THE OPPORTUNITIES AND CHALLENGES FOR MITIGATING CLIMATE CHANGE THROUGH DROUGH...Julius Huho
The economy of Kenya is based on agriculture which in turn depends on rainfall performance. Over 80 percent of the Kenyan population relies on rain-fed agriculture as a livelihood source. Unfortunately, changes in its amount, timing and distribution in the last two decades have influenced the reliability of rainfall for agricultural purposes. The consequence has been recurrent food insecurity in most parts of the country and chronic in the arid and semi arid areas due to frequent droughts. To survive the harsh climatic conditions, the affected communities employ various adaptive strategies. The current study examined the drought adaptive strategies employed by subsistence farmers in the semi arid areas of Kenya in relation to mitigating climate change. Data was obtained from Mukogodo and Central Divisions of Laikipia County, Kenya. Time series was used to analyze rainfall trends. Data on effects of droughts and adaptive strategies was obtained through in-depth interviews. The inhabitants were aware of climate change with 90% and 10% of the respondents attributing the changes to human activities and divine forces, respectively. Rainfall showed a declining trend in Mukogodo Division but an increasing trend in Central Division. Nevertheless, the timing of the "long rains" and the length of the growing season in Central Division showed considerable changes. The consequences were food insecurity and livelihood destruction in the area. The adaptive strategies employed aimed at cushioning farmers against immediate problems but with minimal consideration of climate change mitigation. This paper highlights on the opportunities and challenges of mitigating climate change that farmers had through their day-to-day adaptive strategies.
THE OPPORTUNITIES AND CHALLENGES FOR MITIGATING CLIMATE CHANGE THROUGH DROUGH...Julius Huho
The economy of Kenya is based on agriculture which in turn depends on rainfall performance. Over 80 percent of the Kenyan population relies on rain-fed agriculture as a livelihood source. Unfortunately, changes in its amount, timing and distribution in the last two decades have influenced the reliability of rainfall for agricultural purposes. The consequence has been recurrent food insecurity in most parts of the country and chronic in the arid and semi arid areas due to frequent droughts. To survive the harsh climatic conditions, the affected communities employ various adaptive strategies. The current study examined the drought adaptive strategies employed by subsistence farmers in the semi arid areas of Kenya in relation to mitigating climate change. Data was obtained from Mukogodo and Central Divisions of Laikipia County, Kenya. Time series was used to analyze rainfall trends. Data on effects of droughts and adaptive strategies was obtained through in-depth interviews. The inhabitants were aware of climate change with 90% and 10% of the respondents attributing the changes to human activities and divine forces, respectively. Rainfall showed a declining trend in Mukogodo Division but an increasing trend in Central Division. Nevertheless, the timing of the "long rains" and the length of the growing season in Central Division showed considerable changes. The consequences were food insecurity and livelihood destruction in the area. The adaptive strategies employed aimed at cushioning farmers against immediate problems but with minimal consideration of climate change mitigation. This paper highlights on the opportunities and challenges of mitigating climate change that farmers had through their day-to-day adaptive strategies.
Review on the Cause and Effects of Recurrent Drought on Ethiopian Agriculture...AI Publications
Drought occurs in virtually all climatic zones, but its characteristics vary significantly from one region to another. Ethiopia characteristically sees three seasons of varying amounts of rainfall. The central western regions getting a sufficient amount of rain during the rainy season, but the rest of the country, especially towards the horn being very dry for most time of the year. This also leads that some parts of regions of Ethiopia severely affected by recurrent drought. Recurrent drought caused by deforestation, high population growth, land degradation and soil erosion which intern affect agriculture include crop losses, lower yields in both crop and livestock production, increased livestock deaths and generally it may bring economic, environmental, and social impacts. Different efforts are made by policy maker and government to reduce or mitigate the impact of drought but still the impact is there thus this review show the gap.
The significance of indigenous weather forecast knowledge and practices under...Premier Publishers
This paper discusses the implication of indigenous knowledge-based weather forecasts (IK-BFs) as a tool for reducing risks associated with weather variability and climate change among smallholder farmers on the south eastern slopes of Mount Kilimanjaro in Moshi Rural District of Tanzania. Participatory research approaches and household surveys were used to identify and document past and existing IK-BF practices. Local communities in the study transect use traditional experiences and knowledge to predict impending weather conditions by observing a combination of locally available indicators: plant phenology (40.80%), bird behaviour (21.33%), atmospheric changes (10.40%), insects’ behaviour (7.20%), environmental changes on Kilimanjaro, Pare and Ugweno mountains (4.80%), astronomical indicators (4.8%), animal behaviour (4.00%), water related indicators (3.73%) and traditional calendars (2.93%). The study established that 60% of farmers use and trust IK-BFs over modern science-based forecasts (SCFs). Although about 86.3% of respondents observed some correlation between IK-BFs and SCFs, and 93.6% supported integration of the two sets of information, the nature and extent of their correlation is not yet established. We none the less recommend that IK-BFs be taken into relevant national policies and development frameworks to facilitate agro-ecological conservation for use and delivery of effective weather and climate services to farming communities.
The Indian summer monsoon:Past present and future_Julia Slingo_2010India Water Portal
This presentation on the Indian Summer Monsoon by Julia Slingo of Edinburgh Met Office (United Kingdom) broadly deals with what the monsoon means for the people of India and the basic science of monsoon.
The history of the United Kingdom’s interest in the Indian monsoon is discussed as also the challenges of climate change for India. Some basic facts regarding the Indian socio-economic context are presented to underline the importance of rainfed agriculture and hence the dependence on monsoons.
‘Monsoon’ means ‘season’, and describes a complete reversal of wind regimes during the seasonal cycle. Monsoons are characterised by a pronounced rainy season. Monsoons are driven by changes in the distribution of heating driven primarily by the seasonal cycle of the sun. A thermal contrast between land and sea is required to set up a monsoon. The Indian Monsoon is part of a much larger circulation, the Asian Monsoon.
The United Kingdom's fascination with the meteorology of India is presented. India appeared to offer an ideal natural laboratory for the science, and an ideal space in which to demonstrate the political importance of science in a global age. The British meteorologist Henry Francis Blanford had commented that "Order and regularity are as prominent characteristics of our (India’s) atmospheric phenomena, as are caprice and uncertainty those of their European counterparts."
From the political economy angle the British were of the view that the control of famine through climate prediction would mean that India could be governed more effectively. The presentation thereafter dealt with the changing nature of Indian rainfall and scientific challenges like:
How will the mean monsoon behave?
How will climate change affect the stability of the monsoon?
Will it become more variable?
Will it be less predictable?
What will climate change mean for extreme events?
How will changes in atmospheric composition affect the monsoon?
The IPCC’s 4th Assessment Report has projections of likely shifts in rainfall patterns by 2080. The changing nature of Indian rainfall with climate change is mainly due to the impact of 2xCO2 on the number of rain days and rainfall intensity. There will be a decrease in number of rain days and an increase in rain intensity on days when raining.
According to Slingo et al there will be changes in the intensity of extreme Indian daily rainfall with climate change. But not all models agree with this simple hypothesis. The impact of aerosols on the monsoons is highlighted viz., the pre-monsoon build up of absorbing aerosol from Arabian and Saharan dust, Thar dust and local black carbon sources.
The presentation finally concludes with the thought that there is much still to learn about what controls the monsoon and its variability. Model improvements are vital for making progress in monsoon prediction and impacts of climate change remain hugely uncertain for those reasons.
