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Farzin Parchami-Araghi; Fariborz Abbasi; Keramat Akhavan
Abstract
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 ...
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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%.
2
Farzin Parchami-Araghi; Adnan Sadeghi-Lari
Abstract
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 ...
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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.