This document discusses a study on climate variability, adaptation strategies, and food security in Malawi. Some key points: - Malawi is highly vulnerable to climate change impacts, threatening food security. The study examines adoption of adaptation practices by subsistence farmers. - Using survey data, the study employs a multivariate probit model to analyze adoption of practices like intercropping, tree planting, and fertilizer use, accounting for interdependencies. - Results find climate, land/plot characteristics, wealth, social capital, and institutions influence adoption. Adoption generally increased maize yields, though impacts varied by province, gender, and land size. - The study provides insights into constraints on smallholder adaptation and

1. Solomon Asfaw
(Co-authors: Nancy McCarthy, Leslie Lipper, Aslihan Arslan and Andrea Cattaneo)
Food and Agricultural Organization (FAO)
Agricultural Development Economics Division (ESA)
Rome, Italy
ICABR Conference
June 18-21, Ravello, Italy
Climate Variability, Adaptation Strategy
and Food Security in Malawi

2. • Background
• Research questions
• Why do we do this?
• Methodology and Data
• What we find so far (results)?
• Conclusions
Outline

3. ► Malawi is ranked as one of the twelve most vulnerable
countries to the adverse effects of climate change - subsistence
farmers are most vulnerable to climate related stressors
Background
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Sum of all rainfall stations per year
► Adaptation in the agricultural sector to climate change is
imperative – requiring modification of farmer behaviour and
practices

4. ► At micro (farmer) level, potential adaptation measures include a
wide range of activities; most appropriate will be context specific
and considered climate-smart agriculture (CSA) option.
► In Malawi, measures with high priority in national agricultural plans
and high CSA potential include:
o maize-legume intercropping
o soil and water conservation (SWC),
o tree planting,
o conservation agriculture
o organic fertilizer
o Improved varieties and inorganic fertilizers
► Despite increasing policy prioritization and committed resources,
adoption rates are quite low and knowledge gaps exist as to the
reasons for this limited adoption
Background

5. Research Questions
► What are the binding constraints of adoption of
potential adaptation/risk mitigation measures?
► To what degree is there interdependence between
adoption of different practices at plot level?
► What is the effect of adoption on maize productivity?
► What is the distributional impact, particularly where
households are heterogeneous on key dimensions
such as land holding, gender and geographical
location?

6. Why do we do this?
►Limited research on adoption of multiple practices and little
understanding of complementarities and substitution across
alternative options; yet these are likely to be increasingly
important under climate change.
►The effect of bio-physical and climatic factors in governing
farmers’ adaptation decisions & how they are moderated
through local institutions/govt interventions is poorly
understood. Thus, we need analysis that incorporates:
o Role of climate change - rainfall and temperature
o Role of institutions
o Government interventions
o Bio-physical characteristics
►Limited understanding on the synergies and tradeoffs between
CSA options and food security

7. Estimation strategy (1)
►We use multiple maize plot observations to jointly analyze the
factors that govern the likelihood of adoption of adaptation
measures in Malawi
►A Multivariate Probit (MVP) model:
o There exist household and field level inter-relationships between
adoption decisions involving various adaptation measures
o The choice of technologies adopted more recently by farmers may be
partly depend on earlier technology choices --- path dependence
o Farm households face technology decision alternatives that may be
adopted simultaneously and/or sequentially as complements,
substitutes, or supplements
► Unlike the univarite probit model, MVP captures this inter-
relationship and path dependence of adoption
► Assumes that the unobserved heterogeneity that affects the
adoption of one of the practices may also affect the choice of
other practices
► Error terms from binary adoption decisions can be correlated

8. Data
►World Bank Living Standard Measurement Survey (LSMS-IHS) in
2010/2011 - 12,288 households and about 64% maize producers
►Household level questionnaire and community level survey –
location are recorded with GPS – link to GIS databases
►Historical rainfall and temperature estimates (NOAA-CPC) (1996-
2010)
►Soil Nutrient Availability (Harmonized World Soil Database)
►Malawi 2009 election results at EA level
►Institutional surveys at district level - supply side constraints
o Credit; extension and other information sources; agricultural input and
output markets; public safety nets and micro-insurance programs;
property rights; and donor/NGO programs and projects.

