Journal of Water Research in Agriculture
Year:2018 | Volume:32 | Issue:2 |Papers Count:11

Infrastructure factors provide the main basis for agricultural production and activity under conditions of limited water resources and are considered as strategic requirement for improving this sector. The aim of this research was recognition, determination of importance and level of effectiveness, and investigating the relation of infrastructural components and indicators in improving agricultural water productivity. The method used for this research was field surveying type in which, after documentary and library studies, a self-made questionnaire was developed for interview with professionals and practitioners to gather their opinion on the importance level of different indicators using a Likert Scale, followed by analysis of questions and assumptions. The research indicated the relation and effectiveness level of variables by means of structural equation modeling and Smart PLS software. According to the obtained results, emphasis of infrastructures were on the side of agricultural water supply, while lack of demand oriented approach, process, systematic, and holistic view were among the main problems of the infrastructures. Also, components such as “ downstream instrumentation of the fields and wells with water measurement devices and controlling irrigation water” and “ share of downstream farms equipped with systems for lowering water consumption out of the total cultivated area” were among the most effective factors. It was shown that by taking other variables as constant, agricultural water infrastructures have been effective (about 87. 6%) on improvement of agricultural water productivity, and there is a positive and meaningful relation between each one of the components and related indicators.

A field experiment was conducted in Selseleh County, Lorestan Province, during 2015-16 growing season to evaluate the effects of planting pattern and partial root zone irrigation (alternate furrow irrigation) on water and land productivity under wheat and Persian clover intercropping system. The experiment was carried out as a two-factor factorial with three replications. The first factor was irrigation management at two levels of conventional (I1) and partial root-zone irrigation (I2) based on root depth, and the second was planting pattern including sole wheat (W), sole Persian clover (C), wheat-Persian clover alternate-row intercropping (WC1) and wheat-Persian clover within-row intercropping (WC2) systems. Water consumption, leaf relative water content, grain yield, water productivity and land equivalent ratio were used to evaluate the treatments. Water consumption of all planting patterns in partial root-zone irrigation system was significantly (P≤ 0. 01) lower than that of conventional irrigation. Grain yield of wheat and Persian clover in partial root-zone irrigation was lower than that of conventional irrigation, where wheat and Persian clover yield decreased by 27% and 36%, respectively. However, grain yield and water consumption reductions resulted in higher water productivity (23%, 92%, 51%, and 44% in sole wheat, sole Persian clover, wheat-Persian clover alternate-row intercropping, and within-row intercropping, respectively). Land equivalent ratio of intercropping in partial root-zone irrigation was 19% more than that of conventional irrigation, suggesting that intercropping reduced the negative effect of lower water supply, because relative water content of wheat and Persian clover was higher in intercropping compared to sole cropping. Therefore, partial root-zone irrigation can be suggested as an effective method for increasing water productivity in grain production of wheat and Persian clover.

In order to study the effects of different levels of irrigation, nitrogen fertilizer, and irrigation methods on yield and yield components of onions, a factorial split plot experiment was conducted in a randomized complete block design with three replications for two years at a research farm in Zahak, Iran. Fertilizer treatments were included four levels of nitrogen fertilizer of from urea fertilizer (130, 97. 5, 65 and 32. 5 kg/ha). Onion seedlings were planted in 2014 and 2015 and irrigation was carried out in the form of surface gravity irrigation, surface drip irrigation and subsurface drip irrigation. The highest yield and water productivity were obtained in the subsurface drip irrigation system (28. 42 ton/ha and 5. 9 kg/m3/ha) and the least in surface irrigation (19 ton/ha and 33. 3 kg/m3/ha). Reducing the amount of nitrogen fertilizer to less than plant requirement reduced the yield of bulbs and the efficiency of onions. The highest yield and water productivity were observed in 100% nitrogen application (31. 59 ton/ha and 4. 75 kg/m3/ha) and the lowest was in 25% nitrogen fertilizer treatment (16. 12 ton/ha and 2. 67 kg/m3/ha). The effect of irrigation water on the yield of onion tubers and its efficiency showed that decreasing the depth of irrigation water to values lower than the water requirement of the plant decreases yield and efficiency, but, no significant effect was observed between treatments 100% and 75% of the plant water requirement. Therefore, according to the results, due to the lack of water in the region, irrigation of this plant can be done with 75% of the plant water requirement, without a significant effect on the yield. Also, due to the high production potential in subsurface drip irrigation and reduction of evapotranspiration, this irrigation method could be used for onions in the region. The best treatment for nitrogen fertilizer use is the 100% nitrogen fertilizer requirement, especially in water tension. Considering the contamination of subsurface waters due to the excessive use of nitrogen fertilizers and the prevention of nitrate accumulation in the bulbs, it seems that using drip irrigation improves nitrogen fertilizer management.

