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DRAFT- NOT FOR CITATION
FARM LEVEL COTTON PRODUCTION
CONSTRAINTS IN UGANDA
Rhona Walusimbi
A Contribution to the Strategic Criteria for Rural Investments in Productivity
(SCRIP) Program of the USAID Uganda Mission
The International Food Policy Research Institute (IFPRI)
2033 K Street, N.W. Washington, D.C. 20006
July 2002
Strategic Criteria for Rural Investments in Productivity (SCRIP) is a USAID-
funded program in Uganda implemented by the International Food Policy Research Institute (IFPRI) in collaboration with Makerere University Faculty of Agriculture and Institute for Environment and Natural Resources. The key objective is to provide spatially-explicit strategic assessments of sustainable rural livelihood and land use options for Uganda, taking account of geographical and household factors such as asset endowments, human capacity, institutions, infrastructure, technology, markets & trade, and natural resources (ecosystem goods and services). It is the hope that this information will help improve the quality of policies and investment programs for the sustainable development of rural areas in Uganda. SCRIP builds in part on the IFPRI project Policies for Improved Land Management in Uganda (1999-2002). SCRIP started in March 2001 and is scheduled to run until 2006. The origin of SCRIP lies in a challenge that the USAID Uganda Mission set itself in designing a new strategic objective (SO) targeted at increasing rural incomes. The Expanded Sustainable Economic Opportunities for Rural Sector Growth strategic objective will be implemented over the period 2002-2007. This new SO is a combination of previously separate strategies and country programs on enhancing agricultural productivity, market and trade development, and improved environmental management. Contact in Kampala Contact in Washington, D.C. Simon Bolwig and Ephraim Nkonya Stanley Wood, Project Leader IFPRI, 18 K.A.R. Drive, Lower Kololo IFPRI, 2033 K Street, NW, P.O. Box 28565, Kampala Washington, D.C. 20006-1002, USA Phone: 041-234-613 or 077-591-508 Phone: 1-202-862-5600 Email: [email protected] Email: [email protected]
INTRODUCTION
Cotton is an annual crop that is produced commercially in over 80 countries in the world
located in the tropics and temperate climate zones. (Lundbaek, 2001). It is one of the
most important internationally traded agricultural commodities in terms of volume and
value traded (Serunjogi et al., 2001). Its main commercial uses are in manufacture of
textile and garment, edible oil, soap and livestock feeds.
In Uganda cotton is produced in all regions of the country, however most of the
production is concentrated in the Northern and Eastern regions. Total number of cotton
producers in 2000 was approximately 300, 000- 400, 000. (Gordon and Goodland, 2000).
Cotton is a labour intensive crop especially at weeding, pesticide application and
harvesting stages. Animal traction introduced into the country in the early 1900’s was
widely adopted by cotton farmers in the main cotton producing areas of the country
mainly for land opening. However, the use of this technology was severely curtailed by
cattle rustling that affected many districts in the northern and eastern regions during the
insurgence of the late 1980’s. The use of tractors by small-scale farmers in general
remains very limited, mainly because the farmers cannot afford the technology (APSEC,
2001).
Cotton was introduced as a cash crop in Uganda in 1903 and became its major export
until the 1950’s when it was surpassed by coffee (Serunjogi et al, 2001). At its peak
production of 465, 000 bales of lint in 1969/70, cotton contributed to about 40 percent of
foreign exchange earnings. The industry experienced a dramatic decline in terms of both
acreage and yield from the mid 70’s to the late 80s during which time the country
experienced political and economic turmoil. By 1987/88, production had hit an all time
low of 11,000 bales. The textile and garment industry and other value addition industries
also suffered a similar fate. The causes of decline of farm level production included lack
of credit and farming inputs, lack of extension services, a poor marketing system and a
run down and inefficient ginning system (LMC International, 2002 and Serunjogi et al.
2001)
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These constraints have been addressed by the economic reform programs implemented
in the country from 1987 and significant improvements in production and rural and
foreign exchange incomes have been achieved. However, these gains remain much
below potential levels. Recent studies aimed at identifying strategies to boost Uganda’s
cotton industry, indicate that most of Uganda’s soils and climate are well suited for cotton
production (with the crop having the potential to do well even in marginal areas), yields
of up to five times the current average farm yields can be achieved. Also Uganda’s cotton
is of a high quality and therefore has an assured international market. According to
COMPETE (2002), Uganda’s annual production for the 2000/2001-crop year (100,000
bales) represented only 0.001 percent of world production.
A number of studies have identified several causes of the current low and stagnating
production. Farm level constraints cited include high dependence on hand-hoe
production, limited availability of some key inputs, limited access to credit, ineffective
extension services, and land fragmentation and low producer prices (CDO, 2002). Value
addition constraints include limited access to credit, poor post-harvest quality control,
underutilized and technologically aging ginneries and manufacturing plants, and low
labor productivity (LMC International Ltd., 2002).
Another reason commonly put forward to explain less than expected growth of the
cotton production in Uganda is its low profitability relative to competing crops, which
include beans, maize, cassava, finger millet, sorghum, simsim and soybeans. Studies done
by the APSEC in 1994 and 1998, revealed that, cotton ranked lowest in profitability
compared with its main competing crops in these years.
