Skip to main content

Advertisement

Log in

Integrated assessment of climate change impacts on crop productivity and income of commercial maize farms in northeast South Africa

  • Original Paper
  • Published:
Food Security Aims and scope Submit manuscript

Abstract

Agriculture in South Africa sustains about 70% of the region’s population for food, income and employment, playing an important role for food security and the local economy. The focus of the study was the commercial maize farms of the Free State Province given their importance in the National economy. The Regional Integrated Assessment (phase I) was implemented to assess climate change and adaptation that links climate, crops, economic data and tools developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP). In this context, the “system” is defined as a whole of agronomic and socio-economic factors. Within that framework three core questions were being evaluated: (i) Impacts of climate change under current system; (ii) Impacts of climate change under future system; (iii) The role of adaptation under climate change and the future system. Maize production will decrease between 10% to 16% as a result of projected climate impacts. Also, current agricultural production systems are negatively affected by climate change with an increase in poverty rates between 2% to 3%. The projected adoption of the adapted technology would result in positive increased net returns and a decrease in poverty rate of between 12% and 22%. The results of this study show that implementing adaptation measures, including strategies indicated by the local stakeholders, will have positive impacts on the agricultural production systems and can contribute to support and inform climate change policy decision making such as the development of National Adaptation Plans.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Agricultural Research Council and Land Type Survey Staff (2006) Land types of South Africa: Digital map (1:250 000 scale) and soil inventory datasets, Agricultural Research Council, Institute for Soil, Climate and Water, Pretoria.

  • Allen RG, Perreira LS, Raes D, Smith M (1998) Crop evapotranspiration: Guidelines for computing crop require-ments. Irrigation and drainage paper no. 56, FAO, Rome requirements.

  • Antle, J.M., Homann-Kee Tui, S., Descheemaeker, K., Masikate, M., Valdivia, R. (2017) Using AgMIP regional integrated assessment methods to evaluate climate impact, adaptation, vulnerability and resilience in agricultural systems. In: Lipper L, Zilberman D, McCarthy N, Asfaw S, Branca G (eds) Climate smart agriculture - building resilience to climate change. Springer.

  • Antle, J.M., Stoorvogel, J., & Valdivia, R. (2014). New parsimonious simulation methods and tools to assess future food and environmental security of farm populations. Philosophical Transactions of the Royal Society B, 369. https://doi.org/10.1098/rstb.2012.0280.

  • Antle, J. M. (2011). Parsimonious multi-dimensional impact assessment. American Journal of Agricultural Economics, 93, 1292–1311.

    Article  Google Scholar 

  • Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P. J., Rotter, R. P., Cammarano, D., Brisson, N., Basso, B., Martre, P., Aggarwal, P. K., Angulo, C., Bertuzzi, P., Biernath, C., Challinor, A. J., Doltra, J., Gayler, S., Goldberg, R., Grant, R., Heng, L., Hooker, J., Hunt, L. A., et al. (2013). Uncertainty in simulating wheat yields under climate change. Nature Climate Change, 3, 827–832.

  • Bassu, S., Brisson, N., Durand, J. L., Boote, K., Lizaso, J., Jones, J. W., Rosenzweig, C., Ruane, A. C., et al. (2014). How do various maize crop models vary in their responses to climate change factors? Global change biology. https://doi.org/10.1111/gcb.12520.

  • Beyer, H.L. (2012) Geospatial Modelling Environment (Version 0.7.3.0) available at: http://www.spatialecology.com/gme Accessed 20 March 2019.

  • Challinor, A. J., & Wheeler, T. R. (2008). Crop yield reduction in the tropics under climate change: Process and uncertainties. Agric Forest Meteorol, 148, 343–356.

    Article  Google Scholar 

  • Claessens, L., Antle, J. M., Stoorvogel, J. J., Valdivia, R. O., Thornton, P. K., & Herrero, M. (2012). A method for evaluating climate change adaptation strategies for small-scale farmers using survey, experimental and modeled data. Agricultural Systems, 111, 85–95.

    Article  Google Scholar 

  • Davis, C.L. (2011). Climate Risk and Vulnerability: A Handbook for Southern Africa. Available at: https://www.csir.co.za. Accessed on 20 Mar 2019.

