THE IMPACT OF GOVERNMENT FUNDING ON COFFEE PRODUCTION IN
UGANDA
ABSTRACT
Coffee is the biggest foreign exchange earner for Uganda, a position it has attained for
a number of years. A study has been conducted in the sub-counties of Nabweru and
Nansana in Wakiso district to assess the impact of government funding on coffee
production with specific objectives, to establish the relationship between fertilizer
application, provision of improved coffee seedlings, availability of extension services,
access to infrastructure, and access to market information with coffee production.
Primary data was collected among coffee farmers. Data was cleaned and then analyzed
with the help of Statistical Package for Social Scientists (SPSS) at univariate, bivariate
and multivariate level.
Study findings confirmed that there was fertilizer application, modern coffee seedlings,
provision of extension services and availability of good infrastructure, methods of
coffee spacing, pruning, coffee picking, and drying and nursery bed preparation. Coffee
farmers had access to modern seedlings and farmers had benefited through increased
output seedlings that are resistant to wilt and quick maturity.
In conclusion the researcher recommends involvement of all further trainings on
improved coffee production as well as government supply of materials and improved
coffee seeds to ensure that coffee production are boosted in Uganda.
Key: Words: Foreign exchange, Infrastructure, fertilizers.
1
SECTION ONE: INTRODUCTION
This study is to assess the impact of government funding on coffee production in
Uganda. This chapter will present the background to the study, the statement of the
problem, the general objectives, the objectives of the study, the research questions, the
hypotheses, the scope of the study and the study significance.
Historically, it is well documented that agricultural sector assumes a dominant role in
Uganda’s economy in terms of numbers employed. More importantly, agriculture has
the potential to act as a base for the improvement of the livelihoods of both rural and
urban populations. In rural areas such improvement would be achieved by enhancing
productivity at the primary, i.e. production level. In rural towns and urban areas
agricultural products constitute the base for the creation and expansion of value adding
processing industries catering to domestic and regional markets, and for
packaging/marketing operations, both for domestic and export markets (Oxford Policy
Management, 2002).
Although most of this agriculture is engaged in at the micro subsistence level, its ability
to transform this sector is greatly constrained by a myriad of factors, among them
limited access to formal financing (Robinson, 2006). The bigger and medium term
financial institutions view the smallholder agriculture operators as too risky and too
costly to service while the smaller financial institutions the majority of them being
rural based and thus in a better position to cost effectively serve the agribusinesses with
their proximity lack the financial and technical capacities to do so. There have however
been modest attempts made by a number of rural based financial institutions working at
the micro level to finance agriculture and agribusiness, though their attempts still
remain “pockets of progress”.
New global forces - the economic downturn, a food crisis and climate change are
driving renewed interest in ways to improve the productivity of smallholder farmers in
developing and emerging economies. Small holder farms can be efficient producers on
a per hectare basis, however, limited capital for investment, exposure to risks such as
weather, unreliable markets and market price fluctuations as well as imperfect
knowledge of sustainable approaches and technologies mean most smallholder farmers
are not optimizing potential returns.
2
At the production level, financial services for agriculture can enable farmers to
introduce external or purchased inputs such as: irrigation or other productivityenhancing technologies; finance input and marketing costs; co-finance extension and
information services; bridge the pre-harvest income gap; prevent forced sales of
produce immediately following harvest at low prices; smooth seasonal income flows
through deposit facilities, facilitate access to remittances, and bank overdraft lines; and
eventually to insure against price or yield fluctuations. If agribusinesses cannot access
financial services, their capacity to finance and supply farmers, and to buy and process
farm produce, is restricted (PMA, 2008).
Coffee is one of the world's largest traded commodities with over 90% of coffee
production taking place in developing countries, while consumption takes place mainly
in the developed economies. Worldwide, it is estimated that over 25 million
smallholder producers rely on coffee for a living.
The world coffee exporting and importing countries are organized under an
intergovernmental body, the International Coffee Organization (ICO). Ethiopia remains
the Africa’s leading coffee producer with production totaling followed Uganda.
The international coffee market net growth trend continues to reflect supply tightness
that will prevail over the next several years. The global consumption as of 2010 stands
at 134 million bags compared to the world supply of 131.3 million bags. The buoyant
global coffee consumption growth of about 2.4% per year is mainly attributed to
expanded, emerging markets and the domestic consumption campaigns and
programmes by the coffee producing countries. There is also marked growth in demand
for specialty coffee in niche markets especially in Europe and United States of
America. Therefore, the coffee producing countries will continue to strategically
position themselves in order to benefit from the growth in the market.
Global stocks are also at their lowest in both exporting and importing countries.
Opening stocks in producing countries for coffee year 2010/11 were estimated at 18
million bags and 18 million in importing countries as of June 2011. Dwindling stock
3
levels and a buoyant consumption growth have supported global prices in the short
term.
Uganda is a member of the International Coffee Organization (ICO) which is the main
intergovernmental organization for coffee, bringing together exporting and importing
Governments to tackle the challenges facing the world coffee sector through
international cooperation.
Uganda is fully committed to the ICO’s mission to strengthen the global coffee sector
and promote its sustainable expansion in a market-based environment for the
betterment of all participants in the coffee sector.
Uganda is also a member of Inter-African Coffee Organization (IACO) which is an
intergovernmental
organization
composed
of
25
African
coffee
producing
countries. Uganda associates herself with IACO’s objective of taking up the challenges
associated with coffee of African origin through regional and international
cooperation.
