Trends extreme temperatures_cattle _corridor by brian owoyesigire
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Transcript of Trends extreme temperatures_cattle _corridor by brian owoyesigire
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TRENDS OF EXTREME TEMPERATURE INDICES FOR SELECTED LOCATIONS IN THE CATTLE CORRIDOR OF UGANDA
A presentation to stakeholders inALiCE CONFERENCE 18TH-20TH JUNE
2014 AA
Owoyesigire, B.1,2 D. Mpairwe1, and P. Ericksen3
1Department of Agricultural Production, School of Agricultural Sciences, College of Agricultural and Environmental Sciences (CAES), Makerere University, P. O. Box 7062 Kampala, Uganda2 NARO/ Buginyanya Zonal Agricultural Research and Development Institute (BugiZARDI),Uganda3International Livestock Research Institute ILRI, P.O Box 37009,Nairobi, Kenya
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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 productsand crop yields Heavy pests and disease outbreaks Reduction in soil fertility
Extr
em
es
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Changes in extreme events result in severe socio-economic impacts Extremes can have positive or negative effects
Why temperature extremes?
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What is responsible????? Anthropogenic activities
Natural systems
DeforestationBurning fossils
-Contribute to emissions such as volcanic eruptions and lightning
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Aim of the study
Determine trends of extreme temperature indices in the cattle corridor of Uganda
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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
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Data CollectionData sets from 1970-2010
Daily maximum temperatures
Minimum temperatures
Selected Mbarara, Masindi and Soroti
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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 (Table. 2)
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Table 1: Definitions of selected extreme temperature indices Indices Indicator
nameIndicator definitions Unit
sTXx 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
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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)
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Percentage warm nights (TN90p)
Warm nights revealed;•Significant increasing trends (P<0.05) in Mbarara and MasindiNon-significant decreasing trends (P > 0.05) in Soroti
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DTR was significantly decreasing in Mbarara and Masindi stations.
In Soroti, the trend was significantly increasing (P < 0.05)
Daily Temperature Range (DTR)
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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 livestock species
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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
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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