Growth prediction of Alternanthera philoxeroides under salt stress by application of artificial neural networking

References

• Abdipour M, Younessi-Hmazekhanlu M, Ramazani SHR. 2019. Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.). Indust Crops Prod. 127:185–194.
   Web of Science ®Google Scholar
• Amirjani M. 2010. Effect of salinity stress on growth, mineral composition, proline content, antioxidant enzymes of soybean. Am J Plant Physiol. 5(6):350–360.
   Google Scholar
• Ashraf M, Foolad M. 2007. Roles of glycine betaine and proline in improving plant abiotic stress resistance. Environ Exp Bot. 59(2):206–216.
   Web of Science ®Google Scholar
• Bombik A, Rymuza K, Markowska M, Stankiewicz C. 2007. Variability analysis of selected quantitative characteristics in edible potato varieties. Acta Sci Polon Agric. 6(3):5–15.
   Google Scholar
• Bussay A, van der Velde M, Fumagalli D, Seguini L. 2015. Improving operational maize yield forecasting in Hungary. Agric Syst. 141:94–106.
   Web of Science ®Google Scholar
• Chen L, Yu Y, He X. 2008. Historical invasion and expansion process of Alternaria philoxeroides and its potential spread in China. Biodiversity Sci. 16(6):578–585.
   Google Scholar
• Chen Z, Zou Y, Chen Y, Zhang Z, Xu X. 2010. Effects of fragmentation intensity of perennial roots and their burial depth on sprouting and early growth of Alternanthera philoxeroides (Mart.) Griseb. Agric Sci Technol Hunan. 11(6):103–111.
   Google Scholar
• Croser C, Renault S, Franklin J, Zwiazek J. 2001. The effect of salinity on the emergence and seedling growth of Picea mariana, Picea glauca, and Pinus banksiana. Environ Pollut. 115(1):9–16.
   PubMed Web of Science ®Google Scholar
• Dawson K, Veblen KE, Young TP. 2007. Experimental evidence for an alkali ecotype of Lolium multiflorum, an exotic invasive annual grass in the Central Valley. Biol Invasions. 9(3):327–334.
   Web of Science ®Google Scholar
• Domínguez J, Kumhálová J, Novák P. 2015. Winter oilseed rape and winter wheat growth prediction using remote sensing methods. Plant Soil Environ. 61(9):410–416.
   Web of Science ®Google Scholar
• Dong B-C, Yu G-L, Guo W, Zhang M-X, Dong M, Yu F-H. 2010. How internode length, position and presence of leaves affect survival and growth of Alternanthera philoxeroides after fragmentation? Evol Ecol. 24(6):1447–1461.
   Web of Science ®Google Scholar
• Emamgholizadeh S, Parsaeian M, Baradaran M. 2015. Seed yield prediction of sesame using artificial neural network. Eur J Agron. 68:89–96.
   Web of Science ®Google Scholar
• Fu W-z, Chen Z-y, Huai H-y. 2006. The characteristics of clonal reproduction of rhizome from Alternanthera philoxeroides. Ecologic Sci. 25 (4):316–319.
   Google Scholar
• Grahovac J, Jokić A, Dodić J, Vučurović D, Dodić S. 2016. Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks. Renew Energy. 85:953–958.
   Web of Science ®Google Scholar
• Habib N, Ashraf M, Ali Q, Perveen R. 2012. Response of salt stressed okra (Abelmoschus esculentus Moench) plants to foliar-applied glycine betaine and glycine betaine containing sugarbeet extract. South Afr J Bot. 83:151–158.
   Web of Science ®Google Scholar
• Hediye Sekmen A, Türkan İ, Takio S. 2007. Differential responses of antioxidative enzymes and lipid peroxidation to salt stress in salt-tolerant Plantago maritima and salt-sensitive Plantago media. Physiol Plant. 131(3):399–411.
   PubMed Web of Science ®Google Scholar
• Huang N, Li R, Lin L, Yu Z, Cai G. 2018. Low redundancy feature selection of short term solar irradiance prediction using conditional mutual information and Gauss process regression. Sustainability. 10(8):2889.
