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  

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

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?

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

Effect on crop revenue Variable All crops revenue Tea revenue Maize revenue Rainfall 0.007*** -0.0223*** 0.0043*** Squared rainfall -0.000*** 0.0001 Long term rainfall 0.033 -0.1277 0.0905 Mean temperature -0.5740*** 1.0372*** 0.0411 Squared temperature 0.0164 -0.0249* 0.0031 Long term temperature -8.2395*** 8.8997* -9.800*** Temperature threshold for tea is around 23.50C Effects vary by crop

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

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

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

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

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?

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 -548.89*** -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

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**

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

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    120,000 115.000  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

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

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

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

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

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

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

“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

• 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