Early sowing date as a strategy for improvement of maize yield and maize physiological and phonological characteristics in climate change conditions at Kermanshah Province

Document Type : Research Paper

Author

Abstract

Background and objectives: Climate change can directly affect on worldwide food security because climate change processes (such as increasing of CO2 concentration and temperature and variability of precipitation) have directly affect on crops. Corn such as C4 plants that are sensitive to climate changes. Therefore to reduce the sensitivity of the maize in the face of climate change, we need to apply adaptation strategies. One of the effective strategies is the changes in sowing dates. Many of studies have shown that changes in sowing dates (particularly the use of early planting dates) can reduce the negative effects of climate change.
Materials and methods: This research was conducted in three locations of Kermanshah Province. Accordingly, the future climate in the study areas was generated using long-term (1980-2009) climate data of the baseline (included minimum and maximum temperatures, rainfall and global radiation) and AgMIP technique under two climate scenarios (RCP4.5 and RCP8.5) for the future period of 2040 -2069. Long-term simulation experiments consisted of five sowing dates (5st April, 20st April, 5st May, 20st May, 5st June), three locations (Kermanshah, Kangavar and Eslamabad), two future climate scenarios (RCP4.5 and RCP8.5) in 30 years. In total, around 1350 simulation experiments were carried out. In this study, APSIM crop model was used for simulation of maize growth and yield. In this study, all of the simulations were conducted in potential conditions and water- and nitrogen-limited production situations were not considered in the current study. APSIM model has previously been calibrated and evaluated for SC704 cultivar (this cultivar is the most common cultivated cultivar in the Kermanshah Province). In current research, all the output data was analyzed, graphed and mapped using the R software package (R Core Team, 2016) and OriginPro9.1 (Seifert, 2014).
Results: Average grain yield of Kermanshah Province was 11354 kg ha-1 in the baseline. Results showed that in 2050, on average grain yield was reduced 60.82 and 80.73 % (under RCP4.5 and RCP8.5, respectively) compared to baseline. In different locations, the highest and lowest grain yield in the baseline were recorded in the Kangavar with 13426 kg ha-1 and Kermanshah with 7952.4 kg ha-1. When averaged across locations, the highest grain yield in the climate change conditions was obtained in an early sowing date (5st April) with 7071.2 and 4743 kg ha-1 (under RCP4.5 and RCP8.5, respectively). In future on average (two scenarios) duration of growth period, vegetative and reproductive growth periods (4.7, 4 and 1.7 %, respectively) were decreased compared to the baseline. Also, in future, the number of grains and grain weight were reduced in Kermanshah Province so that under RCP4.5 were 56.5 and 31.8 %, respectively and under RCP8.5 were 78.5 and 59.3 %, respectively. However, these reductions in duration of growth period, vegetative and reproductive growth periods, the number of grains and grain weight were less in 5st April early sowing date than other sowing dates (especially late sowing dates).
Conclusion: Generally, results of the current study indicated that climate change had negative effects on maize yield and maize physiological and phonological characteristics in Kermanshah Province. However, early sowing dates might reduce these negative effects. So that in most cases, 5st April can reduce negative effects on maize yield and maize physiological and phonological characteristics in the future period.

