بهینه‌سازی شرایط تغذیه‌ای و محیطی برای آزادسازی پتاسیم از ایلایت توسط Aspergillus niger و Pseudomonas fluorescens

نویسندگان

1 دانشجوی دکتری گروه علــوم خاک، دانشکده کشاورزی، دانشگاه ارومیه

2 استاد گروه علــوم خاک، دانشکده کشاورزی، دانشگاه ارومیه

3 استادیار گروه علــوم خاک، دانشکده کشاورزی، دانشگاه ارومیه

چکیده

پژوهش حاضر با هدف ارزیابی اثر منابع مختلف کربن بر قابلیت انحلال پتاسیم توسط باکتری Pseudomonas fluorescens و قارچ Aspergillus niger و مدل‌سازی اثر سطوح مختلف متغیرهای زمان انکوباسیون، pH و منبع کربن بر انحلال‌پذیری پتاسیم با استفاده از طرح مرکب مرکزی انجام شد. در مرحله‌ی اول، بر مبنای طرح پلاکت – برمن، تعداد 12 آزمایش با ترکیب سطوح مختلف تعریف شده و تأثیر منابع مختلف کربن شامل گلوکز، ساکارز و فروکتوز در دو سطح 1+ (10 گرم در لیتر) و 1- (5 گرم در لیتر) بر انحلال پتاسیم موجود در ایلایت مورد بررسی قرار گرفت. در مرحله‌ دوم، پس از گزینش منبع کربن مهم و تأثیرگذار، دامنه‌های متفاوتی از متغیرهای مستقل شامل زمان انکوباسیون، pH و منبع کربن در نظر گرفته شده و بر اساس مقادیر کدبندی شده متغیرهای مستقل، طراحی آزمایش صورت پذیرفت. نتایج نشان داد تأثیر منابع کربن بر رهاسازی K توسط باکتری و قارچ معنی‌دار نبود و لذا ﻫﺮ ﮐﺪام از آنﻫﺎ ﻣﯽﺗﻮاﻧﻨﺪ ﺑﻪﻋﻨﻮان ﺟﺎﯾﮕﺰﯾﻦ ﯾﮑﺪﯾﮕﺮ در ﻣﺤﯿﻂ‌ﮐﺸﺖ اﺳﺘﻔﺎده ﺷﻮﻧﺪ. بر مبنای نتایج تحلیل آماری ضرایب مدل طرح مرکب مرکزی، pH اثر مثبت و افزاینده‌ای‌ بر افزایش رهاسازی پتاسیم محلول داشت (0001/0>P). به‌طوری‌که بیشترین آزادسازی پتاسیم توسط باکتری و قارچ به‌ترتیب برابر با 75/109 و 3/170 میلی‌گرم در لیتر، در سطوح مرکزی زمان و منبع کربن، و 36/10 = pH مشاهده شد. بر اساس مقدار ضریب تبیین مدل طرح مرکب مرکزی، به‌ترتیب 91 و 8/87 درصد از تغییرات پتاسیم محلول در حضور باکتری و قارچ توسط این مدل قابل تبیین بود.

کلیدواژه‌ها


عنوان مقاله [English]

Optimizing Nutritional and Culture Medium Conditions for Potassium Release from Illite by Aspergillus niger and Pseudomonas fluorescens

نویسندگان [English]

  • Sanaz Ashrafi-Saeidlou 1
  • Abbas Samadi 2
  • MirHassan Rasouli-Sadaghiani 2
  • Ebrahim Sepher 2
  • M barin 3
1 Ph.D. Student, Soil Science Department, Faculty of Agriculture, Urmia University, Urmia, Iran
2 Prof. Soil Science Department, Faculty of Agriculture, Urmia University, Urmia, Iran
3 Assist. Prof. Soil Science Department, Faculty of Agriculture, Urmia University, Urmia, Iran
چکیده [English]

Abstract
Present research was carried out to evaluate the effect of different carbon sources on K release by Pseudomonas fluorescens and Aspergillus niger and modeling the effects of incubation time, pH and different amounts of carbon source on K release using central composite design. At the first step, 12 experiments were defined with a combination of different levels, and the effects of different carbon sources including glucose, sucrose and fructose at two levels of +1 (10 g.l-1) and -1 (5 g.l-1) on potassium dissolution of illite was studied. After selection of effective carbon source, different ranges of independent variables including incubation time, pH and carbon source were considered and experiment design has been made based on the coded values of them. Results showed that there is no difference between carbon sources, which were used at the first step of experiment, so each of them can be used as alternatives to each other in culture medium. According to the statistical analysis results of central composite design, pH has a positive impact on increasing the soluble potassium release (P>0.001). Maximum potassium release by bacteria and fungus, 109.75 and 170.3 mg.l-1 respectively, observed at central levels of incubation time and carbon source and pH = 10.36. Based on determination coefficient of, 91 and 87.8 percent of soluble potassium changes in the presence of bacteria and fungus can be explained by this central composite design model, respectively.

