FASID
Development Database
2004-09-01
The 2003 REPEAT Survey in Uganda: Results
Takashi Yamano, Dick Sserunkuuma, Keijiro Otsuka,
George Omiat, John Herbert Ainembabazi,
and Yasuharu Shimamura
September 2004
Foundation for Advanced Studies on International Development
The 2003 REPEAT Survey in Uganda: Results
Takashi Yamano*, Dick Sserunkuuma**, Keijiro Otsuka*,
George Omiat**, John Herbert Ainembabazi**, and Yasuharu Shimamura*
September 2004
* Foundation for Advanced Studies on International Development, Japan
** Makerere University, Uganda
Acknowledgements
Support for this research project has been provided by the Ministry of Foreign Affairs
and the Japan International Cooperation Agency (JICA). The study was jointly carried
out in collaboration with the Foundation for Advanced Studies on International
Development (FASID) and Makerere University.
2
Executive Summary
The persistent poverty, land degradation, and low agricultural productivity problems are major
challenges to the government of Uganda and its development partners. FASID, in
collaboration with Makerere University, undertook research on poverty, environment, and
agricultural technologies (REPEAT) with the overall goal of identifying agricultural
technologies and farming systems that will contribute to increased agricultural productivity, the
sustainable use of natural resources, and reduced poverty in Uganda. The research findings
will be used to inform policy makers, development practitioners, and other stakeholders in
formulating and implementing policies and strategies in Uganda.
This report summarizes findings from the 2003 REPEAT Survey. The survey
is based on data collected from 940 households in 94 Local Council 1s that cover most
parts of Uganda, except the north regions where security problems exist.
The report identifies five major findings: First, the report identifies a national
poverty line at $119 per person per year and finds that about 52 percent of rural
households are living in poverty. The poverty profile found by the 2003 REPEAT
Survey will be the baseline profile for future rounds of the REPEAT surveys to measure
changes in poverty over time.
Second, the report finds that soil conservation practices are less practiced under
the Mailo tenure system than the freehold tenure system. Although this could be
because of insecure land rights among farmers under the Mailo tenure system, further
studies are required to explore the reasons behind the low investments under the Mailo
tenure system.
Third, the report indicates very low applications of fertilizer and other soil
conservative practices compared with neighboring countries, such as Kenya. Policies
should promote more fertilizer use (both chemical and organic) by encouraging the
development of inputs markets and by promoting more use of organic fertilizer.
Fourth, the report examines an intensive farming system, called the Organic
Green Revolution technology, which intensifies synergies between improved dairy
production and crop production via animal manure. The evidence shows that more
manure is applied on crops under the improved dairy production system since it is easy
to collect manure from cows that are kept in an isolated area. To help farmers shift
from the extensive farming system that relies on grazing to the intensive farming system
that relies on stall-feeding, policy makers and donors should help farmers obtain
improved dairy cows by providing in-kind credit, or help improve infrastructure to
transport milk from rural areas to urban cities.
3
Fifth, increased farm income may not be sufficient for the poor to escape from
poverty in the long-run. The report finds that, in addition to formal education,
different language skills are important to enter non-farm self-employment activities and
regular wage work. More research is needed to identify the specific skills required in
labor markets to use the education expenditure more efficiently.
Sixth, we find that orphaned young adults aged 15-18 are less likely to be in
school than non-orphaned young adults. Since secondary education is a key factor in
labor markets, the results indicate that orphaned young adults would be disadvantaged
in labor markets. Education among young adults is also important in preventing the
further spread of HIV in Uganda. Thus, it is important to provide help to orphaned
young adults to attain a sufficient level of education.
In sum, the findings in this report suggest some promising agricultural
technologies in Uganda. Over the next several years, the REPEAT project will re-visit
the sampled households and communities so that we can evaluate how some
technologies affect poverty.
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Table of Contexts:
1. Introduction
2. Data
3. Expenditure Profile and Poverty
3.1. Per capita “Cash” Expenditure
3.2. Total Expenditure
3.3. Measuring Poverty
3.4. Income Profile
4. Land Tenure and Investments in Land
4.1. Land Tenure Systems
4.2. Land Rights under Different Tenure Systems
4.3. Evolving Land Rights
4.4. Soil Conservation Practices under Different Tenure Systems
4.5. Tree Planting under Different Tenure Systems
4.6. Soil Fertility
5. Farm Production
5.1. Major Crops in Uganda
5.2. Input Use and Crop Yield
6. Livestock Production
6.1. Livestock Holdings
6.2. Adoption of Improved Cattle
6.3. Adoption of Improved Milk Production and Management
7. Organic Green Revolution in Uganda
7.1. Organic Green Revolution: Mechanism
7.2. Evidence from Uganda
8. Non-farm Income
8.1. Major Non-farm Activities: List
8.2. Participation in Non-farm Activities: Education vs. Language Skills
9. Education
9.1. Schooling System in Uganda
9.2. School Attendance in the Survey
9.3. Schooling of Orphaned Children and Young Adults
10. Conclusion
Reference
5
Tables
Table 1. Sampled Communities and Households
Table 2. Per Capita “Cash” Expenditure by Agro-climatic Zone
Table 3. Home and Total Expenditure by Agro-climatic Zone
Table 4. Poverty Comparison
Table 5. Derivation of a Simplified Food Based Poverty Line
Table 6. Per Capita Income and Income Share by Agro-climatic Zone
Table 7. Land Tenure Systems by Agro-climatic Zone—Plot Level
Table 8. Land Acquisition and Land Tenure and Tenancy—Plot Level
Table 9. Land Rights by Land Tenure and Tenancy—Plot Level
Table 10. Land Owners’ Individualized Rights to Sell and the Tenure System
Table 11. Land Owners’ Individualized Rights to Sell—Plot Level Analysis
Table 12. Soil Conservation Practices—Plot Level Analysis
Table 13. Soil Conservation Practices—LC1 Fixed Effects Model
Table 14. Slush and Burn and Tree Planting—Plot Level Analysis
Table 15. Slush and Burn and Tree Planting— LC1 Fixed Effects Model
Table 16. Soil Nutrient and Texture by Agro-climatic Zone
Table 17. Crop Production—Value Production at the Household Level
Table 18. Percentages of Producer Households of Major Crops
Table 19. Chemical and Organic Fertilizer Application
Table 20. Maize Yield, Improved Seeds, and Fertilizer Application
Table 21. Matoke Yield and Manure Application—Plot Level
Table 22. Livestock Ownership—Household Level
Table 23. Improved and Local Cow Distribution across Agro-climatic Zones
Table 24. Adoption of Improved Cattle—Household Level Analysis
Table 25. Milk Production Management
Table 26. Livestock Production System and Manure Application on Crops
Table 27. Manure Application and Improved Cattle—Plot Level Analysis
Table 28. Maize and Matoke Yields by Fertilizer Application
Table 29. Crop Yield and Fertilizer Application—Plot Level Analysis
Table 30. Non-farm Self-employment and Wage Activities
Table 31. Determinants of Participation in Business and Labor Activities
Table 32. Proportion of Orphans among Children/Young Adults (aged 0-18)
Table 33. School Attendance Ratio
Table 34. Determinants of School Attendance among Children and Young Adults
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Figures
Figure 1. Sampled Communities in Uganda
Figure 2. Cash Expenditure Share by per capita Expenditure Quartile
Figure 3. Distribution of per capita Total Expenditure
Figure 4. Food Expenditure Share
Figure 5. Income Share by per capita Income Quartile
Figure 6. “Organic Green Revolution” in East Africa
Figure 7. Isoquant Curve: Chemical and Organic Fertilizer
Figure 8. School Attendance Ration by Age
Figure 9. School Attendance Ration by Orphan Status and Living Arrangements
7
1.
Introduction
Since 1987, the government of Uganda has implemented fundamental institutional
and economic policy reforms, aiming at achieving macroeconomic stability, removing
distortions in the incentive framework for resource allocation, and increasing microeconomic
efficiency. As a result, Uganda achieved remarkable economic growth and poverty
reduction in the past decade, with absolute poverty declining from 56% to 35% of the
population between 1992 and 1999/2000 (Appleton, 2001).
Notwithstanding this outstanding progress, there is growing concern over whether
this trend is reflected in the improvement of the living standards of the majority of the people,
particularly in rural areas, where 96% of the poor depend mainly on agriculture for their
livelihood. Agricultural productivity stagnated or declined for most farmers during the 1990s
(Pender, et al. 2001; Deininger and Okidi 2001) caused by declining soil fertility, increased
incidence of pests and diseases, and drought conditions. In addition, the long–term effcts of
the AIDS epidemic on rural poverty raises some concerns (Yamano and Jayne, 2004). In
fact, although poverty declined between 1992 and 1999, it has since increased (albeit slightly)
to 38% (8.9 million people) in 2002/2003, driven mostly by worsening poverty in rural areas
particularly among crop farmers. Poverty reduction in rural areas and the northern region as
a whole has also been much less than the rest of the country, and many people interviewed
during the participatory poverty assessment (UPPAP, 2002) felt that poverty was worsening in
their communities, with more movement into poverty than out of it.
The persistent poverty, land degradation, and low agricultural productivity problems
are major challenges to the government of Uganda and its development partners that require
concerted effort to be effectively addressed in a sustainable and equitable manner. To achieve
these goals, the government has laid out an ambitious strategy for addressing poverty, through
the Poverty Eradication Action Plan (PEAP), with a target of reducing the proportion of the
population living in absolute poverty from 44% in 1997 to below 10% in 2017 (MFPED, 2001).
The government recently launched the Plan for Modernization of Agriculture (PMA), which is
one of the pillars of the PEAP. The mission of the PMA is to eradicate poverty by transforming
subsistence agriculture to commercially oriented farming, through re-orienting the poor
subsistence farmers’ production towards the market.
The government recognizes that the success of this strategy will depend on the
uptake of improved agricultural technologies by a significant proportion of farmers so as to
increase total factor productivity and farm income (through the reduction of per unit cost of
production); stimulate growth in non-farm income and employment (through the increased
integration of agriculture and other sectors of the economy); and raise real incomes of the
poor (by lowering food prices) who spend 60 percent of their incomes on food. In this
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regard, the government has among other things resolved to support the generation,
dissemination, and adoption of productivity-enhancing technologies and to improve the
access of smallholder farmers to markets as a means of increasing the marketed share of what
they produce. The government also recognizes that natural resources are critical in
addressing poverty because the majority of the poor depend on the extraction of natural
resources and has, thus, designed a number of policies and strategies with the broad goal of
ensuring that natural resources are used in a sustainable manner to contribute to poverty
reduction.
FASID, in collaboration with Makerere University, undertook research on
poverty, environment, and agricultural technologies (REPEAT) with the overall goal of
identifying agricultural technologies and farming systems that will contribute to
increased agricultural productivity, sustainable use of natural resources and reduced
poverty in Uganda. It is hoped that this knowledge will be used to guide policy
makers, development practitioners, and other stakeholders in formulating and
implementing policies and strategies for sustainable natural resource use, increased
agricultural productivity and reduced poverty in Uganda.
2.
Data
The sample for the REPEAT project largely builds upon and complements a
completed research project on policies for improved land management in Uganda,
conducted by IFPRI and Makerere University between 1999 and 2001 (Pender et al.,
2004). The latter involved a survey of 107 communities selected from two thirds of
regions in Uganda, including more densely populated and more secure areas in the
southwest, central, eastern and parts of northern Uganda and representing seven of the
nine major farming systems of the country (Figure 1). Because of insecurity in the
north and northeastern part of the country, these areas were excluded. The REPEAT
project was conducted in 29 out of 32 districts and in 94 out of 107 Local Counsel 1s
(LC1s) studied by IFPRI. From each LC1, ten households were selected to make a
total of sample 940 households.
Following the IFPRI study, we stratify the sample into six agro-climatic zones:
the bimodal high rainfall zone (mostly the Lake Victoria crescent), the bimodal medium
rainfall zone (most of central and parts of western Uganda), the bimodal low rainfall
zone (lower elevation parts of southwestern Uganda), the uni-modal rainfall zone (much
of northern Uganda), and the southwestern and eastern highlands (1500m a.s.l. and
above).
In Table 1, we present basic characteristics of sampled LC1s and households.
9
In LC1s in the bimodal low rainfall zone and the bimodal medium rainfall zones, the
population density (persons per km2) is low at 100 (column C). In contrast, in the
bimodal high rainfall zone (Lake Victoria crescent), the population density is very high,
and so is in the unimodal rainfall zone. Consistent with the population density, the
average farm size is large in zones with low population densities and small in zones
with high population densities (column F). In the following sections, we examine
characteristics of sampled LC1s and households from different aspect of their
livelihood.
3. Expenditure Profile and Poverty
3.1. Per Capita “Cash” Expenditure
We start this report with describing expenditure profile in rural Uganda and
defining poverty lines for our samples according to $1 a day poverty line by using a
purchasing power parity measure and a national poverty line based on food and
non-food requirements, by following earlier studies on poverty (Ravallion and Bidani,
1994; Appleton, 2001). First, we present “cash” expenditure profiles in each zone
(Table 2). In the survey we asked about cash expenditure on 39 items over the last 12
months, instead of consumption of all items, to reduce interview lengths. We asked for
frequencies of purchase, such as once a week or three times a year, and average
spending per purchase. We evaluate home consumption by taking a difference
between the total food production minus sales of crop, livestock, and livestock products
production. By using such information, we have calculated annual expenditure.
