farzin parchami araghi

Although sesame has traditionally been grown in southern Iran's warm and arid regions, its desirable agronomic traits and economic profitability have led to increased interest in cultivating this crop in the northern regions. However, sesame often exhibits low performance due to poor agronomic management, environmental stress, and limited use of improved varieties. Therefore, the use of improved sesame varieties, mainly those resistant to shattering, along with proper agronomic management, maybe the key to successful cultivation in various parts of the country. The seed of a non-dehiscent sesame genotype (S29) was imported into the country in 2016. Several years of studies related to this genotype confirm that it is resistant to seed shattering. In this regard, the present study was designed and conducted to investigate the effects of planting arrangement and plant density on the performance of a non-dehiscent sesame genotype. The experiment was conducted in research fields located in Mazandaran (Sari) and Ardabil (Moghan) in 2020 and 2021. The effects of three planting row distances (30 cm, 45 cm, and 60 cm) and four plant spacing on the rows (5 cm, 8 cm, 11 cm, and 14 cm), with a minimum density of 12 plants per square meter to a maximum density of 67 plants per square meter, were evaluated in terms of plant height, number of branches, yield components, seed yield and oil percentage of the shattering-resistant sesame genotype. The experiment was set up in a randomized complete block design with split-plot arrangements and three replications in each location. The results showed that the highest plant height in both regions was achieved with a row distance of 45 cm and a plant spacing of 8 cm. Additionally, increasing plant density up to 45 plants per square meter resulted in increased plant height, but further increase in density decreased plant height. Decreasing the distance between planting rows and plant spacing, equivalent to increasing planting density, reduced the number of branches, capsules per plant, and seeds per capsule in both regions. The maximum seed yield in the Moghan region (1465 kg/ha) was obtained from the 45×8 cm arrangement. However, there was no significant difference compared to the yield obtained from the 45×5 cm planting arrangement. In contrast, the highest seed yield in the Sari region (824 kg/ha) was achieved with the planting arrangement of 30×14 cm. Based on regression analysis of seed yield, the optimum density was estimated to be 23 plants per square meter for the Moghan region and 31 plants per square meter for the Sari region. The results indicated that the imported non-shattering sesame genotype did not exhibit desirable potential for cultivation in the Sari region due to its lower average yield than the regional average. However, in the Moghan region, this genotype showed a comparable average yield and even higher maximum yield (250 kg/ha) than the regional average, with the highest yield obtained from the 45×8 cm planting arrangement. Therefore, due to its suitable seed yield performance and the potential for mechanized harvesting, this genotype holds promise for cultivation in the Moghan region.

The basic strategy to mitigate water crisis is to save agricultural water consumption by increasing productivity, which will result in more income for farmers and sustainable production. Due to the economic importance of barley production in the country, it is necessary to study the volume of irrigation water and water productivity to produce this strategic product. Based on extensive field research on irrigation water management and application of different irrigation methods in barley farms, the innovations of this research were: a) measuring water consumed and determining water use efficiency in barley production, b) the up-to-date of the measurements and research findings, c) findings applicability for application in agricultural planning at the national and regional levels, d) the ability to development the findings in barley farms at the national level to improve water use efficiency. The hypotheses of this research are: a) barley irrigation water is various in different regions, b) water applied in barley farms is more than the required one, c) the water use efficiency of barley is different in the main production areas, and d) The applied water of barley is not the same in different irrigation methods. Therefore, the main objective of this study is to determine the water consumed and water use efficiency in barley production; to measure the water applied to barley farms in the main production areas; to compare the water measured in the production areas with the net irrigation requirement; and finally to determine water use efficiency of the barley in the main production areas in the Iran.

 For this purpose, the volume of irrigation water and barley yield in 296 selected farms in 12 provinces (about 75% of the area under cultivation and production of barley in Iran) including Khuzestan, East Azerbaijan, Ardabil, North Khorasan, Fars, Khorasan Razavi, Tehran, Semnan, Markazi, Isfahan, Hamedan and Qazvin were measured directly. Farms in the mentioned provinces were selected to cover various factors such as irrigation method, level of ownership, proper distribution and quality of irrigation water. By carefully monitoring the irrigation program of selected farms during the growing season, the amount of irrigation water for barley during one year was measured. At the end of the season and after determining the average yield of barley during the 2020-2021 year, the values of irrigation water productivity and total water productivity (irrigation+effective rainfall) were determined in selected barley farms in each region. The volume of water supplied was compared with the gross irrigation requirements estimated by the Penman-Monteith method using meteorological data from the last ten years, and compared with the values of the National Water Document. Analysis of variance was used to investigate the possible differences in yield, irrigation water and water productivity in barley production.

