Determinants of Technical, Allocative and Economic Efficienciesamong Dry Season Vegetable Farmers in Sokoto State, Nigeria

B. A. Tsoho1, O. A. Omotesho1, S. A. Salau2 and M. O. Adewumi1

1Department of Agricultural Economics and Farm Management Faculty of Agriculture,University of Ilorin, Ilorin, Nigeria

2Department of Agricultural Economics and Extension Services, College of Agriculture andVeterinary Medicine, Kwara State University, Malete, Nigeria

Telephone: +2348038032456; E-mail: talk2salaushehu@yahoo.com

KEYWORDS Productive Efficiency, Inefficiency, Productivity and Fadama

ABSTRACT This study investigated the determinants of technical, allocative and economic efficiencies among small-scaledry season vegetable farmers in Sudan Savannah (SS) zone of Sokoto State, Nigeria, using a stochastic frontier productionfunction. Structured questionnaires were used to collect data from 155 respondents randomly selected from designated locationsin the project area. The findings revealed that four variables (extension visits, source of irrigation water, crop diversificationand location of the farm), two variables (extension visits and location of the farm) and three variables (extension contact, sourceof irrigation water and crop diversification) were found to be significant at different levels of significance for technical, allocativeand economic efficiency, respectively. These variables were therefore the important policy determinants of inefficiency in studyarea. These results suggest that farmers’ location in vegetable farming and increased investment in extension services couldjointly contribute to an improvement in efficiency of vegetable farmers in the study area.

INTRODUCTION

Nigerian agricultural sector has always beenan important component of the country’s eco-nomy. A sectoral analysis in 2008 of the realGDP indicated that the agricultural sector con-tributed about 42 percent of the GDP, with crop,livestock, forestry and fishery accounting for37.52, 2.65, 1.37, and 0.53 percent respectively(CBN 2008). This implies that the crop sub-sec-tor contributed 89.2 percent of agriculture GDP.

The small holders who are the back bone ofthe agricultural sector produce about 80 percentof the total food requirement (Fayinka 2004),mainly cultivate land by using traditional meth-ods under rain- fed conditions. For example,Dogondaji (2005) asserted that productivityunder rain- fed conditions is very low. For in-stance, yields of rain-fed cereals and cowpeasare about 0.5 t/ha while under fadama condi-tions productivity would increase to between oneand two tonnes/ha for rice, 10 to 15 tonnes foronions, and 5 tonnes/ha for tomatoes. As a re-sult, net farm incomes would increase from35,000 naira under rain fed conditions to at least80,000 naira. Consequently, future growth basedentirely on rain-fed farming is likely to fall shortof the needs of the rapidly growing Nigeria popu-lation which is put at 3.4 percent per annum;this is against the agricultural sector growth of2.8 percent (PCU/FMARD 2004). Thus, the

agricultural growth cannot keep pace with thepopulation growth rate. This apparent disparitybetween the rate of food production and require-ment in Nigeria has led to a food demand-sup-ply gap thus leading to a widening gap betweendomestic food and total food requirement, anincreasing resort to food importation and highrate of increases in food prices (Ojo 2003).

Sokoto state is among the northern area en-dowed with fadama areas where substantialquantities of vegetable are grown under irriga-tion during the dry season (Dogondaji 2005).Bello et al. (1998) observed that the fadama landalthough quite small in area, is one of the mostvaluable agricultural resources in the state. De-spite its vast potentials, fadama productivityincrease is said to be declining (Ajibola 2006).Similarly, productive efficiency for most cropsstill falls under 60 percent (Rahji 2005). Theseshortfalls are attributed to inefficiencies in pro-duction. Therefore, the need for the efficientallocation of productive resources cannot beoveremphasized. Presently, there are very fewfirm-level studies of efficiency in the develop-ing economies, particularly in Nigeria (Rahji2003). As far as the researcher knows, no spe-cific investigation into farm level productiveefficiency involving vegetable production in thefadama is carried out in the study area, as mostof these studies centre on only the resource useefficiency. To bridge the existing gap, this study

© Kamla-Raj 2012 J Agri Sci, 3(2): 113-119 (2012)

examined the determinants of productive effi-ciencies that would help in promoting the muchdesired productivity and output growth in theagricultural sector, particularly among small-scale fadama vegetable producers in the area.