First, we focus on cash expenditure. Because cash expenditure does not
capture self-consumption, it differs from total expenditure systematically for poor
households whose expenditure share of self-consumption is higher than non-poor
households. Despite this drawback, however, the cash expenditure information
provides useful information.
The average per capita cash expenditure is $106. The unimodal rainfall zone
has the highest per capita cash expenditure and the bimodal rainfall zone has the lowest.
We divide the 39 expenditure items into five categories: staple food items, fresh food
items (such as meats), non-fresh food items (such as sugar), non-food items (such
education and medical expenditure), and social activities (such as contributions to
churches or local organizations). The share of non-food items is about 44 percent and
takes the largest share among the five categories. Among the non-food items,
education expenditure takes the largest share: 22 percent of the total cash expenditure.
Shares of other categories are similar across zones.
10
In Figure 2, we present shares of cash expenditure in five categories by the per
capita cash expenditure quartile. We find an expected pattern that poor households
spend a high proportion of expenditure on food items. For instance, households in the
lowest quartile spend about 49 percent of the total cash expenditure on items in food
categories (staple food, fresh food items, and non-fresh food items), while households in
the highest quartile spend only 37 percent. The differences in shares between the
lowest and highest quartiles, however, are not so distinct.
3.2.
Total Expenditure
Next, we evaluate self-consumption of crops, livestock, and livestock products.
The results are presented in Table 3 (column C). The per capita self-consumption
ranges from $32 to $52. We find the highest consumption of home products in the
bimodal low rainfall zone, where the per capita cash expenditure is also the highest.
The relationship between the per capita cash expenditure and home product expenditure,
however, is not clear. This is expected because households with low cash expenditure
have to rely on self-consumption, but at the same time they can consume less in total
than households with high cash expenditure.
By combining the per capita cash and home product expenditure, we obtain per
capita total expenditure (column A). The average per capita total expenditure is $144
among the sampled households. In Figure 3, we present the distribution of per capita
total expenditure. The peak of the distribution is at about $90 a year, and the
distribution has a long tail on the right-hand side. For simplicity, we have truncated
the figure at $500 or top two percent of the distribution.
Lastly, we present a relationship between food expenditure share and total
expenditure (Figure 4). As we expect, the food expenditure share declines as the total
expenditure increases. At the lowest expenditure level, the share is above 65 percent.
As the per capita expenditure increases, it starts declining and levels off at about 55
percent. Although, this may seem a high level of food expenditure share for high cash
expenditure households, it has to be emphasized that sampled households are in rural
areas and their expenditure levels are still low compared with high expenditure
households in urban areas.
3.3.
Measuring Poverty
One of the long-term objectives of the REPEAT project is to measure changes
in poverty over time and examine which factors, especially agricultural technologies,
reduce poverty. In order to measure changes over time, we need to determine a base
11
line poverty rate from the 2003 survey. There are two methods to set a poverty line.
First, we can use an internationally comparable poverty line of $1 a day. A purchasing
power parity exchange rate should be used to reflect living costs in Uganda. Second,
we use a national poverty line which is based on food and non-food requirements. In
this subsection, we examine poverty lines based on both methods.
The purchasing power parity exchange rate is about Shs 478 to the US$1 in
2003 (calculated form World Bank, 2004). This suggests that a $1 a day poverty line
is about Shs 174,470 a year, which is equivalent of $91.8 per person a year by using
official nominal exchange rate. According to this poverty line, we find that about 40
percent of households are poor. The distribution of poverty across agro-climatic zones
is similar to that of the average total expenditure.
Although the $1 a day poverty line is comparable across countries, it does not
have theoretical justification. (Why does it has to be one dollar per day?) Thus, we
also estimate a local poverty line based on calorie requirements by following the
approach of Ravallion and Bidani (1994) and Appleton (2001). They focus on
defining food-related needs and indirectly estimate non-food requirement. The food
requirement is based on costs of obtaining 3,000 kilo calories per day for a male adult in
rural Uganda.1 Appleton (2001) used four nationally representative monitoring
surveys in the 1990s and measured changes in poverty over time. He found that the
percentage of people living below the poverty line declined from 56 percent in 1992 to
44 percent in 1997/98 in Uganda. To define a poverty line, he first defined a food
poverty line and then a poverty line which include non-food requirements as well.
Appleton (2001) used a food-basket of the poorest 50 percent of the population
and calculated the cost of obtaining 3,000 kilo calories from this food-basket in rural
Uganda. We use the same food-basket used by Appleton but apply 2003 prices found
in our survey. The results are reported in Table 5. According to this food-basket, the
cost of obtaining 3,000 kilo calories per day for a month (30 days) is Shs 14,280, which
is equivalent of $7.52 per month. Thus, the annual food poverty line is at $ 90.2 per
male adult. However, not all members in the household need 3,000 kilo calories per
day. Thus, we use adult equivalents (Table A4.2 in Appleton, 2001) to evaluate food
requirements for different age-gender groups.
By following Ravallion and Bidani (1994) and Appleton (2001), the least
non-food requirement is defined as the non-food expenditure of those whose
1 Although 3,000 kilo calories is a high requirement even for a male adult who has to work on farm.
However, we use this requirement to be consistent with Appleton (2001).
12
expenditure is just equal to the food poverty line. This is justified because if
households are sacrificing the food expenditure needed to meet their calorie
requirements for non-food spending, nonfood spending should also be considered as
needed. In our sample, we find that households whose total per adult equivalent is just
at the food poverty line spend about 31.5 percent of total expenditure on nonfood items,
thus the required nonfood spending is about $28.4. From this calculation, we find the
poverty line at $118.6 per person per year.
According to the food poverty line, we find that about 39 percent of sampled
households live in food poverty. This is very close to what Applenton (2001) found by
using four monitoring surveys in Uganda in the 1990s. He estimated that 36 percent of
Ugandans lived below the food poverty line. Because his estimate includes urban
people, the percentage for rural households should be higher, which would be closer to
or above our estimate.
We also find that about 52 percent of households live in poverty. This is also
close to Applenton (2001)’s estimates. He found that the poverty rate declined from 56
percent in 1993/94 to 49 percent in 1997/98. Thus, our estimate is somewhere
between the 1993/94 and 1997/98 levels. However, because our data do not represent
the whole rural population of Uganda and because our questionnaire is different from
the one used in Applenton (2001), we should emphasize that his estimates are not
directly comparable to our estimate. What is clear is that our estimates of total
expenditure and poverty are reasonably close to estimates from much bigger (and more
expensive) national representative surveys. What we hope for the future is to use the
same questionnaire to the same samples in a panel survey and measure a change in
poverty in the future to achieve one objective of the REPEAT project.
3.4.
Income Profile
The previous sub-section describes how many people are poor in rural Uganda
and what they consume but do not provide information on how they earn income and,
therefore, how they could escape from poverty by increasing income. Although, we
will examine each income generating activity in the following sections, it is useful to
take a look at income profile first. Income profile is summarized in Table 6, which
shows average per capita income and income share by different income sources and by
the agro-climatic zones.
The average per capita income for the entire sample is US$118. Income
sources include farm income, livestock income, non-farm activities, and wage income.
Farm income is calculated as production value of farm products minus paid-out costs,
13
which include costs on seeds, fertilizer, hired labor, and rental oxen. Livestock income
is also calculated as production value minus paid-out costs. Production value includes
animal sales and production value of livestock products, such as milk and eggs.
Paid-out costs for livestock production include purchased feeds, expenditure on
artificial insemination (A.I.) service, bull service, and anima health care service.
Non-farm activities include non-farm micro enterprises such as trading various goods
and seasonal labor activities. To obtain reasonable profit from micro enterprises, we
asked for numbers of low- and high-business months in the past 12 months and earnings
and costs per month during the low- and high-business months. Wage income only
includes salaries from jobs that provide constant monthly salary.
Out of the total income, farm income accounts for 60 percent. Among the six
agro-climatic zones, the average per capita income is the highest at $161 in the bimodal
rainfall zone (lower elevation parts of southwestern Uganda), while it is at the lowest
level in the bimodal medium rainfall zone at $96. In the unimodal rainfall zone
(northern Uganda), the share of the farm income is low at 51 percent, whereas shares of
livestock income and wage incomes are high at about 20 and 11 percents, respectively.
Next, we stratify the sample into quartiles by per capita income (Figure 5).
As expected, we find that households in lower income quartiles have larger income
shares coming from farm production than households in higher income quartiles. The
proportion of farm income is over 70 percent among households in the lowest and
second lowest income quartiles. Instead, households in higher income quartiles have
larger income share coming from non-farm activities and wages than households in
lower income quartiles. For instance, households in the highest income quartile have
30 and 18 percents of income coming from non-farm activities and wage income,
respectively.
From this table, we can draw two common findings in studies on non-farm
income in Africa (Reardon, 1997; Jayne et al., 2003). First, farm income is the most
important income source for poor households. Thus, increasing farm productivity is a
crucial issue to secure and increase income for them. In the following sections, we
examine farm and livestock productions and explore possible ways to increase
productivity. Especially, we propose an intensive farming system that integrates
improved dairy production system, organic fertilizer, and crop production. We call this
system as “Organic Green Revolution” technology and describe this technology in
Section 7.
Second, Figure 5 suggests that farm income is not enough to move up income
quartiles. The non-farm income share is high among the highest income quartile.
14
Although the causality could be in both directions (non-farm income increases total
income or high income households have means going into non-farm activities), it is
clear that the access to non-farm income can play a critical role in escaping from the
poverty. In Section 8, we examine determinants of having non-farm jobs at the
individual level.
As mentioned earlier, the incidence of poverty decreased nationally from 56
percent to 49 percent in 1997 (Applenton, 2001). A descriptive analysis indicates that
agricultural production for the market was strongly correlated with the reduction in
poverty. More specifically, households growing cash crops (mainly coffee) account for
half of the poverty reduction achieved between 1992 and 1997. In the following
sections, therefore, we examine various agricultural activities in rural Uganda, so that
we better understand determinants of poverty and identify possible strategies to escape
from it.
4. Land Tenure and Investments in Land
4.1. Land Tenure Systems
As in many other African countries, there exist several land tenure systems in
Uganda. Three major land tenure systems are Mailo, freehold, and customary tenure
systems (Mugambwa, 2002). Mailo was a quasi-freehold tenure system, which was
unique to the kingdom of Buganda. The tenure had its origin in the 1900 Uganda
Agreement between the British colonial administration and the chiefs of Buganda.
Under the agreement, about half of the land in the kingdom of Buganda was allocated to
chiefs and notables as their private property. Because the allocations were expressed
in multiples or fractions of square miles, the term Mailo was adopted. Owners, lacking
labor to till such large areas, had received fees and rents by settling tenants (kibanja) on
their land. The 1928 Busuulu and Envujjo law and subsequent land laws require
landlords to pay tenants full compensation for any investments made by tenants,
protecting tenants from eviction. Further more, kibanja tenants enjoy an inheritable
permanent right of occupancy subject to payment of a fixed annual rent.
Under the colonial administration, three freehold tenure systems were
established through agreements with different kingdoms partly to pave way for
European settlement and partly to bribe chiefs and notables (Mugambwa, 2002).2
2
Freehold tenure system could be disaggregated into three, depending on different agreements with
different kingdoms (Mugambwa, 2002).
These three are freeholds created under the Crown Lands
Ordinance of 1903, the Native freeholds of Ankole and Toro, and the Adjudicated freeholds under the
15
Freehold tenure system is an individualized land tenure system where individual land
rights are relatively well established, although this does not mean land owners have
titles or rights to plant trees as we discuss later in this section.
Finally, there exist customary land tenure systems. In pre-colonial Uganda,
there were three broad customary land tenure systems: communal or tribal tenure, clan
tenure, and normadic tenure (Mugambwa, 2002). In communal or tribal tenure
systems, ownership of the land was vested in the ruler as owner or trustee of the land for
members of the tribe. Under clan tenure, ownership of land was organized according
to clans as opposed to tribes. The last customary tenure system is normadic tenure,
which is prevalent in Karamoja.
To characterize the three major land tenure systems, we present descriptive
information on land acquisition and land rights under the three tenure systems. First,
we present proportions of plots under different tenure systems across agro-climatic
zones in Table 7. Mailo land tenure system is only found in the three bi-modal rainfall
zones that used to belong to the kingdom of Buganda. In the bi-modal low rainfall
zone, there are virtually no customary lands, only freehold and Mailo lands. In the
bi-modal medium and high rainfall zones, all three land tenure systems exist. In the
uni-modal and eastern highlands (northern and eastern parts of Uganda), the customary
land tenure system is dominant, while in the south-western high lands, the freehold
tenure system is dominant. Thus, there are several different land tenure systems
co-exist in rural Uganda, which provide opportunities to compare land rights and
investments across different land tenure systems.
4.2.
