Soil science
Saba Bagherifam; Mohammad Amir Delavar; Payman Keshavarz; Parviz Karami
Abstract
Introduction
Soil is one of the main drivers of global warming through losing carbon in the form of CO2. On the other hand, its ability to sequester carbon is a suitable option for reducing CO2 emissions. Therefore, even few changes in carbon sequestration or decomposition of soil organic carbon ...
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Introduction
Soil is one of the main drivers of global warming through losing carbon in the form of CO2. On the other hand, its ability to sequester carbon is a suitable option for reducing CO2 emissions. Therefore, even few changes in carbon sequestration or decomposition of soil organic carbon affect the global atmospheric CO2 content. Although the soils of arid and semi-arid regions have low organic carbon content, they can sequester substantial amounts of carbon due to the large area of these regions. So, the Rothamsted carbon model was used to predict the impact of future climate changes on the amount of CO2 emissions and low soil organic carbon stocks in the semi-arid arable lands of Razavi Khorasan province. This model is one of the most widely used models for the study of soil organic carbon turnover and has been evaluated in a variety of ecosystems including grasslands, forests and croplands and in various climate regions. The RothC model is consists of five conceptual soil carbon pools, four active fractions and a small amount of inert organic matter (IOM) that is resistant to decay. The active pools splits into: Decomposable Plant Material (DPM), Resistant Plant Material (RPM), Microbial Biomass (BIO) and Humified Organic Matter (HUM). This model is able to reveal the effect of soil texture, temperature, rainfall, evaporation, vegetation and crop management on the soil organic carbon turnover process.
Materials and Methods
The Rothamsted carbon model was calibrated and validated using data measured in 2020 and available data from the long-term field experiments in the semi-arid agricultural lands of Jolge Rokh. Then, by analyzing the climate change of the study area, the impact of climate change until the end of the current century on the amount of CO2 cumulative emissions, total organic carbon (TOC) and active carbon pools model were modeled and compared in the current climate and also climate change conditions.
Results and Discussion
The comparison between the measured and simulated soil organic carbon values by the model shows the potential of the model to provide predictions with acceptable accuracy. The outcome of comparisons revealed that R2, Root Mean Square Error (RMSE), Mean Difference (MD), Mean Absolute Error (MAE) and Model efficiency were 0.97, 2.78, 2.11, 2.33 and 0.70 respectively. Assessment of climate changes in the region (during 1981-2020) showed a decrease in precipitation and a significant increase in temperature over the past 40 years. Climate change simulation was carried out by temperature increasing and decreasing the precipitation until the end of the current century, indicated the decrease of all active carbon pools. It was found that DPM, RPM, BIO, HUM and TOC decreased respectively to 2.41, 2.72, 2.51, 1.04 and 1.32% compared to the current climatic conditions, while the cumulative CO2 emission increased by 1.26%. Temperature rising leads to increase the rate modifying factor (a) by 2.20%, which enhances microbial respiration and decomposition rate of organic carbon and CO2 emissions (carbon output). However, it also increases the ecosystem's net primary productivity (carbon input). Decreases in rainfall and increase in potential evapotranspiration cause a reduction of the rate modifying factor (b) to 0.23%, which on one side reduces the activity of microorganisms and carbon biodegradation; but on the other side, it decreases the vegetation cover and following that reduces CO2 trapping during the photosynthesis process and transfers it to the soil. It seems that in arid and semi-arid climates where the lack of moisture is the most important limiting factor of the plants growth; the role of precipitation in carbon decomposition and sequestration is greater than temperature.
Conclusion
The Rothamsted carbon model is suitable for regional simulations because it requires only easily obtainable inputs. Therefore RothC is an appropriate tool for estimating long-term effects of climate change and agricultural management (such as application of manures, returning plant residues to the soil, crop rotations, conservation tillage etc.). The RothC model validation in the cold semi-arid agricultural lands of the region, shows the ability of model to properly simulate the pattern of organic carbon changes. Also, simulation of soil organic carbon changes under the climate changes conditions indicates an increase in cumulative CO2 emissions and decrease in soil organic carbon pools of the study area. The methodology can be applied to other regional estimations, provided that the relevant data are available. The predictions allowed to identify the land management potential to carbon sequestration. Such information demonstrate a beneficial tool for evaluation of past land management effects on soil organic carbon trends and also estimation of future climate change effects on soil organic carbon stocks and CO2 emissions.
