Abstract Background: The current knowledge does not prepare a precise scientific tool for quantifying the effects of inputs particularly ecofriendly inputs such as superabsorbent polymer (SAP) and humic acid (HA) are being used to increase soil fertility, improve crop performance and finally food production. This study was designed and conducted aimed to suggest an innovative approach not only to identify and quantify the effects of these inputs but also to determine the efficient path among underground/aboveground relationships associated with sesame oil production. Two experiments were conducted at the Research Farm of Ferdowsi University of Mashhad using randomized complete block design with split strip plot arrangement and three replications in two successive cropping years (2015–2016) to evaluate the effects of SAP and HA on Sesamum indicum L. growth characteristics and oil production under two different irrigation levels including: supplying 50 and 100% of the sesame water requirement were allocated to the main plots. Applying of SAP (80 kg ha−1) into the soil and control (no applying SAP) were allocated to the subplots. Foliar application of HA (6 kg ha−1) and control (not applying HA) were allocated to the strip plots. The analysis of variance revealed that the effects of HA and SAP on many sesame traits also soil properties were significant. Result: The fitted structural equation model suggests a direct strong-positive effect of leaf area index (LAI), plant height (PlantH) and water-use efficiency (WUE) on plant architecture construct (PlantArchitecture), soil nitrogen content (SoilN), soil electrical conductivity (SoilEC), and on soil properties construct (SoilProperties), which finally increase the sesame qualitative yield production. The calculation of the standard regression coefficients of the model’s variables revealed that variables including: LAI, WUE and PlantPhysiology have had the most causal effect to defining the yield of sesame oil under the field condition of SAP and HA application. The findings in our study suggest that the direct advantages of SAP and HA application is to increase PlantPhysiology, PlantArchitecture and SoilProperties by 65, 50 and 17 percent, respectively, through contributing to the respective processes. Conclusion: Generally, the coefficient of determination of the suggested model (R2= 0.44) indicates that the model explains 44% of the variations in the sesame qualitative yield. The present study suggests employing the structural equation modeling could be best taken as a precise and practical quantitative modeling approach rather than a specific statistical technique, not only to quantify the effects of inputs and management operations but also helps to profound our understanding to identify the most efficient paths involved to certain process which in turn prepare options to reduce production costs beside to produce healthy food and products.

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