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Limits of conservation agriculture to overcome low crop yields in sub-Saharan Africa

Nature Food volume 1, pages 447–454 (2020)Cite this article

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Abstract

Conservation agriculture (CA) has become a dominant paradigm in scientific and policy thinking about the sustainable intensification of food production in sub-Saharan Africa. Yet claims that CA leads to increasing crop yields in African smallholder farming systems remain controversial. Through a meta-analysis of 933 observations from 16 different countries in sub-Saharan African studies, we show that average yields under CA are only slightly higher than those of conventional tillage systems (3.7% for six major crop species and 4.0% for maize). Larger yield responses for maize result from mulching and crop rotations/intercropping. When CA principles are implemented concomitantly, maize yield increases by 8.4%. The largest yield benefits from CA occur in combination with low rainfall and herbicides. We conclude that although CA may bring soil conservation benefits, it is not a technology for African smallholder farmers to overcome low crop productivity and food insecurity in the short term.

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Fig. 1: Location of the experiments considered in this meta-analysis.
Fig. 2: Effect of CA relative to CT on grain yield for different crop species.
Fig. 3: Effect of CA relative to CT on maize grain yield under different combinations of CA principles.
Fig. 4: Effect of CA relative to CT on crop grain yield in relation to experimental duration.
Fig. 5: Effect of CA relative to CT on crop grain yield as a function of herbicide use in the CA treatment.
Fig. 6: Effect of CA relative to CT on crop grain yield as a function of applied chemical phosphorus fertilizer.
Fig. 7: Effect of CA relative to CT on crop grain yield as a function of average seasonal rainfall at the experimental site.

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Data availability

The data used in this study are available from the Dataverse repository at https://doi.org/10.18167/DVN1/DLTQWR.

Code availability

Scripts used in the literature search and statistical analyses are available from the corresponding author on request.

References

  1. OECD-FAO Agricultural Outlook 2016–2025 59-95 (OECD, 2016).

  2. Van Ittersum, M. K. et al. Can sub-Saharan Africa feed itself? Proc. Natl Acad. Sci. USA 113, 14964–14969 (2016).

    PubMed  Google Scholar 

  3. Garnett, T. et al. Sustainable intensification in agriculture: premises and policies. Science 341, 33–34 (2013).

    ADS  CAS  PubMed  Google Scholar 

  4. Godfray, H. C. J. & Garnett, T. Food security and sustainable intensification. Phil. Trans. R. Soc. B 369, 20120273 (2014).

    PubMed  Google Scholar 

  5. Hobbs, P., Sayre, K. & Gupta, R. The role of conservation agriculture in sustainable agriculture. Phil. Trans. R. Soc. B 363, 543–555 (2008).

    PubMed  Google Scholar 

  6. Pretty, J. & Bharucha, Z. Sustainable intensification in agricultural systems. Ann. Bot. 114, 1571–1596 (2014).

    PubMed  PubMed Central  Google Scholar 

  7. Benites, J. R. & Ashburner, J. E. in Conservation Agriculture: Environment, Farmers Experiences, Innovations, Socio-economy, Policy (eds Garcia-Torres, L. et al.) 139–153 (Kluwer Academic, 2003).

  8. Andersson, J. A. & D’Souza, S. From adoption claims to understanding farmers and contexts: a literature review of Conservation Agriculture (CA) adoption among smallholder farmers in southern Africa. Agric. Ecosyst. Environ. 187, 116–132 (2014).

    Google Scholar 

  9. Kassam, A., Friedrich, T. & Derpsch, R. Global spread of conservation agriculture. Int. J. Environ. Stud. 76, 29–51 (2019).

    CAS  Google Scholar 

  10. Thierfelder, C., Matemba-Mutasa, R. & Rusinamhodzi, L. Yield response of maize (Zea mays L.) to conservation agriculture cropping system in Southern Africa. Soil Till. Res. 146, 230–242 (2015).

    Google Scholar 

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

    Google Scholar 

  12. Stevenson, J. R., Serraj, R. & Cassman, K. G. Evaluating conservation agriculture for small-scale farmers in sub-Saharan Africa and South Asia. Agric. Ecosyst. Environ. 187, 1–10 (2014).

    Google Scholar 

  13. Brouder, S. M. & Gomez-Macpherson, H. The impact of conservation agriculture on smallholder agricultural yields: a scoping review of the evidence. Agric. Ecosyst. Environ. 187, 11–32 (2014).

    Google Scholar 

  14. Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).

    ADS  CAS  PubMed  Google Scholar 

  15. Erenstein, O., Sayre, K., Wall, P., Hellin, J. & Dixon, J. Conservation agriculture in maize- and wheat-based systems in the (sub)tropics: lessons from adaptation initiatives in South Asia, Mexico, and Southern Africa. J. Sustain. Agric. 36, 180–206 (2012).

