MULTIPLE LINEAR REGRESSION ANALYSIS ON THE FACTORS AFFECTING
MAIZE PRODUCTION.
(A CASE STUDY OF TURBO CONSTITUENCY, KENYA (2016-2021)
HAMISI BRIAN DUNCAN
PA100/G/6798/19
A RESEARCH PROPOSAL SUBMITTED IN PARTIAL FULFILMENT OF THE
REQUIREMENTS FOR DEGREE BACHELOR OF SCIENCE IN STATISTICS
DEPARTMENT OF PURE AND APPLIED SCIENCES FACULTY STATISTICS
KIRINYAGA UNIVERSITY
2022
i
DECLARATION
I, the undersigned, declare that this research proposal is my original work and has not been
presented in any other college or university for academic credit.
Signed…………………… Date…………….
HAMISI BRIAN DUNCAN PA100/G/6798/19
This research proposal has been submitted for examination with my approval as university
supervisor.
Signed………………… Date………………….
DR. HURUN GITONGA
Lecturer, School of Pure and Applied Sciences
ii
DEDICATION
I dedicate this work to my loving mum Jane Achieng Asena; my sister Sharon Judith Ongach, my
manager David Kimani at Davin's creation ltd; and my community for allowing me to be a role
model to others inspiring me.
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ACKNOWLEDGEMENT
I want to express my special gratitude to the Almighty God for giving me life, commitment, and
courage to undertake this work. I want to acknowledge my supervisor Dr Harun Gitonga for her
feedback, advice, and patience throughout my study. My thanks also go to all my lecturers for
playing a crucial role in my studies. I also thank my loving parents for their moral and financial
support and prayers throughout the study. I would also like to thank everyone who has contributed
to the success of this work entirely.
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ABSTRACT
The most significant cereal crop in Kenya is maize, which significantly impacts the feed system
and raises household income and the economy. Kenya's primary staple food is maize, which
occupies about 60% of the nation's agricultural area. The variables impacting maize production in
Kenya's Turbo Constituency were subjected to regression analysis in the study. The study's main
objectives are to determine the impact of market demand on maize production in the Turbo
Constituency and To investigate the maize production process in the Turbo Constituency. Farmers,
corn vendors, and future scholars will benefit from the study. According to the ministry of
agriculture, there are more than 6300 maize growers in the turbo constituency. The farmers were
chosen using a straightforward random selection technique. Through a pilot study and pre-test, the
reliability of the research instruments was evaluated in conjunction with the explanatory survey
design. The explanatory survey design necessitates being there and receiving direct replies since
it is a primary tool for measuring variables and investigating correlations among factors and
respondents' attitudes. For assurance, Data from the Statistical Package for Social Software were
reviewed in a manner consistent with qualitative and quantitative analysis using a descriptive and
inferential method of the significant purpose to ensure the analysis's success. There was evidence
that maize-produced influences include geography, labour markets, infrastructure, age, gender,
education level, and other local economic factors. ANOVA analysis was used to assess the climatic
conditions because it was difficult for the respondents to provide correct information or data on
the weather measurement.
v
ABBREVIATION AND ACRONYMS
KARI:
Kenya Agriculture Research Institutes
NCPB:
National Cereal and Produce Board
WPF:
World Food Program
USDA:
United State Department of Agriculture
MT:
Metric Tons
KEPHIS:
Kenya Plant Health Inspectorate Service
IFPRI:
International Food Policy Research Institutes
SSA:
Standard for Sub-Saharan Africa
NALEP:
National Agriculture and Livestock Extension Program
ANOVA:
Analysis Of Variance
SPSS:
Statistical packages for Social Science
vi
Table of Contents
DECLARATION......................................................................................................................................... ii
DEDICATION............................................................................................................................................ iii
ACKNOWLEDGEMENT ......................................................................................................................... iv
ABSTRACT ................................................................................................................................................. v
ABBREVIATION AND ACRONYMS .................................................................................................... vi
CHAPTER ONE: INTRODUCTION ....................................................................................................... 1
1.0 BACKGROUND OF THE STUDY ............................................................................................... 1
1.1.
