The initial batch of Sobol points yields landscapes that, due to the overall data scarcity, is dominated by the mean of the observed values, and the surrogate models have essentially no predictive power. The lack of data at this stage is also reflected in the predicted standard deviation, which is uniformly high at a distance from the Sobol points. As the dataset is extended in batches two to four, the landscapes become smoother and the surrogate models predictive power, i.e., ability to accurately interpolate between acquisitions, increases. In the converged model, the lignin yield and β-O-4 content landscapes have resolved into regions of high and low values. The refinement of the landscapes is accompanied by a corresponding decrease in the predicted standard deviation. In batch four, we obtain a uniformly low standard deviation and approach the lower limit set by the experimental noise built into the surrogate models. At this stage, the landscapes are converging, as qualitatively suggested by the relatively small feature changes observed between the third and fourth batch. We furthermore note that the design space is sampled non-uniformly, as exemplified by the locations of the acquisition points in batches one to four. This non-uniformity results from the exploitation-exploration trade-off in the acquisition functions and leads to a rapid improvement of the surrogate models with added data, which is one of the key features that makes BO an effective approach to experimental design problems.