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Cereal Research Communications

, Volume 47, Issue 1, pp 170–181 | Cite as

Determining the Best Drought Tolerance Indices using Artificial Neural Network (ANN): Insight into Application of Intelligent Agriculture in Agronomy and Plant breeding

  • A. EtminanEmail author
  • A. Pour-Aboughadareh
  • R. Mohammadi
  • L. Shooshtari
  • M. Yousefiazarkhanian
  • H. Moradkhani
Breeding

Abstract

In the present study, efficiency of the artificial neural network (ANN) method to identify the best drought tolerance indices was investigated. For this purpose, 25 durum genotypes were evaluated under rainfed and supplemental irrigation environments during two consecutive cropping seasons (2011–2013). The results of combined analysis of variance (ANOVA) revealed that year, environment, genotype and their interaction effects were significant for grain yield. Mean grain yield of the genotypes ranged from 184.93 g plot–1 under rainfed environment to 659.32 g plot–1 under irrigated environment. Based on the ANN results, yield stability index (YSI), harmonic mean (HM) and stress susceptible index (SSI) were identified as the best indices to predict drought-tolerant genotypes. However, mean productivity (MP) followed by geometric mean productivity (GMP) and HM were found to be accurate indices for screening drought tolerant genotypes. In general, our results indicated that genotypes G9, G12, G21, G23 and G24 were identified as more desirable genotypes for cultivation in drought-prone environments. Importantly, these results could provide an evidence that ANN method can play an important role in the selection of drought tolerant genotypes and also could be useful in other biological contexts.

Keywords

drought stress selection indices ANN analysis durum wheat 

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42976_2019_4701170_MOESM1_ESM.pdf (504 kb)
Determining the Best Drought Tolerance Indices using Artificial Neural Network (ANN): Insight into Application of Intelligent Agriculture in Agronomy and Plant breeding

