DETERMINANTS OF BEANS PRODUCTION IN TANZANIA
A CASE OF KIGOMA REGION
BY
SIKUJUA NJILE SAMWEL
1524010101898
A Research Project Report Submitted to the Eastern Africa Statistical Training
Centre in Partial Fulfillment for the Award of Bachelor Degree in Official
Statistics of Eastern Africa Statistical Training Centre
August, 2021
CERTIFICATION
The undersigned certify that she has read and here by recommend for acceptance by
the Eastern Africa Statistical Training Centre the research project report titled:
Determinants of beans production in Tanzania a case of Kigoma region, in partial
fulfilment of the requirements for the award of Bachelor’s Degree in Official Statistics
of the Eastern Africa Statistical Training Centre.
______________________________
Ms. Nyambilila Minga
(Supervisor)
Date: ________________________
i
DECLARATION AND COPYRIGHT
I, Sikujua Njile Samwel, declare that this research project report is my own original
work, and that it has not been presented and will not presented to any other higher
learning institution for a similar or any other degree award.
Signature: _____________________
Date: _________________________
This research project report is copyright material protected under the Berne
Convention, the Copyright Act of 1999 and other international and national
enactments. No part of this research project report may be reproduced, stored in any
retrieval system, or transmitted in any form or by any means without prior written
permission of the author or EASTC.
ii
ACKNOWLEDGMENTS
First and foremost, I thank God, the Almighty for giving me this opportunity and
granting me the gift of life, grace and strength throughout the capability to successfully
accomplish this research project.
I am grateful to several individuals who have been influenced me in one way or another
and made this piece of work possible. I would wish to state my deep and sincere
appreciation foremost to my supervisor Ms. Nyambilila Minga for her technical
guidance, corrections, suggestions and valuable inputs which helped to shape this
study. Furthermore, I am also thankful to the research project coordinators Mr.
Tuntufye Mwakasisi for his guidance and clarification on research project matters.
Moreover I am grateful to Mr. Leguma Bakari, Mr. Nelson Ndifwa and Mr. Edwin
Magoti for their valuable discussions on my analysis whenever I approached them for
any inquiries. I am also grateful to the entire EASTC staff for their support and services
rendered.
Lastly but not least, special thanks to my family, my friends and my course mates
(Bachelor third year students of 2020/2021) at EASTC for the company and support
they gave me throughout period of study.
iii
DEDICATION
This research report is dedicated to my supervisor, family, relatives, colleagues and
friends for their love, support and aspirations.
iv
LIST OF ABBREVIATIONS
AASS
Annual Agriculture Sample Survey
FAOSTAT
Food and Agriculture Organization Corporate Statistical Database
URT
United Republic of Tanzania
ICRISAT
International Crops Research Institute for the Semi-Arid Tropics
FAO
Food and Agriculture Organization
NBS
National Bureau of Statistics
CIAT
Centro International de Agricultural Tropical (International Centre
for tropical Agriculture)
UN
United Nation
UNESCO
United Nations Education, Scientific Cultural Organization
OECD
Organization for Economic Co-operation and Development
WTO
World Trade Organization
v
ABSTRACT
This study investigates determinants of beans production in Tanzania, a case of
Kigoma region. The source of data from National bureau of Statistics (NBS) in Annual
Agricultural Sample Survey (AASS), 2014/2015. STATA version 15 package was
used as a tool for data analysis whereby multiple regression was used to find out the
determinants of beans production and correlation was to determine the relationship
between planted acres and beans production both during short rain season and long
rain season. The study revealed that majority of farmers perform small scale farming
that use small piece of land and poor technology in beans production The study also
revealed that small scale farmers depend on rainfall because during short rain season
beans tends to be low and long rain season production tends to be high. Furthermore
the study shows that average price tends to be high in short rain season because low
production of beans but in long rain season the average price tends to be low because
of high production of beans. Moreover, The study therefore recommend that the
government should develop strategic policies to promote quality farming method in
order to help farmers from farming more beans, government should employ various
measures to maintain farm price, reliable market and income for their beans output
such as exporting beans to international market.
vi
TABLE OF CONTENTS
CERTIFICATION ........................................................................................................ i
DECLARATION AND COPYRIGHT ........................................................................ ii
ACKNOWLEDGMENTS ...........................................................................................iii
DEDICATION .............................................................................................................iv
LIST OF ABBREVIATIONS ...................................................................................... v
ABSTRACT .................................................................................................................vi
LIST OF TABLES .......................................................................................................xi
LIST OF FIGURES .................................................................................................... xii
CHAPTER ONE .......................................................................................................... 1
INTRODUCTION ....................................................................................................... 1
1.1 Background of the study .................................................................................... 1
1.2 Statement of the problem ................................................................................... 3
1.3 Objectives of the study ....................................................................................... 4
1.3.1 General objective ......................................................................................... 4
1.3.2 Specific objectives ....................................................................................... 4
1.4 Research hypothesis ........................................................................................... 4
CHAPTER TWO ......................................................................................................... 6
LITERATURE REVIEW ............................................................................................ 6
vii
2.1 Introduction ........................................................................................................ 6
2.2 Definitions of terms ............................................................................................ 6
2.3 Theoretical Literature Review ............................................................................ 7
2.4 Empirical Literature Review .............................................................................. 8
2.5 Conceptual frame work .................................................................................... 11
2.6 Research Gap .................................................................................................... 12
RESEARCH METHODOLOGY............................................................................... 13
3.1 Introduction ...................................................................................................... 13
3.2 Study area ......................................................................................................... 13
3.3 Research Approach .......................................................................................... 14
3.4 Target population ............................................................................................. 14
3.5 Data source ....................................................................................................... 14
3.6 Measurement of Variables................................................................................ 15
3.7 Data analysis .................................................................................................... 15
3.7.1 Descriptive analysis ................................................................................... 15
3.7.2 Inferential statistics .................................................................................... 16
CHAPTER FOUR ...................................................................................................... 18
RESULTS AND DISCUSSION OF FINDINGS ...................................................... 18
4.0 Introduction ...................................................................................................... 18
viii
4.1 Descriptive analysis .......................................................................................... 18
4.1.1 Descriptive analysis of planted acres during long rain season. ................. 18
4.1.2 Descriptive analysis of quantity of beans harvested during long rain
season. ................................................................................................................. 18
