




































 AMERICAN INTERNATIONAL JOURNAL OF AGRICULTURAL STUDIES 10(1) (2025), 29-45  

29 

 

      AGRICULTURAL STUDIES 
                                                               AIJAS VOL 10 NO 1 (2025) P-ISSN 2641-4155   E-ISSN 2641-418X 

                                                                                                                      Available online at www.acseusa.org      

                                                                                                                                Journal homepage: https://www.acseusa.org/journal/index.php/aijas 

                Published by American Center of Science and Education, USA 

GENDER DIFFERENTIALS IN FARM LEVEL EFFICIENCY IN 

RICE PRODUCTION AMONG SMALLHOLDER RICE FARMERS 

IN FEDERAL CAPITAL TERRITORY, NIGERIA    
 

 Luka Anthony  (a)1    Christiana Amarachi Ukaoha (b)   

 

(a)Lecturer 1, Department of Agricultural Economics, University of Abuja, Nigeria, E-mail: gqluka11@gmail.com 
(b)Graduate Assistant, Department of Food and Resource Economics, University of Florida, USA; E-mail: christianaukaoha58@gmail.com 
 

 
A R T I C L E I N F O 
 

 

Article History: 
 

Received:18th May 2025 

Reviewed & Revised: 18th May 
to 30th September 2025 

Accepted: 1st October 2024 

Published: 2nd October 2025 

 

 
Keywords: 

 

Farm Level Efficiency Rice,  

Stochastic Frontier, Smallholder Farmers,  
Nigeria  

 

 
JEL Classification Codes: 

 

Q10, Q18 

       

 
Peer-Review Model:  

 

 External peer-review was done through  

double-blind method.  

 
A B S T R A C T 

 
Farm-level efficiency plays a crucial role in achieving the expected agricultural productivity, which can 

lead to sustainability and food security for Nigeria's growing population. However, smallholder rice 

farmers are producing at a level below their potential efficiency. This study evaluated gender 

differentials in farm-level efficiency in rice production among smallholder farmers in the Federal 

Capital Territory, Nigeria. This study employs a random sampling technique. The data were collected 

using well-structured questionnaires, which utilized cross-sectional data from 316 smallholder rice 

farmers. They were analyzed using a stochastic production frontier, stochastic cost frontier, and Tobit 

regression model. The results show that the statistically significant factors influencing technical 

efficiency, allocative efficiency and economic efficiencies of rice production for male and female 

smallholder rice farmers were age (P<0.01), sex (P<0.05), household size (P<0.1), years schooling 

(P<0.05), nonfarm income (P<0.1), access to credit (P<0.01), extension contact (P<0.05), unit price 

(P<0.05) and price information (P<0.1), respectively. The average economic efficiency score index 

obtained by male and female smallholder rice farmers was 51.6% and 55.5%, respectively, and the 

pooled index was 54.6%. The findings of this study suggest that credit facilities should be made available 

to farmers at single digit rate to acquire farm inputs such as seed, fertilizer and agrochemicals timely, 

extension services should be provided to farmers to teach them the application of farm inputs 

appropriately, they should also be encouraged to join cooperative associations to have access to 

production resources easily and market their product collectively to earn higher profit, improve their 

welfare and livelihood. 

 
 

© 2025 by the authors. Licensee ACSE, USA. This open-access article is distributed under the terms 

and conditions of the Creative Commons Attribution (CC BY) license 

(http://creativecommons.org/licenses/by/4.0/).                           

 

INTRODUCTION 

Rice (Oryza sativa L)   is very important crop in Nigeria, it is used for preparation of local delicacies and dishes in our 

homes, and as a special food for ceremonies, and festivals contributing a significant proportion of the food requirements of 

Nigerians (Sanusi et al., 2022). However, the country has not been able to produce sufficient quantity to meet up with 

consumers’ demand for the past years, the production capacity of rice is low and below the national requirements in Nigeria, 

though it is contributing significantly to the food security of the larger population. Smallholder rice farmers mostly produced 

low output and are characterize by inefficient production, aging population and low technological know-how (Aboaba, 

2020). The production of rice in Nigeria rose from 3.7 million metric tons in 2017 to 4.0 million metric tons in 2018, in 

spite the increase in rice production only 57 percent of the 6.7 million metric tons of rice consumed in Nigeria per annum is 

produced locally resulting in a deficit of about 3 million metric tons, which is either smuggled or imported into the country. 

Also in 2020 Nigeria’s rice production was 4.89 million metric tonnes and rose to 5.0 million metric tonnes in 2021. In 

order to stimulate and increase local production, the Government of Nigeria banned importation of rice in 2019 into the 

country. From (2016–2019), Nigeria’s cumulative aggregate agricultural imports between 2016 and 2019 stood at N3.35 

trillion, four times higher than the agricultural export of N 803 billion within the same period. The term efficiency of a firm 

can be defined as its ability to produce the largest possible quantity of output from a given set of inputs. The modern theory 

of efficiency dates back to the pioneering work, who proposed that the efficiency of a firm has two components namely: 

                                                      
1Corresponding author: ORCID ID: 0000-0001-8337-2341 

© 2025 by the authors. Hosting by ACSE. Peer review under the responsibility of the American Center of Science and Education, USA.  

https://doi.org/10.46545/rkcnbe25 

 
To cite this article: Anthony, L., & Ukaoha, C. A. (2025). GENDER DIFFEENTIALS IN FARM LEVEL EFFICIENCY IN RICE PRODUCTION 

AMONG SMALLHOLDER RICE FARMERS IN FEDERAL CAPITAL TERRITORY, NIGERIA. American International Journal of Agricultural 

Studies, 10(1), 29-45. https://doi.org/10.46545/rkcnbe25 

https://doi.org/10.46545/rkcnbe25
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://orcid.org/0000-0001-8337-2341
https://orcid.org/0000-0003-3358-2265


Anthony & Ukaoha, American International Journal of Agricultural Studies 10(1) (2025), 29-45 

  

30 
 

technical and allocative efficiency and the combination of these two components provide a measure of total economic 

efficiency (overall efficiency). Technical efficiency is the ability to produce a given level of output with a minimum quantity 

of inputs. It is measured either as input conserving oriented technical efficiency or output-expanding oriented technical 

efficiency (Ifeanyichukwu et al., 2018). Measurement of farm efficiency via frontier approach has been widely utilized and 

studied (Ifeanyichukwu et al., 2018). The term frontier involves the concept of maximization in which the function sets a 

limit to the range of possible observations. The observation of points below the production frontier for firms producing 

below the maximum possible output can occur, but there cannot be any point above the production frontier given the 

available technology. Deviations from the frontier are attributed to inefficiency.  Despite the contribution of Agriculture to 

the economy, Nigeria’s agricultural sector faces many challenges which impact on its productivity (Aboaba, 2020). These 

include; poor land tenure system, low level of irrigation farming, climate change and land degradation. Others are low 

technology, high production cost and poor distribution of inputs, limited financing, high post-harvest losses, poor access to 

markets and inadequate or low access to credit, despite the increasing importance of formal credit to farmers, rice farmers 

had limited access to credit facilities.  Study by and Addison et al. (2016) showed that that the male and female headed 

households had differences in endowments (land rights, education) and differences access to technologies, factors of 

production, and support services. These differences in turn had implications for the productivity levels and adoption 

capacities of both types of farming households. Gender differentials in relation to farm productivity in subsistence farming 

has been of special interest from the standpoint of public policy in developing countries, as the difference is often viewed 

from the angle of human capital theory and measurement of discrimination. Gender differences in technical efficiency was 

not considered by the authors in the existing literatures in the country for male and female rice farmers, there exists a gap 

on differences in farm level efficiencies across male and female rice farmers. Generally female farmers access to productive 

resources is inadequate and limited them could not have technical training in modern technologies, credit facilities, 

membership of cooperatives, and markets, resulting in low productivity and income inequality between themselves and their 

male counterparts Addison et al. (2016). Hence, bridging these existing gaps for women or female to have access to 

productive and financial facilities is a critical strategy for increasing productivity in the agricultural industry including rice 

sector. Most studies on efficiency focus on technical efficiency (Ebukiba et al., 2020); Ifeanyichukwu et al., 2018) and profit 

efficiency Abdulai, Nkegbe, & Donkoh, 2018; Mabe, Donkoh, & Al-hassan, 2018). The inability of farmers to obtain the 

expected yields could be attributed to the low efficiency level of resource use Tasila Konja et al. (2019) which needs to be 

investigated. The demand for rice in Nigeria has been on increase on daily basis while the supply is low leading to perpetual 

increase in price which is now beyond the capacity of a common man creating demand and supply gap. This study provides 

valuable feedback for policy initiatives to mitigate the gender gap in agricultural productivity as reported by (Sadiq et al., 

2021). Hence, this study provided useful information for policy formulation regarding gender inclusion through estimating 

the identified gap in existing literatures by examining the differences in farm level efficiencies in rice production among 

male and female smallholder rice farmers in the Federal Capital Territory, Nigeria. 

The broad objective of this study is to analyze gender differential in farm level efficiencies in rice production 

among smallholder farmers in Federal Capital Territory, Nigeria. The specific objectives of this study is to estimate the 

gender differences in factors influencing technical efficiency, allocative efficiency and economic efficiency of rice 

production among smallholder rice farmers in the study area. The results show that factors influencing technical efficiency, 

allocative efficiency and economic efficiency were age of the farmers, sex, farming experience, household size, extension 

contact, price information and cooperative association. The results further revealed that average economic efficiency score 

index obtained by male and female smallholder rice farmers were 51.6% and 55.5% and pooled 54.6%, respectively. The 

paper has five sections section one is introduction, section two deals with literature review, section three focuses on materials 

and methods while section four presents the results and discussion and section five draws conclusion and suggested policy 

recommendations from the research. 

 

LITERATURE REVIEW 

Gender is defined as a social construct that identifies the socially expected rights, responsibilities, privileges and obligations 

of males and females. Gender role refers to the socially differentiated roles assigned to males and females (Addison et al., 

2016). Evaluated gender analysis of resource use and efficiency in rice production in Kogi State, Nigeria. The result of the 

analysis revealed that access to productive resources among men and women farmers in the study area is generally low. Yet, 

women access is lower compared to men. The study also, revealed that there is gap in decision making on productive 

resource between" men and women rice farmers: The factors that affects the technical efficiency includes sex, extension 

service, land capital and gender. Evaluated gender gap in agricultural productivity in Nigeria: a commodity level analysis. 

