Irrigation
M.S. Fakhar; A. Kaviani
Abstract
Introduction
Achieving food security in the future with sustainable use of water resources will be a big challenge for the current and future generations. Population increase, economic growth and climate change intensifythe pressure on existing resources. Agriculture is a key consumer of water, and ...
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Introduction
Achieving food security in the future with sustainable use of water resources will be a big challenge for the current and future generations. Population increase, economic growth and climate change intensifythe pressure on existing resources. Agriculture is a key consumer of water, and it is necessary to closely monitor water productivity for it and explore opportunities to increase its productivity. Systematic monitoring of water productivity through the use of remote sensing techniques can help identifying the gaps in water productivity and evaluate appropriate solutions to address these gaps.
Materials and Methods
Qazvin plain is known as a hub of modern agriculture by providing about 5% of the country's agricultural products. Therefore, estimating water demand and water productivity in agricultural management in the region is considered important and necessary. In order to monitor water productivity through access to various data across Africa and the Middle East, the WaPOR database provides the possibility to examine the rate of evapotranspiration, biomass and gross and net biomass volume productivity based on the land use map in the period of years 2009 to 2021. In this database, it is possible to check the mentioned items at three levels with different spatial resolution, which according to the scope of the study, it is possible to check values with a spatial resolution of 250(m). In order to determine the efficiency and accuracy of the land cover classification map of the WaPOR database, the results obtained are examined and compared with the Dynamic World model, which represents a global model with high accuracy. For this purpose, the latest land use map related to 2021 Using the WaPOR database and Dynamic World in the GEE system, it was prepared and based on the classification of the region in order to check the accuracy of the user map of the WaPOR database and to determine the percentage of each class compared to each other. Finally, all estimable indicators were calculated and checked by the WaPOR database during the years 2009 to 2022.
Results and Discussion
The amount of evapotranspiration of the plants covered by the irrigation network in the period of 2009 to 2016 has been associated with a relatively stable trend, but this trend has decreased in 2017 onwards, which is one of the reasons for the decrease in the amount of evapotranspiration in this the period of time and can refer to the lack of water available to the plant due to the limited water resources in recent years. The investigation of the total amount of biomass in different lands shows that during the years 2009 to 2022, this index has been accompanied by a gradual increase in all uses, so that the amount of TBP index in 2020 was 17% more than in 2009. It shows the amount of biomass in different lands. The amount of biomass in the lands covered by the water network is 5 to 6 times higher than that of the rainfed lands. Among the influential parameters in estimating the TBP index, we can mention the amount of evaporation, transpiration, and transpiration, the increase or decrease of each of these parameters will have a significant impact on the estimated amount of biomass. The results showed that the amount of biomass production in the areas covered by the irrigation network largely depends on the high transpiration rate in these areas. From the beginning of 2009 to 2016, the gross amount of biomass water in the lands covered by the irrigation network has been accompanied by an increase, but in 2017, drastic changes in the process of underground changes will decrease the area of the lands covered by the network and many of these lands. It has been turned into fallow and rainfed lands. The analysis of NBWP index also showed that the amount of net productivity in rainfed lands is strongly dependent on the annual increase rate, and much of the crop yield in rainfed lands is dependent on the amount received. Among the influential parameters in estimating the total amount of biomass, we can mention the amount of evaporation, transpiration and transpiration, the increase or decrease of each of these parameters will have a significant impact on the amount of estimated biomass.
Conclusion
WaPOR database data can play an important role in estimating the rate of delayed transpiration and parameters related to water productivity in the region due to its ten-day spatial resolution and the absence of data gaps. In general, the WaPOR database can be used as a guide in the reliable determination of evapotranspiration values and planning related to water resources in the agricultural sector.
M. R. Emdad; A. Tafteh
Abstract
Introduction: SALTMED model is one of the most practical tools for simulating soil salinity and crop production yield. Growth models are important and efficient tools for studying and evaluating the impact of different management conditions and scenarios on water, soil and plant relationships and can ...