4.1.3 Descriptive analysis of harvested acres of beans during long rain season. 19
4.1.4 Descriptive analysis of value sold (Tsh) of beans during long rain season.
............................................................................................................................ 20
4.1.5 Descriptive analysis of planted acres during short rain season. ................ 20
4.1.6 Descriptive analysis of quantity harvested during short rain season. ........ 21
4.1.7 Descriptive analysis of harvested acres during short rain season. ............. 21
4.8 Descriptive analysis of value sold during short rain season. ........................ 22
4.2 Inferential statistics .......................................................................................... 22
Correlation .............................................................................................................. 22
4.2.1
Relationship between quantity harvested and planted acres during .... 23
Short rain season ................................................................................................. 23
4.2.2
Relationship between quantity harvested (beans production) and
planted acres during long rain season ................................................................. 24
4.3 Multiple linear regression................................................................................. 25
4.3.1 Effects of value sold, harvested acres and average price on harvested acres
(beans production) during short rain season. ...................................................... 28
ix
4.3.2 Effects of value sold, harvested acres and average price on harvested acres
(beans production) during long rain season. ....................................................... 31
4.4 Discussion of findings ...................................................................................... 34
4.4.1 Relationship between planted acres and quantity of beans harvested ....... 34
4.4.2 Effects of harvested acres on quantity harvested (beans production)........ 34
4.4.3 Effect of value sold on quantity harvested (beans production) ................. 34
4.4.4 Effect of average price on quantity harvested (beans production) ............ 34
CHAPTER FIVE ....................................................................................................... 36
CONCLUSION AND RECOMMENDATIONS ...................................................... 36
5.0 Introduction ...................................................................................................... 36
5.1 Summary .......................................................................................................... 36
5.2 Conclusion ........................................................................................................ 37
5.3 Recommendations ............................................................................................ 38
5.3.1 General recommendations ......................................................................... 38
5.3.2 Recommendations for further studies ........................................................ 38
REFERENCES .......................................................................................................... 39
LIST OF APPENDICES ............................................................................................ 42
Appendix: Data used .............................................................................................. 42
x
LIST OF TABLES
Table 3. 1 Measurements of Variables....................................................................... 15
Table 4. 1 Planted acres in long rain season. ............................................................. 18
Table 4. 2 Quantity harvested in long season. ........................................................... 19
Table 4. 3 Harvested acres during long rain season. .................................................. 19
Table 4. 4 Value sold during long rain season ........................................................... 20
Table 4. 5 Planted acres during short rain season. ..................................................... 20
Table 4. 6 Quantity harvested during short rain season. ............................................ 21
Table 4. 7 Harvested acres during short rain season. ................................................. 21
Table 4. 8 Value sold during short rain season. ......................................................... 22
Table 4. 9 Quantity harvested and planted acres ....................................................... 23
Table 4. 10 Quantity harvested and area planted ....................................................... 24
Table 4. 11 Model summary for short rain season ..................................................... 25
Table 4. 12 Model summary for long rain season...................................................... 26
Table 4. 13 Multicollinearity for long rain season ..................................................... 26
Table 4. 14 Multicollinearity during short rain season .............................................. 27
Table 4. 15 Multiple linear regression results ............................................................ 29
Table 4. 16 Multiple linear regression results. ........................................................... 32
xi
LIST OF FIGURES
Figure 2.1 Conceptual Framework............................................................................. 12
xii
CHAPTER ONE
INTRODUCTION
1.1 Background of the study
Agriculture plays a fundamental important role in the economic growth and
development predictions most of the majority of developing countries including
Tanzania (World Trade Organization [WTO], 2019). The sector contributing
approximately Gross Domestic Product (29%) in Tanzania (URT, 2017), amongst the
important agricultural subsectors in Tanzania are livestock, fishery, agro-forestry and
crops.
The production of beans is the one of food crop in Tanzania appears popular among
small-scale farmers because beans takes a short duration (2.5-4 months) which permits
production even when rainfall is erratic. Tanzania ranks 5th worldwide in beans
production and is the leading producer of beans in Africa which is produced almost
entirely under intercropped systems with maize and other crops by smallholders
farmers who operate 1 to 5 acres, on the average produced over 70% of national bean
production in Tanzania for own consumption and for markets about 40% of the
harvested are marketed by households (FAOSTAT, 2014).
The production of beans contributes the growth of economy in countries such Africa
countries. Tanzania is the producer of beans in East Africa and largest producer in
Africa (Kilimo Trust, 2013, Larochelle et al., 2017) where beans are the most exported
pulses from Tanzania contributing about 62% of all Tanzanian pulse exports (URT,
1
2016), mainly exported to Netherlands and India also, neighboring countries like
Kenya, Uganda, Rwanda, Burundi, DR Congo, Zambia (Ronner and Giller, 2013).
About 80% of Tanzanians depend on agriculture for their livelihood. Therefore, the
National Development Vision 2025, main national development strategy in Tanzania,
places considerable emphasis on the sector and envisages that by 2025 the economy
will have been transformed from a low productivity agricultural economy to a semiindustrialized one led by modernized and highly productive agricultural activities that
are integrated with industrial and service activities in urban and rural areas.
The Agricultural Sector Development Strategy (ASDS) was adopted in 2001, and gave
rise to the Agricultural Sector Development Program (ASDP) of 2005; and the
Cooperative Development Policy (CDP) of 2002, complemented by a variety of sector
policies. The strategy and the ASDP are embedded in the National Strategy for Growth
and Reduction of Poverty (NSGRP), which is a medium-term plan to realize Vision
2025. Kilimo Kwanza (agriculture first), developed in 2009, provides additional inputs
for the implementation of ASDP and other programs favorable for the agricultural
sector. It is an assertion of the commitment of the government and the private sector
to agricultural development, and it invites all Tanzanians to become part of this
commitment.
The major production areas of beans in Tanzania are in the northern regions; Arusha,
Kilimanjaro and Manyara, the Great Lakes/West; Kagera and Kigoma, the Southern
Highlands; Mbeya, Iringa and Rukwa (Larochelle et al., 2017)
In Kigoma Region agriculture is the main economic activity, employing over 70% of
the population, dominated by small-scale farmers, especially women. The major crops
2
are maize, beans, cassava, rice, bananas, oil palm, coffee, tobacco and various fruits
and vegetables. The production of beans is mostly produced in Kasulu and Kibondo
districts which shows the highest potential in development by looks on the market
potential which contributes to the household food security and the income of the
household (Sustainable Agriculture Kigoma Region Project [SAKiRP],2015).
