The Impact of Small Scale Irrigation on Household Food Security ...

<strong>The</strong> <strong>Impact</strong> <strong>of</strong> <strong>Small</strong> <strong>Scale</strong> <strong>Irrigation</strong> on Household Food Security: <strong>The</strong> Case<strong>of</strong> Filtino and Godino <strong>Irrigation</strong> Schemes in Ada Liben District,East Shoa, EthiopiaAbonesh Tesfaye 1 , Ayalneh Bogale 2 , Regassa E. Namara 31 Ministry <strong>of</strong> Water Resources, 2 Haramaya University, 3 IWMI-Acraabuye_t@yahoo.comAbstractIrrigated production is far from satisfactory inthe country. <strong>The</strong> country's irrigation potential isestimated at 3.7 million hectare, <strong>of</strong> which onlyabout 190,000 hectare (4.3 percent <strong>of</strong> thepotential) is actually irrigated. <strong>The</strong> aim <strong>of</strong> thispaper is to identify the impact <strong>of</strong> small-scaleirrigation on household food security based ondata obtained from 200 farmers in Ada Libendistrict <strong>of</strong> Ethiopia. Different studies revealedthat access to reliable irrigation water can enablefarmers to adopt new technologies and intensifycultivation, leading to increased productivity,overall higher production, and greater returnsfrom farming. In the study area also about 70percent <strong>of</strong> the irrigation users are food securewhile only 20 percent <strong>of</strong> the non-users are foundto be food secure. Access to irrigation enabledthe sample households to grow crops more thanonce a year; to insure increased and stableproduction, income and consumption; andimprove their food security status. <strong>The</strong> studyconcludes that small-scale irrigation is one <strong>of</strong> theviable solutions to secure household food needsin the study area but it did not eliminate the foodinsecurity problem.1. Introduction1.1 BackgroundEthiopia is faced with complex poverty, which isbroad, deep and structural (MoFED, 2002).Despite the importance <strong>of</strong> agriculture in itseconomy, the country has been a food deficitcountry for several decades, with cereal food aidaveraging 14 percent <strong>of</strong> total cereal production(FAO, 2001). <strong>Irrigation</strong> is one means by whichagricultural production can be increased to meetthe growing food demand in Ethiopia(Awulachew et al., 2005). However, in Ethiopiairrigated production is far from satisfactory(Woldeab, 2003). While the country’s irrigationpotential is about 3.7 million hectares (WSDP,2002), the total irrigated area is 190,000 ha in2004, that is only 4.3 percent <strong>of</strong> the potential(FAO, 2005).It was claimed that Ethiopia can not assure foodsecurity for its population with rain fedagriculture alone without a substantivecontribution <strong>of</strong> irrigation. Thus, the government<strong>of</strong> Ethiopia has prepared a water sectordevelopment program to be implemented in 15years between 2002 and 2016. this programassigned a prominent role to the development <strong>of</strong>irrigation in the country for food production(mowr, 2001). this paper reports the results <strong>of</strong> astudy conducted to assess the efficacy <strong>of</strong>irrigation led food insecurity eradication andpoverty reduction policy objectives <strong>of</strong> ethiopiabased on data collected from godino and filtinosmall scale irrigation schemes found in ada libendistrict <strong>of</strong> the oromia regional state <strong>of</strong> ethiopia.1.2 <strong>Irrigation</strong> and Household Food Security:some empirical evidences179

Chamber (1994) based on some empiricalstudies confirms that reliable and adequateirrigation increases employment, i.e., Landlesslaborers as well as small and marginal farmershave more work on more days<strong>of</strong> the year, which ultimately contributes to foodsecurity. A study conducted in 10 Indian villagesin different agro-climatic regions shows thatincreasing irrigation by 40 percent was equallyeffective in reducing poverty (reducing foodinsecurity) as providing a pair <strong>of</strong> bullocks,increasing educational level and increasing wagerates (Singh et al., 1996). Kumar (2003) alsostated that irrigation has significantlycontributed to boosting India's food productionand creating grain surpluses used as droughtbuffer. A study by Hussain et al. (2004)confirms that access to reliable irrigation watercan enable farmers to adopt new technologiesand intensify cultivation, leading to increasedproductivity, overall higher production, andgreater returns from farming. This in turn opensup new employment opportunities; both on farmand <strong>of</strong>f-farm, and can improve incomes,livelihood, and the quality <strong>of</strong> life in rural areas.