The portion of soil water that is easily absorbed by plant and usually leads to maximum yield is defined as "Readily Available Water" and is shown as p. This is an important parameter for irrigation scheduling and is useful for determining the irrigation depth (d) and frequency (i). The value of p depends upon the plant species, growth period, rate of evapotranspiration (ETo), and the type of soil.In the present study the value of p is determined for grain corn variety single cross 704 in 2 stages of growth periods in Karaj. The first stage was from seed establishment till appearance of the sixth leaf and the second stage was after appearance of this leaf till soft grain.'This research was carried out with a split plot design in the research station of Seed and Plant Improvement Institute in Karaj for a period of 3 years. The main plots were allocated to the treatments of the first stage and the levels of p fraction in this stage were 0.30 and 0.60. The subplots for the second stage were irrigated when p values were 0.30, 0.50 and 0.70. The amount of p was kept 0.8 for the last growth stage (from soft grain till harvesting). These 6 treatments were replicated 4 times. In this study production potential parameters as grain yield, ear yield dry mater and weight of 1000 grains were determined. Also by measuring the amount of irrigation water, soil moisture balance and evapotranpiration, the water utilization and application efficiency were obtained.Analysis of the results show that by doing short irrigation intervals (p=0.30) during the first stage could be had a good crop establishment and vegetative growth period, then enable us to reduce the number of irrigation (longer irrigation intervals) (p=0.70) in later growth stages. The amount of consumptive use of water in this treatment was 7898 m3ha-1 and grain yield's was 12 ton ha-1.

One of the most common approaches for farm irrigation management is using soil readily available water and allowable depletion coefficient. The objective of this study was to determine wheat crop response coefficients, critical moisture content, and soil allowable depletion coefficient using a physically based method in three dominant soils under wheat cultivation in Khuzestan province. Treatments included full irrigation and water stress at three levels low, moderate, and high. The highest and lowest values of wheat crop response coefficient were related to silty clay loam (Ky=1.26) and clay loam (Ky=0.96), respectively. Critical soil moisture content was observed in loam soil (0.25 cm3cm-3)> silty clay loam (0.23 cm3cm-3)> clay loam (0.22 cm3cm-3), respectively. Despite the higher critical moisture content in loam, the most soil allowable depletion coefficient was also calculated in loam (0.54). Soil allowable depletion coefficient in silty clay loam and clay loam were 0.44 and 0.42, respectively. The results confirmed the simultaneous effects of soil and plant properties on the availability of soil water for the plants.

For an accurate irrigation schedule, the daily soil water depletion should be estimated during the crop growth period. Soil water depletion is dependent on daily evapotranspiration. In this research, daily evapotranspiration of S.C 704 maize was measured in mini-lysimeters. Estimation of daily evapotranspiration was done by continuous measurement of soil moisture. Leaves stomatal resistance was measured daily, by AP4 Porometer device. Soil water allowable depletion was determined in four growth stages of initial (C1), development (C2), mid (C3), and late (C4), based on the leaves stomatal resistance response. At each growth stage, when leaves stomatal resistance increased relative to the control crops, readily available water was ending and the time was right for new irrigation. The main variables included growth stage effect on crop evapotranspiration and water depletion coefficient, which was investigated in a completely randomized basic design, with three replications. Regression functions (models) were used for simulation of allowable soil water depletion coefficient (P) based on the daily evapotranspiration (ETc). The models were calibrated by daily data at initial and development stages, and were evaluated by daily data in mid and late stages. The FAO-56 linear model was compared with the models introduced in this research. The results showed that maize ETc (S.C 704) in initial, development, mid, and late stages was in the range of 1.5-4.5, 3.9 -7.1, 1.4 -7.5, and 0.2 -2.1 mm.d-1, respectively. The allowable soil water depletion in the mentioned stages was calculated as 0.45, 0.66, 0.61 and 0.7, respectively. Different sensitivity in crop growth stages caused readily available water limit not to be constant during growth period. The ETc increase caused a decrease in P, and decrease in ETc increased P. Linear, exponential, logarithmic, polynomial, power, and FAO-56 linear functions were investigated. Polynomial function with statistical indices of RMSE=0.00035, NRMSE=0.054, ME=0.0008, CRM=-0/000005, R2=0.999 and EF=0.999, was the optimal model in estimation of P coefficient. The reason for weak performance of FAO-56 model was the constant limit for readily available water and mean ETc rate in the growing season. Therefore, the FAO-56 model was modified. The research result was to estimate the soil water allowable depletion coefficient (by using ETc), without daily measurement of soil moisture. This method will be useful in irrigation scheduling, especially those with short intervals.

