USE OF INDIGENOUS KNOWLEDGE IN WEATHER FORECASTING
IN UGANDA
NATIONAL SURVEY
UGANDA NATIONAL METEOROLOGY AUTHORITY
FUNDED BY
WORLD VISION UGANDA & AFRICA CLIMATE CHANGE RESILIENCE ALLIANCE (ACCRA)
PATRICK NGANZI – LEAD CONSULTANT
REVIEWED BY TRACY C. KAJUMBA, MARGARET BARIHAIHI, JAMES BATAZE AND MUJUNI GODFREY
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ABOUT THE AUTHORS
Patrick Nganzi is a Principal Consultant and Team Leader at Regional Capacity Building Partners
(RECABIP). He has over 20 years’ experience in development work gained through full time and short
term engagements (consultancies) in different countries OF Sub-Saharan Africa particularly on Food
Security, HIV and AIDS, Gender, Livelihoods, Climate Change, Environment and Natural Resources
programming with UN agencies like UNFPA, FAO, WFP, UNICEF, UNAIDS, UNDP and International NGOs
like Action Aid, Oxfam, SNV, CAFOD, PLAN International, Concern Universal, CARE International, ZOA,
ICD, etc
Tracy C. Kajumba is the Uganda National programme Coordinator, Africa Climate Change Resilience
Alliance; [email protected]
Margaret Barihaihi is the Deputy International Coordinator, Africa Climate change Resilience Alliance;
[email protected]
James Bataze and Godfrey Mujuni are Senior Meteorology Officers with the Uganda National
Meteorology Authority; [email protected],/ [email protected]
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ACKNOWLEDGEMENTS
We are so grateful for all parties that have contributed to the different parts of this report. Particularly,
we are so grateful for the leadership of World Vision Uganda particularly the ACCRA Team for the
constructive technical advice and guiding team they have been since the inception of the idea to
conduct this study.
Uganda national Meteorology Authority for the collaboration, technical guidance and willingness to
explore other methods that can improve adaptive capacity of communities to the impacts of climate
change
We are also indebted to the data collection teams for gathering data from all the districts in which the
study was conducted.
The research team is very grateful to all people of all the communities visited in this study for their
contribution and enthusiasm to share their knowledge for the benefit of all society. We also express our
profound appreciation to the Chief Administrative Officers (CAOs) and District Sector Heads who took
time off their busy schedules to meet with us, share their perspectives on the subject with us.
ABOUT ACCRA;
ACCRA is a consortium initiative of Oxfam GB, Overseas Development Institute (ODI), Care International,
Save the Children UK, and World Vision International. ACCRA is active in Ethiopia, Mozambique and
Uganda and is funded by the UK Department for International Development (DFID). In Uganda, World
Vision takes lead of the consortium.
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TABLE OF CONTENTS
ABOUT THE AUTHORS ............................................................................................................................................. 2
ACKNOWLEDGEMENTS ............................................................................................................................................ 3
ABOUT ACCRA;...................................................................................................................................................... 3
List of tables ............................................................................................................................................................... 6
LIST OF ABBREVIATIONS ............................................................................................................................................. 7
EXECUTIVE SUMMARY ................................................................................................................................................ 8
C H A P T E R O N E ................................................................................................................................................ 11
1.1
BACKGROUND AND CONTEXT OF THE STUDY ........................................................................................... 11
1.2
OVERALL STUDY OBJECTIVE: .................................................................................................................... 15
SPECIFIC OBJECTIVES OF THE STUDY: ............................................................................................... 15
1.2.1
C H A P T E R T W O ............................................................................................................................................... 16
LITERATURE REVIEW ................................................................................................................................ 16
2.1
2.1.1
FORMS, PRACTICES / APPLICATIONS AND INDICATORS OF IK IN WEATHER FORECASTING IN UGANDA
16
2.1.2
ACCURACY, RELIABILITY AND TRUST IN IK FOR WEATHER FORECASTING ....................................... 21
2.1.3
RELATIONSHIP BETWEEN IK WEATHER FORECASTING AND SCIENTIFIC WEATHER FORECASTING ... 23
Taught through lectures and readings............................................................................................................. 23
2.1.4
POLICY FRAMEWORKS AND INSTITUTIONAL CONSIDERATIONS FOR IK IN WEATHER FORECASTING IN
UGANDA 25
C H A P T E R T H R E E .......................................................................................................................................... 26
METHODOLOGY USED TO CONDUCT THE NATIONAL IK STUDY ................................................................ 26
3.1
3.1.1
CONCEPTUAL AND THEORETICAL FRAMEWORK TO GUIDE THE STUDY ............................................ 26
3.1.2
STUDY PROCESS AND METHODS ....................................................................................................... 27
3.1.3
TOOLS FOR DATA COLLECTION AND ANALYSIS ............................................................................... 28
3.1.4
SAMPLING AND DATA COLLECTION .................................................................................................. 29
3.1.5
SAMPLING SIZE: ................................................................................................................................. 30
3.1.6
QUALITY CONTROL MEASURES ......................................................................................................... 32
C H A P T E R F O U R ............................................................................................................................................. 33
4.1
FINDINGS AND DISCUSSION ....................................................................................................................... 33
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MAJOR LIVELIHOOD ACTIVITIES ........................................................................................................ 33
4.1.1
4.1.2
TYPES OF INDIGENOUS KNOWLEDGE INDICATORS USED TO PREDICT THE ONSET OF THE RAINY
SEASON 35
4.1.3
INDIGENOUS KNOWLEDGE INDICATORS USED TO PREDICT THE ONSET OF THE DRY SEASONS ......... 38
4.1.4
LOCAL INDICATORS THAT ARE SAID TO BE ACCURATE AND REASONS ............................................. 42
4.1.5
LOCAL INDICATORS THAT ARE SAID TO BE INACCURATE AND THE REASONS ................................... 44
4.1.6
PERCEPTIONS OF THE LOCAL COMMUNITIES ON THE APPLICATION AND RELIABILITY OF BOTH
INDIGENOUS KNOWLEDGE AND SCIENTIFIC FORECASTING ............................................................................... 48
4.1.7
KNOWLEDGE AND APPLICATION OF IK WEATHER FORECASTING BY OFFICIALS AT DISTRICT LEVEL
54
C H A P T E R F I V E .............................................................................................................................................. 61
5.0
CONCLUSION AND RECOMMENDATIONS ................................................................................................... 61
References ................................................................................................................................................................ 64
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LIST OF TABLES
TABLE 1 COMPARISON OF IKF AND SCF METHODS ...................................................................................................................... 23
TABLE 2 ACTUAL NUMBER OF RESPONDENTS REACHED BY THE STUDY ..................................................................................... 31
TABLE 3 IK INDICATORS FOR PREDICTING ONSET OF RAINS IN THE CLIMATOLOGICAL ZONES............................................... 35
TABLE 4 NOTES ON IK INDICATORS USED BY COMMUNITIES IN UGANDA TO PREDICT ONSET OF RAINS ............................. 37
TABLE 5 IK INDICATORS FOR PREDICTING ON-SET OF DRY WEATHER IN THE CLIMATOLOGICAL ZONES .............................. 39
TABLE 6 NOTES ON IK INDICATORS USED BY COMMUNITIES TO PREDICT ON-SET OF DRY SEASON ..................................... 40
TABLE 7 INDICATORS THAT ARE SAID TO BE ACCURATE TO PREDICT ON-SET OF RAINY SEASON............................................ 42
TABLE 8 INDICATORS THAT ARE SAID TO BE ACCURATE IN PREDICTING ON-SET OF DRY SEASON ......................................... 43
TABLE 9 INDICATORS THOUGHT TO BE INACCURATE TO PREDICT ON-SET OF RAINY SEASON ................................................. 45
TABLE 10 INDICATORS THAT ARE THOUGHT TO BE INACCURATE TO PREDICT ON-SET OF DRY SEASON ................................. 46
TABLE 11 HOW IK WEATHER FORECAST INFORMATION ASSISTS COMMUNITIES........................................................................ 49
TABLE 12 CHALLENGES OF USING IK IN WEATHER FORECASTING ............................................................................................... 50
TABLE 13 SOLUTIONS TO THE CHALLENGES OF USING IK IN WEATHER FORECASTING ............................................................. 50
TABLE 14 CHALLENGES WITH USING SCIENTIFIC WEATHER FORECASTING METHODS ............................................................... 52
TABLE 15 REASONS WHY GOVERNMENT SHOULD ADOPT IK FOR NATIONAL USE ................................................................... 54
TABLE 17 USE OF IK WEATHER FORECASTING INFORMATION BY DISTRICT OFFICIALS .............................................................. 54
LIST OF FIGURES
FIGURE 1: RESPONDENTS COMPARISON BETWEEN INDIGENOUS AND METEOROLOGICAL FORECASTS’ ACCURACY AND
RELIABILITY .................................................................................................................................................................. 22
FIGURE 2 SHOWING THE MAJOR LIVELIHOODS ACTIVITIES OF THE INTERVIEWED COMMUNITY MEMBERS, OPINION LEADERS
AND FORETELLERS. ....................................................................................................................................................... 34
FIGURE 3 SHOWING WHETHER COMMUNITIES RECEIVE THE MODERN WEATHER FORECAST FROM GOVERNMENT
(ACCORDING TO THE INTERVIEWED COMMUNITY MEMBERS, OPINION LEADERS, IK FORETELLERS) .................................... 51
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LIST OF ABBREVIATIONS
ACCRA
Africa Climate Change Resilience Alliance
UNMA
Uganda National Meteorology Authority
IPCC
Inter Government Panel on Climate Change
CSO
Civil Society Organization
IK
Indigenous Knowledge
IKS
indigenous knowledge systems
MAAIF
Ministry of Agriculture, Animal Industry and Fisheries
SCFs
Seasonal climate forecasts
NAADS
National Agricultural Advisory Services
NARO
National Agricultural Research Organisation
N
Sample size of the population (Households)
RECABIP
Regional Capacity Building Partners
SPSS
Statistical Package for the Social Scientists
UBOS
Uganda Bureau of Statistics
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EXECUTIVE SUMMARY
This study was set to compile indigenous knowledge indicators in forecasting and its application country
wide and investigate elements of traditional knowledge that can be harmonized with science to improve
quality and increase use of both forecasts in planning and decision making for rural farmers in Uganda.
This was a qualitative study covered 335 community members (Focus Group Discussions), 120 Opinion
leaders, foretellers and 53 key informant District officials in addition to extensive literature review. 22
Districts were considered for this study ensuring that all were chosen from within all the 14
climatological zones in Uganda.
The study findings show that the use of Indigenous Knowledge in weather forecasting continues to be
significantly important by the predominantly agricultural communities across the country as reported by
94.6% of the 135 interviewed communities. This importance of IK forecasting was also reaffirmed by the
majority of district officials (97%) of the 53 officials interviewed. The study found most of the indigenous
knowledge and indicators in weather prediction were related to rainfall which is explained by agriculture
being the major livelihood to these rural citizens. Farmers’ major interest is when to plant, what to plant,
where, when to harvest and early warning alerts on food security.
About eighteen (18) major IK indicators for predicting onset of rainy season were reported. However,
those that are still working and reliable included; prevailing Westerly winds which blows from West
towards East (56%), heavy dark cloud formation (54%), the singing or crying of birds in the morning
(42%), un usual increase in temperatures (heat-hot at night) (40%) and trees sprouting (37%)
respectively. Additional twenty two (22) IK indicators for predicting onset of dry season, were also
identified. Those that are still working included; prevailing Easterly winds (winds blowing from East to
West) (42%), Clear sky/white clouds (36%), the Weathering of plants/trees/shading of leaves (26%),
Presences of Butterflies (23%) and Drop in temperatures at night/Coldness at night (21%) respectively,
amongst others.
The study found five common and still reliable indigenous indicators across all climatologically zones to
determine when it is likely to rain or not by farmers. These include; Winds, Clouds or Sky,
Temperature, Birds and Trees/plants. It is important to note that winds, clouds or sky, temperature
are same major atmospheric parameters observed by the meteorologists to prepare the scientific
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seasonal forecast. This is where farmers and scientists could focus on harmonizing to strengthen the
seasonal forecast.
The question as to whether the IK forecasts were working or not compared to the meteorological
forecasts for the communities were largely dependent upon their reliability and accessibility and this
differed from one climatological zone to another. Most of the IK indicators that are no longer reliable as
reported by 74% of the total respondents were attributed to the changing climate and the continuous
disappearance of specific IK indicators such as trees, insects, birds etc due to environmental
degradation. Also, the study found that the elderly or gifted foretellers are major custodians of the
Indigenous knowledge with no documentation; IK only survives on being passed on from generation to
generation. In some cases, people were not having adequate information especially women. By nature,
IK forecasts are shared informally which favours men leaving yet women are at forefront of agricultural
activities but do not get the forecast timely thus it is not helpful.
On the other hand, the majority (91%) of the respondents reported scientific forecasts are also unreliable
and not accurate that is why farmers stick to their IK forecasts in the midst of their increasing challenges.
The main challenges reported regarding scientific forecasts include; inaccuracy and not being realistic
for local conditions (91%); not widely available; few people access the information and not easily
understood by locals etc. Poor accessibility was reported regarding the meteorological forecast, which
is disseminated through FM radio (92.3%), TV (17%), news paper (16%), and mobile phones (5%) which
most rural and illiterate farmers do not access especially women. Therefore the feedback on reliability of
both forecasts were shaped by the differences in individuals’ experience, knowledge, familiarity with
seasonal patterns of rainfall, age, gender, culture, social status and nature of livelihood of the people in
these regions interviewed.
Although the local communities acknowledged that both Indigenous Knowledge of weather forecast
(74%)
and
scientific method of forecasting (91%) had challenges mainly in relation to their
accuracy/reliability, the majority (79%) of all of them would choose to use both methods, leaving only a
smaller proportion who would opt to use traditional knowledge (17%) alone, Their preference to
concurrently use both indigenous knowledge and scientific methods was mainly attributable to the fact
that the two methods would complement each other, and hence improve on accuracy/reliability in
weather prediction. The District officials upon acknowledging the reliability of IK indicators (59%),
strongly rejected the fact that IK was more reliable than scientific method of weather forecasting (58%).
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This was attributable to that fact that IK was not documented anywhere (39%), in addition to no known
policy requiring officials to use IK indicators (96%).
This study suggests the following:

Based on the fact that farmers IK weather forecasting indicators that continue to work for them are
similar parameters meteorologists observe to generate the scientific seasonal forecast, also
acknowledging that both forecasts have strengths and weaknesses, it calls for a technical and
systematic harmonization of the two systems.

