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.
Sh. Amirmoradi; P. Rezvani Moghaddam; A. Koocheki; Shahnaz Danesh; A. Fotovat
Abstract
Introduction: Accumulation of heavy metals in agronomic soils continuously by contaminated waste waters not only causes to contamination of soils but also it affects food quality and security. Cadmium and lead are one of the most important heavy metals due to long permanence and persistence in soil can ...
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Introduction: Accumulation of heavy metals in agronomic soils continuously by contaminated waste waters not only causes to contamination of soils but also it affects food quality and security. Cadmium and lead are one of the most important heavy metals due to long permanence and persistence in soil can cause problems to human and animal health. Some medicinal plants are able to accumulate of heavy metals from contaminated soils. Heavy metals are not able to enter in the essential oil of some aromatic plants. Study of these plants helps human to select them for cultivating the resistant medicinal plants in contaminated soils.
Materials and Methods: This experiment was carried out in the research greenhouse of agriculture faculty of Ferdowsi university of Mashhad in 2011. Seeds were cultivated in planting aprons into peat moss medium. Then the uniform plantlets were transferred into soil in the plastic boxes (30×50×35 cm) at two leaf stage. In each box 6 plantlets were sown with distance of 15 cm on the planting rows and 20 cm between rows. Experiment was set up as factorial on the basis of randomized complete block design with three replications. The first factor was cadmium concentrations consisted of 0,10,20,40 mg per kilogram and the second factor was lead concentrations consisted of 0,100,300 and 600 mg/kg. Plants were irrigated during of15 weeks with cadmium and lead nitrogen nitrate solutions and then irrigated with distilled water. The differences of nitrogen amounts in treatments were compensated with ammonium nitrate on the basis of differences between level of the highest treatment and the treatment which obtained lower amount of nitrogen. Plants were harvested after 180 days at the beginning of flowering. All shoots and roots were weighted separately as fresh weight and then were dried under shading and then were weighted. The essential oil sage was determined by using of 30 grams of dried sage leaves with distillation method with Clevenger. Cadmium and lead contents in shoot and root were measured by wet digestion method (digestion by Perchloric and Nitric acid). Cadmium and lead contents were detected by atomic absorption apparartus. Data were analyzed by MSTATC software and all means were compared by DMRT at 5% of probability.
Result and Discussion: Results argued that fresh weight of sage at 40 mg/kg of cadmium were decreased 4.61% as compare as control. Dry weight of sage decreased at 600 mg/kg of lead 11.08 % as compare of control. Mean comparisons indicated that at the highest concentrations of cadmium and lead fresh and dry weight of sage were dropped. Growth decrement due to toxicity of cadmium causes to photosynthesis and respiration decline, carbohydrate metabolism decreasing and leaf chlorosis. Researchers observed lead ions by interfering with water balance lead to water stress. High concentrations of lead may cause to decrease the availability of water for plant and high concentrations of cadmium causes to disturb the protein synthesis and lead to protein decline in plant cells. Plant height of sage was declined at 40 mg/kg and 600 mg/kg as compared as control 14.17 and 10.83, respectively. Essential oil in sage was dropped in high levels of cadmium and lead as compare of control 12 and 14.51, respectively. Researchers stated that cadmium concentrations of 2,6 and 10 mg/lit and 50,100 and 500 mg/kg of lead had no significant effect on peppermint, but caused to drop the essential oil percentage of dill and basil.
Disturbance of carbon nutrition in plant cells during the photosynthesis process by heavy metals lead to a decrease in the essential content. The most cadmium absorption by sage shoots belonged to 40 mg/kg and 600 mg/kg of cadmium and lead, respectively and then 40 mg/kg cadmium and 300 mg/kg lead were ranked as second treatment. Increase of cadmium and lead concentrations in irrigation water led to increase of these heavy metals into sage shoots. Increase of lead and cadmium concentrations caused to antagonistic effects of cadmium and lead absorption into shoots of sage. In this experiment cadmium and lead concentrations of all treatments were too below to detect by atomic absorption apparatus. In this study cadmium and lead could not enter to essential oil. Researchers stated that high doses of cadmium, lead, zinc and copper concentrations could not enter into essential oil in sage. Some researchers showed that cadmium, lead and copper were not transferred to essential oil of peppermint, dill and basil during the essential oil distillation process. This finding confirmed that selection of medicinal plants as alternative plants with crops in cadmium and lead contaminated soils.
