ORIGINAL_ARTICLE
Evaluation of Direct Methods and Computer Models in Estimating the Infiltration Parameters of the Kostiakov-Lewis Equation in Sugarcane Fields
Introduction: Surface irrigation systems are the oldest and common irrigation method. Surface irrigation is of low cost and energy requirements compared to sprinkler and drip irrigation systems. In general, a main large number of fields' data is needed to show the farm average condition. Infiltration parameters are one of the most important parameters in surface irrigation systems and it has led to increase the irrigation systems efficiency, especially since the characteristics of infiltration vary with time and place. The modified kostiakov-lewis equation is one of the most useful infiltration equations in surface irrigation. In the current study, the infiltration parameters of the modified kostiakov-lewis equation were determined with two sets of usual methods including direct methods such as two-point Elliot and Walker and Input-Output, computer models such as SIPAR_ID and IPARM. Finally, the results were compared with the results of field experiments.
Materials and Methods: The current field was irrigated three times from 14 September to 31 October 2016 at the R 5-22 farm located in Salman Farsi Agro-Industry sugarcane fields with age of Raton 2. To collect the required data, the fields experiments were conducted on nine furrows of 250 m in length, 1.83m in space and 0.04% in slope, which all furrows were irrigated under three events and three inflow (1, 1.5 and 2 l/s), and fields’ data were obtained from experimental measurements during summer and autumn2016 at sugarcane fields of Salman Farsi Agro-Industry %. In the current study, the inflow rate and runoff were measured by W.S.C type 1 and 2 and all furrows divided into 10 stations. The advane time and infiltrated depth were measured at each stations. In this study 18 furrows were considered, nine furrows were used for testing and the other furrows had buffer roles. The furrows were irrigated by closed-end method. In this study, three indicators of infiltrated volume in the root zone, advance time and runoff volume were used to evaluate the accuracy of estimation of infiltration parameters. Surface irrigation model: WinSRFR 4.1.3 was used to simulate irrigation phases and infiltration value in each method. In this study, zero inertia model was used for simulation.
Results and Discussion: Results of this study showed that using the direct methods to estimate the infiltration parameters in WinSRFR 4.1.3 software improves the simulation process significantly. The results of the Two- Point and Input-Output method were showed a little difference with the results of the WinSRFR 4.1.3 software in simulation of the closed-end furrow irrigation process with sugarcane cultivated in furrows. The direct methods for infiltration parameters in furrow irrigation showed more accuracy than computer models in advance time , runoff and infiltrated water volumes. According to the results of this study, the Two-Point method in estimation of advance time with mean of RMSE, MAE and RE of 10.52, 14.91 and 10.1%, infiltrated water volume with mean of RMSE, MAE and RE of 9.6, 7.36 and 7.8 and runoff volume with mean of RMSE, MAE and RE of 8.8%, 8.7% and 1.2%, had a very acceptable performance. Also, the RMSE and RE values of other direct method (input-output method) were 11.4% and 6.8% for infiltrated water volume, respectively, and 1.6 and 0.3% for runoff volume, respectively, shows that this method has high accuracy in estimating these two performance indicators although this method with an average of 25.11% and 27.2% was not able to accurately simulate advance time. On the other hand, the results of computer models showed that the IPARM model with the average mean absolute error and relative error was 23.33, 15.5% of the advance time, 20.02 and 26.7% of the infiltrated volume and 11.81% and 1.8% estimated runoff volume, which was better than the SIPAR_ID model. Although computer models had acceptable performance in estimating infiltration parameters, direct methods performed better due to the use of more input data and data from all stages of irrigation. In general results of this study were showed that, if the direct methods for infiltration equations used Instead of the computers models in the designing, simulation and evaluation of the furrow irrigation systems, increased the accuracy of results to significantly and will improve and increase irrigation performance indicators.
https://jsw.um.ac.ir/article_38739_9beb13e05cd3a2044b00c5dbbcceaad8.pdf
2019-08-23
379
389
10.22067/jsw.v0i0.74330
Infiltration Parameter
Two-Point method
Inflow-outflow method
SIPAR_ID
IPARM
Reza
Mazarei
reza.mazarei1372@gmail.com
1
Shahid Chamran of Ahvaz
LEAD_AUTHOR
AbdAli
Naseri
abdalinaseri@scu.ac.ir
2
Shahid Chamran University, Ahwaz
AUTHOR
amir
soltani mohammadi
a.soltani@scu.ac.ir
3
Shahid Chamran of Ahvaz
AUTHOR
1- Bautista E., Clemmens A.J., and Strelkoff T.S. 2009. Modern analysis of surface irrigation systems with WinSRFR. Agricultural Water Management 96(7): 1146–1154.
1
2- Ebrahimian H., Liaghat A., GhanbarianAlavijeh B., and Abbasi F. 2010. Evaluation of various quick methods for estimating furrow and border infiltration parameters. Irrigation Science 28: 479-488.
2
3- Ebrahimian H. 2014. Soil Infiltration Characteristics in Alternate and Conventional Furrow Irrigation using Different Estimation Methods. Korean Society of Civil Engineers 18(6): 1904-1911.
3
4- Elliot R.L., and Walker W.R. 1982. Field evaluation of furrow infiltration and advance functions. Trans of the ASAE, 25(2): 396-400.
4
5- Gillies M.H., and Smith R.J. 2005. Infiltration parameters from surface irrigation advance and run-off data. Irrigation Science 24(1): 25-35.
5
6- Hanson B.R., Prichard T.L., and Schulbach H. 1993. Estimating furrow infiltration. Agricultural Water Management 24(4): 281–298.
6
7- Kamali P., Ebrahimian H., and Verdinejad V.R. 2015. Evaluation and comparison of multilevel optimization method and IPARM model to estimate infiltration parameters in furrow irrigation. Journal of Water and Irrigation Management 5(1): 43-54. (In Persian)
7
8- Kamali P., and Ebrahimian H. 2017. Comparison and evaluation of different methods for inverse estimation of the infiltration equation parameters in vegetated furrows. Journal of Soil and Water Research 48(1): 39-48.
8
9- Moravejalahkami B., Mostafazadeh-Fard B., Heidarpour M., and Abbasi F.2009. Furrow infiltration and roughness prediction for different furrow inflow hydrographs using a zero-inertia model with a multilevel calibration approach. Biosystems Engineering 103(3): 374–381.
9
10- Moravejalahkami B., Mostafazadeh-Fard B., Heidarpour M., and Abbasi F. 2012. Comparison of Multilevel Calibration and Volume Balance Method for Estimating Furrow Infiltration. Irrigation and Drainage Engineering 138(8): 781-777.
10
11- Rezaverdinejad V., Ahmadi H., Hemmati M., and Ebrahimian H. 2016. Evaluation and Comparison of Different Approaches of Infiltration Parameters Estimation under Different Furrow Irrigation Systems and Inflow Regimes. JWSS. 20(76): 161-176.
11
12- Rodriguez J.A., and Martos J.C. 2010. SIPAR_ID: freeware for surface irrigation parameter identification. Environment Model Software 25: 1487–1488.
12
13- Sayari S., Rahimpour M., and Zounemat-Kermani M. 2017. Numerical modeling based on a finite element method for simulation of flow in furrow irrigation. Paddy and Water Environment 15(4): 879-887.
13
14- Walker W.R. 2005. Multilevel calibration of furrow infiltration and roughness. Journal of Irrigation Drainage Engineering 131(2): 129–136.
14
15- Walker W.R., and Skogerboe G.V. 1987. the theory and practice of surface irrigation. Logan, Utah, Chapter 8, Vol. Balance field design, 81-87.
15
ORIGINAL_ARTICLE
The Effect of Different Irrigation Regimes under Subsurface Drip Irrigation System on Soil Moisture Distribution in Pistachio Orchard
Introduction: Creating a uniform and adequate moisture in the root zone is one of the most challenging issues in irrigated lands. Use of irrigation systems with high water efficiency, such as sub-surface drip irrigation is recommended as a solution to reduce water losses. Information on soil moisture variation is an important factor for managing and designing a subsurface drip irrigation system. This study was conducted to evaluate the soil moisture variation for different irrigation regimes in a pistachio orchards equipped by a subsurface drip irrigation system (SDI).
Materials and Methods: This study was carried out in a two-hectare of 10 years old pistachio orchard located in Semnan province, Iran ( located at 35°28ˊ N, 53°12ˊE and elevation of 1160 m above sea level) during the 2012-2013 growing season. The climate of the studied area is hot desert having an average annual precipitation of approximately 110 mm. Daily meteorological data such as the temperature, relative humidity, wind speed, rainfall, and solar radiation were collected from a meteorology station in farm. The soil was sandy loam textured with average field capacity and permanent wilting point of 12.23 and 5.01%, respectively. Subsurface drip irrigation system was equipped by EuroDrip Company emitters (PC2), inline, to a distance of 80 cm and with a discharge of 26.2 Lit/ hr installed at a depth of 40 cm. In this study, a factorial experiment in split plot design was used with three replications. Three irrigation treatments i.e. control (I1), Irrigation based on irrigation requirement (I2) and I2 plus leaching requirement (I3), and changes in the moisture front were investigated by weight sampling between two drip lines, between the trees rows, on the drip line and out of the drip line of each row, before and after irrigation and in development, middle and late season.
Results and Discussion: For the evaluated irrigation systems, increased levels of irrigation regime resulted in increased moisture content in the root zone. The higher average soil moisture (16.6 %) was measured after irrigation under I3. The I1 irrigation regime did not significantly change the soil moisture content in upper part of emitters before and after irrigation event. Average soil moisture content at different depths showed that the soil moisture content in 75 soil depth was significantly higher than that in 25 and 50 cm soil depth, which can be attributed to higher root water uptake by root in 0-50 cm soil depth. Bilateral impact of irrigation regimes and soil depth showed higher soil moisture content (19.3%) under I3 and 75 cm soil depth which may lead to deep percolation. Bilateral impact of irrigation regimes, soil depth, and time before and after irrigation event also resulted in higher soil moisture content (22.5 %) in 75 cm soil depth after irrigation under I3. The lowest soil water content (10.5 %) was measured in 25 soil depth before irrigation under I1.
Conclusion: The results of this study showed that I2 and I3 irrigation regimes did not show water shortage during growth season (before and after irrigation), but the I1 irrigation regime caused water scarcity. Therefore, the formation of continuous moisture profiles with low moisture in I1 irrigation regime was caused as a result of low irrigation during this period. Accumulation of moisture at depth of 50-75 cm from the soil surface, even under low irrigation conditions I1 irrigation regime, implies that irrigation time is not suitable for irrigation regimes. In general, in order to improve the irrigation management, it is necessary to reduce the irrigation intervals and have a more appropriate distribution of moisture in the soil profile.
https://jsw.um.ac.ir/article_38740_e8bab00b4229ab9febd71591128dbe66.pdf
2019-08-23
391
404
10.22067/jsw.v0i0.78773
leaching Requirement
Moisture Front
Soil depth
subsurface drip irrigation
Irrigation time
hossein
dehghani
dehghanisanij@yahoo.com
1
موسسه تحقیقات فنی و مهندسی کرج
LEAD_AUTHOR
Hamidreza
Haji Agha Bozorgi
bozorgi_ff@yahoo.com
2
Shiraz University
AUTHOR
ali asghar
ghaemi
ghaemiali@yahoo.com
3
?
AUTHOR
1- Allen R.G., Pereira L.S., Raes D., and Smith M. 1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrig. Drain. No, 56. FAO, Rome.
1
2- Amali S., Rolston D.E., Foltun A.E., Hanson B.R., Phen C.J., and Oster I.D. 1999. Soil water variabilityunder subsurface drp irrigation and furrow irrigation. Journal of Irrigation Science 17(4): 151-155.
2
3- Ayers R.S., and Westcot D.W. 1985. Water quality for agriculture (Vol. 29). Rome: Food and Agric Organization of the United Nations.
3
4- Badr A.E., and Aburab M.E. 2013. Soil moisture distribution patterns under surface and subsurface drip irrigation systems in sandy soil using neutron scattering technique. Journal of Irrigation Science 31: 317-332.
4
5- Battam M.A., Sutton B.G., and Boughton D.G. 2003. Soil pits as a simple design aid for subsurface drip irrigation systems. Journal of Irrigation Science 22: 135-141.
5
6- Ben-Gal A., Lazorovitch N., and Shani U. 2004. Subsurface drip irrigation in gravelfilled cavities. Vadose Zone Journal 3: 1407-1413.
6
7- Beniwal R.K., Soni M.L., Yadava N.D., Prakash C., and Talwar H.S. 2006. Effect of irrigation scheduling on moisture and salt distribution and growth of Kagji lime under drip irrigation in arid Rajasthan. Annals of Arid Zone 45(2): 169.
7
8- Cote C.M., Bristow K.L., Charlesworth P.B., Cook F.J., and Thorburn P.J. 2003 Analysis of soil wetting and solute transport in subsurface trickle irrigation. Journal of Irrigation Science 22: 143–156.
8
9- Dehghanisanij H., Agassi M., Anyoji H., Yamamoto T., Inoue M., and Eneji A.E. 2006. Improvement of saline water use under drip irrigation system. Journal of Agricultural Water Management 85: 233–242.
9
10- Dos Santos L.N., Matsura E.E., Gonçalves I.Z., Barbosa E.A., Nazario A.A., Tuta N.F., Elaiuy M.C., Feitosa D.R., and de Sousa A.C. 2016. Water storage in the soil profile under subsurface drip irrigation: Evaluating two installation depths of emitters and two water qualities. Journal of Agricultural Water Management 170: 91-98.
10
11- Douh B., Boujelben A., Khila S., and Mguidiche A.B.H. 2013. Effect of subsurface drip irrigation system depth on soil water content distribution at different depths and different times after irrigation. Larhyss Journal 13: 7-16.
11
12- Farshi A.A., Shariati M.H., Jarollahi R., Ghaemi M.H., Shabifar M., and Tolaei M.M. 1997. Estimated water requirement major plants agricultural and horticultural of country. Soil and Water Research Institute, Publication of Agriculture Education in Karaj, 394p. (In Persian)
12
13- Ghassemzadeh Mojaveri F. 1990. Evaluation of irrigation systems of farms. Mashhad: Astan Quds Razavi. Bhnshr company, 329p. (In Persian)
13
14- Goldhamer D.A., and Beede R. 1993. Result of four years of regulated deficit irrigation on deep rooted pistachio trees. CalifirniaPistachio Industry Annual Report Crop.
14
15- Hosseini Fard S.J., Insight M.N., Sedaghati V., and Akhiani A. 1396. Integrated management of soil fertility and plant nutrition pistachio tree. National Pistachio Research Institute. 101 p.
15
16- Khalili M., Haha Jaribi A., Akbar M., and Zacharyna M. 1391. Determine the moisture profile in underwater drip irrigation. Master's Thesis. Gorgan University of Agricultural Sciences and Natural Resources.
16
17- Kosari H. 2009. Evaluation of soil surface energy balance to estimation of evapotranspiration and its components in surface and sub-surface drip irrigation systems. Irrigation and Drainage Master's thesis, University of Tehran. (In Persian with English abstract)
17
18- Lamm F.R. 2016. Cotton, tomato, corn, and onion production with subsurface drip irrigation: A review. Transactions of the ASABE 59(1): 263-278.
18
19- Lamm F.R., and Camp C.R. 2007. Managing the Challenges of Subsurface Drip Irrigation. Elsevier Publications 473-551.
19
20- Li J., Zhang J., and Rao M. 2004. Wetting patterns and nitrogen distributions as affected by fertigation strategies from a surface point source. Journal of Agricultural Water Management 67: 89–104.
20
21- Mondal P., Biswas R.K., Tewari V.K., Kundu K., and Manisha B. 2007. Investigation on soil wetting patterns of low cost drip irrigation system developed in India. Trends in Applied Sciences Research 2(1): 45-51.
21
22- Patel N., and Rajput T.B.S. 2008. Dynamics and modeling of soil water under subsurface drip irrigated onion. Journal of Agricultural Water Management 95: 1335-1349.