The mean pairwise comparisons was used to examine the extent of gender gap in agricultural productivity, access to inputs 

including other variables; non-parametric quantile regression technique was also applied in order to determine the 

relationship that exist between the use of inputs and gender gaps in total farm outputs. The finding revealed that gender 

there is a gap in farm output which was low with quantity harvested and quantity sold by male managed plots was marginally 

higher than female managed plots by 0.22% and 6.24% respectively. The gender gap in farm productivity are linked to 

farming experience which in longer in favour of men and there are labour market imperfection which is also biased against 

women farmers. A study conducted by Danso-Abbeam et al. (2020) on Gender differentials in technical efficiency of 

Ghanaian cocoa farms using two-stage double bootstrap data envelopment analysis (DEA) procedure in order to estimate 

the technical efficiency level scores for male and female cocoa farm managers. The outcome of the analysis revealed that 

there exists a potential for male and female cocoa farm managers to increase output without changing the quantities of farm 

inputs used. They further applied the Blinder-Oaxaca (B-O) extended version decomposition approach, their findings 

suggested that the female plot managers are on high average less technically efficient when compared with their male 



Anthony & Ukaoha, American International Journal of Agricultural Studies 10(1) (2025), 29-45 

  

31 
 

counterparts. The identified gap in technical efficiency between female and male farmers could be because of disparity in 

their resource endowments. The findings also indicate that differences in educational level attainment, participation in non-

farm activities, and farm size may contribute to the unexplained technical efficiency gap. 

 

MATERIALS AND METHODS 

Materials and Methods 

This study was conducted in Federal Capital Territory, Nigeria. Federal Capital Territory was created and carved out in 

1976 from the Kaduna, Niger, Kwara and Plateau States. Multi-stage sampling technique was adopted and employed for 

this study. The required sample size was determined by the formula and it is specified as follows; 

                                      𝑛 =  
𝑧2. 𝑝. 𝑞

𝑒2
= 316                                                                                            1 

Where; 

𝑛 = Sample Size, Z2= Confidence Level (α = 0.05); p = Proportion of the Population Containing the major interest (29%), 

q = 1-p= 71% and e = error or Precision level. Hence, Z = 1.96;  

                         
(1.96)2. (0.29). (0.71)

(0.05)2
= 316                                                                                 2 

This gives a sample size of 316 smallholder rice farmers in the study area. 

Method of Data Analysis  
Descriptive statistics and Inferential statistics were used for analyzing the collected data from the field. The following 

analytical tools were employed and used to achieve the stated objectives of the study: 

Stochastic Frontier Production Function Model 

This study applied stochastic frontier production function model developed by Aigner et al.  (1977) which is stated as 

follows 

                                            𝑌𝑖 = 𝑓(𝑋𝑖 , 𝛽)𝜖, 𝑖 = 1, … , 𝑁                                                              3 
Where  

                                                 𝜖 = 𝑣𝑖 − 𝑢𝑖                                                                                       4 

The Stochastic Frontier Model is stated thus: 

                                        𝑌𝑖 = 𝐹(𝑋𝑖 , 𝛽) + 𝜖𝑖                                                                                 5 

                                𝑌𝑖 = 𝑓(𝑋1, 𝑋2, 𝑋3, 𝑋4, 𝑋5, 𝑋6, 𝑉 − 𝑈𝑖)                                                        6 

𝐿𝑛𝑌𝑖 = 𝛽0 + ∑ 𝛽𝑖

6

𝑖=1

𝐿𝑛𝑋𝑖 + ⋯ 𝛽𝑛𝐿𝑛𝑋𝑛 + 𝑉 − 𝑈𝑖                                                                    7 

      

The explicit function is stated thus: 

𝑌𝑖 = 𝛽0 + 𝛽1𝐿𝑛𝑋1 + 𝛽2𝐿𝑛𝑋2 + 𝛽3𝐿𝑛𝑋3 + 𝛽4𝑙𝑛𝑋4 + 𝛽5𝐿𝑛𝑋5 + 𝛽6𝐿𝑛𝑋6 + 𝑉𝑖−𝑈𝑖         8 

Where, 

𝑌𝑖 = Total Output of Rice (Kg) 

𝑋1= Seed Input (Kg) 

𝑋2 = Farm Size (Hectares) 

𝑋3 = Quantity of Organic Fertilizer (Kg) 

𝑋4 = Quantity of Inorganic Fertilizer (Kg) 

𝑋5 = Agro-Chemical Input (Litres) 

𝑋6 = Labour Input (Mandays) 

𝛽0 = Constant Term 

𝛽1 − 𝛽6 = Regression Coefficients 

The Technical Inefficiency Component of the Stochastic Frontier Model is stated thus: 

𝑈𝑖 = 𝛼0 − 𝛼1𝑍1 − 𝛼2𝑍2 − 𝛼3𝑍3 − 𝛼4𝑍4 − 𝛼5𝑍5 − 𝛼6𝑍6 − 𝛼7𝑍7 − 𝛼8𝑍8 − 𝛼9𝑍9 − 𝛼10𝑍10

− 𝛼11𝑍11                                                                                                                            9 

Where, 

𝑈𝑖 = 𝑇𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝐼𝑛𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡  

𝑍1= Sex (1, Male; 0, Otherwise) 

𝑍2= Age of rice Farmers (Years) 

𝑍3= Education Level of the rice Farmers (Years Spent Schooling) 

𝑍4= Non-Farm Income (Naira) 

𝑍5= Access to Credit (1, Access; 0, Otherwise) 

𝑍6= Extension Contact (Number of Contact per Month) 

𝑍7= Farming Experience (Years) 

𝑍8= Unit Price of Rice Per Bag  

𝑍9= Price Information Dummy (1, Access; 0, Otherwise) 

𝑍10= Household Size (Number) 

𝑍11= Cooperative Association (1, Access; 0, Otherwise) 

𝛼0 = Constant Term 



Anthony & Ukaoha, American International Journal of Agricultural Studies 10(1) (2025), 29-45 

  

32 
 

𝛼1 − 𝛼11 = Regression Coefficients 

Vi  = Error Term  

Ui =  Measure of Inefficiency 

 

Stochastic Cost Frontier Function 

                           Ci = f(Pi, γ ) exp(Vi + Ui );  i = 1, 2, … , n                                                         10 

               𝐿𝑛𝐶𝑖 = γ 0 + ∑ γ 𝑖

6

𝑖=1

𝐿𝑛𝑃𝑖 + ⋯ γ 𝑛𝐿𝑛𝑃𝑛 + 𝑉 + 𝑈𝑖                                                         11 

The explicit form of the stochastic cost frontier function is specified as:  

LnCi = γ0 + γ1LnP1 + γ2LnP2 + γ3LnP3 + γ4LnP4 + γ5LnP5 + γ6LnP6 + γ7LnP7 + Vi +
Ui                                                                                                                                                                                         12  

Where,  

 Ln = Natural Logarithm  

Ci = Total Cost of Rice Production   

 γ0 = Intercept term   

γ1 − γ6 = Partial Regression Coefficients (Elasticities of Production)  

P1  = Cost of Seed  

P2  = Cost of Land (Rent) 

P3  = Cost of Organic Fertilizer Cow Dung  

P4  = Cost of Inorganic Fertilizer NPK   

P5  = Cost of Agro-chemical Input   

P6  = Cost of Labour Input  

Vi  = Two-sided Idiosyncratic Error Term  

Ui =  Non-negative error term (Measure of Inefficiency) assumed to be truncated below by zero.  

 

The Allocative efficiency of individual farmers is defined in terms of the ratio of the predicted minimum cost (C i*) to 

observed cost (Ci).  Allocative efficiency (AE) of farm-level is computed and obtained by using the relationship: 

                                                     AE =
 Ci ∗

Ci 
 =  exp(μi)                                                                                  13 

Therefore, Allocative efficiency is an inverse function of cost efficiency 
1

𝐶𝐸
 and so ranges between 0 and 1 as used by Okello 

et al., (2019).   

The Allocative Inefficiency Model is Stated as Follows: 

        Ui = α0 − α1 δ1 − α2 δ2 − α3 δ3 − α4 δ4 − α5 δ5 − α6 δ6 − α7 δ7 − α8 δ8 − α9 δ9 − α10 δ10 

− α11 δ11                                                                                                                                   14  
Where:  

Ui = Allocative Inefficiency Component  

δ1 =Sex of Farmers Dummy Variable (1=Male and 0 = Female)  

δ2  =Age of Farmers (Years)  

δ3  = Educational Level (Number of Years of Formal Education)  

δ4 = Non-farm Income (Naira)  

δ5 = Access to Credit (Amount Borrowed in Naira)  

δ6 = Extension Visit (Number of Visit Per Year)  

δ7  = Farming Experience (Years)  

δ8  = Unit Price Per Bag (Naira) 

δ9  = Price Information (Dummy 1, Yes; 0, Otherwise) 

δ10 = Household Size (Number of Persons)  

δ11  = Co-operative Association (1, Access; 0, Otherwise)  

α0 = 𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡  

α1 − α11  = Parameters to be Estimated 

 

Economic Efficiency Model 

Following Haruna et al. (2021) the overall economic efficiency was estimated as: 

𝐸𝐸𝑛 = 𝑇𝐸𝑛 X 𝐴𝐸𝑛                                                                                                                                                                15  

Where,  

EE = Economic Efficiency (Units) 

TE = Technical Efficiency (Units) 

AE = Allocative Efficiency (Units) 

When EE=1, the firm is economically efficient and EE<1 means the firm is economically inefficient. 

 

Tobit Regression Model (ML) 

The Tobit regression model was used to evaluate the socioeconomic factors influencing economic efficiency among 



Anthony & Ukaoha, American International Journal of Agricultural Studies 10(1) (2025), 29-45 

  

33 
 

smallholder rice farmers in the study area. Following as also used by (Aboaba, 2020; Okello et al., 2019; Danso-Abbeam et 

al., 2020; Kamau, 2019).  

The Tobit model is specified as:  

 

𝑦𝑖 = 𝑦∗ = 𝑋𝑖 + 𝜑𝑖+𝜀𝑖                                                                                                                                       16 

The observed 𝑦𝑖  can be defined as: 

𝑦𝑖 =     𝑦∗ 𝑖𝑓 𝑦∗ ≥ 𝜏 

𝑦𝑖 = 𝜏𝑦 𝑖𝑓 𝑦∗ ≤ 𝜏 

            𝑖 = 1,2,3.4 … 𝑛 
 

Where: 

 𝑦𝑖 =  Refers to the observable which is a censored variable that measure economic efficiency among the smallholder rice 

farmers 

𝑦∗ = This is the latent variable which indicates that economic efficiency may be directly observable or it may not be directly 

observable.  

Therefore, Economic efficiency was observed if 𝑦∗ ≥ 𝜏 and unobservable if 𝑦∗ ≤ 𝜏     
𝑋𝑖 = These are the explanatory variables that are in the inefficiency model  

𝜑𝑖 = These are parameters which was estimated 

 𝜀𝑖 = Stochastic, error or disturbance term 

The formula becomes 

1  𝑖𝑓 𝑦∗ ≥ 1 

𝑦𝑖 = 𝑦∗𝑖𝑓 < 𝑦∗ < 1 

0 𝑖𝑓 𝑦∗ ≤ 0 

 

The values were only observed when they fall within the range of 0 and 1. Any value below 0 and above 1 were termed as 

an unobserved latent or hidden variable. 

