Skip to main content

Does minimum tillage improve the livelihood outcomes of smallholder farmers in Zambia?


Minimum tillage (MT) is a farming practice that reduces soil disturbance by limiting tillage only to planting stations. MT is an integral part of Climate Smart Agriculture aimed at raising agricultural productivity, improving farmer livelihoods and building climate resilient farming systems in sub-Saharan Africa. However, there are questions on its suitability for smallholder farmers in the region. This paper assesses the impacts of MT on crop yield and crop income using an endogenous switching regression (ESR) model applied to cross sectional data from 751 fields, of which 17% were under MT in Zambia. The ESR framework accounts for heterogeneity in the decision to adopt MT or not and consistently predicts the outcomes of adopters and non-adopters had they not adopted and adopted, respectively. The results suggest that adopting MT was associated with an average yield gain for maize, groundnut, sunflower, soybean and cotton of 334 kg/ha but it had no significant effects on crop income (from sales and for subsistence) of households in the short-term. These results are partly explained by partial adoption: even among adopters, only 8% of cultivated land was under MT. In these circumstances, although MT confers some yield benefits, the gains may be insufficient to offset the costs of implementation and translate into higher incomes and better livelihood outcomes in the short-term. Additional costs associated with MT include implements, herbicides, and labor for weed control and for land preparation. Assumptions of labor saving from preparing land in the dry season and cost savings by reduced fuel use and weed pressure are aspirational because of the prevalent customary land tenure and communal grazing systems, and because mechanization and the use of herbicides to control weeds remain low among smallholders. Nevertheless, if the longer-term productivity gains from MT are large enough, these may offset the higher implementation costs of MT due to economies of scale and may eventually result in improved incomes and food security. These findings may help to explain the perceived low uptake rates for MT in Zambia and call for lowering implementation costs through extension specific to MT and by adapting MT to local contexts.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2


  1. Because MT involves many different possible tillage practices, these components may have different cost implications. However, data on the direct costs of each possible component were not collected in the survey.

  2. Defined in the Zambian context as the use of reduced or zero mechanical disturbance of the soil through animal-draught or mechanized ripping, zero tillage with jab planters or dibble sticks or planting basins made by hand hoe (Haggblade and Tembo 2003).

  3. I did not use the other two CA practices (crop rotation and residue retention) to focus on the full CA package because the joint uptake of all the three CA principles including MT is much lower (at 1.7%) compared to 17% for MT alone in the sample. Crop rotation and residue retention, are complementary to MT.

  4. MT is generally considered risk reducing, but due to data limitations, risk is not formally considered in this paper.

  5. Due to budget and time constraints, this study was only a cross section and not panel. The latter would have been more appropriate.

  6. This underlies the logic of the Di Falco et al. (2011) IV admissibility test. Because the IV should affect the outcome only through the treatment, it therefore follows that the IV should not directly affect outcomes even for the untreated subsample. This result should hold by construction for the treated sample if the IV is relevant and admissible.

  7. I also estimated the Local Average Treatment Effects (LATE) because the ATT may not be so informative since the adoption of MT is low. The LATE results from Two Stage Least Squares (2SLS) following (Wooldridge 2010) are available from the author upon request. The ATT is still relevant in this case because 17% of the field plots in the sample used MT. Whether that is low adoption at the field level is an open question. The ATT results are better than the LATE results.

  8. cattle =0.7, donkey = 0.5, pigs = 0.2, goats =0.1, chicken = 0.01, duck = 0.06.

  9. The asset value was computed as the sum of the quantity of productive assets, e.g., ploughs, ox-carts, lorries, and bicycles, and their market prices.

  10. I also estimated a LATE as a possible better impact measure compared to ATT on account that MT adoption was low in the sample. The LATE results (available from the author) were not better.


  • Abdulai, A., & Huffman, W. (2014). The adoption and impact of soil and water conservation technology: An endogenous switching regression application. Land Economics, 90(1), 26–43.

