Dynamic Assessment of Global Maize Exposure to Extremely High Temperatures

Abstract

Exposure to extreme heat can severely harm crop growth and development, and it is essential to assess such exposure accurately to minimize risks to crop production. However, the actual distribution of crops and its changes have neither been examined in sufficient detail nor integrated into the assessments of exposure to ensure their accuracy. By examining the distribution of maize at a high resolution through species distribution modeling, we assessed the past and future exposure of maize to temperatures above 37°C worldwide. Such exposure is likely to be widespread and severe, mainly in the subtropics, and may even expand to the mid-latitudes to encompass some major maize-producing areas. Many areas at both high and low latitudes may become exposed for the first time in the next 20 years. By the 2050s, the total area exposed could increase by up to 185% to 308.18 million ha, of which the area exposed for over 60 days may increase nearly sevenfold. The average length of exposure may increase by 69% to 27 days, and areas optimally suited to maize planting may see the fastest increase by up to 772%. Extreme heat can threaten global maize production severely, and measures to mitigate that threat and to adapt to it are urgently needed.

Keywords

Climate change / Exposure to extreme heat / Maize / Maxent model / Potential maize distribution

References

[1] Anandhi A, Steiner JL, Bailey N. A system’s approach to assess the exposure of agricultural production to climate change and variability. Climatic Change, 2016, 136(3–4): 647-659

[2] Araújo MB, Guisan A. Five (or so) challenges for species distribution modelling. Journal of Biogeography, 2006, 33(10): 1677-1688

[3] Badu-Apraku B, Fakorede MAB. Janick J. Breeding early and extra-early maize for resistance to biotic and abiotic stresses in Sub-Saharan Africa. Plant breeding reviews, 2013, New Jersey: John Wiley 123-205

[4] Baker, N.T., and P.D. Capel. 2011. Environmental factors that influence the location of crop agriculture in the conterminous United States. U.S. Geological Survey Scientific Investigations Report 2011–5108. Reston, VA: USGS.

[5] Banziger M, Setimela PS, Hodson D, Vivek B. Breeding for improved abiotic stress tolerance in maize adapted to southern Africa. Agricultural Water Management, 2006, 80(1–3): 212-224

[6] Batjes NH. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma, 2016, 269: 61-68

[7] Blair, D., C. Shackleton, and P. Mograbi. 2018. Cropland abandonment in South African smallholder communal lands: Land cover change (1950–2010) and farmer perceptions of contributing factors. Land 7(4): 121.

[8] Bouwer LM. Have disaster losses increased due to anthropogenic climate change?. Bulletin of the American Meteorological Society, 2011, 92(1): 39-46

[9] Bouwer LM. Projections of future extreme weather losses under changes in climate and exposure. Risk Analysis, 2013, 33(5): 915-930

[10] Cooper M, Gho C, Leafgren R, Tang T, Messina C. Breeding drought-tolerant maize hybrids for the US corn-belt: Discovery to product. Journal of Experimental Botany, 2014, 65(21): 6191-6204

[11] Danielson, J.J., and D.B. Gesch. 2011. Global multi-resolution terrain elevation data 2010 (GMTED2010). U.S. Geological Survey open-file report 2011–1073. Reston, VA: USGS.

[12] Dhondt S, Beckx C, Degraeuwe B, Lefebvre W, Kochan B, Bellemans T, Panis LI, Macharis C, Putman K. Health impact assessment of air pollution using a dynamic exposure profile: Implications for exposure and health impact estimates. Environmental Impact Assessment Review, 2012, 36: 42-51

[13] Eaton WM, Burnham M, Hinrichs CC, Selfa T, Yang S. How do sociocultural factors shape rural landowner responses to the prospect of perennial bioenergy crops?. Landscape and Urban Planning, 2018, 175: 195-204

[14] Elith J, Leathwick JR. Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 2009, 40(1): 677-697

[15] Elith J, Graham CH, Anderson RP, Dudık M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 2006, 29(2): 129-151

[16] Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 2011, 17(1): 43-57

[17] Evans JM, Fletcher RJ, Alavalapati J. Using species distribution models to identify suitable areas for biofuel feedstock production. Global Change Biology Bioenergy, 2010, 2(2): 63-78

[18] FAO (Food and Agriculture Organization of the United Nations). 2018. FAOSTAT. http://www.fao.org/faostat/en/#data/QC. Accessed 25 Sept 2020.

[19] Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, and D. Wiberg. 2008. Global agro-ecological zones assessment for agriculture (GAEZ 2008). Laxenburg, Austria and Rome, Italy: IIASA and FAO.

[20] Fourcade, Y., J.O. Engler, D. Rodder, and J. Secondi. 2014. Mapping species distributions with MAXENT using a geographically biased sample of presence data: A performance assessment of methods for correcting sampling bias. PLoS One 9(5): Article e97122.