Land Rights under Different Tenure Systems
Next, we examine land acquisition modes under the three tenure systems,
separately for land owners, occupants, and tenants. We define occupants as
households who use someone else’s land but do not pay rents. According to Table 8,
land owners basically acquire land through purchase and inheritance. Even under the
customary and Mailo systems, about half of land owners have acquired land through
purchase. Occupants acquire land through various channels. Of which, borrowing is
a major acquisition model for occupants, in addition to inheritance and purchase.
Tenants acquire land through renting (mostly via fixed rents).
Under different tenure systems, households have different degrees of land
rights to sell their plots, plant timber trees (which is valuable), and plant other trees
Kigezi, Bugisu, and Ankole land registration pilot schemes.
16
(which are not as valuable as timber trees in general). In Table 9, we present
individual land rights under different tenure systems, separately for owners, occupants,
and tenants. Over all, we find that, on more than 60 percent of their plots, households
have individualized rights to sell land (i.e., they can sell land without obtaining
approvals from non-household members). Their rights to give land, to plant timber
trees, and to plant non-timber trees closely follow their rights to sell land. In terms of
land titles/certificates, only 4 percent of the entire plots were issued titles/certificates.
We find that under the freehold tenure system, sample households can sell 81
percent of their plots without any approvals. Under the customary and Mailo, they can
sell about 64 and 74 percents of their plots, respectively. Individual rights to plant
trees, however, are quite similar across tenure systems for land owners. But under the
customary and Mailo tenure systems, land occupants have much stronger rights to plant
trees. We examine a relationship tree planting and land tenure systems later in this
section.
4.3. Evolving Land Rights
Although traditional land tenure systems were considered as static and
inefficient in resource allocation, a more recent view considers them as dynamic and
flexible systems that can respond to changes in economic and social environments.
This view is known as the evolutionary theory of land rights (Otsuka and Place, 2001).
The basic implication from this theory is that as land scarcity increases people demand
more land tenure security and, as a result, private property rights in land tend to emerge.
Thus, according to this view, there should be variations in land rights even under the
same tenure system, depending mainly on land scarcity. In Table 10, we present
proportions of households who have individualized land rights to sell land across
agro-climatic zones. The results in Table 10 indeed show variations in the land right
across zones. The customary land tenure system has a large variation in the land right.
In the bimodal medium rainfall zone, the proportion of plots with the land right is 53
percent, while it is 85 percent in the south-western highlands zone. In the
south-western highlands zone, 85 percent of plots are under the freehold tenure system.
Thus, the customary land tenure system may have been influenced by the freehold
tenure system.
Based on this observation, we pose a hypothesis that the freehold tenure system
has neighboring effects to customary and Mailo tenure systems and that because of the
neighboring effects, the customary and Mailo tenure systems become more
individualized. To identify the neighboring effects, we need to control for any other
17
factors, such as population density, that may influence the customary and Mailo tenure
systems.
To test this hypothesis, we estimate Probit model, where the dependent variable
is a dummy variable for an individualized right to sell a plot. To represent the
presence of the freehold tenure system within LC1, we construct a ratio of freehold
plots out of all plots within LC1. Thus, the ratio takes one if all of plots within CL1 is
under freehold and zero if no plots are under freehold within LC1. We estimate this
model at the plot level with Probit first for all plots and later separated for each tenure
system.
The results in Table 11 indicate that there is a strong neighboring effect of the
freehold tenure system on the individualized land right to sell. According to a
simulation, when the proportion of pots under the freehold within LC1 increases from
10 to 30 percent, then the probability of having the individual land right will go up by
five percent under all tenure systems.
To disaggregate the neighboring effect on the freehold tenure system itself and
the neighboring effects on the customary and Mailo tenure systems, we include
interaction terms between the ratio of freehold plots and the customary and Mailo
dummies. The results indicate that the neighboring effect is strong to the freehold
tenure system itself, indicating that the freehold tenure system is more individualized in
LC1s where the ratio of freehold plots is high. The neighboring effects to the
customary and Mailo systems are much stronger than its effects on the freehold. The
interaction terms between the ratio of freehold and the customary and Mailo dummies
are positive and significant. The results are consistent with the view that traditional
land tenure systems are actually dynamic and adoptive to surrounding economic and
social environments.
Turning to the other variables, we find that a plot closer to the homestead also
has a higher probability of being issued the individual right than a plot further away.
We also find that the female headed households have a 20 percent lower probability of
having the right to sell the plot than male headed households. The older the household
is, the higher the probability of having the individualized right to sell. Interestingly the
maximum education level of female members has a positive impact on the
individualized rights to sell but not the maximum education of male members. We
need further investigations to interpret these results.
4.4.
Soil Conservation Practices under Different Tenure Systems
Next, we explore the relationships between soil conservation practices and land
18
tenure systems. In Table 12, we present proportions of plots that are under five soil
conservation practices: chemical fertilizer application, manure application, fallow (at
least once in the last three cropping seasons), mulching, and crop residue application.
Except fallow, the other practices are practices applied during the 2003 first cropping
season, which was completed immediate before the 2003 REPEAT survey.
In general, soil conservation practices are not commonly practiced in Uganda.
We find that only 13 percent of all plots were applied chemical fertilizer. Application
of crop residues is the most widely practiced: 29 percent of plots were applied crop
residues. By stratifying the plots by the tenancy status, we find that soil conservation
practices are practiced more by owners of the plots than occupants or tenants. The
differences are less pronounced in fertilizer application than other practices, such as
manure application and mulching, that are intended for long-term benefits.
Next we stratified the sample by the tenure systems (note that we have
excluded tenants when we stratified by the tenure system because they behave very
differently from owners and occupants.) In Table 12, we find that three soil
conservation practices (fertilizer application, manure application, and fallow) were more
practiced under the freehold tenure system than the customary or Mailo tenure systems.
Fallowing considered a practice that may increase land insecurity under a fragile land
tenure system since followed land appears to be idle. This could be a reason for low
percentages of plots being fallowed under the Mailo tenure system.
On the other hand, mulching and crop residues were more practiced under the
Mailo tenure system than the freehold and customary tenure systems. Some of these
differences among tenure systems could be driven by regional differences. For
instance, areas under the Mailo tenure system are central and western regions near
Victoria Lake. In these areas, mulching and crop residues are commonly applied on
matoke.
To control for regional differences and other factors that affect soil
conservation practices, we estimate regression models where the dependent variables
are dummy variables for the five soil conservation practices (Table 13). We estimate
these models with the LC1 Fixed Effects models to control for observed and unobserved
regional characteristics. The estimation models are linear probability models where
estimated coefficients are interpretable as marginal changes in the probability. (We
have also estimated the models with Probit using LC1 dummies. The results are very
similar to the LC1 Fixed Effects models.)
The results in Table 13 indicate that under the Mailo tenure system, three out of
the five soil conservation practices are about 8 to 10 percent less likely to be practiced
19
than the freehold tenure system. Under the customary tenure system, fallow is about 5
percent less likely to be practiced than the freehold tenure system, the difference is only
significant at the 10 percent level. In general the differences in soil conservation
practice between the freehold and customary tenure systems are not significant. These
results seem to suggest that households in the Mailo tenure system are less interested in
investing in land to improve short-and long-term soil fertility than households under the
freehold land tenure. This could be because of insecure land tenure among under the
Mailo tenure system, despite protections by laws.
The results also indicate that some soil conservation practices are less likely to
be applied on occupant and tenant plots. Fallowing is significantly less practiced on
occupant and tenant plots. Because there are many occupants under the Mailo tenure
system, especially, the result suggest that fallowing is close to 17 percent less likely to
be practice on a occupant plot under the Mailo tenure system than an owner plot under
the freehold tenure system.
The results on other plot characteristics indicate that manure application,
mulching, and crop residue application are less practiced on plots that are farther away
from homestead. This is understandable because of high labor demand to transport
bulky materials from the homestead to plots. The estimated coefficient of the distance
between the plot and homestead is positive and close to significant (t =1.57) on
chemical fertilizer, which is not heavy or bulky as manure. Therefore, this may
suggest that farm households substituting fertilizer to manure on plots that are farther
away from homestead.
4.5.
Tree Planting under Different Tenure Systems
Soil conservation practices that we investigated in the previous sub-section are
short- or medium term investments in soil fertility. To examine the impacts of land
tenure systems on investments, however, long-term investments could be more
appropriate. Tree planting is one example of long term investments that people in rural
areas often make. Yet, because tree planting is a long-term investment, there is a
serious possibility that the causality runs the other way around. In Africa, tree planting
is often considered as a method to enhance tenure security (Besley, 1995; Platteau,
2000; Otsuka and Place, 2001). In Uganda, Place, Ssenteza, and Otsuka (2001)
hypothesize that land investments that are visible to outsiders enhance tenure security,
while invisible land investments (to outsiders) do not. They find evidence that link
planting trees on customary land to expected increase in tenure security.
In this report, however, we do not investigate causal relationships between tree
20
planting and land security because it is quite difficult to obtain definitive answers.
Instead, we only present some descriptive information about tree planting under
different tenure systems. In Table 14, we present proportions of plots under the slush
and burn practice and tree planting (both for non-timber trees and timber trees) under
the three land tenure systems. In Table 14, we find that the slush and burn practice is
more applied on occupant and tenant plots than owner plots. In contrary, non-timber
and timber trees are more planted in owner plots than occupant and tenant plots. When
we stratify the samples by the land tenure systems, we find that both slush and burn
practice and tree planting are more often applied under the customary and Mailo tenure
systems. According to the hypothesis posed by Place, Ssenteza, and Otsuka (2001),
both slush and burn practice and tree planting are “visible” by the outsiders. Thus, the
findings in Table 14 suggest that people under the customary and Mailo tenure systems
are trying to increase land security by clearing field and planting trees on the fields.
However, once we control for regional differences, the results do not stand
(Table 15). In Table 15, we present results from the LC1 Fixed Effects models, where
the dependent variables are dummy variables for slush and burn practice, non-timber
tree planting, and timber tree planting. In fact, we find that slush and burn practice is
less likely to be practiced in customary tenure system once we control for regional
differences across LC1s. We also find that timber trees are less likely to be planted in
occupant and tenant plots. This could be because occupants and tenants do not want to
make a valuable investment, such as planting timber trees, on plots that belong to land
owners.
The results on plot characteristics are as expected. Plots that are acquired a
long time ago have higher probabilities of being planted non-timber or timber trees in
the past and plot that are farther away from homestead are less likely to be planted
non-timber or timber trees than plot that are closer to homestead.
In sum, we find that short-tem land investments are less practiced under the
Mailo tenure system than the freehold tenure system, although we do not find any
differences across land tenure systems in long-term land investments (slush and burn
practice and tree planting). However, as we discussed earlier, land rights within tenure
system are not homogeneous. Thus, what is important is to look at relationships
between land rights and land investments. Because of the endogeneity of the land
rights, however, this analysis is beyond the scope of this report. We hope that we will
extend our research into that direction in the future.
4.6.
Soil Fertility
21
Our next interest is how soil conservation practices affect soil fertility over
time. Ideally, we would need to have a panel data to measure such impacts, and this is
what we are planning to do in the future. With cross section data, we can only show
correlation between soil conservation practices and soil fertility. Unfortunately, we
have not been able to match soil information to the plot information. Thus, we only
present soil fertility information in this report.
We have collected soil samples from one annual crop plot operated by sampled
household in the first cropping season of 2003, preferably a plot of maize or beans.
Where the household produced none of these, an alternative annual crop plot was
selected for soil sampling. The total number of soil samples is 680. Soil samples
collected from the 0-20cm depth were air-dried and ground to pass a 2 mm sieve and
analyzed for pH, Organic Matter (OM), texture, extractable P, K, and Ca according to
Foster (1971) methodology. Extractable P, K, and Ca were measured in a single
ammonium lactate/acetic acid extract buffered at pH 3.8. Soil pH was measured using a
soil to water ration of 1:2.5. The results are presented in Table 12. In the future, we
are planning to combine the soil fertility information with soil conservation practices,
plot and household characteristics, and conduct analyses.
5. Farm Production
5.1. Major Crops in Uganda
We present characteristics of farm production in rural Uganda. Table 13
shows percentages of households producing each crop (column A), the average area
devoted to each crop among all households (column B) and among producers (column
C), the average production value of each crop among all households (column D) and
among produces (column E), and the average return to one acre of land (column F).
Beans and maize are the most and second most common crops produced,
respectively: about three thirds of sampled households produced beans and maize. The
average land devoted to maize and beans are 2.99 and 3.34 acres, respectively.
However, because intercropping is a common practice, especially between beans and
maize, these numbers are overestimating the land devoted to a single crop. Thus, some
parts of areas devoted to maize and beans are double counted. Because it is very
difficult to allocate intercropped areas into each crop, we present the double counted
numbers in Table 13. Maize provides about $66 to its producers and bans about $37.
Thus producers with both crops receive substantial income from intercropping both
crops.
Matooke (plantain) is the third most popular crop produced by 67 percent of
22
sampled households. It occupies the largest cultivated land among all the crops.
Matooke provides the largest income, i.e. production value net of paid-out costs, which
provides $190 to matooke producers on average.