    Article  Google Scholar 

  • Ainembabazi, J. H., & Angelsen, A. (2014). Do commercial forest plantations reduce pressure on natural forests? Evidence from forest policy reforms in Uganda. Forest Policy and Economics, 40, 48–56.

    Article  Google Scholar 

  • Alem, Y., Eggert, H., & Ruhinduka, R. (2015). Improving welfare through climate-friendly agriculture: The case of the system of rice intensification. Environmental and Resource Economics, 62(2), 243–263.

    Article  Google Scholar 

  • Andersson, J. A., & D'Souza, S. (2014). From adoption claims to understanding farmers and contexts: A literature review of conservation agriculture (CA) adoption among smallholder farmers in southern Africa. Agriculture, Ecosystems and Environment, 187, 116–132.

    Article  Google Scholar 

  • Arslan, A., McCarthy, N., Lipper, L., Asfaw, S., & Cattaneo, A. (2014). Adoption and intensity of adoption of conservation farming practices in Zambia. Agriculture, Ecosystems and Environment, 187, 72–86.

    Article  Google Scholar 

  • Arslan, A., McCarthy, N., Lipper, L., Asfaw, S., Cattaneo, A., & Kokwe, M. (2015). Climate smart agriculture? Assessing the adaptation implications in Zambia. Journal of Agricultural Economics, 66(3), 753–780.

    Article  Google Scholar 

  • Asfaw, S., Shiferaw, B., Simtowe, F., & Lipper, L. (2012). Impact of modern agricultural technologies on smallholder welfare: Evidence from Tanzania and Ethiopia. Food Policy, 37(3), 283–295.

    Article  Google Scholar 

  • Blinder, A. S. (1973). Wage discrimination: Reduced form and structural estimates. The Journal of Human Resources, 8(4), 436–455.

    Article  Google Scholar 

  • Brown, B., Nuberg, I., & Llewellyn, R. (2017). Negative evaluation of conservation agriculture: Perspectives from African smallholder farmers. International Journal of Agricultural Sustainability, 15, 467–481.

    Article  Google Scholar 

  • de Janvry, A., Fafchamps, M., & Sadoulet, E. (1991). Peasant household behavior with missing markets: Some paradoxes explained. Economic Journal, 100(409), 1400–1417.

    Article  Google Scholar 

  • Di Falco, S., Veronesi, M., & Yesuf, M. (2011). Does adaptation to climate change provide food security? A micro-perspective from Ethiopia. American Journal of Agricultural Economics, 93(3), 829–846.

    Article  Google Scholar 

  • Droppelmann, K. J., Snapp, S. S., & Waddington, S. R. (2017). Sustainable intensification options for smallholder maize-based farming systems in sub-Saharan Africa. Food Security, 9(1), 133–150.

    Article  Google Scholar 

  • El-Shater, T., Yigezu, Y. A., Mugera, A., Piggin, C., Haddad, A., Khalil, Y., et al. (2016). Does zero tillage improve the livelihoods of smallholder cropping farmers? Journal of Agricultural Economics, 67, 154–172.

    Article  Google Scholar 

  • Feder, G., Just, R. E., & Zilberman, D. (1985). Adoption of agricultural innovations in developing countries: A survey. Economic Development and Cultural Change, 33(2), 255–298.

    Article  Google Scholar 

  • Giller, K. E., Witter, E., Corbeels, M., & Tittonell, P. (2009). Conservation agriculture and smallholder farming in Africa: The heretics’ view. Field Crops Research, 114, 23–34.

    Article  Google Scholar 

  • Grabowski, P. P., Haggblade, S., Kabwe, S., & Tembo, G. (2014). Minimum tillage adoption among commercial smallholder cotton farmers in Zambia, 2002 to 2011. Agricultural Systems, 131, 34–44.