Recalling the fact that the government of Uganda introduced the Entandikwa credit
scheme in 1995 and later modified it to a new Prosperity for All programme known as
“bonna baggagawale”, these schemes have channelled huge amounts of money to
agriculture especially among coffee farmers.
While there has been an increase in funding to the coffee production in Uganda over the
last six years, growth in production is still low in comparison to the neighbouring
countries. In 2008/09, the sector received UShs 223 billion, amounting to 3.8 per cent
of government spending was used to boost coffee production (MoFPED, 2010).
According to MAAIF and UBOS (2011), coffee exports in FY2010/11 were 2.6 million
60-kilogram bags valued at US$338 million representing a 3.7% decline in volume and
value compared to a target of 2.7 million bags. Very few studies have been conducted
to ascertain the impact of government funding on agriculture. To that end therefore, it is
imperative to establish the effect of government funding on coffee production in
Uganda.
The main objective of this study was to establish the effect of government funding on
coffee production in Uganda. But the entire research aimed fulfilling specific objectives
4
like; to find out the relationship between fertilizer application and growth in coffee
production, to find out the relationship between improved coffee seedling provision and
growth in coffee production, To find out the relationship between agriculture extension
services and growth in coffee production, To determine whether availability and
access to infrastructure has an effect on coffee production, To establish whether access
to market information has an effect on coffee production
Conceptual Framework
Figure 1.0: A conceptual framework showing the impact of Government funding on
coffee production in Uganda.
Independent Variables

Dependent Variable
Individual farmer
Impact of Government funding as
characteristics

Agriculture extension services

Fertilizer application

Improved coffee seedlings
measured by GDP contribution of
coffee
provision

Intervening Variables
Availability and accessibility

to infrastructure
Government policies
Tax rateson the coffee
Access
to market explains
information
This above
framework
the impact of government financing
Climateonchanges
production in Uganda with the production of coffee being dependant
government

funding.
Price fluctuations
The findings of the study will provide useful information to the concerned policy
makers such as government, donors, farmers associations and all the stakeholders in the
agricultural sector. The findings of the study will provide an insight to how government
finance can benefit the key production sectors in the country like coffee production.
5
The study will act as a reference for future researchers and scholars who may want to
use it as a secondary source thereby adding on the existing literature.
Uganda as a country is one of the leading coffee exporters in Africa. Considering that
Uganda as agriculture sector has been experiencing increase in the funding, we would
expect a good allocation of the funds to coffee production hence need to find out
whether there is an impact of government funding on the production of coffee.
The study was conducted among coffee farmers, NAADS extension workers, nursery
bed operators and coffee processors in Wakiso district within the selected sub-counties
of Nansana and Nabweru. Nansana is located on the main high way between Kampala
and Hoima approximately 12 kilometres by road, North West of Kampala city. In 2011,
the Uganda Bureau of Statistics estimated the population of Nansana at 89,900 people.
On the other hand, Nabweru sub-county is also found in Wakiso district and it borders
with Kawempe division of Kampala city.
The study covered a period of 10 years starting with 1997 when the Entandikwa Credit
scheme was introduced by the Government of Uganda to support farmers up to 2007
after the inception of NAADS.
The impact of Government funding as measured by GDP contribution of Coffee was
the dependent variable while individual farmer characteristics, agriculture extension
services, fertilizer application, improved coffee seedlings provision, availability and
accessibility to infrastructure as well as access to market information were the
independent variables. The study also covered coffee production reports from major
statistical centres such as UBOS, UCDA and MoFPED.
6
Coffee production refers to quantity and quality of coffee produced as a result of
various inputs such as fertilizer and mechanisation.
Coffee seedlings provision looks at the seedlings which have been provided or
recommended to farmers for usage.
Agriculture extension services refer to services rendered to farmers through
workshops, trainings to provide them with necessary information regarding production
and modernisation of agriculture.
7
SECTION TWO: RESEARCH METHODOLOGY
This chapter discusses the research designs, sampling designs, data sources, data
collection instruments, sample size, data presentation and analysis methods that were
used in the study.
Research design adopted both qualitative and quantitative approaches when collecting
and processing data to establish the impact of Government funding on coffee
production in Uganda. The qualitative approach was adopted because it was the best
way to bring out respondents’ opinions and perceptions on the effects of government
funding on coffee production in Uganda. Further, the approach was preferred because it
was suitable for answering “what, why and how” social as well as institutional
phenomena occur.
The quantitative approach was adopted to measure the magnitude of the various
constructs and variables that were conceptualized in the study. Most importantly,
quantitative studies would lead to the establishment of the effect of Government
funding on coffee production in Uganda through testing the hypotheses. Also,
quantitative studies would lead to the understanding of the strength of each construct in
their contribution to the main independent variable and its relationship with the
dependent variable. A combination of both qualitative and quantitative approaches thus
served as a strong basis for drawing compelling conclusions and recommendations
during the study (Sekaran, 2000).
The sample size of study consisted of 60 respondents comprising of 50 coffee farmers
and 10 stakeholders who include NAADS extension workers (5), nursery bed operators
(3) as well as coffee processors (2).
8
The Sampling design and Sample size of the study adopted purposive sampling
technique where 60 respondents were selected for the study. Purposive sampling is a
technique used where the researcher chooses the sample based on who they think would
be appropriate for the study. The technique was very useful in reaching a targeted
sample quickly and where sampling for proportionality was not the main concern
(Babbie, 2001). Stratified sampling was also used in order to have respondents of
similar characteristics grouped together during the interviews.