  • Dimes, J.P., Du Toit P., (2009) Quantifying water productivity in rainfed cropping systems in Limpopo Province, South Africa. In: Humphreys E, Bayot RS (eds) Increasing the Productivity and Sustainability of Rainfed Cropping Systems of Poor Smallholder Farmers. Proceedings CGIAR Challenge Program on Water and Food International Workshop on Rainfed Cropping Systems, Tamale, Ghana, 22–25 September, 2008, The CGIAR challenge program on water and food, Colombo, Sri Lanka.

  • Durand, J. L., Delusca, K., Boote, K., Lizaso, J., Manderscheid, R., Weigel, H. J., Ruane, A. C., Rosenzweig, C., et al. (2018). How accurately do maize cropmodels simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield? European Journal of Agronomy, 100, 67–75.

    Article  CAS  Google Scholar 

  • Du Toit AS (1996) The quantification of the compensation ability of the maize plant, PhD thesis, Department of Agronomy, Faculty of Agriculture, University of the Free State, South Africa.

  • Du Toit, A. S., Booysen, J., & Human, J. J. (1994a). Evaluation and calibration of CERES-maize 1. Non-linear regression to determine genetic parameters. S Afr J Plant Soil, 11, 96–100.

    Article  Google Scholar 

  • Du Toit, A. S., Booysen, J., & Human, J. J. (1994b). Evaluation and calibration of CERES-maize 2. Phenology prediction values. S Afr J Plant Soil, 11, 121–125.

    Article  Google Scholar 

  • Du Toit, A. S., Booysen, J., & Human, J. J. (1994c). Evaluation and calibration of CERES-maize 3. Row widths for the western Highveld. S Afr J Plant Soil, 11, 153–158.

    Article  Google Scholar 

  • Du Toit, A. S., Booysen, J., & Human, J. J. (1997). Use of linear regression and correlation in the evaluation of CERES3 (maize). S Afr J Plant Soil, 14, 177–182.

    Article  Google Scholar 

  • Du Toit, A. S., Booysen, J., & Human, J. J. (1998). Calibration of CERES3 (maize) to improve silking date prediction values for South Africa. S Afr J Plant Soil, 15, 61–66.

    Article  Google Scholar 

  • Du Toit AS, Prinsloo M.A. (2000) Incorporating prolificacy into CERES-maize prediction of kernel number. Physiology and Modelling kernel set in maize CSSA special publication no. 29, crop science Society of America and American Society of agronomy, Madison, USA.

  • Durand, W. (2016) Crop yield forecast data for South Africa. In: S. Pasetto (ed.) crop yield forecasting: Methodological and institutional aspects. Food and agriculture Organization of the United Nations (FAO), Rome, Italy.

  • Engelbrecht, C. J., Engelbrecht, F. A., & Dyson, L. (2013). High-resolution model-projected changes in mid-tropospheric closed-lows and extreme rainfall events over southern Africa. International Journal of Climatology, 33, 173–187.

    Article  Google Scholar 

  • Ferreira, S.L., Newby, T., and du Preez E. (2006) Use of remote sensing in support of crop area estimates in South Africa. SPRS archives XXXVI-8/W48 workshop proceedings: Remote sensing support to crop yield forecast and area estimates, pp. 51-52.

  • Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S.C., et al. (2013). Evaluation of Climate Models. In Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, XiaY, Bex V, Midgley PM (eds.), Climate Change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge.

  • Freduah, B., MacCarthy, D.S., Adam, M., Ly, M., Ruane, A.C., Timpong-Jones, E.C., Traore, P.S., Boote, K., Porter, C., Adiku, S.G.K. (2019) Sensitivity of maize yield in smallholder systems to climate scenarios in semi-arid regions of West Africa: Accounting for variability in farm management practices. J. Agronomy (in revision).

  • Galmarini, S., Cannon, A. J., Ceglar, A., Christensen, O. B., & Noblet-ducoudré, N. De. (2019). Adjusting climate model bias for agricultural impact assessment: How to cut the mustard. Climate Services, 13(June 2018), 65–69.

  • Grain South Africa - S.A.(2012) Market Reports. Available at: www.grainsa.co.za. Accessed 30 Mar 2019.

  • Hoogenboom, G., Jones, J.W., Wilkens, P.W., Porter, C.H., Boote, K.J., Hunt, L.A., Singh, U., Lizaso, JL, White JW, Uryasev O, Royce FS, Ogoshi R, Gijsman AJ, Tsuji GY, Koo J (2012) Decision support system for Agrotechnology transfer (DSSAT) version 4.5. University of Hawaii, Honolulu, Hawaii.

