TECHNOLOGY ADOPTION ON CORN HARVESTER TOWARDS FARMING
MECHANIZATION IN SAN MARTIN, MALAYBALAY CITY BUKIDNON
SHEILA MAE ALFECHE MEDY
UNDERGRADUATE THESIS PROPOSAL SUBMITTED TO THE FACULTY
OF THE COLLEGE OF AGRICULTURE, DEPARTMENT OF AGRICULTURAL
ECONOMICS, CENTRAL MINDANAO UNIVERSITY, IN PARTIAL
FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE
BACHELOR OF SCIENCE IN AGRICULTURE
MAY 2023
1
Republic of the Philippines
CENTRAL MINDANAO UNIVERSITY
Musuan, Maramag, Bukidnon
College of Agriculture
Department of Agricultural Economics
APPROVAL SHEET
The undergraduate thesis proposal attached here to entitled, “TECHNOLOGY
ADOPTION ON CORN HARVESTER TOWARDS FARMING MECHANIZATION
IN SAN MARTIN, MALAYBALAY CITY BUKIDNON”, prepared and submitted by
SHEILA MAE ALFECHE MEDY, in partial fulfillment of the requirements for the
degree Bachelors of Science in Agriculture (Agricultural Economics), is hereby
endorsed.
OLIVER MICHEAL C. NARRETO, MS
Chair, Thesis Advisory Committee
Date
KAREN DEBBIE R. COSROJAS, PhD
Member, Thesis Advisory Committee
Date
HEIDE P. MAGADAN, MS
Member, Thesis Advisory Committee
___________________
Date
Recommending Approval:
LOWELLA R. ANGCOS, PhD
Department Chair
TEDDY E. COLIPANO, DBA
Research Coordinator
__________________
Date
Date
Approved:
MARIA ESTELLA B. DETALLA, PhD
College Dean
Date
Noted:
JUPITER V. CASAS, PhD
Director for Research
___________________
Date
2
TABLE OF CONTENTS
CHAPTER I .............................................................................................................................. 1
BACKGROUND OF THE STUDY......................................................................................... 1
Introduction........................................................................................................................... 1
Significance of the Study.................................................................................................... 2
Statement of the Problem .................................................................................................. 2
Objectives of the Study ...................................................................................................... 3
Scope and Limitation of the Study .................................................................................... 3
Conceptual Framework ...................................................................................................... 4
Attitudes and Perception analysis ................................................................................ 6
Dependent and Independent Variables ....................................................................... 6
Operational Definition of Terms ......................................................................................... 7
CHAPTER II ............................................................................................................................. 8
REVIEW RELATED LITERATURE ....................................................................................... 8
Mechanization Trend .......................................................................................................... 8
Modifications to Harvesting Techniques and Practices ................................................. 8
Combine Harvester ............................................................................................................. 9
Agricultural Technology Adoption...................................................................................... 9
Technological Factors ....................................................................................................... 10
Household-Specific Factors............................................................................................. 11
Education ........................................................................................................................ 11
Age .................................................................................................................................. 11
Economic Factors ............................................................................................................. 12
Institutional Factors ........................................................................................................... 12
Participation in Seminars / On-Farm Trials / Extension Services Specific to
Combine Harvesting ......................................................................................................... 13
Membership in Associations /Cooperatives .................................................................. 13
Perception and Attitudes Towards the Labor Displacement Caused by Combine
Harvesters .......................................................................................................................... 13
CHAPTER III .......................................................................................................................... 14
METHODOLOGY .................................................................................................................. 14
Geographic Location of the Study .................................................................................. 14
Respondents and Sampling Design ............................................................................... 15
Method of Data Collection................................................................................................ 15
Ethical Consideration ........................................................................................................ 16
Method of Data Analysis .................................................................................................. 16
3
Simple Costs and Returns Analysis ............................................................................... 16
Unpaired T-test .................................................................................................................. 17
Partial Budget Analysis..................................................................................................... 18
Multiple Logistic Regression Analysis ............................................................................ 19
REFERENCES ...................................................................................................................... 20
4
CHAPTER I
BACKGROUND OF THE STUDY
Introduction
Technological change has been a major factor shaping agriculture in the
last 100 years (Schultz & Cochrane, 1964-1979). Internationally, tremendous
changes in production patterns have occurred. While world population more
than doubled between 1950 and 1998 (from 2.6 to 5.9 billion), grain production
per person has increased by about 12 percent, and harvested acreage per
person has declined by half (Brown, Gardner, and Halweil; 1999). These figures
suggest that productivity has increased and agricultural production methods
have changed significantly.
