Technical Efficiency Differentials and Resource-Productivity Analysis

among Smallholder Soybean Farmers in Benue State, Nigeria

Keywords: Technical efficiency, productivity, transcendental logarithmic stochastic frontier model, smallholder soybean production.

ABSTRACT: The importance of soybean as a high protein, primary input in vegetable oil, diary and feed industries is not in doubt. The technical efficiency and resource-productivity of smallholder soybean farmers in Benue State, Nigeria were estimated using cross sectional data obtained on 96 soybean farmers in the empirical analysis. Results obtained with transcendental logarithmic (translog) stochastic frontier model showed that the technical efficiencies varied widely from 0.254 to 0.999 with a mean of 0.718. This indicates that smallholder soybean production was in the irrational stage of production (stage III) as depicted by the returns-to-scale (RTS) of -2.848. Land and fertilizer were effectively allocated and used, as confirmed by each variable having estimated coefficient value between zero and unity, depicting stage II in the production curve. The productivity of the factors can be enhanced by expanding the farm size at the existing level of labour so that the variable of labour used could move from stage III to stage II in the production curve. Labour saving resource and/or practices should be encouraged for productivity and technical efficiency to be enhanced.

108-113 | JRA | 2012 | Vol 1 | No 2

This article is governed by the Creative Commons Attribution License (http://creativecommons.org/

licenses/by/2.0), which gives permission for unrestricted use, non-commercial, distribution and reproduction in all medium, provided the original work is properly cited.

www.jagri.info

Journal of Research in

Agriculture An International Scientific

Research Journal

Authors:

Otitoju MA1, Omole MO2,

Ezihe JAC3, Arene CJ4.

Institution:

1.Agricultural Biotechnology and

Bioresources Development

Department, National

Biotechnology Development

Agency, Abuja, Nigeria.

2. School of Business

Education, Federal College

of Education (Technical),

Bichi, Kano State, Nigeria.

3. Department of Agricultural Economics,

University of Agriculture,

Makurdi, Benue State,

Nigeria.

4. Department of

Agricultural Economics,

University of Nigeria,

Nsukka, Nigeria.

Corresponding author:

Otitoju MA.

Email:

maobanjo@yahoo.co.uk

Phone No:

+2347063036013.

Web Address: http://www.jagri.info

documents/AG0024.pdf.

Dates: Received: 9 Jun 2012 Accepted: 02 Jul 2012 Published: 24 Aug 2012

Article Citation: Otitoju MA, Omole MO, Ezihe JAC, Arene CJ. Technical Efficiency Differentials and Resource-Productivity Analysis among Smallholder Soybean Farmers in Benue State, Nigeria. Journal of Research in Agriculture (2012) 1(2): 108-113

Original Research

Journal of Research in Agriculture

Jou

rn

al of R

esearch

in

A

gricu

ltu

re

An International Scientific Research Journal

109 Journal of Research in Agriculture (2012) 1(2): 108-113

Otitoju et al., 2012

INTRODUCTION

Benue State is the largest producer of soybean in

Nigeria with almost all the tonnage coming from the TIV

dominated zones of the state (Eastern and Northern

Agricultural zones) (Iwe, 2003). The importance of

soybean as a high protein, primary input in vegetable oil,

diary and feed industries is not in doubt (Ayoola, 2001).

The efficacy of the soybean protein has been reported in

comparison to other traditional sources of protein, 1kg of

soybean contained as much as 2kg of boneless meat or 5

dozens of eggs or 45 cups of cow milk (Dashiell, 1993).

It is relatively cheap with 40% protein content compared

to these other sources of protein (Ayoola, 2001).

Soybean is used to make soup condiment called

“dawadawa” by the Hausa, which is also known as „iru‟

or „ogiri‟ in the Yoruba land, as a substitute for “maggi”.

It has also been successfully incorporated into at least

140 traditional food products of different regions and

ethnic groups in Nigeria (Iwe, 2003; Okoruwa, 1999).

Examples of these products are soy-ogi, soy vegetable

soup, soy garri, soy-alibo, soy-akpu, soy-tuwo; soy-

kuruzaki, soy-opa, soy-hatsi and soy-ice cream, etc.

These products have been widely accepted and this has

made the demand for soybean not to keep pace with its

supply.