Scion-rootstock combination and level of salinity affect on the biochemical characteristics of almond cultivars. To evaluate the effect of salinity stress on the biochemical reactions of almond cultivars and genotypes, a factorial experiment was carried out based on completely randomized design (CRD). Treatments included two factors: Factor A: genotypes in four levels (‘ Shokofeh’ , ‘ Sahand’ cultivars, and ‘ 13-40’ genotype budded on GF677 rootstock, and GF677 (without budding)), and Factor B: irrigation water salinity in five levels (0. 5, 2. 5, 4. 9, 7. 3 and 9. 8 dS/m). Total phenolic, antioxidant capacity, soluble carbohydrate, non-soluble carbohydrate, proline, total soluble proteins, hydrogen peroxide, malondialdehyde, other aldehydes, enzymes activity of catalase, ghayacol peroxidase and ascorbat peroxidase were measured at the end of the experiment. Results showed that, in all genotypes, with increasing salinity level (up to 9. 8 dS/m) the content of hydrogen peroxide, malondialdehyde and other aldehydes was increased. Also, the content of total phenolics, antioxidant capacity, soluble carbohydrate, proline, total soluble proteins, enzymes activity of catalase, ghayacol peroxidase and ascorbat peroxidase were higher at the lower salinity levels (2. 5 and 4. 9 ds/m), but their contents were reduced in higher salinity levels. Overall, the highest content of soluble proteins, enzymes activity of catalase, ghayacol peroxidase and ascorbat peroxidase were recorded at salinity level of 7. 3 dS/m, and the highest content of total phenolics, antioxidant capacity, soluble carbohydrate and prolin at salinity level 9. 8 dS/m were observed in ‘ Shokofeh’ cultivar. Also, at salinity levels of 7. 3 and 9. 8 dS/m, the lowest content of hydrogen peroxide, malondialdehyde, other aldehydes, and total non-soluble carbohydrate were observed in ‘ Shokofeh’ cultivar. Finally, ’ Shokofeh’ and ’ Sahand’ budded on GF677 rootstock were recognized as the most tolerant and sensitive cultivars to salinity, respectively.

Physiological study of crops under microclimatic changes is very important to improve their production in the future. This research was done in Tarbiat Modaress University Research Greenhouse as factorial arrangement in randomized complete block design with three replications in one year (2015). The objective was to study the effects of carbon dioxide (500, 900, and 1300 ppm), UV-radiation (UV-A, B, and C with intensity of 18, 25 and 40 µ W. cm2, respectively) and two irrigation water treatments (full irrigation and deficit irrigation i. e. 60 percent of field capacity) on yield and yield components of sweet corn. Plant height showed a direct relation with the amount of irrigation water and decreased about 10 percent under water deficit stress. The interaction of carbon dioxide and water deficit as well as the interaction of UV-radiation and carbon dioxide reduced the number of leaves below the corns, although this decrease was not significant. Water deficit reduced Specific Leaf Area and Leaf Area Index by, respectively, 11% and 19% relative to the control. In full irrigation, carbon dioxide concentration and number of seeds in each row had direct relation, but under water deficit condition, they had indirect relation. Ultraviolet radiations wave length reduction reduced yield. Water deficit reduced Specific Leaf Area and number of seed in corn row. UV-A and B wave length did not significantly affect yield, but UV-C radiation decreased it. In brief, increase in UV wave length under drought conditions led to decrease in leaf number, leaf area, and yield.