There is need for proper documentation of indigenous Knowledge of weather forecasting systems,
clearly indicating how prediction is done, how they are used so that they could be readily accessible
to everyone, as part of official seasonal forecast. This should be done for all meteorological
forecasting zones.

There is need for UNMA and ACCRA to conduct a scientific pilot study to ascertain the efficacy of the
listed common and reliable IK indicators in comparison with the scientific indicators in selected
climatological zones to generate evidence to inform policy on the use of IK in weather forecasting.

Harmonization of the Indigenous knowledge and scientific weather forecasting methods is essential
in enabling concurrency in use of the two methods, achievable through creation of a hybrid weather
forecasting methods/systems.

There is need to sensitize communities and the District officials on how the indigenous knowledge of
weather forecasting and Meteorological forecasts are produced, their relationships, and how
effectively the information can used for planning purposes in different climatological zones.

Efforts to push for a policy on concurrent use of IK indicators and scientific should be taken, this
would go a long way in enabling district officials comfortably use IK indicators and hence provide
relevant advice to the community members.
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CHAPTER ONE
1.1
BACKGROUND AND CONTEXT OF THE STUDY
Climate change and variability continues to affect agriculture, forestry, fisheries, human
settlements, water resources, ecological systems and human health adversely in many parts of
Africa. The agriculture sector still forms the backbone of most economies in Africa with 70% of
people living in sub-Saharan Africa dependent on rain-fed subsistence agriculture (Hellmuth et
al, (2007), Uganda inclusive. Devereux and Edward (2004) observed that countries in East Africa
including Uganda are already among the most food insecure in the world and climate change
and variability exacerbates the situation. Therefore, weather plays a very big role in agricultural
production (Das & Stigter, 2007) which calls for accurate, reliable and timely weather forecasts,
Early warning Systems and other decision-making tools in order to enhance adaptive capacity
and saving lives.
Traditionally, local communities and farmers in Africa and elsewhere have used traditional
knowledge to understand weather and climate patterns in order to make decisions about crop
and irrigation cycles (Makwara, 2013). This knowledge has been gained through many decades
of experience, and has been passed on from previous generations. The knowledge is adapted to
local conditions and needs. Indigenous knowledge provides valuable insights on how
communities have interacted with their local environment (UNEP, 2008). However, increasing
variability in climate has reduced farmers’ confidence in traditional knowledge and has led them
to seek out scientific weather forecasts.
The most commonly produced form of Meteorological forecast for the last few decades in most
countries in Africa is the seasonal climate forecast (SCFs). Although SCFs have significantly
advanced with increased awareness and availability in many parts of Africa (O’Brien and Vogel
2003; Patt et al. 2007; Roncoli et al. 2009), most African countries have not experienced
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significant benefits from using this information to reduce climate impacts.
Local farmer
continually utilize and devise their own indigenous and traditional modes of sustenance. These
conventional approaches, use agreed climate models, to predict future climate scenarios in
most countries. However, these models paint the bigger picture of climate change and provide
estimates for the likely consequences of different future scenarios of human development; they
are not very good at providing information about changes at the local level.
Consequently, in recent years, there has been an increasing realization at international level that
indigenous groups are a valuable source of this local and specific information. Indigenous
peoples are not only keen observers of climate changes but are also actively trying to adapt to
the changing conditions. They have developed intricate systems of gathering, predicting,
interpreting and decision-making in relation to weather, although not documented. In some
instances, people can draw on already existing mechanisms for coping with short term adverse
climatic conditions. Some of these responses may be traditionally included in their normal
subsistence activities, while others may be acute responses, used only in case of critical weather
conditions (Stott and Kettleborough, 2002). This implies that the enhancement of indigenous
capacity is key to the empowerment of local communities and their effective participation in the
development process (Boko et al, 2007 citing Leautier, 2004).
The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (2007) noted
that indigenous knowledge is ‘an invaluable basis for developing adaptation and natural
resource management strategies in response to environmental and other forms of change. This
was reaffirmed at the 32nd Session of the IPCC in 2010: ‘indigenous or traditional knowledge
may prove useful for understanding the potential of certain adaptation strategies that are costeffective, participatory and sustainable.1 The recent IPCC 5th assessment report (2014) highlights
approaches for managing the risks of climate change. Under the social component, sharing and
1
IPCC. 2010. Review of the IPCC Processes and Procedures, report by the Inter-Academy Council (IPCC-XXXII/Doc. 7), 32nd
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use of indigenous knowledge is recommended for education and information option especially
in climate observations, participatory scenario development and integrated assessments. 2
Despite the fact that efforts have been made towards fighting climate change from scientific
views, research and policies directed towards indigenous knowledge and perception are highly
needed, in order to reduce the vulnerability of the rural communities and enhance their cultural
resilience and adaptive capacity. The challenge for governments, organizations and institutions
is to identify policies, decisions and actions that might support those communities and enhance
their efforts to mobilize Indigenous Knowledge for climate change assessment and adaptation
(United Nations, 2013). It is, therefore, important to understand indigenous perceptions of
climate change and their preferences of strategies towards adaptation. It is a fact that IKs have
often been overlooked by western scientific research and development because of their oral
tradition (Warren, 1990). Local indigenous communities should not only be seen as recipients of
technical messages but as the originators of either technical knowledge or improved practice.
In Uganda, the National Meteorology Authority (UNMA) is mandated to provide climate and
weather information for different sectors as well as communities who need the information for
planning and decision making. With support from Africa Climate Change Resilience Alliance
(ACCRA) consortium led by World Vision, UNMA has strengthened access to weather and
climate information in understandable formats and has been supporting downscaling,
developing sector based advisories, translation into local languages and dissemination in
different parts of the country. Impact assessments with beneficiaries are then conducted after
each season to improve the subsequent forecasts. Much as farmers testify getting more
prepared and well informed, it is clear that indigenous knowledge is used simultaneously even
when scientific forecasts are received. Communities prefer to use both the IK and scientific
forecasts because both have short comings and they felt safer using both methods.3 However,
the challenge is how to bring together traditional knowledge and modern science without
2
3
IPCC 2014; Climate Change Impacts, Adaptation and Vulnerability
September, October & November (SON) 2013;Dissemination Impact Assessment
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substituting each other, respecting these two sets of values and builds on their respective
strengths.4
Given the above background, ACCRA and Uganda National Meteorology Authority
commissioned a national study to identify Indigenous knowledge indicators that are still
reliable and used across the different climatology zones, to inform possibilities of using IK and
the scientific forecasts to improve weather forecasts and early warning systems. The study will
also inform a strategy for piloting use of the two methods, using the findings of the study to
enhance learning from the two methods.
4
MS Swaminathan Research Foundation, Chennai, 2008 - unep.org - Linking traditional and scientific knowledge systems on climate
prediction and utilization
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1.2
OVERALL STUDY OBJECTIVE:
The main purpose of this study was to compile indigenous knowledge indicators in forecasting
and its application country wide and investigate elements of traditional knowledge that can be
harmonized with science to increase use of both forecasts in planning and decision making for
rural farmers in Uganda.
1.2.1 SPECIFIC OBJECTIVES OF THE STUDY:
The study had specific objectives as below;
1. To compile a national wide report on indigenous climate forecasting local indicators used in
IK forecasting in different parts of the country (climatological zones)
2. To identify, analyze and document IK weather indicators that work and those that are not
working and the reasons
3. Assesse the perceptions of the local communities on the application and reliability of both
Indigenous Knowledge and scientific forecasting methods in their daily lives in order to
identify the gaps and the needs for improvement.
4. Use the findings and recommendations to develop a strategy document to guide a
comparative pilot study on the use of IK and scientific weather forecasting methods to
generate evidence for policy influence and change.
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CHAPTER TWO
2.1
LITERATURE REVIEW
This chapter presents a review of previous studies carried out on the use of Indigenous
Knowledge (IK) in weather forecasting in Uganda. An overview of the types of indicators of
indigenous knowledge (IK) identified; their application in weather forecasting; their accuracy
and reliability; the relationship between IK and meteorological forecasts; and policy frameworks
and institutional considerations for IK in Uganda are discussed.
2.1.1 FORMS, PRACTICES / APPLICATIONS AND INDICATORS OF IK IN WEATHER FORECASTING
IN UGANDA
IK prediction indicators of the start (on-set) of the rainy season: A Study in Kaabong (
Karamoja) and Soroti by Komutunga et al (2013), among the predominant indicators of
Indigenous knowledge in Kaabong and Soroti, ‘Ebata’ was the most common plant predictor of
rain in Kaabong (33.3%) where as mangoes,engosororot, and etek’ were the most common
plant predictors of rain in Soroti (16.7%). Following the indication by plants, the majority of
households in both Kaabong and Soroti prepared/cleared their gardens as a production
decision (76.9% and 82.4% respectively).
Cattle emerged as the most common animal predictor of rain in Kaabong (26.3%) where as
“esuskusk” and frogs were the most common animal predictors of rain in Soroti (26.3%).
Following the indication by animals, majority of households in both Kaabong and Soroti
prepared their land as a production decision (61.1% and 94.1% respectively).
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‘Elele’ was the most common bird predictor of rain in Kaabong (25%) where as ‘eduduut’ was
the most common bird predictor of rain in Soroti (17.8%). Following the indication by birds,
majority of households in both Kaabong and Soroti prepared their gardens as a production
decision (66.7% and 81.1% respectively).
‘Lomoroko’ was the most common star predictor of rain in Soroti (33.3%). Following the
indication by the stars, half of the households in Soroti diversified to other activities as a
production decision (50%). ‘New moon’ was the most common predictor of rain in Kaabong
(66.7%). Following the indication by the moon, half of the households in Kaabong diversified to
other activities (50%) while the largest fraction in Soroti prepared their land (40%).
‘Grasshoppers’ were the most common insect predictors of rain in Kaabong (20%) where as the
burger was the most common insect predictor of rain in Soroti (33.3%). Following the indication
by insects, the highest proportion of households in Kaabong prepared their land for cultivation
as a production decision (59.4%).
“Foreteller dreams’ were the most common human ailment predictors of rain in Kaabong (70%)
where as ‘measles’ was the most common human ailment predictor of rain in Soroti (42.9%).
Following the indication by human ailments, the highest proportion of households in Kaabong
opted to ritually cleanse people as well as diversify to other activities (20.7%).
The most known rainfall-onset indicator in both Kaabong and Soroti districts was found to be
‘the appearance of dark clouds.’ This was followed by ‘the singing of some specific birds.’ For
the indicators: direction & strength of the wind, aggressive & stubborn behaviour among cattle,
appearance of less dew on grass, sprouting of young shoots of muvule and position of the
moon; the status of using each of these indicators in making a production decision varied
significantly between Kaabong & Soroti districts (p < 0.05). Generally, the majority of the
households had not used the indicators in making a production decision (Komutunga et al,
2013).
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Six regions study by Okonya and Kroschel 2013 in Soroti (Teso region); Masindi (Bunyoro
region); Wakiso (Buganda region); Gulu (Acholi region); Kabale (Kigezi region); and, Kasese
(Tooro region) identified 22 indicators whose details are in Annex 1.
These include winds
blowing from the west to the east, cuckoo birds (Cuculiformes: Cuculidae) start to call, and
winged African termite (Coptotermes formosanus Shiraki) swarms leave their nests. Predictors
of rain in the following days included presence of red clouds in the morning.
Some of these early warning signs, such as watching the behaviour of a herd of cows, require
patience and are not stand-alone thereby requiring a combination of two or more other signs
to be able to make an accurate weather forecast. Acharya (2011) proposed that the behaviour
of animals can reliably be used to predict the onset of the rainy season, upcoming rain, and the
occurrence of floods or typhoons since animals alter their behaviour to suit upcoming natural
dangers. Cattle are believed to sense when the rains are near and they become frisky which is
demonstrated by jumping. The cry and movement of birds to signal the onset of the rains has
been reported by local communities in India5, Tanzania (Chang’a et al 2010) and, Swaziland
(Musa and Omokore 2011), though these are unlikely to be the same species as the birds
observed in Uganda.
Indicators for rain over coming days were not as many as those for the start of the seasons and
included a feeling of excess heat6, presence of many rain clouds, a red sky in the morning,
appearance of fog in the morning, and a feeling of body pains by some individuals. Other early
warning signs reported by respondents were for hailstorms (3% in Kabale and 9% in Soroti) and
ceremonies for rainmakers. Cultural rituals to ask the gods for rain were performed by very few
respondents (6% in Soroti, 3% in Wakiso and Soroti). In Wakiso; the rituals involved drinking a
local brew (malwa), performing cultural dances while drumming, and singing to tree called
nyagalabwami on a sacred hill (sekwa).
5
Acharya 2011 ibid
This is corroborated by similar studies in Kaabong and Soroti by Komutunga et al (2013), NARO and in Rakai by Roncoli et al
(2009).
6
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Rakai study by Orlove et al, 2009, identified the following indicators for the start of rains; an
increase in nighttime temperatures, so that the nights feel uncomfortably warm (many people
speak of hot nights, reporting that they feel sweaty and wish to bathe when they wake in the
morning); shifts in the direction of prevailing winds; the flowering of trees, especially coffee
trees; particular phases of the moon; the appearance of whirlwinds that lift dust and leaves; and
the arrival of migratory birds, particularly the Abyssinian hornbill (Bucorvus abyssinicus). This
bird’s call is said to resemble the Luganda word ggulu, which means “heaven”, and to connote
the phrase ggulu mpa enkuba or “Heaven, send rain.” The whirlwinds also have a symbolic
interpretation: their Luganda name is akazimu, literally “ghost-wind,” and they are understood
to be a manifestation of the ancestor spirits that are active just before the rains, sometimes
fighting among themselves.
Local people give different names to the two rainy seasons, and are familiar with a number of
attributes. They know the typical timing and duration of the seasons. The first rainy season
(toggo in Luganda) is expected to run from March through May; the second season ( ddumbi in
Luganda) runs from September through December. Though they do not describe amounts in
millimeters of precipitation, farmers distinguish the quantity of rain that has fallen during each
season. By scraping soil away with their hands, or digging with hoes, they examine soil moisture
after the onset of the rains to determine when enough has fallen for viable planting (Orlove et
al, 2009).
IK prediction of the start of the dry season
The same study of Okonya and Kroschel 2013, in six regions in Uganda identified Twenty one
distinctive indicators by local communities for forecasting the start of the dry season, but only
few of these indicators were more consistently and frequently used in the different districts.
These included the appearance of bush crickets (Ruspolia baileyi Otte), winds blowing from the
east to the west, the appearance and movement of migratory birds such as cattle egrets
(Bubulcus ibis Linnaeus), and calling by the Bateleur eagle (Terathopius ecaudatus ).
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Appearance and movement of insects as an indicator of the approach of the start of the dry
season was the most mentioned local sign only in the districts of Wakiso (34%), Masindi (31%),
and Kasese (6%). In Wakiso district, nsenene (bush crickets, Ruspolia baileyi Otte) usually appear
in the months of May and November every year and the dry seasons normally start in June and
December. Winds blowing from the east to the west were mentioned in all six districts as a sign
of an upcoming dry season. Indicators of the nearness of the dry season that were specific to a
district were coldness during the night and day, and the presence of red clouds in the morning
hours in Kasese district.
In Masindi district, coldness in the morning and evening, new moon appearing red without a
lining, winds blowing from the west to the south/north, movement of white clouds from the
east to the west and strong winds coming with rain in a storm indicated that the dry season is
near. Indicators like the presence of fog in the morning, strong winds in the morning and
evening, and the moon appearing black were specific only to Wakiso district. Although,
respondents in Masindi district mentioned that the appearance of cumulus humilis clouds in a
clear sky indicated the start of a dry season, meteorologists use these clouds to forecast a dry
day as they produce little or no precipitation (The Weather Channel, 2012). In Kasese district, a
red sunset was taken to indicate the nearness of the dry season but Schott (2006) believes this
forecast is for no rain during the next day.
Winds blowing from the west to the east are the most common sign and this was reported in
five out of the six districts. The appearance of nimbus clouds in the morning and evening used
by communities in all the six districts was the second most mentioned sign, while calling by
birds came third.
Signs for forecasting rains specific to a district were movement of clouds from the west to the
east in Masindi, appearance of algae in Kabale, cows becoming restless in Gulu, visibility of the
ice cap on Mt. Rwenzori in Kasese, sight of a group of small stars in the east in Soroti, and
appearance of millipedes and the presence of dew on grass in Wakiso.
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Other studies, (Okonya and Kroschel, 2013; Orlove et al 2009) note that observations for
forecasting the weather based on the sun, moon, sky/clouds, dew, or fog may be the same in all
districts and even countries since they refer to the same planet earth. Observations based on
certain birds, plants, insects, and animals may however vary from one region to another
because they are unique or specific to a certain locality. The variation in indicators across
regions for prediction of weather events observed in these studies could partly be explained by
the difference in individuals’ experience, knowledge, and culture of the people in these regions.
Although some of these signs may not be consistent, a good number of them do have a
scientific explanation mainly based on pressure (barometric and hydrostatic) zones. Such signs
include a halo around the moon, feeling of body pains and aches, fog in the morning, cows’
behavior, red sky, and wind direction (Acharya, 2011; Schott, 2006; Watts, 2011).
2.1.2 ACCURACY, RELIABILITY AND TRUST IN IK FOR WEATHER FORECASTING
Since agricultural activities in Uganda, are mainly driven by rain, it means crop production to a
large extent depends on right decisions being made on what to plant, when and where which in
turn depends much on the accuracy and reliability of seasonal rainfall forecasting. Officially, the
Meteorological Department is charged with the responsibility for monitoring and predicting
weather and climate in Uganda, including seasonal rainfall forecasting. According to the
Uganda National Meteorological Authority, the first rainy season, run from March to May, is
shorter (3 months), but receives more rain, averaging 436 mm for the season. Rains are more
intense and closely spaced. Both the onset and the end are abrupt. The second season, from
September through December, is longer (4 months) but there is less rain, with a seasonal
average of only 386 mm.
It was noted that recent changes in climate are affecting the use of traditional signs for
forecasting the start of the rainy season. Hence, farmers would profit from modern weather
forecasts provided by governmental institutions. This will enable farmers to make sound
decisions on how to fully exploit the seasonal distribution of rainfall to improve and stabilize
crop yields.
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However, when meteorological climate forecasting deviates from reality and traditional
forecasts, the farmers’ inclination is towards indigenous information for reasons that it blends
well with culture, has been tried and tested over years and is in a language that the farmers
understand (Makwara, 2013). For example in the Soroti – Kaabong study, respondents were
asked to compare between indigenous and meteorological forecasts in terms of accuracy and
reliability. Results in Figure 1 below clearly show that the population rated indigenous forecasts
methods as reliable (58%) compared to only 27% under the meteorological approach
(Komutunga et al 2013).
Figure 1: Accuracy and Reliability comparison between Indigenous and Meteorological forecasts’
58
60
54
50
39
Percentage
40
Reliable
27
30
Somehow reliable
Not reliable
Don’t know
20
15
10
2
4
1
0
Indigenous
Meteorological
Compare Indigenous to Meteorological
Source: Komutunga et al 2013
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2.1.3 RELATIONSHIP BETWEEN IK WEATHER FORECASTING AND SCIENTIFIC WEATHER
FORECASTING
Climate information is generated by two main sources: meteorological seasonal climate
forecasts and indigenous knowledge seasonal forecasts. Table 1 illustrates the methods used to
generate the two forecasts.
Table 1 Comparison of IKF and SCF Methods
INDIGENOUS KNOWLEDGE FORECASTS
SCIENTIFIC SEASONAL CLIMATE FORECASTS
Use biophysical indicators of the environment as well
Use weather and climate models of measurable
as spiritual methods
meteorological data
Forecast methods are seldom documented
Forecast methods are more developed and documented
Up-scaling and down-scaling are usually complex
Up-scaling and down-scaling are relatively simple
Indicators are mostly observed
Indicators are usually measurable
Application of forecast output is less developed
Application of forecast output is more developed
Communication is usually oral
Communication is usually written
Explanation is based on spiritual and social values
Explanation is theoretical
Taught by observation and experience
Taught through lectures and readings
Source: Adopted from Ziervogel, G. and Opere, A. ( 2010).
Researchers on climate change (Hinkel et al. 2007; Laidler and Ikummaq 2008) have recognized,
despite the differences in the criteria used by local farmers and scientists to define seasonal
phenomena, there is also significant overlap between them, making indigenous observations
potentially useful to climate scientists in tracking change. Orlove et al (2009) postulate that
Indigenous Knowledge Systems offer points of connection with climate science. Some of the
components of indigenous knowledge such as historical climatic patterns, weather observations
and regional information—correspond to sources of information that meteorologists use.
This similarity could be used in explaining to farmers how climate forecasts are produced,
thereby enhancing their credibility. It might even be possible to explain that meteorologists rely
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on observations that are similar to traditional signs in order to convey to farmers the way that
scientific forecasters draw on statistical models to make predictions.
The fact remains that Indigenous knowledge practices have been employed successfully in
adapting to climate change impacts among farmers in sub-Saharan Africa. However, it is
important to note that not all indigenous practices are beneficial to the sustainable
development of a local community; and not all indigenous knowledge can provide the right
solution for a given problem (Ajani et al 2013). For example, although there are mixed reactions
to various Indigenous Knowledge indicators in the different climatic zones and a general
mistrust of use of metrological forecasts the population still uses indigenous forecast methods.
(Komutunga et al 2013).
Therefore, before adopting indigenous knowledge, integrating it into development programs or
disseminating it, practices need to be scrutinized for their appropriateness just as any other
technology. In addition to scientific proof, local evidence and the socio-cultural background in
which the practices are embedded also need to be considered in the process of implementation
and evaluation.
Incorporating indigenous knowledge into climate change policies can lead to the development
of effective adaptation strategies that are cost-effective, participatory and sustainable. There is
the need therefore to integrate this local knowledge into formal adaptation policies.
Institutional support, as well as increased access to education, information and technology and
sustainable agricultural development could improve the overall resilience of smallholder
farmers and strengthen their efforts to withstand the overall impacts of changes in climate
variability and long-term climate change.
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2.1.4 POLICY FRAMEWORKS AND INSTITUTIONAL CONSIDERATIONS FOR IK IN WEATHER
FORECASTING IN UGANDA
The government of Uganda has relevant institutional, legal framework and opportunities to
support and promote Indigenous Knowledge (IK). Key among these are the Uganda National
Culture Policy, the National Indigenous Knowledge Policy of the Uganda National Commission
for Science and Technology (UNCST), the regulation on Access to Genetic Resources
and
Benefit Sharing (ABS), the Constitution of Uganda, intellectual property right laws including
trademark laws, and patent laws. The pivotal role of IK to sustainable livelihoods and national
development is well-understood in Uganda, and the need to preserve, integrate, utilize and
promote IK, is articulated in the Uganda National Culture Policy of 2006, sections 7.3. and 7.4
(MGLSD, 2006). Collectively, these policies and laws are there to promote IK and to ensure that
bio-prospectors using IK develop products and share acquired benefits with IK owners.
However, there is no specific policy or formal framework on Indigenous Knowledge in climate
and weather forecasting in Uganda despite its mention in the climate change policy. This study
and the subsequent process of developing a policy brief will add to the existing frame work and
strengthen the use of indigenous knowledge in weather forecasting.
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CHAPTER THREE
3.1
METHODOLOGY USED TO CONDUCT THE NATIONAL IK STUDY
This chapter presents the conceptual and theoretical framework used to guide the study as well
as the methodological approach to the study amongst others.
3.1.1 CONCEPTUAL AND THEORETICAL FRAMEWORK TO GUIDE THE STUDY
Social audit was the overall framework used for this study and it used both theoretical and
functional analyses as they applied to communities in the country. The theoretical analysis was
used to understand the weather forecasting knowledge, myths and beliefs, while the functional
analysis was used to analyze the application of the IK in forecasting and determining their
accuracy and shortfalls.
The theoretical / conceptual analysis framework for the study:
a) Understanding what IK exists in relation to weather forecasting;
b) Appreciating the different methods of IK weather forecasting
c) Establishing who are the custodians of this indigenous weather forecasting knowledge
d) Under what circumstances and considerations does the IK on weather forecasting work?
e) Ascertaining how this IK on weather forecasting has evolved over time
The functional analysis framework involved:
a) Identification of how IK in weather forecasting is done (applied)
b) Identification of who does the weather forecasting using IK
c) Identification of how the IK on weather forecasting is passed-on to other generations
d) Understanding how the IK weather forecasting information is interpreted
e) Identifying strengths, trust, reliance and bottlenecks with IK in weather forecasting given
the changes and variability in climate.
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The social audit framework also emphasized:
a) Gender, family and community dynamics - who does forecasting by gender, social beliefs
and norms
that may
exclude some groups from using and sharing indigenous
knowledge and the involvement of children (boys and girls) in accessing and using IK in
forecasting.
b) Traditions and social institutional frameworks
c) Attitudes and beliefs
d) Policy and regulatory frameworks
3.1.2 STUDY PROCESS AND METHODS
Desk Review:
The assignment was commenced with a comprehensive desk review of all
indigenous knowledge studies in weather and climate forecasting that have been done in the
country and generate a report with a list of IK weather indicators that have been used in various
communities in different climatological zones. More specifically, the following information on
IK in weather forecasting was assembled from secondary data:
a) Global and regional experiences of IK in weather forecasting
b) Forms and practices / applications of IK in weather forecasting
c) Accuracy, reliability and trust in IK for weather forecasting
d) Key drivers of IK on weather forecasting at individual, family and community levels
e) Relationship between IK weather forecasting and scientific weather forecasting
f) Policy frameworks and institutional considerations for IK in weather forecasting
The geographical coverage and data gaps identified in the desk review, was then used to:
1. Design a field study to collect more data from the climatological zones that were found
deficient in IK weather knowledge
2. Structures in a qualitative and quantitative study tools to collect primary data that was
missing as well as triangulate available information where necessary.
The primary data collected covered the following areas among others:
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a) IK indicators used by farmers for weather prediction
b) Interpretations and communication of the IK indicators by farmers
c) Reliability and accuracy of IK indicators for weather predication
Data sources & interviews