<strong>The</strong> same study identified five key dimensions<strong>of</strong> how access to good irrigation watercontributes to socioeconomic uplift <strong>of</strong> ruralcommunities. <strong>The</strong>se are production, income andconsumption, employment, food security, andother social impacts contributing to overallimproved welfare.According to a study carried out on fiveirrigation schemes in Zimbabwe, the schemeswere found to act as sources <strong>of</strong> food security forthe participants and the surrounding communitythrough increased productivity, stableproduction and incomes (Mudima, 1998). <strong>The</strong>same study reported that farmers participating inirrigation schemes never run out <strong>of</strong> food unliketheir counterparts that depend on rain-fedagriculture.Ngigi (2002) disclosed that in Kenya for the twodecades agricultural production has not beenable to keep pace with the increasing population.To address this challenge the biggest potentialfor increasing agricultural production lies in thedevelopment <strong>of</strong> irrigation. According to thesame study, irrigation can assist in agriculturaldiversification, enhance food self sufficiency,increase rural incomes, generate foreignexchange and provide employment opportunitywhen and where water is a constraint. Nigigiconcluded that the major contributions <strong>of</strong>irrigation to the national economy are foodsecurity, employment creation, and improvedforeign exchange earning.A study by IFAD (2005) states that in Ethiopia,the construction <strong>of</strong> small-scale irrigationschemes has resulted in increased production,income and diet diversification in the Oromiaand Southern Nation and Nationalities People(SNNP) regions. According to this study, thecash generated from selling vegetables and otherproduce is commonly used to buy food to coverthe household food demand during the fooddeficit months. <strong>The</strong> same study further addedthat during an interview conducted with somefarmers, it was disclosed that the hungry monthsreduced from 6 to 2 months (July and August)because <strong>of</strong> the use <strong>of</strong> small scale irrigation.Moreover, the increase in diversity <strong>of</strong> cropsacross the schemes and the shift from cereallivestocksystem to cereal-vegetable-livestocksystem is starting to improve the diversity <strong>of</strong>household nutrition through making vegetablespart <strong>of</strong> the daily diet. A study conducted byWoldeab (2003) also identified that in Tigrayregion irrigated agriculture has benefited somehouseholds by providing an opportunity toincrease agricultural production through doublecropping and by taking advantage <strong>of</strong> moderntechnologies and high yielding crops that calledfor intensive farming.However, these studies were descriptive thananalytical in that they did not formally accountfor/ isolate the possible contribution <strong>of</strong> otherconfounding variables such hashousehold/village characteristics, and otherpolicies and interventions that might have aswell contributed to the food security statusdifferences between irrigators and non-irrigators.Moreover, the empirical works in this area arevery scant in Ethiopia in particular and in Africain general. Thus, the study aims to contribute tothe small scale irrigation-food security literatureand to provide policy conclusions and180

implications for future planning <strong>of</strong> irrigationsystems.2. Research methodology2.1. Study area, sample size and samplingtechniquesgodino and filtino small scale irrigation schemesare found in ada liben district and wereconstructed by oromiya irrigation developmentauthority (oida) in 1996 and 1998, respectively(oida, 2000 ). the water source for godinoirrigation scheme is wedecha dam, which has thecapacity to irrigate about 310 ha. while the watersource for filtino irrigation scheme is belbeladam, which has a capacity <strong>of</strong> irrigating 100 ha.the irrigable land in the respective commandareas is distributed to farmers by thegovernment. except few farmers who lease-inadditional irrigable land almost all farmers in thearea own quarter <strong>of</strong> a hectare. the major types <strong>of</strong>crops grown by irrigation are onion, tomato,potato and chick pea among others.Out <strong>of</strong> the 45 Peasant Associations (PA) that arefound in the Ada Liben district, two PAs namelyGodino and Quftu were purposely selectedmainly because <strong>of</strong> availability <strong>of</strong> irrigationschemes. To select sample respondents from thetwo PAs, first the household heads in the twoPAs were identified and stratified in to twostrata: irrigation users and non-users. <strong>The</strong>n thesample respondents from each stratum wereselected randomly using simple randomsampling technique. Since the number <strong>of</strong>household heads in the two groups wasproportional, equal number <strong>of</strong> sample is drawnfrom each group, i.e., 100 household heads wereselected from each group. In total 200household heads were interviewed.2.2. Data collection<strong>The</strong> data required for this study was collectedfrom sample respondents using a semi-structuredquestionnaire. <strong>The</strong> enumerators for the datacollection were selected on the basis <strong>of</strong> theireducational background and their ability <strong>of</strong> thelocal language. One week training was given tothe enumerators about method <strong>of</strong> data collectionand the contents <strong>of</strong> the questionnaire. Datacollection proper was started after pretest wasconducted and modifications were made basedon the feedback from the pretest. Secondaryinformation that could supplement the primarydata was collected from published andunpublished documents obtained from differentgovernmental and non-governmentalorganizations.2.3. Method <strong>of</strong> data analysis<strong>The</strong> study employed both descriptive andeconometric