Statistical modeling is commonly used to relate the performance of potato (Solanum tuberosumL.) to fertilizer requirements. Prescribing optimal nutrient doses is challenging because of the involvement of many variables including weather, soils, land management, genotypes, and severity of pests and diseases. Where sufficient data are available, machine learning algorithms can be used to predict crop performance. The objective of this study was to predict tuber yield and quality (size and specific gravity) as impacted by nitrogen, phosphorus and potassium fertilization as well as weather, soils and land management variables. We exploited a data set of 273 field experiments conducted from 1979 to 2017 in Quebec (Canada). We developed, evaluated and compared predictions from a hierarchical Mitscherlich model,k-nearest neighbors, random forest, neuronal networks and Gaussian processes. Machine learning models returned R2values of 0.49–0.59 for tuber marketable yield prediction, which were higher than the Mitscherlich model R2(0.37). The models were more likely to predict medium-size tubers (R2= 0.60–0.69) and tuber specific gravity (R2= 0.58–0.67) than large-size tubers (R2= 0.55–0.64) and marketable yield. Response surfaces from the Mitscherlich model, neural networks and Gaussian processes returned smooth responses that agreed more with actual evidence than discontinuous curves derived fromk-nearest neighbors and random forest models. When marginalized to obtain optimal dosages from dose-response surfaces given constant weather, soil and land management conditions, some disagreements occurred between models. Due to their built-in ability to develop recommendations within a probabilistic risk-assessment framework, Gaussian processes stood out as the most promising algorithm to support decisions that minimize economic or agronomic risks.