[21] Freire S, Aubrecht C. Integrating population dynamics into mapping human exposure to seismic hazard. Natural Hazards and Earth System Sciences, 2012, 12(11): 3533-3543

[22] Gourdji, S.M., A.M. Sibley, and D.B. Lobell. 2013. Global crop exposure to critical high temperatures in the reproductive period: Historical trends and future projections. Environmental Research Letters 8(2): Article 024041.

[23] Griffiths, P., D. Muller, T. Kuemmerle, and P. Hostert. 2013. Agricultural land change in the Carpathian ecoregion after the breakdown of socialism and expansion of the European Union. Environmental Research Letters 8(4): Article 045024.

[24] Hatfield JL, Boote KJ, Kimball BA, Ziska LH, Izaurralde RC, Ort D, Thomson AM, Wolfe D. Climate impacts on agriculture: Implications for crop production. Agronomy Journal, 2011, 103(2): 351-370

[25] He, Q., G. Zhou, X. Lü, and M. Zhou. 2019. Climatic suitability and spatial distribution for summer maize cultivation in China at 1.5 and 2.0 °C global warming. Science Bulletin 64(10): 690–697.

[26] Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 2005, 25(15): 1965-1978

[27] Hu, X., L. Wu, F. Zhao, D. Zhang, N. Li, G. Zhu, C. Li, and W. Wang. 2015. Phosphoproteomic analysis of the response of maize leaves to drought, heat and their combination stress. Frontiers in Plant Science 6: Article 298.

[28] Hurtt GC, Chini L, Sahajpal R, Frolking S, Bodirsky BL, Calvin K, Doelman JC, Fisk J Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geoscientific Model Development, 2020, 13(11): 5425-5464

[29] IPCC (Intergovernmental Panel on Climate Change). 1996. Climate change 1995: Impacts, adaptations and mitigation of climate change: Scientific‐technical analyses. Contribution of Working Group II to the Second Assessment Report of the Intergovernmental Panel on Climate Change, ed. R.T. Watson, M.C. Zinyowera, and R.H. Moss. Cambridge and New York: Cambridge University Press.

[30] IPCC (Intergovernmental Panel on Climate Change). 2014. Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, ed. C.B. Field, V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, et al. Cambridge and New York: Cambridge University Press.

[31] Kogo, B.K., L. Kumar, R. Koech, and C.S. Kariyawasam. 2019. Modelling climate suitability for rainfed maize cultivation in Kenya using a Maximum Entropy (MaxENT) approach. Agronomy-Basel 9(11): Article 727.

[32] Kullback S, Leibler RA. On information and sufficiency. The Annals of Mathematical Statistics, 1951, 22(1): 79-86

[33] Lobell DB, Hammer GL, McLean G, Messina C, Roberts MJ, Schlenker W. The critical role of extreme heat for maize production in the United States. Nature Climate Change, 2013, 3(5): 497-501

[34] Long SP, Spence AK. Toward cool C4 crops. Annual Review of Plant Biology, 2013, 64: 701-722

[35] Luo QY. Temperature thresholds and crop production: A review. Climatic Change, 2011, 109(3–4): 583-598

[36] Matiu, M., D.P. Ankerst, and A. Menzel. 2017. Interactions between temperature and drought in global and regional crop yield variability during 1961–2014. PLoS One 12(5): Article e0178339.

[37] Monfreda, C., N. Ramankutty, and J.A. Foley. 2008. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles 22 (1): Article GB1022.

[38] Noojipady, P., C.D. Morton, N.M. Macedo, C.D. Victoria, C.Q. Huang, K.H. Gibbs, and L.E.Bolfe. 2017. Forest carbon emissions from cropland expansion in the Brazilian Cerrado biome. Environmental Research Letters 12(2): Article 025004.

[39] Phillips SJ, Dudik M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography, 2008, 31(2): 161-175

[40] Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 2006, 190(3–4): 231-259

[41] Pittore M, Wieland M, Fleming K. Perspectives on global dynamic exposure modelling for geo-risk assessment. Natural Hazards, 2016, 86(S1): 7-30

[42] Prasanna BM. Rajpal VR, Rao SR, Raina SN. Developing and deploying abiotic stress-tolerant maize varieties in the tropics: Challenges and opportunities. Molecular breeding for sustainable crop improvement, 2016, Cham: Springer International Publishing 61-77

[43] Ramirez-Cabral, N.Y.Z., L. Kumar, and F. Shabani. 2017. Global alterations in areas of suitability for maize production from climate change and using a mechanistic species distribution model (CLIMEX). Scientific Reports 7(1): Article 5910.

[44] Ranum P, Peña-Rosas JP, Garcia-Casal MN. Global maize production, utilization, and consumption. Annals of the New York Academy of Sciences, 2014, 1312(1): 105-112

[45] Sacks WJ, Deryng D, Foley JA, Ramankutty N. Crop planting dates: An analysis of global patterns. Global Ecology and Biogeography, 2010, 19(5): 607-620.

