations determine the controls of gardens (Moyo and Tevera,2000).There are also illegitimate forms of transferring land or selling of land or expansion ofplots which is common in dambo gardens. According to Moyo and Tevera (2000) this was asa result of usurpation of powers of traditional leaders to manage land and other naturalresources lead to protest against rules.According to Middleton (2009) community gardens in rural areas face managementchallenges. Most of the participants in community gardens lack gardening skills. Communitygardens attracted members which are political motivated and they tend to influence decisionmaking. Middleton (2009) also noted that community gardens also face the challenge ofwater to irrigate fruits and vegetable during summer. Conflicts over control of land,competition between actors over use of scarce resources such as water because ofpopulation pressure are also common in community gardens .According to Moyo and Tevera(2000) there are conflicts between national institutions and local people for example nationalinstitutions restrict the cultivation of dambo gardens using national institutions.METHODOLOGYBoth qualitative and quantitative data were utilized in this research. A descriptivesurvey design was adopted which incorporated questionnaires, interviews ,secondary datasources and focused group discussions to collect data about selection of beneficiaries,benefits, challenges and coping strategies in community garden projects from beneficiaries,local authorities, locals and leaders. Random and snowball sampling were employed in thestudy of community gardens in Mberengwa. A sample of forty garden beneficiaries wasselected using this procedure. Purposive sampling technique /Snowball were used becausea detailed data from a few cases was needed as the research focused on in-depthinformation which was generated from respondents. This procedure was used to selectgarden committee, local leaders as introduced by an AGRITEX extension worker of thegarden. The extension worker was targeted and introduced other potential respondents withrelevant information on the community garden. Triangulation, descriptive statistical andcontent analysis was used for data analysis.11

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Figure 2. Scheme adopted from Mawere, 2011The above diagram indicated that the majority of gardeners derive about $45 to 80%per annum and about 10% of respondents derive more than $115 per annum.The majority of the respondents indicated that income derived from selling gardenproduce was used to buy grocery and food, followed by other emergencies, agricultureutensils, fees, clothing and livestock and lastly grinding mill charges. This is because thosegardeners earn little income and moneys are not meaningful for purchasing huge assets perannum. Hence income is mainly used to buy cheaper products for daily use especially foodand groceries.6050% of respondents403020100Below $45 $45-80 $80-115 $115 and aboveIncomeFigure 3. Income of respondents per yearSource: field survey, 201213

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)4% 5%7%29% grinding mill charges20%clothing and livestockschool feesagriculture inputs and utencilsgrocery and foodother emergences.35%Figure 4. Assets from incomeSource: field survey, 2012Towards Sustainable Livelihood Development through Community Gardens. Theterm sustainable livelihoods relates to a wide set of issues which encompass much of thebroader debate about the relationships between poverty and environment, (Scoones, 2010).Chambers and Cornway, (1991) state that livelihoods comprise of the capabilities, assets(both material and social resources) and activities required for a means of living. A livelihoodis sustainable when it can cope with, and recover from, stresses and shocks and maintain orenhance its capabilities and assets both now and in the future. In an attempt to make a living,people use a variety of resources such as social networks, capital knowledge and markets toproduce food and marketable commodities and to raise their incomes, (Herbinck andBourdillon 2001, Mutangi, 2010). However when such resources are not available or whenthey are undermined people tend to go under stress and shock. This can be traced to Sen’stheory of entitlements, which postulates that the purpose of development is to improvehuman lives through expanding the range of things a person could do and be, for example,being healthy and well nourished, being knowledgeable and being able to participate in thelife of the community. Development is thus about removing obstacles to what a person cando in life for example illiteracy, ill health, lack of access to income and employmentopportunities, lack of civil and political freedoms, (Zimbabwe Human Development Report2003). Before the inception of the garden, the people of Mberengwa were suffering frommalnutrition and kwashiorkor due to shortage of proteins in their diets. As a result gardeningbrought about rich protein crops such as beans, peas and potatoes. According to FAO(2002) community garden constituted the affected and the vulnerable groups promotingvegetable garden to ensure food and nutrition security, providing medical role of treating HIVrelated symptoms and improving digestion and stimulate appetite. This was also witnessedfrom the Imbahuru garden as the crops like garlic are found in the garden and respondentsdisclosed that they have better diet in their daily consumption.Many households indicated the changes in their quality of life since the inception ofcommunity gardens. They obtained basic food commodities like relish and spices for theirmeals. They have gained skills through workshops conducted for farming purposes whichhelp them to practice good methods of farming and cooperative efforts as they enhancedattitudes interdependency.Respondents also indicated that they were gaining team spirit, financial transactionsand reduced crime as the locals spend their time in gardening activities. Communitygardening become a meeting point for formation clubs and helps to reduce stress as14

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)gardeners exchange ideas and knowledge. Moreover, gardeners enhanced communityparticipation in their projects and improved social ties, networks and relations throughinteraction and by borrowing produce, equipment and sharing moral obligation that improvesunity in their community. Other respondents indicated that they have gained teachings aboutHIV and AIDS while working in the gardens. As a result gardening was seen as centre forpracticing cultivation of different crops from different corners of the world hence reduceslaziness, dependency and promotes diligence, leadership practice and identification oftalents. Therefore community gardens proved to have different benefits as witnessed bypeople of Mberengwa.Factors Influencing Food Security in Community Gardens. The community gardenactivity is an all year round enterprise. This on its own makes the project sustaining to ruralhouseholds. This would be an assurance that something is going to come out be it winter orsummer season. Coupled with that, the crop varieties grown are those that would be on highdemand like fresh ground nuts, round nuts and other vegetable varieties in to an extent thatmarket is readily available. The role of extension officers in monitoring and evaluation of theactivities in the gardens has placed them at the level of intensive small scale commercialfarmers that production is being realized. Field days are promotional days that encouragecompetition and motivation among the gardeners to an extent that it boosts futureproductions in their gardens.403530No.ofRespondents(%)2520151050Managementproblemsinterferencewith outsiderslack ofcooperationlivestock andtheftChallenges Faced in Community GardensFigure 6. Challenges Faced in Community GardensSource: field survey, 2012The majority of respondents indicated that there is lack of cooperation amonggardeners; as a result the communal spirit is affected hence reduced production in gardens.Interference with other outsiders who vandalize fencing followed by livestock and theftaffecting their produce were some of the challenges. Wild animals such as baboons devourgarden crops. Finally respondents showed that they suffer from poor management asleadership lacks knowledge and skills. As Middleton (2009) alludes community gardens inrural areas face management challenges as they are management intensive, time andcapacity to work and organize people to resolve conflicts becomes problematic. Thegardeners also reported that chemicals were lacking to treat their crops resulting in poorharvests. Gardeners were struggling to mobilize finances together for buying chemicals. Thisis because CARE which was helping them in gardening activities handed over the project tothe community hence failed to properly manage.Marketing Challenges. The majority of respondents indicated that they have onaccess to buyers, long distance to market and high transport costs. This is probably due tolack of strategic marketing points where their produce can fetch meaningful prices, and they15

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)are competing with other community gardens, in their ward, nearest gardens and homegardens for market. As a result price for inputs where not in commensuration with incomeafter selling their produce.Coping Strategies. The majority of the respondents indicated that they have set upthree sub-committees on top of the main committees; which are marketing, agronomy andsecurity. The committees were set as to solve the challenges that are likely to occur and toreshuffle committees every year so as avoid conflicts. However other gardens relied onkinship and build friendships. Moyo and Tevera (2009) noted that as the coping strategy inthe community, other gardens engage in intermarriages to build network of kinship assources of cooperation as pre-conflict resolution.CONCLUSIONThe people of Mberengwa benefited the socio-economic services from gardening as itcontinued to be the source of income for the beneficiaries of community. The income derivedhas been used for paying school fees, buying assets, agricultural inputs and buying groceriesand food. Evaluations have confirmed that the benefits resulted in improvement in the qualityof life of the rural people. Field days, monitoring and evaluation by extension workers, andthe practice of gardening all year-round has transformed gardening to sustain food security inthe district. Social benefits like social networks, physical exercises, team spirit and stressreduction and community participation are important for community building and livelihooddevelopment.The challenges being faced in community gardens are theft, poor management andmarketing problems. Challenges such as conflicts and power contestations are inherent andmeasures are needed to solve such problems as they contribute to community breakdown.There should be an increase in community participation through the introduction of othersubsidiary projects linked to gardening such as orchard to enhance diet, household incomeand capacity to absorb shocks and trends like droughts and climatic change. There is needfor desplinary committee to control behaviour and conflicts in community gardens. Thegardening project should be done not in isolation with the Zimbabwe’s agrarian reformprogramme which would provide all forms of capital which capacitated the vulnerable ruraldwellers to enhance food security.REFERENCES[1] Auret, D. (1990). A Decade of Development in Zimbabwe 1980-1990. Gweru MamboPress.[2] Chambers and Conway, G (1991). Sustainable Rural Livelihoods: Practical Conceptsfor the 21 st Century. IDS Discussion paper, no. 296. Institute of Development Studies.[3] Chazovachii, B., Mutami, C. and Phikelele, S. (2012). Mechanized ConservationAgriculture and Food Security in Mberengwa District, Zimbabwe. Unpublished.[4] Ellis, F. (1998) Survey Rural Livelihood Diversity in Developing Countries: Analysis,Policy, Methods. Oxford, Oxford University Press.[5] Eshtayeh, I. and Earis, R. (2006) Lessons learned in how to select Femalebeneficiaries: Backyard agricultural production and Cottage industry activities forwomen, Rome. FAO.[6] FAO (2002) Migrants’ Long Distance Relationships and Social Networks in Dakar:Environment and Urbanization, Rome, FAO.[7] Grigg, D.B. (1974) The Agricultural Systems of the world: An Evolutionary Approach.,New York, Cambridge University Press.[8] Leach, M (1990) Social Organization and Agricultural Innovation: Women’s vegetableProduction in Eastern Sierra Leone, Peasant Household Systems. Internationalworkshop proceedings.[9] Local Harvest (2009) Challenges and Solutions to Community Gardens: AlternativeCommunal Farmer/WPB-FL, Florida.16

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)[10] Mawere, M. (2011) Gold panning in Central Mozambique: A Critical Investigation ofthe Effects of Gold Panning in Manica.IJOPAGG.[11] Middleton, J. (2009) Community Gardening. Columbia, University of Missouri.[12] Moyo, S. and Tevera, D. (2000) Environmental Security in Southern Africa. Harare,SAPES Trust.[13] Mutangi, G. (2010). ‘The changing patterns of farm labour after the Fast Track LandReform Programme: The case of Guruve District’. Livelihoods after Land Reform inZimbabwe Working Paper 13. Livelihoods after Land Reform Project. South Africa:PLAAS.[14] New Farmer (2004) Zimbabwe’s Leading Voice of Agriculture: Volume 9 number 4Harare, Ministry of Agriculture.[15] Rangasamy, A. et al (2002) Farming Systems in the Tropics, New Delhi, Kalyanpublishers.[16] Rukuni, M. et al (2006) Zimbabwe’s Agricultural Revolution Revisited, Harare, UZPublications.[17] Scoones, I. et al (2010) African Issues: Zimbabwe’s Land Reform: Myths andRealities. Harare, Weaver Press.[18] Taylor and Francis (2009) Traditional Home Gardens and Rural Livelihoods inNhema, Zimbabwe: A Sustainable Agro forestry System, Volume 16,Issue 1, 2009.[19] UNDP (1996) Some Basic Facts about Home Gardens, New York, UNDP.[20] World Bank (2007) Delivering on the Pro-poor Growth. Insights and Lessons fromcountry experiences. Washington DC, Macmillan.[21] Zimbabwe Human Development Report (2003). Redirecting our Responses to HIVand AIDS; Towards Reducing Vulnerability, the ultimate war of survival.17

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)SERVICE TRADE AND NON-OIL EXPORT IN NIGERIAMmaduabuchukwu Mkpado, ResearcherDepartment of Agricultural Economics and Extension, Federal University Oye-Ekiti, NigeriaE-mail: mmaduabuchukwu.mkpado@fuoye.edu.ng, mkpado@gmail.comABSTRACTThe paper was set to examine the relationship between different aspects of service trade andnon oil export in Nigeria as well as assess the impact of capacity development on value ofservice trade and the implications for improving value of non oil export in Nigeria. Secondarydata 1980 to 2010 were used. Data were analysed using descriptive statistics, correlationanalysis and regression analysis. Results show that total service trade value in Nigeria hasincreased from $1126.59 million in 1980 to $3076.19 million in 2010. Significant correlationsexisted between the total value of service trade and all the types of service trade except withother service trade value. Road network, government (domestic) capital expenditure onservices, agricultural credit and domestic service GDP positively determine exportableservices. Recommendations include improvement of service GDP and agricultural credit/loanfacilities.KEYWORDSExportable services; Road network; Government expenditure; Agricultural credit.Services constitute one of the key sectors providing significant contribution to economicgrowth and a country’s competitiveness including Nigeria and African as a whole (Table 1).Banking, insurance and finance, tourism, retail and food and beverages, media andentertainment, education and health, and airline industry are some of the sub-sectors in theservices sector. The list also includes electricity and water supply as well as good roadnetworks. A country’s domestic capacity to provide these services will not only improvewelfare of her citizens but also in the long run became a set of assets for attraction of foreignexchange. Table 1 is a breakdown of subsectors contribution to Nigerian Gross DomesticProduct (GDP) in 2010. It showed that the service sector is the second largest contributor tothe country’s GDP. Nigeria Government established the Federal Service Commission(SERVICOM) in 2005 to promote service in the country. Thus, the Government knew thepotentials of using the services sub sector to create wealth and alleviate poverty.Today, tourism is a fast growing foreign exchange earner for countries that havedeveloped or developing the sub sector and its associated services. Kaur (2011) aptly notedthat due to technological progress, since 1980s, international trade in services has beenincreasing rapidly. It has now accounted for twenty per cent of globe trade. Globally, theshare of primary sector and secondary sector has been declining while the share of servicesector is growing rapidly. Through the internet and ecommerce, many communications andinformation processing activities have opened new opportunities for cross border servicetrade, which has strengthened the importance of international service trade.Table 1. Sector contribution to GDP in NigeriaORDER GDP BREAKDOWN FOR NIGERIA % to the GDP 2010Agriculture 40.84Wholesale and Retail Contributed 18.70Crude Petroleum & Natural Gas Contributed 15.85Tele Communication /Postal Services 4.56Manufacturing 4.16Finance & Insurance 3.57Building & Construction 2.00Real Estate 1.74Business & Other Services 0.90Hotel and Restaurant 0.50Solid Minerals 0.34Others 6.83Source: CBN Annual Report 201118

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Trade can play a crucial role in the development of service sectors in Nigeria. Servicesoffer new dynamic opportunities for exports, especially for land-locked countries, whileopening up to imports of services and foreign direct investment is a key mechanism toincrease competition and drive greater efficiency in the provision of services in the domesticeconomy. Trade policy especially liberalization is a very important variable in determining thenature of competition in domestic services sectors. Countries that place restrictions onforeign services providers may limit access to the most efficient suppliers and the besttechnologies and, in turn, deny producers and consumers throughout the economy access tolow cost services or to the types of services that are most appropriate for their needs.Similarly restrictions in overseas markets can act as a constraint on the development ofservices exports for which the country has a comparative advantage (World Bank, 2010).Despite the importance of services and trade in services, few countries in Africa have defineda trade strategy for them. In ECOWAS for instance the ECOWAS common external tariffsare in accordance with trade liberalisation policy (Achike, Mkpado and Arene, 2008). Theongoing liberalisation of trade is a favourable conduction for generating exportable services.Trade in services offers new opportunities for export diversification. The NigerianGovernment through its export promotion council has been encouraging the diversification ofthe domestic foreign exchange earners. Often services are overlooked as a source of exportdiversification and discussions and trade policies are inappropriately focused onmanufactures. Considerable scope remains to expand traditional sectors such as tourism. Inaddition, the telecommunications revolution is making new opportunities available for export,growth and employment. The fastest growing top 100 company in Kenya in 2009 is anexporter of services. It is estimated that each job in such export oriented services sectorssupports an additional 3 or 4 jobs in the wider economy (World Bank, 2010). Exports ofservices appear to be of particular importance for land-locked countries for whomopportunities to diversify into the export of manufactures are more limited by the high costs oftransporting goods. Indeed, over the past 10 years exports of services from non-oil exportingland-locked countries in Africa have increased at a rate more than 3 times faster than theirexports of goods (Brenton, et al 2010).Components of exportable services include:• Transport covers all transport services that are provided by residents of one economyfor those of another and that involve the carriage of passengers, the movement ofgoods (freight), rentals (charters) of carriers with crew, and related supporting andauxiliary services. All modes of transport are considered including sea, air, space, rail,road, inland waterway, and pipelines, as are other supporting and auxiliary services(such as storage and warehousing).• Travel covers primarily the goods and services acquired from an economy by travellersduring visits of less than one year to that economy. The goods and services arepurchased by, or on behalf of, the traveller or provided, without a quid pro quo (that is,are provided as a gift), for the traveller to use or give away. The transportation oftravellers within the economies that they are visiting, where such transportation isprovided by carriers not resident in the particular economy being visited, as well as theinternational carriage of travellers are excluded; both are covered in passengerservices under transport. Also excluded are goods purchased by a traveller for resalein the traveller’s own economy or in any other economy. Travel is divided in twosubcomponents: business travel and personal travel.• Other services comprise external transactions not covered under transport or travel,specifically: communications services, construction services, insurance services,financial services, computer and information services, royalties and licence fees, otherbusiness services, personal, cultural and recreational services, and governmentservices (Permani, 2008).Problem statement. World Bank (2010) has noted that Africa has not been able toincrease its share of global services trade over the past two decades. Nevertheless, theavailable case study and anecdotal evidence suggests that there are considerableopportunities for the expansion of trade in services to the global market and between Africancountries from both the growth of existing flows and from increasing the range of sectors inwhich trade in services takes place.19

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Trade in services can improve not only the performance of the service sector but alsothe whole economy (Arnold et al., 2007 Francois et al., 2007). Despite the increasingimportance of services trade in global economy, there has been limited research on servicetrade (Kaur, 2011). Unfortunately, there is lack of awareness in many developing countriesincluding Nigeria about the key role of services trade in development, and this leads to a lackof coherence in their policies. Lack of coherence deter potential investors and impede tradedespite the absence of impediments to market access. Research is yet to fill such gap.The objectives were to (a) examining performance of Nigerian exportable services (b)examine the relationship between different aspects of service trade and non oil export inNigeria, (c) assess the impact of capacity development on value of exportable services and(d) draw lessons for achieving higher service trade in Nigeria.Conceptual framework. The components of this exportable services include: travelwhich includes goods and services acquired from an economy by non-resident travellersduring visits shorter than one year; transport, which covers all transport services that involvecarriage of passengers, movement of goods, freight, rentals et c.; memo item, which coversall service categories except government services; and others, which include all servicesexcept transportation and travel.Stern (2002) noted that Leamer (1974) identifies three groups of explanatory variables,all of which he applies to trade in goods. The three groups of variables are resourcevariables, development variables and resistance variables. These same classifications wereused by Stern (2002) to distinguish between the possible determinants of trade in services inSouth Africa. The three categories of variable were namely: (a) Resource variables includeHuman capital, Physical capital, Natural resources, Technology; (b) Development variablessuch as or Economic development indicator in terms of GDP, size with respect to producerservices namely financial and insurance services, Export orientation which can be known asexport diversification as well as (d) Resistance variables such as protection, geography andlanguage.In another development, Kaur (2011) employed a gravity model which strength is onsize of GDP to examine determinants of export services of USA with its Asian Partners usingpanel data. However, the study included variables such as openness, corruption index andsimilarities among countries. While all variables were significant at Restricted/PooledEstimation, Fixed Effects Estimation and Random Effects Estimation; openness was notsignificant at Random Effects Estimation (in this study trade liberalisation started in 1986 andthe date frame was 1980-2010 thus liberalisation as a variable is skewed already andmodelling it in the data even as a dummy can lead to bias.In this study one of the challenges/constraints to choice of model and variables is thedata frame of the dependent variable (exportable service performance) which available datastarted from 1980. Thus only factors which variables can have good representationapproximating normal distribution were selected. Trade openness was excluded because italready a major component of the dependent variable.Thus it is specified that total exportable services is a function of some explanatoryvariables:TES =f(GEC, ACGSF , SGDP, Road, ACU, FDI , EPC)+ e......(1)Where:• Total Exportable services (TES) is measured in US dollars,• Government expenditure on services (GEC) is measured in naira,• ACGSF is volume of agricultural credit measured in naira,• Service GDP (SGDP) is the value of GDP produced by service sector,• Federal road net work is the length road networks in Km,• Average capacity utilization (ACU) is the average capacity utilization of electricity inmega watts,• FDI in trading and business services is measured in US Dollar,• Export Promotion Council (EPC) is a dummy variable used to denote structuralchange resulting in liberalization of administration of EPC; zero denotes prior toliberalization (before 1992) and one denotes liberalised era (after 1992).20

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)RESULTS AND DISCUSSIONSTrend in performance of exportable services in Nigeria. Table 2 is an illustration ofperformance of exportable services in Nigeria. From 1980 to 2010 the greater proportion ofexport service performance occurred from 2005 up wards. This is an indication that the postStructural Adjustment Period (SAP) period has experienced more exportable services.Table 2. Trend of Exportable services, Service GDP and non oil Export values in Nigeria*Year19801980-19841985-19891990-19941995-19992000-20042005-20092010TotalTotalservices(Millions $)1126.59(9.817478)679.0492(5.91746)341.0652(2.972155)887.505(7.734012)797.9906(6.953954)2563.81(22.34189)2003.152(17.45613)3076.19(26.80694)11475.35(100)Transportservices(Millions $)911.334(17.37722)454.831(8.672667)97.41716(1.85754)108.1693(2.06256)99.62676(1.899672)351.4354(6.701131)1260.255(24.0304)1961.35(37.3988)5244.419(100)Travelservices(Millions $)67.6688(5.260716)107.9065(8.38888)69.93458(5.436863)27.23927(2.117639)41.5225(3.228047)76.3108(5.932563)324.5456(25.23086)571.176(44.40443)1286.304(100)Otherservices(Millions $)147.587(2.984794)116.3116(2.352282)173.7136(3.513178)752.0964(15.21037)656.8414(13.28394)2136.064(43.19968)418.3514(8.460724)543.664(10.99504)4944.629(100)Memo item:Commercialservices(Millions $)1126.59(10.5829)679.0492(6.378816)341.0652(3.20388)887.505(8.336997)797.9906(7.496121)2563.81(24.08378)1636.051(15.36865)2613.321(24.54887)10645.38(100)service GDPin (MillionsN)4,748.06(0.346211)16864.058(1.229665)20125.904(1.467507)29480.126(2.149582)35271.3(2.571854)60959.552(4.444946)103985.967(7.582275)1100000(80.20796)1371435(100)VAL NONOil EXPORT(Millions N)554.4(0.079694)329.82(0.047411)1782.6(0.256244)4501(0.647007)25830(3.712995)71129.8284(10.22473)195160.074(28.05375)396,377.2(56.97818)695664.9(100)* Values in parenthesis are percentages. Computed from UNTACD DATA baseA closer look at the trend reveals that it is ‘u’ shaped curve indicating a decrease fromthe value in 1980 to 1989 and then increased till 2010. A similar trend characterised thevalues of transport and memo services, while the value for travel fluctuated from 1980 to1984 the similar trend holds from 1985 to 2010. Why the SAP era did witnessed a decreasein export of service trade? It may be that the era was a pro export of goods regimes withrestricted imports. This can reduce degree of openness which has been documented to havea positive relationship with export of service (World Bank, 2010). It is not surprising thatservice GDP trend mirrored the trend of performance of services because the total serviceGDP is a derived function which components can be a reflection export service performance.It is interesting to note that trend in value of non oil export tend to follow the trend in servicetrade. This has implication for this study.The value of non oil export decreased from N554.4 in 1980 to N329.82 million betweenN 1980 and 1984 but increased to 1782.6 million from 1985 to 1989. This could have beeninfluenced by adoption of Structural Adjustment Programme. It latter decreased to 4501million from 1990 to 1994 but increased to N25830 million from 1995 to 19999 and increasedto N71129.8284 million between 2000 and 2004, further increased to N195160.074 between2005 and 2009, and to N396,377.2 million in 2010. The consecutive increases in export ofnon oil products can be attributed to the changes made in the Nigeria Export promotionCouncil (NEPC) to improve its performance especially in dealing with the private sector. TheNEPC Amendment Decree No.64 of 1992 was promulgated to enhance the performance ofthe Council by minimizing bureaucratic bottlenecks and increasing its autonomy in dealingwith members of the organised private sector.21

