climate smart agricultural adaptation measures among smallholder mixed farmers in east africa by...
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Climate smart agricultural adaptation measures among smallholder mixed farmers in East Africa
Gumisiriza M1; Tadesse, T.1; Isubikalu.P2; Kabirizi, J.3 & Zziwa, E4
1Mekelle University, Ethiopia; 2Makerere University, Uganda; 3National Livestock Resources Research Institute, Uganda
4Association for Strengthening Agricultural Research in Eastern and Central Africa, Uganda.
Introduction
Climate change is a major challenge facing smallholder crop-livestock farmers in Uganda.
Various adaptation strategies have been adopted among communities to increase farmers’ resilience to climate change.
However variations occur across several scales regarding: ability to cope with climate change adaptation measures
Discrepancy may be from the individual to farm and plot levels to country levels
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Introduction (cont.)
Various studies have looked into adaptation practices and the ability to cope at given levels.
For instance: the study on “Climate Change Vulnerability and Adaptation Preparedness in Uganda” (Hepworth 2010).
Unfortunately research into the missing adaptation measures in given study areas is still very limited.
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Objectives
Identify and validate farmers’ knowledge and perception on climate change using existing climate data of the study regions.
Find out and document the existing adaptation strategies in the study areas.
Identify the missing adaptation measures in the study areas that exist in other East African countries with the same climatic conditions.
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Materials and MethodsStudy sites
Ngora & Masaka districts were purposively selected to represent the humid and semi-arid climatic regions with smallholder crop-livestock farmers implementing planned adaptation strategies (ASARECA, 2011).
Sampling techniques
Purposive non- random sampling: used to identify 2 districts and 2 villages based on the implementation of the planned adaptation strategies.
Systematic sampling technique: used to select 20 farmer households per village
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Map of Uganda showing study sites
Ngora district
Masaka district
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Materials and Methods (cont.)
Data collection methods and sources
Non–self administered semi-Structured questionnaires
Interview guides
Focus Group discussions
Climate data -- collected from the National Metrological Center (for Masaka) in Kampala & Soroti flying school in Soroti (to represent Ngora)
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Instat software
Ms Excel
(Regression analysis )
Analogue tool
Data analysis packages
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Results and Discussion
Perception of farmers in Soroti and Masaka on annual rainfall totals (1974-2004)
0
10
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100
13.3%
86.7%
66.7%
33.3%
% r
es
po
ns
e
Ngora
Masaka
Increased Reduced Increased Reduced
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Start End Start End1ST Rain fall season 2ND Rain fall season
SEASONAL VARIABILITY (% response)
01020304050607080 Late Early Timely
Per
cen
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Perceptions of farmers on rainfall seasonal variability.
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Average for rainfall totals for every 5 years for Masaka district (1974-2004)
0 1 2 3 4 5 6 7 80
200
400
600
800
1000
1200
f(x) = − 7.23214287161828 x + 944.982857111522R² = 0.0260655031954109
Average for rainfall totals Linear (Average for rainfall totals )
5 Year interval
Av
era
ge
to
tal
rain
fall
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ANOVA
Df SS MS FSignificance
F
Regression 1 1464.509 1464.509 0.133815 0.729475
Residual 5 54721.21 10944.24
Total 6 56185.72
CoefficientsStandard Error t Stat P-value Lower 95%
Intercept 944.9828571 88.41558 10.68797 0.000124 717.7034
X Variable 1 -7.232142872 19.77033 -0.36581 0.729475 -58.0534
Regression analysis results for annual rainfall totals of Masaka district
The regression results show that at R² = 0.026, the rainfall is fairly declining but it is not statistically significant at P=0.73.
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Average for rainfall totals for every 5 years for Soroti district (1974-2004)
1.5 2 2.5 3 3.5 4 4.5 5 5.50
200
400
600
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1000
1200
1400
1600
f(x) = 130.31823530688 x + 775.534705892032R² = 0.790871754115071
Average for rainfall totalsLinear (Average for rainfall totals)
Five Year Interval
Av
era
ge
to
tal
rain
fall
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ANOVA
df SS MS F Significance F
Regression 1 115483.3 115483.3 11.34526 0.043466
Residual 3 30536.97 10178.99
Total 4 146020.3
CoefficientsStandard Error t Stat P-value Lower 95%
Intercept 775.5347 153.7894 5.042836 0.015036 286.1082
X Variable 1 130.3182 38.68992 3.368273 0.043466 7.189629
Regression analysis results for annual rainfall totals of Soroti district
The regression results show that at R² = 0.7909, the rainfall is relatively
declining and also statistically significant at P-value of 0.043.
15Major adaptation strategies in Masaka and Ngora district
Wa
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& s
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Oth
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& s
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Oth
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& S
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So
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Oth
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High rainfall Low rainfall High temperature Low tempera-
ture
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District (%) Masaka District (%) Ngora
Adaptation strategies
Perc
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Water harvesting (left) and Soil & water conservation (right)
Drought tolerant forages (left) Soil fertility management (right)
Missing adaptation strategies Country Adaptation strategies Implemented
Ethiopia Change of planting dates Crop diversification Large scale pasture conservation Use of early warning systems Implementation of awareness-creation
programs regarding the natural environment and climate change
Agro forestry systems (plant multi-purpose trees for fodder, soil fertility & soil fertility improvement).
Kenya Access of credit by smallholder famers to take on adaptation measures
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Missing adaptation strategies
Large scale pasture conservation in Ethiopia
Green house used for vegetable growing in Kenya
Conclusions
Farmers in the study areas have observed a change in the climate which also concurs with the historical climate data analysis results.
Various adaptation measures were identified to be implemented among the farmers in the Masaka and Ngora
Analogue tool assisted in the Identification of a number of adaptation strategies missing in the study areas
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Recommendations and Implications of the study
Implementers of adaptation strategies should consider putting in place, encouraging or supporting the farmers to take on the identified missing adaptation strategies where applicable
.
Researchers can use analogue tool to identify relevant but missing adaptation measures in other areas of the country and implement them.
Acknowledgements
Rockefeller Foundation
RUFORUM
ASARECA
National Livestock Resources Research Institute, Uganda
Mekelle University, Ethiopia
Makerere University, Uganda.
Farmers and extension staff in Ngora and Masaka
Thank you for your attention