To assess the reliability of statistical analysis, we evaluated the sufficiency of the number of measurements needed for both the quantity of irrigation water and the ley yield on the farms. Subsequently, we computed statistical indices, such as the mean and standard deviation. The results showed that the number of measurements of irrigation water and barley yield was to be 296 and 283, respectively, which was more than the number of measurements required for irrigation water (41 dataset) and yield (50 dataset). Therefore, the sufficiency of the data for the statistical analysis was reliable. The results showed that the difference in yield, volume of irrigation water and water productivity indices were significant in the mentioned provinces. The volume of barley irrigation water in the studied areas varied from 1900 to 9300 cubic meters per hectare and its average weight was 4875 cubic meters per hectare. The average barley yield in selected farms varied from 1630 to 7050 kg ha-1 and the average was 3985 kg ha-1. Irrigation water productivity in selected provinces ranged from 0.22 to 1.53 and its weight average was 0.90 kg m-3. Average gross irrigation water requirement in the study areas by the Penman-Monteith method using meteorological data of the last ten years and the national water document were 4710 and 4950 cubic meters per hectare, respectively. Irrigation efficiency of barley fields in the country is estimated at 62-65% without deficit irrigation.

In order to reduce water consumption and improve water productivity, it is suggested to manage water delivery to farms during the season and deliver water rights to them according to crops water requirements. To reduce water losses and enhance productivity in the barley farms, it is suggested the application of modern irrigation systems according to the farms conditions with the suitable operation; and modification and improvement of surface and traditional irrigation methods. Note that, water is only one of several necessary and effective inputs in the optimal and economic production of barley. On the other hand, attention should be paid to the optimal application of other inputs including: seeds, fertilizers, equipment and tools etc.

Providing food security in scarcity conditions of water resources requires macro-planning for the supply, allocation and water consumption in different sections such as agricultural section. In Iran, like in other countries of the world, most fresh water resources are consumed in the agricultural sector. In this situation, one of the effective and practical solutions is the optimal use of irrigation water in the agricultural sector, which consumes the most water. The most basic component for optimal irrigation water management in Iran is the awareness of applied water in the production of various agricultural products under the farmers’ management conditions. Therefore, this study was conducted with the aim of appraising irrigation water management indicators such as seasonal applied water, yield , and irrigation water productivity, total water productivity (irrigation water plus plus effective rainfall ) in Azarbayjan Sharghi, Azarbayjan Gharbi, Ardabil, Alborz, Tehran, Chaharmahal and Bakhtiari, Fars, Qazvin, Markazi, Hamedan, Golastan and Mazandaran provinces as peach and nectarine production hubs in Iran.

In this study, a field survey was conducted to measure applied irrigation water and yield under the gardeners’ management in peach and nectarine production hubs. This indicators was measured in 195 gardenes in Azarbayjan Sharghi, Azarbayjan Gharbi, Ardabil, Alborz, Tehran, Chaharmahal and Bakhtiari, Fars, Qazvin, Markazi, Hamedan, Golastan and Mazandaran provinces with different conditions of climates, irrigation methods (surface and drip), salinity of irrigation water and soil; and different peach and nectarine cultivars during growing season 2018-2019. To measuring irrigation water volume, after determining the inflow of water to the garden by carefully monitoring the garden irrigation time and measuring the irrigated area, the volume of irrigation water applied by peach and nectarine trees in each garden was measured. Crop yield was obtained in three consecutive years and their mean was used in the analysis. Irrigation water productivity (WPIrr) and total water productivity (WPIrr+pe) were calcucated as the ratio of yield to applied water and irrigation water plus effective rainfall, respectively. Then, the effect of modern irrigation methods (surface drip irrigation) on applied water, WPIrr and WPIrr+pe were investigated in the study areas. Analysis of variance was used to investigate the possible difference between yield, applied water and WP among the hubs. Data adequacy was assessed by using the method provided by Sarmad et al. (2001)