The Tobit regression model is explicitly specified as follows: 

 

𝐿𝑛𝑌𝑖 = γ 0 + ∑ φ 𝑖
11
𝑖=1 𝑋𝑖 + ⋯ φ 𝑛𝑋𝑛 + 𝜖𝑖               17 

𝑌𝑖 = 𝜑0 + 𝜑1𝑋1 + 𝜑2𝑋2 + 𝜑3𝑋3 + 𝜑4𝑋4 + 𝜑5𝑋5 + 𝜑6𝑋6 + 𝜑7𝑋7 + 𝜑8𝑋8 + 𝜑9𝑋9 + 𝜑10𝑋10 
 

+𝜑11𝑋11𝜖𝑖                                                                                                                                                                        18   
 

Where  

𝑌𝑖 = (𝑦∗ > 0 if a farmer is economically efficient and if 𝑦∗ ≤ 0  if a farmer is economically not efficient) 

𝜑1 = Sex of the Household Head Dummy (1, Male; 0, otherwise) 

𝜑2 = Age of the Farming Household Head (Years) 

𝜑3 = Education Level (Number of Years in School) 

𝜑4 = Non-Farm Income Level of the Household Head (Naira) 

𝜑5 = Access to Credit Dummy (1, Yes; 0, otherwise) 

𝜑6 = Extension Services (Number of Contact) 

𝜑7 =Farming Experience (Years) 

 𝜑8 = Unit Price Per bag (Naira) 

𝜑9 = Price Information (1, Yes; 0, Otherwise) 

𝜑10 = Household Size (Number) 

𝜑11 = Cooperative Association (1, Yes; 0, Otherwise) 

 

RESULTS 

Gender Differences in Estimates of the Factors Influencing Technical Efficiency of Rice Production among Male and 

Female Smallholder Farmers. Table 1 presents the results of the estimates of the parameters included in the stochastic 

frontier production function using maximum likelihood method of estimation. In the technical efficiency component, six (6) 

variables were included in the model, four (4) variables were statistically significant for male smallholder rice farmers they 

are: seed input, farm size, inorganic fertilizer and labour input. Four variables were also significant for female rice farmers 

they were: seed input, farm size, inorganic fertilizer and labour input. The pooled data show that about four (4) variables 

were statistically significant which includes: farm size, inorganic fertilizer, agrochemical and labour input. The estimated 

coefficient of seed input influence total output of rice production for male smallholder farmers positively and it was 

statistically significant at (P<0.05). Seed input was also significant for female smallholder rice farmers (P<0.01). The 

coefficient of seed input for male smallholder rice farmers was (0.1266), for female rice farmers (0.1626). This implies that 

a percentage change in the quantity of seed input planted as results of more usage holding other variables constant will result 

in the increase in the total output of rice for male and female smallholder farmers by 12.6% and 16.3%, respectively. Farmers 

with access to improved seed varieties stand a chance of having optimum yield per hectare. This result is consistent with 

the findings of Aboaba (2020); Okello et al. (2019) who reported positive relationships between the quantities of seed 

planted and the total output of rice.  Farm size influences the total output of rice for male and female smallholder farmers 



Anthony & Ukaoha, American International Journal of Agricultural Studies 10(1) (2025), 29-45 

  

34 
 

positively and was statistically significant at (P<0.01).  The pooled data show that farm size was also statistically significant 

at (P<0.01) probability level. The coefficient of farm size for male rice farmers (0.4561), for female rice farmers (0.3698) 

and for the pooled data (0.4393) signifies that percentage change in farm size due to farm expansion will lead to 45.2% and 

36.9% increase in total output of rice for male and female smallholder rice farmers and 43.9% increase in the total output 

of rice for the pooled sampled smallholder farmers respectively in the study area. This is because land is the primary factor 

for rice production, as land size increases output of rice will also increase in conformity with predicted apriority expectation. 

This is in line with the findings of Yusuf (2022) who reported positive relationship between farm size and output of rice in 

Kwara State, Nigeria. Addison et al. (2016) also reported positive association between output of rice with the farm size and 

suggested that farmers’ yield is sensitive to farm size in Ahafo Ano North District in Ashanti Region of Ghana. Inorganic 

organic fertilizer influences total output of rice for male and female smallholder farmers positively, the pooled sample 

further show that inorganic fertilizer has a positive influence on the total output of rice among smallholder farmers. The 

coefficient of inorganic fertilizer for male rice farmers (0.1148), and for female rice farmers (0.2345) and for pooled data 

(0.2152) were statistically significant at (P<0.10) for smallholder male rice farmers, (P<0.05) for female smallholder rice 

farmers, and (P<0.01) for pooled sampled respondents, respectively. This implies that a percentage change in the quantity 

of inorganic fertilizer applied to the rice farm by smallholder farmers will result in the increase in total output of rice by 

11.5% for male smallholder farmers, 23.4% increase in the total output for female farmers and 21.5% for pooled sampled 

smallholder rice farmers respectively. Inorganic fertilizer increases yield potential of rice when it is applied timely and 

appropriately as a result of additional nutrient added to the soil that could increase rice yield. This result corroborates with 

positive influence between inorganic fertilizer (chemical fertilizer) and output of rice. Agrochemical influence total output 

of rice positively for male and negatively for female farmers, but it was not statistically significant. The pooled data shows 

that agrochemical influence total output of rice negatively among smallholder rice farmers and it was statistically significant 

at (P<0.05) probability level. The coefficient of agrochemical for pooled sample data (-0.1581) implies that a percentage 

change in the quantity of agrochemical applied to rice farm will result in the decrease in the total output of rice by 15.8%. 

This could be as a result of improper application of agrochemical due to lack of technical know-how which could not give 

an effective result in controlling weeds, pests and diseases. This result is in consonance with Yusuf (2022) who posited that 

there is a negative relationship between agrochemical and output of rice in Kwara State Nigeria. The results are contrary to 

the findings of Gela et al. (2019) and Aboaba (2020) who asserted that agrochemicals had positive and significant influence 

on total output of rice production in West Gondar Zone of Ethiopia and Ogun State, Nigeria respectively. The result is also 

in conformity. Labour input influence total output of rice for male and female farmers positively and it was statistically 

significant at (P<0.01). The pooled sample also show that labour input had positive influence on total output of rice among 

sampled smallholder rice farmers and it was statistically significant at (P<0.01) probability level. The coefficient of labour 

input for male was (0.4322), for female (0.3526), and for pooled sample (0.2654), this indicates that a percentage change in 

the labour input supply for rice production will result in the increase in total output of rice by 43.3% for male rice farmers, 

35.3% for female rice farmers and 26.5% for pooled sample respectively. This is consistent with Amaechina and Eboh 

(2017) who reported that labour had positive influence on rice output in Anambara State, Nigeria. The results are also in 

line with findings of Yusuf (2022) who found positive relationship between labour and output of rice. The technical 

inefficiency component revealed that out of eleven (11) variables that was included in the inefficiency model, eight (8) 

variables were statistically significant in influencing technical efficiency for male smallholder rice farmers, while seven (7) 

variables were statistically significant for female smallholder rice farmers, the pooled data show that eight (8) variables 

were statistically significant in influencing technical efficiency of rice production. The negative sign of the coefficients of 

the variables implies a decrease in the technical inefficiency or increase in technical efficiency of rice production among 

smallholder rice farmers in the study area. Gender of the smallholder rice farmers influence technical inefficiency of rice 

production negatively and it was statistically significant at (P<0.01) probability level, the coefficient of the pooled data (-

0.1472) signifies that a unit change in sex meaning a possibility of farmer being a male rice farmer will result in the increase 

in technical efficiency by 14.7% for male sampled smallholder rice farmers in the study area. This is in line with who 

reported that male farmers are more efficient than female counterparts. Age of the sampled smallholder rice farmers 

influence technical inefficiency positively for male farmers and negatively for female farmers and was statistically 

significant at (P<0.01) respectively. The pooled data revealed that age of the sampled smallholder rice farmers influence 

technical efficiency positively implying that a unit change in the age of female farmer will result in increase in the level of 

technical efficiency in rice production positively. The magnitude of the coefficient of the age of male farmers (1.3201), and 

female farmers (-0.01317) and for pooled data (0.0137) implies that a unit change in the age of the farmers will result in 

decrease in technical efficiency for male smallholder rice farmers by 132%, and increase in technical efficiency for female 

rice farmers by 1.3% and increase in technical efficiency by 1.4% for pooled data. This could occur as the male farmers 

become older they don’t take risk and adopt new innovation and technology thereby rendering them technically inefficient, 

this result show that younger farmers were more efficient because younger farmers adopt new technology and innovation 

easily. This conforms to the findings of Yoezer (2023) who reported that younger farmers are more receptive, better 

knowledge and have management skills compared to the older rice farmers. This result is also in line with the findings of 

Wijaya et al. (2022) who reported that age has negative influence on technical efficiency of rice production in East Java just 

as in the case of female rice farmers, this could be so because as farmers grow older they accumulate experience and enables 

them to be familiar with soil management practices and appropriate resource allocation.  Years of schooling influence 

technical inefficiency for male and female smallholder rice farmers negatively, and it was statistically significant at (P<0.10) 

and (P<0.01) respectively. The pooled data also revealed that number of years schooling influence technical inefficiency 

negatively and it was statistically significant at (P<0.01) probability level. The magnitude of the coefficient of years 

schooling for smallholder male rice farmers (-0.0245), female rice farmers (-0.0505) and for pooled data (-0.1052). This 



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indicated that a unit change in the years of schooling for smallholder rice farmers will result in the increase in the technical 

inefficiency by 2.5% for male rice farmers, 5.1% for female rice farmers, and 10.5% for the pooled data. Years schooling 

indicate more knowledge as years spent in school increases knowledge will also increase and the knowledgeable farmers 

adopt new innovation, takes more risks and also enables them to source for information regarding rice production which 

could make them technically efficient. This is in line with Tasila Konja et al. (2019) and Aboaba (2020) who reported 

negative association of education with technical inefficiency meaning educated farmers are technically more efficient than 

those without education.  Access to credit influence technical inefficiency of rice production among smallholder farmers 

negatively for male and female farmers and likewise pooled data and it was statistically significant at (P<0.05) probability 

level for male rice farmers, and (P<0.01) for female farmers and also (P<0.01) for pooled sample respectively. The 

magnitude of the coefficient of access to credit for male rice farmers (-0.0548), for female rice farmers (-1.2832) and for 

polled data (-0.1026). This implies that a unit change in access to credit facilities by smallholder rice farmers will result in 

5.5% increase in technical efficiency for male farmers, and 128.3% increase in technical efficiency for female rice farmers, 

while for pooled sample it leads to 10.25% increase in technical efficiency among the sampled smallholder farmers in the 

study area. This finding show that access to credit plays an important role in rice production, amount of credit access will 

enable the smallholder rice farmers to purchase farm inputs and all the necessary equipment required to improve rice 

production, which could increase technical efficiency among smallholder farmers. The results of this study is in consonance 

with Ebukiba et al. (2020) and Wijaya et al. (2022) who posited that as farmers has more access to credit will lead to 

accessibility of input easily which could increase output of rice thereby increasing technical efficiency among small scale 

farmers. Extension contact influence technical inefficiency for male smallholder rice farmers negatively and was statistically 

significant at (P<0.01) but not significant for female rice farmers, extension contact was statistically significant (P<0.01) 

for pooled sample data. The magnitude of the coefficient of the extension contact for male smallholder rice farmers was (-

