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38 J Agric Biodivers Res

In Nigeria, the bulk of vegetables are produced underfadama by small holders who cultivate small plots anduse low input technology. In Nigeria, the term fadama isan Hausa name for irrigable land - usually low-lyingplains underlaid by shallow aquifers found along majorriver systems. But fadama lands are under intense and

high demand for competing land uses resulting fromhaphazard use, intensification of agricultural activities byfarmers, pastoralists, fisher folks and others [2]. Underthese conditions, the resource base of the fadama landsdeteriorates with eventual negative effect on vegetableyield. Therefore, vegetable production could be seriouslythreatened by climate change. Climate change affectsagriculture in several ways. It has a direct impact on foodproduction and agricultural productivity [3-5]. Potentialimpacts of climate change on agricultural production willdepend not only on climate per se, but also on theinternal dynamics of agricultural systems, including theirability to adapt to the changes [6]. The need to maintainand/or improve vegetable productivity calls for farmersadoption of mitigation practices. Success in mitigatingclimate change depends on how well agricultural cropsand systems adapt to the changes. African countriesneed tools to adapt and mitigate the adverse effects ofclimate change on vegetable production, quality andyield. Farmers at all levels need to acknowledge theeffects of climate change on vegetables and the possiblemitigation strategies.

Objectives of the study

These are:

i. To identify the adaptation practices adopted byvegetable farmers.ii. To determine the costs and returns andiii. To analyse the determinants of climate changeadaptation practices.

RESEARCH METHODOLOGY

The study was conducted in Ekiti State, which lies withinthe tropical zone in the rain forest and savannah region inthe South Western part of Nigeria. The State enjoys atypical tropical climate with two district seasons; the

raining seasons which lasts roughly from April to Octoberand the dry seasons which prevails for the remainingmonths. Majority of the inhabitants are essentially smallholder farmers who depend largely on agriculture for theirlivelihood.

In the collection of data, a multi stage samplingtechnique was employed. The first stage involvespurposive sampling of four Local Government Areas(L.G.A.) with highest density of leafy vegetable farmersand based on predominance of vegetable production inthose areas, they were purposively selected. The second

stage was the random sampling of two villages from eachLGA while the third stage consisted of the selection offifteen respondents within each village via simple randomsampling technique. In all, a total of one hundred andtwenty (120) leafy vegetable farmers were selected fointerview.

A questionnaire was designed to facilitate the collectionof reliable and specific data. Data were collected inNovember and December of 2012 using a pre-testedstructured questionnaire on climate change adaptationpractices, farmers outputs, production input variables(farm size and labour used) and socio economiccharacteristics of the farmers (age, level of educationfarming experience, household size, credit availability, ofseason income and extension visits). The data obtainedwere analysed using the descriptive statistics, budgetarytechnique and multinomial logit model. Descriptivestatistics such as frequency distribution, percentagesranges and means were used to describe the values othe selected socio-economic variables such as sex, ageeducational level, household size, farm size andinstitutional factors such as access to financial capitalmembership of association or group were used tocompare farmers adaptation to climate change. A totafarm budget approach was undertaken to estimateproduction costs, revenue and gross margin accruable toeach of the farmers. In this study, the gross margin of thefarmers was analysed as well as profitability ratio. Theequations used in estimating the various parameterswere defined below:

TC = TFC + TVCGM = TRTVC

GR = Price of output x yieldNP = TRTCNFI = GMTFCDepreciation = Cost of purchasesalvage value

Useful life

The profitability technique can be expressed as:

Operating expense ratio = TVCGR

Net farm income = NFIGM

Returns/Outlay = NFC

TC = TR- TC

Where: = ProfitFC = Fixed costVC = Variable costTFC = Total cost of productionGM = Gross MarginNFI = Net farm incomeTR = Total revenue from output


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P = Price per outputQ = Total output produced

Model Specification

The Multinomial Logit Model

The multinomial logic used the cumulative distributionfunction (CDF) to explain the behaviour of a dichotomousdependent variable. Given the assumption of normality,the probability that I*i is less than or equal to I i can becomputed by the normal CDF as:

P i = P(Y=1/X)= P (I i* < I i)=P (Z i < B1 + B2 X i)=F (B1 + B2 X i)

Where I* = critical or threshold level of the index, suchthat if I i exceeds I*, the family will adopt, otherwise it willnot. P(Y=1/X) is the probability that an events occursgiven the values of X, or explanatory variable(s) andwhere Z i is the normal variable i.e. Z~N (0, Q2).