JUSTIFICATION AND OBJECTIVES OF THE STUDY
Due its high potential as a foreign exchange and poverty alleviation crop, the
Government of Uganda has placed high emphasis on formulation and implementation of
strategies aimed at increasing productivity and profitability of cotton production.
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(MFPED, 2001, COMPETE, 2001 and CDO, 2001). This paper investigates underlying
causes of farm level cotton production constraints. Specifically it analyzes the
relationship between socioeconomic characteristics of cotton farmers and adoption of
cotton production technologies and assesses profitability of cotton production. The paper
is expected to provide information to policy makers and other stakeholders of agricultural
development and poverty reduction in Uganda.
The remainder of the paper is organized as follows. First a review of factors affecting
adoption of cotton production technologies and of the recent cotton sub sector recovery
programs with an emphasis on crop production technologies is made then the
methodology used in collecting and analyzing the data used in the study is described.
Next, the results obtained are presented and discussed and finally conclusions of the
study are made and policy implications discussed.
LITERATURE REVIEW
The main interventions to revive Uganda’s cotton-sub sector have been part and parcel of
the economic reforms that were implemented in the country from 1987. Government’s
strategy is to revive cotton production and exports through increased competition in
cotton processing and marketing and improved support services.
From 1993 to 1995, the Small Holder Cotton Rehabilitation Project (SCRP), aimed at
strengthening the national cotton-breeding program in order to improve cotton planting
seed quality and its multiplication and to promote the greater use of animal traction was
implemented.
The Cotton Sub-sector Development Project (CSDP) designed to support and build on the
activities of SCRP and other on-going agric sector development programmes was
implemented in 1995 and closed in 2001. Under CSDP, cotton processing and export
marketing were liberalized; a regulatory and promotion body named the Cotton
Development Organization (CDO) was established. The project also implemented
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activities aimed at improving the managerial, technical and operating efficiency in a
creditworthy ginning industry, improving the efficiency and impact of supporting
services through support for national research and extension programmes and improving
the delivery mechanisms and availability of cotton planting seed and production credit.
The project envisaged increases in cotton production through area expansion and yield
increases.
According to the impact assessment report of the CSDP by APSEC (2001), a number of
improved production technologies including seed, agronomic practices, pest management
(IPM) and ox drawn implements (ploughs, planters, seeders and weeders) were generated
under the project. However, some of the agronomic and integrated pest management
technologies have yet to be transferred to farmers and the improved ox-drawn
implements are not yet available commercially (APSEC 2001). The project also
supported the national livestock breeding programme with an aim of production of oxen
for training and sale to farmers, implemented a capacity building programme for micro-
finance institutions and a short term in-kind seeds, pesticides and spray pumps credit
programme. The credit programme implemented by CDO and the Uganda Ginners and
Cotton Exporters Association (UGCEA) provided seeds and pesticides and spray pumps
to farmers on credit at the beginning of the growing season from the 1998/99 seasons.
Recovery was made by ginners at the time of cotton harvest sales. The pesticides program
was however stopped during the 2001/2002-cotton season due to loan recovery problems
caused mainly by avoidance of payment by farmers (Lundbaek, 2002). The likely
reasons for farmers reluctance to repay pesticides credit are that some farmers may have
received pesticides late or not at all, they may have considered their ginners pesticides to
be too expensive, or they may have been defaulting on their sales contracts in order to
obtain higher prices from other buyers.
Several studies report that the cotton sub sector revival programs have resulted into
increased production. Total annual lint production rose from 33,000 bales in 1994/95 to
110,000 bales in 1996/97 mostly due to increase in area planted rather than increase in
yields. (Serunjogi et al., 2001). APSEC, 2002 reports that there have been no clear
5
upward trends in yields since the onset of the cotton sub sector recovery programmes.
Current yields average 300- 400 kg per hectare. This is way below potential on-farm
yields of 1000 kg/ha. Annual production has stagnated at around 100,000 bales from
1997/98 to 2000/01. (CDO, undated). Reasons for this occurrence include continued high
dependence on hand-hoe production, limited availability of some key inputs, limited
access to production credit, ineffective extension services, land fragmentation, declining
soil fertility, insecurity, low producer prices and adverse weather occurrences. (CDO,
2002 and APSEC, 2001).
According to Nanyeenya et al., 1999, the decision for farmers to adopt technologies is
driven by their needs and circumstances that may be technical, social, economic and
agro-ecological in nature. They further add that awareness, of the profitability or
potential benefits of new technologies is necessary to trigger the diffusion of an
agricultural innovation and point out that extension visits, attendance at on farm
demonstrations and exposure to mass media, literacy, level of education and time spent
outside one’s village are some proxies for awareness of new innovations. They stress
however that for the adoption process to be sustained, the new technology must be
compatible with farmers economic resources and supported by institutions responsible for
providing inputs and technical advice.
Many studies on adoption of agricultural technologies have identified that family size,
education of household head, farm size, land tenure status, participation in on-farm
demonstrations, farmer perceptions of technology specific characteristics, non-farm
employment, access to extension and credit are hypothesized to be positively related to
the decision to adopt agricultural technologies.
6
METHODOLOGY
Data
The data used in this study was obtained from a household and plot surveys that were
carried out by the International Food Policy Research Institute (IFPRI) in December 2000
to June 2001. A total of 451 Households were randomly selected from the central,
eastern, northern and western regions of Uganda. From each sampled household, a plot
level survey was conducted to determine the farm management practices of each plot.