The corn mechanization program provides inputs support to qualified
farmers like labor (machine and equipment), seeds, fertilizer, soil conditioner,
chemicals and technical support. Due to its lengthy technology adoption lag,
the Philippine agriculture sector has always been hampered by low productivity
and high production costs (Galang, 2019). As a result, the government has put
in place a number of measures to boost the productivity and profitability of the
nation's crop production—particularly that of rice and corn. One of these
initiatives promotes the mechanization of crop production.
Mechanized corn farms have achieved quality corn translating to higher
market price. The mechanical corn planter assures accuracy of seed and
fertilizer placement and more uniform distribution of seeds (Dela Cruz, R. Sta.
M. 2018).
Mechanization has had a major impact on the demand and supply for
farm labor; the profitability of farming; and the change in the rural landscape,
including rural communities (Mwangi, M., & Kariuki, S. 2015).
Even though farmers are aware of the advantages of using combine
harvesters, which include lower costs and shorter harvesting times, some still
choose to use more traditional harvesting methods, which, if not done properly,
1
can result in rapid quality decline and rising harvest losses. Studies on the use
of the combine harvester in the aforementioned area are often lacking.
Therefore, it is necessary to conduct a study on the use of combine harvesters.
The goal of the current study is to determine the differences in net
benefits between manual harvesting and the use of corn harvesters as well as
the factors that affect farmers' decisions to use corn harvesters.
Significance of the Study
The Philippines will be able to increase the productivity of corn farms by
adopting more mechanization. The usage of combine harvesters is one facet of
mechanization in maize growing. The use of combine harvesters considerably
decreased labor requirements and accelerated harvesting. Farmers' production
and profitability can rise if combine harvesters are used more frequently.
The results of this study may provide significant data that could help
maize farmers in San Martin Malaybalay, Bukidnon, understand how using
combine harvesters and manual harvesting differ in terms of their net
advantages. This could improve their decisions on adopting the said
technology.
The policymakers and governments will also benefit from this research
by learning what influences farmers' decisions to employ combine harvesters.
This might help them enhance and change current policies and initiatives, or
develop new ones, to encourage farmers to utilize combine harvesters.
Similarly, farmers' associations and cooperatives may use the study's
findings to help their members adopt combine harvesters to increase their
farming operations' profitability and efficiency.
Statement of the Problem
Among the problems that snag the implementation of the corn farming
mechanization are the slow adoption of clustering among small corn farmers,
2
unavailability of spare parts of the equipment in the local market and the costly
subsequent repair of machineries. Other constraints cited for the large-scale
corn mechanization are small landholdings and inaccessibility of farms.
The proposed study aims to answer the following questions on
Technology Adoption on Corn Harvester towards Farming Mechanization in
San Martin, Malaybalay City, Bukidnon:
1. What are the differences in net benefits between using a combine
harvester and manual harvesting?
2. What are the factors affecting farmers' decision-making regarding the
adoption of combine harvesters?
Objectives of the Study
The study on Technology Adoption on Corn Harvester towards Farming
Mechanization in San Martin, Malaybalay City, Bukidnon seeks to achieve the
following;
1. To compare the net benefits of using a combine harvester and manual
harvesting, and;
2. To identify the factors that influence farmers' decisions to adopt
combine harvesters.
Scope and Limitation of the Study
This study dealt mainly with the Technology Adoption on Corn Harvester
towards Farming Mechanization in San Martin, Malaybalay City Bukidnon. In
addition to highlighting the contrasts between employing combine harvesters
and manual harvesting in terms of net benefits and harvesting costs, the
researcher seeks to identify the factors that influence farmers' decisions to
adopt combine harvesters.
3
Due to our current situation and limited time, there are some unavoidable
limitations the researcher can experience throughout conducting the study, one
of which is that a significant number of respondents may be uncooperative or
unwilling to participate in data collection. Furthermore, the study is limited to
only 60 respondents in Barangay San Martin only, and it was limited to corn
farmers who had prior experience with the technology, the combine harvester.