The study had a Sample size of 60 respondents was selected in Nabweru and Nansana
sub-counties of Wakiso district and their number was determined using the statistical
inference formula;
n 
Z2
2
pq
e2
………………………………………............................... (1)
Where,
n
Is the total sample size that was selected
Z is the area under the normal curve corresponding to the desired confidence
level

p
2 is the level of significance
is the expected frequency value or true proportion of factor in the population.
e is the maximum difference between the sample mean and the population mean.
q
=(1- p ) is the probability of failure.
The variables were measured by operationally defining concepts for instance the
questionnaire was designed to ask responses about the effect of Government funding on
coffee production in Uganda. These were channelled into observable and measurable
elements to enable development of an index of the concept.
The researcher used both primary and secondary sources. Primary data was obtained
from the respondents using questionnaires and key informant interviews to examine the
effect of Government funding on coffee production in Uganda.
9
Secondary data was obtained from already existing information that included NAADS
reports, Uganda Coffee Development annual reports, Ministry of Agriculture reports,
and Uganda Bureau of Statistics on agricultural production.
Data collection methods are an integral part of research design which involves selection
of both qualitative and quantitative data in form of primary and secondary data (Amin,
2005). Primary data was collected by the researcher from Coffee farmers in Nabweru
sub county while secondary data was obtained from already existing information.
Generally, the researcher collected primary data using quantitative methods and
secondary data using qualitative methods (Forshaw, 2000).
The questionnaire was used on the basis that the variables under study could not be
observed for instance the views, opinions, perceptions and feelings of the respondents.
It was also used because the information was collected from a large sample in a short
period of time yet the respondents could easily read and write (Sekaran, 2003).
In this research, questionnaires with both structured questions and closed ended
questions were administered to a group of 50 farmers. The researcher interviewed the
respondents in Luganda which is the local language spoken in the sub-counties of
Nabweru and Nansana of Wakiso district.
With this method, the researcher interviewed NAADS extension workers, Nursery bed
operators and coffee processors following questions on an interview guide. The
researcher also summarized the responses in a note book reflecting what was being
replied by the respondents.
Secondary data was collected by reviewing existing published and unpublished
information relating to the effect of Government funding on coffee production in
Uganda. Relevant documents such as the internal project quarterly reports, midterm
evaluation reports, project documents, newspapers, journals and magazines were also
reviewed and vital information recorded. This information was used to supplement
other methods of data collection in understanding the core areas and variables of the
study.
10
The field questionnaires were first cleaned to remove errors, and then entered into
Statistical Package for Social Scientists (SPSS) for analysis. Most errors were detected
using descriptive statistics, scatter plots and histograms. These errors were checked by
revisiting the questionnaires and coding missing data as “999”. The blank and not
applicable data was coded “0” and there was also rectifying of the typing errors.
SPSS programme version 17 was used for univariate, bivariate and multivariate
analysis. This version is an improved one unlike the lower versions. It is user-friendly
and can accommodate a big number of variables as well as simultaneous tabulation of
variables.
This type of analysis involved the description of a single variable and its attributes. The
basic way univariate data was presented included creation of frequency distribution of
the individual cases. This was done in a table format, bar graphs and pie charts.
Keeping the dependent variable fixed, the researcher analyzed the impact of
government funding to each of the independent variables. Bar graphs were also used to
explain the bivariate data as well as testing of simple hypotheses of association between
the dependent and independent variables. Bivariate analysis was also used in predicting
a value of the dependent variable using the independent variables. The study results
were presented in form of percentage distribution tables, pie charts, bar graphs,
correlation coefficient as well as the logistic regression model.
At multivariate analysis, a logistic regression model was fitted to determine the impact
of government funding on coffee production in Uganda. A dependent variable with two
outcomes; (1-Increase in coffee production yields, 0-No increase in coffee production
yields) was considered during the study. Independent variables included; NAADS
extension services, fertilizer application, improved coffee seedling provision,
availability and accessibility to infrastructure as well as access to market information.
Below is the logistic regression model that was used;
Y    1 Es   2 FA   3C SD   4 AAI   5 AMI .................................................................(ii )
β 0 is a constant
β i are unknown coefficients of each independent variable.
11
Y is a measure of GDP contribution of Coffee
ES
is the NAADS extension services provided to farmers like training, informing
farmers of appropriate methods and seasons to plant. This variable was measured in
such a way that
outcome 1 was assigned if
NAADS services had contributed
positively to coffee production in terms of yields whereas outcome 0 was assigned if
NAADS services provided did not bring a positive impact.
FA
is the fertilizer application among farmers which include type of fertilizer used,
duration of fertilizer application and yields from fertilizer application. This variable
was measured in such a way that outcome 1 was assigned if the
application of
fertilizers to coffee farmers had contributed positively to coffee production in terms of
yields while outcome 0 was assigned if fertilizer application did not bring a positive
impact in terms of coffee production.
C SD is the availability of
improved coffee seedling distribution that includes sources
of coffee seedling and coffee seedling productivity. This variable was measured in
such a way that outcome 1 was assigned if improved coffee seedling distribution
positively contributed to coffee production while outcome 0 was assigned if improved
coffee seedlings did not bring a positive impact.
