Register      Login
Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
RESEARCH ARTICLE

Modelled impacts of extreme heat and drought on maize yield in South Africa

Robert Mangani A , Eyob Tesfamariam A D , Gianni Bellocchi B and Abubeker Hassen C
+ Author Affiliations
- Author Affiliations

A Department of Plant and Soil Science, University of Pretoria, Private Bag x20, Hatfield 0028, Pretoria, South Africa.

B Gianni Bellocchi, UREP, INRA 63000, Clermont-Ferrand, France.

C Department of Animal and Wildlife Sciences, Faculty of Natural and Agricultural Sciences, University of Pretoria, Private Bag x20, Hatfield 0028, Pretoria 0002, South Africa.

D Corresponding author. Email: eyob.tesfamariam@up.ac.za

Crop and Pasture Science 69(7) 703-716 https://doi.org/10.1071/CP18117
Submitted: 13 September 2017  Accepted: 8 June 2018   Published: 3 July 2018

Abstract

This study assessed two versions of the crop model CropSyst (i.e. EMS, existing; MMS, modified) for their ability to simulate maize (Zea mays L.) yield in South Africa. MMS algorithms explicitly account for the impact of extreme weather events (droughts, heat waves, cold shocks, frost) on leaf development and yield formation. The case study of this research was at an experimental station near Johannesburg where both versions of the model were calibrated and validated by using field data collected from 2004 to 2008. The comparison of EMS and MMS showed considerable difference between the two model versions during extreme drought and heat events. MMS improved grain-yield prediction by ~30% compared with EMS, demonstrating a better ability to capture the behaviour of stressed crops under a range of conditions. MMS also showed a greater variability in response when both versions were forced with scenarios of projected climate change, with increased severity of drought and increased temperature conditions at the horizons 2030 and 2050, which could drive decreased maize yield. Yield was even lower with MMS (8 v. 11 t ha–1 for EMS) at the horizon 2050, relative to the baseline scenario (~13 t ha–1 at the horizon 2000). Modelling solutions accounting for the impact of extreme weather events can be seen as a promising tool for supporting agricultural management strategies and policy decisions in South Africa and globally.

Additional keywords: crop modelling, cropping systems, food security, harvest index, rainfed agriculture.


References

Abraha MG, Savage MJ (2006) Potential impacts of climate change on the grain yield of maize for the Midlands of KwaZulu-Natal, South Africa. Agriculture, Ecosystem Environment 115, 150–160.
Potential impacts of climate change on the grain yield of maize for the Midlands of KwaZulu-Natal, South Africa.Crossref | GoogleScholarGoogle Scholar |

Anderson CJ, Babcock BA, Peng Y, Gassman PW, Campbell TD (2015) Placing bounds on extreme temperature response of maize. Environmental Research Letters 10, 124001
Placing bounds on extreme temperature response of maize.Crossref | GoogleScholarGoogle Scholar |

Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn PJ, Rötter RP, Cammarano D, Brisson N (2013) Uncertainty in simulating wheat yields under climate change. Nature Climate Change 3, 827–832.
Uncertainty in simulating wheat yields under climate change.Crossref | GoogleScholarGoogle Scholar |

Bassu S, Brisson N, Durand JL, Boote K, Lizaso J, Jones JW, Rosenzweig C, Ruane AC, Adam M, Baron C, Basso B (2014) How do various maize crop models vary in their responses to climate change factors? Global Change Biology 20, 2301–2320.
How do various maize crop models vary in their responses to climate change factors?Crossref | GoogleScholarGoogle Scholar |

Bellocchi G, Silvestri N, Mazzoncini M, Menini S (2002) Using the CropSyst model in continuous rainfed maize (Zea mays L.) under alternative management options. Italian Journal of Agronomy 6, 43–56.