22
23- Ragheb H.M.A., Gameh M.A., Ismail S.M., and Abou Al-Rejal N. A. 2011. Water distribution patterns of drip irrigation in sandy calcareous soil as affected by discharge rate and amount of irrigation water. J. King Abdulaziz University Meteorolical Envirnment 22(3): 141.
23
24- Saifi A., Mirlatifi S.M., Dehghanisanij H., and Torabi M. 1393. Effect of irrigation interval on distribution of moisture and salinity in pistachio gardens under underlying drip irrigation conditions (Case study: Sirjan city, Kerman province). Irrigation and Drainage Journal of Iran 8(4): 786-799.
24
25- Sayyari N., Ghahraman B., and Davari K. 2007. Investigation of soil moisture distribution under subsurface drip irrigation system in pistachio gardens (case study: Rafsanjan with saline water). Water, Soil and Plant Research in Agriculture 6: 65-77.
25
26- Sedaghati N., Alizadeh A., Ansari H., and Hosseinifard S.J. 2016. Study of Changes in Soil Moisture and Salinity under Plastic Mulch and Drip Irrigation in Pistachio Trees. Journal of Nuts 7 (1): 21-33.
26
27- Shan Y., Wang W., and Wang C. 2011. Simulated and measured soil wetting patterns for Overlap zone under double points sources of drip irrigation. African Journal of Biotechnology 10(63): 13744-13755.
27
28- Singh D.K., Rajput T.B.S., Sikarwar H.S., Sahoo R.N., and Ahmad T. 2006. Simulation of soil wetting pattern with subsurface drip irrigation from line source. Journal of Agricultural Water Management 83(1): 130-134.
28
29- Thorburn P.J., Cook F.J., and Bristow K.L. 2003. Soil-dependent wetting from trickle emitters: implications for system design and management. Journal of Irrigation Science 22(3): 121-127.
29
ORIGINAL_ARTICLE
Water Quality Assessment of the Beheshtabad River Using Liou Pollution Index and Principal Component Analysis
Introduction: Surface water, especially rivers, are the important sources for drinking water, agricultural and industrial uses. These reservoirs are easily affected by pollution and various activities. The vulnerability of surface water is greater than that the groundwater. Therefore, the importance of water quality evaluation, especially for drinkable water, has increased due to the reduction in its quality and quantity in recent years. Optimal use and conservation of water resources in terms of quantity and quality are the principles of sustainable development of any country. Water quality indices are among the useful tools in water quality assessment and management. The aim of this study was to evaluate the water quality of the Beheshtabad River in Chaharmahal and Bakhtiari Province, Iran by using the Liou Pollution Index and selecting the most important parameters based on Principal Component Analysis (PCA).
Materials and Methods: In this study, 7 water quality parameters including temperature, dissolved oxygen, biological oxygen demand, ammonia nitrogen, electrical conductivity, total suspended solids, and potential hydrogen were measured by standard methods along the river in 7 selected stations for 6 months (April to September 2016). Some of these parameters were measured at the sampling site and others in the laboratory. Then, the values of the Liou Pollution Index were calculated to evaluate the water quality of the Beheshtabad River in different stations. In this study, SPSS software was used to analyze the principal component. In the next step, the appropriateness of the statistical universe was assessed using the Kaiser-Meyer-Olkin test.
Results and Discussion: The results of this study showed that the water quality was good during the study period at sampling stations\, according to the Liou Pollution Index. The value of Liou Pollution Index was in the slightly polluted class in March in station 4. Then, the average of Liou Pollution Index in the Beheshtabad River was compared to different rivers. The result showed that the average of Liou Pollution Index in the studied river is higher than rivers outside Iran. In addition, according to the statistical technique of PCA, two components were introduced as the main component. The first component expressed 57.26% of the total variance and included dissolved oxygen, ammonia nitrogen, biological oxygen demand, electrical conductivity, total suspended solids and potential hydrogen parameters. The second component, temperature, expressed 21.3% of the total variance. Furthermore, the result of comparing the measured quality parameters with the standard value of surface water showed that biological oxygen demand, electrical conductivity, and total suspended solids parameters in some stations were within the standard range and in some others were higher, which indicated a negative result. The best and worst water quality in terms of biological oxygen demand was observed in May and June, respectively. The electrical conductivity in April and May in all stations was within the standard range. However, electrical conductivity was higher than the standard level in June in stations 4 and 5, higher again in July and August in stations 4 to 7, and higher as well in September in stations 2 to 7. The fish farming workshops, industrial pollution and geological survey may be the reasons. The value of potential hydrogen in all of the stations was within the standard range of 6.5 to 9.5. The value of dissolved oxygen was high because of increasing rainfall and stream flows due to the snow melting.
Conclusion: The results of this study showed that the water quality in the Beheshtabad River did not change during the last 6 months (April to September 2016), and water quality was good. In addition, PCA plays an important role in prioritizing the importance of each parameter in the pollution. Therefore, PCA places more important parameters in the first component and less important parameters in the subsequent, respectively. On the other hand, the measurement of physicochemical parameters is important for the study of water quality. This research demonstrates the usefulness and efficiency of the multivariate statistical technique of PCA and the use of indicators for effective management of surface water quality. Therefore, using water resources in the future is possible, and does not endanger their management based on sustainable development.
https://jsw.um.ac.ir/article_38741_20c34f1b22bee81835f141846f358519.pdf
2019-08-23
405
417
10.22067/jsw.v0i0.79102
Chaharmahal and Bakhtiari Province
Physicochemical parameters of water
River pollution index
Water Resources
R.
Zamani-Ahmadmahmoodi
rasoolzamani@yahoo.com
1
Shahrekord University
LEAD_AUTHOR
Ehsan
Fathi
ef.ehsan2012@gmail.com
2
Shahrekord University
AUTHOR
Samira
Bayati
bayatisamira36@yahoo.com
3
Shahrekord University
AUTHOR
Pone
Ghorbani-Dashtaki
ghorbanid_511@yahoo.com
4
Ardakan University
AUTHOR
1- Aminpour Shiani S., Mohammadi M., Khaledian M.R., and Mirroshandel A. 2016. Water quality evaluation of Gazroudbar river using NSFWQI and Liou indices. Journal of Wetland Ecobiology 8(1): 63-74. (In Persian)
1
2- APHA. 1992. Standard methods for the examination of water and wastewater. 18th ed. American Public Health Association, Washington, DC.
2
3- Bahar M.M., Ohmori H., and Yamamuro M. 2008. Relationship between river water quality and land use in a small river basin running through the urbanizing area of Central Japan. Limnology 9(1): 19-26.
3
4- Bouza-Deano R., Ternero-Rodriguez M., and Fernandez-Espinosa A.J. 2008. Trend study and assessment of surface water quality in the Ebro River (Spain). Journal of Hydrology 361(3): 227-239.
4
5- Boyd C.E., and Gautier D. 2000. Effluent composition and water quality standards. Advocate 3: 61-66.
5
6- Camdevyren H., Demyr N., Kanik A., and Keskyn S. 2005. Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs. Ecological Modelling 181: 581-589.
6
7- Effendi H. 2015. River water quality preliminary rapid assessment using pollution index. Procedia Environmental Sciences 33: 562-567.
7
8- Effendi H., and Wardiatno R.Y. 2015. Water Quality Status of Ciambulawung River, Banten Province,Based on Pollution Index and NSF-WQI. Procedia Environmental Sciences 24: 228-237.
8
9- Environmental Protection Agency (EPA). 1996. Quality Criteria for Waters, Washington, DC.
9
10- Fan X., Cui B., Zhao H., Zhang Z., and Zhang H. 2010. Assessment of river water quality in Pearl River Delta using multivariate statistical techniques. Procedia Environmental Sciences 2: 1220-1234.
10
11- Fathi E., Zamani-ahmadmahmoodi R., and Zare Bidaki R. 2018. Water quality assessment of Beheshtabad River at the intersection of Shalamzar Spring with Koohrang River. Journal of Environment and Water Engineering 4(2): 178-183. (In Persian with English abstract)
11
12- Gayoor H., and Montazarei M. 2005. Classification of temperature regime of Iran using PCA and CA. Geography and Development Iranian Journal 2(4): 21-34. (In Persian)
12
13- Hoseinzadeh E., Khorsandi H., Rahimi N., Hoseinzadeh S., and Alipour M. 2013. Evaluation of Aydughmush water quality by national sanitation foundation oundation water quality (NSFWQI) and Liou pollution indices. URMIA Medical Journal 24(2): 156-162. (In Persian with English abstract)
13
14- Jang C.S. 2016. Using probability-based spatial estimation of the river pollution index to assess urban water recreational quality in the Tamsui River watershed. Environmental Monitoring and Assessment 188(1): 36.
14
15- Kelly T.R., Herida J., and Mothes J. 1998. Sampling of the Mackinaw River in central Illinois for physicochemical and bacterial indicators of pollution. Trans Ill State AcadSci 91: 145-154.
15
16- Lausch A., and Herzog F. 2002. Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability. Ecological Indicators 2(1-2): 3-15.
16
17- Liou S.M., Lo S.L., and Hu C.Y. 2003. Application of two-stage fuzzy set theory to river quality evaluation in Taiwan. Water Research 37(6): 1406-1416.
17
18- Mohamed I., Othman F., Ibrahim A.I, AlaaEdin M.E., and Yunus R. 2015. Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia. Environmental Monitoring and Assessment 187(1): 1-12.
18
19- Nazemosadat S.M.J., Baigi B., and Amin S. 2003. Application of the principal component analysis for the regionalization of winter precipitation over Boushehr, Fars and Kohgiloye and Boyerahmad provinces. Journal of Water and Soil Science 7(1): 61-72. (In Persian)
19
20- Noori R., Abdoli M.A., Jalili Ghazizade M., and Samieifard R. 2009. Comparison of neural network and principal component-regression analysis to predict the solid waste generation in Tehran. Iranian Journal of Public Health 38(1): 74-84.
20
21- Noori R., Khakpour A., Omidvar B., and Farokhnia A. 2010. Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems with Applications 37(8): 5856-5862.
21
22- Nosrati K., Derafshi K.H, Gharechahi S., and Rahimi K.H. 2011. Assessment of surface water quality of Haraz-Gharesou watershed using with multivariate techniques. Journal of Earth Science Researches 2(5): 41-55. (In Persian)
22
23- Oram B. 2011. Calculating NSF Water Quality Index (WQI). Wilkes University Center for Environmental Quality Geo Environmental Sciences and Engineering Department.
23
24- Ouyang Y. 2005. Evaluation of river water quality monitoring stations by principal component analysis. Water Research 39(12): 2621-2635.
24
25- Papatheodorou G., Demopouloua G., and Lambrakis N. 2006. A long-term study of temporal hydro chemical data in a shallow lake using multivariate statistical techniques. Ecological Modeling 193: 759-776.
25
26- Pasha Zanoosi H. 2014. Practical Statistics for Environmental and Biological Scientists John Townend. Jahad Daneshgahi Publisher, Mazandaran Branch, Iran.
26
27- Riitters K.H., Oneill R.V., Hunsaker C.T., Wickham J.D., Yankee D.H., Timmins S.P., Jones K.B., and Jackson B.L. 1995. A factor analysis of landscape pattern and structure metrics. Landscape Ecology 10(1): 23-39.
27
28- Sabahi H., Faizi M., Veisi H., and Asilan K.S. 2010. Study on the influence of agricultural activities on water quality of Sikan. Environmental Sciences 7(4): 23-30. (In Persian with English abstract)
28
29- Samantray P., Mishra B.K., Panda C.R., and Rout S.P. 2009. Assessment of water quality index in Mahanadiand Atharabanki Rivers and Taldanda Canal in Paradip area, India. Journal of Human Ecology 26(3): 153-161.
29
30- Sanchez E., Colmenarejo M.F., Vicente J., Rubio A., Garcia M.G., Travieso L., and Borja R. 2007. Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution. Ecological Indicators 7(2): 315-328.
30
31- Seifi A., Mirlatifi S.M., and Riahi H. 2011. Developing a combined model of multiple linear regression- principal component and factor analysis (MLR-PCA) for estimation. Journal of Water and Soil 24(6): 1186-1196. (In Persian with English abstract)
31
32- Shamsaie A., Oreei S., and Sarang A. 2004. The comparison of water indices and zoning quality in Karoon and Dez rivers. Journal of Water and Wastewater 16(3): 39-48. (In Persian)
32
33- Sheykhestani N. 2001. Explanation of surface water quality indices, and their applications in evaluation of quality vulnerability and interpolation of rivers. MSc Thesis in Environmental engineering. Iran University of Science Technology. (In Persian).
33
34- Shih C.H., Chu T.J., Kuo Y.Y., Lee Y.C., Tzeng T.D., and Chang W.T. 2010. Environmental pre-evaluation for eco-leisure: A case study of a restored stream system in Hofanchuken creek of Taipei county,Taiwan. Journal Environment Engineering Management 20(2): 99-108.
34
35- Terrado M., Barcelo D., Tauler R., Borrell E., and Campos S.D. 2010. Surface-water-quality indices for the analysis of data generated by automated sampling networks. TrAC Trends in Analytical Chemistry 29(1): 40-52.
35
36- Yidana S.M., Ophori D., and Banoeng-Yakubo B. 2008. A multivariate statistical analysis of surface water chemistry data-The Ankobra Basin, Ghana. Journal of Environmental Management 86: 80-87.
36
ORIGINAL_ARTICLE
Investigating the Impact of Chicken Feather, Vermicompost and Potassium Humate on the Physical Properties of Soil
Introduction: Destruction of soil structure and reduction of soil organic matter are major problems of cultivated soils which result from improper tillage operations, excessive consumption of chemical fertilizers and low consumption of organic and green fertilizers. One method for maintaining sustainable agriculture is to add organic and inorganic amenders. By producing resistant aggregates, organic matters improve soil structure and enhance soil permeability, FC moisture and water availability capacity. Furthermore, through enhancing organisms’ activities, especially earthworms, organic matters improve soil hydraulic conductivity and reduce bulk density. Organic matters may be added to soil through different way, however, the effect of each one on the soil’s physical properties is different. Chicken feather (CF) is readily available through henhouses and slaughterhouses, however, significant amounts of CF are destroyed by burning and burying them. Potassium Humate (PH) is a potassium salt from humic acid. Humic acid is extracted from various natural sources such as humus, peat, lignite and coal. Vermicompost (VC) is a compost which is produced by a non-thermal process. The impact of CF on different soil properties has not been studied yet. Accordingly, we investigated the impact of adding differing weight percentages of three types of amenders (PH, CF and VC) on the physical properties of soil under wheat cultivation at different moisture levels.
Materials and Methods: The experiment was conducted in factorial form based on randomized complete block design with 27 treatments in three replications. The first factor included the above-mentioned amenders; the second factor included three weight levels of these amenders (0%, 2.5% and 5%); the third factor included three moisture levels (0.5FC, 0.7FC and 0.9FC). The amenders were uniformly mixed with the soil up to the depth of 10 cm; then, wheat seeds were planted and moisture treatments were carried out during the growth period (from late April 2016 to September 2016). The soil moisture of the plots was controlled during the experiment period using the gravimetric method. For investigating the changes in the soil’s physical properties, samples (disturbed and undisturbed) were taken from the plots before and after the experiment. The following physical parameters were measured: bulk density (BD), soil moisture in field capacity (FC), permanent wilting point (PWP), wet aggregate stability (WAS), saturated hydraulic conductivity (KS), penetration resistance (PR), retention curve slope at inflection point (Si), mean weight diameter of aggregates (MWD) and mass-size fractal dimension of aggregates (Dm). Statistical analysis was done by SPSS software and means were compared via Duncan test. Tables and graphs were generated by Excel software.
Results and Discussion: Variance analysis and means comparison indicated that using amenders reduced bulk density for 89%. Reduced bulk density was caused by high keratin (91%) in CF, high porosity and the production of coarse pores in soil. On the other hand, VC with many pores led to increased aggregation and reduced bulk density.
