Adaptation in a Changing Climate:

Practices of Smallholder Farmers in Uganda

Cassidy Bennett

Senior Thesis

Environmental Studies A.B. 2015

Advisor: Dr. Leah VanWey

Institute at Brown for Environment and Society

Deputy Director of Research;

Associate Professor of Sociology

Thesis Abstract

Date: May 3, 2015

Student Name: Cassidy Bennett

Practicum Advisor: Dr. Leah VanWey

Project Title/Subject: Adaptation in a Changing Climate: Practices of Smallholder Farmers in Uganda

Purpose of Research: As climate change takes place and the effects continue to spread and become more intense, a focus on adaptation is an important consideration. Farmers, and especially smallholder farmers, are some of the most susceptible to the effects of climate change. When the rain and weather are unreliable and changing from year to year it is much more difficult to count on consistent or normal yields. Uganda is one developing country that depends on smallholder farmers and is experiencing the effects of climate change. Though Uganda is not a large country, there are diverse climates, types of soil, farming techniques, crops, and other difficulties that farmers encounter. Generally, there are two periods of rain and one dry season. Farmers have worked with these conditions for their entire lives and learned from the generations before them how to best farm in these conditions. We wanted to find out how much climate variation farmers were experiencing, and if they were making changes in their practices in reaction to these climate changes. Methods: In order to address these research questions we used a large social survey data set collected in Uganda, and gridded climate data on precipitation and temperature. The survey data were analyzed using Stata and combined with the climate data. Climate data was mapped and analyzed using ArcGIS, Excel, and Stata. A regression model was used to analyze the variables and determine their significance in changing the use of slash and burn practices, herbicide and pesticide use, and use of inorganic fertilizer. Results, Conclusions, and Recommendations: The results indicate that farmers are responding to variability in recent weather, though primarily to changes in precipitation. This could be because farmers already adjusted to changes in temperature since this was already a factor in 2003 (across the country 14-18 of the core six months of the agricultural season had temperatures at least one standard deviation above the long term average). This research could be greatly influenced by qualitative interviews and further investment. Smallholder farmers in Uganda to have the ability to adapt to climate change, and many are willing and already making changes in order to adapt. These data and results could be much further analyzed, and could be well put to use by the Ugandan government in supporting farmers in adaptation as climate change continues.

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Introduction

As climate change takes place and the effects continue to spread and become more

intense, a focus on adaptation is an important consideration. In the developed countries, where

many of the causes of climate change originate, there are available resources and funds for

adaptation. However, in many developing countries resources are not as readily available to

devote to finding adaptations for climate change effects. These are often the countries most at

risk for weather related disasters or general climate change effects because of their specific

locations (Roberts and Parks, 2007). Farmers, and especially smallholder farmers, are some of

the most susceptible to the effects of climate change. They rely on consistent seasons to know

when and how to best plant and harvest their crops. When the rain and weather are unreliable and

changing from year to year it is much more difficult to count on consistent or normal yields.

One developing country that depends on smallholder farmers and is experiencing the

effects of climate change is Uganda. Though Uganda is not a large country, there are diverse

climates, types of soil, farming techniques, crops, and difficulties that farmers encounter.

Generally, there are two periods of rain and one dry season. Farmers have worked with these

conditions for their entire lives and learned from the generations before them how to best farm in

these conditions. While weather changes on a smaller scale over the course of history are a

constant, the seasons are now changing much more rapidly.

This thesis brings together data from several sources to study the effect of the climate on

farmers throughout Uganda. The data set on the farmers includes data from 2003 and 2013 from

eight different districts in Uganda, and over one hundred small communities. The data include

extensive survey information on the communities, the farmers, their individual plots, and the

soil. Comparing the data between the two years will allow us to determine what, if any, were the

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changes in the practices of the farmers. Combining this with climate data from the time period,

as well as using a baseline from the previous 50 years, will show what changes have occurred in

precipitation and temperature and how they are related to the practices of farmers. Use of

ArcGIS software will allow for specific data and mapping options with the climate data,

analyzing each community’s climate changes and linking them with social survey data. Logistic

regression analyses in Stata software then allow us to relate the farmer behavior to these climate

changes.

Uganda is an appropriate choice through which to study these topics in order to get a

better idea of the effects East Africa as a whole is facing. Many countries in sub-Saharan Africa

are experiencing very low farm productivity already, and as climate change continues, this will

continue to worsen (Omamo, 2005). Uganda has similarly experienced climate changes, and has

importantly seen different patterns of climate and climate change in different regions. Combining

information on practices within households over ten years with climate information for Uganda

over those ten years and including the historical climate information will allow for a

comprehensive overview of the effects that are already occurring and the possibilities for

adaptation and preparing for the future. This is only a small part of so much more work that

could be done with this dataset, especially if qualitative interview data were incorporated.

