Simulating the risk of heat stress on grain maize production under arid and semi-arid conditions

Document Type : Original Article

Authors

1 Department of Agronomy and Plants Breeding, Faculty of Agricultural, Lorestan University, Khorram Abad, Iran

2 Department of Agroecology, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran

Abstract

Introduction:
Heat stress is one of the most important threats and concerns for maize production, which mostly occurs in hot and dry areas. Heat stress reduces grain yield and the plant's photosynthesis rate and increases transpiration. Maize is very sensitive to heat stress and extreme temperatures at the flowering stage because extreme temperatures decrease pollen germination ability, and thus, decrease grain yield. However, there are some strategies to prevent the maize flowering stage from being exposed to heat stress. Careful management practices including adjusting the sowing time and cultivar can be considered as useful strategies to deal with heat stress. Crop simulation models can be used to investigate these practices. Therefore, the present study was carried out to evaluate the risk of heat stress (frequency and intensity of heat) on grain maize of Iran and evaluate the risk window for grain maize using the modeling approach.
Material and methods:
In order to evaluate the risk of heat stress in maize agroecosystems of Iran, a simulation experiment was designed in five regions (Iranshahr, Dezful, Parsabad, Kermanshah, and Kerman), three sowing times (common: farmers sowing time in each region; late: 20 days after common sowing time; early: 20 days before common sowing time), and two cultivars (SC704 and SC260 as late- and early-maturity cultivars, respectively). To do this, the long-term climatic data of each region including minimum and maximum temperatures, rainfall, and radiation were collected from Iran Meteorological Organization. These data were applied as inputs of the crop simulation model. In this study, the APSIM model was employed to simulate the growth and development of the maize plant. In order to assess the risk of heat stress on grain maize, three dimensions including the critical stage of grain maize to extreme temperatures (flowering), frequency of extreme temperatures at the critical stage, and intensity of extreme temperatures at the critical stage were evaluated. Furthermore, the risk window for maize flowering in each region was equal to the first day of the year with a temperature of over 36 °C until the last day of the year with a temperature above 36 °C.
Results and discussion:
The highest risk window of extreme temperatures was recorded in Iranshahr County (183 days) as a hot and dry region and the lowest risk window was simulated in Parsabad (14 days) as a semi-arid and temperate region. Moreover, the percentage of the number of maize flowering days with temperatures above 36 °C and the mean maximum temperature during the flowering period were 63.5% and 37.09 °C, respectively. This issue reduced the grain yield of maize in Iran so that the grain yield was simulated 6196.5 kg ha-1 . However, in the spring season, the early sowing time and the early-maturity cultivar decreased the percentage of the number of maize flowering days with temperatures above 36 °C (37.2%) and mean maximum temperature during the flowering period (35.1 °C) and increased grain yield (7486.9 kg ha-1 ). Overall, in the summer, the percentage of the number of maize flowering days with temperatures above 36 °C and mean maximum temperature during the flowering period were decreased 38.9% and 35.3 °C, respectively, and grain yield was boosted to 7743.6 kg ha-1 under the combination of late sowing time and late-maturity cultivar.
Conclusion:
The results showed that grain maize is currently cultivated by farmers under high-risk conditions of heat stress. In order to reduce the risk and increase grain yield, farmers in each region should apply the optimal sowing times and cultivars according to the growing season.