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)The apparent synergy between service trade and non oil export was tested usingPearson correlation coefficient and significant relationships were recorded. It implied thatfactors which affect export service will likely affect non oil export.Table 3. Correlates of exportable services with non oil export in NigeriaVariable 1 Variable 2PearsonCorrelationP value Significant levelValue of Total services Value of non oil export .707 ** 0.000 0.01 level (2-tailed).Value of Transport Value of non oil export .807 ** 0.000 0.01 level (2-tailed).Value of Travel Value of non oil export .858 ** 0.000 0.01 level (2-tailed).Value of Other services Value of non oil export .130 .487 Not significant (2-tailed).Value of Memo item:Commercial servicesValue of non oil export .598 ** .000 0.01 level (2-tailed)N=31.Source: Author’s computation.Selected variables that can affect exportable services performance. Besidesservice GDP other variables which can affect exportable services include governmenteconomic services capital expenditure, agricultural credit guarantee scheme fund (ACGSF)FDI in business and trade service, road network and average capacity utilization of non oilsector. The trends of these variables are presented in Table 4. Generally, the magnitudes ofvariables have been on the increase since the post SAP era.Table 4. Trend of Selected variables that can affect exportable services performanceYear19801980-19841985-19891990-19941995-19992000-20042005-20092010TotalGovernment ECONOMICSERVICES CAP EXP (N)5,981.1(57.79533)679.0492(6.561648)341.0652(3.295711)887.505(8.575955)797.9906(7.710978)2563.81(24.77408)2003.152(19.35644)3076.19(29.7252)10,348.76(100)ACGSF(N)30,945.0(714.1571)454.831(10.49671)97.41716(2.24822)108.1693(2.496361)99.62676(2.299213)351.4354(8.110522)1260.255(29.08451)1961.35(45.26457)4,333.08(100)FDI IN TRADEBusiness Services($)693.2(1.214744)1,568.3(29.35904)2,262.5(32.71425)2,112.2(8.332497)3,030.1(4.477462)4,929.6(7.649834)9,374.3(8.188725)33,095.3(9.276177)57,065.5(100)Road km8186.4(1.807037)19736.16(4.356489)11829.64(2.611232)30472.07(6.726296)31990.11(7.061382)33044.34(7.294089)27976.84(6.175508)297979.84(65.775)453,029.00(100)average capacityutilization of non oilsector70.1(21.02706)59.94(17.97948)40.74(12.22029)37.6(11.27842)31.83(9.547663)49.18(14.75193)54.092(16.22533)60(17.99748)333.38(100)Values in parenthesis are percentages.Source: Authors computation from CBN publications and UNCTAD data base.Modelling of Determinants of Export Service in Nigeria. In order to produce anacceptable result, normality test was conducted, the time series properties were examinedand cointegrating test was also conducted and finally dynamic approach was used toestimate the model. Almost all the variables in the model are normally distributed. Thisshowed that the emerging result will be acceptable, provided the Durbin Watson statistics iswithin acceptable limit.22

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Table 5. Normality TestVariable Chi-square value RemarksDLTota Export Service 3.9229 [0.1407] normally distributedDDLGOV. Expenditure on services 25.424 [0.0000] ** normally distributedDDLACGSF 6.5624 [0.0376]* normally distributedDDL service GDP 1.0185 [0.6009] Not normally distributedDDL Federal road net work in Km 33.378 [0.0000] ** normally distributedDDL average capacity utilization 15.212 [0.0005] ** normally distributedDDL % FDI in trading and business services 6.3422 [0.0420] * normally distributedExport Promotion Council 70.466 [0.0000] ** normally distributedD=1 st difference, DD=2 nd difference, L=LogSource: Authors computationThe time series properties were examined. The results showed that the variables werestationary within the limits of statistical procedures.Table 6. Unit Root TestVariableADF –ValueDLTotal Exportable services -3.9344* 1(0)DDLGOV. Expenditure on services -7.2927** 1(0)DDL ACGSF -6.9636**1(0)DDL Service GDP -7.3346** 1(0)DDL Federal road net work in Km -8.2251** 1(0)DDL Average capacity utilization -7.0652**1(0)DDL FDI in Trading and Business Services -10.597**1(0)D=1 st difference, DD=2 nd difference, L=LogCritical values: 5%=-3.712 ; 1%=-4.619; Constant and Trend includedThe variables were stationary at different level of differencing. Which is an indicationthat there is no long run relationship between them, also efforts were made to examine thesame issue using E-views and the result showed no cointegrating vectors (Table 7).Table 7. Unrestricted Cointegration Rank Test (Trace)HypothesizedTrace0.05EigenvalueNo. of CE(s)Statistic Critical ValueProb.** RemarksNone 0.671244 94522.75 NA 0.5000 Not significantAt most 1 0.484543 35287.50 NA 0.5000 Not significantTrace test indicates no cointegration at the 0.05 level* denotes rejection of the hypothesis at the 0.05 level**MacKinnon-Haug-Michelis (1999) p-valuesSource: Authors computationTable 8. Dynamic modelling of determinants of export service in Nigeria*Variable Coefficient Std.Error t-value t-prob PartRýConstant 0.15347 0.091812 1.672 0.1228 0.2026DDroad_k 1.7711e-005 7.0897e-006 2.498 0.0296 0.3620DDLGOVExp_1 0.38953 0.11217 3.473 0.0052 0.5230DDLACGSF 0.51568 0.25024 2.061 0.0638 0.2785DDL%FDI trade -0.21811 0.15950 -1.367 0.1966 0.1348exp_prom_co -0.034957 0.12757 -0.274 0.7891 0.0068DDL avecap -1.0020 0.84871 -1.181 0.2627 0.1125DDL service 1.6092 1.2008 1.340 0.2072 0.1404* Rý = 0.675835 F(7, 11) = 3.2762 [0.0388] å = 0.29422 DW = 1.97 RSS = 0.9522181351 for 8 variables and19 observations. Source: Authors’ computation23

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)The results of modelling the determinants of export service in Nigeria is acceptablebecause of its significant regression parameters (see table 5) such F-ratio (3.2762), lowvariance of the estimate (å = 0.29422), high coefficient of determination (0.675835) andacceptable Dobbin Watson statistics (1.97).The significant explanatory variables are road network, government (domestic) capitalexpenditure on services, volume of agricultural credit guarantee fund, proportion of FDIinflow in trade and business and domestic service GDP. Road network is essential to conveygoods and services as well as people from one place to another. Thus foreigners especiallytourists need to move from place to place within the country. The result indicated thatincreasing road network will lead to increase in greater service in Nigeria.Government (domestic) capital expenditure on services such as transport andcommunication, tourism and education positively influenced exportable services in Nigeria. Itis possible that Nigerian Government efforts to catch-up with trend in information andcommunication technology and provision of some basic tourist facilities can boost exportservice performance. Thus the result is an indication that increase in government capitalexpenditure on social and economic services will lead to higher export of service.Volume of agricultural credit guarantee fund was positively related to the service. Manyof Nigerian service include agricultural products. Besides agriculture is a source of foodmaterials for populace and even the travellers especially tourists. Thus increase ininvestment in agricultural credit has a positive relationship with exportable services.FDI inflow in trade and business as expected had positive relationship with exportableservices. Also domestic service GDP had positive relationship with the exportable services.The actual services rendered have the potential of attracting exportable services.Variable that were not significant are average capacity utilization of non oil sector andexport promotion council. Average capacity utilization of the non oil sector althoughsurprisingly not significant can increase exportable services. If the sector production is ofhigh quality, travellers will be willing to obtain such goods and services. The non significant ofthis available indicates that Nigerian non oil production sector need to improve in the qualityof their output to attract foreigners.The Nigerian Export Promotion Council (NEPC) was established through thepromulgation of the Nigerian Export Promotion Decree No. 26 of 1976 and formallyinaugurated in March, 1977. This act was amended by Decree No. 72 of 1979 and furtheramended by the Nigerian Export Promotion Decree No. 41 of 1988 and complimented by theExport (Incentives and Miscellaneous Provisions) Decree No. 18 of 1986. Furthermore, theNigerian Export Promotion Council Amendment Decree No. 64 of 1992 was promulgated toenhance the performance of the Council by minimizing bureaucratic bottlenecks andincreasing autonomy in dealing with members of the Organised Private sector. The Councilhas a governing Board drawn from both the Public and the Private sectors.Other variables which literature has shown can increase export performance includetrade openness as well as information and communication technology. World Bank (2010)noted that a range of empirical studies have shown that openness to trade in services, suchas telecommunications and finance, is associated with greater efficiency and faster growth.Also Harsh (2012) noted that preferential trade agreements especially multilateralagreements has can improve trade in services.CONCLUSIONNigeria needs to increase performance in exportable services because of its manyadvantages. Service trade especially export offers new opportunities for export diversificationwhich can be used to create new job opportunities, wealth and poverty reduction. It can offera way to sustain trade Liberalization and regulation of services sectors because improvedperformance in exportable services will motivate a country to invest more in the service subsector as well as its regulation for better economic and social gains. Exportable services aremechanisms for increasing competition in services as countries will thrive to increase theirexportable services. Greater efficiency and faster economic growth can be achieved by24

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)developing trade in services as the example from Kenya has shown. It can help to increaseeconomic partnership agreements among Africans and with other nations. African countriescan pursue to support coordinated trade and regulatory reform in services together withunilateral, regional and multilateral reforms. The tempo will can be sustained with servicetrade.The paper has examined the relationship between different aspects of service tradeand non oil export in Nigeria as well as assessed the impact of certain variables on value ofservice trade and highlighted the implications for improving service trade and non oil exportin Nigeria. For Nigeria to attract higher service trade it is recommended that she engagementin economic partnership agreements. This will enable her to increase her trading partners.Also, develop domestic capacity to attract FDI in trade and business services, increaseagricultural credit, domestic service GDP and road networks since they impact positively onvalue of exportable services which have strong correlation with export value of non oilproducts.REFERENCES[1] Arnold, J., Javorcik B. S., and Mattoo A. (2007), “The Productivity Effects of ServicesLiberalization: Evidence from the Czech Republic.” World Bank Policy ResearchWorking Paper No. 4730.[2] Brenton, P., Dihel, N., Hinkle, L. and Strychacz N. (2010), Africa’s Trade in Servicesand the Opportunities and Risks of Economic Partnership Agreements Africa TradePolicy Notes Note #6 September,http://siteresources.worldbank.org/INTAFRREGTOPTRADE/Resources/EPAPolicyNoteREVISED.pdf[3] CBN (2011), Central Bank of Nigeria Annual Report 2011 Abuja.[4] Deardorf, A. V. (1994), “Testing Trade Theories and Predicting Trade Flows,” inJones R.W and P. B. Kenen (eds.) (1994) Handbook of International Economics,Amsterdam, North Holland Publishing Co.[5] Francois, J., B. Hoekman and J. Woerz (2007), “Does Gravity Apply to Intangibles?Measuring Barriers to Trade in Services” Paper presented at the CEPII-OECDWorkshop Recent Developments in International Trade in Services, Paris, November.[6] Harsh H.G. (2012), Preferential and Regional Service Agreements: The way forwardfor developing countries? A paper presented at the 7 th TRAPCA annual conferenceheld at Arusha, Tanzania. 22 nd -23 Nov.[7] Kaur S. (2011), “Determinants of Export Services of USA with its Asian Partners: APanel Data Analysis” Eurasian Journal of Business and Economics Vol. 4 No 8 pp101-117.[8] Leamer, E.E. (1974), “The Commodity Composition of International Trade inManufactures: An Empirical Analysis”, Oxford Economic Papers Vol. 24: pp 350-374.[9] Permani, R. (2008), Trade in Services: How is Indonesia’s performance?http://indonesianlabourstudies.wordpress.com/2011/06/08/trade-in-services-how-isindonesias-performance/[10] Stern, M. (2002), Predicting South African Trade in Services Trade and IndustrialPolicy Strategies Annual Forum at Glenburn Lodge, Muldersdrifthttp://www.tips.org.za/iles/564.pdf[11] Saez, S. and Goswam, A. G. (2010), Uncovering Developing Countries’ Performancein Trade in Services. The world Bank Economic Premise, November, No.39http://www.worldbank/EP39 servicetrade.pdf[12] World Bank (2010) Africa’s Trade in Services and Economic Partnership Agreements.Report No.:55747-AFR, July 20, Africa Regionwww.AfricaTradeinServicesandEPAsNEW.pdf25

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)ANALYSIS OF FOOD SECURITY STATUS OF FARMING HOUSEHOLDSIN THE FOREST BELT OF THE CENTRAL REGION OF GHANAJohn K.M. Kuwornu, Demi M. Suleyman, Ditchfield P.K. Amegashie, ResearchersDepartment of Agricultural Economics and AgribusinessUniversity of Ghana, Legon-Accra, GhanaE-mail: jkuwornu@gmail.comABSTRACTThe study seeks to examine the Food Security Status of Farming Households in the ForestBelt of the Central Region of Ghana. A multistage sampling technique was used to select therespondents that were interviewed. In all 134 farming households were interviewed but 120were selected for analysis after removing the questionnaires which were not properlyadministered. The households were selected from eight communities in two districts. Foodconsumption data of 851 individuals in 120 households were used for the analysis. The studyreveals that the majority of the farming households (60%) were found to be food insecure.Further, the Binary Logit Model results reveal that an increase in household’s income, havingaccess to credit as well as increase in the quantity of own farm production improve the foodsecurity status of farming households in the Forest Belt of the Central Region of Ghana.However, holding all other factors constant, increases in non-working member of householdsworsens the food security status of farming households. Most of the food insecurity copingstrategies adopted by household’s are not severe and can only be used to avert the impactof food insecurity on a temporal basis. These results have policy implications for FoodSecurity Status of Farming Households in developing countries.KEYWORDSFarming Households; Food Security; Food Security Index; Logit Model; Forest Belt; Ghana.Various interventions have been made by governments in modernizing agriculture inAfrica which was previously characterized by sluggish growth, low factor productivity,declining terms of trade, and often linked to practices that degrade the environment (Salamaet al., 2010). Since the late 1970s to mid-1980s, many African countries including Ghanahave implemented macroeconomic policies, sectoral and institutional reforms aimed atensuring high and sustainable economic growth, food security and poverty reduction. Thoughin recent times some African countries have recorded some level of growth in the agriculturalsector, however, the sector’s growth remained insufficient to adequately address poverty,attain food security, and lead to sustained GDP growth on the continent (Dessy et al., 2006and World Bank, 2008). Food security and poverty reduction have been a major campaignissues across all political parties, yet provision of enough food to feed the entire populationhas eluded many governments.Food is the basic need and necessity of life that must be satisfied before any otherdevelopmental issue. Inadequate nutrition is considered as measure of poverty in manysocieties or synonymous to poverty (Datt et al., 2000). Helen (2002) noted, food securitymaintains political stability, and ensures peaceful coexistence among people while foodinsecurity results in poor health and reduced performance of both children and adult. Foodsecurity is therefore defined “as a situation when all people, at all times, have physical andeconomic access to sufficient, safe and nutritious food to meet their dietary needs and foodpreferences for a healthy and active life” (FAO, 1996).Ironically, farming households are the most affected in terms of food insecurity andpoverty in Africa especially the smallholder farming households though the rest of thepopulation depends on their production. According to Cruz (2010) and Valdés et al., (2010),majority (more than 80 per cent) of the smallholder farmers in the world are food insecureand depend on land as their primary source of livelihoods. Three out of every four poorpeople leave in rural areas and depend on agriculture either directly or indirectly for theirlivelihood (World Bank, 2008).26

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)In most part of the world and especially in the developing countries, concerns regardingfood security and its related issues are vital for poverty reduction. Attainment of food securityis core problem confronting farming households, especially women and rural populations dueto low productivity in staple crop production, seasonal variability in food supply as well asprice fluctuations. These problems facing farming households come about as a result ofoverreliance on rain-fed agriculture, none or inappropriate usage of chemical inputs as wellas inadequate improved varieties of crops and animal species. Food security of farminghouseholds is of serious concern if Ghana wants to consolidate her macroeconomic gainsbecause; farmers who are vulnerable to food and nutritional insecurity have limited capacityto respond to agricultural programmes.Despite the fact that Ghana made significant achievement towards meeting themillennium development goal one by halving poverty from approximately 51.7 per cent in1991-1992, to 28.5 per cent in 2005-2006 (Ghana Statistical Service (GSS), 2008); the depthof poverty has exacerbated and spread into urban areas (WFP, 2009). Farming householdswere recognized as most affected by poverty among all the economic activities with almosthalf of them (46%) falling below the poverty line (WFP, 2009). According to the statistics ofWorld Food Programme (2009), about 1.2 million people, representing 5% of the populationof Ghana are food insecure and 2 million people are vulnerable to become food insecure inan event of any natural or man-made shock.Recent surge in world food prices, changing climatic pattern resulting in global warmingas well as growing demand for arable land for cultivation of biofuel has worsen the foodsecurity situation in most part of the world especially developing countries and Ghana cannotbe exempted from these current development. Ghana is only self-sufficient in the productionof root and tubers, though production is erratic and fluctuates between scarcity, sufficiencyand glut depending on the quirks of weather, (Ministry of Food and Agriculture (MoFA),2007). Ghana has high deficit in the production of cereals, meat and fish even thoughKuwornu et al., (2011) noted that cereals are the most widely consumed food crop in Ghana.Ghana produces 51% of its cereal needs, 60% of fish requirement, 50% of meat andless than 30% of the raw materials needed for agro-based industries (MoFA, 2007). Ghanahad its fair share of global financial crisis which saw food prices soaring from 2006 in mostpart of the world. Food prices for rice, maize and other cereals increased in Ghana by 20 to30 percent between the last few months of 2007 and the beginning of 2008 (Wodon et al.,2008). As result of the food price increases; 18% of the population whose income is less thanthe costs of the minimum food basket have become more vulnerable and less resilient tofood insecurity (WFP, 2009).The growing demand for arable land for biofuel cultivation is a serious threat toensuring food security in Ghana since evidence in India and other part of the world indicatenegative signals. Recent increases in the prices of energy worldwide has resulted in massiveforeign investments in biofuel production and Ghana has been projected to be among thebiggest producers of Jatropha in Africa by 2015 (IFAD and FAO, 2010). According to the CIAWorld Fact Book, Ghana has 3.99 million hectares of arable land with 2.075 million hectaresunder permanent crops cultivation (www.foodsecurityghana.com).Out of the land under permanent cultivation (2.075 million ha) 769,000 hectares havebeen acquired by foreign companies for biofuel cultivation (www.foodsecurityghana.com).This means 37 percent of Ghana’s cropland has been grabbed for the plantation of jatrophaand in some cases, food crops have been cleared to plant jatropha, leaving farmers with noincome and no source of food.The changing climatic pattern and over reliance on rain-fed agriculture pose a seriousfood security challenge to Ghana. The United Nation estimate has projected that over thenext 20 years demand for food will exceed 50%. Ironically, Boko et al., (2007) revealed thatyields from Africa’s rain-fed farm production may decrease by 50% as result of changes inclimatic conditions by 2020.In the mist of this challenging statistics, the population of Ghana is growing at rate of2.5 percent (3.5% in Central Region) yet agricultural growth is fluctuating. Agricultural sectorof the economy recorded a decline in growth rate from 5.3 in 2010 to 0.8 in 2011 (GSS,27

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)2012). The various sub-sectors in the area of crops, and fishing have recorded decline withonly livestock appreciating marginally. For instance, the crop sub-sector which is keydeterminant of food security has declined for the three consecutive times from 10.2 in 2009,5.0 in 2010 to 3.7 in 2011(GSS, 2012). The growth rate of livestock also declined from 1.5 in2010 to -8.7 which could affect the protein intake of households (GSS, 2012).Available statistics indicate that economy of Ghana is doing well at the macro levelmaking Ghana to be regarded as one of the fastest growing economy in the world. Equallyworth noting are the following: high food prices, changing climatic patterns and growingdemand for land for biofuel cultivation in Ghana. These situations which have made itnecessary to examine the current food security status of farming households who are alreadytrapped in poverty and where vast arable land is used for biofuel (jatropha) cultivation in theForest belt of the Central Region of Ghana. Central Region is the fifth poorest regions inGhana. These developments coupled with the recent high food prices have seriousimplications on the food security status of the region making it one of the vulnerable regionsto food insecurity in Ghana. However, most of food security studies conducted in Ghana areconcentrated in the three northern regions considered the poorest. The few studies on theCentral Region examined the effects of biofuel cultivation on household food security. Muchhas not been done in analyzing the food security status of farming households who are themost food insecure population.Therefore, objectives of the study are threefold: First, to establish the food securitystatus of farming households in the forest belt of Central Region of Ghana; Second, todetermine the factors influencing food security status of farming households in the studyarea; Third, to identify and rank food insecurity coping strategies used by farming householdsin the study area.LITERATURE REVIEWThe term “food security” gained prominence after the World Food Conference in 1974and ever since has become households name and attracted so many definitions from variousorganizations and individual researchers. Food security is defined as “access by all people atall times to enough food for an active and healthy life” (World Bank, 1986 p.8). This definitionprovided a standard for further definitions and addresses the issues of availability,accessibility, as well as utilization of food for healthy living. The World Bank (1986) definitionwas subsequently augmented by FAO to include the nutritional value and foodpreferences.FAO, (1996) defined food security as a situation when all people, at all times,have physical and economic access to sufficient, safe and nutritious food to meet theirdietary needs and food preferences for a healthy and active life.The inclusion of “safe and nutritious” stresses food safety and nutritional compositionwhiles the addition of food preferences” changes the concept of food security from mereaccess to enough food, to access to the food preferred. However, the operational definitionfor food security by Ministry of Food and Agriculture in Ghana is “good quality nutritious foodhygienically packaged, attractively presented, available in sufficient quantities all year roundand located at the right place at affordable prices” (MoFA, 2007 PP.24).Food security wasformerly assumed as adequacy of food supply at the global and national levels until the mid1970’s. This view only takes into account food production oriented variables and overlookedthe multiple forces which come to play to affect access of food.Large amount of food at global level does not guarantee food security at national level.Furthermore, availability of enough food at national level does not necessarily ensurehousehold food security. For example UNDP (1992) noted that calorie supply at global levelin 1990 was over 110 percent compared to the total requirement. Yet in the same period,more than quarter of the world’s population was short of enough food (UNDP, 1992).When an individual or population lacks, or is potentially vulnerable due to the absenceof, one or more factors outlined in the above definition, then it suffers from, or is at risk ofbecoming food insecure. The inclusion of stability of food supply, and food and nutritionsafety in the definition of food security (MoFA, 2007; USAID, 2008) has added additional28