Keywords

Main Subjects

References

1. AgMIP, 2013a. Guide for Running AgMIP Climate Scenario Generation Tools with R
in Windows. AgMIP, URL: http://www.agmip.org/wp-content/uploads/2013/
10/Guide-for-Running-AgMIP-Climate-Scenario-Generation-with-R-v2.3.pdf
2. AgMIP, 2013b. The Coordinated Climate-Crop Modeling Project C3MP: An Initiative of the
Agricultural Model Intercomparison and Improvement Project. C3MP Protocols and
Procedures. AgMIP, URL: http://research.agmip.org/
download/attachments/1998899/C3MP+Protocols+v2.pdf
3. Alexandrov, V.A., and Hoogenboom, G. 2000. The impact of climate variability and change
on crop yield in Bulgaria. Agric. For. Meteorol., 104: 315- 327.
4. Araya, A., Hoogenboom, G., Luedeling, E., Hadgu, K.M., Kisekka, I., and Martorano, L.G.
2015. Assessment of maize growth and yield using crop models under present and future
climate in southwestern Ethiopia. Agric. For Meteorol., 214: 252-265.
5. Bannayan, M., Kobayashi, K., Kim, H.Y., Lieffering, M., Okada, M., and Miura, S. 2005.
Modeling the interactive effects of atmospheric CO2 and N on rice growth and yield. Field
Crops Res., 93: 237-251.
6. Collins, M., Knutti, R., Arblaster, J., Dufresne, J.L., Fichefet, T., Friedlingstein, P., Gao, X.,
Gutowski, W.J., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver, A.J., and
Wehner, M. 2013. Long-term climate change: Projections, commitments and irreversibility.
In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker,
T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Doschung, J., Nauels, A., Xia, Y.,
Bex, V., and Midgley, P.M. Eds. Cambridge University Press, Pp: 1029-1136.
7. Dupuis, I., and Dumas, C. 1990. Influence of temperature stress on in vitro fertilisation and
heat shock protein synthesis in maize (Zea mays L.) reproductive tissues. Plant Physiol., 94:
665–670.
8. Eyni Nargeseh, H., Deihimfard, R., Soufizadeh, S., Haghighat, M., and Nouri, O. 2016.
Predicting the impacts of climate change on irrigated wheat yield in Fars province using
APSIM model. EJCP., 8(4): 203-224. (In Persian)
9. Hatfield, J.L., and Prueger, J.H. 2015. Temperature extremes: effect on plant growth and
development. Weather Clim. Extrem., 10: 4-10.
10. 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. Vol. 1: Overview. University of Hawaii, Honolulu, HI.
11. Huang, J.K., Pray, C., and Rozelle, S. 2002. Enhancing the crops to feed the poor. Nature.,
48: 678– 684.
12. Hulme, M., Barrow, E.M., Arnell, N.W., Harisson, P.A., Jones, T.C., and Dowing, T.E.
1999. Relative impacts of human-induced climate change and natural climate variability.
Nature., 397: 688- 691.
13. IPCC, 2007. Climate change 2007: the physical Science Basis. Contribution of Working
Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change. Cambridge University Press: Cambridge, UK, and New York, NY, USA, 996p.
14. 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., and Smith, C.J. 2003. An overview of
APSIM, a model designed for farming systems simulation. Eur. J. Agron., 18: 267– 288.
15. 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. Glob. Change Biol., 19 (11): 3481-3492.
16. Mera, R.J., Niyogi, D., Buol, G.S., Wilkerson, G.G., and Semazzi, F.H.M. 2006. Potential
individual versus simultaneous climate change effects on soybean (C3) and maize (C4)
crops: An agrotechnology model based study. Global Planet. Change., 54: 163–182.
17. Meza, F.J., Silva, D., and Vigil, H. 2008. Climate change impacts on irrigated maize in
Mediterranean climates: evaluation of double cropping as an emerging adaptation
alternative. Agric. Sys., 98: 21–30.
18. Ministry of Agriculture Jihad, 2013. Agricultural statistics. Iranian Ministry of Agriculture
Jihad, Department of Planning and Economically, Center of Information and
Communication Technology, first volume, Iran. (In Persian)
