The Effect of Magnetized Water on the Efficacy of Nicosulfuron, Yield and Yield Components of Corn (Zea mays L.)

Document Type : Research Article

Authors

Ferdowsi University of Mashhad

Abstract

Introduction: There are many factors that affect the efficacy of herbicides such as formulation, physicochemical properties of the herbicide molecule, morphology and physiology of weeds, application time, and environmental conditions at the time of herbicide application and quality of their carriers. Meanwhile, water is considered as the most important and common carrier in most herbicides in which the activity of herbicide molecules are influenced by its quality. In general, there are many factors that stimulate water quality such as water hardness, pH of water, water temperature, bicarbonate ion content, turbidity of water and organic matter, iron and other substances affect the uptake, transport and optimum performance of some herbicides. Effect of magnetic fields on water is now the subject of an increasingly large research effort because it is known to reduce water hardness. In physic science, it is well established that by passing water through a magnetic device, the water can be influenced. However, the literature review in physical science in different country indicated also that magnetized water induced a change in water physicochemical properties such as conductivity, surface tension, viscosity, vaporization rate, and pH. So this technology was used in different countries who all reported the successful use of magnets in treating water for herbicide application. Using magnetized water in sprayer`s tank for some herbicide which sensitive to hard water, with reduced does, might lead to better weed control.
Material and Methods: To study the effect of passing water of sprayer`s tank through magnetic field on Nicosulfuron efficacy, yield and yield components of Corn (Zea mays L.) two factorial experiments conducted based on randomized complete block design with three replications at the research field of Ferdowsi University of Mashhad ( Lat 36˚15' N, Long 59˚28' E; 985 m Altitude) during 2016-2017. Experimental factors included Nicosulfuron dose in 5 levels: 0, 20, 40, 60 and 80 g a.i. ha-1 (recommended dose) and water type of sprayer`s tank with 3 levels (no magnetic water, 10 and 20 times passed water through magnetic field 650 mT). Magnetized water was produced by a commercial magnet namely AQUA CORRECT (650 Gauss magnetic field). Plots were sprayed on 6-leaf stage of corn by a knapsack sprayer with a flat nozzle no. 8002 at 240 kPa and with delivery volume of 185 liters per hectare. At the end of growth season (physiological maturity of corn), crop height, yield and yield components of corn including plant height, No. of row per ear, ear length, No. of grain per row, No. of total grain per ear, 100 seed weight, grain yield, biological yield and harvest index and total weed density and total weed biomass measured in 2 m2 area of each plot. After Batrlet test and confirmation of homogeneity of error between two years of all data were analyzed by SAS ver. 9.1 and Excel ver. 2007 and treatments were separated using LSD Test. All statements of significance were based on probability of (P ≤ 0.05).
Results and Discussion: According to the results, 10 times of passing water of sprayer`s tank through magnetic field (650 mT) combined to 60 and 80 g a.i. ha-1 Nicosulfuron significantly reduced total weed density and biomass in corn field. The maximum biological yield of crop (42510 Kg ha-1), grain number in ear`s row (45), total number of grain per ear (627.2) and weight of 100 grain of corn (35.5 g) observed in 10 times of passing water of sprayer`s tank through magnetic field (650 mT) with the 60 and 80 g a.i. ha-1 of Nicosulfuron, which were 57, 21, 44, and19% more than the control treatment (no herbicide and no magnetized water), respectively. Also grain yield of corn increased significantly (p ≤ 0.05) in control treatment of water with increasing the Nicosulfuron herbicide doses in both years of this experiment. The maximum yield observed in the combination of maximum Nicosulfuron dose with 10 times passed water of sprayer`s tank through magnetic field (650 mT) in both years. The lowest grain yield observed in the control treatment and the highest ones achieved in 100 percentage of herbicide recommended dose. It was concluded that the best combination treatment was 60 and 80 g a.i. ha-1 of Nicosulfuron and 10 times passing water sprayer`s tank through magnetic field 650 mT for corn.