The results showed that the difference between average volume of water applied by gardeners, yield, WPIrr and WPIrr+pe, in the studied sites were significant at 5% probability level. The average amount of applied water by gardeners in Azarbayjan Sharghi, Azarbayjan Gharbi, Ardabil, Alborz, Tehran, Chaharmahal and Bakhtiari, Fars, Qazvin, Markazi, Hamedan, Golastan and Mazandaran provinces was 8617, 7178, 8140, 8137, 8568, 7763, 8814, 8675, 10806, 6428, 2842 and 2012 m3/ha, respectively, and the average was 6734m3/ha. The yield of peach and nectarine varied from 10 to 50 tons/ha with an average of 20 tons/ha. Irrigation water productivity (WPIrr) varied from 1.6 to 8.6 and its average was 3.06 kg/m3. The average WPIrr+pe for peach and nectarine was 2.44 kg/m3. The results showed that the average applied water for peach and nectarine orchards in the study areas except for Golestan and Mazandaran provinces for surface and drip irrigation methods were 9325 and 7098 m3/ha, respectively, (p<1%). Therefore, in drip irrigation method, applied water was 25% less and WPIrr was 34% higher.

In general, the results of this study provide useful information on irrigation water management indicators in peach and nectarine production to managers and water decision makers within Iran. Accordingly, in order to reduce the volume of irrigation water and improve peach and nectarine water productivity, it is recommended to use drip irrigation method in suitable climatic conditions where irrigation water is of good quality and the technical criteria of design, implementation, operation, and economic considerations are met. Also, training and application methods to improve the performance of surface irrigation to reduce evaporation and applied irrigation water is recommended.

Irrigation performance assessments under actual operation conditions are required as a first step toward improving agricultural water management. In this study, the seasonal irrigation performance of peaches and nectarines was evaluated under actual operation conditions by monitoring 24 orchards, in Ardabil Province (Parsabad and Meshginshahr counties), Iran, during the growing season 2018-2019. The total sum of seasonal applied water and effective precipitation (I + Pe) and the fruit yield ranged from 280-1675 mm and 1.00-32.43 ton ha-1 (with a weighted average of 582 mm and 14.61 ton ha-1), respectively. The mean Relative Irrigation Supply index (RIS) for the drip irrigation method (1.25) was significantly (P < 0.05) lower than the surface irrigation method (1.66). The water productivity indicators were significantly (P < 0.05) affected by the orchardist's skill level, interplantion of trees, tree spacing, irrigation method, and disease/frost (DF) damage. Post-harvest period accounted for a mean proportion of 75, 25, and 15% of the seasonal applied water in orchards with early-season, late-season, and mixed early- and late-season cultivars, respectively. DF damage accounted for an 87 and 85% reduction in fruit yield and water productivity (WPI+Pe), respectively, compared to the orchards without severe DF damage. Under the current technological and economic constraints, most of the study orchards experienced a rational (but yet with low productivity) irrigation water management. Improving irrigation efficiency and fruit yield, controlling DF damage, and implementing regulated deficit irrigation during pre- and post-harvest stages are the most effective approaches to improve water productivity indicators in the study area.

Reliable estimates of the seasonal applied water and irrigation performance assessment indicators under current irrigation and farm management are perquisites for improving water resources management. In this study, seasonal applied water and irrigation performance assessment indicators of winter barley were studied by monitoring 25 farms under actual conditions, in Ardabil Province (Ardabil, Namin, Nir and Kosar counties), Iran, during the growing season 2020-2021. The seasonal estimates of the net irrigation requirement during the growing season 2020-2021 and its 10-year average ranged from 476 to 652 mm and from 403 to 535 mm (with a mean of 530 and 449 mm), respectively, over the study farms. The total sum of seasonal applied water and effective precipitation (I + Pe) and the barley grain yield ranged from 266 to 716 mm and from 0.14 to 4.07 ton ha-1 (with a weighted average, WA, of 475 mm and 2.33 ton ha-1), respectively. As a result of limited irrigation water availability, 3-91% (with a WA of 52%) of the intended yield of irrigated barley in the study area (4.5 ton ha-1) was achieved. Physical and economic water productivity indicators were significantly (P < 0.05) affected by farmer's skill level, crop rotation, type of irrigation water source, and irrigation method. The results indicated that farms with surface water supply showed the highest vulnerability to drought periods. Improving the on-farm water management flexibility and mitigating the negative impacts of hot and windy days during the grain filling stage can improve water productivity indicators in the study area.

