Content uploaded by Rovelito Narita
Author content
All content in this area was uploaded by Rovelito Narita on Mar 27, 2025
Content may be subject to copyright.
1
A Comparative Profitability Analysis of Hybrid and Inbred Rice Farming in
Bicol Region, Philippines
Ma. Victoria P. Balderas1 , Mae SA. Lustre2, Rovelito L. Narita3*
1,2 Faculty, Agricultural Economics, College of Agriculture and Natural Resources
Central Bicol State University of Agriculture, San Jose, Pili, Camarines Sur, 4418 Philippines
mavic.balderas@cbsua.edu.ph
ORCID: https://orcid.org/0009-0002-6479-6266
mae.lustre@cbsua.edu.ph
ORCID: https://orcid.org/0000-0002-5871-3500
3Faculty, Agricultural Economics and Agribusiness, College of Business Administration
Jose Rizal Memorial State University, Znac, Tampilisan, Zamboanga del Norte, 7116 Philippines
Correspondence: rovelito.narita@jrmsu.edu.ph
ORCID: https://orcid.org/0000-0003-2328-4918
Abstract
Several studies conducted on rice has focused on its productivity advantages between hybrid
and non-hybrid varieties, consistently demonstrating the superior yield performance of hybrid
rice. However, while productivity is crucial, profitability remains a more critical factor in
economic analysis. This study extends beyond productivity evaluation by conducting a
comparative economic analysis between the two production systems, emphasizing cost
structures and profitability. Utilizing data from 323 rice farmers (143 hybrid and 180 inbred),
a multiple linear regression analysis was performed to identify key cost components and farm
attributes influencing profitability. Results indicate that hybrid rice exhibits higher
productivity but incurs substantially greater production costs compared to inbred rice. Seed
expenses emerged as the most significant determinant negatively affecting the profit-cost
ratio in hybrid rice farming, followed by fertilizer, fuel, and machinery rental costs. While
hybrid rice generates higher absolute net returns due to increased yield, its profit-cost ratio is
lower than that of inbred rice. Notably, factors such as training attendance, credit access, and
farming experience had negligible effects on profitability, whereas seed type was the only
statistically significant determinant of net returns. To enhance hybrid rice profitability,
policymakers and stakeholders should consider cooperative-based bulk seed purchasing,
development of cost-effective hybrid varieties through localized hybrid seed production, and
implementation of machinery-sharing programs. Accessible community-based credit schemes
and targeted training on precision input use can help mitigate high input costs, ensuring both
economic sustainability and enhanced profitability for rice farmers.
Keywords: Cost structure, Hybrid rice, Inbred rice, Profitability, Profit-cost ratio, Philippines
JEL codes: Q12, Q16, D24.
2
1.0 INTRODUCTION
Significance of the Study
Rice is a staple food for over half of the world’s population and in the Philippines, it
holds a central role in its diet (Tacio, 2021). The country ranks eighth among the top rice
producers globally (DA, 2022) but despite this, it continuously struggles to achieve self-
sufficiency due to low productivity compared to other rice-producing nations. Factors such as
low mechanization and limited adoption of high-yielding varieties contribute to these
challenges (PhilRice, 2015). Consequently, the country often resorts to importing rice to meet
the growing demand.
Rice production plays a crucial role in employment and rural livelihoods.
Approximately 25 percent of the population relies on agriculture, with rice farming serving as
a primary source of livelihood for many families (Balgos & Digal, 2017). As the population
continues to grow, so does the demand for rice, necessitating improvements in productivity
and profitability among rice farmers.
Recent studies have highlighted the potential of hybrid rice varieties to enhance yield
and profitability compared to traditional inbred varieties. Hybrid rice, a result of crossing two
inbred varieties, has been shown to offer higher yields (Panigrahi et al., 2021). In Bangladesh,
Anwar et al. (2021) found that hybrid rice yields 20-25 percent more than inbred varieties and
has better resistance to pests and diseases. However, adoption has been hindered by high
seed costs and insect infestations. Similarly, Rahaman et al. (2022) reported that despite
seasonal fluctuations in labor and input costs, hybrid rice production remained profitable as
returns outweighed expenses. Despite these benefits, hybrid rice adoption remains low due
to multiple barriers. Studies by David (2007) and Chanda et al. (2019) indicate that factors
such as high seed costs, limited access to resources, and knowledge constraints deter farmers
from transitioning to hybrid rice. Wagan et al. (2015) further supported these findings, noting
that while hybrid rice yields are higher, the increased costs of seeds and inputs discourage
widespread adoption.
Determinants of Rice Profitability: Global Insights
Multiple studies have identified key factors influencing rice profitability, many of which
are relevant to understanding the economic viability of hybrid and inbred rice farming in the
Philippines relevant to the present study. For example, education, access to extension
services, and credit facilities significantly enhance profit efficiency by improving farmers’
decision-making, technology adoption, and financial capability to invest in productivity-
enhancing inputs (Yasin et al., 2024; Keghter et al., 2023; Dang, 2017). In the context of the
study, these factors may play a critical role in determining whether hybrid rice, which typically
requires higher input investments, yields greater profitability compared to inbred varieties.
Farm size, high-yielding variety (HYV) adoption, cropping intensity, and diversification into
3
allied activities such as fisheries and livestock contribute to economic viability by ensuring
higher productivity and income stability (Prasad et al., 2020; Chidiebere et al., 2019). This is
particularly relevant as hybrid rice adoption often requires larger-scale investment and
continuous cropping management, which may not be equally feasible for all farmers,
especially smallholders. Understanding how these structural and management factors
influence profitability can provide insight into whether hybrid rice farmers achieve better
returns than those cultivating inbred rice.
