Irrigation
V. Feiziasl
Abstract
Introduction Barley could be grown under low-input and harsh conditions because of its wide adaptability to drought, and heat stresses. Nonetheless, the water stress leads to yield reduction when drought stress occurs during stem elongation and grain filling stages. In rainfed areas, water and heat ...
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Introduction Barley could be grown under low-input and harsh conditions because of its wide adaptability to drought, and heat stresses. Nonetheless, the water stress leads to yield reduction when drought stress occurs during stem elongation and grain filling stages. In rainfed areas, water and heat stress occur together, specifically after anthesis, amplifying the adverse effects of water stress via disrupting water uptake of crops. In this regard, measurement of canopy temperature (Tc) by infrared thermometry is a non-destructive method that can effectively characterize the water status of plants. There is a linear relation between Tc and transpiration, which increases upon stomata closure. Since stomata is very sensitive to environmental variations and moisture reduction in the plant and it is very difficult to measure, therefore, Tc is the preferred factor to determine the crop water status. The Tc was used to calculate the practical Crop Water Stress Index (CWSI) by Idso et al. (1981) and Jackson et al. (1981). Dold et al., (2017) reported a positive significant correlation between CWSI and transpiration, daily soil water content, and plant production. Negative significant correlations between CWSI and pure photosynthesis rate, transpiration, and stomatal conductance were also reported. This study was aimed to: (i) assess the water stress effects on dryland barley genotypes using Tc, (ii) identify the upper limit for Tc affecting performance and reducing barley grain yield, (iii) determine the critical point of water stress, and (iv) apply CWSI to select the most suitable barley genotypes for both rainfed and supplemental irrigation conditions.Materials and Methods To determine the crop water stress index (CWSI) and assess water status of dryland barley genotypes, an experiment was carried out in a split plot arrangement based on randomized complete block design with 15 genotypes in three replications at the Dryland Agricultural Research Institute, Maragheh (46° 45ʹ E, and 37° 26ʹ N), Iran in the 2015-2018 cropping seasons. The main plots included rainfed (as stress conditions), and supplemental irrigation (two times: 50 mm irrigation in the sowing time and 30 mm irrigation in the booting stage) as non-water stress conditions. The sub-plots included 15 barley genotypes (GaraArpa, 71411, Abidar, Ansar, ARM-ICB, ChiCm/An57//Albert, Dobrynya, Kuban-06, Makooei, Redical, Sahand, Sahand/C-25041, Sararood1, Ste/Antares//YEA762 and Valfajr). The barley genotypes were planted by Wintersteiger planter in six-row plots with 8 m long and 1.20 m wide (20 cm row spacing). The sowing rate was 380 seeds per m2 based on the thousand kernel weight (TKW) of each genotype. Seeds were treated by Penconazole fungicide. The planting dates were October 4, 2015, and October 7, 2017. In each plot, two canopy temperatures (Tc) were measured using infrared thermometer Model A-1 in six crop reproductive stages from the half of ear emerged above flag leaf ligule stage (GS55) to the soft dough stage (GS85). Measuring time was between 1:00 to 2:00 pm.Results and Discussion The results indicated that the upper baseline for non–transpiring of dryland barley genotypes (Tc-Ta = 0.0008VPD + 5.89; VPD: vapor pressure deficit) was 5.9 °C (ranged from 5.5 to 6.9) which is equal to 32.4 °C green canopy and 9.0 to 11.1 mm/day evapotranspiration. Non-stressed baseline or lower baseline (Tc-Ta = -2.4662VPD + 9.15; R2 = 0.97**) showed that CWSI threshold value was 0.75 which is equal to 24.3 °C (23.7 to 26.1 °C) Tc under supplemental irrigation and 23.3 to 24.7 °C under water stress conditions. Additionally, CWSI threshold was equal to 7.3 mm/day evapotranspiration and 5.02 kPa VPD. On the other hand, results revealed that when Tc exceeded 25 °C, biological yield, thousand kernel weight (TKW) decreased significantly, followed by grain yield in different barley genotypes. The slope of the CWSI calibration equation (Tc-Ta = -2.4662VPD + 9.15) is often more negative in hot and dry areas, and tends to zero in cold and humid areas. Therefore, its negativity indicates the conditions of moisture stress for barley genotypes in the dryland phase. The CWSI threshold for barley genotypes growth stages happened at 248 (6th June) days from sowing time (4th – 7th October) which is equal to flowering stage (ZGS60). According to CWSI quantity, Ansar, ChiCm/An57//Albert, Sahand/C-25041and Ste/Antares//YEA762 were grouped in the tolerance class under stress (dryland) conditions. However, Abidar, Sahand/C-25041, GaraArpa, ChiCm/An57//Albert and Makooei were placed in the tolerance class under non-stress (supplemental irrigation) conditions.ConclusionThe CWSI could estimate the intensity of heat and water stresses in the grain filling stage for barley genotypes in cold and semi-arid areas. The average of canopy temperature threshold values were 24.8 and 24.0 °C for dryland barley genotypes in supplemental irrigation and dryland conditions, respectively. In addition, these indices could be used to estimate heat and water stress tolerance levels for barley genotypes.