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Literature Review

Adapting to anthropogenic climate change presents new and important challenges.

Climate change impacts increase daily in new ways and we need to respond to that in order to

adapt most efficiently. While everyone will be affected, there are certain groups whose

adaptation to climate change will be critical to the survival of others. One such group is

smallholder farmers. Smallholder farms are small farms, often less than one acre, which are

typically owned and operated by a family. In developing countries, the farmers are often

subsistence farmers and rely on the farm for their food. Many are also able to produce enough to

provide food for others and make a small income. More than 90% of Africa’s agricultural

production depends on subsistence or local food production; 65% of the population are

themselves smallholder farmers that depend on food and income from their farms (IFPRI, 2004).

Smallholders, while they may be easily adaptable in some ways, also face larger threats

and constraints unique or especially intense due to their situation. Smallholders are often

diversified across a wider variety of income sources or crops, but they often have low capital

with which to adjust to risk and changes (World Bank, 2007). This can be good if there is a heat

wave or frost or flood or drought, because some of their crops will be better adapted than others

and they have a better chance of some crops surviving even while others fail. However it can

also mean that if they lose crops, they lose a lot of their income and food source because the

farms are so small and they generally lack insurance. Smallholder farmers are located

predominately in rural areas, which means they face additional disadvantages (World Bank,

2007). Farms are widespread and roads are difficult to traverse, so it can be difficult for farmers

to get their food to markets and for banks to be able to finance or insure them when they are not

easily accessible. These smallholder farmers are essential to feeding the world in a changing

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climate, but need to be able to adapt to the changing climate conditions in order to survive and

thrive.

This paper focuses on the experiences of smallholder farmers in Uganda, called the

“Pearl of Africa” by Winston Churchill due to its bountiful and vast vegetation and variety of

species. The soil is fertile and water is widely available. It is in a tropical area along the equator

with two annual rainy seasons, one having higher rains than the other, and two shorter dry

periods. Temperature stays fairly high throughout the year. Africa as a whole relies heavily on

agriculture in the economy and labor force, and in Uganda in particular, 60% of the work force is

in the agricultural sector (FAO, 2003). Recent climate change could threaten livelihoods of this

large population. Over the entire country, according to the climate data we studied, precipitation

has increased by 3% and temperature by 6% in the last decade relative to the second half of the

twentieth century. With climate change, the reliable wet and dry season system is fluctuating and

upsetting the normal decisions farmers make on planting times and which crops to grow (World

Bank, 2007).

Smallholder Farmers & Rural Livelihoods

Agriculture has changed dramatically in the last fifty years. The changes post-World War

II and industrialization have revolutionized the way we farm, eat, and distribute food in much of

the world. Especially, in developing countries such as the United States, this has meant a

decrease in the number of farmers and the number of farms, and a large increase in the size of

farms (USDA, 2014). However, this is not a phenomenon that occurred everywhere or to every

farmer. As of 2013, the World Bank reports that 47% of the world’s population lives in a rural

area (World Bank, 2013). The World Development Report breaks countries into three groups

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based on their agricultural systems – “one agriculture-based, one transforming, one urbanized”

(World Bank, 2007). It is the agriculture-based countries that continue to be dominated by

smallholder farmers and rely on them to feed the population.

Smallholders are a unique group in their countries as they live in rural areas, and often in

poverty, but own or can work on farming property. While most smallholders have little access to

capital and thus less ability to invest in risky new technologies or crops than do larger farmers,

they often use non-market methods for insuring against risks, including climate-related risks

(World Bank, 2007). They often grow multiple crops instead of just one. This means that if an

unexpected frost hits, or prolonged drought, or flooding, or a certain pest, then there is a higher

diversity of crops planted and a better chance that some of them will survive.