    Google Scholar 

  16. Giller, K. E. et al. Beyond conservation agriculture. Front. Plant Sci. 6, 870 (2015).

    PubMed  PubMed Central  Google Scholar 

  17. Grabowski, P. P., Kerr, J. M., Haggblade, S. & Kabwe, S. Determinants of adoption and disadoption of minimum tillage by cotton farmers in eastern Zambia. Agric. Ecosyst. Environ. 231, 54–67 (2016).

    Google Scholar 

  18. Brown, B., Nuberg, I. & Llewellyn, R. Stepwise frameworks for understanding the utilisation of conservation agriculture in Africa. Agric. Syst. 153, 11–22 (2017).

    Google Scholar 

  19. Dixon, J., Gulliver, A. & Gibbon, D. Farming Systems and Poverty: Improving Farmers’ Livelihoods in a Changing World (FAO, 2001).

  20. Valbuena, D. et al. Conservation agriculture in mixed crop–livestock systems: scoping crop residue trade-offs in Sub-Saharan Africa and South Asia. Field Crops Res. 132, 175–184 (2012).

    Google Scholar 

  21. Frelat, R. et al. Drivers of household food availability in sub-Saharan Africa based on big data from small farms. Proc. Natl Acad. Sci. USA 113, 458–463 (2016).

    ADS  CAS  PubMed  Google Scholar 

  22. Findeling, A., Ruy, S. & Scopel, E. Modeling the effects of a partial residue mulch on runoff using a physically based approach. J. Hydrol. 275, 49–66 (2003).

    ADS  Google Scholar 

  23. Beare, M. H., Cabrera, M. L., Hendrix, P. F. & Coleman, D. C. Aggregate-protected and unprotected organic matter pools in conventional- and no-tillage soils. Soil Sci. Soc. Am. J. 58, 787–795 (1994).

    ADS  Google Scholar 

  24. Tian, G., Brussaard, L. & Kang, B. T. Biological effects of plant residues with contrasting chemical compositions under humid tropical conditions: effects on soil fauna. Soil Biol. Biochem. 25, 731–737 (1993).

    Google Scholar 

  25. Schoenau, J. J. & Campbell, C. A. Impact of crop residues on nutrient availability in conservation tillage systems. Can. J. Plant Sci. 76, 621–626 (1996).

    CAS  Google Scholar 

  26. Ranaivoson, L. et al. Agro-ecological functions of crop residues under conservation agriculture: a review. Agron. Sustain. Dev. 37, 26 (2017).

    Google Scholar 

  27. Bussiere, F. & Cellier, P. Modification of the soil temperature and water content regimes by a crop residue mulch: experiment and modelling. Agric. For. Meteorol. 68, 1–28 (1994).

    ADS  Google Scholar 

  28. Ratnadass, A., Fernandes, P., Avelino, J. & Habib, R. Plant species diversity for sustainable management of crop pests and diseases in agroecosystems: a review. Agron. Sustain. Dev. 32, 273–303 (2012).

    Google Scholar 

  29. Ball, B. C., Bingham, I., Rees, R. M., Watson, C. A. & Litterick, A. The role of crop rotations in determining soil structure and crop growth conditions. Can. J. Soil Sci. 85, 557–577 (2005).

    Google Scholar 

  30. de Oliveira Ferreira, A. et al. Can no-till grain production restore soil organic carbon to levels natural grass in a subtropical Oxisol? Agric. Ecosyst. Environ. 229, 13–20 (2016).

    Google Scholar 

  31. Tuzzin de Moraes, M. et al. Soil physical quality on tillage and cropping systems after two decades in the subtropical region of Brazil. Soil Till. Res. 155, 351–362 (2016).

    Google Scholar 

  32. Corbeels, M., Cardinael, R., Naudin, K., Guibert, H. & Torquebiau, E. The 4 per 1000 goal and soil carbon storage under agroforestry and conservation agriculture systems in sub-Saharan Africa. Soil Till. Res. 188, 16–26 (2019).

    Google Scholar 

  33. Pleasant, J. M. T., Burt, R. F. & Frisch, J. C. Integrating mechanical and chemical weed management in corn (Zea mays). Weed Technol. 8, 217–223 (1994).

    Google Scholar 

  34. Nichols, V., Verhulst, N., Cox, R. & Govaerts, B. Weed dynamics and conservation agriculture principles: A review. Field Crops Res. 183, 56–68 (2015).

    Google Scholar 

  35. Williams, A. et al. Establishing the relationship of soil nitrogen immobilization to cereal rye residues in a mulched system. Plant Soil 426, 95–107 (2018).