Mirhassan Rasoulsiadaghiani; Vali Feiziasl; Ebrahim Sepehr; Mehdi Rahmati; Salman Mirzaee
Abstract
Introduction: In cereal crops, nitrogen is the most important element for maintaining growth status and enhancing grain yield. Nitrogen is an important constituent of the chlorophyll molecule and the carbon-fixing enzyme ribulose-1, 5-bis-phosphate carboxylase/oxygenase. Therefore, providing enough nitrogen ...
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Introduction: In cereal crops, nitrogen is the most important element for maintaining growth status and enhancing grain yield. Nitrogen is an important constituent of the chlorophyll molecule and the carbon-fixing enzyme ribulose-1, 5-bis-phosphate carboxylase/oxygenase. Therefore, providing enough nitrogen to achieve optimal yield is essential. Common chemical analyzes are used to determine the nutrient elements of plants using laboratory methods. Conventional laboratory techniques are expensive, laborious, and time-consuming. Determination of plant biochemical content by remote sensing could be used as an alternative method which reduce the problems of laboratory analyses. Expensive and time-consuming direct determination of the nutritional status of the plant play an important role in the quantitative and qualitative yield of the product. However, exposure to rainfed wheat nutrient stresses (in particular, nitrogen) compared to irrigated wheat resulting in attempts to evaluate these features with acceptable accuracy without the direct measurement. In this regard, remote sensing data and satellite images are of the basic dryland management and optimal wheat production methods. As such, it collects massive information periodically from the surface of the planet, and it is easy to use this timely information to identify the stresses and apply appropriate agronomic methods in order to counteract them or reduce their negative impact on the production of this strategic product. Therefore, the goal of this study was to determine the nitrogen concentration of dryland wheat in the laboratory and its fitting with ETM+ images, evaluate the accuracy of remote sensing in determining the total nitrogen content of the plant and establish a regression relationship to estimate the amount of canopy nitrogen in the plant.
Material and Methods: This research was undertaken in parts of the south of the West Azerbaijan Province in Iran. The sampling was done from 45 dryland wheat fields using a stratified random method in May 2016. The wheat canopy nitrogen was determined using the Kjeldahl method. Satellite images of the ETM+ were downloaded on the USGS website. Then the required pre-processing was performed on images to reduce systematic and non-systematic errors. Statistical analyses were performed by excel and SPSS. Descriptive statistics and correlations were obtained between reflectance data obtained from various satellite bands and nitrogen measured in the laboratory. Correlated variables among the reflectance data of different bands were analyzed by principal component to reduce repeat calculations. The regression relationship between the plant canopy nitrogen and the first principal component has been evaluated using the stepwise regression method. To draw the plant canopy nitrogen, map, the equation was obtained and the ETM+ image has been used for land uses. Finally, the map of canopy N distribution at the studied area was drawn.
