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

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

Changes in extreme events result in severe socio-economic impacts Extremes can have positive or negative effects

Why temperature extremes?

What is responsible????? Anthropogenic activities

Natural systems

DeforestationBurning fossils

-Contribute to emissions such as volcanic eruptions and lightning

Aim of the study

Determine trends of extreme temperature indices in the cattle corridor of Uganda

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

Data CollectionData sets from 1970-2010

Daily maximum temperatures

Minimum temperatures

Selected Mbarara, Masindi and Soroti

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)

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

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)

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

DTR was significantly decreasing in Mbarara and Masindi stations.

In Soroti, the trend was significantly increasing (P < 0.05)

Daily Temperature Range (DTR)

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

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

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