    CAS  Google Scholar 

  36. Vanlauwe, B. et al. A fourth principle is required to define conservation agriculture in sub-Saharan Africa: the appropriate use of fertilizer to enhance crop productivity. Field Crops Res. 155, 10–13 (2014).

    Google Scholar 

  37. Rusinamhodzi, L. et al. A meta-analysis of long-term effects of conservation agriculture on maize grain yield under rain-fed conditions. Agron. Sustain. Dev. 31, 657 (2011).

    Google Scholar 

  38. Steward, P. R. et al. The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: a meta-regression of yields. Agric. Ecosyst. Environ. 251, 194–202 (2018).

    Google Scholar 

  39. Scopel, E., Da Silva, F. A. M., Corbeels, M., Affholder, F. & Maraux, F. Modelling crop residue mulching effects on water use and production of maize under semi-arid and humid tropical conditions. Agronomie 24, 383–395 (2004).

    Google Scholar 

  40. Todd, R. W., Klocke, N. L., Hergert, G. W. & Parkhurst, A. M. Evaporation from soil influenced by crop shading, crop residue and wetting regime. Trans. ASAE 34, 461–466 (1991).

    Google Scholar 

  41. Thierfelder, C. & Wall, P. C. Effects of conservation agriculture on soil quality and productivity in contrasting agro-ecological environments of Zimbabwe. Soil Use Manage. 28, 209–220 (2012).

    Google Scholar 

  42. Thierfelder, C. et al. How climate-smart is conservation agriculture (CA)? Its potential to deliver on adaptation, mitigation and productivity on smallholder farms in southern Africa. Food Secur. 9, 537–560 (2017).

    Google Scholar 

  43. Herrero, M. et al. Smart investments in sustainable food production: revisiting mixed crop-livestock systems. Science 327, 822–825 (2010).

    ADS  CAS  PubMed  Google Scholar 

  44. Nyamangara, J. et al. Effect of conservation agriculture on maize yield in semi-arid areas of Zimbabwe. Exp. Agric. 50, 159–177 (2014).

    Google Scholar 

  45. De Roo, N., Andersson, J. A. & Krupnik, T. J. On-farm trials for development impact? The organisation of research and the scaling of agricultural technologies. Exp. Agric. 55, 163–184 (2019).

    Google Scholar 

  46. Pannell, D. J., Llewellyn, R. S. & Corbeels, M. The farm-level economics of conservation agriculture for resource-poor farmers. Agric. Ecosyst. Environ. 187, 52–64 (2014).

    Google Scholar 

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

    Google Scholar 

  48. Nyagumbo, I., Mkuhlani, S., Mupangwa, W. & Rodriguez, D. Planting date and yield benefits from conservation agriculture practices across southern Africa. Agric. Syst. 150, 21–33 (2017).

    Google Scholar 

  49. Krupnik, T. J. et al. Does size matter? A critical review of meta-analysis in agronomy. Exp. Agric. 55, 200–229 (2019).

    Google Scholar 

  50. Harris, D. & Orr, A. Is rainfed agriculture really a pathway from poverty? Agric. Syst. 123, 84–96 (2014).

    Google Scholar 

  51. Van Bruggen, A. H. C. et al. Environmental and health effects of the herbicide glyphosate. Sci. Total Environ. 616-617, 255–268 (2018).

    ADS  PubMed  Google Scholar 

  52. Cerdeira, A. L., Gazziero, D. L., Duke, S. O. & Matallo, M. B. Agricultural impacts of glyphosate-resistant soybean cultivation in South America. J. Agric. Food Chem. 59, 5799–5807 (2011).

    CAS  PubMed  Google Scholar 

  53. Kirkegaard, J. A. et al. Sense and nonsense in conservation agriculture: principles, pragmatism and productivity in Australian mixed farming systems. Agric. Ecosyst. Environ. 187, 133–145 (2014).

    Google Scholar 

  54. Bai, S. H. & Ogbourne, S. M. Glyphosate: environmental contamination, toxicity and potential risks to human health via food contamination. Environ. Sci. Pollut. Res. Int. 23, 18988–19001 (2016).

    CAS  PubMed  Google Scholar 

  55. Grieser, J., Gommes, R. & Bernardi, M. New LocClim-the local climate estimator of FAO. Geophys. Res. Abstr. 8, 08305 (2006).

    Google Scholar 

  56. Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).

    Google Scholar 

  57. Greenland, S. & O’Rourke, K. On the bias produced by quality scores in meta-analysis, and a hierarchical view of proposed solutions. Biostatistics 2, 463–471 (2001).

    CAS  PubMed  MATH  Google Scholar 

  58. Borenstein, M., Hedges, L. V., Higgins, J. P. & Rothstein, H. R. A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Res. Synth. Methods 1, 97–111 (2010).

