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The flexibility of the Bayesian approach to account for covariates withmeasurement error is combined with semiparametric regression models for a classof continuous, discrete and mixed univariate response distributions withpotentially all parameters depending on a structured additive predictor. Markovchain Monte Carlo enables a modular and numerically efficient implementation ofBayesian measurement error correction based on the imputation of unobservederror-free covariate values. We allow for very general measurement errors,including correlated replicates with heterogeneous variances. The proposal isfirst assessed by a simulation trial, then it is applied to the assessment of asoil-plant relationship crucial for implementing efficient agriculturalmanagement practices. Observations on multi-depth soil information forageground-cover for a seven hectares Alfalfa stand in South Italy were obtainedusing sensors with very refined spatial resolution. Estimating a functionalrelation between ground-cover and soil with these data involves addressingissues linked to the spatial and temporal misalignment and the large data size.We propose a preliminary spatial interpolation on a lattice covering the fieldand subsequent analysis by a structured additive distributional regressionmodel accounting for measurement error in the soil covariate. Results areinterpreted and commented in connection to possible Alfalfa managementstrategies.

Bayesian Measurement Error Correction in Structured Additive  Distributional Regression with an Application to the Analysis of Sensor Data  on Soil-Plant Variability