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ISSN: 2159-5852 (Print) ISSN:2159-5860 (Online)

Vol 3(4), December 2013

Investigating Market Integration and Price Transmission of Different Rice Qualities in Iran… ...... …219-225 Amir Hossein Chizari , Masoud Fehresti Sani and Mohammad Kavoosi Kalashami

Drought Risk Vulnerability Parameters among Wheat Farmers in Mashhad County, Iran...... 227-236 Mojtaba Sookhtanlo, Hesamedin Gholami and Seyyed Reza Es’haghi

Livestock Farming Systems and Cattle Production Orientation in Eastern High Plains of Algeria, Cattle Farming System in Algerian Semi Arid Region...... …………………………237-244 Lounis Semara, Charefeddine Mouffok and Toufik Madani

An Investigation into Credit Receipt and Enterprise Performance among Small Scale Agro Based Enterprises in the Niger Delta Region of Nigeria…...... ……....245-258 Ubon Asuquo Essien, Chukwuemeka John Arene and Noble Jackson Nweze

Investigation of the Potential Market and Estimation of WTP for Insurance of Pistachio Tree Trunk (Case Study Rafsanjan-Iran)………………………………...... ………..259-267 Mostafa Baniasadi , Saeed Yazdani and Habib Allah Salami

The Economic and Welfare Effects of Different irrigation Water Pricing Methods, Case study of khomein Plain in Markazi Province of Iran………...... ………………..269-280 Gholamreza Zamanian, Mehdi Jafari and Shahram Saeedian

Research Performance of Agriculture Faculty Members: A Comparative Study at West Part of Iran...... 281-288 Nematollah Shiri, Nader Naderi and Ahmad Rezvanfar

Islamic Azad University, Rasht Branch, Iran.

Director Manager Dr. Mohammad Sadegh Allahyari Department of Agricultural Management Islamic Azad University, Rasht Branch, Rasht, Iran [email protected] Editor-in-Chief Prof. Mohammad Chizari, Tarbiat Modares University, Iran [email protected] Editorial Board Prof. Saeed Yazdani, University of Tehran, Iran Dr. Mohammad Sadegh Allahyari, Islamic Azad University, Rasht Branch, Rasht, Iran Associ. Prof. R. Saravanan, Central Agricultural University, India Prof. Hanho Kim, Seoul National University, South Korea Associ. Prof. Arvind Kumar, CSK Himachal Pradesh University, India Prof. Ahmad S. Al-Rimawi, Faculty of Agriculture, University of Jordan Dr. Rico Lie, Wageningen University, Netherlands Prof. Nasrolah Molaee, Islamic Azad University, Rasht Branch, Rasht, Iran Associ. Prof. Murat Boyaci, Ege University, Turkey Associ. Prof. Lesli D. Edgar, University of Arkansas, USA Dr. Nav Ghimire, University of Wisconsin- Extension (UW-Extension), USA Associ. Prof. Karim Motamed, University of Guilan, Iran Associ. Prof. Hossein Shabanali Fami, University of Tehran, Iran Prof. Mary S. Holz-Clause, University of Connecticut, USA Dr. Jafar Azizi, Islamic Azad University, Rasht Branch, Rasht, Iran Executive Manager Dr. Hamidreza Alipour, Iran Islamic Azad University, Rasht, Iran Assistant Editor Zahra BagherAmiri [email protected] Abstracting/Indexing EBSCO, Agricola, CABI, AgEcon search, Ulrich's, Cabell's Directory, DOAJ, Google scholar, Index Copernicus, Islamic World Science Citation (ISC), Scientific Information Database (SID), Open-J- Gate, Electronic Journl Library, Electronics Journal Database, Scholar, Magiran, Agris and Scirus.

Global Impact Factor: 0.506 Universal Impact Factor: 1.1764 ICV: 6.12

This journal is published in cooperation with Iranian Association of Agricultural Economic Content Page

Investigating Market Integration and Price Transmission of Different Rice Qualities in Iran… ...... …219-225 Amir Hossein Chizari , Masoud Fehresti Sani and Mohammad Kavoosi Kalashami

Drought Risk Vulnerability Parameters among Wheat Farmers in Mashhad County, Iran...... 227-236 Mojtaba Sookhtanlo, Hesamedin Gholami and Seyyed Reza Es’haghi

Research Performance of Agriculture Faculty Members: A Comparative Study at West Part of Iran...... 281-288 Nematollah Shiri, Nader Naderi and Ahmad Rezvanfar

Socio-Economic Factors Influencing Adoption of Energy–Saving Technologies among Small- holder Farmers: The Case of West Pokot County, Kenya……...... ………………….…..289-301 Andiema Chesang Everlyne, Nkurumwa Oywaya Agnes and Amudavi Mulama David * Corresponding author’s email:[email protected] * Corresponding author’s 3 2 University. 1 Amir HosseinChizari of Different RiceQualitiesinIran Investigating MarketIntegrationandPrice Transmission Assistant Professor, Department of Agricultural Economics,Facultyof Agricultural EconomicsandDevelopment,Tehran Ph.D StudentofDepartment Agricultural Economics,Facultyof Agricultural EconomicsandDevelopment,Tehran Uni Assistant Professor, Department of Agricultural Economics,Facultyof Agriculture, GuilanUniversity. Accepted: 10October2013 Received: 1February2013, Retail price,On-farmprice quality,price, Wholesale Market integration,Rice Keywords: ISSN:2159-5860 (Online) ISSN: 2159-5852(Print) Available onlineon:www.ijamad.com International Journalof Agricultural ManagementandDevelopment(IJAMAD) 1* , MasoudFehrestiSani

Abstract making processassignedseparately. different qualityofrice verities,supportpolicydesignanddecision these tworicemarketsinIran.Itissuggestedthataccording tothe strongly inwholesalepriceandthisshowsintense integrationof wholesale-retail marketforSadriqualityriceimpulses influence other ricequalitiestherateandspeedofthisinfluence islow. Butin Khazar ricerapidlyinfluenceon-farmprices,however, incaseof was studied.Resultsshowthatimpulsesinwholesaleprices during 1999-2009marketconditionsofdifferent qualitiesofrice (S1), Sadridarge yek(S2),Sadrimamooli(S3)andKhazar(K1) case ofthepricericequalities(items)includingSadrimomtaz statistics fromJihad Agriculture Organization ofGuilanProvincein tribution andmarketingofricestrategicproduct.So,usingthe and plannersintheirdecisionmakingsonresearch,production,dis- short termistheimportantconsequencethatcanhelppolicymakers long termandfinallypricetransferringmarketintegrationin margins, causativerelationsamongtheprices,marketintegrationsin the marketconditionsofdifferent qualitiesofriceincludingmarketing international levelqualityhasbeenincreased.Inthiscasestudying income hasbeenincreasedandalsodemandforriceatnational production andnationalgrossincomeofthecountryper-capita world anddomesticmarkets.Furthermore,togetherwithgrowthin in supplyandproportionatelydecreasetherealpriceofrice R more rapidlythanpopulationandthishasbeenledtoincrease ice productioninmostof Asian countrieshasbeenincreased 2 and MohammadKavoosiKalashami 3 versity. 219 International Journal of Agricultural Management and Development, 3(4): 219-225, December, 2013. 220 International Journal of Agricultural Management and Development, 3(4): 219-225, December, 2013. for domesticconsumption. ( imports around400000 to 500000tonsofrice Iran arestillnotevenly balanced,thecountry caput/year. As thedemand andsupplyofricein consumption ofriceisaround28kgper being 3780kg/ha(roughrice). The percaput 2.36 milliontons1993,withtheaverageyield late1980s, riceproductioninIranincreasedto spectively. From1.8milliontonsinthe vary from19°to25°Cand25125mm,re- Mazandaran –duringthericegrowingseason rainfall ofGuilan–whicharesimilartothose rice cultivation. The monthlytemperaturesand province) and230000hainGuilanareunder those inMazandaran(includingareasGorgan Mazandaran. Itisestimatedthat265000ha uted intwoNorthernprovincesofGuilanand ever, morethan80percentofriceareaisdistrib- growth (riceself-sufficiency plan,2006).How- to theendofsecondprogramhashad8.26% 2004) hadbeen2.54milliontonsthatcompared during thethirddevelopmentprogram(2000- program. Average yearlypaddyriceproduction that hashad3.93%growthcomparedtothefirst been 2.25and2.35milliontons,respectively, second developmentprogram(1995-1999)had first developmentprogram(1990-1994)andthe erage yearlyproductionofpaddyriceduringthe water resourcesneededforricecultivation. Av- mentioned yearsaredroughtandshortageof fluctuations inundercultivationlevelduringthe 628100 hectares. The mostcentralreasonsfor 1986-2005 hadbeenchangingfrom471000to cultivation levelofIran'spaddyriceduring tic neediscompensatedbyimporting.Under duced inIraneveryyear. The remainderdomes- more than1.4milliontonsofwhitericeispro- considering theyieldof2400kgwhiterice, 615000 haandriceisgrownin15provinces vation thetotalareaunderriceismorethan lands inIranhavebeendedicatedtoriceculti- More than615thousandhectaresofirrigated centered innorthernprovincesofthecountry. water undercultivationlevelofricehasbeen for Iranianconsumers. This productneedsmore cooked riceoutweighingallotherconsiderations the staplefoodinIran,withqualityof main seedstobeusedbyhumanbeingsandis Rice isfrommilletfamilyanditonethe Investigating MarketIntegrationandPriceTransmission /AmirHosseinChizarietal. INTRODUCTION Agronomic report Yek (S2), varieties are and stemborer. The mostpopularlygrownlocal prone tolodgingandarealsosusceptibleblast (AC), aromaandelongationqualities. They are 60 to63percent,intermediate Amylase Content slender grainandaheadricerecovery(HRR)of weak Culmanddroopyleaves. They havealong characterized bytallstature(125to135cm),a eties, whicharesimilartobasmatitypesand total riceareainIranisstillunderthesevari- cellent qualitytraits,morethan80percentofthe eraging 2.5to3.5tones/ha),becauseoftheirex- Varietal status ferent ricequalitiesinGuilanprovince. tegration atfarm-wholesale-retaillevelfordif- marketing systemsthatfacilitatethemarketin- 110 to125days.Presentstudyexaminestherice varieties, withtheappropriatedurationbeing May to August/September with100-to130-day Normally onecropofriceistakenfrom April/ local and5to7tones/haforimprovedvarieties. 7.5) andyieldsarehigh,at3to3.5tones/hafor irrigated conditionsinnormalsoils(pH7.0– and marketing. Almost allriceisgrownunder ners intheirresearch,production,distribution that canguideandhelppolicymakersplan- tions inshortterm,istheimportantconsequence margins, causativerelations,andmarketintegra- market conditionswhichincludemarketing tional levelhasbeenincreased.Studyingthe demand forqualityriceatnationalandinterna- per capitaincomehasbeenincreasedandalso tion andnationalgrossincomeofthecountries Furthermore, togetherwithgrowthinproduc- price ofriceinworldanddomesticmarkets. supply andproportionatelydecreaseinthereal population andthishasbeenledtoincreasein so on)hasbeenincreasedmorerapidlythan eties, newirrigationsystems,usingfertilizerand (consequence ofusingdifferent modernvari- province, (1996-2006) of different ricevarietiescultivationinGulan is animportantindicator ofoverallmarketper- Market integration Despite thelowyieldsoflocalvarieties(av- Rice productioninmostof Asian countries Spatial pricebehaviorin regional ricemarkets Sadri Mamooli Sadri Momtaz ) (S3) andKhazar(K1). (S1), Sadri Darge to otherones. impulse transfersinpricesfromonemarket mines pricesandcommercialflowalso and costtransferindifferent marketsitdeter- and dependingonthestateofdemand,supply equivalence process)transparencyinmarket ent markets. This definitionincludes(place fined ascommercecapabilityamongdiffer- Engle -Granger’s Co-integrationmethod ones givenanullthreshold rium arefasterthanadjustmentstothepositive price deviationsfromlong-runstableequilib- These resultsshowthatadjustmentstonegative tegration betweenNepalandIndiaanalyzed. toregressive modelaboutCoarsericemarketin- term integration. some adjustments some pauses(longtermintegration).Ofcourse (short termintegration)and/ortogetherwith taneously betransferredtoanothermarket to onechangesinpricesamarketwillsimul- change costsineachoneofthemarkets.One are (OPL). These techniquesassumethatifmarkets have beenformedonthebasisofOnePriceLow actually. Mostofmarketintegrationtechniques another onethatmaytakeplacepotentiallyor fer ofdemandsurplusfromonemarketto porter. Signalsofcommercecapabilityaretrans- of themarketsisexporterandotherim- tween twoeconomiesormarketsandone bility showsthefactthatgoodsisexchangedbe- (1987) the recentyears. According to concerned affair ofmostresearchersduring market places. and ricemarketintegrationbetweendifferent ance analysisfocusesonregionalprice Therefore, animportantpartofmarketperform- contributing toinefficient productmovements. the marketingdecisionsofriceproducersand convey inaccuratepriceinformationdistorting formance. Marketsthatarenotintegratedmay Lee (2002) Sanogo Barrette (2008) One ofco-integrationtests is Analysis ofmarketintegrationhasbeenthe integrated, priceswilldiffer onlyduetoex- Investigating MarketIntegrationandPriceTransmission /AmirHosseinChizarietal. MATERIALSMETHODS AND test. Ifatimeseriesvariable becomessta- et al. market integrationisoftende- (2010) defines thatcommercecapa- took placeconcerninglong applying athresholdau- Engle-Granger’s Barrette and is theycontainlong-termintegration. integrated marketwillbeofthesameorderthat are ofthefirstorder, regression2isestimated: are studiedandifthetwostationaryvariables nation ofthetwotimeserieswillbeasfollows: able willbeof al. there arefixednumbersof ing equation. residuals isstudiedwiththehelpoffollow- error item. At thenextstepstationarystateof ω related to bination ofthemwillalsobe (granger, 1969) lowing formula: FPE amountsarecalculated accordingtothefol- so, atfirst,anyvariableis fittedtotheirlagsand lag foreachvariableofthe combination. To do (FPE) fordeterminingoptimized lengthofthe ity ofGrangerandfinalpredictionerror variables of tionary in marketjwithinthetimet,respectively. method fordeterminingthelengthsoflags bers ofoptimallags,hasoffered asystematic mation andpreventingfromerrorinthenum- of lagsinthemodel.Inorderforreliableesti- estimated forthistest: using thepastamounts.Followingequationsare causes foreachotheriftheycanbepredicted Engle-Granger causality If residualitemsarestationary, thenthetwo P At firstthestationarystateoftwovariables Steps ofthistestareasfollows: Ut=P where In aboveequations,p,q,randsarethelength According tothistest,twovariableswillbe are parametersoftheequationand , 1987) it =φ +ωP a 1t P -α-βP times differencing, thisintegratedvari- it . P and 1t P 1t and jt +e 2t and P Thismethodcombinescausal- . a jt order or t P are priceinmarketiand 2t P 2t or mentionedlinearcombi- are ,thenanylinearcom- I ( a α ) . Ifbothtimeseries and I ( a β ) , thenresidual . And now, if (Engle ω is the φ (1) and (5) (4) (2) (3) (3) et 221 International Journal of Agricultural Management and Development, 3(4): 219-225, December, 2013. 222 International Journal of Agricultural Management and Development, 3(4): 219-225, December, 2013. transmission in changesoftenmarketpricesknowasprice market priceofriceshouldbesimilarlyreflected obstacle totheiroperation,changesintheone and retail levels Price transferringbetweenfarm,wholesale the secondvariable. been fittedonitslagsandnisthelaglengthof relation: optimized lagthatisobtainedforthefollowing least amountforFPE,willshowthelengthof the leastamountforFPE. The modelwiththe entered intheregressionsthat,atfirststep,had optimized lag. Then lagsofothervariableare amount forFPE,willdeterminethelengthof length. Now, theregressionwithleast and ayearlypause multaneous equationssystemisasfollows: lations amongthepricesatdifferent levelsassi- qualities ofrice. This modelthatformulatesre- and wholesalericemarketconsideringdifferent transfer andshorttermintegrationoffarm,retail by quality andayearlypause quality If marketsareefficient andpoliciesarenotan m* isthelengthofoptimizedvariablethathas P Where wehavethefollowings: P Where, T isthesamplesizeandmlag P P P P Ravallion (1985) ri,t-1 rit fi,t-1 fit wi,t-1 wit : Farmpriceofriceproductwith : Wholesale price of riceproductwith : Retailpriceofriceproduct with : Farmpriceofriceproductwith Investigating MarketIntegrationandPriceTransmission /AmirHosseinChizarietal. : Retailpriceofriceproduct with : wholesalepriceofrice productwith (Rafeek, 2003) was usedtostudytheprice . Modeldeveloped i i i quality i quality quality quality (10) (8) (9) (7) (6) i i 2008 and2009. 2007, however, theyhavebeenincreasedin ferences betweenpriceshavebeensmalluntil Among thequalitiesofS2,S3andK1dif- three finalyearsoftheinvestigatedperiod. And thishastakenplacemoreintenselyin pared tootherqualitiesaswellfarmprice. have takenthehighestplacesineachyearcom- investigated period. Wholesale andretailprices dedicated thehighestpricetoitselfduring 1999-2009. Inallprices,theyear2008has retail pricesofselectedricequalitiesduring curred which casetheshorttermintegrationhasoc- and wholesalemarketsfarmersin efficiently intransferringthepriceamongretail said thatwhichqualityofricehasoperatedmore rice. Inanalyzingthesecoefficients itcanbe and retailviceversaindifferent qualitiesof condition fromthelevelsoffarmtowholesale and ayearlypause Figure: 1-4-Farm,Wholesale andRetailpriceofdif- Figures 5to8showanoverviewofmarketing Figures 1to4,presentfarm,wholesaleand ϕ ϕ e ti ij i2 , , ψ and ɛ ti ij (Rapsomanikis and and ψ i2 ferent ricequalities 1999-2009. γ v coefficients showthepricetransfer ij ti : parametersofregressionequations : residualitemsofequations DISCUSSION et al., 2003). 1 Figure: 5-8-Farm-WholesaleandWholesale-Retail Augmented Dickey–Fuller increased. tween farm–wholesalehasintenselybeen pared towholesale-retail,pricediffrences be- has beenchangedin2008and2009,com- tionary trendforallricequalities.However, this sale andwholesale-retailmargins havehadsta- in otherwords,during1999-2007farm-whole- tuations haveoccurredaroundanaverageand fixed pattern.Itisclearthatinallfiguresfluc- qualities, until2007thesemargins havehad wholesale-retail fordifferent qualities.Forall margins in2levelsoffarm-wholesaleand after adifferencing. Table 1showstheresults time serieswereI(1)andtheybecomestationary ADF Investigating MarketIntegrationandPriceTransmission /AmirHosseinChizarietal. 1 margins fordifferent ricequalities. stationary testresultsshowedthatall Source: Researchfindings.

Tests

Table 1: Cointegration tests of farm, wholesale and retail price for different rice

1 2 3 4 5 6 7 8

Wholesale price is not cointegrated with retail price in S1

Wholesale price is not cointegrated with retail price in S2

Wholesale price is not cointegrated with retail price in S3

Wholesale price is not cointegrated with retail price in K1

Farm price is not cointegrated with wholesale price in S1

Farm price is not cointegrated with wholesale price in S2

Farm price is not cointegrated with wholesale price in S3

Farm price is not cointegrated with wholesale price in K1

Null hypothesis

varieties. those ofotherqualities.Estimation these twoshorttermsinK1riceismorethan short term,inotherwords,marketintegrationof price transferofwholesaleandfarmpricesin rice qualities. tions systemofpricetransfermodelindifferent sidered. elements ofdifferent ricequalitieshasbeencon- gating pricetransferrelationsamongmarketing ities. So,applyingsystemequationsforinvesti- farm, wholesaleandretaillevelforallricequal- tests resultsandshowsmarketintegrationin case ofallprices. Table 2supportcointegration prices. InK1andS2thereisbilateralrelationin causality testforfarm,wholesaleandretail markets. a marketinlongtermaretransferredtoother have joinedtogethersothatcreatedimpulsesin integration ispresentinlongtermand,markets made byfarmerconcerningchangesinprices rate. Also, comparedtootherqualities,decisions qualities thisinfluencetakesplacewithaslow by shocksinitspricesandcaseofotherrice that farmpricesinK1ricearequicklyaffected than thoseofotherqualities. These resultsshow tegration inshorttermcaseofK1riceisless prices inshortterm,otherwords,marketin- shows thatpricetransferofwholesaleandretail mation of wholesale andretaillevelsviceversa.Esti- way thepricetransfersfromfarmlevelto it canbesaidthatinallqualitiesofrice,market tegration existbetweendifferent prices.Infact, for K1(wholesale-retailprice)inothercasesin- retail pricesfordifferent ricequalities.Except for cointegrationtestsoffarm,wholesaleand Table 3showsresultsfromsimultaneousequa- Table 2showstheresultsofEngle-Granger ϕ i2 in different qualitiesshowsthat ϕ

ADF statistic i2 and

-2.02 -3.75 -2.17 -3.14 -2.04

-2.56 -1.88

-2.2 ψ i2 coefficients showthe

P-value

0.005

0.04 0.02 0.03

0.04 0.03 0.01 0.06 ψ i2 also 223 International Journal of Agricultural Management and Development, 3(4): 219-225, December, 2013. 224 International Journal of Agricultural Management and Development, 3(4): 219-225, December, 2013. high qualityricetakes place rarely, pricein these twomarketsinrice productofIran. retail priceandthisshows intenseintegrationof sale priceisintenselyaffected byshockson sale-retail marketforSadriqualityrice,whole- price ofK1highyieldingrice. And inwhole- are moreaffected byshakingsonwholesale Source: Researchfindings.

Coefficients Since pricetransferfrom wholesaletofarmin

ψ ψ ψ ψ

ϕ ϕ ϕ ϕ Investigating MarketIntegrationandPriceTransmission /AmirHosseinChizarietal.

γ γ γ γ γ γ

i0 i1 i2 i3 i4 i5

i0 i1 i2 i3

Table 3: Price transmission simultaneous equations system for different rice qualities. Source: Researchfindings.

Tests

12 13 14 15 16 17 18 19 20 21 22 23 24

1 2 3 4 5 6 7 8 9

Table 2: Engle-Granger causality test results for different rice qualities.

0.96 (0.00002)

0.25 (0.00001)

-601.16 (0.71)

1088.5 (0.97)

320.56 (0.69)

-1.04 (0.008)

-1.41 (0.012)

-0.39 (0.003)

-1.59 (0.011)

1.58 (0.012) 0.53 (0.001)

0.9 (0.0002)

1.61 (0.011)

1.65 (0.011)

For S1 retail price is not the causality of farm price For S1 farm price is not the causality of retail price For S1 wholesale price is not the causality of retail price For S1 retail price is not the causality of wholesale price For S1 wholesale price is not the causality of farm price For S1 farm price is not the causality of wholesale price For S2 retail price is not the causality of farm price For S2 farm price is not the causality of retail price For S2 wholesale price is not the causality of retail price For S2 retail price is not the causality of wholesale price For S2 wholesale price is not the causality of farm price For S2 farm price is not the causality of wholesale price For S3 retail price is not the causality of farm price For S3 farm price is not the causality of retail price For S3 wholesale price is not the causality of retail price For S3 retail price is not the causality of wholesale price For S3 wholesale price is not the causality of farm price For S3 farm price is not the causality of wholesale price For K1 retail price is not the causality of farm price For K1 farm price is not the causality of retail price For K1 wholesale price is not the causality of retail price For K1 retail price is not the causality of wholesale price For K1 wholesale price is not the causality of farm price For K1 farm price is not the causality of wholesale price

0.98

Null hypothesis

-0.99 (0.00001)

0.95 (0.00004)

-516.83 (0.47)

1096.9 (1.33)

0.71 (0.0012) 0.38 (0.0069) 593.18 (0.45)

1.12 (0.0001)

-0.49 (0.009)

-0.2 (0.0096)

-0.12 (0.009)

-0.49 (0.001)

0.11 (0.009)

0.25 (0.01)

0.94

S2 union ofricefarmersconcerning richproduct rice transfer. Itseemsthat,bargaining powerof rice (lowerqualities)compared tohighquality tail leveltofarmin caseofhighyielding quently, thistransfertakesplacemorefromre- rapidly transferstowholesalelevelandconse- clusive. Infact,increaseinpriceatretaillevel wholesale levelincaseofsuchqualitiesisex-

-0.066 (0.000002)

0.93 (0.000005)

0.037 (0.00023)

1.12 (0.00003)

-0.36 (0.0026)

0.65 (0.0005)

278.19 (0.65)

1159.5 (0.96)

-0.74 (0.005) -222.49 (0.7)

-0.85 (0.005)

0.56 (0.003)

0.77 (0.005)

0.79 (0.005)

0.97

ADF statistic

5.58 3.13 2.25 2.08 9.32 5.32 6.83 3.53 0.48 0.85 9.54

0.85 0.64 0.86 2.28 1.61 0.57 0.14 0.56 0.83 0.79 0.38

5.4 1.6

P-value

0.06 0.15 0.22 0.23 0.03 0.07 0.05 0.13 0.64 0.45 0.03 0.07

0.85 0.57 0.48 0.22

0.86

0.49 0.82

0.3

0.3 0.6

0.6

0.7

0.015 (0.00005)

-0.18 (0.00059)

-0.08 (0.00002)

0.81 (0.00014)

0.86 (0.00001)

-0.53 (0.0063)

0.19 (0.0007)

0.58 (0.0069)

0.61 (0.0073) 1.2 (0.00003)

-433.8 (0.71)

-0.68 (0.008)

403.7 (0.64)

396.2 (0.7)

0.96

K1 American Journalof Agricultural Economics,68(1), 8- Ravallion,M.(1986). Testing market integration, DOCREP/006/Y5117E/y5117e06.htm. view andapplications,online: http://www.fao.org/ and cashcropmarketsofdevelopingcountries:re- integration andpricetransmissioninselectedfood 7- Rapsomanikis,G.&Hallam,D.(2003).Market ter. Peradeniya. Of Affordability, socioeconomicsandplanningcen- plication ForRiceQualityImprovement And Issue 6- Rafeek,M.(2003).RiceMarketingSystem:Im- Econometrica, 37,424-438. by econometricmodelsandcross-spectralmethods, 5- Granger, C.(1969).Investigatingcausalrelations and testing,Econometrica,(2)55,251-276. tion anderrorcorrection:representation,estimation 4- Engle,R.&Granger, C.W.J. (1987), Co-integra- ics 84,292–307. analysis. American Journal of Agricultural Econom- tween equilibriumandintegrationinspatialprice 3- Barrett,C.B.,Li,J.(2002).Distinguishingbe- ed. London:PalgraveMacmillan. The NewPalgraveDictionaryofEconomics,second 2- Barrett,C.B.(2008).SpatialMarketIntegration. research center. vation inGulanprovince(1996-2006).Iran'sRice 1- Agronomic reportofdifferent ricevarietiesculti- process assignedseparately. support policydesignanddecisionmaking cording tothedifferent qualityofriceverities, ning forhighqualities.Itissuggestedthatac- directions towardsthepolicymakingandplan- cerned executivepowersalsochangetheir mination forricewithhighqualitiesandcon- farmers prioritizebargaining aboutpricedeter- country. So,itisproposedthatunionofrice ture andirreparableharmtoriceeconomyofthe duction inproductionofgoodqualityricefu- rice marketwithlarge quantitieswillcausere- tion tothequalityandmoreattentionsupply the highyieldingqualitiesandpayinglessatten- kets isaninevitablerealityandthat,supporting for thepurposeofattendinginternationalmar- present research,beingcarefulaboutthequality above saidcontentsinintroductionpartofthe thorities isconsiderable. quality thatisalsosupportedbyexecutiveau- The pointtobeconsideredisthataccording Investigating MarketIntegrationandPriceTransmission /AmirHosseinChizarietal. REFERENCES border tradewithIndia,FoodPolicy, 35,312–322. gration andfoodsecurityinNepal: The roleofcross- Sanogo,I.,&Maliki,M.(2010).Ricemarketinte- 9- 102-109. 225 International Journal of Agricultural Management and Development, 3(4): 219-225, December, 2013. * Corresponding author’s email:[email protected] * Correspondingauthor’s Department of Agricultural Extension andEducation,UniversityofTehran, Karaj,Iran. Mojtaba Sookhtanlo,HesamedinGholami Farmers inMashhadCounty, Iran Drought Risk Vulnerability Parametersamong Wheat Accepted: 14 April 2013 Received: 19March2013, degree Vulnerability, Riskaversion farmers, Drought, Wheat Keywords: ISSN:2159-5860 (Online) ISSN: 2159-5852(Print) Available onlineon:www.ijamad.com International Journalof Agricultural ManagementandDevelopment(IJAMAD)

Abstract have hadthehighestvulnerabilitylevel. for crops,salepricesofcropsandthetypelandownership), specting economicvulnerability, riskneutralfarmers(ininsuring highest vulnerabilitylevelunderdroughtconditions. While re- and typeofcultivation;riskaversefarmershavehadthe technical vulnerability;irrigationmethod,cultivationmethod farming activitiesanddependencyongovernmentin social vulnerabilityindicators;educationlevel,collaboratively First Rulewereappliedrespectively. Findingsrevealedthatin degree, formulaofMe-Barand Valdes andmethodofSafety Delphi technique.Formeasuringvulnerabilityandriskaversion of 2009-2011. Vulnerability parametersweredeterminedby degree intheMashhadCounty(Iran)betweendroughtyears farmers categorizedinaccordancewiththeirriskaversion parameters (economic,socialandtechnical)amongwheat Iran. So,thisstudywasinvestigatedthreeriskvulnerability quirements forplanningandreducingimpactsofdroughtin I with theirriskaversiondegreeisoneofthenecessaryre- dentification andanalysisoffarmers’ vulnerabilityassociated * and SeyyedRezaEs’haghi 227 International Journal of Agricultural Management and Development, 3(4): 227-236, December, 2013. 228 International Journal of Agricultural Management and Development, 3(4): 227-236, December, 2013. 2010, Keshavarz Ojha, 2006,Barbier essa, 2010, Wilhelmi erable thattheyear2011 wasthe13 strategy isprovedtobeineffective. Itisconsid- sources aremobilizedinthatparticularregion. state ofemergency isdeclaredandthusallre- drought occursindifferent partsofthecountry, a based oncrisismanagement.Forexample,when current droughtmanagementstrategiesinIranare In Iran,droughtisare-currentphenomenonand nomic, social,andenvironmentalconsequences. drought anditsvulnerabilityto as acombinationoflocation’s exposureto dicators indrought.Droughtriskisbestdefined tic visionforidentifyingofriskvulnerabilityin- natural disastersandprovidesanewrealis- come agriculturalcrisessuchasdroughtor ing interestinhowfarmerscopewithandover- 2011) 2005, BrondizioandMoran,2008, Ajijola studies eters butthisstillhasbeenconsideredbyrare rectly influenceddroughtvulnerabilityparam- their riskaversion(internalfactors)havedi- as farmers’ characteristicsincludinglevelsof is agrowingappreciationthatotherfactorssuch et al., (Hoddinott andQuisumbing2003,Hoogeveen (loans), cropsinsurance, technicalassistance level, accesstogovernmental andbankcredits ers’ incomes,farming landssize,education riety ofresourcessuchas landownership,farm- depending onownershiporaccesstoawideva- sions ofagriculturalandruralsectors most ofthesocioeconomicandtechnicaldimen- County, northeastofIran,andthishasimpacted year thatdroughthadbeenoccurredinMashhad ceptable levelofwell-beingamongfarmers exposure touninsuredriskleadinganunac- vulnerable todrought within alocalityandsameareaarenotevenly tors onfarmers’ vulnerability. However, people graphical situationsandrainfalllevelaskeyfac- et al., Province, 2011) culture Organization ofKhorasan-e-Razavi However, thistypeofdroughtmanagement isaslow-onsetdisasterthathaseco- Drought . Farmers’ capacitiestocopewithdrought, 2005) 2011) (Hoogeveen . Manystudies and vulnerabilityisidentifiedasthe INTRODUCTION . Therefore, thisstudyisanabid- Drought riskvulnerabilityparameters/MojtabaSookhtanloetal. et al et al et al. et al (Slegers 2008) ., 2011) ., 2005,Franke , 2008,Mongi ., 2002,Kapoorand (Gwimbi 2009,Der- highlight geo- th continuous (The Agri- . So,there (Ajijola et al., et al. et al. , , and Zarafshani(20102011) Cyr 2006) parameters ofsocial,economicandtechnical. al. Eakin receives anaveragerainfall of256mm county withanarid-semi-arid andaridclimate Khorasan-e-Razavi agricultural statisticsand informationoffice of vated landofthecountyis56615km This countyconsistsof591villages. The culti- each categoryofriskaversiondegree. gree andcategorizethemaccordingtoit. social andtechnicalvulnerabilityindicators. interests areasfollows: degree intheMashhadCounty(Iran).Particular vulnerable farmersregardingtheirriskaversion purpose ofthisstudywastoidentifythemost uncertainty conditions(drought).So,themain on farmers’ capacitiestorespondstressand ogy, technicalassistance,creditandinsurance) considered importanceofadaptation(technol- farmers’ accesstoresourcesanduseofthem method, householdextensionpackagesand outputs, lowincomelevel,credits,irrigation ing size,andfarmprofit,loans,sellingofcrop use, farmers’ riskmitigationpractices,landhold- mercialization practices,inputandmachinery crop andlivestockmanagementpractices,com- duction andlossestoclimatehazardspests, hold humanresourcesandincomesources,pro- farmers. The varietyofinformationonhouse- on povertyandvulnerabilitylevelamongrural impact ofriskattitudes(levelaversion) sea level. The area ofthiscountyis1490km Province. This county is992–1184 metersabove populous countyin is locatedinNorthEastofIranandthemost County istheMashhadCity. This Countythat Razavi Province,Iran. The capitalofMashhad County (ruralareas)locatedinKhorasan-e- support programs and information,socialnetworking,public - To determinewheatfarmers’ vulnerabilityin - To calculatewheatfarmers’ riskaversionde- - To calculatewheatfarmers’ vulnerability. - To identifywheatfarmersbasedoneconomic, This studywasconductedintheMashhad (2011), Keshavarz MATERIALSMETHODS AND et al are categorizedinthisstudythree . (2006),Deressa(2010), Ajijola (Scoones 1998;Ellis2000;St. Province, 2009) et al. Khorasan-e-Razavi (2011) examined the and . Wheat is . Wheat Sharafi 2 . This (The et 2 . Table 1: Sample size in each district (Mashhad County)

Districts (Bakhsh)

Central

Razaviyeh Ahmadabad

Torghabeh district Total

stages.

farmers. This study is conducted in two main two in conducted is farmers. study This

technique) to calculate vulnerability level of level vulnerability calculate to technique)

was obtained through the first stage (Delphi stage first the through obtained was

third part consisted of vulnerability indicators vulnerability of consisted part third

(according to formula of Safety First Rule). Rule). First The Safety of formula to (according

second part consisted of risk aversion indicators aversion risk of consisted part second

personal and professional characteristics. characteristics. The professional and personal

The first part was to collect data about farmers' about data collect to was part first The

ond questionnaire was consisted of three parts. three of consisted was questionnaire ond

Mashhad County by Delphi technique. technique. sec- The Delphi by County Mashhad

and technical vulnerability indicators in the in indicators vulnerability technical and

determine the most important socioeconomic important most the determine

first questionnaire included open questions to questions open included questionnaire first

were designed and used to gathering data. data. gathering The to used and designed were

mined 293 wheat farmers (Table farmers wheat 293 mined 1).