The term multi-linear logit was coined in the 1930s byChester Bliss and it stand for probability unit. The probitmodel is defined as:

Pr(y =1/X) = (xb)

Where is the standard cumulative normal probabilitydistribution and xb is called the probit score or index.Since xb has a normal distribution, interpreting probit

coefficients thinking in the Z (normal quantile) metric. Theinterpretation of a probit coefficient is that one-unitincrease in the predictor leads to increasing the probitscore by b standard deviations.Learning to think and communicate in the Z metric takespractice and can be confusing to others. We will makeuse of a number of tools developed by Long and Freeseto aid in the interpretation of the results.

The log-likelihood function for multi-linear logit is; InL=wjIn(xjb) + wjIn(1-(xjb)

Where wj denotes optional weights.

The model relating to the intensity of adoption is specifiedas follows:Pi = F(B0+ B1X 1 +B2X2+B3X3+B4X4+B5X5+B6X6+B7X7+B8X8+B9X9+B10X10)

Where,Pi = Adoption status measured as dummy (0 = non-adopters, 1= adopters of indigenous vegetable, 2=adopters of irrigation, 3= adopters of varying plantingtime.)

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X1 = Sex of respondents (1=Male, 0=Female)X2= Age (in year)X3= Educational status of the household head (years)X4=Household sizeX5=Land ownership (1=owned, 0=otherwise)X6=Farm size (ha)

X7= Perceived impact of technology of yield (1=Positive0=Negative)X8=Farming experience (years)X9= off farm income ()X10= Extension visits (1=Yes, 0=No)

The independent variables included are age of thehousehold head in years, farming experience (measuredin years), the number of people in the household, off-farmincome measured in Nigerian naira (N), education ofhousehold head (years of formal education), perceivedimpact of climate on yield (dummies), effective extensioncontacts measured by regularity of visits by extensionagents (dummies) and, farm size. The effect of age on adecision whether to adopt or employ new technologiesmight be negative or positive. Previous studies show thathe age of individuals affect their mental attitude tochange from using or not using a new ideas andinfluences farmers decision in several ways. Youngefarmers have been found to be more knowledgeableabout new practices and may be more willing to bear riskand adopt new technology because of their longerplanning horizons. The older the farmers, the less likelythey are to adopt new practices as he gains confidence inhis old ways and methods. On the other hand, oldefarmers may have more experience, resources, oauthority that may give them more possibilities for trying a

new technology. There is no agreement on the sign ofthis variable as the direction of the effect is location- ortechnology-specific. The same assertion is true offarming experience [7-10]. Education was expected to bepositively related to the adoption of innovations since asfarmers acquire more education, ability to obtainprocess, and use new information improves and they arelikely to adopt [7-9,11,12].

Extension contact was expected to positively influenceadoption as these support services facilitate the uptake onew technologies. The extension contact variableincorporates the information that the farmers obtain ontheir production activities on the importance and

application of innovations through counseling anddemonstrations by extension agents on regular basesThe effect of this information on adoption variesdepending on channel, source, content, motivation, andfrequency. Respondents who are not frequently visited byextension agents have lower possibilities of adoption thanthose frequently visited [9,13-15]. Off-farm income andcredit facilities were expected to influence adoptionpositively. It was considered to be capital that could beused either in the production process or be exchangedfor cash or other productive assets [12,14,16,17]. Lack of


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liquidity could constrain adoption process, Off-farmincome will have a positive effect on adoption by relaxingthe constraint [18,19]. Perceived impact of the technologywas expected to influence positively on adoption asfarmers who perceived the positive effect of a technologyon yield would readily adopt [14,18]. Household size

could have either positive or negative influence onadoption [9,10,12,15,16,20]. A large family size couldreflect the presence of a greater labor force beingavailable to the household for timely farm operations. Onthe other hand, a negative relationship of the variablewith adoption could imply greater use of pressure inrelation to large family. Farm size was hypothesized tohave a positive relationship with technological adoption[9,10,17,18,20].

RESULTS AND DISCUSSION

This section discusses the results of the research asanalyzed by the analytical techniques employed toachieve the study objectives.

Socio- Economic Characteristics of Respondents

The sex distribution of the respondents shows that about53 of the total respondents were male while theremaining 67 respondents were female. This shows thatcultivation of vegetable in the study area is predominantlya female dominated enterprise. Cultivation of vegetable isgenerally accepted to be gender biased. Age of a farmerto a large extent, has an important bearing uponeffectiveness in the performance of various management

and operational duties on the production of leafyvegetables, therefore, it affect adaptation to climaticchange among leafy vegetable farmers. The age of afarmer is crucial to engaging him/her, physically in farming activities. The results show that about 32 respondentswere less than 30years of age, 33 were between the agebrackets of 31-50, and about 31 respondents were 51-70while 24 respondents were above 70 years of age.