1.2 Statement of the problem
Beans is the most important food and cash crop in Tanzania with high in nutrients and
commercial potential. Bean provides high nutrients includes a combination of
carbohydrates (60-65%), proteins (21-25%), fats (less than 2%), vitamins and
minerals. In fact, with increasing health concerns, most people especially the urban
population are reducing consumption of animal proteins, and instead they are turning
to pulses such as common bean due to its low-fat content (Mshenga&Birachi, 2017).
Despite of having policies and strategies in Tanzania on agriculture such as Kilimo
Kwanza (agriculture first), Agriculture Sector Development Program (ASDP) and
coordinated sectorial approach which include programs aimed to ensure increase in
bean production by smallholders for income and food security, the production of beans
in Tanzania have been highly fluctuating. Statistics from Annual Agricultural Sample
Survey shows that 2014/2015 there is decreasing of beans production which does not
meet the demand of people.
A number of studies on beans production had been conducted in Tanzania regions
where beans are produced such as Mbeya, Kagera and Manyara.
3
But there is no study had been conducted in Kigoma region to examine the
determinants of beans production such as planted acres, harvested acres, value sold
and average price of beans. This study explores the determinants of beans production
in Tanzania a case of Kigoma region, the effect of harvested acres, value sold and
average price on beans production.
1.3 Objectives of the study
1.3.1 General objective
The general objective of this study was to analyze the determinants of beans
production in Tanzania, a case of Kigoma region.
1.3.2 Specific objectives
i.
To examine the relationship between Planted acres and beans production
ii.
To examine the effect of harvested acres on beans production
iii.
To examine the effect of value sold on beans production
iv.
To examine the effect of Average price on beans production
1.4 Research hypothesis
1. H0: There is no significance relationship between planted acres and quantity
harvested (beans production)
H1: There is significant relationship between planted acres and quantity
harvested (beans production)
2. H0: There is no significant effect of harvested acres on quantity harvested
(beans production)
H1: There is significant effect of harvested acres on quantity harvested (beans
production)
4
3. H0: There is no significance effect of value sold on quantity harvested (beans
production)
H1: There is significant effect of value sold on quantity harvested (beans
production) beans production
4. H0: There is no significant effect of average price on quantity harvested (beans
production)
H1: There is significant effect of average price on quantity harvested (beans
production)
1.5 Significance of the study
The study will be useful to agriculture sector particularly the legume subsector in
planning appropriate and consistence strategies which will create a comprehensive
awareness to strategies such as smallholders’ farmers and researchers also, will be
beneficial to the farmers for them to improve their farming system and skills for better
quality and improved output.
5
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter focus on the definitions of terms theoretical literature reviews and
empirical literature reviews basing on information from different scholars and other
researchers who carried out their research concerning the determinants of beans
production, also consists of conceptual framework and research gap.
2.2 Definitions of terms
Agriculture
Refers to the science or art of producing crops, practice of cultivating the soil and
raising livestock and in varying degrees for the preparation and advertising of the
resulting products, (Merriam Webster Dictionary, 2018)
Production
Production is the creation of output of goods and services which values and contributes
to the utility of the individual.
Beans
The edible nutritious seed of various plants of the legume family, especially of the ge
nus Phaseolus, (free Dictionary, 2005)
6
2.3 Theoretical Literature Review
There are several factors affects the production of beans, many agriculture production
theories apply to beans production since it is a food crop. These agriculture production
theories are discussed as follows.
Orodho, (2005) discussed the theoretical focus on increasing agricultural output and
improving income distribution in the rural sector may be the only effective way to get
their economies moving. Productivity can be improved by use of high yielding
varieties of seed, application of fertilizer, good farming practices and the development
of intermediate or appropriate technologies to complement labor. A number of critical
factors constrain the productivity of small-scale farmers; these include hostile climate,
poor soils, rapid population growth, limited market opportunities, and a lack of
commitment to rural development by the government (Munyeko, 1994: Orodho, 1984)
Production theory
Production theory explains the relationship between inputs and outputs, which is the
transformation of factor inputs into outputs (Thomas and Maurrice, 2008). Debertin
(2012) defines Production function as the technical relationship that transforms inputs
resources into outputs commodities. According to Rasmussen (2012) the theory of
production economics is special in that the limits of economic behavior are defined by
the technical production possibilities. Production technology is the decisive factor
regarding the quantity produced and how it may be produced. Therefore, a very
important part of the theory of production economics consists of describing the
production technology which defines the framework for the economic behavior.
Production technology is, in its most general form, a description of the relationship
7
between input and produced output. The description of production technical
relationships is based on empirical observation of relationships between inputs and
outputs. Generally, production always includes at least two, and often more, inputs
(Rasmussen, 2012).
2.4 Empirical Literature Review
A study conducted in Tanzania by Letaa et al.(2015) using probity model on Farm
Level Adoption and Spatial Diffusion of Improved Common Bean Varieties in
Southern Highlands of Tanzania revealed that factors such as perceptions about soil
fertility status and plot distance from residence, agricultural wealth, number of
dependents, access to off farm income and years of experience in bean growing,
distance from the village to main road, agricultural credit, significantly influenced the
adoption of the improved varieties. The results further show that the improved varieties
have extensively diffused in the study area, with new improved bean varieties
replacing old ones.
The research carried out by Challa and Tilahun (2014) on the determinants and Impacts
of Modern Agricultural Technology Adoption in West Wollega, Ethiopia using the
logistic regression showed that household heads’ education level, farm size, credit
accessibility, perception of farmers about cost of the inputs and off-farm income
positively and significantly affected the farm households’ adoption decision; while
family size affected their decision negatively and significantly.
8
Teferi et al. (2015) conducted a study on factors that affect the adoption of improved
beans varieties by smallholder farmers in Central Oromia, Ethiopia using logit model,
the findings revealed that adoption of the improved maize varieties among households
was found to be positively influenced by adult-literacy, family size, livestock wealth,
access to output market and credit access for the new varieties. On the other hand,
farmer associations, distance to main markets and fertilizer credit negatively
influenced adoption.
Musimu, (2018) assessed economics of small holder common beans production in
Tanzania specifically in Mbeya. The study uses the data sources from both secondary
and primary data sources obtained by administering a semi structured questionnaire.