Soil science
M. Zangiabadi; manoochehr gorji; P. Keshavarz
Abstract
Introduction: Soil quality can be considered as a comprehensive index for sustainable land management assessment. Studying the most important soil physical properties and combining them as an index of soil physical quality (SPQI) could be used as an appropriate criteria for evaluating and monitoring ...
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Introduction: Soil quality can be considered as a comprehensive index for sustainable land management assessment. Studying the most important soil physical properties and combining them as an index of soil physical quality (SPQI) could be used as an appropriate criteria for evaluating and monitoring soil physical changes. In this regard, this study was conducted to determine the most important soil physical properties and calculate the SPQI of medium to coarse-textured soils of Khorasan-Razavi province.
Materials and Methods: Torogh Agricultural and Natural Resources Research and Education Station of Khorasan-Razavi province is located in south-east of Mashhad city (59° 37' 33"-59° 39' 10" E, 36° 12' 31"-36° 13' 56" N). Soil texture variability in this research station is one of its outstanding features. The soil textures are classified into loam, silt loam, silty clay loam, clay loam, and sandy loam. More than 90% of agricultural soils in Khorasan-Razavi province are classified in these five texture classes. Using the available data, 30 points with different soil textures and OC contents were selected. The soil samples were collected from 0-30 cm soil depth at each point. Intact soil cores (5 cm diameter by 5.3 cm length) were used for sandbox measurements, and disturbed soil samples were used to determine other properties. Required laboratory analysis and field measurements were conducted using standard methods. In this research, 35 soil physical properties as total data set (TDS) including: soil moisture release curve (SMRC) parameters, particle size distribution and five size classes of sand particles, soil bulk and particle density, dry aggregates mean weight diameter (MWD) and stability index (SI), S-index, soil porosity and air capacity, location and shape parameters of soil pore size distribution (SPSD) curves, relative field capacity (RFC), plant available water measured in matric pressure heads of 100 and 330 hPa for the field capacity (PAW100 and PAW330), least limiting water range measured in matric pressure heads of 100 and 330 hPa for the field capacity (LLWR100 and LLWR330), integral water capacity (IWC) and integral energy (EI) of different soil water ranges were measured and calculated for 30 soil samples. The most important soil physical properties were selected using principal component analysis (PCA) method by JMP (9.02) software. Selected physical properties as minimum data set (MDS) were weighted and scored using PCA results and scoring functions, respectively. In this study, three types of linear scoring functions were used. The soil physical quality index (SPQI) was calculated by two scoring and two weighting methods for each soil sample and the differences between these four SPQIs were tested by sensitivity index.
Results and Discussion: Principal component analysis results showed that among 35 soil physical properties (TDS) which were studied at this research, six properties of mean pore diameter (dmean), PAW100, total porosity (PORT), EI LLWR330, SI and PAW330 accounted for about 90% of the variance between soil samples. Weight of the selected properties (MDS) was calculated by the ratio of variation in the data set explained by the PC that contributed the selected property to the total percentage of variation explained by all PCs with eigenvalue ˃ 1. In this research, the parameters of PAW100, total porosity (PORT), SI and PAW330 were scored using scoring function of more is better, EI LLWR330 was scored using scoring function of less is better and dmean was scored using scoring function of optimum by two scoring methods with score ranges of 0.1-1 and 0-1. Considering unweighted and weighted MDS and two ranges of scores, four SPQIs were calculated for each soil sample. The results showed that SPQIs which were calculated by the MDS derived from PCA method and scoring weighted MDS at the range of 0-1, had the highest sensitivity index and could represent the differences between the studied soil samples better than other SPQIs. By this method, maximum and minimum SPQI values for the studied soils were 0.82 and 0.12, respectively. SPQI is a relative comparison criterion to quantify the soil physical quality which could be applied only for the studied soils with specific characteristics.
Conclusion: The results of this research showed that minimum data set (MDS) explained about 90% of the variance between soil samples. Combining MDS into a numerical value called soil physical quality index (SPQI) could be used as a physical comparison criterion for the studied soils. From the SPQI based on the MDS indicator method, soil quality was evaluated quantitatively. Soil samples with grade I, II, III, and IV accounted for 10%, 36.7%, 30%, and 23.3% of the soil samples, respectively.
payman keshavars; majid forouhar; masoud Dadivar
Abstract
Introduction: World cereal demand is growing at present in accordance with the global expansion of human populations. Deficiency of micronutrients in cereal cropping is one of the major worldwide problems. Beside of lowering grain yield, it may cause some healthy problems in human populations. Iron is ...