The marital status is an important factor thatdetermines the per capital income of the farmer. When afarmer has so many wives, there will also be morechildren and this will certainly reduce the farmers percapita income because more number of people will

depend on him for survival hence reducing his realincome. On the other hand, the family member couldserve as source of labour for the farmer on his farm. Witha large family, lesser number of hired labours will beused. This will work if the children are youth. The pool ofthe results has shown that 91 respondents were married,25 were widowed while just four were single. This showsthat vegetable farming is mostly practiced by marriedpeople.

Labour is an important input in production and it takesthe largest share of variable cost of production. Farmersin the area are mostly small holders and they rely heavily

on the household labour supply to carry out both the farmand non-farm (domestic and social) activities. There isalways scarcity of labour at the peak of the seasonbecause of the seasonality of Nigerian agriculturasystem [18]. Labour inadequacy in supply constitutes amajor hindrance to agricultural production. Household

labour size is between the ranges of 7-9 and it has about63 of the total supply of labour.Education is an important determinant of adoption

decisions as well as an item of human capitadevelopment. The number of years of formal educationmeasures the literary level of the farmers. To a greaterdegree, it determines the ability to read and/or write byfarmers. In addition, it affords farmers the opportunity toclearly weigh and compare the advantages anddisadvantages of various innovations or technologyintroduced in order to make a rational decision foadoption.As the farmers level of education increases, itseffect on agricultural production is meant to be positiveThis is due to the fact that an educated farmer is atadvantage in understanding and adopting newtechniques of production. The more educated a farmer isthe more his decision making on the farm is enhanced ashe becomes a better manager of farm resources foincreased productivity from the set of farm inputs. Thedata revealed that over 60% of the respondents wentthrough post secondary education.

Extension visit afforded farmers easy exposure to newtechnologies. The greater the visits by extension agentthe better the farmers are informed about new technology

Investigation revealed that 81 of the respondents hadcontact with extension agents in the last productionseason, while the remaining 39 respondents had no

contact with the extension agent. A greater proportion ofthe respondents that adopted varying planting time werevisited on the last production season while 38 and 23resistant indigenous vegetable adopters and irrigationadopters were also visited. It could therefore be seen thaextension visits is a determinant of adaptation practicesemployed in the face of changing climate in theproduction of leafy vegetables.

Information dissemination is the key lubricant of thewheel of adoption decision process. The source and thefrequency are very germane in innovation adoptionprocess. The major sources and or /channels oinformation in the study area were mass media, friends

and family, extension agents/officers, local farmers andfadama programme. The results have shown that two ofthe respondents were informed through mass media, 25by friends and relatives, 69 by extension officer, and 12by local councillors. The pool of the results has shownthat most of the farmers have been informed throughextension agents.

Adoption of climate change adaptation practices

Farmers were interested in climate change/adaptationpractices but were unable to invest heavily in them due to


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certain constraints such as accessibility and affordabilityof technology and the technicality involved. However,three areas of adopting strategies/techniques wererecognized on the field. These are planting of resistanceindigenous vegetables, using of irrigation method andvarying the planting period. Respondents were thus

classified into non-adopters of climate change adaptationpractice. A farmer is classified as a non-adopter ofadaptation practices if he did not use any of theadaptation techniques. On the other hand, a farmer isclassified as an adopter of climate change mitigationpractices if he uses any one of the adaptation practices.

Adopters of planting indigenous vegetable were 31.6percent, that of irrigation were 19.2 percent and that ofvarying planting period was 45 percent. The high rate ofadoption of planting indigenous vegetables and varyingplanting time among small holder farmers might be due toone or a combination of the following; less labourintensive, highly cost effective and compatibility to mostcrops. The result showed that majority of the people inthe study area invested in varying planting periods,followed by planting indigenous vegetables. Irrigation wasthe least adopted technology because of the costinvolved.

Budgetary analysis

Table 1 show that the revenue accrued to leafy vegetableproduction in the area was N71, 906 among the non-adopters of any adaptation techniques and N 87,390among the irrigation adopters. The revenue value forirrigation was a reflection of the yield results. Therevenue values were N84558 and N72444 among the

adopters of planting indigenous vegetables and varyingtime of planting respectively. Total variable cost was N28,304 among the adopters of irrigation compared to otheradopters categories. It was N20, 593 among the non-adopters. This might be due to non-investment costs. Thegross margin was N61, 777 among the adopters ofplanting indigenous vegetable and N49, 883 amongthose varying planting time. The gross margin valueswere N51, 313 and N59, 086 for non-adopters andadopters of irrigation respectively. The higher value incost observed for planting at varying time wasnecessitated by increased cost of labour and chemicalthat accompanied planting in the planting period. This

suggests the reason behind the returns for varyingplanting time as against non-adaptation,

