عملکرد پتانسیل گندم آبی (Triticum aestivum L.) و تأثیر صفات گیاهی بر آن در شرایط اقلیم کنونی و آینده در سراسر ایران

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشگاه علوم کشاورزی و منابع طبیعی گرگان

2 محقق ارشد مؤسسه ICRISAT در هند و مؤسسه IRD در فرانسه

3 موسسه تحقیقات گیاهپزشکی کشور

چکیده

اصلاح ارقام جدید در جهت افزایش عملکرد در واحد سطح همواره یکی از راهکارهای افزایش تولید محصولات کشاورزی بوده است. شناسایی صفات گیاهی تأثیرگذار بر عملکرد می‏تواند روند اصلاح ارقام جدید را تسریع بخشد. هدف از این مطالعه، شناسایی صفات گیاهی کلیدی در جهت افزایش عملکرد گندم آبی (Triticum aestivum L.) در مناطق تولید گندم در سراسر ایران بود. این مطالعه به‌کمک شبیه‏سازی تأثیر صفات مختلف گیاهی بر عملکرد پتانسیل گندم آبی، توسط مدل SSM-Wheat برای شرایط اقلیم کنونی و آینده انجام شد. برای این منظور از پروتکل پروژه اطلس خلأ عملکرد، موسوم به پروتکل گیگا، در جهت شناسایی پهنه‏های اقلیمی و همچنین شناسایی ایستگاه‏های هواشناسی مهم واقع در مناطق تولید گندم آبی در کشور استفاده شد. برای پیش‏بینی شرایط اقلیم آینده از روش دلتا و سناریوی انتشار RCP4.5 برای سال 2055 استفاده شد. در این مطالعه اثر کاهش و افزایش طول دوره شروع پنجه‏دهی تا شروع ساقه رفتن، طول دوره پر شدن دانه، کارایی استفاده از تشعشع و توسعه سطح برگ بر عملکرد پتانسیل گندم آبی بررسی شد. میزان تأثیر افزایش طول دوره پر شدن دانه به‌عنوان صفت کلیدی بر عملکرد پتانسیل برای اقلیم کنونی 3/15 درصد و برای اقلیم آینده 8/16 درصد بود. افزایش کارایی استفاده از تشعشع در سطح کشور باعث افزایش 7/14 درصدی عملکرد برای اقلیم کنونی و 7/13 درصد برای اقلیم آینده شد. اثر افزایش کارایی استفاده از تشعشع بر عملکرد پتانسیل، در مناطق گرم (GDD>6000) بیشتر از مناطق خنک بود. افزایش طول دوره شروع پنجه‏زنی تا شروع ساقه رفتن، صفتی بود که فقط در مناطق گرم منجر به افزایش عملکرد شد و اثر آن در مناطق خنک یا ناچیز بود و یا کاهش عملکرد پتانسیل را در پی داشت. نتایج این مطالعه می‏تواند در انتخاب صفات کلیدی برای افزایش عملکرد و تسریع تولید ارقام پرمحصول در مناطق مختلف گندم آبی به کار گرفته شوند.

کلیدواژه‌ها


عنوان مقاله [English]

Irrigated Wheat (Triticum aestivum L.) Traits Effects on Potential Yield under Current and Future Climates in Iran

نویسندگان [English]

  • Seyyedmajid Alimagham 1
  • Afshin Soltani 1
  • Vincent Vadez 2
  • Ebrahim Zeinali 1
  • Eskandar Zand 3
1 Gorgan University of Agricultural Sciences and Natural Resources,
2 University of Montpellier
3 Iranian Research Institute of Plant Protection, Agricultural Research, Education and Extension Organization (AREEO)
چکیده [English]

Introduction
Wheat (Triticum aestivum L.) known as a main crop in Iran. It is the main source of calories and protein which directly provides 37 percent of the food calories and 40 percent of daily protein for people in Iran. Breeding to produce new cultivars is always an important way to increase crops yield. New cultivars breeding is a very complex process because there is an interaction between climate and genotype and the time is limited to produce new cultivars adapted to new climates. The target trait identification can accelerate new cultivar breeding process. The objectives of this study were to explore the potential benefit of irrigated wheat traits over the country to increase the yield.
 
