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【Background and Objective】 The APEX model is a comprehensive watershed-scale model for simulating the effects of management practices on agricultural systems and their impacts on water quality, soil erosion, and nutrient cycling. This paper analyzes the sensitivity of its parameters to water status in soil. 【Method】 The analysis is based on data measured from 2016 to 2019 from an irrigation experiment conducted in Jiaodong in Shandong province. Winter wheat was used as the model plant; the Sobol, Morris, and FAST methods were used to analyze the sensitivities of the APEX model parameters associated with crop growth and water stress. We considered the influences of groundwater depth, rainfall and irrigation. 【Result】 When groundwater depth was 1.25 m, the maximum root depth (RDMX) was the most sensitive parameter affecting evapotranspiration, biomass, and yield, while the maximum potential leaf area index (DMLA) was the most sensitive parameter impacting leaf area index (LAI). When the groundwater depth was increased to 5 m, the sensitive parameters influencing crop evapotranspiration and yield differed, with PARM38 (weight coefficient of water stress calculation) and RWPC1 (proportion of root biomass during germination) becoming the most sensitive parameters. Results calculated from all three methods indicated that as irrigation water increased, the sensitivity of RDMX decreased, while the sensitivities of DMLA, DLAI (peak point in growth season), and WA (potential light energy utilization) increased. The sensitivity of RDMX was significantly higher in dry years than in humid years, as opposed to the sensitivity of DMLA. Uncertainty analysis demonstrated that wheat biomass, yield, and evapotranspiration fell within the 5% to 95% confidence interval of the simulated data. 【Conclusion】 The most sensitive parameters identified by the Sobol, Morris, and FAST methods were consistent, although their sensitivity indexes varied with irrigation treatments, rainfall patterns, and groundwater depth. Considering computational efficiency and accuracy, the Morris method is more suitable for parameter sensitivity analysis of the APEX model. These findings provide valuable insights into the application of the APEX to analyze the impact of environmental conditions on crops.

Categorization of soil organic carbon (SOC) into different functional subpools according to their recalcitrance and protective mechanisms helps better understand ecosystems organic carbon (OC) dynamics, and various attempts have been made to explore the suitable experimental fractionation method for such purpose. However, most previous studies neglected the influences of environmental factors on the effectiveness of varying fractionation methods. Density fractionation has shown great promise in elucidating SOC immobilization mechanisms. Here, we compared three varying types of density fractionation methods (density, density + dispersion, and density + other procedures) for categorizing the SOC into three functional pools, that is, active OC (OCactive), moderately stable OC (OCm‐stable), and stable OC, (OCstable) using global data compiled for 95 sites in 31 published studies, and examined the influences of climate (mean annual temperature [MAT] and annual precipitation), vegetation type, and soil properties (soil depth, clay content, and soil type) on SOC fractions determined by the three density fractionation methods. The percentage of OCm‐stable fraction was found to be highest using the density method and lowest using the density + dispersion method, due to differential density ranges between the two methods. At a global scale, the contents of total SOC and its OC fractions decreased with temperature. Precipitation had no apparent influences on the subdivided SOC fractions using either the density + dispersion method or the method of density + other procedures, whereas soil type constrained the effect of precipitation on SOC fractions using the density method. The percentage of OCm‐stable determined by the density + dispersion method was more responsive to MAT and vegetation type than that by the other two methods. The percentage of OCstable determined by the method of density + other procedures was significantly and positively related to the clay content as the OCstable based on this method included small particles. For all the three methods of fractionation, soil type had a greater influence than the clay content on the SOC fractions, especially the OCm‐stable and the OCstable. For soil type characterized by rich metal oxides, both the density method and the method of density + other procedures could be used for SOC fractionation. For soil type rich in nutrients, the density + dispersion method would have higher sensitivity for distinguishing the OCm‐stable. [ABSTRACT FROM AUTHOR]