19. Moini, S., Javadi, S., and Dehghan Manshadi, M. 2011. Feasibility study of solar energy in
Iran and preparing radiation atlas. Recent Advances in Environment, Energy Systems and
Naval Science. 4th International Conference of Environmental and Geological Science and
Engineering. Greece. 1-7.
20. Moradi, R., Koocheki, A., Nassiri Mahallati, M., and Mansoori, H. 2013. Adaptation
strategies for maize cultivation under climate change in Iran: Irrigation and planting data
management. Mitig. Adapt. Strat. Gl., 18: 265-284.
21. Morison, J.I.L., and Morecroft, M.D. 2006. Plant Growth and Climate Change. Blackwell
Publisher, Oxford, England, 213p.
22. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P.,
Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B.,
Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., and
Wilbanks, T. 2010. The next generation of scenarios for climate change. The next generation
of scenarios for climate change research and assessment. Nature., 463(7282): 747-756.
23. Prescott, J.A. 1940. Evaporation from a water surface in relation to solar radiation. T. Roy.
Soc. South Aust., 64(1): 114-118.
24. R Core Team 2016. R: A language and environment for statistical computing. R Foundation
for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
25. Rahimi-Moghaddam, S., Deihimfard, R., Soufizadeh, S., Kambouzia, J., Nazariyan
Firuzabadi, F., and Eyni Nargeseh, H. 2015d. The effect of sowing date on grain yield, yield
components and growth physiological indices of six grain maize cultivars in Iran. J.
Agroecology., 5(1): 72- 83 (In Persian)
26. Rahimi-Moghaddam, S., Deihimfard, R., Soufizadeh, S., Kambouzia, J., Nazariyan
Firuzabadi, F., and Eyni Nargeseh, H. 2015c. Determination of genetic coefficients of some
maize (Zea mays L.) cultivars of Iran for application in crop simulation models. Iran. J. Field
Crops Res., 13(2): 328-339. (In Persian)
27. Rahimi-Moghaddam, S., Kambouzia, J., and Deihimfard, R. 2017b. Estimation of
parameters for some dominant maize (Zea mays L.) cultivars of Iran for using in APSIM
mechanistic model. EJCP., 10(1): 129-147. (In Persian)
28. Rahimi-Moghaddam, S., Kambouzia, J., and Deihimfard, R. 2018a. Adaptation strategies to
lessen negative impact of climate change on grain maize under hot climatic conditions: A
model-based assessment. Agric. For. Meteorol., 253: 1-14.
29. Rowhani, P., Lobell, D.B., Linderman, M., and Ramankutty, N. 2011. Climate variability
and crop production in Tanzania. Agric. For. Meteorol., 151: 449-460.
30. Ruane, A.C., Cecil, L.D., and Horton, R.M. 2013. Climate change impact uncertainties for
maize in Panama: farm information, climate projections, and yield sensitivities. Agric. For.
Meteorol., 170: 132–145.
31. Seifert, E. 2014. OriginPro 9.1: Scientific data analysis and graphing software—software
review. J. Chem. Inf. Model., 54: 1552–1552.
32. Wang, J., Wang, E., Luo, Q., and Kirby, M. 2009. Modeling the sensitivity of wheat growth
and water balance to climate change in Southeast Australia. Clim. Change., 96: 79–96.
33. Wayne, G.P. 2013. The beginner’s guide to representative concentration pathways. Skeptical
Sci., URL: http://www.skepticalscience.com/docs/RCP Guide.
34. Wilby, R.L., Charles, S.P., Zorita, E., Timbal, B., Whetton, P., and Mearns, L.O. 2004.
Guidelines for use of climate scenarios developed from statistical downscaling methods.
Supporting material of the Intergovernmental Panel on Climate Change, available from the
DDC of IPCC TGCIA. Aug 27.
35. Zheng, B., Chenu, K., Dreccer, M.F., and Chapman, S.C. 2012. Breeding for the future: what
are the potential impacts of future frost and heat events on sowing and flowering time
requirements for Australian bread wheat (Triticum aestivium) varieties? Glob. Change Biol.,
18: 2899–2914.
Journal of Crop Production
Volume 10, Issue 4
May 2018
Pages 91-105

Files

Share

How to cite

Statistics