However, they also face a number of constraints. Many advantages that farmers in

developing countries take for granted are unavailable to smallholder farmers – food storage,

easily accessible markets and roads, access to loans, insurance, and credit, and use of modern

technologies and inputs such as nonorganic fertilizer (Morton, 2007). Smallholder farmers are

normally unable to store excess crops and sell them when prices are better or when food is

needed more because they do not have this infrastructure. Increasing populations and land

fragmentation can stress the land and soil, market expansion can create threats in competition,

and a lack of insurance makes them vulnerable to crop disease epidemics (Morton 2007). They

can pool resources with neighbors when possible, but this can be difficult if a shock affects the

whole community (World Bank, 2007). A great way for smallholders to increase income and

diversify their risks is to expand to off farm activities or even to on farm activities that involve

livestock or products such as honey (Peck and Pearce 2005). Research shows that a larger

percentage of income from off-farm activities is positively correlated with a higher total income

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(Ellis, 1999). Yet this can be hard to implement without some outside help because most

smallholder families have low levels of education because the whole family works on the farm.

Increased education in the family could influence off farm activities and income strategies

(Natoya, 2004).

There are many innovative ways for smallholder farmers to invest more in their land and

become less vulnerable to climate change; however many of these require upfront costs that most

farmers cannot make. Financial resources can be and are often an obstacle for smallholders in

their investments and decisions that they make in taking care of their land. Rural areas have a

much harder time accessing credit because banks encounter higher financial costs and constraints

in these areas due to rural locations and related risks (Kloeppinger-Todd and Sharma, 2010).

Farmers may be unable to take out loans because they do not qualify, or because they decide that

the collateral is not worth the risk – what Boucher et al. call “risk-rationing” (2008). Problems of

land tenure and security also make investments difficult. When smallholders do not have a clear

title for their land not only can they not use it as collateral, but also they are much less likely to

invest in land they do not know if they will continue to own it (Deininger and Ali, 2007). Even

when land is owned it is usually not worth enough to use as collateral.

Farming is also difficult to insure or grant credit for because so much is uncertain.

Farmers need to make decisions on what to grow and how much of it to grow without knowing

who else is growing it or what extreme weather could occur to hurt crops during that season

(Peck and Pearce, 2005). Because timing of payments is so variable in agriculture, it is best for

the borrower and the lender if they can operate closely, which is difficult for bigger banks to do

(Peck and Pearce, 2005). This means that many rural areas are left with nowhere to turn for

credit or loans, making the opportunity for them to make further investments in their land much

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more difficult. Especially for less wealthy borrowers, this has led to non-traditional forms of

insurance and credit, such as microfinancing, and cooperatives (World Bank, 2007). Even with

these more local options farmers can be unable to get enough money to make a big difference

without putting up too much collateral.

Insurance is a whole other aspect of market access for smallholders, and can bring further

peace of mind for farmers, especially in light of weather shocks due to climate change.

Traditional forms of insurance often occur between neighbors, but this system can leave the

poorest groups out of luck (World Bank, 2007). Formal insurance is difficult to obtain because it

requires upfront sums of money before the harvest that most farmers do not have, it requires a

level of financial literacy that they often do not have, it is not beneficial every year, and it

requires that a formal insurance system functions in their rural area (World Bank, 2007). One of

the suggestions that Christen and Pearce make in relation to agricultural microfinance is

providing weather indexed insurance at a local level, so this can be more specific than it would

be at a country or district level (Peck and Pearce, 2005). Weather indexed insurance allows for

objective indicators such as rainfall and temperature to estimate risk and shock of different areas;

a difficult measure because it can affect individuals in so many different ways (World Bank,

2007). Insurance providers set certain thresholds for different regions based on risks and they

pay out if the index falls above or below these thresholds, no matter if damage was incurred by

every farmer insured or not. This could be very useful for smallholder farmers, especially those

that do not have a diverse income, because the payouts would be directly related to crop prices

and the weather in their region, and involvement in this type of insurance has even been related

to an increase in input use (Cole et al. 2012). It is better for farmers who do not have to assess

multiple risks or pay bribes to collectors, and for benefits providers so that they can reduce

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overhead and pay only for certain risks, not cover all agricultural risk (Peck and Pearce 2005,

Kloeppinger-Todd, Sharma 2010). If insurance is run well it can benefit all parties and reduce

some of the risks that farmers take on. However, even though weather-indexed insurance has

good benefits for farmers and providers, and even work is done to increase the financial literacy

and trust in insurance, smallholders just do not have the financial liquidity that is required to

purchase insurance (Binswanger-Mkhize 2012, Cole et al 2012).

