APROPOSAL SUBMITTED TO ECONOMICS DEPARTMENT FOR

THE PARTIAL FULLFILMENT OF BACHELOR OF ART (BA)

DEGRE IN ECONOMICS

FACTOR AFFECTING CEREAL PRODUCTION (IN CASE OF

SEKELA WOREDA)

By Abiel Asabu UGR/1536/13

February, 2023
ADDIS ABABA, ETHIOPIA
AKNOWLEDGMENT

First, I thank God for his help to me. Next, I want to thank my Major advisor Assefa
Admassie (phD) for his guidance and genuine help tirelessly giving his time. Without his
sincere help from proposal preparation to this thesis accomplishment, this thesis
research document could not complete successfully. I also want to grant my heartily
thank to sekela woreda agricultural officials for giving full information that I want.
Thirdly, I would like to acknowledge my family for their inspiration in successful
completion of the research as the whole.

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ACRONYMS

ATA Agricultural Transformation Agency

CSA Central Statistical Authority
EEA Ethiopian Economic Association
EOMR Ethiopia Outcome Monitoring Report
FAO Food and Agricultural Organization
HYV High Yield Variety
IFPRI International Food Policy Research Institute

IPCC Intergovernmental Panel on Climate Change

KG Kilogram

MOA Ministry of Agriculture

NBE National Bank of Ethiopia
OCHA United Nations Office For the Coordination of Humanitarian Affairs
OLS Ordinary least square
PFP Partial factor productivity

RVI Rift Valley Institute

TFP Total factor productivity

UC ANR University of California Agriculture and Natural Resource

UN United Nation

USAID United State Agency International Development

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USDA United States Department of Agriculture

WB World Bank

WDR World Development Report

Abstract
In Ethiopia, the agriculture sector has a crucial contribution to the socioeconomic
development of the nation, as most of the population depends on this sector. Identifying
factors affecting cereal production is the most important activities to achieve development.
This study attempted to contribute to the consideration of factors affecting cereal production
in case of sekela woreda. Both descriptive statistics and econometric model were used for
data analysis. Ordinary least square were applied to identify the factors affecting cereal
production. The result of linear regression models shows tha farming experience, education
level, total lands owned, fertilizer, herbicide, farm experience, family size,working time per
day, and improved seed positively and significantly affect quantity of cereal crop production
whereas conflict negatively and significantly affect cereal crop production.
Keywords: Cereal Crop, Sekela Woreda, OLS regression model, Productivity, Ethiopia

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Table of Contents
AKNOWLEDGMENT....................................................................................................................................I
ACRONYMS.................................................................................................................................................II
Abstract........................................................................................................................................................III
CHAPTER ONE.............................................................................................................................................1
1. INTRODUCTION......................................................................................................................................1
2. 1.1. Back Ground of the Study....................................................................................................................1
1.2 Statement of the Problem......................................................................................................................2
1.3 Objectives of the Study.........................................................................................................................4
1.3.1 General objective...........................................................................................................................4
1.3.2 Specific objective..........................................................................................................................4
1.4 Significance of the Study......................................................................................................................5
1.5 Scope of the Study................................................................................................................................5
CHAPTER TWO............................................................................................................................................6
REVIEW LITERATURE...............................................................................................................................6
2.1. Theoretical Review..............................................................................................................................6
2.2.1 Cereal Crop Productivity Performance and Sources of Growth....................................................6
2.2.2 Determinants of cereal crop productivity.......................................................................................8
2.3 Empirical literature review.................................................................................................................11
CHAPTER THREE......................................................................................................................................17
RESEARCH METHODOLOGY....................................................................................................................17
3.1 Description of study area....................................................................................................................17
3.2 Types of data and source of data........................................................................................................17
3.4. Methods of data collection.................................................................................................................19
3.5 Method of data analysis......................................................................................................................19
3.6 Model specification............................................................................................................................19
3.6.1 Description of independent variables used in cereal production..................................................20
CHAPTER FOUR.........................................................................................................................................25
RESULTS AND DISSCUSION.....................................................................................................................25

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 3. 1. Descriptive analysis..........................................................................................................................25
4.2_Econometric analysis.............................................................................................................................33
4.2.1Regression result and its interpretation.........................................................................................34
4.2.1. Diagnostic Tests..........................................................................................................................37
CHAPTER FIVE..........................................................................................................................................40
Conclusion and Recommendation................................................................................................................40
5.1. Conclusion.........................................................................................................................................40
5.2. Recommendation...............................................................................................................................40
Reference......................................................................................................................................................41
Appendix : Questionnaire.............................................................................................................................45

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 CHAPTER ONE

1. INTRODUCTION

2. 1.1. Back Ground of the Study

Agriculture is the main livelihood source for most of the Ethiopian population. Several
cereals and grain legumes are grown in the country by smallholder farmers. Productivity of
grain crops in the country suffers from large yield gaps arising from many factors. Ethiopian
smallholder agriculture is characterized as low-input, low-output, labor-intensive, and rain-
dependent, with a fragmented landholding system. It involves high crop diversity in both time
and space, providing food for family use, and the surplus is taken to the market, which is an
indigenous strategy to maximize benefits while reducing natural risks of crop failure
(Ethiopia Outcome Monitoring Report 2019).In Ethiopia crop growing plays essential role in
scarcity lessening, food security and in general development by taking the share of the 36.2%
of foreign exchange earnings and serves as a means of lively hood for about 83% of the rural
residents (ATA, -2017 and USAID, 2018).

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Given its contributions to the national economy, the agriculture sector is considered as one of
the major sectors driving inclusive, sustainable economic growth in the government’s Ten-
Year Development Plan (TYDP) to be implemented from the 2021 to 2030 periods. The plan
lays out strategies for enhancing agricultural production and productivity, which include
converting unutilized arable land, modernizing production systems, improving uptake of
technology, expanding use of irrigation, mechanization, improving institutional support, and
resilience to climate change. All these strategies are assumed to help Ethiopia realize its
vision of becoming an African beacon of prosperity by 2030 (NPC, 2020).

In Ethiopia, the agriculture sector which is dominated by smallholder farmers still takes a
large share in Ethiopia’s economy as evident from its contribution to national GDP (33.3%)
(NBE, 2019). However, notwithstanding its role in the national economy, smallholder
farming which constitutes large lots of agricultural production in the country is characterized
by a lack of resources, poor agricultural intensification, and vulnerability to production and
marketing-related shocks. Moreover, even if by and large, smallholder farmer’s market
integration is taken as a key for growth in the agricultural sector and a means to reduce
poverty incidence, the vast majority of smallholder farmers in developing nations could not
able to fully exploit the existing potential of the markets and thus, they are constrained by
different problems (G. Tesso, 2017). Corresponding to this, subsistence production systems
continuously dominate the sector in which 95% of total area under agriculture is cultivated by
smallholder farmers who produce more than 90% of total output (USAID, 2018).

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To solve problems sustainably facing smallholder farmers, many countries and international
development agencies focused on modernization and commercialization of the smallholder
agricultural sector as a means for reducing food insecurity and poverty among smallholders
in developing countries (Tesso, 2017). In Ethiopia, cereal production is a dominant form of
agricultural practice over other types of crop production (CSA, 2019). According to CSA
(2019), the percentage area share of crops production shows that cereals constituted
(71.57%), pulses (11.20%), oilseeds (5.17), vegetables (1.67%), root crops (1.60%), fruit
crops (0.83%), and coffee (5.28%) of total crop production area. Out of regional states in the
country, Oromia ranks first both in terms of land area allocation (45.41% of national crop
production area) and crop production (49.24% of national crop production).

In Ethiopia, the agriculture sector has a crucial contribution to the socioeconomic

development of the nation, as most of the population depends on this sector. As the National
Bank of Ethiopia (2022) reported, about 77.2% of the country’s total population lives in rural
areas and gets jobs from agriculture. Even though the share of the agriculture sector in the
gross domestic product (GDP) is declining from time to time, it remains high. The sector
accounted for approximately 32.4% of the total GDP of the country. Crop production takes
the lion’s share of the sector which accounts for 65.6% (National Bank of Ethiopia, 2022).

1.2 Statement of the Problem

The various factors are affecting cereal production in Ethiopia include population growth,
food aid requirements, urbanization, grain market prices, armed conflict, climate-related
factors, climate change, including droughts and irregular rainfall patterns, agricultural
transformation initiatives. International events such as the war in Ukraine also affected
wheat supplies in Ethiopia, impacting the importation of cereals(EEA 2022).