Results and Discussion: The results showed that nitrogen content varied from 1.6% to 0.79%, with an average of 1.11%. The normality data was verified by the Shapiro Wilk test. The results of the Pearson correlation showed that the wheat canopy nitrogen has a high correlation with digital number values of all bands of satellite images except band 4, so that it has the highest and the least correlation with band 2 and band 4, respectively. The correlation between remote sensing data in different bands was also evaluated using bi-plot statistics, which results showed a high correlation between all bands except band 4 with the first one of the principal component (PC1). Therefore, only PC1 data has been used to study the regression relationships between wheat canopy nitrogen and remote sensing data. A regression equation between wheat canopy nitrogen and ZPC1 (R2= 0.71) was developed. ZPC1 is obtained according to the following formula: where ZPC1 is the standardized Z parameter, is the average of PC1 and the ????pc1 is the standard deviation of PC1. Finally, the map of canopy N distribution was drawn to the studied area. According to the results of this study, the application of remote sensing data such as Landsat ETM+ data is a very important variable for improving and managing the prediction of wheat canopy nitrogen.
Conclusion: Overall, the results indicated that the remote sensing data provide more accurate and timely information from the drylands of Iran to manage farm fertilization and prevent the decline in yields at critical points. However, proper management to avoid the fertilizer loss by precise and timely application of N-fertilizer is needed.
V. Feiziasl
Abstract
Introduction Dryland farming is a major agricultural practice in northwest of Iran. Knowledge of soil fertility status in this areas is one of the basic needs of dryland agricultural system. Soil chemical properties play important role for the soil fertility and determined after soil testing. The measurement ...
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Introduction Dryland farming is a major agricultural practice in northwest of Iran. Knowledge of soil fertility status in this areas is one of the basic needs of dryland agricultural system. Soil chemical properties play important role for the soil fertility and determined after soil testing. The measurement of the fertility of soils is usually done by chemical analysis for plant nutrients. Assessing soil fertility is essential to help identify strategies with less environmental impact in order to achieve more sustainable agricultural systems. Unfortunately, many dry farming areas in Iran do not have adequate levels of all the necessary plant nutrients, or conditions in the soil are unfavorable for plant uptake of certain nutrients. Soil scientists focus on using commercial fertilizers and manures (rotation system and conservation tillage) to add nutrients and organic matter to the soil. Soil fertility can be further improved by incorporating cover crops that add organic matter to the soil, which leads to improved soil macro-nutrients and micronutrients, structure and promotes a healthy. Therefore, soil fertility evaluation of Iran dryland regions is most basic decision making tool for the sustainable soil nutrient management in this areas and estimation of capacity of soil to maintain a continuous supply of plant nutrients for a crop production. Evaluation of soil fertility in drylands of the northwest Iran have two objectives 1) Assess nutrient status of soil-crop system 2) Diagnose suspected nutrient imbalances. Materials and Methods This study was carried out in northwest of Iran drylands included: west Azarbayjan, east Azarbayjan, Kordistan and Kermanshah provinces. A total of 674 soil samples were collected from farmer’s fields in east Azarbayjan, weast Azarbayjan, Kurdistan and Kermanshah 414, 97, 90 and 73 samples respectively. The surface soil samples were taken from 0-25 cm depth in each field before the sowing of the rainfed plants in autumn by composite sampling method. Immediately after collection soil samples were dried, grounded, screened through 2 mm sieve, labelled and stored in plastic container. The samples were analyzed for 12 chemical and physical parameters include: soil texture (hydrometer method), pH (saturation paste) and EC (saturated extract), organic carbon (Walkley and Black, 1934), Total N (Kejeltak), calcium carbonate equivalence (acid-neutralizing value), phosphorus (Olsen), potassium (sodium bicarbonate extracted) and iron, zinc, Mn and copper (DTPA extracted). Soil samples were categorized as low, medium and high on the basis of their availability in soils by two Gomes (1985) (equation 1) and common (nutrient classification by critical level method for dryland wheat) methods. Low Medium Equation (1) High Where, , and SD are soil property, average of soil property in all area and standard deviation of soil property, respectively. In order to compare the levels of soil fertility of one province with those of another it is necessary to obtain a single value for each nutrient. Nutrient index value (NIV) was calculated by Parker et al., (1951) method (equation 2) for soil samples of each province or district from the proportion of soils under low, medium and high categories using following equation: Equation (2) Where, , and are number of samples testing low, medium and high category in each province, respectively. If the NIV is less than 1.67, the soil fertility status is low (nutrients or other property) and the value is 1.67-2.33 the fertility status is optimum (sufficiency) nutrients. The value greater than 2.33, the fertility status is high nutrients. Results and Discussion The results showed that, the Gomes (1985) method could not classify the soil properties in all region (population) correctly, due to the tends towards central limit theorem (optimal condition). In the calculation of NIV, the conventional method (critical levels) for classification of soil properties was better than Gomes (1985) method because it was more compatible with the field conditions. Soil salinity and calcium carbonate are not problems in dryland areas seriously. But with increasing amount of calcium carbonate, soil phosphorus, potassium, Fe, Mn, Zn and Cu decreased significantly. But soil phosphorus and Zn deficiencies were more sensitive to increase soil calcium carbonate. Assessment of soil fertility status by NIV showed that, soil organic matter were low (deficient) in west and east Azerbaijan with 92 and 69 percent of those areas. But total nitrogen were optimum (sufficient) in all areas with 98 percent averagely (except east Azarbayjan). This is mainly due to the application of nitrogen fertilizers for dryland wheat production and apply conservation tillage in some areas. Soil phosphorus were evaluated low in two west Azerbaijan (81%) and Kermanshah (67%) provinces, but in east Azerbaijan (68 %) and Kurdistan (85%) were sufficiency or high for wheat production. Potassium was more than sufficiency (high) in 90 percent of all areas averagely. Micronutrients deficiency were observed in some provinces. Deficiency of Fe with 100 and 69 percent in west Azarbayjan and Kurdistan respectively, Mn with 89 percent of west Azarbayjan, Zn 84 percent in east Azarbayjan and Cu with 100 and 87 percent in west and east Azarbayjan respectively. These results suggest that, in addition, nitrogen and phosphorus fertilizer applications should also be important for micronutrient management in dryland areas. Conclusions It can be concluded that, the capability of critical level method is better than Gomes (1985) method in the classification of soil properties. Nutrient index value (NIV) method can be evaluated soil fertility status in Iran dryland conditions. According to this, there is deficiency of Fe, Zn and Cu elements in addition to the P and N nutrients in Iran dryland areas.
Mehdi Kousehlou; Mehdi Rahmati; Iraj Eskandari; Vali Feiziasl
Abstract
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 ...
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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.
vali feiziasl
Abstract
Introduction: Nitrogen is the main component of fertilizer programs necessary for production of high quantity dryland barley. This element is the second limiting factor, after water in dryland areas. So for economic production of barley, the proper nitrogen fertilizer application is essential to increase ...
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Introduction: Nitrogen is the main component of fertilizer programs necessary for production of high quantity dryland barley. This element is the second limiting factor, after water in dryland areas. So for economic production of barley, the proper nitrogen fertilizer application is essential to increase seed quantity and quality in Iran dryland areas. Many researchers have been confirmed that dryland barley yield increased by nitrogen application management. Nitrogen fertilization in dryland areas can increase the use of soil moisture, and improve barley yields to some extent. Different studies have been confirmed interactions between water stress and nitrogen fertilizers on barley, especially under field conditions. From the nitrogen management factors, timing and amount of nitrogen application is known as the most important aspect. This project established in order to study nitrogen rates and nitrogen application time's effects on nitrogen requirement, nitrogen agronomy use efficiency (NUE) and crop characteristics of various dryland barley genotypes in cold and semi cold drylands of Iran.