   Web of Science ®Google Scholar
• Javed Q, Sun J, Azeem A, Ullah I, Huang P, Kama R, Jabran K, Du D. 2019. The enhanced tolerance of invasive Alternanthera philoxeroides over native species under salt-stress in china. Appl Ecol Environ Res. 17(6):14767–14785.
   Web of Science ®Google Scholar
• Jia X, Yang X, Pan X, Li B, Chen J. 2008. Vegetative propagation characteristics of Alternanthera philoxeroides in response to disturbances. Biodiversity Sci. 16(3):229–235.
   Google Scholar
• Kantanantha N, Serban N, Griffin P. 2010. Yield and price forecasting for stochastic crop decision planning. J Agric Biol Environ Stat. 15(3):362–380.
   Web of Science ®Google Scholar
• Keser LH, Visser EJ, Dawson W, Song Y-B, Yu F-H, Fischer M, Dong M, van Kleunen M. 2015. Herbaceous plant species invading natural areas tend to have stronger adaptive root foraging than other naturalized species. Front Plant Sci. 6:273.
   PubMed Web of Science ®Google Scholar
• Khairunniza-Bejo S, Mustaffha S, Ismail WIW. 2014. Application of artificial neural network in predicting crop yield: a review. J Food Sci Eng. 4(1):1.
   Google Scholar
• Khoshnevisan B, Rafiee S, Iqbal J, Shamshirband S, Omid M, Badrul AN, Wa A. 2015. A comparative study between artificial neural networks and adaptive neuro-fuzzy inference systems for modeling energy consumption in greenhouse tomato production: a case study in Isfahan Province. J Agric Sci Technol. 17:49–62.
   Google Scholar
• Kosma DK, Jenks MA. 2007. Eco-physiological and molecular-genetic determinants of plant cuticle function in drought and salt stress tolerance. In Jenks MA, Hasegawa PM, Jain SM, editors. Advances in Molecular Breeding toward Drought and Salt Tolerant Crops. Dordrecht, The Netherlands: Springer; p. 91–120.
   Google Scholar
• Li F, Qiao J, Han H, Yang C. 2016. A self-organizing cascade neural network with random weights for nonlinear system modeling. Appl Soft Comput. 42:184–193.
   Web of Science ®Google Scholar
• Liu C, Yu D. 2009. The bud and root sprouting capacity of Alternanthera philoxeroides after over-wintering on sediments of a drained canal. Hydrobiologia. 623(1):251–256.
   Web of Science ®Google Scholar
• Morais MC, Panuccio MR, Muscolo A, Freitas H. 2012. Salt tolerance traits increase the invasive success of Acacia longifolia in Portuguese coastal dunes. Plant Physiolo Biochem. 55:60–65.
   PubMed Web of Science ®Google Scholar
• Nawaz K, Hussain K, Majeed A, Khan F, Afghan S, Ali K. 2010. Fatality of salt stress to plants: morphological, physiological and biochemical aspects. Afr J Biotechnol. 9(34):5475–5480.
   Web of Science ®Google Scholar
• Nelson GC, Valin H, Sands RD, Havlík P, Ahammad H, Deryng D, Elliott J, Fujimori S, Hasegawa T, Heyhoe E, et al. 2014. Climate change effects on agriculture: economic responses to biophysical shocks. Proc Natl Acad Sci USA. 111(9):3274–3279.
   PubMed Web of Science ®Google Scholar
• Niazian M, Sadat-Noori SA, Abdipour M. 2018. Modeling the seed yield of Ajowan (Trachyspermum ammi L.) using artificial neural network and multiple linear regression models. Ind Crops Prod. 117:224–234.
   Web of Science ®Google Scholar
• Niedbała G. 2019. Application of artificial neural networks for multi-criteria yield prediction of winter rapeseed. Sustainability. 11(2):533.
   Web of Science ®Google Scholar
• Niedbała G, Mioduszewska N, Mueller W, Boniecki P, Wojcieszak D, Koszela K, Kujawa S, Kozłowski RJ, Przybył K. 2016. Use of computer image analysis methods to evaluate the quality topping sugar beets with using artificial neural networks. Eighth International Conference on Digital Image Processing (ICDIP 2016); May 20–22; Chengu, China: International Society for Optics and Photonics.