Due to limitation of available water resources, improving agricultural water productivity has become an inevitable necessity. Therefore, it is important to have reliable estimates of the seasonal applied water and water productivity under current irrigation and farm management. In this paper, the seasonal applied water and physical and economic water productivity of soybean were studied through monitoring 29 farms under actual conditions located at the tail end region of Moghan irrigation and drainage network, Ardabil Province, Iran, during the growing season 2020-2021. The net water requirement estimates of soybean during the growing season 2020-2021 and its 10-year average ranged from 417-719 mm and 457-797 mm with a mean of 539 and 581 mm, respectively, over the studied farms. The total applied water (irrigation + effective precipitation, I + Pe) and the grain yield ranged from 3859-7105 m3 ha-1 and 1.30-2.80 ton ha-1, with a mean of 5664 m3 ha-1 and 2.35 ton ha-1, respectively. The lack of flexibility in water allocations led irrigation schedule to be not adapted with the crop water requirement. The limiting factors of soybean production in the study area caused the observed maximum grain yield to be significantly lower than the potential level of soybean yield in Moghan plain (4.00 ton ha-1). The soybean grain yield exhibited a quadratic correlation with I + Pe. Total water productivity (WPI+Pe) and economic water productivity (WP$) ranged from 0.33 to 0.47 kg m-3 and 21.18 ´ 103 to 48.29 ´ 103 Rials m-3 with a mean of 0.42 kg m-3 and 39.89 ´ 103 Rials m-3, respectively. The mean Israelsen's application efficiency (AE) over initial, development, and mid-season plant growth stages in the study fields were obtained 19, 95, and 100%, respectively.

A proper planting date in canola allows the crop to grow sufficiently and minimizes the damaging effects of stress. On the other hand, early autumn rains and the problem of timely farm preparation and unavailability of farm fields due to summer crops such as peanuts in the Moghan region cause delays in planting dates for autumn crops such as canola. Problems such as supplying primary soil water for crop establishment of canola fields, pest damage such as flea beetles in delayed cultivation, the possibility of damage from cold and frost stress, as well as the decline in yield due to canola exposure to drought and heat stress due to delayed planting are the main problems of canola cultivation in the Moghan region. Because of this, canola transplanting can solve the problems and be a good choice in these kinds of situations.

In order to agronomic evaluation of direct cultivation and transplantation of spring canola in different plant densities under delayed conditions in the Moghan region, an experiment was conducted as a randomized complete block design with three replications in two cropping years, 2019-2020 and 2020-2021. Experimental treatments included direct sowing of seeds as a control at a rate of 6 kg/ha; transplanting with densities of 20, 30, and 40 plants per square meter, each density with both bare-root transplant and potted-root transplant; and also with one seedling or two seedlings in the planting hole. During the experiment, traits including flowering initiation, flowering completion, flowering period, growth period, plant height, first pod height, stem diameter, branch number, pod length, pod thickness, pod number, seed number per pod, 1000-seed weight, and seed yield were recorded. Correlations among traits and path analysis were performed to investigate seed yield's direct and indirect effects.

The results showed that transplanting on average caused a significant decrease in phenological traits such as flowering initiation (145.5 GDD), flowering completion (207.8 GDD), growth period (158.9 GDD), and increased flowering period (156.6 GDD) during the experimental years. Also, transplanting caused a significant increase in stem diameter (2.8 mm), branch number (2.3), pod diameter (0.6 mm), number of pods per plant (158.2), number of seeds per pod (3.3), 1000-seed weight (0.69 g) and seed yield (1894.4 kg/ha). In control treatment as delayed direct sowing, seed yield in total experimental years showed a yield decline of 62.2% (1150.7 kg/ha) compared to transplanting cultivation treatments (3045.1 kg/ha). Comparing the bare-root transplant treatments, it was found that 40 plants per square meter had a higher number of pods per plant, 1000-seed weight, and seed yield than the 30 and 20 treatments and had a significant difference with them. Phenotypic correlation among agronomic traits showed seed yield had a negative and significant correlation with phenological traits: flowering initiation (-0.90**), flowering completion (-0.90**), and growth period (-0.77**) with a positive and non-significant effect on the flowering period (0.52). The results of stepwise regression analysis showed that the number of pods per plant (0.241*) and pod thickness (0.229*) had the most significant direct and positive effects on seed yield, respectively, while flowering completion (-0.559**) had the most negative and significant direct effect on seed yield.

Under delayed conditions, bare-root transplanting of canola with 40 plants per square meter density and one seedling in the planting hole was superior in terms of seed yield and phenological traits compared to potted-root transplanting and direct cultivation and is recommended in Moghan region.