STATEMENT OF PROBLEM .................................................................................................. 2
1.2 SIGNIFICANCE OF THE STUDY................................................................................................. 3
1.3 PURPOSE OF THE STUDY ........................................................................................................... 3
1.4 THE STUDY'S OBJECTIVES ........................................................................................................ 3
1.4.1 The main objective of the study ................................................................................................ 3
1.4.2 Specific objectives of the study ................................................................................................. 3
1.5 HYPOTHESIS FOR RESEARCH .................................................................................................. 3
1.6 JUSTIFICATION OF THE STUDY ............................................................................................... 4
1.7 THE SCOPE OF THE STUDY. ...................................................................................................... 4
1.8. LIMITATIONS OF THE STUDY.................................................................................................. 4
1.9 DEFINITION OF TERMS............................................................................................................... 5
CHAPTER TWO: LITERATURE REVIEW .......................................................................................... 6
2.0 Introduction ....................................................................................................................................... 6
2.1 Maize Production Globally .............................................................................................................. 6
2.2 Maize Production in the Africa........................................................................................................ 7
2.3 Maize production in Kenya .............................................................................................................. 8
2.4 Maize yield in turbo Constituency, Kenya...................................................................................... 9
2.5 Farm inputs ....................................................................................................................................... 9
2.6. Effects of Inputs Factor on Maize Production ............................................................................ 11
2.7. Effect of Climatic Change factor on Maize production .............................................................. 11
2.8. Market prices and market price control by the Government of Kenya ................................... 11
2.9 Market Maize Challenges............................................................................................................... 11
2.10 The Makert Competition .............................................................................................................. 12
2.11. The Effects Of Maize Quantity Produced In Turbo Constituency.......................................... 13
2.12 Conceptual Framework ................................................................................................................ 13
vii
2.13 Research Gap ................................................................................................................................ 15
CHAPTER THREE: RESEARCH METHODOLOGY .......................................................................... 16
3.0 Introduction ..................................................................................................................................... 16
3.1 Source of data .................................................................................................................................. 16
3.2 The target population ..................................................................................................................... 16
3.3 Data collection procedure............................................................................................................... 16
3.3.1 Multiple Linear Regression Analysis Model and Data Processing .......................................... 17
3.4 Classical Assumption Test .............................................................................................................. 18
3.4.1 Normality Test .......................................................................................................................... 18
3.4.2. Multicolinearity Test............................................................................................................... 18
3.4.3. Autocorelation Test ................................................................................................................. 18
3.4.4 Heteroscedasticity Test ............................................................................................................ 19
3.5 Regression Analysis ........................................................................................................................ 19
3.5.1. t-Test......................................................................................................................................... 19
3.5.3. Coefficient of Determination (R-Squared)............................................................................ 20
REFERENCES ..................................................................................Ошибка! Закладка не определена.
APPENDICES ........................................................................................................................................... 23
APPENDIX I: TIME FRAME ............................................................................................................. 23
APEENDIX II: BUDGET ........................................................................................................................ 24
viii
CHAPTER ONE: INTRODUCTION
1.0 BACKGROUND OF THE STUDY
Around 5,000 BC, central Mexico was the site of the invention of maize (Zea Mays, often known
as corn in North America). The crop was brought to Europe in the sixteenth century and quickly
spread to Africa and Asia. It is currently one of the most frequently cultivated crops in temperate
and tropical parts of the world, and it ranks in the top ten most valuable crops worldwide. Over
870 million tons of food were produced globally in 2012 on 158 million hectares of land;
According to estimates from the International Grains Council and the FAO's Agricultural Market
Information System (AIMS), production might reach 990 million tons in 2014–2015 and be
cultivated on about 200 million hectares.
As long as optimal crop management is utilized, maize cultivation is acknowledged as a high-yield
crop in Australia. The amount of solar energy intercepted, the availability of water and nitrogen,
and physiological process limitations are the main variables affecting maize yield potential. Due
to business and economic demands and public expectations, notably in resource utilization
efficiency and environmental management, the industry faces ongoing challenges (Martin et al.,
1991).). Sweet corn for human consumption, and field corn for other applications like animal feed
and biofuels, are both produced from maize. Only about 15% of the maize produced worldwide is
used for food output, primarily for animal feed use. However, the percentage of Mazie made for
food production is more significant in developing nations at 25% and even higher in places like
South East Asia, which is estimated to be between 30 and 40 percent. At the same time, in some
areas of Sub-Saharan Africa, it can reach as high as 70 to 80 per cent.
The crop is a staple diet for an estimated 1 billion people in sub-Saharan Africa, South Asia, and
Latin America. 50% of the population in Africa consumesMaize, with daily consumption rates of
up to 328 grams per person (in Lesotho). Regionally, the cultivation of maize has been viewed as
an essential component of growth in Eastern African nations. Kaliba (1998) concluded that several
difficulties call for more attention from research, extension, and policymakers in the study on
adopting maize production practices in Central Tanzania... It is necessary to connect and expand
research and extension programs to increase the information flow to farmers. Even though maize
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is cultivated in almost all of Kenya's agroecological and technological systems, the maximum
production in lowland areas has the lowest growth potential.
In contrast, significant potential is seen in central highland zones. In almost every agro-ecological
zone in Kenya, Maize is grown using modern techniques like hybrid maize and fertilizers. More
access to agricultural extension services, better soils, increased rainfall, and other factors have all
been linked to an inter-zonal difference. The use of yield-enhancing inputs, which cost slightly
more but shift production and change the entire input-output relationship, is one example of
technical innovation that must be encouraged and requires credit. Small-scale farmers in
developing nations don't seem to be sensitive to requests that seem to be economically reasonable.
Technology may have advanced as a result of risk-taking behaviour and liquidity constraints. Riskaverse producers are more likely to choose conventional technologies, which may promise a higher
average yield with lower variance, instead of new technologies, which may demand a higher
average output but also carry the risk of greater fluctuation at the subsistence level, where mere
survival is at stake. High-interest rates and the unknowns around repayment make people riskaverse. The most dependable way to increase the amount of maize grain needed to feed them. The
nation will enhance corn yields on territory that has previously been farmed. To reach this
objective, several corrective actions must be taken (Jones, 2007).
