In this study, the impacts of groundwater level on nitrate nitrogen accumulation in the vadose zone of a cotton field were investigated. Experiments were conducted in a cotton field at the CAS Ecological Agricultural Experiment Station in Nanpi from 2008 to 2010. A vertical observation well was drilled, and time-domain reflectometry probes and soil solution extractors were installed every 50 cm in the walls of the well to a depth of 5 m. The soil water content was monitored, and soil solution samples were obtained and analyzed every six days throughout the growing seasons during the three studied years. Additionally, a water consumption experiment was conducted, and the topsoil water content and leaf area index were measured in the cotton field. The resulting data were used to estimate parameters for use in a soil hydraulic and nitrate nitrogen movement model, and cotton evapotranspiration was calculated using the Penman–Monteith method. Groundwater level increases and decreases of ±4 m were simulated during a ten-year period using HYDRUS-1D. The results showed significant nitrate nitrogen accumulation in the vadose zone when the groundwater level remained unchanged or decreased, with increased accumulation as the groundwater depth increased. Additionally, increased precipitation and a deeper groundwater level resulted in greater nitrate nitrogen leaching in the cotton root zone. Therefore, irrigation and fertilization strategies should be adjusted based on precipitation conditions and groundwater depth.

Accurate evapotranspiration (ET) monitoring is important for making scientific irrigation decisions. Unmanned aerial vehicle (UAV) remote sensing platforms allow for the flexible and efficient acquisition of field data, providing a valuable approach for large-scale ET monitoring. This study aims to enhance the accuracy and reliability of ET estimation in rice paddies through two synergistic approaches: (1) integrating the energy flux diurnal variations into the Two-Source Energy Balance (TSEB) model, which considers the canopy and soil temperature components separately, for physical estimation and (2) optimizing the flight altitudes and observation times for thermal infrared (TIR) data acquisition to enhance the data quality. The results indicated that the energy flux in rice paddies followed a single-peak diurnal pattern dominated by net radiation (Rn). The diurnal variation in the ratio of soil heat flux (G) to Rn could be well fitted by the cosine function with a max value and peak time (R² > 0.90). The optimal flight altitude and time (50 m and 11:00 am) for improved identification of temperature differentiation between treatments were further obtained through cross-comparison. These adaptations enabled the TSEB model to achieve a satisfactory accuracy in estimating energy flux compared to the single-source SEBAL model, with R² values of 0.8501 for Rn − G and 0.7503 for latent heat (LE), as well as reduced rRMSE values. In conclusion, this study presents a reliable method for paddy field scale ET estimation based on a calibrated TSEB model. Moreover, the integration of ground and UAV multimodal data highlights its potential for precise irrigation practices and sustainable water resource management.

Saline groundwater irrigation is an important way to alleviate the shortage of fresh water resources. In order to find a reasonable saline irrigation method for farmland, an irrigation experiment was conducted with fresh water and saline water at the seedling, jointing, heading, and filling stages. The soil salinity, growth, chlorophyll fluorescence, and yield of summer maize were measured. The results showed that alternating fresh and saline water irrigation led to a smaller increase in soil salinity relative to that irrigation with saline water alone. In addition, different sequences of alternating irrigation also significantly affected the accumulation of soil salinity. The maximum quantum yield, effective quantum yield of photochemical energy conversion, photochemical quenching, and non-photochemical quenching varied greatly at the jointing stage and heading stage. Furthermore, the yield of maize that was irrigated with fresh water at the heading stage (8.53 t ha⁻¹) was greater than that at the jointing (7.69 t ha⁻¹) and filling stages (7.45 t ha⁻¹). Therefore, these findings indicate that in areas where fresh water is scarce, priority should be given to the application of fresh water at the heading stages for summer maize irrigation.

Shortages of water resources and labor make it urgent to improve irrigation efficiency and automation. To respond to this need, this study demonstrates the development of an adaptive border irrigation system. The inflow is adjusted based on the functional relationship between the advance time deviation and the optimal adjustment inflow rate, thereby avoiding the real-time calculation of infiltration parameters required by traditional real-time control irrigation systems. During the irrigation process, the inflow rate is automatically adjusted based only on the advance time deviation of the observation points. The proposed system greatly simplifies the calculation and reduces the requirements for field computing equipment compared with traditional real-time control irrigation systems. Field validation experiments show that the proposed system provides high-quality irrigation by improving the application efficiency, distribution uniformity, and comprehensive irrigation performance by 11.3%, 10.7%, and 11.0%, respectively. A sensitivity analysis indicates that the proposed system maintains a satisfactory irrigation performance for all scenarios of variations in natural parameters, flow rates, and border length. Due to its satisfactory irrigation performance, robustness, facile operation, and economical merit compared with traditional real-time control irrigation systems, the proposed system has the potential to be widely applied.

The zero-inertia model is widely used for simulating surface flow in irrigation systems. This model is accurate when inflow discharge is constant. However, simulation of irrigation systems with rapidly varied inflow discharge is needed due to the development of real time control irrigation technology. Hence, the objective of this study is to validate the zero-inertia model with rapidly varied inflow discharge. For this purpose, twenty-three border irrigation tests at a range of inflow changes on different field slopes and roughness coefficients were conducted. Then, the sensitivity analyses of bed slope, infiltration parameters, and roughness coefficient were examined. The results indicate that the zero-inertia model predictions are in good agreement with field data in both advance/recession times and flow depths. The infiltration parameters were the most sensitive input variable of the zero-inertia model. The input variables have a more considerable impact on the recession phase than the advance phase. Keywords: zero-inertia model, rapidly varied inflow, border irrigation, sensitivity analysis DOI: 10.25165/j.ijabe.20201302.5228 Citation: Liu K H, Jiao X Y, Li J, An Y H, Guo W H, Salahou M K, et al. Performance of a zero-inertia model for irrigation with rapidly varied inflow discharges. Int J Agric & Biol Eng, 2020; 13(2): 175–181.

The shortage of water resources makes it urgent to increase agricultural irrigation efficiency. In response to this need, this study develops a real-time adaptive control irrigation (RACI) system for border irrigation. Avoiding the need for traditional real-time control systems to calculate soil infiltration properties in real-time, RACI adjusts the inflow rate based on the difference between the actual and expected advance time. Simulations of the collected data are used to evaluate and analyze the sensitivity of RACI to input parameters, and field experiments were carried out to further verify the irrigation performance and efficiency. The experimental results indicate that RACI delivers an average application efficiency, distribution uniformity, requirement efficiency, and deep percolation of 0.930, 0.884, 0.954, and 0.070, respectively. A sensitivity analysis of irrigation input parameters (infiltration parameter, roughness coefficient, inflow rate, and slope) shows that the irrigation performance of RACI remains satisfactory even when the parameters are varied. Given the satisfactory irrigation performance and robustness and the more facile operation than traditional real-time control systems, RACI has the potential to become widely used.