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. Andrea Cattaneo
Solomon Asfaw, Romina Cavatassi, Nancy McCarthy, Josh Graff-
Zivin, Adriana Paolantonio, Leslie Lipper
Technical Workshop
February 6, Lilongwe, Malawi
Diversification & Climate Risk Management
Strategies: Evidence from Rural Malawi

2. Outline of the presentation
Part 1 –Rationale of Analysis & Background
Part 2 – Extreme events: how to prepare?
Part 3 – How to adapt in non-extreme cases?
Part 4 – Looking beyond the farm to reduce risk
Part 5 – Bringing all the pieces together

3. 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
Options:
Livelihood diversification,
safety nets, insurance,
farm management
practices
Responding to
extreme events
Managing
increased climate
uncertainty

4. 1. Maize self-sufficiency through increased maize
productivity and reduced post harvest losses
2. Diversification of food production and dietary
diversification for improved nutrition at household
level with focus on Crops, Livestock, and Fisheries
3. Risk management for food stability at national level
ASWAp: Components of Food Security and
Risk Management Focus Area

5. ►Diversification is a central element of risk
management
►Farmers diversify to respond to adverse
shocks (“push”) or because of better
opportunities available off-farm (“pull”)
►We distinguish between farm households that
do not have the option to diversify their
income off-farm, and those that do
What strategy to provide new insights (1)

6. ►For those that cannot diversify off-farm we
examine how to address on-farm:
the impact of changes in climatic variability
and options to address it on-farm
Effects of extreme events leading potentially to
total yield loss
►For those households that can diversify off-farm
we examine options using an econometric
analysis that looks at the role of income
diversification in addressing climatic variability
What strategy to provide new insights (2)

7. Database
We use four data-sets:
1. World Bank Living Standard Measurement Survey
(LSMS-IHS) in 2010/2011 – 12,288 households
o Household level questionnaire and community level
survey linked to GIS databases
2. Historical rainfall and temperature estimates (NOAA-
CPC) for 1983-2012
3. SLM cost data collected by the Climate Smart
Agriculture (EPIC) project
4. Institutional surveys at district level
o Credit, extension and other information sources,
agricultural input and output markets, public safety nets
programs, and donor/NGO programs and projects

8. Climate in Malawi
Figure 1a. Mean rainfall 1983-2012 (mm/year) Figure 1b. Coefficient of variation of rainfall 1983-2012

9. Climate is a determinant of
household vulnerability in Malawi
• Consumption per capita is lower in environments with greater
long term climate variability
• Higher long term mean rainfall is associated with lower
vulnerability to poverty.
• Deviation below the long term mean rainfall pattern in the last
season, increases vulnerability to poverty.
• Other significant variables for reducing vulnerability are
higher household wealth, and access to institutions such as
extension, credit, fertilizer subsidies and social safety nets.

10. Outline of the presentation
Part 1 –Rationale of Analysis & Background
Part 2 – Extreme events: how to prepare?
Part 3 – How to adapt in non-extreme cases?
Part 4 – Looking beyond the farm to reduce risk
Part 5 – Bringing all the pieces together

11. ►how does the projected increase of extreme climate events
(e.g. drought, floods, extreme high temperatures) affect
smallholder adoption of SLM techniques to improve resilience?
►Examples of SLM techniques considered in this model are
box/tied ridges, infiltration trenches, grass embankments, etc
►We distinguish between a case where farmers cooperate in
adopting SLM practices, creating spillover effects, and a case
where they do not
►Examine four potential policy interventions to favor SLM
adoption including interventions that lead to cooperative
outcomes, access to social safety nets (DRR), payments for
carbon sequestration, and access to input subsidies.
Extreme events & SLM adoption for resilience

12. Safety nets may increase adoption of SLM
► Countervailing effects of safety net coverage:
i. reduces loss exposure (decreasing marginal benefit of SLM in
reducing losses)
ii. increases marginal benefit to expanding production (leading to
increase in all inputs, also SLM)
► If farmers cooperate safety nets are more likely to have positive effects on
SLM adotpion (result less dependent on parameter values)
Conventional
inputs
SLMinputs

13. Outline of the presentation
Part 1 –Rationale of Analysis & Background
Part 2 – Extreme events: how to prepare?
Part 3 – How to adapt in non-extreme cases?
Part 4 – Looking beyond the farm to reduce risk
Part 5 – Bringing all the pieces together

14. ►Extreme events, are usually rare. What decisions and
policies are appropriate under “marginal” changes in
climate?
►Although climate change is already happening, farmers
and policymakers have incomplete information on how it
affects local conditions and what is best course of action.
►Diversification is an important autonomous form of
adaptation to risk. How do policies affect incentives to
diversify?
►How are budgetary costs of policies affected by lack of
information?
Climate Uncertainty, Information, and
Adaptation