Bocchiola D, Nana E, Soncini A (2013) Impact of climate change scenarios on crop yield and water footprint of maize in the Po valley of Italy. Agricultural Water Management 116, 50–61.
Impact of climate change scenarios on crop yield and water footprint of maize in the Po valley of Italy.Crossref | GoogleScholarGoogle Scholar |

Bolaños J, Edmeades GO (1996) The importance of the anthesis-silking interval in breeding for drought tolerance in tropical maize. Field Crops Research 48, 65–80.
The importance of the anthesis-silking interval in breeding for drought tolerance in tropical maize.Crossref | GoogleScholarGoogle Scholar |

Bunting ES (1976) Accumulated temperature and maize development in England. The Journal of Agricultural Science 87, 577–583.
Accumulated temperature and maize development in England.Crossref | GoogleScholarGoogle Scholar |

Carter EK, Melkonian J, Riha SJ, Shaw SB (2016) Separating heat stress from moisture stress: analyzing yield response to high temperature in irrigated maize. Environmental Research Letters 11, 094012
Separating heat stress from moisture stress: analyzing yield response to high temperature in irrigated maize.Crossref | GoogleScholarGoogle Scholar |

Chavez E, Conway G, Ghil M, Sadler M (2015) An end-to-end assessment of extreme weather impacts on food security. Nature Climate Change 5, 997–1001.
An end-to-end assessment of extreme weather impacts on food security.Crossref | GoogleScholarGoogle Scholar |

Confalonieri R, Gusberti D, Bocchi S, Acutis M (2006) The CropSyst model to simulate the N balance of rice for alternative management. Agronomy for Sustainable Development 26, 241–249.
The CropSyst model to simulate the N balance of rice for alternative management.Crossref | GoogleScholarGoogle Scholar |

De Jager JM (1994) Accuracy of vegetation evaporation ratio formulae for estimating final wheat yield. Water SA-Pretoria 20, 307–314.

Desclaux D, Roumet P (1996) Impact of drought stress on the phenology of two soybean (Glycine max L. Merr) cultivars. Field Crops Research 46, 61–70.
Impact of drought stress on the phenology of two soybean (Glycine max L. Merr) cultivars.Crossref | GoogleScholarGoogle Scholar |

Donatelli M, Stöckle C, Ceotto E, Rinaldi M (1997) Evaluation of CropSyst for cropping systems at two locations of northern and southern Italy. European Journal of Agronomy 6, 35–45.
Evaluation of CropSyst for cropping systems at two locations of northern and southern Italy.Crossref | GoogleScholarGoogle Scholar |

Duveiller G, Donatelli M, Fumagalli D, Zucchini A, Nelson R, Baruth B (2015) A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios. Theoretical and Applied Climatology 127, 573–585.

Eitzinger J, Thaler S, Schmid E, Strauss F, Ferrise R, Moriondo M, Bindi M, Palosuo T, Rötter R, Kersebaum KC, Olesen JE (2013) Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria. The Journal of Agricultural Science 151, 813–835.
Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria.Crossref | GoogleScholarGoogle Scholar |

Field CB (2012) ‘Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change.’ (Cambridge University Press: Cambridge, UK)

Finger R, Hediger W, Schmid S (2011) Irrigation as adaptation strategy to climate change—a biophysical and economic appraisal for Swiss maize production. Climatic Change 105, 509–528.
Irrigation as adaptation strategy to climate change—a biophysical and economic appraisal for Swiss maize production.Crossref | GoogleScholarGoogle Scholar |

Gornall J, Betts R, Burke E, Clark R, Camp J, Willett K, Wiltshire A (2010) Implications of climate change for agricultural productivity in the early twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences 365, 2973–2989.
Implications of climate change for agricultural productivity in the early twenty-first century.Crossref | GoogleScholarGoogle Scholar |

Hatfield JL, Boote KJ, Kimball BA, Ziska LH, Izaurralde RC, Ort D, Thomson AM, Wolfe D (2011) Climate impacts on agriculture: implications for crop production. Agronomy Journal 103, 351–370.
Climate impacts on agriculture: implications for crop production.Crossref | GoogleScholarGoogle Scholar |

Haverkort AJ, Franke AC, Engelbrecht FA, Steyn JM (2013) Climate change and potato production in contrasting South African agro-ecosystems 1. Effects on land and water use efficiencies. Potato Research 56, 31–50.
Climate change and potato production in contrasting South African agro-ecosystems 1. Effects on land and water use efficiencies.Crossref | GoogleScholarGoogle Scholar |