Coffee, an important high value crop, is produced by 31 percent of the
households. Other major crops in terms of producer income include rice, industrial
crops (cotton, tobacco, etc), fruits, and other vegetables. Although only 3.2 percent of
sampled households produce rice, rice provides the second largest income, $145 to rice
producers. Rice also has the largest return to land, $71 per acre. In terms of high
returns to land, Sweet and Irish potatoes rank second ($48.5 per acre) and third ($35.7
per acre), respectively.
In Table14, we present percentages of households producing major crops by
agro-climatic zones. Maize is the most popular in the bimodal low rainfall zone, 80
percent of households producing maize. But it is also popular in other zones, at least
60 percent of households producing maize in all zones. Matooke follows a similar
pattern: more than half of the households produce matooke, while it is most popular in
the bimodal low rainfall zone. The rest of the crops have more regional variation.
Coffee is produced by more than 40 percent of households in the eastern highlands,
unimodal rainfall, and bimodal high rainfall zones, but not so popular in the other zones.
Sweet potato is produced by more than 70 percent of households in the bimodal
medium and high rainfall zones. Cassava follows a similar distribution pattern as
sweet potato. Rice is mainly produced in the bimodal medium rainfall zone only.
5.2. Input Use and Crop Yield
According to Table 16, only 7 percent of households applied chemical fertilizer
on crops. The proportion of households that applied animal manure is more than
double, about 16 percent. Only in the eastern highlands zone, more than 30 percent of
households applied chemical fertilizer. This may be because this zone is closer to
Kenyan border and can sell maize in Kenya. In this zone, the manure application is
also high: about 23 percent of households applied manure and the average amount of
manure applied was about 141 kgs per acre. In the western parts of the country (the
bimodal low rainfall and the south-western highlands zones), the manure application is
high. We suspect that this is because of the intensified dairy production, which
involves improved dairy cows and intensive feeding. Because the intensive dairy
production keeps animal in a small area, it is much easier to collect manure than
extensive animal grazing. We discuss this issue in Section 7.
23
Maize
In Table 17, we present maize yield across agro-climatic zones. The maize
yield in Uganda is low at 0.77 tons per hectare, despite the fact that over 60 percent of
households adopted improved seeds. A major reason is a low application of fertilizer.
Only 3 percent of households applied chemical fertilizer. The application of manure is
also low at 4 percent. The potential reason for the low application of both chemical
fertilizer and manure could be a low output-input price or other constraints. Further
research is needed by using the profitability analysis on maize.
Matoke (Banana)
Matoke is another major staple crop in Uganda. The estimation of matoke
production is extremely difficult since matoke is harvested through out the year and it is
sold in bunches. Each bunch has a different weight and price. Thus, recall
information of matoke production inheritably comes with measurement errors.
Nonetheless, enumerators were trained to spend extra time to obtain reliable matoke
production information since this is an important crop economically.
The average yield of matoke is 2.1 tons per hectare. In western parts of
Uganda (the bimodal low and medium rainfall and the south-western highlands zones)
the yield is over 2.5 tones per hectare. On matoke, virtually no households apply
chemical fertilizer. Instead, more than 8 percent of households apply manure on
matoke. The quantity of manure application is about 117 kgs per hectare (including all
plots). The quantity of manire application is especially high in the south-western
highlands and the bi-modal low rainfall zone. To understand why manure application
is so high in these two zones, we need to understand the livestock production systems in
these zones. And this is what we do in the next section.
6. Livestock Production
6.1. Livestock Holdings
For farm households in rural Uganda, livestock is an important asset that can
provides income and be disposable in hard times. In terms of livestock ownership,
cattle, goats, and chicken are owned by many households (Table 19). In 2003, 29
percent of households owned local cows. Among the households that owned at least
one local cow, the average number of cows owned is 3 cows. Many households also
had different types of local cattle.
Some households also own improved cows that are cross breeds of local and
European cattle. European cows are bigger and more productive in milk production,
24
while local cows are resistant against local diseases. As a cross breed of the two,
improved cows are productive in milk production and at the same time fairly resistant to
local diseases. Staal and Kaguongo (2003) report that the proportion of improved
cattle was about 5 percent in 1999 but that there are mixed evidences on what the recent
proportion is. Among our samples, 6.9 percent of households owned improved cows
in 2003. In terms of cow population, about 17 percent of cows are cross-bred cows in
2003.
Turning to other animals ownership (Table 19), local chicken is the most
popular livestock animal among the sampled households. About 62 percent of
households owned at least one local chicken in 2003. Among the chicken owners, the
average number of chicken is just below 8. Local goat is the second most popular
livestock animal. About 47 percent of households owned at least one local goat in
2003.
To investigate regional distributions of improved cattle, we calculated
proportions of households who own improved cattle, the average number of improved
cattle, and the average number improved cattle among households who own them
(Table 20). For comparison, we also present the same number for local cattle.
Among the all sampled households, 9.5 percent of households own at least one
improved cattle. The improved cattle are adopted in the eastern highlands zone the
most, followed by the uni-modal zone. In terms of the number of improved cattle
among the owner, however, the south-western highlands zone has the highest number
among the owner. Thus, it seems that in the south-western zone, some households are
specializing in the improved dairy production system. In the bi-modal low rainfall
zone, the number of improved cattle among the owners is also high. Recall that in
these two zones and the eastern highlands zone, we found that the application of manure
is also high (Table 14). We investigate more on the relationship between the adoption
of intensive dairy production and manure application in Section 7. But before
investigating that issue, we estimate the determinants of adopting improved cattle in the
next sub-section.
6.2.
Adoption of Improved Cattle
To investigate which households are adopting improved cattle, we estimate a
simple regression model (Table 21). The results indicate that improved cattle are
adopted in communities where public grazing land is less available. The information
about the size of public grazing land is collected by the community level survey. The
estimated coefficient of this ratio is negative and significant indicating that the
25
probability of adopting improved cattle is high in communities with little public grazing
land.
Household characteristics that are related to male members are found
influential on determining the adoption of improved cattle. For instance, the
maximum education level of male members in the household increases the adoption of
improved cattle, while that of female members has no impact. The numbers of male
adults and boys also increase the adoption. Again the number of female adults and
girls has no impact on the adoption. Field observations suggest, however, that female
members are involved in taking care of improved cattle. Thus, this may suggest that
the adoption of improved cattle, which is a big commitment for rural households, is
determined by male members, especially male household heads, even though daily
managements are conducted by both male and female members.
6.3.
Adoption of Improved Milk Production and Management
Uganda has seen a significant increase in milk production in during the second
half of the 1990s. As a result, for instance, real milk prices appear to have fallen by 36
percent between 1995 and 2000, although projections to 2010 suggest a milk shortage
under a scenario of a reasonable economic growth (Staal and Kaguongo, 2003). Thus,
the dairy production in rural Uganda provides a good opportunity for commercialization
of Ugandan agriculture.
Next, we compare production managements of dairy production of local and
improved milking cows (Table 22). We find that 201 and 52 households produced at
least some milk from local and improved cows, respectively, in the last 12 months prior
to the survey in 2003. Among the 52 households with improved cows, 38.5 percent of
them used stall feeding. The average monthly milk production among households with
improved cows is about 2.5 times more than households with local cows. Households
with improved cows sell about half of produced milk, while households with local cows
sell one fifth of total production. More than half of households with improved cows
sell their milk to individual customers. About 12 percent of them sell their milk at
local markets. But only 5 percent of them sell their milk to dairy cooperatives and
private milk processing factories. For dairy sector in Uganda to expand its milk
production, formal marketing channel has to collect more milk from dairy producers
since local demand for fresh milk is limited.
7. Organic Green Revolution in Uganda
7.1. Organic Green Revolution: Mechanism
26
Like the Agricultural Revolution in 18th century England, the OGR in East
Africa will depend on the stall-feeding of cattle using cultivated feed (e.g., napier grass
and oats, as shown in Figure 6). However, unlike the Asian Green Revolution, the
OGR must rely on organic fertilizer, i.e., manure produced by stall-fed cattle (McIntire
et al., 1992). The use of fodder leaves from agroforestry trees, which have the
capacity to fix nitrogen, may also improve the quality of any manure used as fertilizer.
The cattle involved are the so-called cross-bred cows discussed earlier, which – in some
ways - is reminiscent of the cross between the Taiwanese and Indonesian rice varieties
that helped spark the Asian Green Revolution.3 As with the Asian Green Revolution,
African farmers must use improved crop varieties, which are more responsive to
fertilizer than TVs. We suspect, however, that these varieties are not as high-yielding
as the MVs developed for Asia, because there has been comparatively little adaptive
breeding research undertaken by international agricultural research organizations for the
agro-ecological conditions of Sub-Saharan Africa (Evenson and Gollin, 2003).
Note that stall-feeding of cattle with cultivated feeds does not enhance the total
amount of soil nutrients. In fact, the total amount of nutrients will likely decline
because of the export of nutrients from the plant-soil-animal system through harvested
products and milk. Such a system, however, enhances the internal cycling of nutrients
and, hence, the ability to extract and use soil nutrients (Buresh 1999). The long-term
sustainability of such a system will depend on the inherent amount of soil nutrients
available to be extracted, i.e., native soil fertility, and the rate at which nutrients are
replenished by exogenous sources, such as nitrogen fixed by agroforestry trees and
legume crops.
In short, the OGR seeks the best possible combination of the most desirable
yield-enhancing features of the world’s two, already recognized revolutions in
agriculture. Additionally, the use of manure is particularly appropriate for the fragile
soils found in Sub-Saharan Africa, which have been depleted in the past by their
intensive use without the adequate replenishment of nutrients. In sum, we present a
possible development pathway in Figure 7. Figure 7 show two isoquant curves with
two production inputs: chemical and organic fertilizer. In Asia, because the relative
price of chemical price was low, the Green Revolution took a path that relied heavily on
chemical fertilizer. On the other had, in Africa, the relative price of chemical fertilizer
over organic fertilizer is high, compared with that in Asia. This is especially so, in
3 Because of the prevalence of a chronic disease, trypanosomaiasis, it is economical to keep dairy goats
in some areas.
27
areas where organic fertilizer is available from improved cows, such as highlands in
East Africa. Thus, on the isoquant curve, the optimal combination of chemical and
organic fertilizer is different from Asia, using relatively more organic fertilizer.
Therefore, if the relative price between the two inputs remains high, then the
development pathway should be different from that of Asia.
A critical assumption is whether the relative price in Africa remains high in the
future. We think it would remain high for a while because of high transportation costs
and neat absence of irrigation in Africa. High transportation costs keep fertilizer prices
high, while the near absence of irrigation keeps demand for chemical fertilizer low
because of risks associated with unreliable rainfall.
7.2. Evidence from Uganda
On Manure Application
We first start with the relationship between the livestock production system and
manure application on crops. In Table 23, we stratify the sample into two by the
availability of improved cattle. Among the sample, about 10 percent of households
have at least one improved cattle. Among them, 32 percent of households apply
manure on crops, while only 14 percent of households without improved cattle apply
manure. Thus, thus the difference between the two groups is 18 percent. In terms of
quantity of manure applied, we find a big difference. Households with some improved
cattle apply 792 kgs of manure on crops over a year in total. This is much larger than
144 kgs of manure applied by households without improved cattle. To see if this
difference is driven by either a large number of cattle or land size, we divided the total
amounts of manure by the number of cattle and by the land size. The results indicate
that households with improved cattle apply three times more manure per cattle or per
land than households without improved cattle.
The results in Table 23, however, do not control for many other factors that are
also associated with adoptions of both manure application and improved cattle. Thus,
we employ regression models to investigate this issue further. In Table 24, we present
two models estimated at the plot level. The dependent variable for the first model is a
dummy variable for manure application. And the dependent variable for the second
model is the quantity of manure application in kilograms. As independent variables,
we include numbers of improved and local cattle per hectare along with other plot and
household characteristics. We estimate the first and second models by Probit and Tobit,
respectively.
The results from the first model (column A) indicate that both the numbers of
28
improved and local cattle per hectare increase the probability manure application. The
size of impact, however, is larger from the number of improved cattle than the number
of local cattle. One additional improved cattle would increase the probability of
manure application by 4.4 percent, while one additional local cattle increases the same
probability only by 2 percent. The results from the second model (column B) are
consistent with the results from the first model. Both improved and local cattle
increase the quantity of manure application, but the size of impact is bigger from the
improved cattle than local cattle. On additional improved cattle increases the quantity
of manure application by 837 kgs, while one additional local cattle increases it only by
232 kgs.
The results in Table 24 could be biased because the numbers of improved and
local cattle could be endogenous, correlated with unobserved plot and household
characteristics. Thus, we still need further investigations. However, the results seem
to suggest a strong association between manure application and improved cattle. It is
difficult think that the association between the two does not have any causal
relationships.
Among the other variables, we find that plots that are farther away from the
homestead are less likely to be applied manure. This negative relationship is very
strong with the quantity of manure application. This is easy to accept because manure
is heavy and difficult to transport. Plots that were not cultivated in the previous season
are applied less amounts of manure. The maximum education of female members (but
not male members) has a positive impact on manure application.