The methodology employed in the study were multiple linear regression to examine
economics of small holder common beans production. The results show that the crop
pests and diseases, unreliable rainfall, high price of farm inputs, unreliable market,
shortage of land, price fluctuation and low capital are the major challenges faced beans
producers in the study area. The study also conclude that common beans production is
profitable and contributes significantly in creating cash income and employment in the
study area.
9
Idrisa et al. (2012) in their study examined the determinants of adoption of improved
soybean seeds among farmers in southern Borno State, Nigeria employed Logit model
and Tobit model, they indicated that yield of soybean and distance to source of
improved seeds were statistically significant factors that influenced the likelihood of
adoption of improved soybean seeds among the respondents. Also they reported that
farm size and distance of respondents to source of improved soybean seeds were
statistically significant factors (ρ ≤ 0.01) that influenced the extent of adoption of
improved soybean seeds among the respondents.
Sibiko, (2012) examined the determinants of common bean productivity and efficiency
in Eastern Uganda. The data for the study was generated from Primary data collected
for the 2010/2011 season using personally administered structured questionnaires and
through observation method. The study uses descriptive statistics included the
frequencies, means and standard deviations. It was established that bean productivity
was positively influenced by plot size, ordinary seeds, certified seeds and planting
fertilizers. Also, the study suggests on the need for policy to discourage land
fragmentation, develop road and market infrastructure in rural areas and provide
affordable and easily available credit facilities to improve production efficiency of
bean farms.
10
Karane, (2016) examined the factors influencing on-farm common bean profitability
Tanzania. The data for the study was generated from primary source collected from
the smallholder farmers in the field using structured interview schedules method. The
study employed Multiple Regression Analysis approach and Logistic Regression
method. The result show that age of respondents; gender; yield; selling price (farmgate price); access to credit; and off-farm income affected the gross margin realized
by smallholder farmers. Similarly, age of respondents; gender; family size; education
level (years of schooling); farm-gate price; distance to the market; and off-farm income
influenced the quantity of bean supplied to the market. This implies that, if this study
is positively recognized by bean industry stakeholders, it may significantly contribute
as a source of information for improving bean profitability and food security.
2.5 Conceptual frame work
The conceptual framework was used to show the relationships between dependent and
independent variables of the study. The independent variables planted acres, harvested
acres, average price and value sold and the dependent variable was beans production.
11
Figure 2.1 Conceptual Framework
DEPENDENT
VARIABLE
INDEPENDENT
VARIABLES
Planted acres
Quantity of beans
produced
Average price
Value sold
Harvested acres
2.6 Research Gap
There are different literatures describing production and productivity of beans in many
areas but most of the study has been done in major areas where beans are produced in
Tanzania such as Mbeya, and Manyara. This study was focus on Kigoma region
specifically to study the determinants of beans production in term of planted acres,
value sold, harvested acres and average price. Therefore, the study as different from
many references in term of study area and objectives of the study.
12
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
This chapter presents research strategies and techniques that used in the study. It
describes the study area, research design, sources of data and methods of data analysis.
3.2 Study area
The study area was conducted in Kigoma region which is located on the shores of Lake
Tanganyika at the North - West corner of Tanzania. It shares boundaries with Burundi
and Kagera region to the North, Shinyanga and Tabora regions to the East, Congo to
the West and Rukwa region to the South. It has an area of 45066 Square kilometers
which is equivalent to 4.8% of the total area of Tanzania of which 8029 square
kilometers is water and 37,037 square kilometers is land area.
According to Kigoma region socio-economic profile report shows that agriculture is
the predominant economic sector in Kigoma region. Over 85% of the total population
of the region depend on agriculture for its livelihood. The majority of agricultural
production come from stallholders who employ very little capital. Agricultural
production in Kigoma region depend mostly on natural rains for crop growing. The
major crops are maize, beans, cassava, bananas, groundnuts, oil palm, coffee, cotton
and tobacco. Legumes are important food crops which have traditionally been a source
of protein in Kigoma region. The most popular leguminous crop is beans. Soils
throughout the region are suitable for beans growing.
13
The present study was conducted in Kigoma region. The region was conducted due to
its high potential for producing beans when the statistics shows that Kigoma region
recently produces total production of beans is about 0.8 tons/ha in (AASS, 2014/2015).
The production of beans among other pulses in Kigoma is much higher than in other
region in Tanzania, with a planted area of 99,753 ha.
3.3 Research Approach
The study used quantitative approach involves a cross sectional study that allows data
to be collected from a target population selected at single point in time. The reason for
choosing this design is due to its suitability for description purposes as well as the
determination of the relationship between the variables.
3.4 Target population
The study population were the people in Tanzania specifically in Kigoma region where
the population was 2,127,930 according to 2012 Population housing census, but the
targeted population was all farmers involving in beans production for the year 20142015 in Kigoma according Annual Agriculture Sample Survey (2014-2015 AASS).
3.5 Data source
Secondary data were obtained from Annual Agriculture Sample Survey of 2014/2015
from National Bureau of Statistics (NBS).
14
3.6 Measurement of Variables
Table 3. 1 Measurements of Variables
Variables
Measurement
Dependent variable
Beans production
Ratio
Independent variables
Planted acres
Ratio
Value sold
Ratio
Harvested acres
Ratio
Average price
Ratio
3.7 Data analysis
Data was analyzed using STATA version 15. The study used both descriptive analysis
and inferential analysis for statistical analysis. Whereby descriptive analysis was to
examine the characteristics of the data while inferential statistics was employed to test
the findings.
3.7.1 Descriptive analysis
Descriptive statistics such as comparison of means, maximum, minimum and
summary statistics was used to study the characteristics of the variables.
15
3.7.2 Inferential statistics
In inferential statistics such as correlation and multiple linear regression analysis was
employed, where regression was used to determine the relationship between planted
acres and quantity harvested (beans production) and multiple linear regression was
used to analyze the effects of (independent variables) value sold, planted acres, average
price and harvested acres on (dependent variable) beans production.