The households were sampled from 100 communities, which were selected using a
stratified random sample of communities representing different development pathways
(livelihood strategies). Other communities were purposively selected from Kabale and
Iganga districts where highland Initiative (AHI) and the International Center for Tropical
Agriculture (CIAT) are conducting research.
This study focuses on households that grew cotton as one of their crops (cotton farmers)
in 2000/2001. The sample size was 38 households. Seventy percent were from the
Eastern region, 19 percent from the Northern region and 11 percent from the Central
region. As far as possible, the results from the cotton households are compared with
corresponding results from households that did not grow cotton (non–cotton farmers),
total sample size being 413.
Analysis
Analysis of data was done in three stages the first being a descriptive analysis, the second
analysis of farm-level profitability of cotton and the third, an econometric analysis of
determinants of adoption of selected cotton production technologies.
The descriptive analysis reports on simple descriptive statistics of key socio-economic
factors that influence adoption of improved cotton production technologies.
7
Goss Margin and Returns to Family Labour were used to measure the profitability of
cotton relative to its competing crops. The two measures are calculated as follows:
Gross Margin (Shs/ha) = Gross Value of Output - (Total Material Inputs Costs+
Hired Labour Costs)
Returns to Family Labour (Shs/Md) = Gross Margin Family Labour
Family labour is labour provided by members of the household during a particular
season. Rural labour is normally measured in man-days (person-days) and is equivalent
to the number of hours worked per day. Six hours of work was considered to be a man-
day for adult workers and 3 hours for children between the age of 12 and 16. The crops
considered in this analysis were cotton, beans, maize, cassava, millet and sorghum.
The econometric analysis seeks to determine the factors that influence adoption of the
specified cotton production technologies. The cotton production technologies analyzed in
this study are pesticides, inorganic fertilizer, and the use animal labour for land opening
(ox ploughing). For pesticide and fertilizer, use in second season, was considered as it the
main growing season for cotton. The analytical models used were the probit and the
generalized leased squares (GLS).
Probit regressions have been widely used in studies where the dependent variable is
dichotomous (1= if the farmer adopts, 0 = otherwise). For purposes of this study, probit
model regression was used for pesticides and fertilizer adoption analysis. For the third
analysis involving the use of ox-ploughing by cotton farmers, a Generalized Least
Squares (GLS) was used since the independent variable is continuous i.e. number of
oxen-hours. The GLS procedure gave better results than the OLS procedure. The GLS
procedure is also preferred since it gives robust estimates in the presence of
heteroskedacticity and autocorrelation.
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The variables used in the econometric analysis are drawn from those identified in
literature as having an influence on adoption of crop production technologies. Those
selected were family size, age of household head, farm size, wealth indicators, animal
labour, and access to credit, markets and extension.
The probit models estimated in this study are specified as follows:
Probability to adopt cotton production technologies =f(BX+ ei)
Where B is a vector of coefficients associated with a vector of factors (X) that explain the
change in probability to adopt cotton production technologies.
The Probit and GLS models use cross sectional data, which is normally susceptible to the
heteroskedasticity problem. The Huber-White estimator for standard errors, which is
robust to heteroskedasticity, was used to correct for this violation of the classical
assumption. Multicollinearity was tested using the variance inflation factors (VIF). The
maximum VIF was less than 3 indicating that multicollininearity was not a serious
problem. (Mukherjee, et al., 1998) The functional forms of the independent variables
were determined using the Exploratory Band Regression (EBR), which showed that all
the independent variables were linearly related to the dependent variable as hypothesized.
One of the model estimation limitations was the small sample size, which greatly reduces
the degrees of freedom as more explanatory variables become included in the model.
Hence, many of the conventional factors given in the adoption literature may not have
been included in the model. Omitted variable bias was used to determine its seriousness.
All 3 models passed the regression specification error test indicating that the models were
not faced by the omitted variable bias problem.
The independent variables used in the analyses are defined in Table 1 and working
hypotheses used are described thereafter.
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Table 1: Definition of the explanatory variables used in the econometric analysis
Variable Definition Expected
sign
Family Family size which is the total number of persons in the
household
+
Agehhd Age of the household head measured in years + or -
Farm size measured in acres + Farmsize
Percow Ownership of cattle, measured as a binary variable: 1 if the
farmer owned cattle, 0 otherwise.
+
Oxenhours Animal labour for land opening. Measured in terms of
number of oxen-hours per household in 2000
+
Exthrs Contact with extension agents. Measured as total duration of
extension visits per household in 2000.
+
Kmmkt Market access measured as distance from the farmer’s land
to the nearest output market in km.
-
Nfapplyre
c
Access to credit measured as % of farmers who received
informal credit
+
Kmres Distance from land under crop to the farmer’s residence in
km.
-
Gross Profitability of cotton production measured as net revenue in
Shs/ha
+
Family size is an indicator of labour availability. A larger family size is likely to increase
the probability to adopt cotton production technologies, which are labor intensive in
nature. Farm size has been found to have a positive influence on adoption decisions.
(Feder et al., 1993 and Adesina et al., 1993).
The age of household head (hypothesized to be the key decision maker in a household)
can increase or decrease the probability to adopt crop production technologies. It can be
argued that younger farmers are likely to be more educated and less risk averse due to
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their longer planning horizons and are therefore more likely to have a higher rate of
adoption of crop technologies. Conversely older farmers are likely to have more
experience and higher financial resources to obtain procured inputs.