Meaning and Characteristics ofFadama Lands

Fadama land is a Hausa word already adoptedby the World Bank. It simply means land thatflooded during rainy season. (Baba and Singh,1998). It refers to low lying swampy area con-sisting of fluvial deposits and containing exten-sive exploitable aquifers (Ghandi and Raja-shakara, 1989). In agricultural usage, however,the word fadama commonly refers to all lowlying relatively flat areas either stream less de-pressions or adjacent to the seasonally or pe-rennially-flowing streams and rivers. Accord-ing to Kolawole and Scoones (1994), fadamameans the seasonally flooded or floodable plainsalong the major savanna rivers and/or depres-sions on the adjacent terraces. The word fadamais in contrast to tudu which means the upland.

Fadama in northern Nigeria is synonymousto bas fond in sahel, wali or khor in Sudan anddambo in southern Africa (Scoones, 1992). Thefadama lands have been described by Scoones(1992) as “wetlands in dry lands“ due to theirmoisture retention characteristics within therhizoshere, not only in the wet but also in thedry season. Characteristically, Arnborg (1988)noted, the fadama land is marked by a flush ofnew vegetation at the beginning of the rainyseason before the adjacent uplands turns green,but it is most conspicuous after cessation of therains when it remains prominently green as athe surrounding upland rapidly turns brown andevenly bare. Arnborg added fadama is like “agarden, a little paradise“ in the vast dry landsof northern Nigeria.

The problem is that fadama lands in mostnorthern Nigerian states are usually small, rela-tive to the total cultivable land area. for instance,it has been reported that fadama covers only798,00 ha out of about five million hectares cul-tivated in Bauchi, Kano, Jigawa, Sokoto, Kebbi,Kaduna and Katsina states (Baba and Singh,1998). This represents only 15.77% of the totalarea cultivated, with area varying from as lowas 6.25% in Kano and Jigawa to as high as39.25% in Sokoto and kebbi states. In the present

Sokoto state, it is estimated that 154,524 ha offadama land is under cultivation (SMANR,(1998).

METHODOLOGY

Ar ea of the Study

This study was conducted in Sokoto State.The state lies within two ecological zones; thenorthern portion which lies largely within theSudan savannah ecological zone, the southernportion on the other hand, lies in the northernGuinea Savanna of Nigeria. The state is locatedin the North West geographical zone of Nigeriawithin longitude 11o3-13o50E and latitude 4o-6o 401N. It covers a land area of 2,648.48 squarekilometers (Singh 2000). The area could be clas-sified as one of the semi-arid regions of theworld, where water is probably the most limit-ing factor to agricultural production. There aretwo distinct seasons in the state; the rainy sea-son which starts around May and last till Sep-tember and the dry season from October to April.The annual rainfall is frequently erratic, poorlydistributed and varies from 500mm to 1300mm(SMANR 1998). There are two temperatureextremes; the hot period (March to May) whichis as high as 39oC and the cold harmattanmonths of December to January, when cold,dust-laden winds blow from across Sahara bring-ing down the temperature to as low as 15oc, av-eraging about 27 o C (SMANR 1998). There arefew low-lying valleys or fadama which termi-nate to form streams at their lowest levels. Thesoil consists of ferruginous soil of alluvial ori-gins, as well drained loamy sandy soil. In thefadama, the soil generally consists of finer sand,silt and clay. River Sokoto and River Rima arethe major rivers in the state with their numer-ous tributaries. The major occupation of thepeople of the state includes arable and livestockfarming.

Sampling Procedure and Sample Size

The target population for this study was thefadama vegetable producers in Sokoto state.Sokoto state is divided into two main agro-eco-logical zones by the state Agricultural and Ru-ral Development Authority (SARDA) in conso-nance with ecological characteristics, culturalpractices and project’s policy and administra-

B. A. TSOHO, O. A. OMOTESHO, S. A. SALAU114

tive convenience. These zones are the northernand western zones with a central headquartersin the state capital. Since vegetable is producedin virtually all the areas of the state and in or-der to have a representative sample in achiev-ing the stated objectives, the sampling proce-dure covered the two zones equally. Accordingly,a two-stage sampling procedure was employedin selecting the sample for this study. The firststage involved the random selection of eightvillages in each of agricultural zone of the stateusing the existing SARDA village listing. Thesecond stage was the random selection of tenfarming households in each of the already se-lected village. Accordingly, a sample of onehundred and sixty farming households was col-lected and subsequently analyzed for the study.However, one hundred and fifty- five question-naires were found useful for the study. The sam-pling distribution of the respondents is shownin Table 1.

