The effect of orthophosphate‚ nitrate, and chloride anions on desorption and availability of adsorbed zinc in some calcareous soils

Document Type : Complete scientific research article

Authors

Department of Soil Science of Shahrekord University,

Abstract

Background and Objectives: Nowadays, the use of zinc (Zn) fertilizers has been expanded to satisfy the deficiency of this element and improve the yield and quality of agricultural products. The knowledge about the availability and release of adsorbed Zn after application in soil is necessary to achieve the best fertilization management and soil and water conservation against Zn accumulation in soil. On the other hand, presence of anions in irrigation water, agricultural fertilizers, and sewage sludge can affect adsorption, desorption, and availability of nutrients such as Zn. Zinc adsorption characteristics was usually studied using isotherm coefficients; while availability of adsorbed Zn in soil is important in soil fertility. In this study, the effects of orthophosphate, nitrate, and chloride anions on adsorption and desorption capacity were investigated in five calcareous soil of Chaharmahal - Va - Bakhtiari province.
Materials and Methods: In this study, a solution containing concentrations of 25, 50, 75, 100, 150, and 200 mg l-1 of Zn as ZnSO4 source in the presence of KH2PO4, KNO3 and KCl electrolytes (50 mM) was used. After Zn adsorption in soils, availability and desorbed of Zn was measured by DTPA-TEA and 0.01 M CaCl2, respectively. The amount of Zn desorbed in 0.01M CaCl2 is adsorbed Zn as non-specific. The adsorbed Zn as specific was calculated from the difference between the amounts of adsorbed Zn and desorbed Zn by 0.01 M CaCl2.
Results: According to the results, the highest amount of Zn adsorbed as specific in the presence of all anions. Percentage of adsorbed Zn in all soils as specific ranged from 99.65 to 99.80 in the chloride solution (more than other anions p < 0.05), 99.84 to 99.99 in the nitrate solution, and 99.55 to 99.72 in the orthophosphate solution. Availability of adsorbed Zn ranged from 41 to 43% in orthophosphate solution, 49 to 54% in nitrate solution, and 58 to 61% in chloride solution.
Conclusion: The results showed that the amount of adsorbed Zn as specific was more than amount of adsorbed Zn as non-specific in the presence of all the anions in all studied soils. The result showed that highest amount of available adsorbed Zn was extracted in the presence of chloride, nitrate, and orthophosphate (p < 0.05). About 50% of the Zn adsorbed extracted by DTPA-TEA. In the presence of all studied anions, more than 99% Zn adsorbed as specific. Therefore, Zn adsorbed at specific sites and 0.01 M CaCl2 cannot extracted it. The results of this study showed the application of P and Zn as fertilizers in calcareous soils can lead to a reduction in extracted Zn by DTPA-TEA in treated soils with these nutrients.