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Introduction: World cereal demand is growing at present in accordance with the global expansion of human populations. Deficiency of micronutrients in cereal cropping is one of the major worldwide problems. Beside of lowering grain yield, it may cause some healthy problems in human populations. Iron is an essential micro element in the soil that mainly had been found as the insoluble (Ferric or Fe3+) form. Although ferric iron is relatively insoluble in water, the solubility of total inorganic iron decreases between pH of 7.4 to 8.5 range which is dominant in calcareous soils. It is estimated that nearly half of the world population is affected from Fe deficiency problem. Major reason for widespread occurrence of Fe deficiency in human populations is very little dietary diversity and high consumption of cereal-based foods with very low amount and poor availability of Fe. Bread wheat is the most widely grown cereal grain with 65% (6.5 million hectares) of the total crop cultivated area in calcareous soils of Iran. Most wheat cultivars currently used have been selected for high yields under optimum fertilizer conditions. Consequently, research is needed to select efficient genotypes that will grow and produce under conditions of lower fertilizer input or soil micronutrients deficiencies. This is especially true for the expansion of wheat cultivation which is often growing in calcareous soil of Iran. These soils are characterized with low fertility, high pH value, low organic matter content and low micronutrients availability. Environmental concerns in wheat production for human population indicate that to improve wheat quality and quantity, the zero or possible lowest amounts of chemical fertilizers would be applied. In this regard, the use of iron-efficient genotypes that have also high yield can be considered as a key strategy.
Materials and Methods: In order to investigate Fe efficiency in various wheat genotypes, a factorial experiments a randomized complete block design was carried out with three replications in agricultural and natural resource research and education center of Khorasan Razavi province, Mashhad (Torough Station), Iran, during 2009-2011. Treatments were consisted of two levels of Fe fertilizer (0 and 10 kg h-1 as Fe-EDDHA) and six genotyps of wheat including: three cultivars and one line of bread wheat (Alvand, Falat, Toos, and C75-5, respectively), two species of wheat known as Thriticosecale and Durum. The trial plots’s size was 9×3.6 (32.4 m2). According to the results of soil analysis, total nitrogen, available forms of phosphorus and potassium were 0.05%, 7.2, and 180 mg kg-1, respectively. DTPA extractable of iron, zinc, manganese and copper were 2.4, 0.52, 3.4 and 0.7 mg kg-1, respectively. Soil texture was silt loam. Soil organic carbon percentage and equivalent CaCO3 percentage (T.N.V) were 0.48% and 18.7%, respectively. The electrical conductivity (EC) and pH measured in saturated extract were 1.4 dSm-1 and 8.1, respectively. At defined phonological stage (SG6 based on Fix’s Index), the Fe concentration in shrub was measured. Moreover, grain yield and Fe uptake by grain were determined at the end of ripening stage. Iron use efficiency, agronomic efficiency and apparent recovery efficiency were calculated and studied as dependent variables.
Results and Discussion: The grain yield is the most integrative trait of a particular genotype. The results showed that Fe application increased significantly grain yield by 9.9% in comparison with control. In our research the highest grain yield increase due to Fe application was found in Durum wheat (17.1%), and the lowest grain yield increase, were found in Toos cultivar (4.1% yield increase). Application of Fe increased Fe concentration and uptake in grain about 5.7% and 16.4%, respectively. In terms of iron uptake by grain, Thriticosecale wheat and C75-5 cultivar had the highest (339.6 g ha-1) and the lowest amounts of Fe uptake (260.3 g ha-1), respectively. Also, application of Fe had no significant effects on Fe concentration in shoot. Fe use efficiency in bread wheat genotype, Durum and Thriticosecale wheat was ranked as: Durum < C75-5 < Alvand < Triticale< Falat < Toos. According to our research results, Toos and Falat cultivars and Thriticosecale have higher iron use efficiency than Alvand and C75-5 cultivars and Durum wheat. The results also suggest that to obtain higher yield in Durum wheat, soil and foliar application of Feis more necessary in comparison with other genotypes especially Toos and Falat.
