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. 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. 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.

中文翻译：

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腐植酸和水超吸收性聚合物在生态种植系统中对芝麻的影响：农业结构方程模型的新应用

目前的知识并不能为量化投入的影响提供精确的科学工具，特别是诸如高吸收性聚合物（SAP）和腐殖酸（HA）等生态友好的投入已被用于增加土壤肥力，改善作物性能并最终实现粮食生产。设计和进行这项研究的目的是提出一种创新方法，不仅可以识别和量化这些投入的影响，而且可以确定与芝麻油生产相关的地下/地面关系之间的有效路径。在马什哈德（Mashhad）的Ferdowsi大学研究农场进行了两个实验，采用随机完整区组设计，采用条带状图布置，并在连续两个种植年（2015-2016年）中进行了三次重复，以评估SAP和HA对芝麻的影响。在两个不同灌溉水平下的生长特性和产油量包括：供给芝麻需水量的50％和100％。将SAP（80 kg ha-1）施用到土壤中，并将对照（不施用SAP）分配给子图。将叶面喷施HA（6 kg ha-1）和对照（不施涂HA）分配给带状样地。方差分析表明，HA和SAP对许多芝麻性状的影响以及土壤特性也很显着。拟合的结构方程模型表明，叶面积指数（LAI），植物高度（PlantH）和水分利用效率（WUE）对植物结构构造（PlantArchitecture），土壤氮含量（SoilN），土壤电性具有直接的强正效应。电导率（SoilEC），并在土壤特性构造上（SoilProperties），最终提高了芝麻的定性产量。模型变量的标准回归系数的计算表明，在SAP和HA应用的田间条件下，包括LAI，WUE和PlantPhysiology在内的变量对定义芝麻油的收率影响最大。我们的研究结果表明，SAP和HA应用程序的直接优势是通过对各自的过程做出贡献，可使植物生理学，植物建筑学和土壤属性分别提高65％，50％和17％。通常，建议模型的确定系数（R2 = 0.44）表明该模型解释了芝麻定性产量中44％的变化。