1. TRENDSOFEXTREMETEMPERATURE
INDICESFORSELECTEDLOCATIONSIN THE
CATTLECORRIDOROFUGANDA
A presentation to stakeholders in
ALiCE CONFERENCE 18TH
-20TH
JUNE
2014 AA
Owoyesigire, B.1,2
D. Mpairwe1
, and P. Ericksen3
1
Department of Agricultural Production, School of Agricultural Sciences, College of Agricultural and
Environmental Sciences (CAES), Makerere University, P. O. Box 7062 Kampala, Uganda
2
NARO/ Buginyanya Zonal Agricultural Research and Development Institute (BugiZARDI),Uganda
3
International Livestock Research Institute ILRI, P.O Box 37009,Nairobi, Kenya

2. Climate Change: A Challenge
 Climate change is a reality and No longer a myth
 Climate change manifests in;
 Erratic and destructive rains
 Long drought periods
 Shortage of water and pastures
 Drastic decrease in livestock products
and crop yields
 Heavy pests and disease outbreaks
 Reduction in soil fertility
Extremes

3. Changes in extreme events result in
severe socio-economic impacts
Extremes can have positive or negative
effects
Why temperature extremes?

4. What is responsible?????
 Anthropogenic activities
 Natural
systems
Deforestation
Burning fossils
-Contribute to emissions
such as volcanic eruptions
and lightning

5. Aim of the study
Determine trends of extreme
temperature indices in the cattle
corridor of Uganda

6. The Cattle corridor
 An area of 84,000
km2
 About 6.6 million
people dwell in this
region (UBOS, 2002)
 Accounts for about
90% of the national
livestock herd
 Semi arid conditions
 Most areas experience a bi-modal rainfall patterns
(High levels of variability)
 Grasses interspersed with trees, to forest savannah mosaics
and woodland are the dominant vegetation
Materials and methods

7. Data Collection
Data sets from 1970-2010
 Daily maximum temperatures
 Minimum temperatures
 Selected Mbarara, Masindi and Soroti

8. Data Analysis
 Homogeneity tests using “RHtestV3” software
(Wang and Feng, 2009)
 RClimdex software was used to derive indices
(Zhang and Feng, 2004)
 RClimdex produces 29 annual time series
indices (ETCCDI)
 Selected ONLY six temperature indices

9. Table 1: Definitions of selected extreme temperature indices
Indices Indicatorname Indicatordefinitions Units
TXx Hottest day Monthly maximum value of
daily max temperature
0C
TNx Warmest night Monthly maximum value of
daily min temperature
0C
TN90p Warm nights Percentage of time when
daily min temperature > 90th
percentile
%
TX90p Hot days Percentage of time when
daily max temperature > 90th
percentile
%
DTR Diurnal
Temperature
Range
Monthly mean difference
between daily max and min
temperature
days
WSDI Warm spell
duration index
Annual count of days with
atleast 6 consecutive days
when Tx > 90th percentile
days

10. RESULTS:
Percentage Hot days (TX90p)
Hot days were;
•Significantly increasing
in Mbarara and Masindi
(P < 0.05)
•Increasing and not
significant in Soroti
(P > 0.05)

11. Percentage warmnights
(TN90p)
Warmnights revealed;
•Significant increasing
trends (P<0.05) in Mbarara
and Masindi
Non-significant decreasing
trends (P > 0.05) in Soroti

12. DTR was
significantly
decreasing in
Mbarara and Masindi
stations.
In Soroti, the trend
Daily Temperature Range (DTR)

13. Discussion
 DTR was significantly decreasing in Mbarara and
Masindi. Indicating that daily minimum temperatures (TN)
were raising faster that daily maximum temperatures (TX).
 In Soroti, DTR was significantly increasing (P<0.05)
indicating that daily max. temperatures were raising faster
than daily min.
 Most areas in the cattle corridor are significantly
warming. All biological systems function in specified
temperature ranges.
 Warming conditions most likely to increase heat stress to

14. Conclusion
All temperature indices revealed strong
significant increasing trends in all stations.
Indicating that the cattle corridor continues
to experience warming conditions.
High temperatures are most likely to
increase heat stress to livestock thus
causing a decline in livestock productivity

15. Acknowledgements
We are grateful to DAAD and International
Livestock Research Institute (ILRI) for
funding this study
Special thanks to the Department of
Metereology, Ministry of Water and
Environment for availing us some of the
temperature data sets