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)dimensions to food security. Jradet al., (2010), elaborated on five dimensions of food securityas food availability, food accessibility, food utilization, stability of food supply and food andnutrition safety.Food Availability refers to the physical presence of food which may come from ownproduction, purchases from internal market or import from overseas. Gregory et al., (2005)explained that food availability refers to the existence of food stocks for consumption.Food Access. Household food access is the ability to obtain sufficient food ofguaranteed quality and quantity to meet nutritional requirements of all household members.Here, the food should be at right place at the right time and people should have economicfreedom or purchasing power to buy adequate and nutritious food. Kuwornu et al., (2011),explained that food access is determined by physical and financial resources, as well as bysocial and political factors.Food Utilizatio. This refers to ingestion and digestion of adequate and quality food formaintenance of good health. This means proper biological use of food, requiring a diet thatcontains sufficient energy and essential nutrients, as well as knowledge of food storage,processing, basic nutrition and child care and illness management.Stability of Food Supply: This refers to the continuous supply of adequate food all yearround without shortages. In the mist of growing population, unfavourable climatic patternsand growing demand for biofuel use; constant supply of food will depend on improvedproductivity and availability of proper storage facilities. Means of distribution of food requiredimprovement through provision of motorable roads to food growing areas. The use of storagevan here will be a key element to prevent post-harvest losses to sustain the interest offarmers to grow more to feed the population.Food and Nutrition Safety. Food safety is part of a wide range of issues which gobeyond the avoidance of food-borne biological pathogens, chemical toxicants, and otherhazards (FAO, 2002). There is growing concern of consumers of developed countries aboutthe effects of the food they eat on their health. Consumers expect food not only to meet theirnutritional needs but also should be wholesome and tasty, and to be produced ethicallyrespecting the environment, animal health and welfare. This, however, is not a priority indeveloping countries where the major concerns are access and availability of a nutritious dietthroughout the year at relatively low costs (FAO, 2002). Developing countries are forced tooverlooked food safety due to high poverty and illiteracy rate.Food safety constitutes an effective platform for poverty alleviation, social andeconomic development, while opening and enlarging opportunities for trade. However,ensuring food safety comes with a cost, and excessive food safety requirements may imposeconstraints on production, storage and distribution systems, which may possibly result intrade barriers or impede competitiveness (FAO, 2002).National food security was used to describe whether a country had access to enoughfood to meet dietary energy requirements of her citizens. To some it connotes selfsufficiency,which means a country produces enough food to meet its population’s demand.But broadly, national food security measures the extent to which a country has the means tomake available to its people the food needed or demanded, irrespective of whether the foodis domestically produced or imported (Pinstrup-Andersen, 2009). Food insecurity is theabsence of food security and applies to a wide range of phenomena ranging from famine toperiodic hunger to uncertain food supply (Bokeloh et al., 2009). Food Insecurity is theinability of a household or individuals to meet their daily required food consumption levels inthe face of fluctuating production, food price and income (Moharjan and Chhetri, 2006). Foodinsecurity is therefore caused by various factors some of which are multifaceted.The major single factor responsible for food insecurity in developing and less developcountries has been the high poverty rate among the population. Poverty has becomeendemic in Africa and continues to resist efforts aimed at eradicating it (Arimah, 2004).According to Arimah (2004), fifteen (15) out of the world twenty (20) identified poorestcountries are in Africa. The poverty in Africa has been compounded by conflicts and civil war,political instability, droughts, high external debt and by the rapid rise and spread ofHIV/AIDS. UNDP (1997) defined poverty “as the result of the deprivation of basic capabilities,29

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)which leads to reduced life expectancy, health, participation, personal security,environmental degradation, as well as the absence of real opportunities to lead a valuablelife and valued life”.Several studies have found nutritional status measured through energy and proteinintake as one major indicator of poverty (Srinivasan, 1988; Datt and Jolliffe, 1999; Datt et al.,2000). Other criteria used to identify the poor in the society are household income (Sen,1976; Sadeghi et al., 2002), employment (Fofack, 2002), asset holdings (Grootaert, 1997;Geda et al., 2001) and food consumption expenditures (Greer and Thorbecke, 1986; Fofack,2002).Meeting the food needs of families in Sub-Saharan Africa remains a serious challenge.This challenge emerges due to widespread poverty and conflict (Misselhorn, 2005; Smith etal., 2000; Oldewage-Theron et al., 2006); drought, famine and other negative weatherpatterns exacerbated by global climate change (Rosenzweig et al., 2001); degradation anddeforestation (Baro and Deubel, 2006), increased food prices due to the growth in demandfor biofuels (Trostle, 2008) and low agricultural productivity (Haile, 2005). Combination ofthese factors restricts access to food for many in developing countries.Ghana has been fairly stable in terms of food security on national basis, although,some pockets of food insecurities situations have been recorded in some areas particularly inthe three northern regions. Africa has witnessed severe droughts in 1970, 1983 and 1984 inthe past four decades where between 24 to 30 countries were affected. However, the 1983and 1984 droughts were the most severe causing wide spread famine in Africa requiringmassive humanitarian food aid (Haile, 2005).Ghana was hardly affected by 1983 drought where acute food shortage was recordedand this saw people depending on all kinds of material for survival. Among the food consumeduring this period includes cocoyam comb, rhizome of bamboo, water leafs and unripebananas were substituted for plantain which under normal circumstances were not part ofGhanaian foodstuff. According to Ghana Statistical Service (2008), about 18.2% ofGhanaians who fall below the extreme poverty line are chronically food insecurity. Also about10.3% of those above the extreme poverty line but classified as poor are vulnerable to foodinsecurity depending on the whims of the weather (MOFA, 2010). However, most of the foodsecurity situations in Ghana are more cyclical in nature and are recorded in all the tenregions but Upper East Region, for example is the most vulnerable to transient foodinsecurity.Statistics available suggest that the prevalence rate of malnutrition among childrenbelow the age of five, and women of reproductive age is still high. It states, 22% of childrenare stunted or too short for their age, 7% of children are too thin for their height (WFP, 2009).The Government of Ghana through the Ministry of Food and Agriculture is embarkingon various interventions to revert the situation. Notable among the interventions are fertilizersubsidy which allow farmers to access fertilizer at reduced prices and also provision oflivestock to selected farmers to serve as out growers. The farmers then returned theoffspring of the livestock collected to be given to other farmers in order to expand thescheme. Though the interventions are commendable, they are faced with several challenges.For instance, the fertilizer subsidy comes too late, sometimes several months after farmershave planted their crops hence less effective on the crops.Selection of committed farmers has been a major setback to the livestock improvementprogramme. In most cases farmers selected are perceived to be aligned to particular politicalparties leading to over politicization of the selection processes. This results in distribution ofthe livestock to political cronies rather than committed and experienced farmers. This hasmade the programme less effective and not visible to many.METHODOLOGYGeneral background to the methodology. Several methods have been used byresearchers to establish food security status of households, but notable among them areCost-of-calorie approach and Food Security index approach. Oluyole et al., (2009), examined30

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)the food security status among cocoa farming households of Ondo State, Nigeria andemployed Cost-of Calorie (COC) function proposed by Greer and Thorbecke (1986). Thismethod was also used in similar studies (Ojogho, 2010; Adenegan and Adewusi, 2007). Thefunction is stated as:ln h = a + bC (1),where h denotes food expenditure; C denotes calorie consumption (Kcal). From the COC( a+bL)function, the Cost of minimum recommended energy level, Z was calculated as: Z = e ,where L denotes Recommended Daily Energy Level (Kcal); a is the intercept term; b =coefficient of the calorie consumption. Based on the estimation, a household whose averagecost of daily calorie consumption is equal to or more than Z is said to be food secure while ahousehold with average cost of daily calorie consumption lower than Z is considered foodinsecure. The surplus/shortfall was estimated using the function:P =m1∑G N jj=1Gjis expressed as:(2)( X i− L)/ L ,where P denotes Surplus/Shortfall, L denotes Recommended Daily Per Capita Requirement(2,450Kcal); G Calorie faced by household, X Per Capita Food Consumption Available tojhousehold and N denotes Number of households that are food secure (for surplus index) orfood insecure (for shortfall index).Babatunde et al., (2007) and Omotesho et al., (2010), examined the socio-economiccharacteristic of household in Kwara State, Nigeria , using food security index to determinethe food security status of each household based on the Recommended Daily Calorieapproach. This method (Food Security Index) was also used by several researchers (Khatri-Chhetri and Maharjan, 2006; Omotesho et al., 2006, Arene and Anyaeji, 2010). Householdwhose food security index is greater or equal to the Recommended Daily Calorie Intake wereregarded as food secure and those whose food security index is lower than therecommended Daily Calorie Intake (2260Kcal) were considered food insecure. The methodis outlined in details latter on in this section.Literature has also provided various models for determining factors influencing foodsecurity status of households and key among them as used by researchers are Tobit model(Etim and Solomon, 2010), Probit model (Oluyole et al., 2009) and Logit model as used byBabatunde et al.,(2007). However, the study used Logit model due to its simplicity in theinterpretations of the coefficients. The dependent variable in this case, food security status, isa binary variable which takes a value of one (1) for food secured household and zero (0) forfood insecure household. The cumulative logistic probability model was specified by Pindyckand Rubinfeld, (1981) as:1Pi= F(Zi)= 1+(3),−(α + ∑ βi x )1+eiwhere Piis the probability that an individual is being food secure giveniXi(the explanatoryvariables); and are parameters to be estimated. The log odds of the probability that anindividual is being food secure is given by:pilog( ) = Zi= α + β1 x1+ β2x2+ ....... + βkxk(4)1−pi31

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Various methods for testing ranking of an object have been identified from literatureand notable among them are Garrett’s ranking score techniques, Friedman’s two-wayanalysis of variance and Kendall’s coefficient of concordance. There is close relationbetween Friedman’s test and Kendall’s coefficient of concordance (Legendre, 2005). Theyaddress hypotheses concerning the same data and use Chi squarer test for testing.However, they differ in the formulation of their respective hypothesis. Whereas Friedman’stest focuses on the items being ranked, the hypothesis of Kendall’s test focuses on therankers themselves.Garrett’s ranking score techniques on the other hand uses average score of therankers and arrange them in either ascending or descending order. However, the limitation ofthis method is that it involves a number of steps and it does not test the level of agreementsbetween rankers. Kendall’s coefficient of concordance was employed by this study becausethe Kendall’s () provides the test of agreement of the rankers (respondents), among theirrankings which the Friedman’s and Garrett’s test lacks.Estimating Food Security Index. To establish food security status of farminghouseholds in the study area, the study constructed Food Security Index ( ) and determinedthe food security status of each household based on the food security line using theRecommended Daily Calorie Required approach as used by Babatunde et al., (2007).Households whose Daily Calorie Intake were equal or higher than Recommended DailyCalorie Required were considered food secure households and those whose Daily CalorieIntake were below the Recommended Daily Calorie Required were considered food insecurehouseholds. The Food Security Index is given as:YZ = iiR(5),where represents Food Security Index of i th household, is Actual Daily Calorie Intake ofi th households and R is the Recommended Daily Calorie Requirement of i th household. Toobtain Per Capita Daily Calorie Intake; daily calorie intake of each household was divided byits’ household size. Households’ Per Capita Daily Calorie Requirement was also obtained bydividing the households’ Daily Calorie Requirement by household size. Based on the foodsecurity index estimated, the study further estimated other indices such as food insecuritygap (FIG), headcount ratio (HCR) and Surplus Index (SI). Food Insecurity gap is given by:1n∑G iM i=1(6),where M represents the number of food insecure households and G i is the calorie intakedeficiency for the i th households. G i was further expanded in a form:Yi− RGi= ( ) (7),Rwhere Y and R are as defined previously (above). The headcount ratio (HCR) is given as:MN*100%(8),where N represents the number of households in the sample. The Surplus index (SI) isgiven by:32

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)1Mn∑I = 1R −Yi( )R(9)To determine the Daily Recommended Calorie Requirement or food needs of eachfarming household, the Ghana Statistical Service (GSS) and IFPRI (2000) standard of 2,900kcal was used.The households’ composition or daily food requirement (daily calorie requirement) wasestimated by first of all categorizing members of each household into different age groupsbased on the fact that different age groups have different calorie requirements. The dailyenergy (calorie) requirements of various compositions of the households were converted intoadult equivalent using the equivalent scales as shown in Table 1.Table 1. Recommended Daily Energy Intake and Equivalent ScaleAge Category (years) Average energy allowance per day Equivalent ScaleChildren ( 18) 2900 1.0Source: Ghana Statistical Service (2000).Total household composition or calorie requirement was obtained by multiplying thetotal number of adult in each households by the recommended calorie requirement of2,900kcal (i.e Total Number of adult*2900kcal). The total food requirements for children wereconverted to adult equivalent. This was done by multiplying the total number of childrenbelow the age of six (6) years in each household by Recommended Daily CalorieRequirement of 2900kcal and conversion factor of 0.4.The total number of children between the ages of 6 to 18 years in each household wasalso multiplied by Recommended Daily Calorie Requirement of 2,900kcal and a conversionfactor of 0.7 to obtain their adult equivalent. The total Daily Calorie Requirement for eachhousehold was obtained by summing up the requirement for the three age groups estimatedabove. The procedure was repeated for Recommended Daily Calorie Requirement of 2,260kcal (FAO Ghana).Households’ daily food consumption (Daily Calorie Intake) was obtained fromhousehold own food production and purchases to supplement own food production. The dataon actual food consumed (maize, rice, cassava, and plantain) by each household per weekwas obtained and converted into kilogram. The energy content of 1kg of each foodstuff(maize, cassava, rice and plantain) was obtained from literature as showed in table 2.Table 2. Cereal Equivalent Conversion RatiosFood Crop Calorie/kg Milling ratio Maize equivalent ratioMaize 3,590 0.85 1.00Rice 3,640 0.65 0.92Cassava 1,490 0.40Plantain 1,350Source: Okigbo (1991) and Latham, (1969) [Compiled by Tayie and Lartey (2000). Nutrition and Food ScienceDepartment, University of Ghana, Legon]The total quantity of each food (in kilogram) consumed was then multiplied by theenergy content (e.g. total kilogram of cassava consumed per week *1,490kcal = total kcal ofcassava consumed). This procedure was repeated for rice and plantain. However, due toprocessing and grinding losses, the quantity of maize consumed per week was multiplied bythe energy content (3950kcal) and the milling ratio of 0.85. The total kilocalories of maize,cassava, rice and plantain consumed by each household were summed up and divided by 7to obtain Actual Daily Calorie Intake.33

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Sample Size and Sampling Techniques. A multistage sampling technique was usedto select the respondents that were interviewed. The first stage involves the selection ofdistricts and municipalities from which respondents interviewed were selected. This wasdone using purposive sampling techniques where the districts and municipalities weregrouped into forest and coastal areas. It was followed by writing the names of all the districtsand municipalities in the forest areas on pieces of paper and randomly picking two districts ormunicipalities.The second stage involved selection of communities and villages visited usingpurposive and simple random sampling. This was achieved with the help of the districts’MoFA directorates which grouped the communities into those which have functional FarmerBased Organization (FBO), extension contacts and those who do not have to give fairrepresentation of different groups of farmers. Two communities each were selected fromcommunities with functional FBO and extension contacts and those communities withoutFBO and extension contacts. The third and final stage was the selection of the farminghouseholds that were interviewed. Respondents were selected using simple randomsampling, and data regarding their socio-economic characteristics, food availability, foodaccessibility and access to institutions were obtained for analysis.In all 134 farming households were interviewed but 120 were selected for analysis afterremoving the questionnaires which were not properly administered. The households wereselected from Two (2) districts and Eight (8) communities. In summary, food consumptiondata of 851 individuals in 120 households were used for the analysis.Determining Factors Influencing Food Security Status of Farming Households.Logit regression model was used to determine factors influencing food security status offarming households in the forest parts of Central Region of Ghana and the variables includedin the model are as follows:Table 3. Variables Influencing Food Security Status of the Farming HouseholdsVariable Descriptions MeasurementA prioriExpectationagehh Age of household head Years + / -genderhh Gender of household head Male = 1, Female = 0 +farmsize Farm size Hectares +off-farm Engagement of off-farm activities Yes = 1, No = 0 + / -annincom Annual income GHS +Primary = 1edu_LevLevel of EducationJSS = 2SSCE/WASSE=3+Tertiary = 4aces2crdit Access to credit Yes = 1, No = 0 +lnownership Land ownership Yes = 1, N0 = 0 +ownprod Quantity of Own production Kg +dep Dependency ratio Ratio -Agesquared Age Squared Number +/-Age of household head. The age of household head is expected to impact on his or herlabour supply for food production (Babatunde et al., 2007). Young and energetic householdheads are expected to cultivate larger farms compared to the older and weaker householdhead. It also determines the ability to seek and obtain off-farm jobs and income whichyounger household heads can do better. Arene and Anyaeji (2010), on the other hand, foundolder household heads to be more food secure than the younger household heads. Hencethe expected effects of age of household head on food security could either be positive ornegative.Gender of Household Head. Gender of household head looks at the role played by theindividuals in providing households’ needs including acquisition of food. Household head cantherefore be male or female. Therefore, gender of household head was coded as: 1 formales and 0 for females. Female headed households have higher dependency ratios whichhinders household capacity to allocate labour to on-farm or other income-generating34

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)activities. Also female headed household tend to be older and have fewer years of educationthan male heads of household (FAO, 2012). The expected effect of this variable is positive.Farm Size. Farm size is the total area of land cultivated to food and cash crop byhouseholds, measured in hectares. Positive relationship has been established between farmsize and improvement in households’ income and food security (Jayne et al., 2005;Deininger, 2003). The larger the farm size of the household, the higher the expected level offood production. It is, therefore, expected of a household with a larger farm size to be morefood secure than a household with a smaller farm size, all things being equal. Hence theexpected effect on food security is positive.Engagement in off-Farm Activity. Off-farm activity is an additional work engaged in byhousehold aside farming to supplement household income. Level of off-farm activity caninfluence households’ food security but this can either be positive or negative depending onthe level and gains from the activity (Babatunde et al., 2007). This is because engagement inan activity can bring in money thereby corroborating the food security situation of thehousehold. On the other hand, if farmers spend more of their time on off-farm activities at theexpense of working on their farm and particularly if the wage they earn does notcommensurate with the forgone farm income, their food security situation could be worsened.Therefore, the expected effect on food security could be positive or negative.Total Annual Income of Household. This refers to the sum of earnings of householdfrom both off-farm and on-farm sources (Babatunde el al., 2007). According to Arene andAnyaeji (2010), the more household head engage in gainful employment, the higher he/sheearns income and the greater the chances of being food secure. The income is expected toincrease household’s food production and access to more quantity and quality food. Theexpected effect on food security is, therefore, positive.Level of Educational of Household Head. Education is a social capital which isexpected to have positive influence on household food security. According to Shaikh (2007),the educated individuals have capacity to process and apply the information passed on tothem. Lower educational levels impede access to better job opportunities in the labourmarket, and hamper more profitable entrepreneurship (FAO, 2012). An increase in femaleeducation not only increase their returns but also has the potential of reducing the fertilitylevel of women, improve their productivity as well as contribute positively to the nationalgrowth ( Herzeet al., 1991).The expected effect of this variable on food security is positive.Access to Credit. This is the ability of household to obtain credit both in cash and kindfor either consumption or to support production. Consumption credit increases household’sincome on the short term basis and could increase the consumption basket of households(Babantunde et al., 2007). Production credit, on the other hand, when obtained on time couldincrease chances of household to acquire productive resources (seeds, fertilizers, pesticidesand others) which will boost production and improve food situation in the house. Access tocredit is therefore dummied as one (1) for households that obtained credit in the last yearcropping season and 0 otherwise. The expected effect of access to credit on food security ispositive.Land Ownership. A farmer can own land either through inheritance or outrightpurchase. Jayne at al., (2005) noted that access to land is key strategy to reduce ruralpoverty and ensure food security. Evidence available showed that incident of food insecurityand poverty tends to be more severe in landless rural poor (Kyaw, 2009). Access to credit istherefore dummied as one (1) for households that obtained credit in the last year croppingseason and 0 otherwise. The expected effect of access to credit on food security is positive.Quantity of Own farm Production. This is the total quantity of food and cash cropproduced by households from their own farm (measured in kilogram). Cash crops areincluded based on the fact that they can be sold and money realised from their sale could beused to purchase food for household consumption (Babaundeet al., 2007). The quantity ofhousehold own production increases the probability of food security (Quinoo, 2010; 2009;Pappoe, 2011). Therefore, the expected effect of this variable on food security is positive.Farming Experience. This refers to the number of years household head has engagedin farming. All things being equal, an experienced household head is expected to have moreinsight and ability to diversify his or her production to minimize risk of food shortage. An35

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)experienced farmer is also expected to have adequate knowledge in pest and diseasemanagement as well as good knowledge of weather. Research findings revealed a positiverelationship between farming experience and food security status (e.g., Felekeet al., 2003,Oluyoleet al., 2009). The expected effect of this variable on food security is, therefore,positive.Dependency Ratio. This was measured as total household size divided by the numberof individuals working to support the household. Owing to the scarcity of resources, anincrease in household size especially the non-working members put pressure onconsumption than production (Felekeet al.,2003). An increase in the number of non-workingmember of household or dependency ratio increases the food insecurity level of household(Ojogbo, 2010). The expected effect of this variable on food security is negative.Age Squared. This was obtained by multiplying the age of household head by itself.The inclusion of this variable is as result of nonlinear relationship between age and foodsecurity. As age increases, the food security increases but at decreasing rate. Also as ageincreases, other factors such as farm experience may influence the food security status ofhouseholds. Negative correlation between age squared and food security was revealed inthe findings of Adenegan and Adewusi (2007).The positive effect of age and a negativeeffect of age squared imply as people get older the effect of age is lessoned. A positive effectof age and a positive effect of age squared means that as people get older the effect is agestronger. Therefore, expected effected of age is either positive or negative.EMPIRICAL RESULTSSocioeconomic Characteristics of Households. The socioeconomic characteristicsof households presented in this study are: age, gender, marital status and household size.The data shows wide range of age groups, however close to half (49.2%) of the respondentsare above the age of 50 years. The data also revealed low (16.2%) representation of theyouth. Analysis of gender distribution of the households also revealed majority (85.8%) of thehousehold heads are males and female headed households being in the minority (14.2%).The data revealed a wide range of household size (i.e., 16), with minimum household size of2 and maximum of 18. The mean household size (7.06) was higher than the national ofaverage 4 as stated GSS (2008).Food Security Status of Farming Households in Forest Communities of CentralRegion of Ghana. Food security status of farming households in the study area is presentedin the Table 4. The result indicates majority (60%) of farming households were food insecure.This implies that the study area is potentially food insecure since the number of foodinsecure households (72) is greater than food secure households (48). The mean foodsecurity index of food secure households was found to be 1.42 and food insecurehouseholds were also found to be 0.69. The food insecurity gap implies that on average thefood insecure households consumed 31% less than their daily calorie requirements whilstfood secure households consumed 41% in excess of their daily calorie requirements. Percapital daily calorie requirement was estimated to be 2,275kcal which is lower than thenational weighted average of 2,849 kcal (World Food Program, 2009; www.fao.org).Table 4. Food Security Status of Farming Households in Forest Parts of Central RegionItem DescriptionFood SecureFood InsecurePercentage of Households 40 60Number of Households 48 72Mean (FSI) 1.42 0.69Standard deviation 0.38 0.18Food Insecurity gap/Surplus Index 0.41 0.31Per capita Daily Calorie Allowable 2,275.1336