Climate variability and climate change in pastoral systemsILRI
Presented by P. Ericksen, J. de Leeuw, P. Thornton, M. Said, A. Notenbaert, M. Herrero and A. Ayantunde at the Climate Variability, Pastoralism and Commodity Chains in Ethiopia and Kenya’ (CHAINS) Project Research Planning Meeting, Livestock-Climate Change Collaborative Research Support Program (LCC CRSP), ILRI, Addis Ababa, 21-22 June 2011.
Review on the Cause and Effects of Recurrent Drought on Ethiopian Agriculture...AI Publications
Drought occurs in virtually all climatic zones, but its characteristics vary significantly from one region to another. Ethiopia characteristically sees three seasons of varying amounts of rainfall. The central western regions getting a sufficient amount of rain during the rainy season, but the rest of the country, especially towards the horn being very dry for most time of the year. This also leads that some parts of regions of Ethiopia severely affected by recurrent drought. Recurrent drought caused by deforestation, high population growth, land degradation and soil erosion which intern affect agriculture include crop losses, lower yields in both crop and livestock production, increased livestock deaths and generally it may bring economic, environmental, and social impacts. Different efforts are made by policy maker and government to reduce or mitigate the impact of drought but still the impact is there thus this review show the gap.
The significance of indigenous weather forecast knowledge and practices under...Premier Publishers
This paper discusses the implication of indigenous knowledge-based weather forecasts (IK-BFs) as a tool for reducing risks associated with weather variability and climate change among smallholder farmers on the south eastern slopes of Mount Kilimanjaro in Moshi Rural District of Tanzania. Participatory research approaches and household surveys were used to identify and document past and existing IK-BF practices. Local communities in the study transect use traditional experiences and knowledge to predict impending weather conditions by observing a combination of locally available indicators: plant phenology (40.80%), bird behaviour (21.33%), atmospheric changes (10.40%), insects’ behaviour (7.20%), environmental changes on Kilimanjaro, Pare and Ugweno mountains (4.80%), astronomical indicators (4.8%), animal behaviour (4.00%), water related indicators (3.73%) and traditional calendars (2.93%). The study established that 60% of farmers use and trust IK-BFs over modern science-based forecasts (SCFs). Although about 86.3% of respondents observed some correlation between IK-BFs and SCFs, and 93.6% supported integration of the two sets of information, the nature and extent of their correlation is not yet established. We none the less recommend that IK-BFs be taken into relevant national policies and development frameworks to facilitate agro-ecological conservation for use and delivery of effective weather and climate services to farming communities.
The Indian summer monsoon:Past present and future_Julia Slingo_2010India Water Portal
This presentation on the Indian Summer Monsoon by Julia Slingo of Edinburgh Met Office (United Kingdom) broadly deals with what the monsoon means for the people of India and the basic science of monsoon.
The history of the United Kingdom’s interest in the Indian monsoon is discussed as also the challenges of climate change for India. Some basic facts regarding the Indian socio-economic context are presented to underline the importance of rainfed agriculture and hence the dependence on monsoons.
‘Monsoon’ means ‘season’, and describes a complete reversal of wind regimes during the seasonal cycle. Monsoons are characterised by a pronounced rainy season. Monsoons are driven by changes in the distribution of heating driven primarily by the seasonal cycle of the sun. A thermal contrast between land and sea is required to set up a monsoon. The Indian Monsoon is part of a much larger circulation, the Asian Monsoon.
The United Kingdom's fascination with the meteorology of India is presented. India appeared to offer an ideal natural laboratory for the science, and an ideal space in which to demonstrate the political importance of science in a global age. The British meteorologist Henry Francis Blanford had commented that "Order and regularity are as prominent characteristics of our (India’s) atmospheric phenomena, as are caprice and uncertainty those of their European counterparts."
From the political economy angle the British were of the view that the control of famine through climate prediction would mean that India could be governed more effectively. The presentation thereafter dealt with the changing nature of Indian rainfall and scientific challenges like:
How will the mean monsoon behave?
How will climate change affect the stability of the monsoon?
Will it become more variable?
Will it be less predictable?
What will climate change mean for extreme events?
How will changes in atmospheric composition affect the monsoon?
The IPCC’s 4th Assessment Report has projections of likely shifts in rainfall patterns by 2080. The changing nature of Indian rainfall with climate change is mainly due to the impact of 2xCO2 on the number of rain days and rainfall intensity. There will be a decrease in number of rain days and an increase in rain intensity on days when raining.
According to Slingo et al there will be changes in the intensity of extreme Indian daily rainfall with climate change. But not all models agree with this simple hypothesis. The impact of aerosols on the monsoons is highlighted viz., the pre-monsoon build up of absorbing aerosol from Arabian and Saharan dust, Thar dust and local black carbon sources.
The presentation finally concludes with the thought that there is much still to learn about what controls the monsoon and its variability. Model improvements are vital for making progress in monsoon prediction and impacts of climate change remain hugely uncertain for those reasons.
Climate variability and climate change in pastoral systemsILRI
Presented by P. Ericksen, J. de Leeuw, P. Thornton, M. Said, A. Notenbaert, M. Herrero and A. Ayantunde at the Climate Variability, Pastoralism and Commodity Chains in Ethiopia and Kenya’ (CHAINS) Project Research Planning Meeting, Livestock-Climate Change Collaborative Research Support Program (LCC CRSP), ILRI, Addis Ababa, 21-22 June 2011.
An Empirical Study of Seasonal Rainfall Effect in Calabar, Cross River State,...IOSR Journals
Calabar has been experiencing yearly severe flooding and landslides within and around the metropolis with substantial costs, in terms of loss of lives and destruction of properties. This paper is focus on the empirical study of seasonal rainfall effect in Calabar, Nigeria. Data was gathered through a well designed and articulated oral and written questionnaires, direct and first-hand observation of the environment, and comprehensive interview sessions were carried out with randomly selected Landlords . A total of thirteen thousand (13,000) questionnaires were randomly distributed evenly to some Landlords in twenty six (26) streets of the study Area from January 2012 to October 2013. Twelve thousand four hundred and eigty two (12,482) valid questionnaire were received back. Twelve thousand (12,000) of the inhabitant Landlords reported that, they were not affected negatively. Table 2 shows the total number of buildings that were negatively affected due to flooding in the Area. While rainfall data from 1993– 2012 were collected from the Nigeria Meteorological Station(NIMET), Calabar. A simple descriptive analysis was used. The results shows that, rainfall is one of the Climatic factor that can indicate Climate change and has created ecological destabilization and altered the pattern of the vegetation belt especially in the flood prone areas highlighted, which includes Atimbo, Edim otop, Ekpo Abasi, Ndidem Usang Iso, Goldie , Target, Ebito, Big Qua, Edibe Edibe, Atamunu, Akim Road, Otop Abasi and Ikot Eyo by Paliamentary Road (Calabar). The rainfall pattern has also enhanced wind erosion/desertification, soil erosion and coastal flooding in Calabar. With these impacts, the paper therefore recommends some adaptive and mitigation measures that could help to revert the current situation, otherwise properties and lives will continue to be lost.
20 Ideas for your Website Homepage ContentBarry Feldman
Perplexed about what to put on your website home? Every company deals with this tough challenge. The 20 ideas in this presentation should give you a strong starting point.