On Crop Yield
In Table 25, we present crop yield stratified by the manure application. As we
have seen before, only 4 percent of maize plots were applied manure. In Table 23, we
find that only 20 local maize plots were applied manure out of 728 local maize plots in
total. Among 836 improved maize plots, only 28 plots were applied manure only and
32 plots were applied both manure and chemical fertilizer. Although sample sizes are
small, maize plots that are applied manure shows higher maize yield than plots without
any fertilizer. Among local maize plots, the average maize yield is about 65 percent
higher when manure is applied. Among improved maize plots, the average maize
yield is about 24 percent higher when only manure is applied and about 150 percent
higher when both manure and chemical fertilizer are applied, compared with the
average maize yield without any fertilizer.
Manure is applied more on matoke than maize. In Table 25, we find that
29
about 8 percent of matoke plots are applied manure. The average matoke yield is
about 30 percent higher when manure is applied to matoke. Chemical fertilizer is
almost never applied on matoke.
To control for many more factors and obtain robust results on the associations
of manure application on crop yield, we estimate crop yield functions in Table 26.
Note that the results in Table 26 are not control for the endogeneity of fertilizer
application. For instance, fertilizers could be applied on fertile plots where crop yield
is higher than other plots even without any fertilizer. Thus, estimated coefficients of
manure and chemical fertilizer applications could be biased and should not be
interpreted strictly as causal effects, although we believe that the results suggest some of
causal effects. In the future analysis, there is a need to overcome this problem by
using instrumental variables or the plot level fixed effects model.
Nonetheless the results in Table 26 suggest the importance of manure and
chemical fertilizer application. The results indicate that the maize yield is higher by 50
percent when chemical fertilizer is applied than when no fertilizer is applied. The
maize yield is also 24 percent higher when improved maize seeds are used. Although
the estimated coefficient suggest a 12 percent higher yield associated with manure
application, the result is not precisely estimated. This could be because of the small
sample size of plots with manure application.
On Matoke, the results indicate a strong association between matoke yield and
manure application. Matoke yield is 36 percent higher when manure is applied.
Because we have shown already a strong association between the number of improved
cattle and manure application, the results indicate a link between improved dairy
production system, which involves improved cattle, manure application, and a high
matoke yield, already described in Figure 6.
As discussed already, we still need further investigation to establish causal
relationships between the improved dairy production system, organic fertilizer
application (including manure and compost), and increased crop yield. However, the
intensive farming system, named Organic Green Revolution, seems to suggest a
promising avenue for a significant improvement in agriculture production at least in
highlands in East Africa, where the Asian Green Revolution technology, which requires
chemical fertilizer, irrigation, and high yielding seeds, has not taken place for more than
three decades.
30
8. Non-farm Income4
8.1. Non-farm Activities: List
As we discussed at the beginning of this report, we find that high income
households have a large share of income coming from non-farm activities. In this
section, we describe in what non-farm activities people are engaged and who are going
into non-farm activities. We list non-farm activities, starting from the most common
non-farm activity to the least common (Table 23). The most common non-farm
activity is regular earners. We defined a regular earner as someone who earns constant
monthly wage throughout a year. Among the 940 sampled households, we found 152
individuals are engaged in this activity. About 21 percent of them are women. They
have over 9 years of education and 8 years of experience on average and earn about
$743 over a year.
The other activities are all seasonal. Except farm labors and construction
workers, all other activities are self-employment non-farm activities. Some non-farm
self-employment activities, such as brewing, provide income to women. The average
income generated from brewing was about $319. People who are engaged in brewing
do not have as much education as wage earners but have about the same level of
experience. Women are also quite active in trading farm products, fish, non-food
goods, and timber. In some activities, there may be clear gender barriers against
women. For instance, only men are engaged in trading livestock, carpentry, brick
making, construction, and Boda Boda (bicycle transportation).
Although most of non-farm activities generate more than $200 of income per
year, farm labor only generates $93 per year on average. The average level of
education among farm labors is low at 3.6 years, which is the lowest level among the all
non-farm activities, but the average experience is over 9 years. Thus, the results
confirm the idea that farm labor is an activity for less educated and poor workers and
that increased farm wages, induced from new farm technologies and better marketing,
would help the poor. In the next sub-section, we estimate determinants of going into
non-farm activities, especially focusing on education and language skills.
8.2.
Participation in Non-farm Activities: Education vs. Language Skills
To understand the determinants of non-farm activity participation, we separate
non-farm activities into two: self-employment activities and wage earners.
4
For simplicity, we use the term “non-farm” loosely. We include farm-labor, for instance, in non-farm
activities, although this could be categorized as off-farm activity but not non-farm.
31
Self-employment activities include all the activities listed in Table 23, expect wage
earners. As we have seen in Table 23, wage earners have a high level of education and
earn a high income. And the number of individuals who are wage earners is
substantial compared with other activities. Therefore, we estimate the determinants of
being wage earners and engaged in self-employment activities by using multi-nominal
logit models. We also pay special attention to education and language ability.
Previous studies suggest the importance of education to go into non-farm activities, but
exactly what skills gained from education are important is not yet clear. Thus, we
focus on one of most important skills, language, in this report. The two language
variables are dummy variables for being able to communicate in more than one local
language and in English.
The results indicate that the local language skill is important in having
self-employment business activities but not regular works. Most of self-employment
activities involve trading across regions so that people may need to be able to
communicate in more than one local language. To have a regular work, some
secondary education or above increases the probability of having a regular work
(column B in Table 24). However, once we include a dummy variable for English
language skill, we find that the English ability is an important determinant in having a
regular work, which the dummy variable for the secondary education becomes
insignificant (column D). Thus, we find that the English skill is an important skill
learned in the secondary education and not much of the other skills (otherwise the
secondary education dummy would remain significant). Note, however, that we need
more careful work to determine if the English ability is the only skill that is required in
having a regular work or if the English ability is highly correlated with the secondary
school dummy and just representing the secondary education as a whole.
Note however that the results in Table 24 could be over-estimated as causal
effects on having self-employment activities and regular works because people may
obtain local and English language abilities as they work. Thus the causality may go
from jobs to language abilities instead of language abilities to jobs. Thus, we need
further careful analysis to determine the effects of language abilities to have non-farm
jobs.
The other variables indicate expected results that women are less likely to have
non-farm jobs. Asset value is important to be in self-employment activities but not in
regular works. This may suggest that self-employment activities require start-up
investments, while regular works only require high level of education (or the English
ability). Land size is negatively correlated with both self-employment and regular
32
activities, suggesting that individuals with little land are going into non-farm activities
to earn adequate income.
The analysis suggests the importance of education, including language abilities,
to earn non-farm income. This suggests that education could provide a pathway to
escape from poverty through non-farm activities. The next question, therefore, is what
factors influence investments in education. Especially in Uganda, as well as other
sub-Saharan African countries, many children are becoming orphans because of the
AIDS epidemic and there is a serious concern about investments in their education and
a potential consequence from it.
9. Education
9.1. Schooling System in Uganda
Finally, we examine school attendance among children (aged 7 to 14) and
young adults (aged 15 to 18) in our data. For the long-term economic development in
Uganda, education is one of the most important factors. The current education system
in Uganda is 7-4-2: 7-year primary education followed by 4-yaer lower secondary and
2-yaer upper secondary educations. According to the Ministry of Education and
Sports, the gross enrollment ratio for primary school was 127% in 2003 for all types of
schools (government, private and community schools). The ratios were 125 % for
girls and 130 % for boys in primary education. Thus, the current enrollment ratio for
primary school is quite high in Uganda. Uganda, however, had experienced a long
harsh time since the independence in 1962 and access to school had been devastated for
a long period until in the 1990s. After the take over of the government by the National
Resistance Army, headed by Yoweri Moseveni, in 1986, the situation has changed. In
1997, the Ugandan government introduced Universal Primary Education (UPE), which
eliminated the costs of schooling up to four children per household (of which at least
two had to be girls), and made considerable investments in the education sector. Since
then, Uganda has successfully achieved high participation rates in primary education.
9.2. School Attendance in the survey
Consistent with secondary data, over 90 percent of children aged 7 to 14 in the
data are attending school. Figure 8, shows school attendance rate by age. The total
attendance rate starts from 80 percent at age 7 and increase close to 100 percent at age
11. Then it starts declining at age 14 to 60 percent at age 18. At age 12, some
children start attending secondary school. At age 17, the attendance rate in secondary
school reached its peak at about 45 percent and then declines. Although not reported
33
in this report, we do not find any significant differences between boys and girls in
school attendance (see Yamano, Shimamura, and Sserunkuuma, 2004, for detail).
9.3. Schooling of Orphaned Children and Young Adults
Ever increasing number of orphans due to the AIDS pandemic is raising a great
concern about long-term investment in education among orphans, especially in
Sub-Saharan Africa (World Bank, 1999; UNAIDS, UNICEF, and USAID, 2004).
Responding to this concern, recent studies have examined orphans’ schooling by using
the orphan status (Case, Paxson, and Ableidinger, 2002; Ainsworth and Filmer, 2002)
and adult mortality (Ainsworth, Beegle, and Koda, 2002; Yamano and Jayne, 2003;
Evans and Miguel, 2004). In Uganda, Deininger, Garcia, and Subbarao (2003)
estimated the school attendance and the health investments in foster children, but they
were not able to distinguish orphans and non-orphans among the foster children. In
general, these studies find a significant but small impact of orphanage on school
attendance.
Most of these studies, however, have been focusing on orphans aged 7 to 14
partly because children in this age group were considered vulnerable and partly because
data on orphan status and schooling are available for this age group. For instance,
Demographic and Health Surveys only collect orphan status for children aged up to 14
years old. To slow down the spread of HIV among sexually active young people
through education, however, such education must take place in school. If the targeted
group is not attending schools, such education campaign does not reach the targeted
group. There is also anecdotal evidence that orphaned young adults are more likely to
drop out of school.
Thus, in Figure 9, we show school attendance ratio for three groups of children
and young adults. The first group is a group of non-orphaned children/young adults
who are living with both parents. Their attendance rates are higher than the other two
groups in general. The second group is children/young adults who have at least one
absent parent. An absent parent is alive but is living elsewhere for some reasons. A
parent, mostly a father, may be living in a city to find a job, or a child or young adult
could be sent away from his or her home to work or attend school. In any case, the
attendance rate of this group is lower than the first group, especially at age 17 and 18.
The third group is a group of orphaned children and young adults. Among
children aged 7 to 14, the average attendance rate is almost the same as the first group
of non-orphaned children living with both parents. However, among young adults the
34
attendance rate of orphaned young adults drops significantly. This is an important
finding because previous studies only looked at attendance rates between orphans and
non-orphans who are aged 7 to 14.
In Table 25, we summarize our findings in Figure 9. In Table 25, we find that
about 70 percent of orphaned young adults aged 15 to 18 attend school, while 79
percent of non-orphaned young adults attend school. The difference is slightly larger
among female young adults than among male young adults. When we divide the
households into two groups by the total asset value, the difference is also larger among
young adults in poor households, whose total asset value is below the median. Among
young adults in poor households, the difference between orphaned and non-orphaned
young adults is 15.4 percent, while it is only 6.1 percent among young adults in the
relatively less poor households.
10. Conclusions
This report summarizes findings from the 2003 REPEAT Survey, which was
financed by FASID and implemented in collaboration with Makerere University, in
Uganda. The survey selected 94 LC1s from the 107 LC1s surveyed by IFPRI and
Makerere University in 1999 and 2000. Although the IFPRI survey visited 4 to 5
households per LC1, the 2003 REPEAT Survey added 5 to 6 households, making the
total number of sampled households 940 (10 households per LC1).
This report highlights five major findings. First, although Uganda has seen
impressive improvements in social and economic conditions since the beginning of the
1990s, poverty still persists, especially in rural areas. The report identifies a national
poverty line at $119 per person per year and finds that about 52 percent of rural
households are living in poverty. These figures are comparable to previous figures
established by Appleton (2001) who found 49 to 56 percent of rural households living in
poverty in the 1990s. To reduce poverty in rural areas, policies aiming to increase
income through increased agricultural productivity and non-farm activities should be
given priority. The poverty profile found by the 2003 REPEAT Survey would be the
baseline profile for future rounds of the REPEAT surveys to measure changes in poverty
over time.
Second, the report finds differences in land investments across three major land
tenure systems in Uganda (freehold, customary, and the Mailo tenure systems). Along
with a recent view of the evolving theory of land rights, we find wide variations in
individualized land rights within the customary and Mailo tenure systems. We also
find that soil conservation practices are less likely to be adopted under the Mailo tenure
35
system than the freehold tenure system. Although this could be because of insecure
land rights among tenant farmers under the Mailo tenure system, further studies are
required to explore the reasons behind the low investments under the Mailo tenure
system.
Third, the report shows very low applications of fertilizer and other soil
conservative practices, compared with neighboring countries, such as Kenya. Farmers
in Uganda have been benefiting from the endowment of fertile soil for a long time.
However, soil degradation has become a major problem. Thus, policies that promote
more fertilizer use (both chemical and organic) by encouraging the development of
inputs markets and means to increase organic fertilizer applications should be
considered.
Fourth, the report focuses on an intensive farming system, called the Organic
Green Revolution technology, which appears promising in the highlands of East Africa.