0.4369) and for pooled sample (-0.1052), this implies that a unit change in extension contact will result in the increase in 

technical efficiency by 43.7% for male farmers, and 10.5% increase in technical efficiency for pooled sample in the study 

area. Access to extension services equips smallholder rice farmers with knowledge on farm management practices that could 

lead to increase in their technical efficiency. This is in consonance with the findings of Ismail and Mahmud (2023) who 

asserted that farmers with access to extension services stand a chance of having higher technical efficiency than those 

without access to extension services in rice production. Farming experience influence technical inefficiency for male and 

female smallholder rice farmers negatively and it was statistically significant at (P<0.01) and (P<0.05) respectively. The 

pooled sampled data also revealed that farming experience negatively influence technical efficiency and it was statistically 

significant at (P<0.05) probability level. The magnitude of the coefficient of farming experience for male and female farmers 

was (-0.0620) and (-0.5361) respectively and for pooled data (-0.0041), signifies that a unit change in the years of farming 

experience will result in 6.2% and 53.6% increase in technical efficiency or decrease in technical inefficiency for male and 

female smallholder rice farmers respectively in the study area. The pooled data show that a unit increase years of farming 

experience will lead to 0.4% increase in technical efficiency among smallholder rice farmers. Experience in rice farming 

enables farmers to accumulate knowledge on how to manage their rice farms well and also equips rice farmers with technical 

know-how on how to use the available resources effectively. This result is in conformity who reported that farmers with 

accumulated experience of rice farming tends to be more technically efficient than those farmers that has less farming 

experience in rice production Unit price influence technical inefficiency among smallholder rice farmers negatively for both 

male and female rice farmers and was statistically significant at (P<0.01) respectively. The pooled sample data show that 

unit price of rice was also statistically significant at (P<0.05) probability level. The magnitude of the coefficient of unit price 

for sampled smallholder rice farmers was for male rice farmers (-0.5766), for female rice farmers (-0.0132), and for pooled 

sample was (-0.0686). This implies that a unit change in the unit price of rice per bag will result in the increase in the 

technical efficiency of rice production for male rice farmers by 57.7% and 1.3% for female rice farmers, and also the pooled 

sample show 6.9% increase in technical efficiency among smallholder rice farmers respectively. Increase in unit price may 

increases farmers’ income, this could give them ability to have enough money or capital and capacity to purchase farm 

inputs like fertilizer, improve seed varieties, agrochemicals and even purchase farm machineries that could lead to increase 

in technical efficiency and maximize profit. Unit price has influence on rice productivity in Federal Capital Territory, 

Nigeria. Household size influence technical inefficiency of rice production negatively for male and female smallholder rice 

farmers and was statistically significant at (P<0.01) and (P<0.10) probability levels respectively. The pooled data show that 

household size also influences technical inefficiency negatively and was statistically significant at (P<0.01). The magnitude 

of the coefficient of the household size for the sampled smallholder rice farmers were for male rice farmers (-0.4546), while 

for female rice farmers (-0.0048) and for the pooled sample data was (-0.3361). The implication of these is that a unit change 

in the number of household size will result in the increase in technical efficiency for male rice farmers by 45.5% and 0.5% 

for female rice farmers and 33.6% increase in technical efficiency for sampled pooled data. Household with large number 

of persons has more supply of labour which reduce cost of hiring labour and have a chance of expanding their production 

capacity that could lead to increase in technical efficiency. This result is in conformity with the findings of Yoezer (2023) 

and Ebukiba et al. (2020) who found negative relationships between household size and technical efficiency, this could be 

due to fact that household with larger family size has the capacity to supply more resources in terms of household labour 

for rice output production. Cooperative association influence technical inefficiency negatively in the study area but wasn’t 

significant for male rice farmers but it was statistically significant for female smallholder farmers at (P<0.01) probability 

level. The pooled sample also show that cooperative association was statistically significant at (P<0.01). The magnitude of 

the coefficient of cooperative association for female rice farmers was (-0.2351), while for pooled sample was (-0.1883), this 

implies that a unit change for being a member of cooperative association by female smallholder rice farmer will result in 

increase in technical efficiency in rice production by 23.5% and increase in technical efficiency for pooled sample by 18.8% 



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36 
 

among the sampled rice farmers. Cooperative association helps farmers to come together and market their farm produce, 

pull their resources together and purchase farm inputs in bulk at lower price rate. This result is consistent with the findings 

of Wijaya et al. (2022) who reported that being a member of cooperative association will enable farmers to have access to 

price information and farm inputs at a lower cost because the farmers might purchase their inputs in bulk at a low price rate 

which could enable them to save cost and in turn make them technically efficient and maximize profit that could improve 

their welfare and livelihood. 

 

Table 1. Results of the Maxi mum Likelihood Estimates of the Stochastic Frontier Production Function of Rice Farmers in 

the Study Area 

 
Variable Male Farmers                    Female Farmers               Pooled Sample 

Total Output Coefficients Std 

Error 

Z-

Score 

Coefficients Std Error Z-Score Coefficient Std Error Z-Score 

 Seed 

Inputs 

0.1266**    0.0625      2.03    0.1626***     0.0159     10.25    -0.1119    0.0840     -1.33    

Farm Size 0.4516***    0.0889      5.09    0.3698***    0.1318      2.81    0.4393*** 0.0779      5.64    

Organic 

Fertilizer 

0.0505   0.0763      0.66    -0.0243    0.1509     -0.16    -0.0202    0.0681     -0.30    

Inorganic 

Fertilizer 

0.1148*     0.0627      1.83    0.2345**    0.1137      2.06    0.2152***    0.0631      3.41    

Agro-

Chemical 

0.0322    0.0904      0.36    -0.1742     0.1073    -1.62    -0.1582**    0.0710     -2.23    

Labour 0.4327***     0.0812       5.33    0.3526***     0.1167      3.02    0.2654***    0.0455      5.83 

Constant 3.3108      0.5579 5.93    2.7570***    0.7566      3.64    2.7104 0.4501      6.02    

Technical 

Inefficiency 
Model 

         

Gender --------- ------- ----- --------- ------ --- -.1472***    0.0451     -3.26    

Age 1.3201***   0.3793     3.48    -0.0132***    0.0053     -2.49     -

0.0137***    

0.0035     -3.85    

Years 

Schooling 

-0.0245*     0.0129     -1.90    -0.0505***    0.0151 -3.33    -0.1052***    0.0329     -3.19    

Non-farm 

Income 

-0.0538    0.1552     -0.35    -0.0089    0.0189     -0.47     0.0064    0.0076       0.83    

Access to 

Credit 

-0.0548**    0.0234     -2.34    -1.2832***   0.4544      -2.82    -0.1025***    0.0361     -2.84    

Extension 

Contact 

-0.4369***       0.0459      -9.50     0.0019***    0.0032       0.59    -0.1052***    0.0329     -3.19    

Farming 

Experience 

-0.0620***    0.01767     -3.51    -0.5361**    0.2570     -2.09    -0.0041**    0.0016     -2.43 

Unit Price -0.5766***    0.2320    -2.49    -0.0131***    0.0053  -2.47    -0.0685**    0.0333     -2.06    

Price 

Information 

-0.0373    0.0271     -1.38    0.0151    0.0194      0.78     0.0566    0.0355      1.60    

Household 

Size 

-0.4546***    0.0282    -

16.23    

-0.0048*    0.0028     -1.69    -0.3361***    0.0534     -6.29    

Coop 

Association 

-0.1327    0.4444     -0.29    -0.2351*** 0.9527     -2.47    -0.1883***    0.0359     -5.23    

Diagnostic 

Statistics: 
         

Log 
likelihood 

5.6856   80.2339   18.8692                          

Sigma 

square 

0.1881      0.0477      0.0527      

Gama 0.4442      0.0866      0.5105      

*Significant at 10%, ** Significant at 5%, *** Significant at 1% Probability Levels 

Source: Computed from Field Survey Data (2023) 

 

Distribution of Technical Efficiency Level Differences of Male and Female Smallholder Rice Farmers 

Table 2 presents the summary statistics of technical efficiency scores of male and female smallholder rice farmers.  About 

19.2% of the male smallholder rice farmers, 19.4% of female smallholder rice farmers and 19.9% of the pooled sample has 

a technical efficiency score between 41% to 60 % efficiency levels. About 17% of the male rice farmers, 14.18% of the 

female rice farmers and 15.5% of the pooled sampled smallholder rice farmers attained 61% to 80% level of technical 

efficiency respectively.  Most of the smallholder rice male farmers (48.3%), female farmers (47.76%) and pooled sample 

(47.15%) obtained a technical efficiency score of 81-100% respectively. This implies that most farmers were technically 

efficient to certain level. The minimum technical efficiency score obtained by the smallholder rice male farmer was 0.018, 

female smallholder rice farmer was 0.115 and the pooled sampled smallholder rice farmers was 0.0158. The mean technical 

efficiency obtained by smallholder rice male farmers was 70.2%, while female rice farmers obtained mean technical 

efficiency of 68.9% and pooled sample of 69.7% leaving a gap of 29.8% for improvement for male rice farmers, 31.1% for 

female rice farmers and 30.3% for pooled sample, which need to be filled with the existing technology and innovation. The 

best performing smallholder rice male farmer had the maximum technical efficiency of 100% which is the maximum 

attainable technical efficiency when resources are utilized efficiently, while female rice farmer had 98.9% and the pooled 

sample of 100%. The higher technical efficiency scores for smallholder rice male farmer could be attributed to their higher 



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37 
 

labour participation in rice production and larger farm size. This result is in line with the findings of Addison et al. (2016) 

who reported that male farmers are more technically efficient than female rice farmers in Ashanti Region of Ghana and 

Ethiopia respectively. This distribution of technical efficiency among rice farmers in this study collaborates the findings of 

Mabe et al. (2018); Tasila Konja et al. (2019) and Abdulai et al. (2018) who reported similar finding on technical efficiency 

distribution.  

 

Table 2. Distribution of Technically Efficiency Score among Male and Female Rice Farmers in the Study Area 

 
Technical Efficiency Male Farmers Female Farmers Pooled Sample 

Score Frequency  Percentage Frequency Percentage Frequency Percentage 

0-0.2 10 5.49         8 5.97         24 7.59         

0.21-0.4 18 9.9                          17 12.69        31 9.81        

0.41-0.6 35 19.23        26 19.40        63 19.94        

0.61-0.8 31 17.03        19 14.18        49 15.51        

0.81-1 88 48.35       64 47.76       149 47.15       

Minimum  0.0158    0.1152     0.0158     

Maximum  1.0791 0.9891  1.0791 

Mean TE .7024      .6894      0.6968     

Source: Computed from Field Survey Data (2023) 

 

Gender Differences in Factors Influencing Allocative Efficiency of Rice Production among Smallholder Rice Farmers 

The results of the maximum Likelihood estimates of the parameters of the stochastic cost frontier is presented in Table 3. 