کلیدواژه‌ها [English]

  • "Illite"
  • "Optimization"
  • "Potassium"
  • "Solubilizing Microorganisms"
  • "Carbon Source"
Agam N and Berliner PR, 2006. Dew formation and water vapor adsorbtion in semi-arid environments-A review. Journal of Arid Environments 65: 572-590.
Al-Shammari ET,  Mohammadi K,  Keivani A, Ab Hamid SH, Akib S, Shamshirband S and Petkovic D, 2016.
Prediction of daily dew point temperature using a model combining the support vector machine with firefly
Algorithm. Journal of  Irrigation and Drainage Engineering 142 (5).040160131-9.
 
Amirmojahedi M, Mohammadi K, Shamshirband S, Seyed Danesh A, Mostafaeipour A and Kamsin A, 2016. A hybrid computational intelligence method for predicting dew point temperature. Journal of Environmental Earth Sciences 75:415-426.
Antonopoulos VZ, Papamichail DM, Aschonitis VG and Antonopoulos AV, 2019. Solar radiation estimation methods using ANN and empirical models. Computers and Electronics in Agriculture 160:160-167.‏
Dong  J, Wu  L, Liu  X, Li  Z, Gao Y, Zhang Y and Yang Q , 2020. Estimation of daily dew point temperature by using bat algorithm optimization based extreme learning machine. Applied Thermal Engineering 165: 114569.‏
Deka PC, Patil AP, Kumar PY and Naganna RS, 2018. Estimation of dew point temperature using SVM and ELM for humid and semi-arid regions of India. Journal of Hydraulic Engineering 24:190-197.
Fathollahzadeh Attar N,  Khalili K, Behmanesh J and Khanmohammadi N, 2018. On the reliability of soft computing methods in the estimation of dew point temperature: The case of arid regions of Iran. Journal of Computers and Electronics in Agriculture 153: 334-336.
Friedman JH,1991. Multivariate adaptive regression splines. The Annals of Statistics 19:1–67.                                                       
 Gornicki K and Winiczenko R, 2017. Evaluation of models for the dew point temperature determination. Technical Sciences 20(3): 241-257.
Hill AJ, DawsonTE, Shelef O and Rachmilevitch S, 2015. The role of dew in Negev Desert plants. Oecologia 178(2): 317-327.                                                                                                                                 
Isazadeh M and Rezaei Banafshe M, 2017. Evaluating of the artificial neural network and support vector mechine performance in determining daily evaporation values (Case study: Tabriz and Maragheh Meteorological Stations). Natural Geographical Research 49:151-168.
Lawrence MG, 2005. The relationship between relative humidity and the dew point temperature in moist air. Pp.225-233, American Meteorological Society.
Mehdizadeh S, Behmanesh J and Khalili K, 2017. Application of gene expression programming to predict daily dew point temperature. Applied Thermal Engineering 112: 1097-1107.
Mahmood R and Hubbard KG, 2005. Assessing bias in evapotranspiration and soil moisture estimate due to the use of modeled solar radiation and dew point temperature data. Agricultural and Forest Meteorology 25(2): 71-84.
Rabinson PR, 2000. Temporal trends in United States dew point temperature. Journal of Climatology 20: 985-1002.
Sabziparvar AA and Khattar B, 2015. Evaluated the artificial neural networks and Irmak Empirical Model in estimation net daily solar radiation in cold and semi arid area (Case study: Hamadan). Water and Soil Science- University of Tabriz 25: 37-50. (In Persian with English abstract).
Shank DB, Hoogenboom G and Mcclendon RW, 2008.  Dew point temperature prediction using artificial neural networks. Journal of Applied Meteorology and Climatology 47: 1757-1769                               
Shafei A, Ebrahimi H and Golkar Hamzehi HR, 2011. Determination of the optimum tillage pattern of crop using linear programming (Bashrouieh city). The First Conference of Meteorology and Agricultural Water Management, Nov.21-22, Tehran University, Tehran. (In Persian with English abstract).
Sharifi SF, Rezaverdinejad V and Nourani V, 2016. Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches. Journal of Atmospheric and Solar-Terrestrial Physics 149: 131- 145
Shiri J, Kim S and Kisi O, 2014. Estimation of daily dew point temperature using soft computing techniques. Hydrology Research 45:165-181.
 
Williams MD, Goodrick SL, Grundstein A and Shepherd M, 2015. Comparison of dew point temperature estimation methods in Southwestern Georgia. Journal of Physical Geography 36: 255-267.