Observed variations in the ground electrical conductivity (σ_b*) measurements obtained with EMI instruments depend on several soil proxies that influence crop growth and development such as salinity (σ_e), contents of water (θ_w), clay (w_c) and organic matter (w_om) and bulk density (ρ_b). However, the relative contributions of all these to σ_b* are unknown. This knowledge is needed to improve the planning and interpretation of σ_b* data for precision agriculture applications. Recently, a semi-empirical model has been developed to relate σ_b* measurements taken with an EM38 device to σ_e, θ_w, w_c, w_om and ρ_b and also soil temperature (t). In this work this model was subjected to a global sensitivity analysis (GSA) based on the soil data obtained during two surveys carried out, one in summer and the other in autumn, in an ample irrigated area in SE Spain. On the basis of the multiple linear regression meta-models developed for the σ_b* measurements, these were found to linearly respond to the soil properties (R² between 0.92 and 0.96) and thus, the GSA could be based on their standardised regression coefficients. According to these, the soil characteristics explain the following percentages of variance in σ_b* (PV): 30–34 (σ_e), 8–20 (θ_w), 32–47 (w_c), 0.6–2.6 (w_om), 5.7–7.5 (ρ_b) and 0.3–0.4 (t) with changes from the summer to the autumn season of − 4, − 12 and + 15 in the PV explained by the most influential properties, respectively, σ_e, θ_w and w_c. The results of the GSA will help the planning and interpretation of σ_b* measurements for improving crop management.

在地面电导率观测变化（σ _b *）的测量与EMI仪器获得取决于若干土壤代理这种影响作物生长和发育如盐度（σ _Ë），的水含量（θ_瓦特），粘土（瓦特_Ç）和有机物（瓦特_OM）和堆积密度（ρ _b）。然而，所有这些的相对贡献σ _b *是未知的。需要这方面的知识，以提高规划和解释σ _b*精确农业应用数据。最近，一个半经验模型已经发展到涉及σ _b *与EM38设备进行的测量σ _ë，θ_瓦特，瓦特_Ç，瓦特_OM和ρ _b，并且还土壤温度（吨）。在这项工作中，根据在西班牙东南部一个充足的灌溉区域进行的两次调查（一次是夏季，另一次是秋季）获得的土壤数据，对该模型进行了全球敏感性分析（GSA）。在多元线性回归的元模型为开发基础σ _b*测量，发现它们与土壤特性线性相关（R ²在0.92和0.96之间），因此GSA可以基于它们的标准化回归系数。根据这些，土壤特性解释方差的百分比如下σ _b *（PV）：30-34（σ _ë），8-20（θ_瓦特），32-47（瓦特_Ç），0.6-2.6（瓦特_OM），5.7-7.5（ρ _b）和0.3-0.4（吨）从夏季到秋季季节变化- 4， - 12 PV和+ 15由最有影响力的性质说明，分别σ _ê，θ_瓦特和瓦特_Ç。在GSA的结果将有助于规划和解释σ _b *为改善作物管理的测量。