Even in the absence of these financial mechanisms, behavioral changes on the farm can

allow farmers to adapt better to climate change and even contribute to combatting climate

change. Farmers can coordinate with neighbors to track new weather patterns and share

successful strategies for changing traditional practices to work more effectively in the changing

environment. Some of these changes will require more financing and farmers on their own

already implement some, often times if one change is made on the farm it will lead to more

change in behavior. For example, implementing irrigation on the farm affects other land

management, such as increased use of mulch and crop rotation (Nkonya et al. 2004). For many

practices, the biggest barrier is knowledge, because activities such as composting, mulching,

rotating crops, or letting fields or strips lie fallow do not usually require any extra materials, they

just require the expertise to implement them, which can be learned from a neighbor or nearby

farmer (Nkonya et al. 2004). Another knowledge limitation exists wherein not every farmer

knows that he or she needs to adapt to climate variations, let alone how to adapt. There is a long

process that smallholder households need to go through in order to actually reach the point at

which they can implement adaptation mechanisms.

Previous studies have looked at the changing practices of food insecure households,

taking climate change into account, but not using it as a primary focus. Kristjanson et al’s 2008

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study found that smallholder famers are making changes, but they are usually small and do not

involve innovative new practices (Kristjanson et al 2008). These practice changes include

management of land such as “introducing crop cover, micro-catchments, ridges, rotations,

improved pastures, planting trees, and new technologies such as improved seeds, shorter cycle

varieties, and drought tolerant varieties” (Kristjanson et al 2008). In another study, it was found

that something as simple as having a rain gauge on the farm allowed farmers to manage their

crops better and get better yields (Hellmuth 2007). Drawing on these literatures, we focus on the

relationships of climate and agricultural training to the use of the traditional practices as well as

change in the use of inorganic fertilizers, herbicides, and pesticides – all more serious inputs.

Agriculture, Climate Change, and East Africa

The Intergovernmental Panel on Climate Change (IPCC) has reported on the increase in

greenhouse gas emissions throughout the world, contributed largely by developed countries. This

increase has caused shifts in precipitation, weather, and seasons (Nelson et al. 2013). Africa is

undergoing a variety of effects from climate change. As a very large continent, there will be

different effects everywhere. East Africa will likely have an increase in rain but all of Africa will

suffer from more extreme events, droughts and floods (Collier et al. 2008). Many countries in

sub-Saharan Africa are experiencing very low farm productivity already, and as climate change

continues, this will continue to worsen (Omamo et al. 2005). Higher temperatures will also

accompany global warming and shorten growing seasons and prevent certain crops from growing

at all. Staple crops such as wheat and maize could be especially threatened (Collier et al. 2008).

The yields of these stable crops will be also be affected, research showing that they are already

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decreasing due to increased variability and extreme events. As rain and temperature are likely to

continue increasing in variability, these yields could decrease even further (Collier et al. 2008).

These changes in rainfall and temperature also cause secondary effects, such as

expanding the range of pests, increased resource competition, and even increasing violent and

non-violent social conflict (Ziervogel 2008, Hendrix and Salehyan 2012). Population is still

growing rapidly in Africa, and agricultural productivity cannot keep up (Nelson et al. 2013),

putting additional pressure on smallholders to keep yields and production up. This is a problem

particularly in many East African countries, where there is a very low level of labor productivity

compared to the rest of Africa and the world. As temperature and precipitation variations

increase, food security could fall further (Nelson et al. 2013). Most of the countries in East

Africa, though their soil types and topography vary wildly, rely on agriculture as the main staple

of the economy (Collier et al. 2008). Thus, understanding smallholder adaptation strategies is

essential for planning for the near future in East Africa.

Agriculture, Climate Change, and Uganda

East Africa is a large area and has different topography throughout, but smallholder

farmers are affected similarly by climate change throughout the region. Though Uganda is not a

large country, there are diverse climates, types of soil, farming techniques, crops, and difficulties

that farmers encounter. There is a large amount of variation in the type and amount of fertilizers

or other modern inputs Ugandan farmers use, and to date they use much less than most African

countries (Nkonya 2004). Generally, there are two periods of rain and two shorter dry seasons

(Nkonya 2004). Farmers have worked with these conditions for their entire lives and learned

from the generations before them how to best farm in these conditions. It is unknown, however,

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how and how well they will adapt to today’s rapidly changing climate. It is probable that some

farmers will be able to adapt their techniques moving forward, but many will encounter problems

with changing weather patterns such as flooding or droughts.