AAI is
the availability and accessibility to infrastructure. This involves whether
buyers easily access farmers gardens, the status of the road network as well as who
provides and maintains the road network. Outcome 1 was assigned if infrastructure
accelerated coffee production while outcome 0 was assigned if infrastructure did not.
AMI is
the access to market information that involves regular market information
updates as well as the sources of market information. This variable was measured in
such a way that outcome 1 was assigned if farmers accessed market information while
outcome 0 was assigned if did not.
12
The researcher got an introductory letter from Uganda Martyrs University, introducing
him to Wakiso district officials, community leaders as well as the respondents. The
researcher was consequently granted a permission to conduct the study within their
area.
Pre-testing involves examining individual questions as well as each of the study
instruments very carefully with the aim of ensuring that the questions measure what
they are intended to measure, respondents understand the questions, and creating a
positive impression that motivates respondents to answer (Amin, 2005). Pre-testing was
conducted at Kawanda Agricultural Research Institute to establish the validity and
reliability of the study instrument.
The researcher obtained consent from all the respondents on presentation of an
introductory letter from Uganda Martyrs University, Nkozi. The researcher observed
extreme confidentiality while handling the responses. The respondents were informed
that participation was voluntary and the research had no direct or indirect harm to them.
The study was faced with some limitations like; Non-response errors that the researcher
anticipated in the field while interviewing in the field. The researcher increased on the
sample size to minimize the error in the study. Incomplete information due to time
limitation on the side of respondents. The researcher made some prior appointments
and also left some questionnaires to the respondents which he later picked after filing
them. The research required money for logistical support like transport to the field,
printing of questionnaires, photocopying drafts, printing and binding of final report.
Nevertheless, the researcher sought financial assistance from the relatives to meet these
costs.
13
SECTION THREE: PRESENTATION, ANALYSIS AND INTERPRETATION
OF RESULTS
This chapter presents study findings as well as the interpretation and discussion of the
findings.
3.1 Background characteristics of the respondents
The background characteristics of the respondents in Nabweru and Nansana subcounties are presented in table 3.1.
Table 3.1: Distribution of the respondents according to their sex
Sex of respondents
Frequency
Percentage (%)
Male
42
70
Female
18
30
Total
60
100
The study results indicated that majority of the respondents were males (70%) and only
30% were females. This was due to higher expectation from the returns of the coffee
farming, males being landlords and family heads, and women being submissive to the
partners they would let the husbands respondent on behalf of the family and the fact
that males commanded a big say in managing family resources like proceeds from
coffee
Table 3.2: Distribution of the respondents according to their age
Age of respondent
Frequency
Percentage (%)
Below 20 years
8
13.3
20-30 years
17
28.3
31-40 years
16
26.7
Above 40 years
19
31.7
Total
60
100
It was further found out that most respondents (31.7%) were aged 40 years and above,
(28.3%) were aged between 20-30 years, while (26.7%) who were aged 31-40 years.
Most of the coffee farmers who were mostly the elderly had been in agriculture for
quite some time and had appreciated the benefits of coffee farming and production.
It was interesting to find out that some respondents reported that they had been in
coffee industry since their childhood because the grandparents owned coffee gardens,
14
their own parents were coffee farmers and the respondents were also engaged in coffee
farming since it was a family source of income. It was however observed that majority
of the young people were not engaged in coffee farming because they were at school
and others were engaged in informal employment such as saloon business and Bodaboda riding.
Table 3.3: Distribution of the respondents according to their marital status
Marital status
Frequency
Percentage (%)
Married
32
53.3
Single
16
26.7
Divorced/separated
4
6.70
Widowed
8
13.3
Total
60
100
Results on the marital status of the respondent showed that majority of the respondents
were married (53.3%), followed by those who were still single (26.7%) and widowed
(13.3%). Very few coffee farmers had divorced or separated with their partners (6.7%).
Most of the married respondents had amassed some assets including pieces of land
where they had established coffee plantations. The married respondents had the biggest
percent besides it would be easier to get farm labour because of big family size and
coffee farming was looked at as an alternative source of income to sustain the family
like paying school fees to the family. One widow testified to the researcher that she had
been able to pay school fees of her children after the death of her husband because of
coffee sales.
Table 3.4: Distribution of the respondents according to their Education level
Education level
Frequency
Percentage (%)
No education
12
20.0
Primary
18
30.0
Secondary
23
38.3
University/tertiary
7
11.7
Total
60
100
15
As shown in table 3.4, findings indicated that the biggest proportion of respondents
(38.3%) had attained secondary education followed by those with primary education
(30%). Very few respondents indicated that they never attained formal education (20%)
although most of these used indigenous knowledge in coffee farming. The high levels
of literacy made it easy for farmers to adopt appropriate farming technologies like use
of fertilizers and coffee farming needs an individual to be highly educated hence people
with normal education were also found to be engaged in coffee farming.
Table 3.5: Distribution of the respondents according to time spent in farming
Time spent in coffee farming
Frequency
Percentage (%)
<5 years
7
11.7
5-10 years
11
18.3
11-15 years
10
16.7
16-20 years
14
23.3
Over 20 years
18
30.0
Total
60
100
According to table 4.5, the study findings indicated that some respondents had been
practicing coffee farming as early as seven (7) years old since they would start by
coffee picking, digging in the gardens etc. The statistics showed that the biggest
number of respondent (30%) had an experience of coffee farming of over 20 years,
followed by those respondents (23.3%) who were in coffee farming for 16-20 years and
11.7% of the respondents reported to have spent less than 5years and these were
majorly the people that operated coffee nursery beds of the new coffee breeds because
the indigenous robusta coffee had been attacked by coffee wilt disease.
