Document Type : Research Paper - Modeling

Authors

1 Assistant Professor Department of Production Engineering and Plant Genetics, Faculty of Agriculture, Natural Resources Lorestan University, Khorramabad, Iran

2 Instructor Department of Agricultural science Technical and Vocational University (TVU), Tehran, Iran

Abstract

Introduction
Improving crop yields can meet the projected demand in developed and developing countries as a key and promising solution. To increase dryland wheat production in arid and semi-arid regions such as Iran, researchers and crop breeders need to understand the drought pattern (in terms of time and intensity) occurring in wheat agro-systems because depending on the time and intensity of drought stress, drought stress impacts on different processes and genes of crops. To accurately study the drought pattern of dryland wheat and different cultivars adapted to these conditions, researchers need to design multi-environmental field experiments, which are time-consuming and costly. Under these circumstances, some essential information is limited in the field experiments, especially when done for only a few years, and basically does not indicate fluctuations in the target environments. In contrast, the modeling and simulation approach is a useful method for comprehensive environmental impact assessment.
 
Materials and Methods
This study was conducted to identify and study different drought patterns in 8 locations including Ardebil, Khalkhal, Maragheh, Marand, Meshginshahr, Sarab, Tabriz, and Urmia in the northwest of Iran. APSIM model was used to simulate the growth and development of dryland wheat in the study locations. The long-term climatic included minimum and maximum temperatures, rainfall, and radiation (1980 to 2016) collected from the Meteorological Organization of Iran. These data were used as input to the crop growth simulation model. Water supply and demand index was used to determine different drought patterns. This index is obtained by dividing soil water content to plant water demand (WSDR). After calculating the water supply and demand index for each year and region, the CLARA clustering method was used to classify and group the index. The number of groups obtained by CLARA method was equal to the type of drought patterns of dryland wheat.
 
Results and Discussion
The results showed 4 different drought patterns during the dryland wheat growing season in the study locations.  Drought pattern 1 showed no drought stress or mild drought stress throughout the growing season of dryland wheat. Under drought pattern 2, drought stress started before flowering and ended during seed filling. For drought pattern 3, water stress started from the time of germination but removed during the vegetative growth period. Drought pattern 4 started in the early stages of growth and continued until the end of the dryland wheat growing season. The frequency of occurrence of 1, 2, 3, and 4 drought patterns was 27.8%, 26%, 8% and 38.2%, respectively. On average across locations, transpiration was 160, 137.1, 31, and 86.7 mm for 1, 2, 3, and 4 drought patterns during the wheat growing season. Also, a significant difference was observed among different drought patterns in terms of biomass that biomass under drought patterns 1, 2, 3, and 4 was equal to 13411, 10076, 2097 and, 6435 kg ha-1, respectively. Grain yield under drought pattern types 1, 2, 3 and 4 were 3738.7, 2949.7, 694.6, and 1456.2 kg ha-1, respectively. The highest percentage of light and mild drought patterns (1 and 2) was related to Marand region with 100% and Sarab region experienced more severe drought patterns (3 and 4) than other regions (88.2%). Under different locations and drought patterns, on average across years, the highest grain yield was simulated for Marand under drought pattern 1 (4759 kg ha-1) and the lowest was recorded for Urmia under drought pattern 3 (208.8 kg ha-1).
 
Conclusion
In general, the simulation results showed that 4 different types of drought patterns were observed during the dryland wheat growing period in the study locations. The frequency of occurrence of 4 drought patterns was various in the study locations and the grain yield varied under the drought patterns. However, the highest occurrence was related to drought pattern 4, under which drought stress begun in the early stages of growth and lasted until the end of the dryland wheat growing season. Accordingly, it is suggested to consider various strategies such as using cultivars with short maturity period to avoid drought stress at the end of the season and using cultivars with high resistance to drought stress (especially in the flowering period).
 