9. Variables
North
province
(N=1897)
Central
province
(N= 3697)
Southern
province
(N=5614)
Total
(N=11208)
Long term inputs
Maize-legume intercropping (1=yes) 0.10 0.07 0.35 0.22
Planting tree (1=yes) 0.51 0.27 0.42 0.39
Organic fertilizer (1=yes) 0.07 0.16 0.10 0.12
SWC measures (1=yes) 0.37 0.47 0.46 0.45
Short term inputs
Improved maize seed (1=yes) 0.55 0.53 0.47 0.50
Inorganic fertilizer (1=yes) 0.74 0.78 0.72 0.74
All five 0.001 0.001 0.002 0.001
None 0.03 0.04 0.03 0.03
Adaptation measures – in proportion
Descriptive statistics (1)
NB: No data available on conservation agriculture

10. Variables Mean Std. Dev.
Household demographics and wealth
Age of household head (years) 43.20 16.44
Gender of household head (1=male) 0.75 0.43
Household head highest level of education (years) 5.06 3.96
Livestock ownership (tropical livestock unit (TLU)) 0.61 2.58
Wealth index -0.31 1.73
Agricultural machinery index 0.47 1.29
Plot level characteristics
Land tenure (1= own, 0= rented) 0.90 0.30
Nutrient availability constraints (1-5 scale) 1.45 0.72
Land size (acre) 2.71 2.45
Slop of the plot (0=flat, 1=steep) 0.11 0.31
Climatic ariables
Coefficient of variation of precipitation (1996-2010) 0.25 0.038
Precipitation in the rainy season (mm) 710.6 101.3
Annual mean temperature (deg C) 21.8 1.8
Drought is a top three shock in the past year (1=yes) 0.43 0.49
Institutions and transaction cost indicators
Fertilizers distributed in MT by district per household 1.27 0.47
Distance to major district centre (Km) 118.04 85.82
Seed or fertilizer vender available in the community (1=yes) 0.30 0.45
Village development committees in the community (number) 2.12 3.03
Percentage of plots received extension advice at EA level 49.79 27.73
Collective action index 0.07 1.00
DPP vote as a share of total vote cast 0.69 0.24
Descriptive statistics (2)
Some explanatory variables

11. Estimated covariance matrix of the MVP regression equations
Empirical Results
Improved
seed
Inorganic
fertilizer
Maize-legume
intercropping
Tree
planting
SWC
measures
Inorganic fertilizer 0.227***
Legume intercropping -0.941*** 0.030
Tree planting 0.025 0.041** 0.015
SWC measures
0.028* 0.024 0.013 0.089***
Organic fertilizer 0.227*** -0.108*** 0.066*** 0.049** 0.087***
Likelihood ratio test of rho21 = rho31 = rho41 = rho51 = rho61 = rho32 = rho42 = rho52 =
rho62 = rho43 = rho53 = rho63 = rho54 = rho64 = rho65 = 0: chi2(15) = 2367.65 Prob > chi2
= 0.0000