However, constraints such as high production costs, labor expenses, and price
instability reduce profitability, as seen in multiple studies (Kasim, 2021; Mauki, 2023; Zakaria
et al., 2021). These findings align with concerns in the study, particularly regarding whether
the higher input costs associated with hybrid rice—such as seeds, fertilizers, and pesticides
offset its yield advantage. The extent to which farmers can manage these costs effectively will
be a key determinant of hybrid rice profitability in the Philippine setting.
Additionally, socio-demographic factors such as age, farming experience, and
cooperative membership positively impact returns, while large household sizes and limited
access to inputs pose challenges (Oloyede, 2021; Fakkhong et al., 2016). This suggests that
experienced farmers with better institutional linkages and economies of scale may benefit
more from hybrid rice cultivation, whereas resource-constrained farmers may struggle with
adoption. Examining these dynamics within my study will help assess the inclusivity and
sustainability of hybrid rice adoption.
Finally, studies emphasize the need for institutional support, improved irrigation, and
better access to affordable inputs to enhance profitability in rice farming globally
(Kulyakwave, 2020; Fakkhong et al., 2016). These findings highlight the importance of policy
interventions in this study, particularly in evaluating whether government support
mechanisms—such as subsidies, credit programs, and extension services play a role in making
hybrid rice farming more viable and competitive.
Objectives
This study introduces an innovative approach by integrating econometric modeling to
rigorously assess the determinants of profitability by systematically identifying the cost
structures, and economic inefficiencies unique to Philippine rice farming. It hopes to generate
data-driven insights crucial for formulating more effective and sustainable policy interventions
to addresses a critical gap in the literature and provides actionable recommendations for
enhancing farm-level profitability, rather than merely focusing on yield improvements.
Specifically, this study aims to:
1. calculate the economic cost and returns of cultivating hybrid versus non-hybrid rice
varieties; and
4
2. determine key cost structures, farm characteristics, and farmer attributes that
influence the profitability of both hybrid and non-hybrid rice farming in the area.
Hypotheses of the study
1. There is no significant difference in the economic cost and returns between cultivating
hybrid and non-hybrid rice varieties.
2. Key cost structures, farm characteristics, and farmer attributes do not significantly
influence the profitability of hybrid and non-hybrid rice farming in the area.
2.0 Theoretical Framework
The profitability of hybrid versus inbred rice can be examined through multiple
economic theories that explain the relationship between productivity, costs, and profitability.
Schumpeter’s Innovation Theory (1934, 2013) suggests that while technological
advancements such as hybrid rice can enhance productivity, they also introduce higher cost
structures and competitive constraints. Farmers may hesitate to adopt hybrid rice due to high
input costs, market price fluctuations, and lack of institutional support, making its profitability
uncertain.
Similarly, Baumol’s Cost Disease (Baumol & Bowen, 1967) explains that sectors with
high labor costs and slow technological progress often struggle to achieve proportional
profitability gains despite productivity improvements. In rice farming, even with increased
yields from hybrid rice, rising input costs such as seeds, fertilizers, and labor may erode
potential profits.
Complementing this, Production Theory and the Law of Diminishing Marginal Returns
highlight that while increasing inputs can boost output, excessive input use may result in
diminishing returns, meaning that the higher input intensity of hybrid rice does not always
guarantee higher profitability.
Profit Maximization Theory and Cost-Benefit Analysis ties these theories emphasizing
that farmers make production decisions based on revenue and cost considerations. While
hybrid rice may produce higher yields, its overall profitability depends on whether the
increased production costs are justified by revenue gains. Anchoring this study on these
theories provides a cohesive integrated framework for assessing whether hybrid rice adoption
is financially viable, emphasizing that higher productivity alone does not guarantee higher
profitability due to cost structures, diminishing returns, and market constraints.
3.0 Methodology
Description of the Study Area
Camarines Sur is situated in the southern part of Luzon. Its population was 2,068,244,
2020 Census. It has a land area of 5,266.8 square kilometers (2,034 sq mi), it is the largest
province in the Bicol Region. The Bicol Plain is surrounded by mountains at the center of the
province. The economy of Camarines Sur is mostly agriculture-based, a major producers of
agricultural commodities like rice, corn, feed meal, coconut, sugar, abaca, freshwater fish and
livestock. The study was conducted in the top three rice producing municipalities in the
Rinconada area namely Bula, Nabua, and Buhi.
5
Figure 1. Map of the Philippines showing the study areas in Camarines Sur Source: (Google
Maps, 2025)
Sampling Procedure and Data Collection
The data was gathered through a household survey utilizing a total of 323 rice farmers
in which 180 of them are non-hybrid rice users and 143 hybrid users. The Cochran sampling
technique was utilized to determine the sample. Using the technique, the actual sample size
of the rice farmers was determined using the following formula:
:
Where: =sample size, p=estimate degree of variability, q=1-p (constant), Z=+/- 1.96
(statistical table) or linked to 95% significance level, = margin of error or desired precision
level, and N=population size.
(1)
(2)
6
A pre-tested questionnaire validated by experts through content validation and pilot
testing were used. The instrument was pre-tested to 15 non-respondent samples to ensure
its content validity.
Data on cost structures, profitability, and drivers of profitability were gathered through
a simple recall of their production for the recent cropping cycle, typically within the past six
months.
Informed consent was obtained from all participants, and their confidentiality and
anonymity were maintained throughout the study. Participation was entirely voluntary, with
participants free to withdraw at any time. The study was designed to avoid any harm, and data
was accurately collected and recorded. Respect for participants’ dignity and rights was upheld,
and the research was reviewed and approved by an independent ethics committee to ensure
adherence to ethical guidelines.