The mathematical model of multiple linear regression is given by:
Y= ꞵ0 + ꞵ1X1 + ꞵ2X2 +ꞵ3X3 …+ ꞵkXk + εi
Where
Y=dependent variable
ꞵ0= is the intercept (is the value of dependent variable (Yi) when the all independent
variables (Xi) are zero
ꞵ1, ꞵ2 …ꞵk = coefficient of variable x
εi-the random error term
With respect to the study variables the multiple linear regression will be
Y=ꞵ0+ꞵ1X1 + ꞵ2X2 + ꞵ3X3 + еi
Where:
Y= Beans production
X1= Value sold
X2= Average price
16
X3= Harvested acres
еi = Random error term
17
CHAPTER FOUR
RESULTS AND DISCUSSION OF FINDINGS
4.0 Introduction
This chapter discuss the findings which are based on this study in Kigoma region on
the determinants of beans production, presents and discusses both descriptive statistics
and inferential findings.
4.1 Descriptive analysis
Descriptive analysis includes the summary of mean, maximum, minimum and
summary statistics was used to study the characteristics of the variables as follows;
4.1.1 Descriptive analysis of planted acres during long rain season.
Table 4. 1 Planted acres in long rain season.
Variable
Mean
Max
Min
Planted acres
1.268939
7
0.25
Table 4.1 shows the average, minimum and maximum planted acres during long rain
season. The average planted acres is 1.268939 in acres per each farmer, the maximum
acres planted is 7 acres and the minimum is 0.25 acres.
4.1.2 Descriptive analysis of quantity of beans harvested during long rain
season.
18
Table 4. 2 Quantity harvested in long season.
Variable
Mean
max
Min
Quantity harvested
394.3636
2000
0
Table 4.2 shows the average, minimum and maximum of quantity harvested, average
quantity harvested is 394.3636 kg per each farmer which the maximum quantity
harvested is 2000kg and its minimum is 0kg which means after harvesting there are
some planted areas had no any production.
4.1.3 Descriptive analysis of harvested acres of beans during long rain season.
Table 4. 3 Harvested acres during long rain season.
Variable
Mean
Max
Min
Harvested acres
1.246212
7
0
Table 4.3 shows average, maximum and minimum of harvested acres where by
average harvested acres is 1.246212 acre per each farmer and its maximum and
minimum is 7 acres and 0 acre respectively means that some of the planted acres are
not harvested.
19
4.1.4 Descriptive analysis of value sold (Tsh) of beans during long rain season.
Table 4. 4 Value sold during long rain season
Variable
Mean
Max
Min
Value sold
198314.8
1800000
0
Table 4.4 shows Average, minimum and maximum number of Value sold in terms of
Tsh, which shows that the average Value sold is Tsh 198314.8, minimum value sold
is Tsh 0 which means that there are some amount harvested are not valued and are not
sold during long rain season, the maximum value sold is Tsh 1800000.
4.1.5 Descriptive analysis of planted acres during short rain season.
Table 4. 5 Planted acres during short rain season.
Variable
Mean
min
Max
Planted acres
1.468534
0.25
10
Table 4.5 shows the average, maximum and minimum of planted acres during short
rain season which shows that the average acres per farmer is 1.468534 acres, minimum
planted acres is 0.25 acre and the maximum planted acres is 10 acres.
20
4.1.6 Descriptive analysis of quantity harvested during short rain season.
Table 4. 6 Quantity harvested during short rain season.
Variable
mean
Min
Max
Quantity harvested
572.2457
0
9000
Table 4.6 shows the average, maximum and minimum of quantity harvested during
short rain season which shows that the average quantity harvested per each farmer was
572.2457 kg, maximum quantity harvested was 9000 kg and the minimum quantity
harvested was 0 kg means that there some harvested areas have no production during
short rain season.
4.1.7 Descriptive analysis of harvested acres during short rain season.
Table 4. 7 Harvested acres during short rain season.
Variable
Mean
Max
Min
Harvested acres
1.393103
10
0
Table 4.7 shows average, minimum and maximum number of Harvested acres, average
is 1.393103 acres per each farmer and its maximum and minimum is 10 acres and 0
acres respectively means that some of the planted acres are not harvested.
21
4.8 Descriptive analysis of value sold during short rain season.
Table 4. 8 Value sold during short rain season.
Variable
Mean
Max
Min
Quantity harvested
572.2457
9000
0
Table 4.8 it shows Average, minimum and maximum of value sold where by the
average is Tsh 572.2457, maximum value sold is Tsh 9000 and minimum value sold
is Tsh 0 which means that some quantity are not sold during short rain season.
4.2 Inferential statistics
The study used correlation and regression analysis to check the relationship between
the dependent variable and independent variable. Correlation check the relationship
between dependent variable (beans production) and independent variable (planted
acres), and the regression analysis was used to determine the effect of independent
variables (value sold, average price and harvested acres) on dependent variable (beans
production).
Correlation
Correlation used for quantifying the association between two variables measured on
interval/ratio scale. The study used correlation so as to determine the relationship
between harvested acres and beans production, planted acres and beans production,
average price and beans production in both long rain season and short rain season as
follows;
22
4.2.1
Relationship between quantity harvested and planted acres during
Short rain season
Table 4. 9 Quantity harvested and planted acres
quantity harvested
planted acres
Pearson correlation (r)
0.538
P-value
0.0000
Hypothesis
H0: There is no linear relationship between quantity harvested (beans production) and
area planted
H1: There is a linear relationship between quantity harvested (beans production) and
area planted.
Table 4.9 shows that there is significance positive linear relationship between
harvested acres and area planted, since the p-value was 0.0000 was less than level of
significance (α) = 0.05, from this there is an evidence to reject null hypothesis which
states that there is no linear relationship between quantity harvested and harvested
acres at 5% level of significance and accept the alternative hypothesis which states that
there is linear relationship between quantity harvested and harvested acres.
Furthermore, the result shows positive Pearson’s correlation coefficient is (r=0.538).
23
4.2.2
Relationship between quantity harvested (beans production) and
planted acres during long rain season
Table 4. 10 Quantity harvested and area planted
quantity harvested
planted acres
Pearson correlation (r)
0.6807
P-value
0.0000
Hypothesis
H0: There is no linear relationship between quantity harvested and area planted
H1: There is linear relationship between quantity harvested and area planted
Table 4.10 shows that there is positive relationship between quantity harvested (beans
production) and area planted at 5% level of significance since the p-value = 0.0000
was less than level of significance = 0.05, from the condition that reject null hypothesis
if p-value is less than level of significance, then null hypothesis rejected which states
that there is no linear relationship between quantity harvested and area planted and
accept the alternative hypothesis which states that there is linear relationship between
quantity harvested and area planted.
Furthermore the relationship is strong positive since the Pearson’s correlation
coefficient is 0.6807.
