seyed mohammadreza naghedifar

Cover plants, among the most commonly used plants in the realm of green spaces, are typically characterized by shallow roots. In this research, a simple and flexible algorithm has been introduced for calculating the basal crop coefficient and evaporation coefficient of shallow-rooted plants without the use of (micro-) lysimeters. The presented algorithm requires measurements of moisture at three depths for short-term calculations and only surface layer moisture monitoring for long-term calculations. Using this algorithm, it is possible to calculate the potential evaporation and transpiration of the plant. Furthermore, the presented algorithm is independent of time steps. To evaluate this algorithm, nine experimental plots were utilized, including six Frankenia plots with full coverage and three bare soil plots during the peak water demand period. All experiments were conducted at the educational site of irrigation systems in the vicinity of the meteorological site of Ferdowsi University of Mashhad (FUM). For this purpose, the water content and irrigation of these plots was fulfilled (at most) every 48 hr. The results indicated that the presented algorithm has good capabilities for estimating the basal crop and evaporation coefficients. Additionally, the basal crop coefficient for Frankenia plant was found to be 1, and the evaporation coefficient was 0.58. Therefore, this method can be employed for estimating the water requirements of different plants without using (micro-) lysimeters.

Agriculture is the largest consumer of fresh water in the whole world. Currently, almost 11% of the Earth’s total land surface is arable, which is expected to reach 13% by 2050. Roughly 17% of these lands are subject to modern form of irrigation management, which constitutes about 30-40% of the gross agricultural output. Reducing water consumption and increase water productivity in agriculture, requires a correct understanding of the biological response of crop to water. The ever-increasing growth of the population and the limitation of fresh water resources have led irrigation experts to new and efficient approaches in making decision to increase water productivity. The lack of scheduling in irrigation or their incompatibility with weather conditions, soil, irrigation system, agricultural restrictions and different phenological stages of crop, has caused severe losses in irrigated fields. Crop modeling and field data measurement can help irrigation experts improve irrigation scheduling in field and reduce water losses. Coupling in-situ measurement and crop modeling during the growing season is one of the useful solutions to improve irrigation scheduling in different farm conditions. In this paper, AquaCrop was calibrated for maize (Zea mays L.) in research farm of Ferdowsi University of Mashhad with comprehensive dataset. Variations of soil water content at different depths and also different growth indices was monitored during one growing season. The main novelty of this research is the targeted use of AquaCrop software to reduce water consumption by knowing the phenologically sensitive stages.

In-situ and in-lab measurements along with plant modeling, have been the two main parts of this research. First, the input files of the AquaCrop software were prepared and calibrated for maize during one growing season. The outputs of software including variations of moisture, biomass produced during the growing season and final yield were compared with the values measured in the field. After ensuring the accuracy of calibrated software, an improved irrigation scheduling was investigated. Subsequently, by means of crop modeling, the sensitive intervals of the crop to soil water stress as well as the thresholds of yield reduction in different stages of growing season were determined with the aim of improving irrigation scheduling in maize field. Biomass reduction, dry yield production, and the changes in water use efficiency during the growing season were investigated according to different amounts of moisture reduction in root zone, and irrigation scheduling was fulfilled by applying stress to less sensitive stages. The software was first run in Net Irrigation Requirement mode. Then, different amounts of drought stress were applied to each of the growth stages of the crop, and the other stages were kept constant in non-stressed condition. Dry yield and biomass reduction as well as water productivity changes were obtained for each stage. According to the threshold values and the amount of yield reduction at each stage, it is possible to fine-tune the time and amount of applying stress to crop. Furthermore, according to the moisture profile of the root zone, the drained water was significantly reduced in field. In this research, in order to increase accuracy in moisture measurement and prevent errors, an equation was developed to convert the mV output of sensor to volumetric moisture. At each moisture measurement, the PR2 sensor reports a number in mV for each depth, which is converted to volumetric moisture using device's conversion equation. This equation can vary according to field and soil condition. To develop the conversion equation, six same pots with a height of 40 cm and diameter of 30 cm were filled with the desired field soil, and the access tubes were placed horizontally inside them. The soil inside these pots was completely saturated and exposed to air for drying. During the drying period of the pots, the soil moisture was measured regularly using PR2 sensor and at the same time by weight method. After these measurements, calibration curves for PR2 sensor were obtained using the alpha mixing method for different depths.