The agricultural research and extension activities need to consider additional agronomic
practices along with the cereal crop productivity method in order to increase cereal crop
production, and for the successful promotion, adoption and scaling up of good agronomic
practices and extension should contact farmers individually as well as in group to be awarded

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in terms of cereal crop productivity is suitable to improve household production. Fertilizer
and improved seed in agricultural sectors still remains critically important. Therefore, local
government with together regional should supply fertilizer and improved seed on the time for
farmers through creating awareness on recommended amounts. Fertilizer utilization is
important in order to increase their cereal crop productivity and improve livelihoods of
smallholder’s farmers (Takele A et al. 2020)..
According to World bank Report (2022); several key determinants of cereal production,
including climate and weather patterns, land quality and soil fertility, technological
innovation, market access and infrastructure, as well as the policy environment and
institutional support systems. Understanding these determinants is essential for addressing
global food security challenges and promoting sustainable agricultural development.

According to the most recent report of Ethiopian economic association (2022), determinants
of cereal production such as serve as a valuable resource for understanding the complexities
of cereal production in Ethiopia. In Less Developed Countries (LDCs) in general and Sub-
Saharan Africa (SSA) in particular, economic policy highly depended on agriculture. Poverty
reduction and income growth can mainly be achieved by agricultural growth. It creates
spillover effects to the remaining sectors (WB 2017).

Even though, agriculture is the crucial sector in the national economy, its production and
productivity is unsatisfactory. So, an important way to increase cereal crop productivity and
agricultural productivity is that reducing constraints of agriculture (natural constraints,
economic constraints, social and institutional constraints). These constraints reduced through
diffusion of improved seed, land management practice and training farmers(CSA, 2015).

In the country level there were a lot of researchers tried to identify the determinants of cereal
crop productivity of small holder farmer, like (Endale, 2011), and (Mr. Takele Ayele Mr.
Tesfaye Melaku 2019), they have tried to study on the variables such as improved seed, farm
size, level of education, irrigation. most of researchers are focused in the country level.
Tenaye (2020) observed various farm practices of smallholders, including farm size, labor,

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oxen, precipitation, and other inputs such as seed, fertilizer, type of farm implement, soil
fertility, and financial status, can contribute to time-varying inefficiency effects.

Though many researches were done in country level and on specific cereal type, there is no
significance study were done in specific areas specially in sekela woreda. Cereal production
is not only influenced by environmental and economic factors but also by sociocultural
determinants such as working habit. Amhara region is affecting by conflict which have
fluctuated in intensity over time (RVI 2023). The researcher will include those sociocultural
determinants and threat of conflict on cereal production. The researchers did not focus in
detail about the contribution of herbicides and pesticides on cereal crop productivity. The
researcher will conduct significant research in sekela woreda with determinant of cereal
production. Therefore, this study will be differing from other studies by giving attention to
threat of conflict, sociocultural determinants and other additional inputs those are herbicides
and pesticides on the study area which is taking the specific area of Sekela Woreda.

1.3 Objectives of the Study

1.3.1 General objective

The general objective of this study is to analyze the factor affecting of cereal crop production.

1.3.2 Specific objective

• To examine the factors that affect cereal crop productivity in the study area.

• To give general understanding for policy makers of sekela woreda

agricultural office.

1.4 Significance of the Study

This study would contributes the following significances: The findings of the study wuold
inform evidence-based policymaking at local, regional, and national levels. Policymakers
would utilize this knowledge to design targeted interventions that address specific challenges
faced by cereal producers in Sekela Woreda. It would help to create motivation for farmers

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how to increase their productivity system by applying technical efficiency in study area. And
also, it would benefit the target group of farmers who were involved on the activity through
providing awareness about the problems and prospects of cereal crop productivity in the area.
It will help researcher to get degree and to relate his theoretical knowledge with what is going
on in the real environment. In addition to this, the research paper has significance as anyone
who wants to undertake a study on related issue can use it as a reading material to filling the
gap for the future.

1.5 Scope of the Study

Although there are many rural areas that not studied very well, this study delimited its area of
investigation to Amhara region in west Gojjam Zone specifically in Sekela Woreda. This was
because of studying the overall area of cereal crop productivity required huge amount of time
and finance. Since cereal crop productivity is multi-dimensional and studying all dimensions
required long time and intensive investigation of each aspect. Therefore, the researcher would
focus on determinant of cereals crops productivity by using both primary and secondary data.

CHAPTER TWO

REVIEW LITERATURE

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 2.1. Theoretical Review

2.2.1 Cereal Crop Productivity Performance and Sources of Growth

Cereals account 65 percent of the total share of agricultural GDP and are a major source of
staple food crops in the country. It leads in area planted and volume of production of grain
crops (cereals, pulses, and oilseeds). During the 2020/2021 cropping season, cereals were
cultivated on 10.5 million ha of land, producing 30 million tons. This represented 81.19% and
88.36% of the total area and production for all grains, respectively (CSA, 2021).

Cereal crop productivity involves the use of different practices, which require knowledge,
and skill of application and management. Education was found to have a strong relation with
the cereal crop production as it enhances cereal crop yields. Therefore, due emphasis has to
be given towards strengthening rural farmer’s education at different levels for small farm
households using farmers training centers. Increasing the number of cooperatives
organization in the rural area in which the farmers will be able to get credit are basis in
enhancing the cereal crop production. Further, it is apparent from the study that if farmers get
credit more easily, they would use cereal crop production to enhance cereal crop yields. Thus,
the credit facility should target poor farmers especially those who were not enhancing cereal
crop productivity due to lack of operating capital. This may encourage the farmers to do
commercial farming practice in which they can build their asset to implement the cereal crop
production technologyon their farms (Takele A et al. 2020).

The agriculture sector has been decisive in directing the overall path of the economy through
its significant contribution to food security and serving as a source of foreign exchange with a
share of 76% of merchandise exports and covering over 70% of raw material sources for
domestic agro-processing industries. It also employs 65% of the labor force, provides
livelihood for about 84% of the population, and contributed 32.7% of the total nation’s GDP
in 2019/20 (NBE, 2020).

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 Agricultural production and productivity are very low because of backward agricultural
practices and a low rate of agricultural technology utilization. Cereal crop productivity is a
critical aspect of global food security and agricultural sustainability. Understanding the
sources of growth in cereal crop productivity is essential for ensuring food availability and
improving the livelihoods of millions of people worldwide. Several factors contribute to the
performance and sources of growth in cereal crop productivity, including technological
advancements, agronomic practices, genetic improvements, and environmental factors (FAO
2022).
The performance of cereal crop productivity can be evaluated based on various parameters
such as yield per hectare, resilience to biotic and abiotic stresses, nutritional quality, and
overall sustainability. Over the years, there has been a significant improvement in the
performance of cereal crops due to advancements in agricultural science and technology. The
adoption of modern farming techniques, improved seed varieties, precision agriculture, and
sustainable management practices has contributed to enhanced productivity in cereal crops
(IFPRI 2020)

A firm’s productivity is greatly determined by the type and quality of inputs and how well
these inputs are combined in the production process. The type and quality of inputs represent
the production technology, while the way inputs are combined in the production process
refers to the technical efficiency of the production process (FAO, 2017)

While the social and demographic characteristics of the household do not matter for inter-
specific diversity of cereal crops they grow, these factors do explain variation in infraspecific
diversity, although the direction of effects is not the same for all cereals. As fixed labor
stocks of adult male labor are drawn out of farm production for non-farm activities, inter-
specific diversity in cereals will probably decline. On the other hand, households with higher
proportions of females or female household heads are more likely than others to grow cereal
crops with greater infra-specific diversity. More educated households also maintain more
variety diversity, as more literate communities maintain a greater richness of cereals (Samuel
B. et al. 2023).