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Categorization of Farmers Based on the Major Growing Crops and their FoodSecurity Indices. Farming households in the study area were grouped based on the majortypes of crops they cultivated against their food security indices and presented in Table 5.Farming households were categorized as food crop, Tree crops and vegetable crop farminghouseholds. Though most of the farming households grow one food crop or the other, thecategorization was based on the major source of income and food.Table 5. Categorization of Farmers Based Major Growing Crop and Food Security IndicesCategorization of farmers based on the food security indices and major crops grownFarmer GroupsFood Insecurity Indices of Farming Households0 - 0.25 0.26 – 0.50 0.51 - 0.75 0.76 - 0.99 ≥1TotalFood CropsFreq 0 2 4 5 5 N=16% 0.0 1.7 3.3 4.2 4.2 13.3%Tree CropsFreq 1 6 29 24 43 N=103% 0.85 5.5 24.2 20.0 35.8 85.8%VegetablesFreq 0 0 1 0 0 N=1% 0.0 0.0 0.8 0.0% 0.8 0.8%TotalFreq N=1 N=8 N=34 N=29 N=48 N=120% 0.8% 6.7% 28.3% 24.2% 40.0% 100%Table 5 shows that, majority of the farming households (85.8%) were tree crop farmerswhilst food crop and vegetable farmers constitute 13. 4% and 0.8%, respectively. Among thefood crop farmers, majority of them (68.75%) were food insecure and 37.5% consumed 50%less their daily calorie requirements. The result also revealed 58.3% of tree crop farmerswere food insecure and few (6.8%) consumed 50% less their daily calorie requirements. Theresult also showed very low (0.8%) representation of vegetable farmers which was foodinsecure.Factors Influencing Food Security Status of Farming Households. To determinefactors influencing food security status of farming households, socioeconomic characteristicsof households were regressed on their food security indices and result presented in Table 6.The result showed four variables: total annual income, access to credit, dependency ratioand own food production as relevant in significantly influencing food security status offarming households in the study area. With the exception of dependency ratio which showednegative relationship with food security all the other variables had positive relationship withfood security.Table 6. Marginal Effects of Logit Regression Results of Factors Influencing Food SecurityStatus Farming Households*Variables Marginal effect Standard Error P-valuesAge of household head -0.0594 0.0365 0.104Farm size -0.0028 0.0521 0.957Engagement of off -farm activity -0.1418 0.1688 0.401Total annual income of household 0.0001*** 0.00004 0.002Level of education of household head 0.1058 0.0799 0.185Access to credit 0.4785*** 0.1445 0.001Land ownership 0.1200 0.1514 0.428Dependency ratio -0.1483*** 0.0529 0.005Gender -0.2879 0.1799 0.109Own food production 0.0257*** 0.0087 0.003Agesquared 0.0005 0.0003 0.132Source: Field Survey *** = significant @1%Number of Obs = 120 Wald Chi2 (11) = 29.66Prob> Chi2 = 0.0018 Pseudo R2 = 0.4917Log pseudo likelihood = -41.0503132* The marginal effects are used here (instead of the coefficients) as they denote the marginal changes of thedependent variables as a result of changes in the respective explanatory variables. Please note that the signs ofthe marginal effects are the same as those of the respective coefficients of the explanatory variables.37

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Total annual Household Income. This variable has positive influence on food securitystatus of farming households. The variable has the expected sign and is significant. Thisindicates the higher the income of households, the greater the probability of being foodsecure. This could be expected because, increased in income all things being equal, meansincreased access to food. The value of the marginal effect implies if households’ incomeincrease by One Ghana cedis (GHS 1.00), the probability of the household being food securewill be increased by 0.0001, holding all other things constant, though negligible. This result isconsistent with Babatunde et al., (2007); Adenega and Adewusi (2007); Arene and Anyaeji(2010) who revealed positive and significant relationship between household income andfood security.Access to Credit. This variable was found to have positive influence on food securitystatus of households and met the a priori expectations. This could be expected since creditserves as consumption smoothing mechanism which gives households temporal reliefagainst the effects of food insecurity. The result of the study implies that household thatreceived credit had greater chances of being food secure compared to those who did nothave credit, all things being equal. The value of the marginal effects indicates when ahousehold obtains credit; the probability of that household to be food secure will beincreased by 0.4785. The result of the study is in line with the findings of Pappoe (2011), whofound that access to credit improves the food security status of farming households amongbiofuel producers in the Central Region of Ghana.Household’s Dependency Ratio. Dependency ratio of the household was found to besignificant and had inverse relationship with food security. This is expected because anadditional increase in the number of non-working member of household increases the foodrequirement of households thereby reducing the probability of food security. The marginaleffect of an additional increase in the number of non-working member of a householddecreases the probability of the household being food secure by 0.1483. This finding agreeswith Ojogho (2010), who revealed that dependency ratio increases food insecurity levelamong arable farmers in Edo state of Nigeria. Etim and Patrick (2010) also found thatdependency ratio increases the probability of households being poor which invariablyreduces their food security status. Orewa and Iyanbe (2010) also noted that an increase inthe number of non-working member of household reduces the daily food calorie intake ofrural and low-income urban households in Nigeria.Quantity of Own Production. Quantity of own production was therefore found to bepositive and significant. The positive sign of the variable indicates that the higher the outputlevels of household, the greater the likelihood of food security. The marginal effect of unit(1kg) increase in quantity of household own production increases the probability of foodsecurity by 0.0257. The result of this study is in line with earlier findings of Quinoo (2010),and Pappoe (2010) in the Central Region of Ghana. The finding of the study is alsoconsistent with Babatunde et al., (2007), who obtained the same result among the ruralfarming households in the North -Central Nigeria. Further, Ojogho (2010) noted that loweroutput level of the household increases food insecurity status of arable farmers of Edo State,Nigeria.Prevailing Food Insecurity Coping Strategies Used by Farming Households.The prevailing food insecurity coping strategies adopted by farming households in the studyarea to mitigate effects of food insecurity are presented in Table 7. The result from the tablerevealed the most widely used strategies by the farming households in the study area inorder of importance are eating less preferred food, limiting size of the food consumed,skipping meal within a day and maternal buffering. This implies when households are facedwith food shortage, the immediate strategy they adopt is to eat less preferred and lessexpensive food such as ‘gari’ and possibly ‘kokonte’. As the food insecurity continues otherstrategies which are more severe are used such as reduce the quantity of food consume,skipping meal and parents (usually the mother) forgo their food to enable children haveenough.38

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Table 7. Food Insecurity Coping Strategies Adopted by HouseholdsFood Insecurity Coping Strategies Mean Rank RankEating less preferred food 1.80 1 stLimiting size of food consumed 3.51 2 ndSkipping meal within a day 4.57 3 rdMaternal buffering 5.66 4 thBorrowing money to buy food 5.75 5 thBorrowing of food 5.85 6 thCollecting food from the wild or garden 5.90 7 thSold Asset to buy food 5.94 8 thTravel to search for jobs 6.03 9 thKendall’s W =0.557; X 2 =534.938; df = 8 Sig 0.000Other strategies adopted by households though with difficulties include borrowedmoney to buy food and borrowed food. These strategies were seen by households as loss ofpride and those who practiced these strategies borrowed food or borrowed money fromrelatives and not from a neighbour for fear of being insulted when misunderstanding breakup. Although forest areas are endowed with wild fruit and one would have expectedhousehold to resort to searching for wild fruit when faced with food shortage, it was one ofthe least practiced strategy by the farmers.CONCLUSIONS AND RECOMMENDATIONSThe study seeks to examine the Food Security Status of Farming Households in theForest Belt of the Central Region of Ghana. A multistage sampling technique was used toselect the respondents that were interviewed. In all 134 farming households were interviewedbut 120 were selected for analysis after removing the questionnaires which were not properlyadministered. The households were selected from eight communities in two districts. Foodconsumption data of 851 individuals in 120 households were used for the analysis. The studyreveals that the majority of the farming households (60%) were found to be food insecure.Further, the Binary Logit Model results reveal that an increase in household’s income, havingaccess to credit as well as increase in the quantity of own farm production improve the foodsecurity status of farming households in the Forest Belt of the Central Region of Ghana.However, holding all other factors constant, increases in non-working member of householdsworsens the food security status of farming households. Most of the food insecurity copingstrategies adopted by household’s are not severe and can only be used to avert the impactof food insecurity on a temporal basis.The study provides the following policy recommendations. The government shouldbroaden the pro-poor policies such as School Feeding, and the Livelihood EmpowermentAgainst Poverty intervention programmes to cover large poor households. Next, educationand sensitization of families regarding family planning should be intensified since higherdependency ratio worsens the food security status of farming households. Families shouldbe educated on the need to give birth to number of children they can comfortably cater for.Provision of input such as weedicides, fertilizer, improves seeds and others willmotivate farming households and also increase their productivity, especially those in thecoastal areas would be a step in the right direction. This will increase the volume of foodproduction. This could be done through selling of input at subsidized rate to farmers on creditby MOFA and allow farmers pay in kind with their farm produce. This will serve as source ofmarket to farmers and also contribute to the performance of the government’s buffer stockprogram.Food insecurity coping strategies adopted by the farming households have short termeffect. Therefore, there is the increase the volume of food production as well as improve onaccess to income generating activities that are more sustainable. The research wasconducted for the Forest Belt of the Central Region of Ghana; hence the results may not berepresentative of the food security status of farming households across the country.Therefore, extending this study to cover other Regions of the country is a useful avenue forfuture research.39

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Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)∆ ∆ , , ~, (1)where y and x are price series of a marketing chain. If y and x are integrated of the order oneprocess, I( 1) that are cointegrated then there exist an equilibrium relationship between y andx which is defined by an error correction term. The long run dynamics captured by the errorcorrection term are implicitly symmetric. In order to allow for asymmetric adjustments, theerror correction term can be segmented as follows: , 0 (2) , 0 (3) The resulting asymmetric model is defined as:∆ ∆ , , ~0, (4)This specification is referred to as the Granger and Lee asymmetric model or thestandard asymmetric error correction model. Asymmetry is incorporated by allowing thespeed of adjustment to differ for the positive and negative components of the ErrorCorrection Term (ECT) since the long run relationship captured by the ECT was implicitly+symmetric. Symmetry in equation (4) is tested by determining whether the coefficients (−+ −andβ 2) are identical (that is H0: β2= β2).Von Cramon-Taubadel and Loy (1996) applied an alternative but a more complexmethod to test for price asymmetry. In this methodology, asymmetries specified affects thedirect impact of price increases and decreases as well as adjustments to the equilibriumlevel. Where ∆ x +tand ∆ x −tare the positive and negative changes in xtand the remainingvariables are defined as in equation (4 ).∆ ∆ ∆ , , ~0, (5)A formal test of the asymmetry hypothesis using the above equation is:+ −+ −H0: β1= β1andβ 2= β2. In this case, a joint F-test can be used to determinesymmetry or asymmetry of the price transmission process. The conventional Houck’s modelcommonly used to model asymmetric price transmission can be specified as:β 2∆ = β ∆ + β ∆ + εy + t 1x+ − t 1x−tε σ (6),2~ N(0, ε)wheret∆ x +tand x −t∆ are the positive and negative changes in xt. The explanatory variablex is generated as independent draws from normal distribution with a constant mean and avariance of one. Asymmetry is incorporated by permitting differing speeds of adjustments forthe coefficients of ∆ x +tand ∆ x −tin equation (6) and ε is generated as i.i.d. draws from thestandard normal distribution with a sample size n. ∆ y tcan be obtained using the values forbeta, positive and negative changes in xt(i.e. ∆ x +tand ∆ x −t) and the error term as specifiedin equation 6. Symmetric price transmission is tested by determining whether the coefficients( β + 1and β − + −1) are identical (i.e. H0 : β1= β1).Model Selection. Model selection refers to the problem of choosing the mostappropriate and concise model to express a given data in an abstract fashion. It hasattracted the attention of many researchers for several decades since the introduction of44

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Akaike’s Information Criterion (AIC), which had a fundamental effect on model selectionresearch. Subsequently,several model selection criteria have been introduced. It comes asno surprise that many of those model selection techniques have been employed in manyasymmetric price transmission empirical applications. Most of these model selection methodsadjust for a variation in the number of parameters among models, essentially penalizingmodels with additional parameters. They include the Akaike information criterion (AIC;Akaike, 1973); Consistent Akaike information criterion (CAIC; Bozdogan, 1987); theBayesian information criterion (BIC; Schwarz, 1978); and the Draper’s Information Criteria(DIC; Draper, 1995). The model selection methods are defined as follows:A I C = − 2 l o g ( L ) + 2 p (7),C A IC = − 2 lo g ( L ) + p[(lo g n ) + 1] (8),B I C = − 2 lo g ( L ) + p lo g ( n ) (9),D I C = − 2 l o g ( L ) + p l o g ( n / 2 π ) (10),where L refers to the likelihood under the fitted model, p is the number of parameters in themodel and n is the sample size. Models that minimize the AIC, BIC, CAIC and DIC areselected. AIC differs from CAIC, BIC and DIC in the second term which now takes intoaccount sample size n. Thus CAIC, BIC and DIC allows for asymptotic consistency.Data Generating Process. The study uses simulated data. This study draws from theexperimental design of Acquah (2010) and specifies the Standard ECM (SECM) andcomplex Von Cramon-Taubadel and Loy ECM (CECM) data generating process as followsand with an error variance of 1.∆ y = 0.50∆x − 0.25( y − x) − 0.75( y − x)+ ε+ −t t t −1 t −1∆ y = 0.95∆ x + 0.20∆x − 0.25( y − x) − 0.75( y − x)+ ε+ + + −t t t t −1 t −1The variables in the model remain as defined previously under measuring asymmetricprice transmission.RESULTS AND DISCUSSIONThis section evaluates the importance of model complexity and the relativeperformance of the model selection criteria in recovering the true data generating process bysimulating the effect of sample size and complexity ( number of asymmetric adjustmentparameters) on model selection. Subsequently, the competing models that differ incomplexity are fitted to the simulated data and their ability to recover the true modelmeasured (i.e. Model Recovery Rates). The model recovery rates define the percentage ofsamples in which each competing model provides a better model fit than the other competingmodels. In this study, all recovery rates are derived using 1000 Monte Carlo simulations.Impliedly, the amount of samples in which each model fits better than the other competingmodels is measured out of the 1000 samples and expressed as a percentage. In this context,the values derived from each model by selection methods are derived as the arithmetic meanbased on 1000 samples. For the purpose of brevity, the standard asymmetric error correctionmodel, the complex asymmetric error correction model and the Houck’s model are denotedby SECM, CECM and HKD respectively.In order to simulate the effects of sample size on model selection, this study considersthree sample sizes ranging from small to large corresponding to 50, 150 and 500. Using anerror size of 1, data is generated from the Standard Error Correction Model (SECM) specifiedin equation (4). The results of the Monte Carlo simulations comparing the performance of themodel selection methods are displayed below in Table 1.45

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Generally, inspection of the recovery rates for the different model selection criteriaillustrates the extent to which the true model (SECM) is recovered by each selection criteriaacross the different sample sizes. In the small sample size of 50, the true model wasrecovered at least 78.2 percent across the model selection criteria in the top part of the Table1. At a sample size of 500, the model selection methods recovered 84.6 to 99.3 percent ofthe true model.Table 1. Relative Performance of Model Selection Methods across Sample Size*Sample Size50150500* Based on 1000 Replications.Model FittedMethods CECM HKD SECM(DGP)AIC 118 (17%) 126 ( 4.8% ) 117 (78.2%)BIC 126 (6.3%) 131 (11.9%) 124 (81.8%)CAIC 129 (3.1%) 131 (16.7%) 125 (80.2%)DIC 129 (4.6%) 132 (14.1%) 126 (81.3%)AIC 402 (18.3%) 435 (0%) 401 (81.7%)BIC 416 (2.4%) 444 (0.1%) 412 (97.5%)CAIC 417 (0.8%) 443 (0.1%) 413 (99.1%)DIC 419 (1.5%) 445 (0.1%) 414 (98.4%)AIC 1396 (15.4%) 1517 (0%) 1395 (84.6%)BIC 1417 (1.6%) 1529 (0%) 1411 (98.4%)CAIC 1416 (0.7%) 1527 (0%) 1410 (99.3%)DIC 1419 (1%) 1531 (0%) 1413 (99%)In comparison with the small sample recovery rates, model recoveries of the truemodel improved significantly when the sample size was large. Despite differences inperformance among the model selection criteria, trends holding across the different criteriawere evident in the simulation results. In effect, the performance of the model selectionmethods to select the true model (i.e. recovery rates of SECM) generally increased withincreases in sample size from 50 to 500. However, two distinct patterns can also beobserved in the Table 1. First, the selection criteria that account for sample size (CAIC, BICand DIC) performed similarly to one another and their recovery rates varied strongly as afunction of sample size. Second, although AIC performed well in the small samples, it did notmake substantial gains in recovery rates as the sample size increased. This is not surprisinggiven that AIC does not account for sample size. Additionally, AIC exhibit a slight tendency toselect complex models across the various sample size studied, though the true datagenerating process was the standard error correction model.The observed trends are consistent with previous studies on model selection. Ichikawa(1988)’s simulation results in a factor analysis indicated that the ability of AIC to select a truemodel rapidly increased with sample size but at larger sample sizes it continued to exhibit aslight tendency to select complex models. Similarly, Markon and Krueger (2004) reviewedexisting work on factor analysis and noted that AIC performs relatively well in small samples,but is inconsistent and does not improve in performance in large samples whilst BIC incontrast appears to perform relatively poorly in small samples, but is consistent and improvesin performance with sample size. Fishler et al. (2002) also investigated the performance ofBIC in a factor analysis and their results suggest that BIC performs poorly at small samplesizes, but improves with increasing sample size to eventually choose the correct model withperfect probability.The results further suggest that there was a slight tendency for DIC to outperform BICacross all large sample sizes. Similarly, Markon and Krueger (2004) noted that the DICoutperforms the BIC in a structural equation modeling framework. The tendencies of DIC tooutperform BIC in the asymmetric price transmission modeling framework are betterexplained by the fact that the improved performance of the DIC was the motivation for itsdevelopment and implementation in Draper (1995). There was also a slight tendency for46

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)CAIC to outperform AIC across all sample sizes. This is because CAIC corrects for samplesize whereas AIC fails to take into account sample size.In order to simulate the effects of sample size and model complexity on modelselection this study considers three sample sizes ranging from small to large correspondingto 50, 150 and 500. Using an error size of 1, data is generated from the Complex AsymmetricError Correction Model (CECM) specified in equation (5). The results of the Monte Carlosimulations comparing the performance of the model selection methods are displayed belowin Table 2.Sample Size50150500* Based on 1000 ReplicationsTable 2. Effects of Sample Size on Model Recovery*Model FittedMethods CECM (DGP) HKD SECMAIC 118 (54.5%) 126 (7.6%) 120 (37.9%)BIC 126 (29.3%) 131 (19.6%) 127 (51.1%)CAIC 129 (17.5%) 131 (28.9%) 128 (53.6%)DIC 129 (23.3%) 132 (24.2%) 129 (52.5%)AIC 402 (97%) 435 (0%) 412 (3%)BIC 416 (86%) 444 (0.1%) 423 (13.9%)CAIC 417 (81.3%) 443 (0.5%) 424 (18.2%)DIC 419 (83.6%) 445 (0.2%) 425 (16.2%)AIC 1396 (100%) 1517 (0%) 1434 (0%)BIC 1417 (99.9%) 1529 (0%) 1451 (0.1%)CAIC 1416 (99.9%) 1527 (0%) 1450 (0.1%)DIC 1419 (99.9%) 1531 (0%) 1453 (0.1%)In general, trends in performance across the different model selection criteria as thesample size increases are similar to those observed when the data was simulated from thestandard asymmetric ECM. The ability of the model selection methods to recover the truemodel (DGP) generally increased with sample size as illustrated in Table 2.The relative performance trends of the model selection criteria illustrates that when thetrue model is complex, AIC persistently outperforms CAIC, BIC and DIC across all samplesizes. This was not the case when the true model was the standard asymmetric ECM. Usinga small sample of 50, the top part of Table 2 indicates that AIC recovers 54.5 percent of thetrue data generating process whilst CAIC, BIC, and DIC recovered between 17.5 to 29.3percent of the true model. In large samples of 500, AIC achieve full recovery of 100 percentwhilst CAIC, BIC and DIC achieve 99.9 percent recovery each when the true data generatingprocess is complex. Similarly, previous studies (Lin and Dayton, 1997) found that AIC wassuperior to BIC when the true model was complex in mixture models. Gagne and Dayton(2002) also observed that AIC was more successful when the true model was relativelycomplex in multiple regression analysis.An important point is that comparatively, the model selection methods performed betterwhen the true asymmetric data generating is relatively complex (CECM) and the sample sizeis large than when the true data generating process is the standard error correction model(SECM) and the sample size is large. This is noted when the recovery rates of Table 1 arecompared with Table 2 under sample size of 500. For instance, under a sample size of 500the model selection methods achieve at least 99.9 percent recovery of the true model whenthe data generating process is complex but achieves at least 84.6 percent recovery when thetrue model is the standard asymmetric error correction model.The foregoing discussions point to the fact that another factor that may influence theperformance of the model selection methods is model complexity or the number ofasymmetric adjustment parameters. In large samples, the model selection methodsperformed better when the true asymmetric data generating process is the complex errorcorrection model.In summary, larger sample sizes improve the ability to make correct inferences aboutthe true asymmetric price transmission model. This research notes that an important factor47