Trend analysis of Temperature and Precipitation change in Sokoto State, NigeriaPremier Publishers
In sub Saharan Africa, Nigeria is one of the most vulnerable countries to climate change. In recent years, there was evidence of rising temperature and rainfall variability in almost every part of the country. This paper investigated the behavior of annual temperature and rainfall in Sokoto State, Northwestern Nigeria, over the period from 1970-2015. During this period, there was an upward trend in both temperature and rainfall. Further analysis detected a structural change in temperature and rainfall series at the State level and in the three agro ecological Zones of the State over the period of 1970-2015. The point of climatic shift in the area was detected to occur in 2003 for temperature, and in 1997, for rainfall. Findings from the Chow test used to confirm the shift in climate were statistically significant. This result further supports the hunch that the State witnessed significant changes in climate in recent period. Findings of this Study will have a significant implication for all sectors of the economy, most importantly, Agriculture which is the mainstay of the economy.
Spatial and seasonal variations in rainfall and temperature across Nigeria | ...Innspub Net
This research investigated spatial and seasonal variations in the rainfall and temperature in Nigeria. The study utilised the ex-post facto research design, on the existing 8 climatic zones in Nigeria. Archival data on rainfall and temperature from 1901 to 2017 used for this study were got from Climate Research Unit, University of East Anglia, via Google Earth Version 7.3.2, using 5° x 5° high-resolution gridded CRUTEM 4.03. Statistical analyses of data were carried out using Analysis of Variance (ANOVA) and Mann-Kendall tests. Results indicate that significant differences exist in: rainfall within Nigerian states as determined by ANOVA test at F (35, 4176) = 1596.76 and p = 0.000; temperature within Nigerian states as determined by ANOVA test at F (35, 4176) = 310.73 and p = 0.000; seasonal variations in rainfall within Nigerian states as determined by ANOVA test at F (11, 50532) = 7776.36 and p = 0.000; seasonal variations in temperature within Nigerian states as determined by ANOVA test at F (11, 50532) = 4575.79 and p = 0.000; trends of rainfall across Nigeria; and trends of temperature across Nigeria. While rainfall showed increasing trends, temperature trends were alternately increasing and decreasing. Rainfall and temperature vary spatially and seasonally within Nigeria. The environmental regions have their peculiar rainfall and temperature characteristics. Therefore, this study is of significant importance to agricultural production because understanding regional climatic attributes is an essential environmental part for effective agricultural productivity.
Effect of Rainfall Trend on Yam Yield in Mokwa Local Government Area of Niger...CrimsonpublishersEAES
Agricultural production in Niger State like other states in Nigeria is highly vulnerable to climate changeability. Climate change is predicted to have adverse effects on the agricultural sector of the poorer parts of the world especially sub-Saharan Africa. The aim of the study is to investigate and analyse the effect of rainfall trend on the production of yam in Mokwa local government area of Niger state, Nigeria. For the purpose of this research, data were collected from 100 respondents through the administration of questionnaires. Rainfall data covering a period of thirteen years (2003-2015) were also obtained from College of Agriculture Mokwa weather station, while the yearly yam yield for 16 years (2000-2015) was obtained from Niger State Ministry of Agriculture.
Linear regression models and standardized anomaly index were used to analysis the data gathered. The study showed that the trend of mean annual rainfall in the study area was minimal but significant with R2 value of 0.8 for mean monthly rainfall. A strong relationship between rainfall variation and yam yield exist with r2 value of 0.881. The variation in the yield among the years was moderately significant with R2 value of 0.5064. It also showed a positive response between yam yield and moderate rainfall that was well distributed. Extension agent from ministries of agriculture and ADPs should do more in harnessing relevant information on food production in all the local government areas of Niger state so as to build a robust data bank for further research.
https://www.crimsonpublishers.com/eaes/fulltext/EAES.000512.php
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Please click on link: https://crimsonpublishers.com/
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Please click on: https://crimsonpublishers.com/eaes/
Long-term observed Precipitation Trends in Arid and Semi-arid Lands, Baringo ...Premier Publishers
The research was conducted to validate the pastoralists’ and agro-pastoralists’ claim that there has been an increasingly variable and changing climate in the study area. The station average and Theissen polygon methods were used to estimate the mean areal precipitation of the small (Mogotio and Baringo South Sub-counties) and the large area (Baringo County), respectively. The aim of the current study is to analyse rainfall time series over long term observed precipitation and a wide area, detecting potential trends and assessing their significance. Monthly precipitation data for the period 1974-2003 from six weather stations, located mainly in Mogotio and Baringo South sub-counties and covering 3906km2 were used in the analysis. The data were quality controlled to ensure no missing data and any inconsistencies. Linear regression analysis of the database highlighted that; the trends were predominantly negative, both where the average and Theissen polygon methods were used and over the whole reference period. The negative trends are not significant. This finding implies that the study area has been suffering a precipitation decrease especially in the period under review.
Adaptation to Climate Change and Variability by Gender in Agro-pastoral Commu...IJEAB
Gender division of roles in agro-pastoral societies of semi-arid parts of Tanzania influence the exposure of women and men into various experiences, skills, knowledge, technology and resources, in similar ways to how they are exposed to climate risks and opportunities. This paper examines gender based vulnerability and adaptation strategies to climate change in these communities. The study was undertaken in two villages of Chamwino District in Tanzania. Data collection involved focus group discussions, key informant interviews and household interviews (5%). Rainfall and temperature data for the past 30 years were also analysed. Indicators of climate change and variability were revealed from both climate and social studies. Annual mean rainfall decreased from 700mm in 1980 to 490mm in 2010 while average temperatures were increasing steadily. The findings indicate that recent climatic changes have favoured pest and diseases, which affects crops, livestock and people. Late onset and early end of rain season were also recorded which lengthened the hot season of the year and early drying of water sources. It was further established that, the change in gendered roles affected women and girls more than men and boys because activities related to chores that are women roles were most affected. Responses to climatic stresses also varied by sex because they had been exposed to different skills and experiences. Lack of resources in female headed households increased severity to impacts and hindered their capacity to overcome stresses.
Change in climate and consequent global warming are posing threats to food security in many developing nations including Nigeria because of the climate-dependent nature of agricultural systems and lack of coping capabilities. The spatiotemporal pattern of temperature and rainfall of Nigeria between 1901 and 2005 (105 years), using temperature and rainfall data to support the concept of regional climate change and its impact on Agriculture and food security was in-vestigated. Also assessed was the level of agriculture funding and output in Nigeria. Mean annual air temperature and rain-fall data between 1901- 2005 (105 years) were obtained from Food and Agricultural Organization publications, National Bureau of Statistics, Central Bank of Nigeria bulletin and National Meteorological Agency. Descriptive statistics were used to analyze the data. The results showed that within the period of 105 years, rainfall decreased by 81 mm with increasing temperature of 1.1℃. The unpredictability of rainfall and steadily increasing air temperature were observed from 1971-2005. The total federal budget between 2001 and 2005 averaged 824 billion Naira per year of which very small amount (14.7 billion (1.8%)) went to agricultural sector. The actual spending was 681 billion Naira per year with 11.4 bil-lion Naira for agriculture. Over 25 years, there were low and dramatic walloping of public spending in agriculture relative to large size and importance of agricultural sector in the economy. There is a continuous rise in output from 1987 to 2000 before it dropped in 2001. Land degradation, desert encroachment, drying up of surface waters, coastal inundations, and shift in cultivated crops over time affected the food security in Nigeria. The current available data showed that Nigeria, like most parts of the world is experiencing the basic features of climate change. Therefore, Nigerian government/ private sector partnership should encourage agricultural, industrial and domestic practices which will not contribute to the emission of greenhouse gasses.