Conceptual Framework
The variables associated with the Technology Adoption on Corn
Harvester towards Farming Mechanization in San Martin, Malaybalay City,
Bukidnon is presented in Figure 1. These variables are classified into four
groups:
socioeconomic,
institutional,
perception
and
attitude,
and
physical/technical factors. All of these elements are based on research
presented in the literature review.
Socioeconomic factors include age, education and farm size. Age may
have a negative impact on adoption because older farmers are less willing to
adopt new technologies. Education is also important in increasing the adoption
of combine harvesters; farmers with a higher level of education can have a
better understanding and analysis of the technology. Farm size also influences
adoption because having a larger farm means having more area to harvest,
which is not only time-consuming but also more expensive and labor-intensive
to harvest manually.
Membership in associations or cooperatives, and participation in
combine harvester seminars or on-farm trials are examples of institutional
factors. Farmers' willingness to adopt may also be increased by membership in
associations or cooperatives. Members can influence others by exchanging
positive
information
about
these
machines
through
word-of-mouth.
Participation in seminars and on-farm trials can increase adoption because they
provide more information about the benefits of combine harvesting, causing
them to try it out.
4
Farmers' perceptions and attitudes toward the benefits of combine
harvester, and labor displacement effects of combine harvesting may all play a
role in their decision to adopt combine harvesters. If a farmer sees combine
harvesting as more beneficial, he or she is more likely to adopt it. The farmer's
attitude toward the labor displacement caused by combine harvesting may also
have a significant impact on adoption. If a farmer is more likely to be opposed
to displacing farm laborers in favor of combine harvesting, the farmer is less
likely to adopt the latter. If farmers believe manual harvester-laborers are skilled
at their jobs, they will be less willing to adopt combine harvesters.
Physical and technical factors include the farm's accessibility to combine
harvesting services, as well as the availability of combine harvesters in the
farmer's area. If the farm is accessible to combine harvester services, the
farmer's willingness to adopt the machinery may be influenced. Adoption
increases as combine harvesters become more available or as the number of
combine harvesters in the area increases.
With a greater number of combine harvesters in the area, combine
harvesting services will be more easily accessible and thus more practical than
hiring combine harvester services from other areas. Farmers' adoption
decisions are influenced by the four categories of factors. This will determine
whether or not they will use combine harvesting.
Socio-Economic
•
•
•
Age (-)
Education (+)
Farm Size (+)
Institutional
• Membership in
associations /
cooperatives (+)
• Participation in
seminars / on farm
trials/extension services
specific to combine
harvester (+)
Perception and Attitudes
Adoption
of
Rice Combine
Harvester
• Perception on the
benefits of combine
harvesting (+)
• Attitude towards the
labor displacement
caused by combine
harvesters (-)
Physical/Technical
• Accessibility (+)
• Availability of combine
harvesters (+)
5
Figure 1. Factors Influencing the Adoption of a Combine Harvester. Source:
Adapted from the study of Tolentino (2006)
Attitudes and Perception analysis
Attitudes and Perception analysis was used for the determination
of the farmer respondents’ attitudes and perception towards combine
harvesters. Primarily, this was used to determine farmer’s knowledge
about combine harvesters, a factor which is hypothesized to affect the
decision-making of farmers towards the adoption of this machinery. The
attitudes and the perceptions of the farmer respondents were measured
through the use of a Likert scale composed of questions about their
perception of the benefits, technical characteristics of combine
harvesters, and attitude towards labor displacement caused by combine
harvesters.
Dependent and Independent Variables
The dependent variable is the variable that is affected by other
variables that are measured. These variables are expected to change as
a result of a manipulation of the independent variables in the experiment.
It is the intended effect. On the other hand, an independent variable is a
variable that is stable and unaffected by the other variables being
measured. It refers to an experiment's condition that is systematically
manipulated by the investigator. (Cramer, D., & Howitt, D. (2004) &
(Penslar, R. L., & Porter, J. P. (2010).
In this study, the researcher has four independent variables
(Socio-Economic,
Institutional,
Perception
and
Attitudes,
and
Physical/Technical) on the left side, as shown in figure 1. Independent
variables are those that can be manipulated, controlled, or changed. It
is what the researcher investigates in order to determine its relationship
or effects. The dependent variable (adoption of rice combine harvester)
6
is on the right side, the researcher will measure the outcome of the
experiment to see how the independent variables cause changes in the
value of a dependent variable.
