Determination of the Effects of Irrigation of Sunflower with Traditional and Capillary Movement-Based Real-Time Management Technology on Yield, Quality, and Water Savings
Project no. 23001 · Status: Kapanmış (Yayın bekleniyor) · 2021
Summary
<p>Increased productivity in agricultural production is only possible with irrigation. However, factors such as increasing world population and climate change are increasingly increasing the pressure on water resources. Sustainability of agricultural production requires the effective use of existing water resources. This is possible with the use of advanced innovative technologies in both irrigation methods and irrigation management. For this purpose, the effects of irrigation in different phenological periods on the morphological, physiological, biochemical and molecular properties, yield and quality parameters of the sunflower, whose production has become widespread in the Menemen Plain in recent years, were examined. In the 2-year study conducted at UTAEM (International Agricultural Research and Training Center) in 2022-2023; irrigation treatments were created by taking into account three different phenological periods in sunflower: table formation, flowering and grain filling. The experiment was carried out with three replications according to the randomized blocks trial design. There were a total of 9 treatments in each replication. While 8 of these treatments were traditional irrigation treatments, one (S9) was the treatment of artificial intelligence-supported irrigation based on real-time capillary water movement monitoring. This study is the first study in which these two different irrigation treatments, traditional and innovative, are used together and the effects of these two treatment methods on some physiological, biochemical and molecular responses of sunflower to drought stress are compared for the first time. According to the data obtained from the study, the amount of irrigation water varied between 112.1 mm (S8) and 326.4 mm (S4), and the amount of irrigation water applied to the treatment S9 was 181.0 mm. Although the control treatment S4 was given approximately 80% more water than the amount of water given to the artificial intelligence-supported treatment S9, similar yield results were obtained with the full irrigation subject (S4). The highest yield values were determined in S4 (409.3 kg) and artificial intelligence-supported S9 treatments (399.1 kg). Additionally, the yields obtained from treatments irrigated in two periods (S6>S7>S5) were higher than the yields obtained from treatments irrigated in one term (S3>S2>S1). The highest WUE value was obtained from treatment S8, which depends on rainfall, and the lowest value was obtained from subject S5, which was irrigated during the table formation and flowering periods. The highest oil yields per decare were obtained from S4 (168.5 kg) and S9 treatments (164.1 kg). The highest and lowest plant height and table diameter values were determined from the artificial intelligence-supported treatment S9 and the rainfed treatment S8, respectively. It was observed that the effects of the treatments on the crude oil ratio, harvest index, dry matter amount, number of days to flowering, number of days to physiological maturity, shell ratio and leaf area index of the sunflower plant during the head formation period were statistically insignificant. Among the irrigated treatments, the least leaf water loss was determined in S9. While the lipid peroxidation rate was higher in S9 than in the S4 in all phenological periods, the opposite results were obtained in the amounts of hydrogen peroxide (H2O2). The amount of dehydrin molecule, which plays a role in drought and stress tolerance, increased 7.5 times in S9 compared to S4. It has been shown that irrigation with artificial intelligence technology does not have any negative effects on the activity of water channel proteins. According to the results obtained; it can be said that the artificial intelligence-supported irrigation system has a direct effect on the water intake, photosynthesis, nutrient absorption and general growth processes of plants by mediating the plant's adequate water intake. When all results are evaluated together, it shows that the use of artificial intelligence-supported irrigation technology based on real-time capillary water movement monitoring, which provides serious water savings compared to the control treatment (S4), is important for the protection and sustainability of our water resources.</p>