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What determines farmers’ adaptive capacity? Empirical evidence from Malawi

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Abstract

This paper assesses farmers’ incentives and conditioning factors that hinder or promote adaptation strategies and evaluates their impact on crop productivity by using data from nationally representative sample households in Malawi. We employed multivariate probit (MVP) and multinomial treatment effect (MTE) techniques to model adoption decisions and their yield impact. Exposure to delayed onset of rainfall and greater climate variability was positively associated with the choice of risk-reducing agricultural practices such as tree planting, legume intercropping, and soil and water conservation (SWC) but reduced the use of inputs (such as inorganic fertilizer) whose risk reduction benefits are uncertain. Concerning household adaptive capacity, wealthier households were more likely to adopt both modern and sustainable land management (SLM) inputs and were more likely to adopt SLM inputs on plots that were under greater security of tenure. In terms of system-level adaptive capacity, rural institutions, social capital and supply-side constraints were key in governing selection decisions for all practices considered, but particularly for tree planting and both organic and inorganic fertilizer applications. A combination of practices gave rise to higher yields suggesting that this might be a course of action that would sustain growth of yield in Malawi in the future.

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Notes

  1. Climate smart agricultural practices are defined as those practices that increase adaptive capacity and resilience of farm production in the face of climate shocks thereby improving food security, and which can also mitigate greenhouse gas (GHG) emissions, mainly through increased carbon sequestration in soils (FAO, 2011)

  2. Conservation agriculture is also high in Malawi national agricultural priority plan and considered to have adaptation potential but we lack data on these practices and as a result they are not included in our analysis.

  3. In this paper we focus on the link between vulnerability and adaptive capacity; however, there is also a resilience literature which illustrates the characteristics of systems that achieve a desirable state in the face of change being applicable to social-ecological systems (Folke, 2006; Janssen et al., 2006). Adaptive capacity in the resilience literature (or adaptability) is the capacity of actors in the system to manage and influence resilience (Walker et al. 2004). Hence, adaptive capacity is a concept shared by the resilience and vulnerability literature (Engle, 2011); however, for empirical applications we find the vulnerability framework to be more informative.

  4. At the time of this study, the panel data was not available for analysis and could not be used.

  5. Average of a 10 km radius buffer of decadal sum of daily values per each enumeration area centroid. For more details on ARC2 algorithms see: http://www.cpc.ncep.noaa.gov/products/fews/AFR_CLIM/AMS_ARC2a.pdf

  6. Point extraction per each enumeration area centre point of values of average of a 50 km radius buffer of decadal values.

  7. The HWSD is based on a spatial layer with Soil Mapping Units (SMU) linked to a Microsoft Access .mdb file storing the various parameters for the SMUs. Each SMU is a combination of different subunits, without spatial attributes but showing a different area share. For more information see: http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/

  8. The Malawi Social Action Fund (MASAF) is a project designed to finance self-help community projects and transfer cash through safety net activities.

  9. Democratic Progressive Party (DPP) was the ruling party at the time and the main opposition party was the Malawi Congress Party (MCP). The variables created include vote counts in the constituencies that cover the IHS3 EAs, DPP votes as a share of total votes cast and the MCP votes as a share of total votes.

  10. We are quick to point out that the use of seed or fertilizer, which changes from year to year, can be different from trees and SWC which are more like capital items. The presence of these items on the farm often reflects past decisions much more than current decisions. Howeverx, in the context of Malawi we approach the decision on trees/SWC as a decision to maintain trees and SWC where population densities are high – and thus potentially significant opportunity costs to retaining trees, and even higher costs for maintaining SWC structures. So the existence of SWC/tree does capture the maintenance decision and retaining trees is a yearly decision given fuel wood opportunity costs as well as opportunity costs of land not cultivated in this densely populated context.

  11. Note that the notations for observed variables (V and W) in this adoption specification are the same as those in the output model specification above. The specific variables in these vectors, however, may differ in econometric estimation, as explained below.

  12. We defined onset of the rainy season as a period where 2 dekads of rainfall are greater or equal to 50 mm after December 1st (Tadross et al., 2009).

  13. The household wealth index is constructed using principal components analysis, which uses assets and other ownerships. In this specific case the following variables have been included: number of rooms per-capita in the dwelling, a set of dummy variables accounting for the ownership of dwelling, mortar/pestle (mtondo), bed, table, chair, fan, radio, tape/CD player, TV/VCR, sewing machine, paraffin/kerosene/electric/gas stove, refrigerator, bicycle, car/motorcycle/minibus/lorry, beer brewing drum, sofa, coffee table, cupboard, lantern, clock, iron, computer, fixed phone line, cell phone, satellite dish, air-conditioner, washing machine, generator, solar panel, desk, and a vector of dummy variables capturing access to improved outer walls, roof, floor, toilet, and water source. The household agricultural implement access index is also computed using principal components analysis and covers a range of dummy variables on the ownership of hand hoe, slasher, axe, sprayer, panga knife, sickle, treadle pump, watering can, ox cart, ox plough, tractor, tractor plough, ridger, cultivator, generator, motorized pump, grain mill, chicken house, livestock kraal, poultry kraal, storage house, granary, barn, and pig sty.

  14. The collective action index is constructed from community level indicators using principal components analysis and takes into account the number of activities where community members provided seed money to address the issue, number of activities where members gave money to actually undertake the activity, number of activities for which manual labor was provided, number of activities for which outside funding was sought, and a set of dummy variables accounting for member participation in school construction or maintenance, health clinic construction or maintenance, agricultural or forest or irrigation activities, and law enforcement activities.

  15. One explanation of inverse farm size productivity is related to errors in land measurements, however, contrary to earlier conjectures, Carletto et al. (2013) found that the empirical validity of the inverse relationship hypothesis was strengthened, not weakened, by the availability of better measures of land size collected using GPS devices in Uganda. Given that we also used plot measurements collected using GPS devices, our findings are consistent with Carletto et al. (2013).

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Acknowledgments

This research forms part of the Economic and Policy Innovation for Climate-Smart Agriculture (EPIC) Project (http://www.fao.org/climatechange/epic/en/), supported financially by EU and SIDA. We would also like to acknowledge the World Bank for sharing the Malawi IHS3 dataset with us and particularly Mr. Talip Kilic and Ms. Siobhan Murray of the World Bank for their valuable support during the construction of the dataset. We are grateful to Giulio Marchi, Geospatial Analyst at FAO, for his valuable support for the extraction of the climate data. The authors would also like to thank the staff at the Headquarters and the Malawi office of FAO for their comments and suggestions during the preparation of this paper. Errors are the responsibility only of the authors, and this paper reflects the opinions of the authors, and not the institutions which they represent or with which they are affiliated.

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  1. Food and Agricultural Organization of the United Nations, Agricultural Development Economics Division (ESA), Viale delle Terme di Caracalla, 00153, Rome, Italy

    Solomon Asfaw, Leslie Lipper, Aslihan Arslan & Andrea Cattaneo

  2. LEAD Analytics, Inc., Washington, DC, USA

    Nancy McCarthy

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Asfaw, S., McCarthy, N., Lipper, L. et al. What determines farmers’ adaptive capacity? Empirical evidence from Malawi. Food Sec. 8, 643–664 (2016). https://doi.org/10.1007/s12571-016-0571-0

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