The Pearson correlation coefficient for measured and simulated moisture by software, was 0.84 and the root mean square error was 12 mm. Also, the value of Pearson correlation coefficient for measured and simulated values of biomass was equal to 0.99 and mean square root of error was 1.3 ton/ha. The results showed that the vegetative stage of crop was sensitive to drought stress and caused a significant yield reduction at the end of the growing season. The stage of germination and flowering were less sensitive, such that the decrease of moisture up to 12.3% compared to the Net Irrigation Requirement mode would not change the final yield of the crop. The improvement made in the field resulted in no change in the amount of biomass and dry yield (0.38% increase in biomass and 0.52% increase in dry yield), at the same time 26.6% decrease in depth of irrigation water, 85.6% decrease In drainage and increasing the efficiency of evapotranspirated water, it has increased from 4.66% to 4.67% kg/m3 compared to condition before modify. Before modify, the farm was managed in traditional way.

More research in the field of irrigation scheduling and providing comprehensive instructions to farmers in different weather conditions and different irrigation systems, taking into account different management approaches in allocating water to different parts of the farm along with investigating the effects of saline water irrigation, can be topics for the next research should be for experts in this field.

The fundamental principles of smart irrigation hinges upon precise assessments of soil moisture content within the root zone layer. Various techniques have been developed to ascertain root zone soil moisture content, such as using soil moisture measurement sensors or simulation models. Each one of these methods has its own distinct advantages and disadvantages. Data assimilation encompasses an array of approaches that combine model estimates with the corresponding observed data to derive a more precise estimations of the required data. The purpose of this research is to ascertain the feasibility of reducing the number of depths at which soil moisture measurements were taken and increasing the time interval between two consecutive soil moisture measurements using the Ensemble Kalman filter.

This study was conducted synthetically based on information collected from four farms in Jovein, Khorasan Razavi Province, cultivating sugar beets and corn. Data was collected from four farms during the period of April to November 2020. The numerical solution of the Richards equation with the inclusion of the sink term was used to simulate the soil moisture changes in the root zone layer. To mitigate data assimilation's vulnerability to potential result divergence among members, an identification and correction mechanism, along with handling divergent members, were integrated into the system. This mechanism was found on the sudden model result shift throughout the entire root profile between two consecutive days. Two indicators were used to evaluate the scenarios: a) the sum of covariance matrix diameters at the last simulation time step, and b) the normalized root mean square difference of the soil moisture content within the soil profile, comparing the scenarios with the scenario having the largest number of soil moisture measurement depths and the shortest time interval between two consecutive measurements.

The results indicated that with the application of Ensemble Kalman filter, it is possible to improve the accuracy of the results using a longer time interval between measurements. The Data Assimilation scenarios exhibited a remarkable capability in reducing the diameter of the covariance matrix. This reduction, ranging from 61% to 86%, compared to the open-loop scenario, emphasizes the ability of Ensemble Kalman filter to effectively mitigate uncertainty. The normalized root mean square difference , was notably improved by the Data Assimilation scenarios. The normalized root mean square difference of scenarios ranged from 0.03 to 0.11, while the normalized root mean square difference for the Open Loop was 0.15, highlighting the capacity of Ensemble Kalman filter to minimize discrepancies between simulated and observed soil moisture profiles. Such reductions in normalized root mean square difference values signify the model's improved ability to capture actual soil moisture variations, thus contributing to more reliable predictions and better decision-making in agricultural water management. The application of Ensemble Kalman filter helped to select the proper measurement depths and ultimately to reduce the number of required soil measurement points.