The cost of seed had positive coefficient and was statistically significant at (P<0.01) probability levels for both men and 

female rice farmers in influence total cost of rice production among smallholder farmers. The coefficient of cost of seed for 

male rice farmers was (0.2299), for female rice farmers was (0.1799) and for pooled sampled smallholder rice farmers was 

(0.2139). This implies that percentage change in the cost of seed input will result in the increase in the total cost of rice 

production among smallholder rice farmers by 22.9% for male farmers, 17.9% for female farmers and 21.4% for the pooled 

sample. Seed inputs is very important in rice production it determines the quality of rice yield output, an increase in quantity 

of seed planted increases total cost of rice production. This is line with the findings of Aboaba (2020) who reported positive 

influence of cost of seed on total cost of rice production in Ogun State and Abuja, Nigeria. Cost of land rent influence total 

cost of rice production positively for female rice farmers and for pooled sampled data and it was statistically significant at 

(P<0.01) probability level respectively. The coefficient for cost of land rent for female rice farmers was (0.2185) and for 

pooled sample data was (0.0711). This signifies that percentage change in the cost of land rent will result in the increase in 

the total cost of rice production by 21.9% for the female rice farmers, and 7.1% for the pooled sample data. This could be 

so because land is a vital resource that particularly used in rice production any increase in the rent of land will lead to 

increase in the total cost of production. This is in conformity with the findings of Aboaba (2020); Melese et al. (2019) and 

Gela et al. (2019) who reported positive relationship between cost of land and cost of rice production. Total output had a 

positive influence on the total cost of production for smallholder rice male farmer, and it was statistically significant at 

(P<0.01). The coefficient of total output was (0.3800). This implies that a percentage change in the total output of rice 

produced will result in the increase in total cost of rice production by for male farmers by 38%. This result is in line with 

who reported that as the quantity of total output increases more resources would have been purchased thereby increasing 

the total cost of production for male farmers in the study area.    Cost of organic fertilizer influence total cost of rice 

production among smallholder rice farmers positively for both male and female farmers, the pooled data also revealed that 

cost of organic fertilizer has a positive influence on the total cost of rice production. The coefficient of the cost of organic 

fertilizer for male rice farmers is (0.0935), female rice farmers (0.0884) and was statistically significant at (P<0.01) 

probability levels for male, female rice farmers and pooled sampled data respectively. This implies that percentage change 

in the cost of organic fertilizer will result in the increase in total cost of production for male farmers by 9.4%, for female 

rice farmers by 8.3%, and pooled sample by 8.8% respectively. This is in support of the findings who reported positive 

effect of organic fertilizer on total cost of rice production in Adamawa State, Nigeria. Cost of inorganic fertilizer influence 

total cost of rice production for male and female smallholder rice farmers positively, and it was statistically significant at 

(P<0.01) probability levels respectively. The pooled sample data also show that cost of inorganic fertilizer influence total 

cost of rice production positively and was also significant at (P<0.01). The coefficient of the cost of inorganic fertilizer for 

male smallholder rice farmers was (0.2221), for female smallholder rice farmers was (0.1336) and for pooled sample data 

was (0.1584). This implies that percentage change in the cost of inorganic fertilizer will result in the increase in the total 

cost of rice production for male smallholder rice farmers by 22.2%, for smallholder female farmers by 13.3%, and for pooled 

sample by 15.8% respectively. Fertilizer play an important role in provision of required soil nutrient in rice production, as 

the quantity of fertilizer usage increases the cost will likewise increase and it will in turn result in the increase in the total 

cost of rice production. This result is consistent with the findings who posited that cost of inorganic fertilizer increases total 

cost of rice production.  Cost of agrochemical influence total cost of rice production positively for male and female 

smallholder rice farmers and also positively for the pooled sample data and it was statistically significant at (P< 0.01) 

probability level. The coefficient of the cost of agrochemical for male and female smallholder rice farmers were (0.1147) 

and (0.1321) respectively, and the coefficient of cost of agrochemical for the pooled sampled data was (0.1551). This implies 

that percentage change in the cost of agrochemical will lead to increase in the total cost of rice production among male and 

female farmers by 11.5% and 13.2% respectively. The result of the pooled sample data show that percentage change in the 

cost of agrochemical will result in 15.5% increase in the total cost of rice production among smallholder farmers. More 

https://www.tandfonline.com/doi/full/10.1080/23311932.2019.1651473


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usage of the quantity of the agrochemical in rice farm will result in the increase in the total cost of rice production. This 

finding is consistent with the finding of Sadiq et al. (2021) who asserted that an increase in the cost of agrochemical variable 

results in the increase in the total cost of rice production. Cost of labour input influence the total cost of rice production for 

male and female smallholder rice farmers positively. The pooled sampled data revealed that the cost of labour input influence 

total cost of rice production positively. Cost of labour was statistically significant in influencing total cost of production for 

both male and female smallholder rice farmers and for the pooled sample data at (P<0.01) probability level. The coefficient 

of cost of labour input for male smallholder rice farmers was (0.3637), for female smallholder rice farmers (0.1345) and for 

the pooled sample (0.3033). This signifies that percentage change in the cost of labour input will lead to increase in the total 

cost of rice production for male and female smallholder rice farmers by 36.4% and 13.5% respectively. The pooled sample 

data show an increase in total cost of rice production by 30.3%. Employing more labour force in rice production by 

smallholder rice farmers will result in the increase in total cost of production. Cost of labor is the highest cost involved in 

rice production due to different activities that is involved in the production process. This is in consonance with the findings 

of Aboaba (2020) who corroborates that any increase in the cost of labour will result in the increase in the total cost of rice 

production in the study area. The results are in line with the findings who posited that crop production is relatively sensitive 

to labor as number of labor supply increases the cost will increase thereby resulting in the increase in total cost of production. 

The allocative inefficiency component show that sex of the female smallholder rice farmers influences allocative 

inefficiency negatively and it was statistically significant at (P<0.05), the pooled sample was also statistically significant at 

(P<0.10) probability level. The pooled sample indicated the coefficient of gender influence (i.e) farmer being a male 

smallholder rice farmer will result in the increase in the allocative efficiency by 15.2%. This is in line with the findings who 

found that male farmers were efficient in cost allocation in rice production in Gambia. Age of the sampled smallholder rice 

farmers influence allocative inefficiency positively for male farmers and it was statistically significant at (P<0.10). Age had 

negative coefficient for female rice farmers, the pooled sample also show that age influence allocative inefficiency 

positively. The coefficient of the age for male smallholder rice farmers (0.2050), for female rice farmers (-0.2102) and for 

pooled sampled data (0.2802), this signifies that a unit change in the age of the sampled smallholder farmers will result in 

the decrease in allocative efficiency for the male rice farmers by 20.5% and 21% increase in the allocative efficiency for the 

female smallholder rice farmers, the pooled sampled data show that age of the farmers will result in the decrease in the 

allocative efficiency of the smallholder rice farmer by 28.1% in the study area. This could be because the older farmer has 

less possibility of accepting modern approaches and adoption of technologies, young farmers are more cost efficient than 

older farmers because they are more knowledgeable, risk averse and more versatile in sourcing productive information 

regarding input prices. This is in line with who reported that an increase in age makes older farmers less cost efficient.  Years 

schooling of the sampled smallholder rice farmers influence allocative inefficiency negatively for both male and female 

smallholder rice farmers and was statistically significant at (P<0.05). The pooled sample result show that years of schooling 

also influence allocative inefficiency of the smallholder rice farmers negatively meaning that a unit change in the years of 

schooling will result in the increase in allocative efficiency for male and female smallholder rice farmers by 7% and 26.4%, 

respectively. The pooled sample result also indicated that a unit change in the years of schooling will result in the increase 

in the allocative efficiency by 22.4%. Farmers with formal system of education are able to source, acquire, process and 

comprehend important productive information about input mix and production management practices which could increase 

their ability to make informed decisions to adopt technologies and innovations regarding rice production. This in line with 

Abdul et al. (2017) and Sadiq et al. (2021) who found negative relationship between years of schooling and allocative 

efficiency. Formal education of farmers plays critical role in influencing the efficiency of rice production. Nonfarm-income 

influence allocative inefficiency for male smallholder rice farmers negatively and it was statistically significant at (P<0.1) 

probability level. The pooled sample show that non-farm income also positively influence allocative inefficiency of the 

smallholder rice farmers and it was statistically significant at (P<0.10) probability level. The coefficient of the non-farm 

income for male smallholder rice farmers (-0.0649) and pooled (0.0555). This implies that a unit change in the non-farm 

income will result in the increase in the allocative efficiency for the male rice farmers by 6.5% and 5.5% decrease in the 

allocative efficiency for the pooled sampled farmers. Farmers who were engage in off-farm activities has the ability to earn 

income and improved productive efficiency. This agrees with the finding of Wakili and Isa (2015) who found non-farm 

income to have negative influence on allocative efficiency and posited that farmers with non-farm income are more likely 

to adopt new innovation and technologies like improve seeds varieties, fertilizers and agrochemical than their counterparts. 

Access to credit facilities influence allocative inefficiency negatively for male and female smallholder rice farmers and it 

was statistically significant at (P<0.01) probability level. The pooled sample show that access to credit facilities influence 

allocative inefficiency of rice production negatively and was also statistically significant at (P<0.05). The coefficient of 

access to credit for male smallholder rice farmers (-0.2142), while for female smallholder rice farmers (-0.1535) and for 

pooled sample (-0.1486), this signifies that a unit change in the access to credit facilities will result in the increase in the 

allocative efficiency for male smallholder rice farmers by 21.4% and for female farmers by 15.4% and for pooled sample 

by 14.9%. This could occur because access to credit facilities could enable smallholder rice farmers to purchase their farm 

input at appropriate time when the prices are low enabling the farmers to allocate cost efficiently. This result is consistent 

with the findings of Musa et al. (2015) and Haile (2015) who asserted that farmers who has access to credit for rice 

production can afford the improved-yielding inputs such as improved seed varieties, fertilizers and labor saving inputs like 

herbicides. This could increase rice yield for farmers, reduce production cost that can translate into increase in productivity 

and profitability. Extension contact influence allocative inefficiency for male and female smallholder rice farmers negatively 

and was statistically significant at (P< 0.05) and (P<0.01) respectively. The pooled sample further show that extension 

contact had negative coefficient and was statistically significant at (P<0.1) probability level. The coefficient of the extension 

contact for smallholder male rice farmers (-0.0714), for female rice farmers (-0.5958) and for the pooled sample (-0.1223). 