National Level: The National Meteorology Authority staff were interviewed and
engaged in discussions over previous studies to inform the process. They also gave the
technical and scientific views of the findings to harmonize the usage of IK indicators as
well as the scientific indicators.

District / Sub-county/ Community level: At field level, this study employed both
conventional and participatory methods to collect data from the community and other
technical respondents.

Specific sources of secondary data included; Uganda National Meteorology Authority,
Makerere University, NARO (Agro meteorology section), Directorate of Water
Development (Hydro – meteorology section) Civil Society Organizations (CSOs), weather
and climate Research Projects, Uganda Bureau of Statistics (UBOS), MAAIF, development
partners and other IK weather forecasting studies conducted in Uganda, and East Africa.

Primary data were collected from farmers (males & females), small holders and large
scale farmers. Facilitating organizations that are directly involved in initiatives to
promote IK were also interviewed.
3.1.3 TOOLS FOR DATA COLLECTION AND ANALYSIS
Key Informant Interviews: Key persons (opinion leaders, foretellers etc) were identified as
reference points for the study interview and detailed information about IK on weather
forecasting were gathered. This tool enabled the study to understand and appreciate IK in
various contexts using these key informers were gate keepers of vital information about the
communities’ IK, its application in weather forecasting and how it has changed overtime.
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Semi-structured Interviews/ Focus group Discussions: Particular issues concerning IK on
weather forecasting were discussed to gather opinionated information from various players
across communities. These were guided by a facilitator who ensured that the process was
focused and guided to achieve the intended study purpose.
Questionnaires: These were administered to specific players in the community / technical
institutions in order to do an objective assessment to determine patterns, trends and
relationships of weather forecasting using IK.
PRA tools: weather flow diagrams and weather/climate timelines were conducted to a cross
section of community members to identify changes that had happened overtime in IK and
weather/climate predictions.
3.1.4 SAMPLING AND DATA COLLECTION
Purposive sampling was used to select the districts to be included in the analysis. This study
covered 14 zones out of 16, the 2 zones had been covered in earlier studies. The choice of the
districts was guided by a criteria agreed upon with ACCRA and the Meteorology team during
the inception planning meeting and guided by the literature review report. A representation of
all climatological zones was considered. Below are the sixteen official different zones and the
map of Uganda showing the different rainfall zones:
1. B- Western parts of L. Victoria
2. A2- Eastern parts of L.Victoria
3. B- Central parts
4. CE- Eastern parts of Southwestern
5. CW- Western parts of Southwestern
6. D- South eastern parts
7. E- Lake Kyoga basin
8. F- Central parts of eastern region
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9. G- North eastern parts
10. H- Eastern parts- Central north
11. Western parts of central north
12. J- North western parts
13. K- Western (Bunyoro)
14. L- Northern parts of central western
15. ME- Eastern parts of South central western
16. MW- Western parts of south western
3.1.5 SAMPLING SIZE:
The table below shows the number of respondents originally planned to be interviewed and
those that were actually reached by this study, a total of 135 focus group discussions were
carried out with each FGD having between 6 to 10 members. 120 key informants (opinion
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
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leaders, foretellers), and 53 District officials were interviewed mainly from the department of
Marketing and production, Natural resources, Meteorology, Disaster, NAADS.
Table 2 Actual Number of Respondents Reached by the Study
Total
District Officials
Community
Key informant-
(FGDs + Key
Members-FGD
Opinion leaders,
informants)
foretellers
Climat
District
e zone
Planed
Actual#
Planed
#
Actual#
Planed#
#
Actual
Planed
Actual
#
#
#
Apac
6
6
8
0
14
6
4
1
Tororo
6
12
8
8
14
20
4
3
Buhweju
6
5
8
23
14
28
4
5
Kiryandongo
6
6
8
1
14
7
4
1
Ntungamo
6
3
8
5
14
8
4
3
Bugiri
6
13
8
13
14
26
4
3
Iganga
6
12
8
13
14
25
4
3
Bushenyi
6
4
8
9
14
13
4
4
Bundibudgyo
6
6
8
0
14
6
4
5
Ntoroko
6
3
8
0
14
3
4
2
Kyenjojo
6
6
8
0
14
6
4
2
Mubende
6
4
8
0
14
4
4
1
Mityana
6
3
8
1
14
4
4
1
Kiboga
6
2
8
0
14
2
4
2
Kalangala
6
4
8
12
14
16
4
3
Lwengo
6
6
8
10
14
16
4
3
Serere
6
12
8
11
14
23
4
4
Bududa
6
11
8
7
14
18
4
1
Kaberamaido
6
11
8
7
14
18
4
3
Arua
6
2
8
0
14
2
4
1
Nebbi
6
2
8
0
14
2
4
1
Lira
6
2
8
0
14
2
4
1
132
135
176
120
308
255
88
53
TOTAL
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3.1.6 QUALITY CONTROL MEASURES
During the actual data collection: A series of trainings were held for the data collection teams ahead
of their field activities of collecting data. These trainings emphasized the need to collect accurate and
complete data, how to identify the correct respondents for interview, how to deal with unresponsive /
uncooperative respondents, ethics of data collection among others.
While collecting data in the field, a team of data supervisors were responsible for quality control of the
responses from each completed questionnaire. This was done later in the evening after each day of data
collection. Thereafter, each questionnaire was examined for completeness, as well as the logical flow of
the collected data verified, and at this stage, where problems were identified the responsible persons
who collected the data were contacted to clarify on the data collected.
Data coding: Following the successful data collection process, efforts to code all the qualitative
variables in the questionnaires were done to ensure that such qualitative data/ case stories are
converted into categorical variables so as to enable statistical measures during analysis.
Data entry process: A data entry screen was then developed and customized using renowned statistical
data entry software called Epidata. The software was chosen due to its great ability to handle very large
sums of data and its capability in minimizing errors which usually arise during the data entry process
unlike other software. Furthermore, epidata software allows for development of a customized userfriendly data entry screens which decreases on time spent for data entry. In addition to supporting
double entry verification, Epidata enables various checks (range, jumps etc) and validation for each
variable being entered etc.
Data analysis: Data analysis was performed in the SPSS (Version 16) application where, descriptive
statistics (mean, sum and standard deviations) were generated for all continuous variables while, for
categorical variables, frequencies, valid percentages and cumulative percentages were generated.
Multiple response analysis was also performed for variables where respondents were expected to give
more than one response to a single question. Each variable has been cross-tabulated with the
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climatological zones, so as to get a clear picture of each IK indicator within the different climatological
zones.
CHAPTER FOUR
4.1
FINDINGS AND DISCUSSION
This chapter describes the major livelihoods of the communities in the 14 visited climatological zones;
the different indigenous indicators they use to predict on-set of rainy and dry seasons; categorises the
local indicators that works and these that no-longer works and reasons why; Perceptions of the local
communities on the application and reliability of both Indigenous Knowledge and scientific forecasting,
and ends with analysis of the common and reliable IK weather forecasting indicators used countrywide.
4.1.1 MAJOR LIVELIHOOD ACTIVITIES
In order to fully appreciate the impact of climate change on the livelihood of communities studied and
how they cope within the climatological zones in Uganda, it is vital to first understand the livelihood
activities. As such, figure 1 clearly indicated that most of the communities across all the climatological
zones were predominantly crop farmers, mainly producing maize, beans, potatoes, cassava, bananas,
ground nuts, peas, rice, etc for home consumption and sale. However, most of the communities around
the lake basin (Lake Victoria) were majorly involved in fishing, with some crop husbandry.
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Figure 2 showing the major livelihoods activities of the interviewed community members, opinion
leaders and foretellers.
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
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4.1.2 TYPES OF INDIGENOUS KNOWLEDGE INDICATORS USED TO PREDICT THE ONSET OF THE RAINY SEASON
According to the interviewed community members (FGDs), opinion leaders, and foretellers there were a number of IK indicators currently being
used to predict the onset of the rainy season, the common ones that cut cross all 14 climatological zones included; prevailing Westerly winds
which blows from West towards East (56%), heavy dark cloud formation (54%), the singing of birds (“Okwir Okwir”, “Mbuluku”, “Ekishamututu”,
“Butu” etc ) in the morning (42%), un usual increase in temperatures (hot at night) (40%) and trees sprouting/gaining of leaves, and flowering
(37%) respectively, amongst others as seen on the map of Uganda and the subsequent table 2.
Table 3 IK Indicators for Predicting Onset of Rains in the Climatological Zones
IK INDICATOR
Westerly winds (blowing from West to East)
Heavy dark cloud formation
Birds sing/cry in the morning (“Okwir Okwir”,
“Mbuluku”, “Ekishamututu”, “Butu”)
Unusual increased temperatures (hot at night)
Plants/Trees start flowering, sprouting and gaining
leaves
Frogs croak/cry in the swamps
Red/Black Ants movement's up-hill
Easterly winds (East to west)
Others (specify)
Animals become hyperactive/restless (cattle)
Migratory birds fly over to the community e.g.
“Enyange”)
Thunder / lightening
Mist or fog in swampy areas in the morning
Increased water levels in rivers, lakes etc
Tremors / earthquakes
Dull moon/Un clear moon
Joint pains felt by some elderly
Southerly winds
CLIMATOLOGICAL ZONES
B
%
87.5
50.0
0.0
A
%
50.0
100.0
25.0
CE
%
81.3
43.8
62.5
CW
%
69.4
18.4
53.1
D
%
31.9
63.8
31.9
E
%
61.5
64.1
59.0
F
%
44.4
61.1
38.9
H
%
100.0
100.0
100.0
I
%
50.0
66.7
66.7
J
%
100.0
75.0
75.0
K
%
83.3
33.3
50.0
56.3
50.0
75.0
75.0
62.5
31.3
30.6
49.0
37.7
26.1
20.5
43.6
22.2
11.1
100.0
100.0
100.0
16.7
50.0
50.0
6.3
18.8
0.0
25.0
12.5
12.5
25.0
50.0
0.0
25.0
0.0
50.0
31.3
68.8
0.0
12.5
50.0
6.3
59.2
49.0
2.0
14.3
10.2
0.0
10.1
8.7
40.6
8.7
1.4
2.9
20.5
2.6
7.7
7.7
2.6
15.4
0.0
5.6
0.0
11.1
0.0
50.0
100.0
50.0
0.0
0.0
100.0
0.0
50.0
0.0
0.0
0.0
50.0
0.0
0.0
18.8
25.0
0.0
6.3
0.0
0.0
0.0
25.0
25.0
0.0
12.5
0.0
0.0
6.3
6.3
12.5
0.0
0.0
16.3
10.2
18.4
6.1
6.1
0.0
15.9
4.3
1.4
0.0
0.0
0.0
4.2
2.6
5.1
0.0
0.0
2.4
2.5
0.0
16.7
16.7
5.6
0.0
0.0
0.0
5.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
33.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
35
L
%
0.0
100.0
0.0
ME
%
50.0
75.0
25.0
MW
%
53.3
66.7
26.7
OVERAL
%
56.4
53.6
42.4
83.3
33.3
50.0
50.0
75.0
75.0
40.0
33.3
40.0
37.2
100.0
25.0
0.0
0.0
75.0
0.0
33.3
16.7
0.0
0.0
50.0
33.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
25.0
25.0
0.0
0.0
0.0
0.0
33.3
20.0
0.0
6.7
24.8
20.4
15.2
11.6
11.2
10.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
14.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
25.0
0.0
0.0
0.0
50.0
0.0
0.0
13.3
6.7
6.7
13.3
13.3
0.0
0.0
8.8
8.4
5.2
4.8
4.3
2.4
1.6
VALID N (Respondents-FGD inclusive))
16
4
16
49
69
39
18
2
6
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
KEY for Climatological zones visited:
1.
B
Western part of L.Victoria (e.g, Kampala, Mpigi, Wakiso etc)
2.
A
Central Part (Luwero, Kayunga, Mityana etc)
3.
CE
Eastern part of S. Western (eg, Rakai, Isingiro, Lyantonde, etc)
4.
CW
Western parts of S. Western ( e.g. Ntungamo, Kabale, Bushenyi etc)
5.
D
South Eastern parts (eg, Iganga, Bugiri, Tororo, etc)
6.
E
L. Kyoga Basin (Kamuli, Serere, etc)
7.
F
Central parts of Eastern region (Mbale, Kapchorwa, Sironko,
Bududa etc)
8.
H
Eastern Parts of C. North (Lira, Otuke, Pader, etc)
9.
I
Western parts of C. North (Apac, Gulu Nwoya, etc)
10. J
North Western Parts (Arua, Moyo, Nebbi, etc)
11. K
Includes Masindi, Hoima, Kiryandongo
12. L
Northern parts of C. Western (Kiboga, Nakaseke, Nakasongola, etc)
13. ME
Eastern parts of S. Central Western (Sembabule, Mubende, etc)
14. MW
Western parts of S. Western (Kabarole, Kasese, Bundibugyo, etc)
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
36
4
6
2
4
15
250
Table 4 Notes on IK Indicators Used by Communities in Uganda to Predict onset of Rains
INDICATORS TO PREDICT ONSET OF RAIN
EXPLANATION OF THE INDICTOR
REMARKS
Westerly winds (blowing from West to
East)
Heavy dark cloud formation
Birds singing in the morning (“Okwir
Okwir”, “Mbuluku”, “Ekishamututu”,
“Butu”)
Unusual increased temperatures (hot at
night)
Plants/Trees start sprouting and gaining
leaves
Whenever, strong winds blows from west towards East, then this is an
indication of rainy season soon approaching
When dark clouds form in the sky, it is certain that the rains will fall.
When specific birds (such as “Okwir okwir” in Lango or “Ekishamututu” in
Runyakitara or “Butu” in Luganda etc) sing in the morning, it indicate the
onset of rainy season
Unusual increase in temperature especially at night is a clear indication of
onset of rains especially around the month January through March
Usually big trees are expected to shed their leaves from around January
and by March the trees will be completely bare, devoid of any leaves.
When the leaves of the trees commence sprouting then the rainy season is
near. Usually When the time for the leaves to sprout is due and the leaves
do not come back then it is an indication of a warm season with bad
rainfall. The sprouting of the leaves is also anticipated around June and
when this happens early, then the rainy season will start early.
Greatly used7 and reliable
Frogs croak/cry in the swamps
Whenever frogs are heard at certain periods of the year it is an indication
of the rainy season approaching, mostly in swampy areas. The croaking of