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)that influences the performance of the model selection criteria in addition to sample size ismodel complexity (i.e. number of asymmetric adjustment parameters or the number ofinformative variables in the model). Intuitively, the increase in model recovery of the truemodel can also be interpreted as due to an increase in asymmetric information provided bythe additional variables or additional asymmetric adjustment parameters.CONCLUSIONThe main point of this article is that the performance of model selection methods maybe influenced by model complexity in asymmetric price transmission modeling framework.This study therefore fits competing asymmetric price transmission models that differ incomplexity to simulated data and evaluates the ability of the model selection methods torecover the true model. Monte Carlo simulation results, suggest that the ability of the modelselection methods to select the true model generally increased with increases in sample sizefrom small to large. AIC is superior to CAIC, BIC and DIC when the true model is thecomplex error correction model. An important point is that in large samples, the modelselection methods performed better when the true asymmetric data generating process is thecomplex Error Correction Model (CECM) as compared to the standard ECM data generatingprocess. In effect, this research suggests that model complexity (i.e. number of asymmetricadjustment parameters or the number of informative variables in the model) influences modelrecovery in asymmetric price transmission modeling.REFERENCES[1] Acquah, H.D.(2010). Comparison of Akaike information criteria (AIC) and Bayesianinformation criteria (BIC) in selection of asymmetric price relationships. Journal ofDevelopment and Agricultural Economics Vol. 2(1) pp.001–006.[2] Akaike, H. (1973). Information Theory and an Extension of the Maximum LikelihoodPrinciple. B. N. Petrov and F. Csaki (eds.) 2 nd International Symposium onInformation Theory: 267-81. Budapest: Akademiai Kiado.[3] Bozdogan, H. (1987). Model Selection and Akaike’s Information Criterion (AIC): TheGeneral Theory and Its Analytical Extensions, 52, No. 3, 345-370.[4] Draper, D. (1995). Assessment and Propagation of Model Uncertainty. Journal ofRoyal Statistical Society. Series B (Methodological), 57, No. 1 (1995), pp 45-97.[5] Fishler, E., Grosmann, M., and Messer, H. (2002). Detection of signals by informationtheoretic criteria: general asymptotic performance analysis. IEEE Trans. SignalProcess, 50, pp.1027–1036.[6] Gagne, P. and Dayton, C.M. (2002). Best Regression model using informationcriteria. Journal of Modern Applied Statistical Methods, 1, pp.497-488.[7] Granger, C.W. J. and Lee, T.H. (1989). Investigation of Production, Sales andInventory Relationships using Multicointegration and non-symmetric Error CorrectionModels, Journal of Applied Econometrics, 4, pp. 135- 159.[8] Ichikawa, M. (1988). Empirical assessments of AIC procedure for model selection infactor analysis. Behaviormetrika , 24, pp. 33–40.[9] Lin, T. H., and Dayton, C. M. (1997). Model selection information criteria for nonnestedlatent class models. J. Educat. Behav. Stat., 22, pp. 249–264.[10] Markon, K. E. and Krueger, R. F. (2004). An Empirical Comparison of Information-Theoretic Selection Criteria for Multivariate Behaviour Genetic Models. BehaviourGenetics, 34, (6), pp.593- 609.[11] Schwarz, G. (1978) “Estimating the Dimension of a Model.” Annals of Statistics, 6, pp.461– 464.[12] Von Cramon-Taubadel, S. and Loy, J.-P. (1996). Price Asymmetry in the internationalWheat Market: Comment. Canadian Journal of Agricultural Economics, 44, pp. 311-317.48

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)ECONOMIC ANALYSIS OF FRESHWATER AQUACULTURE PRODUCTION:A COMPARATIVE ANALYSIS OF DIFFERENT PRODUCTION SYSTEMSH. Kumar, Research ScholarR. Singh, ProfessorDepartment of Agricultural EconomicsInstitute of Agricultural Science, Varanasi, IndiaE-mail: hradaykumarbhu@gmail.comABSTRACTIndia produced 8.29 million tonnes of fish in 2010-2011. The industry contributes nearly INR200 trillion to the national economy, forming 1.4 percent of national gross domestic product(GDP) and 5.4 percent of Agricultural GDP. At present, almost 84 percent of the total inlandfish production, in the country is contributed by freshwater aquaculture amounting to 3.9million tonnes in 2008-09. Further, the potential of the vast freshwater resources covering 6.7million hectare is yet to be fully realized. The freshwater aquaculture which began as smallscale activity of stocking ponds with fish seed collected from riverine sources during earlyfifties in rural Bengal has now transformed into a major economic activity in almost all states.There is a further need to make the sector more vibrant so as to achieve the predicted targetof 15 kg per capita fish availability in the country by 2030.KEYWORDSPonds; Fish ponds; Production functions; Aquaculture; Fish; Disease control; Fixed costs.Capture fishery in the country being almost stagnant since last three decades,freshwater sector has been shouldering the major responsibility to meet the increaseddemand for fish. Now, quality fish protein supply, nutrient security of consumers, livelihoodsecurity of producers and traders are all linked with the growth and development of thissector. This study was confined in Eastern Uttar Pradesh which comprises 15 districts.Maharajganj district being the highest fish producing district was selected purposively. A listof all 12 blocks was prepared on the basis of fish production. Two blocks having highest fishproduction viz. Partawal, Mithaura and two blocks with lowest fish production viz.Bridzemanganj, Pharenda, were selected purposively. A list of villages and fisher’s alongwith fish production was prepared and three were categorized in to viz. Private fish ponds,community fish ponds and leased fish ponds. Total of 200, fishers of four blocks wereselected for the study. Primary data were collected with the worked pretesting scheduled.METHODOLOGYAnalytical framework. To workout the cost and returns structure, the tabular analysiswas employed. Thecosts, returns and profit in Maharajganj district (MD) and BHU pondsaquaculture production systems computed on per hectare basis were compared andcontrasted .the cost of human labour was estimated in terms of 8 man hours. The costsmachine labourboth owned and hired were calculated at the prevailing rates. The costs ofponds produced fish seeds and farm yard manure (FYM) were imputed at the market price inthe village including the cost of transportation and other incidental charges, if any. The costof purchased fingerlings (fish seed) fertilizers, lime, feed, disease control chemicals werecalculated based on the actual expenditure incurred .the amount of fixed by the governmentfor irrigation and land revenue was considered for computation of this cost. The rental valueof pond was imputed based on the prevailing rents in the study area. The short term and longterm bank lending rates were used to work out the interest on working and fixed capitalrespectively.The depreciation was calculated by the strait line method.The charges onaccount of minor repairs of implements and machinery during the year were added to thedepreciation charges .the interest on fixed capital and depreciation were apportioned on thebasis of area of land under each crop grown during the year. The gross returns werecomputed were multiplying the quantity of product with respective prices received.49

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)The resource use efficiency was assessed by comparing marginal value product(MVP) with factor cost of the resources .the marginal product (MP) was estimated from theparameters of Cobb-Douglas production function and the geometric mean level of output andinput. Decomposition analysis was employed to figure out the sources contributing to theyield differences between the systems. Dummy variable technique was employed to knowthe nature of technological change between Maharajganj fisher’s pond and BHU pondproduction systems of aquaculture. The output decomposition model as developed byBisaliah (1977) was used for investigating the contribution of various constituent sources tothe productivity difference between the BHU fish ponds and the Maharajganj fisher’s pondsproduction systems. For any two production functions, the total change in the Productivitycould be brought out by shifts in the production parameters that defined the productionfunction itself and by the changes in the input use levels. Therefore, the production functionswere considered as the convenient econometric tools for decomposing the productivitydifference between the BHU fish ponds and the maharajganj fish ponds production systems..Two separate production functions, one for BHU fish ponds production and another formaharajganj fish production systems were fitted as follows. In logarithm form, Cobb-Douglasproduction function for BHU fish ponds is:lnY B = lna B + b B1 lnX B1 + b B2 lnX B2 + b B3 lnX B3 + b B4 lnX B4 + b B5 lnX B5 b B6 lnX B6 + U B …..(1)Logarithm form of Cobb-Douglas production function for maharajganj fish ponds is:lnY M = lna M + b M1 lnX M1 + b M2 lnX M2 + b M3 lnX M3 + b M4 lnX M4 + b M5 lnX M5 + b M6 lnX M6 + U M ….(2)Taking differences between (1) and (2) and adding some terms and subtracting thesame terms.lnY B – lnY M = (lna B – lna M ) +(b B1 lnX B1 – b M1 lnX M1 + b B1 lnX B1 – b B1 lnX B1 ) + (b B2 lnX B2 – b M2 lnX M2 +b B2 lnX B2 – b B2 lnX B2 ) + (b B3 lnX B3 – b M3 lnX M3 + b B3 lnX B3 – b B3 lnX B3 ) + (b B4 lnX B4 –b M4 lnX M4 +b B4 lnX B4 – b B4 lnX B4 ) +( b B5 lnX B5 – b M5 lnX M5 + b B5 lnX B5 – b B5 lnX B5 ) + (b B6 lnX B6 – b M6 lnX M6 +b B6 lnX B6 – b B6 lnX B6 ) + (U B – U M ) …(3)By using logarithm rule equation (13) becomes:ln (Y B /Y M ) = { ln [a B / a M ) } +{ (b B1 – b M1 ) lnX B1 + (b B2 – b M2 ) lnX B2 + (b B3 – b M3 ) lnX B3 +( b B4 –b M4 ) lnX B4 + (b B5 – b M5 ) lnX B5 + (b B6 – b M6 ) lnX B6 } + { b B1 ln (X B1 /X M1 ) + b B2 ln (X B2 / X M2 ) +b B3 ln(X B3 / X M3 ) + b B4 ln (X B4 / X M4 ) + b B5 ln (X B5 / X M5 ) + b B6 ln (X B6 / X M6 ) + [(U B – U M )]……(4),where:Y B = output in quintal/ha (BHU); Y M = output in quintal/ha (MGD);X 1 = Seeds in Rs/ ha; X 2 = feed in Rs/ ha;X 3 = Fertilizer in Rs/ ha; X 4 = Manure in Rs/ ha;X 5 = lime in Rs/ ha; X 6 = Irrigation charges Rs/ha;ln = natural logarithms; u = Error term.This is the decomposition model for decomposing the productivity difference betweenthe BHU fishponds and Maharajganj fish ponds production system. This equation involvesdecomposing the logarithm of ratio of per hectare productivity of the BHU fishponds andMaharajganj fish ponds production systems (LHS). This is approximately a measure ofpercentage change in per hectare output between the BHU fishponds and Maharajganj fishponds production system. The summation of first and the second terms on the right handside of the decomposition model together represented the productivity difference betweenthe BHU fishponds and Maharajganj fish ponds production systems attributable to thedifference in the cultural practices. The third term provided the productivity difference50

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)between the BHU fishponds and Maharajganj fish ponds production systems attributable tothe differences in the input use.Dummy variable Technique. To examine whether the parameters of the productionfunction of BHU ponds production systems were different from those of the Maharajganjfisher’s ponds production systems. Dummy variable technique was used. The followingdummy variable model introducing intercept and slope dummy was specified:Ln Q = ln + b 0 + b 1 In X 1 + b 2 In X 2 + b 3 In X 3 b 4 In X 4 + b 5 In X 5 + b 6 ln X 6 + cD +d 1 (Dln X 1 )+d 2(Dln X 2 )+ d 3 (Dln X 3 ) + d 4 (Dln X 4 ) +d 5 (Dln X 5 ) + d 6 (D ln X 6 ) + ln u…………..(5)Dummy values: D = 1 If it is BHU production systems. D = 0 if it isMaharajganj fisher’spondsproduction systems.RESULTS AND DISCUSSIONCosts and returns in md fishers ponds and bhu fish ponds production system.Cost of aquaculture production system includes both operational as well as fixed cost.operational cost includes the cost of fish seed ,human labour, machine labour ,manures andfertilizers ,irrigation charges insecticide ,lime, feed and interest on working capital .fixed costincludes the rental value of owned pond, rent paid for leased in pond, land revenue, rentalvalue of owned pond, rent paid for leased in community fish pond, rent paid for leased inprivate fish pond, depreciation charges, and interest on fixed capital.Table 1. Comparison of Costs and returns of aquaculture of Maharajganj district fishersponds and BHU fish ponds (Rs/ha)Item Maharajganj BHU fish pondsOperational costsSeed 2270.57 (3.10) 1500.00 (1.61)Feed 3573.57 (4.88) 4750.00 (5.09)Irrigation 3352.80 (4.58) 5000.00 (5.36)Manure 2223.00 (3.04) 4000.00 (4.29)Lime 927.07 (1.26) 1600.00 (1.71)Fertilizers 835.41 (1.14) 3620.00 (3.88)Disease control 447.43 (0.61) 850.00 (0.91)Human labour 7387.72 (10.10) 16300.00 (17.49)Machine labour 514.01 (0.70) 1200.00 (1.28)Miscellaneous expences 242.58 (0.33) 500.00 (0.53)Interest on working capital 2721.76 (3.72) 4915.00 ( 5.27)Sub Total 24495.97 (33.50) 44235.00 (47.48)Fixed costs 41974.25 (57.40) 40446.54 (43.42)10% of Managerial of sub total 6647.01 (9.09) 8468.15 (9.09)Grand Total 73117.23 (100.00) 93149.69 (100.00)Production (Kg/ha) 2191.18 3000.00Gross income 168900.76 240000.00Net income 95783.53 146850.31Note: figures in the parentheses indicate percentages to totalCost of aquaculture production systems of B.H.U fish pond per hectare is given table(1) total cost per hectare Rs. (93149.69) was more when compared to that in Maharajganjfisher’s pondsproduction systems (Rs. 73117.23).The share of Maximum difference wasobserved in the cost of human labour. on an average sample fish farms incurred Rs.16300was incurred towards human labour in BHU fish farm production system while only 7387.12was incurred towards human labour in Maharajganj fish ponds production system. irrigationchargeswas the next important item of expenditure in both the systems of aquacultureproduction which worked out to be Rs.3352.80. (4.58 percent) and Rs.5000.00 (5.36 percent)of total cost, respectively in Maharajganj fisher’s pondsproduction systemsand BHU ponds51

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)production systems. Expenditure incurred on seed per hectare Rs.2270.57 (3.10 percent)was more in the case of Maharajganj fisher’s ponds production systems. compared to thatBHU production systems Rs.1500 per hectare, (1.61 percent) of total cost. The amount spenton FYM, lime fertilizers, feed, machine labuor, expenditure of disease control in BHUproduction systems was more when compared to that MGD production systems table (1).Operational cost per hectare was higher Rs.44235.00 in BHU production systems whencompared to that in MGD production systems Rs. 24495.97. Fixed cost Rs.41974.25 (57.40percent) in MGD production systems was more when compared to that in BHU productionsystems Rs.40446.54 (43.42 percent).Gross income obtained per hectare was more in BHU fish farm than that ofMaharajganj fish ponds. It was Rs.240000 in BHU fish ponds and Rs.168900.76 inMaharajganj fish ponds per hectare. But net income was more in BHU fish farm.it wasRs.146850.31 in BHU fish ponds and Rs.95783.53 in Maharajganj fish ponds per hectare.This was due to the comparatively lower expenditure on production of Maharajganj fishponds production systems.Decomposition of factors contribution to productivity difference between BHUand MD aquaculture production systems. In order to test the difference in the structuralproduction relationship in the parameters defining the production functions for the twosystems, the log linear production function with both intercept and slope dummies wasestimated.This result facilitated of the hypothesis that production parameters defining the BHUfish production systems and MD fisher’s pondsaquaculture production systems are same.The positive estimates of intercept and slope dummy coefficients for all resources impliedthat the output in BHU production systems is significantly higher than that in the MD fisher’spondsaquaculture production systems for a given level of resources .they also implied largerregression coefficients of production with respect to each input under BHU aquacultureproduction systems compared to MD fisher’s pondsaquaculture production systems. Theresult as such offered the required justification for decomposing the factor contributing toproductivity difference between BHU ponds aquaculture production systems and MD fisher’spondsaquaculture production systems.For decomposing the productivity difference between BHU production systems and MDfisher’s pondsaquaculture production systems, the parameters of the per hectare productionfunction and the mean level of input use for the two systems were essential. Hence, theproduction functions for BHU production systems and MD fisher’s pondsaquacultureproduction systems were also estimated separately. The estimates provided in table .Asmuch as 80.10 percent and 69.10 percent of variation in aquaculture output ,respectively, inMD systems and BHU ponds systems was explained by the independent variables. Theconstant term (intercept) in the case of BHU systems was higher than that for the MD fisher’spondsaquaculture production systems. This virtually signified that there was an upward shiftin production function due to technological change associated with BHU ponds. Theproduction regression coefficient of fingerlings (fish seed), feed, manure, lime, waterrecharges, fertilizers, disease control (chemical) ,laboure, Were positive and significant inMD fisher’s pondsaquaculture production systems and BHU ponds production systemsregression coefficient of feed, lime ,fertilizers, disease control (chemical) and human labourewere positive and significant, seed manure, water recharges were negative regressioncoefficient. The output regression coefficients seed water recharges, manure, disease controlitems in case of MD fisher’s pondsaquaculture production systems were relatively greater ascompared to those for BHU ponds production systems. The output regression coefficientsfeed, fertilizers, laboure and lime items in case of BHU ponds production systems wererelatively greater as compared to those for MD fisher’s pondsaquaculture productionsystems. The aquaculture output in MD fisher’s pondsaquaculture production systems wouldincrease by 0.462 percent and 0.020 percent for every one percent increase in the use ofseed and manure.in case of BHU pondssystems, the aquaculture output would increase by0.147 percent, 0.056 percent, 0.569 percent and 0.227 percent for every one percentincrease in the use of feed lime, fertilizers and human laboure. Thus, the major contributionto output in BHU aquaculture production systems came from feed lime and fertilizers.52

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Table 2. Estimated production functions with intercept and slop dummiesPooled MD fisher’s ponds BHU pondsS.Particulars Regression Standard Regression Standard Regression StandardNo.coefficient error coefficient error coefficient error1. Intercept 4.384** 0.309 4.379** 0.659 5.335** 1.6982. Seed (fingerlings) 0.460** 0.048 0.462** 0.083 -0.215 0.1113. feed 0.086** 0.021 0.086** 0.021 0.147 0.0744. manure 0.020 0.023 0.020 0.024 -0.017 0.0195. Water recharges 0.034 0.019 0.034 0.027 -0.081 0.0686. lime 0.006 0.023 0.006 0.020 0.056 0.1477. fertilizers 0.098** 0.022 0.098** 0.024 0.569* 0.2668. Disease control(chemical) 0.062 0.025 0.062 0.024 0.052 0.1109. Human labour 0.213** 0.062 0.212** 0.065 0.227 0.115Intercept 10.278 0.198(a)Seed (fingerlings) 0.023 0.111(b)feed 0.065 0.050(c)manure 0.002 0.056(d) Water recharges 0.013 0.045(e)lime -0.101 0.055(f)fertilizers -0.020 0.054(g)Diseasecontrol(chemical)-0.041 0.058(h)Human labour 0.114* 0.10910.Coefficient of multipledetermination(R2)0.76 0.801 0.69111.Adjusted Coefficient ofmultipledetermination(R2)0.75 0.793 0.46612. F value 67.06 95.783 3.072Note: Figures in parentheses are standard errors.** Significant at 1% level and *Significant at 5% level, respectively.Table 3. Decomposition of productivity difference between the BHU ponds productionsystems and the MD fisher’s ponds production systemsSl. No. Sources of output differences Percentage contributionI. Total difference in output 23.34II. Source of contribution –1. Due to difference in Technology 21.252. Due to difference in input use –a) Seed (fingerlings) 8.44b) Feed -0.83c) Manure -0.07d) Water recharges -0.29e) Lime 0.04f) Fertilizers -7.17g) Disease control(chemical) 0.74h) Human labour 1.90III. Estimated difference in output 22.92Table 4. MVP to MFC ratios of resources in MD and BHU ponds aquaculture productionsystemsSl.MGD MD fisher’s pondsBHU pondsParticularsNo.MVP MFC Ratio MVP MFC Ratio1. Seed (fingerlings)(Kg.) 30.73 117.72 0.26 -32.86 100 -0.322. Feed (Qt.) 7.12 6.00 1.18 7.38 6.00 1.233. Manure (Tonnes) 0.95 112.37 0.008 -3.89 120 -0.034. Lime (Kg) 1.31 8.50 0.15 0.15 6.50 0.0235. Fertilizers (Kg) 32.02 12.00 2.66 39.88 12.00 3.326. Disease control(chemical)(Rs) 41.71 295.17 0.14 17.18 80 0.217. Human labour (man days) 4.60 100.00 0.046 3.2 158.65 0.0253

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)To analyse the scope for intensification of resources in both systems, the marginalvalue products (MVP) of resources are compared with the respective marginal factor cost(MFC). The MVP and MFC ratios for different resources for both the systems were furnishedin Table 2. It is revealed from feed and fertilizer were underutilized as their ratio was morethan one therefore it was conducted that profitability may be increased by offer more andfeed and fertilizer on both ponds .manure was done utilized on BHU ponds as its reach wasnegative while there is great scope to improve the production by offer manure a fishersponds. The decomposition analysis revealed that the per hectare production of MD fisher’sponds aquaculture production systemswas less than that in BHU ponds production systemsof aquaculture 23.34 per cent (Table 3) and the estimated difference 22.92 per centdifference in the productivity of BHU ponds production systems and MD fisher’spondsaquaculture production systems. This implied that aquaculture output could beenhanced by nearly 23.00 Percent if the BHU production systems of aquaculture wereadopted by all the fish growing fishers. The contribution of BHU ponds systems technology tothe productivity difference between the production systems of aquaculture was estimated at21.25 percent.The contribution of differences in input levels to the productivity differences betweenthe BHU systems and MD fisher’s pondsaquaculture production systemswas meager and itwas 23.34 per cent. Seed (8.44 percent), feed (-0.83 percent), fertilizer (-7.17 percent),manure (-0.07 percent), lime (0.04 percent) water recharges (-0.29 percent) expenditure ofdisease control (0.74 percent) and human laboure (1.90 percent) respectively. This impliedthat farmers growing in BHU production systems of aquaculture obtained higher output perhectare than that obtained by the fishers of MD fisher’s pondsaquaculture productionsystemsby spending less on those inputs. Altogether, the total contribution of differences inthe levels of input use to the productivity gap accounted for 23.34 per cent, indicating that theproductivity on MD fisher’s pondsaquaculture production systemscould be increased byabout 22.92 per cent, if the per hectare input use levels on these ponds could be increasedto the same level as on the MD fisher’s pondsaquaculture production systems.CONCLUSIONGross income obtained per hectare was more in BHU fish farm than that of Maharajganjfish ponds. It was Rs.240000 in BHU fish ponds and Rs.168900.76 in Maharajganj fish pondsper hectare. But net income was more in BHU fish farm.it was Rs.146850.31 in BHU fish pondsand Rs.95783.53 in Maharajganj fish ponds per hectare. This was due to the comparativelylower expenditure on production of Maharajganj fish ponds production systems. The share ofMaximum difference was observed in the cost of human labour. on an average sample fishfarms incurred Rs.16300 was incurred towards human labour in BHU fish farm productionsystem while only 7387.12 was incurred towards human labour in Maharajganj fish pondsproduction system.Total cost per hectare Rs.( 93149.69) was more when compared to that inMD fisher’s pondsaquaculture production systems (Rs. 73117.23). Operational cost per hectarewas higher Rs.44235.00 in BHU production systems when compared to that in MD fisher’spondsaquaculture production systemsRs. 24495.97. Fixed cost Rs.41974.25 (57.40 percent) inMD fisher’s pondsaquaculture production systemswas more when compared to that in BHUponds production systemsRs.40446.54 (43.42 percent).The estimated production was significant with high R² for both the BHU ponds productionsystems and MD fisher’s pondsaquaculture production systems. The production regressioncoefficient of fingerlings (fishseeds), feed, manure, lime, water recharges, fertilizers, diseasecontrol (chemical) ,laboure, Were positive and significant in MD fisher’s pondsaquacultureproduction systemsand BHU ponds production systems regression coefficient of feed, lime,fertilizers, disease control (chemical) and human laboure were positive and significant ,seedmanure, water recharges were negative regression coefficient.The technological change inaquaculture production systems has brought 21.25 percent productivity difference between thetwo production systems. The major component of this productivity difference was due to thedifference in systems of production, which contributed to 23.34 percent.54

