




































 

 

In ternationa l
Scholars
Journa ls

 
African Journal of Agricultural Marketing ISSN 2375-1061 Vol. 8 (10), pp. 001-009, October, 2020. Available 
online at www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s) retain the copyright of this article. 
 
 
 

Full Length Research Paper 
 

Factors influencing output commercialisation among 
small scale farmers in Nigeria 

 

Femi Ogundele 
 

Nigerian Institute of Social and Economic Research (NISER). 
 

Accepted 19 September, 2020 

 

Abstract 
This study presented evidence-based analysis of the degree of commercialisation among small scale maize 
producing households in Nigeria as well as factors that determine their levels of output commercialisation 
using the National Bureau of Statistics Generalise Household Survey wave 2 and 3 panel data. Analysis 
involved computation of the household crop commercialisation index (HCCI) and Tobit regression of the 
determinants of output commercialisation. The results are quite revealing and brought to the fore a number 
of significant findings on output commercialisation among small scale farmers in Nigeria. One of such 
critical findings is the challenge faced by women (in terms of ownership and control) as plot management 
and control as well as decision over earnings from crop sales was dominated by male spouse. Also, while 
increased output is necessary for improved commercialisation, finding suggests that this was only achieved 
through extensification while storage constituted critical challenge. Also, the study underscores the 
significance of output price in driving output commercialisation, storage for the future sale only increased 
marginally due to limited capacity of the households to store for long period. This study, therefore, 
recommends gender inclusive agricultural commercialisation policy and the development of market 
infrastructure (storage and transportation).  
 
Keywords: Crop, output, commercialisation, small scale, farmers. 
 
 
INTRODUCTION 
 
The importance of smallholder farmers in transforming 
the agricultural sector in Nigeria is embedded not only 
in their numerical strength but also in their potentials to 
serve as the fulcrum for ensuring food security and 
achieving poverty reduction if properly harnessed. The 
National Agricultural Sample Survey by National Bureau 
of Statistics (2012) put the population of small scales 
farmers in Nigeria at 18.2 million while that of corporate 
farmers was 171. Meanwhile, one of the major 
characteristics feature of smallholder farms in Nigeria is 
the fact that they are widely dispersed with fragmented  
 
 
 
Corresponding author. E-mail: olorunfemiogundele@gmail.com 

holdings thus, making it difficult for provision of market 
support services and proper integration into the market. 
Aside, production is largely rain fed with less than 1% of 
the arable land irrigated; therefore production varies 
with the amount and distribution of rainfall. This type of 
production system couple with lack of appropriate 
storage facilities, make it difficult for small scale farmers 
to cope with seasonal price volatility which often made 
them to sell their produce during harvesting season at 
very low price. In other words, smallholder farmers in 
Nigeria are price takers and thus, the need for an in-
depth understanding of how they can be properly 
integrated into the output market. 
Meanwhile, commercialization of agricultural output as 
a characteristic of agricultural change is more than



 

 

 
 
 
 
whether or not a cash crop is produce to a certain 
extent in a production system. It can take many different 
forms by either occurring on the output side of 
production with increased marketed surplus or on the 
input side with increased use of purchased inputs. Abu 
(2015) posited that smallholder commercialisation 
requires both well-functioning output market and 
efficient and cost effective factor market that reflect 
opportunity cost of farm inputs. 
Commercialization is the outcome of a simultaneous 
decision-making behaviour of farm households in 
production and marketing. Smallholder 
commercialization in Nigeria encompasses: sale of a 
marketable surplus of traditional crops; diversification 
into the production of new crops; introduction of new 
income generating and post-harvest activities such as 
processing of farm produce.  
All though a number of empirical studies on agricultural 
output commercialisation such as (Adenegan et al, 
2013; Oteh and Nwachukwu, 2014; Falola et al., 2014; 
Olanrewaju et al., 2016 and Ugwu and Alimba, 2018) 
exit in Nigeria, they are, however, localised (limited to a 
particular location) and do not considered crop specific 
analysis as proposed in this study. Even though a few 
of them considered factors that drive market 
participation and degree of output commercialisation, 
none of them examine the inter-temporal shift in the 
degree of output commercialisation as well as changes 
in the drivers over time. Therefore, what remains 
empirically contentious is how the inter-temporal 
changes in factors that drive output commercialisation 
determine the degree of integration of farmers into the 
output market in Nigeria. This is very important as study 
by Murithi and Matz (2014) have shown that improved 
output commercialisation could impact positively on 
livelihood generation. Against this backdrop, the study 
examines factors that influence smallholder output 
commercialization among small scale maize farmers in 
Nigeria and how these factors changes over time. 
 