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Understanding the factors that hinder cereal crop productivity is essential in planning and
executing technology related programs for meeting the challenges of cereal crop production
in our country. Therefore, to enhance cereal crop productivity by farmers, it’s important for
policy makers and planners of cereal crop producer to understand farmers need as well as
their ability to cereal crop productivity in order to come up with technology that will suit
them. It is better to encourage cereal crop production because the results of this study
signified that application of agricultural technology increase substantially both the
productivity and income of farmers (Takele Ayele and Negese Tamirat 2020).
Agriculture is the center of policy making. As result government of Ethiopia takes attention
to agricultural sector( Mr. Takele Ayele and Mr. Tesfaye Melaku 2019). farmers’ decision to
adopt fertilizer and the decision on the intensity of use of fertilizer are two independent
decisions. In spite of some region-specific differences, the mos important factors are
household resource endowments (livestock ownership, farm size, etc.), institutional services
(credit and membership to cooperatives), infrastructure (proximity to markets and roads), and
communities’ indigenous knowledge (crop rotation, use of soil and water conservation).
Policies to increase fertilizer uptake could, for instance, include strengthening cooperatives,
access to credits, and infrastructure development (Tefera T. et al. 2020).

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2.2.2 Determinants of cereal crop productivity

The physical input factors

Among the needed farm inputs, seed is recognized and considered having the highest ability
of boosting on-farm productivity potential of all other agricultural inputs. Improved Yields or
output and the productivity since seed determines the actual amount of crop Varieties of seed
are essential agricultural inputs that supports farms to obtain improved Agricultural yields.
The genetic manipulation of selective breeding improved the productivity and value of crops
obtained. Chemical fertilizer is another important input to increase smallholder farm
production. This is because the use of organic and inorganic Fertilizer help to improve the
soil fertility status if soil fertility is not improved the use of other Technologies such as high-
yielding varieties will not have a significant impact (Bihon, 2017)

The physical factors further sub divided into non-human (no labor) physical input and labor
physical are land, seeds, water, fertilizer, pesticides, structure, work, animal’s tools and
machinery and fuel and power other than animal power (FAO 2017)

Environmental factors

cereal production is influenced by various environmental factors that have implications for
land use, water resources, greenhouse gas emissions, biodiversity conservation, and soil
health. Sustainable agricultural practices and technological innovations are essential for
mitigating these environmental impacts while ensuring global food security(IPCC 2019)

The economic factors

Cereal production is influenced by a complex interplay of economic factors that encompass

input costs, market demand and prices, government policies and subsidies, international trade
dynamics, technological advancements, and environmental considerations. Understanding
these economic factors is essential for stakeholders in the cereal production industry to make
strategic decisions that ensure long-term sustainability and profitability (WB 2022).

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 Organization factors Organization factors significantly influence cereal production.

Effective management practices, streamlined supply chain logistics, technology integration,

understanding market dynamics, regulatory compliance, and investment in research and
development are all critical components for optimizing cereal production processes (IFPRI
2020).

Knowledge factors

Cereal production involves various stages, from seed selection and planting to harvesting and
processing. Ignorance factors in cereal production can refer to the lack of knowledge or
understanding in specific areas that may affect the overall efficiency and quality of the
process.(FAO 2020)
To address ignorance factors, it is essential to invest in education, research, and extension
services that promote the adoption of best practices and technologies in cereal production.
This will ultimately contribute to increased productivity, improved crop quality, and
enhanced food security(FAO 2020).

Cultural and motivation factors

According to world bank report (2021) emphasizes on various cultural factors such as
traditional farming practices, indigenous knowledge, social norms, and beliefs that influence
agricultural productivity. It also examines the role of culture in shaping farmers’ attitudes
towards innovation, technology adoption, and risk-taking behavior in agriculture.

Policy issues

Cereal production is influenced by a wide range of policy issues, including environmental

sustainability, climate change, land use, input subsidies, trade, agricultural research and
development, food loss and waste reduction, smallholder farmers’ support, agricultural labor
and workers’ rights, and food safety and quality. Addressing these policy issues is crucial for

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ensuring global food security, environmental protection, and social justice in the cereal sector
(USDA 2022).

Many poor African countries relied on inadequate or little price support programs to manage
agricultural productivity and price fluctuation, which resulted in low crop productivity
growth and increased dependency on food imports. In Ethiopia, the lack of price support
policies resulted in low crop productivity as well as social welfare for producers (Z. Shikur,
2020).

2.3 Empirical literature review

Sex/gender is one of the demographic variables that affect the rural farmer’s use of improved
agricultural technology services. Among improved agricultural technology user households,
56.03% were male-headed households and 19.86% were female-headed households. On the
other hand, non-user male-headed and female-headed households were 16.31% and 7.80%
respectively. The finding shows that the numbers of female-headed participation on improved
technology usage were lower than male-headed households. The study is in line with the
previous studies that confirmed female-headed households are more resource-constrained
(Elias D. et al. 2019).

Several studies since then reported substantial yield increases in maize and tef when applying
NPS fertilizers that include Zn, B or both (Berhe and Marie In press; Haileselassie et al.
2018). These studies however do not show whether the yield increases can be attributed to the
addition of S, B or Zn, as the crops received diferent levels of N and P fertilizers in addition
to the multi-nutrient fertilizer.

An early publication on the 2017 wheat trials in the current paper revealed some but no
signifcant efect of fve-nutrient fertilizers (Elias et al. 2019). Demisse and Bekele (2017)
concluded in a country review that ‘the proft potential is generally much greater with
application of N and P fertilizers compared with K and the secondary and micronutrients,

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particularly Zn for all cereals. In short, all results so far show that crop response to fertilizer
is generally existent and promising, but the evidence that fertilizers that contain S, Zn and B
do better than their conventional equivalents is (at best) mixed.

The study conducted by Oyetunde-Usman and Olagunju (2019) showed a mean technical
efficiency of 52% for farm households in Nigeria. Similarly, the efficiency analysis by
Wongnaa and Awunyo-Vitor (2018) revealed the mean technical efficiency of 58.1% for
maize farmers in Ghana. This study also found that educational level, farming experience,
extension contact, group membership, use of fertilizer, and improved seed enhance technical
efficiency, whilst farm size and land fragmentation decrease technical efficiency among
maize farmers.

Family size of household head is significant variable at 5% of significance level in cereal

crop productivity. The marginal effect (0.084) also reveals keeping all other explanatory
variables constant, a 1% increases in family size increases household probability of cereal
crop production and productivity by 8.4%. This suggests that family size is among the major
variable in influencing decisions of households to participate in cereal crop productivity
(Takele A et al. 2020)..

Households with more labor will be able to engage in a more complex set of activities, but
families with larger sizes may also have higher dependency ratios. More varieties of a crop
may require more time in selecting, storing and managing the seed. On the other hand,
planting varieties and crops that mature at different points in time is a way of coping with
seasonal labor shortages (Samuel B. et al. 2023).

Recent information and communication technology (ICT) and development initiatives in Africa have

been to promote the use of mobile phones that can potentially improve smallholder farmers' access to

information and markets (E. Misaki., 2018). This stress is based on the idea that farming sectors in

developing countries mostly comprise resource poor, small ‐scale subsistence farmers Tadesse &

Bahiigwa ( 2015) who face high transaction costs and have poor access to information that bounds

their market participation. Effective use of ICT devices such as mobile phones is considered ideal in

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reducing asymmetries of information between traders and producers and subsequently reducing

farmers' transaction costs.

According to A stewel ( 2017) on his study on determinants of rice production using multiple

regression models, rice production was positively and significantly influenced by sex of household

head, oxen ownership, land size, labor availability and amount rice seed used. Access to finance is

essential for the further development of maize farming enterprises: - Credit is necessary for maize

farming associations running collection centers, buying products from producers and selling on in

bulk.

According to Kenate B. (2020) analysis of model indicates that household maize production,

significantly affected by education level, total land owned, farming system, age of the HH, family

size, farm experience, distance to the nearest market, other crop production and crop rotation

positively and significantly. According the Henchman model estimation household market

participation decision positively and significantly affected by gender, farming experience , education

level, land allocated for maize, extension contact, market information and transportation equipment.

A study by Amanuel A. and Girma A. (2018) examined the impact of agricultural inputs and
technology adoption on cereal production in Ethiopia. The findings revealed that access to
improved seeds, fertilizers, and modern farming techniques significantly. B. Minten et al.
(2022) examined the role of market access and price incentives in influencing cereal
production decisions among smallholder farmers in Ethiopia. The research underscored the
significance of efficient market linkages and price stability in stimulating increased
production. Dniel T. (2019) assessed the impact of policy interventions on agricultural
development and cereal production outcomes in Ethiopia. The findings showed positive
related of supportive policies that promote investment, infrastructure development, and cereal
production.