Data Treatment
Profitability is analyzed using several financial metrics, such as Net Returns, Net Profit-
Cost Ratio and per unit cost and per unit profit. Net Profit is calculated as the difference
between total revenue and total expenses. Per Unit cost and Profit calculates the profit
generated per kilogram of rice. The Net Profit-Cost ratio is calculated by dividing the net profit
by the total costs incurred in the production process. A higher Net Profit-Cost Ratio indicates
that the farm is more profitable, as it is generating higher profits relative to its expenses.
To determine the impact of each expense category on profitability, Multiple Linear
Regression (MLR) analysis was employed estimating the relationship between the dependent
variable (Net Profit-Cost Ratio) and various independent variables (expense categories) such
as labor cost, fertilizer costs, fuel cost, pesticides, machine rentals and seed costs. The Net
Profit-Cost Ratio is used instead of profit per se to mitigate issues of endogeneity,
multicollinearity, and heteroscedasticity that may arise when using raw net profit versus cost
categories. Separate models were developed for hybrid and non-hybrid rice farming systems
to ensure a comprehensive comparative analysis of each system’s unique financial
characteristics.
The Regression Model for Expense Categories and Profitability:
(P) = β0 + β1(labor cost) + β2(fertilizer costs) + β3(fuel cost)
+ β4(pesticides) + β5(machine rentals) + β6(seed costs) + μ
where: P stands for the net profit-cost ratio; β0 for the intercept of the model; β 1…6 slopes
(coefficients) of each expense category and μ the error term.
Net Returns (Profit) in rice farming, is also subjected to regression analysis with factors
such as Farm Experience, Tenurial Status, Ownership of Machinery, Training Attendance,
Credit Access, Type of Seeds used and Membership in Farmer Organizations to determine the
(3)
7
farm characteristics and farmer attributes that influence profitability. The independent
variables are described as follows:
Farming experience is treated as a continuous variable, capturing the number of years
a farmer has been engaged in rice farming.
Tenurial status is categorized into three groups: (1-owners, 2-tenants, and 3-
leaseholders).
Ownership of farm machinery is included as a binary variable, where 1 indicates
ownership and 0 indicates non-ownership. The role of farm mechanization in improving
efficiency and reducing production costs is explored through this variable.
Training attendance is also a binary variable, coded as 1 if the farmer has attended
training programs on rice farming and 0 if otherwise.
Membership in farmer organizations is another binary variable, where 1 represents
membership and 0 represents non-membership.
Access to credit, also a binary variable (1 for access, 0 for no access), examines whether
financial resources enable farmers to invest in better inputs and technology, ultimately
affecting their profitability.
Type of seeds used is classified as a categorical variable, distinguishing between hybrid
and inbred rice varieties.
The Regression Model for factors affecting Profitability:
(Y) = β0 + β1(Farm Experience) + β2(Tenurial Status) + β3(Ownership of Machinery)
+ β4(Training Attendance) + β5(Credit Access) +β6(Membership in Farmer
Organizations) + β7(Type of Seeds) + μ
where: Y stands for the net returns; β0 for the intercept of the model; β1…7 slopes
(coefficients) of each factor influencing net returns and μ the error term.
Model Specification and Transformation
To ensure robustness and validity, different functional forms (linear, log-linear, linear-
log, and log-log) are tested. The log-log model is identified as the best specification for net
profit cost ratio and expense category based on criteria such as adjusted R², mean squared
error (MSE), log-likelihood ratio, and Akaike information criteria (AIC). The log-log model
addresses heteroscedasticity and multicollinearity better than other models.
For the regression of cost categories on the net profit-cost ratio, a Cobb-Douglas
functional form was used, which was subsequently linearized into a log-log specification. In
this manner it allowed for the interpretation of regression coefficients in terms of elasticity,
(4)
8
meaning that percentage changes in cost inputs could be directly related to percentage
changes in the net profit-cost ratio. On the other hand, the log-linear model was used for the
regression of net profit and its determining factors.
The transformed model is:
ln (NPCRi) = β0 + β1 ln (labor cost) + β2 ln (fertilizer costs) + β3ln (fuel cost)
+ β4ln (pesticides) + β5ln (machine rentals) + β6ln (seed costs) + μi
where: ln (NPCRi) the natural logarithm of net profit-cost ratio; β0 for the intercept of the
model; β1…6 slopes (coefficients) of each expense category and μ the error term.
To account for potential non-linearity, the dependent variable (net profit) was
transformed using the natural logarithm, while the independent variables remained in their
original units.
ln (Y) = β0 + β1(Farm Experience) + β2(Tenurial Status) + β3(Ownership of Machinery)
+ β4(Training Attendance) + β5(Credit Access)+β6(Membership in Farmer
Organizations)+ β7(Type of Seeds) + μ
where: ln (Y) stands for the natural logarithm of the net returns; β0 for the intercept of the
model; β1…7 slopes (coefficients) of each factor influencing net returns and μ the error term.
4.0 RESULTS AND DISCUSSION
Profitability of Hybrid versus Inbred Rice Varieties
Table 1 presents a comparative profitability analysis of Hybrid and Inbred rice farming.
Results indicate that Hybrid rice farming, while entailing higher overall costs, ultimately
generates greater net returns or profit compared to Inbred rice farming. A finding that is
consistent with the findings in (Panigrahi et al., 202; Anwar et al.,2021; Rahaman et al., 2022).
The higher profitability of Hybrid rice is likely attributed to its significantly higher yield
potential, a common characteristic of hybrid varieties. This increased yield compensates for
the elevated expenses associated with Hybrid rice cultivation.