Land cover transition and fragmentation of River Ogba catchment in Benin City...Glory Enaruvbe
This study examined variation in the intensity of land cover transition and the pattern of habitat fragmentation
of River Ogba catchment. Landsat images of 1988, 2002 and 2016 were classified into five categories: low
density urban, high density urban, mixed vegetation, agriculture and dense forest using maximum likelihood
classifier. Intensity analysis approach and landscape metrics were used to analyze the changes and fragmentation
of the land cover. Number of patches, largest patch Index, area-weighted shape index and Euclidean nearest
neighbour were computed. The results show that although mixed vegetation accounted for the largest land cover
category in 1988 and 2002, low density urban dominated the study area in 2016. Intensity analysis of land cover
change in the study area indicates a rising trend. The urban fringe is observed to be highly dynamic zone and this
is primarily driven by changes in agriculture, low density urban and mixed vegetation. The implications of rapid
land cover transition and fragmentation in River Ogba catchment, and especially in the urban fringe, include
threat to biodiversity, food supply and deteriorating environmental conditions. This study provides necessary
insights for developing sustainable strategies for urban landscape planning, administration and governance.
Evaluations of Stream Flow Response to Land use and Land Cover Changes in Wab...IJCMESJOURNAL
Land Use and Land Cover Change (LU/LC) is one of the notable human induced worldwide changes. Hence, understanding the stream flow responses of a watershed to this dynamic change is becoming fundamental for water resources management planning. The study was conducted with the objective to analyses the impact of Land use and Land cover changes on stream flow response of Wabe watershed, in Omo-Gibe basin. Land use and land cover maps were developed using satellite image of Landsat5 TM 1988, Landsat7 ETM+ 2001 and Landsat8 OLI/TIRS 2018 through maximum likelihood algorithm of supervised classification using ERDAS Imagine 2014 and ArcGIS software for satellite image processing and map preparation. A physical based, semi-distributed hydrological model SWAT was used to simulate LU/LC change effects on the stream flow responses of watershed. During the study period the land use and land cover has changed due to natural and anthropogenic activity. The results depicted that there was an incessant expansion of agricultural land, built-up area and forest cover, on the other hand declining of agroforestry; grassland and woodland were happened during from the 1988 to 2018 periods. Due to the occurred LU/LC changes, the mean monthly stream flow were increased by 5.97m3/s for wet season and similarly the dry season flow showed increasing by 0.96m3/s during the study periods from 1988 up to 2018. Generally the result indicated that large changes of the stream flow in the watershed. Hence result notified an urgent intervention, so as to regulate the LU/LC change and to reduce its strong impacts on the stream flow of the Wabe watershed.
Long-term trends of rainfall identify priority zones for targeting climate sm...africa-rising
Presented by Francis Muthoni (IITA), Vincent Odongo (Wageningen University), Justus Ochieng (WorldVeg), Irmgard Hoeschle-Zeledon (IITA) at the European Geosciences Union General Assembly 2018, Vienna, Austria, 8-13 April 2018
Cocoa is known as one of the notable cultivated cash crops of the tropical rainforest of the world that is rain dependent. The study examines the effect of rainfall variation on the yield of cocoa plantation in Ondo State, Nigeria. Data used for the study includes the rainfall data of 15 years from 2000 to 2014 collected from Ondo state agro climatological office as well as cocoa yield data for the same period of time from Ondo State ministry of agriculture and forest resources. Descriptive statistical method was employed to determine the relationship between both variables in which the result shows direct relationship between rainfall and cocoa yield. Results were presented using bar charts and line graph for the time series analysis of the variables. Linear regression statistical analysis was used to predict cocoa yield with certain amount of rainfall with the correlation coefficient ‘r’ value of 0.97 which implies that rainfall changes go a long way to determine the same variation trend in the cocoa yield. Though, not only the quantity of rainfall within the range of rainfall required for the growth of this crop affect the yield but its distribution. A little millimeter of rainfall above or below the required range of rain for cocoa plantation greatly affects cocoa yield.
Agriculture is one of those activities of man that is greatly affected by climate. Therefore, a change in climate would in no small measure impact on agriculture, location notwithstanding. This work as a result examined the impact of climate change on maize and cassava yields in Southeastern Nigeria. Expost-facto research method in the context of quasi experimental research design was adopted for the study. Data for rainfall and temperature were obtained from Nigerian Meteorological Agency (NIMET); and those for crop yields came from Federal Ministry of Agriculture of Nigeria and Agricultural Development Programme (ADP) of selected states. The data were analyzed using descriptive statistics, multiple linear regressions and analysis of variance. Results showed that, there are evidences of climate change in Southeastern Nigeria, with notable fluctuations in the identified trends. Employing the trend analysis represented by the least square line, Abia State rainfall is increasing at 0.1026mm per annum, while Imo State is decreasing at -1.1255 mm per annum. All the states recorded positive slopes in mean temperature which shows an increase in their trends. The multiple regression model showed R2 values that ranged between 0.25 – 0.29 revealing that only 25 %- 29 % of cassava and maize yields could be explained by rainfall and temperature across the states and the result was significant at p<0.05 revealing that cassava and maize yields significantly depended on rainfall and temperature. Crop yields were also significantly different spatially. As a result of the findings the study strongly advocates, development of better and sustained environmental policies that will be beneficial to climate systems while creating sustainable food security.
Similar to Perceived and actual rainfall trends and variability in eastern uganda implications for community preparedness and response (20)
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
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Perceived and actual rainfall trends and variability in eastern uganda implications for community preparedness and response
1. Journal of Natural Sciences Research www.iiste.org
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Perceived and Actual Rainfall Trends and Variability in Eastern
Uganda: Implications for Community Preparedness and Response
Monica K. Kansiime1*
, Stephen K. Wambugu1
and Chris A. Shisanya2
1
School of Agriculture and Enterprise Development, Department of Agribusiness Management and Trade,
Kenyatta University, P.O Box 43844 - 00100, Nairobi, Kenya
2
School of Humanities and Social Sciences, Department of Geography, Kenyatta University, P. O. Box 43844-
00100, Nairobi, Kenya
*Email of the corresponding author: monkansiime@yahoo.co.uk
Abstract
This study assessed the extent of rainfall trends and variability in Eastern Uganda and implications for
community preparedness and response. Regional and national climate studies have been generalized over large
scales and thus are insufficient in capturing variability at local level where management actions occur. This study
used both observational rainfall data for the period 1971 to 2010 and primary data on communities’ perceptions
of changes in rainfall. The study was conducted in three distinct agro-ecologies covering highland, low land and
floodplains. Trends analysis was done using Regression method, while Coefficient of Variation and ANOVA
techniques were used to analyze variability. Rainfall satisfaction index was used to assess farmers’ perceptions.
The results show statistically significant increasing trends (P ≤ 0.05) in annual and seasonal rainfall for highland
areas, and negative, but non-significant trends for low lying areas. Analysis of Variance shows significant within
and between season variations for L. Victoria and less significant variations for Mt. Elgon and SE L. Kyoga
agro-ecologies. However, Mt. Elgon exhibits a very high coefficient of variation for ASON (CV > 30%),
indicating high rainfall variability. Over 90% of the interviewed farming communities perceived change in
rainfall pattern, dating as far back as 10 to 15 years. The rainfall subjective index of 0.19 was obtained, which
indicates that the rainfall situation for the base year of this study was undesirable. Adaptation to the observed
variability may include; development of early warning systems based on a combination of meteorological data
and communities’ knowledge, adoption of crops adapted to water logging or stress conditions for the different
seasons and agro-ecologies, and local institutional preparedness to anticipate and manage the climate variability
induced risks.