Results revealed that consuming CF increased soil moisture to field capacity (FC) (87%). CF had more significant impacts on increasing FC at high moisture levels. Thanks to its keratin structure, feather operates like a sponge which enhances soil porosity; hence, it absorbs more moisture and improves FC. Furthermore, results indicated that increasing the amounts of amenders led to increased soil moisture in PWP (91%). By increasing the amount of amenders in soil, aggregation and soil porosity increased which led to enhanced PWP.
Large amounts of CF, PH and soil moisture (0.9FC) resulted in 3.7 times enhancement of Ks. CF led to the production of large soil pores and reduced soil density which resulted in improved soil structure and increased Ks. Thanks to its adhesion properties, PH increased Ks.
Increasing the amount of amenders and the level of soil moisture in all three types of organic matters (especially CF) caused the 2.5 times enhancement of WAS.
The results revealed that increasing soil moisture and amenders led to reduced Si (101%). Given all three types of amenders, PH had the highest impact on the reduction of Si. Moreover, soil penetration resistance (PR) was reduced as a function of increasing the soil moisture level.
Contrary to the expectation, MWD was reduced as a result of increasing amenders. Furthermore, it was found that, given little soil moisture, increasing the amount of amenders resulted in increased Dm; however, given high soil moisture, increasing the amount of amenders led to decreased Dm. Thus, it should be noted that adding amenders improved the stability of aggregates over long time periods and at high soil moisture levels.
Conclusion: One major strategy for improving soil physical and chemical properties is using modifiers, especially organic matters. In this study, we investigated the impact of chicken feather on physical properties of soil and compared its effect with those of potassium humate and vermicompost under different levels of soil moisture and wheat cultivation.
The results indicated that consuming amenders resulted in reduced Bd but increased FC, PWP, Ks and WAS. In other words, it improved physical properties of soil. Moreover, Si decreased as a result of increasing soil moisture and organic matters. Among the three types of amenders, potassium humate had the highest impact on reducing Si. PR was reduced as a function of increasing soil moisture. However, increasing organic matter led to decreased MWD. Furthermore, it was unexpectedly found that, given low soil moisture, Dm increased as a result of increasing the organic matters weight. Nevertheless, in high levels of soil moisture, Dm decreased as a function of increasing organic matter. Thanks to positive impacts of organic matters (especially CF which is cheaper and more accessible than other amenders) on soil’s physical properties, they are highly recommended for soil improvement. Regarding future studies, investigation of the effect of these amenders on soil chemical properties under different soil textures is suggested.
https://jsw.um.ac.ir/article_38742_2a499d97241e06b335f7f20a66f41cf7.pdf
2019-08-23
417
430
10.22067/jsw.v0i0.72579
Amenders
Chicken Feather
Physical attributes
potassium humate
Vermi Compost
wheat
mohammadreza
dalalian
mdalalian@gmail.com
1
islamic aza university tabriz branch
LEAD_AUTHOR
fatemeh
zabihi
parastoo.zabihi@yahoo.com
2
Tabriz Branch, Islamic Azad University
AUTHOR
anvarossadat
paknejad
anvarpaknejad@yahoo.com
3
Tabriz Branch, Islamic Azad University
AUTHOR
mina
khoshkhan
mina.khoshkhan@gmail.com
4
Tabriz Branch, Islamic Azad University
AUTHOR
1- Aggelides S.M., and Londra P.A. 2000. Effects of compost produced from town wasted and sewage sludge on the physical properties of a loamy and clay soil. Bioresource Technology 71: 253-259.
1
2- Ajwa H.A., and Trout T.J. 2006. Polyacrylamid and water quality effects on infiltration in sandy loam soils. Soil Science Society. American Journal 70:643-650.
2
3- Allison F.E. 1973. Soil organic matter and its role in crop production. Elsevir. Newyork.
3
4- Arriaga F.J., and Lowery B. 2003. Soil physical properties and crop productivity of an eroded soil amended with cattle manure. Soil Science. Copyright @ by Lippincott Williams. Inc.0038-075X/03/16812:888-898.
4
5- ASAE standard S 313.2.1995. Soil cone penetrometer Agricultural Engineering Year Book. P 683.
5
6- Atiyeh R.M., Lee S., Edwards C.A., Aroncon N.Q., and Metzger J.D. 2002. The influence of humic acids derived from earth worm processed organic wastes on plant growth. Bioresour. Technol. 84: 7-14.
6
7- Azarmi R., Torabi M., and Didar R. 2008. Influence of vermicompost on soil chemical and physical properties in tomato field. African Journal of Biothecnology 7: 2397-2401.
7
8- Bagarllo V., and Sgroi A. 2007. Using the simplified falling head technique to detect temporal changes in field- saturated hydrolic conductivity at the surface of a sandy loam soil. Soil Tillage Research 44: 283-294.
8
9- Bauer A., and Bluck A.L. 1992. Organic carbon effects on available water capacity of three soil textural groups. Soil Science. American Journal 56: 248-254
9
10- Bird N.R.A., Perrier E., and Rieu M. 2000. The water retention function for a model of soil structure with pore and solid fractal distribution. Europ J Siol Sci. 51: 55-63.
10
11- Bouyoucos G.J. 1993. Effected of organic matter on waterholding capacity and the wilting point of mineral soils. Soil Science 47: 377-383.
11
12- Chaney K., and Swift R.S. 1986. Studies on aggregate stability: II. The effect on Humic substances on the stability of re-formed soil aggregates. Soil Science Journal 37: 337-343.
12
13- Dexter A.R. 2004. Soil physical quality. Part III. Unsaturated hydraulic conductivity and general conclusions about S-theory, Geoderma, 120:227-239.
13
14- Emami H. 2009. Determination of some hydraulic and mechanical properties using Soil Physical Quality Index (Si), Ph.D. Thesis, Department of Soil Science, Agricultural Faculty, Tehran University, Iran. (In Persian)
14
15- Emerson W.W. 1995. Water retention, organic carbon and soil texture. Australian Journal of soil Research 17: 45-56.
15
16- Grandy A.S., Porter G., and Erich M.S. 2002. Organic amendment and rotation crop effects on three covery of soil organic matter and aggregation in potato cropping systems. Soil Science Society of American Journal 66: 1311-1314.
16
17- Hajabbasi M.A., and Hemmet A. 2000. Tillage impact on aggregate stability and crop productivity in a clay loam soil in central Iran. Soil and Tillage Research 56: 205-212.
17
18- Imbufe A.U., Patti A.F., Surapaneni A., and. Jackson W.R. 2004. Effects of brown coal derived materials on pH and electrical conductivity of an acidic vineyard soil. http:// www. Regional. Org. au.
18
19- Imbufe A.U., Patti A.F., Burrow D., Jackson W.R., and Milner A.D. 2005. Effects of potassium Humate on aggregate stability of two soils from Victoria. Australian Journal. Geoderma 125: 321-330.
19
20- Kay B.D., and Van Den Baygaurt A.J. 2002. Conservation tillage and depth stratification of porosity and soil organic matter. Soil Tillage Research 66: 107-118.
20
21- Kemper W. D., and Chepil W.S. 1965. Size distribution of aggregates. Pp: 499-510. In Black CA (ed.) Methods of Soil Analysis. Part1, Physical and Mineralogical Properties. 1 st edition. ASA, Madison. WI.
21
22- Kemper W.D., and Rosenau R.C. 1986. Size distribution of aggregates. In: Klute, A. Ed, Method of soil Analysis. Part 1, (2 ed.). Agron. Monogr. Vol 9. ASA-SSSA. Madison. WI.PP: 425-442.
22
23- Krishnamoorthy R.V., and Vajranabhaiah S.N. 1986. Biological activity of earthworm casts: an assessment of plant growth promotor leves in the casts. Proceeding of the India Acrlemy of Sciences (Animal Science) 95: 341-351.
23
24- Li Y.Y., and Shao M.A. 2006. Change of soil physical properties under long-term natural vegetation restoration in the loess plateau of china. Arid Environments 64: 77-96.
24
25- Mamedov A.I., Beckmann S., Huang C., and Levy G.J. 2007. Aggregate stability as affected by polyacrylamid molecular weight, soil textureand water quality. SSSA J. 71(6): 1909-1918.
25
26- Mirzaee Talarposhti R., Kambodia J., Sabahi H., and Mahdavi Damgani A. 2009. Effect of organic fertilizers on physicochemical properties of soil and production of tomato dry matter. Iranian Journal of Agricultural Research 7(1): 257-267. (In Persian with English abstract)
26
27- Nyamangara J., Gotosa J., and Mpofu S.E. 2001. Cattle manure effects on structural stability and water retention capacity of a granitic sandy soil in Zimbabwe. Soil Tillage Research 62: 157-162.
27
28- Neyshabouri M.R., Mirzajani M., and Oustan Sh. 2013. The effect of polyacrylamide and organic matter on three structural stability indexes in fine and medium soil texture with different wet and dry cycles, Journal of Water and Soil Science 22(4): 161-172. (In Persian with English abstract)
28
29- Ohu J.O., Ekwue E.I., and Folorunso D.A. 1994. The effect of addition of organic matter on the compaction of a vertisol from northern Nigeria. Soil Technolgy 7: 155-162.
29
30- Reynolds W.D., Elrick D.E. 1990. Ponded infiltration from a single ring. I. Analysis of steady flow. Soil Science Society American Journal 54: 1233-1241.
30
31- Safadoost A., Mosaddeghi M.R., Mahboobi A.A., Nouroozi A., and Asadian G. 2007. Effect of short-term tillage and manure on structural properties of soil. J.sci.and Tech. Agriculture and Natural Resource41: 91-100 (In Persian).
31
32- Sarbazrashid S., Dalalian M.R., and Darbandi S. 2014. Effect of potassium humate and feather with leaching on physical and chemical specifications of saline-sodic soils, M.Sc. Thesis. Department of Soil Science, Agriculture College of Tabriz Branch, Islamic Azad University, Tabriz, Iran. (In Persian)
32
33- Sadegi S., Eslahi N., and Dadashian F. 2015. Recycling chicken feather fibers for the production of absorbent porous keratin foam. The 10th National Conference on Textile Industry of Iran, Isfahan, Faculty of Textile Engineering, Isfahan University of Technology (In Persian).
33
34- Schoenau J.J. 2006. Benefits of long-term application of manure. Advance in Pork Production 17: 153-158.
34
35- Sebahattin A., and Necdet C. 2005. Effects of different levels and application times of humic asid on root and leaf yield and yield components of forage Turnip (Brassica rapa L.) Agronomy Journal 4: 130-133.
35
36- Shirani H., Hajabbasi M.A., Afyuni M., and Hemmat A. 2002. Effect of farmyard and tillage systems on soil physical properties and corn yield in central Iran. Soil Tillage Research. 68:101-108.
36
37- Sloan D.R., Kidder G., and Jacobs R.D. 2003. Poultry manure as a fertilizer. University of Floria Journal. Htt://edis.ifas.ufl.edu.
37
38- Tajik F., Rahimi H., and Pazira E. 2003. Effect of electrical conductivity and sodium adsorption ratio of water on aggregate stability in soils with different organic matter content. Journal Agriculture Science Technology 5: 67-75.
38
39- Tejada M., and Gonzalez J.L. 2006. Crushed cotton gincompost on soil biological properties and rice yield. Europ. Journal Agronomy 25: 22-29.
39
40- Tejada M., and Gonzalez J.L. 2007. Influence of organic amendments on soil structure and soil loss under simulated rain. Soil and Tillage Research 93: 197-205
40
41- Vance W.H., Tisdell J.M., and Mckenzie B.M. 1998. Residual effects of surface application of organic matter and calcium salts on the sub-soil of a red-brown earth. Australian Journal of Experimental Agriculture 38: 595-600.
41
42- Van Genuchten M.Th. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44: 892-898.
42
43- Walker D.J., and Bernal M.P. 2008. The effects of olive mill waste compost and poultry manure on the availability and plant uptake of nutrients in a highly saline soil. Biores. Thechnol. 99: 396-403.
43
ORIGINAL_ARTICLE
The Effect of Interaction of Land Abandonment and Climate Conditions on Restoration of Organic Matter in Primary Soil Particles in the Rangeland of Steppe Regions
Introduction: Although many studies have been done on the effects of agricultural land abandonment, there is very little information about the impact of climate conditions on the restoration of abandoned agricultural lands. Human has changed most of rangelands to agricultural lands causing a decrease in carbon sequestration, depending on land management and tillage operations. One of the methods for rebuilding the land cover is the land abandonment, which results in enhanced organic carbon and decreased CO2 emission. Understanding the storage and dynamics of soil organic matter, especially in relation to changing land use, is fundamental to evaluate the role of soil as a carbon source or sink. After land use change from rangeland to cropland, agricultural practices decrease the C stored in soils and cause a net release of C into the atmosphere, which has strongly influenced the atmospheric CO2 levels and global C balance over the last centuries. For this purpose, this study aimed to assess the effect of interaction between agricultural land abandonment and climatic conditions on organic material reserves of primary soil particles.
Materials and Methods: The study area was located in semi-steppe rangelands of Sheida and Khargosh in about 60 km northwest of Shahrekord city, Chaharmahal-va-Bakhtiari province, central Iran. In this study, four treatments including rangeland, agricultural and cultivated land abandoned in the time series of 10-15 and 15-40 Year were selected. The sample plots were placed in the distance of transects, and the soil samples were collected from 0-30 cm depths with different rainfall conditions from two above-mentioned regions in three replications. For each region, the soil samples were transferred to the laboratory and then analyzed. The selected locations had same soil shape, topography, parent material, and slope. The soil samples of three plots were then combined and 24 samples were prepared. The distribution of carbon and nitrogen concentrations was determined at different soil particle components.
Results and Discussion: The results showed that the rangeland change to cultivated land did not have a significant effect on the amount of organic carbon, total nitrogen, and total carbon to total nitrogen ratio. However, the values of these indicators decreased significantly in the Sheida region. Under all land management, the amount of carbon and nitrogen of soil particles increased with decreasing the particle size from sand to clay. Hence, the abandoned agricultural land and rangelands did not significantly affect the amount of carbon and nitrogen concentration in sand, silt and clay particles. The amount of carbon, however, increased with the abandonment time and non-agronomic activity of carbon in sand and silt particles, although the carbon content of clay particle was not influenced. Agricultural practices may negatively or positively impact natural ecosystem depending on climatic condition and soil quality in unchanged lands. However, despite suitable climatic conditions (in terms of precipitation) and land cover in the rangelands over Sheida, the cultivation adversely influenced the soil quality and organic matter of the unchanged land. Although, the precipitation and soil quality were relatively lower in Khargosh region, the agricultural activities seem not to negatively affect the land quality. Moreover, rangelands change to cultivated lands did not have a significant effect on the amount of soil nitrogen in this region. The greatest nitrogen amount was measured in clay fractions of cultivated and abandoned lands for 40 years, and the minimum nitrogen content was detected in sand particles of lands abandoned for 15 years. The highest and lowest amount of nitrogen over all three fractions was, respectively, found for unchanged and abandoned lands in Sheida region. Therefore, the cultivated land depending on climate condition and management may considerably increase or decrease the organic carbon content in sand, silt and clay particles.
Conclusion: The results indicated that the agricultural land abandonment may differently affect the rangelands restoration measures such as the vegetation reclamation and soil carbon sequestration depending on climatic condition.
https://jsw.um.ac.ir/article_38743_c2a7dd19d1e84d96168621b60f61b59f.pdf
2019-08-23
431
443
10.22067/jsw.v0i0.72947
Agriculture land abandoned
non agro
organic carbon and total nitrogen
soil particle
samira
salari
ssalari46@yahoo.com
1
Shahrekord University
AUTHOR
Mehdi
Pajouhesh
drpajoohesh@gmail.com
2
Shahrekord University
LEAD_AUTHOR
Pejhman
Tahmasebi
pejman.tahmasebi@nres.sku.ac.ir
3
Shahrekord University
AUTHOR
Farzaneh
Nikookhah
f_nikookhah@yahoo.com
4
Shahrekord University
AUTHOR
1- Ahmadi H., Heshmati G.H., Psrkly M., and Nasseri H.R. 2009. Comparison of carbon sequestration in desert and meadow forests to manage sandy land in south of salt lake. Thesis of the Ministry of Science and Research and Technology, Faculty of Agricultural Sciences and Natural Resources, Gorgan University, p. 75. (In Persian with English abstract)
1
2- Anderson D.W., and Paul E.A. 1984. Organo-mineral complexes and their study by radiocarbon dating. Soil Sci. Soc, Am, J., 48: 298-301.