Keywords

References

  1. Anderson, W., 2010. Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar. Field Crops Research. 116(1), 14–22.
  2. Anonymous, 2016. Research center for agriculture and natural resources of Fars Province. 2016. Series reports of yield and stability in early maturity maize hybrids in 2007, 2008, 2011 and 2012. Available online at: http://fars.areeo.ac.ir.
  3. Anonymous, 2017a. Agricultural statistics. Department of planning and economy. Information and communication center. Ministry of agriculture Jihad, Iran. Available online at: https://www.maj.ir/
  4. Anonymous, 2017b. Tehran chamber of commerce, industries, mines and agriculture. economic research department. The trade state of agricultural products and food during the first nine months of 2017. Available online at: http://en.tccim.ir/.
  5. Bontkes, T.S. and Wopereis, M., 2003. Decision Support Tools for Smallholder Agriculture in Sub-Saharan Africa. A Practical Guide. International Center for Soil Fertility and Agricultural Development (IFDC), Muscle Shoals, AL, USA, and Technical Centre for Agricultural and Rural Cooperation (CTA), Wageningen, Netherlands.
  6. Butler, E.E. and Huybers, P., 2013. Adaptation of US maize to temperature variations. Nature Climate Change. 3(1), 68-72.
  7. Carberry, P., Muchow, R. and McCown, R., 1989. Testing the CERES-maize simulation model in a semi-arid tropical environment. Field Crops Research. 20(4), 297–315.
  8. Chenu, K., Deihimfard, R. and Chapman, S.C., 2013. Large-scale characterization of drought pattern: a continentwide modelling approach applied to the Australian wheatbelt—spatial and temporal trends. New Phytologist. 198, 801–820.
  9. Chenu. K., 2014. Characterizing the crop environment–nature, significance and applications. In Sadras, V. and Calderini, D. (Eds.), Crop Physiology: Applications for Genetic Improvement and Agronomy. Academic Press, Massachusetts, USA.
  10. Choukan, R., 2013. Final Report of Yield Trial and Adaptability of Late and Medium Maturing Promising Hybrids of Maize (Final Stage). Seed and Plant Improvement Institute, Iran. (In Persian with English abstract).
  11. Crafts‐Brandner, S.J. and Salvucci, M.E., 2002. Sensitivity of photosynthesis in a C4 plant, maize, to heat stress. Plant Physiology. 129(4), 1773–1780.
  12. Dehghanpour, Z. and Estakhr, A., 2010. Determination of the suitable planting date for new early maturity maize hybrids in second cropping in temprate regions in Fars province. Seed Plant Production Journal. 26(2), 169-191. (In Persian with English abstract).
  13. Deihimfard, R., Mahallati, M.N. and Koocheki, A., 2015. Yield gap analysis in major wheat growing areas of Khorasan province, Iran, through crop modelling. Field Crops Research. 184, 28-38.
  14. Deihimfard, R., Rahimi-Moghaddam, S. and Chenu, K., 2019. Risk assessment of frost damage to sugar beet simulated under cold and semi-arid environments. International Journal of Biometeorology. 13, 1-11.
  15. Dupuis, I. and Dumas, C., 1990. Influence of temperature stress on in vitro fertilization and heat shock protein synthesis in maize (Zea mays L.) reproductive tissues. Plant Physiology. 94(2), 665–670.
  16. Dwyer, L.M., Evanson, L. and Hamilton, R.I., 2003. Maize physiological traits related to grain yield and harvest moisture in mid-to short season environments. Crop Science. 34, 985-992.
  17. Emam, Y., Sedaghat, M. and Bahrani, H., 2013. Responses of maize (SC704) yield and yield components to source restriction. Iran Agricultural Research. 32(1), 31-40. (In Persian with English abstract).
  18. Estakhr, A. and Choukan, R., 2011. Effect of planting date on grain yield and its components and reaction to important maize viruses in Fars Province in some exotic and Iranian maize hybrids. Seed Plant Production Journal. 27(3), 313-333. (In Persian with English abstract).