Ownership of cattle is an indicator of wealth among the people of the main cotton
producing areas in Uganda and is a source of oxen and is likely to have a positive
association with adoption of cotton technologies. The use of animal labour reduces
labour bottlenecks and facilitates increased land opening. It is hypothesized to be
positively associated with the decision to adopt cotton production technologies.
Contact with extension agents will increase the probability of farmers to adopt cotton
production technologies as the contacts increase farmers’ access to information.
Distance to nearest output market is expected to be negatively associated with the
decision to adopt improved cotton production technologies: longer distances to output
markets, which implies declining market access, will reduce farmers’ incentives to adopt
the technologies. Distance of land parcels to residence is expected to be negatively
associated with adoption of cotton production technologies. It is hypothesized that, plots
that are further away from the household are less likely to receive new technologies. This
is because farmers are better able to monitor and supervise innovations on plots that are
closer to their homesteads. Also, costs of transporting bulky technologies to plots further
away from homestead may favour adoption on nearby plots.
Access to credit1 and increasing profitability are also hypothesized to be positively
related to adoption of crop technologies.
1 The percentage of respondents who applied and received informal credit was used as an indicator of market access. The use of informal credit was considered to be more suitable as the levels of access to formal credit were found to be very low among respondents.
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Limitations
The study involved a small sample size (only 38 respondents). This introduces
econometric estimation problems due to limited degrees of freedom. Major cotton
production districts in the North and West Nile regions of the country were not included
in the study because of insecurity. Also, the study utilized cross-sectional data, which
may not give conclusive evidence of cotton production trends. With regard to
profitability analysis, farmers’ lack of record and their tendency to overstate labour costs
(APSEC, 2001) may affect accuracy of results obtained.
RESULTS AND DISCUSSION
Descriptive Statistics
This part of the study reports the socioeconomic characteristics of the sampled cotton-
growing households (cotton farmers) and compares them with those of the non-cotton
growing households (non–cotton farmers). The results are summarized in Tables 2-12, in
Appendix 1 of this report and are discussed in detail below.
Adoption of technologies
Table 2 reports the use of fertilizer, ox ploughing and pesticides among the sampled
cotton farmers. As reported in the literature review section of this study, these
technologies form part of the recommended technology package for cotton production in
Uganda.
Only 5.41 percent of the respondents used inorganic fertilizer. This supports the findings
of other studies (NARO and FAO, 1999 and Nkonya and Kaiizi, 2001), which report low
use levels of this technology among smallholder farmers (less than 10 percent). The low
use of inorganic fertilizer contributes to soil nutrient depletion. The Eastern region of
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Uganda where much of cotton production in the country (and 70 percent of respondents
of this study) is concentrated has been hard hit by soil fertility problems (CDO, 2001and
APSEC, 2001).
Only 35.14 percent of the respondents used pesticides for cotton production. This result
supports findings by APSEC, 2001 that although the pesticides credit system
implemented by CDO and UGCEA greatly improved farmers’ access to pesticides for use
in cotton production, and some farmers did not use them because of late delivery,
diversion to other crops or outright resale of the pesticides. These findings imply a need
to improve the current pesticide supply systems. Sixty three percent of cotton farmers
used oxen for land opening (ox-ploughing) as compared to 24 percent of the non-cotton
farmers
The use of all three technologies was significantly higher (at 10 percent level)) among the
cotton farmers. For pesticides this is most likely due to cotton’s high sensitivity to pests
and hence higher use for this crop and also a result of better input supply systems for
pesticides and access to extension (see Table 5) in the cotton growing areas. For
fertilizers, a likely explanation is that promotion of fertilizer use is higher in the cotton
growing areas. For ox ploughing the result obtained was expected since the practice is
still more widespread in the cotton farming systems (Odogola, 2001). The results also
support the findings of APSEC, 2001 that the availability of ox ploughs from local and
imported sources greatly improved between 1994 and 2001. The availability of oxen has
also improved in this period. (Odogola, 2001).
Yields
Average cotton yields among the sampled cotton farmers were 430 kg per hectare. This
yield does not vary much from World Bank (2000) findings of 300-400 kg per hectare for
this year. Table 3 reports cotton yields with the use of ox ploughing and spraying and
with the use of hand-hoe with no spraying. As expected, the yield with the use ox
ploughing and pesticides is significantly higher than without.
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Farm size and market access
Table 4 reports on average farm size and average distance of each respondents individual
land parcel from the nearest output market and seasonal roads and compares these
parameters with those of non-cotton farmers. The average farm size for the cotton
farmers was 3.2ha and 2.3 ha for non-cotton farmers indicating that both categories were
smallholder farmers, which is predominant in Uganda. For cotton production, small farm
sizes are a disadvantage as competition for land with foods crops is likely to limit
increased output.
Average distance of each respondents parcels to the nearest seasonal road and to the
nearest output market were used as indicators of market access. For cotton farmers the
average distance of parcels to the nearest market was 4.3 km while that for non-cotton
farmers were 5.0 km. Average distance to nearest seasonal road was 1.5 km for cotton
farmers and 1.2 km for the non-cotton farmers. The results were not significantly
different between the 2 groups. The distances are quite long given the predominant
modes of transport to market used in rural areas mainly head poterage and bicycle.