2
3- Bremner J.M., and Mulvaney C.S. 1982. Nitrogen total. Pp: 595-624. In: Page AL. (ed.) Methods of Soil Analysis. Part 2, Chemical Analysis. ASA and SSSA. Madison, WI.
3
4- Bronick C.J., and Lal R. 2005. Manuring and rotation effects on soil organic carbon concentration for different aggregate size fractions on two soils in northeastern Ohio, USA. Soil and Tillage Research 81: 239-252.
4
5- Caravaca F., and Roldan A. 2003. Effect of Eisenia foetida earthworms on mineralization kinetics, microbial biomass, enzyme activities, respiration and labile C fractions of three soils treated with a composted organic residue. Biology and Fertility of Soils 38: 45–51.
5
6- Caravaca F., Masciandro G., and Ceccanti B. 2002. Land use in relation to soil chemical and biochemical properties in a semi-arid Mediterranean environment. Soil and Tillage Research 68: 23-30.
6
7- Christensen B.T., and Sørensen L.H. 1985. The distribution of native and labelled carbon between soil particle size fractions isolated from long-term incubation experiments. Eur. J. Soil Sci. 36: 219-229.
7
8- Christensen B.T. 1987. Decomposability of organic matter in particle size fractions from field soils with straw incorporation. Soil Biology and Biochemistry 19: 429-435.
8
9- Christensen B.T. 1996. Carbon in primary and secondary organomineral complexes. Pp: 97-165. In: Carter MR and Stewart BA (eds). Structure and Organic Matter Storage in Agricultural Soils. CRC Press Inc., Boca Raton, FL.
9
10- Christensen B.T. 2001. Physical fractionation of soil and structural and functional complexity in organic matter turnover. European Journal of Soil Science 52: 345-353.
10
11- Gee G.W., and Bauder J.W. 1986. Particle-size analysis. Pp: 383-411. In: Klute A (ed). Methods of Soil Analysis. Physical and Mineralogical Methods. Part 1(2nd ed),
11
12- Golchin A., and Malakouti M.J. 1999. Maintenance and mobility of soil organic matter. Iranian Journal Soil and Water Science 13(1): 40-53.(In Farsi)
12
13- Gregorich E.G., Kachanoski R.G., and Voroney R.P. 1989. Carbon mineralization in soil size fractions after various amounts of aggregate disruption. Eur. J. Soil Sci. 40: 649-659.
13
14- Haile-Mariam S., Collins H.P., Wright S., and Paul E.A. 2008. Fractionation and long-term laboratory incubation to measure soil organic matter dynamics. Soil Sci. Soc. Am. J. 72: 370-378.
14
15- He N., Wu L., Wang Y., and Han X. 2009. Changes in carbon and nitrogen in soil particle-size fractions along a grassland restoration chronosequence in northern China. Geoderma 150: 302- 308.
15
16- Jafari S., Golchin A., and Tollabi fard A. 2016. The Effect of Land Use Change on the Properties of Physical Components of Organic Matter, Pressure Clay and Aggregate Stability in Some Lands of Khuzestan Province. Iran Water and Soil Research, Period 47, No 3, pp 603-593. (In Persian with English abstract)
16
17- Jagadamma S., and Lal R. 2010. Distribution of organic carbon in physical fractions of soils as affected by agricultural management. Biology and Fertility of Soils 46: 543-554.
17
18- Kandeler E., Stemmer M., and Klimanek E.M. 1999. Response of soil microbial biomass, urease and xylanase within particle size fractions to long-term soil management. Soil Biology and Biochemistry 31: 261-273.
18
19- Lorenz K., Lal R., and Shipitalo M.J. 2008. Chemical stabilization of organic carbon pools in particle size fractions in no-till and meadow soils. Biology and Fertility of Soils 44: 1043-1051.
19
20- Murage E.W., Voroney P.R., Kay B.D., Deen B., and Beyaert R.P. 2007. Dynamics and turnover of soil organic matter as affected by tillage. Soil Sci. Soc. Am. J. 71: 1363_1370.
20
21- Nadal-Romero E., Cammeraat E., Perez-Cardiel E., and Lasanta T. 2016. Effects of secondary succession and afforestation practices on soil properties after cropland abandonment in humid Mediterranean mountain areas. Agriculture, Ecosystems & Environment 228: 91-100.
21
22- Nelson D.W., and Somners L.E. 1982. Total carbon, organic carbon, and organic matter. Pp: 539-579.
22
23- Novara A., Gristina L., Sala G., Galati A., Crescimanno M., Cerdà A., and LaMantia T. 2017. Agricultural land abandonment in Mediterranean environment provides ecosystem services via soil carbon sequestration. Science of the Total Environment 576: 420-429
23
24- Olk D.C., and Gregorich E.G. 2006. Overview of the symposium proceedings, meaningful pools in determining soil carbon. Soil Science Society of America Journal 70: 967-974.
24
25- Preston C.N., Newman R.H., and Rother P. 1994. Using 13C CPMAS NMR to assess effects of cultivation on the organic matter of particle size fractions in a grassland soil. Soil Sci. 157: 26-35.
25
26- Qiu L., Wei X., Zhang X., Cheng J., Gale W., Guo C., and Long T. 2012. Soil organic carbon losses due to land use change in a semiarid grassland. Plant and Soil 355(1-2): 299-309.
26
27- Raiesi F. 2007. The conversion of overgrazed pastures to almond orchards and alfalfa cropping systems may favor microbial indicators of soil quality in Central Iran. Agric Ecosyst Environ 121: 309–318.
27
28- Raiesi F. 2012. Soil properties and C dynamics in abandoned and cultivated farmlands in a semi-arid ecosystem. Plant Soil 351: 161–175
28
29- Salek-Gilani S., Raiesi F., Tahmasebi P., and Ghorbani N. 2013. Soil organic matter in restored rangelands following cessation of rainfed cropping in a mountainous semi-arid landscape. Nutrient Cycling in Agroecosystems 96(2-3): 215-232.
29
30- San Roman Sanz A., Fernandez C., Mouillot F., Ferrat I., Istria D., and Pasqualini V. 2013. Long-term forest dynamics and land use abandonment in the Mediterranean mountains, Coesica France. Ecol. Soc. 18 (2): 38.
30
31- Schahczenski J., and Hill H. 2009. Agriculture, Climate Change and Carbon Sequestration, ATTRA Publications, 16 pp.
31
32- Schuman G.E., Janzen H., and Herrick J.E. 2002. Soil carbon information and potential carbon sequestration by rangelands, Environmental Pollution, Vol 116. Pp: 391-396.
32
33- Six J., Guggenberger G., Paustian K., Haumaier L., Elliott E.T., and Zech W. 2001. Sources and composition of soil organic matter fractions between and within soil aggregate. European Journal of Soil Science: 52(4): 607-618.
33
34- Six J., Paustian K., Elliott E.T., and Combrink C. 2000. Soil structure and organic matter: I. distribution of aggregate-size classes and aggregate-associated carbon. Soil Science Society of America Journal, 64:681–689. Soil Use and Management 21: 38–52.
34
35- Spohn M., Novak T.J., Incze J., and Giani L. 2016. Dynamics of soil carbon, nitrogen, and phosphorus in calcareous soils after land-use abandonment–A chronosequence study. Plant and Soil 401(1-2): 185-196.
35
36- Stemmer M., Gerzabeki M.H., and Kandeler E. 1998. Organic matter and enzyme activity in particlesize fractions of soils obtained after low-energy sonication. Soil Biology and Biochemistry 30: 9-17.
36
37- Wagai R., Mayer L.M., and Kitayama K. 2009. Nature of the occluded low density fraction in soil organic matter studies: A critical review. Soil Science and Plant Nutrition 55: 13-25.
37
38- Wertebach T.M., Hölzel N., Kämpf I., Yurtaev A., Tupitsin S., Kiehl K., and Kleinebecker T. 2017. Soil carbon sequestration due to post‐Soviet cropland abandonment: estimates from a large‐scale soil organic carbon field inventory. Global Change Biology.
38
39- Zhang Z.D., Yang X.M., Drury C.F., Reynolds W.D., and Zhao L.P. 2010. Mineralization of active soil organic carbon in particle size fractions of a Brookston clay soil under no-tillage and mouldboard plough tillage. Canadian Journal of Soil Science 90(4): 551-557.
39
ORIGINAL_ARTICLE
Effect of Sugarcane Bagasse Derived Biochar on Distribution of Zinc Fractions in a Calcareous Soil
Introduction: Zinc is a key micronutrient which takes part in plant physiological functions. One of the extensively wide range abiotic stresses arises from Zn shortage in agricultural calcareous soils. Zn is one of the most prevalent disorders among various crops. Zinc deficiency is very common in most calcareous soils. Different mechanisms are involved in the deficiency of Zn In calcareous soils. The presence of calcium carbonate, lack of organic matter and high pH lead to Zn deficiency. Knowledge on the total Zn contents of in soil gives little information for their bioavailability. In order for better understanding availability of Zn to plant, knowledge about their mobility, and distribution in soil fractions is necessary. Biochar is a carbon-rich material produced by pyrolysis of biomass under oxygen-limited conditions and relatively low temperature. Biochar as a valuable soil amendment has received much attention due to its beneficial effects on carbon sequestration, soil physiochemical properties, soil microbial activity as well as soil fertility. Pyrolysis temperature has a significant influence on biochar physicochemical properties. Furthermore, biochar may alter the distribution of Zn fractions in calcareous soils. The impact of produced biochars at different pyrolysis temperature on distribution of Zn fractions in calcareous soils has been less studied. Therefore, the objective of this research was to evaluate the changes in distribution of Zn fractions in a calcareous soils treated with sugarcane bagasse derived biochars at different pyrolysis temperature.
Materials and Methods: An incubation experiment was carried out in laboratory condition as a factorial experiment based on a randomized complete design with two factors: (1) biochar type in four levels including control (without biochar) and biochar produced at 200 (B200), 350 (B350) and 500 ˚C (B500), (2) biochar application rate in two levels including 1 and 2% (w/w), and in three replications. Biochars were produced at 200, 350 and 500˚C pyrolysis temperatures under slow pyrolysis conditions with a heating rate of 5 °C min−1. Heating at this temperature lasted for 2 h. Then biochars were sieved to pass through 2 mm sieve and some properties were measured using the standard methods. The soil used in this study was sampled from the surface layer (0 to 20 cm depth), then, air-dried and sieved through 2 mm. Biochars produced at 200, 350 and 500˚C were mixed at 1 and 2% (w/w) with the 300 g of soil sample and incubated in ambient temperature at laboratory conditions (25 ± 2°C), for 90 days. Soil moisture content was maintained at 80% of field capacity. The samples were weighted every day and the required amounts of distilled water were added. At the end of incubation period, soil samples were air-dried and soil chemical parameters such as pH, cation exchange capacity (CEC), total organic carbon (TOC) and dissolved organic carbon (DOC) were measured.Chemical fractions of Zn in the incubated soil were determined according to the Tessier fractionation method. The Tessier sequential extraction method categorized Zn into 5 different fractions including: the exchangeable (Exch), bound to carbonate fraction (Car), bound to organic matter (OM), bound to Fe and Mn-oxides (FeMnOx) and residual fraction (Res).
Results and Discussion: Result indicated that application of different biochars significantly increased soil CEC and TOC. Maximum CEC and TOC were measured in B200 and B350 treatments, respectively, while their minimum values were observed in control treatment. In B200 treatments (B200, 1% and B200, 2%), pH significantly decreased compared to control, while this value significantly increased in B350, 1% , B500, 1% and B500, 2% treatments. B350 1% treatment did not have a significant effect on the soil pH. Application of 1 and 2% B200 significantly enhanced DOC (23.9 and 38%, respectively), compared to the control, but increase of DOC in B350 and B500 treatments was not significant compared to the control. Results showed that concentration of exchangeable Zn fraction decreased by 9.3, 19.5 and 9.5 % in B350, 2%, B500, 1% and B500, 2% treatments, respectively, compared to the control. However, B200 treatments (B200, 1% and B200, 2%) caused a significant increase in concentration of exchangeable Zn fractions (12.5 and 21.6%) compared to the control. The concentration of OM and Car Zn fractions increased in all biochar treatments compared to control. The highest concentration of OM and Car Zn fractions was observed after application of 2% B200 and 2% B500, respectively. Results showed that application of B350 and B500 had no significant effect on concentration of FeMnOx Zn fraction, while, this concentration significantly increased after B200 was applied. There were no significant (P ≤0.05) differences in concentration of residual Zn fraction among all the biochar treatments. The mean comparison results showed that the concentration of residual Zn in B200 treatments was significantly (P ≤0.05) lower than B350 and B500 treatments. There were no significant differences in this concentration among B500, B350 and the control treatments. Results revealed that in all treatments, different Zn fractions in the soil were distributed in the following order: Res > FeMnOx > Car > OM > Exch. The largest effect of biochars on the change in distribution of Zn fractions of soil was observed at 2% application rate.
Conclusion: It can be concluded that biochar B200 application could be an effective amendment for improving chemical properties and conversion of Zn from less available fractions to fractions with more bioavailability in the calcareous soil. Moreover, the biochar produced at 350 and 500˚C is better suited for enhancing soil organic carbon and Zn stabilization in calcareous soil.
https://jsw.um.ac.ir/article_38744_841f911cbd548348c5478f403e19e43e.pdf
2019-08-23
445
461
10.22067/jsw.v0i0.75162
Organic amendments
Pyrolysis temperature
Sequential Extraction
Zinc availability
Akbar
Karimi
akbar.karimi84@yahoo.com
1
Shahid Chamran University of Ahvaz
AUTHOR
abdolamir
moezzi
moezzi151@scu.ac.ir
2
Shahid Chamran University of Ahvaz
LEAD_AUTHOR
Mostafa
Chorom
m.chorom@scu.ac.ir
3
Shahid Chamran University of Ahvaz
AUTHOR
Naeimeh
Enayatizamir
n.enayatzamir@scu.ac.ir
4
Shahid Chamran University of Ahvaz
AUTHOR
1. Abbas T., Rizwan M., Ali S., Zia-ur-Rehman M., Qayyum M.F., Abbas F., Hannan F., Rinklebe J., and Ok Y.S. 2017. Effect of biochar on cadmium bioavailability and uptake in wheat (Triticum aestivum L.) grown in a soil with aged contamination. Ecotoxicology and Environmental Safety 140: 37-47.
1
2. Al‐Wabel M.I., Al‐Omran A., El‐Naggar A.H., Nadeem M., and Usman A.R. A. 2013. Pyrolysis temperature induced changes in characteristics and chemical composition of biochar produced from conocarpus wastes. Bioresource Technology 131: 374–379.
2
3. Al‐Wabel M.I., Hussain Q., Usman A.R., Ahmad M., Abduljabbar A., Sallam A.S., and Ok Y.S. 2017. Impact of biochar properties on soil conditions and agricultural sustainability: A review. Land Degradation and Development 29: 2124-2161.
3
4. Boostani H.R. 2018. Effect of organic manures, their biochars and arbuscular mycorrhizae fungi on distribution of zinc chemical fractions in a calcareous soil. Journal of Water and Soil Conservation 24(5): 49-75. (In Persian with English abstract)
4
5. Cantrell K.B., Hunt P.G., Uchimiya M., Novak J.M., and Ro K.S. 2012. Impact of pyrolysis temperature and manure source on physicochemical characteristics of biochar. Bioresource Technology 107: 419-428.
5
6. Carter M.R., and Gregorich E.G. 2008. Soil sampling and methods of analysis (2nd ed). CRC Press. Boca Raton. FL. 1204 p.
6
7. Corre M.D., Schnabel R.R., and Shaffer J.A. 1999. Evaluation of soil organic carbon under forests, cool-season and warm-season grasses in the northeastern US. Soil Biology and Biochemistry 31: 1531-1539.