A closer look at the cost breakdown reveals that Hybrid rice farming incurs higher costs
across various categories. Cash costs, which include expenses like irrigation fees, labor,
fertilizers, and pesticides, are notably greater for hybrid rice (David, 2007; Wagan et al., 2015;
Chanda et al.,2019). This suggests that Hybrid varieties may demand more intensive
management practices and inputs to maximize their yield potential.
While Hybrid rice farming exhibits higher overall costs and greater profit, Inbred rice
farming demonstrates a slightly better net profit-cost ratio. This metric indicates that for every
peso invested, inbred rice cultivation yields a marginally higher return. However, it is crucial
(5)
(6)
9
to remember that this is a ratio, and the absolute profit earned from Hybrid rice remains
superior.
Table 1. Cost and return analysis of hybrid and inbred rice per hectare,
Bicol Region, 2024 (1 Php = 0.017 USD)
ITEM
HYBRID
(n=143)
INBRED
(n=180)
(Php/ha)
(Php/ha)
Returns:
Sales from Palay
62,314.04
50,248.97
Less: Costs
Cash Costs:
Irrigation fee
3,030.91
1,700.00
Machine rental (plowing)
2,287.75
2,143.17
Labor
9,440.08
5,904.99
Fuel
2,226.47
1,314.77
Interest for loans
699.06
868.90
Palay seeds
4,132.55
2,402.29
Fertilizer
11,573.82
9,310.00
Pesticide
4,363.83
3,197.74
Other Farm supplies
541.38
184.63
Total Cash Costs
38,259.85
27,290.31
Non-Cash Costs:
Harvester-thresher share
5,568.78
5,858.42
Depreciation exp
900.00
921.42
Total Non-Cash Costs
6,468.78
6,779.84
TOTAL COST
44,764.63
33,806.83
NET RETURNS (Profit)
17,549.41
16,442.14
a. Gross Profit Margin (%)
28.16
32.72
c. Net Profit-Cost Ratio
0.39
0.49
d. Per Unit Cost (per kilo)
12.21
11.44
e. Per Unit Profit
4.79
5.56
T- statistics comparing hybrid and inbred rice
Table 2 presents the t-test results comparing the economic costs and returns of hybrid
and inbred rice varieties in the Bicol Region for 2024. The results show that hybrid rice farmers
generate significantly higher sales from palay (Php 62,314.04) compared to inbred rice farmers
(Php 50,248.97), with a t-statistic of 22.51 and a p-value of 0.00, indicating a statistically
significant difference. This means that the sales difference between hybrid and inbred rice
really is statistically significant.
The total cost of production for hybrid rice (Php 44,764.63) is also significantly higher
than for inbred rice (Php 33,806.83), with a t-statistic of 25.83 and a p-value of 0.00. As a
result, despite higher productivity, hybrid rice farmers earn only a slightly higher net returns
(Php 17, 549.41) compared to inbred rice farmers (Php 16,442.14). Statistical analysis reveals
10
that this difference in net returns between the two is not significant (t = 3.39, p = 0.078),
indicating that in terms of profitability, hybrid and inbred rice yield comparable financial
outcomes. These findings shows that while hybrid rice enhances productivity, its elevated
production costs offset the potential income gains. This is not a critique of hybrid rice but
rather an important insight for farmers that effective cost management is crucial to
maximizing the financial benefits of hybrid rice cultivation. Optimizing resource use and
minimizing unnecessary expenses can ensure that the productivity advantage of hybrid rice
translates into higher profitability.
Table 2. T-Test Results Comparing Economic Costs and Returns of Hybrid and Inbred Rice Varieties,
Bicol Region, 2024
Category
Mean
(hybrid)
Mean
(inbred)
t-
statistics
Standard
Error
p -
value
Interpretation
Sales from palay
62,314.04
50,248.97
22.51
0.536
0.001
Significant
Total cost
44,764.63
33,806.83
25.83
0.424
0.001
Significant
Net Returns (Profit)
17,549.41,
16,442.14
3.39
0.326
0.078
Not Significant
Influence of Cost Categories on Net Profit-Cost Ratio for Hybrid Rice Varieties
Table 3 presents the regression analysis, for hybrid rice production using net profit-
cost ratio as dependent variable and various cost items as independent variables. The model
is based on the Cobb-Douglas Production functional form; hence, it is linearized, and in the
interpretation, it uses elasticity to measure the responsiveness of the profit-cost ratio to
changes in various cost components.
Seed expenses exert the strongest negative significant influence among the identified
cost items with elasticity of -0.3284, p value of 0.000 and an effect size of 0.57. This means
that a 1% increase in seed costs results in a 0.328% decrease in the profit-cost ratio holding
other factors constant. This signifies a financial burden of hybrid seeds, underscoring the need
for cost-reduction strategies such as subsidies or bulk purchasing arrangements. Wagan et al.
(2015) found that hybrid rice seeds are significantly more expensive than conventional inbred
seeds, making them a major contributor to the overall high production costs of hybrid rice.
Similarly, Chanda et al. (2019) highlighted that the rising price of hybrid seeds and farm inputs
significantly constrains profitability, creating financial challenges for farmers. On the other
side, findings from David (2007) argue that subsidies on hybrid seeds have been largely
ineffective in increasing adoption due to their high cost. Instead, the study suggested
alternative cost-reduction strategies such as cooperative-led bulk seed purchasing or localized
hybrid seed production to mitigate the financial burden on farmers.
Fuel costs also have a substantial significant negative effect (elasticity = -0.2233, p =
0.000, effect size = 0.56), meaning that a 1% rise in fuel expenditure reduces profitability by
0.223%, emphasizing the energy-intensive nature of hybrid rice farming and the potential
benefits of fuel efficiency measures. Anwar et al. (2021) highlighted that the high cost of
11
mechanized operations, including fuel, was a major constraint in hybrid rice adoption in
Bangladesh. They emphasized the need for cost-reduction strategies, such as fuel-efficient
technologies and shared machinery programs, to mitigate this financial burden.