Table 1: Sampling distribution in the study area

Western zone Number Northern zone Numberof respond- of respond-

ents ents

Kamata 10 Tunga 10Wajajuke 9 Taloka 10Jirga 10 Kwargaba 10Kaura Kwasau 10 Sabo Dole 9M/gari 8 Lugu 10Shuni 10 Goronyo 10Salla 10 Gidan AjIkwara 9Saida 10 Gidan Kaima 10

Total 77 Total 78

Grand total = 155 RespondentsSource: Field Survey 2009

Analytical Techniques

Descriptive statistics and Cobb–Douglas sto-chastic production frontier approach was usedto estimate the production function and the de-terminants of technical, allocative and economicefficiencies among vegetable farmers in the state.The stochastic function assumes the presenceof technical inefficiency of production. Thespecification involves a function specified forcross-sectional data, which has an error term,with two components, one to account for ran-dom effects and another to account for techni-cal inefficiency. Hence, the function is definedby:Y

i = X

iβ + (V

i - U

i), i = 1,..., N, ...................................... (1)

Where Yi is the monetary value of vegetable

crop per farmX

i is a kx1 vector of (transformations of the)

input quantities of the i-th firm;β is a vector of unknown parameters to be

estimated;Where Vi are random variables, two-sided

(- ∞ < vi < ∞) normally distributed random errorN ~ (0,δv2), which are assumed to be independ-ent of the Ui that captures the stochastic effectsoutside the farmer’s control (for example, wea-ther, natural disasters, and luck, measurementerrors in production, and other statistical noise).The two components v and u are also assumedto be independent of each other. Thus, to esti-mate a Cobb-Douglas production functions, wemust log all the input and output data beforethe data is analyzed (Coelli 1995).

Thus, to estimate a Cobb-Douglas produc-tion function, we must log all of input and out-put data before the data is analyzed. (Coelli1995). The estimating equation for the stochas-tic function is given as:lnY = B

0 +B

1lnX

1 +B

2lnX

2 + B

3lnX

3 +

B

4lnX

4 + B

5lnX

5 +

B6lnX

6 + V

i –U

i ................................................................ (2)

In fadama farming, land, labour, seeds, fer-tilizers, agro-chemical and irrigation water aregenerally regarded as inputs (Tsoho 2004). Onthe basis of this, land (X

1) in hectare, labour

(X2) in man days, seeds in kg (X

3), fertilizer in

kg (X4), agro-chemical in litres (X

5), and quan-

tity of irrigation water in ha-cm (X6) was in-

cluded in the stochastic frontier models. Tech-nical efficiency of an individual firm is definedin terms of the ratio of the observed output (Y

i)

to the corresponding frontier output (Yi*), given

the available technology, conditional on the lev-els of input used by the firm. That is:Technical efficiency (TE) = Y

i/Y

i* .................................. (3)

TE = Yi/Y

i* which is obtainable by the use of

Frontier 4.1 (Coelli 1996). Based on the indi-vidual farm’s technical efficiency, the mean tech-nical efficiency for the sample is obtained (Rahji2005). The technical, allocative and economicinefficiencies are explained by:U

i =δo +δiZ

1i +δ

2Z

2i +δ

3Z

3i +δ

4Z

4i +δ

5Z

5i+δ

6Z

6i + δ

7Z

7i +

δ8Z

8i +δ

9Z

9i ...................................................................... (4)

WhereU

i represents inefficiency effects;

δo represents the interceptFarm size (Z

1): Farm size was measured in

hectares.Farming experience (Z

2): This was measured

in years and will equally serve as a proxy to theage of the farmer.

ALLOCATIVE AND ECONOMIC EFFICIENCIES AMONG DRY SEASON VEGETABLE FARMERS 115

Educational level (Z3): This variable was

graded for values 1, 2 and 3 to represent Quar-nic, adult and western education, respectively.

Household size (Z4): This was based on the

number of direct family and dependants of thehousehold and was adjusted to adult equivalent.