24
4.3 Multiple linear regression.
Multiple linear regression is a statistical data analysis technique used to determine the
extent to which there is a linear relationship between a dependent variable and more
than one independent variable. Multiple linear regression test at 5% level of
significance, was used to examine the relationship between dependent variable, which
was quantity harvested (beans production), and independent variables (value sold,
harvested acres and average price).
Residual is independent (serial autocorrelation)
Autocorrelation assumption shows that the errors between observed and predicted
values should be normally distributed, this is basically the same as the observations of
the study (or individual data points) to be independent from one another (or
uncorrelated). Durbin-Watson test was used to test the null hypothesis that the
residuals are not linearly auto-correlated against that the residual are serially
correlated. This statistic can vary from 0 to 4 Durbin-Watson statistic showed in Table
4.11 and 4. 12 the value was 1.731 and 1.997 respectively for both short rain season
and long rain season.
Table 4. 11 Model summary for short rain season
Model
R
R
Adjusted Std. Error of Durbin-
Square
R
the Estimate
Watson
Square
1
0.915
0.837
0.837
155073.82389 1.731
25
Table 4. 12 Model summary for long rain season
Model
1
R
0.922
R
Adjusted
Std. Error of
Durbin-
Square
R Square
the Estimate
Watson
0.850
0.850
89365.99143
1.997
No Multicollinearity
Gujarati (2004), Multiple regression assumes that the independent variables are not
highly correlated each other. This assumption is detected by using Variance Inflation
Factor (VIF) values. For the assumption to be meet VIF (Variance inflation factor)
scores to be well should be below 10, and tolerance scores to be above 0.2. Table 4.13
and Table 4.14 shows analysis of collinearity statistics and show that the assumption
has been met, as VIF (Variance inflation factor) scores were below 10. Therefore, the
data have no multicollinearity for both short rain season and long rain season.
Table 4. 13 Multicollinearity for long rain season
Variable
VIF
Value sold
1.4
Harvested acres
1.39
Average price
1.01
Mean VIF
1.27
26
Table 4. 14 Multicollinearity during short rain season
Variable
VIF
Value sold
1.79
Harvested acres
1.74
Average price
1.05
Mean VIF
1.53
There is no any VIF value which is greater than 10, hence no multicolinearity problem
among explanatory variables.
Normal distribution of residuals
Normality assumption can be tested by looking at the distribution of residuals.
Regression assumes the residuals (errors) should be approximately normally
distributed. The residuals for short rain season are translated into log normal
distribution. The log normal distribution is a continuous probability distribution of a
random variable in which logarithm is normally distributed, thus if the random variable
has a lognormal distribution, then has normal distribution. Likewise, if has a normal
distribution, then has a lognormal distribution, then probability function that describes
how the values of a variable are distributed. The study uses Shapiro-wilk to test the
normality assumption when the null hypothesis states that the variable is normally
distributed, reject null hypothesis if p-value is less than level of significance, since the
probability of Shapiro test = 0.27412 for short rain season is greater than level of
significance (α= 0.05) hence data are normal distributed and for long rain season the
27
probability of Shapiro test = 0.66 which is greater than level of significance (α= 0.05)
hence data are normal distributed.
Homoscedasticity (The variance of the residuals is constant)
Homoscedasticty assumption shows that the variation in the residuals (or amount of
error in the model) is similar at each point across the model. In other words, the spread
of the residuals should be fairly constant at each point of the predictor variables or
across the linear model.
Breusch-Pagan test was used to test heteroskedasticity, where by the null hypothesis
is that the variance of the residuals is homogenous or constant means
homoscedasticity. Therefore the p-value = 0.1857 and p-value = 0.1557 for long rain
season and short rain season respectively were very large then the null hypothesis
which states that the variance of the residuals is homogenous was accepted, hence the
variance of the residuals are homogenous for both short rain season and long rain
season.
4.3.1 Effects of value sold, harvested acres and average price on harvested acres
(beans production) during short rain season.
4.3.1.1 Significance of the model
This part shows the probability of F test to test whether or not the model has
explanatory power and R squared (Coefficient of determination) indicates how much
total variation in the dependent variable are explained by the independent variables.
The null hypothesis under F test is that the model is not significance while, alternative
hypothesis is model is significance. So, reject null hypothesis if p- value < 0.05.
Probability of F test = 0.0000
28
Therefore, in this study, gives F test its p-value = 0.0000 that is less than α = 0.05
which means that the model is significance.
R-squared = 0.7141
The study shows that there is 71.41% of value sold, harvested acres and average price
explains quantity harvested (beans production), while 28.59% is explained by other
factors.
4.3.1.2 Table of Coefficients
Table 4. 15 Multiple linear regression results
Quantity harvested
Coefficient
P-value
Harvested acres
0.412855
0.001
Value sold
1.53E-07
0.421
Average price
1.24E-05
0.968
Constant
5.377838
0.000
Y=ꞵ0+ꞵ1X1+ ꞵ2X2 + ꞵ3X3
Log (quantity of beans harvested) = 5.378+0.413 harvested acres+1.53E-07 value
sold+1.24E-05 average price.
Hypothesis
H0: There is no linear relationship between dependent variable and independent
variables
H1: There is linear relationship between dependent variable and independent variables
29
Harvested acres
Table 4.15 results shows that the p value is quite low (0.001) reject the null hypothesis
of no effect of harvested acres on beans production. So there is enough evidence to
suggest that there is positive effect of harvested acres on beans production controlling
for value sold and average price. Moreover the coefficient is positive meaning that as
harvested acres increases also quantity harvested increases. This implies that one acre
increase in harvested acres will lead to percentage change in quantity harvested by
41.29% during short rain season while other factors kept constant.
Value sold
Table 4.15 results shows that the p-value is particularly 0.412855 which is more than
0.05, so there is no enough evidence to reject the null hypothesis of no effect of value
sold on quantity harvested. So there is an evidence to suggest that there is no
significance effect of value sold on quantity harvested (beans production) at 5% level
of significance during short rain season.
Average price
Table 4.15 results shows that the p-value is particularly 0.968 which is more than 0.05,
so there is no enough significance evidence to reject the null hypothesis that, there is
no significance effect of average price on quantity harvested. So there is an evidence
to suggest that there is no significance effect of average price on quantity of beans
harvested (beans production) at 5% level of significance during short rain season.
30
4.3.2 Effects of value sold, harvested acres and average price on harvested acres
(beans production) during long rain season.