1.1.STATEMENT OF PROBLEM
Most of the world's maize producers reported significant losses in the quantity of maize exported,
indicating declining global and regional maize production patterns (Pingali, 2001). Farmers are
discouraged from growing this crop because there is no local or regional market for imported
maize. Even though farmers in the Turbo constituency region have accepted the use of
contemporary technologies and have gotten some training on growing maize through programs
like the NALEP program, maize output has fallen. Several elements affect the Turbo
Constituency's maize production; one of the problems restricting maize production in various parts
of Kenya is acidity, where soil cultivation is encroaching. Another major issue is the cost. This
could be an issue in the research field where transportation costs may rise, or access to the study
area may become more complex.
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1.2 SIGNIFICANCE OF THE STUDY.
Farmers, maize processing and industrial facilities, donor communities, researchers, and Kenya
benefited greatly from the study. Farmers were in a position to identify the causes of their inability
to maximize maize yield, and maize gathering and manufacturing facilities provided insights into
the elements influencing maize. Donor communities would educate to inform about the
difficulties, offer financial assistance, and work with farmers on solutions to the problems now
facing maize-producing farmers. Researchers recommended that to enable future studies founded
on recorded knowledge generation, they should document information on efficient ways to
maximize productivity, which is important for the Kenyan economy.
1.3 PURPOSE OF THE STUDY
The study aimed to identify the factors impacting Kenya's Turbo Constituency's maize output.
1.4 THE STUDY'S OBJECTIVES
1.4.1 The main objective of the study
To develop a multiple linear regression model of the determinants affecting maize production in
the Turbo constituency. Kenya.
1.4.2 Specific objectives of the study
i.
To develop a multiple linear regression model.
ii.
Test the significance of the model parameters.
1.5 HYPOTHESIS FOR RESEARCH
H0: There is no statistically significant correlation between the costs of farm inputs and the
determinant factors affecting maize yield.
H1: There is a statistically significant correlation between the costs of farm inputs and the
determinant factors affecting maize yield.
H0: there is no statistical correlation between maize production and climate change.
H1: there is a statistical correlation between maize production and rainfall.
3
1.6 JUSTIFICATION OF THE STUDY
The study will be conducted to determine the effects of various factors that affect maize yield
among farmers in the turbo constituency and make suggestions on policies to reduce or curb how
those effects will be reduced using a multiple linear regression model. This study will be critical
in understanding the various determinant factors that impact maize production turbo and finding
better ways or sTurboions to deal with it.
1.7 THE SCOPE OF THE STUDY.
The study's primary objective was to find the factors affecting maize output in the Turbo
constituency. The Uasin-Gishu County district of Turbo is widely renowned for its extensive
maize production. The survey, conducted in the Turbo Constituency between March and April,
concentrated on farmers. Data analysis approaches were employed to understand the
fundamentals of maize production from farmers in the Turbo constituency.
1.8. LIMITATIONS OF THE STUDY.
Any other data will not be considered; the study will only look at data from the record books of
turbo farmers. Since the research relies on secondary data gathered from the farmer's database,
the accuracy of the data records won't be known, though. When the instances of farms generated
based on were being recorded, there may have been some bias in the data gathered from the
Farmers Agriculture Association database.
Sometimes handling lectures and projects simultaneously become very difficult; hence it
becomes a major challenge in the study.
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1.9 DEFINITION OF TERMS
Regression Analysis is a powerful statistical method that allows you to examine the relationship
between two or more variables of their interest.
Random Sampling is the part of the sampling technique where each sample has an equal
probability of being chosen.
Capital is a factor of products produced to make other goods and services.
Income- relates to the amount of money, property, and other transfers of value received over a set
period in exchange for services or products
Market- is the total of all the buyers and sellers in the area or region under consideration to
promote business effectiveness and a favourable working environment.
Climate change- is a significant and lasting change in the statistical distribution of weather
patterns over decades to millions of years.
5
CHAPTER TWO: LITERATURE REVIEW
2.0 Introduction
This chapter presents a summary of previous research on the subject of maize production, with a
focus on the market, climatic change, and input availability as major influences on maize farming.
This chapter provides a conceptual and theoretical foundation to guide the investigation.
2.1 Maize Production Globally
The two types of maize produced worldwide archive and yellow cornet (rehan, (2018).). Despite
having distinct visual characteristics, white and yellow corn are medically and genetically
somewhat similar but lack the carotid oil pigments that would otherwise give the grain its yellow
colour. Production and cultivation conditions are essentially the same (Dinolfo, (2022).). 65–70
million tons, or 12–13% of all maize produced worldwide yearly, are produced as white maize.
More than 90% of the world's white maize is grown in impoverished nations, which produce about
a quarter of the world's maize and occupy just under two-fifths of the world's maize-growing area.
In the tropical highlands and subtropical/mid-altitude regions of the developing world, where it
makes up around 40% of the lowland tropical maize acreage, more white than yellow maize is
farmed. (Adejuwon, (2018)). MoreMaize is grown yearly than any other grain, thanks to
widespread global cultivation. 40% of the global harvest is produced in the United States, with
China, Brazil, Mexico, Indonesia, India, France, and Argentina rounding out the top ten producers.