Reference Evapotranspiration is a complex and multivariate phenomenon that depends on several factors and the most accurate way to estimate is lysimeter, though it is costly and time-consuming. Therefore, the main objective of this study was to estimate actual evapotranspiration based on single-source energy balance, i. e. SEBAL and SSEB, and two-source energy balance algorithm, i. e. TSEB, in three sensors MODIS, ETM+ and OLI & TIRS in three steps. In evapotranspiration estimating by SEBAL, Soil Adjusted Vegetation Index and the correction factor for soil effects (L) are particularly important. For this purpose, this index was used as calibration coefficient that is selected based on percentage of vegetation coverage. According to the results, the actual evapotranspiration with L calibrated (L=0/5) had lower error in each of the three sensors (RMSE=1/76, 0/84, and 1/49 mm/day). For verification of calibration results, 30% of the remaining lysimeter data was used. The results of the statistical indices showed significant difference between the predicted data at the 95% level and also in the predictions. Finally, by comparing the three algorithms in the three sensors i. e. MODIS, ETM +, and OLI & TIRS, SSEB algorithm in ETM + sensor was introduced as the best algorithm in Qazvin plain area, at 95% significance level and RMSE of 0/41 mm/day.

Various mathematical models are available for predicting the response of plants to combined water and salinity stress and their share in water uptake. The reduction functions are classified as additive, multiplicative, and conceptual models. In this study, 6 macroscopic reduction functions, namely, Van Genuchten (additive and multiplicative), Dirksen et al., Van Dam et al, Homaee and Skaggs were evaluated in a greenhouse experiment on cherry tomato, var. cherry tomato cluster. This experiment was performed based on a completely randomized design with 3 replicates and 2 levels of salinity (4 and 7 dSm-1). Water stress levels were imposed as matric potential decline during the study at 3 levels of available water depletion (40%, 50%, and 65%). The result of the study indicated that the crop response to water stress and salinity stress was incremental at 4 and 7 dSm-1 salinity levels. Among the multiplicative models, reduction functions of Dirksen model had better fit than others at 4 dSm-1 salinity level (RMSE=0. 15 and ME=0. 14). However, at 7 dSm-1, Van Dam (RMSE=0. 017, ME=0. 09) and Skaggs (RMSE=0. 018, ME=0. 14) had better fit to the measured data.

Excessive withdrawal of groundwater resources in the agricultural sector has led to an increase in cultivated land area and agricultural production, however, it has led to dropping of groundwater levels and drainage of aquifers. Consequently, most of Iran's plains are now considered as forbidden plains for underground water extraction. Therefore, proper policies for management of groundwater resources are essential. For this purpose, the present study aimed to evaluate the effects of economic policies of the agricultural sector on management of ground water resources in Kabodarahang Plain, by using a Positive Mathematical Programming (PMP) and Maximum Entropy (ME). The data and information was collected by referring to the relevant organizations and completing 141 questionnaires with multi-stage cluster sampling by farmers in 2016. The results showed that application of various senarios such as price increases, groundwater allocation, and combination of quotation scenarios simultaneously with increasing prices (combined) at levels of 10%, 20%, and 30% cause reduction in cropped area, decreased net profit of farmers, and reduced water consumption. Also, the results showed that the groundwater price increases scenario was not much effective and only increased the farmers' expenses, but the allocation/quotation polices and the combined policy are recommended as effective policies. Another result revealed that the quotation scenario, compared to the other scenarios, has higher economic returns per cubic meter of water consumption, thus it has been introduced as the best scenario in this research. Finally, it is suggested that the cropping pattern be directed towards products such as wheat and potato that have more gross profit for the water consumed and thus have more economic returns.