Uganda has better access to water than many other countries in Africa; however this

could quickly change as weather becomes more extreme and extended dry seasons or rainy

seasons change water storage and supply (Mukwaya et al., 2011). Although the agriculture sector

in Uganda now only generates twenty percent of the GDP, seventy percent of the country is still

employed by this sector and as of 2010, eighty five percent of the country still lived in rural areas

(Mukwaya et al., 2011). The tenure system in place is similar to elsewhere in East Africa,

causing insecurity about land ownership among farmers and sometimes increasing the likelihood

that they will work the land without considering the future because they do not know what the

future will hold (Nkonya 2004). Some smallholders choose to sell or abandon land and migrate

to urban areas but unfortunately, the urban economy is not able to incorporate this new influx

and people often end up unemployed, living in the slums, and need to farm anyway, finding

small space on the edges of the city (Mukwaya et al., 2011). While this may allow them to

continue growing some food, land pressure is even greater in these areas. Uganda has recognized

that they are encountering problems with decreasing food supplies and there are some

government efforts operating and in the works to support farmers throughout the country, though

they are still vulnerable (Omamo et al. 2005).

Uganda faces additional region-specific challenges throughout the country. In Northern

Uganda, they will focus on rebuilding agriculture after the war with the Lord’s Resistance Army.

Agricultural knowledge was lost when Ugandans were forced into displacement camps during

the war. As they have been repopulating villages post-conflict, the transition back into

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agricultural production is crucial, both for increased food security, and for reintegration and

incorporation for the communities (Birner et al. 2011). This difficult task requires a lot of

support because it is most successful when implemented at small scales and tailored to specific

areas (Birner et al. 2011).

Uganda also experiences differences between rural and urban areas. There is worse

poverty in rural areas even as the urban areas and the slums that surround them grow (Mukwaya

et al. 2012). When rural poverty is worse than urban poverty, people abandon farms and try and

make it in the urban areas, even though the modern sectors have not grown enough to

incorporate all of these people. This move to the city is often unsuccessful for them, and

decreases food production throughout the country (Mukwaya et al. 2012). This problem is

persistent throughout the country, but particularly significant in the south near the capitol and

largest city, Kampala (Mukwaya et al. 2012).

Uganda confronts many unique systemic and agricultural problems that are specific to the

country. Uganda is also still dealing with effects of war in the Northern region and the recovery

of land, people, and farming knowledge in this area. However, many of the problems the farmers

here face are going to be similar if not the same as farmers elsewhere in East Africa. While

programs and adaptation suggestions will need to be tailored to specific communities, not just in

East Africa, but also in Uganda, the overall behavior patterns of how smallholders in Uganda are

adapting or not adapting to climate change will indicate how smallholders elsewhere might be

adapting their behaviors to climate change. Thus, the analysis I present below of farmers in

Uganda is broadly representative of the adaptation process underway in much of Africa and

indeed among smallholders throughout the tropics.

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Research Questions

As we continue to explore this area, we will focus on some questions that have not been

answered in relation to this topic. In Uganda, precipitation has increased by 3% and temperature

by 6% in the last decade relative to the second half of the twentieth century. Which farmers and

communities have experienced the most changes? How have farmers adjusted their agriculture

practices to reflect these community level changes?

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Data and Methods

In order to address these research questions we used a large social survey data set

collected in Uganda, and gridded climate data on precipitation and temperature. The survey data

were analyzed using Stata and combined with the climate data. Climate data was mapped and

analyzed using ArcGIS, Excel, and Stata.

Data and Measures

The social survey data in Uganda were collected as the “Project on Policies for Improved

Land Management in Uganda” in both 2003 and 2013. These data refer to the previous year, so

they cover the 2002 and 2012 agricultural practices. Many groups collaborated on the data

collection, including the International Food Policy Research Institute (IFPRI), the World Bank,

the Makerere University Faculty of Agriculture, the National Agricultural Research

Organization, and the Norwegian Trust Fund. Clark Gray, Assistant Professor of Geography at

the University of North Carolina (UNC), served as Principal Investigator for the 2013 wave of

data collection, funded by a grant from the National Science Foundation (NSF) with

collaboration from Brown University.

The data were collected through a series of four questionnaires in 2003, and five

questionnaires in 2013. While they were collected in 2003 and 2013, the questions asked were

referring to 2002 and 2012, so the years 2002 and 2003 refer to the first set of data and the years

2012 and 2013 refer to the second set. These questionnaires were administered in-person by

interviewers fluent in local languages. One collected information about the community as a

whole and was administered to community leaders. It included general information about

community programs, use of private and public lands, and environmental and resource

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management. The other three surveys provide information about individual households;

collecting extensive information such as household makeup, how the agricultural land and plots

are maintained, and how many crops are harvested and sold. In 2013, an additional module on

reproductive history of women, and prenatal and child care, was added. These surveys were

given in eight districts throughout the country and over one hundred communities within these

districts. The houses were originally interviewed in 2003, and then in 2013 the same places were

interviewed again, it was indicated if the household was the same or if it had split. Some

households split after 2003 and these households were study in their multiple iterations in 2013,

indicating if it was a split household in the 2013 set.