Operational Definition of Terms
Adoption is defined as the act of accepting, embracing, or beginning to
use something as an idea, behavior, characteristic, or principle. Adoption
technology is a term that refers to the acceptance, integration, and use of new
technology in society. Combine harvester, an agricultural machine that reaps,
threshes, and cleans a cereal crop in one operation. Economic factors, a factor
that can affect and influence a person's financial situation, education,
employment status, and income are among of them. Institutional factors are
internal dynamics that reflect the effectiveness of governmental or nongovernmental organizations' performance. Net benefits are determined by
adding all project benefits and subtracting all project costs. Manual harvesting
refers to farmers who cut their crops with a special tool known as a sickle.
7
CHAPTER II
REVIEW RELATED LITERATURE
Maize is one of the most planted grains in the Philippines. The Rice/Corn
Combine Harvester makes the harvesting process easier by combining six
operations such as gathering, transporting, reaping, threshing, cleaning and
bagging into one machine. It is the modern and efficient way of harvesting rice.
It also provides our farmers comfort in operation and ease in maintaining their
machines (ADAMCO, 2015).
Mechanization of Philippine farms is on an upswing, as more farmers
are now showing more willingness to mechanize their farms. Also, the latest
survey by the Philippine Center for Postharvest Development and
Mechanization (PhilMech) showed that the mechanization level of farms in the
Philippines is 1.23 horsepower per hectare (hp/ha). Rice and corn farms had
the highest level of available farm power at 2.31 hp/ha (GOVPH, 2013).
Mechanization Trend
Agricultural mechanization in the Philippines started during the last years
of the Spanish Era in the form of disc harrows, cultivators, plows and corn
planters. The use of farm machinery was significant prior to 1960. After World
War II- four-wheel tractors from US, Britain, Japan and West Germany were
imported. The initial introduction of tractors was used primarily by the sugar
industry. In 1960, power tillers were first introduced exclusively for rice land
preparation and in 1972, locally made power tillers were introduced in the
production of rice and other crops. This paved the way of utilizing other farm
machinery in production of rice and corn crop (Sims, B.G. & Kienzle, J. 2016).
Modifications to Harvesting Techniques and Practices
8
Prior to the development of modern-day machines, agricultural workers
had to harvest crops by performing a series of laborious operations one after
the other. Identified problems in traditional manual rice harvesting were: labor
crisis at peak harvesting period, high harvesting cost as the traditional method
was labor intensive and high labor wages, delayed harvesting due to the
unavailability of labors, more grain/yield losses owing to the over maturity.
Additionally, it was found that employing a combine harvester to harvest
corn reduced crop loss and human drudgery while reducing harvesting time,
cost, and labor requirements. Additional advantages included raising income
through bespoke hiring services and generating a new job opportunity in
technology operation and upkeep. The findings showed that proper maize
harvesting technology must be quickly adopted in developing nations to boost
cropping intensity, productivity, and economic emancipation with fewer inputs
of time, effort, and money.
Combine Harvester
The combine harvester is a versatile machine designed to efficiently
harvest a variety of grain crops. The name derives from its ability to combine
three separate harvesting operations which include:
Reaping, the process of cutting and often also gathering crops at harvest
when they are ripe. Threshing is the process of loosening the edible part of
grain (or other crop) from the chaff to which it is attached. Winnowing is the
process of separating grain from straw. With the use of combine harvester, it
eliminates grain loss during manual cutting of the crop, tying the crop for
carrying, transporting the harvest from incomplete threshing. Combine
harvester reduces the overall cost of production and help farmers to prepare
crop in a very short time.
Agricultural Technology Adoption
9
Defining technology adoption is a difficult task because it varies
depending on the technology being adopted. The first consideration in defining
agricultural technology adoption by farmers is whether adoption is a discrete
state with binary response variables or not (Doss, 2003). That is, the definition
is dependent on whether the farmer is a technology adopter or a non-adopter,
with values 0 and 1 indicating that the response is continuous variable (Challa,
2013). Adoption can also be defined as ongoing. This is commonly used to
determine the extent to which technologies have been adopted. In this
definition, probit models are frequently used. Logit and probit analysis are
commonly used in studies to determine the factors influencing technology
adoption because they provide more detailed information on the characteristics
of farmers who would adopt a particular technology (Mariano et al., 2012).
