مجله پژوهش آب در کشاورزی
سال سی و پنجم شماره 2 (تابستان 1400)

Determining the intra-seasonal sensitivity of maize evapotranspiration to environmental stresses has an important effect on modeling of yield. In this research, the effect of drought and salinity stresses were investigated on the relative evapotranspiration (during initial, development, mid, and late stages) and relative yield of maize, in a field with sandy loam soil texture. Salinity treatments were applied by water with EC of 0.5(S0), 2.1(S1), 3.5(S2), and 5.7(S3)   dS.m-1. Drought treatments included four irrigation levels of 100% (I0), 80% (I1), 60% (I2), and 40% (I3) of the crop water requirement. The experiment was performed as factorial in a randomized complete block design, with three replications. The relative evapotranspiration of maize in the initial, development, middle and final stages was estimated between 63.5-100%, 62.6-100%, 55.2-100%, and 66.4-100%, respectively. The relative yield of maize in the I0S0 to I3S3 treatments was calculated between 42.6-100%. The results showed that salinity and drought stresses reduced both the evapotranspiration and maize yield. Also, evapotranspiration decreased with a steeper slope in sensitive growth stages compared to yield. Effect of the mentioned stresses at sensitive growth stages caused disruption in the flowering and fruiting of maize. In this study, the relative yield of maize was modeled by additive models of Blank, Stuart, Singh and multipliable models of Jensen, Rao, and Minhas. According to the results, Stewart model with sensitivity coefficients (in four growth stages) of 0.227, 0.416, 0.604, 0.14 and Jensen model with sensitivity coefficients of 0.301, 0.41, 0.608, and 0.147 were selected as the optimal models. However, Rao, Blank, Singh, and Minhas models were chosen as the next priorities. Therefore, under salinity and drought stress, the relative yield of maize was modeled based on the amount of evapotranspiration in the growth stages.

In exploitation of low-quality water in arid and semi-arid regions, irrigation management is essential to increase water use efficiency. Determination of crop-water-salinity production function is an essential tool for proper irrigation management. In this study, the AquaCrop model was first evaluated by considering 4 soil and water salinity levels and 4 deficit irrigation levels for the major cereal crops including wheat, barley, and corn in Qazvin Plain. The results showed that the coefficients of determination for wheat, barley, and corn yield were 0.97, 0.86 and 0.91, respectively. Therefore, the model can evaluate the performance in salinity and deficit irrigation conditions with a good approximation. To determine the optimal production functions of each crop, the results of the plant model were compared with three models of linear and nonlinear regression, and artificial neural network. The neural network model was able to estimate the performance compared to the AquaCrop model with lower error and higher correlation (0.99). These values in the linear function for wheat, barley, and corn were 0.98, 0.95, and 0.78 and in the nonlinear function as 0.92, 0.86 and 0.81, respectively. Also, the error calculated in the neural network method for wheat, barley, and maize was 40.16, 62.09, and 57.08 kg, respectively, which were less than the linear model by 75 %, 70 %, and 95 %; and less than the exponential model by 90 %, 85 %, and 93%, respectively. The best trained network for determining the water-salt production function for barley and wheat 5 Nero and for corn 7 Nero was introduced in the single layer structure. Sensitivity analysis on wheat and barley showed that this model had low sensitivity to irrigation and salinity parameters and only corn plant showed a moderate range sensitivity to salinity parameter.

The explosive growth of science and the dissemination of various results have confused researchers in various fields. Simple surveys and reviews of results collected in a specific area seem necessary. Meta-analysis as a structured method and using statistical methods aggregate the results of studies on a subject. In this study, in order to assess the surface and drip fertigation based on crop yield and water and fertilizer productivity, a meta-analysis approach was used. For this purpose, studies of eight databases were recruited, and, after reviewing them, 21 studies had the conditions to enter the meta-analysis. Combined results showed that both surface and drip fertigation methods significantly increased the indicators of crop yield and water and fertilizer productivity. Measuring the effect size of the results for crop yield in surface and drip fertigation methods compared to surface irrigation with traditional fertilization method was equal to 1.32 and 1.25, respectively. The effect size of water productivity for surface and drip fertigation compared to surface irrigation with traditional fertilization methods was 1.24 and 1.74. These values were 2.12 and 1.43, respectively, in terms of fertilizer productivity. Therefore, both surface and drip fertigation methods increase crop yield and water and fertilizer productivity. The results of this study are derived from the quantitative data of preliminary studies, to achieve higher accuracy, it is suggested that further studies be examined by meta-analysis.

