Irrigation
E. Farrokhi; M. Nassiri Mahallati; A. Koocheki; alireza beheshti
Abstract
Introduction: Predicting yield is increasingly important to optimize irrigation under limited available water to enhance sustainable production. Calibrated crop simulation models therefore increasingly are being used an alternative means for rapid assessment of water-limited crop yield over a wide range ...
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Introduction: Predicting yield is increasingly important to optimize irrigation under limited available water to enhance sustainable production. Calibrated crop simulation models therefore increasingly are being used an alternative means for rapid assessment of water-limited crop yield over a wide range of environmental and management conditions. AquaCrop is a multi-crop model that simulates the water-limited yield of herbaceous crop types under different biophysical and management conditions. It requires a relatively small number of explicit and mostly-intuitive parameters to be defined compared to other crop models, and has been validated and applied successfully for multiple crop types across a wide range of environmental and agronomic setting. This study was conducted as a two-year field experiment with the aim of the simulation of water productivity, above ground biomass and fresh and dry yield of tomato using AquaCrop model under different irrigation regimes applied at two growth stages in Mashhad climate conditions.Materials and Methods: A two-year field experiment was conducted during 2016-2017 growing seasons in the experimental field of Ferdowsi University of Mashhad located in Khorasan Razavi province, North East of Iran. The water-driven AquaCrop model developed by FAO was calibrated and validated to simulate water productivity, above-ground biomass and yield of tomato crop under varying irrigation regimes. AquaCrop was calibrated and validated for tomato under full (100% of water requirements) and deficit (75 and 50% of water requirements) irrigation regimes at vegetative (100V, 75V, and 50V) and reproductive stages (100R, 75R, and 50R). Model performance was evaluated in terms of the normalized root mean squared error (NRSME), the Nash–Sutcliffe model efficiency coefficient (EF), Willmott’s index of agreement (d) and coefficient of determination (R2). The drip irrigation method was used for irrigation. The tomato water requirement was calculated using CROPWAT 8.0 software. The irrigation water was supplied based on total gross irrigation and obtained irrigation schedule of CROPWAT. The 2016 and 2017 measured data sets were used for calibration and validation of AquaCrop model, respectively.Results and Discussion: Calibration results showed good agreement between simulated and observed data for water productivity in all treatments with high R2 value (0.93), good ME (0.23), low estimation errors (RMSE=0.09 kgm3) and high d value (0.85). The goodness of fit results showed that measured WP values were closer to simulated WP values for the validation season (2017) than for the calibration season (2016). During calibration, (2016), the model simulated the biomass with good accuracy. The simulated above ground biomass values were close to the observed values during calibration (2016) for all treatments with R2 ranging from 0.92 to 0.99, NRMSE in range of 7.4 to 23%, d varying from 0.94 to 1, and ME ranging from 0.71 to 0.98. Validation results indicated good performance of model in simulating above ground biomass for most of the treatments (0.92 < R2 < 0.98, 6.5% < NRMSE < 21.3%, 0.76 < ME < 0.99). During validation (2017 growing season), overall, the trend of biomass growth (or accumulation) was captured well by model. However, the range of biomass of simulation errors was high, especially in treatments with higher stress. Accurate simulation of the response of yield to water is important for agricultural production, especially in an arid region where agriculture depends closely heavily on irrigation. During validation, the model predicted dry and fresh yield satisfactorily (NRMSE = 15.64% and 11.80% for dry and fresh yield, respectively).Conclusion: In general, the AquaCrop model was able to simulate the observed water productivity, above ground biomass and yield of tomato satisfactorily in both calibration and validation stage. However, the model performance was more accurate in non- and/or moderate stress conditions than in sever water-stress environments. In conclusion, the AquaCrop model could be calibrated to simulate growth and yield of tomato under temperate condition, reasonably well, and become a very useful tool to support decision on when and how much irrigate. This study provides the first estimate of the soil and plant parameter values of AquaCrop for simulation of tomato growth in Iran. Model parameterization is site specific, and thus the applicability of key calibrated parameters must be tested under different climate, soil, variety, irrigation methods, and field management.