   Google Scholar
• Niedbala G, Przybył J, Sęk T. 2007. Prognose of the content of the sugar in roots of sugar-beet with utilization of the techniques regression and neural. Agric Eng. 2(90):225–234.
   Google Scholar
• Ozaslan C, Farooq S, Onen H, Bukun B, Ozcan S, Gunal H. 2016. Invasion potential of two tropical physalis species in arid and semi-arid climates: effect of water-salinity stress and soil types on growth and fecundity. PLoS One. 11(10):e0164369.
   PubMed Web of Science ®Google Scholar
• Park S, Hwang C, Vlek P. 2005. Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agric Syst. 85(1):59–81.
   Web of Science ®Google Scholar
• Peng J, Kim M, Kim Y, Jo M, Kim B, Sung K, Lv S. 2017. Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea. Grassl Sci. 63(3):184–195.
   Web of Science ®Google Scholar
• Pimentel D, McNair S, Janecka J, Wightman J, Simmonds C, O’Connell C, Wong E, Russel L, Zern J, Aquino T, et al. 2001. Economic and environmental threats of alien plant, animal, and microbe invasions. Agric Ecosyst Environ. 84(1):1–20.
   Web of Science ®Google Scholar
• Pintó-Marijuan M, Munné-Bosch S. 2013. Ecophysiology of invasive plants: osmotic adjustment and antioxidants. Trends Plant Sci. 18(12):660–666.
   PubMed Web of Science ®Google Scholar
• Rouifed S, Byczek C, Laffray D, Piola F. 2012. Invasive knotweeds are highly tolerant to salt stress. Environ Manage. 50(6):1027–1034.
   PubMed Web of Science ®Google Scholar
• Safa M, Samarasinghe S, Nejat M. 2015. Prediction of wheat production using artificial neural networks and investigating indirect factors affecting it: case study in Canterbury province, New Zealand. J Agr Sci Technol. 17:791–803.
   Web of Science ®Google Scholar
• StatSoft I. 2001. STATISTICA (data analysis software system), version 6. Tulsa (OK), p. 150.
   Google Scholar
• Wang Q, Wang C, Zhao B, Ma Z, Luo Y, Chen J, Li B. 2006. Effects of growing conditions on the growth of and interactions between salt marsh plants: implications for invasibility of habitats. Biol Invasions. 8(7):1547–1560.
   Web of Science ®Google Scholar
• Wilson JR, Yeates A, Schooler S, Julien MH. 2007. Rapid response to shoot removal by the invasive wetland plant, alligator weed (Alternanthera philoxeroides). Environ Exp Bot. 60(1):20–25.
   Web of Science ®Google Scholar
• Yang C, Yang X, Zhang X, Zhou C, Zhang F, Wang X, Wang Q. 2019. Anatomical structures of alligator weed (Alternanthera philoxeroides) suggest it is well adapted to the aquatic–terrestrial transition zone. Flora. 253:27–34.
   Web of Science ®Google Scholar
• You W-H, Han C-M, Fang L-X, Du D-L. 2016. Propagule pressure, habitat conditions and clonal integration influence the establishment and growth of an invasive clonal plant, Alternanthera philoxeroides. Front Plant Sci. 7:568.
   PubMed Web of Science ®Google Scholar
• You W, Yu D, Xie D, Han C, Liu C. 2014. The invasive plant Alternanthera philoxeroides benefits from clonal integration in response to defoliation. Flora. 209(11):666–673.
   Web of Science ®Google Scholar
• Yu L, Yu D, Liu C, Xie D. 2010. Flooding effects on rapid responses of the invasive plant Alternanthera philoxeroides to defoliation. Flora. 205(7):449–453.
   Web of Science ®Google Scholar
• Zhang G, Patuwo BE, Hu MY. 1998. Forecasting with artificial neural networks: the state of the art. Int J Forecast. 14(1):35–62.
   Web of Science ®Google Scholar
• Zhu J-K. 2000. Genetic analysis of plant salt tolerance using Arabidopsis. Plant Physiol. 124(3):941–948.
   PubMed Web of Science ®Google Scholar