Materials and Methods
This study was performed at potential yield simulation using SSM-Wheat crop model to evaluate different traits impact on irrigated wheat potential yield in Iran. For this purpose, the protocol presented by Global Yield Gape Analysis (GYGA) was used to identify the same climate zones and the main weather stations for irrigated wheat in Iran. The potential yield of irrigated wheat was simulated by SSM-iCrop model for the area covered by each main weather stations. The average potential yield was calculated at the country level by scaling up the simulated results within the area covered by weather stations using the GYGA protocol. All the simulations and calculations were done for existing cultivars and for the cultivars with desired plant traits, identified in this study, under current and future climates. The effect of desired plant on potential yield was quantified by comparison of simulation results between existing cultivars and the cultivars with desired plan traits. Future climate (2055) scenario were created for the sites using the baseline 1986-2005 and the projections for delta mean air temperature (and precipitation) which is the difference between the future air temperature (and precipitation) and baseline air temperature (and precipitation). Deltas of air temperature and precipitation were obtained from the international panel on climate change report which it used 42 GCM model outputs under RCP4.5 climate change scenario to calculate them.
 
Results and Discussions
In this study, the effect of increasing and decreasing of biological days from tillering to stem elongation, biological days from anthesis to philological maturity, the rate of canopy development and radiation use efficiency on irrigated wheat potential yield were evaluated. Increasing biological days from anthesis to philological maturity increased the potential yield in all the regions under current (15.3 %) and future climates (16.8%). The potential yield gain from increasing radiation use efficiency was 14.7% under current climate and 13.7% under future climate. The effect of decreasing biological days from tillering to stem elongation, biological days from anthesis to philological maturity, the rate of canopy development and radiation use efficiency on the potential yield were negative. Monpara (2011) reported that increasing duration of grain filling period was an effective trait to increase wheat yield in India. Yang et al. (2008) demonstrated that the yield of rice increased with increasing cumulative radiation receiving during grain filling period. There was positive correlation between cumulative radiation receiving during grain filling and grain filling duration. With longer stay green duration, the potential yield of wheat increased thereby raising photosynthesis during wheat grain filling period (Spano et al., 2003).
Conclusion
Increasing radiation use efficiency positive effect on potential yield in the regions with warmer climate was higher than the region with lower average temperature over the year. Increasing radiation use efficiency had negative effect on potential yield in some cooler regions. Increasing biological days from tillering to stem elongation just had positive effect on potential yield in the region with warmer climate and its effect was negative in the regions with cool climate. The faster canopy development had no significant effect on potential yield.
 

کلیدواژه‌ها [English]