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Introduction: SALTMED model is one of the most practical tools for simulating soil salinity and crop production yield. Growth models are important and efficient tools for studying and evaluating the impact of different management conditions and scenarios on water, soil and plant relationships and can be used to make or predict appropriate management scenarios according to the region's conditions and to predict plant performance in the field. Since the performance of irrigation scenarios in field conditions are costly and time consuming, and due to the limited water resources in the country and the necessity of optimal water use in agriculture, using the efficient and generic models can be useful tool for simulating crop production and soil salinity variations. This research has been conducted in order to simulate soil salinity and yield production using SALTMED model in Azadegan Plain of Khuzestan province. Materials and Methods: This study was carried out in wheat fields of Azadegan plain in Khuzestan province during 2014-2015 in three regions including Ramseh (as saline soil), Atabieh (as very saline soil) and Hamidieh (as control, non-saline soil). Three 10-hectare plots were selected in each area and a pilot with area of 2000 m2 was used for evaluation and measurement in each plot. First year data were used to calibrate the SALTMED model and second year field data were used to validate the model and to achieve the results in three conditions. The dominant soil texture in the area was clay loam. The quality of used irrigation water with average salinity of 2 dSm-1 was classified as C3-S1(high salinity with low sodium absorption ratio) and had no effect on wheat yield loss. In this study, version 3-04-25(2018) of SALTMED model was used and after calibrating in the first year, the results of simulated wheat grain yield and soil salinity variation values were used for model validation in different regions and in soils with different degrees of salinity, in the second year. Results and Discussion: The average measured and simulated biomass yield in the first year were 6.6 and 6.1 t/ha, respectively. Furthermore, the average of measured and simulated of wheat grain yield was 2.9 and 2.6 t/ha, respectively. Some statistical indices including mean bias error, normalized root mean square error, and root mean square error for grain yield were 0.11, 0.04, and 0.12 t/ha, respectively. The values of the same statistical parameters for biomass were -0.49, 0.1, and 0.61t/ha, respectively. These results showed that the measured values of grain yield and wheat biomass were in good agreement with the simulated values using SALTMED model. The simulated and measured variations of soil salinity at three soil depths of 0-30, 30-60, and 60-90 cm, showed close agreement with each other in three layers. Root mean square error, normalized root mean square error, and mean bias error for soil salinity values were 1.3, 0.20, and -0.06, respectively. After calibrating the model in the first year, to validate this model in the second year, the results of three pilots locations in three regions of Ramseh (saline), Atabieh(very saline) and Hamidieh(non-saline) were used. Comparison of simulated and measured wheat grain yield and biomass values showed that there was no significant difference between simulated and measured values. The simulated values of grain yield and wheat biomass in the three non-saline, saline and very saline soils had high correlation with the measured values, indicating high accuracy and efficiency of this model in simulating grain and biomass yield in different degrees of soil salinity. Moreover, the trend of soil salinity changes simulated by the SALTMED model in three highly saline, saline and non-saline soils (for three soil layers) was close to the measured values. The SALTMED model with normalized root mean square error and mean bias error of 0.18 and -0.13, respectively, showed good accuracy in different salinity conditions. There was no significant difference (5% level) between the measured and simulated salinity values of the different soil layers. The mean standard error at the 0-30, 30-60, and 60-90 cm layers was 1.1, 1.05, and 0.81 dSm-1, respectively. Therefore, based on the results and statistical indices, it was found that SALTMED model had good accuracy and efficiency in simulating yield, biomass and soil salinity under different salinity conditions. Conclusion: According to the results and statistical indices, SALTMED model had good performance and accuracy in simulating grain yield, biomass and soil salinity variations in different soil salinity conditions and so it can be used to predict wheat yield, yield components and soil salinity in different soil condition with different degrees of soil salinity to sustain soil and water and improve water productivity in similar areas.
,fatmeh hashami; Ali Shahnazari; mahmood raeini; ali ghadami firouzabadi; Ebrahim Amiri
Abstract
The research as reported in related to simulation by WOFOST, predominately focused on traditional methods of deficit irrigation such as terms of percentage in full irrigation conditions or as evaluation of growth and development in certain days after irrigation. Also it should be noted that not only ...
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The research as reported in related to simulation by WOFOST, predominately focused on traditional methods of deficit irrigation such as terms of percentage in full irrigation conditions or as evaluation of growth and development in certain days after irrigation. Also it should be noted that not only these researches was based on a year plants, but also there isn’t any research of sunflower. So, in this research the ability of the last version of WOFOST in simulating of sunflower in DI and PRD in %75 and %55 levels is carried out in contrast to FI in two continued year so that crop coefficient of sunflower could be calculated and by this, the productivity of yield in Sari agricultural and natural resources research field could be achieved. The results of calibrations showed that crop coefficient which depends on weather, coordinates of region and physiologic and phonologic of plant is fixed among the simulation and irrigation coefficient are depend on irrigation treatment and their response in development of growth stages. Also the results showed that by decreasing the volume of water which given to plant, AMAXTB and KDIFTB decreased and adversely EFFTB is increase. Simulated seed yield and total biomass had normalized root mean square error (nRMSE) index less than 10%, coefficient of residual mass (CRM) index near zero, modeling efficiency (EF) about 0.98, correlation coefficient (R) about 0.96 and totally comparing the simulation and observation parameters showed that in the most statistical test done in the present study, the result in acceptable range which represented that WOFOST could be able to simulate the responses od sunflower in DI and PRD treatments by calibrated coefficient.