techniques. <strong>The</strong> descriptive analysiswas performed using frequencies, means, andmaximum and minimum values. <strong>The</strong>econometric analysis employed the Heckmantwo-step procedure to identify the impact <strong>of</strong>small scale irrigation on household food securityfrom among possible other household foodsecurity influencing factors.Heckman two-step procedure: Evaluating theimpact <strong>of</strong> a project/program on an outcomevariable using regression analysis can lead tobiased estimate if the underlying process whichgoverns selection into a project/ program is notincorporated in the empirical framework. <strong>The</strong>reason for this is that, the effect <strong>of</strong> the programmay be over (under) estimated if programparticipants are more (less) able due to certainunobservable characteristics, to derive thesebenefits compared to eligible non-participants(Zaman, 2001).To evaluate the impact <strong>of</strong> a program, a modelcommonly employed can be expressed as:Y = Xβ + αI+ u(1)Where Y is the outcome/impact, X is a vector <strong>of</strong>personal exogenous characteristics and I is adummy variable (I=1, if the individualparticipates in the program and 0 otherwise).From this model, the effect <strong>of</strong> the program ismeasured by the estimate <strong>of</strong> α . However, thedummy variable ‘I’ can not be treated asexogenous if the likelihood <strong>of</strong> an individual toparticipate or not to participate in the program is181

ased on an unobserved selection process(Maddala, 1983). Some studies have shown thelimitations <strong>of</strong> applying the classical linearregression methodology to the analysis <strong>of</strong>samples with selectivity bias (Heckman, 1979,Dardis et al. 1994, Sigelman and Zeng, 1999,Maddala, 1992). Application <strong>of</strong> the classicallinear regression model does not guaranteeconsistent and unbiased estimates <strong>of</strong> theparameter. One solution to this problem ineconometrics is the application <strong>of</strong> Heckmantwo-step procedures. It is considered as anappropriate tool to test and control for sampleselection biases (Wooldrige, 2002).<strong>The</strong> Heckman two step procedures involves twoequations. <strong>The</strong> first equation (i.e., the selectionor participation equation) attempts to capture thefactors governing membership in a program.This equation is used to construct a selectivityterm known as the ‘Mills ratio’ which isincluded as independent variable to the secondequation known as response or outcomeequation. If the coefficient <strong>of</strong> the ‘selectivity’term is significant then the hypothesis that theparticipation equation is governed by anunobserved selection process or selectivity biasis confirmed. Moreover, with the inclusion <strong>of</strong>extra term, the coefficient in the second stage‘selectivity corrected’ equation is unbiased(Zaman, 2001). <strong>The</strong>refore, to evaluate theimpact <strong>of</strong> small scale irrigation on householdfood security, we use the Heckman two-stepprocedure.Specification <strong>of</strong> the Heckman two-stepprocedure:Let Zikbe a group <strong>of</strong> K variables whichrepresent the characteristics <strong>of</strong> a household iwhich influences the probability <strong>of</strong> participationin irrigation agriculture measured by a latent*variable Diandγ kare the coefficients whichreflect the effect <strong>of</strong> these variables on theprobability <strong>of</strong> being an irrigation farmer, andXisis a group <strong>of</strong> variables which represent thecharacteristics <strong>of</strong> household i which determinehousehold’s food security ( Ci) and β sare thecoefficients which reflect the effect <strong>of</strong> thesevariables on household food security. Thus, theHeckman two-step procedure takes thefollowing form:K*Di= ∑ γkZik+ ui(2)CiSS =1Ks= 1= ∑ β X + ε Observed only ifisiD * i>0...(3)Where the disturbances u iand εifollow abivariate normal distribution with a zero mean,variance σuand σεrespectively, andcovarianceσ εu. <strong>The</strong>refore, we define adichotomous variable Diwhich takes a value 1when a household is an irrigator and 0otherwise. <strong>The</strong> estimator is based on theconditional expectation <strong>of</strong> the observed variable,household food security (C i) :E*( C / D > 0) = xβ+ σ σ λ( − γz)iiεuε(4)Where λ is the inverse Mills ratio defined asλ( − γZ) = φ( − γZ)/( 1−ϕ( − γZ)); β and γ arethe vectors <strong>of</strong> parameters which measure theeffect <strong>of</strong> variables X and Z, φ and ϕ are thefunctions <strong>of</strong> density and distribution <strong>of</strong> a normal,respectively. <strong>The</strong> expression <strong>of</strong> conditionalexpectation shows thatC iequals xβonly whenthe errors εiand uiare non correlated, i.e.,σεu= 0 ; otherwise, the expectation <strong>of</strong> Ciisaffected by the variable <strong>of</strong> equation 2. Thus,from expression 4 we find that:*(i/ Di> 0) + Vi= xβ+ σu εσελ( − Z) ViCi/ Di> 0=EC/ γ + (5)WhereViis the distributed error term,( 0 , σ ( 1−σ( λ( λ − ))))NuγZε3. Results and discussion3.1. Descriptive Resultsε182