FAO. (2010) (2010) According to FAOSTAT, North America produced the most maize in 2008,
accounting for around 38.8% of the world's total production. Africa (6.7%), Central America
(3.4%), South America (11.2%), Europe (11.1%), Asia (28.5%), and Oceania (0.07%) follow the
next regions in order. China, Brazil, and Argentina make up more than 60%. China accounts for
46% of the world's total production of maize. When these nations are considered, white maize
accounts for over 54% of the total maize acreage and slightly under 60% of the overall maize
production in emerging nations. WhiteMaize, in comparison, has much less value in the
industrialized world. For instance, because the market is so limited, only 1% of the nation's total
maize production, which is by far the most significant producer of the crop in the world, is
produced through contract farming in the United States (Grote, (2021).). First, Central America
omits the Caribbean sub-region, where it is produced in substantial quantities. Second, the northern
6
region of South America, including Colombia and Venezuela, together accounts for nearly 90%
of the region's entire output ofMaize.
2.2 Maize Production in Africa
When the Portuguese brought to Africa in the 16th to 18th centuries, Maize emerged as the
continent's most important source of food and animal feed. Zimbabwe, which until the late 1980s
exported maize, Angola, Ghana, Kenya (Halls, (1997)) World Bank, 2007), Kenya, and other
African countries must dramatically increase their maize production to feed their growing
populations. The FAO/2004/2005 WFP agricultural and food supply assessment states that the
nation's main staple, Maize, has been produced less frequently over time, with most regions
witnessing a 60% decline, a decrease over five years. This resulted from the Lack of cultivation of
the arable lands due to delayed rainfall, increased risk of agricultural Lack, Lack of seeds for
substitute crops, and other factors. Many scientists believe that the potential effects of climate
change on agriculture are very unpredictable, and rain-fed agriculture in Africa is among the most
susceptible to climatic change. The Agriculture Technological Organization's report (WMO) A
multitude of variables, including widespread poverty, bureaucracy, a lack of material and financial
resources, frequent political instability, and environmental degradation, render Africa more
vulnerable to climate change, according to an IPCC study from 2007. The local and global policy
has improved since the World Congress on women in 1975. Yet, the International Assessment of
Agriculture Knowledge, Science, and Technology Development, If development processes,
particularly for the production of maize, are to consider gender issues, it is imperative that these
issues be adequately handled (Scoones, (2009).). Smallholder rural farms in Africa prostate most
of the continent'sMaize. Production occurs in challenging circumstances marked, among other
things, by poor soils, low-yielding varieties, insufficient access to inputs that yields, such as
fertilizers and improved seeds, yield sufficient access to financing by producers, suppliers, and
buyers, and variable climatic and environmental conditions. There is a significantly large postharvest due to poor technology and storage facilities. The entire maize value chain, from input
supply through production to marketing and consumption, is plagued with restrictions that may be
abolished if known. Technology, legal, and marketing innovations could all be effectively and
profitably used (Ranum, (2014).).
7
2.3 Maize production in Kenya
Trans Nzoia, Uasin Gishu, Kakamega, Nakuru, Embu, Nyeri, Kirinyaga, TaitaTaveta, and Kwale
are the principal counties most suitable for the production of maize. About 23 million bags of
maize are produced annually on an estimated 1.4 million hectares of agricultural land). This is less
than the estimated annual consumption of 44 million bags of homegrownMaize (Tarus, (2019).).
Given the limited amount of available arable land, future growth in maize production will depend
primarily on yield enhancements made possible by the broad adoption of productivity-enhancing
technologies, such as better farming practices (Isaac, (2019).).. Producers of Maine have access to
a market through the Kenya Cereals and Produce Board. During the spring harvest, extra maize is
purchased from government granaries. Morocco controls market pricing. Various corn-significant
millers from nearby towns, like Dola millers and Unga millers, are among the buyers. The millers
do not, however, pay a fair price for the maize they purchase. Kenyans eat 97 kilograms of maize
annually, which is the main food source in the nation. Rural farmers no longer have access to
several market services due to a Lack of roads, storage facilities, and substantial post-harvest
storage losses brought on by weevils. Inter Academy Council (2004). These tactics were seldom
effective in Kenya (Tefera, (2011)).
Table: 1.0. Maize production trends in Kenya, 2016 - 2021
Year
2016
2017
2018
2019
2020
2021
Area(Ha)
1,672,600 1,919,71 1,660,712 1,898,185 1,625,305 1,716,800
7
Prod(90kg
bag)