Theoretical Review 
  
Theoretical literatures on agricultural commercialization 
have proceeded along two main perspectives. These 
are the macro and micro perspectives. From the macro 
perspective, the development economists viewed 
agricultural commercialization as gradual replacement 
of integrated farming systems by specialized 
enterprises for crop, livestock, and poultry and 
aquaculture products. Changes in product mix and input 
uses are determined largely by the market forces during 
this transition. They also considered commercialization 
of agricultural production as an endogenous process 
which is usually accompanied by economic growth, 
urbanization and withdrawal of labour from the 
agricultural sector (Pingali and Rosegrant, 1995).  From 

the micro perspective (farm household level), 
agricultural commercialization refers to the process of 
increasing the proportion of agricultural production that 
is sold by farmers (Pradhan et al., 2010). This view was 
held by the Structuralist and the Agricultural Economist. 
According to the Structuralist, agricultural 
commercialization, means more than the marketing of 
agricultural output, it means the product choice and 
input use decisions are based on the principles of profit 
maximization. Commercial reorientation of agricultural 
production can occur for the primary staple cereals as 
well as for the so-called high value cash crops. On the 
input side, commercialization implies that both traded 
and non-traded inputs are valued in terms of their 
market value (Abu, 2015). Commercialization of 
agricultural systems leads to greater market orientation 
of farm production; progressive substitution out of non-
traded inputs in favour of purchased inputs; and the 
gradual decline of integrated farming systems and their 
replacement by specialized enterprises for crop, 
livestock, poultry and aquaculture products. According 
to Manyong et al., (2005), commercialization, can be 
viewed as the movement from a subsistence production 
to a market-based system of production. It involves 
raising the cash earnings of small-scale, agricultural 
related enterprises. In other words, commercialization 
can be brought about by increasing the unit of output, 
raising the value added or both, and producing for 
domestic and foreign markets.  
 
Empirical Review 
 
Determinants of Agricultural Output 
Commercialisation 
 
Empirical studies on the determinants of agricultural 
commercialisation have proceeded along three major 
lines. The first category of these studies adopted the 
Hickman two stage selections approach and estimated 
probit model with emphasis on factors that drive 
farmers to select into a particular market. Most studies 
in this category do not consider crop specific factors. 
The second category adopted other analytical 
approaches such as OLS and multinomial logit. The 
main emphasis here are factors that determine rate of 
participation. Also here, the quantities or values of 
output offer for sale as proportion of total output are 
often used as index of commercialisation. The third 
category considered the case of censor data and the 
need for crop specific analysis. Such studies have 
adopted the tobit model to analyse the determinants of 
commercialisation focusing on the specificity of the 
market for each crops and this current study belongs to 
this third category.  
Studies across the world have revealed some of the major  

 



 

 

 
 