Conclusion: There were various abilities to uptake and use Fe by different wheat genotypes. Fe-efficient genotypes of wheat were Toos and Falat also Triticale. Moreover, these genotypes also had higher grain yield.
mehdi zangiabadi; manoochehr gorji; Mehdi Shorafa; Payman Keshavarz; Saeed Saadat
Abstract
Introduction: Soil physical quality isone of the most important factors affects plants water use efficiency in agricultural land uses. In the literature, some soil physical properties and indices such as S-index, Pore Size Distribution (PSD), porosity, Air Capacity (AC), Plant Available Water (PAW) content, ...
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Introduction: Soil physical quality isone of the most important factors affects plants water use efficiency in agricultural land uses. In the literature, some soil physical properties and indices such as S-index, Pore Size Distribution (PSD), porosity, Air Capacity (AC), Plant Available Water (PAW) content, Least Limiting Water Range (LLWR) and Integral Water Capacity (IWC) were mentioned as the soil physical quality indicators. It has been reported that the soils with the same PAW, LLWR and IWC may have different physical qualities. The index of Integral Energy (EI) of the soil moistureranges may differ between the soils with equal soilmoistureover a defined water content range. This index is defined as the required energy to uptake the unit mass of soil moistureby plants. According to this definition, the soils with low EI would have better physical quality for plants roots growth. In this research, we hypothesized that EI of different soil moistureranges were negatively related to S-index which means the plants required energy to uptake the soil water in the soils with high S-index, is lower than the soils with poor physical quality (less S-index). So we examined our hypothesis in medium to coarse-textured soils of Khorasan-Razavi province (Iran).
Materials and Methods:This research was conducted in Torogh Agricultural and Natural Resources Research and Education Station in Khorasan-Razavi province, north-eastern Iran (59° 37' 33"-59° 39' 10" E, 36° 12' 31"-36° 13' 56" N). Soil textures of this research station, are classified into loam, silt loam, silty clay loam, clay loam, and sandy loam which is as the same in more than 90% of agricultural soils in Khorasan-Razavi province. Thirty points with different soil textures and organic carbon contents were selected. In order to measure different properties of the soil, two soil samples (5 cm diameter × 5.3 cm length core sample and a disturbed soil sample) were collected from 0-30 cm depth of each point. After conducting required laboratory and field measurements using standard methods, the Soil Moisture Release Curve (SMRC) parameters (RETC program), S-index, PAW and LLWR (measured in matric heads of 100 and 330 cm for the field capacity), IWC and EI of mentioned soil moisture ranges were calculated. In this regard, integration calculations were done by Mathcad Prime 3 software. Finally, the relationships between the measured properties and EI values (for PAW100, PAW330, LLWR100, LLWR330 and IWC) were analyzed using Pearson correlation coefficient and stepwise multivariate linear regression by JMP (9.02) statistical software.
Results and Discussion: The S-index of 30 soil samples were between 0.029-0.070 with average of 0.046. These results showed that 90% of studied soil samples had good and very good and 10% had poor physical quality. The lowest average EI values of different soil moisture ranges were observed in sandy loam and silt loam and the highest was observed in silty clay loam soil textures. The EI(IWC) mean value was lower than EI(PAW) and EI(LLWR) mean values which indicated that calculating the EI values based on continuous effects of water uptake physical limitations, resulted in lower required energy for plants to uptake the unit mass of soil moisture . Statistical analysis resulted in significantly negative relations between S-index and two indices of EI(PAW100) and EI(IWC). Multivariate regression analysis showed that EI(PAW100) and EI(LLWR100) could be estimated by shape parameter (n) of SMRC by regression coefficients of 0.95 and 0.22, respectively and the value of EI(IWC) could be estimated by S-index and organic carbon content by regression coefficient of 0.57. The parameter of saturated volumetric water content (θvs) of SMRC and sand percentage were determining factors of EI(PAW330). In this research, it was not obtained the significant relationship between EI(LLWR330) values and measured soil physical properties. According to the results, increment of the shape parameter (n) of SMRC and S-index led to reducing the plants required energy to uptake the unit mass of soil moisture in PAW100 and IWC ranges. We found that EI of different soil moisture ranges could be used to evaluate the soil physical quality between the soils with equal soilmoisture contents.