Extension contact (Z5): Access to extension

education is expected to enhance the farmer’slevel of adaptation to improved technologies.Access to extension was based on the numberof visits by the extension agent.

Land ownership (Z6): The mode of owner-

ship of a fadama farmland is expected to influ-ence the productive efficiency of a farmer. It wasmeasured as a dummy. D= 1 if ownership byinheritance, otherwise, = 0.

Source of irrigation water (Z7): The variable

was measured as dummy. D = 1 if surface wateris used, D = 0 if underground water is utilized.It is expected to have a positive effect on thefarmer’s efficiency.

Crops diversification (Z8): Weather farmer

practiced mixed or sole cropping system. It willbe measured as a dummy. D= 1 if farmer growmore than one crop in the same piece of land,and otherwise, = 0. The variable is expected tohave a positive significant effect on the farmer’sefficiency.

Zonal location of the farmer (Z9): This vari-

able is expected to capture any variability thatmay exits between the two agro-ecological zonesthat make up the study area. The location of thefarmer was measured as a dummy. D= 1, iffarmer is located in northern zone of the stateand otherwise, = 0.

RESULTS AND DISCUSSION

The mean age of the respondents is 43.33years and the modal age is 41-50 years, whichconstituted about one-third of the total respon-dents (Table 2). The age of the farmer accord-ing to Adewumi and Omotesho (2002) is ex-pected to affects his labor productivity and out-put. This agrees with findings of Tsoho (2004).

All the one hundred and fifty- five sampledrespondents in the study area were male. Thismay be due to cultural and religious (Islamic)belief of the people in the area, which prohibitswomen to go out freely and engage in certainactivities such as farming. The study revealedthat more than ninety percent of the respondentswere married, while the remaining were eithersingle or widow(ers), respectively. The mean

Table 2: Socio-economic characteristics of thehouseholds’ heads in the study area

S. Characteristics Frequency PercentageNo.

1. Age of the Respondents 1- 20 years 1 0.65 21-30 years 25 16.13 31-40 years 36 23.22 41-50 years 54 34.84 51-60 years 20 12.90 61-70 years 18 11.61 71-80 years 1 0.65 Total 155 100.00

2. Marital Status of the Respondents Married 145 93.55 Single 4 2.58 Widower/Separated 6 3.87 Total 155 100.0

3. Family Size Respondents 1-10 104 67.9 11-20 39 25.16 21-30 10 6.45 31-40 2 1.30 Total 155 100.00

4. Education Status of the Respondents. Qu‘ranic education 119 76.77 Adult education 12 7.74 Primary education 10 6.45 Secondary education 12 7.74 Tertiary education 2 1.30 Total 155 100.00

5. Respondents Farming Experience 1-10 years 25 16.13 11-20 years 38 24.52 21-30 years 64 41.29 31-40 years 17 10.97 41-50 years 11 7.09 Total 155 100.00

Source: Field survey 2009

family size was 10 persons per respondent andit ranged from 1 to 40. The study also revealedthat 15.49 percent have attained between pri-mary and tertiary education. More than two-third of the respondents have had Qu‘araniceducation. The farmer’s years of experienceranged from 5 to 45 with an average of 23.21years. Farmers experience is expected to have aconsiderable effect on farmer’s productive effi-ciency. Almost all the respondents have inher-ited farming as an occupation, while the remain-ing were introduced to farming by either friendsor relatives. About Ninety percent of the respon-dents have farming as their main occupation andonly ten percent adopts farming as their sec-ondary occupation.

Technical Efficiency analysis

The expected parameters and the related sta-tistical test results obtain from the analysis of

B. A. TSOHO, O. A. OMOTESHO, S. A. SALAU116

the maximum likelihood estimates (MLE) of theCobb-Douglas based stochastic frontier produc-tion function parameters for dry season vegetablefarmers are presented in Table 3.