Materials and Methods: This study was carried out in split-split plot in a RCBD in Dryland Agricultural Research Institute (DARI), Maragheh; where nitrogen application times (fall, 1/2 in fall and 1/2 in spring and 2/3 in fall and 1/3 in spring) were assigned to the main plots, nitrogen rates to sub plot (0, 30, 60, 90 and 120 kg/ha), and 5 dryland barley genotypes to sub-sub plots (Sahand, Abidar, Dayton/Ranny, Alpha/ Gumhuriyet/ Sonja and B-C-74-2)in 4 replications during 2007-2010 years. The Rainfall were 177-498 mm.yr-1 (long term mean is 365 mm.yr-1) in cropping years in DARI station. Soil samples were collected from 0-25 cm for determining total N, P-Olsen, K-Ammonium acetate, TNV, OC, Soil texture, pH, EC and Fe, Mn, Zn and Cu-DTPA before sowing and collected from 0-2, 20-40 and 40-60 cm depths in sub-sub plots in shooting stage (GS32) for determining NO3− andNH4+. Ammonium measurement in the soil KCl extracts were down by spectrophotometry method and colorimetric reaction at 655 nm. Also, Absorption spectrophotometry method was used for determination of nitrate in soil extract based on its UV absorbance at 210 nm. In this method two measurements were carried out; one before (by Zn coated by Cu) and second after reduction of nitrate). Using the difference between these two measurements, concentration of nitrate in the extracts was determined.
Results and Discussion: The results showed that nitrogen application rates significantly increased (p
V. Feiziasl; A. Fotovat; A. Astaraei; A. Lakzian; M.A. Mousavi Shalmani; A. Khorasani
Abstract
Introduction: Nitrogen (N) is one of the most important growth-limiting nutrients for dryland wheat. Mineral nitrogen or ammonium (NH4+) and nitrate (NO3−) are two common forms of inorganic nitrogen that can serve as limiting factors for plant growth. Nitrogen fertilization in dryland area can increase ...
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Introduction: Nitrogen (N) is one of the most important growth-limiting nutrients for dryland wheat. Mineral nitrogen or ammonium (NH4+) and nitrate (NO3−) are two common forms of inorganic nitrogen that can serve as limiting factors for plant growth. Nitrogen fertilization in dryland area can increase the use of soil moisture, and improve wheat yields to some extent. Many researchers have been confirmed interactions between water stress and nitrogen fertilizers on wheat, especially under field conditions. Because of water stress affects forms of nitrogen uptake that leads to disorder in plant metabolism, reduction in grain yield and crop quality in dryland condition. On the other hand, use of suitable methods for determining nitrogen requirement can increase dryland wheat production. However, nitrogen recommendations should be based on soil profile content or precipitation. An efficient method for nitrogen fertilizer recommendation involves choosing an effective soil extractant and calibrating soil nitrogen (Total N, NO3− andNH4+) tests against yield responses to applied nitrogen in field experiments. Soil testing enables initial N supply to be measured and N supply throughout the season due to mineralization to be estimated. This study was carried out to establish relationship between nitrogen forms (Total N, NO3− andNH4+) in soil and soil profile water content with plant response for recommendation of nitrogen fertilizer.
Materials and Methods: This study was carried out in split-split plot in a RCBD in Dryland Agricultural Research Institute (DARI), Maragheh, Iranwhere N application times (fall, 2/3 in fall and 1/3 in spring) were assigned to the main plots, N rates to sub plot (0, 30, 60 and 90 kg/ha), and 7 dryland wheat genotypes to sub-sub plots (Azar2, Ohadi, Rasad and 1-4 other genotypes) in three replications in 2010-2011. Soil samples were collected from 0-20, 20-40, 40-60 and 60-80 cm in sub-sub plots in shooting stage (ZGS32). Ammonium measurement in the soil KCl extracts was down by spectrophotometry method and colorimetric reaction at 655 nm. Also, Absorption spectrophotometry method was used for determination of nitrate in soil extract based on its UV absorbance at 210 nm. In this method two measurements were carried out; one before (by Zn coated by Cu) and second after reduction of nitrate). Using the difference between these two measurements, concentration of nitrate in the extracts was determined. Soil water content was also measured with Diviner 2000 after calibration in 0-20, 20-40, 40-60 and 60-80 cm soil profile in sub-sub plots. After wheat harvest, the most suitable regression model between soil mineral nitrogen (Nm) and soil moisture (θ) was fitted with wheat grain yield by DataFit version 9.0 software.
