نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی آبیاری و آبادانی، دانشکده کشاورزی، دانشگاه تهران، تهران، ایران
2 گروه مهندسی آب، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران
3 گروه آبیاری و زهکشی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران، اهواز، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Sugarcane is a plant that has the most water requirement in the summer when the least rainfall occurs, and there is a need to irrigate this plant. In this research, the simulation and modeling of sugarcane cultivation with a focus on the water-environment-food nexus, utilizing the system dynamics approach, have been conducted at the Hakim Farabi Khuzestan Agro-Industry Company. This research was modeled using Vensim software. The model is an integrated and interconnected simulation of water consumption, product production, drainage water volume, salinity, and soil salinity. The information of three years 2015 to 2017 was used for calibration and the information of two years 2018 to 2019 was used to validate the model. MAE, MBE, and MAPE statistical parameters were used to evaluate the model results. The modeling results showed that the model has high accuracy in the calibration period with an MAE index of 6.31 ton/ha for crop yield, 53.56 mm for water drainage volume, 1.21 dS/m for water drainage salinity, and 0.09 dS/m for soil salinity. Also, the results of the same index in the validation period, which were 3.04 ton/ha for crop yield, 48.76 mm for water drainage volume, 1.11 dS/m for water drainage salinity, and 0.04 dS/m for soil salinity, indicate that the model is highly accurate in simulating the existing conditions. The highest water productivity was achieved at a rate of 3.75 kg/m³ in 2019.
کلیدواژه‌ها [English]
EXTENDED ABSTRACT
The water-energy-food nexus is a term used to describe the interdependent relationship between water, energy, and agricultural production. It also refers to the competition between energy and food production for water resources. The interdependence among water, energy, and food resources means that an increase in demand for one resource can lead to a rise in demand for another. Likewise, the cost of one resource can influence the productivity of another. Water is essential in the water-energy-food nexus because it is irreplaceable. Integrating all the system's drivers under a framework is necessary to achieve a sustainable, safe, and flexible water-energy-food system. This framework emphasizes the importance of social and economic dimensions in developing the water-energy-food system (Hoff, 2011).
The concept of system dynamics involves the changes in input and output components, including the interactions and feedback among elements in the system over time. This method can account for non-linear and cause-and-effect relationships.
Sugarcane is a high-water-demand crop, especially in Khuzestan province, where temperatures can exceed 50 degrees Celsius in the summer, increasing water requirements. Hakim Farabi Agro-Industry Company is one of the eight subsidiary companies of Khuzestan Sugarcane Development and Ancillary Industries Holding, the largest sugar producer in Iran. Farabi Company was selected for this research due to the environmental issues created by the region's sugarcane agro-industries.
This research developed causal diagrams concerning water consumption, crop production and drainage water volume and quality using Vensim software within the system dynamics framework. These diagrams were then transformed into stock and flow models. Flows represent system variables, while stocks represent accumulations within the system. Flows serve as the input and output of stocks, determining their rate of change.
After creating the model, we first conducted a sensitivity analysis. We used data from three years (2015 to 2017) to calibrate the model and data from two years (2018 to 2019) to validate the model. The model validation was based on the parameters to which the model was sensitive. We used MAE, MBE and MAPE statistical indices to evaluate the model.
The sensitivity analysis results provide valuable insights into the model's performance. They indicate that the model is most sensitive to soil moisture parameters at the point of permanent wilting, soil moisture at field capacity, and porosity, with sensitivity indices of 6.014, 1.428, and 1, respectively. This means that small changes in these parameters can significantly affect the model's output. On the other hand, the model is not sensitive to the parameters of root development depth and hydraulic conductivity above the drain pipe. This information is crucial because it helps us to understand the strengths and limitations of the model.
The crop yield simulation results for the calibration period indicate a Mean Absolute Error (MAE) of 6.31, a Mean Bias Error (MBE) of -0.89, and a Mean Absolute Percentage Error (MAPE) of 7.99, demonstrating the model's high accuracy. During the validation period, the MAE is 3.04, the MBE is -3.04, and the MAPE is 3.66, further confirming the model's reliability. The model achieved its highest accuracy in simulating crop yield in 2018, while its lowest accuracy occurred in 2015.
The simulation of drainage volume indicates that the model exhibits relatively high accuracy in estimating this parameter. The results reveal an MAE of 53.56, MBE of 53.56, and MAPE of 3.74 during the calibration period, underscoring its precision. During the validation period, the model demonstrates an MAE of 48.76, an MBE of -22.97, and a MAPE of 3.60, further confirming its high accuracy. The model achieved its highest accuracy in simulating drainage volume in 2017 and its lowest in 2019.
The model's performance in simulating drainage water salinity was assessed using various metrics. During the calibration period, the MAE was 1.21, the MBE was 1.21, and the MAPE was 12.56. For the validation period, the MAE was 1.11, the MBE was 1.11, and the MAPE was 12.40. These results indicate that the model's accuracy is satisfactory. Additionally, the model demonstrated the highest accuracy in 2018 and the lowest in 2016.
The results of the soil salinity simulation indicate that the model demonstrated high accuracy. During the calibration period, the model achieved an MAE of 0.09, MBE of -0.04, and MAPE of 3.65. In the validation period, the model's performance improved further, with an MAE of 0.04, an MBE of -0.04, and a MAPE of 1.94. The model's highest accuracy was recorded as an annual average in 2018, while the lowest accuracy occurred in 2016.
The results obtained from different parts of the model showed high accuracy in simulating existing conditions. This suggests that the model can predict the crop yield, volume, and salinity of drainage water in Hakim Farabi Agro-industry Company.
All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.
The authors would like to thank all participants of the present study.
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The author declares no conflict of interest.
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