    Article  Google Scholar 

  • Haggblade, S., & Tembo, G. (2003). Development, diffusion and impact of conservation farming in Zambia. Food Security Research Project working paper # 8. Lusaka: Food Security Research Project.

  • Heckman, J., Tobias, J. L., & Vytlacil, E. (2001). Four parameters of interest in the evaluation of social programs. Southern Economic Journal, 68(2), 211–223.

    Article  Google Scholar 

  • IMF (2012). Sub-Saharan Africa; Maintaining Growth in an Uncertain World. Regional Economic Outlook. Washington, DC: International Monetary Fund.

  • IPCC. (2014). Impacts, Adaptation, and Vulnerability. Part B: Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In V. R. Barros, C. B. Field, D. J. Dokken, M. D. Mastrandrea, K. J. Mach, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P. R. Mastrandrea, & L. L. White (Eds.), Climate Change 2014. Cambridge: Cambridge University Press.

  • Jahnke, H. (1982). Livestock production systems and livestock development in tropical Africa. Kiel Germany: Kieler wissenschaftsverlag vauk.

  • Jaleta, M., Kassie, M., Tesfaye, K., Teklewold, T., Jena, P. R., Marenya, P., et al. (2016). Resource saving and productivity enhancing impacts of crop management innovation packages in Ethiopia. Agricultural Economics, 47(5), 513–522.

    Article  Google Scholar 

  • Jann, B. (2008). The blinder-Oaxaca decomposition for linear regression models. Stata Journal, 8(4), 453–479.

    Google Scholar 

  • Jayne, T. S., Sitko, N. J., Mason, N. M., & Skole, D. (2018). Input subsidy programs and climate smart agriculture: Current realities and future potential. In L. Lipper, N. McCarthy, D. Zilberman, S. Asfaw, & G. Branca (Eds.), Climate smart agriculture: Building resilience to climate change (pp. 251–273). Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Kassie, M., Shiferaw, B., & Muricho, G. (2011). Agricultural technology, crop income, and poverty alleviation in Uganda. World Development, 39(10), 1784–1795.

    Article  Google Scholar 

  • Kuntashula, E., Chabala, L. M., & Mulenga, B. P. (2014). Impact of minimum tillage and crop rotation as climate change adaptation strategies on farmer welfare in smallholder farming systems of Zambia. Journal of Sustainable Development, 7(4), 95–110.

    Article  Google Scholar 

  • Kuteya, A.N., Lukama, C., Chapoto, A. & Malata, V. (2016). Lessons Learnt from the Implementation of the E-voucher Pilot. Indaba Agricultural Policy Research Institute Policy Brief No. 8. Lusaka: IAPRI.

  • Lokshin, M., & Sajaia, Z. (2004). Maximum likelihood estimation of endogenous switching regression models. Stata Journal, 4(3), 282–289.

    Google Scholar 

  • Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Mazvimavi, K., (2011). Socio-economic analysis of conservation agriculture in southern Africa. Network paper no. 2. Food and Agriculture Organization of the United Nations, Regional Emergency Office for Southern Africa, Rome, Italy. Available

  • Ngoma, H., Mason, N. M., & Sitko, N. J. (2015). Does minimum tillage with planting basins or ripping raise maize yields? Meso-panel data evidence from Zambia. Agriculture, Ecosystems and Environment, 212, 21–29.

    Article  Google Scholar 

  • Ngoma, H., Mulenga, B. P., & Jayne, T. S. (2016). Minimum tillage uptake and uptake intensity by smallholder farmers in Zambia. African Journal of Agricultural and Resource Economics, 11(4), 249–262.

    Google Scholar 

  • Oaxaca, R. (1973). Male-female wage differentials in urban labor markets. International Economic Review, 14(3), 693–709.

    Article  Google Scholar 

  • Pannell, D. J., Llewellyn, R. S., & Corbeels, M. (2014). The farm-level economics of conservation agriculture for resource-poor farmers. Agriculture, Ecosystems and Environment, 187, 52–64.