31,130,44 28,259,72 34,400,86 37,186,50 35,400,50 27,260,
0
1
3
6
3
00
0
Consumption 31,150,00 33,135,00 32,120,00 33,115,00 34,098,00 35,122,
on est.'s 90
0
0
0
kg bags
0
0
00
0
2017 Economic Review of Agriculture as a Source
8
2.4 Maize yield in turbo Constituency, Kenya.
This is partly due to the region's climate, which is given by Uasin Gishu County and has
temperatures ranging from 17 to 29 degrees with an annual rainfall of 13500 mm. The average
yearly temperature is about 250 C. There won't be a need for irrigation because the conditions are
perfect for maize growth. The best seeds for the area are H614, H629, and H6213; they were
created and are intended for use between medium and high elevations (1500 and 2100 m), which
is exactly where they are found. During the growth season, daily highs rarely exceed 28 °C, and
overnight lows can get as low as 8 °C. Rainfall has to fall between 800-1500mm. This variation is
recommended in situations when similar circumstances prevail. According to legend, this variety
is among the most excellent seeds to grow in Kenya's highlands. If all necessary conditions are
satisfied, In Turbo, the yield per hector varies from fhectare0 kg-1 to 5250 kg-1. Given the proper
circumstances, necessary management, and especially excellentMaize in the Turbo region. DAP
fertilizers weighing 185 kilograms (1 pound), CAN top dressing weighing 185 kilograms (1
pound), and certified seeds from Kenya seed are needed based on the soil in the Turbo area. For
example, weddings cost Kshs. 750 per acre in 2016, but Kshs. 1500 in 2018. This is due to the
growing cost of labour.
Furthermore, community farming has become less popular over time. One of the many reasons,
notably in Uasin Gishu, is the soil's acidity. County, which is preventing some Kenyan regions
from producing maize. Fertilization can increase the acidity of soils with insufficient buffering,
especially when using some nitrogenous fertilizers that contain strong acid-forming anions like
sulfate.
2.5 Farm inputs
The following inputs should be made available at the correct time to get the optimum results
from a farm in Turbo:
Government of Canada, Turbo Constituency 2021 A minimum of 2700kg to 4600kg of Mazie
per ha is anticipated when the above items are applied correctly.
As a result, the following harvest totals are anticipated:
9
Table2.4.2: Expected Harvest
Area in ha
1 hectare
10 hectares
Maximum
4600kgs
46000kgs
Minimum
2800kgs
28000kgs
Source: Ministry of Agriculture Turbo Constituency 2021
The Kenya Cereals and Produce Board will pay Kshs for the corn if sold to
them. 55kg-1 the item can bring in:
= 46000kg x Kshs. 55 kg-1
= kHz. 2530,000 (maximum value of farm output)
= 28000kg x Kshs. 55 kg-1
= Kshs. 1,540,000 (minimum value of farm output)
Profits
The farm's anticipated profit is calculated by deducting input from the
output: Maximum Anticipated Revenues Profit Revenue Revenue = Output Inputs
= 2530000-1540000
= Kshs. 990000
The minimum expected profit will be;
= 1540000-990000
= Kshs. 550000
The profit average that is expected will be as follow:
= (550000+2530000)/2
= Kshs. 1540000
10
2.6. Effects of Inputs Factor on Maize Production
A producer's productivity can be influenced by a variety of factors, which can be categorized into
three groups: farm and farmer characteristics, quantity and quality of inputs used, such as land,
labour, and money, fertilizer, and seeds, as well as external influences like government policy
(Biggelaar, (2004).). Capital inputs include, among other things, agricultural equipment, fertilizer,
and seeds. The size, topography, and farm's proximity to markets for its output and input, as well
as the farmer's age, gender, level of education, the size of the household, the accessibility of
extension services, and the availability of credit, are examples of characteristics of the farm and
the farmer (Place, (2007).).
2.7. Effect of Climatic Change factor on Maize production
The state of the soil and meteorological elements, including rainfall, temperature, and humidity,
are called agro-climatic conditions (Falconnier, (2020).). In the past 50 years, more than in any
other comparable time in human history, human activity has modified ecosystems more swiftly
and thoroughly to supply the need for gasoline, food, clean water, and other industrial raw
materials (Xiong, (2016).). Kenya's vice population.
2.8. Market prices and market price control by the Government of Kenya
Through:
(a) The National Cereals and Produce Board's (NCPB) activities include purchasing and selling
at administratively set rates, and the government has pursued its maize pricing and revenue
distribution policies.
(b) Limitations on the export ofMaizeze via fluctuating Ministry of Trade and Industry import
taxes (2010).
2.9 Market Maize Challenges
New private investment in storage facilities might be vulnerable to enormous losses if the NCPB
continued to be a significant market player, provided prices to farmers and millers that did not
increase throughout the marketing season (pan-seasonal prices), or set a small margin between its
buying and selling prices that the Treasury could guarantee as it did for the majority of the 2000s
(Kirimi, (2011).). These and other elements, discussed in more detail below, have slowed down
the pace of private investment in grain marketing facilities. The variation in prices and the scarcity
11
of maize produced by significant producing counties are additional difficulties. These are industrial
corn growers, and you can buy their corn everywhere
October to December. Independent grain investment marketing facilities have slowed due to
these and other factors, which are discussed in more depth below.