Due to the extensive research in agricultural technology adoption,
various variables affecting technology adoption have been studied – with
varying results. In the study of Mwangi and Kariuki (2015) the factors were
classified by into four categories: technological, household specific, economic,
and institutional factors. The characteristics of technology are referred to as
technological factors. Age and education are examples of household specific
factors. Farm size, net gain from adoption, and off-farm income are all economic
factors. The farmer's social group membership, information acquisition, access
to extension services specific to combine harvester, and credit availability are
an example of institutional factors.
Technological Factors
According to Mwangi and Kariuki (2015) the Characteristic of a
technology is a precondition of adopting it. Trialability, or the extent to which a
potential adopter can test something out on a small scale before fully adopting
it, is a major determinant of technology adoption (Doss, 2003). Mignouna et al.,
(2011) found that the characteristics of the technology play a critical role in the
adoption decision process. They contended that farmers who see the
technology as being consistent with their needs and compatible with their
10
environment are more likely to adopt it because they see it as a good
investment.
Household-Specific Factors
Farmers' human capital is thought to have a significant influence on their
decision to adopt new technologies. Most adoption studies have attempted to
quantify human capital by examining the farmer's age and education
(Fernandez-Cornejo & Daberkow, 1994; Fernandez-Cornejo et al., 2007;
Mignouna et al., 2011; Keelan et al., 2014).
Education
Farmers' education is thought to have a positive influence on their
decision to adopt new technology. A farmer's education level improves
his ability to obtain, process, and apply information relevant to the
adoption of a new technology (Mignouna et al., 2011; Lavison 2013;
Namara et al., 2013). For example, Okunlola et al., (2011) discovered
that the level of education had a positive and significant influence on the
adoption of new technologies by fish farmers and Ajewole (2010)
discovered that the level of education had a positive and significant
influence on the adoption of organic fertilizers.
Age
Age is also one of the problems that constraints of adopting new
technology. Several studies have found that being older has a negative
impact on adoption (Ayodele, 2012; Howley, O. Donoghue, & Heanue,
2012). According to Ghosh (2010), younger generations are more likely
to be open to farm mechanization. Older farmers have more farming
experience gained through experimentation and observation. As a result,
they may find it difficult to abandon their old practices in favor of new
technologies. Akudugu et al., (2012), on the other hand, discovered that
11
age assumed a quadratic function in their study of the adoption of
modern agricultural production technologies in Ghana.
Economic Factors
Another factor influencing agricultural technology adoption is farm size.
Ghosh (2010) discovered that farm size is a significant factor in the adoption of
agricultural mechanization in the Burdwan Districts of West Bengal. He stated
that having a larger farm size influences adoption positively. Other studies have
produced comparable results (Akudugu et al., 2012; Mariano et al., 2012).
Farmers who operate on larger farms are more likely to adopt new technologies
because they can afford to dedicate a portion of their land to testing its efficacy
(Uaiene et al., 2009).
The cost of implementing agricultural technology has been identified as
a factor to technology adoption. Previous studies on the determinants of
technology adoption have also identified high technology costs as a barrier to
adoption. Makokha et al., (2001) identified high labor and other input costs,
unavailability of demanded packages, and late delivery as the main constraints
to fertilizer adoption in their study on the determinants of fertilizer and manure
use in maize production in Kiambu county, Kenya. Ouma et al., (2002) identified
labor costs as one of the factors limiting fertilizer and hybrid seed adoption in
Embu County, Kenya. Previous studies on the determinants of technology
adoption have also identified high technology costs as a barrier to adoption.
Makokha et al., (2001) identified high labor and other input costs, unavailability
of demanded packages, and late delivery as the main constraints to fertilizer
adoption in their study on the determinants of fertilizer and manure use in maize
production in Kiambu county, Kenya. Ouma et al., (2002) identified labor costs
as one of the factors limiting fertilizer and hybrid seed adoption in Embu County,
Kenya.
Institutional Factors
12
Belonging to a social group increases social capital by allowing for the
exchange of trust, ideas, and information (Mignouna et al., 2011). Farmers in a
social group learn about the benefits and application of new technology from
one another. According to Uaiene et al., (2009), social network effects are
important for individual decisions and, in the context of agricultural innovations,
farmers share information and learn from one another.