the frog is not an indicative of the intensity of rainfall but rather that it will
rain.
The appearance of red ants moving up-hill in lines indicates the onset of
rains. The same applies to when ants are seen moving down hill to low
areas/streams it’s an indication of very little or no rainfall at all.
When winds are seen to blow from East towards West, it indicate onset of
rains, this was reported by a few communities especially those within
South Eastern parts and Western parts of S. Western Uganda. This is
because Easterlies (dry winds) are seen to first blow and then its preceded
by Westerly which comes with the rains
When rainy season is soon approaching, animals are seen to be super
excited this includes cattle etc. This kind of excitement was also reported
in ducks as seen by their flapping of wings,
When the migratory birds fly into the community in their numbers, it is an
indication that the rainy season is near. An example of these birds are the
Fairly used, reliable
Red/Black Ants movement's uphill/highlands
Easterly winds (East to west)
Animals become hyperactive/restless
(especially cattle)
Migratory birds fly over to the community
such as “Enyange”
7
Greatly used & reliable
Fairly used but reliable
Fairly used but reliable
Fairly used but reliable
Limited use but reliable
Limited use and un reliable
Limited use but reliable
Limited use but reliable
Greatly used, fairly used and limited use refers to the proportion of the population that is reported to use a particular indicator across the climatological zones
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
37
Thunder / lightening
Mist or fog in swampy areas in the
morning/mountain tops
Increased water levels in rivers, lakes etc
Tremors / earthquakes
Dull moon/Un clear moon/sun
Joint pains felt by elderly
“Enyange” which is usually snowy white bird with a yellow beak and black
legs commonly found where cattle graze
When there is more thunder and lightning around the month of March
prior to the rainy season, it is an indication of a very strong rainy season
which will most likely flood the community.
Whenever mist forms in swampy areas especially in the morning hours,
this is indicates that it will soon rain
Increased water levels in the lakes, rivers, is an indicator of onset of rains,
this was mainly reported by communities within the lake Victoria basin
and Western parts of S. Western Uganda
Whenever earthquakes are felt, this is indicative of onset of rains, this was
mainly reported by the communities within the great western rift valleys
i.e. Western parts of S. Western ( e.g. Ntungamo, Kabale, Bushenyi,,
Kabarole, Kasese, Bundibugyo, etc)
Whenever the moon appears cloudy/ unclear, and sometimes with a
yellowish ring/circle around it, this is indicative of rain, it is said that when
the next morning, the sun rises with a similar ring/ circle around it then
this will finally confirm that it will surely rain very soon (1 to 4 days)
When some elders start to feel joint paints/pains in the feet, this an
indication of onset of rains, this was mainly reported within the Eastern
part of South Western (eg, Lwengo, Rakai, Isingiro, etc) and Western parts
of S. Western ( e.g. Ntungamo, Kabale, Bushenyi etc)
Southerly winds
Limited use, reliable
Limited use and reliability
Limited use, reliable
Very limited use and reliability
Very limited use, Not working, unreliable
Very limited use, Not working, unreliable
Very limited use, Not working, unreliable
**Note: the criterion used for remarks is based on the IK indicators reported to be working for the community as in table 5 entitled
“Indicators that are working for communities to predict the ONSET OF THE RAIN season (according to the interviewed community
members, opinion leaders, and foretellers)”
4.1.3 INDIGENOUS KNOWLEDGE INDICATORS USED TO PREDICT THE ONSET OF THE DRY SEASONS
In line with the interviewed community members, opinion leaders, and foretellers, there were a number of IK indicators currently
being used by communities to predict the onset of dry season. The common ones across the 14 zones (ref to the map below)
included ; prevailing Easterly winds (winds blowing from East to West) (42%), Clear sky/white clouds (36%), the Weathering of
plants/trees/shading of leaves (26%), Presences of Butterflies (23%) and Drop in temperatures at night/Coldness at night (21%)
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
38
respectively, amongst others as illustrated in the table below. The explanatory notes and remarks for each IK indicator being used to
predict onset of dry season has been further discussed in details in table 5 of this report:
Table 5 IK Indicators for Predicting on-set of Dry Weather in the Climatological Zones
Easterly winds (winds blowing from East to West)
Clear sky/white clouds
Weathering of plants/trees/shading of leaves
Presences of Butterflies
Drop in temperatures at night/Coldness at night
Dew or mist forms in the morning/cold breeze
Others (Thunder, calm lake waters,)
Flowering of plants in the hills (“Ehongwe”)
High temperatures during day/ Hot morning sunrise
Bees come out of hives
Strong winds/spiral
Red sky at sunset
Migratory birds "White" fly and Leave the community,
"Kamakomati"
Bright/clear moon
Irregular rain
Reduction in water level in lakes, rivers, swamps,
lagoons etc
Movements of eagles from west to east
Caterpillars are seen moving around (“Ndoboozi”)
Known weather patterns/calendar periods
Earth quake
Presence of grasshopper
Cloud movements from (West to East)
VALID N (Respondents-FGD inclusive))
B
A
CE
D
E
F
H
I
J
K
L
%
60.0
53.3
0.0
66.7
60.0
13.3
13.3
0.0
0.0
40.0
6.7
13.3
0.0
CW
%
44.9
34.7
10.2
69.4
10.2
4.1
24.5
59.2
0.0
44.9
16.3
18.4
0.0
%
75.0
37.5
0.0
18.8
12.5
6.3
18.8
0.0
6.3
0.0
0.0
6.3
37.5
%
50.0
25.0
25.0
50.0
50.0
50.0
0.0
0.0
0.0
0.0
0.0
0.0
25.0
%
17.9
40.3
35.8
1.5
13.4
34.3
14.9
4.5
29.9
0.0
7.5
7.5
0.0
%
59.5
32.4
62.2
0.0
21.6
10.8
5.1
13.5
8.1
0.0
10.8
2.7
7.3
%
27.8
27.8
16.7
5.6
0.0
27.8
5.6
0.0
11.1
0.0
16.7
11.1
38.9
%
100.0
100.0
50.0
0.0
0.0
0.0
50.0
50.0
0.0
0.0
0.0
0.0
0.0
%
66.7
50.0
33.3
0.0
50.0
0.0
16.7
16.7
33.3
0.0
0.0
0.0
0.0
%
50.0
50.0
75.0
0.0
25.0
50.0
75.0
0.0
25.0
0.0
50.0
0.0
25.0
%
100.0
0.0
20.0
0.0
20.0
40.0
0.0
0.0
20.0
0.0
20.0
20.0
0.0
%
0.0
50.0
0.0
0.0
50.0
50.0
0.0
0.0
0.0
0.0
50.0
0.0
0.0
0.0
0.0
0.0
25.0
0.0
0.0
13.3
0.0
0.0
14.3
2.0
4.1
3.0
13.4
10.4
2.7
5.4
8.1
5.6
33.3
11.1
50.0
0.0
50.0
0.0
16.7
25.0
0.0
0.0
0.0
20.0
0.0
0.0
0.0
0.0
0.0
0.0
16
0.0
25.0
0.0
0.0
0.0
0.0
4
0.0
0.0
0.0
0.0
0.0
0.0
15
8.2
0.0
0.0
22.4
0.0
2.0
49
1.5
6.0
9.0
0.0
1.5
3.0
67
16.2
2.7
5.4
0.0
0.0
0.0
37
11.1
5.6
5.6
0.0
0.0
0.0
18
50.0
16.7
16.7
0.0
0.0
16.7
0.0
6
25.0
0.0
25.0
0.0
50.0
0.0
4
20.0
80.0
40.0
0.0
20.0
0.0
5
0.0
0.0
0.0
0.0
2
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
39
ME
%
25.0
50.0
0.0
75.0
50.0
25.0
25.0
0.0
0.0
0.0
0.0
MW
0.0
%
21.4
14.3
7.1
14.3
57.1
7.1
57.1
0.0
7.1
0.0
14.3
14.3
6.7
OVERALL
%
41.6
36.2
26.3
23.0
21.0
18.9
17.8
16.0
12.8
11.5
11.1
9.5
7.5
0.0
0.0
0.0
0.0
0.0
14.3
0.0
7.1
7.4
7.4
7.4
0.0
0.0
0.0
0.0
0.0
0.0
2
75.0
0.0
0.0
0.0
0.0
4
7.1
14.3
0.0
0.0
0.0
0.0
14
7.4
7.0
4.9
4.3
2.1
1.2
243
Table 6 Notes on IK Indicators Used by Communities to Predict on-set of Dry Season
INDICATORS TO PREDICT ONSET OF
DRY SEASON
Easterly winds (winds blowing from
East to West)
Clear sky/white clouds
Weathering of plants/trees/shading of
leaves
Presences of Butterflies
Drop in temperatures at night/Coldness
at night
Dew or mist forms in the morning/cold
breeze
Others (Thunder, calm lake waters,)
Flowering of plants in the hills
(“Ehongwe”)
High temperatures during day/ Hot
morning sunrise
Bees come out of hives
Strong winds/spiral
Reddish/brownish sky at sunset
Migratory birds "White" fly and Leave
the community, "Kamakomati"
EXPLANATION OF THE INDICATOR
REMARKS
Whenever prevailing wind blows from East to West, this indicates that it will
not rain, this is because these are dry winds which carry no rains and hence
onset of dry season
Clear sky (blue sky) is a clear indication of no rain while white clouds may
mean very little or no rain, when this is observed then it clearly indicates the
onset of dry season
Whenever trees ( especially big ones such as:- “Mivule” ) starts to shade off
their leaves, this is a clear indication of onset of dry season, this occurs from
around January and by March the trees will be completely bare, when trees
remain bare for longer than expected this is indicative of prolonged dry
season as well.
Being used, reliable
Whenever, butterflies are seen in their numbers, this indicate that there will
be no rains
Whenever there is unusual drop in temperature characterized by coldness
at night, this indicates that dry season is soon starting
When mist/dew form especially In morning hours accompanied with cool
breeze, this indicate no possibility of rains and also signify that the onset of
dry season is about to start. Likewise.
Being used, reliable
Whenever plants such as “ehogwe” start to flower it’s an indication of onset
of dry season, the mango trees have also been proven to flower during
onset of dry season.
When there is hot morning sunrise, with high temperatures during the day
these indicate no rains and also mean that dry season is soon approaching.
Whenever this happens it signifies no rains and hence onset of dry season,
it is alleged that these bees come out to collect nectar from most of the
plants that are flowering at around this time.
Whenever strong winds/ whirlwinds are felt, this is a clear indication of
onset of dry seasons. The whirlwinds do normally occur during hot, calm
days.
Whenever the sky at sunset is reddish/brownish, it’s an indication of no rain
and hence onset of dry season,
When the migratory birds fly usually in large groups and leave the
community, it’s an indication of no rains and therefore onset of dry season
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
40
Being used, reliable
Being used, reliable
Being used, reliable
Being used, reliable
Limited used, reliable
Being used, reliable
Limited use, reliable
Limited use, reliable
Being used, reliable
Limited use, sometimes reliable
Limited use, reliable
Bright/clear moon
When the moon is seen to be clear with no yellowish rings around it, this is
clear indication that it won’t rain and hence onset of dry season
Whenever rains starts getting irregular especially during rainy season, this is
an indication of an end to the rainy season and hence an onset of dry
season
Whenever water levels in lakes, rivers, swamps and lagoons reduce
significantly or even dry up (for cases of swamps, lagoons etc), this is a clear
indication of onset of dry season additionally, since the major source of
water inflow into most of the lagoons/swamps is rainfall, hence with
reduction in the water level in the lagoon, it is a proof of decreased quantity
of rainfall. When lagoons/water sprouts are dry, this indicate drought.
When eagles are seen to fly high up in the sky, this indicative of no rains
and hence onset of dry season
When these caterpillars are seen in their numbers around March/April
before the rainy season, it is a sign of no or little rainfall in the year. When
the caterpillars do not appear, it is a sign of good rains. Usually these
caterpillars are black in color with some white rings all over the body. When
it appears, it chews all the vegetation.
Very limited use, reliable
Known weather patterns/calendar
periods
There are known periods in the year when the weather is always known to
be a particular manner. E.g. In Uganda, the "first-season" rains start in
February/March and continue through mid-June in the south and early
August in the north affecting the main harvest in June-August. Whereas the
"second-season" rains start in July and end in October affecting the
secondary harvest in November-January. However, due to the constant
weather changes, these seasons have equally been affected over time.
limited use, not always reliable
Earthquake
Whenever earthquakes are felt especially at certain known periods in the
year, this is indicative of onset of dry seasons, this was mainly reported by
the communities within the great western rift valleys i.e. Western parts of S.
Western ( e.g. Ntungamo, Kabale, Bushenyi,, Kabarole, Kasese, Bundibugyo,
etc)
When grasshoppers appear in their numbers at certain periods of the year
(especially in the month of April) it is an indication that it may not rain.
When they appear in their numbers around March/April, it means the rains
will not be good or the season. These grasshoppers are always green or
brown in color
Very limited use, unreliable
Irregular rain
Reduction in water level in lakes, rivers,
swamps, lagoons etc
Movements of eagles from west to east
Caterpillars are seen moving around
(“Ndoboozi”)
Presence of grasshoppers in their
numbers
Cloud movements from (East to West)
Limited use, reliable
Limited use, reliable
Limited use, reliable
Limited use, reliable
Very limited use, not working, unreliable
Very limited use, unreliable
**Note: the criterion used for remarks is based on the IK indicators reported to be working for the community as in table 7
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
41
4.1.4 LOCAL INDICATORS THAT ARE SAID TO BE ACCURATE AND REASONS
INDICATORS THAT ARE ACCURATE FOR COMMUNITIES TO PREDICT THE ONSET OF THE RAIN SEASON
The main IK indicators found to be working for the communities in prediction of onset of rain season were; Heavy dark cloud
formation (40%), Westerly winds (blowing form West to East) (39%), Birds cry in the morning (“Okwir Okwir”, “Mbuluku”,
“Ekishamututu”, “Butu”(28%), Unusual increased temperatures (usually hot at night) (25%), Frogs croaking/cry in the swamps (21%),
and Plants/Trees start sprouting and gaining leaves (17%) respectively amongst others as seen in the following table, these IK
indicators were equally reported to be reliable, attributable to the fact that they are commonly used among the community and have
been witnessed to predict weather changes (onset of rains) just fine for decades of time.
Table 7 Indicators that are said to be Accurate to predict on-set of Rainy Season
Heavy dark cloud formation
Westerly winds (blowing form West to East)
Birds cry in the morning (“Okwir Okwir”, “Mbuluku”,
“Ekishamututu”, “Butu”
Unusual increased temperatures (hot at night)
Frogs croak/cry in the swamps
Plants/Trees start flowering, sprouting and gaining leaves
Easterly winds (East to west)
Others (specify)
Thunder / lightening