2.10 The Market Competition
In 1990, the Soviet Union followed the previous year's collapse of the communist regimes in
Eastern Europe. These events marked the beginning of fundamental, systemic changes that
transformed societies, economics, political systems, and institutional arrangements. The
perception of the transition is not restricted to overcoming backward economic structures but
also involves attributes of the social system like freedom of speech and democracy. The initial
phase brought a series of considerable economic shocks in almost all countries that lasted for
several years until macroeconomic
stability was achieved, often by currency reforms after
periods of hyperinflation. By the mid-1990s, the private sector made for an average of 40% of the
transition economies' GDP (Turley, (2013).), reflecting fundamental changes in the micro
economy (Commander et al., 1999). New firms exerted competition on formerly closed markets,
expediting the structural change. Not only domestic firms entered the markets, but also liberal
trade policies allowed for international competition and further increased
the degree of
competition. Government-owned firms were privatized as a reaction to reform pressures. Almost
all countries experienced a series of significant economic shocks during the early phase that lasted
for years before macroeconomic stability was attained, frequently due to currency reforms
following periods of hyperinflation. According to Hare and Turley (2013), by the middle of the
1990s, the private sector accounted for 40% on average of the GDP of transition economies,
suggesting significant modifications to the microeconomy. (Duval, (2006). )
12
2.11. The Effects Of Maize Quantity Produced In Turbo Constituency.
Reducing poverty through the production of Maize in the Turbo Constituency has provided many
people with food and carbs, an essential component of human health. Lunch is provided in primary
schools so children can focus on their studies without wasting time. Numerous tasks are involved
in producing Maize, which has historically fed work for women and young people. Farmers' living
standards improve, especially in rural regions, due to the revenue they receive from harvesting the
product. Production of Maize is crucial for feeding livestock, including dairy cows, pigs, and
poultry. The productized Maize, another essential component of the impact of maize production,
supports the government's efforts to make Kenya a food-secure nation. Fewer people will be
hungry. Increasing maize output can significantly reduce hunger, food security, and poverty by
giving farmers more purchasing power. Increases in agricultural productivity not only lead to
agricultural expansion but can also help reduce poverty in impoverished and rising countries,
where agriculture usually employs the most significant share of the workforce for better
opportunities to build a life. Agricultural work, either as employees or as farm owners. Meanwhile,
individuals who gain from increases in agricultural output go beyond those who work in the sector.
Lower food prices and a more consistent food supply also assist those working in other economic
sectors.
2.12 Conceptual Framework
The following model of study, which outlines the independent and dependent variables of the
study, will serve as the foundation for the investigation. The conceptualization of the framework
views the factors influencing the amount of Maize produced as a dependent variable and maize
production as an independent variable.
13
Figure 2.2: Showing conceptual framework.
The conceptual framework states that agricultural input influences maize production in the turbo
constituency. The primary way that high labour costs affect Maize's productization is by making
it difficult for farmers to hire labour, which impacts maize yield. The price of fertilizer, on the
other hand, affects the outpMaizezeaize because crops that aren't fertilized tend to yield less than
those that are. Given the farmers' meagre incomes, they couldn't afford fertilizer, resulting in low
production. On the other hand, climatic circumstances have an impact on maize output.
Unpredictable rainfall patterns brought on by global warming have impacted Maize's
productization. On the other side, drought affects maize production since it prevents the expansion
of maize plantations.
14
2.13 Research Gap
The study acknowledges a shortage of thorough research examining the precise interactions
between the many aspects of maize growing and the amounts of Maize harvested. No one element
has been recognized as having a greater impact on the part of Maize gathered than another.
According to the investigation, farmers lack technical awareness about all the aspects influencing
their farming practices, which prevents them from being able to justify why, despite using the most
scientific approach, their maize output is declining.
15
CHAPTER THREE: RESEARCH METHODOLOGY
3.0 Introduction
These chapters explain the various methods that were used to assemble statistics. The report's
sampling technique is a quantitative study. Quantitative methods-research techniques used to
evaluate quantitative data-enabled professionals to organize and understand numbers and, in turn,
to make good decisions. The following components are highlighted: research design, sampling
techniques, sample procedure, data collection instruments, data collection methods, data analysis,
presentation, and conclusion.
3.1 Source of data
The data source used in the research will be the Turbo Constituency Ministry of Agriculture office
database recorded (2012-2020). The data will be used to determine the effects of various factors
on maize production and the maize yield size from different farmers in turbo constituency Kenya.
3.2 The target population
A study of a group of people chosen from the general population who share a common trait, such
as age, sex, or health condition, is known as the intended audience study (Chumo, (2013). ). To
assess the factors influencing maize production in the region and their effects on production
amounts, the target population, which included maize farmers in the Constituency, was chosen
from among the respondents who make up the majority of the people in the area. The study chose
farmers from each ward in the Turbo constituency using a straightforward random selection
technique. The study focused on 140 farmers out of the 5210 maize growers in the Constituency,
according to the Turbo Constituency Ministry of Agriculture office (2012). Via way of the seven
wards. This figure represented at least 20 farmers from each neighbourhood, the typical number
of farmers praising Maize commercially in each ward. As farming is primarily done for
commercial interests, the farmers are chosen because they belong to a group of farmers interested
in the production elements.
3.3 Data collection procedure
Data collection is the systematic approach to gathering and measuring information from various
sources to get a complete and accurate picture of an area of interest. Data collection enables a
person or organization to answer relevant questions, evaluate outcomes, and make predictions
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about future probabilities and trends (Jagoe, (2020). ). The data collected and used in this study is
that the data is secondary to the method of documentation. Sources of farm inputs price (turbo
Export maize and maize production.