Participation in Seminars / On-Farm Trials / Extension Services Specific to
Combine Harvesting
Farmers are more likely to adopt new technology if they can attend a
seminar that explains how to use it properly and if extension services are made
available. Farmers learn about the benefits of new technology through
extension services provided by extension agents. The extension agent serves
as a link between the technology's innovators (researchers) and its users. This
helps to reduce transaction costs associated with disseminating information
about new technology to a large and diverse population of farmers (Genius,
Koundouri, Nauges, & Tzouvelekas, 2013).
Membership in Associations /Cooperatives
Another factor that has recently been studied is a farmer's membership
in social groups such as associations and cooperatives. Farmers benefit from
social groups because they share information and learn from one another's
experiences (Uaiene et al., 2009).
Perception and Attitudes Towards the Labor Displacement Caused by
Combine Harvesters
Farmers used combine harvesters due to labor shortages, particularly
during peak harvesting season. Despite the fact that harvesting rice with a
combine is more convenient, farmers are hesitant to adopt due to labor
13
displacement concerns. The majority of manual harvesters are rice farmers'
relatives, neighbors, or close friends. As a result, adopting a combine will affect
not only the laborers' source of income, but also their positive relationship with
rice farmers. Other farm owners are concerned that after using a combine, farm
laborers will no longer be available for land preparation and crop establishment.
CHAPTER III
METHODOLOGY
Geographic Location of the Study
This research will be conducted in Brgy, San Martin, Malaybalay City,
Bukidnon. The location of this study was determined as the area of the study
because it has a relatively good number of corn farmers. The study is aimed to
accomplish in the College of Agriculture, Central Mindanao University,
University Town Musuan Maramaag Bukidnon. This research will be carried out
from January to May 2022.
14
Figure 2. Geographic Location of the Study
Respondents and Sampling Design
The respondents of the study will be the farmers of Malaybalay City,
Bukidnon. The respondents were taken from the list of registered corn farmers
provided by Barangay San Martin.
The study respondents will be composed of 60 registered corn farmers.
A simple random sampling procedure will be used for selecting the participants
of the study. The sampling technique was employed to make sure of unbiased
and fairly equal representation of the variables for the study. This was achieved
through using wheel of names, an online application where you can select
random numbers by spinning. The list of registered farmers provided by the
barangay have already numbers associated with their names. The researcher
will enter their numbers into the application, spin them, and record the results.
Method of Data Collection
Primary data during wet harvest season of this year 2023 will be used in
the study. Primary data is information gathered directly from field interviews with
respondents who are guided through a pre-prepared questionnaire. In the study
there will be two sets of questionnaires. The first set includes information such
as their socioeconomic characteristics; institutional characteristics; questions
regarding their perception, attitudes, and problems encountered regarding their
decision-making process towards the adoption of combine harvesters. The
second set includes questions about the net benefits and harvesting cost
between manual harvesting and combine harvester. Before being relayed to
respondents, items in the questionnaire will be translated into the local dialect.
A letter of consent shall be presented before the actual interview
containing the reason and purpose of conducting the said research study.
15
Ethical Consideration
The study will be conducted observing research ethics. An approval to
conduct face to face interview with a questionnaire guide will be asked from the
barangay captain of Barangay San Martin, Malaybalay City. An Institutional
Ethics Review Committee (IERC) permit will be secured from CMU before the
conduct of the study. The researcher will inform the respondents about the
nature and purpose of the study, and will guarantee the participants that their
identity will remain anonymous. Respondents will have the choice whether to
participate or not, and will be informed about their right not to answer questions
which they think are abhorrent or discriminatory, or to abandon their
participation if ever they feel uncomfortable in the middle of the activity.
The interview will be conducted in the respective houses of the
respondents or any other venue they feel comfortable and secured. The faceto-face interview will only run for about 20 to 30 minutes approximately. In case
of any injury or harm that will be caused by the activity, the compensation shall
be shouldered by the researcher alone, and for any queries about the questions
to be used in the interview, respondents will be informed of the contact details
of the researcher.
