1. EFFECTS OF CLIMATE VARIABILITY AND CHANGE ON HOUSEHOLD WELFARE IN KENYA
Lilian Kirimi
Tegemeo Institute of Agricultural Policy and Development
At The Radisson Blu Hotel, Nairobi, 10th November, 2016.
3rd ReNAPRI Conference: Anticipating Africa’s Agricultural Transformation Pathways in the Context of Climate change: Lessons from the Recent Regional Drought

2. Flooded rural villages-loss of livelihood
Drought
Flooded rural villages-loss of livelihood
Deforestation
Stressed water resources
Land degradation 2

3. Introduction Climate change is now widespread affecting nearly all
countries albeit differently
It is disrupting national economies and affecting lives and
livelihoods
An agenda in global discussions (COP, SDGs )
Discussions on mitigation and adaptation
What do study findings show?
What does evidence show?

4. Study #1 Effects of climate variability and change on agricultural
production: The case of small-scale farmers in Kenya
J. Ochieng, L. Kirimi and M. Mathenge
Using rural household data (2000, 2004, 2007 & 2010) and
historical rainfall & temperature data (1980 to 2010)
Assessed effects of climate variability and change on
Crop revenue (all crops, maize and tea)
Assessed future effects of climate change using rainfall
& temperature predictions

5. Effect on crop revenue Variable All crops revenue Tea revenue
Maize revenue
Rainfall
0.007***
***
0.0043***
Squared rainfall
-0.000***
0.0001
Long term rainfall
0.033
0.0905
Mean temperature
***
1.0372***
0.0411
Squared temperature
0.0164 *
0.0031
Long term temperature
***
8.8997*
-9.800***
Temperature threshold for tea is around 23.50C
Effects vary by crop

6. Future effects on revenue
Year
Level of increase
Climate variable
% change due to rainfall and temperature increase
All crops revenue
Maize revenue
Tea revenue
2020
11%
Rainfall
0.8
0.6
-2.5
10C
Temperature
-14.2
1.1
2.3
2030
26%
0.9
1.2
-5.5
20C
-14.8
2.2
2.4
2040
30%
1.0
1.9
-8.8
2.50C
-15.2
3.3
2.5

7. Study #2
Let it rain: Weather extremes and household welfare in rural Kenya
A. Wineman, N. Mason, J. Ochieng and L. Kirimi
Using rural household data (2004, 2007 & 2010)
Historical rainfall and temperature data
Assessed effects of weather shocks on
Net household income/AE/day
Poverty status
Calories available/AE/day
Extremes: periods of high/low rainfall: >75/<15 cumulative mm pentads
Cumulative degree days over 32 degrees

8. Effect of weather shocks on welfare
Income/AE/day
HH is poor
Calories/AE/day
High rainfall
-6.5
0.02
260.8**
Low rainfall
-25.6**
0.08**
-208.1
High temperature
1.7
-0.03***
100.1
Exposure to rainfall deficits is the most important/most consistently negative weather

9. Effect of shocks on welfare by zones
Income/AE/day
Calories/AE/day
High rainfall
-4.3
259.1**
Low rainfall
-29.1***
-208.1
High temperature
2.1
157.9**
Highlands*high rainfall
-88.5***
152.9
Highlands*low rainfall
46.7
558.7
Lowlands*high rainfall
60.1**
1167.8
Lowlands*low rainfall
59.3
2610
Lowlands*high temperature
0.12
-246
Effects are heterogeneous—vary across agro-ecological regions

10. Mechanisms of weather shock impacts
Income/AE/day
Crop income
Livestock income
Off-farm
income
High rainfall
-4.74
1.4
-0.99
Low rainfall
-17.3***
1.32
-13.0**
High temperature
2.03
0.9
-0.82
Highlands*high rainfall
-63.7***
-9.69
-15.08
Highlands*low rainfall
20.6
6.50
19.6
Lowlands*high rainfall
-2.05
9.65
52.5***
Lowlands*low rainfall
95.7
6.57
-42.97**
Lowlands*high temp
-27.90
5.31
22.71***
Effects are heterogeneous—vary across agro-ecological regions e.g. higher rainfall decreases HH Y in rainier highlands (a concern where CC implies rainfall increase) and low rainfall reduces off-farm Y in midlands (non-farm sector not a perfect safety for HHs in such areas)
What are these off-farm businesses?