Table 3: Estimated stochastic production frontierfunction

Variables Parameters Coeffi- t- cient values

Physical inputs β0

6.573*** 12.9Constant

Land (ha) (X1) β

1 0.149 1.52

Labour (man-days) β2

0.181** 2.42(X

2)

Seeds (Kg) (X3) β

3-0.036 -0.01

Fertilizer (kg) (X4) β

4 0.237*** 3.72

Agrochemical β5

0.078 1.18(litres) (X

5)

Irrigation water in β6

0.303*** 3.66ha-cm (X

6)

Diagnostic StatisticsSigma square (ä2) (δu2+ δv2) 0.253*** 5.420Gamma (ã) (δu2/ δ2) 0.820*** 10.20Lambda (δu/δv) 4.560Log-likelihood function -0.0053Sample size (n) 155

Source: Data Analysis*significant at 10% 2009,*** , significant at 1%, ** significant at 5%

The variance parameters of the productionfunction represented by Sigma-squared (σ2) andGamma (γ) are all significant even at 1%. TheLambda is greater than one (λ = 4.56). Thestatistical significance of Lambda showed thatthere exits sufficient evidence to suggest thattechnical inefficiencies are present in the data.Theoretically, this implies a good fit for theestimated model and correctness of the distri-butional assumptions for the U

i and V

i. The

statistical significance of the sigma-squared alsoindicates a good fit for the model. The estimatedgamma (0.82) shows the amount of the variat-ion in vegetable outputs which results fromtechnical efficiency of the sampled farmers.

The results of the estimated parameters re-vealed that all the coefficients of the physicalvariables except quantity of seeds used, conformto a priori expectation of a positive signs. Thepositive coefficient of land, labour, fertilizer,agro-chemical and irrigation implies that aseach of these variables is increased, ceteris pari-bus, vegetable output increased. The negativesign of the seeds suggest a situation of exces-sive (and, hence, inefficient) use of plantingmaterial in the production of vegetable in the

area. The coefficient of the variable associatedwith land although positive, is statistically notsignificant even at 10 percent level of signifi-cance. The coefficients of the three physical vari-ables; labor, fertilizer and irrigation water areall significant even at 1 percent level of signifi-cance. Therefore, these are the major factorsexplaining vegetable production under fadamain the area. This findings agrees with those ofAjibefun et al. (2002) and Onyenweaku andEffiong (2005).

The Returns to Scale (RTS)

The return to scale (RTS) analysis, whichserves as a measure of total resource productiv-ity, is given in Table 4. The maximum likeli-hood estimates (MLE) of the Cobb-Douglasbased stochastic production function parameterof 0.834 is obtained from the summation of thecoefficients of the estimated inputs (elasticites).It indicated that, vegetable production in thestudy area was in the stage II of the productionsurface. Stage II is the stage of decreasing posi-tive return-to scale, where resources and pro-duction were believes to be efficient.

Table 4: Elasticities and returns to scale of theparameters of stochastic frontier production function

Variables Elasticity

Farm size 0.149Labor 0.181Planting materials -0.036Fertilizer 0.237Agro-chemical 0.078Irrigation water 0.303RTS 0.834

Determinants of Technical, Allocative andEconomic Efficiencies in Dry SeasonVegetable Production

Multiple Regression Analysis (OLS)

Based on the literature on previous studies;nine characteristics are chosen as indicators ofthe farmer’s socio-economic environment andare subsequently used as explanatory variablesin the analysis of productive efficiency for veg-etable production under fadama in the studyarea. The results of the analysis of the relation-ship between the farmer’s characteristics andefficiency indices are presented in Table 5.

ALLOCATIVE AND ECONOMIC EFFICIENCIES AMONG DRY SEASON VEGETABLE FARMERS 117

Table 5: Relationship between transformed efficiencyindices and farm-farmer characteristics

Variable TTE TAE TEE

1 Farm size (ha) -0.017 -0.026 0.053(-0.197) (-0.305) (0.592)

2 Farming experience -0.45 0.88 -0.085(years) (-0.432) (0.818) (-0.770)

3 Education level 0.003 -0.029 0.013(graded) (0.039) (-0.350) (0.148)

4 Household size (no 0.103 -0.079 0.154of persons) (1.007) (-.725) (1.371)

5 Extension contact 0.161 0.110 0.152(no of visits) (1.824)* (1.710)* (1.640)*

6 Land ownership 0.025 0.119 0.044(dummy) (0.300) (1.484) (0.526)

7 Source of irrigation 0.170 -0.006 0.196water (dummy) (2.002)** (-0.073) (2.273)*

8 Crop diversification 0.209 -0.046 0.182(dummy) (2.545)** (-0.558) (2.160)*

9 Zone/location of the -0.156 -0.333 -0.112farmer (dummy) (-1.917)* (-4.132)* (-1.060)

R2 0.42 0.31 0.21n 155 155 155**,* Estimates are significant at 5% and 10% levels ofsignificance respectively.Source: Data Analysis 2009

