www.fao.org/climatechange/epic This presentation was prepared to as background to the knowledge-sharing workshop held in Lilongwe, Malawi, on 5 Feb 2015 ©FAO

1. Solomon Asfaw
Nancy McCarthy, Aslihan Arslan, Andrea Cattaneo
Leslie Lipper, George Phiri et al.
Food and Agriculture Organization of the United Nations (FAO)
Economics and Policy Innovations for Climate-Smart Agriculture (EPIC)
TWG meeting
February 05, 2015
Lilongwe, Malawi
Climate/weather patterns in Malawi &
determinants of adoption of CSA

2. Outline of the presentation
1. CSA project overview and link to ASWAP
2. Climate/weather pattern in Malawi
3. Adoption and impact of CSA practices

3. The CSA project aims to build evidence-based agricultural
development strategies, policies and investment
frameworks for Zambia, Malawi and Vietnam to:
1. sustainably increase agricultural productivity and
incomes,
2. build resilience and the capacity of agricultural and
food systems to adapt to climate change, and
3. seek opportunities to reduce and remove GHGs
compatibly with their national food security and
development goals.
CSA Project Overview
3

4. The Building Blocks
of CSA logical chain
Evaluating CSA practices
Identify barriers
and enabling
factors
Managing
Climate Risk
Defining
coherent
policies
Guiding
Investments
Assessing climate impacts

5. Sustainable Agricultural Land & water Management
Our efforts focus more on sustainable land management.
Specifically how to improve adoption of SLM practices.
CSA project linkage with ASWAp
Technology Generation and Dissemination
Many action points: our work is most relevant for aspects
relating to dissemination of good agricultural practices to
increase productivity

6. ► Key policy relevant questions:
– How heterogeneous is climate risk in Malawi?
– What are the binding constraints of adoption of potential
CSA measures?
– What are the impact of adoption on productivity?
► World Bank Living Standard Measurement Survey (LSMS-
IHS) in 2010/2011 – location are recorded with GPS – link
to GIS database
► Linking rainfall & temperature data to Malawi Integrated
IHS Survey (1983-2012)
– Rainfall (1983-2012): Dekadal (10 days) rainfall data from
Africa Rainfall Climatology v2 (ARC2) of the National
Oceanic and Atmospheric Administration’s Climate
Prediction Center (NOAA-CPC) 6
Assessing Climate Impacts and CSA in Malawi

7. Climate Variables
► Rainfall:
– Growing Season Total
– Coefficient of variation (across 29 years)
– Onset of the rainy season: 2 dekads of ≥ 50mm rainfall
after November 1.
– Dry spells: # dekads with <20mm rain during
germination & ripening
► Temperature:
– Growing season average
– Growing season max
– Indicator if Tmax ≥ 28 °C
References:
Tadross et al. 2009. “Growing-season rainfall and scenarios of future change in southeast Africa:
implications for cultivating maize. “ Climate Research 40: 147-161.
Thornton P., Cramer L. (eds.) 2012. “Impacts of climate change on the agricultural and aquatic systems
and natural resources within the CGIAR’s mandate.” CCAFS Working Paper 23.
7

8. Recent Rainfall Patterns in Malawi (1983-2012)
Figure 1a. Coeff. of variation of
rainfall
Figure 1b. Mean rainfall Figure 1c. Delayed onset of rain
32 33 34 35 36
-18
-17
-16
-15
-14
-13
-12
-11
-10
-9
Delayed Onset of Rain
0.07
0.09
0.11
0.13
0.15
0.17
0.19
0.21
0.23
0.25
0.27

9. 400
650
900
1150
1400 83/84
87/88
91/92
95/96
99/00
03/04
07/08
11/12
Rainy season (Nov-May)
Zomba
400
650
900
1150
1400
Totalrainfall(mm)
83/84
87/88
91/92
95/96
99/00
03/04
07/08
11/12
Rainy season (Nov-May)
Zomba city
400
650
900
1150
1400
Totalrainfall(mm)
83/84
87/88
91/92
95/96
99/00
03/04
07/08
11/12
Rainy season (Nov-May)
Blantyre
400
650
900
1150
1400
83/84
87/88
91/92
95/96
99/00
03/04
07/08
11/12
Rainy season (Nov-May)
Blantyre city
400
650
900
1150
1400
Totalrainfall(mm)
83/84
87/88
91/92
95/96
99/00
03/04
07/08
11/12
Rainy season (Nov-May)
Phalombe
400
650
900
1150
1400
Totalrainfall(mm)
83/84
87/88
91/92
95/96
99/00
03/04
07/08
11/12
Rainy season (Nov-May)
Neno
Note: green dashed line is district's mean over time, red line are fitted values.
Rainfall pattern in selected districts of Malawi
(1983-2012)Rainfall pattern in selected districts of Malawi (1983-2012)
Recent Rainfall Patterns in Malawi
9