  • Intergovernmental Panel on Climate Change – IPCC (2019) Representative Concentration Pathways (RCPs). https://sedac.ciesin.columbia.edu/ddc/ar5_scenario_process/RCPs.html (Verified 25 Oct 2019).

  • Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J., & Ritchie, J. T. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18, 235–265.

    Article  Google Scholar 

  • Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E., Robertson, M. J., Holzworth, D., Huth, N. I., Hargreaves, J. N. G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J. P., Silburn, M., Wang, E., Brown, S., Bristow, K. L., Asseng, S., Chapman, S., McCown, R. L., Freebairn, D. M., & Smith, C. J. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18, 267–288.

    Article  Google Scholar 

  • Kimball, B.A. et al. (2019). Simulation of maize evapotranspiration: An inter-comparison among 29 maize models. Agricultural and Forest Meteorology, 271, 264–284.

  • Long, S. P., Ainsworth, E. A., Leakey, A. D. B., Nosberger, J., & Ort, D. R. (2006). Food for thought: Lower than expected crop yield stimulation with rising CO2 concentrations. Science, 312, 1918–1921.

    Article  CAS  Google Scholar 

  • Maize Information Guide - MIG (2014) http://www.arc.agric.za/arc-gci/Pages/MIG/MIG-2014.aspx Accessed 15 Dec 2018.

  • Martre, P., Wallach, D., et al. (2014). Multimodel ensembles of wheat growth: Many models are better than one. Global change biology, 21. https://doi.org/10.1111/gcb.12768.

  • Meadows, M. E. (2006). Global change and southern Africa. Geographical Research, 44, 135–145.

    Article  Google Scholar 

  • Nel, A. A., & Bloem, A. A. (2006). The delta yield procedure for nitrogen fertilisation of maize in South Africa. South African Journal of Plant and Soil, 23, 203–208.

    Article  Google Scholar 

  • National Planning Commission – NPC (2012) National Development Plan Vision 2030: Our future make it work. Available at: http://www.dac.gov.za Accessed on 10 Feb 2019.

  • Ncube, B., Dimes, J. P., van Wijk, M. T., Twomlow, S. J., & Giller, K. E. (2009). Productivity and residual benefits of grain legumes to sorghum under semi-arid conditions in southwestern Zimbabwe: Unravelling the effects of water and nitrogen using a simulation model. Field Crops Research, 110, 173–184.

    Article  Google Scholar 

  • Ncube, B., Twomlow, S. J., van Wijk, M. T., Dimes, J. P., & Giller, K. E. (2007). Productivity and residual benefits of grain legumes to sorghum under semi-arid conditions in southwestern Zimbabwe. Plant and Soil, 299, 1–15.

    Article  CAS  Google Scholar 

  • Rosenzweig, C., Hillel, D. (2015) Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments — Joint Publication with American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. https://doi.org/10.1142/p970.

  • Ruane, A.C., Winter, J.M., McDermid, S.P., Hudson, N.I. (2015a) AgMIP climate datasets and scenarios for integrated assessment. In handbook of climate change and Agroecosystems: The agricultural model Intercomparison and improvement project (AgMIP) integrated crop and economic assessments, part 1. C. Rosenzweig and D. Hillel, Eds., ICP series on climate change impacts, adaptation, and mitigation Vol. 3. Imperial college press, pp. 45-78, doi:10.1142/9781783265640_0003.

  • Ruane, A. C., Goldberg, R., & Chryssanthacopoulos, J. (2015b). Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agricultural and Forest Meteorology, 200, 233–248.

    Article  Google Scholar 

  • Ruane, A. C., & McDermid, S. P. (2017). Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment. Earth Perspective, 4, 1–20. https://doi.org/10.1186/s40322-017-0036-4.

    Article  Google Scholar 

  • Saseendran, S.A., Ahuja, L.R., Ma, L., Timlin, D., Stockle, C.O., Boote, K.J., Hogenboom, G. (2008) Current water deficit stress simulations in selected agricultural system models. In: Ahuja LR, Reddy VR, Saseendran SA, Yu Q (Eds) Response of crops to limited water: Understanding and modeling water stress effects on plant growth processes. ASA , CSSA, SSSA, Madison, WI, USA.

