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

Background Research questionsWhy do we do this?Methodology and DataWhat we find so far (results)?ConclusionsOutline

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 stressorsBackgroundAdaptation in the agricultural sector to climate change is imperative requiring modification of farmer behaviour and practices

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: maize-legume intercroppingsoil and water conservation (SWC), tree planting, conservation agricultureorganic fertilizer 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

Research QuestionsWhat 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?

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:Role of climate change - rainfall and temperatureRole of institutionsGovernment interventionsBio-physical characteristics

Limited understanding on the synergies and tradeoffs between CSA options and food security

Estimation strategy (1)We use multiple maize plot observations to jointly analyze the factors that govern the likelihood of adoption of adaptation measures in MalawiA Multivariate Probit (MVP) model:There exist household and field level inter-relationships between adoption decisions involving various adaptation measures The choice of technologies adopted more recently by farmers may be partly depend on earlier technology choices --- path dependence 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 adoptionAssumes 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

DataWorld 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 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.

Adaptation measures in proportionDescriptive statistics (1)NB: No data available on conservation agriculture

VariablesNorth province (N=1897)Central province (N= 3697)Southern province (N=5614)Total (N=11208)Long term inputsMaize-legume intercropping (1=yes)0.100.070.350.22Planting tree (1=yes)0.510.270.420.39Organic fertilizer (1=yes) 0.070.160.100.12SWC measures (1=yes)0.370.470.460.45Short term inputsImproved maize seed (1=yes)0.550.530.470.50Inorganic fertilizer (1=yes) 0.740.780.720.74All five0.0010.0010.0020.001None0.030.040.030.03

Descriptive statistics (2)Some explanatory variables

VariablesMeanStd. Dev.Household demographics and wealthAge of household head (years)43.2016.44Gender of household head (1=male)0.750.43Household head highest level of education (years)5.063.96Livestock ownership (tropical livestock unit (TLU))0.612.58Wealth index-0.311.73Agricultural machinery index0.471.29Plot level characteristics Land tenure (1= own, 0= rented)0.900.30Nutrient availability constraints (1-5 scale)1.450.72Land size (acre)2.712.45Slop of the plot (0=flat, 1=steep)0.110.31Climatic ariablesCoefficient of variation of precipitation (1996-2010)0.250.038Precipitation in the rainy season (mm)710.6101.3Annual mean temperature (deg C) 21.81.8Drought is a top three shock in the past year (1=yes)0.430.49Institutions and transaction cost indicatorsFertilizers distributed in MT by district per household1.270.47Distance to major district centre (Km)118.0485.82Seed or fertilizer vender available in the community (1=yes)0.300.45Village development committees in the community (number) 2.123.03Percentage of plots received extension advice at EA level49.7927.73Collective action index 0.071.00DPP vote as a share of total vote cast0.690.24

Estimated covariance matrix of the MVP regression equationsEmpirical Results

ImprovedseedInorganicfertilizerMaize-legume intercroppingTree plantingSWC measures

Inorganic fertilizer0.227***Legume intercropping-0.941***0.030Tree planting0.0250.041**0.015SWC measures0.028*0.0240.0130.089***Organic fertilizer0.227***-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

Barrier to adoption - Multivariate Probit model

Improved SeedInorganic fertilizerOrganic fertilizerLegume intercropTree plantingSWCCoefficient 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(+)(---)(---)(+)

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

Maize productivity by adoption status (kg/acre)Note: Number of observations refers to the number of maize plots. *** p

Identification strategy (2)Random assignment of treatment and control not possibleNo panel data availableDifference-in-Difference (DD) estimator Address time invariant unobservablesCross-sectional data PSM combined with inverse propensity weights (IPW) Address only observable biasInstrumental variable (IV) strategy Address observable and unobservable bias

Impact of adoption on maize yield (log kg/acre) IV estimator

OLSInstrumental Variable (IV) strategySeed (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***0.917Soil and water conservation (1=yes)0.0340.661Precipitation 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 instrumentsCoefficient of variation of precipitation (1996-2010) XXXXXSeed and/or fertilizer vendor in EA (1=yes)XXPercentage of plots received extension advice at EA levelXXFertilizer distributed in MT by district per householdXProportion of land covered by forest by districtXDistrict agriculture extension officer per householdXWeak identification test (Wald F-stat)43.34***21.55***11.92***19.80***24.92***Over identification test (Henson J- stat)0.860.891.220.0090.30

Heterogeneous impact of adoption (ATT) IV estimatorNote: *** p

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

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

THANK YOU!

Contact: Solomon AsfawEmail: solomon.asfaw@fao.org

No data on conservation agriculture and soil and water conservation practices

*No data on conservation agriculture and soil and water conservation practices

*Farmers are more likely to adopt a mix of measures to deal with a multitude of production constraints than adopting a single practice. Past studies assessed the specific technology adoption decision (fertilizer or SWC or improved seed), which fails to account for complementarities and/or substitutability among different practices. Some recent empirical studies of technology adoption decisions assume that farmers consider a set (or bundle) of possible technologies and choose the particular technology bundle that maximizes expected utility. Thus, the adoption decision is inherently multivariate and attempting univariate modeling excludes useful economic information contained in interdependent and simultaneous adoption decisions

*For this study, we primarily focus on households producing maize crop and use the plot level information to conduct the analysis.

*Malawi Social Action Fund (MASAF) is a Project designed to finance self-help community projects and transfer cash through safety net activities.*Adoption of a specific practice is conditioned by whether another practice in the subset has been adopted or not *A valid measure of the impact of adoption of these practices should compare outcomes in households (plots) that adopted the practice to what those outcomes would have been had the same households (plots) not adopted any of the practices - the construction of this unobserved counterfactual is the basic dilemma of impact evaluation. Measuring impact as the difference in mean outcomes between all households adopting the practice and those not adopting the practices, even controlling for other characteristics, may give a biased estimate of adoption impact. This bias arises if there are unobserved characteristics that affect the probability of adoption of the practices which are also correlated with the outcome of interest, in our case maize yield. In the absence of the baseline which is the case for Malawi, the instrumental variables (IV) technique helps to control for these sources of endogeneity.

*Adoption of a specific practice is conditioned by whether another practice in the subset has been adopted or not *Increasing yields is just one of the reasons to adopt a technology, reducing downside loss is the other. In MW, and to some extent ZM, unfortunately for us (but not the farmers), the survey year was pretty close to "normal" across the country, with no farmers facing 2 standard deviations below or above the mean, a couple 1 1/2 SD's... But, if nothing else this needs to be clarified as one (compelling) reason why farmer's adopt certain practices even if they otherwise appear to have limited impact on staple food crop yields.

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