7
8. Dai Sh., Hui Li H., Yang Zh., Dai M., Dong X., Ge X., Sun M., and Shi L. 2018. Effects of biochar amendments on speciation and bioavailability of heavy metals in coal-mine-contaminated soil, Human and Ecological Risk Assessment: An International Journal 24(7): 1887-1900.
8
9. Dehghanian H., Halajnia A., Lakzian A., and Astaraei A.R. 2018. The effect of earthworm and arbuscular mycorrhizal fungi on availability and chemical distribution of Zn, Fe and Mn in a calcareous soil. Applied Soil Ecology 130: 98-103.
9
10. Doelsch E., Masion A., Moussard G., Chevassus-Rosset C., and Wojciechovwicz O. 2010. Impact of pig slurry and green waste compost application on heavy metal exchangeable fractions in tropical soils. Geoderma 155: 390–400.
10
11. Domingues R.R., Trugilho P.F., Silva C.A., de Melo I.C.N., Melo L.C., Magriotis Z.M., and Sanchez-Monedero M.A. 2017. Properties of biochar derived from wood and high-nutrient biomasses with the aim of agronomic and environmental benefits. PloS one 12(5): 0176884.
11
12. El-Mahrouky M., El-Naggar A.H., Usman A.R., and Al-Wabel M. 2015. Dynamics of CO2 emission and biochemical properties of a sandy calcareous soil amended with Conocarpus waste and biochar. Pedosphere 25(1): 46–56.
12
13. Fellet G., Marchiol L., Vedove G.D., and Peressotti A. 2011. Application of biochar on mine tailings: Effects and perspectives for land reclamation. Chemosphere 83(9): 1262–67.
13
14. Gul S., Whalen J.K., Thomas B.W., Sachdeva V., and Deng H. 2015. Physico-chemical properties and microbial responses in biochar-amended soils: mechanisms and future directions. Agriculture, Ecosystems and Environment 206: 46-59.
14
15. Hosseinpur A.R., and Motaghian H.R. 2017. The effect of cow manure and vermicompost application on fractionation and availability of Zinc and Copper in Wheat planting. Journal of Water and Soil 30(6): 2005-2018.
15
16. Ippolito J.A., J.M. Novak W.J. Busscher M. Ahmedna D. Rehrah and Watts D.W. 2012. Switchgrass biochar affects two Aridisols. Journal of Environmental Quality 41: 1123–1130.
16
17. Ippolito J.A., Ducey T.F., Cantrell K.B., Novak J.M., and Lentz R.D. 2016. Designer, acidic biochar influences calcareous soil characteristics. Chemosphere 142: 184–191.
17
18. Karami M., Afyuni M., Khoshgoftarmanesh A.H., Papritz A., and Schulin R. 2009. Grain zinc, iron, and copper concentrations of wheat grown in central Iran and their relationships with soil and climate variables. Journal of Agricultural and Food Chemistry 57(22): 10876-10882.
18
19. Karimi A., Moezzi A., Chorom M., Enayatizamir N. 2019. Investigation of physicochemical characteristics of biochars derived from corn residue and sugarcane bagasse in different pyrolysis temperature. Iranian Journal of Soil and Water Research 50(3): 725-739. (In Persian with English abstract)
19
20. Khadem A., Raiesi F., and Besharati H. 2018. The effect of corn biochar on chemical and microbiological properties of two calcareous soils with clayey and sandy texture. Journal of Soil Management and Sustainable Production 8(1): 25-47. (In Persian with English abstract)
20
21. Laird D., Fleming P., Wang, B., Horton R., and Karlen D. 2010. Biochar impact on nutrient leaching from a Midwestern agricultural soil. Geoderma 158(3-4): 436-442.
21
22. Lehmann J., and Joseph S. (Eds.). (2015). Biochar for environmental management: science, technology and implementation. Routledge.
22
23. Lindsay W.L., and Norvel W.A. 1978. Development of DTPA soil test for zinc, iron, manganese and copper. Soil Science Society of America Journal 42: 421-428.
23
24. Liu X.H., and Zhang X.C. 2012. Effect of biochar on pH of alkaline soils in the Loess Plateau: results from incubation experiments International Journal of Agriculture and Biology 4: 745–750.
24
25. Mete F., Mia S., Dijkstra F.A., Abuyusuf M., and Iqbal Hossain A.S.M. 2015. Synergistic Effects of Biochar and NPK Fertilizer on Soybean Yield in an Alkaline Soil. Pedosphere 25(5): 713-719.
25
26. Moradi N., Rasouli-Sadaghiani M.H., and Sepehr E. 2017. Effect of biochar types and rates on some soil properties and nutrients availability in a calcareous soil. Journal of Water and Soil 31(4): 1232-1246. (In Persian with English abstract)
26
27. Naeem M.A., Khalid M., Aon M., Abbas G., Tahir M., Amjad M., Murtaza B., Yang A., and Akhtar S.S. 2017. Effect of wheat and rice straw biochar produced at different temperatures on maize growth and nutrient dynamics of a calcareous soil. Archives of Agronomy and Soil Science 63(14): 2048-2061.
27
28. Najafi G., Ghobadian B., Tavakoli T., and Yusaf T. 2009. Potential of bioethanol production from agricultural wastes in Iran. Renewable and Sustainable Energy Reviews 13(6-7): 1418-1427.
28
29. Namgay T., Singh B., and Singh B.P. 2010. Influence of biochar application to soil on the availability of As, Cd, Cu, Pb, and Zn to maize (Zea mays L.). Australian Journal of Soil Research 48: 638–647.
29
30. Nelson D.W., and Sommers L.E. 1996. Carbon, organic carbon and organic matter. P 961-1010, In: D.L. Sparks (Ed.), Methods of Soil Analysis. SSSA, Madison.
30
31. Ouyang L., Tang Q., Yu L., and Zhang R. 2014. Effects of amendment of different biochars on soil enzyme activities related to carbon mineralisation. Soil Research 52(7): 706-716.
31
32. Preetha P.S., and Stalin P. 2014. Different Forms of Soil Zinc-their Relationship with Selected Soil Properties and Contribution towards Plant Availability and Uptake in Maize Growing Soils of Erode District, Tamil Nadu. Indian Journal of Science and Technology 7(7): 1018-1025.
32
33. Qi F., Dong Z., Lamb D., Naidu R., Bolan N.S., Ok Y.S., Liu C., Khan N., Johir M.A.H., and Semple K.T. 2017. Effects of acidic and neutral biochars on properties and cadmium retention of soils. Chemosphere 180: 564-573.
33
34. Rengel Z. 2015. Availability of Mn, Zn and Fe in the rhizosphere. Journal of soil science and plant nutrition. Journal of Soil Science and Plant Nutrition 15(2): 397-409.
34
35. Sadegh‐Zadeh F., Parichehreh M., Jalili M., and Bahmanyar M.A. 2018. Rehabilitation of calcareous saline‐sodic soil by means of biochars and acidified biochars. Land Degradation and Development, DOI: 10.1002/ldr.3079.
35
36. Shahbazi K., and Besharati H. 2013. Overview of Agricultural Soil Fertility Status of Iran. Land Management Journal 1(1): 1-15. (In Persian with English abstract)
36
37. Sheng Y., and Zhu L. 2018. Biochar alters microbial community and carbon sequestration potential across different soil pH. Science of the Total Environment 622–623: 1391–1399.
37
38. Singh B., Camps-Arbestain M., and Lehmann J. (Eds.). 2017. Biochar: a guide to analytical methods. Csiro Publishing.
38
39. Song D., Xi X., Huang S., Liang G., Sun J., Zhou W., and Wang X. 2016. Short-term responses of soil respiration and C-cycle enzyme activities to additions of biochar and urea in a calcareous soil. PloS one 11(9): 0161694.
39
40. Song D., Tang J., Xi X., Zhang S., Liang G., Zhou W., and Wang X. 2018. Responses of soil nutrients and microbial activities to additions of maize straw biochar and chemical fertilization in a calcareous soil. European Journal of Soil Biology 84: 1-10.
40
41. Sposito G., Lund L.J., and Chang A.C. 1982. Trace Metal Chemistry in Arid-zone Field Soils Amended with Sewage Sludge: I. Fractionation of Ni, Cu, Zn, Cd, and Pb in Solid Phases 1. Soil Science Society of America Journal 46(2): 260-264.
41
42. Tan X., Liu Y., Gu Y., Zeng G., Wang X., Hu X., Sun Z., and Yang Z. 2015. Immobilization of Cd (II) in acid soil amended with different biochars with a long term of incubation. Environmental Science and Pollution Research 22(16): 12597-12604.
42
43. Tessier A., Campbell P.G., and Bisson M. 1979. Sequential extraction procedure for the speciation of particulate trace metals. Analytical Chemistry 51(7): 844-851.
43
44. Wang P., Zhou D.M., Luo X.S., and Li L.Z. 2009. Effects of Zn-complexes on zinc uptake by wheat (Triticum aestivum) roots: a comprehensive consideration of physical, chemical and biological processes on biouptake. Plant and Soil 316(1-2): 177-192.
44
45. Yang X., Lu K., McGrouther K., Che L., Hu G., Wang Q., Liu X., Shen L., Huang H., Ye Z., and Wang H. 2017. Bioavailability of Cd and Zn in soils treated with biochars derived from tobacco stalk and dead pigs. Journal of Soils and Sediments 17(3): 751-762.
45
46. Yue Y., Cui L., Lin Q., Li G., and Zhao X. 2017. Efficiency of sewage sludge biochar in improving urban soil properties and promoting grass growth. Chemosphere 173: 551–556.
46
47. Zahedifar M. 2017. Sequential extraction of zinc in the soils of different land use types as influenced by wheat straw derived biochar. Journal of Geochemical Exploration 182: 22-31.
47
48. Zeng G., Wu H., Liang J., Guo S., Huang L., Xu P., Liu Y., Yuan Y., He X., and He Y. 2015. Efficiency of biochar and compost (or composting) combined amendments for reducing Cd, Cu, Zn and Pb bioavailability, mobility and ecological risk in wetland soil. Rsc Advances 5(44): 34541-34548.
48
49. Zhao B., O'Connor D., Zhang J., Peng T., Shen Z., Tsang D.C., and Hou D. 2018. Effect of pyrolysis temperature, heating rate, and residence time on rapeseed stem derived biochar. Journal of Cleaner Production 174: 977-987.
49
ORIGINAL_ARTICLE
Effect of Superabsorbent, Nitrogen Fertilizer and Drought Stress on Yield and Water Productivity of Bell Pepper
In this pot experiment, the effects of three levels of zero (A0), three (A1) and five gram (A2) aquasorb superabsorbent per kg of soil, three levels of 70 (W1), 85 (W2) and 100 (W3) percent of irrigation requirements and two levels of 75 (F1) and 100 (F2) percent of nitrogen fertilizer requirements were studied on some traits of bell pepper plant. The experiment was factorial based on randomized complete block design with 18 treatments and three replications. The results showed significant effect of superabsorbent and irrigation treatments on all components except stem diameter. Among the superabsorbent treatments, the highest fruit yields (666.2 gr) and water productivity (12.36 kg/m3) were obtained in A2 treatment. Among the irrigation treatments, the highest values of the mentioned functions were obtained in the W3 and W1 treatments with 621.81 g and 10.57 Kg/m3 respectively. The effect of fertilizer treatments on shoot weight, root and fruit yield was significant. The highest fruit yield was 638.70 g in F2 treatment. The interaction of two variables of water with superabsorbent with effect on fresh and dry weight of shoot and root and on yield and water productivity yielded the highest fruit yield (916.65g) and productivity (14.55kg/m3) in A2W3 treatment. The interaction effects of superabsorbent and fertilizer showed that the highest yield and water productivity were equal to 670.51 grams and 12.44 kg / m3 in A2F2 treatment. The interactions of water and fertilizer showed that the highest yield and water productivity were 625.59 g in W3F2 and 12.32 kg/m3 in W1F2 treatment. The interaction of three superabsorbent, water and fertilizer variables on all studied traits was not significant.
https://jsw.um.ac.ir/article_38745_e3084bf1fcd3779d9c6f223a27de366a.pdf
2019-08-23
463
476
10.22067/jsw.v0i0.76431
Bell pepper
Root
Shoot
superabsorbent
Hamid
Zare Abyaneh
zare@basu.ac.ir
1
Bu-Ali Sina University
AUTHOR
Farzaneh
Heidari
f.heidari90@basu.ac.ir
2
Bu-Ali Sina University
AUTHOR
Gholamreza
Heidari
g.heidari@uok.ac.ir
3
دانشگاه کردستان
AUTHOR
mehdi
jovzi
jovzimehdi@gmail.com
4
Kermanshah Agricultural and Natural Resources Research and Education Center, AREEO, Kermanshah, Iran
LEAD_AUTHOR
1. Abedi Koupai, J., and Mesforoush, M. 2009. Evaluation of Superabsorbent Polymer Application on Yield, Water and Fertilizer Use Efficiency in Cucumber (Cucumis sativus). Iranian Journal of lrrigation and drainage, 2 (3): 100-111. (In Persian with English abstract)
1
2. Adt, S.K., Clifford, S.C., Wanek, W., Jones, H.G., and Popp, M. 2001. Physiological and morphological adaptations of the fruit tree Ziziphus rotundifolia in response to progressive drought stress. Tree Physiology 21: 705-715.
2
3. Afazaty, M., Irandost, M., and Rezaei Estakhroeih, A. 2016. Effect of application of super absorbent on the growth and yield of cucumber under deficit irrigation. Journal of water and irrigation management (Journal of agriculture), 5 (2): 203-214. (In Persian with English abstract)
3
4. Ahrar, M., Delshad, M., and Babalar, M. 2009. Improving water/fertilizer use efficiency of hydroponically cultured greenhouse cucumber by grafting and hydrogel amendment. Journal of Horticultural Sciences, 23 (1): 69-77. (In Persian with English abstract)
4
5. Albaho, M., Bhat, N., Abo, H., and B, Tomas. 2009. Effect of three substrates on growth and yield of two cultivars of capcicum Annum. Eroupean Journal of Scientific Reasearch, 28: 227-233.
5
6. Alkire, B.H., and Simon, J.E. 1993.Water management for Midwestern peppermint (MenthapiperitaL.) growing in highly organic soil. Indiana, USA. Acta Horticulture Journal 344: 544-556.
6
7. Babaee Sabzikar Langaroodi, N., Ashouri, M., Dorodian, H.R., and Azarpour, E. 2013. Study effect of super absorbent application, saline water and irrigation management on yield and yield components of peanut (Arachis hypogaea L.) Scholars Research Library Annals of Biological Research. 4 (1):160-169.
7
8. Bal, W., Zhang, H., Wu, L.Y., and Song, J. 2010. Effects of super- absorbent polymers on the physical and chemical properties of soil following different wetting and drying cycles. Soil Use and management Journal 26: 253-260.
8
9. Dashtbozorg, A., Sayyad, Gh. Kazeminezhad, I., and Mesgarbashi, M. 2013. The Effects of Different Sizes of Particles of a Superabsorbent Polymer on Water Holding Capacity of Two Different Soil Textures. Journal of Agricultural Engineering, 36 (1): 65-75. (In Persian with English abstract)
9
10. Efeoğlu, B., Ekmekçi, Y., and Çiçek, N., 2009. Physiological responses of three maize cultivars to drought stress and recovery. South African Journal of Botany, 75: 34–42.
10
11. Haghighi, M., Mozafariyan, M., and Afifipour, Z. 2014. The Effect of Superabsorbent Polymer and Different Withholding Irrigation Level on Some Qualitative and Quantitative Traits of Tomato (Lycopersicum esculentum). Journal Of Horticulture Science, 28(1): 125-133. (In Persian with English abstract)
11
12. Islam, M.R., Xue, X.Z., Mao, S., Ren, C.Z., Eneji, A.E., and Hu, Y.G. 2011. Effects of water-saving superabsorbent polymer on antioxidant enzyme activities and lipid peroxidation in oat (Avena sativa L.) under drought stress. Journal of the Science of Food and Agriculture 91 (4): 680–686.