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This implies that a unit change in the extension contact will result in 7.1% and 59.6% increase in the allocative efficiency 

for both male and female smallholder rice farmers, the pooled sample indicate 12.2% increase in the allocative efficiency 

for the sampled smallholder rice farmers in the study area. This could occur as a result or due to advices that the extension 

officers may proffer to the rice farmers on how to utilize production inputs such as inorganic fertilizer, agrochemical and 

improved seed varieties in order to avoid waste such that the farmers will not over utilize their available resources and as a 

result it may reduce cost of purchasing input among farmers and improve allocative efficiency. This result is consistent with 

the findings who found negative relationship between extension contact and allocative inefficiency. Farming experience 

influence allocative inefficiency for the smallholder female rice farmers negatively and it was statistically significant at 

(P<0.05) probability level. The pooled sample show that farming experience influence allocative inefficiency negatively 

and it was statistically significant at (P<0.05) probability level. The coefficient of the farming experience for smallholder 

female rice farmers (-0.1925) and for pooled sample (-0.1486), this implies that a unit increase in the years of farming 

experience of the smallholder female rice farmers will result in the increase in the allocative efficiency by 19.3% and 14.9% 

increase in the allocative efficiency for the pooled sample in the study area. Farmers who have been planting rice for so long 

of time will be able to predict accurately when to plant, the type of seed to plant and the amount of inputs to utilize in 

production season. Experience accumulated over the years equips farmers with production knowledge that could make them 

effective in cost allocation in rice production, they will be able to purchase their inputs accordingly and avoid waste of 

resources that could lead to increase in cost of production and decrease profit and efficiency level. This is in consonance 

with Yoezer (2023) who reported negative influence of farming experience on rice production inefficiency and explained 

that experienced farmers understand soil and water management and conservation practices more than farmers that are 

inexperienced in rice production which could lead to increase in their allocative efficiency. Unit price of rice influence 

allocative inefficiency negatively for both male and female smallholder rice farmers and it was statistically significant at 

(P<0.05) probability level. The pooled sample show that unit price had negative coefficient and was also significant at 

(P<0.05) probability level. The coefficient of the unit price for smallholder male rice farmers (-0.0713) and for smallholder 

female rice farmers (-0.1121), while for pooled sample (-0.0418), this implies that a unit increase in the unit price of rice 

per bag will result in the increase in the allocative efficiency of the male and female smallholder rice farmers by 7.1% and 

11.2% respectively, and also 4.2% increase in allocative efficiency of smallholder rice farmers for pooled data in the study 

area. Price of rice produce plays a vital role in the production process and is the major determinant of revenue, as price 

increases farmers profit will also increase thereby enabling the farmer to have enough funds to purchase production inputs. 

This conforms to the findings of Khounthikoumane, Chang, and Lee (2021) who asserted that unit price or selling price has 

influence on revenue in rice production as the revenue increases farmers’ ability to allocate cost efficiently increases.  

Household size influence allocative inefficiency negatively for male and female smallholder rice farmers and it was 

statistically significant at (P<0.01) and (P<0.10) respectively. The pooled sample smallholder rice farmers show that the 

household size was statistically significant at (P<0.01). The coefficient of household size for smallholder male rice farmers 

(-0.3846) for smallholder female rice farmers (-0.0037) and the pooled sample (-0.8362), this signifies that a unit change in 

the number of household size by one person will result in the increase in the allocative efficiency for smallholder male rice 

farmers by 38.5% and 0.4% for the smallholder female rice farmers. The pooled sample show that as household size 

increases by one unit it will result in the increase in the allocative efficiency of the smallholder rice farmers by 3.8%. Larger 

household size supplies more labour for rice production and as a result the farmers will not pay for hired labour due to 

availability of family labour thereby leading to increase in the allocative efficiency of the smallholder rice farmers. This 

supports the findings of Musa et al. (2015) who corroborates that most farmers depend on household supply of labour in 

order to increase rice production due to its availability, it is inexpensive and very easy to allocate for different farm activities 

more especially during the period of rice planting, weeding and harvesting season.  Cooperative association influence 

allocative inefficiency negatively for smallholder female rice farmers and it was statistically significant at (P<0.1). The 

pooled sample show that cooperative association had negative coefficient and was statistically significant at (P<0.05) 

probability level.  The coefficient of the cooperative association for smallholder female rice farmers was (-0.2257) and for 

pooled sample (0.0918), this implies that a unit change in the possibility of the smallholder female rice farmers being a 

member of cooperative association will result in the increase in the allocative efficiency for female rice farmers by 22.5%, 

while for the pooled sample will result in the increase in the allocative efficiency by 9.2%.  Cooperative association helps 

farmers to pull their resources together in order to purchase production input in bulk at a lower price, this could lead to 

reduction in cost of production thereby leading to increase in allocative efficiency of the smallholder rice farmers in the 

study area. This result is in line with the findings of Mwangi et al. (2020) and Okello et al. (2019) who found cooperative 

membership to be significantly influencing allocative efficiency and posited that farmers who belong to cooperative 

association or farmers group can have access to production inputs, credit facilities, agricultural training and also linkage to 

product market and this could improve rice farmers productivity as a result of proper and efficient allocation of available 

resources. 

 

Table 3. Results of the Maximum Likelihood Estimates of the Stochastic Cost Frontier Estimates of Rice Production in the 

Study Area 

 
Items Male Farmers                    Female Farmers Pooled Sample 

Variable Coefficients Std 

Error 

Z-

Score 

Coefficients Std Error Z-

Score 

Coefficients Std 

Error 

Z-Score 

 Cost of Seed 0.2299* **   0.0258      8.91    0.1799***    0.0294      6.12    0.2138***    0.0242 8.82    

Total Output 0.3800***   0.0370      10.26    0.0257    0.0280      0.92    0.1713*** 0.0297      5.77    



Anthony & Ukaoha, American International Journal of Agricultural Studies 10(1) (2025), 29-45 

  

40 
 

Cost of Land 

Rent 

0.0069     0.0313      0.22    0.2185***   0.0267      8.18    0.0711*** 0.0277      2.56    

Cost of 

Organic 

Fertilizer 

0.0934***    0.0346      2.70    0.0837*** 0.0266      3.14    0.0884*** 0.0284      3.11    

Cost of 

Inorganic 

Fertilizer 

0.2221* **   0.0371      5.98    0.1336***   0.0259       5.15    0.15848***   0.0272      5.81    

Agro-chemical 0.1147***    0.0271      4.23    0.13209***    0.0195       6.77    0.1551***   0.1551    7.61    

Cost  of 

Labour 

0.3635***    0.0306     11.85    0.1345***  0.1042      5.06    0.3303*** 0.0202     16.38    

Constant 0.6601    0.2892      2.28    1.3177***   0.0951      5.55    0.7138*** 0.2297      3.11    

Allocative 

Inefficiency  
         

Sex -----    -----     ----    ------    ------     ------    -0.1523***    0.0432     -3.52    

Age  0.2050***     0.0270     7.59    -0.2101***  0.0429       4.90     0.2807***  0.1033      2.72    

Years 

Schooling 

-0.0708**    0.0328     -2.16    -0.2644**   0.12021      -2.20    -0.2244***    0.0452     -4.97    

Non-farm 

Income 

-0.0649*     0.0331      -1.96    -0.0420  0.0681 -0.62     0.0555*   0.0324     1.71    

Access to 

Credit 

-0.2142***   0.0314     -6.83    -0.1535***    0.0495     -3.10    -0.1486** 0.0613     -2.42    

Extension 

Contact 

-0.0713**    0.0313     -2.28    -0.5958***   0.1042     -5.72    -0.1224*  0.0645     -1.90    

Farming 

Experience 

 0.0018    0.0016      1.13    -0.1925**   0.0951 -2.02    -0.1486**  0.0612     -2.42    

Unit Price -0.0713**    0.0313    -2.28    -0.1121**  0.0490     -2.29    -0.0918**    0.0418     -2.20    

Price 

Information 

-0.0163 

 

0.0324      -0.53    -0.0614    0.0674     -0.91     0.0090    0.0224      0.40    

Household Size -.3846***   0.0300    -12.81    -0.0036*  0.0021     -1.80    -0.1836***  0.0384     -4.78    

Coop 

Association 

-0.0155    0.0323     -0.49    -0.2257* 0.1155     -1.95 -0.0918 **   0.0417     -2.20    

Diagnostic 

Statistics  
         

Log likelihood 103.57                          1.4332                          150.153   

Sigma square 0.0872      0.0559        0.01601      

Gama 0.0930   0.0226         

*** Significant at (P<0.01) , ** Significant at   (P<0.05) , * Significant at (P<0.10) 
Source: Computed from Field Survey Data (2023)   

 

Differences in Allocative Efficiency Scores Index for Male and Female Smallholder Rice Farmers in the Study Area  

Table 4 shows the distribution of allocative efficiency score index for male and female smallholder rice farmers. The results 

show that about 32.4% of the sampled male and 14.2% of the female smallholder rice farmers attained the allocative 

efficiency score index between 0.41-0.60, the pooled results indicated that about 24.7% were within the ranges of 0.41-0.6.  

Most (52.7%) of the male and majority (67.9%) of the female smallholder rice farmers attained 0.61-0.80 allocative 

efficiency score index, while pooled sample results revealed 59.2% of the sampled smallholder rice farmers were able to 

obtain allocative efficiency score index between 0.81-10.  The minimum allocative efficiency score attained by male and 

female smallholder rice farmer was 0.001 and 0.017 respectively, the minimum allocative efficiency for pooled sample was 

0.00. The maximum value of the allocative efficiency score index for male and female smallholder rice farmers were 1.0791 

and 0.999 respectively, while the maximum allocative efficiency score index for the pooled sample was 1.0994. This implies 

that male farmers obtained 0.01% above the frontier while the pooled sample show that an individual smallholder rice 

farmers obtained allocative efficiency of 0.99% above the frontier indicating the male rice farmers wasted about 0.017 and 

0.99% for pooled of the cost in rice production respectively. This result is in line with reported cost efficiency of 1.16. The 

mean allocative efficiency score index for both male and female smallholder rice farmers was 0.738 (73.8) and 0.798 

(79.8%), the pooled sample show that the average allocative efficiency score index attained by sampled smallholder rice 

farmers was 0.765 (76.5%). This study shows that female smallholder rice farmers were more efficient in cost allocation 

but with a shortfall or gap of 20.2% inefficiency that needed to be filled to reach maximum cost allocative efficiency and 

perfection while male smallholder rice farmers had about 26.2% inefficiency level that need improvement to reach 

maximum cost allocation efficiency while the pooled sample show that there is a gap of 23.5% allocative inefficiency to 

attain optimum level of cost allocation, which needed to be met through adoption of modern technology or new innovation 

to reach maximum allocative efficiency and achieve optimum output for profit maximization by smallholder rice farmers in 

the study area. This result is in line with Abdul et al. (2017); Yenihebit et al. (2020) and Aboaba (2020) who reported similar 

allocative efficiency. This is also in consonance with the findings of Okello et al. (2019); and Gela et al. (2019) who reported 

higher allocative efficiency of 86% and 75% among rice farmers in Adamawa State, Federal Capital Territory, Nigeria and 

Gulu and Amuru Districts of Northern Uganda. 