  • GYGA protocol
  • late maturity
  • Genotype
  • Environment
  • Radiation Use Efficiency
  • SSM-wheat model
Anderson, W.K., 2010. Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar. Field Crops Research 116: 14-22.
Chmielewski, F.M., Müller, A., and Bruns, E., 2004. Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961–2000. Agricultural and Forest Meteorology 121: 69-78.
Christensen, J.H., Krishna Kumar, K., Aldrian, E., An, S.I., Cavalcanti, I.F.A., de Castro, M., Dong, W., Goswami, P., Hall, A., Kanyanga, J.K., Kitoh, A., Kossin, J., Lau, N.C., Renwick, J., Stephenson, D.B., Xie, S.P., and Zhou, T., 2013. Climate Phenomena and their Relevance for Future Regional Climate Change. In: Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.M., (Eds), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Christopher, J.T., Manschadi, A.M., Hammer, G.L., and Borrell, A.K., 2008. Developmental and physiological traits associated with high yield and stay-green phenotype in wheat. Australian Journal of Agricultural Research 59: 354-364.
Espe, M.B., Cassman, K.G., Yang, H., Guilpart, N., Grassini, P., Van Wart, J., Anders, M., Beighley, D., Harrell, D., Linscombe, S., and McKenzie, K., 2016. Yield gap analysis of US rice production systems shows opportunities for improvement. Field Crops Research 196: 276-283.
Farshi, A., 1998. Water requirements estimation for major agronomic and horticultural plants of Iran. Irans’s Ministry of Agriculture of Iran Agriculture Education Press, Iran. (In Persian)
Fiorani, F., and Schurr, U., 2013. Future scenarios for plant phenotyping. Annual Review of Plant Biology 64: 267-291.
Fletcher, A.L., and Jamieson, P.D., 2009. Causes of variation in the rate of increase of wheat harvest index. Field Crops Research 113: 268-273.
Flohr, B.M., Hunt, J.R., Kirkegaard, J.A., Evans, J.R., Trevaskis, B., Zwart, A., Swan, A., Fletcher, A.L., and Rheinheimer, B., 2018. Fast winter wheat phenology can stabilise flowering date and maximize grain yield in semi-arid mediterranean and temperate environments. Field Crops Research 223: 12-25.
Ghanem, M.E., Marrou, H., and Sinclair, T.R., 2015. Physiological phenotyping of plants for crop improvement. Trends in Plant Science 20: 139-144.
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: 2185-2202.
Hanson, J.D., 1982. Effect of light, temperature and water stress on net photosynthesis in two populations of honey mesquite. Journal of Range Management 455-458.
Hossain, M.A., Araki, H., and Takahashi, T., 2011. Poor grain filling induced by waterlogging is similar to that in abnormal early ripening in wheat in Western Japan. Field Crops Research 123: 100-108.
Koo, J., and Dimes, J., 2010. Generic Soil Profiles for Crop Modeling Applications (HC27). International Food Policy Research Institute, Washington, DC, and University of Minnesota, St. Paul, MN. Available online at http://harvestchoice.org/node/662.
Liu, B., Chen, X., Meng, Q., Yang, H., and van Wart, J., 2017. Estimating maize yield potential and yield gap with agro-climatic zones in China—Distinguish irrigated and rainfed conditions. Agricultural and Forest Meteorology 239: 108-117.
Lollato, R.P., Patrignani, A., Ochsner, T.E., and Edwards, J.T., 2016. Prediction of plant available water at sowing for winter wheat in the southern great plains. Agronomy Journal 108: 745-757.
Martre, P., Quilot-Turion, B., Luquet, D., Memmah, M.M.O.S., Chenu, K., and Debaeke, P., 2015. Model-assisted phenotyping and ideotype design. In: V.O. Sadras, and D.F. Calderini, (Eds.) Crop physiology application for genetic improvement and agronomy. Academic Press/Elsevier Science.
Moeller, C., and Rebetzke, G., 2017. Performance of spring wheat lines near-isogenic for the reduced-tillering ‘tin’trait across a wide range of water-stress environment-types. Field Crops Research 200: 98-113.
Monpara, B.A., 2011. Grain filling period as a measure of yield improvement in bread wheat. Crop Improvement 38: 1-5.
Morison, J.I., 1985. Sensitivity of stomata and water use efficiency to high CO2. Plant, Cell and Environment 8: 467-474.
Ramirez-Villegas, J., and Challinor, A., 2012. Assessing relevant climate data for agricultural applications. Agricultural and Forest Meteorology 161: 26-45.
Reynolds, M., Bonnett, D., Chapman, S.C., Furbank, R.T., Manes, Y., Mather, D.E., and Parry, M.A., 2010. Raising yield potential of wheat. I. Overview of a consortium approach and breeding strategies. Journal of Experimental Botany 62: 439-452.