Conclusion: This Research investigated the relationship of PAW, LLWR and IWC EI values with soil physical properties and S-Index in medium to coarse-textured soils. The results indicated that increment of S-index led to decreasing EI(PAW100) and EI(IWC) indices. According to the results, the shape parameter of SMRC and S-index could be accounted for determining factors of EI(PAW100) and EI(IWC) indices values.
H. Asadi Rahmani; A. Lakzian; J. Ghaderi; P. Keshavarz; H. Haghighatnia; K. Mirzashahi; M. R. Ramezanpour; A. Charati Arayi; A. Mohammadi Torkashvand
Abstract
Intoduction: Plant growth promoting rhizobacteria (PGPR) are a diverse group of bacteria consisting different species like Pseudomonas, Azotobacter, Azospirillum, Flavobacterium, Bacillus and Serratia with ability of enhancing plant growth and yield by different mechanisms. Flavobacteria are aerobic, ...
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Intoduction: Plant growth promoting rhizobacteria (PGPR) are a diverse group of bacteria consisting different species like Pseudomonas, Azotobacter, Azospirillum, Flavobacterium, Bacillus and Serratia with ability of enhancing plant growth and yield by different mechanisms. Flavobacteria are aerobic, gram negative, rod shape bacteria with more than 100 species living in different habitats ranging from soil and water to the foods. There are reports indicating that Flavobacteria are of dominant rhizosphere bacteria with beneficial effects on agricultural crops. Studies in Iran showed that six species of Flavobacterium were isolated and identified from rhizosphere of wheat. The aim of this study was to evaluate the effect of four strains of Flavobacterium on growth and yield of wheat under field conditions.
Materials and Methods: In this study four strains of Flavobacterium F9, F11, F21 and F40 were used. Bacterial strains were propagated in liquid NB growth medium and were used in field experiments. Fields were prepared in Khorasan Razavi, Khuzestan, Fars, Mazandran and Kermanshah and wheat seeds were inoculated with strains and sowed in a randomized complete block design (RCBD) with five treatments (four strains and a un-inoculated control) with four replications. Wheat varieties were Pishtaz in Khorasan and Fars, Marvdasht in Kermanshah, Chamran in Khuzestan and Milan in Mazandaran. Chemical fertilizers were used based on soil analysis. The rate of inoculation was 10 ml of bacteria per kg of seed. Plants were harvested at the end of the experiment and seed yield, total shoot biomass, 1000-seed weight, plant height, number of panicles per m2, number of seeds per panicle and panicle length were measured. Data analysis was performed by SPSS software, and the means were compared at α꞊5% by Duncan test.
Results and discussion: Results of the study showed that bacterial strains increased growth and yield of wheat in all provinces. In Mazandaran, all strains promoted seed yield although the effect of F21 was not significant. F40 had the highest effect on factors measures in the study. In Khuzestan, inoculation had no significant effect of seed yield production, although yield production was increased compared to control treatment. There was a similar trend regarding to other factors. In Khorasan, all factors were increased except for seed yield and 1000-seed weight due to inoculation with Flavobacterium strains. In Fars, inoculation with strain F40 significantly increased seed yield production by 11.5% compared to control treatment. In Kermanshah, seed yield, total biomass and plant height were significantly affected by inoculation with bacterial strains. Results showed that strain F40 was the most effective strain to increase yield of wheat. This study showed that Flavobacterium as a PGPR bacteria is able to positively affect the growth of wheat in Iran. This is in agreement with experiments in other parts of the world. In Khuzestan, bacteria were not effective on growth of wheat probably due to high soil temperature in this province compared to other provinces.
Conclusions: This study revealed that Flavobacteria are present in rhizosphere of wheat in Iran and could improve growth characteristics and yield of wheat in field experiments. Finally, strain F40 was the superior strain which increased seed yield by 15 % compared to control treatment.
P. Keshavarz; M. Forouhar; M. Dadivar
Abstract
Introduction: World cereal demand is growing at the present in accordance with the global expansion of human populations.Bread wheat is the most widely grown cereal grain with 65% (6.5 million hectares) of the total crop cultivated area in Iran. Deficiency of micronutrients in cereal cropping is one ...