<strong>The</strong> variables included in the model are definedin table 1. <strong>The</strong> dependent variable for the firststage <strong>of</strong> the Heckman two-step procedure isparticipation in irrigation. This variable is adummy variable (given a value <strong>of</strong> 1 if thehousehold participates in the irrigation schemeand 0 otherwise) for the second stage <strong>of</strong> themodel household food security status is acontinuous variable measured by the annualfood expenditure in Birr <strong>of</strong> the household peradult equivalent. Before discussing theeconometric results, however, we present someinteresting descriptive results.One <strong>of</strong> the pervasive features <strong>of</strong> food insecurityin Ethiopia is that it is usually seasonal. Itmainly coincides with the active agriculturalseason or wet season. To this effect we havetried to see if there is discernable difference inthe timing <strong>of</strong> food inadequacy between irrigatorsand non-irrigators. Surprisingly, there is nodifference regarding the timing <strong>of</strong> foodshortages between irrigators and non-irrigators(See Figure 1). <strong>The</strong> food shortage months startas early as June (which is the beginning rainyseason and therefore agricultural activities inthe study areas) and extends up to November(which is the beginning <strong>of</strong> harvest season). Nohousehold from the irrigators group has reportedfood shortage in June. September is the mostserious food shortage month among nonirrigators,while October is the peak foodshortage month for irrigators. About half <strong>of</strong> thenon-irrigators reported food shortage in themonth <strong>of</strong> September. However, there is a starkdifference regarding the incidence rate <strong>of</strong>reported food shortage between the two groups.<strong>The</strong> proportion <strong>of</strong> farmers reporting foodshortage in every month is significantly lowerfor irrigators group. It is interesting to note thatirrigation has not eradicated the food insecurityproblem even in this seemingly better <strong>of</strong>f part <strong>of</strong>the country indicating the depth <strong>of</strong> the problem.% <strong>of</strong> households reporting food shortage6050403020100JuneJulyAugustSeptemberOctoberNovemberMonths <strong>of</strong> food shortageIrrigatorsNon-irrigatorsFigure 1. Incidence <strong>of</strong> reported food shortage bymonths<strong>The</strong> irrigators and non-irrigators have slightlydifferent copping mechanisms in the advent <strong>of</strong>food deficit problem (See figure 2). None <strong>of</strong> theirrigators have reported <strong>of</strong>f-farm employment asa coping strategy and also relatively fewerirrigators reported to have used credit as a means<strong>of</strong> copping with food shortage. It must be notedthat using wage employment and consumptioncredit as a strategy to avert food insecurity isconsidered as a distress measure or strategy inEthiopia. <strong>Small</strong> animals (such as sheep, goatsand chicken) is the most important coppingstrategy among both irrigators and nonirrigators.183

% <strong>of</strong> sample farmers reporting50454035302520151050Sales <strong>of</strong>CattleSales <strong>of</strong> <strong>Small</strong>AnimalsOff-farmEmploymentFood shortage copping strategyConsumptionCreditIrrigatorsNon-IrrigatorsFigure 2. Food shortage coping mechanismsBased on how households adapt to the presenceor threat <strong>of</strong> food shortages, the overall CopingStrategy Index (CSI) has been calculated foreach <strong>of</strong> the sample households and the resultingvalues were averaged for irrigators and nonirrigators.It was found that the average CSI forirrigator households is 11.4, while for nonirrigatorsthe corresponding value is 31.4. <strong>The</strong>mean difference is statistically significant (Table2). <strong>The</strong> higher the CSI, the more food-insecureis a household (reference). <strong>The</strong>refore, based onCSI the non-irrigator households are more foodinsecure as compared to irrigator households.<strong>The</strong> calculated food consumption expenditureper adult equivalent values also confirms thefood security status difference between irrigatorsand non-irrigators (table 2). <strong>The</strong> average foodconsumption expenditure per adult equivalentper annum for irrigation user households is1322.4 Birr, while the corresponding figure fornon-users is 774.4 Birr. <strong>The</strong> mean difference isstatistically significant. Moreover, the totalconsumption expenditure (both food and nonfood)for irrigators is almost double that <strong>of</strong> nonirrigators.<strong>The</strong> minimum food consumption expenditureper adult equivalent above which a household isconsidered to be food secure (alternativelybelow which a household is considered as foodinsecure) was calculated based on the estimatedcost <strong>of</strong> acquiring the recommended daily calorieallowance, which was taken as 2200 kcal peradult equivalent per day 15 . This cut-<strong>of</strong>f value isestimated to be Birr 900.0 per adult equivalentper annum. Thus, households having foodconsumption expenditure per adult equivalent <strong>of</strong>less than Birr 900 are considered as foodinsecure, while those earning more than Birr 900are considered to be food secure. Based on thisindicator, again there is substantial difference infood insecurity incidence rate between irrigatorand non-irrigators households (see figure 3).Generally out <strong>of</strong> the 200 sample households 45percent <strong>of</strong> them are food secure and 55 percent<strong>of</strong> them are food insecure.15 This cut-<strong>of</strong>f value was calculated following Greer andThorbecke (1986) food energy intake method <strong>of</strong> measuringhousehold food security184