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)REFRENCES[1] Annual report, Department of Animal Husbandry, Dairying & Fisheries, Ministry ofAgriculture Government of India New Delhi, 2011-12.[2] Bisalaiah,S., decomposition analysis of output change under new productiontechnology in wheat farming: Some implications to returns on investment. IndianJournal of Agricultural Economics, 32(3), pp 193-201, 1977.[3] Balakrishna,A., Economics of Bt cotton in India Journal of Development andAgricultural Economics Vol. 4(5), pp. 119-124, 12 March, 2012.[4] Basavaraja, H., Mahajanashetti, S.B.andSivanagaraju,P., Technological change inpaddy production .A comparative Analysis of traditional and SRI method of cultivation.Indian journal of Agricultural Economics, vol.63 (4), pp 629-640, 2008.[5] Hugar, L. B., and Patil, B. V., Productivity difference between bt and non bt cottonfarms in Karnataka state, India-an Econometric evidence college ofAgriculture,lingasugur road, Raichur, 584101, India, 2007.[6] Maheswari ,R.,Ashok, K. R., and Prahadeeswaran , M.,Precision FarmingTechnology, Adoption Decisions and Productivity of Vegetables in Resource-PoorEnvironments, Agricultural Economics Research Review Vol. 21: pp 415-424, 2008.[7] Sharma, A. and Nizamuddin., Fish production in rainfed area of Uttar Pradesh aregression approach,Journal of Interacademicia. 8(3): pp 441-446, 2004.55

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)ASSESSING THE RETURNS TO SCALE:EVIDENCE FROM FISH FARMERS IN ILORIN, KWARA STATEN.A. Jatto, ResearcherDepartment of Agricultural Economics, Usmanu Danfodiyo University, NigeriaE-mail: nuayjao@yahoo.comABSTRACTThis study assesses the returns to scale as evidenced from fish farmers in Ilorin, Kwara. Arandom sampling technique was adopted in selecting 120 fish farmers from fish farmersassociation of Nigeria Ilorin branch list. The data for the study were collected with the use ofwell structured questionnaire. The result showed the fish farmers were relatively technicallyefficient in their use of resources, with a mean technical inefficiency of 40%. The result alsoshowed that 73% of the fish farmers exhibited increasing returns to scale. On the average;numbers of fingerlings, feeds and labour had slacks of 0.0, 6.5 and 0.4 respectively. Theseimply that inputs could be decreased by those units and still produce the same level ofoutput. Thus, the fish farmers are said to be inefficient in input usage by the said values. Itwas concluded that though average cost is expected to decrease as output increases thereis still room for improvement in productivity of fish farms. With this high level of returns toscale in fish farming, it is recommended that this information should be spread to all thefarmers in the study area and other surrounding communities.KEYWORDSReturns to scale; Data envelopment analysis; Fish farmers.Returns to scale refer to the degree by which level of production changes as a result ofgiven change in the level of all inputs used. Salvatore (1996) stated that there are threedifferent types of returns to scale: constant return to scale (CRS) means when we double allinputs, output is exactly doubled, increasing return to scale (IRS) means when we double allinputs, output is more than doubled and decreasing return to scale (DRS) means when wedouble all inputs, output is less than doubled. Mathematically, the implication of returns toscale can be shown as follow. Let denote a production function as Q = f (K, L). If K and L ismultiplied by, and then Q increases by as indicated in Q = f (K, L). The production functionexhibits CRS, IRS or DRS respectively, is dependent on whether =, > or

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)efficiently. Secondly, returns to scale will provide information of whether expansion of scaleof fish production done by multiplying capital and variable inputs will have economic impact.Returns to scale also imply economies of scale because of duality in production theory (Jehleand Reny 2001; Pindyck and Rubinfeld 1998). The outcome of this study is expected to beable to provide significant contributions for improving fish production.METHODOLOGYThe study was conducted in Ilorin, the kwara state capital, chosen based onpredominance of registered fish farmers in the state. A random sampling technique wasadopted in selecting 120 registered fish farmers. Data was collected by administering astructured questionnaire to the sampled farmers. The analysis was done with dataenvelopment analysis and descriptive statistics.For this study output oriented DEA as designed by Coelli (1996) and used by Jatto etal. (2012) was used to determine how much input mix the farmers would have to change toachieve the output level hat coincides with the best practice frontier. Technical efficiency willbe measured as follows:Max TE =s∑r−1m∑r−1s∑r rjr−1subject to ≤ 1, j = 1,m∑r−1α yrβ xiα yβ xiroior = 1… s; i = 1… m.ijLnα, β are parameters to be estimated and x ij and y ij respectively are the quantities of thei th input and r th output of the j th farm. As the ratio is maximized it would be constrained to beno greater than one. Thus, if TE equals one, then it is perfectly efficient.Inputs = fingerlings (numbers); Feed (kg), Labour (man/days)Output = fish (numbers)Firm = 1…nRESULTS AND DISCUSSIONTable 1 showed the summary statistics of distribution of technical efficiency and returnsto scale. The result showed that majority (55%) of the fish farmers operate at an efficiencyrange of 0.501-0.6. On average, the technical efficiency of the fish farms is 0.60; with lessthan 18 per cent of fish farms still having efficiency less than the average. Therefore, therewas still considerable room for boosting productivity through improving technical efficiencywith the existing technology. It could be done by increasing scale of the fish farm, orincreasing the number of fingerlings.The overall mean technical efficiency which was 0.6 implies that on average fishfarmers observed output was 0.4 less than the maximum output which can be achieved fromthe existing level of inputs. In addition, it is an indication of opportunity for improvement inefficiency which could either increase output or reduce cost of production given the presenttechnology and operating close to the frontier (Jatto et al., 2012). The observed efficiency(0.6) can also be attributed to various factors ranging from technical production constraints,socio-economic and environmental factors. Furthermore, it has been argued by Yusuf andMalomo (2007) that non physical inputs like experience, information asymmetry and other57

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)socioeconomic factors might influence the ability of a farmer to use the available technologyefficiently and this agrees with Ojo (2003).Table 1. Distribution of technical efficiency and returns to scaleDistribution Frequency Percentage0.301-0.4 9 80.401-5.0 12 100.501-0.6 66 550.601-0.7 16 130.701-0.8 5 40.801-0.9 2 20.901-1.0 10 8Total 120 100Average 0.6 –Maximum 1.0 –Minimum 0.3 –Returns to scale Frequency PercentageIncreasing 88 73Constant 18 15Decreasing 14 12Total 120 100Input slacks: This problem arises when it is questionable as to whether a farm is atefficient point on the frontier (Yusuf and Malomo, 2007). If one could reduce the amount ofany of the input used and still produce the same output, it is known as input slack which isalso referred to as input excess.The input slacks provide an accurate indication of technical efficiency of a farm byFarrell in Data Envelopment Analysis. On the average; numbers of fingerlings, feeds andlabour had slacks of 0.0, 6.5 and 0.4 respectively. These imply that inputs could bedecreased by those units and still produce the same level of output. Thus, the fish farmersare said to be inefficient in input usage by the said values. From the result above, numbers offingerlings and labour is more efficiently utilized, while feed is the most underutilized input.This result disagrees with Yusuf and Malomo (2007) whose result showed that feed is moreefficiently utilized and labour is the most underutilized but agrees with Jatto et al. (2012).The returns to scale showed that majority (73%) of the poultry farmers farms areexhibiting increasing returns to scale. This increasing return to scale is an appropriate choiceto increased productivity and the biggest observation is that when there are increasingreturns to scale a firm’s average cost of production is decreasing. The implications of this; isthat If one farm with increasing returns to scale is capable of producing enough output for theentire market then there is a barrier to entry because this one farm could produce a level ofoutput that would satisfy the market at less cost than any other firm.CONCLUSIONFrom the analysis estimated from the returns to scale; the average technical efficiencyof the fish farmers was 0.6 with an inefficiency of 0.4. This showed that fish farmers operateat a moderate efficiency score and exhibited an increasing return to scale. It was concludedwith the state of technology that there is still room for improvement in fish productivity whichcan give a double impact: increase in efficiency leads to increase in productivity. With thishigh level of returns to scale in fish farming, it is recommended that this information shouldbe spread to all the farmers in the study area and other surrounding communities.58

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)REFERENCES[1] Banker, R.D., A. Charnes and W. Cooper (1984).Some Models for EstimatingTechnical and Scale Inefficiency in DEA. Cited in Ajao, O.A. (2010). EmpiricalAnalysis of Agricultural productivity growth in sub-Sahara Africa: 1961- 2003.[2] Belbase, K. and Grabowski, R., 1985.Technical efficiency in Nepalese agriculture,Journal of Development Areas, 19: 515-525.[3] Charnes, A., W. Cooper and E. Rhodes (1978).Measuring the Efficiency of DecisionMaking Units (DMU), European Journal of Operations Research. 2 (6): 429-444.[4] Coelli T., E. Grifell-Tatje, and S. Perelman (2001). Capacity Utilization and Short RunProfit Efficiency: Paper presented at the North American Productivity Workshop, June2000.[5] Coelli, T. J. (1996).A guide to DEAP version 2.1: A Data Envelopment AnalysisComputer Program, CEPA working paper 96/08, university of New England,Australia.[6] Färe, R., S. Grosskopf, M. Norris and Z. Zhang (1994). Productivity Growth,Technical Progress and Efficiency Change in Industrialized Countries, AmericaEconomic Rev., 6(11): 66-83.[7] Jatto, N.A., M.A. Maikasuwa, S. Jabo, U.I. Gunu (2012). Assessing the TechnicalEfficiency level of Poultry Egg Producers in Ilorin, Kwara State: A Data EnvelopmentAnalysis Approach European Scientific Journal 8(27): 110-117.[8] Jehle, G. A. and P.J. Reny (2001).Advanced Microeconomic Theory, Addison-Wesley, Boston.[9] Ngunyen-thi-thanh, H. (2006).Draft Report on the Use of Data Envelopment Analysis:A Comprehensive Text with Models Cited in Theodoridis, A. M., A. Psychoudakis andA. Christofis (2006).Data Envelopment Analysis as a Complement to MarginalAnalysis”. Agricultural economics review 7(2): 55-65.[10] Ojo, S.O. (2003).Productivity and Technical Efficiency of Poultry Egg Production inNigeria, International journal of poultry science 2(6): 459-464.[11] Pindyck, R. S. and D. L. Rubinfeld (1998).Microeconomics, Prentice HallInternational, Inc. Upper Sadle River, New Jersey.[12] Salvatore, D., 1996. Managerial Economics in A Global Economy. McGraw-Hill, NewYork.[13] Seiford, L.M. and R.M. Thrall (1990).Recent Developments in DEA: TheMathematical Programming Approach to Frontier Analysis. Journal ofEconometrics, 46: 7-38.[14] Shapiro, K.H. (1983).Efficiency differentials in peasant agriculture and theirimplications for development policies. Journal of Development Studies 19: 179–190.[15] Theodoridis, A. M., A. Psychoudakis and A. Christofis (2006).Data EnvelopmentAnalysis as a Complement to Marginal Analysis, Agricultural economics review.7(2):55-65.[16] Yusuf, S.A. and O. Malomo (2007).Technical Efficiency of Poultry Egg Production inOgun State: A Data Envelopment Analysis (DEA) approach. International journal ofpoultry science 6(9): 627-629.59

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)ECONOMIC ANALYSIS OF COWPEA PRODUCTION IN NIGERIAAbba M. Wakili, ResearcherDepartment of Agricultural Education, Federal College of Education, NigeriaE-mail: boyaabba@yahoo.comABSTRACTThis study employs a stochastic frontier production function analysis to examine theproductivity and technical efficiency of cowpea production in Adamawa State, Nigeria andalso to identify the factors affecting the technical inefficiency using farm level survey datacollected from 150 cowpea farmers selected using multi stage sampling technique. Findingsfrom the analysis show that cowpea farmers operated on a very small scale and areprofitable. The productivity analysis shows that agro chemicals, fertilizer, farm size and laborwere all positively and significantly related to the technical efficiency. The return to scale(RTS) of 0.9904 shows that cowpea production was in the rational stage of the productionsurface. The technical efficiency varies from 0.1094 to 0.9568 with a mean technicalefficiency of 0.6649, indicating that farmers were operating below the efficiency frontier.Thus, in the short run, there is a scope to increase output by 34%. The inefficiency modelrevealed that education of the farmers; extension visits and access to credit are the mainfactors that affect technical efficiency of the farmers.KEYWORDSCowpeas; Production functions; Family labour; Efficiency; Farm size; Nigeria.Cowpea (Vigna Unguiculata Walp) is a very important crop which is grown in manyparts of Nigeria. It provides protein to rural as well as the urban dwellers as a substitute forthe animal protein. However, cowpea production is generally low as a result of some factorsuch as diseases and pest, drought, insect pest and weeds (Gungula and Garjila, 2005).Nigeria is the largest producer of cowpea in Africa; Agboola (1979) reported that an averageyield of 271.5 kg/ha from the vast area of 3.8 million hectares cultivated to cowpea in Nigeria.In addition Singh and Jackai, (1985) further reported that with the use of improvedtechnologies in cowpea production, yield of 1500-2000 kg/ha can be obtained on sole crops.According to gibbon and pain 1985), increase in demand for cowpea in the past few decadeshas led to the cultivation of cowpea as a sole crop in many parts of the country. Similarly inthe northern part of Adamawa State, Cowpea which is used to be grown in mixture withcereals is now being produced as a sole crop (Sajo and Kadams, 1999). The role ofagriculture is to provide adequate output to assure global food security and enhanceeconomic development, nevertheless agricultural development in Nigeria has suffered a lotof setback due to the shift of emphasis and manpower to petroleum sector. Priority must begiven to small holder farmers because they constitute about 95% of farming household inNigeria and produce most of the food crops consumed in the country (Adesina, 1991).Cowpea is a major food crop and is widely grown in Adamawa state, however, withincreasing population over the years, the demand for the crop had gone up but theproduction has not been increase significantly (Agwu, 2001). This study is therefore toevaluate the profitability and technical efficiency of production of the crop in Adamawa StateNigeria and also identifies the factors affecting the inefficiency in the production process.Analytical framework. The stochastic frontier production function in efficiency studiesis employed in this study. In the Stochastic frontier analysis (SFA), the error term is assumedto have two components parts V and U. The V covers the random effects (random errors onthe production and they are outside the control of the decision unit while the U measures thetechnical inefficiency effects, which are behavior factors that come under the control of thedecision unit. They are controllable errors if efficient management is put in place. Thestochastic frontier analysis is generally preferred for agricultural research for the followingreasons: the inherent variability of agricultural production due to inter play of weather, soil,pests, diseases and environmental factors and many firms are small family owned enterprise60

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)where keeping of accurate records is not always a priority hence available data onproduction are subject to measurement errors. The application of the stochastic frontiermodel for efficiency analysis include: Aigner, et al. (1977) in which the model was applied toU.S. agricultural data. Battese and Corra (1977) applied the technique to the pastoral zone ofeastern Australia. More recently, empirical analysis has been reported by Bravo Ureta andPinheiro (1993).The stochastic frontier production function model is specified as Y= f(Xi,β)+e, where Yis output in a specified unit, X denotes the actual input vector, β is the vector of productionfunction parameters and e is the error term that is decomposed into two components, V andU. the V is a normal random variable that is independently and identically distributed (iid) withzero mean and constant variance .it is introduced to capture the white noise in theproduction, which are due to factors that are not within the influence of the producers. It isindependent of U. the U is a non negative one sided truncation at zero with the normaldistribution (Tadesse and Krishnamoorthy, 1977) it measures the technical inefficiencyrelative to the frontier production function, which is attributed to controllable factors (technicalinefficiency). It is half normal, identically and independently distributed with zero mean andconstant variance. The variance of the random errors ( ) and that of the technicalinefficiency effects ( ) and overall model variance ( ) are related thus: = + , andthe ratio = / is called Gama. Gama measures the total variation of output from thefrontier, which can be attributed to technical inefficiency.The technical efficiency of an individual firm is defined in terms of the observed outputY i to the corresponding frontier output . The is maximum output achievable given theexisting technology and assuming 100% efficiency. It is denoted as: = f(Xij,β) +V, that isTE= Yi / .Also the TE can be estimated by using the expectation of U i conditioned on the randomvariable (V-U) as shown by Battese and Coelli 1988. That is TE = f(Xi,β) + V-U / f(Xi,β) +Vand that 0≤TE≤1.Gross Margin. It was used under the assumption that fixed cost component isnegligible as in the case with subsistence farming and that the analysis is for short term. It isexpressed as:GM=Σ - Σ ….(1),where:GM = gross margin (N/ha); = output of crop (kg); = unit price of the output (100kg); = total revenue from the crop (N/kg); = quantity of the ith input used in kg per hectare; = price per kg of the ith (N/kg); = total cost associated with the ith input per hectare;Σ = summation sign.METHODOLOGYStudy area. The study was based on farm level data on cowpea farmers in AdamawaState, Nigeria. Adamawa state is made up of 21 local government areas divided into fourzones by the Adamawa state Agricultural Development Programme. The state has a tropicalclimate marked by dry and rainy seasons. The major economic activity of the inhabitants isagriculture. The main food crops grown are maize, millet, rice, cowpea/beans, groundnutsweat potatoes and cassava. Farming practice in the study area involves the use of handtools and other simple implements.Data collection and sampling techniques. The data are mainly from primary sourceswere collected from 150 cowpea farmers selected using multi stage sampling techniquesfrom three local government areas. The three local governments are Maiha, Hong and61

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Madagali local government areas are purposively selected because of their prominence incowpea production. Secondly 50 farmers were randomly selected from each of the threelocal government, making a total number of 150 respondents. Data were collected with theuse of a structured questionnaire on inputs, output and income during the production season.Data were also collected on the socio-economic variables such as educational level of thefarmers, farming experience, farm size and age of the farmers.Data analysis. Descriptive statistics (means), gross margin and the stochastic frontierproduction function were used to analyze the socio economic characteristics of the cowpeafarmers, profitability and technical efficiency of cowpea production in the study arearespectively.The production technology of the cowpea farmers was expressed following theadoption of Battese and Coelli, 1988 with the explicit Cob Douglass functional form specifiedas follows:lnY i =β o +β 1 lnX 1i +β 2 ln X 2i +β 3 lnX 3i +β 4 lnX 4i +β 5 lnX 5i +V i - U i ….(2),where:Y = output of cowpea produced (kg);X1= Farm size (ha);X2 = family labour (man-days);X3 = fertilizer (kg);X4 = hercides (litres).The inefficiency model Ui is defined by:Ui = δ 0 +δ 1 Z 1 +δ 2 Z 2 +δ 3 Z 3 +δ 4 Z 4 +δ 5 Z 5 …(3),where: Z 1 Z 2 Z 3 Z 4 Z 5 represent years of formal education, farming experience, extensionvisits, age of the farmer respectively. The socio economic variables were included in themodel to indicate their possible influence on the technical efficiencies of the farmers. The β’sδ’s are scalar parameters to be estimated. The variances of the random errors and that ofthe technical inefficiency effects and overall variance of the model are related, thus, = + and the ratio = / , Gama measures the total variation of output from thefrontier which can be attributed to technical inefficiency (Battese and Corra 1977).RESULTS AND DISCUSSIONThe mean output of the cowpea harvested by farmers was 1,169.45 kg, this indicatesthat the farmers operated at different levels of farm size. The mean age of the cowpea farmeris 37 years, this suggest that cowpea farming is dominated by the youth. The mean years ofeducation shows that on average the highest level of education attained by a farmer isprimary school. Average household size is 5 per household, large household size ensuresadequate supply of family labour. Average farm size is 2 ha and they received only once visitby extension workers, this indicates that farmers operate on a smaller scale and receivedlimited or no extension contact. The labour used in cowpea production had an average of600.00 man-days, the findings indicated that production of cowpea require a lot of labour forefficient productivity. Labour was intensively used which required both the used of hired andfamily labour for more output to be achieved. The average cost of chemicals used in cowpeaproduction was N 1,872, this shows that cowpea production requires a lot of chemical forviable output.Profitability Analysis: cowpea production was a profitable business in the study areaas shown by the average gross margin of N50, 897.12. The cost elements in the totalvariable cost (TVC) include labour cost and the cost of agro chemicals which is N20, 560.78.62

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Table 1. Summary statistics of variables of the stochastic frontier production function forcowpea productionVariablesOutput of cowpea (kg)Age of farmer (Years)Household size ( Number)Farm size (ha)Education (Years)Experience (Years)Extension contac t( Number)Fertilizer ( kg/ha)Cost of Chemicals (Naira)Labour (man-days)Mean1169.4537.3452611.215001872600.21Estimates of stochastic frontier production function: for estimating technicalefficiency stochastic production function approach was used. The parameters of the frontierproduction function were estimated using the maximum likelihood estimation MLE and theresults are presented in table 2.Table 2. Estimates of stochastic frontier production functionVariables Coefficient t-ratioConstantFarm sizeFamily labourChemicalsFertilizer3.46260.66510.03020.01430.072542.2219***4.8785***2.1881**2.8941***2.5825**Inefficiency modelConstantAge of the farmerEducationFarming experienceExtension visitsAccess to creditGenderVariance parametersSigma squareGamaLog likelihood function** Significance at 5 %, ***Significant at 1 %-4.5448-0.2878-8.7457-1.3427-2.0216-0.1454-0.12316.95130.8756-147.69-3.2502***-0.2983-2.7405***-2.6208***-2.4120**-2.3652**-0.74546.8465***4.2312***The estimated stochastic frontier function shows that all the coefficients had theexpected sign, indicating that an increase in these variables will lead to an increase of theoutput. It is also evident from the analysis that the estimate of gama (ϒ) is large andsignificantly different from zero, indicating that a good fit and the correctness of the specifieddistributional assumption. Moreover, the estimate of gama, which is the ratio of the varianceoutput was 0.8756. This means that more than 87% of the variations in output among thecowpea farmers are due to differences in technical efficiency. The variable farm size had acoefficient of 0.6651 and is statistically significant at 1% level, meaning that a 1% increase inthe use of land will increase output by about 6.6 %.similarly, the variable family labour,fertilizer and chemicals are statistically significant at 5% level. This observation is in line witha priori expectation and implies that the output of the farmers in the study area would beexpected to increase with the increasing use of such production inputs. Amaza et al. (2005),and Ebong (2005) also reported a positive and significant relationship between thesevariables and technical efficiency. The return to scale (RTS) which is the summation of all theestimated elasticities of production was 0.9904 and showed decreasing return to scale.This implies that cowpea production is in stage 2 of production surface or decreasingreturn to scale of the production stage. At this stage every additional unit of production inputwould lead to less than proportionate addition to output, therefore the use of input is neededto increase the output of cowpea production.63