The results indicated that, overall, the ex-planatory ability of the variables included in theanalysis is limited (R2 values are generally low)and not all regressions or parameters are sig-nificant. This result agrees with the findings ofseveral researchers who have generally obtainedan R2 value of less than 0.5 in their secondaryanalysis similar to this study. For example, Xuand Jeffrey (1997) obtained an R2 value of 0.21,0.31 and 0.19, respectively.

Extension contact has a positive sign and isstatistically significant in three efficiency indi-ces. The positive and statistically robust rela-tionship between extension and efficiency sup-ports the notion, which implies that farmers whohad more extension visits/teachings are likelyto be more successful in gathering informationand understanding new practices and the use ofmodern inputs which in turn will improve theirEE through higher levels of TE and AE. Theseresults are consistent with the findings of Onyen-weaku and Nwaru (2004) and Rahji (2005).Contrarily, the study disagreed with findings ofthose of Parikh et al. (1995) that have foundextension to be negatively and statistically re-lated to efficiency indices.

Source of irrigation has positive and statisti-cally significant coefficients for both TE and EE.But, the opposite is true for the AE as its rela-tionship is negative and non-significant. The

positive coefficient suggests that farmers whouse surface water to irrigate their fadama veg-etable are technically and economically moreefficient than those who use underground wa-ter. Interestingly, these same categories of farm-ers are however allocatively less efficient. Thisfinding is consistent with Baba and Wando(1998) that the there is a positive and signifi-cant relationship between the source of waterand the efficiency of the farmers.

The study also reveals a positive and statisti-cally significant correlation between cropsgrown and TE and EE. These results indicatethat as diversification increases and more cropsare grown, efficiency increases. Farmers whopracticed mixed cropping exhibited higher lev-els of TE and EE, but, surprisingly, reverse isthe case for transformed AE index. The impli-cation is that greater diversification is associ-ated with higher relative efficiency.

The location coefficient values for the twoefficiency indices (TE and AE) are negative,relatively large in magnitude and have a statis-tically significant effect on efficiency. The loca-tion coefficient for EE although negative is sta-tistically not significant. The coefficients ofhousehold size, farm size, farming experience,educational status of farmers and land tenuresystems are not important in explaining thevariation in TE, AE and EE of farmers in thestudy area.

CONCLUSION

This study described the socio-economiccharacteristics and identifies the determinantsof TE, AE, and EE of vegetable farmers inSokoto State, Nigeria. The results indicated thatwhile the extension contact, source of water, andcrop diversification exhibited higher levels ofpositive significant impact on TE and EE, thelocation of the farm exerts a negative signifi-cant impact on TE. In addition, farm locationhas a statistically positive association with AE.In all, extension contact exerts a uniform im-pact on all the efficiency indices.

RECOMMENDA TIONS

Policymakers should foster the developmentand provision of qualitative extension servicesto the farmers, while promoting the wide adop-tion of pumps use by the farmers.

B. A. TSOHO, O. A. OMOTESHO, S. A. SALAU118

REFERENCES

Adewumi MO, Omotesho OA 2002. An analysis of productionobjective of small rural farming Households in KwaraState, Nigeria. Journal of Rural Development, 25: 201-211.

Ajibefun IA, Battese GE, Daramola AG 2002. Determinantsof technical efficiency in small holder food crop farming:Application of stochastic frontier function. QuarterlyJournal of International Agriculture, 41(3): 225-240.

Ajibola CAA 2006. Resource Use Efficiency in Tomato andPepper Production Under the Fadama DevelopmentProject in Kaduna State. Master’s Thesis ABU,Unpublished. Zaria, Nigeria.

Arnborg, T. (1988). Where Savannah turns into Desert. RuralDevelopment Studies No. 24, Swedish University ofAgricultural Sciences, Uppsalsa.

Baba K M, Wando M A 1998. Impact of membership offadama user associations on resources use, crop yieldand farm income: A case study from two localgovernment areas of Niger State. Nigeria. Journal ofBasic and Applied Science,7: 31-41.