 

Table 4. Distribution of Allocative Efficiency Scores Among Male and Female Rice Farmers in the Study Area 

 
Allocative Efficiency Male Farmers Female Farmers Pooled Sample 

Score Frequency  Percentage Frequency Percentage Frequency Percentage 

0-0.2 18 9.9 6 4.5 24 7.6         

0.21-0.4 2 1.1 12 9.0 14 4.43        

0.41-0.6 59 32.4 19 14.2 78 24.7        



Anthony & Ukaoha, American International Journal of Agricultural Studies 10(1) (2025), 29-45 

  

41 
 

0.61-0.8 7 3.8 6 4.5 13 4.11        

0.81-1 96 52.7 91 67.9 187 59.2       

Minimum  0.001  0.017  0.001     

Maximum  1.09 0.999  1.099 

Mean AE 0.738     0.798  0.765      

Source: Computed from Field Survey Data (2021) 

 

Factors Influencing Economic Efficiency of Rice Production among Male and Female Smallholder Rice Farmers  

The factors influencing economic efficiency using maximum likelihood estimates using tobit regression model is presented 

in Table 5. The factors are shown below: 

Gender of the household head for sampled smallholder rice farmers had positive influence on the economic 

efficiency and was statistically significant for the pooled data (P<0.10). The marginal effect of the pooled sample was 

(0.1393), this implies that if a farmer happened to be male farmers, will result in the probability of increase in the economic 

efficiency of the smallholder rice farmers by 13.9%. This is in line with the findings of Melese et al. (2019) who report. ed 

positive influence of sex on rice production in Ethiopia.   

Age of the smallholder rice farmers has a negative influence on the level of economic efficiency for male rice 

farmers and has a positive influence for female smallholder rice farmers, while the pooled result show that age of the sampled 

smallholder rice farmers influences economic efficiency positively. Age was statistically significant at (P<0.05) for male, 

and (P<0.01) for female rice farmers, and (P<0.10) for pooled sample. The marginal effect of the age for male and female 

smallholder rice farmers was (-0.3736), (0.1898) and pooled (0.0941) respectively. This implies that a unit change in age 

will result in the decrease in economic efficiency of the male smallholder rice farmers by 37.4%, and will result in the 

increase in the probability that the economic efficiency of the female smallholder rice farmers will increase by 18.9%, while 

the pooled results show that a unit change in the age of the sampled smallholder rice farmers will result in the increase in 

the probability that economic efficiency of the smallholder rice farmers will increase by 9.4% in the study area. The 

implication of this result is that younger male farmers were more economically efficient than the older male farmers, this 

could be because younger farmers are more energetic, risk averse and adopt new technology and innovation easily, while 

for female farmers the older farmers were more  economically efficient than the younger ones this could happen because as 

they acquire more experience as a result of their age it could make them familiar with the farm management practices that 

might make them economically efficient than the younger female farmers, this could be so because most younger female 

farmers don’t own their own farm. This result is line with the findings of Aboaba (2020) who asserted that age of the farmers 

had positive effect on economic efficiency of rice production. Years schooling influence economic efficiency for male and 

female smallholder rice farmers positively and was statistically significant at (P<0.05) for both male and female smallholder 

rice farmers respectively. The pooled sample show that years of schooling had positive influence on the economic efficiency 

of the smallholder rice farmers and was statistically significant at (P<0.01). The marginal effect of the years of schooling 

for male rice farmers (0.1533), for the female rice farmers (0.0083) and for the pooled sample was (0.1687), this implies 

that a unit change in the years of schooling for the smallholder rice farmers will result in the increase in the probability of 

economic efficiency by 15.6% for male rice farmers, 0.8% for female rice farmers, and 16.9% for pooled sample 

respectively. Farmers with formal education has the ability to acquire information on input mix that could increase their 

decision-making process on production practices, more educated farmers have the capacity to perceive, interpret and know 

how to respond to any new information regarding new method of production and adopt innovation and improve technologies 

like fertilizers, agrochemical and planting materials faster than their counterparts. This result confirms the finding of Aboaba 

(2020); Okello et al. (2019) and Gela et al. (2019) who reported a positive influence of years of schooling between education 

and economic efficiency and posited that educated farmers are more economically efficient; it enables them to adopt new 

innovation in rice production.  Non-farm income had positive influence on economic efficiency for both male and female 

smallholder rice farmers, but it was only statistically significant for female smallholder rice farmers at (P<0.01). The 

estimated marginal effect for the sampled female smallholder rice farmers was (0.3268), which indicated that a unit change 

in the level of non-farm income will result in the likelihood of the increase in economic efficiency for the female smallholder 

rice farmers by 32.7%. Non-farm income enables smallholder rice farmers to organize their farming activities properly by 

enabling them to have the capacity to purchase farm inputs at appropriate time which could make them economically 

efficient and maximize profit at optimum level of productivity. This result is contrary to the findings of Melese et al. (2019) 

who reported that non-farm income had negative effect on economic efficiency that farmers which are engaged in non-farm 

income activities tends to allocate more of their time to non-farm income generating activities which could make them 

exhibit lower level of economic efficiency. Access to credit influence economic efficiency positively for both male and 

female smallholder rice farmers and was statistically significant at (P<0.01) and (P<0.05) respectively. The pooled sample 

result revealed that access to credit has positive influence on economic efficiency of the sampled smallholder rice farmers 

and it was statistically significant at (P<0.10) probability level. The marginal effect of the access to credit for male and 

female smallholder rice farmers was (0.2797) and (0.634) respectively. This signifies that a unit increase in the amount of 

credit accessed by the male and female smallholder rice farmers will result in the likelihood or probability of the economic 

efficiency for both male and female smallholder rice farmers to increase by 27.9% and 63.4% respectively. The marginal 

effect of access to credit for the pooled sample was (0.0829), which implies that a unit increase in the access to credit will 

lead to the likelihood or probability of the increase in the economic efficiency of the sampled smallholder rice farmers by 

8.3%. Access to credit enables rice farmers to acquire production inputs with ease and timely which could lead to increase 

in their economic efficiency. This is consistent with Kamau (2019) who asserted that farmers that had access to credit for 

agricultural production has the ability to purchase improved yielding inputs such as improved seed varieties and 

agrochemicals which is labour saving like herbicides which can increase crop yield and reduce production costs that can 



Anthony & Ukaoha, American International Journal of Agricultural Studies 10(1) (2025), 29-45 

  

42 
 

translate into increased productivity and profitability. Musa et al. (2015) and Haile (2015) also reported access to credit 

influencing economic efficiency positively.  

Extension contacts influence economic efficiency of the male and female smallholder rice farmers positively and 

was statistically significant at (P<0.01) for both male and female smallholder rice farmers, while the pooled sample results 

show that extension contact has positive effect on the economic efficiency of the rice farmers and was statistically significant 

at (P<0.01) probability level. This result indicates that, smallholder farmers with more access to extension services during 

the cropping season were more economically efficient than those that had less number of extension contact during 

production period. The marginal effect of the extension contact for both male and female smallholder rice farmers were 

(0.2906) and (0.3091), while that of pooled sample was (0.1596), this implies that a unit change in the probability or 

likelihood of having access to extension services by smallholder rice farmers will result in the increase in economic 

efficiency of the male and female smallholder rice farmers by 29.1% and 30.9% respectively, while for the pooled sample 

will result in the increase in economic efficiency of the rice farmers by 15.9%. Access to extension contact could expose 

rice farmers to better training on how to use their available resources adequately and other extension services like knowledge 

and skills on how to use agrochemicals, improved seed varieties and fertilizer application that could lead to increase in the 

farmers’ economic efficiency. This result is consistent with the findings of Melese et al. (2019); Mwangi et al. (2020) who 

reported positive influence of extension services on rice production in their respective study areas. Farming experience 

influence economic efficiency negatively for male and positively for female smallholder farmers and it was statistically 

significant at (P<0.05) and (P<0.10) probability level respectively. The marginal effect of the farming experience of the 

smallholder farmers for male (-0.0557), female farmers (0.3091), and the pooled result was (0.2432), this implies that a unit 

change in the years of farming experience of the smallholder rice farmers will result in the decrease in the likelihood or 

probability of the economic efficiency of the male smallholder rice farmers to decrease by 5.6%, while the economic 

efficiency of the female farmers will increase by 30.9%. The pooled result show that a unit change in the farming experience 

of the smallholder rice farmers will result in the increase in economic efficiency of the sample smallholder rice farmers by 

24.3% in the study area. This could be because farmers acquire more experience over the years and could enable them to 

use farm inputs appropriately that might make them economically efficient. This result is in conformity with the findings of 

Aboaba (2020) who asserted that farming experience has negative influence on economic efficiency in Ogun State, Nigeria. 

The results are also in line with posited that experienced rice farmers are not economically efficient. This could be true 

because experienced farmers might likely depend on their ideas and knowledge rather than adopting new innovation which 

could improve their productivity and efficiency. As shown in Table 5 the coefficient of farming experience is positively 

related with economic efficiency, this confirms the finding of Haruna et al. (2021) who found positive relationship between 

experience and economic efficiency in rice production in Kogi State, Nigeria and asserted that generally it is expected that 

productivity increases with accumulated years of experience and experienced farmers master production techniques and can 

avoid previous mistakes and they are more likely to take informed decisions in order to improve productivity and 

profitability that will in turn increase economic efficiency. Unit price of rice had positive influence on economic efficiency 

for male and female smallholder rice farmers positively and it was statistically significant at (P<0.05) and (P<0.01) 

respectively. The pooled sample also revealed that a unit price of rice per bag was statistically significant at (P<0.01). The 

marginal effect of the unit price for male (0.271), and female (0.040) and pooled sample (0.0232). This implies that a unit 

change in the unit price will lead to increase in the likelihood or probability of the economic efficiency of rice production 

to increase by 27.1% and 4% for male and female smallholder rice farmers respectively, while the pooled sample indicated 

that it will result in the increase in economic efficiency by 2.3% among smallholder rice farmers. Price information influence 

economic efficiency for male smallholder rice farmers positively and it was statistically significant at (P<0.10) probability 

level. Price information was not significant for female farmers and the pooled sample. The marginal effect of the price 

information for sampled male smallholder rice farmers was (0.1188), which signifies that a unit change in the access to price 

information by smallholder rice farmers will result in the in the likelihood or probability of the economic efficiency to 

increase by 11.9%. Access to information enables farmers to acquire information about prices of output which could enable 

them to sale their product at appropriate time that could increase economic efficiency of the male smallholder rice farmers. 