Key words: Rainfall Variability, Rainfall Trends, Farmers’ Perceptions, Eastern Uganda.
1. Introduction
Evidence is emerging that climate change is increasing rainfall variability and the frequency of extreme events
such as drought, floods, and hurricanes (IPCC, 2007). Boko et al. (2007) predict that Africa is likely to warm
across all seasons during this century with annual mean surface air temperatures expected to increase between
3°C and 4°C by 2099, roughly 1.5 times average global temperatures. Projections in East Africa suggest that
increasing temperatures due to climate change will increase rainfall by 5 - 20% from December to February, and
decrease rainfall by 5-10% from June to August by 2050 (Hulme et al., 2001; IPCC, 2007). Analyses from
General Circulation Models (GCM’s) indicate an upward trend in rainfall under global warming over much of
Burundi, Kenya, Rwanda, southern Somali and Uganda (Schreck & Semazzi, 2004; van de Steeg et al., 2009).
Studies conducted in Uganda indicate a general lack of scientific consensus on the trend and distribution of
annual and seasonal rainfall. McSweeney et al. (2008) report an annual rainfall decrease of 3.5% since the
1960s, with annual rainfall due to decline further. McSweeney et al. (2008) further suggest that rains during the
March to May rainy season are falling by 4.7% per decade. However, Government of Uganda (GOU 2007)
indicates that the wetter areas of Uganda, around the Lake Victoria basin and the east and northwest are tending
to become wetter, indicating an increase in rainfall in these areas. Temperature and rainfall simulations by
Goulden (2008) indicate high percentage increases in rainfall for historically dry seasons for many parts of
Uganda. In their study of localized precipitation around Kibale National Park in mid Western Uganda, Stampone
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et al. (2011) found that patterns in annual time series do not reflect the direction and magnitude of seasonal
trends nor the spatial variability in intra-annual rainfall.
On the seasonal scale, GOU (2007) reports increasing erratic onset and cessation of rainfall seasons across the
country in recent years; coupled with increasing frequency of droughts. It has also been observed that falls are
heavier and more violent. Non-governmental organizations working in Uganda also report that farmers recognize
an increasingly erratic rainfall pattern in the first March to May rainy season, causing drought and crop failure,
but also more intense rainfall, especially in the second rains at the end of the year, causing flooding and erosion
(Oxfam, 2008). The spatial variability has been attributed to the complex topography and existence of large
inland water bodies (Bamanya, 2007), La Niña and El Niño phenomena, with La Niña years tending to bring
significant drying and El Niño years heavy rains (GOU, 2009).
Despite these evidences in general trends of rainfall withnin the region and Uganda, this information may not be
relied upon to make policy and management decisions, due to generalization over large scales. General
Circulation Model scenarios are insufficiently precise in terms of spatial resolution or scale of assessment and
fail to reasonably differentiate spatiality (Thornton, et al., 2008). Local level studies conducted in Uganda have
been based on the magnitudes of monthly and seasonal rainfall (Kigobe et al., 2011; Komutunga & Musiitwa,
2001) and the occurrence of dry and wet spells (Bamanya, 2007, Osbahr et al., 2011), with limited focus on the
variability of rainfall within the year and seasons. Yet according to Mukiibi (2001), the magnitude of rainfall is
less critical to farmers’ production than distribution through a season. While, Stampone et al. (2011) assessed
variability in areas around Kibale National Park, a tropical rain forest, results obtained from this area cannot be
easily generalized over other areas in Uganda due to contextual and environmental differences. In addition, the
lack of consensus by previous studies in Uganda calls for location-specific analysis to understand where
variability is highest in order to support local level decision making on adaptation.
This study addressed this information gap by providing empirical evidence of the extent of annual and seasonal
rainfall variability in the context of Eastern Uganda for the 40-year period from 1971 to 2010. The region
comprises of three distinct agro-ecological zones, ranging from low land to highland, and semi arid to sub
humid. Clarity on variability by region and specific agro-ecology is essential to support vulnerable communities
to adapt their food systems to emerging climate variability realities. It was hypothesized that there is no
significant variation in the pattern of seasonal and inter-annual rainfall pattern in the three agro-ecologies of
Eastern Uganda.
2. Study Area and Relevance to Sub-Saharan Africa
Uganda lies in East Africa, astride the equator with its area lying between latitude 40
12’N and 10
29’S and
longitude 290
34’W and 350
0’E (Ojakol, 2001). The country occupies 241,551 square kilometres of largely fertile
arable land. It is bordered to the east by Kenya, to the north by South Sudan, to the west by the Democratic
Republic of Congo, and to the south by Rwanda and Tanzania. The country is located on a plateau, averaging
about 1100 meters (3,250 ft) above sea level sloping down to the Sudanese Plain to the north. Large parts of the
country have fertile soil with regular rainfall and agriculture is the mainstay of both the national economy and
the main source of livelihood for most Ugandans. Subsistence farming is the main source of household income
for the majority of Ugandans. Agricultural products currently still supply nearly all of Uganda's foreign
exchange earnings. The country is divided into four major regions - Western, Central, Eastern and Northern
(GOU, 2010).
This study was carried out in Eastern Uganda. The region comprises 32 districts (GOU, 2010) with a total
population of about 6,301,677 people, which is 25.5% of the total population of Uganda (UBOS, 2002). The
region comprises of three distinct agro-ecological zones (AEZs) - Lake Victoria Crescent and Mbale farm lands
(L.Victoria Crescent); Southern and Eastern Lake Kyoga basin (SE L. Kyoga); and Mt. Elgon high farmlands
(Mt. Elgon) (Wortmann & Eledu, 1999). The AEZs are largely determined by the amount of rainfall, which
drives the agricultural potential and farming systems and range from sub-humid to semi-arid (GRID, 1987).
They also capture variability in altitude, soil productivity, cropping systems, livestock systems, and land use
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intensity. Table 1 shows the AEZ in Eastern Uganda, their biophysical characteristics and their relevance to Sub-
Saharan Africa (SSA).
Although all the AEZs of Uganda are grappling with the effects of climate change and variability, the Eastern
region is most affected. This is attributable to the fact that the region is less socially and economically
developed, and even among the generally poorer parts of Uganda as a whole. It is characterized by a
combination of acute poverty, vulnerability to drought, floods and landslides, and natural resource degradation.
Recent floods in the Teso sub region and landslides in Bududa have led to crop loss and subsequent hunger and
displacement of people (GOU, 2009). These climate challenges combined constrain crop production, increasing
crop failure, thus exacerbating poverty.
3 Materials and Methods
3.1 Data and sampling procedure
Data for the study were collected during August – September 2011 from both primary and secondary sources.
Primary data were obtained from respondents on their perception of the long term rainfall variability and
adequacy. The AEZs formed the study strata and from each, one district was randomly selected for the study.
The districts included in this study are; Mbale, Pallisa and Sironko representing L. Victoria Crescent, SE L.
Kyoga and Mt. Elgon agro-ecologies respectively. Using random sampling technique, nine sub counties were
selected (three per district), and one village per sub-county from which respondents were drawn. Sample size
was obtained using coefficient of variation method (Nassiuma, 2000). Three hundred and fifty three household
surveys were conducted, nine focus group discussions (FGDs) involving 104 community members, and 23 key
informant interviews (KIIs), using structured and semi-structured interview schedules. Observational rainfall
data for these AEZs were obtained from Uganda Meteorological Department, of the Ministry of Lands and
Environment, for the period extending from 1971 to 2010. At least one weather station exists in each of the AEZ
of interest from which data were obtained.