Access to Agricultural Training and Extension Services2
Table 5 reports on participation in agricultural training and skills acquired from the
training from 1990 to 2000. Only 47.2 of the cotton farmers and only 45.4 of the non–
cotton farmers received training in this period. For the cotton farmers, the main skills
acquired were on crop agronomy, soil and water conservation, plant protection and soil
fertility management, micro-finance and agro-forestry. Only 1.0 percent of the
respondents received training in agricultural marketing and none received training in post
harvest management, which is reported as a constraint to increased cotton production.
(COMPETE, 2001 and APSEC, 2001) More members of non-cotton growing households
2 Agricultural training refers to a specific (topical) learning process organized a class-like format involving trainer(s) and many participants and usually takes a long duration per contact.
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acquired agricultural training. The main areas of training reported for this group were
crop agronomy, soil and water conservation, soil fertility management and post harvest
management.
Table 6 reports the respondents’ access to agricultural extension services and on change
in the services since 1990. Only 39.4 percent of the cotton farmers received extension
visits in 2000. The situation was worse among the non-cotton farmers with 29.6 percent
reporting to have received extension visits in the same year. This finding can be
explained by less active extension services among non-cotton farmers due to lower
availability of funding for extension in non-cotton growing districts in the last few years.
About 26.5 percent of the respondents reported that there had been a change in the
amount of extension contact and out of these, over 50 percent reported that there had
been a slight increase in amount of extension services received since 1990.
Respondents were also asked whether there had been a change in the subject of training
offered by extension since 1990 and the nature of the change if any (Table 7). Only about
26.3 percent of the cotton farmers reported a change and the most reported nature of
change for them was increased emphasis on new varieties or crop types and crop disease
control. Approximately twenty seven percent of non-cotton farmers reported a change
and the most reported change was increased education on soil and water conservation and
crop agronomy.
Access to credit
Table 8 reports on percentage of cotton and non-cotton farmers who applied and received
formal and informal credit in 1990 and 2000.3 In both groups of farmers, while there was
a slight increase in rate of application for formal credit between the two years, the
application rates were very low in both years (below 12 percent for cotton farmers and
below 16 percent or non-cotton farmers). The rate of application for informal loans in
3 Formal credit sources include banks, NGOs and other programs. Informal credit sources included traders, mo56ney lenders rotating savings and credit associations, intermediaries, friends and relatives)
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both groups was much higher increasing from 60.0 percent in 1990 to 67.7 percent in
2000 for cotton farmers and 50.2 percent in 1990 to 72.0 percent in 2000 for non-cotton
farmers.
Almost all the cotton farmers (over 93 percent) who applied for informal credit in 1990
and 2000 received it. This proportion was higher among non-cotton farmers (over 97
percent). For formal credit the figures are somewhat lower for both groups.
The results support other studies, which0 observe that in Uganda, formal credit is
generally much less accessible to smallholder crop farmers than informal credit.
Demographic factors and Education levels
Table 9 reports household demographic and education characteristics that are
hypothesized to influence technology adoption. Average family size was 11.6 for cotton
farmers and 10.1 for the non-cotton farmers. The average number of family members
who can contribute to on-farm labor (age 12 and above) was about 6 in both groups of
farmers.
The average age of the household head and their spouses were about 44years and 36
years respectively for cotton farmers and 44 years and 36 years respectively for non-
cotton farmers. There were no significant differences in the two groups for these
variables.
Most household head and their spouses in both groups of farmers were educated up to
primary level only (Table 10). There were no significant differences in family size and
education levels for the cotton and non-cotton farmers
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Non-farm Activities
Non-farm activities are hypothesized to positively affect the decision to adopt crop
production technologies because they are important sources of income and would thus
increase farmer’s access to desired technologies. Tables 11 and 12 show that both cotton
and non-cotton farmers were engaged in non-farm activities (cottage industries and non-
agricultural trade) only to a limited extent.
Profitability Analysis
Table 13 in Appendix 2 shows gross margins in shillings per hectare (Shs/ha) and returns
to family labor in shillings per man-day (Shs/md) for cotton and its main competing
crops. The gross margin results show that in 2000, cotton was less profitable than most of
its competing crops. This can be attributed labour costs that tend to be higher for cotton
than its competing crops, low yields and low producer prices due to low world prices for
cotton in recent years. The finding that maize was less profitable than cotton is
surprising. It can be attributed to the deep decline in maize prices from 2000.
Similar findings on profitability of cotton relative to its competing crops cotton are
reported by APSEC, 1994 and APSEC 1997. Review of literature by (Serunjogi et al.,
2001), Lundbaek 2002 and APSEC, 2001 reveal about four main explanations for
farmers’ continued participation in cotton production in Uganda despite its low
profitability relative to other crops. First, cotton is increasingly becoming a more certain
source of cash than its competing crops. This is because food crops face larger price
variations relative to cotton, which has well defined and increasingly competitive
markets. Second, cotton cash payment comes at a convenient time when there are many
cash expenditures (Christmas, school fees, taxes etc.) Third, in smallholder integrated
farming systems, cotton is a good land opening crop for crops grown after it and fourth;
there may be no viable cash crop alternatives in some areas of the country.
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The results on returns to family labour show that cotton has higher returns to family
labour than it main competing crops. The explanation for this finding is that cotton
farmers depend more on hired labour because of the labour intensive nature of cotton
production.