This farming system intensifies synergies between improved dairy production and crop
production via animal manure and feeding stuff. The evidence shows that more
manure is applied on crops under the improved dairy production system. Because
irrigation systems are virtually absent and chemical fertilizer is very expensive in
Sub-Saharan Africa, the Organic Green Revolution technology seems to be a promising
alternative farming system in highlands of East Africa. Policy makers and donors may
be able to help farmers obtain improved dairy cows by providing in-kind credit as some
NGOs (notable Heifer International or Send A Cow) do in Kenya or FINNIDA did in
Ethiopia or by helping to improve infrastructure to transport milk from rural areas to
urban cities.
Fifth, increased farm income may not be sufficient for the poor to escape from
poverty in the long-run. Considering the importance of non-farm income, the report
examines determinants of participations in non-farm activities. The report finds that,
in addition to formal education, different language skills are important to enter non-farm
self-employment activities and regular wage work. Local language abilities are
important to enter non-farm self-employment activities, while the English language skill
is a key determinant to have regular wage work. More research is needed to identify
the specific skills required in labor markets to use the education expenditure more
efficiently.
Sixth, we find that orphaned young adults aged 15-18 are less likely to be in
school than non-orphaned young adults. Since secondary education is a key factor in
labor markets, the results indicate that orphaned young adults would be disadvantaged
in labor markets. Education among young adults is also important in preventing the
36
further spread of HIV in Uganda. Thus, it is important to provide help to orphaned
young adults to attain a sufficient level of education.
We hope to revisit sampled households repeatedly in the future so that we can
track changes in their poverty status, income profiles, and all income generating
activities because we believe that effective policy making requires solid empirical
evidence of factors affecting the persistency of and escape from poverty.
37
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39
Table 1.
Sampled Communities and Households
Sampled Local Council 1s
Sampled Households
Number of Household
Farm size
Agro-climatic
Number of
Average
Population
zone
LC1s
number of
density
households
size
(C)
(D)
(E)
(F)
Number
Number
Acres
households
(A)
(B)
Number
Number
Bi-Low Rainfall
12
137
100
120
7.5
10.6
Bi-Med Rainfall
20
123
100
199
7.7
9.9
Bi-High Rainfall
31
250
371
309
7.9
4.9
Uni-modal Rainfall
8
171
480
80
7.5
4.4
Eastern Highlands
8
62
98
80
7.1
3.9
SW Highlands
15
99
154
150
7.3
4.4
All
94
163
234
940
7.6
6.5
Persons per
KM2
Note: Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
40
Table 2.
Per Capita “Cash” Expenditure by Agro-climatic Zone
Per capita
Agro-climatic
zone
Cash Expenditure Share
Cash
Fresh food Non-fresh
Expenditure
(A)
Non-food
Social
Staple food
items
food items
items
activities
(B)
(C)
(D)
(E)
(F)
US$
Bi-Low
139
13.5
15.8
11.0
45.3
14.4
Bi-Med
68
10.9
22.6
15.3
43.2
8.0
Bi-High
112
9.3
19.6
15.2
48.1
7.8
Uni
142
18.6
18.8
17.0
40.1
5.5
Eastern High Lands
117
19.0
14.6
17.9
40.0
8.5
SW High Lands
94
11.7
15.3
13.9
41.4
17.6
All
106
12.2
18.6
14.9
44.2
10.1
Note: Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
41
Table 3.
Home and Total Expenditure by Agro-climatic Zone
Per capita expenditure
Agro-climatic
zone
Home Consumption of
own products
(C)
Total Expenditure
(A)
Cash Expenditure
(B)
Bi-Low
Bi-Med
Bi-High
Uni
Eastern High Lands
SW High Lands
191
104
144
176
156
134
- US$ per capita 139
68
112
142
117
94
52
37
32
34
39
40
All
144
106
37
Note: Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone /
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
42
Table 4.
Poverty Comparison
Poverty: Head Count Ratio
Agro-climatic
zone
PPP$1 a day
poverty line Food poverty
Poverty Gap
Squared Poverty
Gap
National
poverty
(A)
(B)
(C)
(D)
(E)
Bi-Low
Bi-Med
Bi-High
Uni
Eastern High Lands
SW High Lands
21.6
48.2
41.7
38.8
26.3
44.7
21.7
48.2
41.7
38.8
26.3
44.0
36.7
56.8
52.8
51.3
42.5
52.0
28.8
39.9
44.0
38.2
39.8
48.3
12.4
20.9
24.4
18.5
24.7
28.7
All
39.9
39.3
52.3
39.8
21.6
Note: Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
43
Table 5.
Derivation of a Simplified Food Based Poverty Line
Quantity
Price
Cost per
Calories
Retention
Calories
per day
ratio
per kg
(A)
(B)
(C)
(D)
(E)
(F)
Matooke (Matoke)
366
0.50
770
28.52
125
3,565
Sweet potatoes
812
0.70
1,020
34.12
100
3,412
Cassava
684
0.89
2,557
9.02
150
1,353
Irish Potatoes
11
0.85
750
0.52
125
65
Rice
7
1.00
3,600
0.06
500
29
Maize (grain)
213
0.90
3,470
2.05
200
409
Millet
158
0.65
3,231
2.56
250
564
Sorghum
163
0.90
3,450
5.25
600
31
Eggs*
66
1.00
86 /egg
23 eggs
100
2300
Milk
12
1.00
640
0.56
202
114
Ghee
18
1.00
8,570
6.30
2,750
173
Passion Fruit
2
0.75
920
0.09
200
17
Sweet Matooke
51
0.56
1,160
2.36
91
214
Tomatoes
4
0.95
200
0.63
160
101
Cabbages
2
0.78
230
0.33
100
33
Beans (fresh)
19
0.75
1,040
0.73
182
133
Beans (dry)
236
0.75
3,300
2.86
260
744
Groundnuts
43
0.93
2,350
0.59
683
403
Sim-sim
89
1.00
5,930
0.45
600
270
Sugar
44
1.00
3,750
0.35
1,000
350
per month
required
per kg
3,000
month
(UShs)
14,280**
Note: * eggs are a substitute for all meats.
** the annual food poverty line is at Shs 171,360 ($90.2) per adult equivalent.
44
Table 6.
Per Capita Income and Income Share by Agro-climatic Zone
Agro-climatic
zone
Per capita
Income
(A)
Farm income
(B)
US$
Income Share
Livestock
Non-farm
income
activities
(C)
(D)
- percent -
Wage income
(E)
Bi-Low
161
62.8
15.2
15.1
6.9
Bi-Med
96
65.1
12.0
16.8
6.0
Bi-High
117
59.6
9.4
21.8
9.1
Uni
142
51.1
19.6
19.8
10.5
Eastern High Lands
108
60.0
15.8
22.5
1.9
SW High Lands
107
62.5
9.8
17.9
9.8
All
118
60.9
12.1
19.2
7.8
Note: Farm income: production value minus feed costs, fertilizer costs, and hired labor and oxen.
Livestock income: sales and home consumption of animals and livestock products, such as milk
and eggs, minus expenditure on purchased feeds, expenditure on A.I., animal health, and hired
labor.
45
Table 7.
Land Tenure Systems by Agro-climatic Zones—Plot Level
Agro-climatic
zone
Total number
Tenure System
of plots
Freehold
Customary
Mailo
(A)
(B)
(C)
(D)
- number -
- percent -
Bi-Low
218
70
0
30
Bi-Med
390
26
57
17
Bi-High
587
19
54
27
Uni
234
15
85
0
Eastern High Lands
257
7
73
0
SW High Lands
648
85
15
0
2,334
41
46
13
All
Note: Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
46
Table 8. Land Acquisition and Land Tenure and Tenancy—Plot Level
Total number
Tenure-Tenancy
of plots
Land Acquisition Mode
Purchased Inherited
(A)
- number -
(B)
(C)
Rented a
(D)
Borrowed Walked in
(E)
(F)
- percent - - percent - - percent - - percent - - percent -
Freehold – Owners
814
55
42
1
0
2
Freehold – Occupants
65
25
12
8
52
3
Freehold – Tenants
88
0
0
92
8
0
Customary – Owners
855
46
54
0
0
0
Customary – Occupants
166
15
43
10
32
0
Customary – Tenants
59
0
0
98
2
0
Mailo – Owners
162
50
48
0
1
1
Mailo – Occupants
123
41
28
4
26
1
Mailo – Tenants
13
8
0
84
8
0
2,345
43
43
8
5
1
All
Note: (a) Only 17 cases out of 187 cases of plots were rented via share-cropping agreements.
rest (170 cases) were rented via fixed rents.
47
The
Table 9.
Land Rights by Land Tenure and Tenancy—Plot Level
Individualized Land Rights a
Total number
Tenure-Tenancy
of plots
To Plant
To sell
To give
timber
trees
(A)
- number -
(B)
(C)
(D)
To plant
Other trees
(E)
Has title/
certificate
(F)
- percent - - percent - - percent - - percent - - percent -
Freehold – Owners
820
81
85
94
93
5
Freehold – Occupants
67
30
31
38
38
0
Freehold – Tenants
88
1
1
5
5
0
Customary – Owners
871
64
74
95
96
4
Customary – Occupants
166
20
50
64
74
1
Customary – Tenants
61
0
8
8
8
0
Mailo – Owners
167
74
75
94
96
14
Mailo – Occupants
125
48
30
67
61
2
Mailo – Tenants
13
8
0
5
5
0
2,334
62
68
82
84
4
All
Note: (a) Can sell, give, plant timber trees, or plant non-timber trees without any approvals from
extended families, land owners, or local authorities.
48
Table 10.
Land Owners’ Individualized Right to Sell and the Tenure System
Agro-climatic
zone
Dominant Tenure
Tenure System (Land Owners Only)
System
Freehold
Customary
Mailo
(A)
(B)
(C)
(D)
- number -
- percent -
Bi-Low
Freehold
84
-
73
Bi-Med
Customary
81
53
62
Bi-High
Customary
85
68
77
Uni
Customary
100
55
-
Eastern High Lands
Customary
79
67
-
Freehold
79
85
-
81
64
73
SW High Lands
All
Note: Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
49
Table 11.
Land Owners’ Individualized Rights to Sell–Plot Level Analysis
Variables
LC1 Characteristics
Ratio of Freehold Plots in LC1
Probit
(A)
Probit
(B)
0.348
(5.86)**
0.011
(2.78)**
-0.078
(1.85)
0.025
(0.88)
0.242
(2.95)**
0.199
(2.03)*
0.096
(0.48)
0.012
(2.92)**
-0.077
(1.83)
0.024
(0.86)
0.042
(1.15)
0.114
(2.65)**
-0.367
(15.37)**
0.017
(0.88)
-0.024
(3.44)**
0.012
(0.64)
-0.062
(0.96)
0.046
(0.61)
-0.363
(15.10)**
0.017
(0.88)
-0.025
(3.54)**
0.010
(0.54)
Ratio of Freehold x Customary
Ratio of Freehold x Mailo
Population Density
(100 persons/km2)
Ratio of public grazing land out of
total LC1 land size
Markets available within LC1 (=1)
Plot Characteristics
Customary (=1 vs. freehold )
Mailo (=1 vs. freehold )
Plot is inherited (=1 vs. purchased )
ln (Years acquired)
ln (Distance from home compound)
ln (Plot size)
Household Characteristics
Female headed (=1 vs. male headed)
-0.196
-0.200
(4.63)**
(4.71)**
ln (Age of household head)
-0.016
-0.012
(0.35)
(0.27)
0.002
0.002
Max. years of male schooling
(0.61)
(0.61)
0.014
0.013
Max. years of female schooling
(4.03)**
(3.91)**
Number of Male adults
0.030
0.030
(3.10)**
(3.13)**
Number of Female adults
-0.035
-0.035
(4.32)**
(4.29)**
1,761
Number of observations
1,761
Note: * and ** indicate 5 and 1 percent significance level, respectively. Estimated coefficients
are marginal changes in probability. Agro-climatic zone dummies are included in the
estimations.
50
Table 12.
Soil Conservation Practices—Plot Level Analysis
Soil Conservation Practices
Fallow in the
Manure
last three
Mulching
application
seasons
(C)
(D)
(E)
Use of crop
residues
- percent -
- percent -
- percent -
- percent -
- percent -
2,345
13
17
14
12
29
1,831
14
19
16
13
31
Occupants
354
7
12
5
10
27
Tenants
160
10
8
9
1
11
879
16
25
17
12
28
1,021
12
13
15
11
30
285
6
13
5
19
41
Total
number of
plots
Fertilizer
application
(A)
(B)
- number All Plots
Owners
Tenure,
tenancy, and
land rights
(F)
Tenants Excluded
Freehold
Customary
Mailo
Note: Numbers in brackets are p-values.