15. Basic Structure of the “marginal” model
• Farmer Objective: choose staple crop and technique
(conservation or traditional) plus up to one diversification
crop to maximize utility within a given Malawian AEZ
subject to a minimum area being dedicated to staples
• Staple Crops: Maize Local & Maize Hybrid
• Diversification Crops: Groundnut Chalimbana, Groundnut
CG7, Beans, & Pigeon Peas
• Based on variance of yield as function of temperature
and rainfall, and covariance of yields between staple
and diversification crops
• Conservation techniques: SWC, Intercropping,
SWC+Intercropping (staple crops only)

16. AEZs in model based on
FAO/IIASA methodology:
Mean Standard
Deviation
tropic-
warm/semiarid
Temperature 22.51 1.41
Rainfall 34.47 34.78
tropic-
warm/subhumid
Temperature 22.98 1.50
Rainfall 37.77 40.17
tropic-cool
Temperature 20.89 1.25
Rainfall 39.85 34.70
tropic-cool: combines tropic-cool/semiarid (light blue) & tropic-
cool/subhumid (green)

17. Important differences across AEZs in Malawi
► It appears that the optimal resource allocation is not that different under
existing climatic conditions
Profit
(MKW/ha)
Crop Choice
Conservation
technique
Baseline 5,578 60% Local Maize & 40% Beans Intercropping
Rain SD +30% 2,430 80% Local Maize & 20% Beans Intercropping
Temp SD + 30% 6,056 90% Hybrid Maize & 10% Groundnut CG7 Intercroping + SWC
Baseline 5,859 60% Local Maize & 40% Beans SWC
Rain SD +30% 7,950 60% Local Maize & 40% Groundnut CG7 Intercropping
Temp SD + 30% 3,627 60% Local Maize & 40% Beans SWC
Baseline 10,719 60% Local Maize & 40% Beans Intercropping
Rain SD +30% 4,980 70% Local Maize & 30% Beans Intercropping
Temp SD + 30% 7,944 90% Hybrid Maize & 10% Groundnut Chalim. Intercropping
Tropic warm/
semi-arid
Tropic warm/
sub-humid
Tropic cool
/semiarid &
subhumid
► With increased climate variability there is a tendency to reduce land
diversification – is maize the safe option?
► Increased climate variability can have large impact on mean profit, and on
optimal cropping decisions

18. Policies affect diversification
► Results are for Tropical Warm/ semi-arid AEZ
Profit
(MKW/ha)
Crop Choice
Conservation
technique
Baseline 5,578 60% Local Maize & 40% Beans Intercropping
Rain SD +30% 2,430 80% Local Maize & 20% Beans Intercropping
Temp SD + 30% 6,056 90% Hybrid Maize & 10% Groundnut CG7 Intercroping + SWC
Baseline 5,880 60% Local Maize & 40% Beans Intercropping
Rain SD +30% 2,645 70% Local Maize & 30% Beans Intercropping
Temp SD + 30% 6,056 90% Hybrid Maize & 10% Groundnut CG7 Intercroping + SWC
Baseline 6,378 80% Local Maize & 20% Pigeon Pea SWC
Rain SD +30% 3,202 80% Local Maize & 20% Pigeon Pea Intercropping
Temp SD + 30% 5,495 90% Hybrid Maize & 10% Pigeon Pea Intercroping + SWC
No policy
Revenue floor
safety net
Rainfall index
insurance
► Both revenue floor and rainfall index insurance can work to smooth
income; however, the budgetary costs depend on farmer information
► `The different policies may reduce incentives to diversify, but not in a
dramatic way.

19. Anticipated vs. unanticipated climate change
► Mean profits can be greatly affected by lack of information under
increased variability of rainfall in sub-humid areas
► An increased variability of temperature in semi-arid areas can have a
negative effect if not anticipated
0
2,000
4,000
6,000
8,000
10,000
12,000
Baseline
RainSD+30%
TempSD+30%
Baseline
RainSD+30%
TempSD+30%
Baseline
RainSD+30%
TempSD+30%
Tropic warm/ semi-
arid
Tropic warm/ sub-
humid
Tropic cool /semiarid
& subhumid
Anticipated climate
change
Unanticipated
climate change

20. ►Policies to address climate risk need to be tailored to
different AEZs
►Information on climate variability available to farmers is
an important element of policy performance
►Proposed policies do not seem to overly affect the
incentive to diversify
Policy implications: risk management under
“marginal” climate change

21. Outline of the presentation
Part 1 –Rationale of Analysis & Background
Part 2 – Extreme events: how to prepare?
Part 3 – How to adapt in non-extreme cases?
Part 4 – Looking beyond the farm to reduce risk
Part 5 – Bringing all the pieces together