A study carried out by Geffersa et al. (2019) showed that sex of the household head, family
size, off-farm income, saving, farmer group participation have significant and positive
association with technical efficiency, while the age of the household head was negatively

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related to technical efficiency. Few studies have been conducted on the impact of improved
technologies on technical efficiency scores in developing countries. A study conducted by
Ahmed et al. (2017) is a good example to estimates the impact of improved maize variety on
technical efficiency of farm households in East Hararghe Zone of Ethiopia using propensity
score matching method. They found that farm households who adopt improved maize
varieties have 4.42% of technical efficiency gain compared with their non-adopter
counterparts.

A study conducted by Ahmed et al. (2017) is a good example to estimates the impact of
improved maize variety on technical efficiency of farm households in East Hararghe Zone of
Ethiopia using propensity score matching method. They found that farm households who
adopt improved maize varieties have 4.42% of technical efficiency gain compared with their
non-adopter counterparts.

The study conducted by Fisseha et al. (2022) have found a positive and significant coefficient
for conventional inputs, such as land, seed, and fertilizer in cereal production, suggesting
cereal output is more responsive to cultivated land, fertilizer, and seed, in that order than
other inputs. The technical efficiency score was found 58%, suggesting the farm households
can improve cereal productivity by about 36% through better use of available input resources
and current technology. This result remarks that there is room to improve cereal productivity
by improving the current technical efficiency of the farm households. Moreover, the results
provide enough evidence that the technical efficiency of farm households is influenced by the
adoption of HYVs and improved management practices. Hence, there is a great scope for
improving the efficiency of farm households by promoting the adoption of modern
technologies. Other sources of significant inefficiency factors include poor managerial
abilities, mobile ownership, group membership and access to modern inputs

Seed supply is also found to be a critical constraint because it is often too expensive for
smallholder producers, and some promoted species produce low yields and are poorly
adapted to production environments (Balehegn et al., 2020). Temesgen Gashaw et al. (2019)

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investigated sustainable agriculture practices and their implications for enhancing resilience, soil

fertility, and biodiversity conservation within Ethiopian cereal farming systems. Fisseha Zegeye et

al. (2021) utilized panel data analysis to identify the determinants of cereal crop productivity in

Ethiopia. The findings revealed that access to improved seeds, fertilizer use, land size, and access to

credit significantly influenced cereal production. Tadesse, G., & Algieri, B. (2019) investigated the

determinants of cereal crop yield in Ethiopia, focusing on wheat and teff production. Their study

found that factors such as soil fertility, irrigation, farm size, and technology adoption were critical

determinants of cereal crop yield.

The study examined by Fisseha et al. (2022) presented the effects of modern cereal crop
varieties and recommended agronomic practices on the technical efficiency of farm
households in cereal production. Our results have shown a low adoption rate of HYVs and
improved agronomic practices. About 34% of farm households applied modern varieties and
recommended rates of seed, fertilizer, and row planting at different scales of intensity. On
average, about 19% of the total area under cereal crop were covered by modern varieties and
recommended practices. The low level of adoption of HYVs and improved agronomic
practices is mainly due to HYVs access constraints, poor quality of seed, and lack of
awareness

2.4 Conceptual Framework

Understanding the multifaceted factors affecting cereal production in Sekela Woreda was crucial for

developing targeted interventions to improve agricultural productivity and food security. By

considering environmental, technological, economic, and social factors within this conceptual

framework, policy makers would develop comprehensive strategies to support sustainable cereal

production in the region. As a result of the literature and empirical reviewed above; the study would

develop the following schematic representation of the conceptual framework.

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Figure 2.1. Conceptual frame work of dependent and independent variables.

Improved
seed Education Family size
level

Farm size

Pesticides

Farming
experience Cereal crops

fertilizer Herbicides

Working time
per day
presence of
conflict

Source: own construction (by taking the theoretical and empirical review)

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 CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Description of study area

Sekela is one of the woredas in the Amhara Region of Ethiopia. Part of the West Gojjam
Zone, Sekela is bordered on the southwest by Bure, on the west by the Agew Awi Zone, on
the north by Mecha, on the northeast by Yilmana Densa, on the east by Kuarit, and on the
southeast by Jabi Tehnan. The administrative center of Sekela is Gish Abay.
Based on the 2007 national census conducted by the Central Statistical Agency of Ethiopia
(CSA), this woreda has a total population of 138,691, an increase of 61.36% over the 1994
census, of whom 69,018 are men and 69,673 women; 6,779 or 4.89% are urban inhabitants.
With an area of 768.83 km2 (296.85 sq mi), Sekela has a population density of 180.39
inhabitants per square kilometre (467.2/sq mi), which is greater than the zone average of

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158.25. The majority of the inhabitants practiced Ethiopian Orthodox Christianity, with
99.97% reporting that as their religion.
Source; sekela woreda administration office

3.2 Types of data and source of data

The researcher would use both primary and secondary data. The Primary data was collected
through questionnaire (illiterate person would be assisted to fill questionnaire) and interview
to identify the determinants of cereal crops productivity at kebele level. The secondary data
was collected from the relevant report of the Woreda bureau of agriculture and rural
development and other related theoretical articles.

3.3 Sampling technique and sample design

The researcher collected data from three kebeles of Sekela Woreda, which contains totally 30
kebeles because studying the whole kebeles needed more finance and it is impossible to get
all available data from each kebele. Three kebeles was selected purposely to get appropriate
data respect to time and finance namely Lichima, Azuri and, enjikusi Then, by using
proportional formula, the researcher selected the respondents from three kebeles households.
After selecting three kebeles purposely, the researcher applied probability sampling methods
of both simple random sampling and proportional formula. The way of collecting the data
from each kebele was by using simple random technique. The sample size was found by
using Yamane taro sample size formula (1987). Sekela woreda comprise of 30 kebeles having
a total of 138,691 populations. The formula of Yamane taro: n=N/1+N (e2)
Where: n=sample size
N=total population of the three kebeles at household level
e=error term, e=10%

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The researcher will use e=10%, to reduce sample size because of time and budget constraints.
N=6745
n=6745/1+6745 (0.1)2=6993/1+6993(0.01)
n=6745/68.45=98.53=99
The proportional formula which helps to select unbiased and desirable number of observation
from each of the 3 kebeles is like this: Ni= (n/N) *NS, where: Ni=total number of observation
in one kebele
n=the total number of households in one kebele
N=the total number of households
In 3 kebeles
NS=the total number of sample size
Therefore, using the above technical formula the proportional number of respondents from
each kebele as follows
From azuri = (1738/6745) (99) =26
From Lichima= (2548/6745) (99) =38
From Enjikusi= (2461/6745) (99) =35

No of Sample Households (%)

Kebeles No of households

38 38.38
Lichima 2548

2461
Enjikusi 35 35.35

1738
Awuri 26 26.26

6745
total 99 100

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3.4. Methods of data collection

A cross-sectional data was collected from randomly selected sample households from
purposely selected kebeles Lichima, Azuri and Enjikusi of sekela woreda was used in this
study. Semi-structured questionnaire consisting of both open and close-ended was asked to
sampled households to collect primary data on household characteristics, crop production,
and its determinant. The primary data was mainly use for analysis and making inferences
determinants of the level of cereal crops production in the study area.

3.5 Method of data analysis

The researcher was used both descriptive method and econometric model to analyze the
collected data. Therefore, for this matter, ordinary least square (OLS) estimation technique
was applied in the study area to differentiate the determinants of the cereal crop productivity.
The reason for using this method is because of the computation procedures of OLS was fairly
for cross sectional data as compared with other econometric methods.