  • Schulze, R.E. (2007). Climate change and the agricultural sector in South Africa: An assessment of findings in the new millennium, ACRU report 55, School of bio-resources and Environmental Hydrology Engineering. Pietermaritzburg: University of KwaZulu-Natal.

    Google Scholar 

  • Schulze, R.E., Hallowes, L.A., Horan, M.J.C., Lumsden, T.G., Pike, A., Thornton-Dibb, S., Warburton, M.L. (2007) South African quaternary catchments database. In: Schulze RE (ed) south African atlas of climatology and Agrohydrology. WaterResearch commission, Pretoria, RSA, WRC report 1489/1/06, section 2.3.

  • Smithers, J., Schulze, R.E. (1995) ACRU agrohydrological modelling system user manual. WRC report. TT 70/95, Water Research Commission, Pretoria.

  • Southern Africa Development Community – SADC (2013) Agricultural & Food Security. Available at: www.sadc.int/themes/agriculture-food-security. Accessed 20 December 2018.

  • Statistics S.A. (2005). Census of commercial agriculture, 2002. Pretoria: Financial and Production Statistics.

  • Statistics S.A. (2010). Census of commercial agriculture 2007. Pretoria: Financial and Production Statistics.

  • Statistics S.A. (2012a). Agricultural Survey 2011. Pretoria: Financial and Production Statistics.

  • Statistics, S.A. (2012b) Poverty profile of South Africa: Application of poverty lines on the LCS 2008/2009.Available at: http://www.statssa.gov.za/publications/Report-03-10-03/Report-03-10-032009.pdf Accessed 10 March 2019.

  • Statistics S.A. (2017). Census of commercial agriculture 2017. Pretoria: Statistics South Africa.

  • Tadross, M.A., Jack, C., & Hewitson, B. C. (2005). On RCM-based projections of change in southern African summer climate. Geophysical Research Letters, 32(23), L23713.

    Article  Google Scholar 

  • Valdivia, R., Antle, J. M., & Stoorvogel, J. J. (2012). Coupling the tradeoff analysis model with a market equilibrium model to analyze economic and environmental outcomes of agricultural production systems. Agricultural Systems, 111, 85–95.

    Article  Google Scholar 

  • Valdivia, R., Antle, J.M., Rosenzweig, C., Ruane, A.C., Vervoort, J., et al (2015) Representative Agricultural Pathways and Scenarios for Regional Integrated Assessment of Climate Change Impacts, Vulnerability, and Adaptation. In Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments, Part 1. C. Rosenzweig and D. Hillel, Eds., ICP Series on Climate Change Impacts, Adaptation, and Mitigation Vol. 3. Imperial College Press, pp. 45–78, doi:10.1142/9781783265640_0003.

  • Van Biljon, J. J., Fouche, D. S., & Botha, A. D. P. (2008). The lower and upper threshold values, biological optimum and mineralization of nitrogen in the main maize producing soils of South Africa. South African Journal of Plant and Soil, 25, 8–13.

    Article  Google Scholar 

  • Zampieri, M., Ceglar, A., Dentener, F., et al. (2019). When will current climate extremes affecting maize production become the norm? Earth's Future, 7, 113–122. https://doi.org/10.1029/2018EF000995.

    Article  Google Scholar 

Download references

Acknowledgments

Research reported in this article was possible owing to generous financial support from the UK Department for International Development, project GB-1-202108 to AgMIP, the Agricultural Model Intercomparison and Improvement Project. Fund disbursement on behalf of AgMIP was facilitated by USDA-ARS, Columbia University, and ICRISAT. Results reported reflect the views of the authors. We also thank the two anonymous reviewers and the Editor for the feedback that helped to improve the manuscript. Funding support was provided by the NASA Earth Sciences Division for the GISS Climate Impacts Group (281945.02.80.01.13).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Davide Cammarano.

Ethics declarations

Conflict of interest

The authors declared that they have no conflict of interest.

Additional information

Agricultural Research Council-Roodeplaat is a former affiliation of Y. G. Beletse

Electronic supplementary material

ESM 1

(DOCX 27 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cammarano, D., Valdivia, R.O., Beletse, Y.G. et al. Integrated assessment of climate change impacts on crop productivity and income of commercial maize farms in northeast South Africa. Food Sec. 12, 659–678 (2020). https://doi.org/10.1007/s12571-020-01023-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12571-020-01023-0

Keywords

Navigation