Data Assimilation successfully diminished the uncertainty of the soil moisture content results, even when utilizing the minimum number of soil moisture measurement depths and maximum time intervals between observations. Both of these findings—increasing the time interval between consecutive measurements and reducing the required number of measurement depths—indicate that with the application of data assimilation, it is possible to decrease the cost of the implementation of the smart irrigation.

Saffron is one of the most valuable spices in the world and due to its high economic value and low water requirement, it is widely cultivated in eastern Iran. In this research, which took place in the research farm of the Ferdowsi University of Mashhad, Iran, in 2021-2022, the AquaCrop model to simulate the yield of the saffron plant was calibrated and validated using field measurements. Therefore, soil moisture, biomass, and plant canopy cover area were measured with a relatively high time resolution during the growing season. Pearson's correlation coefficient, root mean square, error, index of agreement and Nash–Sutcliffe index for moisture simulation were 0.87, 7.8 mm, 0.92 and 0.62 respectively, plant biomass was 0.99, 0.3 t.ha-1, 0.99, 0.98 and also 0.98 , 5%, 0.98 and 0.93 were obtained for canopy cover . The sensitive stages of the saffron plant were determined by examining the changes in daughter corms weight, biomass, and water productivity during different stages of growth in response to water stress, and a revised scenario was proposed to improve field irrigation. By applying this scenario and running the model, the amount of daughter corms weight production increased from 5.550 to 7.693 t.ha-1 and biomass from 7.204 to 9.395 t.ha-1. The water productivity value also increased from 3.50 to 3.69 kg.m-3 and 85 mm was saved in water consumption.

Development of numerical models for management and assessment of irrigation systems is an important step for establishing farm decision support systems. In this study, a coupled model has been developed for simulation of furrow irrigation using 1D fully hydrodynamic form of Saint-Venant equations and 3D fully-form of Richards’ equation. The Saint-Venant equations have been discretized by an explicit scheme while the Richards’ equation has been solved by an implicit scheme. Furthermore, coordinate transformation technique was employed to handle non-orthogonal grids of 3D Richards’ equation. The model was subsequently validated using experimental and numerical data and in all cases acceptable accuracy was observed. Root mean square error and mean absolute error for the advance phase were 0.63 and 2.63 sec, respectively. Furthermore, the maximum root mean square error and the mean absolute error for pressure head distribution were obtained 0.24 and 0.45 m, respectively.  Finally, the proposed model was employed to simulate furrow irrigation for five irrigation events and the results were analyzed. The results showed that the proposed model is able to simulate advance phase of furrow irrigation.

Since many parts of Iran are located in arid and semi-arid regions of the world, groundwater recharge is an important component of the water cycle in these areas. In this paper, groundwater recharge under different irrigation scenarios for thirty logs lied in Neishabour plain was investigated. Daily data, including precipitation, evapotranspiration and leaf area index were used to run HYDRUS-1D software. Thirty observation logs with different depths of water table were selected at different locations in the plain. Analysis of the recharge rate in the wheat-fallow scenario showed that for different soil textures, groundwater recharge is dependent on the number of irrigation applications. For the logs with sandy soil textures, the maximum amount of recharge was obtained in five irrigation events scenario by an average of 325.23 mm year-1. For the logs with sandy clay loam soil texture, the highest recharge rate was obtained in the seven irrigation events scenario by an average of 67.43 mm per year-1. In the logs dug in loamy soil texture, due to the high depth of the soil block, the same recharge rate was obtained at different scenarios. In the case of a double-cropping scenario, groundwater recharge increased due to irrigation of tomato in summer. In this scenario, the highest recharge rate was obtained in observation log drilled in sandy soil texture with a depth of 15 m which was equal to 440.47 mm year-1.