Estimating biophysical potentials in Qazvin irrigation and drainage network using DSSAT crop models

In this research, the biophysical potentials of agricultural crops were analyzed in Qazvin irrigation and drainage network using DSSAT crop models. These models were calibrated and validated using field data or proper input coefficients were selected for the crops without such data. Land Units (LUs) were extracted by overlaying soil mapping units (SMUs) and weather grids using GIS. Weather grids were mapped using Thiessen method assuming each unit as a homogeneous area. Crop growth simulation was carried out using the DSSAT crop models. For each crop, two irrigation regimes (surface and pressurized) and 2-3 planting dates were considered. The results revealed the difference of crop production for each planting date and irrigation regime. Among the major crops in the study area, the values of spatial coefficient of variation were mediocre for Wheat and Barley, but lower for Corn. In relation to Tomato, Soybean and Cotton, significant differences were found between LUs and this issue must be taken in consideration when determining cropping pattern. The index of time coefficient of variation can also be used as a measure of risk in relation to the studied crops. The results of this research can be used as inputs of a spatial planning support system (PSS).In this research, the biophysical potentials of agricultural crops were analyzed in Qazvin irrigation and drainage network using DSSAT crop models. These models were calibrated and validated using field data or proper input coefficients were selected for the crops without such data. Land Units (LUs) were extracted by overlaying soil mapping units (SMUs) and weather grids using GIS. Weather grids were mapped using Thiessen method assuming each unit as a homogeneous area. Crop growth simulation was carried out using the DSSAT crop models. For each crop, two irrigation regimes (surface and pressurized) and 2-3 planting dates were considered. The results revealed the difference of crop production for each planting date and irrigation regime. Among the major crops in the study area, the values of spatial coefficient of variation were mediocre for Wheat and Barley, but lower for Corn. In relation to Tomato, Soybean and Cotton, significant differences were found between LUs and this issue must be taken in consideration when determining cropping pattern. The index of time coefficient of variation can also be used as a measure of risk in relation to the studied crops. The results of this research can be used as inputs of a spatial planning support system (PSS).In this research, the biophysical potentials of agricultural crops were analyzed in Qazvin irrigation and drainage network using DSSAT crop models. These models were calibrated and validated using field data or proper input coefficients were selected for the crops without such data. Land Units (LUs) were extracted by overlaying soil mapping units (SMUs) and weather grids using GIS. Weather grids were mapped using Thiessen method assuming each unit as a homogeneous area. Crop growth simulation was carried out using the DSSAT crop models. For each crop, two irrigation regimes (surface and pressurized) and 2-3 planting dates were considered. The results revealed the difference of crop production for each planting date and irrigation regime. Among the major crops in the study area, the values of spatial coefficient of variation were mediocre for Wheat and Barley, but lower for Corn. In relation to Tomato, Soybean and Cotton, significant differences were found between LUs and this issue must be taken in consideration when determining cropping pattern. The index of time coefficient of variation can also be used as a measure of risk in relation to the studied crops. The results of this research can be used as inputs of a spatial planning support system (PSS).In this research, the biophysical potentials of agricultural crops were analyzed in Qazvin irrigation and drainage network using DSSAT crop models. These models were calibrated and validated using field data or proper input coefficients were selected for the crops without such data. Land Units (LUs) were extracted by overlaying soil mapping units (SMUs) and weather grids using GIS. Weather grids were mapped using Thiessen method assuming each unit as a homogeneous area. Crop growth simulation was carried out using the DSSAT crop models. For each crop, two irrigation regimes (surface and pressurized) and 2-3 planting dates were considered. The results revealed the difference of crop production for each planting date and irrigation regime. Among the major crops in the study area, the values of spatial coefficient of variation were mediocre for Wheat and Barley, but lower for Corn. In relation to Tomato, Soybean and Cotton, significant differences were found between LUs and this issue must be taken in consideration when determining cropping pattern. The index of time coefficient of variation can also be used as a measure of risk in relation to the studied crops. The results of this research can be used as inputs of a spatial planning support system (PSS).