In this study, the seasonal applied water and physical and economic water productivity of soybean were evaluated through monitoring 37 farmers’ fields (with furrow/border irrigation systems) in Moghan Plain, Ardabil Province, Iran, during the 2020-21 growing season. The net soybean water requirement during that growing season and its 10-year mean value ranged from 431-691 mm and 442-671 mm with a mean of 542 and 543 mm, respectively. The mean seasonal total applied water (irrigation + effective precipitation) and the grain yield were 6554 m3 ha-1 and 2.90 ton ha-1, ranging from 5005-10009 m3 ha-1 and 2.05-4.12 ton ha-1, respectively. The mean seasonal total applied water for spring soybean (7906 m3 ha-1) was significantly (P < 0.01) higher than its corresponding value for summer soybean (6390 m3 ha-1). Total water productivity (WPI+Pe) and economic water productivity (WP$) ranged from 0.18 to 0.30 kg m-3 and 15.21 ´ 103 to 62.40 ´ 103 Rials m-3 with a mean of 0.24 kg m-3 and 33.19 ´ 103 Rials m-3, respectively. In most of the studied farms (70% of total cases), the grain yield was higher than the minimum expected threshold for irrigated soybean (2.5 ton ha-1). The results indicated that reasonable levels of grain yield and water productivity indices can be achieved by applying five and three irrigations for spring and summer soybean, respectively. The mean water application efficiency over soybean growth stages in the studied fields ranged between 50-82%.

To study 38 corn hybrids, an experiment based on randomized complete blocks design with three replications at the Moghan and Karj was carried out during the 2020 cropping season. The results showed that there was a significant difference between corn hybrids for grain yield, grains per ear, ear length, rows per ear, 1000-grain weight, ear diameter, and grain depth. Hybrid No. 26 (KLM81027 × K47/3) with the highest yield and ear length was identified as the best hybrid. The correlation between grain yield with grain depth and 1000-grain weight was significant. The heritability value was between 19.01% (grain yield) and 87.59% (row per ear). The regression analysis indicated that rows per ear and ear diameter had positively related to grain yield. Cluster analysis with grain yield, grains per ear, ear length, rows per ear, 1000-grain weight, ear diameter, and grain depth of 38 corn hybrids classified into four different groups, one of which has a hybrid of No. 26 (KLM81027 × K47/3), and No. 37 (TWC647) was the most desirable groups. In addition, hybrids No. 4, 9, 13, 15, 19, 20, 23, 25, 32, 37, and 38 were identified as superior and stable hybrids for cropping in warm dry climates. As a result, it seems that corn hybrids of FAO 600 can be suitable for cropping in warm dry climates, alsothe K74/2-2-1-4-1-1-1 × K3640/3 hybrid was identified as superior and stable.

In this work, the seasonal applied water and physical water productivity of rapeseed (Brassica napus L.) were evaluated through monitoring 26 farmer fields (including 18, five, and three farmer fields with furrow, center-pivot, solid-set irrigation systems, respectively) over Moghan Plain, Ardabil Province, Iran, during the growing season 2019-2020. The rapeseed seasonal water use (irrigation + effective precipitation) and the grain yield value ranged from 2921-6762 m3 ha-1 and 1.00-3.70 ton ha-1, respectively (with a mean of 4798 m3 ha-1 and 2.64 ton ha-1, respectively). The net rapeseed water requirement during the growing season 2019-2020 and its 10-year mean value ranged from 285-399 mm and 282-354 mm, respectively (with a mean of 325 and 304 mm, respectively). The mean seasonal applied water at farmer fields with furrow irrigation (4328 m3 ha-1) was significantly (p < 0.01) higher compared to the farmer fields with center pivot and solid set sprinkler irrigation (2154 and 2633 m3 ha-1, respectively). Total water productivity (WPI+Pe) and irrigation water productivity (WPI) ranged from 0.28 to 0.95 kg m-3 and 0.41 to 1.45 kg m-3, respectively (with a mean of 0.58 and 0.77 kg m-3, respectively). Based on the results of multivariate linear regression analysis, the factors, including irrigation water salinity, sowing date, seeding rate, date of the first irrigation, growth period length, strong winds near harvest time, the latitude of farmer fields, flowrate of delivered water, mean irrigation interval, and rapeseed grain moisture content and impurity rate were recognized as the most important factors affecting the grain yield and the physical water productivity indices.