12
13. Jalili; J., Jalili Kh., and Sohrabi H. 2013. Possibility of Increasing Irrigation Period Without Decreasing Growth of Rose Saplings by Implementing Super Absorbent Polymer Trawat A200 in a Semi Arid Region. Journal of horticulture science, 27 (2): 185-192. (In Persian with English abstract)
13
14. Javadi Khederi, S., Khanjani, M., Ahmad Hoseini, M., Hoseininia, A., and Safari, H. 2016. Effects of drough stress and supper absorbent polymer on susceptibility of pepper to damage Aphis gossypii Glover (Hem.: Aphididae). Journal of Crop Protection, 5 (1) :49-57
14
15. Jhurry, D. 1997. Agricultural Polymers. Food and Agricultural Research council, Reduit, Mauritius, 109-113.
15
16. Kafi, M., and Mahdavi damghani, A. 2000. Resistance mechanisms of plants to environmental stresses. Ferdowsi University Press, Mashhad. (In Persian)
16
17. Karimi, A., and Naderi, M. 2011. Investigating the effects of super absorbent polymer and soil type on yield, water consumption, distance and number of irrigation in corn fodder Journal of Plant Production (Scientific Journal of Agriculture), 34 (1): 69-82. (In Persian with English abstract)
17
18. Keshavarz, L., Farahbakhsh, H., and Golkar, P. 2013. Effect of Hydrogel and Irrigation Regimes on Chlorophyll Content, Nitrogen and Some Growth Indices and Yield of Forage Millet (Pennisetum glaucum L.). Journal of Crop Production and Processing, 3 (9):147-161. (In Persian with English abstract)
18
19. Koohestani, SH., Askari, N., and Maghsoudi, K. 2010. Assessment effects of super absorbent hydro gels on corn yield (Zea mays L.) under drought stress condition. Iran Water Research Journal, 3 (5): 71-78. (In Persian with English abstract)
19
20. Lebaschy, M.H., and Sharifi Ashoorabadi, E. 2004. Growth indices of some medicinal plants under different water stresses. Iranian Journal of Medicinal and Aromatic Plants Research, 20 (3): 249-261. (In Persian with English abstract)
20
21. Lobo, D., Torres, D., Gabriels, D., Rodriguez, N., and Rivero, D. 2006. Effect of organic waste compost and a water absorbent polymeric soil conditioner (hydrogel) on the water use efficiency in a Cspsicum annum (green pepper) cultivation. P. 453-459. 4-7 September. 2006. AGROENVIRON, Ghent, Belgium.
21
22. Lopez-Elias, J., Huez, M. A., Rueda, E.O., Jose Jimenez, L., Fidencio Cruz, B., and Oscar Garrido L. 2013. Use of a hydrophilic polymer in Anaheim pepper (Capsicum annuum L.) under greenhouse conditions. IDESIA (Chile). 31 (2): 77-81.
22
23. Mardani Nezhad, S., Zare Abyaneh, H., Tabatabei, S.H., and Mohammad Khani, A. 2013. Effect of different levels of soil water on root development of chili pepper. Journal of Water Research in Agriculture, 27: 241-253. (In Persian)
23
24. Marques, P.A.A., and Bastos, R.O. 2010. Use of different doses of Hidrogel for sweet pepper seedling production. Pesquisa Aplicada Agrotecnologia, 3(2):59-64.
24
25. Nazarli, H., Zardashti, M.R., Darvishzadeh, R., and Najafi, S. 2010. The effect of water stress and polymer on water use efficiency, yield and several morphological traits of sunflower. Notulae Scientia Biologicae, 2(4): 53-58.
25
26. Parvathy, P.C., and Jyothi, A. 2014. Rheological and Thermal Properties of Saponified Cassava Starchg-Poly (acrylamide) Superabsorbent Polymers Varying in Grafting Parameters and Absorbency. Journal of Applied Polymer Science, 131 (11): 1-11.
26
27. Rafiee Majoumard, Z., Tavili, A., Zehtabian, G.R., Heidary, M., and Soltani Gerd Faramarzi, M. 2012. Effects of super absorbent polymer on Haloxylon aphyllum seedlings growth properties and water consumption in nursary. Journal of Rangeland, 6 (2): 110-119. (In Persian with English abstract)
27
28. Robiul Islam, M.R., Hu, Y., Mao, S., Jia, P., Eneji, A.E., and Xue, X. 2011. Effects of water-saving superabsorbent polymer on antioxidant enzyme activities and lipid peroxidation in corn (Zea max L.) under drought stress. Journal of Science Food and Agriculture 91: 813-819.
28
29. Rostami, F., Gholami sefid kohi, M.A., Shahnazari, A., and Akbarpour, V. 2016. Effect of super absorbent (A200) on some phytochemical characteristics and water productivity under drought stress in Hot Pepper Pplant (Capsicum frutescence). Iran Water Research Journal, 10 (1): 107-114. (In Persian with English abstract)
29
30. Sajjadi, F., Sharifan, H., Hezarjaribi, A., Ghorbani Nasrabad, Gh. 2016. The effect of salinity stress and over irrigation on yield and yield components of green pepper. Journal of water and irrigation management (Journal of agriculture), 6 (1): 89-100. (In Persian with English abstract)
30
31. Salar, N., Farahpour, M, and Bahadori, F. 2005. Investigation of the effect of hydrophilic polymer on irrigation frequency in melon crop. 3th Specialized Seminar on Agricultural Application of superabsorbent Hydrogels, Tehran, Iran.
31
32. Sanatombi, K., and Sharma, G. 2007. MICROPROPAGATION OF CAPSICUM ANNUUM. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 35 (1): 57-64.
32
33. Sayyari, M., and Ghanbari, F., 2012. Effects of Super Absorbent Polymer A200 on the Growth,Yield and Some Physiological Responses in Sweet Pepper (Capsicum Annuum L.) Under Various Irrigation regims. International journal of agricultural and food research. 1(1): 1-11.
33
34. Shangwe, V.D., Magongo, B.N., Masarirambi, M.T., and Manyastsi, A.M. 2010. Effects of irrigation moisture regims on yield and quality of paprika (Cspsicum annum L.). Pysical and Chemistry of the Earth, Parts A/B/C. 35 (13): 717-722.
34
35. Shahriari, A., Noori, S., Asaleh, F., Noori, G.R., and Zaboli, M. 2011. The effects of wastewater, super absorbent and soil texture on the growth of Nitraria schoberi. Journal of Rangeland, 4 (4): 564-573. (In Persian with English abstract)
35
36. Sheikhmoradi, F., Arji, I., Emaeili, A., and Abdosi, V. 2011. Evaluation the Effects of Cycle Irrigation and Super Absorbent on Qualitative Characteristics of Lawn. Journal Of Horticulture Science, 25(2): 170-177. (In Persian with English abstract)
36
37. Sivapalan, S. 2001. Effect of polymer on soil water holding capacity and plant water use efficiency. Proceeding of 10th Australian agronomy conference, Horbat, Tasmania, Australia.
37
38. Taylor, K.C., and Halfacre, R.G. 1986. The effect of hydrophilic polymer on media water retention and nutrient availability to ligustrum lucidum. Horticultural Science, 21: 1159-1161
38
39. Topuz, A., and Ozdemir, F. 2007. Assessment of carotenoids, capsaicinoids and ascorbic acid composition of some selected pepper cultivars (Capsicum annuum L.) grown in Turkey. Journal of Food Composition and Analysis, 20 (7): 596-602.
39
40. Tupitsyn, N.V., Waines, J.G., and Lyashok, A.K. 1986. Water uptake by the root system of the spring wheats Botanicheskaya 3 and Orenburgskaya 7 in relation to their drought resistance. Plant Breeding Abs. 57: 9,815.
40
41. Vahidi, A., alizadeh, A., baghizadeh, A., and Ansari, H. 2018. Effect of Biofertilizer and Chemical Fertilizer Application on Water use Efficiency and Physiological Growth Indices of Henna as Medicinal Plant under Water Deficit Condition. Iranian Journal of Irrigation and Drainage, 12(2): 260-274. (In Persian with English abstract)
41
42. Zangooei Nasab, Sh., Emami, H., Astaraei, A.R., and Yari, A.R. 2013. Effects of stockosorb hydrogel and irrigation intervals on some soil physical properties and growth of haloxylon seedling. Electronic Journal of Soil Management and Sustainable Production, 3(1): 167-182. (In Persian with English abstract)
42
43. Ziaei, A., Moghaddam, M., and Kashefi, B. 2016. The effect of superabsorbent polymers on morphological traits of rosemary (Rosmarinus officinalis) under drought stress. Journal of Science and Technology of Greenhouse Culture, 7 (2): 99-111. (In Persian with English abstract)
43
ORIGINAL_ARTICLE
Changes in Physical Properties of Soils after Five Years of Application of Conventional and Conservation Tillage Practices
Introduction: Soil is one of the nonrenewable resources (in human being life time scale) that is important to be protected. Tillage operations are carried out in a variety of ways, which in general can be divided into two comprehensive classes of conventional and conservation tillage practices. The tillage has a very important impact on soil physical, chemical and biological properties. Different tillage systems can have conflicting effects on soil physical properties, which is thought to reflect the impact of different weather conditions. Therefore, it seems necessary to study the effects of different tillage practices on the soil attributes in different climatic conditions.
Materials and Methods: This experiment was conducted for five years from 2011 to 2016 in a randomized complete block design (RCBD) with repeated measurements in two different locations and four replications. The applied tillage practices included no-till in standing residue (NT1), no-till in entire residue (NT2), chisel plow plus disc harrow (CH), minimum tillage with mulch cultivator (MT) and conventional plowing with moldboard plowing (CT). The experiment was carried out at Dryland Agricultural Research Institute (DARI) in Maragheh. Soil samples were taken at the end of fifth year and then soil texture were determined by hydrometer method, weight and geometric means of aggregates diameters by wet-sieving (MWDwetو GMDwet) and dry-sieving (MWDdry GMDdry) procedures, the stability of 1 to 2 mm aggregates (WAS) by wet-sieving, total soil organic carbon (TOC) by wet oxidizing method, dissolved soil organic carbon (DOC) using carbon analyzer and mass fractal dimension aggregates using Tyler and Wheatcraft model. The soil bulk density (Db) was also measured by intact samples (from two depths of 0-15 cm and 15-30 cm) prepared from the study area using sampling cylinders with a diameter of 5 and a height of 4 cm.
Results and Discussion: In general, the results showed that the interaction of depth and location on Db was significant at 5% probability level. The measured Db in 15-30 cm was greater than the measured Db in a depth of 0-15 cm. Also, in spite of the significance of the main effects of location and tillage and the interaction of tillage-location on soil dissolved organic carbon (DOC), tillage treatments and their interaction effects on total organic carbon (TOC) were not significant. The results showed that conventional tillage, CT, had the highest amount of DOC. However, no-till in entre residue (NT2) and minimum tillage (MT) showed the lowest amount of DOC. Further, the main effects of tillage practices on MWDdry and GMDdry were significant at 5% probability level. No-till (NT1 and NT2) practices had the highest MWDdry with values of 1.17 and 1.25 mm. Tillage practices and location had no significant effect on WAS, Dm, and MWDwet and GMDwet.
Conclusion: It seems that the reason that DOC content of CT was higher than conservation tillage practices is due to the preservation of crop residues on the soil surface in conservation and no-till systems and less mixing of them with soil and consequently their less decomposition. While in conventional tillage, plant residues were mixed with soil, and the effect of biological degradation increased soil DOC. The greater MWDdry in NT1 and NT2 practices suggests that tillage, even at a minimum or reduced state, breaks down the aggregates and produces smaller particles or aggregates. It also seems that the main reason for GMDdry reduction in minimum tillage is due to the further degradation of aggregates by the tillage agent. Therefore, to better and more accurately observe the effects of different types of tillage, sampling should be done at the end of each growing season.
https://jsw.um.ac.ir/article_38746_77fc8ec094118615e8d813679d10ec55.pdf
2019-08-23
477
478
10.22067/jsw.v0i0.76818
Aggregates stability
Bulk density
Dissolved Organic Carbon
Fractal Dimension
Mehdi
Kousehlou
m.kooselou633@gmail.com
1
University of Maragheh
AUTHOR
Mehdi
Rahmati
mehdirmti@gmail.com
2
University of Maragheh
LEAD_AUTHOR
Iraj
Eskandari
eskandari1343@yahoo.com
3
Dryland Agricultural Research Institute
AUTHOR
Vali
Feiziasl
vfeiziasl@yahoo.com
4
Dryland Agricultural Research Institute
AUTHOR
1- Abbasi H., Khodaverdiloo H., Ghorbani Dashtaki S., and Ahmadi Moghaddam P. 2014. The effects of some tillage methods on soil physical quality index in arid and semiarid region. Journal of Agricultural Mechanization 1(2): 37-45. (In Persian with English abstract)
1
2- Afyuni M., and Mosadeghi M. R. 2001. Effect of tillage practices on soil physical properties and bromide translocation. Journal of Science and Technology of Agriculture and Natural Resources 5(3): 39-53. (In Persian)
2
3- Alvarez R., and Steinbach H. 2009. A review of the effects of tillage systems on some soil physical properties, water content, nitrate availability and crops yield in the Argentine Pampas. Soil and Tillage Research 104(1): 1-15.
3
4- Ataee A., Gorji M., and Parvizi Y. 2015. Evaluation of the suitability of fractal dimension of soil aggregates in assessing different soil management practices. Journal of Soil Researches 28(4): 701-712. (In Persian)
4
5- Bahrani M.J., Raufat M.H., and Ghadiri H. 2007. Influence of wheat residue management on irrigated corn grain production in a reduced tillage system. Soil and Tillage Research 94(2): 305-309.
5
6- Beare M., Hendrix P., and Coleman D. 1994. Water-stable aggregates and organic matter fractions in conventional-and no-tillage soils. Soil Science Society of America Journal 58(3): 777-786.
6
7- Ding Q., and Ding W. 2007. Comparing stress wavelets with fragment fractals for soil structure quantification. Soil Tillage Research 93: 316–323.
7
8- Eghball B., Mielke L.N., Calvo G.A., and Wilhelm W.W. 1993. Fractal description of soil fragmentation for various tillage methods and crop sequences. Soil Science Society of America Journal 57(5): 1337-1341.
8
9- Eynard A., Schumacher T.E., Lindstrom M.J., and Malo D. D. 2004. Aggregate sizes and stability in cultivated South Dakota prairie Ustolls and Usterts. Soil Science Society of America Journal 68(4): 1360-1365.
9
10- Gbadamosi J. 2013. Impact of different tillage practices on soil moisture content, soil bulk density and soil penetration resistance in oyo metropolis, Oyo state, Nigeria. Transnational Journal of Science and Technology 3(9): 50-57.
10
11- Gee G.W., and Or. D. 2002. Particle-size analysis. In: J.H. Dane and G.C. Topp, editors, Methods of soil analysis. Part 4. SSSA Book Ser. 5. SSSA Madison, WI. p. 255–293.
11
12- Ghaffari Nejad S.A. 2018. Long-term soil tests to evaluate soil fertility management methods. Journal Management System 5(2): 99-112.
12
13- Ghasemi Abaolmaleki Y., GHajar Sepanlou M., and Bahmanyar M.A. 2015. The effect of different tillage methods on some soil physical properties. Journal of Soil Researches 29(4): 309-320. (In Persian)
13
14- Grossman R., and Reinsch T. 2002. 2.1 Bulk density and linear extensibility. In: J.H. Dane and G.C. Topp, editors, Methods of soil analysis. Part 4. SSSA Book Ser. 5. SSSA Madison, WI. p. 201-228.
14
15- Gülser C. 2006. Effect of forage cropping treatments on soil structure and relationships with fractal dimensions. Geoderma,131(1-2): 33-44.