 
determinants of output commercialisation; however, 
these determinants differ within country due to 
heterogeneous conditions by smallholder farmers. As 
such, in several countries output commercialization 
have been associated with several factors prominent 
among which are landholding size, ownership of 
livestock assets, education, technology, rural 
infrastructure, transaction costs, ownership of 
agricultural implements, market information, wealth, 
family labour force, etc. (Asfaw et.al, 2010, Abu, 2015).   
These factors can be broadly grouped in two categories 
namely household specific and external factors which 
have been broken further into eight by Afework and 
Geta (2016). Household specific factors include quality 
and quantity of household resources such as land, 
labour and capital. The external factors are factors that 
are beyond the smallholder‟s control. They include 
variables such as technological change, population 
growth, introduction of new commodities, infrastructure, 
market facilitating institutions, and public policies, agro-
climatic variables, development of non-farm sector and 
broader economy, rising labour opportunity costs. Sigei, 
et al. (2014) also disaggregate these external factors to 
institutional, market and included political stability of the 
nation, natural disaster and calamities as other external 
factor.  
In a study by Martey, et al (2014), a truncated Tobit 
regression model was used to ascertain determinants of 
commercialization in Effutu Municipality in Ghana with 
major focus on market information. The study analysed 
trends in maize and cassava production using both 
primary and secondary data. They found the extents of 
maize and cassava commercialization as 0.53 and 0.72 
respectively and total agricultural commercialization of 
the two crops as 0.66.  Gender, education, market 
information, farm size, access to land and non-farm 
income were found as significant determinants of 
commercialization of agriculture. In a related study by 
Kabiti et al (2106), Tobit model was adopted to examine 
factors driving smallholder commercialisation in 
Zimbabwe. The paper found that varying factors were 
responsible for the observed level of input and output 
commercialisation and recommended increased public 
and private sector contribution through training and 
financial support. 
Empirical studies in Nigeria on smallholder agricultural 
commercialization are in agreement with some findings 
in other countries. Onoja, et al. (2012) in a study of 
determinants of market participation in Nigerian small-
scale fishery sector used primary data and logistic 
regression model. The authors observed the 
significance of household size, distant to the nearest 
marketing channel, price of the commodity and sex of 
the fish farmer/marketer as determinants of fish 
farmers‟ participation in the market. The authors 
observed the importance of market access noting that 
those who live close to better roads and have more 

frequent and direct contacts with the market appeared 
more willing to produce more systematically for the 
market, while those with poor market access have little 
incentive to produce crops other than those required for 
domestic consumption. Okezie, et al (2012) studied 
factors driving commercialization processes in Abia 
State, Nigeria using panel data, observed some 
degrees of market orientation and identified off-farm 
income as important sources of agricultural 
commercialization. Inputs such as labour, fertilizer and 
planting materials are farm-level determinants of 
commercialization. They sell their labour and invest 
proceeds in subsistence production which implies 
subsistence agriculture tending to input side 
commercialization. 
Also, in another study by Adenegan et al. (2013), factors 
determining cassava farmers‟ market orientation in Nigeria 
were examined with the use of primary data and personal 
observations. The study adopted a combination of Probit 
and Tobit models as well as truncated regression model. 
The study observed the importance of market information, 
education and distance in the household‟s decision on the 
quantity of produce to take to the market. Age was also 
found to be a significant factor. Another study by Oteh and 
Nwachukwu (2014) examined the determinants of 
commercialization of cassava producing households and 
used household commercialization index and multiple 
regression models. Analysis of their primary data showed 
that less than 2 per cent of the households have very high 
commercial orientation. They also found out that output 
value, farm size, nearness to market, membership of 
cooperative and farming experience were significant 
determinants of commercialization in the study area. 
Other studies in Nigeria such as Olanrewaju et al., (2016) 
observed an improved level of commercialisation among 
smallholder farming households in Southwest Nigeria with 
an average household commercialisation index of 0.83 
while Ugwu and Alimba (2018) computed 
commercialisation index for smallholder farmers in 
Southeast Nigeria and found that the index was higher for 
cassava and rice. According to them, major determinants 
of commercialisation include gender, household size, 
processing cost and distance to the market. While studies 
in Nigeria revealed varying degree of output 
commercialisation by smallholder farmers as well as 
variation in the driving factors across the country, none of 
the above studies, however, considered the temporal 
dynamics in household output commercialisation and the 
associated factors which is the main focus of this study. 

 
STUDY APPROACH AND METHODOLOGY 
 
Data Collection  
 
This study made use of secondary data which consisted 
of a set of panel data by National Bureau of Statistics in 
two nationwide surveys conducted under the Generalized   



 

 

 
 
 
 
Household Survey (GHS) wave 2 and 3 in 2013 and 
2015 respectively. The GHS data sets used in this 
study covered pre and post planting season. These two 
data sets provided an ambient for analysing the effects 
agricultural transformation that have taken place in the 
country with respect to commercialization of agricultural 
output among small scale maize producing households. 
 