Bello HM, Singh BR, Garba UA 1998. Improving strategiesfor fadama farming in Sokoto. Nigerian Journal ofRural Sociology, 2: 17-20.

Bravo-Ureta B, Pinheiro A 1997. Efficiency analysis ofdeveloping country agriculture: A review of the frontierfunction literature. Agriculture and ResourceEconomics Review, 22(1): 88-101.

Central Bank 2008. Annual Report and Statement ofAccounts for the year ended 31st December 2008.

Coelli TJ 1995. Recent developments in frontier modellingand efficiency measurement. Australian Journal ofAgricultural Economics, 39(3): 219- 245.

Coelli TJ 1996. A Guide to FRONTIER Version 4.1: AComputer Program for Stochastic Frontier Productionand Cost Function Estimation. CEPA Working Paper96/07. University of New England, Armidale, Australia.

Dogondaji SD 2005. Economics of Dry Season OnionProduction and Marketing in Sokoto and KebbiStates. A Ph.D. Thesis submitted to the Department ofAgricultural Economics and Extension, Usman DanFodio University, Sokoto.

Fayinka FA 2004. Food Security in Production in Nigeria:Challenges under Democratic Dispensation. Paperpresented at the 9th ARMTI Annual Lecture held onMarch 24, 2004.

Ghandi I, Rajashakara BG 1989. Sokoto Agricultural andRuralDevelopment Authority. An insight into thefadama development. A paper presentedat a workshop

on fadama and irrigation development in Nigeria, atBauchi, Nigeria.October 20-23, 1989.

Kolawole, A, Scoones I 1994. Fadama lands use andsustainability in northern Nigeria: An overview. In: AKolawole, I Scoones, MO Awogbade, JP Voh (Eds.):Strategies for the sustainable use of fadama in NorthernNigeria. CSER/ABU (Zaria) and IIED (London), pp.29-34.

Ojo SO 2003. Productivity and Technical Efficiency ofpoultry Eggs Production in Nigeria. InternationalJournal of Poultry Science, 2 (6): 459-464.2003. From<www.pdf.com> (Retrieved on 5 January 2006).

Onyenweaku CE, Nwaru, JC 2004. Application of stochasticfrontier production functions to the measurement oftechnical efficiency in food production in Imo state,Nigeria. Nigeria Agricultural Journal (In press).

Onyenweaku CE, Effiong EO 2005. Technical efficiency andits determinants in pig production in Akwa Ibom state,Nigeria. International Journal of AgriculturalDevelopment, 6: 51-57.

Parikh A, Ali, F, Shah MK 1995. Measurement of economicefficiency in Pakistani agriculture. American Journalof Agricultural Economics, 77: 675-685.

Project Coordinating Unit and Federal Ministry ofAgriculture, (2004)-PCU/FMA. Crop Production Datain Nigeria. pp. 3-10.

Project Coordinating Unit and Federal Ministry of Agriculture2004-PCU/FMA. Crop Production Data in Nigeria.pp. 3-10.

Rahji MAY 2005. Determinants of efficiency differentials inlowland rice production systems in Niger state, Nigeria.Ibadan Journal of Agricultural Research, 1(1): 7-17.

Scoones I 1992. Wetlands in drylands: key resource foragricultural and pastoral production in Africa. IIEDNo.38 Drylands Network Programme.

Singh BR 2000. Fadama Lands of Sokoto State and theirAgricultural Potential. A Seminar Paper Presented atFaculty of Agriculture, University of Sokoto, May 19.

SMANR 1998. Sustainable Agricultural, Environmental andRural Development. A Survey of Report Prepared bySokoto State Ministry of Agriculture and NaturalResources, March, 14th, P. 149.

Tsoho BA 2004. Economics of Tomato Production underSmall-scale Irrigation in Sokoto State.M.Sc Thesis,Unpublished. Submitted to the Dept. of Agric.Economics and Farm Management, University ofIlorin, Ilorin, Nigeria.

Xu X, Jeffrey SR 1998. Efficiency and technical progress inmodern agriculture: Evidence from rice production inChina. Journal of Agricultural Economics, 18: 157-165.

ALLOCATIVE AND ECONOMIC EFFICIENCIES AMONG DRY SEASON VEGETABLE FARMERS 119