15
16- Hajabbasi M.A., Mirlohi A.F., and Sadrarhami M. 1999. Tillage effects on some physical properties of soil and maize yield in Lavark research farm. Journal of Water and Soil Science (Journal of Sciences and Technology of Agriculture and Natural Resources) 3(3): 13-24. (In Persian)
16
17- Ishaq M., Ibrahim M., and Lal R. 2002. Tillage effects on soil properties at different levels of fertilizer application in Punjab, Pakistan. Soil and Tillage Research 68(2): 93-99.
17
18- Jabro J.D., Iversen W.M., Stevens W.B., Evans R.G., Mikha M.M., and Allen B.L. 2015. Effect of three tillage depths on sugarbeet response and soil penetrability resistance. Agronomy Journal 107(4): 1481-1488.
18
19- Jabro J.D., Iversen W.M., Stevens W.B., Evans R.G., Mikha M.M., and Allen B.L. 2016. Physical and hydraulic properties of a sandy loam soil under zero, shallow and deep tillage practices. Soil and Tillage Research 159: 67-72.
19
20- Jabro J., Stevens W., Iversen W., and Evans R. 2011. Bulk density, water content, and hydraulic properties of a sandy loam soil following conventional or strip tillage. Applied engineering in agriculture 27(5): 765-768.
20
21- Karuma A., Mtakwa P., Amuri N., Gachene C.K., and Gicheru P. 2014. Tillage effects on selected soil physical properties in a maize-bean intercropping system in Mwala District, Kenya. International scholarly research notices, 2014: PMC4897449.
21
22- Kemper W.D., and Rosenau R.C. 1986. Aggregate stability and size distribution. In: Method of Soil Analysis. Part 1. Physical and Mineralogical Methods, Soil Sci. Soc. Am. Agron. 9: 425-440.
22
23- Khurshid K., Iqbal M., Arif M.S., and Nawaz A. 2006. Effect of tillage and mulch on soil physical properties and growth of maize. International Journal of Agriculture and Biology 8(5): 593-596.
23
24- Kubar K.A., Huang L., Lu J., Li X., Xue B., and Yin Z. 2018. Integrative effects of no-tillage and straw returning on soil organic carbon and water stable aggregation under rice-rape rotation. Chilean Journal of Agricultural Research 78(2): 205-215.
24
25- Kutlu T., Ersahin S., and Yetgin B. 2008. Relations between solid fractal dimension and some physical properties of soils formed over alluvial and colluvial deposits. Journal of Food, Agriculture and Environment 6(3): 445-449
25
26- Nelson D.W., and Sommers L.E. 1996. Total carbon, organic carbon and organic matter. In: D.L. Sparks (Ed.). Methods of Soil Analyses. Part 3. Chemical Methods. SSSA. Madison, WI. pp. 961-1010.
26
27- Nimmo J.R., and Perkins K.S. 2002. 2.6 Aggregate stability and size distribution. In: J.H. Dane and G.C. Topp, editors, Methods of soil analysis. Part 4. SSSA Book Ser. 5. SSSA Madison, WI. p. 317-328.
27
28- Olson K.R., Ebelhar S.A., and Lang J.M. 2013. Effects of 24 years of conservation tillage systems on soil organic carbon and soil productivity. Applied and Environmental Soil Science 2013.: 617504.
28
29- Paustian K., Collins H.P., and Paul E.A. 1997. Management controls on soil carbon: 1997, CRC Press: Boca Raton, FL, USA. p.
29
30- Peixoto R., Coutinho H., Madari B., Machado P., Rumjanek N., Van Elsas J., Seldin L., and Rosado A. 2006. Soil aggregation and bacterial community structure as affected by tillage and cover cropping in the Brazilian Cerrados. Soil and Tillage Research 90(1): 16-28.
30
31- Pirmoradian N., Sepaskhah A.R., and Hajabbasi M.A. 2005. Application of fractal theory to quantify soil aggregate stability as influenced by tillage treatments. Biosystems Enginering 90(2): 227-234.
31
32- Prosperini N., and Perugini D. 2008. Particle size distributions of some soils from the Umbria Region (Italy): fractal analysis and numerical modelling. Geoderma 145(3-4): 185-195.
32
33- Rousta M. 2009. Effects of different tillage methods on soil organic matter content and aggregate stability. Iranian Journal of Soil Research (Formerly Soil and Water Sciences) 23(1): 61-67.
33
34- Sarker J.R., Singh B.P., Cowie A.L., Fang Y., Collins D., Badgery W., and Dalal R.C. 2018. Agricultural management practices impacted carbon and nutrient concentrations in soil aggregates, with minimal influence on aggregate stability and total carbon and nutrient stocks in contrasting soils. Soil and Tillage Research 178: 209-223.
34
35- Skaggs T., Arya L., Shouse P., and Mohanty B. 2001. Estimating particle-size distribution from limited soil texture data. Soil Science Society of America Journal 65(4): 1038-1044.
35
36- Tyler S.W., and Wheatcraft S.W. 1992. Fractal scaling of soil particle-size distributions: analysis and limitations. Soil Science Society of America Journal 56(2): 362-369.
36
37- Yoder R.E. 1936. A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. Agronomy Journal 28(5): 337-351.
37
38- Zhao S.W., S J., Yang Y.H., Liu N., Wu J., and Shangguan Z. 2006. A fractal method of estimating soil structure changes under different vegetations on Ziwuling Mountains of the Loess plateau, China. China Agricaltural Science Journal 5(7): 530-538.
38
39- Zimmermann M., Leifeld J., and Fuhrer J. 2007. Quantifying soil organic carbon fractions by infrared-spectroscopy. Soil Biology and Biochemistry 39(1): 224-231.
39
ORIGINAL_ARTICLE
Isolation of Silicate Minerals-Solubilizing Bacteria from Tobacco (Nicotiana tabacum L.) Rhizosphere and Evaluation of their Efficiency on Soil Potassium Release
Introduction: Potassium (K) is one of the major essential macronutrients for plant growth. Soil has rich reserves of K, among which only 1–2% can be directly absorbed by plants. It may be more economically viable to transform the fixed slow-release K into available K that can be absorbed by plants. The ability of some microorganisms to dissolve soil K-bearing minerals, such as micas is an important feature for increasing the yield of high-K-demand crops such as tobacco. Also, these microorganisms have both economic and environmental advantage. A large number of saprophytic bacteria such as Bacillus mucilaginosus and fungal strains such as Aspergillus spp. are known for their potential in releasing insoluble native K-source in soil into a plant available nutrient pool. Tobacco (Nicotiana spp.) is one of the most important industrial crops. K plays a vital role in increasing the tobacco yield and controlling quality parameters such as leaf combustibility that is one of the key criteria taken into account by the tobacco industry for assessing quality. Thus, high ranges of K fertilizers are applied in tobacco fields based on plant K requirement to build up soil K in tobacco producing countries. Increasing cost of the fertilizers and environmental risks necessitates alternate means to fertilizers such as application of microorganisms. The use of chemical K fertilizers can be reduced by exploiting the potential of bio-inoculants which are inexpensive and eco-friendly. Information related to K-solubilizing microorganisms in tobacco rhizosphere and their suitability in increasing the available K in tobacco-cultivated soils is not well-documented. Hence, the present study was conducted to screen the KSB isolates from tobacco-cultivated soils and evaluate their potential in dissolving K bearing silicate minerals and increasing soil available potassium.
Materials and Methods: Soil samples were randomly collected from the rhizosphere of tobacco from 25 different locations in northwest of Iran. The serial dilutions of the soil samples were made up to 10-4 and 5 µl of diluted soil suspension plated on Aleksandrov medium plates (on the agar-based culture medium). Aleksandrov medium contained 5.0 g Glucose, 0.5 g MgSO4.7H2O, 0.1g CaCO3, 0.006 g FeCl3, 2.0 g Ca3PO4, 2.0 g insoluble mica powder as potassium source and 20.0 g agar in 1 liter of deionized water. The plates were incubated at 28±2°C in incubator for 10 days. Finally, nine isolates of potassium silicate solubilizing bacteria were isolated and purified. Solid and liquid Aleksandrov media were applied for qualitative (Solubility Index = Diameter of zone of clearance/ Diameter of growth) and quantitative (K content) evaluation, respectively, based on the completely randomized design (CRD) with three replication. Liquid Aleksandrov medium containing 2 g L-1 of mica and feldspar mixture, was inoculated with bacterial isolates. Bacterial isolates creating high solubility index and releasing more K from K-bearing minerals into liquid medium, were selected as effective isolates. In order to evaluate the efficiency of the potent bacterial isolates for increasing soil available K, an experiment was conducted with three replication and eight potent bacterial isolates along with a control (non-inoculated soil). Sterilized soil samples were inoculated with bacterial isolates separately and incubated at 25°C, with 75% field capacity moisture levels for 90 days. After incubation, available K in soil samples were extracted with Ammonium Acetate 1M. Variance of solubility index, K concentration into liquid Aleksandrov medium and soil available K were analyzed using SPSS (Statistical Package for the Social Sciences). Student-Newman-Keuls (SNK) test comparisons were also used to compare available soil K using SPSS 16.0.
Results and Discussion: Eight KSBs isolates, including KSB20, KSB30, KSB40, KSB22, KSB42, KSB90, KSB92 and KSB10, were isolated and purified as effective isolates for dissolving mica and feldspar minerals. Most isolates were gram-positive, rod-shaped, and white in appearance. The studied isolates, except KSB22, KSB40 and KSB20, had α-amylase enzyme activity. Bacterial isolates, including KSB20, KSB30, KSB42 and KSB10, were significantly superior in sucrose and glucose hydrolysis. The isolate of KSB10 also had fluorescence properties. The highest solubility index (2.8, 2.7 and 2.5) was obtained from the activity of KSB22, KSB42 and KSB10 isolates in solid Aleksandrov medium, respectively. The highest concentration of potassium into liquid Aleksandrov medium was found for the KSB42 and KSB10 isolates (9.40 mg L-1). The KSB42 and KSB10 isolates increased medium K concentration approximately three times more than non-inoculated medium. In addition, KSB42 and KSB10 isolates were more effective in releasing potassium from soil potassium-bearing minerals. The amount of available potassium in soil incubated with KSB42 and KSB10 isolates increased by 44 and 46 mg kg-1 compared to the control, respectively.
Conclusion: Among bacterial isolates purified from the tobacco rhizosphere, the KSB42 and KSB10 isolates increased more significantly the solubility of potassium minerals and potassium availability in soil compared to other isolates. These bacteria isolates increased potassium concentration into Aleksandrov liquid medium by more than three times and also increased soil available potassium by about 44 to 46 mg kg-1 compared with the control. As a result, these isolates (KSB42 and KSB10) can be used as a bio-fertilizer to reduce potassium fertilizer application and increase the quality of tobacco after field experiments.
https://jsw.um.ac.ir/article_38747_d7d3ee4499edbdf601e1589990a22afc.pdf
2019-08-23
489
500
10.22067/jsw.v0i0.78382
Potassium
Potassium solubilizing bacterium
Silicates minerals
Tobacco rhizosphere
R.
Ranjbar
ranjbarrahim14@gmail.com
1
Urmia University
LEAD_AUTHOR
Ebrahim
Sepehr
e.sepehr@urmia.ac.ir
2
Urmia University
AUTHOR
Abbas
Samadi
a.samadi@ac.ir
3
Urmia
AUTHOR
MirHasan
Rasouli Sadaghiani
m.rsadaghiani@urmia.ac.ir
4
Urmia University
AUTHOR
Mohsen
Barin
m.barin@urmia.ac.ir
5
Urmia University
AUTHOR
behnam
Dovlati
b.dovlati@urmia.ac.ir
6
Urmia University
AUTHOR
1- Adesemoye A.O., Torbert H.A., and Kloepper J.W. 2008. Enhanced plant nutrient use efficiency with PGPR and AMF in an integrated nutrient management system. Canadian Journal of Microbiology 54(10): 876-86.
1
2- Ashrafi Saeidloo S., and Rasouli Sadaghiani M.H. 2017. The role of silicate-solubilizing microorganisms on potassium release kinetics from K-bearing minerals. Iranian Journal of Soil and Water Research 48(3): 639-649. (In Persian)
2
3- Bagyalakshmi B., Ponmurugan P., and Marimuthu S. 2012. Influence of potassium solubilizing bacteria on crop productivity and quality of tobacco (Camellia sinensis). African Journal of Agricultural Research 7(30): 4250-4259.
3
4- Bhattacharyya P.N., Dutta P., Mausomi Madhab P., Phukan I.K., Sarmah S.R., and Pathak S.K. 2016. Isolation of potash mobilizing microorganisms in tobacco soil and evaluation of their efficiency in potash nutrition in tobacco: a novel approach. Two and a Bud 63(1): 8-12.
4
5- Bozhinova R. 2012. Effect of long-term potassium fertilization on the chemical composition of oriental tobacco. Journal of Central European Agriculture 13(3): 510-518.
5
6- Deaker R., Kecskes M.L., Rose M.T., Amprayn K., Ganisan K., Tran T.K.C., Vu T.N., Phan T.C, Hien N.T., and Kennedy I.R. 2011. Practical methods for the quality control of inoculant bio-fertilizers. ACIAR Monograph Series No.147, Australian Center for International Agricultural Research: Canberra.
6
7- Ebrahimi Karim-Abad R., Rasouli-Sadaghiani M.H., and Barin M. 2016. Isolation of phosphate-solubilizing microorganisms from wheat rhizosphere and evaluation of their solubilizing potential in presence of two insoluble phosphate sources. Soil Applied Research 3(2): 29-41. (In Persian with English abstract)
7
8- Friedrich S., Platonova N.P., Karavaiko G.I., Stichel E., and Glombitza F. 2004. Chemical and microbiological solubilization of silicates. Acta Biotechnology 1: 187–196.
8
9- Hu X.F., Chen J., and Guo J.F. 2006. Two phosphate and potassium solubilizing bacteria isolated from Tiannu Mountain, Zhejiang, China. World Journal of Microbiology and Biotechnology 22: 983-990.
9
10- Kasana R.C., Panwar N.R., Burman U, Pandey C.B., and Kumar P. 2017. Isolation and identification of two potassium solubilizing fungi from arid soil. International Journal of Current Microbiological and Applied Science 6(3): 1752-1762.
10
11- Khoshrou B., Sarikhani M.R., and Aliasgharzad N. 2013. Molecular and biochemical identification of the bacterial isolates used in common biofertilizers in Iran. Water and Soil Science 25 (4/2): 13-26.
11
12- Liu D., Lian B., and Dong H. 2012. Isolation of Paenibacillus sp. and assessment of its potential for enhancing mineral weathering. Geomicrobiology Journal 29: 413-421.
12
13- Liu W., Xu X., Wu X., Yang Q., Luo Y., and Christie P. 2006. Decomposition of silicate minerals by Bacillus mucilaginosus in liquid culture. Environmental Geochemistry and Health 28: 133–140.
13
14- Malinovskaya I.M., Kosenko L.V., Votselko S.K., and Podgorskii V.S. 1990. Role of Bacillus mucilaginosus polysaccharide in degradation of silicate minerals. Microbiology 59: 49–55.
14
15- McLean E.O., and Watson M.E. 1985. Soil measurement of plant- available potassium. P. 277-308. In R.D. Munson (ed.) Potassium in agriculture. ASA, CSSA and SSSA, Madison, WI.
15
16- Nihala J.P.P. 2017. Solubilization of Insoluble Potassium by Different Microbial Isolates in vitro Condition. International Journal of Current Microbiology and Applied Sciences 6: 3600-3607.
16
17- Parmar P., and Sindhu S.S. 2013. Potassium solubilization by rhizosphere bacteria: Influence of nutritional and environmental conditions. Journal of Microbiological Research 3(1): 25-31.
17
18- Ponmurugan P., and Gopi C. 2006. In vitro production of growth regulators and phosphate activity by phosphate solubilizing bacteria. African Journal of Biotechnology 5: 348-350.
18
19- Rasouli Sadaghiani M, Sadeghi S, Barin M, Sepehr E, and Dovlati B. 2017. The effect of silicate solubilizing bacteria on potassium release from mica minerals and its uptake by corn plants. Journal of Water and Soil Science 20(78): 89-102. (In Persian with English abstract)
19
20- Richmond M.D., Pearce R.C., and Bailey W.A. 2016. Dark fire- cured tobacco response to potassium and application method. Tobacco Science 53: 12-15.