Likewise, machine rental costs show a notable significant negative relationship
(elasticity = -0.0963, p value = 0.001, effect size = 0.46), where a 1% increase in rental expenses
leads to a 0.096% decline in profitability, pointing to the financial strain of mechanization and
the need for cooperative-based machinery sharing programs.
Labor costs also negatively impact profitability (elasticity = -0.0935, p = 0.032, effect
size = 0.22). This tells that at 0.05 level of significance a 1% increase in labor cost decreases
profitability by 0.9%. This conforms to the study of Bolotaolo (2021) which found that labor
costs have a negative impact on rice yield. Higher labor costs lead to reduced productivity as
farmers struggle to afford the necessary workforce. In contrary the study of Chau et al. (2022)
found out that increased labor intensity in rice production positively impacts Technical
Efficiency. Higher TE is often associated with higher profitability because it indicates that a
farm is operating closer to its production frontier, i.e., producing the maximum possible
output from the available resources. Farmers who engage more intensively in the production
process tend to achieve higher efficiency and profitability.
Additionally, fertilizer costs have an elasticity of -0.0719 (p = 0.024, effect size = 0.24),
meaning a 1% increase in fertilizer spending results in a 0.072% decline in the profit-cost ratio.
On the other hand, pesticide expenses do not significantly affect profitability (elasticity = -
0.0470, p = 0.472, effect size = 0.02), indicating that changes in pesticide expenditures have
only a minimal impact on farm returns. Findings by Rapusas et al. (2003) found that hybrid
rice varieties like Mestizo are more resistant to pests and diseases compared to traditional
rice varieties. This resistance can lead to higher productivity and reduced reliance on
pesticides, which aligns with the finding that pesticide expenses do not significantly affect
profitability.
The model’s robustness is supported by an Akaike Information Criterion (AIC) of -19.85
and a log-likelihood ratio of 16.92, confirming the explanatory power of the included variables.
The adjusted R² of 88.21% indicates that the model explains a substantial portion of the
variations in profitability of hybrid rice production.
12
Table 3: Regression Analysis on the Influence of Cost Categories on the Profit-Cost Ratio of
Hybrid Rice Farms, Bicol Region, 2024 (n=143)
Dependent Var: ln (Profit-cost ratio)
Variables
Coefficient
Standard
Error
p -value
Effect Size
(partial eta
squared)
Constant
6.3510
0.8708
0.000
Labor
-0.0935
0.0403
0.032
0.22
Fertilizer
-0.0719
0.0292
0.024
0.24
Fuel
-0.2233
0.0451
0.000
0.56
Pesticides
-0.0470
0.0641
0.472
0.02
Machine rentals
-0.0963
0.0236
0.001
0.46
Cost of Seeds
-0.3284
0.0649
0.000
0.57
Adjusted R2(%)
88.21
AIC1
-19.85
Loglikelihood ratio
16.92
1AIC: Akaike Information Criterion
Influence of Cost Categories on Net Profit-Cost Ratio for Non-Hybrid Rice Varieties
The regression analysis on the influence of cost categories on the profit-cost ratio of
non-hybrid rice farms is shown in Table 4. In this model, the dependent variable is the natural
logarithm of the profit-cost ratio, and the analysis includes also the natural log of the
independent variables such as labor cost, fertilizer cost, fuel cost, pesticides, machine rentals,
and the cost of seeds. As mentioned, this utilizes the Cobb Douglas Production Function
regression method which linearizes variables for better model fit.
The results show that labor, fertilizer, and machine rentals have significant elasticities
with respect to the profit-cost ratio. Specifically, a 1% increase in labor costs is associated with
a 0.2507% decrease in the profit-cost ratio, a 1% increase in fertilizer costs is associated with
a 0.3536% decrease in the profit-cost ratio, and a 1% increase in machine rental costs is
associated with a 0.1187% decrease in the profit-cost ratio. These variables have p-values of
0.000, indicating strong statistical significance. The effect sizes (partial eta squared) for these
variables are 0.529, 0.403, and 0.226, respectively, suggesting that these factors explain a
substantial portion of the variance in the profit-cost ratio.
Comparing to the cost structure in the two production systems, it turns out that only
the cost of seeds differs as it is a significant factor that contributes to profitability in hybrid
rice farming while insignificant in inbred rice farming. On the other hand, fuel and the cost of
seeds do not have statistically significant elasticities with respect to the profit-cost ratio, in
inbred rice farms, with p-values of 0.886 and 0.333 respectively. Pesticides have a marginally
significant elasticity, with a 1% increase in pesticide costs associated with a 0.0788% decrease
in the profit-cost ratio, having a p-value of 0.043.
13
These findings are corroborated by existing literature. For instance, the study of
Prakash (2017) found that hybrid rice production incurred the highest production cost per
hectare compared to inbred and organic rice, highlighting the significant impact of labor and
fertilizer costs on profitability.
The adjusted R-squared value of 74.54% indicates that the model explains a large
portion of the variability in the profit-cost ratio, and the AIC (Akaike Information Criterion)
value of 11.14 suggests a good fit for the model.