Fatemeh Rakhsh; Ahmad Golcchin
Abstract
Introduction: Mobilization and stabilization of organic matter in soils represent a set of complex processes involving the processing and decomposition of organic matter by diverse communities of soil fauna and microorganisms, as well as chemical-physical interactions with mineral particles of soil. ...
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Introduction: Mobilization and stabilization of organic matter in soils represent a set of complex processes involving the processing and decomposition of organic matter by diverse communities of soil fauna and microorganisms, as well as chemical-physical interactions with mineral particles of soil. Clay minerals have high effects on the soil organic matter dynamics. Clay minerals with the physical protection of organic matter play an important role in reducing the rate of decomposition of organic matter. The effects of soil texture on the soil organic matter dynamics have been investigated in many studies, but the effects of exchangeable cations and clay types on mineralization of organic nitrogen and microbial biomass nitrogen have not been given much attention. For this reason, the aim of this study was to evaluate the effects of types and clay contents and exchangeable cations on the mineralization of organic nitrogen and microbial biomass nitrogen.
Material and Methods: Appropriate amounts of homoionic Na-, Ca- and Al- clays from Georgia kaolinite, Illinois illite and Wyoming montmorillonite were mixed with pure sand to prepare artificial soils with different clay contents, exchangeable cations, and clay types. The artificial soils have zero, 5 and 10% clay from Georgia kaolinite, Illinois illite and Wyoming montmorillonite that their clay minerals saturated with Ca, Na and Al. Alfalfa plant residues were incorporated into the artificial soils and the soils were inoculated with microbes from a natural soil and incubated for 60 days and concentration of NH4-N and NO3-N were measured every 15 days. In the artificial soil samples, microbial biomass nitrogen was measured by the fumigation-extraction method in the end time of incubation period.
Results and Discussion: The results of this study showed that the percentage of mineralized nitrogen in the two-month incubation period, was higher in the pure sand than in soils containing 5% and 10% clay, indicating that clay contents influence the capacity of soils to protect and store organic nitrogen. Microbial biomass nitrogen increased as the amount of clay in the soil increased. The highest and lowest amounts of microbial biomass nitrogen measured in soils with 10% clay (9.26 mg per 50 g dry soil) and pure sand (4.31 mg per 50 g dry soil), respectively. There was a significant influence of exchangeable cations on the percentage of mineralized nitrogen and microbial biomass nitrogen. The microbial biomass nitrogen and the percentage of mineralized nitrogen were highest in Ca-soils and lowest in Al-soils. The percentage of mineralized organic nitrogen in two months of incubation period was highest in soils with Georgia kaolinite clay and lowest in soil with Wyoming montmorillonite clay. The amounts of microbial biomass nitrogen in soils with Wyoming montmorillonite clay were lower than soils with Georgia kaolinite and Illinois illite clays. The percentage of mineralized organic nitrogen increased as the incubation period increased. The results of this study indicated that organic nitrogen mineralization rates and microbial biomass nitrogen were affected by types and clay contents and exchangeable cations and interaction of organic matter with clays and is an important process as it slows soil organic matter decomposition.
Conclusions: Mixing the alfalfa residues with artificial soils and incubation samples allowed to study the effects of types and clay contents and exchangeable cations on the percentage of NH4+-N, NO3--N, mineralized nitrogen, and microbial biomass nitrogen. Soils with different clay contents have different surface areas and cation exchange capacities; therefore, it is concluded that organic nitrogen storage of soils is, partly, controlled by the surface areas, cation exchange capacity and physical protection provided by the soils. Nitrogen mineralization and the amounts of microbial biomass nitrogen were different in soils with different exchangeable cations. It is concluded that exchangeable cations exert their influence on microbial biomass and hence nitrogen dynamics by controlling the size and activity of the microbial population through modifying the physicochemical characteristics of microbial habitats. Since various clay minerals have different specific surface areas and cation exchange capacity and the physicochemical changes induced in the soil environment as a result of variations of exchangeable cations is much greater in soils with higher cation exchange capacity and specific surface area. It seems the effects of clay mineralogy on the dynamics of organic materials and microbial biomass, in part, arise from the type of exchangeable cations present on the exchange sites of the clay minerals.