The analysis presented here draws from the household survey data, the survey on the

individual plots, and the survey on all the plots. Data were cleaned and manipulated to generate

measures of agricultural practices undertaken by each household (regardless of on which plot the

practice was used), and measures of household characteristics. We wanted to highlight the

changes that farmers made in between 2003 and 2013 to see if they were adapting to climate

change. Thus our key dependent variable is the use of certain farming practices, and particularly

the initiation or discontinuation of certain practices between 2003 and 2013. The survey asked

about 27 different types of practices, which we initially grouped into eight different types of

practices – irrigation, traditional practices, use of fallow, mulch, machines, herbicides or

pesticides, integrated pest management, and inorganic inputs – as shown in Table 1.

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Table 1 Agricultural Practices: New Groupings and Original Codes

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The data included every crop that any of the farmers in the study grew, so we focused on

the crops with the biggest influence and prevalence. These five crops were bananas, beans,

maize, cassava, and sweet potato. As Table 2 shows, their numbers were much larger in the

districts than the other top nine crops.

Crop Number of Farmers

Growing This Crop in

Original Data Set

Bananas* 1633

Beans* 808

Maize* 595

Cassava* 560

Sweet Potato* 418

Coffee 287

Groundnut 203

Millet 173

Irish Potato 78

Our key independent variables are measures of climate and differences in climate

between 2002 and 2012. This information was downloaded from the University of Delaware’s

site as raster files and specified to Uganda at the 0.5 x 0.5 degree for the years 1951-2012 (see

figure 2 for examples of the raster maps in ArcGIS). Using ArcGIS, the locations of the survey

communities were overlaid on the climate data and then historical weather data were extracted

for every community. These data included monthly mean temperature (degrees Celsius) and

monthly total precipitation (mm) for each month in each year 1951-2012. We focused on the

months of March through August, during which the two rainy seasons fall. We averaged these

monthly temperature and precipitation measures for the years 1951-2000 to establish a half-

century baseline of historical climate data for the middle of the agricultural season. There are two

Table 2 Crop Representation in the

Data (*Top five – chosen for study).

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rainy seasons in Uganda In Southern Uganda these are fairly distinct, with a dry season between,

but in the North, the dry season is more of a small dip between the two more wet seasons

(Uganda: Climate and Agriculture). This is also clearly demonstrated in the climate data that we

gathered, as can be seen in Figure 1 – the map of the community points and climate graphs of

three districts in each area.

Our analysis focuses on variation across communities experiencing different weather

during the years immediately preceding each survey. We expressed the recent weather in terms

of deviations from the long-term averages. Once the long-term averages and standard deviations

were established using Stata, we found the differences between the long-term mean and the

monthly average temperature or total rainfall for each of our six focal months in each of the

years 2000 – 2002 and 2010 – 2012. Then the number of times during the three years prior to

each survey that a month fell above or below one standard deviation from the mean was

calculated (maximum eighteen, minimum zero). These data show the amount of climate shock

that farmers experienced in their region in the two years before, and during the year that data was

collected about.

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Figure 1: Precipitation Trends: Map of the

community points and graphs of precipitation records in

these areas.

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We also include other control variables in our models – the number of plots, the total area

of the plots, whether or not they grew any of the top five crops – beans, maize, cassava, sweet

potatoes, and bananas - the size of the household, the age of the head of the household, if any

member of the household had received any agricultural training, and if any member of the house

is employed in a non-agricultural sector.

Analytic Approach

We used a logistic regression model, regressing use of each of the three practices on

climate and control variables. We did not include the 2003 climate variables, instead including

the agricultural practices in 2003 (which reflect previous climate conditions) and focusing on the

effect that the climate variability in 2010-2012 had on practices in 2013. Logit coefficients

presented in Table 5 thus show the relationship between the independent variables and the log-

odds of the household using the practice in 2013 given the value of the other variables we were

testing. Results significant at the 0.05 and 0.10 level are shown in this table.

Figure 2: ArcGIS Climate Maps: Raster maps in Arc GIS of

temperature (left, orange) and precipitation (right, blue) in

August 2006.