3.6 Model specification

The cereal crops productivity model includes these explanatory variables in the form of
multiple linear regression function:
CCP=β0+β1imsd+β2fams+β3fe+β4edu+β5fs+β6fex+β7ps+β8cf+β9hs+β10wtpd Ui
Where: CCP= cereal crop productivity
IMSD=improved seed
FAMS=farm size
FE=fertilizer
EDU=education
FS= family size
FEX= farm experience
CF= conflict
WTPD= working time per day
PS=pesticide

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 HS=herbicide
B0=constant term
Bi=coefficient of explanatory variables
Ui=error term (residual term)

3.6.1 Description of independent variables used in cereal production

Improved seed; Seed is a key input for improving crop production and productivity.
Increasing the quality of seeds can increase the yield potential of the crop by significant folds
and thus, is one of the most economical and efficient inputs to agricultural development
(FAO, 2022). The Ethiopian agricultural sector has an important opportunity to further
develop the seed production sector. Generation and transfer of new technologies are critical
prerequisites for agricultural development particularly for an agrarian based economy such as
Ethiopia. Seed, especially that of improved varieties, is an essential input for increasing crop
productivity. This suggests the need to place much emphasis on sustainable and efficient seed
production systems. Access to ‘good seeds’ is certainly vital for farmers’ livelihood.
Characters of good seeds are universal in many aspects and should be realized for better
production. However, good varieties mean different things for different stakeholders/farmers,
and they are very much location, timing and market specific. Therefore, promotion of good
(improved) varieties is a different issue from promoting and securing good quality seeds.
Integration between the two is necessary, but they need to be clearly understood as separate
matter.( Girma Abebe & Amanuel Alemu 2017).

Education level of the household head: it is a categorical variable and measured by status
of sample household head education level. Educated households are expected to have better
exposure to information, egger to accept new technologies for their production, make better
use of their viable resources which enhance their certeral production. Education improves the
distribution ability of decision makers by allowing them to think critically and use
information sources efficiently. Producers with more education should be conscious of more
sources of information, and more efficient in assessing and interpreting information about

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innovations than those with less education. Education was found to positively affect adoption
of improved maize varieties in West shoa, Ethiopia ( silvanus ,2014). Aman et al, ( 2014)
states that education increases the ability of farmers to get and analyze relevant market
information which would improve the managerial ability of the farmers in terms of better
formulation and execution of farm plans, and acquiring better information to improve their
farming performance. It is also in conformity with (Enete and Igbokwe, 2016) who argued
that education will endow the household with better production and managerial skills which
could lead to increased participation in the farm.
Conflict; can significantly impact cereal production in various ways, affecting both the
quantity and quality of crops grown. The presence of conflict can disrupt agricultural
activities, leading to reduced planting, harvesting, and overall productivity. In regions
affected by conflict, farmers may face challenges such as landmines, looting, and destruction
of infrastructure, and displacement, all of which can hinder their ability to cultivate cereals
effectively. Additionally, conflicts often result in food insecurity and limited access to
essential resources like seeds, fertilizers, and machinery, further exacerbating the decline in
cereal production (OCHA 2020). One of the primary ways in which conflict affects cereal
production is through the destruction of agricultural infrastructure. Conflict zones are often
characterized by damaged or inaccessible roads, bridges, irrigation systems, and storage
facilities. This destruction not only impedes farmers’ ability to transport goods to markets but
also limits their capacity to store and preserve harvested crops effectively. As a result, cereal
production may decrease due to the lack of proper infrastructure support (IFPRI 2021).
Pesticides; are crucial part of agriculture and human health worldwide irrespective of the
potential risks and hazards they have. They support the production of the growing food
supply and improve human health by controlling problematic pests. The overall aim of this
study was to improve our understanding of the practices of pesticide uses, benefits, potential
risks, and effects in selected areas of Ethiopia. We specifically assessed (1) farmers’
knowledge of pesticide uses and benefits, (2) perceived environmental, human, and animal
health effects, and (3) estimated the effects of pesticide application frequencies on wheat and
teff yields using 775 farm household survey data. The results showed that about 99% of the

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households surveyed, in general use at least one type of the chemical pesticides, and 89%,
93%, 84%, and 15% of the households, in particular use pesticides for the controls of weeds,
fungi, insects, and rodents, respectively, sometimes during a crop production season. In
addition, a substantial number of farmers also use various traditional practices and new
varieties as pest control methods. Well above 80% of households reported that the uses of
chemical pesticides have solved the problems of crop pests and have also increased crop
production and productivity on their farms. (Dagne M. & Lemma Z. 2022).
Herbicides; are increasingly adopted as a response to labor shortages in rapidly transforming
economies, such as India and China. While agricultural and economic transformation is also
happening in Africa (Reardon et al. 2015; Frankema 2014), it is estimated that only 5 percent
of the cropped area of Africa receives applications of agro-chemicals (Gianessi 2013). It is
argued that herbicides have been underused in Africa, leading to significant productivity
losses through, for example, hand-weeding being performed late or not frequently enough
(Gianessi 2013). However, few authors have comprehensively examined this issue for Africa.
Herbicide use on cereals is found to significantly save on weeding, traditionally a major
agricultural task. In consequence, the increased use of herbicides has important implications
on labor markets in rural Ethiopia where the large majority of its population makes a living. In
the research reported on here, we use a double-hurdle model from a large-scale survey of
producers of teff, the most widely grown cereal in Ethiopia (Seneshaw T. et al. 2016)
Family size: this is a continuous variable measured in number of family members by adult
equivalent. Having large and productive family size could increase crop production through
proper labor division, on time weeding sand harvest. Besides, small and efficient family size
could increase crop production by devoting all their time for farm activities as well as by
employing agricultural input. Household size plays an important role in cereal production and
most farmers depend mainly on family labor. So, that it is expected to have a positive effect
in quality of cereal production (Richard, 2016).
Working time per day; is a crucial determinant of cereal production. By dedicating
sufficient hours to agricultural activities, farmers can enhance efficiency, increase yields,
maintain crop health, optimize harvest timing, improve weather resilience, and adopt

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innovative practices all of which contribute to overall productivity in cereal farming (USDA
2020).
Farm size: is continuous variable measured in hectare and it was expected to affect the
household participation decision and volume of cereal production. Farmers with bigger
cultivable land were found to participate more because of their ability to produce bigger
volumes that ensured production surpluses. Hauwa (2017) found farm size to be an influential
asset that leads to higher production volumes and positively influences farmers’ cereal
production. It was also in conformity with abera (2014)who noted that the larger the total
farm size, the larger the area allocated to the crop production thereby increasing the quantity
of produce available for sale and thus the per unit transaction costs will be lower due to the
economics of scale.
Farming Experience: is a continuous variable measured in number of years of household
head engaged in cereal production. This variable was used as a proxy for availability of active
labor force in the household. This variable was expected to affect farmers‟ decisions to
participate in production positively. Family size was expected to have positive relationship
with cereal production and the extent of participation in the cereal market in household head
(Agete, 2014). 39 Households who have better experience in maize production and marketing
is assumed to acquire knowledge and ability through continuous learning which help them to
actively participate in marketing of maize and produce more amounts to maize ton supply to
the market than those with less experience (Yallew, 2016).
variable measurement Expected sign
improved seed Continuous, Kg positive
farm size Continuous, hectare positive
fertilizer Continuous, Kg positive
education Categories, educational level positive
family size Continuous, Number positive
farm experience Continuous, Farming year positive
conflict Dummy ( 1=yes, 0=no) negative
working time per day Continuous, hour positive
pesticide Continuous, Liter positive

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herbicide Continuous, Liter positive
cereal crop productivity continuous, Quintal Will affected by
independent variables

CHAPTER FOUR

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 RESULTS AND DISSCUSION
This chapter deals with presentation and analysis of the data gathered from respondents of
primary data through questioner and interviewee method and also Secondary data sources are
from office of sekela woreda agriculture and rural development. Generally, in this the
analysis of factor affecting cereal production in amhara region in the case of sekela woreda
performed in two ways, descriptive and econometrics analysis.

3. 1. Descriptive analysis

3.1.1. Family size

FS Freq. Percent Cum.

2 3 3.03 3.03

3 19 19.19 22.22

4 14 14.14 36.36

5 17 17.17 53.54

6 30 30.3 83.84

7 15 15.15 98.99

8 1 1.01 100

Total 99 100 100

Source: own survey 2024

The above table indicates that the majority of the respondents (30.3% households) have 6
family members, 19.19% households have 3 family members, 17.17% of households have 3
family members, 15.15% of family members have 7 family members, 14.14% of households
have 4 family members, 3.03% of households have 2 family members, and 1.01% of
household has 8 family member. Based on this table, above 75% of households have more
than four family members.

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 3.1.2. Educational background of respondents

EDU Freq. Percent Cum.

0 73 73.74 73.74

1 17 17.17 90.91

2 9 9.09 100

Total 99 100 100

Source: own survey 2024

The above table indicates that the majority of respondents (73.74% of households) are
illiterate, 17.175 of households have completed primary education, and 9.09% of households
have completed secondary education. This indicates the majority of the people contributed in
agricultural activities are illiterates

3.1.3. Farm experience of respondents

FEX Freq. Percent Cum.