M. Biria; Abdulamir Moezzi; H. AmeriKhah
Abstract
Introduction: Among wide variety of soil pollutants including heavy metals, acidic precipitation and other toxicants, the importance of heavy metals due to their pollution capacity has received growing attention in recent years. These metals enters into soil through municipal and industrial sewage as ...
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Introduction: Among wide variety of soil pollutants including heavy metals, acidic precipitation and other toxicants, the importance of heavy metals due to their pollution capacity has received growing attention in recent years. These metals enters into soil through municipal and industrial sewage as well as direct application of fertilizer and pesticides. High cadmium and lead concentration in soil lead to severe environmental pollution. Such pollution not only has a destructive effect on crop yield but also endangers human being and other creatures’ health after entering in their food chain. Several physical, chemical and biological methods used to reduce the adverse effect of high concentration of heavy metals in soil. In spite of the hight cost, these methods are not always suitable for reclamation of small area and mostly have side effect on physico-chemical and biological characters of soil, after application. Biochar produced by thermal decomposition of biomass in the absence or presence of low oxygen. These material due to their high spacific surface area and high cation exchange capacity may have great ability to absorb charged material including heavy metals. Therefore in this study attempt is made to evaluate the effect of sugarcane bagasse –derived biochar in improving maize plant growth in cadmium and lead contaminated soils.
Material and methods: This study was carried out during the year 2014 in two separate experiments in Shahid Chamran university. The treatments in each case consisted of two levels of sugarcane bagasse made biochar (0 and 4 percent by weight) in combination with each soil, properly contaminated with 50 and 100 mg cadmium per kg soil in first experiment and 500 and 1000 mg lead per kg soil in the second. The treated soils were applied to pot and arranged in a complete randomized block designe and replicated 3 times. Prior to introduction of soil to pots, the heavy metal contaminated soils with moisture content around 70 percent of F.C. were incubated for 30 days. During incubation period sugarcane bagasse was dried, milled, sieved, compacted and subjected to traditional furnace at 550 oc for 3 hours on low pyrolysis. The furnace temperature was controlled manually using lesser thermometer. The furnace cooled down and the collected sugarcane bagasse made biochar sieved again. The incubated soil mixed with proper amount of sugarcane bagasse made biochar and incubated under previous condition for 45 days. The treated soils were poured to the labeled pots and 3 maize seeds were sown in each pot and two weeks after emergence thinned to one plant per pot. Nineteen days after sowing, the height of the plants and chlorophyll index were recorded and plants were harvested and leaf area of each plant was recorded, maize root content of each pot were carefully separated from soil and along with shoot property washed, dried, weighed and after milling subjected to chemical analysis. Prior to sowing maize seeds some of physic- chemical properties of untreated soil were estimated. Furthermore few charactoristics of sugarcane bagasse made biochar including pH and EC in 1 : 10 solution of biochar to water recorded. N, C, H, O concentration were estimated by elementary analyzer. Cation exchange capacity of sugarcane bagasse made biochar was measured by ammonium acetate method. Moreover its functional group determined by FT-IR method. Specific surface area estimated as per Branuar Emmet Teller (BET) method. Sugarcane bagasse made biochar image was obtained from scanning electron microscope. Cadmium and lead concentration in root and shoots were estimated by atomic absorption spectrometer after wet digestion. SAS software was used for statistical analysis data which fallowed by Duncan test to compare the mean values.
Results and discussion: The results showed that implementation of cadmium and lead led to decrease in chlorophyll index, leaf area, height of plant and root and shoot dry weight significantly. But the sharp decline in the concentration of cadmium and lead in root and shoot after sugarcane bagasse made biochar application improved chlorophyll index, leaf area, height of plant, root and shoot dry weight. Application of 4% Sugarcane bagasse made biochar, decreased transfer factor (TF) and bioaccumulation factor (BF) of these elements compared to control. The results showed high capability of sugarcane bagasse made biochar to absorb cadmuim and lead and reduce their availability to plant respectively. In fact application of sugarcane bagasse made biochar dwindled cadmium and lead absorption as well as their transfer factor and bioaccumulation factor, and hence improved plant growth.
Conclusion: The results obtained after sugarcane bagasse made biochar application mainly initiated due to high cation exchange capacity of which eventually was created by large number of functional groups in its high specific surface area (table 2) to stabilize cadmium and lead and render them unavailable to plant and hence improve its growth.
M. Mohammadi; B. Ghahraman; K. Davary; H. Ansari; A. Shahidi
Abstract
Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop ...