% <strong>of</strong> the households9080706050403020100Food SecureFoodInsecureHousehold food securitystatusIrrigatorsNon-IrrigatorsFigure 3. Household food security status differentiated by access to irrigationWhen comparing other indicators <strong>of</strong> welfarebetween irrigation and non-irrigators,statistically significant differences were detected(Table 3). For example, irrigators have smallhousehold size, higher level <strong>of</strong> education, largelivestock holding size, and better quality(fertility) cultivable land. <strong>The</strong> irrigators had alsobetter access to extension and credit services(Table 4). In conclusion, the descriptiveanalyses indicate that irrigators are better <strong>of</strong>f interms <strong>of</strong> food security status and other welfareindicators. But is this due solely to access toirrigation? Other observable and unobservablevariables might have contributed to the observedfood security status difference between irrigatorsand non-irrigators. <strong>The</strong>refore, we know turn tothe presentation <strong>of</strong> Heckman’s two stageregression model to show the impact <strong>of</strong> access toirrigation on food security while controlling forthe effects <strong>of</strong> other observable and unobservableconfounding factors.3.2. Econometric Analysis ResultsDeterminants <strong>of</strong> likelihood <strong>of</strong> access toirrigation: <strong>The</strong> first stage <strong>of</strong> the Heckman modelpredicts the probability <strong>of</strong> access to the irrigationscheme <strong>of</strong> a household. Among the observablehypothesized variables, those that significantlyinfluenced the probability <strong>of</strong> participating inirrigation farming include nearness to the watersource, household siz size <strong>of</strong> cultivated land,livestock holding, the quality <strong>of</strong> land owned by afarmer and access to credit (Table 5). <strong>The</strong>relationship between household size andparticipation in irrigation project is non-linear.As the size <strong>of</strong> a household increases by one adultequivalent, the probability <strong>of</strong> access to irrigationdecreases by 30.4% but only up certain pointbeyond which a unit increase in household sizestarts increasing the likelihood <strong>of</strong> participationin irrigation. As the size <strong>of</strong> cultivated areaincreases the probability <strong>of</strong> being an irrigatordecreases. This may imply that irrigators tendintensify their cultivated land, while rain-fedfarmers try to put more land under cultivation.185

Irrigators have significantly more livestock thantheir rain-fed only farmers. <strong>The</strong>y also possessmore fertile land.Determinants <strong>of</strong> household food security: <strong>The</strong>significance <strong>of</strong> the lambda term in the secondstage <strong>of</strong> the Heckman procedure, confirms thepresence <strong>of</strong> selectivity bias (Table 6). Asexpected, access to irrigation had significantimpact on household food security. In the studyarea irrigation enable households to grow cropsmore than once a year, to insure increased andstable production, income and consumptionthereby improving food security status <strong>of</strong> thehousehold. This result is consistent with thefinding <strong>of</strong> Abebaw (2003). <strong>The</strong> other variablesthat significantly enhance household foodsecurity are experience (as indicate by farmersage in years), access to extension service, andsize <strong>of</strong> cultivated land. .<strong>The</strong> relationship between household size andfood security is non-linear (see the coefficientsfor household size and its square variable). <strong>The</strong>negative and significant coefficient <strong>of</strong> householdsize reveals that larger household size leads t<strong>of</strong>ood insecurity, but only up to a certain point.<strong>The</strong> coefficient <strong>of</strong> the variable indicates that asthe household size increases by one adultequivalent the food consumption expenditure <strong>of</strong>the household decreases by 391.9 Birr. Thisresult is consistent with the finding <strong>of</strong> Mulugeta(2002) and Yilma (2005). Contrary to othersimilar studies (Belayneh, 2005), in this studyfemale headed households had better foodsecurity status than the male headed households.<strong>The</strong> coefficient <strong>of</strong> the variable shows that whenthe head <strong>of</strong> the household is male, foodconsumption expenditure <strong>of</strong> the householddecreases by 331.1 Birr. <strong>The</strong> possiblejustification for this inverse relationship couldbe that though male headed households are in abetter position to pool resource to increaseproduction, they might spent more money onnonfood expenses rather than spending on fooditems to meet the household’s food needs.