Animals become hyperactive/restless (cattle)
B
%
9.1
63.6
A
%
36.4
54.5
18.2
27.3
9.1
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
CE
%
31.3
50.0
75.0
CW
%
13.0
58.7
17.4
D
%
53.7
22.4
28.4
E
%
45.0
47.5
40.0
F
%
72.2
27.8
22.2
H
%
37.5
56.3
25.0
28.3
56.5
32.6
4.3
4.3
25.4
1.5
7.5
20.9
10.4
14.9
7.5
15.0
12.5
10.0
10.3
7.7
2.5
22.2
50.0
100.0
6.3
6.3
37.5
6.5
50.0
I
%
33.3
50.0
16.7
J
%
50.0
50.0
50.0
K
%
42.9
14.3
33.3
16.7
50.0
50.0
57.1
14.3
25.0
14.3
100.0
42
16.7
16.7
ME
%
100.0
MW
%
28.6
14.3
11.1
11.1
16.7
L
%
14.3
28.6
100.0
100.0
100.0
100.0
14.3
42.9
50.0
OVERALL
%
39.6
39.1
28.0
25.3
21.3
17.3
12.4
10.7
8.5
7.1
Red/Black Ants movement's up-hill
Increased water levels in rivers, lakes etc
Migratory birds fly over to the community (“Enyange”-white in
color)
Known weather patterns/calendar known periods in the year
Mist or fog in swampy areas in the morning
Tremors / earthquakes
VALID N (Respondents-FGD inclusive))
18.8
36.4
11
10.9
13.0
2.2
16
4.3
4.3
46
6.0
1.5
7.5
2.5
2.6
5.1
4.5
4.5
5.1
67
40
5.6
6.2
5.4
5.4
22.2
16.7
28.6
14.3
18
2
6
4
7
1
7
3.6
2.7
0.9
225
Source: National IK Study for predicting weather patterns in Uganda, World Vision (2015)
INDICATORS THAT ARE WORKING FOR COMMUNITIES TO PREDICT THE ONSET OF DRY SEASON
The main IK indicators found to be working for the communities in prediction of onset of dry season were; Clear sky/white clouds
(28%), Easterly winds (winds blowing from East to West) (26%), Weathering of plants/trees/shading of leaves (20%), Presences of
Butterflies (18%), formation of dew or mist in the morning alongside cold morning breeze (14%), and Flowering of plants in the hills
(“Ehongwe”, mango trees etc) (13%) respectively amongst others as seen in the following table, these IK indicators were similarly
reported to be reliable, attributable to the fact that they are commonly used among the community and have been witnessed to
predict weather changes (onset of dry season) just fine for over decades of time.
Table 8 Indicators that are said to be Accurate in Predicting on-set of Dry Season
Clear sky/white clouds
Easterly winds (winds blowing from East to West)
Weathering of plants/trees/shading of leaves
Presences of Butterflies
Dew or mist forms in the morning/cold breeze
Flowering of plants in the hills (“Ehongwe”)
Strong winds/spiral
Drop in temperatures at night/Coldness at night
High temperatures during day/ Hot morning sunrise
B
%
22.2
44.4
55.6
A
%
CE
%
50
57.1
57.1
28.6
28.6
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
CW
%
17.02
31.9
4.3
53.2
2.1
53.2
2.1
10.6
2.1
D
%
32.35
2.9
32.4
E
%
27.78
52.8
33.3
F
%
33.3
16.2
4.4
11.8
11.8
20.6
13.9
2.8
16.7
8.3
8.3
38.9
5.6
H
%
50
100.0
27.8
I
%
J
%
K
%
40
60.0
20.0
25
75.0
75.0
33.3
75.0
33.3
100.0
100.0
33.3
100.0
20.0
50.0
5.6
43
L
%
ME
%
25.0
MW
%
33.3
16.7
50.0
16.7
OVERALL
28.2
25.9
19.9
18.1
13.9
13.4
12.5
12.5
9.3
Bees come out of hives
Reduction in water level in river, lakes etc
Irregular rain
Migratory birds leave the community
Caterpillars are seen moving around (“Ndoboozi”)
Movements of eagles from west to east
Bright/clear moon
Red sky at sunset
Calm lake water
Cloud movements from (West to East)
Presence of grasshopper
VALID N (Respondents-FGD inclusive))
11.1
11.1
28.6
27.7
44.4
11.1
33.3
7.1
7.1
2.1
6.4
10.6
8.8
10.3
2.9
10.3
1.5
7.4
2.9
8.3
5.6
8.3
2.8
22.2
33.3
11.1
5.6
16.7
50.0
18
2
25.0
16.7
20.0
25.0
33.3
16.7
5
4
6
100.0
100.0
16.7
1
6
2.8
9
14
47
68
36
8.3
7.9
6.9
5.6
5.1
5.1
4.6
4.2
1.4
0.5
216
Source: National IK Study for predicting weather patterns in Uganda, World Vision (2015)
4.1.5 LOCAL INDICATORS THAT ARE SAID TO BE INACCURATE AND THE REASONS
The question as to whether these IK indicators were not working for the communities was largely dependent upon their reliability and
this differed from one climatological zone to another. An example is the use of Heavy dark cloud formation (P-value = 0.708) which
was majorly reported nationally by about 40% of all respondents to be working for the communities, and yet it’s equally topping the
list for those IK indicators not working (18%). These findings clearly show that indeed these IK indicators work but some of their
reliability do greatly differ from one climatological zone to another, and also depends on the expertise of the users.
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
44
Table 9 Indicators thought to be inaccurate to predict on-set of Rainy Season
Heavy dark cloud formation
Others (migratory birds, position of sun,
dull moon, cold winds/breeze, ducks
flapping wings etc)
Birds cry in the morning (“Okwir Okwir”,
“Mbuluku”, “Ekishamututu”, “Butu”
Unusual increased temperatures (hot at
night)
Westerly winds (blowing form West to
East)
Plants/Trees start flowering, sprouting
and gaining leaves
Mist or fog in swampy areas in the
morning
Tremors / earthquakes
Red/Black Ants movement's up-hill
Thunder / lightening
Rainbow formation
Whirl wind/Spiral wind
Easterly winds (East to west)
Frogs croak/cry in the swamps
Animals become hyperactive/restless
(cattle)
White birds fly over to the community
(“Enyange”)
Increased water levels in rivers, lakes etc
B
A
CE
CW
D
E
F
H
I
J
K
L
ME
MW
%
%
%
26.7
13.3
%
17.5
7.5
%
15.6
18.8
%
27.3
14.7
%
11.1
38.9
%
50.0
%
%
20.0
%
%
%
%
66.7
10.0
9.4
9.1
11.1
50.0
26.7
2.5
15.6
6.1
16.7
33.3
7.5
9.4
15.2
5.6
50.0
33.3
6.7
5.0
10.9
15.2
5.6
50.0
6.7
7.5
13.8
5.6
50.0
6.7
33.3
32.5
12.5
6.7
6.7
5.1
23.1
10.0
2.5
1.6
3.1
9.4
6.3
3.1
6.3
3.1
1.6
2.5
3.1
16.7
16.7
16.7
100.0
50.0
40.0
26.7
16.7
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
1.6
25.0
20.0
25.0
40.0
100.0
OVERALL
%
30.0
40.0
18.0
16.5
10.0
14.1
50.0
11.7
20.0
11.2
9.3
25.0
7.8
20.0
7.3
6.8
6.8
6.8
6.3
5.9
5.4
3.9
20.0
2.4
50.0
6.1
5.7
2.9
11.1
25.0
50.0
20.0
40.0
10.0
6.1
25.0
25.0
5.6
3.0
1.5
45
VALID N (Respondents-FGD inclusive))
6
1
5
40
64
33
18
2
4
4
5
1
2
10
195
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
Table 10 Indicators that are thought to be inaccurate to predict on-set of dry season
Earthquake/Tremors
Others (Migratory birds, Southerlies, prolonged
rain season respectively etc)
Clear sky/white clouds
Dew or mist forms in the morning/cold breeze
Strong winds/spiral
Easterly winds (winds blowing from East to West)
Known weather patterns/calendar known periods
in the year
Drop in temperatures at night/Coldness at night
High temperatures during day/ Hot morning
sunrise
Weathering of plants/trees/shading of leaves
Red sky at sunset
Bright/clear moon
Cloud movements from (West to East)
Irregular rain
Movements of eagles from west to east
Flowering of plants in the hills (“Ehongwe”)
Presences of Butterflies
Reduction in water level in river, lakes etc
Caterpillars are seen moving around
(“Ndoboozi”)
Bees come out of hives
Presence of grasshopper
VALID N (Respondents-FGD inclusive))
B
A
CE
CW
D
E
F
H
I
J
K
L
ME
MW
OVERALL
%
%
%
%
53.7
%
1.8
10.5
%
3.3
32.3
%
%
%
%
%
%
%
%
%
14.6
2.4
19.5
9.8
19.6
17.9
7.1
10.7
14.3
3.3
23.3
5.9
5.9
23.5
5.9
35.3
2.4
2.4
14.3
14.3
3.3
2.4
9.8
7.3
2.4
7.1
5.4
3.6
7.1
7.1
1.8
1.8
1.8
13.3
3.3
6.7
66.7
66.7
40.0
40.0
40.0
20.0
0.0
33.3
20.0
2.4
4.9
2.4
10.0
16.7
17.6
14.0
13.8
33.3
50.0
25.0
28.6
14.3
33.3
33.3
25.0
42.9
14.3
8.7
6.4
14.3
5.8
5.2
4.1
3.5
3.5
3.5
2.3
2.3
1.7
1.2
5.9
5.9
3.3
6.7
33.3
33.3
50.0
25.0
6.7
25.0
1.8
33.3
2.4
3
5
41
0.6
56
30
17
2
4
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
12.2
12.2
11.0
11.0
11.0
46
4
3
7
172
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47
4.1.6 PERCEPTIONS OF THE LOCAL COMMUNITIES ON THE APPLICATION AND RELIABILITY OF
BOTH INDIGENOUS KNOWLEDGE AND SCIENTIFIC FORECASTING
Meaning of IK indicators other than prediction of weather: In an attempt to establish whether IK
indicators could be meaning something else other than weather prediction, the respondents (community
members and opinion leaders/foretellers) interviewed, only 36.5% of them (178) agreed that indeed a few
of these IK indicators mean something else other than prediction of weather, in most cases these IK
indicators were indicative of mainly famine /food shortage and even death especially when onset of
adverse dry season and floods were foreseen, additionally some of the IK were being used by the
community to foretell bountiful harvest/production, others also believed that some of the IK indicators
meant a bad omen e.g. the moon tilting to the left- means death.
Who does the IK forecasting?: The main category of people found to be practising IK forecasting were
the farmers as reported by 75% of all the respondents, this was so because agriculture is the predominant
activity which is practised by farmers (both males and females) and therefore knowledge of the current
weather changes/ forecast are vital, this would enable farmers to foretell when to plant, weed, harvest, and
even store food in anticipation for prolonged drought. Additionally the elders in the community were also
reported by more than a half (52%) of all respondents to be the ones involved in IK weather forecasting,
this was attributable to the assumption that the elders have an immeasurable knowledge of weather
prediction got through their lifetime experiences and also their forefathers.
All the above IK foretellers came to know about this forecasting mainly through knowledge which is
acquired in the course of experiences as suggested by 85% of all the respondents, however, foretellers
were also reported to be naturally endowed (13%) with the knowledge of IK forecasting.
Communication of IK weather forecast information: IK weather forecast information was established
to be mainly communicated among the community members informally through conversations with other
locals/farmers mainly at water collection points, drinking places, churches e.t.c as reported by 88% of all
the respondents, this was followed by organized village meetings/ group learning sessions (32%) and
observation from other farmers (7.2%). Other sources of IK weather forecasting information included
media (7.2%). The frequency of communicating the IK weather forecasting information was very often
(51%) among the community members, this is because IK indicators are widely available and used by the
communities.
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48
The main category of people who received IK information first were the men (42%) and women (19%)
respectively, the men were reported to be mainly moving up and down (to social gatherings, drinking
joints, cattle rearing) and hence ending up being more informed than the women who often stayed at
home, also given the African traditional setting where men are the family heads/authority figure, they are
in most cases required to receive IK information first and then in turn inform their families. However, 13%
of all respondents also reported that elders were the first to receive IK information, this was attributable to
the fact that elders are looked at as the source of IK information and thus expected to be well experienced
with IK weather foretelling techniques. The use of IK forecast information in communities was mostly by
the farmers (84%), and pastoralists (30%) respectively, both men and women were equally using the IK
forecast information especially in farming as reported by 65% of all respondents. This was because both
farmers and pastoralists regardless of gender are affected by the weather changes and require rain water
for a number reasons such as crops, animals,, domestic use etc.
Importance of IK weather forecast information to the community: The major importance of IK
weather forecast information to the community was established by this study to be mainly helping
farmers make decisions in regard to preparation for planting, weeding, and harvesting of crops, this was
reported by the preponderance of all the respondents (95%), additionally this information also acted/
provided as an early warning signs to the community members thereby enabling them to prepare and
deal with the anticipated adverse weather conditions such prolonged droughts and floods etc, these
preparations would curtail food storage, repairing of weak/leaking house roof tops etc. As seen in the
table below
Table 11 How IK weather forecast information assists Communities
Use of IK weather forecast information
1. Helps in preparation for planting, harvesting/ planning and decision making
Yes
94.6%
Valid N
242
2. Acts as early warning signs/one can prepare to deal with harsh weather conditions
such as droughts, floods etc
3. Helps in knowing when to store crops (in anticipation for dry season)
17.8%
242
6.6%
242
2.5%
243
4.Others (Specify)
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
Challenges in the use of IK forecasts: As much as the indigenous knowledge of weather forecasting is
currently being practiced by communities across the country, the greater percentage (90%) of all the
interviewed respondents reported some challenges in regard to use of IK forecast. The major and most
crucial challenge was its unreliability which was misleading farmers since the predicted weather outcome
is not always true, thus resulting into wastage of seeds due to delays in rain. This was also followed by the
fact that IK weather forecast is un predictable due to changes in weather over time (39%) and also the
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49
continuous disappearance of IK weather forecasting indicators such as trees, insects, birds etc arising from
environmental degradation by man (4.3%) as seen in the table below.
Table 12 Challenges of Using IK in weather forecasting
Challenges of using IK
1. Unreliable and sometimes may be misleading
Yes (%)
73.8%
Valid N
210
39.0%
210
3. Continuous disappearance of IK indicators (such as trees, insects, birds etc
4.3%
210
4. Others (Specify)
5.7%
210
2. Un predictability due to changes in weather over time
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
Solutions to the challenges of using IK weather forecast:
As suggested by all the interviewed
respondents, the main solution to the challenges of using IK is the adoption of scientific methods of
weather forecasting (29%), this according to them would be much more reliable if and only if both
methods are concurrently used. Additionally a significant proportion (22%) of all respondents also made
known that farmers should be patient and wait for a confirmation of presence of rain before actual
planting of their seeds, this according to them would solve the challenges of unreliability of IK weather