Irrigation
E. Farrokhi; M. Nassiri Mahallati; A. Koocheki; alireza beheshti
Abstract
Introduction: The modeling approach for the simulation of the growth and development of tomatoes in Iran has been overlooked. Calibrated crop simulation models, therefore, are increasingly being used as an alternative means for the rapid assessment of water-limited crop yield over a wide range of environmental ...
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Introduction: The modeling approach for the simulation of the growth and development of tomatoes in Iran has been overlooked. Calibrated crop simulation models, therefore, are increasingly being used as an alternative means for the rapid assessment of water-limited crop yield over a wide range of environmental and management conditions. AquaCrop is a multi-crop model that simulates the water-limited yield of herbaceous crop types under different biophysical and management conditions. It requires a relatively small number of explicit and mostly intuitive parameters to be defined compared to other crop models and has been validated and applied successfully for multiple crop types across a wide range of environmental and agronomic settings. This study was conducted as a two-year field experiment with the aim of the simulation of soil water content, evapotranspiration, and green canopy cover of tomato using AquaCrop model under different irrigation regimes at two growth stages in Mashhad climate conditions. Materials and Methods: A field experiment was conducted over two consecutive seasons (2016-2017) in the experimental field of Ferdowsi University of Mashhad, located in Khorasan Razavi province, North East of Iran. The experiment was laid out in a split-plot design with different irrigation regimes at the vegetative and at the reproductive stage as the main and subplot factors, replicated thrice. In total, 27 plots of 4.5×3 m (13.5 m2) were used at a planting density of 2.7 plants per m2. Seedlings were planted in a zigzag pattern into twin rows, with a distance of 1.5 m between them, so there were four twin rows of three meters in each plot. The distance between tomato plants within each twin-row was 0.5 meters. A buffer zone spacing of 3 and 1.5 m was provided between the main plots and subplots, respectively. The following experimental factors were studied: three irrigation regimes (100= 100% of water requirement, 75= 75% of water requirement, 50= 50% of water requirement) and two crop growth stages (V= vegetative stage and R= Reproductive stage). The drip irrigation method was used for irrigation. The tomato water requirement was calculated using CROPWAT 8.0 software. The irrigation water was supplied based on total gross irrigation and obtained irrigation schedule of CROPWAT. Model accuracy was evaluated using statistical measures, e.g., R2, normalized root means square error (NRMSE), model efficiency (E.F.), and d-Willmott. The 2016 and 2017 measured soil and canopy data sets were used for calibration and validation of the AquaCrop model, respectively. Results and Discussion: For a water-driven model, such as AquaCrop, it is important to evaluate its effectiveness in simulating soil water content. During calibration (2016), the model simulated the soil water content with good accuracy. The simulated soil water content values were close to the observed values during calibration (2016) for all treatments with R2 ranging from 0.90 to 0.97, NRMSE in range of 8.47 to 17.96%, d varying from 0.76 to 0.99, and M.E. ranging from 0.87 to 0.96. Validation results indicated the good performance of the model in simulating soil water content for most of the treatments (0.79<R2<0.99, 10.04%<NRMSE<18.65%, 0.77<ME<0.92). Appropriate parameterization of canopy cover curve is critical for the model to provide accurate estimates of soil evaporation, crop transpiration, biomass, and yield. In general, the calibration results showed good agreement between simulated and observed data for canopy cover development in all treatments with high R2 values (>0.87), good E.F. (>0.61), low estimation errors (RMSE, ranging from only 4.5 to 9.2) and high d values (>0.92). Conclusion: The AquaCrop model (version 6.1) was calibrated and validated for modeling soil water content, evapotranspiration, and green canopy cover for tomatoes under drought stress conditions. In general, soil water content, evapotranspiration, and green canopy cover of tomato were simulated by AquaCrop model with acceptable accuracy in both calibration and validation stages. However, the model performance was more accurate in no and/or moderate stress conditions than in severe water stress environments. In conclusion, the AquaCrop model could be calibrated to simulate the growth and soil water content of tomatoes under temperate conditions reasonably well and become a very useful tool to support the decision on when and how much irrigate. For R2, values > 0.90 were considered very well, while values between 0.70 and 0.90 were considered good. Values between 0.50 and 0.70 were considered moderately well, while values less than 0.50 were considered poor. Root mean square error ranges from 0 to positive infinity and expresses in the units of the studied variable. An RMSE approaching 0 indicates good model performance.