Table 4: Regression Analysis on the Influence of Cost Categories on the Profit-Cost Ratio of
Non-Hybrid Rice Farms, Camarines Sur, 2024 (n=180)
Dependent Var: ln (Profit-cost ratio)
Variables
Coefficient
Standard
Error
p -value
Effect Size
(partial eta
squared)
Constant
6.6299
0.7699
0.000
Labor
-0.2507
0.0307
0.000
0.529
Fertilizer
-0.3536
0.0569
0.000
0.403
Fuel
-0.0045
0.0313
0.886
0.000
Pesticides
-0.0788
0.0380
0.043
0.067
Machine rentals
-0.1187
0.0285
0.000
0.226
Cost of Seeds
-0.0694
0.0710
0.333
0.015
Adjusted R2(%)
74.54
AIC1
11.14
Loglikelihood ratio
1.43
1AIC: Akaike Information Criterion
Factors Affecting Net Returns
In this section of the study, log net return was used as the response variable to account
for potential non-linearity in the relationship between profitability and the explanatory
variables. Among the independent variables, farming experience, tenurial status (land
ownership), ownership of machinery and training attendance, got a negative insignificant
coefficient. This signifies these variables does not necessarily translate into higher profitability
in rice farming (Table 5).
In contrast, membership in a farmer’s organization shows a positive coefficient but still
insignificant. Meaning that being part of a farmer’s organization might offer some advantages,
such as better access to resources and information, but its direct impact on profitability is not
strongly supported by the data.
14
Access to credit, which is often assumed to be a positive factor in farm productivity
and profitability, has a negative coefficient though statistically insignificant. This result imply
that credit access alone is not a sufficient driver of profitability.
The most notable finding in this study is the impact of seed type (hybrid or not) on
profitability. With a positive coefficient of 0.05661, a p-value of 0.001, and an effect size of
0.035, this variable is the only statistically significant determinant of net returns. The result
indicates that farmers who use hybrid seeds tend to have significantly higher profitability than
those who do not. This finding aligns with existing literature that highlights the productivity
benefits of hybrid rice varieties, which typically have higher yields and better resistance to
pests and diseases, leading to greater net returns.
The Akaike Information Criterion (AIC) value of -422.63 suggests that the model has a
relatively good fit, with a lower AIC indicating better model performance. The log-likelihood
ratio of 226.24 indicates the overall explanatory power of the model. A higher log-likelihood
value suggests that the estimated model better explains the observed data compared to a null
model. Since the model has an adjusted R² of 61%, the log-likelihood value further supports
the notion that the included variables explain a significant portion of the variability in net
returns, even though only the type of seed used was found to be statistically significant.
Table 5. Multiple Linear Regression Result on the Impact of Farm Characteristics and Farmer
Profiles on Profitability of Rice Production, Bicol Region (n=323)
Dependent Variable: Log (Net Returns)
Variables
Coefficient
Standard
Error
p -
value
Effect Size
(partial eta
squared)
Constant
9.41078
0.0200
0.000
Farming Experience
-0.00001
0.0009
0.985
Tenurial status
-0.00263
0.0140
0.851
Ownership of machinery
-0.02055
0.0137
0.135
Training Attendance
-0.01267
0.0191
0.508
Membership in farmer’s organization
0.03310
0.0193
0.088
Access to Credit
-0.17695
0.0137
0.199
Type of seed used (hybrid or not)
0.05661
0.0165
0.001
0.035
Adjusted R2(%)
61.00
AIC1
-422.63
Loglikelihood ratio
226.24
1AIC: Akaike Information Criterion
5.0 Conclusion
The study confirms that though hybrid rice farms demonstrate higher productivity, its
profitability is not necessarily superior to that of non-hybrid rice since its revenue is offset by
higher production cost specific on labor, fertilizer, machinery rentals and seed costs. However,
the net profit-cost ratio is slightly more favorable for inbred rice farmers, suggesting that they
15
achieve marginally higher returns per peso invested. Regression analysis highlights that the
cost of seeds, fuel, machine rentals, labor, and fertilizers significantly impact the profit-cost
ratio of hybrid rice farming. Among these, seed expenses have the most substantial negative
effect. Additionally, the type of seed used (hybrid or inbred) is the only statistically significant
determinant of net returns, confirming the superior profitability of hybrid rice production. The
study found out that hybrid rice technology offers economic advantages but requires
significant investment in inputs and resources.
6.0 Recommendation
Given that the study confirms hybrid rice has significantly higher productivity but also
significantly higher costs, which ultimately reduce profitability, solutions should focus on cost
reduction without compromising yield. Farmers can adopt several cost-reduction strategies,
such as cooperative bulk purchasing and localized hybrid seed production, optimizing fertilizer
use by following site-specific recommendations, using certified but affordable seeds to
balance cost and productivity, improving labor efficiency through mechanization and better
work management, reducing dependency on pesticides by implementing integrated pest
management (IPM), and adopting efficient irrigation practices to minimize water costs.
Extension services and training programs related to precision input use can help farmers
optimize resource use and maximize profitability.
In addition to these on-farm strategies, government support and policy interventions
are crucial. Providing subsidies for key inputs such as seeds and fertilizers can help lower
production costs. Expanding farmers’ access to low-interest credit can ease financial burdens
and encourage investment in cost-effective technologies. Investing in mechanization support
through shared service facilities or rental schemes can improve efficiency while reducing labor
expenses. Strengthening market linkages and implementing price stabilization measures can
help ensure fair prices for farmers, preventing income losses due to market fluctuations.
Further research on cost-effective hybrid rice production is necessary to identify best
practices and innovations that can lower costs while maintaining high yields. Studies on
sustainable farming techniques, efficient input utilization, and alternative cost-saving
measures should be encouraged to enhance the economic viability of hybrid rice farming.