  19. Goldani, M., Rezvani, M.P., Nassiri, M.M. and Kaffi, M., 2011. Radiation use efficiency and phenological and physiological characteristics in hybrids of maize (Zea may L.) on response to different densities. Journal of Plant Production Research. 18, 1-26. (In Persian with English abstract).
  20. Gourdji, S.M., Sibley, AM. and Lobell, D.B., 2013. Global crop exposure to critical high temperatures in the reproductive period: historical trends and future projections. Environmental Research Letters. 8(2), 1-11.
  21. Hammer, G.L., van Oosterom, E., McLean, G., Chapman, S.C., Broad, I., Harland, P. and Muchow, R.C., 2010. Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. ‎Journal of Experimental Botany. 61(8), 2185–2202.
  22. Hasanzadeh, M.H. and Dehghanpour, Z., 2010. Final report of the study of yield and compatibility in early maturity maize hybrids. Center of Agriculture research and nature resources of Khorasan Razavi province. Iran. (In Persian with English abstract).
  23. He, D., Wang, E., Wang, J. and Lilley, J.M., 2017. Genotype× environment× management interactions of canola across China: a simulation study. Agricultural and Forest Meteorology. 247, 424-433.
  24. He, D., Wang, E., Wang, J. and Robertson, M.J., 2017. Data requirement for effective calibration of process-based crop models. Agricultural and Forest Meteorology. 234, 136–148.
  25. Holzworth, D.P., Huth, N.I., Zurcher, E.J., Herrmann, N.I., McLean, G., Chenu, K., van Oosterom, E.J., Snow, V., Murphy, C., Moore, A.D. and Brown, H., 2014. APSIM—evolution towards a new generation of agricultural systems simulation. Environmental Modelling and Software. 62, 327–350.
  26. Hoogenboom, G., Jones, J.W., Porter, C.H., Wilkens, P.W., Boote, K.J., Batchelor, W.D., Hunt, L.A. and Tsuji, G.Y., (Editors). 2003. Decision Support System for Agrotechnology Transfer Version 4.0. Volume 1: Overview. University of Hawaii, Honolulu, Hawaii.
  27. Kamara, A.Y., Ekeleme, F., Chikoye, D. and Omoigui, L.O., 2009. Planting Date and cultivar effects on grain yield in dryland corn production. Agronomy Journal. 101, 91–98.
  28. Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N., Meinke, H., Hochman, Z. and McLean, G., 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy. 18(3), 267–288.
  29. Liu, Z., Hubbard, K. G., Lin, X. and Yang, X., 2013. Negative effects of climate warming on maize yield are reversed by the changing of sowing date and cultivar selection in Northeast China. Global Change Biology. 19(11), 3481-3492.
  30. Madadizadeh, M., 2017. Simulation of growth and development of current maize (Zea mays L.) hybrids under different nitrogen levels in Kerman Province, Iran. Shahid Beheshti University, Tehran, Iran. (In Persian with English abstract).
  31. Makowski, D., Naud, C., Jeffroy, M.H., Barbttin, A. and Monod, H., 2006. Global sensitivity analysis for calculating the contribution of genetic parameters to the variance of crop model prediction. Reliability Engineering & System Safety. 91, 1142-1147.
  32. Moeinirad, A., Pirdashti, H., Eaghanehpour, F. and Mokhtarpour, H., 2013. Effecct of sowing date and plant density on phenology, morphology and yield of Maize cv. KSC704 in Gorgan. Journal of Research in Crop Sciences. 19, 41- 56. (In Persian).
  33. Moradi, R., Koocheki, A., Mahallati, M.N. and Mansoori, H., 2013. Adaptation strategies for maize cultivation under climate change in Iran: irrigation and planting date management. Mitigation and Adaptation Strategies for Global Change. 18(2), 265-284.
  34. Naderi, F., Siadat, S.A. and Rafiee, M., 2010. Effect of planting date and plant density on grain yield and yield components of two maize hybrids as second crop in Khorram Abad. Iranian Journal of Crop Sciences. 12(1), 31- 41. (In Persian).