forecast, where farmers have to replant because all the seeds planted earlier on in anticipation of the rains
have been destroyed by the continuous scorching sunshine. See table below
Table 13 Solutions to the Challenges of using IK in weather forecasting
1. Adopting scientific methods of weather forecast
2. Patience/Wait and confirm presence of rains before planting seeds
3. Restoration of nature (a forestation, swamp reclamation etc)
4. Others (Document IK, use more than one IK indicator))
5. Leave it up to God/ no guarantee/no solution
6. Plan for other interventions (irrigation)
7. Not encroaching on wetlands
8. Plant seeds in bits
Yes (%)
29.4%
21.6%
21.2%
15.0%
9.8%
8.8%
8.2%
8.2%
Valid N
194
194
193
193
194
194
194
194
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
Reception of modern/scientific weather forecast information from government by
communities
Nationally, reception of modern/scientific weather forecast from the government was found to be high as
seen by 88% of all respondents who were in total agreement. Noticeably, the reception of modern
weather forecast from government was seen to be relatively low in the areas of Masindi, hoima,
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50
Kiryandongo etc and Western parts of Central North as represented in the subsequent table. Furthermore,
most of these scientific weather forecasting information were received mainly through radio
announcements
(97.3%),
followed
by
newspapers
(16%),
television
(T.V)
(15%),
government
workers/community based workers (6%), and Phones-SMS (5%)respectively.
This kind of information was established to be helpful to the community majorly through aiding in the
preparation for the planting season as reported by 93% of all the respondents, other usefulness of
scientific weather information by government was the fact that it provides the local members with
environmental protection tips (3%) with a minimal proportion (3%) reporting that such information was
not helpful due to its inaccuracy amongst others.
Figure 3 showing whether communities receive the modern weather forecast from government
(according to the interviewed community members, opinion leaders, IK foretellers)
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
Challenges with using the scientific weather forecast information: Just like indigenous knowledge
(IK), the scientific weather forecast information has challenges which are currently being faced by
community members; this was seen by majority of all respondents (93%) agreeing that indeed there are
challenges with using the scientific weather forecast information, leaving only 7% who suggested
otherwise. Out of the many challenges reported, the most outstanding one was the fact that scientific
weather information in most cases misleads people due to its unreliability and inaccuracy in weather
prediction this was suggested by 91% of all the interviewed respondents, because of this inaccuracy,
farmers usually spend more on re-buying seeds and replanting following delays in rains contrary to what
was predicted by the scientific weather forecast. The other challenge was that few people actually have
access to the scientific weather forecast information since it’s not widely available (7.2%), this study also
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
51
confirmed that its reception is greatly influenced by the ownership/availability of a fully functioning radio.
As seen in the table below.
Table 14 Challenges with using Scientific weather forecasting Methods
Challenges with using scientific weather forecasting
1. Most times mislead people - it's not accurate/realistic
(YES %)
91.0%
Valid N
223
2. It’s not widely available/ Few people access the information
7.6%
224
3. Others (delay in reception of information, not easily understood by locals etc)
5.4%
222
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
Preference in using indigenous knowledge and scientific methods to predict change in
weather
In a bid to determine the respondents’ choice of use between the indigenous knowledge and scientific
methods in predicting weather change, the community members, opinion leaders and foretellers were
asked, and this study established that Nationally, the majority (79%) of all the respondents would
choose to use both methods, leaving only a smaller proportion who would opt to use traditional
knowledge (17%), with very few (4%) who wished to use scientific methods of weather forecasting
respectively as graphically illustrated in the subsequent figure.
The preference to concurrently use both indigenous knowledge and scientific methods was mainly
attributed to the fact that both of the two methods would complement each other, that is to say, when
scientific method is used, it would be compared against IK indicators before making final decisions and
vice versa, this would improve upon the reliability and accuracy in weather forecast. Additionally the
respondents also noted that in case one of the methods failed, the probability of the other working would
be very high. The interviewed district officials were also in complete agreement, but added that there
should be an initiative to document and promote IK use and need for policy formulation in mainstreaming
IK if indeed they are to be concurrently used.
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52
Figure 4 Responses to the Question given the opportunity, what would be the choice to predict change in
weather, scientific, IK or both? (According to the interviewed community members, opinion leaders,
foretellers)
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
Adoption of Indigenous Knowledge (IK) Weather Forecasting By Government:
Nearly all the respondents (97%) interviewed felt that IK weather forecasting should be adopted by the
government for national use, this according to them was mainly because when IK is in cooperated with
scientific methods of weather forecast, one can be assured of an effective prediction (35%). They also
noted that this will help in improving weather predictability/ agricultural planning both by government
and communities. IK was also considered by 14% of the respondents to be easily applicable and
understandable by the local community in addition to being locally /widely available (9%)
respectively. Other reasons also included; IK will be developed and published to all by government,
communities will be sensitized by government on the effective use of the IK indicators amongst
others as seen in the table below.
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Table 15 Reasons why Government should adopt IK for National Use
1. When incorporated with scientific we are sure of the forecast
2. Helps in improved weather predictability/ planning, planting
3. Others (Specify)
4. IK is easy to apply and understand by the local community
5. IK indicators are locally/widely available (trees, animals, insects, winds etc)
6. IK was first before scientific methods
YES %
35.20%
31.70%
20.00%
14.30%
8.70%
5.20%
Valid N
230
230
230
230
230
230
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
4.1.7 KNOWLEDGE AND APPLICATION OF IK WEATHER FORECASTING BY OFFICIALS AT
DISTRICT LEVEL
District Local Government staff
Out of the 53 district officials interviewed, about two thirds (66%) had knowledge or used IK on
weather forecasting in their various sectors, their knowledge was focused around IK being used to
predict onset of weather seasons as envisaged by prevailing wind directions, cloud formations,
shedding and sprouting of tree leaves etc, it was noted that most of these district officials were
using IK individually but not within their respective sectors/departments. As a matter of fact when
asked on how they use IK weather forecasting in their sectors, close up to a half (49%) of all the
interviewed district officials reported that they were not using IK within their sectors/departments, with
only a few suggesting that they used it for agricultural planning purposes i.e. to know when to engage in
agricultural activities (30%) and advising farmers on planting, harvesting seasons (24%), in addition to
agricultural management of crops ( such as drying, storage, spraying, transportation etc (12%) respectively
as seen in the table below.
Table 16 Use of IK weather forecasting information by District Officials
Use of IK weather forecasting information
1. NOT USED
2. Planning purposes (when to engage in agric activities)
3.Advise farmers on planting, harvesting
5. others (specify)
Agric management of crops (drying, storage, spraying transportation etc)
4. Help farmers in conservation of environment (a forestation, reclamation
etc
YES (%)
48.9%
30.0%
24.0%
14.0%
12.0%
6.0%
Valid
N
47
50
50
50
50
50
Source: National IK Study for predicting weather patterns in Uganda, ACCRA (2015)
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54
Reception of IK weather forecast information by the district Officials:
Reception of IK weather
forecast information by the district officials was mainly through rural communities (37%) and informally
(28%) respectively, there was no proper official channel for receiving this kind of information this therefore
makes the frequency of receiving such information to be as and when it’s required. This study also
established that IK weather forecast information was being communicated to the community by the
district officials, mainly through informal means (38%) and community meetings (24%) etc respectively.
Although 38% of the interviewed district officials reported that they were not communicating IK
information, this was attributable to a number of factors among which was absence of policy or guidelines
regarding the use of IK in weather prediction as reported by almost all the officials (96%).
Reliability of IK indicators and scientific weather forecasting methods according to district
officials: The majority (59%) of the interviewed district officials felt that IK indicators were reliable and
efficient with a quarter of them (25%) rating it to be somehow reliable, thus only a smaller proportion
(16%) considered IK indicators not to be reliable. However, when asked as to whether IK indicators were
more reliable than the scientific weather forecasts, the majority (58%) of the interviewed district officials
disagreed with only 36% concurring to the fact that IK indicators were more reliable than the scientific
weather forecast, this was attributable to IK being locally and widely available and its ability to correctly
predict weather most of the times whereas, scientific in most cases is not accurate resulting into
disappointments and loss of trust from the public. The district officials also noted that the use of more
than just one IK indicator in predicting weather would greatly increase on its reliability as compared to
when only one IK indicator is used to generalize weather predictions. This finding has clearly shown that a
significant proportion of community members including district officials have lost trust in the use of
scientific weather forecasting due to its unreliability and constant inaccuracies.
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Figure 5 showing reliability & efficiency of IK indicators and whether they are more reliable than scientific
weather forecast (according to the interviewed District officials)
Source: National IK Study for predicting weather patterns in Uganda, World Vision (2015)
Hindrances from using the IK weather forecasting by Districts: According the interviewed district
officials, IK weather forecasting is not being used by the districts mainly because it’s not reliable (41%) in
addition to IK not have been researched/documented (39%) and not scientific in nature(20%). However,
this study also revealed that given the fact that there is no any known policy or guidelines regarding the
use of IK weather forecasting as reported by 96% of the interviewed district officials, this has therefore
deterred them from using the IK weather forecasting.
They however, suggested that concurrent use of scientific and IK weather forecasting indicators would
improve on reliability and accuracy of weather prediction given that IK indicators are well researched and
documented first. They also noted that IK indicators can be used to enhance scientific methods in addition
to IK being widely available. Below are a selected few of the suggestions on concurrent use of scientific
and IK weather forecasting indicators in their own words
Both methods should be used because they all work”, “scientific method of weather forecasting could be
used to confirm the IK and help create awareness among communities”, “Policy formulation and
strengthening in addition to targeting resourceful persons for extensive study for both methods” , “both
methods should be used to enhance weather forecasting” etc
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Analysis of common and reliable Indigenous indicators for seasonal forecasting country wide
Based on these study findings plus the previous studies, an attempt has been made to present a country
wide picture analysis of commonly used and reliable Indigenous indicators for seasonal weather
forecasting in Uganda (Figure 6&7 ..below and in a table in appendix 1 . Overall, rainfall came out as the
major climatic parameter that was commonly perceived by communities interviewed to have had major
changes with severe impact on their livelihoods. As a result, most of the indigenous knowledge and
indicators in weather prediction were related to rainfall more than temperature prediction. This is also true
in the previous studies reviewed. As discussed earlier, this may be due to the people’s dependence on
rainfall for agriculture more than temperature for their livelihoods.
The map of Uganda showing the distribution of key IK indicators that predict rainy seasons across
the 14 climatological zones
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57
The map of Uganda showing the distribution of key IK indicators that predict dry seasons across
the 14 climatological zones
The study found five common and still reliable indigenous indicators across all climatologically zones to
determine when it is likely to rain or not by farmers. These include; Winds, Clouds or Sky, Temperature,
Birds and Trees/plants. It is important to note that winds, clouds or sky, temperature are same major
atmospheric parameters observed by the meteorologists to prepare the scientific seasonal forecast. This is
where farmers and scientists should connect to improve seasonal forecasts.

