شبیه‌سازی عملکرد دانه و کارایی مصرف آب در ارقام غالب ذرت تحت شرایط محدودیت آب و تغییر اقلیم

بوم شناسی کشاورزی

مقاله 7، دوره 13، شماره 1 - شماره پیاپی 47، خرداد 1400، صفحه 103-115   

نوع مقاله: علمی - پژوهشی

شناسه دیجیتال (DOI): 10.22067/jag.v13i1.80923

نویسندگان

ندا شریفی حداد¹؛ رضا دیهیم فرد^{* 2}؛ امید نوری²؛ سجاد رحیمی مقدم³

¹گروه کشاورزی اکولوژیک، پژوهشکده علوم محیطی، دانشگاه شهید بهشتی، ایران.

²گروه کشاورزی اکولوژیک، پژوهشکده علوم محیطی، دانشگاه شهید بهشتی، ایران

³گروه زراعت و اصلاح نباتات، دانشکده کشاورزی، دانشگاه لرستان، ایران.

چکیده

به‌منظور شبیه­سازی عملکرد و کارایی مصرف آب در ارقام ذرت آزمایشی تحت شرایط تغییر اقلیم و محدودیت آب در استان خوزستان (مناطق اهواز، ایذه، دزفول و بستان) طراحی شد. اقلیم آینده (2070-2040) در این مناطق با استفاده از مدل گردش عمومی Miroc5، تحت سناریوی اقلیمی RCP4.5 و روشAgMIP  پیش­بینی شد. همچنین از مدل APSIM برای شبیه­سازی رشد و نمو گیاه ذرت در دوره گذشته (2010-1980) و آینده (2070-2040) استفاده گردید. نتایج نشان داد که به‌طور متوسط عملکرد دانه و کارایی مصرف آب ذرت در استان خوزستان در آینده نسبت به گذشته به‌ترتیب 2- و 7/5- درصد کاهش و میانگین دما در طول فصل رشد و تبخیر و تعرق 6/12+ و 9/0+ درصد افزایش خواهد داشت. به‌علاوه نتایج نشان داد که اگر کشاورزان یک آبیاری مناسب (آبیاری 10 دور) به‌کار ببرند، این موضوع باعث افزایش کارایی مصرف آب (42 درصد) و کاهش تبخیر و تعرق (8/3 درصد) خواهد شد. این سطح بهینه آبیاری در دوره آینده به‌همراه یک رقم دیررس (سینگل کراس 704) می­تواند بهترین نتیجه را در استان خوزستان از نظر عملکرد دانه (26/7999 کیلوگرم در هکتار) و کارایی مصرف آب (94/16 کیلوگرم بر میلی‌متر در هکتار) داشته باشد.

کلیدواژه‌ها

تبخیر- تعرق؛ رژیم آبیاری؛ مدل APSIM

مراجع

Abbas Torki, T., Mojaddam, M., and Abadouz, G.R., 2011. Study of water stress on morphological characters of corn (Zea mays L.) hybrids in south Khouzestan condition. In First National Conference on Climate Change and its Impact on Agriculture and the Environment, Urmia, Iran, 24 July 2011, p. 1836-1842. (In Persian with English Summary)

Anonymous, 2014. Agricultural statistics. Iranian Ministry of Agriculture Jihad. Department of Planning and Economically. Center of Information and Communication Technology. Iran. p. 158. (In Persian)

Bannayan, M., Crout, N.M.J., and Hoogenboom, G., 2003. Application of the CERES-wheat model for within–season prediction of wheat yield in United Kingdom. Agronomy Journal 95: 114–125.

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 Research 93: 237-251.

Boote, K.J., 2011. Crop adaptation to climate change. In: S.S. Yadav, R.J. Redden, J.L. Hatfield, H. Lotze-Campen and A.E. Hall (Eds.). Improving soybean cultivars for adaptation to climate change and climate variability: Wiley-Blackwell Press. p. 370-395.

Dashtbozorgi, A., Alijani, B., Jafarpur, Z., and Shakiba, A., 2015. Simulatiing extreme temperature indicators based on RCP scenarios: the case of Khuzestan Province. Geography and Environmental Hazards 4: 105–123 (In Persian with Englosh Summary)

Dong, B., Shi, L., Shi, C., Qiao, Y., Liu, M., and Zhang, Z., 2011. Grain yield and water use efficiency of two types of winter wheat cultivars under different water regimes. Agricultural Water Management 99: 103-110.

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 Physiology 94: 665–670.

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. Electronic Journal of Crop Production8(4): 203-224. (In Persian with English Summary)

Huang, J.K., Pray, C., and Rozelle, S., 2002. Enhancing the crops to feed the poor. Nature 48: 678–684.