Analytical Approach 
 
Measuring the Extent of Output Commercialization 
 
For purposes of measuring the extent of crop 
commercialization, this study adopted the approach 
used by Govereh et al., (1999) in a study on 
smallholder commercialization in east and southern 
Africa and which was also used by Strasberg et al, 
(1999) to examining the effects of crop 
commercialization in Kenya. This approach has been 
used by Martey, Al-Hassan and Kuwornu (2012) and 
Olanrewaju et al, (2016). The advantage of this 
approach over other approaches such as profitability 
index, efficiency and productivity index is that apart 
from showing the dynamics of output commercialization 
among the households, it also measures the extent of 
integration of the farming households into the output 
markets. Where panel data is available as the case of 
this study, the index can be used to measure inter-
temporal shift in household levels of commercialization.  
 
The Household Crop Commercialization Index (HCCI) 
is defined mathematically as: 








n

i

ii

n

i

isi

qP

qP

HCCIi

1

1
 X 100   

................................................. (1) 
Where HCCIi is household crop commercialization 
index 
Pi is market price for crop i 
qi  is quantity of crop i offer for sale by the household 
qi  is quantity of crop i harvested by the household 
 
The index above can be interpreted as the ratio of the 
gross value or quantities of crop sold to the gross value 
or total quantities of the crop produced in that particular 
year. This index measures the extent to which 
household crop production is oriented toward the 
market. A value of zero would signify a totally 
subsistence oriented household; the closer the index is 
to 100, the higher the degree of commercialisation.The 
advantage of this approach is that commercialisation is 
treated as a continuum thereby avoiding crude 
distinction between “commercialised” and “non-

commercialized” households. The HCCIieffectively bring 
subsistence food production to the centre of 
discussions about commercialisation. 
 
Analysing the Determinants of Agricultural Output 
Commercialization 
 
Most studies have modeled agricultural 
commercialisation as a two-step analytical approaches 
involving the unobservable decision to commercialise 
and the observed degree or extent of commercialisation 
(Alene et al., 2008). This is the Heckman selection 
approaches which usually ask question “conditional on 
selection into the market and what influences the 
amount supplied by farmers and in most cases, probit 
models are estimated which may not properly capture 
some of the characteristics of a censored data. 
However, in order to properly address the many and 
varied research questions in this study, the Tobit 
regression approach was adopted following Martey, Al-
Hassan and Kumornu, (2012). Although there are 
divers‟ issues related to agricultural commercialisation 
and its determinants as found empirical literatures, in 
order to address trends in commercialisation at 
household level, the central explanatory variables will 
be those related to the farmer household and farm 
characteristics and markets factors.  
 
The general implicit model is simply specified as: 
 
Yj

*
 = f(H, F, M), )      ....................................................... 

(2)  
 
Where, Yj

* 
is defined as percentage of output soldand F, 

H and M are vectors of households, farm characteristics 
and market factors capable of explaining them. Details 
of the various components of each of the explanatory 
variables are listed in Table 1. 
The Tobit regression approach wasemployed to 
quantify the magnitude and direction of the effects of 
the factors influencing commercialisation of the 
smallholder producers (Alene et al., 2008). The Tobit or 
censored normal regression model assumes that the 
observed dependent variables for observations j = 1,…, 
n satisfy:  
Yj = Max (Yj

*
,0 )     

    (3) 
TheYj

*
s  are latent variables generated by the classical 

linear regression model:  
Y*j  = β' Xj+ Uj , Yj = { {Y*j Y*j > 0  and 0 if Y*j  ≤ 0  
   (4) 
Where  Xjdenotes vector of regressorsincluding1 for the 
intercept and β isthe corresponding vector of 
parameters. The model errors Uj are assumed to be 
independently normally distributed:  Uj  ˜ N (0, σ2) 



 

 

 
 
 
           
         Table 1. Description and Measurement of Variables. 