Ahmad Reza Razavi; Mahdi Nassiri Mahallati; Alireza Koocheki; Alireza Beheshti
Abstract
Introduction: Climate change (CC) is one of the most important concerns for mankind in the current century. Increasing CO2 concentration and the proof of the greenhouse effect theory in which the type and composition of atmospheric gases which influence the earth temperature, are among undeniable facts ...
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Introduction: Climate change (CC) is one of the most important concerns for mankind in the current century. Increasing CO2 concentration and the proof of the greenhouse effect theory in which the type and composition of atmospheric gases which influence the earth temperature, are among undeniable facts makes the future climate change more possible. Impacts of Global warming on hydrological cycles and precipitation patterns would be more prominent in arid and semi-arid regions of the earth. For the arid and semi-arid nature and the poverty more fraction of Afghanistan suffer from, it is likely that the impacts of CC on the country will be more intense. This is while there is no credible and reliant research addressing the impacts of CC on agriculture and food security sector of Afghanistan. Studying the impacts of CC on agriculture, future changes in agroclimatic indices and application of crop growth simulation models intensively require a precise and adequate sets of meteorological data. Because of many reasons, Afghanistan's historical meteorological data coverage is really weak. In this research the applicability of AgMERRA as a gauge-satellite based dataset for filling the Afghanistan in-situ meteorological gaps is evaluated via goodness of fit measures, patterns of seasonal changes and the probability distribution functions.
Materials and Methods: This study is conducted on four major stations of Afghanistan (Kabul, Herat, Mazar Sharif and Qandahar in the east, west, north and south of the country, respectively) (Fig. 1 and table 1) which had the best in-situ meteorological data coverage. Observed Maximum (Tmax) and Minimum temperature (Tmin) and precipitation (PRCP) data is collected via Afghanistan Meteorological Authority (AMA) or other sources. AgMERRA database downloaded with .nc4 format and extracted with R statistical software or Panoply ver. 4.8.4, dependently. Then five goodness of fit (GOF) measures (RMSE, NRMSE, MBE, R2 and d) are calculated according to the equations 1 to 5. There are different norms and indices to measure the validity of a models, some based on Pearson correlation coefficient (R and R2) which indicate the degree of correlation between observed and predicted data but have some amounts of sensitivity to extreme values (outliers). Although, many other measures are considered to overcome the weaknesses but it is hard to distinguish the best.
Results and Discussion: The results of this research indicated the good potency, effectiveness and ability of AgMERRA for gap-filling of in-situ meteorological data and producing spatiotemporal data series. Several studies in this area have almost the same results. It is reported that AgMERRA is the most applicable dataset for reflecting precipitation data comparing with ERA-Interim, ERA-Interim/Land and JRA-55 datasets. Comparisons via NRMSE shows great (>10%) and good (>20%) amounts in all stations and temporal scales. Among other stations, Mazar Shrif showed the best conformity between AgMERRA and observed data, while Kabul station had the weakest, probably due to complex topographic situation of the Kabul airport station. The amounts of R2 for predicting temperature (Tmax and Tmin) were more than 0.86 in daily, 14-days and monthly temporal scales. The lowest amount of the coefficient of determination was obtained at Qandahar station for Tmean in daily temporal scale (R2=0.8) and the highest amount obtained for daily Tmax at Mazar Sharif station (R2=0.947). R2 for daily PRCP were inadequate, but increasing to adequate amounts in 14-days and monthly temporal scales. The highest spatiotemporal amount of Tmax,Tmin and Tmean was obtained in daily scale and the lowest amount was obtained for Tmean (1.8 and 0.9, respectively). The Index of agreement (d), also had adequate amounts for 14-days and monthly PRCP (>0.87). The amount of MBE for precipitation in Herat, Mazar Sharif and Kabul stations were negative, while it was positive in Qandahar station with a hot and dry climate. AgMERRA could show a good compliance with changes of observed seasonal patterns, however, some amount of over and under-estimates are obvious especially for Kabul station. This compliance with in-situ observed patterns was acceptable for daily temporal scale, although AgMERRA was unable to predict some of the fluctuations in probability distribution composition (with the range of 1 °C), especially fot Tmax and Tmin, but fot Tmean the fluctuations simulated well.