Dry farming is an important method of crop production, particularly in semi-arid regions, where crop yield strongly depends on precipitation amount and its temporal variation during the growing season. Supplemental irrigation is a suitable practice for mitigating moisture stress and, consequently, improving crop yield in rainfed agriculture. This study was conducted to estimate the proper time of supplemental irrigation in rainfed land by measuring soil water content during growth period, in 2016-2017. A field experiment was conducted using two plowing directions (along the slope and on contour lines) in plots with 2 m × 5 m dimensions, in a rainfed farm with 10 % slope. Volumetric soil water content (SWC) was measured in root zone by a TDR set at 10-day interval during growth period, from November 2016 to June 2017. Based on the results, SWC in the plots plowed on contour lines was about 6 % higher than the plots plowed along slope, and this difference was statistically different (P< 0. 05). The mean readily available water (RAW) during growth period in the plots plowed on contour lines (6. 07 mm) was about 2. 5 times higher than the plots plowed along slope (2. 25 mm) and showed significant difference with it (P< 0. 05). The highest RAW was observed at stem elongation and the lowest value was found at grain germination, at which stage, about 6. 7 mm water deficit was observed in rainfed wheat. Thus, application of supplemental irrigation at this stage can efficiently prevent water stress and improve wheat grain yield in this semi-arid region.

Since the measurement of soil moisture retention curve and its index points like field capacity and permanent wilting point are costly and time-consuming using direct methods, one of the quick and low costly estimation methods of these properties is estimating of those properties with respect to soil basic properties including particle size distribution and organic matter content as well as bulk density. In this study, index points of soil moisture retention curve such as field capacity, permanent wilting point and available water were estimated using MLP type neural network with three activate functions and regressions methodology with respect to soil basic properties. In order to conduct the aforementioned issue, 60 soil samples were collected from the case study, the region of Robatkarim of Karaj city, with an area of 24 ha. Then, the moisture content of the index points of soil moisture retention curve was measured by using the pressure plate. Further soil basic properties including the percentages of clay, sand, bulk density and organic matter as well as lime and the moisture content of the index points were measured using the aforementioned methodology. The results of the investigations showed that the artificial neural networks by sigmoid activate function a network with 6 neurons in the input layer and 10 neurons in the hidden layer as well as 3 neurons in the output layer with Performance indicators R2 and RMSE variations of 0.6197–0.8902 and 1.6846–2.0225, respectively was introduced as the efficient network which had the best performance in estimation of wanted outputs in comparison with another activate functions (method of artificial neural network) and regression method. Percentage of lime according to results of sensitivity analysis is considered as an effective factor in the range of soils texture tested.

In order to investigate the water uptake limit of maize at different growth stages and scheduling the irrigation time, research was conducted in Imam Khomeini International University as a factorial experiment in a randomized complete block design. In the control treatment, the water requirement of the plant was supplied. But stress treatments included applying water stress (up to the temporary wilting point), at growth stages of 6-leaf, 12-leaf, flowering and seeds doughing. In the period of water stress, stomatal resistance, canopy temperature and meteorological parameters were measured. Based on the response of crop parameters to water stress, the readily available water (RAW) and crop water stress index (CWSI) were determined. The amount of RAW and CWSI at the 6-leaf, 12-leaf, flowering and doughing stages, were calculated equal to 45, 66, 61 and 70 percent and 0. 37, 0. 54, 0. 63, and 0. 47, respectively. The results showed that crop sensitivity to water stress, was different at maize growth stages. The flowering stage of maize was the most sensitive stage to water stress. Therefore, based on crop response, the irrigation time was determined at the time of water stress. Also, by calculating the exact volume of water required and applying variable irrigation intervals during growth stages, the crop water requirement to be supplied and irrigation losses were prevented. Under these conditions, due to the different sensitivity of water absorption at growth stages, it was possible to increase the water productivity.