Agro-hydrological models play an important role in water resource management. However, their predictions always suffer from various sources of uncertainty, including model structures, parameters, and input and output data. Model structural uncertainty is caused by the fact that the model cannot perfectly represent the natural processes involved in the studied system. Parameter uncertainty indicates that many model parameters are not directly measurable or can only be obtained with unknown errors. Measurement uncertainty in input and output data is due to unknown measurement errors and incommensurability errors. Hence, it is important to assess the degree of uncertainty involved in agro-hydrologic modeling. The generalized likelihood uncertainty estimation (GLUE) method has been widely used for uncertainty analysis in hydrologic modeling because of its simplicity, ease of implementation, and less strict statistical assumptions about model errors. In GLUE, parameter uncertainty accounts for all sources of the model uncertainty. The drawback of the GLUE is its prohibitive computational burden imposed by its random sampling strategy, which hinders the efficient application of the method. In this study, a hybrid high-dimensional uncertainty analysis method was developed, combining GLUE with an evolutionary optimization algorithm, Unified Particle Swarm Optimization (UPSO), to improve the computational efficiency of the GLUE framework. UPSO is a modification of Particle Swarm Optimization (PSO) that aggregates its local and global variants, combining their exploration and exploitation capabilities without additional objective function evaluations. The hybrid GLUE-UPSO framework was used for uncertainty analysis of SWAP distributed sub-daily agro-hydrological modeling for a sugarcane farming system with combinational free/controlled subsurface drainage management.

The source code of the SWAP model was modified and extended to consider the duration of the irrigation events, simulation of sub-daily reference evapotranspiration, sub-daily precipitation interception, ratooning, and implementation of subsurface controlled drainage during the simulation period. The GLUE-UPSO framework was coded in FORTRAN and C++ and integrated into SWAP source code. The developed framework was applied to a dataset collected from a field with a combinational free/controlled (70-cm depth) subsurface drainage management located at Shoaybiyeh Sugarcane Agro-industrial company farms, Khuzestan province, Iran. The simulation was performed from 2010-07-19 to 2011-12-11 (481 days) for planted sugarcane (CP48-103 cultivar). A soil profile of 550 cm depth (depth of impermeable layer) was specified during simulations. The soil profile was divided into two layers. To consider the heterogeneity of irrigation scheduling at different parts of the studied field, the field area ( 21 ha) was divided into ten homogeneous simulation units, termed as hydrotopes. Hydrotopes have similar agro-hydrological properties except for irrigation scheduling. The model was calibrated, using the measured soil moisture profile, soil solute concentration profile, groundwater level, subsurface drainage outflow, drainage outflow salinity, Leaf Area Index (LAI), cane yield, and sucrose yield in a parallel manner. The weighted average of simulated values derived for each hydrotopes was compared with the corresponding measured data. Totally, 45 parameters were estimated through the GLUE-UPSO framework. The accuracy of the model in calibration and validation stages was evaluated, normalized root mean square error NRMSE and Nash-Sutcliffe model efficiency coefficient EF. The behavioral parameters were identified, using NRMSE > 0.2 for solute transport (soil water solute concentration and drainage outflow salinity) and EF > 0.7 for hydrological (soil water content, water table level, and drainage outflow) and biophysical (cane yield, sucrose yield, and LAI) simulations. For each parameter set, the objective function values were used as the likelihood measure to calculate the corresponding likelihood weights. The 95% prediction uncertainty (95PPU) bands were calculated at the 2.5% and 97.5% levels of the cumulative posterior distribution (realized from the weighted behavioral parameter sets) of the simulated state/flux variables.

The results revealed a significant nonuniqueness 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 model's hydrology (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) components enveloped 41-87%, 18-67%, and 75-100% of the corresponding total observed data (including both calibration and validation datasets), respectively, with a r-factor (the ratio of the average thickness of the 95PPU band to the standard deviation of the corresponding measured variable) of 0.71-1.14, 0.33-1.14, and 0.84-0.98. The results indicated that the hybrid GLUE-UPSO framework offers an efficient alternative to provide traditional calibrated parameters as well as uncertainty analysis of computationally expensive hydrologic models.