Ivey CT, Carr DE (2012) Tests for the joint evolution of mating system and drought escape in Mimulus. Annals of Botany 109, 583–598.
Tests for the joint evolution of mating system and drought escape in Mimulus.Crossref | GoogleScholarGoogle Scholar |

Keeling PL, Greaves JA (1990) Effects of temperature stresses on corn: opportunities for breeding and biotechnology. In ‘Proceedings 45th Annual Corn and Sorghum Research Conference’. Chicago, IL. (Ed. D Wilkinson) pp. 29–42. (American Seed Trade Association: Washington, DC)

Kenny GJ, Harrison PA (1992) The effects of climate variability and change on grape suitability in Europe. Journal of Wine Research 3, 163–183.
The effects of climate variability and change on grape suitability in Europe.Crossref | GoogleScholarGoogle Scholar |

Klein T, Samourkasidis A, Athanasiadis IN, Bellocchi G, Calanca P (2017) webXTREME: R-based web tool for calculating agroclimatic indices of extreme events. Computers and Electronics in Agriculture 136, 111–116.
webXTREME: R-based web tool for calculating agroclimatic indices of extreme events.Crossref | GoogleScholarGoogle Scholar |

Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529, 84–87.
Influence of extreme weather disasters on global crop production.Crossref | GoogleScholarGoogle Scholar |

Lobell DB, Bänziger M, Magorokosho C, Vivek B (2011) Nonlinear heat effects on African maize as evidenced by historical yield trials. Nature Climate Change 1, 42–45.
Nonlinear heat effects on African maize as evidenced by historical yield trials.Crossref | GoogleScholarGoogle Scholar |

Lobell DB, Hammer GL, McLean G, Messina C, Roberts MJ, Schlenker W (2013) The critical role of extreme heat for maize production in the United States. Nature Climate Change 3, 497–501.
The critical role of extreme heat for maize production in the United States.Crossref | GoogleScholarGoogle Scholar |

McMaster GS, Wilhelm WW (1997) Growing degree-days: one equation, two interpretations. Agricultural and Forest Meteorology 87, 291–300.
Growing degree-days: one equation, two interpretations.Crossref | GoogleScholarGoogle Scholar |

Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA (2010) The next generation of scenarios for climate change research and assessment. Nature 463, 747–756.
The next generation of scenarios for climate change research and assessment.Crossref | GoogleScholarGoogle Scholar |

Motha RP (2011) The impact of extreme weather events on agriculture in the United States. In ‘Challenges and opportunities in agrometeorology’. pp. 397–407. (Springer: Dordrecht, The Netherlands) http://link.springer.com/chapter/10.1007/978-3-642-19360-6_30

Nelson GC, Valin H, Sands RD, Havlík P, Ahammad H, Deryng D, Elliott J, Fujimori S, Hasegawa T, Heyhoe E, Kyle P (2014) Climate change effects on agriculture: economic responses to biophysical shocks. Proceedings of the National Academy of Sciences of the United States of America 111, 3274–3279.
Climate change effects on agriculture: economic responses to biophysical shocks.Crossref | GoogleScholarGoogle Scholar |

Nhemachena C (2009) Agriculture and future climate dynamics in Africa: Impacts and adaptation options. PhD Dissertation in Environmental Economics, University of Pretoria, South Africa.

Orlowsky B, Seneviratne SI (2012) Global changes in extreme events: regional and seasonal dimension. Climatic Change 110, 669–696.
Global changes in extreme events: regional and seasonal dimension.Crossref | GoogleScholarGoogle Scholar |

Palosuo T, Kersebaum KC, Angulo C, Hlavinka P, Moriondo M, Olesen JE, Patil RH, Ruget F, Rumbaur C, Takáč J, Trnka M (2011) Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. European Journal of Agronomy 35, 103–114.
Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models.Crossref | GoogleScholarGoogle Scholar |

Porter JR, Semenov AM (2005) Crop responses to climatic variation. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 360, 2021–2035.
Crop responses to climatic variation.Crossref | GoogleScholarGoogle Scholar |

Powell JP, Reinhard S (2016) Measuring the effects of extreme weather events on yields. Weather and Climate Extremes 12, 69–79.
Measuring the effects of extreme weather events on yields.Crossref | GoogleScholarGoogle Scholar |

Rafiee M, Shakarami G (2010) Water use efficiency of corn as affected by every other furrow irrigation and planting density. World Applied Sciences Journal 11, 826–829.