Keywords

Main Subjects

Asgari, K., Dastan, S., Ajm Norouzi, H., & Ghanbari Malidarreh, A. 2016. Effects of grain growth characteristic and yield components on Wheat yield in Golestan province's climatic condition. Journal of Plant Production Science, 6(2), 33-40. [In Persian].
Blum, A. 2010. Plant Breeding for Water-Limited Environments. Springer, London. pp. 1–210.
Borras, L., & Otegui, M.E. 2001. Maize kernel weight response to postflowering source–sink ratio. Crop Science, 41(6), 1816-1822.
Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., & Bussiere, F. 2003. An overview of the crop model STICS. European Journal of Agronomy, 18(3), 309-332.
Chenu, K. 2015. Characterizing the crop environment–nature, significance and applications. Crop Physiology. Applications for Genetic Improvement and Agronomy.
Chenu, K., Chapman, S.C., Tardieu, F., McLean, G., Welcker, C., & Hammer, G.L. 2009. Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a “Gene-to-phenotype” modeling approach. Genetics, 183, 1507–1523.
Chenu, K., Deihimfard, R., & 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.
Daei Alhagh, D., Rashidi, V., Aharizad, S., Farahvash, F., & Mirshekari, B. 2022. Yield stability analysis of advanced spring wheat genotypes under non-stress and drought stress conditions. Plant Productions, 44(4), 489-502. [In Persian]
Deihimfard, R., Mahallati, M.N., & Koocheki, A. 2015. Yield gap analysis in major wheat growing areas of Khorasan province, Iran, through crop modelling. Field Crops Research, 184, 28-38.
Deihimfard, R., Rahimi-Moghaddam, S., Collins, B., & Azizi, K. 2022. Future climate change could reduce irrigated and rainfed wheat water footprint in arid environments. Science of the Total Environment, 807, 150991.
Dodig, D., Zorić, M., Kandić, V., Perović, D., & Šurlan-Momirović, G. 2012. Comparison of responses to drought
stress of 100 wheat accessions and landraces to identify opportunities for improving wheat drought resistance. Plant Breeding, 131, 369–379.
Ehdaie, B., Layne, A.P., & Waines, J.G. 2012. Root system plasticity to drought influences grain yield in bread
wheat. Euphytica, 186, 219–232.
FAO. 2016. United Nations food and Agricultural Organization. Agricultural Data available on world wid. http://faostat3.fao.org/download/Q/QC/E.
Fischer, R.A. 2011. Wheat physiology: a review of recent developments. Crop & Pasture Science, 62, 95–114.
Hammer, G., Cooper, M., Tardieu, F., Welch, S., Walsh, B., van Eeuwijk, F., Chapman, S., & Podlich, D. 2006. Models for navigating biological complexity in breeding improved crop plants. Trends in Plant Science, 11, 587–593.
Hoogenboom, G., Jones, J.W., Porter, C.H., Wilkens, P.W., Boote, K.J., Batchelor, W.D., Hunt, L.A., & Tsuji, G.Y. (Editors). 2003. Decision Support System for Agrotechnology Transfer Version 4.0. Vol. 1: Overview. University of Hawaii, Honolulu, HI.
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., & Smith, C.J. 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18, 267– 288.
Kholová, J., McLean, G., Vadez, V., Craufurd, P., & Hammer, G.L. 2013. Drought stress characterization of post-rainy season (rabi) sorghum in India. Field Crops Research, 141, 38-46.
Kumar, U., Joshi, A.K., Kumari, M., Paliwal, R., Kumar, S., & Röder, M.S. 2010. Identification of QTLs for stay green trait in wheat (Triticum aestivum L.) in the ‘Chirya 3’בSonalika’population. Euphytica, 174, 437–445.
Lobell, D.B., Hammer, G.L., Chenu, K., Zheng, B., McLean, G., & Chapman, S.C. 2015. The shifting influence of drought and heat stress for crops in northeast Australia. Global Change Biology, 21(11), 4115-4127.
Manschadi, A.M., Christopher, J., & Hammer, G.L. 2006. The role of root architectural traits in adaptation of wheat to water-limited environments. Functional Plant Biology, 33, 823–837
Moini, S., Javadi, S., & Dehghan Manshadi, M. 2011. Feasibility study of solar energy in Iran and preparing radiation atlas. Recent Advances in Environment, Energy Systems and Naval Science: Proceedings of the 4th International Conference on Environmental and Geological Science and Engineering. pp. 1–7.
Naderi, A., & Eslahi, M.R. 2019. Evaluation of susceptibility of some phenological stages of wheat genotypes in response to drought stress. Plant Productions, 42(1), 133-148. [In Persian]
OECD. 2014. Climate change, water and agriculture: Towards resilient systems. IWA Publishing.
 
Prescott, JA, 1940. Evaporation from a water surface in relation to solar radiation. Transactions of the Royal Society of South Australia, 64, 114-118.
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/.
 
Rahimi-Moghaddam, S., Kambouzia, J., & 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., Kambouzia, J., 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.
Seifert, E. 2014. OriginPro 9.1: Scientific Data Analysis and Graphing Software—Software Review. Journal of Chemical Information and Modeling, 54, 1552–1552.
Slafer, G.A. 2003. Genetic basis of yield as viewed from a crop physiologist’s perspective. Annals of Applied Biology, 142, 117–128.
Spielmeyer, W., Hyles, J., Joaquim, P., Azanza, F., Bonnett, D., Ellis, M., Moore, C., & Richards, R.A. 2007. A QTL on chromosome 6A in bread wheat (Triticum aestivum L.) is associated with longer coleoptiles, greater seedling vigour and final plant height. Theoretical and Applied Genetics, 115, 59–66.
 
Talebifar, M., Taghizadeh, R., & Kamal kivi, S.E. 2015. Determination of relationships between yield and yield components in wheat varieties under water deficit stress in different growth stages through Path analysis. Agronomy Journal (Pajouhesh & Sazandegi), 108, 107-113. [In Persian].
Tardieu, F. 2012. Any trait or trait-related allele can confer drought tolerance: just design the right drought scenario. Journal of Experimental Botany, 63, 25–31.
Zhang, J., Zhang, S., Cheng, M., Jiang, H., Zhang, X., Peng, C., Lu, X., Zhang, M., & Jin, J. 2018. Effect of drought on agronomic traits of rice and wheat: a meta-analysis. International Journal of Environmental Research and Public Health. 15 (5), 839.