Cochran's test the size of sample was deter- was sample of size the test Cochran's

applied to access the respondents and using and respondents the access to applied

Razaviyeh) and and Razaviyeh) Torghabeh (capital: Torghabeh).

Central (capital: Mashhad), Razaviyeh (capital: Razaviyeh Mashhad), (capital: Central

(Figure 1): 1): (Figure Malekabad), (capital: Ahmadabad

into four districts (Bakhsh), with their capitals their with (Bakhsh), districts four into

the year 2009-2011. divided year is the County Mashhad

gion was severely affected by drought during affected drought by severely was gion

who live in in live who

sample of this study consisted of wheat farmers wheat of consisted study this of sample

the dominant crop in the region, so the statistical the so region, the in crop dominant the

Twointerview of methods and questionnaires

Awas sampling random stratified proportional Mashhad

Figure 1: Area ofstudy(MashhadCounty, Khorasan-e-RazaviProvince,Iran) Drought risk vulnerability parameters / Mojtaba Sookhtanlo et al. et Sookhtanlo Mojtaba / parameters vulnerability risk Drought

population

Statistical County. re- selected The

2574 1320 1086

5940

960 Reference: StatisticalCentreofIran(2012).

Sample size

125

293

53 50

same. These indicators used to design the next the design to used same. indicators These

tors, so the indicators could not be weighted the weighted be not could indicators the so tors,

must express the relative importance of indica- of importance relative the express must

phasized in the questionnaire that, weighing that, questionnaire the in phasized

tance) to 10 (the highest importance). It was em- was It importance). highest (the 10 to tance)

weigh the indicators from 0 (the lowest impor- lowest (the 0 from indicators the weigh

farmers’ experts. could by They vulnerability

importance) (W importance)

a section for determining the weight (relative weight the determining for section a

nomic, social and technical indicators and also and indicators technical and social nomic,

tionnaire was consisted of final confirmed eco- confirmed final of consisted was tionnaire

confirmed by them. In the third step, the ques- the step, third the In them. by confirmed

were edited to send again to the experts to be to experts the to again send to edited were

tionnaire including the primary indicators which indicators primary the including tionnaire

Acquired data were used to design another ques- another design to used were data Acquired

technical indicators with the most frequency. most the with indicators technical

categorize common major social, economic and economic social, major common categorize

questionnaire data were used to determine and determine to used were data questionnaire

tributed among experts. In the next step, first step, next the In experts. among tributed

ability indicators at Mashhad County) were dis- were County) Mashhad at indicators ability

important socioeconomic and technical vulner- technical and socioeconomic important

open-ended questions (i.e. determine the most the determine (i.e. questions open-ended

area of study. of area Aincluding questionnaire primary

programs or activities related to drought in the in drought to related activities or programs

extension experts who were directly engaged in engaged directly were who experts extension

ple who had field research about drought or drought about research field had who ple

naires and their data was used. peo- used. was were data They their and naires

among which, 31 experts resend the question- the resend experts 31 which, among

perts to us. Finally 45 experts were chosen were experts 45 Finally us. to perts

in the research process to introduce other ex- other introduce to process research the in

words, we asked the experts who were known were who experts the asked we words,

experts related to the study objectives. In other In objectives. study the to related experts

2010)

ies ies

the study region as used in many previous stud- previous many in used as region study the

and weigh major indicators of vulnerability in vulnerability of indicators major weigh and

cludes usage of the Delphi technique to identify to technique Delphi the of usage cludes

First stage (Delphi technique): in- stage technique): This (Delphi stage First

(Kaly and Pratt 2000; Dercon 2004; Deressa 2004; Dercon 2000; Pratt and (Kaly

. Snowball method was used to determine to used was method Snowball .

i =1… n) of each indicator in indicator each of n) =1… 229 International Journal of Agricultural Management and Development, 3(4): 227-236, December, 2013. 230 International Journal of Agricultural Management and Development, 3(4): 227-236, December, 2013. fined as: (2001), Ajetomobi andBinuomote(2006) and Bernard(1988),SekarRamasamy the familyinworkingof agriculture). dard rateinIran). least caloriesupplyinone yearinRial(thestan- lowing: activities)) three yearsofagriculturalandnon-agricultural of thefarmer(j)’s annualincome(inthepast income offarmer(j),Sj:Thestandard deviation Critical incomeleveloffarmer(j),Ej:Expected applied inthisstudy. Inthisformula: the studiedregion,mentionedformulawas and lackofvalidcategorizeddatabasesin accurate dataneededforothercommonmethods degree offarmers.Becauselackaccessto studies inordertodeterminetherisk-aversion Ajijola Rule formulawasused. and Valdez(2005) amount wasdeterminedbymethodof social, economicandtechnicalvulnerability neutral andrisktaker).Furthermore,farmers’ them inthreegroups(namelyriskadverse, ers’ riskaversiondegreeandalsocategorizes Safety FirstRulewasusedtocalculatethefarm- another questionnairewasused.Formulaof sion degreeandvulnerabilitylevel):Inthisstage stage questionnaire. DMG =( The weightedcropdamagevariablewasde- - CHI: Number ofchildren(activemembers - FAM: The household'sfarmsize(Hectare). - 7955936: The percapitacostofsupplyingthe The partsoftheaboveformulasareasfol- (R j:Risk-aversiondegree offarmer(j),E*j: R j=[E*j–Ej]/[Sj],1,2…,n To calculateriskaversiondegreeSafetyFirst Second stage(determiningfarmers'riskaver- E* =7955936(FAM -CHI/2)+DPT –

Risk- aversion coefficient 0.1 ≤ R j ≤ 1 -0.1 ≤ R j ≤ 0.1 -1 ≤ R j ≤ -0.1 Total et al. Σ E = VP (1+DMG) – TC k i (2011) DMG (UAR +UAR') Drought riskvulnerabilityparameters/MojtabaSookhtanloetal. .

Table 2: Status of the respondents, by the riskaversion degree. i ) /( used thismethodintheir Σ Randhir (1991),Parikh k i )

Status of wheat farmers Me-Bar and

Risk neutral

Risk averse

Risk taker

- of Iran inpreviousstudies(forexamplethe and itssuccessfulapplicationinotherregionsof ing othercommonmethodsinthestudiedregion ering thelackofreliableresourcesdataand tors isaffecting droughtvulnerability. Consid- formula basedonsubjectiveassessmentoffac- should bemeasuredquantitatively. Mentioned itative conceptforwhichcomparingsocieties Valdez (2005)statedthatvulnerabilityisaqual- nerability parametersinthisstudy. Me-Barand sessment ofsocio-economicandtechnicalvul- (2005) wasconsideredtobeappropriateforas- formula suggestedbyMe-Barand Valdez year (IRR). weighted average. due tolossesandabnormalincidentsasa non-agricultural activities(IRR). tivities otherthanwheatcultivation(IRR). formal institutions(IRR). Province and n) /2,C rameter amount, Wi= each parameterweight) sum oftotalvulnerabilityweight,Pi=eachpa- assessment. So, thisformulawasappliedforvulnerability method forthecountryconditionwasproved. Province) theapplicabilityandefficiency ofthis factor parameters) to eachparameter(10), n: The numberof each (V= eachfarmervulnerabilityamount,C Information whichisaprerequisiteforapply- Among vulnerabilityassessmentmethods,a - - DMG: The proportionoffarmer’s damage - Total valueofwheatproduction(IRR). - UAR': The beneficiaries’ annualincomefrom - UAR: The farmers’ annualincomefromac- - DPT: Farmer’s debtamounttoformalandin- C Also, inthisformula: V =1/C (W TC: Total wheatproductioncostinthesame Sharafi andZarafshani(2011) 0 = ∑W max : The maximumweightthatcanbelong 0 i < W 0 , ∑W ∑ (P Keshavarz max i

Frequency i = (W W × n i )

178 293

52 63 max × n)/2,C et al . (2011)

percent in Kermanshah

100

18 21 61 0 = (W in Fars max 0 × = was calculatedaccordingtoSafetyFirstRule Risk-Aversion degree ofrespondents ing amongtherespondentswere31to40years. 1.14 hectaresandthemostexperiencesoffarm- The averageareaofeachfarmerfarmlandswas related tothefarmerswhohad47hectares. extent offarmlands,thehighestfrequencywas between 21to30years. Also, bylookingtothe wheat cultivationamongtherespondentswere higher thandiploma. The mostexperiencesof only 9%ofstatisticalpopulationhadadegree also 21%ofwheatfarmerswereilliterateand level whichconstituted33%ofthesampleand farmers educationlevelwassecondary were women. The mostfrequencyofthewheat Personal andprofessional characteristics

Social parameter indicators

Social esteem Membership in rural associations / organizations Dependency to government Attending in extension education programs Education level Farming collaborative activities Family members collaboration The level of related to farming religious believe Participation in rural development programs Total

Economic parameter indicators

Insuring crops Extension agents’ economic advices Farmers’ incomes Amount of liquidity Pre-sale crops to middlemen Sale price of crops Land ownership type Farming lands Size Access to governmental and bank credits Total In thetable(2),risk-aversioncoefficient (Rj) Among farmers,84.5%weremenand15.5

Table 3: The amount and weight of economic parameter indicators in three farmers' groups

Table 4: Amount and weight of social parameter indicators in farmers groups Drought riskvulnerabilityparameters/MojtabaSookhtanloetal. RESULTS

(loans)

weight (W

indicators

Indicators

Weight of

4.93 5.10 4.77 5.52 5.35 6.06 4.81 3.82 4.64

6.12 5.46 4.95 4.65 3.75 5.24 4.82 4.60 5.41

) mental andbankcredits(loans)(W tive activities(W social parameterindicators(farmingcollabora- (W economic parameterindicators(insuringcrops Indicators weightofparameters: weight (∑W dicators ofanyparameter, totalvulnerability the tables(3,4and5).First,formeasuringin- nical parametersofvulnerabilityareshownin Parameters ofvulnerability tral and18%wererisktaker. spondents wereriskaverse,23%neu- formula. Basedonthefindings,61%ofre- nomic advices(W ∑W Findings showedthatexpertsbelieved Findings relatedtoeconomic,socialandtech-

i i

) =6.12), regionalextensionexpertswitheco- Indicators amountinfarmersgroups(P Indicators amountinfarmersgroups(P

farmers (P i

farmers (P

Risk taker = (W

Risk taker

2.63 1.98 3.27 1.65 2.29 2.21 2.50 1.85 1.90

1.98 2.08 2.12 2.65 2.67 1.77 1.54 1.63 3.25

- max i ) wascalculated. × n)/2=(109)45

i1 i

)

) =6.06), attendinginextension i =5.46), andaccesstogovern-

farmers (P

Risk neutral

farmers (P

Risk neutral

2.63 2.40 2.65 3.15 1.94 1.67 2.15 2.13 2.25

3.49 1.78 1.54 2.03 1.71 2.89 2.75 1.57 2.68

)

farmers (P

Risk averse

2.56 1.84 2.62 1.60 3.43 2.76 1.97

2.51

2.11

2.87 1.59 2.17 2.47 1.63 1.80 3.26 2.04 2.74 i =5.41)),

i3 i i )

) )

) 231 International Journal of Agricultural Management and Development, 3(4): 227-236, December, 2013. 232 International Journal of Agricultural Management and Development, 3(4): 227-236, December, 2013. level (W and harvestingtimes(P technical parameterindicators(planting,saving (P indicators (dependencytogovernment amount ofliquidity(2.65)),thesocialparameter (3.25), pre-salecropstomiddlemen(2.67)and cess togovernmentalandbankcredits(loans) farmers, theeconomicparameterindicators(ac- ability: Indicators amountinparametersofvulner- target regions. order toexplainparametersofvulnerabilityin tively werethemostimportantindicatorsin (rain-fed /watery)(P oration offamilymembers(P 6.06), irrigationmethod(W cators (cultivationtype(rain-fed/watery)(W education programs(W method (traditional/mechanized)(P highest scores. respectively werethreeindicatorswhichhad pests anddiseasescontrol(W

Technical parameter indicators

Cultivation type (rain-fed/ watery) Cultivation pattern (spring / autumn) Cultivation method (traditional/ mechanized) Use of drought resistant varieties Irrigation method Planting, saving and harvesting times Use of chemical fertilizers Weeds, pests and diseases control Tillage implements Total Considering thefindingsamongrisktaker Among riskneutralfarmers,theeconomicpa- i1 =3.27), socialesteem(P

Table 5: The amount and weight of technical parameter indicators in groups of wheat farmers .

Vulnerability amount

Economic vulnerability Social vulnerability Technical vulnerability Total vulnerability i =5.35)) andtechnicalparameterindi- Drought riskvulnerabilityparameters/MojtabaSookhtanloetal. i1 i1 =3.03) andcultivation

Total 6: total vulnerability amounts in farmers groups. =3.05), cultivationtype i =5.52) andeducation i1 ii =5.65) andweeds, =2.63) andcollab- i1 i =5.29)), respec- =2.50)) andthe i1 =2.96)),

Indicators

weight i =

6.06 4.84 5.12 4.94 5.65 4.71 4.55 5.29 3.84

) eter indicators(Irrigationmethod(P tivities (P type (rain-fed/watery)(P method (P technical parameterindicators(irrigation ship type(P sale priceofcrops(P (P cultivation method(traditional/mechanized) (P rameter indicators(Landownershiptype among riskaversefarmers,theeconomicpa- had thehighestintensityduringdrought. Also tery) (P cation level(P indicators. ers havehadthehighestvulnerabilityinthese rank andmeansthatduringdrought,thesefarm- programs (P cators (participationinruraldevelopment (P government (P (P to governmentalandbankcredits(loans) tional/ mechanized)(P rameter indicators(insuringcrops(P

Risk taker

farmers i3 i2 i3 i3

2.13 2.25 2.59 6.97 =2.74)), thesocialparameterindicators(edu- =3.26), insuringcrops(P =2.65) andsocialesteem(P =3.02) andcultivationtype(rain-fed/wa- Indicators amountinfarmersgroups(P

farmers (P

Risk taker i3 =3.00)), respectivelyhadthehighest

3.03 1.90 2.96 2.51 2.46 3.05 2.19 2.64 2.36 i3 farmers groups i2 i2

- =3.42), cultivationmethod(tradi- i 3=2.76) anddependencyto

Risk neutral =2.75)), thesocialparameterindi- =3.15), dependencytogovernment i3 i3

farmers =3.43), farmingcollaborativeac- =2.62)) andt

2.34 2.32 2.83 7.49

)

farmers (P

Risk neutral i2 =2.89) andlandowner- i3 =3.02) andcultivation

2.37 2.13 2.98 2.59

2.90 2.97 3.19 3.22

3.11 i3

- =3.00)), respectively

Risk averse he techn i3

farmers =2.87) andaccess

2.30 2.38 2.90 7.58 i2

) =2.63)) andthe

farmers (P

Risk averse ical param- i2 i3

3.00 2.46 3.02 2.73 3.42 2.87 2.88 2.90 2.63 =3.49), =3.42),

i3 i )

) Farmers groups

Risk taker farmers

Risk neu farmers

Risk averse farmers farmers iscalculated: example, economicvulnerabilityinriskaverse plied tocalculatetotalvulnerabilityamount.For Total vulnerability risk averse,theyhardly adopt thenewadvises vulnerability. Although mostofthefarmersare tive appropriatemechanisms toreducetheir ers arebeingextremelystressed tofindalterna- of thefarmersarevulnerable. Therefore, farm- that iscritical. As theresultspointedout,most mitigation totheseverecontinuousimpactsof that itisaharshrealityofIranagricultureand The lengthofdroughtinstudiedregionimplies drought vulnerabilityinMashhadCounty(Iran). nerable groups andrisktakerfarmersweretheleastvul- risk aversefarmerswerethemostvulnerable taker farmers. Also intechnicalvulnerability, and thelowestvulnerabilitywasamongrisk vulnerability wasamongriskaversefarmers With respecttosocialvulnerability, thehighest and thelowestwasamongrisktakerfarmers. vulnerability wasamongriskneutralfarmers 95.63/45 =2.13 2.08) +(4.95×2.12)+…+(5.423.25)= V =1/C Formula ofMe-Barand Valdez (2005)wasap- This paperdescribesaninvestigationof According totable6,thehighesteconomic

tral groups.

Access to governmental and bank credits (loans) Pre-sale crops to middlemen Amount of liquidity

Insuring crops Sale price of crops Land ownership type

Land ownership type Insuring crops Access to governmental and bank credits (loans) 0

Economic vulnerability ∑ (P DISCUSSION i W Drought riskvulnerabilityparameters/MojtabaSookhtanloetal. i ) =(6.12×1.98)+(5.45×

Table 7: A summary of indicators priority in farmers groups.

Dependency to government Social esteem Family members collaboration

Attending in extension education programs Dependency to government Social esteem

Education level Farming collaborative activities Dependency to government

Social vulnerability farmers groups shown intable7. farmers. Someotherparts offindingsare can besaidthattheyare themostvulnerable named socialandtechnical parameters.So,it most vulnerablegroupintwoparameters most vulnerablegroup,becausetheywerethe nomic parameter. Riskaversefarmerswerethe were theonlymostvulnerablegroupineco- nical parameters,whileriskneutralfarmers parameters namedeconomic,socialandtech- the leastvulnerabilityinallthree findings revealedthatrisktakerfarmershad not beenappropriateforthesegroups.Insum, rial, educationalandsupportprogramshave agers inthestudiedregionandthusmanage- been consideredbypolicymakersandman- drought ondifferent farmers'groupshavenot ability level.Inotherwords,variouseffects of farmers’ riskaversiondegreeandtheirvulner- made isthattherearelationshipbetween these groupswouldbeinefficient. ity parametersandthusunspecificsupportsfrom completely haddifferent andgeneralvulnerabil- taker, riskneutralandaverse,theywould we categorizedtheminthreegroupsnamedrisk various riskaversiondegreeisdifferent, sowhen and amountofvulnerabilityamongfarmerswith they currentlydo.Findingsimplythatthekind ers shouldsignificantlyactdifferent fromwhat with potentialrisks. This meansthatpolicymak- The interestingconclusionwhichcouldbe

Planting, saving and harvesting times Cultivation type (rain-fed / watery) Cultivation method (traditional/ mechanized)

Tillage implements Weeds, pests and disease control Irrigation method

Irrigation method Cultivation method (traditional /mechanized) Cultivation type (rain-fed / watery)

Technical vulnerability 233 International Journal of Agricultural Management and Development, 3(4): 227-236, December, 2013. 234 International Journal of Agricultural Management and Development, 3(4): 227-236, December, 2013. phasized by three farmersgroups,itcanbesaidthatasem- et al crops insurancefund rural banks,moregovernmentalattentionto on farmerslivelihoodlevel,establishingsmall granting gratuitousorlowinterestloansbased nomic vulnerability, mechanismssuchas age andreducenegativeimpactsofdroughteco- drought reliefprograms should bebasedonthe able. The resultsofthisstudycanimplythat mon diseasesduringdrought isrecommend- and helpingfarmerstocontrol pestsandcom- such asdraining,pressuredirrigationsystems for sustainabledevelopmentofwaterresources water requirements,providinginfrastructures conditions assubstitutionsforcropswithhigh farmers andalsocompatiblewithcontinental species whicharesuitableforeachgroupof identification andpromotionofvarieties effects ontheirtechnical vulnerability. Hence, tion type(rain-fed/watery)havehadthemost method (traditional / mechanized)andcultiva- averse farmers,irrigationmethod,cultivation trol andirrigationmethodamongrisk neutral farmers,weeds,pestsanddiseasescon- on theirtechnicalvulnerability. Among risk tional/ mechanized)havehadthemosteffect fed/watery) andcultivationmethod(tradi- and harvestingtimes,cultivationtype(rain- cators forrisktakerfarmers,planting,saving nomic vulnerability. to governmenthavehadthemosteffect oneco- (2010) Nelson andEscalante(2004) Zarafshani (2011) ency togovernment(consistentwith revealed thatamongrisktakerfarmers,depend- regarded ashighpriorityactions. oping andenrichinglocalcreditfundsshouldbe farmers whichisconsistentwith teem andeducationlevelamongriskaverse grams, dependencytogovernmentandsociales- farmers, attendinginextensioneducationpro- on socialvulnerability. Among riskneutral members’ collaborationhavehadthemosteffect tent with Considering thecommonindicatorsinall With respecttotechnicalvulnerabilityindi- With respecttosocialvulnerability, findings . 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Ph.D Studentof Animal Production, Departmentof Agriculture and Animal Science,Setif 1University, Algeria. 1 , CharefeddineMouffok ISSN:2159-5860 (Online) ISSN: 2159-5852(Print) Available onlineon:www.ijamad.com International Journalof Agricultural ManagementandDevelopment(IJAMAD)

Abstract 50% offarms). the modeltypefrequentlyencounteredinregion(overthan context prefermixedsystemswhenbeefsystemwas (dairy orbeef)inlessthan20%ofsituations.Farmersour showed thatthebreederswereorientedtowardsspecialization mixed system,dairysystemandbeefsystem. These results balanced mixedsystem(dairy-beef),beefsystem,dairy types ofcattleproductiveorientationhavebeenidentified,the coding inSPSS[19.2010].Followingthisapproach,five categorical principalcomponentsanalysis(CATPCA) ofoptimal as aresultoffunctionaltypologyestablishedusingtheprocedure and preferredmarketingpolicies. The modelhasbeenemerged tionship betweenthemaintenanceofdifferent typesofcattle adopted byfarmersinaregionthroughtheanalysisofrela- least twocows. The approachtakenwastoidentifyallsystems farm, andthefarmerproposedtoinvestigationmusthaveat selection ofbreederswasbasedtoexistencecattleonthe regard, 165farmsrandomlyidentifiedwereinvestigated. The T 2* and Toufik Madani of cattleherdsineasternhighplains Algeria. Inthis his studywasanattempttodeviseproductiveorientations 3 tation 237 International Journal of Agricultural Management and Development, 3(4): 237-244, December, 2013. 238 International Journal of Agricultural Management and Development, 3(4): 237-244, December, 2013. and incomeveryimportantforsocialstability ing cattleassumealsotherolesofjobcreation and Mouffok, 2008) sets formanyfarmers cash incomeandtheanimalsarelivingas- (2008) (Srairi ent agro-ecologicalzonesofcountries problems ofadaptationexoticbreedsindiffer- opment ofastrongdairyactivity, inparticular, byaconstraintwhichopposesthedevel- context performance ofthecattlesectorin Algerian searchers haveattemptedtoexplainthepoor of milkrequirementsareimported.Severalre- most withimportedpowderedmilkwhere60% plied. However, thedairyindustryoperatesal- as severalpoliciesandactionshavebeenap- center ofoccupationpublicauthorities,well tion especiallydairycattlewerealwaysatthe nomic andsocialfarmerspractical farmers' practices,itcomestotechnical,eco- approaches, onefocusedontheanalysisof ances. Manyauthorssuggesttwoconceptual tors influencingtheelaborationofperform- therefore essentialtounderstandbetterthefac- temic visiononcattlefarmsprospectionis imals andlivestocksystemsimplanted.Sys- technical choicesandespeciallythetypeofan- duction in Algerian’s farmsrequires changesin activity guided bythesearchforefficiency improvement duction requiredforintensivedairyfarm. Livestock andtheirproductsprovidedirect Researches onlivestockhavealwaysbeen , providethatthedeficiencyofmilkpro- Livestock FarmingSystemsandCattleProductionOrientation/LounisSemaraetal. et al. (Dedieu, 2009) , 2009) INTRODUCTION . In Algeria, animalproduc- and thelackoffodderpro- (FAO/ILRI, 1995) . Madani andMouffok Figure 1:Localisation ofstudiedarea (Chapman (Madani . Breed- et as muchinformationaboutthelivestock,but this surveywastocollectamongthosesurveyed with morethan150questions. The objectiveof farms andfunctionningpracticesofcattleherd) ics offarmersfamily, structureandresourcesof consisting ofthreecomponents(socioeconom- tigations havebeendevelopedinaquestionnaire which increasesfromnorthtosouth. The inves- Bouarraridj departments(Figure1). eastern highplainsof Algeria, SetifandBordj investigation werelocatedintwoprovincesofthe visited. Livestockfarmsthatarethesubjectofour sample of165farmswererandomlychosenand to plusthan90%ofallbreeders.Inthisregard,a study region,thesecategoriesoffarmscorrespond investigation musthaveatleasttwocows.In cattle breedingactivitiesandfarmproposedfor lection offarmers’ wasbasedonexistenceof ducted amongfarmersandherdersofcattle.Se- Methodological approach planning strategiesadopted. identify pathwaystoexplainmanagementand organization ofcattleproductionsystemsand gerian Easternhighplainsfarmstoanalyzethe some technicalandeconomicpracticesinof Al- ing systems.Itsaimsthroughtheadjustmentof tion tocharacterizationdiversityofcattlefarm- decisions tempts tounderstandhowfarmersmaketheir al., We alsoselectedscaleofariditygradient Investigation inasinglepassagewascon- This researchcanbeconsideredasacontribu- 2008; Dufumier, 1996) MATERIALSMETHODS AND (Shalloo et al., 2004) and theotherat- . means andstandarddeviation. tion procedure. All variableswaspresentedby forced bytwo-stepclusterautomaticclassifica- calves saleage). This categorizationwasrein- uct commercialization(amountofsoldmilkand such astypedebreedingchoiceandcattleprod- scribing practice-policiesadoptedbyfarmers ferent modalitiesofqualitativevariablesde- effective maleandfemalecalves)thedif- effective, beefcattleeffective, heifers effective, scribing thestructureofcattleherd(dairycows lationship betweenquantitativevariablesde- appropriate toresearchaimsanalysisthere- (19.2010) software. This procedurewasmost (CATPCA) optimalcodingprocedure ofSPSS Categorical PrincipalComponents Analysis Diagnostics tools of cattleandstrategiesdeveloped. ment andthediversityoffunctionningpractices also variablesrelatedtotheproductionenviron- LU :LivestockUnit

Mean Standard error of mean Standard deviation Minimum Maximum A graphicstypologywasestablishedusing Livestock FarmingSystemsandCattleProductionOrientation/LounisSemaraetal. C :Cattle;S Sheep ;G:GaotP :Poultry.

Variable

Breeding species

Milk soled

Age of calf sale

Table 2: Farming system and economic practices of farms

Table 1: Data of cattle categories number in all farms’

Cattle (LU)

12.63

10.00 02.00 71.45

0.78

C.S.G C.S C.P C

Total Part of Never

Pre weaning After weaning Old age As needed

Cows

45.00

7.69 0.46 5.49 2.00

Modality have developedanewtrendtotheassociation than 5%ofcasesandabout6.7%producers tion ofcattle-sheep-goatwasobservedinless (46.7%) orcattle-sheep(41.8%). The associa- Description ofeconomicpractices sity ofcattleproductionpolicies. farms, whichwasthefirstindicatorofdiver- positional structureofcattleherdbetween recorded reflectsahighdivergence inthecom- 1.5±4.0 youngbeef. A large standarddeviation permanent presenceof2.5±2.80heifers’ and the totalcattlepopulation. Those farmsmarkthe and thiscategoryrepresentsmorethan60%of LU. The numberofcowswas7.6±5.4byfarm that allfarmsexploitaverageherdof12.6±10.0 farm wassummarizedintable1.Resultsshow Effective andstructure ofcattleherd Overall characteristicsoffarms

Heifer

14.00

2.53 0.22 2.80 0.00 Theses farmsweremostlycattlealone The descriptiveanalysisofcattleherdsizeby

40.00

Beef

1.53 0.32 4.01 0.00

Percentage (%) RESULTS

Male Calf

41.8

46.7

45.4 49.7

12.5 13.5 64.4 10.0

4.8

6.7

4.9

18.00

2.02 0.20 2.55 0.00

Femelle Calf

10.00

2.13 0.17 2.10 0.00 239 International Journal of Agricultural Management and Development, 3(4): 237-244, December, 2013. 240 International Journal of Agricultural Management and Development, 3(4): 237-244, December, 2013. all farmsvisited)prefer to exploitthepotential Type 1.Dairysystem velopment andintegration ofmilkproduction. farms" includesdifferent levelsoforientation,de- cattle herd(Figure2andtable4). The term"dairy tle systemaccordingtoproductiveorientationof classification hasdemonstratedfivetypesofcat- Cattle farmingsystemsidentified lated todairyparameters’ (Table 3). correlated tobeefnumberandnegativelycorre- plains 19%oftotalvariationandwaspositively ber ofcows’ andheifers. The secondaxisex- variables relatedtodairyactivitiessuchasnum- of dairyorientation.Itwashighlycorrelatedto of totalvariation.Itwasinterpretedasanaxis total variance.Firstaxisrepresentedabout32% (CATPCA) wasdefinedtwoaxeswith43%of Statistical modelpresentation Multivariate analysis economic needs(sellingasneeded). calves assavingmoneytomobilizewhentheir their weaning.Inaddition,10%ofproducersusing a marketingofmalecalvesweredoneshortlyafter grammed earlybeforeweaningand13.5%ofcases ited declarethatthesaleofmalecalveswaspro- year ofage). Therefore, about12.5%offarmsvis- that calvesweresoldatlaterage(morethanone large partoffarmerssurveyed(64.4%)announce producers refusethesaleofmilk. Therefore, a the localandregionalmarketonly4.9%of cialize respectivelyallorpartofmilkproducedin shows that45.4%and49.7%offarmerscommer- of cattlewithintensivepoultryproduction. Cattle breedersinthissystem (about15%of The approachadoptedenhancedwithtwostep Categorical PrincipalComponents Analysis The analysisofeconomicpractice(Table 2) Livestock Farming Systems and Cattle Production Orientation / / Livestock FarmingSystemsandCattleProductionOrientation

centage ofexplainedvariancedoesnotcorrespondtothesumondimensions b. Duetothepresenceofmultiplenominalvariables.propervalueandtotalper- a. The totalvalueof Alpha ofCronbachisbasedonthetotalpropervalue

Total

Dimension Alpha of Cronbach of Alpha

Table 3: Model parameters’ of CATPCA

0.809 0.809

0.409

0.691 a generally alarge farmsdistinguishedbyprac- This systemrepresentsa small samplewhichare sonably balanced,milk andbeefproduction. two different functions,complementaryandrea- veyed. Inthisfirstmodel, thecattlecomplete beef cattle) Type 3.Balancedmixedsystem(Dairyand of calvesatanearlyage. number ofbeef(1.2±1.3)perfarms’ duetosold highest numberofdairycows(10±8)andalow ploited ruminants. This systempromotesthe cattle herdrepresentsmorethan80%ofallex- mark thistypeoffarmsin65%situationand in orderof17±12LU.Cattlealonebreeding economic needs). The livestockexploitedwas uncertainties (saleofcalvesaccordingtothe planned actusedtocopewitheconomic come. The fatteningoftheirscalveswasanun- producing andsalesmilkwastheiressentialin- adopt strategiesofmixedcattleproductionbut the region.Inthislivestocksystem,farmers oriented milk) Type 2.Dairymixedsystem(Dairy-beefcattle sured bythepresenceof7±6dairycows. cattle (over90%). The milkproductionwasen- LU/farm) characterizedbyalarge dominanceof formed byalowersizeofherd(10±8.5 production ofpoultry. Animal materialis ized (cattlealone)orassociatedwithintensified fore theirweaning.Livestockismostlyspecial- of breedersaco-productthatgetsridrapidlybe- calves bornonthefarmswereforthiscategory order toensurehighestpossibleincome.Male ization ofallmilkproducedontheirfarmsin concern ofthesefarmerswasthecommercial- of animalsindairyproduction. The principal

It wasrecordedinonly4%oftotalfarmssur- This typecoversabout20%ofcattlefarmsin

Proper Value Proper

3.41

1.55

2.52 b

Lounis Semaraetal.

