1
reza saeidi; AbdolMajid Liaghat
Abstract
This research aimed to simulate the yield of maize cv. S. C 704 under conditions of separate application of salinity stress at different growth stages in mini-lysimeter, in Qazvin area, Iran. The experiment was performed as factorial and in a completely randomized design. Soil salinity treatments, as ...
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This research aimed to simulate the yield of maize cv. S. C 704 under conditions of separate application of salinity stress at different growth stages in mini-lysimeter, in Qazvin area, Iran. The experiment was performed as factorial and in a completely randomized design. Soil salinity treatments, as the main factor, included four levels of 1.7(S1), 3(S2), 5(S3) and 7(S4) dS.m-1. The sub-factors included different growth stages as follows: one-stage at 6-leaves (C1), flowering (C2), and milk stage (C3); and two-stages of C1C2, C1C3 and C2C3. By combining saline water (from a salt marsh) with a well fresh water (0.5 dS.m-1), saline water was prepared according to the treatments. Irrigation was done in a way that the salinity of input and output water from the mini-lysimeters was equal. The control treatment was irrigated with fresh water. By combining the water uptake reduction functions, the derived models were presented and evaluated for simulating yield reduction coefficient (α). The stress application data in one and two-growth stages were used for models calibration and validation, respectively. Applying the highest salinity level led to decrease in dry matter yield from 157.2 g. plant-1 (in S1 treatment) to 115.9, 53.2, 77.7, 86.1, 97 and 46.5 g. plant-1 in the C1, C2, C3, C1C2, C1C3 and C2C3 treatments, respectively. The results showed that crop sensitivity was different in one-stage and two-stage stress application. Salinity stress at flowering (C2) and milk stage (C3) had a more negative effect relative to C1C2 and C1C3. In this research, Van Genuchten's additive model and Dirksen-Maas-Hoffman's multipliable model could be recommended as the optimal models for crop yield simulation. Also, application of two-stage salinity stress (up to level of 7 dS.m-1) in C1C2 and C1C3, had higher yield relative to application of one-stage stress in C2 and C3 growth stages.
7
reza saeidi
Abstract
For irrigation planning, parameters such as actual crop water needs (transpiration) and water losses (evaporation) are considered. In this research, for management of deficit irrigation, the amounts of maize evapotranspiration components were simulated under water stress conditions. Water stress was ...
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For irrigation planning, parameters such as actual crop water needs (transpiration) and water losses (evaporation) are considered. In this research, for management of deficit irrigation, the amounts of maize evapotranspiration components were simulated under water stress conditions. Water stress was applied by reducing the soil water, relative to the readily available water. Four treatments were defined as depletion of the available soil water by 40% (I0), 55% (I1), 70% (I2), and 85% (I3). The amounts of maize evapotranspiration and its components (transpiration and evaporation rates separately) were measured in a mini-lysimeter. The seasonal total values of evapotranspiration and components of transpiration and evaporation were equal to 443, 319 and 124 mm (I0), 401, 282 and 119 mm (I1), 303, 211 and 92 mm (I2), and 201, 127 and 74 mm (I3), respectively. Soil water deficiency reduced the evapotranspiration and its components relative to the normal conditions (treatment I0). Reduction of evaporation losses was favorable point in this deficit irrigation method (long irrigation interval). Transpiration and evaporation values were simulated based on the evapotranspiration data (in I0), evapotranspiration stress coefficient (Ks), and crop growth stage sensitivity (Kpi). For this purpose, we used the linear, exponential, logarithmic, polynomial, and power functions as the regression models. By using the actual data, unknown coefficients in the functions were estimated by SPSS software and regression models were generated. Statistical analyses showed that the linear function (R2= 0.91) and polynomial function (R2= 0.874) were the optimal models for estimation of transpiration and evaporation components (under water stress conditions), respectively. The actual water requirement of crop and evaporation losses can be estimated more accurately by separate estimation of evapotranspiration components. This would provide a suitable criterion for irrigation planning and calculation of water use efficiency.