22. ► Up to here we have looked at policies relating to on-farm
strategies to address climate change because many
smallholder households’ options are “confined” to the farm
► However, historically, off-farm income diversification has
played an important role in improving rural household
welfare
► We examine determinants of diversification accounting for
the fact that farmers diversify to respond to adverse shocks
(“push”) or because of better opportunities available off-
farm (“pull”)
► Constructed diversification indices for cropland, labour, and
income and estimate the role of institutions, household
characteristics, and climate variables in driving diversification
Determinants of Diversification

23. Diversification Indices: Concepts
On-farm diversification can be expressed by:
Land diversification index: based on number of crop species planted
and the area allocated during the 2009-10 agricultural season
Labour diversification based on person-days per year allocated by all
household members to: on-farm agriculture, off-farm wage labour and
self-employment in household enterprise.
Including off-farm diversification is done by computing:
Income diversification index measured by assessing the share of
farm income derived from 9 different possible sources, ranging from
crop, fishery and livestock incomes, to off-farm labor, rents,
remittances, and cash transfers.
We can then estimate the relative importance of different
forms of diversification in improving household welfare

24. Margalef Index
Figure 2a. Cropland diversification Figure 2b. Labour diversification Figure 2c. Income diversification
Geographical distribution of land, labour
and income diversification

25. Summary: Determinants of Diversification
►Labour, income and cropland diversification is higher in
environments with greater climate variability
►Higher mean rainfall is associated with greater
diversification of income
►In general, those with greater household wealth have
greater levels of diversification, indicating that these are
“pull” factors
►Access to institutions such as extension, credit and
fertilizer subsidies leads to greater labour and income
diversification.

26. Variance
Expected
consumption
Labour diversification index 0.114* 0.610***
Income diversification index -0.154*** 0.429***
Cropland diversification index 0.013 0.186***
Coefficient of variation of rainfall, 1983-2010 -0.375** -1.212***
Long term mean rainfall, 1983-2010 (mm) -0.000 0.000***
Rainfall anomaly, 2009-10 rainy season 0.165*** -0.271***
Agricultural extension and development officers per household in the
district 0.002 0.009***
Number of microfinance institution 0.008*** 0.024***
Fertilizers distributed per household (MT) -0.003 0.004
ln(MASAF wages paid in 2008-09 season (million MKW/HH)) -0.590 12.542***
Microfinance*CV rainfall
Safety-net*CV rainfall
Extension service*CV rainfall
Fertilizer distributed*CV rainfall
Observations 7,020 7,020
R-squared 0.017 0.442
Note: Robust standard errors in parentheses based on EA level clusters. *** p<0.01, ** p<0.05, * p<0.1.
How do different forms of diversification perform?
Effect of diversification on vulnerability components: variance of consumption
(𝑽 𝑪𝒊 ) and expected consumption per capita (𝑬(𝑪𝒊))

27. ►On-farm Labour diversification leads to higher, but
also less stable consumption; overall the impact is
still to reduce vulnerability to poverty
►Income diversification opportunities should be
prioritized, since it leads to higher and more stable
consumption
►Access to extension and fertilizer subsidies have the
strongest positive impacts on income diversification
of the institutions considered
►A shock such as a rainfall anomaly leads to lower
future expected consumption and to higher
variability
Summary: Effects of Diversification on Welfare

28. Outline of the presentation
Part 1 –Rationale of Analysis & Background
Part 2 – Extreme events: how to prepare?
Part 3 – How to adapt in non-extreme cases?
Part 4 – Looking beyond the farm to reduce risk
Part 5 – Bringing all the pieces together

29. ► For extreme events: important to have policies & institutions to
support effective coordination and cooperation for adoption of
SLM to increase resilience
►For “Marginal” changes in climate: policy options need to be
tailored to agro-ecological conditions, and better information on
changes in climate variability is crucial in reducing losses through
changes in cropping decisions and SLM practices
►Policies and institutions play an important role in determining
levels diversification, and their outcome & costs depends on
information available to framers
►Income diversification opportunities should be prioritized, since it
has the strongest impacts on reducing vulnerability to poverty
• Among institutions, access to extension and fertilizer subsidies have
the strongest positive impacts on income diversification
Overall Conclusions and Policy Implications

30. Knowledge Sharing
• Results for extreme events & for “marginal” are very
preliminary: would be very interested in establishing
a dialogue to improve the results and tailor to policy
needs
• Availability of data: Data is available for interested
researchers
• Availability of simulation model: The “marginal”
climate change model is implemented in Excel for
ease of use

31. Thank you!
If interested in the CSA evidence-base :
www.fao.org/climatechange/epic