Conclusion: Fresh and dry weight of Sage in the condition of contaminated soil by 100 mg/kg cadmium and 600 mg/kg lead were declined 4.61 and 5.16 % as compare as control, respectively. At the highest doses of cadmium and lead the essential oil of sage were dropped but, these heavy metals were not detected in essential oil. So, it is seems that this medicinal plant may be applied in the contaminated soil or in the condition of using of contaminated irrigated water by cadmium and lead.
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.
A. Lashkari; Mohammad Bannayan Aval; A. Koocheki; A. Alizadeh; Y. S. Choi; S.-K. Park
Abstract
Introduction: Consistency and transparency in climate data and methods facilitate comparisons across regions or between models in each of these assessments, particularly when market linkages between regions are emphasized (14 and 15). However, the density and quality of stationary climate data varies ...
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Introduction: Consistency and transparency in climate data and methods facilitate comparisons across regions or between models in each of these assessments, particularly when market linkages between regions are emphasized (14 and 15). However, the density and quality of stationary climate data varies widely through space and time, with the best coverage in developed countries and less reliable coverage in the Tropics and Southern Hemisphere (15). So, several groups have collected these data and constructed harmonized, global gridded datasets at monthly resolution. However, these require weather generators synthesize daily resolution before they may be applied to crop models and are therefore likely to miss events that are important for the calibration and validation of agricultural models. Regional gridded observational networks have also been created (e.g., E-Obs in Europe, (8); APHRODITEin Asia, (21)), however many regions and variables are not covered by any such network and inter comparing sites between regions with different methodologies introduces inconsistencies (). Recently, AgMERRA climate forcing dataset provide daily, high-resolution, continuous, meteorological series over the 1980–2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA) with in situ and remotelysensed observational datasets fortemperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparisonto a network of 2324 agriculturalregion stations from the Hadley Integrated Surface Dataset (HadISD) (5).Therfore, this research was done in order to investigate the possibility of using AgMERRA climate forcing dataset to estimate missing data in in-situ daily temperature and precipitation observations in Mashhad plain.
Materials and Methods: The study area was Mashhad plain in KhorasanRazavi province, located in the northeast of Iran. Climate data corresponding to Mashhad plain extracted by means of geographical characteristics of Mashhad (for the 1980-2010 periods) and Golmakan (1987-2010 period) stations from AgMERRA dataset. The goodness of fit of AgMERRA climate forcing dataset was done by means of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) tests and R2. The root mean-squared error (RMSE) is computed to measure the coincidence between measured and modelled values and Mean Bias Error (MBE) is simply to examine the overall model error.Furthermore, probability distribution function of observed daily data and AgMERRA data for both Golmakan and Mashhad stations calculated. Eventually, mean and variance of AgMERRA and in-situ data were calculated to have a more accurate comparison of simulated and observed data.
Results: Results indicated that AgMERRA dataset has a good performance in estimating daily maximum and minimum temperature in Mashhad Plain. RMSE, MAE and MBE for daily precipitation illustrated a good performance of AgMERRA data. However, R2 value was 0.43 and 0.25 for Mashhad and Golmakan stations, respectively. Although the probability distribution function of daily maximum and minimum temperature and precipitation indicated the same trend for both studied stations, comparison of mean and variance of observed daily maximum and minimum temperature and precipitation and AgMERRA data for Mashhad and Golmakan stations showed different results. The difference between mean of AgMERRA and observed daily maximum temperature for Mashhadand Golmakan stations was 3.42 and 2.10 C°, respectively. It was 4.68 and 3.05 C° for minimum daily temperature for Mashhad and Golmakan, respectively, and the difference between mean of AgMERRA and observed daily precipitation was 0.06 and 0.28 mm.day-1 for Mashhad and Golmakan, respectively.