4.3.2.1 Significance of the model
This part shows the probability of F test to test whether or not the model is significance
and R squared (Coefficient of determination) indicates how much of the total variation
in the dependent variable, can be explained by the independent variables. The null
hypothesis under F test is that the model is not significance while, alternative
hypothesis is model is significance. So, reject null hypothesis if p-value < 0.05.
Probability of F test = 0.0000
Therefore, in this study, gives F test its p-value = 0.0000 that is less than α = 0.05
which means that the model is significance.
R-squared = 0.9185
The study shows that there are 91.85% of value sold, harvested acres and average price
explains quantity of beans harvested (beans production), while 8.15% is not explained
by other factors.
31
4.3.2.2 Table of Coefficients
Table 4. 16 Multiple linear regression results.
quantity harvested
Coefficient
p-value
harvested acres
116.3489
0.0000
value sold
.0009881
0.0000
average price
.0124656
0.883
Constant
12.14269
0.906
Hypothesis
H0: There is no linear relationship between dependent variable and independent
variables
H1: There is linear relationship between dependent variable and independent variables
Harvested acres
Table 4.16 shows that the p value is quite low (0.0000) reject the null hypothesis of no
effect of harvested acres on beans production. So there is an evidence to suggest that
there is an effect of harvested acres on beans production during long rain season
controlling for value sold and average price. More over the coefficient is positive
meaning that as harvested acres increases also quantity harvested increases. This
implies that one acre increase in harvested acres will increase quantity harvested by
116.3489 kg during long rain season while other factors kept constant.
32
Value sold
Table 4.16 shows that the p value is particularly 0.0000 which is less than 0.05, so
there is enough statistical evidence to reject the null hypothesis for no effect of value
sold on quantity harvested. So there is an evidence to suggest that there is an effect of
value sold on beans production during long rain season controlling for harvested acres
and average price. More over the coefficient is positive meaning that as value sold
Tshs increases also quantity harvested increases. This implies that one Tshs increase
in value sold will increase quantity harvested by .0009881 Kg during long rain season
while other factors kept constant.
Average price
Table 4.16 shows that the p value is particularly 0.883 which is more than 0.05, so
there is no enough evidence to reject the null hypothesis that, there is no effect of
average price on quantity harvested. So there is an evidence to suggest that there is no
significance effect of average price on quantity harvested (beans production) at 5%
level of significance during long rain season.
33
4.4 Discussion of findings
This section presents the discussion of findings for each specific objective that
analyzed to determine effects of beans production in Tanzania and the relationship
between planted acres and quantity harvested.
4.4.1 Relationship between planted acres and quantity of beans harvested
Finding revealed that planted acres had a positive significance relationship with
quantity harvested (beans production) in both long rain season and short rain season,
furthermore, the relationship was positive statistically significant (P-value = 0.000).
This study were similar to the study conducted by (Teferi et al. 2015; Idrisa et al. 2012)
found that planted acres were significance positive relationship on improved soybean
production but contrary of those Challa and Tilahun (2014) which suggested that were
negative significance relationship between planted acres and beans production.
4.4.2 Effects of harvested acres on quantity harvested (beans production).
Findings revealed that harvested acres had a positive significantly and positively effect
on beans production in both short and long rain season (P-value = 0.0000, p= 0.001)
respectively.
4.4.3 Effect of value sold on quantity harvested (beans production)
Findings revealed that value sold ha d no statistical significance effect on beans
production during short (p-value = 0.421) but had statistical significance effect on
beans production during long rain season (p-value = 0.0000).
4.4.4 Effect of average price on quantity harvested (beans production)
Findings revealed that average price had no statistical significance effect on bean
production during short and long rain season (p value = 0.968 and p- value = 0.883)
34
respectively. These results contrary with Karane, (2016) which suggested that average
price in the market had significance effect on the productivity of beans and improving
beans production.
35
CHAPTER FIVE
CONCLUSION AND RECOMMENDATIONS
5.0 Introduction
This chapter presents the summary, conclusions and recommendations of the research
project results and areas for further research. The conclusion is based on the objectives
of the study, and the recommendations based on the findings of the study.
5.1 Summary
The study intended to account for the determinants of beans production in Tanzania
specifically in Kigoma region. The study used cross sectional data on average price,
value sold, harvested acres, quantity harvested and planted acres. The data collected
were secondary data from the Annual Agricultural Sample Survey (AASS 2014/2015).
The study used multiple linear regression to determine the effect of harvested acres,
value sold and average price on beans production.
The study had four objectives, which were to examine the relationship between planted
acres and quantity harvested (beans production), the study revealed that there were
positive relationship between planted acres and quantity harvested both short rain
season and long rain season, secondly were to examine the effect of harvested acres
on quantity harvested (beans production), the study revealed that were positive effect
of harvested acres on quantity harvested in both short rain season and long rain season.
Third were to examine the effect of value sold on beans production, the results revealed
that value sold had no significance effect on beans production in short rain season but
had effect during long rain season. Lastly were to examine the effect of average price
36
on beans production, the results revealed that average price had no significance effect
on beans production in both short rain season and long rain season.
5.2 Conclusion
The first objective was to determine the relationship between planted acres and beans
production. The results shows that there were the positive relationship between area
planted and quantity harvested (beans production), hence concluding that as planted
acres increases also quantity harvested increases and decrease in planted acres also
decrease in quantity harvested in both short rain season and long rain season in Kigoma
region.
The second objective was to examine the effect of harvested acres on beans production.
The results shows that harvested acres was significance effect on beans production,
hence concluding that harvested acres had an effect on beans production in both short
rain season and long rain season in Kigoma region.
Third research objective was to determine the effect of value sold and average price
on beans production. The results showed that value sold was significance effect during
short rain season and insignificance during long rain season on beans production,
hence concluding that value sold had no significance effect on beans production in
short rain season but had effect during long rain season in Kigoma region.
Lastly, the findings reported that average price had no significance effect. Thus,
concluding that average price had no significance effect on beans production at
Kigoma region both short rain season and long rain season.
37
5.3 Recommendations
From the conclusion on the determinants of beans production by harvested acres, value
sold and average price made above the following recommendation drawn.
5.3.1 General recommendations
Government should formulate price policy for beans product and set price to encourage
farmers to produce more beans.
Government should employ various measures to maintain farm price and income
above what market would, include tariffs or import quotas, export subsidies, direct
payments to farmers and limitations on production.