References

  1. Bagheri, S., Gheysari, M., Ayoubi, S., Lavaee, N. 2012. Silage maize yield prediction using artificial neural networks. Int. J. Plant Prod. 19:77–96.Google Scholar
  2. Bahrami, F., Arzani, A., Karimi, V. 2014. Evaluation of yield-based drought tolerance indices for screening safflower genotypes. Agron. J. 106:1219–1224.CrossRefGoogle Scholar
  3. Bouslama, M., Schapaugh, W.T. 1984. Stress tolerance in soybean: 1. Evaluation of three screening techniques for heat and drought tolerance. Crop Sci. 24:933–937.CrossRefGoogle Scholar
  4. Ebrahimi, M., Ebrahimie, E. 2010. Sequence-based prediction of enzyme thermostability through bioinformatics algorithms. Curr. Bioinform. 5:195–203.CrossRefGoogle Scholar
  5. Farshadfar, E., Sutka, J. 2002. Multivariate analysis of drought tolerance in wheat substitution lines. Cereal. Res. Commun. 31:33–39.Google Scholar
  6. Fernandez, G.C.J. 1993. Effective selection criteria for assessing plant stress tolerance. In: Kuo, C.G. (ed), Proceeding of the AFCTWS Adaptation of food crops to temperature and water stress; Shanhua, Taiwan, pp. 257–270.Google Scholar
  7. Fischer, R.A., Maurer, R. 1978. Drought resistance in spring wheat cultivars: 1. Grain yield response. Aust. J. Agric. Res. 29:897–912.CrossRefGoogle Scholar
  8. Food and Agriculture Organization, FAO. 2013. http://www.fao.org/faostat/en/
  9. Gavuzzi, P., Rizza, F., Palumbo, M., Campaline, R.G., Ricciardi, F.L., Borghi, G. 1997. Evaluation of field and laboratory predictors of drought and heat tolerance in winter cereals. Plant Sci. 77:523–531.Google Scholar
  10. Gholipoor, M., Rohani, A., Torani, S. 2012. Optimization of traits to increasing barley grain yield using an artificial neural network. Int. J. Plant. Prod. 7:1–18.Google Scholar
  11. Hsiao, H.W., Tasi, M.S., Wang, S.C. 2006. Spatial data mining of colocation patterns for decision support in agriculture. Asian Journal of Health and Info. Sci. 1:61–72.Google Scholar
  12. Khalili, M., Naghavi, M.R., Pour-Aboughadareh, A.R., Talebzadeh, S.J. 2012. Evaluating of drought stress tolerance based on selection indices in spring canola cultivars (Brassica napus L.). J. Agric. Sci. 4:78–85.Google Scholar
  13. Khalili, M., Pour-Aboughadareh, A., Naghavi, M.R., Mohammad-Amini, E. 2014. Evaluation of drought tolerance in safflower genotypes based on drought tolerance indices. Not. Bot. Horti. Agrobot. Cluj-Napoca. 42:214–218.Google Scholar
  14. Khashei, M., Bijari, M. 2010. An artificial neural network (p, d, q) model for time series forecasting. Expert. Syst. Appl. 37:479–489.CrossRefGoogle Scholar
  15. Loss, S.P., Siddique, K.H.M. 1994. Morphological and physiological traits associated with wheat yield increases in Mediterranean environments. Adv. Agron. 52:229–276.CrossRefGoogle Scholar
  16. Mohammadi, R. 2016. Efficiency of yield-based drought tolerance indices to identify tolerant genotypes in durum wheat. Euphytica 211:71–89.CrossRefGoogle Scholar
  17. Moosavi, S.S., Yazdi-Samadi, B., Naghavi, M.R., Zali, A.A., Dashti, H., Pourshahbazi, A. 2008. Introduction of new indices to identify relative drought tolerance and resistance in wheat genotypes. Desert. 12:165–178.Google Scholar
  18. Naghavi, M.R., Pour-Aboughadareh, A.R., Khalili, M. 2013. Evaluation of drought tolerance indices for screening some of corn (Zea mays L.) cultivars under environmental conditions. Not. Sci. Biol. 5:388–393.CrossRefGoogle Scholar
  19. Pour-Siahbidi, M.M., Pour-Aboughadareh, A. 2013. Evaluation of grain yield and repeatability of drought tolerance indices for screening chickpea (Ciceraritinum L.) genotypes under rainfed conditions. Iranian J. Genetics. Plant. Breed. 2:28–37.Google Scholar
  20. Ravari, S.Z., Dehghani, H., Naghavi, H. 2015. Assessment of salinity indices to identify Iranian wheat varieties using an artificial neural network. Ann. Appl. Biol. 168:185–194.CrossRefGoogle Scholar
  21. Robert, N. 2002. Comparison of stability statistics for yield and quality traits in bread wheat. Euphytica. 128:333–341.CrossRefGoogle Scholar
  22. Rosielle, A.A., Hamblin, J. 1981. Theoretical aspects of selection for yield in stress and non-stress environment. Crop. Sci. 21:943–946.CrossRefGoogle Scholar
  23. Safa, M., Samarasinghe, S., Nejat, M. 2015. Prediction of wheat production using artificial neural networks and investigating factors affecting it: case study in Canterbury province, New Zealand. J. Agr. Sci. Tech. 17:791–803.Google Scholar
  24. Shekoofa, A., Emam, Y., Shekoufa, N., Ebrahimi, M., Ebrahimie, E. 2014. Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: A new avenue in intelligent agriculture. Plos One. 9:e97288.CrossRefGoogle Scholar
  25. Shi, C., Dong, B., Qiao, Y., Guan, X., Si, F., Zheng, X., Liu, M. 2014. Physiological and morphological basis of improved water-use-efficiency in wheat from partial root-zone drying. Crop Sci. 54:2745–2751.CrossRefGoogle Scholar
  26. Tahmasbi, G., Heydarnezhadian, J., Pour-Aboughadareh, A. 2013. Evaluation of yield and yield components in some of promising wheat lines. Intl. J. Agri. Crop Sci. 20:2379–2384.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2019

Authors and Affiliations

  • A. Etminan
    • 1
    Email author
  • A. Pour-Aboughadareh
    • 2
  • R. Mohammadi
    • 3
  • L. Shooshtari
    • 1
  • M. Yousefiazarkhanian
    • 4
  • H. Moradkhani
    • 1
  1. 1.Department of Biotechnology and Plant Breeding, Kermanshah BranchIslamic Azad UniversityKermanshahIran
  2. 2.>Department of Agronomy and Plant Breeding, Faculty of AgricultureUniversity of TehranKarajIran
  3. 3.Dryland Agricultural Research Institute, Sararood branch, Agricultural ResearchEducation and Extension Organization (AREEO)KermanshahIran
  4. 4.Agricultural Jihad OrganizationQazvinIran

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