Variable name  Description  Measurement  

Sex  Sex of household head Dummy ( Male=1) 

Age Age of household head Years  

Marital Status Marital Status of household head Dummy (married=1) 

Location Location of households Dummy (Rural=1) 

Crop pattern Cropping pattern adopted by households Dummy (mixed cropping=1) 

Land Area Total land area cultivated to maize by households Hectares  

Store sale Quantity of output stored for future sale Kg 

Store seed Quantity of output store for seed next year Kg  

Plot manage Who control and manages the plot Dummy (female spouse=1) 

Price  Log of market price of maize output N/kg 

 
 
An observation of 0‟s on the dependent variable could 
mean either a “true” 0 or censored data or Yj  would 
always equal Y*j   and the true model would be linear 
regression and not Tobit. Following from the 
aforementioned discussion, the empirical model for 
quantifying the factors which influence the intensity of crop 
output commercialisation is specified as follows: 

 
Yj

*  
= β0 + β1Sex  + β2Age + β3Martstat + β4Loc + β5Croppat + 

β6Area + β7SSale  + β8SSeedj + β9PMN+ β10logP 

      

           (5)  
Where Yj

* 
is the proportion of output of maize that was 

sold. The explanatory variables are as defined in Table 
1. 
 
Marginal Effects 
 
Tobit model parameters do not directly correspond to 
changes in the dependent variable brought about by 
changes in independent variables. According to Greene 
(2003), the marginal effect on the intensity of output 
commercialisation due to changes in the explanatory 
variable is given as follows: 

 



































X j
E

'
'

Xj

Xj

j*Y

   

     (6) 
Thus, Equations 1 and 6 was estimated for small scale 
maize, producing households 
 
RESULTS AND DISCUSSIONS OF THE 
ANALYSES 
 
Changes in Household Socio-Economic and Farm 
Characteristics 
 
The analysis of changes in household and farm charac- 

teristics over the two periods of the study was carried 
out for maize growing households and the results 
presented in Table 2. The results indicated the 
dominance of male headed households in the sample 
and a marginal increase of about 1.5 per cent during 
wave 3. Similarly, the number of household heads that 
were married also increased marginally by about 0.5 
per cent. Crop management practices indicated a 
gradual movement from mixed cropping pattern to 
mono cropping between wave 2 and 3.  Management 
and control of plots continued to be dominated by male 
spouse as proportion of female managed plots within 
the households declined by about 5 per cent during 
wave 3. Similarly, male spouse continued to dominate 
control over earnings from sales of crop as the 
proportion of female spouse that have control over 
earnings from sale of crop fell significantly from 22 per 
cent during wave 2 to 4 per cent during wave 3.  
Storage of crop continued to pose critical challenge as 
the proportion of households that store harvested crop 
fell more than 200 per cent between wave 2 and 3. 
Major reasons for storage of crop included the need to 
make provision for household food consumption, future 
market price speculation as well as provision for next 
season seeds. Meanwhile, the proportion of households 
that store harvest for future market increased marginally 
though  household food security remained the most 
critical reason for storage. The location of households 
could be one of the critical factors for output 
commercialisation. Table 2 indicated that over 90 per 
cent of the households were located in rural areas.The 
location of households was used as a proxy for distance 
to the market which plays significant role in market 
access and participation. The result indicated that more 
than 90 per cent of the households were located in rural 
areas. As such with the dearth of rural transport and 
storage infrastructure the households are likely to face 
some structural constraints that will affect their level of 
market participation.In spite of this, Market participation 
for maize growing households in Nigeria improved 
significantly between wave 2 and 3 as the proportion of  



 

 

 
 
 
 

Table 2. Households and Farm Characteristics by Wave.   

Variables  2011 / 2012 –wave 2 2014/2015 –wave 3 

Frequency  Percentage  Frequency  Percentage  

Sex of household head 
- Female 
-  Male   

 
224 
1625 

 
12.11 
87.89 

 
135 
1031 

 
11.58 
88.42 

Marital Status 
- Not married 
- Married        

 
287 
1563 

 
15.51 
84.49 

 
171 
995 

 
14.67 
85.33 

Sold  Harvested crop 533 28.81 600 51.66 

Crop sales by sex 
- Female 

- Male  

 
94 
439 

 
41.96 
27.00 

 
76 
524 

 
56.30 
50.82 

Cropping pattern 
- Mono cropping  

- Mixed cropping  

 
249 
1601 

 
13.46 
86.54 

 
249 
917 

 
21.36 
78.64 

Plot manager  
- Male spouse 

- Female spouse 
-  Others  

 
1604 
171 
42 
 

 
88.28 
9.41 
2.31 

 
950 
41 
11 

 
94.81 
4.09 
1.10 

Control over earning from crop sales  
- Male spouse 
- Female spouse 
- Others  

 
401 
117 
6 

 
76.53 
22.33 
1.15 

 
952 
41 
9 

 
95.01 
4.09 
0.90 

Store harvested crops 609 32.92 137 11.75 

Main purpose of storage 

- Food  
- Future sale 
- Seed  

 
409 
71 
118 

 
68.89 
11.87 
19.73 

 
85 
17 
35 

 
62.04 
12.41 
25.56 

Location of households  
- Urban  
- Rural  

 
157 
1693 

 
8.50 
91.50 

 
91 
1075 

 
7.08 
92.20 

Source: NBS-GHS 2011/2012 and 2014/2015 Wave 2 (n=1850); Wave 3(n=1166). 