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Results and Findings

Though the dataset started off large and messy, we were able to use it to find interesting

results about smallholder farmers and how climate influences the decisions they make in their

farming practices. Of the eight different groups of types of practices we created – irrigation,

traditional practices, use of fallow, mulch, machines, herbicides or pesticides, integrated pest

management, and inorganic inputs – three that changed between 2002 and 2012 and represented

a continuum from traditional to modern practices were traditional (slash and burn) practices,

herbicides or pesticides, and inorganic inputs. These three changes in practice were all affected

by a few different variables, though not those that we most expected would affect the

smallholder’s choices.

We ran regressions to estimate the relationships between key independent variables and

smallholders’ use of these practices in 2013. In each case, we controlled for their use of the

practice in 2003, meaning that the coefficients on other variables represent their relationship with

the change in use of the practice over the decade. The results of these regression models are

shown in Table 5. Interestingly, the use of a practice in 2003 is not consistently significantly

related to the use of the practice in 2013, suggesting there is some variability in behavior

reflecting experimentation and/or responsiveness to inter-annual variability.

The focus of our analysis is on the impacts of recent weather patterns on use of these

practices. We seek to examine how farmers are responding to recent weather anomalies (relative

to long-term averages) as an indication of likely adaptation to climate change in the absence of

additional interventions. As shown in Table 3 (Descriptive Statistics I – Climate Variability),

Uganda has already been experiencing hotter conditions than the long-term averages and drier

conditions in some places. In this recent period, farmers are responding by increasing use of

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herbicides and pesticides during years when it has been drier and decreasing their use when it has

been wetter. Their probability of using traditional (slash and burn) methods or of using inorganic

fertilizer, however, is unrelated to the recent weather anomalies. In contrast to studies in other

areas in the world, all of the changes in practices seem to be unrelated to temperature anomalies.

These results, however, could reflect that practices have already changed in response to

temperature changes. That is, farmers have already effectively adapted to a hotter climate (both

2003 and 2013 showed a range of 11-18 in the number of above average months in the past three

agricultural seasons, suggesting no change in the weather patterns between the two survey dates,

see Table 3).

Beyond the focus on weather, we examined the relationship between other farmer

behaviors and characteristics and the change in the use of the three types of practice between

2003 and 2013. Looking first at whether changing practices reflected previous crop choices, we

examine the relationship between planting each of the five most common crops (separated into

the first and second rainy periods) and use of the slash/burn, herbicide and pesticides, and

inorganic fertilizer. Whether or not they grew a certain crop in the first or second season affected

all of the practices in some way. Their use of traditional slash and burn methods was impacted if

they grew maize in the first season, and significantly impacted if they grew it in the second

season as well. Growing bananas also significantly impacted their use of traditional slash and

burn methods. Growing cassava in the second season also significantly impacted the change in

the use of herbicides and pesticides. As demand for these crops increases, especially

international demand for bananas, farmers produce them as a monoculture and do not rotate

them, making it much easier for pests to target them (Lunder 2013). It is also possible that the

prevalence of pests is increasing with the large variation in rainfall and farmers are trying to

24

compensate for this. Use of inorganic fertilizer was additionally related to growing beans in the

first season. This is interesting because beans should not need that much fertilizer as they are

legumes and can fix nitrogen themselves, suggesting that they might be attempting to further

increase yields, or alleviate deficiencies in a different macronutrient. Growing maize in the

second season is also significantly related to increasing use of inorganic fertilizer, reflecting

either inherently low soil fertility or the high nitrogen demands of maize production.

Turning to the characteristics of the household and farmer, the area of the farm, the size

of the household, and the participation of anyone in the household in a non-agricultural activity

did not have any significant effect on any of the practices. This could have been because the farm

areas were similar because smallholder farmers all operate on similarly sized plots of land

(Figure 3). Whereas large variations in land could make a large difference in practices used

because certain practices could be much more difficult or even more expensive if they are

practiced on larger swaths of land, the small variations in land size are unlikely to affect what

practices they use. Similarly, household size was largely concentrated (Figure 4) and no

background literature indicated that this would be an important factor in how a household makes

a decision. The age of the head of the household was significantly related to the use of inorganic

fertilizer only. This could be because the older farmers are used to farming in a traditional way

and are uninterested in changing and the younger farmers are more excited and willing to adopt

new methods in their farming. Participation in a non-agricultural activity was something that we

had expected might have some effect because it could mean that the household has access to

more funds, as the literature does show that households with a non-agricultural income have

higher incomes that might allow them to bear the cost of the inorganic fertilizer or modern

herbicides and pesticides. These factors did not have significant coefficients in the regression

25

model. However, someone in the house having received agricultural training was significantly

positively related to herbicide and pesticide use. This is something we would have expected. If a

household receives some agricultural training than they will know more about herbicides and

pesticides, how effective they may be for their crop, how to use them, where to get them, and

maybe even help purchasing them.