5.3.2 Recommendations for further studies
This study considered only three variables that influence beans production in Kigoma
region but there are many other relevant factors or variables that could influence beans
production such as cost of inputs, effect of climatic change (rainfall, temperature) and
social-economic factors. Further studies may be carried out to determine the effects of
climatic change on beans production.
This study did not focus on the determinants of beans productivity and efficiency
among stallholder farmers, it may also be important for future research to examine the
determinants of beans productivity and efficiency.
The study employed secondary data of 2014/2015 Annual Agricultural Sample Survey
(AASS) but similar study could be undertaken by using the similar determinants but
different data such as from Household Budget Survey and Agriculture Census.
38
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41
LIST OF APPENDICES
Appendix: Data used
Long rain season Data
Planted
Harvest
Quantity
Value
Average
acres
ed acres
harvested
sold
price
3
3
400
255000
850
1
1
500
160000
800
1.5
1.5
900
712500
950
1
1
200
0
0.5
0.5
120
48000
0.5
0.5
300
0
1
1
200
0
0.5
0.5
60
0
1
1
300
1500
1500
2
2
200
120000
1200
0.5
0.5
100
0
1
1
120
32000
800
0.75
0.75
15
22500
1500
2
2
300
0
1
1
360
192000
800
0.5
0.5
80
80000
1000
1
1
100
0
800
42
1.25
1.25
720
480000
1000
1
1
120
120000
1000
1
1
300
160000
800
2
2
2000
1800000
1200
0.25
0.25
400
360000
1200
2
2
1248
988000
1000
1
1
180
12000
120
3
3
1000
800000
1000
2
2
360
240000
1000
0.5
0.5
300
240000
1200
1
1
400
3400
1700
2
2
100
96000
1200
1
1
200
120000
1200
3
3
400
0
1
1
100
0
1
1
480
144000
1200
0.5
0.5
100
40000
1000
2
2
800
540000
900
1.5
1.5
300
0
3
3
1560
1440000
0.25
0.25
60
0
1.5
1.5
810
500000
0.5
0.5
100
0
1000
1000
43
1
1
480
360000
1000
1
1
100
1280
1600
0.5
0.5
80
0
0.5
0.5
40
0
2
2
100
0
0.5
0.5
100
0
2
1.5
1300
0
0.5
0.5
40
0
1
1
200
0
1
1
100
0
2
2
300
3600
0.25
0.25
100
0
0.5
0.5
200
240000
1200
2
2
800
270000
900
1.5
1.5
1395
775000
1000
7
7
2000
1000000
1000
0.5
0.5
100
0
1
1
200
0
2
2
700
450000
0.5
0.5
600
0
0.5
0.5
100
72000
1200
1.5
1.5
420
210000
1000
0.5
0.5
80
0
1800
1500
44
1
0
0
0
1
1
100
0
1
1
100
0
Short rain season Data
Planted Harvested
Quantity
Value sold Average
acres
acres
harvested
price
0.5
0.5
700
7200
1
1
100
0
0.5
0.5
100
60000
600
1
1
520
223600
860
0.25
0.25
60
0
0.5
0.5
100
105000
1
1
200
0
0.5
0.5
40
0
3
3
1300
10800
1200
2
2
400
740000
1850
4
4
240
400000
2000
1.5
1.5
160
0
2
2
200
0
0.25
0.25
200
0
0.5
0.5
40
0
1800
1500
45
0.5
0
0
0
1
1
300
0
3
3
1500
5000
500
0.5
0.5
200
120000
1200
0.5
0.5
300
0
2
2
7000
20000
2000
3
3
1500
10000
2000
0.5
0.5
200
0
1
1
60
0
1
1
100
140000
2000
1
1
360
288000
1200
1.5
1.5
900
6000
1200
1
1
300
0
0.5
0.5
3.5
0
1
1
100
0
0.5
0.5
120
0
1
1
200
225000
1.5
1.5
100
0
1
1
200
0
3
3
200
0
0.25
0.25
100
0
1.5
1.5
100
80000
1.5
1.5
60
0
1500
1600
46
1.25
1
360
240000
1000
1
1
40
0
1
1
120
40000
1000
3
3
180
96000
1200
0.5
0.5
120
132000
1100
1.5
1.5
100
0
1.5
0.5
80
0
1
1
100
0
0.5
0.5
300
0
2
2
3750
3325000
950
4
4
1700
21600
1800
2.5
2.5
100
0
0.5
0.5
300
160000
800
0.25
0.25
300
240000
1200
2
0
0
0
3
3
600
400000
1000
10
10
9000
6000000
1000
2
2
200
1800
1200
1
1
90
90000
2000
1.5
1.5
2970
224000
800
0.5
0.5
300
250000
1000
2
2
1404
1684800
1200
1
1
60
0
47
0.5
0.5
20
0
2
0
0
0
7
7
100
0
0.5
0.5
60
0
1
1
200
150000
0.5
0.5
40
0
2.5
2.5
1000
1000000
10
10
200
0
0.5
0
0
0
1
1
200
300000
0.5
0.5
400
0
1
1
360
432000
1200
1.5
1.5
260
54000
900
0.5
0.5
60
0
1
1
300
0
0.75
0.75
100
0
3
3
1320
960000
0.25
0.25
80
0
1
1
200
120000
1200
2
2
1200
1200000
1500
1.5
1.5
480
240000
1000
4
4
6000
75000
2500
1
1
240
100000
1000
1500
1000
2000
1000
48
0.5
0.5
40
0
1
1
400
0
0.5
0.5
300
0
0.5
0.5
100
0
2
2
800
1000000
1.5
1.5
100
0
0.5
0.5
40
0
5
5
5000
360000
0.5
0.5
80
0
0.5
0.5
200
2500
0.5
0.5
200
0
3.35
3.35
328
0
0.5
0.5
80
0
3
3
600
360000
0.5
0
0
0
0.5
0.5
60
0
0.5
0.5
100
0
3
2.5
500
360000
1200
1
1
800
300000
1500
0.25
0.25
20
0
1
1
200
0
1
1
200
0
1
1
200
0
2000
1200
2500
1200
49
1.5
0
0
0
2
2
700
0
0.5
0.5
66
0
1
1
1000
1020000
1
1
240
0
1
1
600
0
0.5
0.5
120
0
0.25
0.25
9
0
0.25
0.25
40
0
1200
50