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Introduction: World cereal demand is growing at the present in accordance with the global expansion of human populations.Bread wheat is the most widely grown cereal grain with 65% (6.5 million hectares) of the total crop cultivated area in Iran. Deficiency of micronutrients in cereal cropping is one of the major worldwide problems. Zinc (Zn) is an essential micronutrient for plants. It plays a key role as a structural constituent or regulatory co-factor of a wide range of different enzymes and proteins in many important biochemical pathways. Nearly half of the world’s cereal-growing areas are affected by soil zinc deficiency, particularly in calcareous soils of arid and semiarid regions. High pH levels and bicarbonate anion concentration in these soils are the major factors resulting in low availability of Zn. About 40% of the soils, used for wheat production in Iran are Zn-deficient, which results in a decrease in growth and wheat grain yield under field conditions. Although application of zinc fertilizers is a common practice to correct Zn deficiency, growing varieties with high Zn efficiency has been reported to be a more sustainable approach. There is significant genetic variation both within and between plant species in their ability to maintain significant growth and yield under Zn deficiency conditions. Plant response to Zn deficiency and Zn fertilization are two distinct concepts. Knowing about these variations, can be very essential and useful for making correct fertilizer recommendation.
Materials and Methods: In order to investigate Zn efficiency in various wheat genotypes, a factorial experiment as a randomized complete block design was carried out with three replications in agricultural research center of Khorasan razavi (Torough Station), during 2009-2011. Treatments consisted of two levels of Zn fertilizer (0 and 40 kg/h as ZnSO4) and six genotyps of wheat including: three cultivars and one line of bread wheat (Alvand, Falat, Toos, and C75-5 respectively), two species of wheat known as Thriticosecale and Durum. The plot size was 9*3.6 (32.4 m2). Soil fertility status showed 0.05% nitrogen, 7.2 mgkg-1 phosphorus, 180 mgkg-1 potassium and 0.52 mgkg-1 DTPA extractable zinc. At defined phonological stage (SG6 based on Fix’s Index) Zn concentration in shrub was measured. Also grain yield and Zn uptake by grain were determined at the end of ripening stage. Zinc use efficiency, agronomic efficiency and apparent recovery efficiency were calculated according to “Graham, et al.”, “Craswell and Godwin” and “Raun and Johnson” respectively. Zinc use efficiency can be defined as the ratio of grain yield or shoot dry matter yield produced under Zn deficiency to that produced under Zn fertilization.
Results and Discussion: Grain yield is the most integrative trait of a particular genotype. The results showed that Zn application increased significantly grain yield by 12.61% in comparison with control. This result is supported by Ziayeian and Malakouti (1999). Who reported that Zn application significantly increased the wheat yield (17%). In our research the highest grain yield increase due to Zn application was found in durum wheat (23.5%), and the lowest grain yield increase, were found in Toos cultivar (1.3% yield increase). Cakmak and et al (1997) also obtained more yield with the application of zinc in durum wheat. Application of Zn increased Zn concentration and uptake in grain, 8.6% and 21.5% respectively. Also, application of Zn significantly increased Zn concentration in shoot (36.5%) over the control. Similarly, Moshiri et al (2010) reported increase of Zn concentration in shoot with application of Zn fertilizer. Zn use efficiency in bread wheat genotype, Durum and Thriticosecale wheat was ranked as: Durum < C75-5 < Alvand < Falat < Triticale ~ Toos. The findings of Khoshgoftarmanesh et al (2004) showed that, Durum wheat is Zn inefficient genotype. According to our research results, Toos and Falat cultivars and Thriticosecale have higher efficiency than Alvand and C75-5 cultivars and Durum wheat. The results also suggest that to obtain higher yield in Durum wheat, soil and foliar application of Zn is more necessary in comparison with other genotypes especially Toos and Thriticosecale.
Conclusion: wheat genotypes were different in their response to Zn deficiency and Zn supply. Thriticosecale and Toos were the most Zn efficient genotypes, whereas Durum and C75-5 were the most responding to Zn supply. So, without considering these differences, accurate fertilizer recommendation cannot be achieved. For organic farming and low input agriculture systems in regions similar to this experiment location (Torough Station), Thriticosecale and Toos could be suggested. However, for improvement of wheat grain yield and achieve desired quality in calcareous soil, most of the time, it is necessary to use the Zinc fertilizers.