11. Mechanisms of weather shock impacts
Calories/AE/day
Field crops
Vegetables
& fruits
Livestock products
Purchases
High rainfall
165.03*
91.21*
5.94
-17.90
Low rainfall
***
-63.14
-75.14***
355.30***
High temperature
41.21
119.8***
13.47**
-14.87
Highlands*high rainfall
77.9
-178.5
124.27
101.25
Highlands*low rainfall
217.29
408.90*
79.96
-61.15
Lowlands*high rainfall
1,695
-554
26.67
-116.4
Lowlands*low rainfall
1,142
646.3
147.5*
620.9
Lowlands*high temp
209.9
-411.6**
-63.11***
14.52
Households able to smooth consumption with a pivot to food market

12. Mitigating factors against low rainfall
Income per day
(1) (2)
High rainfall
-0.05
0.01
Low rainfall
-0.48***
-0.06
High temperature
0.05**
0.15*
Credit availability (lagged)
-0.43
Credit*low rainfall
0.39**
Participation in savings group
0.38*
Savings group*low rainfall
0.23**

13. Study #3 Rapid assessment of food situation in Kenya (Sept/Oct 2016)
Understand status and factors affecting maize supply
Current regional drought, La nina?
Francis Karin—Tegemeo Institute

14. Maize production in Kenya
Regions
Achieved 2015
2016 Long Rains Targets
2016 Totals-Estimates
Area LR
Prod LR
Total Area
Prod LR 2016 Ha
Bags (90 Kgs)
Rift Valley
593,982
18,907,821
618,470
19,968,960
602,320
16,764,639
Nyanza
228,638
4,572,589
240,080
5,203,650
226,410
2,680,350
Central/Nrb
112,314
2,000,622
118,200
2,105,325
99,125
1,763,055
Western
220,561
7,350,055
231,930
7,768,000
214,780
6,261,324
Eastern
242,900
2,014,467
254,430
2,861,460
208,290
1,508,600
Coast Region
143,668
2,294,485
151,010
2,535,750
126,480
1,094,190
North Eastern
 52,000
 130,000
 120,000  
Total
1,542,063
37,270,039
1,614,120
40,563,145
1,477,405
30,187,158
Domestic production from LR lower than expected: 30.2 million versus 40.6 million 90-kg bags
Mainly due to depressed rainfall and poor rainfall distribution
Little effect of disease and pests (head smut and MLND)
La Niña expected in the last quarter of 2016 (Oct to Dec): negative effects on production; thus reduced SR harvest.
Shortfall needs to be filled from imports, particularly from UG and TZ

15. Maize imports from Uganda
Busia
Suam/Kitale
2015
2016
Jan
21,516
52,365
9,678
2,927
Feb
193,491
39,177
10,399
8,576
Mar
192,331
9,269
14,002
9,828
Apr
165,719
8,625
7,424
689
May
107,371
13,060
19,582
3,000
Jun
76,963
9,836
17,828
1,116
Jul
126,950
58,327
11,357 -
Aug
215,402
104,039
3,466 37
Sep
168,974
16,859
7,204
Total
1,268,717
294,698
110,595
33,377
Decline
77%
70%
Overall
76%
Low prospects for imports from UG
TZ: export bans and permit restrictions

16. Prospects of imports from the region
FAO estimates: 36 countries (28 in Africa) are in need of external food assistance
Kenya, Uganda, Mozambique, Malawi, Burundi, DRC
Due to drought and persisting conflicts
Thus low prospects of getting maize supplies from the region
Implications?
Intra-regional trade
Easing trade restrictions
Distribution of food

17. Conclusion/recommendations
Current and future effects of climate variability and change are real (future effects may be larger)
Affecting different aspects of household welfare
Effects vary across agro-regions, livelihood activities and crops
Analysis of effects of climate variability & change and weather shocks and requires a comprehensive approach, considering
Different types of climate variables and shocks
Multiple aspects of welfare
Heterogeneous effects
Crops have different rainfall and temperature thresholds
Geography matters

18. Conclusion/recommendations
Heterogeneity of effects implies need to use a combination of mitigation and adaptation strategies
Building resilience and adaptation capacity
Reducing gas emissions
Climate smart agriculture
Extreme weather affects household welfare mainly through crop production
Policies to prioritize development of crops with enhanced tolerance for extreme weather

19. Conclusion/recommendations
During periods of low rainfall, non-farm economy may not serve as a safety for income
Agriculture-linked businesses?
Diversify to other businesses?
Bankable?
Access to markets is an important safety net for households during periods of low rainfall
Need for well functioning markets and ease of market access
Access to financial services has potential to improve household resilience to weather shocks
Ease of access: physical infrastructure; competitive market environment; easing of trade restrictions

20. Conclusion/recommendations
Where households lack capacity, policy makers need to remain vigilant of such shocks and be prepared to offer support
Need to strengthen early warning systems
Need for better use of information from the systems for early planning

21. “Climate change brings not only bad news but also a lot of potential
“Climate change brings not only bad news but also a lot of potential. The winners will be those who are prepared for change and know how to adapt.”- CIAT 2011
Thank you 19

22. • High rainfall: Cumulative millimeter pentads above
Key
variables:
WELFARE
• Net household (HH) income per adult equivalent per day
• HH is poor
• Poverty gap
• Poverty severity
• Calories available per
adult equivalent per day
• HH is ‘energy deficient’
EXTREME WEATHER
(main growing season)
• High rainfall: Cumulative
75 mm
millimeter pentads below
• Heat: Cumulative degree
• High winds: Cumulative
m/s
millimeter pentads above •
Low rainfall: Cumulative
15 mm
days above 32 °C (daytime)
wind speed days above 5 22