Econometric Estimation
The results of the probit and the GLS models on factors affecting adoption of pesticides,
fertilizer and ox ploughing are summarized in Tables 14 to 16 in Appendix 3.
Ox ploughing, market access, farm size, family size and age of household head are the
key factors that influence the adoption of pesticide use among cotton farmers (Table14).
As expected, increases in use of ox-ploughing, and family size are likely to increase the
farmers’ probability to adopt pesticide use. Increase in age of household head influences
this probability negatively. The explanation for this result is that younger farmers are
likely to be more educated and less risk averse and hence are more likely to have a higher
rate of adoption. The results show that an increase in farm size and increased market
access are likely to reduce the probability to adopt pesticide use. These results are
surprising as the reverse was expected. A possible explanation for the result on market
access is that with increased market access cotton farmers are likely to divert use of
material inputs to crops with higher market demand.
According to the results, access to credit and extension and distance of parcels to
residence, wealth (ownership of cattle) and profitability do not significantly influence
adoption of pesticide use among cotton farmers.
Market access, ox ploughing and age of household head are the factors that significantly
influence adoption of fertilizer. Age of household head is positively associated with the
probability to adopt fertilizer while use of ox ploughing and market access are negatively
associated. A feasible explanation for the result on ox ploughing is that farmers who use
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oxen will benefit from organic manure supply from the oxen and will therefore have a
lower need for using inorganic fertilizer. Older farmers are more likely to adopt
fertilizers, as they are more likely to be in a better position to buy purchased inputs
because they are more likely to have higher financial resources. Distances of farmland
from residence, farm size, and access to extension and profitability are not significantly
associated with adoption of inorganic fertilizer use.
Adoption of ox ploughing is positively associated with farm size, ownership of oxen and
market access while the reverse is true for family size. (Table 16) Smaller families are
more likely to reduce their labor constraints by employing animal power. Distances of
land parcels from residence, access to extension, age of household head and profitability
of cotton appear not to be significantly associated with adoption of ox ploughing.
CONCLUSIONS AND RECOMMENDATIONS
The study, which was based on data on farm level production of cotton and other crops in
2000, found that all of the cotton farmers were small-scale farmers with an average land
holding of 3.2 hectares. Small farm sizes are likely to limit increased cotton output due
competition with other farm enterprises. Average cotton yields among the farmers were
420 kg/ha, is much below potential on-farm yields of1000 kg/ha.
The study also found that there was low fertilizer and pesticides use among cotton
farmers. Only 5.4 percent used inorganic fertilizer and only 35.14 percent used
pesticides. Sixty three percent used animal draught power for tillage, which indicates a
recovery in use of oxen for tillage from the late 1980s when the use of the technology
was severely curtailed by cattle rustling.
Access to agricultural training and extension was also found to be low. Only 47 percent
of the cotton farmers reported to have received agricultural training between 1990 and
2000 and only 39.4 percent had contact with extension agents in 2000. The study made a
comparison with non-cotton farmers and found that their situation was similar and
19
implied low access to agricultural training and extension for Ugandan smallholder
farmers in general. Also, cotton farmers seem to have limited extension training in use
some important crop production practices for example, use of fertilizer and proper post
harvest management. The policy implication of these findings is the need for increased
availability and quality of extension services for farmers.
Involvement in non-farm income which is an important source of additional income for
farmers and would assist farmers to obtain desired technologies was found to be very low
for both cotton and non-cotton farmers. The study also found that access to formal loans
was much lower than access to informal loans. This points to the need to promote
suitable rural non-farm activities and to increase access to formal credit or explore viable
ways in which informal credit institutions can be used to channel credit to smallholder
farmers.
The analysis of profitability of cotton relative to its main competing crops (gross margin
analysis) revealed that in 2000 cotton was less profitable than most of its competing
crops. Available literature shows the same status in earlier years. Likely reasons why
farmers continue to grow cotton under such circumstances are its reliable market, good
timing of the cash income, its property of being a good land opening crop or lack of
alternative income sources. Not withstanding, cotton is likely to become more profitable
if the on-gong government led interventions to improve productivity are sustained.
Probit models were used to determine the factors that influence adoption of pesticides and
fertilizer. Use of oxen for tillage and family size positively influence adoption of
pesticide use. The policy implication for this finding is that labour saving technologies
for pesticide application should be developed and promoted and use of oxen for tillage
should continue to be promoted among cotton farmers.
Younger farmers are more likely to adopt pesticides as they are be more educated and are
less risk adverse. The descriptive analysis however showed that the cotton and non-
cotton farmers tended to be older (average age of 44 yrs) and most adult household
20
members were educated up to primary level only. This points to the need to increase
agricultural extension and training among smallholder farmers.
Increased market access and use of oxen for tillage appear to be positively associated
with the adoption of fertilizers. Also, older farmers are more likely to adopt fertilizer use.
The Generalized Leased Square analysis revealed that market access, farm size, family
size and ownership of oxen positively influence adoption of ox ploughing. The finding
on farm size points to the need to promote larger scale cotton production through group
farming by smallholders as is currently practiced in Kasese district.
Profitability and access to extension were found to be positively related to adoption of the
three technologies discussed above, however this relationship was not statistically
significant. Access to credit is hypothesized to have a positive influence on adoption of
agricultural technologies but this study found that it only influenced adoption of
pesticides but the finding was not statistically significant as well.