The agro-hydrologic simulations are subject to varying degrees of uncertainty. In this regard, uncertainty analysis can provide useful insights into the level of robustness of the model outputs. In this study, a hybrid multi-objective uncertainty analysis scheme, GLUE-UPSO, based on strained Monte-Carlo simulations was developed, combining Generalized Likelihood Uncertainty Estimation (GLUE) and Unified Particle Swarm Optimization (UPSO). The developed scheme was used for uncertainty analysis of distributed agro-hydrological modeling with SWAP for a sugarcane farming system with subsurface drainage located at Shoaybiyeh Sugarcane Agro-industrial Company farms, Khuzestan, Iran. The results highlighted the strong nonuniqueness of most of the calibrated parameters and the importance of uncertainty analysis of the SWAP simulations. Strong parameter correlations revealed the need for the simultaneous calibration of the model parameters against diverse calibration data. The 95% prediction uncertainty bands obtained for the model's hydrology (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) components enveloped 67%-90%, 23%-71%, and 75%-100% of the corresponding observed data (including both calibration and validation datasets), respectively, with an r-factor of 0.58-1.34, 0.43-1.07, and 0.70-0.98. The results of the study indicated the acceptable level of model uncertainty and the capability of the developed framework for simultaneous calibration and uncertainty quantification of model components.

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.

Despite the high capability of field-scale agro-hydrological models to simulate plant growth interactions with water and solute transport in agricultural systems, their application to real conditions of large sugarcane fields in Khuzestan province faces many challenges, including spatial heterogeneity of irrigation scheduling across the field, the difficulty of determining initial and boundary conditions as well as several unknown model parameters, and data–intensiveness of calibration procedure. This work aimed to implement the agro-hydrological modeling under real operational conditions of large fields with surface/subsurface drainage. In this work, a distributed agro-hydrological modeling scheme was developed through the application of a modified version of the SWAP model and an improved variant of the Unified Particle Swarm Optimization (UPSO) algorithm with capability of sub-daily calibration and simulation of controlled drainage. The developed model was applied to a sugarcane field with subsurface drainage with planted sugarcane (CP48-103 cultivar) in Imam Khomeini Sugarcane Agro-industrial company farms, during 2010-07-19 to 2011-12-11 (481 days). The results revealed the reasonable performance of the developed modeling scheme in retrieving the measured soil moisture, groundwater level, subsurface drainage outflow (with an EF of 0.901, 0.827, and 0.877 for calibration dataset; and 0.514, 0.798, and 0.672 for validation dataset, respectively), soil water solute concentration, subsurface drainage outflow salinity (with a NRMSE of 0.039 and 0.096 for calibration dataset; and 0.154 and 0.046 for validation dataset, respectively), Leaf Area Index, cane yield, and sucrose yield (with an EF of 0.995, 0.999, and 0.972, respectively).

Realistic agro-hydrological modeling of the sugarcane fields with subsurface drainage in Khuzestan Province, Iran, is a challenging problem, due to rapid fluctuations of the shallow groundwater and, hence, water balance components, and significant size (~ 20 ha) of the fields. In this work, a distributed agro-hydrological modeling scheme was developed through the application of a modified version of the SWAP model and an improved variant of Unified Particle Swarm Optimization (UPSO) algorithm with capability of sub-daily calibration and simulation of controlled drainage. The developed modeling scheme was applied to a sugarcane (CP48-103 cultivar) field with controlled drainage (at 90 cm below ground level) in Imam Khomeini Sugarcane Agro-industrial Company farms, during 2010-2011 (481 days). The results demonstrated the success of the developed modeling scheme in retrieving the measured soil moisture, groundwater level, subsurface drainage outflow (with an EF of 0.829, 0.922, and 0.857 for calibration dataset; and 0.877, 0.781, and 0.712 for validation dataset, respectively), soil water solute concentration, subsurface drainage outflow salinity (with a NRMSE of 0.124 and 0.079 for calibration dataset; and 0.152 and 0.072 for validation dataset, respectively), Leaf Area Index, cane yield, and sucrose yield (with an EF of 0.997, 0.993, and 0.988, respectively). Based on the solute balance components simulated throughout the simulation period, ~ 30.10 ton salt ha-1 was added to the soil due to saline irrigation water, and ~ 45.25 ton salt ha-1 was discharged into the receiving water bodies via surface/subsurface field drains.