Conclusion: According to the results of the study, AgMERRA showed an acceptable potency to simulate the in-situ meteorological data in four major studied stations of Afghanistan. According to the stochastic nature of PRCP, the variable showed the weakest results in daily temporal scale but acceptable in 14-days and monthly. Given the weak coverage of in-situ meteorological data of Afghanistan, AgMERRA could be a valid dataset for producing well scaled spatiotemporal data series to be used in agroclimatic, CC and crop growth modeling studies.
Rooholla Moradi; Alireza Koocheki; Mehdi Nassiri; Hamed Mansoori
Abstract
Introduction: The latest report of the Intergovernmental Panel on Climate Change (IPCC) states that future emissions of greenhouse gases (GHGs) will continue to increase and cause climatic change (16). These conditions are also true for Iran. The three greenhouse gases associated with agriculture are ...
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Introduction: The latest report of the Intergovernmental Panel on Climate Change (IPCC) states that future emissions of greenhouse gases (GHGs) will continue to increase and cause climatic change (16). These conditions are also true for Iran. The three greenhouse gases associated with agriculture are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). The three GHGs associated with agriculture CO2, CH4, and N2O differ in their effectiveness in trapping heat and in their turnover rates in the atmosphere. This environmental change will have serious impacts on different growth and development processes of crops. Increasing temperature could affect physiological processes such as photosynthesis, respiration and partitioning of photoassimilates. Farmers are not able to change or manage the climatic conditions, but some factors such as soil, water, seed and agricultural practices can be managed to reduce the adverse impacts of climate change (32). Mitigation and adaptation are two known ways for reducing the negative impacts of climate change. Mitigation strategies are associated with decreasing greenhouse gas (GHG) emissions through management practices such as reducing chemical fertilizer application, mechanization, increasing carbon storage in agroecosystems, planting biofuel crops and moving towards organic farming (42), etc.
Material and Methods: This study was carried out at the experimental field of the Ferdowsi University of Mashhad in 2011 and was repeated in 2012. The Research Station (36°16´N, 59°36´E) is located at about 985 m a.s.l. Average temperature and precipitation rate of the research station in two years are shown in Figure. 1. The three-factor experiment was set up in a strip-split-plot arranged in a randomized complete block design with three replications. The experimental treatments were tillage systems (conventional and reduced tillage) and residual management (remaining and leaving of maize residual) assigned to main plots and different levels of N fertilizer (0, 150, 300 and 450 kg urea ha-1) was randomized as a subplot in tillage treatment. The seedbed preparation was made based on common practices at the location. Plot size under the trial was 4 m × 3 m so as to get 70 cm inter row spacing. Maize seeds (single-cross 704 cultivar) were hand sown in May for two years. The ideal density of the crops was considered as spacing 20 cm inter plant. As soon as the seeds were sown, irrigation continued every 10 days. No herbicides or chemical fertilizers were applied during the course of the trials and weeding was done manually when necessary. Measurement of CO2 emissions was performed by the closed chamber method. For this purpose, PVC plastic rings (20 cm in diameter and 30 cm height) were scattered on each of the plots. The chambers were placed in soil for two hours and the gathered air was collected by 10 ml vacuum syringe. Then, the samples were transferred to the laboratory and CO2 was measured using GC-mass.
Results and Discussion: The results showed that CO2 emissions for conventional tillage was about 15 and 10% higher than the reduced tillage in 2011 and 2012, respectively. The CO2 emissions can be taken as indicators of soil tillage effects on the soil ecosystem, because CO2 emissions are closely connected to the microbial turnover and the physical accessibility of organic matter to microbes. These parameters were more available in the conventional tillage than the reduced tillage. CO2 emissions were strongly higher in the remaining residual condition rather than leaving condition in two years. CO2 emissions in the remaining residual condition was about 4.36 and 5.37 times higher than that of the leaving residual condition in 2011 and 2012, respectively. The microbial respiration and humidity of soil in the remaining residual condition is higher than that of the leaving residual condition. CO2 emission was elevated with increasing the rate of N fertilizer. The N fertilizer can increase the microbial activity of the soil. Cover cropping and N fertilization can increase CO2 emissions in full and reduced tilled soils by increasing the amount of crop residue returned to the soil. The results showed that CO2 emissions in 2011 were higher than 2012 in all treatments. The residual treatment had more effect on daily CO2 emission in comparison with tillage and N fertilizer treatments in both years. The trait was higher under conventional tillage, residue remaining and higher N fertilizer levels compared to reduced tillage, residue leaving and lower N fertilizer application. Linear regression for air temperature and mean CO2 emission illustrated that there was a positive correlation between air temperature and CO2 emission.