Efforts had been made by various successive

governments to correct supply deficits in agricultural

production in Nigeria (Olaitan, 2007), soybean

production inclusive. Examples of these efforts through

policies are the Institute of Agricultural Research (IAR)

at Ahmadu Bello University, Zaria, Nigeria who

generated the samsoy varieties; University of

Agriculture, Makurdi established in 1988 which has as

part of her mandates to increase the production of crops

in the Northern Nigeria and more recently Vegetable Oil

Development Programme (VODEP) launched in 2002 to

address five oil-producing crops; cocoa, oil palm, cotton,

ground nut and soybean (FMARD, 2006). VODEP is

assumed to increase the production of these crops to

meet the 300,000-400,000 tonnes per annum supply

deficit of vegetable oil (PRCU, 2003). Also the

International Institute of Tropical Agriculture (IITA)

Ibadan also tried in the generation of better adaptable

varieties (Tgx and Tgm). These generated varieties are

assumed to replace the old Malayan variety. With all

these policies and programmes the demand/supply gap is

yet to be bridged. It has been observed that these policies

are inconsistent and usually short-lived. As identified by

Idachaba (2000), inconsistent policies are the major

source of poor performance of Nigeria Agriculture.

Central to the inability of the Nigerian soybean to correct

the supply deficits is the issue of efficiency of the

soybean farmers in the use of available resources or

technology. Ajibefun, (2006) opined that efficiency of

production is central to raising production and

productivity in the African agriculture. The efficient use

of available technology is what is referred to as technical

efficiency (Obwona, 2006).

Arene and Okpupara, (2006) defined technical

efficiency as the maximisation of the ratio of output to

input. In his own view Lovell (1993) explained technical

efficiency to mean the ability to avoid waste by

producing as much output as input usage allows, or by

using little inputs as output of production allows. Bishop

and Toussaint (1958) defined productivity as the ratio of

valuable output to valuable inputs.

Most soybean production is under smallholder

agricultural systems. These smallholder soybean farmers

cultivate about two hectares on the average, use

traditional implements like hoes and machetes, and often

do not use appropriate quantity of fertilizers, and

appropriate spacing. The purpose of this study was to

investigate the technical efficiency and resource-

productivity of smallholder soybean farmers located

within Eastern and Northern agricultural zones of Benue

State, Nigeria, the major soybean producing area of the

state.

METHODOLOGY:

Following the 2005/2006 production period, a

survey was conducted in Benue State, the largest

producer of soybean in Nigeria. The two agricultural

zones (Northern and Eastern) were purposively selected

being the major soybean-producing areas of Benue State.

A multi-stage random sampling technique was used for

the selection of the respondents. Two districts were

selected from each zone. Sixteen villages were chosen in

the two zones (8 villages in each zone) as the study area.

Six smallholder soybean farmers were selected from

each village. A total of 96 respondents were used for the

study. A structured questionnaire and/or interview

schedule was used to collect data for the study. Data on

inputs - labour, land and fertilizer and soybean yields

collected from smallholder farmers were used in the

technical efficiency and productivity analysis.

Benue State is located in the middle belt of

Nigeria, approximately between latitudes 6.3o N to 8.1o N

and longitudes 8°E to 10o E. The state is blessed with two

major rivers namely River Benue and River Katsina-Ala.

She has a total land area of 32, 866.25 squared

kilometres (BNARDA, 2000). According to 2006 census

by the National Population Commission, the state has a

population of about 4,219,244 million (Nigerian Muse,

2007) She is referred to as the “food basket of the

nation” because of the abundance of agricultural

resources in the state.

The production technology of the farmers was

assumed to be specified by the transcendental

logarithmic (translog) stochastic frontier production

function, which is stated as:

u> 0

Where:

ln represents the natural logarithm;

βS are parameters estimated;

Σ stands for summation;

j represents the input variables in the second-order term

of the translog model.

Yi = output of soybean harvested for the sample ith

farmer (in kilogrammes);

X1 =total labour used (in man-days);

X2 = total land area planted to soybean (in hectares);

X3=total fertilizer used in soybean production

(in kilogrammes);

Vi = random errors that are assumed to be independent

and identically distributed as N (0, σv2) random

variables; and

Ui = non-negative technical inefficiency effects that are

assumed to be independently distributed among

themselves and between Vis such that Ui is defined by the

truncation of N (0, σv2) distribution.

TE = Exp (-Ui). Technical efficiencies (TE) vary

between zero and one.

Journal of Research in Agriculture (2012) 1(2): 108-113 110

Otitoju et al., 2012

Table 2: Loglikehood - ratio (LR) test of null hypothesis

Null Hypothesis Likelihood-ratio Test statistic Critical value Decision

H0: γ =0 - 31.626 67.04 12.59* Reject H0

* Critical value was obtained from the chi-square (χ2) table.

Variable Sample mean Standard Deviation Minimum value Maximum value

Output (Kilogrammes) 1741.11 1081.99 300 5550

Land (Hectares) 2.19 1.20 0.5 5.5

Labour (man-days) 416.96 222.42 121 1021

Fertilizer (Kilogrammes) 134.22 168.75 50 750

Table 1: Summary statistics for variables used in the productivity and technical efficiency analysis

Source: Computed from field data, 2007

33

1

3

1

;lnln2

1lnln UiViXjXiijxiioyi

i ji

The Maximum Likelihood Estimates (MLE) for all the

parameters of the SFA was estimated with FRONTIER

version 4.1 computer programme (Coelli, 1996).