<strong>The</strong> regression result also shows that as thecultivated land size increases, a household isable to increase and diversify the quantity andtype <strong>of</strong> crop produced, which may in turn lead toincreased consumption and household foodsecurity. <strong>The</strong> coefficient <strong>of</strong> the land size variableshows that as the household gets one morehectare <strong>of</strong> land food consumption expenditure <strong>of</strong>the household increases by 85 Birr. This result isconsistent with the findings <strong>of</strong> Mulugeta (2002),Ayalew (2003), Abebaw (2003) and Yilma(2005).Access to extension service and nearness to thewater source are also found to have a positiverelationship with household food security. <strong>The</strong>positive effect <strong>of</strong> access to extension servicemay indicate that in the study area, thosehouseholds who get technical advice andtraining or those who participated in fielddemonstrations are well aware <strong>of</strong> the advantage<strong>of</strong> agricultural technologies and adopt newtechnologies and produce more, therebyimproving the household food security status.<strong>The</strong> nearness to the water source may be asurrogate variable for access to irrigation. It hasalready been shown that to the irrigationscheme, significantly improves household’s foodsecurity status. <strong>The</strong> possible other justificationcould be that the nearness to water source mayproxy the location <strong>of</strong> the farms in relation to theirrigation water source . <strong>The</strong>refore, householdswho are closer to the irrigation scheme do notincur much cost to access their farm so they canfollow up the farm activity closely andfrequently and may get a better yield.4. Conclusion and Implications<strong>The</strong> variables that significantly predict access toirrigation are: household size, size <strong>of</strong> cultivatedland, livestock holding, farmers’ perception <strong>of</strong>soil fertility status, access to credit, nearness tothe water source and household size square. <strong>The</strong>variables that reduce the probability <strong>of</strong> access toirrigation are large household size, largecultivated area and access to credit. Rain-fedfarmers tend to have large cultivated area. <strong>The</strong>negative relationship between access to creditand access to irrigation may be explained by thefact that: (1) in Ethiopia, the institutional creditsusually give priority to rain-fed agriculture, and(2) the demand for credit among farmers withaccess to irrigation may be lower for they cansatisfy cash needs through sales from theirirrigated crops.186

<strong>The</strong> variables that increase the probability <strong>of</strong>participation <strong>of</strong> farmers in irrigation farminginclude large livestock holding size, ownership<strong>of</strong> relatively fertile land and nearness to watersource. Obviously, those households that aresituated near the water source are more likely toparticipate in irrigation scheme. However, itdoes not mean that placement <strong>of</strong> an irrigationscheme in the village is solely governed byhydrological considerations. It involves politicalprocess and power relations.In the study area the use <strong>of</strong> small-scale irrigationcontributes significantly to improve householdfood security. In addition to access to irrigation,access to irrigation, household size, sex <strong>of</strong> thehousehold head, size <strong>of</strong> cultivated land, andaccess to extension service significantlyinfluence the food security status <strong>of</strong> a farmhousehold.<strong>The</strong> relationship between a household foodsecurity status and household size is non-linear(see the signs for the variables household sizeand the square <strong>of</strong> household size). As the size <strong>of</strong>a household increases the per capita foodexpenditure decreases, but up to a point, afterwhich the per capita food expenditure starts toincrease as the household size increases.Contrary to expectation, female headedhouseholds are less likely to be food insecure ascompared to male headed households. Thisneeds further investigation, however, tentativelyit may be explained by differences in theexpenditure behavior <strong>of</strong> male and femalefarmers-female members <strong>of</strong> a farm householdtend to spend more on food items to guaranteethe food needs <strong>of</strong> the family before anythingelse. Another possible explanation may be thatthe male members <strong>of</strong> a female-headed householdmay have gainful employment elsewhere thuscontributing to household food security.Size <strong>of</strong> cultivated land and household foodsecurity are positively related indicating largerfarm size improves household food security.Households with large farm size are found to befood secure; however, there may not be apossibility <strong>of</strong> expanding cultivated land size anymore because <strong>of</strong> increasing family size anddegradation <strong>of</strong> the existing farm land. <strong>The</strong>refore,household must be trained as to how to increaseproduction per unit area (productivity).Access to extension service is also positivelyrelated to household food security. Extensionworkers could play a key role in transferringknowledge to the rural people easily there byimproving production and consumption.Capacity building <strong>of</strong> the existing ones andtraining more extension workers might helpaddress the issue.ReferenceAbebaw, Shimeles. 