2 6 6.06 6.06

3 4 4.04 10.1

4 3 3.03 13.13

5 12 12.12 25.25

6 9 9.09 34.34

7 9 9.09 43.43

8 12 12.12 55.56

9 7 7.07 62.63

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 10 12 12.12 74.75

11 5 5.05 79.8

12 7 7.07 86.87

13 1 1.01 87.88

14 4 4.04 91.92

15 3 3.03 94.95

16 1 1.01 95.96

17 2 2.02 97.98

19 2 2.02 100

Total 99 100 100

Source: own survey 2024

As indicated on the above table 25.25% of respondents have 1-5 years of farm experience,
49.49% of respondents have 6-10 years of farm experience, 20.2% of respondents have 11-15
years of farm experience, and 5.06% of respondents have 16-20 years of farm experience.
This indicates the majority of the people contributed in agricultural activities have 5-10 years
of farm experience.

3.1.4. Fertilizer distribution of respondents

FE Freq. Percent Cum.

30 7 7.07 7.07

35 3 3.03 10.1

40 14 14.14 24.24

45 23 23.23 47.47

47 2 2.02 49.49

50 46 46.46 95.96

55 3 3.03 98.99

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 60 1 1.01 100

Total 99 100 100

Source: own survey 2024

As survey indicated the farmers of study area all respondents used fertilizer. Majority of
respondents (46.46%) used 50kg fertilizer per hectare, 23.23% of respondents used 45kg
fertilizer per hectare, 14.14% of respondents used 40kg fertilizer per hectare, 7.07% of
respondents used 30kg fertilizer per hectare, 3.03% of respondent used 55kg fertilizer per
hectare, 3.03% of respondents used 35kg fertilizer per hectare, 2.02% of respondents used
47kg fertilizer per hectare, and 1.01% of respondent used 60kg fertilizer per hectare. There
was shortage of fertilizer because of conflict and inflation as respondents indicated during the
survey.

3.1.5. Farm size owned by the respondents

FAMS Freq Percent Cum.

1 7 7.07 7.07

1.2 1 1.01 8.08

1.3 5 5.05 13.13

1.4 3 3.03 16.16

1.5 11 11.11 27.27

1.6 2 2.02 29.29

1.7 1 1.01 30.3

2 42 42.42 72.73

2.4 1 1.01 73.74

2.5 2 2.02 75.76

2.6 5 5.05 80.81

2.7 12 12.12 92.93

3 7 7.07 100

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 Total 99 100 100

Source: own survey 2024

Based on the above table 42.42% of respondents have 2 hectares of land, 30.3% of
respondents have 1-1.7 hectares of land, and 27.27% of respondents have 2.4-3 hectares.

3.1.6. Improved seed distribution

IMSD Freq. Percent Cum.

30 15 15.15 15.15

32 1 1.01 16.16

35 20 20.2 36.36

37 19 19.19 55.56

40 42 42.42 97.98

45 2 2.02 100

Total 99 100 100

Source: own survey 2024

Based on the above table all of respondents used improved seed. 42.42% of respondents used
40kg improved seed per hectare, 20.2% of respondents used 35kg improved seed per hectare,
19.19% of respondents used 37kg improved seed per hectare, 15.15% of respondents used
30kg improved seed per hectare, 2.02 % of respondents used 45kg improved seed per hectare,
and 1.01% of respondent used 32kg improved see per hectare. There was shortage of
improved seed because of conflict and inflation as respondents indicated during the survey.

3.1.7. Working time per day of respondents

WTPD Freq. Percent Cum.

7 20 20.2 20.2

8 19 19.19 39.39

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 9 57 57.58 96.97

10 3 3.03 100

Total 99 100 100

Source: own survey 2024

Majority (57.58%) of respondents work nine hours per day, 20.2% of respondents work 7
hours per day, 19.9% of respondents work eight hours per day, and 3.03% of respondents
work ten hours per day. The researcher proved that all days of the week are not working days.
Saturday and Sunday are commonly out of working days. In addition to those two days there
are selected days those are out of working days every month based on their religion
command.

3.1.8. Herbicide

HS Freq. Percent Cum.

1 15 15.15 15.15

2 26 26.26 41.41

3 58 58.59 100

Total 99 100 100

Source: own survey 2024

Majority of respondents (58.59%) used 3 litters herbicide per hectare, 26.26% of respondents
used 2 litter herbicide per hectare, and 15.15% of respondents used 1 litter herbicide. The use
of herbicides provides several benefits for farmers. They help maintain clean and weed-free
fields, preventing weed competition for resources like water, nutrients and sunlight. This
allows crops to grow more efficiently, resulting in increased yield and improved quality.
Herbicides also help reduce labor costs associated with manual weed removal and mechanical
cultivation. In addition, they contribute to soil conservation by minimizing soil erosion
caused by excessive tillage (UC ANR 2022).

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 3.1.9 Summary

Variable Obs Mean Std. Dev. Min Max

CCP 99 5.217172 1.829689 2 9

IMSD 99 36.91919 3.705145 30 45
FAMS 99 2.00101 0.550231 1 3
FE 99 45.74747 6.270018 30 60
EDU 99 0.353535 0.643858 0 2
FS 99 5.020202 1.484476 2 8
FEX 99 8.414141 3.90711 2 19
CF 99 0.737374 0.442301 0 1
WTPD 99 8.434343 0.847103 7 10
HS 99 2.434343 0.744526 1 3
PS 99 0 0 0 0

Mean of approximately 5.22 indicates that, on average, the cereal crop productivity per
quintal is around 5.22 units. The mean provides a measure of the central tendency of the data,
representing the typical value. Standard deviation of around (1.83) measures the spread or
dispersion of the data points around the mean. A standard deviation of approximately 1.83
suggests that the data points are dispersed or deviate from the mean by an average of 1.83
units. This indicates the variability or spread in cereal crop productivity values. The range
represents the difference between the highest and lowest values in the sample. In this case,
the range of 7 suggests that the difference between the highest and lowest cereal crop
productivity values is 7 units. This provides insight into the spread of values and the extent of
variability within the sample.

Mean of approximately 36.92 suggests that, on average, the quantity of improved seed per
kilogram is around 36.92 units. The mean provides a measure of central tendency, giving an
idea of the typical value of improved seed per kilogram in the sample. Standard deviation of
about measures the dispersion or spread of data points around the mean. With a standard

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deviation of approximately 3.71, it implies that the values of improved seed per kilogram
vary from the mean by an average of 3.71 units. This indicates the degree of variability or
dispersion in the sample. The range provides the difference between the highest and lowest
values in the data set. In this context, the values of improved seed per kilogram range from 30
to 45. This gives insight into the spread of values and the range of variation within the
sampled group.

Mean of approximately 2 indicates that, on average, the farm size per hectare is around 2.00
units. The mean provides a measure of central tendency, giving an idea of the typical farm
size in terms of hectares in the sampled group. The standard deviation measures the
dispersion or spread of data points around the mean. With a standard deviation of
approximately 0.55, it implies that the values of farm size per hectare vary from the mean by
an average of 0.55 units. This indicates the degree of variability or dispersion in the sampled
data. The range provides the difference between the highest and lowest values in the sampled
population. In this context, the values of farm size per hectare range from 1 to 3.
Mean of approximately 45.75 suggests that, on average, the quantity of fertilizer per kilogram
is around 45.75 units. The mean provides a measure of central tendency, giving an idea of the
typical amount of fertilizer used per kilogram in the collected data set. The standard deviation
measures the dispersion or spread of data points around the mean. With a standard deviation
of approximately 6.27, it implies that the values of fertilizer per kilogram vary from the mean
by an average of 6.27 units. This indicates the degree of variability or dispersion in the
collected data set. The range provides the difference between the highest and lowest values
in the collected data set. In this context, the values of fertilizer per kilogram range from 30 to
60.
Mean of around 0.35 suggests that, on average, the education level in the collected data set
leans towards the lower end, closer to illiteracy. The standard deviation measures the
dispersion or spread of data points around the mean. With a standard deviation of
approximately 0.64, it implies that the values representing education level vary from the