Results and Discussion: The best model between soil N forms (nitrate, ammonium and mineral nitrogen) was calibrated between mineral nitrogen (Nm) and soil moisture (θ) with crop response (Y=a+bN_m+c ln〖(θ)〗+dN_m^2+eln〖(θ)〗^2+fN_m ln〖(θ)〗) that explained 80% of dryland wheat yield variations. In this model, the contributions of mineral nitrogen (NO3− +NH4+) were 26%, soil moisture 50% and their interactions 24%. According to this model, the effect of soil moisture on production of grain yield was 2.3 folds greater than the mineral N. These results are most suitable for sampling and calibration of mineral nitrogen in 0-40 cm in dryland wheat stem elongation (ZGS32). Critical value of soil mineral N was 41 kg/ha, equal to 18.0 mg Nm/kg in this layer for obtaining higher grain yield (over 2500 kg/ha). According to regression model, application of 50 kg N/ha in autumn was able to provide Nm critical level in 0-40 cm layer for dryland wheat genotypes under experimental conditions. Also simulation model showed that nitrogen fertilizer increased grain yield and it is more than the soil mineral nitrogen. If the soil mineral nitrogen is 20 kg/ha or less in 0-40 cm soil layer, there may be increase of grain yield up to 4000 kg/ha through the application of nitrogen fertilizers. Therefore, increasing of mineral nitrogen in the soil profile up to 20 kg/ha is not appropriate for wheat production in Northwest of Iran drylands.
Conclusion: It can be concluded that, there is a relationship between soil nitrogen and moisture content with dryland wheat response and suggested model can be used for nitrogen recommendations for dryland wheat. According to the model, the effects of nitrogen fertilizer application on grain yield were much more than the effect of soil mineral nitrogen. Therefore, the increasing of soil nitrogen storage is not recommended in dryland conditions.
M.A. Mousavi Shalmani; A. Lakzian; A. Khorasani; V. Feiziasl; A. Mahmoudi; A. Borzuee; N. Pourmohammad
Abstract
In order to assessment of water quality and characterize seasonal variation in 18O and 2H in relation with different chemical and physiographical parameters and modelling of effective parameters, an study was conducted during 2010 to 2011 in 30 different ponds in the north of Iran. Samples were collected ...
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In order to assessment of water quality and characterize seasonal variation in 18O and 2H in relation with different chemical and physiographical parameters and modelling of effective parameters, an study was conducted during 2010 to 2011 in 30 different ponds in the north of Iran. Samples were collected at three different seasons and analysed for chemical and isotopic components. Data shows that highest amounts of δ18O and δ2H were recorded in the summer (-1.15‰ and -12.11‰) and the lowest amounts were seen in the winter (-7.50‰ and -47.32‰) respectively. Data also reveals that there is significant increase in d-excess during spring and summer in ponds 20, 21, 22, 24, 25 and 26. We can conclude that residual surface runoff (from upper lands) is an important source of water to transfer soluble salts in to these ponds. In this respect, high retention time may be the main reason for movements of light isotopes in to the ponds. This has led d-excess of pond 12 even greater in summer than winter. This could be an acceptable reason for ponds 25 and 26 (Siyahkal county) with highest amount of d-excess and lowest amounts of δ18O and δ2H. It seems light water pumped from groundwater wells with minor source of salt (originated from sea deep percolation) in to the ponds, could may be another reason for significant decrease in the heavy isotopes of water (18O and 2H) for ponds 2, 12, 14 and 25 from spring to summer. Overall conclusion of multiple linear regression test indicate that firstly from 30 variables (under investigation) only a few cases can be used for identifying of changes in 18O and 2H by applications. Secondly, among the variables (studied), phytoplankton content was a common factor for interpretation of 18O and 2H during spring and summer, and also total period (during a year). Thirdly, the use of water in the spring was recommended for sampling, for 18O and 2H interpretation compared with other seasons. This is because of function can be explained more by variables and there are more variables compare with other two seasons. Fourthly, potassium concentration in spring and total volume of water in summer would be most appropriate variables for interpretation of data during these seasons
V. Feiziasl; A. Fotovat; A. Astaraei; A. Lakzian; M.A. Mousavi Shalmani
Abstract
In order to determination of water stress threshold and dryland wheat genotypes water status in different nitrogen managements, this experiment was carried out in split split plot RCBD design in three replications in 2010-2011 cropping year. Treatments included: N application time (whole fertilization ...