It must be reiterated that the policy recommendations of this study are based on a small
sample size and solely on cross sectional data. Further research based on a larger sample
size and covering all the main cotton growing districts and use time series analysis is
likely to strengthen the policy recommendations made.
21
APPENDIX 1 Table 2: Use of Selected Crop Production Technologies Among Cotton and Non- Cotton Farmers (2000)
% Households using: Cotton Farmers (N=38)
Non-Cotton Farmers (N=413)
Inorganic fertilizer 5.41 1.53 Pesticides 35.14 9.16 Ox-ploughing 63.16 24.70
P value =0.074
Notes (i) P value of the chi-statistic (χ2 ) comparing the percentages in the cotton and non-cotton farmers
Table 3: Cotton Yields (2000) Sample size Yield
(kg/ha)) Std dev.
Average national yield 38 430.9 Av. Yields with use of ox-ploughing + pesticides 6 639.7 Av. Yields with use of hand-hoe, without spray 25 129 Table 4: Farm size and distance from farm to nearest market and seasonal road Cotton Farmer (N=38) Non cotton Farmers (N=413) Paired T test* Av. farm size (ha) 3.2 (1.89) 2.5 (2.77) 0.68 Av. distance of farmers land to nearest output market (km)
4.9 (4.81)
5.0 (5.88)
0.90
Av. distance of farmers land to nearest seasonal Rd (km)
1.5 (5.56)
1.2 (4.25)
0.70
* Paired T-test is a statistical test comparing farm size and market access for cotton and non-cotton farmers. Figures in parentheses are standard deviations of the corresponding means
22
Table 5: Skills acquired from Agricultural training by members of sampled Cotton and Non-Cotton growing Households (1990-2000) Participation in agricultural training since 1990 % reporting Yes
Cotton farmers (N=38) 47.23
Non Cotton farmers (N=413) 45.48 p value =
Skills/ knowledge acquired Cotton Farmers (N=38) Non Cotton Farmers (N=414) % household members reporting Crop Agronomy 75.40 69.13 Plant protection 58.70 53.25 Animal health 16.25 52.86 Animal breeding 20 39.0 Range/ pasture management 20 36.90 Animal feed 0.0 52.23 Mechanization 0.0 0.0 Soil fertility management 35.83 62.73 Post harvest management 0.0 54.76 Micro finance 40 50.66 Soil &water conservation 72.5 62.16 Agro forestry 32.22 53.58 Agricultural Marketing 100 46.43 Agricultural processing 0.0 20.0 Cost of production 6.67 48.21 Livestock management 20 50.60 Horticulture 0.0 0.0 Rainfall 26.67 36.26 Rainfall 26.67 36.26 Note: The statistical test comparing percentages of respondents who acquired agric. skills among the cotton and non-cotton farmers showed that there were no significant differences. Table 6: Access of Households to Extension Services & Change Since 1990
Change in extension services since 1990 Amount of contact
Received extension visits in 2000 % yes
Av. No of extension visits (2000)
Av duration of extension visits in hrs. (2000)
Total duration of extension visits in hrs. (2000)
Changed % yes
Significant decrease
Slight decrease
Slight increase
Significant increase
Cotton Farmers (N=38)
39.4
4.2 ( 19.4)
5.2 (1.3)
13.2
26.32
9.09
0.00
54.55
27.27
Non Cotton Farmers (N=413)
29.85
2.0 (6.0)
0.34 (0.98)
3.4
27.25
2.56
8.55
56.41
19.66
Paired T-test
0.05 0.59
0.29 0.000 0.04 0.81 0.81
0.81 0.81
Paired T-test is a statistical test comparing access to extension services among cotton and non-cotton farmers. Figures in parentheses are standard deviations of the corresponding means.
23
Table 7: Nature of change in Extension Services between 1990 and 2000. Change Cotton Farmers (N=11) Non Cotton Farmers (N=106) % reporting change Increased education on SWC 1.1 10.37 Reduced emphasis on SWC 0.0 1.9 Increased emphasis on pest mgt 0.0 4.7 Increased emphasis on soil fertility mgt 0.0 3.78 Increased emphasis on crop agronomy 0.0 10.4 Increased emphasis on crop disease control 9.09 1.9 Increased emphasis on new varieties/ crop types
18.18 6.6
Shift between crops 18.18 8.5 Shift in emphasis from production of traditional cash crops/ livestock to other income generating activities
0.0
4.7
Increased emphasis on income generating activities
0.0 5.7
Increased frequency of visits by extension agents
9.1 47
Note: The statistical test comparing percentages in the cotton and non-cotton farmers showed that there were no significant differences. Table 8: Access to Credit in 1990 and 2000
Cotton Farmers
(N=38) Non Cotton Farmers (N=413)
Paired T-test*
% of households that applied for formal credit (1990) 0.00 4.94 0.26 % of households that applied & received formal credit (1990)
0.00
84.62.