2003. Dimensions anddeterminants <strong>of</strong> food security among ruralhouseholds in Dire Dawa, Eastern Ethiopia. AnMSc <strong>The</strong>sis Presented to the School <strong>of</strong> GraduateStudies <strong>of</strong> Alemaya University. 152p.Awulachew, B., Merrey, J., Kamara, B., VanKoppen, B., Penning de Vries, F., andBoelee, E. 2005. Experiences andopportunities for promoting small scale microirrigation and rain water harvesting for foodsecurity in Ethiopia. Working paper 98.IWMI (International Water ManagementInstitute) Addis Ababa, Ethiopia.Belayneh Belete. 2005. Analysis <strong>of</strong> foodinsecurity causes: the case <strong>of</strong> rural farmhouseholds in Metta woreda, easternEthiopia. An MSc <strong>The</strong>sis Presented to theSchool <strong>of</strong> Graduate Studies <strong>of</strong> AlemayaUniversity. 130pChamber, R. 1994. <strong>Irrigation</strong> against RuralPoverty. pp. 32-33. In Socio- EconomicDimension and <strong>Irrigation</strong>, ed., R.K. Gujar.Jaipur. India: Printwell.Cho, S., D. Newman and J. Bowker. 2005.Measuring rural homeowners’ willingness topay for land conservation easements. Forestpolicy economics 7:757-770CBE (Commercial Bank <strong>of</strong> Ethiopia). 2006.International Banking Division.Dardis, H., Soberon and D. Patro. 1994.Analysis <strong>of</strong> leisure expenditure in the United187

States. <strong>The</strong> Proceeding <strong>of</strong> the AmericanCouncil on Consumer Interest 39,194-200.FAO (Food and Agricultural Organization <strong>of</strong> theUnited Nations), 2001. <strong>The</strong> state <strong>of</strong> food andagriculture. pp. 18-20. World Review Part I.Rome. Italy.FAO (Food and Agricultural Organization <strong>of</strong> theUnited Nations).2005. Global informationand early warning system on food andagriculture. World food program. Specialreport. Italy, Rome.Green, W. 2003. Econometric Analysis. 5 th ed.Prentice Hall. Inc, New York.Heckman, J., 1979. Sample selection bias as aspecification error. Econometrica. 47(1):153-162.Hussain, I., R.E., Namara, and S. Madar. 2004.Water for food security for the poor. Acollection <strong>of</strong> thematic papers. AsianDevelopment Bank. Colombo, Sri Lanka.IFAD (International Fund for AgriculturalDevelopment). 2005. Special countryprogram phase II (SCP II) interim evaluationreport number 1643-ET. Ethiopia.Kumar, D. 2003. Food security and sustainableagriculture in India. pp. 1-2. <strong>The</strong> watermanagement challenge. Working paper 60.International Water Management Institute.Colombo, Sri Lanka.Madalla, G.S. 1983. Limited Dependent andQualitative Variables in Econometrics.Cambridge University Press. UnitedKingdom.Madalla, G.S. 1992. Introduction toEconometrics. pp. 341. 2 nd ed. CambridgeUniversity Press. United Kingdom.MoFED (Ministry <strong>of</strong> Finance and EconomicDevelopment).2002.Sustainable developmentand poverty reduction program. pp. 1-87.Addis Ababa, Ethiopia.MoWR (Ministry <strong>of</strong> Water Resources).2001.<strong>Irrigation</strong> development strategy (Component<strong>of</strong> the water sector development program).Draft report. Ethiopia, Addis Ababa.Mudima, K.1998. Socio economic impact <strong>of</strong>smallholder irrigation development inZimbabwe: A case study <strong>of</strong> five successfulirrigation schemes. Private irrigation in SubSaharan Africa. International WaterManagement Institute. Colombo, Sri Lanka.Mulugeta Tefera. 2002. Determinants <strong>of</strong>household food security in Eastern Oromiya,Ethiopia: <strong>The</strong> case <strong>of</strong> Boke District <strong>of</strong>Western Hararge Zone. An MSc <strong>The</strong>sisPresented to the School <strong>of</strong> Graduate Studies<strong>of</strong> Alemaya University. 151p.Ngigi, S.2002. Review <strong>of</strong> irrigation developmentin Kenya. pp. 35-37. International WaterManagement Institute. Colombo, Sri Lanka.Reilly, B. 1990. Occupational endogeneity andgender wage differentials for young workers:An empirical analysis using Irish data. <strong>The</strong>economic and social review. 21(3): 311-328Singh, B., B. N. Singh, and A. Singh. 1996.Effect <strong>of</strong> mulch and irrigation on yield <strong>of</strong>Indian mustard on dry terraces in Alfisols.Indian Journal <strong>of</strong> Agricultural Science 60(7):477-479Sigelman, L., and L. Zeng. 1999. Analyzingcensored and sample selected data with Tobitand Heckit models. Political analysis 8:167-182Teshome, W. 2003. <strong>Irrigation</strong> practices, stateintervention and farmers Life-Worlds indrought-prone Tigray. pp. 2-53. PhdDissertation, Wageningen University, theNetherlands.Wooldridge, M. 2002. Econometric Analysis <strong>of</strong>Cross Section and Panel Data. pp. 551-565.Massachusetts Institute <strong>of</strong> Technology.London, England.188