7.0 Literature Cited
Adhikari, A., Dumre, A., Sharma, M. D. (2019). Comparative study of input use, Productivity
and profitability of improved and hybrid rice in Kapilvastu, Nepal. Acta
Scientific Agriculture, 3(1), 94-100. DOI: 10.31080/ASAG.2019.03.0305
Anwar, M., Zulfiqar, F., Ferdous, Z., Tsusaka, T. W., Datta, A. (2021). Productivity,
profitability, efficiency, and land utilization scenarios of rice cultivation: An
assessment of hybrid rice in Bangladesh. Sustainable Production and
Consumption, (26), 752-758. DOI: https://doi.org/10.1016/j.spc.2020.12.035.
16
Baumol, W. J. (1967). Macroeconomics of unbalanced growth: the anatomy of urban
crisis. The American economic review, 57(3), 415-426. Accessed from:
https://www.jstor.org/stable/1812111
Balgos, C., Digal, L. (2017). Employment generation potential of the rice value chain: The
case of Mlang, North Cotabato in Mindanao. Philippine Institute for
Development Studies (PIDS). Philippine Journal of Development, 43(1),1-23.
[Retrieved from:
https://pidswebs.pids.gov.ph/CDN/PUBLICATIONS/pidspjd2016-1_rice.pdf]
Bolotaolo, K. (2021). Rice Farm Productivity in the Philippines: An SUR Application.
International Journal of Multidisciplinary Research and Publications (IJMRAP),
(3)8, 39-42. [Retrieved from: https://ijmrap.com/wp-
content/uploads/2021/02/IJMRAP-V3N8P68Y21.pdf]
Bravo-Ureta, B., Higgins, D., Arslan, A. (2020). Irrigation infrastructure and farm productivity
in the Philippines: A stochastic Meta-Frontier analysis. World
development, (135),105073. DOI:
https://doi.org/10.1016/j.worlddev.2020.105073
Chanda, S., Ali, M., Haque, M., Abdullah, M., Sarwar, A. (2019). Cost of production and cost
benefit analysis of different rice in Sirajganj district. Asian Journal of Crop, Soil
Science and Plant Nutrition, 1(1),7-14. DOI:
https://doi.org/10.18801/ajcsp.010119.02
Chau, N., Ahamed, T. (2022). Analyzing Factors That Affect Rice Production Efficiency and
Organic Fertilizer Choices in Vietnam. Sustainability, 14(14), 8842.
https://doi.org/10.3390/su14148842
Chidiebere-Mark, N., Ohajianya, D., Obasi, P., Onyeagocha, S. (2019). Profitability of rice
production in different production systems in Ebonyi State, Nigeria. Open
Agriculture, 4(1), 237-246. DOI: https://doi.org/10.1515/opag-2019-0022
DA Press Office. (2022). Philippine rice production, varieties and research: What we know so
far. Department of Agriculture. [Retrieved from:
https://www.da.gov.ph/from-business-mirror-philippine-rice-production-
varieties-and-research-what-we-know-so-far/]
Dang, N. H. (2017). Profitability and profit efficiency of rice farming in Tra Vinh province,
Vietnam. Review of Integrative Business and Economics Research, (6),191.
Retrieved from: https://buscompress.com/uploads/3/4/9/8/34980536/riber_6-
s1_sp_b17-201_191-201.pdf
17
David, C. C. (2007). The Philippine Hybrid Rice Program: A case for redesign and scaling
down (No. RPS 2006-03). Philippine Institute of Development Studies.
Accessed at: RePEc:phd:rpseri:rps_2006-03
Department of Agriculture. (2022). Philippine rice production, varieties and research: What
we know so far. Retrieved from: https://www.da.gov.ph/from-business-
mirror-philippine-rice-production-varieties-and-research-what-we-know-so-
far/
Digal, L., Placencia, S. (2020). Factors affecting the adoption of hybrid and high yielding rice
varieties in the Philippines. Agricultural Research, 9(1), 1-8.
DOI:10.1007/s40003-019-00403-z
Fakkhong, S., Suwanmaneepong, S., (2016). Determinants of Profitability of Rice Farming in
Peri-Urban Area, Bangkok, Thailand. In The Asian Conference on the Social
Sciences. The International Academic Forum. Retrieved from:
https://papers.iafor.org/wp-
content/uploads/papers/acss2016/ACSS2016_27913.pdf
Goswami, K., Thapa, D. B., Sandilya, J., Deka, N. (2023). An assessment of economic
profitability of black rice (Oryza sativa L. indica) production in Assam,
India. Journal of Applied Research on Medicinal and Aromatic Plants, (34),
100488. DOI: https://doi.org/10.1016/j.jarmap.2023.100488
Ho, P. T., Hung, P. X., Tien, N. D. (2023). Effects of varieties and seasons on cost efficiency in
rice farming: A stochastic meta frontier approach. Asian Journal of Agriculture
and Rural Development, 13(2),120-129. DOI: 10.22004/ag.econ.342402
Johnson, J. M., Becker, M., Dossou-Yovo, E. R., Saito, K. (2024). Enhancing rice yields, water
productivity, and profitability through alternate wetting and drying
technology in farmers’ fields in the dry climatic zones of West
Africa. Agricultural Water Management, (304),109096. DOI:
https://doi.org/10.1016/j.agwat.2024.109096 .
Kasim C., M. (2021). An Empirical Analysis of Profitability of Rice Cultivation In Kuttanad
Wetland System, Southern Kerala. Journal of Rural Development, 40(5), 634–
649. https://doi.org/10.25175/jrd/2021/v40/i5/170695
Kulyakwave, D. Peter, S., Wen Yu., Muyobozi, S. (2020). Profitability Analysis of Rice
Production, Constraints and Consumption Shares by Small-Scale Producers in
Tanzania. Asian Journal of Agricultural Extension, Economics & Sociology 37
(4),1-12. https://doi.org/10.9734/ajaees/2019/v37i430280.