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Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop model predicts crop productivity, water requirement, and water use efficiency under water-limiting and saline water conditions. This model has been tested and validated for different crops such as maize, sunflower and wheat (T. aestivum L.) under diverse environments. In most of arid and semi-arid regions water shortage is associated with reduction in water quality (i.e. increasing salinity). Plants in these regions in terms of water quality and quantity may be affected by simultaneous salinity and water stress. Therefore, in this study, the AquaCrop model was evaluated under simultaneous salinity and water stress. In this study, AquaCrop Model (v4.0) was used. This version was developed in 2012 to quantify the effects of salinity. Therefore, the objectives of this study were: i) evaluation of AquaCrop model (v4.0) to simulate wheat yield and water use efficiency under simultaneous salinity and water stress conditions in an arid region of Birjand, Iran and ii) Using different treatments for nested calibration and validation of AquaCrop model.
Materials and Methods: This study was carried out as split plot design (factorial form) in Birjand, east of Iran, in order to evaluate the AquaCrop model.Treatments consisted of three levels of irrigation water salinity (S1, S2, S3 corresponding to 1.4, 4.5, 9.6 dS m-1) as main plot, two wheat varieties (Ghods and Roshan), and four levels of irrigation water amount (I1, I2, I3, I4 corresponding to 125, 100, 75, 50% water requirement) as sub plot. First, AquaCrop model was run with the corresponding data of S1 treatments (for all I1, I2, I3, and I4) and the results (wheat grain yield, average of soil water content, and ECe) were considered as the “basic outputs”. After that and in the next runs of the model, in each step, one of the inputs was changed while the other inputs were kept constant. The interval of variation of the inputs was chosen from -25 to +25% of its median value. After changing the values of input parameters, the model outputs were compared with the “basic outputs” using the sensitivity coefficient (Sc) of McCuen, (1973). Since there are four irrigation treatments for each salinity treatment, the model was calibrated using two irrigation treatments for each salinity treatment and validated using the other two irrigation treatments. In fact, six different cases of calibration and validation for each salinity treatment were [(I3 and I4), (I2 and I4), (I1 and I4), (I2 and I3), (I1 and I3), and (I1 and I2) for calibration and (I1 and I2), (I1 and I3), (I2 and I3), (I1 and I4), (I2 and I4), and (I3 and I4) for validation, respectively]. The model was calibrated by changing the coefficients of water stress (i.e. stomata conductance threshold (p-upper) stomata stress coefficient curve shape, senescence stress coefficient (p-upper), and senescence stress coefficient curve shape) for six different cases. Therefore, the average relative error of the measured and simulated grain yield was minimized for each case of calibration. After calibrating the model for each salinity treatment, the model was simultaneously calibrated using six different cases for three salinity treatments as a whole.
Results and Discussion: Results showed that the sensitivity of the model to crop coefficient for transpiration (KcTr), normalized water productivity (WP*), reference harvest index (HIo), θFC, θsat, and maximum temperature was moderate. The average value of NRMSE, CRM, d, and R2 for soil water content were 11.76, 0.055, 0.79, and 0.61, respectively and for soil salinity were 24.4, 0.195, 0.72, and 0.57, respectively. The model accuracy for simulation of soil water content was more than for simulation of soil salinity. In general, the model accuracy for simulation yield and WP was better than simulation of biomass. The d (index of agreement) values were very close to one for both varieties, which means that simulated reduction in grain yield and biomass was similar to those of measured ones. In most cases the R2 values were about one, confirming a good correlation between simulated and measured values. The NRMSE values in most cases were lower than 10% which seems to be good. The CRM values were close to zero (under- and over-estimation were negligible). Based on higher WP under deficit irrigation treatments (e.g. I3) compared to full irrigation treatments (e.g. I1 and I2), it seems logical to adopt I3 treatment, especially in Birjand as a water-short region, assigning the remaining 25% to another piece of land. By such strategy, WP would be optimized at the regional scale.
Conclusion: The AquaCrop was separately and simultaneously nested calibrated and validated for all salinity treatments. The model accuracy under simultaneous case was slightly lower than that for separate case. According to the results, if the model is well calibrated for minimum and maximum irrigation treatments (full irrigation and maximum deficit irrigation), it could simulate grain yield for any other irrigation treatment in between these two limits. Adopting this approach may reduce the cost of field studies for calibrating the model, since only two irrigation treatments should be conducted in the field. AquaCrop model can be a valuable tool for modelling winter wheat grain yield, WP and biomass. The simplicity of AquaCrop, as it is less data dependent, made it to be user-friendly. Nevertheless, the performance of the model has to be evaluated, validated and fine-tuned under a wider range of conditions and crops.
Keywords: Biomass, Plant modeling, Sensitivity analysis