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In order to determination of water stress threshold and dryland wheat genotypes water status in different nitrogen managements, this experiment was carried out in split split plot RCBD design in three replications in 2010-2011 cropping year. Treatments included: N application time (whole fertilization of N at planting time , and its split fertilization as 2/3 at planting time and 1/3 in early spring), N rates (0, 30, 60 and 90 kg ha-1) and 7 wheat genotypes. Also these genotypes were grown in supplemental irrigation condition for calculation of crop water stress index (CWSI) parameters. Canopy temperature (Tc) was measured in flowering and early milking stages. Crop water stress index (CWSI) was calculated. A non-water stressed baseline (lower baseline) were fitted as Tc-Ta=4.523-3.761×VPD; R2=0.92 and non-transpiring baseline (upper baseline) determined 6 ºC for rainfed wheat genotypes. Water stress threshold was 0.4 and crossing of that occurred 8 days before heading stage. In water stress threshold boundary, was depleted 60 mm available water from 0 to 50 cm soil depth. There was negative significant relationship (p >0.01) between CWSI and grain yield in all treatments and different nitrogen rates. Nitrogen application reduced water stress and increased grain yield of rainfed wheat genotypes. Ohadi and Rasad genotypes showed highest resistance to water stress and high grain yield production for N30 in split and planting time application, respectively. Cereal4 and Rasad genotypes were suitable for N60 application in split and planting time application, respectively.
V. Feiziasl
Abstract
Abstract
In order to determine the critical level and classify the soil Zn in Western Azerbaijan, Eastern Azerbaijan, Kurdistan and Kermanshah dryland areas, a study was conducted in a complete randomized block design having 4 treatments (0, 5, 10 and 15 kg.ha-1 of zinc sulfate) with three replications ...
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Abstract
In order to determine the critical level and classify the soil Zn in Western Azerbaijan, Eastern Azerbaijan, Kurdistan and Kermanshah dryland areas, a study was conducted in a complete randomized block design having 4 treatments (0, 5, 10 and 15 kg.ha-1 of zinc sulfate) with three replications for four years (1998-2002).When the experiment ended, the crop and soil data uniformity test were performed for all experimental sites. The results of these experiments were interpreted by different methods including: Cate-Nelson graphical method, Cate-Nelson two and three classes ANOVA models, Mitshcherlich equation, plant response column order procedure and interaction chi-square methods. The results showed that the Zn critical levels using the mentioned methods were 0.75, 0.55, 0.65, 0.61, 0.80 and 0.66 mg.kg-1, respectively. Different Zn critical levels calculated by different soil testing interpretation methods were compared by using contingency table. The results showed that Cate-Nelson two classes ANOVA model with 0.55 mg.kg-1 Zn and 0.47 predictability value was a better model for determining the Zn critical level than all other models for Northwestern dryland region of Iran. Using different soil testing interpretation methods for determining the Zn critical levels it was concluded that all the values were to some extent similar; however, Cate-Nelson two classes ANOVA model seemed to be more suitable for this purpose.
Key words: Zn critical level, dryland wheat, Western Azerbaijan, Eastern Azerbaijan, Kurdistan, Kermanshah.