Winds: from the communities’ responses, the change of wind direction and speed are important
features they use to forecast. During the dry season the wind blows in a particular direction and as the
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rainfall is about to begin, the wind direction changes. For example the Westerly winds which blows
from West towards East came top as an indication of rains coming soon; whilst the Easterly winds
blowing East to west indicates onset of dry season.

Clouds: The heavy dark clouds formation which indicates that rain will fall while clear sky/white clouds
predict onset of dry season.

Temperature: the unusual increased temperature (Hot at night) especially January through March
indicates onset of rain and a drop in temperatures at night/Coldness at night predicts onset of dry
season.

Birds: specific birds singing or crying in the morning indicate onset rainy season, while migratory birds
indicate the start of dry season. The type of birds varies from one region to another because they are
unique or specific to a certain locality (refer to Annex.1.).

Trees/plants: specific trees shade leaves or plants weather as a sign of dry season approaching. While
when sprouting of leaves begin, it indicates onset of rains. Same as birds, trees/plants vary from one
region to another because they are unique or specific to a certain locality (refer to Annex.1.).

Other indicators found commonly used but for specific localities included; Dew, or Fog in the
morning, Insects, Frogs, Animals like cows’ behaviour, stars, moon, feeling of body pains and
aches, among others
The variation in indigenous indicators across climatological zones for prediction of weather events
observed in could partly be explained by:

The differences in individuals’ experience, knowledge, familiarity with seasonal patterns of rainfall, age,
gender, culture and nature of livelihood of the people in these regions interviewed.

The study found that the elderly or foretellers are major custodians of the Indigenous knowledge.
There is no documentation; IK only survives on being passed on from generation to generation. In
some cases, people were not having adequate information. This puts IK at a risk of extinction in some
regions.

Much as communities still rely and believe in the IK indicators, most of the indicators are not
considered reliable anymore than in the past. Communities reported that most indicators may come at
the times expected but nothing happens which is attributed to changes in the climate. For example the
heavy dark clouds appeared on top as greatly used and reliable, but at the same time it was flagged
top among the indicators not working in some regions. Therefore this affected the results on
application of some indicators.
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
Particular indicators like birds, insects, animals, frogs and trees/plants have become extinct in some
regions due to environmental degradation. So with time, certain indigenous knowledge practices will
become outmoded because of rapid changes in the environment or the socio-economic.