Conclusion: In essence, the results showed that CO2 emissions for conventional tillage were higher than that of reduced tillage in two years. Remaining residual condition had strongly higher CO2 emission rather than leaving condition. CO2 emission was elevated with increasing the rate of N fertilizer.
M. Ghaemi; A. Astaraei; M. Nassiri Mahalati; S.H. Sanaeinejad; H. Emami
Abstract
Successful implementation of soil and crop management program requires quantitative knowledge of site characteristics and interactions that affect crop yield. Soil properties are one of the most important site variables affecting within- field yield variability. The objective of this research was to ...
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Successful implementation of soil and crop management program requires quantitative knowledge of site characteristics and interactions that affect crop yield. Soil properties are one of the most important site variables affecting within- field yield variability. The objective of this research was to identify intercorrelations among soil properties (chemical, physical and biological) using principal component analysis (PCA) and their relationships with maize yield variability in field. Site variables (18) and maize yield were measured in selected parts of Astan Quds agricultural fields in Mashhad city. The principal component analysis was used to reduce the site variables numbers and remove multicollinearity among variables. The first four PCs with eigenvalues>1 accounted for > 67% of variability in measured soil properties. Soil properties were grouped in four PCs as: (PC1) Soil highly descriptive fertility potential, (PC2) Soil moderately descriptive fertility potential, (PC3) Soil permeability potential, (PC4) Soil aggregation potential. The results showed that the factor of soil highly descriptive fertility potential explained 43% of variance and accounted for 77% of the yield variability in the field. Principal component analysis allows explaining a major part of crop yield variability by removing the multicollinearity.
J. Fallahi; P. Rezvani Moghaddam; M. Nassiri Mahallati; Mohammad Ali Behdani
Abstract
Climate change by increasing concentrations of greenhouse gases, particularly carbon dioxide, has led to increase attention to the carbon sequestration through the restoration and protection of vegetation cover. In this regards, ecosystems of arid regions have a special importance. In this study the ...
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Climate change by increasing concentrations of greenhouse gases, particularly carbon dioxide, has led to increase attention to the carbon sequestration through the restoration and protection of vegetation cover. In this regards, ecosystems of arid regions have a special importance. In this study the effects of reconstruction and conservation, on soil carbon sequestration of the region of the International Carbon Sequestration Project in Hussein Abad, South Khorasan province of Iran was investigated by a simulation approach using RothC model. In addition, the effects of climate change (increasing temperature and decreasing rainfall) on soil carbon sequestration potential was studied. In the studied area, replanting was done in 2004 and then soil samples were taken every two months during 2010-2011. After collecting the required input data for RothC model (climate, soil and management input data), the model was evaluated and validated for the study area. Moreover, soil carbon sequestration was studied under climate change condition. The simulation results revealed that the RothC model is applicable in rangelands of dry and warm regions, because it estimated the soil carbon changes over the time with proper accuracy. The amounts of model performance index, R2 and RMSE were 0.98, 98% and 0.01, respectively. Simulation study indicated that soil carbon storage will increase from 2011 to 2050 and will be affected by climate change and protection programs. Based on model estimation the amounts of soil carbon in preotected areas will be higher than non-protected areas. Moreover, in non-climate change scenario the amounts of soil carbon will be higher than climate change scenario in 2050.
F. Akbarnejad; A. Astaraei; A. Fotovat; M. Nasiri Mahalati
Abstract
Recently Application of municipal solid waste compost and sewage sludge on the farm land had received considerable attention. These organic wastes provides a valuable source of organic matter and enhances crop yield and soil fertility by improving soil physical, chemical and biological properties. To ...