This study aimed to derive the daily air temperature disaggregation parameters for some regions of Iran and to compare the performance of some daily-to-subdaily air temperature disaggregation Models. For this purpose, air temperature disaggregation models, including WAVE I, WAVE II, WCALC, ERBS, ESRA, and TM models were calibrated and their performance were compared, using long-term daily and three-hourly weather data obtained from 12 different synoptic weather stations in Iran. The results indicated that compared to the WAVE I, WAVE II, WCALC, ERBS, and ESRA models, the calibrated TM model had the best performance to disaggregate daily air temperature with a model efficiency (EF) of 0.9770 to 0.9877. Compared to air temperature disaggregation models with an arbitrary value for the time of maximum and minimum air temperature, the models in which the above mentioned times are described as a function of sunrise and/or sunset had better performance in describing the diurnal variations in air temperature. The use of the WAVE II model can be recommended for the regions with no subdaily weather data needed for calibration of the disaggregation models.This study aimed to derive the daily air temperature disaggregation parameters for some regions of Iran and to compare the performance of some daily-to-subdaily air temperature disaggregation Models. For this purpose, air temperature disaggregation models, including WAVE I, WAVE II, WCALC, ERBS, ESRA, and TM models were calibrated and their performance were compared, using long-term daily and three-hourly weather data obtained from 12 different synoptic weather stations in Iran. The results indicated that compared to the WAVE I, WAVE II, WCALC, ERBS, and ESRA models, the calibrated TM model had the best performance to disaggregate daily air temperature with a model efficiency (EF) of 0.9770 to 0.9877. Compared to air temperature disaggregation models with an arbitrary value for the time of maximum and minimum air temperature, the models in which the above mentioned times are described as a function of sunrise and/or sunset had better performance in describing the diurnal variations in air temperature. The use of the WAVE II model can be recommended for the regions with no subdaily weather data needed for calibration of the disaggregation models.This study aimed to derive the daily air temperature disaggregation parameters for some regions of Iran and to compare the performance of some daily-to-subdaily air temperature disaggregation Models. For this purpose, air temperature disaggregation models, including WAVE I, WAVE II, WCALC, ERBS, ESRA, and TM models were calibrated and their performance were compared, using long-term daily and three-hourly weather data obtained from 12 different synoptic weather stations in Iran. The results indicated that compared to the WAVE I, WAVE II, WCALC, ERBS, and ESRA models, the calibrated TM model had the best performance to disaggregate daily air temperature with a model efficiency (EF) of 0.9770 to 0.9877. Compared to air temperature disaggregation models with an arbitrary value for the time of maximum and minimum air temperature, the models in which the above mentioned times are described as a function of sunrise and/or sunset had better performance in describing the diurnal variations in air temperature. The use of the WAVE II model can be recommended for the regions with no subdaily weather data needed for calibration of the disaggregation models.This study aimed to derive the daily air temperature disaggregation parameters for some regions of Iran and to compare the performance of some daily-to-subdaily air temperature disaggregation Models. For this purpose, air temperature disaggregation models, including WAVE I, WAVE II, WCALC, ERBS, ESRA, and TM models were calibrated and their performance were compared, using long-term daily and three-hourly weather data obtained from 12 different synoptic weather stations in Iran. The results indicated that compared to the WAVE I, WAVE II, WCALC, ERBS, and ESRA models, the calibrated TM model had the best performance to disaggregate daily air temperature with a model efficiency (EF) of 0.9770 to 0.9877. Compared to air temperature disaggregation models with an arbitrary value for the time of maximum and minimum air temperature, the models in which the above mentioned times are described as a function of sunrise and/or sunset had better performance in describing the diurnal variations in air temperature. The use of the WAVE II model can be recommended for the regions with no subdaily weather data needed for calibration of the disaggregation models.This study aimed to derive the daily air temperature disaggregation parameters for some regions of Iran and to compare the performance of some daily-to-subdaily air temperature disaggregation Models. For this purpose, air temperature disaggregation models, including WAVE I, WAVE II, WCALC, ERBS, ESRA, and TM models were calibrated and their performance were compared, using long-term daily and three-hourly weather data obtained from 12 different synoptic weather stations in Iran. The results indicated that compared to the WAVE I, WAVE II, WCALC, ERBS, and ESRA models, the calibrated TM model had the best performance to disaggregate daily air temperature with a model efficiency (EF) of 0.9770 to 0.9877. Compared to air temperature disaggregation models with an arbitrary value for the time of maximum and minimum air temperature, the models in which the above mentioned times are described as a function of sunrise and/or sunset had better performance in describing the diurnal variations in air temperature. The use of the WAVE II model can be recommended for the regions with no subdaily weather data needed for calibration of the disaggregation models.