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mean by an average of 0.64 units. This indicates the degree of variability or dispersion in the
collected data set. The range provides the difference between the highest and lowest values in
the education. In this context, the values representing education level range from 0 to 2,
corresponding to illiteracy, primary education, and secondary education, respectively.
The average family size is approximately 5.02 members per family. This suggests that, on
average, families in this population tend to have around five members. The standard
deviation of around 1.48 indicates the amount of variation or spread in the data. In this case,
it indicates that most family sizes are within about 1.48 members above or below the mean of
5.02. So, there is some variability in family sizes around the average. The values range from
2 to 8 members per family. This tells us the minimum and maximum sizes of families in the
population.
The average farming experience among farmers is approximately 8.41 years. This indicates
that, on average, farmers in this population have been involved in farming for around 8.41
years. The standard deviation of about 3.91 indicates the amount of variation or spread in the
farming experience data. In this case, it suggests that most farmers' experience falls within
about 3.91 years above or below the mean of 8.41. So, there's some variability in the farming
experience among the farmers. The values range from 2 to 19 years of farming experience.
This tells us the minimum and maximum years of experience among the farmers in the
population.
The average working time per day for households is approximately 8.43 hours. This suggests
that, on average, households in this population spend around 8.43 hours per day on farming
activities. The standard deviation of approximately 0.85 indicates the amount of variation or
spread in the working time data. In this case, it indicates that most households' working times
fall within about 0.85 hours above or below the mean of 8.43. So, there's relatively low
variability in working time among households. The values range from 7 to 10 hours of
working time per day. This tells us the minimum and maximum hours of work per day among
the households in the population.
The average amount of herbicide used per hectare is approximately 2.43 litters. This suggests
that, on average, a certain quantity of herbicide is applied to each hectare of land. The

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standard deviation of roughly 0.74 indicates the amount of variation or spread in the
herbicide usage data. In this case, it suggests that most herbicide usage falls within about 0.74
litters above or below the mean of 2.43. So, there is some variability in the amount of
herbicide used per hectare. The values range from 1 to 3 litters of herbicide per hectare. This
tells us the minimum and maximum amounts of herbicide applied per hectare in the context
being measured.
Pesticide usage has a constant value of 0 for all 99 observations, it indicates that none of the
observations involved the application of pesticide. In other words, in the context being
observed or measured, there was no usage of pesticide across all the instances or samples.

4.2_Econometric analysis

This section is devoted to the discussion of the ordinary least square method by to analyze
regression results with the aim of addressing the determinants of cereal crops productivity in
terms of some qualitative and quantitative variables. In particular, the purpose of the model is
to determine the factors that explain the cereal crops productivity.

4.2.1Regression result and its interpretation

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 Source SS df MS Number of obs = 99
F(9, 89) = 163.26
Model 309.342944 9 34.3714382 Prob > F = 0.0000
Residual 18.7378643 89 .210537801 R-squared = 0.9429
Adj R-squared = 0.9371
Total 328.080808 98 3.34776335 Root MSE = .45884

CCP Coef. Std. Err. t P>|t| [95% Conf. Interval]

IMSD .051848 .0198854 2.61 0.011 .0123361 .0913598

FAMS .2053041 .1012893 2.03 0.046 .0040444 .4065637
FE .0366775 .0105002 3.49 0.001 .0158139 .0575412
EDU .5851628 .089919 6.51 0.000 .4064957 .7638298
FS .232864 .062519 3.72 0.000 .1086401 .3570879
FEX .0652602 .0172227 3.79 0.000 .0310389 .0994814
CF -1.647379 .1261035 -13.06 0.000 -1.897944 -1.396814
WTPD .347305 .107179 3.24 0.002 .1343425 .5602674
HS .281103 .1077564 2.61 0.011 .0669932 .4952127
ps 0 (omitted)
_cons -3.109598 .7160233 -4.34 0.000 -4.532321 -1.686874

CCP= -3.1+0.052IMSD+0.205FAMS+0.037FE+0.58EDU+0.23FS+0.065FEX-

1.65CF+0.35WTPD+0.28HS

Interpretation

The overall significance is test refers the effects of all explanatory variable on dependent
variable jointly it can understand based on Prob> F. If Prob> F less than0.05 (5%) then the
explanatory variables included in the model are jointly statistically significant. As a result
show above in the table Prob> F<0.05 which is less than 5% for all independent variables
then, there are statistically significant.
The goodness of fit of the model is measured by coefficient of determination, which shows
the percentage or power of explained variable to express by the explanatory variables. As the
above regression result table shows that R-squared= 0.9429, which implies 94% of output

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function is explained by the selected explanatory variables. In other words 94% of variation
of the dependent variable is due to the variation of the independent variable which are
included in the model and the remaining variation 0.6 (6%) is explained by the variable
which are not included in the model. If the value of adjusted R-squared is higher, the greatest
the goodness of fit of the regression plans to be the sample observation. Therefore, the
adjusted R-squared (09371) obtained in the regression model reveals that there is good fitness
of values for a given result.
The constant term B0(-3.11) indicates that, amount of cereal crop productivity before using
these available extra variables. The result shows cereal crop productivity in the study area is
very low without using essential inputs that come from different sources like fertilizer,
improved seed, education, year of experience, family size, working time per day, pesticide,
herbicide, and others. The estimated coefficients of all explanatory variables have signs that
with prior expectation on cereal crop productivity, and they are statistically significant at all.

The coefficient of farm size is statistically significant, and the positive coefficient it states
that, keeping other things constant if farm size of the household increased by 1 hectare, the
mean production cereal crops increased by 0.205. On the other hand the elasticity or
responsiveness of output with respect to farm size is0.205. This show that other things remain
constant, a 1 unit /hectare change in land size leads to on average about 0.205 unit/quintal
increases in the output of farmers. This means that, the size of land increase or decrease leads
to agricultural output to increase or decrease, other things being unchanged.
The coefficient of improved seed is statistically significant, and it has positive sign on cereal
crop; this indicates that, if the use of improved seed increases by 1 unit the amount cereal
crop’s production increase by0.052 on average. On the other hand regression result shows
that improved seed positively affects agricultural output, and its elasticity or responsiveness
of output with respect to improved seed is 0.052 It shows that ceteries paribus, as the use of
improved seed increases by one unit, output of farmers increase by 0.052 quintal. This means
that, the use of improved seed increase the productivity of cereal crops, other things being
unchanged.

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The coefficient of fertilizer on the regression result shows that fertilizer positively affects
cereal crop production in the study area. The elasticity or responsiveness of cereal crop
productivity with respect to fertilizer is 0.037. And also it shows that if fertilizer increase by 1
kg, mean cereal crop production of farmers increase by 0.037quintal compared with the
production of cereal crops those farmers produced in the study area before they used
fertilizers. This implies that there was a difference in the production of cereal crop
productivity produce by using and without using fertilizer in the study area.

The coefficient of education on the regression result shows that education positively affects
cereal crop production in the study area. The elasticity or responsiveness of cereal crop
productivity with respect to education is 0.58. And also it shows that if the farmer get
education, mean cereal crop production of farmers increase by 0.58 quintal compared with
the production of cereal crops those farmers produced in the study area who are illiterate.
This implies that there was a difference in the production of cereal crop productivity between
educated and illiterate farmers.
The coefficient of family size is statistically significant, and the positive coefficient it states
that, keeping other things constant if family size of the household increased by 1, the mean
production cereal crops increased by 0.23 unit/quintal. On the other hand the elasticity or
responsiveness of output with respect to family size is0.23. This means that, the size of
family increase or decrease leads to agricultural output to increase or decrease, other things
being unchanged.
The coefficient of farm experience on the regression result shows that year of farm
experience positively affects cereal crop production in the study area. The elasticity or
responsiveness of cereal crop productivity with respect to year of farm experience is 0.065.
And also it shows that if the farmer year of farm experience increased by 1 year, mean cereal
crop production of farmers increase by 0.065 quintal. This implies that year of farm
experience has positive impact on cereal production.
The coefficient of war/conflict regression result represents the negative relationship between
the dependent variable (cereal production) and the independent variable (conflict/war). A

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negative coefficient indicates that as the level of conflict or war increases, cereal production
tends to decrease. The magnitude of the coefficient (-1.65) suggests that there is a strong
negative relationship between the two variables. In simpler terms, if there is conflict or war,
cereal production is expected to decrease by 1.65 units, assuming all other factors remain
constant.
The coefficient of working time per day in the regression analysis is 0.35, which implies
that a one-hour increase in working time per day is associated with a 0.35-unit/quintal
increase in cereal production, assuming all other variables in the model are held constant.
This implies that working time has a positive and statistically significant impact on cereal
production.
The coefficient of herbicide is statistically significant, and also its coefficient is positive.
This shows that available use herbicides increased by 11ittre, cereal crop production increase
by 0.28. It implies that, if other things being unchanged, the farmers would increase their
cereal crop productivity by using selective weed killer chemicals (herbicide) in the given
study area.
The coefficient of a pesticide is 0, it signifies that there is no effect or impact of the
herbicide on the dependent variable in the model.there is no insect affection to use pesticide
in the study area during the study period. In statistical terms, the coefficient represents the
change in the dependent variable for a one-unit change in the independent variable.
Therefore, when the coefficient is 0, it implies that there is no change in the dependent
variable associated with any change in the pesticide level.