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Recently Application of municipal solid waste compost and sewage sludge on the farm land had received considerable attention. These organic wastes provides a valuable source of organic matter and enhances crop yield and soil fertility by improving soil physical, chemical and biological properties. To evaluate the influences of municipal solid waste compost (MSWC) and sewage sludge (SS) on chemical properties of soil an experiment was conducted with Municipal solid waste compost at 0, 15, 30 ton/ha (C0, C15 and C30) and sewage sludge at 0, 15, 30 ton/ha (S0, S15 and S30) in a factorial experiment based on completely randomized design with three replications in greenhouse of Faculty of Agriculture, Ferdowsi University of Mashhad. Results showed that municipal solid waste compost and sewage sludge and their interaction effects had significant effects on soil chemical properties. With increasing amounts of municipal solid waste compost and sewage sludge, organic carbon and electrical conductivity of soil increased. Portion of Sewage sludge compared to municipal solid waste compost in increasing of organic nitrogen is lower. The most amount of soil organic nitrogen was observed in municipal solid waste treatments. Also use of these wastes together decreased soil acidity.
M. Rahimizadeh; A. Zare Feizabadi; A. Kashani; A.R. Koocheki; M. Nassiri Mahallati
Abstract
Abstract
This study was conducted under cold climate condition in Khorasan during 2006-2008 growing seasons to evaluation of soil fertility in wheat-based double cropping systems under different rate of nitrogen and return of crop residues. A randomized complete block design with split-split plot arrangement ...
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Abstract
This study was conducted under cold climate condition in Khorasan during 2006-2008 growing seasons to evaluation of soil fertility in wheat-based double cropping systems under different rate of nitrogen and return of crop residues. A randomized complete block design with split-split plot arrangement and three replicates was used. Main plots were five crop rotations namely: wheat-wheat, potato-wheat, silage corn-wheat, clover-wheat and sugar beet-wheat. Four sub plots were, N fertilizer rates in preceding crop including no N (control), 50% lower than recommended N rate, recommended N rate and 50% more than recommended N rate. The two sub-sub plots were preceding crop residue return including: no residue return (control) and 50% residue return. Results showed that soil nitrogen content was not affected by crop rotation, nitrogen rate and return of crop residues. Soil phosphorus content at 30-cm depth was significantly affected by preceding crop of wheat. Although, nitrogen rate and crop residue return were not influenced on soil phosphorus. Our results indicated that soil potassium content observed for the clover and wheat, respectively. There was a significantly interaction between preceding crop and return of crop residue for soil organic carbon in the 30 to 60 cm depth. But, soil organic carbon was not affected by preceding crop and nitrogen rate in the first year of experiment.
Keywords: Crop rotation, Nitrogen, Phosphorus, Organic carbon, Wheat
A. Lakziyan; A. Halajnia; M. Nasiri Mahalati; F. Nikbin
Abstract
Abstract
Arsenic (As) contamination of soils is a global problem. Legumes are often used in remediation of contaminated sites because of their capacity to fix nitrogen. In the present study, the effects of inoculation of bean (by Rhizobium leguminosarum bv. phaseoli) on plant uptake and tolerance to ...
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Abstract
Arsenic (As) contamination of soils is a global problem. Legumes are often used in remediation of contaminated sites because of their capacity to fix nitrogen. In the present study, the effects of inoculation of bean (by Rhizobium leguminosarum bv. phaseoli) on plant uptake and tolerance to arsenic were investigated. An experiment with a factorial arrangement, two levels of inoculation (with and without inoculation) and five levels of arsenic concentrations (0, 2.5, 5, 7.5 and 10 μM) in completely randomized design with three replications was carried out in a sand culture system in a green house condition. The results showed that the dried shoot weight was increased significantly by inoculation treatments. However, dried root weight and plant height were not affected by inoculation. Dried shoot weight was significantly decreased by increasing arsenic concentrations. The least dried shoot weight was observed in 5, 7.5 and 10 μM of arsenic. The response of dry root weight and plant height to arsenic concentrations was similar to dried shoot weight. The least nodule number was observed in 5 μM arsenic treatment and nodule number in other treatments was not affected by arsenic concentration. Arsenic concentration in shoot increased by Rhizobium leguminosarum bv. phaseoli inoculation. The concentration of arsenic in bean shoot increased by increasing arsenic concentrations in medium. However the highest concentration of arsenic in root and the least nodule number were observed in 5 μM arsenic.
Key words: Arsenic, Sand culture, Nodulation Inoculation