Investigation of Sugarcane Water Footprint Index Components in Free Drainage, Controlled Drainage and Water Requirement Conditions (Case study: Salman Farsi Agro-industry)
Identifying the amount of water used to produce agricultural products is of great importance, and it can be very effective in recognizing and providing appropriate solutions to reduce water consumption in agriculture. In this study, in order to investigate the water consumption of sugarcane in Khuzestan province for sugarcane production, the water footprint index in two 25-hectare farms (free drainage and controlled drainage) from Salman Farsi agro-industry unit was used. Using the collected and available information, the AquaCrop model was calibrated. Then four irrigation scenarios (100, 110, 85, and 70% of water requirement) were implemented. Based on the results, the water footprint index was recalculated. The results showed that the amount of water required for sugarcane production in the field with free drainage was 258 cubic meters per ton. Of this amount, 12% was green water, 72% blue water, and 16% gray water. Using controlled drainage, this amount was reduced to 222 cubic meters per ton, of which green, blue, and gray water were 16, 69, and 15 percent, respectively. The results of the model showed that in scenarios I1 and I2, the water footprint index in controlled drainage is 18% lower than the free drainage. This value is 18% and 19% for I3 and I4, respectively. Comparison of the results showed that in the controlled drainage condition and supply of 85% of the plant water requirement, the water footprint index decreases by 23% compared to the normal (which is running) condition, which is the best option among the studied scenarios.
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Planning the Water requirement supply of Pasture shrubs under different Irrigation systems using plant Water Stress Index
Gholamreza Bostanian *, Mohammad Albaji, , Seed Boroomand Nasab, Naser Alemzadeh Ansari
Journal of Extension and Development of Watershed Managment, -
Evaluating Remote Sensing Technique and Machine Learning Algorithms in Estimating Sugarcane Evapotranspiration
Mohammad Alavi, Mohammad Albaji *, Mona Golabi, , Saeid Homayouni
Journal of Water and Irrigation Management,