3.2 Analytical methods
To identify local rainfall variability in the study areas, this study quantified trends and variability in total
seasonal and annual rainfall derived from monthly rainfall observations. Data were first evaluated for
discontinuities by inspection of each time series and then tested for homogeneity using the Student’s t-test (von
Storch & Zwiers, 1999) and found to be homogenous. Trend analysis was done to reveal the general movement
of the rainfall pattern, examining evidence of any changes in the trend of rainfall amounts. Such patterns were
investigated by use of both graphical and statistical methods. Graphical methods were used as a tool for
visualization of temporal variation of annual rainfall amounts over the study period – 1971 to 2010. Regression
analysis was done to determine the magnitude, direction and significance of the trends in annual and seasonal
rainfall for each sample district. The regression equation was defined as:
Yi = βo + β1Xi + ε
Where Y = total annual rainfall, and X = time measure in years. It was hypothesized that there is no trend in the
amount of rainfall over time. Thus the null hypothesis was stated as; H0: β1 = 0. Variability of annual and
seasonal rainfall was assessed using Coefficient of Variation (CV), and Analysis of Variance (ANOVA)
techniques. In addition, first moments of variation (minimum, maximum, mean, and standard deviation) were
obtained using descriptive analysis. Standard rainfall anomalies were plotted against time (in years) to visualize
the time series variation of annual and seasonal rainfall about the mean.
In order to determine communities’ perceptions of rainfall trends and variability, respondents were asked two
sets of questions. The first was asking farmers if they have observed any change in rainfall pattern, and if so,
how many years back they had noticed this change. The second set consisted of asking farmers their perception
of rainfall adequacy in the preceding agricultural season (August – November 2010, the base season for this
study). The questions asked on rainfall adequacy included; whether rain came and stopped on time, whether
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there was enough rain at the beginning and during the growing season and whether it rained at harvest time (see
Table 2). The responses for these questions were dichotomized in such a way that those who responded “on
time” coded into one and others (early /late) into zero. The responses were summed and divided by the number
of rain related questions (nine in this case). So the most favourable rainfall outcome is one and the least is zero.
Quisumbing (2003) in the study of food aid and child nutrition in Ethiopia followed a similar approach in
generating a rainfall satisfaction index. In the same token, Demeke and Zeller (n.d) used the same approach to
study impacts of rainfall shock on smallholder food security in Ethiopia.
4 Results
4.1 Rainfall Variables
Objective rainfall variables were computed based on observational rainfall data to obtain maximum, minimum
and mean annual and seasonal rainfall as well as the standard deviation and coefficients of variation (CV). Table
3 shows summary information of annual and seasonal rainfall variables by AEZ. The two seasons recognized by
farming communities were used to compute seasonal rainfall variables. That is, 1st
season stretching from March
to June (MAMJ), and 2nd
season from August to November (ASON). From the table, it is observed that the
mean annual rainfall in Eastern Uganda varies from 1374mm in parts SE L. Kyoga to 2058mm in Mt. Elgon.
Mean seasonal rainfall varies from 522mm to 905mm in SE L. Kyoga and Mt. Elgon respectively. Results
further show highest rainfall amounts both on the annual and seasonal scales in Mt. Elgon and lowest in SE L.
Kyoga agro-ecologies. Results of CV for annual and seasonal rainfall amount show CVs less than 30% for all
locations, except ASON season for Mt Elgon which shows a CV of 38%. The highest coefficients of variation
are noted for Mt. Elgon area for both annual and seasonal rainfall. In addition, the variation is higher for ASON
as compared to MAMJ for all locations.
In addition to observational rainfall variables, a subjective index was obtained from asking farmers a series of
questions related to rainfall adequacy in the previous growing season, in order to understand their perceptions of
rainfall variability and how it relates to actual variation computed from weather stations. Over 90% of the
farmers interviewed had perceived change in rainfall pattern, dating as far back as 10 to 15 years. The rainfall
subjective index of 0.19 was obtained, which indicates that during the growing season of August – November
2010 (the base year for this research), the rainfall situation was undesirable (Table 4).
Farmers’ generally reported late on set of rain, poor distribution within the season, and sometimes early
cessation. In particular, they noted that the first season had shifted from a start in early March to mid or late
March and now ended in June rather than May. Meanwhile, they claimed the second season had shifted from a
start in August to September and now ended in November rather than December. During the past 15 years,
farmers highlighted specific problems of variability in the duration, timings and intensity of the rains, including
in winds and heavy rains at the start of the seasons, such as in 2004, 2006 and 2007. In the lower lying areas (SE
L. Kyoga), respondents highlighted drought in the first season as an increasing problem, and more frequent flash
floods as a result of increased rainfall intensity. In the highland areas (Mt. Elgon and part of L. Victoria
Crescent), increased rainfall intensity leading to increased ground water and water logging and landslides was
reported. Comparing means across the sample locations indicates no significant differences in people’s
perception of climate variability (P<0.05).
4.2 Annual and Seasonal Rainfall Trends
Graphical visualization of annual rainfall data for the period 1971 to 2010 in the studied agro-ecological zones is
presented in Figures 1. There is an observed increasing trend of total annual rainfall for L. Victoria Crescent and
Mt Elgon and a decreasing trend for SE L. Kyoga. Annual and seasonal rainfall totals were regressed against
time scale and regression results are shown in Table 5. The results show statistically significant increasing trends
(P ≤ 0.05) in annual and seasonal rainfall for Mt. Elgon. Negative, but non-significant trend is observed for
annual rainfall for SE L. Kyoga. On the seasonal scale, MAMJ rainfall shows a negative trend, while ASON
shows increasing trend for L. Victoria Crescent and SE L. Kyoga. For Mt. Elgon, both MAMJ and ASON show
increasing trends.
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4.3 Annual and Seasonal Rainfall Variability
The year-to-year variation of annual and seasonal rainfall over the studied agro-ecological zones was expressed
in terms of normalised rainfall anomaly (Figures 2 and 3). This analysis of rainfall variability shows significant
anomalies in annual rainfall in the recent past (2000 to 2010). While Mt. Elgon and L. Victoria Crescent seem to
have received above average rainfall, SE L. Kyoga received more or less below average in the years from 2000
to 2010. Analysis of variance (ANOVA) shows significant variation in inter-annual rainfall as well as across
AEZs. L. Victoria Crescent exhibits significant within and between season variations as shown in Table 7.
5 Discussion
Descriptive study results show that Eastern Uganda experiences a bimodal seasonal pattern: the long rainy
season starts around March and runs through to June, with the peak centred on March to May; the short rains run
from August and taper off in November. Mean annual rainfall varies from 1374 mm in SE L. Kyoga to 2058 mm
in Mt. Elgon. In comparison, the average long-term annual rainfall for Uganda is 1318 mm, which is considered
adequate to support agricultural activities (Osbahr et al., 2011). This implies that Eastern Uganda receives
adequate rainfall to support agriculture. Despite this seemingly desirable rainfall situation, the study shows
significant variation in its distribution both on the annual and seasonal scales. Statistically significant increasing
trends in annual rainfall are observed for Mt. Elgon and L. Victoria Crescent AEZs, and negative but non-
significant trends for SE L. Kyoga basin. Seasonal trends indicate decreasing rainfall for MAMJ rainfall for L.
Victoria Crescent and SE L. Kyoga, while in Mt. Elgon it is increasing, while ASON rainfall is increasing in all
the three agro-ecologies.