16
3.2 Fertilizer use on coffee production in Nabweru and Nansana Sub-counties
Fertilizer use on coffee production in Nabweru and Nansana sub-counties is presented
in figure 3.1
Figure 3.1: Distribution of respondents according to fertilizer application
The pie chart in figure 3.1, indicates that majority of them (78.3%) used fertilizes,
while a few of the farmers (21.7%) did not use. This implies that farmers were applying
fertilizers though the biggest percentage of farmers was using the locally made organic
manure like cow dung, compost manure from matooke peelings.
Table 3.6: Percentage distribution on the type of fertilizers
Type of fertilizers used
Frequency
Percentage (%)
Organic manure
31
66.0
NPK
9
19.1
UPK
4
8.50
Urea
3
6.40
Total
47
100
Results in table 3.6, indicated that majority of the respondent (66%) used organic/ local
manure, followed by those who used NPK fertilizer (19.1%), while the least used
fertilizer was urea (6.4%). The biggest number of the respondents using organic manure
was due to the fact that coffee farmers were applying compost manure/ locally made
manure like from cow dung. From the discussion with the farmers, they reported that
they got information about organic manure application from NAADS and it was
affordable though they purchased modern fertilizers especially NPK and UPK from
National Agricultural Research Organization (NARO) and agricultural drug shops.
17
Farmers received training from NAADS on the preparation of organic manure like cow
dung and coffee husks in the financial year 2011/2012.
3.2.1 Duration of fertilizer application on the coffee farms.
The farmers in coffee industry were asked to ascertain the duration of fertilizers. The
results are presented in the figure 3.2.
Figure 3.2: Duration of fertilizer application and coffee production
According to the pie chart in figure 3.2, results indicate that majority of the respondents
(44.7%) applied fertilizers once in a year; followed by those who applied fertilizers
three times in one year (38.3%), while (17.0%) of the respondents applied fertilizers
twice a year. While in the field it was observed that farmers were using local manure
like compost manure and cow dung which they applied more often. Farmers reported
that modern fertilizers like NPK and UPK were applied once in a year because they had
a high concentration of nutrients and the chemicals were expensive. Fertilizers that
were applied twice or even three times in a year were majorly organic since they were
cheaper to buy or access amongst most farmers.
18
3.2.2 Government support in form of fertilizers
The responses from farmers on whether they received government support in form of
fertilizers are presented in table 3.7.
Table 4.7: Distribution of the respondents according to fertilizer support received
Fertilizers support received
Frequency
Percentage (%)
Received fertilizer
12
25.5
Did not receive fertilizers
35
74.5
Total
47
100
The respondents were also asked if they received government support in form of
fertilizers and majority indicated that they never received fertilizers (74.5%) while a
few respondents indicated that they received fertilizers (25.5%). This implies that few
farmers received government support in terms of fertilizers. Most of the beneficiaries
were model farmers who received free fertilizers from NAADS for demonstration to
other farmers in the area while the rest of the farmers were buying for themselves.
4.3 Coffee seedling provision in Nabweru and Nansana Sub-counties
Percentage distribution on coffee seedling provision in the sub-counties of Nabweru
and Nansana is presented in table 3.8
Table 3.8: Distribution of the respondents according to improved seedlings
Improved seedlings
Frequency
Percentage (%)
Used modern seedlings
48
80.0
Never used modern seedlings
12
20.0
Total
60
100
Statistics indicated that majority of the coffee farmers used modern coffee seedlings
(80%) with an exception of a few who never used modern coffee seedlings (20%) but
instead planted robusta coffee. Those who planted modern coffee got them from
nursery beds, National Agricultural Research Organization in Kawanda as well as
Coffee Research Institute, Mukono-Kituza Uganda. These coffee improved seedlings
included; Kituza R1, R2, R3, R4, R5, R6, and R7.
Table 3.9: Distribution of the respondents according source of improved seedlings
Sources of coffee seedlings
Frequency
19
Percentage (%)
Bought
34
69.4
Government support
10
20.4
Other Donations
5
10.2
Total
49
100
Results in table 3.9 indicate that the majority of coffee farmers (69.4%) bought their
coffee seedlings followed by (20.4%) farmers who got their coffee seedling from the
government through NAADS coordinators. The least number of the coffee farmers
(10.2%) got their seedlings through donations for instance the politician donated
improved coffee seedlings during campaigns in 2011. Majority of those who bought
modern coffee seedlings got them from either the National Agricultural Research
Organization located in Kawanda and others got them from nursery bed operators.
3.3.1 Impacts of modern seedlings on coffee productivity
Coffee farmers in Nabweru and Nansana were asked about the impact of modern coffee
seedlings and the findings are presented in table 3.10.
Table 3.10: Distribution of the respondents according to impacts of improved
seedlings.
Impacts of modern seedlings
Frequency
Percentage (%)
Positive effects
39
81.2
No improvement
9
18.8
Total
48
100
The farmers in Nabweru and Nansana sub-counties informed the researcher that the
improved coffee seedlings positively affected the production (81.2%) while a few of
them did not notice any change in production (18.8%). This was attributed to the fact
that some farmers poorly cared for their farms, some thought they did not need, or it
was as a result of poor soils, or else it could have come from not following the
instructions given by the extension workers e.t.c.