Household size as presented in Table 5 influence economic efficiency positively for male smallholder rice farmers and 

negatively for female smallholder rice farmers, the pooled sample revealed that household size had positive influence on 

economic efficiency. The marginal effect of household size for male and female smallholder rice farmers were (0.0196) and 

(-0.3255) respectively. This implies that a unit change in the number of household size for male rice farmers will result in 

the increase in the likelihood or probability of the economic efficiency of rice production to increase by 1.9% and decrease 

in economic efficiency for female rice farmers by 32.6%. Male farmers with larger household size has more supply of family 

labour for rice production but in the case of female smallholder rice farmers, household size has negative effect on the 

economic efficiency, this could be that larger household size could make the available resources to be diverted for children 

school fees, hospital bills and feeding that may affect economic efficiency of the female smallholder rice farmers. The 

marginal effect for the pooled sample was 0.1462, this means a unit change in the number of household size will result in 

the increase in the probability of the economic efficiency to increase by 14.6%. As household size increases there will be 

availability of labour supply which could make farmers not to source for hired labour and as a result there will be cost 

savings which could lead to increase in economic efficiency. This is in line with Melese et al. (2019) who opined that a 

larger household size guarantees availability of family labor for farm operations to be accomplished in time in Ethiopia, the 

results further agrees with the findings of Okello et al. (2019) who reported that household size had negative relationship 

with farm efficiency level in Uganda. The result is also in consonance with Aboaba (2020) who posited that household size 

decreases economic efficiency of rice farmers in Ogun State, Nigeria. Cooperative association influence economic 

efficiency of rice production for female smallholder rice farmers positively and it was statistically significant at (P<0.10) 



Anthony & Ukaoha, American International Journal of Agricultural Studies 10(1) (2025), 29-45 

  

43 
 

probability level. The pooled sample results also show that cooperative association had positive influence on economic 

efficiency of rice production among smallholder rice farmers. The marginal effect of the cooperative association for female 

smallholder rice farmers and pooled results was 0.0826 and 0.1248 respectively. This connotes that a unit change in the 

possibility of the female rice farmers and pooled sample being a member of cooperative association will result in the increase 

in the likelihood or probability of the economic efficiency to increase by 8.3% and 12.5% in the study area. This is in line 

with the findings of Yusuf (2022) who asserted that membership of farmers’ cooperative association has positive influence 

on rice production efficiency in Patigi Local Government Area of Kwara State Nigeria.   

 

Table 5. Maximum Likelihood Estimates of the Tobit Regression Model of the Factors Influencing Economic Efficiency of 

Rice Production in the Study Area 

 
Variable 

Economic 

Efficiency 

                  Male Farmers                    Female Farmers                 Pooled Sample 

 Coeffici

ents 

Std 

Error 

t-

value 

ME    Coeffici

ents 

Std 

Error 

t-

value 

ME Coefficie

nts 

Std 

Error 

t-

value 

ME 

Sex ------ -------      -----    ------   ------- ------      ----    ----- 0.1122*     0.0665      1.69 0.1393    
Age -0.0050**    0.0022     -2.26    -0.3736 0.0787***   0.0302      2.61    0.1898    -0.0233*    0.0124 -1.88 0.0941    

Years of 

Schooling 

0.5626**    0.2817      2.00    0.1533 1.1862**    0.5576      2.13    0.0083 0.3545***    0.0609 5.82 0.1687 

Non-Farm 

Income 

0.0127    0.0149      0.84    0.4131    1.0302***    0.2738      3.76    0.3268   8.60e-0   7.22e-7 1.19 0.0126 

Access to 
Credit 

0.2797 
***   

0.1005     2.78    0.2797 1.1448**      0.4915      2.33    0.0160 0.1906*    0.1141 1.67 0.0829 

Extension 
Contact 

0.0043*    0.0023     1.82    0.2906 0.6949*    0.3843      1.81    0.1634 0.1462*** 0.0332 4.40 0.1596 

Farming 

Experience 

-0.2556** 0.1208     -2.12    0.0557 2.8334*    1.6149      1.75    0.3091 0.0608*   0.0320 1.90 0.2432 

Unit Price 0.1953**    0.0949 2.06    0.2713 0.0528***    0.0178      2.96    0.0404    0.6433*** 0.1778 3.62 0.0232    

Price 

Information 

0.2906* 0.1674      1.74    0.1188 -0.1193     0.2468     - 0.48 0.0276 0.0170    0.1251 0.14 -0.1906 

Household 

Size 

0.4131*** 0.1316       3.14    0.0196    -0.0083*    0.0049      -1.68    -0.3255 0.3140*** 0.0645 4.87 0.1462 

Coop 
Association 

0.0267 0.0474      0.56    0.0979 0.3229*     0.1722      1.88       0.0826 0.1879***    0.0669 2.81 0.1248 

 Sigma 0.2403       0.8692      0.890    

Log 

likelihood 

-38.138                             -138.568    -328.88    

Prob >ch2 0.240    0.831      0.859    

*Significant at 10%, ** Significant at 5%, *** Significant at 1% Probability Levels 

Source: Computed from Field Survey Data (2023) 

 

Distribution of Economic Efficiency Scores Among Male and Female Rice Farmers  

The distribution of economic efficiency score level for male and female smallholder rice farmers is presented in Table 6. 

The results also show that 14.8% of the male smallholder farmers and 20.9% of the female rice farmers had an economic 

efficiency between the ranges of 0.21-40, while 17.4% of the pooled sampled smallholder rice farmers were within the range 

of 0.21-0.40. About 37.9% of the sampled male smallholder rice farmers and 30.6% of the female smallholder rice farmers 

attained economic efficiency level of 0.4-0.60.  About 34.8% of the pooled sampled smallholder rice farmers had economic 

efficiency within the ranges of 0.41-0.60 in the study area. The results further show that about 25.3%, 31.3% of the sampled 

male and female smallholder rice farmers attained economic efficiency score between 0.81-10, while the pooled sample 

revealed that 27.9% of the smallholder rice farmers had an economic efficiency within the ranges of 0.81-10. The maximum 

economic efficiency score attained by male and female rice farmers was 1.04 and 0.987 respectively and the pooled was 

1.003. This implies that the best performing male rice farmer obtained economic efficiency 100.4% and the best performing 

female rice farmer attained economic efficiency of 98.7%, while pooled sample show that the best performing smallholder 

rice farmer attained 100%. The average economic efficiency level attained by the sampled smallholder rice farmers were 

male (0.516) 51.6%, female (0.555) 55.5%, and the pooled sample was (0.545) 54.5% in the study area.  There exists an 

economic inefficiency gap of 49.5% for male rice farmers, 45.5% for female rice farmers, and 46.5% for pooled sample that 

need improvement to be filled up with existing technology to attain perfection in the level of economic efficiency among 

sampled smallholder rice farmers. This result is in line with the findings of Okello et al. (2019) who reported 75% economic 

efficiency among rice farmers in Gulu and Amuru districts of northern Uganda and Haruna et al. (2021) who reported 

economic efficiency of 66% for male and 56% for female rice farmers in Kogi State, Nigeria and the results is contrary to 

the findings of Aboaba (2020) who reported higher average economic efficiency of 94% for rice farmers in Ogun state, 

Nigeria. 

 

Table 6. Distribution of Economic Efficiency Scores Among Male and Female Rice Farmers in the Study Area 

 
Economic Efficiency Male Farmers Female Farmers Pooled Sample 

Score Frequency  Percentage Frequency Percentage Frequency Percentage 

0-0.20 40 22.0 23 17.16        63 19.9        



Anthony & Ukaoha, American International Journal of Agricultural Studies 10(1) (2025), 29-45 

  

44 
 

0.21-0.40 27 14.8 28        20.9        55 17.4        

0.41-0.60 44 24.2 21 15.7        65 20.6        
0.61-0.80 25 13.7 20 14.9        45 14.2        

0.81-1.00 46 25.3 42 31.3       88 27.9       

Minimum  0.001  0.008  0.008     
Maximum  1.041 0.987  1.003 

Mean EE 0.516 0.555  0.545 

Source: Computed from Field Survey Data (2023) 
 

CONCLUSIONS 

This study evaluated gender differences in farm level efficiencies in rice production among smallholder rice farmers in 

Federal Capital Territory, Nigeria. Generally, the farmers were efficient to certain level but there was a shortfall in obtaining 

the maximum farm level efficiency for both male and female rice farmers, male farmers were more efficient in technical 

efficiency while female farmers were more efficient in both allocative and economic efficiency. The study shows that the 

seed inputs, farm size, inorganic fertilizer, and labour input had relationship with technical efficiency. The mean technical 

efficiency score obtained by the male and female smallholder rice farmers was 70.2%, and 69.7% respectively. The socio-

economic factors that had significant relationship with technical inefficiencies for male and female smallholder farmers 

were: sex, age, years schooling, and access to credit, extension contact, farming experience, unit price, cooperative 

association and household size. The study further revealed that the significant factors influencing total cost of rice production 

for male smallholder farmers were: cost of seed, total output, cost of organic fertilizer, cost of inorganic fertilizer, cost of 

agrochemical, cost of land rent and cost of labour, the average allocative efficiency score obtained by smallholder rice 

farmers were 73.8% and 79.8% respectively. The socioeconomic factors influencing allocative efficiency for male and 

female smallholder rice farmers were sex, age, years schooling, access to credit, extension contact, farming experience, unit 

price, household size and cooperative association. The significant factors influencing economic efficiencies of the male and 

female smallholder rice farmers were: age, years schooling, access to credit, extension contact, farming experience, unit 

price, price information, nonfarm income, household size and cooperative association. The average economic efficiency 

score index obtained by male smallholder rice farmers was 51.6% while female smallholder farmers obtained 55.5%.

 There exists an economic inefficiency gap of 49.5% for male rice farmers, 45.5% for female rice farmers, and 

46.5% for pooled sample that need improvement to be filled up with existing technology to attain perfection in the level of 

economic efficiency among sampled male and female smallholder rice farmers. Theoretically, this study agreed with the 

theory of production which implies that more usage of farm inputs led to increase in total out of rice and generally leads to 

increase in farm level efficiency, efficient management of production resources could result to maximum profit. Therefore, 

the study made the following policy recommendations: Production inputs like improved seed varieties, inorganic fertilizer 

and agrochemical should be provided to male and female rice farmers by government of Nigeria or Non-Governmental 

Organizations at affordable price or subsidized rate and timely to increase productivity and minimize cost among rice 

producers that will enable them to maximize profit. Credit facilities should be provided at single digit by government and 

financial institutions in order to enable farmers to purchase inputs at affordable price to earn more profit and improve their 

welfare and livelihood. Extension services should be made available to farmers to teach them about the application of farm 

inputs appropriately and they should also be encouraged to join cooperative membership to have access to resources easily. 

Training on additional source of non-farm income should be provided to female rice farmers to enable them to be financially 

independent to make them fully engaged in rice farming. In the course of this research work the researcher encountered 

difficulties in collecting data from the respondent due higher level of illiteracy among the smallholder farmers and lack of 

availability of research funds. Further research should be conducted on resource use efficiency among smallholder farmers, 

how farm level efficiency affects family food security and livelihood. 

 

 
Author Contributions: Conceptualization, L.A.; Methodology, L.A.; Software, L.A.; Validation, L.A.; Formal Analysis, L.A.; Investigation, L.A.; 
Resources, L.A.; Data Curation, L.A.; Writing – Original Draft Preparation, L.A.; Writing – Review & Editing, L.A.; Visualization, L.A.; Supervision, 

L.A.; Project Administration, L.A.; Funding Acquisition, L.A. Authors have read and agreed to the published version of the manuscript.  

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 
groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Acknowledgments: Not Applicable  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 
Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   

 

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