Igbadun, H.E., Mahoo, H.F., Tarimo, A.K., and Salim, B.A., 2006. Crop water productivity of an irrigated maize crop in Mkoji sub-catchment of the Great Ruaha River Basin. Tanzania. Agricultural Water Management 85: 141-150.

IPCC, 2014. Climate Change: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Core Writing Team, R.K. Pachauri and L.A. Meyer (Eds.)). IPCC. Geneva, Switzerland. p. 151.

Izadi Darbandi, E., 2012. Evolution of drought stress and nitrogen rate on redroot pigweed (Amaranthus retroflexus) and corn (Zea mays) competition. Agronomy Journal(Pajouhesh and Sazandegi) 94: 68-74. (In Persian with English Summary)

Kang, Y., Khan, S., and Ma, X., 2015. Analysing climate change impacts on water productivity of cropping systems in the Murray Darling Basin, Australia. Irrigation and Drainag64: 443-453.

Karandish, F., Kalanaki, M., and Saberali, S.F., 2017. Projected impacts of global warming on cropping calendar and water requirement of maize in a humid climate. Archives of Agronomy and Soil Science 63(1): 1-13.

Kirkegaard, J.A., Lilley, J.M., Howe, G.N., and Graham, J.M., 2007. Impact of subsoil water use on wheat yield. Crop and Pasture Science 58: 303–315.

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: 3481-3492.

Maa, L., Ahujaa, L.R., Islamb, A., Trout C.T.J., Saseendrand, S.A., and Malone, R.W., 2017. Modeling yield and biomass responses of maize cultivars to climate change under full and deficit irrigation. Agricultural Water Management 180: 88–98.

Manschadi, A.M., Soufizadeh, S., and Deihimfard, R., 2010. The role and importance of simulation modeling in improving crop production in Iran. In: Proceedings of 11^th Iranian Crop Science Congress, Shahid Beheshti University, Tehran, Iran, 24 July 2010, p. 234-247. (In Persian with English Summary)

Martin de Santa Olalla, F., Dominguez-Padilla, A., and Lopez, R., 2004. Production and quality of the onion crop (Allium cepa) cultivated under controlled deficit irrigation conditions in a semi-arid climate. Agricultural Water Management 68: 77-89.

Medrano, H., Tomás, M., Martorell, S., Flexas, J., Hernández, E., Rosselló, J., Pou, A., J. Escalona, M., and Bota, J., 2015. From leaf to whole-plant water use efficiency (WUE) in complex canopies: limitations of leaf WUE as a selection target. The Crop Journal 3: 220-228.

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. Agricultural Systems 98: 21–30.

Moradi, R., Koocheki, A., Nassiri Mahallati, M., and Mansoory, H., 2013. Adaptation strategies for maize cultivation under climate change in Iran: Irrigation and planting data management. Mitigation andAdaptation Strategies for Global Change 18: 265-284.

Oktem, A., Simsek, M., and Oktem, G., 2003. Deficit irrigation effects on sweet corn (Zea mays saccharata Sturt) with drip irrigation system in a semi-arid region: I. Water-yield relationship. Agricultural Water Management 61: 63-74.

Rahimi Moghaddam, S., and Azizi, K., 2017. Early sowing date as a strategy for improvement of maize yield and maize physiological and phonological characteristics in climate change conditions at Kermanshah Province. Electronic Journal of Crop Production 10(40): 129-147. (In Persian with English Summary)

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.

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: 105570.

Rahimi-Moghaddam, S., Eyni-Nargeseh, H., Ahmadi, S.A.K., and Azizi, K., 2021. Towards withholding irrigation regimes and drought-resistant genotypes as strategies to increase canola production in drought-prone environments: A modeling approach. Agricultural Water Management 243: 106487.

Sabziparvar, A.A., and Tabari, H., 2010. Regional estimation of reference evapotranspiration in arid and semi-arid regions. Journal of Irrigation and Drainage Engineering 136(10): 724–731.

Seifert, E., 2014. OriginPro 9.1: Scientific data analysis and graphing software—software review. Journal of Chemical Information and Modeling 54: 1552–1552.

Soltani, A., and Hoogenboom, G., 2007. Assessing crop management options with crop simulation models based on generated weather data. Field Crops Research 103: 198- 207.

Wakrim, R., Aganchich, B., Tahi, H., Serraj, R., and Wahbi, S., 2005. Comparative effects of partial root drying (PRD) and regulated deficit irrigation (RDI) on water relations and water use efficiency in common bean (Phaseolus vulgaris L.). Agriculture, Ecosystems and Environment 106: 275-287.