0.36
% of households that applied for informal credit (1990)
60.00 50.17 0.86
% of households that applied & received informal credit (1990)
93.33
98.68
0.14
% of households that applied for formal credit (2000) 11.76 15.91 0.52 % of households that applied & received formal credit (2000)
75.00
79.00
0.46
% of households that for informal credit (2000)
67.65
71.97
0.59
% of households that applied & received informal credit (2000)
100
97.07 0.54
* Paired T-test is a statistical test comparing access to formal and informal credit among cotton and non-cotton farmers
24
Table 9: Demographic Characteristics of Cotton & Non-Cotton Farmers in 2000. Household member characteristics Cotton farmers (N=38) Non cotton farmers (N=413) Paired
T-Test* Av total family size 11.63 (5.8) 10.12 (5.05) 0.08 Av no. of children (>12 yrs) 6.47 (3.8) 6.13 (13.87) 0.61 Av age of household head 43.61 (13.66) 44.14 (13.45) 0.81 Av age of spouse 35.7 (10.38) 33.6 (12.02) 0.70 Paired T-test is a statistical test comparing family size and age of household heads and their spouses among cotton and non-cotton farmers. Figures in parentheses are standard deviations of the corresponding means.
Table10: Education Level of Household Head and Spouse among Cotton & Non-
Cotton Farmers in 2000.
Spouses Household head Education category Cotton farmers
(N=38) Non cotton farmers (N=413)
Cotton farmers (N=38)
Non cotton Farmers (N=413)
% reporting No schooling 8.95 5.17 3.4 2.7 Some primary 0.00 1.98 3.4 0.6 Completed primary
40.74 40.75 35.9 40.3
Some secondary 8.14 18.05 11.6 19.5 O level 16.51 8.52 24 14.9 A level 0.00 1.79 8.1 6.1 Graduate/Post graduate
0.00 0.48 4.0 1.5
College 0.00 0 0.4 Too young 0.00 0.31 0 0.6 P value* 0.50 0.52
*P- value of χ2 comparing education levels of spouses and household heads of cotton and non-cotton farmers.
25
Table 11: Main Primary Activities of Adult Household Members of sampled Cotton
And Non-Cotton Farmers (2000). Primary Activity Cotton farmers (N=38) Non cotton farmers (N=413) (Percentage reporting activity) Crop Production 81.25 70.50 Other agric. Activities* 2.08 2.81 Livestock 0.69 2.74 Non agric trade 2.08 2.74 Cottage Industries** 1.39 1.44 Farm wage employment 9.72 0.87 Student 11.4 11.54 House work 0.0 0.07 Others 2.78 5.12 * Includes fishing, agric. output processing, local beer brewing & agric. input/output trade ** Includes carpentry, craft, masonry, tailoring, photography, barber, shoe repair, bicycle & farm implement repairs/ maintenance. P value χ2 = 0.218 Table 12: Main Secondary Activities of Adult Household Members of sampled Cotton And Non-Cotton Farmers (2000). Secondary Activity Cotton Farmers (N=38) Non-Cotton Farmers (N=413) % reporting activity Crop production 7.76 17.50 Other agric activities * 4.31 6.59 Livestock 17.26 14.02 Non agric. Trade 3.45 6.02 Cottage industries ** 6.03 2.82 Farm wage employment 0.00 2.35 Student 0.00 0.38 House work 1.72 46.33 Others P value of χ2 = 0.007 * Includes fishing, agric. output processing, local beer brewing & agric. input/output trade ** Includes carpentry, craft, masonry, tailoring, photography, barber, shoe repair, bicycle & farm implement repairs/ maintenance.
26
APPENDIX 2 Table 13: Relative profitability of Cotton and Competing Crops (2000)
Crop Gross Margin (Shs/Ha)
Returns to family labour (Shs/Md)
Cotton 47,602 8,280 Beans 86, 466 608 Maize 46,831 608 Cassava 185,354 1,060 Millet 120,111 528 Sorghum 14,394 578
27
APPENDIX 3 Table 14: Determinants of Adoption of Pesticides among Cotton Farmers in Uganda
Factor Impact P Use of animal draught power + *** Distance of land parcel from residence + NS Distance to nearest market + ** Farm size - *** Access to credit + NS Family size + *** Access to Extension services + NS Age of household head - * Ownership of cattle + NS Profitability of cotton + NS
Notes: (i) P shows the significance of the impact of the associated factor. *, **and*** mean the impact is
significant at 10%, 5% and 1% level respectively. NS means that the impact is not significant, at least at 10% level.
(ii) + means that the impact of the associated factor is positive and –means the associated factor is
negative. Table 15: Determinants of Adoption of Fertilizers among Cotton Farmers in Uganda
Factor Impact P Distance of land parcel from residence - NS Distance of land parcel to nearest output market (market access) + ** Use of animal draught power - * Farm size + NS Access to extension services + NS Age of household head + ** Profitability of cotton + NS
Notes: (i) P shows the significance of the impact of the associated factor. *, **and*** mean the impact is
significant at 10%, 5% and 1% level respectively. NS means that the impact is not significant at least at 10% level.
(ii) + means that the impact of the associated factor is positive and –means the associated factor is
negative.
28
Table 16: Determinants of Use of Ox-Ploughing in Uganda
Factor Impact P Distance of land parcel from residence - NS Distance to nearest market - ** Farm size + *** Access to Extension services - NS Age of household head + NS Ownership of oxen + ** Family size - *** Profitability of cotton - NS
Notes: (i) P shows the significance of the impact of the associated factor. *, **and*** mean the impact is significant at 10%, 5% and 1% level respectively. NS means that the impact is not significant at least at 10% level.
(ii) + means that the impact of the associated factor is positive and – means the associated factor is
negative.
29
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