Keywords


1- Abbasi Surki A., Rouhi H.R., Moradi A., Zainali N., and Alimoradi J. 2017. Methods for overcoming seed dormancy in jimsonweed (Datura stramonium L. Journal of Field Crop Science Special Issue: 35-41. (In Persian with English Abstract)
2- Abin A., and Eslami S.V. 2009. Influence of maternal environment on salinity and drought tolerance of annual sowthistle (Sonchus oleraceus L.) at germination and emergence stage. Iranian Weed Research Journal 2: 1- 12. (In Persian with English Abstract)
3- Alm D.M., McGiffen J.R.M.E., and Hersketh J.D. 1991. Weed phenology. In Predicting Crop Phenology 191-218.
4- Arana M.V., de Miguel, L.C. and Sanchez, R.A. 2006. A phytochrome-dependent embryonic factor modulates gibberellin responses in the embryo and micropylar endosperm of Datura ferox seeds. Planta 223: 847-857.
5- Boonman J. G. 1993. East Africa's grasses and fodders: Their ecology and husbandry. Kluwer Academic Publishers, Dordrecht, the Netherlands.
6- Cardina J., Catherine P., Herms D., Herms A., and Forcella F. 2007. Evaluating Phonological Indicators for Predicting Giant Foxtail (Setaria faberi) Emergence. Weed Science 55: 455-464.
7- Clements D.R., and Ditommaso A. 2011. Climate change and weed adaptation: can evolution of invasive plants lead to greater range expansion than forecasted? Weed Research 51: 227-240.
8- Donato L., Edite S., Masin R., Calha I., Zanin G., Fernandez- Quintanilla C., and Dorado J. 2013. Estimation and Comparison of Base Temperatures for Germination of European Populations of Velvetleaf (Abutilon theophrasti) and Jimsonweed (Datura stramonium). Weed Science 61: 443–451.
9- Donohue, K. 2002. Germination timing influences natural selection on life-history characters in Arabidopsis thaliana. Ecology 83(4): 1006-1016.
10- Fenner M., and Thompson K. 2006. The ecology of seeds. Cambridge: Cambridge University Press. P. 260
11- Forcella F., Benech Arnold, R.L., Sanchez R., and Ghersa, C.M. 2000. Modeling seedling emergence. Field Crop Research 67: 123-139.
12- Forouzesh S., Oveisi M., Alizadeh H., Rahimian Mashhadi H., and Farokhi Z. 2017. Comparison of phenological development of Avena ludoviciana ecotypes. Iranian Journal of Field Crop Science 48: 601-613.
13- Ghersa C.M., and Holt J.S. 1995. Using phenology prediction in weed management: a review. Weed Research 35: 461-470.
14- Hess M., Barralis G., Bleiholder H., Eggers T.H., Hack H., and Stauss R. 1997. Use of the extended BBCH scale – general for the descriptions of the growth stages of mono – and dicotyledonous weed species. Weed Research 37: 433-441.
15- Karimmojeni H., Rahimianmashhadi H., Alizadeh H.M., Nasirimahhallati M., and Zand E. 2007. Estimation of corn yield loss due to single and single species of jimson weed and rough cocklebur using empirical models based on density. Electronic Journal of Crop Production 1: 127-136. (In Persian with English Abstract)
16- Kawabata O., and Nishimoto R.K. 2003.Temperature and rhizome chain effect on sprouting of purple nutsedge (Cyperus rotundus) ecotypes. Weed Science 51: 348–355.
17- Khanjani M., Mahmoodi, S., and Jamil mohammadi. 2009. Effect of density and relative time of emergence of Jimsonweed (Datura stramonium L.) on yield and yield components of Chitti been (Phaseolus vulgaris L.). Electronic Journal of Crop Production 89: 215-228. (In Persian with English Abstract)
18- Kirwa E.C., Njoroge K., Chemining’wa G.N., and Mnene N. 2016. Ecological effects on the flowering phenology of Cenchrus ciliaris L. collections from the arid and semiarid lands of Kenya. African Journal of Agricultural Research 11: 1008-1018.
19- Leblanc M.L., Cloutier D.C., Stewart K., and Hamel C. 2003. The use of thermal time to model common lambsquarters (Chenopodium album) seedling emergence in corn. Weed Science 51: 718-724.
20- Masin R., Loddo D., Benvenuti S., Otto S., and Zanin G. 2012. Modeling weed emergence in Italian maize fields. Weed Science 60: 254-259.
21- Mickelson J.A., and Grey W. E. 2006. Effect of soil water content on wild oat (Avena fatua) seed mortality and seedling emergence. Weed Science 52: 255-262.
22- Najafi H. 2013. Biology and management of Iranian weeds. Agricultural Research Organization. 455 pp.
23- Rowse H.R., and Finch-Savage W.E. 2003. Hydrothermal threshold models can describe the germination response of carrot (Daucus carota) and onion (Allium cepa) seed populations across both sub- and supra-optimal temperatures. New Phytologist 158: 101–108.
24- Sans F.X., and Masalles R.M. 1994. Life-history variation in the annual arable weed Diplotaxis erucoides (Cruciferae). Canadian Journal Botany 72: 10-19.
25- Soltani A., Robertson M.J., Torabi B., Yousefi-Daz M., and Sarparast R. 2006. Modeling seedling emergence in chickpea as influenced by temperature and sowing depth. Agriculture Forest Meteorological 138: 156-167.
26- Sultan S.E., and Bazzaz F.A. 1993. Phenotypic plasticity in Polygonum persicaria. I. Diversity and uniformity in genotypic norms of reaction to light. Evolution 47: 1009-1031.
27- Veblen K.E. 2012. Savanna glade hotspots: Plant community development and synergy with large herbivores. Journal of Arid Environment 78: 119-127.
28- Wang J.Y. 1960. A critique of the heat unit approach to plant response studies. Ecology 41: 785-790.
29- Zand E., Rahimian H., Koocheki A. R., Khalaghani J., Moosavi K., and Ramezani K. 2004. Weed ecology (Translation). Jehade Daneshgahi of Mashhad Press.
30- Zand E., Baghestani M.A., Nezamabadi N., Minbashi M., and Hadizadeh M.H. 2009. A review on the last list of herbicides and the most important weeds of Iran. Weed Research Journal 1: 83-99. (In Persian with English Abstract)
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