20
Table 3.11: Effects of improved coffee seedlings on productivity
Effects of improved coffee seedlings
Frequency
Percentage (%)
Quick maturity
6
14.6
More yields
19
46.3
Resistant to coffee wilt
7
17.1
Marketable produce/ seedlings
9
22.0
Total
41
100
Results in table 3.11 indicated that majority of the
farmers realized increased
yields(46.3%) from the planting of improved coffee seedlings followed by those who
indicated that it was a marketable produce (22.0%) and resistant to coffee wilt disease
(17.1%). The rest of the respondents also noted that improved coffee seedlings had an
advantage of quick maturity (14.6%). Since the study also involved nursery bed
operators, those in the business of selling seedlings reported that there was a high
market for improved seedlings since farmers expected quick yield from those improved
seedlings like Kituza R1, R2, R3 and R5 even farmers who had received post
harvesting information like proper drying made their coffee seedlings more marketable.
3.4 Agriculture extension services on coffee production
On the agricultural extension services on coffee production, farmers were asked if they
ever received agricultural extension services on coffee production and results indicated
that majority of the farmers (58.3%) had received agricultural extension services while
41.7% of the respondents had never received agricultural extension service.
3.4.1 Providers of extension service to coffee farmers
Results on Agricultural extension service providers among coffee farmers are provided
in the table 3.12.
Table 3.12: Providers of extension service to coffee farmers
Extension service providers
Frequency
Percentage (%)
NAADS service providers
13
37.1
Agricultural officer
14
40.0
NGOs
7
20.0
Others
1
2.90
Total
35
100
21
The biggest number of coffee farmers in Nabweru and Nansana Sub-counties (40%) got
information on extension services from the sub-county agricultural officers, followed
by (37.1%) who got information on coffee farming from NAADS service providers
while (20%) got information on coffee farming from NGOs like Agency for Integrated
Rural Development (AFIRD) and farmer groups.
3.4.2 Form of extension services provided to coffee farmers in Nabweru and
Nansana Sub-counties
Results on the type of extension services provided to coffee farmers are provided in the
table 3.13.
Table 4.13: Extension services provided to coffee farmers.
Extension services provided
Frequency
Percentage (%)
Coffee spacing
15
44.1
Pruning
8
23.5
Coffee picking and drying
6
17.6
Nursery bed preparation
2
5.9
Weeding and spraying
3
8.8
Total
34
100
Findings in the table 3.13 showed that majority of the respondents (44.1%) got
knowledge about coffee spacing, followed by pruning (23.5%), coffee picking and
drying (17.6%) but the least number of farmers had benefited from nursery bed
preparation (5.9%). Field findings further indicated that farmers were taught modern
drying methods like using local mats and canvas mats locally known as “Ntundubale”
such that stones are not mixed with dried coffee, that others were taught how to cement
their compounds only those specifically mean’t for drying coffee. However it was
noticed that those who did not manage cementing used cow dung and clay to smear
their compounds. Among farmers who benefited from the training under weeding and
spraying, said that spraying was much better since it killed pests like nematodes
attacking coffee farms and other caterpillars eating up coffee leaves.
22
3.4.3 Benefits of extension services to coffee farmers
The coffee farmers indicated that they had benefited from the extension services and
results are presented in the figure 3.3.
Figure 3.3: Percentage distribution of benefits of extension services to coffee
farmers
The majority of the coffee farmers who benefitted from the extension services in the
area of increased produce was 35.3%, followed by those who benefited in the area of
increased knowledge which was 32.4% and increased sales (26.5%). Among those
who benefited from increased produce were able to fetch more profits which even
assisted them to buy more land and paying for their children.
3.5 Availability and accessibility to infrastructure among coffee farmers in
Nabweru and Nansana Sub-counties
Respondents involved in coffee farming industry were interviewed on whether the
buyers had access to their farms and results are provided in figure 3.4.
23
Figure 3.4: Percentage distribution of respondents on whether coffee buyers easily
access coffee farms
According to the results in figure 3.4, majority of the farmers (85%) said that buyers
had direct access to their coffee farms because many were small scale coffee farmers
who sold their produce to brokers who moved from home to home buying coffee while
other farmers took their produce to the nearby coffee traders in trading centres.
However a handful of respondents (15%) reported that buyers did not have direct
access to their farms. In relation to this point, it was observed that majority of the
farmers had good murram roads and pathways that enabled coffee buyers to reach the
farms.
4.5.1 Status of the road network to coffee markets.
Coffee farmers were also asked about the status of the road network to the markets and
results are presented in the table 3.14
Table 3.14: Percentage distribution of the status of road network to coffee markets
Status of road network
Frequency
Percentage
Poor
14
23.7
Average
28
47.5
Good
17
28.8
Total
59
100.0
24
According to the responses of the respondents, the researcher found out that majority of
the respondents (47.5%) said that their road network to the markets was average
implying that they were passable followed by (28.8%) who reported that their road
network was good because it was managed by the central government that always
provided graders for levelling. Only 23.7% of the respondents reported that their roads
are poor and are hardly passable, these referred to those roads deep in villages that are
renovated by the communities through their own mobilisation called “Bulungi bwansi”.
3.5.2 Road maintenance in Nabweru and Nansana sub-counties
The field respondents were also asked on the bodies responsible for maintaining road
network in their areas as presented in figure 3.4.
