Journal of Soil and Plant Interactions
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1- Department of Irrigation, College of Agriculture, Isfahan University of Technology
2- Department of Irrigation, College of Agriculture, Isfahan University of Technology ,gheysari@cc.iut.ac.ir
2- Department of Irrigation, College of Agriculture, Isfahan University of Technology ,
Abstract: (71 Views)
Background and Objective: Given declining water resources in Iran, particularly in Isfahan Province, assessing forage maize production potential and water productivity—despite its relatively high water requirement but strategic importance—has become essential. This study aimed to determine the optimal planting date and to evaluate yield potential and water productivity across different regions of Isfahan.
Methods: Maize growth was simulated for 18 planting dates using 13 years of climatic data and the CERES-Maize model for 20 calibrated hybrids across dominant soil texture classes. After selecting optimal planting date for nine selected stations, production potential and water productivity were assessed for two calibrated hybrids (SC606 and SC704) using detailed soil profile data.
Results: Climatic analysis indicated that eastern stations had potential yields below the provincial average. The combined effects of climate and soil texture revealed that the highest yield was occurred in Daran (26.2 ton ha-1) for SC606, while the lowest was obtained in Zarrin‑Shahr (17.2 ton ha-1) for SC704. Dry biomass water productivity ranged from 3.75 to 5.55 kg m-3.
Conclusion: The western regions of the province, due to their higher elevation, greater solar radiation, and favorable temperatures, exhibited the highest production and water productivity. Conversely, poorer water and soil quality in the central and eastern regions substantially reduced yield and water productivity. Accordingly, the western parts were identified as suitable areas for forage maize production because of favorable climatic conditions and the absence of soil salinity constraints. Furthermore, the simulation results indicated that planting date has a significant effect on maize yield and water productivity. In the suitable regions of the province, planting from late May to late July (depending on the hybrid), not only enhance yield and water productivity, but can also reduce the risk of late‑season cold stress.
Methods: Maize growth was simulated for 18 planting dates using 13 years of climatic data and the CERES-Maize model for 20 calibrated hybrids across dominant soil texture classes. After selecting optimal planting date for nine selected stations, production potential and water productivity were assessed for two calibrated hybrids (SC606 and SC704) using detailed soil profile data.
Results: Climatic analysis indicated that eastern stations had potential yields below the provincial average. The combined effects of climate and soil texture revealed that the highest yield was occurred in Daran (26.2 ton ha-1) for SC606, while the lowest was obtained in Zarrin‑Shahr (17.2 ton ha-1) for SC704. Dry biomass water productivity ranged from 3.75 to 5.55 kg m-3.
Conclusion: The western regions of the province, due to their higher elevation, greater solar radiation, and favorable temperatures, exhibited the highest production and water productivity. Conversely, poorer water and soil quality in the central and eastern regions substantially reduced yield and water productivity. Accordingly, the western parts were identified as suitable areas for forage maize production because of favorable climatic conditions and the absence of soil salinity constraints. Furthermore, the simulation results indicated that planting date has a significant effect on maize yield and water productivity. In the suitable regions of the province, planting from late May to late July (depending on the hybrid), not only enhance yield and water productivity, but can also reduce the risk of late‑season cold stress.
Type of Study: Research |
Subject:
Modeling of soil-water-plant relations and root water uptake
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Journal of Soil and Plant Interactions by http://jspi.iut.ac.ir is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.
This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.