3.3.1 Multiple Linear Regression Analysis Model and Data Processing
Multiple linear regression analysis is the technique utilized for analysis. Numerous linear
regression analysis techniques were applied to describe the relationship and the degree of influence
the independent factors had on the dependent variable. In this study, multiple linear regression
analysis is performed to identify factors influencing maize pMaizetion, maize expMaize, and
maize trade. To accomplish the study's goal, qualitative and quantitative analyses were used.
Regression analysis is a quantitative research method used when the study involves modelling and
analyzing several variables, where the relationship includes a dependent variable and one or more
independent variables. In simple terms, regression analysis is a quantitative method used to test
the nature of relationships between a dependent variable and one or more independent variables.
There are two kinds of linear regression analysis:
 Simple linear regression: Regression analysis with one independent variable, with the
general formulation:
 y = 𝑎 + 𝑏1 𝑥1 +∈
 Multiple linear regression: Regression analysis with two or more independent
variables, with the general formulation:
Y = 𝑎 + 𝑏1 𝑥1 + 𝑏2 𝑥2 + ⋯ + 𝑏𝑛 𝑥𝑛+ 𝜖
The regression equation function, in addition to predicting the dependent variable value (Y), can
also be used to determine the direction and magnitude of influence Independent variable (X) on
the Dependent variable (Y).
This research used multiple regression analysis because of these research three independent
variables. The regression equation in this research is as follows:
Y = ∝ +𝛽1 𝑋1 + 𝛽2 𝑋2 + 𝛽3 𝑋3 +∈
Explanation: Y = total production (maize production)
a = constants
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e = error
β= regression coefficient
x1 = labour input
x2 = amount of fertilizer
x3, = land size
3.4 Classical Assumption Test
Testing the classical assumptions used are normality test, multicollinearity, Autocorrelation, and
Heteroscedasticity in detail can be explained as follows:
3.4.1 Normality Test
This normality test aims to test whether the independent variable or both are usually distributed in
the regression model of the dependent variable. According to Agus Tri Bakasi and Imamudin
Yuliadi (2014), to detect whether the residual is normally distributed or not by comparing the value
of Jarque Bera (JB) with the table, namely:
 If the probability of Jarque Bera (JB) > 0,05, then the residual is normally distributed.
 If the probability of Jarque Bera (JB) < 0,05, then the residual is not normally
distributed.
3.4.2. Multicollinearity Test
Multicollinearity is a linear relationship between independent variables in the regression model. If
the linear relationship between free X variable in multiple regression is perfect correlation, then
the variables said Perfect multicollinearity. Multicollinearity detection can see the value of
Variance Inflation Factors (VIF). The testing criterion is that if the value of VIF <10, there is no
multicollinearity among independent variables. Otherwise, if the value of VIF >10, there is
multicollinearity among independent variables.
3.4.3. Autocorrelation Test
The autocorrelation Test is used to find out whether or not the deviation of classical assumptions;
Autocorrelation is the correlation between residuals in an elevation with other observations on the
regression model, and Autocorrelation is a condition where there has been a correlation between
this year's residual with the error rate of the previous year, to know the presence or absence of an
18
autocorrelation disease in a prototype, can be seen according to the Durbin-Watson set of data with
the Breusch-Godfrey Test.
The Durbin-Watson test (DW test), with the following circumstances, is the process that is
typically frequently used:
 If d < dL or d > 4-dL, hypothesis 0 is not rejected, which means there is Autocorrelation.
 If d is located between dU and (4-dU), then hypothesis 0 is rejected, which means there
is no autocorrelation.
 If d is located between dL and dU or between (4-dU) and (4-dL), then hypothesis 0 is
rejected, which means there is no autocorrelation. Then do not result in a definite
conclusion.
3.4.4 Heteroscedasticity Test
Heteroscedasticity is a hard word to pronounce, but it doesn't need to be challenging to understand.
Put, Heteroscedasticity (also spelt Heteroscedasticity) relates to the circumstance in which the
variability of the variable is unequal across the range of the values of a second variable that predict
it, and Heteroscedasticities is the residual residuals for all observations in the regression model.
The heteroskedastic test is to know the existence of deviations from the terms of the classical
assumption on the regression model, in which the regression model must be met in the absence of
the Heteroscedasticity test.
3.5 Regression Analysis
Linear regression analysis is a statistical technique for modelling and investigating the effect of
one or more independent variables on a single response variable.
3.5.1. t-Test
The T-Test is used to determine the influence of each independent variable partially. T-Test shows
how far the influence of the independent variables is in explaining the dependent variable. The
significance of independent variables to dependent variables can be seen from the Sig value. At
the 0.05 (5%)
Significance level, assuming the independent variable has a constant value.
Hypothesis:
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 If the probability βi > 0.05, Not significant
 If the probability βi < 0.05, Significant
t=
̃1 − 𝛽0
𝛽
√∑(𝑥1 − 𝑥̅ )2
3.5.3. Coefficient of Determination (R-Squared)
The R2 test is a value that shows how much the independent variable will explain the variable
dependent variable, R2 in the regression equation is susceptible to the addition of independent
variables. Where more independent variables are involved, the value of R2 will be greater because
that is the use of R2 adjusted on multiple linear regression analysis (Prawoto, 2016) if the value of
the coefficient of determination = 0 (Adjusted R2 = 0), variable Y's variation cannot be explained
by variable X. In contrast, if R2 = 1, it means the variation of variable Y as a whole can be explained
by variable X. In other words, If Adjusted R2 approaches 1, then the independent variable will be
able to explain the changed variant of the dependent variable. If Adjusted R2 approaches 0, the
independent variable cannot define the dependent variable.