[46] Sanchez B, Rasmussen A, Porter JR. Temperatures and the growth and development of maize and rice: A review. Global Change Biology, 2014, 20(2): 408-417

[47] Schroth G, Ruf F. Farmer strategies for tree crop diversification in the humid tropics: A review. Agronomy for Sustainable Development, 2013, 34(1): 139-154

[48] Shabani F, Kotey B. Future distribution of cotton and wheat in Australia under potential climate change. Journal of Agricultural Science, 2016, 154(2): 175-185

[49] Shi P. Disaster risk science, 2019 2 Singapore and Beijing: Springer and Beijing Normal University Press

[50] Shi P, Sun S, Wang M, Li N, Wang JA, Jin YY, Gu XT, Yin WX. Climate change regionalization in China (1961–2010). Science China Earth Sciences, 2014, 57(11): 2676-2689

[51] Stone P. Basra AS. The effects of heat stress on cereal yield and quality. Crop responses and adaptations to temperature stress, 2001, Binghamton, New York: Food Products Press 243-291.

[52] Su, P., A. Zhang, R. Wang, J.A. Wang, Y. Gao, and F. Liu. 2021. Prediction of future natural suitable areas for rice under Representative Concentration Pathways (RCPs). Sustainability 13(3): Article 1580.

[53] Sun JS, Zhou GS, Sui XH. Climatic suitability of the distribution of the winter wheat cultivation zone in China. European Journal of Agronomy, 2012, 43: 77-86

[54] Swastika, D.K.S., F. Kasim, W. Sudana, R. Hendayana, K. Suhariyanto, R. Gerpacio, and P. Pingali. 2004. Maize in Indonesia: Production systems, constraints, and research priorities. Texcoco, Mexico: CIMMYT (International Maize and Wheat Improvement Center).

[55] Teixeira EI, Fischer G, van Velthuizen H, Walter C, Ewert F. Global hot-spots of heat stress on agricultural crops due to climate change. Agricultural and Forest Meteorology, 2013, 170: 206-215

[56] Thrasher, B., and R. Nemani. 2012. NASA earth exchange global daily downscaled projections (NEX-GDDP). https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp. Accessed 28 Aug 2017.

[57] UNISDR (United Nations Office for Disaster Risk Reduction) Global assessment report on disaster risk reduction 2015 – Making development sustainable: The future of disaster risk management, 2015, New York: United Nations

[58] Ureta C, González-Salazar C, González EJ, Álvarez-Buylla ER, Martínez-Meyer E. Environmental and social factors account for Mexican maize richness and distribution: A data mining approach. Agriculture, Ecosystems and Environment, 2013, 179: 25-34

[59] Ureta C, Martínez-Meyer E, Perales HR, Álvarez-Buylla ER. Projecting the effects of climate change on the distribution of maize races and their wild relatives in Mexico. Global Change Biology, 2012, 18(3): 1073-1082

[60] van Bussel LGJ, Stehfest E, Siebert S, Müller C, Ewert F. Simulation of the phenological development of wheat and maize at the global scale. Global Ecology and Biogeography, 2015, 24(9): 1018-1029

[61] Wahid A, Gelani S, Ashraf M, Foolad MR. Heat tolerance in plants: An overview. Environmental and Experimental Botany, 2007, 61(3): 199-223

[62] Wang, B., P.Y. Feng, D.L. Liu, and C. Waters. 2020. Modelling biophysical vulnerability of wheat to future climate change: A case study in the eastern Australian wheat belt. Ecological Indicators 114: Article 106290.

[63] Wang, R., Y. Jiang, P. Su, and J.A. Wang. 2019. Global spatial distributions of and trends in rice exposure to high temperature. Sustainability 11(22): Article 6271.

[64] Warszawski L, Frieler K, Huber V, Piontek F, Serdeczny O, Schewe J. The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): Project framework. Proceedings of the National Academy of Sciences, 2014, 111(9): 3228-3232

[65] Yan JZ, Yang ZY, Li ZH, Li XB, Xin LJ, Sun LX. Drivers of cropland abandonment in mountainous areas: A household decision model on farming scale in Southwest China. Land Use Policy, 2016, 57: 459-469

[66] Yue Y, Zhang P, Shang Y. The potential global distribution and dynamics of wheat under multiple climate change scenarios. Science of the Total Environment, 2019, 688: 1308-1318

[67] Zabel, F., B. Putzenlechner, and W. Mauser. 2014. Global agricultural land resources – A high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. PLoS One 9(9): Article e107522.

[68] Zhang GQ, Liu CW, Xiao CH, Xie RZ, Ming B, Hou P, Liu GZ, Xu WJ Optimizing water use efficiency and economic return of super high yield spring maize under drip irrigation and plastic mulching in arid areas of China. Field Crops Research, 2017, 211: 137-146

[69] Zhang L, Yang BY, Li S, Hou YY, Huang DP. Potential rice exposure to heat stress along the Yangtze River in China under RCP8.5 scenario. Agricultural and Forest Meteorology, 2018, 248: 185-196

[70] Zhang L, Zhang Z, Chen Y, Wei X, Song X. Exposure, vulnerability, and adaptation of major maize-growing areas to extreme temperature. Natural Hazards, 2018, 91(3): 1257-1272