4.2.1. Diagnostic Tests

4.2.1.1 Multicollinearity test

The term multicollinearity is the existence of a perfect or exact linear relationship among
some or all-explanatory variables of regression model multicollinearity is a question of
degree and not of kind. The meaningful distinction is not between the presence and the
absence of multi co linearity, but between it is various degrees (Guajarati, 2004).To detect

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multicollinearity, the researcher used VIF or Variance Inflation Factor .The larger the mean
value of VIF, the more similar variables occurred. AS the rule of thumb, if the mean VIF
exceeds 10, then the variable is highly collinear (Gujarati, 2004)

. vif

Variable VIF 1/VIF

FS 4.01 0.249421
WTPD 3.84 0.260622
HS 3.00 0.333778
IMSD 2.53 0.395754
FEX 2.11 0.474447
FE 2.02 0.495647
EDU 1.56 0.640945
CF 1.45 0.690581
FAMS 1.45 0.691651

Mean VIF 2.44

The result above shows the mean value of VIF is 2.44, which is less than 10. So there is no of
Multicollinearity problem between explanatory variables. Therefore, it is possible to estimate
the individual effect of the variables on dependent variable.
4.2.1.2 Heteroscedasticity Test
The assumption of heteroscedasticity states that the variation of each random terms around its
zero mean is not constant and changes as the explanatory variable changes regardless of the
sample size that whether it increase, decrease or remain constant, but does not mean that it
affects the unbiasedness and consistency properties of OLS estimators rather it results the
variance of coefficient of OLS to be incorrect and inefficient. By taking in to consideration,
one of the assumptions in regression analysis which is the error up has a constant variance. If
the error term does not have constant variance, there is a heteroscedasticity problem.

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This is the test of the variance of the error term /disturbance term/ under classical linear
regression model assumptions error are homoscedastic (constant variance). The nature of the
variance of the error term is judge by Breusch-pagan test.
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of CCP

chi2(1) = 0.12
Prob > chi2 = 0.7321

If P>chi2 is less than the chosen level (5%) of significance, then reject the null hypothesis
and accept the alternative hypothesis that means there is the problem of heteroscedasticity. If
P>chi2 is greater than the chosen level of significance, then accept the null hypothesis that is
no heteroscedasticity problem. Therefore, from the above Stata result Prob>chi2= 0.7321
which is greater than 5% level of significance. Therefore accept the null hypothesis and reject
the alternative hypothesis, and there is no heteroscedascity problem in the model.

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 CHAPTER FIVE

Conclusion and Recommendation

5.1. Conclusion
Cereal crop production is the main source of food for our country’s population; because the
country’s economy is depend on agriculture. Therefore it is the center of policy making to.
As result government of Ethiopia takes attention to agricultural sector. This study analyzed a
onetime visit cross-sectional data on 99 representative samples of farmers at household level
to empirically access the determinants of cereal crops production in sekela woreda. For this
purpose the ordinary least square method used. In this study the descriptive analysis and the
econometric model were applied.
For econometric analysis the ordinary least square model was employed so as to identify the
factor affecting cereal production in three purposely selected kebeles in sekela woreda. The
dependent variable that was the being cereal production was regressed against the ten
explanatory variables. The study found that the independent variables like improved seed,
farm size, fertilizer, education level, family size, year of farm experience,working time per
day, and herbicide were positively affected the productivity of the cereal crops in the study
area. The variable war/conflict were negatively affected the productivity of the cereal crops
in the study area.
Finally the sample respondents were asked to mention the major problem that faced by
farmers. They indicated that, there are some problems that affect cereal crop productivity like

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back ward technology, inadequate market, poor infrastructure, and absence of enough inputs
with regarding to primary data gathered through questioner and interview. The researcher
concluded that the availability of inputs such as improved seed, fertilizer, herbicide, farm
size, are important for expanding the cereal crops production in the study area.

5.2. Recommendation

This part deals with some recommendation drawn based on the results of the study in which
the small holder farmers in the study area should focus in order to promote cereal production.
As the result indicates most of the factor affecting are significant on cereal crop production in
the study area.
Therefore, the researcher recommends correcting these problems in two ways; The first one is
government: the government should be solving problems of cereal crop productivity by
offering inputs like improved seed, fertilizer, access of education, and herbicide. And also
intervene in some affairs like problem related to stability this means government should
stand on the side of farmers how they success.
On the other hand farmers should be take training from agricultural professionals about what
amount of inputs used for their land in order produce better yields. Farmers should use
modern agricultural inputs those are more effective when they are combined with
complimentary inputs like improved seed, fertilizer, access of education, and herbicide. To
improve land productivity the small holder farmers should use land management practice
method like terracing and tree planting even the farm land is narrow.
Generally, the researcher recommend that body of government and farmers should work
together to maintain effective and sustainable growth on agricultural system, and also either
government or farmers should their own responsibilities rather attack one another when
create problems on cereal crops. For example government should be give necessary inputs far
farmers on time, and farmers should be use the inputs appropriately and should report to rural
agricultural office if unnecessary condition exist.
In the last but not the least, the researcher recommends that it would be better for other
researchers to increase their sample size to gain significant variables of education level, and
irrigation in their study.

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Appendix : Questionnaire

ADDIS ABABA UNIVERSITY

COLLEGE OF BUSINESS AND ECONOMICS

DEPARTMENT OF ECONOMICS

BACHELOR DEGREE PROGRAM

Questionnaire

The questionnaire is prepared by fourth year economics student, Abiel Asabu from the
college of business and economics in Addis Ababa university. The purpose of this
questionnaire is to collect primary data for the proposed research “factor affecting cereal
production in Western gojjam Zone of Amhara region (in case of sekela woreda), to
determine the situation of cereal crop productivity.

Thanks for your willingness to give correct data!!

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Instruction
Put “X” on the brackets if your idea matches with the question, provide your statement for
the space provided and choose the best with is listed below.
I Personal information
 Sex of respondents male female
 Age of individual …………
 Marital status married single Divorced
Education level ………………………………
Household family size ………………………
II Questions related to objectives
1 Do you have land withholding rights Yes No
2. If you say yes how many hectares of land do you have.................?
3. How many hectares of land were cultivated last year...........................?
4. Do you use fertilizer? Yes No
5. If your answer is yes, how many quantities used in kg/hec? ……………………………
6. Do you use improved seed? Yes No
7. Do you use irrigation? Yes No
8. Do you use herbicides? Yes No
9. Do you use pesticides? Yes No

10. If your answer is yes in question number (8 and 9), how much amount used in
litters/hector?
 Herbicides……………………..
 Pesticides……………………….
11. What is the contribution of pesticides and herbicides for your cereal crop productivity?
High Low Medium Very high
12. Which of the following inputs greatly contribute for your productivity?(choose more than
one answer)
Fertilizer Pesticides and herbicides Improved seed Irrigation
13. How many quintals Produced yearly per hectare?

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 Wheat......... Barely……… Chickpea………………
Teff……... Maize…… Others………………..
14. What types of crop do you produce mostly? (You can choose more than one answer)
Chickpea Barely
Teff Maize Pepper Others ……………………….
Total
15. Is there conflict around you?
yes No
16. Which of the following determinates are significantly affecting the productivity of your
farming? (you can choose more than one answer)
 Shortage of rain fall
 Shortage of inputs / factors
 Back ward technology
 conflict
 Other …………………………………

 THANKS FOR ALL!

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