RESULTS:

The presence of technical inefficiency effects

using the generalised likelihood ratio test is given in

table 2. The test statistic computed had a value of 67.04.

The null hypothesis (there is no technical inefficiency in

smallholder soybean production, H0: γ =0) was rejected

at 5% level of significance, indicating that the

coefficients of the frontier production function are

significantly different from the average production

function estimated with the Ordinary Least Sqaures

(OLS) model (Battese and Coelli, 1988; Ojo, 2003).

Hence, translog model was the preferred model. The

Maximum Likelihood Estimates (MLE) of the

parameters of the stochastic frontier model are presented

in table 3. The signs of the coefficients of land and

fertilizer were positive, but all the coefficients of the

three input variables considered in this study were

significant at 5% level. The estimated gamma parameter

(γ) of translog model of 0.999 indicated that about 99%

of the variation (or differential) in soybean output among

the farmers was due to technical inefficiency.

The estimated elasticities of independent

variables of the translog model (table 4) shows that land

and fertilizer exhibited positive decreasing returns-to-

scale in soybean production, indicating the variables

allocation and use were in the stage of economic range of

the production function (stage II). The elasticity of

labour demonstrated negative decreasing returns-to-scale

in soybean production indicating it was over utilized,

which depicts stage III of the production schema. This

might partly due to the availability of family labour,

which is predominant in Nigerian agriculture. The

returns-to-scale parameter (-2.848), indicated a negative

decreasing returns-to-scale which is less than zero. This

implies that small scale soybean production was in stage

III of the production region. At this stage, every addition

to the production inputs would lead to less than

proportionate addition to output. This stage III never

denotes stage of economic production. The productivity

of the factors could be enhanced by expanding the farm

size at the existing level of labour so that the variable of

111 Journal of Research in Agriculture (2012) 1(2): 108-113

Otitoju et al., 2012

OLS Transloga Variable Parameter Coefficient t-ratio Coefficient t-ratio

Constant β0 0.262 (0.113) 2.323* 20.091 (1.665) 12.068* Ln (labour) β1 -6.576 (4.064) -1.618 -4.314 (0.606) -7.114*

Ln (land) β2 0.960 (0.264) 3.641* 0.961 (0.112) 8.615*

Ln (fertilizer) β3 0.219 (0.593) 0.369 0.505 (0.172) 2.935*

[Ln (labour)]2 β11 0.541 (0.369) 1.467 0.340 (0.0564) 6.032*

[Ln (land)]2 β22 -0.826 (0.385) -2.215* -0.595 (0.0721) -8.251*

[Ln ( fertilizer)]2 β33 0.453 (0.0132) 0.344 -0.0570 (0.0437) -1.305

Ln (labour) x Ln (Land) β12 0.211 (0.0518) 4.063* 0.182 (0.0135) 13.50* Ln (labour) x Ln (fertilizer) β13 -0.044 (0.112) -0.397 0.0851 (0.0304) -2.800*

Ln (land) x Ln (fertilizer) β23 -0.029 (0.104) 0.285 0.808 (0.0271) 2.978*

Total variance σs2 0.126 0.867 (0.162) 5.365*

Gamma γ - 0.999

(0.0000000235)

4.262 x 107*

Loglikelihood function Llf -31.626 1.893

Table 3: The maximum likelihood estimates (MLE) of the stochastic frontier production function for

smallholder soybean farmers

* Significant at 5% level aPreferred model

OLS means Ordinary Least Square

Translog means Transcendental Logarithmic

labour used could move from stage III to stage II in the

production curve.

Table 5 shows the technical efficiency estimates

for smallholder soybean farmers. The predicted technical

efficiencies differ substantially among the farmers,

ranging between 0.254 and 0.999, with the mean

technical efficiency estimated to be 0.718. It shows that

about 79% (79.17%) of the farmers had technical

efficiency exceeding 0.60 and about 21% (20.80%) had

technical efficiency of less than 0.60.

CONCLUSION

This study observed that technical efficiency of

smallholder soybean farmers varied due to the presence

of inefficiency of soybean farmers in the use of

productive resources in their production activities. The

technical efficiencies of smallholder soybean farmers

clustered around 0.61 and 0.70 range. Although the

farmers were small-scale and resource poor, they were

fairly efficient in the use of their resources as the result

predicted. The mean technical efficiency score was

0.718. This indicates that technical efficiency can be

increased by about 28% through better use of available

resources, while using the present technology. Labour

saving practices has to be introduced for productivity and

technical efficiency to be improved in soybean

production.

