Climate Variability, Adaptation Strategy and Food Security in Malawi

1. Climate Variability, Adaptation Strategy and Food Security in Malawi 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

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

3. Background • 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 • Adaptation in the agricultural sector to climate change is imperative – requiring modification of farmer behaviour and practices

4. Background • 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 intercropping • soil and water conservation (SWC), • tree planting, • conservation agriculture • organic 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

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: • Role of climate change - rainfall and temperature • Role of institutions • Government interventions • 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: • 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 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 • 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. Descriptive statistics (1) Adaptation measures – in proportion NB: No data available on conservation agriculture

10. Descriptive statistics (2) Some explanatory variables

11. Empirical Results Estimated covariance matrix of the MVP regression equations

12. Barrier to adoption - Multivariate Probit model

13. Summary of Findings: Adoption • 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

14. Impact of adoption on maize yield 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.

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

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

17. Heterogeneous impact of adoption (ATT) – IV estimator Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are clustered at EA.

18. Summary of Findings: Impact • 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 impactin key dimensions such as land holding, gender and geographical location

19. Conclusions and Implications • 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

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