Multinomial logit estimates

The results as shown in Table 2 revealed that the loglikelihood value was -62.19 and chi-square value was78.99. The values supported the fitness of the model.The results further revealed that the level of educationpositively and significantly influenced the adoption of thethree identified adaptation practices. It was significant at5% level of probability for each of the practices. This

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implies that increased level of education would increasethe farmers adoption of climate change adaptationpractices. In the same vein, while farm size and amountof credit were positively and significantly influenced byirrigation adoption, extension visits influenced varyingtime of planting. Increased farm size and amount of credi

would increase the probability of farmers adoption oirrigation practices, while increased number of extensionvisits would increase the adoption of varying time oplanting.

Age was a positive determinant of adoption oindigenous vegetable. A one unit increase in age of thefarmers increased probability of planting indigenousvegetable by about 0.5 percent. This implied that theolder a farmer is, the greater the likelihood that he wouldadopt the planting of indigenous vegetable. This resultmight suggest the insistence of older farmers to continueto plant indigenous vegetable. Contrarily, age negativelyinfluenced the adoption of irrigation practices. A one uniincrease in age of farmers decreased the probability ousing irrigation by about 0.3 percent. This implies thatthe older a farmer is, the less the likelihood that he wouldadopt irrigation practices. This might not be unconnectedwith increased labour demand and equipment that attendto irrigation practices; this agreed with [18].

Conclusion

Adoption of any agricultural practice and mitigationpractices is crucial to development of agriculture which isa vital sector in the economy. The study assessed thecost and returns of farmersadaptation to climate changein the production of leafy vegetable in Ekiti State of

Nigeria. Results from the data analysis have indicatedthat female farmers dominated the production ovegetables. Farmers used various adaptation practiceslike varying the planting period, planting resistantindigenous vegetable and the use of irrigation technologyon the plots. The costs and returns analysis have shownthat vegetable production in the area of study isprofitable. The results from the multinomial logit modehave indicated that the major determinants of farmersadaptation to climate change were age, educational levelyear of schooling farm size and amount of credit. Themore a farmer spends time in school the more he is likelyto apply adaptation practices.

The analysis have further shown that the use ofirrigation and planting resistant breeds of indigenousvarieties and varying the time of planting have aided thesustainability of the vegetable in the face of the changingclimate. However, policy thrust should take advantage oage of farmers for effective adoption of relevantechnologies that will improve their livelihoods. The studyconcluded that vegetable production in the area shouldbe pursued while extension agents should be on hand toadvice the farmers of ways of cutting costs in thepurchase and use of irrigation equipments on the farmingplots


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Table 1:Costs and Returns per hectare under Different Adaptation Methods.

Item Non-adaptation Planting indigenousvegetable

Irrigation Varyingplanting time

Revenue (N) 71,906 84,558 87,390 72,444

Variable cost(N)

Seed 2,966 2,782 2,702 2,700

Fertilizer 2,648 1,010 1,648 1,333

Labour 11,221 13, 993 17,972 12,655

Chemicals 2,555 2,998 3,700 3,000

Other expenses 1,200 1,997 2,280 2,870

Total variablecost

20,593 22,781 28,304 22,560

Fixed cost (N)

Rent 1,200 1,250 1,470 1,300

Depreciation 650 630 2,100 980

Total fixed cost 1,850 1,880 3,570 2,280

Gross margin 51,313 61,777 59,086 49,883Net income 49,463 59,897 55,516 47,603

Source:Field survey, 2012.

Table 2:Multinomial logistic model.

Variable Planting indigenousvegetable

Irrigation Varying planting time

Age 0.4520* (0.0011) -0.2955*** (0.0941) 0.2811 (0.0043)

Education level 0.7314** (0.3112) 0.3520** (0.0053) 0.7000**(0.1114)

Farm size 0.1342 (0.0191) 0.8930* (0.007) 0.1852 (0.0062)

Household size 0.0117 (0.0071) 0.0025 (0.0940) 0.0111 (0.0011)Credit amount 0.3179 (0.0333) 0.0014* (0.111) 0.0869 (0.1341)

Off farm income 0.0091 (0.0009) 0.1934 (0.6113) 1.2113 (0.0260)

Extension visit 0.7122 (0.1134) 0.3933 (0.2221) 0.3331***(0.0413)

Farm expenses 0.0034 (0.0714) 0.2116 (0.2341) 0.6413 (0.0001)

Chi squared 78.99

Log likelihood -62.19

Restricted loglikelihood

-116.24

Source: Data analysis, 2012***= significant at 1%; **= significant at 5% and * = significant at 10%; Figures in parenthesis are standard errors.

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