ACKNOWLEDGEMENT

This article emanates from the M.Sc. dissertation

of the corresponding author. He wishes to express

worthy thanks to Prof. C. J. Arene who supervised the

work and also Mrs. J. A. C. Ezihe and Mr. Mathias

Omole for their valuable contributions which helped to

sharpen the focus of the work.

REFERENCES

Ajibefun IA. 2006. Linking socio-economic and policy

variables to technical efficiency of traditional

agricultural production: Empirical evidence from

Nigeria. A poster paper prepared for the 26th conference

of the International Association of Agricultural

Economists, August 12-18, 2006. Queensland Australia.

Arene CJ and Okpupara BC. 2006. Economics of

agricultural production, resource use and development:

An introduction to the micro and macro level

perspectives,Nsukka, Nigeria, Prize publishers.

Ayoola GB. 2001. Essays in the agricultural economy: A

book of readings in agricultural policy and

administration in Nigeria. 1:1-5. Ibadan: TMA

publishers.

Battese GE and Coelli TJ. 1988. Prediction of firm

level technical efficiencies with a generalised frontier

production function and panel data. Journal of

Economics. 6:21-37.

Benue State Agricultural and Rural Development

Authority (BNARDA). 2000. Agricultural production

recommendation for Benue. Extension bulletin 3.1-20.

Journal of Research in Agriculture (2012) 1(2): 108-113 112

Otitoju et al., 2012

Variable Elasticity

Labour -4.314

Land 0.961

Fertilizer 0.505

Returns-to-scale (RTS) -2.848

Table 4: Elasticities of Production and

Returns-to-Scale for Smallholder Soybean Farmers

Source: Computed from field data, 2007

Efficiency level Frequency Percentage

0.91 - 1.00 17 17.71

0.81 - 0.90 15 15.63

0.71 - 0.80 13 13.54

0.61 - 0.70 31 32.29

0.51 - 0.60 13 13.54

0.41 - 0.50 4 4.16

< 0.40 3 3.13

Total 96 100.00

Mean 0.718 Minimum value 0.254 Maximum value 0.999

Table 5: Frequency Distribution of Technical

Efficiency Estimates of Smallholder Soybean

Farmers

Bishop CC and Toussaint WD. 1958. Introduction to

agricultural economic analysis.New York: John Wiley &

Sons.

Coelli TJ. 1996. A guide to FRONTIER version 4.1: a

computer program for stochastic frontier production and

cost function estimation, CEPA working paper 96/07.

University of New England, Armidale, Australia.

Dashiell KE. 1993. Soybean production and utilization

in Nigeria. A paper presented at the National workshop

on small scale and industrial level processing of soybean

held at International Institute of Tropical Agriculture,

Ibadan, Nigeria, 27 - 29 July.

Federal Ministry of Agriculture and Rural

Development Document (FMARD) 2006. (not dated).

Retrieved September 2, from http://www.nigeria.gov.ng

Idachaba FS. 2000. Desirable and workable agricultural

policies for Nigeria in the first decade of the 21st

century. Topical issues in Nigerian agriculture series,

University of Ibadan, Nigeria.

Iwe MO. 2003. The science and technology of soybean:

chemistry, nutrition, processing and utilization. Enugu,

Nigeria: Rejoin communication services Ltd.

Lovell CAK. 1993. Production frontiers and production

efficiency. In: H. O. Fried, C. A. K. Lovell, and S. S.

Schmidt (eds.) The measurement of productive

efficiency. New York: OxfordUniversity Press.

Nigerian Muse. 2007. Provisional figures for 2006

Nigerians census. Retrieved August 6, 2006 from

http://www.nigerianmuse.com.

Obwona M. 2006. Determinants of technical efficiency

differentials amongst small and medium scale farmers in

Uganda.A case of tobacco growers.AERC research paper

152, Nairobi, Kenya.

Ojo SO. 2003. Productivity and technical efficiency of

poultry egg production in Nigeria. International Journal

of Poultry Science 2(6):459-464.

Okoruwa AE. 1999. Soybean processing and utilization

for healthy nutrition in West Africa.IDRC/IITA soybean

utilization project phase III final report, May

1994 - September 1999.

Olaitan SO. 2007. Sustaining reforms in agriculture for

the achievement of the millennium goal in food

production, employment creation and poverty reduction.

A paper presented at the national conference of faculty of

education held between 6 -9 August, 2007, University of

Nigeria, Nsukka.

Presidential Research and Communication Unit

(PRCU). 2003. Recent development in the agricultural

sector.May 7, 2003. Retrieved September 2, 2006 from

http://www.nigeriafirst.com.

113 Journal of Research in Agriculture (2012) 1(2): 108-113

Otitoju et al., 2012

Submit your articles online at www.jagri.info

Advantages

Easy online submission Complete Peer review Affordable Charges Quick processing Extensive indexing You retain your copyright

submit@jagri.info

www.jagri.info/Sumit.php.