Explain variance variance Explain

42.67 42.67

19.42

31.55 b farms shownheremakeupthemodelfre- oriented beef) Type 4.BeefMixedSystem(Dairy-beef cattle 6.14±3.02 cowsand1.57±0.98beefperfarm. mals exploitedcharacterizedbythepresenceof herd. Cattlerepresentsabout50%ofallani- of fiftyewesandabouttengoatsnearthecattle (18.5 LU/farminaverage)marksthebreeding In thisregard,livestockherdwasimportant crops andlivestockoffered amultiplereturns. pared totheotherduelarge diversificationof special attentiontothetypeofproductioncom- sale ofmalecalves. These farmsdonotgive tices ofthepartialmarketingmilkandearly This systemdominatesthestudyregionand Livestock Farming Systems and Cattle Production Orientation / / Livestock FarmingSystemsandCattleProductionOrientation Figure 3:Characteristics ofcattlefarmingsystems’ Figure 2:Graphicalpresentationofobtainedmodel 7.42±5.21 cowsand2.44±2.78beef. cattle herddefinedbythepresenceof stock exploitedperfarmwasformedbythe tle alone. Approximately 70%oftotallive- sheep, therestoffarmersexploitsmostlycat- sheep havesubsequentlymorethanthirty longing tothisgroupcombinethecattlewith LU perfarm.Over60%ofbreederswhobe- to milk. A ruminantlivestockcontains18±13 was basedontheearningsofbeefcompared farming practicesasfarfarmersreasoning production. Sucklingcalveswasapriorityin cattle systembutmoredirectedtothebeef farms). These farmersadoptpoliciesofmixed high plainsof Algerian (morethan56%of quently foundinthecontextofeastern Lounis Semaraetal. 241 International Journal of Agricultural Management and Development, 3(4): 237-244, December, 2013. 242 International Journal of Agricultural Management and Development, 3(4): 237-244, December, 2013. ket andlackoftechnical backstopping).Inthis bility offarmproductprice intheinternalmar- several economicandtechnical problems(insta- able agriculturalenvironment characterizedby Beef). These producers operateinanunfavor- adopt mixedcattlefarmingsystems(Dairy- farmers inthecontextof Algerian semiaridarea ± 6.4cowsand5.7±13.9beefs. livestock distinguishedoperatedbybreeding6.8 separately formmorethan90%oftheoverall animal herd(morethan19LU). The cattleherd specialized farmers(cattleonly).Ithasabiggest breeders exploitinparallelsheepifwerenot suckling offuturebeef.Inthesesituations,¾ farms, themilkcannotbesold.Itwasvaluedin the centerofinterestpolicymakersonthese regions. Ifreproductionandfatteningcalvesare to themodelofsucklecattlesystemintemperate frequency (lessthan5%offarmers)issimilar Type 5.Beefsystem LU: Livestockunit

Variable

Land

Fodder production

Livestock

Cattle The studyshowedthatmorethan80%of This categoryoffarmsencounteredwithalow Livestock Farming Systems and Cattle Production Orientation / / Livestock FarmingSystemsandCattleProductionOrientation

Arable Land

Fodder land Grass land

Livestock Unit Ewes (head) Goats (head)

LU Cattle Cows (head) Beefs (head) Heifers (head) Calf Male Calf Femele DISCUSSION

Modality

Table 4: Characteristics of cattle farming system

Dairy system

24.7 ±23.9

10.4 ±8.6 7.9 ±22.6

2.2 ±4.7 1.5 ±2.5

0.0 ±0.0

9.2 ±7.5 6.9 ±5.6 0.8 ±1.2 1.9 ±1.6 0.8 ±0.9 1.4 ±1.7

(14.5%)

Type 1

Dairy Mixed

23.6 ±29.9

17.2 ±12.1 18.9 ±36.5

14.3 ±10.2

2.5 ±4.6 1.1 ±1.9

9.9 ±7.9 1.2 ±1.3 3.2 ±2.9 1.6 ±1.7 2.1 ±1.9

(20.0%)

System

0.0±0.0

Type 2 the survivaloffarm maximizing productionisasecondarygoalafter gle product(milkorbeef).Inthiscondition ment oftheproductivitytheirsfarmstoasin- pushing farmerslogicallytoavoidtheattach- these livestocksystems.Suchobstaclesare sources offoodforherdswhichhousedunder tions, forestgrazingorcerealsresiduesare of forageautonomy(Figure3).Inseveralsitua- ers toachieveasatisfactoryofintra-farmlevel production forvariousreasonpreventingfarm- farms, lackareasofgrasslandandfeebleforage farmers tocontinuetheiractivities.Onthese calves bornonthefarmcanencouragethese Only theprofitsgeneratedbyfatteningof production andmarketingofmilkisunsecured. profitability ofcattlelivestockspecializesinthe is lowandunfavorabletotheseconstraints,the particular environmentwheremilkproduction and Jacquerie(2004) sition tothespecialization activities ever, intemperatecountrieswithhighpredispo- tria aremixed(Dairy-Beef) duetovariousrea- 20% offarmsrespectively inBelgiumand Aus-

Cattle Farming System

Mixed System

41.5 ±71.9

45.3 ±22.8

Balanced

18.3 ±6.0

10.2 ±3.7

13.6±7.8

2.0 ±2.6 1.6 ±1.8

6.1 ±3.0 1.9 ±1.4 1.9 ±1.5 1.6 ±1.0 2.0 ±1.0

Type 3

(4.2%) Lounis Semaraetal.

Beef Mixed reported that25%and

23.9 ±33.2

18.0 ±13.2 35.4 ±47.4

12.7 ±8.9

2.5 ±4.3 0.9 ±1.6

0.3 ±1.9

7.4 ±5.2 3.0 ±2.6 1.7 ±3.0 2.4 ±2.8 2.5 ±2.3 (Abbas, 2004)

(56.4%)

System

Type 4

19.4 ±23.7 12.7 ±15.6

17.5 ±23.2 Chatellier

5.7 ±13.9

11.9 ±9.9

1.0 ±1.4 0.4 ±0.7

0.0 ±0.0

6.4 ±6.4

0.0 ±0.0 2.9 ±4.9 1.0 ±2.1

System

Type 5

(4.8%)

Beef . How- nus, 2003) over thelastyears. the saleofmilkatapricesubstantiallyimproved stability andconsistencyofincomeprovidedby Diary-Beef systemorientedmilkinsearchof (Dairy-Beef) orientedbeefevolvedgraduallyto number ofthesefarmswereinmixedsystem duction andotheradvantages. An important vate dairiestobenefitasubventionformilkpro- farms soldallmilkproducedtothepublicorpri- sponds perfectlytofarms‘’cattlealone’’. These ented towardstheproductionofmilkcorre- cattle forfattening. mixed anddairyfarmssupply50%ofyoung duced comesin35%fromdairycattle,and established byLivestockInstitute,meatpro- France forexampleandaccordingtoareport farms havealsocontributedinthesepolicies.In and lackofmilkconservationinstrumentsin culty ofintegrationonmilkcollectionnetworks vestment andmodernizationoffarm. A diffi- promotes thecreationoffundsusingfornewin- activities, whilethesellingofbeefandcalves life familyexpensesanddailycostoflivestock Partial saleofmilkguaranteedthecoverage the efficiency ofworkorganization infarms. profitability ofbeefproductionagainstmilkand ers havemeltedtheirpoliciesarestilleconomic (over 56%).Logicalapproachwhichthesefarm- model dominantcattlebreedinginthestudyarea ented towardsbeefproductionisthesystem The earlysaleofyoungmales bornonthefarm specialized farmsarerarely onlycattlefarms. cattle herdsorincattle-poultry farms. These farms withcattle-sheepbreeding dominatedby oriented todairyproduction. scale irrigatedfarmingwhichispredominantly Tunisia mixedsystemscanbefoundinsmall- sons. In Tunisia, accordingto in beefproductionsystems milk productionsystemswillcausealterations systems arecloselyconnectedandchangesin production systems.Milkandbeef be carriedoutwithfeweranimalsthaninbeef beef productionincombinationwithmilkcan medium-sized farms. The reasonforthisisthat mixed dairy-beefsystemscanbeobservedin The mixedsystem(Dairy-Beef)butmoreori- The mixedsystem(Dairy-Beef)butmoreori- The dairysystemcanbeencounteredonlyin Livestock Farming Systems and Cattle Production Orientation / / Livestock FarmingSystemsandCattleProductionOrientation . Jaouad (2004) (Christel andMag- Jaoad (2004) report thatin , a vival ofthefarm. speculation wasasecondarygoalafterthesur- ized. However, maximizationofproductionper breeder whatevermannerwithwhichitisorgan- potential ofherd,weretheobjectives its byreducingcostsandoptimizingproduction ical toassimilatethatthemaximizationofprof- such systemcomparedtotheother. Soitislog- several factorsthatorientedbreederstofavorite production strategies,itwastheinteractionof semi aridareaprefermixedsystems.Incattle (Srairi Physical productionand economic performance. non-irrigated dairyfarms in southern Australia. 1. son, I.R.(2008).Pastureand foragecropsystemsfor 2- Chapman,D.F., Kenny, S.N.,Beca,D.,&John- niennes, 62,169-173. développement durable.Cahiersoptionsméditeran- zones céréalièresSemi-arides:Pouruneapprochede 1- Abbas, K.(2004).LaJachèrePâturéedansles 2000; Srairi 1998) of thefeedispurchased on verylimitedareas(lessthan5ha)thatmuch breeding ofcalvesorbeeffattening,arebased and socioculturalreasons.InMaghreb,the these practicesarelargely inflictedbytraditional sources. Itisreasonablealsotoassumethat tensive onlimitedareasorwithoutforagere- breed inthisenvironmentareconductedex- perate regions.However, cows’ oflocalorcross following theexamplesucklingsystemsintem- orization ofmilkproducinginsucklingcalves farms fortechnicalreasonsrelatingtotheval- cept thatthesaleofmilkisneverdoneonthese ization arriveathigheconomicperformance. farms’ directlycommittedtothewayofspecial- used infodderproduction.Inthisregiononly was observedinlarge farms policy. InMorocco,thespecializeddiarysystem stable sourcesofincomethathaveguidedthis not tocallintoquestionbutratherthesearchfor duced bydairycows.Farmsstructurefactoris offers morefacilityinthesaleofallmilkpro- Clearly, breedersinconditionsof Algerian In beefsystem,itwasabsolutelynormaltoac- et al. or inirrigatedperimetersmallholders’ , 2003) et al. CONCLUSION REFERENCES , 2003) Lounis Semaraetal. that 100%ofarablelandwas . (Jemai andSaadani, (Srairi andKessab, 243 International Journal of Agricultural Management and Development, 3(4): 237-244, December, 2013. 244 International Journal of Agricultural Management and Development, 3(4): 237-244, December, 2013. naire despaystropicaux, gharb aumaroc.Revued'élevage etdemédecinevétéri- gies deseleveursdebovinsdans lepérimètreirriguédu Production deLaitet/ou Viande: DiversitédesStraté- Sraïri,M.T.,13- Leblond,J.M.,&Bourbouze, A. (2003). 11, 321-326. Institut NationalDeLaRecherche Agronomique-, spécialisées auMaroc.Productions Animales-Paris- et modalitésdeproductionlaitièredanssixétables 14- Sraïri,M. T., &Kessab,B.(1998).Performances Prod, 41(2),835–843. gion: Casestudyfrommorocco. Trop Anim Health tems inanirrigatedperimeterandasuburbanre- & Faye,B.(2009). A comparisonofdairycattlesys- 13- Sraïri,M.T., Kiade,N.,Lyoubi, R.,Messad,S., M. J.Norusis. 12- SPSS,Inc.(2010).SPSSGLM18.0.Chicago: 1945-1959. dairy systemmodel.JournalofDairyScience.87, (2004). Descriptionandvalidationofthemoorepark 11- Shalloo,L.,Dillon,P., Rath,M.,& Wallace, M. Revue Elev. Méd. Vét. Paystrop,61(2),97-107. Mmontbéliardes enrégionsemiaridealgérienne. laitière etperfomancesdereproductiondesvaches 10- Madani, T., &Mouffok, C.(2008).Production 39, 39-56. nord ouestdela Tunisie. Optionsméditerranéennes. systèmes d’élevagedansleszonesmontagneusesdu 9- Jemai, A., &Saadani, Y. (2000).Evolutiondes Cahiers OptionsMéditeranniennes,62,421-424. en Tunisie etcontraintesalimentairesfourragères. 8- Jaoad,M.(2004).Dynamiquedescheptelsbovins de l’élevage. de viandebovineen 7- FrenshLivestockInstitute.(2005).Laproduction Feb. to02March1995. Roundtable heldatILRI, Addes Ababa, Ethiopia, 27 egy forlowincomecountries.Proceedingsofa 6- FAO/ILRI. (1995).Livestockdevelopmentstrat- monde. Cahiersd'Agriculture,15(6),584-588. agricoles etpluriactivitédesagriculteursdansletiers 5- Dufumier, M.(2006).Diversitédesexploitations - CarrefourProductionsanimales2009. vage etincertitudessurl’avenir. CRA-W &FUSAGx Dedieu, B.(2009). Adaptation des systèmesd’éle- 4- INRA Prod. Anim. 17(4),315-333. différenciés delaréformePAC dejuin2003. des exploitationslaitièreseuropéennesetleseffets 3- Chatellier, V., &Jacquerie, V. (2004).Ladiversité Agricultural Systems,97(3),108-125. Livestock Farming Systems and Cattle Production Orientation / / Livestock FarmingSystemsandCattleProductionOrientation france.Le 56 (3-4), 177-186. dossier economie Lounis Semaraetal. * Corresponding author’s email:[email protected] * Corresponding author’s Department of Agricultural Economics, UniversityofNigeria,Nsukka. Ubon Asuquo Essien,ChukwuemekaJohn Arene andNobleJacksonNweze in theNiger DeltaRegionofNigeria amongSmallScale Agro BasedEnterprises Performance An InvestigationintoCredit ReceiptandEnterprise Niger deltaregion,Nigeria enterprises, ance, Agro-based Access, Enterpriseperform- Credit amountreceived, Keywords: Accepted: 2September2013 Received: 26July2013, ISSN:2159-5860 (Online) ISSN: 2159-5852(Print) Available onlineon:www.ijamad.com International Journalof Agricultural ManagementandDevelopment(IJAMAD)

Abstract the federalgovernmentofNigeriainregion. go alongwayincomplementingtheamnestyprogramme of made tobeeasilyaccessibleandefficiently utilized. This will but enhancedgreaterperformance.Formalcredit shouldbe capital. Formalcreditwaslessaccessiblethaninformal nificant resultswithage,size,income,collateralandsocial hurdle forformalcredit,whilethesecondreportedsig- education, age,size,andcollateralaresignificantforthefirst capital aresignificantforthesecondhurdle.Similarly, gender, hurdle, whereasgender, size,income,guarantorandsocial gender, ageandsocialcapitalaresignificantforthefirst area. Analyses ofinformalcreditamountreceivedrevealthat that borrowedfrominformalandformalcreditmarketsinthe addition tothet-testexamineperformanceofenterprises current ratioandreturnoncapitalemployedwereusedin credit receivedbytheenterprises.Financialratiossuchas examine thefactorsaffecting amountofinformalandformal formal creditrespectively. The Heckmanmodelwasusedto and 96agrobasedenterprisesthataccessedinformal technique wasadoptedinselecting264agrobasedenterprises in theNigerDeltaregionofNigeria. A multistagesampling T enterprise performancebysmallscaleagrobasedenterprises he studywasdesignedtoanalyzecreditreceiptand 245 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. 246 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. across incomedistributions growth aswelldirectandindirectbenefits, financial systemshavehelpeddeliveredrapid nomic growthandpovertyreduction.Strong sidered financialdevelopmentvitalforeco- resources andriskcoping services forefficient inter-temporal transfersof oping countrieslackaccesstoadequatefinancial ception thatmicroandsmallscalefirmsindevel- productivity anddeclinedprivateinvestments phere ofpoliticallyunstableenvironment,eroded sector, withtheeconomyoperatingunderatmos- sector oftheeconomy, includingsmallbusiness gated negativenominalandrealshocksinevery tional companies and governmentagencies,youthsmultina- between youthsandcommunityleaders, volatile, resultinginyouthrestiveness,conflicts the region challenges tosustainablehumandevelopmentin sive poverty, hencegivingbirthtoformidable revenues barelytouchNigerDeltaownperva- omy, butitalsoportraysaparadoxasthevastoil and hasbecometheengineofNigeria’s econ- cern. The regionproducesimmenseoilwealth caused increasingnationalandinternationalcon- fronting theNigerDeltaregionofNigeriahave (Ministry ofNigerDelta Affair, 2011) 2007) for expandingtrade the incomegrowthforpoorfamilies,asmuch to financeisrightlyseenasakeyunlocking growth ofthepoor. Henceacross Africa, access ity bydisproportionatelyboostingtheincome cate thatfinancialdevelopmentreducesinequal- and sustainableways creasing theirproductivityinmanysignificant scale firmsmaylackmuchprospectsforin- out well-functioningfinancialmarkets,small credit programsaimedat improvingaccessto developing countrygovernments, havesetup ciated withthesmallloans thatsuitthem,most able collateralandhigh transaction costsasso- in lendingtosmallfirmsduetheirlackofvi- mercial bankstypicallyhaveminimuminterest these reasons,andthefactthattraditionalcom- Studies ondevelopingeconomieshavecon- In thisregard,policymakershaveheldthecon- A littleoverfourdecades,theissuescon- An InvestigationintoCreditReceiptandEnterprisePer . Beck andDemirguc-Kunt, (2005) (UNDP, 2006) INTRODUCTION (UNDP, 2006) (Honohan andBeck,2007) (Nwaru, 2004) (Besley, 1995) (Honohan andBeck, . Peoplearemore . These propa- . These . Basedon . With- . indi- . government interventions failures impedetheirgrowth,thusjustifying nomic development,butmarketandinstitutional on thepremisesthattheyareengineofeco- Olaniran, 2011) sence ofwellfunctioningfinancialmarkets and conducttheirinter-temporal tradeintheab- highly riskyenvironmentinsureagainstrisk firms intheNigerDelta,oftenoperating need forabetterunderstandingofhowthese cial institutionsisaconvincingevidenceofthe ever, thefailure region ofNigeria. and enterpriseperformance intheNigerDelta therefore setsouttoinvestigate creditreceipt backed byempiricalevidence. The study Niger Deltamaybedeficient,sinceitisnot against theirresponseinaregionsuchasthe to thecreditmarketandfactorsmilitating information onhow Agro-based firmsrespond late creditpolicieswithoutsubstantial conflict context. Therefore, attempttoformu- among smallagrobasedenterprisesinapost- fluence offinancingispopular, butlacking influence performance? Assessment ofthein- extent hascreditadvancedtotheseenterprises for small Agro-based enterprises,buttowhat institutions intheNigerDelta,thereishope gence ofmanyformalandinformalfinancial and Olaniran,2011) (Ministry ofNigerDelta Affairs, 2011) country whoseeconomicsisailing and lowstandardoflivingpeopleina exacerbated poverty, hunger, unemployment credit proving microandsmallscalefirms’ accessto Nigeria asamoreefficient mechanismofim- systems arebeingdevelopedandpromotedin poverty inasustainableway, innovativecredit critical rolethatcreditcanplayinalleviating sponse tothesefailuresandinrecognitionofthe formed atveryabysmallevel Delta. prises andperformanceinpostconflictNiger credit receiptbysmallscaleagro-basedenter- quate informationrelatingtoempiricalissueson the creditmarketpresupposeslackofade- credit Efforts targeted atsmallbusinessesarebased Evidently, smallscaleenterpriseshaveper- (Arene, 1993;CBN,2010) (CBN, 2010) formance / / formance of governmentsupportedfinan- . This lowperformancehas bnAsuquoEssienetal. Ubon . This inefficient natureof . Consideringtheemer- (Gomez, 2008) . (Hassan and (Hassan . Inre- . How- gion variesfromthehotequatorialforesttype fairs (2011), theclimateofNigerDeltaRe- lations oflessthan5,000(Ojameruaye,2008). 13,329 settlements;94%ofwhichhavepopu- lation of27millionpeople,185LGAs,about states, withanareaof112,000 sq.km,apopu- Cross River, Delta,Edo,Ondo,ImoandRivers region, namely; Abia, Akwa Ibom,Bayelsa, Nine ofNigeria’s constituentstatesmakeupthe east oftheGreenwichmeridian(Tawan, 2006). north oftheequatorandlongitudes5º1''7º2'' Nigeria. Itliesbetweenlatitudes4º2''and6º2'' According totheMinistryofNigerDelta Af- The studyareawastheNigerDeltaRegionof An InvestigationintoCreditReceiptandEnterprisePer

Source: FieldSurvey, 2012

Total

Tertiary

Secondary

Primary Primary

No Formal Education Education Formal No

Level of Formal Education Formal of Level

Mean

Total

13-15 13-15

10-12 10-12 7-9 7-9

Table 1: Distribution of small scale Agro-based enterprises by their Socio-economic characteristics. 4-6 4-6

1-3 1-3

YearsExperience Borrowing of

Total

No Access No

Formal Credit Credit Formal

Informal Credit Informal

Accessibility of Credit Market Credit of Accessibility

Total

Female

Male

Gender

Mean

Total

21-24

17-20 17-20

13-16 13-16

9-12 9-12

5-8 5-8

1-4

Age Variables

MATERIALSMETHODS AND

Frequency

Informal Credit Borrower Credit Informal

3.79

5.35

264

102

264

155

439

264

184

264

146

6 Enterprises Enterprises Indonesia largest producerofpalmoilafterMalaysiaand sixth exporterofcrudeoilandthirdasworld’s of MarchtoOctober. and eightmonthsoftheyear, fromthemonths season isrelativelylong,lastingbetweenseven type intheObuduplateauarea.Further, thewet in thenorthernhighlandsandcoolmontane in thesouthernlowlandstohumidtropical in thearea yam andorangeareproducedinlarge quantities pineapple andfish,also;cocoa,cashew, rice, ther, theDeltaleadsinproductionoftimber, While cassavaresourcescanstimulatethe

The regionhashugeoilreservesandranks Percentage

formance / / formance

26.52 26.52

38.64 38.64

31.44 31.44

28.79 28.79

58.71 58.71

17.99

21.86

60.13

30.30 30.30

69.70 69.70

14.39 14.39

25.76 25.76

55.30 55.30

3.14 3.14

1.52 1.52

3.03 3.03

7.95 7.95

0.76 0.76

1.52 1.52

2.27 2.27

100

100 100

(Omafonmwan andOdia,2009) ₦₦ (Omafonmwan andOdia,2009) bnAsuquoEssienetal. Ubon

Formal CreditBorrower

Frequency

4.09

6.92

26 41

Enterprises

Percent-

18.87

29.17 29.17

55.21

36.45

63.54

12.50

13.54

27.08

42.71

42.7

1.04

1.49

2.08

5.21

8.33

2.08

100

age 37 . Fur- . 247 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. 248 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. namely manufacturing,services andtradingsec- fice. This listwasstratifiedintothreesectors the LocalGovernmentBusiness registrationof- and MediumScaleEnterprises Associations and based enterpriseswasobtainedfromtheSmall ident ineachstateandbyoralinterview. and MediumScaleEnterprise Associations res- istry ofEconomicDevelopment/trade,theSmall was possiblewiththehelpofstaff oftheMin- Areas wereBrass, Warri North,andPhalga This selected forthestudy. The LocalGovernment three states,fromwhichoneeachwasrandomly each werepurposivelyselectedfromofthe States. Further, threelocalGovernment Areas based The StateswereBayelsa,DeltaandRiver tion ofeconomicactivitieswhichare Agro- purposively selectedbasedonhighconcentra- Rivers, ImoandOndostates,threestateswere Akwa Ibom,Bayelsa,Crossriver, Delta,Edo, this study. Ofthe9NigerDeltaStatesof Abia, institutions. in theregionareformalandinformalcredit Further, themajorlendingcreditinstitutions tion bringing andnotviaformaltrainingoreduca- and skillstransferredchieflythroughfamilyup- on manualartisanaltechnologies,localinputs throughout theregion,arethattheybased industries foundinvaryingproportions gion. The maincharacteristicsofthese prises arefoundalmosteverywhereinthere- distillation etc.SmallandMediumscaleenter- (garri, fufuandstarchfromcassava),localgin materials), palmoilprocessing,foodprocessing weaving, mat-making,thatchmaking(roofing the areaincludecanoecarving,pottery, cloth gion someyearsback. Traditional industriesin (Igbuzor, 2006) management, andpooreducationalstructure cation, powerandfuel,housing,poorwaste poor accesstowater, transport,telecommuni- nies haveimpactedontheenvironmentwith ing andagriculturebutactivitiesofoilcompa- built ontheregionshugebambooresources. sive furniture,buildingandcraftindustriescanbe garri, chips,flour, glucose,starchandpellets;mas- oflocalprocessingindustriesforfufu, growth In thethirdstage,alistofSmallScale Agro- A multistagesamplingtechniquewasusedin The majoroccupationofthepeopleisfish- An InvestigationintoCreditReceiptandEnterprisePer (Ministry ofNigerDelta Affairs, 2011) ; thisleadtoconflictinthere- . Threshold indexequation= (a) following equations: study. The Heckmanmodelisillustratedbythe tios andthet-testwerealsoemployedin etc. The Heckmanmodel,selectedfinancialra- statistics suchasfrequency, means,percentages, Data analysis cause ofrandomsampling. tence werematchedbyanotherfirminframebe- from theframe. That is,thosefirmsnotinexis- missing datawasdealtwithbymatchedsample firms ornon-respondentswereencountered. The plemented, notwithstanding,howevermissing incomplete answers. Though meticulouslyim- drop questionnaireswithinconsistentaswell tionnaires wasmadeinothertodetermineand actual datacollection,examinationoftheques- statement afterthepilottesting.Following considered onthequestionnaireandproblem incomplete response.Changeswerehowever ing oftheinstrumenttoavoidinconsistencyand tionnaires. This wastoensureclearunderstand- personnel wereusedforpre-testingoftheques- mented duringthepilotstudywheresame data collectioninstruments. This wasimple- research assistantswerebriefedontheuseof tionnaire andoralinterview. mary sourcesthroughtheuseofstructuredques- formal creditwereusedfordetailedstudy. informal creditand96enterprisesthataccessed hundred andsixtyfourenterprisesthataccessed along creditsourcelines.Onthewhole,two thermore, the360enterpriseswerestratified prises wereselectedfromthethreestates.Fur- each state.Inall,threehundredandsixtyenter- three sectorsineachlocalGovernment Areas of and twentyenterpriseswereselectedfromthe was randomlyselectedforstudy. Onehundred each oftheselectedenterprisesfromsector Poultry Feedsanddrugs- Trading. Twenty of Restaurants andColdRoomServices-Services; Bakery andCapentry/furniture-Manufacturing; making itsix. The enterprisesselectedwere domly selectedfromeachofthethreesectors, tors, outofwhichtwoenterprisetypeswereran- Data wereanalyzedbytheuseofdescriptive Before undertakingtheactualdatacollection, Data fromthestudywereobtainedpri- Index Equationd formance / / formance bnAsuquoEssienetal. Ubon i * = X / Ii β {1 ifd 1 +U i , U i * > 0,andis0 i ~N(0,1)...... has accesstocredit,0otherwise V~N(0,δ (b) Amount ofCreditreceived: in thecooperative. erative society, hencethe number ofpeople mal credit,itdescribes membership ofcoop- acquainted withlender, 0otherwise.Forfor- lender. Measured asdummy, 1ifborroweris describes borrowersacquaintancewith and 0ifnot) be paid.(Binary;1ifguarantorwasavailable lateral toaccesscredit,0otherwise) (Measured asDummy:1iffirmprovidedcol- asset thateasestheapprovalofformalcredit in Naira) prises fromsalesinthelastoneyear(Measured borrowed. the borrowerpaysasinterestcharges onmoney prise valuedinNaira) worth oftheenterprise;totalassetsenter- tence. Measuredinyears). ber ofyearsthatthebusinesshasbeeninexis- ceiving formaleducation). number ofyearstheentrepreneurspentinre- owner/manager offirm.Measuredbythetotal level offormaleducationattainedbythe female. dummy, takesthevalueof1formaleand0 below: has accesstocredit,0otherwise if d Threshold equation: t t Where Where Further, financialperformanceofenterprise SOC=Social Capital(Forinformalcredit;it GUA= A personwhopledgesthatadebtwill COL= Collateral(Definedasanyvaluable INC= Incomeoffirm(Receiptstheenter- INT=Interest Amount; thisisthetotalamount SIZ= EnterpriseSize(Thisdescribesthe AGE= Enterprise Age (Definesthetotalnum- EDU=Entrepreneur’s Education.(Thisisthe GEN (Genderoftheentrepreneur. Definedas Other variablesinthemodelweredefined t t An InvestigationintoCreditReceiptandEnterprisePer i * i * i = amountofcreditreceivedifrespondenti * = amountofcreditreceived = amountofcreditreceivedifrespondenti = amountofcreditreceived ≤ 0 2 d d ) i i = Probabilityofaccesstocredit = Probabilityofaccesstocredit t i ={t 1 * if d i =1. Oifd t * =X / 2i β 2 +V i =0 i , credit market. from theinformalcredit market thantheformal that mostmaleentrepreneurs tendtoborrow rower entrepreneursare females. This implies and 36.46%ofinformalformalcreditbor- from theformalcreditmarket.Further, 30.30% mal creditmarketwhereas63.54%borrowed the maleentrepreneursborrowedfrominfor- range of1-4years. terpart. The most common agefellwithinthe older thantheirinformalcreditborrowercoun- that borrowedfromtheformalcreditmarketare most ofthesmallscaleagro-basedenterprises rower enterprisesrespectively. This impliesthat and 6.92fortheinformalformalcreditbor- under 12yearsofage. The meanagesare5.35 83.33% offormalcreditenterpriseborrowerare of informalcreditenterpriseborrowerand as shownintable1belowrevealsthat95.45% based enterprisesaccordingtoageofenterprise Socio-economic characteristicsofrespondents than currentliabilities(orclaims)againstit. prises inthestudyareahasmorecurrentassets one meantthatsmallscaleagrobasedenter- maturing inthecurrentyear. Ratiosgreaterthan penses, incometaxliabilityandlongtermdebts included creditors,billspayable,accruedex- year wereincludedincurrentliabilitiesandthey expenses. Obligationsthatmaturedwithinthe marketable securities,inventories,andprepaid verted intocashwithinthelastyear, suchas Naira (₦) ued inNaira(₦) efficiency. capital employed.Higherratiosimpliedgreater based enterprisesinthestudyareahaveused Profit after Tax/Capital employed was analysedusingthe: Gender ofrespondentsshowthat69.70% The distributionofsampledsmallscaleagro- Current Assets includedcashandassetscon- CL =Currentliabilitiesofenterprisevaluedin Where CA =Current Asset ofenterpriseval- 2- Current Ratio=CA/CL These ratiosindicatedhowsmallscaleagro 1- Returns onCapitalEmployedRatio=Net (2) CurrentRatio (1) ReturnonCapitalemployed(ROCE) RESULTSDISCUSSION AND formance / / formance Doan bnAsuquoEssienetal. Ubon et al. (2010) explain that 249 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. 250 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. macro-economic instability tial ofthesurvivingfirmsduringperiods availability ofcreditincreasesthegrowthpoten- ries toavoidstockingoutduringcriseswhile access tofundingareablebuildupinvento- education. This issignificantlyhighandconsis- entrepreneur hadoneformofprimarytotertiary preneurs and98.96%offormalcreditborrower rowing forabout4years. mal creditenterpriseborrowershavebeenbor- enterprise borroweris1.5yearswhereasinfor- ther, averageborrowingageforformalcredit have beenborrowingformorethan3years.Fur- prises whoaccessinformalandformalcredit erative societies,personalsavingsandrelations. 93% accessloanfromothersourceslikeco-op- scale farmershaveaccesstobasicloanwhile Edo State,findingsshowthatonly7%ofsmall farming inEtsakoLocalGovernment Area of appraising financialconstraintstosmallscale flow problems resource allocationandreducetheimpactofcash ternal resourcesisneededtoensureflexibilityin formal orinformalcreditmarket. Access toex- 17.99% oftheenterprisesdonothaveaccessto have accesstoformalcreditmarket.Further, access totheinformalcreditmarket;only21.86% tion butplaysaroleinexplainingloansize. gender doesnotreallymatterincreditparticipa- *** P ˂0.01,**P ˂ 0.05,*P ˂0.10 Source: EstimatedFromField SurveyData,2012

Const GEN EDU AGE SIZ INC INT SOC Also, 96.6%ofinformalcreditborrowerentre- Further, morethan80%ofagro-basedenter- While 60.13%ofagro-basedenterpriseshave While An InvestigationintoCreditReceiptandEnterprisePer

cadnRsurd0564 dutdRsurd0.415245 Adjusted R-squared 0.526944 McFadden R-squared o-ieiod-9298Aak rtro 114.5192 Akaike criterion -49.25958 Log-likelihood

-8.58228e-09

Coefficient

8.46569e-08

-6.2595e-09

-0.0435472

0.0694754

-1.35722

0.32658

2.58999 (Bigsten

Table 2: Estimated determinants of informal credit access (First Hurdle). et al.