    Article  Google Scholar 

  • Powlson, D. S., Stirling, C. M., Jat, M. L., Gerard, B. G., Palm, C. A., Sanchez, P. A., & Cassman, K. G. (2015). Reply to 'No-till agriculture and climate change mitigation'. Nature Climate Change, 5(6), 489–489.

    Article  Google Scholar 

  • Powlson, D. S., Stirling, C. M., Thierfelder, C., White, R. P., & Jat, M. L. (2016). Does conservation agriculture deliver climate change mitigation through soil carbon sequestration in tropical agro-ecosystems? Agriculture, Ecosystems and Environment, 220, 164–174.

    Article  CAS  Google Scholar 

  • Shumba, E. M., Waddington, S. R., & Rukuni, M. (1992). Tine tillage, with atrazine weed control, to permit earlier planting of maize by smallholder farmers in Zimbabwe. Experimental Agriculture, 28, 443–452.

    Article  CAS  Google Scholar 

  • Singh, I., Squire, L., & Strauss, J. (1986). Agricultural household models: Extensions, applications, and policy. USA: Johns Hopkins University Press.

    Google Scholar 

  • Thierfelder, C., & Wall, P. C. (2010). Investigating conservation agriculture (CA) systems in Zambia and Zimbabwe to mitigate future effects of climate change. Journal of Crop Improvement, 24(2), 113–121.

    Article  Google Scholar 

  • Thierfelder, C., Rusinamhodzi, L., Ngwira, A. R., Mupangwa, W., Nyagumbo, I., Kassie, G. T., et al. (2015a). Conservation agriculture in southern Africa: Advances in knowledge. Renewable Agriculture and Food Systems, 30(4), 328–348.

    Article  Google Scholar 

  • Thierfelder, C., Matemba-Mutasa, R., & Rusinamhodzi, L. (2015b). Yield response of maize (Zea Mays L.) to conservation agriculture cropping system in southern Africa. Soil and Tillage Research, 146, 230–242.

    Article  Google Scholar 

  • Thierfelder, C., Matemba-Mutasa, R., Bunderson, W. T., Mutenje, M., Nyagumbo, I., & Mupangwa, W. (2016). Evaluating manual conservation agriculture systems in southern Africa. Agriculture, Ecosystems and Environment, 222, 112–124.

    Article  Google Scholar 

  • Thierfelder, C., Chivenge, P., Mupangwa, W., Rosenstock, T. S., Lamanna, C., & Eyre, J. X. (2017). How climate-smart is conservation agriculture (CA)? – Its potential to deliver on adaptation, mitigation and productivity on smallholder farms in southern Africa. Food Security, 9(3), 537–560.

    Article  Google Scholar 

  • UNEP. (2013). The emissions gap report 2013. Nairobi: United Nations Environmental Programme (UNEP).

    Google Scholar 

  • Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). Cambridge: MIT Press.

    Google Scholar 

Download references


This work was funded by the Norwegian Agency for Development Cooperation through the Center for International Forestry Research (CIFOR) [agreement no. GLO-3945 QZA 13/0545]. Additional funding from USAID through the Innovation Lab for Food Security Policy is acknowledged. An earlier version of this paper was published as part of my PhD thesis at the School of Economics and Business at the Norwegian University of Life Sciences (NMBU). I thank Arild Angelsen, three reviewers and the editors of Food Security for their very helpful comments and suggestions on the paper.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Hambulo Ngoma.

Ethics declarations

Conflict of interest

The author declares no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ngoma, H. Does minimum tillage improve the livelihood outcomes of smallholder farmers in Zambia?. Food Sec. 10, 381–396 (2018).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Minimum tillage
  • Impact assessment
  • Crop yield
  • Crop income
  • Endogenous switching
  • Zambia

JEL classifications

  • D1
  • Q12
  • O33