In the current experimental study, economic analysis of deficit irrigation was conducted for an orange orchard and results were evaluated based on the highests net income. This experiment was performed as a split plot in the form of a randomized complete block design with five replications in 2018. The main treatments were two types of irrigation management including variable irrigation interval and 4 days irrigation interval. Sub factor was 4 irrigation levels including 100%, 80%, 65% and 50% of soil readily available water (RAW). For economic evaluation, production, cost and income functions and optimum water use depths were calculated using English method. Results showed that irrigation water depth for maximum yield for two types of irrigation management was 148. 3 and 186. 5 mm, respectively. When land is limiting, the best irrigation water depths were 146. 2 mm and 184. 0 mm. When water is limiting, the irrigation water depths are 109. 3 and 136. 9 mm, respectively, which caused more than 16percent increase in net income per unit volume of water both two types of irrigation managements. With this amount of saved water, if land is not limited, the cultivated area can be increased up to 36% and 36%. Results showed that the optimum irrigation water management (in unlimited conditions) is 4 days irrigation interval and 80%RAW irrigation depth. For this situation, the yield was 55 ton/ha. The net income for this case is more than 483 million Rials per hectare and B/C equals to 1. 78. The highest net income (for no land limitation and irrigation depth equivalent to full irrigation), is irrigation as large as Ww (water limitation conditions) that net income for the without and 4 days irrigation interval are more than 476 and 611 million Rials, respectively. This case generates the most net income per unit volume of used water. Results showed that in both unlimited condition and in water limitation condition, having an irrigation schedule and also applying deficit irrigation has a positive effect on the gardener's income that for deficit irrigation in unlimited condition 1492 and in water limitation condition 1369 m3/ha water is needed.

Infiltration is the most important soil physical characteristic which its direct measurement is laborious, time consuming and expensive. The purpose of this study is to estimate steady infilterability rate, using Neuro-Fuzzy, Neural Network and Multivariate Linear Regression models. Consequently, steady infilterability rate, was measured using double rings in 100 points in Dehgolan region, Kurdistan Province, Iran. Soil physical (porosity, bulk density, sand, silt and clay) and topography characteristics were measured as readily available properties and used to estimate steady infilterability rate, The data were divided into two sets of terrain (70% of the data) and test (30% of the data). The models based on input type were categorized into type 1 (physical characteristics) and 2 (soil physical and topography characteristics). The results based on mean bias error (MBE), Root Mean Square Error (RMSE), Mean Error (ME), Relative Standard Error (RSE) and Relative Improvement (RI) showed that the Neuro-Fuzzy model (type 1 respectively with statistics 0. 24, 2. 01, 0. 46, 4. 04 and 46. 65) (type 2 respectively with statistics-0. 1, 1. 24, 0. 23, 1. 54 and 58. 62) has the most accuracy of steady infilterability rate, estimation. Also was observed using topography data as input together with soil physical characteristics can lead to improvement of the estimation accuracy of steady infilterability rate.

Increasing the concentration of heavy metals in soil has adverse effects on ecosystem and causes serious damage to humans. Quantifying pollution can be helpful for soil management. In this study, the pollution of heavy metals has been studied and quantified in agricultural lands around some industrial units at Ardabil plain. For this, we selected 9 industrial units and prepared 46 soil samples (0 to 30 cm). Clay, sand and silt percentages, soil organic carbon content, pH and EC were measured. The heavy metals were extracted by digestion using HNO3 and HCl and the concentration of Pb, Zn, Cu and Cd were measured by AAS. Pollution index (Pi), comprehensive pollution index (Pj), ecological risk (Er) and potential ecological risk (RI) were calculated. The average concentration of heavy metals varied from 0.724 mgkg-1 of Cd to 120.58 mg/kg-1 of Cu. All regions had Pi greater than 2 and showed mild pollution except region 4 which had slight pollution. The pollution index of Cd had the highest value among all heavy metals. Pj had the lowest (1.268) and highest (3.636) mean values in regions 2 and 5, respectively. Region 2 had slight pollution class, regions 1, 6 and 7 had a mild class and regions 3, 4, 5, 8 and 9 had a moderate class. The ecological risk of Pb, Zn and Cu was lower than 3.0 in all regions and ErCd was between 120 and 240 for regions 3, 5, 8 and 9 increasing a serious pollution class and greater than 240 indicating a severe pollution class for other areas. All regions were in the serious pollution class according to the RI index. There is a significant difference between the concentration of Pb (sig. 5%) and Zn and Cu (sig.1%) in all regions. It shows differences between regions cannot be related to parent material and human activities have resulted in an increase in metal concentration.