1.06843e-07 7.12192e-08 1.68174e-07 , 2003)

Std. Error (Atieno, 2009)

0.0310248 0.0376283

0.572032 0.426915

0.33906 . Firmswith . In

-3.1791 -1.4036

-0.0879 -0.0510

0.5709

1.8464 0.7923

7.6387

Z agro-based enterprisestoaccessinformalcredit. of theprobabilitydecisionsmallscale included inthemodelwereabletoexplain52% which impliesthatalltheexplanatoryvariables gave theMcFaddenR-Squaredofabout0.52 model. The estimatedprobitregressionmodel estimates ofthefirstpartHeckmann based enterprises(FirstHurdle) Informal credit accessbysmallscaleagro agro-based enterprisestoaccessinformalcredit. of theprobabilitydecisionsmallscale included inthemodelwereabletoexplain52% which impliesthatalltheexplanatoryvariables gave theMcFaddenR-Squaredofabout0.52 model. The estimatedprobitregressionmodel lihood estimatesofthefirstpartHeckmann resort tosmallscaleenterpriseactivities. find employmentintheformalsectorandthus most peoplewiththislevelofeducationfailedto borrower entrepreneurs. This mayimplythat mented withtrainingcomparedtoformalcredit had achievedsecondaryeducationlevelsupple- rowed fromtheinformalcreditmarkethowever among thestates.Mostentrepreneurswhobor- erage of54%,althoughmarkeddifferences exist about 78%,slightlyhigherthanthenationalav- adult literacystatusoftheNigerDeltastatesis tent with The coefficient ofthefirsthurdleindicates The table2presentsthemaximumlikelihood The table2belowpresentsthemaximumlike- formance / / formance MNDA (2004)

-6.51762e-010 -8.93619e-010

8.81479e-09

0.00723403

-0.0045343

0.676166

-0.10347

Slope*

- bnAsuquoEssienetal. Ubon which indicatesthatthe

p-value

0.56806 0.00148 0.16043 0.06484 0.42816 0.92996 0.95930 0.00001

***

* scale entrepreneurialactivitiescomparedto be attributedtothefactthatmenengageinlarge entrepreneurs inthestudyarea. This resultcould decreases amongmalesmallscaleagro-based that theprobabilityofaccessinginformalcredit enterprises inthestudyarea. The resultreveals cess creditbythesmallscaleagro-based cally significantwithrespecttodecisionac- (GEN at1%)isnegativelysignedandstatisti- d’voir. Onthe other sonance withfindingsof in Kenya. The resultforSocialCapitalisincon- Atieno (2001) reported corroboratestheresearchfindingsof entrepreneur. The resultforEnterprise Age as and trustinbusinessbetweenthelender tionship andhencewillbringaboutconfidence increase inthesocialcapital,willenhancerela- implies increaseinexperienceandgrowth. Also, priori expectationsasincreaseinenterpriseage to creditincreasestoo.Resultisinlinewitha borrower enterprise,thechancetohaveaccess ship withlenderincreasesforinformalcredit sult impliesthatasEnterprise Age andrelation- agro-based enterprisesinthestudyarea. The re- ity toaccessinformalcreditbysmallscale nificant withrespecttothedecisionorprobabil- (SOC at1%)arepositiveandstatisticallysig- enterprise Age (AGEat10%)andsocialcapital (probit model)indicatesthattheco-efficient of access tocredit. The resultofthefirsthurdle how agivenvariableaffects thelikelihoodof An InvestigationintoCreditReceiptandEnterprisePer *** P ˂0.01,**P ˂0.05,*P ˂0.10 Source: EstimatedFromField SurveyData,2012

Const GEN EDU AGE SIZ INC INT GUA SOC

h-qae8 9459pvle0.012695 p-value 19.43569 Chi-square(8) o-ieiod-3522Aak rtro 6670.444 Akaike criterion -3325.222 Log-likelihood

Table 3: Estimated determinants of informal credit amount received (Second Hurdle). and

-0.000692011

Coefficient

-0.0544053

0.0195456 Mwangi andOumar(2012) hand, coefficient ofGender

-848.123 -5078.49

-175070

70627.9

62183.5

422993 Togba (2009)

Std. Error

0.0001709 0.0607931

0.006454

24359.89

85407.5 8435.06 9499.96 in Cote

189919

117559 increase innumberofmaleentrepreneurswill prises inthestudyarea. The resultimpliesthat credit receivedbysmallscaleagro-basedenter- negatively relatedtotheamountofInformal efficient ofGenderissignificantat5%leveland reveals anormaldistributedregressionresidual The estimatedtruncated Tobit regressionmodel Truncated Tobit modelarepresentedintable3. ceived bysmallscaleagrobasedenterprises. variable influencesinformalcreditamountre- scale agro basedenterprises(SecondHurdle) Informal credit amountreceived bysmall cess toinformalcreditinthestudyarea. small scaleagro-basedenterprisestohaveac- icy variablesthatimpactonthedecisionof and Genderappeartobethemostimportantpol- ficient intheestimatedmodel,SocialCapital ever, basedonthemagnitudeofslopeco-ef- every unitincreaseinmalerespondents.How- chance toaccessinformalcreditoccursfor and SocialCapital,while10%reductioninthe by 67%foreveryunitincreaseinEnterprise Age The probabilityofaccessincreasesby0.7%and additional unitincreaseinthedecisionvariables. changes intheprobabilityofaccesstocreditfor quate enoughforinvestment. women henceinformalcreditmaynotbeade- The resultofthe Tobit modelrevealsthatco- The maximumlikelihoodestimatesofthe The secondhurdleindicateshowadecision The marginal effect oftheProbitmodelshow formance / / formance

-2.0498 -0.1005 -0.5346

-4.0476 -0.8949

0.3719

3.0283

2.5527 3.5981

Z bnAsuquoEssienetal. Ubon

0.000202

0.004045

0.00039

0.00032

0.04038

p-value

0.70998

0.91991 0.59294

0.37083

***

** 251 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. 252 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. around theneighborhood andtheborrowersac- Informal creditsuppliers areusuallywithinand to increaseininformalcredit amountsupplied. implying thatincreasein socialcapitalwilllead with aprioriexpectation. A directrelationship, signed andsignificantat1%level. This isinline research reportsof a “lastresort.Findingsagreewiththeempirical financing, andthendebt,lastlyraisingequityas which postulatesthatfirmsfirstpreferinternal The resultreflectsthepeckingordertheory increase willbereinvestedintothebusiness. come, willtendtoborrowlessbecausethe ceived decreases.Firmswhohaveincreasedin- income increasesinformalcreditamountre- of informalcreditreceived;therefore,asfirm’s direct relationshipbetweenincomeandamount study area. The resultimpliesthatthereisanin- to smallscaleagro-basedenterprisesinthe related totheamountofinformalcreditsupplied the firmissignificantat1%levelandnegatively model revealsthatthecoefficients ofincome found amongthepeople. over anyotherlendingsourcetypeastheyare credit suppliershaveinformationaladvantage as aformofsecurityfortheirloans.Informal value ofassetsownedbytherespondents’ assets will disbursefundsbasedoninformation prises inthestudyarea.Informalcreditsuppliers credit receivedbysmallscaleagro-basedenter- will leadtoincreaseinamountofinformal sult impliesthatincreaseinsizeofenterprise in consonancewithaprioriexpectation. The re- positively signedandsignificantat1%. This is ities intheamountsborrowed. differences partlyaccountforthegenderdispar- nancial Institutionshencecredit–source-related preneurs borrowfromNGOsandNon-bankfi- (2000) stantiated bythefindingsof formal creditmarketsmore. This resultissub- unlike men,hencetheytendtopatronizethein- women borrowsmalleramountsforbusinesses This resultcouldbeattributedtothefactthat ceived frominformalcreditsourcesinthearea. lead toadecreaseintheamountofcreditre- Uruguay, and The SocialCapitalco-efficient ispositively Furthermore, theresultof Truncated Torbit Further, theco-efficient ofenterprisesizeis An InvestigationintoCreditReceiptandEnterprisePer that agreaterproportionoffemaleentre- Nwaru et al. Kedir (2004) Kimuyu andOmiti et al. in Nigeria. (2009) in (2002) that ofGenderisconsistent with this studyissubstantiatedbythefindingsof enabling himhaveaccesstocredit. The resultof quaintance withthelendergoesalongwayto Mwangi andOuma(2012) corroborates theresearch findingsof result forfirmsize, income andageoffirm lative requirementsforexternal financing. The a firm’s debtratio willthereforereflectitscumu- ship betweenincreasedincomeandcreditaccess; order theorybestexplainsthepositiverelation- cess toinvestiblefunds.Further, thepecking stimulate greaterinvestment,henceac- ence inbusiness;Firmsizeisaconcessionto cause increaseinEnterprise Age impliesExperi- This resultisinlinewithaprioriexpectationsbe- neurs toaccesscreditthanfemaleentrepreneurs. ther, thereisgreaterchanceformaleentrepre- greater thechancestoaccessformalcredit.Fur- prise Size,andvalueofCollateralincreases,the as Gender, Education,Enterprise Age, Enter- study area. The implicationoftheresultisthat, by smallscaleagro-basedenterprisesinthe decision orprobabilitytoaccessformalcredit and statisticallysignificantwithrespecttothe at 10%),Collateral(COL at5%)areallpositive terprise age(Ageat5%),EnterpriseSize(SIZ der (GENat10%),Education(EDU5%),En- (Probit model)indicatesthatcoefficient ofGen- dent entrepreneur. The resultofthe first hurdle amount offormalcreditreceivedbytherespon- cate howadecisionvariableinfluencesthe formal credit. Those inthesecondhurdleindi- a givenvariableaffects thelikelihoodtoaccess from formalcreditinstitutions. scale agro-basedenterprisestoaccesscredit 87% oftheprobabilitydecisionsmall included inthemodelwhereabletoexplain which impliesthatalltheexplanatoryvariables gave theMacFaddenR-Squaredofabout0.87 tive. The estimatedProbitregressionmodel tained forformalcreditaccessinthefirstobjec- model. Again, theresultissimilartothatob- estimates ofthefirstpartHeckman based enterprises(FirstHurdle) Access toformalcredit bysmallscaleagro The coefficient ofthefirsthurdleshowshow The table4presentsthemaximumlikelihood nOu tt,NgraadFtsiwhile in OsunState,Nigeriaand Fatoski formance / / formance bnAsuquoEssienetal. Ubon in Kenya. Ajagbe Lawal et al. et al. of theenterpises.Ifthese assetsareliquidated, inventory willincrease,hence increaseinassets as sizeoftheenterprises increases,thestockof sets ownedbytherespondents. This isbecause burse fundsbasedoninformationvalueofas- the studyarea.Formalcreditsupplierswilldis- ceived bysmallscaleagro-basedenterprisesin lead toincreaseinamountofformalcreditre- sult impliesthatincreaseinsizeofenterprisewill in consonancewithaprioriexpectation. The re- positively signedandsignificantat5%. This is creases withyearsofbusinessdealings. for theloanamountheisgivingoutastrustin- loan access.Itisaformofsecurityforthelender business isusuallyperceivedasanincentiveto credit astheirageincreases.Numberofyearsin enterprises intheregiontendtoreceivemore by theenterprisesinregion.Itimpliesthat lationship withformalcreditamountreceived and significantat10%level. This isadirectre- credit institutions. 89% ofCreditamountreceivedfromformal included inthemodelwhereabletoexplain which impliesthatalltheexplanatoryvariables gave theMacFaddenR-Squaredofabout0.89 model. The estimated Tobit regressionmodel estimates ofthesecondpartHeckman (2012) *** P ˂0.01,**P ˂0.05,*P ˂0.10 Source: EstimatedFromFieldSurveyData,2012

Const GEN EDU AGE SIZ INC INT COL SOC

cadnRsurd0857 dutdRsurd0.732665 Adjusted R-squared 0.875574 McFadden R-squared o-ieiod-.395Aak rtro 33.67189 Akaike criterion -7.835945 Log-likelihood Further, theco-efficient ofenterprisesizeis The coefficient ofageenterpriseispositive The table5presentsthemaximumlikelihood

Table 4: Estimated determinants of access to formal credit by small scale Agro-based enterprises (First Hurdle). An InvestigationintoCreditReceiptandEnterprisePer in Osun.

Coefficient

8.42606e-07 5.63213e-08 2.67719e-07

0.0594197

0.174152

-24.6353

1.16775

1.17937

16.4717

3.26089e-07 2.97144e-07 5.06448e-07

Std. Error

0.0921485

0.0490305

0.841978

0.545003

10.8426

7.81895 credit amountsupplied. This isconsistent , de- Collateral thereforeisagreat incentivetoformal formal credittheenterprise willbeabletoaccess. equate securityforloan, the moreamountof This impliesthatthemoreavailabilityofad- coefficient ofcollateralispositiveat1%level. mal sector. Itenhances easyaccesstofunds. The and search reportsof resort. Findingsagreewiththeempiricalre- ing, andthendebt,lastlyraisingequityasa“last postulates thatfirmsfirstpreferinternalfinanc- result reflectsthepeckingordertheorywhich abnitio, irrespectiveofincreasedincome. The up businessesmaynotnecessarilygoforcredit will bereinvestedintothebusiness. Also, start- will tendtoborrowlessbecausetheincrease decreases. Firmswhohaveincreasedincome, come increasesformalcreditamountreceived of formalcreditreceived;therefore,asfirm’s in- direct relationshipbetweenincomeandamount study area. The resultimpliesthatthereisanin- by smallscaleagro-basedenterprisesinthe related totheamountofformalcreditreceived the firmissignificantat1%levelandnegatively model revealsthatthecoefficients ofincome more creditamountallthingsbeingequal. eventuality. Therefore enterprisesizewillattract they canbeusedtorepayloansincasedofany Collateral isaprerequisiteforcreditsinthefor- Further, theresultoftruncated Torbit

-2.2721

1.3869 1.8899 2.1640 2.5840 0.1895 0.5286 2.1066

1.2119 Nwaru

Z formance / / formance et al. Kedir (2004)

7.7302e-08 5.1670e-09 2.4561e-08 bnAsuquoEssienetal. Ubon

0.0159772

0.0054513

0.136854

0.108199

Slope*

1 et al. in Nigeria. (2009)

0.05877 0.03047 in Uruguay,

0.03515

0.16547

0.00977

p-value

0.84967 0.59707

0.2255

** **

* 253 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. 254 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. received creditfromformal andinformalcredit ratio forsmallscaleagro-based enterprisesthat the liquidityoffirmis poor. current liabilitiesexceed current assets,andthus bilities. A ratiooflessthan1.00indicatesthat ratio givescurrentassetsrelativetolia- quacy offinancialperformance. The current analysis iscommonlyusedtointerprettheade- less adequatethanitmighthavebeen.Ratio firms intheregion,particularlysmallfirms,was Delta madeclearthatthefinancialpositionof their financialposition.InstabilityintheNiger novation intheregionisheavilydependentupon an entrepreneurialengineofjobcreationandin- prises tocopewithturbulence,andprovide Performance ofenterprisesintheregion in Kenya. by thefindingsof market. The resultofthisstudyissubstantiated able toaccessmorefundsfromtheformalcredit themselves intocooperativegroups,theywillbe Therefore asmoresmallscaleenterprisesform groups ratherthanindividualsforbusiness. tions wouldusuallywanttoloanmoney sirable andconsistent.Formalcreditinstitu- consonant withaprioriexpectation.Itisde- signed andsignificantat10%level. This isin sirable andinlinewithaprioriexpectation. Table 6 and7thereforerepresentthecurrent The abilityofsmallscaleagrobasedenter- The SocialCapitalco-efficient ispositively An InvestigationintoCreditReceiptandEnterprisePer *** P ˂0.01,**P ˂0.05,*P ˂0.10 Source: EstimatedFromFieldSurveyData,2012

Const GEN EDU AGE SIZ INC INT GUA SOC

h-qae8 8046pvle7.42e-06 p-value 38.03476 Chi-square(8) o-ieiod-0931Aak rtro 2038.702 Akaike criterion -1009.351 Log-likelihood McFadden R-squared 0.89543

1.97263e+06

1.95356e+06

Coefficient

Table 5: Estimated determinants of formal credit amount received

-0.0179366 Mwangi andOuma(2012)

-0.017621

0.095229

3295.75 57680.5

30044.9

205785

Std. Error

0.039657 0.004246 0.131784

31285.2 30835.5

15976.2

666081 333579

494901 Bangladesh. findings of sult ofthiswork by larger creditamount,ceterisparibusthere- ance througheconomies ofscaleoccasioned tic offormalloansshouldenhanceperform- large sizecreditamount whichischaracteris- for meaningfulproduction.Ifwellemployed, always larger than informalcreditanduseful expectation. This isbecauseformalcredit is expected,desirableandinlinewithapriori from theinformalcreditsources. This result small formal creditsourcesperformbetterthanthose jority ofenterpriseswhoreceivedcreditfrom ratio. Further, theimplicationofthisisthatma- informal creditsourceswithsimilarcurrent (45.45%) ofenterpriseswhoreceivedfromthe ligations comparedtothesmallerpercentage ratio >5.99 credit fromformalsourceswithcurrent number (51.04%)oftheenterprisesthatreceived their currentobligations.Fromthese,agreater 60% oftheenterprisesinregioncanmeetup not meetuptheircurrentobligations.Morethan This impliesthat study area,hadacurrentratiooflessthanone. ceived fromtheformalcreditmarketin market and25%oftheenterprisesthatre- that receivedcreditfromtheinformal sources intheregion. The tablerevealsthat29.55%ofenterprises scale enterpriseswhoreceivedcredit formance / / formance are wellabletomeetupcurrentob- Majumder andRahman (2011)

-2.9615

-4.2235 -0.1337

0.6169 0.1053 1.8706 2.4013

3.9474 1.8806

Z these groupsofenterprisescan- bnAsuquoEssienetal. Ubon corroborates theresearch

0.00306

0.00313

0.00008

0.03671

0.06140

0.06003

p-value

0.53730 0.91610

0.89363

***

* in Source: Estimated FromFieldSurveyData, 2012. do notactuallyvary. the informalcreditmarket,theirperformance percentage ofenterprisesthatborrowedfrom up currentobligationscomparedtoalesser from formalcreditmarketwereabletomeet though majorityofenterprisesthatborrowed the informalcreditmarket. That is,even ket andenterprisesthataccessedfundsfrom prises thatborrowedfromformalcreditmar- no difference inper cal isinsignificant(.579),implyingthatthere borrowed fromtheformalcreditmarket. The t- the informalcreditmarketandenterprisesthat prises, thatis,enterprisesborrowedfrom currentratiobythetwogroupsofenter-mean Difference inmeansofcurrent ratio

0.00-0.10 0.11-0.20 0.21-0.30 0.31-0.40 0.41-0.50 0.51-0.60 0.61-0.70 0.71-0.80 0.81-0.90 0.91-1.00 >1.00 Total Source: EstimatedFromFieldSurveyData,2012.

Table 8: Return on capital employed for

0.00-0.99 1.00-1.99 2.00-2.99 3.00-3.99 4.00-4.99 5.00-5.99 >5.99 Total There isnosignificantdifference between

Category An InvestigationintoCreditReceiptandEnterprisePer

Category

Table revealsthat19.69%ofinformalcredit Further, tables8and9representthereturnon Category

Table 7: Current ratio for informal credit formance / / formance

credit borrower enterprise.

borrower enterprises

Frequency

Frequency bnAsuquoEssienetal. Ubon

120 264

70 96

78 41

3 2 3

3 2 1

6 5 8 6

- - -

Percentage

72.916

3.125 2.083 3.125

3.125 2.083 1.041

29.545 15.530

45.454

12.5

2.272 1.893 3.636

2.27

- - - 255 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. 256 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. orates thatof cial institutionsinthearea. and efficient monitoringofloan-usebyfinan- sources. This resultmaybeduetoconsistent cessed fundsfromtheinformalcredit more efficiently thanenterprisesthatac- depended onformal level at1%level,implyingthatenterprises The T-cal is-3.57. This is significantat1% that borrowedfromtheinformalcreditmarket. from theformalcreditmarketandenterprises of enterprises,thatis,enterprisesborrowed return oncapitalemployedbythetwogroups employed Difference betweenmeansofreturn oncapital Majumder andRahman(2011) Again theresultofthisworkcorroboratesthat those thatreceivedfromtheinformalmarket. however aremoreefficient inuseofcapitalthan ceived creditfromtheformalmarket prises inthestudyarea.Moreenterprisesthatre- resources amongsmallscaleagrobasedenter- credit borrower, hencethereisinefficient useof ROCE belowthelendingrateforformal jority oftheenterprisesfrombothgrouphavean Against thisbackdrop,theresultimpliesthatma- ing rateareobservedtobeashigh25%. this isnotobtainableinthebanksasbanklend- the countryisfixedatabout12.5%,however, The currentcommercialbankborrowingratein ployed (ROCE)shouldbeaboveborrowingrate. and above.Intheory, thereturnoncapitalem- credit sourcehaveareturnoncapitalof0.100 Informal creditbysmall scale agrobasedenter- variables influencingthe probability ofaccessing and genderarestatistically significantdecision analysis revealsthatenterprise age,socialcapital in thestudyarea. The Probitmodelregression acquisition bysmallscaleagrobasedenterprises analyze thetwostagedecisionofcreditaccessand Nigeria. The study usestheHeckmanmodelto the performanceofenterprisesinNigerDelta cess tocredit,actualcreditamountaccessedand fluencing smallscaleagrobasedenterprisesac- Bangladesh. The studywasconductedtoidentifyfactorsin- There isasignificantdifference inthemean An InvestigationintoCreditReceiptandEnterprisePer Majumder andRahman(2011) CONCLUSION credit sourcesperformed The result in Bangladesh. corrob- in demand: evidencefromOyo state,Nigeria. Am. J. (2012). Determinantsofsmall-scale enterprisecredit 1-Ajagbe, F. A., Oyelere,B. A., & Ajetomobi, J.O. an earlierdraft. committee fortheirconstructivecriticismsof members ofthedepartmentalpostgraduate sis supervisors. The authorsaregratefulto going Ph.Dthesis. The co-authorsarethethe- credit totheentrepreneurs. needs ofthelendingagentsandduration cated forthere-assessmentofcollateral trepreneurs inthestudyarea,advo- would affect theiraccesstocreditpositively. mented foragrobasedentrepreneursasthis or outfitsnearertotheseentrepreneurs. deavor tolocatesomeofthelendinginstitutions increase awarenessonmattersrelatingtocredit. priate informationsharing,riskreductionand cooperative societiesasthiswillensureappro- that entrepreneursinthestudyareashouldform prises accesstocredit,thestudyrecommended this willenhanceperformanceinthesector. ing policiesinothertofavourstart-upbusinesses; stitutions shouldendeavourtoreviewtheirlend- thatoperatorsofformalcreditin- recommended mal andinformalcreditsources.Itwastherefore factors affect creditamountobtainedfromfor- influence formalcreditamountaccessed,similar that apartfromtheageofenterprisewhich terprises intheregion.Itwashoweverobserved actual formalcreditamountreceivedbytheen- eral werevariablesthatsignificantlyinfluenced age, gender, enterprisesize,incomeandcollat- had accesstoformalcreditsources,whereas credit accessbythosegroupsofenterprisesthat and socialcapitalsignificantlyinfluencesformal credit amountreceivedbytheseenterprises. ital significantlyinfluencedtheactualinformal income oftheenterprise,guarantorandsocialcap- prises inthestudyarea,whereasenterprisesize, This paperformspartofthefirstauthor’s on- 4- To increasecreditamountreceivedbyen- 3- Adult educationprogrameshouldbeimple- 2- Operatorsofcreditinstitutionsshoulden- 1- To improvesmallscaleagrobasedenter- Similarly, gender, age,enterprise size,income formance / / formance AKNOWLEDGEMENT REFERENCES bnAsuquoEssienetal. 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(2005). rogramme 257 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. 258 International Journal of Agricultural Management and Development, 3(4): 245-258, December, 2013. New York. DPP 2005,1-20. Development Goals. Annual Session,13-24June, Investing intheLDCsto Achieve theMillennium UNCDF (2005).UNCFBusinessPlan2005-2007. 29- UnitedNationsCapitalDevelopmentFund ber, 2009,Dakar, Senegal,1-30. 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Ubon 2 1 * Corresponding author’s email:[email protected] * Corresponding author’s Mostafa Baniasadi Rafsanjan-Iran) WTP for InsuranceofPistachio Tree Trunk (CaseStudy Investigation ofthePotentialMarketandEstimation Rafsanjan valuation, WTP, Logitmodel, Pistachio tree,Contingent Keywords: Accepted: 26 August 2013 Received: 11 February2013, Professors of Agricultural Economics,Departmentof Agricultural Economics, UniversityofTehran MSc Graduatedof Agricultural Economics,Departmentof Agricultural Economics,UniversityofTehran. ISSN:2159-5860 (Online) ISSN: 2159-5852(Print) Available onlineon:www.ijamad.com International Journalof Agricultural ManagementandDevelopment(IJAMAD) 1 *, Saeed Yazdani

Abstract 2 C WTP calculatedinthisstudycanbeused. tachio gardeners.Inordertodeterminepremium inRafsanjan, mium ofpistachiotreeisoffered toreduceriskandlossofpis- high riskdestructionofpistachiotrees,itissuggestedthatpre- and 1183.3 IRRpertreerespectively. Consideringresultsand part, Anar andKashkuiehhasbeenestimatedby1953,3255.8 that willingnesstopaypremiumofpistachiotreeincentral interview with184pistachiogardenersin2012.Resultssuggest been used.Researchdatawereobtainedbyfieldmethodand of contingentvaluationanddoubleboundeddichotomoushave sanjan locatedinKermanprovince.Forthispurpose,methods estimate willingnesstopaypremiumforpistachiotreeinRaf- fecting willingnesstowardsinsuranceofpistachiotreeandto to insurethetree. This studyisaimedtoinvestigatefactorsaf- Thus, inordertoreducelossincurredontrees,itisnecessary Pistachio treehasbeenindangerofdestructionanddryness. duction offruitsinthecountrythereforefarmersincuraloss. for producinggardenproducts,naturaldisastersdamagepro- products intheworldbutdespiteexcellentconditionIran and Habib Allah Salami accounted asacountrythatproducesthirteengarden apacity ofgardenproductionsinIranissuchthat 2 259 International Journal of Agricultural Management and Development, 3(4): 259-267, December, 2013. 260 International Journal of Agricultural Management and Development, 3(4): 259-267, December, 2013. allocated togardensubsector ment and80%oftotalagriculturalexpertsare total agriculturalproduction,13%ofemploy- tural landsareplantedforgardens.17.5%of producing 14gardenproducts.17%ofagricul- among firsttoseventhcountriesintheworldfor cording currentstatistics,Iranhasbeenranked development ofnumerousgardenproducts. Ac- climatic varietieshaveprovidedproductionand ucts, Iranhasaspecialpotential.Capacityand 2007) istry 2009) producers willbeprevented producers inriskycondition, lossesincurredon tion inrisktakingsothat bycooperationwith agricultural productsisa strategyforparticipa- participate ininsurancesystems.Insuranceof to takeanactiveroleinriskmanagementand the incipientstagewhichcanencouragefarmers is necessaryforitsdevelopmentespeciallyin into ahighriskone plant diseasesthatchangesactivityinthissector as flood,hail,coolingandwarming,pests wide rangeofnaturaleventsanddangerssuch sector reliesinthenaturehighlyandfacesa tivities. themostimportantdifference isthatthis ferent fromothercommercialandproductiveac- the highestrisk. Agricultural productionisdif- economical activities,agriculturalactivityfaces face alwayswithriskofdryingandruin. Among in Rafsanjancounty. Also thepistachiotrees weather, technologicalandmarketuncertainties is pronetovariousrisksdueproduction, surance ofagriculturalproducts phenomenon inagriculturalproductionsisin- uncertainty, one ofthewaystocopewiththis of agriculture.Consideringriskyconditionand plays areallyimportantroleinthedevelopment their gardeners’ households.Riskmanagement risks affect theincomestabilityandwelfareof 2004) and Iranrespectively this producthasbeen34and60%intheworld tribution ofthiscitytofertileplantingareafor world, IranandKermanprovincesuchthatcon- the maincenterofpistachioproductionin sanjan withplantingareaof110000 hectaresis Rafsanjan isfamousthroughouttheworld.Raf- As oneofpolesinproductiongardenprod- . Publicsupporttoagriculturalinsurance . Inrecentyears,productionofpistachio Investigation ofthePotentialMarketandEstimation . PistachioproductioninIranand INTRODUCTION (Anderson 2003) (Mirzaee andHeidari (Agricultural Min- (Nelson and (Kiani Rad . All these . All sanjan countyofIran. the demandfornewtree trunk insuranceinRaf- In thispaper, theCVMmethod isusedtostudy insurance, thereisnotanyresearchbackground. tors on Willingness to adoptionofthis tial marketfornewinsuranceandeffective fac- trunk), Willingness to pay, analysisofthepoten- In topicoftreeinsurance(destruction von Braun2005) 2011) Loehman 1987) nomical developmentprogram tors isaccountedasoneofgoalsfourtheco- Assuring fruitfultreeandotherproductivefac- sumed forthistreemakethedamagedouble. direct costs,opportunitycostandtimecon- loss willbeincurredonfarmers.Inadditionto natural disastersafterpayingmoneyforyears, to haveafruitfultree.Ifthistreeisdestroyedby in danger. Muchmoneyhasbeenspentforyears tachio treeisoneofthosethathavealwaysbeen no seriousresearcheshavebeendoneyet.Pis- ance hasnotbeenperformedinIranandalso, particular productorgood what individualswouldbewillingtopayfora erature usesCVMmethodwhichelicitsdirectly cost-effectiveness to nameafew ing, cost-benefitanalysis,travelcostand tempted tosolvethisprobleme.g.hedonicpric- non-market good.Numerousmethodshaveat- tempting topriceaproductwhichispublicor One mayrunintoparticulardifficulties whenat- demand curvesastaughtinMicroeconomics. finding theintersectionpointofsupplyand for aproductisnotalwaysasstraightforward premium) willbeestimated.Establishingaprice tree insurance(including;determiningpriceand Then, thewillingnesstopayofgardenersfor adoption ofgardenersforthenewinsurance. market andeffective factorsonwillingnessto pose ofthestudyisinvestigationpotential ment inthecountryasascientificbase. The pur- order thatthisnewservicewillstartandimple- of treetrunkinsuranceneedstomanystudiesin for fruittreesofthecountryduetonewsubject trees inthecountry. Codificationofsuchpattern sary todesignawideinsurancesystemforfruit on producinggardenproducts,itseemsneces- country, legalobligationandinordertoinvest Ahsan . Therefore, highriskconditioninthe fWP/MostafaBaniasadietal. of WTP et al . (1989) . Unfortunately, treetrunkinsur- . Muchofthecurrent WTP lit- found thatrecordofcon- (Wright et al. (Asfaw and (Baniasadi , 2009) . surances (exceptinsurance offruittree).Forex- that majorityofstudiesare aroundagriculturalin- tries somestudieshavebeen doneinthissubject, for fruittreetrunkinsurance. But,inothercoun- about insurancemarketandthewillingnesstopay percent oftheirincome.However, thereisnostudy est incomequintilearewillingtopayup11.4 per capitamonthandrespondentsinthepoor- average respondentsarewillingtopayNAD48 (around 90percentoftheaveragefamilysize).On average arewillingtoinsure3.2individuals join theproposedhealthinsuranceschemeandon percent oftheuninsuredrespondentsarewillingto method. The findings ofthisstudyshowthat87 double boundedcontingentvaluation(DBCV) health insuranceproductinNamibia,usingthe and hencethepotentialmarketfornewlow-cost alyze thewillingnesstopayforhealthinsurance circle ofhealthinsurance, to payforspeciesofinsurances.Forexamplein of potentialmarketandestimationwillingness potential demandratherthaneffective demand. agricultural insurancecanonlyberegardedasa would notchooseinsuranceandthedemandof amount ofsubsidy, thevastmajorityoffarmers In thevoluntaryinsuranceandnoracertain surance behaviorisinfluencedbymanyfactors. is thatunderthepresentstage,agriculturalin- ences ofindividualfarmer. The researchresult stern UtilityModeltoanalyzetheriskprefer- behavior. Hewithusing Von. Norman-Morgen- cultural insuranceandthefactorsaffecting their lyzes characteristicsoffarmerbehaviorinagri- Shaik and Atwood (2003) the fluctuationofprofitandincomechanges. tural productsisoneofthemainwaystoreduce insurance. Hebelievedthatinsuranceofagricul- nificant factorsonadoptionoftheagricultural frontation withthedangerisasoneofsig- with buyingtheinsurance. cows, levelofincome,andextentthefarm there isadirectrelationshipamongnumberof in Indiandairysection. The resultsshowedthat alyzed explanatoryfactorsofbuyinginsurance their products. bigger farmhavemorewillingnesstoinsure showed thatproducersofmoreefficiency and ucts byusingLogitmodelandtheresults factors ondemandofinsuranceforcottonprod- Moreover, therearemanystudiesaboutscrutiny Investigation ofthePotentialMarketandEstimation Ogursov andMarcel(2006) Wright examined effective Wang (2010) et al. , (2009) ana- an- an- of necessarysamplefrom societystudiedin the mainfactorincontingentvaluationmethod gained inCVMbychoicetechnique,whichis surance. Value ofgoodsorservice(WTP)is is usedtodeterminethevalueofthiskindin- insurance inIran,contingentvaluationmethod not supplyandthereisanymarketforthis ample (Hanemann (Mitchell andCarson1989) more efficient thansingleboundedtechnique Double boundeddichotomous isstatistically bounded methodisusedwhendataarenormal. method frompistachiogardenersofRafsanjan. tained byquestionnairein2010-2011 inafield formation requiredforthisresearchhasbeenob- farmers topayfortreeinsuranceisestimated.In- insurance shouldbestudied. Then willingnessof in firststep,factorsaffecting acceptanceoftree Since mostoffarmersresistagainstnewideas, of farmersforacceptingthisnewinsurance. aimed toinvestigatefactorsaffecting willingness perform treeinsurancedesign,thisstudyis performed onascientificbasis.Infirststepto searches inorderthatthisnewinitiativeis lating suchmodelfortreesrequiresnumerousre- both oftheirparticipationand WTP. knowledge ofallowancesignificantlyinfluenced cept. The acceptanceofpremiumandfarmers’ rent premiumisintherangethatfamerscanac- WTP. The resultsofthisstudyshowthatthecur- factors influencingfarmers’ participationinand willingness topay(WTP)forcowinsuranceand time by method wasused. This methodusedforthefirst ble boundeddichotomouschoice(DBDC) valuation methods method (CVM)isoneofthetypicalnon-market economic valuation.Contingentvaluation ing willingnesstopayforinsuranceisakindof gardeners forpistachiotreesinsurance.Estimat- premium isestimationofwillingnesstopay methods fordeterminetemporaryofinsurance for insurancepremiuminIran,oneofthebest of treesdestruction’ inordertodeterminearate et al. Considering newnessoftreeinsurance,formu- Due tolackoftimeseriesdatameasurerisk , 2003) MATERIALSMETHODS AND Fengli Xiu fWP/MostafaBaniasadietal. of WTP Hanemann . Becauseinsuranceoftreetrunkis et al. , 1991) et al (Turner . (2012) et al. . To determinenumber et al. . Inthisstudy, dou- (1991) analyze farmers’ , 2001;Batmane . Double 261 International Journal of Agricultural Management and Development, 3(4): 259-267, December, 2013. 262 International Journal of Agricultural Management and Development, 3(4): 259-267, December, 2013. factors ofindividuals.and vidual's income;Sisavectorofothereco-social tree ineverydistrictofthecountyisdetermined: based-on percentofunder-sown levelofeach with usingequation(2)thenumberofsamples ated difference inutility(∆U)duetotheeffect uted randomlyindependent ofeachother. Cre- variables withaverage0 thathavebeendistrib- equation the coefficient ofvariation WTP infollowing and thensamplevolumeiscalculatedbyusing some questionnaireiscompletedasapre-test ber ofsampleisdetermined.InCV method,first pistachio gardenersofRafsanjancounty),num- been supplied). After choiceofsociety(thatare ance ofpistachiotreeasaservicethathasnot tence newgoodorservice(suchas;theinsur- which isinfluencedbyexistenceornon-exis- CVM, researchermustdetermineofthesociety shows it which hedoesn'tuseinsuranceandrelation(3) tree trunkinsuranceismorethanthecasein The amountofcreatedutilityduetotheusage new insurancecausesutilitytobecreatedforhim. tled asproposedamount(B)andthisusagefrom his agriculturalincometotreetrunkinsuranceti- Each gardenerisreadytopaysomeamountof assumed thatgardenerbearutilityfunctions. county. Indouble-boundedchoicemethod,itis level ofunder-sown pistachioinRafsanjan tachio levelin1stdistrict,andHisthewhole in 1stdistrict,histheamountofunder-sown pis- U (1,Y- B;S)+ɛ tion studies able amountofdis0.3-0.5incontingentvalua- between WTP calculatedandreal WTP. Accept- WTP, distheerrorpercentageofdifference isthecoefficient ofvariation equal to1.96), statistic (thatinlevelof5%isapproximately 1998; PattanayakandEvanMercer1998) Where Uisindirectutilityfunction, Y isindi- Where, Where, nisthesamplevolume,tt-student Investigation ofthePotentialMarketandEstimation (Amirnejhad (Mitchell andCarson1989) n 1 (Mitchell andCarson1989) is thenumberofnecessarysample 1 ≥ U(0,Y; S)+ɛ et al ɛ ., 2006;Judge 1 and 0 ɛ 0 are random : . Then, : et al. (2) (1) (3) , A= culated fromrelation(7): Heberlein, 1979) 2006; Hanemann is expressedasrelation(5) ceptance oftheproposedamountbyindividual According tothelogitmodel,probabilityofac- gardener wasusedtodeterminethepremium. scriptive variablesontheamountof WTP of model forstudyingtheeffect ofdifferent de- ent variablewithdualselection.Hence,logit studying the WTP ofindividualshasadepend- Batmane + β ∆U =U(1,Y- B;S)–U(0,Y;S)+(ɛ respectively andalso number ofdestroyedtrees inthepreviousyear, effect ondamagereduction(Likertscale) and extension, qualitativevariable ofnewinsurance tree, dummyvariableofcropinsurancecontract income, yieldofpistachio,numberpistachio able ofliteracy, dummyvariableofagricultural imum bid(B) merical integrationwithintherangeof0tomax- this methodiscalculatedfromrelation6bynu- aggregation. The expectedamountof WTP in tations withtheory, statisticaleffectiveness and protects thestabilityandcompatibilityoflimi- cated mean WTP isused,becausethismethod order tocalculate WTP amethodknownastrun- andθ>0.In mated anditisexpectedthatβ≤0 arecoefficients thatcanbeesti- andθ ables. β in thispaperitincludessomeeco-socialvari- function withastandardlogisticdifference and relation (4) of usingtreetrunkinsuranceiscalculatedfrom β y Where Where Bisbidamountvariableand A iscal- Where Double-bounded questionnairestructurein , β α nt nt + M , β Σ nt ce i +β β , fWP/MostafaBaniasadietal. of WTP α F et al., β i M η ei is interceptofmodeland ce (Amirnejhad , i ( = M β ∆U t1 α ce are ageofgardener, dummyvari- 1995) +β +β ) isaccumulativedistribution : (Amirnejhad a ei et al. M M M : a + β ei a +β , et al. M , 1991;Bishopand dL t1 dL M (Amirnejhad M , dL , 2006) M t1 + β dI et al. 1 , -ɛ M dI 0 M ) y β : , a dI M , , 2006; + β β nt dL et al. , , M y β (7) (5) (6) (4) M ce dI y , , , variables andtheircoefficients. tial probabilityandinitialvalueofindependent probability ofbidacceptancedependsontheini- by individualwhenX ity ofbidacceptancefortreetrunkinsurance plained thepercentagechangeinprobabil- tory variable(X and marginal effect. The elasticityofkexplana- portant inlogitmodelresultsincludeelasticity form. The interpretationoftwoparameterisim- model couldbeestimatedinlinearorlogarithmic al (X The marginal effect ofkexplanatory variable vidual whenXkamountchangedbyoneunit. acceptance forgreenchickenbuyingbyindi- the percentagechangeinprobabilityofbid percentage. Also, themarginal effect showed M 2011; KavoosiKalashami Source: researchfindings