10. 32 33 34 35 36
-18
-17
-16
-15
-14
-13
-12
-11
-10
-9
Maximum Temperature CoV
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.03
0.03
32 33 34 35 36
-18
-17
-16
-15
-14
-13
-12
-11
-10
-9
Average Maximum Temperature
24.05
24.60
25.16
25.71
26.27
26.82
27.38
27.93
28.49
29.04
29.60
Recent Temprature Patterns in Malawi
10

11. First Message
1. There are significance differences in rainfall and
temperature variability across geographical
regions in Malawi and this have important
implications for agricultural planning.
2. Projection about future climate trend adds
another layer of uncertainty
11

12. 12
► Given heterogeneity in climate risk:
– What are the options to adapt to climate risk?
– And how they will be targeted?
► Adoption of potential CSA practices:
– Soil and Water Conservation
– Organic Fertilizer Use
– Tree Use
– Maize-legume Intercropping
– Inorganic Fertilizer Use
– Improved Maize Seeds
Adaptation to Climate Risk

13. Variables North Central South Total
Maize-legume intercropping 10 7 35 22
Planting tree 51 27 42 39
Organic fertilizer 7 16 10 12
SWC measures 37 47 46 45
Improved maize seed 55 53 47 50
Inorganic fertilizer 74 78 72 74
All six 0.1 0.1 0.2 0.1
None 3 4 3 3
Adoption of potential CSA measures on maize plots – in %
Adoption of Potential CSA Measures
NB: No data available on conservation agriculture from LSMS -ISA
13

14. CSA practices more likely to be adopted in
presence of high climate variability in Malawi
14
Rainfall variability
during the rainy
season
(1989-2011)
Avg. delay in the
onset of the rainy
season
(1989-2011)
Use of improved
maize varieties
0.0462** 0.0114
Maize-legume
intercropping
0.0816*** 0.2034***
Use of SWC
structures
0.1106*** 0.0429**
Tree planting 0.2368*** 0.2408***

15. Climactic variables, access to rural institutions and social
capital play an important role in adoption of most practices.
►Exposures to climate variability and delayed onset
increases use of SLM measures, but reduces the use
inorganic fertilizer.
►Collective action and institutions (esp. Extension) be
key in determining adoption of SLM practices
►Better tenure security increases the use of SLM
strategies and reduces inorganic fertilizer and improved
seed.
►Implications for targeting and overcoming barriers to
adoption at the household or systemic levels.
Summary of Findings: Adoption
15

16. Adoption
Difference (%)
Maize-legume intercrop 25.6 (3.3)***
Trees -3.5(0.6)
SWC measures -1.9(0.3)
Improved seed 37.8 (5.2)***
Inorganic fertilizer 83.0 (7.4)***
Impact of Adoption on Maize Yield
Maize productivity by adoption status (kg/acre)
Note: *** p<0.01, ** p<0.05, * p<0.1. t-stat in parenthesis.
16
► Effectiveness of practices varies by exposure to climatic risk
• Greater benefits from the SLM practices in areas of higher
exposure and sensitivity;
• Improved seed and fertilizer perform better in areas of lower
climate variability

17. Policy Implications
► Many activities in ASWAp have CSA properties
► A wide range of agricultural practices & changes to
management can be CSA but the best CSA options vary
by location & CC impact
►Improving CSA aspects of ASWAp will require much
greater attention to building local level capacity to
identify & implement best CSA options
►FISP clearly key levers – they can be improved to better
support ;
– Complementarity with SLM and other inputs
►Achieving SLM adoption requires more attention to
removing barriers - link to extension, collective action
and land tenure 17

18. Resources
Climate Variability, Adaptation
Strategies and Food Security in
Malawi
ESA Working Paper, FAO, June 2014
www.fao.org/3/a-i3906e.pdf

19. Thank you!
For further information on the CSA
evidence-base :
www.fao.org/climatechange/epic