12. Barrier to adoption - Multivariate Probit model
Improved
Seed
Inorganic
fertilizer
Organic
fertilizer
Legume
intercrop
Tree planting SWC
Coefficient of variation of precipitation (1996-2010) (+++) (---) (++) (--)
Precipitation in the 08/09 season (mm) (+++) (+++) (---) (---) (---)
Annual mean temperature in 08/09 year(deg C) (---) (---) (---)
Drought is a top three shock in survey year (yes=1) (---) (--) (+++) (+++) (+++)
Plot size (acre) (---) (+++) (+++) (+++)
Land tenure (1= own, 0= rented) (---) (---) (+++) (++) (+++)
Slop of the plot (1=steep/hilly) (---) (+++) (+++)
Nutrient availability constraint (1-4 scale, 5= non-soil) (+++) (---)
Wealth index (+++) (+++) (---)
Agricultural machinery index (+++) (+++) (+++) (---) (+++) (+++)
Livestock in TLU (+++) (---) (+++)
Seed and/or fertilizer vendor in EA (1=yes) (+) (++) (---) (---) (--)
Percentage of plots received extension advice at EA level (+++) (---) (---) (+++)
Distance to major centre (km) (---)
Number of village development committees (+++) (++) (+++) (++) (+++)
Collective action index (+++) (+++) (++) (+)
DPP votes as a hare of total votes case (+++) (---) (--) (+++)
Price of maize (MKW/kg) (+++) (+++) (-) (++)
Fertilizer distributed in MT by district per hh (+++) (+++) (---) (--)
Proportion of land covered by forest by district (+++) (+++)
Microfinance & donor agri projects operating in district (+++) (+) (---) (---) (+++)
MASAF wages paid out in district in 08/09 season (---) (+++) (+++) (+++) (---) (+++)
Northern Province (Ref: Southern province) (---) (---) (-) (+++) (---)
Central province (+) (---) (---) (+)

13. ► Adopting a specific practice is conditioned by whether another
practice has been adopted or not –interdependency between
adoption decision - complimentarity or substitutability
► Climate risk: Favorable rainfall increases probability of adopting
practices with short-term return; unfavorable rainfall increases
likelihood of adopting measures with longer term benefits.
► Land tenure: increases the likelihood to adopt strategies that will
capture the returns in the long run and reduces the demand for
short-term inputs.
► Social capital and supply side constraints: Collective action and
informal institutions matter in governing farmers adoption
decisions to adopt
► Plot characteristics and household wealth: are important
determinants of adoption of adaptation measures
Summary of Findings: Adoption

14. Variables
North
province
(N=1897)
Central
province
(N= 3697)
Southern
province
(N=5614)
Total
(N=11208)
Maize-legume intercrop
No 601.2 347.5 587.6 496.4
Yes 1164.7 460.1 821.7 807.8
Difference (%) 93.7(12.5)*** 32.4(4.3)*** 39.8(8.1)*** 62.7(16.7)***
Tree planting
No 693.3 350.5 667.1 546.5
Yes 628.1 370.7 675.8 594.3
Difference (%) -9.4(2.2)** 5.7(1.2) 1.3(0.3) 8.7(2.9)***
SWC measures
No 647.1 381.5 600.2 540.5
Yes 681.3 328.3 754.0 595.1
Difference (%) 5.3(1.1) -13.9(3.8)*** 25.6(5.5)*** 10.1(3.4)***
Improved seed
No 646.4 294.9 566.8 493.6
Yes 671.0 410.2 785.4 634.7
Difference (%) 3.8(0.8) 39.1(8.3)*** 38.6(7.9)*** 28.6(9.0)***
Inorganic fertilizer
No 536.5 267.2 339.6 352.7
Yes 701.8 380.4 797.2 636.8
Difference (%) 30.8(5.0)*** 42.3(6.7)*** 134.7(15.0)*** 80.5(15.9)***
Maize productivity by adoption status (kg/acre)
Note: Number of observations refers to the number of maize plots. *** p<0.01, ** p<0.05, * p<0.1. t-stat in parenthesis.
Impact of adoption on maize yield

15. Identification strategy (2)
► Random assignment of treatment and control not possible
► No panel data available
o Difference-in-Difference (DD) estimator
 Address time invariant unobservables
► Cross-sectional data
o PSM combined with inverse propensity weights (IPW) –
 Address only observable bias
o Instrumental variable (IV) strategy
 Address observable and unobservable bias