Age (year) Level of education (Years of education) Agricultural income (10 million rials) under-sown level (hectare) Yield (tone per hectare) Number of pistachio tree in 2010 Source: research findings

Question In aboverelation,theextentofchangein Elasticity ofanexplanatoryvariableex-

Answer Frequency Percentage ., 2012) ei k , ) isasrelation(9) M Investigation ofthePotentialMarketandEstimation t1 are theiraverages,respectively. Logit : Table 1:Statisticalcharacteristics (economic-social)ofquantitativevariable.

Variables k ) isasrelation(8)

Whether have been destroyed your

trees because of existing risks? Table 2:Willingnesstoadoptionofpistachiotreeinsurance k amount changedbyone (Kavoosi Kalashami

93.5

172

Yes et al. , 2012) (Hayati :

Average

4361

50.4

23.7 et al

7.9

4.8 1.3 (9) (8) et .,

6.5

12 questions relatedtothesubject. Table 2,showedsomereportsfromresponsesof to adoptionforinsuranceofthisriskornot. tree andifso,whetherthereisanywillingness whether thereisanydestructionriskofpistachio tachio treeinsurance,firstweshouldsee pistachio treesoftheunder-question individuals. income, under-sown level,yieldandnumberof variables ofage,leveleducation,agricultural 184 questionnairesareaccomplished. County aredetermined170individualsandthen pre-test, thenecessarysamplesofRafsanjan tionnaires requiredinRafsanjancounty. Inthis plished asapre-testtodeterminealloftheques- and professors.First,29questionnairesisaccom- and CVM,alsocorrespondingwithexperts area, otherquestionnairesrelatedtotheinsurance naire isprovidedbyanalyzingconditionsofeach time inIranhasbeenperformed;CVMquestion- tionnaire, withregardtothestudythatforfirst Koshkoo’iyeh. Beforethecompletingofques- district andsuburbs, Anar, Noghand done inRafsanjancountywhereincludesCentral As canbeseenintable2,destructionriskofpis- To Investigationofwillingnesstopayforpis- Table 1showssomeofthestatistics aboutthe Collecting dataandresearchesprocesswas

0.12 0.06

Min

100

0.6

0 RESULTSDISCUSSION AND

Whether you desire to insure your

trees? (willingness to insurance) fWP/MostafaBaniasadietal. of WTP

60000

Max

300

80 18

71.7

132

Yes

deviation

Standard

8429.2

13.1

37.9

5.8

7.4 0.7

Coefficient of

variation

28.3

0.26 0.73 1.61 1.55 0.56 1.93 263 International Journal of Agricultural Management and Development, 3(4): 259-267, December, 2013. 264 International Journal of Agricultural Management and Development, 3(4): 259-267, December, 2013. (Batemen WTP ofgardenerswithregardtothepre-test The firstbidisbased-onmeanofmaximum double boundeddichotomouschoicemethod. tree thatissuggestedwhichclose-endedand WTP amountofgardenerstoinsurepistachio mand thisnewkindofinsurance. 72%) havewillingnesstoinsuretheirtreesandde- asked inthissample,132ofthem(approximately In accordingtotable3,among184gardeners gardeners willacceptthenewkindofinsurance. risk oftheirtrees. Therefore, itisexpectedthat as 93.5%ofgardenerswerefacedwithdestruction tachio treeisreportedinahighlevelofthesample first bidamount.Iftheamountisac- dian ofwillingnesstopayisconsideredasthe Source: researchfindings The mainquestionofquestionnaireisthe

amounts

(Rials)

2000 1000 4000

Bid Source: researchfindings.*** P ˂0.01,**P ˂0.05,*P ˂0.10 Investigation ofthePotentialMarketandEstimation

Intercept Age DVL Agri-income Yield (tone per hectare) NPT DVCICE QVIEODR (Likert scale) Number of destroyed tree in 2010 Bid amount (10 Rials) Anar district Koshkoo’iyeh district Nogh district Percentage of right predictions Likelihood ratio statistic P-value of Likelihood ratio et al., Table 4:Effective factorsonprobabilityofadoptionpistachiotreetrunkinsurance

Frequency 1995)

Variable

67 51 28 Table 3: The resultsofacceptingbidamountsbygardeners

Acceptatio . Generallyinpretest,me-

Percentage

36.4 43.6 41.8

Coefficient

-0.00006*

-0.016***

0.00003

0.53****

-0.004*

0.68**

-1.95*

-0.80*

0.73* 0.44*

0.93* 0.52*

Frequency

0.20

117

66 39

Not acceptation ing totheresultsofestimatedmodel,variables chio gardeners(approximately35.9%). tions isnotacceptedby66ofownerspista- (approximately 64.1%)andnoneofthesugges- accepted by118 ofownerspistachiogardens As generally, atleastoneofthe3suggestionsis them (approximately63.6%)didnotacceptit. 36.4%) acceptedthefirstsuggestionand117 of asked tointerview, 67ofthem(approximately tion ofgardenersforbidamount. showed bidamountandadoptionornotadop- bid amount the secondbidamountwillbehalfoffirst as thesecondbid.Iffirstbidisnotaccepted, cepted, doubleoffirstbidamountisconsidered Table 4reportsresultsofthemodel. Accord- According totable3,from184gardeners

t-student

statistic

0.009

-2.38 -1.60

-2.33

-2.97

-2.18

Percentage

1.97 2.47 2.24

3.63 4.31

0.52 1.53 fWP/MostafaBaniasadietal. of WTP

Logit model

63.6 56.4 58.2 (Batemen

Elasticity at

0.0004

mean

-0.51

-0.17

-0.46

-0.08

0.33 0.18 0.36

0.22 0.95

0.02 0.06

Frequency 60.15 0.01

- 70 et al.

184

117

67 , 1995)

Marginal

Total

0.000009

-0.00001

-0.0008

effect

-0.004

-0.13

0.16 0.10

0.21 0.12

0.04

0.11

Percentage

- . Table3,

100 100 100 respectively. is 0.95and-0.37forKoshkoo’iyehNogh, With regardtorelationbelow, theamountof A equation differently. Then, WTP isestimated. two thesedistrictsshouldhavetoputintoan Nogh andKoshkoo’iyeh,onlycoefficients of Central sectionand Anar. hectare ofpistachiogardenis2228212IRRin achieved. Calculatedinsurancepremiumforper of atree,thepremiumforperhectareare that withmultiplythisnumberatexpected WTP of thepistachiotreesforperhectareis866 garding tosamplestudied,averageofnumber district anditssuburbs Anar section.Inre- surance iscalculated2573IRRforcentral pected WTP ofgardenersforpistachiotreesin- expected WTP isobtainedfromrelationbelow: for analysisofthenextresults. The amountof is 70%. Therefore, consideredmodelisreliable bid amountisdone. variable onprobabilitypercentageofacceptable effects ofchangerelatedtoeverydescriptive percentage aresignificant. Therefore, analyzing sign andorderlyinlevelof10,1,5,1 ance effect ondamagereductionwithpositive contract andqualitativevariableofnewinsur- variable ofperiodextensioncropinsurance pistachio, numberofpistachiotrees,dummy dummy variableofagriculturalincome,yield nificant. Also, dummyvariableofliteracy, trees inlevelof1%withanegativesignaresig- of ageinlevel10%andnumberpistachio A Where, A To estimate WTP ofgardenersindistricts According torelationabove,theamountofex- The percentageofcorrectpredictionmodel Nogh Kosh Investigation ofthePotentialMarketandEstimation =A+ =A+ Source: researchfindings

District

Central section and Anar Koshkoo’iyeh Nogh β β Kosh β Kosh Nogh and =0.95 =-0.37 β Nogh Table 5: The resultsofestimationexpectedWTP are dummyvariables

-0.37

0.42 0.95 strict, respectively. and 1453.9rialsinKoshkoo’iyehNoghre- pistachio treeinsurancearecalculated3547.6 model. According torelationbelow, WTP for coefficients ofKoshkoo’iyehandNoghin sults of WTP estimatedareshownintable5. Koshkoo’iyeh andNogh,respectively. The re- culated 3072568and1259164IRRin mium forperhectareofpistachiogardenarecal- trees forperhectare,expectedinsurancepre- ber ofpistachiotreesper hectare (866trees),the tively. Also, withregardtotheaverageofnum- 1453.9 IRRinKoshkoo’iyeh andNogh,respec- suburbs and Anar district,butitis3547.6 and deners is2573IRRincentraldistrictandits nificant difference. The expected WTP ofgar- than centralsection,whereas Anar hasnosig- cant differences in WTP fortheinsurancerather surveys, NoghandKoshkoo’iyehhavesignifi- Koshkoo’iyeh havebeenstudied. According to central anditssuburbs, Anar, Nogh, and destruction. To estimatingof WTP, 4districtof pistachio treeencounterwithhighriskof sanjan countyandIran. The resultsshowthat tance ofpistachioproductineconomyRaf- of thistree,under-sown levelandtheimpor- tree isdoneinregardtohighriskofdestruction in RafsanjancountyofIran.Selectingpistachio factors onadoptionofpistachiotreeinsurance for theinsurance,alsoanalyzingeffective the maximumwillingnesstopayofgardeners ket ofinsurancepistachiotreetrunk,estimate

A According toaverageofnumberpistachio Purpose ofthestudyisexaminepotentialmar- fWP/MostafaBaniasadietal. of WTP

WTP for per tree (Rials) CONCLUSION

2573 3548 1454 265 International Journal of Agricultural Management and Development, 3(4): 259-267, December, 2013. 266 International Journal of Agricultural Management and Development, 3(4): 259-267, December, 2013. surance contractconsiderably dependson surance. Extensionofthe periodofcropin- positive influenceonaccepting pistachiotreein- surance contractextension hassignificantand tree insurance. Variable coefficient ofcropin- ance fundshouldmakepositiveattitudetowards service anditsrequirements,agriculturalinsur- area andintroducingmakingknownthis experts andpromoteroftheinsuranceineach plying thiskindofinsurance,withsending ance anditsbidprices. Therefore, beforesup- insurance influencesonacceptingoftreeinsur- ability. Positiveattitudetonewserviceoftree they willaccepttheinsurancewithhigherprob- esquire) becausetheyhavemoreincome,so begun fromwealthygardeners(greatownersor imental ofthetreeinsurance’s project,itis for low-incomegardeners. To performingexper- come levelsbysupportingproductionespecially ciency oftreeinsuranceisreinforcingthein- Therefore, apolicyadvicetosupplymoreeffi- price tousenewserviceforpestachiotrees. deners hassignificanteffect onacceptingthebid cording toconsideredelasticities,incomeofgar- willingness ofgardenerswillbedetermined. Ac- detection itsproblems,degreeofefficacy and perimental testofpistachiotreeinsuranceand mining thefairinsurancepremium. To doex- as atemporaryinsurancepremiumuntildeter- of gardenerstoinsuretheirpistachiotreestrunks mon methods,itissuggestedtoconsider WTP and todetermineofinsurancepremiumbycom- due toinadequateinformationmeasurerisk surance isnecessary. According totheresults, system, suchastreetrunkinsuranceandthisin- for them,asaresult,itisrequiredsupporting pistachio treesanditistheonlyincomesource come gardeners(yeomanfarmer)thatownfew their trees. The problemwassevereforlow-in- ing ofnumberpistachiotreethatshouldcut and everyyeargardenerencounterwithwither- that thedestructionriskofpistachiotreeishigh new insurance. As aboveissaid,itconcluded ance fundcanusethispotentialandsupply insurance inthecountyandagriculturalinsur- tential markettodemandofpistachiotreetrunk 1259164, respectively. Therefore, thereisapo- and Noghareestimated2228218,3072568, three districtofcentralsection,Koshkoo’iyeh, average ofwillingnesstopayforinsurancein Investigation ofthePotentialMarketandEstimation versity press. to cost-benefitanalysis.Cambridge: CambridgeUni- Applied environmentaleconomics, aGISapproach 7- Batmane,I.J.,Lovett, A., &Brainard,J.S.(2003). Tehran. partment of Agricultural Economics,Universityof College of Agriculture andNaturalResources,De- trees inKermanprovince.MScthesis,University ingness toadoptionforinsuranceofselectedfruit 6- Baniasadi,M.(2012).Effective factorsonwill- finance andeconomics,5(3),241-253. areas ofEthiopia.Internationaljournalhealthcare of communityhealthinsuranceschemesintherural health carefinancing:newevidenceontheprospect 5- Asfaw, A., &vonBraun,J.(2005).Innovationsin ing and Agricultural Economics, 49(31),147-177. ability in Australian agriculture. ReviewofMarket- 4- Anderson, J.R.(1979). Impactsofclimatevari- 665-675. valuation method.EcologicalEconomics,58(4), value ofnorthforestsIranbyusingacontingent Ahmadian, M.(2006).Estimatingtheexistence 3- Amirnejad, H.,Khalilian, S., Assareh, M.H.,& Journal of Agricultural Economics,69(3),520-529. Toward atheoryofagriculturalinsurance. American 2- Ahsan, S.A., Ali A.G., &Kurian,N.G.(1987). andinformationtechnology. statistical program ofgardenproducts. Tehran: Office of 1- Agricultural ministry. (2009).Resultsofthesample ness topayforeverytreebasedonareas’ features. country willbecategorizedandestimatedwilling- for fruittrees.So,itissuggestedthatthewhole deners encounterwithvariousdestructionrisks agricultural, andnaturalhazardseacharea,gar- According tothecharacteristicsofgeographical, in areaswhichoverlapthesamecharacteristics. cannot beusedforothertreesandareasunless it usable for gardeners' attention. The resultsofthisstudyare general performanceofinsurancefundtoattract rectly theinfluenceofsatisfiedclientfrom tract andsatisfiesit. Therefore, itshowsindi- economic logic,soclientbenefitsfromthecon- institution. Insurance Fundtherebyweappreciatefromthis This studywassupportedbythe Agricultural fWP/MostafaBaniasadietal. of WTP ACKNOWLEDGMENT pistachio treeinRafsanjancounty, so REFERENCES Rafsanjan County).6 management inpistachio production(casestudy 19- Mirza’ee,H.R.,&Chizari, A.H. 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Gholamreza Zamanian in MarkaziProvince ofIran Water PricingMethods,CaseStudyofKhomeinPlain The Economicand Welfare EffectsofDifferent irrigation * Corresponding author’s email:[email protected] * Corresponding author’s 3 2 1 programming, Khomeinplain Water pricing,Mathematical Economic andwelfareeffects, Keywords: Accepted: 24October2013 Received: 22July2013, Assistant ProfessorinEconomics, UniversityofSistanandBaluchestan,Iran. Graduate studentof Agricultural Economics,UniversityofSistanandBaluchestan, Iran. Ph.D Studentof Agricultural Economics,UniversityofSistanandBaluchestan, Iran. ISSN:2159-5860 (Online) ISSN: 2159-5852(Print) Available onlineon:www.ijamad.com International Journalof Agricultural ManagementandDevelopment(IJAMAD) 1 , MehdiJafari

Abstract 2 price rangeof198to853Rials. optimal allocationandpromotingthewaterefficiency inthe the blocktariff inplaceofvolumetricpricingmethodtoreachthe policies. According tothefinaloutcomes,itissuggestedapply native approachinsteadofPMP tobetteranalyzeofagricultural province inIran.ResultsshowthattheEMP canbeabetteralter- agricultural period2011/2012 inKhomeinplainofMarkazi water pricingapproachesintheagriculturalsectorduring analysis tostudytheeconomicandwelfareimpactsofalternative Econometric MathematicalProgramming(EMP)inacomparative paper usesthepositiveMathematicalProgramming(PMP)and the optimalallocationandsocialjustice. To thispurpose, is usingthedifferent waterpricingapproachestherebyobtaining globe. Oneofthebestknownsolutionsproposedbyeconomists mand speciallyinrecentdecadesalmostallregionsofthe T * andShahramSaeedian have causedanincreasinggapbetweenwatersupplyandde- he scarcityofwaterresourcesandsupplylimitation, 3 269 International Journal of Agricultural Management and Development, 3(4): 269-280, December, 2013. 270 International Journal of Agricultural Management and Development, 3(4): 269-280, December, 2013. The EconomicandWelfare EffectsofDifferentirrigation Frija tems, waterlawsandcommercialplans(see of irrigationwatermanagement,pricingsys- tivity usedsomepolicieslikedecentralization the optimumallocationandrisingwaterproduc- studies showthatgovernments,inordertoreach lutions arechanging. A substantialnumberof water betweenconsumersasmoresuitableso- managing thewaterdemandandreallocating concentrating onproductivityimprovement, agement patternwithbrandnewpolicieslikeas extracting. To solvetheseproblems,water man- projects hasincreasedthemarginal costofwater also theexternalitiesofconstructinghugewater difficulty offindingnewwaterresourcesand tion arenotpossibleanymore. Additionally, the species, ecosystemsandwaterresourcespollu- lowed previously, duetoimpairmentlossesof tinuing theexpansionistpolicieswhichwerefol- increscent waterdemandinIran.However, con- one ofthemainapproachestoprovidewiththis tion ofnon-renewablewaterresourceshasbeen man predicted creasing growthofthisdemandcanbeeasily water isprovidedfromfreshandthein- nowadays, morethan70percentofirrigation fits ofthispolicy. Ithastobementionedthat, economic developmentweresomeofthebene- producing morefoodandelectricityrural ing healthyandreliablewaterresources, ploiting newwaterresourcescameup.Increas- primarily, thestrategyofdiscoveringandex- mand facedadazzlingspeed. To dealwiththis, growth andimprovinglifestandards,waterde- Veettil, 2011 Tiwari andDinar, 2002; Tsure, 2004;Roe,2005; Dinar andMaria,2005;Johansson significantly. The results of ternative croppingsystemscanaffect theresults type, waterlaws,structuralframeworksandal- agricultural environmentasthespecialirrigation variables anddifferent existentcharacteristicsin these studiesisthattherelationshipbetween of waterpricingsystems, non-optimumwater are notdefinedproperly, itleads toinefficiency environmental conditions andwhenwaterlaws ers willingnesstopaycould beinfluencedby During recentdecades,asfortopopulation et al. et al. (Jafari 2013) (2010 and2011) (2008), Herrera INTRODUCTION for surveys). What emerges from . Extendingtheexploita- mentioned thatfarm- et al. Liao (2004), Speel- et al. et al. (2007), , 2002; average water resourcesincreased13.1millionm3on gion, annualexploitationfromunderground happened during2008to2011 inkhomeinre- that despitethemoderateandextremedrought water resourcesstockandrainfall,surveysshow considering thesubstantialrelationshipbetween water demandandcontrarytoitsshortage. With as extendingagriculturalactivities,facesrising and alsoincreasingpopulationgrowthaswell agricultural regionasfor240mmannualrainfall semi-dry climate.Khomeinasaflatandtalented and particularlyKhomeinregionhaveadry More than60percentofIran,Markazyprovince Asadi tion ofMarkaziprovince,2012) noted thatefficiency improvementandwater al- policies aretobeintroduced. But,itistobe has tobeincreasedsubstantially oralternative the waterdemandslightly. So,thewater price Iran, increasingthepriceofthisinputdecreases ticity ofwaterdemandinagriculturalsector ent researchersinIran. These policieshavebeeninvestigatedbydiffer- tary inputstaxesorproductunavoidable. of waterdemand-sidepoliciesascomplemen- nies withhugewaterlossesmaketheapplying water resourcesandweakmanagementcompa- distributing costs. The extensionlimitationof often higherthanthepriceandproviding allocation ofwateranditsmarginal returnis not enoughmotivationtoefficient andeconomic based onwaterconsumptionvolume;thereis derlying crop. As inthissystem,pricingisnot Distribution of Water" lawandregardingtheun- agricultural sectorisdoneonthebasisof"Justly problems. CurrentlywaterpricinginIranian creasing thecurrentpricesencountersmany cally. The actofpricingthisscarceinputandin- regarded asafreecommodityinIran,histori- dried inthenearfuture liters insecondwaterthataresolikelytoget tains, fiveriversand5soiled-damshave1to10 and shallowwells,79Ghanats,21naturalfoun- rivers and13soiled-dams. And also349deep wells, 172Ghanats,57naturalfountains,38 to completelydryingof164deepandshallow water resources ditures andfinallyinappropriateevaluationof allocation, increasingtrade-off costsandexpen- Water PricingMethods/ et al. (Mosayebi andMaleki,2012) (2007) (Fragoso andMarques,2013) showed thatasforlowelas- Hossain zad(2004) (Agricultural organiza- Mehdi Jafarietal. . Water hasbeen which led and . The EconomicandWelfare EffectsofDifferentirrigation sented as farmers isthesumofindividual demandspre- qj(w) maximize theprofitisas price isshownby cates themarketpriceof mand functionoffarmeris water withprice concave function, j=1, 2,...,n an inputofwater, theprofitisdefinedas: products. Supposeafarmwithnproductsand come fromthemarketdemandofagricultural The water demandandsupply Analytical framework five concludes. ical resultsanddiscussionfinallyinpart empirical models.Partfourpresentstheempir- work. Partthree,elaboratesthedataand lows: Inparttwowediscussheanalyticalframe- plain. The restofthearticleisorganized asfol- enue, costsandotherinputsdemandinKhomein among irrigatedagriculturalcrops,farmers'rev- ods impactsonwaterdemand,allocation going tostudythedifferent waterpricingmeth- action tothepolicies. To thisend,paperis plications inagriculturalsectorandfarmers're- seeking togetagoodintuitionaboutpolicyim- in laboratoryconditionsandthepolicymakeris It isnotpossibletoexaminealternativepolicies dition, individualattitudesandcharacteristics. The farmers'reactionisdependedonfarmcon- on thefarmers'reactiontoappliedpolicies. result ofapolicyoritsimpactdependshighly instruments isnotconceivable.Moreover, the location withoutsuitableeconomicpoliciesand The individualwaterdemand isspecifiedby Where Where Generally, thedemandforIrrigationwateris and theaggregativewater demandforall fj(qj)=yj qj(w) shows theamountofentering w . Inotherwords,thewaterde- j is anascendingandstrictly w is productionyield, . Essentialprerequisiteto j-th product andwater pj indi- (3) (1) (2) imprecise estimations water priceinsmallscales, thismethodcauses availability ofinformation andthevariabilityof tity. However, duetosomeproblems likeasun- observed informationofwaterpriceandquan- can beextractedbyregressionanalyzingofthe the usualapproach,irrigationwaterdemand amounts of levels whichallowstoobtaindifferent allocative ject tomaximizingtheprofitatdifferent water water demandfunctioncouldbeobtainedsub- frame. Also, inboth ofthesecases,theIrrigation can becombinedintoanon-linearprogramming production functionswithlinearprogramming process. Inwords,acombinationofnon-linear and cropsthatusethewaterintheirproduction variables, constraints,infinitepuechasedinputs it. This strategycanbeappliedformoreinputs, factor ofwater, andshowstheshadowpriceof p[f(x+∆)-f(x)] come fromusing∆unitmorewateris water. When theyusewaterat the farmerswillingnesstopayfor∆unitmore which seemsimportantisthatwehavetoknow supposing itlimitedinxliter. Here,thething sidering itasafreecommodityandalsowith by maximizingprofitconditionas: cating thewaterbetweenproductscanbesolved is binding.Inotherwords,theproblemofallo- and itsvalueispositiveifthewaterconstraint water. This priceiscalledShadowofwater for consumingadditionalunitsofirrigation maximum pricethatfarmersarewillingtopay which isduetothelittleamount∆,indeed enue is Where Which itslagrangianformisas Water demandcouldbemeasuredwithcon- Water PricingMethods/ p×f(x) λ here isacoefficient ofconstraining qj with shadowpriceofwater . The additionalincome . Expectedly, theadditionalin- (Tsure, 2005) Mehdi Jafarietal. x level, theirrev- . pf(x) λ (6) (5) . In (4) 271 International Journal of Agricultural Management and Development, 3(4): 269-280, December, 2013. 272 International Journal of Agricultural Management and Development, 3(4): 269-280, December, 2013. The EconomicandWelfare EffectsofDifferentirrigation ing rience adownfallbytakingthismethodofpric- increasing profit,sothetotalwelfarewillexpe- Since thisdecreasingprofitisgreaterthanthat multaneously decreasesthefarmers'profit. provide producerswithpositiveprofitandsi- ginal costcurvetowardtheaveragecould ing thewaterpricebymovingthroughmar- placed undertheaveragecost tion isdecreasingandthemarginal costcurve irrigation projects,whentheaveragecostfunc- marginal costofwater. This happensInthegreat derived fromdemandfunctiondeterminesthe marginal costfunctionandthedescendingslope Water pricing As itwasmentionedby maximization ofproducerandfarmerswelfare. age pricingcostbecauseitdoesnotensurethe cost, butthispolicyisnotefficient intheaver- the farmerisabletoreturnoverallwater function andaveragecost.Bytheway, despite price issetupfollowingtheexploiteddemand ing theaveragepricingcosts.Inthiscase,water cover theircostswhichoftenresultsindecreas- ing thesuppliers'costsincreasesinorderto given tothesuppliers.Inlong-run,financ- activity inlong-run,subsidieshavegottobe meet thefixedcostsandinordertocontinue Therefore, therealprofitofsupplierdoesnot and Dinar(1997) specified levelofwaterconsumption; twocom- volumetric tariffs areusedproportionalto an cial region;blockingmethod inwhichvariable irrigation methodand seasoninaspe- come asforthekindandamountofirrigation, gion. Usuallythedifferences inirrigationcosts on thebasisofirrigationmethodsusedinre- process ;regionalmethodthatwaterispriced puts (exceptwater)usedintheproduction water valuingisdonebasedonproductsorin- sumpted; input-outputapproachthatirrigation rectly byestimatingthewatervolumecon- under which,thewatercostsaremeasureddi- across theworldincludingvolumetricapproach ternative waterpricingmethodsareapplied management andcollectingexactdata. Thus, al- some prohibitiveoperationslikemonitoringand tion butimplementingthismethodrequires marginal costcangivetheoptimalwateralloca- The intersectionbetweenthenon-decreasing (Tsure, 2005) , waterpricingonthebasisof . Hence,accordingto Tsure (2005) (w*