Method of Data Analysis
This study will utilize different method in analyzing the answer of the
respondents, based on the set objectives. For the first objective, cost and
return and partial budget analysis computation will be used. For the second
objective, a descriptive analysis will be used in describing the socioeconomic
characteristics of the farmer. Multiple logistic regression is use to determine
which factors were significant in influencing farmers' decision-making process
regarding the adoption of combine harvesters.
Simple Costs and Returns Analysis
16
This analysis is used to determine the benefits and costs of combine
harvester and manual harvesting. Primarily, this is used to determine the
differences in the costs of combine harvester and manual harvesting. Cost and
return items for rice production are also computed to determine the effect of the
harvesting methods on the net farm income of the farmers. The variables that
were used in this analysis are the total farm revenue (TR), total farm cost (TC)
and net farm income (NFI), which was the difference between the two former
variables and plus/minus any gain/loss on the sale of capital assets (Aragon et
al., 2010 as cited in the study of Tolentino, 2006). This was given by the formula:
NFI = TR – TC
where:
NFI = net farm income (in PhP)
TR = total revenue (in PhP)
TC = total cost (in PhP)
The revenue from corn production will be categorized into cash and noncash revenues. Cash revenue was from the sale of corn (in cash) while noncash revenue came from the home consumption of corn. Costs were classified
as cash and non-cash costs. Cash costs included expenses on inputs, machine
rental, and labor. Non-cash costs include expenses such as depreciation of
fixed assets (buildings, tools, and equipment), and the shares of production of
the landlord, thresher, and harvester.
Unpaired T-test
Unpaired T-test is a statistical comparison of the means and standard
deviations of two unrelated or independent samples. In this study, the
researcher wants to compare the profitability between combine harvester and
manual harvesting to identify which from the two is more profitable.
Assuming that both groups have equal variance, the test statistics are:
17
Where:
x̄1: Mean value of the first group
x̄2: Mean value of the second group
n1: Size of the first group
n2: Size of the second group
S1: Standard deviation of the first group
S2: Standard deviation of the second group
The p-value can then be determined from the table with the t distribution. The
number of degrees of freedom is given by;
df = n1 + n2 - 2
where:
n1: Size of the first group
n2: Size of the second group
Partial Budget Analysis
Partial budgeting was use to compare profitability between manual
harvesting and combine harvester. The format for this analysis is given in the
table below.
Table 1. Partial Budget Analysis Format
ADDED RETURNS
ADDED COSTS
TOTAL ADDED RETURNS (I)
Total Added Costs (III)
REDUCED COSTS
REDUCED RETURNS
TOTAL REDUCED COSTS (II)
Total Reduced Returns (IV)
SUBTOTAL (A = I + II)
SUBTOTAL (B = III + IV)
18
NET GAIN (A-B)
Multiple Logistic Regression Analysis
To determine which factors were significant in influencing farmers'
decision-making process regarding the adoption of combine harvesters,
multiple logistic regression analysis where used. The researcher used multiple
logistic regression because of the reason that the study has more than two
measurement variables, which is the dependent variable and the independent
variables. The researcher used it to identify about which independent variables
have a major effect on the dependent variable. The dummy variable in this
study represents the farmers’ choice of harvesting method:
z=
1 farmers adopt combine harvester
0 farmer practices manual harvesting
The analysis equation is;
𝑧 = β0 - β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 - β7X7 + β8X8 + β9X9 + ε
Where:
z = adoption of combine harvester
X1 = farmer’s age (in years)
X2 = farmer’s education (in years)
X3 = farmer’s farm size (in ha)
X4 = membership on cooperatives/associations
X5 = participation in combine harvester seminars or on-farm trials
X6 = Perception on the benefits of combine harvesting (total number of
questions with strongly agree/agree responses)
X7 = Attitude towards the labor displacement caused by combine harvesters
(total number of questions with strongly agree/agree responses)
X8 = Combine Harvester Accessibility
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X9 = Availability of combine harvester
ε = disturbance term
Positive coefficient of the independent variable (xi) indicated the
increasing use of rice combine harvester technology including higher education
level, larger farm size, membership on cooperatives/associations, participation
in combine harvester , seminars or on-farm trials, perception in combine
harvesting combine harvester accessibility and availability of combine
harvester while age and attitude towards the labor displacement caused by
combine harvesters will have a negative impact on the adoption of the
technology.
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