Figure 3.4: Percentage distribution of bodies responsible for road maintenance
Accoring to figure (3.4), the researcher found out that 66.7% of the respondents
reported that roads in the communities are maintained by the local government, Also
results showed (30%) of the respondents said that roads are maintained by community’s
administration, that is to say individuals who use them to reach their gardens while only
(3.3%) reported that roads are maintained by UNRA. This mans that government
maintian roads for easy transportation of agricultural produce including coffeeproduce.
25
3.6 Access to market information among coffee farmers in Nabweru and Nansana
sub-counties
Respondents were asked to state if they had access to market information and results
are presented in figure 3.5.
Figure 3.5: Access to market information among coffee farmers in Nabweru and
Nansana sub-counties
Findings indicated that majority of the farmers (72.9%) indicated that they had access
to market information while (27.1%) did not have access to market information. The
biggest percentage indicate that farmers were well informed about the prices of coffee,
hence, they were able to get enough profits and the revenue from coffee would be
increased, since coffee is bought and sold at proper prices as set by government.
3.6.1 Sources of market information
Results on sources of market information are presented on the table 3.15.
Table 3.15: Percentage distribution of sources of market information
Source of information
Frequency
Percentage (%)
Radio
5
11.4
Farmer Groups
21
47.7
NAADS Extension Officers
1
2.30
Buyers
17
38.6
Total
44
100.0
26
Findings in table 3.15 indicated that majority of the respondents (47.7%) were reported
to have accessed market information though farmer groups, followed by those who
mentioned buyers (38.6%). Some respondents mentioned accessing market information
from the radio (11.4%) while only one respondent indicated that he received market
information from NAADS extension officer.
3.7 DATA NAALYSIS
3.7.1 Correlation between independent factors and Coffee production in terms of
yields
A correlation test between independent factors and increase in coffee production in
terms of yields was done to establish if there is any relationship as presented in table
3.8. The independent factors included fertilizer usage, improved coffee seedlings,
agricultural extension services, access and availability of infrastructure as well as
access to market information.
Results indicate that there was a strong positive correlation between fertilizer
application and increase in coffee yields (r=0.805*), on the other hand p=0.001<0.05,
this implies that the variables was positively significant, keeping all other variables
constant, we reject the null hypothesis and conclude that there is a significant
relationship between fertilizer application and increase in coffee yields.
On the study about the relationship between modern coffee seedlings in relation to
coffee production, a weak positive correlation was found (0.344*), while p=0.018<0.05
implying that if modern coffee seedlings are used, the production of coffee is
increased positively but to a smaller extent. This was attributed to poor soils and
changes in the climate like dry spells.
On the other hand p=0.018<0.05, this implied that the variable was positively
significant, hence we reject the null hypothesis and conclude that there is a significant
relationship between improved coffee seedlings and growth in coffee production.
On the relationship between extension services and coffee production, a weak positive
correlation was found out(r=0.344*), while p=0.015<0.05, means that provision and
availability of extension services brought about an increase in coffee yields though to a
small extent. This was attributed to the fact that some of the farmers never followed
properly the methods of coffee farming that were taught to them.
27
On the other hand p=0.015<0.05, this implied that the variable was positively
significant, hence we reject the null hypothesis and conclude that there is a significant
relationship between extension services and growth in coffee production.
Similarly, there was a positive weak correlation between availability of infrastructure
with increase in coffee production (r=0.261*), while p=0.044<0.05, implying that
availability and accessibility of infrastructure boosts coffee production to a smaller
extent. This was partly because some roads were affected by weather especially in a
rainy season.
On the other hand p=0.044<0.05, this implied that the variable was positively
significant, hence we reject the null hypothesis and conclude that infrastructure
contributes to growth in coffee production.
The findings indicated that there was no statistical relationship between access to
market information and increase in coffee production in terms of yields (r=-0.108*),
while p=0.412>0.05 implies that access to market informant does not necessarily boost
coffee production.
A weak negative correlation was identified -0.108, this implied that access to market
information, had no influence to coffee production. The variable was insignificant and
hence we accept the null hypothesis since (p=0.412>0.05) indicates clearly the
insignificance of the variable.
28
Table 3.16: Correlation between independent factors and Coffee production (yields)
Correlations
Fertilizer usage
Modern coffee
seedlings
Extension services
Infrastructure
Market information
Increase in Coffee
production(yields)
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
Fertilizer
usage
1.000
0.000
60
0.021
0.875
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
60
-0.017
0.898
60
-0.145
0.27
60
-0.167
0.201
60
0.805**
0.001
60
Modern
coffee
seedlings
Extension
services
Market
Increase in Coffee
Infrastructure information production(yields)
1.000
0.000
60
0.039
0.768
60
-0.067
0.613
60
0.087
0.508
60
0.344*
0.018
60
1.000
0.000
60
0.039
0.768
60
0.086
0.594
60
0.344*
0.015
60
1.000
0.000
60
0.084
0.606
60
0.261*
0.044
60
**. Correlation is significant at the 0.01 level (2-tailed) *.Correlation is significant at the 0.05 level (2-tailed).
29
1.000
0.000
60
-0.108
0.412
60
1.000
0.000
60
3.8 Results from multivariate analysis
The findings presented below are from logistic regression model predicting the odds of
determinants of coffee production in terms of yields.
Table 3.17: Regression of the independent variables against the dependent
variable
Determinants
of
coffee Odds
production(yields)
[95%
Conf.