 
 
 
households that sold harvested crop increased from 
about 29 per cent in wave 2 to about 52 per cent in 
wave 3. This is probably driven by increased price of 
the commodity over the two periods (Table 2).  
 
Households Characteristics and Crop 
Commercialisation Dynamics  
 
The summary of the descriptive statistics presented in 
Table 3 showed a progressive increase in average age 
of household head between wave 2 and 3. Also, 
Average quantity of output harvested by households 
increased by about 20.75 per cent during wave 3.  This 
may be as a result of the significant increase in average 
area cultivated by the households which rose from 0.95 
in wave 2 to 1.75 during wave 3. Average price of a kg 
of maize also increased from N7.35 in wave 2 to N8.75 
during wave 3. As such the proportion of output sold 
which is an index of household crop commercialisation 
(HCCI) increased significantly by 32 per cent between 
wave 2 and 3. Olanrewaju et al (2016) estimated HCCI 
of 0.83 among smallholder farming households in the 

Southwest, Nigeria which further confirmed the positive 
changes in HCCI in Nigeria.Given the improved 
changes in HCCI, therefore, what are the factors that 
accounted for the observed commercialisation 
dynamics between the two waves? The result of the 
analysis is presented in Table 3. 
 
Crop Output Commercialization Dynamics and Its 
Determinants 
 
The result of the Tobit regression of the determinants of 
output commercialisation among maize growing 
households for the two waves is presented in Table 4. 
Sincethe linear regression of the parameters of Tobit 
Model  do not directly correspond to changes in the 
dependent variable brought about by changes in 
independent variables, the marginal effect of the 
intensity of output commercialisation due to changes in 
the explanatory variable was estimated following Green 
(2003). Seven variables were found to significantly 
influence output commercialisation during both waves. 
These include sex of household head, marital status of  



 

 

 
 
 
 

Table 3. Descriptive Statistics of Variables.  

Variables  2011/2012- wave 2 2014/2015- wave 3 

N  Mean value N  Mean value 

Mean age of household head (yrs) 1850 53.10 1166 57.60 

Average quantity of output harvested (kg) 1850 2921 1166 3527 

Average quantity sold 533 1314 600 2716 

Proportion of output sold (HCCI) 533 0.45 600 0.77 

Average area of land cultivated (ha) 1.850 0.95 1166 1.75 

Average price of output (N/kg) 1850 7.35 1166 8.75 

Source: NBS-GHS 2011/2012 and 2014/2015. 

 
 
 

Table 4. Marginal Effects of Tobit Regression of Determinants of Output Commercialisation. 

Variables  20111/2012 – wave 2 2014/2015 – wave 3 

Coeff.  Std. err  t-values  Coeff.  Std. err t-values  

Sex of household head 
- female =1  

 
0.0405 

 
0.056 

 
0.72 

 
0.48** 

 
0.179 

 
2.70 

Age of household head 0.0007 0.0007 0.95 -0.003 0.002 -1.37 

Marital status 
- Married =1 

 
0.014 

 
0.053 

 
0.27 

 
-0.449** 

 
0.173 

 
--2.59 

Location of Household 
- Rural =1 

 
0.028 

 
0.039 

 
0.71 

 
-0.318 

 
0,173 

 
-1.83 

Cropping pattern 
- Mixed cropping =1 

 
-0.047 

 
0.052 

 
-0.89 

 
-0.043 

 
0.147 

 
-0.66 

Land area cultivated  -0.022** 0.007 -2.94 0.021** 0.006 3.22 

Storage for sale  0.084** 0.028 2.91 0.099** 0.028 3.50 

Storage for seed  -0.135** 0.0259 -5.22 -0.073 0.072 -1.01 

Plot manager 
- Female spouse = 1 

 
0.071** 

 
0.032 

 
2.21 

 
0.083** 

 
0.032 

 
2.60 

Log of price 0.069** 0.009 7.35 0.069** 0.008 7.79 

Constant 0.019 0.124 0.16 -0.033 0.114 -0.29 

No of observations   314 137   

Log likelihood   98.868 -12.77   

Pseudo R
2
   -1.0443 0.50   

Sources: NBS-GHS 2011/2012 and 2014/2015 ** significant at 1%. 