-1 - , , ɛ -1 t u(ú , w Mehdi Jafarietal. e , wtvalues,the ɛ t -1 ) andelastic- u) -1 u -1 } and σ (ɛ jts t ) . The EconomicandWelfare EffectsofDifferentirrigation ured accordingtothefollowingequation large-scales likeourcase. This indexismeas- we useEntropyDiversityindexasitisusedfor and Mishra,2008) to thisend some ofthemostfamouseindexesthatareused Entropy andcorrectedconcentrationindexare ping plan.Shanonandbor, Simpson,Herfindal, indexes forcalculatingthediversityofacrop- area andgrossincome. There existNumerous ity. Inthisequation,ifthe Where Source: Researchfindings Machinery (hour) Herbicide(kg) Animal fertilizer(kg) Chemical fertilizer(kg) Labor(hour) Irrigated corn Alfalfa Onion Potato Bean Dry pea Dry barely Irrigated barely Dry wheat Irrigated wheat Machinery (rial) Herbicide(kg/ha) Animal fertilizer(kg/ha) Chemical Fertilizerazot(kg/ha) Chemical Fertilizerphospat(kg/ha) Labor(rial) Inputs Irrigated corn Alfalfa Onion Potato Bean Dry pea Dry barely Irrigated barely Dry wheat Irrigated wheat Yield variable X i (Karbasi indicates thesownareaofactiv- : et al. EI , 2010) is greaterthanzero, Table 1:Descriptivestatisticsofvariables . Inthispaper, (Chang 360163.8 (18) 63269.5 6566.5 5627.6 5945.9 4773.6 4933.3 3113.7 37100 40000 19470 mean 0.683 136.2 118.4 1000 6500 6000 2100 5850 5944 5944 5527 8696 5000 2568 2615 1411 1157 199 370 9.8 districts andvillagesover2011-2012 agricul- lation accordingtonumberoffarmersinrural sampling andgiventheCochran-Orcutformu- were gatheredthroughathree-stagestratified chemical fertilizerandmaureherbicide which areincludedas:water, labor, machinery, consumption requiredforproducingcrops collect thedatarelatedtoquantityofinputs lying statisticalpopulationandinorderto modated 7543farmers,wasusedastheunder- Data zero orlessthanit,thereisnocroppingdiversity. the croppingdiversityishighandifitequalto In thecurrentstudy, Khomeinplainaccom- 453615.4 74904.9 1708.2 1840.5 169.41 Water PricingMethods/ 1164.6 12890 651.8 0.589 198.8 1421 6208 3250 2325 5658 1.03 84.7 68.8 33.6 56.6 909 889 250 852 SD 0 0 0 0 0 0 0 19845.24 4926.1 4328.1 5911.3 36400 40000 1000 3180 6000 2100 5850 5944 5370 4560 4820 5000 3333 1000 2000 4926 83.3 63.5 68.9 Min 750 150 200 125 200 8.7 0 0 Mehdi Jafarietal. 984926.1 6775431 42000 14428 40000 20000 32000 216.4 318.8 171.9 7215 7575 7215 1000 7000 6000 2100 5850 5944 5948 4890 5010 5000 5000 1350 8000 7000 4000 8000 max 10.8 1.7 275 International Journal of Agricultural Management and Development, 3(4): 269-280, December, 2013. 276 International Journal of Agricultural Management and Development, 3(4): 269-280, December, 2013. The EconomicandWelfare EffectsofDifferentirrigation reproducing thePMP modelandalsodecreasing changing thecroppingpatternisonbasisof PMP andtheEMP modelsareshowingthat Results ofEMP andPMP land area,farmprofitandtotalwelfare. pricing policiesonwaterconsumption,irrigated section isrelatedtosurveythealternativewater farmers' behaviorinthebestway. The second choose amodelwhichisabletoexplainthe EMP approachesusingobserveddataaimedto first sectioncomparestheresultsfromPMP and ported inthe Table 1. zation databank The descriptivestatisticsarere- Markazi province'sJihad-e-Agricultureorgani- under studyregionwasderivedfromthe sown areaandtheproductionquantityof termined. Also, theinformationrelatedto rural district,thesampleofeachvillagewasde- farmers numberandsamplesizerelatedtoeach atic samplingmethodsothatonthebasisof Farmers ofeachvillagewerechosenbysystem- questionnaires) ruraldistricts,respectively. loo, Rastagh,SalehanandGalezan(Totally 250 cheshme, Khoramdasht, Ashena khor, Hamze tionnaires weredistributedamongChahar tural year, 36,41,30,32,39,45and27ques- Results ofmeasuringtheEntropyindexfor The resultsarepresentedintwosections. The Source: Research findings Dual valueofland(Rial/hectare) Water consumption('000 M3) Entropy Index Total sown area Irrigated corn Alfalfa Onion Potato Bean Dry pea Dry barely Irrigated barely Dry wheat Irrigated wheat Activity Table 2:ComparativeresultsofPMP andEMP modelsincomparisontothebaseyear. RESULTS base year(Hectare) Sown areainthe 0.6576 71685 23558 26241 1494 3496 2708 9422 8531 266 151 114 35 24 policy changes.Evaluatingtheirrigationwater model topredictfarmers'behaviorregarding is morethanPMP, butitisamoresuitable of shadowprice. Although, theEMP curvature ure 2wherethesownareaisstatedasafunction substitution. This resultismoreobviousinfig- effect inPMP modelforsimulationofproducts that thewaterconstrainthasamoreconsiderable more flexiblecurvethanEMP whichindicates price ineitherofthemodels. centage ofirrigatedlandversustheshadow evaluate thewaterdemandquantityandper- PMP modelsinFigures1and2respectively, we In ordertosurveyandcomparetheEMP and assumptions whichareessentialforsimulation. but itcanbeusedforcreatingpricingscenario cial forchoosingthebestpolicyanalysismodel mand andirrigatedlandareaisnotonlybenefi- Irrigation water demand profit andtotalwelfareisdiscussed. water consumption,theirrigatedarea,farm impact ofalternativewaterpricingpolicieson second sectionoftheresults,evaluation EMP modelswithobserveddata. Also inthe compare theresultsexploitedfromPMP and which modelpredictsthefarmers'behavior, we EMP modelreproduction.Inthenextstep,asfor the diversityofcroppingpatternisbasedon As showninFigure1,thePMP patternhasa Given thatEvaluatingtheirrigationwaterde- Water PricingMethods/ PMP 0.6576 71685 23558 26241 1494 3496 2708 9422 8531 266 151 114 Models 35 24 EMP 0.6572 68385 20106 26241 1498 3500 2700 9450 8533 293 157 110 0 0 Mehdi Jafarietal. EMP-base -3.51 -14.8 -100 -100 0.02 0.27 10.5 0.03 0.11 -0.3 -4.8 -34 0.3 0 The EconomicandWelfare EffectsofDifferentirrigation comes outas0.049.Interestingly, inthelastpart is between382and420.3theelasticity availability rangeof12500to15000M3,price ing thepricechanges.Inthirdpartand more changingisexpectedfromfarmersregard- elasticity increasesto0.017whichmeansthat M3, priceisintherangeof198.2to382and part ofwaterdemandcurvei.e.,15000to17500 the pricechangingpercentage.Insecond centage inwaterconsumptionismuchlessthan of 0.008.Inthispricerange,thechangingper- range of0to198rialswhichhaveanelasticity 25000 M3, The shadowpriceofwaterisinthe assumptions whichareessentialforsimulation. but itcanbeusedforcreatingpricingscenario eficial tochoosethebestpolicyanalysismodel demand andirrigatedlandareaisnotonlyben- In thefirstpartofdemandcurvei.e.,17500to Figure 1:DerivedIrrigationWater Demandfrom Source: Researchfindings Block Pricing Tariff 4 Tariff 3 Tariff 2 Tariff 1 Policy Table 4:Theeconomiceffects ofalternativeirrigationwaterpricingpoliciesusingtheEMP Source: Researchfindings 4 3 2 1 Number PMP andEMP models Table 3: The waterdemandelasticityresultedfromforeach Welfare Changes Water Demand 10000-12500 12500-15000 15000-17500 17500-25000 -52.1% -42% -24% -30% -33% shadow price. Sown area model Water shadowprice -17% -50% -38% -20% -16% cies onwaterconsumptioninallovertheregion, policies Evaluation oftheirrigationwater pricing cent ofwatercostscoveragewassimulated. three partsandineachpart50,100150per- block method,thewatercostsweredividedto each cubicmeterofirrigationwater. Forthe 382, 420.3and853.3rialsasoptimalpricesfor volumetric tariff, simulationwasdonefor198, ing thevolumetricandblocktariff. Inthe rigation waterpricingpolicieswasdoneregard- objective ofthispaperthesimulationir- sumption regardingtothepricechanges. 0.108 whichisthelargest changeinthecon- of demandcurve,theelasticityincreasesto 420.3-853 382-420.3 198.2-382 In thissection,theeffect ofwaterpricingpoli- As fortotheaforementionedresultsand Figure 2:Percentageofirrigatedlandareafrom 0-198 Water PricingMethods/ Water consumption PMP andEMP models -21% -23% -19% -14% -8% Demand elasticity 0.108 0.049 0.017 0.008 Mehdi Jafarietal. Total Profit -1.5% -26% -40% -25% -4% 277 International Journal of Agricultural Management and Development, 3(4): 269-280, December, 2013. 278 International Journal of Agricultural Management and Development, 3(4): 269-280, December, 2013. The EconomicandWelfare EffectsofDifferentirrigation gramming modelismore suitableanditissug- understood thateconometric mathematicalpro- water demandandirrigated wateramounts,itis producing theobserved valuesandalsothe metric Mathematicalprogramming(EMP)inre- Mathematical Programming(PMP)andEcono- results ofthetwomodelsincludingPositive to 80,62andlessthan50percent,respectively. and 853.3tariffs will reducetheirrigatedlands the sownareas.Furthermore,placing382,420.3 cent watersavingfor1percentofreductionin pricing tariff, blocktariff allowstohave a3per- water consumption. Therefore, forthislevelof the irrigatedlandareastherebyreducing that increasingwaterpriceresultsinreducing is 83%.Onthehand,attentionhastobepaid volumetric tariff is84%andunderblocktariff profit. The sownareainthetariff of198rialsfor water has11% and26%morereductionof 420.3 and853.3rialsforeachcubicmeterof than volumetriconesothatthisdifference in tariffs, theprofitreductioninblocktariff isless most identicalbutinthehigherlevelsofpricing and volumetrictariffs effect onfarmprofit isal- can besaidthatinthelowertariffs, theblock duction, respectively. Consideringtheresults,it umetric methodwhichshows1.5%and4%re- of 198and382rialsforeachcubicmetervol- reduction offarmprofitisrelatedtothetariffs policies inuppertariffs isnegligible. The less As itisseen,thedifference betweenthesetwo is 8%andintheblocktariff, itreachesto21%. volumetric tariff of198rials,thewatersaving in comparisontothevolumetrictariff. Inthe iff providesamoresatisfyingtotalwelfarelevel case) whichshowsthatgenerally, theblocktar- methods evenexceeds20percent(for Tariff 4 The distancebetweenblockandvolumetric shows theleastreductionintotalwelfare. cent reductionproportionaltothebaseyear and inthesecondplace,blocktariff with30per- tariff of198rials,hadtheleastreduction24% plus consumerwelfare)underthevolumetric Table 4. tested. The finaloutcomesarefeaturedinthe gross profitandtotalwelfareofthesocietyis In abriefsummarizing,giventhecomparative As featuredin Table 4,totalwelfare(supplier CONCLUSION matical programmingastools forincorporationof (2007). Normative,positive andeconometricmathe- 3- Buysse,J., Van Huylenbroeck,G., & Lauwers,L. and Development(Specialagricultural policy),15:58. downstream of Taleghan dam. Agricultural Economic waterpricinginIran: Irrigation A casestudyonland 2-Asadi, H.,Soltani,Gh.,&torkamaani,J.(2007). Organization ofMarkaziProvince. 1- Agricultural Static Yearbook, (2012), Agricultural irrigation watermanagentsystem. encial policiestoreconstructaprogressive gion orothersimilarplainsarethemostinflu- tarrif whichisappropriateforthekhomeinre- of determiningtheratewaterpriceasblock the waterrulesandpresentingasuitablepattern water resourcesdistributionandalsocorrecting ment. Inthisline,managementandplanningthe improve theirrigationwaterdemandmanage- country) andconsolidatedwhicharelikelyto with reformingtheeconomicstructureof proaches tolong-runmarginal costcompanied increasing (sothat,theaveragewaterpriceap- curate planningandschedulingthewaterprice water demand,theyshouldgoforwardwithac- there appearsthatforproperlymanagingthe to waterissue.Giventheafformantioned issues, icy makersandadministrativeauthoritiesrelated eficiaries ofsurfacewaterandalsothepol- but thisplanwillfaceseriousreactionsbyben- it needstoinceasethewaterpricesignificantly influencial reductioninirrigationwaterdemand, taine thewaterresourcesandmanagement of suppliersandconsumers.So,inordertosus- account offarmers'profitandthetotalwelfare provement andwatersavingwithtakinginto pable toinfluencetheallocation,efficiency im- that theblockpricingpolicyisconsiderablyca- tive irrigationwaterpricingpoliciesindicates each other. The simulationanalysisofalterna- resources conservationwhichareoppositeto economic efficiency, reducingcosts,justiceand pricing policiesoftenareseekingobjectivesas structural andinstitutionalsituation. Also the gation sectorareextremelyaffected bythelocal, ulation resultsshowthatpricingpoliciesinirri- on farmersbehavior. Ontheotherhand,sim- simulation oftheeffects ofagriculturalpolicies gested tousthisapproachbetteranalyzethe Water PricingMethods/ REFERENCES Mehdi Jafarietal. The EconomicandWelfare EffectsofDifferentirrigation R.L. (2004). An application ofthecontingentvalu- 13- Herrera,P.A., vanHuylenbroeck, G.,&Espinel, Implementations. Kluwer Academic Publihers. book ofOperationsResearch Models, Algorithms and C. 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Water Policy, 13(5),1-14. tion waterrights:lessonsfromSouth Africa and Huylenbroeck, G.(2011). The importanceofirriga- 27- Speelman,S.,Frija, A., Buysse,J.,& Van- ment andDevelopmentEconomics,15,465-483. water inLimpopoprovince,South Africa. Environ- system onsmallholderirrigators’ willingnesstopay & D’haese,L.(2010a). The impactofthewaterrights 26- Speelman,S.,Farolfi,Frija, A., D’Haese,M., 4, 1-17. equity, efficiency, andsustainability. Water Policy, is aneconomicgood:Howtousepricespromote Water PricingMethods/ Mehdi Jafarietal. 3 2 1 * Corresponding author’s email:[email protected] * Corresponding author’s Nematollah Shiri A ComparativeStudyat West PartofIran Research Performanceof Agricul Accepted: 18October2013 Received: 16September2013, Members Faculty acteristics, Agriculture sonal andProfessionalChar- Research Performance,Per- Keywords: Assistant Professor, Department of Agricultural Extension&Education, Razi University, Kermanshah,Iran. Professor, Department of Agricultural Extension&Education,University ofTehran, Karaj,Iran. Ph.D. Student,Departmentof Agricultural Extension&Education,RaziUniversity, Kermanshah,Iran. ISSN:2159-5860 (Online) ISSN: 2159-5852(Print) Available onlineon:www.ijamad.com International Journalof Agricultural ManagementandDevelopment(IJAMAD) 1* , NaderNaderi

Abstract 2 and Ahmad Rezvanfar and Ahmad B agricultural colleges. promote researchperformanceamongfacultymembersof sound programsinhigheragriculturaleducationsystemto variables. Findingsofthisstudycanpavethewayforformulating experience, academicdegree,educationalgroupandgender difference betweenresearchperformancebasedonage,work of meancomparisonsshowedthattherewassignificant of agriculturalcollegesinwestpartIranwasweak.Results present statusofresearchperformanceamongfacultymembers statistics withSPSSWin20 software.Resultsshowedthatthe experts. The datawasanalyzedusingdescriptiveandinferential tionnaire whichitsvaliditywasconfirmedbythepanelof sampling method. The maininstrumentinthisstudywasques- selected asthesampleusingproportionatestratifiedrandom Razi andKurdistanatIran,which116 facultymemberswere members intheagriculturalcollegesofuniversitiesIlam, The statisticalpopulationofthisstudyconsistedallfaculty faculty membersofagriculturalcollegesinwestpartIran. present studycomparestheresearchperformanceamong ased onpersonalandprofessionalcharacteristics,the 3 ture FacultyMembers: 281 International Journal of Agricultural Management and Development, 3(4): 281-288, December, 2013. 282 International Journal of Agricultural Management and Development, 3(4): 281-288, December, 2013. ticles, bookchaptersandmonographies clude refereedpublications,libraryandfieldar- a broaddefinition,researchperformancecanin- vious indicatorofdevelopmentinanycountry in practicecanbeconsideredasthemostob- production ofknowledgeanditsapplication ports published researchreports andusedresearchre- all completedresearchreports, thenumberof dicators derivedfromthesumofnumber formance withcalculatingandcombiningthein- the overridinggoalsofuniversity demic researchexcellenceisconsideredasoneof centuryperspective,thepromotionofaca- first ies haveusedthenumber ofcategoriessuchas, (Zainab, 2000) (Ransdell, 2001) ties portant roleintheacademicrankingofuniversi- of theacademicperformancethatplaysanim- performanceistheoneofmainaspects search (FAO, 1997) et al. (Blonedell, 2001;Kotrlik (Najafipour as themainindicatorofsuccessinuniversities promotion, tenureandsalaryalsomeasured search performanceplaysanimportantrolein Because, inhighereducationsystem,there- patents, citationsofarticlesandrewards and internationaljournals,presentations, such as:researchreportspublishedinnational search performancealsocancoverscategories that includesseveralindicators tion institutionsisamultidimensionalconcept performance inuniversitiesandhighereduca- and centers velop agriculturalhighereducationinstitutions using researchfindingscansignificantlyde- and exchangeofideas,scientificmeetings higher educationcenterswithnewparadigms universities' scientificmembersandagricultural dergone severalchanges. Therefore, aligning the other research,theagriculturalscienceshaveun- to maintainandenhancetheresearchquality the higheragriculturaleducationsystemneeds sponse tovariousfieldsofagriculturalscience, Scientific andtechnicalcapabilityinthe Researchers mainlymeasureresearchper- (Jung, 2012;ShinandCumming,2010) , 2009) (Wichian Research Performanceof Agriculture FacultyMembers et al. (Movahedi . Duetoincreasingchangesinre- . FAO notesthat,consistentwith INTRODUCTION . et al. , 2009) . Intheotherdefinitions,re- , 2009) . As such,inthetwenty- et al et al . Inturn,moststud- ., 2012) (Tien, 2007) ., 2002; Wichian (Tien, 2007) . Research . Re- . In . document every fourfacultymembershaveproduceda was 13,568casesandshowsthatonaverage, number ofdocumentsindexedin2008atIran, countries intheregion yielded firstrank,whencomparedtoother Turkey hasaconsiderabledistance fromIran, and Cilliers,2001; Taylor, 2001) Sax 2007; ZhaoandRitchie,2006;Bowen,2005; mings, 2010; Wichian, 2009;LawandChon, Hedjazi andBehravan,2011; ShinandCum- capitals andthefieldisready forahugescien- the capacity, talentandimportant intellectual gard, researchersbelieve thatIran,however, has (Hosseinpour, 2011) search anddevelopment more thananythingelsewithdeepeningtheirre- As societiesdevelop,theymustimproveitsposition velopment ofcommunitiesiscriticallyimportant. and researchprojects. performance, i.e.,books,articles,conferences most importantandbasicindicatorsofresearch tural collegesinwestpartofIran,withusing performance amongfacultymembersofagricul- study, therefore,wealsoevaluatetheresearch are stillnotenough tors suggestthattheutilityoftheseindicators comparative comparisonoftheresearchindica- research activitiesatIran,butonaglobalscale, that therehasbeenrelativelysuitablegrowthof Although, intherecentyears,wecanobserve to currentgapthanotherparties knowledge production,theyaremoreresponsive search facilitiesandalsohaveimportantmissionof centers haverequiredresources,specialists,re- fore, becauseuniversitiesandhighereducation faculty membersinuniversities ects toassesstheresearchperformanceamong books, articles,conferencesandresearchproj- at Thai publicuniversities and research improve theirdynamicproductionofthescience rations, theyaremoreinclinedtoincreasingly mission towardrealizationofthenationalaspi- Moreover, due touniversitieshaveimportant the universitiesandresearchinstitutions capability ofresearchersisconcentratedin In Iran,morethan70percentoftheresearch The importanceofresearchonthegrowthandde- et al ., 2002;Changsrisang,Bouden (Saburi, 2009) (Karimian / NematollahShirietal. . According tostatistics,the (Karimian (Karimian (Saburi, 2009) et al. . Thesameratiois40 (Wichian , 2011) (Toreghi, 2005) . Inthepresent et al (Jung, 2012; et al et al. . Inthisre- ., 2011) ., 2011) . There- , 2009) . . . . puttarak, 2008; Wichian 2004; Smebyand Try, 2005;Ouimet instrument fordatacollection wasaquestion- sampling method(n=116). The mainresearch lected viatheproportionate stratifiedrandom University, 39KurdistanUniversity), werese- (Patten, 2002),116 (26IlamUniversity, 51Razi (47) atIran(N=137).Usingthesamplingtable universities, Ilam(31),Razi(59)andKurdistan consisted ofallagriculturalfacultymembers search paradigm. The statisticalpopulation tive-survey studiesandusedquantitativere- professional characteristics. faculty membersbasedontheirpersonaland performance amongfacultymembers; and professionalcharacteristics; study areasfollow: Iran. Also, thederivedspecificobjectivesof members ofagriculturalcollegesinwestpart pare theresearchperformanceamongfaculty characteristics, thepresentstudyaimedtocom- fore, withfocusonpersonalandprofessional sonal andprofessionalcharacteristics. There- part ofIranwerestudiedaccordingtotheirper- ulty membersofagriculturalcollegesinwest this study, theresearchperformanceamongfac- of theresearchproblemandextantliterature,in possible asotherfields.Giventheimportance of highereducation,holisticdevelopmentisas in thelightofaneffective andefficient system structions. Already, itisgenerallyacceptedthat of Iran’s universitiesandhighereducationin- research performanceamongfacultymembers study theeffects ofthesecharacteristicsonthe ance, todate,thereisnostudyprofoundly fessional characteristicsintheresearchperform- istics enced bypersonalandprofessionalcharacter- found thatknowledgeproductionisinflu- this questionformdifferent perspectivesand Iran isnotenough?Researchersaddressed is thatwhythegrowthofacademicresearchin tific leaps,butnowmorethaneverthequestion This studycategorizesinappliedanddescrip- 3-Compare theresearchperformanceamong 2- Investigatethecurrentstatusofresearch 1- Investigatethefacultymembers'personal Despite theimportanceofpersonalandpro- MATERIALSMETHODS AND (Callcut Research Performanceof Agriculture FacultyMembers et al ., 2004;CastillandCano, et al. , 2009;Jung,2012) , 2005; Lert- . ity, whilebookislastpriority. Overall, theav- performance, conference islocatedattopprior- among thefourindicators ofmeasuringresearch sented in Table 2. tors tomeasuretheresearchperformanceispre- used. The resultsof theprioritizationofindica- conferences, researchprojects,andbookswere the facultymembers,fourindicatorsofarticles, Research performance were shownin Table 1. fessional characteristicsoffacultymembers ence andFoodIndustry. Otherpersonalandpro- (n=3) wereworkingintheDepartmentofSci- and PlantBreeding2.6percentofthem were workingintheDepartmentof Agronomy 25 percentofthefacultymembers(29cases) 10 years.Furthermore,basedonthefindings, work experiencestratum10yearsandlessthan which mostofthem(60.3%)categorizedinthe (SD= 7.47)andwiththeagerange1to30years, ence ofthefacultymemberswas10.16years 39 to48years. Also, theaverageworkexperi- of them(45.7%)categorizedintheagestratum with theagerange29to67years,whichmost ulty memberswas40.5years(SD=8.19)and Personal andprofessional characteristics (Tests ofmeancomparison)statistics. mean andstandarddeviation)inferential parts ofdescriptive(Frequency, percentage, software wasusedtoanalyzethedataintwo psychology ofuniversity Tehran. SPSSWin20 education facultymembersand pert indepartmentofagriculturalextensionand questionnaire wasassessedthroughpanelofex- form ofadocumentarystudy. Validity ofthe extracted frompersonalandresearchfilesinthe ance offacultymembersin2011 and2012was ance. The dataconcerningtheresearchperform- project andbook)tomeasureresearchperform- indicators (i.e.,article,conference,research ture, inthesecondsection,weappliedfour tics. Through asystematicreviewofthelitera- includes personalandprofessionalcharacteris- naire consistedoftwoparts,whichfirstsection Based onthefindingspresented in Table 2, In ordertoassesstheresearchperformanceof Based onthefindings,averageageoffac- / NematollahShirietal. RESULTS 283 International Journal of Agricultural Management and Development, 3(4): 281-288, December, 2013. 284 International Journal of Agricultural Management and Development, 3(4): 281-288, December, 2013. faculty members.Finally, facultymemberswho likely toshowresearchperformancethanother ademic degreeofassociateprofessor, aremore members. Facultymembers,whopossessanac- higher researchperformancethanotherfaculty work experienceclassof11 to20years,show other facultymembers.Facultymemberswith more, havemoreresearchperformancethan bers wholocatedintheageclassof59yearsand group. According torankingmean,facultymem- perience, academicdegreeandeducational ance offacultymembersbasedonage,workex- significant difference intheresearchperform- Wallis test(Table 3). As findings show, thereis tional groupvariables,weappliedKruskal- ence, university, academicdegreeandeduca- of facultymembersbasedonage,workexperi- on personalandprofessional characteristics Comparison oftheresearch performancebased part ofIranisweak. faculty membersofagriculturalcollegesinwest gest thattheresearchperformanceamong cient ofvariation1.87. These findingssug- was 2.71(lowerthanmean=5.28)withacoeffi- erage researchperformanceoffacultymembers In ordertocomparetheresearchperformance Research performance(Total) Book Research project Conference Article Indicators Table 2:Prioritizationofindicators Research Performanceof Agriculture FacultyMembers University Academic degree Marital status Gender Variable Table1: Descriptivestatisticsofrespondentsregardingtheirpersonaland Kurdistan Razi Ilam Professor Associate Assistant Single Married Female Male professional characteristics professional assessing researchperformance Category Mean 2.71 0.43 1.65 6.32 2.37 ing thedevelopmentprocessthroughknowledge education systemhavecrucialroleofaccelerat- able ofgraduateuniversity. of facultymembersbasedonthegroupingvari- nificant difference intheresearchperformance sented in Table 5,indicatethatthereisnosig- faculty members(Table 5). The resultspre- order tocomparetheresearchperformanceof dependent variableintoindependentt-testin members thantheircounterparts. der withhigherperformanceofmalefaculty ference betweenfacultymembersontheirgen- sabbatical. However, therewassignificantdif- members basedontheirmaritalstatusandusing ference intheresearchperformanceoffaculty findings indicatethatthereisnosignificantdif- Mann-Whitney Test (Table 4).Surprisingly, our and usingsabbaticalvariables,weapplied ulty membersbasedongender, maritalstatus, performance thanothertheircounterparts. extension andeducation,havemoreresearch were workinginthedepartmentofagricultural Faculty membersofIranianhigheragricultural Finally, weincludegraduateuniversityasin- To comparetheresearchperformance offac- 1.87 0.60 1.56 4.60 2.22 C.V. Frequency 102 108 39 51 26 20 96 6 8 8 / NematollahShirietal. 0.50 0.00 0.00 0.00 0.00 Min DISCUSSION fessional characteristics Percent (%) 33.6 44.0 22.4 87.9 17.2 82.8 93.1 10.56 19.00 13.50 5.2 6.9 6.9 3.00 9.00 Max Priority 4 3 1 2 - Table 3:Comparisonofresearchperformancerespondentsrelatedtotheirpersonalandprofessional ** P ˂0.05,*P ˂0.10 Educational group Academic degree University Work experience Age Independent variable (2005) vious studies,suchas formance. This finding iscorrespondswithpre- more likelytoshowhigherlevelsofresearchper- of facultymembers.Oldermemberswere tor, hasamajor roleintheresearchperformance promoting researchperformance. of universitiestodevelopcoherentprogramsfor research performanceandwouldhelpplanners personal andprofessionalfactorsaffecting the study couldincreaseourunderstandingofthe professional characteristics.Findingsofthis in westpartofIran,basedontheirpersonaland among facultymembersofagriculturalcolleges conducted tocomparetheresearchperformance ically important.Inthisregard,thepresentstudy the academicsuccessandperformancearecrit- Therefore, understandingthefactorsthataffect production anditstransfertotheirclients. Castill andCano(2004) formance amongfaculty membersofagricul- that oneofthereasonsfor thepoorresearchper- Findings showedthattheage,asapersonalfac- . Hence,asourfindings show, weargue Research Performanceof Agriculture FacultyMembers Food Science Agri. Economics Horticulture Soil Science Agri. Mechanics Animal Science Irrigation Plant Protection Agronomy Agri. Extension Professor Associate Assistant Kurdistan Razi Ilam more than21 11-20 up to10 more than59 49-58 39-48 up to38 Category Callcut and Smeby and Try et al., (2004), characteristics Frequency 102 10 23 13 29 39 51 26 35 70 53 49 11 11 3 5 7 8 7 6 8 7 7 and Callcut work togetheranduse exchangetheirexpe- ulty memberswillhave moreopportunitiesto which, allexperiencedand lessexperiencedfac- ticipatory cultureshould beencouraged,in ence and,inthisregard,wesuggestthatthepar- in westpartofIranistheirweakworkexperi- among facultymembersofagriculturalcolleges of theotherreasonsforpoorperformance can bedovetailedwithofthestudiessuchas, performance thantheircounterparts. This finding with moreworkexperiencehaveresearch ance offacultymembers,sothatmembers plays animportantroleintheresearchperform- older facultymembers. members canbenefitfromtheexperiencesof tematic programinwhichyoungerfaculty tural highereducationsystemtodevelopasys- study encouragesplannersofIranianagricul- age ontheresearchperformance. Therefore, this tural collegesinwestpartofIranistheeffect of Results showedthattheworkexperience,also, Jung (2012) et al Ranking Mean 50.33 53.00 12.14 61.30 41.75 64.26 49.58 68.18 69.48 92.71 82.08 90.31 54.62 52.32 59.23 66.35 67.64 70.13 51.25 86.93 80.00 62.00 47.58 ., (2004),CastillandCano(2004) / NematollahShirietal. . Hence,wecanstatethatone Wallis Test Kruskal- 27.268** 13.628** 11.491** 8.266* 2.761 Significant Level 0.001 0.003 0.251 0.016 0.003 285 International Journal of Agricultural Management and Development, 3(4): 281-288, December, 2013. 286 International Journal of Agricultural Management and Development, 3(4): 281-288, December, 2013. sistent with ance thantheircounterparts. This findingiscon- professor wereasbeingmoreresearchperform- members withanacademicdegreeofassociate contribute toresearchperformance.Faculty gree, asaprofessionalfactor, cansignificantly riences. and knowledgeproduction. should bemoreactivein thefieldofresearch would expectthatassistant facultymembers as increaseinsalaryandwelfarefacilities,ifwe higher educationsystemcantakemeasuressuch Therefore, theplannersofIranianagricultural tive toconductresearchinorderbeupgraded. degree ofprofessor, and,inturn,havenoincen- ulty members,already, possessesanacademic professor haveagreatdistancefromthosefac- ulty memberswithacademicdegreeofassistant to proceedfurtherpromotions,however, fac- academic degreeofassociateprofessorinorder portant incentiveforfacultymemberswith fessor academicdegreecouldbethemostim- the majorityofthem.Inthisregard,havingpro- formance islowacademicdegree(Assistant)in one ofthefactorscontributingtotheirpoorper- in assistantprofessordegree,wecansaythat cultural collegesinwestpartofIranwereplaced given thatmostfacultymembersamongagri- Our findingsindicatedthattheacademicde- Results showedthatthe educational groupof Table 4.Comparisonofresearchperformancerespondentsrelatedtotheirgender, maritalsta- ** P <0.01. ** P <0.01. portunities Using studyingop- Marital status Gender Independent variable Graduate University Independent Variable Table 5.Comparisonofresearchperformancerespondentsrelatedtotheirgraduateuniversity. Research Performanceof Agriculture FacultyMembers Smeby and Try (2005) Category No Yes Single Married Female Male Abroad Interior Category tus, andusingsabbatical. . Hence, Frequency Frequency 108 99 17 20 96 8 39 77 Ouimet (2005) of probablymuchwork in thehomeandother work inuniversities,i.e., beingasbusybecause faced withtwomajorobstacles forscientific the femalefacultymembers atIranhavebeen of counterparts, whichiscongruentwithfindings to showhigherresearchperformancethantheir in that,malefacultymembersweremorelikely significantly affect intheresearchperformance, facilities requiredfortheknowledgeproduction. members withprovidingresearchequipmentand prove theresearchperformanceofallfaculty agricultural highereducationsystemcanim- Therefore, itisrecommendedthatplannersof mainly appliedanon-experimentaldesign. tions ofsocial,culturalandeconomic,are to theyareofteninterestedthefarmingcondi- and productiveinpoorlaboratoryfacilities,due cultural extensionandeducationaremoreactive searchers whoareworkinginthefieldofagri- they areprimarilyneeded. Accordingly, re- and theconditionsfacilitiesforresearchthat ing couldbeduetothenatureoffarmingfields performance thantheircounterparts. This find- extension andeducationshowmoreresearch were workinginthedepartmentofagricultural research performance.Facultymemberswho faculty memberscanhaveamajorroleintheir Finally, ourresults showedthatthegendercan Castill andCano(2004),Jung(2012) Mean 12.44 Ranking 9.96 Mean 56.60 69.56 50.65 60.14 23.63 61.08 7.610 7.416 SD . This findingmay bebecauseof / NematollahShirietal. Kruskal-Wal- 153.000*** -1.685 653.500 803.000 lis Test t Significant level Significant 0.095 Level 0.142 0.251 0.002 and search Williams, H.A.(2002).Factorsassociatedwith re- 12- Kotrlik,J.W., Bartlett, J.E.,Higgins,C.C.,& university. Iranianhighereducation,3(4), 35-63. search andproductofscientific inmedicalsciences (2011). Investigation ofbarriersandchallengesre- 11- Karimian,Z.,Sabbaghian,&Sadaghpor, B.S. ucation studies,2(4),1-13. 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* Corresponding author’s email: [email protected] email: author’s Corresponding *

2 1 Andiema ChesangEverlyne Case of West PokotCounty, Kenya Saving Technologies amongSmallholder Farmers: The Socio-Economic Factors Influencing A

ergy-saving technologies Adoption, Smallholder, En- Keywords:

Director, Biovision Trust, Africa Kenya. Nairobi, Health, and Food for Science Insect icipe-African C/O