Discussion and Conclusion: This research showed that using AgMERRA climate forcing dataset could be a reliable tool to estimate missing data of in-situtemperature observations. Although the performance of AgMERRA dataset was good for daily precipitation, distribution of simulated precipitation compare with observed precipitation was different. Concerning AgMERRA precipitation data some points have to keep in mind that precipitation in arid and semi-arid regions tends to be more variable in time than in humid regions. In fact, the distinctive features of arid and semiarid regions affect precipitation modeling on a discrete event basis and a continuous basis (7, 10, 13).Results of this research illustrated the same trend and it revealed that AgMERRAdataset could not simulate the precipitation distribution in Mashhad plain. It seems that comparing AgMERRAprecipitation data with OPHRODITE dataset and other dataset can give us more accurate vision about AgMERRA dataset. Furthermore, it seems that it is needed to do more researches regarding investigation of performances of crop model results by using AgMERRA dataset as climate data input, because this dataset was released for agricultural application.
A.R. Koocheki; V. Mokhtari; Sh. Taherabadi; S. Kalantari
Abstract
Abstract
In order to investigate the response of two species of P. ovata and P. psyllium to water deficit. The experiment was conducted during 2009 growing seasons in the Agriculture research Station Ferdowsi of Mashhad. For this purpose a split plot experiment based on complete randomized block design ...
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Abstract
In order to investigate the response of two species of P. ovata and P. psyllium to water deficit. The experiment was conducted during 2009 growing seasons in the Agriculture research Station Ferdowsi of Mashhad. For this purpose a split plot experiment based on complete randomized block design with three replications was used. Four irrigation levels (4000, 3000, 2000, 1000 m3/ha) allocated in the main plots and two species of plantago (p. ovate, p. psyillium) were as sub plots. Criteria such as spike length, number of spikes per plant, number of seeds per spike, 1000-seed weight, straw and seed yield were measured accordingly. Three quality characters namely amount of mucilage, swelling factor and swelling rate per gram mucilage were also measured. The results indicated that number of spikes per plant, number of seeds per spike, 1000-seed weight, seed yield were significantly affected by irrigation levels. The maximum value of spike length, number of spikes per plant, number of seeds per spike, 1000-seed weight was obtained in irrigation level of 4000 (m3/ha), and the maximum value of straw and seed yield in p. ovatea was obtained in irrigation levels of 4000 (m3/ha) and 3000 (m3/ha), respectively and to p. psyllium was obtained in irrigation level of 3000 (m3/ha) and 4000 (m3/ha), respectively. The maximum amount of mucilage and swelling factor were obtained for both species were obtained irrigation level of 2000(m3/ha) and the maximum swelling rate per gram mucilage for both species were obtained irrigation level of 1000 (m3/ha).
Keywords: Water deficit, Yield, yield components, P. ovate, p. psyllium, Mucilage
A.R. Koocheki; M. Jahani; L. Tabrizi; A.A. Mohammadabadi
Abstract
Abstract
In order to investigate the effect of biofertilizer, chemical fertilizer and Plant Density on Yield and Corm Criteria of Saffron, an experiment was conducted as complete randomized block design in a factorial arrangement with three replications under field condition in agricultural college, ...
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Abstract
In order to investigate the effect of biofertilizer, chemical fertilizer and Plant Density on Yield and Corm Criteria of Saffron, an experiment was conducted as complete randomized block design in a factorial arrangement with three replications under field condition in agricultural college, Ferdowsi university of Mashhad during 3 years of 2007,2008,2009. treatments included Nitroxin biofertilizer ( a mixture of free-living nitrogen-fixing bacteria Azospirilum sp./ Azotobacter sp.), Dalfard fertilizer ( a commercialized saffron fertilizer with 12% N from Urea and nitrate sources, 8% P, 4%K and also Zn, Cu, Mg, Fe, Chelates) and control with five corm density 4, 6, 8, 10 and 12 t ha-1. Results indicated that effect of fertilizer treatment was significant on number of flower , dried and fresh flower weight, dried and fresh stigma weight during experimental year. Effect of fertilizer treatment was significant on number of corm, dried and fresh corm weight during second experimental year. The highest number of flower, dried flower weight, dried stigma weight and dried corm weight was shown in Dalfard and the lowest number of flower, dried flower weight, dried stigma weight and dried corm weight was obtain in Nitroxin. The effect of different densities was significant on number of flower, fresh flower weight, dried flower weight and dried stigma weight during second year. The highest dried stigma weight was obtained in 8 t ha-1 in second and third year. Regression results showed that with increasing the number of corm, dried corm weight was decreased.
Keywords: Nitroxin, Dalfard, Saffron
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