  35. Nassiri, M., Koocheki, A., Kamali, G.A. and Shahandeh, H., 2006. Potential impact of climate change on rainfed wheat production in Iran: (Potentieller Einfluss des Klimawandels auf die Weizenproduktion unter Rainfed-Bedingungen im Iran). Archives of Agronomy and Soil Science. 52(1), 113-124.
  36. Nassiri, M.M., 2008. Modeling crop growth Processes. Mashhad University Press, Mashhad, Iran. (In Persian).
  37. Prescott, J., 1940. Evaporation from a water surface in relation to solar radiation. Transactions of The Royal Society of South Australia. 64(1), 114–118
  38. Rahimi-Moghaddam, S., 2013. Determination of genetic coefficients of some maize (Zea mays L.) cultivars in Iran to be applied in crop simulation models. M.Sc. Thesis. Shahid Beheshti University, Tehran, Iran. (In Persian with English abstract).
  39. Rahimi-Moghaddam, S., Deihimfard, R., Soufizadeh, S., Kambouzia, J., Nazariyan Firuzabadi, F. and Eyni Nargeseh, H., 2015. Determination of genetic coefficients of some maize (Zea mays L.) cultivars of Iran for application in crop simulation models. Iranian Journal of Field Crops Research. 13(2), 328-339. (In Persian with English abstract).
  40. Rahimi-Moghaddam, S., Kambouzia, J. and Deihimfard, R., 2018. Adaptation strategies to lessen negative impact of climate change on grain maize under hot climatic conditions: a model-based assessment. Agricultural and Forest Meteorology. 253, 1-14.
  41. Rahimi-Moghaddam, S., Kambouzia, J. and Deihimfard, R., 2019. Optimal genotype × environment × management as a strategy to increase grain maize productivity and water use efficiency in water-limited environments and rising temperature. Ecological Indicators. 107, 1-16.
  42. Saberi, A., Ghoshchi, F., Sirani, S. and Safahani, A., 2008. Effect of plant density and planting pattern on grain yield of maize cv. KSC704 in Gorgan. Seed Plant Production Journal. 19, 96-111. (In Persian).
  43. Salehi, B., 2005. Effect of row spacing and plant density on grain yield and yield components in maize (cv. Sc 704) in Miyaneh. Iranian Journal of Crop Sciences. 6(4), 383-395. (In Persian with English abstract).
  44. Seifert, E., 2014. OriginPro 9.1: scientific data analysis and graphing software—software review. Journal of Chemical Information and Modeling. 54(5), 1552–1552.
  45. Sharma, D.L., D’Antuono, M.F., Anderson, W.K., Shackley, B.J., Zaicou-Kunesch, C.M. and Amjad, M., 2008. Variability of optimum sowing time for wheat yield in Western Australia. Crop and Pasture Science. 59(10), 958–970.
  46. Shi, P., Tang, L., Wang, L., Sun, T., Liu, L., Cao, W. and Zhu, Y., 2015. Post-heading heat stress in rice of south China during 1981-2010. PLOS ONE. 10(6), e0130642. DOI: https://doi.org/10.1371/journal.pone.0130642.
  47. Stone, P., 2000. The Effects of Heat Stress on Cereal Yield and Quality. In A.S. Basra eds. Crop Responses and Adaptations to Temperature Stress. Food Products Press, Inghamton, USA.
  48. Tebaldi, C. and Lobell, D., 2018. Differences, or lack thereof, in wheat and maize yields under three low‐warming scenarios open access differences, or lack thereof, in wheat and maize yields under three low‐warming scenarios. Environmental Research Letters. 13(6), 065001.
  49. Teixeira, E.I., Fischer, G., van Velthuizen, H., Walter, C. and Ewert, F., 2013. Global hot-spots of heat stress on agricultural crops due to climate change. Agricultural and Forest Meteorology. 170, 206-215.
  50. Wallach, D. and Goffinet, B., 1987. Mean squared error of prediction in models for studying economic and agricultural systems. Biometrics. 43, 561–576.
  51. Watson, J., Zheng, B., Chapman, S. and Chenu, K., 2017. Projected impact of future climate on water-stress patterns across the Australian wheatbelt. Journal of Experimental Botany. 68(21-22), 5907-5921.
  52. Willmott, C.J., 1982. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society. 63, 1309–1313.
Environmental Sciences
Volume 18, Issue 3
October 2020
Pages 85-105

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