Diversification is in the Detail: Accounting for Crop System Heterogeneity to Inform Diversification Policies in Malawi and Zambia

Abstract

Crop diversification is a common agricultural policy objective. However, the determinants and impacts of crop diversification are heterogeneous and depend on a range of crop-specific characteristics. Index-based measurements of crop diversification, common in the agriculture economics literature, are unable to account for this heterogeneity. Using two national panel surveys from Malawi and Zambia, we develop a multinomial treatment effects model to examine the determinants of adopting seven discreet cropping systems and their impacts on maize productivity and crop income stability. The results of this approach are compared to those obtained when using a Gini–Simpsons index. Differences between the two empirical approaches highlight the importance of accounting for cropping system heterogeneity when conducting analyses of crop diversification to inform policy. For example, in Zambia, we show that levels of output market competition have no significant effect on diversification when diversification is measured as an index. However, at a cropping system level increased private trader activity is associated with a reduced probability of adopting subsistence-oriented cropping systems and an increased probability of adopting more productive and commercialised systems.

Acknowledgements

We thank the Flanders Cooperation which supported this analysis through the project entitled “Strengthening integrated adaptation planning and implementation in Southern Africa smallholder agricultural systems to support food security” grant number GDCP/SFS/001/FLA.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplementary Materials are available for this article which can be accessed via the online version of this journal available at https://doi.org/10.1080/00220388.2020.1769072.

Notes

1. The countries reviewed were Kenya, Tanzania, Malawi, Uganda, Rwanda, Zimbabwe, South Africa, Mozambique, Ethiopia, Zambia, Burundi, South Sudan, Madagascar, and Eswatini. Of these only Madagascar did not specifically mention diversification.

2. These include, among others, the Herfindahl Index, the Gini-Simpsons Index, the Herfindahl Index, and the Shannon spatial index.

3. Refers to Tables A1a–Tables A1c in the Appendix for the summary statistics.

4. For example, it is probable that a high level of diversification, often involving complex cropping systems, will be mainly adopted by high-skilled farmers with technical abilities. If the empirical model fails to account for this, the coefficient linked to the diversification variable will be upward biased, as it will likely capture the effect of the innate ability of the farmer independently of the cropping system adopted.

5. For Zambia, the leave out mean is constructed account similarities in land tenure. This is done by identifying whether the household holds a land size above or below the median of the sample and constructing the instrument using the average level of diversification of households in the same village holding similar land size.

6. The SPI index is defined over the agricultural/rainfall seasons of the two countries. This is 6 months for both Malawi and Zambia. The month of reference is the last month of the rainfall season for both countries, which is April for Zambia and Malawi.

7. The estimated coefficients show a similar when considering a threshold of 1.65 (results available under request).

Additional information

Funding

This work was supported by the Flanders Cooperation [grant number GDCP/SFS/001/FLA].