Figure 3: Histogram of the area of the

land owned by a household.

Figure 4: Histogram of the amount of

people living in a household.

26

Table 3: Descriptive Statistics I – Climate variability

27

Table 4: Descriptive Statistics II – Household characteristics and agricultural practices.

28

Table 5: Regression model results

29

Conclusions

The analysis we undertook offered interesting conclusions, and offered many more

questions that could be addressed both through this quantitative data and with further qualitative

data research. We found some interaction between the changing climate and a change in farmer’s

practices, but not as much as expected.

The results indicate that farmers are responding to variability in recent weather in a few

ways. Use of pesticides and herbicides increased in drier years and decreased in wet years.

Temperature did not seem to affect change in practices between 2003 and 2013, but this could be

because that change took place previous to 2003. The temperature in the six primary months of

the agricultural season (March through August) was higher than one standard deviation above

the mean for fourteen to eighteen of the eighteen months in 2000-2002 throughout the region and

for eleven to fourteen of the eighteen months form 2010-2012. While we focused on the change

from 2003 to 2013, the changes they made in response to temperature could have taken place

before the 2003 study, something that would not reflect in these responses. In 1988, Matlon and

Kristjanson had already completed a study looking at how farmers coped with variability of

rainfall, demonstrating that farmers have been adapting for decades, which makes the possibility

that farmers had already made changes in practices by 2003 even more likely.

Studies show that East Africa will continue to have an increase in rain, temperature, and

extreme events such as drought and flooding (Collier et al. 2008). If the results from these data

hold, this will mean an increased in use of herbicides and pesticides in the future. This may be

hindered if financing for farmers does not improve, as herbicides and pesticides can be a

financial burden on the household. As the data is only quantitative, we cannot know for sure why

certain farmers decided to use more herbicides and pesticides in the drier seasons, and less in the

30

wetter seasons. This could be further explored through interviews, or even in studying the

interactions of certain results with other variables within this quantitative dataset.

Changes in practices could also be deterred because of lack of training. The use of

herbicides and pesticides was slightly increased if a member of the household had taken part in

any agricultural training, and was not significant in the use of inorganic fertilizers or traditional

practices. Perhaps if training were more widespread or available it could influence these practice

changes more drastically, especially because the literature indicated that training programs

should be influencing farmers to change practices in order to better adapt (World Bank 2007).

The literature also showed that engagement in non-agricultural activities would allow families to

increase resources and invest more in the farm, which meant I expected this to influence these

practices more than it did in these results (Natoya 2004). Non-agricultural activities should be

bringing in more income to the household and allowing for more access to training or resources

to advance the farm, but this did not seem to be the case with these three practices in the study

areas.

This research is an interesting step towards more that could be found in this data, and that

could be enhanced further with qualitative additions and study of interactions. As we saw that the

age of the head of the household significantly impacted the use of inorganic fertilizer, it seems

that younger households may be more willing to make changes. Was this same thing true with

any other practices? Are younger households overall more willing to make changes? Do they

have better access to resources? These questions could be looked at in the data further through

interactions, and even more could be discovered if the farmers were interviewed about this. As

previously mentioned, it would also be interesting to further explore the effect of the climate

change in the few years before the 2002 data was collected in 2003 in order to determine if

31

changes were made then due to climate change. This could also be enhanced through interviews

with farmers on why they make certain decisions and what considerations go into these decision-

making processes.

We were able to answer the questions on whether or not farmers are adapting to climate

change, and which practices they were changing as a result of the climate changes, but we still

cannot answer the underlying questions of why exactly they are adapting. If we know more about

why they choose to adapt, and how, then it will be easier to support them in future adaptation

measures. It is important that programs for farmers critically affected by climate change are

established and administrative capacity is increased dramatically so that farmers can benefit from

local modeling systems and can receive specific recommendations on how to adapt to the climate

change that affects their area (Nelson et al. 2013). Even within this one country, we can see

variation in climate, so this is even more important at the regional East Africa area. While we

support farmers in adaptation, it is also crucial that mitigation increase immediately so that the

effects do not continue to be exacerbated.

Smallholder farmers in Uganda to have the ability to adapt to climate change, and many

are willing and already making changes in order to adapt. However, this needs to continue to be

a subject of research as climate continues to change more dramatically. Hopefully research can

lead to policy changes that support farmers in the most effective ways so that not only they

continue to survive, but so that they can support and feed their communities.

32

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