It is important to assess the uncertainties involved in agro-hydrologic simulations because they are subject to varying degrees of uncertainty. Uncertainty analysis of the agro-hydrological models can provide useful insights into the degree of confidence in the model results. In this study, uncertainty analysis of a distributed application of the SWAP model to a sugarcane field with subsurface controlled drainage was conducted using a hybrid uncertainty analysis scheme, combining Generalized Likelihood Uncertainty Estimation (GLUE) and Unified Particle Swarm Optimization (UPSO). The results revealed a high variability of the calibrated parameters and the necessity of an uncertainty assessment for the SWAP simulations. Strong parameter correlations highlighted the need for calibration of the model parameters against diverse calibration data in a simultaneous manner. The 95% prediction uncertainty bands obtained for the  hydrological (soil water content, water table level, sub-surface drainage outflow), solute transport (soil water solute concentration and sub-surface drainage outflow salinity), and biophysical (leaf area index, cane, and sucrose dry yield) simulations enveloped 73-80%, 45-58%, and 75-100% of the corresponding total observed data (including both calibration and validation datasets), respectively, with an r-factor (the ratio of the average thickness of the 95PPU band to the standard deviation of the corresponding measured variable) of 0.83-0.98, 1.43-1.96, and 0.75-1.11. The thickness of the derived 95PPU bands for the biophysical simulations showed an increasing trend over the simulation period.

Carla is a medicinal plant whose fruit is used to treat diabetes. In this study, the effect of water stress and biochar on the qualitative and quantitative parameters of Carla plant was investigated. The experiment was performed in greenhouse conditions in a factorial manner and in a completely randomized design with three replications. Treatments included three irrigation water treatments (50%, 75%, and 100% of the water requirement) and four biochar treatments (zero, 1.25%, 2.5%, and 5% by weight of potting soil). During the growing season, water treatments were applied by weekly weighing of the pots and replenishing the water loss up to Field Capacity. The amount of water added to each pot was measured. Harvest at 50 days after planting was done once a week. A total of five harvests were performed. In each harvest, quantitative parameters including plants height, number, weight, diameter and length of fruit, and qualitative parameters of fruit sugar content, greenness index, and leaf area index in each pot were carefully measured. Also, the yield and efficiency of irrigation water consumption at the end of the planting season was determined in each treatment. The results showed that the effects of irrigation water and biochar levels were significant (at 1% and 5% probability) on the measured parameters. With decrease in irrigation water, the amount of quantitative parameters and plant yield decreased. The highest value of the measured parameters was obtained from 100% irrigation water treatment, which was not significantly different from the 75% irrigation water treatment. The use of biochar up to 2.5% by weight of soil increased the parameters. Use of the right amount of biochar reduces the negative effects of stress and improves plant growth compared with the control treatment. Therefore, use of biochar for plants, especially when the plant is under water stress, can be recommendable. In order to reduce the amount of water used in greenhouses and improve plant growth and yield, biochar use is advisable, however, its use under field conditions should be tested.

Lack of fresh water resources has led to production of salt tolerant species. This study was conducted in 2019-2020 to investigate the effect of irrigation water salinity on forage yield, plant height, and shoot ash content of Salicornia bigelovii and native Salicornia ecotypes including Bushehr (S. sinus persica), Central Plateau, Gorgan and Urmia of S. persica under irrigation with Persian Gulf (60 dS m-1) and groundwater (20 dS m-1), in Bushehr and Yazd conditions, respectively. Results showed that there were significant differences between the species and ecotypes in terms of fresh and dry weight of forage in both conditions. The highest fresh and dry weight of forage were related to Bushehr ecotype in both conditions, but the lowest of these traits were in S. bigelovii in Yazd and in Gorgan and Urmia ecotypes in Bushehr conditions. The amount of fresh forage for Bushehr ecotype were about 9333 and 22940 g m-2 in Bushehr and Yazd, respectively. In general, plant height under seawater irrigation conditions varied from 23.0 cm to 35.5 cm in Bushehr condition, and 56.5-78.0 cm in Yazd condition under saline groundwater. Regardless of species and ecotypes, the average shoot ash content in Bushehr and Yazd conditions were, respectively, 53.83% and 47.76%. Based on the results of the study, Bushehr ecotype could be considered as superior for planting and forage production in the southern coastal strip. In Yazd condition, due to better water quality, all species and ecotypes produced high fodder yield. However, due to high water requirement of Salicornia, its production in arid areas, especially in the central regions of the country, is not recommended.

The purpose of this study was to gain a better understanding of the future of water in the agricultural sector of the Sistan region. In this study, to determine the final drivers, we relied on Future Studies techniques and, by using the Expert Panel method, 23 drivers were identified and seven main drivers affecting the Sistan water crisis were used to write the scenarios by Delphi method. These factors included drought, dependence on external water resources, the lack of sufficient attention to water resources management, careless cultivation of crops with high water requirement, inappropriate irrigation methods, technological weakness of agriculture, and free water charges in the agriculture sector. Also, 86% of the Sistan region will be exposed to a severe water crisis in the future according to the hazard zonation map. Furthermore, probable conditions for each of these factors were presented, and using that, 3 scenarios with high acclimatization include the first, second, and third scenarios for the water crisis in Sistan, which were designed using the Wizard Scenario software. In these scenarios, three situations including: possible state (if the drivers remain in the current state), optimistic state (improvement of the condition), and pessimistic state (weakening of the condition) were designed for each of the factors. Cultivation of crops with high water requirement were in a pessimistic state, free water and drought in a possible state, and the other drivers in an optimistic state. In these scenarios, optimistic conditions were superior to others and formed most of the assumptions. It is expected that these scenarios would improve water and agricultural conditions in the Sistan Plain.