This result confirms earlier studies by Basalirwa (1995) who predict an increase of approximately 10-20% in
rainfall for high ground areas, and more drying conditions for low areas like Uganda’s cattle corridor. Other
studies in Uganda and government analysis papers also confirm these results indicating increasing trends in
inter-annual rainfall, and decreasing trends in March-April-May (MAM) rainfall (GOU, 2007; Goulden, 2008;
Hepworth & Goulden, 2008; Osbahr et al., 2011). Although McSweeney et al. (2008) report decrease in annual
rainfall, they agree that the MAM rainfall is decreasing.
Analysis of Variance shows significant within and between season variations for L. Victoria and less significant
variations for Mt. Elgon and SE L. Kyoga. However, Mt. Elgon exhibits a very high coefficient of variation for
ASON (CV > 30%). According to Araya and Stroosnijder (2011), a CV > 30% is an indicator of large rainfall
variability. This may be linked to the 2007 El Nino rains that characterised the OND season in Uganda (GOU,
2009). Links between El Nino and climate variability have also been suggested by other studies (e.g. Anyah &
Semazzi, 2007). Shisanya et al. (2011) also report above normal rainfall during OND season than preceding
MAM rainfall in ASALs of Kenya during El Nino years. In terms of variability, seasonal rainfall in Eastern
Uganda varies a lot around the mean, with occasions of subsequent below average rainfall. The variations are
more pronounced for ASON than MAMJ seasons, and in the years from 2006 to 2010. Mutai et al. (1998), and
Phillips and McIntyre (2000) also observed that OND variability is stronger than MAM.
Farmers’ perceptions of climate variability are in line with actual climatic data, noting variability in the duration,
timing and distribution within seasons, including in winds and heavy rains at the start of the seasons. This is a
common finding from other studies on perceptions of resource users of climate change such as in the Sahel
(Mertz et al., 2009), Nile basin of Ethiopia (Deressa et al., 2008), Zambia (Nyanga et al., 2011), semi-arid
central Tanzania (Slegers, 2008); Uganda (Magrath, 2010), and Asia (Marin, 2010), where farmers perceived
increased variability of rainfall and shifts in the growing seasons. Osbahr et al. (2011) indicates that the potential
crop growing period is shrinking. Seasonal distribution of rainfall affects the decisions made by farming
households on what type of crops to grow and land management practices to adopt (Komutunga and Musiitwa,
2001). In addition, excessive rains both in intensity and duration lead to water logging conditions that negatively
affect crops and pasture (GOU, 2007; Komutunga & Musiitwa, 2001). For example drought in 2008 caused an
average reduction in yield of 50% of simsim, sorghum, groundnuts, cassava and maize in Uganda (Ocowunb,
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2009). Heavy rainfall experienced between 2006 and 2010 is responsible for massive floods in the low land areas
and numerous landslides in the mountainous regions in Eastern Uganda (GOU, 2009).
In related studies in East Africa, Recha et al. (2012) report that persistence of below normal rainfall is a great
risk to people’s livelihood in Tharaka district in Kenya, where majority of people have been left vulnerable to
hunger and famine. Similar observations have been reported by various scholars studying, for example intra-
seasonal factors, such as the timing of the onset of first rains affecting crop-planting regimes (Tennant &
Hewitson, 2002), the distribution and length of period of rain during the growing season (Mortimore & Adams,
2001), and the effectiveness of the rains in each precipitation event (Usman & Reason, 2004), are the real criteria
that affect the effectiveness and success of farming. IPCC (2007) reported that changes in rainfall amount and
patterns also affect soil erosion rates and soil moisture, both of which are important for crop yields.
From the farmers’ perspective, this uncertainty in addition to increasing food insecurity due to crop failure, it
generally increases the cost of production as sometimes farmers have to re-plough and replant destroyed crop
fields. As noted by Olupot John (37 years) from Kadengerwa village, Pallisa district: “We’ve stopped adopting
seasonal planting, because it’s useless. Now we just try all the time. We used to plant in March, and that would
be it. Now we plant and plant again. We waste a lot of seeds that way, and our time and energy. Sometimes
we’ve hired labour and end up losing all that money for preparing land”.
6 Conclusions
This study sought to provide empirical evidence of the extent of annual and seasonal rainfall variability in
EasternUganda. The null hypothesis being tested was thus: Ho: There is no variation in the pattern of annual and
seasonal rainfall in Eastern Uganda. Contrary to the expected direction of trends and variability, this study found
significant variation in the amount and distribution of annual and seasonal rainfall. This is attributed to increase
in extremes of rainfall on the annual scale such as high intensity rainfall and droughts thus affecting the
variability. Significance of the variations varies by agro-ecological zone, attributed to variations in altitude,
cropping systems and land use intensity in the specific locations. High land areas showed increasing amounts
and higher variability in rainfall as opposed to low lying areas which showed decreasing amounts and less
variability within and between seasons. Greater variation was observed for ASON, than MAMJ season for all the
locations. Communities’ perceptions of rainfall adequacy were in line with observational data, where they
acknowledged late rainfall onset, mid season droughts and early cessation, especially in the first season, and
increase in high intensity rainfall, and climate related disasters such as floods, droughts and landslides during
second season. Several others studies conducted in Uganda and SSA in general are in agreement with results of
this study confirming increasing rainfall trends and variability for ASON, and decreasing trends for MAMJ
season.
Study findings have the following implications: First, significant within season variability negatively affects crop
and livestock production, where in extreme cases there has been total crop failure either due to prolonged
droughts or heavy erosive rainfall or floods washing away the crop. An example is the long rains in Uganda in
2007, 2008 and 2010. It is evident that ASON rains are increasing in magnitude and variability as compared to
MAMJ rains, and farming communities are aware of the changing trends in seasonal rainfall, which greatly
affects their farming decisions. Adaptation to the observed variability may include; development of early
warning systems based on a combination of meteorological data and communities’ knowledge, adoption of crops
adapted to water logging or stress conditions for the different seasons and locations, and local institutional
preparedness to anticipate and manage the climate variability induced risks.
Acknowledgements
This paper is prepared as part of the PhD research work for the corresponding author, investigating the
effectiveness of technological options for reducing production risks under variable climatic conditions in Eastern
Uganda. The study was partially funded by Climate, Food and Farming Research Network (CLIFF) of the
University of Copenhagen, Denmark. The authors acknowledge the financial support by CLIFF. The authors also
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thank the Uganda Meteorological Department for providing the rainfall data for this study. The support and
participation of farming communities in Mbale, Pallisa and Sironko, and respective district extension personnel
and leaders during data collection is also acknowledged. Lastly, the authors acknowledge the contributions of the
anonymous reviewers of this paper for their technical input.