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Table 3. Elasticity of production and return to scaleVariableFarm sizeFamily labourChemicalsFertilizerRTSElasticity of production0.66510.03020.01430.07250.9904The inefficiency model also revealed that the variable education and farmingexperience are statistically significant at 1% level, meaning that education of farmers andtheir experience affect technical efficiency. The implication is that farmers that areexperienced, with high level of education and have more extension contact tend to be moreefficient in farming and hence increase in the output level which is consistence with thefindings of Amaza and Olayemi, (2000), while extension visits and access to credit issignificant at 5% level which is also in consistence with the findings of Onyenweaku, et al.(2005).Technical Efficiency Analysis. The technical efficiency analysis is presented in table4. The Technical efficiency of the sampled farmers is less than one (i.e. 100%) indicating thatall the farmers are producing below the maximum efficiency frontier. The farmers technicalefficiency ranged from 0.3318 to 0.9801 with a mean technical efficiency of 0.6649. Thedistribution of the technical efficiency shows that 54% of the farmer had technical efficiencyof 70% above while about 46% of the farmers had technical efficiency of below 70%. Thedistributions of the technical efficiency suggest that in the short run, there is a scope ofincreasing cowpea production by about 40%.Table 4. Frequency distribution of technical efficiencyEfficiency level freq Percentage0.00-0.190.20-0.290.30-0.390.40-0.490.50-0.590.60-0.690.70-0.790.80-0.890.90-1.00346111726411033.31.32.19.310.617.329.320.65.3Mean 0.6649, Min 0.3318, Max 0.9801CONCLUSIONCowpea production is a profitable venture, the return to scale indicates decreasingreturn to scale, this also indicates that all inputs were used within the rational stage ofproduction surface and therefore its production is inefficient in the study area. The technicalinefficiency is also found in the production process. Farmers are also technically inefficient, inorder to improve the technical efficiency of the farmers; the government should enhance itsextension services and provision of credit facilities in order for the farmers boost cowpeaproduction through financing its agricultural activities.REFERENCES[1] Agboola,S.A (1979). An Agricultural Atlas of Nigeria, Oxford University Press,London. pp 95-97.[2] Agwu, A.E (2001). Commercialization of agricultural extension services deliver inNigeria. Prospects and problems proceeding of the seventh annual nationalconference of the agricultural extension society of Nigeria.64

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)[3] Aigner,D.J,Lovell, C.A.K and Schmidts, (1977). Formulation and estimation ofstochastic frontier production model. J.Econometrics, 6: 21-37.[4] Amaze, P.S, Kwaghe, P.V and Ojo, N. (2005). Determinants of wheat production andtechnical efficiency in the Chad Basin Development area Nigeria. Nigeria Journal ofAgric. Food Environ 2:1-6.[5] Amaze PS and Olayemi JK 2000. The influence of education and extension contact infood crop production in Gombe state, Nigeria. Nigeria Journal of Agricbus. RuralDevelop. 1: 80-92.[6] Battesse, G.E and Corra,G.S 1977. Estimation of production frontier model withapplication to the pastoral of eastern austrilia. Australian Journal of AgriculturalEconomics,2 :169-179.[7] Bravo-Ureta,B.E and Pinheiro, A.E. (1993). Efficiency analysis of developing countryagriculture. A Review of the frontier function literature. Agricultural and ResourceEconomics Review, 22 :88-101.[8] Ekunme,PA and Emokaro, CO( 2009).Technical efficiency of catfish farmers inKaduna, Nigeria. Journal of Applied Sciences Research 5(7):80-85.[9] Ebong, V.O. 2005).Resource use efficiency in oil palm production in Akwa IbomState, Nigeria. J. Cult. Develop. 7:23-38.[10] Ebong VO, Okoro US and Effiong, EO 2009. Determinants of technical efficiency ofurban farming in uyo metropolis of Akwa Ibom State. Nigeria Journal of Agric andSocial Sciences.[11] Gungula,D.T andGarjila, Y (2005). The effects of phosphorus application on growthand yield of cowpea in yola. Journal of Sustainable Development in AgricultureEnvironment 1(1).[12] Gibbon, D and Pain, A (1985).crops of the drier regions of the tropics. LongmanGroup. Singapore pp. 111-112.[13] Sajo,A.A and Kadams, A.M (1999). Food and cash crops in A.A. Adebayo and A.I.Tukur (eds). Adamawa State in Maps,Yola, Nigeria. Paraclete Publishers pp 37-40.[14] Singh, S.R and Jakai, E.N (1985). Insect pest and of cowpea in Africa: Their LifeCycle, Economic Importance and Potential Control. In singh,R.S and Rachie,K.O(eds).Cowpea research production and utilization (CRPU) John Willey,S.R and SonsLtd pp 217-231.[15] Tadesse, B. and Krishnamoorthy, 1977. Technical efficiency in paddy farms of tamilnadu: An analysis based on farm size and ecology zone. Agric. Eco. 16: 185-192.[16] Omonona, B.T,Egbetokun, A.O and Akanbi, A.T (2010). Farmers resource-use andtechnical efficiency in cowpea production in Nigeria. Economic Analysis and PolicyVol 40. No1.[17] Ojo,SO and Ehinmowo, OO (2010). Economic analysis of Kolanut production inNigeria. Journal of Social Science 22(1):1-5.[18] Onyenweaku, C.E, Igwe, K.C and Mbanasor, J.A( 2005). Application of a stochasticfrontier production function to the measurement of technical efficiency in yamproduction in Nasarawa State, Nigeria. Nigeria Journal of Sustainable Trop. AgricRes. 13:20-25.65

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)MOVEMENT POPULATION IN THE SECOND OF XX ANDBEGINNING OF XXI CENTURY: THE CASE NORTHEASTERN MONTENEGROGoran Rajović, Jelisavka Bulatović, ResearchersCollege of Textile Design, Technology and Management, Belgrade, SerbiaE-mail: dkgoran.rajovic@gmail.com, jelisavka.bulatovic@gmail.comPhone: 0038161/19-24-850, 003861/ 3082651ABSTRACTThis paper analyzes population trends northeast of Montenegro in the second half of thetwentieth and early twenty-first century. The population has increased in the period 1948-2003 to 2.16%, but with a tendency to decline from 1981. The population in are period 1981-2003, decreased by 14,674, or 21.16 %. Parameters of natural population indicate a negativetrend. So the natural increase in 2003 in the municipality was Andrijevica - 4.6 ‰, in themunicipality of Plav 4.21 ‰, and Berane 9.29 ‰, significantly lower than in the beginning ofthe seventies. Population migration indicates uneven density and population concentration.The existence of a large number of settlements up to 500 populations (81) is not suitable formodern flow to vital economic development of the region.KEYWORDSNortheastern Montenegro; Density of population; Natural change; Migration.Northeastern Montenegro covers an area of 1486 km² and the population census in2003 there lived 54 658 inhabitants, or 36.8 in/ km². It covers three municipalities: Berane,Andrijevica and Plav. The paper provides a review and interpretation of the basic parametersof population trends northeastern of Montenegro in the second half of the twentieth and earlytwenty-first century.Unlike the nineties of the last century, the population of the region during the seventies,moving out to a much lesser extent, we can explain the material well-being of the formerYugoslavia. Specifically, the seventies of the last century, many remained in my memory asa period when the well-earned and well-lived. In this regard, we should not be surprised thatin most walks of socialism remained in my memory as the past is better than the presentmeager (Bolčić and Milić, 2002). But in the early eighties of the last century, Yugoslaveconomy began to show signs of crisis. In this regard, the fall in the population of northeasternregion of Montenegro, at that time, it seems to us quite expected. In fact, manycompanies have started to noticeably reduce the workforce, and the process of job creationhas slowed. It is also a time of mass migration of population from rural to urban areas, ortemporary work abroad.Nineties of the last century, represent an extremely complex period in the social life ofour population. In addition to long-term demographic factors on the development of theregion seemed a series of major historical events. "The disintegration of the formerYugoslavia, the war in the region, the sanctions of the international community, the socialand political changes, the deep economic crisis, military intervention, political developments,institutional crisis... Feeling, above all, economic and existential uncertainty, the basiccharacteristics of people's lives during this period that the individual and psychological NEW”(Tucović and Stevanović, 2007). The account should be taken of the consequences oftransition in 2000, the most important being the increase of unemployment, poverty,increased mortality rates, shorter life expectancy.Migration of the population is characterized by uneven settlement density andpopulation concentration. The classification of settlements in northeastern Montenegro bypopulation size in 2003, show that in the region of the village had as many as 18 to 100people, or 81 to 500 village residents. These settlements are characterized by demographicexhaustion of resources, due to the negative net migration, and because of the lack ofbiological population replacement, as well as age and education structure of the population.While the population of the village a little off, on the other hand there is a strongconcentration of population in Berane, Luge Beranske, Gusinje and Plav, which results in66

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)demographic imbalance and large differences in population density and population betweenspatial entities, with particular demographic, economic and social polarization.RESEARCH METHODOLOGYThis paper has several objectives. First of all, the analysis of the existing literature aimsto establish the number and density of the population of northeastern Montenegro. The nextgoal is to show the change in population in the region. And finally, we need to identify thereasons and motives of population movements and highlight the factors that have led to themigration of the population.The methodology is primarily based on an analysis of the existing literature on thepopulation of the region and statistics. From the existing literature, we used both domesticand those published in the international literature. On this occasion of the internationalpublication emphasize this: Holmes (1971), Foord (1975), Parr (1987), Zah (1994), van derLaan (1998), Artis and Romani & Surinach (2000). There are literature monographs onpopulation, proceedings and textbooks. Were studied and written sources on the internet.The scientific explanation of terms, we applied two methods are used: analytic and synthetic.Analytical methods are considered some of the dimensions of the research topic, and asynthetic whole, the interconnections between the case and suggested measures that derivethere from.RESULTS AND DISCUSSIONNumber and population density. The population of Northeastern Montenegro ischaracterized by steadily declining in relation to the dynamics of the population. This in 1948the population of the region seemed 14.17% of the population and 8.12% in 2003.The percentage increase of population, accounted for 1948-2003, 43.96%. However,northeastern Montenegro shows significant deviations from these population dynamics.Thus, the percentage increase in population during the period amounted to 1948-2003,2.16%, but with a tendency to decline from 1981. Namely, in the period 1981-1991population of Northeastern Montenegro is reduced from – 0, 63% to - 6.31%, from 1991-2003 - 6.31% to - 15.9%. The general conclusion is that the Northeastern Montenegro, hadover a period of extreme depopulation of 1981, which had a negative impact on the overallsocial and economic developments, and that means the population decline in the near pastthirty years. The population in northeastern Montenegro, in are period 1981-2003 decreasedby 14,674, or 21,16%.Table 1. Change of population in Montenegro and the region in the period 1948-2003– 1948. 1953. 1961. 1971. 1981. 1991. 2003.POPULATIONMontenegro 377.189 419.873 471.896 529.604 584.310 615.037 673.094Region 53.477 57.973 62.993 68.893 69.332 64.954 54.658The percentage share of the population of MontenegroRegion 14,18 13,81 13,35 13,01 11,87 10,56 8,12The percentage increase or decrease in population– 1948/53. 1953/61. 1961/71. 1971/81. 1981/91. 1991/2003. 2003/48.Montenegro42.684 52.023 57.708 54.706 30.727 58.060 295.905Region11,32%4.4968,41%12,40%5.0208,66%12,23%5.9009,37%10,33%- 439-0,63%5,26%- 4.378-6,31%9,44%-10.296-15,9Source: Statistical Office of Montenegro, Census of Population (appropriate year), calculations by.43,96%1.1812,16 %Under the influence of general demographic principles, but also many geographical,historical, socio-economic factors in northeastern Montenegro demographics presentsignificant spatial differences. "Urbanization and industrialization, and geographicenvironment and unfavorable, as the dominant factors of population transfer, led to the67

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)emergence of the concentration of people in a favorable area, and the depopulation of theneighboring mountain of unfavorable areas, or to discharge them from the population andtheir" drip "in one of the first, the more favorable areas "(Jaćimović, 1989).Based on the demonstrated tendency of the forward movement of the total populationin northeastern Montenegro, it is possible to single out one hand and on the otherdepopulated areas of population concentration areas (see map no. 1). The depopulation ofareas which include 85 from a total of 113 villages, or 72.81% (1082 km²), the total area ofthe region (1.1486 km²), census 1971 lived 37 851 inhabitants (59.94% of total population),and in 2003.year 9578 population (17.52% of total population). Therefore, depopulation isevident in the demographic sphere in ...... its lack of natural regeneration, changes indistribution and density.... (Spasovska and Ilić, 1989). For example, pronounceddepopulation in rural areas, and who could not keep the population was (an index for theperiod 1971-2003, settlements Kurikuće 28.8, Dulipolje 29.0; Seoca 30.0, Bastahe 38.5;Kralje 40.3, Upper Ržanica 45.2...). Areas of population concentration in growth ofpopulation, 1971 census they were living in 31 042 people (45.06% of total population), and45 080 inhabitants in 2003 (82.47% of total population). A substantial population growth inthat period, record the settlement in the vicinity of Berane: Dolac (index 212.5), Pešca (index197.9), Luge (index 150.6), Beran Selo (index 162.9), Lužac (index 107, 5).Figure 1. Zone of concentration and zone depopulation in northeastern MontenegroThe general conclusion is that the depopulation of the north-eastern Montenegro, afterthe 1971 settlement was higher in remote mountainous areas and municipal centers, whilegrowth had community centers, which lie along important roads, especially the road, and onein the valley widening which the overall living conditions were more favorable (Bakić et al1991). Thus, the hallmark of are modern development of rural areas in the region, given thatthe process of depopulation processes of industrialization and urbanization. . Age groups,due to migration and the reduction of fertility change and take on unfavorable characteristics,reduces the proportion of younger and older increases the proportion of the population. In68

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)both cases, the disturbed age structure has a reverse effect on the movement of thepopulation (the size of reproductive contingent), but also to all other structures of thepopulation (the size of contingent employment, population, compulsory school contingent,contingent dependent population ratio)( see more Rajović and Bulatović, 2012).Table 2. Movement of the total population in northeastern Montenegro 1971-2003SpaceTotal population IndexArea in km 2 Density (the population km2 )1971. 2003. 71/03.1971. 2003.Zone concentration 37.851 45.080 395,2 1.082 76,8 8,85Zone depopulation 31.042 9.578 68,9 404,1 35,0 111,6Total 68.893 54.658 126,0 1.486 46,4 36,8Source: Statistical Office of Montenegro, Census of Population (appropriate year), calculations byGeneral population density is one of the basic demographic characteristics that indicatethe spatial distribution of population. In northeastern Montenegro, it was reduced from 46.4in/km 2 (in 1971) to 36.8 in/km 2 (in 2003). In areas of depopulation density is reduced from35.0 in/km 2 (in 1971) to 8.85 in/km 2 (in 2003). In some rural areas of hill and mountain areasit is extremely low, for example: Cecuni 2.7 in/km 2 (in 1971 10.0 in / km 2 ), Kuti 3.3 in /km 2 (in1971 8, 8 in/km 2 ), Vuča 2.6 in/km 2 (in 1971 15.4 in/km 2 ). In contrast, in zones ofconcentration of population density increased from 76.8 in/km 2 in 1971 to 111.6 in/km 2 in2003. Densely populated, urban areas in addition, characterized the settlement in the vicinityof urban centers and municipal Berane and Plav: Pešca 1497.5 in/km 2 ; Gusinje 808.3 in/km 2 ; lower Luge 607.5 in/km 2 ; Dolac 175, 8 in/km 2 ; Budimlja 173.4 in/km 2 .Formed from such a density, we can state the following:1. First that the distribution of the population in northeastern Montenegro in 1971 was incorrelation with the impact of geographic relationships of natural conditions (physical)type and2. Second those rural settlements are still a source of power of the population (Bakić et al1991).Beginning of the eighties was the decisive moment. That in this period begin with thepreservation of rural settlements, construction of traffic infrastructure, development of smallbusinesses, today northeastern Montenegro, would not confirm the model selected asrepresentative (typical), as in all categories figures as part of the dominant and widespreadoccurrence and trends in rural areas of Montenegro, which is treated and consideredunderdeveloped. With great certainty, it can be argued, that this distribution of the populationin northeastern Montenegro had its causes in the economic underdevelopment, but alsoadverse effects on the natural growth and migration, which will show the following analysis.Natural movement of population. The population in northeastern Montenegrodepended on the balance of natural and migratory movements. The population issue, inaddition to the rural exodus and the concentration of population in municipal areas, came tothe fore the ongoing process of reducing population growth.The birth rate in the municipalities of Berane, Andrijevica and Plav for decades has atendency to decline. The birth rate of 22.2 ‰ in municipalities Berane and Andrijevica and25.4 ‰ in the municipality of Plav, 1971 shows that for every 1,000 inhabitants in themunicipalities of Berane and Andrijevica 22.2 babies born in the municipality of Plav 25.4babies while in 2003, 11.1 children born in the municipality Andrijevica, 12.5 in Berane and12.9 in the municipality of Plav (by the division of the People's Republic of Montenegro to themunicipalities of 15.04.1960.godine Andrijevica municipality was abolished and thenconnected communication Ivangrad. From then until 1991, official statistics provide uniquedata for Ivangrad or municipality Berane, which applies to the municipality Andrijevica)(Rajović and Bulatović, 2013).So in terms of territorial distribution of fertility in northeastern Montenegro, we can drawthe following conclusions:1. The birth rate would be more likely, that there is a higher standard of living, betterconditions of employment, housing, education, childcare and69

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)2. That of Berane, Andrijevica and Plav no longer an inexhaustible source of labor forceand population.Despite falling birth rates, death rates show that for every 1,000 inhabitants in 1971 inthe municipalities of Berane and died Andrijevica 6.2, 3.0 and the municipality of Plavpopulation. So, there was an increase in the mortality rate. This is confirmed by data for 2003and indicates that the mortality rate ranged from 3.21 deaths in Berane, 8.69 in themunicipality of Plav Municipality and 15.7 Andrijevica. The biggest change in the relationshipbetween fertility and mortality, and thus change the rate of natural increase had Andrijevicamunicipalities. These municipalities had negative population growth in 2003 -4.6‰. In themunicipality of Plav population growth that year was 4.21‰, and Berane 9.29 ‰. In thefuture we should expect a stagnation of population growth rate, due to the migration andadaptation of the current population in the region, a new way of life and plan members in thefamily)(see more Rajović and Bulatović, 2013).The changes that have occurred in our society in the last decade of the last century, inbetween census 1971 and 2003.years, were affected by changes in population trends in theregion. In addition to mechanical and natural movement of the population was under theinfluence of social change, namely the social crisis. If we take into account the determinantsof fertility decline: decrease in the share of agricultural population (9.1% of the total ruralpopulation), housing, health care, social protection, later marriage, and the changes thathave occurred in this period, the apparent the social impact of the crisis on the level of thebirth rate. Another consideration, and transition effects, the most important being theincrease of unemployment, poverty, increased mortality rates, shorter life expectancy. Thereduction in the already small number of live births are affected just unemployment, a verypoor financial situation and social instability. On the other hand, the mortality rate hassteadily increased due to inadequate health care, lack of medicines, poor diet, but alsobecause of the increase in the proportion of the population over 60 years (Stojšin, 2004).As a basic form of existence, the whole family during the emergence of developmentnortheastern of Montenegro was a pillar of the organization of life and economic activities.Some reasons for its closure are deep, as the reasons for termination of life in many ruralareas of our country. Probably the wrong attitude of society towards the family andrelationships in it and caused the disturbed relations in other spheres of life and work. Not atthis point, you can get into all of the importance of family in the development of populationand economy of the region. "Modern science has neglected the role of family in shapingeconomic - demographic processes, which makes the totality of these processes can notexplain, and some of them receive a stencil - an abstract form" (Boonefozc, 1968).Population growth is the result of natural relations of movement and migrationprocesses. If the region does not make any migration of the population, then the growth rateand population growth were the same, that there would be a territorial population balance."However, this situation actually exists nowhere" (Ilić, 1973). There is not in the northeasternpart of Montenegro. Therefore, the municipality Berane, Andrijevica and Plav has verycomplex demographic components related to population growth. In addition, to note thatthese components are territorially unevenly distributed causing the demographic imbalance,unstable economic conditions. These facts, as well as uneven economic development,compared to other regions of Montenegro, causing significant migration movements. Theseprocesses are 70's of last century were intense. "Therefore, their amounts in the generalpublic are often taken as an important proof of the vitality of our socio-economic system.However, in our opinion, the right score can be obtained if the process is put in an objectiveframework or, if you locate the temporal, geographical and socio-economic "(Ilić, 1973). Howlong and to what extent the rate of population growth in the region should fall very hard tosay because we do not have the necessary indicators of economic development in thefuture. But if the population growth rate is still declining, may be considered space in the timeto get into a lot of difficult economic situation, due to demographic aging and reduce theworking population.Migration. From the aspect of nationality, it is possible to partition the migration:external (mobility across national borders) and internal (within the country). "The fact is that70

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)most developed migration in contemporary urban stage especially since the beginning of theseventies of the twentieth century to the present time" (Stamenković, 1996). By their naturewere radical and immediately noticeable. Changing the image of the village in a short periodof time, the effects are achieved with a different sign, occur primarily in rural areas and areborn out of poverty. It is not very well known that it occurred where space is economicallydeveloped. With the rugged northeastern mountains of Montenegro, the whole are familydown to the valley, moving closer to roads and easier life in urban areas. "This movementresembles a river that is constantly going down are mountain or the constant wind flow fromhigher to lower areas") (Pavlović and Radivojević & Lazić, 2008). Whether you are coming orinhabited part of the Northeast Montenegro from local or distant parts, whether they areleaving the region in search of a better life, the residents had a strong influence ondevelopments in the area. Arriving, they brought new customs, system of construction,aesthetics and culture of living. Leaving, we changed the image of the village, as removingthe previous spatial relationships, and creating opportunities for someone new and notadapted to the environment adapts in space, which is happening quite often.Their impact is evident in all areas of the territorial space of the complex considered inany relevant geographic features of the settlement (demographic, morph-physiognomic andfunctional). In this sense, is characterized by continuous changes in demographiccharacteristics (1948-53477, 1961- 62993, 1971-68993, 1981- 69332, 1991-64954 and 2003-54658 inhabitants), morph-physiognomic structure (modern functional zoning, types ofhouses…) and regional-functional characteristics (increase in functional capacity anddevelopment of new external functions - industrial, tourism….).Group of important and characteristic features of population migration northeast ofMontenegro in the last forty years, belonging to the following:1. Changes in the territorial structure of the immigration population,2. Matching period of industrial development with the continuing dominance of themigration phase of migration,3. Significant representation of labor migration (temporary work) population abroad and4. Developed and diversified regional daily movement of workers, pupils and students toother places in the same municipality, other municipalities in Montenegro, another ofthe Republic (Serbia) or a foreign country or to an unknown place of work or schooling.SpaceTable 3. Indigenous and migrant population in municipalities with respect to the totalpopulation of the region in 2003TotalSince the birthof lives in thesame placeTotalimmigrantSettlers fromthe territory ofa municipalityDisplaced fromothermunicipalities ofthe sameRepublic -AutonomousProvinceMigrants fromother Republic- AutonomousProvinceBroj % Broj % Broj % Broj % Broj %Andrijevica 5.785 4.427 76,53 1.358 23,47 559 9,66 612 10,58 187 3,23Berane 35.068 28.088 80,10 6.980 19,9 3.559 10,15 2.062 5,88 1.359 3,88Plav 13.805 11.711 84,83 2.094 15,17 1.257 9,11 386 2,79 451 3,27Region 54.658 44.226 80,91 10.432 19,09 5.375 9,83 3.060 5,60 1.997 3,66Source: Statistical Office of Montenegro, Census of Population (appropriate year), calculations by.The territorial structure of the studied population migration geo-space, suggests thefollowing structural and developmental characteristics:1. Major presence in the region has an indigenous population of 80.91% by municipalitiesAndrijevica 76.53%, 80.10% Berane and Plav 84.83% compared to the total populationin 2003,2. Total immigrant population in the region is 19.09%, have a major presence, settlersfrom the territory of a municipality 5375 or 9.83%, followed by settlers from the71