Lecturer,University, of Egerton Department Extension, & Education Agricultural Kenya. Graduate Student, Department of Agricultural Education & Extension, Egerton University, of Egerton Department Extension, & Student, Education Graduate Agricultural Kenya. Accepted: 1November2013 Received: 16September2013, ISSN:2159-5860 (Online) ISSN: 2159-5852(Print) Available onlineon:www.ijamad.com International Journalof Agricultural ManagementandDevelopment(IJAMAD) 1 , NkurumwaOywaya Agnes Abstract F of usetheMaendeleostove. spondents onthereasonsfornon-adoptionanddiscontinuance be furtherinvestigationintotheadoptionbehaviour ofthere- promotion anddisseminationofMaendeleostove. There should and developmentpartnersputinplaceaprogramme forthe relying onbiomass,thisstudyrecommendsthatthe government the county, andtheprojectedincreaseinnumber ofpeople Given therelativelylowadoptionlevelofMaendeleo stovein highest influenceonthenon-adoptionofMaendeleo stove. The resultsshowedthattheageofrespondentshad research methodologywithex-postfactodesignwasemployed. stove intheruralsettingofKapenguriaDivision. A survey cio-economic factorsinfluencingtheadoptionofMaendeleo of suchinnovations. This studytherefore,soughttoassessso- institutional barriersareconsideredtocontributelowuptake valuable innovations?Socio-cultural,economic,politicaland in thisstudywaswhatmakespotentialusersnotutilizesuch novation hasremainedlow. An importantquestioninvestigated the Maendeleostovetechnology;adoptionlevelofthisin- multiple benefitsandtheinstitutionalpromotionalefforts of on localforests.However, despitethedemonstratedtechnological wood householdsuseforenergy, andultimatelyreducepressure northern ofKenya,withthegoalreducingquantity and promotedinKenyamoreso, West PokotCounty, veloped. A uniquecookstovenamed Maendeleowasdeveloped efforts, severalenergy-conserving technologies havebeende- adverse environmentaleffects inKenya. As partofinnovation an imbalanceindemandandsupplyconsequentlyresulting in Kenya. The heavyrelianceonthebiomassenergy hasexerted and spaceheatingforover80percentofhouseholdsliving uel woodprovidesthemainsourceofenergy forcooking 2 and Amudavi MulamaDavid doption of 3 Energy– 289 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. 290 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. five decades stitutions andfundingagencies,formorethan from governments,organizations, scientificin- nities indifferent partsoftheworldwithsupport grams facilitatedandimplementedforcommu- There havebeenseveralimprovedstovepro- for slowadoptionrates. Maendeleostoveisa systems ofthestoveprograms areresponsible of thehousehold,andlack ofpropermonitoring to theuseoftraditionalhearths strumental incombatingthenegativeeffects related isoneofthestrategiesperceivedasin- countries fusion ofimprovedcookstovesindeveloping manner developing worldhavetobemetinasustainable mand ofahousehold,yetenergy needsofthe mass ininefficient waysincreasesfuelwoodde- of theusersandruralenvironment has anegativeimpactonthesocialwell-being mass fuelsfordomesticenergy consumption dependence oftheworld’s populationonbio- Nations, 2009;Inayat,2011) energy consumption ergy accountsformorethan90%ofallhousehold gies should encourageinnovationsinnewtechnolo- nomic conditionsindevelopingcountries ing theargument by scholarsthatthepooreco- prevent thetechnologyadoption,notwithstand- the characteristicsofinnovationitselfthat cial, culturalandeconomicfactors,aswell However, theremaybearangeofpersonal,so- as fastinitiallyanticipated rate ofadoptiontechnologiespromotedisnot stove programmesseemtoberatherclear, the and environmentalbenefitsoftheimproved (Inayat, 2011) of improvedcookingtechnology lack ofknowledgeaboutthecostsandbenefits Mohamed, 2003;Bikram, 2008) tanayak, 2012) and regionalclimatebenefits household health,localenvironmentalquality, have potentialfordeliveringtripledividends: social, economicandenvironmentalconcern cook stovesinthedevelopingcountriesisof A numberofsocio-economic factorssuchas The adoptionandcontinueduseofimproved In mostdevelopingcountries,biomass-baseden- (Lundvall, 2007) (Ndung’u, 2009) (Reddy, 2008) INTRODUCTION . Inparticularsuchinnovations . Although thesocial,economic, (FAO, 2010;Field,2010) . . The inventionanddif- . . The useofbio- (Lewis andPat- (Rwiza, 2009) , incomelevel (FAO, 2010). (Muneer and (United . The . than woulddoyoungerfarmers. An analysis of better abletoevaluatetechnology information and experienceintheuse ofthestoveandare knowledge oftheMaendeleo stovetechnology sense thatolderfarmersovertimehavegained Maendeleo stovetechnologyadoptioninthe novations. A farmer’s ageisexpectedtoincrease with mixedresultstoinfluenceadoptionofin- moted foruptake. adoption ofpracticesortechnologiesbeingpro- fluence overtheadoptionofinfluence of thevariablesthathavevaryingdegreesin- in, householdsizeandfarm respondents, leveloftheireducation,household deleo stovetechnology–namelyageofthetarget tant factorsinfluencingadoptionoftheMaen- socio-economic factorsasbeingthemostimpor- technologies. pothesized asinfluencingadoptionofthese the technologyandinstitutionalfactorsarehy- number offarmers’ characteristics,attributesof search findingsby or inametalcladding(Mandeleoportable).Re- mud andstonesurroundedstoveinthekitchen and potsupportsonthestoveliner, builtintoa which incorporatesadoorforfuelandairintake liner, (madeofapotterycylinderfiredinkiln), sumption. The basiccomponentisthestove as oneofthestrategiestoreducefuelwoodcon- stove thatwasdevelopedinKenyathe1980’s (Jeanette over theadoptionofchangedpracticese,g land size,allhavevaryingdegreesofinfluence ucation, householdincome,sizeand cook stoves.Demographicfactorssuchasage,ed- correlated withtheadoptionofenergy-saving that thesocio-eco ulations isusingthisstove rural communities-only4%ofthetargeted pop- stove hasremainedatalowlevelofusewithin been producedonamorecommercialbasis,the moted inKenyafornearlytwentyyearsandhas the Maendeleostovetechnologyhasbeenpro- of upto50%isachievable.Despitethefactthat With properuse;afuelwoodsavingefficiency compared toathree-stonefirecommonlyused. 43 percentandproducingupto60%lesssmoke stove couldprovidefuelwoodsavingsofupto The ageofapotentialadopterhasbeenfound, Much emphasishasbeenplacedonseveral et al., 2010) nomic factors Were positively Karin Ndung’u, (2009) . et al. size. These are size. These (Ingwe, 2007) (2007) show the observed some . A Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. Belay, 2001;Rogers,2003) sociated withit,andenhanceitsadoption technology tocleardoubtsanduncertaintiesas- nically andeconomicallyassessthenew cation thefarmerwouldbeinapositiontotech- technologies becausewithhigherlevelofedu- to influencetheadoptiondecisionofmany to adoptinnovationsearlyinhislifecycle The youngerthefarmer, themorelikelyheis between adoptionratesandanadopter’s age. searchers havefoundamoredirectrelationship ogy usearelikelytoadoptit quate informationaboutknowledgeoftechnol- influences adoption.Farmerswhohaveade- tion thatismodeledtobeuseful)whichinturn mation (processeddata)toknowledge(informa- Technical informationandthefrequencyofex- adoption oftheMaendeleo stovetechnology. been identifiedasamajor factorimpactingthe extension staff. Inthiscontext,awareness has farmer tocontact,communitygroupand sources ofinformationbeingfamilymembers, introduced inthestudyareawashigh,main technology inwhichtherespondentshadbeen cated people. Awareness oftheMaendeleostove Maendeleo stovetechnologythandoun-edu- people havemoreknowledgeaboutbenefitsof awareness, itisassumedthatmoreeducated necessary thatmoreeducationequatestogreater Maendeleo stovetechnology. Although itisnot awareness abouttheprosandconsofusing ucation washypothesizedasaproxyformore speed thantheuneducated benefit oftheinnovationinquestionatafaster cated peoplewereexpectedtounderstandthe (Inayat, 2011) (Aneani (Diederen technologies (forexample; tween ageandadoptionratesofimprovedstove searchers findingnosignificantrelationshipbe- evidence onthisrelationship,withsomere- stove technology. However, thereisconflicting associated withtheadoptionofMaendeleo this studyshowsthatageoftherespondentis were touseefficient cookingtechnologies cated) respondentswere,themorelikelythey Rollins, 2009) 2002; LockieandRockloff, 2004) Education helpsthetransformationofinfor- et al., et al., . Inthepresentstudy, levelofed- . Educationalstatusisassumed 2012) 2003) . The moreaware(edu- . . Traditionally,edu- Cary (Makame, 2007; (Abebaw and et al., . Otherre- 2001, et al., stove technologyunless otherattributesofthe expected toincreaseadoption oftheMaendeleo adoption. This perceivedcosttherefore,may be influence ontherateand speedoftechnology high initialcost.Lowcosthasapositive to beadoptedthanwouldtechnologywith Technology withlowinitialcostismorelikely respondents foundthestovetobeexpensive. Maendeleo stovewasaffordable asnoneofthe ning costs. The study revealedthatcostofthe the ideaisexpensiveinbothinitialandrun- thrusts foradoptinganinnovation,especiallyif that economicmotivationisoneofthemain (Rwiza, 2009) in andusingimprovedstoves holds mayhavehigherprobabilityofinvesting on adoptionoftechnologiesaswealthierhouse- ity andmaybeexpectedtohavepositiveeffect tact withextensionvisitsthandidnon-adopters. adopters. Adopters hadsignificantlyhighercon- in termsofgapbetweenadoptersandnon- with formaleducationandnon-formal icant difference observedbetweenrespondents mer beingmoreeducated. There wasnosignif- young andtheolderrespondents,withfor- of schoolingtherespondentsamong There wasasignificantdifference intheyears cation mayincreasethespeedofadoption. using biomassfuels,andtherefore,formaledu- aware oftheenvironmentalandhealtheffects of cated respondentsmaybeassumedtomore nology tor influencingthedecisiontoadopttech- a newtechnologyremainsveryimportantfac- ability ofthestoveuser. Costofadopting economic purchasingpowerandinstallation Maendeleo stovetechnology;thisreferedtothe the presentstudyfocusedoncostof Kenya shillingsthehouseholdearnsperyear. hold wasmeasuredastotalsumofmoneyin ogy andothertechnologies fluencing adoptionofMaendeleostovetechnol- posure tothisinformationareimportantinin- be negativelyaffected. a stoveistooexpensive,adoptiondecisionswill used asenergy sourceandtypeofcookstove relationship withthehousehold’s typeoffuel Household incomehasaunidirectional,linear Household incomeisanindicatorofprosper- 2007) (Huh andKim,2008) . Inthestudyarea,moreedu- . Householdincomeofahouse- Rogers (2003) (Fernandez-Cornejo . Itfollowsthatif (Inayat, 2011) assumed . 291 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. 292 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. landholdings cook stovesthanwouldhouseholdswithsmall holding willbemorelikelytoadoptimproved be assumedthatahouseholdwithlarge land- innovations larger farmsaremorelikelytoadoptrelativelynew ship betweenfarmsizeandadoption.Farmerswith has focused.Moststudiesfindapositiverelation- factors onwhichtheempiricaladoptionliterature the Maendeleostovetechnology. will affect positivelythedecisionofadopting expected, therefore,thatalarger householdsize to adopttheMaendeleostovetechnology. Itis and morefuelwoodhencewillbeinclined household membersrequiringuseoflarge pans larger householdswillcookmorefoodforthe (cost, size,perceivedbenefits, biomassflexibil- household size,landsize); stove-relatedfactors tors (i.e.age,education, householdincome, Maendeleo stoveinclude: socio-economicfac- that mayhaveinfluenceontheadoptionof slow diffusion process oftechnologies.Factors tions revealspossibleexplanationaboutthe A literaturereview onthediffusion ofinnova- adoption isnotasfasthopedandanticipated. grammes seemtoberatherclear, therateof mental benefitsoftheimprovedstovepro- stove technology. would bemorelikelytoadoptimprovedcook pected thatahouseholdwithlarge landholding energy savingMaendeleostoves.Itwasex- availability offuelandhencetheabilitytouse for woodproductionwhichinturnmayaffect farmer’s abilitytosetasideaportionoftheland study, farmsizemaybeexpectedtoinfluencea Maendeleo stovetechnology. Inthepresent large landholdingwillbemorelikelytoadopt County. Itwasexpectedthatahouseholdwith nant ofsocio-economicstatusin West Pokot that acquisitionoflandisanimportantdetermi- including thisvariableinthemodelwasfact stove used have apositiveinfluenceonthemodelof stove technology. Familysizeisexpectedto tive influenceontheadoptionofMaendeleo tively influencedutilizationofthetechnology. technology orotherextraneousvariablesnega- Land sizeisoneofthefirstandmostwidelyused Although thesocial,economic,andenviron- Household sizewasexpectedtohaveaposi- (Diederen, (Inayat, 2011) (Field, 2010) et al. . Itisassumedthat . The importanceof , 2003) . Thus, itcan Pokot County. The nullhypothesiswas:H scale farmersinKapenguriaDivisionof West adoption oftheMaendeleostoveamongsmall- fluence ofsocio-economiccharacteristicson purpose ofthisstudywastodeterminethein- of theMaendeleostovesinstudyarea. The tors wereusedtoexamineissuesinthecontext land tenure,membershiptogroups). These fac- tutional factors(accesstoextensionservices, ity, operatabilityandquality)and,finallyinsti- Maendeleo stove. statistically significantinfluenceonadoptionof farmers’ socio-economiccharacteristicshaveno many similaritiesinterms oftheirfarmingsys- Pokot sub-county. The foursub-locationshad respectively; ofKapenguria Division, West each) Kaibos,Kipkorinya, Chepkotiand Talau namely; Kaibosand Talau (with2sub-locations practice mixedfarming. ists andthoseofthelarger partofKapenguria toralists; Chepareriaresidentsareagropastoral- locations with82sub-locations. Kapenguria, Kongelai,ChepareriaandSook;23 grade. The County hasfourdivisionsnamely: centigrade toamaximumof34degreescenti- tures rangefromaminimumof15degrees 700mm to1600mmperannum. The tempera- normally unevenlydistributedrangingfrom county hasabimodalrainfallpatternwhichis lowest is1550metresabovesealevel. The reaches upto2550metresabovesealevel,the within themountainousCheranganiHills,which geographical features. The southeastpartfalls Valley Province. The countyvariesgreatlyin which isapproximately5%oftheareaRift 35049’E. Itcoversanareaof2317.5sqkm, and 107’Nbetweenlongitudes34037’E der withUgandabetweenlatitudes24040’N County whichliesalongKenya’s westernbor- could onlybestudiedretrospectively. on-going andthefactorsinfluencingadoption Maendeleo stoveshadalreadyoccurredandis suitable forthisstudyasthedisseminationof research design. This designwasfoundtobe The studywasconductedin2locations Kongelai andSook’s residentsarepurelypas- The surveywasconductedin West Pokot The studyemployedanex-postfactosurvey MATERIALSMETHODS AND 01 : Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. Hosmer andLemeshow, 2000) nary dependentvariable ation betweenexplanatoryvariablesandabi- standard methodforunderstandingtheassoci- logit regressionmodel. The logitmodelisa the inferentialstatisticaltoolusedwas quency counts,percentagesandmeans,while tistics. The descriptivestatisticsusedwerefre- were boththedescriptiveandinferentialsta- study areasisdepictedintable1. proved stoves. when itcomestotheadoptionanduseofim- Studies indicatethatwomenhaveacentralstake of childrenisalmostdoneentirelybywomen. the householdbecausecookingandtakingcare respondents whoareincharge ofcookingfor tionnaire wasadministeredtoadultfemale puter randomizerprogram. A structured ques- through simplerandomsamplingusingcom- sampling list160householdswereselected Extension staff andthelocalleaders.From sub-locations withthehelpof Agricultural households whichwasgeneratedfromthefour highly inefficient openfirestoves. demand forfirewoodtousecookingwith of destructiontreesbecausethereisahigh fast growingpopulationhasacceleratedtherate comers fromthedrierareasofdistrict. The agriculturally andalsoduetoinfluxofnew population becauseitisahighpotentialarea itan, rural-urbansettingwitharapidincreasein 16,131 households. The divisionisacosmopol- comprised of82,057peoplewithatotal cations. The accessiblepopulationofthisstudy of 335.6sqkmwith9locationsand28sub-lo- smallholder farmers. The divisioncoversanarea agricultural sectorintheregionisdominatedby tem andthesocio-culturalenvironment. The The analyticaltoolsemployedinthisstudy The distributionoftherespondentsin The samplingframeconsistedofalist Total Talau Location Kaibos Location Location Table 1:Distributionofrespondentsbylocationandsub-location. (Greene, 2008; . The objective . The Kipkorinya Sub-location Chepkoti Sub-location Kaibos Sub-location Talau Sub-location Sub-location hold sizeandfarmsize. level ofeducation,householdincome,house- socio-economic characteristics;namelyage, relation tothefiveexplanatoryvariablesin adoption ofMaendeleostovewasconsideredin X2,………., Xk. the coefficients oftheindependentvariablesX1, = ß is theintercepttermand does notadoptMaendeleostovetechnology. ogy and(1-Y)istheprobabilitythatahousehold household adoptstheMaendeleostovetechnol- tion inthesample. Y istheprobability thata of events able affects theprobabilityofoccurrence tool toinvestigatehoweachindependentvari- model wasappliedasthemostappropriate non-adoption, thebinarylogisticregression variable, withtheoptionofeitheradoptionor nology isadichotomousorbinarydependent Since theadoptionofMaendeleostovetech- adoption ofMaendeleostovetechnology. pendent andindependentvariablesintermsof and thetrendofrelationshipbetweende- of theresearchwastounderstanddegree ent variablesandisspecifiedasfollows: study areaisinfluencedbyasetofindepend- ingly, Maendeleostovetechnologyinthe technology atthehouseholdlevel. Accord- ables intheadoptionofMaendeleostove ship betweendependentandindependentvari- explore thedegreeanddirectionofrelation- Maendeleo stovetechnology. Thus ithelpsto nomic factorsinfluencingtheadoptionof tic regressionmodelexploresthesocio-eco- income (shillings);+ (level ofeducationattained);+ Y i The modelcanthenbeexpressedasfollows: In theanalysisofhypothesisthisstudy Where thesubscriptimeansithobserva- / 1-Y 0 + ß i 1 = ß (x (Long andFreese,2006) 1 = Age (inyears); + 0 + ß No. ofRespondents 1 x 1i + ß ß 4 160 (x 37 43 41 39 2 x 4 β1, β2,……….,βk 2i = Farmsize(inacres) + ß ß ß 3 3 2 x (x (x 3i 3 2 = Household +..... +ß . The logis- . The = Education k are x β0 Yi ki . 293 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. 294 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. gory of31-60yearswhiletherest(17)werein (83%) oftherespondentswereinagecate- egories asshownin Table 2. The majority The respondentsweregroupedintosixagecat- was 69yearswithameanof42(±0.903)years. respondents was19yearswhilethemaximum with regardstoageoftherespondents. age inyears. Table 2summarizestheresults Age oftherespondents respondents Socio-economic characteristicsofthe continuous variablemeasuredinacres. sons livinginthehouseholdandlandsizeisa tinuous variablemeasuredbythenumberofper- of thelast12months.Householdsizeisacon- measured inKenyashillingsbasedonestimates Household incomeisacontinuousvariable was usedasareferencecategoryvariable. farmer wereused,wherenon-formaleducation senting thelevelofeducationattainedby ary, 4=tertiary. Fourdummyvariablesrepre- = non-formaleducation2primary3second- level attainedbythehouseholdwascodedas1 + Findings revealedthattheminimumageof The respondentswereaskedtoindicatetheir Age wasmeasuredinyears.Highesteducation ß 5 (x 5 RESULTSDISCUSSION AND Mean 42.31,se0.903,median42,mode49,stddev11.43, minimum19maximum69. Mean 42.31,se0.903,median 42,mode49,stddev11.43, minimum19maximum69. Total >60 51 –60 41 –50 31 –40 21 –30 <21 Age categories(Years) = Householdsize(persons). Total Tertiary Secondary Primary No formaleducation Level offormalEducation Table 2:Frequencydistributionof therespondentsbyage. Table 3:Levelofeducationattainedbytherespondents. Frequency 160 33 43 47 23 Frequency 9 5 160 118 14 17 11 stove technologybytheagriculturalextension technical informationabouttheMaendeleo Maendeleo stovetechnology, duetothelimited but, lackedinformationonthebenefitsof were awareoftheMaendeleostovetechnology new technologies. the decisionfarmerwouldmakeinadopting years offarmingexperienceandhencethebetter that theolderfarmerswere,moretheir adoption offarmtechnologies.Heconcluded nificant positiverelationshipbetweenageand a studyofNigerianwomenfarmersfoundsig- are consistentwithresearchworkdoneby through theextensionservices. These findings promoted inthecountylast30years the Maendeleostovetechnologyhavingbeen spondents hadpriorexposuretothebenefitsof the Maendeleostovetechnology. dents intheagecategoryof31–60yearsadopted tions fromthestudyrevealedthatrespon- the agegroupof30yearsandbelow. Observa- Mignouna mation thanyoungerfarmers. and arebetterabletoevaluatetechnologyinfor- have gainedfarmingknowledgeandexperience adoption inthesensethatolderfarmersovertime farmer’s ageisexpectedtoincreasetechnology The youngergenerationsontheotherhand The observedresultsindicatethattheolderre- Percent 100.0 Percent 20.6 26.9 29.4 14.4 5.6 3.1 100.0 10.6 73.8 6.9 8.8 et al., (2011) Cumulative Percent Cumulative Percent which indicatedthata 100.0 100.0 91.3 80.6 94.4 73.9 46.9 17.5 6.9 3.1 Okunade (2007) in Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. (Aneani sociated withitandenhanceitsadoption technology tocleardoubtsanduncertaintiesas- nically andeconomicallyassessthenew cation thefarmerwouldbeinapositiontotech- technologies becausewithhigherlevelofedu- to influencetheadoptiondecisionofmany level education.Educationalstatusisassumed (73.8%) oftherespondentshadattainedprimary mal educationwhileahigherpercentage (6.9%) ofrespondentshadneverreceivedfor- 3. The findingsindicatedthatonlyaminority ucation, thefindingswereasindicatedin Table education, secondaryeducationandtertiaryed- ranged fromnoformaleducationatall,primary attained. highest levelofformaleducationthattheyhad Level ofeducationtherespondents like thechepkubebrooder, rocketstovesetc. dents optedforthealternativeimprovedstoves or wouldbreakquickly. As aresult;therespon- the respondentsfearedstovewouldnotwork lack ofconfidencethatthestoveisdurableas ity oftheMaendeleostovesinstudyareaand agents. This wasacceleratedbytheun-availabil- spondents withformaleducationmaybeas- The levelofeducationtherespondents The respondentswereaskedtoindicatethe Mean 4.0±0.297,median3,mode 1,minimum0,andmaximum17. Mean 7.5;SE0.239;median7,mode8,stddev3.033,minimum 1andmaximum18. Total Above 13 10-12 members 7-9 members 4-6 members 1-3 members Number ofMembers Total 10.1-15 5.1-10.0 1.0-5.0 <1.0 Size ofLand(inacres) et al., 2012) . Inthestudyarea,re- Table 5:Frequencydistributionofthelandsize. Table 4:Frequencydistributionof thehouseholdsize. Frequency Frequency 160 88 64 160 2 6 31 55 55 10 9 ogy sincehouseholdswhoadoptedtheMaen- of adoptionthe level ofeducationisnotamajordeterminant However, thestudyfindingsindicatedthat sion toadoptsometechnologies years offormaleducationintheoveralldeci- counter balancethenegativeeffect oflack cultural productiontechnologiesbecauseitcan critical inpromotingadoptionofmodernagri- nical informationthroughextensionservicesis over timetherebyfacilitatingadoption. Tech- assessment frompurelysubjectivetoobjective formance hencemaychangeindividual’s duces th to informationthroughextensionservicesre- in theStateDepartmentof Agriculture. Access among others,bytheHomeEconomicsOfficers field days,demonstrations,farmers’ trainings agricultural extensionpathwayssuchasfarmers’ stove technologyaredisseminatedthroughthe mation abouttheenergy-saving Maendeleo agricultural extensionpathways.Technical infor- gotten thetechnicalinformationthrough efits oftheMaendeleostovetechnologyhaving mal educationhowever, wereawareoftheben- speed ofadoption. The respondentswithnofor- therefore, formaleducationmayincreasethe and healtheffects ofusingbiomassfuels,and sumed tobemoreawareoftheenvironmental Percent Percent 100.0 100.0 19.4 34.4 34.4 e uncertaintyaboutatechnology’s per- 55 40 1.2 3.8 6.3 5.5 (n=160) Maendeleo Cumulative Percent Cumulative Percent . 100.0 94.5 75.1 40.7 100.0 98.8 6.3 95 40 (Kubok, 2007) stove technol- . 295 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. 296 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. technology wasbecauseoftheinflexibilityna- used boththetraditionalandMaendeleostove holder’s. Another reasonwhysomerespondents a potwhosediameterwaslarger thanthe deleo stovetechnologywhentheywantedtouse specific diameter. They couldnotuse theMaen- Maendeleo stovetechnologyhadpotholdersof cient stovedesign. Their reasonwasthatthe tained it,insteadreturnedtotheirformerineffi- Maendeleo stovetechnologythoughtheyre- with morehouseholdmembersdidnotusethe The studyfindingsindicatedthathouseholds ditures andwithlarger families for thosewithhigherbaselinefueluseandexpen- tion ofenergy-saving cookstovewouldbehigher line fueluse. Thus, itwasalsoexpectedthatadop- cook stoves. and maybemoreinclinedtoadoptimproved members haveahigherdemandforenergy fuel sumed thatfamilieswithalarge numberof Maendeleo stovetechnologyasitmaybeas- have apositiveinfluenceontheadoptionof the studyarea,householdsizewasexpectedto had tenormoremembersinthehouseholds.In nine orfewermemberswhiletherest(24.9%) eight. The majority(75.1%)ofhouseholdshad ber ofpeoplelivingwithinthehouseholdswas between 1and18members. The averagenum- findings indicatedthathouseholdsizesranged sponses weresummarizedin Table 4. The study of peoplelivingintheirhomesandre- respondents wereaskedtoindicatethenumber all thepeoplelivingwithinthathousehold. The Distribution ofhouseholdsize of education. deleo stovetechnologycutacrossallthelevels Fuel savingsareroughlyproportionaltobase- The householdsizereferredtothenumberof 18,630,000. Mean 116,438 ±8,801;median 100,000;mode50,000;stddev111,330; minimum5,000 andmaximum Total >200,000 150,001-200,000 100,001-150,000 50,001-100,000 >50,000 Income categories(KShs.) Table 6: Annual householdincome. (Ostrom, 2010) Frequency 160 18 13 21 50 58 . high costofbuyingorgatheringfuelandthein- Maendeleo stovetechnologybecauseofthe landholding wouldbemorelikelytoadoptthe energy savingstoves availability offuelandhencetheabilitytouse for woodproductionwhichinturnmayaffect farmer’s abilitytosetasideaportionoftheland acres. Farmsizemaybeexpectedtoinfluencea ity (5%)ownedlandthatwasmorethanfive farms thatwerebelowfiveacreswhileaminor- acres. The majority(95%)ofthehouseholdshad acres andrangedbetweenzeroseventeen land areaownedbythehouseholdswasfour respondents. 10 showstheaveragesizeoflandownedby size oflandtheyownedasahousehold. Table Distribution offarmsize modate number andsizesofpotsthestovecouldaccom- turn toformerstovedesign)wasdictatedbythe problem. The acceptanceorabandonment(re- their traditionalstoveasitdoesnothavethis stove andutensilsmayleadsomeuserstokeep nology. Therefore incompatibilitybetweena ficulty cookingusingtheMaendeleostovetech- holders whilecookingugaliwhichtheyhaddif- ogy hadeithermodifiedthepotrestsorused that hadadoptedtheMaendeleostovetechnol- tional three-stonefiresbut,thefewhouseholds ibility therespondentsre-deployedtradi- fire onthetraditionalstoveaswell. cook twopotsoffoodsimultaneouslytomake pot holder. This makesrespondentswhowantto ture ofMaendeleostovetechnologywithone It wasexpectedthathouseholdswithsmall An analysisofthedatashowedthatmean The respondentswereaskedtoindicatethe To solvetheproblemofholder-pot incompat- Percent 100.0 13.1 31.2 36.3 11.2 (Rwiza, 2009) 8.2 (n=160). Cumulative Percent (Makame, 2007) . 100.0 88.8 80.6 67.5 36.3 . Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. ability ofinvestingin and usingimproved as wealthierhouseholds may havehigherprob- have positiveeffect onadoptionoftechnologies indicator ofprosperityand maybeexpectedto than KShs.150,000.Householdincomeisan while therest(19.4%)hadanincomeofmore households earnedlessthanKShs.150,000 18,630,000. The majority (80.6%)ofthe imum ofKShs.5,000andamaximum holds wasKShs.116,438 (±8,801),withamin- sented asgivenintable6. household incomeswascategorizedandpre- members ofthehouseholdperyear. Dataon of moneyinKenyashillingsasearnedbyall Household income wood shortage. contrary becausetheystillfelthadnofuel wood comparedtotheircounterpartsbut,the equate spaceforestablishingwoodlotsfuel Maendeleo stovetechnologysincetheyhadad- ings likewisewereexpectedtoadoptthe deleo stoves.Householdswithlarge landhold- stove becauseofunavailabilitytheMaen- nology andacceptedit,theycouldnothavethe ized thebenefitsofMaendeleostovetech- wood. Despitethefactthattheyhadlaterreal- adequate landtoestablishwoodlotsforfuel The averageannualincomeforthehouse- Household incomewasmeasuredastotalsum 18,630,000. Mean 116,438 ±8,801;median100,000;mode50,000;stddev111,330; minimum5,000andmaximum Significance Chi-square Nagelkerke RSquare Cox &SnellRSquare -2 Loglikelihood Overall percentageprediction Number ofobservations Constant Farm size Household size Income dummy Tertiary dummy Secondary educationdummy Primary educationdummy Age ofrespondent Variable name Table 7:Logisticregressionanalysisofsocio-economicfactorsinfluencingadoption Maendeleo stove(dependentvariable1=adopterofMS;0=non-adopter). 205.211 16.371 -2.733 -0.032 0.130 0.097 0.132 1.385 0.881 0.572 0.046 0.113 0.22 51.9 160 β scale farmers. determined theadoption decisionofthesmall- indicated thattheindependent variablesjointly tically significantchi-squareof16.371(P <0.05) 2log likelihoodestimateof205.211 withastatis- The resultsareindicatedintable7. stove. The - significant influenceonadoptionofMaendeleo farmers’ socio-economiccharacteristicshaveno the technology. variables negativelyinfluencedutilizationof attributes ofthetechnologyorotherextraneous the Maendeleostovetechnologyunlessother fore, maybeexpectedtoincreaseadoptionof technology adoption. This perceivedcostthere- a positiveinfluenceontherateandspeedof initial cost adopted thanthatwithtechnologyhigh ogy withlowinitialcostismorelikelytobe technology lesscostlyandaffordable. Technol- their incomelevelfoundtheMaendeleostove vealed thatalltherespondentsirrespectiveof most consideritconfidential. The studyre- formation onincomefromtherespondentsas of afarmer, itisdifficult tocollectreliablein- most importantpointeroftheeconomicstatus stoves 1.149 0.094 0.063 0.152 0.974 0.889 0.704 0.017 S.E. The modelasawholeexplained between9.7 The studyinvestigatedthehypothesisthat (Inayat, 2011) (Rogers, 2003) 5.652 1.449 0.251 0.752 2.022 0.983 0.660 7.022 Wald RESULTS . Although incomeisthe 0.017 0.229 0.616 0.386 0.155 0.321 0.417 0.008 Sig. . Lowinitialcosthas Exp(B) 0.065 1.120 1.141 3.994 2.414 1.771 1.047 .969 297 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. 298 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. strength declinesthereby reducingthefarmer’s As theageoffarmer increasesthephysical negative relationshipbetween ageandadoption. cocoa farmersinGhana, reportedastatistically of somecocoaproductiontechnologiesby farmers. evaluate technologyinformationthanyounger knowledge andexperiencearebetterableto older farmersovertimehavegainedfarming to increasetechnologyadoptioninthesensethat which indicatedthatafarmer’s ageisexpected search workdoneby the same. These findingsareconsistentwithre- younger respondentswhowerenotsensitizedon area morethantwentyyearsagounlikethe stove technologyhavingbeenbroughtupinthe prior exposuretothebenefitsofMaendeleo the olderrespondentsinthisstudymayhavehad younger ones. The observedresultsindicatethat was higheramongolderrespondentsthan age suggeststhatadoptionofMaendeleostove in theoddsratioby1.047times. ratio increasedby0.046whichledtoanincrease dent gotolderbyoneyear, thelogofodds adopting theMaendeleostove. As therespon- influenced thelikelihoodofrespondent the respondent(P <0.05). This meansthatage significant determinantofadoptionwasage the modelwassignificant(P <0.05). rectly predictedbythemodel. The interceptof strated that51.9percentofthecaseswerecor- by thepredictors. The resultsfurtherdemon- tion ofMaendeleostove)wasexplainedjointly in thedichotomousdependentvariable(adop- plying thatabout13.0percentofthevariation pseudo R-squaredwasestimatedtobe0.130im- percent (CoxandSnellRsquare)the the studiesby farmers andtheiradoptionbehavior. However, who reportednorelationshipbetweenageof (2006) farmer wouldmakeinadoptingtechnologies. experience andhencethebetterdecision farmers were,themoretheiryearsoffarming technologies. Heconcludedthattheolder lationship betweenageandadoptionoffarm women farmersfoundasignificantpositivere- The positiveandsignificantcontributionof The socio-economicmodelindicatedthatthe These findingsdiffer fromthoseof in hisstudyoffoddershrubsfarmers Okunade (2007) Aneani et al. Mignouna in astudyofNigerian (2012) et al. on adoption Wambugu (2011) shown byinconsistentresults whether ornottoadoptnewtechnologiesas landholding sizeonhouseholds’ decisions findings intheliteratureoninfluenceof positive, negativeorneutral. There aremixed The effect offarm size fected bytheother is becausefarmsizecanaffect andinturnbeaf- tural innovationsandtechnologies(Seeforinstance tant determinantofadoptiondifferent agricul- farm sizeasthefirstandprobablymostimpor- cooking fuelbythehouseholds. stove isdependentonmeetingabasicneedof this studyfindingssinceuseoftheMaendeleo to assesstheusefulnessofanewtechnology ucation iscriticalinenablingatechnologyuser are notabletofitwellonthestoves. While ed- households usebiggerutensilstocookwhich on thefueluse,thiswasnotcasesincelarger clined toadopttheimprovedstovesminimize families withalarge householdwillbemorein- from thestudy. Though itmaybeassumedthat of thestoveused;thisiscontrarytoresults pected tohaveapositiveinfluenceonthemodel ship withtheadoptionof theMaendeleostove dicate thatfarmsizehad anegativerelation- found farmsizetobepositively relatedtoadoption. Kassie Doss andMorris,2001; (Makame, 2007) Fernandez-Cornejo, (1996)andKasenge(1998) and Ritchie(2007) ings by Maendeleo stove. Though fromresearchfind- influencing thedecisionofafarmertoadopt and householdsizetendedtobelessprobablein level ofeducation,householdincome,farmsize more technologies. economic resourcestheywouldopttoadopt takers andthatsincetheyarestillaccumulating proved technologicalpracticesastheyarerisk younger peoplearemorelikelytoadoptim- of anewtechnologyistypicallyyoungeras stove. more skepticalaboutthebenefitsofMaendeleo may bemoreconservative,lessflexibleand ability tousenewtechnologyalsoolderfarmers Much empiricaladoptionliteraturefocuseson However, findingsfromthe current studyin- According tothefindingsofanalysis, Akudugu et al. Inayat (2011) (2009); Waithaka et al. , thisisinconsistentwiththe factors influencing concluded thattheadopter , householdsizeisex- (2012) and on adoptioncouldbe Daku, 2002) in thestudiesby and et al. Marchionni adoption. (2007). . This Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. Harper sistent withfindingsby of requiredfuelwood. These findingsarecon- using asmallportionofthefarmforsupply ergy demandforthehouseholdcanbemet technology sincecookingfueltomeettheen- degree ofcontactfarmers withextension portant totechnologyadoption. The greater the these findingsisthatextension visitsareim- been slowandinefficient. The implicationof through theextensionserviceappearstohave the years.However, technologytransfer government andNGOshasbeendecliningover by themainextensionserviceprovidersin ogy. DeliveryofextensionservicesinKenya and promotingtheMaendeleostovetechnol- other stakeholdershavebeendisseminating tions suchastheMinistry’s of Agriculture and adoption. Inthelast30yearsmanyorganiza- evant informationthatpromotestechnology creates theplatformforacquisitionofrel- area. Access toextensionsservicestherefore source ofagriculturalinformationinthestudy the Ministryof Agriculture arethemajor ity ofthestoves. Maendeleo stovetechnologyandnon-availabil- technical informationonthebenefitsof they wereawareofthestoveduetolack Maendeleo stovetechnologyatallalthough is durable. The restoftherespondentshadno lent, etc.),andlackofconfidencethatthestove non pots, thestovedesigncouldnotallowforother not accommodatealarge numberandsizesof among thereasonsgivenwere:stovecould that promptedthemtoabandonthestoveand sign. The respondentsquotedvariousreasons using itandreturnedtotheirformerstovede- had adoptedthecookstoveabandoned low becauseabouthalfoftherespondentswho among thesmall-scalefarmerssurveyedwas income levelmaynotlimitaffordability. they areaffordable tothepotentialusersand it isnotafactorinfluencinguseofthestovesas Household income,fromthefindings,showthat tionship betweenadoptionandfarmsize. Agricultural extensionservicesprovidedby The levelofadoptiontheMaendeleostove ‐ cooking attributes(heat,light,insectrepel- et al. (1990) CONCLUSION who foundnegativerela- Yaron et al. (1992); and which helpedimprovetheanalysisinthispaper. vided bytheeditorandanonymousreviewers like toacknowledgethehelpfulcommentspro- field datacollection. The authorwouldalso tience andcooperationduringtheperiodof thanks totherespondentsfortheirtime,pa- direct contributionstothisstudy. Special staff West PokotCountyfortheirdirectandin- thanks areduetotheMinistryof Agriculture versity. Myprofoundacknowledgementand nious environmentduringmystayattheUni- for theirmoral,adviceandcreatingaharmo- tension EgertonUniversityandmycolleagues Department of Agricultural EducationandEx- Quarterly JournalofInternational Agriculture, 40 Southwestern Ethiopia: An applicationoflogit. encing adoptionofhighyielding maizevarietiesin 1- Abebaw, D.,&Belay, K.(2001).Factorsinflu- adopters. ucated, youngandold;allappeartobepotential technology. Richandpoor, educatedanduned- farmers fromadoptingtheMaendeleostove none ofthesefeaturespreventssmallholder The findingsareimportantastheyshowthat adoption oftheMaendeleostovetechnology. and farmsize)hadnosignificantinfluenceon of education,householdincome,size, ogy. Othersocio-economiccharacteristics(level with adoptionoftheMaendeleostovetechnol- spondent’s age,hadasignificantassociation economic characteristicsconsideredonlythere- among smallholderfarmers. Among thesocio- the energy-saving Maendeleostovetechnology socio-economic factorsinfluencingadoptionof probability ofitsadoption. uncertainties concerningittoincreasethe deleo stovetechnologytoclearanydoubtsand well astheprofitabilitystatusofMaen- ity ofneededresources,marketandpricesas with necessaryinformationabouttheavailabil- the extensionagentwouldprovidefarmer technologies. Frequentvisitstothefarmerby farmers beinginfluencedtoadoptagricultural personnel, thegreaterispossibilitiesof I amgratefultoalltheacademicstaff ofthe The focusofthestudywastodetermine ACKNOWLEDGEMENTS REFERENCES 299 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. 300 International Journal of Agricultural Management and Development, 3(4): 289-301, December, 2013. Socio-Economic FactorsInfluencing Adoption ofEnergy–Saving/AndiemaChesangEverlyneetal. No. 36.EconomicResearch Service.UnitedStates Economic Performance. 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Articles in preparation or articles submitted for publication, unpublished observations, personal communications, etc. should not be included in the reference list but should only be mentioned in the article text (e.g., A. Kingori, University of Nairobi, Kenya, personal communication). Journal names are abbreviated according to Chemical Abstracts. Authors are fully responsible for the accuracy of the references. Short Communications Short Communications are limited to a maximum of two figures and one table. They should present a complete study that is more limited in scope than is found in full-length papers. The items of manuscript preparation listed above apply to Short Communications with the following differences: (1) Abstracts are limited to 100 words; (2) instead of a separate Materials and Methods section, experimental procedures may be incorporated into Figure Legends and Table footnotes; (3) Results and Discussion should be combined into a single section. Proofs and Reprints: Electronic proofs will be sent (e-mail attachment) to the corresponding author as a PDF file. Page proofs are considered to be the final version of the manuscript. With the exception of typographical or minor clerical errors, no changes will be made in the manuscript at the proof stage. Because IJAMAD will be published freely online to attract a wide audience), authors will have free electronic access to the full text (in both HTML and PDF) of the article. Authors can freely download the PDF file from which they can print unlimited copies of their articles. Copyright: Submission of a manuscript implies: that the work described has not been published before (except in the form of an abstract or as part of a published lecture, or thesis) that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher. Fees and Charges: Authors are required to pay a $100 handling fee. مجله بین المللی مدیریت و توسعـه کشاورزی قابل دسترس در سایت www.ijamad.com شماره استاندارد بین المللی چـاپ: 2159-5852 شماره استاندارد بین المللی آنالین: 2251-5860