Riboni M, Robustelli Test A, Galbiati M, Tonelli C, Conti L (2014) Environmental stress and flowering time: the photoperiodic connection. Plant Signaling & Behavior 9, e29036
Environmental stress and flowering time: the photoperiodic connection.Crossref | GoogleScholarGoogle Scholar |

Rötter RP, Carter TR, Olesen JE, Porter JR (2011) Crop–climate models need an overhaul. Nature Climate Change 1, 175–177.
Crop–climate models need an overhaul.Crossref | GoogleScholarGoogle Scholar |

Rötter RP, Palosuo T, Kersebaum KC, Angulo C, Bindi M, Ewert R, Ferrise R, Hlavinka P, Moriondo M, Nendel CO, Olesen JE (2012) Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models. Field Crops Research 133, 22–36.
Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models.Crossref | GoogleScholarGoogle Scholar |

Saini HS, Westgate ME (1999) Reproductive development in grain crops during drought. Advances in Agronomy 68, 59–96.
Reproductive development in grain crops during drought.Crossref | GoogleScholarGoogle Scholar |

Setter TL, Flannigan BA, Melkonian J (2001) Loss of kernel set due to water deficit and shade in maize. Crop Science 41, 1530–1540.
Loss of kernel set due to water deficit and shade in maize.Crossref | GoogleScholarGoogle Scholar |

Sharp RG, Else MA, Cameron RW, Davies WJ (2009) Water deficits promote f lowering in Rhododendron via regulation of pre and post initiation development. Scientia Horticulturae 120, 511–517.
Water deficits promote f lowering in Rhododendron via regulation of pre and post initiation development.Crossref | GoogleScholarGoogle Scholar |

Shaw RH (1983) Estimates of yield reductions in corn caused by water and temperature stress. In ‘Crop reaction to water and temperature stress in humid, temperate climates’. pp. 49–65. (Westview Press: New York)

Singletary GW, Banisadr R, Keeling PL (1994) Heat stress during grain filling in maize: effects on carbohydrate storage and metabolism. Functional Plant Biology 21, 829–841.
Heat stress during grain filling in maize: effects on carbohydrate storage and metabolism.Crossref | GoogleScholarGoogle Scholar |

Soil Classification Working Group (1991) ‘Soil Classification: A taxonomic system for South Africa.’ (Department of Agricultural Development: Pretoria, South Africa)

Soil Survey Staff (2014) ‘Keys to Soil Taxonomy.’ 12th edn (USDA-Natural Resources Conservation Service: Washington, DC)

Sommer R, Kienzler K, Conrad C, Ibragimov N, Lamers J, Martius C, Vlek P (2008) Evaluation of the CropSyst model for simulating the potential yield of cotton. Agronomy for Sustainable Development 28, 345–354.
Evaluation of the CropSyst model for simulating the potential yield of cotton.Crossref | GoogleScholarGoogle Scholar |

Sommer R, Glazirina M, Yuldashev T, Otarov A, Ibraeva M, Martynova L, Bekenov M, Kholov B, Ibragimov N, Kobilov R, Karaev S, Sultonov M, Khasanova F, Esanbekov M, Mavlyanov D, Isaev S, Abdurahimov S, Ikramov R, Shezdyukova L, de Pauw E (2013) Impact of climate change on wheat productivity in Central Asia. Agriculture, Ecosystems & Environment 178, 78–99.
Impact of climate change on wheat productivity in Central Asia.Crossref | GoogleScholarGoogle Scholar |

Stöckle CO, Martin MA, Campbell GS (1994) CropSyst, a cropping systems simulation model: water/nitrogen budgets and crop yield. Agricultural Systems 46, 335–359.
CropSyst, a cropping systems simulation model: water/nitrogen budgets and crop yield.Crossref | GoogleScholarGoogle Scholar |

Stöckle CO, Cabelguenne M, Debaeke P (1997) Comparison of CropSyst performance for water management in southwestern France using sub models of different levels of complexity. European Journal of Agronomy 7, 89–98.
Comparison of CropSyst performance for water management in southwestern France using sub models of different levels of complexity.Crossref | GoogleScholarGoogle Scholar |

Stöckle CO, Donatelli M, Nelson R (2003) CropSyst, a cropping systems simulation model. European Journal of Agronomy 18, 289–307.
CropSyst, a cropping systems simulation model.Crossref | GoogleScholarGoogle Scholar |

Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2007) Going to the extremes: An intercomparison of model-simulated historical and future changes in extreme events. Climatic Change 79, 185–211.