IK prediction indicators have their own limitations by nature. For example the study found
communities are not able to establish which exact dates is the onset of rains or the intensity or
cessation dates - this keeps communities in probabilities which fail effective decision making on when
to plant and what type of crops to try.
That is why communities are seeking support from the
meteorological services to complement their efforts.
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CHAPTER FIVE
5.0
CONCLUSION AND RECOMMENDATIONS
Conclusion
This study was set to compile indigenous knowledge indicators in forecasting and its application country
wide and investigate elements of traditional knowledge that can be harmonized with science to improve
quality and increase use of both forecasts in planning and decision making for rural farmers in Uganda.
In summary, the use of Indigenous Knowledge in weather forecasting continues to be significantly
important by the predominantly agricultural communities across the country as reported by 94.6% of the
335 interviewed communities. This importance of IK forecasting was also reaffirmed by the majority of
district officials (97%) of the 53 officials interviewed. The study found most of the indigenous knowledge
and indicators in weather prediction were related to rainfall which is explained by agriculture being the
major livelihood to these rural citizens. Farmers’ major interest is when to plant, what to plant, where,
when to harvest and early warning alerts on food security.
About eighteen (18) major IK indicators for predicting onset of rainy season were reported. However,
those that are still working and reliable included; prevailing Westerly winds which blows from West
towards East (56%), heavy dark cloud formation (54%), the singing or crying of birds in the morning
(42%), un usual increase in temperatures (heat-hot at night) (40%) and trees sprouting (37%) respectively.
Additional twenty two (22) IK indicators for predicting onset of dry season, were also identified. Those that
are still working included; prevailing Easterly winds (winds blowing from East to West) (42%), Clear
sky/white clouds (36%), the Weathering of plants/trees/shading of leaves (26%), Presences of Butterflies
(23%) and Drop in temperatures at night/Coldness at night (21%) respectively, amongst others.
The study found five common and still reliable indigenous indicators across all climatologically zones to
determine when it is likely to rain or not by farmers. These include; Winds, Clouds or Sky, Temperature,
Birds and Trees/plants. It is important to note that winds, clouds or sky, temperature are same major
atmospheric parameters observed by the meteorologists to prepare the scientific seasonal forecast. This is
where farmers and scientists could focus on harmonizing to strengthen the seasonal forecast.
The question as to whether the IK forecasts were working or not compared to the meteorological forecasts
for the communities were largely dependent upon their reliability and accessibility and this differed from
one climatological zone to another. Most of the IK indicators that are no longer reliable as reported by
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74% of the total respondents were attributed to the changing climate and the continuous disappearance
of specific IK indicators such as trees, insects, birds etc due to environmental degradation. Also, the study
found that the elderly or gifted foretellers are major custodians of the Indigenous knowledge. There is no
documentation; IK only survives on being passed on from generation to generation. In some cases, people
were not having adequate information. This mix renders IK unreliable and puts IK at a risk of extinction in
some regions. By nature, it was also found that IK forecasts are shared informally which in most cases
favours men leaving out women due to their domestic gender roles that limit their movements. Yet
women are at forefront of agricultural activities but do not get the forecast timely thus it is not helpful in
most times. Other related challenges with IK forecasting, it is not able to tell when exactly the rains are
starting or ending and the intensity of the rains.
On the other hand, the majority (91%) of the respondents reported scientific forecasts are also unreliable
and not accurate that is why farmers stick to their IK forecasts in the midst of their increasing challenges.
The main challenges reported regarding scientific forecasts include; inaccuracy and not being realistic for
local conditions (91%); not widely available; few people access the information and not easily understood
by locals etc. Poor accessibility was reported regarding the meteorological forecast, which is disseminated
through FM radio (92.3%), TV (17%), news paper (16%), and mobile phones (5%) which most rural and
illiterate farmers do not access especially women. Therefore the feedback on reliability of both forecasts
were shaped by the differences in individuals’ experience, knowledge, familiarity with seasonal patterns of
rainfall, age, gender, culture, social status and nature of livelihood of the people in these regions
interviewed.
Although the local communities acknowledged that both Indigenous Knowledge of weather forecast
(74%)
and
scientific method of forecasting (91%) had challenges mainly in relation to their
accuracy/reliability, the majority (79%) of all of them would choose to use both methods, leaving only a
smaller proportion who would opt to use traditional knowledge (17%) alone, Their preference to
concurrently use both indigenous knowledge and scientific methods was mainly attributable to the fact
that the two methods would complement each other, and hence improve on accuracy/reliability in
weather prediction. Furthermore, nearly all of the respondents (97%) felt that the government should
adopt the use of Indigenous Knowledge (IK) weather forecasting.
Noticeably, the interviewed District officials upon acknowledging the reliability of IK indicators (59%),
strongly rejected the fact that IK was more reliable than scientific method of weather forecasting (58%).
This was attributable to that fact that IK was not documented anywhere (39%), in addition to no known
policy requiring officials to use IK indicators (96%). The use of IK indicators in district sectors was limited
as seen by close up to a half (49%) of all the interviewed district officials who reported that they were not
using IK within their sectors/departments, with only a few suggesting that they used it for agricultural
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62
planning purposes i.e. to know when to engage in agricultural activities (30%) and advising farmers on
planting, harvesting seasons (24%).
In conclusion, IK indicators have for years been and still continue to be widely used by the local
community members across the country, this study therefore was timely, coming at a time when the users
of IK who are predominant agriculturalist require proper documentation of its effective use. In addition to
forging a way of concurrent use of IK indicators, and modern scientific weather forecasting systems which
has for long not been well communicated to the farmers. The weather predictions based on more than
one IK indicator was reported to be much more reliable than forecasts based on just one IK indicator
Recommendations
This study therefore recommends the following:

Based on the fact that farmers IK weather forecasting indicators that continue to work for them are
similar parameters meteorologists observe to generate the scientific seasonal forecast, also
acknowledging that both forecasts have strengths and weaknesses, it calls for a technical and
systematic harmonization of the two systems.

There is need for proper documentation of indigenous Knowledge of weather forecasting systems,
clearly indicating how prediction is done, how they are used so that they could be readily accessible to
everyone, as part of official seasonal forecast. This should be done for all meteorological forecasting
zones.

There is need for UNMA and ACCRA to conduct a scientific pilot study to ascertain the efficacy of the
listed common and reliable IK indicators in comparison with the scientific indicators in selected
climatological zones to generate evidence to inform policy on the use of IK in weather forecasting.

Harmonization of the Indigenous knowledge and scientific weather forecasting methods is essential in
enabling concurrency in use of the two methods, achievable through creation of a hybrid weather
forecasting methods/systems.

There is need to sensitize communities and the District officials on how the indigenous knowledge of
weather forecasting and Meteorological forecasts are produced, their relationships, and how effectively
the information can used for planning purposes in different climatological zones.

Efforts to push for a policy on concurrent use of IK indicators and scientific should be taken, this would
go a long way in enabling district officials comfortably use IK indicators and hence provide relevant
advice to the community members.
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Annex 1
SUMMARY OF IDENTIFIED INDICATORS IN ALL CLIMATOLOGICAL ZONES IN UGANDA
Climatological
Region
District(s)
Covered
On set of rainy season Predictors
Dry Season Predictors
A1- Western
parts of L.
Victoria (e.g.
Kampala,
Mpigi, Wakiso
etc)
Wakiso
Appearance of nimbus clouds in the
morning and evening/night;
Appearance and movement of insects
(butterflies, red caterpillars, western
honey bees, Apis mellifera Linnaeus
(Hymenoptera: Apidae), bush crickets,
Ruspolia baileyi Otte (Orthoptera:
Tettigoniidae), nsenene in Luganda;
Appearance of migratory birds (cattle
egrets Bubulcus ibis Linnaeus,
bisege in Runyoro, ichule-deka or
ariaabong in Ateso); Appearance of the
rainbow frequently; Moon appears black
in color; Moon appears bright; Strong
winds in the morning and evening;
Appearance of fog in the morning
Birds like cuckoos, ducks, tutu or
tongufu in Acholi, ekirikint in
Ateso and the grey crowned crane
(Balearica regulorum) start to call;
Termite swarms also known as
African flying white ants
(Coptotermes formosanus Shiraki)
leave their nests;
Uphill movement of African army
ants (Dorylus sp.); Appearance of
migratory birds (ichule-deka in
Ateso); Frogs in swampy areas start
croaking at night; A feeling of excess
heat during the night and day;
Occurrence of thunderstorms
A2- Eastern
parts of
L.Victoria (eg,
Jinja, Iganga,
Mayuge, etc)
B- Central
parts (eg,
Luwero,
Kayunga,
Mityana)
CE- Eastern
parts of
Southwestern
(eg, Rakai,
8
Rakai
An increase in night time
temperatures, so that the nights feel
uncomfortably warm; shifts in the
direction of prevailing winds; the
8
Dry Season predictors were not explicit in the Rakai study. This would need further investigation.
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Isingiro,
Lyantonde,
etc)
CW- Western
parts of
Southwestern
(eg,
Ntungamo,
Kabale,
Bushenyi,
Mbarara, etc)
Kabale
flowering of trees, especially coffee
trees; particular phases of the moon;
the appearance of whirlwinds that lift
dust and leaves; and the arrival of
migratory birds, particularly the
Abyssinian hornbill (Bucorvus
abyssinicus).
Winds blowing from west to east;
Appearance of nimbus clouds in the
morning and evening/night9; Termite
swarms also known as African flying
white ants
(Coptotermes formosanus Shiraki)
leave their nests;
Uphill movement of African army
ants (Dorylus sp.); Winds blowing
from the east to the west; A feeling
of excess heat during the night and
day; Movement of clouds from the
east to the west; Algae swell,
dampen and become more visible
Winds blowing from the east to the
west10
D- South
eastern parts
(eg, Iganga,
Bugiri, Tororo,
etc)
E- Lake Kyoga
basin (Kamuli,
Serere, etc)
F- Central
parts of
eastern region
(Mbale,
Kapchorwa,
Sironko, etc)
G- North
eastern parts
(Moroto,
Kotido,
Kaabong, etc)
Kaabong11
, Soroti
Winds blowing from west to east
(S12); Appearance of nimbus clouds
in the morning and evening/night
(S); Birds like cuckoos, ducks, tutu or
tongufu in Acholi, ekirikint in
Ateso and the grey crowned crane
(Balearica regulorum) start to call;
Termite swarms also known as
African flying white ants
Winds blowing from the east to the west
(S); Appearance of birds (black eagle,
African pied wagtail Motacilla aguimp
Dumont, osukusuku or okwir in Ateso (S) ;
Appearance of migratory birds (cattle
egrets Bubulcus ibis Linnaeus,
bisege in Runyoro, ichule-deka or
ariaabong in Ateso); (S) Winds blowing
from the west to the east; A clear sky;
9
The most frequently reported on indicator across all the researched regions and perhaps the one most linked to
meteorological weather forecasts.
10
The only dry season predictor for Kabale
11
We only retrieved animal and plant predictors of the wet season for Kaabong. Need for further dry season predictors for
Kaabong
12
S & K used to denote Soroti and Kaabong respectively
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(Coptotermes formosanus Shiraki)
leave their nests (S); Appearance of
migratory birds (ichule-deka in
Ateso) (S); Winds blowing from the
east to the west (S)
Uphill movement of African army
ants (Dorylus sp.) (S); Presence of
cool winds (S); Occurrence of
whirlwinds (S); New leaves of trees
sprout (S); When a group of small
stars is in the east13 (S); Ebata (K);
Mangoes (S); Cattle behavior (K);
Frogs (S) ‘Elele14’ (K); ‘eduduut’ (S)
Trees shed their leaves (S)
‘Lomoroko15’ (S); New Moon (K);
Grasshopers (K); burger (S); Foreteller
dreams (K); Measles (S); The
appearance of dark clouds (S,K)
H- Eastern
parts- Central
north (Lira,
Otuke, Pader,
etc)
I- Western
parts of
central north
(Apach, Gulu
Nwoya, etc)
Gulu
Winds blowing from west to east;
Appearance of nimbus clouds in the
morning and evening/night; Birds
like cuckoos, ducks, tutu or tongufu
in Acholi, ekirikint in
Ateso and the grey crowned crane
(Balearica regulorum) start to call;
Frogs in swampy areas start croaking
at night; Moon appears
white/grey/bright with visible ring
and one side of the moon is black;
Winds blowing from the south to the
north; Cattle are restless and start
jumping16;
Winds blowing from the east to the west;
Appearance of birds (black eagle, African
pied wagtail Motacilla aguimp
Dumont, osukusuku or okwir in Ateso;
Appearance of migratory birds (cattle
egrets Bubulcus ibis Linnaeus,
bisege in Runyoro, ichule-deka or
ariaabong in Ateso); Singing/calling of
birds (Bateleur eagle, Terathopius
ecaudatus, koga in Acholi);
Masindi
Winds blowing from west to east;
Appearance of nimbus clouds in the
morning and evening/night; Birds
like cuckoos, ducks, tutu or tongufu
in Acholi, ekirikint in
Ateso and the grey crowned crane
(Balearica regulorum) start to call;
Appearance and movement of insects
(butterflies, red caterpillars, western
honey bees, Apis mellifera Linnaeus
(Hymenoptera: Apidae), bush crickets,
Ruspolia baileyi Otte (Orthoptera:
Tettigoniidae), nsenene in Luganda;
Winds blowing from the east to the west;
J- North
western parts
(Arua, Moyo,
Nebbi, etc)
K- Includes
(Masindi,
Hoima,
Kiryandongo,
etc
13
Only reported in Soroti among the investigated districts
Elele and Eduduut are types of birds in Kaabong and Soroti respectively
15
Lomoroko is a Star in Ateso - Soroti
16
Reported only in Gulu
14
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Frogs in swampy areas start croaking
at night; Winds blowing from the
east to the west; Moon appears
white/grey/bright with visible ring
and one side of the moon is black;
Movement of clouds from the east to
the west; Occurrence of
thunderstorms; Presence of cool
winds; Winds blowing from the
south to the north; Occurrence of
whirlwinds; New leaves of trees
sprout; Movement of clouds from the
west to the east17;
Appearance of birds (black eagle, African
pied wagtail Motacilla aguimp
Dumont, osukusuku or okwir in Ateso;
Appearance of migratory birds (cattle
egrets Bubulcus ibis Linnaeus,
bisege in Runyoro, ichule-deka or
ariaabong in Ateso); Singing/calling of
birds (Bateleur eagle, Terathopius
ecaudatus, koga in Acholi); Winds
blowing from the west to the east; A clear
sky; Trees shed their leaves; A lot of
coldness in the morning and evening;
Winds blowing from the north to the
south; Movement of cumulus clouds from
the east to the west; Strong winds coming
with rain in a storm; Warm winds blowing;
Winds blowing from west to east;
Appearance of nimbus clouds in the
morning and evening/night; Termite
swarms also known as African flying
white ants
(Coptotermes formosanus Shiraki)
leave their nests;
Uphill movement of African army
ants (Dorylus sp.); Appearance of
migratory birds (ichule-deka in
Ateso); Occurrence of thunderstorms;
Presence of cool winds; Ice cap on
Mount Rwenzori is visible
Appearance and movement of insects
(butterflies, red caterpillars, western
honey bees, Apis mellifera Linnaeus
(Hymenoptera: Apidae), bush crickets,
Ruspolia baileyi Otte (Orthoptera:
Tettigoniidae), nsenene in Luganda;
Winds blowing from the east to the west;
Presence of red clouds at sunset
L- Northern
parts of
central
western
(Nakasongola,
Nakaseke,
Kiboga, etc)
ME- Eastern
parts of South
central
western
(Sembabule,
Mubende, etc)
MW- Western
parts of south
western
(Kabarole,
Kasese,
Bundibugyo,
etc)
17
Kasese
This is the opposite of what was said for Kabale and Masindi for the same prediction of the wet season.
ACCRA | USING INDIGENOUS KNOWLEDGE (IK) IN WEATHER PREDICTION IN UGANDA
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