20
21- Sadeghi Azad S., Rasouli-Sadaghiani M.H., Barin B., Sepehr M., Dovlti D., and Vahedi R. 2018. Influence of K- Solubilizing Fungi on Potassium Release from Silicate Minerals and some Growth Indices of Corn (Zea mays L.). Applied Soil Research 6(3): 96-108. (In Persian with English abstract)
21
22- Sarikhani M.R., Madani O., and Oustan Sh. 2017. Study on potassium release from mica minerals and its alteration as influenced by microbial inoculation. Journal of Water and Soil 30(3): 900-914. (In Persian with English abstract)
22
23- Sarikhani M.R., Khoshru B, and Oustan Sh. 2016. Efficiency of some bacterial strains in potassium release from mica and phosphate solubilization under in vitro conditions. Geomicrobiology Journal 33 (9): 832-838.
23
24- Schaad N.W., Jones J.B., Chun W. 2001. Laboratory guide for identification for plant pathogenic bacteria. 3nd ed. The American Phytopathological society, Minnesota USA.
24
25- Sessitsch A., Kuffner M., Kidd P., Vangronsveld J., Wenzel W.W., Fallmann K., and Puschenreiter M. 2013. The role of plant-associated bacteria in the mobilization and phyto-extraction of trace elements in contaminated soils. Soil Biology and Biochemistry 60: 182-194.
25
26- Sheng X.F., and Huang W.E. 2002. Mechanism of potassium relase from feldspar affected by the strain NBT of silicate bacterium. Acta Pedologica Sinica 39 (6): 863-871.
26
27- Singh G., Biswas D.R., and Marwaha T.S. 2010. Mobilization of potassium from waste mica by plant growth promoting rhizobacteria and its assimilation by maize (Zea mays) and wheat (Triticum aestivum L.): a hydroponics study under phytotron growth chamber. Journal of Plant Nutrition 33(8): 1236-1251.
27
28- Subhashini D.V. 2013. Effect of bio-inoculation of AM fungi and PGPR on the growth, yield and quality of FCV tobacco (Nicotiana tabacum) in vertisols. Indian Journal of Agricultural Science 83(6): 667-672.
28
29- Subhashini D.V. 2014. Growth promotion and increased potassium uptake of tobacco by potassium-mobilizing bacterium frateuria aurantia grown at different potassium levels in vertisols. Communication in Soil Science and Plant Analysis 46(2): 210-220.
29
30- Subhashini D.V., Anuradha M., Reddy D., and Vasanthi J. 2016. Development of bioconsortia for optimizing nutrient supplementation through microbes for sustainable tobacco production, International Journal of Plant Production 10 (4): 479-490.
30
31- Sugumaran P., and Janarthanam B. 2007. Solubilization of potassium containing minerals by bacteria and their effect on plant growth. World Journal of Agricultural Sciences, 3: 350-355.
31
32- Vann M.C., Fisher L.R., Jordan D.L., Hardy D.H., Smith W.D., and Stewart A.M. 2012. The effect of potassium rate on the yield and quality of flue-cured tobacco (Nicotiana tabacum L). Tobacco Science 49: 14–20.
32
33- Velazquez E., Silva R.L., Ramirez-Bahena M.H., and Piex A. 2016. Diversity of potassium solubilizing microorganisms and their interactions with plants. P. 99-110. In V.S. Meena, B.R. Maurya, J.P. Verma, R.S. Meena (eds) Potassium solubilizing microorganisms for sustainable agriculture. Springer, New Delhi.
33
34- Zhang C., and Kong F. 2014. Isolation and identification of potassium-solubilizing bacteria from tobacco rhizospheric soil and their effect on tobacco plants. Applied Soil Ecology 82: 18-25.
34
ORIGINAL_ARTICLE
Evaluation and Application of Different Observational (Land and Satellite) Datasets Over Iran
Introduction: Precipitation has an important role not only in the variety of scientific applications including climate change, climate simulations, weather modeling, and forecasting but also in decision making such as water management, hydrology, agriculture, drought, and crisis management. Different temporal resolutions and coverages of data are required for this and other applications. For example, long term meteorological data are needed for monitoring the climate variability and trends and for climate simulation assessments in local and global scales. Also, present data are used to assimilate into forecast models to improve the predictions. Historical and present precipitation data are the main requirements to monitor and predict droughts which help to early warning system and water management decisions in a country. The recent rainfall data are also the primary input of hydrological models to flood forecast in a basin. The accurate estimation of precipitation amount is vital for these applications.
Materials and Methods: However, rainfall is discontinuous and varies greatly both in time and space which makes it parallel with difficulties in the actual measurements. The two main sources of observational precipitation datasets are ground-based rain gauge measurements and space-based remote sensing satellite estimations each one with its own limitations and strengths. Historically, rain-gauge measurements have been considered as the “ground truth”, but they have mostly limited to land surface, the measurements are sparse or nonexistent in some regions like deserts or high topographic areas. Although rain gauges measure rainfall directly, their data are only representative for a limited spatial extent and may be subjected to some errors caused by local effects such as topography or wind-induced undercatch. An alternative approach which can provide relatively homogenous estimates with complete coverage over most of the globe is based on using satellite observations. Therefore, satellite data are capable to estimate precipitation over the oceans and over remote areas where few or no ground measurements are available. The satellite-based precipitation estimates are derived mainly from visible, infrared (IR) and passive microwave (PMW) radiances which are measured by satellites. Although the visible channels cannot be used at night, the IR data are available in fine spatial resolution (about 3-4 km) with high temporal sampling (15 min) which are provided by geosynchronous satellites. Another source of data is PMW that can be used to estimate rainfall more directly. Low-altitude polar-orbiting satellites serve to measure the PMW data. Although, the microwave sensors can penetrate into the clouds and provide more information about the cloud characteristics such as water vapor, cloud particles, and structure of hydrometeors, but at the expense of temporal sampling. In recent years, different algorithms have been developed using the combination of the IR, Visible (VIS) and PWM observations to provide more accurate rainfall estimations in high spatial and temporal resolutions. To demonstrate the similarities and differences between the spatial distribution of different satellite-based and gauge-based precipitation datasets over Iran we compared seven different datasets. For comparisons all datasets are regridded to 0.25-degree latitude longitude spatial resolutions. Then the spatial distribution of the mean and relative standard deviations of annual precipitation of these datasets have been calculated. We also used more than 2000 rain gauges to evaluate the selected datasets. To reduce error only 228 pixels, include at least 3 rain gauges are used for comparisons of spatial average of monthly, seasonal and annual precipitation of gauge and seven datasets.
Results and Discussion: The results showed a large amount of differences in annual precipitation between seven selected datasets. The most differences pronounce in wet areas in the north of Alborz Mountain, in the semi-arid and arid regions of the central desert and in the high mountainous areas of the southern Zagros. The reason for these differences is that not only satellite-based but gauge-based datasets have large uncertainties estimating areal precipitation in such high topographic areas. The satellite products are prone to some errors arising from not fully understood physical process, sampling error and parameter estimation. Therefore, verification of precipitation datasets is one of the most important parts of the data development and refinements. In this paper, the spatial distribution of seven different global-observational precipitation datasets over Iran are compared for the period 2003-2007. At first all datasets were regridded to 0.25° spatial resolutions using linear interpolation method. Then, the mean and relative standard deviation of annual precipitation of the datasets were calculated to analyze the spatial discrepancies between datasets. The areal average of annual precipitation and the contribution of seasonal precipitation were calculated for comparison purposes. The results showed that areal average of annual and seasonal precipitation for 228 selected pixels for PERSIANN-CDR, TRMM, and GPCP which are satellite-based and gauge adjusted datasets are more similar to the rain gauge data than other datasets. The results for the above datasets are even better than CRU and APHRODITE which are gauge-based datasets.
Conclusion: The results showed that the satellite estimates are not capable to show the precipitation (detection and amount) over the coast of Caspian Sea and the high areas of the Zagros Mountain as well as other parts of the country. There are some useful recommendations for data users at the end of this paper. In fact, in this paper our spatial focus is on Iran and we introduced a web address which data users can access freely from one of the most popular and widely used satellite-based products in easy-to-use format only for Iran. The results show considerable differences between the datasets. The difference is about 0.8 times of mean annual precipitation (about 300 mm in a year) for the coast of Caspian Sea. The satellite-based estimations were less accurate over the coast of Caspian Sea and high mountainous area of the southwest of Zagros comparing to other parts of the country. While spring precipitation shows maximum contributions in annul precipitation for in-situ datasets, winter precipitation shows maximum contribution in annual precipitation for other datasets. The results showed that areal average of monthly, seasonal and annual precipitation over 228 selected pixels for PERIANN-CDR, TRMM and GPCP were consistent with rain gauge data. CMORPH and PERSIANN underestimate areal average of monthly and seasonal precipitation over the pixels.
https://jsw.um.ac.ir/article_38748_2088a1ac20b6d196c57ae2d3e262f501.pdf
2019-08-23
501
520
10.22067/jsw.v0i0.78832
Evaluation
Iran
Remote sensing
Precipitation Datasets
Satellite-based Precipitation
Ali
Chavoshian
chavoshian@gmail.com
1
Civil Engineering, Faculty of Engineering, Iran University of Science and technology, Tehran, Iran
AUTHOR
P.S.
Katiraie-Boroujerdy
sima_katiraie@yahoo.com
2
Tehran North Branch, Islamic Azad University
LEAD_AUTHOR
1- Adler R., Sapiano M., Huffman G., Bolvin D., Gu G., Wang J., and Ferraro R. 2016. The new version 2.3 of the global precipitation climatology project (GPCP) monthly analysis product. University of Maryland, April.
1
2- Adler R., Huffman G., Chang A., Ferraro R., Xie P., Janowiak J., and Bolvin D. 2003. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology 4(6): 1147-1167.
2
3- Aonashi K., Awaka J., Hirose M., Kozu T., Kubota T., Liu G., and Takahashi N. 2009. GSMaP passive microwave precipitation retrieval algorithm: Algorithm description and validation. Journal of the Meteorological Society of Japan. Ser. II 87: 119-136.
3
4- Ashouri H., Hsu K.L., Sorooshian S., Braithwaite D. K., Knapp K.R., Cecil L.D., and Prat O.P. 2015. PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society 96(1): 69-83.
4
5- Beck H.E., Van Dijk A.I.J.M., Levizzani V., Schellekens J., Miralles D.G., Martens B., and Roo A.D. 2017. MSWEP: 3-hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. Hydrology and Earth System Sciences 21(1):589-615.
5
6- Ebert E.E. 2007. Methods for Verifying Satellite Precipitation Estimates. In: Levizzani V., Bauer P., Turk F.J. (eds) Measuring Precipitation From Space. Advances In Global Change Research, vol 28. Springer, Dordrecht.
6
7- Funk C., Peterson P., Landsfeld M., Pedreros D., Verdin J., Shukla S., and Hoell A. 2015. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific data, 2, 150066.
7
8- Galindo, Francisco J, & Palacio, Juan. (1999). Estimating the instabilities of N correlated clocks: REAL OBSERVATORIO DE LA ARMADA (SPAIN).
8
9- Golian S., Moazami S., Kirstetter P.E., and Hong Y. 2015. Evaluating the performance of merged multi-satellite precipitation products over a complex terrain. Water Resources Management 29(13): 4885-4901.
9
10- Harris I.P.D.J., Jones P.D., Osborn T.J., and Lister D.H. 2014. Updated high‐resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. International journal of climatology 34(3): 623-642.
10
11- Hong Y., Hsu K.L., Sorooshian S., and Gao X. 2004. Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. Journal of Applied Meteorology 43(12): 1834-1853.
11
12- Huffman G.J., Bolvin D.T., Nelkin E.J., Wolff D.B., Adler R.F., Gu G., and Stocker E.F. 2007. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of hydrometeorology 8(1): 38-55.
12
13- Javanmard S., Yatagai A., Nodzu MI., BodaghJamali J., and Kawamoto H. 2010. Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran. Advances in Geosciences 25: 119-125.
13
14- Joyce R.J., Janowiak J.E., Arkin P.A., and Xie P. 2004. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology 5(3): 487-503.
14
15- Katiraie-Boroujerdy P.S., Asanjan A.A., Hsu K.L., and Sorooshian S. 2017. Intercomparison of PERSIANN-CDR and TRMM-3b42v7 precipitation estimates at monthly and daily time scales. Atmospheric Research 193: 36-49.
15
16- Katiraie-Boroujerdy P.S., Ashouri H., Hsu K.L., and Sorooshian S. 2017. Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR. Theoretical and Applied Climatology 130(1-2): 249-260.
16
17- Katiraie-Boroujerdy P.S., Nasrollahi N., Hsu K.L., and Sorooshian S. 2013. Evaluation of satellite-based precipitation estimation over Iran. Journal of Arid Environments 97: 205-219.
17
18- Kubota T., Shige S., Hashizume H., Aonashi K., Takahashi N., Seto S., and Nakagawa K. 2007. Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation. IEEE Transactions on Geoscience and Remote Sensing 45(7): 2259-2275.
18
19- Moazami S., Golian S., Hong Y., Sheng C., and Kavianpour M.R. 2016. Comprehensive evaluation of four high-resolution satellite precipitation products under diverse climate conditions in Iran. Hydrological Sciences Journal 61(2): 420-440.
19
20- Moazami S., Golian S., Kavianpour M. R., and Hong Y. 2013. Comparison of PERSIANN and V7 TRMM Multi-satellite Precipitation Analysis (TMPA) products with rain gauge data over Iran. International Journal of Remote Sensing 34(22): 8156-8171.
20
21- Rudolf B., and Schneider U. 2005. Calculation of gridded precipitation data for the global land-surface using in-situ gauge observations. P. 231-247, Paper presented at the Proc. Second Workshop of the Int. Precipitation Working Group, October 2004, Monterey,Germany, EUMETSAT, ISBN 92-9110-070-6, ISSN 1727-432X, 231-247.
21
22- Schamm K., Ziese M., Becker A., Finger P., Meyer-Christoffer A., Schneider U., and Stender P. 2014. Global gridded precipitation over land: A description of the new GPCC First Guess Daily product. Earth System Science Data 6(1): 49-60.
22
23- Schneider U., Becker A., Finger P., Meyer-Christoffer A., Ziese M., and Rudolf B. 2014. GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theoretical and Applied Climatology 115(1-2): 15-40.
23
24- Sorooshian S., AghaKouchak A., Arkin P., Eylander J., Foufoula-Georgiou E., Harmon R., Skahill B. 2011. Advanced concepts on remote sensing of precipitation at multiple scales. Bulletin of the American Meteorological Society 92(10): 1353-1357.
24
25- Sorooshian S., Hsu K.L., Gao X., Gupta H.V., Imam B., and Braithwaite D. 2000. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bulletin of the American Meteorological Society 81(9): 2035-2046.
25
26- Sun Q., Miao C., Duan Q., Ashouri H., Sorooshian S., and Hsu K.L. 2018. A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Reviews of Geophysics 56(1): 79-107.
26
27- Ushio T., Sasashige K., Kubota T., Shige S., Okamoto K., Aonashi K., and Kachi M. 2009. A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. Journal of the Meteorological Society of Japan. Ser. II, 87: 137-151.
27
28- Wilks D.S. 2006. Statistical Methods in the Atmospheric Sciences. Burlington, MA: Academic Press.
28
29- Willmott C.J., and Robeson S.M. 1995. Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology 15(2): 221-229.
29
30- Xie P., and Arkin P.A. 1997. Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bulletin of the American Meteorological Society 78(11): 2539-2558.
30
31- Yatagai A., Kamiguchi K., Arakawa O., Hamada A., Yasutomi N., and Kitoh A. 2012. APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bulletin of the American Meteorological Society 93(9): 1401-1415.
31
32- Zhang X., Alexander L., Hegerl G.C., Jones P., Tank A.K., Peterson T.C., and Zwiers F.W. 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change 2(6): 851-870.
32