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Agricultural and Forestry Meteorology, 151:425–436
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Table 1: Agro-Ecological Zones in Eastern Uganda, their biophysical characteristics and their relevance
to SSA
Characteristic Agro-Ecological Zone
L. Victoria Crescent SE L. Kyoga Mt. Elgon
Soils Petric Plinthosols (Acric) Gleysols (for Kumi area) Vertisols
Mean altitude
(m.a.s.l)
1174 1075 1299 - 1524
Population
density
166.3/km2 (431/sq mi) 252/km2 (650/sq mi) 770/km2 (2,000/sq mi)
Mean annual
rainfall
Bimodal high rainfall >1,200
mm/year
Bimodal high rainfall >1,200
mm/year
Bimodal high rainfall
(>1,200 mm/year)
Farming systems Montane (Millet/ Cotton)
System
Teso Systems Montane (Millet/ Cotton)
System,
Major crops Arabica coffee, banana,
cotton, maize, bean, wheat,
millet, rice, Irish potato,
sweet potato
Cotton, finger millet,
sorghum, groundnut,
sesame, sweet potato,
cassava, Robusta coffee,
beans, maize
Arabica coffee, banana,
cotton, maize, bean, wheat,
millet, rice, Irish potato,
sweet potato
Major climate
related disasters
Mid to high ground areas
vulnerable to floods and
landslides
Lies in the low land areas
and in the cattle corridor
vulnerable to
droughts
Lies in so called highland
areas of Uganda vulnerable
to landslides and water
logging
SSA countries
with similar
biophysical
characteristics
West-central (Democratic
Republic of the Congo,
Congo, etc.), and coastal
region of western Africa
Countries along the desert
margin (e.g., Burkina Faso,
Northern and central
Nigeria, Namibia, central
Sudan)
Highlands of Cameroon,
Ethiopia, Kenya, Malawi,
Rwanda, and Tanzania
(southern highlands)
Sample districts
and location
Mbale
(000
57’N, 340
20’E)
Pallisa
(010
01’N, 330
43’E)
Sironko
(010
14’N, 340
15’E)
Representative
weather station
and location
Tororo
(0.930
N, 33.970
E)
Soroti
(1.720
N, 33.620
E)
Sipi
(1.330
N, 34.370
E)
Source: Adapted and modified from Komutunga and Musitwa (2001) and Wasige (2009)
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Table 2: Rainfall Satisfaction Index Construction
During the growing season preceding the
last main harvest:*
Codes Recorded into:
1 Did the rainfall come on time? 1=on time; 2=too early;
3=too late
On time
Others (2 and 3)
1
0
2 Was there enough rain on your fields at
the beginning of the rainy season?
1=enough; 2=too little;
3=too much
Enough
Others (2 and 3)
1
0
3 Was there enough rain on your fields
during the growing season?
1=enough; 2=too little;
3=too much
Enough
Others (2 and 3)
1
0
4 Did the rains stop on time on your
fields?
1=on time; 2=too late;
3=too early
On time
Others (2 and 3)
1
0
5 Did it rain near the harvest time? 1 = no; 2 = yes No
Others (2)
1
0
6 Number of rainfall days 1=No change; 2=Reduced;
3= Increased
No change
Others (2 and 3)
1
0
7 Frequency of heavy rains 1=No change; 2=Reduced;
3=Increased
No change
Others (2 and 3)
1
0
8 Frequency of dry spells 1=No change; 2=Reduced;
3=Increased
No change
Others (2 and 3)
1
0
9 Duration of the growing season 1=No change; 2=Reduced;
3=Increased
No change
Others (2 and 3)
1
0
* Reference was made to August - November 2010 rainy season
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Table 3: Summary of Rainfall Variables for the Study Locations
Agro-Ecological
Zone
Rainfall Annual
(1971-2010)
Mar-Jun
(MAMJ)
Aug-Nov
(ASON)
L. Victoria Crescent Minimum (mm) 1018 445 283
Maximum (mm) 2068 932 840
Mean (mm) 1503 659 522
Std. Dev. 226 123 137
Coef. of Variation 15.04 18.66 26.25
SE L. Kyoga Minimum (mm) 895 306 271
Maximum (mm) 1844 936 74
Mean (mm) 1368 574 539
Std. Dev. 231 14 115
Coef. of Variation. 16.89 2.44 21.34
Mt. Elgon Minimum (mm) 1409 528 420
Maximum (mm) 3001 1287 2546
Mean (mm) 2058 812 905
Std. Dev. 349 166 328
Coef. of Variation 16.96 20.44 38.24
Source: Authors’ computation based on observational rainfall data from Meteorological department
Table 4: Rainfall Subjective Index
During the main growing season of 2010 Mean (Std. Dev.)
L. Victoria
Crescent
SE L. Kyoga Mt. Elgon Overall
1. Did the rainfall come on time? 0.26 (0.44) 0.10 (0.30) 0.13 (0.33) 0.16 (0.37)
2. Was there enough rain at the beginning of
the rainy season?
0.52 (0.50) 0.12 (0.32) 0.25 (0.44) 0.30 (0.46)
3. Was there enough rain during the growing
season?
0.56 (0.50) 0.18 (0.38) 0.31 (0.46) 0.35 (0.48)
4. Did the rains stop on time? 0.23 (0.42) 0.07 (0.25) 0.18 (0.39) 0.16 (0.37)
5. Did it rain near the harvest time? 0.02 (0.14) 0.88 (0.32) 0.04 (0.19) 0.31 (0.46)
6. Did the number of rainfall days change? 0.26 (0.44) 0.01 (0.10) 0.03 (0.17) 0.10 (0.30)
7. Did the frequency of heavy rains change? 0.30 (0.46) 0.01 (0.10) 0.01 (0.10) 0.11 ()0.31
8. Did the frequency of dry spells change? 0.07 (0.25) 0.00 (0.00) 0.03 (0.17) 0.03 (0.18)
9. Did the duration of the growing season
change?
0.38 (0.49) 0.23 (0.42) 0.08 (0.27) 0.23 (0.42)
Average 0.29 (0.40) 0.18 (0.24) 0.12 (0.28) 0.19 (0.37)
F-value 1.69
P-value 0.21
F crit (5%) 3.40
Source: Field data, 2011
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Table 5: Summary Statistics of the Regression Analysis
AEZ Rainfall Coef. Std. Err. t stat P value R2
L. Victoria Crescent Annual 4.320 2.950 1.465* 0.151 0.052
MAMJ -0.258 1.726 -0.150 0.882 0.001
ASON 2.847 1.870 1.522* 0.136 0.057
SE L. Kyoga Annual -1.502 3.068 -0.490 0.063 0.006
MAMJ -3.090 2.033 -1.520* 0.137 0.057
ASON 1.472 1.592 0.924* 0.361 0.022
Mt. Elgon Annual 4.436 4.617 0.961* 0.341 0.023
MAMJ 3.134 2.167 1.446* 0.156 0.052
ASON 7.466 4.411 1.693* 0.099 0.070
* Significant at Alpha level = 0.05
Source: Field data, 2011
Table 7: ANOVA of Annual and Seasonal Rainfall
Source of Variation SS MS F-value F crit.
Annual Inter-annual 6324334 158108 1.636* 1.545
Inter-AEZs 12231595 6115797 63.263* 3.111
Total 26289740
Mbale Between Seasons 384928 384928 22.707* 3.960
Within Seasons 1356130 16951
Total 1741058
Pallisa Between Seasons 23783 23783 1.340 3.960
Within Seasons 1419921 17749
Total 1443704
Sironko Between Seasons 178077 178077 2.636 3.960
Within Seasons 5404352 67554
Total 5582429
*Significant at Alpha level =0.05
Source: Field data, 2011
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(a)
(b)
(c)
Figure 2: Trends in annual rainfall anomalies relative to the 1971-2010 mean rainfall for (a) L. Victoria,
(b) SE L. Kyoga and (c) Mt. Elgon
Source: Field data, 2011
-3
-2
-1
0
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2
3
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1973
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(a)
(b)
(c)
Figure 3: Trend in seasonal rainfall anomalies relative to the 1971-2010 mean rainfall for (a) L. Victoria,
(b) SE L. Kyoga and (c) Mt. Elgon
Source: Field data, 2011
-4
-2
0
2
4
6
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1973
1975
1977
1979
1981
1983
1985
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1989
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1993
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2009
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