 
 
 
household, area cultivated, storage for future sale, 
storage for seed, plot management by female spouse 
and output price. The direction of the effects of some of 
the variables however varied between the two waves.  
Headship of households has implication for output 
commercialisation. Results indicated that household 
headed by female significantly improved 
commercialisation particularly during wave 3 when 
output price increased. Also, married household tends 
to significantly influence output commercialisation than 
single headed households though excessive expansion 
in household size may increase the proportion of output 
reserved for household consumption thereby reducing 
output commercialisation.  The coefficient of area of 
land cultivated was negative for wave 2 but positive for 
wave 3. This underscores the progressive increase in 
average area of land cultivated from 0.95 during wave 2 
to 1.75 in wave 3.  Storage for future sale in anticipation 

for price increase positively enhanced output 
commercialization during the two waves.  This shows 
that opportunity for farmers to store their output over a 
period of time will assist them to overcome the problem 
of selling at lower prices that usually accompany the 
harvesting period due to market glut.  Storage for seed 
for the next growing season can, however, be inimical 
to output commercialization as the coefficient carries 
negative sign for the two waves and was highly 
significant particularly during wave 2.  The proportion of 
households that stored output increased from 19.73 in 
wave 2 to 25.56 during wave 3 and this could be traced 
to increase in output price which drives households to 
increase the quantity of output offer for sale during the 
year thereby cutting down on quantity of output stored 
for seed purpose.  When plot is manage and control by 
female spouse even in male headed household, output 
commercialisation significantly increases. The result was 



 

 

 
 
 
 
consistent for the two waves.  Finally, output price was 
found to be a positive and significant determinant of 
output commercialisation during the two waves. The 
stability of these variables over the two period calls for a 
renewedfocus and policy attention targeting on them as 
a way of driving output commercialisation among 
smallholder farmers in Nigeria. 
 Even though the location of the household (proxy for 
distance to market) was not significant probably due to 
the fact that 0ver 90 per cent of the household are 
located in rural areas, the variable exert negative 
influence on output commercialisation indicating 
structural constraints on market access by households 
in rural areas. Similarly, practice of mixed cropping 
reduces commercialisation though it was not significant. 
Mixed cropping enhances diversification and can 
increase total harvested  output from a given land area 
but where it is not properly managed, it can also  
reduce crop specific output which can in turn lead to 
reduction in the quantity offer for sale by households. 
Table 2 indicated that the proportion of household 
practicing mixed cropping reduced by about 8 per cent 
during wave 3. 
 
CONCLUSION 
 
This study has brought to the fore a number of 
significant findings that characterised output 
commercialisation among smallholder farmers in 
Nigeria. The study further underscored one of the 
critical challenges faced by women participation in 
agriculture (issue of ownership and control). Findings 
from this study suggested that plot management and 
control as well as decision over earnings from sales of 
output continued to be dominated by the male 
spouse.Though there was significant improvement in 
market participation over the two periods, storage of 
output constituted critical problem. These were further 
exacerbated by the location of the house (90% in rural 
areas) which imposed serious structural constraints 
occasioned by the dearth of critical market 
infrastructure such as transport and storage in the rural 
areas.Findings also indicated that though increased 
output enhanced commercialisation, output increased 
was only achieved through extensification (increase in 
area cultivated) and finally, the study underscored the 
significant of output price in driving output 
commercialisation nevertheless. 
 
ACKNOWLEDGEMENT 
 
The study from where this article emanated had 
benefited immensely form NISER quality control 
mechanisms which include pair review of the proposal 
and the draft report by colleagues as well as NISER in-
house seminar on the proposal and the draft report. 

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