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

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

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

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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

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

► 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)

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Assessing Climate Impacts and CSA in Malawi

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.

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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

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-9Delayed Onset of Rain

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Rainy season (Nov-May)

Zomba

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Rainy season (Nov-May)

Zomba city

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Rainy season (Nov-May)

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Rainy season (Nov-May)

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Rainy season (Nov-May)

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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

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32 33 34 35 36-18

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-9Maximum Temperature CoV

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-9Average Maximum Temperature

24.05

24.60

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25.71

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27.38

27.93

28.49

29.04

29.60

Recent Temprature Patterns in Malawi

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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

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► 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

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

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CSA practices more likely to be adopted in presence of high climate variability in Malawi

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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***

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

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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.

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► 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

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

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Resources

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

Thank you!

For further information on the CSA

evidence-base :

www.fao.org/climatechange/epic