Tingem M, Rivington M (2009) Adaptation for crop agriculture to climate change in Cameroon: turning on the heat. Mitigation and Adaptation Strategies for Global Change 14, 153–168.
Adaptation for crop agriculture to climate change in Cameroon: turning on the heat.Crossref | GoogleScholarGoogle Scholar |

Todorovic M, Albrizio R, Zivotic L, Saab MTA, Stöckle C, Steduto P (2009) Assessment of AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agronomy Journal 101, 509–521.
Assessment of AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes.Crossref | GoogleScholarGoogle Scholar |

Tubiello FN, Donatelli M, Rosenzweig C, Stockle CO (2000) Effects of climate change and elevated CO2 on cropping systems: Model predictions at two Italian locations. European Journal of Agronomy 13, 179–189.
Effects of climate change and elevated CO2 on cropping systems: Model predictions at two Italian locations.Crossref | GoogleScholarGoogle Scholar |

van der Velde M, Tubiello FN, Vrieling A, Bouraoui F (2012) Impacts of extreme weather on wheat and maize in France: evaluating regional crop simulations against observed data. Climatic Change 113, 751–765.
Impacts of extreme weather on wheat and maize in France: evaluating regional crop simulations against observed data.Crossref | GoogleScholarGoogle Scholar |

van Keulen H, Penning de Vries FWT, Drees EM (1982) A summary model for crop growth. In ‘Simulation of plant growth and crop production’. pp. 87–97. (Pudoc: Wageningen, The Netherlands)

van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF, Masui T (2011) The representative concentration pathways: An overview. Climatic Change 109, 5–31.
The representative concentration pathways: An overview.Crossref | GoogleScholarGoogle Scholar |

Villalobos F, Tardieu F, Bellocchi G, de Melo e Abreu JP, Parent B, Morales A, Zaka S, Louarn G, Borras D, Martin R, Leolini L, Moriondo M, Donatelli M, Confalonieri R, Bindi M, Testi L (2015) Report on modelling approaches for simulating the impact of extreme events on agricultural production. EU-FP7 MODEXTREME (http://modextreme.org). Available at: http://prodinra.inra.fr/ft?id={A6CBE276-3AA4-47D6-B4B0-B6F4D1FBBD80} (accessed 20 February 2016)

Wilcke RAI, Mendlik T, Gobiet A (2013) Multi-variable error correction of regional climate models. Climatic Change 120, 871–887.
Multi-variable error correction of regional climate models.Crossref | GoogleScholarGoogle Scholar |

Wolf J, Evans LG, Semenov MA, Eckersten H, Iglesias A (1996) Comparison of wheat simulation models under climate change. I. Model calibration and sensitivity analyses. Climate Research 7, 253–270.
Comparison of wheat simulation models under climate change. I. Model calibration and sensitivity analyses.Crossref | GoogleScholarGoogle Scholar |

Woli P, Jones JW, Ingram KT, Fraisse CW (2012) Agricultural reference index for drought (ARID). Agronomy Journal 104, 287–300.
Agricultural reference index for drought (ARID).Crossref | GoogleScholarGoogle Scholar |

Zinyengere N, Crespo O, Hachigonta S (2013) Crop response to climate change in Southern Africa: A comprehensive review. Global and Planetary Change 111, 118–126.
Crop response to climate change in Southern Africa: A comprehensive review.Crossref | GoogleScholarGoogle Scholar |

Zinyengere N, Crespo O, Hachigonta S, Tadross M (2014) Local impacts of climate change and agronomic practices on dry land crops in Southern Africa. Agriculture, Ecosystems & Environment 197, 1–10.
Local impacts of climate change and agronomic practices on dry land crops in Southern Africa.Crossref | GoogleScholarGoogle Scholar |