Document Type : Original Article
Authors
1 Assistant professor, Agricultural Engineering Research Department, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Mashhad, Iran
2 2. Master’s degree, Agricultural Engineering Research Department, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Mashhad, Iran
Abstract
Introduction
Water, as a vital and limited resource in agriculture, plays a decisive role in crop production. With the growing population and water resource constraints, optimizing water use in agriculture has become a major challenge. The use of modern technologies and real-time meteorological data can improve irrigation management and enhance water productivity. In recent years, efforts have been made to develop irrigation systems based on meteorological data. These systems use information such as temperature, humidity, precipitation, and evapotranspiration to estimate crop water requirements more accurately. However, many of these systems are still in the experimental and developmental stages, requiring field evaluations to assess their performance and efficiency. This study aimed to evaluate an intelligent irrigation planning system for wheat cultivation in the Mashhad region using real-time meteorological data.
Methodology
The study was conducted over two years in the Mashhad region, focusing on wheat cultivation. The irrigation treatments included: (1) irrigation based on real-time meteorological data from the intelligent system, (2) irrigation supplying 80% of the water volume used in the first treatment, and (3) irrigation supplying 120% of the water volume used in the first treatment. Water productivity was calculated based on irrigation volume and effective rainfall. A paired T-test was used to compare the treatments. Data on crop yield, water consumption, and water productivity were collected and analyzed.
Results and Discussion
The results showed that wheat yield in the treatment based on the intelligent system increased compared to the average yield in Mashhad farms. However, this treatment did not show an advantage in reducing water consumption, as it used more water than the regional average. Water productivity in this treatment was also not superior to the average wheat farms in the region. The intelligent system estimated water requirements to be more than double those calculated using data from the Mashhad meteorological station. In terms of usability, the system was simple and user-friendly for experts but required further optimization for farmers with limited technical knowledge.
In the first year, surface irrigation showed that the 80% treatment had a significantly lower yield (4890 kg/ha) compared to the intelligent system (6600 kg/ha). The 120% treatment (6285 kg/ha) did not differ significantly from the intelligent system. In drip irrigation, the 120% treatment had the highest yield (6345 kg/ha), but it was not significantly different from the intelligent system (6170 kg/ha). In the second year, a 60% treatment was added. Surface irrigation showed that the 80% treatment (3550 kg/ha) had a significantly lower yield than the intelligent system (4680 kg/ha), while the 60% treatment (4600 kg/ha) did not differ significantly. The 120% treatment (6000 kg/ha) showed a significant difference compared to the intelligent system.
Water consumption in the first year for surface irrigation was highest in the intelligent system (100% treatment), while the 80% and 120% treatments used 67% and 90% of this volume, respectively. In drip irrigation, the 100% treatment used the most water (4560 m³/ha), while the 80% and 120% treatments used 2840 and 4180 m³/ha, respectively. Water productivity in surface irrigation was highest in the 120% treatment (0.723 kg/m³), while in drip irrigation, the 80% treatment had the highest productivity (1.63 kg/m³).
The intelligent system overestimated water requirements compared to data from the Mashhad meteorological station, primarily due to differences in minimum and maximum temperatures, relative humidity, and sunshine hours. The system's performance was rated as good for maximum temperature, moderate for relative humidity, and poor for minimum temperature and sunshine hours.
Conclusion
The intelligent irrigation scheduling system showed potential for improving wheat yield but did not reduce water consumption or enhance water productivity significantly. The system's overestimation of water requirements highlights the need for algorithmic improvements. While the system was user-friendly for experts, it requires further optimization for farmers with limited technical knowledge. This study serves as a preliminary step toward developing more efficient irrigation systems based on real-time meteorological data.
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