𝑅2 = 1 −
∑(𝑦𝑖 − 𝑦̃ )2
∑(𝑦𝑖 − 𝑦̅ )2
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References
Adejuwon, J. O. ((2018)). Assessment of the changing pattern in Maize cultivation in Sokoto-Rima River
Basin, Nigeria. Journal of Applied Sciences and Environmental Management, 22(9), 1433-1437.
Biggelaar, C. D. ((2004).). The global impact of soil erosion on productivity. I. Absolute and relative
erosion-induced yield losses. NA, NA.
Chumo, M. K. ( (2013). ). Determining Factors That Affect Maize Production In Turbo Constituency,
Kenya (Doctoral dissertation, University of Nairobi). Determining Factors That Affect Maize
Production In Turbo Constituency, Kenya (Doctoral dissertation, University of Nairobi)., n.pages.
Dinolfo, M. I. ((2022).). Fusarium in Maize during harvest and storage: a review of species involved,
mycotoxins, and management strategies to reduce contamination. . European Journal of Plant
Pathology, 1-16.
Duval, R. &. ((2006). ). The effects of EMU on structural reforms in labour and product markets. n.p,
n.page.
Falconnier, G. N. ((2020).). Modelling climate change impacts maize yields under low nitrogen input
conditions in sub‐Saharan Africa. . Global change biology, 26(10), 5942-5964.
Grote, U. F. ( (2021).). Food security and the dynamics of wheat and maize value chains in Africa and
Asia. Frontiers in Sustainable Food Systems, 4, 617009., NA.
Halls, A. J. ((1997)). Wetlands, biodiversity, and the Ramsar Convention: the role of the convention on
wetlands in the conservation and wise use of biodiversity. . In Ramsar Convention Bureau,
Gland, Switzerland, (Vol. 13).
Isaac, M. M. ( (2019).). . Effects Of Government Input Subsidy Program On Maize Production In UasinGishu County. NA, NA.
Jagoe, K. R. ((2020). ). Sharing the burden: Shifts in family time use, agency, and gender dynamics after
introducing new cookstoves in rural Kenya. Energy Research & Social Science, 64, 101413.,
n.pages.
Kirimi, L. S. ((2011).). A farm gate-to-consumer value chain assessment of Kenya's maize marketing
system. NA, n.p.
Place, F. A. ((2007).). Understanding rural poverty and investment in agriculture: An assessment of
integrated quantitative and qualitative research in western Kenya. World Development, 35(2),
312-325.
Ranum, P. P.‐R.‐C. ( (2014).). Global maize production, utilization, and consumption. Annals of the New
York academy of sciences, 1312(1), 105-112.
Rehan, S. S. ((2018).). Characteristics of white, yellow, and purple corn accessions: the phenolic profile,
textural, rheological properties, and muffin-making potential. Journal of food science and
technology, 55(6), 2334-2343.
21
Scoones, I. ((2009).). The politics of global assessments: the case of the International Assessment of
Agricultural Knowledge, Science and Technology for Development (IAASTD). . The Journal of
Peasant Studies, 36(3), 547-571.
Tarus, C. B. ((2019).). Maize Crisis: A Position Paper on Strategies for Addressing Challenges Facing Maize
Farming In Kenya. . East African Scholars Publisher, 2(3), 149-158.
Tefera, T. K. ( (2011)). The metal silo: An effective grain storage technology for reducing maize postharvest insect and pathogen Maizes whiMaizeproving smallholder farmers' food security in
developing countries. Crop protection, 30(3), 240-245.
Turley, G. ((2013).). Transition Economies: The first two decades1. In Handbook of the Economics and
Political Economy of Transition (pp. 17-33). Routledge., (pp. 17-33).
Xiong, W. S. ((2016).). Calibration‐induced uncertainty of the EPIC model to estimate climate change
impact on global maize yield. . Journal of Advances in Modeling Earth Systems, 8(3), 1358-1375.
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APPENDICES
APPENDIX I: TIME FRAME
Months
Topic
selection
Research
development
objectives
Discussions
with the
supervisor
Development
of
proposal(draft
1)
Consultations
with the
supervisor
Proposal
defend
Proposal
amendment
Research
instruments
piloting
Data
collection
Data analysis
Development
of research
projects
Consultations
with
supervisor
Correction of
project
Final defend
Final
projection
Final copies
submission
WORK PLAN.
Chart representing the work plan of the research study
September October November December January February march
23
APPENDIX II: BUDGET
Source: Fictitious data, for illustration purposes only
Requirement
quantity
laptop
1 pcs
Photocopy of the final
40 pages@ Ksh 5
proposal
Binding of the final
I pcs @ 200
proposal
Printing hardcopy
Ksh 10 per page
total
24
amount
28000
200
200
400
28800