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Assessing Traditional and Innovative Fertilization Approaches for Rice Yield in Dry Direct-Seeding Systems (Case Study: Khuzestan Province)
A.r. Jafarnejadi, A. Gilani, F. Meskini-Vishkaee, M. Hoseini Chaleshtori,
Volume 29, Issue 3 (10-2025)
Abstract
Rice, as one of the world's most strategic crops, plays a vital role in global food security. This study investigated the effects of different nutrition management approaches on yield and water productivity in dry direct-seeded rice cultivation (local Anbouri Red Dwarf cultivar) at Shavoor Research Station in Khuzestan Province. The experiment was conducted in a randomized complete block design with four treatments, including 1) Farmer's conventional practice, 2) Soil test-based fertilization, 3) Soil test-based fertilization + supplementary nutrition, and 4) 25% reduced chemical fertilizers + biofertilizers, with three replications. Results demonstrated that the supplementary nutrition (4270 kgha-1) and biofertilizer with 25% chemical fertilizer reduction (4356 kgha-1) treatments increased yield by 17% and 19.3 %, respectively, compared to conventional practice (3651 kgha-1). This improvement was primarily attributed to increased panicles per m² (10-14%) and enhanced nutrient uptake efficiency. The biofertilizer treatment also showed the highest water productivity (0.25 kg m-³) and the best benefit-cost ratio (23.25). Economic analysis confirmed that combining biofertilizers with 25% chemical fertilizer reduction significantly reduced costs while maintaining yield. These findings suggest that integrating soil testing with either biofertilizers or stage-specific nutrition represents an effective strategy for enhancing yield, improving water use efficiency, and reducing dependence on chemical inputs in dry-seeded rice cultivation. These methods can be recommended as sustainable models for farmers in arid regions like Khuzestan, which face salinity challenges and water resource limitations.

Comparison and Analysis of Empirical Equations for Estimating Time of Concentration (A Case Study in Khuzestan Province)
M. Tahvilian, S. Eslamian, A.r. Gohari, M. Jamali,
Volume 29, Issue 3 (10-2025)
Abstract
Time of concentration (Tc) is one of the key parameters in hydrological studies, playing a critical role in flood control structure design, runoff simulation, and water resource management. This study evaluates the performance of seven empirical equations—Bransby-Williams, California, Giandotti, Kirpich, Pilgrim, Rational Hydrograph (SCS), and Carter—in estimating Tc across 35 sub-watersheds in Khuzestan Province, Iran. To assess the accuracy, six sub-watersheds with reliable rainfall-runoff data were selected, and observational Tc values were calculated. The estimated results from the empirical formulas were then compared with observed data using statistical indices such as RMSE, ME, and the Nash–Sutcliffe Efficiency (NSE). The findings revealed that the Kirpich equation provided the most accurate and reliable estimates, with RMSE = 2 hours, ME = 0.44 hours, and NSE = 0.91. Subsequently, all seven models were applied to estimate Tc for the remaining sub-watersheds. Finally, a concentration time zoning map was generated, which can serve as a practical tool for hydraulic design, flood risk analysis, and optimal water resource planning in Khuzestan Province.

Spatial Distribution of Soil Quality Index in Saline and Sodic Soils of Urmia Lake Using Kriging and IDW Methods
H. Rezazadeh, P. Alamdari, S. Rezapour, M. S. Askari,
Volume 29, Issue 3 (10-2025)
Abstract
Soil quality assessment plays a crucial role in sustainable land management, particularly in degraded areas such as saline and sodic soils. This study aimed to determine the spatial distribution of the Soil Quality Index (SQI) in saline and sodic soils around Lake Urmia using two geostatistical interpolation methods: Kriging and Inverse Distance Weighting (IDW). A total of 82 soil samples were collected from a depth of 0–30 cm, and 24 physical, chemical, and heavy metal properties were analyzed. The Soil Quality Index was calculated based on both linear and non-linear approaches. Principal Component Analysis (PCA) was used to identify a Minimum Data Set (MDS), including: calcium carbonate equivalent, EC, clay percentage, BD, silt percentage, organic carbon, Pb, and cadmium, which explained more than 78% of the total variance. The results indicated that the SQI showed moderate spatial variability across the study area, with a decreasing trend from west to east. Comparison of the interpolation methods revealed that Kriging performed better in the linear model, while IDW showed higher accuracy in the non-linear approach. The best-fitted theoretical model was spherical, with a range of influence varying between 6,130 and 20,610 meters. Overall, integrating the Soil Quality Index with geostatistical methods provides a powerful tool for understanding spatial variability and supporting effective planning in saline and sodic soils.

Evaluating the Feasibility of Direct Flood Damage Estimation Using the HEC-FIA Model in the Semirom Watershed
N. Haseli Nasrabadi, R. Modarres, S. Soltani,
Volume 29, Issue 3 (10-2025)
Abstract
Floods are among the most frequent and destructive natural disasters worldwide, causing significant damage to human infrastructure and the environment each year. This study aims to assess the direct damages caused by flooding using the HEC-FIA model in the Semirom watershed. In the first step, flood inundation maps were generated using the HEC-RAS model based on digital elevation model (DEM) data, hydrological inputs, and Manning’s roughness coefficients under both steady and unsteady flow conditions. These maps were then converted into a format compatible with the HEC-FIA software and integrated with economic, land use, and population data to estimate flood damages. The economic database included updated information on agricultural, horticultural, residential, and industrial land uses, partly obtained through field surveys. The flood event of March 11, 2006, was selected as the base flood, and damage analyses were performed for various return periods. The results indicated that the agricultural sector suffered the most damage. In the base year flood, agricultural damages exceeded 821 billion IRR, while structural damages were estimated at approximately 3 billion IRR. In the 1000-year return period, agricultural damages rose to 1,427 billion IRR, and structural damages increased to 44 billion IRR. Analysis of shorter return periods showed a significant decrease in damages, with no structural damage observed in the 10-year return period or less, although agricultural areas remained vulnerable. The findings suggest that the HEC-FIA model has a high capability in estimating direct flood damages across spatial and temporal scales and can serve as an effective tool for flood risk management and planning.

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