51
Table 13. Soil Conservation Practices—LC1 Fixed Effects Model
Variables
Land Tenure-Tenancy
Customary (=1)
Mailo Land (=1)
Occupant Plot (=1)
Tenant Plot (=1)
Plot Characteristics
ln (Years acquired)
ln (Distance to plot)
ln (Plot size)
Female headed (=1)
ln (Age of household
Max. years of male
Max. years of female
Number of Male adults
Number of Female
Constant
Fertilizer
application
Manure
application
(A)
(B)
Fallow in
the last 3
seasons
(C)
0.040
(1.60)
0.014
(0.41)
-0.041
(1.79)
-0.061
(1.97)*
0.010
(0.35)
-0.089
(2.33)*
-0.048
(1.89)
-0.096
(2.80)**
-0.030
(2.68)**
0.008
(1.57)
0.007
(0.50)
0.001
(0.04)
0.037
(1.26)
-0.004
(1.52)
0.005
(1.91)
0.010
(1.75)
-0.005
(0.79)
0.019
(0.19)
-0.016
(1.32)
-0.018
(3.37)**
0.010
(0.70)
-0.015
(0.53)
0.011
(0.33)
-0.003
(1.29)
0.010
(3.51)**
0.005
(0.74)
-0.008
(1.21)
0.186
(1.69)
Mulching
Use of crop
residues
(D)
(E)
-0.050
(1.96)
-0.084
(2.37)*
-0.085
(3.58)**
-0.093
(2.95)**
0.022
(0.95)
-0.109
(3.41)**
-0.020
(0.93)
-0.043
(1.50)
0.045
(1.51)
0.040
(0.96)
-0.060
(2.18)*
0.006
(0.16)
-0.008
(0.71)
0.024
(4.72)**
0.068
(5.04)**
-0.024
(0.92)
0.020
(0.67)
0.002
(0.89)
0.001
(0.29)
0.000
(0.05)
0.005
(0.75)
0.011
(0.11)
0.002
(0.25)
-0.027
(6.04)**
0.040
(3.32)**
-0.030
(1.27)
0.027
(1.00)
0.005
(2.10)*
0.001
(0.47)
-0.011
(1.95)
0.010
(1.71)
-0.004
(0.05)
0.023
(1.80)
-0.120
(20.49)**
0.069
(4.40)**
-0.011
(0.36)
-0.025
(0.71)
0.003
(1.07)
0.004
(1.35)
-0.014
(1.91)
-0.004
(0.62)
0.450
(3.82)**
2320
96
2320
96
Number of obs
2320
2320
2320
Number of LC1s
96
96
96
Note: * and ** indicate 5 and 1 percent significance level, respectively.
52
Table 14.
Slush and Burn and Tree Planting—Plot Level Analysis
Tenure, tenancy, and
land rights
Total number
Slush and Burn
of plots
Ever Planted Trees
(A)
(B)
Other Trees
(C)
Timber trees
(D)
- number -
- percent -
- percent -
- percent -
All Plots
2,345
24
34
13
Owners
1,831
23
38
15
Occupants
354
30
27
8
Tenants
160
28
0
1
879
15
28
9
1,021
30
40
18
285
29
49
14
Tenants Excluded
Freehold
Customary
Mailo
Note: Numbers in brackets are p-values.
53
Table 15. Slush and Burn and Tree Planting—LC1 Fixed Effects Model
Variables
Land Tenure –Tenancy
Customary (=1 vs. Freehold)
Mailo Land (=1 vs. Freehold)
Occupant Plot (=1 vs. Owner)
Tenant Plot (=1 vs. Owner)
(A)
Planting
non-timber trees
(B)
Planting timber
trees
(C)
-0.079
(2.67)**
-0.017
(0.41)
0.041
(1.49)
0.057
(1.54)
-0.029
(1.22)
-0.025
(0.77)
-0.013
(0.57)
0.007
(0.22)
-0.005
(0.15)
0.034
(0.79)
-0.073
(2.59)**
-0.091
(2.40)*
Slush and Burn
Plot Characteristics
ln (Years acquired)
-0.007
0.043
(0.51)
(4.10)**
ln (Distance from homestead)
0.007
-0.027
(1.22)
(5.77)**
ln (Plot size)
0.046
0.095
(2.89)**
(7.55)**
Female headed (=1)
-0.047
-0.042
(1.54)
(1.75)
-0.019
-0.016
ln (Age of household head)
(0.55)
(0.56)
0.008
-0.000
Max. years of male schooling
(2.86)**
(0.00)
Max. years of female schooling
0.007
0.003
(2.52)*
(1.24)
Number of Male adults
-0.014
-0.009
(1.97)*
(1.65)
Number of Female adults
-0.005
-0.002
(0.73)
(0.42)
Constant
0.238
0.072
(2.00)*
(0.76)
Number of observations
2320
2320
Number of households
96
96
Note: * and ** indicate 5 and 1 percent significance level, respectively.
54
0.116
(8.62)**
-0.073
(12.13)**
0.089
(5.51)**
-0.020
(0.66)
-0.022
(0.62)
0.004
(1.54)
0.003
(1.00)
-0.016
(2.21)*
0.008
(1.10)
0.160
(1.32)
2320
96
Table 16.
Soil Nutrient and Texture by Agro-climatic Zone
Soil Nutrient
Agro-climatic
zone
Soil Texture
PH
OM
P
K
Ca
Sand
Clay
Silt
(A)
(B)
(C)
(D)
(E)
(F)
(G)
(H)
%
Mg/kg
Cmolc/kg
-%-
Bi-Low
6.30
5.17
70.1
0.99
3.68
57.7
30.7
11.6
Bi-Med
6.32
5.40
148.2
0.80
4.31
65.2
22.9
11.8
Bi-High
6.39
3.79
32.1
0.72
3.12
62.4
27.7
9.9
Uni
6.26
5.53
103.9
1.62
6.55
45.2
39.0
15.8
E Highlands
6.30
5.44
83.4
1.52
6.31
37.7
44.1
18.2
SW Highlands
5.64
6.94
61.5
1.23
3.18
53.4
25.9
20.7
All
6.27
4.91
70.1
0.99
4.07
57.3
29.7
12.9
Note: * Animal manure includes compost.
Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
55
Table 17.
Crop Production– Value Production at the Household Level
Percentage
Area devoted
of producer
Production value
Producers
Production
Producers
value per
households
All
only
All
only
acre
(A)
(B)
(C)
(D)
(E)
(F)
%
acres
acres
US$
US$
US$/acre
Beans
75.8
3.34
4.33
28.3
36.7
8.5
Maize
74.9
2.99
3.90
50.3
65.5
16.8
Matooke (Matoke)
67.3
8.25
11.8
132.5
189.8
16.1
Sweat potato
51.7
0.62
1.14
29.9
55.3
48.5
Cassava
48.4
1.64
3.06
30.5
56.9
18.6
Coffee
31.0
2.66
7.64
19.7
56.2
7.4
Groundnuts
26.4
0.48
1.77
9.9
36.3
20.5
Millet
19.9
0.27
1.29
5.7
27.5
21.3
Sorghum
15.2
0.24
1.55
4.6
29.1
18.8
Peas
13.2
0.23
1.68
3.6
26.4
15.7
Irish potato
12.3
0.16
1.30
5.8
46.4
35.7
Fruits
9.6
n.a.
n.a.
7.6
79.0
n.a.
Industrial crops
8.4
0.44
4.19
10.1
96.5
23.0
Other vegetables
7.6
0.20
2.45
6.0
70.9
28.9
Rice
3.2
0.07
2.05
4.7
145.3
70.9
Wheat
1.3
0.01
0.68
0.2
12.8
18.8
56
Table 18.
Percentages of Producer Households of Major Crops
Agro-climatic
Percentage of producer households
Maize
Matooke
Coffee
S. Potato
Cassava
Rice
(A)
(B)
(C)
(D)
(E)
(F)
Bi-Low
80.0
81.7
22.5
45.0
45.0
0
Bi-Med
76.9
54.3
13.1
72.4
64.8
13.1
Bi-High
79.9
68.0
42.4
70.6
63.4
1.9
Uni
72.5
67.5
42.5
23.8
37.5
3.8
Eastern High Lands
61.3
75.0
56.3
5.0
16.3
0
SW High Lands
67.3
68.7
20.7
32.0
18.7
0
All
74.9
67.3
31.0
51.7
48.4
3.2
zone
Note: Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
57
Table 19.
Chemical and Organic Fertilizer Application
Agro-climatic
zone
Adoption
of
improved
seeds
(A)
Percentage of households
using
Fertilizer application
Chemical
fertilizer
Animal
Manure*
Chemical
fertilizer
Animal
Manure*
(B)
(C)
(D)
(E)
- percent -
- percent -
- kgs/acre -
Bi-Low
72.9
2.5
20.8
0.1
138.8
Bi-Med
53.7
4.0
4.5
0.3
9.8
Bi-High
56.7
2.6
14.6
0.3
71.0
Uni
74.1
10.0
10.0
0.7
68.1
E Highlands
83.7
32.5
22.5
9.8
140.8
SW Highlands
52.5
9.3
27.3
4.6
89.7
All
61.0
7.1
15.6
1.9
75.3
Note: * Animal manure includes compost.
Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
58
Table 20.
Maize Yield, Improved Seed, and Fertilizer Application
Maize
Agro-climatic
Percentage of maize plots
Average fertilizer
applied fertilizer
application on maize plots
Chemical
fertilizer
Animal
Manure*
Chemical
fertilizer
Animal
Manure*
- tons/ha -
Adoption
of
improved
seeds
- percent -
- percent -
- percent -
- kgs/acre -
- kgs/acre -
Bi-Low
0.58
72.9
0.5
2.6
0
5.9
Bi-Med
1.01
53.7
0.9
2.0
0
3.7
Bi-High
0.82
56.7
1.4
2.6
0.3
19.7
Uni
0.58
74.1
5.9
4.2
0.8
45.0
E Highlands
0.71
83.7
15.3
9.2
6.7
65.2
SW Highlands
0.57
52.5
5.4
9.9
3.1
28.9
All
0.77
61.0
3.2
4.1
1.1
21.4
zone
Yield
Note: * Animal manure includes compost.
Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
59
Table 21.
Matoke Yield and Fertilizer Application—Plot Level
Percentage of
matoke plots applied
animal manure*
Average manure
- tons/ha -
- percent -
- kgs / ha -
Bi-Low
2.95
11.2
189.7
Bi-Med
2.59
3.2
31.0
Bi-High
1.38
10.3
152.7
Uni
1.20
3.1
37.4
Eastern Highlands
1.30
7.3
90.8
SW Highlands
3.93
11.5
154.0
All
2.12
8.3
117.2
Agro-climatic
zone
Matoke yield
application on
matooke plots
Note: * Animal manure includes compost.
Bi-low rainfall zone (lower elevation parts of southwestern Uganda), Bi-Medium
Rainfall zone (most of central and parts of western Uganda), Bi-High Rainfall zone
(mostly the Lake Victoria crescent), Uni-modal Rainfall zone (much of northern
Uganda), SW Highlands (1500m a.s.l. and above), and Eastern Highlands (1500m a.s.l.
and above).
60
Table 22.
Livestock Ownership—Household Level
In 2003
% of
households
Average
Average
number owned
number owned
among owners
Average Price
%
number
number
US$
Local cows
29.3
0.90
3.08
105
Local bulls
10.3
0.22
2.09
112
Local young bulls
11.5
0.27
2.37
53
Local heifers
15.6
0.34
2.16
79
Local calves
18.4
0.38
2.07
37
Improved cows
6.9
0.18
2.58
200
Improved bulls
1.5
0.02
1.29
153
Improved young bulls
2.6
0.06
2.38
63
Improved heifers
3.6
0.10
2.88
158
Improved calves
4.7
0.11
2.25
42
Local goats
46.8
2.16
4.63
11
Improved goats
2.2
0.16
7.14
16
Sheep
8.8
0.29
3.23
11
Local chicken
61.6
4.7.
7.68
1.4
Improved chicken
1.6
0.64
39.9
1.6
Local pigs
21.6
0.50
2.33
16
Improved pigs
0.6
0.08
1.33
21
Donkeys
0.3
0.05
1.67
22
Ducks
5.6
0.23
4.15
1.6
Cattle
Other Animals
61
Table 23.
Improved and Local Cow Distribution across Agro-climatic Zone
Improved Cattle
Agro-climatic
zone
% of
households
(A)
Local Cattle
Average
Average
number
% of
number among HHs households
who own
(B)
(C)
(D)
Average
Average
number
number among HHs
who own
(E)
(F)
Bi-Low
11.2
0.4
3.5
37.8
3.2
8.6
Bi-Med
3.7
0.1
2.3
51.3
3.1
6.0
Bi-High
11.0
0.3
2.8
28.4
1.3
4.5
Uni
13.8
0.2
1.7
51.3
2.1
4.2
Eastern High Lands
15.0
0.3
1.9
52.5
1.2
2.3
SW High Lands
7.4
0.4
4.8
24.8
1.4
5.6
All
9.5
0.3
2.8
37.7
2.0
5.2
62
Table 24.
Adoption of Improved Cattle – Household Level Analysis
Probit
Variables
(A)
Household Characteristics
Max. years of male schooling
0.004
(1.88)*
0.003
(1.20)
0.009
(1.82)*
-0.005
(0.85)
0.011
(2.06)**
-0.004
(0.63)
-0.023
(1.03)
0.019
(1.91)*
0.032
(4.48)**
Max. years of female schooling
Number of Male adults
Number of Female adults
Number of Boys
Number of Girls
Female headed household (=1)
ln(Land size in acre)
ln (Asset value)
Community Level Characteristics
Market outside LC 1 (=1)
0.001
(0.07
-0.105
(3.24)**
Included
Ratio of public grazing land out of total LC1
land size
Agro-climatic zone dummies
Number of observations
939
Note: * and ** indicate 10 and 5 percent significance level, respectively. Estimated
coefficients are marginal changes in probability.