Reynolds, M.P., Calderini, D., Condon, A., and Vargas, M., 2007. Association of source/sink traits with yield, biomass and radiation use efficiency among random sister lines from three wheat crosses in a high-yield environment. Journal of Agricultural Science 145: 3-16.
Ribeiro, R.V., Machado, E.C., and Oliveira, R.F.D., 2006. Temperature response of photosynthesis and its interaction with light intensity in sweet orange leaf discs under non-photorespiratory condition. Ciência e Agrotecnologia 30: 670-678.
Richards, R.A., 2000. Selectable traits to increase crop photosynthesis and yield of grain crops. Journal of Experimental Botany 51: 447-458.
Salehi, F., 2012. Desired Food Basket for Iranian People. 2012. Andisheh Mandegar Press, Iran. 58 p. (In Persian)
Sinclair, T.R., 2011. Challenges in breeding for yield increase for drought. Trends in Plant Science 16: 289-293.
Sinclair, T.R., Messina, C.D., Beatty, A., and Samples, M., 2010. Assessment across the United States of the benefits of altered soybean drought traits. Agronomy Journal 102: 475-482.
Soltani, A., and Galeshi, S., 2002. Importance of rapid canopy closure for wheat production in a temperate sub-humid environment: experimentation and simulation. Field Crops Research 77: 17-30.
Soltani, A., Maddah, V., and Sinclair, T.R., 2013. SSM-Wheat: a simulation model for wheat development, growth and yield. International Journal of Plant Production 7: 711-740.
Soltani, A., and Sinclair, T.R., 2012 a. Identifying plant traits to increase chickpea yield in water-limited environments. Field Crops Research 133: 186-196.
Soltani, A., and Sinclair, T.R., 2012 b. Optimizing chickpea phenology to available water under current and future climates. European Journal of Agronomy 38: 22-31.
Soltani, A., and Sinclair, T.R., 2012 c. Modeling physiology of crop development, growth and yield. CABI Press. 322 p.
Soltani, A., and Sinclair, T.R., 2015. A comparison of four wheat models with respect to robustness and transparency: simulation in a temperate, sub-humid environment. Field Crops Research 175: 37-46.
Spano, G., Di Fonzo, N., Perrotta, C., Platani, C., Ronga, G., Lawlor, D.W., Napier, J.A., and Shewry, P.R., 2003. Physiological characterization of ‘stay green’mutants in durum wheat. Journal of Experimental Botany 54: 1415-1420.
Sultana, H., Ali, N., Iqbal, M.M., and Khan, A.M., 2009. Vulnerability and adaptability of wheat production in different climatic zones of Pakistan under climate change scenarios. Climatic Change 94: 123-142.
Tao, F., and Zhang, Z., 2013. Climate change, wheat productivity and water use in the North China Plain: A new super-ensemble-based probabilistic projection. Agricultural and Forest Meteorology 170: 146-165.
van Bussel, L.G., Grassini, P., Van Wart, J., Wolf, J., Claessens, L., Yang, H., Boogaard, H., de Groot, H., Saito, K., Cassman, K.G., and van Ittersum, M.K., 2015. From field to atlas: upscaling of location-specific yield gap estimates. Field Crops Research 177: 98-108.
Van Vuuren, D.P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J.F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J., and Rose, S.K., 2011. The representative concentration pathways: An overview. Climatic Change 109: 5–3.
Wang, B., Li Liu, D., Asseng, S., Macadam, I., and Yu, Q., 2017 b. Modelling wheat yield change under CO2 increase, heat and water stress in relation to plant available water capacity in eastern Australia. European Journal of Agronomy 90: 152-161.
Wang, B., Li Liu, D., Asseng, S., Macadam, I., Yang, X., and Yu, Q., 2017 a. Spatiotemporal changes in wheat phenology, yield and water use efficiency under the CMIP5 multimodel ensemble projections in eastern Australia. Climate Research 72: 83-99.
Weigand, C., and Analyst, M., 2011. Wheat import projections towards 2050. US Wheat Associates, USA.
Willenbockel, D., 2011. Exploring food price scenarios towards 2030 with a global multi-region model. Oxfam Policy and Practice: Agriculture, Food and Land 11: 19-62.
www.dssat.net. Available at 2018/08/23.
www.esrl.noaa.gov/gmd/ccgg/trends. Available at 2018/08/23.
www.fao.org/faostat/en/#data/OA. Available at 2018/08/23.
www.yieldgap.org. Available at 2018/08/23.
www.yieldgap.org/web/guest/cz-ted. Available at 2018/08/23.
Yang, W., Peng, S., Dionisio-Sese, M.L., Laza, R.C., and Visperas, R.M., 2008. Grain filling duration, a crucial determinant of genotypic variation of grain yield in field-grown tropical irrigated rice. Field Crops Research 105: 221-227.