| dc.description.abstract | Abstract
Extreme temperatures and rainfall patterns are being experienced in many parts of the
world including Eastern Africa. These have been associated with droughts, floods,
cold/hot spells, cyclones, among others that have had devastating socio-economic
impacts. Thus extreme climate variability and change will in future have serious impacts
on future sustainability of our socio economic systems. The objective of this study was
to assess the potential impacts of extreme climate variability and change on livestock in the
Arid and Semi-Arid Lands (ASALs) of Kenya, with specific reference to Turkana, Marsabit,
Samburu, and Isiolo Counties, using past, present, and future patterns of rainfall and
temperature extremes.
Rainfall and temperature data used were obtained from IGAD Climate Prediction and
Application Centre (ICPAC) while gridded observations used were from Climate
Research Unit (CRU), University of Anglia. ICPAC and CRU data were for the period
1961-2013 and 1901-2013 respectively. The climate projection data sets were obtained
from ICPAC for the period 2006-2100. The data were subjected to various trend
methods in order to delineate the temporal patterns of rainfall characteristics at specific
locations. The trend methods adopted included graphical, regression, and non-parametric
approaches based on Mann-Kendal statistics. Gaussian Kernel density distribution was
used to assess the changes in the mean, variance, skewness and kurtosis coefficients, and
extremes in rainfall and surface air temperature. Spectral analysis was further used to
determine the cycle of extremes over the study area. The standardized precipitation index
was used to determine the past, present, and future abnormal wet and dry conditions and their
effect on cattle population. The skill of the models was examined by the use of root mean
square error, correlation analysis, model bias, and standard deviation. Graphical methods
were then used to examine the probable effect of future climate on cattle farming.
It was evident from the study that both maximum and minimum temperatures are increasing
at all locations as have been observed at many locations worldwide. The highest increase in
seasonal mean of surface air temperature ranging from 0.33-1.450C was observed for June-
August season. Results from rainfall analyses did not delineate any homogenous
changing patterns at all locations and seasons, however, increase in drought risk was
vi
evident at most locations within the study area when recent mean rainfall (1991-2013)
was compared with the means of 1901-30, 1931-60, and 1961-90. Some changes in the
pattern of temperature and rainfall extremes were also evident from the patterns of
higher order time series moments which included skewness and kurtosis. It was observed
that the recurrences of extremes were centered on 2.3, 3.5, 5.5, and 9-10 years which were
attributed to Quasi-biannual oscillation, El Nino, and sun spot cycle. The study observed that
during the period of abnormal wetness, cattle populations were higher than those of the
abnormal dryness thus climate affects cattle population. An ensemble of the models was
found to have a better skill in replicating the observation and hence was used for analysis of
future climate. The wet and dry conditions and temperature are projected to increase in the
future in all the scenarios used in this study. Cattle farming are likely to be affected
negatively in terms of high temperatures resulting to severe thermal heat comfort as well as
severe dry conditions. Hence development of an adaptation mechanism is necessary to cattle
farming in the ASALs of Kenya.
The result from this study can be used in the planning and management of the livestock
sector in the ASALs of Kenya and support national sustainable development planning.
The SPI tool is recommended for monitoring and forecasting abnormal wetness and dryness
over the ASALs of Kenya to improve the timely identification of the emerging extreme
conditions to be action by the government. Livestock farming should be addressed
appropriately using the expected future climatic conditions over the ASALs of Kenya. The
study information can be used by the policy makers to develop policies that can address the
problem of high livestock mortality due to extreme weather and climate conditions in the
country. Further studies on the effect of climate change on other aspects of livestock
such as forage as well as a methodology way to distinguish human factors from climate
factors that affect livestock farming are recommended.
vii | |