16. OLS Instrumental Variable (IV) strategy
Seed (1) Fertilizer (2) Legume (3) Trees (4) SWC (5)
Improved maize seed (1=yes) 0.135*** 0.611***
Log of inorganic fertilizer (kg/ha) 0.365*** 1.596*
Maize-legume intercropping (1=yes) 0.720*** 2.128*
Perennial trees (1=yes) 0.282*** 0.917
Soil and water conservation (1=yes) 0.034 0.661
Precipitation in the last rainy season (mm) (+++) (++) (+) (+++) (+++) (+++)
Annual mean temperature (deg C) (---) (---) (--) (---) (---)
Drought is a top three shock in survey year (yes=1) (---) (---) (--_) (---) (--)
Plot size (acre) (---) (--) (---) (---) (---) (---)
Slop of the plot (1=steep/hilly) (--) (-) (-)
Nutrient availability constraint (1-4 scale, 5= non-soil) (---) (---) (---) (---) (---) (--)
Wealth index (+++) (+++) (+++) (+++)
Education of the head (years) (+) (++) (+)
Education of the spouse (1=yes) (+++) (+++) (+++) (+++) (+++)
Age of the head (years) (--) (---) (--)
Gender of the head (1=male) (--) (-)
Excluded instruments
Coefficient of variation of precipitation (1996-2010) X X X X X
Seed and/or fertilizer vendor in EA (1=yes) X X
Percentage of plots received extension advice at EA level X X
Fertilizer distributed in MT by district per household X
Proportion of land covered by forest by district X
District agriculture extension officer per household X
Weak identification test (Wald F-stat) 43.34*** 21.55*** 11.92*** 19.80*** 24.92***
Over identification test (Henson J- stat) 0.86 0.89 1.22 0.009 0.30
Impact of adoption on maize yield (log kg/acre) – IV estimator

17. Improved
seed
Inorganic
fertilizer
Legume
intercropping
Tree
planting
SWC
measures
Province
North -0.26 -0.53 2.20* 0.22 7.81
Central 0.59 1.34*** -5.23** 0.29 0.19
South 0.94 1.32*** 0.63** 0.05 0.29
Gender of head
Male 1.29 0.63*** 2.29* 0.51 0.59
Female 2.09** 0.55*** 0.03 2.63** 1.05
Median land size
Small 1.24* 0.11 3.03** 0.81 1.28
Large 2.27* 1.27*** 1.48 1.13 -0.57
Heterogeneous impact of adoption (ATT) – IV estimator
Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are clustered at EA.

18. ► On average adoption of three of the five farm management
practices (short term) have a positive and statistically significant
impact on maize yield.
► Average precipitation is positively correlated with maize yield
whereas drought and high temperature are negatively
correlated
► Plot characteristics , household wealth and human capital are
positively correlated with maize productivity
► Heterogeneous impact in key dimensions such as land holding,
gender and geographical location
Summary of Findings: Impact

19. ► Place matters (and CC makes it even more important)! Plot characteristics,
agro-ecology, local institutions and climate regime key factors affecting adoption
of practices with adaptation potential
► Given importance of adopting a package of practices for adaptation (e.g. SLM);
need to get better understanding of complementarities/substitution- this
method is one approach
► Given importance of climate on adoption of practices with short
(seeds/fertilizer) vs. long (trees, SWC, legume) term returns; need to improve
access to reliable climate forecast information is key to facilitating adaptation -
farmers to new sources of information on climate variability will be important;
► Heterogeneity in yield benefits from adoption of different practices across farm
size, gender and agro-ecology – suggests possible heterogeneity in
synergies/tradeoffs between food security/adaptation.
► Not surprising that fertilizer/seeds gives maize yield effect, but we need to know
more about implications for yield variance. We have not estimated the impact
on reducing yield variability in the face of variable climate conditions
Conclusions and Implications

20. THANK YOU!
Contact: Solomon Asfaw
Email: solomon.asfaw@fao.org