عوامــل اقتصــادی اجتماعــی موثــر بــر پذیــرش فناوریهــای ذخیــره انــرژی در میــان کشــاورزان خــرده پــا مطالعه موردی شهرستان پوکوت غربی، کنیا

آندیما چیسنگ اورلین 1*، آِنکوروموا اویوایا آگنس 2 و اموداوی موالما دیوید 3

تاریخ دریافت: 25 شهریور 1392 ســوختهای جنگلــی مهمتریــن منبــع تامیــن انــرژی بــرای پخــت و پــز و تاریخ تایید: 10 آبان 1392 گرمایــش بیــش از 80 درصــد خانوارهــای کنیــا میباشــند. اتــکای شــدید بــه ســوختهای زیســتی منجــر بــه عــدم تعــادل در عرضــه و تقاضــا و در نتیجــه اثــرات نامطلــوب زیســت محیطــی در کنیــا شــده اســت. بــه عنــوان یــک تــالش خالقانــه چندیــن فنــاوری ذخیــره انــرژی در کنیــا بــه ویــژه در پوکــوت غربــی بــه وجــود آمــده نظیــر یــک اجــاق خــوراک پــزی ویــژه بــه نــام Maendeleo کــه چکیده بــا هــدف کاهــش مقــدار هیــزم مــورد اســتفاده در خانوارهــا و درنهایــت کاهــش فشــار بــر جنگلهــای محلــی ترویــج شــده اســت. گرچــه عــالوه بــر شــرح مجله بین المللی مدیریت و توسعه کشاورزی، سال سوم، شماره 4 ، ) 1392 ( نمایــش منافــع چندگانــه فنــاوری و تالشهــای ترویجــی ســازمانها دربــاره فنــاوری اجــاق خــوراک پــزی ســطح پذیــرش ایــن نــوآوری در حــد پایینــی باقــی مانــد ســوال مهمــی کــه در ایــن مطالعــه مــورد بررســی قــرار گرفــت ایــن بــود کــه چــرا کاربــران بالقــوه چنیــن نــوآوری ارزشــمندی را مــورد اســتفاده قــرار نمیدهنــد. موانــع اجتماعــی، فرهنگــی، اقتصــادی، سیاســی و ســازمانی بــه عنــوان دالیــل ســطح پذیــرش پاییــن ایــن نــوآوری مــورد توجــه قــرار گرفتــه اســت بنابرایــن ایــن مطالعــه در جســتجوس عوامــل اجتماعــی- اقتصــادی موثــر بــر پذیــرش اجــاق خوراکپــزی Maendeleo در نواحــی روســتایی کاپنگوریــا میباشــد. روش تحقیــق پیمایشــی بــا طــرح علــی قیاســی در ایــن مطالعــه بــه کار گرفتــه شــده اســت نتایــج نشــان داد کــه ســن پاســخگویان بیشــترین تاثیــر را بــر روی عــدم پذیــرش داشــته اســت. بــا توجــه بــه ســطح ًنســبتا پاییــن پذیــرش نــوآوری و افزایــش تعــداد افــراد متکــی بــر ســوخت زیســتی ایــن مطالعــه پیشــنهاد میکنــد کــه دولــت و شــرکای توســعهای، برنامــهای را در جهــت ترویــج و اشــاعه ایــن نــوآوری تدویــن نماینــد و تحقیقــات بیشــتری واژگــان کلیـــدی: میبایســت دربــاره رفتــار پذیــرش پاســخگویان دربــاره دالیــل عــدم پذیــرش و پذیـرش، خـرده پـا، فناوریهـای ذخیـره عــدم تــداوم اســتفاده از ایــن نــوآوری صــورت پذیــرد. انرژی

1 کارشناسی ارشد، گروه آموزش و توسعه کشاورزی، دانشگاه اگرتون، کنیا 2 مدرس گروه آموزش و توسعه کشاورزی، دانشگاه اگرتون، کنیا 3 مدیر اتحادیه بیوویژن آفریقا، نایروبی، کنیا 8 * ایمیل نویسنده مسئول: [email protected] مجله بین المللی مدیریت و توسعـه کشاورزی قابل دسترس در سایت www.ijamad.com شماره استاندارد بین المللی چـاپ: 2159-5852 شماره استاندارد بین المللی آنالین: 2251-5860

عملکرد پژوهشی اعضای هیأت علمی کشاورزی مطالعه مقایسهای در غرب ایران

نعمتاله شیری1*، نادر نادری2 و احمد رضوانفر3

تاریخ دریافت: 25 شهریور 1392 پژوهـش حاضـر بهمنظـور مقایسـه عملکـرد پژوهشـی اعضـای هیـأت علمـی تاریخ تایید: 26 مهر 1392 دانشـکدههای کشـاورزی غـرب ایـران براسـاس ویژگیهـای فـردی و حرفـهای آنهـا انجـام شـد. جامعـه آمـاری پژوهـش شـامل تمـام اعضـای هیـأت علمـی دانشـکدههای کشـاورزی دانشـگاههای ایـالم، رازی و کردسـتان در ایـران بودنـد کـه تعـداد 116 نفـر از آنهـا بـه روش نمونهگیـری طبقـهای تصادفی با انتسـاب متناسـب بـرای مطالعـه انتخـاب شـدند. ابـزار اصلـی پژوهـش بـرای جمـعآوری چکیده دادههـا پرسشـنامه بـود. تجزیـه و تحلیـل دادههـا در دو بخـش آمـار توصیفـی و اسـتنباطی بـا اسـتفاده از نرمافـزار SPSSWin20 انجام شـد. نتایج پژوهش نشـان داد کـه وضعیـت موجـود عملکـرد پژوهشـی اعضای هیـأت علمی دانشـکدههای مجله بین المللی مدیریت و توسعه کشاورزی، سال سوم، شماره 4 ، ) 1392 ( کشـاورزی غـرب ایـران ضعیف بـود. نتایـج مقایسـه میانگینها نشـان داد که بین عملکـرد پژوهشـی اعضـای هیـأت علمـی دانشـکدههای کشـاورزی غـرب ایران براسـاس متغیرهای سـن، سـابقه خدمت، مرتبه علمی، گروه آموزشـی و جنسـیت اختـالف معنـیداری وجود داشـت. نتایـج این مطالعه دسـتاوردهای مناسـبی برای واژگــان کلیـــدی: کمـک بـه برنامهریـزان نظـام آموزش عالـی کشـاورزی در جهت ارتقـای عملکرد عملکرد پژوهشی، ویژگیهای فردی و حرفهای، اعضای هیأت علمی کشاورزی پژوهشـی اعضـای هیـأت علمـی دانشـکدههای کشـاورزی دارد.

1 دانشجوی دکتری گروه ترویج و آموزش کشاورزی، دانشگاه رازی، کرمانشاه، ایران 2 استادیار گروه ترویج و آموزش کشاورزی، دانشگاه رازی، کرمانشاه، ایران 3 استاد گروه ترویج و آموزش کشاورزی، دانشگاه تهران، کرج، ایران 7 * ایمیل نویسنده مسئول: [email protected] مجله بین المللی مدیریت و توسعـه کشاورزی قابل دسترس در سایت www.ijamad.com شماره استاندارد بین المللی چـاپ: 2159-5852 شماره استاندارد بین المللی آنالین: 2251-5860

اثرات اقتصادی و رفاهی روشهای مختلف قیمتگذاری آب مطالعه موردی دشت خمین، استان مرکزی، ایران

غالمرضا زمانیان1 ، مهدی جعفری2* و شهرام سعیدیان3

تاریخ دریافت: 31 تیر 1392 كميابـي منابـع آبـي و عـدم توانايـي انسـان در توليـد آب برخالف ديگـر محصوالت، موجـب شـده اسـت كـه فاصله بيـن عرضه و تقاضـاي آب به ويـژه در دهههـاي اخير تاریخ تایید: 2 آبان 1392 بـه شـدت زيـاد شـده و در اغلـب مناطـق دنيـا كمبـود عرضـه به وجـود آيـد. یکی از راه حلهـای ارائـه شـده توسـط اقتصاددانان، اسـتفاده از شـیوههای قیمتگـذاری آب، در راسـتای دسـتیابی بـه تخصیـص بهینـه و عدالـت اجتماعی اسـت. بدیـن منظور در ایـن مطالعـه با یـک رویکـرد مقایسـهای بیـن روشهـای برنامهریزی ریاضـی مثبت چکیده )PMP( و برنامهریـزی ریاضـی اقتصادسـنجی)EMP(، بـه بررسـی اثـرات رفاهـی و اقتصـادی روشهـای مختلـف قیمتگـذاری آب در بخـش کشـاورزی، طـی فصـل مجله بین المللی مدیریت و توسعه کشاورزی، سال سوم، شماره 4 ، ) 1392 ( زراعـی91-1390 در منطقـه دشـت خمیـن پرداخته شـده اسـت. نتایج نشـان میدهد کـه میتـوان از روش EMP بـه عنوان جایگزین مناسـب بـرای روش PMP در تحلیل سیاسـتهای کشـاورزی اسـتفاده کرد. با توجه نتایج بدسـت آمده، پیشـنهاد میشـود کـه از روش قیمتگـذاری مبتنـی بـر تعرفـه بلوکـی بـه جـای روش قیمتگـذاری واژگــان کلیـــدی: حجمـی، جهت دسـتیابی به تخصیـص بهینه و بهبـود کارایی آب، در محـدوده قیمتی اثرات اقتصادی و رفاهی، قیمتگذاری 198 تـا 853 ریـال بـه عنـوان یـک روش ایـدهال جهت دسـتیابی به اهـداف پیشرو آب، برنامهریزی ریاضی، دشت خمین در منطقـه مورد اسـتفاده قـرار گیرد.

1 استادیار گروه اقتصاد دانشگاه سیستان و بلوچستان 2 دانشجوی دکتری اقتصاد کشاورزی دانشگاه سیستان و بلوچستان 3 کارشناسی ارشد اقتصاد کشاورزی دانشگاه سیستان و بلوچستان 6 * ایمیل نویسنده مسئول: [email protected] مجله بین المللی مدیریت و توسعـه کشاورزی قابل دسترس در سایت www.ijamad.com شماره استاندارد بین المللی چـاپ: 2159-5852 شماره استاندارد بین المللی آنالین: 2251-5860

بررسی بازار بالقوه و برآورد WTP برای بیمه تنه درختان پسته مطالعه موردی رفسنجان- ایران

مصطفی بنی اسدی1*، سعید یزدانی 2و حبیب اهلل سالمی 2

تاریخ دریافت: 23 بهمن 1391 علیرغـم شـرایط کمنظیـر ایـران در تولیـد محصـوالت باغـی، مخاطـرات طبیعـی تاریخ تایید: 4 شهریور 1392 همـواره بـه تولیـد میـوه در کشـور خسـارت زده و کشـاورزان از ایـن بابـت متضـرر میشـوند. درخـت پسـته نیـز همـواره در معـرض خطر نابودی و خشـک شـدن بوده اسـت. بنابراین جهت کاهش خسـارت ناشـی از نابودی درختان، ضرورت وجود بیمه تنـه درخت احسـاس میشـود. هـدف از انجـام این مطالعه بررسـی وجود بـازار بالقوه بـرای بیمـه تنـه درختـان پسـته و بـرآورد تمایـل بـه پرداخـت حـق بیمه بـرای این چکیده درخـت در شهرسـتان رفسـنجان واقـع در اسـتان کرمان میباشـد. بـرای این منظور از روش ارزشگـذاری مشـروط و انتخـاب دوگانه دوبعدی اسـتفاده شـد. دادههای این تحقیـق بهصـورت میدانـی و از طریـق مصاحبـه با 184 باغدار پسـته در سـال 2012 مجله بین المللی مدیریت و توسعه کشاورزی، سال سوم، شماره 4 ، ) 1392 ( بدسـت آمـده اسـت. نتایج حاکی از آن اسـت که مقـدار تمایل به پرداخـت برای حق بیمـه درخـت پسـته در سـه بخـش مرکـزی و انار، کشـکوئیه و نـوق بهترتیـب برابر بـا 2573، 3548 و 1454 ریـال بـه ازای هـر درخـت بـرآورد گردید. با توجـه به نتایج واژگــان کلیـــدی: مطالعـه و وجـود ریسـک باالی نابـودی درخت پسـته، بهمنظـور کاهش خسـارت و درخـــت پســـته، ارزشـــگذاری مشـــروط، ریسـک باغـداران پسـته، ارائه بیمه تنه درخت پسـته پیشـنهاد میشـود. همچنین تا تمایـــل بـــه پرداخـــت، مـــدل الجیـــت، زمـان محاسـبه حق بیمـه منصفانه، تمایل به پرداخت محاسـبه شـده در این مطالعه، رفســـنجان بهعنـوان حق بیمه پیشـنهاد میشـود.

1 دانشآموخته کارشناسی ارشد اقتصاد کشاورزی، گروه اقتصاد کشاورزی، دانشگاه تهران 2 استاد اقتصاد کشاورزی، گروه اقتصاد کشاورزی، دانشگاه تهران 5 * ایمیل نویسنده مسئول: [email protected] مجله بین المللی مدیریت و توسعـه کشاورزی قابل دسترس در سایت www.ijamad.com شماره استاندارد بین المللی چـاپ: 2159-5852 شماره استاندارد بین المللی آنالین: 2251-5860

پژوهشـی در اجرای سـرمایهگذاری و دریافـت وام در بین واحدهای سـرمایهگذاری کوچـک زراعـی در منطقه دلتای نیجـر، نیجریه

اوبون آسوکو اسین*، چوکوومکا جان آرن و نوبل جکسون اِ ِنوز

تاریخ دریافت: 4 مرداد 1392 ایـن مطالعـه بـه منظـور تحلیـل اجـرای سـرمایه گـذاری و دریافـت وام بـا تاریخ تایید: 11 شهریور 1392 سـرمایهگذاری در واحـد کوچـک مبتنـی بـر زراعـت در ناحیـه دلتـای نیجـر در نیجریـه انجـام گرفتـه اسـت. از تکنیـک نمونهبرداری چنـد مرحلـهای در انتخاب 264 و 96 سـرمایهگذار زراعـی کـه بـه ترتیـب بـه وامهای رسـمی و غیررسـمی دسترسـی داشـتند، اسـتفاده شـد. مدل همکن برای امتحـان فاکتورهـای موثر بر میـزان وامهـای رسـمی و غیررسـمی دریافت شـده برای سـرمایهگذاری اسـتفاده چکیده شـده اسـت. عـالوه بـر آزمـون T تسـت کـه اجـرای سـرمایهگذاری را آزمـون میکـرد کـه از بنگاههـای اعتباری رسـمی و غیررسـمی در منطقه قـرض گرفته مجله بین المللی مدیریت و توسعه کشاورزی، سال سوم، شماره 4 ، ) 1392 ( شـده بـود نسـبتهای مالـی نظیر نسـبت جاری و نسـبت بازگشـت سـرمایهی به کاررفتـه، مـورد اسـتفاده قـرار گرفته شـد. تحلیـل میـزان وام غیررسـمی دریافت شـده آشـکار کرد که جنسـیت، سـن و سـرمایه اجتماعی برای مانع اول معنیدار هسـتند درحالـی که جنسـیت، انـدازه، درآمـد، ضمانت و سـرمایه اجتماعـی برای مانع دوم معنیدار هسـتند. به طور مشـابه جنسـیت، آموزش، سـن، اندازه و وثیقه بـرای مانـع اول در وام رسـمی معنیدار هسـتند. درصورتی که کـه نتایج معنیدار واژگــان کلیـــدی: گـزارش شـده بـرای مانع دوم با سـن، اندازه، درآمـد، ضمانت و سـرمایه اجتماعی میــزان وام دریافــت شــده، دسترســی وام، در ارتبـاط اسـت. وام رسـمی نسـبت بـه وام غیررسـمی کمتر در دسـترس بود اما اجــرای ســرمایهگذاری، ســرمایهگذاری مبتنــی بــر کشــت، ناحیــه دلتــای نیجــر، افزایـش کارایـی بیشـتری را به همراه داشـته اسـت. باید به گونهای باشـد که وام نیجریه رسـمی قابلیت دسترسـی آسـان و اسـتفاده موثری داشـته باشـد.

گروه اقتصاد کشاورزی، دانشگاه نیجریه، انسوکا 4 * ایمیل نویسنده مسئول: [email protected] مجله بین المللی مدیریت و توسعـه کشاورزی قابل دسترس در سایت www.ijamad.com شماره استاندارد بین المللی چـاپ: 2159-5852 شماره استاندارد بین المللی آنالین: 2251-5860

نظامهای بهرهبرداری دامی و جهت گیریهای تولید دامی در دشتهای شرقی الجزایر، نظام بهرهبرداری دامی در منطقه نیمهخشک الجزایر لوئیس سمارا 1، چارفدین موفوک 2* و توفیک مادانی 3

تاریخ دریافت: 21 اردیبهشت 1392 ایـن مطالعـه تالشـی بـود در جهت ایجـاد رویکردهـای بهره ور در بیـن گلههای تاریخ تایید: 26 شهریور 1392 دام در دشـتهای شـرقی الجزایـر بـه این منظـور 165 نفر از کشـاورزان به طور تصادفـی انتخـاب و مـورد مطالعـه قـرار گرفتنـد. انتخـاب پرورشدهنـدگان بـر اسـاس وجـود دام در مزرعـه بـود و کشـاورزان موردنظـر میباید حداقـل دو گاو در مزرعـه داشـته باشـند. رهیافـت بـه کار گرفتـه شـده در جهت شناسـایی همه نظامهـای بهرهبـرداری پذیرفتـه شـد بوسـیله کشـاورزان در منطقـه از طریـق چکیده تجزیـه و تحلیـل رابطـه بیـن نگهـداری انـواع مختلـف دامهـا و سیاسـتهای بازاریابـی ترجیحـی بـود. در نتیجـه نـوع شناسـی کارکـردی مـدل پژوهـش بـا اسـتفاده از روش آمـاری تحلیـل مولفههـای اصلی طبقـهای کدگـذاری بهینه در مجله بین المللی مدیریت و توسعه کشاورزی، سال سوم، شماره 4 ، ) 1392 ( spss بـه دسـت آمـد به دنبـال این رهیافـت پنج نـوع جهتگیری بهـرهور تولید دام شناسـایی شـد نظـام مختلـط متعـادل )گوشـتی - شـیری( ، نظـام مختلـط گاوهـای گوشـتی، نظـام مختلـط گاوهـای شـیری، سیسـتم شـیری و سیسـتم گوشـتی. نتایـج نشـان داد کـه پرورشدهنـدگان در کمتـر از 20درصـد مـوارد به تخصصـی شـدن تولیـد تمایـل داشـتند )شـیری یـا گوشـتی(. در حالی کـه نظام واژگــان کلیـــدی: دامـــداری، نوشنــــاسی، نظـــام بهرهبـــرداری، مختلـط گوشـتی بیشـترین توجـه را در منطقه به خـود اختصـاص داده بود )بیش دام و مدیریـــت از 50 درصـد کشـاورزان(.

1 دانشجوی دکتری تولید دام، گروه علوم دامی و کشاورزی، دانشگاه ستیف، الجزیره 2 استادیار گروه علوم دامی و کشاورزی، دانشگاه ستیف، الجزیره 3 استاد گروه علوم دامی و کشاورزی، دانشگاه ستیف، الجزیره 3 * ایمیل نویسنده مسئول: [email protected] مجله بین المللی مدیریت و توسعـه کشاورزی قابل دسترس در سایت www.ijamad.com شماره استاندارد بین المللی چـاپ: 2159-5852 شماره استاندارد بین المللی آنالین: 2251-5860

پارامترهــای آســیبپذیری ریســک میــان کشــاورزان گندمکـــار در شهرســتان مشــهد، ایــران

مجتبی سوختانلو، حسامالدین غالمی* و سید رضا اسحاقی

تاریخ دریافت: 27 اسفند 1391 شناسـایی و تحلیل آسـیبپذیری کشـاورزان با توجه به درجه ریسـکگریزی تاریخ تایید: 25 فروردین 1392 آنـان یکـی از اقدامـات ضـروری بـرای برنامهریـزی و کاهش اثرات خشکسـالی در ایـران محسـوب میشـود، آنچنانکـه بتوانـد سـازگاری کشـاورزان را جهـت مواجهـه بـا پیامدهـای خشکسـالی افزایـش دهـد. بنابرایـن، ایـن مطالعـه بـه بررسـی سـه پارامتـر آسـیبپذیری )اقتصـادی، اجتماعـی و فنـی( در میـان کشـاورزان گنـدمکار شهرسـتان مشـهد )ایـران( میپـردازد کـه بـا توجـه درجـه چکیده ریسکگریزیشـان گروهبنـدی شـدهاند و بـا خشکسـالی سـالهای 1386 تـا 1389 مواجـه بودنـد. پارامترهـای آسـیبپذیری از طریـق روش دلفـی تعییـن مجله بین المللی مدیریت و توسعه کشاورزی، سال سوم، شماره 4 ، ) 1392 ( شـد. بـرای اندازهگیـری میـزان آسـیبپذیری و درجه ریسـکگریزی، بـه ترتیب فرمـول Me-Bar & Valdes و روش قاعـده اول اطمینـان اسـتفاده شـد. یافتهها، نشـان داد که در شـاخصهای آسـیبپذیری اجتماعی؛ شـاخصهای سطح سواد، انجـام فعالیتهـای جمعـی در کشـت و وابسـتگی بـه دولـت و در شـاخصهای آسـیبپذیری فنـی؛ شـاخصهای روشهـای آبیاری، اسـتفاده از روش کشـت و نـوع کشـت، کشـاورزان ریسـکگریز باالتریـن سـطح آسـیبپذیری در شـرایط خشکسـالی را داشـتند. در حالیکـه، بـا توجـه بـه شـاخصهای آسـیبپذیری واژگــان کلیـــدی: اقتصـادی، کشـاورزان ریسـکخنثی )در بیمـه محصـوالت کشـاورزی، قیمـت خشکســـالی، کشـــاورزان گندمکــــار، فـروش محصـوالت و نـوع مالکیـت زمیـن(، باالتریـن سـطح آسـیبپذیری را آســـیبپذیری، درجـــه ریســـکگریزی داشتند.

گروه ترویج و آموزش کشاورزی، دانشگاه تهران، کرج، ایران 2 * ایمیل نویسنده مسئول: [email protected] مجله بین المللی مدیریت و توسعـه کشاورزی قابل دسترس در سایت www.ijamad.com شماره استاندارد بین المللی چـاپ: 2159-5852 شماره استاندارد بین المللی آنالین: 2251-5860

بررسـی یکپارچگـی بازار و انتقـال قیمت کیفیتهـای مختلف برنج ایران

امیر حسین چیذری1*، مسعود فهرستی ثانی 2 و محمد کاووسی کالشمی 3

تاریخ دریافت: 12 بهمن 1391 تولیـد برنـج در اکثـر کشـورهای آسـیایی )پیامـد اسـتفاده از واریتههـای مـدرن، تاریخ تایید: 18 مهر 1392 سیسـتمهای آبیـاری جدیـد، اسـتفاده از کـود و غیـره( سـریعتر از جمعیت رشـد کـرده اسـت. درنتیجـه عرضـه برنـج افزایـش یافتـه و متناسـب بـا آن قیمـت واقعـی برنـج در بـازار جهانـی و بـازار داخلـی کاهـش یافتـه اسـت. از طرفـی با رشـد تولیـد و درآمـد ناخالـص ملـی کشـور درآمد سـرانه افزایـش یافتـه و تقاضا بـرای برنـج بـا کیفیـت در سـطح ملـی و بینالمللـی افزایـش یافتـه اسـت. در چکیده صـورت وجـود کیفیـت پاییـن کـه عـالوه بـر نـوع واریتـه در نتیجـه مدیریـت نامناسـب پـس از برداشـت نیـز میتوانـد رخ دهـد سـبب میشـود کل اقتصـاد برنـج سـالیانه میـزان معنـی داری زیـان ببینـد. در این صـورت بررسـی وضعیت مجله بین المللی مدیریت و توسعه کشاورزی، سال سوم، شماره 4 ، ) 1392 ( بـازار کیفیتهـای مختلـف برنـج شـامل حاشـیههای بازاریابی، روابـط علی میان قیمتهـا، پیوسـتگی بازارهـا در بلندمـدت و ًنهایتـا انتقـال قیمـت و پیوسـتگی بـازار در کوتاهمـدت دسـتاوردی اسـت بـا اهمیـت کـه میتوانـد سیاسـتگذاران و برنامهریـزان در زمینـه تصمیمگیـری درخصـوص پژوهـش، تولیـد، توزیـع و بازاریابـی محصول اسـتراتژیک برنج یاری رسـاند. لذا با اسـتفاده از آمار سـازمان جهـاد کشـاورزی اسـتان گیـالن درخصـوص قیمـت کیفیتهـای )ارقـام( برنـج شـامل صـدری ممتـاز، صدری درجـه یک، صـدری معمولی و خزر طی سـالهای 2009-1999 بـه بررسـی وضعیت بـازار کیفیتهـای مختلف برنج پرداخته شـد. نتایـج حاکـی از ایـن اسـت کـه بـروز تکانههـا در قیمتهـای عمدهفروشـی در برنـج خـزر بهسـرعت بـر قیمتهـای سـرمزرعه تأثیر میگـذارد در حالـی که در سـایر کیفیتهـای برنـج ایـن تأثیـر با میـزان و سـرعت کمتری اسـت. در حالی واژگــان کلیـــدی: یکپارچگــی بــازار، کیفیــت برنــج، قیمــت کـه در بـازار عمـده - خـرده برنجهای بـا کیفیت صـدری بروز تکانههـا بر قیمت عمدهفروشــی، قیمــت خردهفروشــی و خـرده به شـدت بر قیمـت عمده تأثیر میگذارد و نشـاندهنده پیوسـتگی شـدید قیمــت ســرمزرعه ایـن دو بـازار در محصـول برنـج در ایران اسـت.

1 استادیار گروه اقتصاد کشاورزی، دانشکده اقتصاد و توسعه کشاورزی، دانشگاه تهران. 2 دانشجوی دکتری، گروه اقتصاد کشاورزی، دانشکده اقتصاد و توسعه کشاورزی، دانشگاه تهران 3 استادیار گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه گیالن. 1 * ایمیل نویسنده مسئول: [email protected] Aims and Scopes International Journal of Agricultural Management and Development (IJAMAD) is an international journal publishing Research Papers, Short Communications and Review on Agricultural Management and related area. The journal covers all related topics on agricultural management and development. Papers are welcome reporting studies in all aspects of agricultural management and development including: Decision-making Education and training Environmental policy and management Family and social enterprise Farm Management Farming Systems Farm Structures Rural tourism and recreation Strategic planning Information and communication technology in agriculture

www.ijamad.com This journal is published in cooperation with Iranian Association of Agricultural Economic

Published by: Islamic Azad University, Rasht Branch, Iran