    PubMed  Google Scholar 

  59. Lajeunesse, M. J. On the meta‐analysis of response ratios for studies with correlated and multi‐group designs. Ecology 92, 2049–2055 (2011).

    Google Scholar 

  60. Duval, S. & Tweedie, R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56, 455–463 (2000).

    CAS  PubMed  MATH  Google Scholar 

  61. Lin, L. & Chu, H. Quantifying publication bias in meta-analysis. Biometrics 74, 785–794 (2018).

    MathSciNet  PubMed  MATH  Google Scholar 

  62. Base SAS 9.3 Procedures Guide (SAS Institute, 2011).

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Acknowledgements

This work was implemented as part of the CGIAR Research Programs on Climate Change, Agriculture and Food Security (CCAFS) and Maize, which are carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.

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Authors and Affiliations

  1. Sustainable Intensification Program (SIP), International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya

    Marc Corbeels

  2. Agro-ecology and Sustainable Intensification of Annual Crops (AIDA), French Agricultural Research Centre for International Development (CIRAD), University of Montpellier, Montpellier, France

    Marc Corbeels, Krishna Naudin & Philippe Letourmy

  3. Innovation Systems for the Drylands (ISD), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Dar es Salaam, Tanzania

    Anthony M. Whitbread

  4. Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Georg August University of Göttingen, Göttingen, Germany

    Ronald Kühne

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Contributions

M.C. conceived the project and developed it with A.M.W., R.K and K.N. M.C. and K.N. contributed to the literature research, extracted data from publications and constructed the database. P.L., M.C. and R.K. conducted the analyses. M.C. wrote the manuscript draft and all authors contributed to the interpretation of the results and writing of the final paper.

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Correspondence to Marc Corbeels.

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Extended data

Extended Data Fig. 1 Random-effects model with explanatory covariates.

Results of the random-effects model developed to determine the influence of explanatory covariates on the CA to CT yield ratio.

Extended Data Fig. 2 Effect of CA relative to CT on crop grain yield as a function of soil texture for different regimes of average seasonal rainfall at the experimental site.

a, < 400mm; b, 400–800mm; c) 800–1200m and d) > 1200mm. Values are mean effect sizes and error bars show the 95% CI. The number of observations and total number of studies for each category are shown in parentheses. The mean effect sizes were considered significant if the 95% CI does not include 0. The CA effect on yield is significantly lower on medium-texture soils than on coarse- (P<0.005) and fine-textured soils (P<0.02) under the 800–1200mm rainfall regime, and the effect on coarse-textured soils is significantly higher than on medium- (P<0.05) and fine-textured soils (P<0.01) under the >1200mm rainfall regime, determined via paired Student’s t-tests.

Extended Data Fig. 3 Operational forms of no- and reduced tillage employed by smallholder farmers in sub Saharan Africa.

Different operational forms of no- and reduced tillage employed by smallholder farmers in sub Saharan Africa (source: CIRAD and CIMMYT).

Extended Data Fig. 4 Effect of CA relative to CT on crop grain yield as a function of type of reduced tillage in the CA treatment.

Values are mean effect sizes and error bars show the 95% CI. The number of observations and total number of studies for each category are shown in parentheses. The mean effect sizes were considered significant if the 95% CI does not include 0. The CA effect on yield is significantly higher under no-tillage than under minimum tillage (P<0.005) and basins/permanent beds (P<0.05), determined via paired Student’s t-tests.

Extended Data Fig. 5 Effect of CA relative to CT on crop grain yield as a function of type of field trial.

Values are mean effect sizes and error bars show the 95% CI. The number of observations and total number of studies for each category are shown in parentheses. The mean effect sizes were considered significant if the 95% CI does not include 0. The CA effect on yield is significantly (P<0.05) higher in on-farm trials than on-station trials.

Extended Data Fig. 6 Funnel plot on the marginal deviations from the random-effects model added to the average logarithmic yield ratio for maize (as reference, solid vertical line).

The diagonal lines represent the 95% CI limits around the effect size logratio. Each point represents an observation (n=933), open blue circles from on-farm studies (n=605), open red circles from on-station studies (n=328). Skewness TS is -0.03, P=0.67 (all observations), 0.18, P=0.07 (on-farm observations) and −0.26, P=0.05 (on-station observations).

Extended Data Fig. 7 Boxplots of logarithmic weights by the inverse of variance of the individual observations in the on-farm (n = 605) versus on-station (n = 328) studies.

The inverse variance weight is significantly (P<0.0001) smaller in on-farm studies than in on-station studies (paired Student’s t-test).

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Corbeels, M., Naudin, K., Whitbread, A.M. et al. Limits of conservation agriculture to overcome low crop yields in sub-Saharan Africa. Nat Food 1, 447–454 (2020). https://doi.org/10.1038/s43016-020-0114-x

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