Keghter, V. S., Malel, M. F., Birma, J., Tijesu, O. G., Musa, S. P., Blessing, O., Nandom, M.
(2023). Profitability and technical efficiency analysis of rice production in
18
Quan’Pan Local Government area of Plateau State, Nigeria. Turkish Journal of
Agriculture-Food Science and Technology, 11(6), 1096-1105.
DOI: https://doi.org/10.24925/turjaf.v11i6.1096-1105.5827
Mauki, C., Jeckoniah, J., Massawe, G. (2023). Smallholder rice farmers profitability in
Agricultural Marketing Co-operative Societies in Tanzania: A case of Mvomero
and Mbarali districts. Heliyon, 9(6), e17039.
DOI: https://doi.org/10.1016/j.heliyon.2023.e17039
Oloyede, W. O., Muhammad-Lawal, A., Amolegbe, K. B., Olaghere, I. L., Joseph, I. A. (2021).
Comparative analysis of the profitability of rice production systems in Kwara
State, Nigeria. Agrosearch, 20(2), 82-101. DOI: 10.4314/agrosh.v20i2.7
Panigrahi, S. Purohit,S., Sibarama, S. (2021) Time Series Forecasting of Price of Agricultural
Products Using Hybrid Methods, Applied Artificial Intelligence, (35)15: 1388-
1406, DOI: https://doi.org/10.1080/08839514.2021.1981659
Philippine Rice Research Institute. (2015, November 11). PH rice productivity secrets
revealed. Philippine Rice Research Institute. Retrieved from:
https://www.philrice.gov.ph/ph-rice-productivity-secrets-revealed/
Philippine Rice Research Institute. (2023). New rice varieties approved for commercial
release. Philippine Rice Research Institute. Retrieved from:
https://www.philrice.gov.ph/ready-for-2023-onwards/
Philippine Rice Research Institute. (2025). Rice Profitability Indicator. Retrieved from
https://www.philrice.gov.ph/ricelytics/incomes
Prakash, A., Singh, H., Shekhawat, R., Sandu, S. (2017). Comparative analysis on economic
efficiency of hybrid and inbred rice production in Udham Singh Nagar District
of Uttarakhand. Journal of Economics, Management and Trade, 18(4),1-7.
https://doi.org/10.9734/JEMT/2017/35469
Prasad, H. N., Das, G., & Chakraborty, G. (2020). Economic Viability of Rainfed Agriculture: A
Case Study of Southern Assam. Journal of Rural Development, 39(3), 292–309.
https://doi.org/10.25175/jrd/2020/v39/i3/158763
Rahaman, M. S., Sarkar, M. A. R., Rahman, M. C., Deb, L., Rashid, M. M., Reza, M. S.,
Siddique, M. A. B. (2022). Profitability analysis of paddy production in
different seasons in Bangladesh: Insights from the Haor. International Journal
of Agriculture Environment and Food Sciences, 6(3), 327-339.
DOI: https://doi.org/10.31015/jaefs.2022.3.1
19
Rapusas, H. R., Santiago, G. C., Rillon, J. P., Villanueva, A. E., Duque, U. B., Cruz, D., Martin, A.
R. (2003) Resistance of promising hybrids to insect pests and
diseases. Philippine Entomologist, 17(2).
https://www.ukdr.uplb.edu.ph/journal-articles/5723
Schumpeter, J. A. (1976). Capitalism, socialism and democracy. 1st ed. Routledge. eBook
ISBN: 9780203202050 page 460. DOI:
https://doi.org/10.4324/9780203202050
Shah, M. M., Grant, W. J., Stocklmayer, S. (2016). Farmer innovativeness and hybrid rice
diffusion in Bangladesh. Technological Forecasting and Social Change, 108:
54-62. DOI: http://dx.doi.org/10.1016/j.techfore.2016.04.015
Shafi, S., Shafi, I., Zaffar, A., Zargar, S. M., Shikari, A. B., Ranjan, A., Sofi, P. A. (2023). The
resilience of rice under water stress will be driven by better roots: Evidence
from root phenotyping, physiological, and yield experiments. Plant Stress,
100211. DOI: https://doi.org/10.1016/j.stress.2023.100211
Salam, M. A., & Sarker, M. N. I. (2023). Impact of hybrid variety adoption on the
performance of rice farms in Bangladesh: A propensity score matching
approach. World Development Sustainability, (2), 100042.
DOI: https://doi.org/10.1016/j.wds.2023.100042
Tacio, H. D. (2021). Rice: The staple food of Filipinos. Edge Davao. Retrieved from:
https://edgedavao.net/agri-trends/2021/09/rice-the-staple-food-of-filipinos-
the-staple-food-of-filipinos/
Tripathi, A., Sardar, S., Shyam, H. S. (2023). Hybrid crops, income, and food security of
smallholder families: Empirical evidence from poor states of
India. Technological Forecasting and Social Change, (191),122532.
DOI: https://doi.org/10.1016/j.techfore.2023.122532
Wagan, S. A., Noonari, S. M., Memon, I. N., Bhatti, M. A., Kalwar, G. Y., Sethar, A. A., Jamro,
A. S. (2015). Comparative economic analysis of hybrid rice v/s conventional
rice production in district Badin Sindh province Pakistan. Journal of
Environment and Earth Science, 5 (3),76-89. DOI: 10.13140/RG.2.1.3627.8245
Yasin, M. A., Yasin, M. A., Ullah, S., Ali, R., Munir, S. (2024). Profitability of Organic Versus
Conventional Rice Production: An Evidence from South Asia. Journal of
Economic Impact, 6(3),210-217.
DOI: https://doi.org/10.52223/econimpact.2024.6304
21