Keywords


1.Du, J., Fang,J., XU, W., and Shi, P. 2013. Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province, China. Stochastic Environmental Research and Risk Assessment, 27: 377-387.
2.Eghtedari, M., Bazrafshan, J., Shafe, M., and Hejabi, S. 2016. Prediction of streamflow drought using SPI and Markov chain in Kharkheh’s basin. J. Water Soil Cons. 23: 2. 115-130.
3.Eslahi, M., Sobhani, B., and Pourasghar, F. 2014. Studying and applying the Standardized Precipitation Evapotranspiration Index (Case study: Tabriz Meteorological Station). J. Clim. Res. 19: 23-28. (In Persian)
4.Ghabaei Sough, M., Zare Abyaneh, H., Mosaedi, A., and Samadi, S.Z. 2016. Assessment of Humidity Conditions and Trends Based on Standardized Precipitation Evapotranspiration Index (SEPI) Over Different Climatic Regions of Iran. J. Water Soil. 30: 5. 1700-1717. (In Persian)
5.Ghorbani, Kh., Salari Jazi, M., and Abdolhosseini, M. 2015. Feasibility Study of the Precipitation of Annual Drought Based on Drought Conditions in the Spring Season. Iran. J. Irrig. Drain. 9: 636-645. 
6.Guerreiro, M.J., Lajinha, T., and Abreu, I. 2008. Flood analysis with the standardized precipitation index (SPI).
7.Hao, Z., and AghaKouchak, A. 2014. A nonparametric multivariate multi-index drought monitoring framework. J. Hydrometeorol. 15: 1. 89-101.
8.Hatefi, A., Mosaedi, A., and Jabbari Nooghabi, M. 2016. The role of evapotranspiration in meteorological drought monitoring in some climate regions of Iran. J. Water Soil Cons. 23: 2. 1-21.
9.Heydari, A. 2000. Real-time flood forecasting and flood control. 4th Conference on Dam Construction, Tehran, Iran. (In Persian)
 10.Hosseini Pazhouh, N. 2014. Studying the possibility of using SPI in the analysis of flood occurrence threshold. Case study: Kasilian basin. M.Sc. Thesis, Imam Khomeini International University. (In Persian)
11.Mashayekhi, T. 2001. Historical flood in Iran. Iranian national committee of large dams. No. 38, 89p. (In Persian)
12.Mckee, T.B., Doesken, N.J., and Kleist, J. 1993. The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society Boston, MA, Pp: 179-183.
13.Mostafazadeh, R., and Zabihi, M. 2016. Comparison of SPI and SPEI indices to meteorological drought assessment using R programming (Case study: Kurdistan Province). J. Earth Space Physic. 34: 3. 633-643. (In Persian)
14.Nadi, M., Bazrafshan, J., Pourtahmasi, K., and Najafi Hersini, F. 2015. Relationship between oak’s tree-ring and climate indices (in regional and global scales) in Javanroud region, Kermanshah. J. Water Soil Cons.
22: 3. 57-71.
15.Nadi, M., Pourtahmasi, K., Bazrafshan, J., and Braeuning, A. 2016. Two century tree ring reconstruction of drought using Multivariate Standardized Precipitation Index (MSPI) in Javanroud-Kermanshah region. J. Water Soil Cons. 22: 6. 99-116.
16.Pappenberger, F., Wetterhall, F., Dutra, E., Di Giuseppe, F., Bogner, K., Alfieri, L., and Cloke, H.L. 2013. Seamless forecasting of extreme events on a global scale. Climate and Land Surface Changes in Hydrology, edited by: Boegh, E., Blyth, E., Hannah, D.M., Hisdal, H., Kunstmann, H., Su, B., and Yilmaz, K.K., IAHS Publication, Gothenburg, Sweden, Pp: 3-10.
17.Salehi, M. 2014. Flood forecasting using artificial neural network and time series. M.Sc. Thesis, shahid bahonar university of kerman. (In Persian)
 18.Seiler, R., Hayes, M., and Bressan, L. 2002. Using the standardized precipitation index for flood risk monitoring. Inter. J. Climatol. 22: 1365-1376.
19.Shadmani1, M., Marofi, S., Mohammadi, K., and Sabziparvar, A.A. 2011. Regional flood discharge modeling in Hamedan province using Artificial Neural Network. J. Water Soil Cons. 18: 4. 21-42. (In Persian)
20.Shokoohi, A., Hosseini Pazhouh, N., and Bakhtiari, A. 2017. Flood forecasting using daily scale SPI. J. Civil Environ. Engine. In Press. (In Persian)
21.Vicente-Serrano, S.M., Begueria, S., and Lopez-Moreno, J.I. 2010. A Multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index-SPEI. J. Clim. 23: 7. 1696-1718.
22.Yaghoobzadeh, M., Ahmadi, M., Seyyed Kaboli, H., Zamani, Gh.R., and Amirabadizadeh, M. 2017. The evaluation of effect of climate change on agricultural drought using ETDI and SPI indexes. J. Water Soil Cons. 24: 4. 43-61.
23.Yazdanpanahi, A., Ahmadaali, K., and Hosseini Pazhouh, N. 2017. Study on spatial-temporal variation of SPEI (Case study: Razavi Khorasan province), 3rd International Conference on Agricultural Engineering and Natural Resources, Tehran, Iran. (In Persian)
24.Zhang, Q., Li, Q., Singh, V.P., Shi, P., Huang, Q., and Sun, P. 2018. Nonparametric integrated agrometeorological drought monitoring: Model development and application. Journal of Annual Drought Based on Drought Conditions in the Spring Season. Iran. J. Irrig. Drain. 9: 636-645.