Integrating computational fluid dynamics and field experiments to optimize mixing and irrigation strategies for biogas slurry application in Chinese yam

Maximizing the agricultural value of biogas slurry while mitigating its environmental risks requires precise blending and irrigation, yet optimized methodologies remain underdeveloped. This study employs Computational Fluid Dynamics (CFD) numerical simulation to analyze the fluid dynamics of three designed mixed fertilizer pipeline structures. It investigates how mixing sequence affects fertilizer blending efficiency within pipes of identical diameter. The realizable k- ε model is used for fluid simulation, with the velocity-based coefficient of variation (CV) serving as the evaluation metric for blending uniformity. Results indicate that the stepwise mixing structure b (anaerobic liquid–water-trace elements sequence) achieves optimal fertilizer mixing uniformity with a flow velocity of 6.65 m/s and a relative standard deviation (CV) of 6.47%. Based on this structure, an integrated biogas slurry irrigation machine featuring high integration and intelligent control was assembled, incorporating components such as solenoid valves, centrifugal pumps, and smart controllers. A two-factor experiment was conducted with four concentrations (25%-100%) and three irrigation lower limits (65%-85% field capacity), statistical analysis using two-factor ANOVA revealed significant effects of slurry concentration, irrigation lower limit, and their interaction on crop performance indicators, and the entropy weight method was used to identify the optimal irrigation strategy. Field trial results indicate that irrigation at 75% slurry concentration + 75% field capacity (F3T2) yields superior Chinese yam production and starch content. The entropy weight method also confirms comprehensively evaluated five key indicators-soil moisture content, yam yield, soluble protein content, starch content, and economic returns-confirming F3T2 as the optimal irrigation scheme for Chinese yam (evaluation value: 0.97). This work establishes a coherent framework linking engineering optimization to precision agronomy, offering a replicable strategy for transforming agricultural waste into a resource with enhanced efficiency and economic return.