I would say it depends on your research question(s). CFA is typically used for studying measurement (psychometric) properties (e.g., factor structure, reliability, validity) of observed variables and correlations between latent variables, whereas SEM is more focused on causal (directional) relationships between latent variables in a structural path model.

A Structural Equation Model (SEM) is a statistical model that aims to examine the relationships between multiple latent (unobserved) variables and observed variables in a dataset. One common approach in SEM is to use Confirmatory Factor Analysis (CFA) to assess the measurement model, which examines the relationships between latent variables and their indicators.

Whether or not CFA is required in SEM for soil analysis depends on the specific research question and data at hand. In general, CFA is used when the research question involves examining the validity of a measurement instrument, such as a survey or questionnaire, where the latent variables are the constructs being measured and the observed variables are the items on the survey or questionnaire.

In soil analysis, the research question may involve examining the relationships between multiple soil properties, such as pH, nutrient content, and soil texture. In this case, CFA may not be necessary since the latent variables (i.e., soil properties) are already well-established and do not require validation. However, if the research question involves examining the relationships between soil properties and observed variables, such as plant growth or soil erosion, then CFA may be necessary to ensure that the observed variables accurately reflect the latent variables of interest.

In summary, whether or not CFA is required in SEM for soil analysis depends on the specific research question and data being analyzed.