Due to shortage of water, use of wastewater for irrigation is becoming more important. Also, using natural and non-toxic materials that increase fertilizer application efficiency and decrease nitrate leaching has gained increasing attention. The aims of this study were to investigate the effect of two sizes for particles, i. e. 1-1. 68 mm and 53-63 μ m, and two application rates (20 and 60 grams per kilogram of soil) of natural zeolite of clinoptilolite (Cp) and surfactant-modified zeolite (SMZ) on soil nitrate and ammonium leaching and wheat plant performance under irrigation by treated urban wastewater. The experiment was conducted in 2015-16 growing season. The experimental soils were placed in Polyethylene columns with 11 cm inner diameter and 65 cm height. The treatments were factorial combination of the variables in completely randomized design with three replications. The results showed that the total amount of released NO3-N from columns modified by SMZ and CP were 32% and 21% lower as compared to the control treatment. Also, these amendments improved wheat plant growth. There was no significant effect on nitrate leaching due to the particle size of the two soil amendments. CP and SMZ amendments are eco-friendly materials and results of the study showed that they were effective in controlling soil nitrate leaching and improving wheat plant growth.

To prevent water stress in plants and have sustainable water management in the field, fast and accurate determination of irrigation time is one of the most important issues. Measuring soil moisture and leaf surface temperature are two methods of determining time of irrigation. In this research, by combination of these two methods, a model for planning and management of forage maize irrigation (cultivar SC-701) is presented. The air relative humidity (RH) and temperature (Ta), leaf surface temperature (TL), and soil moisture content (SM) were measured in 2013 and, by using artificial neural network model and multiple stepwise method, a regression model was developed. Experiments were carried out in 2014 with five treatments including 100%, 85%, 75%, 65%, and 35% total available water (TAW), with four replications, Irrigation was carried out when soil moisture content reached the treatments moisture level. Measurements of the previous year were repeated and the model was calibrated. The results of the first year showed a correlation (R2=0. 87) between the parameters RH, Ta, TL, Ta-TL as independent variable and SM as the dependent variable. Then, using three input parameters of air temperature, leaf surface temperature, and relative humidity, Determination Coefficient of soil moisture content model was calculated as R2= 0. 92. In this model, soil moisture has an inverse relation with (Ta) and (TL-Ta) variables, but a direct relation with RH. Soil moisture content was compared using the model for the second year treatments and compared with the measured values. The difference in soil moisture content measured and estimated by the model at the peak solar radiation time (at noon) was less than ± 10%. The model estimated 75% TAW treatment data well, with very small difference compared to the measured value.

Water resources scarcity and increasing pollution and the rapid increase in water demand due to population growth and higher livelihood standards along with climate change have created a widespread scope for water crisis in Iran and the world. Accordingly, water in agricultural production has become a limiting factor. Hence, farmers have adopted various strategies to reduce the effects of water scarcity in their agricultural practices. The purpose of this survey was to investigate farmers' beliefs and adaptation strategies for managing agricultural water under conditions of water scarcity and effective factors in selection of farmers' adaptation methods in Mamassani County of Fars province. The statistical population of this study was 4033 irrigated farmers. In order to select the sample, a random-quota sampling method was used. A sample of 351 farmers was selected based on the Krejcie and Morgan's table. The results of the study showed that the farms distance to the city center, agricultural experience, awareness of the consequences of danger, sense of commitment, risk taking, and access to credit could explain 42. 2% of farmers' changing beliefs about water scarcity. Regression results of the factors influencing the selection of adaptation strategies showed that variables of farm size, intention for water conservation, risk salience, and social capital could predict 27% of the variability of adaptation strategies.