Ratio
Std. Err.
p-value
Interval]
1.37
0.22
0.05**
1.02
1.99
1.658
0.224
0.000**
0.151
2.546
1.440
0.191
0.006**
0.457
4.760
1.374
0.220
0.048**
0.188
1.716
0.570
0.397
0.417
0.134
2.260
Fertilizer usage
No(RC)
Yes
Modern coffee seedling provision
No(RC)
Yes
Agricultural extension services
No(RC)
Yes
Availability & accessibility to
infrastructure
No(RC)
Yes
Access to market information
No(RC)
Yes
** Statistically significant (p<0.05)
At multivariate analysis, the effect of government funding on coffee production were
studied using binary logistic regression model and the results in table 3.17 show
that
coffee farmers who used fertilizers were 1.37 times more likely to have an increased
production in terms of yields unlike their counterparts who never applied fertilizers
(reference category). The application of fertilizers and increase in yields among coffee
farmers was statistically significant (p=0.05**) at 95% confidence interval. The
fertilizers that were used included NPK, UPK, urea and organic manure.
30
In the same vein, coffee farmers that planted modern coffee seedlings were 1.65 times
more likely to have increased coffee yields than their counterparts who never planted
improved coffee species like Kituza R1, R2, R3, R4, R5, R6 and R7. The P value of
0.000 indicates a strong statistical relationship.
For coffee farmers who received extension services, they were more likely to have an
increased coffee yields (OR=1.440) than those who never received such services. This
is confirmed by the level of significance (p=0.006) which is less than the critical value
of 0.05 at 95% confidence interval. It is significant when the roads are goods, farmers
altitude is busted and in the end according to OR -1.4 then the coffee production will be
increased by 1.4 times and more to this good road attract more buyers to access the
farmer so that every buyer produced is sold out indeed the P-value =0.043 indicated
that good infrastructure contribute positive to the (GDP contribution).
Indeed it was only access to market information that was not statistically significant
(p=0.417>p=0.05) in terms of increase in coffee yields after performing a logistic
regression.
31
SECTION FOUR: SUMMARY OF FINDINGS
In this chapter, the conclusions and recommendations of the study are presented. The
summary presents the short presentation of the findings, conclusions involve a
summary of the most significant issues found out in the study and their discussion and
recommendations are proposed as priority actions that could be undertaken by
government of Uganda and other stakeholders to boost coffee production in Uganda.
4.2 Summary of findings
Results from the background characteristics indicated that majority of the coffee
farmers who responded to the study were males (70%) and few were females (30%).
Similarly, it was found out that majority of the respondents were married (53.3%)
followed by those who were single (26.7%).
Majority of the respondents had attained secondary education (38.3%) and majority had
also been in coffee farming for over 20 years (30%). Farmers had access to extension
services and had benefited from a number of trainings like coffee spacing, pruning,
coffee picking, and drying and nursery bed preparation.
Coffee farmers were reported to be using fertilizers like NPK, UPK, Urea and organic
manure. The latter is locally made from the decomposed wastes and the effectiveness of
every fertilizer depended on the number of times a certain type of fertilizer was applied
though some were reported to have been applied once a year or even more than that.
Coffee farmers had access to modern seedlings and farmers had benefited through
increased output seedlings that are resistant to wilt and quick maturity. Infrastructural
development had improved although 23.7% of the respondents reported that they had
poor roads and hardly passable.
Study findings confirmed that fertilizer application, modern coffee seedlings, extension
services and good infrastructure were significant variables in that coffee yields
increased, while market information was insignificant since it had no or negative
impact on the quality of coffee produces by farmers.
32
4.3 Conclusion
Coffee is one of the Uganda main exports, implies that concerted efforts are needed
to keep the performance high and this is possible through provision of fertilizer to
farmers by government, use of improved coffee seedlings, availability of extension
service like modern farming methods, infrastructural development like constructing
roads. These innovations are even possible in situations where majority of the coffee
farmers are farming on a small scale as the case of Nabweru and Nakasongola.
4.4 Recommendations
Basing on the study findings and analysis, the study has a number of recommendations
that have implications on policy design and implementation related to coffee farming
and its productivity and they include the following;
There is need for involvement of all stakeholders in enacting the national policy on
fertilizers. This policy will specifically formalize fertilizer trade, create awareness of
fertilizer use, create a private sector driven fertilizer market system, and promote
optimal use of organic and inorganic fertilizers, supporting domestic fertilizer
production as well as establishing a regulatory and monitoring systems of fertilizer
products.
Training, sensitisation and mobilisation of coffee farmers should be encouraged and
should continue at all times since it forms the foundation for economic growth in
Uganda. This is a form of extension services which should emphasize specifically care
for coffee plantations as well as coffee drying and processing.
Material supply by the government of Uganda especially modern coffee seedlings,
fertilizers and herbicides should be timely and match the planting and harvesting season
to avoid mismatch and resource wastage and be distributed to all farmers. This will
enable farmers to grow on time and be able to harvest high output.
33
4.5 Areas for further studies
Since the study only focussed on coffee farmers in Nabweru and Nansana sub-counties
in Wakiso district, the small sample could have determined much the outcomes which
led to some insignificant effects of government funding on coffee production. To that
end therefore, a comprehensive study perhaps covering all coffee farmers in Uganda is
recommended so that it could give a national picture of the impact of government
funding on coffee production.
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Forshaw, M (2000). Your Undergraduate Psychology Project: A BPS Guide Blackwell
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34
Sekaran, U., (2000). Research Methods for Business. USA: Hemttage Publishing
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