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)territories of other municipalities in Montenegro 3060 or 5.60%, and finally, immigrantsfrom Serbia and other state 1997 or 3.66%.3. Fluctuations in the level of participation of individual territorial categories are negligible,except for the categories of immigrant population from the same municipality and4. Highlighted the apparent displacement of the population in the short geographicdistance.Per iodization of immigration in the northeastern part of Montenegro, is determined bythe pace of socio-economic development, because the phase of the urban socio-economicdevelopment coincides with periods of immigration. Namely, in are period before 1940 andmoved to the region 89 persons or 0.85% of the total number of immigrants (-26 Andrijevicaor 1.91%, Berane - 53 or 0.76%, Plav 10, or 0, 48%), 1941-1960 1300 persons or 12.47%(Andrijevica - 256 or 18.85%, Berane - 894 or 12.81%, Plav -150 or 7.16%). In the period1961-1970, the number of settlers in the region amounted to 1221 persons or 11.70%(Andrijevica - 166 or 12.22%, Berane - 951 or 13.62%, Plav -104 or 4.97%).SpaceAndrijevicaTotalimmigrant1.358 26Berane 6.980 53Plav 2.094 10Region 10.432 89Table 4. Per iodization and the volume of immigration1940 andbeforeNumber1941-1960 1961-970 1971-1980 1981-1991 1991-2003 Unknown% Numbe Numbe Numbe Numbe Numbe% % % % %rrrrr1,9 18,8 12,2 12,3 16,6 27,8256 166 167 226 3781 520440,7 12,8 13,6 13,9 14,4 17,1894 951 971 1.006 1.1946 121110,421,2150 7,16 104 4,97 112 5,35 209 9,98 445850,8 12,4 11,7 11,9 13,8 19,31.300 1.221 1.250 1.441 2.0175 70823Source: Statistical Office of Montenegro, Census of Population (appropriate year), calculations byNumberIn the period 1971-1980, the number of settlers in the region amounted to 1250persons or 11.98% (Andrijevica - 167 or 12.30%, Berane - 971 or 13.91%, Plav -112 or5.35%). In the period 1981-1991, the number of settlers in the region amounted to 1441persons or 13.82% (Andrijevica - 226 or 16.64%, Berane - 1006 or 14.41%, Plav -209 or9.98%). The largest volume of immigration is related to the period 1991-2003 and thenmoved into the region 2017 persons or 19.33% (Andrijevica - 378 or 27.84%, Berane - 1194or 17.11%, or 21 -445 Plav, 25%). Therefore, the scope immigration related to the period ofindustrial development since the beginning of the 60s of last century onwards that culminatedin the early 90 of last century. Highlight the extent of the migration periods: 1981-1991. -1441 or 13.82%, and 1991-2003. - 2017 or 19.33%.The exact number of displaced inhabitants of Montenegro and their descendantsaround the world, certainly, we can not have definitive data. Various sources mention a figureof 90,000 to 120,000 Montenegrin emigrants. The fact is that there is no institution inMontenegro, which has accurate data on the number of Montenegrin emigrants abroad, andtherefore not considered in the geo-space. However, all agree that this number is not smalland it is increasing every year (Rajović, 2011). According to the census of 2003, 54 816citizens of Montenegro was temporarily working abroad, which was 8.84% compared to thetotal population of Montenegro. In relation to the total population of the municipality, themunicipality of Plav temporarily working abroad was 57.4%, 16.9% Berane and Andrijevica12.0% of the population (htpp://www.cg.dijaspora@cg.yu). In the meantime are ceased tooperate the State Union of Serbia and Montenegro which means that the status has changedand people who are from Montenegro and live and work in Serbia. The exact number ofpeople, it is difficult to give precise figures, but various estimates suggest that at least thisnumber is between 60,000 and 80,000. Reasons for not determine exact number ofMontenegrin emigrants were numerous, and especially emphasize, illegal migration thathave characterized the ex-Yugoslav space, as well as recording people with Montenegrin1391.9111.0643.114%10,2427,3850,8129,8572

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)space in the receiving countries as Yugoslavs, Serbia and Montenegro citizens and ex-Yugoslavs (htpp://www.cg.dijaspora@cg.yu). "There is no doubt that the departure of many,especially young people, is much more complex socio-geographical problem. State of theeconomy, level of industry and failure of agricultural development are decisive effect on themigration process. Job opportunities, job creation and the amount of personal income, arethe causes that affect the process of movement of labor abroad. The desire to earn a shorttime to buy an apartment, made a house, bought the estate, car, tractor or other machinery,are common and expressed motivations of migration abroad"(Rodić, 1972). Spatial effects ofour workers abroad are numerous and conspicuous in the region (spatial and functionalchanges in the organization of rural backyards, modern types of rural houses, commercialbuildings).The importance of the study of commuting between the village stems from thecomplexity of the relationship of commuting and migration of people to the outcome of thechange of residence. Commuters are often potential migrants, and people with previousexperience of migration, a daily migration for short distances most common method ofadjusting the alternative migration (Holmes, 1971; Zax, 1994, Artis and Surinach & Romani,2000).Daily migrants considered geo-space, which are the subject of our interest, can bedivided into two categories: workers (2534 or 52.33%) and school youth - students (2,318 or47.67%). The workers usually commute to the workplace in urban areas by the center of themunicipality. The modernization of the economic structure, as a result of the transition fromthe dominance of production dominance of the service sector, in particular the developmentof information technology, leading to changes in the spatial distribution of commuting (Vander Laan, 1998). Of the total number of commuters (4852), workers who are employed orwork in other places in the same municipality within the region is - 60.22%, the secondMontenegrin municipality of -31.89%, the Republic or another foreign country - 3,95% and anunfamiliar area of waste also 3.95% of workers. Quantitative indicators of the relations ofpeople commuting by activity in a certain way about the organization are functional division inthe economy and relations between different branches of activity" (Stamenković, 1989).Intense population growth of secondary sector of the economy (27.80%) multi-influencedphenomenon of commuting, for accelerated development of the secondary sector follows thedecline of the primary (13.78%), on the one hand, while on the other, in parallel with thesecondary sector, developed following the tertiary (19.82%) and quaternary (24.88%). Thisfinding, among other things, vividly illustrated, the daily migration of the active population byoccupation (see table no. 5).Table 5. Commuting active population by occupation 2003Active who are employed work inSpaceOther settlements Another municipality Second Republic or Unknown place ofTotal within the municipality of Montenegro a foreign country workNumber % Number % Number % Number %Andrijevica 669 349 52,17 292 43,65 13 1,94 15 2,24Berane 1.551 975 62,86 460 29,66 71 4,58 45 2,90Plav 314 202 64,33 56 17,83 16 5,10 40 12,74Region 2.534 1.526 60,22 808 31,89 100 3,95 100 3,95Source: Statistical Office of Montenegro, Census of Population (appropriate year), calculations byStrengthening the spatial mobility of population in the northeastern part of Montenegrois following growth in the daily movement of students. Of the total number of commutingstudents (2,318), students who study in other places in the same municipality within theregion is 66.01%, the second Montenegrin municipality of 17.33%, other foreign country orthe Republic 14.50 % and an unfamiliar area of waste education 1.77% of their students."Spatial distribution of daily mobility ..... school youth in municipalities ... inseparable from thenatural and geographical features, geopolitical situation and the current level of socio-73

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)economic development, hence the important differences between them in terms of volume ofcommuting "(Stamenković, 1989) (see table no. 6).Table 6. Commuting students in 2003Students are educated inSpaceOther settlements Another municipality Second Republic or a Unknown placeTotal within the municipality of Montenegro foreign country of workNumber % Number % Number % Number %Andrijevica 458 288 59,38 162 33,40 29 5,98 6 1,24Berane 1.358 994 73,20 109 8,03 233 17,16 22 1,62Plav 475 248 52,21 140 29,47 74 15,58 13 2,74Region 2.318 1.530 66,01 411 17,33 336 14,50 41 1,77Source: Statistical Office of Montenegro, Census of Population (appropriate year), calculations by.Until are advent of commuting between the different levels of the hierarchy within thenetwork of settlements brought a number of factors. One is the desire to live on the secondlevel of the hierarchy than the one in which the worker is employed (either in terms of theopportunities it offers a level of hierarchy or the presence of relatives, friends). In this case,the worker is willing to submit commuting costs, including the time needed (spent) on thedaily journey. Another factor is the fact that an individual employed in a particular level of thehierarchy can not afford the cost of living at that level, but can not afford the cost of living inthe second level, along with the cost of commuting. The third factor includes the possiblebenefits derived from the physical separation of work and residence (Parr, 1987).Rue picture of the distribution of population in the region is difficult to assess. Amongthe external factors, not economic migration an important role in choosing the type of spatialmobility of the population and the migration flows have space organization. Imply a set oforganizational factors that may influence or control to facilitate migration between the twospaces (Swindle and Ford, 1975). It is useful to ask why the border village of 500 inhabitants.This is the minimum number of inhabitants will assure the development of certain centralfunctions, which will serve a wider area (Simonović and Ribar, 1993). Although thepopulation of the rural villages is divide into two groups: (0 - 100 and 100 -500 people) forboth can be said to belong to a group of rural settlements which are substantially flat. In thisfirst, size of the group (18 settlements) has further depletion trend of space, a second group(63 villages), this trend is mitigated.Today is very unevenly distributed network of settlements in northeastern Montenegro,make settlements with small populations. Most of them are from 100 - 500 (63 settlements),followed by 500-1000 (18 villages) and over 1000 (16 settlements). It is noticeable lack ofsettlements with over 2000 people (only 4 settlements with over 2000 inhabitants: 12 651Berane; Luge Beranske 2011; Gusinje 3015; Plav 5554). Only in these settlements, we cantalk about the real potential for the development of central functions, and this size appears asother important Joints in numerical terms (the symbols used in Table 7 ♦ village belongs tothe municipality Berane ♣ village belongs to the municipality of Plav ▼ village belongs to themunicipality Andrijevica).Table 7. Distribution of the population to population in urban areas0 - 100Villages Population Villages PopulationBastahe ♦ 70 Murovac ♦ 59Veliđe ♦ 29 Poroča ♦ 92Vuče ♦ 26 Praćevac ♦ 49Zagrad ♦ 55 Rujišta ♦ 56Jašovići♦ 33 Skakavac ♦ 89Kuti ▼ 49 Novšići ♣ 87Lješnica ♦ 64 Cecuni ▼ 77Oblo Brdo ▼ 69 Lazi ♦ 99Orah ♦ 90 Tmušići ♦ 39100 -50074

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)Azanje ♦ 146 Velika ♣ 417Andželati ▼ 146 Lubnice ♦ 256Babino ♦ 446 Luge Andrijevičke ▼ 165Božići▼ 292 Marsenić Rijeka ▼ 414Bojovići ▼ 137 Mašte ♦ 210Bor ♦ 317 Mezgalji ♦ 208Bubanje ♦ 212 Dolja ♣ 315Vrševo ♦ 475 Dosuđe ♣ 438Glavica ♦ 130 Orahovo ♦ 165Gnjili Potok ▼ 118 Pahulj ♦ 141Godočelje ♦ 243 Ponor ♦ 146Gornje Zaostro ♦ 236 Prisoja ▼ 387Gračanica ▼ 336 Radmuževići ♦ 106Dašča Rijeka♦ 195 Rovca ♦ 105Dobro Dole ♦ 272 Savin Bor ♦ 449Trepča ▼ 267 Seoca ▼ 125Donje Zaostro♦ 149 Sjenožeta ▼ 121Dragosava ♦ 173 Slatina ▼ 419Dulipolje♦ 135 Đurička Rijeka ♣ 438Đulići ▼ 130 Ulotina ▼ 284Zagrađe ♦ 296 Crljevine ♦ 118Zagroje ♦ 330 Crni Vrh ♦ 146Zabrđe ▼ 342 Štitari ♦ 288Javorova ♦ 170 Višnjevo ♣ 190Johovica ♦ 258 Gornja Rženica ♣ 269Jošanica ▼ 166 Grnčar ♣ 360Kaludra ♦ 267 Kolenovići ♣ 484Kalica ♦ 250 Mašnica ♣ 314Košutići ▼ 143 Trepča ▼ 267Kralje ▼ 268 Skič ♣ 443Kruščica ♦ 109 Kurikuće ♦ 115Crljevine ♦ 118500 - 1000Donja Vrbica ♦ 831 Radmanci ♦ 646Hoti ♣ 585 Trešnjevo ▼ 600Vinicka ♦ 639 Tucanje ♦ 655Meteh ♣ 586 Bogajići ♣ 599Goražde ♦ 599 Kruševo ♣ 505Gornja Vrbica ♦ 833 Murino ♣ 580Dapsići ♦ 779 Petnjica ♦ 778Donja Ržen ica ♦ 829 Petnjik ♦ 713Lagatori ♦ 969 Lužac ♦ 8421000 -2000Beran Selo ♦ 1.568 Trpezi ♣ 1.416Budimlja ♦ 1.745 Vusanje♣ 1.887Dolac ♦ 1.335 Vojno Selo ♣ 1.036Pešca ♦ 1.857 Prnjavor ♣ 1.306Andrijevica ▼ 1.193 Buče ♦ 1.048Brezojevica ♣ 1.035 Martinovići ♣ 1.312Preko 2000Berane ♦ 12.651 Plav ♣ 5.554Luge Beranske ♦ 2.011 Gusinje ♣ 3.015Source: Statistical Office of Montenegro, Census of Population (appropriate year), calculations by.The existing network of settlements is a consequence of the no uniform density andconcentration of population. A large number of settlements up to 500 people (81 settlements)are not suitable for modern developments vital for economic development. Namely, there is alack of rural villages with rural center of over 1000 inhabitants (municipality Andrijevica) as acategory that would connect the primary rural settlements of the municipality, with the centerof the region - Berane.Historical experience shows that the village still giving the city a vital workforce, thebest people and the demographic they zoom in and refreshed. The villages are a long heldtradition, positive character traits, maintains love for the preservation of national values(Bakić, 1988). Taking all this into account subjective factors of decision making and planning75

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)would have to bear in mind all these facts, and commitments to ensure their planningactivities of the agents that will keep the life in the villages and preserve the demographicvitality, as prerequisites to secure national life on the cliff north-eastern region ofMontenegro. Such a conception of the network of settlements would have created favorablesocio-economic conditions for the spatially homogeneous development of all parts of themunicipal territory, and that means a region as a whole.NortheasternMontenegroBERANEANDRIJEVICABERANEPLAVBudimlja BeranDolacLuge Pešc TrpeziVillageBučeANDRIJEVICA612VojnoVillageVusanjeGusinjePrnjavorBrezojevicMartinović17Figure 2. Functional system of settlements in northeastern Montenegro:1. Berane as a regional center2. Municipal centers3. Community centers in rural villages with over 1000 population4. Other rural settlements• The numbers in circles indicate the village in a given territorial unitThe foregoing facts suggest the following conclusion:1. Migrations are one of many factors in the evolution and transformation of the region,2. Evident correspondence between migration flows and industrial development,3. Contemporary migration flows in complex geographical regions have the highestrepresentation of commuting workers, students, labor migration for temporary workabroad and4. Notes the lack of rural settlements with the center, as a category that would connectthe primary rural settlements of the region with the center - Berane.In a variety of demographic changes caused by migration, as well as some relevantmorph-physiognomic and functional changes, such as:76

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)1. Significant increase in urban population, and in connection with the representation ofnew urban facilities and2. Regional-functional development, which is achieved through a gradual change ofeconomic structure of urban settlements (Stamenković and Baćević, 1992).The spatial development of urban settlements is to achieve the expansion in theperipheral, the construction of residential, commercial and recreational facilities. Generalurban plan is planning to functional zoning is constituted. Make it a center of urban areas,residential areas, recreational and work. This is accomplished using urban territory it is afunctional need zone conditions of urban life.Regional Development is functional mark major changes. They are reflected in thedecline of primary and strengthening secondary, tertiary and quaternary functions of urbansettlements. The main change is reflected in the fact that the leading agricultural functionsceded its place industry (transfer of agricultural to an industrial population). At the same time,due to increased mechanical influx of population, mostly from the surrounding rural areas,there is a transfer of rural to an urban population (Stamenković and Baćević, 1992). Somefurther analysis needs to show what is in today's economic conditions more acceptable andreasonable. Life according is to scattered small remote rural areas or urban settlement anddevelopment of industry in them. The urban areas of the region are still far from the actualextent of urban development.The overall data presented in this northeastern part of Montenegro, can serve as agood framework to display the size of the chosen model in the context of global events. Thestudied region is one of the underdeveloped regions, where the dispersion due to themorphological structure of the picked-there were significant disparities in the relative size andgrowth trends in municipal centers (Berane, Andrijevica and Plav) and other settlements inthe considered area. As the shattered village, structured by dense fragments (hamlet),remote and scattered on the territory of the corresponding region, the northeastern part ofMontenegro is an interesting and distinctive way, fit into a systematic picture of the village ofMontenegro. "Because it takes such a medium supplemented with new and more effectiveactivities this achieving a more balanced economic development at the country level, whichis one of the primary goals of local economic development. Positive examples of local andregional development, with well-defined strategy, were recorded in the following areas:Werttenberg Baden in Germany, Lorraine in France, Westphalia in the UK, Veneto and Friuliin Italy, Slovenia nearest us. This development concept is practical, since it includes all whowant to cooperate; it does not cost much and gives results, which is of particular importancefor underdeveloped countries "(Vojnović and Riznić &Borić, 2009).CONCLUSIONResults of analysis of population trends northeastern of Montenegro in the second halfof the twentieth and early twenty-first century, suggests the following conclusions:1. The population has increased in the period 1948-2003 to 53 477 to 54 658 population.Looking generally in relation to the 1948 population of the region increased by 2.16% in2003, but with a tendency to decline from 1981.2. On the basis of demonstrated tendencies in the movement of the total population in theregion can be identified: depopulation zone and zones of concentration of population.The depopulation of areas which include 85 from a total of 113 villages, or 72.81%(1082 km2), the total area of the considered geographic space (1.1486 km2), census1971 lived 37 851 inhabitants (59.94% of total population), and 2003, 9578 people(17.52% of total population). Areas of concentration in growth of population, census1971 they lived in 31 042 people (45.06% of total population), and 45 080 inhabitants in2003 (82.47% of total population).3. Parameters of natural increase are negative tendencies. The birth rate decreases,mortality increases. Thus, the birth rate is decreasing since the beginning of theseventies. So in 2003 the municipality Andrijevica was -4.6‰, in the municipality ofPlav 4.21‰, and Berane 9.29‰.77

Russian Journal of Agricultural and Socio-Economic Sciences, 1(13)4. Migration of population indicates an uneven population density and concentration ofpopulation. A large number of settlements up to 500 people (81 settlements) are notsuitable for modern vital flow of economic development in the region. It is noticeablelack of settlements with over 2000 people (only 4 settlements with over 2000inhabitants: 12 651 Berane, Luge Beranske 2011, 3015 Gusinje, Plav 5554). Only inthese settlements, we can talk about the real potential for the development of centralfunctions, and this size appears as another important fact in numerical terms.Finally, the demographic-economic problems of North-Eastern Montenegro should beviewed realistically, without undue optimism, pessimism and even less. The process ofgeneral and qualitative transformation of the region will be relatively very slow and timeconsuming. So you should work on it patiently, but persistently and continuously.REFERENCES[1] Bolčić S., Milić A.(2002), Serbia at the end of the millennium-destruction of societyand changes in daily life, “Filip Višnjić”, Belgrade.[2] Tucović O., Stevanović, R (2007), Natural population of Belgrade in the second half ofthe twentieth and early twenty-first century, Geographical Institute “Jovan Cvijić“,Serbian Academy of Sciences and Arts, Volume 57:144.[3] Statistical Office of Montenegro (2011), Census (appropriate year).[4] Holmes J.(1971), External Commuting as a Prelude to Suburbanization, Annals of theAssociation of American Geographers, Volume 61(4): 774-790.[5] Swindell K., Ford,R. (1975), Places, Migrants and Organization: Some Observationson Population Mobility, Geografiska Annaler, Series B, Human Geography, Volume57(1): 68-76.[6] Parr J. B. (1987), Interaction in an Urban system: Aspects of Trade and Commuting",Economic Geogruphy, Volume 63( 3): 223-240.[7] Zax J. (1994), When Is a Move Migration?, Regional Science and Urban Economics,Volume 24 (3):341-360.[8] Van Der Laan L. (1998), Changing Urban Systems: An Empirical Analysis at TwoSpatial Levels, Regional Studies, Volume 32 (3):235-248.[9] Artis J., Romani, J., Surinach, J.(2000), Determinants of Individual Commuting inCatalonia, 1986-1991: Theory and Empirical Evidence, Urban Studies, Volume37(8):1431-1450.[10] Jaćimović B. (1989), Influence of depopulation of changes in agricultural structureSouthern region, Institute of Geographical Science, Proceedings, Volume 36: 46.[11] Spasovska M., Ilić, J. (1989), Problems of demographic development and thedepopulation of rural areas in the SR Serbia, Geography Faculty of Science,Proceedings, Volume 36:69.[12] Bakić R. et al (1991), Geography of Montenegro – population, "University for" Book.1,Nikšić.[13] Rajović G., Bulatović, J.(2012), Some economic-geographic factors development ofthe example rural areas northeastern Montenegro, Russian Journal of Agriculturaland Socio- Economic Sciences, Number 9( 9): 3-20.[14] Rajović G., Bulatović, J. (2013), Analysis of shange in population structure: The CaseNortheastern Montenegro, Journal of studies in social siences, Volume 2(1):1-30.[15] Stojšin S. (2004), Society in transition and change population trends - example ofVojvodina, Sociological review, Volume 38(1-2): 355.[16] Boonefozc E.(1968), Le monde est il surpleuple, “DII Press“, Nashate.[17] Ilić J. (1973), Basic and dynamic characteristics of the regional growth of populationin Serbia after World War II, Institute of Geographical Science, Proceedings, Volume20:118-119.[18] Stamenković Đ.S. (1996), Migration as a factor in the transformation of the settlementSvilajnac, Journal of Serbian Geographical Society, Volume 76: 14.78

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