WSDP (Water sector Development Program).2002. Water sector development program2002-2016, Volume II: Main report. AddisAbaba, Ethiopia.Yilma Muluken. 2005. Measuring ruralhousehold food security status and itsdeterminants in the Benishangul GumuzeRegion: <strong>The</strong> case <strong>of</strong> Asosa Woreda. An MSc<strong>The</strong>sis Presented to the School <strong>of</strong> GraduateStudies <strong>of</strong> Alemaya University. pp. 134.Zaman, H. 2001. Assessing the poverty andvulnerability impact <strong>of</strong> micro credit inBangladesh. pp. 34-36. A case study <strong>of</strong>BRAC. Office <strong>of</strong> the chief economist andsenior vice president (DECVP). <strong>The</strong> WorldBank.Table 1. Definition <strong>of</strong> model variablesVariablecodeVariabletype Variable definition Mean Std.ExpectedsignACCIRRG Dummy Access to irrigation <strong>of</strong> the household PositiveHEADAGE Continuous Age <strong>of</strong> household head in years 48.0 13.5 PositiveHEADAGE2 Continuous Age <strong>of</strong> the household head square PositiveHHSIZEAE Continuous Household size in adult equivalent 4.7 1.7 NegativeHHSIZEAE2 Continuous Household size in adult equivalent square PositiveEDUCATA Category Education <strong>of</strong> the household head /illiterate,read and write, grade 1-4, grade 5-8 and grade>8/PositiveSEXHEAD Dummy Sex <strong>of</strong> the household head (1=male, 0=female) PositiveCUTLAND Continuous Cultivated land size in hectare 1.5 1.2 PositiveLIVESTOC Continuous Total livestock holding in TLU 6.7 4.2 PositiveDISMARKE Continuous Distance from the market place in km 6.7 2.1 NegativeSOILFERT Dummy Farmers’ perception <strong>of</strong> soil fertility status (1=Positivefertile, 0= infertile)SUPPEX Dummy Access to extension service (1= access, 0=noPositiveaccess)CREDIT Dummy Access to credit (1=access, 0=no access) PositiveNEARNESS Continuous Nearness <strong>of</strong> households to water source in km 13.0 9.7 PositiveTable 2. Comparison <strong>of</strong> consumption expenditure per adult equivalent between irrigators andnon-irrigatorsUserNonuserMean Std Mean StdMD t - valueFood consumptionexpenditure 1322.3 563.4 774.4 369.7 547.8 8.0 ***Total expenditure 1,780.3 946.4 955.6 434.5 824.7 7.9 ***Coping strategy index 11.4 13.9 31.4 16.1 19.93 9.1 ***Source: survey result (2006)*** indicates significance level at 1 percent.189

Table 3. Summary <strong>of</strong> descriptive statistics <strong>of</strong> sample households by access to irrigation/continuous variables/UserNonuserMean Std Mean StdMD t - valueHEADAGE 46.8 14.4 49.5 12.5 2.7 1.4HHSIZEAE 4.3 1.7 5.1 1.8 0.7 3.0 ***DEPRATIO 0.4 0.1 0.5 0.1 0.0 3.1 ***CUTLAND 1.5 1.5 1.4 0.7 0.1 0.9LIVESTOC 7.3 3.4 5.0 2.6 2.2 3.6 ***TOTPRODUC 13,689.1 21,706.8 2,255.4 3,487.0 11,433.7 5.2 ***TOTEXPEN 1,780.3 946.4 955.6 434.5 824.7 7.9 ***DISMARKE 7.3 2.2 6.1 1.9 1.2 4.0 ***Source: Survey result (2006)*** indicates significance level at 1 percent.Table 4. Summary <strong>of</strong> descriptive statistics <strong>of</strong> sample households by access to irrigation/discrete variables/Variable User Nonuser Total χ2EDUCATAGORY 0.007***Illiterate 69 58 127Read and write 1 13 14Grade 1-4 3 7 10Grade 5-8 15 15 30Grade >8 12 7 19SEXHEAD 0.6Female 7 9 16Male 93 91 184SUPPEX 0.002***Access to extension 67 45 112No access to extension 33 55 88CREDIT 0.01***Access to credit 31 48 79No access to credit 69 52 121SOILFERT 0.001***Fertile 93 67 160Infertile 7 33 40Source: Survey result (2006)*** indicates significance level at 1 percent.190

Table 5. Estimation result <strong>of</strong> the Binary Probit model and its Marginal EffectVariable Coefficient Marginal effectCONSTANT 2.634(0.203)1.050(0.203)AGEHEAD -0.861(0.248)-0.343(0.248)HHSIZEAE -0.764(0.021) ** -0.304(0.021)SEXHEAD 0.414(0.438)0.165(0.438)EDUCATAGORY -0.293(0.764)-0.117(0.764)DISMARKE -0.324(0.673)-0.129(0.673)CUTLAND -0.604(0.004) *** -0.241(0.004)LIVESTOC 0.362(0.000) *** 0.144(0.000)SOILFERT 0.838(0.019) *** 0.334(0.019)SUPPEX -0.427(0.169)-0.170(0.169)CREDIT -0.615(0.024) ** -0.245(0.024)NEARNESS 0.403(0.008) *** 0.160(0.008)AGEHEAD2 0.722(0.302)0.288(0.302)HHSIZEAE2 0.687(0.034) ** 0.274(0.034)Dependent variableAccess to irrigationWeighting variableOneNumber <strong>of</strong> Observations 193Logliklihood function -69.13Restricted log likelihood -133.65Chi squared 129.03Degree <strong>of</strong> freedom 13Significance level 0.00Source: Model out put (2006)*** and** are level <strong>of</strong> significance at 1 percent and 5 percent respectivelyValues in parenthesis are p values191

Table 6. Estimation Result <strong>of</strong> the Selection Equation and its Marginal EffectVariable Coefficient Marginal effect1553.936576.882-331.133CONSTANT 1553.936(0.000) *** (0.000) ***ACCIRRIG 576.882(0.000) *** (0.000) ***AGEHEAD 14.918(0.348)14.918(0.348)HHSIZEAE -391.676(0.000) *** -391.676(0.000) ***SEXHEAD -331.133(0.001) *** (0.001) ***EDUCATAGORY 1.736(0.930)1.736(0.930)DISMARKE 13.567(0.378)13.567(0.378)CUTLAND 85.751(0.058) * 85.751(0.058) *LIVESTOC -5.063(0.717)-5.063(0.717)SOILFERT -47.613(0.534)-47.613(0.534)SUPPEX 117.729(0.069) * 117.729(0.069) *CREDIT -44.539(0.429)-44.539(0.429)NEARNESS 9.602(0.009) *** 9.602(0.009) ***AGEHEAD2 -0.112(0.441)-0.112(0.441)HHSIZEAE2 25.607(0.001) *** 25.607(0.001) ***LAMBDA -243.448(0.041) **Dependent variableNumber <strong>of</strong> Observations 193Selection rule is: User =1Log-L = -1395.69Restricted (b=0) Log -L = -1489.70R-squared = 0.58Correlation <strong>of</strong> disturbance inregression and selection criteria(Rho)Total food (Total food expenditureper adult equivalent per annum)-0.67Prob value = 0.00Source: model out put (2006)*** ** and * show level <strong>of</strong> significance at 1percent, 5 percent and 10 percent probability level.Values in parenthesis are p values192