63
Table 25.
Milk Production Management
Local Cows
(A)
Improved Cows
(B)
201
52
0
38.5
Average number of milking cows
1.99
2.13
Monthly milk production in liter
103.7
257.8
Proportion of milk sold (%)
20.4
47.5
Individual customers
25.6
54.5
Local markets
4.0
11.9
Restaurants/ Hotels
4.3
5.9
Cooperative/ Private Processing
4.0
5.0
No buyer type
62.1
22.7
1.2
20.9
A.I. service ($)
0
1.3
Bull service ($)
0.4
1.8
Health service ($)
21.8
18.6
Number of Milk producers
Stall feeding (%)
Main buyer type
Total Expenditure
Purchased Feeds ($)
64
Table 26.
Livestock Production System and Manure Application on Crops
Livestock
Production System
Number of
households
Percentage
of
households
using
manure
Quantity of Manure applied on
farm
total
per cattle
per acre
- number (%) Local vs. Crossbred(CB) Cattle
Local Cows Only
848 (90.2)
Some CB Cows
75 (9.8)
Difference
-%-
kgs
kgs/cattle
kgs/acre
13.8
31.5
+ 17.7
144.4
791.6
+647.2
68.8
224.9
+156.1
59.7
228.4
+168.7
Grazing vs. Stall feeding
Grazing only
865 (92.0)
Some Stall-feeding
75 (8.0)
Difference
14.7
25.3
+10.6
181.9
498.3
+316.4
84.8
192.3
+107.5
66.3
173.7
+107.4
65
Table 27.
Manure Application and Improved Cattle–Plot Level Analysis
Manure Application (=1)
Quantity of Manure
Probit
Tobit
(A)
(B)
0.044
(2.69)**
0.020
(4.07)**
837.4
(3.30)**
231.8
(3.54)**
-0.029
(1.54)
0.004
(0.41)
-0.027
(6.08)**
-0.014
(1.18)
-975.1
(2.45)*
5.702
(0.04)
-458.8
(5.93)**
-234.1
(1.25)
Variables
Number of Cattle per Acre
Improved cattle per acre
Local cattle per acre
Plot Characteristics
Not cultivated in 2002 (=1)
ln (Years since acquired)
ln (Distance from home compound)
ln (Plot size)
Household Characteristics
Female headed household (=1)
0.003
(0.14)
-0.037
ln (Age of household head)
(1.49)
0.001
Max. years of male schooling
(0.27)
Max. years of female schooling
0.005
(2.42)*
Number of Male adults
-0.007
(1.52)
Number of Female adults
0.005
(1.01)
ln (Asset Value in Shs)
0.008
(1.31)
Yes
Agro-climatic zone dummies
Number of observations
1,970
Note: * and ** indicate 5 and 1 percent significance level, respectively.
are marginal changes in probability.
66
62.73
(0.20)
-447.6
(1.09)
-15.14
(0.49)
85.39
(2.65)**
-113.3
(1.46)
109.2
(1.35)
261.2
(2.72)**
Yes
1,970
Estimated coefficients
Table 28.
Maize and Matoke Yields by Fertilizer Application
Crop Yield with and without organic fertilizer
All
Crop (Seed Type)
Without any
With organic
With organic
fertilizer
fertilizer
and chemical
fertilizer
(A)
(B)
(C)
(D)
- tons / hectare Maize (Local)
Maize (Improved)
Matooke
0.71
0.70
1.15
(n = 728)
(n = 696)
(n = 20)
0.84
0.79
0.98
1.97
(n = 836)
(n = 776)
(n = 28)
(n = 32)
2.24
2.20
2.88
n.a.
(n = 1,761)
(n = 1,608)
(n = 141)
67
n.a.
Table 29.
Crop Yield and Fertilizer Application– Plot Level Analysis
Maize Yield
Matoke Yield
(A)
(B)
0.118
(0.62)
0.503
(2.18)*
0.241
(3.32)**
0.359
(2.91)**
n.a.
-0.032
(0.59)
-0.016
(0.65)
-0.151
(2.32)*
0.136
(2.22)*
-0.002
(0.06)
-0.212
(3.76)**
Variables
Number of Cattle per Acre
Manure Application (=1)
Chemical Fertilizer Application (=1)
Improved Seeds (=1)
Plot Characteristics
ln (Years since acquired)
ln (Distance from home compound)
ln (Plot size)
Household Characteristics
Female headed household (=1)
0.002
(0.02)
-0.101
ln (Age of household head)
(0.71)
0.023
Max. years of male schooling
(1.93)
Max. years of female schooling
-0.006
(0.48)
Number of Male adults
0.132
(4.44)**
Number of Female adults
-0.056
(1.85)
ln (Asset Value in Shs)
0.064
(1.56)
0.127
Second Season (=1)
(1.85)
5.072
Constant
(7.55)**
Agro-climatic zone dummies
Yes
Number of observations
1,460
Note: * and ** indicate 5 and 1 percent significance level, respectively.
are marginal changes in probability.
68
n.a.
0.103
(0.89)
-0.338
(2.38)*
0.021
(1.86)
-0.007
(0.61)
-0.016
(0.59)
0.018
(0.64)
0.166
(4.39)**
-0.003
(0.04)
5.796
(9.26)**
Yes
1,738
Estimated coefficients
Table 30.
Non-farm Self-Employment and Wage Activities
Age
Number of % of
individuals female
Years of Years of Income
education experience (USD)
(A)
(B)
(C)
(D)
(E)
(F)
Wage earner
Brewing
Trading farm product
Farm labor
General-kisok owner
Trading Livestock
152
85
82
80
59
16
21
47
15
25
15
0
38
38
36
35
38
33
9.3
5.1
6.3
3.6
6.3
6.9
8.3
7.8
6.5
9.4
4.9
4.8
743
319
587
93
676
762
Charcoal burning
Trading Fish
Carpentry
Trading non-food goods
Brick making
Construction
Boda Boda
Trading Timber
Others
15
15
14
13
12
12
10
10
84
27
20
0
15
0
0
0
10
31
34
36
36
42
34
35
34
40
37
6.3
5.3
6.5
7.8
5.7
8.3
6.2
7.4
5.9
5.6
6.8
6.5
7.2
8.6
10.8
5.9
7.3
7.4
198
307
319
751
376
380
395
630
494
Note: 1$=1,850Shs
69
Table 31.
Determinants of Participation in Business and Labor Activities
Multi-nominal Logit
Multi-nominal Logit
Business
(A)
Regular (B)
Business
(C)
Regular (D)
0.10
(0.50)
0.20
(0.83)
-0.07
(0.23)
0.19
(0.42)
-0.14
(0.25)
1.23
(2.38)*
0.07
(0.35)
0.14
(0.49)
-0.15
(0.47)
-0.28
(0.57)
-1.08
(1.63)
0.29
(0.46)
0.41
(2.68)**
0.18
(0.63)
0.40
(2.63)**
0.10
(0.53)
0.10
(0.36)
1.12
(2.64)**
-1.19
(4.23)**
0.10
(3.05)**
0.00
(4.37)**
-2.09
(4.08)**
0.18
(3.25)**
0.00
(3.35)**
-1.18
(4.20)**
0.10
(3.04)**
0.00
(4.35)**
-2.05
(4.04)**
0.19
(3.26)**
0.00
(3.32)**
0.04
(1.76)+
-0.03
(1.21)
0.00
(0.05)
-0.08
(1.24)
0.11
(1.76)+
-0.24
(2.44)*
0.05
(1.15)
0.08
(1.70)+
-0.18
(1.24)
-0.17
(1.75)+
0.00
0.00
-0.26
(1.57)
0.04
(1.72)+
-0.03
(1.22)
0.00
(0.01)
-0.08
(1.27)
0.11
(1.73)+
-0.24
(2.43)*
0.04
(0.98)
0.07
(1.64)
-0.14
(0.99)
-0.18
(1.83)+
-0.01
(0.10)
-0.28
(1.65)+
Included
Included
Included
Included
Education
Some primary (=1)
Primary completed (=1)
Some secondary and
above (=1)
Languages
Can communicate in More
than one local language (=1)
English (=1)
Individual Characteristics
Female (=1)
Age
Age*Age
HH Characteristics
Highest education
among female
Highest education
among male
Number of female
Number of male
ln (Asset-value in
Shs)
ln (Land in acres)
Local Counsel 1 Dummies
Psedu R2
Observations
0.30
2,694
0.29
2,694
Note: Dummies for relationships to the head and marital status are also included in the model but not
reported here. ** indicates 1% significance level; * indicates 5% significance level; + indicates 10%
significance level.
70
Table 32.
Proportion of Orphans among Children/Young Adults (aged 0-18)
All
Non-
Orphans
Orphans
Maternal
Paternal
Double
(A)
(B)
(C)
(D)
(E)
100.0
88.7
3.0
5.7
2.7
Girls
49.4
43.9
1.2
2.8
1.4
Boys
50.6
44.8
1.7
2.8
1.2
Both parents
69.0
69.0
Father only
5.7
4.3
Mother only
9.5
6.1
Neither
15.9
9.3
1.6
2.3
Child
78.7
75.1
1.4
2.3
Grandchild
13.3
9.1
0.7
2.1
1.4
Other relatives
6.6
3.6
0.7
1.1
1.2
Non-relatives
1.4
0.9
0.2
0.2
0.1
All
Living with
1.4
3.4
2.7
Relationship to head
71
Table 33.
School Attendance Ratio
Children (aged 7-14)
Non-
Orphans
orphans
Young Adults (aged 15-18)
Dif.
Non-
B-A
orphans
Orphans
Dif.
E-D
(A)
(B)
(C)
(D)
(E)
(F)
93.8
92.9
-0.9
79.0
70.3
-8.7
Girls
93.3
94.1
0.8
78.8
69.2
-9.6
Boys
94.4
91.9
-2.5
79.3
71.4
-7.9
Poor (lower half)
91.8
87.6
-4.2
73.9
58.5
-15.4
Non-poor (upper half)
95.3
96.2
0.9
82.0
75.9
-6.1
All
Gender
Household Wealth
Living Arrangements
Living with both parents
94.7
At least one absent parent
91.2
83.5
92.9
65.5
70.3
** indicates 5% significance level; * indicates 10% significant level.
(a) The sampled households are divided into two groups based on the total value of the
assets and livestock each household owns.
72
Table 34. Determinants of School Attendance among Children and Young Adults
(Household Fixed Effect)
Children Aged 7-14
Young Adults Aged 15-18
(A)
(B)
-0.030
-0.254
(1.01)
(2.97)**
Non-orphans with at least
-0.048
-0.396
one absent parent (=1)
(1.82)*
(4.96)**
0.002
0.021
(0.18)
(0.46)
Child Characteristics
Orphan (=1)
Girl (=1)
Age 9-10 (=1)
Age 11-12 (=1)
Age 13-14 (=1)
0.128
(7.26)**
0.129
(7.17)**
0.111
(6.19)**
-0.167
Age 16 (=1)
(2.97)**
-0.157
Age 17 (=1)
(3.05)**
-0.361
Age 18 (=1)
(6.29)**
R-squared
0.06
0.21
Households
716
433
1,863
691
Children
** indicates 5% significance level; * indicates 10% significant level.
73
Figure 1.
Sampled Communities in Uganda
74
100%
Social
activities
80%
Non-food
items
60%
Non-fresh
food items
40%
Fresh food
items
20%
Staple
food
0%
1-25
26-50
51-75
76-100
percentile
Figure 2. Cash Expenditure Share by Per Capita Cash Expenditure Quartile
75
.006
0
.002
Density
.004
Median ($102)
0
Figure 3.
100
300
200
Per capita total expenditure (US$)
Distribution of Per Capita Total Expenditure
76
400
500
70
Food expenditure share
65
60
55
50
0
Figure 4.
100
200
Per capita total Expenditure
Food Expenditure Share
77
300
400
10 0%
Wage
income
8 0%
Non-farm
activities
6 0%
Livestock
income
4 0%
2 0%
Farm
income
0%
1 -2 5
2 6-5 0
5 1- 75
Figure 5. Income Share by Per Capita Income Quartile
78
76 -1 00
percentile
Napier Grass, Oats,
and Fodder Leaves
Feeds
Manure
Dairy Cows
and Goats
Manure
Crop residues
Figure 6.
Crop production
“Organic Green Revolution” in East Africa
79
Manure
Input
Isoquant Curves
Africa’s Pathway?
Asian Green Revolution
P2
P1
Chemical Fertilizer Input
Figure 7. Isoquant Curve: Organic and Chemical Fertilizer
The relative price of chemical fertilizer over manure has been low in Asia than
in Africa (P1 < P2).
80
%
100
80
60
all
40
primary
secondary
20
0
7
Figure 8.
8
9
10
11
12
School Attendance Ratio by Age
81
13
14
15
16
17
18 age
%
100
80
60
Non-orphans with both parents
40
Non-orphans with at least one absent parent
20
Orphans
0
7
Figure 9.
8
9
10
11
12
13
14
15
16
17
18 age
School Attendance Ratio by Orphan Status and Living Arrangements
82